02242nas a2200169 4500008004100000022001400041245016700055210006900222260008100291300000800372490000600380520150400386100002101890700001701911700001901928856012501947 2010 eng d a1752-050900aSystems approach to investigating host-pathogen interactions in infections with the biothreat agent Francisella. constraints-based model of Francisella tularensis0 aSystems approach to investigating hostpathogen interactions in i a236 GRAYS INN RD, FLOOR 6, LONDON WC1X 8HL, ENGLANDbBIOMED CENTRAL LTDcAUG a1180 v43 a

Background: Francisella tularensis is a prototypic example of a pathogen for which few experimental datasets exist, but for which copious high-throughout data are becoming available because of its re-emerging significance as biothreat agent. The virulence of Francisella tularensis depends on its growth capabilities within a defined environmental niche of the host cell. Results: We reconstructed the metabolism of Francisella as a stoichiometric matrix. This systems biology approach demonstrated that changes in carbohydrate utilization and amino acid metabolism play a pivotal role in growth, acid resistance, and energy homeostasis during infection with Francisella. We also show how varying the expression of certain metabolic genes in different environments efficiently controls the metabolic capacity of F. tularensis. Selective gene-expression analysis showed modulation of sugar catabolism by switching from oxidative metabolism (TCA cycle) in the initial stages of infection to fatty acid oxidation and gluconeogenesis later on. Computational analysis with constraints derived from experimental data revealed a limited set of metabolic genes that are operational during infection. Conclusions: This integrated systems approach provides an important tool to understand the pathogenesis of an ill-characterized biothreat agent and to identify potential novel drug targets when rapid target identification is required should such microbes be intentionally released or become epidemic.

1 aRaghunathan, Anu1 aShin, Sookil1 aDaefler, Simon uhttp://library.ncl.res.in/content/systems-approach-investigating-host-pathogen-interactions-infections-biothreat-agent-002247nas a2200253 4500008004100000022001400041245009900055210006900154260010300223300002500326490000600351520128400357653002701641653002601668653001801694653002401712653002301736653002501759653002701784653001701811100002001828700002101848856012401869 2015 eng d a1664-302X00aCell scale host-pathogen modeling: another branch in the evolution of constraint-based methods0 aCell scale hostpathogen modeling another branch in the evolution aPO BOX 110, EPFL INNOVATION PARK, BUILDING I, LAUSANNE, 1015, SWITZERLANDbFRONTIERS MEDIA SAcOCT aArticle Number: 10320 v63 a

Constraint-based models have become popular methods for systems biology as they enable the integration of complex, disparate datasets in a biologically cohesive framework that also supports the description of biological processes in terms of basic physicochemical constraints and relationships. The scope, scale, and application of genome scale models have grown from single cell bacteria to multi cellular interaction modeling; host-pathogen modeling represents one of these examples at the current horizon of constraint-based methods. There are now a small number of examples of host-pathogen constraint-based models in the literature, however there has not yet been a definitive description of the methodology required for the functional integration of genome scale models in order to generate simulation capable host-pathogen models. Herein we outline a systematic procedure to produce functional host-pathogen models, highlighting steps which require debugging and iterative revisions in order to successfully build a functional model. The construction of such models will enable the exploration of host-pathogen interactions by leveraging the growing wealth of omic data in order to better understand mechanism of infection and identify novel therapeutic strategies.

10aconstraint-based model10aflux balance analysis10ahost-pathogen10amathematical models10aomics-technologies10aoptimization methods10asalmonella typhimurium10atuberculosis1 aJamshidi, Neema1 aRaghunathan, Anu uhttp://library.ncl.res.in/content/cell-scale-host-pathogen-modeling-another-branch-evolution-constraint-based-methods-100454nas a2200133 4500008004100000245005600041210005600097260000800153300001400161490000800175100002100183700002600204856009000230 2017 eng d00aModelling natural and synthetic biological networks0 aModelling natural and synthetic biological networks cOCT a1221-12220 v1131 aRaghunathan, Anu1 aAthale, Chaitanya, A. uhttp://library.ncl.res.in/content/modelling-natural-and-synthetic-biological-networks02450nas a2200193 4500008004100000245025800041210006900299260000800368490000700376520146600383653013101849100002501980700002602005700002702031700002502058700003102083700002102114856012102135 2017 eng d00aScalable metabolite supplementation strategy against antibiotic resistant pathogen Chromobacterium violaceum induced by NAD(+)/NADH(+) imbalanceA scalable metabolite supplementation strategy against antibiotic resistant pathogen Chromobacterium violaceu0 aScalable metabolite supplementation strategy against antibiotic  cAPR0 v113 a

