Large-scale k-mer-based analysis of the informational properties of genomes, comparative genomics and taxonomy

Information theoretic approaches are ubiquitous and effective in a wide variety of bioinformatics applications. In comparative genomics, alignment-free methods, based on short DNA words, or k-mers, are particularly powerful. We evaluated the utility of varying k-mer lengths for genome comparisons by analyzing their sequence space coverage of 5805 genomes in the KEGG GENOME database. In subsequent analyses on four k-mer lengths spanning the relevant range (11, 21, 31, 41), hierarchical clustering of 1634 genus-level representative genomes using pairwise 21- and 31-mer Jaccard similarities best recapitulated a phylogenetic/taxonomic tree of life with clear boundaries for superkingdom domains and high subtree similarity for named taxons at lower levels (family through phylum). By analyzing ~14.2M prokaryotic genome comparisons by their lowest-common-ancestor taxon levels, we detected many potential misclassification errors in a curated database, further demonstrating the need for wide-scale adoption of quantitative taxonomic classifications based on whole-genome similarity.

Efficacy of SPG-ODN 1826 Nanovehicles in Inducing M1 Phenotype through TLR-9 Activation in Murine Alveolar J774A.1 Cells: Plausible Nano-Immunotherapy for Lung Carcinoma

Alveolar macrophages are the first line of defense against intruding pathogens and play a critical role in cancer immunology. The Toll-like receptor (TLR) family mediates an important role in recognizing and mounting an immune response against intruding microbes. TLR-9 is a member of the intracellular TLR family, which recognizes unmethylated CG motifs from the prokaryotic genome. Upon its activation, TLR-9 triggers downstream of the MyD-88-dependent transcriptional activation of NF-κB, and subsequently results in abundant inflammatory cytokines expression that induces a profound inflammatory milieu. The present exploratory investigation aimed at elucidating the potency of schizophyllan for entrapping ODN 1826 (SPG-ODN 1826)-mediated stimulation of TLR-9 in provoking an inflammatory-type response in murine alveolar macrophages. Schizophyllan (SPG), a representative of the β-glucan family, was used in the present study as a nanovehicle for endosomal trafficking of CpG ODN 1826. TEM analysis of SPG-ODN 1826 nanovehicles revealed that the prepared nanovehicles are spherical and have an average size of about 100 nm. Interestingly, SPG-ODN 1826 nanovehicles were competent in delivering their therapeutic payload within endosomes of murine alveolar macrophage (J774A.1) cells. Exposure of these nanovehicles within LPS stimulated J774A.1, resulted in a significant provocation of reactive oxygen species (ROS) (p < 0.01) in comparison to CpG ODN 1826 alone. Moreover, the formulated nanovehicles succeeded in generating a profound Th1-based cytokine profile constituted by enhanced expression of IFN-γ (p < 0.001) and IL-1β (p < 0.001) inflammatory cytokines. These findings clearly indicated the immunostimulatory potential of SPG-ODN 1826 nanovehicles for inducing the Th1-type phenotype, which would certainly assist in skewing M2 phenotype into the much-desired M1 type during lung cancer.

Mechanisms for Chromosome Segregation in Bacteria

The process of DNA segregation, the redistribution of newly replicated genomic material to daughter cells, is a crucial step in the life cycle of all living systems. Here, we review DNA segregation in bacteria which evolved a variety of mechanisms for partitioning newly replicated DNA. Bacterial species such as Caulobacter crescentus and Bacillus subtilis contain pushing and pulling mechanisms that exert forces and directionality to mediate the moving of newly synthesized chromosomes to the bacterial poles. Other bacteria such as Escherichia coli lack such active segregation systems, yet exhibit a spontaneous de-mixing of chromosomes due to entropic forces as DNA is being replicated under the confinement of the cell wall. Furthermore, we present a synopsis of the main players that contribute to prokaryotic genome segregation. We finish with emphasizing the importance of bottom-up approaches for the investigation of the various factors that contribute to genome segregation.

