Biology subjects

Polak, P.

Publications and source records attributed to Polak, P..

17 recordsLinked to original sources

bioRxiv2025

Molecular basis of polyadenylated RNA fate determination in the nucleus

Eukaryotic genomes generate a plethora of polyadenylated (pA+) RNAs1,2, that are packaged into ribonucleoprotein particles (RNPs). To ensure faithful gene expression, functional pA+ RNPs, including protein-coding RNPs, are exported to the cytoplasm, while transcripts within non-functional pA+ RNPs are degraded in the nucleus1-4. How cells distinguish these opposing fates remains unknown. The DExD-box ATPase UAP56/DDX39B is a central component of functional pA+ RNPs, promoting their docking to the nuclear pore complex (NPC)-anchored transcription and export complex 2 (TREX-2) (ref.5,6), which triggers transcript release from UAP56 to facilitate export (ref.7,8). Here, we uncover that the Poly(A) tail exosome targeting (PAXT) connection9 harbors its own TREX-2-like module, which releases pA+ RNAs from UAP56 for decay by the nuclear exosome. The core of this module consists of a LENG8-PCID2-SEM1 (LENG8-PS) trimer, which we show is structurally and functionally equivalent to the central GANP-PCID2-SEM1 (GANP-PS) trimer of TREX-2. Mutagenesis and transcriptomic data demonstrate that the nuclear fate of pA+ RNPs is governed by the contending actions of nucleoplasmic PAXT and NPC-associated TREX-2, which interpret RNA-bound UAP56 as a signal for RNA decay or export, respectively. As RNA targets of PAXT are generally short and intron-poor, we propose an overall model for pA+ RNP fate determination, whereby the distinct sub-nuclear localizations of PAXT and TREX-2 govern the degradation of short non-functional pA+ RNAs while allowing export of their longer and functional counterparts.

molecular biology↗

bioRxiv2025

Learning the cellular origins of cancer using single-cell chromatin landscapes

Deciphering the cell of origin (COO) of different cancers is critical for understanding tumor development and improving diagnostic and therapeutic strategies in oncology. Previous studies demonstrated that the COO chromatin accessibility landscape shapes the genomic distribution of cancer somatic mutations. We leveraged machine learning, 559 single-cell chromatin accessibility cellular profiles, and 2,734 whole genome sequencing patient samples to predict the COO of 36 cancer subtypes with high robustness and accuracy, confirming both the known anatomical and cellular origins of numerous human cancers, often at cell subset resolution. Importantly, our data-driven approach predicts that basal cells give rise to small cell lung cancers, challenging the traditional view of neuroendocrine COO. Our study also highlights distinct cellular trajectories during cancer development of different histological subtypes and uncovers an intermediate metaplastic state during tumorigenesis for multiple gastrointestinal cancers, which have important implications for cancer prevention, early detection, and treatment stratification.

cancer biology↗

bioRxiv2025

A Broad Survey and Functional Analysis of Immunoglobulin Loci Variation in Rhesus Macaques

Rhesus macaques (RMs) are a vital model for studying human disease and invaluable to pre-clinical vaccine research, particularly for the study of broadly neutralizing antibody responses. Such studies require robust genetic resources for antibody-encoding genes within the immunoglobulin (IG) loci. The complexity of the IG loci has historically made them challenging to characterize accurately. To address this, we developed novel experimental and computational methodologies to generate the largest collection to date of integrated antibody repertoire and long-read genomic sequencing data in 106 Indian origin RMs. We created a comprehensive resource of IG heavy and light chain variable (V), diversity (D), and joining (J) alleles, as well as leader, intronic, and recombination signal sequences (RSSs), including the curation of 1474 novel alleles, unveiling tremendous diversity, and expanding existing IG allele sets by 60%. This publicly available, continually updated resource (https://vdjbase.org/reference_book/Rhesus_Macaque) provides the foundation for advancing RM immunogenomics, vaccine discovery, and translational research.

