Biology subjects

Jaroszewski, L.

Publications and source records attributed to Jaroszewski, L..

11 recordsLinked to original sources

bioRxiv2026

Evolutionary history of ligand binding by the LRR domain of innate immunity receptors: the story of the TLR2 cavity

Toll-like receptors (TLRs) are vital components of the innate immune system, recognizing both exogenous pathogens signals (PAMPs) and internal stress signals (DAMPs). TLR2 is unique among the human (Homo sapiens) TLR family members, as it contains a large cavity for binding hydrophobic ligands, such as lipoteichoic acid (LTA) and di/triacyl lipopeptides (Pam2/3CSK4). This study analyzed the structural phylogeny of cavity presence in the TLR2 lineage in vertebrates (vTLR) enabled by AI protein structure predictions and explored the potential convergent evolution of similar features in invertebrates (iTLRs). Analysis of AI models of TLR2s shows that this cavity is consistently present in TRL2 orthologs across jawed vertebrates (Gnathostomata). In jawless vertebrates (Cyclostomatha), these cavities were found in lamprey (Petromyzon marinus) TLR2 model, but only in some extant hagfish (Myxini), suggesting an ancestral origin in basal vertebrates followed by lineage-specific losses. TLR2 paralogs were found in several species, with a similar central cavity but potentially different ligand specificities. In silico ligand docking showed Pam2CSK4 binds to this cavity in all TLRs and paralogs consistently, demonstrating the conserved function of the ligand-binding pocket in gram-positive bacteria recognition across TLR2 branches. Changes in the TLR2 cavity size and shape in some vertebrate groups show the evolution of this DAMP recognition mechanism adapted to its respective pathogens. iTLRs form a separate phylogenetic branch with distinct structural features, but in literature some are considered to be TLR2 orthologs. Indeed, TLRs from some species of Helobdella and Ciona, contain a cavity with some similarity to that in the vTLR2 lineage. However, detailed structural comparisons of their location in the LRR domain and the structural details of the models suggest that their cavities have developed independently from that in TLR2s. Smaller cavities are present in other branches of the LRR family, but show different locations, shapes, and features, indicating that the binding of small ligands in the internal cavities within the LRR domains evolved multiple times in the LRR domain family history.

bioinformatics↗

bioRxiv2024

Divergence of the Individual repeats in the leucine-rich repeat domains of human Toll-like receptors explain their diversity and functional adaptations.

Toll-like receptors (TLRs) are best known pattern recognition receptors of innate immunity, detecting a broad range of pathogen-associated molecular patterns (PAMPs), and endogenous danger associated molecular patterns (DAMPs), initiating inflammatory and antimicrobial responses. TLRs contain two specialized domains, a signaling domain (TIR), and a receptor domain composed of tandem repeats of short 20-30 amino acid segments called Leucine-rich Repeats (LRRs). LRR domains, often paired with other domains, are widespread in all kingdoms of life, invariably forming highly similar, solenoid-like three dimensional structures. Despite this structural conservation, LRR domains overall, and receptor domains of TLRs in particular, exhibit remarkable diversity in binding specificity, recognizing diverse ligands such as lipoproteins, nucleic acids, polysaccharides, other proteins and protein complexes. To understand how this conserved scaffold can accommodate such binding diversity, we performed an in-depth analysis of sequential and structural conservation of individual repeats within the LRR domain of each of the ten human TLRs. We demonstrate that the small variations in repeat lengths and local sequence patterns lead to subtle, but critical structural adaptations, such as changes in local curvature, emergence of loops, cavities and interaction interfaces, each contributing to recognition of specific ligands and formation of the receptor complexes. TLR polymorphisms in human populations can further fine-tune the specificity and strength of ligand recognition, influencing how individuals respond to different pathogens and cell damage causing diseases. We show that in most cases the interfaces with the ligands show high level of polymorphism, suggesting a potential for diverse immune responses to infections among human populations while interfaces with other proteins in the receptor complex are more conserved, pointing to the importance of conserving the overall structure of the signaling pathways. By studying how divergence in LRR repeats affects TLRs structure and function, we provide deeper insights into TLR recognition mechanisms, and a better understanding of the mechanism of evolutionary adaptability of immune recognition systems, both along the evolution of vertebrates, and across the human population. Significance statementHuman Toll-like receptors (hTLRs) play a key role in the innate immune response, recognizing diverse danger molecular patterns through their receptor domains, that consist of tandem repeats of a structural unit called a leucine rich repat (LRR). They are also an example of functionally diverse paralogous family where, despite the overall sequence and structure similarity, each member develops its specific function. Our study reveals that subtle modifications of dividual repeats, without disrupting the overall structure of the receptor domain, form secondary patterns defining the functional specificity of each TLR, enabling them to recognize and respond to a broad array of pathogen and disease associated molecular patterns. These findings offer a new perspective on how sequence variability within conserved protein domains can drive functional evolution and adaptation, with implications both for understanding immune receptor functional adaptation and their fast evolution as well as more general problem of functional diversification in paralogous families.

