Biology subjects

Kacprowski, T.

Publications and source records attributed to Kacprowski, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Benchmarking precision matrix estimation methods for differential co-expression network analysis

BackgroundGene expression profiling is widely used to investigate disease mechanisms, but classical approaches such as differential expression or pairwise correlation analyses provide limited interpretability. Network-based differential co-expression methods that model conditional dependencies through partial correlations offer richer insights, yet their application in high-dimensional settings requires estimation of precision matrices. Numerous precision matrix estimation methods (PMEMs) have been proposed, but their relative performance under various conditions remains unclear. ResultsSimulated gene expression datasets with known ground truth correlation structures were used to benchmark a broad set of PMEMs. Performance was strongly affected by data characteristics, including covariance structure, matrix density, covariance values, sample size-to-dimension ratio, and sampling distribution. Among the evaluated methods, GLassoElnetFast consistently showed the highest accuracy in recovering differential edges, although high signal-to-noise ratios and sufficient sample sizes remain essential for reliable inference. ConclusionsEvaluation across diverse simulation conditions demonstrated that no single metric or condition was sufficient to assess PMEM performance. Therefore, previous less extensive evaluations risked misleading conclusions. Our simulation and benchmarking framework supports future method development and ensures reproducible evaluation of newly developed approaches.

bioinformatics↗

bioRxiv2025

Regulation of the transcriptome, miRNAs, and alternative splicing in a FSGS zebrafish injury model

BackgroundFocal Segmental Glomerulosclerosis (FSGS) is a severe kidney disorder with complex and not yet fully understood pathogenesis. Alternative splicing (AS) - the generation of distinct protein isoforms from the same gene - might play a critical role by the regulation of gene functions and disease development. MethodsTo investigate the role of AS in FSGS, we used a zebrafish model, which mimics key human FSGS features, including foot process effacement, matrix accumulation, podocyte detachment and parietal epithelial cell activation. We performed total RNA sequencing of isolated zebrafish glomeruli and whole larvae, followed by integrative bioinformatic analysis to identify AS events and regulatory miRNAs. ResultsOur data revealed a downregulation of essential podocyte genes (nphs1, nphs2, podxl, wt1) and an inhibition of pathways associated with nephron development and cytoskeletal organization. We also observed increased expression of the transcription factor stat3 and disease-associated miRNAs such as miR-21 and miR-193. AS analysis identified approximately [~]7,000 splicing events, primarily exon skipping ([~]80%), affecting genes such as nphs1, magi2, and ptpro. A total of 136 and 612 alternatively spliced genes were found at 5 and 6 days post-fertilization (dpf), respectively. Isoform switch analysis uncovered 70 genes affected by AS in FSGS, including epb41l5 (linked to podocyte adhesion), fgfr1a (fibroblast growth signaling), and members of the SRSF splicing factor family (e.g., srsf3a). ConclusionsThese findings emphasize the importance of transcriptional and post-transcriptional regulation, including AS, in FSGS pathogenesis. Furthermore, they support the zebrafish model as a valuable system for identifying novel mechanisms and potential therapeutic targets for kidney diseases.

cell biology↗

bioRxiv2024

RNA sequencing depth guidelines for the study of alternative splicing

A key parameter in the experimental design of RNA-seq projects is the choice of sequencing depth. Considering a limited budget, one needs to find a tradeoff between the number of samples and the sensitivity of the analysis, particularly concerning lowly expressed genes. While previous studies have proposed a lower bound for the comprehensive analysis of differential gene expression, for the analysis of alternative splicing, it has only been proposed for human adipose tissue. However, alternative splicing differs across tissues and conditions. We analyzed publicly available and newly generated deep-sequenced paired-end RNA-seq samples (between 150 and >500 million reads, read length 50-150 bp) from human buffy coat cells and diverse sets of tissues, including gluteal subcutaneous fat, heart, and hypothalamus. Our results show that the sequencing depth typically used in published cohorts is not sufficient to comprehensively capture the landscape of alternative splicing. This motivates the use of deeper sequencing or long-read technologies in future studies. Toward this goal, we offer guidelines for choosing a suitable sequencing depth. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=177 SRC="FIGDIR/small/617406v2_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@1aecd9org.highwire.dtl.DTLVardef@1b3d0fborg.highwire.dtl.DTLVardef@5d15c4org.highwire.dtl.DTLVardef@140035b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Identification of transcriptional regulators using a combined disease module identification and prize-collecting Steiner tree approach

