Biology subjects

Przytycka, T. M.

Publications and source records attributed to Przytycka, T. M..

At least 19 recordsLinked to original sources

bioRxiv2026

Combined inference of known and novel mutational signatures with ReDeNovo

Mutational signatures represent characteristic mutational patterns imprinted on the genome by mutagenic processes. They can provide information about the impact of the environmental and endogenous cellular processes on tumor mutations and can suggest treatment. Analysis of presence and strength of mutational signatures in cancer genomes has become a cornerstone in analysis of new and legacy cancer data. However, a precise inference of novel (de novo) signatures requires a large set of genomes, and methods focusing on estimating the presence of previously defined signatures are unable to uncover potential novel signatures that might emerge in new data. Thus, reliable methods to address these challenges are needed. We formally define the Combined Mutational Signature Inference Problem (CMSI) for the identification of known signatures and the inference of novel signatures in cancer data. CMSI represents non-convex optimization, and we provide a heuristic algorithm, ReDeNovo, to solve it efficiently. We extensively validated ReDeNovo on simulated data, evaluating its ability to precisely estimate presence and exposure to known signatures and to discover of novel signatures. On both tasks ReDeNovo outperformed existing approaches. In real biological data, ReDeNovo identified signatures missed by previous analyses and defined a new signature related to UV light exposure. ReDeNovo method provides a new and powerful tool to uncover mutational signatures. ReDeNovo is available from https://github.com/ncbi/redenovo.

genomics↗

bioRxiv2026

Toward identification of common DNA repair process in mutational signatures

Mutational signatures are characteristic patterns of mutation frequencies assumed to be generated by specific mutagenic processes. A growing catalog of mutational signatures exists, but tools to systematically infer relationships between them remain limited. A mutational signature can be viewed as the combined outcome of two processes: DNA damage and DNA repair. Since cancer therapies often target DNA repair, inferring DNA repair pathways is important for treatment design, even when the mutagenic process is unknown. Here, we model the DNA repair step as a transformation, called RePrint, from damaged nucleotides to repair-related mutation patterns conditioned on the damage. We demonstrate that RePrint similarity is indicative of shared DNA repair mechanisms, enabling guilt-by-association prediction of DNA repair pathways. Using experimentally annotated signatures from environmental exposures and CRISPR gene knockouts as gold standards, we demonstrate that RePrint-based clustering consistently outperforms signature-based clustering across multiple evaluation metrics. We validate several guilt-by-association predictions with literature evidence, demonstrating RePrints ability to identify shared repair mechanisms even among signatures with divergent mutational profiles. RePrint provides the first approach to systematically transfer DNA repair information between signatures, opening doors to understanding signatures of unknown origin and informing therapeutic strategies. An open-source implementation is available at https://github.com/wojtowicz-lab/RePrintPy

cancer biology↗

bioRxiv2025

Connecting spatial regions to clinical phenotypes by transferring knowledge from bulk patient data

Spatially resolved transcriptomics (SRT) technology has enabled a new level of knowledge about tumors. Many critical tumor properties, such as invasiveness and growth, depend on both specific transcriptomic changes in tumor cells and the tumor microenvironment. However, computational methods to study clinical phenotypes of spatial regions, such as hazard and drug response, have not yet been developed. Since clinical phenotypes are measured at the patient level and not at the level of spatial regions, such a method would require transferring knowledge from the patient level domain to the spot level domain. To overcome this challenge, we developed SpacePhenotyper. Our approach uses algebraic spectral techniques to transfer the predictive relationship between gene expression and clinical phenotypes from bulk gene expression data to SRT data. Our approach captures a gene expression pattern that is predictive of the phenotype of interest in the form of a vector, the "Eigen-Patient," which is then used to quantify the phenotype in spatial spots. After extensively validating SpacePhenotyper on simulated and real data, we utilize it to study how the spatial heterogeneity of breast cancer tumors influences residual cancer burden after treatment. By assigning relative quantities of clinical phenotypes to spatial locations, SpacePhenotyper has proven a powerful tool for the identification and interpretation of transcriptional changes over spatial regions and of spatially-regulated patterns of cellular states. SpacePhenotyper is implemented in Python. The source code and data sets used for and generated during this study are available at https://github.com/ncbi/SpacePhenotyper

