Survey of drug resistance associated gene mutations in Mycobacterium tuberculosis, ESKAPE and other bacterial species

Machine learning-based classification reveals distinct clusters of non-coding genomic allelic variations associated with Erm-mediated antibiotic resistance

The erythromycin resistance RNA methyltransferase (erm) confers cross-resistance to all therapeutically important macrolides, lincosamides, and streptogramins (MLS phenotype). The expression of erm is often induced by the macrolide-mediated ribosome stalling in the upstream co-transcribed leader sequence, thereby triggering a conformational switch of the intergenic RNA hairpins to allow the translational initiation of erm. We investigated the evolutionary emergence of the upstream erm regulatory elements and the impact of allelic variation on erm expression and the MLS phenotype. Through systematic profiling of the upstream regulatory sequences across all known erm operons, we observed that specific erm subfamilies, such as ermB and ermC, have independently evolved distinct configurations of small upstream ORFs and palindromic repeats. A population-wide genomic analysis of the upstream ermB regions revealed substantial non-random allelic variation at numerous positions. Utilizing machine learning-based classification coupled with RNA structure modeling, we found that many alleles cooperatively influence the stability of alternative RNA hairpin structures formed by the palindromic repeats, which, in turn, affects the inducibility of ermB expression and MLS phenotypes. Subsequent experimental validation of 11 randomly selected variants demonstrated an impressive 91% accuracy in predicting MLS phenotypes. Furthermore, we uncovered a mixed distribution of MLS-sensitive and MLS-resistant ermB loci within the evolutionary tree, indicating repeated and independent evolution of MLS resistance. Taken together, this study not only elucidates the evolutionary processes driving the emergence and development of MLS resistance but also highlights the potential of using non-coding genomic allele data to predict antibiotic resistance phenotypes.

IMPORTANCE

Antibiotic resistance (AR) poses a global health threat as the efficacy of available antibiotics has rapidly eroded due to the widespread transmission of AR genes. Using Erm-dependent MLS resistance as a model, this study highlights the significance of non-coding genomic allelic variations. Through a comprehensive analysis of upstream regulatory elements within the erm family, we elucidated the evolutionary emergence and development of AR mechanisms. Leveraging population-wide machine learning (ML)-based genomic analysis, we transformed substantial non-random allelic variations into discernible clusters of elements, enabling precise prediction of MLS phenotypes from non-coding regions. These findings offer deeper insight into AR evolution and demonstrate the potential of harnessing non-coding genomic allele data for accurately predicting AR phenotypes.

Identification of novel single nucleotide variants in the drug resistance mechanism of Mycobacterium tuberculosis isolates by whole-genome analysis

BACKGROUND

Tuberculosis (TB) represents a major global health challenge. Drug resistance in Mycobacterium tuberculosis (MTB) poses a substantial obstacle to effective TB treatment. Identifying genomic mutations in MTB isolates holds promise for unraveling the underlying mechanisms of drug resistance in this bacterium.

METHODS

In this study, we investigated the roles of single nucleotide variants (SNVs) in MTB isolates resistant to four antibiotics (moxifloxacin, ofloxacin, amikacin, and capreomycin) through whole-genome analysis. We identified the drug-resistance-associated SNVs by comparing the genomes of MTB isolates with reference genomes using the MuMmer4 tool.

RESULTS

We observed a strikingly high proportion (94.2%) of MTB isolates resistant to ofloxacin, underscoring the current prevalence of drug resistance in MTB. An average of 3529 SNVs were detected in a single ofloxacin-resistant isolate, indicating a mutation rate of approximately 0.08% under the selective pressure of ofloxacin exposure. We identified a set of 60 SNVs associated with extensively drug-resistant tuberculosis (XDR-TB), among which 42 SNVs were non-synonymous mutations located in the coding regions of nine key genes (ctpI, desA3, mce1R, moeB1, ndhA, PE_PGRS4, PPE18, rpsA, secF). Protein structure modeling revealed that SNVs of three genes (PE_PGRS4, desA3, secF) are close to the critical catalytic active sites in the three-dimensional structure of the coding proteins.

CONCLUSION

This comprehensive study elucidates novel resistance mechanisms in MTB against antibiotics, paving the way for future design and development of anti-tuberculosis drugs.

Antimicrobial resistance in ocular infection: A review

Antimicrobial resistance (AMR) is a global public health threat with significant impact on treatment outcomes. The World Health Organization's Global Action Plan on AMR recommended strengthening the evidence base through surveillance programs and research. Comprehensive, timely data on AMR for organisms isolated from ocular infections are needed to guide treatment decisions and inform researchers and microbiologists of emerging trends. This article aims to provide an update on the development of AMR in ocular organisms, AMR in bacterial ocular infections and on AMR stewardship programs globally. The most common ocular pathogens are Pseudomonas aeruginosa, Staphylococcus spp., Streptococcus pneumoniae, and Haemophilus influenzae in ocular infections. A variety of studies and a few surveillance programs worldwide have reported on AMR in these infections over time. Fluoroquinolone resistance has increased particularly in Asia and North America. For conjunctivitis, the ARMOR cumulative study in the USA reported a slight decrease in resistance to ciprofloxacin. For keratitis, resistance to methicillin has remained stable for S. aureus and CoNS, while resistance to ciprofloxacin has decreased for MRSA globally. Methicillin-resistance and multidrug resistance are also emerging, requiring ongoing monitoring. Antimicrobial stewardship (AMS) programmes have a critical role in reducing the threat of AMR and improving treatment outcomes. To be successful AMS must be informed by up-to-date AMR surveillance data. As a profession it is timely for ophthalmology to act to prevent AMR leading to greater visual loss through supporting surveillance programmes and establishing AMS.

