Somnath Tagore

RNAi--a tool for target finding in new drug development.

RNAi (RNA interference) refers to the introduction of homologous double stranded RNA (dsRNA) to specifically target a genes product, resulting in null or hypomorphic phenotypes. Long double-stranded RNAs (dsRNAs; typically >200 nt) can be used to silence the expression of target genes in a variety of organisms and cell types (e.g., worms, fruit flies, and plants). The long dsRNAs enter a cellular pathway that is commonly referred to as the RNA interference (RNAi) pathway. RNAi is being considered as an important tool not only for functional genomics, but also for gene-specific therapeutic activities that target the mRNAs of disease-related genes. RNAi plays a very important role in endogenous cellular processes, such as heterochromatin formation, developmental control and serves as an antiviral defense mechanism. RNAi has shown great potential for use as a tool for target finding in new drug development, molecular biological discovery, analysis and therapeutics. RNAi pathway is involved in post-transcription silencing, transcriptional silencing and epigenetic silencing as well as its use as a tool for forward genetics and therapeutics.

Proteomics: Technologies for Protein Analysis

Proteomics technologies have produced an abundance of drug targets, which is creating a bottleneck in drug development process. There is an increasing need for better target validation for new drug development and proteomic technologies are contributing to it. Identifying a potential protein drug target within a cell is a major challenge in modern drug discovery; techniques for screening the proteome are, therefore, an important tool. Major difficulties for target identification include the separation of proteins and their detection. These technologies are compared to enable the selection of the one by matching the needs of a particular project. There are prospects for further improvement, and proteomics technologies will form an important addition to the existing genomic and chemical technologies for new target validation. Proteomics is applicable for protein analysis and bioinformatics based analysis gives the comprehensive molecular description of the actual protein component. Bioinformatics is being increasingly used to support target validation by providing functionally predictive information mined from databases and experimental datasets using a variety of computational tools. This review is focused on key technologies for proteomics strategy and their application in protein analysis.

Species scaling and extrapolation.

The various scaling methodologies and molecular features analysis were applied to new dataset to predict human pharmacokinetics studies. Whereas the predictive accuracies demonstrated across all of the various methodologies were lower for this higher clearance compound dataset, scaling from species continued to be an accurate methodology, and human volume of distribution was similarly well predicted regardless of scaling methodology. Also, extrapolation is the method for constructing new data points given a set of discrete data points. Methods estimate is reasonably reliable for short times, but for longer times, the estimate is liable to become less accurate. Species Scaling and Extrapolation are useful for acquiring toxicological data- epidemiological and experimental study. Animal studies help us to understand toxicity characteristics of a chemical before human exposure is allowed, whereas the epidemiological method generally does not. Species scaling and extrapolation from animals is necessary in many cases which helps in dealing with the so-called human risks more properly.

Analyzing methods for path mining with applications in metabolomics

Metabolomics is one of the key approaches of systems biology that consists of studying biochemical networks having a set of metabolites, enzymes, reactions and their interactions. As biological networks are very complex in nature, proper techniques and models need to be chosen for their better understanding and interpretation. One of the useful strategies in this regard is using path mining strategies and graph-theoretical approaches that help in building hypothetical models and perform quantitative analysis. Furthermore, they also contribute to analyzing topological parameters in metabolome networks. Path mining techniques can be based on grammars, keys, patterns and indexing. Moreover, they can also be used for modeling metabolome networks, finding structural similarities between metabolites, in-silico metabolic engineering, shortest path estimation and for various graph-based analysis. In this manuscript, we have highlighted some core and applied areas of path-mining for modeling and analysis of metabolic networks.

Phylogenomics: evolution and genomics intersection

Phylogenomics is the analysis of genomes of a group of closely related species. Almost all functional prediction methods rely on the identification, characterisation and quantification of sequence similarity between the gene of interest and genes for which functional information is available. This is the new evolved branch that is developed from the ongoing genome sequencing projects that have led to a phylogenetic approach based on genome-scale data. The use of large data sets in phylogenomic analysis results in a global increase in resolution owing to a decrease in sampling error.

