The epidemiological surveillance of SARS-CoV-2 by means of whole-genome sequencing has revealed the emergence and co-existence of multiple viral lineages or subtypes throughout the world. Moreover, it has been shown that several subtypes of this virus display particular phenotypes, such as increased transmissibility or reduced susceptibility to neutralizing antibodies, leading to the denomination of Variants of Interest (VOI) or Variants of Concern (VOC). Thus, subtyping of SARS-CoV-2 is a crucial step for the surveillance of this pathogen. Here, we present Covidex, an open-source, alignment-free machine learning subtyping tool. It is a shiny web app that allows an ultra-fast and accurate classification of SARS-CoV-2 genome sequences into the three most used nomenclature systems (GISAID, Nextstrain, Pango lineages). It also categorizes input sequences as VOI or VOC, according to current definitions.

Summary: Covidex is an open-source, alignment-free machine learning subtyping tool for viral species. It is a shiny app that allows a fast and accurate classification in pre-defined clusters for SARS-CoV-2 and FMDV genome sequences. The user can also build its own classification models with the Covidex model generator. Availability: Covidex is open-source, cross-platform compatible, and is available under the terms of the GNU General Public License v3 ( http://www.gnu.org/licenses/gpl.txt ). Covidex is available via SourceForge https://sourceforge.net/projects/covidex or the web application https://cacciabue.shinyapps.io/shiny2/ Contact: cacciabue.marco@inta.gob.ar ; marcocacciabue@yahoo.com

Inflammatory bowel disease (IBD) is the most common form of intestinal inflammation associated with a dysregulated immune system response to the commensal microbiota in a genetically susceptible host. IBD includes ulcerative colitis (UC) and Crohn’s disease (CD), both of which are remarkably heterogeneous in their clinical presentation and response to treatment. This translates into a notable diagnostic challenge, especially in underdeveloped countries where IBD is on the rise and access to diagnosis or treatment is not always accessible for chronic diseases. The present work characterized, for the first time in our region, epigenetic biomarkers and gut microbial profiles associated with UC and CD patients in the Buenos Aires Metropolitan area and revealed differences between non-IBD controls and IBD patients. General metabolic functions associated with the gut microbiota, as well as core microorganisms within groups, were also analyzed. Additionally, the gut microbiota analysis was integrated with relevant clinical, biochemical and epigenetic markers considered in the follow-up of patients with IBD, with the aim of generating more powerful diagnostic tools to discriminate phenotypes. Overall, our study provides new insights into data analysis algorithms to promote comprehensive phenotyping tools using quantitative and qualitative analysis in a transkingdom interactions network context.

Background: Globally, ulcerative colitis (UC) is the most common form of intestinal inflammation, which is believed to be the result of a deregulated immune system response to commensal microbiota in a genetically susceptible host. Multicellular organisms rely heavily on their commensal symbiotic microbiota, whose composition is closely related to intrinsic local characteristics and regulated or modified by environmental factors. In the present study we aim to describe the unknown gut microbiota of patients with UC in comparison with healthy individuals in order to find novel biomarkers for UC in our region. Methods: We evaluated 46 individuals, 26 healthy non-UC controls and 20 UC patients, from the metropolitan area of Buenos Aires (BA), Argentina. Clinical features, biochemical tests and anthropometric measurements were determined. Fecal samples were collected and DNA was extracted for microbiota analysis. The hypervariable regions V3-V4 of the bacterial 16SR gene were sequenced using a MiSeq platform and sequences were analyzed using the QIIME2 environment. In addition, we looked for differential functional pathways using PICRUSt and compared the performance of three machine learning models to discriminate the studied individuals, using taxa and functional annotations. Results: All UC patients were under clinical treatment with 70% of individuals in remission. We found no significant differences in gut microbiota richness or evenness between UC patients and non-UC controls (alpha diversity). Remarcably, microbial compositional structure within groups (beta diversity) showed differences: At the phylum level, Verrucomicrobia was overrepresented in controls while Actinobacteria was distinctive of UC patients; At the genus level Bacteroides and Akkermancia were significantly more abundant among controls while Eubacterium and Collinsella in UC patients. In addition, our results showed that carbohydrates metabolism was preponderant in UC patients, not observing a distinctive biochemical pathway for the healthy non-UC controls. Finally, in order to define a robust classifying method in our population, we evaluated the capability of three machine learning models to classify individuals. Our results reinforced the idea of functional compensation in microbiome communities, as models that used KEGG orthologs annotations had better capabilities than taxonomy to distinguish UC patients. Conclusions: Our study provides new knowledge on the differences and similarities of the gut microbiota of UC patients as compared to non-UC controls of our population. This allows not only the association of local changes in gut microbial diversity with the pathology process, but also the future development of personalized nutritional and pharmacological therapies through the use of omic strategies describing the metagenomic profiles of the Argentine population.

The plant RLK/Pelle family is one of the largest kinase groups and includes key regulators of development, environmental signaling, and stress responses. Transposable elements (TEs) also shape plant genome structure and can contribute to regulatory variation, yet their landscape around RLK/Pelle loci remains poorly explored in large, repeat-rich crop genomes. Here, we identified 2196 protein kinases in the sunflower (Helianthus annuus) XRQ reference genome and 702 in non-reference contigs assembled from reads unmapped to XRQ. Within this total kinome, 2289 kinases were assigned to RLK/Pelle subfamilies, of which 763 also fulfilled the architecture-based criteria for structurally supported RLKs. The broader RLK/Pelle set was used for TE-association analyses. Genome-wide analyses showed that RLK/Pelle loci occupy non-random TE environments that depend on TE superfamily and relative position. TIR, LTR/Copia, TRIM, and Mutator elements were enriched among TEs overlapping RLK/Pelle genes, whereas downstream regions showed enrichment of LTR/Copia elements and underrepresentation of LTR/Gypsy elements. TE associations also differed among RLK/Pelle duplication classes, with the strongest signal involving TRIM elements and tandemly duplicated genes. Integration of public RNA-seq datasets revealed a more restricted transcriptional component: ten flanking TE–RLK/Pelle pairs showed significant positive expression correlations in drought or Sclerotinia sclerotiorum stress datasets. Motif enrichment analyses further identified NAC- and TCP-associated sequence features in selected TE subsets. Together, these findings reveal position- and context-dependent TE associations around sunflower RLK/Pelle loci and identify candidate TE–RLK/Pelle relationships for future functional validation.