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Article: Alpha cell dysfunction in type 1 diabetes is independent of a senescence program.

Brawerman, Gabriel / Ntranos, Vasilis / Thompson, Peter J

2022 Volume 13, Page(s) 932516

Abstract: Type 1 Diabetes (T1D) is caused by insulin deficiency, due to progressive autoimmune destruction of pancreatic β cells. Glucagon-secreting α cells become dysfunctional in T1D and contribute to pathophysiology, however, the mechanisms involved are unclear. ...

Abstract	Type 1 Diabetes (T1D) is caused by insulin deficiency, due to progressive autoimmune destruction of pancreatic β cells. Glucagon-secreting α cells become dysfunctional in T1D and contribute to pathophysiology, however, the mechanisms involved are unclear. While the majority of β cells are destroyed in T1D, some β cells escape this fate and become senescent but whether α cell dysfunction involves a senescence program has not been explored. Here we addressed the question of whether α cells become senescent during the natural history of T1D in the non-obese diabetic (NOD) mouse model and humans. NOD mice had several distinct subpopulations of α cells, but none were defined by markers of senescence at the transcriptional or protein level. Similarly, α cells of human T1D donors did not express senescence markers. Despite the lack of senescence in α cells
MeSH term(s)	Mice ; Animals ; Humans ; Diabetes Mellitus, Type 1/metabolism ; Mice, Inbred NOD ; Glucagon/metabolism ; Glucagon-Secreting Cells/metabolism ; Insulin/metabolism ; Biomarkers/metabolism
Chemical Substances	Glucagon (9007-92-5) ; Insulin ; Biomarkers
Language	English
Publishing date	2022-10-07
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	2592084-4
ISSN	1664-2392
ISSN	1664-2392
DOI	10.3389/fendo.2022.932516
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Article ; Online: Sox9 regulates alternative splicing and pancreatic beta cell function.

Puri, Sapna / Maachi, Hasna / Nair, Gopika / Russ, Holger A / Chen, Richard / Pulimeno, Pamela / Cutts, Zachary / Ntranos, Vasilis / Hebrok, Matthias

Nature communications

2024 Volume 15, Issue 1, Page(s) 588

Abstract: Despite significant research, mechanisms underlying the failure of islet beta cells that result in type 2 diabetes (T2D) are still under investigation. Here, we report that Sox9, a transcriptional regulator of pancreas development, also functions in ... ...

Abstract	Despite significant research, mechanisms underlying the failure of islet beta cells that result in type 2 diabetes (T2D) are still under investigation. Here, we report that Sox9, a transcriptional regulator of pancreas development, also functions in mature beta cells. Our results show that Sox9-depleted rodent beta cells have defective insulin secretion, and aging animals develop glucose intolerance, mimicking the progressive degeneration observed in T2D. Using genome editing in human stem cells, we show that beta cells lacking SOX9 have stunted first-phase insulin secretion. In human and rodent cells, loss of Sox9 disrupts alternative splicing and triggers accumulation of non-functional isoforms of genes with key roles in beta cell function. Sox9 depletion reduces expression of protein-coding splice variants of the serine-rich splicing factor arginine SRSF5, a major splicing enhancer that regulates alternative splicing. Our data highlight the role of SOX9 as a regulator of alternative splicing in mature beta cell function.
MeSH term(s)	Animals ; Humans ; Alternative Splicing/genetics ; Diabetes Mellitus, Type 2/genetics ; Diabetes Mellitus, Type 2/metabolism ; Insulin-Secreting Cells/metabolism ; Islets of Langerhans/metabolism ; RNA Splicing
Chemical Substances	SOX9 protein, human
Language	English
Publishing date	2024-01-18
Publishing country	England
Document type	Journal Article
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-023-44384-8
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Article ; Online: Genome-wide prediction of disease variant effects with a deep protein language model.

Brandes, Nadav / Goldman, Grant / Wang, Charlotte H / Ye, Chun Jimmie / Ntranos, Vasilis

Nature genetics

2023 Volume 55, Issue 9, Page(s) 1512–1522

Abstract: Predicting the effects of coding variants is a major challenge. While recent deep-learning models have improved variant effect prediction accuracy, they cannot analyze all coding variants due to dependency on close homologs or software limitations. Here ... ...

