Unique k-mers as Strain-Specific Barcodes for Phylogenetic Analysis and Natural Microbiome Profiling

The interplay of gut microbiota and heavy metals in multiple sclerosis patients

Multiple Sclerosis (MS) is a chronic inflammatory disease characterized by central nervous system (CNS). In this study, the concentration of heavy metals was measured in stool samples of MS patients by Inductively Coupled Plasma-Mass Spectroscopy (ICP-MS) method and compared with healthy people. Also, another goal of this study is to investigate the alteration of the gut microbiome of MS patients by metagenomics technique based on the 16S rRNA gene sequencing. The IL-10 ELISA assay showed no significant differences between the serum level of the IL-10 in the patients and the control group (p = 0.510). Heavy metal measurement by ICP-MS showed significantly higher levels of arsenic (As, Mean = 32.77 μg/kg), nickel (Ni, Mean = 7.154 μg/kg), manganese (Mn, Mean = 3723 μg/kg), and zinc (Zn, Mean = 5508 μg/kg) in the stool samples of the MS group compared to the control group, while concentrations of iron (Fe, Mean = 9585 μg/kg), lead (Pb, Mean = 18.54 μg/kg), titanium (Ti, Mean = 69.69 μg/kg), and tin (Sn, Mean = 13.92 μg/kg) were significantly lower. The result of gut microbiome analysis showed an increase in the abundance of the Verrumicrobiaceae, Lachnospiraceae and Ruminococcaceae families was considerably increased in MS patients compared to the control group (p < 0.05). This study reports that high levels of heavy metals such as Ars, Ni, Mn, and Zn, deficiency of Fe, Pb, Ti, and Sn, and alteration of the gut microbiome are involved in the pathogenesis of MS. The novelty of this study lies in its multi-faceted approach to understanding MS by integrating the measurement of heavy metals in stool samples with the analysis of gut microbiome alterations, thereby providing comprehensive insights into heavy metals, the gut microbiome, and potential therapeutic avenues. This study suggests several potential applications and practical implications based on its findings regarding heavy metals, gut microbiome alterations, and IL-10 levels in MS. First, the identification of elevated levels of specific heavy metals and deficiencies in others may lead to targeted screening and monitoring, informing preventive strategies for MS patients. Additionally, the observed gut microbiome changes could facilitate the development of microbiome-based therapies, such as probiotics or dietary interventions, aimed at restoring microbial balance. Finally, exploring the interplay between heavy metals, gut microbiome, and immune response may guide the creation of novel therapeutic interventions, ultimately enhancing treatment efficacy and providing new avenues for managing MS, thereby alleviating the burden of this chronic condition.

Advancing microbial diagnostics: a universal phylogeny guided computational algorithm to find unique sequences for precise microorganism detection

Sequences derived from organisms sharing common evolutionary origins exhibit similarity, while unique sequences, absent in related organisms, act as good diagnostic marker candidates. However, the approach focused on identifying dissimilar regions among closely-related organisms poses challenges as it requires complex multiple sequence alignments, making computation and parsing difficult. To address this, we have developed a biologically inspired universal NAUniSeq algorithm to find the unique sequences for microorganism diagnosis by traveling through the phylogeny of life. Mapping through a phylogenetic tree ensures a low number of cross-contamination and false positives. We have downloaded complete taxonomy data from Taxadb database and sequence data from National Center for Biotechnology Information Reference Sequence Database (NCBI-Refseq) and, with the help of NetworkX, created a phylogenetic tree. Sequences were assigned over the graph nodes, k-mers were created for target and non-target nodes and search was performed over the graph using the depth first search algorithm. In a memory efficient alternative NoSQL approach, we created a collection of Refseq sequences in MongoDB database using tax-id and path of FASTA files. We queried the MongoDB collection for the target and non-target sequences. In both the approaches, we used an alignment free sliding window k-mer-based procedure that quickly compares k-mers of target and non-target sequences and returns unique sequences that are not present in the non-target. We have validated our algorithm with target nodes Mycobacterium tuberculosis, Neisseria gonorrhoeae, and Monkeypox and generated unique sequences. This universal algorithm is a powerful tool for generating diagnostic sequences, enabling the accurate identification of microbial strains with high phylogenetic precision.

Discovery of novel treponemes associated with pododermatitis in elk (Cervus canadensis)

Pododermatitis, also known as treponeme-associated hoof disease (TAHD), presents a significant challenge to elk (Cervus canadensis) populations in the northwestern USA, with Treponema spp. consistently implicated in the lesion development. However, identifying species-specific Treponema strains from these lesions is hindered by its culture recalcitrance and limited genomic information. This study utilized shotgun sequencing, in silico genome reconstruction, and comparative genomics as a culture-independent approach to identify metagenome-assembled Treponema genomes (MATGs) from skin scraping samples collected from captive elk experimentally challenged with TAHD. The genomic analysis revealed 10 new MATGs, with 6 representing novel genomospecies associated with pododermatitis in elk and 4 corresponding to previously identified species-Treponema pedis and Treponema phagedenis. Importantly, genomic signatures of novel genomospecies identified in this study were consistently detected in biopsy samples of free-ranging elk diagnosed with TAHD, indicating a potential etiologic association. Comparative metabolic profiling of the MATGs against other Treponema genomes showed a distinct metabolic profile, suggesting potential host adaptation or geographic uniqueness of these newly identified genomospecies. The discovery of novel Treponema genomospecies enhances our understanding of the pathogenesis of pododermatitis and lays the foundation for the development of improved molecular surveillance tools to monitor and manage the disease in free-ranging elk.IMPORTANCETreponema spp. play an important role in the development of pododermatitis in free-ranging elk; however, the species-specific detection of Treponema from pododermatitis lesions is challenging due to culture recalcitrance and limited genomic information. The study utilized shotgun sequencing and in silico genome reconstruction to identify novel Treponema genomospecies from elk with pododermatitis. The discovery of the novel Treponema species opens new avenues to develop molecular diagnostic and epidemiologic tools for the surveillance of pododermatitis in elk. These findings significantly enhance our understanding of the genomic landscape of the Treponemataceae consortium while offering valuable insights into the etiology and pathogenesis of emerging pododermatitis in elk populations.

