Posts Tagged ‘cancer cells’

Tumor Organoids Used to Speed Cancer Treatment

Reporter: Irina Robu, PhD

Collecting cancer cells from patients and growing them into 3-D mini tumors could make it possible to quickly screen large numbers of potential drugs for ultra-rare cancers. Preliminary success with a new high-speed, high-volume approach is already guiding treatment decisions for some patients with recurring hard-to-treat cancers.

A London-based team labelled how a “tumor-in-a-dish” approach positively forecasted drug responses in cancer patients who previously took part in clinical trials. That study was a major development in a new research area focused on “organoids” — tiny 3-D versions of the brain, gut, lung and other organs grown in the lab to probe basic biology or test drugs.

UCLA cancer biologist Alice Soragni and her colleagues developed a high-volume, automated method to rapidly study drug responses in tumor organoids grown from patient cells. By studying mini tumors grown on a plate with 96 tiny test tubes, her team can screen hundreds of compounds at once and classify promising candidates within a time frame that is therapeutically actionable. According to Dr. Soragni, the method seemed to work for various kinds of ovarian cancer. It was shown that the lab-grown organoids mimicked how tumors in the body look and behave. And even in cases when mini tumors had a hard time growing in a dish, scientists still acknowledged potential drug candidates.
Up to now, the UCLA team has produced organoids from 35 to 40 people with various types of sarcoma which will allow them to classify tumors that won’t respond to conventional therapy. This proves useful for people with recurrent metastases, where it’s not clear if we’re doing anything for their overall survival or giving them more toxicity.



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3-D visualization of cancer cells

Larry H. Bernstein, MD, FCAP, Curator



Cancer cells in 3D

What researchers miss on glass slides
February 22, 2016


A spheroid of many lung cancer cells illustrates a diversity of behaviors. (credit: Welf and Driscoll et al./Developmental Cell)


Cancer cells don’t live on glass slides. Yet the vast majority of images related to cancer biology come from the cells being photographed on flat, two-dimensional surfaces — images sometimes used to draw conclusions about the behavior of cells that normally reside in a more complex environment.

Now a new high-resolution microscope, presented (open access) February 22 in Developmental Cell, makes it possible to visualize cancer cells in 3D and record how they are signaling to other parts of their environment — revealing previously unappreciated biology of how cancer cells survive and disperse within living things. Based on ”microenvironmental selective plane illumination microscopy” (meSPIM),  the new microscope is designed to image cells in microenvironments free of hard surfaces near the sample.

“There is clear evidence that the environment strongly affects cellular behavior — thus, the value of cell culture experiments on glass must at least be questioned,” says senior author Reto Fiolka, an optical scientist at theUniversity of Texas Southwestern Medical Center. “Our microscope is one tool that may bring us a deeper understanding of the molecular mechanisms that drive cancer cell behavior, since it enables high-resolution imaging in more realistic tumor environments.”


This image shows the extracted surfaces of two cancer cells. (Left) A lung cancer cell colored by actin intensity near the cell surface. Actin is a structural molecule that is integral to cell movement. (Right) A melanoma cell colored by PI3-kinase activity near the cell surface. PI3K is a signaling molecule that is key to many cell processes. (credit: Welf and Driscoll et al./Developmental Cell)

Hidden protrusions from cancer cells

In their study, Fiolka and colleagues, including co-senior author Gaudenz Danuser, and co-first authors Meghan Driscoll and Erik Welf, also of UT Southwestern, used their microscope to image different kinds of skin cancer cells from patients. They found that in a 3D environment (where cells normally reside), unlike a glass slide, multiple melanoma cell lines and primary melanoma cells (from patients with varied genetic mutations) form many small protrusions called blebs.

One hypothesis is that this blebbing may help the cancer cells survive or move around and could thus play a role in skin cancer cell invasiveness or drug resistance in patients.


This is a melanoma cell (red) embedded in a 3-D collagen matrix (white). A 100 x 100 x 100 μm cube is shown, with one corner cut away to show the interaction of the cell with the collagen. (credit: Welf and Driscoll et al./Developmental Cell)

The researchers say that this is a first step toward understanding 3D biology in tumor microenvironments. But since these kinds of images may be too complicated to interpret by the naked eye alone, the next step will be to develop powerful computer platforms to extract and process the information.

The microscope control software and image analytical code are freely available to the scientific community.

The authors were supported by the Cancer Prevention Research Institute of Texas and the National Institutes of Health.

Abstract of Quantitative Multiscale Cell Imaging in Controlled 3D Microenvironments

The microenvironment determines cell behavior, but the underlying molecular mechanisms are poorly understood because quantitative studies of cell signaling and behavior have been challenging due to insufficient spatial and/or temporal resolution and limitations on microenvironmental control. Here we introduce microenvironmental selective plane illumination microscopy (meSPIM) for imaging and quantification of intracellular signaling and submicrometer cellular structures as well as large-scale cell morphological and environmental features. We demonstrate the utility of this approach by showing that the mechanical properties of the microenvironment regulate the transition of melanoma cells from actin-driven protrusion to blebbing, and we present tools to quantify how cells manipulate individual collagen fibers. We leverage the nearly isotropic resolution of meSPIM to quantify the local concentration of actin and phosphatidylinositol 3-kinase signaling on the surfaces of cells deep within 3D collagen matrices and track the many small membrane protrusions that appear in these more physiologically relevant environments.

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Swansea University Uses Artificial Intelligence to Detect Cancer. Reporter: Evelina Cohn Budu Ph.D.

Swansea University is a well known and respected University in UK, a research -led institution with an excellent reputation for the quality of its student experience.

Their new type of research involving algorithms and mathematics in evaluation and identification of cancer cells.