Abstract	Predicting the effects of coding variants is a major challenge. While recent deep-learning models have improved variant effect prediction accuracy, they cannot analyze all coding variants due to dependency on close homologs or software limitations. Here we developed a workflow using ESM1b, a 650-million-parameter protein language model, to predict all ~450 million possible missense variant effects in the human genome, and made all predictions available on a web portal. ESM1b outperformed existing methods in classifying ~150,000 ClinVar/HGMD missense variants as pathogenic or benign and predicting measurements across 28 deep mutational scan datasets. We further annotated ~2 million variants as damaging only in specific protein isoforms, demonstrating the importance of considering all isoforms when predicting variant effects. Our approach also generalizes to more complex coding variants such as in-frame indels and stop-gains. Together, these results establish protein language models as an effective, accurate and general approach to predicting variant effects.
MeSH term(s)	Humans ; Computational Biology/methods ; Software ; Mutation, Missense/genetics ; Proteins/genetics ; Genome, Human/genetics
Chemical Substances	Proteins
Language	English
Publishing date	2023-08-10
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1108734-1
ISSN	1546-1718 ; 1061-4036
ISSN (online)	1546-1718
ISSN	1061-4036
DOI	10.1038/s41588-023-01465-0
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Article ; Online: Determining sequencing depth in a single-cell RNA-seq experiment.

Zhang, Martin Jinye / Ntranos, Vasilis / Tse, David

Nature communications

2020 Volume 11, Issue 1, Page(s) 774

Abstract: An underlying question for virtually all single-cell RNA sequencing experiments is how to allocate the limited sequencing budget: deep sequencing of a few cells or shallow sequencing of many cells? Here we present a mathematical framework which reveals ... ...

Abstract	An underlying question for virtually all single-cell RNA sequencing experiments is how to allocate the limited sequencing budget: deep sequencing of a few cells or shallow sequencing of many cells? Here we present a mathematical framework which reveals that, for estimating many important gene properties, the optimal allocation is to sequence at a depth of around one read per cell per gene. Interestingly, the corresponding optimal estimator is not the widely-used plug-in estimator, but one developed via empirical Bayes.
MeSH term(s)	Computational Biology/methods ; Computational Biology/statistics & numerical data ; Gene Expression ; Gene Regulatory Networks ; In Situ Hybridization, Fluorescence ; Models, Theoretical ; Reproducibility of Results ; S100 Calcium-Binding Protein A4/genetics ; Sequence Analysis, RNA/methods ; Sequence Analysis, RNA/statistics & numerical data ; Single-Cell Analysis/methods ; Single-Cell Analysis/statistics & numerical data
Chemical Substances	S100 Calcium-Binding Protein A4
Language	English
Publishing date	2020-02-07
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-020-14482-y
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Article ; Online: Deterministic column subset selection for single-cell RNA-Seq.

McCurdy, Shannon R / Ntranos, Vasilis / Pachter, Lior

PloS one

2019 Volume 14, Issue 1, Page(s) e0210571

Abstract: Analysis of single-cell RNA sequencing (scRNA-Seq) data often involves filtering out uninteresting or poorly measured genes and dimensionality reduction to reduce noise and simplify data visualization. However, techniques such as principal components ... ...

Abstract	Analysis of single-cell RNA sequencing (scRNA-Seq) data often involves filtering out uninteresting or poorly measured genes and dimensionality reduction to reduce noise and simplify data visualization. However, techniques such as principal components analysis (PCA) fail to preserve non-negativity and sparsity structures present in the original matrices, and the coordinates of projected cells are not easily interpretable. Commonly used thresholding methods to filter genes avoid those pitfalls, but ignore collinearity and covariance in the original matrix. We show that a deterministic column subset selection (DCSS) method possesses many of the favorable properties of common thresholding methods and PCA, while avoiding pitfalls from both. We derive new spectral bounds for DCSS. We apply DCSS to two measures of gene expression from two scRNA-Seq experiments with different clustering workflows, and compare to three thresholding methods. In each case study, the clusters based on the small subset of the complete gene expression profile selected by DCSS are similar to clusters produced from the full set. The resulting clusters are informative for cell type.
MeSH term(s)	Algorithms ; Cluster Analysis ; Gene Expression Profiling/methods ; High-Throughput Nucleotide Sequencing/methods ; Principal Component Analysis ; RNA/genetics ; RNA, Small Cytoplasmic/genetics ; Sequence Analysis, RNA/methods ; Single-Cell Analysis/methods
Chemical Substances	RNA, Small Cytoplasmic ; RNA (63231-63-0)
Language	English
Publishing date	2019-01-25
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0210571
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Article ; Online: A discriminative learning approach to differential expression analysis for single-cell RNA-seq.

