Biology subjects

Kossinna, P.

Publications and source records attributed to Kossinna, P..

11 recordsLinked to original sources

bioRxiv2026

Somatic Genome Diversification and Clonal Evolution of Pathogenic B Cells in Systemic Autoimmunity

Systemic autoimmune rheumatic diseases are characterized by persistent immune activation and clinical heterogeneity, yet the molecular processes sustaining and diversifying pathogenic immune states remain incompletely understood. Here, we investigate somatic genome diversification as a potential driver of immune variation and disease progression. Using Systemic Lupus Erythematosus as a model disease, we generated a subset-resolved map of somatic mutations across B-cell subsets from 35 patients. Double-negative (DN) B cells carried the highest mutational burden, which was associated with disease duration and immunosuppressive therapy, rather than with disease activity. Integration with single-cell transcriptomes linked DN mutations to dysregulated signalling and proteostasis. Notably, DN cells harboured mutations in genes recurrently altered in B-cell lymphomas. Together, these findings identify somatic genome diversification as a feature of pathogenic B-cell subsets and provide a resource for understanding how chronic stimulation and therapeutic pressure shape the clonal evolution of autoimmunity. KEY POINTSO_LIDouble-negative B cells in SLE carry the highest somatic mutational burden among circulating B-cell subsets and show evidence of treatment-associated mutational processes. C_LIO_LIPathogenic variants in DN cells are acquired de novo or through selective expansion of pre-existing variant-bearing clones. C_LIO_LIAcquisition of lymphoma-relevant mutations by DN cells is common, supporting a molecular-level linkage between chronic systemic autoimmunity and B-cell malignancies. C_LIO_LIThe high-depth, subset-resolved map provides a foundational resource for the functional prioritization of variants and the development of targeted sequencing strategies for monitoring pathogenic clones. C_LI

genomics↗

bioRxiv2026

Extra-lineage tissue programs define the transcription states of human pancreatic cancer

Cancers acquire alternate transcriptional states as they evolve, but the origins, timing and determinants of this plasticity are poorly understood in many tumours. We investigated the transcriptional states of pancreatic cancer by integrating [~]1000 tumour-enriched genomes and transcriptomes from 464 patients combined with scRNA-seq, multiome profiling, and spatial proteomics. Four epithelial states covering the spectrum of lineage plasticity were identified (Classical-1, Classical-2, Basal-1, Basal-2). Comparing these states to normal and pan-cancer human single cell atlases showed each state reflects distinct tissue programs found in other malignancies. Single cell analysis uncovered that the main transcription state of this disease (Classical-1) emerges before KRAS mutations. Spatial proteomics from patients and cancer-free donors showed that the Classical-1 program emerges during acinar-to-ductal metaplasia, and also unexpectedly, in normal ducts without disrupting their morphology. Overall, these findings link the extensive lineage plasticity potential of this organ to the origins of the transcriptional states.

cancer biology↗

bioRxiv2025

iSHARC: Integrating scMultiome data for heterogeneity and regulatory analysis in cancer

SummaryThe 10x Genomics single cell Multiome (scMultiome) assay enables the simultaneous profiling of chromatin accessibility and gene expression from the same nucleus, and has increasingly been utilized in revealing cellular heterogeneity and gene regulation in cancers. However, a dedicated bioinformatics pipeline specifically designed for this type of data is still lacking. Here we present iSHARC, a streamlined pipeline for quality control, modality integration, clustering, cell type annotation, and regulatory mechanism analysis of individual scMultiome data, as well as for integrating multiple samples. The main advantages of iSHARC are: 1) easy implementation, execution and extension through a modular Snakemake workflow management system; 2) flexible analysis and parameters customization via a single configuration file; and 3) comprehensive accessibility by providing different access points to results and detailed summary reports from a single run. Availability and implementationThis pipeline is an open-source software under the MIT license and it is freely available at https://github.com/yzeng-lol/iSHARC. Contactyong.zeng@uhn.ca or hansen.he@uhn.ca or mathieu.lupien@uhn.ca Supplementary informationSupplementary data are appended.

bioinformatics↗

bioRxiv2024

Stabilized marker gene identification and functional annotation from single-cell transcriptomic data

With the rapid emergence of single-cell transcriptomics datasets, reproducible marker genes and functional annotation of cell type or state is becoming increasingly important. Conventional methods that rely on differential gene expression (DEG) analysis lack both consistency across datasets and functional annotations of selected markers. Here, we present scSCOPE, an R- based platform that utilizes stabilized LASSO (Least Absolute Shrinkage and Selection Operator) feature selection, bootstrapped co-expression networks, and pathways enrichment to identify reproducible and functionally relevant marker genes and associated pathways for cell type identification and functional annotation in scRNAseq datasets. Using 8 scRNAseq datasets of immune cell types from human and mouse tissues generated by different sequencing technologies, we show that scSCOPE outperforms other popular methods by automatically identifying marker genes and pathways with the highest consistency across all datasets. We also demonstrate that scSCOPEs gene co-expression and pathway analyses provide in-depth molecular insights into the functionality of identified marker genes. We anticipate that scSCOPE will greatly improve cell type/state annotation and accelerate the design of experimental validation and functional investigations on cell heterogeneity.

