Determining the potential clinical value of panel-based pharmacogenetic testing in patients with chronic pain or gastroesophageal reflux disease.

We aimed to determine the potential value of panel-based pharmacogenetic (PGx) testing in patients with chronic pain or gastroesophageal reflux disease (GERD) who underwent single-gene PGx testing to guide opioid or proton pump inhibitor (PPI) therapy, respectively. Of 448 patients included (chronic pain, n = 337; GERD, n = 111), mean age was 57 years, 68% were female, and 73% were white. Excluding opiates for the pain cohort and PPIs for the GERD cohort, 76.6% of patients with pain and 71.2% with GERD were prescribed at least one additional medication with a high level of PGx evidence, most commonly ondansetron or selective serotonin reuptake inhibitors. The most common genes that could inform PGx drug prescribing were CYP2C19, CYP2D6, CYP2C9, and SLCO1B1. Our findings suggest that patients with chronic pain or GERD are commonly prescribed drugs with a high level of evidence for a PGx-guided approach, supporting panel-based testing in these populations.

The Mycobacterium tuberculosis regulatory network and hypoxia.

We have taken the first steps towards a complete reconstruction of the Mycobacterium tuberculosis regulatory network based on ChIP-Seq and combined this reconstruction with system-wide profiling of messenger RNAs, proteins, metabolites and lipids during hypoxia and re-aeration. Adaptations to hypoxia are thought to have a prominent role in M. tuberculosis pathogenesis. Using ChIP-Seq combined with expression data from the induction of the same factors, we have reconstructed a draft regulatory network based on 50 transcription factors. This network model revealed a direct interconnection between the hypoxic response, lipid catabolism, lipid anabolism and the production of cell wall lipids. As a validation of this model, in response to oxygen availability we observe substantial alterations in lipid content and changes in gene expression and metabolites in corresponding metabolic pathways. The regulatory network reveals transcription factors underlying these changes, allows us to computationally predict expression changes, and indicates that Rv0081 is a regulatory hub.

Pridopidine activates neuroprotective pathways impaired in Huntington Disease.

Pridopidine has demonstrated improvement in Huntington Disease (HD) motor symptoms as measured by secondary endpoints in clinical trials. Originally described as a dopamine stabilizer, this mechanism is insufficient to explain the clinical and preclinical effects of pridopidine. This study therefore explored pridopidine's potential mechanisms of action. The effect of pridopidine versus sham treatment on genome-wide expression profiling in the rat striatum was analysed and compared to the pathological expression profile in Q175 knock-in (Q175 KI) vs Q25 WT mouse models. A broad, unbiased pathway analysis was conducted, followed by testing the enrichment of relevant pathways. Pridopidine upregulated the BDNF pathway (P = 1.73E-10), and its effect on BDNF secretion was sigma 1 receptor (S1R) dependent. Many of the same genes were independently found to be downregulated in Q175 KI mice compared to WT (5.2e-7 < P < 0.04). In addition, pridopidine treatment upregulated the glucocorticoid receptor (GR) response, D1R-associated genes and the AKT/PI3K pathway (P = 1E-10, P = 0.001, P = 0.004, respectively). Pridopidine upregulates expression of BDNF, D1R, GR and AKT/PI3K pathways, known to promote neuronal plasticity and survival, as well as reported to demonstrate therapeutic benefit in HD animal models. Activation of S1R, necessary for its effect on the BDNF pathway, represents a core component of the mode of action of pridopidine. Since the newly identified pathways are downregulated in neurodegenerative diseases, including HD, these findings suggest that pridopidine may exert neuroprotective effects beyond its role in alleviating some symptoms of HD.

Improving prediction of helix-helix packing in membrane proteins using predicted contact numbers as restraints.

One of the challenging problems in tertiary structure prediction of helical membrane proteins (HMPs) is the determination of rotation of α-helices around the helix normal. Incorrect prediction of helix rotations substantially disrupts native residue-residue contacts while inducing only a relatively small effect on the overall fold. We previously developed a method for predicting residue contact numbers (CNs), which measure the local packing density of residues within the protein tertiary structure. In this study, we tested the idea of incorporating predicted CNs as restraints to guide the sampling of helix rotation. For a benchmark set of 15 HMPs with simple to rather complicated folds, the average contact recovery (CR) of best-sampled models was improved for all targets, the likelihood of sampling models with CR greater than 20% was increased for 13 targets, and the average RMSD100 of best-sampled models was improved for 12 targets. This study demonstrated that explicit incorporation of CNs as restraints improves the prediction of helix-helix packing. Proteins 2017; 85:1212-1221. © 2017 Wiley Periodicals, Inc.

