Comprehensive Characterization of Cancer Driver Genes and Mutations.

Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Sequence- and structure-based analyses identified >3,400 putative missense driver mutations supported by multiple lines of evidence. Experimental validation confirmed 60%-85% of predicted mutations as likely drivers. We found that >300 MSI tumors are associated with high PD-1/PD-L1, and 57% of tumors analyzed harbor putative clinically actionable events. Our study represents the most comprehensive discovery of cancer genes and mutations to date and will serve as a blueprint for future biological and clinical endeavors.

A Distinct Type of Pilus from the Human Microbiome.

Pili are proteinaceous polymers of linked pilins that protrude from the cell surface of many bacteria and often mediate adherence and virulence. We investigated a set of 20 Bacteroidia pilins from the human microbiome whose structures and mechanism of assembly were unknown. Crystal structures and biochemical data revealed a diverse protein superfamily with a common Greek-key ß sandwich fold with two transthyretin-like repeats that polymerize into a pilus through a strand-exchange mechanism. The assembly mechanism of the central, structural pilins involves proteinase-assisted removal of their N-terminal ß strand, creating an extended hydrophobic groove that binds the C-terminal donor strands of the incoming pilin. Accessory pilins at the tip and base have unique structural features specific to their location, allowing initiation or termination of the assembly. The Bacteroidia pilus, therefore, has a biogenesis mechanism that is distinct from other known pili and likely represents a different type of bacterial pilus.

Defining the risk of SARS-CoV-2 variants on immune protection.

The global emergence of many severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants jeopardizes the protective antiviral immunity induced after infection or vaccination. To address the public health threat caused by the increasing SARS-CoV-2 genomic diversity, the National Institute of Allergy and Infectious Diseases within the National Institutes of Health established the SARS-CoV-2 Assessment of Viral Evolution (SAVE) programme. This effort was designed to provide a real-time risk assessment of SARS-CoV-2 variants that could potentially affect the transmission, virulence, and resistance to infection- and vaccine-induced immunity. The SAVE programme is a critical data-generating component of the US Government SARS-CoV-2 Interagency Group to assess implications of SARS-CoV-2 variants on diagnostics, vaccines and therapeutics, and for communicating public health risk. Here we describe the coordinated approach used to identify and curate data about emerging variants, their impact on immunity and effects on vaccine protection using animal models. We report the development of reagents, methodologies, models and notable findings facilitated by this collaborative approach and identify future challenges. This programme is a template for the response to rapidly evolving pathogens with pandemic potential by monitoring viral evolution in the human population to identify variants that could reduce the effectiveness of countermeasures.

Detection of a SARS-CoV-2 variant of concern in South Africa.

Continued uncontrolled transmission of SARS-CoV-2 in many parts of the world is creating conditions for substantial evolutionary changes to the virus1,2. Here we describe a newly arisen lineage of SARS-CoV-2 (designated 501Y.V2; also known as B.1.351 or 20H) that is defined by eight mutations in the spike protein, including three substitutions (K417N, E484K and N501Y) at residues in its receptor-binding domain that may have functional importance3-5. This lineage was identified in South Africa after the first wave of the epidemic in a severely affected metropolitan area (Nelson Mandela Bay) that is located on the coast of the Eastern Cape province. This lineage spread rapidly, and became dominant in Eastern Cape, Western Cape and KwaZulu-Natal provinces within weeks. Although the full import of the mutations is yet to be determined, the genomic data-which show rapid expansion and displacement of other lineages in several regions-suggest that this lineage is associated with a selection advantage that most plausibly results from increased transmissibility or immune escape6-8.

Protein structure, amino acid composition and sequence determine proteome vulnerability to oxidation-induced damage.

Oxidative stress alters cell viability, from microorganism irradiation sensitivity to human aging and neurodegeneration. Deleterious effects of protein carbonylation by reactive oxygen species (ROS) make understanding molecular properties determining ROS susceptibility essential. The radiation-resistant bacterium Deinococcus radiodurans accumulates less carbonylation than sensitive organisms, making it a key model for deciphering properties governing oxidative stress resistance. We integrated shotgun redox proteomics, structural systems biology, and machine learning to resolve properties determining protein damage by γ-irradiation in Escherichia coli and D. radiodurans at multiple scales. Local accessibility, charge, and lysine enrichment accurately predict ROS susceptibility. Lysine, methionine, and cysteine usage also contribute to ROS resistance of the D. radiodurans proteome. Our model predicts proteome maintenance machinery, and proteins protecting against ROS are more resistant in D. radiodurans. Our findings substantiate that protein-intrinsic protection impacts oxidative stress resistance, identifying causal molecular properties.

