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Non-classically secreted proteins (NCSPs) are proteins that are located in the extracellular environment, although there is a lack of known signal peptides or secretion motifs. They usually perform different biological functions in intracellular and extracellular environments, and several of their biological functions are linked to bacterial virulence and cell defence. Accurate protein localization is essential for all living organisms, however, the performance of existing methods developed for NCSP identification has been unsatisfactory and in particular suffer from data deficiency and possible overfitting problems. Further improvement is desirable, especially to address the lack of informative features and mining subset-specific features in imbalanced datasets. In the present study, a new computational predictor was developed for NCSP prediction of gram-positive bacteria. First, to address the possible prediction bias caused by the data imbalance problem, ten balanced subdatasets were generated for ensemble model construction. Then, the F-score algorithm combined with sequential forward search was used to strengthen the feature representation ability for each of the training subdatasets. Third, the subset-specific optimal feature combination process was adopted to characterize the original data from different aspects, and all subdataset-based models were integrated into a unified model, NonClasGP-Pred, which achieved an excellent performance with an accuracy of 93.23 %, a sensitivity of 100 %, a specificity of 89.01 %, a Matthew’s correlation coefficient of 87.68 % and an area under the curve value of 0.9975 for ten-fold cross-validation. Based on assessment on the independent test dataset, the proposed model outperformed state-of-the-art available toolkits. For availability and implementation, see: http://lab.malab.cn/~wangchao/softwares/NonClasGP/.

As genome sequencing efforts are unveiling the genetic diversity of the biosphere with an unprecedented speed, there is a need to accurately describe the structural and functional properties of groups of extant species whose genomes have been sequenced, as well as their inferred ancestors, at any given taxonomic level of their phylogeny. Elaborate approaches for the reconstruction of ancestral states at the sequence level have been developed, subsequently augmented by methods based on gene content. While these approaches of sequence or gene-content reconstruction have been successfully deployed, there has been less progress on the explicit inference of functional properties of ancestral genomes, in terms of metabolic pathways and other cellular processes. Herein, we describe PathTrace, an efficient algorithm for parsimony-based reconstructions of the evolutionary history of individual metabolic pathways, pivotal representations of key functional modules of cellular function. The algorithm is implemented as a five-step process through which pathways are represented as fuzzy vectors, where each enzyme is associated with a taxonomic conservation value derived from the phylogenetic profile of its protein sequence. The method is evaluated with a selected benchmark set of pathways against collections of genome sequences from key data resources. By deploying a pangenome-driven approach for pathway sets, we demonstrate that the inferred patterns are largely insensitive to noise, as opposed to gene-content reconstruction methods. In addition, the resulting reconstructions are closely correlated with the evolutionary distance of the taxa under study, suggesting that a diligent selection of target pangenomes is essential for maintaining cohesiveness of the method and consistency of the inference, serving as an internal control for an arbitrary selection of queries. The PathTrace method is a first step towards the large-scale analysis of metabolic pathway evolution and our deeper understanding of functional relationships reflected in emerging pangenome collections.

Plasmids are the key element in horizontal gene transfer in the microbial community. Recently, a large number of experimental and computational methods have been developed to obtain the plasmidomes of microbial communities. Distinguishing transmissible plasmid sequences, which are derived from conjugative or at least mobilizable plasmids, from non-transmissible plasmid sequences in the plasmidome is essential for understanding the diversity of plasmids and how they regulate the microbial community. Unfortunately, due to the highly fragmented characteristics of DNA sequences in the plasmidome, effective identification methods are lacking. In this work, we used information entropy from information theory to assess the randomness of synonymous codon usage over 4424 plasmid genomes. The results showed that for all amino acids, the choice of a synonymous codon in conjugative and mobilizable plasmids is more random than that in non-transmissible plasmids, indicating that transmissible plasmids have different sequence signatures from non-transmissible plasmids. Inspired by this phenomenon, we further developed a novel algorithm named PlasTrans. PlasTrans takes the triplet code sequences and base sequences of plasmid DNA fragments as input and uses the convolutional neural network of the deep learning technique to further extract the more complex signatures of the plasmid sequences and identify the conjugative and mobilizable DNA fragments. Tests showed that PlasTrans could achieve an AUC of as high as 84–91%, even though the fragments only contained hundreds of base pairs. To the best of our knowledge, this is the first quantitative analysis of the difference in sequence signatures between transmissible and non-transmissible plasmids, and we developed the first tool to perform transferability annotation for DNA fragments in the plasmidome. We expect that PlasTrans will be a useful tool for researchers who analyse the properties of novel plasmids in the microbial community and horizontal gene transfer, especially the spread of resistance genes and virulence factors associated with plasmids. PlasTrans is freely available via https://github.com/zhenchengfang/PlasTrans

Here, we report the draft genome sequence of Dissulfurirhabdus thermomarina SH388. Improved phylogenetic and taxonomic analysis of this organism using genome-level analyses supports assignment of this organism to a novel family within the phylum Desulfobacterota. Additionally, comparative genomic and phylogenetic analyses contextualize the convergent evolution of sulfur disproportionation and potential extracellular electron transfer in this organism relative to other members of the Desulfobacterota.

