Archive for the ‘Gene Regulation’ Category

Biomolecular Condensates: A new approach to biology originated @MIT – Drug Discovery at DewPoint Therapeutics, Cambridge, MA gets new leaders, Ameet Nathwani, MD (ex-Sanofi, ex-Novartis) as Chief Executive Officer and Arie Belldegrun, PhD (ex-Kite Therapeutics) on R&D

Curator & Reporter: Aviva Lev-Ari, PhD, RN


Hooked by the science, Arie Belldegrun joins a group of influentials who believe Dewpoint may have the key to the next big thing in biotech

A new approach to biology

“The real voyage of discovery consists, not in seeking new landscapes, but in having new eyes.” Marcel Proust

Starting with the study of P granules in C.elegans embryos in 2009, Tony Hyman, working with his collaborators like Frank Julicher, Cliff Brangwynne, Simon Alberti, Mike Rosen, and Rohit Pappu, began to unravel the mysteries of biomolecular condensates. These scientists realized that P granules behave like liquid droplets that form by phase separation (think of oil droplets in salad dressing) and called them condensates.

In subsequent studies, they found to their surprise that many compartments inside cells had the behavior of condensates: they are liquid-like and form by phase separation.

Inspired by the work of Tony and his colleagues, Richard Young, Phillip Sharp, and Arup Chakraborty at MIT applied these approaches to the study of gene expression, similarly shedding light on many important questions in gene control.

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Press releases and Dewpoint in the news

  • Dewpoint Therapeutics Appoints Ameet Nathwani as Chief Executive Officer


  • New York Times interviews Rick Young and Amy Gladfelter on the role of condensate “droplets” in COVID-19

    New York Times

  • Dewpoint Therapeutics raises $77 million to go after ‘undruggable’ diseases

    Boston Globe

  • Hooked by the science, Arie Belldegrun joins a group of influentials who believe Dewpoint may have the key to the next big thing in biotech

    Endpoint News

  • Dewpoint Therapeutics to put ‘pedal to the metal’ with $77M round


  • Dewpoint Therapeutics Raises $77M Series B Financing to Advance the Development of Drugs That Target Biomolecular Condensates


  • 21 biotech startups that are set to take off, according to top VCs

    Business Insider

  • Proteins — and labs — coming together to prevent Rett Syndrome

    Whitehead Institute

  • Dewpoint Therapeutics Collaborates with Merck to Evaluate Novel Approach for the Treatment of HIV


  • Discovery of how cancer drugs find their targets could lead to a new toolset for drug development

    Whitehead Institute



Other related article published in this Online Open Access Scientific Journal include: 

Economic Potential of a Drug Invention (Prof. Zelig Eshhar, Weitzman Institute, registered the patent) versus a Cancer Drug in Clinical Trials: CAR-T as a Case in Point, developed by Kite Pharma, under Arie Belldegrun, CEO, acquired by Gilead for $11.9 billion, 8/2017.


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Most significant article published in the Society of Evolution, Medicine and Public Health won Prize: polygenic scores, polygenic adaptation, and human phenotypic differences

Reporter: Aviva Lev-Ari, PhD, RN 


UPDATED on 8/30/2020

Analysis of polygenic risk score usage and performance in diverse human populations


A historical tendency to use European ancestry samples hinders medical genetics research, including the use of polygenic scores, which are individual-level metrics of genetic risk. We analyze the first decade of polygenic scoring studies (2008–2017, inclusive), and find that 67% of studies included exclusively European ancestry participants and another 19% included only East Asian ancestry participants. Only 3.8% of studies were among cohorts of African, Hispanic, or Indigenous peoples. We find that predictive performance of European ancestry-derived polygenic scores is lower in non-European ancestry samples (e.g. African ancestry samples: t = −5.97, df = 24, p = 3.7 × 10−6), and we demonstrate the effects of methodological choices in polygenic score distributions for worldwide populations. These findings highlight the need for improved treatment of linkage disequilibrium and variant frequencies when applying polygenic scoring to cohorts of non-European ancestry, and bolster the rationale for large-scale GWAS in diverse human populations.



