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Pull at Cancer’s Levers

Curator: Larry H. Bernstein, MD, FCAP

 

Driving Cancer Immunotherapy 

The Stakes in Immuno-Oncology Are Too High for Researchers to Pull at Cancer’s Levers Blindly. Researchers Need a System.

  • Within the past decade or so, a revolutionary idea has emerged in the minds of scientists, physicians, and medical experts. Instead of using man-made chemicals to treat cancer, let us instead unleash the power of our own bodies upon the malignancy.

    This idea is the inspiration behind cancer immunotherapy, which is, according to most experts, a therapeutic approach that involves training the immune system to fight off cancer. In the words of one expert, cancer immunotherapy means “taking the immune system’s inherent properties and turbo charging those to fight cancer.”

    Cancer immunotherapy technologies are being developed to accomplish
    several tasks:

    • Enhance the molecular targeting of cancer cells
    • Report the rate of killing by specific immune agents
    • Direct immune cells toward tumor destruction.

    Since its inception, the field has evolved, and it continues to do so. It began with in vivo investigations of tumor growth and development, and it progressed through laboratory investigations of cellular morphology and survival curves. And now it is adopting pathway analysis to guide therapeutic development and improve patient care.

    To begin to understand cancer immunotherapy, one must understand how the immune system targets tumor cells. One of the prominent adaptive components of the immune system is the T cell, which responds to perceived threats through the massive increase in clonal T cells targeted in some way toward the diseased cell or pathogen.

  • The T-Cell Repertoire

    Adaptive Biotechnologies’ immunoSEQ Assay, a high-throughput research platform for immune system profiling, is designed to generate sequencer-ready libraries using highly optimized primer sets in a multiplex PCR format that targets T- and B-cell receptor genes. This image depicts how the assay’s two-step PCR process can be used to quantify the clonal diversity of immune cells.

    Immunologists call this process VJD rearrangement. It happens during T-lymphocyte development and affects three gene regions, the variable (V), the diversity (D), and the joining (J) regions. This rearrangement of the genetic code allow for the structural diversity in T-cell receptors responsible for antigenic specificity including antigenic targets on tumor cells. In the case of cancer, specificity is complicated because the tumor is actually part of the body itself, one of the reasons cancers naturally evade detection.

    The specificity problem would always hinder attempts to goad the immune system into attacking cancer, scientists realized, unless technologies emerged that could efficiently track the clonal diversity of T cells inside patients. Existing technologies, such as spectratyping, were inadequate.  In 2007, when Dr. Robins and his collaborators began developing the technology, only 10,000 T-cell receptor sequences had been reported in all the literature using older methodologies.

    “The immunology field of the time had no connection with high-throughput sequencing,” notes Dr. Robins, recalling his days as a computational biologist for the Fred Hutchinson Cancer Research Center. “It became clear that instead of using this old technology to look at T-cell receptors, we could just directly sequence them—if we could amplify them correctly.”

    With its first experiment, Dr. Robins’ team ended up with six million T-cell receptor sequences. “Our approach,” Dr. Robins modestly suggests, “kind of changed the scale of what we were able to do.” The team went on to develop advanced multiplex sequencing technology, doing work that essentially started the field of immune sequencing. “Previously,” maintains Dr. Robins, “no one had ever been able to quantitatively do a multiplex PCR.”

    Adaptive Biotechnologies’ product, the ImmunoSEQ® assay, uses several hundred primer pairs to quantify the clonal diversity of T cells. Using this technology, researchers and clinicians can focus on T-cell clones that are expanded specifically in or near a tumor or that are circulating in the blood stream.

    “You obviously can’t get a serial sample of the tumor,” explains Dr. Robins, “but you can get serial samples of blood,” allowing for immune cell repertoire tracking during the progression of a disease. The technology is already being used to assess leukemias in the clinic, directly tracking the leukemia itself based on the massive clonal expansion of a single cancerous B or T cell.

    Eventually, Dr. Robins’ team hopes to monitor serial changes in T cell clones before, during, and after therapeutic intervention. The team has even developed a tumor infiltrating lymphocyte (TIL) assay to examine clones that are attracted to tumors.

     

Circulating Tumor Cells

“Years ago, they were just interested in what was happening in the tumor,” says Daniel Adams, senior research scientist at Creatv MicroTech. “Now people have realized that the immune system is reacting to the tumor.”

Scientists such as Adams have been tracking tumor cells and tumor-modified stromal cells, as well as components of the non-adaptive immune system, directly within the bloodstream to examine changes that occur over time.

“We can’t go back in to re-biopsy the patient every year, or every time there is a recurrence,” says Adams, “It’s just not feasible.”

That is why Creatv MicroTech, with locations in Maryland and New Jersey, has developed the CellSieve, a mechanical cell filter. The CellSieve, which improves on older technology through better polymers and engineering, isolates circulating tumor cells (CTCs) and stromal cells in order to capture them for further clinical analysis.

Isolation, culture and expansion of cells isolated on CellSieve™. (A) MCF-7 cells spiked into vacutainers, isolated by filtration and cultured on CellSieve for 2-3 weeks. A 3 dimensional cluster attributed to this cell line is seen on the filter. (green=anti-cytokeratin, blue=DAPI) (B) PANC-1 cells spiked into vacutainers, isolated by filtration and grown on CellSieve for 2-3 weeks. PANC-1 is seen growing as a monolayer on the filter. (C) SKBR3 cells are spiked into blood, filtered by CellSieve. The CTCs are identified by presence of anti-cytokeratin and anti-EpCAM, and absence of anti-CD45. After CTCs are counted, cells are subtyped by HER2 FISH. (D) SKBR3 cells are spiked into vacutainers, isolated by filtration and grown on CellSieve for 2-3 weeks. Expanded colonies were directly analyzed as a whole colony and as individual cells, molecularly by HER2/CR17 FISH probes. (E) Circulating stromal cell, e.g. a 70 µm giant cancer associated macrophage can be identified for clinical use, myeloid marker in red. (F) A cell of interest can be identified and restained with immunotherapeutic biomarkers, e.g. PD-L1 (green) and PD-1 (purple). (G) After filtration, cells were identified with histopathological stains (e.g. H&E) for cytological analysis. (H) After H&E, external cell structures were analyzed by SEM. [Creatv MicroTech].

 

“As a patient goes through therapy, the patient’s resistance builds, and the cancer recurs in different subpopulations,” states Adams. “And after a few years, the original tumor mass is no longer applicable to what is growing in the patient farther down the road.”

Although CTCs are exceedingly rare in the bloodstream, with just one or two in every 5 to 10 mL of blood, and although these cells have a very low viability, the surviving CTCs have a high prognostic value.

“We looked at 30 to 40 breast cancer patients over two years,” reports Adams. “And we showed that if you have a dividing CTC, you have a 90% chance of dying in two years and a 100% chance of dying within two and half years.”

Furthermore, the immune system response can be tracked, says Adams, by examining stromal cells, which can also be collected with the CellSieve filtration device. That is, these cells can be collected serially. Much recent evidence supports the conclusion that stromal cells in the tumor environment co-evolve with the tumor, suggesting that stromal marker changes reflect tumor changes.

“There is this plethora of stromal cells and tumor cells out there in the circulation for you to look at,” declares Adams. “Once the cells are isolated, you can subject them to pathological approaches, biomarker approaches, or molecular approaches—or all of the above.”

A MicroTech Creatv study published in the Royal Society of Chemistry showed the efficacy of following up CTC isolation with techniques such as fluorescence in situ hybridization (FISH), histopathological analysis, and cell culture.

Cancer-Killing Assays

Diverse mechanisms are at play in cancer biology. Our understanding of these mechanisms contributes to a couple of virtuous cycles. It strengthens and is strengthened by diagnostic approaches, such as immune- and tumor-cell monitoring. The same could be said of therapeutic approaches. Cancer biology will inform and be informed by cancer immunotherapies such as adoptive cell transfer. To maintain the virtuous cycle, however, it will be necessary to conduct in vitro testing.

“There is no doubt that immunotherapy is going to play a major role in the treatment of cancer,” says Brandon Lamarche, Ph.D., technical communicator and scientist at ACEA Biosciences. “Regardless of what the route is, what is going to have to happen in terms of the research area is that you need an effective cell-killing assay.”

ACEA Biosciences, a San Diego-based company, has developed a microtiter plate that is coated with gold electrodes across 75% of the well bottoms. When the microtiter plates are placed in the company’s xCELLigence plate reader, the electrodes enable the detection of changes in cell morphology and viability through electrical impedance.

“The instrument provides a weak electric potential to the electrodes on the plate, so you get electrons flowing between these electrodes,” explains Dr. Lamarche. Researchers can then apply reagents or non-adherent immune cell suspensions to adherent cancer cells and examine the effect.

Dr. Lamarche asserts that the xCELLigence system overcomes problems that bedevil competing cell-killing assays. These problems include leaky and radioactive labels, such as chromium 51, and assays that can only provide users with an endpoint for cell killing. “With xCELLigence,” he insists, “you’re getting the full spectrum of what’s happening, and there’s all kinds of subtleties in the cell-killing curves that are very informative in terms of the biology.”

ACEA would like to see the xCELLigence system become the new standard in cell-killing assays from standard research to clinical testing on patient tumors. Dr. Lamarche envisions a day when patient tumor cells are quickly screened with therapeutic scenarios to determine the most efficacious killing option. “xCELLigence technology,” he suggests, “enables you to quickly sample a broad spectrum of conditions with a very simple workflow.”

Bioinformatics of Immuno-Oncology

From monitoring to treatment modalities, the field of cancer immunotherapy is aided by bioinformatics-minded data-mining experts, such as the analysts at Thompson-Reuters who are compiling data archives and applying advanced analytics to find new targets. “Essentially,” says Richard Harrison Ph.D., the company’s chief scientific officer for the life science division, “for every stage within pharmaceutical drug development, we have a database associated with that.”

The analysts at Thompson-Reuters curate and compile databases such as MedaCore and Cortellis, which they provide to their clients to help them with their research and clinical studies. “We can take customer data, and using our tools and our pathway maps, we can help them understand what their data is telling them,” explains Dr. Harrison.

Matt Wampole, Ph.D., a solutions scientist at Thompson-Reuters, spends his days reaching out and working with customers to help them understand and better use the company’s products. “Bench researchers,” he points out, “don’t necessarily know what is upstream of whatever expression change might be leading to a particular change in regulation.” Dr. Wampole indicates that he is part of a “solution team” that aids clients in determining important signaling cascades, regulators, and so on.

“We have a group of individuals who are very ‘skilling’ experts in the field,” Dr. Wampole continues, “including experts in the areas such as biostatistics, data curation, and data analytics. These experts help clients identify models, stratify patients, understand mechanisms, and look into disease mechanisms.”

Dr. Harrison sums up the Thompson-Reuters approach as follows: “We look for master regulators that can serve as both targets and biomarkers.” By examining the gene signatures from both the patient and from curated datasets, in the case of cancer immunotherapy, they hope to segregate patients according to what drugs will work best for them.

  • “We are working with a number of pharmaceutical companies to put our approach into practice for clinical trials,” informs Dr. Harrison. The approach has already been applied in several studies, including one that used data analysis of cell lines to help predict drug response in patients. Another study helped stratify glioblastoma patients.

  • Tumor-Targeted Delivery Platform

    PsiOxus Therapeutics, which is focused on immune therapeutics in oncology, has developed a patented platform for tumor-targeted delivery based on its oncolytic vaccine, Enadenotucirev (EnAd), which can be delivered systemically via intravenous administration.

    According to company officials, EnAd’s anti-cancer scope can be expanded by adding new genes, thereby enabling the creation of a broad range of unique immuno-oncology therapeutics. In a recent study conducted at the University of Oxford, researchers led by Philip G. Jakeman, Ph.D., sought to improve the models for evaluating cancer therapeutics by introducing ex vivo methodologies for research into colorectal cancer.

    The ex vivo approach utilized was able to exploit a major advantage by preserving the three-dimensional architecture of the tumor and its associated compartments, including immune cells. The study, which was presented at the International Summit on Oncolytic Viral Therapeutics in Quebec, showed the tissue slice model can provide a novel means to assessing an oncolytic vaccine in a system that more accurately recapitulates human tumors, provide a more stringent test for oncolytic viruses, such as EnAd, and allow study of the human immune cells within the tumor 3D context.

    By maintaining the components of the tumor immune microenvironment, this new methodology could become useful in analyzing anti-viral responses within tumors, or even in evaluating therapeutics that target immunosuppressive tumor micro-environments, noted the Oxford team.

