U2AF1 S34F enhances tumorigenic potential of lung cells by exhibiting synergy with KRAS mutation and altering response to environmental stress.

Although U2AF1 S34F is a recurrent splicing factor mutation in lung adenocarcinoma (ADC), U2AF1 S34F alone is insufficient for producing tumors in previous models. Because lung ADCs with U2AF1 S34F frequently have co-occurring KRAS mutations and smoking histories, we hypothesized that tumor-forming potential arises from U2AF1 S34F interacting with oncogenic KRAS and environmental stress. To elucidate the effect of U2AF1 S34F co-occurring with a second mutation, we generated human bronchial epithelial cells (HBEC3kt) with co-occurring U2AF1 S34F and KRAS G12V . Transcriptome analysis revealed that co-occurring U2AF1 S34F and KRAS G12V differentially impacts inflammatory, cell cycle, and KRAS pathways. Subsequent phenotyping found associated suppressed cytokine production, increased proliferation, anchorage-independent growth, and tumors in mouse xenografts. Interestingly, HBEC3kts harboring only U2AF1 S34F display increased splicing in stress granule protein genes and viability in cigarette smoke concentrate. Our results suggest that U2AF1 S34F may potentiate transformation by granting precancerous cells survival advantage in environmental stress, permitting accumulation of additional mutations like KRAS G12V , which synergize with U2AF1 S34F to transform the cell.

The TSC22D, WNK, and NRBP gene families exhibit functional buffering and evolved with Metazoa for cell volume regulation.

The ability to sense and respond to osmotic fluctuations is critical for the maintenance of cellular integrity. We used gene co-essentiality analysis to identify an unappreciated relationship between TSC22D2, WNK1, and NRBP1 in regulating cell volume homeostasis. All of these genes have paralogs and are functionally buffered for osmo-sensing and cell volume control. Within seconds of hyperosmotic stress, TSC22D, WNK, and NRBP family members physically associate into biomolecular condensates, a process that is dependent on intrinsically disordered regions (IDRs). A close examination of these protein families across metazoans revealed that TSC22D genes evolved alongside a domain in NRBPs that specifically binds to TSC22D proteins, which we have termed NbrT (NRBP binding region with TSC22D), and this co-evolution is accompanied by rapid IDR length expansion in WNK-family kinases. Our study reveals that TSC22D, WNK, and NRBP genes evolved in metazoans to co-regulate rapid cell volume changes in response to osmolarity.

Intricate MIB1-NOTCH-GATA6 Interactions in Cardiac Valvular and Septal Development.

Genome-wide association studies and experimental mouse models implicate the MIB1 and GATA6 genes in congenital heart disease (CHD). Their close physical proximity and conserved synteny suggest that these two genes might be involved in analogous cardiac developmental processes. Heterozygous Gata6 loss-of-function mutations alone or humanized Mib1 mutations in a NOTCH1-sensitized genetic background cause bicuspid aortic valve (BAV) and a membranous ventricular septal defect (VSD), consistent with MIB1 and NOTCH1 functioning in the same pathway. To determine if MIB1-NOTCH and GATA6 interact in valvular and septal development, we generated compound heterozygote mice carrying different Mib1 missense (Mib1K735R and Mib1V943F) or nonsense (Mib1R530X) mutations with the Gata6STOP/+ heterozygous null mutation. Combining Mib1R530X/+ or Mib1K735R/+ with Gata6STOP/+ does not affect Gata6STOP/+ single mutant phenotypes. In contrast, combining Mib1V943F/+ with Gata6STOP/+ decreases the incidence of BAV and VSD by 50%, suggesting a suppressive effect of Mib1V943F/+ on Gata6STOP/+. Transcriptomic and functional analyses revealed that while the EMT pathway term is depleted in the Gata6STOP/+ mutant, introducing the Mib1V943F variant robustly enriches this term, consistent with the Mib1V943F/+ phenotypic suppression of Gata6STOP/+. Interestingly, combined Notch1 and Gata6 insufficiency led to a nearly fully penetrant VSD but did not affect the BAV phenotype, underscoring the complex functional relationship between MIB1, NOTCH, and GATA6 in valvular and septal development.