Transcription factors play important roles in maintaining normal biological function, and their dys-regulation can lead to the development of diseases. Identifying candidate transcription factors involved in disease pathogenesis is thus an important task for deriving mechanistic insights from gene expression data. We developed Transcriptional Regulator Identification using Prize-collecting Steiner trees (TRIPS), a workflow for identifying candidate transcriptional regulators from case-control expression data. In the first step, TRIPS combines the results of differential expression analysis with a disease module identification step to retrieve perturbed subnetworks comprising an expanded gene list. TRIPS then solves a prize-collecting Steiner tree problem on a gene regulatory network, thereby identifying candidate transcriptional modules and transcription factors. We compare TRIPS to relevant methods using publicly available disease datasets and show that the proposed workflow can recover known disease-associated transcription factors with high precision. Network perturbation analyses demonstrate the reliability of TRIPS results. We further evaluate TRIPS on Alzheimers disease, diabetic kidney disease, and prostate cancer single-cell omics datasets. Overall, TRIPS is a useful approach for prioritizing transcriptional mechanisms for further downstream analyses.

bioinformatics↗

bioRxiv2023

On the limits of 16S rRNA gene-based metagenome prediction and functional profiling

Molecular profiling techniques such as metagenomics, metatranscriptomics or metabolomics offer important insights into the functional diversity of the microbiome. In contrast, 16S rRNA gene sequencing, a widespread and cost-effective technique to measure microbial diversity, only allows for indirect estimation of microbial function. To mitigate this, tools such as PICRUSt2, Tax4Fun2, PanFP and MetGEM infer functional profiles from 16S rRNA gene sequencing data using different algorithms. Prior studies have cast doubts on the quality of these predictions, motivating us to systematically evaluate these tools using matched 16S rRNA gene sequencing, metagenomic datasets, and simulated data. Our contribution is threefold: (i) using simulated data, we investigate if technical biases could explain the discordance between inferred and expected results; (ii) considering human cohorts for type 2 diabetes, colorectal cancer and obesity, we test if health-related differential abundance measures of functional categories are concordant between 16S rRNA gene-inferred and metagenome-derived profiles and; (iii) since 16S rRNA gene copy number is an important confounder in functional profiles inference, we investigate if a customised copy number normalisation with the rrnDB database could improve the results. Our results show that 16S rRNA gene-based functional inference tools generally do not have the necessary sensitivity to delineate health-related functional changes in the microbiome and should thus be used with care. Furthermore, we outline important differences in the individual tools tested and offer recommendations for tool selection.

bioinformatics↗

bioRxiv2023

ATF6 activation alters colonic lipid metabolism causing tumor-associated microbial adaptation

