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Posts Tagged ‘Transcriptomics’


Cancer Genomics: Multiomic Analysis of Single Cells and Tumor Heterogeneity

Curator: Stephen J. Williams, PhD

 

scTrio-seq identifies colon cancer lineages

Single-cell multiomics sequencing and analyses of human colorectal cancer. Shuhui Bian et al. Science  30 Nov 2018:Vol. 362, Issue 6418, pp. 1060-1063

To better design treatments for cancer, it is important to understand the heterogeneity in tumors and how this contributes to metastasis. To examine this process, Bian et al. used a single-cell triple omics sequencing (scTrio-seq) technique to examine the mutations, transcriptome, and methylome within colorectal cancer tumors and metastases from 10 individual patients. The analysis provided insights into tumor evolution, linked DNA methylation to genetic lineages, and showed that DNA methylation levels are consistent within lineages but can differ substantially among clones.

Science, this issue p. 1060

Abstract

Although genomic instability, epigenetic abnormality, and gene expression dysregulation are hallmarks of colorectal cancer, these features have not been simultaneously analyzed at single-cell resolution. Using optimized single-cell multiomics sequencing together with multiregional sampling of the primary tumor and lymphatic and distant metastases, we developed insights beyond intratumoral heterogeneity. Genome-wide DNA methylation levels were relatively consistent within a single genetic sublineage. The genome-wide DNA demethylation patterns of cancer cells were consistent in all 10 patients whose DNA we sequenced. The cancer cells’ DNA demethylation degrees clearly correlated with the densities of the heterochromatin-associated histone modification H3K9me3 of normal tissue and those of repetitive element long interspersed nuclear element 1. Our work demonstrates the feasibility of reconstructing genetic lineages and tracing their epigenomic and transcriptomic dynamics with single-cell multiomics sequencing.

Fig. 1 Reconstruction of genetic lineages with scTrio-seq2.

Global SCNA patterns (250-kb resolution) of CRC01. Each row represents an individual cell. The subclonal SCNAs used for identifying genetic sublineages were marked and indexed; for details, see fig. S6B. On the top of the heatmap, the amplification or deletion frequency of each genomic bin (250 kb) of the non-hypermutated CRC samples from the TCGA Project and patient CRC01’s cancer cells are shown.

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Fig. 1 Reconstruction of genetic lineages with scTrio-seq2.

Global SCNA patterns (250-kb resolution) of CRC01. Each row represents an individual cell. The subclonal SCNAs used for identifying genetic sublineages were marked and indexed; for details, see fig. S6B. On the top of the heatmap, the amplification or deletion frequency of each genomic bin (250 kb) of the non-hypermutated CRC samples

 

 

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5:00 – 5:45 PM Early Diagnosis Through Predictive Biomarkers, NonInvasive Testing

Reporter: Stephen J. Williams, Ph.D.

 

Diagnosing cancer early is often the difference between survival and death. Hear from experts regarding the new and emerging technologies that form the next generation of cancer diagnostics.

Moderator: Heather Rose, Director of Licensing, Thomas Jefferson University
Speakers:
Bonnie Anderson, Chairman and CEO, Veracyte @BonnieAndDx
Kevin Hrusovsky, Founder and Chairman, Powering Precision Health @KevinHrusovsky

Bonnie Anderson and Veracyte produces genomic tests for thyroid and other cancer diagnosis.  Kevin Hrusovksy and Precision Health uses peer reviewed evidence based medicine to affect precision medicine decision.

Bonnie: aim to get a truth of diagnosis.  Getting tumor tissue is paramount as well as properly preserved tissue.  They use deep RNA sequencing  and machine learning  in their clinically approved tests.

Kevin: Serial biospace entrepreneur.  Two diseases, cancer and neurologic, have been diseases which have been hardest to get reproducible and validated biomarkers of early disease.  He concentrates on protein biomarkers.

Heather:  FDA has recently approved drugs for early disease intervention.  However the use of biomarkers can go beyond patient stratification in clinical trials.

Kevin: 15 approved drugs for MS but the markers are scans looking for brain atrophy which is too late of an endpoint.  So we need biomarkers of early disease progression.  We can use those early biomarkers of disease progression so pharma can target those early biomarkers and or use those early biomarkers of disease progression  for endpoint

Bonnie: exciting time in the early diagnostics field. She prefers transcriptomics to DNA based methods such as WES or WGS (whole exome or whole genome sequencing).  It was critical to show data on the cost savings imparted by their transcriptomic based thryoid cancer diagnostic test for payers to consider this test eligible for reimbursement.

Kevin: There has been 20 million  CAT scans for  cancer but it is estimated 90% of these scans led to misdiagnosis. Biomarker  development  has revolutionized diagnostics in this disease area.  They have developed a breakthrough panel of ten protein biomarkers in serum which he estimates may replace 5 million mammograms.

All panelists agreed on the importance of regulatory compliance and the focus of new research should be on early detection.  In addition they believe that Dr. Gotlieb’s appointment to the FDA is a positive for the biomarker development field, as Dr. Gotlieb understands the potential and importance of early detection and prevention of disease.  Kevin also felt Dr. Gotlieb understands the importance of incorporating biomarkers as endpoints in clinical trials.  Over 750 phase 1,2, and 3 clinical trials use biomarker endpoints but the pharma companies still need to prove the biomarkers clinical relevance to the FDA.They also agreed it would be helpful to involve advocacy groups in putting more pressure on the healthcare providers and policy makers on this importance of diagnostics as a preventative measure.

In addition, the discovery and use of biomarkers as disease endpoints has led to a resurgence of Alzheimer’s disease drug development by companies which have previously given up on these type of neurodegenerative diseases.

Kevin feels proteomics offers great advantages over DNA-based diagnostics, especially in cancer such as ovarian cancer, where a high degree of specificity for a diagnostic test is required to ascertain if a woman should undergo prophylactic oophorectomy.  He suggests that a new blood-based protein biomarker panel is being developed for early detection of some forms of ovarian cancer.

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Please see related articles on Live Coverage of Previous Meetings on this Open Access Journal

LIVE – Real Time – 16th Annual Cancer Research Symposium, Koch Institute, Friday, June 16, 9AM – 5PM, Kresge Auditorium, MIT

Real Time Coverage and eProceedings of Presentations on 11/16 – 11/17, 2016, The 12th Annual Personalized Medicine Conference, HARVARD MEDICAL SCHOOL, Joseph B. Martin Conference Center, 77 Avenue Louis Pasteur, Boston

Tweets Impression Analytics, Re-Tweets, Tweets and Likes by @AVIVA1950 and @pharma_BI for 2018 BioIT, Boston, 5/15 – 5/17, 2018

BIO 2018! June 4-7, 2018 at Boston Convention & Exhibition Center

https://pharmaceuticalintelligence.com/press-coverage/

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RNA Modification

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

New RNA Modification Added to Epitranscriptomic Library   

GEN News Highlights  Feb 17, 2016    http://www.genengnews.com/gen-news-highlights/new-rna-modification-added-to-epitranscriptomic-library/81252376/

 

http://www.genengnews.com/Media/images/GENHighlight/109124_web3461772372.jpg

 

In 1956, Francis Crick—co-discoverer of DNA’s helical structure—postulated what is now considered to be a central doctrine of the biological sciences stating that “The central dogma of molecular biology deals with the detailed residue-by-residue transfer of sequential information. It states that such information cannot be transferred back from protein to either protein or nucleic acid.” What Crick was suggesting was that DNA makes RNA and, in turn, RNA makes protein.

In the time since the initial proposal of the central dogma, scientists have come to understand that there are not only instances of reverse information flow from RNA to DNA, but chemical alterations to RNA structures that can have a profound effect on gene regulation. The discovery of these alterations has added a critical dimension to how scientists view the genetic code and recently spawned an entirely new field of study within molecular biology: the epitranscriptome.

Now, a recent study by scientists at the University of Chicago and Tel Aviv University has revealed evidence that provides a promising new lever in the control of gene expression. The researchers describe a small chemical modification to RNA that can significantly boost the conversion of genes to proteins.

The findings from this study were published recently in Nature through an article entitled “The dynamic N1-methyladenosine methylome in eukaryotic messenger RNA.”

“Epigenetics, the regulation of gene expression beyond the primary information encoded by DNA, was thought until recently to be mediated by modifications of proteins and DNA,” explained co-senior study author Gidi Rechavi, Ph.D., chair in oncology at Tel Aviv University’s Sackler Faculty of Medicine and head of the Cancer Research Center at Sheba Medical Center. “The new findings bring RNA to a central position in epigenetics.”

“This discovery further opens the window on a whole new world of biology for us to explore,” added co-senior study author Chuan He, Ph.D., professor in the department of chemistry and investigator within the Howard Hughes Medical Institute at the University of Chicago. “These modifications have a major impact on almost every biological process.”

Previously, Dr. He’s laboratory discovered the first RNA demethylase that reverses the most prevalent mRNA methylation N6-methyladenosine (m6A), implying that the addition and removal of the methyl group could dramatically affect these messengers and the outcome of gene expression—as also seen for DNA and histones—which subsequent research found to be true.

In the current study, the investigators described a second functional mRNA methylation, N1-methyladenosine (m1A). Like m6A, the small modification is evolutionarily conserved and common, present in humans, rodents, and yeast. However, its location and effect on gene expression reflect a new form of epitranscriptome control.

“The discovery of m1A is extremely important, not only because of its own potential in affecting biological processes but also because it validates the hypothesis that there is not just one functional modification,” Dr. He stated. “There could be multiple modifications at different sites where each may carry a distinct message to control the fate and function of mRNA.”

From their findings, the research team estimates that that m1A may be present on transcripts of more than one out of three expressed human genes—suggesting that m1A, like m6A, may be a mechanism by which cells rapidly boost the expression of hundreds or thousands of specific genes.

“mRNA is the perfect place to regulate gene expression because they can code information from transcription and directly impact translation—you can add a consensus sequence to a group of genes and use a modification of the sequence to readily control several hundred transcripts simultaneously,” Dr. He said. “If you want to rapidly change the expression of several hundred or a thousand genes, this offers the best way.”

The scientists were excited by their findings and have plans for future studies that will examine the role of m1A methylation in human development, for diseases such as diabetes and cancer, and its potential as a target for therapeutic uses.

“This study represents a breakthrough discovery in the exciting, nascent field of the ‘epitranscriptome,’ which is how RNAs are regulated, akin to the genome and the epigenome,” commented Christopher Mason, Ph.D., associate professor at Weill Cornell Medicine, who was not affiliated with the study. “What is important about this work is that m6A was recently found to enrich at the ends of genes, and now we know that m1A is what is helping regulate the beginning of genes, and this opens up many questions about revealing the ‘epitranscriptome code’ just like the histone code or the genetic code.”

 

The dynamic N1-methyladenosine methylome in eukaryotic messenger RNA

Dan DominissiniSigrid NachtergaeleSharon Moshitch-MoshkovitzNitzan Kol, et al.
Nature(2016 10 Feb )      http://dx.doi.org:/10.1038/nature16998      http://www.nature.com/nature/journal/vaop/ncurrent/full/nature16998.html

Gene expression can be regulated post-transcriptionally through dynamic and reversible RNA modifications. A recent noteworthy example is N6-methyladenosine (m6A), which affects messenger RNA (mRNA) localization, stability, translation and splicing. Here we report on a new mRNA modification, N1-methyladenosine (m1A), that occurs on thousands of different gene transcripts in eukaryotic cells, from yeast to mammals, at an estimated average transcript stoichiometry of 20% in humans. Employing newly developed sequencing approaches, we show that m1A is enriched around the start codon upstream of the first splice site: it preferentially decorates more structured regions around canonical and alternative translation initiation sites, is dynamic in response to physiological conditions, and correlates positively with protein production. These unique features are highly conserved in mouse and human cells, strongly indicating a functional role for m1A in promoting translation of methylated mRNA.

 

Figure 1: Development of m1A-seq to map a newly identified constituent of mammalian mRNA.