bioinformatics↗

bioRxiv2023

IBAS: Interaction-bridged association studies discovering novel genes underlying complex traits

The contribution of genetic variants to a complex phenotype may be mediated by various forms of complicated interactions. Currently, the discovery of genetic variants underlying interaction is limited, partly due to that the real interaction patterns are diverse and unknown, whereas exhaustively examining all potential combinations confers the risk of overfitting and instability. We propose IBAS, Interaction-Bridged Association Study, a new model using statistical learning techniques to extract representations of interaction patterns in transcriptome data, which act as a mediator for the next genotype-phenotype association test. Using simulated perturbation experiments, it is demonstrated that IBAS is more robust to noise than similar mediation-based protocols replying on single-genes, i.e., transcriptome-wide association studies (TWAS). By applying IBAS to real genotype-phenotype and expression data, we reported additional genes underlying complex traits as well as their biological annotations. IBAS unlocks the power of integrating gene-gene interactions in association mapping without concerning overfitting and instability.

bioinformatics↗

bioRxiv2023

XA4C: eXplainable representation learning via Autoencoders revealing Critical genes

Machine Learning models have been frequently used in transcriptome analyses. Particularly, Representation Learning (RL), e.g., autoencoders, are effective in learning critical representations in noisy data. However, learned representations, e.g., the "latent variables" in an autoencoder, are difficult to interpret, not to mention prioritizing essential genes for functional follow-up. In contrast, in traditional analyses, one may identify important genes such as Differentially Expressed (DiffEx), Differentially Co-Expressed (DiffCoEx), and Hub genes. Intuitively, the complex gene-gene interactions may be beyond the capture of marginal effects (DiffEx) or correlations (DiffCoEx and Hub), indicating the need of powerful RL models. However, the lack of interpretability and individual target genes is an obstacle for RLs broad use in practice. To facilitate interpretable analysis and gene-identification using RL, we propose "Critical genes", defined as genes that contribute highly to learned representations (e.g., latent variables in an autoencoder). As a proof-of-concept, supported by eXplainable Artificial Intelligence (XAI), we implemented eXplainable Autoencoder for Critical genes (XA4C) that quantifies each genes contribution to latent variables, based on which Critical genes are prioritized. Applying XA4C to gene expression data in six cancers showed that Critical genes capture essential pathways underlying cancers. Remarkably, Critical genes has little overlap with Hub or DiffEx genes, however, has a higher enrichment in a comprehensive disease gene database (DisGeNET), evidencing its potential to disclose massive unknown biology. As an example, we discovered five Critical genes sitting in the center of Lysine degradation (hsa00310) pathway, displaying distinct interaction patterns in tumor and normal tissues. In conclusion, XA4C facilitates explainable analysis using RL and Critical genes discovered by explainable RL empowers the study of complex interactions. Author SummaryWe propose a gene expression data analysis tool, XA4C, which builds an eXplainable Autoencoder to reveal Critical genes. XA4C disentangles the black box of the neural network of an autoencoder by providing each genes contribution to the latent variables in the autoencoder. Next, a genes ability to contribute to the latent variables is used to define the importance of this gene, based on which XA4C prioritizes "Critical genes". Notably, we discovered that Critical genes enjoy two properties: (1) Their overlap with traditional differentially expressed genes and hub genes are poor, suggesting that they indeed brought novel insights into transcriptome data that cannot be captured by traditional analysis. (2) The enrichment of Critical genes in a comprehensive disease gene database (DisGeNET) is higher than differentially expressed or hub genes, evidencing their strong relevance to disease pathology. Therefore, we conclude that XA4C can reveal an additional landscape of gene expression data.

bioinformatics↗

bioRxiv2023

An expression-directed linear mixed model (edLMM) discovering low-effect genetic variants

Detecting genetic variants with low effect sizes using a moderate sample size is difficult, hindering downstream efforts to learn pathology and estimating heritability. In this work, by utilizing informative weights learned from training genetically predicted gene expression models, we formed an alternative approach to estimate the polygenic term in a linear mixed model (LMM). Our LMM estimates the genetic background by incorporating their relevance to gene expression. Our protocol, expression-directed linear mixed model (edLMM), enables the discovery of subtle signals of low-effect variants using moderate sample size. By applying edLMM to cohorts of around 5,000 individuals with either binary (WTCCC) or quantitative (NFBC1966) traits, we demonstrated its power gain at the low-effect end of the genetic etiology spectrum. In aggregate, the additional low-effect variants detected by edLMM substantially improved estimation of missing heritability. edLMM moves precision medicine forward by accurately detecting the contribution of low-effect genetic variants to human diseases.