The Voice of Prof. Marcus W. Feldman

You might be interested in the paper “interpreting polygenic scores, polygenic adaptation, and human phenotypic differences” by N. Rosenberg, M. Edge, J. Pritchard, and M. Feldman, published in Evolution, Medicine and Public Health  (2019).    Rosenberg and Pritchard are my former PhD students, both full professors at Stanford, and M.Edge is a student of Rosenberg.


On Aug 28, 2020, at 4:36 PM, Horowitz, Barbara Natterson <natterson-horowitz@fas.harvard.edu> wrote:

Dear Dr. Rosenberg,

It is my pleasure in my role as President of the International Society for Evolution, Medicine and Public Health to inform you that your 2019 EMPH article, “Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences” has won The George C. Williams Prize which is awarded each year to the first author of the most significant article published in the Society’s flagship journal, Evolution, Medicine and Public Health.  

The Prize recognizes the contributions of George C. Williams to evolutionary medicine and aims to encourage and highlight important research in this growing field. It includes $5,000 and an invitation to present at the online lecture series, Club EvMed. The Prize is made possible by donations from Doris Williams, Randolph Nesse, and other supporters of EMPH.

The winning article:


Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences

Evolution, Medicine, and Public Health, Volume 2019, Issue 1, 2019, Pages 26–34, https://doi.org/10.1093/emph/eoy036
27 December 2018

Article history



Recent analyses of polygenic scores have opened new discussions concerning the genetic basis and evolutionary significance of differences among populations in distributions of phenotypes. Here, we highlight limitations in research on polygenic scores, polygenic adaptation and population differences. We show how genetic contributions to traits, as estimated by polygenic scores, combine with environmental contributions so that differences among populations in trait distributions need not reflect corresponding differences in genetic propensity. Under a null model in which phenotypes are selectively neutral, genetic propensity differences contributing to phenotypic differences among populations are predicted to be small. We illustrate this null hypothesis in relation to health disparities between African Americans and European Americans, discussing alternative hypotheses with selective and environmental effects. Close attention to the limitations of research on polygenic phenomena is important for the interpretation of their relationship to human population differences.


We are currently witnessing a surge in public interest in the intersection of evolutionary genetics with such topics as cognitive phenotypes, disease, race and heritability of human traits [1–7]. This attention emerges partly from recent advances in genomics, including the introduction of polygenic scores—the aggregation of estimated effects of genome-wide variants to predict the contribution of a person’s genome to a phenotypic trait [8–10]—and a new focus on polygenic adaptations, namely adaptations that have occurred by natural selection on traits influenced by many genes [11–13].

Theories involving natural selection have long been applied in the scientific literature to explain mean phenotypic differences among human populations [14–16]. Although new tools for statistical analysis of polygenic variation and polygenic adaptation provide opportunities for studying human evolution and the genetic basis of traits, they also generate potential for misinterpretation. In the past, public attention to research on human variation and its possible evolutionary basis has often been accompanied by claims that are not justified by the research findings [17]. Recognizing pitfalls in the interpretation of new research on human variation is therefore important for advancing discussions on associated sensitive and controversial topics.

The contribution of polygenic score distributions to phenotype distributions. Two populations are considered, populations 1 (red) and 2 (blue). Each population has a distribution of genetic propensities, which are treated as accurately estimated in the form of polygenic scores (left). The genetic propensity distribution and an environment distribution sum to produce a phenotype distribution (right). All plots have the same numerical scale. (A) Environmental differences amplify an underlying difference in genetic propensities. (B) Populations differ in their phenotypes despite having no differences in genetic propensity distributions. (C) Environmental differences obscure a difference in genetic propensities opposite in direction to the difference in phenotype means. (D) Similarity in phenotype distributions is achieved despite a difference in genetic propensity distributions by an intervention that reduces the environmental contribution for individuals with polygenic scores above a threshold. (E) Within populations, heritability is high, so that genetic variation explains the majority of phenotypic variation; however, the difference between populations is explained by an environmental difference. Panels (A–C and E) present independent normal distributions for genotype and environment that sum to produce normal distributions for phenotype. In (D), (genotype, environment) pairs are simulated from independent normal distributions and a negative constant—reflecting the effect of a medication or other intervention—is added to environmental contributions associated with simulated genotypic values that exceed a threshold


These limitations illustrate that much of the complexity embedded in use of polygenic scores—the effects of the environment on phenotype and its relationship to genotype, the proportion of variance explained, and the peculiarities of the underlying GWAS data that have been used to estimate effect sizes—is obscured by the apparent simplicity of the single values computed for each individual for each phenotype. Consequently, in using polygenic scores to describe genomic contributions to traits, particularly traits for which the total contribution of genetic variation to trait variation, as measured by heritability, is low—but even if it is high (Fig. 1E)—a difference in polygenic scores between populations provides little information about potential genetic bases for trait differences between those populations.