     

 

Deciphering the Cancer Transcriptome

A Rogue’s Gallery of Malignant Outliers May Hide in Transcriptome Profiles That Emphasize Averages

http://www.genengnews.com/gen-articles/deciphering-the-cancer-transcriptome/5729/

 

The key link between genomic instability and cancer progression is transcriptome dynamics. The shifts in transcriptome dynamics that contribute to cancer evolution may come down to statistical outliers. [iStock/zmeel]

  • In recent years, scientists have adopted a gene-centric view of cancer, a tendency to see each malignant transformation as the consequence of alterations in a discrete number of genes or pathways. These alterations are, fortunately, absent from healthy cells, but they pervert malignant cells.

    The gene-centric view takes in molecular landscapes illuminated by genomic and transcriptomic technologies. For example, genomes can be cost-effectively sequenced within hours. Such capabilities have made it possible to interrogate associations between genotypes and phenotypes for increasing numbers of conditions, and to collect data from progressively larger patient groups.

    As genomic and transcriptomic technologies rise, they reveal much—but much remains hidden, too. Perhaps these technologies are less like the sun and more like the proverbial streetlight, the one that narrows our searches because we’re inclined to stay in the light, even though what we hope to find may lie in the shadows.

    “Each individual study that looks at the cancer transcriptome is impressive and tells a convincing story, but if we put several high-quality papers together, there are very few genes that overlap,” says Henry H. Heng, Ph.D., professor of molecular medicine, genetics, and pathology at Wayne State University. “This shows that something is wrong.”

  • Distinct Karyotypes

    One of the major observations in Dr. Heng’s lab is that the intra- and intertumor cellular heterogeneity results in nearly every cancer cell having a unique, distinct karyotype, that is, an important but often ignored genotype. “Biological systems need a lot of heterogeneity,” notes Dr. Heng. “People like to think that this is noise, but heterogeneity is a fundamental buffer system for biological function to be achievable. Moreover, it is the key agent for cellular adaptation.”

    To capture the degree of genomic heterogeneity at the genome level and its impact on cancer cell growth, Dr. Heng and colleagues performed serial dilutions to isolate single mouse ovarian surface epithelial cells that had undergone spontaneous transformation. Spectral karyotyping revealed that within a short timeframe each of these unstable cells exhibited a very distinct karyotype. In these unstable cells, cloning at the level of the karyotype was not possible.

    Stable cells exhibited a normal growth distribution, i.e., no subset of stable cells contributed disproportionately to the overall growth of the cell population. In contrast, unstable cell populations showed a non-normal growth distribution, with few cells contributing most to the cell population’s growth. For example, a single unstable colony contributed more than 70% to the cell population’s growth. This finding suggests that although average profiles can be used to describe non-transformed cells, they cannot be taken to represent the biology of malignant cells.

    “Most people who study the transcriptome want to get rid of the noise, but the noise is in fact the strategy that cancer uses to be successful,” explains Dr. Heng. “Each individual cancer cell is very weak but together the entity becomes very robust.”

    In a recent model that Dr. Heng and colleagues proposed, system inheritance visualizes chromosomes not merely as the vehicle for transmitting genetic information, but as the genetic network organizer that shapes the physical interactions between genes in the three-dimensional space. Based on this model, individual genes represent parts of the system. The same genes can be reorganized to form different systems, and chromosomal instability becomes more important than the contribution of individual genes and pathways to cancer biology.

    The vital link between genomic instability and cancer progression is transcriptome dynamics, and the shifts in those dynamics that contribute to cancer evolution may come down to statistical outliers.

    “Transcriptome studies rarely focus on single-cell analyses, which means important outliers are frequently ignored,” declares Dr. Heng. “This preoccupation with uninformative averages explains why we have learned so little despite having examined so many transcriptomes.”

  • Chimeras and Fusion Genes

    “Our focus is on chimeric RNA molecules,” says Laising Yen, Ph.D, assistant professor of pathology at Baylor College of Medicine. “This category of RNAs is very special because their sequences come from different genes.”

    In a study that was designed to capture chimeric RNAs in prostate cancer, Dr. Yen and his colleagues performed high-throughput sequencing of the transcriptomes from human prostate cancer samples. “We found far more chimeric RNAs, in terms of abundance, and a number of species that are not seen in normal tissue,” reports Dr. Yen. This approach identified over 2,300 different chimeric RNA species. Some of these chimeras were present in prostate cancer cell lines, but not in primary human prostate epithelium cells, which points toward their relevance in cancer.

    “Most of these chimeric RNAs do not have a genomic counterpart, which means that they could be produced by trans-splicing,” explains Dr. Yen. During trans-splicing, individual RNAs are generated and trans-spliced together as a single RNA, which provides a mechanism for generating a chimera.

    “The other possibility is that in cancer cells, where gene–gene boundaries are known to become broken, chimeras can be formed by cis-splicing from a very long transcript that encodes several neighboring genes located on the same chromosome,” informs Dr. Yen. Chimeric RNAs formed by either of these two mechanisms can potentially translate into fusion proteins, and these aberrant proteins may have oncogenic consequences.

    Another effort in Dr. Yen’s laboratory focuses on chromosomal aberrations in ovarian cancer. One of the hallmarks of ovarian cancer is the high degree of genomic rearrangement and the increased genomic instability.

    “When we looked at ovarian cancers, we did not find as many chimeric RNAs,” notes Dr. Yen. “But we found many fusion genes.” Gene fusions, similarly to chimeric RNAs, increase the diversity of the cellular proteome, which could be used selectively by cancer cells to increase their rates of proliferation, survival, and migration.

    A recent study in Dr. Yen’s lab identified BCAM-AKT2, a recurrent fusion gene that is specific and unique to high-grade serous ovarian cancer. BCAM-AKT2 is the only fusion gene in this malignancy that was proved to be translated into a fusion kinase in patients, which points toward its functional significance and potential therapeutic value.

    “Recurrent fusion genes, which are repeatedly found in many patients in precisely made forms, indicate that there is a reason that they are present,” concludes Dr. Yen. “This might have important therapeutic implications.”

  • Context-Specific Patterns

    “We contributed to a study of tumor gene expresssion that we are currently revisiting because so much more data has become available,” says Barbara Stranger, Ph.D., assistant professor, Institute for Genomics and Systems Biology, University of Chicago. “The data is being processed in homogenized analytic pipelines, and we can look at many more tumor types across the Cancer Genome Atlas than a few years ago.”

    Previously, Dr. Stranger and colleagues performed expression quantitative trait loci (eQTL) analyses to examine mRNA and miRNA expression in breast, colon, kidney, lung, and prostate cancer samples. This approach identified 149 known cancer risk loci, 42 of which were significantly associated with expression of at least one transcript.

    Causal alleles are being prioritized using a fine-mapping strategy that integrated the eQTL analysis with genome-wide DNAseI hypersensitivity profiles obtained from ENCODE data. These analyses are focusing on capturing differences across tumors and on performing comparisons with normal tissue, and one of the challenges is the lack of normal tissue from the same patients.

    “But still there is a lot of power in these analyses because they are based on large-scale genomic datasets. Also, these tumor datasets can be compared with large-scale normal tissue genomics datasets, such as the NIH’s Genotype-Tissue Expression (GTEx) project,” clarifies Dr. Stranger. “This helps us characterize differences between those tumors and normal tissue in terms of the genetics of gene regulation.”

    An ongoing effort in Dr. Stranger’s laboratory involves elucidating how the effect of genetic polymorphisms is shaped by context. Stimulated cellular states, cell-type differences, cellular senescence, and disease are some of the contexts that are known to impact genetic polymorphisms.

    “We have seen a lot of context specificity,” states Dr. Stranger. “Our observations suggest that a genetic polymorphism can have a specific effect in regulating a particular gene or transcript in one context, and another effect in another context.”

    Another example of cellular context is sex, and an active area of investigation in Dr. Stranger’s lab proposes to dissect the manner in which sex differences shape the regulatory effects of genetic polymorphisms.

    “Thinking about sex-specific differences is not very different from thinking about a different cellular environment,” notes Dr. Stranger.

    The expression of specific transcription factors can be determined by sex; consequently, a polymorphism that interacts with a transcription factor may have functional outcomes that can be seen in only one of the sexes.

    “There are gene-level and gene-splicing differences that we see in normal tissues between males and females, and we want to take the same approach and look at the cancer context to see whether the genetic regulation of gene expression and transcript splicing is different between individuals and whether it has a sex bias,” concludes Dr. Stranger. “Finally, we want to see how that differs in cancer relative to normal tissues.”

    Early Clinical Impact

An increasing number of clinicians are adding the cancer transcriptome to their precision medicine program. They have found that the transcriptome is important in identifying clinically impactful results. [iStock/DeoSum]

“Over the last two years,” says Andrew Kung, M.D., Ph.D., chief of the Division of Pediatric Hematology, Oncology, and Stem Cell Transplantation at Columbia University Medical Center, “we have included the cancer transcriptome as part of our precision medicine program.” Dr. Kung and colleagues developed a clinical genomics test that includes whole-exome sequencing of tumors and normal tissue and RNA-seq of the tumor.

“Our results show that the transcriptome is very important in identifying clinically impactful results,” asserts Dr. Kung. “The technology has really moved from a research tool to real clinical application.” In fact, the test has been approved by New York State for use in cancer patients.

The data from transcriptome profiling has enabled identification of translocations, verification of somatic alterations, and assessment of expression levels of cancer genes.  Dr. Kung and his colleagues are using genomic information for initial diagnosis and prognostic decisions, as well as the investigation of potentially actionable alterations and the monitoring of disease response.

To gain insight into gene-expression changes, transcriptome analysis usually compares two different types of tissues or cells. For example, analyses may attempt to identify differentially expressed genes in cancer cells and normal cells.

“In patients with cancer, we usually do not have access to the normal cell of origin, making it harder to identify the genes that are over- or under-expressed,” explains Dr. Kung. “Fortunately, the vast amounts of existing gene-expression data allow us to identify genes whose expression are most changed relative to models built on the expression data aggregated across large existing datasets.”

These genomic technologies were first used to augment the care of pediatric patients at Columbia. The technologies were so successful that they attracted philanthropic funding, which is being used to expand access to genomic testing to all children with high-risk cancer across New York City.

Other related articles published in this Open Access Online Scientific Journal include the following:

Immunotherapy in Combination, 2016 MassBio Annual Meeting  03/31/2016 8:00 AM – 04/01/2016 3:00 PM Royal Sonesta Hotel, Cambridge, MA

Live Press Coverage: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/04/01/plenary-session-immunotherapy-in-combination-2016-massbio-annual-meeting-03312016-800-am-04012016-300-pm-royal-sonesta-hotel-cambridge-ma/

 

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Summary of Transcription, Translation ond Transcription Factors

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

 

Proteins are integral to the composition of the cytoskeleton, and also to the extracellular matrix.  Many proteins are actually enzymes, carrying out the transformation of some substrate, a derivative of the food we ingest.  They have a catalytic site, and they function with a cofactor – either a multivalent metal or a nucleotide. Proteins also are critically involved in the regulation of cell metabolism, and they are involved in translation of the DNA code, as they make up transcription factors (TFs). There are 20 essential amino acids that go into protein synthesis that are derived from animal or plant protein.   Protein synthesis is carried out by the transport of mRNA out of the nucleus to the ribosome, where tRNA is paired with a matching amino acid, and the primary sequence of a protein is constructed as a linear string of amino acids.

This is illustrated in the following three pictures:

protein synthesis

protein synthesis

mcell-transcription-translation

mcell-transcription-translation

transcription_translation

transcription_translation

Proteins synthesized at distal locations frequently contain intrinsically disordered segments. These regions are generally rich in assembly-promoting modules and are often regulated by post-translational modifications. Such proteins are tightly regulated but display distinct temporal dynamics upon stimulation with growth factors. Thus, proteins synthesized on-site may rapidly alter proteome composition and act as dynamically regulated scaffolds to promote the formation of reversible cellular assemblies.
RJ Weatheritt, et al. Nature Structural & Molecular Biology 24 Aug, 2014; 21: 833–839 http://dx.do.orgi:/10.1038/nsmb.2876

An overview of the potential advantages conferred by distal-site protein synthesis

An overview of the potential advantages conferred by distal-site protein synthesis

Turquoise and red filled circle represents off-target and correct interaction partners, respectively. Wavy lines represent a disordered region within a distal site synthesis protein. Grey and red line in graphs represents profiles of t…  http://www.nature.com/nsmb/journal/v21/n9/carousel/nsmb.2876-F5.jpg

In the the transcription process an RNA sequence is read.  This is essential for protein synthesis through the ordering of the amino acids in the primary structure. However, there are microRNAs and noncoding RNAs, and there are transcription factors.  The transcription factors bind to chromatin, and the RNAs also have some role in regulating the transcription process. (see picture above)

Transcription factors (TFs) interact dynamically in vivo with chromatin binding sites. Four different techniques are currently used to measure their kinetics in live cells,

  1. fluorescence recovery after photobleaching (FRAP),
  2. fluorescence correlation spectroscopy (FCS),
  3. single molecule tracking (SMT) and
  4. competition ChIP (CC).

