HGDiscovery: an online tool providing functional and phenotypic information on novel variants of homogentisate 1,2- dioxigenase

Alkaptonuria (AKU), a rare genetic disorder, is characterized by the accumulation of homogentisic acid (HGA) in the body. Affected individuals lack functional levels of an enzyme required to breakdown HGA. Mutations in the homogentisate 1,2-dioxygenase (HGD) gene cause AKU and they are responsible for deficient levels of functional HGD, which, in turn, leads to excess levels of HGA. Although HGA is rapidly cleared from the body by the kidneys, in the long term it starts accumulating in various tissues, especially cartilage. Over time (rarely before adulthood), it eventually changes the color of affected tissue to slate blue or black. Here we report a comprehensive mutation analysis of 111 pathogenic and 190 non-pathogenic HGD missense mutations using protein structural informa-tion. Using our comprehensive suite of graph-based signature methods, mCSM complemented with sequence-based tools, we studied the functional and molecular consequences of each mutation on protein stability, inter-action and evolutionary conservation. The scores generated from the structure and sequence-based tools were used to train a supervised machine learning algorithm with 89% accuracy. The empirical classifier was used to generate the variant phenotype for novel HGD missense mutations. All this information is deployed as a user friendly freely available web server called HGDiscovery (https://biosig.lab.uq.edu.au/hgdiscovery/)

Empirical ways to identify novel Bedaquiline resistance mutations in AtpE.

Clinical resistance against Bedaquiline, the first new anti-tuberculosis compound with a novel mechanism of action in over 40 years, has already been detected in Mycobacterium tuberculosis. As a new drug, however, there is currently insufficient clinical data to facilitate reliable and timely identification of genomic determinants of resistance. Here we investigate the structural basis for M. tuberculosis associated bedaquiline resistance in the drug target, AtpE. Together with the 9 previously identified resistance-associated variants in AtpE, 54 non-resistance-associated mutations were identified through comparisons of bedaquiline susceptibility across 23 different mycobacterial species. Computational analysis of the structural and functional consequences of these variants revealed that resistance associated variants were mainly localized at the drug binding site, disrupting key interactions with bedaquiline leading to reduced binding affinity. This was used to train a supervised predictive algorithm, which accurately identified likely resistance mutations (93.3% accuracy). Application of this model to circulating variants present in the Asia-Pacific region suggests that current circulating variants are likely to be susceptible to bedaquiline. We have made this model freely available through a user-friendly web interface called SUSPECT-BDQ, StrUctural Susceptibility PrEdiCTion for bedaquiline (http://biosig.unimelb.edu.au/suspect_bdq/). This tool could be useful for the rapid characterization of novel clinical variants, to help guide the effective use of bedaquiline, and to minimize the spread of clinical resistance.M.K was funded by the Melbourne Research Scholarship. D.B.A was funded by a Newton Fund RCUK-CONFAP Grant awarded by The Medical Research Council (MRC) and Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) (MR/M026302/1), the Jack Brockhoff Foundation (JBF 4186, 2016), and a C. J. Martin Research Fellowship from the National Health and Medical Research Council (NHMRC) of Australia (APP1072476). The Vietnam genomic dataset was funded by a NHMRC Australia grant (APP1056689) to SJD and KEH. Supported in part by the Victorian Government's OIS Program

Methods used in the spatial analysis of tuberculosis epidemiology: a systematic review

