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BackgroundA mechanistic understanding of the spread of SARS-CoV-2 and diligent tracking of ongoing mutagenesis are of key importance to plan robust strategies for confining its transmission. Large numbers of available sequences and their dates of transmission provide an unprecedented opportunity to analyze evolutionary adaptation in novel ways. Addition of high-resolution structural information can reveal the functional basis of these processes at the molecular level. Integrated systems biology-directed analyses of these data layers afford valuable insights to build a global understanding of the COVID-19 pandemic.ResultsHere we identify globally distributed haplotypes from 15,789 SARS-CoV-2 genomes and model their success based on their duration, dispersal, and frequency in the host population. Our models identify mutations that are likely compensatory adaptive changes that allowed for rapid expansion of the virus. Functional predictions from structural analyses indicate that, contrary to previous reports, the Asp614Gly mutation in the spike glycoprotein (S) likely reduced transmission and the subsequent Pro323Leu mutation in the RNA-dependent RNA polymerase led to the precipitous spread of the virus. Our model also suggests that two mutations in the nsp13 helicase allowed for the adaptation of the virus to the Pacific Northwest of the USA. Finally, our explainable artificial intelligence algorithm identified a mutational hotspot in the sequence of S that also displays a signature of positive selection and may have implications for tissue or cell-specific expression of the virus.ConclusionsThese results provide valuable insights for the development of drugs and surveillance strategies to combat the current and future pandemics.

Small proteins (10 200 amino acids aa in length) encoded by short open reading frames (sORF) play important regulatory roles in various biological processes, including tumor progression, stress response, flowering, and hormone signaling. However, ab initio discovery of small proteins has been relatively overlooked. Recent advances in deep transcriptome sequencing make it possible to efficiently identify sORFs at the genome level. In this study, we obtained 2.6 million expressed sequence tag (EST) reads from Populus deltoides leaf transcriptome and reconstructed full-length transcripts fromthe EST sequences. We identified an initial set of 12,852 sORFs encoding proteins of 10 200 aa in length. Three computational approaches were then used to enrich for bona fide protein-coding sORFs from the initial sORF set: (1) codingpotential prediction, (2) evolutionary conservation between P. deltoides and other plant species, and (3) gene familyclustering within P. deltoides. As a result, a high-confidence sORF candidate set containing 1469 genes was obtained. Analysis of the protein domains, non-protein-coding RNA motifs, sequence length distribution, and protein mass spectrometry data supported this high-confidence sORF set. In the high-confidence sORF candidate set, known protein domains were identified in 1282 genes (higher-confidence sORF candidate set), out of which 611 genes, designated as highest-confidence candidate sORF set, were supported by proteomics data. Of the 611 highest-confidence candidate sORF genes, 56 were newto the current Populus genome annotation. This study not only demonstrates that there are potential sORF candidates to be annotated in sequenced genomes, but also presents an efficient strategy for discovery of sORFs in species with no genome annotation yet available.

An important challenge in microbial ecology is developing methods that simultaneously examine the physiology of organisms at the molecular level and their ecosystem level interactions in complex natural systems. We integrated extensive proteomic, geochemical, and biological information from 28 microbial communities collected from an acid mine drainage environment and representing a range of biofilm development stages and geochemical conditions to evaluate how the physiologies of the dominant and less abundant organisms change along environmental gradients. The initial colonist dominates across all environments, but its proteome changes between two stable states as communities diversify, implying that interspecies interactions affect this organism's metabolism. Its overall physiology is robust to abiotic environmental factors, but strong correlations exist between these factors and certain subsets of proteins, possibly accounting for its wide environmental distribution. Lower abundance populations are patchier in their distribution, and proteomic data indicate that their environmental niches may be constrained by specific sets of abiotic environmental factors. This research establishes an effective strategy to investigate ecological relationships between microbial physiology and the environment for whole communities in situ.

A proteomics-based approach to simultaneously document the strain membership and microbial physiology of the dominant Geobacter community members during in situ acetate amendment of the U-contaminated Rifle, CO, and aquifer is reported. The evolving Geobacter strain composition might be linked to changes in protein abundance over the course of biostimulation and might reflect changes in metabolic functioning.

The analysis of near-complete population (composite) genomic sequences for coexisting acidophilic iron-oxidizing Leptospirillum group II and III bacteria (phylum Nitrospirae) and an extrachromosomal plasmid from a Richmnd Mine, Iron Mountain, Ca, acid mine drainage biofilm is presented. Both the bacterial groups are found to have genes for community-essential functions, including carbon fixation and biosynthesis of vitamins, fatty acids, and biopolymers and multiple pathways for osmotic protection.