A comparison of data from each of these techniques raises an important question:

  • do measured transcription kinetics reflect biologically functional interactions at specific sites (i.e. working TFs) or
  • do they reflect non-specific interactions (i.e. playing TFs)?

There are five key unresolved biological questions related to

  • the functionality of transient and prolonged binding events at both
  • specific promoter response elements as well as non-specific sites.

In support of functionality,

  • there are data suggesting that TF residence times are tightly regulated, and
  • that this regulation modulates transcriptional output at single genes.

In addition to this site-specific regulatory role, TF residence times

  • also determine the fraction of promoter targets occupied within a cell
  • thereby impacting the functional status of cellular gene networks.
  • TF residence times, then, are key parameters that could influence transcription in multiple ways.

Quantifying transcription factor kinetics: At work or at play? Mueller F., et al.  http://dx.doi.org:/10.3109/10409238.2013.833891

Dr. Virginie Mattot works in the team “Angiogenesis, endothelium activation and Cancer” directed by Dr. Fabrice Soncin at the Institut de Biologie de Lille in France where she studies the roles played by microRNAs in endothelial cells during physiological and pathological processes such as angiogenesis or endothelium activation. She has been using Target Site Blockers to investigate the role of microRNAs on putative targets.

A few years ago, the team identified

  • an endothelial cell-specific gene which
  • harbors a microRNA in its intronic sequence.

They have since been working on understanding the functions of

  • both this new gene and its intronic microRNA in endothelial cells.

While they were searching for the functions of the intronic microRNA,

  • theye identified an unknown gene as a putative target.

The aim of my project was to investigate if this unknown gene was actually a genuine target and

  • if regulation of this gene by the microRNA was involved in endothelial cell function.

They had already shown the endothelial cell phenotype is associated with the inhibition of the intronic microRNA.
They then used miRCURY LNA™ Target Site Blockers to demonstrate

  • the expression of this unknown gene is actually controlled by this microRNA.
  • the microRNA regulates specific endothelial cell properties through regulation of this unknown gene.

MicroRNA function in endothelial cells – Solving the mystery of an unknown target gene using Target Site Blockers to investigate the role of microRNAs on putative targets

We first verified that this TSB was functional by analyzing

  • the expression of the miRNA target against which the TSB was directed
  • we then showed the TSB induced similar phenotypes as those when we inhibited the microRNA in the same cells.

Target Site Blockers were shown to be efficient tools to demonstrate the specific involvement of

  • putative microRNA targets
  • in the function played by this microRNA.

Some genes are known to have several different alternatively spliced protein variants, but the Scripps Research Institute’s Paul Schimmel and his colleagues have uncovered almost 250 protein splice variants of an essential, evolutionarily conserved family of human genes. The results were published July 17 in Science.

Focusing on the 20-gene family of aminoacyl tRNA synthetases (AARSs),

  • the team captured AARS transcripts from human tissues—some fetal, some adult—and showed that
  • many of these messenger RNAs (mRNAs) were translated into proteins.

Previous studies have identified several splice variants of these enzymes that have novel functions, but uncovering so many more variants was unexpected, Schimmel said. Most of these new protein products

  • lack the catalytic domain but retain other AARS non-catalytic functional domains.

This study fundamentally effects how we view protein-synthesis, according to  Michael Ibba (who was not involved in the work), The Scientist reported. “The unexpected and potentially vast expanded functional networks that emerge from this study have the potential to influence virtually any aspect of cell growth.”

The team—comprehensively captured and sequenced the AARS mRNAs from six human tissue types using high-throughput deep sequencing. They next showed that a proportion of these transcripts, including those missing the catalytic domain, indeed resulted in stable protein products:

  • 48 of these splice variants associated with polysomes.

In vitro translation assays and the expression of more than 100 of these variants in cells confirmed that

  • many of these variants could be made into stable protein products.

The AARS enzymes—of which there’s one for each of the 20 amino acids—bring together an amino acid with its appropriate transfer RNA (tRNA) molecule. This reaction allows a ribosome to add the amino acid to a growing peptide chain during protein translation. AARS enzymes can be found in all living organisms and are thought to be among the first proteins to have originated on Earth.

One goal of human genetics is to understand how the information for precise and dynamic gene expression programs is encoded in the genome. The interactions of transcription factors (TFs) with DNA regulatory elements clearly

  • play an important role in determining gene expression outputs, yet
  • the regulatory logic underlying functional transcription factor binding is poorly understood.

An important question in genomics is to understand how a class of proteins called ‘‘transcription factors’’ controls the expression level of other genes in the genome in a cell type-specific manner – a process that is essential to human development. One major approach to this problem is to study where these transcription factors bind in the genome, but this does not tell us about the effect of that binding on gene expression levels and

  • it is generally accepted that much of the binding does not strongly influence gene expression.

DA Cusanovich et al. PLoS Genet 2014;10(3):e1004226.  http://dx.doi.org:/10.1371/journal.pgen.1004226

We knocked down 59 TFs and chromatin modifiers in one HapMap lymphoblastoid cell line

  • to evaluate the context of functional TF binding.

We then identified genes whose expression was affected by the knockdowns

  • by intersecting the gene expression data with transcription factor binding data
    (based on ChIP-seq and DNase-seq)
  • within 10 kb of the transcription start sites of expressed genes.

This combination of data allowed us to infer functional TF binding.
Only a small subset of genes bound by a factor were

  • differentially expressed following the knockdown of that factor,
  • suggesting that most interactions between TF and chromatin
  • do not result in measurable changes in gene expression levels
  • of putative target genes.

We found that functional TF binding is enriched

  • in regulatory elements that harbor a large number of TF binding sites,
  • at sites with predicted higher binding affinity, and
  • at sites that are enriched in genomic regions annotated as ‘‘active enhancers.’’

We aim to be able to predict the expression pattern of a gene based on its regulatory
sequence alone.

Combining a TF knockdown approach with TF binding data can help us to

  • distinguish functional binding from non-functional binding

This approach has previously been applied to the study of human TFs, although for the most part studies have only focused on

  • the regulatory relationship of a single factor with its downstream targets.

The FANTOM consortium knocked down 52 different transcription factors in

  • the THP-1 cell line, an acute monocytic leukemia-derived cell line, and
  • used a subset of these to validate certain regulatory predictions based on binding motif enrichments.

We and others previously studied the regulatory architecture of gene expression in

  • the model system of HapMap lymphoblastoid cell lines (LCLs) using both
  • binding map strategies and QTL mapping strategies.

We now sought to use knockdown experiments targeting transcription factors in a HapMap LCL

  • to refine our understanding of the gene regulatory circuitry of the human genome.

Therefore, We integrated the results of the knockdown experiments with previous data on TF binding to

  • better characterize the regulatory targets of 59 different factors and
  • to learn when a disruption in transcription factor binding
  • is most likely to be associated with variation in the expression level of a nearby gene.

Gene expression levels following the knockdown were compared to

  • expression data collected from six samples that were transfected with negative control siRNA.

Depending on the factor targeted, the knockdowns resulted in

  • between 39 and 3,892 differentially expressed genes at an FDR of 5%
    (Figure 1B; see Table S3 for a summary of the results).

The knockdown efficiency for the 59 factors ranged

  • from 50% to 90% (based on qPCR; Table S1).

The qPCR measurements of the knockdown level were significantly

  • correlated with estimates of the TF expression levels
  • based on the microarray data (P =0.001; Figure 1C).

 

Did the factors tended to have a consistent effect (either up- or down-regulation)

  • on the expression levels of genes they purportedly regulated?

All factors we tested are associated with both up- and down-regulation of downstream targets (Figure 6).

While there is compelling evidence for our inferences, the current chromatin functional annotations

  • do not fully explain the regulatory effects of the knockdown experiments.

For example, the enrichments for binding in ‘‘strong enhancer’’ regions of the genome range from 7.2% to 50.1% (median = 19.2%),

  • much beyond what is expected by chance alone, but far from accounting for all functional binding.

A slight majority of downstream target genes were expressed at higher levels

  • following the knockdown for 15 of the 29 factors for which we had binding information (Figure 6B).

The factor that is associated with the largest fraction (68.8%) of up-regulated target genes following the knockdown is EZH2,

  • the enzymatic component of the Polycomb group complex.

On the other end of the spectrum was JUND, a member of the AP-1 complex, for which

  • 66.7% of differentially expressed targets were down-regulated following the knockdown.

Our results, combined with the previous work from our group and others make for a complicated view

  • of the role of transcription factors in gene regulation as
  • it seems difficult to reconcile the inference from previous work that
  • many transcription factors should primarily act as activators with the results presented here.

One somewhat complicated hypothesis, which nevertheless can resolve the apparent discrepancy, is that

  • the ‘‘repressive’’ effects we observe for known activators may be
  • at sites in which the activator is acting as a weak enhancer of transcription and
  • that reducing the cellular concentration of the factor
  • releases the regulatory region to binding by an alternative, stronger activator.

Integrative study of Arabidopsis thaliana metabolomic and transcriptomic data
with the interactiveMarVis-Graph software

M Landesfeind, A Kaever, K Feussner, C Thurow, C Gatz, I Feussner and P Meinicke
PeerJ 2:e239;   http://dx.doi.org /10.7717/peerj.239

High-throughput technologies notoriously generate large datasets often including data from different omics platforms. Each dataset contains data for several thousand experimental markers, e.g., mass-to-charge ratios in mass spectrometry or spots in DNA microarray analysis. An experimental marker is associated with an intensity profile which may include several measurements according to different experimental conditions (Dettmer, Aronov & Hammock, 2007).

The combined analysis and visualization of data from different high-throughput technologies remains a key challenge in bioinformatics.We present here theMarVis-Graph software for integrative analysis of metabolic and transcriptomic data. All experimental data is investigated in terms of the full metabolic network obtained from a reference database. The reactions of the network are scored based on the associated data, and

  • sub-networks, according to connected high-scoring reactions, are identified.

Finally, MarVis-Graph scores the detected sub-networks,

  • evaluates them by means of a random permutation test and
  • presents them as a ranked list.

Furthermore, MarVis-Graph features an interactive network visualization that provides researchers with a convenient view on the results.

The key advantage ofMarVis-Graph is the analysis of reactions detached from their pathways so that

  • it is possible to identify new pathways or
  • to connect known pathways by previously unrelated reactions.

TheMarVis-Graph software is freely available for academic use and can be downloaded at: http://marvis.gobics.de/marvis-graph.

Significant differences or clusters may be explained by associated annotations, e.g., in terms of metabolic pathways or biological functions. During recent years, numerous specialized tools have been developed to aid biological researchers in automating all these steps (e.g., Medina et al., 2010; Kaever et al., 2009; Waegele et al., 2012). Comprehensive studies can be performed by combining technologies from different omics fields. The combination of transcriptomic and proteomic data sets revealed a strong
correlation between both kinds of data (Nie et al., 2007) and supported the detection of complex interactions, e.g., in RNA silencing (Haq et al., 2010). Moreover, correlations
were detected between RNA expression levels and metabolite abundances (Gibon et al., 2006). Therefore, tools that integrate, analyze and visualize experimental markers from different platforms are needed. To cope with the complexity of genome-wide studies, pathway models are utilized extensively as a simple abstraction of the underlying complex mechanisms. Set Enrichment Analysis (Subramanian et al., 2005) and Over-Representation Analysis (Huang, Sherman & Lempicki, 2009) have become state-of-the-art tools for analyzing large-scale datasets: both methods evaluate predefined sets of entities, e.g., the accumulation of differentially expressed genes in a pathway.