BACKGROUND: The leading edge of the global problem of antibiotic resistance necessitates novel therapeutic strategies. This study develops a novel systems biology driven approach for killing antibiotic resistant pathogens using benign metabolites. RESULTS: Controlled laboratory evolutions established chloramphenicol and streptomycin resistant pathogens of Chromobacterium. These resistant pathogens showed higher growth rates and required higher lethal doses of antibiotic. Growth and viability testing identified malate, maleate, succinate, pyruvate and oxoadipate as resensitising agents for antibiotic therapy. Resistant genes were catalogued through whole genome sequencing. Intracellular metabolomic profiling identified violacein as a potential biomarker for resistance. The temporal variance of metabolites captured the linearized dynamics around the steady state and correlated to growth rate. A constraints-based flux balance model of the core metabolism was used to predict the metabolic basis of antibiotic susceptibility and resistance. CONCLUSIONS: The model predicts electron imbalance and skewed NAD/NADH ratios as a result of antibiotics - chloramphenicol and streptomycin. The resistant pathogen rewired its metabolic networks to compensate for disruption of redox homeostasis. We foresee the utility of such scalable workflows in identifying metabolites for clinical isolates as inevitable solutions to mitigate antibiotic resistance.

10aAntibiotic resistance; Flux balance analysis; Flux variability analysis; Metabolism; Metabolomic; NAD; NADH; Redox homeostasis1 aBanerjee, Deepanwita1 aParmar, Dharmeshkumar1 aBhattacharya, Nivedita1 aGhanate, Avinash, D.1 aPanchagnula, Venkateswarlu1 aRaghunathan, Anu uhttp://library.ncl.res.in/content/scalable-metabolite-supplementation-strategy-against-antibiotic-resistant-pathogen01850nas a2200169 4500008004100000245009800041210006900139260000800208300001000216490000800226520121500234100003201449700002501481700003101506700002101537856012201558 2018 eng d00a Integrated constraints based analysis of an engineered violacein pathway in escherichia coli0 aIntegrated constraints based analysis of an engineered violacein cSEP a10-190 v1713 aStrategies towards optimal violacein biosynthesis, a potential drug molecule, need systems level coordination of enzymatic activities of individual genes in a multigene operon vioABCDE. Constraints-based flux balance analysis of an extended iAF1260 model (iAF1260vio) with a reconstructed violacein module predicted growth and violacein yields in Escherichia coli accurately. Shadow price (SP) analysis identified tryptophan metabolism and NADPH as limiting. Increased tryptophan levels in Delta pgi & Delta pheA were validated using in silico gene deletion analysis. Phenotypic phase plane (PhPP) analysis highlighted sensitivity between tryptophan and NADPH for violacein synthesis at molar growth yields. A synthetic VioABCDE operon (SYNO) sequence was designed to maximize Codon Adaptive Index (CAI: 0.9) and tune translation initiation rates (TIR: 2-50 fold higher) in E. coli. All pSYN E. coli transformants produced higher violacein, with a maximum six-fold increase in yields. The rational design E. coli: Delta pheA SYN: gave the highest violacein titers (33.8 mg/I). Such integrated approaches targeting multiple molecular hierarchies in the cell can be extended further to increase violacein yields.1 aImmanuel, Selva, Rupa Chris1 aBanerjee, Deepanwita1 aRajankar, Mayooreshwar, P.1 aRaghunathan, Anu uhttp://library.ncl.res.in/content/integrated-constraints-based-analysis-engineered-violacein-pathway-escherichia-coli01530nas a2200145 4500008004100000245005500041210005400096260000800150300001200158490000900170520107500179100002101254700002001275856008901295 2018 eng d00aIntegrated host-pathogen metabolic reconstructions0 aIntegrated hostpathogen metabolic reconstructions cJAN a197-2170 v17163 aThe science and art of Genome scale metabolic network reconstructions have been explicitly documented in the literature for organisms across all the three kingdoms of life. Constraints-based models derived from such reconstructions have been used to assess metabolic phenotypes of their complex connections to genotype accurately. The problem of infectious disease is complex due to the multifactorial response of the host to the pathogen. Systems biology approaches and modeling allow one to study, understand, and predict emergent properties of such complex responses. The integration of the host and pathogen metabolic networks and the subsequent merger of their stoichiometric matrices is nontrivial and requires understanding of both pathogen and host metabolism and physiologies. The protocol here describes the detailed process of network and stoichiometric matrix merger using a salmonella-mouse macrophage model. The protocol also discusses the interfacial and objective functions required to actually embark on the analysis of host-pathogen interaction models.1 aRaghunathan, Anu1 aJamshidi, Neema uhttp://library.ncl.res.in/content/integrated-host-pathogen-metabolic-reconstructions02197nas a2200289 4500008004100000022001400041245008700055210006900142260000800211300001400219490000800233520125800241653001401499653002301513653002501536653002001561653002801581653001001609100002901619700002501648700003001673700002001703700003101723700001701754700002101771856011501792 2018 eng d a0006-291X00aIntegrative analysis of rewired central metabolism in temozolomide resistant cells0 aIntegrative analysis of rewired central metabolism in temozolomi cJAN a2010-20160 v4953 a