Deciphering Abnormal Platelet Subpopulations in COVID-19, Sepsis and Systemic Lupus Erythematosus through Machine Learning and Single-Cell Transcriptomics.

This study focuses on understanding the transcriptional heterogeneity of activated platelets and its impact on diseases such as sepsis, COVID-19, and systemic lupus erythematosus (SLE). Recognizing the limited knowledge in this area, our research aims to dissect the complex transcriptional profiles of activated platelets to aid in developing targeted therapies for abnormal and pathogenic platelet subtypes. We analyzed single-cell transcriptional profiles from 47,977 platelets derived from 413 samples of patients with these diseases, utilizing Deep Neural Network (DNN) and eXtreme Gradient Boosting (XGB) to distinguish transcriptomic signatures predictive of fatal or survival outcomes. Our approach included source data annotations and platelet markers, along with SingleR and Seurat for comprehensive profiling. Additionally, we employed Uniform Manifold Approximation and Projection (UMAP) for effective dimensionality reduction and visualization, aiding in the identification of various platelet subtypes and their relation to disease severity and patient outcomes. Our results highlighted distinct platelet subpopulations that correlate with disease severity, revealing that changes in platelet transcription patterns can intensify endotheliopathy, increasing the risk of coagulation in fatal cases. Moreover, these changes may impact lymphocyte function, indicating a more extensive role for platelets in inflammatory and immune responses. This study identifies crucial biomarkers of platelet heterogeneity in serious health conditions, paving the way for innovative therapeutic approaches targeting platelet activation, which could improve patient outcomes in diseases characterized by altered platelet function.

Unusual structural and functional features of TpLRR/BspA-like LRR proteins.

Leucine Rich Repeat (LRR) domains, are present in hundreds of thousands of proteins across all kingdoms of life and are typically involved in protein-protein interactions and ligand recognition. LRR domains are classified into eight classes and when examined in three dimensions seven, of them form curved solenoid-like super-helices, also described as toruses, with a beta sheet on the concave (inside) and stacked alpha-helices on the convex (outside) of the torus. Here we present an overview of the least characterized 8th class of LRR proteins, the TpLRR-like LRRs, named after the Treponema pallidum protein Tp0225. Proteins from the TpLRR class differ from the proteins in all other known LRR classes by having a flipped curvature, with the beta sheet on the convex side of the torus and irregular secondary structure instead of helices on the opposite, now concave site. TpLRR proteins also present highly divergent sequence pattern of individual repeats and can associate with specific types of additional domains. Several of the characterized proteins from this class, specifically the BspA-like proteins, were found in human bacterial and protozoan pathogens, playing an important role in the interactions between the pathogens and the host immune system. In this paper we surveyed all existing experimental structures and selected AlphaFold models of the best-known proteins containing this class of LRR repeats, analyzing the relation between the pattern of conserved residues, specific structural features and functions of these proteins.

US National Institutes of Health Prioritization of SARS-CoV-2 Variants.

Since late 2020, SARS-CoV-2 variants have regularly emerged with competitive and phenotypic differences from previously circulating strains, sometimes with the potential to escape from immunity produced by prior exposure and infection. The Early Detection group is one of the constituent groups of the US National Institutes of Health National Institute of Allergy and Infectious Diseases SARS-CoV-2 Assessment of Viral Evolution program. The group uses bioinformatic methods to monitor the emergence, spread, and potential phenotypic properties of emerging and circulating strains to identify the most relevant variants for experimental groups within the program to phenotypically characterize. Since April 2021, the group has prioritized variants monthly. Prioritization successes include rapidly identifying most major variants of SARS-CoV-2 and providing experimental groups within the National Institutes of Health program easy access to regularly updated information on the recent evolution and epidemiology of SARS-CoV-2 that can be used to guide phenotypic investigations.

Gender differences in smoking-induced changes in the tumor immune microenvironment.