Multidrug-resistant tuberculosis

Tuberculosis (TB) remains the foremost cause of death by an infectious disease globally. Multidrug-resistant or rifampicin-resistant TB (MDR/RR-TB; resistance to rifampicin and isoniazid, or rifampicin alone) is a burgeoning public health challenge in several parts of the world, and especially Eastern Europe, Russia, Asia and sub-Saharan Africa. Pre-extensively drug-resistant TB (pre-XDR-TB) refers to MDR/RR-TB that is also resistant to a fluoroquinolone, and extensively drug-resistant TB (XDR-TB) isolates are additionally resistant to other key drugs such as bedaquiline and/or linezolid. Collectively, these subgroups are referred to as drug-resistant TB (DR-TB). All forms of DR-TB can be as transmissible as rifampicin-susceptible TB; however, it is more difficult to diagnose, is associated with higher mortality and morbidity, and higher rates of post-TB lung damage. The various forms of DR-TB often consume >50% of national TB budgets despite comprising <5-10% of the total TB case-load. The past decade has seen a dramatic change in the DR-TB treatment landscape with the introduction of new diagnostics and therapeutic agents. However, there is limited guidance on understanding and managing various aspects of this complex entity, including the pathogenesis, transmission, diagnosis, management and prevention of MDR-TB and XDR-TB, especially at the primary care physician level.

A cluster of six respiratory cultures positive for Mycobacterium xenopi -Clinical characteristics and genomic characterization

Mycobacterium xenopi is a slow growing non-tuberculous mycobacterium (NTM) isolated from water systems and has been associated with pseudo-outbreaks and pulmonary infections in humans. We observed a cluster of six respiratory cultures positive for M. xenopi within a six-month period at our institution, approximately double our normal isolation rate of this organism. Only three of the six cases met clinical, radiographic, and microbiologic criteria for NTM infection. An investigation led by our hospital's Healthcare Epidemiology and Infection Program found no epidemiologic link between the six patients. Three isolates underwent whole-genome sequencing (WGS) and phylogenetic analysis confirmed they were non-clonal. In vitro susceptibility data found the isolates were sensitive to macrolides, moxifloxacin, and rifabutin. Our findings suggest that isolation of M. xenopi from pulmonary specimens may be increasing, further defines the genomic population structure of this potentially emerging infection, and establishes WGS as a useful tool for outbreak investigation strain typing.

A Comprehensive Bioinformatics Resource Guide for Genome-Based Antimicrobial Resistance Studies

The use of high-throughput sequencing technologies and bioinformatic tools has greatly transformed microbial genome research. With the help of sophisticated computational tools, it has become easier to perform whole genome assembly, identify and compare different species based on their genomes, and predict the presence of genes responsible for proteins, antimicrobial resistance, and toxins. These bioinformatics resources are likely to continuously improve in quality, become more user-friendly to analyze the multiple genomic data, efficient in generating information and translating it into meaningful knowledge, and enhance our understanding of the genetic mechanism of AMR. In this manuscript, we provide an essential guide for selecting the popular resources for microbial research, such as genome assembly and annotation, antibiotic resistance gene profiling, identification of virulence factors, and drug interaction studies. In addition, we discuss the best practices in computer-oriented microbial genome research, emerging trends in microbial genomic data analysis, integration of multi-omics data, the appropriate use of machine-learning algorithms, and open-source bioinformatics resources for genome data analytics.

The Role of Next-Generation Sequencing (NGS) in the Management of Tuberculosis: Practical Review for Implementation in Routine

Next-generation sequencing (NGS) has modernized the field of tuberculosis (TB) research by enabling high-throughput sequencing of the entire genome of Mycobacterium tuberculosis (MTB), which is the causative agent of TB. NGS has provided insights into the genetic diversity of MTB, which are crucial for understanding the evolution and transmission of the disease, and it has facilitated the identification of drug-resistant strains, enabling rapid and accurate tailoring of treatment. However, the high cost and the technical complexities of NGS currently limit its widespread use in clinical settings. International recommendations are thus necessary to facilitate the interpretation of polymorphisms, and an experimental approach is still necessary to correlate them to phenotypic data. This review aims to present a comparative, step-by-step, and up-to-date review of the techniques available for the implementation of this approach in routine laboratory workflow. Ongoing research on NGS for TB holds promise for improving our understanding of the disease and for developing more efficacious treatments.

Discrete Dynamics of Warhead Modulation on Covalent Inhibition of Oxyr: A QM/MM Study

The bacterial transcriptional factor OxyR, a peroxide sensor conserved in bacterial virulence pathways, has the capability to exhibit exceptional reactivity toward hydrogen peroxide (H₂O₂). H₂O₂ is essential for oxidizing cysteine thiolates to maintain cellular redox homeostasis and is dispensable for bacterial growth that can potentially mitigate drug resistance, thus underlining OxyR as a valuable target. We employ quantum mechanics/molecular mechanics (QM/MM) umbrella sampling (US) simulations at the DFTB3/MM level of theory and propose a reaction mechanism with four potential covalent inhibitors. The potential of mean force reveals the direct role of intrinsic reactivity of inhibitors, for instance, benzothiophenes and modified experimental inhibitors with methyl oxo-enoate warhead-activated carbonyl samples in the first step of reaction, which shed light on the significance of proton transfer indispensable for full inhibition, whereas the nitrile inhibitor undergoes a stepwise mechanism with a small proton-transfer energy barrier and lower imaginary frequencies that materialize instantly after nucleophilic attack. To unveil the molecular determinants of respective binding affinities, transition states along the reaction path are optimized and characterized with B3LYP 6-31+G(d,p). Furthermore, the post-simulation analysis indicates the catalytic triad (His130/Cys199/Thr129), thermodynamically favored for inhibition, which restricts water molecules from acting as the potential source of protonation/deprotonation. This study thus serves as a preamble to add variation in the proposed structures and unveils the impact of functional groups lying in warheads that modulate the kinetics of proton transfer, which will certainly aid to design more selective and efficient irreversible inhibitors of OxyR.

