A Combined Approach Reveals a Regulatory Mechanism Coupling Src's Kinase Activity, Localization, and Phosphotransferase-Independent Functions.

Multiple layers of regulation modulate the activity and localization of protein kinases. However, many details of kinase regulation remain incompletely understood. Here, we apply saturation mutagenesis and a chemical genetic method for allosterically modulating kinase global conformation to Src kinase, providing insight into known regulatory mechanisms and revealing a previously undiscovered interaction between Src's SH4 and catalytic domains. Abrogation of this interaction increased phosphotransferase activity, promoted membrane association, and provoked phosphotransferase-independent alterations in cell morphology. Thus, Src's SH4 domain serves as an intramolecular regulator coupling catalytic activity, global conformation, and localization, as well as mediating a phosphotransferase-independent function. Sequence conservation suggests that the SH4 domain regulatory interaction exists in other Src-family kinases. Our combined approach's ability to reveal a regulatory mechanism in one of the best-studied kinases suggests that it could be applied broadly to provide insight into kinase structure, regulation, and function.

sccomp: Robust differential composition and variability analysis for single-cell data.

Cellular omics such as single-cell genomics, proteomics, and microbiomics allow the characterization of tissue and microbial community composition, which can be compared between conditions to identify biological drivers. This strategy has been critical to revealing markers of disease progression, such as cancer and pathogen infection. A dedicated statistical method for differential variability analysis is lacking for cellular omics data, and existing methods for differential composition analysis do not model some compositional data properties, suggesting there is room to improve model performance. Here, we introduce sccomp, a method for differential composition and variability analyses that jointly models data count distribution, compositionality, group-specific variability, and proportion mean-variability association, being aware of outliers. sccomp provides a comprehensive analysis framework that offers realistic data simulation and cross-study knowledge transfer. Here, we demonstrate that mean-variability association is ubiquitous across technologies, highlighting the inadequacy of the very popular Dirichlet-multinomial distribution. We show that sccomp accurately fits experimental data, significantly improving performance over state-of-the-art algorithms. Using sccomp, we identified differential constraints and composition in the microenvironment of primary breast cancer.

Closing the gap: Systematic integration of multiplexed functional data resolves variants of uncertain significance in BRCA1, TP53, and PTEN.

Clinical interpretation of missense variants is challenging because the majority identified by genetic testing are rare and their functional effects are unknown. Consequently, most variants are of uncertain significance and cannot be used for clinical diagnosis or management. Although not much can be done to ameliorate variant rarity, multiplexed assays of variant effect (MAVEs), where thousands of single-nucleotide variant effects are simultaneously measured experimentally, provide functional evidence that can help resolve variants of unknown significance (VUSs). However, a rigorous assessment of the clinical value of multiplexed functional data for variant interpretation is lacking. Thus, we systematically combined previously published BRCA1, TP53, and PTEN multiplexed functional data with phenotype and family history data for 324 VUSs identified by a single diagnostic testing laboratory. We curated 49,281 variant functional scores from MAVEs for these three genes and integrated four different TP53 multiplexed functional datasets into a single functional prediction for each variant by using machine learning. We then determined the strength of evidence provided by each multiplexed functional dataset and reevaluated 324 VUSs. Multiplexed functional data were effective in driving variant reclassification when combined with clinical data, eliminating 49% of VUSs for BRCA1, 69% for TP53, and 15% for PTEN. Thus, multiplexed functional data, which are being generated for numerous genes, are poised to have a major impact on clinical variant interpretation.

Collateral fitness effects of mutations.

The distribution of fitness effects of mutation plays a central role in constraining protein evolution. The underlying mechanisms by which mutations lead to fitness effects are typically attributed to changes in protein specific activity or abundance. Here, we reveal the importance of a mutation's collateral fitness effects, which we define as effects that do not derive from changes in the protein's ability to perform its physiological function. We comprehensively measured the collateral fitness effects of missense mutations in the Escherichia coli TEM-1 ß-lactamase antibiotic resistance gene using growth competition experiments in the absence of antibiotic. At least 42% of missense mutations in TEM-1 were deleterious, indicating that for some proteins collateral fitness effects occur as frequently as effects on protein activity and abundance. Deleterious mutations caused improper posttranslational processing, incorrect disulfide-bond formation, protein aggregation, changes in gene expression, and pleiotropic effects on cell phenotype. Deleterious collateral fitness effects occurred more frequently in TEM-1 than deleterious effects on antibiotic resistance in environments with low concentrations of the antibiotic. The surprising prevalence of deleterious collateral fitness effects suggests they may play a role in constraining protein evolution, particularly for highly expressed proteins, for proteins under intermittent selection for their physiological function, and for proteins whose contribution to fitness is buffered against deleterious effects on protein activity and protein abundance.