cancer biology↗

bioRxiv2025

Cell type specific allometry controls sex-differences in Drosophila body size

Species and sex-specific differences in organ size are fundamental features of animal biology, yet the mechanisms that drive these differences remain debated. Adult female Drosophila are larger than males. While most organs are present across both sexes, the underlying mechanisms driving sex-specific organ and body size scaling of Drosophila remain unclear. Using single-nucleus transcriptomes from the Fly Cell Atlas, combined with experimental validation, we show that different Drosophila organs scale through distinct strategies, including cell size, cell number, or a combination of both, in an allometric rather than uniform manner. Larger female flight muscles develop from more myoblasts than in males, while cardiomyocyte numbers are the same despite forming a larger heart in females. Female fat body cells are larger and express more ribosomal protein-coding mRNAs, supporting increased cell size. In contrast, males have a greater number of fat body cells. Together, this sex-specific allometry in cell size and number define the cellular basis for differences in body and organ size between sexes in Drosophila. By uncovering how a conserved developmental system produces sex-specific proportions through distinct cellular strategies, our work offers a framework for dissecting sex differences in other species and systems.

cell biology↗

bioRxiv2024

Stochastic modelling of single-cell gene expression adaptation reveals non-genomic contribution to evolution of tumor subclones

Cancer progression is an evolutionary process driven by the selection of cells adapted to gain growth advantage. We present the first formal study on the adaptation of gene expression in subclonal evolution. We model evolutionary changes in gene expression as stochastic Ornstein-Uhlenbeck processes, jointly leveraging the evolutionary history of subclones and single-cell expression data. Applying our model to sublines derived from single cells of a mouse melanoma revealed that sublines with distinct phenotypes are underlined by different patterns of gene expression adaptation, indicating non-genetic mechanisms of cancer evolution. Interestingly, sublines previously observed to be resistant to anti-CTLA-4 treatment showed adaptive expression of genes related to invasion and non-canonical Wnt signaling, whereas sublines that responded to treatment showed adaptive expression of genes related to proliferation and canonical Wnt signaling. Our results suggest that clonal phenotypes emerge as the result of specific adaptivity patterns of gene expression.

cancer biology↗

bioRxiv2023

Exploring tumor-normal cross-talk with TranNet: role of the environment in tumor progression

There is a growing awareness that tumor-adjacent normal tissues used as control samples in cancer studies do not represent fully healthy tissues. Instead, they are intermediates between healthy tissues and tumors. The factors that contribute to the deviation of such control samples from healthy state include exposure to the tumor-promoting factors, tumor-related immune response, and other aspects of tumor microenvironment. Characterizing the relation between gene expression of tumor-adjacent control samples and tumors is fundamental for understanding roles of microenvironment in tumor initiation and progression, as well as for identification of diagnostic and prognostic biomarkers for cancers. To address the demand, we developed and validated TranNet, a computational approach that utilizes gene expression in matched control and tumor samples to study the relation between their gene expression profiles. TranNet infers a sparse weighted bipartite graph from gene expression profiles of matched control samples to tumors. The results allow us to identify predictors (potential regulators) of this transition. To our knowledge, TranNet is the first computational method to infer such regulation. We applied TranNet to the data of several cancer types and their matched control samples from The Cancer Genome Atlas (TCGA). Many predictors identified by TranNet are genes associated with regulation by the tumor microenvironment as they are enriched in G-protein coupled receptor signaling, cell-to-cell communication, immune processes, and cell adhesion. Correspondingly, targets of inferred predictors are enriched in pathways related to tissue remodelling (including the epithelial-mesenchymal Transition (EMT)), immune response, and cell proliferation. This implies that the predictors are markers and potential stromal facilitators of tumor progression. Our results provide new insights for the relationships between tumor adjacent control sample, tumor and the tumor environment. Moreover, the set of predictors identified by TranNet will provide a valuable resource for future investigations. The TranNet method was implemented in python, source codes and the data sets used for and generated during this study are available at the Github site https://github.com/ncbi/TranNet.

systems biology↗

bioRxiv2022

Emergent dynamics of adult stem cell lineages from single nucleus and single cell RNA-Seq of Drosophila testes

