The need for a comparative analysis of natural metagenomes stimulated the development of new methods for their taxonomic profiling. Alignment-free approaches based on the search for marker k-mers turned out to be capable of identifying not only species, but also strains of microorganisms with known genomes. Here, we evaluated the ability of genus-specific k-mers to distinguish eight phylogroups of Escherichia coli (A, B1, C, E, D, F, G, B2) and assessed the presence of their unique 22-mers in clinical samples from microbiomes of four healthy people and four patients with Crohn’s disease. We found that a phylogenetic tree inferred from the pairwise distance matrix for unique 18-mers and 22-mers of 124 genomes was fully consistent with the topology of the tree, obtained with concatenated aligned sequences of orthologous genes. Therefore, we propose strain-specific “barcodes” for rapid phylotyping. Using unique 22-mers for taxonomic analysis, we detected microbes of all groups in human microbiomes; however, their presence in the five samples was significantly different. Pointing to the intraspecies heterogeneity of E. coli in the natural microflora, this also indicates the feasibility of further studies of the role of this heterogeneity in maintaining population homeostasis.

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.

The development of alignment-free approaches using short k-mers as barcodes has revo-lutionized taxonomic analysis, enabling bacterial identification at the phylogroup level even within natural communities. We applied this approach to characterize Escherichia coli intraspecific diversity in human gut microbiomes using publicly available datasets. By estimating the relative abundance of eight E. coli phylogroups defined by their 18-mer markers in 558 fecal samples, we employed correlation analysis and machine learning techniques to assess the discriminatory power of their patterns under different host phys-iological states. We compared E. coli phylogroup distribution between microbiomes of healthy individuals and patients with chronic colorectal disorders, tracked their dynamic alterations following prolonged antibiotic exposure with subsequent probiotic-assisted recovery, and evaluated changes induced by a physiologically beneficial dietary interven-tion for obesity. As a result, binary classification methods distinguished most compared datasets, while correlation analysis revealed shifts in phylogroups connectivity networks and their bimodal interactions with Prevotella, suggesting intra- and inerspecific epistatic relationships. Finally, a technique based on nonlinear dimensionality reduction visual-ized all distinct clusters of all sets, providing a chance to test the feasibility of using intra-specific phylotyping of E. coli for medical applications.

The advent of alignment-free k-mer barcoding has revolutionized taxonomic analysis, enabling bacterial identification at phylogroup resolution within natural communities. We applied this approach to characterize Escherichia coli intraspecific diversity in human gut microbiomes using publicly available datasets representing diverse human physiological states. By estimating the relative abundance of eight E. coli phylogroups defined by their 18-mer markers in 558 fecal samples, we compared their distribution between gut microbiomes of healthy individuals, patients with chronic bowel diseases and volunteers subjected to various external interventions. Across all datasets, phylogroups exhibited bidirectional abundance shifts in response to host physiological changes, indicating an inherent bimodality in their adaptive strategies. Correlation analysis of phylogroup persistence revealed positive intraspecific connectivity networks and dependence of their patterns on both acute interventions like antibiotic or probiotic treatment and chronic bowel disorders. Along with predominantly negative correlations with Bacteroides, we observed a transition from positive to negative associations with Prevotella in Prevotella-rich microbiomes. Several interspecific correlations individually established by E. coli phylogroups with dominant taxa suggest their potential role in shaping intraspecific networks. Machine learning techniques statistically confirmed an ability of phylogroup patterns to discriminate the physiological state of the host and virtual diagnostic assays opened a way to optimize intraspecific phylotyping for medical applications.