Development of m1A-seq to map a newly identified constituent of mammalian mRNA.

http://www.nature.com/nature/journal/vaop/ncurrent/carousel/nature16998-f1.jpg

a, Chemical structures of m1A and m6A. Methyl groups (-CH3) are in red and the positive charge (+) on m1A is in blue. b, LC-MS/MS quantitation of m1A, m6A and Ψ in human and mouse mRNA isolated from the indicated cell types. …

 

Figure 3: m1A occurs in GC-rich sequence contexts and in genes with structured 5′ UTRs.

m1A occurs in GC-rich sequence contexts and in genes with structured 5′ UTRs.

http://www.nature.com/nature/journal/vaop/ncurrent/carousel/nature16998-f3.jpg

a, Sequence frequency logo for a set of 192 adenosines in peak areas that have a higher mismatch rate in immunoprecipitation relative to input (FC ≥ 6) in HepG2 demonstrates the GC-rich context of m1A. b, Length-adjusted minimum free energy…

 

Figure 5: m1A in mRNA is a dynamic modification that responds to changing physiological and stress conditions, and varies between tissues.

m1A in mRNA is a dynamic modification that responds to changing physiological and stress conditions, and varies between tissues.

http://www.nature.com/nature/journal/vaop/ncurrent/carousel/nature16998-f5.jpg

a, LC-MS/MS quantification of m1A (left, grey) and m6A (right, black) in mRNA of untreated and glucose-starved (upper panels) or heat shock-treated (lower panels) HepG2 cells, presented as percentage of unmodified A. Mean values ± s.e.m…

 

RNA modification discovery suggests new code for control of gene expression

A new cellular signal discovered by a team of scientists at the University of Chicago and Tel Aviv University provides a promising new lever in the control of gene expression.    Gene expression study

The study, published online Feb. 10 in the journal Nature, describes a small chemical modification that can significantly boost the conversion of genes to proteins. Together with other recent findings, the discovery enriches a critical new dimension to the “Central Dogma” of molecular biology: the epitranscriptome.

“This discovery further opens the window on a whole new world of biology for us to explore,” said Chuan He, the John T. Wilson Distinguished Service Professor in Chemistry, Howard Hughes Medical Institute investigator and senior author of the study. “These modifications have a major impact on almost every biological process.”

The central dogma of molecular biology describes the cellular pathway where genetic information from DNA is copied into temporary RNA “transcripts,” which provide the recipe for the production of proteins. Since Francis Crick first postulated the theory in 1956, scientists have discovered a multitude of modifications to DNA and proteins that regulate this process.

Only recently, however, have scientists focused on investigating dynamic modifications that specifically target the RNA step. In 2011, He’s group discovered the first RNA demethylase that reverses the most prevalent mRNA methylation N6-methyladenosine, or m6A, implying that the addition and removal of the methyl group could dramatically affect these messengers and impact the outcome of gene expression, as also seen for DNA and histones. Subsequently, scientists discovered that the dynamic and reversible methylation of m6A dramatically controlled the metabolism and function of most cellular messenger RNA, and thus, the production of proteins.

In the new Nature study, researchers from UChicago and Tel Aviv University describe a second functional mRNA methylation, N1-methyladenosine, or m1A. Like m6A, the small modification is evolutionarily conserved and common, and present in humans, rodents and yeast, the authors found. But its location and effect on gene expression reflect a new form of epitranscriptome control and suggest an even larger cellular “control panel.”

“The discovery of m1A is extremely important, not only because of its own potential in affecting biological processes, but also because it validates the hypothesis that there is not just one functional modification,” He said. “There could be multiple modifications at different sites where each may carry a distinct message to control the fate and function of mRNA.”

The researchers estimated that m1A was present on transcripts of more than one out of three expressed human genes. Methylated genes exhibited enhanced translation compared to unmethlyated genes, producing protein levels nearly twice as high in all cell types. This increase suggests that m1A, like m6A, may be a mechanism by which cells rapidly boost the expression of hundreds or thousands of specific genes, perhaps during important processes such as cell division, differentiation or under stress.

“mRNA is the perfect place to regulate gene expression, because they can code information from transcription and directly impact translation; you can add a consensus sequence to a group of genes and use a modification of the sequence to readily control several hundred transcripts simultaneously,” He said. “If you want to rapidly change the expression of several hundred or a thousand genes, this offers the best way.”

However, despite their complementary effects, m1A and m6A exert their influence on mRNA through different pathways. While studies have found that m6A localizes predominantly to the tail of messenger RNA molecules, increasing their translation and turnover rate, m1A was found largely near the start codon of mRNA transcripts, where protein translation begins. The different mechanisms could allow for finer tuning of post-transcriptional gene expression, or the selective activation of particular genes in different physiological situations.

“This study represents a breakthrough discovery in the exciting, nascent field of the ‘epitranscriptome,’ which is how RNAs are regulated, akin to the genome and the epigenome,” said Christopher Mason, associate professor at Weill Cornell Medicine, who was not affiliated with the study. “What is important about this work is that m6A was recently found to enrich at the ends of genes, and now we know that m1A is what is helping regulate the beginning of genes, and this opens up many questions about revealing the ‘epitranscriptome code’ just like the histone code or the genetic code.”

Future studies will examine the role of m1A methylation in human development, diseases such as diabetes and cancer, and its potential as a target for therapeutic uses.


Citation: “The dynamic N1-methyladenosine methylome in eukaryotic messenger RNA,” Nature, Feb. 10, 2016, by Chuan He, Dan Dominissini, Sigrid Nachtergaele, Qing Dai, Dali Han, Wesley Clark, Guanqun Zheng, Tao Pan and Louis Dore from the University of Chicago, and Sharon Moshitch-Moshkovitz, Eyal Peer, Nitkan Kol, Moshe Shay Ben-Haim, Ayelet Di Segni, Mali Salmon-Divon, Oz Solomon, Eran Eyal, Vera Hershkovitz, Ninette Amariglio and Gideon Rechavi from Tel Aviv University. DOI: 10.1038/nature16998

Funding: National Institutes of Health, Howard Hughes Medical Institute, Flight Attendant Medical Research Institute, Israel Science Foundation, Israeli Centers of Excellence Program, Ernest and Bonnie Beutler Research Program, Chicago Biomedical Consortium, Damon Runyon Cancer Research Foundation and Kahn Family Foundation.

– See more at: http://news.uchicago.edu/article/2016/02/16/rna-modification-discovery-suggests-new-code-control-gene-expression#sthash.HX6wUgKW.dpuf

RNA modifications and epitranscriptomics conference   
University of Chicago, Chicago, Illinois, US   September 8-9, 2016
The meeting is aimed at bringing in students and postdocs as well as faculty involved in RNA modification and epitranscriptome research.  In addition to talks, there will be a poster session and reception.

Topics

  • M6A mRNA methylation
  • Biological functions of m6A RNA methylation
  • Dynamic RNA modifications

Registration will open on March 1, 2016

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Insights into Brain Structure

Larry H. Bernstein, MD, FCAP, Curator

FPBI

 

Can Big Genomic Data Reveal the Fundamental Units of the Brain?

Aaron Kroll     http://www.bio-itworld.com/2016/1/20/can-big-genomic-data-reveal-fundamental-units-brain.html

January 20, 2016 | An adult mouse’s brain, an object not much bigger than the last joint of your pinky finger, contains around 75 million neurons. At the Allen Institute for Brain Science in Seattle, the Mouse Cell Types program, led by Hongkui Zeng, is trying to figure out just how many varieties of neurons make up this vast complex, and what makes each one unique.

Zeng’s research focuses on the primary visual cortex, a tiny sliver of the brain where signals from the eyes are processed and interpreted. Because vision is a relatively well-defined process, it’s thought to be a good model for connecting the behavior of individual neurons to larger brain functions.

“You really can’t understand a system until you understand its parts,” says Bosiljka Tasic, a founding member of the Mouse Cell Types program.

This month, Zeng’s team published a study in Nature Neuroscience that takes advantage of new technological developments to get a fine-grained look at the molecular toolkits of single neurons. Using newly refined methods to isolate single cells, Zeng’s lab collected over 1,600 brain cells from the visual cortexes of adult mice, intact and in good shape for sequencing. With advances in highly parallel, unbiased RNA sequencing, the group was able to measure each cell’s entire “transcriptome”―the array of RNA molecules that indicate which genes are actively producing proteins―at a depth that reveals even the scarcest RNA traces.

To a shocking extent, those parts are still a mystery. Many supposed cell types are based on little more than what you can see through a microscope: a neuron’s shape, or the pattern of rootlike dendrites extending from its body. These morphological traits, though important, are hard to see in full, and even harder to track methodically across thousands or millions of cells.

“We think this is probably the most comprehensive survey of a cortical area,” says Tasic, who co-led the study with her colleague Vilas Menon. “Many studies that are coming out now do very shallow sequencing… We wanted to go deeper.” With a median of 8.7 million sequencing reads per cell, the authors discovered a wealth of new RNA markers that define discrete groups of neurons. Some of these markers suggest that known cell types in the brain can be split into smaller sub-categories. A few even stake out rare types of neurons that may be new to science.

Yet the data collected for this study also confirms that the brain’s biology is neither tidy nor easy to unravel.

“There is this obsession in the field, and in many other areas of biology, that people always want cleanliness and discreteness,” Tasic says. Instead, her efforts to classify neurons have shown that “types” can be slippery, and many cells straddle the line between closely related groups. As projects like this one seek to redefine cell types for the genomics age, scientists will have to face these ambiguities and consider what they can tell us about the nature of the brain.

Patterns within Patterns

Whole transcriptomes provide an impressive amount of data with which to organize cells, but that data is hard to interpret in an unbiased way. “We’re trying, in some sense, to solve two problems simultaneously,” says Vilas Menon, co-lead author of the paper. “We’re trying to cluster the genes, and also to cluster the cells.”

To disentangle these problems, the team performed an iterative analysis. First, their software looked for RNA markers that diverged most widely between different cells, using those markers to sort all the cells in the study into large clusters. Then, they wiped the slate clean, looking for brand-new markers within each cluster to split the cells step by step into smaller groups. The smallest possible divisions, in which no new RNA markers could strongly distinguish cells from one another, became the group’s proposed “cell types.”

The researchers used two different computational methods to define clusters, but both revealed the same basic hierarchy of types. “In general, the higher level splits correspond to what’s already known for these broad classes of neurons,” says Menon. For instance, the first split simply divided all the neurons in their data from a handful of other cell types present in the brain, like the glial cells that support the brain’s physical structure. The second split separated GABAergic cells, which mostly damp down chemical signals in the brain, from glutamatergic cells, which mostly spark and amplify signals.

Beyond this point, the patterns became more revealing. Within the glutamatergic cells, for example, later clustering tended to split neurons according to how deeply they were embedded in the cortex. A mouse’s primary visual cortex is organized in six layers, and the Allen Institute’s transcriptome data suggests that the neurons in each layer may be closely related to one another, or have similar functions that require the same genes to be activated. Yet the GABAergic cells did not split out so naturally by layer, implying that their development may follow very different rules.

At the narrowest levels of clustering, the genes that defined cell types sometimes came as complete surprises. Within a group of GABAergic neurons known for producing high levels of the hormone somatostatin, the authors found a subtype of cells expressing an additional gene called Chodl. “Nobody has ever heard of this marker Chodl,” says Tasic. “But it’s the most beautiful pattern you’ve ever seen, because it’s only in that cell type. This is the beauty of transcriptomics.”

With luck, genes like Chodl will provide new clues to the roles of specific cell types. If no other neurons make use of this gene, it’s reasonable to think it may have a very specialized function. But even if that’s not the case, highly unique markers like Chodl are invaluable for studying neurons more closely, letting scientists design new molecular and genetic tools to target single cell types for follow-up research.

“I see this as a first step in allowing us to selectively manipulate cell types,” says Tasic. “And then you can do all sorts of things to those cells. You can label them specifically, and study their morphology. You can perturb them. You can inactivate them. I think this will be the way to truly understand what these different cells do.”

Mountains and Ridges

“Technically, this is a very impressive achievement,” says Joshua Sanes, a neurobiologist at the Harvard Center for Brain Science. “It’s using a really nice combination of state-of-the-art methods to address what, to me, is a big problem in neurobiology.”

