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Icelandic Population Genomic Study Results by deCODE Genetics come to Fruition: Curation of Current genomic studies

Reporter/Curator: Stephen J. Williams, Ph.D.

 

UPDATED on 9/6/2017

On 9/6/2017, Aviva Lev-Ari, PhD, RN had attend a talk by Paul Nioi, PhD, Amgen, at HMS, Harvard BioTechnology Club (GSAS).

Nioi discussed his 2016 paper in NEJM, 2016, 374:2131-2141

Variant ASGR1 Associated with a Reduced Risk of Coronary Artery Disease

Paul Nioi, Ph.D., Asgeir Sigurdsson, B.Sc., Gudmar Thorleifsson, Ph.D., Hannes Helgason, Ph.D., Arna B. Agustsdottir, B.Sc., Gudmundur L. Norddahl, Ph.D., Anna Helgadottir, M.D., Audur Magnusdottir, Ph.D., Aslaug Jonasdottir, M.Sc., Solveig Gretarsdottir, Ph.D., Ingileif Jonsdottir, Ph.D., Valgerdur Steinthorsdottir, Ph.D., Thorunn Rafnar, Ph.D., Dorine W. Swinkels, M.D., Ph.D., Tessel E. Galesloot, Ph.D., Niels Grarup, Ph.D., Torben Jørgensen, D.M.Sc., Henrik Vestergaard, D.M.Sc., Torben Hansen, Ph.D., Torsten Lauritzen, D.M.Sc., Allan Linneberg, Ph.D., Nele Friedrich, Ph.D., Nikolaj T. Krarup, Ph.D., Mogens Fenger, Ph.D., Ulrik Abildgaard, D.M.Sc., Peter R. Hansen, D.M.Sc., Anders M. Galløe, Ph.D., Peter S. Braund, Ph.D., Christopher P. Nelson, Ph.D., Alistair S. Hall, F.R.C.P., Michael J.A. Williams, M.D., Andre M. van Rij, M.D., Gregory T. Jones, Ph.D., Riyaz S. Patel, M.D., Allan I. Levey, M.D., Ph.D., Salim Hayek, M.D., Svati H. Shah, M.D., Muredach Reilly, M.B., B.Ch., Gudmundur I. Eyjolfsson, M.D., Olof Sigurdardottir, M.D., Ph.D., Isleifur Olafsson, M.D., Ph.D., Lambertus A. Kiemeney, Ph.D., Arshed A. Quyyumi, F.R.C.P., Daniel J. Rader, M.D., William E. Kraus, M.D., Nilesh J. Samani, F.R.C.P., Oluf Pedersen, D.M.Sc., Gudmundur Thorgeirsson, M.D., Ph.D., Gisli Masson, Ph.D., Hilma Holm, M.D., Daniel Gudbjartsson, Ph.D., Patrick Sulem, M.D., Unnur Thorsteinsdottir, Ph.D., and Kari Stefansson, M.D., Ph.D.

N Engl J Med 2016; 374:2131-2141June 2, 2016DOI: 10.1056/NEJMoa1508419

Abstract
Article
References
Citing Articles (22)
Metrics

BACKGROUND

Several sequence variants are known to have effects on serum levels of non–high-density lipoprotein (HDL) cholesterol that alter the risk of coronary artery disease.

METHODS

We sequenced the genomes of 2636 Icelanders and found variants that we then imputed into the genomes of approximately 398,000 Icelanders. We tested for association between these imputed variants and non-HDL cholesterol levels in 119,146 samples. We then performed replication testing in two populations of European descent. We assessed the effects of an implicated loss-of-function variant on the risk of coronary artery disease in 42,524 case patients and 249,414 controls from five European ancestry populations. An augmented set of genomes was screened for additional loss-of-function variants in a target gene. We evaluated the effect of an implicated variant on protein stability.

RESULTS

We found a rare noncoding 12-base-pair (bp) deletion (del12) in intron 4 of ASGR1, which encodes a subunit of the asialoglycoprotein receptor, a lectin that plays a role in the homeostasis of circulating glycoproteins. The del12 mutation activates a cryptic splice site, leading to a frameshift mutation and a premature stop codon that renders a truncated protein prone to degradation. Heterozygous carriers of the mutation (1 in 120 persons in our study population) had a lower level of non-HDL cholesterol than noncarriers, a difference of 15.3 mg per deciliter (0.40 mmol per liter) (P=1.0×10−16), and a lower risk of coronary artery disease (by 34%; 95% confidence interval, 21 to 45; P=4.0×10−6). In a larger set of sequenced samples from Icelanders, we found another loss-of-function ASGR1 variant (p.W158X, carried by 1 in 1850 persons) that was also associated with lower levels of non-HDL cholesterol (P=1.8×10−3).

CONCLUSIONS

ASGR1 haploinsufficiency was associated with reduced levels of non-HDL cholesterol and a reduced risk of coronary artery disease. (Funded by the National Institutes of Health and others.)

 

Amgen’s deCODE Genetics Publishes Largest Human Genome Population Study to Date

Mark Terry, BioSpace.com Breaking News Staff reported on results of one of the largest genome sequencing efforts to date, sequencing of the genomes of 2,636 people from Iceland by deCODE genetics, Inc., a division of Thousand Oaks, Calif.-based Amgen (AMGN).

Amgen had bought deCODE genetics Inc. in 2012, saving the company from bankruptcy.

There were a total of four studies, published on March 25, 2015 on the online version of Nature Genetics; titled “Large-scale whole-genome sequencing of the Icelandic population[1],” “Identification of a large set of rare complete human knockouts[2],” “The Y-chromosome point mutation rate in humans[3]” and “Loss-of-function variants in ABCA7 confer risk of Alzheimer’s disease[4].”

The project identified some new genetic variants which increase risk of Alzheimer’s disease and confirmed some variants known to increase risk of diabetes and atrial fibrillation. A more in-depth post will curate these findings but there was an interesting discrete geographic distribution of certain rare variants located around Iceland. The dataset offers a treasure trove of meaningful genetic information not only about the Icelandic population but offers numerous new targets for breast, ovarian cancer as well as Alzheimer’s disease.

View Mark Terry’s article here on Biospace.com.

“This work is a demonstration of the unique power sequencing gives us for learning more about the history of our species,” said Kari Stefansson, founder and chief executive officer of deCode and one of the lead authors in a statement, “and for contributing to new means of diagnosing, treating and preventing disease.”

The scale and ambition of the study is impressive, but perhaps more important, the research identified a new genetic variant that increases the risk of Alzheimer’s disease and already had identified an APP variant that is associated with decreased risk of Alzheimer’s Disease. It also confirmed variants that increase the risk of diabetes and a variant that results in atrial fibrillation.
The database of human genetic variation (dbSNP) contained over 50 million unique sequence variants yet this database only represents a small proportion of single nucleotide variants which is thought to exist. These “private” or rare variants undoubtedly contribute to important phenotypes, such as disease susceptibility. Non-SNV variants, like indels and structural variants, are also under-represented in public databases. The only way to fully elucidate the genetic basis of a trait is to consider all of these types of variants, and the only way to find them is by large-scale sequencing.

Curation of Population Genomic Sequencing Programs/Corporate Partnerships

Click on “Curation of genomic studies” below for full Table

Curation of genomic studies
Study Partners Population Enrolled Disease areas Analysis
Icelandic Genome

Project

deCODE/Amgen Icelandic 2,636 Variants related to: Alzheimer’s, cardiovascular, diabetes WES + EMR; blood samples
Genome Sequencing Study Geisinger Health System/Regeneron Northeast PA, USA 100,000 Variants related to hypercholestemia, autism, obesity, other diseases WES +EMR +MyCode;

– Blood samples

The 100,000 Genomes Project National Health Service/NHS Genome Centers/ 10 companies forming Gene Consortium including Abbvie, Alexion, AstraZeneca, Biogen, Dimension, GSK, Helomics, Roche,   Takeda, UCB Rare disorders population UK Starting to recruit 100,000 Initially rare diseases, cancer, infectious diseases WES of blood, saliva and tissue samples

Ref paper

Saudi Human Genome Program 7 centers across Saudi Arabia in conjunction with King Abdulaziz City Science & Tech., King Faisal Hospital & Research Centre/Life Technologies General population Saudi Arabia 20,000 genomes over three years First focus on rare severe early onset diseases: diabetes, deafness, cardiovascular, skeletal deformation Whole genome sequence blood samples + EMR
Genome of the Netherlands (GoNL) Consortium consortium of the UMCG,LUMCErasmus MCVU university and UMCU. Samples where contributed by LifeLinesThe Leiden Longevity StudyThe Netherlands Twin Registry (NTR), The Rotterdam studies, and The Genetic Research in Isolated Populations program. All the sequencing work is done by BGI Hong Kong. Families in Netherlands 769 Variants, SNV, indels, deletions from apparently healthy individuals, family trios Whole genome NGS of whole blood no EMR

Ref paper in Nat. Genetics

Ref paper describing project

Faroese FarGen project Privately funded Faroe Islands Faroese population 50,000 Small population allows for family analysis Combine NGS with EMR and genealogy reports
Personal Genome Project Canada $4000.00 fee from participants; collaboration with University of Toronto and SickKids Organization; technical assistance with Harvard Canadian Health System Goal: 100,000 ? just started no defined analysis goals yet Whole exome and medical records
Singapore Sequencing Malay Project (SSMP) Singapore Genome Variation Project

Singapore Pharmacogenomics Project

Malaysian 100 healthy Malays from Singapore Pop. Health Study Variant analysis Deep whole genome sequencing
GenomeDenmark four Danish universities (KU, AU, DTU and AAU), two hospitals (Herlev and Vendsyssel) and two private firms (Bavarian Nordic and BGI-Europe). 150 complete genomes; first 30 published in Nature Comm. ? See link
Neuromics Consortium University of Tübingen and 18 academic and industrial partners (see link for description) European and Australian 1,100 patients with neuro-

degenerative and neuro-

muscular disease

Moved from SNP to whole exome analysis Whole Exome, RNASeq

References

  1. Gudbjartsson DF, Helgason H, Gudjonsson SA, Zink F, Oddson A, Gylfason A, Besenbacher S, Magnusson G, Halldorsson BV, Hjartarson E et al: Large-scale whole-genome sequencing of the Icelandic population. Nature genetics 2015, advance online publication.
  2. Sulem P, Helgason H, Oddson A, Stefansson H, Gudjonsson SA, Zink F, Hjartarson E, Sigurdsson GT, Jonasdottir A, Jonasdottir A et al: Identification of a large set of rare complete human knockouts. Nature genetics 2015, advance online publication.
  3. Helgason A, Einarsson AW, Gumundsdottir VB, Sigursson A, Gunnarsdottir ED, Jagadeesan A, Ebenesersdottir SS, Kong A, Stefansson K: The Y-chromosome point mutation rate in humans. Nature genetics 2015, advance online publication.
  4. Steinberg S, Stefansson H, Jonsson T, Johannsdottir H, Ingason A, Helgason H, Sulem P, Magnusson OT, Gudjonsson SA, Unnsteinsdottir U et al: Loss-of-function variants in ABCA7 confer risk of Alzheimer’s disease. Nature genetics 2015, advance online publication.

Other post related to DECODE, population genomics, and NGS on this site include:

Illumina Says 228,000 Human Genomes Will Be Sequenced in 2014

CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics & Computational Genomics

CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics and Computational Genomics – Part IIB

Human genome: UK to become world number 1 in DNA testing

Synthetic Biology: On Advanced Genome Interpretation for Gene Variants and Pathways: What is the Genetic Base of Atherosclerosis and Loss of Arterial Elasticity with Aging

Genomic Promise for Neurodegenerative Diseases, Dementias, Autism Spectrum, Schizophrenia, and Serious Depression

Sequencing the exomes of 1,100 patients with neurodegenerative and neuromuscular diseases: A consortium of 18 European and Australian institutions

University of California Santa Cruz’s Genomics Institute will create a Map of Human Genetic Variations

Three Ancestral Populations Contributed to Modern-day Europeans: Ancient Genome Analysis

Impact of evolutionary selection on functional regions: The imprint of evolutionary selection on ENCODE regulatory elements is manifested between species and within human populations

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Depth Underwater and Underground

Writer and Curator: Larry H. Bernstein, MD, FCAP 

 

Introduction

Deep diving for mammals is dangerous for humans and land based animals for too long, and it has dangerous consequences, most notable in nitrogen emboli  with very deep underwater diving. Other mammals live in water and have adapted to a water habitat.  This is another topic that needs further exploration.

Deep diving has different meanings depending on the context. Even in recreational diving the meaning may vary:

In recreational diving, a depth below about 30 metres (98 ft), where nitrogen narcosis becomes a significant hazard for most divers, may be considered a “deep dive”

In technical diving, a depth below about 60 metres (200 ft) where hypoxic breathing gas becomes necessary to avoid oxygen toxicity may be considered a “deep dive”.

Early experiments carried out by Comex S.A. (Compagnie maritime d’expertises) using hydrox and trimix attained far greater depths than any recreational technical diving. One example being the Comex Janus IV open-sea dive to 501 metres (1,644 ft) in 1977. The open-sea diving depth record was achieved in 1988 by a team of Comex divers who performed pipe line connection exercises at a depth of 534 metres (1,752 ft) in the Mediterranean Sea as part of the Hydra 8 program. These divers needed to breathe special gas mixtures because they were exposed to very high ambient pressure (more than 50 times atmospheric pressure).

Then there is the adaptation to the water habitat as a living environment. The two main types of aquatic ecosystems are marine ecosystems and freshwater ecosystems.

http://en.wikipedia.org/wiki/Deep_diving

Marine ecosystems are part of the earth’s aquatic ecosystem. The habitats that make up this vast system range from the productive nearshore regions to the barren ocean floor. The marine waters may be fully saline, brackish or nearly fresh. The saline waters have a salinity of 35-50 ppt (= parts per thousand). The freshwater has a salinity of less than 0.5 ppt. The brackish water lies in between these 2. Marine habitats are situated from the coasts, over the continental shelf to the open ocean and deep sea. The ecosystems are sometimes linked with each other and are sometimes replacing each other in other geographical regions. The reason why habitats differ from another is because of the physical factors that influence the functioning and diversity of the habitats. These factors are temperature, salinity, tides, currents, wind, wave action, light and substrate.

Marine ecosystems are home to a host of different species ranging from planktonic organisms that form the base of the marine food web to large marine mammals. Many species rely on marine ecosystems for both food and shelter from predators. They are very important to the overall health of both marine and terrestrial environments. Coastal habitats are those above the spring high tide limit or above the mean water level in non-tidal waters.  They are close to the sea and include habitats such as coastal dunes and sandy shores, beaches , cliffs and supralittoral habitats. Coastal habitats alone account for approximately 30% of all marine biological productivity.

http://www.marbef.org/wiki/marine_habitats_and_ecosystems

All plant and animal life forms are included from the microscopic picoplankton all the way to the majestic blue whale, the largest creature in the sea—and for that matter in the world. It wasn’t until the writings of Aristotle from 384-322 BC that specific references to marine life were recorded. Aristotle identified a variety of species including crustaceans, echinoderms, mollusks, and fish.
Today’s classification system was developed by Carl Linnaeus external link as an important tool for use in the study of biology and for use in the protection of biodiversity. Without very specific classification information and a naming system to identify species’ relationships, scientists would be limited in attempts to accurately describe the relationships among species. Understanding these relationships helps predict how ecosystems can be altered by human or natural factors.

Preserving biodiversity is facilitated by taxonomy. Species data can be better analyzed to determine the number of different species in a community and to determine how they might be affected by environmental stresses. Family, or phylogenetic, trees for species help predict environmental impacts on individual species and their relatives.

http://marinebio.org/oceans/marine-taxonomy/

For generations, whales and other marine mammals have intrigued humans. 2,400 years ago, Aristotle, a Greek scientist and philosopher, recognized that whales are mammals, not fish, because they nurse their young and breathe air like other mammals. There are numerous myths and legends surrounding marine mammals. The Greeks believed that killing a dolphin was as bad as murdering a human. An Amazon legend said that river dolphins came to shore dressed as men to woo pretty girls during fiestas. During the Middle Ages, there were numerous legends surrounding the narwhals’ amazing tusk, which was thought to have come from the unicorn.

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Marine mammals evolved from their land dwelling ancestors over time by developing adaptations to life in the water. To aid swimming, the body has become streamlined and the number of body projections has been reduced. The ears have shrunk to small holes in size and shape. Mammary glands and sex organs are not part of the external physiology, and posterior (hind) limbs are no longer present.

Mechanisms to prevent heat loss have also been developed. The cylindrical body shape with small appendages reduces the surface area to volume ratio of the body, which reduces heat loss. Marine mammals also have a counter current heat exchange mechanism created by convergent evolution external link where the heat from the arteries is transferred to the veins as they pass each other before getting to extremities, thus reducing heat loss. Some marine mammals also have a thick layer of fur with a water repellent undercoat and/or a thick layer of blubber that can’t be compressed. The blubber provides insulation, a food reserve, and aids with buoyancy. These heat loss adaptations can also lead to overheating for animals that spend time out of the water. To prevent overheating, seals or sea lions will swim close to the surface with their front flippers waving in the air. They also flick sand onto themselves to keep the sun from directly hitting their skin. Blood vessels can also be expanded to act as a sort of radiator.

One of the major behavioral adaptations of marine mammals is their ability to swim and dive. Pinnipeds swim by paddling their flippers while sirenians and cetaceans move their tails or flukes up and down.

Some marine mammals can swim at relatively high speeds. Sea lions swim up to 35 kph and orcas can reach 50 kph. The fastest marine mammal, however, is the common dolphin, which reaches speeds up to 64 kph. While swimming, these animals take very quick breaths. For example, fin whales can empty and refill their huge lungs in less than 2 seconds. Marine mammals’ larynx and esophagus close automatically when they open their mouths to catch prey during dives. Oxygen is stored in hemoglobin in the blood and in myoglobin in the muscles. The lungs are also collapsible so that air is pushed into the windpipe preventing excess nitrogen from being absorbed into the tissues. Decreasing pressure can cause excess nitrogen to expand in the tissues as animals ascend to shallower depths, which can lead to decompression sickness,  aka “the bends.” Bradycardia, the reduction of heart rate by 10 to 20%, also takes place to aid with slowing respiration during dives and the blood flow to non-essential body parts. These adaptations allow sea otters to stay submerged for 4 to 5 minutes and dive to depths up to 55 m. Pinnipeds can often stay down for 30 minutes and reach average depths of 150-250 m. One marine mammal with exceptional diving skills is the Weddell seal, which can stay submerged for at least 73 minutes at a time at depths up to 600 m. The length and depth of whale dives depends on the species. Baleen whales feed on plankton near the surface of the water and have no need to dive deeply so they are rarely seen diving deeper than 100 m external link. Toothed whales seek larger prey at deeper depths and some can stay down for hours at depths of up to 2,250 m external link.

http://marinebio.org/oceans/marine-mammals/

Human Experience

Albert Behnke: Nitrogen Narcosis

Casey A. Grover and David H. Grover
The Journal of Emergency Medicine, 2014; 46(2):225–227
http://dx.doi.org/10.1016/j.jemermed.2013.08.080

As early as 1826, divers diving to great depths noted that descent often resulted in a phenomenon of intoxication and euphoria. In 1935, Albert Behnke discovered nitrogen as the cause of this clinical syndrome, a condition now known as nitrogen narcosis. Nitrogen narcosis consists of the development of euphoria, a false sense of security, and impaired judgment upon underwater descent using compressed air below 34 atmospheres (99 to 132 feet). At greater depths, symptoms can progress to loss of consciousness. The syndrome remains relatively unchanged in modern diving when compressed air is used. Behnke’s use of non-nitrogencontaining gas mixtures subsequent to his discovery during the 1939 rescue of the wrecked submarine USS Squalus pioneered the use of non-nitrogencontaining gas mixtures, which are used by modern divers when working at great depth to avoid the effects of nitrogen narcosis.

Behnke’s first duty station as a licensed physician was as assistant medical officer for Submarine Division 20 in San Diego, which was then commanded by one of the Navy’s rising stars, Captain Chester W. Nimitz of World War II fame.
In this setting, Dr. Behnke spent his free time constructively by learning to dive, using the traditional ‘‘hard-hat’’ gear aboard the USS Ortalon, a submarine rescue vessel to which he also rotated. Diving was not a notable specialty of the Navy at the time, and the service was slow in developing the infrastructure for it. Dr. Behnke devoted his efforts to research on the topic of diving medicine, as well as developing a more sound understanding of the biophysics of diving. In 1932, he wrote a letter to the Surgeon General describing some of his observations on arterial gas embolism, which earned him some accolades from the Navy and resulted in his transfer to Harvard’s School of Public Health as a graduate fellow. After 2 years at Harvard, the Navy assigned duty to Dr. Behnke at the Navy’s submarine escape training tower at Pearl Harbor. He worked extensively here on developing techniques for rescuing personnel from disabled submarines on the sea floor. In 1937, he was one of three Navy physicians assigned to the Navy’s Experimental Diving Unit. This team worked on improving the rescue system, plus updating the diving recompression tables originally developed by the British in 1908.

The intoxicating effects of diving were first described by a French physician named Colladon in 1826, who reported that descent in a diving bell resulted in his feeling a ‘‘state of excitement as though I had drunk some alcoholic liquor’’.
The etiology of this phenomenon remained largely unknown until the 1930s, when the British military researcher Damant again highlighted the issue, and reported very unpredictable behavior in his divers during descents as deep as 320 feet during the British Admiralty Deep Sea diving trials. Two initial theories arose as to the etiology for this effect, the first being from psychological causes by Hill and Phillip in 1932, and the second being from oxygen toxicity by Haldane in 1935.

Dr. Behnke and his colleagues at the Harvard School of Public Health had another idea as to the etiology of this phenomenon. In 1935, based on observation of individuals in experiments with a pressure chamber, Dr. Behnke published an article in the American Journal of Physiology in which he posited that nitrogen was the etiology of the intoxicating effects of diving.

Nitrogen narcosis, described as ‘‘rapture of the deep’’ by Jacques Cousteau, still remains a relatively common occurrence in modern diving, despite major advances in diving technology since Behnke’s initial description of the pathophysiologic cause of the condition in 1935. The development of symptoms of this condition varies from diver to diver, but usually begins when a depth of 4 atmospheres (132 feet) is reached in divers using compressed air. More sensitive divers can develop symptoms at only 3 atmospheres (99 feet), and other divers may not be affected up to depths as high as 6 atmospheres (198 feet). Interestingly, tolerance to nitrogen narcosis can be developed by frequent diving and exposure to the effects of compressed air at depth.

  1. Acott C. A brief history of diving and decompression illness. SPUMS J 1999;29:98–109.
    2. Bornmann R. Dr. Behnke, founder of UHMS, dies. Pressure 1992; 21:14.
    3. Behnke AR, Thomson RM, Motley P. The psychologic effects from breathing air at 4 atmospheric pressures. Am J Physiol 1935; 112:554–8.
    4. Behnke AR, Johnson FS, Poppen JR, Motley P. The effect of oxygen on man at pressures from 1 to 4 atmospheres. AmJ Physiol 1934; 110:565–72.

Exhaled nitric oxide concentration and decompression-induced bubble formation: An index of decompression severity in humans?

J.-M. Pontier, Buzzacott, J. Nastorg, A.T. Dinh-Xuan, K. Lambrechts
Nitric Oxide 39 (2014) 29–34
http://dx.doi.org/10.1016/j.niox.2014.04.005

Introduction: Previous studies have highlighted a decreased exhaled nitric oxide concentration (FE NO) in divers after hyperbaric exposure in a dry chamber or following a wet dive. The underlying mechanisms of this decrease remain however unknown. The aim of this study was to quantify the separate effects of submersion, hyperbaric hyperoxia exposure and decompression-induced bubble formation on FE NO after a wet dive.
Methods: Healthy experienced divers (n = 31) were assigned to either

  • a group making a scuba-air dive (Air dive),
  • a group with a shallow oxygen dive protocol (Oxygen dive) or

a group making a deep dive breathing a trimix gas mixture (deep-dive).
Bubble signals were graded with the KISS score. Before and after each dive FE NO values were measured using a hand-held electrochemical analyzer.
Results: There was no change in post-dive values of FE NO values (expressed in ppb = parts per billion) in the Air dive group (15.1 ± 3.6 ppb vs. 14.3 ± 4.7 ppb, n = 9, p = 0.32). There was a significant decrease in post-dive values of FE NO in the Oxygen dive group (15.6 ± 6 ppb vs. 11.7 ± 4.7 ppb, n = 9, p = 0.009). There was an even more pronounced decrease in the deep dive group (16.4 ± 6.6 ppb vs. 9.4 ± 3.5 ppb, n = 13, p < 0.001) and a significant correlation between KISS bubble score >0 (n = 13) and percentage decrease in post-dive FE NO values (r = -0.53, p = 0.03). Discussion: Submersion and hyperbaric hyperoxia exposure cannot account entirely for these results suggesting the possibility that, in combination, one effect magnifies the other. A main finding of the present study is a significant relationship between reduction in exhaled NO concentration and dive-induced bubble formation. We postulate that exhaled NO concentration could be a useful index of decompression severity in healthy human divers.

Brain Damage in Commercial Breath-Hold Divers

Kiyotaka Kohshi, H Tamaki, F Lemaıtre, T Okudera, T Ishitake, PJ Denoble
PLoS ONE 9(8): e105006 http://dx.doi.org:/10.1371/journal.pone.0105006

Background: Acute decompression illness (DCI) involving the brain (Cerebral DCI) is one of the most serious forms of diving related injuries which may leave residual brain damage. Cerebral DCI occurs in compressed air and in breath-hold divers, likewise. We conducted this study to investigate whether long-term breath-hold divers who may be exposed to repeated symptomatic and asymptomatic brain injuries, show brain damage on magnetic resonance imaging (MRI).
Subjects and Methods: Our study subjects were 12 commercial breath-hold divers (Ama) with long histories of diving work in a district of Japan. We obtained information on their diving practices and the presence or absence of medical problems, especially DCI events. All participants were examined with MRI to determine the prevalence of brain lesions.
Results: Out of 12 Ama divers (mean age: 54.965.1 years), four had histories of cerebral DCI events, and 11 divers demonstrated ischemic lesions of the brain on MRI studies. The lesions were situated in the cortical and/or subcortical area (9 cases), white matters (4 cases), the basal ganglia (4 cases), and the thalamus (1 case). Subdural fluid collections were seen in 2 cases. Conclusion: These results suggest that commercial breath-hold divers are at a risk of clinical or subclinical brain injury which may affect the long-term neuropsychological health of divers.

Decompression illness

Richard D Vann, Frank K Butler, Simon J Mitchell, Richard E Moon
Lancet 2010; 377: 153–64

Decompression illness is caused by intravascular or extravascular bubbles that are formed as a result of reduction in environmental pressure (decompression). The term covers both arterial gas embolism, in which alveolar gas or venous gas emboli (via cardiac shunts or via pulmonary vessels) are introduced into the arterial circulation, and decompression sickness, which is caused by in-situ bubble formation from dissolved inert gas. Both syndromes can occur in divers, compressed air workers, aviators, and astronauts, but arterial gas embolism also arises from iatrogenic causes unrelated to decompression. Risk of decompression illness is
affected by immersion, exercise, and heat or cold. Manifestations range from itching and minor pain to neurological symptoms, cardiac collapse, and death. First aid treatment is 100% oxygen and definitive treatment is recompression to increased pressure, breathing 100% oxygen. Adjunctive treatment, including fluid administration and prophylaxis against venous thromboembolism in paralyzed patients, is also recommended. Treatment is, in most cases, effective although residual deficits can remain in serious cases, even after several recompressions.

Bubbles can have mechanical, embolic, and biochemical effects with manifestations ranging from trivial to fatal. Clinical manifestations can be caused by direct effects from extravascular (autochthonous) bubbles such as mechanical distortion of tissues causing pain, or vascular obstruction causing stroke-like signs and symptoms. Secondary effects can cause delayed symptom onset up to 24 h after surfacing. Endothelial damage by intravascular bubbles can cause capillary leak, extravasation of plasma, and haemoconcentration. Impaired endothelial function, as measured by decreased effects of vasoactive compounds, has been reported in animals and might occur in man. Hypotension can occur in severe cases. Other effects include platelet activation and deposition, leucocyte-endothelial adhesion, and possibly consequences of vascular occlusion believed to occur in thromboembolic stroke such as ischaemia-reperfusion injury, and apoptosis.

Classification of initial and of all eventual manifestations of decompression illness in 2346 recreational diving accidents reported to the Divers Alert Network from 1998 to 2004 For all instances of pain, 58% consisted of joint pain, 35% muscle pain, and 7% girdle pain. Girdle pain often portends spinal cord involvement. Constitutional symptoms included headache, lightheadedness, inappropriate fatigue, malaise, nausea or vomiting, and anorexia. Muscular discomfort included stiffness, pressure, cramps, and spasm but excluded pain. Pulmonary manifestations included dyspnoea and cough.

Other than depth and time, risk of decompression sickness is affected by other factors that affect inert gas exchange and bubble formation, such as immersion (vs dry hyperbaric chamber exposure), exercise, and temperature. Immersion decreases venous pooling and increases venous return and cardiac output. Warm environments improve peripheral perfusion by promoting vasodilation, whereas cool temperatures decrease perfusion through vasoconstriction. Exercise increases both peripheral perfusion and temperature. The effect of environmental conditions on risk of decompression sickness is dependent on the phase of the pressure exposure. Pressure, exercise, immersion, or a hot environment increase inert gas uptake and risk of decompression sickness. During decom-pression these factors increase inert gas elimination and therefore decrease the risk of decompression sickness. Conversely, uptake is reduced during rest or in a cold environment, hence a diver resting in a cold environment on the bottom has decreased risk of decompression sickness. Rest or low temperatures during decompression increase the risk. If exercise occurs after decompression when super-saturation is present, bubble formation increases and risk of decompression sickness rises.

Exercise at specific times before a dive can decrease the risk of serious decompression sickness in animals and incidence of venous gas emboli in both animals and man. The mechanisms of these effects are unknown but might involve modulation of nitric oxide production and effects on endothelium. Venous gas emboli and risk of decompression sickness increase slightly with age and body-mass index.

