Iñaki Comas

Background Nontuberculous mycobacteria (NTM) are an increasingly common group of pathogens that remain challenging to diagnose and treat effectively. The lack of standardization of NTM management, from identification to antibiotic resistance prediction, results in imperfect correlations between treatment and outcomes. This study characterizes the genetic heterogeneity of a previously uncharacterized NTM during a 29-month bacteremia with acquired drug resistance. Results In contrast to the initial diagnostic result identifying M. nebraskense, a rare NTM causing disease in humans, whole genome sequencing (WGS) identified Mycobacterium sp. SMC-2, a species with only one publicly available genome. High-resolution analysis of variants revealed 444 unique SNPs and 26 indels in 12 longitudinal isolates, with the highest number of low-frequency mutations between 3-5% frequency. Seven candidate drug-resistance mutations across five evolutionary trajectories showed frequency shifts that correlated with changes in minimum inhibitory concentrations to the corresponding antibiotics. These included a 23S rRNA clarithromycin-resistance SNP detected at 7% frequency when phenotypic resistance emerged, suggesting that low-frequency variants drive subpopulation evolution. Acquisition of drug resistance during therapy was associated with several low-frequency mutations in genes associated with resistance to antibiotics, including clarithromycin and quinolones, in other NTM species. Conclusion This study highlights the importance of low-frequency variants as drivers of intra-patient bacterial population diversity, allowing subpopulations to adapt to antibiotic pressure and ultimately contributing to treatment failure. Additionally, it underscores their potential implications for the development of molecular diagnostic tests for NTM resistance prediction.

Elucidating the evolution and epidemiology of Mycobacterium tuberculosis requires comprehensive characterization of its genomic diversity; however, short-read sequencing fails to resolve part of this variation. Here, we assembled 216 complete genomes from clinical isolates in the Valencia Region, Spain, using long-read sequencing. This dataset, mostly encompassing Lineage 4, provides a refined map of M. tuberculosis genetic diversity across evolutionary scales. Complete genomes uncover a median of 312 (−1 to 792) additional SNPs per pairwise comparison, revealing an estimated evolutionary rate 1.44-fold higher than that inferred from short-read mapping. This diversity is concentrated in discrete hotspots, particularly within the pe/ppe gene family, where gene conversion is a major driver of nucleotide diversity. While most PE/PPE epitopes remain highly conserved, suggesting strong purifying selection, some involved in vaccine candidates are affected by gene conversion, with unknown consequences. At the epidemiological scale, additional resolution is gained from SNPs previously masked and newly resolved indels and structural variation, refining genetic transmission networks. Finally, at the within-host level, the use of patient-specific reference genomes allows us to capture genuine diversity during infection, showing that previous approaches led to false positive calls. Together, these findings delineate the landscape of M. tuberculosis genomic diversity and provide a framework for more accurate inference of pathogen evolution, host–pathogen interactions, and transmission dynamics.

Wastewater (WW) genomics can track SARS-CoV-2 circulation beyond clinical testing, but its ability to reflect clinical diversity and capture severity-linked mutations remains unclear. Here, we integrated 845 clinical genomes and 22 wastewater genomes from Valencia, Spain, across matched metropolitan and hospital catchments. We compared matched WW and clinical sequencing for lineage and mutation surveillance at two levels: metropolitan and hospital. Then, we tested WW sensitivity to detect mutations statistically associated with hospitalization status in regional (n = 4,843), national (n = 10,052) and supranational (n = 39,099) clinical datasets. WW surveillance captured the dominant Omicron background when collapsing lineages into parental lineages constellation but had limited sensitivity for fine-scale sublineage diversity. Performance was strongly catchment dependent: metropolitan wastewater best represented broader community circulation, whereas hospital wastewater was noisier but detected KP.3 months before its appearance in routine metropolitan clinical surveillance. Across clinical datasets, hospitalisation-associated substitutions showed limited reproducibility, although the national and supranational analyses converged on receptor-binding-domain substitutions D405N, K417N and R408S. Networks showed coupling between G252V in NTD with those RBD substitutions involved in immune escape and receptor engagement. Finally, integrating regional to supranational GWAS with interaction networks and wastewater detection prioritised mutations supported by at least two independent association layers, that includes mutations in the Spike, especially in RBD, and the wastewater-exclusive candidate S:V445P, which was missed by contemporaneous clinical sequencing. Overall, WW genomics preferentially recovers the common mutational backbone of SARS-CoV-2 circulation and can highlight important changes missed by clinical sampling, making it a complementary tool for real-time prioritisation of viral evolutionary change.

Even though our meta-analysis ranks Mycobacterium tuberculosis genomes among the bacterial pathogens that are most straightforward to assemble, most available assemblies rely on short-read sequencing and contain genomic blind spots that miss functionally important genes. Complete genomes are essential for functional genomics, particularly for identifying small ORF-encoded proteins (SEPs; ≤100 amino acids), which can play critical biological roles yet are frequently missed by standard annotations. Here, we generated complete long-read assemblies for six clinical reference strains representing lineage 1 and the more pathogenic lineage 2, followed by comparative genomic and proteogenomic analyses. We additionally provide software to predict comprehensive sets of mycobacteria-specific PE and PPE family genes, including lineage-specific variants. Using parallel accumulation–serial fragmentation mass spectrometry, we detected approximately two-thirds of each strain’s annotated proteome from unfractionated cell extracts. Extending our proteogenomic framework across related strains, and rigorously controlling proteogenomic discovery rates using entrapment strategies, we revealed 12–24 previously unannotated proteins per strain, predominantly SEPs, 56–60 alternative translation start sites, and 9–17 expressed pseudogenes. Newly identified proteins included conserved and lineage-specific SEPs, an antitoxin, candidate antimicrobial peptides, and novel proteins under purifying selection. Overall, applying proteogenomics to phylogenomically selected clinical reference strains provides a valuable approach for discovering candidate diagnostics or therapeutics, as illustrated here for a WHO-listed critical bacterial pathogen.

