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Start Over Author "P. Rai" Journal nature communications
127 results on '"P. Rai"'

4. Widespread exposure to SARS-CoV-2 in wildlife communities

Author

Goldberg, Amanda R., Langwig, Kate E., Brown, Katherine L., Marano, Jeffrey M., Rai, Pallavi, King, Kelsie M., Sharp, Amanda K., Ceci, Alessandro, Kailing, Christopher D., Kailing, Macy J., Briggs, Russell, Urbano, Matthew G., Roby, Clinton, Brown, Anne M., Weger-Lucarelli, James, Finkielstein, Carla V., and Hoyt, Joseph R.

Published
2024
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5. Local incomplete combustion emissions define the PM2.5 oxidative potential in Northern India

Author

Bhattu, Deepika, Tripathi, Sachchida Nand, Bhowmik, Himadri Sekhar, Moschos, Vaios, Lee, Chuan Ping, Rauber, Martin, Salazar, Gary, Abbaszade, Gülcin, Cui, Tianqu, Slowik, Jay G., Vats, Pawan, Mishra, Suneeti, Lalchandani, Vipul, Satish, Rangu, Rai, Pragati, Casotto, Roberto, Tobler, Anna, Kumar, Varun, Hao, Yufang, Qi, Lu, Khare, Peeyush, Manousakas, Manousos Ioannis, Wang, Qiyuan, Han, Yuemei, Tian, Jie, Darfeuil, Sophie, Minguillon, Mari Cruz, Hueglin, Christoph, Conil, Sébastien, Rastogi, Neeraj, Srivastava, Atul Kumar, Ganguly, Dilip, Bjelic, Sasa, Canonaco, Francesco, Schnelle-Kreis, Jürgen, Dominutti, Pamela A., Jaffrezo, Jean-Luc, Szidat, Sönke, Chen, Yang, Cao, Junji, Baltensperger, Urs, Uzu, Gaëlle, Daellenbach, Kaspar R., El Haddad, Imad, and Prévôt, André S. H.

Published
2024
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7. Development of pathophysiologically relevant models of sickle cell disease and β-thalassemia for therapeutic studies

Author

Gupta, Pragya, Goswami, Sangam Giri, Kumari, Geeta, Saravanakumar, Vinodh, Bhargava, Nupur, Rai, Akhila Balakrishna, Singh, Praveen, Bhoyar, Rahul C., Arvinden, V. R., Gunda, Padma, Jain, Suman, Narayana, Vanya Kadla, Deolankar, Sayali C., Prasad, T. S. Keshava, Natarajan, Vivek T., Scaria, Vinod, Singh, Shailja, and Ramalingam, Sivaprakash

Published
2024
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9. Coordinated cortical thickness alterations across six neurodevelopmental and psychiatric disorders

Author

Hettwer, MD, Larivière, S, Park, BY, van den Heuvel, OA, Schmaal, L, Andreassen, OA, Ching, CRK, Hoogman, M, Buitelaar, J, van Rooij, D, Veltman, DJ, Stein, DJ, Franke, B, van Erp, TGM, Jahanshad, N, Thompson, PM, Thomopoulos, SI, Bethlehem, RAI, Bernhardt, BC, Eickhoff, SB, and Valk, SL

Subjects
Mental Health, Brain Disorders, Neurosciences, Behavioral and Social Science, Mental health, Humans, Cerebral Cortex, Connectome, Mental Disorders, Cerebellum, Attention, Magnetic Resonance Imaging, ENIGMA ADHD Working Group, ENIGMA Autism Working Group, ENIGMA Bipolar Disorder Working Group, ENIGMA Major Depression Working Group, ENIGMA OCD Working Group, ENIGMA Schizophrenia Working Group
Abstract

Neuropsychiatric disorders are increasingly conceptualized as overlapping spectra sharing multi-level neurobiological alterations. However, whether transdiagnostic cortical alterations covary in a biologically meaningful way is currently unknown. Here, we studied co-alteration networks across six neurodevelopmental and psychiatric disorders, reflecting pathological structural covariance. In 12,024 patients and 18,969 controls from the ENIGMA consortium, we observed that co-alteration patterns followed normative connectome organization and were anchored to prefrontal and temporal disease epicenters. Manifold learning revealed frontal-to-temporal and sensory/limbic-to-occipitoparietal transdiagnostic gradients, differentiating shared illness effects on cortical thickness along these axes. The principal gradient aligned with a normative cortical thickness covariance gradient and established a transcriptomic link to cortico-cerebello-thalamic circuits. Moreover, transdiagnostic gradients segregated functional networks involved in basic sensory, attentional/perceptual, and domain-general cognitive processes, and distinguished between regional cytoarchitectonic profiles. Together, our findings indicate that shared illness effects occur in a synchronized fashion and along multiple levels of hierarchical cortical organization.

