03135nas a2200157 4500008004100000245012300041210006900164260000800233490000600241520251900247100002002766700002202786700002302808700002102831856012502852 2015 eng d00aIdentification of Th1/Th2 regulatory switch to promote healing response during Leishmaniasis: a computational approach0 aIdentification of Th1Th2 regulatory switch to promote healing re cDEC0 v13 a

Leishmania devices its survival strategy by suppressing the host’s immune functions. The antigen molecules produced by Leishmania interferes with the host’s cell signaling cascades and consequently changes the protein expression pattern of the antigen-presenting cell (APC). This creates an environment suitable for the switching of the T-cell responses from a healing Th1 response to a non-healing Th2 response that is favorable for the continued survival of the parasite inside the host APC. Using a reconstructed signaling network of the intracellular and intercellular reactions between a Leishmania infected APC and T-cell, we propose a computational model to predict the inhibitory effect of the Leishmania infected APC on the T-cell and to identify the regulators of this Th1-/Th2-switching behavior as observed during Leishmania infection. In this work, we hypothesize that a complete removal of the parasite could only be achieved with a simultaneous up-regulation of the healing Th1 response and stimulation of nitric oxide (NO) production from the APCs, and downregulation of the non-healing Th2 response and thereby propose several unique combinations of protein molecules that could elicit this anti-Leishmania immune response. Our results indicate that TLR3 may play a positive role in eliciting NO synthesis, while TLR2 may be responsible for inhibiting an anti-Leishmania immune response. Also, TLR3 overexpression (in the APC), when combined with SHP2 inhibition (in the T cell), produces an anti-Leishmania response that is better than the conventional IFN-gamma or IL12 treatment. A similar anti-Leishmania response is also obtained in another combination where TLR3 (in APC) is overexpressed, and SHC and MKP (of T cell) are inhibited and activated, respectively. Through our study, we also observe that Leishmania infection may induce an upregulation of IFN-beta production from the APC that may lead to an upregulation of the RAP1 and SOCS3 proteins inside the T cell, the potential inhibitors of MAPK and JAK-STAT signaling pathways, respectively, via the TYK2-mediated pathway. This study not only enhances our knowledge in understanding the Th1/Th2 regulatory switch to promote healing response during leishmaniasis but also helps to identify novel combinations of proteins as potential immunomodulators. Electronic supplementary material The online version of this article (doi:10.1186/s13637-015-0032-7) contains supplementary material, which is available to authorized users.

1 aGanguli, Piyali1 aChoudhury, Saikat1 aChoudhury, Shomeek1 aSarkar, Ram, Rup uhttps://library.ncl.res.in/content/identification-th1th2-regulatory-switch-promote-healing-response-during-leishmaniasis02553nas a2200157 4500008004100000245014400041210006900185260000800254300001400262490000700276520191700283100001802200700002602218700002102244856013002265 2017 eng d00aIntegrative machine learning strategy for improved prediction of essential genes in Escherichia coli metabolism using flux-coupled features0 aIntegrative machine learning strategy for improved prediction of cAUG a1584-15960 v133 aPrediction of essential genes helps to identify a minimal set of genes that are absolutely required for the appropriate functioning and survival of a cell. The available machine learning techniques for essential gene prediction have inherent problems, like imbalanced provision of training datasets, biased choice of the best model for a given balanced dataset, choice of a complex machine learning algorithm, and data-based automated selection of biologically relevant features for classification. Here, we propose a simple support vector machine-based learning strategy for the prediction of essential genes in Escherichia coli K-12 MG1655 metabolism that integrates a non-conventional combination of an appropriate sample balanced training set, a unique organism-specific genotype, phenotype attributes that characterize essential genes, and optimal parameters of the learning algorithm to generate the best machine learning model (the model with the highest accuracy among all the models trained for different sample training sets). For the first time, we also introduce flux-coupled metabolic subnetwork-based features for enhancing the classification performance. Our strategy proves to be superior as compared to previous SVM-based strategies in obtaining a biologically relevant classification of genes with high sensitivity and specificity. This methodology was also trained with datasets of other recent supervised classification techniques for essential gene classification and tested using reported test datasets. The testing accuracy was always high as compared to the known techniques, proving that our method outperforms known methods. Observations from our study indicate that essential genes are conserved among homologous bacterial species, demonstrate high codon usage bias, GC content and gene expression, and predominantly possess a tendency to form physiological flux modules in metabolism.1 aNandi, Sutanu1 aSubramanian, Abhishek1 aSarkar, Ram, Rup uhttps://library.ncl.res.in/content/integrative-machine-learning-strategy-improved-prediction-essential-genes-escherichia-coli02648nas a2200217 4500008004100000245016900041210006900210260000800279300002400287490000600311520182800317100002102145700002002166700001802186700002102204700002302225700001902248700001702267700001902284856012702303 2018 eng d00aIntegrative approaches to understand the Mastery in manipulation of host cytokine networks by protozoan parasites with emphasis on Plasmodium and Leishmania species0 aIntegrative approaches to understand the Mastery in manipulation cFEB aArticle Number: 2960 v93 acDiseases by protozoan pathogens pose a significant public health concern, particularly in tropical and subtropical countries, where these are responsible for significant morbidity and mortality. Protozoan pathogens tend to establish chronic infections underscoring their competence at subversion of host immune processes, an important component of disease pathogenesis and of their virulence. Modulation of cytokine and chemokine levels, their crosstalks and downstream signaling pathways, and thereby influencing recruitment and activation of immune cells is crucial to immune evasion and subversion. Many protozoans are now known to secrete effector molecules that actively modulate host immune transcriptome and bring about alterations in host epigenome to alter cytokine levels and signaling. The complexity of multi-dimensional events during interaction of hosts and protozoan parasites ranges from microscopic molecular levels to macroscopic ecological and epidemiological levels that includes disrupting metabolic pathways, cell cycle (Toxoplasma and Theileria sp.), respiratory burst, and antigen presentation (Leishmania spp.) to manipulation of signaling hubs. This requires an integrative systems biology approach to combine the knowledge from all these levels to identify the complex mechanisms of protozoan evolution via immune escape during host-parasite coevolution. Considering the diversity of protozoan parasites, in this review, we have focused on Leishmania and Plasmodium infections. Along with the biological understanding, we further elucidate the current efforts in generating, integrating, and modeling of multi-dimensional data to explain the modulation of cytokine networks by these two protozoan parasites to achieve their persistence in host via immune escape during host-parasite coevolution.1 aMahanta, Anusree1 aGanguli, Piyali1 aBarah, Pankaj1 aSarkar, Ram, Rup1 aSarmah, Neelanjana1 aPhukan, Saurav1 aBora, Mayuri1 aBaruah, Shashi uhttps://library.ncl.res.in/content/integrative-approaches-understand-mastery-manipulation-host-cytokine-networks-protozoan02587nas a2200241 4500008004100000022001400041245011400055210006900169260000800238300001200246490000800258520175700266653002402023653002402047653002802071653001302099653002402112653002102136653001802157100001902175700002102194856013002215 2023 eng d a1617-461500aIdentification of potential microRNAs regulating metabolic plasticity and cellular phenotypes in glioblastoma0 aIdentification of potential microRNAs regulating metabolic plast cJAN a161-1810 v2983 a