Scalable Microbial Strain Inference in Metagenomic Data Using StrainFacts

While genome databases are nearing a complete catalog of species commonly inhabiting the human gut, their representation of intraspecific diversity is lacking for all but the most abundant and frequently studied taxa. Statistical deconvolution of allele frequencies from shotgun metagenomic data into strain genotypes and relative abundances is a promising approach, but existing methods are limited by computational scalability. Here we introduce StrainFacts, a method for strain deconvolution that enables inference across tens of thousands of metagenomes. We harness a “fuzzy” genotype approximation that makes the underlying graphical model fully differentiable, unlike existing methods. This allows parameter estimates to be optimized with gradient-based methods, speeding up model fitting by two orders of magnitude. A GPU implementation provides additional scalability. Extensive simulations show that StrainFacts can perform strain inference on thousands of metagenomes and has comparable accuracy to more computationally intensive tools. We further validate our strain inferences using single-cell genomic sequencing from a human stool sample. Applying StrainFacts to a collection of more than 10,000 publicly available human stool metagenomes, we quantify patterns of strain diversity, biogeography, and linkage-disequilibrium that agree with and expand on what is known based on existing reference genomes. StrainFacts paves the way for large-scale biogeography and population genetic studies of microbiomes using metagenomic data.

Lacticaseibacillus paracasei: Occurrence in the Human Gut Microbiota and K-Mer-Based Assessment of Intraspecies Diversity

Alignment-free approaches employing short k-mers as barcodes for individual genomes have created a new strategy for taxonomic analysis and paved a way for high-resolution phylogeny. Here, we introduce this strategy for the Lacticaseibacillus paracasei species as a taxon requiring barcoding support for precise systematics. Using this approach for phylotyping of L. paracasei VKM B-1144 at the genus level, we identified four L. paracasei phylogroups and found that L. casei 12A belongs to one of them, rather than to the L. casei clade. Therefore, we propose to change the specification of this strain. At the genus level we found only one relative of L. paracasei VKM B-1144 among 221 genomes, complete or available in contigs, and showed that the coding potential of the genome of this "rare" strain allows its consideration as a potential probiotic component. Four sets of published metagenomes were used to assess the dependence of L. paracasei presence in the human gut microbiome on chronic diseases, dietary changes and antibiotic treatment. Only antibiotics significantly affected their presence, and strain-specific barcoding allowed the identification of the main scenarios of the adaptive response. Thus, suggesting bacteria of this species for compensatory therapy, we also propose strain-specific barcoding for selecting optimal strains for target microbiomes.

Suppression of Escherichia coli Growth Dynamics via RNAs Secreted by Competing Bacteria

With the discovery of secreted RNAs, it has become apparent that the biological role of regulatory oligonucleotides likely goes beyond the borders of individual cells. However, the mechanisms of their action are still comprehended only in general terms and mainly for eukaryotic microRNAs, which can interfere with mRNAs even in distant recipient cells. It has recently become clear that bacterial cells lacking interference systems can also respond to eukaryotic microRNAs that have targets in their genomes. However, the question of whether bacteria can perceive information transmitted by oligonucleotides secreted by other prokaryotes remained open. Here we evaluated the fraction of short RNAs secreted by Escherichia coli during individual and mixed growth with Rhodospirillum rubrum or Prevotella copri, and found that in the presence of other bacteria E. coli tends to excrete oligonucleotides homologous to alien genomes. Based on this observation, we selected four RNAs secreted by either R. rubrum or P. copri, together with one E. coli-specific oligonucleotide. Both fragments of R. rubrum 23S-RNA suppressed the growth of E. coli. Of the two fragments secreted by P. copri, one abolished the stimulatory effect of E. coli RNA derived from the 3'-UTR of ProA mRNA, while the other inhibited bacterial growth only in the double-stranded state with complementary RNA. The ability of two RNAs secreted by cohabiting bacteria to enter E. coli cells was demonstrated using confocal microscopy. Since selected E. coli-specific RNA also affected the growth of this bacterium, we conclude that bacterial RNAs can participate in inter- and intraspecies signaling.

Bioinformatics Methods in Medical Genetics and Genomics

Medical genomics relies on next-gen sequencing methods to decipher underlying molecular mechanisms of gene expression. This special issue collects materials originally presented at the “Centenary of Human Population Genetics” Conference-2019, in Moscow. Here we present some recent developments in computational methods tested on actual medical genetics problems dissected through genomics, transcriptomics and proteomics data analysis, gene networks, protein–protein interactions and biomedical literature mining. We have selected materials based on systems biology approaches, database mining. These methods and algorithms were discussed at the Digital Medical Forum-2019, organized by I.M. Sechenov First Moscow State Medical University presenting bioinformatics approaches for the drug targets discovery in cancer, its computational support, and digitalization of medical research, as well as at “Systems Biology and Bioinformatics”-2019 (SBB-2019) Young Scientists School in Novosibirsk, Russia. Selected recent advancements discussed at these events in the medical genomics and genetics areas are based on novel bioinformatics tools.