Global analysis of aging-related protein structural changes uncovers enzyme-polymerization-based control of longevity

Evaluating forest depletion and structural change effects on environmental sustainability in Pakistan: Through the lens of the load capacity factor

The pace of species extinction and deforestation has increased dramatically due to the substantial increase in global environmental degradation. This trend is approaching the crucial temperature threshold of 2 °C and calls for more attention. Although previous research has observed the individual impacts of forest depletion, structural change, economic growth, and urbanization on various sustainability outcomes, there has been no previous research into their interrelationships with an emphasis on the load capacity factor (LCF). Furthermore, no previous study has examined the environmental impacts of the abovementioned variables by contrasting the results of LCF and CO2 emissions in Pakistan. Therefore, this research suggests a theoretical framework that integrates these concepts, provides a roadmap for an effective and sustainable mitigation strategy for Pakistan and compares LCF results with CO2 emissions. Using the time-series data from 1970 to 2021, a unique and sophisticated dynamic Autoregressive Distributed Lag (DARDL) technique, the authors found that (i) a 1 % rise in forest depletion leads to a decline in load capacity factor by 0.026 %. (ii) A one per cent upsurge in structural change fosters environmental sustainability by raising the load capacity factor by 0.084 %. (iii) An increase of 1 % in economic growth dwindles the load capacity factor by 0.027 %. (iv) A one per cent surge in urbanization enhances the load capacity factor by 0.029 %. The findings suggest that Pakistan's Government should promote afforestation by emphasizing the constructive role of structural change in achieving environmental sustainability.

Replicative aging in yeast involves dynamic intron retention patterns associated with mRNA processing/export and protein ubiquitination

Saccharomyces cerevisiae (baker's yeast) has yielded relevant insights into some of the basic mechanisms of organismal aging. Among these are genomic instability, oxidative stress, caloric restriction and mitochondrial dysfunction. Several genes are known to have an impact on the aging process, with corresponding mutants exhibiting short- or long-lived phenotypes. Research dedicated to unraveling the underlying cellular mechanisms can support the identification of conserved mechanisms of aging in other species. One of the hitherto less studied fields in yeast aging is how the organism regulates its gene expression at the transcriptional level. To our knowledge, we present the first investigation into alternative splicing, particularly intron retention, during replicative aging of S. cerevisiae. This was achieved by utilizing the IRFinder algorithm on a previously published RNA-seq data set by Janssens et al. (2015). In the present work, 44 differentially retained introns in 43 genes were identified during replicative aging. We found that genes with altered intron retention do not display significant changes in overall transcript levels. It was possible to functionally assign distinct groups of these genes to the cellular processes of mRNA processing and export (e.g., YRA1) in early and middle-aged yeast, and protein ubiquitination (e.g., UBC5) in older cells. In summary, our work uncovers a previously unexplored layer of the transcriptional program of yeast aging and, more generally, expands the knowledge on the occurrence of alternative splicing in baker's yeast.

FLiPPR: A Processor for Limited Proteolysis (LiP) Mass Spectrometry Datasets Built on FragPipe

Here, we present FLiPPR, or FragPipe LiP (limited proteolysis) Processor, a tool that facilitates the analysis of data from limited proteolysis mass spectrometry (LiP-MS) experiments following primary search and quantification in FragPipe. LiP-MS has emerged as a method that can provide proteome-wide information on protein structure and has been applied to a range of biological and biophysical questions. Although LiP-MS can be carried out with standard laboratory reagents and mass spectrometers, analyzing the data can be slow and poses unique challenges compared to typical quantitative proteomics workflows. To address this, we leverage the fast, sensitive, and accurate search and label-free quantification algorithms in FragPipe and then process its output in FLiPPR. FLiPPR formalizes a specific data imputation heuristic that carefully uses missing data in LiP-MS experiments to report on the most significant structural changes. Moreover, FLiPPR introduces a new data merging scheme (from ions to cut-sites) and a protein-centric multiple hypothesis correction scheme, collectively enabling processed LiP-MS datasets to be more robust and less redundant. These improvements substantially strengthen statistical trends when previously published data are reanalyzed with the FragPipe/FLiPPR workflow. As a final feature, FLiPPR facilitates the collection of structural metadata to identify correlations between experiments and structural features. We hope that FLiPPR will lower the barrier for more users to adopt LiP-MS, standardize statistical procedures for LiP-MS data analysis, and systematize output to facilitate eventual larger-scale integration of LiP-MS data.

Agglomeration: when folded proteins clump together

Protein self-association is a widespread phenomenon that results in the formation of multimeric protein structures with critical roles in cellular processes. Protein self-association can lead to finite protein complexes or open-ended, and potentially, infinite structures. This review explores the concept of protein agglomeration, a process that results from the infinite self-assembly of folded proteins. We highlight its differences from other better-described processes with similar macroscopic features, such as aggregation and liquid-liquid phase separation. We review the sequence, structural, and biophysical factors influencing protein agglomeration. Lastly, we briefly discuss the implications of agglomeration in evolution, disease, and aging. Overall, this review highlights the need to study protein agglomeration for a better understanding of cellular processes.