02243nas a2200169   4500008004100000022001400041245016700055210006900222260008100291300000800372490000600380520150400386100002101890700001701911700001901928856012601947       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<p>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.</p>1 aRaghunathan, Anu1 aShin, Sookil1 aDaefler, Simon  uhttps://library.ncl.res.in/content/systems-approach-investigating-host-pathogen-interactions-infections-biothreat-agent-002248nas a2200253   4500008004100000022001400041245009900055210006900154260010300223300002500326490000600351520128400357653002701641653002601668653001801694653002401712653002301736653002501759653002701784653001701811100002001828700002101848856012501869       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<p>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.</p>10aconstraint-based model10aflux balance analysis10ahost-pathogen10amathematical models10aomics-technologies10aoptimization methods10asalmonella typhimurium10atuberculosis1 aJamshidi, Neema1 aRaghunathan, Anu  uhttps://library.ncl.res.in/content/cell-scale-host-pathogen-modeling-another-branch-evolution-constraint-based-methods-100455nas a2200133   4500008004100000245005600041210005600097260000800153300001400161490000800175100002100183700002600204856009100230       2017                        eng d00aModelling natural and synthetic biological networks0 aModelling natural and synthetic biological networks  cOCT  a1221-12220 v1131 aRaghunathan, Anu1 aAthale, Chaitanya, A.  uhttps://library.ncl.res.in/content/modelling-natural-and-synthetic-biological-networks02451nas a2200193   4500008004100000245025800041210006900299260000800368490000700376520146600383653013101849100002501980700002602005700002702031700002502058700003102083700002102114856012202135       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<p>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.</p>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  uhttps://library.ncl.res.in/content/scalable-metabolite-supplementation-strategy-against-antibiotic-resistant-pathogen01851nas a2200169   4500008004100000245009800041210006900139260000800208300001000216490000800226520121500234100003201449700002501481700003101506700002101537856012301558       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  uhttps://library.ncl.res.in/content/integrated-constraints-based-analysis-engineered-violacein-pathway-escherichia-coli01531nas a2200145   4500008004100000245005500041210005400096260000800150300001200158490000900170520107500179100002101254700002001275856009001295       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  uhttps://library.ncl.res.in/content/integrated-host-pathogen-metabolic-reconstructions02198nas a2200289   4500008004100000022001400041245008700055210006900142260000800211300001400219490000800233520125800241653001401499653002301513653002501536653002001561653002801581653001001609100002901619700002501648700003001673700002001703700003101723700001701754700002101771856011601792       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<p>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.</p>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  uhttps://library.ncl.res.in/content/integrative-analysis-rewired-central-metabolism-temozolomide-resistant-cells01307nas a2200145   4500008004100000245012400041210006900165260000800234300001400242490000800256520072100264100002500985700002101010856013001031       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  uhttps://library.ncl.res.in/content/knowledge-attitude-and-practice-antibiotic-use-and-antimicrobial-resistance-study-post-red02546nas a2200157   4500008004100000022001400041245014100055210006900196260000900265300002900274490000700303520190900310100002502219700002102244856012302265       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<p>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.</p>1 aBanerjee, Deepanwita1 aRaghunathan, Anu  uhttps://library.ncl.res.in/content/constraints-based-analysis-identifies-nad-recycling-through-metabolic-reprogramming01855nas a2200205   4500008004100000245011700041210006900158260000900227300000600236490000600242520110000248100003201348700002501380700003001405700001601435700001601451700003101467700002101498856013001519       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<p>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.</p>