Stimulation of subsurface microorganisms to induce reductive immobilization of metals is a promising approach for bioremediation, yet the overall microbial community response is typically poorly understood. Here we used proteogenomics to test the hypothesis that excess input of acetate activates complex community functioning and syntrophic interactions among autotrophs and heterotrophs. A flow-through sediment column was incubated in a groundwater well of an acetate-amended aquifer and recovered during microbial sulfate reduction. De novo reconstruction of community sequences yielded near-complete genomes of Desulfobacter (Deltaproteobacteria), Sulfurovum- and Sulfurimonas-like Epsilonproteobacteria and Bacteroidetes. Partial genomes were obtained for Clostridiales (Firmicutes) and Desulfuromonadales-like Deltaproteobacteria. The majority of proteins identified by mass spectrometry corresponded to Desulfobacter-like species, and demonstrate the role of this organism in sulfate reduction (Dsr and APS), nitrogen fixation and acetate oxidation to CO2 during amendment. Results indicate less abundant Desulfuromonadales, and possibly Bacteroidetes, also actively contributed to CO2 production via the tricarboxylic acid (TCA) cycle. Proteomic data indicate that sulfide was partially re-oxidized by Epsilonproteobacteria through nitrate-dependent sulfide oxidation (using Nap, Nir, Nos, SQR and Sox), with CO2 fixed using the reverse TCA cycle. We infer that high acetate concentrations, aimed at stimulating anaerobic heterotrophy, led to the co-enrichment of, and carbon fixation in Epsilonproteobacteria. Results give an insight into ecosystem behavior following addition of simple organic carbon to the subsurface, and demonstrate a range of biological processes and community interactions were stimulated. [ABSTRACT FROM AUTHOR]

Dehalococcoides ethenogenes strain 195 (DE195) was grown in a sustainable syntrophic association with Desulfovibrio vulgaris Hildenborough (DVH) as a co-culture, as well as with DVH and the hydrogenotrophic methanogen Methanobacterium congolense (MC) as a tri-culture using lactate as the sole energy and carbon source. In the co- and tri-cultures, maximum dechlorination rates of DE195 were enhanced by approximately three times (11.0±0.01 μmol per day for the co-culture and 10.1±0.3 μmol per day for the tri-culture) compared with DE195 grown alone (3.8±0.1 μmol per day). Cell yield of DE195 was enhanced in the co-culture (9.0±0.5 × 107 cells per μmol Cl− released, compared with 6.8±0.9 × 107 cells per μmol Cl− released for the pure culture), whereas no further enhancement was observed in the tri-culture (7.3±1.8 × 107 cells per μmol Cl− released). The transcriptome of DE195 grown in the co-culture was analyzed using a whole-genome microarray targeting DE195, which detected 102 significantly up- or down-regulated genes compared with DE195 grown in isolation, whereas no significant transcriptomic difference was observed between co- and tri-cultures. Proteomic analysis showed that 120 proteins were differentially expressed in the co-culture compared with DE195 grown in isolation. Physiological, transcriptomic and proteomic results indicate that the robust growth of DE195 in co- and tri-cultures is because of the advantages associated with the capabilities of DVH to ferment lactate to provide H2 and acetate for growth, along with potential benefits from proton translocation, cobalamin-salvaging and amino acid biosynthesis, whereas MC in the tri-culture provided no significant additional benefits beyond those of DVH. [ABSTRACT FROM AUTHOR]

Leptospirillum spp. are widespread members of acidophilic microbial communities that catalyze ferrous iron oxidation, thereby increasing sulfide mineral dissolution rates. These bacteria play important roles in environmental acidification and are harnessed for bioleaching-based metal recovery. Known members of the Leptospirillum clade of the Nitrospira phylum are Leptospirillum ferrooxidans (group I), Leptospirillum ferriphilum and "Leptospirillum rubarum" (group II), and Leptospirillum ferrodiazotrophum (group III). In the Richmond Mine acid mine drainage (AMD) system, biofilm formation is initiated by L. rubarum; L. ferrodiazotrophum appears in later developmental stages. Here we used community metagenomic data from unusual, thick floating biofilms to identify distinguishing metabolic traits in a rare and uncultivated community member, the new species "Leptospirillum group IV UBA BS." These biofilms typically also contain a variety of Archaea, Actinobacteria, and a few other Leptospirillum spp. The Leptospirillum group IV UBA BS species shares 98% 16S rRNA sequence identity and 70% average amino acid identity between orthologs with its closest relative, L. ferrodiazotrophum. The presence of nitrogen fixation and reverse tricarboxylic acid (TCA) cycle proteins suggest an autotrophic metabolism similar to that of L. ferrodiazotrophum, while hydrogenase proteins suggest anaerobic metabolism. Community transcriptomic and proteomic analyses demonstrate expression of a multicopper oxidase unique to this species, as well as hydrogenases and core metabolic genes. Results suggest that the Leptospirillum group IV UBA BS species might play important roles in carbon fixation, nitrogen fixation, hydrogen metabolism, and iron oxidation in some acidic environments. [ABSTRACT FROM AUTHOR]

