Biology subjects

Kohler, D.

Publications and source records attributed to Kohler, D..

2 recordsLinked to original sources

bioRxiv2022

SARS-CoV-2 exposure in Norwegian rats (Rattus norvegicus) from New York City

Millions of Norway rats (Rattus norvegicus)inhabit New York City (NYC), presenting the potential for transmission of SARS-CoV-2 from humans to rats and other wildlife. We evaluated SARS-CoV-2 exposure among 79 rats captured from NYC during the fall of 2021. Results showed that 13 of 79 rats (16.5%) tested IgG or IgM positive, and partial genomes of SARS-CoV-2 were recovered from four rats that were qRT-PCR positive. Using a virus challenge study, we also showed that Alpha, Delta, and Omicron variants can cause robust infections in wild-type Sprague Dawley (SD) rats, including high level replications in the upper and lower respiratory tracts and induction of both innate and adaptive immune responses. Additionally, the Delta variant resulted in the highest infectivity. In summary, our results indicated that rats are susceptible to infection with Alpha, Delta, and Omicron variants, and rats in the NYC municipal sewer systems have been exposed to SARS-CoV-2. Our findings highlight the potential risk of secondary zoonotic transmission from urban rats and the need for further monitoring of SARS-CoV-2 in those populations. ImportanceSince its emergence causing the COVID-19 pandemic, the host tropism expansion of SARS-CoV-2 raises a potential risk for reverse-zoonotic transmission of emerging variants into rodent species, including wild rat species. In this study, we presented both genetic and serological evidence for SARS-CoV-2 exposure in wild rat population from New York City, and these viruses are potentially linked to the viruses during the early stages of the pandemic. We also demonstrated that rats are susceptible to additional variants (i.e., Alpha, Delta, and Omicron) predominant in humans and that the susceptibility to different variants vary. Our findings highlight the potential risk of secondary zoonotic transmission from urban rats and the need for further monitoring of SARS-CoV-2 in those populations.

microbiology↗

bioRxiv2022

MSstatsPTM: Statistical relative quantification of post-translational modifications in bottom-up mass spectrometry-based proteomics

Liquid chromatography coupled with bottom up mass spectrometry (LC-MS/MS)-based proteomics is increasingly used to detect changes in post-translational modifications (PTMs) in samples from different conditions. Analysis of data from such experiments faces numerous statistical challenges. These include the low abundance of modified proteoforms, the small number of observed peptides that span modification sites, and confounding between changes in the abundance of PTM and the overall changes in the protein abundance. Therefore, statistical approaches for detecting differential PTM abundance must integrate all the available information pertaining to a PTM site, and consider all the relevant sources of confounding and variation. In this manuscript we propose such a statistical framework, which is versatile, accurate, and leads to reproducible results. The framework requires an experimental design, which quantifies, for each sample, both peptides with post-translational modifications and peptides from the same proteins with no modification sites. The proposed framework supports both label-free and tandem mass tag (TMT)-based LC-MS/MS acquisitions. The statistical methodology separately summarizes the abundances of peptides with and without the modification sites, by fitting separate linear mixed effects models appropriate for the experimental design. Next, model-based inferences regarding the PTM and the protein-level abundances are combined to account for the confounding between these two sources. Evaluations on computer simulations, a spike-in experiment with known ground truth, and three biological experiments with different organisms, modification types and data acquisition types demonstrate the improved fold change estimation and detection of differential PTM abundance, as compared to currently used approaches. The proposed framework is implemented in the free and open-source R/Bioconductor package MSstatsPTM.

bioinformatics↗