While manually curated pathways are convenient and easy to interpret, experimental studies have shown that all metabolic and signaling pathways are heavily interconnected (Kunkel & Brooks, 2002; Laule et al., 2003). Data from biomolecular databases support these studies: the metabolic network of Arabidopsis thaliana in the KEGG database (Kanehisa et al., 2012; Kanehisa & Goto, 2000) contains 1606 reactions from which 1464 are connected in a single sub-network (>91%), i.e., they
share a metabolite as product or substrate. In the AraCyc 10.0 database (Mueller, Zhang & Rhee, 2003; Rhee et al., 2006), more than 89% of the reactions are counted in a single sub-network. In both databases, most other reactions are completely disconnected. Additionally, Set Enrichment Analyses can not identify links between the predefined sets easily. This becomes even more important when analyzing smaller pathways as provided by the MetaCyc (Caspi et al., 2008; Caspi et al., 2012) database. Moreover, methods that utilize pathways as predefined sets ignore reactions and related biomolecular entities (e.g., metabolites, genes) which are not associated with a single pathway. For example, this affects 4000 reactions in MetaCyc and 2500 in KEGG, respectively (Altman et al., 2013). Therefore, it is desirable to develop additional methods

  • that do not require predefined sets but may detect enriched sub-networks in the full metabolic network.

While several tools support the statistical analysis of experimental markers from one or more omics technologies and then utilize variants of Set Enrichment Analysis (Xia et al., 2012; Chen et al., 2013; Howe et al., 2011),

  • no tool is able to explicitly search for connected reactions that include
  • most of the metabolites, genes, and enyzmes with experimental evidence.

However, the automatic identification of sub-networks has been proven useful in other contexts, e.g., in the analysis of protein–protein-interaction networks (Alcaraz et al., 2012; Baumbach et al., 2012; Maeyer et al., 2013).

MarVis-Graph imports experimental markers from different high-throughput experiments and

  • analyses them in the context of reaction-chains in full metabolic networks.

Then, MarVis-Graph scores the reactions in the metabolic network

  • according to the number of associated experimental markers and
  • identifies sub-networks consisting of subsequent, high-scoring reactions.

The resulting sub-networks are

  • ranked according to a scoring method and visualized interactively.

Hereby, sub-networks consisting of reactions from different pathways may be identified to be important

  • whereas the single pathways may not be found to be significantly enriched.

MarVis-Graph may also connect reactions without an assigned pathway

  • to reactions within a particular pathway.

TheMarVis-Graph tool was applied in a case-study investigating the wound response in Arabidopsis thaliana to analyze combined metabolomic and transcriptomic high-throughput data.

Figure 1 Schema of the metabolic network representation in MarVis-Graph. Metabolite markers are shown in gray, metabolites in red, reactions in blue, enzymes in green, genes in yellow, transcript markers in pink, and pathways in turquoise color. The edges are shown in black with labels that comply with the biological meaning. The orange arrows depict the flow of score for the initial scoring (described in section “Initial Scoring”). (not shown)

In MarVis-Graph, metabolite markers obtained from mass-spectrometry experiments additionally contain the experimental mass. The experimental mass has to be
calculated based on the mass-to-charge ratio (m/z-value) and specific isotope- or adduct-corrections (Draper et al., 2009) by means of specialized tools, e.g.,MarVis-Filter
(Kaever et al., 2012).

For each transcript marker the corresponding annotation has to be given. In DNA microarray experiments, each spot (transcript marker) is specific for a gene and can
therefore be used for annotation. For other technologies an annotation has to be provided by external tools.

In MarVis-Graph, each reaction is scored initially based on the associated experimental data (see “Initial scoring”). This initial scoring is refined (see “Refining the scoring”) and afterwards reactions with a score below a user-defined threshold are removed. The network is

  • decomposed into subsequent high-scoring reactions that constitute the sub-networks.

The weight of each experimental marker (see “Experimental markers”) is equally distributed over all metabolites and genes associated with the metabolite marker or
transcript marker, respectively. For all vertices, this is repeated as illustrated in Fig. 1 until the weights are accumulated by the reactions.

The initial reaction scores are used as input scoring for the random walk algorithm. The algorithm is performed as described by Glaab et al. (2012) with a user-defined
restart-probability r (default value 0.8). After convergence of the algorithm, reactions with a score lower than the user-defined threshold t (default value t = 1−r) are removed from the reaction network. During the removal process,

  • the network is decomposed into pairwise disconnected sub-networks containing only high-scoring reactions.

In the following, a resulting sub-network is denoted by a prime: G′ = (V′,L′) with V′ = M′ ∪C′ ∪R′ ∪E′ ∪G′ ∪T′ ∪P′.

The scores of the identified sub-networks can be assessed using a random permutation test, evaluating the marker annotations under the null hypothesis of being connected
randomly. Here, the assignments

  • from metabolite markers to metabolites and from transcript markers to genes are randomized.

For each association between a metabolite marker and a metabolite,

  • this connection is replaced by a connection between a randomly chosen metabolite marker and a randomly chosen metabolite.

The random metabolite marker is chosen from the pool of formerly connected metabolite markers. Each connected transcript marker

  • is associated with a randomly chosen gene.

Choosing from the list of already connected experimental markers ensures that

  • the sum of weights from the original and the permuted network are equal.

This method differs from the commonly utilized XSwap permutation (Hanhij¨arvi, Garriga & Puolam¨aki, 2009) that is based on swapping endpoints of two random edges. The main difference of our permutation method is that it results in a network with different topological structure, i.e., different degree of the metabolite and gene nodes.

Finally, the sub-networks are detected and scored with the same parameters applied for the original network. Based on the scores of the networks identified in the random
permutations, the family-wise-error-rate (FWER) and false-discovery-rate (FDR) are calculated for each originally identified sub-network.

MarVis-Graph was applied in a case study investigating the A. thaliana wound response. Data from a metabolite fingerprinting (Meinicke et al., 2008) and a DNA microarray
experiment (Yan et al., 2007) were imported into a metabolic network specific for A. thaliana created from the AraCyc 10.0 database (Lamesch et al., 2011). The metabolome
and transcriptome have been measured before wounding as control and at specific time points after wounding in wild-type and in the allene oxide synthase (AOS) knock-out
mutant dde-2-2 (Park et al., 2002) of A. thaliana Columbia (see Table 1). The AOS mutant was chosen, because AOS catalyzes the first specific step in the biosynthesis of the hormone jasmonic acid, which is the key regulator in wound response of plants (Wasternack & Hause, 2013).

Both datasets have been preprocessed with theMarVis-Filter tool (Kaever et al., 2012) utilizing the Kruskal–Wallis p-value calculation on the intensity profiles. Based on the ranking of ascending p-values,

  • the first 25% of the metabolite markers and 10% of the transcript markers have been selected for further investigation (Data S2).

The filtered metabolite and transcript markers were imported into the metabolic network. For metabolite markers, metabolites were associated

  • if the metabolite marker’s detected mass differs from the metabolites monoisotopic mass by a maximum of 0.005u.

Transcript markers were linked to the genes whose ID equaled the ID given in the CATMA database (Sclep et al., 2007) for that transcript marker.

Table 2 Vertices in the A. thaliana specific metabolic network after import of experimental markers. Number of objects in the metabolic network
in absolute counts and relative abundances. For experimental markers, the with annotation column gives the number of metabolite markers and
transcript markers that were annotated with a metabolite or gene, respectively. The direct evidence column contains the number of metabolites
and genes, that are associated with a metabolite marker or transcript marker. For enzymes, this is the number of enzymes encoded by a gene with
direct evidence. The number of vertices with an association to a reaction is given in the with reaction column. In the last column, this is given for
associations to metabolic pathways. (not shown)

MarVis-Graph detected a total of 133 sub-networks. The sub-networks were ranked according to size Ss, diameter Sd, and sum-of-weights Ssow
scores (Table S4). Interestingly, the different rankings show a high correlation with all pairwise correlations higher than 0.75 (Pearson correlation
coefficient) and 0.6 (Spearman rank correlation).

Allene-oxide cyclase sub-network
In all rankings, the sub-network allene-oxide cyclase (named after the reaction with the highest score in this sub-network) appeared as top candidate.

This sub-network is constituted of reactions from different pathways related to fatty acids. Figure 2 shows a visualization of the sub-network.
Jasmonic acid biosynthesis. The main part of the sub-network is formed by reactions from the “jasmonic acid biosynthesis” (PlantMetabolic Network, 2013)
resulting in jasmonic acid (jasmonate). The presence of this pathway is very well established because of its central role in mediating the plants wound response
(Reymond & Farmer, 1998; Creelman, Tierney & Mullet, 1992). Additionally, metabolites and transcripts from this pathway were expected to show prominent
expression profiles because AOS, a key enzyme in this pathway, is knocked-out in themutant plant. Jasmonic acid derivatives and hormones.

Jasmonic acid derivatives and hormones. Jasmonate is a precursor for a broad variety of plant hormones (Wasternack & Hause, 2013), e.g., the derivative (-)-
jasmonic acid methyl ester (also Methyl Jasmonic Acid; MeJA) is a volatile, airborne signal mediating wound response between plants (Farmer&Ryan, 1990).
Reactions from the jasmonoyl-amino acid conjugates biosynthesis I (PMN, 2013a) pathway connect jasmonate to different amino acids, including L-valine,
L-leucine, and L-isoleucine. Via these amino acids, this sub-network is connected to the indole-3-acetylamino acid biosynthesis (PMN, 2013b) (IAA biosynthesis).
Again, this pathway produces a well known plant hormone: Auxine (Woodward & Bartel, 2005). Even though, jasmonate and auxin are both plant hormones, their
connection in this subnetwork is of minor relevance because amino acid conjugates are often utilized as active or storage forms of signaling molecules.While
jasmonoyl-amino acid conjugates represent the active signaling form of jasmonates, IAA amino acid conjugates are the storage form of this hormone (Staswick et al.,
2005).

polyhydroxy fatty acids synthesis

polyhydroxy fatty acids synthesis

 

Figure 2 Schema of the allene-oxide cyclase sub-network. Metabolites are shown in red, reactions in blue, and enzymes in green color. Metabolites and reactions without direct experimental evidence are marked by a dashed outline and a brighter color while enzymes without experimental evidence are hidden. The metabolic pathways described in section “Resulting sub-networks” are highlighted with different colors. The orange and green parts indicate the reaction chains required to build jasmonate and its amino acid conjugates. The coloring of pathways was done manually after export from MarVis-Graph.

The ω-3-fatty acid desaturase should catalyze a reaction from linoleate to α-linolenate. Metabolite markers that match the mass of crepenynic acid do also match α-linolenate
because both molecules have the same sum-formula and monoisotopic mass. As mentioned above, MarVis-Graph compiled the metabolic network for this study
from the AraCyc database version 10.0. On June 4th, a curator changed the database to remove theΔ12-fatty acid dehydrogenase prior to the release of AraCyc version 11.0.

The presented new software tool MarVis-Graph supports the investigation and visualization of omics data from different fields of study. The introduced algorithm for
identification of sub-networks is able to identify reaction-chains across different pathways and includes reactions that are not associated with a single pathway. The application of MarVis-Graph in the case study on A. thaliana wound response resulted in a convenient graphical representation of high-throughput data which allows the analysis of the complex dynamics in a metabolic network.

 

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Transcription Modulation

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

 

This portion of the transcription series deals with transcription factors and the effects of their binding on metabolism. This also has implications for pharmaceutical target identification.

The Functional Consequences of Variation in Transcription Factor Binding
DA. Cusanovich, B Pavlovic, JK. Pritchard*, Y Gilad*
1 Department of Human Genetics, 2 Howard Hughes Medical Institute, University of Chicago, Chicago, IL 3 Departments of Genetics and Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA.
PLoS Genet 2014;10(3):e1004226.  http://dx.doi.org:/10.1371/journal.pgen.1004226

One goal of human genetics is to understand how the information for precise and dynamic gene expression programs is encoded in the genome. The interactions of transcription factors (TFs) with DNA regulatory elements clearly

  • play an important role in determining gene expression outputs, yet
  • the regulatory logic underlying functional transcription factor binding is poorly understood.

An important question in genomics is to understand how a class of proteins called ‘‘transcription factors’’ controls the expression level of other genes in the genome in a cell type-specific manner – a process that is essential to human development. One major approach to this problem is to study where these transcription factors bind in the genome, but this does not tell us about the effect of that binding on gene expression levels and

  • it is generally accepted that much of the binding does not strongly influence gene expression.

To address this issue, we artificially reduced the concentration of 59 different transcription factors in the cell and then

  • examined which genes were impacted by the reduced transcription factor level.

Our results implicate some attributes

  • that might influence what binding is functional, but they also suggest that
  • a simple model of functional vs. non-functional binding may not suffice.

Many studies have focused on characterizing the genomic locations of TF binding, but

  • it is unclear whether TF binding at any specific locus has
  • functional consequences with respect to gene expression output.

We knocked down 59 TFs and chromatin modifiers in one HapMap lymphoblastoid cell line

  • to evaluate the context of functional TF binding.

We then identified genes whose expression was affected by the knockdowns

  • by intersecting the gene expression data with transcription factor binding data
    (based on ChIP-seq and DNase-seq)
  • within 10 kb of the transcription start sites of expressed genes.

