Microbes form fundamental bases of every Earth ecosystem. As their key survival strategies, some microbes adapt to broad ranges of environments, while others specialize to certain habitats. While ecological roles and properties of such “generalists” and “specialists” had been examined in individual ecosystems, general principles that govern their distribution patterns and evolutionary processes have not been characterized. Here, we thoroughly identified microbial generalists and specialists across 61 environments via meta-analysis of community sequencing data sets and reconstructed their evolutionary histories across diverse microbial groups. This revealed that generalist lineages possess 19-fold higher speciation rates and significant persistence advantage over specialists. Yet, we also detected three-fold more frequent generalist-to-specialist transformations than the reverse transformations. These results support a model of microbial evolution in which generalists play key roles in introducing new species and maintaining taxonomic diversity.

Typical analyses of mass spectrometry data only identify amino acid sequences that exist in reference databases. This restricts the possibility of discovering new peptides such as those that contain uncharacterized mutations or originate from unexpected processing of RNAs and proteins. De novo peptide sequencing approaches address this limitation but often suffer from low accuracy and require extensive validation by experts. Here, we develop SMSNet, a deep learning-based de novo peptide sequencing framework that achieves >95% amino acid accuracy while retaining good identification coverage. Applications of SMSNet on landmark proteomics and peptidomics studies reveal over 10,000 previously uncharacterized HLA antigens and phosphopeptides, and in conjunction with database-search methods, expand the coverage of peptide identification by almost 30%. The power to accurately identify new peptides of SMSNet would make it an invaluable tool for any future proteomics and peptidomics studies, including tumor neoantigen discovery, antibody sequencing, and proteome characterization of non-model organisms. Graphical Abstract Highlights Deep learning-based hybrid de novo sequencing with database search strategy. Accurate identifications via ability to revise confidence scores and amino acids. Discovery of >10,000 potential new HLA antigens and human phosphopeptides. A dataset of >26 million annotated HCD spectra from Q Exactive instruments.

Immunotherapy is an emerging approach in cancer treatment that activates the host immune system to destroy cancer cells expressing unique peptide signatures (neoepitopes). Administrations of cancer-specific neoepitopes in the form of synthetic peptide vaccine have been proven effective in both mouse models and human patients. Because only a tiny fraction of cancer-specific neoepitopes actually elicits immune response, selection of potent, immunogenic neoepitopes remains a challenging step in cancer vaccine development. A basic approach for immunogenicity prediction is based on the premise that effective neoepitope should bind with the Major Histocompatibility Complex (MHC) with high affinity. In this study, we developed MHCSeqNet, an open-source deep learning model, which not only outperforms state-of-the-art predictors on both MHC binding affinity and MHC ligand peptidome datasets but also exhibits promising generalization to unseen MHC class I alleles. MHCSeqNet employed neural network architectures developed for natural language processing to model amino acid sequence representations of MHC allele and epitope peptide as sentences with amino acids as individual words. This consideration allows MHCSeqNet to accept new MHC alleles as well as peptides of any length. The improved performance and the flexibility offered by MHCSeqNet should make it a valuable tool for screening effective neoepitopes in cancer vaccine development.

Background and objectives: Gut dysbiosis that resulted from the alteration between host-microbe interaction might worsen obesity-induced systemic inflammation. Gut microbiota manipulation by supplementation of prebiotic inulin may reverse metabolic abnormalities and improve obesity. This study aimed to determine whether inulin supplementation improved intestinal microbiota and microbial functional pathways in children with obesity. Methods: Children with obesity whose BMI above median + 2SDs were recruited to a randomized, double-blinded placebo-controlled study. The participants aged 7–15 years were assigned to inulin supplement extracted from Thai Jerusalem artichoke (intervention), maltodextrin (placebo), and dietary fiber advice groups. All participants received similar monthly conventional advice and follow-up for 6 months. Fecal samples were collected for gut microbiome analysis using 16S rRNA sequencing. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States was performed to infer microbial functional pathways. Results: One hundred and forty-three children with available taxonomic and functional pathway abundance profiles were evaluated. A significant increase in alpha-diversity was observed in the inulin group. Inulin supplementation substantially enhanced Bifidobacterium, Blautia, Megasphaera, and several butyrate-producing bacteria, including Agathobacter, Eubacterium coprostanoligenes, and Subdoligranulum, compared to the other groups. The inulin group showed a significant difference in functional pathways of proteasome and riboflavin metabolism. These changes correlated with clinical and metabolic outcomes exclusively in the inulin group. Conclusions: Inulin supplementation significantly promoted gut bacterial diversity and improved gut microbiota dysbiosis in children with obesity. The modulation of functional pathways by inulin suggests its potential to establish beneficial interactions between the gut microbiota and host physiology. Inulin supplementation could be a strategic treatment to restore the balance of intestinal microbiota and regulate their functions in childhood obesity.