bioinformatics↗

bioRxiv2024

Global atlas of predicted functional domains in Legionella pneumophila Dot/Icm translocated effectors

Legionella pneumophila utilizes the Dot/Icm type IVB secretion system to deliver hundreds of effector proteins inside eukaryotic cells to ensure intracellular replication. Our understanding of the molecular functions of this largest pathogenic arsenal known to the bacterial world remains incomplete. By leveraging advancements in 3D protein structure prediction, we provide a comprehensive structural analysis of 368 L. pneumophila effectors, representing a global atlas of predicted functional domains summarized in a database (https://pathogens3d.org/legionella-pneumophila). Our analysis identified 157 types of diverse functional domains in 287 effectors, including 159 effectors with no prior functional annotations. Furthermore, we identified 35 unique domains in 30 effector models that have no similarity with experimentally structurally characterized proteins, thus, hinting at novel functionalities. Using this analysis, we demonstrate the activity of thirteen domains, including three unique folds, predicted in L. pneumophila effectors to cause growth defects in the Saccharomyces cerevisiae model system. This illustrates an emerging strategy of exploring synergies between predictions and targeted experimental approaches in elucidating novel effector activities involved in infection.

microbiology↗

bioRxiv2023

Deciphering Abnormal Platelet Subpopulations in Inflammatory Diseases through Machine Learning and Single-Cell Transcriptomics

IntroductionThe transcriptional heterogeneity of activated platelets, play a significant role in contributing to negative outcomes in sepsis, COVID-19, and autoimmune diseases such as systemic lupus erythematosus (SLE). Despite this, our understanding of these heterogeneous platelet responses remains limited. In this study, we aim to investigate the diverse transcriptional profiles of activated platelets in these diseases, with the goal of deciphering this platelet heterogeneity for new therapeutic strategies to target abnormal and pathogenic platelet subtypes. Materials and methodsWe obtained the single cell transcriptional profiles of blood platelets from patients with COVID-19, sepsis, and SLE. Utilizing machine learning algorithms, Deep Neural Network (DNN) and eXtreme Gradient Boosting (XGB), we discerned the distinct transcriptomic signatures indicative of fatal versus survival clinical outcomes. Our methodological framework incorporated source data annotations and platelet markers and used SingleR and Seurat for detailed profiling. Additionally, we implemented Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and visualization, aiding in the detection of various platelet subtypes and their correlation with disease status and patient outcomes. ResultsOur study identified distinct platelet subpopulations that are associated with disease severity. We demonstrated that alterations in platelet transcription patterns can exacerbate endotheliopathy, potentially heightening the risk of coagulation in fatal patients. Moreover, these changes can also influence lymphocyte function, indicating a more extensive role for platelets in inflammatory and immune responses. ConclusionsEnhanced transcriptional heterogeneity in activated platelets is linked to adverse outcomes in conditions such as sepsis, COVID-19, and autoimmune diseases. The discovery of these unique platelet subpopulations paves the way for innovative therapeutic strategies targeting platelet activation, which could potentially improve patient outcomes. Summary sentenceSingle-Cell RNA Sequencing Analysis of Platelets from COVID-19, Sepsis, and SLE Reveals Disease Signatures and Treatment Options to Prevent Patient Mortality. Graphical AbstractsO_LIThe platelet to T cell ratio proportion in PBMC was identified as the most potent predictor for distinguishing survivors from fatal patients, underscores the potential of this ratio as a prognostic biomarker. C_LIO_LIThe discovery of different platelet subgroups, especially active coagulation, hypoxic, and quiescent clusters, in fatal COVID-19 patients, indicates potential targeted treatment strategies. C_LIO_LIIn patients with severe and fatal conditions, we observed three key phenomena: the aggregation of platelets with monocytes, the amplification of endothelial dysfunction by platelets, and a decrease in lymphocyte activation and differentiation due to platelets. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=197 SRC="FIGDIR/small/572680v1_ufig1.gif" ALT="Figure 1"> View larger version (64K): org.highwire.dtl.DTLVardef@141ca55org.highwire.dtl.DTLVardef@4ad1aborg.highwire.dtl.DTLVardef@b400forg.highwire.dtl.DTLVardef@9ad44e_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