SYNDEEP: a deep learning approach for the prediction of cancer drugs synergy

Drug combinations can be the prime strategy for increasing the initial treatment options in cancer therapy. However, identifying the combinations through experimental approaches is very laborious and costly. Notably, in vitro and/or in vivo examination of all the possible combinations might not be plausible. This study presented a novel computational approach to predicting synergistic drug combinations. Specifically, the deep neural network-based binary classification was utilized to develop the model. Various physicochemical, genomic, protein-protein interaction and protein-metabolite interaction information were used to predict the synergy effects of the combinations of different drugs. The performance of the constructed model was compared with shallow neural network (SNN), k-nearest neighbors (KNN), random forest (RF), support vector machines (SVMs), and gradient boosting classifiers (GBC). Based on our findings, the proposed deep neural network model was found to be capable of predicting synergistic drug combinations with high accuracy. The prediction accuracy and AUC metrics for this model were 92.21% and 97.32% in tenfold cross-validation. According to the results, the integration of different types of physicochemical and genomics features leads to more accurate prediction of synergy in cancer drugs.

Bioinformatics toolbox for exploring target mutation-induced drug resistance

Drug resistance is increasingly among the main issues affecting human health and threatening agriculture and food security. In particular, developing approaches to overcome target mutation-induced drug resistance has long been an essential part of biological research. During the past decade, many bioinformatics tools have been developed to explore this type of drug resistance, and they have become popular for elucidating drug resistance mechanisms in a low cost, fast and effective way. However, these resources are scattered and underutilized, and their strengths and limitations have not been systematically analyzed and compared. Here, we systematically surveyed 59 freely available bioinformatics tools for exploring target mutation-induced drug resistance. We analyzed and summarized these resources based on their functionality, data volume, data source, operating principle, performance, etc. And we concisely discussed the strengths, limitations and application examples of these tools. Specifically, we tested some predictive tools and offered some thoughts from the clinician's perspective. Hopefully, this work will provide a useful toolbox for researchers working in the biomedical, pesticide, bioinformatics and pharmaceutical engineering fields, and a good platform for non-specialists to quickly understand drug resistance prediction.

CRISPR-Cas-Guided Mutagenesis of Chromosome and Virulence Plasmid in Shigella flexneri by Cytosine Base Editing

Shigella is a Gram-negative bacterium that invades the human gut epithelium. The resulting infection, shigellosis, is the deadliest bacterial diarrheal disease. Much of the information about the genes dictating the pathophysiology of Shigella, both on the chromosome and the virulence plasmid, was obtained by classical reverse genetics. However, technical limitations of the prevalent mutagenesis techniques restrict the generation of mutants in a single reaction to a small number, preventing large-scale targeted mutagenesis of Shigella and the subsequent assessment of phenotype. We adopted a CRISPR-Cas-dependent approach, where a nickase Cas9 and cytidine deaminase fusion is guided by single guide RNA (sgRNA) to introduce targeted C→T transitions, resulting in internal stop codons and premature termination of translation. In proof-of-principle experiments using an mCherry fluorescent reporter, we were able to generate loss-of-function mutants in both Escherichia coli and Shigella flexneri with up to 100% efficacy. Using a modified fluctuation assay, we determined that under optimized conditions, the frequency of untargeted mutations introduced by the Cas9-deaminase fusion was in the same range as spontaneous mutations, making our method a safe choice for bacterial mutagenesis. Furthermore, we programmed the method to mutate well-characterized chromosomal and plasmid-borne Shigella flexneri genes and found the mutant phenotype to be similar to those of the reported gene deletion mutants, with no apparent polar effects at the phenotype level. This method can be used in a 96-well-plate format to increase the throughput and generate an array of targeted loss-of-function mutants in a few days. IMPORTANCE Loss-of-function mutagenesis is critical in understanding the physiological role of genes. Therefore, high-throughput techniques to generate such mutants are important for facilitating the assessment of gene function at a pace that matches systems biology approaches. However, to our knowledge, no such method was available for generating an array of single gene mutants in an important enteropathogen-Shigella. This pathogen causes high morbidity and mortality in children, and antibiotic-resistant strains are quickly emerging. Therefore, determination of the function of unknown Shigella genes is of the utmost importance to develop effective strategies to control infections. Our present work will bridge this gap by providing a rapid method for generating loss-of-function mutants. The highly effective and specific method has the potential to be programmed to generate multiple mutants in a single, massively parallel reaction. By virtue of plasmid compatibility, this method can be extended to other members of Enterobacteriaceae.

Food for Thought: Proteomics for Meat Safety

Foodborne bacteria interconnect food and human health. Despite significant progress in food safety regulation, bacterial contamination is still a serious public health concern and the reason for significant commercial losses. The screening of the microbiome in meals is one of the main aspects of food production safety influencing the health of the end-consumers. Our research provides an overview of proteomics findings in the field of food safety made over the last decade. It was believed that proteomics offered an accurate snapshot of the complex networks of the major biological machines called proteins. The proteomic methods for the detection of pathogens were armed with bioinformatics algorithms, allowing us to map the data onto the genome and transcriptome. The mechanisms of the interaction between bacteria and their environment were elucidated with unprecedented sensitivity, specificity, and depth. Using our web-based tool ScanBious for automated publication analysis, we analyzed over 48,000 scientific articles on antibiotic and disinfectant resistance and highlighted the benefits of proteomics for the food safety field. The most promising approach to studying safety in food production is the combination of classical genomic and metagenomic approaches and the advantages provided by proteomic methods with the use of panoramic and targeted mass spectrometry.

