Heparan sulfate (HS) chains play crucial biological roles by binding to various signaling molecules including fibroblast growth factors (FGFs). Distinct sulfation patterns of HS chains are required for their binding to FGFs/FGF receptors (FGFRs). These sulfation patterns are putatively regulated by biosynthetic enzyme complexes, called GAGOSOMES, in the Golgi. While the structural requirements of HS-FGF interactions have been described previously, it is still unclear how the FGF-binding motif is assembled in vivo. In this study, we generated HS structures using biosynthetic enzymes in a sequential or concurrent manner to elucidate the potential mechanism by which the FGF1-binding HS motif is assembled. Our results indicate that the HS chains form ternary complexes with FGF1/FGFR when enzymes carry out modifications in a specific manner.

Heparan sulfate (HS) proteoglycans regulate a number of biological functions in many systems. Most of the functions of HS are attributed to its unique structure, consisting of sulfated and non-sulfated domains, arising from the differential presence of iduronyl and glucuronyl residues along the polysaccharide chain. A single glucuronyl C5-epimerase enzyme acts on heparan sulfate precursor, converts glucuronyl residues into iduronyl residues and modulates subsequent biosynthetic steps in vivo. The ratios of non-sulfated epimers within the polysaccharide chain have been calculated by resolving radiolabeled GlcA-AManR and IdoA-AManR disaccharides using a tedious paper chromatography technique. Radioactive assay, based on measuring either the release or incorporation of 3H at C5 carbon of uronyl residues of 3H-labeled HS precursor substrate, has been in use over three decades to characterize the action of HS C5-epimerase. We have developed a non-radioactive assay to estimate the epimerase activity through resolving GlcA-AManR and IdoA-AManR disaccharides on HPLC in conjunction with hydrogen/deuterium exchange upon epimerization protocol-liquid chromatography mass spectrometry (DEEP-LC-MS). Utilizing this new, non-radioactive based assay, DEEP-LC-MS, we were able to determine the extent of both forward and reverse reaction on the same substrate catalyzed by C5-epimerase. Results from this study also provide insights into the action of C5-epimerase and provide an opportunity to delineate snapshots of biosynthetic events that occur during the HSPG assembly in the Golgi.

In biological systems, individual phenotypes are typically adopted by multiple genotypes. Examples include protein structure phenotypes, where each structure can be adopted by a myriad individual amino acid sequence genotypes. These genotypes form vast connected ‘neutral networks’ in genotype space. The size of such neutral networks endows biological systems not only with robustness to genetic change, but also with the ability to evolve a vast number of novel phenotypes that occur near any one neutral network. Whether technological systems can be designed to have similar properties is poorly understood. Here we ask this question for a class of programmable electronic circuits that compute digital logic functions. The functional flexibility of such circuits is important in many applications, including applications of evolutionary principles to circuit design. The functions they compute are at the heart of all digital computation. We explore a vast space of 1045 logic circuits (‘genotypes’) and 1019 logic functions (‘phenotypes’). We demonstrate that circuits that compute the same logic function are connected in large neutral networks that span circuit space. Their robustness or fault-tolerance varies very widely. The vicinity of each neutral network contains circuits with a broad range of novel functions. Two circuits computing different functions can usually be converted into one another via few changes in their architecture. These observations show that properties important for the evolvability of biological systems exist in a commercially important class of electronic circuitry. They also point to generic ways to generate fault-tolerant, adaptable and evolvable electronic circuitry.

Heparan sulfate proteoglycans (HSPGs) are essential players in several steps of tumor-associated angiogenesis. As co-receptors for several pro-angiogenic factors such as VEGF and FGF, HSPGs regulate receptor-ligand interactions and play a vital role in signal transduction. Previously, we have employed an enzymatic strategy to show the importance of cell surface HSPGs in endothelial tube formation in vitro. We have recently found several fluoro-xylosides that can selectively inhibit proteoglycan synthesis in endothelial cells. The current study demonstrates that these fluoro-xylosides are effective inhibitors of endothelial tube formation in vitro using a matrigel based assay to simulate tumor-associated angiogenesis. These first generation scaffolds offer a promising stepping-stone to the discovery of more potent fluoro-xylosides that can effectively neutralize tumor growth.