Arterial gas embolism should be suspected if a diver has a new onset of altered consciousness, confusion, focal cortical signs, or seizure during ascent or within a few minutes after surfacing from a compressed gas dive.

If the diver spends much time at depth and might have absorbed substantial inert gas before surfacing, arterial gas embolism and serious decompression sickness can coexist, and in such cases, spinal cord manifestations can predominate. Other organ systems, such as the heart, can also be affected, but the clinical diagnosis of gas embolism is not reliable without CNS manifestations. Arterial gas embolism is rare in altitude exposure; if cerebral symptoms occur after altitude exposure, the cause is usually decompression sickness.

Nondermatomal hypoaesthesia and truncal ataxia are common in neurological decompression sickness and can be missed by cursory examination. Pertinent information includes level of consciousness and mental status, cranial nerve function, and motor strength. Coordination can be affected disproportionately, and abnormalities can be detected by assessment of finger-nose movement, and, with eyes open and closed, ability to stand and walk and do heel-toe walking backwards and forwards. Many of these simple tests can be done on the scene by untrained companions.

Panel: Differential diagnosis of decompression illness
Inner-ear barotrauma
Middle-ear or maxillary sinus overinfl ation
Contaminated diving gas and oxygen toxic effects
Musculoskeletal strains or trauma sustained before, during, or after diving
Seafood toxin ingestion (ciguatera, pufferfish, paralytic shellfish poisoning)
Immersion pulmonary edema
Water aspiration
Decompression chamber

Decompression chamber

Decompression chamber. fluidic or pneumatic ventilator is shown at the left. The infusion pump is contained within a plastic cover, in which 100% nitrogen is used to decrease the fi re risk in the event of an electrical problem. The monitor screen is outside the chamber and can be seen through the viewing port. Photo from Duke University Medical Center, with permission.

Long-term outcomes of 69 divers with spinal cord decompressionsickness, by manifestation
n %
No residual symptoms 34 49·3
Any residual symptom 35 50·7
Mild paraesthesias, weakness, or pain 14 20·3
Some impairment of daily activities 21 30·4
Difficulty walking 11 15·9
Impaired micturition 13 18·8
Impaired defecation 15 21·7
Impaired sexual function 15 21·7

Decompression illness occurs in a small population but is an international problem that few physicians are trained to recognise or manage. Although its manifestations are often mild, the potential for permanent injury exists in severe cases, especially if unrecognised or inadequately treated. Emergency medical personnel should be aware of manifestations of decompression illness in the setting of a patient with a history of recent diving or other exposure to substantial pressure change, and should contact an appropriate consultation service for advice.

Diving Medicine: Contemporary Topics and Their Controversies

Michael B. Strauss and Robert C. Borer, Jr
Am J Emerg Med 2001; 19:232-238
http://dx.doi.org:/10.1053/ajem.2001.22654

SCUBA diving is a popular recreational sport. Although serious injuries occur infrequently, when they do knowledge of diving medicine and/or where to obtain appropriate consultation is essential. The emergency physician is likely to be the first physician contact the injured diver has. We discuss 8 subjects
in diving medicine which are contemporary, yet may have controversies associated with them. From this information the physician dealing primarily with the injured diver will have a basis for understanding and managing, as
well as where to find additional help, for his/her patients’ diving injuries.

Over the past 10 years, new knowledge and equipment improvements have made diving safer and more enjoyable. Estimates of actively participating sports divers show a striking increase over this time interval while the number of SCUBA diving deaths annually has remained nearly level at approximately 100. A further indicator of recreational diving safety is that reflected in the nearly constant number of diving injuries (1000 per annum) over the most recent 5 reported years, or approximately 0.53 to 3.4 incidents/10,000 dives.

Divers Alert Network.
The Divers Alert Network (DAN) is a nonprofit organization directed and staffed by experts in the specialty of diving medicine.6 DAN provides immediate consultation for both divers and physicians in the diagnosis and initial management of diving injuries. This 24-hour service is available free world-wide through a dedicated emergency telephone line: 1-919-684-4326. The DAN staff will also identify the nearest appropriate recompression treatment facility and knowledgeable physicians for an expedient referral. General diving medical inquiries can be answered during normal weekday hours either through an information telephone line: 1-919-684-2948 or through an interactive web site http://www.diversalertnetwork.org.

Use of 100% Oxygen for Initial, on the Scene, Management of Diving Accidents
The breathing of pure oxygen is crucial for the initial management of the diving related problems of arterial gas embolism (AGE), decompression sickness (DCS), pulmonary barotrauma (thoracic squeeze), aspiration pneumonitis, and hypoxic encephalopathy associated with near drowning. In 1985, Dick reported that in many cases the neurologic symptoms of AGE and DCS were resolved with the immediate breathing of pure oxygen on the surface. The breathing of pure oxygen reduces bubble size by increasing the differential pressure for the inert gas to diffuse out of the bubble and it also speeds the washout of inert gas from body tissues. The early elimination of the bubble prevents hypoxia and the interaction of the bubble with the blood vessel lining. This interaction leads to secondary problems of capillary leak, bleeding, inflammation, ischemia, and cell death. These secondary problems are the reasons not all DCS symptoms resolve with recompression chamber treatment. The immediate use of pure oxygen for the medical management of these diving problems is analogous to the use of cardiopulmonary resuscitation for the witnessed cardiac arrest; the sooner initiated the better the results.

Diving Education

Medical Fitness for Diving

Asthma has the potential risk for AGE. Neuman reviewed the subject of asthma and diving. He and his coauthors recommend that asthmatics who are asymptomatic, not on medications and have no exercised induced abnormality on pulmonary function studies be allowed to dive.

Conditions leading to loss of consciousness, such as insulin dependent diabetes and epilepsy, can result in drowning. Carefully controlled diving studies in diabetics, who are free from complications, are now defining the safe requirements for diving. Epilepsy remains as a disqualification except in individuals with a history of febrile seizures ending prior to 5 years of age.

Availability of Hyperbaric Oxygen Treatment Facilities

The availability of these chambers makes it possible for divers who become symptomatic after SCUBA diving to readily receive recompression treatment. This is important because the closer the initiation of recompression treatment to the onset of DCS (and AGE) signs and symptoms, the greater the likelihood of full recovery.

Improved Diving Equipment

Mixed and Rebreather Gas Diving
Mixed gas diving involves changing the breathing gas from air which has 20% oxygen to higher oxygen percentages (nitrox). As the amount of oxygen is increased in the gas mixture, the amount of the inert gas (nitrogen) is reduced. With oxygen enriched air there is less tissue deposition of inert gas per unit of time under water for any given depth. However, because of increased oxygen partial pressures, the seizure threshold for oxygen toxicity is lowered. For normal sports diving activities, oxygen toxicity with mixed gas diving is only a theoretical concern.

Decompression Illness is More Than Bubbles

When AGE occurs, DCS symptoms may be concurrent or appear during or after recompression treatment even though the decompression tables were not violated on the dive. When DCS occurs in this situation it appears resistant to recompression treatment (Neuman) perhaps because of the inflammatory reaction generated by the bubble-blood vessel interaction from the AGE. In cases of DCI where components of both DCS and AGE are suspected, the diver should be observed for a minimum of 24 hours after the recompression treatment is completed for the delayed onset of DCS.

No theory of DCS discounts the primary role of bubbles in this condition. However, new information suggests that there are precursors to bubble formation and post-bubbling events that occur as a consequence of the bubbles. As mentioned earlier, venous gas emboli are a common occurrence diving ascent and ordinarily are filtered out harmlessly by the lungs. Precursors to DCS include stasis, dehydration and too rapid of ascents. These conditions allow the ubiquitous VGE to enlarge, coalesce and occlude the venous side of the circulation. Massive venous bubbling to the lungs can cause pulmonary vessel obstruction described as the chokes. If right to left shunts occur in the heart, VGE can become AGE to the brain. If the arterial flow is slow enough and/or the gradients large enough, autochthonus (ie, spontaneous) bubbles can form in the arterial circulation and lead to any of the consequences of AGE. In such situations it could be difficult to determine whether the DCI event was from AGE or DCS even after careful analysis of the dive profile. Hollenbeck’s model for diving paraplegia includes the setting of venous stasis (Batson’s plexus of veins) in the spinal canal, bubble formation, bubble enlargement possibly from off gassing of the spinal cord, blood vessel occlusion, and venous side infarctions of the spinal cord.
Contemporary Management of DCS

Problem Intervention Effect
Bubble Recompression
with HBO
Reduce bubble size
1. Washout inert gas.
2. Change bubble composition by diffusion.
Stasis and dehydration Hydration: oral fluids if alert, IV fluids otherwise. Improve blood flow.
InflammationCell Ischemia ? Anti-inflammatory medicationsHBO Reduce interaction between bubble and blood vessel endothelium.
Improve oxygen availability to hypoxic tissues, reduce edema and also reduces the interaction between bubble and blood vessel endothelium.

.

Conclusions

We anticipate that in the future there will be further improvements for the safety and enjoyment of the recreational SCUBA diver. For example, the dive computer of the future will be able to individualize dive profiles for different personal medical parameters such as age, body composition and fitness level. Diver locators could quickly target a missing diver and save time and gas consumption as well as prevent serious diving mishaps. Drugs may be developed that would minimize the effect of bubbles interacting with body tissues and prevent DCS and AGE.

Extracorporeal membrane oxygenation therapy for pulmonary decompression illness

Yutaka Kondo, Masataka Fukami and Ichiro Kukita
Kondo et al. Critical Care 2014; 18:438 http://ccforum.com/content/18/3/438/10.1186/cc13935

Pulmonary decompression illness is rarely observed in clinical settings, and most patients die prior to hospitalization. We administered ECMO therapy to rescue a patient, even though this therapy has rarely been reported with good outcome in patients with decompression illness. In addition, we had to select venovenous ECMO even with the patient showing right ventricular failure. A lot of physicians may select venoarterial ECMO if the patient shows right ventricular failure, but the important physiological mechanism of pulmonary decompression illness is massive air embolism in the pulmonary arteries, and the bubbles diminish within the first 24 hours. The management of decompression illness therefore differs substantially from the usual right-sided heart failure.

Extremes of barometric pressure

Jane E Risdall, David P Gradwell
Anaesthesia and Intensive Care Medicine 16:2
Ascent to elevated altitude, commonly achieved through flight, by climbing or by residence in highland regions, exposes the individual to reduced ambient pressure. Although there are physical manifestations of this exposure as a consequence of Boyle’s law, the primary physiological challenge is of hypobaric hypoxia. The acute physiological and longer-term adaptive responses of the cardiovascular, respiratory, hematological and neurological systems to altitude are described, together with an outline of the presentation and management of acute mountain sickness, high-altitude pulmonary edema and high-altitude cerebral edema. While many millions experience modest exposure to altitude as a result of flight in pressurized aircraft, fewer individuals are exposed to increased ambient pressure. The pressure changes during diving and hyperbaric exposures result in greater changes in gas load and gas toxicity. Physiological effects include the consequences of increased work of breathing and redistribution of circulating volume. Neurological manifestations may be the direct result of pressure or a consequence of gas toxicity at depth. Increased tissue gas loads may result in decompression illness on return to surface or subsequent ascent in flight.

  • understand the physical effects of changes in ambient pressure and the physiological consequences on the cardiovascular respiratory and neurological systems
  • gain an awareness that exposure to reduced ambient pressure produces both acute and more chronic effects, with differing signs, symptoms and time to onset at various altitudes
  • develop an awareness of the toxic effects of ‘inert’ gases at increased ambient pressures and the pathogenesis and management of decompression illness

Decompression illness According to Henry’s law, at a constant temperature the amount of gas which dissolves in a liquid is proportional to the pressure of that gas or its partial pressure, if it is part of a mixture of gases. Breathing gases at increased ambient pressure will increase the amount of each gas dissolved in the fluid phases of body tissues. On ascent this excess gas has to be given up. If the ascent is controlled at a sufficiently slow rate, elimination will be via the respiratory system. If the ascent is too fast, excess gas may come out of solution and form free bubbles in the tissues or circulation. Bubbles may contain any of the gases in the breathing mixture, but it is the presence of inert gas bubbles (nitrogen or helium) that are thought most likely to give rise to problems, since the elimination of excess oxygen is achieved by metabolism as well as ventilation. These bubbles may act as venous emboli or may trigger inflammatory tissue responses giving rise to symptoms of decompression illness (DCI). Signs and symptoms of DCI may appear up to 48 hours after exposure to increased ambient pressure and include joint pains, motor and sensory deficits, dyspnoea, cough and skin rashes.

Neurological effects of deep diving

Marit Grønning, Johan A. Aarli
Journal of the Neurological Sciences 304 (2011) 17–21
http://dx.doi.org:/10.1016/j.jns.2011.01.021

Deep diving is defined as diving to depths more than 50 m of seawater (msw), and is mainly used for occupational and military purposes. A deep dive is characterized by the compression phase, the bottom time and the decompression phase. Neurological and neurophysiologic effects are demonstrated in divers during the compression phase and the bottom time. Immediate and transient neurological effects after deep dives have been shown in some divers. However, the results from the epidemiological studies regarding long term neurological effects from deep diving are conflicting and still not conclusive.

Possible immediate neurological effects of deep diving
Syndrome Pressure
Hyperoxia/oxygen seizures >152 kPa (5 msw)
HypoxiaHypercapnia
Nitrogen narcosis >354 kPa (25 msw)
High pressure nervous syndrome >1.6 MPa (150 msw)
Neurological decompression sickness

Neurological effects have been demonstrated, both clinically and neurophysiologically in divers during the compression phase and the bottom time. Studies of divers before and after deep dives have shown immediate and transient neurological effects in some divers. However, the results from the epidemiological and clinical studies regarding long term neurological effects from deep diving are conflicting and still not conclusive. Prospective clinical studies with sufficient power and sensitivity are needed to solve this important issue.

Today deep diving to more than 100 msw is routinely performed globally in the oil- and gas industry. In the North Sea remote underwater intervention and maintenance is performed by the use of remotely operated vehicles (ROV), both in conjunction to and as an alternative to manned underwater operations. There will, however, always be a need for human divers in the technically more advanced underwater operations and for contingency repair operations.

P300 latency indexes nitrogen narcosis

Barry Fowler, Janice Pogue and Gerry Porlier
Electroencephalography, and clinical Neurophysiology, 1990, 75:221-229

This experiment investigated the effects of nitrogen narcosis on reaction time (RT) and P300 latency and amplitude, Ten subjects breathed either air or a non-narcotic 20% oxygen-80% helium (heliox) mixture in a hyperbaric chamber at 6.5, 8.3 and 10 atmospheres absolute (ATA), The subjects responded under controlled accuracy conditions to visually presented male or female names in an oddball paradigm. Single-trial analysis revealed a strong relationship between RT and P300 latency, both of which were slowed in a dose-related manner by hyperbaric air but not by heliox. A clear-cut dose-response relationship could not be established for P300 amplitude. These results indicate that P300 latency indexes nitrogen narcosis and are interpreted as support for the slowed processing model of inert gas narcosis.

Adaptation to Deep Water Habitat

Effects of hypoxia on ionic regulation, glycogen utilization and antioxidative ability in the gills and liver of the aquatic air-breathing fish Trichogaster microlepis

Chun-Yen Huang, Hui-Chen Lina, Cheng-Huang Lin
Comparative Biochemistry and Physiology, Part A 179 (2015) 25–34
http://dx.doi.org/10.1016/j.cbpa.2014.09.001

We examined the hypothesis that Trichogaster microlepis, a fish with an accessory air-breathing organ, uses a compensatory strategy involving changes in both behavior and protein levels to enhance its gas exchange ability. This compensatory strategy enables the gill ion-regulatory metabolism to maintain homeostasis during exposure to hypoxia. The present study aimed to determinewhether ionic regulation, glycogen utilization and antioxidant activity differ in terms of expression under hypoxic stresses; fish were sampled after being subjected to 3 or 12 h of hypoxia and 12 h of recovery under normoxia. The air-breathing behavior of the fish increased under hypoxia. No morphological modification of the gills was observed. The expression of carbonic anhydrase II did not vary among the treatments. The Na+/K+-ATPase enzyme activity did not decrease, but increases in Na+/K+-ATPase protein expression and ionocyte levels were observed. The glycogen utilization increased under hypoxia as measured by glycogen phosphorylase protein expression and blood glucose level, whereas the glycogen content decreased. The enzyme activity of several components of the antioxidant system in the gills, including catalase, glutathione peroxidase, and superoxidase dismutase, increased in enzyme activity. Based on the above data, we concluded that T. microlepis is a hypoxia-tolerant species that does not exhibit ion-regulatory suppression but uses glycogen to maintain energy utilization in the gills under hypoxic stress. Components of the antioxidant system showed increased expression under the applied experimental treatments.

Divergence date estimation and a comprehensive molecular tree of extant cetaceans

Michael R. McGowen , Michelle Spaulding, John Gatesy
Molecular Phylogenetics and Evolution 53 (2009) 891–906
http://dx.doi.org/10.1016/j.ympev.2009.08.018

Cetaceans are remarkable among mammals for their numerous adaptations to an entirely aquatic existence, yet many aspects of their phylogeny remain unresolved. Here we merged 37 new sequences from the nuclear genes RAG1 and PRM1 with most published molecular data for the group (45 nuclear loci, transposons, mitochondrial genomes), and generated a supermatrix consisting of 42,335 characters. The great majority of these data have never been combined. Model-based analyses of the supermatrix produced a solid, consistent phylogenetic hypothesis for 87 cetacean species. Bayesian analyses corroborated odontocete (toothed whale) monophyly, stabilized basal odontocete relationships, and completely resolved branching events within Mysticeti (baleen whales) as well as the problematic speciose clade Delphinidae (oceanic dolphins). Only limited conflicts relative to maximum likelihood results were recorded, and discrepancies found in parsimony trees were very weakly supported. We utilized the Bayesian supermatrix tree to estimate divergence dates among lineages using relaxed-clock methods. Divergence estimates revealed rapid branching of basal odontocete lineages near the Eocene–Oligocene boundary, the antiquity of river dolphin lineages, a Late Miocene radiation of balaenopteroid mysticetes, and a recent rapid radiation of Delphinidae beginning [1]10 million years ago. Our comprehensive,  time calibrated tree provides a powerful evolutionary tool for broad-scale comparative studies of Cetacea.

Mitogenomic analyses provide new insights into cetacean origin and evolution

Ulfur Arnason, Anette Gullberg, Axel Janke
Gene 333 (2004) 27–34
http://dx.doi.org:/10.1016/j.gene.2004.02.010

The evolution of the order Cetacea (whales, dolphins, porpoises) has, for a long time, attracted the attention of evolutionary biologists. Here we examine cetacean phylogenetic relationships on the basis of analyses of complete mitochondrial genomes that represent all extant cetacean families. The results suggest that the ancestors of recent cetaceans had an explosive evolutionary radiation 30–35 million years before present. During this period, extant cetaceans divided into the two primary groups, Mysticeti (baleen whales) and Odontoceti (toothed whales). Soon after this basal split, the Odontoceti diverged into the four extant lineages, sperm whales, beaked whales, Indian river dolphins and delphinoids (iniid river dolphins, narwhals/belugas, porpoises and true dolphins). The current data set has allowed test of two recent morphological hypotheses on cetacean origin. One of these hypotheses posits that Artiodactyla and Cetacea originated from the extinct group Mesonychia, and the other that Mesonychia/Cetacea constitutes a sister group to Artiodactyla. The current results are inconsistent with both these hypotheses. The findings suggest that the claimed morphological similarities between Mesonychia and Cetacea are the result of evolutionary convergence rather than common ancestry.

The order Cetacea traditionally includes three suborders: the extinct Archaeoceti and the recent Odontoceti and Mysticeti. It is commonly believed that the evolution of ancestral cetaceans from terrestrial to marine (aquatic) life was accompanied by a fast and radical morphological adaptation. Such a scenario may explain why it was, for a long time, difficult to morphologically establish the position of Cetacea in the mammalian tree and even to settle whether Cetacea constituted a monophyletic group.

Biochemical analyses in the 1950s  and 1960s had shown a closer relationship between cetaceans and artiodactyls (even-toed hoofed mammals) than between cetaceans and any other eutherian order and karyological studies in the late 1960s and early 1970s unequivocally supported cetacean monophyly (Arnason, 1969, 1974). The nature of the relationship between cetaceans and artiodactyls was resolved in phylogenetic studies of mitochondrial (mt) cytochrome b (cytb) genes (Irwin and Arnason, 1994; Arnason and Gullberg, 1996) that placed Cetacea within the order Artiodactyla itself as the sister group of the Hippopotamidae (see also Sarich, 1993). The Hippopotamidae/ Cetacea relationship was subsequently supported in studies of nuclear data (Gatesy et al., 1996; Gatesy, 1997) and statistically established in analysis of complete mt genomes (Ursing and Arnason, 1998). The relationship has also been confirmed in analyses of combined nuclear and mt sequences (Gatesy et al., 1999; Cassens et al., 2000) and in studies of short interspersed repetitive elements (SINEs). Artiodactyla and Cetacea are now commonly referred to as Cetartiodactyla.

Previous analyses of the complete cytb gene of more than 30 cetacean species (Arnason and Gullberg, 1996) identified five primary lineages of recent cetaceans, viz., Mysticeti and the four odontocete lineages Physeteridae (sperm whales), Platanistidae (Indian river dolphins), Ziphiidae (beaked whales) and Delphinoidea (iniid river dolphins, porpoises, narwhals and dolphins). However, these studies left unresolved the relationships of the five lineages as well as those between the three delphinoid families Monodontidae (narwhals, belugas), Phocoenidae (porpoises) and Delphinidae (dolphins). Similarly, the relationships between the four mysticete families Balaenidae (right whales), Neobalaenidae (pygmy right whales), Eschrichtiidae (gray whales) and Balaenopteridae (rorquals) were not conclusively resolved in analyses of cytb genes.

Fig. (not shown). Cetartiodactyl relationships and the estimated times of their divergences. The tree was established on the basis of maximum likelihood analysis of the concatenated amino acid (aa) sequences of 12 mt protein-coding genes. Length of alignment 3610 aa. Support values for branches A–H are shown in the insert.
Cetruminantia (branch A) receives moderate support and Cetancodonta (B) strong support. Cetacea (C) splits into monophyletic Mysticeti (baleen whales) and monophyletic Odontoceti (toothed whales). Odontoceti has four basal lineages, Physeteridae (sperm whales: represented by the sperm and pygmy sperm whales), Ziphiidae (beaked whales: bottlenose and Baird’s beaked whales), Platanistidae (Indian river dolphins: Indian river dolphin) and Delphinoidea. Delphinoidea encompasses the families Iniidae (iniid river dolphins: Amazon river dolphin, La Plata dolphin), Monodontidae (narwhals/belugas: narwhal), Phocoenidae (porpoises: harbour porpoise) and Delphinidae (dolphins: white-beaked dolphin). The common odontocete branch and the branches separating the four cetacean lineages are short. These relationships are therefore somewhat unstable (cf. Section 3.1 and Table 1). Iniid river dolphins (F) are solidly nested within the Delphinoidea (E). Thus, traditional river dolphins (Platanistidae + Iniidae) do not form a monophyletic unit. Molecular estimates of divergence times (Sanderson 2002) were based on two calibration points, A/C-60 and O/M-35 (cf. Section 3.2). Due to the short lengths of internal branches, some estimates for these divergences overlap. NJ: neighbor joining; MP: maximum parsimony; LBP: local bootstrap probability; QP: quartet puzzling. The bar shows the number of aa substitutions per site.

The limited molecular resolution among basal cetacean lineages has been known for some time. Studies of hemoglobin and myoglobin (Goodman, 1989; Czelusniak et al., 1990) have either joined Physeteridae and Mysticeti to the exclusion of Delphinoidea (myoglobin data) or Mysticeti and Delphinoidea to the exclusion of Physeteridae (hemoglobin data). Thus, neither of the data sets identified monophyletic Odontoceti by joining the two odontocete lineages (Physeteridae and Delphinoidea) to the exclusion of Mysticeti. A similar instability was recognized and cautioned against in analyses of some mt data, notably, sequences of rRNA genes (Arnason et al., 1993b). The suggestion (Milinkovitch et al., 1993) of a sister group relationship between Physeteridae and the mysticete family Balaenopteridae (rorquals) was based on a myoglobin data set (which joins Physeteridae and Mysticeti to the exclusion of Delphinoidea) that was complemented with partial data of the mt 16S rRNA gene.

The cetancodont divergence times calculated using A/C-60 and O/M-35 as references have been included in Fig. 1. As a result of the short branches separating several cetacean lineages, the estimates of these divergences overlap. The same observation has been made in calculations based on SINE flanking sequences (Nikaido et al., 2001). There is a general consistency between the current and the flanking sequence datings, except for those involving the Balaenopteridae, which are somewhat younger in our analysis than in the SINEs study. The currently estimated age of the divergence between Hippopotamus and Cetacea (c53.5 MYBP) is consistent with the age (>50 MY) of the oldest archaeocete fossils identified so far (Bajpai and Gingerich, 1998). This suggests that the ages allocated to the two references, A/C-60 (the divergence between ruminant artiodactyls and cetancodonts) and O/M-35 (the divergence between odontocetes and mysticetes) are reasonably accurate.

The dating of the divergence between the blue and fin whales is of interest regarding hybridization between closely related mammalian species. Previous molecular analyses (Arnason et al., 1991b; Spilliaert et al., 1991) demonstrated the occurrence of hybridization between these two species. These studies, which were based on three hybrids (one female and two males), showed that either species could be the mother or father in these hybridizations. The two male hybrids had rudimentary testes, whereas the female hybrid was in her second pregnancy. This suggests that the blue and fin whales may be close to the limit for permissible species hybridization among mammals.

The current data set has allowed examination of the coherence between the molecular results and two prevalent morphological hypotheses related to cetacean evolution. The first hypothesis, which in essence originates from Van Valen (1966, 1968), postulates that monophyletic Artiodactyla and monophyletic Cetacea evolved separately from the extinct Palaeocene group Mesonychia. This hypothesis was recently reinforced in a morphological study (Thewissen et al., 2001) that included mesonychians, two archaeocete taxa (Ambuloocetus and Pakicetus) and some extant and fossil artiodactyls. The study of Thewissen et al. (2001) showed a sister group relationship between monophyletic Artiodactyla and monophyletic Cetacea, with Mesonychia as the basal sister group of Artiodactyla/Cetacea, a conclusion consistent with the palaeontological age of Mesonychia relative to that of Artiodactyla and Cetacea. The second hypothesis favours a sister group relationship between Mesonychia and Cetacea with the Mesonychia/Cetacea clade as the sister group of monophyletic Artiodactyla (O’Leary and Geisler, 1999; see also Gatesy and O’Leary, 2001).

Although the position of Mesonychia differs in the two morphological hypotheses, both correspond to a sister group relationship between Cetacea and monophyletic Artiodactyla among extant cetartiodactyls. Thus, both hypotheses can be tested against the current data set. The result of such a test has been included in Table 1, topology (m)(not shown). As evident, both these morphological hypotheses are incongruent with the mitogenomic findings.

Morphological studies have not provided an answer to the question whether mysticetes and odontocetes had separate origins among the archaeocetes (Fordyce and de Muizon, 2001). However, the long common cetacean branch and the short branches separating the five extant cetacean lineages strongly suggest an origin of modern cetaceans from the same archaeocete group (probably the Dorudontidae).

The limbs of Ambulocetus constitute somewhat of an evolutionary enigma. As evident in Thewissen et al.’s (1994) paper, Ambulocetus has very large hind limbs compared to its forelimbs, a difference that is less pronounced in later silhouette drawings of the animal. It is nevertheless evident that evolution from the powerful hindlimbs of Ambulocetus to their rudimentation in archaeocetes constitutes a remarkable morphological reversal if Ambulocetus is connected to the cetacean branch after the separation of the hippopotamid and cetacean lineages.

For natural reasons, systematic schemes have traditionally been based on external morphological characteristics. The rates of morphological and molecular evolution are rarely (if ever) strictly correlated, however, and this may give rise to inconsistency between traditional systematics and molecular findings. The emerging consensus that the order Cetacea resides within another traditional order, Artiodactyla, makes apparent the incongruity in cetartiodactyl nomenclature (Graur and Higgins, 1994). In this instance, a possible solution for maintaining reasonable consistency between nomenclature and phylogeny would be to recognize Cetartiodactyla as an order with three suborders: Suina, Tylopoda and Cetruminantia. According to such a scheme, Cetacea would (together with the Hippopotamidae) constitute a parvorder within the infraorder Cetancodonta.

Cytochrome b and Bayesian inference of whale phylogeny

Laura May-Collado, Ingi Agnarsson
Molecular Phylogenetics and Evolution 38 (2006) 344–354
http://dx.doi.org//10.1016/j.ympev.2005.09.019

In the mid 1990s cytochrome b and other mitochondrial DNA data reinvigorated cetacean phylogenetics by proposing many novel

and provocative hypotheses of cetacean relationships. These results sparked a revision and reanalysis of morphological datasets, and the collection of new nuclear DNA data from numerous loci. Some of the most controversial mitochondrial hypotheses have now become benchmark clades, corroborated with nuclear DNA and morphological data; others have been resolved in favor of more traditional views. That major conflicts in cetacean phylogeny are disappearing is encouraging. However, most recent papers aim specifically to resolve higher-level conflicts by adding characters, at the cost of densely sampling taxa to resolve lower-level relationships. No molecular study to date has included more than 33 cetaceans. More detailed molecular phylogenies will provide better tools for evolutionary studies. Until more genes are available for a high number of taxa, can we rely on readily available single gene mitochondrial data? Here, we estimate the phylogeny of 66 cetacean taxa and 24 outgroups based on Cytb sequences. We judge the reliability of our phylogeny based on the recovery of several deep-level benchmark clades. A Bayesian phylogenetic analysis recovered all benchmark clades and for the Wrst time supported Odontoceti monophyly based exclusively on analysis of a single mitochondrial gene. The results recover the monophyly, with the exception of only one taxa within Cetacea, and the most recently proposed super- and subfamilies. In contrast, parsimony never recovered all benchmark clades and was sensitive to a priori weighting decisions. These results provide the most detailed phylogeny of Cetacea to date and highlight the utility of both Bayesian methodology in general, and of Cytb in cetacean phylogenetics. They furthermore suggest that dense taxon sampling, like dense character sampling, can overcome problems in phylogenetic reconstruction.

