Seasonal shift in gut microbiome diversity in wild Sichuan takin (Budorcas tibetanus) and environmental adaptation.

In this study, we investigated the change in microbiome composition of wild Sichuan takin (Budorcas tibetanus) during winter and spring and analyzed the physiological implications for such changes. Diversity analyses of the microbiome (average 15,091 high-quality reads per sample) in 24 fecal samples (15 from winter, 9 from spring) revealed that spring samples had higher species diversity and were compositionally different from winter samples (P < 0.05). Taxonomic composition analysis showed that the relative abundance increased in spring for Patescibacteria (2.7% vs. 0.9% in winter, P < 0.001) and Tenericutes (1.9% vs. 1% in winter, P < 0.05). Substantial increases in relative abundance of Ruminococcaceae and Micrococcaceae were identified in spring and winter, respectively. Mann-Whitney U and ANCOM identified seven differentially abundant genera: Enterococcus, Acetitomaculum, Blautia, Coprococcus 1, Lachnospiraceae UCG 008, Ruminococcus 2 and Ralstonia. All seven genera were significantly more abundant in spring (average 0.016-1.2%) than winter (average 0-0.16%), with the largest difference found in Ruminococcus (1.21% in spring vs. 0.16% in winter). The other six genera were undetectable in winter. Functional prediction and pathway analysis revealed that biosynthesis of cofactors (ko01240) had the highest gene count ratios in the winter, followed by the two-component system (ko02020). Seasonal variation affects the gut microbiomes in wild Sichuan takins, with winter associated with lower species diversity and spring with enrichment of cellulose-degrading genera and phytopathogens. Such changes were crucial in their adaptation to the environment, particularly the difference in food abundance.

KOMB: K-core based de novo characterization of copy number variation in microbiomes.

Characterizing metagenomes via kmer-based, database-dependent taxonomic classification has yielded key insights into underlying microbiome dynamics. However, novel approaches are needed to track community dynamics and genomic flux within metagenomes, particularly in response to perturbations. We describe KOMB, a novel method for tracking genome level dynamics within microbiomes. KOMB utilizes K-core decomposition to identify Structural variations (SVs), specifically, population-level Copy Number Variation (CNV) within microbiomes. K-core decomposition partitions the graph into shells containing nodes of induced degree at least K, yielding reduced computational complexity compared to prior approaches. Through validation on a synthetic community, we show that KOMB recovers and profiles repetitive genomic regions in the sample. KOMB is shown to identify functionally-important regions in Human Microbiome Project datasets, and was used to analyze longitudinal data and identify keystone taxa in Fecal Microbiota Transplantation (FMT) samples. In summary, KOMB represents a novel graph-based, taxonomy-oblivious, and reference-free approach for tracking CNV within microbiomes. KOMB is open source and available for download at https://gitlab.com/treangenlab/komb.

Myelin like electrogenic filamentation and Liquid Microbial Fuel Cells Dataset.

Biofilm at water-oil interface of hypoxic water columns of microcosms, prepared from a lacustrine sample, that used diesel as a carbon source was found to show electrogenic properties. These microcosms named, Liquid Microbial Fuel Cells (L-MFCs) were electrically characterized using a custom electronic analyzer; accurate determination of voltage (V), power density (W/m 2), and current density (A/m2) for both charge and discharge phases was carried out. The instrument made it possible to carry out cell characterizations using resistive loads between 0 Ω (Ohm) and 10 kΩ. During the hypoxic and electrogenic phase, the synthesis of a system of "bacterial piping induction", produced filaments of hundreds of micrometers in which the microbial cells are hosted. Ultrastructural microscopy collected by scanning (SEM), transmission (TEM), immunofluorescence, Thunder Imager 3D, confocal laser scanning (CLSM) microscopy revealed a "myelin like" structure during filamentation processes; this "myelin like" structure exhibited cross-reactivity towards different epitopes of the myelin basic protein (MBP) and Claudin 11 (O4) of human oligodendrocytes. The disclosure of these filamentation processes could be helpful to describe further unconventional microbial structures in aquatic ecosystems and of the animal world. The data that support the findings of this study are openly available in at https://data.mendeley.com/datasets/7d35tj3j96/1.

Genetic divergence and functional convergence of gut bacteria between the Eastern honey bee Apis cerana and the Western honey bee Apis mellifera.