The gut microbiome of the healthy population in Kolkata, India, is a reservoir of antimicrobial resistance genes emphasizing the need of enforcing antimicrobial stewardship

Antimicrobial resistance (AMR) alleviation warrants antimicrobial stewardship (AS) entailing the indispensability of epidemiological surveillance. We undertook a small-scale surveillance in Kolkata to detect the presence of antimicrobial resistance genes (ARGs) in the healthy gut microbiome. We found that it was a reservoir of ARGs against common antibiotics. A targeted Polymerase Chain Reaction (PCR) and sequencing-based ARGs detection against tetracyclines, macrolides, trimethoprim, sulfamethoxazole, aminoglycosides, amphenicol, and mobile genetic element (MGE) markers were deployed in 25 fecal samples. Relative abundance and frequency of ARGs were calculated. We detected markers against all these classes of antibiotics. 100% samples carried aminoglycoside resistance marker and int1U. A comparison with our previously published diarrheal resistome from the same spatial and temporal frame revealed that a higher diversity of ARGs were detected in the community and a higher rate of isolation of tetC, msrA, tmp, and sul-2 was found. The presence of common markers in the two cohorts proves that the gut microbiome has been contaminated with ARGs and which are being disseminated among different ecosystems. This is an issue of discerning concern for public health. The study raises an alarming picture of the AMR crisis in low-middle and emergent economies. It emphasizes the strict enforcement of AS in the community.

In silico analyses of isoniazid and streptomycin resistance-associated mutations in Mycobacterium tuberculosis

Multi-drug resistant tuberculosis is categorised by the World Health Organisation (WHO) as a public health crisis. In silico techniques were used to probe the structural basis of Mycobacterium tuberculosis resistance to isoniazid and streptomycin. Isoniazid resistance-associated mutations in InhA were predicted to reduce the binding affinity of NADH to InhA, without affecting INH-NAD (competitive-inhibitor) binding. Perturbation of the mutated residues was predicted (with the AlloSigMA server) to modulate the free energy of allosteric modulation of key binding site residues F41, F149, Y158 and W222. These results suggest that allosteric modulation of the protein structure may be key to the mechanism by which isoniazid resistance-associated mutations act. Mutations in the methyltransferase glucose-inhibited division gene B (GidB) are associated with streptomycin resistance. Molecular docking was carried out to predict the structure of the GidB bound to its substrate (s-adenosyl methionine). The effects of streptomycin resistance-associated mutations in GidB on protein stability and substrate binding were predicted (using SDM and mCSM-lig). All GidB mutants were predicted to disfavour SAM binding.

Droplets microfluidics platform-A tool for single cell research

Cells are the most basic structural and functional units of living organisms. Studies of cell growth, differentiation, apoptosis, and cell-cell interactions can help scientists understand the mysteries of living systems. However, there is considerable heterogeneity among cells. Great differences between individuals can be found even within the same cell cluster. Cell heterogeneity can only be clearly expressed and distinguished at the level of single cells. The development of droplet microfluidics technology opens up a new chapter for single-cell analysis. Microfluidic chips can produce many nanoscale monodisperse droplets, which can be used as small isolated micro-laboratories for various high-throughput, precise single-cell analyses. Moreover, gel droplets with good biocompatibility can be used in single-cell cultures and coupled with biomolecules for various downstream analyses of cellular metabolites. The droplets are also maneuverable; through physical and chemical forces, droplets can be divided, fused, and sorted to realize single-cell screening and other related studies. This review describes the channel design, droplet generation, and control technology of droplet microfluidics and gives a detailed overview of the application of droplet microfluidics in single-cell culture, single-cell screening, single-cell detection, and other aspects. Moreover, we provide a recent review of the application of droplet microfluidics in tumor single-cell immunoassays, describe in detail the advantages of microfluidics in tumor research, and predict the development of droplet microfluidics at the single-cell level.

Meta-analysis of sputum microbiome studies identifies airway disease-specific taxonomic and functional signatures

Introduction. Studying taxonomic and functional signatures of respiratory microbiomes provide a better understanding of airway diseases.Gap Statement. Several human airway metagenomics studies have identified taxonomic and functional features restricted to a single disease condition and the findings are not comparable across airway diseases due to use of different samples, NGS platforms, and bioinformatics databases and tools.Aim. To study the microbial taxonomic and functional components of sputum microbiome across airway diseases and healthy smokers.Methodology. Here, 57 whole metagenome shotgun sequencing (WMSS) runs coming from the sputum of five airway diseases: asthma, bronchiectasis, chronic obstructive pulmonary diseases (COPD), cystic fibrosis (CF), tuberculosis (TB), and healthy smokers as the control were reanalysed using a common WMSS analysis pipeline.Results. Shannon's index (alpha diversity) of the healthy smoker group was the highest among all. The beta diversity showed that the sputum microbiome is distinct in major airway diseases such as asthma, COPD and cystic fibrosis. The microbial composition based on differential analysis showed that there are specific markers for each airway disease like Acinetobacter bereziniae as a marker for COPD and Achromobacter xylosoxidans as a marker of cystic fibrosis. Pathways and metabolites identified from the sputum microbiome of these five diseases and healthy smokers also show specific markers. 'ppGpp biosynthesis' and 'purine ribonucleosides degradation' pathways were identified as differential markers for bronchiectasis and COPD. In this meta-analysis, besides bacteria kingdom, Aspergillus fumigatus was detected in asthma and COPD, and Roseolovirus human betaherpesvirus 7 was detected in COPD. Our analysis showed that the majority of the gene families specific to the drug-resistant associated genes were detected from opportunistic pathogens across all the groups.Conclusion. In summary, the specific species in the sputum of airway diseases along with the microbial features like specific gene families, pathways, and metabolites were identified which can be explored for better diagnosis and therapy.

