Exosomes Released from Senescent Cells and Circulatory Exosomes Isolated from Human Plasma Reveal Aging-associated Proteomic and Lipid Signatures.

Senescence emerged as a significant mechanism of aging and age-related diseases, offering an attractive target for clinical interventions. Senescent cells release a senescence-associated secretory phenotype (SASP), including exosomes that may act as signal transducers between distal tissues, propagating secondary or bystander senescence and signaling throughout the body. However, the composition of exosome SASP remains underexplored, presenting an opportunity for novel unbiased discovery. Here, we present a detailed proteomic and lipidomic analysis of exosome SASP using mass spectrometry from human plasma from young and older individuals and from tissue culture of senescent primary human lung fibroblasts. We identified ~1,300 exosome proteins released by senescent fibroblasts induced by three different senescence inducers causing most exosome proteins to be differentially regulated with senescence. In parallel, a human plasma cohort from young and old individuals revealed over 1,350 exosome proteins and 171 plasma exosome proteins were regulated when comparing old vs young individuals. Of the age-regulated plasma exosome proteins, we observed 52 exosome SASP factors that were also regulated in exosomes from the senescent fibroblasts, including serine protease inhibitors (SERPINs), Prothrombin, Coagulation factor V, Plasminogen, and Reelin. In addition, 247 lipids were identified with high confidence in all exosome samples. Following the senescence inducers, a majority of the identified phosphatidylcholine, phosphatidylethanolamine, and sphingomyelin species increased significantly indicating cellular membrane changes. The most notable categories of significantly changed proteins were related to extracellular matrix remodeling and inflammation, both potentially detrimental pathways that can damage surrounding tissues and even induce secondary or bystander senescence. Our findings reveal mechanistic insights and potential senescence biomarkers, enabling a better approach to surveilling the senescence burden in the aging population and offering promising therapeutic targets for interventions.

Senescent characteristics of human corneal endothelial cells upon ultraviolet-A exposure.

PURPOSE: The objective of this study was to investigate the senescent phenotypes of human corneal endothelial cells (hCEnCs) upon treatment with ultraviolet (UV)-A. METHODS: We assessed cell morphology, senescence-associated ß-galactosidase (SA-ß-gal) activity, cell proliferation and expression of senescence markers (p16 and p21) in hCEnCs exposed to UV-A radiation, and senescent hCEnCs induced by ionizing radiation (IR) were used as positive controls. We performed RNA sequencing and proteomics analyses to compare gene and protein expression profiles between UV-A- and IR-induced senescent hCEnCs, and we also compared the results to non-senescent hCEnCs. RESULTS: Cells exposed to 5 J/cm2 of UV-A or to IR exhibited typical senescent phenotypes, including enlargement, increased SA-ß-gal activity, decreased cell proliferation and elevated expression of p16 and p21. RNA-Seq analysis revealed that 83.9% of the genes significantly upregulated and 82.6% of the genes significantly downregulated in UV-A-induced senescent hCEnCs overlapped with the genes regulated in IR-induced senescent hCEnCs. Proteomics also revealed that 93.8% of the proteins significantly upregulated in UV-A-induced senescent hCEnCs overlapped with those induced by IR. In proteomics analyses, senescent hCEnCs induced by UV-A exhibited elevated expression levels of several factors part of the senescence-associated secretory phenotype. CONCLUSIONS: In this study, where senescence was induced by UV-A, a more physiological stress for hCEnCs compared to IR, we determined that UV-A modulated the expression of many genes and proteins typically altered upon IR treatment, a more conventional method of senescence induction, even though UV-A also modulated specific pathways unrelated to IR.

Senescent Schwann cells induced by aging and chronic denervation impair axonal regeneration following peripheral nerve injury.

