Biology subjects

Wasserman, W. W.

Publications and source records attributed to Wasserman, W. W..

16 recordsLinked to original sources

bioRxiv2026

Functional, transcriptomic, and proteomic profiles of human primary and stem cell-derived beta cells in a state of high insulin production and increased fragility

Insulin production is a cardinal feature of pancreatic {beta} cells. Studies in rodents show that {beta} cells can switch between low and high insulin gene activity states and that elevated insulin production makes {beta} cells more vulnerable to stresses associated with diabetes. In people, genetically elevated insulin production increases the risk of type 1 diabetes. Via effects on obesity, hyperinsulinemia contributes to the pathogenesis of type 2 diabetes. Here, we characterize {beta} cells in low and high INS gene activity states sorted from primary human islets transduced with INS-GFP adenovirus and differentiated INS-EGFP knock-in embryonic stem cells (SC{beta} cells). We profile {beta} cell function, protein synthesis, resilience to diabetes associated stress, single {beta} cell transcriptomes and their co-activity networks, and purified {beta} cell proteomes. We show that human {beta} cells transition between distinct states. High INS cells have elevated maturity marker mRNAs and proteins, increased protein translation, are larger, but also more susceptible to cell death when exposed to diabetes-relevant stresses. We also catalogue thousands of differences in proteins in high INS stem cell-derived {beta} cells compared directly with high INS primary {beta} cells. Our study improves our understanding of the delicate balance between insulin production and {beta} cell resilience and guides the engineering of better {beta} cells. Blurbtranscriptional, proteomic, and functional analyses of insulin gene expression states in human {beta} cells from donor islets and stem cells Key findingsO_LIWe identify high and low INS gene activity states in human insulin-producing cells from donor islets and embryonic stem cell differentiations. C_LIO_LIWe characterize the relationship between insulin production and fragility, demonstrating that increased insulin production comes at a cost of reduced resilience to multiple stresses. C_LIO_LIFunctional, transcriptomic, and proteomic analyses identify similarities and differences between how primary and stem cell-derived {beta} cells manage stress and insulin production. C_LIO_LIWe report a comprehensive side-by-side proteomic analysis of purified primary and stem cell- derived {beta} cells in the high INS state and identify differences in protein production and secretion machinery, providing a roadmap for making better {beta} cells. C_LI

physiology↗

bioRxiv2025

A prioritized medium-throughput screen on human stem cell derived insulin secreting beta cells identifies FGF4, FGF5, FGF8F, FGF19 and FGF21 as protective factors

AbstractStem cell-derived {beta}-like cells (SC{beta} cells) are a potential alternative to cadaveric {beta} cells for replacement therapy in type 1 diabetes. However, SC{beta} cells face a multitude of stresses that must be overcome. Both {beta} cells and SC{beta} cells reside in complex microtissues and can therefore be modulated by hundreds of autocrine/paracrine signals within these islets or spheroids. Here, we leveraged multi-omics data from late-stage SC{beta} cells and human islets to map ligand-receptor pairs and generate a prioritized list of ligands for high-content SC{beta} cell survival screening. Our medium-throughput screen tracked cell number, cell death, and INS production over several days using automated, high-content imaging. Members of the fibroblast growth factor (FGF) family significantly prevented cytokine-induced cell death, with the top validated hits being FGF4, FGF5, FGF19, FGF21, and FGF8F. With these results, there is the potential to improve SC{beta} cell survival in vitro via readily targetable pathways and to produce a more robust product for translation to the clinic.

molecular biology↗

bioRxiv2022

A new type of transcriptional reprogramming by an IRF4 mutation in lymphoma

SUMMARY PARAGRAPHDisease-causing mutations in genes encoding transcription factors (TFs) are a recurrent finding in hematopoietic malignancies and might involve key regulators of lineage adherence and cellular differentiation1-3. Such mutations can affect TF-interactions with their cognate DNA-binding motifs4, 5. Whether and how TF-mutations impact upon the nature of binding to TF composite elements (CE) and influence their interaction with other TFs is unclear. Here, we report a new mechanism of TF alteration in human lymphomas with perturbed B cell identity. It is caused by a recurrent somatic missense mutation c.295T>C (p.Cys99Arg; p.C99R) targeting the center of the DNA-binding domain of Interferon Regulatory Factor 4 (IRF4), a key TF in immune cell-differentiation and -activation6, 7. IRF4-C99R fundamentally alters IRF4 DNA-binding, with loss-of-binding to canonical IRF motifs and neomorphic gain-of-binding to canonical and non-canonical IRF composite elements (CEs). Furthermore, IRF4-C99R thoroughly modifies IRF4 function, by blocking IRF4-dependent plasma cell induction, and up-regulating disease-specific genes in a non-canonical Activator Protein-1 (AP-1)-IRF-CE (AICE)-dependent manner. Our data explain how a single arginine mutation creates a complex switch of TF specificity and gene regulation. These data open the possibility of designing specific inhibitors to block the neomorphic, disease-causing DNA-binding activities of a mutant transcription factor.

