The source and fate of Mycobacterium tuberculosis complex in wastewater and possible routes of transmission

Antimicrobial resistance landscape in a metropolitan city context using open drain wastewater-based metagenomic analysis

One Health concept recognizes the inextricable interactions of diverse ecosystems and their subsequent effect on human, animal and plant health. Antimicrobial resistance (AMR) is a major One Health concern and is predicted to cause catastrophes if appropriate measures are not implemented. To understand the AMR landscape in a south Indian metropolitan city, metagenomic analysis of open drains was performed. The data suggests that in January 2022, macrolide class of antibiotics contributed the highest resistance of 40.1% in the city, followed by aminoglycoside- 24.4%, tetracycline- 11.3% and lincosamide- 6.7%. The 'mutations in the 23S rRNA gene conferring resistance to macrolide antibiotics' were the major contributor of resistance with a prevalence of 39.7%, followed by '16s rRNA with mutation conferring resistance to aminoglycoside antibiotics'- 22.2%, '16S rRNA with mutation conferring resistance to tetracycline derivatives'- 9.2%, and '23S rRNA with mutation conferring resistance to lincosamide antibiotics'- 6.7%. The most prevalent antimicrobial resistance gene (ARG) 'mutations in the 23S rRNA gene conferring resistance to macrolide antibiotics' was present in multiple pathogens including Escherichia coli, Campylobacter jejuni, Acinetobacter baumannii, Streptococcus pneumoniae, Pseudomonas aeruginosa, Neisseria gonorrhoeae, Klebsiella pneumoniae and Helicobacter pylori. Most of the geographical locations in the city showed a similar landscape for AMR. Considering human mobility and anthropogenic activities, such an AMR landscape could be common across other regions too. The data indicates that pathogens are evolving and acquiring antibiotic resistance genes to evade antibiotics of multiple major drug classes in diverse hosts. The outcomes of the study are relevant not only in understanding the resistance landscape at a broader level but are also important for identifying the resistant drug classes, the mechanisms of gaining resistance and for developing new drugs that target specific pathways. This kind of surveillance protocol can be extended to regions in other developing countries to assess and combat the problem of antimicrobial resistance.

Systematic tracking of nitrogen sources in complex river catchments: Machine learning approach based on microbial metagenomics

Tracking nitrogen pollution sources is crucial for the effective management of water quality; however, it is a challenging task due to the complex contaminative scenarios in the freshwater systems. The contaminative pattern variations can induce quick responses of aquatic microorganisms, making them sensitive indicators of pollution origins. In this study, the soil and water assessment tool, accompanied by a detailed pollution source database, was used to detect the main nitrogen pollution sources in each sub-basin of the Liuyang River watershed. Thus, each sub-basin was assigned to a known class according to SWAT outputs, including point source pollution-dominated area, crop cultivation pollution-dominated area, and the septic tank pollution-dominated area. Based on these outputs, the random forest (RF) model was developed to predict the main pollution sources from different river ecosystems using a series of input variable groups (e.g., natural macroscopic characteristics, river physicochemical properties, 16S rRNA microbial taxonomic composition, microbial metagenomic data containing taxonomic and functional information, and their combination). The accuracy and the Kappa coefficient were used as the performance metrics for the RF model. Compared with the prediction performance among all the input variable groups, the prediction performance of the RF model was significantly improved using metagenomic indices as inputs. Among the metagenomic data-based models, the combination of the taxonomic information with functional information of all the species achieved the highest accuracy (0.84) and increased median Kappa coefficient (0.70). Feature importance analysis was used to identify key features that could serve as indicators for sudden pollution accidents and contribute to the overall function of the river system. The bacteria Rhabdochromatium marinum, Frankia, Actinomycetia, and Competibacteraceae were the most important species, whose mean decrease Gini indices were 0.0023, 0.0021, 0.0019, and 0.0018, respectively, although their relative abundances ranged only from 0.0004 to 0.1 %. Among the top 30 important variables, functional variables constituted more than half, demonstrating the remarkable variation in the microbial functions among sites with distinct pollution sources and the key role of functionality in predicting pollution sources. Many functional indicators related to the metabolism of Mycobacterium tuberculosis, such as K24693, K25621, K16048, and K14952, emerged as significant important factors in distinguishing nitrogen pollution origins. With the shortage of pollution source data in developing regions, this suggested approach offers an economical, quick, and accurate solution to locate the origins of water nitrogen pollution using the metagenomic data of microbial communities.