Drug resistant Mycobacterium tuberculosis in Oman: resistance-conferring mutations and lineage diversity

Background

The Sultanate of Oman is country a low TB-incidence, with less than seven cases per 10⁵ population detected in 2020. Recent years have witnessed a persistence in TB cases, with sustained incidence rate among expatriates and limited reduction among Omanis. This pattern suggests transmission from the migrant population. The present study examined the genetic profile and drug resistance-conferring mutations in Mycobacterium tuberculosis collected from Omanis and expatriates to recognise possible causes of disease transmission.

Methods

We examined M. tuberculosis cultured positive samples, collected from Omanis (n = 1,344) and expatriates (n = 1,203) between 2009 and 2018. These isolates had a known in vitro susceptibility profile to first line anti-TB, Streptomycin (SM), Isoniazid (INH), Rifampicin (RIF), Ethambutol (EMB) and Pyrazinamide (PZA). The diversity of the isolates was assessed by spacer oligo-typing (spoligotyping). Drug resistance-conferring mutations resulted from full-length sequence of nine genes (katG, inhA, ahpc, rpoB, rpsL, rrs, embB, embC, pncA) and their phenotypic relationship were analysed.

Results

In total, 341/2192 (13.4%), M. tuberculosis strains showed resistance to any drug, comprising mono-resistance (MR) (242, 71%), poly-resistance (PR) (40, 11.7%) and multi-drug resistance (MDR) (59, 17.3%). The overall rate of resistance among Omanis and expatriates was similar; however, MDR and PZA^R were significantly higher among Omanis, while INH^R was greater among expatriates. Mutations rpsL K43R and rpoB S450L were linked to Streptomycin (SM^R) and Rifampicin resistance (RIF^R) respectively. Whereas, katG S315T and inhA -C15T/G-17T were associated with Isoniazid resistance (INH^R). The resistance patterns (mono-resistant, poly-resistant and MDR) and drug resistance-conferring mutations were found in different spoligo-lineages. rpsL K43R, katG S315T and rpoB S450L mutations were significantly higher in Beijing strains.

Conclusions

Diverse drug resistant M. tuberculosis strains exist in Oman, with drug resistance-conferring mutations widespread in multiple spoligo-lineages, indicative of a large resistance reservoir. Beijing's M. tuberculosis lineage was associated with MDR, and multiple drug resistance-conferring mutations, favouring the hypothesis of migration as a possible source of resistant lineages in Oman.

Sensitivity of different DNA extraction methods and PCR to detect resistance in patients with leprosy stratified by the bacilloscopic index

Introduction

Antimicrobial resistance in leprosy is an emerging problem, and the quantitative impact of low bacilloscopic indexes (BIs) on the sensitivity of molecular tests is unknown. We aimed to evaluate the sensitivity of gene sequencing for the detection of mutations related to antimicrobial resistance in Mycobacterium leprae in patients with low BIs using an analytical model.

Methods

Patients with leprosy were included and divided into two groups depending on their BIs (≥ 2+ and < 2+). The sensitivities of the two DNA extraction methods were compared after amplifying and sequencing the repetitive element (RLEP), folP1, rpoB and gyrA in M. leprae.

Results

We included 56 patients with leprosy: 35 had BIs less than 2+ (22 had negative slit-skin smear [SSS] results) and 21 patients had BIs greater than or equal to 2+. The sensitivity of the amplification of the RLEP target and the gene sequencing of folP1, rpoB and gyrA was 50 to 70% lower in patients with a BI less than 2+ and was significantly reduced in patients with lower BIs for all targets (p < 0.001). One patient had a mutation in the folP1 gene, and 14 patients had mutations in the gyrA gene, but no mutations related to antimicrobial resistance were found.

Conclusions

We can conclude that the sensitivity of molecular tests is directly related to the BI, but these tests can still detect up to 20% of the targets in patients with BIs < 2+. New strategies to improve the sensitivity for detecting antimicrobial resistance in leprosy patients and reasonable clinical criteria for follow-up and the introduction of alternative treatments must be developed.

Deciphering Isoniazid Drug Resistance Mechanisms on Dimeric Mycobacterium tuberculosis KatG via Post-molecular Dynamics Analyses Including Combined Dynamic Residue Network Metrics

Resistance mutations in Mycobacterium tuberculosis (Mtb) catalase peroxidase protein (KatG), an essential enzyme in isoniazid (INH) activation, reduce the sensitivity of Mtb to first-line drugs, hence presenting challenges in tuberculosis (TB) management. Thus, understanding the mutational imposed resistance mechanisms remains of utmost importance in the quest to reduce the TB burden. Herein, effects of 11 high confidence mutations in the KatG structure and residue network communication patterns were determined using extensive computational approaches. Combined traditional post-molecular dynamics analysis and comparative essential dynamics revealed that the mutant proteins have significant loop flexibility around the heme binding pocket and enhanced asymmetric protomer behavior with respect to wild-type (WT) protein. Heme contact analysis between WT and mutant proteins identified a reduction to no contact between heme and residue His270, a covalent bond vital for the heme-enabled KatG catalytic activity. Betweenness centrality calculations showed large hub ensembles with new hubs especially around the binding cavity and expanded to the dimerization domain via interface in the mutant systems, providing possible compensatory allosteric communication paths for the active site as a result of the mutations which may destabilize the heme binding pocket and the loops in its vicinity. Additionally, an interesting observation came from Eigencentrality hubs, most of which are located in the C-terminal domain, indicating relevance of the domain in the protease functionality. Overall, our results provide insight toward the mechanisms involved in KatG-INH resistance in addition to identifying key regions in the enzyme functionality, which can be used for future drug design.