Human and viral membrane-associated E3 ubiquitin ligases MARCH1 and MIR2 recognize different features of CD86 to downregulate surface expression.

Immune-stimulatory ligands, such as major histocompatibility complex molecules and the T-cell costimulatory ligand CD86, are central to productive immunity. Endogenous mammalian membrane-associated RING-CHs (MARCH) act on these and other targets to regulate antigen presentation and activation of adaptive immunity, whereas virus-encoded homologs target the same molecules to evade immune responses. Substrate specificity is encoded in or near the membrane-embedded domains of MARCHs and the proteins they regulate, but the exact sequences that distinguish substrates from nonsubstrates are poorly understood. Here, we examined the requirements for recognition of the costimulatory ligand CD86 by two different MARCH-family proteins, human MARCH1 and Kaposi's sarcoma herpesvirus modulator of immune recognition 2 (MIR2), using deep mutational scanning. We identified a highly specific recognition surface in the hydrophobic core of the CD86 transmembrane (TM) domain (TMD) that is required for recognition by MARCH1 and prominently features a proline at position 254. In contrast, MIR2 requires no specific sequences in the CD86 TMD but relies primarily on an aspartic acid at position 244 in the CD86 extracellular juxtamembrane region. Surprisingly, MIR2 recognized CD86 with a TMD composed entirely of valine, whereas many different single amino acid substitutions in the context of the native TM sequence conferred MIR2 resistance. These results show that the human and viral proteins evolved completely different recognition modes for the same substrate. That some TM sequences are incompatible with MIR2 activity, even when no specific recognition motif is required, suggests a more complicated mechanism of immune modulation via CD86 than was previously appreciated.

MaveRegistry: a collaboration platform for multiplexed assays of variant effect.

SUMMARY: Multiplexed assays of variant effect (MAVEs) are capable of experimentally testing all possible single nucleotide or amino acid variants in selected genomic regions, generating 'variant effect maps', which provide biochemical insight and functional evidence to enable more rapid and accurate clinical interpretation of human variation. Because the international community applying MAVE approaches is growing rapidly, we developed the online MaveRegistry platform to catalyze collaboration, reduce redundant efforts, allow stakeholders to nominate targets and enable tracking and sharing of progress on ongoing MAVE projects. AVAILABILITY AND IMPLEMENTATION: MaveRegistry service: https://registry.varianteffect.org. MaveRegistry source code: https://github.com/kvnkuang/maveregistry-front-end.

Novel drivers and modifiers of MPL-dependent oncogenic transformation identified by deep mutational scanning.

The single transmembrane domain (TMD) of the human thrombopoietin receptor (TpoR/myeloproliferative leukemia [MPL] protein), encoded by exon 10 of the MPL gene, is a hotspot for somatic mutations associated with myeloproliferative neoplasms (MPNs). Approximately 6% and 14% of JAK2 V617F- essential thrombocythemia and primary myelofibrosis patients, respectively, have "canonical" MPL exon 10 driver mutations W515L/K/R/A or S505N, which generate constitutively active receptors and consequent loss of Tpo dependence. Other "noncanonical" MPL exon 10 mutations have also been identified in patients, both alone and in combination with canonical mutations, but, in almost all cases, their functional consequences and relevance to disease are unknown. Here, we used a deep mutational scanning approach to evaluate all possible single amino acid substitutions in the human TpoR TMD for their ability to confer cytokine-independent growth in Ba/F3 cells. We identified all currently recognized driver mutations and 7 novel mutations that cause constitutive TpoR activation, and a much larger number of second-site mutations that enhance S505N-driven activation. We found examples of both of these categories in published and previously unpublished MPL exon 10 sequencing data from MPN patients, demonstrating that some, if not all, of the new mutations reported here represent likely drivers or modifiers of myeloproliferative disease.

