Biology subjects

Elyanow, R.

Publications and source records attributed to Elyanow, R..

8 recordsLinked to original sources

bioRxiv2025

Shared TCRs in peripheral blood offer robust celiac disease classification independent of gluten Intake

Background and AimsCeliac disease (CeD) is a chronic digestive autoimmune disorder affecting approximately 1% of the worldwide population. It is driven by T cells activated by specific HLA-DQ2 or HLA-DQ8 molecules leading to the destruction of intestinal villi. We aimed to characterize shared CeD-specific T cells in patients on and off a gluten-free diet (GFD) from a large cohort of cases and controls. MethodsWe performed bulk TCR{beta} immune sequencing of the peripheral blood of 1,604 biopsy-confirmed CeD patients (1,339 on GFD, 265 on normal diet) and 1,100 controls. We identified over 300 TCR{beta}s enriched in CeD cases versus controls in an HLA-aware manner, controlling for CeD risk alleles. ResultsCeD-associated TCR{beta}s were found to be more predictive of disease than previously characterized gliadin and glutenin-binding TCRs in a validation cohort. Furthermore, the clonal breadth of these TCR{beta}s was associated with increased intestinal damage. Immune sequencing of the peripheral blood also uncovered repertoire-level differences between CeD patients and controls. CeD patients displayed significantly higher productive clonality compared to age-matched controls as well as expansion of TCR{beta}s specific to cytomegalovirus (CMV) and Epstein-Barr Virus (EBV). ConclusionsThese findings underscore the value of unbiased immune repertoire sequencing to identify novel biomarkers for autoimmune disease and to discover new disease mechanisms which can improve both diagnosis and treatment of disease.

immunology↗

bioRxiv2024

Large-scale statistical mapping of T-cell receptor β sequences to Human Leukocyte Antigens

T-cell receptors (TCRs) interacting with peptides presented by human leukocyte antigens (HLAs) are the foundation of the adaptive immune system but population-level analysis of TCR-HLA interactions is lacking. Here we statistically associate[~] 106 public TCRs to specific HLAs using the TCR{beta} repertoires sampled from 4,144 HLA-genotyped subjects. The TCRs we associate are specific to unique HLA allotypes, not allelic groups, and to the paired -{beta} heterodimer of class II HLAs though exceptions are observed. This specificity permits highly accurate imputation of 248 class I and II HLAs from the TCR{beta} repertoire. Notably, 45 HLA-DP and -DQ heterodimers lack associated TCRs because they likely arise from non-functional trans-complementation. The public class I and II HLA-associated TCRs we identify are primarily expressed on CD8+ and CD4+ memory T cells, respectively, which are responding to various common antigens. Our results recapitulate fundamental biology, provide insights into the functionality of HLAs and demonstrate the power and potential of population-level TCR repertoire sequencing.

immunology↗

bioRxiv2024

Identifying immune signatures of common exposures through co-occurrence of T-cell receptors in tens of thousands of donors

BackgroundMemory T cells are records of clonal expansion from prior immune exposures such as infections, vaccines and chronic diseases. Some of the receptors of these expanded T cell clones in a typical immune repertoire are highly public (present in many individuals) because they respond to the same peptide from a prevalent immune exposure, presented by the same Human Leukocyte Antigen (HLA) allele. Only a tiny fraction of public T-cell receptor {beta} sequences (TCRs) have known associations with exposures or specific peptides. MethodsWe mined the TCR repertoires of tens of thousands of donors to define "ECOclusters": clusters of public TCRs that tend to occur in the same donors. First, we built models to infer donor HLA type from the TCR repertoire, then associated public TCRs with HLA alleles. Next, we derived co-occurrence clusters of TCRs responding to antigens presented by the same HLA allele, then combined those clusters by co-occurrence across HLA alleles. Each such cross-HLA ECOcluster putatively represents a public TCR signature of a single exposure. ResultsWe constructed sensitive, specific models to predict the presence of 220 HLA alleles from TCR repertoires and clustered 8,618,285 HLA allele-associated TCRs to define 11,058 ECOclusters. Using serologically labeled repertoires, we identified ECOclusters associated with HSV-1, HSV-2, EBV, Parvovirus, Toxoplasma gondii, Cytomegalovirus and SARS-CoV-2, and constructed sensitive, specific classifiers of exposure. ECOclusters represent a step toward deciphering the ledger of immune exposure history encoded by the T-cell repertoire.

immunology↗

bioRxiv2020

Ecological load and balancing selection in circumboreal barnacles

Acorn barnacle adults experience environmental heterogeneity at various spatial scales of their circumboreal habitat, raising the question of how adaptation to high environmental variability is maintained in the face of strong juvenile dispersal and mortality. Here we show that 4% of genes in the barnacle genome experience balancing selection across the entire range of the species. Many of these genes harbor mutations maintained across 2 million years of evolution between the Pacific and Atlantic oceans. These genes are involved in ion regulation, pain reception, and heat tolerance, functions which are essential in highly variable ecosystems. The data also reveal complex population structure within and between basins, driven by the trans-Arctic interchange and the last glaciation. Divergence between Atlantic and Pacific populations is high, foreshadowing the onset of allopatric speciation, and suggesting that balancing selection is strong enough to maintain functional variation for millions of years in the face of complex demography.