cancer biology↗

bioRxiv2022

CAFE (Cohort Allele Frequency Estimation) Pipeline: A workflow to generate a variant catalogue from Whole Genome Sequences

Today, several projects are working toward reducing inequities and improving health care for individuals affected with rare genetic diseases from diverse populations. One route to reduce inequities is to generate variant catalogues for diverse populations. To that end, we developed the variant catalogue pipeline, an open-source pipeline implemented in the Nextflow framework. The variant catalogue pipeline includes detection of single nucleotide variants, small insertions and deletions, mitochondrial variants, structural variants, mobile element insertions, and short tandem repeats. Sample and variant quality control, allele frequency calculation (for whole and sex-stratified cohorts) and annotation steps are also included, delivering vcf files with annotated variants and their frequency in the cohort. Successful application of the variant catalogue pipeline to 100 publicly available human genomes is described. We hope that, by making this pipeline available, more under-represented populations benefit from enhanced capacity to generate high-quality variant catalogues.

bioinformatics↗

bioRxiv2022

ExplaiNN: interpretable and transparent neural networks for genomics

Sequence-based deep learning models, particularly convolutional neural networks (CNNs), have shown superior performance on a wide range of genomic tasks. A key limitation of these models is the lack of interpretability, slowing down their adoption by the genomics community. Current approaches to model interpretation do not readily reveal how a model makes predictions, can be computationally intensive, and depend on the implemented architecture. Here, we introduce ExplaiNN, an adaptation of neural additive models[1] for genomic tasks wherein predictions are computed as a linear combination of multiple independent CNNs, each consisting of a single convolutional filter and fully connected layers. This approach brings together the expressiveness of CNNs with the interpretability of linear models, providing global (cell state level) as well as local (individual sequence level) biological insights into the data. We use ExplaiNN to predict transcription factor (TF) binding and chromatin accessibility states, demonstrating performance levels comparable to state-of-the-art methods, while providing a transparent view of the models predictions in a straightforward manner. Applied to de novo motif discovery, ExplaiNN identifies equivalent motifs to those obtained from specialized algorithms across a range of datasets. Finally, we present ExplaiNN as a plug-and-play platform in which pretrained TF binding models and annotated position weight matrices from reference databases can be easily combined. We expect that ExplaiNN will accelerate the adoption of deep learning by biological domain experts in their daily genomic sequence analyses.

genomics↗

bioRxiv2022

Allele Dispersion Score: Quantifying the range of allele frequencies across populations, based on UMAP

Genomic variation plays a crucial role in biology, serving as a base for evolution - allowing for adaptation on a species or population level. At the individual level, however, specific alleles can be implicated in diseases. To interpret genetic variants identified in an individual potentially affected with a rare genetic disease, it is fundamental to know the population frequency of each allele, ideally in an ancestry matched cohort. Equity in human genomics remains a challenge for the field, and there are not yet cohorts representing most populations. Currently, when ancestry matched cohorts are not available, pooled variant libraries are used, such as gnomAD, the Human Genome Diversity Project (HGDP) or the 1,000 Genomes Project (now known as IGSR: International Genome Sample Resource). When working with a pooled collection of variant frequencies, one of the challenges is to determine efficiently if a variant is broadly spread across populations or appears selectively in one or more populations. While this can be accomplished by reviewing tables of population frequencies, it can be advantageous to have a single score that summarizes the observed dispersion. This score would not require classifying individuals into populations, which can be complicated if it is a homogenous population, or can leave individuals excluded from all the predefined population groups. Moreover, a score would not display fine-scaled population information, which could have privacy implications and consequently be inappropriate to release. Therefore, we sought to develop a scoring method based on a Uniform Manifold Approximation and Projection (UMAP) where, for each allele, the score can range from 0 (the variant is limited to a subset of close individuals within the whole cohort) to 1 (the variant is spread among the individuals represented in the cohort). We call this score the Allele Dispersion Score (ADS). The scoring system was implemented on the IGSR dataset, and compared to the current method consisting in displaying variant frequencies for several populations in a table. The ADS correlates with the population frequencies, without requiring grouping of individuals.

genomics↗

bioRxiv2021

In silico discovery of small molecules for efficient stem cell differentiation into definitive endoderm