BET inhibitor resistance emerges from leukaemia stem cells.

Bromodomain and extra terminal protein (BET) inhibitors are first-in-class targeted therapies that deliver a new therapeutic opportunity by directly targeting bromodomain proteins that bind acetylated chromatin marks. Early clinical trials have shown promise, especially in acute myeloid leukaemia, and therefore the evaluation of resistance mechanisms is crucial to optimize the clinical efficacy of these drugs. Here we use primary mouse haematopoietic stem and progenitor cells immortalized with the fusion protein MLL-AF9 to generate several single-cell clones that demonstrate resistance, in vitro and in vivo, to the prototypical BET inhibitor, I-BET. Resistance to I-BET confers cross-resistance to chemically distinct BET inhibitors such as JQ1, as well as resistance to genetic knockdown of BET proteins. Resistance is not mediated through increased drug efflux or metabolism, but is shown to emerge from leukaemia stem cells both ex vivo and in vivo. Chromatin-bound BRD4 is globally reduced in resistant cells, whereas the expression of key target genes such as Myc remains unaltered, highlighting the existence of alternative mechanisms to regulate transcription. We demonstrate that resistance to BET inhibitors, in human and mouse leukaemia cells, is in part a consequence of increased Wnt/ß-catenin signalling, and negative regulation of this pathway results in restoration of sensitivity to I-BET in vitro and in vivo. Together, these findings provide new insights into the biology of acute myeloid leukaemia, highlight potential therapeutic limitations of BET inhibitors, and identify strategies that may enhance the clinical utility of these unique targeted therapies.

Genomics 2 Proteins portal: A resource and discovery tool for linking genetic screening outputs to protein sequences and structures.

Recent advances in AI-based methods have revolutionized the field of structural biology. Concomitantly, high-throughput sequencing and functional genomics technologies have enabled the detection and generation of variants at an unprecedented scale. However, efficient tools and resources are needed to link these two disparate data types - to "map" variants onto protein structures, to better understand how the variation causes disease and thereby design therapeutics. Here we present the Genomics 2 Proteins Portal (G2P; g2p.broadinstitute.org/): a human proteome-wide resource that maps 19,996,443 genetic variants onto 42,413 protein sequences and 77,923 structures, with a comprehensive set of structural and functional features. Additionally, the G2P portal generalizes the capability of linking genomics to proteins beyond databases by allowing users to interactively upload protein residue-wise annotations (variants, scores, etc.) as well as the protein structure to establish the connection. The portal serves as an easy-to-use discovery tool for researchers and scientists to hypothesize the structure-function relationship between natural or synthetic variations and their molecular phenotype.

Minimum information and guidelines for reporting a multiplexed assay of variant effect.

Multiplexed assays of variant effect (MAVEs) have emerged as a powerful approach for interrogating thousands of genetic variants in a single experiment. The flexibility and widespread adoption of these techniques across diverse disciplines have led to a heterogeneous mix of data formats and descriptions, which complicates the downstream use of the resulting datasets. To address these issues and promote reproducibility and reuse of MAVE data, we define a set of minimum information standards for MAVE data and metadata and outline a controlled vocabulary aligned with established biomedical ontologies for describing these experimental designs.

Defining and Reducing Variant Classification Disparities.