1 aImmanuel, Selva, Rupa Chris1 aGhanate, Avinash, D.1 aParmar, Dharmeshkumar, S.1 aYadav, Ritu1 aUthup, Riya1 aPanchagnula, Venkateswarlu1 aRaghunathan, Anu  uhttps://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/s12045-021-1227-804148nas a2200589   4500008004100000022001400041245012400055210006900179260000800248300001400256490000700270520236200277653001302639653001002652653001202662653003602674653002302710653003402733100002102767700001702788700002102805700002102826700002102847700001902868700001602887700002302903700001702926700002702943700001902970700001902989700002103008700001803029700002003047700001903067700001903086700002103105700002203126700001903148700002203167700001903189700001803208700001903226700002303245700001803268700002003286700002403306700002603330700002803356700002103384700002603405856012703431       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<p>
	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.&amp; 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/).</p>
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  uhttps://library.ncl.res.in/content/tale-two-waves-delineating-diverse-genomic-and-transmission-landscapes-driving-covid-1906441nas a2200865   4500008004100000245020100041210007000242260000800312490000700320520376100327100003104088700002604119700001804145700001804163700002504181700002604206700002004232700001904252700001604271700002104287700002704308700001904335700002204354700001904376700002604395700001904421700002004440700002104460700002004481700002004501700002304521700002404544700002204568700002504590700002704615700001804642700002004660700002104680700001604701700002004717700001504737700002904752700002104781700002104802700001904823700002704842700002904869700001804898700001804916700002604934700003204960700001904992700001905011700002005030700002305050700001605073700002205089700001905111700002005130700001605150700003105166700001605197700001805213700002505231700002105256700001505277700001905292700002005311700002405331700002805355700001705383700002005400700002005420856013505440       2024                        eng d00aImmunogenicity of SARS-CoV-2 vaccines BBV152 (COVAXIN®) and ChAdOx1 nCoV-19 (COVISHIELD™) in seronegative and seropositive individuals in India: a multicentre, nonrandomised observational study0 aImmunogenicity of SARSCoV2 vaccines BBV152 COVAXIN® and ChAdOx1   cMAR0 v223 a<p style="box-sizing: inherit; line-height: 1.5; margin: 1.2rem 0px; color: rgb(33, 33, 33); font-family: BlinkMacSystemFont, -apple-system, &quot;Segoe UI&quot;, Roboto, Oxygen, Ubuntu, Cantarell, &quot;Fira Sans&quot;, &quot;Droid Sans&quot;, &quot;Helvetica Neue&quot;, sans-serif; font-size: 16px;">
	<strong class="sub-title" style="box-sizing: inherit;">Background:&nbsp;</strong>There are limited global data on head-to-head comparisons of vaccine platforms assessing both humoral and cellular immune responses, stratified by pre-vaccination serostatus. The COVID-19 vaccination drive for the Indian population in the age group 18-45 years began in April 2021 when seropositivity rates in the general population were rising due to the delta wave of COVID-19 pandemic during April-May 2021.</p>
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	<strong class="sub-title" style="box-sizing: inherit;">Methods:&nbsp;</strong>Between June 30, 2021, and Jan 28, 2022, we enrolled 691 participants in the age group 18-45 years across four clinical sites in India. In this non-randomised and laboratory blinded study, participants received either two doses of Covaxin® (4 weeks apart) or two doses of Covishield™ (12 weeks apart) as per the national vaccination policy. The primary outcome was the seroconversion rate and the geometric mean titre (GMT) of antibodies against the SARS-CoV-2 spike and nucleocapsid proteins post two doses. The secondary outcome was the frequency of cellular immune responses pre- and post-vaccination.</p>