Improving methods for human embryonic stem cell differentiation represents a challenge in modern regenerative medicine research. Using drug repurposing approaches, we discover small molecules that regulate the formation of definitive endoderm. Among them are inhibitors of known processes involved in endoderm differentiation (mTOR, PI3K, and JNK pathways) and a new compound, with an unknown mechanism of action, capable of inducing endoderm formation in the absence of growth factors in the media. Optimization of the classical protocol by including this compound achieves the same differentiation efficiency with a 90% cost reduction. The gene expression profile induced by the compound suggests that it is an inhibitor of the MYC pathway. The proposed in silico procedure for candidate molecule selection has broad potential for improving stem cell differentiation protocols.

bioinformatics↗

bioRxiv2020

Biologically-relevant transfer learning improves transcription factor binding prediction

BackgroundDeep learning has proven to be a powerful technique for transcription factor (TF) binding prediction, but requires large training datasets. Transfer learning can reduce the amount of data required for deep learning, while improving overall model performance, compared to training a separate model for each new task. ResultsWe assess a transfer learning strategy for TF binding prediction consisting of a pre-training step, wherein we train a multi-task model with multiple TFs, and a fine-tuning step, wherein we initialize single-task models for individual TFs with the weights learned by the multi-task model, after which the single-task models are trained at a lower learning rate. We corroborate that transfer learning improves model performance, especially if in the pre-training step the multi-task model is trained with biologically-relevant TFs. We show the effectiveness of transfer learning for TFs with [~]500 ChIP-seq peak regions. Using model interpretation techniques, we demonstrate that the features learned in the pre-training step are refined in the fine-tuning step to resemble the binding motif of the target TF (i.e. the recipient of transfer learning in the fine-tuning step). Moreover, pre-training with biologically-relevant TFs allows single-task models in the fine-tuning step to learn features other than the motif of the target TF. ConclusionsOur results confirm that transfer learning is a powerful technique for TF binding prediction.

bioinformatics↗

bioRxiv2020

Cross-species examination of X-chromosome inactivation highlights domains of escape from silencing

BackgroundX-chromosome inactivation (XCI) in eutherian mammals is the epigenetic inactivation of one of the two X chromosomes in XX females in order to compensate for dosage differences with XY males. Not all genes are inactivated, and the proportion escaping from inactivation varies between human and mouse (the two species that have been extensively studied). ResultsWe used DNA methylation to predict the XCI status of X-linked genes with CpG islands across 12 different species: human, chimp, bonobo, gorilla, orangutan, mouse, cow, sheep, goat, pig, horse and dog. We determined the XCI status of 342 CpG islands on average per species, with most species having 80-90% of genes subject to XCI. Mouse was an outlier, with a higher proportion of genes subject to XCI than found in other species. Sixteen genes were found to have discordant X-chromosome inactivation statuses across multiple species, with five of these showing primate-specific escape from XCI. These discordant genes tended to cluster together within the X chromosome, along with genes with similar patterns of escape from XCI. CTCF- binding, ATAC-seq signal and LTR repeats were enriched at genes escaping XCI when compared to genes subject to XCI; however, enrichment was only observed in three or four of the species tested. LINE and DNA repeats showed enrichment around subject genes, but again not in a consistent subset of species. ConclusionsIn this study we determined XCI status across 12 species, showing mouse to be an outlier with few genes that escape inactivation. Inactivation status is largely conserved across species. The clustering of genes that change XCI status across species implicates a domain-level control. In contrast, the relatively consistent, but not universal correlation of inactivation status with enrichment of repetitive elements or CTCF binding at promoters demonstrates gene-based influences on inactivation state. This study broadens enrichment analysis of regulatory elements to species beyond human and mouse.

genetics↗

bioRxiv2020

GeneBreaker: Variant simulation to improve the diagnosis of Mendelian rare genetic diseases

Mendelian rare genetic diseases affect 5-10% of the population, and with over 5,300 genes responsible for ~7,000 different diseases, they are challenging to diagnose. The use of whole genome sequencing (WGS) has bolstered the diagnosis rate significantly. Effective use of WGS relies upon the ability to identify the disrupted gene responsible for disease phenotypes. This process involves genomic variant calling and prioritization, and is the beneficiary of improvements to sequencing technology, variant calling approaches, and increased capacity to prioritize genomic variants with potential pathogenicity. As analysis pipelines continue to improve, careful testing of their efficacy is paramount. However, real-life cases typically emerge anecdotally, and utilization of clinically sensitive and identifiable data for testing pipeline improvements is regulated and limiting. We identified the need for a gene-based variant simulation framework which can create mock rare disease scenarios, utilizing known pathogenic variants or through the creation of novel gene-disrupting variants. To fill this need, we present GeneBreaker, a tool which creates synthetic rare disease cases with utility for benchmarking variant calling approaches, testing the efficacy of variant prioritization, and as an educational mechanism for training diagnostic practitioners in the expanding field of genomic medicine. GeneBreaker is freely available at http://GeneBreaker.cmmt.ubc.ca.