Unlike heritability, which ranges from 0 to 1 and therefore makes it obvious that the remaining contribution to phenotypic variation is summarized by its difference from 1, the limited explanatory role of genetics is not embedded in the nature of the polygenic scores themselves. Although polygenic scores encode knowledge about specific genetic correlates of trait variation, they do not change the conceptual framework for genetic and environmental contribution to population differences. Attributions of phenotypic differences among populations to genetic differences should therefore be treated with as much caution as similar genetic attributions from heritability in the pre-genomic era.


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Reporter and Curator: Dr. Sudipta Saha, Ph.D.


In-vitro fertilisation (IVF) is now regarded as a huge clinical success which has benefitted an estimated 16 million parents, at the time the development not only sparked moral outrage but led to political and legislative constraints. Patients undergoing IVF may be presented with numerous assisted reproductive treatments purportedly increasing the chances of pregnancy. Such commercialised “IVF add-ons” often come at high costs without clinical evidence of validity. Additionally, long-term studies of children born through IVF have historically been scarce and inconsistent in their data collection. This has meant that potential genetic predispositions, such as increased body fat composition and blood pressure, as well as congenital abnormalities long associated with IVF births, lack proof of causality.


With Preimplantation genetic testing mutated embryos are automatically discarded, whereas CRISPR could correct mutations to increase the number of viable embryos for implantation. Moreover, in instances where all embryos in a given cycle are destined to develop with severe or lethal mutations, CRISPR could bring success for otherwise doomed IVF treatments. Genetic screening programs offered to couples in hot-spot areas of carrier frequency of monogenic disorders have had huge success in alleviating regional disease burdens. Carried out since the 1970s these programs have altered the course of natural evolution, but few would dispute their benefits in preventing heritable disease transmission.


Mutations are as inevitable as death and taxes. Whilst age is considered one of the largest factors in de-novo mutation generation, it appears that these are inherited primarily from the paternal line. Thus, the paternal age of conception predominantly determines the mutation frequency inherited by children. Whereas advanced maternal age is not associated with mutagenic allele frequency but chromosomal abnormalities. The risk of aneuploidy rises steadily in mothers over the age of 26. Although embryos are screened for aneuploidy prior to implantation, with so many other factors simultaneously being screened the probability of having enough embryos remaining to allow for 50% rate of blastocyte development in-vitro are often fairly low.


Despite IVF being used routinely for over 40 years now, it’s not abundantly clear if, or how often, IVF may introduce genomic alternations or off-target affects in embryos. Likewise, scientists and clinicians are often unable to scrutinise changes produced through natural cellular processes including recombination and aging. So, it may be OK to do controlled experiments on using CRISPR to try and prevent multi-generational suffering. But, there has to be a long term investigation on the side effects of germline genome editing. Science has advanced a lot but still there are lot of things that are yet to be described or discovered by science. Trying to reduce human suffering should not give rise to new bigger sufferings and care must be taken not to create a Frankenstein.






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Evolution of the Human Cell Genome Biology Field of Gene Expression, Gene Regulation, Gene Regulatory Networks and Application of Machine Learning Algorithms in Large-Scale Biological Data Analysis

Curator & Reporter: Aviva Lev-Ari, PhD, RN



The Scientific Frontier is presented in Deciphering eukaryotic gene-regulatory logic with 100 million random promoters

Boer, C.G., Vaishnav, E.D., Sadeh, R. et al. Deciphering eukaryotic gene-regulatory logic with 100 million random promotersNat Biotechnol (2019) doi:10.1038/s41587-019-0315-8


