Genomic transmission clusters and circulating lineages of Mycobacterium tuberculosis among refugees residing in refugee camps in Ethiopia.

BACKGROUND: Understanding the transmission dynamics of Mycobacterium tuberculosis (Mtb) could benefit the design of tuberculosis (TB) prevention and control strategies for refugee populations. Whole Genome Sequencing (WGS) has not yet been used to document the Mtb transmission dynamics among refugees in Ethiopia. We applied WGS to accurately identify transmission clusters and Mtb lineages among TB cases in refugee camps in Ethiopia. METHOD AND DESIGN: We conducted a cross-sectional study of 610 refugees in refugee camps in Ethiopia presenting with symptoms of TB. WGS data of 67 isolates was analyzed using the Maximum Accessible Genome for Mtb Analysis (MAGMA) pipeline; iTol and FigTree were used to visualize phylogenetic trees, lineages, and the presence of transmission clusters. RESULTS: Mtb culture-positive refugees originated from South Sudan (52/67, 77.6%), Somalia (9/67, 13.4%). Eritrea (4/67, 6%), and Sudan (2/67, 3%). The majority (52, 77.6%) of the isolates belonged to Mtb lineage (L) 3, and one L9 was identified from a Somalian refugee. The vast majority (82%) of the isolates were pan-susceptible Mtb, and none were multi-drug-resistant (MDR)-TB. Based on the 5-single nucleotide polymorphisms cutoff, we identified eight potential transmission clusters containing 23.9% of the isolates. Contact investigation confirmed epidemiological links with either family or social interaction within the refugee camps or with neighboring refugee camps. CONCLUSION: Four lineages (L1, L3, L4, and L9) were identified, with the majority of strains being L3, reflecting the Mtb L3 dominance in South Sudan, where the majority of refugees originated from. Recent transmission among refugees was relatively low (24%), likely due to the short study period. The improved understanding of the Mtb transmission dynamics using WGS in refugee camps could assist in designing effective TB control programs for refugees.

Site-directed mutagenesis of Mycobacterium tuberculosis and functional validation to investigate potential bedaquiline resistance-causing mutations.

Molecular detection of bedaquiline resistant tuberculosis is challenging as only a small proportion of mutations in candidate bedaquiline resistance genes have been statistically associated with phenotypic resistance. We introduced two mutations, atpE Ile66Val and Rv0678 Thr33Ala, in the Mycobacterium tuberculosis H37Rv reference strain using homologous recombineering or recombination to investigate the phenotypic effect of these mutations. The genotype of the resulting strains was confirmed by Sanger- and whole genome sequencing, and bedaquiline susceptibility was assessed by minimal inhibitory concentration (MIC) assays. The impact of the mutations on protein stability and interactions was predicted using mutation Cutoff Scanning Matrix (mCSM) tools. The atpE Ile66Val mutation did not elevate the MIC above the critical concentration (MIC 0.25-0.5 µg/ml), while the MIC of the Rv0678 Thr33Ala mutant strains (> 1.0 µg/ml) classifies the strain as resistant, confirming clinical findings. In silico analyses confirmed that the atpE Ile66Val mutation minimally disrupts the bedaquiline-ATP synthase interaction, while the Rv0678 Thr33Ala mutation substantially affects the DNA binding affinity of the MmpR transcriptional repressor. Based on a combination of wet-lab and computational methods, our results suggest that the Rv0678 Thr33Ala mutation confers resistance to BDQ, while the atpE Ile66Val mutation does not, but definite proof can only be provided by complementation studies given the presence of secondary mutations.

A Bayesian approach to estimate the probability of resistance to bedaquiline in the presence of a genomic variant.