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	<strong class="sub-title" style="box-sizing: inherit;">Findings:&nbsp;</strong>When compared to pre-vaccination baseline, both vaccines elicited statistically significant seroconversion and binding antibody levels in both seronegative and seropositive individuals. In the per-protocol cohort, Covishield™ elicited higher antibody responses than Covaxin® as measured by seroconversion rate (98.3% vs 74.4%, p &lt; 0.0001 in seronegative individuals; 91.7% vs 66.9%, p &lt; 0.0001 in seropositive individuals) as well as by anti-spike antibody levels against the ancestral strain (GMT 1272.1 vs 75.4 binding antibody units/ml [BAU/ml], p &lt; 0.0001 in seronegative individuals; 2089.07 vs 585.7 BAU/ml, p &lt; 0.0001 in seropositive individuals). As participants at all clinical sites were not recruited at the same time, site-specific immunogenicity was impacted by the timing of vaccination relative to the delta and omicron waves. Surrogate neutralising antibody responses against variants-of-concern including delta and omicron was higher in Covishield™ recipients than in Covaxin® recipients; and in seropositive than in seronegative individuals after both vaccination and asymptomatic infection (omicron variant). T cell responses are reported from only one of the four site cohorts where the vaccination schedule preceded the omicron wave. In seronegative individuals, Covishield™ elicited both CD4+ and CD8+ spike-specific cytokine-producing T cells whereas Covaxin® elicited mainly CD4+ spike-specific T cells. Neither vaccine showed significant post-vaccination expansion of spike-specific T cells in seropositive individuals.</p>
1 aAsokan, Mangaiarkarasi, S.1 aJoan, Roshni, Florina1 aBabji, Sudhir1 aDayma, Girish1 aNadukkandy, Prajitha1 aSubrahmanyam, Vinutha1 aPandey, Archana1 aMalagi, Girish1 aArya, Pooja1 aMahajan, Vibhuti1 aBhavikatti, Jayateerth1 aPawar, Ketakee1 aThorat, Aishwarya1 aShah, Priyanki1 aGoud, Ramakrishna, B.1 aRoy, Bishnudeo1 aRajukutty, Shon1 aImmanuel, Sushil1 aAgarwal, Dhiraj1 aSaha, Sankhanil1 aShivaraj, Akshatha1 aPanikulam, Patricia1 aShome, Rajeshwari1 aGulzar, Shah-E-Jahan1 aSharma, Anusmrithi, U.1 aNaik, Ajinkya1 aTalashi, Shruti1 aBelekar, Madhuri1 aYadav, Ritu1 aKhude, Poornima1 aV, Mamatha1 aShivalingaiah, Sudarshan1 aDeshmukh, Urmila1 aBhise, Chinmayee1 aJoshi, Manjiri1 aInbaraj, Leeberk, Raja1 aChandrasingh, Sindhulina1 aGhose, Aurnab1 aJamora, Colin1 aKarumbati, Anandi, S.1 aSundaramurthy, Varadharajan1 aJohnson, Avita1 aRamesh, Naveen1 aChetan, Nirutha1 aParthiban, Chaitra1 aAhmed, Asma1 aRakshit, Srabanti1 aAdiga, Vasista1 aD'souza, George1 aRale, Vinay1 aGeorge, Carolin, Elizabeth1 aJohn, Jacob1 aKawade, Anand1 aChaturvedi, Akanksha1 aRaghunathan, Anu1 aDias, Mary1 aBhosale, Anand1 aRaghu, Padinjat1 aShashidhara, L., S.1 ayakarnam, Annapurna, V.1 aBal, Vineeta1 aKang, Gagandeep1 aMayor, Satyajit  uhttps://library.ncl.res.in/content/immunogenicity-sars-cov-2-vaccines-bbv152-covaxin%C2%AE-and-chadox1-ncov-19-covishield%E2%84%A202291nas a2200265   4500008004100000022001400041245012000055210006900175260000900244300000800253490000700261520139700268653000601665653002901671653002801700653001801728653001701746653002601763653002601789100002401815700001801839700002301857700002101880856012401901       2025                        eng d  a0959-399300aAdvances in metabolic engineering and fermentation for 3-hydroxypropionic acid biosynthesis: a comprehensive review0 aAdvances in metabolic engineering and fermentation for 3hydroxyp  cSEP   a3520 v413 a<p>
	The grand challenge in biobased Manufacturing Lies in achieving the sustainable, economically competitive conversion of renewable biomass into high-value Chemicals capable of replacing fossil-derived products. Among these, 3-hydroxypropionic acid (3-HP) has emerged as a top-tier target-an exceptionally versatile platform molecule. It finds applications in the synthesis of acrylic acid, 1,3-propanediol, and other derivatives, positioning it as a potential cornerstone for bio-based plastics. This review consolidates the latest breakthroughs in microbial 3-HP production, encompassing advanced strain engineering, pathway rewiring, cofactor optimization, metabolic modeling, and flux balance analysis. We critically examine strategies to overcome inherent metabolic and physiological constraints, including byproduct suppression, redox balancing, and tolerance engineering. Emerging approaches-such as dynamic regulation of metabolic flux, control of cell morphology and density, and integration of co-production pathways-are highlighted for their capacity to boost yields and process robustness. Additionally, we address the fermentation process innovations targeting enhanced productivity, substrate efficiency, minimal nutrient input, and industrially relevant titres. Collectively, these insights Chart a clear path toward the scalable, sustainable biomanufacturer of 3-HP.</p>