This combination of data allowed us to infer functional TF binding.
Only a small subset of genes bound by a factor were

  • differentially expressed following the knockdown of that factor,
  • suggesting that most interactions between TF and chromatin
  • do not result in measurable changes in gene expression levels
  • of putative target genes.

We found that functional TF binding is enriched

  • in regulatory elements that harbor a large number of TF binding sites,
  • at sites with predicted higher binding affinity, and
  • at sites that are enriched in genomic regions annotated as ‘‘active enhancers.’’

We aim to be able to predict the expression pattern of a gene based on its regulatory
sequence alone. However, the regulatory code of the human genome is much more complicated than

  • the triplet code of protein coding sequences, and is highly context-specific,
  • depending on cell-type and other factors.

Moreover, regulatory regions are not necessarily organized into

  • discrete, easily identifiable regions of the genome and
  • may exert their influence on genes over large genomic distances

Genomic studies addressing questions of the regulatory logic of the human genome have largely taken one of two approaches.

  1. collecting transcription factor binding maps using techniques such as ChIPseq
    and DNase-seq
  2. mapping various quantitative trait loci (QTL), such as gene expression levels
    (eQTLs) [7], DNA methylation (meQTLs) [8] and chromatin accessibility (dsQTLs)

Cumulatively, binding map studies and QTL map studies have

  • led to many insights into the principles and mechanisms of gene regulation.

However, there are questions that neither mapping approach on its own is well equipped to address. One outstanding issue is

  • the fraction of factor binding in the genome that is ‘‘functional’’,
    which we define here to mean that
  • disturbing the protein-DNA interaction leads to a measurable
  • downstream effect on gene regulation.

Transcription factor knockdown could be used to address this problem, whereby

  • the RNA interference pathway is employed to greatly reduce
  • the expression level of a specific target gene by using small interfering RNAs (siRNAs).

The response to the knockdown can then be measured by collecting RNA after the knockdown and

  • measuring global changes in gene expression patterns
  • after specifically attenuating the expression level of a given factor.

Combining a TF knockdown approach with TF binding data can help us to

  • distinguish functional binding from non-functional binding

This approach has previously been applied to the study of human TFs, although for the most part studies have only focused on

  • the regulatory relationship of a single factor with its downstream targets.

The FANTOM consortium knocked down 52 different transcription factors in

  • the THP-1 cell line, an acute monocytic leukemia-derived cell line, and
  • used a subset of these to validate certain regulatory predictions based on binding motif enrichments.

We and others previously studied the regulatory architecture of gene expression in

  • the model system of HapMap lymphoblastoid cell lines (LCLs) using both
  • binding map strategies and QTL mapping strategies.

We now sought to use knockdown experiments targeting transcription factors in a HapMap LCL

  • to refine our understanding of the gene regulatory circuitry of the human genome.

Therefore, We integrated the results of the knockdown experiments with previous data on TF binding to

  • better characterize the regulatory targets of 59 different factors and
  • to learn when a disruption in transcription factor binding
  • is most likely to be associated with variation in the expression level of a nearby gene.

Gene expression levels following the knockdown were compared to

  • expression data collected from six samples that were transfected with negative control siRNA.

The expression data from all samples were normalized together using

  • quantile  normalization followed by batch correction using the RUV-2 method.

We then performed several quality control analyses to confirm

  1. that the quality of the data was high,
  2. that there were no outlier samples, and
  3. that the normalization methods reduced the influence of confounders

In order to identify genes that were expressed at a significantly different level

  • in the knockdown samples compared to the negative controls,
  • we used likelihood-ratio tests within the framework of a fixed effect linear model.

Following normalization and quality control of the arrays,

  • we identified genes that were differentially expressed between
  • the three knockdown replicates of each factor and the six controls.

Depending on the factor targeted, the knockdowns resulted in

  • between 39 and 3,892 differentially expressed genes at an FDR of 5%
    (Figure 1B; see Table S3 for a summary of the results).

The knockdown efficiency for the 59 factors ranged

  • from 50% to 90% (based on qPCR; Table S1).

The qPCR measurements of the knockdown level were significantly

  • correlated with estimates of the TF expression levels
  • based on the microarray data (P =0.001; Figure 1C).

Reassuringly, we did not observe a significant correlation between

  • the knockdown efficiency of a given factor and
  • the number of genes classified as differentially expressed foci.

Because we knocked down 59 different factors in this experiment

  • we were able to assess general patterns associated with the perturbation of transcription factors
  • beyond merely the number of affected target genes.

Globally, despite the range in the number of genes we identified as

  • differentially expressed in each knockdown,
  • the effect sizes of the differences in expression were relatively modest and
  • consistent in magnitude across all knockdowns.

The median effect size following the knockdown experiment for genes classified as

  • differentially expressed at an FDR of 5% in any knockdown was
  • a 9.2% difference in expression level between the controls and the knockdown (Figure 2),
  • while the median effect size for any individual knockdown experiment ranged between 8.1% and 11.0%.
    (this was true whether we estimated the knockdown effect based on qPCR (P = 0.10; Figure 1D) or microarray (P = 0.99; not shown) data.

Nor did we observe a correlation between

  • variance in qPCR-estimated knockdown efficiency (between replicates) and
  • the number of genes differentially expressed (P = 0.94; Figure 1E).

We noticed that the large variation in the number of differentially expressed genes

  • extended even to knockdowns of factors from the same gene family.

Figure 1. Differential expression analysis.
(a) Examples of differential expression analysis results for the genes HCST and IRF4. The top two panels are ‘MA plots’ of the mean Log2(expression level) between the knockdown arrays and the controls for each gene (x-axis) to the Log2(Fold-Change) between the knockdowns and controls (y-axis). Differentially expressed genes at an FDR of 5% are plotted in yellow (points 50% larger). The gene targeted by the siRNA is highlighted in red. The bottom two panels are ‘volcano plots’ of the Log2(Fold-Change) between the knockdowns and controls (x-axis) to the P-value for differential expression (y-axis). The dashed line marks the 5% FDR threshold. Differentially expressed genes at an FDR of 5% are plotted in yellow (points 50% larger). The red dot marks the gene targeted by the siRNA.
(b) Barplot of number of differentially expressed genes in each knockdown experiment.
(c) Comparison of the knockdown level measured by qPCR (RNA sample collected 48 hours posttransfection) and the knockdown level measured by microarray.
(d) Comparison of the level of knockdown of the transcription factor at 48 hrs (evaluated by qPCR; x-axis) and the number of genes differentially expressed in the knockdown experiment (y-axis).
(e) Comparison of the variance in knockdown efficiency between replicates for each transcription factor (evaluated by qPCR; x-axis) and the number of differentially expressed genes in the knockdown experiment (y-axis).

Differential expression analysis

Differential expression analysis

http://dx.doi.org:/10.1371/journal.pgen.1004226.g001

Figure 2. Effect sizes for differentially expressed genes.
Boxplots of absolute Log2(fold-change) between knockdown arrays and control arrays for all genes identified as differentially expressed in each experiment. Outliers are not plotted. The gray bar indicates the interquartile range across all genes differentially expressed in all knockdowns. Boxplots are ordered by the number of genes differentially expressed in each experiment. Outliers were not plotted.

Effect sizes for differentially expressed genes

Effect sizes for differentially expressed genes

http://dx.doi.org:/10.1371/journal.pgen.1004226.g002

Knocking down SREBF2 (1,286 genes differentially expressed), a key regulator of cholesterol homeostasis,

  • results in changes in the expression of genes that are
  • significantly enriched for cholesterol and sterol biosynthesis annotations.

While not all factors exhibited striking enrichments for relevant functional categories and pathways,

  • the overall picture is that perturbations of many of the factors
  • primarily affected pathways consistent with their known biology.

In order to assess functional TF binding, we next incorporated

  • binding maps together with the knockdown expression data.

We combined binding data based on DNase-seq footprints in 70 HapMap LCLs, reported by Degner et al. (Table S5)

  • and from ChIP-seq experiments in LCL GM12878, published by ENCODE.

We were thus able to obtain genome wide binding maps for a total of 131 factors that were either

  • directly targeted by an siRNA in our experiment (29 factors) or were
  • differentially expressed in one of the knockdown experiments.

We classified a gene as a bound target of a particular factor when

  • binding of that factor was inferred within 10kb of the transcription start site (TSS) of the target gene.

Using this approach, we found that the 131 TFs were bound

  • in proximity to a median of 1,922 genes per factor (range 11 to 7,053 target genes).

We considered binding of a factor to be functional if the target gene

  • was differentially expressed after perturbing the expression level the bound transcription factor.

We then asked about the concordance between

  • the transcription factor binding data and the knockdown expression data.
  •  the extent to which differences in gene expression levels following the knockdowns
  • might be predicted by binding of the transcription factors
  • within the putative regulatory regions of the responsive genes. and also
  • what proportion of putative target (bound) genes of a given TF were
  • differentially expressed following the knockdown of the factor.

Focusing only on the binding sites classified using the DNase-seq data
(which were assigned to a specific instance of the binding motif, unlike the ChIP data),

  • we examined sequence features that might distinguish functional binding.

In particular, whether binding at conserved sites was more likely to be functional  and

  • whether binding sites that better matched the known PWM for the factor were more likely to be functional.

We did not observe a significant shift in the conservation of functional binding sites (Wilcoxon rank sum P = 0.34),

  • but we did observe that binding around differentially expressed genes occurred at sites
  • that were significantly better matches to the canonical binding motif.

Figure 3. Intersecting binding data and expression data for each knockdown.
(a) Example Venn diagrams showing the overlap of binding and differential expression for the knockdowns of HCST and IRF4 (the same genes as in Figure 1).
(b) Boxplot summarizing the distribution of the fraction of all expressed genes that are bound by the targeted gene or downstream factors.
(c) Boxplot summarizing the distribution of the fraction of bound genes that are classified as differentially expressed, using an FDR of either 5% or 20%.

Intersecting binding data and expression data for each knockdown

Intersecting binding data and expression data for each knockdown

http://dx.doi.org:/10.1371/journal.pgen.1004226.g003

Considering bound targets determined from either the ChIP-seq or DNase-seq data, we observed that

  • differentially expressed genes were associated with both
  • a higher number of binding events for the relevant factors within 10 kb of the TSS (P,10216; Figure 4A)
  • as well as with a larger number of different binding factors
    (considering the siRNA-targeted factor and any TFs that were DE in the knockdown; P,10216; Figure 4B).

Figure 4. Degree of binding correlated with function. Boxplots comparing
(a) the number of sites bound, and
(b) the number of differentially expressed transcription factors binding events near functionally or non-functionally bound genes. We considered binding for siRNA-targeted factor and any factor differentially expressed in the knockdown.
(c) Focusing only on genes differentially expressed in common between each pairwise set of knockdowns we tested for enrichments of functional binding (y-axis). Pairwise comparisons between knockdown experiments were binned by the fraction of differentially expressed transcription factors in common between the two experiments. For these boxplots, outliers were not plotted.

Degree of binding correlated with function

Degree of binding correlated with function

http://dx.doi.org:/10.1371/journal.pgen.1004226.g004

We examined the distribution of binding about the TSS. Most factor binding was concentrated

  • near the TSS whether or not the genes were classified as differentially expressed (Figure 5A).
  • the distance from the TSS to the binding sites was significantly longer for differentially expressed genes (P,10216; Fig. 5B).

Figure 5. Distribution of functional binding about the TSS.
(a) A density plot of the distribution of bound sites within 10 kb of the TSS for both functional and non-functional genes. Inset is a zoom-in of the region +/21 kb from the TSS (b) Boxplots comparing the distances from the TSS to the binding sites for functionally bound genes and non-functionally bound genes. For the boxplots, 0.001 was added before log10 transforming the distances and outliers were not plotted.

Distribution of functional binding about the TSS

Distribution of functional binding about the TSS

http://dx.doi.doi:/10.1371/journal.pgen.1004226.g005

We investigated the distribution of factor binding across various chromatin states, as defined by Ernst et al. This dataset lists

  • regions of the genome that have been assigned to different activity states
  • based on ChIP-seq data for various histone modifications and CTCF binding.

For each knockdown, we separated binding events

  • by the genomic state in which they occurred and then
  • tested whether binding in that state was enriched around differentially expressed genes.

After correcting for multiple testing of genes that were differentially expressed.