genomics↗

bioRxiv2020

Demonstrating the utility of flexible sequence queries against indexed short reads with FlexTyper

Across the life sciences, processing next generation sequencing data commonly relies upon a computationally expensive process where reads are mapped onto a reference sequence. Prior to such processing, however, there is a vast amount of information that can be ascertained from the reads, potentially obviating the need for processing, or allowing optimized mapping approaches to be deployed. Here, we present a method termed FlexTyper which facilitates a "reverse mapping" approach in which high throughput sequence queries, in the form of k-mer searches, are run against indexed short-read datasets in order to extract useful information. This reverse mapping approach enables the rapid counting of target sequences of interest. We demonstrate FlexTypers utility for recovering depth of coverage, and accurate genotyping of SNP sites across the human genome. We show that genotyping unmapped reads can correctly inform a samples population, sex, and relatedness in a family setting. Detection of pathogen sequences within RNA-seq data was sensitive and accurate, performing comparably to existing methods, but with increased flexibility. We present two examples of ways in which this flexibility allows the analysis of genome features not well-represented in a linear reference. First, we analyze contigs from African genome sequencing studies, showing how they distribute across families from three distinct populations. Second, we show how gene-marking k-mers for the killer immune receptor locus allow allele detection in a region that is challenging for standard read mapping pipelines. The future adoption of the reverse mapping approach represented by FlexTyper will be enabled by more efficient methods for FM-index generation and biology-informed collections of reference queries. In the long-term, selection of population-specific references or weighting of edges in pan-population reference genome graphs will be possible using the FlexTyper approach. FlexTyper is available at https://github.com/wassermanlab/OpenFlexTyper. Author SummaryIn the past 15 years, next generation sequencing technology has revolutionized our capacity to process and analyze DNA sequencing data. From agriculture to medicine, this technology is enabling a deeper understanding of the blueprint of life. Next generation sequencing data is composed of short sequences of DNA, referred to as "reads", which are often shorter than 200 base pairs making them many orders of magnitude smaller than the entirety of a human genome. Gaining insights from this data has typically leveraged a reference-guided mapping approach, where the reads are aligned to a reference genome and then post-processed to gain actionable information such as presence or absence of genomic sequence, or variation between the reference genome and the sequenced sample. Many experts in the field of genomics have concluded that selecting a single, linear reference genome for mapping reads against is limiting, and several current research endeavors are focused on exploring options for improved analysis methods to unlock the full utility of sequencing data. Among these improvements are the usage of sex-matched genomes, population-specific reference genomes, and emergent graph-based reference pan-genomes. However, advanced methods that use raw DNA sequencing data to inform the choice of reference genome and guide the alignment of reads to enriched reference genomes are needed. Here we develop a method termed FlexTyper, which creates a searchable index of the short read data and enables flexible, user-guided queries to provide valuable insights without the need for reference-guided mapping. We demonstrate the utility of our method by identifying sample ancestry and sex in human whole genome sequencing data, detecting viral pathogen reads in RNA-seq data, African-enriched genome regions absent from the global reference, and HLA alleles that are complex to discern using standard read mapping. We anticipate early adoption of FlexTyper within analysis pipelines as a pre-mapping component, and further envision the bioinformatics and genomics community will leverage the tool for creative uses of sequence queries from unmapped data.

bioinformatics↗

bioRxiv2019

ExpansionHunter Denovo: A computational method for locating known and novel repeat expansions in short-read sequencing data

Expansions of short tandem repeats are responsible for over 40 monogenic disorders, and undoubtedly many more pathogenic repeat expansions (REs) remain to be discovered. Existing methods for detecting REs in short-read sequencing data require predefined repeat catalogs. However recent discoveries have emphasized the need for detection methods that do not require candidate repeats to be specified in advance. To address this need, we introduce ExpansionHunter Denovo, an efficient catalog-free method for genome-wide detection of REs. Analysis of real and simulated data shows that our method can identify large expansions of 41 out of 44 pathogenic repeats, including nine recently reported non-reference REs not discoverable via existing methods. ExpansionHunter Denovo is freely available at https://github.com/Illumina/ExpansionHunterDenovo

bioinformatics↗

bioRxiv2018

Introduction to Genomic Analysis Workshop: A catalyst for engaging life-science researchers in high throughput analysis