Proper differentiation of sperm from germline stem cells, essential for production of the next generation, requires dramatic changes in gene expression that drive remodeling of almost all cellular components, from chromatin to organelles to cell shape itself. Here we provide a single nucleus and single cell RNA-seq resource covering all of spermatogenesis in Drosophila starting from in-depth analysis of adult testis single nucleus RNA-seq (snRNA-seq) data from the Fly Cell Atlas (FCA) study (Li et al., 2022). With over 44,000 nuclei and 6,000 cells analyzed, the data provide identification of rare cell types, mapping of intermediate steps in differentiation, and the potential to identify new factors impacting fertility or controlling differentiation of germline and supporting somatic cells. We justify assignment of key germline and somatic cell types using combinations of known markers, in situ hybridization, and analysis of extant protein traps. Comparison of single cell and single nucleus datasets proved particularly revealing of dynamic developmental transitions in germline differentiation. To complement the web-based portals for data analysis hosted by the FCA, we provide datasets compatible with commonly used software such as Seurat and Monocle. The foundation provided here will enable communities studying spermatogenesis to interrogate the datasets to identify candidate genes to test for function in vivo.

developmental biology↗

bioRxiv2021

Influence network model uncovers new relations between biological processes and mutational signatures

There has been a growing appreciation recently that mutagenic processes can be studied through the lenses of mutational signatures, which represent characteristic mutation patterns attributed to individual mutagens. However, the causal link between mutagens and observed mutation patterns remains not fully understood, limiting the utility of mutational signatures. To gain insights into these relationships, we developed a network-based method, named GO_SCPLOWENEC_SCPLOWSO_SCPLOWIGC_SCPLOWNO_SCPLOWETC_SCPLOW that constructs a directed network among genes and mutational signatures. The approach leverages a sparse partial correlation among other statistical techniques to uncover dominant influence relations between the activities of network nodes. Applying GO_SCPLOWENEC_SCPLOWSO_SCPLOWIGC_SCPLOWNO_SCPLOWETC_SCPLOW to cancer data sets, we uncovered important relations between mutational signatures and several cellular processes that can shed light on cancer related mutagenic processes. Our results are consistent with previous findings such as the impact of homologous recombination deficiency on a clustered APOBEC mutations in breast cancer. The network identified by GO_SCPLOWENEC_SCPLOWSO_SCPLOWIGC_SCPLOWNO_SCPLOWETC_SCPLOW also suggest an interaction between APOBEC hypermutation and activation of regulatory T Cells (Tregs) and a relation between APOBEC mutations and changes in DNA conformation. GO_SCPLOWENEC_SCPLOWSO_SCPLOWIGC_SCPLOWNO_SCPLOWETC_SCPLOW also exposed a possible link between the SBS8 signature of unknown aetiology and the nucleotide excision repair pathway. GO_SCPLOWENEC_SCPLOWSO_SCPLOWIGC_SCPLOWNO_SCPLOWETC_SCPLOW provides a new and powerful method to reveal the relation between mutational signatures and gene expression. GO_SCPLOWENEC_SCPLOWSO_SCPLOWIGC_SCPLOWNO_SCPLOWETC_SCPLOW is freely available at https://github.com/ncbi/GeneSigNet.

systems biology↗

bioRxiv2021

Mutational Signatures as Sensors of Environmental Exposures: Role of Smoking in COVID-19 Vulnerabilities