Automated metabolic pathway reconstruction based on structural grammars

T bacterial genus Streptomyces has long been appreciated for its ability to produce various kinds of medically important secondary metabolites such as antibiotic actinorhodin (ACT) in S. coelicolor and anti-tumor doxorubicin (DXR) in S. peucetius. Although traditional random mutation has been one of the most widely-practiced strategies for Streptomyces strain improvement, genome sequencing, targeted-gene disruption, and omics-guided reverse engineering approaches were successfully used to identify, analyze, and modify specific biosynthetic and regulatory genes involved in most of the secondary metabolites in Streptomyces species. Here, I present an example of rational polyketide pathway redesign strategies through Streptomyces genome engineering. Recursive comparative transcriptome analyses using S. coelicolor microarrays, followed by sequential targeted-gene disruptions of independently-functioning regulatory as well as precursor flux-controlling systems could be synergistically optimized for ACT and DXR productions in Streptomyces species.R progress in the field of metabolomics has created an opportunity to advance our understanding of physiological and pathological processes. It also created a number of bioinformatics challenges associated with data analysis and interpretation. Experience with genomic data has shown that automated annotations linking genes, transcripts and proteins to published biomedical literature is useful for a wide range of bioinformatics applications including pathway analysis and identification of disease genes. With this in mind we recently developed Metab2MeSH, a web-based tool that uses a statistical approach (Fisher’s exact test) to annotate compounds with Medical Subject Headings (MeSH) used by the National Library of Medicine to manually index articles for MEDLINE/PubMed. The resulting data set contains statistically significant associations (p-value<0.005) between compounds and MeSH terms linked to PubMed articles (http://metab2mesh.ncibi.org).C genomic and metabonomic approaches based on culture-independent (e.g., pyrosequencing, FISH, DGGE) and culture-dependent methods together with 1H-NMR and GC-MS/SPME analyses were used to determine the metabolic changes triggered by gut microbiota and dietary variation. This new “integrated” approach lead to understand the “collaboration” between human host and microorganisms in relation to phenotype, diet and diseases. The impact of the diet on the gut microbiota of children having cow’s milk protein allergy (CMPA) and celiac disease (CD) was determined. Fecal slurry and urine of two groups of children were analyzed: the first one is CMPA children before and after 2 months of the hydrolyzed and ultra-filtered whey protein formula (eHF) with lactose intake in the diet; and the second one, symptom-free CD children, who had been on a gluten free diet (GFD) for at least 2 years. Children without known food intolerance (healthy children; HC) were also studied. The addition of lactose to eHF formula is able to positively modulate the composition of microbiota by increasing the total fecal counts of Lactobacillus/Bifidobacteria and decreasing that of Bacteroides/Clostridia. The GFD lasting at least two years did not completely restore the microbiota of the CD children. The levels of Lactobacillus, Enterococcus and Bifidobacteria were significantly higher in HC than in CD children. On the contrary, Bacteroides, Staphylococcus, Salmonella, Shighella and Klebsiella were higher in CD compared to HC children. As showed by GC-MS/SPME and 1H-NMR, significant differences between molecules belonging to short chain fatty acids (SCFAs), esters, alcohols, aldehydes, ketones, monosaccharides and amino acids, before and after the lactose intake or GFD were found. Some molecules seems to be metabolic signatures of food allergy and intolerance.T fate of the last intermediate of glycolysis, phosphoenolpyruvate (PEP), controls much of cellular metabolism, e.g. the balance of glycolysis and gluconeogenesis. How are the key enzymes consuming PEP controlled? Here we examine this issue in the bacterium Escherichia coli and the budding yeast Saccharomyces cerevisiae. In both organisms, removal of glucose results in a paradoxical increased in PEP, which goes up the most of any canonical metabolite. What mechanisms lead this product of glucose to rise when glucose is removed? Enzyme activity can be regulated at the level of transcription, translation, degradation, covalent modification, and allostery.We show that allostery predominates in both organisms, with PEP consumption activated in an ultrasensitive (switch-like) manner by the upstream glycolytic intermediate fructose-1,6-bisphosphate. Mutations that eliminate this regulation do not impair growth on steady glucose, but they render the microbes defective in gluconeogenesis and ingrowth in an oscillating glucose environment. Thus, microbial central carbon metabolism is intrinsically programmed with ultrasensitive feed-forward