Proteomic assessment of serum biomarkers of longevity in older men

Development and characterization of a first-in-class adjustable-dose gene therapy system

INTRODUCTION

Despite significant potential, gene therapy has been relegated to the treatment of rare diseases, due in part to an inability to adjust dosage following initial administration. Other significant constraints include cost, specificity, antigenicity, and systemic toxicity of current generation technologies. To overcome these challenges, we developed a first-in-class adjustable-dose gene therapy system, with optimized biocompatibility, localization, durability, and cost.

METHODS

A lipid nanoparticle (LNP) delivery system was developed and characterized by dynamic light scattering for size, zeta potential, and polydispersity. Cytocompatibility and transfection efficiency were optimized in vitro using primary human adipocytes and preadipocytes. Durability, immunogenicity, and adjustment of expression were evaluated in C57BL/6 and B6 albino mice using in vivo bioluminescence imaging. Biodistribution was assessed by qPCR and immunohistochemistry; therapeutic protein expression was quantified by ELISA.

RESULTS

Following LNP optimization, in vitro transfection efficiency of primary human adipocytes reached 81.3 % ± 8.3 % without compromising cytocompatibility. Critical physico-chemical properties of the system (size, zeta potential, polydispersity) remained stable over a broad range of genetic cassette sizes (1,871-6,203 bp). Durable expression was observed in vivo over 6 months, localizing to subcutaneous adipose tissues at the injection site with no detectable transgene in the liver, heart, spleen, or kidney. Gene expression was adjustable using several physical and pharmacological approaches, including cryolipolysis, focused ultrasound, and pharmacologically inducible apoptosis. The ability of transfected adipocytes to express therapeutic transgenes ranging from peptides to antibodies, at potentially clinically relevant levels, was confirmed in vitro and in vivo.

CONCLUSION

We report the development of a novel, low-cost therapeutic platform, designed to enable the replacement of subcutaneously administered protein treatments with a single-injection, adjustable-dose gene therapy.

Plasma proteomic signature of human longevity

The identification of protein targets that exhibit anti-aging clinical potential could inform interventions to lengthen the human health span. Most previous proteomics research has been focused on chronological age instead of longevity. We leveraged two large population-based prospective cohorts with long follow-ups to evaluate the proteomic signature of longevity defined by survival to 90 years of age. Plasma proteomics was measured using a SOMAscan assay in 3067 participants from the Cardiovascular Health Study (discovery cohort) and 4690 participants from the Age Gene/Environment Susceptibility-Reykjavik Study (replication cohort). Logistic regression identified 211 significant proteins in the CHS cohort using a Bonferroni-adjusted threshold, of which 168 were available in the replication cohort and 105 were replicated (corrected p value <0.05). The most significant proteins were GDF-15 and N-terminal pro-BNP in both cohorts. A parsimonious protein-based prediction model was built using 33 proteins selected by LASSO with 10-fold cross-validation and validated using 27 available proteins in the validation cohort. This protein model outperformed a basic model using traditional factors (demographics, height, weight, and smoking) by improving the AUC from 0.658 to 0.748 in the discovery cohort and from 0.755 to 0.802 in the validation cohort. We also found that the associations of 169 out of 211 proteins were partially mediated by physical and/or cognitive function. These findings could contribute to the identification of biomarkers and pathways of aging and potential therapeutic targets to delay aging and age-related diseases.

Proteomic changes induced by longevity-promoting interventions in mice

Using mouse models and high-throughput proteomics, we conducted an in-depth analysis of the proteome changes induced in response to seven interventions known to increase mouse lifespan. This included two genetic mutations, a growth hormone receptor knockout (GHRKO mice) and a mutation in the Pit-1 locus (Snell dwarf mice), four drug treatments (rapamycin, acarbose, canagliflozin, and 17α-estradiol), and caloric restriction. Each of the interventions studied induced variable changes in the concentrations of proteins across liver, kidney, and gastrocnemius muscle tissue samples, with the strongest responses in the liver and limited concordance in protein responses across tissues. To the extent that these interventions promote longevity through common biological mechanisms, we anticipated that proteins associated with longevity could be identified by characterizing shared responses across all or multiple interventions. Many of the proteome alterations induced by each intervention were distinct, potentially implicating a variety of biological pathways as being related to lifespan extension. While we found no protein that was affected similarly by every intervention, we identified a set of proteins that responded to multiple interventions. These proteins were functionally diverse but tended to be involved in peroxisomal oxidation and metabolism of fatty acids. These results provide candidate proteins and biological mechanisms related to enhancing longevity that can inform research on therapeutic approaches to promote healthy aging.

