Independent genomic polymorphisms in the PknH serine threonine kinase locus during evolution of the Mycobacterium tuberculosis Complex affect virulence and host preference.

Species belonging to the Mycobacterium tuberculosis Complex (MTBC) show more than 99% genetic identity but exhibit distinct host preference and virulence. The molecular genetic changes that underly host specificity and infection phenotype within MTBC members have not been fully elucidated. Here, we analysed RD900 genomic region across MTBC members using whole genome sequences from 60 different MTBC strains so as to determine its role in the context of MTBC evolutionary history. The RD900 region comprises two homologous genes, pknH1 and pknH2, encoding a serine/threonine protein kinase PknH flanking the tbd2 gene. Our analysis revealed that RD900 has been independently lost in different MTBC lineages and different strains, resulting in the generation of a single pknH gene. Importantly, all the analysed M. bovis and M. caprae strains carry a conserved deletion within a proline rich-region of pknH, independent of the presence or absence of RD900. We hypothesized that deletion of pknH proline rich-region in M. bovis may affect PknH function, having a potential role in its virulence and evolutionary adaptation. To explore this hypothesis, we constructed two M. bovis 'knock-in' strains containing the M. tuberculosis pknH gene. Evaluation of their virulence phenotype in mice revealed a reduced virulence of both M. bovis knock-in strains compared to the wild type, suggesting that PknH plays an important role in the differential virulence phenotype of M. bovis vs M. tuberculosis.

Epitopemap: a web application for integrated whole proteome epitope prediction.

BACKGROUND: Predictions of MHC binding affinity are commonly used in immunoinformatics for T cell epitope prediction. There are multiple available methods, some of which provide web access. However there is currently no convenient way to access the results from multiple methods at the same time or to execute predictions for an entire proteome at once. RESULTS: We designed a web application that allows integration of multiple epitope prediction methods for any number of proteins in a genome. The tool is a front-end for various freely available methods. Features include visualisation of results from multiple predictors within proteins in one plot, genome-wide analysis and estimates of epitope conservation. CONCLUSIONS: We present a self contained web application, Epitopemap, for calculating and viewing epitope predictions with multiple methods. The tool is easy to use and will assist in computational screening of viral or bacterial genomes.

Whole-Genome sequencing in routine Mycobacterium bovis epidemiology - scoping the potential.

Mycobacterium bovis the main agent of bovine tuberculosis (bTB), presents as a series of spatially-localised micro-epidemics across landscapes. Classical molecular typing methods applied to these micro-epidemics, based on genotyping a few variable loci, have significantly improved our understanding of potential epidemiological links between outbreaks. However, they have limited utility owing to low resolution. Conversely, whole-genome sequencing (WGS) provides the highest resolution data available for molecular epidemiology, producing richer outbreak tracing, insights into phylogeography and epidemic evolutionary history. We illustrate these advantages by focusing on a common single lineage of M. bovis (1.140) from Northern Ireland. Specifically, we investigate the spatial sub-structure of 20 years of herd-level multi locus VNTR analysis (MLVA) surveillance data and WGS data from a down sampled subset of isolates of this MLVA type over the same time frame. We mapped 2108 isolate locations of MLVA type 1.140 over the years 2000-2022. We also mapped the locations of 148 contemporary WGS isolates from this lineage, over a similar geographic range, stratifying by single nucleotide polymorphism (SNP) relatedness cut-offs of 15 SNPs. We determined a putative core range for the 1.140 MLVA type and SNP-defined sequence clusters using a 50 % kernel density estimate, using cattle movement data to inform on likely sources of WGS isolates found outside of core ranges. Finally, we applied Bayesian phylogenetic methods to investigate past population history and reproductive number of the 1.140 M. bovis lineage. We demonstrate that WGS SNP-defined clusters exhibit smaller core ranges than the established MLVA type - facilitating superior disease tracing. We also demonstrate the superior functionality of WGS data in determining how this lineage was disseminated across the landscape, likely via cattle movement and to infer how its effective population size and reproductive number has been in flux since its emergence. These initial findings highlight the potential of WGS data for routine monitoring of bTB outbreaks.

Protein dielectric constants determined from NMR chemical shift perturbations.