An authenticated U87MG clonal glioblastoma cell line was investigated to identify a sub-population of neurospheroidal (NSP) cells within the main epithelial population (U87MG). The NSP cells sorted using Fluorescence Assisted Cell Sorting (FACS) showed varied morphology, 30% lower growth rates, 40% higher IC50 values for temozolomide drug and could differentiate into the glial cell type (NDx). Metabolite profiling using HR-LCMS identified glucose, glutamine and serine in both populations and tryptophan only in U87MG as growth limiting substrates. Glycine, alanine, glutamate and proline were secreted by U87MG, however proline and glycine were re-utilized in NSP. Exo-metabolite profiling and phenotypic microarrays identified differential metabolism of primary carbon sources glucose and derived pyruvate for U87MG; glutamine and derived glutamate metabolism in NSP. Differential mRNA abundance of AKT1, PTEN, PIK3CA controlling metabolism, drug efflux, nutrient transport and epigenetic control MDM2 are potentially critical in shaping DNA methylation effects of temozolomide. Our study provides a new insight into the combined effect of these factors leading to temozolomide resistance in NSP. (C) 2017 Elsevier Inc. All rights reserved.

10aGlutamine10aMetabolic rewiring10aMetabolite profiling10amRNA abundances10aTemozolomide resistance10aU87MG1 aImmanuel, Selva, Rupa C.1 aGhanate, Avinash, D.1 aParmar, Dharmeshkumar, S.1 aMarriage, Fiona1 aPanchagnula, Venkateswarlu1 aDay, Nap, J.1 aRaghunathan, Anu uhttp://library.ncl.res.in/content/integrative-analysis-rewired-central-metabolism-temozolomide-resistant-cells01306nas a2200145 4500008004100000245012400041210006900165260000800234300001400242490000800256520072100264100002500985700002101010856012901031 2018 eng d00aKnowledge, attitude and practice of antibiotic use and antimicrobial resistance: a study post the 'Red Line' initiative0 aKnowledge attitude and practice of antibiotic use and antimicrob cMAY a1866-18770 v1143 aAntimicrobial resistance (AMR) is propagated by irrational use of antibiotics by healthcare practitioners and the uninformed public. This study assessed a select cohort of 504 urban Indians for their knowledge, awareness and practice of antibiotic use and AMR. Forty seven per cent were unaware of the differences between over-the-counter drugs and antibiotics. One in four believes that dose-skipping does not contribute to AMR. One in ten tends to self-medicate. One in five bought medicines without prescription or started an antibiotic course by calling a doctor. Our results mandate educational campaigns, stewardship and surveillance at the national level for prudent antimicrobial use in the Indian community.1 aBanerjee, Deepanwita1 aRaghunathan, Anu uhttp://library.ncl.res.in/content/knowledge-attitude-and-practice-antibiotic-use-and-antimicrobial-resistance-study-post-red02545nas a2200157 4500008004100000022001400041245014100055210006900196260000900265300002900274490000700303520190900310100002502219700002102244856012202265 2019 eng d a1932-620300aConstraints-based analysis identifies NAD(+) recycling through metabolic reprogramming in antibiotic resistant Chromobacterium violaceum0 aConstraintsbased analysis identifies NAD recycling through metab cJAN  aArticle Number: e02100080 v143 a