Like the researchers at the Allen Institute, Sanes is interested in the problem of defining cell types. (Both his group and Hongkui Zeng’s receive funding from the national BRAIN Initiative, which has provided grants for big data-gathering projects to attack this question.) It’s a vexing issue, both because it requires such an immense amount of data to address, and because biology again and again rejects easy categories.

To Sanes, one of the most interesting aspects of Tasic and Menon’s paper is their decision to point out neurons with traits of more than one cell type. Unlike other groups that may exclude ambiguous data from analysis, the Allen Institute accepted cells with “intermediate” transcriptomes as important findings of their study. In some cases―most notably, a class of glutamatergic neurons in layer four of the cortex―these intermediate cells are so abundant that two or more supposedly separate “types” almost seem to merge together.

“That could mean that, although some cells are in types, there’s a certain amount of slipperiness,” says Sanes. “It’s been pretty hard to define neurons in a way that will help research move forward.”

It’s possible that some classes of neurons don’t exist in discrete types at all, but include a spectrum of cells expressing different mixes of the same genes. Or transcriptomes may just not be the best way to define cell types―because neurons of the same type change their RNA arsenals depending on their stage of development, or the chemical signals they’re responding to.

“Some parts of the overall phenotypic landscape may have features of a continuum,” says Tasic, but that doesn’t mean that her group’s proposed cell types are not useful ways of thinking about neurobiology. “If there are two mountains that are connected by a ridge, there are still two mountains. The fact that you have a ridge is fine. Maybe that’s biology.”

From Rosetta Stones to Searchable Databases

Tasic, Menon, and their colleagues identified 49 cell types altogether, but the number is less important than the process that produced it. Almost certainly, there are still new cell types to discover, and perhaps further divisions within the types the Allen Institute has identified.

“I think it’s extremely unlikely they’ve gotten all the types,” says Sanes. “It’s terrific, but it’s not like you should think of this as a complete catalogue.” To isolate single neurons, the Allen Institute used a method called FACS, which relies on sampling many different strains of transgenic mice to collect both abundant and rare cell types. The authors agree that this approach leaves open the possibility that some rare types were not sampled, and future studies will use different methods of capturing single cells, adding yet more data to the mix. (At his lab, Sanes is working with a new method called Drop-seq, which the Allen Institute also plans to adopt.)

For work like this to be meaningful, it’s not necessary for the Allen Institute to come up with a complete encyclopedia of cell types on its own. What is essential is that the data be made easily available to neuroscientists everywhere, to compare with their own studies and gradually refine with new discoveries.

Today, this is far from assured. A lot of research on cell types is only available through journal articles, and there are few standards for formatting data so it can be shared and understood across institutions. This is apparent in some of the detective work that Zeng’s team did to see if their proposed cell types matched any previously identified types. Tasic, Menon, and colleagues trawled through the scientific literature looking for what they called “Rosetta stones,” unique molecular features that could clearly be seen in their own transcriptome data.

In the future, this work could be made almost automatic, especially as objective data types like RNA sequencing information become more common. Just a few weeks ago, many of the first recipients of BRAIN Initiative grants―including both Zeng and Sanes―met in Bethesda, Md., to discuss plans for sharing neurobiological data, and ways to make that data more uniform and searchable.

“I think the BRAIN Initiative has been helpful in drawing attention and funding,” says Sanes. “The NIH is doing everything it can to ensure data sharing, and I think the community is going along with that well.”

In the meantime, Zeng’s group has released their raw transcriptome data to GEO, an NIH-supported database of RNA information, and made an annotated version of their data available online on the Allen Institute website. Tasic and Menon hope that outside researchers will use these resources to design more detailed studies of specific neuron types. Neuroscience is still in the earliest stages of data gathering, but to truly understand the brain, scientists will eventually have to make the leap into exploring function, cell type by cell type.

“We can find genes that are differentially expressed at the level of the whole brain, but we really don’t know what these genes do,” Tasic says. “Once you see that this gene is expressed in a specific type, you can formulate a hypothesis.”

 

http://casestudies.brain-map.org/celltaxb

 

Adult mouse cortical cell taxonomy revealed by single cell transcriptomics

Bosiljka Tasic, et al.

Nature Neuroscience(2016)   http://dx.doi.org:/10.1038/nn.4216

Nervous systems are composed of various cell types, but the extent of cell type diversity is poorly understood. We constructed a cellular taxonomy of one cortical region, primary visual cortex, in adult mice on the basis of single-cell RNA sequencing. We identified 49 transcriptomic cell types, including 23 GABAergic, 19 glutamatergic and 7 non-neuronal types. We also analyzed cell type–specific mRNA processing and characterized genetic access to these transcriptomic types by many transgenic Cre lines. Finally, we found that some of our transcriptomic cell types displayed specific and differential electrophysiological and axon projection properties, thereby confirming that the single-cell transcriptomic signatures can be associated with specific cellular properties.

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Layers of Human Brain

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Human Brain Peeled Back to Its Transcriptional Core

http://www.genengnews.com/gen-news-highlights/human-brain-peeled-back-to-its-transcriptional-core/81251987/

 

http://www.genengnews.com/Media/images/GENHighlight/thumb_Nov17_2015_AllenIst_HumanBrain6811542443.jpg

The Allen Human Brain Atlas, a data set derived from analyses of tissue samples such as the one shown here, was used in an investigation of differential transcription across 132 structures in six individual brains. The investigation revealed that a set of just 32 gene-expression signatures defines, in large part, a common network architecture that is conserved across the human population. [Allen Institute for Brain Science]

 

The human brain has so many organizational layers that you might wonder whether there is, deep down, a core that we all share, however diverse our brains are in other respects. It turns out that there is a core, report scientists at the Allen Institute for Brain Science. This core, the scientists say, is transcriptional and surprisingly compact—just 32 gene-expression signatures.

The Allen Institute scientists decided that the highly stereotyped character of the human brain implied that a conserved molecular program was responsible for the brain’s development, cellular structure, and function. “So much research focuses on the variations between individuals, but we turned that question on its head to ask, what makes us similar?” explained Ed Lein, Ph.D., investigator at the Allen Institute for Brain Science. “What is the conserved element among all of us that must give rise to our unique cognitive abilities and human traits?”

Using a microarray profiling dataset from the Allen Human Brain Atlas, Dr. Lein and colleagues found that many genes showed highly consistent patterns of transcriptional regulation across brain regions as quantified using a metric called differential stability (DS). DS is the tendency for a gene to exhibit reproducible differential expression relationships across brain structures.

This approach allowed the investigators to identify molecular patterns that dominate gene expression in the human brain and appear to be common to all individuals. The investigators detailed their work November 16 in the journal Nature Neuroscience, in an article entitled, “Canonical genetic signatures of the adult human brain.”

“[We assessed] reproducibility of gene expression patterning across 132 structures in six individual brains, revealing mesoscale genetic organization,” wrote the authors. “The genes with the highest differential stability are highly biologically relevant, with enrichment for brain-related annotations, disease associations, drug targets and literature citations.”

“[These genes appear to] represent a functionally critical set whose transcriptional regulation is tightly controlled,” the authors continued. “Taking this concept of conserved patterning from genes to gene networks, we demonstrate the existence of a relatively small (32) set of consensus coexpression gene networks that explain most (90.1%, ρ > 0.4) transcriptional variation across adult brain regions.”

In other words, most of the patterns of gene usage across all 20,000 genes could be characterized by just 32 expression patterns. While many of these patterns were similar in human and mouse, the dominant genetic model organism for biomedical research, many genes showed different patterns in human. Surprisingly, genes associated with neurons were most conserved across species, while those for the supporting glial cells showed larger differences.

The most highly stable genes—the genes that were most consistent across all brains—include those that are associated with diseases and disorders like autism and Alzheimer’s and include many existing drug targets. These patterns provide insights into what makes the human brain distinct and raise new opportunities to target therapeutics for treating disease.

Finally, the investigators noted that highly consistent transcriptional architecture in neocortex is correlated with resting state functional connectivity. According to the investigators, this suggests a link between conserved gene expression and functionally relevant circuitry.

“The human brain is phenomenally complex,” said Christof Koch, Ph.D., president and CSO at the Allen Institute for Brain Science. “There could easily have been thousands of patterns, or none at all. This gives us an exciting way to look further at the functional activity that underlies the uniquely human brain.”

 

Canonical genetic signatures of the adult human brain

Michael HawrylyczJeremy A MillerVilas MenonDavid FengTim DolbeareAngela L Guillozet-BongaartsAnil G Jegga, et al.

Nature Neuroscience (2015)          http://dx.doi.org:/10.1038/nn.4171

The structure and function of the human brain are highly stereotyped, implying a conserved molecular program responsible for its development, cellular structure and function. We applied a correlation-based metric called differential stability to assess reproducibility of gene expression patterning across 132 structures in six individual brains, revealing mesoscale genetic organization. The genes with the highest differential stability are highly biologically relevant, with enrichment for brain-related annotations, disease associations, drug targets and literature citations. Using genes with high differential stability, we identified 32 anatomically diverse and reproducible gene expression signatures, which represent distinct cell types, intracellular components and/or associations with neurodevelopmental and neurodegenerative disorders. Genes in neuron-associated compared to non-neuronal networks showed higher preservation between human and mouse; however, many diversely patterned genes displayed marked shifts in regulation between species. Finally, highly consistent transcriptional architecture in neocortex is correlated with resting state functional connectivity, suggesting a link between conserved gene expression and functionally relevant circuitry.

 

Genetic variability in the regulation of gene expression in ten regions of the human brain

Adaikalavan RamasamyDaniah TrabzuniSebastian GuelfiVibin VargheseColin SmithRobert WalkerTisham DeUK Brain Expression ConsortiumNorth American Brain Expression Consortium,  et al.

Nature Neuroscience  2014;  17; 1418–1428    http://dx.doi.org:/10.1038/nn.3801

Germ-line genetic control of gene expression occurs via expression quantitative trait loci (eQTLs). We present a large, exon-specific eQTL data set covering ten human brain regions. We found thatcis-eQTL signals (within 1 Mb of their target gene) were numerous, and many acted heterogeneously among regions and exons. Co-regulation analysis of shared eQTL signals produced well-defined modules of region-specific co-regulated genes, in contrast to standard coexpression analysis of the same samples. We report cis-eQTL signals for 23.1% of catalogued genome-wide association study hits for adult-onset neurological disorders. The data set is publicly available via public data repositories and via http://www.braineac.org/. Our study increases our understanding of the regulation of gene expression in the human brain and will be of value to others pursuing functional follow-up of disease-associated variants.

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Summary and Perspectives: Impairments in Pathological States: Endocrine Disorders, Stress Hypermetabolism and Cancer


Summary and Perspectives: Impairments in Pathological States: Endocrine Disorders, Stress Hypermetabolism and Cancer

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

This summary is the last of a series on the impact of transcriptomics, proteomics, and metabolomics on disease investigation, and the sorting and integration of genomic signatures and metabolic signatures to explain phenotypic relationships in variability and individuality of response to disease expression and how this leads to  pharmaceutical discovery and personalized medicine.  We have unquestionably better tools at our disposal than has ever existed in the history of mankind, and an enormous knowledge-base that has to be accessed.  I shall conclude here these discussions with the powerful contribution to and current knowledge pertaining to biochemistry, metabolism, protein-interactions, signaling, and the application of the -OMICS to diseases and drug discovery at this time.

The Ever-Transcendent Cell

Deriving physiologic first principles By John S. Torday | The Scientist Nov 1, 2014
http://www.the-scientist.com/?articles.view/articleNo/41282/title/The-Ever-Transcendent-Cell/

Both the developmental and phylogenetic histories of an organism describe the evolution of physiology—the complex of metabolic pathways that govern the function of an organism as a whole. The necessity of establishing and maintaining homeostatic mechanisms began at the cellular level, with the very first cells, and homeostasis provides the underlying selection pressure fueling evolution.

While the events leading to the formation of the first functioning cell are debatable, a critical one was certainly the formation of simple lipid-enclosed vesicles, which provided a protected space for the evolution of metabolic pathways. Protocells evolved from a common ancestor that experienced environmental stresses early in the history of cellular development, such as acidic ocean conditions and low atmospheric oxygen levels, which shaped the evolution of metabolism.