BACKGROUND: Bedaquiline is a core drug for treatment of rifampicin-resistant tuberculosis. Few genomic variants have been statistically associated with bedaquiline resistance. Alternative approaches for determining the genotypic-phenotypic association are needed to guide clinical care. METHODS: Using published phenotype data for variants in Rv0678, atpE, pepQ and Rv1979c genes in 756 Mycobacterium tuberculosis isolates and survey data of the opinion of 33 experts, we applied Bayesian methods to estimate the posterior probability of bedaquiline resistance and corresponding 95% credible intervals. RESULTS: Experts agreed on the role of Rv0678, and atpE, were uncertain about the role of pepQ and Rv1979c variants and overestimated the probability of bedaquiline resistance for most variant types, resulting in lower posterior probabilities compared to prior estimates. The posterior median probability of bedaquiline resistance was low for synonymous mutations in atpE (0.1%) and Rv0678 (3.3%), high for missense mutations in atpE (60.8%) and nonsense mutations in Rv0678 (55.1%), relatively low for missense (31.5%) mutations and frameshift (30.0%) in Rv0678 and low for missense mutations in pepQ (2.6%) and Rv1979c (2.9%), but 95% credible intervals were wide. CONCLUSIONS: Bayesian probability estimates of bedaquiline resistance given the presence of a specific mutation could be useful for clinical decision-making as it presents interpretable probabilities compared to standard odds ratios. For a newly emerging variant, the probability of resistance for the variant type and gene can still be used to guide clinical decision-making. Future studies should investigate the feasibility of using Bayesian probabilities for bedaquiline resistance in clinical practice.

Predicting compound-protein interaction using hierarchical graph convolutional networks.

MOTIVATION: Convolutional neural networks have enabled unprecedented breakthroughs in a variety of computer vision tasks. They have also drawn much attention from other domains, including drug discovery and drug development. In this study, we develop a computational method based on convolutional neural networks to tackle a fundamental question in drug discovery and development, i.e. the prediction of compound-protein interactions based on compound structure and protein sequence. We propose a hierarchical graph convolutional network (HGCN) to encode small molecules. The HGCN aggregates a molecule embedding from substructure embeddings, which are synthesized from atom embeddings. As small molecules usually share substructures, computing a molecule embedding from those common substructures allows us to learn better generic models. We then combined the HGCN with a one-dimensional convolutional network to construct a complete model for predicting compound-protein interactions. Furthermore we apply an explanation technique, Grad-CAM, to visualize the contribution of each amino acid into the prediction. RESULTS: Experiments using different datasets show the improvement of our model compared to other GCN-based methods and a sequence based method, DeepDTA, in predicting compound-protein interactions. Each prediction made by the model is also explainable and can be used to identify critical residues mediating the interaction.

Variants in Bedaquiline-Candidate-Resistance Genes: Prevalence in Bedaquiline-Naive Patients, Effect on MIC, and Association with Mycobacterium tuberculosis Lineage.

Studies have shown that variants in bedaquiline-resistance genes can occur in isolates from bedaquiline-naive patients. We assessed the prevalence of variants in all bedaquiline-candidate-resistance genes in bedaquiline-naive patients, investigated the association between these variants and lineage, and the effect on phenotype. We used whole-genome sequencing to identify variants in bedaquiline-resistance genes in isolates from 509 bedaquiline treatment naive South African tuberculosis patients. A phylogenetic tree was constructed to investigate the association with the isolate lineage background. Bedaquiline MIC was determined using the UKMYC6 microtiter assay. Variants were identified in 502 of 509 isolates (98.6%), with the highest (85%) prevalence of variants in the Rv0676c (mmpL5) gene. We identified 36 unique variants, including 19 variants not reported previously. Only four isolates had a bedaquiline MIC equal to or above the epidemiological cut-off value of 0.25 µg/mL. Phylogenetic analysis showed that 14 of the 15 variants observed more than once occurred monophyletically in one Mycobacterium tuberculosis (sub)lineage. The bedaquiline MIC differed between isolates belonging to lineage 2 and 4 (Fisher's exact test, P = 0.0004). The prevalence of variants in bedaquiline-resistance genes in isolates from bedaquiline-naive patients is high, but very few (<2%) isolates were phenotypically resistant. We found an association between variants in bedaquiline resistance genes and Mycobacterium tuberculosis (sub)lineage, resulting in a lineage-dependent difference in bedaquiline phenotype. Future studies should investigate the impact of the presence of variants on bedaquiline-resistance acquisition and treatment outcome.