regulation to enable rapid adaptation to changing environmental conditions.R 13C-labeled tracers have been incorporated into metabolomic studies to complement the conventional 1H NMRbased metabolomic studies that result in concentration profiles of metabolites. These Stable Isotope Resolved Metabolomics (SIRM) studies produce comprehensive metabolic data that unequivocally quantifies whether the metabolite concentrations changes discovered in conventional metabolomic studies are due to increased or decreased pathway production. Cell culture is an ideal biosystem to apply this relatively new methodology because all major substrates in the central compartment (i.e., media) and tissue can be quantified, and thus accounting for all input and output to the biosystem. Cultured hepatocytes prove especially useful in demonstrating the effectiveness of untargeted SIRM because their normal function is to create glucose under fasted conditions, and thus consumption rates for glucose are often ambiguous based on concentration data alone since the carbon source could be a multitude of compounds found in the media. The comparison of rat and human cultured hepatocytes will be discussed using several 13C-labeled substrates added to the media, in order to elucidate the metabolome of these two species’ liver cells in 2D culture (Winnike et al., 2011). A strategy for targeted metabolomics will be described using a human cell model for resistance chronic myelogenous leukemia, the Myl and Myl-R cell lines. In this approach, an initial conventional 1H NMR-based metabolomic analysis of Myl and Myl-R cells revealed that creatine is 7-fold higher in the resistant cell-line, MylR (Dewar et al., 2010). Glycine is a substrate for creatine, and therefore, 2-13C-glycine was used in a targeted SIRM study to elucidate the mechanism of increased creatine in the Myl-R, and propose a potential mechanism of chemoresistance.A parasites are responsible for high impact human diseases such as malaria, toxoplasmosis and cryptosporidiosis. These obligate intracellular pathogens are dependent on both de novo lipid biosynthesis as well as the uptake of host lipids for biogenesis of parasite membranes and the membranes of vacuoles within which they reside. Genome annotations and biochemical studies indicate that apicomplexan parasites can synthesize fatty acids via a number of different biosynthetic pathways that are differentially compartmentalized. However, the relative contribution of each of these biosynthetic pathways to total fatty acid composition of intracellular parasite stages remains poorly defined. Here we use a combine metabolomics with genetic and biochemical approaches to delineate the contribution of fatty acid biosynthetic pathways in Toxoplasma gondii. Metabolic labeling studies with 13C-glucose and 13C-acetate showed that intracellular tachyzoites synthesized a range of long and very long chain fatty acids (C14:0-26:1). Genetic disruption of type II fatty acid synthase (FASII) resulted in greatly reduced synthesis of saturated fatty acids up to eighteen carbons long, leading to reduced intracellular growth that was partially restored by addition long chain fatty acids. In contrast, synthesis of very long chain fatty acids was primarily dependent on a fatty acid elongation system comprising three elongases, two reductases and a dehydratase that were localized to the endoplasmic reticulum. The function of these enzymes was confirmed by metabolomics and heterologous expression in yeast. This elongase pathway appears to have a unique role in generating very long unsaturated fatty acids (C26:1) that cannot be salvaged from the host.T Clostridia are a diverse group of Gram-positive bacteria that include several pathogens and many terrestrial species that produce solvents and organic acids through fermentation of a variety of carbon sources. However, the knowledge about carbohydrate utilization pathways and their regulation in Clostridium spp. is rather limited. Accurate projection of known carbohydrate catabolic pathways across diverse bacteria with complete genomes is quite challenging due to frequent variations in components of these pathways.A 70% of newly diagnosed cases of invasive breast cancer in the U.S. will be estrogen receptor i positive (ER+). Endocrine therapy is the least toxic and most effective means to manage the hormone-dependent breast cancer in these patients, administered as an antiestrogen, e.g., Tamoxifen (TAM) or Faslodex (Fulvestrant; ICI 182,780) or an aromatase inhibitor (AI), e.g., Letrozole (LET). TAM produces a 26% proportional reduction in mortality; however, advanced ER+ breast cancer that has become resistant to endocrine therapy remains a significant clinical problem.We have shown that antiestrogen resistant breast cancer cells over-express X-Box Binding Protein 1 (XBP1) and glucose regulated protein-78 (GRP78;BiP), two integral signaling components of the unfolded protein response (UPR).XBP1 can regulate glucose homeostasis, and as glucose levels fall, GRP78 activates the UPR. Antiestrogen