  • 19 knockdowns showed significant enrichment for binding in ‘‘strong enhancers’’
  • four knockdowns had significant enrichments for ‘‘weak enhancers’’,
  • eight knockdowns showed significant depletion of binding in ‘‘active promoters’’ ,
  • six knockdowns had significant depletions for ‘‘transcription elongation’’,

Did the factors tended to have a consistent effect (either up- or down-regulation)

  • on the expression levels of genes they purportedly regulated?

All factors we tested are associated with both up- and down-regulation of downstream targets (Figure 6).

A slight majority of downstream target genes were expressed at higher levels

  • following the knockdown for 15 of the 29 factors for which we had binding information (Figure 6B).

The factor that is associated with the largest fraction (68.8%) of up-regulated target genes following the knockdown is EZH2,

  • the enzymatic component of the Polycomb group complex.

On the other end of the spectrum was JUND, a member of the AP-1 complex, for which

  • 66.7% of differentially expressed targets were down-regulated following the knockdown.

Figure 6. Magnitude and direction of differential expression after knockdown.
(a) Density plot of all Log2(fold-changes) between the knockdown arrays and controls for genes that are differentially expressed at 5% FDR in one of the knockdown experiments as well as bound by the targeted transcription factor.
(b) Plot of the fraction of differentially expressed putative direct targets that were up-regulated in each of the knockdown experiments.

Magnitude and direction of differential expression after knockdown

Magnitude and direction of differential expression after knockdown

http://dx.doi.org:/10.1371/journal.pgen.1004226.g006

We found no correlation between the number of paralogs and the fraction of bound targets that were differentially expressed. We also did not observe a significant correlation when we considered whether

  • the percent identity of the closest paralog might be predicative of
  • the fraction of bound genes that were differentially expressed following the knockdown (Figure S8).

While there is compelling evidence for our inferences, the current chromatin functional annotations

  • do not fully explain the regulatory effects of the knockdown experiments.

For example, the enrichments for binding in ‘‘strong enhancer’’ regions of the genome range from 7.2% to 50.1% (median = 19.2%),

  • much beyond what is expected by chance alone, but far from accounting for all functional binding.

In addition to considering

  • the distinguishing characteristics of functional binding, we also examined
  • the direction of effect that perturbing a transcription factor had on the expression level of its direct targets.

We specifically addressed whether

  • knocking down a particular factor tended to drive expression of its putatively direct (namely, bound) targets up or down,
  • which can be used to infer that the factor represses or activates the target, respectively.

Transcription factors have traditionally been thought of primarily as activators, and previous work from our group is consistent with that notion. Surprisingly, the most straightforward inference from the present study is that

  • many of the factors function as repressors at least as often as they function as activators.
  1. EZH2 had a negative regulatory relationship with the largest fraction of direct targets (68.8%),
    consistent with – the known role of EZH2 as the active member of the Polycomb group complex PC2
  2. while JUND seemed to have a positive regulatory relationship with the largest fraction of direct targets (66.7%),
    and with – the biochemical characterization of the AP-1 complex (of which JUND is a component) as a transactivator.

More generally, however, our results, combined with the previous work from our group and others make for a complicated view

  • of the role of transcription factors in gene regulation as
  • it seems difficult to reconcile the inference from previous work that
  • many transcription factors should primarily act as activators with the results presented here.

One somewhat complicated hypothesis, which nevertheless can resolve the apparent discrepancy, is that

  • the ‘‘repressive’’ effects we observe for known activators may be
  • at sites in which the activator is acting as a weak enhancer of transcription and
  • that reducing the cellular concentration of the factor
  • releases the regulatory region to binding by an alternative, stronger activator.

To more explicitly address the effect that our proximity-based definition of target genes might have on our analyses, we reanalyzed

  • the overlap between factor binding and differential expression following the knockdowns
  • using an independent, empirically determined set of target genes.

Thurman et al. used correlations in DNase hypersensitivity between

  • intergenic hypersensitive sites and promoter hypersensitive sites across diverse tissues
  • to assign intergenic regulatory regions to specific genes,
  • independently of proximity to a particular promoter.

We performed this alternative analysis in which we

  • assigned binding events to genes based on the classification of Thurman et al.

We then considered the overlap between binding and differential expression in this new data set. The results were largely

  • consistent with our proximity-based observations.

A median of 9.5% of genes that were bound by a factor were

  • also differentially expressed following the knockdown of that factor
    (compared to 11.1% when the assignment of binding sites to genes is based on proximity).

From the opposite perspective, a median of 28.0% of differentially expressed genes were bound by that factor
(compared to 32.3% for the proximity based definition). The results of this analysis are summarized in Table S7.

Our results should not be considered a comprehensive census of regulatory events in the human genome. Instead, we adopted a gene-centric approach,

  • focusing only on binding events near the genes for which we could measure expression
  • to learn some of the principles of functional transcription factor binding.

In light of our observations a reassessment of our estimates of binding may be warranted. In particular, because functional binding is skewed away from promoters (our system is apparently not well-suited to observe functional promoter binding, perhaps because of protection by large protein complexes),

  • a more conservative estimate of the fraction of binding that is indeed functional would not consider data within the promoter.

Importantly, excluding the putative promoter region from our analysis (i.e. only considering a window .1 kb from the TSS and ,10 kb from the TSS)

  • does not change our conclusions.

Considering this smaller window,

  • a median of 67.0% of expressed genes are still classified as bound by
  1. either the knocked down transcription factor or
  2. a downstream factors that is differentially expressed in each experiment,

yet a median of only 8.1% of the bound genes are

  • also differentially expressed after the knockdowns.

Much of what distinguishes functional binding (as we define it) has yet to be explained. We are unable to explain much of the differential expression observed in our experiments by the presence of least one relevant binding event. This may not be altogether surprising, as

  • we are only considering binding in a limited window around the transcription start site.

To address these issues, more factors should be perturbed to further evaluate the robustness of our results and to add insight. Together, such studies will help us develop a more sophisticated understanding of functional transcription factor binding in particular, the gene regulatory logic more generally.

Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale

E Shmelkov, Z Tang, I Aifantis, A Statnikov*
Biology Direct 2011; 6(15).  http://www.biology-direct.com/content/6/1/15

Recently the biological pathways have become a common and probably the most popular form of representing biochemical information for hypothesis generation and validation. These maps store wide knowledge of complex molecular interactions and regulations occurring in the living organism in a simple and obvious way, often using intuitive graphical notation. Two major types of biological pathways could be distinguished.

  1. Metabolic pathways incorporate complex networks of protein-based interactions and modifications, while
  2. signal transduction and transcriptional regulatory pathways are usually considered to provide information on mechanisms of transcription

While there are a lot of data collected on human metabolic processes,

  • the content of signal transduction and transcriptional regulatory pathways varies greatly in quality and completeness.

An indicative comparison of MYC transcriptional targets reported in ten different pathway databases reveals that these databases differ greatly from each other (Figure 1). Given that MYC is involved

  • in the transcriptional regulation of approximately 15% of all genes,

one cannot argue that the majority of pathway databases that contain

  • less than thirty putative transcriptional targets of MYC are even close to complete.

More importantly, to date there have been no prior genome-wide evaluation studies (that are based on genome-wide binding and gene expression assays) assessing pathway databases

Background: While pathway databases are becoming increasingly important in most types of biological and translational research, little is known about the quality and completeness of pathways stored in these databases. The present study conducts a comprehensive assessment of transcriptional regulatory pathways in humans for seven well-studied transcription factors:

  1. MYC,
  2. NOTCH1,
  3. BCL6,
  4. TP53,
  5. AR,
  6. STAT1,
  7. RELA.

The employed benchmarking methodology first involves integrating

  • genome-wide binding with functional gene expression data
  • to derive direct targets of transcription factors.

Then the lists of experimentally obtained direct targets

  • are compared with relevant lists of transcriptional targets from 10 commonly used pathway databases.

Results: The results of this study show that for the majority of pathway databases,

  • the overlap between experimentally obtained target genes and
  • targets reported in transcriptional regulatory pathway databases is
  • surprisingly small and often is not statistically significant.

The only exception is MetaCore pathway database which

  • yields statistically significant intersection with experimental results in 84% cases.

The lists of experimentally derived direct targets obtained in this study can be used

  • to reveal new biological insight in transcriptional regulation,  and we
  • suggest novel putative therapeutic targets in cancer.

Conclusions: Our study opens a debate on validity of using many popular pathway databases to obtain transcriptional regulatory targets. We conclude that the choice of pathway databases should be informed by

  • solid scientific evidence and rigorous empirical evaluation.

In the current study we perform

(1) an evaluation of ten commonly used pathway databases,

  • assessing the transcriptional regulatory pathways, considered in the current study as
  • the interactions of the type ‘transcription factor-transcriptional targets’.

This involves integration of human genome wide functional microarray or RNA-seq gene expression data with

  • protein-DNA binding data from ChIP-chip, ChIP-seq, or ChIP-PET platforms
  • to find direct transcriptional targets of the seven well known transcription factors:
  • MYC, NOTCH1, BCL6, TP53, AR, STAT1, and RELA.

The choice of transcription factors is based on their important role in oncogenesis and availability of binding and expression data in the public domain.

(2) the lists of experimentally derived direct targets are used to assess the quality and completeness of 84 transcriptional regulatory pathways from four publicly available (BioCarta, KEGG, WikiPathways and Cell Signaling Technology) and six commercial (MetaCore, Ingenuity Pathway Analysis, BKL TRANSPATH, BKL TRANSFAC, Pathway Studio and GeneSpring Pathways) pathway databases.

(3) We measure the overlap between pathways and experimentally obtained target genes and assess statistical significance of this overlap, and we demonstrate that experimentally derived lists of direct transcriptional targets

  • can be used to reveal new biological insight on transcriptional regulation.

We show this by analyzing common direct transcriptional targets of

  • MYC, NOTCH1 and RELA
  • that act in interconnected molecular pathways.

Detection of such genes is important as it could reveal novel targets of cancer therapy.

Figure 1 Number of genes in common between MYC transcriptional targets derived from ten different pathway databases. Cells are colored according to their values from white (low values) to red (high values). (not shown)

statistical methodology for comparison

statistical methodology for comparison

Figure 2 Illustration of statistical methodology for comparison between a gold-standard and a pathway database

Since we are seeking to compare gene sets from different studies/databases, it is essential to transform genes to standard identifiers. That is why we transformed all
gene sets to the HUGO Gene Nomenclature Committee approved gene symbols and names. In order to assess statistical significance of the overlap between the resulting gene sets, we used the hypergeometric test at 5% a-level with false discovery rate correction for multiple comparisons by the method of Benjamini and Yekutieli. The alternative hypothesis of this test is that two sets of genes (set A from pathway
database and set B from experiments) have greater number of genes in common than two randomly selected gene sets with the same number of genes as in sets A and B. For example, consider that for some transcription factor there are 300 direct targets in the pathway database #1 and 700 in the experimentally derived list (gold-standard), and their intersection is 16 genes (Figure 2a). If we select on random from a total of
20,000 genes two sets with 300 and 700 genes each, their overlap would be greater or equal to 16 genes in 6.34% times. Thus, this overlap will not be statistically significant at 5% a-level (p = 0.0634). On the other hand, consider that for the pathway database #2, there are 30 direct targets of that transcription factor, and their intersection with the 700-gene gold-standard is only 6 genes. Even though the size of this intersection is rather small, it is unlikely to randomly select 30 genes (out of 20,000) with an overlap greater or equal to 6 genes with a 700-gene gold-standard (p = 0.0005, see Figure 2a). This overlap is statistically significant at 5% a-level.

We also calculate an enrichment fold change ratio (EFC) for every intersection between a gold-standard and a pathway database. For a given pair of a gold-standard and a pathway database, EFC is equal to the observed number of genes in their intersection, divided by the expected size of intersection under the null hypothesis (plus machine epsilon, to avoid division by zero). Notice however that larger values of EFC may correspond to databases that are highly incomplete and contain only a few relations. For example, consider that for some transcription factor there are 300 direct targets in the pathway database #1 and 50 in the experimentally derived list (gold-standard), and their intersection is 30 genes (Figure 2b). If we select on random from a total of 20,000 genes two sets with 300 and 50 genes each, their expected overlap under the null hypothesis will be equal to 0.75. Thus, the EFC ratio will be equal to 40 (= 30/0.75). On the other hand, consider that for the pathway database #2, there are 2 direct
targets of that transcription factor, and their intersection with the 50-gene gold-standard is only 1 gene. Even though the expected overlap under the null hypothesis will be equal to 0.005 and EFC equal to 200 (5 times bigger than for the database #1), the size of this intersection with the gold-standard is 30 times less than for database #1 (Figure 2b).