Heparanomics is the study of all the biologically active oligosaccharide domain structures in the entire heparanome and the nature of interactions among these domains and their protein ligands. Structural elucidation of heparan sulfate and heparin oligosaccharides is a major obstacle in advancing structure-function relationships and the study of heparanomics. There are several factors that exacerbate challenges involved in the structural elucidation of heparin and heparan sulfate. Therefore, there is a great interest in developing novel strategies and analytical tools to overcome the barriers in decoding the enigmatic heparanome. This review article focuses on the applications of isotopes, both radioisotopes and stable isotopes, in the structural elucidation of the complex heparanome at the disaccharide or oligosaccharide level using liquid chromatography, nuclear magnetic resonance spectroscopy and mass spectrometry. This review article also outlines the utility of isotopes in determining the substrate specificity of biosynthetic enzymes that eventually dictate the emergence of biologically active oligosaccharides.

Heparan sulfate proteoglycans (HSPGs) play vital roles in many steps of angiogenesis under physiological and pathological conditions. HSPGs on endothelial cell surfaces act as coreceptors for a variety of pro-angiogenic growth factors such as FGF and VEGF and anti-angiogenic factors such as endostatin. However, the fine structural requirements of these binding interactions are dependent on the sulfation patterns of HSPGs. Previous studies have shown that Heparitinases, heparin lyases isolated from flavobacterium heparinum, can cleave heparan sulfate chains. These enzymes have been shown to reduce tumor—derived neovascularization in vivo in mice. However, the results from these experiments could not conclusively pinpoint the origin of the HS fragments. Thus, in this study we utilized an in vitro assay to assess the differential effects of Heparitinase I (Hep I) and Heparitinase III (Hep III) on endothelial tube formation. Hep III was found to be a more potent inhibitor of tube formation than Hep I. In conclusion, differential cleavage of endothelial cell surface bound HS can affect the extent of inhibition of tube formation.

Heparan sulfate proteoglycans (HSPGs) play vital roles in every step of tumor progression allowing cancer cells to proliferate, escape from immune response, invade neighboring tissues, and metastasize to distal sites away from the primary site. Several cancers including breast, lung, brain, pancreatic, skin, and colorectal cancers show aberrant modulation of several key HS biosynthetic enzymes such as 3-O Sulfotransferase and 6-O Sulfotransferase, and also catabolic enzymes such as HSulf-1, HSulf-2 and heparanase. The resulting tumor specific HS fine structures assist cancer cells to breakdown ECM to spread, misregulate signaling pathways to facilitate their proliferation, promote angiogenesis to receive nutrients, and protect themselves against natural killer cells. This review focuses on the changes in the expression of HS biosynthetic and catabolic enzymes in several cancers, the resulting changes in HS fine structures, and the effects of these tumor specific HS signatures on promoting invasion, proliferation, and metastasis. It is possible to retard tumor progression by modulating the deregulated biosynthetic and catabolic pathways of HS chains through novel chemical biology approaches.

Protein–protein interactions form the basis for a vast majority of cellular events, including signal transduction and transcriptional regulation. It is now understood that the study of interactions between cellular macromolecules is fundamental to the understanding of biological systems. Interactions between proteins have been studied through a number of high-throughput experiments and have also been predicted through an array of computational methods that leverage the vast amount of sequence data generated in the last decade. In this review, I discuss some of the important computational methods for the prediction of functional linkages between proteins. I then give a brief overview of some of the databases and tools that are useful for a study of protein–protein interactions. I also present an introduction to network theory, followed by a discussion of the parameters commonly used in analysing networks, important network topologies, as well as methods to identify important network components, based on perturbations.