resistant breast cancer cells (MCF7/LCC9) utilize prosurvival UPR to maintain a higher level of basal autophagy compared to sensitive cells (MCF7/LCC1) that can provide raw materials to promote cell survival under stress from therapeutic insults. Abundance of metabolites from antiestrogen sensitive and resistant cells was compared and analyzed using UPLC-MS. Changes in selected metabolites were independently validated. Our findings indicate that resistant MCF7/LCC9 cells have an abundance of cAMP compared to MCF7/LCC1 cells. Glucose uptake in MCF/LCC9 control cells was 28-fold higherwhen compared to MCF7/LCC1 control cells, yet ATP levels in MCF7/LCC9 cells was 40% lower compared with MCF7/LCC1 control cells. cAMP has been recently identified as a potent inducer of autophagy. Our findings suggest that antiestrogen resistant breast cancer cells may have higher glucose (from the Warburg effect) and energy requirements, resulting in increased cAMPthat helps to maintain survival via autophagy under basal or treatment conditions. These metabolic adaptations are critical to the coordinated signaling from the UPR that both suppresses apoptosis and activates a prosurvival autophagy. Further studies will help to uncover the signaling mechanism involved in regulating the pathways that connect autophagy and metabolic pathways to maintain cell survival in resistance. The overall goal of this study is to provide more affordable diagnostic tools and to identify effective therapies and reliable biomarkers to predict accurately the response to antiestrogen therapy.R emergences of glycobiology, glycotechnology and glycomics have been clarifying enormous roles of carbohydrates in both physiological and pathological recognition systems. Glycan arrays have become important tools for the analysis of carbohydrate–biomacromolecule interactions such as the specificities of lectins, antibodies, cells, and viruses. However, the critical limitations of glycan array applications are restricted epitopes available from both synthesis and isolation and less mode of glycan presentation on the array surface. Conventional glycan arrays are based on two dimensional (2D) surface chemistries that result in low signal intensity and substantial non-specific binding of target proteins because of an insufficient number of binding sites and the presence of surface-protein interactions. We present here cytomimetic glycan microarray platforms based on glycopolymer immobilization and glycosylated liposome immobilization strategies. First, an oriented and density controlled glyco-marcroligand array formation was demonstrated by end-point immobilization of glycopolymer imprinted with boronic acid ligands in different sizes. Glycoarray and SPR results confirmed the same trend of density-dependent binding of specific lectins. Second, liposomes carrying ganglioside and lipid-triphenylphosphine as anchor lipid were printed onto azide-modified glass slide via Staudinger ligation. Specific lectin and toxin bindings onto liposomal glycan arrays, containing GM1 and GM3 in different densities, were confirmed by florescence scanning. The reported glycan array platforms present multivalent glycans in defined orientation and density configurations that are critical for glycan recognition. It is, thus, uniquely useful tool for probing the ligand specificities of glycan-binding molecules and for molecular and cellular proteomic applications.T public availability of high throughput datasets from a variety of biological sources has prompted the creation of a multitude of databases that significantly facilitate biomedical research. However, although each individual experiment may successfully address a focused set of hypotheses, a more comprehensive understanding of biological processes may emerge by integrating results from multiple datasets into a coherent framework. Furthermore, knowledge about higher-order, more clinically relevant aspects of any complex disease may be only achieved by these kind of analyses. Unfortunately, such integrative approaches to complex diseases have not been yet realized in part due to the lack of a theoretical framework and appropriate analytical tools to implement them.M neoplasms (MPNs) are a kind of bone marrow diseases and Ten-Eleven Transcription 2 (TET2) mutations are found in MPN patients.Located on 4q24, TET2 can catalyze the conversion from 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC) in DNA. TET2 mutations,such as nonsense mutations, out-of-frame insertions, deletions, and splice site mutations, alter catalytic activity andlead to low level of 5hmC. However, it was unclear why some MPN patients with wild type TET2 also revealed both low level of 5hmC.To explore other factors affecting the 5hmc level, we investigated the microRNAs’ post-transcriptional regulation of TET2 and its associated genes using 11 bioinformatics resources in our previous study. It was found that TET2 is associatedeight genes and they are targets of miR-152 and miR-29b.Importantly, DNMT-1,an associated gene, is a DNA methyltransferase