@article { ISI:000282259600001, title = {Systems approach to investigating host-pathogen interactions in infections with the biothreat agent Francisella. constraints-based model of Francisella tularensis}, journal = {BMC Systems Biology}, volume = {4}, year = {2010}, month = {AUG}, pages = {118}, publisher = {BIOMED CENTRAL LTD}, address = {236 GRAYS INN RD, FLOOR 6, LONDON WC1X 8HL, ENGLAND}, abstract = {

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.

}, issn = {1752-0509}, doi = {10.1186/1752-0509-4-118}, author = {Raghunathan, Anu and Shin, Sookil and Daefler, Simon} } @article { ISI:000363379900001, title = {Cell scale host-pathogen modeling: another branch in the evolution of constraint-based methods}, journal = {Frontiers in Microbiology}, volume = {6}, year = {2015}, month = {OCT}, pages = {Article Number: 1032}, publisher = {FRONTIERS MEDIA SA}, address = {PO BOX 110, EPFL INNOVATION PARK, BUILDING I, LAUSANNE, 1015, SWITZERLAND}, abstract = {

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.

}, keywords = {constraint-based model, flux balance analysis, host-pathogen, mathematical models, omics-technologies, optimization methods, salmonella typhimurium, tuberculosis}, issn = {1664-302X}, doi = {10.3389/fmicb.2015.01032}, author = {Jamshidi, Neema and Raghunathan, Anu} } @article {44160, title = {Modelling natural and synthetic biological networks}, journal = {Current Science}, volume = {113}, year = {2017}, month = {OCT}, pages = {1221-1222}, type = {Article}, author = {Raghunathan, Anu and Athale, Chaitanya A.} } @article {43934, title = {Scalable metabolite supplementation strategy against antibiotic resistant pathogen Chromobacterium violaceum induced by NAD(+)/NADH(+) imbalanceA scalable metabolite supplementation strategy against antibiotic resistant pathogen Chromobacterium violaceu}, journal = {BMC Syst Biol. }, volume = {11}, year = {2017}, month = {APR}, abstract = {

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.

}, keywords = {Antibiotic resistance; Flux balance analysis; Flux variability analysis; Metabolism; Metabolomic; NAD; NADH; Redox homeostasis}, doi = {10.1186/s12918-017-0427-z}, author = {Banerjee, Deepanwita and Parmar, Dharmeshkumar and Bhattacharya, Nivedita and Ghanate, Avinash D. and Panchagnula, Venkateswarlu and Raghunathan, Anu} } @article {45413, title = { Integrated constraints based analysis of an engineered violacein pathway in escherichia coli}, journal = {Biosystems }, volume = {171}, year = {2018}, month = {SEP}, pages = {10-19}, type = {Article}, abstract = {Strategies 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.}, doi = {10.1016/j.biosystems.2018.06.002}, author = {Immanuel, Selva Rupa Christinal and Banerjee, Deepanwita and Rajankar, Mayooreshwar P. and Raghunathan, Anu} } @article {44508, title = {Integrated host-pathogen metabolic reconstructions}, journal = {Methods in Molecular Biology}, volume = {1716}, year = {2018}, month = {JAN}, pages = {197-217}, abstract = {The 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.}, doi = {10.1007/978-1-4939-7528-0_9}, author = {Raghunathan, Anu and Jamshidi, Neema} } @article {44220, title = {Integrative analysis of rewired central metabolism in temozolomide resistant cells}, journal = {Biochemical and Biophysical Research Communications}, volume = {495}, year = {2018}, month = {JAN}, pages = {2010-2016}, type = {Article}, abstract = {

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.

}, keywords = {Glutamine, Metabolic rewiring, Metabolite profiling, mRNA abundances, Temozolomide resistance, U87MG}, issn = {0006-291X}, doi = {10.1016/j.bbrc.2017.12.073}, author = {Immanuel, Selva Rupa C. and Ghanate, Avinash D. and Parmar, Dharmeshkumar S. and Marriage, Fiona and Panchagnula, Venkateswarlu and Day, Nap J. and Raghunathan, Anu} } @article {44377, title = {Knowledge, attitude and practice of antibiotic use and antimicrobial resistance: a study post the {\textquoteright}Red Line{\textquoteright} initiative}, journal = {Current Science}, volume = {114}, year = {2018}, month = {MAY}, pages = {1866-1877}, abstract = {Antimicrobial 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.}, doi = {10.18520/cs/v114/i09/1866-1877}, author = {Banerjee, Deepanwita and Raghunathan, Anu} } @article {45520, title = {Constraints-based analysis identifies NAD(+) recycling through metabolic reprogramming in antibiotic resistant Chromobacterium violaceum}, journal = {PLoS One}, volume = {14}, year = {2019}, month = {JAN }, pages = {Article Number: e0210008}, type = {Article}, abstract = {

