PathTracer Comprehensively Identifies Hypoxia-Induced Dormancy Adaptations in Mycobacterium tuberculosis.

Mining large-scale data to discover biologically relevant information remains a challenge despite the rapid development of bioinformatics tools. Here, we have developed a new tool, PathTracer, to identify biologically relevant information flows by mining genome-wide protein-protein interaction networks following integration of gene expression data. PathTracer successfully mines interactions between genes and traces the most perturbed paths of perceived activities under the conditions of the study. We further demonstrated the utility of this tool by identifying adaptation mechanisms of hypoxia-induced dormancy in Mycobacterium tuberculosis (Mtb).

Hidden duplicates: 10s or 100s of Indian trials, registered with ClinicalTrials.gov, have not been registered in India, as required by law.

BACKGROUND: This study's primary goal was based on the fact that since 15 June 2009 it has been mandatory to register regulatory trials running in India with Clinical Trials Registry-India (CTRI). Were all such trials, registered with ClinicalTrials.gov (CTG) after 2009, that included India as a location, also registered with CTRI? We first had to determine how to correctly identify a trial that was registered in both the registries, but that lacked the relevant secondary ID. Therefore the secondary goal of this study was to identify the best method to do this. METHODS: We used a control set of 1013 trials that cross-referenced a record in the other registry. We used two algorithms to-in a blinded fashion-identify CTRI matches for the 1013 CTG records. 80% of the predictions were correct. Using the same methodology, we identified matches for the CTG trials without known CTRI matches. We then used a logistic regression model to predict which of these matches were correct. RESULTS: (i) 3664 CTG records listed India as a location, but did not list any CTRI ID, and were not identified by any CTRI records either. (ii) The best single field to find a CTRI match for a CTG trial was the title field. (iii) Between 50 and 300 of 581 relevant CTG trials were not registered with CTRI. CONCLUSIONS: This is the first study to use hidden duplicates to determine that the law on trial registration has been broken (in India). Similar studies need to be done for trials run in other countries.

An analysis of deficiencies in the data of interventional drug trials registered with Clinical Trials Registry - India.

BACKGROUND: Clinical Trials Registry - India (CTRI) was established in July 2007 and today hosts thousands of trials, a significant fraction of them registered in the last couple of years. We wished to undertake an up-to-date analysis of specific fields of the registered trials. In doing so we discovered problems with the quality of the data, which we describe in this paper. METHODS: We downloaded CTRI records and reformatted the data into an SQLite database, which we then queried. We also accessed ClinicalTrials.gov records as needed. RESULTS: We discovered various categories of problems with the data in the CTRI database, including (1) a lack of clarity in the classification of Types of Study, (2) internal inconsistencies, (3) incomplete or non-standard information, (4) missing data, (5) variations in names or classification, and (6) incomplete or incorrect details of ethics committees. For most of these problems, error rates have been calculated, over time. Most were found to be in single digits, although others were significantly higher. We suggest how data quality in future editions of CTRI could be improved, including (1) a more elaborate and structured way of classifying the Type of Study, (2) the use of logic rules to prevent internal inconsistencies, (3) less use of free text fields and greater use of drop-down menus, (4) more fields to be made compulsory, (5) the pre-registration of individuals' and organizations' names and their subsequent selection from drop-down menus while registering a trial, and (6) more information about each ethics committee, including (a) its address and (b) linking the name of the trial site to the relevant ethics committee. As we discuss problems with the data of specific fields, we also examine - where possible - the quality of the data in the corresponding fields in ClinicalTrials.gov, the largest clinical trial registry in the world. CONCLUSIONS: It is a scientific and ethical obligation to correctly record all information pertaining to each trial run in India. CTRI is a valuable database that has proved its worth in terms of improving the record of trials in the country. The suggestions made herein would improve it further.

Identification of a gene signature for discriminating metastatic from primary melanoma using a molecular interaction network approach.

Understanding the biological factors that are characteristic of metastasis in melanoma remains a key approach to improving treatment. In this study, we seek to identify a gene signature of metastatic melanoma. We configured a new network-based computational pipeline, combined with a machine learning method, to mine publicly available transcriptomic data from melanoma patient samples. Our method is unbiased and scans a genome-wide protein-protein interaction network using a novel formulation for network scoring. Using this, we identify the most influential, differentially expressed nodes in metastatic as compared to primary melanoma. We evaluated the shortlisted genes by a machine learning method to rank them by their discriminatory capacities. From this, we identified a panel of 6 genes, ALDH1A1, HSP90AB1, KIT, KRT16, SPRR3 and TMEM45B whose expression values discriminated metastatic from primary melanoma (87% classification accuracy). In an independent transcriptomic data set derived from 703 primary melanomas, we showed that all six genes were significant in predicting melanoma specific survival (MSS) in a univariate analysis, which was also consistent with AJCC staging. Further, 3 of these genes, HSP90AB1, SPRR3 and KRT16 remained significant predictors of MSS in a joint analysis (HR = 2.3, P = 0.03) although, HSP90AB1 (HR = 1.9, P = 2 × 10-4) alone remained predictive after adjusting for clinical predictors.