Both gender and smoking are correlated with prevalence and outcomes in many types of cancers. Tobacco smoke is a known carcinogen through its genotoxicity but can also affect cancer progression through its effect on the immune system. In this study, we aim to evaluate the hypothesis that the effects of smoking on the tumor immune microenvironment will be influenced differently by gender using large-scale analysis of publicly available cancer datasets. We used The Cancer Genomic Atlas (TCGA) datasets (n = 2724) to analyze effects of smoking on different cancer immune subtypes and the relative abundance of immune cell types between male and female cancer patients. We further validated our results by analyzing additional datasets, including Expression Project for Oncology (expO) bulk RNA-seq dataset (n = 1118) and single-cell RNA-seq dataset (n = 14). Results of our study indicate that in female patients, two immune subtypes, C1 and C2, are respectively over and under abundant in smokers vs. never smokers. In males, the only significant difference is underabundance of the C6 subtype in smokers. We identified gender-specific differences in the population of immune cell types between smokers and never smokers in all TCGA and expO cancer types. Increased plasma cell population was identified as the most consistent feature distinguishing smokers and never smokers, especially in current female smokers based on both TCGA and expO data. Our analysis of existing single-cell RNA-seq data further revealed that smoking differentially affects the gene expression profile of cancer patients based on the immune cell type and gender. In our analysis, female and male smokers show different smoking-induced patterns of immune cells in tumor microenvironment. Besides, our results suggest cancer tissues directly exposed to tobacco smoke undergo the most significant changes, but all other tissue types are affected as well. Findings of current study also indicate that changes in the populations of plasma cells and their correlations to survival outcomes are stronger in female current smokers, with implications for cancer immunotherapy of women smokers. In conclusion, results of this study can be used to develop personalized treatment plans for cancer patients who smoke, particularly women smokers, taking into account the unique immune cell profile of their tumors.

Group 1 metabotropic glutamate receptor expression defines a T cell memory population during chronic Toxoplasma infection that enhances IFN-gamma and perforin production in the CNS.

Within the brain, a pro-inflammatory response is essential to prevent clinical disease due to Toxoplasma gondii reactivation. Infection in the immunocompromised leads to lethal Toxoplasmic encephalitis while in the immunocompetent, there is persistent low-grade inflammation which is devoid of clinical symptoms. This signifies that there is a well-balanced and regulated inflammatory response to T. gondii in the brain. T cells are the dominant immune cells that prevent clinical disease, and this is mediated through the secretion of effector molecules such as perforins and IFN-γ. The presence of cognate antigen, the expression of survival cytokines, and the alteration of the epigenetic landscape drive the development of memory T cells. However, specific extrinsic signals that promote the formation and maintenance of memory T cells within tissue are poorly understood. During chronic infection, there is an increase in extracellular glutamate that, due to its function as an excitatory neurotransmitter, is normally tightly controlled in the CNS. Here we demonstrate that CD8+ T cells from the T. gondii-infected brain parenchyma are enriched for metabotropic glutamate receptors (mGluR's). Characterization studies determined that mGluR+ expression by CD8+ T cells defines a distinct memory population at the transcriptional and protein level. Finally, using receptor antagonists and agonists we demonstrate mGluR signaling is required for optimal CD8+ T cell production of the effector cytokine IFNγ. This work suggests that glutamate is an important environmental signal of inflammation that promotes T cell function. Understanding glutamate's influence on T cells in the brain can provide insights into the mechanisms that govern protective immunity against CNS-infiltrating pathogens and neuroinflammation.

Health disparities in COVID-19: immune and vascular changes are linked to disease severity and persist in a high-risk population in Riverside County, California.

BACKGROUND: Health disparities in underserved communities, such as inadequate healthcare access, impact COVID-19 disease outcomes. These disparities are evident in Hispanic populations nationwide, with disproportionately high infection and mortality rates. Furthermore, infected individuals can develop long COVID with sustained impacts on quality of life. The goal of this study was to identify immune and endothelial factors that are associated with COVID-19 outcomes in Riverside County, a high-risk and predominantly Hispanic community, and investigate the long-term impacts of COVID-19 infection. METHODS: 112 participants in Riverside County, California, were recruited according to the following criteria: healthy control (n = 23), outpatients with moderate infection (outpatient, n = 33), ICU patients with severe infection (hospitalized, n = 33), and individuals recovered from moderate infection (n = 23). Differences in outcomes between Hispanic and non-Hispanic individuals and presence/absence of co-morbidities were evaluated. Circulating immune and vascular biomarkers were measured by ELISA, multiplex analyte assays, and flow cytometry. Follow-up assessments for long COVID, lung health, and immune and vascular changes were conducted after recovery (n = 23) including paired analyses of the same participants. RESULTS: Compared to uninfected controls, the severe infection group had a higher proportion of Hispanic individuals (n = 23, p = 0.012) than moderate infection (n = 8, p = 0.550). Disease severity was associated with changes in innate monocytes and neutrophils, lymphopenia, disrupted cytokine production (increased IL-8 and IP-10/CXCL10 but reduced IFNλ2/3 and IFNγ), and increased endothelial injury (myoglobin, VCAM-1). In the severe infection group, a machine learning model identified LCN2/NGAL, IL-6, and monocyte activation as parameters associated with fatality while anti-coagulant therapy was associated with survival. Recovery from moderate COVID infection resulted in long-term immune changes including increased monocytes/lymphocytes and decreased neutrophils and endothelial markers. This group had a lower proportion of co-morbidities (n = 8, p = 1.0) but still reported symptoms associated with long COVID despite recovered pulmonary function. CONCLUSION: This study indicates increased severity of COVID-19 infection in Hispanic individuals of Riverside County, California. Infection resulted in immunological and vascular changes and long COVID symptoms that were sustained for up to 11 months, however, lung volume and airflow resistance was recovered. Given the immune and behavioral impacts of long COVID, the potential for increased susceptibility to infections and decreased quality of life in high-risk populations warrants further investigation.

