Puspo, J. A. | Search bioRxiv

bioRxiv2021

Microbiome dysbiosis regulates the level of energy production under anaerobic condition

The microbiome of the anaerobic digester (AD) regulates the level of energy production. To assess the microbiome dysbiosis in different stages of anaerobic digestion, we analyzed 16 samples dividing into four groups (Group-I = 2; Group-II = 5; Group-III = 5 and Group-IV = 4) through whole metagenome sequencing (WMS). The physicochemical analysis revealed that highest CH4 production (74.1%, on Day 35 of digestion) was associated with decreased amount of non-metal (phosphorus and sulfur) and heavy metals (chromium, lead and nickel). The WMS generated 380.04 million reads mapped to ~ 2800 distinct bacterial, archaeal and viral genomes through PathoScope (PS) and MG-RAST (MR) analyses. The PS analysis detected 768, 1421, 1819 and 1774 bacterial strains in Group-I, Group-II, Group-III and Group-IV, respectively which were represented by Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Spirochaetes and Fibrobacteres (> 93.0% of the total abundances). The archaeal fraction of the AD microbiomes was represented by 343 strains, of which 95.90% strains shared across these metagenomes. The indicator species analysis showed that Methanosarcina vacuolate, Dehalococcoides mccartyi, Methanosarcina sp. Kolksee and Methanosarcina barkeri were the highly specific for energy production in Group-III and Group-IV. However, most of the indicator phylotypes displayed reduced abundance in the initial stage of biogas production (Group-I and Group-II) compared to their increased relative abundances in Group-IV (Day 35). The correlation network analysis showed that different strains of Euryarcheota and Firmicutes phyla were associated with highest level (74.1%) of energy production (Group-IV). In addition to taxonomic dysbiosis, top CH4 producing microbiomes showed increased genomic functional activities related to one carbon and biotin metabolism, oxidative stress, proteolytic pathways, MT1-MMP pericellular network, acetyl-CoA production, motility and chemotaxis. This study reveals distinct changes in composition and diversity of the AD microbiomes including different indicator species, and their genomic features that are highly specific for energy production.

microbiology↗

bioRxiv2020

Comprehensive annotations of the mutational spectra of SARS-CoV-2 spike protein: a fast and accurate pipeline

In order to explore nonsynonymous mutations and deletions in the spike (S) protein of SARS-CoV-2, we comprehensively analyzed 35,750 complete S protein gene sequences from across six continents and five climate zones around the world, as documented in the GISAID database as of June 24th, 2020. Through a custom Python-based pipeline for analyzing mutations, we identified 27,801 (77.77 % of spike sequences) mutated strains compared to Wuhan-Hu-1 strain. 84.40% of these strains had only single amino-acid (aa) substitution mutations, but an outlier strain from Bosnia and Herzegovina (EPI_ISL_463893) was found to possess six aa substitutions. The D614G variant of the major G clade was found to be predominant across circulating strains in all climates. We also identified 988 unique aa substitution mutations distributed across 660 positions within the spike protein, with eleven sites showing high variability - these sites had four types of aa variations at each position. Besides, 17 in-frame deletions at four major regions (three in N-terminal domain and one just downstream of the RBD) may have possible impact on attenuation. Moreover, the mutational frequency differed significantly (p= 0.003, Kruskal-Wallis test) among the SARS-CoV-2 strains worldwide. This study presents a fast and accurate pipeline for identifying nonsynonymous mutations and deletions from large dataset for any particular protein coding sequence and presents this S protein data as representative analysis. By using separate multi-sequence alignment with MAFFT, removing ambiguous sequences and in-frame stop codons, and utilizing pairwise alignment, this method can derive nonsynonymus mutations (Reference:Position:Strain). We believe this will aid in the surveillance of any proteins encoded by SARS-CoV-2, and will prove to be crucial in tracking the ever-increasing variation of many other divergent RNA viruses in the future.

evolutionary biology↗