10a110a3-hydroxypropionaldehyde10a3-hydroxypropionic acid10a3-Propanediol10aFermentation10aflux balance analysis10aMetabolic engineering1 aJoshi, Harshvardhan1 aIsar, Jasmine1 aRangaswamy, Vidhya1 aRaghunathan, Anu  uhttps://library.ncl.res.in/content/advances-metabolic-engineering-and-fermentation-3-hydroxypropionic-acid-biosynthesis02711nas a2200253   4500008004100000022001400041245012900055210006900184260000900253300001100262490000800273520181700281653002302098653002902121653001702150653001502167653003902182653002502221100002102246700002502267700001602292700002102308856012802329       2025                        eng d  a0006-291X00aIntegrated analysis of mitochondrial ETC inhibition reveals genotype-specific heterogeneity of drug response in glioblastoma0 aIntegrated analysis of mitochondrial ETC inhibition reveals geno  cNOV   a1527980 v7873 a<p>
	Glioblastoma (GBM) is among the most aggressive brain cancers, driven by genetic diversity and resistance to therapy. Mitochondrial metabolism-and in particular the electron transport chain (ETC)-has emerged as both a key weakness and a source of variable drug response. To investigate this, we integrated constraint-based metabolic modeling (CBM), high-resolution drug profiling, and genomic sequencing across three GBM cell models: LN229, U87MG, and neurospheres (NSP). Modeling predicted distinct ETC vulnerabilities, which were confirmed experimentally using inhibitors against Complexes I-V. Sensitivity to rotenone varied sharply: NSP cells were most vulnerable (IC50 = 0.007 mu M), LN229 showed intermediate sensitivity (0.021 mu M), and U87MG remained highly resistant (1.816 mu M). Across inhibitors, LN229 consistently showed steep dose-response slopes, U87MG maintained flat curves, and NSP displayed selective weaknesses. By incorporating slope (m) and Instantaneous Inhibitory Potential (IIP), median-effect analysis captured dynamic drug-response behaviour's that IC50 values alone overlooked. Genomic sequencing revealed striking differences in mutational burden: U87MG and NSP carried 354 and 307 single nucleotide polymorphisms (SNPs), respectively, compared with 141 in LN229. Several non-synonymous mutations were directly linked to altered drug sensitivity, including L194S, Y50 N, and L46V in LN229; S456L, A466T, and Y629F in U87MG; and the NSP-specific R159Q. Notably, mutations near catalytic sites correlated with changes in slope and IIP, providing mechanistic insight into therapeutic response. Together, these results show how genetic variation reshapes ETC function and drug sensitivity in GBM, offering a predictive framework for mutation-informed, personalized therapy.</p>
10aDrug dose response10aElectron transport chain10aGlioblastoma10aIC50 value10aInstantaneous inhibitory potential10aMitochondrial genome1 aBelekar, Madhuri1 aKavatalkar, Vijendra1 aYadav, Ritu1 aRaghunathan, Anu  uhttps://library.ncl.res.in/content/integrated-analysis-mitochondrial-etc-inhibition-reveals-genotype-specific-heterogeneity03451nas a2200433   4500008004100000245014700041210006900188260000900257300000800266490000700274520201300281653002002294653003402314653001702348653002202365653001702387653001502404100001802419700004002437700001702477700002302494700001702517700001702534700002402551700001302575700002002588700002602608700002602634700002102660700003202681700002302713700002302736700001902759700002502778700003102803700002402834700002702858856013202885       2025                        eng d00aPrecision integrated identification of predictive first-trimester metabolomics signatures for early detection of gestational diabetes mellitus0 aPrecision integrated identification of predictive firsttrimester  cNOV   a4340 v243 a<p>
