Integrated sequence and -omic features reveal novel small proteome of Mycobacterium tuberculosis.

Bioinformatic studies on small proteins are under-represented due to difficulties in annotation posed by their small size. However, recent discoveries emphasize the functional significance of small proteins in cellular processes including cell signaling, metabolism, and adaptation to stress. In this study, we utilized a Random Forest classifier trained on sequence features, RNA-Seq, and Ribo-Seq data to uncover small proteins (smORFs) in M. tuberculosis. Independent predictions for the exponential and starvation conditions resulted in 695 potential smORFs. We examined the functional implications of these smORFs using homology searches, LC-MS/MS, and ChIP-seq data, testing their expression in diverse growth conditions, and identifying protein domains. We provide evidence that some of these smORFs could be part of operons, or exist as upstream ORFs. This expanded data resource for the proteins of M. tuberculosis would aid in fine-tuning the existing protein and gene regulatory networks, thereby improving system-wide studies. The primary goal of this study was to uncover and characterize smORFs in M. tuberculosis through bioinformatic analysis, shedding light on their functional roles and genomic organization. Further investigation of these potential smORFs would provide valuable insights into the genome organization and functional diversity of the M. tuberculosis proteome.

Rho-dependent transcription termination is the dominant mechanism in Mycobacterium tuberculosis.

Intrinsic and Rho-dependent transcription termination mechanisms regulate gene expression and recycle RNA polymerase in bacteria. Both the modes are well studied in Escherichia coli, and a few other organisms. The understanding of Rho function is limited in most other bacteria including mycobacteria. Here, we highlight the dominance of Rho-dependent termination in mycobacteria and validate Rho as a key regulatory factor. The lower abundance of intrinsic terminators, high cellular levels of Rho, and its genome-wide association with a majority of transcriptionally active genes indicate the pronounced role of Rho-mediated termination in Mycobacterium tuberculosis (Mtb). Rho modulates the termination of RNA synthesis for both protein-coding and stable RNA genes in Mtb. Concordantly, the depletion of Rho in mycobacteria impact its growth and enhances the transcription read-through at 3' ends of the transcription units. We demonstrate that MtbRho is catalytically active in the presence of RNA with varied secondary structures. These properties suggest an evolutionary adaptation of Rho as the efficient and preponderant mode of transcription termination in mycobacteria.

Tumor relevant protein functional interactions identified using bipartite graph analyses.

An increased surge of -omics data for the diseases such as cancer allows for deriving insights into the affiliated protein interactions. We used bipartite network principles to build protein functional associations of the differentially regulated genes in 18 cancer types. This approach allowed us to combine expression data to functional associations in many cancers simultaneously. Further, graph centrality measures suggested the importance of upregulated genes such as BIRC5, UBE2C, BUB1B, KIF20A and PTH1R in cancer. Pathway analysis of the high centrality network nodes suggested the importance of the upregulation of cell cycle and replication associated proteins in cancer. Some of the downregulated high centrality proteins include actins, myosins and ATPase subunits. Among the transcription factors, mini-chromosome maintenance proteins (MCMs) and E2F family proteins appeared prominently in regulating many differentially regulated genes. The projected unipartite networks of the up and downregulated genes were comprised of 37,411 and 41,756 interactions, respectively. The conclusions obtained by collating these interactions revealed pan-cancer as well as subtype specific protein complexes and clusters. Therefore, we demonstrate that incorporating expression data from multiple cancers into bipartite graphs validates existing cancer associated mechanisms as well as directs to novel interactions and pathways.

Integrating multi-omics data to investigate pseudogene expression in Mycolicibacterium smegmatis.

Large-scale -omics data now allows for investigating genome-wide functional elements. Using RNA-Seq data, we tested the expression of 134 pseudogenes which do not show duplicated protein coding genes in the M. smegmatis genome. We observe significant expression and translation of 28 pseudogenes. Further examination using RNA-Seq reads suggested the sequencing errors in many pseudogenes. These include some of the functionally relevant genes such as recN and manB. We propose that the analysis of transcriptional and translational landscape using multi-dimensional -omics data could shed light on the current annotations of the bacterial pseudogenes.