A Genomic Island of Vibrio cholerae Encodes a Three-Component Cytotoxin with Monomer and Protomer Forms Structurally Similar to Alpha-Pore-Forming Toxins.

Alpha-pore-forming toxins (α-PFTs) are secreted by many species of bacteria, including Escherichia coli, Aeromonas hydrophila, and Bacillus thuringiensis, as part of their arsenal of virulence factors, and are often cytotoxic. In particular, for α-PFTs, the membrane-spanning channel they form is composed of hydrophobic α-helices. These toxins oligomerize at the surface of target cells and transition from a soluble to a protomer state in which they expose their hydrophobic regions and insert into the membrane to form a pore. The pores may be composed of homooligomers of one component or heterooligomers with two or three components, resulting in bi- or tripartite toxins. The multicomponent α-PFTs are often expressed from a single operon. Recently, motility-associated killing factor A (MakA), an α-PFT, was discovered in Vibrio cholerae. We report that makA is found on the V. cholerae GI-10 genomic island within an operon containing genes for two other potential α-PFTs, MakB and MakE. We determined the X-ray crystal structures for MakA, MakB, and MakE and demonstrated that all three are structurally related to the α-PFT family in the soluble state, and we modeled their protomer state based on the α-PFT AhlB from A. hydrophila. We found that MakA alone is cytotoxic at micromolar concentrations. However, combining MakA with MakB and MakE is cytotoxic at nanomolar concentrations, with specificity for J774 macrophage cells. Our data suggest that MakA, -B, and -E are α-PFTs that potentially act as a tripartite pore-forming toxin with specificity for phagocytic cells. IMPORTANCE The bacterium Vibrio cholerae causes gastrointestinal, wound, and skin infections. The motility-associated killing factor A (MakA) was recently shown to be cytotoxic against colon, prostate, and other cancer cells. However, at the outset of this study, the capacity of MakA to damage cells in combination with other Mak proteins encoded in the same operon had not been elucidated. We determined the structures of three Mak proteins and established that they are structurally related to the α-PFTs. Compared to MakA alone, the combination of all three toxins was more potent specifically in mouse macrophages. This study highlights the idea that the Mak toxins are selectively cytotoxic and thus may function as a tripartite toxin with cell type specificity.

Actin Cross-Linking Effector Domain of the Vibrio vulnificus F-Type MARTX Toxin Dominates Disease Progression During Intestinal Infection.

Vibrio vulnificus is an opportunistic pathogen that causes gastroenteritis and septicemia in humans. The V. vulnificus multifunctional-autoprocessing repeats-in-toxin (MARTX) toxin is a pore-forming toxin that translocates multiple functionally independent effector domains into target cells and an essential virulence factor for fatal disease. The effector repertoire delivered and thus the mechanism of action of the toxin can differ dramatically across V. vulnificus isolates. Here, we utilize a strain of V. vulnificus that carries an F-type MARTX toxin that delivers an actin cross-linking domain (ACD) and four other effector domains. We demonstrate that ACD is the primary driver of virulence following intragastric infection and of bacterial dissemination to distal organs. We additionally show that ACD activates the transcription of intermediate early response genes in cultured intestinal epithelial cells (IECs). However, the genes activated by ACD are suppressed, at least in part, by the codelivered Ras/Rap1-specific endopeptidase (RRSP). The transcriptional response induced by strains translocating only RRSP results in a unique transcriptional profile, demonstrating that the transcriptional response to V. vulnificus is remodeled rather than simply suppressed by the MARTX toxin effector repertoire. Regardless, the transcriptional response in the intestinal tissue of infected mice is dominated by ACD-mediated induction of genes associated with response to tissue damage and is not impacted by RRSP or the three other effectors codelivered with ACD and RRSP. These data demonstrate that while other effectors do remodel early intestinal innate immune responses, ACD is the dominant driver of disease progression by ACD+ V. vulnificus during intestinal infection.