Genetic variants and their association with phenotypic resistance to bedaquiline in Mycobacterium tuberculosis: a systematic review and individual isolate data analysis.

BACKGROUND: Bedaquiline is a crucial drug for control of rifampicin-resistant tuberculosis. Molecular drug resistance assays could facilitate effective use of bedaquiline and surveillance of drug resistance emergence. To facilitate molecular assay development, we aimed to identify genomic markers of bedaquiline resistance. METHODS: In this systematic review and individual isolate analysis, we searched Europe PubMed Central and Scopus for studies published from the inception of each database until Oct 19, 2020, that assessed genotypic and phenotypic bedaquiline resistance in clinical or non-clinical Mycobacterium tuberculosis isolates. All studies reporting on the assessment of variants in the four genes of interest (Rv0678, atpE, pepQ, and Rv1979c) and phenotypic bedaquiline data in both clinical and non-clinical samples were included. We collated individual isolate data from eligible studies to assess the association between genomic variants with phenotypic bedaquiline resistance, using a standardised method endorsed by WHO. Risk of bias of the extracted data was independently assessed by two authors using the Quality Assessment of Diagnostic Accuracy Studies tool for clinical studies and Systematic Review Center for Laboratory Animal Experimentation tool for animal studies. The primary outcome was to identify mutations associated with resistance in four genes of interest (Rv0678, atpE, pepQ, and Rv1979c); for each genomic variant, the odds ratio (OR), 95% CI, and p value were calculated to identify resistance markers associated with bedaquiline resistance. This study is registered with PROSPERO, CRD42020221498. FINDINGS: Of 1367 studies identified, 41 published between 2007 and 2020 were eligible for inclusion. We extracted data on 1708 isolates: 1569 (91·9%) clinical isolates and 139 (8·1%) non-clinical isolates. We identified 237 unique variants in Rv0678, 14 in atpE, 28 in pepQ, and 11 in Rv1979c. Most clinical isolates with a single variant reported in Rv0678 (229 [79%] of 287 variants), atpE (14 [88%] of 16 variants), pepQ (32 [100%] of 32 variants), or Rv1979c (115 [98%] of 119 variants) were phenotypically susceptible to bedaquiline. Except for the atpE 187G→C (OR ∞, [95% CI 13·28-∞]; p<0·0001) and Rv0678 138_139insG (OR 6·91 [95% CI 1·16-47·38]; p=0·016) variants, phenotypic-genotypic associations were not significant (p≥0·05) for any single variant in Rv0678, atpE, pepQ, and Rv1979c. INTERPRETATION: Absence of clear genotypic-phenotypic associations for bedaquiline complicates the development of molecular drug susceptibility tests. A concerted global effort is urgently needed to assess the genotypic and phenotypic drug susceptibility of M tuberculosis isolates, especially in patients who have received unsuccessful bedaquiline-containing regimens. Treatment regimens should be designed to prevent emergence of bedaquiline resistance and phenotypic drug susceptibility tests should be used to guide and monitor treatment. FUNDING: Research Foundation Flanders, South African Medical Research Council, Department of Science and Innovation - National Research Foundation, National Institute of Health Institute of Allergy and Infectious Diseases, and Doris Duke Charitable Foundation.

Maternal Ambient Exposure to Atmospheric Pollutants during Pregnancy and Offspring Term Birth Weight in the Nationwide ELFE Cohort.