Prof Paul Rees in collaboration with specialists from US, Germany, London and Newcastle, from the University College of Engineering, conducted a research in which the technology of fingerprint recognition software was taught to recognize cells and pinpoint them. “The algorithm recognizes the specific cells of interest by giving examples of the cells to be identified ” he said. After learning how the cells look like, the algorithm can identify target cells in a new population.
Along with these characteristics, the group of researchers can identify the age of a cell, which is very important to determine the cycle of a certain cell in a certain moment.

The research is an international collaboration with the Broad Institute of MIT  and Harvard In Cambridge Massachusetts USA, Hemholtz Zentrum in Munich, The Francis Crick Institute in London and Newcastle Upon Tyne University . The group’s paper entitled “Label -free cell cycle analysis for high-throughput imaging flow cytometry”, was published in Nature Communications on January 7, 2016.

Prof. Dr. Fabian Theis of the Helmholz Zentrum Munich , expert in “Computational Modelling in Biology” said that this discovery can open up a completely new perspective in responding to different research questions, not only cell analysis.

The computer uses algorithms to learn what cells look like

Source: http://

Here is an example of how the artificial intelligence can be used to find an algorithm.

sources : in

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H2S-mediated protein sulfhydration in stress reveals metabolic reprogramming

Larry H. Bernstein, MD, FCAP, Curator




Quantitative H2S-mediated protein sulfhydration reveals metabolic reprogramming during the Integrated Stress Response

” data-author-inst=”CaseWesternReserveUniversityUnitedStates”>Bo-JhihGuan, 

Ilya Bederman
Department of Pediatrics, Case Western Reserve University, Cleveland, United States
No competing interests declared

” data-author-inst=”CaseWesternReserveUniversityUnitedStates”>IlyaBederman, 

Mithu Majumder
Department of Pharmacology, Case Western Reserve University, Cleveland, United States
No competing interests declared

” data-author-inst=”CaseWesternReserveUniversityUnitedStates”>MithuMajumder, et al.
eLife 2015;10.7554/eLife.10067

