Biology subjects

Dutta, J.

Publications and source records attributed to Dutta, J..

5 recordsLinked to original sources

bioRxiv2022

Human influenza A virus H1N1 1 in marine mammals in California, 2019

From 2011-2018, we conducted surveillance in marine mammals along the California coast for influenza A virus (IAV), frequently detecting anti-influenza antibodies and intermittently detecting IAV. In spring 2019, this pattern changed. Despite no change in surveillance intensity, we detected IAV RNA in 10 samples in March and April, mostly in nasal and rectal swabs from northern elephant seals (Mirounga angustirostris). Although virus isolation was unsuccessful, IAV sequenced from one northern elephant seal nasal swab showed close genetic identity with pandemic H1N1 IAV subclade 6B.1A.1 that was concurrently circulating in humans in the 2018/19 influenza season. This represents the first report of human A(H1N1)pdm09 IAV in northern elephant seals since 2010, suggesting IAV continues to spill over from humans to pinnipeds.

microbiology↗

bioRxiv2022

A transformer model for predicting cognitive impairment from sleep

Sleep disturbances are known to be aggravated with normal aging. Additionally, sleep disruptions have a potentially bidirectional causal relationship with dementia due to neurodegenerative diseases like Alzheimers disease. Predictive techniques that can automatically detect cognitive impairment from an individuals sleep data have broad clinical and biological significance. Here, we present a deep learning approach based on a transformer architecture to predict cognitive status from sleep electroencephalography (EEG) data. This work uses data from N = 1, 502 subjects from the Multi-Ethnic Study of Atherosclerosis (MESA) cohort. Our transformer model achieves 70.22% accuracy at the binary classification task for distinguishing cognitively normal and impaired subjects based on their sleep EEG. Our method outperforms traditional feature handcrafting, which has an overall accuracy of 57.61% for the same task. We use a sparse regression model to understand and interpret the information captured by each learned feature from our transformer model. To our knowledge, this is the first effort to use deep learning to predict cognitive impairment from sleep metrics.

neuroscience↗

bioRxiv2020

Respiratory tract explant infection dynamics of influenza A virus in California sea lions, northern elephant seals, and rhesus macaques and severe acute respiratory syndrome virus-2 in rhesus macaques

To understand susceptibility of wild California sea lions and Northern elephant seals to influenza A virus (IAV), we developed an ex vivo respiratory explant model and used it to compare infection kinetics for multiple IAV subtypes. We first established the approach using explants from colonized rhesus macaques, a model for human IAV. Trachea, bronchi, and lungs from 11 California sea lions, 2 Northern elephant seals and 10 rhesus macaques were inoculated within 24 hours post-mortem with 6 strains representing 4 IAV subtypes. Explants from the 3 species showed similar IAV infection kinetics with peak viral titers 48-72 hours post-inoculation that increased by 2-4 log10 plaque forming units (PFU)/explant relative to the inoculum. Immunohistochemistry localized IAV infection to apical epithelial cells. These results demonstrate that respiratory tissue explants from wild marine mammals support IAV infection. In the absence of the ability to perform experimental infections of marine mammals, this ex vivo culture of respiratory tissues mirrors the in vivo environment and serves as a tool to study IAV susceptibility, host-range, and tissue tropism. ImportanceAlthough influenza A virus can infect marine mammals, a dearth of marine mammal cell lines and ethical and logistical challenges prohibiting experimental infections of living marine mammals means that little is known about IAV infection kinetics in these species. We circumvented these limitations by adapting a respiratory tract explant model first to establish the approach with rhesus macaques and then for use with explants from wild marine mammals euthanized for non-respiratory medical conditions. We observed that multiple strains representing 4 IAV subtypes infected trachea, bronchi, and lungs of macaques and marine mammals with variable peak titers and kinetics. This ex vivo model can define infection dynamics for IAV in marine mammals. Further, use of explants from animals euthanized for other reasons reduces use of animals in research.

microbiology↗

bioRxiv2020

Docosahexaenoic acid (DHA), a nutritional supplement, modulates steroid insensitivity in asthma