BACKGROUND: Studies have reported associations between maternal exposure to atmospheric pollution and lower birth weight. However, the evidence is not consistent and uncertainties remain. We used advanced statistical approaches to robustly estimate the association of atmospheric pollutant exposure during specific pregnancy time windows with term birth weight (TBW) in a nationwide study. METHODS: Among 13,334 women from the French Longitudinal Study of Children (ELFE) cohort, exposures to PM2.5, PM10 (particles < 2.5 µm and <10 µm) and NO2 (nitrogen dioxide) were estimated using a fine spatio-temporal exposure model. We used inverse probability scores and doubly robust methods in generalized additive models accounting for spatial autocorrelation to study the association of such exposures with TBW. RESULTS: First trimester exposures were associated with an increased TBW. Second trimester exposures were associated with a decreased TBW by 17.1 g (95% CI, -26.8, -7.3) and by 18.0 g (-26.6, -9.4) for each 5 µg/m3 increase in PM2.5 and PM10, respectively, and by 15.9 g (-27.6, -4.2) for each 10 µg/m3 increase in NO2. Third trimester exposures (truncated at 37 gestational weeks) were associated with a decreased TBW by 48.1 g (-58.1, -38.0) for PM2.5, 38.1 g (-46.7, -29.6) for PM10 and 14.7 g (-25.3, -4.0) for NO2. Effects of pollutants on TBW were larger in rural areas. CONCLUSIONS: Our results support an adverse effect of air pollutant exposure on TBW. We highlighted a larger effect of air pollutants on TBW among women living in rural areas compared to women living in urban areas.

Prioritizing the provision of urban ecosystem services in deprived areas, a question of environmental justice.

The distribution of urban ecosystem services (UES) is often uneven across socioeconomic groups, leading to environmental justice issues. Understanding the distribution of UES across a landscape can help managers ensure an equitable distribution of services. While many past studies have focused on the distribution of green spaces in relation to socioeconomic variables, this research analyzes the distribution of UES provided by these green spaces. This research quantified air pollution removal, atmospheric carbon reduction, and surface runoff mitigation provided by urban trees in Strasbourg city (France). The provision of these three UES was studied at the census block scale by creating an index of UES delivery, which was contrasted with a constructed social deprivation index. Our results show that there is no significant association between the delivery of UES and social deprivation. Some deprived populations benefit from high UES delivery. Results also suggest that mapping associations between UES delivery and social deprivation should be integrated with future development plans to enhance the equitable distribution of UES. This study provides insights into the French context where studies about the distribution of UES at a small-area level remain lacking.

Nickel spreading assessment in New Caledonia by lichen biomonitoring coupled to air mass history.

Lichen biomonitoring and air mass trajectories were used to study the influence of mining activities in the atmospheric dispersion of metallic elements to assess the exposure of the population to dust emitted by mining activities. A map of forward trajectory densities from open mine surfaces throughout New Caledonia was computed and allowed to identify three preferred wind directions (trade wind, bent trade winds and oceanic winds) that could arise in mining particles dispersion all over New Caledonia. Areas where an air quality monitoring would be advisable to evaluate the exposure of the population to the Nickel dusts have been identified. Lichens collected around the industrial mining site KNS and in North Provence of New Caledonia were analysed for their Ni, Co, Cr, Zn and Ti contents. Backward trajectories were simulated from the lichen sampling point using FLEXTRA fed with ECMWF meteorological data, and densities of trajectories having overflown a mine were calculated. Ratio metal/Ti was then plotted as a function of air mass trajectory densities having overflown open pits. A positive correlation between trajectory densities and titanium-normalized metal in lichen for Ni, Co, Cr was highlighted, indicating that mining is a source of dispersion of these metals. For Zn, which is a tracer of fossil fuel or biomass (wood) combustion activity, no correlation was found. Graphical abstract.

Capacity building for whole genome sequencing of Mycobacterium tuberculosis and bioinformatics in high TB burden countries.

