Cross-species prediction of essential genes in insects.

MOTIVATION: Insects possess a vast phenotypic diversity and key ecological roles. Several insect species also have medical, agricultural and veterinary importance as parasites and disease vectors. Therefore, strategies to identify potential essential genes in insects may reduce the resources needed to find molecular players in central processes of insect biology. However, most predictors of essential genes in multicellular eukaryotes using machine learning rely on expensive and laborious experimental data to be used as gene features, such as gene expression profiles or protein-protein interactions, even though some of this information may not be available for the majority of insect species with genomic sequences available. RESULTS: Here, we present and validate a machine learning strategy to predict essential genes in insects using sequence-based intrinsic attributes (statistical and physicochemical data) together with the predictions of subcellular location and transcriptomic data, if available. We gathered information available in public databases describing essential and non-essential genes for Drosophila melanogaster (fruit fly, Diptera) and Tribolium castaneum (red flour beetle, Coleoptera). We proceeded by computing intrinsic and extrinsic attributes that were used to train statistical models in one species and tested by their capability of predicting essential genes in the other. Even models trained using only intrinsic attributes are capable of predicting genes in the other insect species, including the prediction of lineage-specific essential genes. Furthermore, the inclusion of RNA-Seq data is a major factor to increase classifier performance. AVAILABILITY AND IMPLEMENTATION: The code, data and final models produced in this study are freely available at https://github.com/g1o/GeneEssentiality/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integrating microbial metagenomics and physicochemical parameters and a new perspective on starter culture for fine cocoa fermentation.

Cocoa beans used for chocolate production are fermented seeds of Theobroma cacao obtained by a natural fermentation process. The flavors and chemical compounds produced during the fermentation process make this step one of the most important in fine chocolate production. Herein, an integrative analysis of the variation of microbial community structure, using a shotgun metagenomics approach and associated physicochemical features, was performed during fermentation of fine cocoa beans. Samples of Forastero variety (FOR) and a mixture of two hybrids (PS1319 and CCN51) (MIX) from Bahia, Brazil, were analyzed at 7 different times. In the beginning (0 h), the structures of microbial communities were very different between FOR and MIX, reflecting the original plant-associated microbiomes. The highest change in microbial community structures occurred at the first 24 h of fermentation, with a marked increase in temperature and acetic acid concentration, and pH decrease. At 24-48 h both microbial community structures were quite homogenous regarding temperature, acetic acid, succinic acid, pH, soluble proteins and total phenols. During 72-96 h, the community structure resembles an acidic and warmer environment, prevailing few acetic acid bacteria. Taxonomic richness and abundance at 72-144 h exhibited significant correlation with temperature, reducing sugars, succinic, and acetic acids. Finally, we recommend that dominant microbial species of spontaneous fine cocoa fermentations should be considered as inoculum in accordance with the farm/region and GMP to maintain a differential organoleptic feature for production of fine chocolate. In our study, a starter inoculum composed of Acetobacter pausterianus and Hanseniaspora opuntiae strains is indicated.

POTION: an end-to-end pipeline for positive Darwinian selection detection in genome-scale data through phylogenetic comparison of protein-coding genes.

BACKGROUND: Detection of genes evolving under positive Darwinian evolution in genome-scale data is nowadays a prevailing strategy in comparative genomics studies to identify genes potentially involved in adaptation processes. Despite the large number of studies aiming to detect and contextualize such gene sets, there is virtually no software available to perform this task in a general, automatic, large-scale and reliable manner. This certainly occurs due to the computational challenges involved in this task, such as the appropriate modeling of data under analysis, the computation time to perform several of the required steps when dealing with genome-scale data and the highly error-prone nature of the sequence and alignment data structures needed for genome-wide positive selection detection. RESULTS: We present POTION, an open source, modular and end-to-end software for genome-scale detection of positive Darwinian selection in groups of homologous coding sequences. Our software represents a key step towards genome-scale, automated detection of positive selection, from predicted coding sequences and their homology relationships to high-quality groups of positively selected genes. POTION reduces false positives through several sophisticated sequence and group filters based on numeric, phylogenetic, quality and conservation criteria to remove spurious data and through multiple hypothesis corrections, and considerably reduces computation time thanks to a parallelized design. Our software achieved a high classification performance when used to evaluate a curated dataset of Trypanosoma brucei paralogs previously surveyed for positive selection. When used to analyze predicted groups of homologous genes of 19 strains of Mycobacterium tuberculosis as a case study we demonstrated the filters implemented in POTION to remove sources of errors that commonly inflate errors in positive selection detection. A thorough literature review found no other software similar to POTION in terms of customization, scale and automation. CONCLUSION: To the best of our knowledge, POTION is the first tool to allow users to construct and check hypotheses regarding the occurrence of site-based evidence of positive selection in non-curated, genome-scale data within a feasible time frame and with no human intervention after initial configuration. POTION is available at http://www.lmb.cnptia.embrapa.br/share/POTION/.