Although gene-finding in bacterial genomes is relatively straightforward, the automated assignment of gene function is still challenging, resulting in a vast quantity of hypothetical sequences of unknown function. But how prevalent are hypothetical sequences across bacteria, what proportion of genes in different bacterial genomes remain unannotated, and what factors affect annotation completeness? To address these questions, we surveyed over 27 000 bacterial genomes from the Genome Taxonomy Database, and measured genome annotation completeness as a function of annotation method, taxonomy, genome size, 'research bias' and publication date. Our analysis revealed that 52 and 79 % of the average bacterial proteome could be functionally annotated based on protein and domain-based homology searches, respectively. Annotation coverage using protein homology search varied significantly from as low as 14 % in some species to as high as 98 % in others. We found that taxonomy is a major factor influencing annotation completeness, with distinct trends observed across the microbial tree (e.g. the lowest level of completeness was found in the Patescibacteria lineage). Most lineages showed a significant association between genome size and annotation incompleteness, likely reflecting a greater degree of uncharacterized sequences in 'accessory' proteomes than in 'core' proteomes. Finally, research bias, as measured by publication volume, was also an important factor influencing genome annotation completeness, with early model organisms showing high completeness levels relative to other genomes in their own taxonomic lineages. Our work highlights the disparity in annotation coverage across the bacterial tree of life and emphasizes a need for more experimental characterization of accessory proteomes as well as understudied lineages.

The MocR-subfamily transcription factors (MocR-TFs) characterized by the GntR-family DNA-binding domain and aminotransferase-like sensory domain are broadly distributed among certain lineages of Bacteria. Characterized MocR-TFs bind pyridoxal 5′-phosphate (PLP) and control transcription of genes involved in PLP, gamma aminobutyric acid (GABA) and taurine metabolism via binding specific DNA operator sites. To identify putative target genes and DNA binding motifs of MocR-TFs, we performed comparative genomics analysis of over 250 bacterial genomes. The reconstructed regulons for 825 MocR-TFs comprise structural genes from over 200 protein families involved in diverse biological processes. Using the genome context and metabolic subsystem analysis we tentatively assigned functional roles for 38 out of 86 orthologous groups of studied regulators. Most of these MocR-TF regulons are involved in PLP metabolism, as well as utilization of GABA, taurine and ectoine. The remaining studied MocR-TF regulators presumably control genes encoding enzymes involved in reduction/oxidation processes, various transporters and PLP-dependent enzymes, for example aminotransferases. Predicted DNA binding motifs of MocR-TFs are generally similar in each orthologous group and are characterized by two to four repeated sequences. Identified motifs were classified according to their structures. Motifs with direct and/or inverted repeat symmetry constitute the majority of inferred DNA motifs, suggesting preferable TF dimerization in head-to-tail or head-to-head configuration. The obtained genomic collection of in silico reconstructed MocR-TF motifs and regulons in Bacteria provides a basis for future experimental characterization of molecular mechanisms for various regulators in this family.

Firmicutes and Bacteroidetes are the predominant bacterial phyla colonizing the healthy human large intestine. Whilst both ferment dietary fibre, genes responsible for this important activity have been analysed only in the Bacteroidetes , with very little known about the Firmicutes . This work investigates the carbohydrate-active enzymes (CAZymes) in a group of Firmicutes , Roseburia spp. and Eubacterium rectale , which play an important role in producing butyrate fr om dietary carbohydrates and in health maintenance. Genome sequences of 11 strains representing E. rectale and four Roseburia spp. were analysed for carbohydrate-active genes. Following assembly into a pan-genome, core, variable and uniqu e genes were identified. The 1840 CAZyme genes identified in the pan-genome were assigned to 538 orthologous groups, of which only 26 were present in all strains, indicating considerable inter- strain variability. This analysis was used to categorize the 11 strains into four carbohydrate utilization ecotypes (CUEs), which were shown to correspond to utilization of diffe rent carbohydrates for growth. Many glycoside hydrolase genes were found linked to genes encoding oligosaccharide transporters and regulatory elements in the genomes of Roseburia spp. and E. rectale ,forming distinct polysaccharide utilization loci (PULs). Whilst PULs are also a common feature in Bacteroidetes , key differences were noted in these Firmicutes , including the absence of close homologues of Bacteroides polysaccharide utilization genes, hence we refer to Gram-positive PULs (gpPULs). Most CAZyme genes in the Roseburia / E. rectale group are organized into gpPULs. Variation in gpPULs can explain the high degree of nutritional specialization at the species level within this group.