Systematic mapping of genetic interactions in Caenorhabditis elegans identifies common modifiers of diverse signaling pathways

The MAST kinase KIN-4 carries out mitotic entry functions of Greatwall in C. elegans

MAST-like, or Greatwall (Gwl), an atypical protein kinase related to the evolutionarily conserved MAST kinase family, is crucial for cell cycle control during mitotic entry. Mechanistically, Greatwall is activated by Cyclin B-Cdk1 phosphorylation of a 550 amino acids-long insertion in its atypical activation segment. Subsequently, Gwl phosphorylates Endosulfine and Arpp19 to convert them into inhibitors of PP2A-B55 phosphatase, thereby preventing early dephosphorylation of M-phase targets of Cyclin B-Cdk1. Here, searching for an elusive Gwl-like activity in C. elegans, we show that the single worm MAST kinase, KIN-4, fulfills this function in worms and can functionally replace Greatwall in the heterologous Xenopus system. Compared to Greatwall, the short activation segment of KIN-4 lacks a phosphorylation site, and KIN-4 is active even when produced in E. coli. We also show that a balance between Cyclin B-Cdk1 and PP2A-B55 activity, regulated by KIN-4, is essential to ensure asynchronous cell divisions in the early worm embryo. These findings resolve a long-standing puzzle related to the supposed absence of a Greatwall pathway in C. elegans, and highlight a novel aspect of PP2A-B55 regulation by MAST kinases.

Transition to Piscivory Seen Through Brain Transcriptomics in a Juvenile Percid Fish: Complex Interplay of Differential Gene Transcription, Alternative Splicing, and ncRNA Activity

Pikeperch (Sander Lucioperca) belongs to main predatory fish species in freshwater bodies throughout Europe playing the key role by reducing planktivorous fish abundance. Two size classes of the young-of-the-year (YOY) pikeperch are known in Europe and North America. Our long-term fish survey elucidates late-summer size distribution of YOY pikeperch in the Lipno Reservoir (Czechia) and recognizes two distinct subcohorts: smaller pelagic planktivores heavily outnumber larger demersal piscivores. To explore molecular mechanisms accompanying the switch from planktivory to piscivory, we compared brain transcriptomes of both subcohorts and identified 148 differentially transcribed genes. The pathway enrichment analyses identified the piscivorous phase to be associated with genes involved in collagen and extracellular matrix generation with numerous Gene Ontology (GO), while the planktivorous phase was associated with genes for non-muscle-myosins (NMM) with less GO terms. Transcripts further upregulated in planktivores from the periphery of the NMM network were Pmchl, Pomcl, and Pyyb, all involved also in appetite control and producing (an)orexigenic neuropeptides. Noncoding RNAs were upregulated in transcriptomes of planktivores including three transcripts of snoRNA U85. Thirty genes mostly functionally unrelated to those differentially transcribed were alternatively spliced between the subcohorts. Our results indicate planktivores as potentially driven by voracity to initiate the switch to piscivory, while piscivores undergo a dynamic brain development. We propose a spatiotemporal spreading of juvenile development over a longer period and larger spatial scales through developmental plasticity as an adaptation to exploiting all types of resources and decreasing the intraspecific competition.

Understanding genetic variants in context

Over the last three decades, human genetics has gone from dissecting high-penetrance Mendelian diseases to discovering the vast and complex genetic etiology of common human diseases. In tackling this complexity, scientists have discovered the importance of numerous genetic processes - most notably functional regulatory elements - in the development and progression of these diseases. Simultaneously, scientists have increasingly used multiplex assays of variant effect to systematically phenotype the cellular consequences of millions of genetic variants. In this article, we argue that the context of genetic variants - at all scales, from other genetic variants and gene regulation to cell biology to organismal environment - are critical components of how we can employ genomics to interpret these variants, and ultimately treat these diseases. We describe approaches to extend existing experimental assays and computational approaches to examine and quantify the importance of this context, including through causal analytic approaches. Having a unified understanding of the molecular, physiological, and environmental processes governing the interpretation of genetic variants is sorely needed for the field, and this perspective argues for feasible approaches by which the combined interpretation of cellular, animal, and epidemiological data can yield that knowledge.

DRMY1 promotes robust morphogenesis in Arabidopsis by sustaining the translation of cytokinin-signaling inhibitor proteins

Robustness is the invariant development of phenotype despite environmental changes and genetic perturbations. In the Arabidopsis flower bud, four sepals robustly initiate and grow to a constant size to enclose and protect the inner floral organs. We previously characterized the mutant development-related myb-like 1 (drmy1), where 3-5 sepals initiate variably and grow to different sizes, compromising their protective function. The molecular mechanism underlying this loss of robustness was unclear. Here, we show that drmy1 has reduced TARGET OF RAPAMYCIN (TOR) activity, ribosomal content, and translation. Translation reduction decreases the protein level of ARABIDOPSIS RESPONSE REGULATOR7 (ARR7) and ARABIDOPSIS HISTIDINE PHOSPHOTRANSFER PROTEIN 6 (AHP6), two cytokinin-signaling inhibitors that are normally rapidly produced before sepal initiation. The resultant upregulation of cytokinin signaling disrupts robust auxin patterning and sepal initiation. Our work shows that the homeostasis of translation, a ubiquitous cellular process, is crucial for the robust spatiotemporal patterning of organogenesis.

MRCK-1 activates non-muscle myosin for outgrowth of a unicellular tube in Caenorhabditis elegans

The formation and patterning of unicellular biological tubes is essential for metazoan development. It is well established that vascular tubes and neurons use similar guidance cues to direct their development, but the downstream mechanisms that promote the outgrowth of biological tubes are not well characterized. We show that the conserved kinase MRCK-1 and its substrate the regulatory light chain of non-muscle myosin, MLC-4, are required for outgrowth of the unicellular excretory canal in C. elegans. Ablation of MRCK-1 or MLC-4 in the canal causes severe truncations with unlumenized projections of the basal membrane. Structure-function analysis of MRCK-1 indicates that the kinase domain, but not the small GTPase-binding CRIB domain, is required for canal outgrowth. Expression of a phosphomimetic form of MLC-4 rescues canal truncations in mrck-1 mutants and shows enrichment at the growing canal tip. Moreover, our work reveals a previously unreported function for non-muscle myosin downstream of MRCK-1 in excretory canal outgrowth that may be conserved in the development of seamless tubes in other organisms.