Some long standing debates are all but resolved: our understanding of deeper level cetacean phylogeny has grown strong. However, the strong focus of most recent studies, aiming specifically to resolve these higher level conflicts by adding mostly characters rather than taxa, has left our understanding of lower level relationships among whale species lagging behind. Mitogenomic data, for example, is available only for 16 cetacean species, and no molecular study to date has included more than 33 cetaceans. It seems timely to focus on more detailed (genus, and species level) molecular phylogenies. These will provide better tools for detailed evolutionary studies, and are necessary to test existing morphological phylogenetic hypotheses, and current cetacean classification.

We judge the reliability of our phylogeny based on the recovery of the previously mentioned benchmark clades, in addition to the less controversial clades Perissodactyla, Euungulata (sensu Waddell et al., 2001; Perissodactyla+ Cetartiodactyla), Cetacea, and Mysticeti. Because Cytb is thought to be most reliable at lower taxonomic levels (due to high substitution rates), recovering ‘known’ deeper clades gives credibility to these new findings which have not been addressed by studies using few taxa. We compare the performance of Bayesian analyses versus parsimony under four different models, and briefly examine the sensitivity of the results to taxon sampling. We use our results to discuss agreement and remaining conflict in cetacean phylogenetics, and provide comments on current classification.

The Bayesian analysis recovered all seven benchmark clades. Support for five of the benchmark clades is high (100 posterior probabilities) but rather low for Cetancodonta (79) and marginal for the monophyly of Odontoceti. The analysis also recovered all but one family level, and most sub- and superfamily level cetacean taxa. The results broadly corroborate current cetacean classiffcation, while also pointing to some lower-level groups that may need redefinition.

Many recent cetacean phylogenetic studies include relatively few taxa, in part due to a focus on generating more characters to resolve higher level phylogenetics. While addressing crucial questions and providing the backbone for lower level phylogenies, such studies have limited utility for classification, and for comparative evolutionary studies. In some cases sparse taxon sampling may also confound the results. Of course, taxon sampling is usually simply constrained by the availability of character data, but for some reason many studies have opted to include only one, or a few outgroup taxa, even if many are available.

We find that as long as outgroup taxon sampling was extensive, Bayesian analyses of Cytb recovered all the a priori identified benchmark clades. When only a few outgroups were chosen, however, the Bayesian analysis negated Odontoceti monophyly, as have many previous parsimony analyses of mitochondrial DNA. Furthermore, in almost every detailed comparison possible our results mirror the findings O’Leary et al. (2004), the most ‘character-complete’ (but including relatively few cetacean taxa) analysis to date (37,000 characters from morphology, SINE, and 51 gene fragments). This result gives credibility to our findings, including previously untested lower level clades.

  • Monophyly and placement of Mysticeti (baleen whales).
  • Monophyly of Odontoceti (toothed whales)
  • Delphinoids
  • River Dolphins
  • Beaked and sperm whales

A major goal of phylogenetics is a phylogeny of life (i.e., many taxa), based on multiple lines of evidence (many characters of many types). However, when phylogenies based on relatively few characters can be judged reliable based on external evidence (taxonomic congruence with other phylogenies using many characters, but few taxa), they seem like very promising and useful ‘first guess’ hypotheses. The evolution of sexual dimorphism, echolocation, social behavior, and whistles and other communicative signals, and major ecological shifts (e.g., transition to fresh water) are among the numerous interesting questions in cetacean biology that this phylogeny can help answer.

Deep-diving sea lions exhibit extreme bradycardia in long duration dives

Birgitte I. McDonald1, and Paul J. Ponganis
The Journal of Experimental Biology (2014) 217, 1525-1534 http://dx.doi.org:/10.1242/jeb.098558

Heart rate and peripheral blood flow distribution are the primary determinants of the rate and pattern of oxygen store utilization and ultimately breath-hold duration in marine endotherms. Despite this, little is known about how otariids (sea lions and fur seals) regulate heart rate (fH) while diving. We investigated dive fH in five adult female California sea lions (Zalophus californianus) during foraging trips by instrumenting them with digital electrocardiogram (ECG) loggers and time depth recorders. In all dives, dive fH (number of beats/duration; 50±9 beats min−1) decreased compared with surface rates (113±5 beats min−1), with all dives exhibiting an instantaneous fH below resting (<54 beats min−1) at some point during the dive. Both dive fH and minimum instantaneous fH significantly decreased with increasing dive duration. Typical instantaneous fH profiles of deep dives (>100 m) consisted of:

(1) an initial rapid decline in fH resulting in the lowest instantaneous fH of the dive at the end of descent, often below 10 beats min−1 in dives longer than 6 min in duration;
(2) a slight increase in fH to ~10–40 beats min−1 during the bottom portion of the dive; and
(3) a gradual increase in fH during ascent with a rapid increase prior to surfacing.

Thus, fH regulation in deep-diving sea lions is not simply a progressive bradycardia. Extreme bradycardia and the presumed associated reductions in pulmonary and peripheral blood flow during late descent of deep dives should

(a) contribute to preservation of the lung oxygen store,
(b) increase dependence of muscle on the myoglobin-bound oxygen store,
(c) conserve the blood oxygen store and
(d) help limit the absorption of nitrogen at depth.

This fH profile during deep dives of sea lions may be characteristic of deep-diving marine endotherms that dive on inspiration as similar fH profiles have been recently documented in the emperor penguin, another deep diver that dives on inspiration.

The resting ƒH measured in this study (54±6 beats min−1) was lower than predicted for an animal of similar size (~80 beats min−1 for an 80 kg mammal). In part, this may be due to the fact that the sea lions were probably sleeping. The resting ƒH in our study was also lower than previous measurements in captive juvenile California sea lions (87±17 beats min−1, average mass 30 kg)  and wild Antarctic fur seals (78±5 beats min−1, body mass 30–50 kg). However, we found a significant negative relationship between mass and resting ƒH even with our small sample size of five sea lions (resting ƒH = –0.58 Mb +100.26, r2=0.81, F1,3=12.37, P=0.039). For a 30 kg sea lion, this equation predicts a resting ƒH of 83 beats min−1, which is similar to what was measured previously in juvenile sea lions, suggesting this equation may be useful in estimating resting ƒH in sea lions.

The sea lions exhibited a distinct sinus arrhythmia fluctuating between a minimum of 42±9 and a maximum of 87±12 beats min−1, comparable to the sinus arrhythmias described in other diving birds and mammals, including sea lions. The minimum instantaneous ƒH during the sinus arrhythmia was similar to the mean minimum ƒH in dives less than 3 min (37±7 beats min−1), indicating that in dives less than 3 min (estimated cADL), ƒH only decreased to levels observed during exhalation at rest. This is consistent with observations in emperor penguins and elephant seals, where it was proposed that in dives shorter than the aerobic dive limit (ADL) the reduction in ƒH is regulated by a mechanism of cardiorespiratory control similar to that governing the respiratory sinus arrhythmia, with a further reduction only occurring in dives longer than the ADL.

Fig. 3. (not shown) Instantaneous fH and dive depth profiles of a California sea lion (CSL12_2). Data are from (A) a short, shallow dive (1.3 min, 45 m), (B) a mid-duration dive (4.8 min, 239 m) and (C) a long-duration dive (8.5 min, 305 m). Minimum instantaneous fH reached 37 beats min−1 in the short dive
(A) 19 beats min−1 in the mid-duration dive
(B) and 7 beats min−1 in the long duration dive
(C) Prominent features typical of mid- and long-duration dives include

  • a surface interval tachycardia (pre- and post-dive);
  • a steady rapid decrease in fH during initial descent;
  • a gradual decline in fH towards the end of descent with the lowest fH of the dive at the end of descent;
  • a slight increase and sometimes variable fH during the bottom portion of the dive; and
  • a slow increase in fH during ascent,
  • often ending in a rapid increase just before surfacing.

We obtained the first diving ƒH data from wild sea lions on natural foraging trips, demonstrating how they regulate ƒH over a range of dive durations. Sea lions always decreased dive ƒH from surface ƒH values; however, individual sea lions exhibited different dive ƒH, accounting for a significant amount of the variation in the relationship between dive duration and ƒH (intra-individual correlation: 75–81%)). The individual differences in dive ƒH exhibited in this study suggest that different dive capacities of individual sea lions may partially account for the range of dive strategies exhibited in a previous study (Villegas-Amtmann et al., 2011). Despite the individual differences in ƒH, the pattern of the dive ƒH response was similar in all the sea lions. As predicted, sea lions only consistently displayed a true bradycardia on mid- to long- duration dives (>4 min) (Fig. 5A). Additionally, as seen in freely diving phocids, dive ƒH and minimum ƒH were negatively related to dive duration, with the longest duration dives having the lowest dive ƒH and displaying the most intense bradycardia, often below 10 beats min−1 (Fig. 5A,B).

Profiles of mean fH at 10 s intervals of dives

Profiles of mean fH at 10 s intervals of dives

Fig 4.  Profiles of mean fH at 10 s intervals of dives for (A) six duration categories and (B) five depth categories. Standard error bars are shown. Data were pooled from 461 dives performed by five sea lions. The number of dives in each category and the number of sea lions performing the dives in each category are provided in the keys.

The mild bradycardia and the dive ƒH profiles observed in the shorter duration dives (<3 min) were similar to those observed in trained juvenile California sea lions and adult Stellar sea lions, but much more intense than ƒH observed in freely diving Antarctic fur seals. Surprisingly, although dive ƒH of trained Steller sea lions was similar, Steller sea lions regularly exhibited lower minimum ƒH, with minimum ƒH almost always less than 20 beats min−1 in dives less than 2 min in duration. In the wild, California sea lions rarely exhibited a minimum ƒH less than 20 beats min−1 in similar duration dives (Fig. 5B), suggesting greater blood oxygen transport during these natural short-duration dives.

Fig. 5. (not shown)  fH decreases with increasing dive duration. Dive duration versus (A) dive fH (total number of beats/dive duration), (B) minimum instantaneous fH and (C) bottom fH (total beats at bottom of dive/bottom time) for California sea lions (461 dives from five sea lions).

Although California sea lions are not usually considered exceptional divers, they exhibited extreme bradycardia, comparable to that of the best diving phocids, during their deep dives. In dives greater than 6 min in duration, minimum ƒH was usually less than 10 beats min−1 and sometimes as low as 6 beats mins−1 (Fig. 5B), which is similar to extreme divers such as emperor penguins (3 beats min−1), elephant seals (3 beats min−1), grey seals (2 beats min−1) and Weddell seals (<10 beats min−1), and even as low as what was observed in forced submersion studies. Thus, similar to phocids, the extreme bradycardia exhibited during forced submersions is also a routine component of the sea lion’s physiological repertoire, allowing them to perform long-duration dives.

While the degree of bradycardia observed in long dives of California sea lions was similar to the extreme bradycardia observed in phocids, the ƒH profiles were quite different. In general, phocid ƒH decreases abruptly upon submergence. The intensity of the initial phocid bradycardia either remains relatively stable or intensifies as the dive progresses, and does not start to increase until the seal begins its ascent. In contrast, the ƒH profiles of sea lions were more complex, showing a more gradual decrease during descent, with the minimum ƒH of the dive usually towards the end of descent (Figs 3, 6). There was often a slight increase in ƒH during the bottom portion of the dive, and as soon as the sea lions started to ascend, the ƒH slowly started to increase, often becoming irregular during the middle of ascent, before increasing rapidly as the sea lion approached the surface.

Fig. 6. (not shown) Instantaneous fH and dive depth profiles of the longest dive (10.0 min, 385 m) from a California sea lion (CSL12_1). During this dive, instantaneous fH reached 7 beats min−1 and was less than 20 beats min−1 for over 5.5 min. Post-dive fH was high in the first 0.5–1 min after surfacing, but then declined to ~100 beats min−1 towards the end of the surface interval.

Implications for pulmonary gas exchange

The moderate dive ƒH in short, shallow dives compared with the much slower ƒH of deep long-duration dives suggests more pulmonary blood flow and greater potential for reliance on lung O2. Most of these dives were to depths of less than 100 m (well below the estimated depth of lung collapse near 200 m), so maintenance of a moderate ƒH during these dives may allow sea lions to maximise use of the potentially significant lung O2 stores (~16% of total body O2 stores) throughout the dive. This is supported by venous blood O2 profiles, where, occasionally, there was no decrease in venous blood O2 between the beginning and end of the dive; this can only occur if pulmonary gas exchange continues throughout the dive. Greater utilization of the lung O2 store in sea lions is consistent with higher dive ƒH in other species that both dive on inspiration and typically perform shallow dives (dolphins, porpoises, some penguin species), and in deeper diving species when they perform shallow dives (emperor penguins).

In deeper dives of sea lions, although ƒH was lower and bradycardia more extreme, the diving ƒH profiles suggest that pulmonary gas exchange is also important. In long-duration dives, even though ƒH started to decrease upon or shortly after submergence, the decrease was not as abrupt as in phocids. Additionally, in long deep dives, despite having overall low dive ƒH, there were more heart beats before resting ƒH was reached compared with short, shallow dives. In dives less than 3 min in duration, there were ~10–15 beats until instantaneous ƒH reached resting values. In longer duration dives (>3 min), there were usually ~30–40 beats before instantaneous ƒH reached resting values. We suggest the greater number of heart beats early in these deeper dives enables more gas exchange and blood O2 uptake at shallow depths, thus allowing utilisation of the postulated larger respiratory O2 stores in deeper dives The less abrupt decline in ƒH we observed in sea lions is similar to the more gradual declines documented in emperor penguins and porpoises, where it has also been proposed that the gradual decrease in ƒH allows them to maximise pulmonary gas exchange at shallower depths. However, as sea lions swam deeper, ƒH decreased further (Figs 3, 6), and by 200 m depth (the approximate depth of lung collapse, instantaneous ƒH was 14 beats min−1. Such an extreme decline in ƒH in conjunction with increased pulmonary shunting due to lung compression at greater depths will result in minimization of both O2 and N2 uptake by blood, even before the depth of full lung collapse (100% pulmonary shunt) is reached.

Implications for blood flow

ƒH is often used as a proxy to estimate blood flow and perfusion during diving because of the relative ease of its measurement. This is based on the assumption that stroke volume does not change during diving in sea lions, and, hence, changes in ƒH directly reflect changes in cardiac output. As breath-hold divers maintain arterial pressure while diving, changes in cardiac output should be associated with changes in peripheral vascular resistance and changes in blood flow to tissues. In Weddell seals, a decrease in cardiac output of ~85% during forced submersions resulted in an 80–100% decrease in tissue perfusion in all tissues excluding the brain, adrenal glands and lung. Sea lions exhibited extremely low instantaneous ƒH values that often remained low for significant portions of the dive (Figs 4, 6), suggesting severe decreases in tissue perfusion in dives greater than 5 min in duration. In almost all dives greater than 6 min in duration, instantaneous ƒH reached 10 beats min−1, and stayed below 20 beats min−1 for more than a minute. At a ƒH of 20 beats min−1, cardiac output will be ~36% of resting cardiac output and only about 18% of average surface cardiac output. At these levels of cardiac suppression, most of this flow should be directed towards the brain and heart.

Conclusions

We successfully obtained diving ƒH profiles from a deep-diving otariid during natural foraging trips. We found that

(1) ƒH decreases during all dives, but true and more intense bradycardia only occurred in longer duration dives and
(2) in the longest duration dives, ƒH and presumed cardiac output were as low as 20% of resting values.

We conclude that, although initial high ƒH promotes gas exchange early in deep dives, the extremely low ƒH in late descent of deep dives (a) preserves lung O2, (b) conserves blood O2, (c) increases the dependence of muscle on myoglobin-bound O2 and (d) limits N2 absorption at depth. This ƒH profile, especially during the late descent/early bottom phase of deep dives is similar to that of deep-diving emperor penguins, and may be characteristic of deep diving endotherms that dive on inspiration.

Dive duration was the fixed effect in all models, and to account for the lack of independence caused by having many dives from the same individual, individual (sea lion ID) was included as a random effect. Covariance and random effect structures of the full models were evaluated using Akaike’s information criterion (AIC) and examination of residual plots. AICs from all the tested models are presented with the best model in bold.

Additionally, dives were classified as short-duration (less than 3 min, minimum cADL), mid-duration (3–5 min, range of cADLs) or long-duration (>5 min) dives. Differences in pre-dive ƒH, dive ƒH, minimum ƒH, post-dive ƒH, and heart beats to resting between the categories were investigated using mixed effects ANOVA, followed by post hoc Tukey tests. In all models, dive duration category was the fixed effect and individual (sea lion ID) was included as a random effect. Model fit was accessed by examination of the residuals. All means are expressed ±s.d. and results of the Tukey tests were considered significant at P<0.05. Statistical analysis was performed in R.

Investigating Annual Diving Behaviour by Hooded Seals (Cystophora cristata) within the Northwest Atlantic Ocean

Julie M. Andersen, Mette Skern-Mauritzen, Lars Boehme
PLoS ONE 8(11): e80438. http://dx.doi.org:/10.1371/journal.pone.0080438

With the exception of relatively brief periods when they reproduce and molt, hooded seals, Cystophora cristata, spend most of the year in the open ocean where they undergo feeding migrations to either recover or prepare for the next fasting period. Valuable insights into habitat use and diving behavior during these periods have been obtained by attaching Satellite Relay Data Loggers (SRDLs) to 51 Northwest (NW) Atlantic hooded seals (33 females and 18 males) during icebound fasting periods (200422008). Using General Additive Models (GAMs) we describe habitat use in terms of First Passage Time (FPT) and analyze how bathymetry, seasonality and FPT influence the hooded seals’ diving behavior described by maximum dive depth, dive duration and surface duration. Adult NW Atlantic hooded seals exhibit a change in diving activity in areas where they spend .20 h by increasing maximum dive depth, dive duration and surface duration, indicating a restricted search behavior. We found that male and female hooded seals are spatially segregated and that diving behavior varies between sexes in relation to habitat properties and seasonality. Migration periods are described by increased dive duration for both sexes with a peak in May, October and January. Males demonstrated an increase in dive depth and dive duration towards May (post-breeding/pre-molt) and August–October (post-molt/pre-breeding) but did not show any pronounced increase in surface duration. Females dived deepest and had the highest surface duration between December and January (post-molt/pre-breeding). Our results suggest that the smaller females may have a greater need to recover from dives than that of the larger males. Horizontal segregation could have evolved as a result of a resource partitioning strategy to avoid sexual competition or that the energy requirements of males and females are different due to different energy expenditure during fasting periods.

Novel locomotor muscle design in extreme deep-diving whales

P. Velten, R. M. Dillaman, S. T. Kinsey, W. A. McLellan and D. A. Pabst
The Journal of Experimental Biology 216, 1862-1871
http://dx.doi.org:/10.1242/jeb.081323

Most marine mammals are hypothesized to routinely dive within their aerobic dive limit (ADL). Mammals that regularly perform deep, long-duration dives have locomotor muscles with elevated myoglobin concentrations that are composed of predominantly large, slow-twitch (Type I) fibers with low mitochondrial volume densities (Vmt). These features contribute to extending ADL by increasing oxygen stores and decreasing metabolic rate. Recent tagging studies, however, have challenged the view that two groups of extreme deep-diving cetaceans dive within their ADLs. Beaked whales (including Ziphius cavirostris and Mesoplodon densirostris) routinely perform the deepest and longest average dives of any air-breathing vertebrate, and short-finned pilot whales (Globicephala macrorhynchus) perform high-speed sprints at depth. We investigated the locomotor muscle morphology and estimated total body oxygen stores of several species within these two groups of cetaceans to determine whether they

(1) shared muscle design features with other deep divers and
(2) performed dives within their calculated ADLs.

Muscle of both cetaceans displayed high myoglobin concentrations and large fibers, as predicted, but novel fiber profiles for diving mammals. Beaked whales possessed a sprinterʼs fiber-type profile, composed of ~80% fast-twitch (Type II) fibers with low Vmt. Approximately one-third of the muscle fibers of short-finned pilot whales were slow-twitch, oxidative, glycolytic fibers, a rare fiber type for any mammal. The muscle morphology of beaked whales likely decreases the energetic cost of diving, while that of short-finned pilot whales supports high activity events. Calculated ADLs indicate that, at low metabolic rates, both beaked and short-finned pilot whales carry sufficient onboard oxygen to aerobically support their dives.

Serial cross-sections of the m. longissimus dorsi of Mesoplodon densirostris

Serial cross-sections of the m. longissimus dorsi of Mesoplodon densirostris

Fig. Serial cross-sections of the m. longissimus dorsi of Mesoplodon densirostris (A–D) and Globicephala macrorhynchus (E–H). Scale bars, 50μm. Muscle sections stained for the alkaline (A,E) and acidic (B,F) preincubations of myosin ATPase were used to distinguish Type I and II fibers. Muscle sections stained for succinate dehydrogenase (C,G) and α-glycerophosphate dehydrogenase (D,H) were used to distinguish glycolytic (gl), oxidative (o) and intermediate (i) fibers.

Previous studies of the locomotor muscles of deep-diving marine mammals have demonstrated that these species share a suite of adaptations that increase onboard oxygen stores while slowing the rate at which these stores are utilized, thus extending ADL. Their locomotor muscles display elevated myoglobin concentrations and are composed predominantly of large Type I fibers. Vmt are also lower in deep divers than in shallow divers or athletic terrestrial species. The results of this study indicate that beaked whales and short-finned pilot whales do not uniformly display these characteristics and that each possesses a novel fiber profile compared with those of other deep divers.

The phylogeny of Cetartiodactyla: The importance of dense taxon sampling, missing data, and the remarkable promise of cytochrome b to provide reliable species-level phylogenies

Ingi Agnarsson, Laura J. May-Collado
Molecular Phylogenetics and Evolution 48 (2008) 964–985
http://dx.doi.org:/10.1016/j.ympev.2008.05.046

We perform Bayesian phylogenetic analyses on cytochrome b sequences from 264 of the 290 extant cetartiodactyl mammals (whales plus even-toed ungulates) and two recently extinct species, the ‘Mouse Goat’ and the ‘Irish Elk’. Previous primary analyses have included only a small portion of the species diversity within Cetartiodactyla, while a complete supertree analysis lacks resolution and branch lengths limiting its utility for comparative studies. The benefits of using a single-gene approach include rapid phylogenetic estimates for a large number of species. However, single-gene phylogenies often differ dramatically from studies involving multiple datasets suggesting that they often are unreliable. However, based on recovery of benchmark clades—clades supported in prior studies based on multiple independent datasets—and recovery of undisputed traditional taxonomic groups, Cytb performs extraordinarily well in resolving cetartiodactyl phylogeny when taxon sampling is dense. Missing data, however, (taxa with partial sequences) can compromise phylogenetic accuracy, suggesting a tradeoff between the benefits of adding taxa and introducing question marks. In the full data, a few species with a short sequences appear misplaced, however, sequence length alone seems a poor predictor of this phenomenon as other taxa.

The mammalian superorder Cetartiodactyla (whales and eventoed ungulates) contains nearly 300 species including many of immense commercial importance (cow, pig, and sheep) and of conservation interest and aesthetic value (antelopes, deer, giraffe, dolphins, and whales) (MacDonald, 2006). Certain members of this superorder count among the best studied organisms on earth, whether speaking morphologically, behaviorally, physiologically or genetically. Understanding the interrelationships among cetartiodactyl species, therefore, is of obvious importance with equally short sequences were not conspicuously misplaced. Although we recommend awaiting a better supported phylogeny based on more character data to reconsider classification and taxonomy within Cetartiodactyla, the new phylogenetic hypotheses provided here represent the currently best available tool for comparative species-level studies within this group. Cytb has been sequenced for a large percentage of mammals and appears to be a reliable phylogenetic marker as long as taxon sampling is dense. Therefore, an opportunity exists now to reconstruct detailed phylogenies of most of the major mammalian clades to rapidly provide much needed tools for species-level comparative studies.

Our results support the following relationship among the four major cetartiodactylan lineages (((Tylopoda ((Cetancodonta (Ruminantia + Suina))), with variable support. This arrangement has not been suggested previously, to our knowledge (see review in O’Leary and Gatesy, 2008 and discussion).

Relationships among clades within Cetancodonta are identical to those found by May-Collado and Agnarsson (2006).

Within Ruminantia all our analyzes suggest the following relationships among families: (((((Tragulidae((((Antilocapridae(((Giraffidae(( Cervidae(Moschidae + Bovidae))))) with relatively high support, supporting the subdivision of Ruminantia into Tragulina and Pecora.
In the rare cases where our results are inconsistent with benchmark clades, ad hoc explanations seem reasonable. The placement of M. meminna (Tragulidae) within Bovidae is likely an artifact of missing data, although remarkably it is the only conspicuous misplacement of a species across the whole phylogeny at the family level (while three species appear to be misplaced at the subfamily level within Cervidae in the full analysis, see Fig. 5a). This is supported by the fact that the placement of Moschiola receives low support, and the removal of Moschiola prior to analysis increases dramatically the support for clades close to where it nested (not shown, analysis available from authors), suggesting it had a tendency to ‘jump around’. Two other possibilities cannot be ruled out, however. One, that possibly the available sequence in Genbank may be mislabeled. And second, it should be kept in mind that the validity of Tragulidae has never been tested with molecular data including more than two species.

Oxygen and carbon dioxide fluctuations in burrows of subterranean blind mole rats indicate tolerance to hypoxic–hypercapnic stresses

Imad Shams, Aaron Avivi, Eviatar Nevo
Comparative Biochemistry and Physiology, Part A 142 (2005) 376 – 382
http://dx.doi.org:/10.1016/j.cbpa.2005.09.003

The composition of oxygen (O2), carbon dioxide (CO2), and soil humidity in the underground burrows from three species of the Israeli subterranean mole rat Spalax ehrenbergi superspecies were studied in their natural habitat. Two geographically close populations of each species from contrasting soil types were probed. Maximal CO2 levels (6.1%) and minimal O2 levels (7.2%) were recorded in northern Israel in the breeding mounds of S. carmeli in a flooded, poor drained field of heavy clay soil with very high volumetric water content. The patterns of gas fluctuations during the measurement period among the different Spalax species studied were similar. The more significant differentiation in gas levels was not among species, but between neighboring populations inhabiting heavy soils or light soils: O2 was lower and CO2 was higher in the heavy soils (clay and basaltic) compared to the relatively light soils (terra rossa and rendzina). The extreme values of gas concentration, which occurred during the rainy season, seemed to fluctuate with partial flooding of the tunnels, animal digging activity, and over-crowded breeding mounds inhabited by a nursing female and her offspring. The gas composition and soil water content in neighboring sites with different soil types indicated large differences in the levels of hypoxic–hypercapnic stress in different populations of the same species. A growing number of genes associated with hypoxic stress have been shown to exhibit structural and functional differences between the subterranean Spalax and the aboveground rat (Rattus norvegicus), probably reflecting the molecular adaptations that Spalax went through during 40 million years of evolution to survive efficiently in the severe fluctuations in gas composition in the underground habitat.

map of the studied sites

map of the studied sites

Schematic map of the studied sites: S. galili (2n =52): 1— Rehania (chalk); 2— Dalton (basaltic); S. golani (2n =54): 3— Majdal Shams (terra tossa); 4—Masa’ada (basaltic soils); S. carmeli (2n =58): 5— Al-Maker (heavy clay); 6— Muhraqa (terra rossa).

Comparison of gas composition (O2 and CO2) and water content between light and heavy soils inhabited by S. carmeli

Comparison of gas composition (O2 and CO2) and water content between light and heavy soils inhabited by S. carmeli

Comparison of gas composition (O2 and CO2) and water content between light and heavy soils inhabited by S. carmeli, Al-Maker (heavy soil) and Muhraqa (light soil). AverageTSD of measurements in the burrows of approximately 10 animals at a given date is presented. **p <0.01, T-test and Mann– Whitney test).

Subterranean mammals, which live in closed underground burrow systems, experience an atmosphere that is different from the atmosphere above-ground. Gas exchange between these two atmospheres depends on diffusion through the soil, which in turn, depends on soil particle size, water content, and burrow depth. Heavy soils (clay and basaltic), hold water and have little air space for gas diffusion. A large deviation from external gas composition is found in the burrows of Spalax living in these soil types. The maximal measured concentration of CO2 was 6.1% in Spalax breeding mounds, which is one of the highest concentrations among studied mammals in natural conditions. At the same time 7.2% O2 was measured in water saturated heavy clay soil

seasonal variation from August to March in mean O2, CO2, and soil water content

seasonal variation from August to March in mean O2, CO2, and soil water content

Example of seasonal variation from August to March in mean O2, CO2, and soil water content (VWC) in the Al-Maker population (2n =58, heavy soil). Values are presented as mean TSD.

In this study new data were presented for a wild mammal that survives in an extreme hypoxic–hypercapnic environment. Interestingly, the very low concentrations of O2 experienced by Spalax are correlated with the expression pattern of hypoxia related genes.  So far, we have shown higher and longer-term mRNA expression of erythropoietin, the main factor that regulates the level of circulating red blood cells, in subterranean Spalax compared to the above-ground rat in response to hypoxic stress, as well as differences in the response of erythropoietin to hypoxia in different populations of Spalax experiencing different hypoxic stress in nature. We also demonstrated that erythropoietin pattern of expression is different in Spalax than in Rattus throughout development, a pattern suggesting more efficient hypoxic tolerance in Spalax starting as early as in the embryonic stages. Furthermore, vascular endothelial growth factor (VEGF), which is a critical angiogenic factor that responds to hypoxia, is constitutively expressed at maximal levels in Spalax muscles, the most energy consuming tissue during digging. This level is 1.6-fold higher than in Rattus muscles and is correlated with significantly higher blood vessel concentration in the Spalax muscles compared to the Rattus muscles. Likewise, myoglobin the globin involved in oxygen homeostasis in skeletal muscles, exhibits different expression pattern under normoxia and in response to hypoxia in Spalax muscles compared to rat muscles as well as between different populations of Spalax exposed to different hypoxic stress in nature (unpublished results). Similarly, neuroglobin, a brain-specific globin involved in reversible oxygen binding, i.e., presumably in cellular homeostasis, is expressed differently in the Spalax brain compared to Rattus brain. Like erythropoietin and myoglobin also neuroglobin is expressed differently in Spalax populations experiencing different oxygen supply (unpublished results). Furthermore, Spalax p53 harbors two amino acid substitutions in its binding domain, which are identical to mutations found in p53 of human cancer cells. These substitutions endow Spalax p53 with several-fold higher activation of cell arrest and DNA repair genes compared to human p53 and favor activation of DNA repair genes over apoptotic genes. The study of specific tumoral variants indicates that such preference of growth arrest over apoptosis possibly results as a response to the hypoxic environmental stress known in tumors. Differences in the structure of other molecules related to homeostasis, namely, hemoglobin, haptoglobin (Nevo, 1999), and cytoglobin (unpublished) were also observed in Spalax.

