Biology subjects

Sheynkman, G. M.

Publications and source records attributed to Sheynkman, G. M..

17 recordsLinked to original sources

bioRxiv2026

Targeted proteomics informed by long-read RNA-sequencing reveals protein isoform expression associated with bone mineral density

The expanding catalog of sQTLs from projects like GTEx has established that genetic risk for complex traits is frequently mediated through RNA splicing. Yet it is generally unknown whether disease-associated splice events produce stable, distinct protein isoforms and whether they are functionally consequent. Even the deepest shotgun proteomics efforts only capture a minority of annotated splice events; isoform-specific peptides are systematically under-sampled by nature of being lower abundance and intrinsically harder to detect. Here, we implement a genetics-guided targeted mass spectrometry (MS) strategy to detect splice-derived protein isoforms prioritized by sQTL colocalization. Using internal standard parallel reaction monitoring (IS-PRM), we targeted 288 peptides corresponding to 166 putative BMD-associated isoforms in differentiating osteoprogenitor cells and patient-derived primary mesenchymal stem cells (MSCs). Targeted acquisition increased recovery of isoform-informative peptides relative to conventional DDA, rescued splice events that were undetected or sparsely sampled under global acquisition, and confirmed protein-level expression of genetically prioritized isoforms in clinical material. Together, this work establishes that targeted MS is a useful approach to support validation of protein expression in studies of the genetics of splicing.

systems biology↗

bioRxiv2026

Sperm development requires TMC5-dependent mechanoresponsive calcium entry

Spermiogenesis, the transformation of round spermatids into streamlined, fertilization-competent sperm, represents cellular morphogenesis at its most extreme. Despite decades of effort, spermiogenesis remains difficult to recapitulate outside the testis, highlighting the importance of cues within the native tissue environment. The nature of these cues, however, remains poorly understood. Here, we discover that mechanical stimulation elicits Ca2+ entry in spermatids and identify transmembrane channel-like protein 5 (TMC5) as a principal mediator of this response. In both mouse and human testes, TMC5 is selectively expressed in spermatids and localized to the plasma membrane. TMC5 associates with TMC7 and calcium- and integrin-binding (CIB) proteins, mirroring TMC-CIB mechanotransduction complexes in sensory organs. Mechanistically, loss of Tmc5 blocks mechanically evoked Ca2+ entry, leading to disruptions in cytoskeletal remodeling and failure of CREM [&tau] dependent transcriptional programs required for spermiogenesis. Thus, TMC5 is required to couple mechanical cues to sperm development, with direct implications for male infertility.

cell biology↗

bioRxiv2026

Isocall enables scalable transcript identification from long-read RNA-sequencing data

Long-read RNA sequencing directly resolves the full structures of RNA transcripts. Advances in throughput now enable the generation of deeply sequenced cohorts of hundreds of samples, making joint transcript discovery across large datasets possible. However, existing transcript identification methods were designed for small datasets, which limits their applicability at this scale. Here, we present Isocall, a scalable and deterministic computational method for jointly calling transcripts from multiple PacBio long-read RNA sequencing samples. Isocall converts aligned full-length non-concatemer reads into compact per-sample transcript profiles, merges these profiles across samples, and jointly identifies known and novel transcripts supported by reads in the analyzed dataset. Filtering is tunable: presets provide coarse control and individual parameters, including relative abundance and internal priming thresholds, provide fine control. Isocall demonstrated high precision in our accuracy benchmarks, including the WTC11 SIRV spike-in controls, for which Isocall reported 0-2 false-positive transcripts per sample across the three SIRV mixes at default settings. To demonstrate scalability, we applied Isocall to 206 samples, totalling 3.5 billion raw reads, from the Human Pangenome Reference Consortium. After parallelized pbmm2 alignment and Isocall profile, the call step performed joint calling across the entire dataset in 25 minutes, using 1.3 GB of peak memory and 8 threads. Finally, in Genome in a Bottle samples with matched SNP genotypes, splice-site polymorphisms provide an additional measure of call accuracy: Isocall recovered 337 polymorphic splice sites, including a de novo donor site in BTN3A1 that corresponds to a complete isoform switch on the mutant allele.