HERVK-mediated regulation of neighboring genes: implications for breast cancer prognosis

Human endogenous retroviruses (HERVs) are the remnants of ancient retroviral infections integrated into the human genome. Although most HERVs are silenced or rendered inactive by various regulatory mechanisms, they retain the potential to influence the nearby genes. We analyzed the regulatory map of 91 HERV-Ks on neighboring genes in human breast cancer and investigated the impact of HERV-Ks on the tumor microenvironment (TME) and prognosis of breast cancer. Nine RNA-seq datasets were obtained from GEO and NCBI SRA. Differentially expressed genes and HERV-Ks were analyzed using DESeq2. Validation of high-risk prognostic candidate genes using TCGA data. These included Overall survival (multivariate Cox regression model), immune infiltration analysis (TIMER), tumor mutation burden (maftools), and drug sensitivity analysis (GSCA). A total of 88 candidate genes related to breast cancer prognosis were screened, of which CD48, SLAMF7, SLAMF1, IGLL1, IGHA1, and LRRC8A were key genes. Functionally, these six key genes were significantly enriched in some immune function-related pathways, which may be associated with poor prognosis for breast cancer (p = 0.00016), and the expression levels of these genes were significantly correlated with the sensitivity of breast cancer treatment-related drugs. Mechanistically, they may influence breast cancer development by modulating the infiltration of various immune cells into the TME. We further experimentally validated these genes to confirm the results obtained from bioinformatics analysis. This study represents the first report on the regulatory potential of HERV-K in the neighboring breast cancer genome. We identified three key HERV-Ks and five neighboring genes that hold promise as novel targets for future interventions and treatments for breast cancer.

Population serum proteomics uncovers a prognostic protein classifier for metabolic syndrome

Metabolic syndrome (MetS) is a complex metabolic disorder with a global prevalence of 20%-25%. Early identification and intervention would help minimize the global burden on healthcare systems. Here, we measured over 400 proteins from ∼20,000 proteomes using data-independent acquisition mass spectrometry for 7,890 serum samples from a longitudinal cohort of 3,840 participants with two follow-up time points over 10 years. We then built a machine-learning model for predicting the risk of developing MetS within 10 years. Our model, composed of 11 proteins and the age of the individuals, achieved an area under the curve of 0.774 in the validation cohort (n = 242). Using linear mixed models, we found that apolipoproteins, immune-related proteins, and coagulation-related proteins best correlated with MetS development. This population-scale proteomics study broadens our understanding of MetS and may guide the development of prevention and targeted therapies for MetS.

Novel pyroptosis-associated genes signature for predicting the prognosis of sarcoma and validation

BACKGROUND

Sarcoma is a rare mesenchymal malignant tumor. Recently, pyroptosis has been reported to be a mode of programmed cell death. Nonetheless, levels of pyroptosis-associated genes in sarcoma and its relevance to prognostic outcomes are yet to be elucidated.

RESULTS

Sarcoma cases were classified into two subtypes with regards to differentially expressed genes. We established a profile composed of seven genes and classified the sarcoma patients into low- and high-risk groups through least absolute shrinkage and selection operator Cox regression. Survival rate of low-risk sarcoma patients was markedly higher, relative to high-risk group (P<0.001). In combination with clinical features, the risk score was established to be an independent predictive factor for OS of sarcoma patients. Chemotherapeutic drug sensitivity response analysis found 65 drugs with higher drug sensitivity in low-risk, than in high-risk group and 14 drugs with higher drug sensitivity in the high-risk patient group, compared with low-risk patient group. In addition, functional enrichment, pathway and gene mutation of the two modules were analyzed. Finally, we used qRT-PCR to detect the expression of seven pyroptosis-related genes in tumor cells, and human skeletal muscle cells, compared with human skeletal muscle cells, PODXL2, LRRC17, GABRA3, SCUBE3 and RFLNB genes show high expression levels in tumor cells, while IGHG2 and hepatic leukemia factor show low expression levels in tumor cells.