Stress, adaptation, and speciation in the evolution of the blind mole rat, Spalax, in Israel

Eviatar Nevo
Molecular Phylogenetics and Evolution 66 (2013) 515–525
http://dx.doi.org/10.1016/j.ympev.2012.09.008

Environmental stress played a major role in the evolution of the blind mole rat superspecies Spalax ehrenbergi, affecting its adaptive evolution and ecological speciation underground. Spalax is safeguarded all of its life underground from aboveground climatic fluctuations and predators. However, it encounters multiple stresses in its underground burrows including darkness, energetics, hypoxia, hypercapnia, food scarcity, and pathogenicity. Consequently, it evolved adaptive genomic, proteomic, and phenomic complexes to cope with those stresses. Here I describe some of these adaptive complexes, and their theoretical and applied perspectives. Spalax mosaic molecular and organismal evolution involves reductions or regressions coupled with expansions or progressions caused by evolutionary tinkering and natural genetic engineering. Speciation of Spalax in Israel occurred in the Pleistocene, during the last 2.00–2.35 Mya, generating four species associated intimately with four climatic regimes with increasing aridity stress southwards and eastwards representing an ecological speciational adaptive trend: (Spalax golani, 2n = 54?S. galili, 2n = 52?S. carmeli, 2n = 58?S. judaei, 2n = 60). Darwinian ecological speciation occurred gradually with relatively little genetic change by Robertsonian chromosomal and genic mutations. Spalax genome sequencing has just been completed. It involves multiple adaptive complexes to life underground and is an evolutionary model to a few hundred underground mammals. It involves great promise in the future for medicine, space flight, and deep-sea diving.

Stress is a major driving force of evolution (Parsons, 2005; Nevo, 2011). Parsons defined stress as the ‘‘environmental factor causing potential injurious changes to biological systems with a potential for impacts on evolutionary processes’’. The global climatic transition from the middle Eocene to the early Oligocene (45–35 Ma = Million years ago) led to extensive convergent evolution underground of small subterranean mammals across the planet (Nevo, 1999; Lacey et al., 2000; Bennett and Faulkes, 2000; Begall et al., 2007). The subterranean ecotope provided small mammals with shelter from predators and extreme aboveground climatic stressful fluctuations of temperature and humidity. However, they had to evolve genomic adaptive complexes for the immense underground stresses of darkness, energy for burrowing in solid soil, low productivity and food scarcity, hypoxia, hypercapnia, and high infectivity. These stresses have been described in Nevo (1999, 2011) and Nevo et al. (2001); and Nevo list of Spalax publication at http://evolution.haifa.ac.il with many cited references relevant to these stresses).

blind subterranean mole rat of the Spalax ehrenbergi superspecies

blind subterranean mole rat of the Spalax ehrenbergi superspecies

The blind subterranean mole rat of the Spalax ehrenbergi superspecies in Israel. An extreme example of adaptation to life underground

Circadian rhythm and genes

adaptive circadian genes. We identified the circadian rhythm of Spalax
(Nevo et al., 1982) and described, cloned, sequenced, and expressed several circadian genes in Spalax. These include Clock, MOP3, three Period (Per), and cryptochromes (Avivi et al., 2001, 2002, 2003). The Spalax circadian genes are differentially conserved, yet characterized by a significant number of amino acid substitutions. The glutamine-rich area of Clock, which is assumed to function in circadian rhythmicity, is expanded in Spalax compared with that of mice and humans and is different in amino acid composition from that of rats. All three Per genes of Spalax oscillate with a periodicity of 24 h in the suprachaismatic nucleus, eye, and Harderian gland and are expressed in peripheral organs. Per genes are involved in clock resetting. Spalax Per 3 is unique in mammals though its function is still unresolved. The Spalax Per genes contribute to the unique adaptive circadian rhythm to life underground. The cryptochrome (Cry) genes, found in animals and plants, act both as photoreceptors and as ingredients of the negative feedback mechanism of the biological Clock. The CRY 1 protein is significantly closer to the human homolog than to that of mice, as was also shown in parts of the immunogenetic system. Both Cry 1 and Cry 2 mRNAs were found in the SCN, eye, harderian gland, and in peripheral tissues. Remarkably, the distinctly hypertrophied harderian gland is central in Spalax’s unique underground circadian rhythmicity (Pevet et al., 1984).

  • Spalax eye mosaic evolution
  • Gene expression in the eye of Spalax
  • Brain evolution in Spalax to underground stresses
  • Spalax: four species in Israel

The morphological, physiological, and behavioral Spalax eye patterns are underlain by gene expression representing regressive and progressive associated transcripts. Regressive transcripts involve B-2 microglobulin, transketolase, four keratins, alpha enolase, and different heat shock proteins. Several proteins may be involved in eye degeneration. These include heat shock protein 90alpha (hsp90alpha), found also in the blind fish Astyanax mexicanus, two transcripts of programmed cell death proteins, oculospanin, and peripherin 2, both belonging to the Tetraspanin family, in which 60 different mutations cause eye degeneration in humans. Several progressive transcripts in the Spalax eye are found in the retina of many mammals involving gluthatione, peroxidase 4, B spectrin, and Ankyrin; the last two characterize rod cells in the retina. Some transcripts are involved in metabolic processing of retinal, a vertebrate key component in phototransduction, and a relative of vitamin A.

cross section of the developing eye of the mole rat

cross section of the developing eye of the mole rat

Light micrographs showing cross section of the developing eye of the mole rat Spalax ehrenbergi. (A) Optic cup and lens vesicle initially develop normally (x100). (B) Eye at a later embryonic stage. Note appearance of iris-ciliary body rudiment (arrows), and development of the lens nucleus (L). ON, optic nerve (x100). (C) Eye at a still later fetal stage. Note massive growth of the iris-ciliary body complex colobomatous opening (arrow) (x100). (D) Early postnatal stage. The iris-ciliary body complex completely fills the chamber. The lens is vascularized and vacuolated (x100). (E) Adult eye. Eyelids are completely closed and pupil is absent. Note atrophic appearance of the optic disc region (arrow) (x65). (F) Higher magnification of the adult retina. The different retinal layers are retained: PE, pigment epithelium: RE, receptor layer; ON, outer nuclear layer: IN, inner nuclear layer; GC, ganglion cell layer (x500) (from Sanyal et al., 1990, Fig. 1).

The brains of subterranean mammals underwent dramatic evolution in accordance with underground stresses for digging and photoperiodic perception associated with vibrational, tactile, vocal, olfactory, and magnetic communication systems replacing sight, as is seen in Spalax. The brain of Spalax is twice as large as that of the laboratory rat of the same body size. The somatosensory region in the isocortex of Spalax is 1.7 times, the thalamic nuclei 1.3 times, and the motor cortex 3.1 times larger than in the sighted laboratory rat Rattus norvegicus matched to body size.

The ecological stress determinant in Spalax brain evolution is highlighted by the four species of the Spalax ehrenbergi superspecies in Israel. They differentiated chromosomally (by means of Robertsonian mutations and fission), allopatrically, and clinally southwards into four species associated with different climatic regimes, following the gradient of increasing aridity stress and decreasing predictability southwards towards the desert: Spalax galili (2n = 52) ->S. golani (2n = 54)->S. carmeli (2n = 58)->S. judaei (2n = 60), and eastwards S. galili ->S. golani (2n = 52–>54) (Fig. 2). This chromosomal speciation trend southwards is associated with the regional aridity stress southwards (and eastwards) in Israel, budding new species adapted genomically, proteomically, and phenomically (i.e., in morphology, physiology, and behavior) to increasing stresses of higher solar radiation, temperature, and drought southwards (Nevo, 1999; Nevo et al., 2001; Nevo
list of Spalax at http://evolution.haifa.ac.il). A uniquely recent discovery of incipient sympatric ecological speciation at a microscale in Spalax triggered by local stresses occurs within Spalax galili.

retinal input to primary visual structures in Spalax

retinal input to primary visual structures in Spalax

Relative degree of retinal input to primary visual structures in Spalax, hamster, rat, and Spalacopus cyanus (South American Octodontidae, ‘‘coruro’’). These rodents are of similar body size (120–140 g). B. Relative degree of change in the proportions of retinal input to different primary visual structures in Spalax compared with measures obtained in other rodents. A relative progressive development in Spalax is seen in structures involved in photoperiodic and neuroendocrine functions (SCN, BNST).The main regressive feature is the drastic relative reduction of retinal input to the superior colliculus. The main regressive feature is the drastic reduction of retinal input to the superior colliculus. The relative size of other visual structures in Spalax is modified compared to that of the other species. c. Comparison of the absolute size (volume, mm3 x 10-4) of visual structures in Spalax and other rodents. The size of the SCN is equivalent in all species. The vLGN and dLGN are reduced by 87–93% in Spalax. The retino-recipient layers of the superior colliculus are reduced by 97%. Abbreviations: SCN: suprachiasmatic nucleus; BNST: bed nucleus of the stria terminalis; dLGN: dorsal lateral geniculate nucleus; SC: superior colliculus [From Cooper et al., 1993 (Fig 3)].

Subterranean life has a high energetic cost if an animal has to burrow in order to obtain its food. For a 150 g Thomomys bottae, burrowing 1 m may be 360–3400 times more expensive energetically than moving the same distance on the surface (Vleck, 1979). Mean rates of oxygen consumption during burrowing at 22 oC are from 2.8 to 7.1 times the RMR. Vleck developed a model examining the energetics of foraging by burrowing and found that, in the desert, Thomomys adjusts the burrow segment length to minimize the cost of burrowing. Since burrowing becomes less economic as body size increases, Vleck (1981) predicted that the maximum possible body size that a subterranean mammal can attain depends on a balance between habitat productivity and the cost of burrowing in local soils. Vleck’s cost of burrowing hypothesis has been verified in multiple cases. Heth (1989) demonstrated longer burrows in the rendzina soil and shorter ones in the terra rossa soil, associating lower productivity in the former for Spalax.

Food is a limiting factor for subterranean mammals. The abundance and distribution of food explain some of the ecological, physiological, and behavioral characteristics of subterranean mammals. In a field test of Spalax foraging strategy, we concluded that Spalax was a generalist due to the constraints of the subterranean ecotope. Restricted foraging time primarily during the winter when soil is wet, and the high energetic investment of tunneling to get to food items is significantly reduced than in summertime.
We also identified a decrease in the basic metabolic rate towards the desert, i.e., economizing energetics. The maintenance of adequate O2 transport in a subterranean mammal confronting hypoxia requires adaptation along the O2 transport system, achieved by increasing the flow of O2 in the convection systems (ventilation and perfusion) and by reduction of oxygen pressure (PO2) gradients at the diffusion barriers (lung blood, blood-tissue (Arieli, 1990). The PO2 gradient between blood capillaries and respiring mitochondria capillaries is large, and any adaptation at this level could be significant for O2 transport. Reduction of diffusion distance in a muscle can be achieved, like in Spalax, by increasing the number of capillaries that surround muscle fiber or by reducing fiber areas.

Geographic distribution in Israel of the four chromosomal species belonging to the S. ehrenbergi superspecies

Geographic distribution in Israel of the four chromosomal species belonging to the S. ehrenbergi superspecies

Geographic distribution in Israel of the four chromosomal species belonging to the S. ehrenbergi superspecies that are separated by narrow hybrid zones (2n = 52, 54, 58, and 60, now named as S. galili, S. golani, S. carmeli, and S. judaei, respectively; see Nevo et al., 2001).

Spalacid evolution, based on mtDNA, is driven by climatic oscillations and stresses. The underground ecotope provided subterranean mammals with shelter from extreme climate (temperature and humidity) fluctuations, and predators. However, they had to extensively and intensively adapt to the multiple underground stresses (darkness, energetic, low productivity and
food scarcity, hypoxia, hypercapnia, and high infectivity). All subterranean mammals, including spalacids as an extreme case, share convergent molecular and organismal adaptations to their shared unique underground ecotope. Evolution underground, as exemplified here in spalacids, led to mosaic molecular and organismal evolutionary syndromes to cope with multiple stresses.

Speciation involves all rates – from gradual to rapid. Subterranean mammals, with the spalacid example discussed above, provide uniquely rich evolutionary global tests of speciation and adaptation, convergence, regression, progression, and mosaic evolutionary processes. Adaptation and speciation underground was one of the most dramatic natural experiments verifying Darwinian evolution.

The Spalax genome sequencing has just been completed. It is being analyzed and will soon be published in 2012. This will be a milestone in understanding how numerous mammals across the globe, who found underground shelter from climatic fluctuations and stresses above ground, cope with the new suite of stresses they encountered underground, demanding a new engineering overhaul on all organizational levels, selecting for adaptive complexes to cope with the new underground stresses. The main current and future challenges are to compare and contrast genome sequences and identify the genomic basis of adaptation and speciation.

This global Cenozoic experiment could answer the following open questions: How heterozygous is the whole genome? How prevalent are retrotransposons and what is their functional role? How many genes are involved in the Spalax genome and how are they regulated? What are the genic and regulatory networks resisting the multiple stresses underground? How much of the Spalax genome is conserved and how much is reorganized to cope with the underground stresses? How is the solitary blind mole rat, Spalax, different from the social naked mole rat Heterocephalus? How are the processes of reduction, expansion, and genetic tinkering and engineering reflected across the genome? How effective is copy number variation in regulation? Is there similarity in the transcriptomes of subterranean mammals? How could we harness the rich genome repertoire of Spalax to revolutionize medicine, especially in the realm of hypoxia tolerance and the related major diseases of the western world, e.g., cancer, stroke, and cardiovascular diseases? What is the phylogenetic origin of Spalax? How much of the Spalax genome represents its phylogenetic roots and how much of coding and noncoding genomic regions are shared with other subterranean mammals across the globe in adapting to life underground?

The Atmospheric Environment of the Fossorial Mole Rat (Spalax Ehrenbergi): Effects of Season, Soil Texture, Rain, Temperature and Activity

  1. Arieli
    Comp Biochen Physiol. 1978; 63A:569-5151. The fossorial mole rat (Spalax ehrenbergi) may inhabit heavy soil with low gas permeability.
  2. Air composition in burrows in heavy soil deviates from atmospheric air more than that of burrows in light soil.
  3. In winter and spring O2 and CO2 concentrations in breeding mounds were 16.5% O2 and 2.5-3x CO2 and the extreme values measured were 14.0% O2 and 4.8% Cot.
  4. Hypoxia and hypercapnia in the burrow develop shortly after rain and when ambient temperature drops.
  5. Composition of the burrows air is influenced by the solubility of CO2 in soil water and by faster penetration of oxygen than outflowing of CO2.

Hypo-osmotic stress-induced physiological and ion-osmoregulatory responses in European sea bass (Dicentrarchus labrax) are modulated differentially by nutritional status

Amit Kumar Sinha, AF Dasan, R Rasoloniriana, N Pipralia, R Blust, G De Boeck
Comparative Biochemistry and Physiology, Part A 181 (2015) 87–99
http://dx.doi.org/10.1016/j.cbpa.2014.11.024

We investigated the impact of nutritional status on the physiological, metabolic and ion-osmoregulatory performance of European sea bass (Dicentrarchus labrax)when acclimated to seawater (32 ppt), brackishwater (20 and 10 ppt) and hyposaline water (2.5 ppt) for 2 weeks. Following acclimation to different salinities, fish were either fed or fasted (unfed for 14 days). Plasma osmolality, [Na+], [Cl−] and muscle water contentwere severely altered in fasted fish acclimated to 10 and 2.5 ppt in comparison to normal seawater-acclimated fish, suggesting ion regulation and acid–base balance disturbances. In contrast to feed-deprived fish, fed fish were able to avoid osmotic perturbation more effectively. This was accompanied by an increase in Na+/K+-ATPase expression and activity, transitory activation of H+-ATPase (only at 2.5 ppt) and down-regulation of Na+/K+/2Cl− gene expression. Ammonia excretion rate was inhibited to a larger extent in fasted fish acclimated to low salinities while fed fish were able to excrete efficiently. Consequently, the build-up of ammonia in the plasma of fed fish was relatively lower. Energy stores, especially glycogen and lipid, dropped in the fasted fish at low salinities and progression towards the anaerobic metabolic pathway became evident by an increase in plasma lactate level. Overall, the results indicate no osmotic stress in both feeding treatments within the salinity range of 32 to 20 ppt. However, at lower salinities (10–2.5 ppt) feed deprivation tends to reduce physiological, metabolic, ion-osmo-regulatory and molecular compensatory mechanisms and thus limits the fish’s abilities to adapt to a hypo-osmotic environment.

The absence of ion-regulatory suppression in the gills of the aquatic air-breathing fish Trichogaster lalius during oxygen stress

Chun-Yen Huang, Hsueh-Hsi Lin, Cheng-Huang Lin, Hui-Chen Lin
Comparative Biochemistry and Physiology, Part A 179 (2015) 7–16
http://dx.doi.org/10.1016/j.cbpa.2014.08.017

The strategy for most teleost to survive in hypoxic or anoxic conditions is to conserve energy expenditure, which can be achieved by suppressing energy-consuming activities such as ion regulation. However, an air-breathing fish can cope with hypoxic stress using a similar adjustment or by enhancing gas exchange ability, both behaviorally and physiologically. This study examined Trichogaster lalius, an air-breathing fish without apparent gill modification, for their gill ion-regulatory abilities and glycogen utilization under a hypoxic  treatment. We recorded air-breathing frequency, branchial morphology, and the expression of ion-regulatory proteins (Na+/K+-ATPase and vacuolar-type H+-ATPase) in the 1st and 4th gills and labyrinth organ (LO), and the expression of glycogen utilization (GP, glycogen phosphorylase protein expression and glycogen content) and other protein responses (catalase, CAT; carbonic anhydrase II, CAII; heat shock protein 70, HSP70; hypoxia-inducible factor-1α, HIF-1α; proliferating cell nuclear antigen, PCNA; superoxidase dismutase, SOD) in the gills of T. lalius after 3 days in hypoxic and restricted conditions. No morphological modification of the 1st and 4th gills was observed. The air breathing behavior of the fish and CAII protein expression both increased under hypoxia. Ion-regulatory abilities were not suppressed in the hypoxic or restricted groups, but glycogen utilization was enhanced within the groups. The expression of HIF-1α, HSP70 and PCNA did not vary among the treatments. Regarding the antioxidant system, decreased CAT enzyme activity was observed among the groups. In conclusion, during hypoxic stress, T. lalius did not significantly reduce energy consumption but enhanced gas exchange ability and glycogen expenditure.

The combined effect of hypoxia and nutritional status on metabolic and ionoregulatory responses of common carp (Cyprinus carpio)

Sofie Moyson, HJ Liew, M Diricx, AK Sinha, R Blusta, G De Boeck
Comparative Biochemistry and Physiology, Part A 179 (2015) 133–143
http://dx.doi.org/10.1016/j.cbpa.2014.09.017

In the present study, the combined effects of hypoxia and nutritional status were examined in common carp (Cyprinus carpio), a relatively hypoxia tolerant cyprinid. Fish were either fed or fasted and were exposed to hypoxia (1.5–1.8mgO2 L−1) at or slightly above their critical oxygen concentration during 1, 3 or 7 days followed by a 7 day recovery period. Ventilation initially increased during hypoxia, but fasted fish had lower ventilation frequencies than fed fish. In fed fish, ventilation returned to control levels during hypoxia, while in fasted fish recovery only occurred after reoxygenation. Due to this, C. carpio managed, at least in part, to maintain aerobic metabolism during hypoxia: muscle and plasma lactate levels remained relatively stable although they tended to be higher in fed fish (despite higher ventilation rates). However, during recovery, compensatory responses differed greatly between both feeding regimes: plasma lactate in fed fish increased with a simultaneous breakdown of liver glycogen indicating increased energy use, while fasted fish seemed to economize energy and recycle decreasing plasma lactate levels into increasing liver glycogen levels. Protein was used under both feeding regimes during hypoxia and subsequent recovery: protein levels reduced mainly in liver for fed fish and in muscle for fasted fish. Overall, nutritional status had a greater impact on energy reserves than the lack of oxygen with a lower hepatosomatic index and lower glycogen stores in fasted fish. Fasted fish transiently increased Na+/K+-ATPase activity under hypoxia, but in general ionoregulatory balance proved to be only slightly disturbed, showing that sufficient energy was left for ion regulation.

The effect of temperature and body size on metabolic scope of activity in juvenile Atlantic cod Gadus morhua L.

Bjørn Tirsgaard, Jane W. Behrens, John F. Steffensen
Comparative Biochemistry and Physiology, Part A 179 (2015) 89–94
http://dx.doi.org/10.1016/j.cbpa.2014.09.033

Changes in ambient temperature affect the physiology and metabolism and thus the distribution of fish. In this study we used intermittent flow respirometry to determine the effect of temperature (2, 5, 10, 15 and 20 °C) and wet body mass (BM) (~30–460 g) on standard metabolic rate (SMR, mg O2 h−1), maximum metabolic rate (MMR, mg O2 h−1) and metabolic scope (MS, mg O2 h−1) of juvenile Atlantic cod. SMR increased with BM irrespectively of temperature, resulting in an average scaling exponent of 0.87 (0.82–0.92). Q10 values were 1.8–2.1 at temperatures between 5 and 15 °C but higher (2.6–4.3) between 2 and 5 °C and lower (1.6–1.4) between 15 and 20 °C in 200 and 450 g cod. MMR increased with temperature in the smallest cod (50 g) but in the larger cod MMR plateaued between 10, 15 and 20 °C. This resulted in a negative correlation between the optimal temperature for MS (Topt) and BM, Topt being respectively 14.5, 11.8 and 10.9 °C in a 50, 200 and 450 g cod. Irrespective of BM cold water temperatures resulted in a reduction (30–35%) of MS whereas the reduction of MS at warm temperatures was only evident for larger fish (200 and 450 g), caused by plateauing of MMR at 10 °C and above. Warm temperatures thus seem favorable for smaller (50 g) juvenile cod, but not for larger conspecifics (200 and 450 g).

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Digital Trials and Research Models of the Future 

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Leslie Kelly Hall, Senior Vice President, Policy, Healthwise

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Sanjay Udoshi, Healthcare Product Strategy, Oracle

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8:00AM 11/13/2014 – 10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston

REAL TIME Coverage of this Conference by Dr. Aviva Lev-Ari, PhD, RN – Director and Founder of LEADERS in PHARMACEUTICAL BUSINESS INTELLIGENCE, Boston http://pharmaceuticalintelligence.com

8:00 A.M. Welcome from Gary Gottlieb, M.D.

Opening Remarks:

Partners HealthCare is the largest healthcare organization in Massachusetts and whose founding members are Brigham and Women’s Hospital and Massachusetts General Hospital. Dr. Gottlieb has long been a supporter of personalized medicine and he will provide his vision on the role of genetics and genomics in healthcare across the many hospitals that are part of Partners HealthCare.

Opening Remarks and Introduction

Scott Weiss, M.D., M.S. @PartnersNews
Scientific Director, Partners HealthCare Personalized Medicine;
Associate Director, Channing Laboratory/
Professor of Medicine, Harvard Medical School 
@harvardmed

Welcome

Engine of innovations

  • lower cost – Accountable care
  • robust IT infrastructure on the Unified Medical Records
  • Lab Molecular Medicine and Biobanks
  • 1. Lab Molecular medicine
  • 2. Biobank
  • 3. Translations Genomics: RNA Sequencing
  • 4. Medical Records integration of coded diagnosis linked to Genomics

BIOBANKS – Samples and contact patients, return actionable procedures

LIFE STYLE SURVEY – supplements the medical record

GENOTYPING and SEQUENCING – less $50 per sequence available to researcher / investigators

RECRUITMENT – subject to biobank, own Consents – e-mail patient – consent online consenting — collects 16,000 patients per month – very successful Online Consent

LAB Molecular Medicine – CLIA — genomics test and clinical care – EGFR identified as a bio-marker to cancer in 3 month a test was available. Best curated medical exon databases Emory Genetics Lab (EMVClass) and CHOP (BioCreative and MitoMAP and MitoMASTER). Labs are renowned in pharmacogenomics and interpretability.

IT – GeneInsight – IT goal Clinicians empowered by a workflow geneticist assign cases, data entered into knowledge base, case history, GENEINSIGHT Lab — geneticists enter info in a codified way will trigger a report for the Geneticist – adding specific knowledge standardized report enters Medical Record. Available in many Clinics of Partners members.

Example: Management of Patient genetic profiles – Relationships built between the lab and the Clinician

Variety of Tools are in development

GenInsight Team –>> Pathology –>> Sunquest Relationship

The Future

Genetic testing –>> other info (Pathology, Exams, Life Style Survey, Meds, Imaging) — Integrated Medical Record

Clinic of the Future-– >> Diagnostics – Genomics data and Variants integrated at the Clinician desk

Gary Gottlieb, M.D. @PartnersNews
President and CEO, Partners HealthCare

Translational Science
Partners 6,000 MDs, MGH – 200 years as Teaching Hospital of HMS, BWH – magnets in HealthCare

2001  – Center for Genomics was started at Partners, 2008 Genomics and Other Omis, Population Health, PM – Innovations at Partners.

Please Click on Link  Video on 20 years of PartnersHealthcare

Video of Dr. Gottlieb at ECRI conference 2012

Why is personalized medicine  important to Partners?

From Healthcare system to the Specific Human Conditions

  • Lab translate results to therapy
  • Biobank +50,000 specimens links to Medical Records of patients – relevant to Clinician, Genomics to Clinical Applications

Questions from the Podium

  • test results are not yet available online for patients
  • clinicians and liability – delays from Lab to decide a variant needs to be reclassified – alert is triggered. Lab needs time to accumulated knowledge before reporting a change in state.
  • Training Clinicians in above type of IT infrastructure: Labs around the Nations deal with VARIANT RECLASSIFICATION- physician education is a must, Clinicians have access to REFERENCE links.
  • All clinicians accessing this IT infrastructure — are trained. Most are not yet trained
  • Coordination within Countries and Across Nations — Platforms are Group specific – PARTNERS vs the US IT Infrastructure — Genomics access to EMR — from 20% to 70% Nationwide during the Years of the Obama Adm.
  • Shakeout in SW linking Genetic Labs to reach Gold Standard

Click to see Advanced Medical Education Partners Offers

 

– See more at: http://personalizedmedicine.partners.org/Education/Personalized-Medicine-Conference/Program.aspx#sthash.qGbGZXXf.dpuf

@HarvardPMConf

#PMConf

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@PartnersNews

@MassGeneral

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@harvardmed

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1:45PM 11/12/2014 – 10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston

Reporter: Aviva Lev-Ari, PhD, RN

 

REAL TIME Coverage of this Conference by Dr. Aviva Lev-Ari, PhD, RN – Director and Founder of LEADERS in PHARMACEUTICAL BUSINESS INTELLIGENCE, Boston http://pharmaceuticalintelligence.com

 

1:45 p.m. Panel Discussion – Oncology

Oncology

There has been a remarkable transformation in our understanding of the molecular genetic basis of cancer and its treatment during the past decade or so. In depth genetic and genomic analysis of cancers has revealed that each cancer type can be sub-classified into many groups based on the genetic profiles and this information can be used to develop new targeted therapies and treatment options for cancer patients. This panel will explore the technologies that are facilitating our understanding of cancer, and how this information is being used in novel approaches for clinical development and treatment.

Oncology

Opening Speaker & Moderator:

Lynda Chin, M.D.
Department Chair, Department of Genomic Medicine
MD Anderson Cancer Center     @MDAnderson   #endcancer

  • Who pays for personalized medicine?
  • potential of Big data, analytics, Expert systems, so not each MD needs to see all cases, Profile disease to get same treatment
  • business model: IP, Discovery, sharing, ownership — yet accelerate therapy
  • security of healthcare data
  • segmentation of patient population
  • management of data and tracking innovations
  • platforms to be shared for innovations
  • study to be longitudinal,
  • How do we reconcile course of disease with personalized therapy
  • phenotyping the disease vs a Patient in wait for cure/treatment

Panelists:

Roy Herbst, M.D., Ph.D.    @DrRoyHerbstYale

Ensign Professor of Medicine and Professor of Pharmacology;
Chief of Medical Oncology, Yale Cancer Center and Smilow Cancer Hospital     @YaleCancer

Development new drugs to match patient, disease and drug – finding the right patient for the right Clinical Trial

  • match patient to drugs
  • partnerships: out of 100 screened patients, 10 had the gene, 5 were able to attend the trial — without the biomarker — all 100 patients would participate for the WRONG drug for them (except the 5)
  • patients wants to participate in trials next to home NOT to have to travel — now it is in the protocol
  • Annotated Databases – clinical Trial informed consent – adaptive design of Clinical Trial vs protocol
  • even Academic MD can’t read the reports on Genomics
  • patients are treated in the community — more training to MDs
  • Five companies collaborating – comparison of 6 drugs in the same class
  • if drug exist and you have the patient — you must apply personalized therapy

 

Lincoln Nadauld, M.D., Ph.D.
Director, Cancer Genomics, Huntsman Intermountain Cancer Clinic @lnadauld @intermountain

  • @Stanford, all patients get Tumor profiles Genomic results, interpretation – deliver personalized therapy
  • Outcomes from Genomics based therapies
  • Is survival superior
  • Targeted treatment – Health economic impact is cost lower or not for same outcome???
  • genomic profiling of tumors: Genomic information changes outcome – adverse events lower
  • Path ways and personalized medicine based on Genomics — integration not yet been worked out

Question by Moderator: Data Management

  • Platform development, clinical knowledge system,
  • build consortium of institutions to share big data – identify all patients with same profile

 

 

 

 

See more at  http://personalizedmedicine.partners.org/Education/Personalized-Medicine-Conference/Program.aspx#sthash.qGbGZXXf.dpuf

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9:20AM 11/12/2014 – 10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston

Reporter: Aviva Lev-Ari, PhD, RN

 

REAL TIME Coverage of this Conference by Dr. Aviva Lev-Ari, PhD, RN – Director and Founder of LEADERS in PHARMACEUTICAL BUSINESS INTELLIGENCE, Boston http://pharmaceuticalintelligence.com

9:20 a.m. Panel Discussion – Genomic Technologies

Genomic Technologies

The greatest impetus for personalized medicine is the initial sequencing of the human genome at the beginning of this Century. As we began to recognize the importance of genetic factors in human health and disease, efforts to understand genetic variation and its impact on health have accelerated. It was estimated that it cost more than two billion dollars to sequence the first human genome and reduction in the cost of sequence became an imperative to apply this technology to many facets of risk assessment, diagnosis, prognosis and therapeutic intervention. This panel will take a brief historical look back at how the technologies have evolved over the last 15 years and what the future holds and how these technologies are being applied to patient care.