immunology↗

bioRxiv2024

An unbiased comparison of immunoglobulin sequence aligners

Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is critical for our under-standing of the adaptive immune systems dynamics in health and disease. Reliable analysis of AIRR-seq data depends on accurate Immunoglobulin (Ig) sequence alignment. Various Ig sequence aligners exist, but there is no unified benchmarking standard representing the complexities of AIRR-seq data, obscuring objective comparisons of aligners across tasks. Here, we introduce GenAIRR, an efficient simulation framework for generating Ig sequences alongside their ground truths. GenAIRR realistically simulates the intricacies of V(D)J recombination, somatic hypermutation, and an array of sequence corruptions. We comprehensively assessed prominent Ig sequence aligners across various metrics, unveiling unique performance characteristics for each aligner. The GenAIRR-produced datasets, combined with the proposed rigorous evaluation criteria, establish a solid basis for unbiased benchmarking of immunogenetics computational tools. It sets up the ground for further improving the crucial task of Ig sequence alignment, ultimately enhancing our understanding of adaptive immunity.

bioinformatics↗

bioRxiv2023

Dual agonistic and antagonistic roles of ZC3H18 provides for co-activation of distinct nuclear RNA decay pathways

The RNA exosome is a versatile ribonuclease. In the nucleoplasm of mammalian cells, it is assisted by its adaptors the Nuclear EXosome Targeting (NEXT) complex and the PolyA eXosome Targeting (PAXT) connection. Via its association with the ARS2 and ZC3H18 proteins, NEXT/exosome is recruited to capped and short unadenylated transcripts. Conversely, PAXT/exosome was considered to target longer and adenylated substrates via their poly(A) tails. Here, mutational analysis of the core PAXT component ZFC3H1 uncovers a separate branch of the PAXT pathway, which targets short adenylated RNAs and relies on a direct ARS2-ZFC3H1 interaction. We further demonstrate that similar acidic-rich short linear motifs of ZFC3H1 and ZC3H18 compete for a common ARS2 epitope. Consequently, while promoting NEXT function, ZC3H18 antagonizes PAXT activity. We suggest that this unprecedented organization of RNA decay complexes provides co-activation of NEXT and PAXT at loci with abundant production of short exosome substrates.

molecular biology↗

bioRxiv2022

IGHV allele similarity clustering improves genotype inference from adaptive immune receptor repertoire sequencing data

In adaptive immune receptor repertoire analysis, determining the germline variable (V) allele associated with each T- and B-cell receptor sequence is a crucial step. This process is highly impacted by allele annotations. Aligning sequences, assigning them to specific germline alleles, and inferring individual genotypes are challenging when the repertoire is highly mutated, or sequence reads do not cover the whole V region. Here, we propose an alternative naming scheme for the V alleles as well as a novel method to infer individual genotypes. We demonstrate the strength of the two by comparing their outcomes to other genotype inference methods and validated the genotype approach with independent genomic long read data. The naming scheme is compatible with current annotation tools and pipelines. Analysis results can be converted from the proposed naming scheme to the nomenclature determined by the International Union of Immunological Societies (IUIS). Both the naming scheme and the genotype procedure are implemented in a freely available R package (PIgLET). To allow researchers to explore further the approach on real data and to adapt it for their future uses, we also created an interactive website (https://yaarilab.github.io/IGHV_reference_book).

immunology↗

bioRxiv2022

Altered somatic hypermutation patterns in COVID-19 patients classifies disease severity

The success of the human body in fighting SARS-CoV-2 infection relies on lymphocytes and their antigen receptors. Identifying and characterizing clinically relevant receptors is of utmost importance. We report here the application of a machine learning approach, utilizing B cell receptor repertoire sequencing data from severely and mildly infected individuals with SARS-CoV-2 compared with uninfected controls. In contrast to previous studies, our approach successfully stratifies non-infected from infected individuals, as well as disease level of severity. The features that drive this classification are based on somatic hypermutation patterns, and point to alterations in the somatic hypermutation process in COVID-19 patients. These features may be used to build and adapt therapeutic strategies to COVID-19, in particular to quantitatively assess potential diagnostic and therapeutic antibodies. These results constitute a proof of concept for future epidemiological challenges.

immunology↗

bioRxiv2022

COOBoostR: an extreme gradient boosting-based tool for robust tissue or cell-of-origin prediction of tumors