Meta-analysis of host response networks identifies a common core in tuberculosis.

Tuberculosis remains a major global health challenge worldwide, causing more than a million deaths annually. To determine newer methods for detecting and combating the disease, it is necessary to characterise global host responses to infection. Several high throughput omics studies have provided a rich resource including a list of several genes differentially regulated in tuberculosis. An integrated analysis of these studies is necessary to identify a unified response to the infection. Such data integration is met with several challenges owing to platform dependency, patient heterogeneity, and variability in the extent of infection, resulting in little overlap among different datasets. Network-based approaches offer newer alternatives to integrate and compare diverse data. In this study, we describe a meta-analysis of host's whole blood transcriptomic profiles that were integrated into a genome-scale protein-protein interaction network to generate response networks in active tuberculosis, and monitor their behaviour over treatment. We report the emergence of a highly active common core in disease, showing partial reversals upon treatment. The core comprises 380 genes in which STAT1, phospholipid scramblase 1 (PLSCR1), C1QB, OAS1, GBP2 and PSMB9 are prominent hubs. This network captures the interplay between several biological processes including pro-inflammatory responses, apoptosis, complement signalling, cytoskeletal rearrangement, and enhanced cytokine and chemokine signalling. The common core is specific to tuberculosis, and was validated on an independent dataset from an Indian cohort. A network-based approach thus enables the identification of common regulators that characterise the molecular response to infection, providing a platform-independent foundation to leverage maximum insights from available clinical data.

Efficacy of ß-lactam/ß-lactamase inhibitor combination is linked to WhiB4-mediated changes in redox physiology of Mycobacterium tuberculosis.

Mycobacterium tuberculosis (Mtb) expresses a broad-spectrum ß-lactamase (BlaC) that mediates resistance to one of the highly effective antibacterials, ß-lactams. Nonetheless, ß-lactams showed mycobactericidal activity in combination with ß-lactamase inhibitor, clavulanate (Clav). However, the mechanistic aspects of how Mtb responds to ß-lactams such as Amoxicillin in combination with Clav (referred as Augmentin [AG]) are not clear. Here, we identified cytoplasmic redox potential and intracellular redox sensor, WhiB4, as key determinants of mycobacterial resistance against AG. Using computer-based, biochemical, redox-biosensor, and genetic strategies, we uncovered a functional linkage between specific determinants of ß-lactam resistance (e.g. ß-lactamase) and redox potential in Mtb. We also describe the role of WhiB4 in coordinating the activity of ß-lactamase in a redox-dependent manner to tolerate AG. Disruption of WhiB4 enhances AG tolerance, whereas overexpression potentiates AG activity against drug-resistant Mtb. Our findings suggest that AG can be exploited to diminish drug-resistance in Mtb through redox-based interventions.

Unbiased Identification of Blood-based Biomarkers for Pulmonary Tuberculosis by Modeling and Mining Molecular Interaction Networks.

Efficient diagnosis of tuberculosis (TB) is met with multiple challenges, calling for a shift of focus from pathogen-centric diagnostics towards identification of host-based multi-marker signatures. Transcriptomics offer a list of differentially expressed genes, but cannot by itself identify the most influential contributors to the disease phenotype. Here, we describe a computational pipeline that adopts an unbiased approach to identify a biomarker signature. Data from RNA sequencing from whole blood samples of TB patients were integrated with a curated genome-wide molecular interaction network, from which we obtain a comprehensive perspective of variations that occur in the host due to TB. We then implement a sensitive network mining method to shortlist gene candidates that are most central to the disease alterations. We then apply a series of filters that include applicability to multiple publicly available datasets as well as additional validation on independent patient samples, and identify a signature comprising 10 genes - FCGR1A, HK3, RAB13, RBBP8, IFI44L, TIMM10, BCL6, SMARCD3, CYP4F3 and SLPI, that can discriminate between TB and healthy controls as well as distinguish TB from latent tuberculosis and HIV in most cases. The signature has the potential to serve as a diagnostic marker of TB.

Identifying and Tackling Emergent Vulnerability in Drug-Resistant Mycobacteria.