Background: Tuberculosis (TB) transmission often occurs within a household or community, leading to heterogeneous spatial patterns. However, apparent spatial clustering of TB could reflect ongoing transmission or co-location of risk factors and can vary considerably depending on the type of data available, the analysis methods employed and the dynamics of the underlying population. Thus, we aimed to review methodological approaches used in the spatial analysis of TB burden. Methods: We conducted a systematic literature search of spatial studies of TB published in English using Medline, Embase, PsycInfo, Scopus and Web of Science databases with no date restriction from inception to 15 February 2017. The protocol for this systematic review was prospectively registered with PROSPERO (CRD42016036655). Results: We identified 168 eligible studies with spatial methods used to describe the spatial distribution (n = 154), spatial clusters (n = 73), predictors of spatial patterns (n = 64), the role of congregate settings (n = 3) and the household (n = 2) on TB transmission. Molecular techniques combined with geospatial methods were used by 25 studies to compare the role of transmission to reactivation as a driver of TB spatial distribution, finding that geospatial hotspots are not necessarily areas of recent transmission. Almost all studies used notification data for spatial analysis (161 of 168), although none accounted for undetected cases. The most common data visualisation technique was notification rate mapping, and the use of smoothing techniques was uncommon. Spatial clusters were identified using a range of methods, with the most commonly employed being Kulldorff's spatial scan statistic followed by local Moran's I and Getis and Ord's local Gi(d) tests. In the 11 papers that compared two such methods using a single dataset, the clustering patterns identified were often inconsistent. Classical regression models that did not account for spatial dependence were commonly used to predict spatial TB risk. In all included studies, TB showed a heterogeneous spatial pattern at each geographic resolution level examined. Conclusions: A range of spatial analysis methodologies has been employed in divergent contexts, with all studies demonstrating significant heterogeneity in spatial TB distribution. Future studies are needed to define the optimal method for each context and should account for unreported cases when using notification data where possible. Future studies combining genotypic and geospatial techniques with epidemiologically linked cases have the potential to provide further insights and improve TB control

Structure guided prediction of Pyrazinamide resistance mutations in pncA

Abstract: Pyrazinamide plays an important role in tuberculosis treatment; however, its use is complicated by side-effects and challenges with reliable drug susceptibility testing. Resistance to pyrazinamide is largely driven by mutations in pyrazinamidase (pncA), responsible for drug activation, but genetic heterogeneity has hindered development of a molecular diagnostic test. We proposed to use information on how variants were likely to affect the 3D structure of pncA to identify variants likely to lead to pyrazinamide resistance. We curated 610 pncA mutations with high confidence experimental and clinical information on pyrazinamide susceptibility. The molecular consequences of each mutation on protein stability, conformation, and interactions were computationally assessed using our comprehensive suite of graph-based signature methods, mCSM. The molecular consequences of the variants were used to train a classifier with an accuracy of 80%. Our model was tested against internationally curated clinical datasets, achieving up to 85% accuracy. Screening of 600 Victorian clinical isolates identified a set of previously unreported variants, which our model had a 71% agreement with drug susceptibility testing. Here, we have shown the 3D structure of pncA can be used to accurately identify pyrazinamide resistance mutations. SUSPECT-PZA is freely available at: http://biosig.unimelb.edu.au/suspect_pza/

Mycobacterium tuberculosis genome mutations and fitness cost: molecular and epidemiological modelling of functional implications

© 2021 Malancha KarmakarIdentification of Mycobacterium tuberculosis (Mtb) increasingly involves characterising large sections of genetic material, such as through whole genome sequencing. While some mutations identified through these techniques are well characterised and strongly associated with anti-tuberculous drug resistance, such molecular methods frequently identify mutations with unknown significance or limited understanding of associated functional biological pathways. In this PhD, I have developed computational protein structural tools and mathematical models of TB transmission, that use genomic data to understand the impact of genomic changes and predict the consequences with regards to transmissibility and drug susceptibility of Mtb. Drug resistant mutations often carry both a selective advantage and a fitness cost, which can be reflected by the changes in protein structure and function. I developed a pipeline that captured the molecular consequences of coding mutations on protein stability, dynamics and interactions. Using my pipeline to evaluate the mechanistic consequences of mutations, I applied it to the real-time genomic analysis of a Victorian tuberculosis patient. The analysis led to identification of a novel resistant strain and altered patient treatment – the first reported use of structural information to guide clinical resistance detection. The information was then used to inform a compartmental epidemiological model of Mtb transmission in order to understand the rise of drug resistance in two high TB-incidence setting. Using a adaptive metropolis algorithm, I estimated drug resistance amplification proportions for two first-line anti-tuberculosis drugs, and explored how structural changes may alter the fitness landscape and transmission dynamics. The work highlighted the power of combining genomic, epidemiological and structural information in the fight against tuberculosis, and presents examples of application across the spectrum from laboratory, clinical and programmatic contexts. This work has further laid the foundation to rapidly apply and translate this approach to other infectious and non-infectious diseases

Empirical ways to identify novel Bedaquiline resistance mutations in AtpE.