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.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0210008}, author = {Banerjee, Deepanwita and Raghunathan, Anu} } @article {47113, title = {Integrated genetic and metabolic landscapes predict vulnerabilities of temozolomide resistant glioblastoma cells}, journal = {npj Systems Biology and Applications}, volume = {7}, year = {2021}, month = {JAN }, pages = {2}, type = {Article}, abstract = {

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.

}, doi = {10.1038/s41540-020-00161-7}, author = {Immanuel, Selva Rupa Christinal and Ghanate, Avinash D. and Parmar, Dharmeshkumar S. and Yadav, Ritu and Uthup, Riya and Panchagnula, Venkateswarlu and Raghunathan, Anu} } @article {47783, title = {Rise of the superbugs: what we need to know overview of antimicrobial resistance}, journal = {Resonance}, volume = {26}, year = {2021}, month = {SEP}, pages = {1251 - 1266}, type = {Article}, abstract = {Bacteria 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.}, isbn = {0973-712X}, doi = {10.1007/s12045-021-1227-8}, url = {https://doi.org/10.1007/s12045-021-1227-8}, author = {Sengupta, Durba and Raghunathan, Anu} } @article {48819, title = {Tale of two waves: Delineating diverse genomic and transmission landscapes driving the COVID-19 pandemic in Pune, India}, journal = {Journal of Infection and Public Health}, volume = {16}, year = {2023}, month = {AUG}, pages = {1290-1300}, type = {Article}, abstract = {

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 {\textquoteleft}{\textquoteleft}band of five{\textquoteright}{\textquoteright} 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/).

}, keywords = {COVID-19, Delta, Omicron, SARS-CoV-2 genomic surveillance, Variant of concern, Whole Genome Sequencing (WGS)}, issn = {1876-0341}, doi = {10.1016/j.jiph.2023.06.004}, author = {Niveditha, Divya and Khan, Soumen and Khilari, Ajinkya and Nadkarni, Sanica and Bhalerao, Unnati and Kadam, Pradnya and Yadav, Ritu and Kanekar, Jugal B. and Shah, Nikita and Likhitkar, Bhagyashree and Sawant, Rutuja and Thakur, Shikha and Tupekar, Manisha and Nagar, Dhriti and Rao, Anjani G. and Jagtap, Rutuja and Jogi, Shraddha and Belekar, Madhuri and Pathak, Maitreyee and Shah, Priyanki and Ranade, Shatakshi and Phadke, Nikhil and Das, Rashmita and Joshi, Suvarna and Karyakarte, Rajesh and Ghose, Aurnab and Kadoo, Narendra and Shashidhara, L. S. and Monteiro, Joy Merwin and Shanmugam, Dhanasekaran and Raghunathan, Anu and Karmodiya, Krishanpal} } @article {49464, title = {Immunogenicity of SARS-CoV-2 vaccines BBV152 (COVAXIN{\textregistered}) and ChAdOx1 nCoV-19 (COVISHIELD{\texttrademark}) in seronegative and seropositive individuals in India: a multicentre, nonrandomised observational study}, journal = {Lancet Regional Health - Southeast Asia}, volume = {22}, year = {2024}, month = {MAR}, type = {Article}, abstract = {

Background:\ 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.

Methods:\ 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{\textregistered} (4 weeks apart) or two doses of Covishield{\texttrademark} (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.

Findings:\ 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{\texttrademark} elicited higher antibody responses than Covaxin{\textregistered} as measured by seroconversion rate (98.3\% vs 74.4\%, p \< 0.0001 in seronegative individuals; 91.7\% vs 66.9\%, p \< 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 \< 0.0001 in seronegative individuals; 2089.07 vs 585.7 BAU/ml, p \< 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{\texttrademark} recipients than in Covaxin{\textregistered} 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{\texttrademark} elicited both CD4+ and CD8+ spike-specific cytokine-producing T cells whereas Covaxin{\textregistered} elicited mainly CD4+ spike-specific T cells. Neither vaccine showed significant post-vaccination expansion of spike-specific T cells in seropositive individuals.