Background

Emergence of drug resistant varieties of tuberculosis is posing a major threat to global tuberculosis eradication programmes. Although several approaches have been explored to counter resistance, there has been limited success due to a lack of understanding of how resistance emerges in bacteria upon drug treatment. A systems level analysis of the proteins involved is essential to gain insights into the routes required for emergence of drug resistance.

Results

We derive a genome-scale protein-protein interaction network for Mycobacterium tuberculosis H37Rv from the STRING database, with proteins as nodes and interactions as edges. A set of proteins involved in both intrinsic and extrinsic drug resistance mechanisms are identified from literature. We then compute shortest paths from different drug targets to the set of resistance proteins in the protein-protein interactome, to derive a sub-network relevant to study emergence of drug resistance. The shortest paths are then scored and ranked based on a new scheme that considers (a) drug-induced gene upregulation data, from microarray experiments reported in literature, for the individual nodes and (b) edge-hubness, a network parameter which signifies centrality of a given edge in the network. High-scoring paths identified from this analysis indicate most plausible pathways for the emergence of drug resistance. Different targets appear to have different propensities for four drug resistance mechanisms. A new concept of 'co-targets' has been proposed to counter drug resistance, co-targets being defined as protein(s) that need to be simultaneously inhibited along with the intended target(s), to check emergence of resistance to a given drug.

Conclusion

The study leads to the identification of possible pathways for drug resistance, providing novel insights into the problem of resistance. Knowledge of important proteins in such pathways enables identification of appropriate 'co-targets', best examples being RecA, Rv0823c, Rv0892 and DnaE1, for drugs targeting the mycolic acid pathway. Insights obtained about the propensity of a drug to trigger resistance will be useful both for more careful identification of drug targets as well as to identify target-co-target pairs, both implementable in early stages of drug discovery itself. This approach is also inherently generic, likely to significantly impact drug discovery.

Background

Tuberculosis still remains one of the largest killer infectious diseases, warranting the identification of newer targets and drugs. Identification and validation of appropriate targets for designing drugs are critical steps in drug discovery, which are at present major bottle-necks. A majority of drugs in current clinical use for many diseases have been designed without the knowledge of the targets, perhaps because standard methodologies to identify such targets in a high-throughput fashion do not really exist. With different kinds of 'omics' data that are now available, computational approaches can be powerful means of obtaining short-lists of possible targets for further experimental validation.

Results

We report a comprehensive in silico target identification pipeline, targetTB, for Mycobacterium tuberculosis. The pipeline incorporates a network analysis of the protein-protein interactome, a flux balance analysis of the reactome, experimentally derived phenotype essentiality data, sequence analyses and a structural assessment of targetability, using novel algorithms recently developed by us. Using flux balance analysis and network analysis, proteins critical for survival of M. tuberculosis are first identified, followed by comparative genomics with the host, finally incorporating a novel structural analysis of the binding sites to assess the feasibility of a protein as a target. Further analyses include correlation with expression data and non-similarity to gut flora proteins as well as 'anti-targets' in the host, leading to the identification of 451 high-confidence targets. Through phylogenetic profiling against 228 pathogen genomes, shortlisted targets have been further explored to identify broad-spectrum antibiotic targets, while also identifying those specific to tuberculosis. Targets that address mycobacterial persistence and drug resistance mechanisms are also analysed.

Conclusion

The pipeline developed provides rational schema for drug target identification that are likely to have high rates of success, which is expected to save enormous amounts of money, resources and time in the drug discovery process. A thorough comparison with previously suggested targets in the literature demonstrates the usefulness of the integrated approach used in our study, highlighting the importance of systems-level analyses in particular. The method has the potential to be used as a general strategy for target identification and validation and hence significantly impact most drug discovery programmes.

Background

Several types of networks, such as transcriptional, metabolic or protein-protein interaction networks of various organisms have been constructed, that have provided a variety of insights into metabolism and regulation. Here, we seek to exploit the reaction-based networks of three organisms for comparative genomics. We use concepts from spectral graph theory to systematically determine how differences in basic metabolism of organisms are reflected at the systems level and in the overall topological structures of their metabolic networks.