Background: Multiplexed Assays of Variant Effects (MAVEs) can test all possible single variants in a gene of interest. The resulting saturation-style data may help resolve variant classification disparities between populations, especially for variants of uncertain significance (VUS). Methods: We analyzed clinical significance classifications in 213,663 individuals of European-like genetic ancestry versus 206,975 individuals of non-European-like genetic ancestry from All of Us and the Genome Aggregation Database. Then, we incorporated clinically calibrated MAVE data into the Clinical Genome Resource's Variant Curation Expert Panel rules to automate VUS reclassification for BRCA1, TP53, and PTEN . Results: Using two orthogonal statistical approaches, we show a higher prevalence ( p ≤5.95e-06) of VUS in individuals of non-European-like genetic ancestry across all medical specialties assessed in all three databases. Further, in the non-European-like genetic ancestry group, higher rates of Benign or Likely Benign and variants with no clinical designation ( p ≤2.5e-05) were found across many medical specialties, whereas Pathogenic or Likely Pathogenic assignments were higher in individuals of European-like genetic ancestry ( p ≤2.5e-05). Using MAVE data, we reclassified VUS in individuals of non-European-like genetic ancestry at a significantly higher rate in comparison to reclassified VUS from European-like genetic ancestry ( p =9.1e-03) effectively compensating for the VUS disparity. Further, essential code analysis showed equitable impact of MAVE evidence codes but inequitable impact of allele frequency ( p =7.47e-06) and computational predictor ( p =6.92e-05) evidence codes for individuals of non-European-like genetic ancestry. Conclusions: Generation of saturation-style MAVE data should be a priority to reduce VUS disparities and produce equitable training data for future computational predictors.

Mapping MAVE data for use in human genomics applications.

The large-scale experimental measures of variant functional assays submitted to MaveDB have the potential to provide key information for resolving variants of uncertain significance, but the reporting of results relative to assayed sequence hinders their downstream utility. The Atlas of Variant Effects Alliance mapped multiplexed assays of variant effect data to human reference sequences, creating a robust set of machine-readable homology mappings. This method processed approximately 2.5 million protein and genomic variants in MaveDB, successfully mapping 98.61% of examined variants and disseminating data to resources such as the UCSC Genome Browser and Ensembl Variant Effect Predictor.

Guidelines for releasing a variant effect predictor.

Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released to date, and there is tremendous variability in their underlying algorithms and outputs, and in the ways in which the methodologies and predictions are shared. This leads to considerable challenges for end users in knowing which VEPs to use and how to use them. Here, to address these issues, we provide guidelines and recommendations for the release of novel VEPs. Emphasising open-source availability, transparent methodologies, clear variant effect score interpretations, standardised scales, accessible predictions, and rigorous training data disclosure, we aim to improve the usability and interpretability of VEPs, and promote their integration into analysis and evaluation pipelines. We also provide a large, categorised list of currently available VEPs, aiming to facilitate the discovery and encourage the usage of novel methods within the scientific community.

Expression-based segmentation of the Drosophila genome.

BACKGROUND: It is generally accepted that gene order in eukaryotes is nonrandom, with adjacent genes often sharing expression patterns across tissues, and that this organization may be important for gene regulation. Here we describe a novel method, based on an explicit probability model instead of correlation analysis, for identifying coordinately expressed gene clusters ('coexpression segments'), apply it to Drosophila melanogaster, and look for epigenetic associations using publicly available data. RESULTS: We find that two-thirds of Drosophila genes fall into multigenic coexpression segments, and that such segments are of two main types, housekeeping and tissue-restricted. Consistent with correlation-based studies, we find that adjacent genes within the same segment tend to be physically closer to each other than to the adjacent genes in different segments, and that tissue-restricted segments are enriched for testis-expressed genes. Our segmentation pattern correlates with Hi-C based physical interaction domains, but segments are generally much smaller than domains. Intersegment regions (including those which do not correspond to physical domain boundaries) are enriched for insulator binding sites. CONCLUSIONS: We describe a novel approach for identifying coexpression clusters that does not require arbitrary cutoff values or heuristics, and find that coexpression of adjacent genes is widespread in the Drosophila genome. Coexpression segments appear to reflect a level of regulatory organization related to, but below that of physical interaction domains, and depending in part on insulator binding.

An Atlas of Variant Effects to understand the genome at nucleotide resolution.