Endoplasmic reticulum unfolded protein responses (UPRER) contribute to cancer development and the activating transcription factor 6 (ATF6) is involved in microbiota-dependent tumorigenesis. Here, we substantiate the clinical relevance of ATF6 in early-onset and late colorectal cancer patient cohorts. Transcriptional analysis in intestinal epithelial cells (IEC) of ATF6 transgenic mice (nATF6IEC) identified bacteria-specific changes in cellular metabolism enriched for fatty acid biosynthesis. Untargeted metabolomics and isotype-labeling confirmed ATF6-related enrichment of long chain fatty acids in colonic tissue of patients, mice and organoid cultures. FASN inhibition and microbiota transfer in germ-free nATF6IEC mice confirmed the causal involvement of ATF6-induced lipid alterations in tumorigenesis. The selective expansion of tumor-relevant microbial taxa was mechanistically linked to long chain fatty acid exposure, using bioorthogonal non-canonical amino acid tagging (BONCAT) and growth analysis of Desulfovibrio isolates. We postulate chronic ATF6 signaling in the epithelium to select for tumor-promoting microbiota by altering lipid metabolism. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/565267v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@be3756org.highwire.dtl.DTLVardef@231da4org.highwire.dtl.DTLVardef@16f3aaeorg.highwire.dtl.DTLVardef@1758fe3_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical Abstract:C_FLOATNO Chronic ATF6 signaling in the colonic epithelium alters lipid metabolism to select a tumor-promoting microbiota C_FIG O_LIBiallelic expression of activated ATF6 (p50 nuclear fragment) in intestinal epithelial cells (nATF6IEC) induces spontaneous colon tumors in SPF but not GF mice C_LIO_LIMechanistically, biallelic SPF nATF6IEC mice alter colonic lipid metabolism, including the upregulation of LCFAs and Fasn C_LIO_LIInhibition of FASN prevents colon tumor formation in mice, and reduces the tumor-promoting potential of the intestinal microbiota (FMT) C_LIO_LIExposure of fl/fl control mouse microbiota to LCFAs ex vivo translationally activates tumor-associated bacteria, including Desulfovibrio fairfieldensis C_LIO_LIHuman CRC patients show ATF6 upregulation, FASN co-occurrence and increased LCFAs in T tissue C_LIO_LIATF6 activity links with CRC-associated microbiota in patients, including Desulfovibrio C_LI Created with BioRender.com nATF6: activated activating transcription factor 6; LCFA: long-chain fatty acids; SAFA: saturated fatty acids; Fasn: fatty acid synthase; C75 i.p.: intraperitoneal injection of the Fasn inhibitor C75.

cancer biology↗

bioRxiv2023

Alternative splicing impacts microRNA regulation within coding regions

MicroRNAs (miRNAs) are small non-coding RNA molecules that bind to target sites in different gene regions and regulate post-transcriptional gene expression. Approximately 95% of human multi-exon genes can be spliced alternatively, which enables the production of functionally diverse transcripts and proteins from a single gene. Through alternative splicing, transcripts might lose the exon with the miRNA target site and become unresponsive to miRNA regulation. To check this hypothesis, we studied the role of miRNA target sites in both coding and noncoding regions using six cancer data sets from The Cancer Genome Atlas (TCGA). First, we predicted miRNA target sites on mRNAs from their sequence using TarPmiR. To check whether alternative splicing interferes with this regulation, we trained linear regression models to predict miRNA expression from transcript expression. Using nested models, we compared the predictive power of transcripts with miRNA target sites in the coding regions to that of transcripts without target sites. Models containing transcripts with target sites perform significantly better. We conclude that alternative splicing does interfere with miRNA regulation by skipping exons with miRNA target sites within the coding region.

bioinformatics↗

bioRxiv2022

Inference of differential gene regulatory networks from gene expression data using boosted differential trees

Diseases can be caused by molecular perturbations that induce specific changes in regulatory interactions and their coordinated expression, also referred to as network rewiring. However, the detection of complex changes in regulatory connections remains a challenging task and would benefit from the development of novel non-parametric approaches. We developed a new ensemble method called BoostDiff (boosted differential regression trees) to infer a differential network discriminating between two conditions. BoostDiff builds an adaptively boosted (AdaBoost) ensemble of differential trees with respect to a target condition. To build the differential trees, we propose differential variance improvement as a novel splitting criterion. Variable importance measures derived from the resulting models are used to reflect changes in gene expression predictability and to build the output differential networks. BoostDiff outperforms existing differential network methods on simulated data evaluated in two different complexity settings. We then demonstrate the power of our approach when applied to real transcriptomics data in COVID-19 and Crohns disease. BoostDiff identifies context-specific networks that are enriched with genes of known disease-relevant pathways and complements standard differential expression analyses. BoostDiff is available at https://github.com/gihannagalindez/boostdiff_inference. Author SummaryGene regulatory networks, which comprise the collection of regulatory relationships between transcription factors and their target genes, are important for controlling various molecular processes. Diseases can induce perturbations in normal gene co-expression patterns in these networks. Detecting differentially co-expressed or rewired edges between disease and healthy biological states can be thus useful for investigating the link between specific disease-associated molecular alterations and phenotype. We developed BoostDiff (boosted differential trees), an ensemble method to derive differential networks between two biological contexts. Our approach applies a boosting scheme using differential trees as base learner. A differential tree is a new tree structure that is built from two expression datasets using a splitting criterion called the differential variance improvement. The resulting BoostDiff model learns the most differentially predictive features which are then used to build the directed differential networks. BoostDiff outperforms other differential network methods on simulated data and outputs more biologically meaningful results when evaluated on real transcriptomics datasets. BoostDiff can be applied to gene expression data to reveal new disease mechanisms or identify potential therapeutic targets.