Understanding the Genetic Diversity of Mycobacterium africanum Using Phylogenetics and Population Genomics Approaches

A total of two lineages of Mycobacterium tuberculosis var. africanum (Maf), L5 and L6, which are members of the Mycobacterium tuberculosis complex (MTBC), are responsible for causing tuberculosis in West Africa. Regions of difference (RDs) are usually used for delineation of MTBC. With increased data availability, single nucleotide polymorphisms (SNPs) promise to provide better resolution. Publicly available 380 Maf samples were analyzed for identification of “core-cluster-specific-SNPs,” while additional 270 samples were used for validation. RD-based methods were used for lineage-assignment, wherein 31 samples remained unidentified. The genetic diversity of Maf was estimated based on genome-wide SNPs using phylogeny and population genomics approaches. Lineage-based clustering (L5 and L6) was observed in the whole genome phylogeny with distinct sub-clusters. Population stratification using both model-based and de novo approaches supported the same observations. L6 was further delineated into three sub-lineages (L6.1–L6.3), whereas L5 was grouped as L5.1 and L5.2 based on the occurrence of RD711. L5.1 and L5.2 were further divided into two (L5.1.1 and L5.1.2) and four (L5.2.1–L5.2.4) sub-clusters, respectively. Unassigned samples could be assigned to definite lineages/sub-lineages based on clustering observed in phylogeny along with high-confidence posterior membership scores obtained during population stratification. Based on the (sub)-clusters delineated, “core-cluster-specific-SNPs” were derived. Synonymous SNPs (137 in L5 and 128 in L6) were identified as biomarkers and used for validation. Few of the cluster-specific missense variants in L5 and L6 belong to the central carbohydrate metabolism pathway which include His6Tyr (Rv0946c), Glu255Ala (Rv1131), Ala309Gly (Rv2454c), Val425Ala and Ser112Ala (Rv1127c), Gly198Ala (Rv3293) and Ile137Val (Rv0363c), Thr421Ala (Rv0896), Arg442His (Rv1248c), Thr218Ile (Rv1122), and Ser381Leu (Rv1449c), hinting at the differential growth attenuation. Genes harboring multiple (sub)-lineage-specific “core-cluster” SNPs such as Lys117Asn, Val447Met, and Ala455Val (Rv0066c; icd2) present across L6, L6.1, and L5, respectively, hinting at the association of these SNPs with selective advantage or host-adaptation. Cluster-specific SNPs serve as additional markers along with RD-regions for Maf delineation. The identified SNPs have the potential to provide insights into the genotype–phenotype correlation and clues for endemicity of Maf in the African population.

Recent Advances and Mechanistic Insights into Antibacterial Activity, Antibiofilm Activity, and Cytotoxicity of Silver Nanoparticles

The substantial increase in multidrug-resistant (MDR) pathogenic bacteria is a major threat to global health. Recently, the Centers for Disease Control and Prevention reported possibilities of greater deaths due to bacterial infections than cancer. Nanomaterials, especially small-sized (size ≤10 nm) silver nanoparticles (AgNPs), can be employed to combat these deadly bacterial diseases. However, high reactivity, instability, susceptibility to fast oxidation, and cytotoxicity remain crucial shortcomings for their uptake and clinical application. In this review, we discuss various AgNPs-based approaches to eradicate bacterial infections and provide comprehensive mechanistic insights and recent advances in antibacterial activity, antibiofilm activity, and cytotoxicity (both in vitro and in vivo) of AgNPs. The mechanistic of antimicrobial activity involves four steps: (i) adhesion of AgNPs to cell wall/membrane and its disruption; (ii) intracellular penetration and damage; (iii) oxidative stress; and (iv) modulation of signal transduction pathways. Numerous factors affecting the bactericidal activity of AgNPs such as shape, size, crystallinity, pH, and surface coating/charge have also been described in detail. The review also sheds light on antimicrobial photodynamic therapy and the role of AgNPs versus Ag⁺ ions release in bactericidal activities. In addition, different methods of synthesis of AgNPs have been discussed in brief.

The Biological and Clinical Aspects of a Latent Tuberculosis Infection

Tuberculosis (TB), caused by bacilli from the Mycobacterium tuberculosis complex, remains a serious global public health problem, representing one of the main causes of death from infectious diseases. About one quarter of the world’s population is infected with Mtb and has a latent TB infection (LTBI). According to the World Health Organization (WHO), an LTBI is characterized by a lasting immune response to Mtb antigens without any TB symptoms. Current LTBI diagnoses and treatments are based on this simplified definition, although an LTBI involves a broad range of conditions, including when Mtb remains in the body in a persistent form and the immune response cannot be detected. The study of LTBIs has progressed in recent years; however, many biological and medical aspects of an LTBI are still under discussion. This review focuses on an LTBI as a broad spectrum of states, both of the human body, and of Mtb cells. The problems of phenotypic insusceptibility, diagnoses, chemoprophylaxis, and the necessity of treatment are discussed. We emphasize the complexity of an LTBI diagnosis and its treatment due to its ambiguous nature. We consider alternative ways of differentiating an LTBI from active TB, as well as predicting TB reactivation based on using mycobacterial “latency antigens” for interferon gamma release assay (IGRA) tests and the transcriptomic analysis of human blood cells.

