Transmitted HIV-1 drug resistance in a large international cohort using next-generation sequencing: results from the Strategic Timing of Antiretroviral Treatment (START) study.

OBJECTIVES: The aim of this analysis was to characterize transmitted drug resistance (TDR) in Strategic Timing of Antiretroviral Treatment (START) study participants by next-generation sequencing (NGS), a sensitive assay capable of detecting low-frequency variants. METHODS: Stored plasma from participants with entry HIV RNA > 1000 copies/mL were analysed by NGS (Illumina MiSeq). TDR was based on the WHO 2009 surveillance definition with the addition of reverse transcriptase (RT) mutations T215N and E138K, and integrase strand transfer inhibitor (INSTI) surveillance mutations (Stanford HIVdb). Drug resistance mutations (DRMs) detected at three thresholds are reported: > 2%, 5% and 20% of the viral population. RESULTS: Between 2009 and 2013, START enrolled 4684 antiretroviral therapy (ART)-naïve individuals in 35 countries. Baseline NGS data at study entry were available for 2902 participants. Overall prevalence rates of TDR using a detection threshold of 2%/5%/20% were 9.2%/5.6%/3.2% for nucleoside reverse transcriptase inhibitors (NRTIs), 9.2%/6.6%/4.9% for non-NRTIs, 11.4%/5.5%/2.4% for protease inhibitors (PIs) and 3.5%/1.6%/0.1% for INSTI DRMs and varied by geographic region. Using the 2% detection threshold, individual DRMs with the highest prevalence were: PI M46IL (5.5%), RT K103NS (3.5%), RT G190ASE (3.1%), T215ISCDVEN (2.5%), RT M41L (2.2%), RT K219QENR (1.7%) and PI D30N (1.6%). INSTI DRMs were detected almost exclusively below the 20% detection threshold, most commonly Y143H (0.4%), Q148R (0.4%) and T66I (0.4%). CONCLUSIONS: Use of NGS in this study population resulted in the detection of a large proportion of low-level variants which would not have been detected by traditional Sanger sequencing. Global surveillance studies utilizing NGS should provide a more comprehensive assessment of TDR prevalence in different regions of the world.

Hypermutator Pseudomonas aeruginosa Exploits Multiple Genetic Pathways To Develop Multidrug Resistance during Long-Term Infections in the Airways of Cystic Fibrosis Patients.

Pseudomonas aeruginosa exploits intrinsic and acquired resistance mechanisms to resist almost every antibiotic used in chemotherapy. Antimicrobial resistance in P. aeruginosa isolates recovered from cystic fibrosis (CF) patients is further enhanced by the occurrence of hypermutator strains, a hallmark of chronic infections in CF patients. However, the within-patient genetic diversity of P. aeruginosa populations related to antibiotic resistance remains unexplored. Here, we show the evolution of the mutational resistome profile of a P. aeruginosa hypermutator lineage by performing longitudinal and transversal analyses of isolates collected from a CF patient throughout 20 years of chronic infection. Our results show the accumulation of thousands of mutations, with an overall evolutionary history characterized by purifying selection. However, mutations in antibiotic resistance genes appear to have been positively selected, driven by antibiotic treatment. Antibiotic resistance increased as infection progressed toward the establishment of a population constituted by genotypically diversified coexisting sublineages, all of which converged to multidrug resistance. These sublineages emerged by parallel evolution through distinct evolutionary pathways, which affected genes of the same functional categories. Interestingly, ampC and ftsI, encoding the ß-lactamase and penicillin-binding protein 3, respectively, were found to be among the most frequently mutated genes. In fact, both genes were targeted by multiple independent mutational events, which led to a wide diversity of coexisting alleles underlying ß-lactam resistance. Our findings indicate that hypermutators, apart from boosting antibiotic resistance evolution by simultaneously targeting several genes, favor the emergence of adaptive innovative alleles by clustering beneficial/compensatory mutations in the same gene, hence expanding P. aeruginosa strategies for persistence.

Genetic relationship between bacteria isolated from intraoperative air samples and surgical site infections at a major teaching hospital in Ghana.

BACKGROUND: In low- and middle-income countries (LMICs) the rate of surgical site infections (SSI) is high, leading to negative patient outcomes and excess healthcare costs. A causal relationship between airborne bacteria in the operating room and SSI has not been established, at a molecular or genetic level. We studied the relationship between intraoperative airborne bacteria and bacteria causing SSI in an LMIC. METHODS: Active air sampling using a portable impactor was performed during clean or clean-contaminated elective surgical procedures. Active patient follow-up consisting of phone calls and clinical examinations was performed 3, 14 and 30 days after surgery. Bacterial isolates recovered from SSI and air samples were compared by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF) identification, ribotyping, whole genome sequencing (WGS), and metagenomic analysis. RESULTS: Of 128 included patients, 116 (91%) completed follow-up and 11 (9%) developed SSI. Known pathogenic bacteria were isolated from intraoperative air samples in all cases with SSI. A match between air and SSI isolates was found by MALDI-TOF in eight cases. Matching ribotypes were found in six cases and in one case both WGS and metagenomic analysis showed identity between air- and SSI-isolates. CONCLUSION: The study showed high levels of intraoperative airborne bacteria, an SSI-rate of 9% and a genetic link between intraoperative airborne bacteria and bacteria isolated from SSIs. This indicates the need for awareness of intraoperative air quality in LMICs.