bioinformatics↗

bioRxiv2022

The Limits of Molecular Signatures for Pancreatic Ductal Adenocarcinoma Subtyping

Molecular signatures have been suggested as biomarkers to classify pancreatic ductal adenocarcinoma (PDAC) into two, three or four subtypes. Since the robustness of existing signatures is controversial, we performed a systematic evaluation of three established signatures for PDAC stratification across eight publicly available datasets. Clustering revealed inconsistency of subtypes across independent datasets and in some cases a different number of PDAC subgroups than in the original study, casting doubt on the actual number of existing subtypes. Next, we built nine classification models to investigate the ability of the signatures for tumor subtype prediction. The overall classification performance ranged from [~]35% to [~]90% accuracy, suggesting instability of the signatures. Notably, permuted subtypes and random gene sets achieved very similar performance. Cellular decomposition and functional pathway enrichment analysis revealed strong tissue-specificity of the predicted classes. Our study highlights severe limitations and inconsistencies that can be attributed to technical biases in sample preparation and tumor purity, suggesting that PDAC molecular signatures do not generalize across datasets. How stromal heterogeneity and immune compartment interplay in the diverging development of PDAC is still unclear. Therefore, a more mechanistic or at least multi-omic approach seems necessary to extract more robust and clinically exploitable insights.

bioinformatics↗

bioRxiv2022

Systematic analysis of alternative splicing in time course data using Spycone

During disease progression or organism development, alternative splicing (AS) may lead to isoform switches (IS) that demonstrate similar temporal patterns and reflect the AS co-regulation of such genes. Tools for dynamic process analysis usually neglect AS. Here we propose Spycone (https://github.com/yollct/spycone), a splicing-aware framework for time course data analysis. Spycone exploits a novel IS detection algorithm and offers downstream analysis such as network and gene set enrichment. We demonstrate the performance of Spycone using simulated and real-world data of SARS-CoV-2 infection.

bioinformatics↗

bioRxiv2022

Alternative splicing analysis benchmark with DICAST

Alternative splicing is a major contributor to transcriptome and proteome diversity in health and disease. A plethora of tools have been developed for studying alternative splicing in RNA-seq data. Previous benchmarks focused on isoform quantification and mapping. They neglected event detection tools, which arguably provide the most detailed insights into the alternative splicing process. DICAST offers a modular and extensible framework for the analysis of alternative splicing integrating 11 splice-aware mapping and eight event detection tools. We benchmark all tools extensively on simulated as well as whole blood RNA-seq data. STAR and HISAT2 demonstrated the best balance between performance and run time. The performance of event detection tools varies widely with no tool outperforming all others. DICAST allows researchers to employ a consensus approach to consider the most successful tools jointly for robust event detection. Furthermore, we propose the first reporting standard to unify existing formats and to guide future tool development.

bioinformatics↗

bioRxiv2021

Functional enrichment of alternative splicing events with NEASE reveals insights into tissue identity and diseases

Alternative splicing (AS) is an important aspect of gene regulation. Nevertheless, its role in molecular processes and pathobiology is far from understood. A roadblock is that tools for the functional analysis of AS-set events are lacking. To mitigate this, we developed NEASE, a tool integrating pathways with protein-protein and domain-domain interactions to functionally characterize AS events. We show in four application cases how NEASE can identify pathways contributing to tissue identity and cell type development, and how it highlights splicing-related biomarkers. With a unique view on AS, NEASE generates unique and meaningful biological insights complementary to classical pathways analysis.