Understanding the connection between protein structure and function requires a quantitative understanding of electrostatic effects. Structure-based electrostatic calculations are essential for this purpose, but their use has been limited by a long-standing discussion on which value to use for the dielectric constants (ε(eff) and ε(p)) required in Coulombic and Poisson-Boltzmann models. The currently used values for ε(eff) and ε(p) are essentially empirical parameters calibrated against thermodynamic properties that are indirect measurements of protein electric fields. We determine optimal values for ε(eff) and ε(p) by measuring protein electric fields in solution using direct detection of NMR chemical shift perturbations (CSPs). We measured CSPs in 14 proteins to get a broad and general characterization of electric fields. Coulomb's law reproduces the measured CSPs optimally with a protein dielectric constant (ε(eff)) from 3 to 13, with an optimal value across all proteins of 6.5. However, when the water-protein interface is treated with finite difference Poisson-Boltzmann calculations, the optimal protein dielectric constant (ε(p)) ranged from 2 to 5 with an optimum of 3. It is striking how similar this value is to the dielectric constant of 2-4 measured for protein powders and how different it is from the ε(p) of 6-20 used in models based on the Poisson-Boltzmann equation when calculating thermodynamic parameters. Because the value of ε(p) = 3 is obtained by analysis of NMR chemical shift perturbations instead of thermodynamic parameters such as pK(a) values, it is likely to describe only the electric field and thus represent a more general, intrinsic, and transferable ε(p) common to most folded proteins.

Exploring virulence in Mycobacterium bovis: clues from comparative genomics and perspectives for the future.

Here we provide a summary of a plenary lecture delivered on Mycobacterium bovis, the bovine TB bacillus, at the M. bovis 2022 meeting held in Galway, Ireland, in June 2022. We focus on the analysis of genetic differences between M. bovis and the human pathogen Mycobacterium tuberculosis as a route to gain knowledge on what makes M. bovis function as an animal pathogen. We provide a brief historical background around M. bovis and comparative virulence experiments with M. tuberculosis, before moving to what we have learned from the studies of the M. bovis genome sequence. We discuss the need to translate knowledge on the molecular basis of virulence in M. bovis into improved control of bovine tuberculosis.

Highly perturbed pKa values in the unfolded state of hen egg white lysozyme.

The majority of pK(a) values in protein unfolded states are close to the amino acid model pK(a) values, thus reflecting the weak intramolecular interactions present in the unfolded ensemble of most proteins. We have carried out thermal denaturation measurements on the WT and eight mutants of HEWL from pH 1.5 to pH 11.0 to examine the unfolded state pK(a) values and the pH dependence of protein stability for this enzyme. The availability of accurate pK(a) values for the folded state of HEWL and separate measurements of mutant-induced effects on the folded state pK(a) values, allows us to estimate the pK(a) values of seven acidic residues in the unfolded state of HEWL. Asp-48 and Asp-66 display pK(a) values of 2.9 and 3.1 in our analysis, thus representing the most depressed unfolded state pK(a) values observed to date. We observe a strong correlation between the folded state pK(a) values and the unfolded state pK(a) values of HEWL, thus suggesting that the unfolded state of HEWL possesses a large degree of native state characteristics.

Toward fast determination of protein stability maps: experimental and theoretical analysis of mutants of a Nocardiopsis prasina serine protease.

The stability of serine proteases is of major importance for their application in industrial processes. Here we study the determinants of the stability of a Nocardiopsis prasina serine protease using fast residual activity assays, a feature classification algorithm, and structure-based energy calculation algorithms for 121 micropurified mutant enzyme clones containing multiple point mutations. Using a multivariate regression analysis, we deconvolute the data for the mutant clones and find that mutations of residues Asn47 and Pro124 are deleterious to the stability of the enzyme. Both of these residues are situated in loops that are known to be important for the stability of the highly homologous α-lytic protease. Structure-based energy calculations with PEATSA give a good general agreement with the trend of experimentally measured values but also identify a number of clones that the algorithm fails to predict correctly. We discuss the significance of the results in relation to the structure and function of closely related proteases, comment on the optimal experimental design when performing high-throughput experiments for characterizing the determinants of protein stability, and discuss the performance of structure-based energy calculations with complex data sets such as the one presented here.

DataPipeline: automated importing and fitting of large amounts of biophysical data.

Raw data from experiments across the biological sciences comes in a large variety of text formats. In small or medium sized laboratories researchers often use an assorted collection of software to interpret, fit, and visualize their data. The spreadsheet is commonly the core component of such a workflow. The limitations of such programs for large amounts of heterogeneous data can be frustrating. We report the construction of DataPipeline, a desktop and command-line application that automates the tasks of importing, fitting, and plotting of text-based data. The software is designed to simplify the process of importing text data from various sources using simple configuration files to describe raw file formats. Once imported, curve fitting can be performed using custom fitting models designed by the user inside the application. Fitted parameters can be grouped together as new datasets to be fitted to other models and experimental uncertainties propagated to give error estimates. This software will be useful for processing of data from high through-put biological experiments or for rapid visualization of pilot data without the need for a chain of different programs to carry out each step. DataPipeline and source code is available under an open source license. The software can be freely downloaded at http://code.google.com/p/peat/downloads/list.