Genome-wide identification of the context-dependent sRNA expression in Mycobacterium tuberculosis.

BACKGROUND: Tuberculosis remains one of the leading causes of morbidity and mortality worldwide. Therefore, understanding the pathophysiology of Mycobacterium tuberculosis is imperative for developing new drugs. Post-transcriptional regulation plays a significant role in microbial adaptation to different growth conditions. While the proteins associated with gene expression regulation have been extensively studied in the pathogenic strain M. tuberculosis H37Rv, post-transcriptional regulation involving small RNAs (sRNAs) remains poorly understood. RESULTS: We developed a novel moving-window based approach to detect sRNA expression using RNA-Seq data. Overlaying ChIP-seq data of RNAP (RNA Polymerase) and NusA suggest that these putative sRNA coding regions are significantly bound by the transcription machinery. Besides capturing many experimentally validated sRNAs, we observe the context-dependent expression of novel sRNAs in the intergenic regions of M. tuberculosis genome. For example, ncRv11806 shows expression only in the stationary phase, suggesting its role in mycobacterial latency which is a key attribute to long term pathogenicity. Also, ncRv11875C showed expression in the iron-limited condition, which is prevalent inside the macrophages of the host cells. CONCLUSION: The systems level analysis of sRNA highlights the condition-specific expression of sRNAs which might enable the pathogen survival by rewiring regulatory circuits.

Revisiting intrinsic transcription termination in mycobacteria: U-tract downstream of secondary structure is dispensable for termination.

In Escherichia coli, G/C-rich hairpin structure followed by a U-tract in the 3' region of the nascent RNA are crucial determinants for intrinsic or factor independent transcription termination. In mycobacteria, there is a scarcity of such intrinsic terminators. However, secondary structures having G/C-rich stem devoid of any U's or with suboptimal U-tracts were identified earlier as terminators and found to be functional both in vitro and in vivo. Two different observations - that a mycobacterial RNA polymerase (RNAP) does not function at intrinsic terminators devoid of U-tracts and the identification of an altogether new motif for termination in mycobacteria necessitated re-examining a number of putative terminators for their function as terminators. When these in silico identified non-canonical terminators were subjected to experimental validation, they were found to dissociate RNA from the elongating RNAP. Termination is observed when the U-tracts were reduced, or totally absent both in vitro and in vivo. Our results, thus indicate that the presence of U-tract following the G/C-rich stem in an intrinsic terminator may not be an essential determinant for transcription termination in mycobacteria.

Computational Identification of the Proteins Associated With Quorum Sensing and Biofilm Formation in Mycobacterium tuberculosis.

With prolonged therapy and increased instances of drug resistance, tuberculosis is viewed as a serious infectious disease causing high mortality. Emerging concepts in Mycobacterium tuberculosis pathogenicity include biofilm formation, which endows bacterial survival in the host for a long time. To tackle chronic tuberculosis infection, a detailed understanding of the bacterial survival mechanisms is crucial. Using comparative genomics and literature mining, 115 M. tuberculosis proteins were shortlisted for their likely association with biofilm formation or quorum sensing. These include essential genes such as secA2, lpqY-sugABC, Rv1176c, and Rv0195, many of which are also known virulence factors. Furthermore, the functional relationship among these proteins was established by considering known protein-protein interactions, regulatory interactions, and gene expression correlation data/information. Graph centrality and motif analyses predicted the importance of proteins, such as Rv0081, DevR, RegX3, Rv0097, and Rv1996 in M. tuberculosis biofilm formation. Analysis of conservation across other biofilm-forming bacteria suggests that most of these genes are conserved in mycobacteria. As the processes, such as quorum sensing, leading to biofilm formation involve diverse pathways and interactions between proteins, these system-wide studies provide a novel perspective toward understanding mycobacterial persistence.

Transcription facilitated genome-wide recruitment of topoisomerase I and DNA gyrase.