Researchers in the life sciences are increasingly faced with the task of obtaining compute resources and training to analyze large, high-throughput technology generated datasets. As demand for compute resources has grown, high performance computing (HPC) systems have been implemented by research organizations and international consortiums to support academic researchers. However, life science researchers lack effective time-of-need training resources for utilization of these systems. Current training options have drawbacks that inhibit the effective training of researchers without experience in computational analysis. We identified the need for flexible, centrally-organized, easily accessible, interactive, and compute resource specific training for academic HPC use. In our delivery of a modular workshop series, we provided foundational training to a group of researchers in a coordinated manner, allowing them to further pursue additional training and analysis on compute resources available to them. Efficacy measures indicate that the material was effectively delivered to a broad audience in a short time period, including both virtual and on-site students. The practical approach to catalyze academic HPC use is amenable to diverse systems worldwide.

scientific communication and education↗

bioRxiv2017

Altered transcription factor binding events predict personalized gene expression and confer insight into functional cis-regulatory variants

Deciphering the functional roles of cis-regulatory variants is a critical challenge in genome analysis and interpretation. We hypothesize that altered transcription factor (TF) binding events are a central mechanism by which cis-regulatory variants impact gene expression. We present TF2Exp, the first gene-based framework (to our knowledge) to predict the impact of altered TF binding on personalized gene expression based on cis-regulatory variants. Using data from lymphoblastoid cell lines, TF2Exp models achieved suitable performance for 3,060 genes. Alterations within DNase I hypersensitive, CTCF-bound, and tissue-specific TF-bound regions were the greatest contributors to the models. Our cis-regulatory variant-based TF2Exp models performed as well as the state-of-the-art SNP-based models, both in cross-validation and external validation. In addition, unlike SNP-based models, our TF2Exp models have the unique advantages to evaluate impact of uncommon variants and distinguish the functional roles of variants in linkage disequilibrium, showing broader utility for future human genetic studies.

bioinformatics↗

bioRxiv2017

The interface of malignant and immunologic clonal dynamics in high-grade serous ovarian cancer

High-grade serous ovarian cancer exhibits extensive intratumoral heterogeneity coupled with widespread intraperitoneal disease. Despite this, metastatic spread of tumor clones is non-random, implying the existence of local microenvironmental factors that shape tumor progression. We interrogated the molecular interface between tumor-infiltrating lymphocytes (TIL) and cancer cells in 143 samples from 21 patients using whole-genome sequencing, immunohistochemistry, histologic image analysis, gene expression profiling, and T- and B-cell receptor sequencing. We identify 3 immunologic response categories, which frequently co-exist within individual patients. Furthermore, epithelial CD8+ TIL were inversely associated with malignant cell diversity, evidenced by subclonal neoepitope elimination and spatial tracking between tumor and T-cell clones. Intersecting mutational signatures and immune analysis showed that foldback inversion genomic aberrations lead to worse outcomes even in the presence of cytotoxic TIL (n=433). Thus, regional variation in immune contexture mirrors the pattern of intraperitoneal malignant spread, provoking new perspectives for treatment of this challenging disease.

cancer biology↗

bioRxiv2017

The SIN3A histone deacetylase complex is required for a complete transcriptional response to hypoxia

Cells adapt to environmental changes, including fluctuations in oxygen levels, through the induction of specific gene expression programs. To identify genes regulated by hypoxia at the transcriptional level, we pulse-labeled HUVEC cells with 4-thiouridine and sequenced nascent transcripts. Then, we searched genome-wide binding profiles from the ENCODE project for factors that correlated with changes in transcription and identified binding of several components of the Sin3A co-repressor complex, including SIN3A, SAP30 and HDAC1/2, proximal to genes repressed by hypoxia. SIN3A interference revealed that it participates in the downregulation of 75% of the hypoxia-repressed genes in endothelial cells. Unexpectedly, it also blunted the induction of 47% of the upregulated genes, suggesting a role for this corepressor in gene induction. In agreement, ChIP-seq experiments showed that SIN3A preferentially localizes to the promoter region of actively transcribed genes and that SIN3A signal was enriched in hypoxia-repressed genes, prior exposure to the stimulus. Importantly, SINA3 occupancy was not altered by hypoxia in spite of changes in H3K27ac signal. In summary, our results reveal a prominent role for SIN3A in the transcriptional response to hypoxia and suggest a model where modulation of the associated histone deacetylase activity, rather than its recruitment, determines the transcriptional output.

genomics↗