Forebrain commissure formation in zebrafish embryo requires the binding of KLC1 to CRMP2.

Formation of the corpus callosum (CC), anterior commissure (AC), and postoptic commissure (POC), connecting the left and right cerebral hemispheres, is crucial for cerebral functioning. Collapsin response mediator protein 2 (CRMP2) has been suggested to be associated with the mechanisms governing this formation, based on knockout studies in mice and knockdown/knockout studies in zebrafish. Previously, we reported two cases of non-synonymous CRMP2 variants with S14R and R565C substitutions. Among the, the R565C substitution (p.R565C) was caused by the novel CRMP2 mutation c.1693C > T, and the patient presented with intellectual disability accompanied by CC hypoplasia. In this study, we demonstrate that crmp2 mRNA could rescue AC and POC formation in crmp2-knockdown zebrafish, whereas the mRNA with the R566C mutation could not. Zebrafish CRMP2 R566C corresponds to human CRMP2 R565C. Further experiments with transfected cultured cells indicated that CRMP2 with the R566C mutation could not bind to kinesin light chain 1 (KLC1). Knockdown of klc1a in zebrafish resulted in defective AC and POC formation, revealing a genetic interaction with crmp2. These findings suggest that the CRMP2 R566C mutant fails to bind to KLC1, preventing axonal elongation and leading to defective AC and POC formation in zebrafish and CC formation defects in humans. Our study highlights the importance of the interaction between CRMP2 and KLC1 in the formation of the forebrain commissures, revealing a novel mechanism associated with CRMP2 mutations underlying human neurodevelopmental abnormalities.

Harnessing genetic interactions for prediction of immune checkpoint inhibitors response signature in cancer cells.

Genetic interactions (GIs) refer to two altered genes having a combined effect that is not seen individually. They play a crucial role in influencing drug efficacy. We utilized CGIdb 2.0 (http://www.medsysbio.org/CGIdb2/), an updated database of comprehensively published GIs information, encompassing synthetic lethality (SL), synthetic viability (SV), and chemical-genetic interactions. CGIdb 2.0 elucidates GIs relationships between or within protein complex models by integrating protein-protein physical interactions. Additionally, we introduced GENIUS (GENetic Interactions mediated drUg Signature) to leverage GIs for identifying the response signature of immune checkpoint inhibitors (ICIs). GENIUS identified high MAP4K4 expression as a resistant signature and high HERC4 expression as a sensitive signature for ICIs treatment. Melanoma patients with high expression of MAP4K4 were associated with decreased efficacy and poorer survival following ICIs treatment. Conversely, overexpression of HERC4 in melanoma patients correlated with a positive response to ICIs. Notably, HERC4 enhances sensitivity to immunotherapy by facilitating antigen presentation. Analyses of immune cell infiltration and single-cell data revealed that B cells expressing MAP4K4 may contribute to resistance to ICIs in melanoma. Overall, CGIdb 2.0, provides integrated GIs data, thus serving as a crucial tool for exploring drug effects.