catalyze the methylation, in whicha methyl group is added to cytosine and 5mC is formed.Involvement of microRNAs may explain the low 5hmC level in MPN patients with wild type TET2.D liver injury (DILI) is the leading cause of drug failure. The prevalence of serious adverse effects is due in part to inefficient and inaccurate biomarkers of toxicity in preclinical studies. ALT and BILI are the commonly measured parameters to assess liver injury. However, ALT elevation can occur without signs of liver injury so it not specific enough. BILI only elevates upon severe liver damage. Therefore, there is a need for more specific biomarkers of liver injury and dysfunction. The omics methods have the potential to detect early biomarkers of toxicity in biofluid samples. In an effort to identify biomarkers of DILI, metabolomics and transcriptomics data were acquired on urine and serum samples in two separate studies. In the first study, Sprague Dawley rats were dosed with 0, 100 or 1250 mg acetaminophen (APAP)/kg body weight. Urine, serum, and tissue were collected 6 hr, 1 d, 3 d, and 7 d post-dosing. Metabolites in pathways involving oxidative stress, bile acids, and lipid ketones were altered. The transcriptomics data indicated genes within the same pathways were altered. In the second study, rats were dosed with 0, 50, or 2000 mg carbon tetrachloride (CCl4)/kg body weight and samples collected 6 hr, 1 d, and 3 d post-dosing. Similar to the results in the APAP study, metabolites and genes involved in oxidative stress, bile acids, and lipid ketones were altered. The arginine metabolism and glycolysis pathways were also affected following administration of CCl4. Omics technologies can provide potential new biomarkers and pathway information.C cell proliferation depends on increased supply of nutrients including carbon sources and molecular oxygen. However, solid tumors frequently outgrow the blood supply, resulting in insufficient supply of oxygen, glucose and glutamine. Particularly, carbon sources are critical for the generation of ATP and building blocks, and for the maintenance of intracellular redox. Two metabolic features of cancers are the Warburg effect and glutaminolysis, underlines the importance of carbon utilization. While hypoxic adaptation of cancer cells has been well studied, how cancer cells respond to lack of carbon sources remain elusive. Using microarray technology, we compared the gene expression profiles of Hep3B cells under a series of defined culture conditions. Data analysis reveals that depletion of glucose and depletion of glutamine have dramatically different effects on transcriptional reprogramming. This observation suggests that glucose and glutamine are two different types of carbon sources, each having some specific metabolic roles in cell proliferation. Analysis of differentially expressed genes and their functional networks reveals that lack of either glucose or glutamine may lead to inhibition of multiple anabolic pathways and cell growth. Considering metabolite homeostasis in cancer cells, we are trying to validate and interpret the data, expecting to eventually establish a systemic view of the sensing, signaling, transcription reprogramming and metabolic reprogramming of cancer cells in response to carbon source insufficiency. A better understanding of the molecular mechanisms that link the carbon source sensing, signaling, transcriptional reprogramming and adaptive metabolic reprogramming may pinpoint novel targets for drug development and cancer prevention.M methods hold promise as part of aplatform which is highly complementary to other systems biology tools such as proteomics, transcriptomics and genomics. The two main analytical platforms employed are nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). These two platforms have unique characteristics which suggest that acquiring data from both platforms would be advantageous. In this presentation, a systematic relationship between serum biomarkers from model systems studies in vivo will first be examined. The successful application of NMR and GC-MS to these model systems serve as a template for clinicalbiomarkers discovery using a combination of NMR and GC-MS in cancer studies. Data will be presented from cohorts of pancreatic, colon and brain tumors patients.Analysis of these biomarkers is performed using apattern-driven approach, and as such analytical techniques which are both quantitative and high-throughput are favoured. The outcome is information with respect to a ‘biopattern’ of disease without the requirement for comprehensively attempting to characterize the entire metabolome. Furthermore, the resulting multivariate data is characterized by concerted changes in multiple markers, in contrast with traditional biomarker-driven approaches that rely on single markers. Finally, several challenges in the evolution of the field will be discussed, including interpreting coherent biological meaning from a combination of both NMR and MS data, and reliable assessment of candidate markers using multivariate statistics.