Wastewater-based surveillance as a tool for public health action: SARS-CoV-2 and beyond

Wastewater-based surveillance (WBS) has undergone dramatic advancement in the context of the coronavirus disease 2019 (COVID-19) pandemic. The power and potential of this platform technology were rapidly realized when it became evident that not only did WBS-measured SARS-CoV-2 RNA correlate strongly with COVID-19 clinical disease within monitored populations but also, in fact, it functioned as a leading indicator. Teams from across the globe rapidly innovated novel approaches by which wastewater could be collected from diverse sewersheds ranging from wastewater treatment plants (enabling community-level surveillance) to more granular locations including individual neighborhoods and high-risk buildings such as long-term care facilities (LTCF). Efficient processes enabled SARS-CoV-2 RNA extraction and concentration from the highly dilute wastewater matrix. Molecular and genomic tools to identify, quantify, and characterize SARS-CoV-2 and its various variants were adapted from clinical programs and applied to these mixed environmental systems. Novel data-sharing tools allowed this information to be mobilized and made immediately available to public health and government decision-makers and even the public, enabling evidence-informed decision-making based on local disease dynamics. WBS has since been recognized as a tool of transformative potential, providing near-real-time cost-effective, objective, comprehensive, and inclusive data on the changing prevalence of measured analytes across space and time in populations. However, as a consequence of rapid innovation from hundreds of teams simultaneously, tremendous heterogeneity currently exists in the SARS-CoV-2 WBS literature. This manuscript provides a state-of-the-art review of WBS as established with SARS-CoV-2 and details the current work underway expanding its scope to other infectious disease targets.

Tuberculosis prevention: current strategies and future directions

BACKGROUND

An estimated one fourth of the world's population is infected with Mycobacterium tuberculosis, and 5-10% of those infected develop tuberculosis in their lifetime. Preventing tuberculosis is one of the most underutilized but essential components of curtailing the tuberculosis epidemic. Moreover, current evidence illustrates that tuberculosis manifestations occur along a dynamic spectrum from infection to disease rather than a binary state as historically conceptualized. Elucidating determinants of transition between these states is crucial to decreasing the tuberculosis burden and reaching the END-TB Strategy goals as defined by the WHO. Vaccination, detection of infection, and provision of preventive treatment are key elements of tuberculosis prevention.

OBJECTIVES

This review provides a comprehensive summary of recent evidence and state-of-the-art updates on advancements to prevent tuberculosis in various settings and high-risk populations.

SOURCES

We identified relevant studies in the literature and synthesized the findings to provide an overview of the current state of tuberculosis prevention strategies and latest research developments.

CONTENT

We present the current knowledge and recommendations regarding tuberculosis prevention, with a focus on M. bovis Bacille-Calmette-Guérin vaccination and novel vaccine candidates, tests for latent infection with M. tuberculosis, regimens available for tuberculosis preventive treatment and recommendations in low- and high-burden settings.

IMPLICATIONS

Effective tuberculosis prevention worldwide requires a multipronged approach that addresses social determinants, and improves access to tuberculosis detection and to new short tuberculosis preventive treatment regimens. Robust collaboration and innovative research are needed to reduce the global burden of tuberculosis and develop new detection tools, vaccines, and preventive treatments that serve all populations and ages.

Metagenomic next-generation sequencing for Mycobacterium tuberculosis complex detection: a meta-analysis

Objective

Metagenomic next-generation sequencing (mNGS) has been gradually applied to the diagnosis of tuberculosis (TB) due to its rapid and highly sensitive characteristics. Despite numerous studies on this subject, their results vary significantly. Thus, the current meta-analysis was performed to assess the performance of the mNGS on tuberculosis.