CONCLUSIONS

Our research suggest that pyroptosis is closely associated with sarcoma, and these findings confirm that pyroptosis-associated seven genes have a critical role in sarcoma and are potential prognostic factors for sarcoma.

The Potential of Metabolomics in Biomedical Applications

The metabolome offers a dynamic, comprehensive, and precise picture of the phenotype. Current high-throughput technologies have allowed the discovery of relevant metabolites that characterize a wide variety of human phenotypes with respect to health, disease, drug monitoring, and even aging. Metabolomics, parallel to genomics, has led to the discovery of biomarkers and has aided in the understanding of a diversity of molecular mechanisms, highlighting its application in precision medicine. This review focuses on the metabolomics that can be applied to improve human health, as well as its trends and impacts in metabolic and neurodegenerative diseases, cancer, longevity, the exposome, liquid biopsy development, and pharmacometabolomics. The identification of distinct metabolomic profiles will help in the discovery and improvement of clinical strategies to treat human disease. In the years to come, metabolomics will become a tool routinely applied to diagnose and monitor health and disease, aging, or drug development. Biomedical applications of metabolomics can already be foreseen to monitor the progression of metabolic diseases, such as obesity and diabetes, using branched-chain amino acids, acylcarnitines, certain phospholipids, and genomics; these can assess disease severity and predict a potential treatment. Future endeavors should focus on determining the applicability and clinical utility of metabolomic-derived markers and their appropriate implementation in large-scale clinical settings.

In vivo Pooled Screening: A Scalable Tool to Study the Complexity of Aging and Age-Related Disease

Biological aging, and the diseases of aging, occur in a complex in vivo environment, driven by multiple interacting processes. A convergence of recently developed technologies has enabled in vivo pooled screening: direct administration of a library of different perturbations to a living animal, with a subsequent readout that distinguishes the identity of each perturbation and its effect on individual cells within the animal. Such screens hold promise for efficiently applying functional genomics to aging processes in the full richness of the in vivo setting. In this review, we describe the technologies behind in vivo pooled screening, including a range of options for delivery, perturbation and readout methods, and outline their potential application to aging and age-related disease. We then suggest how in vivo pooled screening, together with emerging innovations in each of its technological underpinnings, could be extended to shed light on key open questions in aging biology, including the mechanisms and limits of epigenetic reprogramming and identifying cellular mediators of systemic signals in aging.

Aging biomarkers and the brain

Quantifying biological aging is critical for understanding why aging is the primary driver of morbidity and mortality and for assessing novel therapies to counter pathological aging. In the past decade, many biomarkers relevant to brain aging have been developed using various data types and modeling techniques. Aging involves numerous interconnected processes, and thus many complementary biomarkers are needed, each capturing a different slice of aging biology. Here we present a hierarchical framework highlighting how these biomarkers are related to each other and the underlying biological processes. We review those measures most studied in the context of brain aging: epigenetic clocks, proteomic clocks, and neuroimaging age predictors. Many studies have linked these biomarkers to cognition, mental health, brain structure, and pathology during aging. We also delve into the challenges and complexities in interpreting these biomarkers and suggest areas for further innovation. Ultimately, a robust mechanistic understanding of these biomarkers will be needed to effectively intervene in the aging process to prevent and treat age-related disease.

A neuronal blood marker is associated with mortality in old age

Neurofilament light chain (NfL) has emerged as a promising blood biomarker for the progression of various neurological diseases. NfL is a structural protein of nerve cells, and elevated NfL levels in blood are thought to mirror damage to the nervous system. We find that plasma NfL levels increase in humans with age (n = 122; 21-107 years of age) and correlate with changes in other plasma proteins linked to neural pathways. In centenarians (n = 135), plasma NfL levels are associated with mortality equally or better than previously described multi-item scales of cognitive or physical functioning, and this observation was replicated in an independent cohort of nonagenarians (n = 180). Plasma NfL levels also increase in aging mice (n = 114; 2-30 months of age), and dietary restriction, a paradigm that extends lifespan in mice, attenuates the age-related increase in plasma NfL levels. These observations suggest a contribution of nervous system functional deterioration to late-life mortality.