How transcription factors (TFs) interpret cis-regulatory DNA sequence to control gene expression remains unclear, largely because past studies using native and engineered sequences had insufficient scale. Here, we measure the expression output of >100 million synthetic yeast promoter sequences that are fully random. These sequences yield diverse, reproducible expression levels that can be explained by their chance inclusion of functional TF binding sites. We use machine learning to build interpretable models of transcriptional regulation that predict ~94% of the expression driven from independent test promoters and ~89% of the expression driven from native yeast promoter fragments. These models allow us to characterize each TF’s specificity, activity and interactions with chromatin. TF activity depends on binding-site strand, position, DNA helical face and chromatin context. Notably, expression level is influenced by weak regulatory interactions, which confound designed-sequence studies. Our analyses show that massive-throughput assays of fully random DNA can provide the big data necessary to develop complex, predictive models of gene regulation.

The Evolution of the Human Cell Genome Biology Field of Gene Expression, Gene Regulation, Gene Regulatory Networks and Application of Machine Learning Algorithms in Large-Scale Biological Data Analysis is presented in the following Table


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8 Gertz, J., Siggia, E. D. & Cohen, B. A. Analysis of combinatorial cis-regulation in synthetic and genomic promoters. Nature 457, 215–218 (2009).
16 Wunderlich, Z. & Mirny, L. A. Different gene regulation strategies revealed by analysis of binding motifs. Trends Genet. 25, 434–440 (2009).
27 Hesselberth, J. R. et al. Global mapping of protein–DNA interactions in vivo by digital genomic footprinting. Nat. Methods 6, 283–289 (2009).
29 Hartley, P. D. & Madhani, H. D. Mechanisms that specify promoter nucleosome location and identity. Cell 137, 445–458 (2009).
51 Gibson, D. G. et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6, 343–345 (2009).
58 Segal, E. & Widom, J. From DNA sequence to transcriptional behaviour: a quantitative approach. Nat. Rev. Genet. 10, 443–456 (2009).
2 Yuan, Y., Guo, L., Shen, L. & Liu, J. S. Predicting gene expression from sequence: a reexamination. PLoS Comput. Biol. 3, e243 (2007).
46 Hibbs, M. A. et al. Exploring the functional landscape of gene expression: directed search of large microarray compendia. Bioinformatics 23, 2692–2699 (2007).
25 Liu, X., Lee, C. K., Granek, J. A., Clarke, N. D. & Lieb, J. D. Whole-genome comparison of Leu3 binding in vitro and in vivo reveals the importance of nucleosome occupancy in target site selection. Genome Res. 16, 1517–1528 (2006).
34 Roberts, G. G. & Hudson, A. P. Transcriptome profiling of Saccharomyces cerevisiae during a transition from fermentative to glycerol-based respiratory growth reveals extensive metabolic and structural remodeling. Mol. Genet. Genomics 276, 170–186 (2006).
48 Tanay, A. Extensive low-affinity transcriptional interactions in the yeast genome. Gen. Res. 16, 962–972 (2006).
53 Tong, A. H. & Boone, C. Synthetic genetic array analysis in Saccharomyces cerevisiae. Methods Mol. Biol. 313, 171–192 (2006).
57 Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).
62 Chua, G. et al. Identifying transcription factor functions and targets by phenotypic activation. Proc. Natl Acad. Sci. USA 103, 12045–12050 (2006).
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28 Bernstein, B. E., Liu, C. L., Humphrey, E. L., Perlstein, E. O. & Schreiber, S. L. Global nucleosome occupancy in yeast. Genome Biol. 5, R62 (2004).
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45 Harbison, C. T. et al. Transcriptional regulatory code of a eukaryotic genome. Nature 431, 99–104 (2004).
60 Kent, N. A., Eibert, S. M. & Mellor, J. Cbf1p is required for chromatin remodeling at promoter-proximal CACGTG motifs in yeast. J. Biol. Chem. 279, 27116–27123 (2004).
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24 Conlon, E. M., Liu, X. S., Lieb, J. D. & Liu, J. S. Integrating regulatory motif discovery and genome-wide expression analysis. Proc. Natl Acad. Sci. USA 100, 3339–3344 (2003).
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To access each reference as a live link, go to the number in the first column in the Table and look it up in the List of References in the Link, below


Author information

C.G.D. and A.R. drafted the manuscript, with all authors contributing. C.G.D. analyzed the data. C.G.D., E.D.V., E.L.A. and R.S. performed the experiments. A.R. and N.F. supervised the research.