BackgroundEnvironmental exposures such as smoking are widely recognized risk factors in the emergence of lung diseases including lung cancer and acute respiratory distress syndrome (ARDS). However, the strength of environmental exposures is difficult to measure, making it challenging to understand their impacts. On the other hand, some COVID-19 patients develop ARDS in an unfavorable disease progression and smoking has been suggested as a potential risk factor among others. Yet initial studies on COVID-19 cases reported contradictory results on the effects of smoking on the disease - some suggest that smoking might have a protective effect against it while other studies report an increased risk. A better understanding of how the exposure to smoking and other environmental factors affect biological processes relevant to SARS-CoV-2 infection and unfavorable disease progression is needed. ApproachIn this study, we utilize mutational signatures associated with environmental factors as sensors of their exposure level. Many environmental factors including smoking are mutagenic and leave characteristic patterns of mutations, called mutational signatures, in affected genomes. We postulated that analyzing mutational signatures, combined with gene expression, can shed light on the impact of the mutagenic environmental factors to the biological processes. In particular, we utilized mutational signatures from lung adenocarcinoma (LUAD) data set collected in TCGA to investigate the role of environmental factors in COVID-19 vulnerabilities. Integrating mutational signatures with gene expression in normal tissues and using a pathway level analysis, we examined how the exposure to smoking and other mutagenic environmental factors affects the infectivity of the virus and disease progression. ResultsBy delineating changes associated with smoking in pathway-level gene expression and cell type proportions, our study demonstrates that mutational signatures can be utilized to study the impact of exogenous mutagenic factors on them. Consistent with previous findings, our analysis showed that smoking mutational signature (SBS4) is associated with activation of cytokine-mediated signaling pathways, leading to inflammatory responses. Smoking related changes in cell composition were also observed, including the correlation of SBS4 with the expansion of goblet cells. On the other hand, increased basal cells and decreased ciliated cells in proportion were associated with the strength of a different mutational signature (SBS5), which is present abundantly but not exclusively in smokers. In addition, we found that smoking increases the expression levels of genes that are up-regulated in severe COVID-19 cases. Jointly, these results suggest an unfavorable impact of smoking on the disease progression and also provide novel findings on how smoking impacts biological processes in lung.

systems biology↗

bioRxiv2021

Fly Cell Atlas: a single-cell transcriptomic atlas of the adult fruit fly

The ability to obtain single cell transcriptomes for stable cell types and dynamic cell states is ushering in a new era for biology. We created the Tabula Drosophilae, a single cell atlas of the adult fruit fly which includes 580k cells from 15 individually dissected sexed tissues as well as the entire head and body. Over 100 researchers from the fly community contributed annotations to >250 distinct cell types across all tissues. We provide an in-depth analysis of cell type-related gene signatures and transcription factor markers, as well as sexual dimorphism, across the whole animal. Analysis of common cell types that are shared between tissues, such as blood and muscle cells, allowed the discovery of rare cell types and tissue-specific subtypes. This atlas provides a valuable resource for the entire Drosophila community and serves as a comprehensive reference to study genetic perturbations and disease models at single cell resolution.

genomics↗

bioRxiv2020

RepairSig: Deconvolution of DNA damage and repaircontributions to the mutational landscape of cancer

Many mutagenic processes leave characteristic imprints on cancer genomes known as mutational signatures. These signatures have been of recent interest regarding their applicability in studying processes shaping the mutational landscape of cancer. In particular, pinpointing the presence of altered DNA repair pathways can have important therapeutic implications. However, mutational signatures of DNA repair deficiencies are often hard to infer. This challenge emerges as a result of deficient DNA repair processes acting by modifying the outcome of other mutagens. Thus, they exhibit non-additive effects that are not depicted by the current paradigm for modeling mutational processes as independent signatures. To close this gap, we present RO_SCPLOWEPAIRC_SCPLOWSO_SCPLOWIGC_SCPLOW, a method that accounts for interactions between DNA damage and repair and is able to uncover unbiased signatures of deficient DNA repair processes. In particular, RO_SCPLOWEPAIRC_SCPLOWSO_SCPLOWIGC_SCPLOW was able to replace three MMR deficiency signatures previously proposed to be active in breast cancer, with just one signature strikingly similar to the experimentally derived signature. As the first method to model interactions between mutagenic processes, RO_SCPLOWEPAIRC_SCPLOWSO_SCPLOWIGC_SCPLOW is an important step towards biologically more realistic modeling of mutational processes in cancer. The source code for RO_SCPLOWEPAIRC_SCPLOWSO_SCPLOWIGC_SCPLOW is publicly available at https://github.com/ncbi/RepairSig.

bioinformatics↗

bioRxiv2020

Reconstruction of Gene Regulatory Networks by integrating biological model and a recommendation system