Biomarkers of mitochondrial dysfunction in autism spectrum disorder: A systematic review and meta-analysis.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting 1 in 36 children and is associated with physiological abnormalities, most notably mitochondrial dysfunction, at least in a subset of individuals. This systematic review and meta-analysis discovered 204 relevant articles which evaluated biomarkers of mitochondrial dysfunction in ASD individuals. Significant elevations (all p < 0.01) in the prevalence of lactate (17%), pyruvate (41%), alanine (15%) and creatine kinase (9%) were found in ASD. Individuals with ASD had significant differences (all p < 0.01) with moderate to large effect sizes (Cohen's d' ≥ 0.6) compared to controls in mean pyruvate, lactate-to-pyruvate ratio, ATP, and creatine kinase. Some studies found abnormal TCA cycle metabolites associated with ASD. Thirteen controlled studies reported mitochondrial DNA (mtDNA) deletions or variations in the ASD group in blood, peripheral blood mononuclear cells, lymphocytes, leucocytes, granulocytes, and brain. Meta-analyses discovered significant differences (p < 0.01) in copy number of mtDNA overall and in ND1, ND4 and CytB genes. Four studies linked specific mtDNA haplogroups to ASD. A series of studies found a subgroup of ASD with elevated mitochondrial respiration which was associated with increased sensitivity of the mitochondria to physiological stressors and neurodevelopmental regression. Lactate, pyruvate, lactate-to-pyruvate ratio, carnitine, and acyl-carnitines were associated with clinical features such as delays in language, social interaction, cognition, motor skills, and with repetitive behaviors and gastrointestinal symptoms, although not all studies found an association. Lactate, carnitine, acyl-carnitines, ATP, CoQ10, as well as mtDNA variants, heteroplasmy, haplogroups and copy number were associated with ASD severity. Variability was found across biomarker studies primarily due to differences in collection and processing techniques as well as the intrinsic heterogeneity of the ASD population. Several studies reported alterations in mitochondrial metabolism in mothers of children with ASD and in neonates who develop ASD. Treatments targeting mitochondria, particularly carnitine and ubiquinol, appear beneficial in ASD. The link between mitochondrial dysfunction in ASD and common physiological abnormalities in individuals with ASD including gastrointestinal disorders, oxidative stress, and immune dysfunction is outlined. Several subtypes of mitochondrial dysfunction in ASD are discussed, including one related to neurodevelopmental regression, another related to alterations in microbiome metabolites, and another related to elevations in acyl-carnitines. Mechanisms linking abnormal mitochondrial function with alterations in prenatal brain development and postnatal brain function are outlined. Given the multisystem complexity of some individuals with ASD, this review presents evidence for the mitochondria being central to ASD by contributing to abnormalities in brain development, cognition, and comorbidities such as immune and gastrointestinal dysfunction as well as neurodevelopmental regression. A diagnostic approach to identify mitochondrial dysfunction in ASD is outlined. From this evidence, it is clear that many individuals with ASD have alterations in mitochondrial function which may need to be addressed in order to achieve optimal clinical outcomes. The fact that alterations in mitochondrial metabolism may be found during pregnancy and early in the life of individuals who eventually develop ASD provides promise for early life predictive biomarkers of ASD. Further studies may improve the understanding of the role of the mitochondria in ASD by better defining subgroups and understanding the molecular mechanisms driving some of the unique changes found in mitochondrial function in those with ASD.

MEMO1 binds iron and modulates iron homeostasis in cancer cells.

Mediator of ERBB2-driven cell motility 1 (MEMO1) is an evolutionary conserved protein implicated in many biological processes; however, its primary molecular function remains unknown. Importantly, MEMO1 is overexpressed in many types of cancer and was shown to modulate breast cancer metastasis through altered cell motility. To better understand the function of MEMO1 in cancer cells, we analyzed genetic interactions of MEMO1 using gene essentiality data from 1028 cancer cell lines and found multiple iron-related genes exhibiting genetic relationships with MEMO1. We experimentally confirmed several interactions between MEMO1 and iron-related proteins in living cells, most notably, transferrin receptor 2 (TFR2), mitoferrin-2 (SLC25A28), and the global iron response regulator IRP1 (ACO1). These interactions indicate that cells with high-MEMO1 expression levels are hypersensitive to the disruptions in iron distribution. Our data also indicate that MEMO1 is involved in ferroptosis and is linked to iron supply to mitochondria. We have found that purified MEMO1 binds iron with high affinity under redox conditions mimicking intracellular environment and solved MEMO1 structures in complex with iron and copper. Our work reveals that the iron coordination mode in MEMO1 is very similar to that of iron-containing extradiol dioxygenases, which also display a similar structural fold. We conclude that MEMO1 is an iron-binding protein that modulates iron homeostasis in cancer cells.

Novel ancestry-specific primary open-angle glaucoma loci and shared biology with vascular mechanisms and cell proliferation.