The reduction of evolution to cell biology may answer the perennially unresolved question of why organisms return to their unicellular origins during the life cycle.

As primitive protocells evolved to form prokaryotes and, much later, eukaryotes, changes to the cell membrane occurred that were critical to the maintenance of chemiosmosis, the generation of bioenergy through the partitioning of ions. The incorporation of cholesterol into the plasma membrane surrounding primitive eukaryotic cells marked the beginning of their differentiation from prokaryotes. Cholesterol imparted more fluidity to eukaryotic cell membranes, enhancing functionality by increasing motility and endocytosis. Membrane deformability also allowed for increased gas exchange.

Acidification of the oceans by atmospheric carbon dioxide generated high intracellular calcium ion concentrations in primitive aquatic eukaryotes, which had to be lowered to prevent toxic effects, namely the aggregation of nucleotides, proteins, and lipids. The early cells achieved this by the evolution of calcium channels composed of cholesterol embedded within the cell’s plasma membrane, and of internal membranes, such as that of the endoplasmic reticulum, peroxisomes, and other cytoplasmic organelles, which hosted intracellular chemiosmosis and helped regulate calcium.

As eukaryotes thrived, they experienced increasingly competitive pressure for metabolic efficiency. Engulfed bacteria, assimilated as mitochondria, provided more bioenergy. As the evolution of eukaryotic organisms progressed, metabolic cooperation evolved, perhaps to enable competition with biofilm-forming, quorum-sensing prokaryotes. The subsequent appearance of multicellular eukaryotes expressing cellular growth factors and their respective receptors facilitated cell-cell signaling, forming the basis for an explosion of multicellular eukaryote evolution, culminating in the metazoans.

Casting a cellular perspective on evolution highlights the integration of genotype and phenotype. Starting from the protocell membrane, the functional homolog for all complex metazoan organs, it offers a way of experimentally determining the role of genes that fostered evolution based on the ontogeny and phylogeny of cellular processes that can be traced back, in some cases, to our last universal common ancestor.  ….

As eukaryotes thrived, they experienced increasingly competitive pressure for metabolic efficiency. Engulfed bacteria, assimilated as mitochondria, provided more bioenergy. As the evolution of eukaryotic organisms progressed, metabolic cooperation evolved, perhaps to enable competition with biofilm-forming, quorum-sensing prokaryotes. The subsequent appearance of multicellular eukaryotes expressing cellular growth factors and their respective receptors facilitated cell-cell signaling, forming the basis for an explosion of multicellular eukaryote evolution, culminating in the metazoans.

Casting a cellular perspective on evolution highlights the integration of genotype and phenotype. Starting from the protocell membrane, the functional homolog for all complex metazoan organs, it offers a way of experimentally determining the role of genes that fostered evolution based on the ontogeny and phylogeny of cellular processes that can be traced back, in some cases, to our last universal common ancestor.

Given that the unicellular toolkit is complete with all the traits necessary for forming multicellular organisms (Science, 301:361-63, 2003), it is distinctly possible that metazoans are merely permutations of the unicellular body plan. That scenario would clarify a lot of puzzling biology: molecular commonalities between the skin, lung, gut, and brain that affect physiology and pathophysiology exist because the cell membranes of unicellular organisms perform the equivalents of these tissue functions, and the existence of pleiotropy—one gene affecting many phenotypes—may be a consequence of the common unicellular source for all complex biologic traits.  …

The cell-molecular homeostatic model for evolution and stability addresses how the external environment generates homeostasis developmentally at the cellular level. It also determines homeostatic set points in adaptation to the environment through specific effectors, such as growth factors and their receptors, second messengers, inflammatory mediators, crossover mutations, and gene duplications. This is a highly mechanistic, heritable, plastic process that lends itself to understanding evolution at the cellular, tissue, organ, system, and population levels, mediated by physiologically linked mechanisms throughout, without having to invoke random, chance mechanisms to bridge different scales of evolutionary change. In other words, it is an integrated mechanism that can often be traced all the way back to its unicellular origins.

The switch from swim bladder to lung as vertebrates moved from water to land is proof of principle that stress-induced evolution in metazoans can be understood from changes at the cellular level.

http://www.the-scientist.com/Nov2014/TE_21.jpg

A MECHANISTIC BASIS FOR LUNG DEVELOPMENT: Stress from periodic atmospheric hypoxia (1) during vertebrate adaptation to land enhances positive selection of the stretch-regulated parathyroid hormone-related protein (PTHrP) in the pituitary and adrenal glands. In the pituitary (2), PTHrP signaling upregulates the release of adrenocorticotropic hormone (ACTH) (3), which stimulates the release of glucocorticoids (GC) by the adrenal gland (4). In the adrenal gland, PTHrP signaling also stimulates glucocorticoid production of adrenaline (5), which in turn affects the secretion of lung surfactant, the distension of alveoli, and the perfusion of alveolar capillaries (6). PTHrP signaling integrates the inflation and deflation of the alveoli with surfactant production and capillary perfusion.  THE SCIENTIST STAFF

From a cell-cell signaling perspective, two critical duplications in genes coding for cell-surface receptors occurred during this period of water-to-land transition—in the stretch-regulated parathyroid hormone-related protein (PTHrP) receptor gene and the β adrenergic (βA) receptor gene. These gene duplications can be disassembled by following their effects on vertebrate physiology backwards over phylogeny. PTHrP signaling is necessary for traits specifically relevant to land adaptation: calcification of bone, skin barrier formation, and the inflation and distention of lung alveoli. Microvascular shear stress in PTHrP-expressing organs such as bone, skin, kidney, and lung would have favored duplication of the PTHrP receptor, since sheer stress generates radical oxygen species (ROS) known to have this effect and PTHrP is a potent vasodilator, acting as an epistatic balancing selection for this constraint.

Positive selection for PTHrP signaling also evolved in the pituitary and adrenal cortex (see figure on this page), stimulating the secretion of ACTH and corticoids, respectively, in response to the stress of land adaptation. This cascade amplified adrenaline production by the adrenal medulla, since corticoids passing through it enzymatically stimulate adrenaline synthesis. Positive selection for this functional trait may have resulted from hypoxic stress that arose during global episodes of atmospheric hypoxia over geologic time. Since hypoxia is the most potent physiologic stressor, such transient oxygen deficiencies would have been acutely alleviated by increasing adrenaline levels, which would have stimulated alveolar surfactant production, increasing gas exchange by facilitating the distension of the alveoli. Over time, increased alveolar distension would have generated more alveoli by stimulating PTHrP secretion, impelling evolution of the alveolar bed of the lung.

This scenario similarly explains βA receptor gene duplication, since increased density of the βA receptor within the alveolar walls was necessary for relieving another constraint during the evolution of the lung in adaptation to land: the bottleneck created by the existence of a common mechanism for blood pressure control in both the lung alveoli and the systemic blood pressure. The pulmonary vasculature was constrained by its ability to withstand the swings in pressure caused by the systemic perfusion necessary to sustain all the other vital organs. PTHrP is a potent vasodilator, subserving the blood pressure constraint, but eventually the βA receptors evolved to coordinate blood pressure in both the lung and the periphery.

Gut Microbiome Heritability

Analyzing data from a large twin study, researchers have homed in on how host genetics can shape the gut microbiome.
By Tracy Vence | The Scientist Nov 6, 2014

Previous research suggested host genetic variation can influence microbial phenotype, but an analysis of data from a large twin study published in Cell today (November 6) solidifies the connection between human genotype and the composition of the gut microbiome. Studying more than 1,000 fecal samples from 416 monozygotic and dizygotic twin pairs, Cornell University’s Ruth Ley and her colleagues have homed in on one bacterial taxon, the family Christensenellaceae, as the most highly heritable group of microbes in the human gut. The researchers also found that Christensenellaceae—which was first described just two years ago—is central to a network of co-occurring heritable microbes that is associated with lean body mass index (BMI).  …

Of particular interest was the family Christensenellaceae, which was the most heritable taxon among those identified in the team’s analysis of fecal samples obtained from the TwinsUK study population.

While microbiologists had previously detected 16S rRNA sequences belonging to Christensenellaceae in the human microbiome, the family wasn’t named until 2012. “People hadn’t looked into it, partly because it didn’t have a name . . . it sort of flew under the radar,” said Ley.

Ley and her colleagues discovered that Christensenellaceae appears to be the hub in a network of co-occurring heritable taxa, which—among TwinsUK participants—was associated with low BMI. The researchers also found that Christensenellaceae had been found at greater abundance in low-BMI twins in older studies.

To interrogate the effects of Christensenellaceae on host metabolic phenotype, the Ley’s team introduced lean and obese human fecal samples into germ-free mice. They found animals that received lean fecal samples containing more Christensenellaceae showed reduced weight gain compared with their counterparts. And treatment of mice that had obesity-associated microbiomes with one member of the Christensenellaceae family, Christensenella minuta, led to reduced weight gain.   …

Ley and her colleagues are now focusing on the host alleles underlying the heritability of the gut microbiome. “We’re running a genome-wide association analysis to try to find genes—particular variants of genes—that might associate with higher levels of these highly heritable microbiota.  . . . Hopefully that will point us to possible reasons they’re heritable,” she said. “The genes will guide us toward understanding how these relationships are maintained between host genotype and microbiome composition.”

J.K. Goodrich et al., “Human genetics shape the gut microbiome,” Cell,  http://dx.doi.org:/10.1016/j.cell.2014.09.053, 2014.

Light-Operated Drugs

Scientists create a photosensitive pharmaceutical to target a glutamate receptor.
By Ruth Williams | The Scentist Nov 1, 2014
http://www.the-scientist.com/?articles.view/articleNo/41279/title/Light-Operated-Drugs/

light operated drugs MO1

light operated drugs MO1

http://www.the-scientist.com/Nov2014/MO1.jpg

The desire for temporal and spatial control of medications to minimize side effects and maximize benefits has inspired the development of light-controllable drugs, or optopharmacology. Early versions of such drugs have manipulated ion channels or protein-protein interactions, “but never, to my knowledge, G protein–coupled receptors [GPCRs], which are one of the most important pharmacological targets,” says Pau Gorostiza of the Institute for Bioengineering of Catalonia, in Barcelona.

Gorostiza has taken the first step toward filling that gap, creating a photosensitive inhibitor of the metabotropic glutamate 5 (mGlu5) receptor—a GPCR expressed in neurons and implicated in a number of neurological and psychiatric disorders. The new mGlu5 inhibitor—called alloswitch-1—is based on a known mGlu receptor inhibitor, but the simple addition of a light-responsive appendage, as had been done for other photosensitive drugs, wasn’t an option. The binding site on mGlu5 is “extremely tight,” explains Gorostiza, and would not accommodate a differently shaped molecule. Instead, alloswitch-1 has an intrinsic light-responsive element.

In a human cell line, the drug was active under dim light conditions, switched off by exposure to violet light, and switched back on by green light. When Gorostiza’s team administered alloswitch-1 to tadpoles, switching between violet and green light made the animals stop and start swimming, respectively.

The fact that alloswitch-1 is constitutively active and switched off by light is not ideal, says Gorostiza. “If you are thinking of therapy, then in principle you would prefer the opposite,” an “on” switch. Indeed, tweaks are required before alloswitch-1 could be a useful drug or research tool, says Stefan Herlitze, who studies ion channels at Ruhr-Universität Bochum in Germany. But, he adds, “as a proof of principle it is great.” (Nat Chem Biol, http://dx.doi.org:/10.1038/nchembio.1612, 2014)

Enhanced Enhancers

The recent discovery of super-enhancers may offer new drug targets for a range of diseases.
By Eric Olson | The Scientist Nov 1, 2014
http://www.the-scientist.com/?articles.view/articleNo/41281/title/Enhanced-Enhancers/

To understand disease processes, scientists often focus on unraveling how gene expression in disease-associated cells is altered. Increases or decreases in transcription—as dictated by a regulatory stretch of DNA called an enhancer, which serves as a binding site for transcription factors and associated proteins—can produce an aberrant composition of proteins, metabolites, and signaling molecules that drives pathologic states. Identifying the root causes of these changes may lead to new therapeutic approaches for many different diseases.