Figure 3 Comparison between different pathway databases and experimentally derived gold-standards for all considered transcription factors. Value in a given cell is a number of overlapping genes between a gold-standard and a pathway-derived gene set. Cells
are colored according to their values from white (low values) to red (high values). Underlined values in red represent statistically significant intersections. (not shown)

Figure 4 Summary of the pathway databases assessment. Green cells represent statistically significant intersections between experimentally derived gold-standards and transcriptional regulatory pathways. White cells denote results that are not statistically significant. Numbers are the enrichment fold change ratios (EFC) calculated for each intersection. (not shown)

At the core of this study was creation of gold-standards of transcriptional regulation in humans that can be compared with target genes reported in transcriptional regulatory pathways. We focused on seven well known transcription factors and obtained gold-standards

  • by integrating genome-wide transcription factor-DNA binding data (from ChIP-chip, ChIP-seq, or ChIP-PET platforms)
  • with functional gene expression microarray and RNA-seq data.

The latter data allows to survey changes in the transcriptomes on a genome-wide scale

  • after the inhibition or over-expression of the transcription factor in question.

However, change in the expression of a particular gene could be caused either by the direct effect of the removal or introduction of a given transcription factor, as well as by an indirect effect, through the change in expression level of some other gene(s). It is essential

  • to integrate data from these two sources to
  • obtain an accurate list of gene targets that are directly regulated by a transcription factor.

It is worth noting that tested pathway databases typically do not give distinction between cell-lines, experimental conditions, and other details relevant to experimental systems in which data were obtained. These databases in a sense propose a ‘universal’ list of transcriptional targets. However, it is known that

  • transcriptional regulation in a cell is dynamic and works differently for different systems and stimuli.

This accentuates the major limitation of pathway databases and emphasizes

  • importance of deriving a specific list of transcriptional targets for the current experimental system.

In this study we followed the latter approach by developing gold-standards for specific cell characterized biological systems and experimental conditions.

The approach used here  for building gold-standards of direct mechanistic knowledge has several limitations. (see article).  Nevertheless, our results suggest that multiple transcription factors can co-operate and control both physiological differentiation and malignant transformation, as demonstrated utilizing combinatorial gene-profiling for

  • NOTCH1, MYC and RELA targets.

These studies might lead us to multi-pathway gene expression “signatures”

  • essential for the prediction of genes that could be targeted in cancer treatments.

In agreement with this hypothesis, several of the genes identified in our analysis have been suggested to be putative therapeutic targets in leukemia, with either preclinical or clinical trials underway (CDK4, CDK6, GSK3b, MYC, LCK, NFkB2, BCL2L1, NOTCH1).

Single-molecule tracking in live cells reveals distinct target-search strategies of transcription factors in the nucleus

I Izeddin†, V Récamier†‡, L Bosanac, II Cissé, L Boudarene, et al.
1Functional Imaging of Transcription, Institut de Biologie de l’Ecole Normale Supérieure (IBENS), Inserm, and CNRS UMR; 2Laboratoire Kastler Brossel, CNRS UMR, Departement de Physique et Institut de Biologie
de l’Ecole Normale Supérieure (IBENS), Paris, Fr; 3Transcription Imaging Consortium, Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, US; + more.
Biophysics and structural biology | Cell biology eLife 2014;3:e02230. http://dx.doi.org:/10.7554/eLife.02230

Transcription factors are

  • proteins that control the expression of genes in the nucleus, and
  • they do this by binding to other proteins or DNA.

First, however, these regulatory proteins need to overcome the challenge of

  • finding their targets in the nucleus, which is crowded with other proteins and DNA.

Much research to date has focused on measuring how fast proteins can diffuse and spread out throughout the nucleus. However these measurements only make sense if these proteins have access to the same space within the nucleus.

Now, Izeddin, Récamier et al. have developed a new technique to track

  • single protein molecules in the nucleus of mammalian cells.

A transcription factor called c-Myc and another protein called P-TEFb

  • were tracked and while they diffused at similar rates,
  • they ‘explored’ the space inside the nucleus in very different ways.

Izeddin, Récamier et al. found that c-Myc explores the nucleus in a so-called ‘non-compact’ manner: this means that it

  • can move almost everywhere inside the nucleus, and has an equal chance
  • of reaching any target regardless of its position in this space.

P-TEFb, on the other hand, searches

  • the nucleus in a ‘compact’ way.

This means that it is constrained to follow a specific path

  • through the nucleus and is therefore guided to its potential targets.

Izeddin, Récamier et al. explain that

  • the different ‘search strategies’ used by these two proteins
  • influence how long it takes them to find their targets and
  • how far they can travel in a given time.

These findings, together with information about

  • where and when different proteins interact in the nucleus,

will be essential to understand how the organization of the genome within the nucleus

  • can control the expression of genes.

The next challenge will now be to

  • uncover what determines a
  • protein’s search strategy in the nucleus, as well as
  • the potential ways that this strategy might be regulated.

Mueller et al., 2010; Normanno et al., 2012). These transient interactions are essential to ensure a fine regulation of binding site occupancy—by competition or by altering the TF concentration—but must also be persistent enough to enable the assembly of multicomponent complexes (Dundr, 2002; Darzacq and Singer, 2008; Gorski et al., 2008; Cisse et al., 2013).
In parallel to the experimental evidence of the fast diffusive motion of nuclear factors, our understanding of the intranuclear space has evolved from a homogeneous environment to an organelle where spatial arrangement among genes and regulatory sequences play an important role in transcriptional control (Heard and Bickmore, 2007). The nucleus of eukaryotes displays a hierarchy of organized structures (Gibcus and Dekker, 2013) and is often referred to as a
crowded environment.
How crowding influences transport properties of macromolecules and organelles in the cell is a fundamental question in quantitative molecular biology. While a restriction of the available space for diffusion can slow down transport processes, it can also channel molecules towards their targets increasing their chance to meet interacting partners. A widespread observation in quantitative cell biology is that the diffusion of molecules is anomalous, often attributed to crowding in the nucleoplasm, cytoplasm, or in the membranes of the cell (Höfling and Franosch, 2013). An open debate remains on how to determine whether diffusion is anomalous or normal (Malchus and Weiss, 2009; Saxton, 2012), and the mechanisms behind anomalous diffusion (Saxton, 2007). The answer to these questions bears important consequences for the understanding of the biochemical reactions of the cell.
The problem of diffusing molecules in non-homogenous media has been investigated in different fields. Following the seminal work of de Gennes (1982a), (1982b) in polymer physics, the study of diffusivity of particles and their reactivity has been generalized to random or disordered media (Kopelman, 1986; Lindenberg et al., 1991). These works have set a framework to interpret the mobility of macromolecular complexes in the cell, and recently in terms of kinetics of biochemical reactions (Condamin et al., 2007). Experimental evidence has also been found, showing the influence
of the glass-like properties of the bacterial cytoplasm in the molecular dynamics of intracellular processes (Parry et al., 2014). These studies demonstrate that the geometry of the medium in which diffusion takes place has important repercussions for the search kinetics of molecules. The notion of compact and non-compact exploration was introduced by de Gennes (1982a) in the context of dense polymers and describes two fundamental types of diffusive behavior. While a non-compact explorer leaves a significant number of available sites unvisited, a compact explorer performs a redundant
exploration of the space. In chemistry, the influence of compactness is well established to describe dimensional effects on reaction rates (Kopelman, 1986).
In this study, we aim to elucidate the existence of different types of mobility of TFs in the eukaryotic nucleus, as well as the principles governing nuclear exploration of factors relevant to transcriptional control. To this end, we used single-molecule (SM) imaging to address the relationship between the nuclear geometry and the search dynamics of two nuclear factors having distinct functional roles: the proto-oncogene c-Myc and the positive transcription elongation factor (P-TEFb). c-Myc is a basic helix-loop-helix DNA-binding transcription factor that binds to E-Boxes; 18,000 E-boxes are found in the genome, and c-Myc affects the transcription of numerous genes (Gallant and Steiger, 2009).
Recently, c-Myc has been demonstrated to be a general transcriptional activator upregulating transcription of nearly all genes (Lin et al., 2012; Nie et al., 2012). P-TEFb is an essential actor in the transcription regulation driven by RNA Polymerase II. P-TEFb is a cyclin-dependent kinase, comprising a CDK9 and a Cyclin T subunit. It phosphorylates the elongation control factors SPT5 and NELF to allow productive elongation of class II gene transcription (Wada et al., 1998). The carboxy-terminal domain (CTD) of the catalytic subunit RPB1 of polymerase II is also a major target of P-TEFb (Zhou et al., 2012). c-Myc and P-TEFb are therefore two good examples of transcriptional regulators binding to numerous sites in the nucleus; the latter binds to the transcription machinery itself and the former directly to DNA.

Single particle tracking (SPT) constitutes a powerful method to probe the mobility of molecules in living cells (Lord et al., 2010). In the nucleus, SPT has been first employed to investigate the dynamics of mRNAs (Fusco et al., 2003; Shav-Tal et al., 2004) or for rheological measurements of the nucleoplasm using inert probes (Bancaud et al., 2009). Recently, the tracking of single nuclear factors has been facilitated by the advent of efficient in situ tagging methods such as Halo
tags (Mazza et al., 2012). An alternative approach takes advantage of photoconvertible tags (Lippincott-Schwartz and Patterson, 2009) and photoactivated localization microscopy (PALM) (Betzig et al., 2006; Hess et al., 2006). Single particle tracking PALM (sptPALM) was first used to achieve high-density diffusion maps of membrane proteins (Manley et al., 2008). However, spt-PALM experiments have typically been limited to proteins with slow mobility (Manley et al., 2008) or those that undergo restricted motions (Frost et al., 2010; English et al., 2011).

Recently, by inclusion of light-sheet illumination, it has been used to determine the binding characteristics of TFs to DNA (Gebhardt et al., 2013). In this study, we developed a new sptPALM procedure adapted for the recording of individual proteins rapidly diffusing in the nucleus of mammalian cells. We used the photoconvertible fluorophore Dendra2 (Gurskaya et al., 2006) and took advantage of tilted illumination (Tokunaga et al., 2008). A careful control of the photoconversion rate minimized the background signal due to out-of-focus activated molecules, and we could thus follow the motion of individual proteins freely diffusing within the nuclear volume. With this sptPALM technique, we recorded large data sets (on the order of 104 single translocations in a single imaging session), which were essential for a proper statistical analysis of the search dynamics.
We applied our technique to several nuclear proteins and found that diffusing factors do not sense a unique nucleoplasmic architecture: c-Myc and P-TEFb adopt different nuclear space-exploration strategies, which drastically change the way they reach their specific targets. The differences observed between the two factors were not due to their diffusive kinetic parameters but to the geometry of their exploration path. c-Myc and our control protein, ‘free’ Dendra2, showed free diffusion in a three-dimensional nuclear space. In contrast, P-TEFb explored the nuclear volume by sampling a space of reduced dimensionality, displaying characteristics of exploration constrained in fractal structures.
The role of the space-sampling mode in the search strategy has long been discussed from a theoretical point of view (de Gennes, 1982a; Kopelman, 1986; Lindenberg et al., 1991). Our experimental results support the notion that it could indeed be a key parameter for diffusion-limited chemical reactions in the closed environment of the nucleus (Bénichou et al., 2010). We discuss the implications of our observations in terms of gene expression control, and its relation to the spatial organization of genes within the nucleus.

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Introduction to Translational Medicine (TM) – Part 1: Translational Medicine


Introduction to Translational Medicine (TM) – Part 1: Translational Medicine

Author and Curator: Larry H Bernstein, MD, FCAP

and

Curator: Aviva Lev-Ari, PhD, RN 

 

This document in the Series A: Cardiovascular Diseases e-Series Volume 4: Translational and Regenerative Medicine,  is a measure of the postgenomic and proteomic advances in the laboratory to the practice of clinical medicine.  The Chapters are preceded by several videos by prominent figures in the emergence of this transformative change.  When I was a medical student, a large body of the current language and technology that has extended the practice of medicine did not exist, but a new foundation, predicated on the principles of modern medical education set forth by Abraham Flexner, was sprouting.  The highlights of this evolution were:

  • Requirement for premedical education in biology, organic chemistry, physics, and genetics.
  • Medical education included two years of basic science education in anatomy, physiology, pharmacology, and pathology prior to introduction into the clinical course sequence of the last two years.
  • Post medical graduate education was an internship year followed by residency in pediatrics, OBGyn, internal medicine, general surgery, psychiatry, neurology, neurosurgery, pathology, radiology, and anesthesiology, emergency medicine.
  • Academic teaching centers were developing subspecialty centers in ophthalmology, ENT and head and neck surgery, cardiology and cardiothoracic surgery, and hematology, hematology/oncology, and neurology.
  • The expansion of postgraduate medical programs included significant postgraduate funding for programs by the National Institutes of Health, and the NIH had faculty development support in a system of peer-reviewed research grant programs in medical and allied sciences.

