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Here, we present a peptide-based linear mixed models tool—PBLMM, a standalone desktop application for differential expression analysis of proteomics data. We also provide a Python package that allows streamlined data analysis workflows implementing the PBLMM algorithm. PBLMM is easy to use without scripting experience and calculates differential expression by peptide-based linear mixed regression models. We show that peptide-based models outperform classical methods of statistical inference of differentially expressed proteins. In addition, PBLMM exhibits superior statistical power in situations of low effect size and/or low sample size. Taken together our tool provides an easy-to-use, high-statistical-power method to infer differentially expressed proteins from proteomics data.
Acute kidney injury is associated with mortality in COVID-19 patients. However, host cell changes underlying infection of renal cells with SARS-CoV-2 remain unknown and prevent understanding of the molecular mechanisms that may contribute to renal pathology. Here, we carried out quantitative translatome and whole-cell proteomics analyses of primary renal proximal and distal tubular epithelial cells derived from human donors infected with SARS-CoV-2 or MERS-CoV to disseminate virus and cell type–specific changes over time. Our findings revealed shared pathways modified upon infection with both viruses, as well as SARS-CoV-2-specific host cell modulation driving key changes in innate immune activation and cellular protein quality control. Notably, MERS-CoV infection–induced specific changes in mitochondrial biology that were not observed in response to SARS-CoV-2 infection. Furthermore, we identified extensive modulation in pathways associated with kidney failure that changed in a virus- and cell type–specific manner. In summary, we provide an overview of the effects of SARS-CoV-2 or MERS-CoV infection on primary renal epithelial cells revealing key pathways that may be essential for viral replication.
The ancestral SARS-CoV-2 strain that initiated the Covid-19 pandemic at the end of 2019 has rapidly mutated into multiple variants of concern with variable pathogenicity and increasing immune escape strategies. However, differences in host cellular antiviral responses upon infection with SARS-CoV-2 variants remain elusive. Leveraging whole-cell proteomics, we determined host signaling pathways that are differentially modulated upon infection with the clinical isolates of the ancestral SARS-CoV-2 B.1 and the variants of concern Delta and Omicron BA.1. Our findings illustrate alterations in the global host proteome landscape upon infection with SARS-CoV-2 variants and the resulting host immune responses. Additionally, viral proteome kinetics reveal declining levels of viral protein expression during Omicron BA.1 infection when compared to ancestral B.1 and Delta variants, consistent with its reduced replication rates. Moreover, molecular assays reveal deferral activation of specific host antiviral signaling upon Omicron BA.1 and BA.2 infections. Our study provides an overview of host proteome profile of multiple SARS-CoV-2 variants and brings forth a better understanding of the instigation of key immune signaling pathways causative for the differential pathogenicity of SARS-CoV-2 variants.
Background: Alternative polyadenylation (APA) refers to the regulated selection of polyadenylation sites (PASs) in transcripts, which determines the length of their 3′ untranslated regions (3′UTRs). We have recently shown that SRSF3 and SRSF7, two closely related SR proteins, connect APA with mRNA export. The mechanism underlying APA regulation by SRSF3 and SRSF7 remained unknown.
Results: Here we combine iCLIP and 3′-end sequencing and find that SRSF3 and SRSF7 bind upstream of proximal PASs (pPASs), but they exert opposite effects on 3′UTR length. SRSF7 enhances pPAS usage in a concentration-dependent but splicing-independent manner by recruiting the cleavage factor FIP1, generating short 3′UTRs. Protein domains unique to SRSF7, which are absent from SRSF3, contribute to FIP1 recruitment. In contrast, SRSF3 promotes distal PAS (dPAS) usage and hence long 3′UTRs directly by counteracting SRSF7, but also indirectly by maintaining high levels of cleavage factor Im (CFIm) via alternative splicing. Upon SRSF3 depletion, CFIm levels decrease and 3′UTRs are shortened. The indirect SRSF3 targets are particularly sensitive to low CFIm levels, because here CFIm serves a dual function; it enhances dPAS and inhibits pPAS usage by binding immediately downstream and assembling unproductive cleavage complexes, which together promotes long 3′UTRs.
Conclusions; We demonstrate that SRSF3 and SRSF7 are direct modulators of pPAS usage and show how small differences in the domain architecture of SR proteins can confer opposite effects on pPAS regulation.
We present measurements of exclusive ensuremathπ+,0 and η production in pp reactions at 1.25GeV and 2.2GeV beam kinetic energy in hadron and dielectron channels. In the case of π+ and π0 , high-statistics invariant-mass and angular distributions are obtained within the HADES acceptance as well as acceptance-corrected distributions, which are compared to a resonance model. The sensitivity of the data to the yield and production angular distribution of Δ (1232) and higher-lying baryon resonances is shown, and an improved parameterization is proposed. The extracted cross-sections are of special interest in the case of pp → pp η , since controversial data exist at 2.0GeV; we find \ensuremathσ=0.142±0.022 mb. Using the dielectron channels, the π0 and η Dalitz decay signals are reconstructed with yields fully consistent with the hadronic channels. The electron invariant masses and acceptance-corrected helicity angle distributions are found in good agreement with model predictions.
The mammalian target of rapamycin and the integrated stress response are central cellular hubs regulating translation upon stress. The precise proteins and pathway specificity of translation targets of these pathways remained largely unclear. We recently described a new method for quantitative translation proteomics and found that both pathways control translation of the same sets of proteins.