Clinical resistance against Bedaquiline, the first new anti-tuberculosis compound with a novel mechanism of action in over 40 years, has already been detected in Mycobacterium tuberculosis. As a new drug, however, there is currently insufficient clinical data to facilitate reliable and timely identification of genomic determinants of resistance. Here we investigate the structural basis for M. tuberculosis associated bedaquiline resistance in the drug target, AtpE. Together with the 9 previously identified resistance-associated variants in AtpE, 54 non-resistance-associated mutations were identified through comparisons of bedaquiline susceptibility across 23 different mycobacterial species. Computational analysis of the structural and functional consequences of these variants revealed that resistance associated variants were mainly localized at the drug binding site, disrupting key interactions with bedaquiline leading to reduced binding affinity. This was used to train a supervised predictive algorithm, which accurately identified likely resistance mutations (93.3% accuracy). Application of this model to circulating variants present in the Asia-Pacific region suggests that current circulating variants are likely to be susceptible to bedaquiline. We have made this model freely available through a user-friendly web interface called SUSPECT-BDQ, StrUctural Susceptibility PrEdiCTion for bedaquiline (http://biosig.unimelb.edu.au/suspect_bdq/). This tool could be useful for the rapid characterization of novel clinical variants, to help guide the effective use of bedaquiline, and to minimize the spread of clinical resistance.M.K was funded by the Melbourne Research Scholarship. D.B.A was funded by a Newton Fund RCUK-CONFAP Grant awarded by The Medical Research Council (MRC) and Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) (MR/M026302/1), the Jack Brockhoff Foundation (JBF 4186, 2016), and a C. J. Martin Research Fellowship from the National Health and Medical Research Council (NHMRC) of Australia (APP1072476). The Vietnam genomic dataset was funded by a NHMRC Australia grant (APP1056689) to SJD and KEH. Supported in part by the Victorian Government's OIS Program

Uncovering the Molecular Drivers of NHEJ DNA Repair-Implicated Missense Variants and Their Functional Consequences

Variants in non-homologous end joining (NHEJ) DNA repair genes are associated with various human syndromes, including microcephaly, growth delay, Fanconi anemia, and different hereditary cancers. However, very little has been done previously to systematically record the underlying molecular consequences of NHEJ variants and their link to phenotypic outcomes. In this study, a list of over 2983 missense variants of the principal components of the NHEJ system, including DNA Ligase IV, DNA-PKcs, Ku70/80 and XRCC4, reported in the clinical literature, was initially collected. The molecular consequences of variants were evaluated using in silico biophysical tools to quantitatively assess their impact on protein folding, dynamics, stability, and interactions. Cancer-causing and population variants within these NHEJ factors were statistically analyzed to identify molecular drivers. A comprehensive catalog of NHEJ variants from genes known to be mutated in cancer was curated, providing a resource for better understanding their role and molecular mechanisms in diseases. The variant analysis highlighted different molecular drivers among the distinct proteins, where cancer-driving variants in anchor proteins, such as Ku70/80, were more likely to affect key protein–protein interactions, whilst those in the enzymatic components, such as DNA-PKcs, were likely to be found in intolerant regions undergoing purifying selection. We believe that the information acquired in our database will be a powerful resource to better understand the role of non-homologous end-joining DNA repair in genetic disorders, and will serve as a source to inspire other investigations to understand the disease further, vital for the development of improved therapeutic strategies