The sequence, structural, and functional diversity within a protein family and implications for specificity and safety: The case for ETX_MTX2 insecticidal proteins.

The need for sustainable insect pest control is driving the investigation and discovery of insecticidal proteins outside of the typical 3-domain Cry protein family from Bacillus thuringiensis (Bt). Examples include Cry35 and Cry51 that belong to protein families (Toxin_10, ETX_MTX2) sharing a common ß-pore forming structure and function with known mammalian toxins such as epsilon toxin (ETX). Although ß-pore forming proteins are related to mammalian toxins, there are key differences in sequence and structure that lead to organism specificity that is useful in the weight-of-evidence approach for safety assessment. Despite low overall amino acid sequence identity among ETX_MTX2 proteins, sequence and structural similarities are found in the tail region responsible for the shared oligomerization and pore formation functions (causing the "relatedness"). Conversely, most of the sequence and structural diversity is located in the head region that is likely responsible for differential receptor binding and target species specificity (e.g., insecticidal vs. mammalian). Therefore, inclusion of a domain-based protein characterization approach that includes bioinformatic and functional comparisons of conserved and diverse domains will enhance the overall weight of evidence safety assessment of proteins including recently reported Cry51 protein variants (Cry51Aa1, Cry51Aa2, and Cry51Aa2.834_16).

Genome Sequencing and Analysis of Geographically Diverse Clinical Isolates of Herpes Simplex Virus 2.

UNLABELLED: Herpes simplex virus 2 (HSV-2), the principal causative agent of recurrent genital herpes, is a highly prevalent viral infection worldwide. Limited information is available on the amount of genomic DNA variation between HSV-2 strains because only two genomes have been determined, the HG52 laboratory strain and the newly sequenced SD90e low-passage-number clinical isolate strain, each from a different geographical area. In this study, we report the nearly complete genome sequences of 34 HSV-2 low-passage-number and laboratory strains, 14 of which were collected in Uganda, 1 in South Africa, 11 in the United States, and 8 in Japan. Our analyses of these genomes demonstrated remarkable sequence conservation, regardless of geographic origin, with the maximum nucleotide divergence between strains being 0.4% across the genome. In contrast, prior studies indicated that HSV-1 genomes exhibit more sequence diversity, as well as geographical clustering. Additionally, unlike HSV-1, little viral recombination between HSV-2 strains could be substantiated. These results are interpreted in light of HSV-2 evolution, epidemiology, and pathogenesis. Finally, the newly generated sequences more closely resemble the low-passage-number SD90e than HG52, supporting the use of the former as the new reference genome of HSV-2. IMPORTANCE: Herpes simplex virus 2 (HSV-2) is a causative agent of genital and neonatal herpes. Therefore, knowledge of its DNA genome and genetic variability is central to preventing and treating genital herpes. However, only two full-length HSV-2 genomes have been reported. In this study, we sequenced 34 additional HSV-2 low-passage-number and laboratory viral genomes and initiated analysis of the genetic diversity of HSV-2 strains from around the world. The analysis of these genomes will facilitate research aimed at vaccine development, diagnosis, and the evaluation of clinical manifestations and transmission of HSV-2. This information will also contribute to our understanding of HSV evolution.

Accurate Prediction of Contact Numbers for Multi-Spanning Helical Membrane Proteins.

Prediction of the three-dimensional (3D) structures of proteins by computational methods is acknowledged as an unsolved problem. Accurate prediction of important structural characteristics such as contact number is expected to accelerate the otherwise slow progress being made in the prediction of 3D structure of proteins. Here, we present a dropout neural network-based method, TMH-Expo, for predicting the contact number of transmembrane helix (TMH) residues from sequence. Neuronal dropout is a strategy where certain neurons of the network are excluded from back-propagation to prevent co-adaptation of hidden-layer neurons. By using neuronal dropout, overfitting was significantly reduced and performance was noticeably improved. For multi-spanning helical membrane proteins, TMH-Expo achieved a remarkable Pearson correlation coefficient of 0.69 between predicted and experimental values and a mean absolute error of only 1.68. In addition, among those membrane protein-membrane protein interface residues, 76.8% were correctly predicted. Mapping of predicted contact numbers onto structures indicates that contact numbers predicted by TMH-Expo reflect the exposure patterns of TMHs and reveal membrane protein-membrane protein interfaces, reinforcing the potential of predicted contact numbers to be used as restraints for 3D structure prediction and protein-protein docking. TMH-Expo can be accessed via a Web server at www.meilerlab.org .

BCL::SAXS: GPU accelerated Debye method for computation of small angle X-ray scattering profiles.