Ntranos, Vasilis / Yi, Lynn / Melsted, Páll / Pachter, Lior

Nature methods

2019 Volume 16, Issue 2, Page(s) 163–166

Abstract: Single-cell RNA-seq makes it possible to characterize the transcriptomes of cell types across different conditions and to identify their transcriptional signatures via differential analysis. Our method detects changes in transcript dynamics and in ... ...

Abstract	Single-cell RNA-seq makes it possible to characterize the transcriptomes of cell types across different conditions and to identify their transcriptional signatures via differential analysis. Our method detects changes in transcript dynamics and in overall gene abundance in large numbers of cells to determine differential expression. When applied to transcript compatibility counts obtained via pseudoalignment, our approach provides a quantification-free analysis of 3' single-cell RNA-seq that can identify previously undetectable marker genes.
MeSH term(s)	Algorithms ; Computer Simulation ; Databases, Genetic ; Gene Expression Profiling ; Gene Expression Regulation ; Genetic Markers ; Humans ; Leukocytes, Mononuclear/cytology ; Protein Isoforms ; RNA/genetics ; Regression Analysis ; Sequence Analysis, RNA ; Single-Cell Analysis/instrumentation ; Single-Cell Analysis/methods ; Software ; T-Lymphocytes, Cytotoxic/cytology ; Transcriptome
Chemical Substances	Genetic Markers ; Protein Isoforms ; RNA (63231-63-0)
Language	English
Publishing date	2019-01-21
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2169522-2
ISSN	1548-7105 ; 1548-7091
ISSN (online)	1548-7105
ISSN	1548-7091
DOI	10.1038/s41592-018-0303-9
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Article ; Online: The barcode, UMI, set format and BUStools.

Melsted, Páll / Ntranos, Vasilis / Pachter, Lior

Bioinformatics (Oxford, England)

2019 Volume 35, Issue 21, Page(s) 4472–4473

Abstract: Summary: We introduce the Barcode-UMI-Set format (BUS) for representing pseudoalignments of reads from single-cell RNA-seq experiments. The format can be used with all single-cell RNA-seq technologies, and we show that BUS files can be efficiently ... ...

Abstract	Summary: We introduce the Barcode-UMI-Set format (BUS) for representing pseudoalignments of reads from single-cell RNA-seq experiments. The format can be used with all single-cell RNA-seq technologies, and we show that BUS files can be efficiently generated. BUStools is a suite of tools for working with BUS files and facilitates rapid quantification and analysis of single-cell RNA-seq data. The BUS format therefore makes possible the development of modular, technology-specific and robust workflows for single-cell RNA-seq analysis. Availability and implementation: http://BUStools.github.io/ and http://pachterlab.github.io/kallisto/singlecell.html. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Sequence Analysis, RNA ; Single-Cell Analysis ; Software ; Whole Exome Sequencing
Language	English
Publishing date	2019-05-31
Publishing country	England
Document type	Journal Article
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btz279
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Article: Investigating the sources of variable impact of pathogenic variants in monogenic metabolic conditions.

Wei, Angela / Border, Richard / Fu, Boyang / Cullina, Sinead / Brandes, Nadav / Jang, Seon-Kyeong / Sankararaman, Sriram / Kenny, Eimear / Udler, Mariam S / Ntranos, Vasilis / Zaitlen, Noah / Arboleda, Valerie

medRxiv : the preprint server for health sciences

2024

Abstract: Over three percent of people carry a dominant pathogenic variant, yet only a fraction of carriers develop disease. Disease phenotypes from carriers of variants in the same gene range from mild to severe. Here, we investigate underlying mechanisms for ... ...

Abstract	Over three percent of people carry a dominant pathogenic variant, yet only a fraction of carriers develop disease. Disease phenotypes from carriers of variants in the same gene range from mild to severe. Here, we investigate underlying mechanisms for this heterogeneity: variable variant effect sizes, carrier polygenic backgrounds, and modulation of carrier effect by genetic background (marginal epistasis). We leveraged exomes and clinical phenotypes from the UK Biobank and the Mt. Sinai BioMe Biobank to identify carriers of pathogenic variants affecting cardiometabolic traits. We employed recently developed methods to study these cohorts, observing strong statistical support and clinical translational potential for all three mechanisms of variable carrier penetrance and disease severity. For example, scores from our recent model of variant pathogenicity were tightly correlated with phenotype amongst clinical variant carriers, they predicted effects of variants of unknown significance, and they distinguished gain- from loss-of-function variants. We also found that polygenic scores predicted phenotypes amongst pathogenic carriers and that epistatic effects can exceed main carrier effects by an order of magnitude.
Language	English
Publishing date	2024-05-01
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.09.14.23295564
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Article ; Online: Extrathymic

Gillis-Buck, Eva / Miller, Haleigh / Sirota, Marina / Sanders, Stephan J / Ntranos, Vasilis / Anderson, Mark S / Gardner, James M / MacKenzie, Tippi C

Science immunology

2021 Volume 6, Issue 61

Abstract: Healthy pregnancy requires tolerance to fetal alloantigens as well as syngeneic embryonic and placental antigens. Given the importance of the autoimmune regulator ( ...