Paradox of complex diversity: Challenges in the diagnosis and management of bacterial keratitis

Bacterial keratitis continues to be one of the leading causes of corneal blindness in the developed as well as the developing world, despite swift progress since the dawn of the "anti-biotic era". Although, we are expeditiously developing our understanding about the different causative organisms and associated pathology leading to keratitis, extensive gaps in knowledge continue to dampen the efforts for early and accurate diagnosis, and management in these patients, resulting in poor clinical outcomes. The ability of the causative bacteria to subdue the therapeutic challenge stems from their large genome encoding complex regulatory networks, variety of unique virulence factors, and rapid secretion of tissue damaging proteases and toxins. In this review article, we have provided an overview of the established classical diagnostic techniques and therapeutics for keratitis caused by various bacteria. We have extensively reported our recent in-roads through novel tools for accurate diagnosis of mono- and poly-bacterial corneal infections. Furthermore, we outlined the recent progress by our group and others in understanding the sub-cellular genomic changes that lead to antibiotic resistance in these organisms. Finally, we discussed in detail, the novel therapies and drug delivery systems in development for the efficacious management of bacterial keratitis.

Wastewater-Based Surveillance of Antibiotic Resistance Genes Associated with Tuberculosis Treatment Regimen in KwaZulu Natal, South Africa

Essential components of public health include strengthening the surveillance of infectious diseases and developing early detection and prevention policies. This is particularly important for drug-resistant tuberculosis (DR-TB), which can be explored by using wastewater-based surveillance. This study aimed to use molecular techniques to determine the occurrence and concentration of antibiotic-resistance genes (ARGs) associated with tuberculosis (TB) resistance in untreated and treated wastewater. Raw/untreated and treated (post-chlorination) wastewater samples were taken from three wastewater treatment plants (WWTPs) in South Africa. The ARGs were selected to target drugs used for first- and second-line TB treatment. Both conventional polymerase chain reaction (PCR) and the more advanced droplet digital PCR (ddPCR) were evaluated as surveillance strategies to determine the distribution and concentration of the selected ARGs. The most abundant ARG in the untreated wastewater was the rrs gene, associated with resistance to the aminoglycosides, specifically streptomycin, with median concentration ranges of 4.69–5.19 log copies/mL. In contrast, pncA gene, associated with resistance to the TB drug pyrazinamide, was the least detected (1.59 to 2.27 log copies/mL). Resistance genes associated with bedaquiline was detected, which is a significant finding because this is a new drug introduced in South Africa for the treatment of multi-drug resistant TB. This study, therefore, establishes the potential of molecular surveillance of wastewater for monitoring antibiotic resistance to TB treatment in communities.

Application of Computational Methods in Understanding Mutations in Mycobacterium tuberculosis Drug Resistance

The emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb) impedes the End TB Strategy by the World Health Organization aiming for zero deaths, disease, and suffering at the hands of tuberculosis (TB). Mutations within anti-TB drug targets play a major role in conferring drug resistance within Mtb; hence, computational methods and tools are being used to understand the mechanisms by which they facilitate drug resistance. In this article, computational techniques such as molecular docking and molecular dynamics are applied to explore point mutations and their roles in affecting binding affinities for anti-TB drugs, often times lowering the protein’s affinity for the drug. Advances and adoption of computational techniques, chemoinformatics, and bioinformatics in molecular biosciences and resources supporting machine learning techniques are in abundance, and this has seen a spike in its use to predict mutations in Mtb. This article highlights the importance of molecular modeling in deducing how point mutations in proteins confer resistance through destabilizing binding sites of drugs and effectively inhibiting the drug action.

Analysis of the application of a gene chip method for detecting Mycobacterium tuberculosis drug resistance in clinical specimens: a retrospective study

Most Mycobacterium tuberculosis (Mtb) resistant to rifampicin (RIF) has mutations in the rpoB gene, while most Mtb resistant to isoniazid (INH) has mutations in the katG gene or inhA promoter. We used gene chip technology to detect mutations in these genes to determine the resistance of Mtb to RIF and INH. A total of 4148 clinical specimens with sputum smear positivity for acid-fast bacilli (AFB) were detected. Then, taking the results of the drug sensitivity test (DST) as the reference standard, the detection efficiency of sputum samples from different grades of positive smears was compared in detail. We found that the sensitivity of the gene chip method for detecting sputum samples with a grade ≥ AFB 2 + was higher than that of sputum samples with a grade ≤ AFB 1 + (P < 0.05). When the grade of the sample was ≤ AFB 1 +, the sensitivity of the gene chip method was 72.6% for RIF, 67.3% for INH, and 60.0% for MDR-TB. When the grade of the sample was ≥ AFB 2 +, the sensitivity of the gene chip method was 84.5% for RIF, 78.2% for INH, and 73.9% for MDR-TB. The results show that gene chip technology can be directly used to diagnose drug-resistant tuberculosis in clinical specimens, and the diagnostic efficiency for the detection of sputum specimens with a grade ≥ AFB 2 + is better than that of other sputum specimens.

Bacterial evolution during human infection: Adapt and live or adapt and die

Microbes are constantly evolving. Laboratory studies of bacterial evolution increase our understanding of evolutionary dynamics, identify adaptive changes, and answer important questions that impact human health. During bacterial infections in humans, however, the evolutionary parameters acting on infecting populations are likely to be much more complex than those that can be tested in the laboratory. Nonetheless, human infections can be thought of as naturally occurring in vivo bacterial evolution experiments, which can teach us about antibiotic resistance, pathogenesis, and transmission. Here, we review recent advances in the study of within-host bacterial evolution during human infection and discuss practical considerations for conducting such studies. We focus on 2 possible outcomes for de novo adaptive mutations, which we have termed "adapt-and-live" and "adapt-and-die." In the adapt-and-live scenario, a mutation is long lived, enabling its transmission on to other individuals, or the establishment of chronic infection. In the adapt-and-die scenario, a mutation is rapidly extinguished, either because it carries a substantial fitness cost, it arises within tissues that block transmission to new hosts, it is outcompeted by more fit clones, or the infection resolves. Adapt-and-die mutations can provide rich information about selection pressures in vivo, yet they can easily elude detection because they are short lived, may be more difficult to sample, or could be maladaptive in the long term. Understanding how bacteria adapt under each of these scenarios can reveal new insights about the basic biology of pathogenic microbes and could aid in the design of new translational approaches to combat bacterial infections.

