Discovery of Virus-Host interactions using bioinformatic tools.

Viruses are a diverse biological group capable of infecting several hosts such as bacteria, plants, and animals, including humans. Viral infections constitute a threat to the human population as they may cause high mortality rates, decrease food production, and generate large economical losses. Viruses co-evolve with their hosts and this constant evolution must be clarified to better predict possible viral outbreaks, and to develop improved diagnostic methods and therapeutical approaches. In this review, we summarize several viral databases that store key information retrieved from a variety of omics approaches. Furthermore, we explore the use of such databases to predict Virus-Host interactions through artificial intelligence algorithms, focusing on the latest methodologies to characterize biological networks.

SARS-CoV-2 Membrane Protein: From Genomic Data to Structural New Insights.

Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2) is composed of four structural proteins and several accessory non-structural proteins. SARS-CoV-2's most abundant structural protein, Membrane (M) protein, has a pivotal role both during viral infection cycle and host interferon antagonism. This is a highly conserved viral protein, thus an interesting and suitable target for drug discovery. In this paper, we explain the structural nature of M protein homodimer. To do so, we developed and applied a detailed and robust in silico workflow to predict M protein dimeric structure, membrane orientation, and interface characterization. Single Nucleotide Polymorphisms (SNPs) in M protein were retrieved from over 1.2 M SARS-CoV-2 genomes and proteins from the Global Initiative on Sharing All Influenza Data (GISAID) database, 91 of which were located at the predicted dimer interface. Among those, we identified SNPs in Variants of Concern (VOC) and Variants of Interest (VOI). Binding free energy differences were evaluated for dimer interfacial SNPs to infer mutant protein stabilities. A few high-prevalent mutated residues were found to be especially relevant in VOC and VOI. This realization may be a game-changer to structure-driven formulation of new therapeutics for SARS-CoV-2.

The Role of Serratomolide-like Amino Lipids Produced by Bacteria of Genus Serratia in Nematicidal Activity.

Bursaphelenchus xylophilus, also known as pinewood nematode (PWN), is the pathogenic agent of pine wilt disease (PWD), which affects pine trees around the world. Infection spread globally through international wood commerce and locally by vector beetles, threatening the wood world economy. As climate changes, more countries are becoming susceptible to PWD and, to prevent disease spread and limit economic and ecological losses, better knowledge about this pathogenic agent is needed. Serratia strains, present in the endophytic community of pine trees and carried by PWN, may play an important role in PWD. This work aimed to better understand the interaction between Serratia strains and B. xylophilus and to assess the nematicidal potential of serratomolide-like molecules produced by Serratia strains. Serrawettin gene presence was evaluated in selected Serratia strains. Mortality tests were performed with bacteria supernatants, and extracted amino lipids, against Caenorhabditis elegans (model organism) and B. xylophilus to determine their nematicidal potential. Attraction tests were performed with C. elegans. Concentrated supernatants of Serratia strains with serratamolide-like lipopeptides were able to kill more than 77% of B. xylophilus after 72 h. Eight specific amino lipids showed a high nematicidal activity against B. xylophilus. We conclude that, for some Serratia strains, their supernatants and specific amino lipids showed nematicidal activity against B. xylophilus.

Guardians of the Cell: State-of-the-Art of Membrane Proteins from a Computational Point-of-View.

Membrane proteins (MPs) encompass a large family of proteins with distinct cellular functions, and although representing over 50% of existing pharmaceutical drug targets, their structural and functional information is still very scarce. Over the last years, in silico analysis and algorithm development were essential to characterize MPs and overcome some limitations of experimental approaches. The optimization and improvement of these methods remain an ongoing process, with key advances in MPs' structure, folding, and interface prediction being continuously tackled. Herein, we discuss the latest trends in computational methods toward a deeper understanding of the atomistic and mechanistic details of MPs.

Genome Sequences of Serratia Strains Revealed Common Genes in Both Serratomolides Gene Clusters.

Serratia strains are ubiquitous microorganisms with the ability to produce serratomolides, such as serrawettins. These extracellular lipopeptides are described as biocides against many bacteria and fungi and may have a nematicidal activity against phytopathogenic nematodes. Serrawettins W1 and W2 from different strains have different structures that might be correlated with distinct genomic organizations. This work used comparative genomics to determine the distribution and the organization of the serrawettins biosynthetic gene clusters in all the 84 publicly available genomes of the Serratia genus. The serrawettin W1 and W2 gene clusters' organization was established using antiSMASH software and compared with single and short data previously described for YD25T Serratia. Here, the serrawettin W1 gene clusters' organization is reported for the first time. The serrawettin W1 biosynthetic gene swrW was present in 17 Serratia genomes. Eighty different coding sequence (CDS) were assigned to the W1 gene cluster, 13 being common to all clusters. The serrawettin W2 swrA gene was present in 11 Serratia genomes. The W2 gene clusters included 68 CDS with 24 present in all the clusters. The genomic analysis showed the swrA gene constitutes five modules, four with three domains and one with four domains, while the swrW gene constitutes one module with four domains. This work identified four genes common to all serrawettin gene clusters, highlighting their essential potential in the serrawettins biosynthetic process.