The Hsp90 Molecular Chaperone as a Global Modifier of the Genotype-Phenotype-Fitness Map: An Evolutionary Perspective

Global modifier genes influence the mapping of genotypes onto phenotypes and fitness through their epistatic interactions with genetic variants on a massive scale. The first such factor to be identified, Hsp90, is a highly conserved molecular chaperone that plays a central role in protein homeostasis. Hsp90 is a "hub of hubs" that chaperones proteins engaged in many key cellular and developmental regulatory networks. These clients, which are enriched in kinases, transcription factors, and E3 ubiquitin ligases, drive diverse cellular functions and are themselves highly connected. By contrast to many other hub proteins, the abundance and activity of Hsp90 changes substantially in response to shifting environmental conditions. As a result, Hsp90 modifies the functional impact of many genetic variants simultaneously in a manner that depends on environmental stress. Studies in diverse organisms suggest that this coupling between Hsp90 function and challenging environments exerts a substantial impact on what parts of the genome are visible to natural selection, expanding adaptive opportunities when most needed. In this Perspective, we explore the multifaceted role of Hsp90 as global modifier of the genotype-phenotype-fitness map as well as its implications for evolution in nature and the clinic.

Pleiotropy, epistasis and the genetic architecture of quantitative traits

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

The Y-ome Conundrum: Insights into Uncharacterized Genes and Approaches for Functional Annotation

The ever-increasing availability of genome sequencing data has revealed a substantial number of uncharacterized genes without known functions across various organisms. The first comprehensive genome sequencing of E. coli K12 revealed that more than 50% of its open reading frames corresponded to transcripts with no known functions. The group of protein-coding genes without a functional description and/or a recognized pathway, beginning with the letter "Y", is classified as the "y-ome". Several efforts have been made to elucidate the functions of these genes and to recognize their role in biological processes. This review provides a brief update on various strategies employed when studying the y-ome, such as high-throughput experimental approaches, comparative omics, metabolic engineering, gene expression analysis, and data integration techniques. Additionally, we highlight recent advancements in functional annotation methods, including the use of machine learning, network analysis, and functional genomics approaches. Novel approaches are required to produce more precise functional annotations across the genome to reduce the number of genes with unknown functions.

Selection for robust metabolism in domesticated yeasts is driven by adaptation to Hsp90 stress

Protein folding both promotes and constrains adaptive evolution. We uncover this surprising duality in the role of the protein-folding chaperone heat shock protein 90 (Hsp90) in maintaining the integrity of yeast metabolism amid proteotoxic stressors within industrial domestication niches. Ethanol disrupts critical Hsp90-dependent metabolic pathways and exerts strong selective pressure for redundant duplications of key genes within these pathways, yielding the classical genomic signatures of beer and bread domestication. This work demonstrates a mechanism of adaptive canalization in an ecology of major economic importance and highlights Hsp90-dependent variation as an important source of phantom heritability in complex traits.

Mutational robustness and the role of buffer genes in evolvability

Organisms rely on mutations to fuel adaptive evolution. However, many mutations impose a negative effect on fitness. Cells may have therefore evolved mechanisms that affect the phenotypic effects of mutations, thus conferring mutational robustness. Specifically, so-called buffer genes are hypothesized to interact directly or indirectly with genetic variation and reduce its effect on fitness. Environmental or genetic perturbations can change the interaction between buffer genes and genetic variation, thereby unmasking the genetic variation's phenotypic effects and thus providing a source of variation for natural selection to act on. This review provides an overview of our understanding of mutational robustness and buffer genes, with the chaperone gene HSP90 as a key example. It discusses whether buffer genes merely affect standing variation or also interact with de novo mutations, how mutational robustness could influence evolution, and whether mutational robustness might be an evolved trait or rather a mere side-effect of complex genetic interactions.

DRMY1 promotes robust morphogenesis by sustaining the translation of cytokinin signaling inhibitor proteins

Robustness is the invariant development of phenotype despite environmental changes and genetic perturbations. In the Arabidopsis flower bud, four sepals robustly initiate and grow to constant size to enclose and protect the inner floral organs. We previously characterized the mutant development related myb-like1 ( drmy1 ), where 3-5 sepals initiate variably and grow to different sizes, compromising their protective function. The molecular mechanism underlying this loss of robustness was unclear. Here, we show that drmy1 has reduced TARGET OF RAPAMYCIN (TOR) activity, ribosomal content, and translation. Translation reduction decreases the protein level of ARABIDOPSIS RESPONSE REGULATOR7 (ARR7) and ARABIDOPSIS HISTIDINE PHOSPHOTRANSFER PROTEIN 6 (AHP6), two cytokinin signaling inhibitors that are normally rapidly produced before sepal initiation. The resultant upregulation of cytokinin signaling disrupts robust auxin patterning and sepal initiation. Our work shows that the homeostasis of translation, a ubiquitous cellular process, is crucial for the robust spatiotemporal patterning of organogenesis.

Strategies for dissecting the complexity of neurodevelopmental disorders

Neurodevelopmental disorders (NDDs) are associated with a wide range of clinical features, affecting multiple pathways involved in brain development and function. Recent advances in high-throughput sequencing have unveiled numerous genetic variants associated with NDDs, which further contribute to disease complexity and make it challenging to infer disease causation and underlying mechanisms. Herein, we review current strategies for dissecting the complexity of NDDs using model organisms, induced pluripotent stem cells, single-cell sequencing technologies, and massively parallel reporter assays. We further highlight single-cell CRISPR-based screening techniques that allow genomic investigation of cellular transcriptomes with high efficiency, accuracy, and throughput. Overall, we provide an integrated review of experimental approaches that can be applicable for investigating a broad range of complex disorders.

(Epi)genomic adaptation driven by fine geographical scale environmental heterogeneity after recent biological invasions

Elucidating processes and mechanisms involved in rapid local adaptation to varied environments is a poorly understood but crucial component in management of invasive species. Recent studies have proposed that genetic and epigenetic variation could both contribute to ecological adaptation, yet it remains unclear on the interplay between these two components underpinning rapid adaptation in wild animal populations. To assess their respective contributions to local adaptation, we explored epigenomic and genomic responses to environmental heterogeneity in eight recently colonized ascidian (Ciona intestinalis) populations at a relatively fine geographical scale. Based on MethylRADseq data, we detected strong patterns of local environment-driven DNA methylation divergence among populations, significant epigenetic isolation by environment (IBE), and a large number of local environment-associated epigenetic loci. Meanwhile, multiple genetic analyses based on single nucleotide polymorphisms (SNPs) showed genomic footprints of divergent selection. In addition, for five genetically similar populations, we detected significant methylation divergence and local environment-driven methylation patterns, indicating the strong effects of local environments on epigenetic variation. From a functional perspective, a majority of functional genes, Gene Ontology (GO) terms, and biological pathways were largely specific to one of these two types of variation, suggesting partial independence between epigenetic and genetic adaptation. The methylation quantitative trait loci (mQTL) analysis showed that the genetic variation explained only 18.67% of methylation variation, further confirming the autonomous relationship between these two types of variation. Altogether, we highlight the complementary interplay of genetic and epigenetic variation involved in local adaptation, which may jointly promote populations' rapid adaptive capacity and successful invasions in different environments. The findings here provide valuable insights into interactions between invaders and local environments to allow invasive species to rapidly spread, thus contributing to better prediction of invasion success and development of management strategies.