Monitoring for SARS-CoV-2 drug resistance mutations in broad viral populations

The search for drugs against COVID-19 and other diseases caused by coronaviruses focuses on the most conserved and essential proteins, mainly the main (Mpro) and the papain-like (PLpro) proteases and the RNA-dependent RNA polymerase (RdRp). Nirmatrelvir, an inhibitor for Mpro, was recently approved by FDA as a part of a two-drug combination, Paxlovid, and many more drugs are in various stages of development. Multiple candidates for the PLpro inhibitors are being studied, but none have yet progressed to clinical trials. Several repurposed inhibitors of RdRp are already in use. We can expect that once anti-COVID-19 drugs become widely used, resistant variants of SARS-CoV-2 will emerge, and we already see that for the drugs targeting SARS-CoV-2 RdRp. We hypothesize that emergence of such variants can be anticipated by identifying possible escape mutations already present in the existing populations of viruses. Our group previously developed the coronavirus3D server (https://coronavirus3d.org), tracking the evolution of SARS-CoV-2 in the context of the three-dimensional structures of its proteins. Here we introduce dedicated pages tracking the emergence of potential drug resistant mutations to Mpro and PLpro, showing that such mutations are already circulating in the SARS-CoV-2 viral population. With regular updates, the drug resistance tracker provides an easy way to monitor and potentially predict the emergence of drug resistance-conferring mutations in the SARS-CoV-2 virus.

bioinformatics↗

bioRxiv2021

Increased frequency of recurrent in-frame deletions in new expanding lineages of SARS CoV-2 reflects immune selective pressure

Most of the attention in the surveillance of evolution of SARS-CoV-2 has been centered on single nucleotide substitutions in the spike glycoprotein. We show that in-frame deletions (IFDs) also play a significant role in the evolution of viral genome. The percentage of genomes and lineages with IFDs is growing rapidly and they co-occur independently in multiple lineages, including emerging variants of concerns. IFDs distribution is correlated with spike mutations associated with immune escape and concentrated in proteins involved in interactions with the host immune system. Structural analysis suggests that IFDs remodel viral proteins surfaces at common epitopes and interaction interfaces, affecting the virus interactions with the immune system. We hypothesize that the increased frequency of IFDs is an adaptive response to elevated global population immunity. SummaryMonitoring of SARS-CoV-2 genome evolution uncovers increased frequency and non-random distribution of in-frame deletions in recently emerged lineages.

evolutionary biology↗

bioRxiv2020

The interplay of SARS-CoV-2 evolution and constraints imposed by the structure and functionality of its proteins

Fast evolution of the SARS-CoV-2 virus provides us with unique information about the patterns of genetic changes in a single pathogen in the timescale of months. This data is used extensively to track the phylodynamic of the pandemics spread and its split into distinct clades. Here we show that the patterns of SARS-CoV-2 virus mutations along its genome are closely correlated with the structural features of the coded proteins. We show that the foldability of proteins 3D structures and conservation of their functions are the universal factors driving evolutionary selection in protein-coding genes. Insights from the analysis of mutation distribution in the context of the SARS-CoV-2 proteins structures and functions have practical implications including evaluating potential antigen epitopes or selection of primers for PCR-based COVID-19 tests.

evolutionary biology↗

bioRxiv2020

The crystal structure of nsp10-nsp16 heterodimer from SARS CoV-2in complex with S-adenosylmethionine