Correspondence to Carl G. de Boer or Aviv Regev.

Ethics declarations

Competing interests

A.R. is an SAB member of Thermo Fisher Scientific, Neogene Therapeutics, Asimov, and Syros Pharmaceuticals, an equity holder of Immunitas, and a founder of and equity holder in Celsius Therapeutics. All other authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Cite this article

Boer, C.G., Vaishnav, E.D., Sadeh, R. et al. Deciphering eukaryotic gene-regulatory logic with 100 million random promoters. Nat Biotechnol (2019) doi:10.1038/s41587-019-0315-8

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NSPR1 and DEC2 genes: Survival on 4.5 hours of Sleep per night: A mutation in the β1-adrenergic receptor gene in humans who require fewer hours of sleep than most, ADRB1 + neurons are active during rapid eye movement (REM) sleep and wakefulness


Reporter: Aviva Lev-Ari, PhD, RN



Mutant neuropeptide S receptor reduces sleep duration with preserved memory consolidation

 See all authors and affiliations

Science Translational Medicine  16 Oct 2019:
Vol. 11, Issue 514, eaax2014
DOI: 10.1126/scitranslmed.aax2014


Sleep is a crucial physiological process for our survival and cognitive performance, yet the factors controlling human sleep regulation remain poorly understood. Here, we identified a missense mutation in a G protein–coupled neuropeptide S receptor 1 (NPSR1) that is associated with a natural short sleep phenotype in humans. Mice carrying the homologous mutation exhibited less sleep time despite increased sleep pressure. These animals were also resistant to contextual memory deficits associated with sleep deprivation. In vivo, the mutant receptors showed increased sensitivity to neuropeptide S exogenous activation. These results suggest that the NPS/NPSR1 pathway might play a critical role in regulating human sleep duration and in the link between sleep homeostasis and memory consolidation.

It is possible that drugs could be developed to target either the NSPR1 or DEC2 genes, as a treatment for insomnia or other sleep disorders. However, further understanding of exactly how these genes function would be required before this stage. Both are involved in brain function, so targeting them could lead to negative neural side effects.



Volume 103, Issue 6, 25 September 2019, Pages 1044-1055.e7

A Rare Mutation of β1-Adrenergic Receptor Affects Sleep/Wake Behaviors


  • A mutation in ADRB1 leads to natural short sleep trait in humans
  • Mice engineered with same mutation have similar short sleep behavior as humans
  • Activity of dorsal pons ADRB1 + neurons associates with REM sleep and wakefulness
  • Mutation increases the population activity of dorsal pons ADRB1 + neurons


Sleep is crucial for our survival, and many diseases are linked to long-term poor sleep quality. Before we can use sleep to enhance our health and performance and alleviate diseases associated with poor sleep, a greater understanding of sleep regulation is necessary. We have identified a mutation in the β 1-adrenergic receptor gene in humans who require fewer hours of sleep than most. In vitro, this mutation leads to decreased protein stability and dampened signaling in response to agonist treatment. In vivo, the mice carrying the same mutation demonstrated short sleep behavior. We found that this receptor is highly expressed in the dorsal pons and that these ADRB1 + neurons are active during rapid eye movement (REM) sleep and wakefulness. Activating these neurons can lead to wakefulness, and the activity of these neurons is affected by the mutation. These results highlight the important role of β 1-adrenergic receptors in sleep/wake regulation.


Additional SOURCES

Second Gene Mutation that Lets People Survive on Less Sleep


Other related articles on Circadian Rhythm and Sleep published in this Open Access Online Scientific Journal include the following:


2017 Nobel Prize in Physiology or Medicine jointly to Jeffrey C. Hall (ex-Brandeis, University of Maine), Michael Rosbash (Brandeis University) and Michael W. Young (Rockefeller University in New York) for their discoveries of molecular mechanisms controlling the circadian rhythm

Curator: Aviva Lev-Ari, PhD, RN



Patient-Reported Outcomes Study, Presented at SLEEP 2018, Provides Confirmatory Real-World Evidence of the Previously Presented 7-hour Action of REMfresh®, the First Continuous Release and Absorption Melatonin™

Reporter: Gail S. Thornton, PhD(c)



Clinically Studied, Continuous Release and Absorption Melatonin, REMfresh, Designed to Give Patients Up to 7 Hours of Sleep Support

Reporter: Gail S. Thornton, M.A.