SAGPAR: Structural Grammar-based automated pathway reconstruction

In-silico metabolic engineering is a very useful branch of systems biology for modeling, analysis and prediction of various outcomes of metabolic pathways. It can also be used for detecting interactions and dynamics within a network. Various protocols have been proposed for modeling a pathway. But most of these protocols have various disadvantages and shortcomings with respect to automated pathway modeling and analysis. In the present article, we have proposed a novel algorithm for automated pathway reconstruction. We have also made a comparative study of our algorithm with other standard protocols and discussed its advantages over others. We present StructurAl Grammar-based automated PAthway Reconstruction (SAGPAR), a fast and robust algorithm that generates any metabolic pathway using some given structural representations of metabolites. Users can model any pathway based on some pre-required features that are asked as an input by the algorithm. The algorithm also takes into considerations various thermodynamic thresholds and structural properties while modeling a pathway. The given algorithm has been tested on the standard pathway datasets of 25 pathways of Mycoplasma pneumoniae M129 and 24 pathways of Homo sapiens. The dataset is taken from KEGG and PubChem Compound data repositories. SAGPAR performs much better than some already present metabolic pathway analysis tools like Copasi, PHT, Gepasi, Jarnac and Path-A.

Blood stage parasites: sufficient to induce protective immunity.

Merozoites are the surface antigens and variant antigens expressed on the surface of malaria-infected erythrocytes (including PfEMP1) are both targets of protective antibody responses. The mechanism of the modified immune response was observed after subpatent infections. Subpatently infected mice had increased antigen-specific T-cell responses; they were not better protected than patently infected mice. The study of human volunteers, the absence of detectable malaria-specific antibodies probably reflects the extremely low parasite doses used for immunization. Induction of this type of immunity by immunizing with low doses of purified antigens from whole parasites may be an alternative but highly effective vaccine strategy.

Simulating an Infection Growth Model in Certain Healthy Metabolic Pathways of Homo sapiens for Highlighting Their Role in Type I Diabetes mellitus Using Fire-Spread Strategy, Feedbacks and Sensitivities

Disease Systems Biology is an area of life sciences, which is not very well understood to date. Analyzing infections and their spread in healthy metabolite networks can be one of the focussed areas in this regard. We have proposed a theory based on the classical forest fire model for analyzing the path of infection spread in healthy metabolic pathways. The theory suggests that when fire erupts in a forest, it spreads, and the surrounding trees also catch fire. Similarly, when we consider a metabolic network, the infection caused in the metabolites of the network spreads like a fire. We have constructed a simulation model which is used to study the infection caused in the metabolic networks from the start of infection, to spread and ultimately combating it. For implementation, we have used two approaches, first, based on quantitative strategies using ordinary differential equations and second, using graph-theory based properties. Furthermore, we are using certain probabilistic scores to complete this task and for interpreting the harm caused in the network, given by a ‘critical value’ to check whether the infection can be cured or not. We have tested our simulation model on metabolic pathways involved in Type I Diabetes mellitus in Homo sapiens. For validating our results biologically, we have used sensitivity analysis, both local and global, as well as for identifying the role of feedbacks in spreading infection in metabolic pathways. Moreover, information in literature has also been used to validate the results. The metabolic network datasets have been collected from the Kyoto Encyclopedia of Genes and Genomes (KEGG).

Automated Reconstruction of Metabolic Pathways of Homo Sapiensinvolved in the Functioning of GAD1 and GAD2 Genes based on StructuralGrammars

Modeling and analyzing the architecture of metabolic networks using various computational strategies can be successfully used for studying their internal metabolic dynamics as well as predicting missing links in diseased networks. In the present work, we have implemented our algorithm based on structural grammars, for automated metabolic pathway reconstruction and modeling in metabolic pathways responsible for coding genes responsible for the cause of Type 1 Diabetes mellitus (T1D) in Homo sapiens. We have especially implemented our algorithm for studying the metabolic pairs responsible for the functioning of GAD1 and GAD2 genes. We have also used the algorithm for automated reconstruction of glutamate metabolism, β-alanine metabolism, taurine & hypotaurine metabolism and butanoate metabolism pathway datasets. We have also used the algorithm for missing and multiple link prediction as well as nodal point analysis for all the four metabolic pathways with 90.4-100% accuracy.