Elucidating the mechanistic impact of single nucleotide variants in model organisms

Understanding how genetic variation propagate to differences in phenotypes in individuals is an ongoing challenge in genetics. Genome-wide association studies have allowed for the identification of many trait-associated genomic loci. However, they are limited in their inability to explain the altered cellular mechanism. Genetic variation can drive disease by altering a range of mechanisms, including signalling networks, TF binding, and protein folding. Understanding the impact of variants on such processes has key implications in therapeutics, drug development, and more. This thesis aims to utilise computational predictors to shed light on how cellular mechanisms are altered in the context of genetic variation and better understand how they drive both molecular and organism-level phenotypes. Many binding events in the cell are mediated by short stretches of sequence motifs. The ability to discover these underlying rules of binding could greatly aid our understanding of variant impact. Kinase–substrate phosphorylation is one of the most prominent post-translational modifications (PTMs) which is mediated by such motifs. We first describe a computational method which utilises interaction and phosphorylation data to predict sequence preferences of kinases. Our method was applied to 57% of human kinases capturing known well-characterised and novel kinase specificities. We experimentally validate four understudied kinases to show that predicted models closely resemble true specificities. We further demonstrate that this method can be applied to different organisms and can be used for other phospho-recognition domains. The described approach allows for an extended repertoire of sequence specificities to be generated, particularly in organisms for which little data is available. TF-DNA binding is another mechanism driven by sequence motifs, which is key for the tight regulation of gene expression and can be greatly altered by genetic variation. We have comprehensively benchmarked current methods used to predict non-coding variant effects on TF-DNA binding by employing over 20,000 compiled allele-specific ChIP-seq variants across 94 TFs. We show that machine learning-based approaches significantly outperform more rudimentary methods such as the position weight matrix. We further note that models for many TFs with distinct binding specificities were unable to accurately assess the impact of variants. For these TFs, we explore alternative mechanisms underlying TF-binding, such as methylation, co-operative binding, and DNA shape that drive poor performance. Our results demonstrate the complexity of predicting non-coding variant effects and the importance of incorporating alternative mechanisms into models. Finally, we describe a comprehensive effort to compile and benchmark state-of-the-art sequence and structure-based predictors of mutational consequences and predict the effect of coding and non-coding variants in the reference genomes of human, yeast, and E. coli. Predicted mechanisms include the impact on protein stability, interaction interfaces, and PTMs. These variant effects are provided through mutfunc, a fast and intuitive web tool by which users can interactively explore pre-computed mechanistic variant impact predictions. We validate computed predictions by analysing known pathogenic disease variants and provide mechanistic hypotheses for causal variants of unknown function. We further use our predictions to devise gene-level functionality scores in human and yeast individuals, which we then used to perform gene-phenotype associations and uncover novel gene-phenotype associations

Microevolution of Serial Clinical Isolates of Cryptococcus neoformans var. grubii and C. gattii

We thank the Broad Institute Sequencing Platform for generating the Illumina sequences. We thank Chen-Hsin Yu for helping on the data processing of the phenotypic tests. We acknowledge the South African National Institute for Communicable Diseases’ GERMS-SA surveillance network through which these isolates were originally collected. This project has been funded in whole or in part by the following U.S. Health and Human Services grants from the National Institute of Allergy and Infectious Diseases: U19 AI110818 (Broad Institute), R01 AI93257 (J.R.P.), R01 AI73896 (J.R.P.), and R01 AI025783 (T.G.M.). R.A.F. was supported by the Wellcome Trust. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The content is solely our responsibility and does not necessarily represent the official views of the funders. The use of product names in this manuscript does not imply their endorsement by the U.S. Department of Health and Human Services. The findings and conclusions in this article are those of the authors and do not necessarily represent the views of the CDC.Peer reviewedPublisher PD

Correction to: Two years later: Is the SARS-CoV-2 pandemic still having an impact on emergency surgery? An international cross-sectional survey among WSES members