Caenorhabditis elegans as an In Vivo Model Organism to Elucidate Teratogenic Effects

Exogenous teratogens contribute to approximately 10% of the human abnormality with exposure occurrence during the prenatal and fetal period. However, the assessment methods and underlying mechanism remain unclear. The nematode Caenorhabditis elegans has been recognized as one of the ideal model animals for toxicologic research as convenient culture, low cost, and complete phenotypes and genomic profiling. This chapter describes the protocols about the estimations on the teratogenic effects using nematodes as model organisms, including the growth, development, behavior, reproduction, energy balance, and transgenes.

A dual sgRNA library design to probe genetic modifiers using genome-wide CRISPRi screens

Mapping genetic interactions is essential for determining gene function and defining novel biological pathways. We report a simple to use CRISPR interference (CRISPRi) based platform, compatible with Fluorescence Activated Cell Sorting (FACS)-based reporter screens, to query epistatic relationships at scale. This is enabled by a flexible dual-sgRNA library design that allows for the simultaneous delivery and selection of a fixed sgRNA and a second randomized guide, comprised of a genome-wide library, with a single transduction. We use this approach to identify epistatic relationships for a defined biological pathway, showing both increased sensitivity and specificity than traditional growth screening approaches.

The long-term effects of genomic selection: 2. Changes in allele frequencies of causal loci and new mutations

Genetic selection has been applied for many generations in animal, plant, and experimental populations. Selection changes the allelic architecture of traits to create genetic gain. It remains unknown whether the changes in allelic architecture are different for the recently introduced technique of genomic selection compared to traditional selection methods and whether they depend on the genetic architectures of traits. Here, we investigate the allele frequency changes of old and new causal loci under 50 generations of phenotypic, pedigree, and genomic selection, for a trait controlled by either additive, additive and dominance, or additive, dominance, and epistatic effects. Genomic selection resulted in slightly larger and faster changes in allele frequencies of causal loci than pedigree selection. For each locus, allele frequency change per generation was not only influenced by its statistical additive effect but also to a large extent by the linkage phase with other loci and its allele frequency. Selection fixed a large number of loci, and 5 times more unfavorable alleles became fixed with genomic and pedigree selection than with phenotypic selection. For pedigree selection, this was mainly a result of increased genetic drift, while genetic hitchhiking had a larger effect on genomic selection. When epistasis was present, the average allele frequency change was smaller (∼15% lower), and a lower number of loci became fixed for all selection methods. We conclude that for long-term genetic improvement using genomic selection, it is important to consider hitchhiking and to limit the loss of favorable alleles.

A dual sgRNA library design to probe genetic modifiers using genome-wide CRISPRi screens

Mapping genetic interactions is essential for determining gene function and defining novel biological pathways. We report a simple to use CRISPR interference (CRISPRi) based platform, compatible with Fluorescence Activated Cell Sorting (FACS)-based reporter screens, to query epistatic relationships at scale. This is enabled by a flexible dual-sgRNA library design that allows for the simultaneous delivery and selection of a fixed sgRNA and a second randomized guide, comprised of a genome-wide library, with a single transduction. We use this approach to identify epistatic relationships for a defined biological pathway, showing both increased sensitivity and specificity than traditional growth screening approaches.

Leveraging the genetic correlation between traits improves the detection of epistasis in genome-wide association studies

Epistasis, commonly defined as the interaction between genetic loci, is known to play an important role in the phenotypic variation of complex traits. As a result, many statistical methods have been developed to identify genetic variants that are involved in epistasis, and nearly all of these approaches carry out this task by focusing on analyzing one trait at a time. Previous studies have shown that jointly modeling multiple phenotypes can often dramatically increase statistical power for association mapping. In this study, we present the "multivariate MArginal ePIstasis Test" (mvMAPIT)-a multioutcome generalization of a recently proposed epistatic detection method which seeks to detect marginal epistasis or the combined pairwise interaction effects between a given variant and all other variants. By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact-thus, potentially alleviating much of the statistical and computational burden associated with conventional explicit search-based methods. Our proposed mvMAPIT builds upon this strategy by taking advantage of correlation structure between traits to improve the identification of variants involved in epistasis. We formulate mvMAPIT as a multivariate linear mixed model and develop a multitrait variance component estimation algorithm for efficient parameter inference and P-value computation. Together with reasonable model approximations, our proposed approach is scalable to moderately sized genome-wide association studies. With simulations, we illustrate the benefits of mvMAPIT over univariate (or single-trait) epistatic mapping strategies. We also apply mvMAPIT framework to protein sequence data from two broadly neutralizing anti-influenza antibodies and approximately 2,000 heterogeneous stock of mice from the Wellcome Trust Centre for Human Genetics. The mvMAPIT R package can be downloaded at https://github.com/lcrawlab/mvMAPIT.

Synonymous Variants of Uncertain Silence

Synonymous variants, traditionally regarded as silent mutations due to their lack of impact on protein sequence, structure and function, have been the subject of increasing scrutiny. This commentary explores the emerging evidence challenging the notion of synonymous variants as functionally inert. Analysis of the activity of 70 synonymous variants in the HIV Tat transcription factor revealed that 50% of the variants exhibited significant deviations from wild-type activity. Our analysis supports previous work and raises important questions about the broader impact of non-silent synonymous variants in human genes. Considering the potential functional implications, the authors propose classifying such variants as "synonymous variants of uncertain silence" (sVUS), highlighting the need for cautious interpretation and further investigations in clinical and genetic testing settings.

Genetic interactions under the microscope

Traditional genetic interaction screens profile phenotypes at aggregate level, missing interactions that may influence the distribution of single cells in specific states. Here, Heigwer and colleagues use an imaging approach to generate a large-scale high-resolution genetic interaction map in Drosophila cells and demonstrate its utility for understanding gene function.

A global genetic interaction network by single-cell imaging and machine learning

Cellular and organismal phenotypes are controlled by complex gene regulatory networks. However, reference maps of gene function are still scarce across different organisms. Here, we generated synthetic genetic interaction and cell morphology profiles of more than 6,800 genes in cultured Drosophila cells. The resulting map of genetic interactions was used for machine learning-based gene function discovery, assigning functions to genes in 47 modules. Furthermore, we devised Cytoclass as a method to dissect genetic interactions for discrete cell states at the single-cell resolution. This approach identified an interaction of Cdk2 and the Cop9 signalosome complex, triggering senescence-associated secretory phenotypes and immunogenic conversion in hemocytic cells. Together, our data constitute a genome-scale resource of functional gene profiles to uncover the mechanisms underlying genetic interactions and their plasticity at the single-cell level.

