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The filamentous mycoparasitic fungus Trichoderma asperelloides (Hypocreales, Ascomycota, Dikarya) strain T 203 was isolated from soil in Israel by the Ilan Chet group in the 1980s. As it has been the subject of laboratory, greenhouse, and field experiments and has been incorporated into commercial agricultural preparations, its genome has been sequenced and analyzed.

SummaryChemsearch is a cross-platform server application for developing and managing a chemical compound library and associated data files, with an interface for browsing and search that allows for easy navigation to a compound of interest, similar compounds or compounds that have desired structural properties. With provisions for access control and centralized document and data storage, Chemsearch supports collaboration by distributed teams.Availability and implementationChemsearch is a free and open-source Flask web application that can be linked to a Google Workspace account. Source code is available at https://github.com/gem-net/chemsearch (GPLv3 license). A Docker image allowing rapid deployment is available at https://hub.docker.com/r/cgemcci/chemsearch.

Cancer progression is an evolutionary process. During this process, evolving cancer cell populations encounter restrictive ecological niches within the body, such as the primary tumor, circulatory system, and diverse metastatic sites. Efforts to prevent or delay cancer evolution-and progression-require a deep understanding of the underlying molecular evolutionary processes. Herein we discuss a suite of concepts and tools from evolutionary and ecological theory that can inform cancer biology in new and meaningful ways. We also highlight current challenges to applying these concepts, and propose ways in which incorporating these concepts could identify new therapeutic modes and vulnerabilities in cancer.

The Center for Genetically Encoded Materials (C-GEM) is an NSF Phase I Center for Chemical Innovation that comprises six laboratories spread across three university campuses. Our success as a multi-institution research team demanded the development of a software infrastructure, GEM-NET, that allows all C-GEM members to work together seamlessly-as though everyone was in the same room. GEM-NET was designed to support both science and communication by integrating task management, scheduling, data sharing, and collaborative document and code editing with frictionless internal and public communication; it also maintains security over data and internal communications. In this Article, we document the design and implementation of GEM-NET: our objectives and motivating goals, how each component contributes to these goals, and the lessons learned throughout development. We also share open source code for several custom applications and document how GEM-NET can benefit users in multiple fields and teams that are both small and large. We anticipate that this knowledge will guide other multi-institution teams, regardless of discipline, to plan their software infrastructure and utilize it as swiftly and smoothly as possible.

Recent studies have revealed the mutational signatures underlying the somatic evolution of cancer, and the prevalences of associated somatic genetic variants. Here we estimate the intensity of positive selection that drives mutations to high frequency in tumors, yielding higher prevalences than expected on the basis of mutation and neutral drift alone. We apply this approach to a sample of 525 head and neck squamous cell carcinoma exomes, producing a rank-ordered list of gene variants by selection intensity. Our results illustrate the complementarity of calculating the intensity of selection on mutations along with tallying the prevalence of individual substitutions in cancer: while many of the most prevalently-altered genes were heavily selected, their relative importance to the cancer phenotype differs from their prevalence and from their P value, with some infrequent variants exhibiting evidence of strong positive selection. Furthermore, we extend our analysis of effect size by quantifying the degree to which mutational processes (such as APOBEC mutagenesis) contributes mutations that are highly selected, driving head and neck squamous cell carcinoma. We calculate the substitutions caused by APOBEC mutagenesis that make the greatest contribution to cancer phenotype among patients. Lastly, we demonstrate via in vitro biochemical experiments that the APOBEC3B protein can deaminate the cytosine bases at two sites whose mutant states are subject to high net realized selection intensities-PIK3CA E545K and E542K. By quantifying the effects of mutations, we deepen the molecular understanding of carcinogenesis in head and neck squamous cell carcinoma.