Although few therapies for human diseases aim to alter gene expression, the outstanding examples—including antiestrogens for hormone-positive breast cancer, antiandrogens for prostate cancer, and PPAR-γ agonists for type 2 diabetes—demonstrate the benefits that can be achieved through targeting gene-control mechanisms.  Now, thanks to recent papers from laboratories at MIT, Harvard, and the National Institutes of Health, researchers have a new, much bigger transcriptional target: large DNA regions known as super-enhancers or stretch-enhancers. Already, work on super-enhancers is providing insights into how gene-expression programs are established and maintained, and how they may go awry in disease.  Such research promises to open new avenues for discovering medicines for diseases where novel approaches are sorely needed.

Super-enhancers cover stretches of DNA that are 10- to 100-fold longer and about 10-fold less abundant in the genome than typical enhancer regions (Cell, 153:307-19, 2013). They also appear to bind a large percentage of the transcriptional machinery compared to typical enhancers, allowing them to better establish and enforce cell-type specific transcriptional programs (Cell, 153:320-34, 2013).

Super-enhancers are closely associated with genes that dictate cell identity, including those for cell-type–specific master regulatory transcription factors. This observation led to the intriguing hypothesis that cells with a pathologic identity, such as cancer cells, have an altered gene expression program driven by the loss, gain, or altered function of super-enhancers.

Sure enough, by mapping the genome-wide location of super-enhancers in several cancer cell lines and from patients’ tumor cells, we and others have demonstrated that genes located near super-enhancers are involved in processes that underlie tumorigenesis, such as cell proliferation, signaling, and apoptosis.

Super-enhancers cover stretches of DNA that are 10- to 100-fold longer and about 10-fold less abundant in the genome than typical enhancer regions.

Genome-wide association studies (GWAS) have found that disease- and trait-associated genetic variants often occur in greater numbers in super-enhancers (compared to typical enhancers) in cell types involved in the disease or trait of interest (Cell, 155:934-47, 2013). For example, an enrichment of fasting glucose–associated single nucleotide polymorphisms (SNPs) was found in the stretch-enhancers of pancreatic islet cells (PNAS, 110:17921-26, 2013). Given that some 90 percent of reported disease-associated SNPs are located in noncoding regions, super-enhancer maps may be extremely valuable in assigning functional significance to GWAS variants and identifying target pathways.

Because only 1 to 2 percent of active genes are physically linked to a super-enhancer, mapping the locations of super-enhancers can be used to pinpoint the small number of genes that may drive the biology of that cell. Differential super-enhancer maps that compare normal cells to diseased cells can be used to unravel the gene-control circuitry and identify new molecular targets, in much the same way that somatic mutations in tumor cells can point to oncogenic drivers in cancer. This approach is especially attractive in diseases for which an incomplete understanding of the pathogenic mechanisms has been a barrier to discovering effective new therapies.

Another therapeutic approach could be to disrupt the formation or function of super-enhancers by interfering with their associated protein components. This strategy could make it possible to downregulate multiple disease-associated genes through a single molecular intervention. A group of Boston-area researchers recently published support for this concept when they described inhibited expression of cancer-specific genes, leading to a decrease in cancer cell growth, by using a small molecule inhibitor to knock down a super-enhancer component called BRD4 (Cancer Cell, 24:777-90, 2013).  More recently, another group showed that expression of the RUNX1 transcription factor, involved in a form of T-cell leukemia, can be diminished by treating cells with an inhibitor of a transcriptional kinase that is present at the RUNX1 super-enhancer (Nature, 511:616-20, 2014).

Fungal effector Ecp6 outcompetes host immune receptor for chitin binding through intrachain LysM dimerization 
Andrea Sánchez-Vallet, et al.   eLife 2013;2:e00790 http://elifesciences.org/content/2/e00790#sthash.LnqVMJ9p.dpuf

LysM effector

LysM effector

http://img.scoop.it/ZniCRKQSvJOG18fHbb4p0Tl72eJkfbmt4t8yenImKBVvK0kTmF0xjctABnaLJIm9

While host immune receptors

  • detect pathogen-associated molecular patterns to activate immunity,
  • pathogens attempt to deregulate host immunity through secreted effectors.

Fungi employ LysM effectors to prevent

  • recognition of cell wall-derived chitin by host immune receptors

Structural analysis of the LysM effector Ecp6 of

  • the fungal tomato pathogen Cladosporium fulvum reveals
  • a novel mechanism for chitin binding,
  • mediated by intrachain LysM dimerization,

leading to a chitin-binding groove that is deeply buried in the effector protein.

This composite binding site involves

  • two of the three LysMs of Ecp6 and
  • mediates chitin binding with ultra-high (pM) affinity.

The remaining singular LysM domain of Ecp6 binds chitin with

  • low micromolar affinity but can nevertheless still perturb chitin-triggered immunity.

Conceivably, the perturbation by this LysM domain is not established through chitin sequestration but possibly through interference with the host immune receptor complex.

Mutated Genes in Schizophrenia Map to Brain Networks
From www.nih.gov –  Sep 3, 2013

Previous studies have shown that many people with schizophrenia have de novo, or new, genetic mutations. These misspellings in a gene’s DNA sequence

  • occur spontaneously and so aren’t shared by their close relatives.

Dr. Mary-Claire King of the University of Washington in Seattle and colleagues set out to

  • identify spontaneous genetic mutations in people with schizophrenia and
  • to assess where and when in the brain these misspelled genes are turned on, or expressed.

The study was funded in part by NIH’s National Institute of Mental Health (NIMH). The results were published in the August 1, 2013, issue of Cell.

The researchers sequenced the exomes (protein-coding DNA regions) of 399 people—105 with schizophrenia plus their unaffected parents and siblings. Gene variations
that were found in a person with schizophrenia but not in either parent were considered spontaneous.

The likelihood of having a spontaneous mutation was associated with

  • the age of the father in both affected and unaffected siblings.

Significantly more mutations were found in people

  • whose fathers were 33-45 years at the time of conception compared to 19-28 years.

Among people with schizophrenia, the scientists identified

  • 54 genes with spontaneous mutations
  • predicted to cause damage to the function of the protein they encode.

The researchers used newly available database resources that show

  • where in the brain and when during development genes are expressed.

The genes form an interconnected expression network with many more connections than

  • that of the genes with spontaneous damaging mutations in unaffected siblings.

The spontaneously mutated genes in people with schizophrenia

  • were expressed in the prefrontal cortex, a region in the front of the brain.

The genes are known to be involved in important pathways in brain development. Fifty of these genes were active

  • mainly during the period of fetal development.

“Processes critical for the brain’s development can be revealed by the mutations that disrupt them,” King says. “Mutations can lead to loss of integrity of a whole pathway,
not just of a single gene.”

These findings support the concept that schizophrenia may result, in part, from

  • disruptions in development in the prefrontal cortex during fetal development.

James E. Darnell’s “Reflections”

A brief history of the discovery of RNA and its role in transcription — peppered with career advice
By Joseph P. Tiano

James Darnell begins his Journal of Biological Chemistry “Reflections” article by saying, “graduate students these days

  • have to swim in a sea virtually turgid with the daily avalanche of new information and
  • may be momentarily too overwhelmed to listen to the aging.

I firmly believe how we learned what we know can provide useful guidance for how and what a newcomer will learn.” Considering his remarkable discoveries in

  • RNA processing and eukaryotic transcriptional regulation

spanning 60 years of research, Darnell’s advice should be cherished. In his second year at medical school at Washington University School of Medicine in St. Louis, while
studying streptococcal disease in Robert J. Glaser’s laboratory, Darnell realized he “loved doing the experiments” and had his first “career advancement event.”
He and technician Barbara Pesch discovered that in vivo penicillin treatment killed streptococci only in the exponential growth phase and not in the stationary phase. These
results were published in the Journal of Clinical Investigation and earned Darnell an interview with Harry Eagle at the National Institutes of Health.

Darnell arrived at the NIH in 1956, shortly after Eagle  shifted his research interest to developing his minimal essential cell culture medium, still used. Eagle, then studying cell metabolism, suggested that Darnell take up a side project on poliovirus replication in mammalian cells in collaboration with Robert I. DeMars. DeMars’ Ph.D.
adviser was also James  Watson’s mentor, so Darnell met Watson, who invited him to give a talk at Harvard University, which led to an assistant professor position
at the MIT under Salvador Luria. A take-home message is to embrace side projects, because you never know where they may lead: this project helped to shape
his career.

Darnell arrived in Boston in 1961. Following the discovery of DNA’s structure in 1953, the world of molecular biology was turning to RNA in an effort to understand how
proteins are made. Darnell’s background in virology (it was discovered in 1960 that viruses used RNA to replicate) was ideal for the aim of his first independent lab:
exploring mRNA in animal cells grown in culture. While at MIT, he developed a new technique for purifying RNA along with making other observations

  • suggesting that nonribosomal cytoplasmic RNA may be involved in protein synthesis.

When Darnell moved to Albert Einstein College of Medicine for full professorship in 1964,  it was hypothesized that heterogenous nuclear RNA was a precursor to mRNA.
At Einstein, Darnell discovered RNA processing of pre-tRNAs and demonstrated for the first time

  • that a specific nuclear RNA could represent a possible specific mRNA precursor.

In 1967 Darnell took a position at Columbia University, and it was there that he discovered (simultaneously with two other labs) that

  • mRNA contained a polyadenosine tail.

The three groups all published their results together in the Proceedings of the National Academy of Sciences in 1971. Shortly afterward, Darnell made his final career move
four short miles down the street to Rockefeller University in 1974.

Over the next 35-plus years at Rockefeller, Darnell never strayed from his original research question: How do mammalian cells make and control the making of different
mRNAs? His work was instrumental in the collaborative discovery of

  • splicing in the late 1970s and
  • in identifying and cloning many transcriptional activators.

Perhaps his greatest contribution during this time, with the help of Ernest Knight, was

  • the discovery and cloning of the signal transducers and activators of transcription (STAT) proteins.

And with George Stark, Andy Wilks and John Krowlewski, he described

  • cytokine signaling via the JAK-STAT pathway.

Darnell closes his “Reflections” with perhaps his best advice: Do not get too wrapped up in your own work, because “we are all needed and we are all in this together.”

Darnell Reflections - James_Darnell

Darnell Reflections – James_Darnell

http://www.asbmb.org/assets/0/366/418/428/85528/85529/85530/8758cb87-84ff-42d6-8aea-96fda4031a1b.jpg

Recent findings on presenilins and signal peptide peptidase

By Dinu-Valantin Bălănescu

γ-secretase and SPP

γ-secretase and SPP

Fig. 1 from the minireview shows a schematic depiction of γ-secretase and SPP

http://www.asbmb.org/assets/0/366/418/428/85528/85529/85530/c2de032a-daad-41e5-ba19-87a17bd26362.png

GxGD proteases are a family of intramembranous enzymes capable of hydrolyzing

  • the transmembrane domain of some integral membrane proteins.

The GxGD family is one of the three families of

  • intramembrane-cleaving proteases discovered so far (along with the rhomboid and site-2 protease) and
  • includes the γ-secretase and the signal peptide peptidase.

Although only recently discovered, a number of functions in human pathology and in numerous other biological processes

  • have been attributed to γ-secretase and SPP.

Taisuke Tomita and Takeshi Iwatsubo of the University of Tokyo highlighted the latest findings on the structure and function of γ-secretase and SPP
in a recent minireview in The Journal of Biological Chemistry.

  • γ-secretase is involved in cleaving the amyloid-β precursor protein, thus producing amyloid-β peptide,

the main component of senile plaques in Alzheimer’s disease patients’ brains. The complete structure of mammalian γ-secretase is not yet known; however,
Tomita and Iwatsubo note that biochemical analyses have revealed it to be a multisubunit protein complex.

  • Its catalytic subunit is presenilin, an aspartyl protease.

In vitro and in vivo functional and chemical biology analyses have revealed that

  • presenilin is a modulator and mandatory component of the γ-secretase–mediated cleavage of APP.

Genetic studies have identified three other components required for γ-secretase activity:

  1. nicastrin,
  2. anterior pharynx defective 1 and
  3. presenilin enhancer 2.