Primary open-angle glaucoma (POAG), a leading cause of irreversible blindness globally, shows disparity in prevalence and manifestations across ancestries. We perform meta-analysis across 15 biobanks (of the Global Biobank Meta-analysis Initiative) (n = 1,487,441: cases = 26,848) and merge with previous multi-ancestry studies, with the combined dataset representing the largest and most diverse POAG study to date (n = 1,478,037: cases = 46,325) and identify 17 novel significant loci, 5 of which were ancestry specific. Gene-enrichment and transcriptome-wide association analyses implicate vascular and cancer genes, a fifth of which are primary ciliary related. We perform an extensive statistical analysis of SIX6 and CDKN2B-AS1 loci in human GTEx data and across large electronic health records showing interaction between SIX6 gene and causal variants in the chr9p21.3 locus, with expression effect on CDKN2A/B. Our results suggest that some POAG risk variants may be ancestry specific, sex specific, or both, and support the contribution of genes involved in programmed cell death in POAG pathogenesis.

A dose-response model for statistical analysis of chemical genetic interactions in CRISPRi screens.

An important application of CRISPR interference (CRISPRi) technology is for identifying chemical-genetic interactions (CGIs). Discovery of genes that interact with exposure to antibiotics can yield insights to drug targets and mechanisms of action or resistance. The objective is to identify CRISPRi mutants whose relative abundance is suppressed (or enriched) in the presence of a drug when the target protein is depleted, reflecting synergistic behavior. Different sgRNAs for a given target can induce a wide range of protein depletion and differential effects on growth rate. The effect of sgRNA strength can be partially predicted based on sequence features. However, the actual growth phenotype depends on the sensitivity of cells to depletion of the target protein. For essential genes, sgRNA efficiency can be empirically measured by quantifying effects on growth rate. We observe that the most efficient sgRNAs are not always optimal for detecting synergies with drugs. sgRNA efficiency interacts in a non-linear way with drug sensitivity, producing an effect where the concentration-dependence is maximized for sgRNAs of intermediate strength (and less so for sgRNAs that induce too much or too little target depletion). To capture this interaction, we propose a novel statistical method called CRISPRi-DR (for Dose-Response model) that incorporates both sgRNA efficiencies and drug concentrations in a modified dose-response equation. We use CRISPRi-DR to re-analyze data from a recent CGI experiment in Mycobacterium tuberculosis to identify genes that interact with antibiotics. This approach can be generalized to non-CGI datasets, which we show via an CRISPRi dataset for E. coli growth on different carbon sources. The performance is competitive with the best of several related analytical methods. However, for noisier datasets, some of these methods generate far more significant interactions, likely including many false positives, whereas CRISPRi-DR maintains higher precision, which we observed in both empirical and simulated data.

Genomic loci involved in sensing environmental cues and metabolism affect seasonal coat shedding in Bos taurus and Bos indicus cattle.

Seasonal shedding of winter hair at the start of summer is well studied in wild and domesticated populations. However, the genetic influences on this trait and their interactions are poorly understood. We use data from 13,364 cattle with 36,899 repeated phenotypes to investigate the relationship between hair shedding and environmental variables, single nucleotide polymorphisms, and their interactions to understand quantitative differences in seasonal shedding. Using deregressed estimated breeding values from a repeated records model in a genome-wide association analysis (GWAA) and meta-analysis of year-specific GWAA gave remarkably similar results. These GWAA identified hundreds of variants associated with seasonal hair shedding. There were especially strong associations between chromosomes 5 and 23. Genotype-by-environment interaction GWAA identified 1,040 day length-by-genotype interaction associations and 17 apparent temperature-by-genotype interaction associations with hair shedding, highlighting the importance of day length on hair shedding. Accurate genomic predictions of hair shedding were created for the entire dataset, Angus, Hereford, Brangus, and multibreed datasets. Loci related to metabolism and light-sensing have a large influence on seasonal hair shedding. This is one of the largest genetic analyses of a phenological trait and provides insight into both agriculture production and basic science.

Genetic Interactions in Various Environmental Conditions in Caenorhabditis elegans.

