Hoagland, C. | bioRxiv Science

bioRxiv2023

Chronic alcohol consumption dysregulates innate immune response to SARS-CoV-2 in the lung

Alcohol consumption is widespread with over half of the individuals over 18 years of age in the U.S. reporting alcohol use in the last 30 days. Moreover, 9 million Americans engaged in binge or chronic heavy drinking (CHD) in 2019. CHD negatively impacts pathogen clearance and tissue repair, including in the respiratory tract, thereby increasing susceptibility to infection. Although, it has been hypothesized that chronic alcohol consumption negatively impacts COVID-19 outcomes; the interplay between chronic alcohol use and SARS-CoV-2 infection outcomes has yet to be elucidated. Therefore, in this study we investigated the impact of chronic alcohol consumption on SARS-CoV-2 anti-viral responses in bronchoalveolar lavage cell samples from humans with alcohol use disorder and rhesus macaques that engaged in chronic drinking. Our data show that in both humans and macaques, the induction of key antiviral cytokines and growth factors was decreased with chronic ethanol consumption. Moreover, in macaques fewer differentially expressed genes mapped to Gene Ontology terms associated with antiviral immunity following 6 month of ethanol consumption while TLR signaling pathways were upregulated. These data are indicative of aberrant inflammation and reduced antiviral responses in the lung with chronic alcohol drinking.

immunology↗

bioRxiv2022

Quantifying physiological trait variation with automated hyperspectral imaging in rice

Advancements in hyperspectral imaging (HSI) and establishment of dedicated plant phenotyping facilities have enabled researchers to gather large quantities of plant spectral images with the aim of inferring target phenotypes non-destructively. However, large volumes of data that result from HSI and corequisite specialized methods for analysis may prevent plant scientists from taking full advantage of these systems. Here, we explore estimation of physiological traits in 23 rice accessions using an automated HSI system. Under contrasting nitrogen conditions, HSI data are used to classify treatment groups with [≥] 83% accuracy by utilizing support vector machines. Out of the 14 physiological traits collected, leaf-level nitrogen content (N, %) and carbon to nitrogen ratio (C:N) could also be predicted from the hyperspectral imaging data with normalized root mean square error of predictions smaller than 14% (R2 of 0.88 for N and 0.75 for C:N). This study demonstrates the potential of using an automated HSI system to analyze genotypic variation for physiological traits in a diverse panel of rice; to help lower barriers of application of hyperspectral imaging in the greater plant science research community, analysis scripts used in this study are carefully documented and made publicly available. HIGHLIGHTData from an automated hyperspectral imaging system are used to classify nitrogen treatment and predict leaf-level nitrogen content and carbon to nitrogen ratio during vegetative growth in rice.

plant biology↗