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.

Protein-coding genes evolved codon usage bias due to the combined but uneven effects of adaptive and nonadaptive influences. Studies in model fungi agree on codon usage bias as an adaptation for fine-tuning gene expression levels; however, such knowledge is lacking for most other fungi. Our comparative genomics analysis of over 450 species supports codon usage and transfer RNAs (tRNAs) as coadapted for translation speed and this is most likely a realization of convergent evolution. Rather than drift, phylogenetic reconstruction inferred adaptive radiation as the best explanation for the variation of interspecific codon usage bias. Although the phylogenetic signals for individual codon and tRNAs frequencies are lower than expected by genetic drift, we found remarkable conservation of highly expressed genes being codon optimized for translation by the most abundant tRNAs, especially by inosine-modified tRNAs. As an application, we present a sequence-to-expression neural network that uses codons to reliably predict highly expressed transcripts. The kingdom Fungi, with over a million species, includes many key players in various ecosystems and good targets for biotechnology. Collectively, our results have implications for better understanding the evolutionary success of fungi, as well as informing the biosynthetic manipulation of fungal genes.

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.

Ancestral sequence reconstruction (ASR) infers predicted ancestral states for sites within sequences and can constrain the functions and properties of ancestors of extant protein families. Here, we compare the likely sequences of inferred nitrogenase ancestors to extant nitrogenase sequence diversity. We show that the most-likely combinations of ancestral states for key substrate channel residues are not represented in extant sequence space, and rarely found within a more broadly defined physiochemical space-supporting that the earliest ancestors of extant nitrogenases likely had alternative substrate channel composition. These differences may indicate differing environmental selection pressures acting on nitrogenase substrate specificity in ancient environments. These results highlight ASR's potential as an in silico tool for developing hypotheses about ancestral enzyme functions, as well as improving hypothesis testing through more targeted in vitro and in vivo experiments.

Summary

Chemsearch 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 implementation

Chemsearch 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.

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.

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.

Calculation of the log-likelihood stands as the computational bottleneck for many statistical phylogenetic algorithms. Even worse is its gradient evaluation, often used to target regions of high probability. Order O(N)-dimensional gradient calculations based on the standard pruning algorithm require O(N2) operations, where N is the number of sampled molecular sequences. With the advent of high-throughput sequencing, recent phylogenetic studies have analyzed hundreds to thousands of sequences, with an apparent trend toward even larger data sets as a result of advancing technology. Such large-scale analyses challenge phylogenetic reconstruction by requiring inference on larger sets of process parameters to model the increasing data heterogeneity. To make these analyses tractable, we present a linear-time algorithm for O(N)-dimensional gradient evaluation and apply it to general continuous-time Markov processes of sequence substitution on a phylogenetic tree without a need to assume either stationarity or reversibility. We apply this approach to learn the branch-specific evolutionary rates of three pathogenic viruses: West Nile virus, Dengue virus, and Lassa virus. Our proposed algorithm significantly improves inference efficiency with a 126- to 234-fold increase in maximum-likelihood optimization and a 16- to 33-fold computational performance increase in a Bayesian framework.

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.