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Farhat, Maha

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Farhat

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Maha

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Farhat, Maha
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    Discovery of a unique Mycobacterium tuberculosis protein through proteomic analysis of urine from patients with active tuberculosis
    (Elsevier, 2017) Pollock, Nira; Dhiman, Rakesh; Daifalla, Nada; Farhat, Maha; Campos-Neto, Antonio
    Identification of pathogen-specific biomarkers present in patients' serum or urine samples can be a useful diagnostic approach. In efforts to discover Mycobacterium tuberculosis (Mtb) biomarkers we identified by mass spectroscopy a unique 21-mer Mtb peptide sequence (VVLGLTVPGGVELLPGVALPR) present in the urines of TB patients from Zimbabwe. This peptide has 100% sequence homology with the protein TBCG_03312 from the C strain of Mtb (a clinical isolate identified in New York, NY, USA) and 95% sequence homology with Mtb oxidoreductase (MRGA423_21210) from the clinical isolate MTB423 (identified in Kerala, India). Alignment of the genes coding for these proteins show an insertion point mutation relative to Rv3368c of the reference H37Rv strain, which generated a unique C-terminus with no sequence homology with any other described protein. Phylogenetic analysis utilizing public sequence data shows that the insertion mutation is apparently a rare event. However, sera from TB patients from distinct geographical areas of the world (Peru, Vietnam, and South Africa) contain antibodies that recognize a purified recombinant C-terminus of the protein, thus suggesting a wider distribution of isolates that produce this protein.
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    Genomic Analysis Identifies Targets of Convergent Positive Selection in Drug Resistant Mycobacterium tuberculosis
    (2013) Farhat, Maha; Shapiro, B Jesse; Kieser, Karen; Sultana, Razvan; Jacobson, Karen R; Victor, Thomas C; Warren, Robin M; Streicher, Elizabeth M; Calver, Alistair; Sloutsky, Alex; Kaur, Devinder; Posey, Jamie E; Plikaytis, Bonnie; Oggioni, Marco R; Gardy, Jennifer L; Johnston, James C; Rodrigues, Mabel; Tang, Patrick K C; Kato-Maeda, Midori; Borowsky, Mark L; Muddukrishna, Bhavana; Kreiswirth, Barry N; Kurepina, Natalia; Galagan, James; Gagneux, Sebastien; Birren, Bruce; Rubin, Eric; Lander, Eric S; Sabeti, Pardis; Murray, Megan
    Mycobacterium tuberculosis is successfully evolving antibiotic resistance, threatening attempts at tuberculosis epidemic control. Mechanisms of resistance, including the genetic changes favored by selection in resistant isolates, are incompletely understood. Using 116 newly and 7 previously sequenced M. tuberculosis genomes, we identified genomewide signatures of positive selection specific to the 47 resistant genomes. By searching for convergent evolution, the independent fixation of mutations at the same nucleotide site or gene, we recovered 100% of a set of known resistance markers. We also found evidence of positive selection in an additional 39 genomic regions in resistant isolates. These regions encode pathways of cell wall biosynthesis, transcriptional regulation and DNA repair. Mutations in these regions could directly confer resistance or compensate for fitness costs associated with resistance. Functional genetic analysis of mutations in one gene, ponA1, demonstrated an in vitro growth advantage in the presence of the drug rifampicin.
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    A phylogeny-based sampling strategy and power calculator informs genome-wide associations study design for microbial pathogens
    (BioMed Central, 2014) Farhat, Maha; Shapiro, B Jesse; Sheppard, Samuel K; Colijn, Caroline; Murray, Megan
    Whole genome sequencing is increasingly used to study phenotypic variation among infectious pathogens and to evaluate their relative transmissibility, virulence, and immunogenicity. To date, relatively little has been published on how and how many pathogen strains should be selected for studies associating phenotype and genotype. There are specific challenges when identifying genetic associations in bacteria which often comprise highly structured populations. Here we consider general methodological questions related to sampling and analysis focusing on clonal to moderately recombining pathogens. We propose that a matched sampling scheme constitutes an efficient study design, and provide a power calculator based on phylogenetic convergence. We demonstrate this approach by applying it to genomic datasets for two microbial pathogens: Mycobacterium tuberculosis and Campylobacter species. Electronic supplementary material The online version of this article (doi:10.1186/s13073-014-0101-7) contains supplementary material, which is available to authorized users.
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    Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production
    (Public Library of Science, 2009) Colijn, Caroline; Brandes, Aaron; Zucker, Jeremy Daniel Hofeld; Lun, Desmond S.; Weiner, Brian; Farhat, Maha; Cheng, Tan-Yun; Moody, David; Murray, Megan; Galagan, James E.
    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.
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    Genome-wide discovery of epistatic loci affecting antibiotic resistance in Neisseria gonorrhoeae using evolutionary couplings
    (Springer Science and Business Media LLC, 2018-12-03) Schubert, Benjamin; Maddamsetti, Rohan; Nyman, Jackson; Farhat, Maha; Marks, Debora S.
    Genome analysis should allow the discovery of interdependent loci that together cause antibiotic resistance. In practice however, the vast number of possible epistatic interactions erodes statistical power. Here, we extend an approach that has been successfully used to identify epistatic residues in proteins to infer genomic loci that are strongly coupled. This approach reduces the number of tests required for an epistatic genome-wide association study of antibiotic resistance and increases the likelihood of identifying causal epistasis. We discovered 38 loci and 240 epistatic pairs that influence the minimum inhibitory concentrations of five different antibiotics in 1,102 isolates of Neisseria gonorrhoeae that were confirmed in a second dataset of 495 isolates. Many known resistance-affecting loci were recovered; however, the majority of associations occurred in unreported genes, such as murE. About half of the discovered epistasis involved at least one locus previously associated with antibiotic resistance, including interactions between gyrA and parC. Still, many combinations involved unreported loci and genes. While most variation in minimum inhibitory concentrations could be explained by identified loci, epistasis substantially increased explained phenotypic variance. Our work provides a systematic identification of epistasis affecting antibiotic resistance in N. gonorrhoeae and a generalizable approach for epistatic genome-wide association studies.