Small angle X-ray scattering (SAXS) is an experimental technique used for structural characterization of macromolecules in solution. Here, we introduce BCL::SAXS--an algorithm designed to replicate SAXS profiles from rigid protein models at different levels of detail. We first show our derivation of BCL::SAXS and compare our results with the experimental scattering profile of hen egg white lysozyme. Using this protein we show how to generate SAXS profiles representing: (1) complete models, (2) models with approximated side chain coordinates, and (3) models with approximated side chain and loop region coordinates. We evaluated the ability of SAXS profiles to identify a correct protein topology from a non-redundant benchmark set of proteins. We find that complete SAXS profiles can be used to identify the correct protein by receiver operating characteristic (ROC) analysis with an area under the curve (AUC) > 99%. We show how our approximation of loop coordinates between secondary structure elements improves protein recognition by SAχS for protein models without loop regions and side chains. Agreement with SAXS data is a necessary but not sufficient condition for structure determination. We conclude that experimental SAXS data can be used as a filter to exclude protein models with large structural differences from the native.

CASP10-BCL::Fold efficiently samples topologies of large proteins.

During CASP10 in summer 2012, we tested BCL::Fold for prediction of free modeling (FM) and template-based modeling (TBM) targets. BCL::Fold assembles the tertiary structure of a protein from predicted secondary structure elements (SSEs) omitting more flexible loop regions early on. This approach enables the sampling of conformational space for larger proteins with more complex topologies. In preparation of CASP11, we analyzed the quality of CASP10 models throughout the prediction pipeline to understand BCL::Fold's ability to sample the native topology, identify native-like models by scoring and/or clustering approaches, and our ability to add loop regions and side chains to initial SSE-only models. The standout observation is that BCL::Fold sampled topologies with a GDT_TS score > 33% for 12 of 18 and with a topology score > 0.8 for 11 of 18 test cases de novo. Despite the sampling success of BCL::Fold, significant challenges still exist in clustering and loop generation stages of the pipeline. The clustering approach employed for model selection often failed to identify the most native-like assembly of SSEs for further refinement and submission. It was also observed that for some ß-strand proteins model refinement failed as ß-strands were not properly aligned to form hydrogen bonds removing otherwise accurate models from the pool. Further, BCL::Fold samples frequently non-natural topologies that require loop regions to pass through the center of the protein.

BCL::MP-fold: Membrane protein structure prediction guided by EPR restraints.

For many membrane proteins, the determination of their topology remains a challenge for methods like X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy. Electron paramagnetic resonance (EPR) spectroscopy has evolved as an alternative technique to study structure and dynamics of membrane proteins. The present study demonstrates the feasibility of membrane protein topology determination using limited EPR distance and accessibility measurements. The BCL::MP-Fold (BioChemical Library membrane protein fold) algorithm assembles secondary structure elements (SSEs) in the membrane using a Monte Carlo Metropolis (MCM) approach. Sampled models are evaluated using knowledge-based potential functions and agreement with the EPR data and a knowledge-based energy function. Twenty-nine membrane proteins of up to 696 residues are used to test the algorithm. The RMSD100 value of the most accurate model is better than 8 Å for 27, better than 6 Å for 22, and better than 4 Å for 15 of the 29 proteins, demonstrating the algorithms' ability to sample the native topology. The average enrichment could be improved from 1.3 to 2.5, showing the improved discrimination power by using EPR data.

BCL::Fold--protein topology determination from limited NMR restraints.

When experimental protein NMR data are too sparse to apply traditional structure determination techniques, de novo protein structure prediction methods can be leveraged. Here, we describe the incorporation of NMR restraints into the protein structure prediction algorithm BCL::Fold. The method assembles discreet secondary structure elements using a Monte Carlo sampling algorithm with a consensus knowledge-based energy function. New components were introduced into the energy function to accommodate chemical shift, nuclear Overhauser effect, and residual dipolar coupling data. In particular, since side chains are not explicitly modeled during the minimization process, a knowledge based potential was created to relate experimental side chain proton-proton distances to Cß -Cß distances. In a benchmark test of 67 proteins of known structure with the incorporation of sparse NMR restraints, the correct topology was sampled in 65 cases, with an average best model RMSD100 of 3.4 ± 1.3 Å versus 6.0 ± 2.0 Å produced with the de novo method. Additionally, the correct topology is present in the best scoring 1% of models in 61 cases. The benchmark set includes both soluble and membrane proteins with up to 565 residues, indicating the method is robust and applicable to large and membrane proteins that are less likely to produce rich NMR datasets.