Sequencing has revealed hundreds of millions of human genetic variants, and continued efforts will only add to this variant avalanche. Insufficient information exists to interpret the effects of most variants, limiting opportunities for precision medicine and comprehension of genome function. A solution lies in experimental assessment of the functional effect of variants, which can reveal their biological and clinical impact. However, variant effect assays have generally been undertaken reactively for individual variants only after and, in most cases long after, their first observation. Now, multiplexed assays of variant effect can characterise massive numbers of variants simultaneously, yielding variant effect maps that reveal the function of every possible single nucleotide change in a gene or regulatory element. Generating maps for every protein encoding gene and regulatory element in the human genome would create an 'Atlas' of variant effect maps and transform our understanding of genetics and usher in a new era of nucleotide-resolution functional knowledge of the genome. An Atlas would reveal the fundamental biology of the human genome, inform human evolution, empower the development and use of therapeutics and maximize the utility of genomics for diagnosing and treating disease. The Atlas of Variant Effects Alliance is an international collaborative group comprising hundreds of researchers, technologists and clinicians dedicated to realising an Atlas of Variant Effects to help deliver on the promise of genomics.

Minimum information and guidelines for reporting a Multiplexed Assay of Variant Effect.

Multiplexed Assays of Variant Effect (MAVEs) have emerged as a powerful approach for interrogating thousands of genetic variants in a single experiment. The flexibility and widespread adoption of these techniques across diverse disciplines has led to a heterogeneous mix of data formats and descriptions, which complicates the downstream use of the resulting datasets. To address these issues and promote reproducibility and reuse of MAVE data, we define a set of minimum information standards for MAVE data and metadata and outline a controlled vocabulary aligned with established biomedical ontologies for describing these experimental designs.

Mutation patterns in cancer genomes.

Recent large-scale cancer sequencing studies have focused primarily on identifying cancer-associated genes, but as an important byproduct provide "passenger mutation" data that can potentially illuminate the mutational mechanisms at work in cancer cells. Here, we explore patterns of nucleotide substitution in several cancer types using published data. We first show that selection (negative or positive) has affected only a small fraction of mutations, allowing us to attribute observed trends to underlying mutational processes rather than selection. We then show that the increased CpG mutation frequency observed in some cancers primarily occurs outside of CpG islands and CpG island shores, thus rejecting the hypothesis that the increase is a byproduct of island or shore methylation followed by deamination. We observe an A-->G vs. T-->C mutational asymmetry in some cancers similar to one that has been observed in germline mutations in transcribed regions, suggesting that the mutation process may be influenced by gene expression. We also demonstrate that the relative frequency of mutations at dinucleotide "hotspots" can be used as a tool to detect likely technical artifacts in large-scale studies.

Integrating deep mutational scanning and low-throughput mutagenesis data to predict the impact of amino acid variants.

BACKGROUND: Evaluating the impact of amino acid variants has been a critical challenge for studying protein function and interpreting genomic data. High-throughput experimental methods like deep mutational scanning (DMS) can measure the effect of large numbers of variants in a target protein, but because DMS studies have not been performed on all proteins, researchers also model DMS data computationally to estimate variant impacts by predictors. RESULTS: In this study, we extended a linear regression-based predictor to explore whether incorporating data from alanine scanning (AS), a widely used low-throughput mutagenesis method, would improve prediction results. To evaluate our model, we collected 146 AS datasets, mapping to 54 DMS datasets across 22 distinct proteins. CONCLUSIONS: We show that improved model performance depends on the compatibility of the DMS and AS assays, and the scale of improvement is closely related to the correlation between DMS and AS results.

Identifying New Hybrid Insulin Peptides (HIPs) in Type 1 Diabetes.

In 2016 Delong et al. discovered a new type of neoepitope formed by the fusion of two unrelated peptide fragments. Remarkably these neoepitopes, called hybrid insulin peptides, or HIPs, are recognized by pathogenic CD4+ T cells in the NOD mouse and human pancreatic islet-infiltrating T cells in people with type 1 diabetes. Current data implicates CD4+ T-cell responses to HIPs in the immune pathogenesis of human T1D. Because of their role in the immune pathogenesis of human T1D it is important to identify new HIPs that are recognized by CD4+ T cells in people at risk of, or with, T1D. A detailed knowledge of T1D-associated HIPs will allow HIPs to be used in assays to monitor changes in T cell mediated beta-cell autoimmunity. They will also provide new targets for antigen-specific therapies for T1D. However, because HIPs are formed by the fusion of two unrelated peptides there are an enormous number of potential HIPs which makes it technically challenging to identify them. Here we review the discovery of HIPs, how they form and discuss approaches to identifying new HIPs relevant to the immune pathogenesis of human type 1 diabetes.