Advances in genomics and transcriptomics accompanying the rapid accumulation of omics data have provided new tools that have transformed and expanded the traditional concepts of model fungi. Evolutionary genomics and transcriptomics have flourished with the use of classical and newer fungal models that facilitate the study of diverse topics encompassing fungal biology and development. Technological advances have also created the opportunity to obtain and mine large datasets. One such continuously growing dataset is that of the Sordariomycetes, which exhibit a richness of species, ecological diversity, economic importance, and a profound research history on amenable models. Currently, 3,574 species of this class have been sequenced, comprising nearly one-third of the available ascomycete genomes. Among these genomes, multiple representatives of the model genera Fusarium, Neurospora, and Trichoderma are present. In this review, we examine recently published studies and data on the Sordariomycetes that have contributed novel insights to the field of fungal evolution via integrative analyses of the genetic, pathogenic, and other biological characteristics of the fungi. Some of these studies applied ancestral state analysis of gene expression among divergent lineages to infer regulatory network models, identify key genetic elements in fungal sexual development, and investigate the regulation of conidial germination and secondary metabolism. Such multispecies investigations address challenges in the study of fungal evolutionary genomics derived from studies that are often based on limited model genomes and that primarily focus on the aspects of biology driven by knowledge drawn from a few model species. Rapidly accumulating information and expanding capabilities for systems biological analysis of Big Data are setting the stage for the expansion of the concept of model systems from unitary taxonomic species/genera to inclusive clusters of well-studied models that can facilitate both the in-depth study of specific lineages and also investigation of trait diversity across lineages. The Sordariomycetes class, in particular, offers abundant omics data and a large and active global research community. As such, the Sordariomycetes can form a core omics clade, providing a blueprint for the expansion of our knowledge of evolution at the genomic scale in the exciting era of Big Data and artificial intelligence, and serving as a reference for the future analysis of different taxonomic levels within the fungal kingdom. [ABSTRACT FROM AUTHOR]

Immune heterogeneity in wild host populations indicates that disease-mediated selection is common in nature. However, the underlying dynamic feedbacks involving the ecology of disease transmission, evolutionary processes, and their interaction with environmental drivers have proven challenging to characterize. Plague presents an optimal system for interrogating such couplings: Yersinia pestis transmission exerts intense selective pressure driving the local persistence of disease resistance among its wildlife hosts in endemic areas. Investigations undertaken in colonial India after the introduction of plague in 1896 suggest that, only a decade after plague arrived, a heritable, plague-resistant phenotype had become prevalent among commensal rats of cities undergoing severe plague epidemics. To understand the possible evolutionary basis of these observations, we developed a mathematical model coupling environmentally forced plague dynamics with evolutionary selection of rats, capitalizing on extensive archival data from Indian Plague Commission investigations. Incorporating increased plague resistance among rats as a consequence of intense natural selection permits the model to reproduce observed changes in seasonal epidemic patterns in several cities and capture experimentally observed associations between climate and flea population dynamics in India. Our model results substantiate Victorian era claims of host evolution based on experimental observations of plague resistance and reveal the buffering effect of such evolution against environmental drivers of transmission. Our analysis shows that historical datasets can yield powerful insights into the transmission dynamics of reemerging disease agents with which we have limited contemporary experience to guide quantitative modeling and inference.

