Genotyping methods and genome sequencing are indispensable to reveal genomic structure of bacterial species displaying high level of genome plasticity. However, reconstruction of genome or assembly is not straightforward due to data complexity, including repeats, mobile and accessory genetic elements of bacterial genomes. Moreover, since the solution to this problem is strongly influenced by sequencing technology, bioinformatics pipelines, and selection criteria to assess assemblers, there is no systematic way to selecta priorithe optimal assembler and parameter settings. To assembly the genome ofPseudomonas aeruginosastrain AG1 (PaeAG1), short reads (Illumina) and long reads (Oxford Nanopore) sequencing data were used in 13 different non-hybrid and hybrid approaches. PaeAG1 is a multiresistant high-risk sequence type 111 (ST-111) clone that was isolated from a Costa Rican hospital and it was the first report of an isolate ofP. aeruginosacarrying both blaVIM-2 and blaIMP-18 genes encoding for metallo-β-lactamases (MBL) enzymes. To assess the assemblies, multiple metrics regard to contiguity, correctness and completeness (3C criterion, as we define here) were used for benchmarking the 13 approaches and select a definitive assembly. In addition, annotation was done to identify genes (coding and RNA regions) and to describe the genomic content of PaeAG1. Whereas long reads and hybrid approaches showed better performances in terms of contiguity, higher correctness and completeness metrics were obtained for short read only and hybrid approaches. A manually curated and polished hybrid assembly gave rise to a single circular sequence with 100% of core genes and known regions identified, >98% of reads mapped back, no gaps, and uniform coverage. The strategy followed to obtain this high-quality 3C assembly is detailed in the manuscript and we provide readers with an all-in-one script to replicate our results or to apply it to other troublesome cases. The final 3C assembly revealed that the PaeAG1 genome has 7,190,208 bp, a 65.7% GC content and 6,709 genes (6,620 coding sequences), many of which are included in multiple mobile genomic elements, such as 57 genomic islands, six prophages, and two complete integrons with blaVIM-2 and blaIMP-18 MBL genes. Up to 250 and 60 of the predicted genes are anticipated to play a role in virulence (adherence, quorum sensing and secretion) or antibiotic resistance (β-lactamases, efflux pumps, etc). Altogether, the assembly and annotation of the PaeAG1 genome provide new perspectives to continue studying the genomic diversity and gene content of this important human pathogen.

Chronic gastrointestinal (GI) diseases are the most common diseases in captive common marmosets (Callithrix jacchus). Despite standardized housing, diet and husbandry, a recently described gastrointestinal syndrome characterized by duodenal ulcers and strictures was observed in a subset of marmosets sourced from the New England Primate Research Center. As changes in the gut microbiome have been associated with GI diseases, the gut microbiome of 52 healthy, non-stricture marmosets (153 samples) were compared to the gut microbiome of 21 captive marmosets diagnosed with a duodenal ulcer/stricture (57 samples). No significant changes were observed using alpha diversity metrics, and while the community structure was significantly different when comparing beta diversity between healthy and stricture cases, the results were inconclusive due to differences observed in the dispersion of both datasets. Differences in the abundance of individual taxa using ANCOM, as stricture-associated dysbiosis was characterized byAnaerobiospirillumloss andClostridium perfringensincreases. To identify microbial and serum biomarkers that could help classify stricture cases, we developed models using machine learning algorithms (random forest, classification and regression trees, support vector machines and k-nearest neighbors) to classify microbiome, serum chemistry or complete blood count (CBC) data. Random forest (RF) models were the most accurate models and correctly classified strictures using either 9 ASVs (amplicon sequence variants), 4 serum chemistry tests or 6 CBC tests. Based on the RF model and ANCOM results,C. perfringenswas identified as a potential causative agent associated with the development of strictures.Clostridium perfringenswas also isolated by microbiological culture in 4 of 9 duodenum samples from marmosets with histologically confirmed strictures. Due to the enrichment ofC. perfringensin situ, we analyzed frozen duodenal tissues using both 16S microbiome profiling and RNAseq. Microbiome analysis of the duodenal tissues of 29 marmosets from the MIT colony confirmed an increased abundance ofClostridiumin stricture cases. Comparison of the duodenal gene expression from stricture and non-stricture marmosets found enrichment of genes associated with intestinal absorption, and lipid metabolism, localization, and transport in stricture cases. Using machine learning, we identified increased abundance ofC. perfringens, as a potential causative agent of GI disease and intestinal strictures in marmosets.

The majority of SARS-CoV-2 genomes obtained during the pandemic were derived by amplifying overlapping windows of the genome (‘tiled amplicons’), reconstructing their sequences and fitting them together. This leads to systematic errors in genomes unless the software is both aware of the amplicon scheme and of the error modes of amplicon sequencing. Additionally, over time, amplicon schemes need to be updated as new mutations in the virus interfere with the primer binding sites at the end of amplicons. Thus, waves of variants swept the world during the pandemic and were followed by waves of systematic errors in the genomes, which had significant impacts on the inferred phylogenetic tree. Here we reconstruct the genomes from all public data as of June 2024 using an assembly tool called Viridian ( https://github.com/iqbal-lab-org/viridian ), developed to rigorously process amplicon sequence data. With these high-quality consensus sequences we provide a global phylogenetic tree of 4,471,579 samples, viewable at https://viridian.taxonium.org . We provide simulation and empirical validation of the methodology, and quantify the improvement in the phylogeny.