Although it is well known that epistasis plays an important role in many evolutionary processes (e.g., speciation, evolution of sex), our knowledge on the frequency and prevalent sign of epistatic interactions is mainly limited to unicellular organisms or cell cultures of multicellular organisms. This is even more pronounced in regard to how the environment can influence genetic interactions. To broaden our knowledge in that respect we studied gene-gene interactions in a whole multicellular organism, Caenorhabditis elegans. We screened over one thousand gene interactions, each one in standard laboratory conditions, and under three different stressors: heat shock, oxidative stress, and genotoxic stress. Depending on the condition, between 7% and 22% of gene pairs showed significant genetic interactions and an overall sign of epistasis changed depending on the condition. Sign epistasis was quite common, but reciprocal sign epistasis was extremally rare. One interaction was common to all conditions, whereas 78% of interactions were specific to only one environment. Although epistatic interactions are quite common, their impact on evolutionary processes will strongly depend on environmental factors.

Interactions between genes involved in physiological dysregulation and axon guidance: role in Alzheimer's disease.

Dysregulation of physiological processes may contribute to Alzheimer's disease (AD) development. We previously found that an increase in the level of physiological dysregulation (PD) in the aging body is associated with declining resilience and robustness to major diseases. Also, our genome-wide association study found that genes associated with the age-related increase in PD frequently represented pathways implicated in axon guidance and synaptic function, which in turn were linked to AD and related traits (e.g., amyloid, tau, neurodegeneration) in the literature. Here, we tested the hypothesis that genes involved in PD and axon guidance/synapse function may jointly influence onset of AD. We assessed the impact of interactions between SNPs in such genes on AD onset in the Long Life Family Study and sought to replicate the findings in the Health and Retirement Study. We found significant interactions between SNPs in the UNC5C and CNTN6, and PLXNA4 and EPHB2 genes that influenced AD onset in both datasets. Associations with individual SNPs were not statistically significant. Our findings, thus, support a major role of genetic interactions in the heterogeneity of AD and suggest the joint contribution of genes involved in PD and axon guidance/synapse function (essential for the maintenance of complex neural networks) to AD development.

A knowledge graph approach to predict and interpret disease-causing gene interactions.

BACKGROUND: Understanding the impact of gene interactions on disease phenotypes is increasingly recognised as a crucial aspect of genetic disease research. This trend is reflected by the growing amount of clinical research on oligogenic diseases, where disease manifestations are influenced by combinations of variants on a few specific genes. Although statistical machine-learning methods have been developed to identify relevant genetic variant or gene combinations associated with oligogenic diseases, they rely on abstract features and black-box models, posing challenges to interpretability for medical experts and impeding their ability to comprehend and validate predictions. In this work, we present a novel, interpretable predictive approach based on a knowledge graph that not only provides accurate predictions of disease-causing gene interactions but also offers explanations for these results. RESULTS: We introduce BOCK, a knowledge graph constructed to explore disease-causing genetic interactions, integrating curated information on oligogenic diseases from clinical cases with relevant biomedical networks and ontologies. Using this graph, we developed a novel predictive framework based on heterogenous paths connecting gene pairs. This method trains an interpretable decision set model that not only accurately predicts pathogenic gene interactions, but also unveils the patterns associated with these diseases. A unique aspect of our approach is its ability to offer, along with each positive prediction, explanations in the form of subgraphs, revealing the specific entities and relationships that led to each pathogenic prediction. CONCLUSION: Our method, built with interpretability in mind, leverages heterogenous path information in knowledge graphs to predict pathogenic gene interactions and generate meaningful explanations. This not only broadens our understanding of the molecular mechanisms underlying oligogenic diseases, but also presents a novel application of knowledge graphs in creating more transparent and insightful predictors for genetic research.

Deciphering the mitophagy receptor network identifies a crucial role for OPTN (optineurin) in acute myeloid leukemia.