2017 award recipients including Thomas S. Kilduff, PhD, Director, Center for Neuroscience at SRI International in Menlo Park, California

Reporter: Aviva Lev-Ari, PhD, RN



Sleep and Memory

Curator: Larry H. Bernstein, MD, FCAP



Sleep Science

Curator: Larry H. Bernstein, MD, FCAP



Genetic Link to Sleep and Mood Disorders

Curator: Larry H. Bernstein, MD, FCAP



Sleep Apnea Insular Glutamate and GABA Levels

Curator: Larry H. Bernstein, MD, FCAP



Fat, Sleep and the Gut

Curator: Larry H. Bernstein, MD, FCAP



23andMe Genome-Wide Association Study on Human propensity to Get up early or Sleep in the Morning

Reporter: Aviva Lev-Ari, PhD, RN



Sleep Quality, Amyloid and Cognitive Decline

Curator: Larry H. Bernstein, MD, FCAP



Study Shows Learning Is Best Enhanced During Sleep – Jewish Business News

Reporter: Aviva Lev-Ari, PhD, RN



Beta-Blockers Cause Lack Of Restful Sleep – Life Extension

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Topical Antispasmodics conducive for Uninterrupted Sleep – A Potential Cardiovascular Chrono-therapeutics

Reporter: Aviva Lev-Ari, PhD, RN



Prolonged Wakefulness: Lack of Sufficient Duration of Sleep as a Risk Factor for Cardiovascular Diseases – Indications for Cardiovascular Chrono-therapeutics

Curator: Aviva Lev-Ari, PhD, RN



Sleep Apnea and Non-invasive positive Pressure Breathing

Curator: Larry H. Bernstein, MD, FCAP



How Might Sleep Apnea Lead to Serious Health Concerns like Cardiac and Cancer?

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2019 Warren Alpert Foundation Award goes to Four Scientists for Seminal Discoveries in OptoGenetics – Illuminating the Human Brain

Reporter: Aviva Lev-Ari, PhD, RN







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Cancer Genomics: Multiomic Analysis of Single Cells and Tumor Heterogeneity

Curator: Stephen J. Williams, PhD


scTrio-seq identifies colon cancer lineages

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

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

Science, this issue p. 1060


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

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

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

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

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



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The second annual PureTech Health BIG (Brain-Immune-Gut) Summit 2019 – By invitation only –

Selected Tweets from  #BIGAxisSummit

by @pharma_BI @AVIVA1950

for @pharmaceuticalintelligence.com

Reporter: Aviva Lev-Ari, PhD, RN


January 30 – February 1, 2019

The second annual PureTech Health BIG Summit brings together an elite ensemble of leading scientific researchers, investors, and CEOs and R&D leaders from major pharmaceutical, technology, and biotech companies.

The BIG Summit is designed to stimulate ideas that will have an impact on existing pipelines and catalyze future interactions among a group of delegates that represent leaders and innovators in their fields.

Please follow the discussion on Twitter using #BIGAxisSummit

By invitation only; registration is non-transferable.

For more information, please contact PureTechHealthSummit@PureTechHealth.com






(Subject to Change)

PureTech Health BIG Summit 2019 Agenda_FINALv2_WEBSITE.jpg

“Almost starting to understand immunology at this thought-provoking @PureTechh #BIGAxisSummit. Great Speakers.”

-tweet by Simone Fishburn, BioCentury @SimoneFishburn




Selected Tweets from  #BIGAxisSummit

by @pharma_BI @AVIVA1950

for @pharmaceuticalintelligence.com

Gail S. Thornton Selections

Luke Timmerman‏ @ldtimmerman 7h7 hours ago

Back for final sessions at #BIGAxisSummit. @PureTechH Jim Harper of Sonde Health talking about how voice data — pacing, fine motor articulation, oscillation — can point the way to objective, quantitative measures for detecting and monitoring depression.