Methods

PubMed, Embase, Web of Science, and The Cochrane Library were searched up to June 21, 2023. Studies utilizing the mNGS for tuberculosis detection were included. The risk of bias was assessed by QUADAS-2, and a meta-analysis was performed with STATA14.0 software.

Results

Seventeen studies comprising 3,205 specimens were included. The combined sensitivity and specificity of mNGS for clinical specimens were 0.69[0.58-0.79] and 1.00[0.99-1.00], respectively. Subgroup analysis identified sequencing platform, diagnostic criteria, study type, sample size, and sample types as potential sources of heterogeneity. Cerebrospinal Fluid (CSF) has a lower sensitivity of 0.58 (0.39-0.75). In a population with a 10% prevalence rate, the accuracy of sensitivity reached 94%.

Conclusion

Metagenomic next-generation sequencing technology exhibits high sensitivity and speed in diagnosing Mycobacterium tuberculosis. Its application in mono and mixed infections peoples shows promise, and mNGS is likely to be increasingly used to address challenges posed by Mycobacterium tuberculosis complexes in the future.

Surveillance of multidrug-resistant tuberculosis in sub-Saharan Africa through wastewater-based epidemiology

The spread of multidrug-resistant tuberculosis (MDR-TB) is a serious public health issue, particularly in developing nations. The current methods of monitoring drug-resistant TB (DR-TB) using clinical diagnoses and hospital records are insufficient due to limited healthcare access and underreporting. This study proposes using Wastewater-Based Epidemiology (WBE) to monitor DR-TB in six African countries (Ghana, Nigeria, Kenya, Uganda, Cameroon, and South Africa) and examines the impact of treated wastewater on the spread of TB drug-resistant genes in the environment. Using droplet-digital polymerase chain reaction (ddPCR), the study evaluated untreated and treated wastewater samples in selected African countries for TB surveillance. There was a statistically significant difference in concentrations of genes conferring resistance to TB drugs in wastewater samples from the selected countries (p-value<0.05); South African samples exhibited the highest concentrations of 4.3(±2,77), 4.8(±2.96), 4.4(±3,10) and 4.7(±3,39) log copies/ml for genes conferring resistance to first-line TB drugs (katG, rpoB, embB and pncA respectively) in untreated wastewater. This may be attributed to the higher prevalence of TB/MDR-TB in SA compared to other African countries. Interestingly, genes conferring resistance to second-line TB drugs such as delamanid (ddn gene) and bedaquiline (atpE gene) were detected in relatively high concentrations (4.8(±3,67 and 3.2(±2,31 log copies/ml for ddn and atpE respectively) in countries, such as Cameroon, where these drugs are not part of the MDR-TB treatment regimens, perhaps due to migration or the unapproved use of these drugs in the country. The gene encoding resistance to streptomycin (rrs gene) was abundant in all countries, perhaps due to the common use of this antibiotic for infections other than TB. These results highlight the need for additional surveillance and monitoring, such as WBE, to gather data at a community level. Combining WBE with the One Health strategy and current TB surveillance systems can help prevent the spread of DR-TB in populations.

Occurrence and prevalence of antibiotic resistance genes and pathogens in an industrial park wastewater treatment plant