Introduction: The functional relevance of intra-species diversity in natural microbial communities remains largely unexplored. The guts of two closely related honey bee species, Apis cerana and A. mellifera, are colonised by a similar set of core bacterial species composed of host-specific strains, thereby providing a good model for an intra-species diversity study. Objectives: We aim to assess the functional relevance of intra-species diversity of A. cerana and A. mellifera gut microbiota. Methods: Honey bee workers were collected from four regions of China. Their gut microbiomes were investigated by shotgun metagenomic sequencing, and the bacterial compositions were compared at the species level. A cross-species colonisation assay was conducted, with the gut metabolomes being characterised by LC-MS/MS. Results: Comparative analysis showed that the strain composition of the core bacterial species was host-specific. These core bacterial species presented distinctive functional profiles between the hosts. However, the overall functional profiles of the A. cerana and A. mellifera gut microbiomes were similar; this was further supported by the consistency of the honey bees' gut metabolome, as the gut microbiota of different honey bee species showed rather similar metabolic profiles in the cross-species colonisation assay. Moreover, this experiment also demonstrated that the gut microbiota of A. cerana and A. mellifera could cross colonise between the two honey bee species. Conclusion: Our findings revealed functional differences in most core gut bacteria between the guts of A. cerana and A. mellifera, which may be associated with their inter-species diversity. However, the functional profiles of the overall gut microbiomes between the two honey bee species converge, probably as a result of the overlapping ecological niches of the two species. Our findings provide critical insights into the evolution and functional roles of the mutualistic microbiota of honey bees and reveal that functional redundancy could stabilise the gene content diversity at the strain-level within the gut community.

Avances de la bioinformática en Costa Rica: vista retrospectiva y perspectivas / Bioinformatics advances in Costa Rica: retrospective view and perspectives

Resumen Introducción: La disciplina científica de la bioinformática tiene el potencial de generar aplicaciones innovadoras para las sociedades humanas. Costa Rica, pequeña en tamaño y población en comparación con otros países de América Latina, ha ido adoptando la disciplina de manera progresiva. El reconocer los avances permite determinar hacia dónde puede dirigirse el país en este campo, así como su contribución a la región latinoamericana. Objetivo: En este manuscrito se reporta evidencia de la evolución de la bioinformática en Costa Rica, para identificar debilidades y fortalezas que permitan definir acciones a futuro. Métodos: Se realizaron búsquedas en bases de datos de publicaciones científicas y repositorios de secuencias, así como información de actividades de capacitación, redes, infraestructura, páginas web y fuentes de financiamiento. Resultados: Se observan avances importantes desde el 2010, incluyendo un aumento en oportunidades de entrenamiento y número de publicaciones, aportes significativos a las bases de datos de secuencias y conexiones por medio de redes. Sin embargo, ciertas áreas, como la masa crítica y la financiación requieren más desarrollo. La comunidad científica y sus patrocinadores deben promover la investigación basada en bioinformática, invertir en la formación de estudiantes de posgrado, aumentar la formación de profesionales, crear oportunidades laborales para carreras en bioinformática y promover colaboraciones internacionales a través de redes. Conclusiones: Se sugiere que para experimentar los beneficios de las aplicaciones de la bioinformática se deben fortalecer tres aspectos clave: la comunidad científica, la infraestructura de investigación y las oportunidades de financiamiento. El impacto de tal inversión sería el desarrollo de proyectos ambiciosos pero factibles y colaboraciones extendidas dentro de la región latinoamericana. Esto permitiría realizar contribuciones significativas para abordar los desafíos globales y la aplicación de nuevos enfoques de investigación, innovación y transferencia de conocimiento para el desarrollo de la economía, dentro de un marco de ética de la investigación.

Abstract Introduction: The scientific discipline of bioinformatics has the potential to generate innovative applications for human societies. Costa Rica, small in size and population compared to other Latin American countries, has been progressively adopting the discipline. Recognizing progress makes it possible to determine where the country can go in this field, as well as its contribution to the Latin American region. Objective: This manuscript reports evidence of the evolution of bioinformatics in Costa Rica, to identify weaknesses and strengths allowing future actions plans. Methods: We searched databases of scientific publications and sequence repositories, as well as information on training activities, networks, infrastructure, web pages and funding sources. Results: Important advances have been observed since 2010, such as increases in training opportunities and the number of publications, significant contributions to the sequence databases and connections through networks. However, areas such as critical mass and financing require further development. The scientific community and its sponsors should promote bioinformatics-based research, invest in graduate student training, increase professional training, create career opportunities in bioinformatics, and promote international collaborations through networks. Conclusions: It is suggested that in order to experience the benefits of bioinformatics applications, three key aspects must be strengthened: the scientific community, the research infrastructure, and funding opportunities. The impact of such investment would be the development of ambitious but feasible projects and extended collaborations within the Latin American region and abroad. This would allow significant contributions to address global challenges and the implementation of new approaches to research, innovation and knowledge transfer for the development of the economy, within an ethics of research framework.