The selective autophagic degradation of mitochondria via mitophagy is essential for preserving mitochondrial homeostasis and, thereby, disease maintenance and progression in acute myeloid leukemia (AML). Mitophagy is orchestrated by a variety of mitophagy receptors whose interplay is not well understood. Here, we established a pairwise multiplexed CRISPR screen targeting mitophagy receptors to elucidate redundancies and gain a deeper understanding of the functional interactome governing mitophagy in AML. We identified OPTN (optineurin) as sole non-redundant mitophagy receptor and characterized its unique role in AML. Knockdown and overexpression experiments demonstrated that OPTN expression is rate-limiting for AML cell proliferation. In a MN1-driven murine transplantation model, loss of OPTN prolonged overall median survival by 7 days (+21%). Mechanistically, we found broadly impaired mitochondrial respiration and function with increased mitochondrial ROS, that most likely caused the proliferation defect. Our results decipher the intertwined network of mitophagy receptors in AML for both ubiquitin-dependent and receptor-mediated mitophagy, identify OPTN as a non-redundant tool to study mitophagy in the context of leukemia and suggest OPTN inhibition as an attractive therapeutic strategy.Abbreviations: AML: acute myeloid leukemia; CRISPR: Clustered Regularly Interspaced Short Palindromic Repeats; CTRL: control; DFP: deferiprone; GI: genetic interaction; KD: knockdown; KO: knockout; ldMBM, lineage-depleted murine bone marrow; LFC: log2 fold change; LIR: LC3-interacting region; LSC: leukemic stem cell; MAGeCK: Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout; MDIVI-1: mitochondrial division inhibitor 1; MOI: multiplicity of infection; MOM: mitochondrial outer membrane; NAC: N-acetyl-L-cysteine; OA: oligomycin-antimycin A; OCR: oxygen consumption rate; OE: overexpression; OPTN: optineurin; PINK1: PTEN induced putative kinase 1; ROS: reactive oxygen species; SEM: standard error of the mean; TCGA: The Cancer Genome Atlas; TEM: transmission electron microscopy; UBD: ubiquitin-binding domain; WT: wild type.

High Lethality of Mycobacterium tuberculosis Infection in Mice Lacking the Phagocyte Oxidase and Caspase1/11.

Immune networks that control antimicrobial and inflammatory mechanisms have overlapping regulation and functions to ensure effective host responses. Genetic interaction studies of immune pathways that compare host responses in single and combined knockout backgrounds are a useful tool to identify new mechanisms of immune control during infection. For disease caused by pulmonary Mycobacterium tuberculosis (Mtb) infections, which currently lacks an effective vaccine, understanding the genetic interactions between protective immune pathways may identify new therapeutic targets or disease-associated genes. Previous studies have suggested a direct link between the activation of NLRP3-Caspase1 inflammasome and the NADPH-dependent phagocyte oxidase complex during Mtb infection. Loss of the phagocyte oxidase complex alone resulted in increased activation of Caspase1 and IL-1ß production during Mtb infection, resulting in failed disease tolerance during the chronic stages of disease. To better understand this interaction, we generated mice lacking both Cybb, a key subunit of the phagocyte oxidase, and Caspase1/11. We found that ex vivo Mtb infection of Cybb-/-Caspase1/11-/- macrophages resulted in the expected loss of IL-1ß secretion but an unexpected change in other inflammatory cytokines and bacterial control. Mtb infected Cybb-/-Caspase1/11-/- mice rapidly progressed to severe TB, succumbing within 4 weeks to disease characterized by high bacterial burden, increased inflammatory cytokines, and the recruitment of granulocytes that associated with Mtb in the lungs. These results uncover a key genetic interaction between the phagocyte oxidase complex and Caspase1/11 that controls protection against TB and highlight the need for a better understanding of the regulation of fundamental immune networks during Mtb infection.

Reproducibility metrics for context-specific CRISPR screens.

CRISPR screens are used extensively to systematically interrogate the phenotype-to-genotype problem. In contrast to early CRISPR screens, which defined core cell fitness genes, most current efforts now aim to identify context-specific phenotypes that differentiate a cell line, genetic background, or condition of interest, such as a drug treatment. While CRISPR-related technologies have shown great promise and a fast pace of innovation, a better understanding of standards and methods for quality assessment of CRISPR screen results is crucial to guide technology development and application. Specifically, many commonly used metrics for quantifying screen quality do not accurately measure the reproducibility of context-specific hits. We highlight the importance of reporting reproducibility statistics that directly relate to the purpose of the screen and suggest the use of metrics that are sensitive to context-specific signal. A record of this paper's transparent peer review process is included in the supplemental information.

Widespread epistasis among beneficial genetic variants revealed by high-throughput genome editing.

