Mycobacterial cell envelope components have been a major focus of research due to their unique features that confer intrinsic resistance to antibiotics and chemicals apart from serving as a low-permeability barrier. The complex lipids secreted by Mycobacteria are known to evoke/repress host-immune response and thus contribute to its pathogenicity. This study focuses on the comparative genomics of the biosynthetic machinery of cell wall components across 21-mycobacterial genomes available in GenBank release 179.0. An insight into survival in varied environments could be attributed to its variation in the biosynthetic machinery. Gene-specific motifs like ‘DLLAQPTPAW’ of ufaA1 gene, novel functional linkages such as involvement of Rv0227c in mycolate biosynthesis; Rv2613c in LAM biosynthesis and Rv1209 in arabinogalactan peptidoglycan biosynthesis were detected in this study. These predictions correlate well with the available mutant and coexpression data from TBDB. It also helped to arrive at a minimal functional gene set for these biosynthetic pathways that complements findings using TraSH.

Metabolic interactions within microbial communities are essential for the efficient degradation of complex organic compounds, and underpin natural phenomena driven by microorganisms, such as the recycling of carbon-, nitrogen-, and sulfur-containing molecules. These metabolic interactions ultimately determine the function, activity and stability of the community, and therefore their understanding would be essential to steer processes where microbial communities are involved. This is exploited in the design of microbial fuel cells (MFCs), bioelectrochemical devices that convert the chemical energy present in substrates into electrical energy through the metabolic activity of microorganisms, either single species or communities. In this work, we analyzed the evolution of the microbial community structure in a cascade of MFCs inoculated with an anaerobic microbial community and continuously fed with a complex medium. The analysis of the composition of the anodic communities revealed the establishment of different communities in the anodes of the hydraulically connected MFCs, with a decrease in the abundance of fermentative taxa and a concurrent increase in respiratory taxa along the cascade. The analysis of the metabolites in the anodic suspension showed a metabolic shift between the first and last MFC, confirming the segregation of the anodic communities. Those results suggest a metabolic interaction mechanism between the predominant fermentative bacteria at the first stages of the cascade and the anaerobic respiratory electrogenic population in the latter stages, which is reflected in the observed increase in power output. We show that our experimental system represents an ideal platform for optimization of processes where the degradation of complex substrates is involved, as well as a potential tool for the study of metabolic interactions in complex microbial communities.

Leprosy, caused by Mycobacterium leprae , has plagued humanity for thousands of years and continues to cause morbidity, disability and stigmatization in two to three million people today. Although effective treatment is available, the disease incidence has remained approximately constant for decades so new approaches, such as vaccine or new drugs, are urgently needed for control. Research is however hampered by the pathogen’s obligate intracellular lifestyle and the fact that it has never been grown in vitro . Consequently, despite the availability of its complete genome sequence, fundamental questions regarding the biology of the pathogen, such as its metabolism, remain largely unexplored. In order to explore the metabolism of the leprosy bacillus with a long-term aim of developing a medium to grow the pathogen in vitro , we reconstructed an in silico genome scale metabolic model of the bacillus, GSMN-ML. The model was used to explore the growth and biomass production capabilities of the pathogen with a range of nutrient sources, such as amino acids, glucose, glycerol and metabolic intermediates. We also used the model to analyze RNA-seq data from M . leprae grown in mouse foot pads, and performed Differential Producibility Analysis to identify metabolic pathways that appear to be active during intracellular growth of the pathogen, which included pathways for central carbon metabolism, co-factor, lipids, amino acids, nucleotides and cell wall synthesis. The GSMN-ML model is thereby a useful in silico tool that can be used to explore the metabolism of the leprosy bacillus, analyze functional genomic experimental data, generate predictions of nutrients required for growth of the bacillus in vitro and identify novel drug targets.

