Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is one of the top 10 causes of death worldwide. Drug-resistant tuberculosis (DR-TB) poses a major threat to the World Health Organization's “End TB” strategy which has defined its target as the year 2035. In 2019, there were close to 0.5 million cases of DRTB, of which 78% were resistant to multiple TB drugs. The traditional culture-based drug susceptibility test (DST - the current gold standard) often takes multiple weeks and the necessary laboratory facilities are not readily available in low-income countries. Whole genome sequencing (WGS) technology is rapidly becoming an important tool in clinical and research applications including transmission detection or prediction of DR-TB. For the latter, many tools have recently been developed using curated database(s) of known resistance conferring mutations. However, documenting all the mutations and their effect is a time-taking and a continuous process and therefore Machine Learning (ML) techniques can be useful for predicting the presence of DR-TB based on WGS data. This can pave the way to an earlier detection of drug resistance and consequently more efficient treatment when compared to the traditional DST.

Background: Whole genome sequencing (WGS) holds great potential for the management and control of tuberculosis. Accurate analysis of samples with low mycobacterial burden, which are characterized by low (40%) levels of contamination, is challenging. We created the MAGMA (Maximum Accessible Genome for Mtb Analysis) bioinformatics pipeline for analysis of clinical Mtb samples. Methods and results: High accuracy variant calling is achieved by using a long seedlength during read mapping to filter out contaminants, variant quality score recalibration with machine learning to identify genuine genomic variants, and joint variant calling for low Mtb coverage genomes. MAGMA automatically generates a standardized and comprehensive output of drug resistance information and resistance classification based on the WHO catalogue of Mtb mutations. MAGMA automatically generates phylogenetic trees with drug resistance annotations and trees that visualize the presence of clusters. Drug resistance and phylogeny outputs from sequencing data of 79 primary liquid cultures were compared between the MAGMA and MTBseq pipelines. The MTBseq pipeline reported only a proportion of the variants in candidate drug resistance genes that were reported by MAGMA. Notable differences were in structural variants, variants in highly conserved rrs and rrl genes, and variants in candidate resistance genes for bedaquiline, clofazmine, and delamanid. Phylogeny results were similar between pipelines but only MAGMA visualized clusters. Conclusion: The MAGMA pipeline could facilitate the integration of WGS into clinical care as it generates clinically relevant data on drug resistance and phylogeny in an automated, standardized, and reproducible manner.

A previous study in Pará, Northern Brazil, described a strain of Mycobacterium tuberculosis with a unique genotype (SIT2517/T1) associated with multidrug-resistant tuberculosis (MDR-TB). To improve our understanding of MDR-TB transmission dynamics of these strains within this region, we performed phenotypic and genotypic drug susceptibility testing (pDST/gDST), 24-loci mycobacterial interspersed repetitive units (MIRU-VNTR) genotyping, whole-genome sequencing (WGS) and geo-epidemiology analysis. Of the 28 SIT2517/T1 isolates, 19 (67.9%) could be genotyped by 24-loci MIRU-VNTR and 15 by WGS. All belonged to sublineage 4.1.1.3, distinct from other representative Lineage 4 isolates identified in Brazil. The MDR phenotype determined by pDST was confirmed by gDST, the latter also demonstrating the presence of additional mutations conferring pre-extensively drug-resistance (pre-XDR). Discrepancies between gDST and pDST were observed for pyrazinamide and fluoroquinolones. Thirteen out of 15 isolates analyzed by WGS were clustered when applying a 12 single nucleotide polymorphisms (SNPs) cutoff. The SIT2517/T1 isolates were distributed across the metropolitan regions of Belém and Collares municipalities, showing no geographic clustering. WGS-transmission network analysis revealed a high likelihood of direct transmission and the formation of two closely linked transmission chains. This study highlights the need to implement TB genomic surveillance in the Brazilian Amazon region.

Mycobacterium tuberculosis (Mtb) Central Asian Strain (CAS) Lineage 3 (L3) genotype is predominantly found in East-Africa, Central-Asia, Western-Asia, and South-Asia; however, a new spoligotyping CAS/SIT2545 was found in northern regions of Brazil. We aimed to characterize and describe the genetic diversity and perform a phylogenetic assessment of this novel genotype. We performed 24-MIRU-VNTR loci and Whole-genome sequencing (WGS) of six Brazilian isolates previously spoligotyped. The libraries were prepared using a Nextera-XT kit and sequenced in a NextSeq 550 Illumina instrument. We performed lineage assignment and genomic characterization. From publicly available genomes of Mtb L3 and other lineages, we created a robust dataset to run the MTBSeq pipeline and perform a phylogenetic analysis. MIRU-VNTR and WGS confirmed CAS/SIT2545 belongs to L3. Out of 1691 genomes, 1350 (79.83%) passed in quality control (genomic coverage > 95%). Strain 431 differed in 52 single nucleotide variants (SNV), confirming it does not belong to the same transmission chain. The eight genomes from a global dataset clustered closer to Brazilian strains differed in >52 SNVs. We hypothesized L3 and L1 were introduced in Brazilian Northern in the same historical event; however, there is a need for additional studies exploring the genetic diversity of Mtb Brazilian Northern.

Next-generation targeted genome sequencing allows the analysis of regions of interest within a genome. While it is possible to incorporate targeted sequencing into whole-genome sequencing (WGS) bioinformatics pipelines, there remains a gap in accurately converting WGS metrics into precise target sequencing metrics and filtering the raw BED files to the targeted regions. Here, we introduce the target-methylseq-qc pipeline (https://doi.org/10.5281/zenodo.13147688), designed to (i) collect metrics from alignment files generated in targeted methylation sequence analysis, using the picard_profiler mode, and (ii) filtering bedGraph files to features overlapping with the reference BED file, using the bed_filter mode. Both of these modes are subworkflows written using the Nextflow (Di Tommaso et al., 2017) workflow language.

In cystic fibrosis, bacteria–bacteriophage interaction in the lower airways is poorly understood. We present the near-complete genome of the uncultured Siphovirus-like bacteriophage, Staphylococcus aureus phage COMBAT-CF_PAR1, isolated from the lower airways. The genome spans 41,510 bp with 33.45% guanine–cytosine content and contains 65 open reading frames.