BACKGROUND: Whole genome sequencing (WGS) is increasingly used for Mycobacterium tuberculosis (Mtb) research. Countries with the highest tuberculosis (TB) burden face important challenges to integrate WGS into surveillance and research. METHODS: We assessed the global status of Mtb WGS and developed a 3-week training course coupled with long-term mentoring and WGS infrastructure building. Training focused on genome sequencing, bioinformatics and development of a locally relevant WGS research project. The aim of the long-term mentoring was to support trainees in project implementation and funding acquisition. The focus of WGS infrastructure building was on the DNA extraction process and bioinformatics. FINDINGS: Compared to their TB burden, Asia and Africa are grossly underrepresented in Mtb WGS research. Challenges faced resulted in adaptations to the training, mentoring and infrastructure building. Out-of-date laptop hardware and operating systems were overcome by using online tools and a Galaxy WGS analysis pipeline. A case studies approach created a safe atmosphere for students to formulate and defend opinions. Because quality DNA extraction is paramount for WGS, a biosafety level 3 and general laboratory skill training session were added, use of commercial DNA extraction kits was introduced and a 2-week training in a highly equipped laboratory was combined with a 1-week training in the local setting. INTERPRETATION: By developing and sharing the components of and experiences with a sequencing and bioinformatics training program, we hope to stimulate capacity building programs for Mtb WGS and empower high-burden countries to play an important role in WGS-based TB surveillance and research.

Role of empiric treatment in hospitalized patients with Xpert MTB/RIF-negative presumptive pulmonary tuberculosis: A prospective cohort study.

OBJECTIVES: The ability of clinical algorithms to identify tuberculosis disease and the impact of empiric treatment on survival in people with a negative Xpert MTB/RIF (Xpert) result remains poorly documented. METHODS: Hospitalized Xpert-negative patients (125 initiated on empiric tuberculosis treatment based on a clinical algorithm and 125 in whom tuberculosis treatment was not started) were enrolled. Sputum samples were evaluated for Mycobacterium tuberculosis by culture. All study participants were followed up for 6 months. RESULTS: Xpert-negative inpatients in whom empiric tuberculosis treatment was initiated were more likely to have microbiological confirmed tuberculosis compared to those in whom empiric tuberculosis treatment was not started (24.8% vs 6.4%, p=0.0001). Six-month risk of death was 5.2%, but the risk was twice as high in people with bacteriological confirmation of TB (10.3% vs 4.3%, p=0.12). Cardinal symptoms of TB were associated with bacteriological confirmation and a decision to start empiric treatment. The positive predictive value of the clinical algorithm was 24.8% and empiric treatment did not affect 6-month risk of death (5.6% vs 4.8%, p=0.78). CONCLUSIONS: Clinical algorithm identifies the majority of confirmed tuberculosis cases among Xpert-negative inpatients. Empiric treatment did not impact survival and resulted in substantial overtreatment. The more sensitive Xpert Ultra assay should be used to eliminate the need for empiric tuberculosis treatment.

Is atmospheric pollution exposure during pregnancy associated with individual and contextual characteristics? A nationwide study in France.

BACKGROUND: Exposure to atmospheric pollutants is a danger for the health of pregnant mother and children. Our objective was to identify individual (socioeconomic and behavioural) and contextual factors associated with atmospheric pollution pregnancy exposure at the nationwide level. METHOD: Among 14 921 women from the French nationwide ELFE (French Longitudinal Study of Children) mother-child cohort recruited in 2011, outdoor exposure levels of PM2.5, PM10 (particulate matter <2.5 µm and <10 µm in diameter) and NO2 (nitrogen dioxide) were estimated at the pregnancy home address from a dispersion model with 1 km resolution. We used classification and regression trees (CART) and linear regression to characterise the association of atmospheric pollutants with individual (maternal age, body mass index, parity, education level, relationship status, smoking status) and contextual (European Deprivation Index, urbanisation level) factors. RESULTS: Patterns of associations were globally similar across pollutants. For the CART approach, the highest tertile of exposure included mainly women not in a relationship living in urban and socially deprived areas, with lower education level. Linear regression models identified different determinants of atmospheric pollutants exposure according to the residential urbanisation level. In urban areas, atmospheric pollutants exposure increased with social deprivation, while in rural areas a U-shaped relationship was observed. CONCLUSION: We highlighted social inequalities in atmospheric pollutants exposure according to contextual characteristics such as urbanisation level and social deprivation and also according to individual characteristics such as education, being in a relationship and smoking status. In French urban areas, pregnant women from the most deprived neighbourhoods were those most exposed to health-threatening atmospheric pollutants.