Gene Regulatory Networks (GRNs) control many aspects of cellular processes including cell differentiation, maintenance of cell type specific states, signal transduction, and response to stress. Since GRNs provide information that is essential for understanding cell function, the inference of these networks is one of the key challenges in systems biology. Leading algorithms to reconstruct GRN utilize, in addition to gene expression data, prior knowledge such as Transcription Factor (TF) DNA binding motifs or results of DNA binding experiments. However, such prior knowledge is typically incomplete hence resulting in missing values. Therefore, the integration of such incomplete prior knowledge with gene expression to elucidate the underlying GRNs remains difficult. To address this challenge we introduce NetREX-CF - Regulatory Network Reconstruction using EXpression and Collaborative Filtering - a GRN reconstruction approach that brings together a modern machine learning strategy (Collaborative Filtering model) and a biologically justified model of gene expression (sparse Network Component Analysis based model). The Collaborative Filtering (CF) model is able to overcome the incompleteness of the prior knowledge and make edge recommends for building the GRN. Complementing CF, the sparse Network Component Analysis (NCA) model can use gene expression data to validate the recommended edges. Here we combine these two approaches using a novel data integration method and show that the new approach outperforms the currently leading GRN reconstruction methods. Furthermore, our mathematical formalization of the model has lead to a complex optimization problem of a type that has not been attempted before. Specifically, the formulation contains{ell} 0 norm that can not be separated from other variables. To fill this gap, we introduce here a new method Generalized PALM (GPALM) that allows us to solve a broad class of non-convex optimization problems and prove its convergence.

bioinformatics↗

bioRxiv2020

Modeling gene expression evolution with EvoGeneX uncovers differences in evolution of species, organs and sexes

While DNA sequence evolution has been well studied, the expression of genes is also subject to evolution, yet the evolution of gene expression is currently not well understood. Recently, new tissue/organ-specific gene expression datasets spanning several organisms across the tree of life, have become available, providing the opportunity to study gene expression evolution in more detail. While a theoretical model to study the evolution of continuous traits exists, in practice computational methods often cannot confidently distinguish between alternative evolutionary scenarios. This lack of power has been attributed to modest numbers of species considered in these studies. We hypothesised that biological replicates can be used to increase predictive power of these models. With this in mind, we introduce EvoGeneX, a computationally efficient method to uncover the mode of gene expression evolution based on the Ornstein-Uhlenbeck process. Importantly, in addition to modelling expression variations between species, EvoGeneX models within-species variation. Furthermore, to facilitate comparative analysis of gene expression evolution, we introduce a formal approach, based on Michaelis-Menten equation, to measure the dynamics of evolutionary divergence of a group of genes in terms of groups asymptotic divergence level and rate. Finally, we used these tools to preform the first analysis the evolution of gene expression across different body parts, species, and sexes of the Drosophila genus. Our analysis revealed that neutral expression evolution can be confidently rejected in favor of purifying selection in nearly half of the genes. In addition, we quantified differences in the evolutionary dynamics of male and female gonads and uncovered interesting examples of adaptive gene expression evolution.

evolutionary biology↗

bioRxiv2019

Bioinformatics Pipeline using JUDI: Just Do It

Large-scale data analysis in Bioinformatics requires executing several software in a pipelined fashion. Generally each stage in a pipeline takes considerable computing resources and several workflow management systems (WMS), e.g., Snakemake and Nextflow, have been developed to ensure in case of multiple invocation of the pipeline, only the bare minimum stages that are affected by the changes across invocations get executed. However, when the pipeline needs to be executed with different settings of parameters, e.g., thresholds, underlying algorithms, etc. these WMS require significant scripting to ensure an optimal execution of the pipeline. We developed JUDI on top of a Python based WMS, DoIt, for a systematic handling of pipeline parameter settings based on the principles of DBMS that simplifies plug-and-play scripting. The effectiveness of JUDI is demonstrated in a pipeline for analyzing large scale HT-SELEX data for transcription factor and DNA binding where JUDI reduces scripting by a factor of five.\n\nAvailabilityhttps://github.com/ncbi/JUDI

bioinformatics↗

bioRxiv2019

Network-based approaches elucidate differences within APOBEC and clock-like signatures in breast cancer