bioinformatics↗

bioRxiv2026

eSIG-Net: Accurate prediction of single-mutation induced perturbations on protein interactions using a language model

Most proteins exert their functions in complex with other interactors. Single mutations can exhibit a profound impact on perturbing protein interactions, leading to human disease. However, predicting the effect of single mutations on protein interactions remains a major computational challenge. Deep learning, particularly protein language models or transformers, has become an effective tool in bioinformatics for protein structure prediction. However, the functional divergence of mutations makes it difficult to predict their interaction perturbation profiles. To address this fundamental challenge, we present eSIG-Net (edgetic mutation Sequence-based Interaction Grammar Network), a novel sequence-based "Interaction Language Model" for predicting protein interaction alterations caused by single mutations. eSIG-Net combines various protein sequence embeddings, introduces a mutation-encoding module with syntax and evolutionary insights, and employs contrastive learning to evaluate mutation-induced interaction changes. eSIG-Net significantly outperforms current state-of-the-art sequence-based and structure-based prediction methods at predicting mutational impact on protein interactions. We highlight examples where eSIG-Net nominates causal variants with high confidence and elucidates their functional role under relevant biological contexts. Together, eSIG-Net is a first-in-kind "interaction language model" that can accurately predict interaction-specific rewiring by single mutations with only sequence information, and exhibits generalizability across biological contexts.

bioinformatics↗

bioRxiv2026

Beyond gene length: Exon-intron architecture and isoform potential in the evolution of eukaryotic complexity

Alternative splicing is a major source of human transcriptomic and phenotypic variation, yet its evolutionary contribution to genomic complexity remains unresolved. It has been shown that a mean gene length can be the most basic but remarkably efficient proxy for multicellular genome complexity, whereas mean protein length is not, as it plateaus abruptly early in eukaryotic evolution. Here, we show that, across 2,683 genomes, exon count continues to increase beyond this transition and then rapidly saturates at [~]10 exons per gene, supporting its role as an additional dimension of genomic complexity linked to exon-intron architecture. A simple stochastic exon-splitting model reproduces the observed biphasic exon-number growth pattern and identifies minimal exon length as a key determinant.

evolutionary biology↗

bioRxiv2025

Sample-specific haplotype-resolved protein isoform characterization via long-read RNA-seq-based proteogenomics

Protein isoform inference from bottom-up mass spectrometry (MS) relies on database search strategies that assume the reference protein database accurately reflects the full repertoire of genetic and transcriptomic states present in the sample being analyzed. Long-read RNA sequencing (lrRNA-seq) now enables simultaneous recovery of complete transcript (splice) structures and the genetic variants present on each molecule, offering a direct route to allele-specific isoforms. Yet, this capability has not been fully leveraged to improve MS-based proteogenomics workflows. Here, we develop an end-to-end workflow for constructing and searching haplotype-resolved, sample-specific proteomes using matched lrRNA-seq and MS data. We benchmark phasing algorithms on PacBio lrRNA-seq from Genome-in-a-Bottle samples and identify methods that achieve high phasing accuracy and completeness. Our Snakemake pipeline leverages existing methods to perform variant calling, read-based phasing, transcript discovery, haplotype-resolved proteome construction, MS search, and downstream annotation. To demonstrate its utility, we apply the workflow to an induced pluripotent stem cell line (WTC11) and to an osteoblast differentiation time course. We show that sample-specific haplotype-resolved databases enable the detection of variant and splice peptides, allele-specific protein isoforms, and linked variants not detectable with reference proteomes. Together, our results demonstrate that lrRNA-seq-based phasing is feasible and effective for proteogenomics and provide a practical framework for allele-resolved proteome characterization in dynamic or disease-relevant settings.