Genomic Technologies

Opening Speaker and Moderator:

George Church, Ph.D.
Professor of Genetics, Harvard Medical School; Director, Personal Genomics

Genomic Technologies and Sequencing

  • highly predictive, preventative
  • non predictive

Shareable Human Genomes Omics Standards

$800 Human Genome Sequence – Moore’s Law does not account for the rapid decrease in cost of Genome Sequencing

Genome Technologies and Applications

  • Genia nanopore – battery operated device
  • RNA & protein traffic
  • Molecular Stratification Methods – more than one read, sequence ties
  • Brain Atlas  – transcriptome of mouse brains
  • Multigenics – 700 genes: hGH therapies

Therapies

  • vaccine
  • hygiene
  • age

~1970 Gene Therapy in Clinical Trials

Is Omic technologies — a Commodity?

  • Some practices will have protocols
  • other will never become a commodity

 

Panelists:

Sam Hanash, M.D., Ph.D. @MDAndersonNews

Director, Red & Charline McCombs Institute for Early Detection & Treatment of Cancer MD Anderson Cancer Center

Heterogeneity among Cancer cells. Data analysis and interpretation is very difficult, back up technology

Proteins and Peptides before analysis with spectrometry:

  • PM  – Immunotherapy approaches need be combined with other techniques
  • How modification in protein type affects disease
  • amplification of an aberrant protein – when that happens cancer developed. Modeling on a CHip of peptide synthesizer

Mark Stevenson @servingscience

Executive Vice President and President, Life Sciences Solutions
Thermo Fisher Scientific

Issues of a Diagnostics Developer:

  • FDA regulation, need to test on several tissues
  • computational environment
  • PCR, qPCR – cost effective
  • BGI – competitiveness

Robert Green, MD @BrighamWomens

Partners, Health Care Personalized Medicine — >>Disclosure: Illumina and three Pharmas

Innovative Clinical Trial: Alzheimer’s Disease, integration of sequencing with drug development

  • Population based screening with diagnosis
  • Cancer predisposition: Cost, Value, BRCA
  • epigenomics technologies to be integrated
  • Real-time diagnostics
  • Screening makes assumption on Predisposition
  • Public Health view: Phenotypes in the Framingham Studies: 64% pathogenic genes were prevalent – complication based in sequencing.

Questions from the Podium:

  • Variants analysis
  • Metastasis different than solid tumor itself – Genomics will not answer issues related to tumor in special tissues variability

 

 

 

 

– See more at: http://personalizedmedicine.partners.org/Education/Personalized-Medicine-Conference/Program.aspx#sthash.qGbGZXXf.dpuf

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Multiple Lung Cancer Genomic Projects Suggest New Targets, Research Directions for Non-Small Cell Lung Cancer

Curator, Writer: Stephen J. Williams, Ph.D.

UPDATED 08/11/2025

Human Curation vs. AI tools: ChatGPT & Knowledge Graphs [KG] Output: A case study for the following original curation:

  • Multiple Lung Cancer Genomic Projects Suggest New Targets, Research Directions for Non-Small Cell Lung Cancer

https://pharmaceuticalintelligence.com/2014/09/05/multiple-lung-cancer-genomic-projects-suggest-new-targets-research-directions-for-non-small-cell-lung-cancer/

 

This update was performed by the following methods:
A. GPT 5 Text analysis and Reasoning
B. Insertion of Knowledge Graph on topic Curation of Genomic Analysis from Non Small Cell Lung Cancer Studies  from Nodus Labs using InfraNodus software
C. Domain Knowledge Expert evaluation of the Update outcomes
This article has the following Structure:
Part A: Introduction to LLM, Knowledge Graph software InfraNodus, ChatGPT5 and Background Information on curated material for Test Case
Part B: InfraNodus Analysis of manual curation and Knowledge Graph Creation
Part C: Chat GPT 5 Analysis of Manually Curated Material
Part D: Curation entitled Multiple Lung Cancer Genomic Projects Suggest New Targets, Research Directions for Non-Small Cell Lung Cancer originally published on 09/05/2014
Results of Article Update with GPT 5
1. GPT5 alone was not able to understand the goal of the article, namely to determine knowledge gaps in a particular research area involving 5 genomic studies on lung cancer patients
2. GPT5 alone was not able to group concepts or comonalities between biological pathways unless supplied with a manually curated list of KEGG pathways from a list of mutated genes.  However this precluded any effect that fusion proteins had on the analysis and so GPT5 would only concentrate on mutated genes commonly found in literature
3. GPT was not able to access some of the open Access databases like NCBI Gene Ontology database
Results of Article Update with KnowledgeGraph presentation to GPT 5
4. As the Knowledge Graph understood the importance of fusion proteins and transversions, the knowledgegraph augmented the GPT analysis and so enriched the known pathways as well as could correctly identify the less represented pathways in the knowledge graph
5.  This led to the identification of many novel signaling pathways not identified in the original analysis, and was able to perform this task with ease and speed

6. GPT with InfraNodus Analysis was able to propose pertinent questions for future research (the goal of the original curation) such as:

  • How does the interaction between [[EGFR]] mutations and sex-specific gene alterations, including [[RBM10]], influence treatment outcomes in lung adenocarcinoma?
  • How does the intersection of mutational patterns from smoking influence pathway activation in NSCLC, and can identifying these interactions improve targeted therapy development?
Novelty in comparison to Original article published on 09/05/2014
7. it appears that manual curation is necessary to assist in the building of relevant knowledge graphs in the biomedical fields to augment generative AI analysis
8. by itself, generative AI is not optimized for inference of higher concepts from biomedical text, and therefore, at this point, requires the input from human curators developing domain-specific knowledge graphs
9.  The combination of ChatGPT5 and Knowledge graphs of this manually curated biomedical text added a further layer of complexity of gaps of knowledge not seen in the original curations including the need to study noncanonical signaling pathways like WNT and Hedgehog in smoker versus nonsmoker cohorts of lung cancer patients

A Comparison of Manual Expert-Curative and an LLM-based analysis of Knowledge Gaps  in Non Small Lung Cancer Whole Exome Sequencing Studies and a Use Case Example of Chat GPT 5

Part A: Introduction to LLM, Knowledge Graph software InfraNodus, ChatGPT5 and Background Information on curated material for Test Case

The development of Large Language Models (LLMs), together with development of knowledge graphs, have facilitated the ability to analyze text and determine the relationships among the various concepts contained within series of texts.  These concepts and relationships can be visualized, and new insights inferred from these visualizations.  As a result, this type of analysis suggests new directions and lines of research.

 

Alternatively,  these types of visualizations can also reveal gaps in knowledge which should be addressed. A new type of LLM and visualization tools have been developed to understand the gaps in knowledge in biomedical text.

Nodus Labs InfrNodus AI Knowledge Graph Software Tools Allow Text Relationship Visualization and Integrated AI Functionality

 

Infranodus makes knowlegde graphs from text and then is able to visualize the relationships between concepts (or nodes).  In doing so, the tool also highlights the various knowledge gaps (or large differences between nodes) which can be used to investigate new hypotheses and research directions of previously univestigated relationships between concepts.  This generates new research questions, in which these gaps can be used as prompts in the software’s integrated AI tool.  The AI tool, much like a GPT, returns recommendations for research to be conducted in the area.

https://infranodus.com/

In addition, the InfraNodus software can detect if text is too biased on a particular concept or conclusion, and using a GPT3 or GPT4, can determine if the nodes are too dispersed and will recommend which gaps should be focused on.

The software can upload any biomedical text in various formats

A full demonstration is on their website but a good summary is found on their Youtube site at

https://www.youtube.com/watch?v=wCEhiIJsmrg

A couple of use cases include

 

 

Previously we had manually curated and analyzed the knowledge gaps from a series of publications on whole exome sequencing  of biopsied tumors from cohorts of non small lung cancer patients. This curation (from 2016) is seen in the lower half of this updated link below and I separated with a bar and highlighted in Yellow as Text for AI Analysis.

https://pharmaceuticalintelligence.com/2014/09/05/multiple-lung-cancer-genomic-projects-suggest-new-targets-research-directions-for-non-small-cell-lung-cancer/

A literature analysis of the driver mutations found in five NSLC exome sequencing projects:

  1. Comprehensive genomic characterization of squamous cell lung cancersNature 2012, 489(7417):519-525.
  2. A genomics-based classification of human lung tumorsScience translational medicine 2013, 5(209):209ra153.
  3. Govindan R, Ding L, Griffith M, Subramanian J, Dees ND, Kanchi KL, Maher CA, Fulton R, Fulton L, Wallis J et alGenomic landscape of non-small cell lung cancer in smokers and never-smokersCell 2012, 150(6):1121-1134.
  4. Imielinski M, Berger AH, Hammerman PS, Hernandez B, Pugh TJ, Hodis E, Cho J, Suh J, Capelletti M, Sivachenko A et alMapping the hallmarks of lung adenocarcinoma with massively parallel sequencingCell 2012, 150(6):1107-1120.
  5. Peifer M, Fernandez-Cuesta L, Sos ML, George J, Seidel D, Kasper LH, Plenker D, Leenders F, Sun R, Zander T et alIntegrative genome analyses identify key somatic driver mutations of small-cell lung cancerNature genetics 2012, 44(10):1104-1110.

 

were performed.

The purpose of this analysis was to uncover biological functions related to the sets of mutated genes with limited research publications in the area of  non small cell lung cancer.  The identification of such biological functions would represent a gap in knowledge in this disease.  In addition, this analysis attempted to find new lines of research or potential new biotargets to investigate for lung cancer therapy.

 

 

 

However this manual method is time consuming and may miss relationships not defined in a GO ontology or gene knowledgebases.

Therefore we turned to an AI-driven approach:

  1. Using InfraNodus ability to develop a knowledge graph based on our curation and determine if the AI platform could infer knowledge gaps
  2. Utilize Chat GPT5 to analyze the same curated set to determine if OpenAI analysis would lead to the similar analysis from curated material
  3. Determine if combining a knowledge graph within GPT would lead to a higher level of analysis

See below (Part D) of this update for the curated studies which were included in this analysis and the text which was entered into both InfraNodus and Chat GPT5. 

As a summary, it seems that manual curation is necessary to assist in the building of relevant knowledge graphs in the biomedical fields to augment generative AI analysis.  In addition, it appears that , by itself, generative AI is not optimized for inference of higher concepts from biomedical text, and therefore, at this point, requires the input from human curators developing domain-specific knowledge graphs.

 

Part B. InfraNodus Analysis of manual curation and Knowledge Graph Creation

Methods: 

Text of the curation was copied and directly pasted into the text analysis module of InfraNodus.  There was no editing of words however genes in the curation were linked to their GeneCard entry. GeneCards is a database run by the Weizmann Institute.  InfraNodus utilizes a combination of LLMs and its own GraphRAG system to provide insights from text analysis. While it leverages various models, including those from OpenAI and Anthropic, it’s not limited to a single LLM. Instead, InfraNodus integrates these models within its GraphRAG framework, which enhances their capabilities by adding a relational understanding of the context through a knowledge graph.

InfraNodus then autogenerates a knowledge graph and returns entities and relationships between entities.  InfraNodus offers the opportunity to modify the knowledge graph however for this analysis we used the first graph InfraNodus generated.  Inspection of this graph (as shown below) was deemed reasonable.

 

Results

The knowledge graph of the input text is shown below:

InfraNodus generated Knowledge Graph of 5 WES Non Smal Cell Lung Cancer studies involving smokers and non smokers

 

Four main concepts were returned: tumors, genes, literature, and mutations.

A snapshot of the Analysis window is given below.  It should be noted that InfraNodus felt there needed to be more connections between Pathway and Mutational Patterns.

An InfraNodus reposrt with Knowlege Graph on Whole Exome Sequencing studies in NSCLC to determine mutational spectrum in smokers versus non smokers

Auto generated summary report

Context name: text_250808T0144

Created on: aug 7, 2025 9:47 pm

Last updated on: aug 7, 2025 10:10 pm

Main concepts:

[[tumors]], analysis, [[mutations]], identify, [[lung]], [[genes]]

Main topics:

  1. Tumor Genomics: [[tumors]] [[lung]] reveal
  2. Genetic Alterations: identify [[genes]] study
  3. Pathway Analysis: analysis pathway literature
  4. Mutation Patterns: [[mutations]] [[egfr]] [[rbm10]]

Structural gap (topics to connect):

  1. Pathway Analysis: analysis pathway
  2. Smoking Influence: mutational [[smoking]]

Topical connectors:

alk clinical [[egfr]] mutational pathway [[paper]] found key literature study [[genomic]] reveal [[transversion]]

 

Top relations / ngrams:

1) [[lung]] [[tumors]]

2) alk fusion

3) link function

4) eml alk

5) function [[gene_ontology]]

Modulary: 0.47

Relations:

InfraNodus identified 744 relations between entities (nodes)

A list of some of the more frequent are given here:

source target occurrences weight betweenness
[[lung]] [[tumors]] 8 24 0.4676
analysis pathway 5 12 0.2291
significantly [[genes]] 5 9 0.1074
significantly [[mutated]] 4 12 0.0281
[[mutated]] [[genes]] 4 12 0.0847
[[transversion]] high 3 12 0.0329
[[smoking]] history 3 10 0.0352
study identify 3 9 0.2051
mutational pattern 3 9 0.0921
[[rbm10]] [[mutations]] 3 8 0.1776
literature analysis 3 7 0.2218
[[egfr]] [[mutations]] 3 7 0.2139
[[transversion]] group 3 7 0.0259
enriched cohort 3 6 0.0219
[[whole_exome_sequencing]] [[tumors]] 3 6 0.3485
identify [[genes]] 3 6 0.2268
including analysis 3 5 0.1985
alteration [[genes]] 3 4 0.1298
[[tumors]] analysis 3 4 0.5192
alk fusion 2 15 0.0671
link function 2 14 0.0269
function [[gene_ontology]] 2 13 0.0054

 

Notice how the betweenness or importance of connection of disparate concepts vary but are high between concepts like tumors and analysis, or lung and tumor, however many important linked concepts like alk and fusion may have low betweenness but are mentioned frequently and have a much higher weight or closeness to each other. Gene-mutations-transversions-smoking seem to have a high correspondence to each other

For a full list please see the file

InfraNodus relations five WES studies on NSCLC and smoking

sjwilliams_text_250808T0144_nodedataon NSCLCWESstudies

Generated the following responses using AI to bridge the gap between

  1. Mutation Patterns: [[mutations]][[egfr]][[rbm10]]and
  2. Genetic Alterations: identify[[genes]]study:The recent comprehensive studies on lung adenocarcinoma have significantly advanced our understanding of the genetic landscape by identifying key mutations and their intricate interactions. Notably, EGFR and RBM10 exhibit distinct mutational patterns, with RBM10 inactivations being notably enriched in male cohorts. This gender-linked enrichment underscores a potential differential oncogenic pathway involving ERBB2 and RB1 alterations.Moreover, these projects emphasize the quest to map significant gene alterations within lung adenocarcinoma. The identification of such genes not only corroborates prior reports but also expands upon them by highlighting new connections between mutation signatures and clinical factors like smoking history. These findings are crucial as they can inform future therapeutic targeting strategies, ensuring that personalized treatment approaches consider both gender-specific genomic enrichments and mutation-driven tumorigenesis pathways elucidated through rigorous analyses.elaborate

 

<MainTopics>

  1. Tumor Genomics: [[tumors]], [[lung]], reveal, base, [[genomic]], [[data]], classification, subtype,
  2. Genetic Alterations: identify, [[genes]], study, alteration, reported, significantly, [[lung_adenocarcinoma]], key,
  3. Pathway Analysis: analysis, pathway, literature, including, activation, target, [[nsclc]], [[patients]],
  4. Mutation Patterns: [[mutations]], [[egfr]], [[rbm10]], male, enriched, cohort, [[erbb2]], noted,
  5. Smoking Influence: mutational, [[smoking]], [[transversion]], pattern, high, notably, [[smokers]], show,
  6. Research Strategies: found, [[paper]], [[omics]], unique, profile, result, multus, characterization,
  7. Targeted Therapy: alk, [[lung_cancer]], fusion, [[therapy]], ro, eml, include, targeted,
  8. Mutation Signatures: clinical, [[tumor]], approach, mutation, sample, signature, gender, specific,
  9. Gene Function: link, [[gene]], note, function, author, [[gene_ontology]],

</MainTopics>

 

<TopicalGap>:

  1. Genetic Alterations: identify [[genes]]
  2. Mutation Patterns: [[mutations]] [[egfr]]

</TopicalGap>

 

 

questions generated using AI to help you explore “alk, clinical, [[egfr]], mutational, pathway, [[paper]], found, key, literature, study, [[genomic]], reveal, [[transversion]]…”:How do mutational patterns, specifically EGFR mutations and transversions related to smoking history, influence the effectiveness of targeted therapies in NSCLC patients?elaborate

 

ideas generated using AI to help you explore “alk, clinical, [[egfr]], mutational, pathway, [[paper]], found, key, literature, study, [[genomic]], reveal, [[transversion]]…”:Develop a predictive model that utilizes genomic data and smoking history to forecast patient response to targeted therapies. This model would identify key mutational signatures linked to EGFR and other genes, highlighting the impact of smoking-induced transversions on drug efficacy.elaborate

 

Project Notes

 

The recent comprehensive studies on lung adenocarcinoma have significantly advanced our understanding of the genetic landscape by identifying key mutations and their intricate interactions. Notably, EGFR and RBM10 exhibit distinct mutational patterns, with RBM10 inactivations being notably enriched in male cohorts. This gender-linked enrichment underscores a potential differential oncogenic pathway involving ERBB2 and RB1 alterations.

 

Moreover, these projects emphasize the quest to map significant gene alterations within lung adenocarcinoma. The identification of such genes not only corroborates prior reports but also expands upon them by highlighting new connections between mutation signatures and clinical factors like smoking history. These findings are crucial as they can inform future therapeutic targeting strategies, ensuring that personalized treatment approaches consider both gender-specific genomic enrichments and mutation-driven tumorigenesis pathways elucidated through rigorous analyses.”

 

<ConceptualGateways>

alk

clinical

[[egfr]]

mutational

pathway

[[paper]]

found

key

literature

study

[[genomic]]

reveal

[[transversion]]

</ConceptualGateways>

 

 

How do mutational patterns, specifically EGFR mutations and transversions related to smoking history, influence the effectiveness of targeted therapies in NSCLC patients?

 

The report from the NCI Bulletin outlines significant advancements in understanding lung cancer through genome sequencing projects. These studies have revealed a plethora of genetic and epigenetic alterations across various forms of lung tumors, including adenocarcinomas, squamous cell carcinomas, and small cell lung cancers. Notably, some identified alterations could be targeted by existing therapies, providing potential new avenues for treatment.Dr. Meyerson emphasizes the complexity of these genetic changes, highlighting that distinct mechanisms inactivating genes can vary between tumors. The report also notes gaps in knowledge regarding non-coding DNA alterations, which comprise a major part of the human genome.Key findings include:1. Comprehensive genomic analyses revealing unique driver mutations in lung adenocarcinoma, such as those affecting MET and ERBB2, alongside significant mutations in known cancer drivers like TP53 and KRAS.2. A classification system based on genomic data enabling more accurate patient stratification—achieving a 75% classification rate of lung cancer subtypes.3. Smoking history is shown to influence mutational patterns significantly, with smokers exhibiting a higher incidence of point mutations compared to never-smokers.Moreover, the integration of genomic data and pathway analysis highlighted recurrent mutations across various pathways related to tumorigenesis, suggesting new therapeutic targets and underscoring the importance of personalized medicine approaches that factor in gender-specific mutation distributions.This synthesis of findings not only corroborates earlier studies but also extends our understanding of the interplay between genomic alterations, smoking habits, and clinical outcomes in lung cancer. Future research is needed to explore the implications of these findings further and to develop targeted therapies that leverage the identified genetic alterations.

 

  1. Tumor Genomics
  2. Genetic Alterations
  3. Pathway Analysis
  4. Mutation Patterns

 

Main Ideas Summary from Infranodus:

 

The report from the NCI Bulletin highlights significant developments in lung cancer genomics through five genome sequencing projects. These studies have uncovered various genetic and epigenetic alterations across multiple forms of lung cancer, including adenocarcinomas, squamous cell cancers, and small cell lung cancers. Notably, these alterations may correspond to existing drugs, offering new avenues for targeted therapy.

Key findings include the identification of unique mutations in genes like NF1, MET, and ERBB2, with particular attention to smoking’s role in mutational patterns. The analysis shows that smokers exhibit a higher incidence of specific point mutations, marking a distinct divergence in mutation profiles compared to never-smokers.

The research also emphasizes the need for continued exploration of the data to link mutation signatures with clinical outcomes, particularly regarding gender-specific differences in mutation prevalence. Emerging insights suggest that pathways involving MYC and alterations in splicing factors could be vital targets for future therapeutic strategies.

Overall, the studies collectively underscore an urgent need for integrating genomic data into clinical practice, which can lead to more personalized treatment approaches for lung cancer patients by considering both the genetic landscape of tumors and their histories, such as smoking exposure. The ongoing efforts to map gene alterations within lung adenocarcinoma not only reinforce existing knowledge but also reveal new potential therapeutic targets that can significantly impact patient outcomes.

 

Questions to Explore from InfraNodus:

 

How does the interaction between [[EGFR]] mutations and sex-specific gene alterations, including [[RBM10]], influence treatment outcomes in lung adenocarcinoma?

 

 

How does the intersection of mutational patterns from smoking influence pathway activation in NSCLC, and can identifying these interactions improve targeted therapy development?

 

Part C: Chat GPT 5 Analysis of Manually Curated Material

Now let us see how Chat GPT5 analyzed the same material.

Methods

 

Nice — I’ll work from the KEGG-pathway list shown in your text (the table of pathways with PubMed counts) and call out major KEGG pathways that are not represented there.

Important caveat: “Not represented in the table” below means not listed in the KEGG-pathway hits shown in your pasted literature analysis, not that these pathways never play a role in lung cancer. Absence from that table can reflect lack of reporting in the specific WES/WGS studies or the PubMed keyword search used — it’s not proof the pathway is uninvolved biologically.

The above Important caveat was very important because it showed that ChatGPT5 could understand what my experimental goals were, or at least understood the problem I was presenting to ChatGPT5!

Major KEGG pathways missing from the provided KEGG list

 

(However Note that GPT did NOT take into account the mutational spectrum of the studies or the gene lists supplied in the curation)

 

These are commonly cited cancer-related KEGG pathways that do not appear in the table you gave:

  • Wnt signaling pathway
  • Notch signaling pathway
  • Hedgehog signaling pathway
  • TGF-beta signaling pathway
  • Hippo signaling pathway
  • p53 signaling pathway(note: TP53 is mutated in many tumors, so p53 biology is implicated in the text, but “p53 signaling” does not appear in that KEGG hits table)
  • Apoptosis (intrinsic/extrinsic apoptotic pathways)
  • Autophagy
  • NF-kappa B signaling pathway
  • Ubiquitin-mediated proteolysis / proteasome
  • DNA repair pathways (several separate KEGG entries):
    • Homologous recombination
    • Mismatch repair
    • Nucleotide excision repair
    • Base excision repair
    • Fanconi anemia pathway
  • Cellular metabolism pathways commonly in cancer:
    • Glycolysis / Gluconeogenesis
    • Citrate cycle (TCA cycle) / Oxidative phosphorylation
    • Fatty acid metabolism / beta-oxidation
    • Pentose phosphate pathway
  • Immune-signaling / antigen processing:
    • T cell receptor signaling pathway
    • B cell receptor signaling pathway
    • Antigen processing and presentation
  • Autocrine/paracrine inflammation/chemokine signaling beyond the one low-count entry (general cytokine networks other than the single “cytokine–cytokine receptor interaction” hit)
  • Signal transduction modules sometimes separate in KEGG:g. Calcium signaling was listed in your table, but other modules like cGMP–PKG signaling or PKA/PKC broader signaling networks are not.

 

Quick summary

The KEGG table in your text highlights many cancer and receptor-tyrosine/MAPK/PI3K related pathways (cell cycle, MAPK, ErbB, JAK-STAT, focal adhesion, etc.). What’s not listed are numerous developmental morphogen pathways (Wnt/Notch/Hedgehog/TGF-β/Hippo), many DNA-repair and core metabolic pathways, canonical apoptosis/autophagy/ubiquitin pathways, and broad adaptive-immune signaling pathways.

If you want, I can:

  • (A) cross-check this against the full KEGG pathway list and produce a definitive “KEGG universe → present vs absent in your table” spreadsheet, or
  • (B) prioritize which of the missing pathways you’d most want investigated in lung tumor sequencing data and suggest genes to look for.

Which follow-up would you prefer?

 

Now taking out the KEGG list I gave GPT in order to force GPT to do a similar but unbiased analysis

 

From the studies you’ve shared, we can identify which KEGG pathways are under-represented (i.e., appear less frequently altered than expected) in the altered genes in lung cancer.

To do this, we note that the researchers used KEGG and other pathway analysis tools (PathScan, literature curation, proteomics) to see which biological pathways the mutated genes mapped to.
While many well-known cancer pathways were found to be activated (MAPK, PI3K-AKT, mTOR, cell cycle, oxidative stress, etc.), a few key pathways were reported as unexpectedly low in mutation frequency despite their known role in cancer biology.

KEGG Pathways Under-Represented in Altered Genes in Lung Cancer

From the descriptions in Papers 1–5, these are the main pathways that appear under-represented:

  1. MAPK signaling pathway
    • Normally, MAPK is a major cancer driver pathway. In this dataset, MAPK activity was high in proteomics, but many tumors lacked mutations in MAPK pathway genes.
    • This means MAPK activation is often non-mutational (possibly via upstream signaling, epigenetic changes, or ligand activation), so in mutation-based KEGG enrichment it is under-represented.
  2. PI3K-Akt signaling pathway
    • Like MAPK, the PI3K-Akt pathway was highly activated in proteomics, but often without mutations in PI3K pathway genes.
    • Under-represented in gene mutation lists, despite strong functional activation.
  3. mTOR signaling pathway
    • Functional activation was observed (especially when STK11 was mutated), but most tumors had no mTOR-pathway gene mutations.
    • Under-represented when looking only at altered genes.
  4. JAK-STAT signaling pathway
    • Mentioned as novel pathway alterations in some tumors, but in KEGG enrichment analysis, JAK-STAT genes did not appear as a dominant mutated group—implying low mutation frequency compared to other cancer types.
  5. Oxidative phosphorylation & metabolic pathways
    • While oxidative stress response genes (e.g., KEAP1) were sometimes mutated, the oxidative phosphorylation KEGG pathway itself did not emerge as mutation-enriched, suggesting functional changes occur via other mechanisms (e.g., hypoxia signaling).
  6. Histone modification / chromatin remodeling pathways
    • While some epigenetic regulators (ARID1A, CREBBP, EP300, MLL) were mutated, KEGG’s “Chromatin remodeling” and “Histone modification” groupings were less represented than expected given the role of epigenetics in lung cancer, indicating epigenetic dysregulation is often non-mutational.

Why They Are Under-Represented

  • Proteomics revealed activation without DNA mutation (e.g., ligand-driven activation, phosphorylation cascades).
  • Epigenetic or post-translational modifications can activate pathways without altering the coding sequence.
  • Tumor heterogeneity: some pathways are only altered in subsets of tumors and thus don’t reach statistical enrichment.
  • Limitations of mutation-based KEGG analysis: pathways that are activated downstream or by non-coding alterations are missed.

Summary Table

Pathway (KEGG ID) Representation in Mutated Genes Reason for Under-Representation
hsa04010 MAPK signaling Low Activated via non-mutational mechanisms
hsa04151 PI3K-Akt signaling Low Functional activation w/o gene mutations
hsa04150 mTOR signaling Low Activation often via upstream STK11 loss or growth factor signaling
hsa04630 JAK-STAT signaling Low Rarely mutated, altered mostly via cytokine/growth factor changes
hsa00190 Oxidative phosphorylation Low Functional metabolic shift via hypoxia & ROS stress, not mutations
Chromatin remodeling / histone modification Low Mostly altered via epigenetic changes, not coding mutations

If you want, I can cross-reference the exact KEGG IDs and run a quick gene-to-pathway mapping using the genes listed in the reports so we can get a precise statistical under-representation list. That way it’s backed by an enrichment p-value rather than just qualitative observation.

 

I tried to re-run the KEGG-backed analysis you requested, but the KEGG REST endpoints/web pages aren’t reachable from my browsing tool right now (the web.run calls to KEGG returned an internal/safety error). I can’t produce a properly-sourced, KEGG-ID–backed enrichment report unless I can successfully query KEGG (or a comparable pathway service) for the exact pathway membership.

Two clear, immediate options — tell me which you prefer and I’ll proceed right away:

Option 1 — Proceed with a KEGG-backed run using an alternate route (recommended)
I’ll attempt a small number of web.run search queries (to public pathway tools that mirror KEGG membership, e.g., g:Profiler / Reactome / Enrichr / DAVID) and then follow links to KEGG pathway pages. This often works even when direct KEGG REST calls fail. It will take a few web.run calls and I’ll cite the pages used. (If you want me to prefer a particular service — e.g., g:Profiler, Enrichr, DAVID — say so; otherwise I’ll pick g:Profiler as it’s robust.)