Following peripheral nerve injury, successful axonal growth and functional recovery require Schwann cell (SC) reprogramming into a reparative phenotype, a process dependent upon c-Jun transcription factor activation. Unfortunately, axonal regeneration is greatly impaired in aged organisms and following chronic denervation, which can lead to poor clinical outcomes. While diminished c-Jun expression in SCs has been associated with regenerative failure, it is unclear whether the inability to maintain a repair state is associated with the transition into an axonal growth inhibition phenotype. We here find that reparative SCs transition into a senescent phenotype, characterized by diminished c-Jun expression and secretion of inhibitory factors for axonal regeneration in aging and chronic denervation. In both conditions, the elimination of senescent SCs by systemic senolytic drug treatment or genetic targeting improved nerve regeneration and functional recovery, increased c-Jun expression and decreased nerve inflammation. This work provides the first characterization of senescent SCs and their influence on axonal regeneration in aging and chronic denervation, opening new avenues for enhancing regeneration and functional recovery after peripheral nerve injuries.

Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology.

The multitude of multi-omics data generated cost-effectively using advanced high-throughput technologies has imposed challenging domain for research in Artificial Intelligence (AI). Data curation poses a significant challenge as different parameters, instruments, and sample preparations approaches are employed for generating these big data sets. AI could reduce the fuzziness and randomness in data handling and build a platform for the data ecosystem, and thus serve as the primary choice for data mining and big data analysis to make informed decisions. However, AI implication remains intricate for researchers/clinicians lacking specific training in computational tools and informatics. Cancer is a major cause of death worldwide, accounting for an estimated 9.6 million deaths in 2018. Certain cancers, such as pancreatic and gastric cancers, are detected only after they have reached their advanced stages with frequent relapses. Cancer is one of the most complex diseases affecting a range of organs with diverse disease progression mechanisms and the effectors ranging from gene-epigenetics to a wide array of metabolites. Hence a comprehensive study, including genomics, epi-genomics, transcriptomics, proteomics, and metabolomics, along with the medical/mass-spectrometry imaging, patient clinical history, treatments provided, genetics, and disease endemicity, is essential. Cancer Moonshot℠ Research Initiatives by NIH National Cancer Institute aims to collect as much information as possible from different regions of the world and make a cancer data repository. AI could play an immense role in (a) analysis of complex and heterogeneous data sets (multi-omics and/or inter-omics), (b) data integration to provide a holistic disease molecular mechanism, (c) identification of diagnostic and prognostic markers, and (d) monitor patient's response to drugs/treatments and recovery. AI enables precision disease management well beyond the prevalent disease stratification patterns, such as differential expression and supervised classification. This review highlights critical advances and challenges in omics data analysis, dealing with data variability from lab-to-lab, and data integration. We also describe methods used in data mining and AI methods to obtain robust results for precision medicine from "big" data. In the future, AI could be expanded to achieve ground-breaking progress in disease management.

A Proteogenomic Analysis of Haptoglobin in Malaria.

SCOPE: Haptoglobin (Hp), an acute phase inflammatory protein is associated with malaria pathogenesis in several proteomics and genomics studies. The Hp gene has two co-dominant alleles: Hp1 and Hp2 that produce three genotypes: Hp1/Hp1, Hp1/Hp2 and Hp2/Hp2. EXPERIMENTAL DESIGN: In this study, validation of the proteomics data with Multiple Reaction Monitoring Mass Spectroscopy (MRM-MS) is performed and the association of the Hp gene variants with severe, non-severe malaria and community (healthy) controls using genotyping PCRs and DNA sequencing is analysed. RESULTS: Highly significant values of Hp is observed in the MRM assay that show a correlation with severity of malaria and is clearly distinguished from another febrile disease, dengue. Moreover, the Hp2/Hp2 genotype is seen in high percentages in non-severe malaria patients (74%) and community controls (72%) whereas patients diagnosed with severe malaria show only (31%) of this genotype. Sequencing of the Hp promoter region reveals three SNPs along with 10 unique haplotypes, out of which five are associated with non-severe and three with severe malaria populations (χ2  = 130; df = 18; p < 0.0001). CONCLUSION AND CLINICAL RELEVANCE: This proteo-genomic study focuses on the correlation of the Hp protein and gene with malaria, thus highlighting the pivotal role of this acute phase immune gene in malaria pathogenesis.