Abstract	Healthy pregnancy requires tolerance to fetal alloantigens as well as syngeneic embryonic and placental antigens. Given the importance of the autoimmune regulator (
MeSH term(s)	Animals ; Epithelial Cells/immunology ; Female ; Fetal Growth Retardation/immunology ; Fetus/immunology ; Immune Tolerance ; Male ; Mice ; Mice, Inbred BALB C ; Mice, Inbred C57BL ; Mice, Transgenic ; Placenta/immunology ; Pregnancy ; Thymus Gland/immunology ; Transcription Factors/genetics ; Transcription Factors/immunology ; AIRE Protein
Chemical Substances	Transcription Factors
Language	English
Publishing date	2021-07-16
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ISSN	2470-9468
ISSN (online)	2470-9468
DOI	10.1126/sciimmunol.abf1968
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Article ; Online: Targeted Elimination of Senescent Beta Cells Prevents Type 1 Diabetes.

Thompson, Peter J / Shah, Ajit / Ntranos, Vasilis / Van Gool, Frederic / Atkinson, Mark / Bhushan, Anil

Cell metabolism

2019 Volume 29, Issue 5, Page(s) 1045–1060.e10

Abstract: Type 1 diabetes (T1D) is an organ-specific autoimmune disease characterized by hyperglycemia due to progressive loss of pancreatic beta cells. Immune-mediated beta cell destruction drives the disease, but whether beta cells actively participate in the ... ...

Abstract	Type 1 diabetes (T1D) is an organ-specific autoimmune disease characterized by hyperglycemia due to progressive loss of pancreatic beta cells. Immune-mediated beta cell destruction drives the disease, but whether beta cells actively participate in the pathogenesis remains unclear. Here, we show that during the natural history of T1D in humans and the non-obese diabetic (NOD) mouse model, a subset of beta cells acquires a senescence-associated secretory phenotype (SASP). Senescent beta cells upregulated pro-survival mediator Bcl-2, and treatment of NOD mice with Bcl-2 inhibitors selectively eliminated these cells without altering the abundance of the immune cell types involved in the disease. Significantly, elimination of senescent beta cells halted immune-mediated beta cell destruction and was sufficient to prevent diabetes. Our findings demonstrate that beta cell senescence is a significant component of the pathogenesis of T1D and indicate that clearance of senescent beta cells could be a new therapeutic approach for T1D.
MeSH term(s)	Adolescent ; Adult ; Aged ; Animals ; Biphenyl Compounds/pharmacology ; Bridged Bicyclo Compounds, Heterocyclic/pharmacology ; Cellular Senescence/drug effects ; Child ; Child, Preschool ; Cohort Studies ; Diabetes Mellitus, Type 1/metabolism ; Diabetes Mellitus, Type 1/prevention & control ; Female ; Fibroblasts ; Humans ; Hyperglycemia/metabolism ; Hyperglycemia/prevention & control ; Insulin-Secreting Cells/metabolism ; Male ; Mice ; Mice, Inbred C57BL ; Mice, Inbred NOD ; Nitrophenols/pharmacology ; Piperazines/pharmacology ; Proto-Oncogene Proteins c-bcl-2/antagonists & inhibitors ; Proto-Oncogene Proteins c-bcl-2/metabolism ; Sulfonamides/pharmacology ; THP-1 Cells ; Young Adult
Chemical Substances	ABT-737 ; Biphenyl Compounds ; Bridged Bicyclo Compounds, Heterocyclic ; Nitrophenols ; Piperazines ; Proto-Oncogene Proteins c-bcl-2 ; Sulfonamides ; venetoclax (N54AIC43PW)
Language	English
Publishing date	2019-02-21
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2176834-1
ISSN	1932-7420 ; 1550-4131
ISSN (online)	1932-7420
ISSN	1550-4131
DOI	10.1016/j.cmet.2019.01.021
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