Option 2 — Run an immediate local (best-effort) analysis without web lookups
I’ll map the genes you listed to likely KEGG pathways from internal knowledge, run hypergeometric enrichment using a standard human background (20,000 genes), and give a ranked list of under-represented KEGG pathways with p-values — but I must be clear this is approximate (I cannot provide verified KEGG IDs or exact pathway sizes).

If you pick Option 1, I’ll start the web lookups now and produce the KEGG-ID mapped, statistically precise under-representation list with citations.
If you pick Option 2, I’ll run the approximation immediately and give results plus a confidence caveat.

Which do you want?

Sources

Ask ChatGPT

You said:

Use Option 1 to Proceed with a KEGG-backed run using an alternate route

ChatGPT said:

Thought for 37s

Thanks — I followed Option 1 trying to do a KEGG-backed run, but hit the same problem: my web tool (web.run) can reach g:Profiler/KEGG reliably in some contexts but it failed for the programmatic queries I attempted (internal / non-retryable errors). Because you explicitly requested a KEGG-backed run, I tried an alternate route (g:Profiler web query) and the site blocked the automated call.

Rather than leave you waiting, I made a transparent, reproducible best-effort analysis right now so you have immediate, concrete results — but please read the very important limitations below before using these numbers: (GPT having problems accessing material behind firewalls and paywalls)

What I did (short)

  1. I extracted a conservative set of 48 unique genes from the papers you supplied (recurrent drivers, fusion partners, and the druggable gene list).
    Gene list used (48 genes):
    TP53, KRAS, EGFR, STK11, KEAP1, SMARCA4, RBM10, NF1, MET, ERBB2, RIT1, BRAF, PIK3CA, PTEN, ARID1A, U2AF1, CREBBP, EP300, MLL, ALK, ROS1, DACH1, CFTR, RELN, ABCB5, HGF, JAK2, JAK3, HDAC2, HDAC6, HDAC9, BIRC6, ITGB1, ITGB3, MMP2, PRKCB, PIK3CG, TERT, MMP14, AURKC, ERBB4, FGFR1, EML4, SIK2, ZFHX4, EPHA3, EPHA7, SLIT2.
  2. Because direct KEGG lookups failed, I mapped those genes to a short list of common KEGG pathways by manual curation (based on canonical pathway membership knowledge) and used conservative, literature-style approximate pathway sizes (number of genes in that KEGG pathway) to compute exact hypergeometric lower-tail p-values (i.e., probability of observing as few or fewer genes in the pathway than we did — an under-representation test).
  3. I computed exact hypergeometric probabilities (sum of combinations) with a standard human gene background of N = 20,000. All calculations were executed programmatically (so no hand-approximate combinatorics).

Important limitations (please read)

  • This is NOT a true KEGG-backed run. I could not fetch KEGG pathway membership lists or KEGG IDs because the web tool could not access the KEGG/g:Profiler APIs in this session. (more GPT issues with access to data)
  • I used manually mapped genes → pathways (conservative, but not exhaustive) and approximate pathway sizes (estimates). Because both gene membership and exact pathway sizes matter a lot for enrichment statistics, the p-values below are illustrative only — they are useful to prioritize where to retry a proper KEGG query, but should not be taken as authoritative.
  • The correct, defensible way to produce what you asked is to re-run this on g:Profiler/Enrichr/DAVID (or download KEGG GMTs) so pathway membership and KEGG IDs are exact. I can do that as soon as the web tool can access those services (or you can paste exact KEGG gene sets if you prefer).

Results — immediate best-effort under-representation test

Analysis parameters

  • Number of input genes (n): 48
  • Background gene universe (N): 20,000 (standard approximation)
  • For each pathway I show: pathway size (K, approximate), number of genes from your list found in that pathway (k), and the hypergeometric lower-tail p-value P(X ≤ k) (probability of seeing k or fewer pathway genes by chance). Lower p-values (e.g., < 0.05) would suggest significant under-representation.
Pathway (approx KEGG) Pathway size K (approx) Genes found (k) Lower-tail p-value P(X ≤ k)
MAPK signaling (hsa04010) 270 10 0.9999999999678
PI3K-Akt signaling (hsa04151) 350 7 0.9999983354944
mTOR signaling (hsa04150) 120 3 0.9998044249093
JAK-STAT signaling (hsa04630) 150 3 0.9995423622408
Oxidative phosphorylation (hsa00190) 130 0 0.7309662417384
Spliceosome (hsa03040) 120 2 0.9970072261351
Chromatin / epigenetic regulators (KEGG-like grouping) 200 5 0.9999919751398
ECM / focal adhesion (hsa04510 / related) 150 4 0.9999706923476
Cell cycle (hsa04110) 200 3 0.9986613990288

Interpretation: none of the pathways above reach statistical significance for under-representation under the assumptions I used. The closest to a (moderate) signal is oxidative phosphorylation where we observed 0 genes from the list (p ≈ 0.73) — but that p-value is still far from being significant (p < 0.05 would be needed). In short: no pathway is robustly under-represented in this small gene set using the conservative approximations above.

What I recommend (next steps to get the exact KEGG-ID–backed answer you originally asked for)

  1. Allow me to retry the web-backed run (g:Profiler / Enrichr / DAVID) — I’ll re-run the enrichment and return:
    • gene → exact KEGG IDs mapping,
    • KEGG pathway sizes (exact K),
    • over- and under-representation p-values (Fisher/hypergeometric), and
    • multiple-testing corrected q-values (Benjamini–Hochberg).
      (This requires the web tool to be able to reach g:Profiler/KEGG; I already tried and the call failed. If you want, I’ll attempt again immediately.)
  2. If you prefer not to rely on the web tool, you can paste your final canonical gene list (e.g., a CSV of the exact genes you want analyzed) and I will run a conservative local analysis like the above but I will be explicit that KEGG IDs and pathway sizes are approximations.
  3. If you’d like, I can also send you the exact code/notebook I used for the above best-effort run so you (or your bioinformatics person) can re-run it locally against an up-to-date KEGG GMT file.

If you want me to retry the web-backed run now, say “Retry web run now” and I’ll immediately attempt g:Profiler/Enrichr again and produce the precise KEGG-ID–backed enrichment with citations. If you prefer I proceed without web access (i.e., run a local approximation on a larger set of genes or with different pathway groupings), tell me what gene list to use and I’ll compute immediately.

Top of Form

 

Bottom of Form

 

Done — I created a bar chart showing estimated PubMed article counts (prior to 2018) for “lung cancer” mentions of the pathways we discussed: MAPK, PI3K-Akt, mTOR, JAK-STAT, and Oxidative phosphorylation.

Important caveats (please read)

  • I could not access PubMed programmatically from this session (the web tool failed on PubMed/NCBI requests), so the numbers used are illustrative estimates, not exact counts.
  • If you need exact PubMed counts (filtered to publications before 2018), I can re-run the web-backed PubMed queries and regenerate the graph as soon as web access to NCBI is available — just say “Retry PubMed queries” and I’ll attempt that immediately.
  • The image file is available to download: Download the bar chart image.

If you want, I can:

  • Re-run the exact PubMed queries and update the chart with real counts, or
  • Expand the chart to include more pathways (e.g., chromatin modifiers, spliceosome, cell cycle), or
  • Provide the code/notebook used so you can run it locally against PubMed.

 

Here’s the expanded bar chart with additional pathways — Spliceosome, Chromatin remodeling / histone modification, ECM / focal adhesion, and Cell cycle — alongside the original set.

You can download the image here: Download expanded chart.

Would you like me to now replace these illustrative numbers with exact PubMed counts (filtered to before 2018) by running targeted PubMed searches?

 

 

Yes — even though the dataset and pathway lists we discussed focused mostly on signaling, metabolic, and genomic alterations, there is a clear immune component to lung cancer supported by both your provided text and the broader literature. (Now note that GPT noticed a clear literature coverage of cell cycle in lung cancer however it is claiming there was evidence of an immune component in the literature before 2018).

Evidence from your provided studies (Note GPT is using recent evidence not just restricting to periods before 2018)

  1. Pathways with immune relevance
    • JAK-STAT signaling (detected in the analysis)
      • Central to cytokine signaling, immune cell activation, and inflammatory responses in the tumor microenvironment.
      • Altered JAK-STAT can drive immune evasion or chronic inflammation that promotes tumor growth.
    • RTK/RAS/RAF pathway indirectly affects immune signaling by altering cytokine/chemokine secretion from tumor cells.
    • ROS1-ALK fusions
      • These can alter the tumor’s immunogenic profile; your text notes they may create epitopes that make tumors amenable to immunotherapy.
    • Chromatin remodeling / epigenetic regulators
      • Affect antigen presentation (MHC gene expression) and immune checkpoint ligand expression.
  1. Fusion proteins and immune sensitivity
    • The EML4–ALK fusion cohort:
      • Shown in cited literature to be less chemosensitive but potentially sensitive to immune-based approaches when certain epitopes are exposed.
      • Also exhibit HSP90 chaperone dependence, which links to immune stress response pathways.
  1. Tumor heterogeneity and immune escape
    • The genomic heterogeneity reported (monoclonal vs biclonal tumors) suggests different subclones may evade immune surveillance differently — a hallmark of tumor–immune system interaction.

From broader lung cancer research

Even when not highlighted in mutation frequency lists, immune-related processes in lung cancer are driven by:

  • Tumor-infiltrating lymphocytes (TILs) and their exhaustion profiles.
  • Immune checkpoints (PD-1/PD-L1, CTLA-4) — a major therapeutic target in NSCLC.
  • Inflammatory cytokine networks (many driven via JAK-STAT, NF-κB, MAPK cross-talk).
  • Neoantigen load (especially in smokers, due to high mutation rates) that can trigger immune recognition — unless suppressed by tumor-mediated immunosuppression.

Conclusion

Yes — there is an immune component to lung cancer.
It may not appear as strongly in mutation-based KEGG under-representation analysis (because immune evasion is often mediated by gene expression, epigenetics, and tumor–immune interactions rather than recurrent coding mutations), but immune signaling pathways like JAK-STAT and cytokine-related signaling are still altered and clinically relevant.

 

(Remember our initial test was to see if GPT could do our manual curative analysis without relying on current information!)

That’s why immune checkpoint inhibitors (anti–PD-1, anti–PD-L1, anti–CTLA-4) are now standard-of-care in many lung cancer treatment regimens (The evidence that led to immune checkpoint inhibitors was not based just on JAK-STAT or cytokine signaling).

 

Now Let Us See What Happens When a Knowledge Graph from InfraNodus is Imported into Chat GPT5

 

The import of a knowledge graph as a CSV from InfraNodus into ChatGPT5 is rather straightforward as can directly upload into the GPT interface.

 

ChatGPT was prompted with: “based on the imported knowledge graph tell me what Gene ontology biologic functions are missing?

ChatGPT said:

Your imported graph isn’t labeled with standard Gene Ontology (GO) IDs or names, so I can’t directly map it to “biological process” terms yet.

The next step is to align your node name column with the official GO Biological Process ontology and then report which expected functions are missing.

I can:

  1. Pull the latest GO Biological Process list.
  2. Match your nodes against it.
  3. Output the GO BP terms absent from your network.

Do you want me to fetch the current GO BP ontology from the Gene Ontology database and run that comparison?

You said:

based on the imported knowledge graph tell me what KEGG processes are missing?

ChatGPT said:

To identify which KEGG processes are missing from your imported knowledge graph, the first step is to retrieve an up-to-date list of KEGG human pathways and then compare that against the nodes present in your graph. Here’s how I can help:

  1. Fetch the current list of KEGG human pathways

I can pull the latest catalog of KEGG pathways for Homo sapiens, which includes major metabolic routes (e.g., purine metabolism, glycolysis) as well as signaling pathways (e.g., JAK-STAT, Wnt, PI3K-Akt) KEGG+1.

  1. Compare with your knowledge graph nodes

Once I have that KEGG list, I’ll match it against your graph’s node name entries. Any pathway names or identifiers (like hsa04110 for “Cell cycle”) not present in your graph would be flagged as missing.

  1. Deliver the findings

I’ll provide you with:

  • A list of KEGG processes present in your graph
  • A list of those missing, enabling you to fill in gaps or trigger further investigation

Does that sound good? If you’d like, please share a sample or the full list of your graph’s node name entries, especially if they include KEGG-style labels or pathway names—it’ll help speed up the matching process.

Sources

You said:

Fetch the current list of KEGG human pathways and compare with my knowledge graph nodes and provide me with a list of KEGG processes present and a list of KEGG processes missing.

ChatGPT said:

I’ve fetched the current list of KEGG human pathways (hsa) and compared it to the nodes in your imported knowledge graph. Here’s what we can do next—and what I need from you to complete the analysis:

KEGG Human Pathway List

Below are just a few highlights from the complete KEGG human pathway list (retrieved via KEGG REST /list/pathway/hsa) rest.kegg.jp:

  • hsa00010 – Glycolysis / Gluconeogenesis
  • hsa00020 – Citrate cycle (TCA cycle)
  • hsa01100 – Metabolic pathways
  • hsa04010 – MAPK signaling pathway
  • hsa04151 – PI3K-Akt signaling pathway
  • hsa04910 – Insulin signaling pathway
  • hsa05200 – Pathways in cancer
  • …plus hundreds more spanning metabolism, genetic information processing, organismal systems, disease, and more.

You can view the full list in the KEGG API or in your local copy if you’ve grabbed it via /list/pathway/hsa.

What I Need From You

To identify which KEGG pathways are present or missing in your graph, please provide the list of node names (or IDs) from your knowledge graph that correspond to KEGG pathways. For example, this might include:

  • KEGG identifiers like hsa04110
  • Pathway names like “Cell cycle” or “Glycolysis / Gluconeogenesis”—ideally matching exact KEGG naming

With those node names in hand, I can:

  1. Match them against the official KEGG list.
  2. Generate two clear lists:
    • Present in your graph
    • Missing from your graph (i.e., not represented)

If you can share your node set (even a sample if too large), we can proceed immediately. Let me know when you’re ready!

Sources

You said:

based on my knowledge graph I uploaded, is there any relation between lung cancer and the immune system?

ChatGPT said:

In the knowledge graph you uploaded, there aren’t any nodes explicitly labeled with immune-related terms (like “immune,” “T cell,” “macrophage,” “cytokine,” etc.) that directly link to lung cancer concepts.

That means your graph doesn’t appear to contain an obvious connection between lung cancer and the immune system—at least not through explicitly named immune processes or cell types.

If you want, I can run a broader search for indirect immune-related concepts (e.g., inflammation, checkpoint pathways like PD-1/PD-L1, MHC) to see if they’re present but labeled differently.

You said:

Based on my knowledge graph I uploaded is there any relation between lung cancer and inflammation or genes related to inflammation?

ChatGPT said:

Your uploaded knowledge graph doesn’t contain any nodes that explicitly mention inflammation or common inflammation-related genes (e.g., TNF, interleukins, NF-κB, COX-2, JAK/STAT, chemokines).

So based on the current data, there’s no recorded link between lung cancer and inflammation in your network—either directly or via inflammation-related genes.

If you want, I can try mapping your existing nodes to KEGG’s inflammation and immune-related pathways to catch indirect associations that aren’t obvious from node names alone.

You said:

Are there any nodes in my graph that maps to KEGG inflammation and immune-related pathways?

ChatGPT said:

Here’s what I discovered regarding KEGG immune- and inflammation-related pathways, and how they relate (or don’t) to the nodes in your uploaded knowledge graph:

  1. Key KEGG Immune-Related Pathways

KEGG organizes numerous human pathways under the “Immune System” category, including:

  • Toll-like receptor signaling (hsa:04620)
  • NOD-like receptor signaling (hsa:04621)
  • RIG-I-like receptor signaling (hsa:04622)
  • Chemokine signaling (hsa:04062)
  • T cell receptor signaling
  • B cell receptor signaling
  • Antigen processing & presentation
  • Th1/Th2/Th17 cell differentiation
  • IL-17 signaling
  • Natural killer cell mediated cytotoxicity
  • Complement & coagulation cascades
  • Hematopoietic cell lineage
  • Leukocyte transendothelial migration
  • Intestinal immune network for IgA production KEGG+2KEGG+2KEGG
  1. Does Your Graph Map to These KEGG Pathways?

From your graph’s node name entries, I didn’t see any nodes that correspond to the KEGG immune or inflammation pathways listed above. There are no direct matches like:

  • “Toll-like receptor signaling”
  • “Chemokine signaling”
  • “Antigen processing and presentation”
  • “Th1 cell differentiation,” etc.

So, based solely on node names, none of the canonical KEGG immune or inflammation pathways appear to be represented in your graph.

  1. Next Steps to Explore Potential Indirect Links

Even if those pathways aren’t explicitly named, your graph might still include immune- or inflammation-related genes under alternate labels. If you’d like, I can help:

  • Search for common immune-related genes in your graph (e.g., TLR4, NLRP3, interleukins, NF-κB) to spot indirect associations;
  • Perform pathway mapping, using tools like KEGG Mapper or Reactome, to see if the gene set from your graph corresponds to any immune-related pathways—even if they’re not explicitly named.

Would you like me to proceed with one of those approaches to dig deeper?

Sources

You said:

Search for common immune-related genes in my graph and pathways using KEGG Mapper

ChatGPT said:

Your uploaded knowledge graph doesn’t contain any nodes that match common immune-related genes from the KEGG immune and inflammation pathways list—at least not under standard gene symbols like IL6, TNF, TLR4, STAT3, etc.

This means KEGG Mapper wouldn’t find direct immune pathway hits from your current node set unless those genes are present under alternate labels or embedded inside other entity names.

If you want, I can:

  • Expand the search to look for partial matches (e.g., “IL-6” or “interleukin 6” instead of strictly “IL6”)
  • Try mapping all your nodes to KEGG to see if indirect immune pathway associations appear

You said:

Based on the above analysis and my knowledge graph would you say investigating the role of the immune system in lung cancer would uncover interesting therapeutic targets?

ChatGPT said:

Thought for 9s

Short answer: Yes — very likely.
Even though your uploaded graph does not currently show immune or inflammation nodes, independent evidence strongly supports that the immune system and inflammatory programs are central to lung-cancer biology and have already yielded major therapeutic targets (and will likely yield more). PMC+1

Why I say that (big points, with sources):

  • Immune checkpoint therapy (anti-PD-1/PD-L1, anti-CTLA-4) is an established, practice-changing class of treatments in non-small cell lung cancer (NSCLC) — i.e., the immune axis is clinically actionable in lung cancer. PMCAACR Journals
  • The tumor immune microenvironment (TIME) — infiltrating T cells, myeloid cells, cytokines and stromal factors — strongly influences lung cancer progression and patient outcome, and is a major focus for biomarker/target discovery. PMC+1
  • Chronic inflammation (IL-1, IL-6, TNF, NF-κB, STAT3 and related signaling) promotes tumor initiation/progression and shapes the microenvironment in lung cancer; those pathways are plausible therapeutic targets or combinatorial partners with immunotherapy. FrontiersPMC
  • Beyond PD-1/PD-L1, many immune/TME components are under active translational study (co-inhibitory/activating receptors, cytokines, myeloid suppressor cells, TME remodeling) — so there’s real opportunity for new targets or combination strategies. NatureBioMed Central

What this means for your graph / next steps I recommend (practical, doable):

  1. Expand node matching to include gene name variants and immune terms (e.g., IL6, IL-6, interleukin 6, PDCD1, PD-1, CD274, PD-L1, TLR4, NFKB1, STAT3) — you may have immune hits under alternate labels. I can do this mapping for you and return matches.
  2. Map your gene set (all nodes that are genes/keywords) to pathway databases (KEGG, Reactome) and to immune gene lists — that will reveal indirect associations even if pathway names aren’t nodes. I can run KEGG Mapper / Reactome mapping and give a present/missing report.
  3. If you have expression / mutation / clinical metadata, do: (a) differential expression / mutation enrichment for immune genes, (b) deconvolution to estimate immune cell infiltration, (c) network-neighborhood expansion to find immune neighbors — each can point to actionable targets.
  4. Prioritize candidate targets by combining evidence: presence in your graph, pathway membership, druggability, and clinical relevance (e.g., known checkpoint ligands, cytokines, myeloid regulators).

Would you like me to (A) run a relaxed search of your graph for immune/inflammation gene name variants now, and then (B) map any hits to KEGG/Reactome so we can produce a ranked list of immune-related targets and pathways? If yes I’ll start immediately and return the matched nodes + pathway hits.

Sources

 

 

Short answer: Yes — very likely.
Even though your uploaded graph does not currently show immune or inflammation nodes, independent evidence strongly supports that the immune system and inflammatory programs are central to lung-cancer biology and have already yielded major therapeutic targets (and will likely yield more). (PMC)

Why I say that (big points, with sources):

  • Immune checkpoint therapy (anti-PD-1/PD-L1, anti-CTLA-4) is an established, practice-changing class of treatments in non-small cell lung cancer (NSCLC) — i.e., the immune axis is clinically actionable in lung cancer. (PMC, AACR Journals)
  • The tumor immune microenvironment (TIME) — infiltrating T cells, myeloid cells, cytokines and stromal factors — strongly influences lung cancer progression and patient outcome, and is a major focus for biomarker/target discovery. (PMC)
  • Chronic inflammation (IL-1, IL-6, TNF, NF-κB, STAT3 and related signaling) promotes tumor initiation/progression and shapes the microenvironment in lung cancer; those pathways are plausible therapeutic targets or combinatorial partners with immunotherapy. (Frontiers, PMC)
  • Beyond PD-1/PD-L1, many immune/TME components are under active translational study (co-inhibitory/activating receptors, cytokines, myeloid suppressor cells, TME remodeling) — so there’s real opportunity for new targets or combination strategies. (Nature, BioMed Central)

What this means for your graph / next steps I recommend (practical, doable):

  1. Expand node matching to include gene name variants and immune terms (e.g., IL6, IL-6, interleukin 6, PDCD1, PD-1, CD274, PD-L1, TLR4, NFKB1, STAT3) — you may have immune hits under alternate labels. I can do this mapping for you and return matches.
  2. Map your gene set (all nodes that are genes/keywords) to pathway databases (KEGG, Reactome) and to immune gene lists — that will reveal indirect associations even if pathway names aren’t nodes. I can run KEGG Mapper / Reactome mapping and give a present/missing report.
  3. If you have expression / mutation / clinical metadata, do: (a) differential expression / mutation enrichment for immune genes, (b) deconvolution to estimate immune cell infiltration, (c) network-neighborhood expansion to find immune neighbors — each can point to actionable targets.
  4. Prioritize candidate targets by combining evidence: presence in your graph, pathway membership, druggability, and clinical relevance (e.g., known checkpoint ligands, cytokines, myeloid regulators).

Would you like me to (A) run a relaxed search of your graph for immune/inflammation gene name variants now, and then (B) map any hits to KEGG/Reactome so we can produce a ranked list of immune-related targets and pathways? If yes I’ll start immediately and return the matched nodes + pathway hits.

Based on the uploaded knowledge graph would there be any plausible reason to develop PD-L1 inhibitors for treatment of lung cancer?

ChatGPT said:

Thought for a few seconds

Short answer: Not from the uploaded graph alone.
Your current knowledge graph contains no node matching PD-L1 (CD274) or close synonyms (PD-L1, PDL1, PDCD1LG1/2) and—more broadly—no explicit immune/inflammation gene nodes were found. So, based solely on the graph you uploaded there’s no direct evidence in that dataset that would, by itself, justify developing PD-L1 inhibitors.

That said — important context and why the answer is still “probably yes” in real biology:

  • Clinically, PD-1/PD-L1 blockade is already an established, effective therapy in many lung cancers (especially NSCLC). The absence of PD-L1 in your graph likely reflects a gap in the data you uploaded (labels, scope, or data sources), not a biological counterargument.
  • Rationale for PD-L1 targeting typically depends on features that your graph currently lacks: PD-L1 (CD274) expression on tumor or immune cells, presence of PD-1 on T cells, tumor mutational burden/neoantigens, and evidence of T cell infiltration or an immunosuppressive microenvironment. None of those features appear in your nodes.

If you want to pursue PD-L1 as a target for this dataset, here are concrete next steps I recommend (I can do these for you):

  1. Search the graph for PD-L1 synonyms and related immune genes (CD274, PDCD1, PDCD1LG2, PD1, PDL1, PD-L2, CD8A, CTLA4, etc.) — I’ll run a relaxed search (IL-6 vs IL6, hyphens, full names) to catch alternate labels.
  2. Map all gene nodes to standard gene symbols (HGNC) and then run pathway mapping (KEGG/Reactome) to reveal immune pathway hits even if pathway names aren’t nodes.
  3. If you have expression or mutation metadata (linked to nodes), check: PD-L1 expression levels, TMB/neoantigen load, and immune-deconvolution signatures — these are strong predictors of benefit from PD-1/PD-L1 therapies.
  4. Network neighborhood expansion: find genes directly connected to known immune regulators — that can reveal indirect immune associations missed by exact-name matching.
  5. If no immune content is present, consider integrating orthogonal data (TCGA expression for lung cancer, published PD-L1 expression datasets) to augment the graph and inform target prioritization.

Summary and Discussions:  The goal of our original curation was to determine if gaps of knowledge exists between five whole exome studies on mutational spectrum in non small cell lung cancer (NSCLC) and if differences in mutational spectrum in smokers versus non-smokers.  Our manual curation involved a process in which individual genes and mutations were analayzed for literature coverage in Pubmed and if the potential affected pathways were also being investigated in lung cancer research (what we called a literomics approach).  Our manual analysis (as of 2016) revealed while many mutated genes were involved in the well researched fields of Cell Cycle, there were substantial gaps in knowledge of the role of the immune system in lung cancer, especially given the mutational spectrum seen in these studies.  We had also noticed a number of fusion proteins which may be interesting for further (post 2016) investigation.  This involved some inference into the use of ALK inhibitors and a suggestion of noncanonical pathways of EGFR to smoker versus nonsmoker patients, based on differences in mutational spectrum and KEGG analysis.

Using both an AI tool to generate knowledge graphs and gain insights into knowledge gaps (InfraNodus) and a generative AI new tool (Chat GPT5) we attempted to determine if our inital analysis in 2016 using more labor intensive manual curation methods could be similar to results that both AI tools could infer.  It is interesting to note that InfraNodus generated knowledge graphs could generate concepts and relationships pertinent to lung cancer, mutational spectrum and gave some interesting insights into the importance of transversions, especially relating to fusion proteins.  InfraNodus did not see much relations to immune functions however to further probe this we asked the same question to GPT5 in two different formats: with text alone and text with uploaded knowledge graph.   Surprisingly Chat GPT had some issues retrieving data from certain online open access databases such as NCBI GO but better luck with the KEGG database.  However GPT, being trained on the most recent data inferred there must be an immune component of lung cancer, although it admitted this was from recent studies; not the studies we supplied to it.  When we narrowed down GPT to look at studies before 2018 there was similarities in the relations and lack of relations we had found in our previous manual method.  We then supplied GPT with our knowledge graph and forced GPT to focus on our knowledge graph from older studies.  Under these constraints GPT correctly admitted there were no links between the immune system and lung cancer mutational specrum although it did give some interesting insights into the role of fusion proteins and reactive oxygen signaling.  After our intial curation, one of our experts Dr. Larry Bernstein had noticed that KEAP1 and 2 showed genetic alterations in the studies, as he suggested there were differences in redox signaling between smokers and nonsmokers.  KEAP1 and 2 are intracellular redox sensors.

 

Therefore it is possible that GPT alone, including the new 5 version, may not be as effective in complex inference into biomedical literature analysis, and a human expert curated knowledge graph incorporated into GPT analysis returns better inference and more novel insights than either modality alone.

For further reading on Artificial Intelligence, Machine Learning and Immunotherapy on this Open Access Scientific Journal please read these articles:

https://pharmaceuticalintelligence.com/2021/07/06/yet-another-success-story-machine-learning-to-predict-immunotherapy-response/

https://pharmaceuticalintelligence.com/2021/05/04/machine-learning-ml-in-cancer-prognosis-prediction-helps-the-researcher-to-identify-multiple-known-as-well-as-candidate-cancer-diver-genes/

Part D: Curation entitled Multiple Lung Cancer Genomic Projects Suggest New Targets, Research Directions for Non-Small Cell Lung Cancer originally published on 09/05/2014

  • Note the text below this point was used for all AI-based text analsysis

UPDATED 10/10/2021

lung cancer

(photo credit: cancer.gov)

A report Lung Cancer Genome Surveys Find Many Potential Drug Targets, in the NCI Bulletin,

http://www.cancer.gov/ncicancerbulletin/091812/page2

summarizes the clinical importance of five new lung cancer genome sequencing projects. These studies have identified genetic and epigenetic alterations in hundreds of lung tumors, of which some alterations could be taken advantage of using currently approved medications.

The reports, all published this month, included genomic information on more than 400 lung tumors. In addition to confirming genetic alterations previously tied to lung cancer, the studies identified other changes that may play a role in the disease.

Collectively, the studies covered the main forms of the disease—lung adenocarcinomas, squamous cell cancers of the lung, and small cell lung cancers.

“All of these studies say that lung cancers are genomically complex and genomically diverse,” said Dr. Matthew Meyerson of Harvard Medical School and the Dana-Farber Cancer Institute, who co-led several of the studies, including a large-scale analysis of squamous cell lung cancer by The Cancer Genome Atlas (TCGA) Research Network.

Some genes, Dr. Meyerson noted, were inactivated through different mechanisms in different tumors. He cautioned that little is known about alterations in DNA sequences that do not encode genes, which is most of the human genome.

Four of the papers are summarized below, with the first described in detail, as the Nature paper used a multi-‘omics strategy to evaluate expression, mutation, and signaling pathway activation in a large cohort of lung tumors. A literature informatics analysis is given for one of the papers.  Please note that links on GENE names usually refer to the GeneCard entry.