bioinformatics↗

bioRxiv2021

Genome-wide analysis of mitochondrial DNA copy number reveals multiple loci implicated in nucleotide metabolism, platelet activation, and megakaryocyte proliferation

Mitochondrial DNA copy number (mtDNA-CN) measured from blood specimens is a minimally invasive marker of mitochondrial function that exhibits both inter-individual and intercellular variation. To identify genes involved in regulating mitochondrial function, we performed a genome-wide association study (GWAS) in 465,809 White individuals from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (UKB). We identified 133 SNPs with statistically significant, independent effects associated with mtDNA-CN across 100 loci. A combination of fine-mapping, variant annotation, and co-localization analyses were used to prioritize genes within each of the 133 independent sites. Putative causal genes were enriched for known mitochondrial DNA depletion syndromes (p = 3.09 x 10-15) and the gene ontology (GO) terms for mtDNA metabolism (p = 1.43 x 10-8) and mtDNA replication (p = 1.2 x 10-7). A clustering approach leveraged pleiotropy between mtDNA-CN associated SNPs and 41 mtDNA-CN associated phenotypes to identify functional domains, revealing three distinct groups, including platelet activation, megakaryocyte proliferation, and mtDNA metabolism. Finally, using mitochondrial SNPs, we establish causal relationships between mitochondrial function and a variety of blood cell related traits, kidney function, liver function and overall (p = 0.044) and non-cancer mortality (p = 6.56 x 10-4).

genetics↗

bioRxiv2020

PySCNet: A tool for reconstructing and analyzing gene regulatory network from single-cell RNA-Seq data

SummaryThe Advanced capacities of high throughput single cell technologies have facilitated a great understanding of complex biological systems, ranging from cell heterogeneity to molecular expression kinetics. Several pipelines have been introduced to standardize the scRNA-seq analysis workflow. These include cell population identification, cell marker detection and cell trajectory reconstruction. Yet, establishing a systematized pipeline to capture regulatory relationships among transcription factors (TFs) and genes at the cellular level still remains challenging. Here we present PySCNet, a python toolkit that enables reconstructing and analyzing gene regulatory networks (GRNs) from single cell transcriptomic data. PySCNet integrates competitive gene regulatory construction methodologies for cell specific or trajectory specific GRNs and allows for gene co-expression module detection and gene importance evaluation. Moreover, PySCNet offers a user-friendly dashboard website, where GRNs can be customized in an intuitive way. AvailabilitySource code and documentation are available: https://github.com/MingBit/PySCNet Contactming.wu@tum.de

bioinformatics↗

bioRxiv2020

sPLINK: A Federated, Privacy-Preserving Tool as a Robust Alternative to Meta-Analysis in Genome-Wide Association Studies

Genome-wide association studies (GWAS) have been widely used to unravel connections between genetic variants and diseases. Larger sample sizes in GWAS can lead to discovering more associations and more accurate genetic predictors. However, sharing and combining distributed genomic data to increase the sample size is often challenging or even impossible due to privacy concerns and privacy protection laws such as the GDPR. While meta-analysis has been established as an effective approach to combine summary statistics of several GWAS, its accuracy can be attenuated in the presence of cross-study heterogeneity. Here, we present sPLINK (safe PLINK), a user-friendly tool, which performs federated GWAS on distributed datasets while preserving the privacy of data and the accuracy of the results. sPLINK neither exchanges raw data nor does it rely on summary statistics. Instead, it performs model training in a federated manner, communicating only model parameters between cohorts and a central server. We verify that the federated results from sPLINK are the same as those from aggregated analyses conducted with PLINK. We demonstrate that sPLINK is robust against heterogeneous data (phenotype and confounding factors) distributions across cohorts while existing meta-analysis tools considerably lose accuracy in such scenarios. We also show that sPLINK achieves practical runtime, in order of minutes or hours, and acceptable network bandwidth consumption for chi-square and linear/logistic regression tests. Federated analysis with sPLINK, thus, has the potential to replace meta-analysis as the gold standard for collaborative GWAS. The user-friendly, readily usable sPLINK tool is available at https://exbio.wzw.tum.de/splink.