The sulfhydration of cysteine residues in proteins is an important mechanism involved in diverse biological processes. We have developed a proteomics approach to quantitatively profile the changes of sulfhydrated cysteines in biological systems. Bioinformatics analysis revealed that sulfhydrated cysteines are part of a wide range of biological functions. In pancreatic β cells exposed to endoplasmic reticulum (ER) stress, elevated H2S promotes the sulfhydration of enzymes in energy metabolism and stimulates glycolytic flux. We propose that transcriptional and translational reprogramming by the Integrated Stress Response (ISR) in pancreatic β cells is coupled to metabolic alternations triggered by sulfhydration of key enzymes in intermediary metabolism.
Posttranslational modification is a fundamental mechanism in the regulation of structure and function of proteins. The covalent modification of specific amino acid residues influences diverse biological processes and cell physiology across species. Reactive cysteine residues in proteins have high nucleophilicity and low pKa values and serve as a major target for oxidative modifications, which can vary depending on the subcellular environment, including the type and intensity of intracellular or environmental cues. Oxidative environments cause different post-translational cysteine modifications, including disulfide bond formation (-S-S-), sulfenylation (-S-OH), nitrosylation (-S-NO), glutathionylation (-S-SG), and sulfhydration (-S-SH) (also called persulfidation) (Finkel, 2012; Mishanina et al., 2015). In the latter, an oxidized cysteine residue included glutathionylated, 60 sulfenylated and nitrosylated on a protein reacts with the sulfide anion to form a cysteine persulfide. The reversible nature of this modification provides a mechanism to fine tune biological processes in different cellular redox states. Sulfhydration coordinates with other post-translational protein modifications such as phosphorylation and nitrosylation to regulate cellular functions (Altaany et al., 2014; Sen et al., 2012). Despite great progress in bioinformatics and advanced mass spectroscopic techniques (MS), identification of different cysteine-based protein modifications has been slow compared to other post-translational modifications. In the case of sulfhydration, a small number of proteins have been identified, among them the glycolytic enzyme glyceraldehyde phosphate dehydrogenase, GAPDH (Mustafa et al., 2009). Sulfhydrated GAPDH at Cys150 exhibits an increase in its catalytic activity, in contrast to the inhibitory effects of nitrosylation or glutathionylation of the same cysteine residue (Mustafa et al., 2009; Paul and Snyder, 2012). The biological significance of the Cys150 modification by H2S is not well-studied, but H2S could serve as a biological switch for protein function acting via oxidative modification of specific cysteine residues in response to redox homeostasis (Paul and Snyder, 2012). Understanding the physiological significance of protein sulfhydration requires the development of genome-wide innovative experimental approaches. Current methodologies based on the modified biotin switch technique do not allow detection of a broad spectrum of sulfhydrated proteins (Finkel, 2012). Guided by a previously reported strategy (Sen et al., 2012), we developed an experimental approach that allowed us to quantitatively evaluate the sulfhydrated proteome and the physiological consequences of H2S synthesis during chronic ER stress. The new methodology allows a quantitative, close-up view of the integrated cellular response to environmental and intracellular cues, and is pertinent to our understanding of human disease development.
The ER is an organelle involved in synthesis of proteins followed by various modifications. Disruption of this process results in the accumulation of misfolded proteins, causing ER stress (Tabas and Ron, 2011; Walter and Ron, 2011), which is associated with development of many diseases ranging from metabolic dysfunction to neurodegeneration (Hetz, 2012). ER stress induces transcriptional, translational, and metabolic reprogramming, all of which are interconnected through the transcription factor Atf4. Atf4 increases expression of genes promoting adaptation to stress via their protein products. One such gene is the H2S-producing enzyme, γ-cystathionase (CTH), previously shown to be involved in the signaling pathway that negatively regulates the activity of the protein tyrosine phosphatase 1B (PTP1B) via sulfhydration (Krishnan et al., 2011). We therefore hypothesized that low or even modest levels of reactive oxygen species (ROS) during ER stress may reprogram cellular metabolism via H2S-mediated protein sulfhydration (Figure 1A).
In summary, sulfhydration of specific cysteines in proteins is a key function of H2S (Kabil and Banerjee, 2010; Paul and Snyder, 2012; Szabo et al., 2013). Thus, the development of tools that can quantitatively measure genome-wide protein sulfhydration in physiological or pathological conditions is of central importance. However, a significant challenge in studies of the biological significance of protein sulfhydration is the lack of an approach to selectively detect sulfhydrated cysteines from other modifications (disulfide bonds, glutathionylated thiols and sulfienic acids) in complex biological samples. In this study, we introduced the BTA approach that allowed the quantitative assessment of changes in the sulfhydration of specific cysteines in the proteome and in individual proteins. BTA is superior to other reported methodologies that aimed to profile cysteine modifications, such as the most commonly used, a modified biotin switch technique (BST). BST was originally designed to study protein nitrosylation and postulated to differentiate free thiols and persulfides (Mustafa et al., 2009). A key advantage of BTA over the existing methodologies, is that the experimental approach has steps to avoid false-positive and negative results, as target proteins for sulfhydration. BST is commonly generating such false targets for cysteine modifications (Forrester et al., 2009; Sen et al., 2012). Using mutiple validations, our data support the specificity and reliability of the BTA assay for analysis of protein sulfhydration both in vitro and in vivo. With this approach, we found that ATF4 is the master regulator of protein sulfhydration in pancreatic β cells during ER stress, by means of its function as a transcription factor. A large number of protein targets have been discovered to undergo sulfhydration in β cells by the BTA approach. Almost 1,000 sulfhydrated cysteine- containing peptides were present in the cells under the chronic ER stress condition of treatment with Tg for 18 h. Combined with the isotopic-labeling strategy, almost 820 peptides on more than 500 proteins were quantified in the 405 cells overexpressing ATF4. These data show the potential of the BTA method for further systematic studies of biological events. To our knowledge, the current dataset encompasses most known sulfhydrated cysteine residues in proteins in any organism. Our bioinformatics analyses revealed sulfhydrated cysteine residues located on a variety of structure-function domains, suggesting the possibility of regulatory mechanism(s) mediated by protein sulfhydration. Structure and sequence analysis revealed consensus motifs that favor sulfhydration; an arginine residue and alpha-helix dipoles are both contributing to stabilize sulfhydrated cysteine thiolates in the local environment.
Pathway analyses showed that H2S-mediated sulfhydration of cysteine residues is that part of the ISR with the highest enrichment in proteins involved in energy metabolism. The metabolic flux revealed that H2S promotes aerobic glycolysis associated with decreased oxidative phosphorylation in mitochondria during ER stress in β cells. The TCA cycle revolves by the action of the respiratory chain that requires oxygen to operate. In response to ER stress, mitochondrial function and cellular respiration are down-regulated to limit oxygen demand and to sustain mitochondria. When ATP production from the TCA cycle becomes limited and glycolytic flux increases, there is a risk of accumulation of lactate from pyruvate. One way to escape accumulation of lactate is the mitochondrial conversion of pyruvate to oxalacetic acid (OAA) by pyruvate carboxylase. This latter enzyme was found to be sulfhydrated, consistent with the notion that sulfhydration is linked to metabolic reprogramming towards glycolysis.
The switch of energy production from mitochondria to glycolysis is known as a signature of hypoxic conditions. This metabolic switch has also been observed in many cancer cells characterized as the Warburg effect, which contributes to tumor growth. The Warburg effect provides advantages to cancer cell survival via the rapid ATP production through glycolysis, as well as the increased conversion of glucose into anabolic biomolecules (amino acid, nucleic acid and lipid biosynthesis) and reducing power (NADPH) for regeneration of antioxidants. This metabolic response of tumor cells contributes to tumor growth and metastasis (Vander Heiden et al., 2009). By analogy, the aerobic glycolysis trigged by increased H2S production could give β cells the capability to acquire ATP and nutrients to adapt their cellular metabolism towards maintaining ATP levels in the ER (Vishnu et al., 2014), increasing synthesis of glycerolphospholipids, glycoproteins and protein (Krokowski et al., 2013b), all important components of the ISR. Similar to hypoxic conditions, a phenotype associated with most tumors, the decreased mitochondria function in β cells during ER stress, can also be viewed as an adaptive response by limiting mitochondria ROS and mitochondria-mediated apoptosis. We therefore view that the H2S-mediated increase in glycolysis is an adaptive mechanism for survival of β cells to chronic ER stress, along with the improved ER function and insulin production and folding, both critical factors controlling hyperglycemia in diabetes. Future work should determine which are the key proteins targeted by H2S and thus contributing to metabolic reprogramming of β cells, and if and how insulin synthesis and secretion is affected by sulfhydration of these proteins during ER stress.
Abnormal H2S metabolism has been reported to occur in various diseases, mostly through the deregulation of gene expression encoding for H2S-generating enzymes (Wallace and Wang, 2015). An increase of their levels by stimulants is expected to have similar effects on sulfhydration of proteins like the ATF4- induced CTH under conditions of ER stress. It is the levels of H2S under oxidative conditions that influence cellular functions. In the present study, ER stress in β cells induced elevated Cth levels, whereas CBS was unaffected. The deregulated oxidative modification at cysteine residues by H2S may be a major contributing factor to disease development. In this case, it would provide a rationale for the design of therapeutic agents that would modulate the activity of the involved enzymes.