Do organisms need an impact factor? Citations of key biological resources including model organisms reveal usage patterns and impact.

Research resources like transgenic animals and antibodies are the workhorses of biomedicine, enabling investigators to relatively easily study specific disease conditions. As key biological resources, transgenic animals and antibodies are often validated, maintained, and distributed from university based stock centers. As these centers heavily rely largely on grant funding, it is critical that they are cited by investigators so that usage can be tracked. However, unlike systems for tracking the impact of papers, the conventions and systems for tracking key resource usage and impact lag behind. Previous studies have shown that about 50% of the resources are not findable, making the studies they are supporting irreproducible, but also makes tracking resources difficult. The RRID project is filling this gap by working with journals and resource providers to improve citation practices and to track the usage of these key resources. Here, we reviewed 10 years of citation practices for five university based stock centers, characterizing each reference into two broad categories: findable (authors could use the RRID, stock number, or full name) and not findable (authors could use a nickname or a common name that is not unique to the resource). The data revealed that when stock centers asked their communities to cite resources by RRID, in addition to helping stock centers more easily track resource usage by increasing the number of RRID papers, authors shifted from citing resources predominantly by nickname (~50% of the time) to citing them by one of the findable categories (~85%) in a matter of several years. In the case of one stock center, the MMRRC, the improvement in findability is also associated with improvements in the adherence to NIH rigor criteria, as determined by a significant increase in the Rigor and Transparency Index for studies using MMRRC mice. From this data, it was not possible to determine whether outreach to authors or changes to stock center websites drove better citation practices, but findability of research resources and rigor adherence was improved.

Why are some plants taller? Researchers on the unveiling of genetic variation associated with complex quantitative phenotypes.

Francisco Pereira Lobo, Giovanni Marques de Castro, and Felipe Campelo are part of an international team of collaborators that developed CALANGO, a comparative genomics tool to investigate quantitative genotype-phenotype relationships. Their Patterns article highlights how the tool integrates species-centric data to perform genome-wide search and detect genes potentially involved in the emergence of complex quantitative traits across species. Here, they talk about their view of data science, their experience with interdisciplinary research, and the potential applications of their tool.

CALANGO: A phylogeny-aware comparative genomics tool for discovering quantitative genotype-phenotype associations across species.

Living species vary significantly in phenotype and genomic content. Sophisticated statistical methods linking genes with phenotypes within a species have led to breakthroughs in complex genetic diseases and genetic breeding. Despite the abundance of genomic and phenotypic data available for thousands of species, finding genotype-phenotype associations across species is challenging due to the non-independence of species data resulting from common ancestry. To address this, we present CALANGO (comparative analysis with annotation-based genomic components), a phylogeny-aware comparative genomics tool to find homologous regions and biological roles associated with quantitative phenotypes across species. In two case studies, CALANGO identified both known and previously unidentified genotype-phenotype associations. The first study revealed unknown aspects of the ecological interaction between Escherichia coli, its integrated bacteriophages, and the pathogenicity phenotype. The second identified an association between maximum height in angiosperms and the expansion of a reproductive mechanism that prevents inbreeding and increases genetic diversity, with implications for conservation biology and agriculture.

An Integrative View of the Phyllosphere Mycobiome of Native Rubber Trees in the Brazilian Amazon.