Novel putative polyethylene terephthalate (PET) plastic degrading enzymes from the environmental metagenome.

Several plastic degrading enzymes have been described in the literature, most notably PETases that are capable of hydrolyzing polyethylene terephthalate (PET) plastic. One of them, the PETase from Ideonella sakaiensis, a bacterium isolated from environmental samples within a PET bottle recycling site, was a subject of extensive studies. To test how widespread PETase functionality is in other bacterial communities, we used a cascade of BLAST searches in the JGI metagenomic datasets and showed that close homologs of I. sakaiensis PETase can also be found in other metagenomic environmental samples from both human-affected and relatively pristine sites. To confirm their classification as putative PETases, we verified that the newly identified proteins have the PETase sequence signatures common to known PETases and that phylogenetic analyses group them with the experimentally characterized PETases. Additionally, docking analysis was performed in order to further confirm the functional assignment of the putative environmental PETases.

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

The unprecedented pace of the sequencing of the SARS-CoV-2 virus genomes provides us with unique information about the genetic changes in a single pathogen during ongoing pandemic. By the analysis of close to 200,000 genomes we show that the patterns of the SARS-CoV-2 virus mutations along its genome are closely correlated with the structural and functional features of the encoded proteins. Requirements of foldability of proteins' 3D structures and the conservation of their key functional regions, such as protein-protein interaction interfaces, are the dominant factors driving evolutionary selection in protein-coding genes. At the same time, avoidance of the host immunity leads to the abundance of mutations in other regions, resulting in high variability of the missense mutation rate along the genome. "Unexplained" peaks and valleys in the mutation rate provide hints on function for yet uncharacterized genomic regions and specific protein structural and functional features they code for. Some of these observations have immediate practical implications for the selection of target regions for PCR-based COVID-19 tests and for evaluating the risk of mutations in epitopes targeted by specific antibodies and vaccine design strategies.

FATCAT 2.0: towards a better understanding of the structural diversity of proteins.

FATCAT 2.0 server (http://fatcat.godziklab.org/), provides access to a flexible protein structure alignment algorithm developed in our group. In such an alignment, rotations and translations between elements in the structure are allowed to minimize the overall root mean square deviation (RMSD) between the compared structures. This allows to effectively compare protein structures even if they underwent structural rearrangements in different functional forms, different crystallization conditions or as a result of mutations. The major update for the server introduces a new graphical interface, much faster database searches and several new options for visualization of the structural differences between proteins.

Coronavirus3D: 3D structural visualization of COVID-19 genomic divergence.

MOTIVATION: As the COVID-19 pandemic is spreading around the world, the SARS-CoV-2 virus is evolving with mutations that potentially change and fine-tune functions of the proteins coded in its genome. RESULTS: Coronavirus3D website integrates data on the SARS-CoV-2 virus mutations with information about 3D structures of its proteins, allowing users to visually analyze the mutations in their 3D context. AVAILABILITY AND IMPLEMENTATION: Coronavirus3D server is freely available at https://coronavirus3d.org.

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 in subgroups defined by cancer type and stage, gender and age brackets, patient's ethnicity or vice versa find dominant cancer type, gender or age groups for specific three-dimensional mutation patterns.

Comparison of algorithms for the detection of cancer drivers at subgene resolution.

Understanding genetic events that lead to cancer initiation and progression remains one of the biggest challenges in cancer biology. Traditionally, most algorithms for cancer-driver identification look for genes that have more mutations than expected from the average background mutation rate. However, there is now a wide variety of methods that look for nonrandom distribution of mutations within proteins as a signal for the driving role of mutations in cancer. Here we classify and review such subgene-resolution algorithms, compare their findings on four distinct cancer data sets from The Cancer Genome Atlas and discuss how predictions from these algorithms can be interpreted in the emerging paradigms that challenge the simple dichotomy between driver and passenger genes.