}, doi = {10.1016/j.lansea.2024.100361.}, author = {Asokan, Mangaiarkarasi S. and Joan, Roshni Florina and Babji, Sudhir and Dayma, Girish and Nadukkandy, Prajitha and Subrahmanyam, Vinutha and Pandey, Archana and Malagi, Girish and Arya, Pooja and Mahajan, Vibhuti and Bhavikatti, Jayateerth and Pawar, Ketakee and Thorat, Aishwarya and Shah, Priyanki and Goud, Ramakrishna B. and Roy, Bishnudeo and Rajukutty, Shon and Immanuel, Sushil and Agarwal,Dhiraj and Saha, Sankhanil and Shivaraj, Akshatha and Panikulam, Patricia and Shome, Rajeshwari and Gulzar, Shah-E-Jahan and Sharma, Anusmrithi U. and Naik, Ajinkya and Talashi, Shruti and Belekar, Madhuri and Yadav, Ritu and Khude, Poornima and V, Mamatha and Shivalingaiah, Sudarshan and Deshmukh, Urmila and Bhise, Chinmayee and Joshi, Manjiri and Inbaraj, Leeberk Raja and Chandrasingh, Sindhulina and Ghose, Aurnab and Jamora, Colin and Karumbati, Anandi S. and Sundaramurthy, Varadharajan and Johnson, Avita and Ramesh, Naveen and Chetan, Nirutha and Parthiban, Chaitra and Ahmed, Asma and Rakshit, Srabanti and Adiga, Vasista and D{\textquoteright}souza, George and Rale, Vinay and George, Carolin Elizabeth and John, Jacob and Kawade, Anand and Chaturvedi, Akanksha and Raghunathan, Anu and Dias, Mary and Bhosale, Anand and Raghu, Padinjat and Shashidhara, L. S. and yakarnam, Annapurna V. and Bal, Vineeta and Kang, Gagandeep and Mayor, Satyajit} } @article {49871, title = {Advances in metabolic engineering and fermentation for 3-hydroxypropionic acid biosynthesis: a comprehensive review}, journal = {World Journal of Microbiology \& Biotechnology}, volume = {41}, year = {2025}, month = {SEP }, pages = {352}, type = {Review}, abstract = {

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.

}, keywords = {1, 3-hydroxypropionaldehyde, 3-hydroxypropionic acid, 3-Propanediol, Fermentation, flux balance analysis, Metabolic engineering}, issn = {0959-3993}, doi = {10.1007/s11274-025-04554-w}, author = {Joshi, Harshvardhan and Isar, Jasmine and Rangaswamy, Vidhya and Raghunathan, Anu} } @article {49845, title = {Integrated analysis of mitochondrial ETC inhibition reveals genotype-specific heterogeneity of drug response in glioblastoma}, journal = {Biochemical and Biophysical Research Communications}, volume = {787}, year = {2025}, month = {NOV }, pages = {152798}, type = {Article}, abstract = {

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{\textquoteright}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.

}, keywords = {Drug dose response, Electron transport chain, Glioblastoma, IC50 value, Instantaneous inhibitory potential, Mitochondrial genome}, issn = {0006-291X}, doi = {10.1016/j.bbrc.2025.152798}, author = {Belekar, Madhuri and Kavatalkar, Vijendra and Yadav, Ritu and Raghunathan, Anu} } @article {49971, title = {Precision integrated identification of predictive first-trimester metabolomics signatures for early detection of gestational diabetes mellitus}, journal = {Cardiovascular Diabetology}, volume = {24}, year = {2025}, month = {NOV }, pages = {434}, type = {Article}, abstract = {

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 (\< 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.

}, keywords = {First trimester, Gestational diabetes mellitus, Indian women, Mass spectrometry, Metabolomics, Prediction}, doi = {10.1186/s12933-025-02978-0}, author = {Sharma, Sapna and Subrahmanyam, Yalamanchili Venkata and Gupta, Payal and Vadivel, Sangeetha and Deepa, Mohan and Tandon, Ansh and Sreedevi, Sreekumar and Ram, Uma and Narad, Priyanka and Parmar, Dharmeshkumar and Anjana, Ranjit Mohan and Raghunathan, Anu and Balasubramanyam, Muthuswamy and Mohan, Viswanathan and Sengupta, Abhishek and Adamski, Jerzy and Saravanan, Ponnusamy and Panchagnula, Venkateswarlu and Usharani, Dandamudi and Gokulakrishnan, Kuppan} }