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CRACKING THE CODE OF HUMAN LIFE: Recent Advances in Genomic Analysis and Disease – Part IIC

CRACKING THE CODE OF HUMAN LIFE: Recent Advances in Genomic Analysis and Disease – Part IIC

Author: Larry H. Bernstein, MD, FCAP, Triplex Medical Science

Part I: The Initiation and Growth of Molecular Biology and Genomics – Part I From Molecular Biology to Translational Medicine: How Far Have We Come, and Where Does It Lead Us?

Part II: CRACKING THE CODE OF HUMAN LIFE is divided into a three part series.

Part IIA. “CRACKING THE CODE OF HUMAN LIFE: Milestones along the Way” reviews the Human Genome Project and the decade beyond.

Part IIB. “CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics & Computational Genomics” lays the manifold multivariate systems analytical tools that has moved the science forward to a groung that ensures clinical application.

Part IIC. “CRACKING THE CODE OF HUMAN LIFE: Recent Advances in Genomic Analysis and Disease “ will extend the discussion to advances in the management of patients as well as providing a roadmap for pharmaceutical drug targeting.

To be followed by:
Part III will conclude with Ubiquitin, it’s role in Signaling and Regulatory Control.

Part IIC of series on CODE OF HUMAN LIFE
CRACKING THE CODE OF HUMAN LIFE: Recent Advances in Genomic Analysis and Disease

This final paper of Part II concludes a thorough review of the scientific events leading to the discovery of the human genome, the purification and identification of the components of the chromosome and the DNA structure and role in regulation of embryogenesis, and potential targets for cancer.

The first two articles, Part IIA, Part IIB,  go into some depth to elucidate the problems and breakthoughs encountered in the Human Genome Project, and the construction of a 3-D model necessary to explain interactions at a distance.

Part IIC, the final article, is entirely concerned with clinical application of this treasure trove of knowledge to resolving diseases of epigenetic nature in the young and the old, chronic inflammatory diseases, autoimmune diseases, infectious disease, gastrointestinal disorders, neurological and neurodegenerative diseases, and cancer.

CRACKING THE CODE OF HUMAN LIFE: Recent Advances in Genomic Analysis and Disease – Part IIC

1. Gene Links to Heart Disease


Recently, large studies have identified some of the genetic basis for important common diseases such as heart disease and diabetes, but most of the genetic contribution to them remains undiscovered. Now researchers at the University of Massachusetts Amherst led by biostatistician Andrea Foulkes have applied sophisticated statistical tools to existing large databases to reveal substantial new information about genes that cause such conditions as high cholesterol linked to heart disease.

Foulkes says, “This new approach to data analysis provides opportunities for developing new treatments.” It also advances approaches

  • to identifying people at greatest risk for heart disease. Another important point is that our method is straightforward to use with freely
  • available computer software and can be applied broadly to advance genetic knowledge of many diseases.

The new analytical approach she developed with cardiologist Dr. Muredach Reilly at the University of Pennsylvania and others is called “Mixed modeling of Meta-Analysis P-values” or MixMAP. Because it makes use of existing public databases, the powerful new method

  • represents a low-cost tool for investigators.
  • MixMAP draws on a principled statistical modeling framework and the vast array of summary data now available from genetic association
  • studies to formally test at a new, locus-level, association.

While that traditional statistical method looks for one unusual “needle in a haystack” as a possible disease signal, Foulkes and colleagues’

  • new method uses knowledge of DNA regions in the genome that are likely to
  • contain several genetic signals for disease variation clumped together in one region.
  • Thus, it is able to detect groups of unusual variants rather than just single SNPs, offering a way to “call out” gene
  • regions that have a consistent signal above normal variation. News/Identify Genes Linked to Heart Disease/

2. Apolipoprotein(a) Genetic Sequence Variants

The LPA gene codes for apolipoprotein(a), which, when linked with low-density lipoprotein particles, forms lipoprotein(a) [Lp(a)] —

  • a well-studied molecule associated with coronary artery disease (CAD). The Lp(a) molecule has both atherogenic and thrombogenic effects in vitro , but the extent to which these translate to differences in how atherothrombotic disease presents is unknown.

LPA contains many single-nucleotide polymorphisms, and 2 have been identified by previous groups as being strongly associated with

  • levels of Lp(a) and, as a consequence, strongly associated with CAD.

However, because atherosclerosis is thought to be a systemic disease, it is unclear to what extent Lp(a) leads to atherosclerosis in other arterial beds (eg, carotid, abdominal aorta, and lower extremity),

  • as well as to other thrombotic disorders (eg, ischemic/cardioembolic stroke and venous thromboembolism).

Such distinctions are important, because therapies that might lower Lp(a) could potentially reduce forms of atherosclerosis beyond the coronary tree.

To answer this question, Helgadottir and colleagues compiled clinical and genetic data on the LPA gene from thousands of previous

  • participants in genetic research studies from across the world. They did not have access to Lp(a) levels, but by knowing the genotypes for
  • 2 LPA variants, they inferred the levels of Lp(a) on the basis of prior associations between these variants and Lp(a) levels. [1]

Their studies included not only individuals of white European descent but also a significant proportion of black persons, in order to

  • widen the generalizability of their results.

Their main findings are that LPA variants (and, by proxy, Lp(a) levels) are associated with

  • CAD,
  • peripheral arterial disease,
  • abdominal aortic aneurysm,
  • number of CAD vessels,
  • age at onset of CAD diagnosis, and
  • large-artery atherosclerosis-type stroke.

They did not find an association with

  • cardioembolic or small-vessel disease-type stroke;
  • intracranial aneurysm;
  • venous thrombosis;
  • carotid intima thickness; or,
  • in a small subset of individuals, myocardial infarction.