Movement of the transcription machinery along a template alters DNA topology resulting in the accumulation of supercoils in DNA. The positive supercoils generated ahead of transcribing RNA polymerase (RNAP) and the negative supercoils accumulating behind impose severe topological constraints impeding transcription process. Previous studies have implied the role of topoisomerases in the removal of torsional stress and the maintenance of template topology but the in vivo interaction of functionally distinct topoisomerases with heterogeneous chromosomal territories is not deciphered. Moreover, how the transcription-induced supercoils influence the genome-wide recruitment of DNA topoisomerases remains to be explored in bacteria. Using ChIP-Seq, we show the genome-wide occupancy profile of both topoisomerase I and DNA gyrase in conjunction with RNAP in Mycobacterium tuberculosis taking advantage of minimal topoisomerase representation in the organism. The study unveils the first in vivo genome-wide interaction of both the topoisomerases with the genomic regions and establishes that transcription-induced supercoils govern their recruitment at genomic sites. Distribution profiles revealed co-localization of RNAP and the two topoisomerases on the active transcriptional units (TUs). At a given locus, topoisomerase I and DNA gyrase were localized behind and ahead of RNAP, respectively, correlating with the twin-supercoiled domains generated. The recruitment of topoisomerases was higher at the genomic loci with higher transcriptional activity and/or at regions under high torsional stress compared to silent genomic loci. Importantly, the occupancy of DNA gyrase, sole type II topoisomerase in Mtb, near the Ter domain of the Mtb chromosome validates its function as a decatenase.

Differential enrichment of regulatory motifs in the composite network of protein-protein and gene regulatory interactions.

BACKGROUND: An important aspect of molecular interactions is the dynamics associated with growth conditions. Intuitively, not all possible interactions take place together all the time in a cell as only a subset of genes is expressed based on environmental conditions. RESULTS: Large scale gene expression data of Escherichia coli was analyzed to understand the dynamics exhibited at expression level. A large compendium of gene expression datasets, which covers about 466 growth conditions, was used for the analysis. Using gene expression data, genes of E. coli were profiled into three classes: Widely expressed, Conditionally expressed and Rarely expressed. Further, dynamics associated with molecular interactions were analysed by studying changing importance of motifs in the composite networks across growth conditions. CONCLUSIONS: Our analysis of large scale gene expression data suggests conditional expression of genes which brings about befitting responses for a given growth environment. We observe a range of importance for network motifs across conditions which can be correlated with a specific function. Our study therefore suggests rewiring of molecular interactions driven by gene expression changes depending on the conditional needs.

Identification of self-consistent modulons from bacterial microarray expression data with the help of structured regulon gene sets.

Identification of bacterial modulons from series of gene expression measurements on microarrays is a principal problem, especially relevant for inadequately studied but practically important species. Usage of a priori information on regulatory interactions helps to evaluate parameters for regulatory subnetwork inference. We suggest a procedure for modulon construction where a seed regulon is iteratively updated with genes having expression patterns similar to those for regulon member genes. A set of genes essential for a regulon is used to control modulon updating. Essential genes for a regulon were selected as a subset of regulon genes highly related by different measures to each other. Using Escherichia coli as a model, we studied how modulon identification depends on the data, including the microarray experiments set, the adopted relevance measure and the regulon itself. We have found that results of modulon identification are highly dependent on all parameters studied and thus the resulting modulon varies substantially depending on the identification procedure. Yet, modulons that were identified correctly displayed higher stability during iterations, which allows developing a procedure for reliable modulon identification in the case of less studied species where the known regulatory interactions are sparse.

Understanding communication signals during mycobacterial latency through predicted genome-wide protein interactions and boolean modeling.