bioinformatics↗

bioRxiv2025

Perplexity as a Metric for Isoform Diversity in the Human Transcriptome

Characterizing the extensive isoform diversity revealed by long-read RNA-sequencing remains challenging. After removal of technical artifacts, existing pipelines apply arbitrary expression thresholds that filter out bona fide transcript structures, misrepresenting diversity and hindering reproducibility. Instead of discarding isoforms, we propose a fundamentally distinct approach to quantifying isoform diversity using perplexity-the effective number of isoforms for a gene, derived from Shannon entropy-wherein every isoform, including low-abundance ones, contributes proportionally to a genes diversity. Analyzing 124 ENCODE4 PacBio datasets spanning 55 human cell types, we show that perplexity provides interpretable and reproducible isoform diversity measurements across genes, regulatory levels, and tissues.

genomics↗

bioRxiv2025

Quantitative isoform profiling using deep coverage long-read RNA sequencing across early endothelial differentiation

PacBio long-read RNA sequencing resolves transcripts with greater clarity than short-read technologies, yet its quantitative performance remains under-evaluated at scale. Here, we benchmark the high-throughput PacBio Kinnex platform against Illumina short-read RNA-seq using matched, deeply sequenced datasets across a time course of endothelial cell differentiation. Compared to Illumina, Kin-nex achieved comparable gene-level quantification and more accurate transcript discovery and transcript quantification. While Illumina detected more transcripts overall, many reflected potentially unstable or ambiguous estimates in complex genes. Kinnex largely avoids these issues, producing more reliable differential transcript expression (DTE) calls, despite a mild bias against short transcripts (shorter than 1.25 kb). When correcting Illumina for inferential variability, Kinnex and Illumina quantifications were highly concordant, demonstrating equivalent performance. We also benchmarked long-read tools, nominating Oarfish as the most efficient for our Kinnex data. Together, our results establish Kinnex as a reliable platform for full-length transcript quantification.

bioinformatics↗

bioRxiv2024

Protein sequencing with single amino acid resolution discerns peptides that discriminate tropomyosin proteoforms

Protein variants of the same gene--proteoforms--can have high molecular similarity yet exhibit different biological functions. Thus, identifying unique peptides that unambiguously map to proteoforms can provide crucial biological insights. In humans, four human tropomyosin (TPM) genes produce similar proteoforms that can be challenging to distinguish with standard proteomics tools. For example, TPM1 and TPM2 share 85% sequence identity, with amino acid substitutions that play unique roles in muscle contraction and myopathies. In this study, we evaluated the ability of the recently released Platinum single-molecule protein sequencer to detect proteoform-informative peptides. Platinum employs fluorophore-labeled recognizers that reversibly bind to cognate N-terminal amino acids (NAAs), enabling polypeptide sequencing within nanoscale apertures of a semiconductor chip that can accommodate single peptide molecules. As a proof of concept, we evaluated the ability of Platinum to distinguish three main types of proteoform variation: paralog-level, transcript-level, and post-translational modification (PTM). We distinguished paralogous TPM1 and TPM2 peptides differing by a single isobaric residue (leucine/isoleucine). We also distinguished tissue-specific TPM2 spliceforms. Notably, we found that a phosphotyrosine-modified peptide displayed reduced recognizer affinity for tyrosine, showing sensitivity to PTMs. This study paves the way for the targeted detection of proteoform biomarkers at the single molecule level. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/621980v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@122c6eaorg.highwire.dtl.DTLVardef@315f7forg.highwire.dtl.DTLVardef@1748af2org.highwire.dtl.DTLVardef@4e2b21_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2024

IS-PRM-based peptide targeting informed by long-read sequencing for alternative proteome detection