The WHO recommends integrating interventions to address the devastating TB/HIV co-epidemics in South Africa, yet integration has been poorly implemented and TB/HIV control efforts need strengthening. Identifying infected individuals is particularly difficult in rural settings. We used mathematical modeling to predict the impact of community-based, integrated TB/HIV case finding and additional control strategies on South Africa's TB/HIV epidemics. We developed a model incorporating TB and HIV transmission to evaluate the effectiveness of integrating TB and HIV interventions in rural South Africa over 10 years. We modeled the impact of a novel screening program that integrates case finding for TB and HIV in the community, comparing it to status quo and recommended TB/HIV control strategies, including GeneXpert, MDR-TB treatment decentralization, improved first-line TB treatment cure rate, isoniazid preventive therapy, and expanded ART. Combining recommended interventions averted 27% of expected TB cases (95% CI 18-40%) 18% HIV (95% CI 13-24%), 60% MDR-TB (95% CI 34-83%), 69% XDR-TB (95% CI 34-90%), and 16% TB/HIV deaths (95% CI 12-29). Supplementing these interventions with annual community-based TB/HIV case finding averted a further 17% of TB cases (44% total; 95% CI 31-56%), 5% HIV (23% total; 95% CI 17-29%), 8% MDR-TB (68% total; 95% CI 40-88%), 4% XDR-TB (73% total; 95% CI 38-91%), and 8% TB/HIV deaths (24% total; 95% CI 16-39%). In addition to increasing screening frequency, we found that improving TB symptom questionnaire sensitivity, second-line TB treatment delays, default before initiating TB treatment or ART, and second-line TB drug efficacy were significantly associated with even greater reductions in TB and HIV cases. TB/HIV epidemics in South Africa were most effectively curtailed by simultaneously implementing interventions that integrated community-based TB/HIV control strategies and targeted drug-resistant TB. Strengthening existing TB and HIV treatment programs is needed to further reduce disease incidence.

The genomic analyses of pediatric acute lymphoblastic leukemia (ALL) subtypes, particularly T-cell and B-cell lineages, have been pivotal in identifying potential therapeutic targets. Typical genomic analyses have directed attention toward the most commonly mutated genes. However, assessing the contribution of mutations to cancer phenotypes is crucial. Therefore, we estimated the cancer effects (scaled selection coefficients) for somatic substitutions in T-cell and B-cell cohorts, revealing key insights into mutation contributions. Cancer effects for well-known, frequently mutated genes like NRAS and KRAS in B-ALL were high, which underscores their importance as therapeutic targets. However, less frequently mutated genes IL7R, XBP1, and TOX also demonstrated high cancer effects, suggesting pivotal roles in the development of leukemia when present. In T-ALL, KRAS and NRAS are less frequently mutated than in B-ALL. However, their cancer effects when present are high in both subtypes. Mutations in PIK3R1 and RPL10 were not at high prevalence, yet exhibited some of the highest cancer effects in individual T-cell ALL patients. Even CDKN2A, with a low prevalence and relatively modest cancer effect, is potentially highly relevant for the epistatic effects that its mutated form exerts on other mutations. Prioritizing investigation into these moderately frequent but potentially high-impact targets not only presents novel personalized therapeutic opportunities but also enhances the understanding of disease mechanisms and advances precision therapeutics for pediatric ALL. [ABSTRACT FROM AUTHOR]

Introduction: Identification of molecular biomarkers in the saliva and serum of oral cavity cancer patients represents a first step in the development of essential and efficient clinical tools for early detection and post‐treatment monitoring. We hypothesized that molecular analyses of paired saliva and serum samples from an individual would likely yield better results than analyses of either serum or saliva alone. Materials and Methods: We performed whole‐transcriptome and small non‐coding RNA sequencing analyses on 32 samples of saliva and serum collected from the same patients with oral squamous cell carcinoma (OSCC) and healthy controls (HC). Results: We identified 12 novel saliva and serum miRNAs and a panel of unique miRNA and mRNA signatures, significantly differentially expressed in OSCC patients relative to HC (log2 fold change: 2.6–26.8; DE: 0.02–0.000001). We utilized a combined panel of the 10 top‐deregulated miRNAs and mRNAs and evaluated their putative diagnostic potential (>87% sensitivity; 100% specificity), recommending seven of them for further validation. We also identified unique saliva and serum miRNAs associated with OSCC and smoking history (OSCC smokers vs. never‐smokers or HC: log2 fold change: 22–23; DE: 0.00003–0.000000001). Functional and pathway analyses indicated interactions between the discovered OSCC‐related non‐invasive miRNAs and mRNAs and their targets, through PI3K/AKT/mTOR signaling. Conclusion: Our data support our hypothesis that using paired saliva and serum from the same individuals and deep sequencing analyses can provide unique combined mRNA and miRNA signatures associated with canonical pathways that may have a diagnostic advantage relative to saliva or serum alone and may be useful for clinical testing. We believe this data will contribute to effective preventive care by post‐treatment monitoring of patients, as well as suggesting potential targets for therapeutic approaches. [ABSTRACT FROM AUTHOR]