Insect antimicrobial peptides: potential weapons to counteract the antibiotic resistance

Misuse and overuse of antibiotics have contributed in the last decades to a phenomenon known as antibiotic resistance which is currently considered one of the principal threats to global public health by the World Health Organization. The aim to find alternative drugs has been demonstrated as a real challenge. Thanks to their biodiversity, insects represent the largest class of organisms in the animal kingdom. The humoral immune response includes the production of antimicrobial peptides (AMPs) that are released into the insect hemolymph after microbial infection. In this review, we have focused on insect immune responses, particularly on AMP characteristics, their mechanism of action and applications, especially in the biomedical field. Furthermore, we discuss the Toll, Imd, and JAK-STAT pathways that activate genes encoding for the expression of AMPs. Moreover, we focused on strategies to improve insect peptides stability against proteolytic susceptibility such as D-amino acid substitutions, N-terminus modification, cyclization and dimerization.

Akim A, Chong PP. Approaches for Mitigating Microbial Biofilm-Related Drug Resistance: A Focus on Micro- and Nanotechnologies

Biofilms play an essential role in chronic and healthcare-associated infections and are more resistant to antimicrobials compared to their planktonic counterparts due to their (1) physiological state, (2) cell density, (3) quorum sensing abilities, (4) presence of extracellular matrix, (5) upregulation of drug efflux pumps, (6) point mutation and overexpression of resistance genes, and (7) presence of persister cells. The genes involved and their implications in antimicrobial resistance are well defined for bacterial biofilms but are understudied in fungal biofilms. Potential therapeutics for biofilm mitigation that have been reported include (1) antimicrobial photodynamic therapy, (2) antimicrobial lock therapy, (3) antimicrobial peptides, (4) electrical methods, and (5) antimicrobial coatings. These approaches exhibit promising characteristics for addressing the impending crisis of antimicrobial resistance (AMR). Recently, advances in the micro- and nanotechnology field have propelled the development of novel biomaterials and approaches to combat biofilms either independently, in combination or as antimicrobial delivery systems. In this review, we will summarize the general principles of clinically important microbial biofilm formation with a focus on fungal biofilms. We will delve into the details of some novel micro- and nanotechnology approaches that have been developed to combat biofilms and the possibility of utilizing them in a clinical setting.

Drug-sensitivity test and analysis of drug-resistant mutations in Mycobacterium tuberculosis isolates from Kashgar, China

Introduction

In recent years, drug-resistant Mycobacterium tuberculosis strains have gradually become widespread. Most drug resistance is related to specific mutations. We investigated M. tuberculosis drug resistance in the Kashgar area, China.

Methods

The drug-susceptibility test was conducted to clinical isolates of M. tuberculosis. Genomic-sequencing technology was used for the drug-resistant strains and the significance of DNA sequencing as a rapid aid for drug-resistance detection and the diagnosis method was evaluated.

Results

The resistance rates of clinical isolates to rifampicin (RFP), isoniazid (INH), streptomycin (SM), ethambutol (EMB), and ofloxacin (OFX) were, respectively, 4.4%, 12.3%, 8.8%, 2.6%, and 3.5%. The single- and multi-drug resistance rates were, respectively, 80.0% and 20.0%. The resistance genes RopB, katG, InhA, RpsL, rrs, gyrA, and embB displayed codon mutations, while InhA was mutated in its promoter region. Kappa scores, evaluating the consistency between DNA sequencing and the resistance ratio methods for the detection of isolates’ resistance to RFP, INH, SM, OFX, and EMB, were 1, 0.955, 0.721, 0.796, and 1, respectively.

Conclusion

The resistance rate of INH and SM is relatively high in the Kashgar area. Detection of mutations in RopB, katG, InhA, RpsL, rrs, gyrA, and embB by DNA sequencing can predict drug resistance of M. tuberculosis strains with high sensitivity and specificity, and can be used for diagnosis.

HARP: a database of structural impacts of systematic missense mutations in drug targets of Mycobacterium leprae

Computational Saturation Mutagenesis is an in-silico approach that employs systematic mutagenesis of each amino acid residue in the protein to all other amino acid types, and predicts changes in thermodynamic stability and affinity to the other subunits/protein counterparts, ligands and nucleic acid molecules. The data thus generated are useful in understanding the functional consequences of mutations in antimicrobial resistance phenotypes. In this study, we applied computational saturation mutagenesis to three important drug-targets in Mycobacterium leprae (M. leprae) for the drugs dapsone, rifampin and ofloxacin namely Dihydropteroate Synthase (DHPS), RNA Polymerase (RNAP) and DNA Gyrase (GYR), respectively. M. leprae causes leprosy and is an obligate intracellular bacillus with limited protein structural information associating mutations with phenotypic resistance outcomes in leprosy. Experimentally solved structures of DHPS, RNAP and GYR of M. leprae are not available in the Protein Data Bank, therefore, we modelled the structures of these proteins using template-based comparative modelling and introduced systematic mutations in each model generating 80,902 mutations and mutant structures for all the three proteins. Impacts of mutations on stability and protein-subunit, protein-ligand and protein-nucleic acid affinities were computed using various in-house developed and other published protein stability and affinity prediction software. A consensus impact was estimated for each mutation using qualitative scoring metrics for physicochemical properties and by a categorical grouping of stability and affinity predictions. We developed a web database named HARP (a database of Hansen's Disease Antimicrobial Resistance Profiles), which is accessible at the URL - https://harp-leprosy.org and provides the details to each of these predictions.