Methodology/Principal Findings

Metabolome-based reaction networks of Mycobacterium tuberculosis, Mycobacterium leprae and Escherichia coli have been constructed based on the KEGG LIGAND database, followed by graph spectral analysis of the network to identify hubs as well as the sub-clustering of reactions. The shortest and alternate paths in the reaction networks have also been examined. Sub-cluster profiling demonstrates that reactions of the mycolic acid pathway in mycobacteria form a tightly connected sub-cluster. Identification of hubs reveals reactions involving glutamate to be central to mycobacterial metabolism, and pyruvate to be at the centre of the E. coli metabolome. The analysis of shortest paths between reactions has revealed several paths that are shorter than well established pathways.

Conclusions

We conclude that severe downsizing of the leprae genome has not significantly altered the global structure of its reaction network but has reduced the total number of alternate paths between its reactions while keeping the shortest paths between them intact. The hubs in the mycobacterial networks that are absent in the human metabolome can be explored as potential drug targets. This work demonstrates the usefulness of constructing metabolome based networks of organisms and the feasibility of their analyses through graph spectral methods. The insights obtained from such studies provide a broad overview of the similarities and differences between organisms, taking comparative genomics studies to a higher dimension.

Mycobacterium tuberculosis is the focus of several investigations for design of newer drugs, as tuberculosis remains a major epidemic despite the availability of several drugs and a vaccine. Mycobacteria owe many of their unique qualities to mycolic acids, which are known to be important for their growth, survival, and pathogenicity. Mycolic acid biosynthesis has therefore been the focus of a number of biochemical and genetic studies. It also turns out to be the pathway inhibited by front-line anti-tubercular drugs such as isoniazid and ethionamide. Recent years have seen the emergence of systems-based methodologies that can be used to study microbial metabolism. Here, we seek to apply insights from flux balance analyses of the mycolic acid pathway (MAP) for the identification of anti-tubercular drug targets. We present a comprehensive model of mycolic acid synthesis in the pathogen M. tuberculosis involving 197 metabolites participating in 219 reactions catalysed by 28 proteins. Flux balance analysis (FBA) has been performed on the MAP model, which has provided insights into the metabolic capabilities of the pathway. In silico systematic gene deletions and inhibition of InhA by isoniazid, studied here, provide clues about proteins essential for the pathway and hence lead to a rational identification of possible drug targets. Feasibility studies using sequence analysis of the M. tuberculosis H37Rv and human proteomes indicate that, apart from the known InhA, potential targets for anti-tubercular drug design are AccD3, Fas, FabH, Pks13, DesA1/2, and DesA3. Proteins identified as essential by FBA correlate well with those previously identified experimentally through transposon site hybridisation mutagenesis. This study demonstrates the application of FBA for rational identification of potential anti-tubercular drug targets, which can indeed be a general strategy in drug design. The targets, chosen based on the critical points in the pathway, form a ready shortlist for experimental testing.

Synopsis

M. tuberculosis, a deadly human pathogen, owes many of its unique qualities to its thick, waxy coat, containing fatty acids called mycolic acids. Several front-line drugs used for treating tuberculosis indeed inhibit mycolic acid synthesis. Understanding the biochemical pathway that makes these compounds is therefore of great interest. Availability of the genome sequence and various computational methods enable us to study pathways as whole functional units, rather than having to infer from the study of individual proteins. Here, we present a comprehensive identification of the components of the mycolic acid pathway and represent it mathematically based on reaction stoichiometry. Such models are amenable to perturbations and simulations using flux balance analysis, allowing the study of pathways from a metabolic capacity perspective, and yielding information about reaction fluxes. The perturbations studied here are in silico gene knock-outs and drug effects, which led us to identify genes essential to the pathway and hence for survival of the pathogen. The results are in good agreement with essentiality determined through experimental genetics. Such essential genes can be good targets for drug design, especially when they do not have homologues in the human proteome. FBA followed by sequence analyses have resulted in identification of potential anti-tubercular drug targets.