GUNC: detection of chimerism and contamination in prokaryotic genomes

Genomes are critical units in microbiology, yet ascertaining quality in prokaryotic genome assemblies remains a formidable challenge. We present GUNC (the Genome UNClutterer), a tool that accurately detects and quantifies genome chimerism based on the lineage homogeneity of individual contigs using a genome’s full complement of genes. GUNC complements existing approaches by targeting previously underdetected types of contamination: we conservatively estimate that 5.7% of genomes in GenBank, 5.2% in RefSeq, and 15–30% of pre-filtered “high-quality” metagenome-assembled genomes in recent studies are undetected chimeras. GUNC provides a fast and robust tool to substantially improve prokaryotic genome quality.

Application of CRISPR/Cas9 System for Plasmid Elimination and Bacterial Killing of Bacillus cereus Group Strains

The CRISPR-Cas system has been widely applied in prokaryotic genome editing with its high efficiency and easy operation. We constructed some “scissors plasmids” via using the temperature-sensitive pJOE8999 shuttle plasmid, which carry the different 20nt (N20) guiding the Cas9 nuclease as a scissors to break the target DNA. We successfully used scissors plasmids to eliminate native plasmids from Bacillus anthracis and Bacillus cereus, and specifically killed B. anthracis. When curing pXO1 and pXO2 virulence plasmids from B. anthracis A16PI2 and A16Q1, respectively, we found that the plasmid elimination percentage was slightly higher when the sgRNA targeted the replication initiation region (96–100%), rather than the non-replication initiation region (88–92%). We also tried using a mixture of two scissors plasmids to simultaneously eliminate pXO1 and pXO2 plasmids from B. anthracis, and the single and double plasmid-cured rates were 29 and 14%, respectively. To our surprise, when we used the scissor plasmid containing two tandem sgRNAs to cure the target plasmids pXO1 and pXO2 from wild strain B. anthracis A16 simultaneously, only the second sgRNA could guide Cas9 to cleave the target plasmid with high efficiency, while the first sgRNA didn't work in all the experiments we designed. When we used the CRISPR/cas9 system to eliminate the pCE1 mega-virulence plasmid from B. cereus BC307 by simply changing the sgRNA, we also obtained a plasmid-cured isogenic strain at a very high elimination rate (69%). The sterilization efficiency of B. anthracis was about 93%, which is similar to the efficiency of plasmid curing, and there was no significant difference in the efficiency of among the scissors plasmids containing single sgRNA, targeting multi-sites, or single-site targeting and the two tandem sgRNA. This simple and effective curing method, which is applicable to B. cereus group strains, provides a new way to study these bacteria and their virulence profiles.

Explainability in transformer models for functional genomics

The effectiveness of deep learning methods can be largely attributed to the automated extraction of relevant features from raw data. In the field of functional genomics, this generally concerns the automatic selection of relevant nucleotide motifs from DNA sequences. To benefit from automated learning methods, new strategies are required that unveil the decision-making process of trained models. In this paper, we present a new approach that has been successful in gathering insights on the transcription process in Escherichia coli. This work builds upon a transformer-based neural network framework designed for prokaryotic genome annotation purposes. We find that the majority of subunits (attention heads) of the model are specialized towards identifying transcription factors and are able to successfully characterize both their binding sites and consensus sequences, uncovering both well-known and potentially novel elements involved in the initiation of the transcription process. With the specialization of the attention heads occurring automatically, we believe transformer models to be of high interest towards the creation of explainable neural networks in this field.

GUNC: Detection of Chimerism and Contamination in Prokaryotic Genomes

Genomes are critical units in microbiology, yet ascertaining quality in prokaryotic genomes remains a formidable challenge. We present GUNC (the Genome UNClutterer), a tool that accurately detects and quantifies genome chimerism based on the lineage homogeneity of individual contigs using a genome’s full complement of genes. GUNC complements existing approaches by targeting previously underdetected types of contamination: we conservatively estimate that 5.7% of genomes in GenBank, 5.2% in RefSeq, and 15-30% of pre-filtered ‘high quality’ metagenome-assembled genomes in recent studies are undetected chimeras. GUNC provides a fast and robust tool to substantially improve prokaryotic genome quality. Source code (GPLv3+): https://github.com/grp-bork/gunc