Eddie Martucci

 @EddieMartucci 5h5 hours ago

Paul Biondi at #BIGAxisSummit : What makes big deals happen is financial, and *deep conviction* of a big future fit. Disproportionate valuation from bidders is expected.

Love this. We often reduce everything to mathematical analyses to champion or ridicule deals. Not that simple


PureTech Health Plc‏ @PureTechH Jan 31

Bob Langer (@MIT) asks how #lymphatics affected by #aging. Santambrogio: typically blame aging #immune cells for increased disease, but aging affects lymphatics too (less efficient trafficking shown). Rejuvenating these could affect several aging-related diseases #BigAxisSummit


PureTech Health Plc‏ @PureTechH Jan 31

Viviane Labrie (@VAInstitute) discusses why the appendix has been identified as a potential starting point for #parkinsons #BIGAxisSummit


PureTech Health Plc‏ @PureTechH Jan 31

Chris Porter (@MIPS_Australia) notes #lymphatics is major route for trafficking #immune cells that surveil gut and respond to immune & #autoimmune stimuli. This is key in #BIGAxis interactions and why lymphatics-targeted therapies could enhance #immunomodulation #BIGAxisSummit


Dr. Stephen J. Williams Selections






Dr. Irina Robu Selection






Dr. Sudipta Saha Selection








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2,000 human brains yield clues to how genes raise risk for mental illnesses

Reporter: Irina Robu, PhD

It’s one thing to detect sites in the genome associated with mental disorders; it’s quite another to discover the biological mechanisms by which these changes in DNA work in the human brain to boost risk. In their first concerted effort to tackle the problem, 15 collaborating research teams of the National Institutes of Health-funded PsychENCODE Consortium evaluated data of 2000 human brains which might yield clues to how genes raise risk for mental illnesses.

Applying newly uncovered secrets of the brain’s molecular architecture, they established an artificial intelligence model that is six times better than preceding ones at predicting risk for mental disorders. They also identified several hundred previously unknown risk genes for mental illnesses and linked many known risk variants to specific genes. In the brain tissue and single cells, the researchers identified patterns of gene expression, marks in gene regulation as well as genetic variants that can be linked to mental illnesses.

Dr. Nenad Sestan of Yale University explained that “ the consortium’s integrative genomic analyses elucidate the mechanisms by which cellular diversity and patterns of gene expression change throughout development and reveal how neuropsychiatric risk genes are concentrated into distinct co-expression modules and cell types”. The implicated variants are typically small-effect genetic variations that fall within regions of the genome that don’t code for proteins, but instead are thought to regulate gene expression and other aspects of gene function.

In addition to the 2000 postmortem human brains, researchers examined brain tissue from prenatal development as well as people with schizophrenia, bipolar disorder,  and typical development compared findings with parallel data from non-human primates. Their findings indicate that gene variants linked to mental illnesses exert more effects when they jointly form “modules”, communicating genes with related functions and at specific developmental time points that seem to coincide with the course of illness. Variability in risk gene expression and cell types increases during formative stages in early prenatal development and again during the teen years. However, in postmortem brains of people with a mental illness, thousands of RNAs were found to have anomalies.

According to NIMH, Geetha Senthil the multi-omic data resource caused by the PsychENCODE collaboration will pave a path for building molecular models of disease and developmental processes and may offer a platform for target identification for pharmaceutical research.


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Individuals without angiographic CAD but with hiPRS remain at significantly elevated risk of mortality after cardiac catheterization

Reporter: Aviva Lev-Ari, PhD, RN


A genome-wide Polygenic risk scores (PRS) improves risk stratification when added to traditional risk factors and coronary angiography. Individuals without angiographic CAD but with hiPRS remain at significantly elevated risk of mortality.



Coronary artery disease (CAD) is influenced by genetic variation and traditional risk factors. Polygenic risk scores (PRS), which can be ascertained before the development of traditional risk factors, have been shown to identify individuals at elevated risk of CAD. Here, we demonstrate that a genome-wide PRS for CAD predicts all-cause mortality after accounting for not only traditional cardiovascular risk factors but also angiographic CAD itself.