genetics↗

bioRxiv2022

cLD: Rare-variant disequilibrium between genomic regions identifies novel genomic interactions

Linkage disequilibrium (LD) is a fundamental concept in genetics; critical for studying genetic associations and molecular evolution. However, LD measurements are only reliable for common genetic variants, leaving low-frequency variants unanalyzed. In this work, we introduce cumulative LD (cLD), a stable statistic that captures the rare-variant LD between genetic regions, which reflects more biological interactions between variants, in addition to lack of recombination. We derived the theoretical variance of cLD using delta methods to demonstrate its higher stability than LD for rare variants. This property is also verified by bootstrapped simulations using real data. In application, we find cLD reveals an increased genetic association between genes in 3D chromatin interactions, a phenomenon recently reported negatively by calculating standard LD between common variants. Additionally, we show that cLD is higher between gene pairs reported in interaction databases, identifies unreported protein-protein interactions, and reveals interacting genes distinguishing case/control samples in association studies.

bioinformatics↗

bioRxiv2021

Stabilized COre gene and Pathway Election uncovers pan-cancer shared pathways and a cancer specific driver

Approaches systematically characterizing interactions via transcriptomic data usually follow two systems: (1) co-expression network analyses focusing on correlations between genes; (2) linear regressions (usually regularized) to select multiple genes jointly. Both suffer from the problem of stability: a slight change of parameterization or dataset could lead to dramatic alternations of outcomes. Here, we propose Stabilized Core gene and Pathway Election, or SCOPE, a tool integrating bootstrapped LASSO and co-expression analysis, leading to robust outcomes insensitive to variations in data. By applying SCOPE to six cancer expression datasets (BRCA, COAD, KIRC, LUAD, PRAD and THCA) in The Cancer Genome Atlas, we identified core genes capturing interaction effects in crucial pan-cancer pathways related to genome instability and DNA damage response. Moreover, we highlighted the pivotal role of CD63 as an oncogenic driver and a potential therapeutic target in kidney cancer. SCOPE enables stabilized investigations towards complex interactions using transcriptome data. Availabilityhttps://github.com/QingrunZhangLab/SCOPE

bioinformatics↗

bioRxiv2021

Explainable autoencoder-based representation learning for gene expression data

Modern machine learning methods have been extensively utilized in gene expression data analysis. In particular, autoencoders (AE) have been employed in processing noisy and heterogenous RNA-Seq data. However, AEs usually lead to "black-box" hidden variables difficult to interpret, hindering downstream experimental validation and clinical translation. To bridge the gap between complicated models and biological interpretations, we developed a tool, XAE4Exp (eXplainable AutoEncoder for Expression data), which integrates AE and SHapley Additive exPlanations (SHAP), a flagship technique in the field of eXplainable AI (XAI). It quantitatively evaluates the contributions of each gene to the hidden structure learned by an AE, substantially improving the expandability of AE outcomes. By applying XAE4Exp to The Cancer Genome Atlas (TCGA) breast cancer gene expression data, we identified genes that are not differentially expressed, and pathways in various cancer-related classes. This tool will enable researchers and practitioners to analyze high-dimensional expression data intuitively, paving the way towards broader uses of deep learning. AvailabilityOpen source at https://github.com/QingrunZhangLab/Explainable-Deep-Autoencoder. Contactsqingrun.zhang@ucalgary.ca and wliao@ucalgary.ca.

bioinformatics↗

bioRxiv2020

kTWAS: integrating kernel-machine with transcriptome-wide association studies improves statistical power and reveals novel genes

The power of genotype-phenotype association mapping studies increases greatly when contributions from multiple variants in a focal region are meaningfully aggregated. Currently, there are two popular categories of variant aggregation methods. Transcriptome-wide association studies (TWAS) represent a category of emerging methods that select variants based on their effect on gene expressions, providing pretrained linear combinations of variants for downstream association mapping. In contrast, kernel methods such as SKAT model genotypic and phenotypic variance using various kernel functions that capture genetic similarity between subjects, allowing non-linear effects to be included. From the perspective of machine learning, these two methods cover two complementary aspects of feature engineering: feature selection/pruning, and feature modeling. Thus far, no thorough comparison has been made between these categories, and no methods exist which incorporate the advantages of TWAS and kernel-based methods. In this work we developed a novel method called kTWAS that applies TWAS-like feature selection to a SKAT-like kernel association test, combining the strengths of both approaches. Through extensive simulations, we demonstrate that kTWAS has higher power than TWAS and multiple SKAT-based protocols, and we identify novel disease-associated genes in WTCCC genotyping array data and MSSNG (Autism) sequence data. The source code for kTWAS and our simulations are available in our GitHub repository (https://github.com/theLongLab/kTWAS).

bioinformatics↗