A collaborative environment for developing and validating predictive tools for protein biophysical characteristics.

The exchange of information between experimentalists and theoreticians is crucial to improving the predictive ability of theoretical methods and hence our understanding of the related biology. However many barriers exist which prevent the flow of information between the two disciplines. Enabling effective collaboration requires that experimentalists can easily apply computational tools to their data, share their data with theoreticians, and that both the experimental data and computational results are accessible to the wider community. We present a prototype collaborative environment for developing and validating predictive tools for protein biophysical characteristics. The environment is built on two central components; a new python-based integration module which allows theoreticians to provide and manage remote access to their programs; and PEATDB, a program for storing and sharing experimental data from protein biophysical characterisation studies. We demonstrate our approach by integrating PEATSA, a web-based service for predicting changes in protein biophysical characteristics, into PEATDB. Furthermore, we illustrate how the resulting environment aids method development using the Potapov dataset of experimentally measured ΔΔGfold values, previously employed to validate and train protein stability prediction algorithms.

Capturing, sharing and analysing biophysical data from protein engineering and protein characterization studies.

Large amounts of data are being generated annually on the connection between the sequence, structure and function of proteins using site-directed mutagenesis, protein design and directed evolution techniques. These data provide the fundamental building blocks for our understanding of protein function, molecular biology and living organisms in general. However, much experimental data are never deposited in databases and is thus 'lost' in journal publications or in PhD theses. At the same time theoretical scientists are in need of large amounts of experimental data for benchmarking and calibrating novel predictive algorithms, and theoretical progress is therefore often hampered by the lack of suitable data to validate or disprove a theoretical assumption. We present PEAT (Protein Engineering Analysis Tool), an application that integrates data deposition, storage and analysis for researchers carrying out protein engineering projects or biophysical characterization of proteins. PEAT contains modules for DNA sequence manipulation, primer design, fitting of biophysical characterization data (enzyme kinetics, circular dichroism spectroscopy, NMR titration data, etc.), and facilitates sharing of experimental data and analyses for a typical university-based research group. PEAT is freely available to academic researchers at http://enzyme.ucd.ie/PEAT.

Corrigendum: The TbD1 Locus Mediates a Hypoxia-Induced Copper Response in Mycobacterium bovis.

[This corrects the article DOI: 10.3389/fmicb.2022.817952.].

The TbD1 Locus Mediates a Hypoxia-Induced Copper Response in Mycobacterium bovis.

The Mycobacterium tuberculosis complex (MTBC) contains the causative agents of tuberculosis (TB) in mammals. The archetypal members of the MTBC, Mycobacterium tuberculosis and Mycobacterium bovis, cause human tuberculosis and bovine tuberculosis, respectively. Although M. tuberculosis and M. bovis share over 99.9% genome identity, they show distinct host adaptation for humans and animals; hence, while the molecular basis of host adaptation is encoded in their genomes, the mechanistic basis of host tropism is still unclear. Exploration of the in vitro phenotypic consequences of known genetic difference between M. bovis and M. tuberculosis offers one route to explore genotype-phenotype links that may play a role in host adaptation. The TbD1 ("Mycobacterium tuberculosis deletion 1 region") locus encompasses the mmpS6 and mmpL6 genes. TbD1 is absent in M. tuberculosis "modern" lineages (Lineages 2, 3, and 4) but present in "ancestral" M. tuberculosis (Lineages 1 and 7), Mycobacterium africanum lineages (Lineages 5 and 6), newly identified M. tuberculosis lineages (Lineages 8 and 9), and animal adapted strains, such as M. bovis. The function of TbD1 has previously been investigated in M. tuberculosis, where conflicting data has emerged on the role of TbD1 in sensitivity to oxidative stress, while the underlying mechanistic basis of such a phenotype is unclear. In this study, we aimed to shed further light on the role of the TbD1 locus by exploring its function in M. bovis. Toward this, we constructed an M. bovis TbD1 knockout (ΔTbD1) strain and conducted comparative transcriptomics to define global gene expression profiles of M. bovis wild-type (WT) and the ΔTbD1 strains under in vitro culture conditions (rolling and standing cultures). This analysis revealed differential induction of a hypoxia-driven copper response in WT and ΔTbD1 strains. In vitro phenotypic assays demonstrated that the deletion of TbD1 sensitized M. bovis to H2O2 and hypoxia-specific copper toxicity. Our study provides new information on the function of the TbD1 locus in M. bovis and its role in stress responses in the MTBC.

