Fast batch searching for protein homology based on compression and clustering

Unveiling the mysteries: Functional insights into hypothetical proteins from Bacteroides fragilis 638R

Humans benefit from a vast community of microorganisms in their gastrointestinal tract, known as the gut microbiota, numbering in the tens of trillions. An imbalance in the gut microbiota known as dysbiosis, can lead to changes in the metabolite profile, elevating the levels of toxins like Bacteroides fragilis toxin (BFT), colibactin, and cytolethal distending toxin. These toxins are implicated in the process of oncogenesis. However, a significant portion of the Bacteroides fragilis genome consists of functionally uncharacterized and hypothetical proteins. This study delves into the functional characterization of hypothetical proteins (HPs) encoded by the Bacteroides fragilis genome, employing a systematic in silico approach. A total of 379 HPs were subjected to a BlastP homology search against the NCBI non-redundant protein sequence database, resulting in 162 HPs devoid of identity to known proteins. CDD-Blast identified 106 HPs with functional domains, which were then annotated using Pfam, InterPro, SUPERFAMILY, SCANPROSITE, SMART, and CATH. Physicochemical properties, such as molecular weight, isoelectric point, and stability indices, were assessed for 60 HPs whose functional domains were identified by at least three of the aforementioned bioinformatic tools. Subsequently, subcellular localization analysis was examined and the gene ontology analysis revealed diverse biological processes, cellular components, and molecular functions. Remarkably, E1WPR3 was identified as a virulent and essential gene among the HPs. This study presents a comprehensive exploration of B. fragilis HPs, shedding light on their potential roles and contributing to a deeper understanding of this organism's functional landscape.

Age-related alterations in metabolome and microbiome provide insights in dietary transition in giant pandas

We conducted UPLC-MS-based metabolomics, 16S rRNA, and metagenome sequencing on the fecal samples of 44 captive giant pandas (Ailuropoda melanoleuca) from four age groups (i.e., Cub, Young, Adult, and Old) to comprehensively understand age-related changes in the metabolism and gut microbiota of giant pandas. We characterized the metabolite profiles of giant pandas based on 1,376 identified metabolites, with 152 significantly differential metabolites (SDMs) found across the age groups. We found that the metabolites and the composition/function of the gut microbiota changed in response to the transition from a milk-dominant diet in panda cubs to a bamboo-specific diet in young and adult pandas. Lipid metabolites such as choline and hippuric acid were enriched in the Cub group, and many plant secondary metabolites were significantly higher in the Young and Adult groups, while oxidative stress and inflammatory related metabolites were only found in the Old group. However, there was a decrease in the α-diversity of gut microbiota in adult and old pandas, who exclusively consume bamboo. The abundance of bacteria related to the digestion of cellulose-rich food, such as Firmicutes, Streptococcus, and Clostridium, significantly increased from the Cub to the Adult group, while the abundance of beneficial bacteria such as Faecalibacterium, Sarcina, and Blautia significantly decreased. Notably, several potential pathogenic bacteria had relatively high abundances, especially in the Young group. Metagenomic analysis identified 277 CAZyme genes including cellulose degrading genes, and seven of the CAZymes had abundances that significantly differed between age groups. We also identified 237 antibiotic resistance genes (ARGs) whose number and diversity increased with age. We also found a significant positive correlation between the abundance of bile acids and gut bacteria, especially Lactobacillus and Bifidobacterium. Our results from metabolome, 16S rRNA, and metagenome data highlight the important role of the gut microbiota-bile acid axis in the regulation of age-related metabolism and provide new insights into the lipid metabolism of giant pandas. IMPORTANCE The giant panda is a member of the order Carnivora but is entirely herbivorous. The giant panda's specialized diet and related metabolic mechanisms have not been fully understood. It is therefore crucial to investigate the dynamic changes in metabolites as giant pandas grow and physiologically adapt to their herbivorous diet. This study conducted UPLC-MS-based metabolomics 16S rRNA, and metagenome sequencing on the fecal samples of captive giant pandas from four age groups. We found that metabolites and the composition/function of gut microbiota changed in response to the transition from a milk-dominant diet in cubs to a bamboo-specific diet in young and adult pandas. The metabolome, 16S rRNA, and metagenome results highlight that the gut microbiota-bile acid axis has an important role in the regulation of age-related metabolism, and our study provides new insights into the lipid metabolism of giant pandas.

Flexible protein database based on amino acid k-mers

Identification of proteins is one of the most computationally intensive steps in genomics studies. It usually relies on aligners that do not accommodate rich information on proteins and require additional pipelining steps for protein identification. We introduce kAAmer, a protein database engine based on amino-acid k-mers that provides efficient identification of proteins while supporting the incorporation of flexible annotations on these proteins. Moreover, the database is built to be used as a microservice, to be hosted and queried remotely.

ProtPCV: A Fixed Dimensional Numerical Representation of Protein Sequence to Significantly Reduce Sequence Search Time

Protein sequence is a wealth of experimental information which is yet to be exploited to extract information on protein homologues. Consequently, it is observed from publications that dynamic programming, heuristics and HMM profile-based alignment techniques along with the alignment free techniques do not directly utilize ordered profile of physicochemical properties of a protein to identify its homologue. Also, it is found that these works lack crucial bench-marking or validation in absence of which their incorporation in search engines may appears to be questionable. In this direction this research approach offers fixed dimensional numerical representation of protein sequences extending the concept of periodicity count value of nucleotide types (2017) to accommodate Euclidean distance as direct similarity measure between two proteins. Instead of bench-marking with BLAST and PSI-BLAST only, this new similarity measure was also compared with Needleman-Wunsch and Smith-Waterman. For enhancing the strength of comparison, this work for the first time introduces two novel benchmarking methods based on correlation of "similarity scores" and "proximity of ranked outputs from a standard sequence alignment method" between all possible pairs of search techniques including the new one presented in this paper. It is found that the novel and unique numerical representation of a protein can reduce computational complexity of protein sequence search to the tune of O(log(n)). It may also help implementation of various other similarity-based operation possible, such as clustering, phylogenetic analysis and classification of proteins on the basis of the properties used to build this numerical representation of protein.