title: Sequencing-relative to hybridization-based transcriptomics approaches better define Mycobacterium tuberculosis stress-response regulons.
abstract: Mycobacterium tuberculosis (Mtb) infections cause tuberculosis (TB), an infectious disease which causes ∼1.5 million deaths annually. The ability of this pathogen to evade, escape and encounter immune surveillance is fueled by its adaptability. Thus, Mtb induces a transition in its transcriptome in response to environmental changes. Global transcriptome profiling has been key to our understanding of how Mtb responds to the different stress conditions it faces during its life cycle. While this was initially achieved using microarray technology, RNAseq is now widely employed. It is important to understand the correlation between the large amount of microarray based transcriptome data, which continues to shape our understanding of Mtb stress networks, and newer data being generated using RNAseq. We assessed how well the two platforms correlate using three well-defined stress conditions: diamide, hypoxia, and re-aeration. The data used here was generated by different individuals over time using distinct samples, providing a stringent test of platform correlation. While correlation between microarrays and sequencing was high upon diamide treatment, which causes a rapid reprogramming of the transcriptome, RNAseq allowed a better definition of the hypoxic response, characterized by subtle changes in the magnitude of gene-expression. RNAseq also allows for the best cross-platform reproducibility.
authors.affiliation: Divisions of Bacteriology & Parasitology, Tulane National Primate Research Center, Covington LA, USA; Department of Microbiology & Immunology, Tulane University School of Medicine, New Orleans, LA, USA.
authors.forename: Ashley V
authors.initials: AV
authors.lastname: Veatch
authors.affiliation: Department of Biostatistics & Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans LA, USA.
authors.forename: Tianhua
authors.initials: T
authors.lastname: Niu
authors.affiliation: Department of Pathobiological Sciences, Louisiana State University School of Veterinary Medicine, Baton Rouge, LA, USA.
authors.forename: John
authors.initials: J
authors.lastname: Caskey
authors.affiliation: Divisions of Bacteriology & Parasitology, Tulane National Primate Research Center, Covington LA, USA.
authors.forename: Amanda
authors.initials: A
authors.lastname: McGillivray
authors.affiliation: Divisions of Bacteriology & Parasitology, Tulane National Primate Research Center, Covington LA, USA.
authors.forename: Uma Shankar
authors.initials: US
authors.lastname: Gautam
authors.affiliation: Department of Pathobiological Sciences, Louisiana State University School of Veterinary Medicine, Baton Rouge, LA, USA.
authors.forename: Ramesh
authors.initials: R
authors.lastname: Subramanian
authors.affiliation: Department of Pathobiological Sciences, Louisiana State University School of Veterinary Medicine, Baton Rouge, LA, USA.
authors.forename: K Gus
authors.initials: KG
authors.lastname: Kousoulas
authors.affiliation: Divisions of Microbiology, Tulane National Primate Research Center, Covington LA, USA; Department of Pathobiological Sciences, Louisiana State University School of Veterinary Medicine, Baton Rouge, LA, USA.
authors.forename: Smriti
authors.initials: S
authors.lastname: Mehra
authors.affiliation: Divisions of Bacteriology & Parasitology, Tulane National Primate Research Center, Covington LA, USA; Department of Microbiology & Immunology, Tulane University School of Medicine, New Orleans, LA, USA. Electronic address: dkaushal@tulane.edu.
authors.forename: Deepak
authors.initials: D
authors.lastname: Kaushal
mesh_terms: D001426:Bacterial Proteins / Q000235:genetics; D004268:DNA-Binding Proteins; D003958:Diamide / Q000494:pharmacology; D020869:Gene Expression Profiling / Q000379:methods*; D015964:Gene Expression Regulation, Bacterial / Q000187:drug effects; D006801:Humans; D009169:Mycobacterium tuberculosis / Q000187:drug effects / Q000235:genetics* / Q000378:metabolism / Q000472:pathogenicity; D015588:Observer Variation; D020411:Oligonucleotide Array Sequence Analysis; D010084:Oxidation-Reduction; D018384:Oxidative Stress; D010100:Oxygen / Q000378:metabolism; D011494:Protein Kinases / Q000235:genetics; D012329:RNA, Bacterial / Q000235:genetics*; D018085:Regulon; D015203:Reproducibility of Results; D017423:Sequence Analysis, RNA; D012808:Sigma Factor / Q000235:genetics; D013312:Stress, Physiological; D013997:Time Factors; D059467:Transcriptome / Q000187:drug effects; D014376:Tuberculosis / Q000382:microbiology
chemical_list: D001426:Bacterial Proteins; D004268:DNA-Binding Proteins; C542863:DosR protein, Mycobacterium tuberculosis; D012329:RNA, Bacterial; C120328:SigH protein, bacteria; D012808:Sigma Factor; D003958:Diamide; D011494:Protein Kinases; D010100:Oxygen
keywords: Correlation; Microarray; RNAseq; Stress response; Tuberculosis