Temporal control of acute protein aggregate turnover by UBE3C and NRF1-dependent proteasomal pathways.

A hallmark of neurodegenerative diseases is the progressive loss of proteostasis, leading to the accumulation of misfolded proteins or protein aggregates, with subsequent cytotoxicity. To combat this toxicity, cells have evolved degradation pathways (ubiquitin-proteasome system and autophagy) that detect and degrade misfolded proteins. However, studying the underlying cellular pathways and mechanisms has remained a challenge, as formation of many types of protein aggregates is asynchronous, with individual cells displaying distinct kinetics, thereby hindering rigorous time-course studies. Here, we merge a kinetically tractable and synchronous agDD-GFP system for aggregate formation with targeted gene knockdowns, to uncover degradation mechanisms used in response to acute aggregate formation. We find that agDD-GFP forms amorphous aggregates by cryo-electron tomography at both early and late stages of aggregate formation. Aggregate turnover occurs in a proteasome-dependent mechanism in a manner that is dictated by cellular aggregate burden, with no evidence of the involvement of autophagy. Lower levels of misfolded agDD-GFP, enriched in oligomers, utilizes UBE3C-dependent proteasomal degradation in a pathway that is independent of RPN13 ubiquitylation by UBE3C. Higher aggregate burden activates the NRF1 transcription factor to increase proteasome subunit transcription, and subsequent degradation capacity of cells. Loss or gain of NRF1 function alters the turnover of agDD-GFP under conditions of high aggregate burden. Together, these results define the role of UBE3C in degradation of this class of misfolded aggregation-prone proteins and reveals a role for NRF1 in proteostasis control in response to widespread protein aggregation.

Social Determinants of Hepatitis C Virus Infection in the United States, 2016-2021.

This cross-sectional study assessed hepatitis C virus (HCV) antibody and RNA test results performed from 2016 to 2021 at a large US clinical reference laboratory. When individual patient factors (ie, income, education, and race/ethnicity) were not available, estimates from the US Census were linked to the residential zip code. The final analytic cohort comprised 19,543,908 individuals with 23,233,827 HCV antibody and RNA test results. An analysis of progressively increasing poverty quintiles demonstrated an increasing trend in both HCV antibody positivity (from 2.6% in the lowest quintile to 6.9% in the highest, P < 0.001 for trend) and HCV RNA positivity (from 1.0% to 3.6%, P < 0.001 for trend). Increasing levels of education were associated with a decreasing trend in both HCV antibody positivity (from 8.4% in the least educated quintile to 3.0% in the most, P < 0.001 for trend) and HCV RNA positivity (from 4.7% to 1.2%, P < 0.001 for trend). Persistent differences in positivity rates by these social determinants were observed over time. HCV antibody and RNA positivity rates were nearly identical in predominantly Black non-Hispanic, Hispanic, and White non-Hispanic zip codes. However, after adjustment for all other factors in the study, residents of predominantly Black non-Hispanic and Hispanic zip codes were significantly less likely to test positive for HCV RNA (adjusted odds ratios [AOR]: 0.51, 95% confidence interval [CI]: 0.51-0.52; AOR: 0.46, 95% CI: 0.46-0.46, respectively). These findings may benefit targeted intervention initiatives by public health agencies.

UniProtExtractR: an app and R package for easily extracting protein-specific UniProtKB information and fine-tuning organelle resolution.

Summary: UniProtKB is a publicly accessible database of annotated protein features for numerous organisms; however, globally extracting protein entry information for data visualization and categorization can be challenging. While the UniProtKB entry syntax maintains database consistency, it simultaneously obscures key terms within long character strings. To increase accessibility, UniProtExtractR is both an app and R package that extracts desired information across nine UniProtKB categories: DNA binding, Pathway, Transmembrane, Signal peptide, Protein families, Domain [FT], Motif, Involvement in disease, and Subcellular location [CC]. The app features interactive frequency tables that globally summarize both the original UniProtKB input query as well as the extracted/changed entry values. Moreover, UniProtExtractR includes a tractable mapping algorithm to define custom organelle-level resolution. UniProtExtractR exists as a freely accessible Shiny app that requires no coding experience as well as R package, the code of which is entirely open source. Availability and implementation: UniProtExtractR source code and user manual, including example files and troubleshooting, is available at https://github.com/alex-bio/UniProtExtractR. The Shiny app is hosted at https://harperlab.connect.hms.harvard.edu/uniprotextractR.

Dual proteome-scale networks reveal cell-specific remodeling of the human interactome.

Thousands of interactions assemble proteins into modules that impart spatial and functional organization to the cellular proteome. Through affinity-purification mass spectrometry, we have created two proteome-scale, cell-line-specific interaction networks. The first, BioPlex 3.0, results from affinity purification of 10,128 human proteins-half the proteome-in 293T cells and includes 118,162 interactions among 14,586 proteins. The second results from 5,522 immunoprecipitations in HCT116 cells. These networks model the interactome whose structure encodes protein function, localization, and complex membership. Comparison across cell lines validates thousands of interactions and reveals extensive customization. Whereas shared interactions reside in core complexes and involve essential proteins, cell-specific interactions link these complexes, "rewiring" subnetworks within each cell's interactome. Interactions covary among proteins of shared function as the proteome remodels to produce each cell's phenotype. Viewable interactively online through BioPlexExplorer, these networks define principles of proteome organization and enable unknown protein characterization.