A Transcriptional Cofactor Regulatory Network for the C. elegans Intestine

Chromatin modifiers and transcriptional cofactors (collectively referred to as CFs) work with DNA-binding transcription factors (TFs) to regulate gene expression. In multicellular eukaryotes, distinct tissues each execute their own gene expression program for accurate differentiation and subsequent functionality. While the function of TFs in differential gene expression has been studied in detail in many systems, the contribution of CFs has remained less explored. Here we uncovered the contributions of CFs to gene regulation in the Caenorhabditis elegans intestine. We first annotated 366 CFs encoded by the C. elegans genome and assembled a library of 335 RNAi clones. Using this library, we analyzed the effects of individually depleting these CFs on the expression of 19 fluorescent transcriptional reporters in the intestine and identified 216 regulatory interactions. We found that different CFs interact specifically with different promoters, and that both essential and intestinally expressed CFs exhibit the highest proportion of interactions. We did not find all members of CF complexes acting on the same set of reporters but instead found diversity in the promoter targets of each complex component. Finally, we found that previously identified activation mechanisms for the acdh-1 promoter use different CFs and TFs. Overall, we demonstrate that CFs function specifically rather than ubiquitously at intestinal promoters and provide an RNAi resource for reverse genetic screens.

Chromosomally Unstable Gastric Cancers Overexpressing Claudin-6 Disclose Cross-Talk between HNF1A and HNF4A, and Upregulated Cholesterol Metabolism

(1) Abnormally increased expression of claudin-6 in gastric cancer is considered a prognostic marker of the chromosomal unstable molecular subtype. However, a detailed molecular profile analysis of differentially expressed genes and affected pathways associated with claudin-6 increased (Cldn6^high) expression has not been assessed. (2) The TCGA Stomach Adenocarcinoma Pan-Cancer Atlas Data was evaluated using Cytoscape's Gene Mania, MCODE, and Cytohubba bioinformatic software. (3) 96.88% of Cldn6^high gastric cancer tumors belonging to the chromosomal unstable molecular subtype are associated with a worse prognosis. Cldn6expression coincided with higher mutations in TP53, MIEN1, STARD3, PGAP3, and CCNE1 genes compared to Cldn6^low expression. In Cldn6^high cancers, 1316 genes were highly expressed. Cholesterol metabolism was the most affected pathway as APOA1, APOA2, APOH, APOC2, APOC3, APOB-100, LDL receptor-related protein 1/2, Sterol O-acyltransferase, STARD3, MAGEA-2, -3, -4, -6, -9B, and -12 genes were overexpressed in Cldn6^high gastric cancers; interestingly, APOA2 and MAGEA9b were identified as top hub genes. Functional enrichment of DEGs linked HNF-4α and HNF-1α genes as highly expressed in Cldn6^high gastric cancer. (4) Our results suggest that APOA2 and MAGEA9b could be considered as prognostic markers for Cldn6^high gastric cancers.

Caenorhabditis elegans as a Model for the Effects of Phytochemicals on Mitochondria and Aging

The study of aging is an important topic in contemporary research. Considering the demographic changes and the resulting shifts towards an older population, it is of great interest to preserve youthful physiology in old age. For this endeavor, it is necessary to choose an appropriate model. One such model is the nematode Caenorhabditis elegans (C. elegans), which has a long tradition in aging research. In this review article, we explore the advantages of using the nematode model in aging research, focusing on bioenergetics and the study of secondary plant metabolites that have interesting implications during this process. In the first section, we review the situation of aging research today. Conventional theories and hypotheses about the ongoing aging process will be presented and briefly explained. The second section focuses on the nematode C. elegans and its utility in aging and nutrition research. Two useful genome editing methods for monitoring genetic interactions (RNAi and CRISPR/Cas9) are presented. Due to the mitochondria's influence on aging, we also introduce the possibility of observing bioenergetics and respiratory phenomena in C. elegans. We then report on mitochondrial conservation between vertebrates and invertebrates. Here, we explain why the nematode is a suitable model for the study of mitochondrial aging. In the fourth section, we focus on phytochemicals and their applications in contemporary nutritional science, with an emphasis on aging research. As an emerging field of science, we conclude this review in the fifth section with several studies focusing on mitochondrial research and the effects of phytochemicals such as polyphenols. In summary, the nematode C. elegans is a suitable model for aging research that incorporates the mitochondrial theory of aging. Its living conditions in the laboratory are optimal for feeding studies, thus enabling bioenergetics to be observed during the aging process.

Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease

Dysregulation of gene expression in Alzheimer's disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, AD-induced regulatory changes across brain cell types remains uncharted. To address this, we integrated single-cell multi-omics datasets to predict the gene regulatory networks of four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes, in control and AD brains. Importantly, we analyzed and compared the structural and topological features of networks across cell types and examined changes in AD. Our analysis shows that hub TFs are largely common across cell types and AD-related changes are relatively more prominent in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are also highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes. The further disease-module-drug association analysis suggests cell-type candidate drugs and their potential target genes. Finally, our network-based machine learning analysis systematically prioritized cell type risk genes likely involved in AD. Our strategy is validated using an independent dataset which showed that top ranked genes can predict clinical phenotypes (e.g., cognitive impairment) of AD with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals cell-type gene dysregulation in AD.

From systems to structure - using genetic data to model protein structures

Understanding the effects of genetic variation is a fundamental problem in biology that requires methods to analyse both physical and functional consequences of sequence changes at systems-wide and mechanistic scales. To achieve a systems view, protein interaction networks map which proteins physically interact, while genetic interaction networks inform on the phenotypic consequences of perturbing these protein interactions. Until recently, understanding the molecular mechanisms that underlie these interactions often required biophysical methods to determine the structures of the proteins involved. The past decade has seen the emergence of new approaches based on coevolution, deep mutational scanning and genome-scale genetic or chemical-genetic interaction mapping that enable modelling of the structures of individual proteins or protein complexes. Here, we review the emerging use of large-scale genetic datasets and deep learning approaches to model protein structures and their interactions, and discuss the integration of structural data from different sources.