Published
2022

10. Knowledge integration and decision support for accelerated discovery of antibiotic resistance genes

Author

Youn, Jason, Rai, Navneet, and Tagkopoulos, Ilias

Subjects
Information and Computing Sciences, Medical Microbiology, Biomedical and Clinical Sciences, Biological Sciences, Antimicrobial Resistance, Emerging Infectious Diseases, Biodefense, Infectious Diseases, Minority Health, Machine Learning and Artificial Intelligence, Infection, Anti-Bacterial Agents, Drug Resistance, Bacterial, Escherichia coli, Escherichia coli Infections, Humans, Salmonella enterica
Abstract

We present a machine learning framework to automate knowledge discovery through knowledge graph construction, inconsistency resolution, and iterative link prediction. By incorporating knowledge from 10 publicly available sources, we construct an Escherichia coli antibiotic resistance knowledge graph with 651,758 triples from 23 triple types after resolving 236 sets of inconsistencies. Iteratively applying link prediction to this graph and wet-lab validation of the generated hypotheses reveal 15 antibiotic resistant E. coli genes, with 6 of them never associated with antibiotic resistance for any microbe. Iterative link prediction leads to a performance improvement and more findings. The probability of positive findings highly correlates with experimentally validated findings (R2 = 0.94). We also identify 5 homologs in Salmonella enterica that are all validated to confer resistance to antibiotics. This work demonstrates how evidence-driven decisions are a step toward automating knowledge discovery with high confidence and accelerated pace, thereby substituting traditional time-consuming and expensive methods.

Published
2022

13. Modulation-doping a correlated electron insulator

Author

Mondal, Debasish, Mahapatra, Smruti Rekha, Derrico, Abigail M., Rai, Rajeev Kumar, Paudel, Jay R., Schlueter, Christoph, Gloskovskii, Andrei, Banerjee, Rajdeep, Hariki, Atsushi, DeGroot, Frank M. F., Sarma, D. D., Narayan, Awadhesh, Nukala, Pavan, Gray, Alexander X., and Aetukuri, Naga Phani B.

Published
2023
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14. mRNA vaccine quality analysis using RNA sequencing

Author

Gunter, Helen M., Idrisoglu, Senel, Singh, Swati, Han, Dae Jong, Ariens, Emily, Peters, Jonathan R., Wong, Ted, Cheetham, Seth W., Xu, Jun, Rai, Subash Kumar, Feldman, Robert, Herbert, Andy, Marcellin, Esteban, Tropee, Romain, Munro, Trent, and Mercer, Tim R.

Published
2023
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17. Machine learning-driven multifunctional peptide engineering for sustained ocular drug delivery

Author

Hsueh, Henry T., Chou, Renee Ti, Rai, Usha, Liyanage, Wathsala, Kim, Yoo Chun, Appell, Matthew B., Pejavar, Jahnavi, Leo, Kirby T., Davison, Charlotte, Kolodziejski, Patricia, Mozzer, Ann, Kwon, HyeYoung, Sista, Maanasa, Anders, Nicole M., Hemingway, Avelina, Rompicharla, Sri Vishnu Kiran, Edwards, Malia, Pitha, Ian, Hanes, Justin, Cummings, Michael P., and Ensign, Laura M.

Published
2023
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19. Loss-of-function genomic variants highlight potential therapeutic targets for cardiovascular disease.