We here present COOBoostR (https://github.com/SWJ9385/COOBoostR), a computational method designed for the putative prediction of tissue-or cell-of-origin of various cancer types. COOBoostR leverages regional somatic mutation density information and chromatin mark features to be applied to an extreme gradient boosting-based machine-learning algorithm. COOBoostR ranks chromatin marks from various tissue and cell types which best explain the somatic mutation density landscape of any sample of interest. Through integrating either ChIP-seq based chromatin data or bulk/single cell chromatin accessibility data along with regional somatic mutation density data derived from normal cells/tissue, precancerous lesions, and cancer types, we show that COOBoostR outperforms existing random forest-based methods in prediction speed with comparable or better tissue or cell-of-origin prediction performance. In addition, our results suggest a dynamic somatic mutation accumulation at the normal tissue or cell stage which could be intertwined with the changes in open chromatin marks and enhancer sites. These results further represent chromatin marks shaping the somatic mutation landscape at the early stage of mutation accumulation, possibly even before the initiation of precancerous lesions or neoplasia.

bioinformatics↗

bioRxiv2021

Diversity and distribution of sulfur metabolism in the human gut microbiome and its association with colorectal cancer

Microbial sulfidogenesis produces genotoxic hydrogen sulfide (H2S) in the human gut using inorganic (sulfate) and organic (taurine/cysteine/methionine) substrates, however the majority of studies have focused on sulfate reduction using dissimilatory sulfite reductases (Dsr). Recent evidence implicates microbial sulfidogenesis as a potential trigger of colorectal cancer (CRC), highlighting the need for comprehensive knowledge of sulfur metabolism within the human gut. Here we show that microbial sulfur metabolism is more abundant and diverse than previously studied and is statistically associated with CRC. Using ~17,000 bacterial genomes from publicly available stool metagenomes, we studied the diversity of sulfur metabolic genes in 667 participants across different health statuses: healthy, adenoma, and carcinoma. Sulfidogenic genes were harbored by 142 bacterial genera and both organic and inorganic sulfidogenic genes were associated with carcinoma. Significantly, anaerobic sulfite reductases were twice as abundant as dsr. We identified twelve potential pathways for reductive taurine metabolism including novel pathways, and prevalence of organic sulfur metabolic genes indicate these substrates may be the most abundant source of microbially derived H2S. Our findings significantly expand knowledge of microbial sulfur metabolism in the human gut, and highlight key gaps that limit understanding of its potential contributions to the pathogenesis of CRC.

microbiology↗

bioRxiv2021

T Cell Receptor Beta (TRB) Germline Variability is Revealed by Inference From Repertoire Data

BackgroundT and B cell receptor (TCR, BCR) repertoires constitute the foundation of adaptive immunity. Adaptive immune receptor repertoire sequencing (AIRR-seq) is a common approach to study immune system dynamics. Understanding the genetic factors influencing the composition and dynamics of these repertoires is of major scientific and clinical importance. The chromosomal loci encoding for the variable regions of TCRs and BCRs are challenging to decipher due to repetitive elements and undocumented structural variants. MethodsTo confront this challenge, AIRR-seq-based methods have recently been developed for B cells, enabling genotype and haplotype inference and discovery of undocumented alleles. However, this approach relies on complete coverage of the receptors variable regions, whereas most T cell studies sequence a small fraction of that region. Here, we adapted a B cell pipeline for undocumented alleles, genotype, and haplotype inference for full and partial TCR sequences. The pipeline also deals with gene assignment ambiguities, which is especially important in the analysis of data-sets of partial sequences. ResultsWe identified 39 undocumented polymorphisms in T cell receptor Beta V (TRBV) and 31 undocumented 5 UTR sequences. A subset of these inferences was also observed using independent genomic approaches. We found that a single nucleotide polymorphism differentiating between the two documented T cell receptor Beta D2 (TRBD2) alleles is strongly associated with dramatic changes in the expressed repertoire. ConclusionsWe reveal a rich picture of germline variability, and demonstrate how a single nucleotide polymorphism dramatically affects the composition of the whole repertoire. Our findings provide a basis for annotation of TCR repertoires for future basic and clinical studies.

immunology↗

bioRxiv2021

Genome-wide mutational signatures of immunological diversification in normal lymphocytes