Background: The SARS-CoV-2 pandemic is still ongoing and a major challenge for health care services worldwide. In the first WSES COVID-19 emergency surgery survey, a strong negative impact on emergency surgery (ES) had been described already early in the pandemic situation. However, the knowledge is limited about current effects of the pandemic on patient flow through emergency rooms, daily routine and decision making in ES as well as their changes over time during the last two pandemic years. This second WSES COVID-19 emergency surgery survey investigates the impact of the SARS-CoV-2 pandemic on ES during the course of the pandemic. Methods: A web survey had been distributed to medical specialists in ES during a four-week period from January 2022, investigating the impact of the pandemic on patients and septic diseases both requiring ES, structural problems due to the pandemic and time-to-intervention in ES routine. Results: 367 collaborators from 59 countries responded to the survey. The majority indicated that the pandemic still significantly impacts on treatment and outcome of surgical emergency patients (83.1% and 78.5%, respectively). As reasons, the collaborators reported decreased case load in ES (44.7%), but patients presenting with more prolonged and severe diseases, especially concerning perforated appendicitis (62.1%) and diverticulitis (57.5%). Otherwise, approximately 50% of the participants still observe a delay in time-to-intervention in ES compared with the situation before the pandemic. Relevant causes leading to enlarged time-to-intervention in ES during the pandemic are persistent problems with in-hospital logistics, lacks in medical staff as well as operating room and intensive care capacities during the pandemic. This leads not only to the need for triage or transferring of ES patients to other hospitals, reported by 64.0% and 48.8% of the collaborators, respectively, but also to paradigm shifts in treatment modalities to non-operative approaches reported by 67.3% of the participants, especially in uncomplicated appendicitis, cholecystitis and multiple-recurrent diverticulitis. Conclusions: The SARS-CoV-2 pandemic still significantly impacts on care and outcome of patients in ES. Well-known problems with in-hospital logistics are not sufficiently resolved by now; however, medical staff shortages and reduced capacities have been dramatically aggravated over last two pandemic years

Evolutionary constraint and disease associations of post-translational modification sites in human genomes.

Interpreting the impact of human genome variation on phenotype is challenging. The functional effect of protein-coding variants is often predicted using sequence conservation and population frequency data, however other factors are likely relevant. We hypothesized that variants in protein post-translational modification (PTM) sites contribute to phenotype variation and disease. We analyzed fraction of rare variants and non-synonymous to synonymous variant ratio (Ka/Ks) in 7,500 human genomes and found a significant negative selection signal in PTM regions independent of six factors, including conservation, codon usage, and GC-content, that is widely distributed across tissue-specific genes and function classes. PTM regions are also enriched in known disease mutations, suggesting that PTM variation is more likely deleterious. PTM constraint also affects flanking sequence around modified residues and increases around clustered sites, indicating presence of functionally important short linear motifs. Using target site motifs of 124 kinases, we predict that at least ∼180,000 motif-breaker amino acid residues that disrupt PTM sites when substituted, and highlight kinase motifs that show specific negative selection and enrichment of disease mutations. We provide this dataset with corresponding hypothesized mechanisms as a community resource. As an example of our integrative approach, we propose that PTPN11 variants in Noonan syndrome aberrantly activate the protein by disrupting an uncharacterized cluster of phosphorylation sites. Further, as PTMs are molecular switches that are modulated by drugs, we study mutated binding sites of PTM enzymes in disease genes and define a drug-disease network containing 413 novel predicted disease-gene links

Uncovering Phosphorylation-Based Specificities through Functional Interaction Networks

Protein kinases are an important class of enzymes involved in the phosphorylation of their targets, which regulate key cellular processes and are typically mediated by a specificity for certain residues around the target phospho-acceptor residue. While efforts have been made to identify such specificities, only ∼30% of human kinases have a significant number of known binding sites. We describe a computational method that utilizes functional interaction data and phosphorylation data to predict specificities of kinases. We applied this method to human kinases to predict substrate preferences for 57% of all known kinases and show that we are able to reconstruct well-known specificities. We used an in vitro mass spectrometry approach to validate four understudied kinases and show that predicted models closely resemble true specificities. We show that this method can be applied to different organisms and can be extended to other phospho-recognition domains. Applying this approach to different types of posttranslational modifications (PTMs) and binding domains could uncover specificities of understudied PTM recognition domains and provide significant insight into the mechanisms of signaling networks.ISSN:1535-9476ISSN:1535-948

Biochemical consequences PTM variation.