Apolipoprotein(a) Genetic Sequence Variants Associated With Systemic Atherosclerosis and Coronary Atherosclerotic Burden but Not With Venous Thromboembolism. Helgadottir A, Gretarsdottir S, Thorleifsson G, et al.    J Am Coll Cardiol. 2012;60:722-729

English: Structure of the LPA protein. Based o...

English: Structure of the LPA protein. Based on PyMOL rendering of PDB 1i71. (Photo credit: Wikipedia)

Micrograph of an artery that supplies the hear...

Micrograph of an artery that supplies the heart with significant atherosclerosis and marked luminal narrowing. Tissue has been stained using Masson’s trichrome. (Photo credit: Wikipedia)

Genomic Blueprint of the Heart

Scientists at the Gladstone Institutes have revealed the precise order and timing of hundreds of genetic “switches” required to construct a fully

  • functional heart from embryonic heart cells — providing new clues into the genetic basis for some forms of congenital heart disease.

In a study being published online today in the journal Cell, researchers in the laboratory of Gladstone Senior Investigator Benoit Bruneau, PhD,

  • employed stem cell technology, next-generation DNA sequencing and computing tools to piece together the instruction manual, or “genomic
  • blueprint” for how a heart becomes a heart. These findings offer renewed hope for combating life-threatening heart defects such as arrhythmias (irregular heart beat) and ventricular septal defects (“holes in the heart”).

ScienceDaily (Sep. 13, 2012)

They approach heart formation with a wide-angle lens by

  • looking at the entirety of the genetic material that gives heart cells their unique identity.

The news comes at a time of emerging importance for the biological process called “epigenetics,” in which a non-genetic factor impacts a cell’s genetic

  • makeup early during development — but sometimes with longer-term consequences. All of the cells in an organism contain the same DNA, but the
  • epigenetic instructions encoded in specific DNA sequences give the cell its identity. Epigenetics is of particular interest in heart formation, as the
  • incorrect on-and-off switching of genes during fetal development can lead to congenital heart disease — some forms of which may not be apparent until adulthood.

the scientists took embryonic stem cells from mice and reprogrammed them into beating heart cells by mimicking embryonic development in a petri dish. Next, they extracted the DNA from developing and mature heart cells, using an advanced gene-sequencing technique called ChIP-seq that lets scientists “see” the epigenetic signatures written in the DNA.

Map of Heart Disease Death Rates in US White M...

Map of Heart Disease Death Rates in US White Males from 2000-2004 (Photo credit: Wikipedia)

Estimated propability of death or non-fatal my...

Estimated propability of death or non-fatal myocardial-infarction over one year corresponding ti selectet values of the individual scores. Ordinate: individual score, abscissa: Propability of death or non-fatal myocardial infarction in 1 year (in %) (Photo credit: Wikipedia)

simply finding these signatures was only half the battle — we next had to decipher which aspects of heart formation they encoded

To do that, we harnessed the computing power of the Gladstone Bioinformatics Core. This allowed us to take the mountains of data collected from

  • gene sequencing and organize it into a readable, meaningful blueprint for how a heart becomes a heart.” Map the Genomic Blueprint of the Heart.  ScienceDaily.

Performance of transcription factor identification tools from differential gene expression data

A three step process is a clear way to establish belief in the performance of transcription factor identification tools

  • from differential gene expression data.
  • identify several types of differential gene expression data sets where the stimulus or trigger is clearly know
  • identify the transcription factors most likely associated with the sets expression data.
  • perform an upstream analysis from the identified transcription factor.

If the transcription factor and upstream analysis tools can trace the signal cascade back to the stimulus, the tools are

  • clearly producing relevant results, and belief in the performance of the analysis tools is established.

At this point, the tools can be directed with confidence to more challenging analyses such as

  • developed resistance or pathway elucidation.

The performance of IPA‘s new Transcription Factor and Upstream analysis tools was evaluated on the following datasets (processing details below):

  • TGFb stimulation, 1 hour, A549 lung adenocarcinoma cell line
  • BMP2 stimulation, 1 hour, Mouse Embryonic Stem Cell E14Tg2A.4
  • TNFa stimulation, 1 hour primary murine hepatocytes

For each of the above datasets, an upstream analysis from the identified transcription factors correctly identified the stimulus. IPA’s tools were very

  • easy to use and the
  • analysis time for the above experiments was less than one minute.

The performance, speed, and ease of use can only be characterized as very good, perhaps leading to breakthroughs when extended and used creatively. Ingenuity’s new transcription factor analysis tool in IPA, coupled with Ingenuity’s established upstream grow tools,  should be strongly considered for every lab analyzing differential expression data.

Differential expression data was obtained from CEL files using the Matlab functions:

affyrma, genelowvalfilter, genevarfilter, mattest, and mavolcanoplot.

Rick Stanton, Pathway Analysis Consultant

3. miR-200a regulates Nrf2 activation by targeting Keap1 mRNA in breast cancer cells.

Eades G, Yang M, Yao Y, Zhang Y, Zhou Q. J Biol Chem. 2011 Nov 25;286(47):40725-33. Epub 2011 Sep 16. regulates Nrf2 activation by targeting Keap1 mRNA in breast cancer cells.

NF-E2-related factor 2 (Nrf2) is an important transcription factor that

  • activates the expression of cellular detoxifying enzymes.

Nrf2 expression is largely regulated through the association of Nrf2 with Kelch-like ECH-associated protein 1 (Keap1), which

  • results in cytoplasmic Nrf2 degradation.