The rubber tree, Hevea brasiliensis, is a neotropical Amazonian species. Despite its high economic value and fungi associated with native individuals, in its original area in Brazil, it has been scarcely investigated and only using culture-dependent methods. Herein, we integrated in silico approaches with novel field/experimental approaches and a case study of shotgun metagenomics and small RNA metatranscriptomics of an adult individual. Scientific literature, host fungus, and DNA databases are biased to fungal taxa, and are mainly related to rubber tree diseases and in non-native ecosystems. Metabarcoding retrieved specific phyllospheric core fungal communities of all individuals, adults, plantlets, and leaves of the same plant, unravelling hierarchical structured core mycobiomes. Basidiomycotan yeast-like fungi that display the potential to produce antifungal compounds and a complex of non-invasive ectophytic parasites (Sooty Blotch and Flyspeck fungi) co-occurred in all samples, encompassing the strictest core mycobiome. The case study of the same adult tree (previously studied using culture-dependent approach) analyzed by amplicon, shotgun metagenomics, and small RNA transcriptomics revealed a high relative abundance of insect parasite-pathogens, anaerobic fungi and a high expression of Trichoderma (a fungal genus long reported as dominant in healthy wild rubber trees), respectively. Altogether, our study unravels new and intriguing information/hypotheses of the foliar mycobiome of native H. brasiliensis, which may also occur in other native Amazonian trees.

Unraveling potential enzymes and their functional role in fine cocoa beans fermentation using temporal shotgun metagenomics.

Cocoa beans fermentation is a spontaneous process, essential for the generation of quality starting material for fine chocolate production. The understanding of this process has been studied by the application of high-throughput sequencing technologies, which grants a better assessment of the different microbial taxa and their genes involved in this microbial succession. The present study used shotgun metagenomics to determine the enzyme-coding genes of the microbiota found in two different groups of cocoa beans varieties during the fermentation process. The statistical evaluation of the most abundant genes in each group and time studied allowed us to identify the potential metabolic pathways involved in the success of the different microorganisms. The results showed that, albeit the distinction between the initial (0 h) microbiota of each varietal group was clear, throughout fermentation (24-144 h) this difference disappeared, indicating the existence of selection pressures. Changes in the microbiota enzyme-coding genes over time pointed to the distinct ordering of fermentation at 24-48 h (T1), 72-96 h (T2), and 120-144 h (T3). At T1, the significantly more abundant enzyme-coding genes were related to threonine metabolism and those genes related to the glycolytic pathway, explained by the abundance of sugars in the medium. At T2, the genes linked to the metabolism of ceramides and hopanoids lipids were clearly dominant, which are associated with the resistance of microbial species to extreme temperatures and pH values. In T3, genes linked to trehalose metabolism, related to the response to heat stress, dominated. The results obtained in this study provided insights into the potential functionality of microbial community succession correlated to gene function, which could improve cocoa processing practices to ensure the production of more stable quality end products.

Exploring antibiotic resistance in environmental integron-cassettes through intI-attC amplicons deep sequencing.

INTRODUCTION: Freshwater ecosystems provide propitious conditions for the acquisition and spread of antibiotic resistance genes (ARGs), and integrons play an important role in this process. MATERIAL AND METHODS: In the present study, the diversity of putative environmental integron-cassettes, as well as their potential bacterial hosts in the Velhas River (Brazil), was explored through intI-attC and 16S rRNA amplicons deep sequencing. RESULTS AND DISCUSSION: ORFs related to different biological processes were observed, from DNA integration to oxidation-reduction. ARGs-cassettes were mainly associated with class 1 mobile integrons carried by pathogenic Gammaproteobacteria, and possibly sedentary chromosomal integrons hosted by Proteobacteria and Actinobacteria. Two putative novel ARG-cassettes homologs to fosB3 and novA were detected. Regarding 16SrRNA gene analysis, taxonomic and functional profiles unveiled Proteobacteria, Bacteroidetes, Firmicutes, and Actinobacteria as dominant phyla. Betaproteobacteria, Alphaproteobacteria, and Actinobacteria classes were the main contributors for KEGG orthologs associated with resistance. CONCLUSIONS: Overall, these results provide new information about environmental integrons as a source of resistance determinants outside clinical settings and the bacterial community in the Velhas River.