Author

Nielsen, Jonas B, Rom, Oren, Surakka, Ida, Graham, Sarah E, Zhou, Wei, Roychowdhury, Tanmoy, Fritsche, Lars G, Gagliano Taliun, Sarah A, Sidore, Carlo, Liu, Yuhao, Gabrielsen, Maiken E, Skogholt, Anne Heidi, Wolford, Brooke, Overton, William, Zhao, Ying, Chen, Jin, Zhang, He, Hornsby, Whitney E, Acheampong, Akua, Grooms, Austen, Schaefer, Amanda, Zajac, Gregory JM, Villacorta, Luis, Zhang, Jifeng, Brumpton, Ben, Løset, Mari, Rai, Vivek, Lundegaard, Pia R, Olesen, Morten S, Taylor, Kent D, Palmer, Nicholette D, Chen, Yii-Der, Choi, Seung H, Lubitz, Steven A, Ellinor, Patrick T, Barnes, Kathleen C, Daya, Michelle, Rafaels, Nicholas, Weiss, Scott T, Lasky-Su, Jessica, Tracy, Russell P, Vasan, Ramachandran S, Cupples, L Adrienne, Mathias, Rasika A, Yanek, Lisa R, Becker, Lewis C, Peyser, Patricia A, Bielak, Lawrence F, Smith, Jennifer A, Aslibekyan, Stella, Hidalgo, Bertha A, Arnett, Donna K, Irvin, Marguerite R, Wilson, James G, Musani, Solomon K, Correa, Adolfo, Rich, Stephen S, Guo, Xiuqing, Rotter, Jerome I, Konkle, Barbara A, Johnsen, Jill M, Ashley-Koch, Allison E, Telen, Marilyn J, Sheehan, Vivien A, Blangero, John, Curran, Joanne E, Peralta, Juan M, Montgomery, Courtney, Sheu, Wayne H-H, Chung, Ren-Hua, Schwander, Karen, Nouraie, Seyed M, Gordeuk, Victor R, Zhang, Yingze, Kooperberg, Charles, Reiner, Alexander P, Jackson, Rebecca D, Bleecker, Eugene R, Meyers, Deborah A, Li, Xingnan, Das, Sayantan, Yu, Ketian, LeFaive, Jonathon, Smith, Albert, Blackwell, Tom, Taliun, Daniel, Zollner, Sebastian, Forer, Lukas, Schoenherr, Sebastian, Fuchsberger, Christian, Pandit, Anita, Zawistowski, Matthew, Kheterpal, Sachin, Brummett, Chad M, Natarajan, Pradeep, Schlessinger, David, Lee, Seunggeun, Kang, Hyun Min, Cucca, Francesco, and Holmen, Oddgeir L

Subjects
Liver, Humans, Cardiovascular Diseases, Lipids, Receptors, LDL, Gene Targeting, Gene Silencing, Genome, Human, Biological Specimen Banks, Genome-Wide Association Study, Molecular Targeted Therapy, United Kingdom, Loss of Function Mutation, Phenomics
Abstract

Pharmaceutical drugs targeting dyslipidemia and cardiovascular disease (CVD) may increase the risk of fatty liver disease and other metabolic disorders. To identify potential novel CVD drug targets without these adverse effects, we perform genome-wide analyses of participants in the HUNT Study in Norway (n = 69,479) to search for protein-altering variants with beneficial impact on quantitative blood traits related to cardiovascular disease, but without detrimental impact on liver function. We identify 76 (11 previously unreported) presumed causal protein-altering variants associated with one or more CVD- or liver-related blood traits. Nine of the variants are predicted to result in loss-of-function of the protein. This includes ZNF529:p.K405X, which is associated with decreased low-density-lipoprotein (LDL) cholesterol (P = 1.3 × 10-8) without being associated with liver enzymes or non-fasting blood glucose. Silencing of ZNF529 in human hepatoma cells results in upregulation of LDL receptor and increased LDL uptake in the cells. This suggests that inhibition of ZNF529 or its gene product should be prioritized as a novel candidate drug target for treating dyslipidemia and associated CVD.

Published
2020

20. Accelerated knowledge discovery from omics data by optimal experimental design.

Author

Wang, Xiaokang, Rai, Navneet, Merchel Piovesan Pereira, Beatriz, Eetemadi, Ameen, and Tagkopoulos, Ilias

Subjects
Escherichia coli, Bacterial Proteins, Membrane Proteins, Molecular Chaperones, Anti-Bacterial Agents, Disinfectants, Computational Biology, Gene Expression Regulation, Bacterial, Models, Biological, Research Design, Stress, Physiological, Machine Learning, Gene Expression Regulation, Bacterial, Models, Biological, Stress, Physiological
Abstract

How to design experiments that accelerate knowledge discovery on complex biological landscapes remains a tantalizing question. We present an optimal experimental design method (coined OPEX) to identify informative omics experiments using machine learning models for both experimental space exploration and model training. OPEX-guided exploration of Escherichia coli's populations exposed to biocide and antibiotic combinations lead to more accurate predictive models of gene expression with 44% less data. Analysis of the proposed experiments shows that broad exploration of the experimental space followed by fine-tuning emerges as the optimal strategy. Additionally, analysis of the experimental data reveals 29 cases of cross-stress protection and 4 cases of cross-stress vulnerability. Further validation reveals the central role of chaperones, stress response proteins and transport pumps in cross-stress exposure. This work demonstrates how active learning can be used to guide omics data collection for training predictive models, making evidence-driven decisions and accelerating knowledge discovery in life sciences.