Conversely, little is known concerning the regulation of Keap1 expression. Until now, a regulatory role for microRNAs (miRs) in controlling Keap1 gene expression had not been characterized. By using miR array-

  • based screening, we observed miR-200a silencing in breast cancer cells and
  • demonstrated that upon re-expression, miR-200a
  • targets the Keap1 3′-untranslated region (3′-UTR), leading to Keap1 mRNA degradation. Loss of this regulatory mechanism may
  • contribute to the dysregulation of Nrf2 activity in breast cancer. Previously, we have identified epigenetic repression of miR-200a

in breast cancer cells. Here, we find that treatment with epigenetic therapy, the histone deacetylase inhibitor suberoylanilide hydroxamic acid, restored miR-200a expression and reduced Keap1 levels. This reduction in Keap1 levels corresponded with

  • Nrf2 nuclear translocation
  • and activation of Nrf2-dependent NAD(P)H-quinone oxidoreductase 1 (NQO1) gene transcription.

Moreover, we found that Nrf2 activation inhibited the anchorage-independent growth of breast cancer cells. Finally, our in vitro observations were confirmed in a model of carcinogen-induced mammary hyperplasia in vivo. In conclusion, our study demonstrates

  • that miR-200a regulates the Keap1/Nrf2 pathway in mammary epithelium, and we find that epigenetic therapy can restore miR-200a
  • regulation of Keap1 expression,
  • reactivating the Nrf2-dependent antioxidant pathway in breast cancer.

Nuclear factor-like 2  (erythroid-derived 2, also known as NFE2L2 or Nrf2, is a transcription factor that in humans is encoded by the NFE2L2 gene.[1])  NFE2L2 induces the expression of various genes including those that encode for several antioxidant enzymes, and it may play a physiological role in the regulation of oxidative stress. Investigational drugs that target NFE2L2 are of interest as potential therapeutic interventions for

  • oxidative-stress related pathologies.

4. Highly active zinc finger nucleases by extended modular assembly

MS Bhakta, IM Henry, DG Ousterout, KT Das, et al.  Corresponding author; email: active zinc finger nucleases by extended modular assembly

Zinc finger nucleases (ZFNs) are important tools for genome engineering. Despite intense interest by many academic groups,

  • the lack of robust non-commercial methods has hindered their widespread use. The modular assembly (MA) of ZFNs from
  • publicly-available one-finger archives provides a rapid method to create proteins that can recognize a very broad spectrum of DNA sequences.

However, three- and four-finger arrays often fail to produce active nucleases. Efforts to improve the specificity of the one-finger archives have not increased the success rate above 25%, suggesting that the MA method might

  • be inherently inefficient due to its insensitivity to context-dependent effects.

Here we present the first systematic study on the effect of array length on ZFN activity.  ZFNs composed of six-finger MA arrays produced mutations at 15 of 21 (71%) targeted

  • loci in human and mouse cells. A novel Drop-Out Linker scheme was used to rapidly assess three- to six-finger combinations,
  • demonstrating that shorter arrays could improve activity in some cases. Analysis of 268 array variants revealed that half of

MA ZFNs of any array composition that exceed an ab initio

  • B-score cut-off of 15 were active.
  • MA ZFNs are able to target more DNA sequences with higher success rates than other methods.

This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date
After six months, it is available under a Creative Commons License (Attribution-NonCommercial 3.0 Unported License), as described at modular_assembly/


These insightful reviews are based on the strategic data and insights from Thomson Reuters Cortellis™ for Competitive Intelligence.  (A Review of April-June 2012). INNOVATION: PERSONALIZED MEDICINE IN THE PIPELINE/ Cortellis™ for Competitive Intelligence/APRIL-JUNE 2012

The majority of diseases are complex and multi-factorial, involving multiple genes interacting with environmental factors. At the genetic level,

  • information from genome-wide association studies that elucidate common patterns of genetic variation across various human populations,
  • in addition to profiling, technologies can be utilized in discovery research to provide snapshots of genes and expression profiles that are controlled
  • by the same regulatory mechanism and are altered between healthy and diseased states.

The characterization of genes that are abnormally expressed in disease tissues could further be employed as

  • diagnostic markers,
  • prognostic indicators of efficacy and/or toxicity, or as
  • targets for therapeutic intervention.

As the defining catalyst that exponentially paved the way for personalized medicine, information from the published genome sequence revealed that much of the genetic variations in humans are concentrated in about 0.1 percent of the over 3 billion base pairs in the haploid DNA. Most of these variations involve substitution of a single nucleotide for another at a given location in the genetic sequence, known as single nucleotide polymorphism (SNP).

  • Combinations of linked SNPs aggregate together to form haplotypes and
  • together these serve as markers for locating genetic variations in DNA sequences.

SNPs located within the protein-coding region of a gene or within the control regions of DNA that regulate a gene’s activity could

  • have a substantial effect on the encoded protein and thus influence phenotypic outcomes.

Analyzing SNPs between patient population cohorts could highlight specific genotypic variations which can be correlated with specific phenotypic variations in disease predisposition and drug responses.

Prior to the genomic revolution, many of the established therapies were directed against less than 500 drug targets, with many of the top selling drugs acting on well defined protein pathways. However, the sequencing of the human genome has massively expanded the pool of molecular targets that could be exploited in unmet medical needs and currently, of the approximately 22,300 protein-coding genes in the human code, it has been estimated that up to 3000 are druggable. Furthermore, genomic technologies such as

  • high-throughput sequencing
  • and transcription profiling,

can be used to identify and validate biologically relevant target molecules, or can be applied to cell-based and mice disease models or directly to in vivo human tissues,

  • helping to correlate gene targets with phenotypic traits of complex diseases.

This is particularly important, as

  • insufficient validation of target gene/proteins in complex diseases may be a contributing factor in the decline in R&D productivity.

Personalized medicine no doubt is already having a tremendous impact on drug development pipelines. According to a study conducted by the Tufts Center for the Study of Drug Development, more than 90 percent of biopharmaceutical companies now utilize at least some

  • genomics-derived targets in their drug discovery programs.