The period after the late 1980s saw a rapid expansion of research in genomics and drug development to treat emerging threats of infectious diseases as US had a large worldwide involvement after the end of the Vietnam War, and drug resistance was increasingly encountered (malaria, tick borne diseases, salmonellosis, pseudomonas aeruginosa, staphylococcus aureus, etc.).

Moreover, the post-millenium found a large, dwindling population of veterans who had served in WWII and Vietnam, and cardiovascular, musculoskeletal,  dementias, and cancer were now more common.  The Human Genome Project was undertaken to realign the existing knowledge of gene structure and genetic regulation with the needs for drug development, which was languishing in development failures due to unexpected toxicities.

A substantial disconnect existed between diagnostics and pharmaceutical development, which had been over-reliant on modification of known organic structures to increase potency and reduce toxicity.  This was about to change with changes in medical curricula, changes in residency programs and physicians cross-training in disciplines, and the emergence of bio-pharma, based on the emerging knowledge of the cell function, and at the same time, the medical profession was developing an evidence-base for therapeutics, and more pressure was placed on informed decision-making.

The great improvement in proteomics came from GCLC/MS-MS and is described in the video interview with Dr. Gyorgy Marko-Varga, Sweden, in video 1 of 3 (Advancing Translational Medicine).  This is a discussion that is focused on functional proteomics role in future diagnostics and therapy, involving a greater degree of accuracy in mass spectrometry (MS) than can be obtained by antibody-ligand binding, and is illustrated below, the last emphasizing the importance of information technology and predictive analytics

Thermo ScientificImmunoassays and LC–MS/MS have emerged as the two main approaches for quantifying peptides and proteins in biological samples. ELISA kits are available for quantification, but inherently lack the discriminative power to resolve isoforms and PTMs.

To address this issue we have developed and applied a mass spectrometry immunoassay–selected reaction monitoring (Thermo Scientific™ MSIA™ SRM technology) research method to quantify PCSK9 (and PTMs), a key player in the regulation of circulating low density lipoprotein cholesterol (LDL-C).

A Day in the (Future) Life of a Predictive Analytics Scientist

 

By Lars Rinnan, CEO, NextBridge   April 22, 2014

A look into a normal day in the near future, where predictive analytics is everywhere, incorporated in everything from household appliances to wearable computing devices.

During the test drive (of an automobile), the extreme acceleration makes your heart beat so fast that your personal health data sensor triggers an alarm. The health data sensor is integrated into the strap of your wrist watch. This data is transferred to your health insurance company, so you say a prayer that their data scientists are clever enough to exclude these abnormal values from your otherwise impressive health data. Based on such data, your health insurance company’s consulting unit regularly gives you advice about diet, exercise, and sleep. You have followed their advice in the past, and your performance has increased, which automatically reduced your insurance premiums. Win-win, you think to yourself, as you park the car, and decide to buy it.

In the clinical presentation at Harlan Krumholtz’ Yale Symposium, Prof. Robert Califf, Director of the Duke University Translational medicine Clinical Research Institute, defines translational medicine as effective translation of science to clinical medicine in two segments:

  1. Adherence to current standards
  2. Improving the enterprise by translating knowledge

He says that discrepancies between outcomes and medical science will bridge a gap in translation by traversing two parallel systems.

  1. Physician-health organization
  2. Personalized medicine

He emphasizes that the new basis for physician standards will be legitimized in the following:

  1. Comparative effectiveness (Krumholtz)
  2. Accountability

Some of these points are repeated below:

WATCH VIDEOS ON YOUTUBE

https://www.youtube.com/watch?v=JFdJRh9ZPps#t=678  Harlan Krumholtz

https://www.youtube.com/watch?v=JFdJRh9ZPps#t=678  complexity

https://www.youtube.com/watch?v=JFdJRh9ZPps#t=678  integration map

https://www.youtube.com/watch?v=JFdJRh9ZPps#t=678  progression

https://www.youtube.com/watch?v=JFdJRh9ZPps#t=678  informatics

An interesting sidebar to the scientific medical advances is the huge shift in pressure on an insurance system that has coexisted with a public system in Medicare and Medicaid, initially introduced by the health insurance industry for worker benefits (Kaiser, IBM, Rockefeller), and we are undertaking a formidable change in the ACA.

The current reality is that actuarially, the twin system that has existed was unsustainable in the long term because it is necessary to have a very large pool of the population to spread the costs, and in addition, the cost of pharmaceutical development has driven consolidation in the industry, and has relied on the successes from public and privately funded research.

https://www.youtube.com/watch?v=X6J_7PvWoMw#t=57  Corbett Report Nov 2013

(1979 ER Brown)  UCPress  Rockefeller Medicine Men

https://www.youtube.com/watch?v=X6J_7PvWoMw#t=57   Liz Fowler VP of Wellpoint (designed ACA)

I shall digress for a moment and insert a video history of DNA, that hits the high points very well, and is quite explanatory of the genomic revolution in medical science, biology, infectious disease and microbial antibiotic resistance, virology, stem cell biology, and the undeniability of evolution.

DNA History

https://www.youtube.com/watch?v=UUDzN4w8mKI&list=UUoHRSQ0ahscV14hlmPabkVQ

As I have noted above, genomics is necessary, but not sufficient.  The story began as replication of the genetic code, which accounted for variation, but the accounting for regulation of the cell and for metabolic processes was, and remains in the domain of an essential library of proteins. Moreover, the functional activity of proteins, at least but not only if they are catalytic, shows structural variants that is characterized by small differences in some amino acids that allow for separation by net charge and have an effect on protein-protein and other interactions.

Protein chemistry is so different from DNA chemistry that it is quite safe to consider that DNA in the nucleotide sequence does no more than establish the order of amino acids in proteins. On the other hand, proteins that we know so little about their function and regulation, do everything that matters including to set what and when to read something in the DNA.

Jose Eduardo de Salles Roselino

Chapters 2, 3, and 4 sequentially examine:

  • The causes and etiologies of cardiovascular diseases
  • The diagnosis, prognosis and risks determined by – biomarkers in serum, circulating cells, and solid tissue by contrast radiography
  • Treatment of cardiovascular diseases by translation of science from bench to bedside, including interventional cardiology and surgical repair

These are systematically examined within a framework of:

  • Genomics
  • Proteomics
  • Cardiac and Vascular Signaling
  • Platelet and Endothelial Signaling
  • Cell-protein interactions
  • Protein-protein interactions
  • Post-Translational Modifications (PTMs)
  • Epigenetics
  • Noncoding RNAs and regulatory considerations
  • Metabolomics (the metabolome)
  • Mitochondria and oxidative stress

 

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Reporter: Ritu Saxena, Ph.D.

Diabetes currently affects more than 336 million people worldwide, with healthcare costs by diabetes and its complications of up to $612 million per day in the US alone.  The islets of Langerhans, miniature endocrine organs within the pancreas, are essential regulators of blood glucose homeostasis and play a key role in the pathogenesis of diabetes.  Islets of Langerhans are composed of several types of endocrine cells.  The α- and β-cells are the most abundant and also the most important in that they secrete hormones (glucagon and insulin, respectively) crucial for glucose homeostasis (Bosco D, et al, Diabetes, May 2010;59(5):1202-10).

Diabetes is a ‘bihormonal’ disease, involving both insulin deficiency and excess glucagon.  For decades, insulin deficiency was considered to be the sole reason for diabetes; however, recent studies emphasize excess glucagon as an important part of diabetes etiology.  Thus, insulin-secreting β cells and glucagon-secreting α cells maintain physiological blood glucose levels, and their malfunction drives diabetes development.  Increasing the number of insulin-producing β cells while decreasing the number of glucagon-producing α cells, either in vitro in donor pancreatic islets before transplantation into type 1 diabetics or in vivo in type 2 diabetics, is a promising therapeutic avenue.  A huge leap has been taken in this direction by the researchers at the University of Pennsylvania (Philadelphia, PA) in collaboration with Oregon Health and Science University (Portland, OR), USA by demonstrating that α to β cell reprogramming could be promoted by manipulating the histone methylation signature of human pancreatic islets.  In fact, the treatment of cultured pancreatic islets with a histone methyltransferase inhibitor leads to colocalization of both glucagon and insulin and glucagon and insulin promoter factor 1 (PDX1) in human islets and colocalization of both glucagon and insulin in mouse islets.  The research findings were published in the Journal of Clinical Investigation.

Study design: First step was to study and analyze the epigenetic and transcriptional landscape of human pancreatic human pancreatic α, β, and exocrine cells using ChIP and RNA sequencing.  Study design for determination of the transcriptome and differential histone marks included the dispersion and FACS to of human islets to obtain cell populations highly enriched for α, β, and exocrine (duct and acinar) cells.  Then, chromatin was prepared for ChIP analysis using antibodies for histone modifications, H3K4me3 (represents gene activation) and H3K27me3 (represents gene repression).  RNA-Sequencing analysis was then performed to determine mRNA and lncRNA.  Sample purity was confirmed using qRT-PCR of insulin and glucagon expression levels of the individual α and β cell population revealing high sample purity.

Results:

  • Long noncoding transcripts: Long noncoding RNA molecules have been implicated as important developmental regulators, cell lineage allocators, and contributors to disease development.  The authors discovered 12 cell–specific and 5 α cell–specific noncoding (lnc) transcripts, indicative of the valuable research resource represented from transcriptome data.  Recently discovered lncRNA molecules in islets are regulated during development and dysregulated in type 2 diabetic islets.
  • Monovalent histone modification landscapes shared among three cell types:  Monovalent H3K4me3-enriched regions, indicative of gene activation, were identified and compared in α, β, and exocrine cells.  Strikingly, the vast majority of monovalently H3K4me3-marked genes were shared among the 3 pancreatic cell lineages (83%–95%), reflecting both their related function in protein secretion and common embryonic descent. Similarly, a high degree of overlap was observed in H3K27me3 modification patterns in all the three cell types (73%–83%).
  • Bivalent histone modifications (H3K4me3 and H3K27me3) were high in α cells: Bernstein colleagues observed bivalent marks to be common in undifferentiated cells, such as ES cells and pluripotent progenitor cells, and in most cases, one of the histone modification marks was lost during differentiation, accompanying lineage specification (Bernstein BE, et al, Cell, 21 Apr 2006; 125(2):315-26).  α cells exhibited many more genes bivalently marked, followed by β cells and exocrine cells.  Bivalent state was remarkably similar to that of hESC, suggesting a more plastic epigenomic state for α cells.
  • Monovalent histone modifications were high in β cells: Thousands of the genes that were in bivalent state in α cells were in a monovalent state, carrying only the activating or repressing mark.
  • Inhibition of histone methyltransferases led to partial cell-fate conversion: Adenosine dialdehye (Adox), a drug that interferes with histone methylation and decreases H3K27me3, when administered in human islet tissue, led to decrease of H3K27me3 enrichment at the 3 gene loci that are originally expressed bivalently in α cells and monovalently in β cells:  MAFA, PDX1 and ARX.  Adox resulted in the occasional cooccurrence of glucagon and insulin granules within the same islet cell, which was not observed in untreated islets.  Thus, inhibition of histone methyltransferases leads to partial endocrine cell-fate conversion.

Conclusion:  α cells have been reprogrammed into β cell fate in various mouse models.  The reason, as proposed by the authors, might be the presence of more bivalently marked genes that confers a more plastic epigenomic state of the cells that probably drives them to the β cell fate.  Therefore, using epigenomic information of different cell types in pancreatic islets and harnessing it for subsequent manipulation of their epigenetic signature could be utilized to reprogram cells and hence provide a path for diabetes therapy.

Source: Bramswig NC, et al, Epigenomic plasticity enables human pancreatic α to β cell reprogramming. J Clin Invest, 22 Feb 2013. pii: 66514.

Related reading on Pharmaceutical Intelligence:

Junk DNA codes for valuable miRNAs: non-coding DNA controls Diabetes

Therapeutic Targets for Diabetes and Related Metabolic Disorders

Reprogramming cell fate

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

2013 Genomics: The Era Beyond the Sequencing of the Human Genome: Francis Collins, Craig Venter, Eric Lander, et al.

Genome-Wide Detection of Single-Nucleotide and Copy-Number Variation of a Single Human Cell

SNAP: Predict Effect of Non-synonymous Polymorphisms: How well Genome Interpretation Tools could Translate to the Clinic

Genomic Endocrinology and its Future

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