Microbial ferrous iron [Fe(II)] oxidation leads to the formation of iron-rich macroscopic aggregates ("iron snow") at the redoxcline in a stratified lignite mine lake in east-central Germany. We aimed to identify the abundant Fe-oxidizing and Fe-reducing microorganisms likely to be involved in the formation and transformation of iron snow present in the redoxcline in two basins of the lake that differ in their pH values. Nucleic acid- and lipid-stained microbial cells of various morphologies detected by confocal laser scanning microscopy were homogeneously distributed in all iron snow samples. The dominant iron mineral appeared to be schwertmannite, with shorter needles in the northern than in the central basin samples. Total bacterial 16S rRNA gene copies ranged from 5.0×108 copies g (dry weight)-1 in the acidic central lake basin (pH 3.3) to 4.0×1010 copies g (dry weight)-1 in the less acidic (pH 5.9) northern basin. Total RNA-based quantitative PCR assigned up to 61% of metabolically active microbial communities to Fe-oxidizing- and Fe-reducing-related bacteria, indicating that iron metabolism was an important metabolic strategy. Molecular identification of abundant groups suggested that iron snow surfaces were formed by chemoautotrophic iron oxidizers, such as Acidimicrobium, Ferrovum, Acidithiobacillus, Thiobacillus, and Chlorobium, in the redoxcline and were rapidly colonized by heterotrophic iron reducers, such as Acidiphilium, Albidiferax-like, and Geobacter-like groups. Metaproteomics yielded 283 different proteins from northern basin iron snow samples, and protein identification provided a glimpse into some of their in situ metabolic processes, such as primary production (CO2 fixation), respiration, motility, and survival strategies. [ABSTRACT FROM AUTHOR]

Small proteins (10-200 amino acids [aa] in length) encoded by short open reading frames (sORF) play important regulatory roles in various biological processes, including tumor progression, stress response, flowering, and hormone signaling. However, ab initio discovery of small proteins has been relatively overlooked. Recent advances in deep transcriptome sequencing make it possible to efficiently identify sORFs at the genome level. In this study, we obtained ⇔1/42.6 million expressed sequence tag (EST) reads from Populus deltoides leaf transcriptome and reconstructed full-length transcripts from the EST sequences. We identified an initial set of 12,852 sORFs encoding proteins of 10-200 aa in length. Three computational approaches were then used to enrich for bona fide protein-coding sORFs from the initial sORF set: (1) coding-potential prediction, (2) evolutionary conservation between P. deltoides and other plant species, and (3) gene family clustering within P. deltoides. As a result, a high-confidence sORF candidate set containing 1469 genes was obtained. Analysis of the protein domains, non-protein-coding RNA motifs, sequence length distribution, and protein mass spectrometry data supported this high-confidence sORF set. In the high-confidence sORF candidate set, known protein domains were identified in 1282 genes (higher-confidence sORF candidate set), out of which 611 genes, designated as highest-confidence candidate sORF set, were supported by proteomics data. Of the 611 highest-confidence candidate sORF genes, 56 were new to the current Populus genome annotation. This study not only demonstrates that there are potential sORF candidates to be annotated in sequenced genomes, but also presents an efficient strategy for discovery of sORFs in species with no genome annotation yet available. [ABSTRACT FROM AUTHOR]

The unprecedented scientific achievements in combating the COVID-19 pandemic reflect a global response informed by unprecedented access to data. We now have the ability to rapidly generate a diversity of information on an emerging pathogen and, by using high-performance computing and a systems biology approach, we can mine this wealth of information to understand the complexities of viral pathogenesis and contagion like never before. These efforts will aid in the development of vaccines, antiviral medications, and inform policymakers and clinicians. Here we detail computational protocols developed as SARS-CoV-2 began to spread across the globe. They include pathogen detection, comparative structural proteomics, evolutionary adaptation analysis via network and artificial intelligence methodologies, and multiomic integration. These protocols constitute a core framework on which to build a systems-level infrastructure that can be quickly brought to bear on future pathogens before they evolve into pandemic proportions., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

One of the most promising methods for large-scale studies of protein interactions is isolation of an affinity-tagged protein with its in vivo interaction partners, followed by mass spectrometric identification of the copurified proteins. Previous studies have generated affinity-tagged proteins using genetic tools or cloning systems that are specific to a particular organism. To enable protein-protein interaction studies across a wider range of Gram-negative bacteria, we have developed a methodology based on expression of affinity-tagged "bait" proteins from a medium copy-number plasmid. This construct is based on a broad-host-range vector backbone (pBBR1MCS5). The vector has been modified to incorporate the Gateway DEST vector recombination region, to facilitate cloning and expression of fusion proteins bearing a variety of affinity, fluorescent, or other tags. We demonstrate this methodology by characterizing interactions among subunits of the DNA-dependent RNA polymerase complex in two metabolically versatile Gram-negative microbial species of environmental interest, Rhodopseudomonas palustris CGA010 and Shewanella oneidensis MR-1. Results compared favorably with those for both plasmid and chromosomally encoded affinity-tagged fusion proteins expressed in a model organism, Escherichia coli.