Abstract Background The broadly accepted pattern of rapid evolution of reproductive genes is primarily based on studies of animal systems, although several examples of rapidly evolving genes involved in reproduction are found in diverse additional taxa. In fungi, genes involved in mate recognition have been found to evolve rapidly. However, the examples are too few to draw conclusions on a genome scale. Results In this study, we performed microarray hybridizations between RNA from sexual and vegetative tissues of two strains of the heterothallic (self-sterile) filamentous ascomycete Neurospora intermedia, to identify a set of sex-associated genes in this species. We aligned Expressed Sequence Tags (ESTs) from sexual and vegetative tissue of N. intermedia to orthologs from three closely related species: N. crassa, N. discreta and N. tetrasperma. The resulting four-species alignments provided a dataset for molecular evolutionary analyses. Our results confirm a general pattern of rapid evolution of fungal sex-associated genes, compared to control genes with constitutive expression or a high relative expression during vegetative growth. Among the rapidly evolving sex-associated genes, we identified candidates that could be of importance for mating or fruiting-body development. Analyses of five of these candidate genes from additional species of heterothallic Neurospora revealed that three of them evolve under positive selection. Conclusions Taken together, our study represents a novel finding of a genome-wide pattern of rapid evolution of sex-associated genes in the fungal kingdom, and provides a list of candidate genes important for reproductive isolation in Neurospora.

To test the inferences of spotted microarray technology against a biochemically well-studied process, we performed transcriptional profiling of conidial germination in the filamentous fungus, Neurospora crassa. We first constructed a 70 base oligomer microarray that assays 3366 predicted genes. To estimate the relative gene expression levels and changes in gene expression during conidial germination, we analyzed a circuit design of competitive hybridizations throughout a time course using a Bayesian analysis of gene expression level. Remarkable consistency of mRNA profiles with previously published northern data was observed. Genes were hierarchically clustered into groups with respect to their expression profiles over the time course of conidial germination. A functional classification database was employed to characterize the global picture of gene expression. Consensus motif searches identified a putative regulatory component associated with genes involved in ribosomal biogenesis. Our transcriptional profiling data correlate well with biochemical and physiological processes associated with conidial germination and will facilitate functional predictions of novel genes in N.crassa and other filamentous ascomycete species. Furthermore, our dataset on conidial germination allowed comparisons to transcriptional mechanisms associated with germination processes of diverse propagules, such as teliospores of the phytopathogenic fungus Ustilago maydis and spores of the social amoeba Dictyostelium discoideum.

Background: The detection of small yet statistically significant differences in gene expression in spotted DNA microarray studies is an ongoing challenge. Meeting this challenge requires careful examination of the performance of a range of statistical models, as well as empirical examination of the effect of replication on the power to resolve these differences. Results: New models are derived and software is developed for the analysis of microarray ratio data. These models incorporate multiplicative small error terms, and error standard deviations that are proportional to expression level. The fastest and most powerful method incorporates additive small error terms and error standard deviations proportional to expression level. Data from four studies are profiled for the degree to which they reveal statistically significant differences in gene expression. The gene expression level at which there is an empirical 50% probability of a significant call is presented as a summary statistic for the power to detect small differences in gene expression. Conclusions: Understanding the resolution of difference in gene expression that is detectable as significant is a vital component of experimental design and evaluation. These small differences in gene expression level are readily detected with a Bayesian analysis of gene expression level that has additive error terms and constrains samples to have a common error coefficient of variation. The power to detect small differences in a study may then be determined by logistic regression.