Evidence for local and international spread of Mycobacterium avium subspecies paratuberculosis through whole genome sequencing of isolates from the island of Ireland.

We describe application of whole genome sequencing (WGS) to a collection of 197 Mycobacterium avium subsp paratuberculosis (MAP) isolates gathered from 122 cattle herds across 27 counties of the island of Ireland. We compare WGS to MAP diversity quantified using mycobacterial interspersed random unit - variable number tandem repeats (MIRU-VNTR). While MIRU-VNTR showed only two major types, WGS could split the 197 isolates into eight major groups. We also found six isolates corresponding to INMV 13, a novel MIRU-VNTR type for Ireland. Evidence for dispersal of MAP across Ireland via cattle movement could be discerned from the data, with mixed infections present in several herds. Furthermore, comparisons of MAP WGS data from Ireland to data from Great Britain and continental Europe revealed many instances of close genetic similarity and hence evidence for international transmission of infection. BEAST MASCOT structured coalescent analyses, with relaxed and strict molecular clocks, estimated the substitution rate to be 0.10-0.13 SNPs/site/year and disclosed greater transitions per lineage per year from Europe to Ireland, indicating transmission into Ireland. Our work therefore reveals new insight into the seeding of MAP infection across Ireland, highlighting how WGS can inform policy formulation to ultimately control MAP transmission at local, national and international scales.

On the development of protein pKa calculation algorithms.

Protein pK(a) calculation methods are developed partly to provide fast non-experimental estimates of the ionization constants of protein side chains. However, the most significant reason for developing such methods is that a good pK(a) calculation method is presumed to provide an accurate physical model of protein electrostatics, which can be applied in methods for drug design, protein design, and other structure-based energy calculation methods. We explore the validity of this presumption by simulating the development of a pK(a) calculation method using artificial experimental data derived from a human-defined physical reality. We examine the ability of an RMSD-guided development protocol to retrieve the correct (artificial) physical reality and find that a rugged optimization landscape and a huge parameter space prevent the identification of the correct physical reality. We examine the importance of the training set in developing pK(a) calculation methods and investigate the effect of experimental noise on our ability to identify the correct physical reality, and find that both effects have a significant and detrimental impact on the physical reality of the optimal model identified. Our findings are of relevance to all structure-based methods for protein energy calculations and simulation, and have large implications for all types of current pK(a) calculation methods. Our analysis furthermore suggests that careful and extensive validation on many types of experimental data can go some way in making current models more realistic.

Remeasuring HEWL pK(a) values by NMR spectroscopy: methods, analysis, accuracy, and implications for theoretical pK(a) calculations.

Site-specific pK(a) values measured by NMR spectroscopy provide essential information on protein electrostatics, the pH-dependence of protein structure, dynamics and function, and constitute an important benchmark for protein pK(a) calculation algorithms. Titration curves can be measured by tracking the NMR chemical shifts of several reporter nuclei versus sample pH. However, careful analysis of these curves is needed to extract residue-specific pK(a) values since pH-dependent chemical shift changes can arise from many sources, including through-bond inductive effects, through-space electric field effects, and conformational changes. We have re-measured titration curves for all carboxylates and His 15 in Hen Egg White Lysozyme (HEWL) by recording the pH-dependent chemical shifts of all backbone amide nitrogens and protons, Asp/Glu side chain protons and carboxyl carbons, and imidazole protonated carbons and protons in this protein. We extracted pK(a) values from the resulting titration curves using standard fitting methods, and compared these values to each other, and with those measured previously by ¹H NMR (Bartik et al., Biophys J 1994;66:1180­1184). This analysis gives insights into the true accuracy associated with experimentally measured pK(a) values. We find that apparent pK(a) values frequently differ by 0.5­1.0 units depending upon the nuclei monitored, and that larger differences occasionally can be observed. The variation in measured pK(a) values, which reflects the difficulty in fitting and assigning pH-dependent chemical shifts to specific ionization equilibria, has significant implications for the experimental procedures used for measuring protein pK(a) values, for the benchmarking of protein pK(a) calculation algorithms, and for the understanding of protein electrostatics in general.

Progress in the prediction of pKa values in proteins.