Influence of socioeconomic deprivation on the relation between air pollution and beta-agonist sales for asthma.

BACKGROUND: Air pollution triggers asthma attacks hours to days after exposure. It remains unclear whether socioeconomic deprivation modulates these effects. Investigation of these interactions requires adequate statistical power, obtainable by using either a sufficient number of observations or very sensitive indicators of asthma attacks. Using a small-area temporal ecologic approach, we studied the short-term relations between ambient air pollution and sales of short-acting beta-agonist (SABA) drugs, a frequent and specific treatment for control of asthma attacks in children and young adults, and then tested the influence of deprivation on these relations. METHODS: The study took place in Strasbourg, France in 2004. Health insurance funds provided data on 15,121 SABA sales for patients aged 0 to 39 years. Deprivation was estimated by small geographic areas using an index constructed from census data. Daily average ambient concentrations of particulate matter (particles with an aerodynamic diameter < 10 microm [PM(10)]), nitrogen dioxide (NO(2)), and ozone (O(3)) were modeled on a small-area level. Adjusted case-crossover models were used for statistical analysis. RESULTS: Increased of 10 microg/m(3) in ambient PM(10), NO(2), and O(3) concentrations were associated, respectively, with increases of 7.5% (95% confidence interval [CI], 4 to 11.2%), 8.4% (95% CI, 5 to 11.9%), and 1% (95% CI, - 0.3 to 2.2%) in SABA sales. Deprivation had no influence on these relations. CONCLUSION: The associations observed are consistent with those reported by studies focusing on SABA use. Similar studies in other settings should confirm whether the lack of interaction with deprivation is due to specific local conditions.

Air pollution, asthma attacks, and socioeconomic deprivation: a small-area case-crossover study.

With few exceptions, studies of short-term health effects of air pollution use pollutant concentrations that are averaged citywide as exposure indicators. They are thus prone to exposure misclassification and consequently to bias. Measurement of the relations between air pollution and health, generally and in specific populations, could be improved by employing more geographically precise exposure estimates. The authors investigated short-term relations between ambient air pollution estimated in small geographic areas (French census blocks) and asthma attacks in Strasbourg, France, in 2000-2005--in the general population and in populations with contrasting levels of socioeconomic deprivation. Emergency health-care networks provided data on 4,683 telephone calls made for asthma attacks. Deprivation was estimated using a block-level index constructed from census data. Hourly concentrations of particulate matter less than 10 microm in aerodynamic diameter (PM(10)), sulfur dioxide, nitrogen dioxide, and ozone were modeled by block with ADMS-Urban software. Adjusted case-crossover analyses showed that asthma calls were positively but not significantly associated with PM(10) (for a 10-microg x m(-3) increase, odds ratio (OR) = 1.035, 95% confidence interval (CI): 0.997, 1.075), sulfur dioxide (OR = 1.056, 95% CI: 0.979, 1.139), and nitrogen dioxide (OR = 1.025, 95% CI: 0.990, 1.062). No association was observed for ozone (OR = 0.998, 95% CI: 0.965, 1.032). Socioeconomic deprivation had no significant influence on these relations.