The phenotypic effect of any genetic variant can be altered by variation at other genomic loci. Known as epistasis, these genetic interactions shape the genotype-phenotype map of every species, yet their origins remain poorly understood. To investigate this, we employed high-throughput genome editing to measure the fitness effects of 1,826 naturally polymorphic variants in four strains of Saccharomyces cerevisiae. About 31% of variants affect fitness, of which 24% have strain-specific fitness effects indicative of epistasis. We found that beneficial variants are more likely to exhibit genetic interactions and that these interactions can be mediated by specific traits such as flocculation ability. This work suggests that adaptive evolution will often involve trade-offs where a variant is only beneficial in some genetic backgrounds, potentially explaining why many beneficial variants remain polymorphic. In sum, we provide a framework to understand the factors influencing epistasis with single-nucleotide resolution, revealing widespread epistasis among beneficial variants.

Epistasis decreases with increasing antibiotic pressure but not temperature.

Predicting mutational effects is essential for the control of antibiotic resistance (ABR). Predictions are difficult when there are strong genotype-by-environment (G × E), gene-by-gene (G × G or epistatic) or gene-by-gene-by-environment (G × G × E) interactions. We quantified G × G × E effects in Escherichia coli across environmental gradients. We created intergenic fitness landscapes using gene knock-outs and single-nucleotide ABR mutations previously identified to vary in the extent of G × E effects in our environments of interest. Then, we measured competitive fitness across a complete combinatorial set of temperature and antibiotic dosage gradients. In this way, we assessed the predictability of 15 fitness landscapes across 12 different but related environments. We found G × G interactions and rugged fitness landscapes in the absence of antibiotic, but as antibiotic concentration increased, the fitness effects of ABR genotypes quickly overshadowed those of gene knock-outs, and the landscapes became smoother. Our work reiterates that some single mutants, like those conferring resistance or susceptibility to antibiotics, have consistent effects across genetic backgrounds in stressful environments. Thus, although epistasis may reduce the predictability of evolution in benign environments, evolution may be more predictable in adverse environments. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

Polycomb Alterations in Acute Myeloid Leukaemia: From Structure to Function.

Epigenetic dysregulation is a hallmark of many haematological malignancies and is very frequent in acute myeloid leukaemia (AML). A cardinal example is the altered activity of the Polycomb Repressive Complex 2 (PRC2) due to somatic mutations and deletions in genes encoding PRC2 core factors that are necessary for correct complex assembly. These genetic alterations typically lead to reduced histone methyltransferase activity that, in turn, has been strongly linked to poor prognosis and chemoresistance. In this review, we provide an overview of genetic alterations of PRC components in AML, with particular reference to structural and functional features of PRC2 factors. We further review genetic interactions between these alterations and other AML-associated mutations in both adult and paediatric leukaemias. Finally, we discuss reported prognostic links between PRC2 mutations and deletions and disease outcomes and potential implications for therapy.

AD-Syn-Net: systematic identification of Alzheimer's disease-associated mutation and co-mutation vulnerabilities via deep learning.

Alzheimer's disease (AD) is one of the most challenging neurodegenerative diseases because of its complicated and progressive mechanisms, and multiple risk factors. Increasing research evidence demonstrates that genetics may be a key factor responsible for the occurrence of the disease. Although previous reports identified quite a few AD-associated genes, they were mostly limited owing to patient sample size and selection bias. There is a lack of comprehensive research aimed to identify AD-associated risk mutations systematically. To address this challenge, we hereby construct a large-scale AD mutation and co-mutation framework ('AD-Syn-Net'), and propose deep learning models named Deep-SMCI and Deep-CMCI configured with fully connected layers that are capable of predicting cognitive impairment of subjects effectively based on genetic mutation and co-mutation profiles. Next, we apply the customized frameworks to data sets to evaluate the importance scores of the mutations and identified mutation effectors and co-mutation combination vulnerabilities contributing to cognitive impairment. Furthermore, we evaluate the influence of mutation pairs on the network architecture to dissect the genetic organization of AD and identify novel co-mutations that could be responsible for dementia, laying a solid foundation for proposing future targeted therapy for AD precision medicine. Our deep learning model codes are available open access here: https://github.com/Pan-Bio/AD-mutation-effectors.