The pK(a) -cooperative aims to provide a forum for experimental and theoretical researchers interested in protein pK(a) values and protein electrostatics in general. The first round of the pK(a) -cooperative, which challenged computational labs to carry out blind predictions against pK(a) s experimentally determined in the laboratory of Bertrand Garcia-Moreno, was completed and results discussed at the Telluride meeting (July 6-10, 2009). This article serves as an introduction to the reports submitted by the blind prediction participants that will be published in a special issue of PROTEINS: Structure, Function and Bioinformatics. Here, we briefly outline existing approaches for pK(a) calculations, emphasizing methods that were used by the participants in calculating the blind pK(a) values in the first round of the cooperative. We then point out some of the difficulties encountered by the participating groups in making their blind predictions, and finally try to provide some insights for future developments aimed at improving the accuracy of pK(a) calculations.

epitopepredict: a tool for integrated MHC binding prediction.

A key step in the cellular adaptive immune response is the presentation of antigens to T cells. Computational prediction of T cell epitopes has many applications in vaccine design and immuno-diagnostics. This is the basis of immunoinformatics, which allows in silico screening of peptides before experiments are performed. With the availability of whole genomes for many microbial species it is now feasible to computationally screen whole proteomes for candidate peptides. epitopepredict is a programmatic framework and command line tool designed to aid this process. It provides access to multiple binding prediction algorithms under a single interface and scales for whole genomes using multiple target MHC alleles. A web interface is provided to assist visualization and filtering of the results. The software is freely available under an open-source license from https://github.com/dmnfarrell/epitopepredict.

Performance and Agreement Between WGS Variant Calling Pipelines Used for Bovine Tuberculosis Control: Toward International Standardization.

Whole genome sequencing (WGS) and allied variant calling pipelines are a valuable tool for the control and eradication of infectious diseases, since they allow the assessment of the genetic relatedness of strains of animal pathogens. In the context of the control of tuberculosis (TB) in livestock, mainly caused by Mycobacterium bovis, these tools offer a high-resolution alternative to traditional molecular methods in the study of herd breakdown events. However, despite the increased use and efforts in the standardization of WGS methods in human tuberculosis around the world, the application of these WGS-enabled approaches to control TB in livestock is still in early development. Our study pursued an initial evaluation of the performance and agreement of four publicly available pipelines for the analysis of M. bovis WGS data (vSNP, SNiPgenie, BovTB, and MTBseq) on a set of simulated Illumina reads generated from a real-world setting with high TB prevalence in cattle and wildlife in the Republic of Ireland. The overall performance of the evaluated pipelines was high, with recall and precision rates above 99% once repeat-rich and problematic regions were removed from the analyses. In addition, when the same filters were applied, distances between inferred phylogenetic trees were similar and pairwise comparison revealed that most of the differences were due to the positioning of polytomies. Hence, under the studied conditions, all pipelines offer similar performance for variant calling to underpin real-world studies of M. bovis transmission dynamics.

Improving the analysis of NMR spectra tracking pH-induced conformational changes: removing artefacts of the electric field on the NMR chemical shift.

pH-induced chemical shift perturbations (CSPs) can be used to study pH-dependent conformational transitions in proteins. Recently, an elegant principal component analysis (PCA) algorithm was developed and used to study the pH-dependent structural transitions in bovine beta-lactoglobulin (betaLG) by analyzing its NMR pH-titration spectra. Here, we augment this analysis method by filtering out changes in the NMR chemical shift that stem from effects that are electrostatic in nature. Specifically, we examine how many CSPs can be explained by purely electrostatic effects arising from titrational events in betaLG. The results show that around 20% of the amide nuclei CSPs in betaLG originate exclusively from "through-space" electric field effects. A PCA of NMR data where electric field artefacts have been removed gives a different picture of the pH-dependent structural transitions in betaLG. The method implemented here is well suited to be applied on a whole range of proteins, which experience at least one pH-dependent conformational change. Proteins 2010. (c) 2009 Wiley-Liss, Inc.

Titration_DB: storage and analysis of NMR-monitored protein pH titration curves.

NMR-monitored pH titration experiments are routinely used to measure site-specific protein pKa values. Accurate experimental pKa values are essential in dissecting enzyme catalysis, in studying the pH-dependence of protein stability and ligand binding, in benchmarking pKa prediction algorithms, and ultimately in understanding electrostatic effects in proteins. However, due to the complex ways in which pH-dependent electrostatic and structural changes manifest themselves in NMR spectra, reported apparent pKa values are often dependent on the way that NMR pH-titration curves are analyzed. It is therefore important to retain the raw NMR spectroscopic data to allow for documentation and possible re-interpretation. We have constructed a database of primary NMR pH-titration data, which is accessible via a web interface. Here, we report statistics of the database contents and analyze the data with a global perspective to provide guidelines on best practice for fitting NMR titration curves. Titration_DB is available at http://enzyme.ucd.ie/Titration_DB. Proteins 2010. (c) 2009 Wiley-Liss, Inc.