Published
2020

26. Multi-modal molecular programs regulate melanoma cell state

Author

Andrews, Miles C., Oba, Junna, Wu, Chang-Jiun, Zhu, Haifeng, Karpinets, Tatiana, Creasy, Caitlin A., Forget, Marie-Andrée, Yu, Xiaoxing, Song, Xingzhi, Mao, Xizeng, Robertson, A. Gordon, Romano, Gabriele, Li, Peng, Burton, Elizabeth M., Lu, Yiling, Sloane, Robert Szczepaniak, Wani, Khalida M., Rai, Kunal, Lazar, Alexander J., Haydu, Lauren E., Bustos, Matias A., Shen, Jianjun, Chen, Yueping, Morgan, Margaret B., Wargo, Jennifer A., Kwong, Lawrence N., Haymaker, Cara L., Grimm, Elizabeth A., Hwu, Patrick, Hoon, Dave S. B., Zhang, Jianhua, Gershenwald, Jeffrey E., Davies, Michael A., Futreal, P. Andrew, Bernatchez, Chantale, and Woodman, Scott E.

Published
2022
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29. Gene expression based inference of cancer drug sensitivity

Author

Chawla, Smriti, Rockstroh, Anja, Lehman, Melanie, Ratther, Ellca, Jain, Atishay, Anand, Anuneet, Gupta, Apoorva, Bhattacharya, Namrata, Poonia, Sarita, Rai, Priyadarshini, Das, Nirjhar, Majumdar, Angshul, Jayadeva, Ahuja, Gaurav, Hollier, Brett G., Nelson, Colleen C., and Sengupta, Debarka

Published
2022
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32. The androgen receptor is a therapeutic target in desmoplastic small round cell sarcoma

Author

Lamhamedi-Cherradi, Salah-Eddine, Maitituoheti, Mayinuer, Menegaz, Brian A., Krishnan, Sandhya, Vetter, Amelia M., Camacho, Pamela, Wu, Chia-Chin, Beird, Hannah C., Porter, Robert W., Ingram, Davis R., Ramamoorthy, Vandhana, Mohiuddin, Sana, McCall, David, Truong, Danh D., Cuglievan, Branko, Futreal, P. Andrew, Velasco, Alejandra Ruiz, Anvar, Nazanin Esmaeili, Utama, Budi, Titus, Mark, Lazar, Alexander J., Wang, Wei-Lien, Rodriguez-Aguayo, Cristian, Ratan, Ravin, Livingston, J. Andrew, Rai, Kunal, MacLeod, A. Robert, Daw, Najat C., Hayes-Jordan, Andrea, and Ludwig, Joseph A.

Published
2022
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34. Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli.

Author

Kim, Minseung, Rai, Navneet, Zorraquino, Violeta, and Tagkopoulos, Ilias

Subjects
Escherichia coli, Computational Biology, Environment, Models, Biological, Models, Biological
Abstract

A significant obstacle in training predictive cell models is the lack of integrated data sources. We develop semi-supervised normalization pipelines and perform experimental characterization (growth, transcriptional, proteome) to create Ecomics, a consistent, quality-controlled multi-omics compendium for Escherichia coli with cohesive meta-data information. We then use this resource to train a multi-scale model that integrates four omics layers to predict genome-wide concentrations and growth dynamics. The genetic and environmental ontology reconstructed from the omics data is substantially different and complementary to the genetic and chemical ontologies. The integration of different layers confers an incremental increase in the prediction performance, as does the information about the known gene regulatory and protein-protein interactions. The predictive performance of the model ranges from 0.54 to 0.87 for the various omics layers, which far exceeds various baselines. This work provides an integrative framework of omics-driven predictive modelling that is broadly applicable to guide biological discovery.