Whole genome pyrosequencing of rare hepatitis C virus genotypes enhances subtype classification and identification of naturally occurring drug resistance variants.

BACKGROUND: Infection with hepatitis C virus (HCV) is a burgeoning worldwide public health problem, with 170 million infected individuals and an estimated 20 million deaths in the coming decades. While 6 main genotypes generally distinguish the global geographic diversity of HCV, a multitude of closely related subtypes within these genotypes are poorly defined and may influence clinical outcome and treatment options. Unfortunately, the paucity of genetic data from many of these subtypes makes time-consuming primer walking the limiting step for sequencing understudied subtypes. METHODS: Here we combined long-range polymerase chain reaction amplification with pyrosequencing for a rapid approach to generate the complete viral coding region of 31 samples representing poorly defined HCV subtypes. RESULTS: Phylogenetic classification based on full genome sequences validated previously identified HCV subtypes, identified a recombinant sequence, and identified a new distinct subtype of genotype 4. Unlike conventional sequencing methods, use of deep sequencing also facilitated characterization of minor drug resistance variants within these uncommon or, in some cases, previously uncharacterized HCV subtypes. CONCLUSIONS: These data aid in the classification of uncommon HCV subtypes while also providing a high-resolution view of viral diversity within infected patients, which may be relevant to the development of therapeutic regimens to minimize drug resistance.

Randomized Placebo-Controlled, Biomarker-Stratified Phase Ib Microbiome Modulation in Melanoma: Impact of Antibiotic Preconditioning on Microbiome and Immunity.

Gut-microbiota modulation shows promise in improving immune-checkpoint blockade (ICB) response; however, precision biomarker-driven, placebo-controlled trials are lacking. We performed a multicenter, randomized placebo-controlled, biomarker-stratified phase I trial in patients with ICB-naïve metastatic melanoma using SER-401, an orally delivered Firmicutesenriched spore formulation. Fecal microbiota signatures were characterized at baseline; patients were stratified by high versus low Ruminococcaceae abundance prior to randomization to the SER-401 arm (oral vancomycin-preconditioning/SER-401 alone/nivolumab + SER-401), versus the placebo arm [placebo antibiotic/placebo microbiome modulation (PMM)/nivolumab + PMM (NCT03817125)]. Analysis of 14 accrued patients demonstrated that treatment with SER-401 + nivolumab was safe, with an overall response rate of 25% in the SER-401 arm and 67% in the placebo arm (though the study was underpowered related to poor accrual during the COVID-19 pandemic). Translational analyses demonstrated that vancomycin preconditioning was associated with the disruption of the gut microbiota and impaired immunity, with incomplete recovery at ICB administration (particularly in patients with high baseline Ruminococcaceae). These results have important implications for future microbiome modulation trials. Significance: This first-of-its-kind, placebo-controlled, randomized biomarker-driven microbiome modulation trial demonstrated that vancomycin + SER-401 and anti-PD-1 are safe in melanoma patients. Although limited by poor accrual during the pandemic, important insights were gained via translational analyses, suggesting that antibiotic preconditioning and interventional drug dosing regimens should be carefully considered when designing such trials.

Comparative analysis of Mycobacterium and related Actinomycetes yields insight into the evolution of Mycobacterium tuberculosis pathogenesis.

BACKGROUND: The sequence of the pathogen Mycobacterium tuberculosis (Mtb) strain H37Rv has been available for over a decade, but the biology of the pathogen remains poorly understood. Genome sequences from other Mtb strains and closely related bacteria present an opportunity to apply the power of comparative genomics to understand the evolution of Mtb pathogenesis. We conducted a comparative analysis using 31 genomes from the Tuberculosis Database (TBDB.org), including 8 strains of Mtb and M. bovis, 11 additional Mycobacteria, 4 Corynebacteria, 2 Streptomyces, Rhodococcus jostii RHA1, Nocardia farcinia, Acidothermus cellulolyticus, Rhodobacter sphaeroides, Propionibacterium acnes, and Bifidobacterium longum. RESULTS: Our results highlight the functional importance of lipid metabolism and its regulation, and reveal variation between the evolutionary profiles of genes implicated in saturated and unsaturated fatty acid metabolism. It also suggests that DNA repair and molybdopterin cofactors are important in pathogenic Mycobacteria. By analyzing sequence conservation and gene expression data, we identify nearly 400 conserved noncoding regions. These include 37 predicted promoter regulatory motifs, of which 14 correspond to previously validated motifs, as well as 50 potential noncoding RNAs, of which we experimentally confirm the expression of four. CONCLUSIONS: Our analysis of protein evolution highlights gene families that are associated with the adaptation of environmental Mycobacteria to obligate pathogenesis. These families include fatty acid metabolism, DNA repair, and molybdopterin biosynthesis. Our analysis reinforces recent findings suggesting that small noncoding RNAs are more common in Mycobacteria than previously expected. Our data provide a foundation for understanding the genome and biology of Mtb in a comparative context, and are available online and through TBDB.org.