Alternative splicing is a major contributor of transcriptomic complexity, but the extent to which transcript isoforms are translated into stable, functional protein isoforms is unclear. Furthermore, detection of relatively scarce isoform-specific peptides is challenging, with many protein isoforms remaining uncharted due to technical limitations. Recently, a family of advanced targeted MS strategies, termed internal standard parallel reaction monitoring (IS-PRM), have demonstrated multiplexed, sensitive detection of pre-defined peptides of interest. Such approaches have not yet been used to confirm existence of novel peptides. Here, we present a targeted proteogenomic approach that leverages sample-matched long-read RNA sequencing (LR RNAseq) data to predict potential protein isoforms with prior transcript evidence. Predicted tryptic isoform-specific peptides, which are specific to individual gene product isoforms, serve as "triggers" and "targets" in the IS-PRM method, Tomahto. Using the model human stem cell line WTC11, LR RNAseq data were generated and used to inform the generation of synthetic standards for 192 isoform-specific peptides (114 isoforms from 55 genes). These synthetic "trigger" peptides were labeled with super heavy tandem mass tags (TMT) and spiked into TMT-labeled WTC11 tryptic digest, predicted to contain corresponding endogenous "target" peptides. Compared to DDA mode, Tomahto increased detectability of isoforms by 3.6-fold, resulting in the identification of five previously unannotated isoforms. Our method detected protein isoform expression for 43 out of 55 genes corresponding to 54 resolved isoforms. This LR RNA seq-informed Tomahto targeted approach, called LRP-IS-PRM, is a new modality for generating protein-level evidence of alternative isoforms - a critical first step in designing functional studies and eventually clinical assays.

molecular biology↗

bioRxiv2023

Single-cell long-read mRNA isoform regulation is pervasive across mammalian brain regions, cell types, and development

RNA isoforms influence cell identity and function. Until recently, technological limitations prevented a genome-wide appraisal of isoform influence on cell identity in various parts of the brain. Using enhanced long-read single-cell isoform sequencing, we comprehensively analyze RNA isoforms in multiple mouse brain regions, cell subtypes, and developmental timepoints from postnatal day 14 (P14) to adult (P56). For 75% of genes, full-length isoform expression varies along one or more axes of phenotypic origin, underscoring the pervasiveness of isoform regulation across multiple scales. As expected, splicing varies strongly between cell types. However, certain gene classes including neurotransmitter release and reuptake as well as synapse turnover, harbor significant variability in the same cell type across anatomical regions, suggesting differences in network activity may influence cell-type identity. Glial brain-region specificity in isoform expression includes strong poly(A)-site regulation, whereas neurons have stronger TSS regulation. Furthermore, developmental patterns of cell-type specific splicing are especially pronounced in the murine adolescent transition from P21 to P28. The same cell type traced across development shows more isoform variability than across adult anatomical regions, indicating a coordinated modulation of functional programs dictating neural development. As most cell-type specific exons in P56 mouse hippocampus behave similarly in newly generated data from human hippocampi, these principles may be extrapolated to human brain. However, human brains have evolved additional cell-type specificity in splicing, suggesting gain-of-function isoforms. Taken together, we present a detailed single-cell atlas of full-length brain isoform regulation across development and anatomical regions, providing a previously unappreciated degree of isoform variability across multiple scales of the brain.

genetics↗

bioRxiv2023

Long read proteogenomics to connect disease-associated sQTLs to the protein isoform effectors of disease