A lymphocyte suffers many threats to its genome, including programmed mutation during differentiation, antigen-driven proliferation and residency in diverse microenvironments. After developing protocols for single-cell lymphocyte expansions, we sequenced whole genomes from 717 normal naive and memory B and T lymphocytes and hematopoietic stem cells. Lymphocytes carried more point mutations and structural variation than stem cells, accruing at higher rates in T than B cells, attributable to both exogenous and endogenous mutational processes. Ultraviolet light exposure and other sporadic mutational processes generated hundreds to thousands of mutations in some memory lymphocytes. Memory B cells acquired, on average, 18 off-target mutations genome-wide for every one on-target IGV mutation during the germinal center reaction. Structural variation was 16-fold higher in lymphocytes than stem cells, with ~15% of deletions being attributable to off-target RAG activity. One Sentence SummaryThe mutational landscape of normal lymphocytes chronicles the off-target effects of programmed genome engineering during immunological diversification and the consequences of differentiation, proliferation and residency in diverse microenvironments.

genomics↗

bioRxiv2020

Machine learning analysis of naïve B-cell receptor repertoires stratifies celiac disease patients and controls

Celiac disease (CeD) is a common autoimmune disorder caused by an abnormal immune response to dietary gluten proteins. The disease has high heritability. HLA is the major susceptibility factor, and the HLA effect is mediated via presentation of deamidated gluten peptides by disease-associated HLA-DQ variants to CD4+ T cells. In addition to gluten-specific CD4+ T cells the patients have antibodies to transglutaminase 2 (autoantigen) and deamidated gluten peptides. These disease-specific antibodies recognize defined epitopes and they display common usage of specific heavy and light chains across patients. Interactions between T cells and B cells are likely central in the pathogenesis, but how the repertoires of naive T and B cells relate to the pathogenic effector cells is unexplored. To this end, we applied machine learning classification models to naive B cell receptor (BCR) repertoires from CeD patients and healthy controls. Strikingly, we obtained a promising classification performance with an F1 score of 85%. Clusters of heavy and light chain sequences were inferred and used as features for the model, and signatures associated with the disease were then characterized. These signatures included amino acid (AA) 3-mers with distinct bio-physiochemical characteristics and enriched V and J genes. We found that CeD-associated clusters can be identified and that common motifs can be characterized from naive BCR repertoires. The results may indicate a genetic influence by BCR encoding genes in CeD. Analysis of naive BCRs as presented here may become an important part of assessing the risk of individuals to develop CeD. Our model demonstrates the potential of using BCR repertoires and in particular, naive BCR repertoires, as disease susceptibility markers.

immunology↗

bioRxiv2019

Tumor mutational landscape is a record of the pre-malignant state

Chromatin structure has a major influence on the cell-specific density of somatic mutations along the cancer genome. Here, we present a pan-cancer study in which we searched for the putative cancer cell-of-origin of 2,550 whole genomes, representing 32 cancer types by matching their mutational landscape to the regional patterns of chromatin modifications ascertained in 104 normal tissue types. We found that, in almost all cancer types, the cell-of-origin can be predicted solely from their DNA sequences. Our analysis validated the hypothesis that high-grade serous ovarian cancer originates in the fallopian tube and identified distinct origins of breast cancer subtypes. We also demonstrated that the technique is equally capable of identifying the cell-of-origin for a series of 2,044 metastatic samples from 22 of the tumor types available as primaries. Moreover, cancer drivers, whether inherited or acquired, reside in active chromatin regions in the respective cell-of-origin. Taken together, our findings highlight that many somatic mutations accumulate while the chromatin structure of the cell-of-origin is maintained and that this historical record, captured in the DNA, can be used to identify the often elusive cancer cell-of-origin.

genomics↗

bioRxiv2019

Somatic mutation landscape reveals differential variability of cell-of-origin for primary liver cancer