Antibiotic resistance genes (ARGs) and pathogens are emerging environmental pollutants that pose a threat to human health and ecosystem. Industrial park wastewater treatment plants (WWTPs) treat large amounts of comprehensive wastewater derived from industrial production and park human activity, which is possible a source of ARGs and pathogens. Therefore, this study investigated the occurrence and prevalence of ARGs, ARGs hosts and pathogens and assesses the ARGs health risk in the biological treatment process in a large-sale industrial park WWTP using metagenomic analysis and omics-based framework, respectively. Results show that the major ARG subtypes are multidrug resistance genes (MDRGs), macB, tetA(58), evgS, novA, msbA and bcrA and the ARGs main hosts were genus Acidovorax, Pseudomonas, Mesorhizobium. In particular, all determined ARGs genus level hosts are pathogens. The total removal percentage of ARGs, MDRGs and pathogens were 12.77 %, 12.96 % and 25.71 % respectively, suggesting that the present treatment could not efficiently remove these pollutants. The relative abundance of ARGs, MDRGs and pathogens varied along biological treatment process that ARGs and MDRGs were enriched in activated sludge and pathogens were enriched in both secondary sedimentation tank and activated sludge. Among 980 known ARGs, 23 ARGs (e.g., ermB, gadX and tetM) were assigned into risk Rank I with characters of enrichment in the human-associated environment, gene mobility and pathogenicity. The results indicate that industrial park WWTPs might serve as an important source of ARGs, MDRGs, and pathogens. These observations invite further study of the origination, development, dissemination and risk assessment of industrial park WWTPs ARGs and pathogens.

Contribution of pollution gradient to the sediment microbiome and potential pathogens in urban streams draining into Lake Victoria (Kenya)

In sub-Saharan Africa (SSA), urban rivers/streams have long been subjected to anthropogenic pollution caused by urbanization, resulting in significantly altered chemical and biological properties of surface water and sediments. However, little is known about the diversity and structure of river microbial community composition and pathogens, as well as how they respond to anthropogenic inputs. High-throughput 16S rRNA amplicon sequencing and PICRUSt predictive function profiling were used in this study to conduct a comprehensive analysis of the spatial bacterial distribution and metabolic functions in sediment of two urban streams (Kisat and Auji) flowing through Kisumu City, Kenya. Results revealed that sediment samples from the highly urbanized mid and lower stream catchment zones of both streams had significantly higher levels of total organic carbon (TOC), total nitrogen (TN), total phosphorous (TP) than the less urbanized upper catchment zone, and were severely polluted with toxic heavy metals lead (Pb), cadmium (Cd), and copper (Cu). Differential distribution of Actinobacteria, Proteobacteria, Chloroflexi, and Verrucomicrobia in sediment bacterial composition was detected along stream catchment zones. The polluted mid and lower catchment zones were rich in Actinobacteria and Proteobacteria, as well as a variety of potential pathogenic taxa such as Corynebacterium, Staphylococcus, Cutibacterium, Turicella, Acinetobacter, and Micrococcus, as well as enteric bacteria such as Faecalibacterium, Shewanella, Escherichia, Klebsiella, Enterococcus, Prevotella, Legionella, Vibrio and Salmonella. Furthermore, PICRUSt metabolic inference analysis revealed an increasing enrichment in the sediments of genes associated with carbon and nitrogen metabolism, disease pathogenesis, and virulence. Environmental factors (TOC, Pb, Cd, TN, pH) and geographical distance as significant drivers of sediment bacterial community assembly, with the environmental selection to play a dominant role. In polluted river catchment zone sediment samples, Pb content was the most influential sediment property, followed by TOC and Cd content. Given the predicted increase in urbanization in SSA, further alteration of surface water and sediment microbiome due to urban river pollution is unavoidable, with potential long-term effects on ecosystem function and potential health hazards. As a result, this study provides valuable information for ecological risk assessment and management of urban rivers impacted by diffuse and point source anthropogenic inputs, which is critical for future proactive and sustainable urban waste management, monitoring, and water pollution control in low-income countries.