22 Leprosy, caused by Mycobacterium leprae, has plagued humanity for thousands 23 of years and continues to cause morbidity, disability and stigmatization in two 24 to three million people today. Although effective treatment is available, the 25 disease incidence has remained approximately constant for decades so new 26 approaches, such as vaccine or new drugs, are urgently needed for control.27 Research is however hampered by the pathogen's obligate intracellular lifestyle 28 and the fact that it has never been grown in vitro. Consequently, despite the 29 availability of its complete genome sequence, fundamental questions regarding 30 the biology of the pathogen, such as its metabolism, remain largely unexplored.31 In order to explore the metabolism of the leprosy bacillus with a long-term aim 32 of developing a medium to grow the pathogen in vitro, we reconstructed an in 33 silico genome scale metabolic model of the bacillus, GSMN-ML. The model was 2 34 used to explore the growth and biomass production capabilities of the pathogen 35 with a range of nutrient sources, such as amino acids, glucose, glycerol and 36 metabolic intermediates. We also used the model to analyze RNA-seq data 37 from M. leprae grown in mouse foot pads, and performed Differential Producibility 38 Analysis (DPA) to identify metabolic pathways that appear to be active during 39 intracellular growth of the pathogen, which included pathways for central carbon 40 metabolism, co-factor, lipids, amino acids, nucleotides and cell wall synthesis. 41The GSMN-ML model is thereby a useful in silico tool that can be used to 42 explore the metabolism of the leprosy bacillus, analyze functional genomic 43 experimental data, generate predictions of nutrients required for growth of the 44 bacillus in vitro and identify novel drug targets. 46Author Summary 47 Mycobacterium leprae, the obligate human pathogen is uncultivable in axenic 48 growth medium, and this hinders research on this pathogen, and the 49 pathogenesis of leprosy. The development of novel therapeutics relies on the 50 understanding of growth, survival and metabolism of this bacterium in the host, 51 the knowledge of which is currently very limited. Here we reconstructed a 52 metabolic network of M. leprae-GSMN-ML, a powerful in silico tool to study 53 growth and metabolism of the leprosy bacillus. We demonstrate the application 54 of GSMN-ML to identify the metabolic pathways, and metabolite classes that 55 M. leprae utilizes during intracellular growth. 56 57 Key words 58 Mycobacterium leprae; metabolism; metabolic network; genome scale model; 59 flux balance analysis; differential producibility analysis 60 61 Introduction 62 The mycobacterial genus includes two of the greatest scourges of humanity:63 Mycobacterium tuberculosis and M. leprae, responsible for tuberculosis (TB) and 64 leprosy respectively. Leprosy is one of the oldest plagues of mankind yet 65 remains prevalent in developing countries. In 1981, the World Health 66 Organization (WHO) recommended multidrug treatment (MDT) of d...

The role of alternative promoter usage in tissue-specific gene expression has been well established; however, its role in complex diseases is poorly understood. We performed cap analysis of gene expression (CAGE) sequencing from the left ventricle of a rat model of hypertension, the spontaneously hypertensive rat (SHR), and a normotensive strain, Brown Norway to understand the role of alternative promoter usage in complex disease. We identified 26,560 CAGE-defined transcription start sites in the rat left ventricle, including 1,970 novel cardiac transcription start sites. We identified 28 genes with alternative promoter usage between SHR and Brown Norway, which could lead to protein isoforms differing at the amino terminus between two strains and 475 promoter switching events altering the length of the 5′ UTR. We found that the shift in Insr promoter usage was significantly associated with insulin levels and blood pressure within a panel of HXB/BXH recombinant inbred rat strains, suggesting that hyperinsulinemia due to insulin resistance might lead to hypertension in SHR. Our study provides a preliminary evidence of alternative promoter usage in complex diseases.

The role of alternative promoter usage in tissue specific gene expression has been well established, however, its role in complex diseases is poorly understood. We performed cap analysis of gene expression (CAGE) tag sequencing from the left ventricle (LV) of a rat model of hypertension, the spontaneously hypertensive rat (SHR), and a normotensive strain, the Brown Norway (BN) to understand role of alternative promoter usage in complex disease. We identified 26,560 CAGE-defined transcription start sites (TSS) in the rat LV, including 1,970 novel cardiac TSS resulting in new transcripts. We identified 27 genes with alternative promoter usage between SHR and BN which could lead to protein isoforms differing at the amino terminus between two strains. Additionally, we identified 475 promoter switching events where a shift in TSS usage was within 100bp between SHR and BN, altering length of the 5 prime UTR. Genomic variants located in the shifting promoter regions showed significant allelic imbalance in F1 crosses, confirming promoter shift. We found that the insulin receptor gene (Insr) showed a switch in promoter usage between SHR and BN in heart and liver. The Insr promoter shift was significantly associated with insulin levels and blood pressure within a panel of BXH/HXB recombinant inbred (RI) rat strains. This suggests that the hyperinsulinemia due to insulin resistance might lead to hypertension in SHR. Our study provides a preliminary evidence of alternative promoter usage in complex diseases.

Anvaya is a workflow environment for automated genome analysis that provides an interface for several bioinformatics tools and databases, loosely coupled together in a coordinated system, enabling the execution of a set of analyses tools in series or in parallel. It is a client-server workflow environment that has an advantage over existing software as it enables extensive pre & post processing of biological data in an efficient manner. "Anvaya" offers the user, novel functionalities to carry out exhaustive comparative analysis via "custom tools," which are tools with new functionality not available in standard tools, and "built-in PERL parsers," which automate data-flow between tools that hitherto, required manual intervention. It also provides a set of 11 pre-defined workflows for frequently used pipelines in genome annotation and comparative genomics ranging from EST assembly and annotation to phylogenetic reconstruction and microarray analysis. It provides a platform that serves as a single-stop solution for biologists to carry out hassle-free and comprehensive analysis, without being bothered about the nuances involved in tool installation, command line parameters, format conversions required to connect tools and manage/process multiple data sets at a single instance.