Structure of a DNA polymerase alpha-primase domain that docks on the SV40 helicase and activates the viral primosome.

DNA polymerase alpha-primase (pol-prim) plays a central role in DNA replication in higher eukaryotes, initiating synthesis on both leading and lagging strand single-stranded DNA templates. Pol-prim consists of a primase heterodimer that synthesizes RNA primers, a DNA polymerase that extends them, and a fourth subunit, p68 (also termed B-subunit), that is thought to regulate the complex. Although significant knowledge about single-subunit primases of prokaryotes has accumulated, the functions and regulation of pol-prim remain poorly understood. In the SV40 replication model, the p68 subunit is required for primosome activity and binds directly to the hexameric viral helicase T antigen, suggesting a functional link between T antigen-p68 interaction and primosome activity. To explore this link, we first mapped the interacting regions of the two proteins and discovered a previously unrecognized N-terminal globular domain of p68 (p68N) that physically interacts with the T antigen helicase domain. NMR spectroscopy was used to determine the solution structure of p68N and map its interface with the T antigen helicase domain. Structure-guided mutagenesis of p68 residues in the interface diminished T antigen-p68 interaction, confirming the interaction site. SV40 primosome activity of corresponding pol-prim mutants decreased in proportion to the reduction in p68N-T antigen affinity, confirming that p68-T antigen interaction is vital for primosome function. A model is presented for how this interaction regulates SV40 primosome activity, and the implications of our findings are discussed in regard to the molecular mechanisms of eukaryotic DNA replication initiation.

TB database: an integrated platform for tuberculosis research.

The effective control of tuberculosis (TB) has been thwarted by the need for prolonged, complex and potentially toxic drug regimens, by reliance on an inefficient vaccine and by the absence of biomarkers of clinical status. The promise of the genomics era for TB control is substantial, but has been hindered by the lack of a central repository that collects and integrates genomic and experimental data about this organism in a way that can be readily accessed and analyzed. The Tuberculosis Database (TBDB) is an integrated database providing access to TB genomic data and resources, relevant to the discovery and development of TB drugs, vaccines and biomarkers. The current release of TBDB houses genome sequence data and annotations for 28 different Mycobacterium tuberculosis strains and related bacteria. TBDB stores pre- and post-publication gene-expression data from M. tuberculosis and its close relatives. TBDB currently hosts data for nearly 1500 public tuberculosis microarrays and 260 arrays for Streptomyces. In addition, TBDB provides access to a suite of comparative genomics and microarray analysis software. By bringing together M. tuberculosis genome annotation and gene-expression data with a suite of analysis tools, TBDB (http://www.tbdb.org/) provides a unique discovery platform for TB research.

Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production.

Metabolism is central to cell physiology, and metabolic disturbances play a role in numerous disease states. Despite its importance, the ability to study metabolism at a global scale using genomic technologies is limited. In principle, complete genome sequences describe the range of metabolic reactions that are possible for an organism, but cannot quantitatively describe the behaviour of these reactions. We present a novel method for modeling metabolic states using whole cell measurements of gene expression. Our method, which we call E-Flux (as a combination of flux and expression), extends the technique of Flux Balance Analysis by modeling maximum flux constraints as a function of measured gene expression. In contrast to previous methods for metabolically interpreting gene expression data, E-Flux utilizes a model of the underlying metabolic network to directly predict changes in metabolic flux capacity. We applied E-Flux to Mycobacterium tuberculosis, the bacterium that causes tuberculosis (TB). Key components of mycobacterial cell walls are mycolic acids which are targets for several first-line TB drugs. We used E-Flux to predict the impact of 75 different drugs, drug combinations, and nutrient conditions on mycolic acid biosynthesis capacity in M. tuberculosis, using a public compendium of over 400 expression arrays. We tested our method using a model of mycolic acid biosynthesis as well as on a genome-scale model of M. tuberculosis metabolism. Our method correctly predicts seven of the eight known fatty acid inhibitors in this compendium and makes accurate predictions regarding the specificity of these compounds for fatty acid biosynthesis. Our method also predicts a number of additional potential modulators of TB mycolic acid biosynthesis. E-Flux thus provides a promising new approach for algorithmically predicting metabolic state from gene expression data.