Published
2016

37. Somatostatin receptor 2 expression in nasopharyngeal cancer is induced by Epstein Barr virus infection: impact on prognosis, imaging and therapy

Author

Lechner, Matt, Schartinger, Volker H., Steele, Christopher D., Nei, Wen Long, Ooft, Marc Lucas, Schreiber, Liesa-Marie, Pipinikas, Christodoulos P., Chung, Grace Tin-Yun, Chan, Yuk Yu, Wu, Feng, To, Ka-Fai, Tsang, Chi Man, Pearce, Wayne, Morelli, Daniele, Philpott, Martin, Masterson, Liam, Nibhani, Reshma, Wells, Graham, Bell, Christopher G., Koller, Julia, Delecluse, Susanne, Yip, Yim Ling, Liu, Jacklyn, Forde, Cillian T., Forster, Martin D., Jay, Amrita, Dudás, József, Krapp, Annika, Wan, Simon, Uprimny, Christian, Sprung, Susanne, Haybaeck, Johannes, Fenton, Tim R., Chester, Kerry, Thirlwell, Christina, Royle, Gary, Marafioti, Teresa, Gupta, Rajeev, Indrasari, Sagung Rai, Herdini, Camelia, Slim, Mohd Afiq Mohd, Indrawati, I., Sutton, Liam, Fles, Renske, Tan, Bing, Yeong, Joe, Jain, Amit, Han, Shuting, Wang, Haitao, Loke, Kelvin S. H., He, Wan, Xu, Ruilian, Jin, Hongtao, Cheng, Zhiqiang, Howard, David, Hwang, Peter H., Le, Quynh-Thu, Tay, Joshua K., West, Robert B., Tsao, Sai Wah, Meyer, Tim, Riechelmann, Herbert, Oppermann, Udo, Delecluse, Henri-Jacques, Willems, Stefan M., Chua, Melvin L. K., Busson, Pierre, Lo, Kwok Wai, Wollmann, Guido, Pillay, Nischalan, Vanhaesebroeck, Bart, and Lund, Valerie J.

Published
2021
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40. Single cell T cell landscape and T cell receptor repertoire profiling of AML in context of PD-1 blockade therapy

Author

Abbas, Hussein A., Hao, Dapeng, Tomczak, Katarzyna, Barrodia, Praveen, Im, Jin Seon, Reville, Patrick K., Alaniz, Zoe, Wang, Wei, Wang, Ruiping, Wang, Feng, Al-Atrash, Gheath, Takahashi, Koichi, Ning, Jing, Ding, Maomao, Beird, Hannah C., Mathews, Jairo T., Little, Latasha, Zhang, Jianhua, Basu, Sreyashi, Konopleva, Marina, Marques-Piubelli, Mario L., Solis, Luisa M., Parra, Edwin Roger, Lu, Wei, Tamegnon, Auriole, Garcia-Manero, Guillermo, Green, Michael R., Sharma, Padmanee, Allison, James P., Kornblau, Steven M., Rai, Kunal, Wang, Linghua, Daver, Naval, and Futreal, Andrew

Published
2021
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42. cAMP-dependent regulation of HCN4 controls the tonic entrainment process in sinoatrial node pacemaker cells

Author

Fenske, Stefanie, Hennis, Konstantin, Rötzer, René D., Brox, Verena F., Becirovic, Elvir, Scharr, Andreas, Gruner, Christian, Ziegler, Tilman, Mehlfeld, Verena, Brennan, Jaclyn, Efimov, Igor R., Pauža, Audrys G., Moser, Markus, Wotjak, Carsten T., Kupatt, Christian, Gönner, Rasmus, Zhang, Rai, Zhang, Henggui, Zong, Xiangang, Biel, Martin, and Wahl-Schott, Christian

Published
2020
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45. Telomere dysfunction activates YAP1 to drive tissue inflammation

Author

Chakravarti, Deepavali, Hu, Baoli, Mao, Xizeng, Rashid, Asif, Li, Jiexi, Li, Jun, Liao, Wen-ting, Whitley, Elizabeth M., Dey, Prasenjit, Hou, Pingping, LaBella, Kyle A., Chang, Andrew, Wang, Guocan, Spring, Denise J., Deng, Pingna, Zhao, Di, Liang, Xin, Lan, Zhengdao, Lin, Yiyun, Sarkar, Sharmistha, Terranova, Christopher, Deribe, Yonathan Lissanu, Blutt, Sarah E., Okhuysen, Pablo, Zhang, Jianhua, Vilar, Eduardo, Nielsen, Ole Haagen, Dupont, Andrew, Younes, Mamoun, Patel, Kalyani R., Shroyer, Noah F., Rai, Kunal, Estes, Mary K., Wang, Y. Alan, Bertuch, Alison A., and DePinho, Ronald A.

Published
2020
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