Structure guided prediction of Pyrazinamide resistance mutations in pncA

Abstract: Pyrazinamide plays an important role in tuberculosis treatment; however, its use is complicated by side-effects and challenges with reliable drug susceptibility testing. Resistance to pyrazinamide is largely driven by mutations in pyrazinamidase (pncA), responsible for drug activation, but genetic heterogeneity has hindered development of a molecular diagnostic test. We proposed to use information on how variants were likely to affect the 3D structure of pncA to identify variants likely to lead to pyrazinamide resistance. We curated 610 pncA mutations with high confidence experimental and clinical information on pyrazinamide susceptibility. The molecular consequences of each mutation on protein stability, conformation, and interactions were computationally assessed using our comprehensive suite of graph-based signature methods, mCSM. The molecular consequences of the variants were used to train a classifier with an accuracy of 80%. Our model was tested against internationally curated clinical datasets, achieving up to 85% accuracy. Screening of 600 Victorian clinical isolates identified a set of previously unreported variants, which our model had a 71% agreement with drug susceptibility testing. Here, we have shown the 3D structure of pncA can be used to accurately identify pyrazinamide resistance mutations. SUSPECT-PZA is freely available at: http://biosig.unimelb.edu.au/suspect_pza/

Organic Cultivation of Tomato in India with Recycled Slaughterhouse Wastes: Evaluation of Fertilizer and Fruit Safety

Environmental and health safety of recycled slaughterhouse wastes-derived fertilizer and the produce obtained through its application is not well understood. Waste bovine blood and rumen digesta were mixed, cooked and sun-dried to obtain bovine-blood-and-rumen-digesta-mixture (BBRDM, NPK 30.36:1:5.75). 1.26 ± 0.18 log CFU mL−1 fecal coliforms were recovered in BBRDM. E. coli O157:H7, Mycobacteria, Clostridium sp., Salmonella sp., Bacillus sp. and Brucella sp. were absent. No re-growth of pathogens was observed after 60 days storage in sealed bags and in the open. However, prions and viruses were not evaluated. Heavy metals (Pb, Cr, Cd, Cu, Zn, As, Ni, Mn) concentrations in BBRDM were within internationally permissible limits. BBRDM was applied for field cultivation of tomato during 2012–2013 and 2013–2014. Lycopene and nitrate contents of BBRDM-grown tomatoes were higher than Diammonium phosphate (DAP) + potash-grown tomatoes because BBRDM supplied 2.5 times more the amount of nitrogen than DAP (NPK 18:46:0) + potash (NPK 0:0:44). Heavy metals and nitrate/nitrite concentrations in tomatoes were within internationally acceptable limits. BBRDM-grown tomatoes showed no mutagenic activity in the Ames test. Sub-acute toxicity tests on Wistar rats fed with BBRDM-grown tomatoes did not show adverse clinical picture. Thus, no immediate environmental or health risks associated with BBRDM and the tomatoes produced were identified

Bioinformatics approaches to predict mutation effects in the binding site of the proangiogenic molecule {CD}93

The transmembrane glycoprotein CD93 has been identified as a potential new target to inhibit tumor angiogenesis. Recently, Multimerin-2 (MMRN2), a pan-endothelial extracellular matrix protein, has been identified as a ligand for CD93, but the interaction mechanism between these two proteins is yet to be studied. In this article, we aim to investigate the structural and functional effects of induced mutations on the binding domain of CD93 to MMRN2. Starting from experimental data, we assessed how specific mutations in the C-type lectin-like domain (CTLD) affect the binding interaction profile. We described a four-step workflow in order to predict the effects of variations on the inter-residue interaction network at the PPI, based on evolutionary information, complex network metrics, and energetic affinity. We showed that the application of computational approaches, combined with experimental data, allowed us to gain more in-depth molecular insights into the CD93–MMRN2 interaction, offering a platform for developing innovative therapeutics able to target these molecules and block their interaction. This comprehensive molecular insight might prove useful in drug design in cancer therapy