Asthmatics with poor steroid responsiveness are now found to use health services at higher frequency and contribute to socio-economic burden disproportionately. We have previously shown that a ω-6 fatty acid metabolite leads to a severe and steroid insensitive asthma-like condition in mice. Here, we investigated the role of retinoid-x-receptor gamma (RXRγ) and Docosahexaenoic acid (DHA), a ω3 fatty acid rexinoid ligand of RXR, on the features of steroid insensitivity in asthmatic condition. RXRγ was found to be reduced in the lungs of human asthmatics and mice with steroid insensitive allergic airway inflammation. RXRγ knockdown in naïve mice led to spontaneous asthma like features whereas RXRγ knockdown in allergic mice led to steroid insensitive asthma features. We observed while RXRγ binds to the glucocorticoid receptor (GR) gene and regulates its transcription, DHA increases the GRα expression in human bronchial epithelial cells and reverses the steroid insensitive features in mice with allergic airway inflammation. Docosahexaenoic acid (DHA), a ligand of RXR, was reduced in the sera of steroid-insensitive asthmatics. We conclude that DHA may prove to be a promising steroid sensitizing agent for the treatment of steroid insensitive asthmatics.Summary The molecular regulation of glucocorticoid receptor by retinoid-x-receptor gamma (RXRgama) has an implication in steroid insensitive asthma as we found that Docosahexaenoic acid (DHA), a nutritional supplement and natural ligand of RXRgamma, improves steroid sensitivity in steroid insensitive mice model of asthma and DHA levels are found to be low in steroid insensitive asthmatic patients.Competing Interest StatementThe authors have declared no competing interest.Abbreviations usedAAIAllergic airway inflammationAHRAirway hyper-responsivenessATRAAll-trans retinoic acidAUArbitrary unitsBALBronchoalveolar lavageBEAS-2BHuman bronchial epithelial cellsCECockroach allergen extractChIPChromatin Immunoprecipitation9CRA9-cis retinoic acidDEXDexamethasoneDHADocosahexaenoic acidDMSODimethyl sulfoxideELISAEnzyme-linked immunosorbent assayGAPDHGlyceraldehyde 3-phosphate dehydrogenaseGLucGaussia luciferaseGRαGlucocorticoid receptor alphaGREGlucocorticoid receptor elementH & EHaematoxylin and eosinIFNγInterferon gammaIgEImmunoglobulinE ILInterleukinKCKeratinocyte chemoattractant15-LOX15-lipoxygenaseMCF-7Human breast adenocarcinoma cell lineMCP1-αMonocyte chemoattractant protein1-αNF-κBNuclear Factor kappa-light-chain-enhancer of activated B cellsO.EOverexpressionOVAOvalbuminp38-MAPKp38 mitogen-activated protein kinasePBSPhosphate buffered salineRARRetinoic acid receptorPDTCPyrrolidinedithiocarbamateRXRERexinoid receptor elementRXRγRetinoid-x-receptor gamma13-S-HODE13-hydroxyoctadecadienoic acidSEMStandard error meansiRNASmall interfering RNASTAT-6Signal transducer and activator of transcription 6ThT helperVEHVehicleView Full Text

immunology↗

bioRxiv2018

Avian influenza viruses in wild birds: virus evolution in a multi-host ecosystem

Wild ducks and gulls are the major reservoirs for avian influenza A viruses (AIVs). The mechanisms that drive AIV evolution are complex at sites where various duck and gull species from multiple flyways breed, winter or stage. The Republic of Georgia is located at the intersection of three migratory flyways: Central Asian Flyway, East Asian/East African Flyway and Black Sea/Mediterranean Flyway. For six consecutive years (2010-2016), we collected AIV samples from various duck and gull species that breed, migrate and overwinter in Georgia. We found substantial subtype diversity of viruses that varied in prevalence from year to year. Low pathogenic (LP)AIV subtypes included H1N1, H2N3, H2N5, H2N7, H3N8, H4N2, H6N2, H7N3, H7N7, H9N1, H9N3, H10N4, H10N7, H11N1, H13N2, H13N6, H13N8, H16N3, plus two H5N5 and H5N8 highly pathogenic (HP)AIVs belonging to clade 2.3.4.4. Whole genome phylogenetic trees showed significant host species lineage restriction for nearly all gene segments and significant differences for LPAIVs among different host species in observed reassortment rates, as defined by quantification of phylogenetic incongruence, and in nucleotide diversity. Hemagglutinin clade 2.3.4.4 H5N8 viruses, circulated in Eurasia during 2014-2015 did not reassort, but analysis after its subsequent dissemination during 2016-2017 revealed reassortment in all gene segments except NP and NS. Some virus lineages appeared to be unrelated to AIVs in wild bird populations in other regions with maintenance of local AIV viruses in Georgia, whereas other lineages showed considerable genetic inter-relationship with viruses circulating in other parts of Eurasia and Africa, despite relative under-sampling in the area.\n\nImportanceWaterbirds (e.g., gulls/ducks) are natural reservoirs of avian influenza viruses (AIVs) and have been shown to mediate dispersal of AIV at inter-continental scales during seasonal migration. The segmented genome of influenza viruses enables viral RNA from different lineages to mix or re-assort when two viruses infect the same host. Such reassortant viruses have been identified in most major human influenza pandemics and several poultry outbreaks. Despite their importance, we have only recently begun to understand AIV evolution and reassortment in their natural host reservoirs. This comprehensive study illustrates of AIV evolutionary dynamics within a multi-host ecosystem at a stop-over site where three major migratory flyways intersect. Our analysis of this ecosystem over a six-year period provides a snapshot of how these viruses are linked to global AIV populations. Understanding the evolution of AIVs in the natural host is imperative to both mitigating the risk of incursion into domestic poultry and potential risk to mammalian hosts including humans.

microbiology↗