By coexpression of presenilin with the other three components, the authors managed to

  • reconstitute γ-secretase activity.

Tomita and Iwatsubo determined using the substituted cysteine accessibility method and by topological analyses, that

  • the catalytic aspartates are located at the center of the nine transmembrane domains of presenilin,
  • by revealing the exact location of the enzyme’s catalytic site.

The minireview also describes in detail the formerly enigmatic mechanism of γ-secretase mediated cleavage.

SPP, an enzyme that cleaves remnant signal peptides in the membrane

  • during the biogenesis of membrane proteins and
  • signal peptides from major histocompatibility complex type I,
  • also is involved in the maturation of proteins of the hepatitis C virus and GB virus B.

Bioinformatics methods have revealed in fruit flies and mammals four SPP-like proteins,

  • two of which are involved in immunological processes.

By using γ-secretase inhibitors and modulators, it has been confirmed

  • that SPP shares a similar GxGD active site and proteolytic activity with γ-secretase.

Upon purification of the human SPP protein with the baculovirus/Sf9 cell system,

  • single-particle analysis revealed further structural and functional details.

HLA targeting efficiency correlates with human T-cell response magnitude and with mortality from influenza A infection

From www.pnas.org –  Sep 3, 2013 4:24 PM

Experimental and computational evidence suggests that

  • HLAs preferentially bind conserved regions of viral proteins, a concept we term “targeting efficiency,” and that
  • this preference may provide improved clearance of infection in several viral systems.

To test this hypothesis, T-cell responses to A/H1N1 (2009) were measured from peripheral blood mononuclear cells obtained from a household cohort study
performed during the 2009–2010 influenza season. We found that HLA targeting efficiency scores significantly correlated with

  • IFN-γ enzyme-linked immunosorbent spot responses (P = 0.042, multiple regression).

A further population-based analysis found that the carriage frequencies of the alleles with the lowest targeting efficiencies, A*24,

  • were associated with pH1N1 mortality (r = 0.37, P = 0.031) and
  • are common in certain indigenous populations in which increased pH1N1 morbidity has been reported.

HLA efficiency scores and HLA use are associated with CD8 T-cell magnitude in humans after influenza infection.
The computational tools used in this study may be useful predictors of potential morbidity and

  • identify immunologic differences of new variant influenza strains
  • more accurately than evolutionary sequence comparisons.

Population-based studies of the relative frequency of these alleles in severe vs. mild influenza cases

  • might advance clinical practices for severe H1N1 infections among genetically susceptible populations.

Metabolomics in drug target discovery

J D Rabinowitz et al.

Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ.
Cold Spring Harbor Symposia on Quantitative Biology 11/2011; 76:235-46.
http://dx.doi.org:/10.1101/sqb.2011.76.010694 

Most diseases result in metabolic changes. In many cases, these changes play a causative role in disease progression. By identifying pathological metabolic changes,

  • metabolomics can point to potential new sites for therapeutic intervention.

Particularly promising enzymatic targets are those that

  • carry increased flux in the disease state.

Definitive assessment of flux requires the use of isotope tracers. Here we present techniques for

  • finding new drug targets using metabolomics and isotope tracers.

The utility of these methods is exemplified in the study of three different viral pathogens. For influenza A and herpes simplex virus,

  • metabolomic analysis of infected versus mock-infected cells revealed
  • dramatic concentration changes around the current antiviral target enzymes.

Similar analysis of human-cytomegalovirus-infected cells, however, found the greatest changes

  • in a region of metabolism unrelated to the current antiviral target.

Instead, it pointed to the tricarboxylic acid (TCA) cycle and

  • its efflux to feed fatty acid biosynthesis as a potential preferred target.

Isotope tracer studies revealed that cytomegalovirus greatly increases flux through

  • the key fatty acid metabolic enzyme acetyl-coenzyme A carboxylase.
  • Inhibition of this enzyme blocks human cytomegalovirus replication.

Examples where metabolomics has contributed to identification of anticancer drug targets are also discussed. Eventual proof of the value of

  • metabolomics as a drug target discovery strategy will be
  • successful clinical development of therapeutics hitting these new targets.

 Related References

Use of metabolic pathway flux information in targeted cancer drug design. Drug Discovery Today: Therapeutic Strategies 1:435-443, 2004.

Detection of resistance to imatinib by metabolic profiling: clinical and drug development implications. Am J Pharmacogenomics. 2005;5(5):293-302. Review. PMID: 16196499

Medicinal chemistry, metabolic profiling and drug target discovery: a role for metabolic profiling in reverse pharmacology and chemical genetics.
Mini Rev Med Chem.  2005 Jan;5(1):13-20. Review. PMID: 15638788 [PubMed – indexed for MEDLINE] Related citations

Development of Tracer-Based Metabolomics and its Implications for the Pharmaceutical Industry. Int J Pharm Med 2007; 21 (3): 217-224.

Use of metabolic pathway flux information in anticancer drug design. Ernst Schering Found Symp Proc. 2007;(4):189-203. Review. PMID: 18811058

Pharmacological targeting of glucagon and glucagon-like peptide 1 receptors has different effects on energy state and glucose homeostasis in diet-induced obese mice. J Pharmacol Exp Ther. 2011 Jul;338(1):70-81. http://dx.doi.org:/10.1124/jpet.111.179986. PMID: 21471191

Single valproic acid treatment inhibits glycogen and RNA ribose turnover while disrupting glucose-derived cholesterol synthesis in liver as revealed by the
[U-C(6)]-d-glucose tracer in mice. Metabolomics. 2009 Sep;5(3):336-345. PMID: 19718458

Metabolic Pathways as Targets for Drug Screening, Metabolomics, Dr Ute Roessner (Ed.), ISBN: 978-953-51-0046-1, InTech, Available from: http://www.intechopen.com/books/metabolomics/metabolic-pathways-as-targets-for-drug-screening

Iron regulates glucose homeostasis in liver and muscle via AMP-activated protein kinase in mice. FASEB J. 2013 Jul;27(7):2845-54.
http://dx.doi.org:/10.1096/fj.12-216929. PMID: 23515442

Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery

Drug Discov. Today 19 (2014), 171–182     http://dx.doi.org:/10.1016/j.drudis.2013.07.014

Highlights

  • We now have metabolic network models; the metabolome is represented by their nodes.
  • Metabolite levels are sensitive to changes in enzyme activities.
  • Drugs hitchhike on metabolite transporters to get into and out of cells.
  • The consensus network Recon2 represents the present state of the art, and has predictive power.
  • Constraint-based modelling relates network structure to metabolic fluxes.

Metabolism represents the ‘sharp end’ of systems biology, because changes in metabolite concentrations are

  • necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs.

To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of

  • the human metabolic network that include the important transporters.

Small molecule ‘drug’ transporters are in fact metabolite transporters, because

  • drugs bear structural similarities to metabolites known from the network reconstructions and
  • from measurements of the metabolome.

Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia:

(i) the effects of inborn errors of metabolism;

(ii) which metabolites are exometabolites, and

(iii) how metabolism varies between tissues and cellular compartments.

However, even these qualitative network models are not yet complete. As our understanding improves

  • so do we recognise more clearly the need for a systems (poly)pharmacology.

Introduction – a systems biology approach to drug discovery

It is clearly not news that the productivity of the pharmaceutical industry has declined significantly during recent years

  • following an ‘inverse Moore’s Law’, Eroom’s Law, or
  • that many commentators, consider that the main cause of this is
  • because of an excessive focus on individual molecular target discovery rather than a more sensible strategy
  • based on a systems-level approach (Fig. 1).
drug discovery science

drug discovery science

Figure 1.

The change in drug discovery strategy from ‘classical’ function-first approaches (in which the assay of drug function was at the tissue or organism level),
with mechanistic studies potentially coming later, to more-recent target-based approaches where initial assays usually involve assessing the interactions
of drugs with specified (and often cloned, recombinant) proteins in vitro. In the latter cases, effects in vivo are assessed later, with concomitantly high levels of attrition.

Arguably the two chief hallmarks of the systems biology approach are:

(i) that we seek to make mathematical models of our systems iteratively or in parallel with well-designed ‘wet’ experiments, and
(ii) that we do not necessarily start with a hypothesis but measure as many things as possible (the ’omes) and

  • let the data tell us the hypothesis that best fits and describes them.

Although metabolism was once seen as something of a Cinderella subject,

  • there are fundamental reasons to do with the organisation of biochemical networks as
  • to why the metabol(om)ic level – now in fact seen as the ‘apogee’ of the ’omics trilogy –
  •  is indeed likely to be far more discriminating than are
  • changes in the transcriptome or proteome.

The next two subsections deal with these points and Fig. 2 summarises the paper in the form of a Mind Map.

metabolomics and systems pharmacology

metabolomics and systems pharmacology

http://ars.els-cdn.com/content/image/1-s2.0-S1359644613002481-gr2.jpg

Metabolic Disease Drug Discovery— “Hitting the Target” Is Easier Said Than Done

David E. Moller, et al.   http://dx.doi.org:/10.1016/j.cmet.2011.10.012

Despite the advent of new drug classes, the global epidemic of cardiometabolic disease has not abated. Continuing

  • unmet medical needs remain a major driver for new research.

Drug discovery approaches in this field have mirrored industry trends, leading to a recent

  • increase in the number of molecules entering development.

However, worrisome trends and newer hurdles are also apparent. The history of two newer drug classes—

  1. glucagon-like peptide-1 receptor agonists and
  2. dipeptidyl peptidase-4 inhibitors—

illustrates both progress and challenges. Future success requires that researchers learn from these experiences and

  • continue to explore and apply new technology platforms and research paradigms.

The global epidemic of obesity and diabetes continues to progress relentlessly. The International Diabetes Federation predicts an even greater diabetes burden (>430 million people afflicted) by 2030, which will disproportionately affect developing nations (International Diabetes Federation, 2011). Yet

  • existing drug classes for diabetes, obesity, and comorbid cardiovascular (CV) conditions have substantial limitations.

Currently available prescription drugs for treatment of hyperglycemia in patients with type 2 diabetes (Table 1) have notable shortcomings. In general,

Therefore, clinicians must often use combination therapy, adding additional agents over time. Ultimately many patients will need to use insulin—a therapeutic class first introduced in 1922. Most existing agents also have

  • issues around safety and tolerability as well as dosing convenience (which can impact patient compliance).

Pharmacometabolomics, also known as pharmacometabonomics, is a field which stems from metabolomics,

  • the quantification and analysis of metabolites produced by the body.

It refers to the direct measurement of metabolites in an individual’s bodily fluids, in order to

  • predict or evaluate the metabolism of pharmaceutical compounds, and
  • to better understand the pharmacokinetic profile of a drug.

Alternatively, pharmacometabolomics can be applied to measure metabolite levels

  • following the administration of a pharmaceutical compound, in order to
  • monitor the effects of the compound on certain metabolic pathways(pharmacodynamics).

This provides detailed mapping of drug effects on metabolism and

  • the pathways that are implicated in mechanism of variation of response to treatment.

In addition, the metabolic profile of an individual at baseline (metabotype) provides information about

  • how individuals respond to treatment and highlights heterogeneity within a disease state.

All three approaches require the quantification of metabolites found

relationship between -OMICS

relationship between -OMICS

http://upload.wikimedia.org/wikipedia/commons/thumb/e/eb/OMICS.png/350px-OMICS.png

Pharmacometabolomics is thought to provide information that

Looking at the characteristics of an individual down through these different levels of detail, there is an

  • increasingly more accurate prediction of a person’s ability to respond to a pharmaceutical compound.
  1. the genome, made up of 25 000 genes, can indicate possible errors in drug metabolism;
  2. the transcriptome, made up of 85,000 transcripts, can provide information about which genes important in metabolism are being actively transcribed;
  3. and the proteome, >10,000,000 members, depicts which proteins are active in the body to carry out these functions.