<p><b>(A)</b> Negative selection of PTM regions is apparent in different modification types, in central residues modified by PTMs (left) and in flanking regions. <b>(B)</b> PTM regions often contain multiple types of modifications. <b>(C)</b> Negative selection is stronger in regions with clustered PTMs. <b>(D)</b> Variation analysis of kinase binding motifs reveals 24 kinases whose motif-breaker sites are negatively selected in the population (14 kinases), enriched in PTM-specific disease mutations (19 kinases) or both (9 kinases, shown in boldface). Motif-breaker sites are protein residues that disrupt kinase binding motifs when substituted. <b>(E)</b> Network of kinase-substrate interactions mediated by motif-breaker sites of the 24 kinases. Disease gene interactions are shown in red and black dots represent kinases with significant motif-breaker sites. Boxplot shows that disease genes have more interactions with motif-breaker sites than other proteins. <b>(F)</b> Protein residues highlighted as motif-breaker sites of the 24 kinases, shown relative to PTM site. Motif-breaker sites accumulate within 3 residues and are enriched in R,K,Q,E amino acids. Expected values from amino acid weighted permutations are shown with error bars indicating ±1 s.d.</p

Enriched disease mutations and drug interactions of PTM regions.

<p><b>(A)</b> Wordcloud summarizing 152 disease genes with significant enrichment of PTM SNVs from ActiveDriver analysis (PAD list, FDR <i>p</i><0.05). Letter size shows number of PTM mutations in disease. <b>(B)</b> An example of a disease gene from ActiveDriver analysis. The <i>PTPN11</i> gene encoding the protein phosphatase SHP2 includes a Noonan syndrome-associated mutation hotspot in the SH2 domain of the protein. ActiveDriver shows that the 23 mutations significantly coincide with a cluster of poorly characterised phosphorylation sites (red circles), predicting a disease mechanism of aberrant protein activation. <b>(C)</b> Drug-protein-disease network shows PAD genes with PTM mutations whose upstream enzymes are known and druggable with approved pharmaceuticals, highlighting candidates for drug repurposing screens. Only experimentally predicted enzymes bound to significantly disease-mutated PTM sites are shown.</p

Biological context of evolutionary constraint in PTM regions.

<p><b>(A)</b> Negative selection of PTM regions is apparent across human tissues and ubiquitously expressed genes, as 90% of tissue-specific groups of proteins have significantly more rare substitutions in PTM regions. Tissues are ranked by proportion of rare substitutions in PTM sites, and expected proportions in the entire protein group are shown in red boxplots. <b>(B)</b> Pathway analysis visualised as an enrichment map reveals 400 biological processes and pathways with significant PTM-specific selection (FDR <i>p</i><0.05). Most processes (90%) show negative selection in PTM regions and ∼75% of processes are also over-represented in PTM-associated disease genes. Nodes indicate processes and pathways and edges show overlaps in annotated genes. Selection in the two population datasets is indicated by node and edge colors (light blue and orange for pathways with negative and positive PTM selection, respectively; dark blue and red for PTM-selected pathways with disease association). <b>(C)</b> An example of PTM-associated disease substitutions enriched in significantly selected pathways. The Gene Ontology process of protein modifications (GO:0031401) is enriched in PTM-specific mutations of a wide range of diseases. Word size corresponds to disease annotation frequency.</p

Negative selection of post-translational modification (PTM) regions in human genomes and importance in disease.

<p><b>(A)</b> ∼130,000 experimental PTM sites of four types were merged into ∼55,000 PTM regions. <b>(B-C)</b> Specific negative selection in PTM regions is apparent in relatively higher frequency of rare substitutions and lower ratio of non-synonymous variants to synonymous variants (K_a/K_s). Boxplots represent comparisons of PTM and non-PTM sequence in 100 bins of proteins with matched tolerance to variation. <b>(D)</b> Negative selection of PTM regions is a distinct evolutionary trend not confounded by other genomic factors. PTM-associated predictors are shown in red with the variable corresponding to PTM regions ranked third after conservation and codon bias. <b>(E)</b> Known disease mutations from the HGMD database are enriched in PTM regions. While central PTM sites appear at an expected mutation rate in this global analysis, amino acid weighted sampling reveals an enrichment of PTM sites (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004919#pgen.1004919.s017" target="_blank">S15 Fig.</a>). <b>(F)</b> Disease-associated substitutions in PTM regions are often predicted to be benign by mutation function predictors. Total number of variants scored by each method is shown on each bar.</p