BackgroundPrimary liver tissue cancers display consistent increase in global disease burden and mortality. Identification of cell-of-origins for primary liver cancers would be a necessity to expand options for designing relevant therapeutics and preventive medicine for these cancer types. Previous reports on cell-of-origin for primary liver cancers was mainly from animal studies, and integrative research utilizing human specimen data was poorly established.\n\nMethodsWe analyzed a whole-genome sequencing data set for a total of 363 tumor and progenitor tissues along with 423 normal tissue epigenomic marks to predict the cell-of-origin for primary liver cancer subtypes.\n\nResultsDespite the mixed histological features, the predicted cell-of-origin for mixed hepatocellular carcinoma / intrahepatic cholangiocarcinoma were uniformly predicted as a hepatocytic origin. Individual sample-level prediction revealed differential level of cell-of-origin heterogeneity depending on the primary liver cancer types, with more heterogeneity observed in intrahepatic cholangiocarcinomas. Additional analyses on the whole genome sequencing data of hepatic progenitor cells suggest these progenitor cells might not a direct cell-of-origin for liver cancers.\n\nConclusionsThese results provide novel insights on the heterogeneous nature and potential contributors of cell-of-origin predictions for primary liver cancers.

genomics↗

bioRxiv2018

A comprehensive analysis of RNA sequences reveals macroscopic somatic clonal expansion across normal tissues

Cancer genome studies have significantly advanced our knowledge of somatic mutations. However, how these mutations accumulate in normal cells and whether they promote pre-cancerous lesions remains poorly understood. Here we perform a comprehensive analysis of normal tissues by utilizing RNA sequencing data from [~]6,700 samples across 29 normal tissues collected as part of the Genotype-Tissue Expression (GTEx) project. We identify somatic mutations using a newly developed pipeline, RNA-MuTect, for calling somatic mutations directly from RNA-seq samples and their matched-normal DNA. When applied to the GTEx dataset, we detect multiple variants across different tissues and find that mutation burden is associated with both the age of the individual and tissue proliferation rate. We also detect hotspot cancer mutations that share tissue specificity with their matched cancer type. This study is the first to analyze a large number of samples across multiple normal tissues, identifying clones with genomic aberrations observed in cancer.

genomics↗

bioRxiv2018

Mosaic deletion patterns of the human antibody heavy chain gene locus

Analysis of antibody repertoires by high-throughput sequencing is of major importance in understanding adaptive immune responses. Our knowledge of variations in the genomic loci encoding antibody genes is incomplete, mostly due to technical difficulties in aligning short reads to these highly repetitive loci. The partial knowledge results in conflicting V-D-J gene assignments between different algorithms, and biased genotype and haplotype inference. Previous studies have shown that haplotypes can be inferred by taking advantage of IGHJ6 heterozygosity, observed in approximately one third of the population. Here, we propose a robust novel method for determining V-D-J haplotypes by adapting a Bayesian framework. Our method extends haplotype inference to IGHD- and IGHV-based analysis, thereby enabling inference of complex genetic events like deletions and copy number variations in the entire population. We generated the largest multi individual data set, to date, of naive B-cell repertoires, and tested our method on it. We present evidence for allele usage bias, as well as a mosaic, tiled pattern of deleted and present IGHD and IGHV nearby genes, across the population. The inferred haplotypes and deletion patterns may have clinical implications for genetic predispositions to diseases. Our findings greatly expand the knowledge that can be extracted from antibody repertoire sequencing data.

genomics↗

bioRxiv2017

Accurate Discrimination of 23 Major Cancer Types via Whole Genome Somatic Mutation Patterns

The two strongest factors predicting a human cancers clinical behaviour are the primary tumours anatomic organ of origin and its histopathology. However, roughly 3% of the time a cancer presents with metastatic disease and no primary can be determined even after a thorough radiological survey. A related dilemma arises when a radiologically defined mass is sampled by cytology yielding cancerous cells, but the cytologist cannot distinguish between a primary tumour and a metastasis from elsewhere.\n\nHere we use whole genome sequencing (WGS) data from the ICGC/TCGA PanCancer Analysis of Whole Genomes (PCAWG) project to develop a machine learning classifier able to accurately distinguish among 23 major cancer types using information derived from somatic mutations alone. This demonstrates the feasibility of automated cancer type discrimination based on next-generation sequencing of clinical samples. In addition, this work opens the possibility of determining the origin of tumours detected by the emerging technology of deep sequencing of circulating cell-free DNA in blood plasma.

cancer biology↗