Pharmacometabolomics complements the omics with

  • direct measurement of the products of all of these reactions, but with perhaps a relatively
  • smaller number of members: that was initially projected to be approximately 2200 metabolites,

but could be a larger number when gut derived metabolites and xenobiotics are added to the list. Overall, the goal of pharmacometabolomics is

  • to more closely predict or assess the response of an individual to a pharmaceutical compound,
  • permitting continued treatment with the right drug or dosage
  • depending on the variations in their metabolism and ability to respond to treatment.

Pharmacometabolomic analyses, through the use of a metabolomics approach,

  • can provide a comprehensive and detailed metabolic profile or “metabolic fingerprint” for an individual patient.

Such metabolic profiles can provide a complete overview of individual metabolite or pathway alterations,

This approach can then be applied to the prediction of response to a pharmaceutical compound

  • by patients with a particular metabolic profile.

Pharmacometabolomic analyses of drug response are

Pharmacogenetics focuses on the identification of genetic variations (e.g. single-nucleotide polymorphisms)

  • within patients that may contribute to altered drug responses and overall outcome of a certain treatment.

The results of pharmacometabolomics analyses can act to “inform” or “direct”

  • pharmacogenetic analyses by correlating aberrant metabolite concentrations or metabolic pathways to potential alterations at the genetic level.

This concept has been established with two seminal publications from studies of antidepressants serotonin reuptake inhibitors

  • where metabolic signatures were able to define a pathway implicated in response to the antidepressant and
  • that lead to identification of genetic variants within a key gene
  • within the highlighted pathway as being implicated in variation in response.

These genetic variants were not identified through genetic analysis alone and hence

  • illustrated how metabolomics can guide and inform genetic data.

en.wikipedia.org/wiki/Pharmacometabolomics

Benznidazole Biotransformation and Multiple Targets in Trypanosoma cruzi Revealed by Metabolomics

Andrea Trochine, Darren J. Creek, Paula Faral-Tello, Michael P. Barrett, Carlos Robello
Published: May 22, 2014   http://dx.doi.org:/10.1371/journal.pntd.0002844

The first line treatment for Chagas disease, a neglected tropical disease caused by the protozoan parasite Trypanosoma cruzi,

  • involves administration of benznidazole (Bzn).

Bzn is a 2-nitroimidazole pro-drug which requires nitroreduction to become active. We used a

  • non-targeted MS-based metabolomics approach to study the metabolic response of T. cruzi to Bzn.

Parasites treated with Bzn were minimally altered compared to untreated trypanosomes, although the redox active thiols

  1. trypanothione,
  2. homotrypanothione and
  3. cysteine

were significantly diminished in abundance post-treatment. In addition, multiple Bzn-derived metabolites were detected after treatment.

These metabolites included reduction products, fragments and covalent adducts of reduced Bzn

  • linked to each of the major low molecular weight thiols:
  1. trypanothione,
  2. glutathione,
  3. g-glutamylcysteine,
  4. glutathionylspermidine,
  5. cysteine and
  6. ovothiol A.

Bzn products known to be generated in vitro by the unusual trypanosomal nitroreductase, TcNTRI,

  • were found within the parasites,
  • but low molecular weight adducts of glyoxal, a proposed toxic end-product of NTRI Bzn metabolism, were not detected.

Our data is indicative of a major role of the

  • thiol binding capacity of Bzn reduction products
  • in the mechanism of Bzn toxicity against T. cruzi.

 

 

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Compilation of References in Leaders in Pharmaceutical Intelligence about proteomics, metabolomics, signaling pathways, and cell regulation


Compilation of References in Leaders in Pharmaceutical Intelligence about
proteomics, metabolomics, signaling pathways, and cell regulation

Curator: Larry H. Bernstein, MD, FCAP

 

Proteomics

  1. The Human Proteome Map Completed
    Reporter and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/08/28/the-human-proteome-map-completed/
  1. Proteomics – The Pathway to Understanding and Decision-making in Medicine
    Author and Curator, Larry H Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/06/24/proteomics-the-pathway-to-understanding-and-decision-making-in-medicine/
  1. Advances in Separations Technology for the “OMICs” and Clarification of Therapeutic Targets
    Author and Curator, Larry H Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2012/10/22/advances-in-separations-technology-for-the-omics-and-clarification-of-therapeutic-targets/
  1. Expanding the Genetic Alphabet and Linking the Genome to the Metabolome
    Author and Curator, Larry H Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2012/09/24/expanding-the-genetic-alphabet-and-linking-the-genome-to-the-metabolome/
  1. Synthesizing Synthetic Biology: PLOS Collections
    Reporter: Aviva Lev-Ari
    https://pharmaceuticalintelligence.com/2012/08/17/synthesizing-synthetic-biology-plos-collections/

 

Metabolomics

  1. Extracellular evaluation of intracellular flux in yeast cells
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    https://pharmaceuticalintelligence.com/2014/08/25/extracellular-evaluation-of-intracellular-flux-in-yeast-cells/ 
  2. Metabolomic analysis of two leukemia cell lines. I.
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    http://pharmaceuticalintelligence.com/2014/08/23/metabolomic-analysis-of-two-leukemia-cell-lines-_i/ 
  3. Metabolomic analysis of two leukemia cell lines. II.
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    https://pharmaceuticalintelligence.com/2014/08/24/metabolomic-analysis-of-two-leukemia-cell-lines-ii/ 
  4. Metabolomics, Metabonomics and Functional Nutrition: the next step in nutritional metabolism and biotherapeutics
    Reviewer and Curator, Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/08/22/metabolomics-metabonomics-and-functional-nutrition-the-next-step-in-nutritional-metabolism-and-biotherapeutics/ 
  5. Buffering of genetic modules involved in tricarboxylic acid cycle metabolism provides homeomeostatic regulation
    Larry H. Bernstein, MD, FCAP, Reviewer and curator
    https://pharmaceuticalintelligence.com/2014/08/27/buffering-of-genetic-modules-involved-in-tricarboxylic-acid-cycle-metabolism-provides-homeomeostatic-regulation/

 

Metabolic Pathways

  1. Pentose Shunt, Electron Transfer, Galactose, more Lipids in brief
    Reviewer and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/08/21/pentose-shunt-electron-transfer-galactose-more-lipids-in-brief/
  2. Mitochondria: More than just the “powerhouse of the cell”
    Reviewer and Curator: Ritu Saxena
    https://pharmaceuticalintelligence.com/2012/07/09/mitochondria-more-than-just-the-powerhouse-of-the-cell/
  3. Mitochondrial fission and fusion: potential therapeutic targets?
    Reviewer and Curator: Ritu saxena
    https://pharmaceuticalintelligence.com/2012/10/31/mitochondrial-fission-and-fusion-potential-therapeutic-target/ 
  4. Mitochondrial mutation analysis might be “1-step” away
    Reviewer and Curator: Ritu Saxena
    https://pharmaceuticalintelligence.com/2012/08/14/mitochondrial-mutation-analysis-might-be-1-step-away/
  5. Selected References to Signaling and Metabolic Pathways in PharmaceuticalIntelligence.com
    Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/08/14/selected-references-to-signaling-and-metabolic-pathways-in-leaders-in-pharmaceutical-intelligence/
  6. Metabolic drivers in aggressive brain tumors
    Prabodh Kandal, PhD
    https://pharmaceuticalintelligence.com/2012/11/11/metabolic-drivers-in-aggressive-brain-tumors/ 
  7. Metabolite Identification Combining Genetic and Metabolic Information: Genetic association links unknown metabolites to functionally related genes
    Author and Curator: Aviva Lev-Ari, PhD, RD
    https://pharmaceuticalintelligence.com/2012/10/22/metabolite-identification-combining-genetic-and-metabolic-information-genetic-association-links-unknown-metabolites-to-functionally-related-genes/
  8. Mitochondria: Origin from oxygen free environment, role in aerobic glycolysis, metabolic adaptation
    Author and curator:Larry H Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2012/09/26/mitochondria-origin-from-oxygen-free-environment-role-in-aerobic-glycolysis-metabolic-adaptation/
  9. Therapeutic Targets for Diabetes and Related Metabolic Disorders
    Reporter, Aviva Lev-Ari, PhD, RD
    https://pharmaceuticalintelligence.com/2012/08/20/therapeutic-targets-for-diabetes-and-related-metabolic-disorders/
  10. Buffering of genetic modules involved in tricarboxylic acid cycle metabolism provides homeomeostatic regulation
    Larry H. Bernstein, MD, FCAP, Reviewer and curator
    https://pharmaceuticalintelligence.com/2014/08/27/buffering-of-genetic-modules-involved-in-tricarboxylic-acid-cycle-metabolism-provides-homeomeostatic-regulation/
  11. The multi-step transfer of phosphate bond and hydrogen exchange energy
    Curator:Larry H. Bernstein, MD, FCAP,
    https://pharmaceuticalintelligence.com/2014/08/19/the-multi-step-transfer-of-phosphate-bond-and-hydrogen-exchange-energy/
  12. Studies of Respiration Lead to Acetyl CoA
    Author and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/08/18/studies-of-respiration-lead-to-acetyl-coa/
  13. Lipid Metabolism
    Author and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/08/15/lipid-metabolism/
  14. Carbohydrate Metabolism
    Author and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/08/13/carbohydrate-metabolism/
  15. Prologue to Cancer – e-book Volume One – Where are we in this journey?
    Author and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/04/13/prologue-to-cancer-ebook-4-where-are-we-in-this-journey/
  16. Introduction – The Evolution of Cancer Therapy and Cancer Research: How We Got Here?
    Author and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/04/04/introduction-the-evolution-of-cancer-therapy-and-cancer-research-how-we-got-here/
  17. Inhibition of the Cardiomyocyte-Specific Kinase TNNI3K
    Author and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2013/11/01/inhibition-of-the-cardiomyocyte-specific-kinase-tnni3k/
  18. The Binding of Oligonucleotides in DNA and 3-D Lattice Structures
    Author and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2013/05/15/the-binding-of-oligonucleotides-in-dna-and-3-d-lattice-structures/
  19. Mitochondrial Metabolism and Cardiac Function
    Author and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2013/04/14/mitochondrial-metabolism-and-cardiac-function/
  20. How Methionine Imbalance with Sulfur-Insufficiency Leads to Hyperhomocysteinemia
    Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2013/04/04/sulfur-deficiency-leads_to_hyperhomocysteinemia/
  21. AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth In Vivo
    Author and Curator: SJ. Williams
    https://pharmaceuticalintelligence.com/2013/03/12/ampk-is-a-negative-regulator-of-the-warburg-effect-and-suppresses-tumor-growth-in-vivo/
  22. A Second Look at the Transthyretin Nutrition Inflammatory Conundrum
    Author and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2012/12/03/a-second-look-at-the-transthyretin-nutrition-inflammatory-conundrum/
  23. Overview of Posttranslational Modification (PTM)
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/07/29/overview-of-posttranslational-modification-ptm/
  24. Malnutrition in India, high newborn death rate and stunting of children age under five years
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/07/15/malnutrition-in-india-high-newborn-death-rate-and-stunting-of-children-age-under-five-years/
  25. Update on mitochondrial function, respiration, and associated disorders
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/07/08/update-on-mitochondrial-function-respiration-and-associated-disorders/
  26. Omega-3 fatty acids, depleting the source, and protein insufficiency in renal disease
    Larry H. Bernstein, MD, FCAP, Curator
    https://pharmaceuticalintelligence.com/2014/07/06/omega-3-fatty-acids-depleting-the-source-and-protein-insufficiency-in-renal-disease/ 
  27. Late Onset of Alzheimer’s Disease and One-carbon Metabolism
    Reporter and Curator: Dr. Sudipta Saha, Ph.D.
    https://pharmaceuticalintelligence.com/2013/05/06/alzheimers-disease-and-one-carbon-metabolism/
  28. Problems of vegetarianism
    Reporter and Curator: Dr. Sudipta Saha, Ph.D.
    https://pharmaceuticalintelligence.com/2013/04/22/problems-of-vegetarianism/

 