A major fraction of loci identified by genome-wide association studies (GWASs) lead to alterations in alternative splicing, but interpretation of how such alterations impact proteins is hindered by the technical limitations of short-read RNA-seq, which cannot directly link splicing events to full-length transcript or protein isoforms. Long-read RNA-seq represents a powerful tool to define and quantify transcript isoforms, and recently, infer protein isoform existence. Here we present a novel approach that integrates information from GWAS, splicing QTL (sQTL), and PacBio long-read RNA-seq in a disease-relevant model to infer the effects of sQTLs on the ultimate protein isoform products they encode. We demonstrate the utility of our approach using bone mineral density (BMD) GWAS data. We identified 1,863 sQTLs from the Genotype-Tissue Expression (GTEx) project in 732 protein-coding genes which colocalized with BMD associations (H4PP [≥] 0.75). We generated deep coverage PacBio long-read RNA-seq data (N=[~]22 million full-length reads) on human osteoblasts, identifying 68,326 protein-coding isoforms, of which 17,375 (25%) were novel. By casting the colocalized sQTLs directly onto protein isoforms, we connected 809 sQTLs to 2,029 protein isoforms from 441 genes expressed in osteoblasts. Using these data, we created one of the first proteome-scale resources defining full-length isoforms impacted by colocalized sQTLs. Overall, we found that 74 sQTLs influenced isoforms likely impacted by nonsense mediated decay (NMD) and 190 that potentially resulted in the expression of new protein isoforms. Finally, we identified colocalizing sQTLs in TPM2 for splice junctions between two mutually exclusive exons, and two different transcript termination sites, making it impossible to interpret without long-read RNA-seq data. siRNA mediated knockdown in osteoblasts showed two TPM2 isoforms with opposing effects on mineralization. We expect our approach to be widely generalizable across diverse clinical traits and accelerate system-scale analyses of protein isoform activities modulated by GWAS loci.

bioinformatics↗

bioRxiv2022

Characterization of protein isoform diversity in human umbilical vein endothelial cells (HUVECs) via long-read proteogenomics

Endothelial cells (ECs) comprise the lumenal lining of all blood vessels and are critical for the functioning of the cardiovascular system. Their phenotypes can be modulated by protein isoforms. To characterize the isoform landscape within ECs, we applied a long read proteogenomics approach to analyze human umbilical vein endothelial cells (HUVECs). Transcripts delineated from PacBio sequencing serve as the basis for a sample-specific protein database used for downstream MS analysis to infer protein isoform expression. We detected 53,836 transcript isoforms from 10,426 genes, with 22,195 of those transcripts being novel. Furthermore, the predominant isoform in HUVECs does not correspond with the accepted "reference isoform" 25% of the time, with vascular pathway-related genes among this group. We found 2,597 protein isoforms supported through unique peptides, with an additional 2,280 isoforms nominated upon incorporation of long-read transcript evidence. We characterized a novel alternative acceptor for endothelial-related gene CDH5, suggesting potential changes in its associated signaling pathways. Finally, we identified novel protein isoforms arising from a diversity of splicing mechanisms supported by uniquely mapped novel peptides. Our results represent a high resolution atlas of known and novel isoforms of potential relevance to endothelial phenotypes and function. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/490813v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@86ca88org.highwire.dtl.DTLVardef@15ab038org.highwire.dtl.DTLVardef@e7553borg.highwire.dtl.DTLVardef@18cbae8_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2022

Transformation of alignment files improves performance of variant callers for long-read RNA sequencing data

Long-read RNA sequencing (lrRNA-seq) produces detailed information about full-length transcripts, including novel and sample-specific isoforms. Furthermore, there is opportunity to call variants directly from lrRNA-seq data. However, most state-of-the-art variant callers have been developed for genomic DNA. Here, there are two objectives: first, we perform a mini-benchmark on GATK, DeepVariant, Clair3, and NanoCaller primarily on PacBio Iso-Seq, data, but also on Nanopore and Illumina RNA-seq data; second, we propose a pipeline to process spliced-alignment files, making them suitable for variant calling with DNA-based callers. With such manipulations, high calling performance can be achieved using DeepVariant on Iso-seq data.

bioinformatics↗

bioRxiv2022

Genetic regulation of human aortic smooth muscle cell gene expression and splicing predict causal coronary artery disease genes