However, pipeline analysis from Cortellis for Competitive Intelligence suggests that there is still a scientific gap that has resulted in difficulty optimizing these novel genomic targets into the clinical R&D portfolios of major pharmaceutical companies, particularly outside the oncology field. Selected examples of personalized medicine product candidates in clinical development include (see TABLE 4).

Table 4: Selected Personalized Medicines in Clinical Development
(DATA are Derived from Cortellis for Competitive Intelligence & Thomson Reuters IntegritySM)
http://Thomson for Competitive Intelligence/IntegritySM/Table_4_Selected_Personalized_Medicines_in_Clinical_Development/


The paucity of actual targeted therapy examples, especially outside oncology, suggest

  • that integration of the personalized medicine paradigm into biopharmaceutical R&D is still fraught with challenges.

Despite the fact that the Human genome Project has been completed for over ten years, the broader application of genomics with drug development

  • still remains unrealized, and is hampered by a number of scientific challenges. One of the major obstacles stems from
  • incomplete association of genomic alterations with complex disease pathways and the phenotypic consequences.

As the modality of most complex diseases are multi-factorial, understanding how each genomic driver event plays a role in disease and the

  • interaction/interdependence with other genetic and environmental factors is important for
  • determining the rationale for targeted prevention or treatment of the disease.

Mutations found in Melanomas may shed light on Cancer Growth

Gina Kolata. New York Times.

Mutations in Melanoma are in regions that control genes, not in the genes themselves. The mutations are exactly the type caused by exposure to ultraviolet light.  The findings are reported in two papers in

The findings do not suggest new treatments, but they help explain how melanomas – and possibly – other cancers – develop and what drives their growth. This is a modification found in the “dark matter”, according to Dr. Levi A. Garraway,  the 99 percent of DNA in a region that regulates genes. A small control region was mutated in 7 out of 10 of the tumors, commonly of one or two tiny changes.
A German Team led by Rajiv Kumar (Heidelberg) and Dirk Schadendorf (Essen) looked at a family whose members tended to get melanomas.  Their findings indicate that those inherited with the mutations might be born with cells that have taken the first step toward cancer.
The mutations spur cells to make telomerase, that keeps the cells immortal by preventing them from losing the ends of their chromosome, the telomere. Abundant telomerase occurs in 90 percent of cancers, according to Immaculata De Vivo at Harvard Medical School.
The importance of the findings is that the mechanism of telomerase involvement in cancer is now within view. But it is not clear how to block the telomerase production in cancer cells.
A slight mutation in the matched nucleotides c...

A slight mutation in the matched nucleotides can lead to chromosomal aberrations and unintentional genetic rearrangement. (Photo credit: Wikipedia)


This discussion addresses the issues raised about the direction to follow in personalized medicine. Despite the amount of work necessary to bring the clarity that is sought after, the experiments and experimental design is most essential.

  • The arrest of ciliogenesis in ovarian cancer cell lines compared to wild type (WT) ovarian epithelial cells, and
  •  The link to suppressing ciliogenesis by AURA protein and CHFR at the base of the cilium, which disappears at mitosis or with proliferation.
  •  There is no accumulation by upregulation of PDGF under starvation by the cancer cells compared to the effect in WT OSE.

Here we have a systematic combination of signaling events tied to changes in putative biomarkers that occur synchronously in Ov cancer cell lines.

These changes are identified with changes in

  • proliferation,
  • loss of ciliary structure, and
  • proliferation.

In this described scenario,

  • WT OSE cells would be arrested, and
  • it appears that they would take the path to apoptosis (under starvation).

Even without more information, this cluster is what one wants to have in a “syndromic classification”. The information used to form the classification entails the identification of strong ‘signaling-related’ biomarkers. The Gli2 peptide has to be part of this.

In principle, a syndromic classification would be ideally expected to have no less than 64 classes. If the classification is “weak”, then the class frequencies would be close to what one would expect in the WT OSE. In this case, in reality,

  • several combinatorial classes would have low frequency, and
  • others would be quite high.

This obeys the classification rules established by feature identification, and the information gain described by Solomon Kullback and extended by Akaike.

Does this have to be the case for all different cancer types? I don’t think so. The cells are different in ontogenesis.  In this case, even the WT OSE have mesenchymal features and so, are not fully directed to epithelial expression.  This happens to be the case in actual anatomic expression of the ovary.  On the other hand, one would expect shared features of the

  • ovary,
  • testes,
  • thyroid,
  • adrenals, and
  • pituitary.

There is biochemical expression in terms of their synthetic function – TPN organs. I would have to put the liver into that broad class. Other organs – skeletal muscle & heart – transform substrate into energy or work.  (Where you might also put intestinal smooth muscle).

They have to have different biomarker expressions, even though they much less often don’t form neoplasms. (Bone is not just a bioenergetic force. It is maintained by muscle action. It forms sarcomas. But there has to be a balance between bone removal by osteoclasts and refill by osteoblasts.)

Viewpoint: What we have learned

  1. The Watson-Crick model proposed in 1953 is limited for explaining fully genome effects
  2. The Pauling triplex model may have been prescient because of a more full anticipation of molecular bonding variants
  3. A more adequate triple-helix model has been proposed and is consistent with a compact genome in the nucleus

The structure of the genome is not as we assumed – based on the application of Fractal Geometry.  Current body of evidence is building that can reveal a more complete view of genome function.

  • transcription
  • cell regulation
  • mutations


I have just completed a most comprehensive review of the Human Genome Project. There are key research collaborations, problems in deciphering the underlying structure of the genome, and there are also both obstacles and insights to elucidating the complexity of the final model.