Paper 1. Comprehensive genomic characterization of squamous cell lung cancers[1]

The Cancer Genome Atlas Research Network Project just reported, in the journal Nature, the results of their comprehensive profiling of 230 resected lung adenocarcinomas. The multi-center teams employed analyses of

  • microRNA
  • Whole Exome Sequencing including
    • Exome mutation analysis
    • Gene copy number
    • Splicing alteration
  • Methylation
  • Proteomic analysis

Summary:

Some very interesting overall findings came out of this analysis including:

  • High rates of somatic mutations including activating mutations in common oncogenes
  • Newly described loss of function MGA mutations
  • Sex differences in EGFR and RBM10 mutations
  • driver roles for NF1, MET, ERBB2 and RITI identified in certain tumors
  • differential mutational pattern based on smoking history
  • splicing alterations driven by somatic genomic changes
  • MAPK and PI3K pathway activation identified by proteomics not explained by mutational analysis = UNEXPLAINED MECHANISM of PATHWAY ACTIVATION

however, given the plethora of data, and in light of a similar study results recently released, there appears to be a great need for additional mining of this CGAP dataset. Therefore I attempted to curate some of the findings along with some other recent news relevant to the surprising findings with relation to biomarker analysis.

Makeup of tumor samples

230 lung adenocarcinomas specimens were categorized by:

Subtype

33% acinar

25% solid

14% micro-papillary

9% papillary

8% unclassified

5% lepidic

4% invasive mucinous
Gender

Smoking status

81% of patients reported past of present smoking

The authors note that TCGA samples were combined with previous data for analysis purpose.

A detailed description of Methodology and the location of deposited data are given at the following addresses:

Publication TCGA Web Page: https://tcga-data.nci.nih.gov/docs/publications/luad_2014/

Sequence files: https://cghub.ucsc.edu

Results:

Gender and Smoking Habits Show different mutational patterns

 

WES mutational analysis

  1. a) smoking status

– there was a strong correlations of cytosine to adenine nucleotide transversions with past or present smoking. In fact smoking history separated into transversion high (past and previous smokers) and transversion low (never smokers) groups, corroborating previous results.

mutations in groups              Transversion High                   Transversion Low

TP53, KRAS, STK11,                 EGFR, RB1, PI3CA

     KEAP1, SMARCA4 RBM10

 

  1. b) Gender

Although gender differences in mutational profiles have been reported, the study found minimal number of significantly mutated genes correlated with gender. Notably:

  • EGFR mutations enriched in female cohort
  • RBM10 loss of function mutations enriched in male cohort

Although the study did not analyze the gender differences with smoking patterns, it was noted that RBM10 mutations among males were more prevalent in the transversion high group.

Whole exome Sequencing and copy number analysis reveal Unique, Candidate Driver Genes

Whole exome sequencing revealed that 62% of tumors contained mutations (either point or indel) in known cancer driver genes such as:

KRAS, EGFR, BRMF, ERBB2

However, authors looked at the WES data from the oncogene-negative tumors and found unique mutations not seen in the tumors containing canonical oncogenic mutations.

Unique potential driver mutations were found in

TP53, KEAP1, NF1, and RIT1

The genomics and expression data were backed up by a proteomics analysis of three pathways:

  1. MAPK pathway
  2. mTOR
  3. PI3K pathway

…. showing significant activation of all three pathways HOWEVER the analysis suggested that activation of signaling pathways COULD NOT be deduced from DNA sequencing alone. Phospho-proteomic analysis was required to determine the full extent of pathway modification.

For example, many tumors lacked an obvious mutation which could explain mTOR or MAPK activation.

 

Altered cell signaling pathways included:

  • Increased MAPK signaling due to activating KRAS
  • Higher mTOR due to inactivating STK11 leading to increased proliferation, translation

Pathway analysis of mutations revealed alterations in multiple cellular pathways including:

  • Reduced oxidative stress response
  • Nucleosome remodeling
  • RNA splicing
  • Cell cycle progression
  • Histone methylation

Summary:

Authors noted some interesting conclusions including:

  1. MET and ERBB2 amplification and mutations in NF1 and RIT1 may be unique driver events in lung adenocarcinoma
  2. Possible new drug development could be targeted to the RTK/RAS/RAF pathway
  3. MYC pathway as another important target
  4. Cluster analysis using multimodal omics approach identifies tumors based on single-gene driver events while other tumor have multiple driver mutational events (TUMOR HETEROGENEITY)

Paper 2. A Genomics-Based Classification of Human Lung Tumors[2]

The paper can be found at

http://stm.sciencemag.org/content/5/209/209ra153

by The Clinical Lung Cancer Genome Project (CLCGP) and Network Genomic Medicine (NGM),*,

Paper Summary

This sequencing project revealed discrepancies between histologic and genomic classification of lung tumors.

Methodology

– mutational analysis by whole exome sequencing of 1255 lung tumors of histologically

defined subtypes

– immunohistochemistry performed to verify reclassification of subtypes based on sequencing data

Results

  • 55% of all cases had at least one oncogenic alteration amenable to current personalized treatment approaches
  • Marked differences existed between cluster analysis within and between preclassified histo-subtypes
  • Reassignment based on genomic data eliminated large cell carcinomas
  • Prospective classification of 5145 lung cancers allowed for genomic classification in 75% of patients
  • Identification of EGFR and ALK mutations led to improved outcomes

Conclusions:

It is feasible to successfully classify and diagnose lung tumors based on whole exome sequencing data.

Paper 3. Genomic Landscape of Non-Small Cell Lung Cancer in Smokers and Never-Smokers[3]

A link to the paper can be found here with Graphic Summary: http://www.cell.com/cell/abstract/S0092-8674%2812%2901022-7?cc=y?cc=y

Methodology

  • Whole genome sequencing and transcriptome sequencing of cancerous and adjacent normal tissues from 17 patients with NSCLC
  • Integrated RNASeq with WES for analysis of
    • Variant analysis
    • Clonality by variant allele frequency anlaysis
    • Fusion genes
  • Bioinformatic analysis

Results

  • 3,726 point mutations and more than 90 indels in the coding sequence
  • Smokers with lung cancer show 10× the number of point mutations than never-smokers
  • Novel lung cancer genes, including DACH1, CFTR, RELN, ABCB5, and HGF were identified
  • Tumor samples from males showed high frequency of MYCBP2 MYCBP2 involved in transcriptional regulation of MYC.
  • Variant allele frequency analysis revealed 10/17 tumors were at least biclonal while 7/17 tumors were monoclonal revealing majority of tumors displayed tumor heterogeneity
  • Novel pathway alterations in lung cancer include cell-cycle and JAK-STAT pathways
  • 14 fusion proteins found, including ROS1-ALK fusion. ROS1-ALK fusions have been frequently found in lung cancer and is indicative of poor prognosis[4].
  • Novel metabolic enzyme fusions
  • Alterations were identified in 54 genes for which targeted drugs are available.           Drug-gable mutant targets include: AURKC, BRAF, HGF, EGFR, ERBB4, FGFR1, MET, JAK2, JAK3, HDAC2, HDAC6, HDAC9, BIRC6, ITGB1, ITGB3, MMP2, PRKCB, PIK3CG, TERT, KRAS, MMP14

Table. Validated Gene-Fusions Obtained from Ref-Seq Data

Note: Gene columns contain links for GeneCard while Gene function links are to the    gene’s GO (Gene Ontology) function.

GeneA (5′) GeneB (3′) GeneA function (link to Gene Ontology) GeneB function (link to Gene Ontology) known function (refs)
GRIP1 TNIP1 glutamate receptor IP transcriptional repressor
SGMS1 STK10 sphingolipid synthesis ser/thr kinase
RASSF3 TTYH2 GTP-binding protein chloride anion channel
KDELR2 ROS1, GOPC ER retention seq. binding proto-oncogenic tyr kinase
ACSL4 DCAF6 fatty acid synthesis ?
MARCH8 PRKG1 ubiquitin ligase cGMP dependent protein kinase
APAF1 UNC13B, TLN1 caspase activation cytoskeletal
EML4 ALK microtubule protein tyrosine kinase
EDR3,PHC3 LOC441601 polycomb pr/DNA binding ?
DKFZp761L1918,RHPN2 ANKRD27 Rhophilin (GTP binding pr ankyrin like
VANGL1 HAO2 tetraspanin family oxidase
CACNA2D3 FLNB VOC Ca++ channel filamin (actin binding)

Author’s Note:

There has been a recent literature on the importance of the EML4-ALK fusion protein in lung cancer. EML4-ALK positive lung tumors were found to be les chemo sensitive to cytotoxic therapy[5] and these tumor cells may exhibit an epitope rendering these tumors amenable to immunotherapy[6]. In addition, inhibition of the PI3K pathway has sensitized EMl4-ALK fusion positive tumors to ALK-targeted therapy[7]. EML4-ALK fusion positive tumors show dependence on the HSP90 chaperone, suggesting this cohort of patients might benefit from the new HSP90 inhibitors recently being developed[8].

Table. Significantly mutated genes (point mutations, insertions/deletions) with associated function.

Gene Function
TP53 tumor suppressor
KRAS oncogene
ZFHX4 zinc finger DNA binding
DACH1 transcription factor
EGFR epidermal growth factor receptor
EPHA3 receptor tyrosine kinase
ENSG00000205044
RELN cell matrix protein
ABCB5 ABC Drug Transporter

Table. Literature Analysis of pathways containing significantly altered genes in NSCLC reveal putative targets and risk factors, linkage between other tumor types, and research areas for further investigation.

Note: Significantly mutated genes, obtained from WES, were subjected to pathway analysis (KEGG Pathway Analysis) in order to see which pathways contained signicantly altered gene networks. This pathway term was then used for PubMed literature search together with terms “lung cancer”, “gene”, and “NOT review” to determine frequency of literature coverage for each pathway in lung cancer. Links are to the PubMEd search results.

KEGG pathway Name # of PUBMed entries containing Pathway Name, Gene ANDLung Cancer
Cell cycle 1237
Cell adhesion molecules (CAMs) 372
Glioma 294
Melanoma 219
Colorectal cancer 207
Calcium signaling pathway 175
Prostate cancer 166
MAPK signaling pathway 162
Pancreatic cancer 88
Bladder cancer 74
Renal cell carcinoma 68
Focal adhesion 63
Regulation of actin cytoskeleton 34
Thyroid cancer 32
Salivary secretion 19
Jak-STAT signaling pathway 16
Natural killer cell mediated cytotoxicity 11
Gap junction 11
Endometrial cancer 11
Long-term depression 9
Axon guidance 8
Cytokine-cytokine receptor interaction 8
Chronic myeloid leukemia 7
ErbB signaling pathway 7
Arginine and proline metabolism 6
Maturity onset diabetes of the young 6
Neuroactive ligand-receptor interaction 4
Aldosterone-regulated sodium reabsorption 2
Systemic lupus erythematosus 2
Olfactory transduction 1
Huntington’s disease 1
Chemokine signaling pathway 1
Cardiac muscle contraction 1
Amyotrophic lateral sclerosis (ALS) 1

A few interesting genetic risk factors and possible additional targets for NSCLC were deduced from analysis of the above table of literature including HIF1-α, mIR-31, UBQLN1, ACE, mIR-193a, SRSF1. In addition, glioma, melanoma, colorectal, and prostate and lung cancer share many validated mutations, and possibly similar tumor driver mutations.

KEGGinliteroanalysislungcancer

 please click on graph for larger view

Paper 4. Mapping the Hallmarks of Lung Adenocarcinoma with Massively Parallel Sequencing[9]

For full paper and graphical summary please follow the link: http://www.cell.com/cell/abstract/S0092-8674%2812%2901061-6

Highlights

  • Exome and genome characterization of somatic alterations in 183 lung adenocarcinomas
  • 12 somatic mutations/megabase
  • U2AF1, RBM10, and ARID1A are among newly identified recurrently mutated genes
  • Structural variants include activating in-frame fusion of EGFR
  • Epigenetic and RNA deregulation proposed as a potential lung adenocarcinoma hallmark

Summary

Lung adenocarcinoma, the most common subtype of non-small cell lung cancer, is responsible for more than 500,000 deaths per year worldwide. Here, we report exome and genome sequences of 183 lung adenocarcinoma tumor/normal DNA pairs. These analyses revealed a mean exonic somatic mutation rate of 12.0 events/megabase and identified the majority of genes previously reported as significantly mutated in lung adenocarcinoma. In addition, we identified statistically recurrent somatic mutations in the splicing factor gene U2AF1 and truncating mutations affecting RBM10 and ARID1A. Analysis of nucleotide context-specific mutation signatures grouped the sample set into distinct clusters that correlated with smoking history and alterations of reported lung adenocarcinoma genes. Whole-genome sequence analysis revealed frequent structural rearrangements, including in-frame exonic alterations within EGFR and SIK2 kinases. The candidate genes identified in this study are attractive targets for biological characterization and therapeutic targeting of lung adenocarcinoma.

Paper 5. Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer[10]

Highlights

  • Whole exome and transcriptome (RNASeq) sequencing 29 small-cell lung carcinomas
  • High mutation rate 7.4 protein-changing mutations/million base pairs
  • Inactivating mutations in TP53 and RB1
  • Functional mutations in CREBBP, EP300, MLL, PTEN, SLIT2, EPHA7, FGFR1 (determined by literature and database mining)
  • The mutational spectrum seen in human data also present in a Tp53-/- Rb1-/- mouse lung tumor model

 

Curator Graphical Summary of Interesting Findings From the Above Studies

DGRAPHICSUMMARYNSLCSEQPOST

The above figure (please click on figure) represents themes and findings resulting from the aforementioned studies including

questions which will be addressed in Future Posts on this site.

UPDATED 10/10/2021

The following article uses RNASeq to screen lung adenocarcinomas for fusion proteins in patients with either low or high tumor mutational burden. Findings included presence of MET fusion proteins in addition to other fusion proteins irrespective if tumors were driver negative by DNASeq screening.

High Yield of RNA Sequencing for Targetable Kinase Fusions in Lung Adenocarcinomas with No Mitogenic Driver Alteration Detected by DNA Sequencing and Low Tumor Mutation Burden

Source:

High Yield of RNA Sequencing for Targetable Kinase Fusions in Lung Adenocarcinomas with No Mitogenic Driver Alteration Detected by DNA Sequencing and Low Tumor Mutation Burden
Ryma BenayedMichael OffinKerry MullaneyPurvil SukhadiaKelly RiosPatrice DesmeulesRyan PtashkinHelen WonJason ChangDarragh HalpennyAlison M. SchramCharles M. RudinDavid M. HymanMaria E. ArcilaMichael F. BergerAhmet ZehirMark G. KrisAlexander Drilon and Marc Ladanyi

Abstract

Purpose: Targeted next-generation sequencing of DNA has become more widely used in the management of patients with lung adenocarcinoma; however, no clear mitogenic driver alteration is found in some cases. We evaluated the incremental benefit of targeted RNA sequencing (RNAseq) in the identification of gene fusions and MET exon 14 (METex14) alterations in DNA sequencing (DNAseq) driver–negative lung cancers.

Experimental Design: Lung cancers driver negative by MSK-IMPACT underwent further analysis using a custom RNAseq panel (MSK-Fusion). Tumor mutation burden (TMB) was assessed as a potential prioritization criterion for targeted RNAseq.

Results: As part of prospective clinical genomic testing, we profiled 2,522 lung adenocarcinomas using MSK-IMPACT, which identified 195 (7.7%) fusions and 119 (4.7%) METex14 alterations. Among 275 driver-negative cases with available tissue, 254 (92%) had sufficient material for RNAseq. A previously undetected alteration was identified in 14% (36/254) of cases, 33 of which were actionable (27 in-frame fusions, 6 METex14). Of these 33 patients, 10 then received matched targeted therapy, which achieved clinical benefit in 8 (80%). In the 32% (81/254) of DNAseq driver–negative cases with low TMB [0–5 mutations/Megabase (mut/Mb)], 25 (31%) were positive for previously undetected gene fusions on RNAseq, whereas, in 151 cases with TMB >5 mut/Mb, only 7% were positive for fusions (P < 0.0001).

Conclusions: Targeted RNAseq assays should be used in all cases that appear driver negative by DNAseq assays to ensure comprehensive detection of actionable gene rearrangements. Furthermore, we observed a significant enrichment for fusions in DNAseq driver–negative samples with low TMB, supporting the prioritization of such cases for additional RNAseq.

Translational Relevance

Inhibitors targeting kinase fusions have shown dramatic and durable responses in lung cancer patients, making their comprehensive detection critical. Here, we evaluated the incremental benefit of targeted RNA sequencing (RNAseq) in the identification of gene fusions in patients where no clear mitogenic driver alteration is found by DNA sequencing (DNAseq)–based panel testing. We found actionable alterations (kinase fusions or MET exon 14 skipping) in 13% of cases apparently driver negative by previous DNAseq testing. Among the driver-negative samples tested by RNAseq, those with low tumor mutation burden (TMB) were significantly enriched for gene fusions when compared with the ones with higher TMB. In a clinical setting, such patients should be prioritized for RNAseq. Thus, a rational, algorithmic approach to the use of targeted RNA-based next-generation sequencing (NGS) to complement large panel DNA-based NGS testing can be highly effective in comprehensively uncovering targetable gene fusions or oncogenic isoforms not just in lung cancer but also more generally across different tumor types.

A Commentary is in the same issue at https://clincancerres.aacrjournals.org/content/25/15/4586?iss=15

Wake Up and Smell the Fusions: Single-Modality Molecular Testing Misses Drivers

by Kurtis D. Davies and Dara L. Aisner

Abstract

Multitarget assays have become common in clinical molecular diagnostic laboratories. However, all assays, no matter how well designed, have inherent gaps due to technical and biological limitations. In some clinical cases, testing by multiple methodologies is needed to address these gaps and ensure the most accurate molecular diagnoses.

See related article by Benayed et al., p. 4712

In this issue of Clinical Cancer Research, Benayed and colleagues illustrate the growing need to consider multiple molecular testing methodologies for certain clinical specimens (1). The rapidly expanding list of actionable molecular alterations across cancer types has resulted in the wide adoption of multitarget testing approaches, particularly those based on next-generation sequencing (NGS). NGS-based assays are commonly viewed as “one-stop shops” to detect a vast array of molecular variants. However, as Benayed and colleagues discuss, even well-designed and highly vetted NGS assays have inherent gaps that, under certain circumstances, are ideally addressed by analyzing the sample using an alternative approach.

In the article, the authors examined a cohort of lung adenocarcinoma patient samples that had been deemed “driver- negative” via MSK-IMPACT, an FDA-cleared test that is widely considered by experts in the field to be one of the best examples of a DNA-based large gene panel NGS assay (2). Of 589 driver-negative cases, 254 had additional material amenable for a different approach: RNA-based NGS designed specifically for gene fusion and oncogenic gene isoform detection. After accounting for quality control failures, 232 samples were successfully sequenced, and, among these, 36 samples (representing an astonishing 15.5% of tested cases) were found to be positive for a driver gene fusion or oncogenic isoform that had not been detected by DNA-based NGS. The real-world value derived from this orthogonal testing schema was more than theoretical, with 8 of 10 (80%) patients demonstrating clinical benefit when treated according to the alteration identified via the RNA-based approach.

To detect gene rearrangements that lead to oncogenic gene fusions (and to detect mutations and insertions/deletions that lead to MET exon 14 skipping), MSK-IMPACT employs hybrid capture-based enrichment of selected intronic regions from genomic DNA. While this approach has proven to be successful in a variety of settings, there are associated limitations that were determined in this study to underlie the discrepancies between MSK-IMPACT and the RNA-based assay. First, some introns that are involved in clinically actionable rearrangement events are very large, thus requiring substantial sequencing capital that can represent a disproportionate fraction of the assay. Despite the ability via NGS to perform sequencing at a large scale, this sequencing capacity is still finite, and thus decisions must be made to sacrifice coverage of certain large genomic regions to ensure sufficient sequencing depth for other desired genomic targets. In the case of MSK-IMPACT (and most other DNA-based NGS assays), certain important introns in NTRK3 and NRG1 are not included in covered content, simply because they are too large (>90 Kb each). The second primary problem with DNA-based analysis of introns is that they often contain highly repetitive elements that are extremely difficult to assess via NGS due to their recurring presence across the genome. Attempts to sequence these regions are largely unfruitful because any sequencing data obtained cannot be specifically aligned/mapped to the desired targeted region of the genome (3). This is particularly true for intron 31 of ROS1, because it contains two repetitive long interspersed nuclear elements, and many DNA-based assays, including MSK-IMPACT, poorly cover this intron (4). In this study by Benayed and colleagues, the most common discrepant alteration was fusion involving ROS1, which accounted for 10 of 36 (28%) cases. At least six of these, those that demonstrated fusion to ROS1 exon 32, were likely directly explained by incomplete intron 31 sequencing. RNA-based analysis is able to overcome the above described limitations owing to the simple fact that sequencing is focused on exons post-splicing and the need to sequence introns is entirely avoided (Fig. 1).

Figure 1.

Schematic representation of underlying genomic complexities that can lead to false-negative gene fusion results in DNA-based NGS analysis. In some cases, RNA-based approaches may overcome the limitations of DNA-based testing.

Lack of sufficient intronic coverage could not account for all of the discrepancies between DNA-based and RNA-based analysis however. Six samples in the cohort were found to be positive for MET exon 14 skipping based on RNA. In five of these, genomic alterations in MET introns 13 or 14 were observed, however they did not conform to canonical splice site alterations and thus were not initially called (although this was addressed by bioinformatics updates). In RNA-based testing, however, determination of exon skipping is simplified such that, regardless of the specific genomic alteration that interferes with splicing, absence of the exon in the transcript is directly observed (5). In another two of the discrepant cases, tumor purity was observed to be low in the sample, meaning that the expected variant allele frequency (VAF) for a genomic event would also likely be low, potentially below detectable levels. However, overexpression of the fusions at the transcript level was theorized to compensate for low VAF (Fig. 1). Additional explanations for discordant findings between the assays included sample-specific poor sequencing in selected introns and complex rearrangements that hindered proper capture (Fig. 1).

The take home message from Benayed and colleagues is simply this: there is no perfect assay that will detect 100% of the potential actionable alterations in patient samples. Even an extremely well designed, thoroughly vetted, and FDA-cleared assay such as MSK-IMPACT will have inherent and unavoidable “holes” due to intrinsic limitations. The solution to this dilemma, as adeptly described by Benayed and colleagues, is additional testing using a different approach. While in an ideal world every clinical tumor sample would be tested by multiple modalities to ensure the most comprehensive clinical assessment, the reality is that these samples are often scant and testing is fiscally burdensome (and often not reimbursed). Therefore, algorithms to determine which samples should be reflexed to secondary assays after testing with a primary assay are critical for maximizing benefit. In this study, the first algorithmic step was lack of an identified driver (because activated oncogenic drivers tend to exist exclusively of each other), which amounted to 23% of samples tested with the primary assay. In addition, the authors found a significantly higher rate of actionable gene fusions in samples with a low (<5 mut/Mb) tumor mutational burden, meaning that this metric, which was derived from the primary assay, could also be used to help inform decision making regarding additional testing. While this scenario is somewhat specific to lung cancer, similar approaches could be prescribed on a cancer type–specific basis.

These findings should be considered a “wake-up call” for oncologists in regard to the ordering and interpretation of molecular testing. It is clear from these and other published findings that advanced molecular analysis has limitations that require nuanced technical understanding. As this arena evolves, it is critical for oncologists (and trainees) to gain an increased comprehension of how to identify when the “gaps” in a test might be most clinically relevant. This requires a level of technical cognizance that has been previously unexpected of clinical practitioners, yet is underscored by the reality that opportunities for effective targeted therapy can and will be missed if the treating oncologist is unaware of how to best identify patients for whom additional testing is warranted. This study also highlights the mantra of “no test is perfect” regardless of prestige of the testing institution, number of past tests performed, or regulatory status. NGS, despite its benefits, does not mean all-encompassing. It is only through the adaptability of laboratories to utilize knowledge such as is provided by Benayed and colleagues that advances in laboratory medicine can be quickly deployed to maximize benefits for oncology patients.

References:

  1. Comprehensive genomic characterization of squamous cell lung cancers. Nature 2012, 489(7417):519-525.
  2. A genomics-based classification of human lung tumors. Science translational medicine 2013, 5(209):209ra153.
  3. Govindan R, Ding L, Griffith M, Subramanian J, Dees ND, Kanchi KL, Maher CA, Fulton R, Fulton L, Wallis J et al: Genomic landscape of non-small cell lung cancer in smokers and never-smokers. Cell 2012, 150(6):1121-1134.
  4. Takeuchi K, Soda M, Togashi Y, Suzuki R, Sakata S, Hatano S, Asaka R, Hamanaka W, Ninomiya H, Uehara H et al: RET, ROS1 and ALK fusions in lung cancer. Nature medicine 2012, 18(3):378-381.
  5. Morodomi Y, Takenoyama M, Inamasu E, Toyozawa R, Kojo M, Toyokawa G, Shiraishi Y, Takenaka T, Hirai F, Yamaguchi M et al: Non-small cell lung cancer patients with EML4-ALK fusion gene are insensitive to cytotoxic chemotherapy. Anticancer research 2014, 34(7):3825-3830.
  6. Yoshimura M, Tada Y, Ofuzi K, Yamamoto M, Nakatsura T: Identification of a novel HLA-A 02:01-restricted cytotoxic T lymphocyte epitope derived from the EML4-ALK fusion gene. Oncology reports 2014, 32(1):33-39.
  7. Yang L, Li G, Zhao L, Pan F, Qiang J, Han S: Blocking the PI3K pathway enhances the efficacy of ALK-targeted therapy in EML4-ALK-positive nonsmall-cell lung cancer. Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine 2014.
  8. Workman P, van Montfort R: EML4-ALK fusions: propelling cancer but creating exploitable chaperone dependence. Cancer discovery 2014, 4(6):642-645.
  9. Imielinski M, Berger AH, Hammerman PS, Hernandez B, Pugh TJ, Hodis E, Cho J, Suh J, Capelletti M, Sivachenko A et al: Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell 2012, 150(6):1107-1120.
  10. Peifer M, Fernandez-Cuesta L, Sos ML, George J, Seidel D, Kasper LH, Plenker D, Leenders F, Sun R, Zander T et al: Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer. Nature genetics 2012, 44(10):1104-1110.

Other posts on this site which refer to Lung Cancer and Cancer Genome Sequencing include:

Multi-drug, Multi-arm, Biomarker-driven Clinical Trial for patients with Squamous Cell Carcinoma called the Lung Cancer Master Protocol, or Lung-MAP launched by NCI, Foundation Medicine, and Five Pharma Firms

US Personalized Cancer Genome Sequencing Market Outlook 2018 –

Comprehensive Genomic Characterization of Squamous Cell Lung Cancers

International Cancer Genome Consortium Website has 71 Committed Cancer Genome Projects Ongoing

Non-small Cell Lung Cancer drugs – where does the Future lie?

Lung cancer breathalyzer trialed in the UK

Diagnosing Lung Cancer in Exhaled Breath using Gold Nanoparticles

Multi-drug, Multi-arm, Biomarker-driven Clinical Trial for patients with Squamous Cell Carcinoma called the Lung Cancer Master Protocol, or Lung-MAP launched by NCI, Foundation Medicine, and Five Pharma Firms

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From Genomics of Microorganisms to Translational Medicine

Reporter and Curator: Demet Sag, PhD

Pharmacogenomics needs new materials that are inert against the host and specifically  active to modulate molecular metabolism towards wanted homeostasis of the physiological system.  These can come from natural resources or men-made.  That is why we must know the origin  to  improve.     Recently, Synthetic Biology, even though it is a developing upcoming field, it is generating mile stones for applications in the clinic, the biotechnology industry and in basic molecular research. As  a result, it created a multidisciplinary expertise from scientists to engineers.  Among other things extending the search to first life on Earth is one of the many alternatives.  Here I like to present how synthetic biology can be initiated onto Translational Medicine from adiscovery of molecules from the sea.

Microorganisms played a role in evolution to start a life.  99 % of our genome is related to microbial organisms. initially there was a classical  Microbiology, then evolved to Industrial Microbiology and Biotechnology then Microbial Genomics and now Microbiome and Health became the focus.  Finally,  the circle is getting tide into how microbiome involved with healthy and disease state of human? How they can be used that is what it really means to include microorganisms into human health for diagnostics and targeted therapies?

Or should we start from  scarcity?

Microbiology is my first formal education and  building block.  Simple but help to understand system biology and  the mechanism of life in a nut shell.   The closest field is embryonic stem cell biology for building “synthesizing” a whole new organism.  Then  system biology and developmental biology also gain interest.

The real  remember the month of October in 2001 when DOE reported that they sequenced 23 organisms in Walnut Creek.  Having seen presentation to identify microorganisms through complex crystal structure assays through chemical pathway  at the Microbial Genomics Meeting organized by ASM in Monterey, CA in 2001.

Discovery of microorganisms in marine life like in Mediterranean Sea, containing 38% salt,is very similar with finding circulating disease making cells.   Yet, they are similar since both search for a specific needle in the pile.  Furthermore, the unique behavior of enzymes from microbial organisms such as Taq polymerase or restriction enzymes made it possible for us to develop new technologies for copying and propagating significant sequences.  When these early molecular biology methods are combined with the power of genomics and knowledge of unique structures in molecular physiology, it is possible to design better and sensitive sensors or build an organism to rptect or fix the need of the body.  neither sensors nor synthesized organism model are complete since one is missing the basic element of life “transformation of information” the other is missing the integrity that once nature provided in a single simple cell.

Having sensory smart chip/band/nanomolecule to redesign the cells may also possible if only we know the combination.  Thus, we have options to deliver if we know what to be carried.

An external file that holds a picture, illustration, etc.<br /><br /><br /><br /><br /> Object name is marinedrugs-11-00700-g002.jpg

(Figure: The combined strategy of gene-based screening and bioactivity-based screening for marine microbial natural products (MMNPs) discovery, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3705366/figure/marinedrugs-11-00700-f002/)

As we come across, novel pathways or primary pathways of physiology gain significant interest to determine marine microbial compound for therapeutics since they are further away from the evolution three that gives an advantage for biomedical/translational scientist to avoid most part of th eimmune responses such as inflammation, toxicity. Yes, indeed these are not scientific tails but true since currently, 16 of 20 marine antitumor compounds under clinical trial are derived from microbial sources because marine microorganisms are a major source for MMNP discovery.  However, isolation of these organisms.  For example, pretreatment methods, enrichment, physical, and chemical techniques (e.g., dry heat, exposure to 1%–1.5% phenol, sucrose-gradient centrifugation, and filtration through cellulose membrane filters) can be applied to increase especially the less abundant specific groups of marine microorganisms, . A variety of pretreatment methods including recovery of these microorganisms.  This reminds me ecosystem of the soil, since in soil the trouble is identifying the specific culture among millions of others.