Coronary artery disease (CAD) is the leading cause of death worldwide. Recent meta-analyses of genome-wide association studies (GWAS) have identified over 175 loci associated with CAD. The majority of these loci are in non-coding regions and are predicted to regulate gene expression. Given that vascular smooth muscle cells (SMCs) play critical roles in the development and progression of CAD, we hypothesized that a subset of the CAD GWAS risk loci are associated with the regulation of transcription in distinct SMC phenotypes. Here, we measured gene expression in SMCs isolated from the ascending aortas of 151 ethnically diverse heart transplant donors in quiescent or proliferative conditions and calculated the association of their expression and splicing with [~]6.3 million imputed single nucleotide polymorphism (SNP) markers across the genome. We identified 4,910 expression and 4,412 splice quantitative trait loci (sQTL) that represent regions of the genome associated with transcript abundance and splicing. 3,660 of the eQTLs had not been observed in the publicly available Genotype-Tissue Expression dataset. Further, 29 and 880 of the eQTLs were SMC- and sex-specific, respectively. To identify the effector transcript(s) regulated by CAD GWAS loci, we used four distinct colocalization approaches and identified 84 eQTL and 164 sQTLs that colocalized with CAD loci, highlighting the importance of genetic regulation of mRNA splicing as a molecular mechanism for CAD genetic risk. Notably, 20% and 35% of the eQTLs were unique to quiescent or proliferative SMCs, respectively. Two CAD loci colocalized with a SMC sex-specific eQTL (AL160313.1 and TERF2IP) and another locus colocalized with SMC-specific eQTL (ALKBH8). Also, 27% and 37% of the sQTLs were unique to quiescent or proliferative SMCs, respectively. The most significantly associated CAD locus, 9p21, was an sQTL for the long non-coding RNA CDKN2B-AS1, also known as ANRIL, in proliferative SMCs. Collectively, these results provide evidence for the molecular mechanisms of genetic susceptibility to CAD in distinct SMC phenotypes.

genetics↗

bioRxiv2019

ORF Capture-Seq: a versatile method for targeted identification of full-length isoforms

Most human protein-coding genes are expressed as multiple isoforms. This in turn greatly expands the functional repertoire of the encoded proteome. While at least one reliable open reading frame (ORF) model has been assigned for every gene, the majority of alternative isoforms remains uncharacterized experimentally. This is primarily due to: i) vast differences of overall levels between different isoforms expressed from common genes, and ii) the difficulty of obtaining contiguous full-length ORF sequences. Here, we present ORF Capture-Seq (OCS), a flexible and cost-effective method that addresses both challenges for targeted full-length isoform sequencing applications using collections of cloned ORFs as probes. As proof-of-concept, we show that an OCS pipeline focused on genes coding for transcription factors increases isoform detection by an order of magnitude, compared to unenriched sample. In short, OCS enables rapid discovery of isoforms from custom-selected genes and will allow mapping of the full set of human isoforms at reasonable cost.

genomics↗

bioRxiv2017

Full-length mRNA sequencing uncovers a widespread coupling between transcription and mRNA processing

The multifaceted control of gene expression requires tight coordination of regulatory mechanisms at transcriptional and post-transcriptional level. Here, we studied the interdependence of transcription, splicing and polyadenylation events on single mRNA molecules by full-length mRNA sequencing. In MCF-7 breast cancer cells, we found 2,700 genes with interdependent alternative transcription, splicing and polyadenylation events, both in proximal and distant parts of mRNA molecules. The analysis of three human primary tissues revealed similar patterns of interdependency between transcription and mRNA processing events. We predict thousands of novel Open Reading Frames from the sequence of full-length mRNAs and obtained evidence for their translation by shotgun proteomics. The mapping database rescued 358 previously unassigned peptides and improved the assignment of others. By recognizing sample-specific amino-acid changes and novel splicing patterns, full-length mRNA sequencing improved proteogenomics analysis of MCF-7 cells. Our findings demonstrate that our understanding of transcriptome complexity is far from complete and provides a basis to reveal largely unresolved mechanisms that coordinate transcription and mRNA processing.

molecular biology↗