MicroRNAs (miRNAs) play important role in regulating cellular metabolism, and are currently being explored in cancer. As metabolic reprogramming in cancer is a major mediator of phenotypic plasticity, understanding miRNA-regulated metabolism will provide opportunities to identify miRNA targets that can regulate oncogenic phenotypes by taking control of cellular metabolism. In the present work, we studied the effect of differentially expressed miRNAs on metabolism, and associated oncogenic phenotypes in glioblastoma (GBM) using patient-derived data. Networks of differentially expressed miRNAs and metabolic genes were created and analyzed to identify important miRNAs that regulate major metabolism in GBM. Graph network-based approaches like network diffusion, backbone extraction, and different centrality measures were used to analyze these networks for identification of potential miRNA targets. Important metabolic processes and cellular phenotypes were annotated to trace the functional responses associated with these miRNA-regulated metabolic genes and associated phenotype networks. miRNA-regulated metabolic gene subnetworks of cellular phenotypes were extracted, and important miRNAs regulating these phenotypes were identified. The most important outcome of the study is the target miRNA combinations predicted for five different oncogenic phenotypes that can be tested experimentally for miRNA-based therapeutic design in GBM. Strategies implemented in the study can be used to generate testable hypotheses in other cancer types as well, and design context-specific miRNA-based therapy for individual patient. Their usability can be further extended to other gene regulatory networks in cancer and other genetic diseases.

10aCellular phenotypes10aFunctional analysis10aGlioblastoma metabolism10amicroRNA10amiRNA-based therapy10aNetwork analysis10aTranscriptome1 aBhowmick, Rupa1 aSarkar, Ram, Rup uhttps://library.ncl.res.in/content/identification-potential-micrornas-regulating-metabolic-plasticity-and-cellular-phenotypes02500nas a2200217 4500008004100000022001400041245008500055210006900140260000800209300001200217490000700229520177900236653002002015653002702035653003402062653001502096653001902111100002002130700002102150856011102171 2025 eng d a2214-552400aImpact of gravitational forces on Red Blood Cell dynamics in biofluid suspension0 aImpact of gravitational forces on Red Blood Cell dynamics in bio cNOV a197-2100 v473 a

The growing interest in space exploration and human spaceflight has highlighted the critical challenges posed by microgravity on human physiology. Among these, a significant issue is space anemia, which adversely affects Red Blood Cells (RBC) and alters its behavior. RBC depends on biofluids, for their systemic transport, a process that experiences disruption in the microgravity environment. This study aims to quantitatively address the puzzle of how red blood cells are influenced by gravity when they are suspended in bio-fluid. Dissipative Particle Dynamics (DPD) approach was used to model blood and the cell by applying gravity as an external force along the vertical axis and varied from 0g to 2g during parameter sweeps. Key metrics, including Elongation and Deformation indices, pitch angle, and normalized center of mass, were utilized to assess cellular behavior. Results revealed that gravity induces shape changes and spatial alignment in red blood cells. The Elongation Index and the normalized center of mass declined linearly with the applied gravity. Correlation analysis showed a strong correlation between applied gravity and the aforementioned variables. Additionally, forces acting on the cell, such as drag, shear stress, and solid forces, diminished as gravitational force increased. Further analysis indicates that increasing gravity affected the cell's velocity, resulting in prolonged proximity to vessel walls and intensified viscous interactions with surrounding fluid particles, thereby triggering morphological changes. This study provides crucial insights into the biophysical effects of gravity on the red blood cell and presents a significant step toward understanding cellular dynamics under altered gravitational conditions.

10aAltered gravity10aComputational modeling10aDissipative Particle dynamics10aFluid flow10aRed Blood Cell1 aMurali, Anirudh1 aSarkar, Ram, Rup uhttps://library.ncl.res.in/content/impact-gravitational-forces-red-blood-cell-dynamics-biofluid-suspension