SARS-CoV-2 is a member of the coronaviridae family and is the etiological agent of the respiratory Coronavirus Disease 2019. The virus has spread rapidly around the world resulting in over two million cases and nearly 150,000 deaths as of April 17, 2020. Since no treatments or vaccines are available to treat COVID-19 and SARS-CoV-2, respiratory complications derived from the infections have overwhelmed healthcare systems around the world. This virus is related to SARS-CoV-1, the virus that caused the 2002-2004 outbreak of Severe Acute Respiratory Syndrome. In January 2020, the Center for Structural Genomics of Infectious Diseases implemented a structural genomics pipeline to solve the structures of proteins essential for coronavirus replication-transcription. Here we show the first structure of the SARS-CoV-2 nsp10-nsp16 2-O-methyltransferase complex with S-adenosylmethionine at a resolution of 1.80 [A]. This heterodimer complex is essential for capping viral mRNA transcripts for efficient translation and to evade immune surveillance.

biochemistry↗

bioRxiv2020

Large scale analysis of smoking-induced changes in the tumor immune microenvironment

Tobacco smoke is a known carcinogen, mostly due to its genotoxicity, but its effects on the host immune system are also playing an important role. Here, we leveraged recent results on the immune landscape of cancer based on The Cancer Genomic Atlas (TCGA) data analysis and compared the proportions of major classes of tumor-infiltrating immune cells (TIICs) between smokers and never smokers in ten TCGA cancer types. We show that statistically significant changes can be identified in all ten cancers, with increased plasma cell populations and the modified ratio of activated to resting TIICs being the most consistent features distinguishing smokers and never-smokers across different cancers, with both being correlated with survival outcomes. Analysis of existing single-cell RNA-seq data further showed that smoking differentially affects the gene expression profile of cancer patients based on the immune cell type. The smoking-induced changes in the patterns of immune cells and their correlations to survival outcomes are stronger in female smokers.

cancer biology↗

bioRxiv2019

Difference contact maps: from what to why in the analysis of the conformational flexibility of proteins

Protein structures, usually visualized in various highly idealized forms focusing on the three-dimensional arrangements of secondary structure elements, can also be described as lists of interacting residues or atoms and visualized as two-dimensional distance or contact maps. We show that contact maps provide an ideal tool to describe and analyze differences between structures of proteins in different conformations. Expanding functionality of the PDBFlex server and database developed previously in our group, we describe how analysis of difference contact maps (DCMs) can be used to identify critical interactions stabilizing alternative protein conformations, recognize residues and positions controlling protein functions and build hypotheses as to molecular mechanisms of disease mutations.\n\nAuthor SummaryProtein folding is driven, and the three-dimensional structures of proteins are defined, by the non-local interactions between amino acid chains. However, the usual visualizations of protein structures focus on the overall chain topology and do not provide much information about the interactions defining them. Here we explore contact map visualization of protein structures, which directly focuses on interactions stabilizing the structure and thus can be easily used to understand effects of mutations on protein structure. We show that contact map visualization is particularly useful for comparing alternative conformations of proteins and identifying critical positions controlling functional flexibility.

bioinformatics↗

bioRxiv2018

Cancer3D 2.0: interactive analysis of 3D patterns of cancer mutations in cancer subsets

Our knowledge of cancer genomics exploded in last several years, providing us with detailed knowledge of genetic alterations in almost all cancer types. Analysis of this data gave us new insights into molecular aspects of cancer, most important being the amazing diversity of molecular abnormalities in individual cancers. The most important question in cancer research today is how to classify this diversity to identify subtypes that are most relevant for treatment and outcome prediction for individual patients. The Cancer3D database at http://www.cancer3d.org gives an open and user-friendly way to analyze cancer missense mutations in the context of structures of proteins they are found in and in relation to patients clinical data. This approach allows users to find novel candidate driver regions for specific subgroups, that often cannot be found when similar analyses are done on the whole gene level and for large, diverse cohorts. Interactive interface allows user to visualize the distribution of mutations is subgroups defined by cancer type and stage, gender and age brackets, patients ethnicity, or vice versa find dominant cancer type, gender or age groups for specific three-dimensional mutation patterns.

bioinformatics↗