Acute, Sublethal, and Developmental Toxicity of Kratom (Mitragyna speciosa Korth.) Leaf Preparations on Caenorhabditis elegans as an Invertebrate Model for Human Exposure

Kratom (Mitragyna speciosa Korth.) is a tree native to Southeast Asia with stimulant and opioid-like effects which has seen increased use in Europe and North America in recent years. Its safety and pharmacological effects remain under investigation, especially in regard to developmental and generational toxicity. In the current study, we investigated commercial kratom preparations using the nematode Caenorhabditis elegans as a translational model for toxicity and pharmacological effects. The pure alkaloids mitragynine and 7-hydroxymitragynine as well as aqueous, ethanolic, and methanolic extracts of three commercial kratom products were evaluated using a battery of developmental, genotoxic, and opioid-related experiments. As determined previously, the mitragynine and 7-hydroxymitragynine content in kratom samples was higher in the alcoholic extracts than the aqueous extracts. Above the human consumption range equivalent of 15-70 µg/mL, kratom dose-dependently reduced brood size and health of parent worms and their progeny. 7-hydroxymitragynine, but not mitragynine, presented with toxic and developmental effects at very high concentrations, while the positive control, morphine, displayed toxic effects at 0.5 mM. Kratom and its alkaloids did not affect pumping rate or interpump interval in the same way as morphine, suggesting that kratom is unlikely to act primarily via the opioid-signalling pathway. Only at very high doses did kratom cause developmental and genotoxic effects in nematodes, indicating its relative safety.

Co-expression network analysis of environmental canalization in the ascidian Ciona

BACKGROUND

Canalization, or buffering, is defined as developmental stability in the face of genetic and/or environmental perturbations. Understanding how canalization works is important in predicting how species survive environmental change, as well as deciphering how development can be altered in the evolutionary process. However, how developmental gene expression is linked to buffering remains unclear. We addressed this by co-expression network analysis, comparing gene expression changes caused by heat stress during development at a whole-embryonic scale in reciprocal hybrid crosses of sibling species of the ascidian Ciona that are adapted to different thermal environments.

RESULTS

Since our previous work showed that developmental buffering in this group is maternally inherited, we first identified maternal developmental buffering genes (MDBGs) in which the expression level in embryos is both correlated to the level of environmental canalization and also differentially expressed depending on the species' gender roles in hybrid crosses. We found only 15 MDBGs, all of which showed high correlation coefficient values for expression with a large number of other genes, and 14 of these belonged to a single co-expression module. We then calculated correlation coefficients of expression between MDBGs and transcription factors in the central nervous system (CNS) developmental gene network that had previously been identified experimentally. We found that, compared to the correlation coefficients between MDBGs, which had an average of 0.96, the MDBGs are loosely linked to the CNS developmental genes (average correlation coefficient 0.45). Further, we investigated the correlation of each developmental to MDBGs, showing that only four out of 62 CNS developmental genes showed correlation coefficient > 0.9, comparable to the values between MDBGs, and three of these four genes were signaling molecules: BMP2/4, Wnt7, and Delta-like.

CONCLUSIONS

We show that the developmental pathway is not centrally located within the buffering network. We found that out of 62 genes in the developmental gene network, only four genes showed correlation coefficients as high as between MDBGs. We propose that loose links to MDBGs stabilize spatiotemporally dynamic development.

The long-term effects of genomic selection: 1. Response to selection, additive genetic variance, and genetic architecture

Background

Genomic selection has revolutionized genetic improvement in animals and plants, but little is known about its long-term effects. Here, we investigated the long-term effects of genomic selection on response to selection, genetic variance, and the genetic architecture of traits using stochastic simulations. We defined the genetic architecture as the set of causal loci underlying each trait, their allele frequencies, and their statistical additive effects. We simulated a livestock population under 50 generations of phenotypic, pedigree, or genomic selection for a single trait, controlled by either only additive, additive and dominance, or additive, dominance, and epistatic effects. The simulated epistasis was based on yeast data.

Results

Short-term response was always greatest with genomic selection, while response after 50 generations was greater with phenotypic selection than with genomic selection when epistasis was present, and was always greater than with pedigree selection. This was mainly because loss of genetic variance and of segregating loci was much greater with genomic and pedigree selection than with phenotypic selection. Compared to pedigree selection, selection response was always greater with genomic selection. Pedigree and genomic selection lost a similar amount of genetic variance after 50 generations of selection, but genomic selection maintained more segregating loci, which on average had lower minor allele frequencies than with pedigree selection. Based on this result, genomic selection is expected to better maintain genetic gain after 50 generations than pedigree selection. The amount of change in the genetic architecture of traits was considerable across generations and was similar for genomic and pedigree selection, but slightly less for phenotypic selection. Presence of epistasis resulted in smaller changes in allele frequencies and less fixation of causal loci, but resulted in substantial changes in statistical additive effects across generations.

Conclusions

Our results show that genomic selection outperforms pedigree selection in terms of long-term genetic gain, but results in a similar reduction of genetic variance. The genetic architecture of traits changed considerably across generations, especially under selection and when non-additive effects were present. In conclusion, non-additive effects had a substantial impact on the accuracy of selection and long-term response to selection, especially when selection was accurate.

Gene regulatory network inference in long-lived C. elegans reveals modular properties that are predictive of novel aging genes

We design a “wisdom-of-the-crowds” GRN inference pipeline and couple it to complex network analysis to understand the organizational principles governing gene regulation in long-lived glp-1/Notch Caenorhabditis elegans. The GRN has three layers (input, core, and output) and is topologically equivalent to bow-tie/hourglass structures prevalent among metabolic networks. To assess the functional importance of structural layers, we screened 80% of regulators and discovered 50 new aging genes, 86% with human orthologues. Genes essential for longevity—including ones involved in insulin-like signaling (ILS)—are at the core, indicating that GRN's structure is predictive of functionality. We used in vivo reporters and a novel functional network covering 5,497 genetic interactions to make mechanistic predictions. We used genetic epistasis to test some of these predictions, uncovering a novel transcriptional regulator, sup-37, that works alongside DAF-16/FOXO. We present a framework with predictive power that can accelerate discovery in C. elegans and potentially humans.

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Gene-regulatory inference provides global network of long-lived animals

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The large-scale topology of the network has an hourglass structure

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Membership to the core of the hourglass is a good predictor of functionality

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Discovered 50 novel aging genes, including sup-37, a DAF-16 dependent gene

Genetic landscape of T cells identifies synthetic lethality for T-ALL

To capture the global gene network regulating the differentiation of immature T cells in an unbiased manner, large-scale forward genetic screens in zebrafish were conducted and combined with genetic interaction analysis. After ENU mutagenesis, genetic lesions associated with failure of T cell development were identified by meiotic recombination mapping, positional cloning, and whole genome sequencing. Recessive genetic variants in 33 genes were identified and confirmed as causative by additional experiments. The mutations affected T cell development but did not perturb the development of an unrelated cell type, growth hormone-expressing somatotrophs, providing an important measure of cell-type specificity of the genetic variants. The structure of the genetic network encompassing the identified components was established by a subsequent genetic interaction analysis, which identified many instances of positive (alleviating) and negative (synthetic) genetic interactions. Several examples of synthetic lethality were subsequently phenocopied using combinations of small molecule inhibitors. These drugs not only interfered with normal T cell development, but also elicited remission in a model of T cell acute lymphoblastic leukaemia. Our findings illustrate how genetic interaction data obtained in the context of entire organisms can be exploited for targeted interference with specific cell types and their malignant derivatives.