Individuals who underwent coronary angiography and were enrolled in an institutional biobank were included; those with prior myocardial infarction or heart transplant were excluded. Using a pruning-and-thresholding approach, a genome-wide PRS comprised of 139 239 variants was calculated for 1503 participants who underwent coronary angiography and genotyping. Individuals were categorized into high PRS (hiPRS) and low-PRS control groups using the maximally selected rank statistic. Stratified analysis based on angiographic findings was also performed. The primary outcome was all-cause mortality following the index coronary angiogram.


Individuals with hiPRS were younger than controls (66 years versus 69 years; P=2.1×10-5) but did not differ by sex, body mass index, or traditional risk-factor profiles. Individuals with hiPRS were at significantly increased risk of all-cause mortality after cardiac catheterization, adjusting for traditional risk factors and angiographic extent of CAD (hazard ratio, 1.6; 95% CI, 1.2–2.2; P=0.004). The strongest increase in risk of all-cause mortality conferred by hiPRS was seen among individuals without angiographic CAD (hazard ratio, 2.4; 95% CI, 1.1–5.5; P=0.04). In the overall cohort, adding hiPRS to traditional risk assessment improved prediction of 5-year all-cause mortality (area under the receiver-operating curve 0.70; 95% CI, 0.66–0.75 versus 0.66; 95% CI, 0.61–0.70; P=0.001).


A genome-wide PRS improves risk stratification when added to traditional risk factors and coronary angiography. Individuals without angiographic CAD but with hiPRS remain at significantly elevated risk of mortality.



*A list of all Regeneron Genetics Center members is given in the Data Supplement.

Guest Editor for this article was Christopher Semsarian, MBBS, PhD, MPH.

The Data Supplement is available at https://www.ahajournals.org/doi/suppl/10.1161/CIRCGEN.118.002352.

Scott M. Damrauer, MD, Department of Surgery, Hospital of the University of Pennsylvania, 3400 Spruce St, Silverstein 4, Philadelphia, PA 19104. Email 

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The HFE H63D variant confers an increased risk for hypertension, no increased risk for adverse cardiovascular events or substantial left ventricular remodeling

Reporter: Aviva Lev-Ari, PhD, RN


The HFE H63D variant confers an increased risk for hypertension per allele and, given its frequency, accounts for a significant number of cases of hypertension. However, there was no increased risk for adverse cardiovascular events or substantial left ventricular remodeling.


HFE H63D Polymorphism and the Risk for Systemic Hypertension, Myocardial Remodeling, and Adverse Cardiovascular Events in the ARIC Study

Originally publishedHypertension. 2018;0:HYPERTENSIONAHA.118.11730

H63D has been identified as a novel locus associated with the development of hypertension. The quantitative risks for hypertension, cardiac remodeling, and adverse events are not well studied. We analyzed white participants from the ARIC study (Atherosclerosis Risk in Communities) with H63D genotyping (N=10 902). We related genotype status to prevalence of hypertension at each of 5 study visits and risk for adverse cardiovascular events. Among visit 5 participants (N=4507), we related genotype status to echocardiographic features. Frequencies of wild type (WT)/WT, H63D/WT, and H63D/H63D were 73%, 24.6%, and 2.4%. The average age at baseline was 54.9±5.7 years and 47% were men. Participants carrying the H63D variant had higher systolic blood pressure (P=0.004), diastolic blood pressure (0.012), and more frequently had hypertension (P<0.001). Compared with WT/WT, H63D/WT and H63D/H63D participants had a 2% to 4% and 4% to 7% absolute increase in hypertension risk at each visit, respectively. The population attributable risk of H63D for hypertension among individuals aged 45 to 64 was 3.2% (95% CI, 1.3–5.1%) and 1.3% (95% CI, 0.0–2.4%) among individuals >65 years. After 25 years of follow-up, there was no relationship between genotype status and any outcome (P>0.05). H63D/WT and H63D/H63D genotypes were associated with small differences in cardiac remodeling. In conclusion, the HFE H63D variant confers an increased risk for hypertension per allele and, given its frequency, accounts for a significant number of cases of hypertension. However, there was no increased risk for adverse cardiovascular events or substantial left ventricular remodeling.


The online-only Data Supplement is available with this article at https://www.ahajournals.org/doi/suppl/10.1161/HYPERTENSIONAHA.118.11730.

Correspondence to Scott D. Solomon, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115. Email 

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