evolutionary biology↗

bioRxiv2020

STARCH: Copy number and clone inference from spatial transcriptomics data

Tumors are highly heterogeneous, consisting of cell populations with both transcriptional and genetic diversity. These diverse cell populations are spatially organized within a tumor, creating a distinct tumor microenvironment. A new technology called spatial transcriptomics can measure spatial patterns of gene expression within a tissue by sequencing RNA transcripts from a grid of spots, each containing a small number of cells. In tumor cells, these gene expression patterns represent the combined contribution of regulatory mechanisms, which alter the rate at which a gene is transcribed, and genetic diversity, particularly copy number aberrations (CNAs) which alter the number of copies of a gene in the genome. CNAs are common in tumors and often promote cancer growth through upregulation of oncogenes or downregulation of tumor-suppressor genes. We introduce a new method STARCH (Spatial Transcriptomics Algorithm Reconstructing Copy-number Heterogeneity) to infer CNAs from spatial transcriptomics data. STARCH overcomes challenges in inferring CNAs from RNA-sequencing data by leveraging the observation that cells located nearby in a tumor are likely to share similar CNAs. We find that STARCH outperforms existing methods for inferring CNAs from RNA-sequencing data without incorporating spatial information.

bioinformatics↗

bioRxiv2020

NetMix: A network-structured mixture model for reduced-bias estimation of altered subnetworks

A classic problem in computational biology is the identification of altered subnetworks: subnetworks of an interaction network that contain genes/proteins that are differentially expressed, highly mutated, or otherwise aberrant compared to other genes/proteins. Numerous methods have been developed to solve this problem under various assumptions, but the statistical properties of these methods are often unknown. For example, some widely-used methods are reported to output very large subnetworks that are difficult to interpret biologically. In this work, we formulate the identification of altered subnetworks as the problem of estimating the parameters of a class of probability distributions which we call the Altered Subset Distribution (ASD). We derive a connection between a popular method, jActiveModules, and the maximum likelihood estimator (MLE) of the ASD. We show that the MLE is statistically biased, explaining the large subnetworks output by jActiveModules. We introduce NetMix, an algorithm that uses Gaussian mixture models to obtain less biased estimates of the parameters of the ASD. We demonstrate that NetMix outperforms existing methods in identifying altered subnetworks on both simulated and real data, including the identification of differentially expressed genes from both microarray and RNA-seq experiments and the identification of cancer driver genes in somatic mutation data. AvailabilityNetMix is available online at https://github.com/raphael-group/netmix. Contactbraphael@princeton.edu

bioinformatics↗

bioRxiv2019

netNMF-sc: Leveraging gene-gene interactions for imputation and dimensionality reduction in single-cell expression analysis

MotivationSingle-cell RNA-sequencing (scRNA-seq) enables high throughput measurement of RNA expression in individual cells. Due to technical limitations, scRNA-seq data often contain zero counts for many transcripts in individual cells. These zero counts, or dropout events, complicate the analysis of scRNA-seq data using standard analysis methods developed for bulk RNA-seq data. Current scRNA-seq analysis methods typically overcome dropout by combining information across cells, leveraging the observation that cells generally occupy a small number of RNA expression states. ResultsWe introduce netNMF-sc, an algorithm for scRNA-seq analysis that leverages information across both cells and genes. netNMF-sc combines network-regularized non-negative matrix factorization with a procedure for handling zero inflation in transcript count matrices. The matrix factorization results in a low-dimensional representation of the transcript count matrix, which imputes gene abundance for both zero and non-zero entries and can be used to cluster cells. The network regularization leverages prior knowledge of gene-gene interactions, encouraging pairs of genes with known interactions to be close in the low-dimensional representation. We show that netNMF-sc outperforms existing methods on simulated and real scRNA-seq data, with increasing advantage at higher dropout rates (e.g. above 60%). Furthermore, we show that the results from netNMF-sc - including estimation of gene-gene covariance - are robust to choice of network, with more representative networks leading to greater performance gains. AvailabilitynetNMF-sc is available at github.com/raphael-group/netNMF-sc. Contactbraphael@princeton.edu

genomics↗

bioRxiv2017

Identifying structural variants using linked-read sequencing data

Structural variation, including large deletions, duplications, inversions, translocations, and other rearrangements, is common in human and cancer genomes. A number of methods have been developed to identify structural variants from Illumina short-read sequencing data. However, reliable identification of structural variants remains challenging because many variants have breakpoints in repetitive regions of the genome and thus are difficult to identify with short reads. The recently developed linked-read sequencing technology from 10X Genomics combines a novel barcoding strategy with Illumina sequencing. This technology labels all reads that originate from a small number (~5-10) DNA molecules ~50Kbp in length with the same molecular barcode. These barcoded reads contain long-range sequence information that is advantageous for identification of structural variants. We present Novel Adjacency Identification with Barcoded Reads (NAIBR), an algorithm to identify structural variants in linked-read sequencing data. NAIBR predicts novel adjacencies in a individual genome resulting from structural variants using a probabilistic model that combines multiple signals in barcoded reads. We show that NAIBR outperforms several existing methods for structural variant identification - including two recent methods that also analyze linked-reads - on simulated sequencing data and 10X whole-genome sequencing data from the NA12878 human genome and the HCC1954 breast cancer cell line. Several of the novel somatic structural variants identified in HCC1954 overlap known cancer genes.

genomics↗