In the post genomic era, high throughput data augment stoichiometric flux balance models to compute accurate metabolic flux states, growth and energy phenotypes. Investigating altered metabolism in the context of evolved resistant genotypes potentially provide simple strategies to overcome drug resistance and induce susceptibility to existing antibiotics. A genome-scale metabolic model (GSMM) for Chromobacterium violaceum, an opportunistic human pathogen, was reconstructed using legacy data. Experimental constraints were used to represent antibiotic susceptible and resistant populations. Model predictions were validated using growth and respiration data successfully. Differential flux distribution and metabolic reprogramming were identified as a response to antibiotics, chloramphenicol and streptomycin. Streptomycin resistant populations (StrpR) redirected tricarboxylic acid (TCA) cycle flux through the glyoxylate shunt. Chloramphenicol resistant populations (ChlR) resorted to overflow metabolism producing acetate and formate. This switch to fermentative metabolism is potentially through excess reducing equivalents and increased NADH/NAD ratios. Reduced proton gradients and changed Proton Motive Force (PMF) induced by antibiotics were also predicted and verified experimentally using flow cytometry based membrane potential measurements. Pareto analysis of NADH and ATP maintenance showed the decoupling of electron transfer and ATP synthesis in StrpR. Redox homeostasis and NAD(+) cycling through rewiring metabolic flux was implicated in re-sensitizing antibiotic resistant C. violaceum. These approaches can be used to probe metabolic vulnerabilities of resistant pathogens. On the verge of a post-antibiotic era, we foresee a critical need for systems level understanding of pathogens and host interaction to extend shelf life of antibiotics and strategize novel therapies.

1 aBanerjee, Deepanwita1 aRaghunathan, Anu uhttp://library.ncl.res.in/content/constraints-based-analysis-identifies-nad-recycling-through-metabolic-reprogramming01854nas a2200205 4500008004100000245011700041210006900158260000900227300000600236490000600242520110000248100003201348700002501380700003001405700001601435700001601451700003101467700002101498856012901519 2021 eng d00aIntegrated genetic and metabolic landscapes predict vulnerabilities of temozolomide resistant glioblastoma cells0 aIntegrated genetic and metabolic landscapes predict vulnerabilit cJAN  a20 v73 a

Metabolic reprogramming and its molecular underpinnings are critical to unravel the duality of cancer cell function and chemo-resistance. Here, we use a constraints-based integrated approach to delineate the interplay between metabolism and epigenetics, hardwired in the genome, to shape temozolomide (TMZ) resistance. Differential metabolism was identified in response to TMZ at varying concentrations in both the resistant neurospheroidal (NSP) and the susceptible (U87MG) glioblastoma cell-lines. The genetic basis of this metabolic adaptation was characterized by whole exome sequencing that identified mutations in signaling pathway regulators of growth and energy metabolism. Remarkably, our integrated approach identified rewiring in glycolysis, TCA cycle, malate aspartate shunt, and oxidative phosphorylation pathways. The differential killing of TMZ resistant NSP by Rotenone at low concentrations with an IC50 value of 5 nM, three orders of magnitude lower than for U87MG that exhibited an IC50 value of 1.8 mM was thus identified using our integrated systems-based approach.

1 aImmanuel, Selva, Rupa Chris1 aGhanate, Avinash, D.1 aParmar, Dharmeshkumar, S.1 aYadav, Ritu1 aUthup, Riya1 aPanchagnula, Venkateswarlu1 aRaghunathan, Anu uhttp://library.ncl.res.in/content/integrated-genetic-and-metabolic-landscapes-predict-vulnerabilities-temozolomide-resistant01411nas a2200157 4500008004100000020001400041245008500055210006900140260000800209300001600217490000700233520092600240100002001166700002101186856004601207 2021 eng d a0973-712X00aRise of the superbugs: what we need to know overview of antimicrobial resistance0 aRise of the superbugs what we need to know overview of antimicro cSEP a1251 - 12660 v263 aBacteria often cause infections, and we take antibiotics to help us recover. Antibiotics are like magic pills that have saved millions of lives. However, the overuse of antibiotics is now making bacteria evolve fast and evade these antibiotics. A few bacteria like the purple one in the petri plate (Figure 1) have evolved a way to dodge and not get killed by one particular kind of antibiotic. The day might come soon when not a single antibiotic would work, and we could die of even tiny paper cuts. In this article, we discuss what antibiotics are, how they target bacteria, and why bacteria are suddenly becoming resistant to antibiotics. We include a list of ten points that each of us must follow and a pledge for everyone to take, to help stop the spread of antibiotic resistance. A small questionnaire is included that we would like you all to answer. Together we can win the battle against antibiotic resistance.1 aSengupta, Durba1 aRaghunathan, Anu uhttps://doi.org/10.1007/s12045-021-1227-804147nas a2200589 4500008004100000022001400041245012400055210006900179260000800248300001400256490000700270520236200277653001302639653001002652653001202662653003602674653002302710653003402733100002102767700001702788700002102805700002102826700002102847700001902868700001602887700002302903700001702926700002702943700001902970700001902989700002103008700001803029700002003047700001903067700001903086700002103105700002203126700001903148700002203167700001903189700001803208700001903226700002303245700001803268700002003286700002403306700002603330700002803356700002103384700002603405856012603431 2023 eng d a1876-034100aTale of two waves: Delineating diverse genomic and transmission landscapes driving the COVID-19 pandemic in Pune, India0 aTale of two waves Delineating diverse genomic and transmission l cAUG a1290-13000 v163 a