A systematic analysis of genetic interactions and their underlying biology in childhood cancer

Childhood cancer is a major cause of child death in developed countries. Genetic interactions between mutated genes play an important role in cancer development. They can be detected by searching for pairs of mutated genes that co-occur more (or less) often than expected. Co-occurrence suggests a cooperative role in cancer development, while mutual exclusivity points to synthetic lethality, a phenomenon of interest in cancer treatment research. Little is known about genetic interactions in childhood cancer. We apply a statistical pipeline to detect genetic interactions in a combined dataset comprising over 2,500 tumors from 23 cancer types. The resulting genetic interaction map of childhood cancers comprises 15 co-occurring and 27 mutually exclusive candidates. The biological explanation of most candidates points to either tumor subtype, pathway epistasis or cooperation while synthetic lethality plays a much smaller role. Thus, other explanations beyond synthetic lethality should be considered when interpreting genetic interaction test results.

A new era in functional genomics screens

The past 25 years of genomics research first revealed which genes are encoded by the human genome and then a detailed catalogue of human genome variation associated with many diseases. Despite this, the function of many genes and gene regulatory elements remains poorly characterized, which limits our ability to apply these insights to human disease. The advent of new CRISPR functional genomics tools allows for scalable and multiplexable characterization of genes and gene regulatory elements encoded by the human genome. These approaches promise to reveal mechanisms of gene function and regulation, and to enable exploration of how genes work together to modulate complex phenotypes.

Identify differential genes and cell subclusters from time-series scRNA-seq data using scTITANS

Time-series single-cell RNA sequencing (scRNA-seq) provides a breakthrough in modern biology by enabling researchers to profile and study the dynamics of genes and cells based on samples obtained from multiple time points at an individual cell resolution. However, cell asynchrony and an additional dimension of multiple time points raises challenges in the effective use of time-series scRNA-seq data for identifying genes and cell subclusters that vary over time. However, no effective tools are available. Here, we propose scTITANS (https://github.com/ZJUFanLab/scTITANS), a method that takes full advantage of individual cells from all time points at the same time by correcting cell asynchrony using pseudotime from trajectory inference analysis. By introducing a time-dependent covariate based on time-series analysis method, scTITANS performed well in identifying differentially expressed genes and cell subclusters from time-series scRNA-seq data based on several example datasets. Compared to current attempts, scTITANS is more accurate, quantitative, and capable of dealing with heterogeneity among cells and making full use of the timing information hidden in biological processes. When extended to broader research areas, scTITANS will bring new breakthroughs in studies with time-series single cell RNA sequencing data.

Next Steps in Integrative Biology: Mapping Interactive Processes Across Levels of Biological Organization

Emergent biological processes result from complex interactions within and across levels of biological organization, ranging from molecular to environmental dynamics. Powerful theories, database tools, and modeling methods have been designed to characterize network connections within levels, such as those among genes, proteins, biochemicals, cells, organisms and species. Here, we propose that developing integrative models of organismal function in complex environments can be facilitated by taking advantage of these methods to identify key nodes of communication across levels of organization. Mapping key drivers or connections among levels of organization will provide data and leverage to model potential rule-sets by which organisms respond and adjust to perturbations at any level of biological organization.

Minimized combinatorial CRISPR screens identify genetic interactions in autophagy

Combinatorial CRISPR-Cas screens have advanced the mapping of genetic interactions, but their experimental scale limits the number of targetable gene combinations. Here, we describe 3Cs multiplexing, a rapid and scalable method to generate highly diverse and uniformly distributed combinatorial CRISPR libraries. We demonstrate that the library distribution skew is the critical determinant of its required screening coverage. By circumventing iterative cloning of PCR-amplified oligonucleotides, 3Cs multiplexing facilitates the generation of combinatorial CRISPR libraries with low distribution skews. We show that combinatorial 3Cs libraries can be screened with minimal coverages, reducing associated efforts and costs at least 10-fold. We apply a 3Cs multiplexing library targeting 12,736 autophagy gene combinations with 247,032 paired gRNAs in viability and reporter-based enrichment screens. In the viability screen, we identify, among others, the synthetic lethal WDR45B-PIK3R4 and the proliferation-enhancing ATG7-KEAP1 genetic interactions. In the reporter-based screen, we identify over 1,570 essential genetic interactions for autophagy flux, including interactions among paralogous genes, namely ATG2A-ATG2B, GABARAP-MAP1LC3B and GABARAP-GABARAPL2. However, we only observe few genetic interactions within paralogous gene families of more than two members, indicating functional compensation between them. This work establishes 3Cs multiplexing as a platform for genetic interaction screens at scale.

Cryptic genetic variation in a heat shock protein modifies the outcome of a mutation affecting epidermal stem cell development in C. elegans

A fundamental question in medical genetics is how the genetic background modifies the phenotypic outcome of mutations. We address this question by focusing on the seam cells, which display stem cell properties in the epidermis of Caenorhabditis elegans. We demonstrate that a putative null mutation in the GATA transcription factor egl-18, which is involved in seam cell fate maintenance, is more tolerated in the CB4856 isolate from Hawaii than the lab reference strain N2 from Bristol. We identify multiple quantitative trait loci (QTLs) underlying the difference in phenotype expressivity between the two isolates. These QTLs reveal cryptic genetic variation that reinforces seam cell fate through potentiating Wnt signalling. Within one QTL region, a single amino acid deletion in the heat shock protein HSP-110 in CB4856 is sufficient to modify Wnt signalling and seam cell development, highlighting that natural variation in conserved heat shock proteins can shape phenotype expressivity.

How the genetic background modifies the expression of mutations is a key question that is addressed in this study in the context of seam cell development in Caenorhabditis elegans isolates. One amino acid deletion in a conserved heat shock protein is sufficient to shape phenotype expressivity upon mutation of a GATA transcription factor.