Signaling Pathways

  1. Introduction to e-Series A: Cardiovascular Diseases, Volume Four Part 2: Regenerative Medicine
    Larry H. Bernstein, MD, FCAP, writer, and Aviva Lev- Ari, PhD, RN  https://pharmaceuticalintelligence.com/2014/04/27/larryhbernintroduction_to_cardiovascular_diseases-translational_medicine-part_2/
  2. Epilogue: Envisioning New Insights in Cancer Translational Biology
    Series C: e-Books on Cancer & Oncology
    Author & Curator: Larry H. Bernstein, MD, FCAP, Series C Content Consultant
    https://pharmaceuticalintelligence.com/2014/03/29/epilogue-envisioning-new-insights/
  3. Ca2+-Stimulated Exocytosis:  The Role of Calmodulin and Protein Kinase C in Ca2+ Regulation of Hormone and Neurotransmitter  Writer and Curator: Larry H Bernstein, MD, FCAP and Curator and Content Editor: Aviva Lev-Ari, PhD, RN
    https://pharmaceuticalintelligence.com/2013/12/23/calmodulin-and-protein-kinase-c-drive-the-ca2-regulation-of-hormone-and-neurotransmitter-release-that-triggers-ca2-stimulated-exocy
  4. Cardiac Contractility & Myocardial Performance: Therapeutic Implications of Ryanopathy (Calcium Release-related Contractile Dysfunction) and Catecholamine Responses
    Author, and Content Consultant to e-SERIES A: Cardiovascular Diseases: Justin Pearlman, MD, PhD, FACC
    Author and Curator: Larry H Bernstein, MD, FCAP and Article Curator: Aviva Lev-Ari, PhD, RN
    https://pharmaceuticalintelligence.com/2013/08/28/cardiac-contractility-myocardium-performance-ventricular-arrhythmias-and-non-ischemic-heart-failure-therapeutic-implications-for-cardiomyocyte-ryanopathy-calcium-release-related-contractile/
  5. Role of Calcium, the Actin Skeleton, and Lipid Structures in Signaling and Cell Motility
    Author and Curator: Larry H Bernstein, MD, FCAP Author: Stephen Williams, PhD, and Curator: Aviva Lev-Ari, PhD, RN
    https://pharmaceuticalintelligence.com/2013/08/26/role-of-calcium-the-actin-skeleton-and-lipid-structures-in-signaling-and-cell-motility/
  6. Identification of Biomarkers that are Related to the Actin Cytoskeleton
    Larry H Bernstein, MD, FCAP, Author and Curator
    https://pharmaceuticalintelligence.com/2012/12/10/identification-of-biomarkers-that-are-related-to-the-actin-cytoskeleton/
  7. Advanced Topics in Sepsis and the Cardiovascular System at its End Stage
    Author and Curator: Larry H Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2013/08/18/advanced-topics-in-Sepsis-and-the-Cardiovascular-System-at-its-End-Stage/
  8. The Delicate Connection: IDO (Indolamine 2, 3 dehydrogenase) and Cancer Immunology
    Demet Sag, PhD, Author and Curator
    https://pharmaceuticalintelligence.com/2013/08/04/the-delicate-connection-ido-indolamine-2-3-dehydrogenase-and-immunology/
  9. IDO for Commitment of a Life Time: The Origins and Mechanisms of IDO, indolamine 2, 3-dioxygenase
    Demet Sag, PhD, Author and Curator
    https://pharmaceuticalintelligence.com/2013/08/04/ido-for-commitment-of-a-life-time-the-origins-and-mechanisms-of-ido-indolamine-2-3-dioxygenase/
  10. Confined Indolamine 2, 3 dioxygenase (IDO) Controls the Homeostasis of Immune Responses for Good and Bad
    Author and Curator: Demet Sag, PhD, CRA, GCP
    https://pharmaceuticalintelligence.com/2013/07/31/confined-indolamine-2-3-dehydrogenase-controls-the-hemostasis-of-immune-responses-for-good-and-bad/
  11. Signaling Pathway that Makes Young Neurons Connect was discovered @ Scripps Research Institute
    Reporter: Aviva Lev-Ari, PhD, RN
    https://pharmaceuticalintelligence.com/2013/06/26/signaling-pathway-that-makes-young-neurons-connect-was-discovered-scripps-research-institute/
  12. Naked Mole Rats Cancer-Free
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2013/06/20/naked-mole-rats-cancer-free/
  13. Amyloidosis with Cardiomyopathy
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2013/03/31/amyloidosis-with-cardiomyopathy/
  14. Liver endoplasmic reticulum stress and hepatosteatosis
    Larry H Bernstein, MD, FACP
    https://pharmaceuticalintelligence.com/2013/03/10/liver-endoplasmic-reticulum-stress-and-hepatosteatosis/
  15. The Molecular Biology of Renal Disorders: Nitric Oxide – Part III
    Curator and Author: Larry H Bernstein, MD, FACP
    https://pharmaceuticalintelligence.com/2012/11/26/the-molecular-biology-of-renal-disorders/
  16. Nitric Oxide Function in Coagulation – Part II
    Curator and Author: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2012/11/26/nitric-oxide-function-in-coagulation/
  17. Nitric Oxide, Platelets, Endothelium and Hemostasis
    Curator and Author: Larry H Bernstein, MD, FACP
    https://pharmaceuticalintelligence.com/2012/11/08/nitric-oxide-platelets-endothelium-and-hemostasis/
  18. Interaction of Nitric Oxide and Prostacyclin in Vascular Endothelium
    Curator and Author: Larry H Bernstein, MD, FACP
    https://pharmaceuticalintelligence.com/2012/09/14/interaction-of-nitric-oxide-and-prostacyclin-in-vascular-endothelium/
  19. Nitric Oxide and Immune Responses: Part 1
    Curator and Author:  Aviral Vatsa PhD, MBBS
    https://pharmaceuticalintelligence.com/2012/10/18/nitric-oxide-and-immune-responses-part-1/
  20. Nitric Oxide and Immune Responses: Part 2
    Curator and Author:  Aviral Vatsa PhD, MBBS
    https://pharmaceuticalintelligence.com/2012/10/28/nitric-oxide-and-immune-responses-part-2/
  21. Nitric Oxide and iNOS have Key Roles in Kidney Diseases – Part II
    Curator and Author: Larry H Bernstein, MD, FACP
    https://pharmaceuticalintelligence.com/2012/11/26/nitric-oxide-and-inos-have-key-roles-in-kidney-diseases/
  22. New Insights on Nitric Oxide donors – Part IV
    Curator and Author: Larry H Bernstein, MD, FACP
    https://pharmaceuticalintelligence.com/2012/11/26/new-insights-on-no-donors/
  23. Crucial role of Nitric Oxide in Cancer
    Curator and Author: Ritu Saxena, Ph.D.
    https://pharmaceuticalintelligence.com/2012/10/16/crucial-role-of-nitric-oxide-in-cancer/
  24. Nitric Oxide has a ubiquitous role in the regulation of glycolysis -with a concomitant influence on mitochondrial function
    Curator and Author: Larry H Bernstein, MD, FACP
    https://pharmaceuticalintelligence.com/2012/09/16/nitric-oxide-has-a-ubiquitous-role-in-the-regulation-of-glycolysis-with-a-concomitant-influence-on-mitochondrial-function/
  25. Nitric Oxide and Immune Responses: Part 2
    Author and Curator: Aviral Vatsa, PhD, MBBS
    https://pharmaceuticalintelligence.com/2012/10/28/nitric-oxide-and-immune-responses-part-2/
  26. Mitochondrial Damage and Repair under Oxidative Stress
    Author and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2012/10/28/mitochondrial-damage-and-repair-under-oxidative-stress/
  27. Is the Warburg Effect the cause or the effect of cancer: A 21st Century View?
    Curator and Author: Larry H Bernstein, MD, FACP
    https://pharmaceuticalintelligence.com/2012/10/17/is-the-warburg-effect-the-cause-or-the-effect-of-cancer-a-21st-century-view/
  28. Targeting Mitochondrial-bound Hexokinase for Cancer Therapy
    Curator and Author: Ziv Raviv, PhD, RN 04/06/2013
    https://pharmaceuticalintelligence.com/2013/04/06/targeting-mitochondrial-bound-hexokinase-for-cancer-therapy/
  29. Ubiquinin-Proteosome pathway, autophagy, the mitochondrion, proteolysis and cell apoptosis
    Curator and Author: Larry H Bernstein, MD, FACP
    https://pharmaceuticalintelligence.com/2012/10/30/ubiquinin-proteosome-pathway-autophagy-the-mitochondrion-proteolysis-and-cell-apoptosis/
  30. Ubiquitin-Proteosome pathway, Autophagy, the Mitochondrion, Proteolysis and Cell Apoptosis: Part III
    Curator and Author: Larry H Bernstein, MD, FACP
    https://pharmaceuticalintelligence.com/2013/02/14/ubiquinin-proteosome-pathway-autophagy-the-mitochondrion-proteolysis-and-cell-apoptosis-reconsidered/
  31. Biochemistry of the Coagulation Cascade and Platelet Aggregation – Part I
    Curator and Author: Larry H Bernstein, MD, FACP
    https://pharmaceuticalintelligence.com/2012/11/26/biochemistry-of-the-coagulation-cascade-and-platelet-aggregation/

 

Genomics, Transcriptomics, and Epigenetics

  1. What is the meaning of so many RNAs?
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/08/06/what-is-the-meaning-of-so-many-rnas/
  2. RNA and the transcription the genetic code
    Larry H. Bernstein, MD, FCAP, Writer and Curator
    https://pharmaceuticalintelligence.com/2014/08/02/rna-and-the-transcription-of-the-genetic-code/
  3. A Primer on DNA and DNA Replication
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/07/29/a_primer_on_dna_and_dna_replication/
  4. Pathology Emergence in the 21st Century
    Author and Curator: Larry Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/08/03/pathology-emergence-in-the-21st-century/
  5. RNA and the transcription the genetic code
    Writer and Curator, Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/08/02/rna-and-the-transcription-of-the-genetic-code/
  6. Commentary on Biomarkers for Genetics and Genomics of Cardiovascular Disease: Views by Larry H Bernstein, MD, FCAP
    Author: Larry H Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/07/16/commentary-on-biomarkers-for-genetics-and-genomics-of-cardiovascular-disease-views-by-larry-h-bernstein-md-fcap/
  7. Observations on Finding the Genetic Links in Common Disease: Whole Genomic Sequencing Studies
    Author an Curator: Larry H Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2013/05/18/observations-on-finding-the-genetic-links/
  8. Silencing Cancers with Synthetic siRNAs
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    https://pharmaceuticalintelligence.com/2013/12/09/silencing-cancers-with-synthetic-sirnas/
  9. Cardiometabolic Syndrome and the Genetics of Hypertension: The Neuroendocrine Transcriptome Control Points
    Reporter: Aviva Lev-Ari, PhD, RN
    https://pharmaceuticalintelligence.com/2013/12/12/cardiometabolic-syndrome-and-the-genetics-of-hypertension-the-neuroendocrine-transcriptome-control-points/
  10. Developments in the Genomics and Proteomics of Type 2 Diabetes Mellitus and Treatment Targets
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    https://pharmaceuticalintelligence.com/2013/12/08/developments-in-the-genomics-and-proteomics-of-type-2-diabetes-mellitus-and-treatment-targets/
  11. CT Angiography & TrueVision™ Metabolomics (Genomic Phenotyping) for new Therapeutic Targets to Atherosclerosis
    Reporter: Aviva Lev-Ari, PhD, RN
    https://pharmaceuticalintelligence.com/2013/11/15/ct-angiography-truevision-metabolomics-genomic-phenotyping-for-new-therapeutic-targets-to-atherosclerosis/
  12. CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics & Computational Genomics
    Genomics Curator, Larry H Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/08/30/cracking-the-code-of-human-life-the-birth-of-bioinformatics-computational-genomics/
  13. Big Data in Genomic Medicine
    Author and Curator, Larry H Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2012/12/17/big-data-in-genomic-medicine/
  14.  From Genomics of Microorganisms to Translational Medicine
    Author and Curator: Demet Sag, PhD
    https://pharmaceuticalintelligence.com/2014/03/20/without-the-past-no-future-but-learn-and-move-genomics-of-microorganisms-to-translational-medicine/
  15.  Summary of Genomics and Medicine: Role in Cardiovascular Diseases
    Author and Curator, Larry H Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/01/06/summary-of-genomics-and-medicine-role-in-cardiovascular-diseases/

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