Background: Modern response to pandemics, critical for effective public health measures, is shaped by the availability and integration of diverse epidemiological outbreak data. Tracking variants of concern (VOC) is integral to understanding the evolution of SARS-CoV-2 in space and time, both at the local level and global context. This potentially generates actionable information when integrated with epidemiological outbreak data.Methods: A city-wide network of researchers, clinicians, and pathology diagnostic laboratories was formed for genome surveillance of COVID-19 in Pune, India. The genomic landscapes of 10,496 sequenced samples of SARS-CoV-2 driving peaks of infection in Pune between December-2020 to March-2022, were determined. As a modern response to the pandemic, a ``band of five'' outbreak data analytics approach was used. This integrated the genomic data (Band 1) of the virus through molecular phylogenetics with key outbreak data including sample collection dates and case numbers (Band 2), demographics like age and gender (Band 3-4), and geospatial mapping (Band 5).Results: The transmission dynamics of VOCs in 10,496 sequenced samples identified B.1.617.2 (Delta) and BA(x) (Omicron formerly known as B.1.1.529) variants as drivers of the second and third peaks of infection in Pune. Spike Protein mutational profiling during pre and post-Omicron VOCs indicated differential rank ordering of high-frequency mutations in specific domains that increased the charge and binding properties of the protein. Time-resolved phylogenetic analysis of Omicron sub-lineages identified a highly divergent BA.1 from Pune in addition to recombinant X lineages, XZ, XQ, and XM. Conclusions: The band of five outbreak data analytics approach, which integrates five different types of data, highlights the importance of a strong surveillance system with high-quality meta-data for understanding the spatiotemporal evolution of the SARS-CoV-2 genome in Pune. These findings have important implica-tions for pandemic preparedness and could be critical tools for understanding and responding to future outbreaks.& COPY; 2023 Published by Elsevier Ltd on behalf of King Saud Bin Abdulaziz University for Health Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

10aCOVID-1910aDelta10aOmicron10aSARS-CoV-2 genomic surveillance10aVariant of concern10aWhole Genome Sequencing (WGS)1 aNiveditha, Divya1 aKhan, Soumen1 aKhilari, Ajinkya1 aNadkarni, Sanica1 aBhalerao, Unnati1 aKadam, Pradnya1 aYadav, Ritu1 aKanekar, Jugal, B.1 aShah, Nikita1 aLikhitkar, Bhagyashree1 aSawant, Rutuja1 aThakur, Shikha1 aTupekar, Manisha1 aNagar, Dhriti1 aRao, Anjani, G.1 aJagtap, Rutuja1 aJogi, Shraddha1 aBelekar, Madhuri1 aPathak, Maitreyee1 aShah, Priyanki1 aRanade, Shatakshi1 aPhadke, Nikhil1 aDas, Rashmita1 aJoshi, Suvarna1 aKaryakarte, Rajesh1 aGhose, Aurnab1 aKadoo, Narendra1 aShashidhara, L., S.1 aMonteiro, Joy, Merwin1 aShanmugam, Dhanasekaran1 aRaghunathan, Anu1 aKarmodiya, Krishanpal uhttp://library.ncl.res.in/content/tale-two-waves-delineating-diverse-genomic-and-transmission-landscapes-driving-covid-19