Studies of cancer mutations typically focus on identifying cancer driving mutations. However, in addition to the mutations that confer a growth advantage, cancer genomes accumulate a large number of passenger somatic mutations resulting from normal DNA damage and repair processes as well as mutations triggered by carcinogenic exposures or cancer related aberrations of DNA maintenance machinery. These mutagenic processes often produce characteristic mutational patterns called mutational signatures. Understanding the etiology of the mutational signatures shaping a cancer genome is an important step towards understanding tumorigenesis. Considering mutational signatures as phenotypes, we asked two complementary questions (i) what are functional pathways whose gene expression profiles are associated with mutational signatures, and (ii) what are mutated pathways (if any) that might underlie specific mutational signatures? We have been able to identify pathways associated with mutational signatures on both expression and mutation levels. In particular, our analysis provides novel insights into mutagenic processes in breast cancer by capturing important differences in the etiology of different APOBEC related signatures and the two clock-like signatures. These results are important for understanding mutagenic processes in cancer and for developing personalized drug therapies.

bioinformatics↗

bioRxiv2019

Identifying Drug Sensitivity Subnetworks with NETPHLIX

Phenotypic heterogeneity in cancer is often caused by different patterns of genetic alterations. Understanding such phenotype-genotype relationships is fundamental for the advance of personalized medicine. One of the important challenges in the area is to predict drug response on a personalized level. The pathway-centric view of cancer significantly advanced the understanding of genotype-phenotype relationships. However, most of network identification methods in cancer focus on identifying subnetworks that include general cancer drivers or are associated with discrete features such as cancer subtypes, hence cannot be applied directly for the analysis of continuous features like drug response. On the other hand, existing genome wide association approaches do not fully utilize the complex proprieties of cancer mutational landscape. To address these challenges, we propose a computational method, named NETPHLIX (NETwork-to-PHenotpe mapping LeveragIng eXlusivity), which aims to identify mutated subnetworks that are associated with drug response (or any continuous cancer phenotype). Utilizing properties such as mutual exclusivity and interactions among genes, we formulate the problem as an integer linear program and solve it optimally to obtain a set of genes satisfying the constraints. NETPHLIX identified gene modules significantly associated with many drugs, including interesting response modules to MEK1/2 inhibitors in both directions (increased and decreased sensitivity to the drug) that the previous method, which does not utilize network information, failed to identify. The genes in the modules belong to MAPK/ERK signaling pathway, which is the targeted pathway of the drug.

bioinformatics↗

bioRxiv2018

Accurate sub-population detection and mapping across single cell experiments with PopCorn

The identification of sub-populations of cells present in a sample, and the comparison of such sub-populations across samples are among the most frequently performed analyzes of single cell data. Current tools for these kind of data however fall short in their ability to adequately perform these tasks. We introduce a novel method, PopCorn (single cell sub-Populations Comparison), allowing for the identification of sub-populations of cells present within individual experiments while simultaneously performing sub-populations mapping across these experiments. PopCorn utilizes several novel algorithmic solutions enabling the execution of these tasks with unprecedented precision. As such, PopCorn provides a much needed tool for comparative analysis of populations of single-cells.

bioinformatics↗

bioRxiv2018

Novel analysis of HT-SELEX data elucidates the role of DNA shape in transcription factor binding

Understanding the principles of DNA binding by transcription factors (TFs) is of primary importance for studying gene regulation. Recently, several lines of evidence suggested that both DNA sequence and shape contribute to TF binding. However, the question if in the absence of any sequence similarity to the binding motif, DNA shape can still increase probability of binding was yet to be addressed.\n\nTo address this challenge, we developed Co-SELECT, a computational approach to analyze the results of in vitro HT-SELEX experiments for TF-DNA binding. Specifically, the presence of motif-free sequences in late HT-SELEX rounds and their enrichment in weak binders allowed us to detect evidence for the role of DNA shape features in TF binding.\n\nOur approach revealed that, even in the absence of the sequence motif, TFs have propensity to weakly bind to DNA molecules enriched in specific shape features. Surprisingly, we also found that some properties of DNA shape contribute to promiscuous binding of all tested TF families. Strikingly, such promiscuously bound shapes correspond to the most frequent shape formed by the DNA. We propose that this promiscuous binding facilitates diffusing of TFs along the DNA molecule before it is locked in its binding site.

bioinformatics↗