The global mechanisms and associated molecular alterations that occur in drug-resistant mycobacteria are poorly understood. To address this, we obtain genomics data and then construct a genome-scale response network in isoniazid-resistant Mycobacterium smegmatis and apply a network-mining algorithm. Through this, we decipher global alterations in an unbiased manner and identify emergent vulnerabilities in resistant bacilli, of which redox response was prominent. Using phenotypic profiling, we find that resistant bacilli exhibit collateral sensitivity to several compounds that block antioxidant responses. We find that nanogram/milliliter concentrations of ebselen, vancomycin, and phenylarsine oxide, in combination with isoniazid, are highly effective against Mycobacterium tuberculosis H37Rv and three clinical drug-resistant strains. Dynamic measurements of cytoplasmic redox potential revealed a surprisingly diminished capacity of clinical drug-resistant strains to counteract oxidative stress, providing a mechanistic basis for efficient and synergistic mycobactericidal activity of the drug combinations. Ebselen and vancomycin appear to be promising repurposable drugs.

Dissecting the structural and functional features of the Luteinizing hormone receptor using receptor specific single chain fragment variables.

The Luteinizing hormone receptor (LHR) has a large extracellular domain (amino acid residues, a.a.1-355) and a transmembrane domain (TMD; a.a. 356-699), essential for hormone binding and signaling, respectively. The LHR hinge region (a.a. 256-355) connects the two domains and acts as an activating switch for the receptor by an unknown mechanism. LHR hinge-specific Single chain fragment variables (ScFv) stimulated cAMP production by the stable and transiently transfected cell lines expressing LHR in a hormone-independent manner and the C-terminal region of LHR hinge (a.a. 313-349) was identified as the probable epitope for one agonistic ScFv. This epitope attained a helical conformation upon agonistic ScFv binding and the activity of the ScFv was dependent on Y331 sulfation. ScFv was also able to activate TMD mutants, D578Y and A593P, reemphasizing the role of TM helix VI in LHR activation.

Complete Genome Sequences of an Escherichia coli Laboratory Strain and Trimethoprim-Resistant (TMP32XR) Mutant Strains.

We report the whole-genome sequences of an Escherichia coli laboratory wild-type strain and trimethoprim-resistant strains (two biological replicates, TMP32XR1 and TMP32XR2). Compared to the U00096.3 strain, a widely used strain in laboratory experiments, the laboratory wild-type strain and the drug-resistant strains evolved from this (TMP32XR1 and TMP32XR2) are 13, 24, and 37 bp longer, respectively.

Complete Genome Sequences of a Mycobacterium smegmatis Laboratory Strain (MC2 155) and Isoniazid-Resistant (4XR1/R2) Mutant Strains.

We report the whole genome sequences of a Mycobacterium smegmatis laboratory wild-type strain (MC(2) 155) and mutants (4XR1, 4XR2) resistant to isoniazid. Compared to Mycobacterium smegmatis MC(2) 155 (NC_008596), a widely used strain in laboratory experiments, the MC(2) 155, 4XR1, and 4XR2 strains are 60, 128 and 93 bp longer, respectively.

Automatic classification of protein structures using physicochemical parameters.

Protein classification is the first step to functional annotation; SCOP and Pfam databases are currently the most relevant protein classification schemes. However, the disproportion in the number of three dimensional (3D) protein structures generated versus their classification into relevant superfamilies/families emphasizes the need for automated classification schemes. Predicting function of novel proteins based on sequence information alone has proven to be a major challenge. The present study focuses on the use of physicochemical parameters in conjunction with machine learning algorithms (Naive Bayes, Decision Trees, Random Forest and Support Vector Machines) to classify proteins into their respective SCOP superfamily/Pfam family, using sequence derived information. Spectrophores™, a 1D descriptor of the 3D molecular field surrounding a structure was used as a benchmark to compare the performance of the physicochemical parameters. The machine learning algorithms were modified to select features based on information gain for each SCOP superfamily/Pfam family. The effect of combining physicochemical parameters and spectrophores on classification accuracy (CA) was studied. Machine learning algorithms trained with the physicochemical parameters consistently classified SCOP superfamilies and Pfam families with a classification accuracy above 90%, while spectrophores performed with a CA of around 85%. Feature selection improved classification accuracy for both physicochemical parameters and spectrophores based machine learning algorithms. Combining both attributes resulted in a marginal loss of performance. Physicochemical parameters were able to classify proteins from both schemes with classification accuracy ranging from 90-96%. These results suggest the usefulness of this method in classifying proteins from amino acid sequences.

Resonance Energy Transfer between protein and rhamnolipid capped ZnS quantum dots: application in in-gel staining of proteins.

The interaction of proteins with quantum dots is an interesting field of research. These interactions occur at the nanoscale. We have probed the interaction of Bovine Serum Albumin (BSA) and Candida rugosa lipase (CRL) with rhamnolipid capped ZnS (RhlZnSQDs) using absorption and fluorescence spectroscopy. Optical studies on mixtures of RhlZnSQDs and proteins resulted in Förster's Resonance Energy Transfer (FRET) from proteins to QDs. This phenomenon has been exploited to detect proteins in agarose gel electrophoresis. The activity of the CRL was unaffected on the addition of QDs as revealed by zymography.