Regardless of the case,  nutrients are the key for selecting and isolating any organisms but specifically, as a result any marine microbes have specific nutrient requirements for growth (e.g., sponge extract ) or chemical (e.g., siderophores, signal molecules, non-traditional electron donors, and electron acceptors.  This also should remind us subject of Biology 101 Essential Vitamins and Minerals.  What we eat who we are.

For example, Bruns et al. employed technique where they employed different carbon substrates (agarose, starch, laminarin, xylan, chitin, and glucose) at low concentrations (200 μM each) so that they can  improve the cultivation efficiency of bacteria from the Gotland Deep in the central Baltic Sea. As a result of this growth medium they were able to elevate yield, which is created higher cultivation efficiencies (up to 11% in fluid media) compared to other studies.

Yet, another component must be addressed that is culture medium such as ionic strength for a microbila growth. For example, Tsueng et al. study on marine actinomycete genus Salinispora that can produce bioactive secondary metabolites such as desferrioxamine, saliniketals, arenamides, arenimycin and salinosporamide.  However, they observed that  three species of SalinisporaS. arenicolaS. tropica, and S. pacifica require a high ionic strength but  S. arenicolahas a lower growth requirement for ionic strength than S. tropica and S. pacificaUsing after assaying them against sodium chloride-based and lithium chloride-based media. As  aresult, there is a specificity for growth. 

In addition, energy must be supported imagine that in marine organisms the metabolism is very unique, may be slow and possibly.  However, the main criteria is  most of them grow under low oxygen conditions like tumors.  Warburg effect posed a  problem for human but helped microorganisms to survive and evolve.  One’s weakness the other’s strength make a great teamwork for solving diseases of human kind es especially for cancer. 

This reminds us to utilize minerals, electrons specifically after all the simplest form of carbon metabolism based on biochemical pathways like Crebs cycle, one carbon metabolism and amino acid metabolism etc. Even though 90% of human body made up off microbial origin there are microorganisms that are not cultured yet.

The irony is less than 1% of microorganisms can be cultured.  Furthermore, they are not included for representing the total phylogenetic diversity. Therefore, majority of work concentrated on finding and cultivating the uncultured majority of the microbial world for MMNPs’.  For example,  an uncultivated bacterial symbiont of the marine sponge Theonella swinhoei  producing many antitumor compounds such as pederin, mycalamide A, and onnamide A.

In any conditions if any living needs to be recognized and remembered, their place would be either on top or the bottom of the stack. Microbiome searches for specificity among tone of other organisms to recognize the disease, changes in cell differentiation and pathways or marine microbiologist search for uncommon scarce organisms. Yet, both of them are beneficial with their unique way.

Then what is the catch or fuss?  The catch is screening to identify what makes this organism unique that can be use for human health. Translational medicine may start from the beginning of life from microorganisms created.  This can be called with its newly coined named”synthetic biology” but if we go further than the conventional screening methods which include bioactivity-guided screening and gene-guided screening  and increase the power with genomics we may call it “synthetic genomics”.

As  a result these signature sequences establishes the “unique” biomarkers  or therpaeutics to be used for drug discovery, making vaccines, and remodulating the targeted cells. How?

These microorganisms secrete these metabolites or proteins to their growth medium just like a soluble protein, if you will like a inflammation factor or any other secreted protein of our human body cells. Collecting substrate or extract the pellet could be the choice.   in a nut shell this require at least three steps: First, finding the bioactivity, apply bioactivity-guided screening for direct detection of  the activity such as antimicrobial, antitumor, antiviral, and antiparasitic activities.  Second, a bioinformatic assessment of the secondary metabolite biosynthetic potential in the absence of fully assembled pathways or genome sequences. Third, application on cell lines and possible onto model organisms can improve the process of MMNP discovery so that allow us to prioritize strains for fermentation studies and chemical analysis. 

In summary, establish the culture growth, analyze bioactivity and discover the new gene product to be used.  Here is an example, first they  isolated Marinispora sp from the saline culture.  Next step,  identify new sources of bioactive secondary metabolites, gene-guided screening has been deployed to search target genes associated with NPs biosynthetic pathways, e.g., the fragments between ketosynthase and methylmalonyl-CoA transferase of polyketides (PKS) type I, enediyne PKS ketosynthase gene, O-methyltransferase gene, P450 monooxygenase gene, polyether epoxidase gene, 3-hydroxyl-3-methylglutaryl coenzyme A reductase gene, dTDP-glucose-4,6-dehydratase (dTGD) gene, and halogenase gene. The, apply bioinformatics that includes synthesizing the knowledge with  homology-based searches and phylogenetic analyses, gene-based screening  to predict new secondary metabolites discovered by isolates or environments.  Finally, identify the sequnce for PCR and use against a cell line or model organisms. In this case,  CNQ-140 based on significant antibacterial activities  against drug-resistant pathogens (e.g., MRSA) and impressive and selective cancer cell cytotoxicities (0.2–2.7 μM of MIC50 values) against six melanoma cell lines provided significant outcome. They are recognized as antitumor antibiotics with a new structural class, marinomycins A–D

This is a great method but there are two botle necks: 1. 99% of microbial organisms are not cultured in the labs. 2. Finding the optimum microbial growth and screening takes time. Thus, assesments can me done through metagenomics.  However, metagenomics has its shortcomings since on face of living unless applications applied in vivo in vitro results may not be valid.  The disadvantage of  metagenomics can be listed as:  1. inability of efficient acquisition of intact gene fragment,  2. incompatibility of expression elements such as promoter in a heterologous host.  On the pther hand, there can be possible resolution to avoid these factors  so metagenomics-based MMNP discovery can be plausable such as development  in  synthetic biology by large DNA fragment assembly techniques for artificial genome synthesis and synthetic microbial chassis suitable for different classes of MMNP biosynthesis.

However, many gene clusters have been identified by combined power of genomics and biioinformatics for MNP discovery.  This is  mainly necessary since  secondary metabolites usually biosynthesized by large multifunctional synthases that acts in a sequential assembly lines like adding carboxylic acid and amino acid building blocks into their products.  

 References

Simmons TL, Coates RC, Clark BR, Engene N, Gonzalez D, Esquenazi E, Dorrestein PC, Gerwick W

Proc Natl Acad Sci U S A. 2008 Mar 25; 105(12):4587-94.

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Silencing Cancers with Synthetic siRNAs

Larry H. Bernstein, MD, FCAP, Reviewer and Curator

Article ID #91: Silencing Cancers with Synthetic siRNAs. Published on 12/9/2013

WordCloud Image Produced by Adam Tubman

http://pharmaceuticalinnovation.com/2012-12-09/larryhbern/Silencing Cancers with Synthetic siRNAs

The challenge of cancer drug development has been marker by less than a century of development of major insights into the know of biochemical pathways and the changes in those pathways in a dramatic shift in enrgy utilization and organ development, and the changes in those pathways with the development of malignant neoplasia.  The first notable change is the Warburg Effect (attributed to the 1860 obsevation by Pasteur that yeast cells use glycolysis under anaerobic conditions).  Warburg also referred to earlier work by Meyerhoff, in a ratio of CO2 release to O2 consumption, a Meyerhoff ratio.  Much more was elucidated after the discovery of the pyridine nucleotides, which gave understanding of glycolysis and lactate production with a key two enzyme separation at the forward LDH reaction and the back reentry to the TCA cycle.  But the TCA cycle could be used for oxidative energy utilization in the mitochondria by oxidative phosphorylation elucidated by Peter Mitchell, or it can alternatively be used for syntheses, like proteins and lipid membrane structures.

A brilliant student in Leloir’s laboratory in Brazil undertook a study of isoenzyme structure in 1971, at a time that I was working under Nathan O. Kaplan on the mechanism of inhibition of mitochondrial malate dehydrogenase. In his descripton, taking into account the effect of substrates upon protein stability (FEBS) could be, in a prebiotic system, the form required in order to select protein and RNA in parallel or in tandem in a way that generates the genetic code (3 bases for one amino acid). Later, other proteins like reverse transcriptase, could transcribe it into the more stable DNA. Leloir had just finished ( a few years before 1971 but, not published by these days yet) a somehow similar reasoning about metabolic regions rich in A or in C or .. G or T.  He later spent time in London to study the early events in the transition of growing cells linked to ion fluxes, which he was attracted to by the idea that life is so strongly associated with the K (potassium) and Na (sodium) asymmetry.   Moreover, he notes that while DNA is the same no matter the cell is dead or alive,  and therefore,  it is a huge mistake to call DNA the molecule of life. In all life forms, you will find K reach inside and Na rich outside its membrane. On his return to Brazil, he accepted a request to collaborate with the Surgery department in energetic metabolism of tissues submitted to ischemia and reperfusion. This led me back to Pasteur and Warburg effects and like in Leloir´s time, he worked with a dimorphic yeast/mold that was considered a morphogenetic presentation of the Pasteur Effect.  His findings were as follows. In absence of glucose, a condition that prevents the yeast like cell morphology, which led to the study of an enzyme “half reaction”. The reaction that on the half, “seen in our experimental conditions did not followed classical thermodynamics” (According to Collowick & Kaplan (of your personal knowledge) vol. I See Utter and Kurahashi in it). This somehow contributed to a way of seeing biochemistry with modesty. The second and more strongly related to the Pasteur Effect was the use an entirely designed and produced in our Medical School Coulometer spirometer that measures oxygen consumption in a condition of constant oxygen supply. At variance with Warburg apparatus and Clark´s electrode, this oxymeters uses decrease in partial oxygen pressure and decrease electrical signal of oxygen polarography to measure it (Leite, J.V.P. Research in Physiol. Kao, Koissumi, Vassali eds Aulo Gaggi Bologna, 673-80-1971). “With this, I was able to measure the same mycelium in low and high “cell density” inside the same culture media. The result shows, high density one stops mitochondrial function while low density continues to consume oxygen (the internal increase or decrease in glycogen levels shows which one does or does not do it). Translation for today: The same genome in the same chemical environment behave differently mostly likely by its interaction differences. This previous experience fits well with what  I have to read by that time of my work with surgeons.  Submitted to total ischemia tissues mitochondrial function is stopped when they already have enough oxyhemoglobin (1) Epstein, Balaban and Ross Am J Physiol.243, F356-63 (1982) 2) Bashford , C. L, Biological membranes a practical approach Oxford Was. P 219-239 (1987).”

Of course, the world of medical and pharmaceutical engagement with this problem, though changed in focus, has benefitted hugely from “The Human Genome Project”, and the events since the millenium, because of technology advances in instrumental analysis, and in bioinformatics and computational biology.  This has lead to recent advances in regenerative biology with stem cell “models”, to advances in resorbable matrices, and so on.  We proceed to an interesting work that applies synthetic work with nucleic acid signaling to pharmacotherapy of cancer.

Synthetic RNAs Designed to Fight Cancer

Fri, 12/06/2013 Biosci Technology
Xiaowei Wang and his colleagues have designed synthetic molecules that combine the advantages of two experimental RNA therapies against cancer. (Source: WUSTL/Robert J. Boston)In search of better cancer treatments, researchers at Washington University School of Medicine in St. Louis have designed synthetic molecules that combine the advantages of two experimental RNA therapies.  The study appears in the December issue of the journal RNA.
 RNAs play an important role in how genes are turned on and off in the body. Both siRNAs and microRNAs are snippets of RNA known to modulate a gene’s signal or shut it down entirely. Separately, siRNA and microRNA treatment strategies are in early clinical trials against cancer, but few groups have attempted to marry the two.   “These are preliminary findings, but we have shown that the concept is worth pursuing,” said Xiaowei Wang, assistant professor of radiation oncology at the School of Medicine and a member of the Siteman Cancer Center. “We are trying to merge two largely separate fields of RNA research and harness the advantages of both.”
 “We designed an artificial RNA that is a combination of siRNA and microRNA, The showed that the artificial RNA combines the functions of the two separate molecules, simultaneously inhibiting both cell migration and proliferation. They designed and assembled small interfering” RNAs, or siRNAs,  made to shut down– or interfere with– a single specific gene that drives cancer.  The siRNA molecules work extremely well at silencing a gene target because the siRNA sequence is made to perfectly complement the target sequence, thereby
  • silencing a gene’s expression.
Though siRNAs are great at turning off the gene target, they also have potentially dangerous side effects:
  • siRNAs inadvertently can shut down other genes that need to be expressed to carry out tasks that keep the body healthy.
 According to Wang and his colleagues, siRNAs interfere with off-target genes that closely complement their “seed region,” a short but important
  • section of the siRNA sequence that governs binding to a gene target.
 “We can never predict all of the toxic side effects that we might see with a particular siRNA,” said Wang. “In the past, we tried to block the seed region in an attempt to reduce the side effects. Until now,
  • we never tried to replace the seed region completely.”
 Wang and his colleagues asked whether
  • they could replace the siRNA’s seed region with the seed region from microRNA.
Unlike siRNA, microRNA is a natural part of the body’s gene expression. And it can also shut down genes. As such, the microRNA seed region (with its natural targets) might reduce
  • the toxic side effects caused by the artificial siRNA seed region. Plus,
  • the microRNA seed region would add a new tool to shut down other genes that also may be driving cancer.
 Wang’s group started with a bioinformatics approach, using a computer algorithm to design
  • siRNA sequences against a common driver of cancer,
  • a gene called AKT1 that encourages uncontrolled cell division.
They used the program to select siRNAs against AKT1 that also had a seed region highly similar to the seed region of a microRNA known to inhibit a cell’s ability to move, thus
  • potentially reducing the cancer’s ability to spread.
In theory, replacing the siRNA seed region with the microRNA seed region also would combine their functions
  • reducing cell division and
  • movement with a single RNA molecule.
 Of more than 1,000 siRNAs that can target AKT1,
  • they found only three that each had a seed region remarkably similar to the seed region of the microRNA that reduces cell movement.
 They then took the microRNA seed region and
  • used it to replace the seed region in the three siRNAs that target AKT1.
The close similarity between the two seed regions is required because
  • changing the original siRNA sequence too much would make it less effective at shutting down AKT1.
 They dubbed the resulting combination RNA molecule “artificial interfering” RNA, or aiRNA. Once they arrived at these three sequences using computer models,
  1. they assembled the aiRNAs and
  2. tested them in cancer cells.
 One of the three artificial RNAs that they built in the lab
  • combined the advantages of the original siRNA and the microRNA seed region that was transplanted into it.
This aiRNA greatly reduced both
  1. cell division (like the siRNA) and
  2. movement (like the microRNA).
And to further show proof-of-concept, they also did the reverse, designing an aiRNA that
  1. both resists chemotherapy and
  2. promotes movement of the cancer cells.
 “Obviously, we would not increase cell survival and movement for cancer therapy, but we wanted to show how flexible this technology can be, potentially expanding it to treat diseases other than cancer,” Wang said.
Source: WUSTL

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Understanding of OVO-like proteins (OVOL), which are members of the zinc finger protein family, serve as transcription factors to regulate gene expression in various differentiation processes and are involved in epithelial development and differentiation in a wide variety of organisms. Thus, comparative genomic analysis among three different OVOL genes (OVOL1-3) in vertebrates may shed a light onto this crucial gene for development of molecular diagnostics and targeted therapies.

The Figure is from:

Genomics. Author manuscript; available in PMC 2010 June 29.

Published in final edited form as: Genomics. 2002 September; 80(3): 319–325.

Analysis of mouse and human OVOL2 gene products. (A) The 5′ end sequences of the mouse Ovol2B cDNA and the deduced OVOL2B protein. The “#” symbol indicates the position of an internal methionine previously mistaken as the initiation codon [14]. (B) Deduced amino acid sequences of OVOL2A proteins in mouse (mOvol2A) and human (hOvol2A). The “*” symbol indicates amino acid identity. The four C2H2 zinc fingers are underlined. The predicted NLS sequences are boxed. Sequences common to mouse OVOL2A and OVOL2B start at the brackets in (A) and (B). Human OVOL2B starts at the internal methionine (bold). Shown in bold and italics are positions where our predicted sequence differs from the previously reported sequence [14]. (C) Phylogenetic analysis of OVO proteins. cOvo, C. elegans OVO (GenBank acc. no. AF134806); dOvo, Drosophila OVO (GenBank acc. no. X59772); mOvol1, mouse OVOL1 (GenBank acc. no. AF134804); hOvol1, human OVOL1 (GenBank acc. no. AF016045); mOvol2, mouse OVOL2 (GenBank acc. no.AY090537); hOvol2, human OVOL2 (GenBank acc. no. AK022284); mOvol3, mouse OVOL3 (GenBank acc. no. BF714064); hOvol3, human OVOL3 (GenBank acc. no. AD001527).

The Ovo gene family encodes evolutionarily conserved proteins contain four DNA-binding C2H2 zinc fingers at the C termini and possess transcriptional regulatory activities in diverse array of organisms from Caenorhabditis elegansDrosophilaZebrafish, chick, and mammals.  Drosophila ovo, the founding member of the family, acts genetically downstream of Wg (fly Wnt homolog) and DER (fly epidermal growth factor receptor homolog) signaling pathways and is required for epidermal denticle formation and oogenesis.

OVOL proteins are characterized by the presence of hypervariable ID regions.

A. Mouse OVOL1 has ID residues in the first 100 amino acids. B. Mouse OVOL2 possesses ID residues in the first 50 amino acids with a glycine-rich and serine rich region as marked in red color. C. Mouse OVOL3 has ID segments within the N-terminal 100 residues. DDrosophila OVO is intrinsically disordered with large patches of residue biasness as indicated by the red color. We used DISOPRED2 software [47] for the prediction of ID regions. The horizontal line indicates the ordered/disordered threshold for the default false positive rate of 5%. The ‘filter’ curve represents the outputs from DISOPRED2 and the ‘output’ curve represents the outputs from a linear support vector machine (SVM) classifier (DISOPREDsvm). The outputs from DISOPREDsvm are included to indicate shorter as low confidence predictions of disorder.

doi:10.1371/journal.pone.0039399.g001, http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0039399)

This gene family is identified as mammalian Ovol (Ovo-like) genes, including Ovol1(movo1), Ovol2 (movo2), and Ovol3 (movo3) in mice and OVOL1OVOL2, andOVOL3 in humans. Ovol1 is the most studied compared to Ovol2 and Ovol3.

Kumar A, Bhandari A, Sinha R, Sardar P, et al. (2012) Molecular Phylogeny of OVOL Genes Illustrates a Conserved C2H2 Zinc Finger Domain Coupled by Hypervariable Unstructured Regions. PLoS ONE 7(6): e39399. doi:10.1371/journal.pone.0039399

http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039399

Phylogenetic history of OVOL proteins using the Bayesian method. A. Full-length OVOL proteins. B. Selected region of OVOL proteins.

Posterior probabilities scores are depicted by various color balls. The placozoan OVOL protein (e_gw1.4.509.1) was used as the outgroup in this phylogenetic tree. Red x indicates sequence position, which did not accord with species phylogeny. BFL: B. floridae (lancelet), SPU: S. purpuratus (sea urchin), NVE: N. vectensis (sea anemone), HRO: H. robusta (annelids), LGI: L. gigantean (molluscs) and TAD: T. adhaerens(placozoan). Trees in figures 7A and 7B are generated using the MrBayes 3.2 [53] from alignments supplied in supplementary Files S1 and S2, respectively. (Figure 7 of doi:10.1371/journal.pone.0039399.g007, http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0039399)

Fine scale analysis of gene expression in Drosophila melanogaster gonads reveals Programmed cell death 4 promotes the differentiation of female germline stem cells” Amy C Cash and Justen Andrews* BMC Developmental Biology 2012, 12:4 doi:10.1186/1471-213X-12-4 

”Regulatory and functional interactions between ovarian tumor and ovo during Drosophila oogenesis. “ Shannon Hinson, Janette Pettus, Rod N Nagoshi Mechanisms of Development Volume 88, Issue 1, 1 October 1999, Pages 3–14  http://www.sciencedirect.com/science/article/pii/S0925477399001677

Molecular phylogeny of OVOL genes illustrates a conserved C2H2 zinc finger domain coupled by hypervariable unstructured regions.” Kumar ABhandari ASinha RSardar PSushma MGoyal PGoswami CGrapputo A. PLoS One. 2012;7(6):e39399. doi: 10.1371/journal.pone.0039399. Epub 2012 Jun 21.

Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.” Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ.

Nucleic Acids Res. 1997 Sep 1; 25(17):3389-402.

Ovol1:

 

 

Chromosomal localization of OVOL1 gene from selected vertebrates, flanked by a set of conserved marker genes.   SIPA1: signal-induced proliferation-associated 1; RELA: v-rel reticuloendotheliosis viral oncogene homolog A (avian); KAT5: K (lysine) acetyltransferase; SNX32: sorting nexin 32; MUS81: MUS81 endonuclease homolog (S. cerevisiae); BANF1: barrier to autointegration factor 1; EXOC6B: exocyst complex component 6B; DYSF: dysferlin, limb girdle muscular dystrophy 2B; COL4A5: collagen, type IV, alpha 5; DAK: dihydroxyacetone kinase 2 S. cerevisiae homolog.   doi:10.1371/journal.pone.0039399.g003, http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0039399)

 

Drosophila ovo/svb (dovo) is required for epidermal cuticle/denticle differentiation and is genetically downstream of the wg signaling pathway, so does a mouse homolog of dovomovo1.  Also, Li group showed that movo1 promoter is activated by the lymphoid enhancer factor 1 (LEF1)/β-catenin complex, a transducer of wnt signaling. Simply these data showed movo1 is a developmental target of wnt signaling during hair morphogenesis in mice, and there is a conserved regulatory pathway at wg/wnt-ovolink in epidermal appendage. human OVOL1 has been identified as a gene that is responsive to TGF-β1/BMP7 treatment via a Smad4-dependent pathway (Kowanetz et al., 2004).

Ovo1 li“Characterization of a human homolog (OVOL1) of the Drosophila ovo gene, which maps to chromosome 11q13.” Chidambaram A, Allikmets R, Chandrasekarappa S, Guru SC, Modi W, Gerrard B, Dean M. Mamm Genome. 1997 Dec;8(12):950-1.nks Wnt signaling with N-cadherin localization during neural crest migrationDevelopment 2010 137 (12) 1981-1990.

“Id2 and Id3 define the potency of cell proliferation and differentiation responses to transforming growth factor beta and bone morphogenetic protein.” Marcin Kowanetz

Ulrich ValcourtRosita Bergström,Carl-Henrik Heldin and Aristidis Moustakas*

. Mol. Cell. Biol., 24 (2004), pp. 4241–4254 http://mcb.asm.org/content/24/10/4241

“The LEF1/β-catenin complex activates movo1, a mouse homolog of Drosophila ovo required for epidermal appendage differentiation” Baoan LiDouglas R. MackayQian DaiTony W. H. LiMahalakshmi NairMagid Fallahi, Christopher P. SchonbaumJudith FantesAnthony P. MahowaldMarian L. Waterman,Elaine Fuchs, and Xing DaiPNAS  vol. 99 no. 9  Baoan Li,  6064–6069. http://www.pnas.org/content/99/9/6064.abstract?ijkey=a7d2985635ef09ca63982ae4397ef325aa46252b&keytype2=tf_ipsecsha

 

“Expression of murine novel zinc finger proteins highly homologous to Drosophila ovo gene product in testis.” Masu Y, Ikeda S, Okuda-Ashitaka E, Sato E, Ito S. FEBS Lett. 1998 Jan 16;421(3):224-8.

The ovo gene required for cuticle formation and oogenesis in flies is involved in hair formation and spermatogenesis in mice” Xing Dai, Christopher Schonbaum, Linda Degenstein, Wenyu Bai,Anthony Mahowald, and Elaine Fuchs. (1998) Genes Dev. 12, 3452–3463 http://www.ncbi.nlm.nih.gov/pubmed/9808631

 

OvoL2 at the Junction of Decisions:

 

Brain development is fascinating and complex since cranial neurulation is an integral component of brain morphogenesis  and there are factors present outside of the neuroepithelium can also affect the morphogenesis of the cranial neural tube.  Previous studies revealed Ovol2 expression in brain, testis, and epithelial tissues such as skin and intestine of adult mice (Li et al., 2002a).

 

 

OVOL2 orthologs identified in vertebrates by comparing chromosomal localization.

RRBP1: ribosome binding protein 1 homolog; BANF2: barrier to autointegration factor 2; SNX5: sorting nexin 5; CSRP2BP: CSRP2 binding protein; SEC23B: protein transport protein Sec23B; POLR3F: polymerase (RNA) III (DNA directed) polypeptide F; RBBP9: Retinoblastoma-binding  protein 9; DTD1: D-tyrosyl-tRNA deacylase 1. doi:10.1371/journal.pone.0039399.g004, http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0039399)

 

Neural/non-neural cell fate decisions is carried by bone morphogenetic protein (BMP) signaling, which inhibits precocious neural differentiation and allows for proper differentiation of mesoderm, endoderm, and epidermis, during early embryonic development. There are many unknown in this mechanism yet the expression of Ovol2, which encodes an evolutionarily conserved zinc finger transcription factor, is down-regulated during neural differentiation of mouse embryonic stem cells since null Ovol2 in embryonic stem cells facilitates neural conversion and inhibits mesendodermal differentiation, whereas Ovol2 overexpression gives rise to the opposite phenotype. Furthermore, the studies also prove BMP4 and ovo2 interacted to rescue these changes. If BMP4 is provided,  Ovol2 knockdown partially rescues the neural inhibition.  Mechanism studies show the regulation pattern between these BMP and Ovol2.  BMP4 directly regulates Ovol2 expression through the binding of Smad1/5/8 to the second intron of the Ovol2 gene. Thus, Ovol2 acts downstream of BMP pathway.  In addition, in vivo chick studies presented that when Ovol2 is ectopically expressed the prospective neural plate represses the expression of the definitive neural plate marker cSox2In the chick embryo.  Also, lack of Ovol2 prevented increase BMP4 expression.  During early germ layer development there is an important comment between neuroectoderm and mesendoderm provided by Ovol2.

In addition, Ovol2 acts in downstream of key developmental signaling pathways including Wg/Wnt and BMP/TGF-β.  Based on findings from chromatin immunoprecipitation, luciferase reporter, and functional rescue assays, Wells group demonstrated that Ovol2 directly represses two critical downstream targets, c-Mycand Notch1.  Hence, this action suppresses keratinocyte transient proliferation and terminal differentiation.  Like a twilight zone to choose when to proliferate and when to resist differentiation.

Ovol

Ovol2, a Mammalian Homolog of Drosophila ovo: Gene Structure, Chromosomal Mapping, and Aberrant Expression in Blind-Sterile Mice.” Baoan Li, Qian Dai, Ling Li, Mahalakshmi Nair, Douglas R. Mackay, Xing DaiGenomics Volume 80, Issue 3, September 2002, Pages 319–325.

Ovol2 directly represses two critical downstream targets, c-Myc and Notch1, thereby suppressing keratinocyte transient proliferation and terminal differentiation, respectively

Wells JLee BCai AQKarapetyan ALee WJRugg ESinha SNie QDai X.

J Biol Chem. 2009 Oct 16;284(42):29125-35. doi: 10.1074/jbc.M109.008847. Epub 2009 Aug 21.

The zinc finger transcription factor Ovol2 acts downstream of the bone morphogenetic protein pathway to regulate the cell fate decision between neuroectoderm and mesendoderm.” Zhang T, Zhu Q, Xie Z, Chen Y, Qiao Y, Li L, Jing N. J Biol Chem. 2013 Mar 1;288(9):6166-77. doi: 10.1074/jbc.M112.418376. Epub 2013 Jan 14.

The mouse Ovol2 gene is required for cranial neural tube development”  Douglas R. MackayaMing Hua,   Baoan LiaCatherine RhéaumeaXing Dai.l Developmental Biology Volume 291, Issue 1, 1 March 2006, Pages 38–52. 

Ovol3

While tracing the OVOL genes, we identified a third OVOL gene, OVOL3, in a wide array of mammals including humans (chromosome 19), chimpanzees (chromosome 19), mice (chromosome 7), rats (chromosome 1), cows (chromosome 18), pigs (chromosome 6), and opossums (chromosome 4) with a conserved synteny. The conserved synteny comprises an octet of genes, LIN37-PRODH2-KIRREL2-APLP11-NKF3ID-LPFN3​-SDHAF1-CLIF3,on one side and POLR2L-CAPSN1-COX7A1 on the other side of OVOL3 in a region of about 400 kb (Figure 5 of http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0039399).

Synteny analysis of OVOL3 genes illustrates the loss of OVOL3a after duplication event and maintenance of paralogous OVOL3b in fishes.

LIN37: lin-37 homolog (C. elegans); PRODH2: proline dehydrogenase (oxidase) 2; KIRREL2: kin of IRRE like 2 (Drosophila); APLP1: amyloid beta (A4) precursor-like protein 1; NFKBID: nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, delta; LRFN3: leucine rich repeat and fibronectin type III domain containing 3; SDHAF1: succinate dehydrogenase complex assembly factor 1; CLIP3: CAP-GLY domain containing linker protein 3; POLR2I: polymerase (RNA) II (DNA directed) polypeptide I, 14.5 kDa; CAPNS1: calpain, small subunit 1; COX7A1: cytochrome c oxidase subunit VIIa polypeptide 1 (muscle); DMPK: dystrophia myotonica-protein kinase; HLCS: holocarboxylase synthetase; AMOT: angiomotin; REXO2: REX2 RNA exonuclease 2 homolog (S. cerevisiae). (Reference from doi:10.1371/journal.pone.0039399.g005 , http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0039399)

There are not many studies on Ovol 3but my interest in this region lies on Chromosome 19 since “it has the highest gene density of all human chromosomes and the large clustered gene families, corresponding high G + C content, CpG islands and density of repetitive DNA indicate a chromosome rich in biological and evolutionary significance.”

The DNA sequence and biology of human chromosome 19.” Grimwood J, et al.  Nature. 2004 Apr 1;428(6982):529-35.

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