This is because of frequent observations of molecular problems in folding and other interactions between nucleotides that challenge the sufficiency of the original DNA model proposed by Watson and Crick. This has come about because of breakthrough innovation in technology and in computational methods.

Radoslav Bozov •

Molecular biology and growth was primarily initiated on biochemical structural paradigms aiming to define functional spatial dynamics of molecules via assignation of various types of bondings – covalent and non-covalent – hydrogen, ionic , dipole-dipole, hydrophobic interactions.

  • Lab techniques based on z/m paradigm allowed separation, isolation and identification of bio substances with a general marker identity finding correlation between physiological/cellular states.
  • The development of electronic/x-ray technologies allowed zooming in nano space without capturing time.
  • NMR technology identified the existence of space topology of initial and final atomic states giving a highly limited light on time – energy axis of atomic interactions.
  • Sequence technology and genomic perturbations shed light on uncertainty of genomic dynamics and regulators of functional ever expanding networks.
  • Transition state theory coupled to structural complexity identification and enzymatic mechanisms ran up parallel to work on various phenomena of strings of nucleotides (oligomers and polymers) – illusion/observation of constructing models on the dynamics of protein-dna-rna interference.
  • The physical energetic constrains of biochemistry were inapplicable in open biological systems. Biologists have accepted observation as a sole driver towards re-evaluating models.
  • The separation of matter and time constrains emerged as deviation of energy and space constrains transforming into the full acceptance of code theory of life. One simple thing was left unnoticed over time –
  • the amount of information of quantum matter within a single codon is larger than that of a single amino acid. This violated all physical laws/principles known to work with a limited degree of certainty.
  • The limited amount of information analyzed by conventional sequence identity led to the notion of applicability of statistical measures of and PCR technology. Mutations were identified over larger scale of data.
  • Quantum chemistry itself is being limited due discrete space/energy constrains, thus it transformed into concepts/principles in biology that possess highly limited physical values whatsoever.
  • The central dogma is partially broken as a result of
  1. regulatory constrains
  2. epigenetic phenomena and
  3. iRNA.

Large scale code computational data run into uncertainty of the processes of evolution and its consequence of signaling transformation. All drugs were ‘lucky based’ applicability and/or discovery with largely unpredictable side effect over time.

Other Related articles on this Open Access Online Sceintific Journal include the following:

Big Data in Genomic Medicine  lhb

BRCA1 a tumour suppressor in breast and ovarian cancer – functions in transcription, ubiquitination and DNA repair S Saha

Computational Genomics Center: New Unification of Computational Technologies at Stanford A Lev-Ari

Personalized medicine gearing up to tackle cancer ritu saxena

Differentiation Therapy – Epigenetics Tackles Solid Tumors sj Williams

Mechanism involved in Breast Cancer Cell Growth: Function in Early Detection & Treatment A Lev-Ari

The Molecular pathology of Breast Cancer Progression tilde barliya

Gastric Cancer: Whole-genome reconstruction and mutational signatures A Lev-Ari

Paradigm Shift in Human Genomics – Predictive Biomarkers and Personalized Medicine – Part 1 ( A Lev-Ari        

LEADERS in Genome Sequencing of Genetic Mutations for Therapeutic Drug Selection in Cancer Personalized Treatment: Part 2 A Lev-Ari

Personalized Medicine: An Institute Profile – Coriell Institute for Medical Research: Part 3 A Lev-Ari

Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders @ ALA Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders/

GSK for Personalized Medicine using Cancer Drugs needs Alacris systems biology model to determine the in silico effect of the inhibitor in its “virtual clinical trial” A Lev-Ari

Recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes in serous endometrial tumors S Saha

Personalized medicine-based cure for cancer might not be far away ritu saxena

Human Variome Project: encyclopedic catalog of sequence variants indexed to the human genome sequence A Lev-Ari

Prostate Cancer Cells: Histone Deacetylase Inhibitors Induce Epithelial-to-Mesenchymal Transition sjwilliams

Inspiration From Dr. Maureen Cronin’s Achievements in Applying Genomic Sequencing to Cancer Diagnostics A Lev-Ari

The “Cancer establishments” examined by James Watson, co-discoverer of DNA w/Crick, 4/1953 A Lev-Ari

Directions for genomics in personalized medicine lhb

How mobile elements in “Junk” DNA promote cancer. Part 1: Transposon-mediated tumorigenesis. Sjwilliams

Mitochondria: More than just the “powerhouse of the cell” eritu saxena

Mitochondrial fission and fusion: potential therapeutic targets? Ritu saxena

Mitochondrial mutation analysis might be “1-step” away ritu saxena

mRNA interference with cancer expression lhb

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English: Cancer cells photographed by camera a...

Reported by: Dr. Venkat S. Karra, Ph.D.

Cancer remains the second leading cause of death by disease. Hundreds of new medicines to treat cancer are now being developed for lessening the burden of cancer to patients, their families and society.

Biopharmaceutical researchers are now working on 981 medicines for cancer. Many are high-tech weapons to fight the disease, while some involve innovative research into using existing medicines in new ways, the report says.

Recent developments in early detection and a steady stream of new and improved treatments suggesting that cancer is a manageable chronic disease (not a deadly one any more). Families and patients alike are with increasing expectations from the industry for more and better treatment options and America’s biopharmaceutical research companies are responding to that.

America’s biopharmaceutical research companies are working on many new cutting-edge approaches to fight cancer. They include:

• A medicine that interferes with the metabolism of cancer cells by depriving them of the energy provided by glucose.
• A medicine for acute myeloid leukemia (AML) that inhibits cancer cells with a mutation found in about a third of AML sufferers.
• A therapy that uses nanotechnology to target the delivery of medicines to cancer cells, potentially overcoming some limitations of existing treatments.

Read more….


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