Background/Objectives: The impact of COVID-19 in Africa has been controversial. Data from African countries are heterogeneous and generally scarce. Many regions are also highly endemic for other infectious diseases like malaria, and it has been suggested that the low incidence and mortality of COVID-19 in malaria-endemic regions could have been related to cross-immunity between malaria and SARS-CoV-2. The aim of this study was to determine the prevalence of SARS-CoV-2 and circulating variants as well as the frequency of co-infections with malaria in Equatorial Guinea. Methods: We conducted antigen tests for SARS-CoV-2 and microscopy malaria examinations in 1556 volunteer participants at six health centres in Bioko and Bata from June to October 2021 and performed SARS-CoV-2 whole-genome sequencing on positive samples to determine the diversity and origin of circulating variants. Results: We report 3.03% of SARS-CoV-2 and 22.25% of malaria prevalence over the sampling period; SARS-CoV-2 cases were found at a similar frequency in all age groups, whereas malaria was most frequent in children and teenagers. Six cases of malaria and SARS-CoV-2 co-infection were found, representing 0.37% prevalence. Genome sequences of 43 SARS-CoV-2 isolates are reported, most of which belong to the lineage Delta and, according to pandemic-scale phylogenies, were introduced from Europe on multiple occasions. Conclusions: This study is relevant in providing first-time estimates of the real prevalence of SARS-CoV-2 in this malaria-endemic country, with the identification of circulating variants, their origin, and co-occurrence with malaria. These data regarding the impact of the pandemic and co-infection with endemic diseases are relevant in future pandemics preparedness.

Advances in short-read sequencing have enhanced our understanding of Mycobacterium tuberculosis complex (MTBC), but fail to capture its complete genomic diversity. We applied long-read sequencing to 216 isolates from the Valencia Region (Spain) and generated high-quality, complete genomes, revealing detailed insights into MTBC evolution across timescales. Complete genome comparisons increased the estimated evolutionary rate by 1.5-fold, resulting in a median of 312 (–1 to 792) additional SNPs per pairwise comparison. Multiple diversity hotspots were identified, mostly in the pe/ppe genes and driven by gene conversion. However, most PE/PPE epitopes were hyperconserved, with notable exceptions involving vaccine candidates. Incorporating previously undetected SNPs and indels improved resolution in transmission analyses. Furthermore, patient-specific reference mapping validates only 5–10% of within-host diversity detected by standard pipelines, indicating substantial overestimation in previous studies. These findings expand our view of MTBC diversity and have important implications for understanding host-pathogen interactions, epidemiology, and transmission dynamics.

Mycobacterium tuberculosis, the bacterium responsible for the Tuberculosis (TB) disease, remains a leading global infectious disease killer, and genomic epidemiology is essential for understanding its transmission dynamics. Computational limitations prevent comprehensive phylogenetic analysis of the publicly available Mycobacterium tuberculosis genomes. Here, we create UShER-TB, a comprehensive pipeline for scalable phylogenomic MTB analysis. We processed 129,312 MTB genomes to construct a comprehensive global phylogeny capturing unprecedented genomic diversity. UShER-TB achieved high accuracy in transmission cluster reconstruction. The comprehensive phylogeny also facilitated identification of putative novel lineages and sublineages, and successful placement of ancient DNA samples. The UShER-TB platform enables real-time phylogenomic analysis of new genomes, revealing transmission hotspots and introduction patterns at global scales. Our approach overcomes longstanding computational barriers, providing researchers with efficient tools for TB genomic surveillance especially for resource-limited settings where TB burden is highest.

The assembly of the gut resistome in early life is key to infant health. Specific perinatal factors such as cesarean section (C-section), antibiotic exposure and lack of breastfeeding practices are detrimental to proper microbial development and increase the antimicrobial resistance genes (ARGs). Using 265 gut longitudinal metagenomes from 66 mother-infant pairs, we investigated how perinatal factors influence the acquisition and dynamics of ARGs during the first year of life. Our findings reveal that Bifidobacterium plays a crucial role in modulating the infant resistome, with its high relative abundance being associated with a lower ARG load. Exclusive breastfeeding during the first month of life accelerates the reduction of ARGs and ensures a lower resistome burden at six months. Moreover, early breastfeeding cessation correlates with a higher ARG load, underscoring its long-term influence on microbial resilience. Importantly, we identify exclusive breastfeeding as a key strategy to mitigate the impact of C-section delivery on the infant gut resistome, counteracting the early-life antibiotic exposure associated with this procedure and the resulting resistance acquisition. By promoting a microbiome enriched in Bifidobacterium, breastfeeding may help suppress ARG-carrying taxa, reducing the risk of resistance dissemination. Our findings underscore the importance of breastfeeding as a natural intervention to shape the infant microbiome and resistome. Supporting breastfeeding through public health policies could help limit the spread of antimicrobial resistance in early life.