Modeling human brain tumors in flies, worms, and zebrafish: From proof of principle to novel therapeutic targets

For decades, cell biologists and cancer researchers have taken advantage of non-murine species to increase our understanding of the molecular processes that drive normal cell and tissue development, and when perturbed, cause cancer. The advent of whole-genome sequencing has revealed the high genetic homology of these organisms to humans. Seminal studies in non-murine organisms such as Drosophila melanogaster, Caenorhabditis elegans, and Danio rerio identified many of the signaling pathways involved in cancer. Studies in these organisms offer distinct advantages over mammalian cell or murine systems. Compared to murine models, these three species have shorter lifespans, are less resource intense, and are amenable to high-throughput drug and RNA interference screening to test a myriad of promising drugs against novel targets. In this review, we introduce species-specific breeding strategies, highlight the advantages of modeling brain tumors in each non-mammalian species, and underscore the successes attributed to scientific investigation using these models. We conclude with an optimistic proposal that discoveries in the fields of cancer research, and in particular neuro-oncology, may be expedited using these powerful screening tools and strategies.

Simultaneous learning of individual microRNA-gene interactions and regulatory comodules

BACKGROUND

MicroRNAs (miRNAs) function in post-transcriptional regulation of gene expression by binding to target messenger RNAs (mRNAs). Because of the key part that miRNAs play, understanding the correct regulatory role of miRNAs in diverse patho-physiological conditions is of great interest. Although it is known that miRNAs act combinatorially to regulate genes, precise identification of miRNA-gene interactions and their specific functional roles in regulatory comodules remains a challenge. We developed THEIA, an effective method for simultaneously predicting miRNA-gene interactions and regulatory comodules, which group functionally related miRNAs and genes via non-negative matrix factorization (NMF).

RESULTS

We apply THEIA to RNA sequencing data from breast invasive carcinoma samples and demonstrate its effectiveness in discovering biologically significant regulatory comodules that are significantly enriched in spatial miRNA clusters, biological pathways, and various cancers.

CONCLUSIONS

THEIA is a theoretically rigorous optimization algorithm that simultaneously predicts the strength and direction (i.e., up-regulation or down-regulation) of the effect of modules of miRNAs on a gene. We posit that if THEIA is capable of recovering known clusters of genes and miRNA, then the clusters found by our method not previously identified by literature are also likely to have biological significance. We believe that these novel regulatory comodules found by our method will be a springboard for further research into the specific functional roles of these new functional ensembles of miRNAs and genes,especially those related to diseases like breast cancer.

Genetic interaction analysis in microbial pathogens: unravelling networks of pathogenesis, antimicrobial susceptibility and host interactions

Genetic interaction (GI) analysis is a powerful genetic strategy that analyzes the fitness and phenotypes of single- and double-gene mutant cells in order to dissect the epistatic interactions between genes, categorize genes into biological pathways, and characterize genes of unknown function. GI analysis has been extensively employed in model organisms for foundational, systems-level assessment of the epistatic interactions between genes. More recently, GI analysis has been applied to microbial pathogens and has been instrumental for the study of clinically important infectious organisms. Here, we review recent advances in systems-level GI analysis of diverse microbial pathogens, including bacterial and fungal species. We focus on important applications of GI analysis across pathogens, including GI analysis as a means to decipher complex genetic networks regulating microbial virulence, antimicrobial drug resistance and host-pathogen dynamics, and GI analysis as an approach to uncover novel targets for combination antimicrobial therapeutics. Together, this review bridges our understanding of GI analysis and complex genetic networks, with applications to diverse microbial pathogens, to further our understanding of virulence, the use of antimicrobial therapeutics and host-pathogen interactions. .

Emergence and propagation of epistasis in metabolic networks

Epistasis is often used to probe functional relationships between genes, and it plays an important role in evolution. However, we lack theory to understand how functional relationships at the molecular level translate into epistasis at the level of whole-organism phenotypes, such as fitness. Here, I derive two rules for how epistasis between mutations with small effects propagates from lower- to higher-level phenotypes in a hierarchical metabolic network with first-order kinetics and how such epistasis depends on topology. Most importantly, weak epistasis at a lower level may be distorted as it propagates to higher levels. Computational analyses show that epistasis in more realistic models likely follows similar, albeit more complex, patterns. These results suggest that pairwise inter-gene epistasis should be common, and it should generically depend on the genetic background and environment. Furthermore, the epistasis coefficients measured for high-level phenotypes may not be sufficient to fully infer the underlying functional relationships.

Network Modeling in Biology: Statistical Methods for Gene and Brain Networks

The rise of network data in many different domains has offered researchers new insight into the problem of modeling complex systems and propelled the development of numerous innovative statistical methodologies and computational tools. In this paper, we primarily focus on two types of biological networks, gene networks and brain networks, where statistical network modeling has found both fruitful and challenging applications. Unlike other network examples such as social networks where network edges can be directly observed, both gene and brain networks require careful estimation of edges using covariates as a first step. We provide a discussion on existing statistical and computational methods for edge esitimation and subsequent statistical inference problems in these two types of biological networks.

H3K9me selectively blocks transcription factor activity and ensures differentiated tissue integrity

The developmental role of histone H3K9 methylation (H3K9me), which typifies heterochromatin, remains unclear. In Caenorhabditis elegans, loss of H3K9me leads to a highly divergent upregulation of genes with tissue and developmental-stage specificity. During development H3K9me is lost from differentiated cell type-specific genes and gained at genes expressed in earlier developmental stages or other tissues. The continuous deposition of H3K9me2 by the SETDB1 homolog MET-2 after terminal differentiation is necessary to maintain repression. In differentiated tissues, H3K9me ensures silencing by restricting the activity of a defined set of transcription factors at promoters and enhancers. Increased chromatin accessibility following the loss of H3K9me is neither sufficient nor necessary to drive transcription. Increased ATAC-seq signal and gene expression correlate at a subset of loci positioned away from the nuclear envelope, while derepressed genes at the nuclear periphery remain poorly accessible despite being transcribed. In conclusion, H3K9me deposition can confer tissue-specific gene expression and maintain the integrity of terminally differentiated muscle by restricting transcription factor activity.

Translational genomics and beyond in bipolar disorder

Genome-wide association studies (GWAS) have revealed multiple genomic loci conferring risk of bipolar disorder (BD), providing hints for its underlying pathobiology. However, there are still remaining questions to answer. For example, discordance exists between BD heritability estimated with earlier epidemiological evidence and that calculated based on common GWAS variations. Where is the "missing heritability"? How can we explain the biology of the disease based on genetic findings? In this review, we summarize the accomplishments and limitations of current BD GWAS, and discuss potential reasons for the "missing heritability." In addition, progresses of research for the biological mechanisms underlying BD genetic risk using brain tissues, reprogrammed cells, and model animals are reviewed. While our knowledge of BD genetic basis is significantly promoted by these efforts, the complexities of gene regulation in the genome, the spatial-temporal heterogeneity during brain development, and the limitations of different experimental models should always be considered. Notably, several genes have been widely studied given their relatively well-characterized involvement in BD (e.g., CACAN1C and ANK3), and findings of these genes are summarized to both outline possible biological mechanisms of BD and describe examples of translating GWAS discoveries into the pathophysiology.