	Background and aimGestational diabetes mellitus (GDM), a common pregnancy-related metabolic disorder, often goes undiagnosed until the second trimester, limiting early intervention opportunities. Given the higher prevalence of GDM in India, there is a critical need to investigate metabolomic biomarkers among Asian Indians, who exhibit greater insulin resistance and are predisposed to developing type 2 diabetes at an earlier age. This study aimed to identify early pregnancy metabolomic signatures predictive of GDM. MethodsAmong 2115 pregnant women from the STratification of Risk of Diabetes in Early pregnancy (STRiDE) study, we performed untargeted metabolomic profiling using UPLC-MS/MS at early pregnancy (&lt; 16 weeks) plasma samples from 100 women-comprising 50 with GDM and 50 normal (without GDM) based on oral glucose tolerance test (OGTT) at 24-28 weeks. Statistical and machine learning approaches, including logistic regression and random forest (RF), were applied to identify GDM-associated metabolites and construct predictive models. Pathway enrichment analysis was conducted using KEGG database annotations. ResultsA total of 49 metabolites were significantly associated with GDM, primarily involving lipid classes such as phosphatidylcholines, sphingomyelins, and triacylglycerols. RF analysis identified a panel of eight metabolites that achieved best predictive performance (AUC 0.880; 95% CI: 0.809-0.951) for GDM. When combined with conventional clinical risk factors, the integrated model showed comparable prediction of GDM with AUC 0.88;: 95% CI: 0.810-0.952). Enrichment analysis highlighted dysregulated pathways including glycerophospholipid and sphingolipid metabolism, autophagy, and insulin resistance. ConclusionThis study demonstrates the utility of early-pregnancy metabolomic profiling for predicting GDM in Indian women. The eight-metabolite panel offers a promising tool for early risk stratification of GDM, warranting validation in diverse populations.</p>
10aFirst trimester10aGestational diabetes mellitus10aIndian women10aMass spectrometry10aMetabolomics10aPrediction1 aSharma, Sapna1 aSubrahmanyam, Yalamanchili, Venkata1 aGupta, Payal1 aVadivel, Sangeetha1 aDeepa, Mohan1 aTandon, Ansh1 aSreedevi, Sreekumar1 aRam, Uma1 aNarad, Priyanka1 aParmar, Dharmeshkumar1 aAnjana, Ranjit, Mohan1 aRaghunathan, Anu1 aBalasubramanyam, Muthuswamy1 aMohan, Viswanathan1 aSengupta, Abhishek1 aAdamski, Jerzy1 aSaravanan, Ponnusamy1 aPanchagnula, Venkateswarlu1 aUsharani, Dandamudi1 aGokulakrishnan, Kuppan  uhttps://library.ncl.res.in/content/precision-integrated-identification-predictive-first-trimester-metabolomics-signatures-early01887nas a2200205   4500008004100000022001400041245017700055210006900232260000900301300001600310490000700326520107800333100001901411700001901430700004001449700001901489700002101508700002101529856013101550       2026                        eng d  a2470-134300aEngineering pichia pastoris as a heterologous platform for non-ribosomal peptide biosynthesis of the β-lactam intermediate δ-(L-α-Aminoadipyl)-L-cysteinyl-D-valine (ACV)0 aEngineering pichia pastoris as a heterologous platform for nonri  cJUN   a32038-320480 v113 a<p>
	The production of nonribosomal peptides (NRPs) in non-native hosts remains challenging due to the size and complexity of the biosynthetic enzymes. Here, we report the first successful reconstruction of the delta-(L-alpha-aminoadipyl)-L-cysteinyl-D-valine (ACV) pathway in Pichia pastoris. By introducing codon-optimized versions of the pcbAB and npgA genes directly into the genome, we were able to rebuild the minimal pathway required for ACV synthesis, which we confirmed using LC-MS/MS. Although the starting strain produced only small amounts of ACV (similar to 5 ng/mL), yields increased 10-fold after adaptive evolution (similar to 50 ng/mL) and further improved with precursor supplementation (similar to 60 ng/mL). Transcriptomic analysis showed that the engineered strain reorganized its metabolism to meet the energy and precursor demands of NRPS activity. Overall, this study suggests that P. pastoris can serve as a practical, proof-of-concept host for complex NRP biosynthesis and lays the groundwork for future engineering of beta-lactam pathways.</p>
1 aJogi, Shraddha1 aJagtap, Rutuja1 aSubrahmanyam, Yalamanchili, Venkata1 aSharma, Monika1 aKulkarni, Mahesh1 aRaghunathan, Anu  uhttps://library.ncl.res.in/content/engineering-pichia-pastoris-heterologous-platform-non-ribosomal-peptide-biosynthesis-%CE%B2