bioinformatics↗

bioRxiv2020

Comparative single-cell trajectory network enrichment identifies pseudo-temporal systems biology patterns in hematopoiesis and CD8 T-cell development

Single cell transcriptomics (scRNA-seq) technologies allow for investigating cellular processes on an unprecedented resolution. While software packages for scRNA-seq raw data analysis exist, no method for the extraction of systems biology signatures that drive different pseudo-time trajectories exists. Hence, pseudo-temporal molecular sub-network expression profiles remain undetermined, thus, hampering our understanding of the molecular control of cellular development on a single cell resolution. We have developed Scellnetor, the first network-constraint time-series clustering algorithm implemented as interactive webtool to identify modules of genes connected in a molecular interaction network that show differentiating temporal expression patterns. Scellnetor allows selecting two differentiation courses or two developmental trajectories for comparison on a systems biology level. Scellnetor identifies mechanisms driving hematopoiesis in mouse and mechanistically interpretable subnetworks driving dysfunctional CD8 T-cell development in chronic infections. Scellnetor is the first method to allow for single cell trajectory network enrichment for systems level hypotheses generation, thus lifting scRNA-seq data analysis to a systems biology level. It is available as an interactive online tool at https://exbio.wzw.tum.de/scellnetor/.

bioinformatics↗

bioRxiv2020

BiCoN: Network-constrained biclustering of patients and omics data

MotivationUnsupervised learning approaches are frequently employed to identify patient subgroups and biomarkers such as disease-associated genes. Thus, clustering and biclustering are powerful techniques often used with expression data, but are usually not suitable to unravel molecular mechanisms along with patient subgroups. To alleviate this, we developed the network-constrained biclustering approach BiCoN (Biclustering Constrained by Networks) which (i) restricts biclusters to functionally related genes connected in molecular interaction networks and (ii) maximizes the difference in gene expression between two subgroups of patients. ResultsOur analyses of non-small cell lung and breast cancer gene expression data demonstrate that BiCoN clusters patients in agreement with known cancer subtypes while discovering gene subnetworks pointing to functional differences between these subtypes. Furthermore, we show that BiCoN is robust to noise and batch effects and can distinguish between high and low load of tumor-infiltrating leukocytes while identifying subnetworks related to immune cell function. In summary, BiCoN is a powerful new systems medicine tool to stratify patients while elucidating the responsible disease mechanism. AvailabilityPyPI package: https://pypi.org/project/bicon Web interface: https://exbio.wzw.tum.de/bicon Contactolga.lazareva@tum.de Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2019

MS Atlas - A molecular map of brain lesion stages in progressive multiple sclerosis

Multiple sclerosis (MS) is a chronic inflammatory neurodegenerative disorder of the central nervous system with an untreatable late progressive phase in a high percentage of patients. Molecular maps of different stages of brain lesion evolution in patients with progressive MS (PMS) are missing but critical for understanding disease development and to identify novel targets to halt progression. We introduce the first MS brain lesion atlas (msatlas.dk), developed to address the current challenges of understanding mechanisms driving the fate of PMS on lesion basis. The MS Atlas gives means for testing research hypotheses, validating candidate biomarkers and drug targets. The MS Atlas data base comprises comprehensive high-quality transcriptomic profiles of 73 brain white matter lesions at different stages of lesion evolution from 10 PMS patients and 25 control white matter samples from five patients with non-neurological disease. The MS Atlas was assembled from next generation RNA sequencing of post mortem samples using strict, conservative preprocessing as well as advanced statistical data analysis. It comes with a user-friendly web interface, which allows for querying and interactively analyzing the PMS lesion evolution. It fosters bioinformatics methods for de novo network enrichment to extract mechanistic markers for specific lesion types and pathway-based lesion type comparison. We describe examples of how the MS Atlas can be used to extract systems medicine signatures. We also demonstrate how its interface can interactively condense and visualize the atlas content. This compendium of mechanistic PMS white matter lesion profiles is an invaluable resource to fuel future multiple sclerosis research and a new basis for treatment development.

systems biology↗