Antimicrobial Resistance in Aquaculture Environments: Unravelling the Complexity and Connectivity of the Underlying Societal Drivers

Food production environments in low- and middle-income countries (LMICs) are recognized as posing significant and increasing risks to antimicrobial resistance (AMR), one of the greatest threats to global public health and food security systems. In order to maximize and expedite action in mitigating AMR, the World Bank and AMR Global Leaders Group have recommended that AMR is integrated into wider sustainable development strategies. Thus, there is an urgent need for tools to support decision makers in unravelling the complex social and environmental factors driving AMR in LMIC food-producing environments and in demonstrating meaningful connectivity with other sustainable development issues. Here, we applied the Driver-Pressure-State-Impact-Response (DPSIR) conceptual framework to an aquaculture case study site in rural Bangladesh, through the analysis of distinct social, microbiological, and metagenomic data sets. We show how the DPSIR framework supports the integration of these diverse data sets, first to systematically characterize the complex network of societal drivers of AMR in these environments and second to delineate the connectivity between AMR and wider sustainable development issues. Our study illustrates the complexity and challenges of addressing AMR in rural aquaculture environments and supports efforts to implement global policy aimed at mitigating AMR in aquaculture and other rural LMIC food-producing environments.

Mycobacterium tuberculosis Transmission in High-Incidence Settings-New Paradigms and Insights

Tuberculosis has affected humankind for thousands of years, but a deeper understanding of its cause and transmission only arose after Robert Koch discovered Mycobacterium tuberculosis in 1882. Valuable insight has been gained since, but the accumulation of knowledge has been frustratingly slow and incomplete for a pathogen that remains the number one infectious disease killer on the planet. Contrast that to the rapid progress that has been made in our understanding SARS-CoV-2 (the cause of COVID-19) aerobiology and transmission. In this Review, we discuss important historical and contemporary insights into M. tuberculosis transmission. Historical insights describing the principles of aerosol transmission, as well as relevant pathogen, host and environment factors are described. Furthermore, novel insights into asymptomatic and subclinical tuberculosis, and the potential role this may play in population-level transmission is discussed. Progress towards understanding the full spectrum of M. tuberculosis transmission in high-burden settings has been hampered by sub-optimal diagnostic tools, limited basic science exploration and inadequate study designs. We propose that, as a tuberculosis field, we must learn from and capitalize on the novel insights and methods that have been developed to investigate SARS-CoV-2 transmission to limit ongoing tuberculosis transmission, which sustains the global pandemic.

TNF and HNRNPL Related Immunoregulatory Long non-coding RNA (THRIL) and long intergenic noncoding RNA-p21 (lincRNA-p21) as potential useful biomarkers for the diagnosis of tuberculosis

Early diagnosis is crucial in controlling tuberculosis globally and in developing countries with the emergence of drug-resistant Mycobacterium tuberculosis strains. Long non-coding RNAs (lncRNAs) are promising tuberculosis diagnostic biomarkers. Two lncRNA diagnostic markers, lncRNA THRIL and lincRNA-p21, were studied as tuberculosis diagnostic biomarkers. This cross-sectional study was conducted at the Center of Respiratory Diseases of LAQUINTINIE hospital and the National Veterinary Laboratory of Douala from December 2020 to August 2021. The ability of lncRNAs to distinguish between 19 healthy controls, 15 latent tuberculosis, and 21 active tuberculosis was estimated using quantitative polymerase chain reaction and Receiver Operating Characteristic curve analysis. Our analysis showed that lncRNA THRIL and lincRNA-p21 were significantly upregulated (P &lt;0.05) in active and latent tuberculosis compared with healthy controls. LincRNA-p21 expression was significantly increased (P &lt;0.05) in active tuberculosis compared with latent tuberculosis, whereas lncRNA THRIL was not significantly affected (P ≥0.05). Both lncRNA THRIL and lincRNA-p21 showed excellent performance in classifying latent tuberculosis and healthy controls (AUC = 92.86%). Furthermore, lncRNA THRIL was good at discriminating active tuberculosis from healthy controls (AUC = 89.79%), while lincRNA-p21 showed excellent discriminating performance (AUC = 100%). LncRNA THRIL was identified as a poor discriminator of latent tuberculosis from active tuberculosis (AUC = 64.28%), while lincRNA-p21 showed excellent diagnostic performance in this distinction (AUC = 92.86%). Our cross-sectional study suggests that lncRNA THRIL and lincRNA-p21 are promising tuberculosis diagnostic biomarkers that can differentiate between latent and active infection.