Machine Learning to Identify Gene Interactions from High-Throughput Mutant Crosses

Advances in molecular genetics through high-throughput gene mutagenesis and genetic crossing have enabled gene interaction mapping across whole genomes. Detecting gene interactions in even small microbial genomes relies on measuring growth phenotypes in thousands of crossed strains followed by statistical analysis to compare single and double mutants. The preferred computational approach is to use a multiplicative model that factors phenotype scores of single gene mutants to identify gene interactions in double mutants. Here we present how machine learning models that consider the characteristics of the phenotypic data improve on the classical multiplicative model. Importantly, machine learning improves the selection of cutoff values to identify gene interactions from phenotypic scores.

Inhibition of PfMYST Histone Acetyltransferase Activity Blocks Plasmodium falciparum Growth and Survival

One of the major barriers in the prevention and control of malaria programs worldwide is the growing emergence of multidrug resistance in Plasmodium parasites, and this necessitates continued efforts to discover and develop effective drug molecules targeting novel proteins essential for parasite survival. In recent years, epigenetic regulators have evolved as an attractive drug target option owing to their crucial role in survival and development of Plasmodium at different stages of its life cycle. PfMYST, a histone acetyltransferase protein, is known to regulate key cellular processes, such as cell cycle progression, DNA damage repair, and antigenic variation, that facilitate parasite growth, adaptation, and survival inside its host. With the aim of assessing the therapeutic potential of PfMYST as a novel drug target, we examined the effect of NU9056 (an HsTIP60 inhibitor) on the rate of parasite growth and survival. In the present study, by using a yeast complementation assay, we established that PfMYST is a true homolog of TIP60 and showed that NU9056 can inhibit PfMYST catalytic activity and kill P. falciparum parasites in culture. Inhibiting the catalytic activity of PfMYST arrests the parasite in the trophozoite stage and inhibits its further transition to the schizont stage, eventually leading to its death. Overall, our study provides proof of concept that PfMYST catalytic activity is essential for parasite growth and survival and that PfMYST can be a potential target for antimalarial therapy.

A Novel Mapping Strategy Utilizing Mouse Chromosome Substitution Strains Identifies Multiple Epistatic Interactions That Regulate Complex Traits

The genetic contribution of additive vs. non-additive (epistatic) effects in the regulation of complex traits is unclear. While genome-wide association studies typically ignore gene-gene interactions, in part because of the lack of statistical power for detecting them, mouse chromosome substitution strains (CSSs) represent an alternate approach for detecting epistasis given their limited allelic variation. Therefore, we utilized CSSs to identify and map both additive and epistatic loci that regulate a range of hematologic- and metabolism-related traits, as well as hepatic gene expression. Quantitative trait loci (QTL) were identified using a CSS-based backcross strategy involving the segregation of variants on the A/J-derived substituted chromosomes 4 and 6 on an otherwise C57BL/6J genetic background. In the liver transcriptomes of offspring from this cross, we identified and mapped additive QTL regulating the hepatic expression of 768 genes, and epistatic QTL pairs for 519 genes. Similarly, we identified additive QTL for fat pad weight, platelets, and the percentage of granulocytes in blood, as well as epistatic QTL pairs controlling the percentage of lymphocytes in blood and red cell distribution width. The variance attributed to the epistatic QTL pairs was approximately equal to that of the additive QTL; however, the SNPs in the epistatic QTL pairs that accounted for the largest variances were undetected in our single locus association analyses. These findings highlight the need to account for epistasis in association studies, and more broadly demonstrate the importance of identifying genetic interactions to understand the complete genetic architecture of complex traits.

Report of the National Cancer Institute and the Eunice Kennedy Shriver National Institute of Child Health and Human Development-sponsored workshop: gynecology and women's health-benign conditions and cancer

The Division of Cancer Prevention and the Division of Cancer Biology at the National Cancer Institute and the Gynecologic Health and Disease Branch in the National Institute of Child Health and Human Development organized a workshop in April 2019 to explore current insights into the progression of gynecologic cancers from benign conditions. Working groups were formed based on 3 gynecologic disease types: (1) Endometriosis or Endometrial Cancer and Endometrial-Associated Ovarian Cancer, (2) Uterine Fibroids (Leiomyoma) or Leiomyosarcoma, and (3) Adenomyosis or Adenocarcinoma. In this report, we highlight the key questions and current challenges that emerged from the working group discussions and present potential research opportunities that may advance our understanding of the progression of gynecologic benign conditions to cancer.

Identification of 6-(piperazin-1-yl)-1,3,5-triazine as a chemical scaffold with broad anti-schistosomal activities

Background: Schistosomiasis, caused by infection with blood fluke schistosomes, is a neglected tropical disease of considerable importance in resource-poor communities throughout the developing world. In the absence of an immunoprophylactic vaccine and due to over-reliance on a single chemotherapy (praziquantel), schistosomiasis control is at risk should drug insensitive schistosomes develop. In this context, application of in silico virtual screening on validated schistosome targets has proven successful in the identification of novel small molecules with anti-schistosomal activity.   Methods: Focusing on the Schistosoma mansoni histone methylation machinery, we herein have used RNA interference (RNAi), ELISA-mediated detection of H3K4 methylation, homology modelling and in silico virtual screening to identify a small collection of small molecules for anti-schistosomal testing. A combination of low to high-throughput whole organism assays were subsequently used to assess these compounds' activities on miracidia to sporocyst transformation, schistosomula phenotype/motility metrics and adult worm motility/oviposition readouts. Results: RNAi-mediated knockdown of smp_138030/smmll-1 (encoding a histone methyltransferase, HMT) in adult worms (~60%) reduced parasite motility and egg production. Moreover, in silico docking of compounds into Smp_138030/SmMLL-1's homology model highlighted competitive substrate pocket inhibitors, some of which demonstrated significant activity on miracidia, schistosomula and adult worm lifecycle stages together with variable effects on HepG2 cells. Particularly, the effect of compounds containing a 6-(piperazin-1-yl)-1,3,5-triazine core on adult schistosomes recapitulated the results of the smp_138030/smmll-1 RNAi screens. Conclusions: The biological data and the structure-activity relationship presented in this study define the 6-(piperazin-1-yl)-1,3,5-triazine core as a promising starting point in ongoing efforts to develop new urgently needed schistosomicides.