About 90% of the people infected with Mycobacterium tuberculosis carry latent bacteria that are believed to get activated upon immune suppression. One of the fundamental challenges in the control of tuberculosis is therefore to understand molecular mechanisms involved in the onset of latency and/or reactivation. We have attempted to address this problem at the systems level by a combination of predicted functional protein:protein interactions, integration of functional interactions with large scale gene expression studies, predicted transcription regulatory network and finally simulations with a boolean model of the network. Initially a prediction for genome-wide protein functional linkages was obtained based on genome-context methods using a Support Vector Machine. This set of protein functional linkages along with gene expression data of the available models of latency was employed to identify proteins involved in mediating switch signals during dormancy. We show that genes that are up and down regulated during dormancy are not only coordinately regulated under dormancy-like conditions but also under a variety of other experimental conditions. Their synchronized regulation indicates that they form a tightly regulated gene cluster and might form a latency-regulon. Conservation of these genes across bacterial species suggests a unique evolutionary history that might be associated with M. tuberculosis dormancy. Finally, simulations with a boolean model based on the regulatory network with logical relationships derived from gene expression data reveals a bistable switch suggesting alternating latent and actively growing states. Our analysis based on the interaction network therefore reveals a potential model of M. tuberculosis latency.

Delineation of key regulatory elements identifies points of vulnerability in the mitogen-activated signaling network.

Drug development efforts against cancer are often hampered by the complex properties of signaling networks. Here we combined the results of an RNAi screen targeting the cellular signaling machinery, with graph theoretical analysis to extract the core modules that process both mitogenic and oncogenic signals to drive cell cycle progression. These modules encapsulated mechanisms for coordinating seamless transition of cells through the individual cell cycle stages and, importantly, were functionally conserved across different cancer cell types. Further analysis also enabled extraction of the core signaling axes that progressively guide commitment of cells to the division cycle. Importantly, pharmacological targeting of the least redundant nodes in these axes yielded a synergistic disruption of the cell cycle in a tissue-type-independent manner. Thus, the core elements that regulate temporally distinct stages of the cell cycle provide attractive targets for the development of multi-module-based chemotherapeutic strategies.

Prediction of conditional gene essentiality through graph theoretical analysis of genome-wide functional linkages.

The genome of an organism characterizes the complete set of genes that it is capable of encoding. However, not all of the genes are transcribed and translated under any defined condition. The robustness that an organism exhibits to environmental perturbations is partly conferred by the genes that are constitutively expressed under all the conditions, and partly by a subset of genes that are induced under the defined conditions. The conditional importance of genes in conferring robustness can be understood in the context of the functional attributes of these genes and their correlations to the defined environmental conditions. However, a priori prediction of such genes for a given condition is yet not possible. We have attempted such predictions by integrating the available gene expression data with genome-wide functional linkages through the well known centrality-lethality correlations in graph theory. We make use of three distinct concepts of centrality, namely, degree, closeness and betweenness, which yield mutually complementary information. We then demonstrate the efficacy of combined graph theoretical and machine learning approaches in ranking essential nodes from a large network of genome-wide functional linkages, which yields predictions with high accuracy. We therefore perceive such predictions as highly useful in applications such as defining and prioritizing drug targets.

Dynamic changes in protein functional linkage networks revealed by integration with gene expression data.

Response of cells to changing environmental conditions is governed by the dynamics of intricate biomolecular interactions. It may be reasonable to assume, proteins being the dominant macromolecules that carry out routine cellular functions, that understanding the dynamics of protein:protein interactions might yield useful insights into the cellular responses. The large-scale protein interaction data sets are, however, unable to capture the changes in the profile of protein:protein interactions. In order to understand how these interactions change dynamically, we have constructed conditional protein linkages for Escherichia coli by integrating functional linkages and gene expression information. As a case study, we have chosen to analyze UV exposure in wild-type and SOS deficient E. coli at 20 minutes post irradiation. The conditional networks exhibit similar topological properties. Although the global topological properties of the networks are similar, many subtle local changes are observed, which are suggestive of the cellular response to the perturbations. Some such changes correspond to differences in the path lengths among the nodes of carbohydrate metabolism correlating with its loss in efficiency in the UV treated cells. Similarly, expression of hubs under unique conditions reflects the importance of these genes. Various centrality measures applied to the networks indicate increased importance for replication, repair, and other stress proteins for the cells under UV treatment, as anticipated. We thus propose a novel approach for studying an organism at the systems level by integrating genome-wide functional linkages and the gene expression data.