Prebiotics enhance the biotransformation and bioavailability of ginsenosides in rats by modulating gut microbiota.

BACKGROUND: Gut microbiota mainly function in the biotransformation of primary ginsenosides into bioactive metabolites. Herein, we investigated the effects of three prebiotic fibers by targeting gut microbiota on the metabolism of ginsenoside Rb1 in vivo. METHODS: Sprague Dawley rats were administered with ginsenoside Rb1 after a two-week prebiotic intervention of fructooligosaccharide, galactooligosaccharide, and fibersol-2, respectively. Pharmacokinetic analysis of ginsenoside Rb1 and its metabolites was performed, whilst the microbial composition and metabolic function of gut microbiota were examined by 16S rRNA gene amplicon and metagenomic shotgun sequencing. RESULTS: The results showed that peak plasma concentration and area under concentration time curve of ginsenoside Rb1 and its intermediate metabolites, ginsenoside Rd, F2, and compound K (CK), in the prebiotic intervention groups were increased at various degrees compared with those in the control group. Gut microbiota dramatically responded to the prebiotic treatment at both taxonomical and functional levels. The abundance of Prevotella, which possesses potential function to hydrolyze ginsenoside Rb1 into CK, was significantly elevated in the three prebiotic groups (P < 0.05). The gut metagenomic analysis also revealed the functional gene enrichment for terpenoid/polyketide metabolism, glycolysis, gluconeogenesis, propanoate metabolism, etc. CONCLUSION: These findings imply that prebiotics may selectively promote the proliferation of certain bacterial stains with glycoside hydrolysis capacity, thereby, subsequently improving the biotransformation and bioavailability of primary ginsenosides in vivo.

Deep metabolome: Applications of deep learning in metabolomics.

In the past few years, deep learning has been successfully applied to various omics data. However, the applications of deep learning in metabolomics are still relatively low compared to others omics. Currently, data pre-processing using convolutional neural network architecture appears to benefit the most from deep learning. Compound/structure identification and quantification using artificial neural network/deep learning performed relatively better than traditional machine learning techniques, whereas only marginally better results are observed in biological interpretations. Before deep learning can be effectively applied to metabolomics, several challenges should be addressed, including metabolome-specific deep learning architectures, dimensionality problems, and model evaluation regimes.

A catalog of HLA type, HLA expression, and neo-epitope candidates in human cancer cell lines.

Cancer cell lines are a tremendous resource for cancer biology and therapy development. These multipurpose tools are commonly used to examine the genetic origin of cancers, to identify potential novel tumor targets, such as tumor antigens for vaccine devel-opment, and utilized to screen potential therapies in preclinical studies. Mutations, gene expression, and drug sensitivity have been determined for many cell lines using next-generation sequencing (NGS). However, the human leukocyte antigen (HLA) type and HLA expression of tumor cell lines, characterizations necessary for the development of cancer vaccines, have remained largely incomplete and, such information, when available, has been distributed in many publications. Here, we determine the 4-digit HLA type and HLA expression of 167 cancer and 10 non-cancer cell lines from publically available RNA-Seq data. We use standard NGS RNA-Seq short reads from "whole transcriptome" sequencing, map reads to known HLA types, and statistically determine HLA type, heterozygosity, and expression. First, we present previously unreported HLA Class I and II genotypes. Second, we determine HLA expression levels in each cancer cell line, providing insights into HLA downregulation and loss in cancer. Third, using these results, we provide a fundamental cell line "barcode" to track samples and prevent sample annotation swaps and contamination. Fourth, we integrate the cancer cell-line specific HLA types and HLA expression with available cell-line specific mutation information and existing HLA binding prediction algorithms to make a catalog of predicted antigenic mutations in each cell line. The compilation of our results are a fundamental resource for all researchers selecting specific cancer cell lines based on the HLA type and HLA expression, as well as for the development of immunotherapeutic tools for novel cancer treatment modalities.