Modelling biological age based on plasma peptides in Han Chinese adults

The schizophrenia risk gene C4 induces pathological synaptic loss by impairing AMPAR trafficking

Neuroimmune interactions play a significant role in regulating synaptic plasticity in both the healthy and diseased brain. The complement pathway, an extracellular proteolytic cascade, exemplifies these interactions. Its activation triggers microglia-dependent synaptic elimination via the complement receptor 3 (CR3). Current models of pathological complement activity in the brain propose that accelerated synaptic loss resulting from overexpression of C4 (C4-OE), a gene associated with schizophrenia, follows this pathway. Here, we report that C4-mediated cortical hypoconnectivity is CR3-independent. Instead, C4-OE triggers impaired GluR1 trafficking through an intracellular mechanism involving the endosomal protein SNX27, resulting in pathological synaptic loss. Moreover, C4 circuit alterations in the prefrontal cortex, a brain region associated with neuropsychiatric disorders, were rescued by increasing neuronal levels of SNX27, which we identify as an interacting partner of this neuroimmune protein. Our results link excessive complement activity to an intracellular endo-lysosomal trafficking pathway altering synaptic plasticity.

Cross-platform proteomics signatures of extreme old age

In previous work, we used a SomaLogic platform targeting approximately 5000 proteins to generate a serum protein signature of centenarians that we validated in independent studies that used the same technology. We set here to validate and possibly expand the results by profiling the serum proteome of a subset of individuals included in the original study using liquid chromatography tandem mass spectrometry (LC-MS/MS). Following pre-processing, the LC-MS/MS data provided quantification of 398 proteins, with only 266 proteins shared by both platforms. At 1% FDR statistical significance threshold, the analysis of LC-MS/MS data detected 44 proteins associated with extreme old age, including 23 of the original analysis. To identify proteins for which associations between expression and extreme-old age were conserved across platforms, we performed inter-study conservation testing of the 266 proteins quantified by both platforms using a method that accounts for the correlation between the results. From these tests, a total of 80 proteins reached 5% FDR statistical significance, and 26 of these proteins had concordant pattern of gene expression in whole blood generated in an independent set. This signature of 80 proteins points to blood coagulation, IGF signaling, extracellular matrix (ECM) organization, and complement cascade as important pathways whose protein level changes provide evidence for age-related adjustments that distinguish centenarians from younger individuals. The comparison with blood transcriptomics also highlights a possible role for neutrophil degranulation in aging.

Tissue fibroblasts are versatile immune regulators: An evaluation of their impact on the aging process

Fibroblasts are abundant stromal cells which not only control the integrity of extracellular matrix (ECM) but also act as immune regulators. It is known that the structural cells within tissues can establish an organ-specific immunity expressing many immune-related genes and closely interact with immune cells. In fact, fibroblasts can modify their immune properties to display both pro-inflammatory and immunosuppressive activities in a context-dependent manner. After acute insults, fibroblasts promote tissue inflammation although they concurrently recruit immunosuppressive cells to enhance the resolution of inflammation. In chronic pathological states, tissue fibroblasts, especially senescent fibroblasts, can display many pro-inflammatory and immunosuppressive properties and stimulate the activities of different immunosuppressive cells. In return, immunosuppressive cells, such as M2 macrophages and myeloid-derived suppressor cells (MDSC), evoke an excessive conversion of fibroblasts into myofibroblasts, thus aggravating the severity of tissue fibrosis. Single-cell transcriptome studies on fibroblasts isolated from aged tissues have confirmed that tissue fibroblasts express many genes coding for cytokines, chemokines, and complement factors, whereas they lose some fibrogenic properties. The versatile immune properties of fibroblasts and their close cooperation with immune cells indicate that tissue fibroblasts have a crucial role in the aging process and age-related diseases.

Cross-platform proteomics signatures of extreme old age

In previous work we used a Somalogic platform targeting approximately 5000 proteins to generate a serum protein signature of centenarians that we validated in independent studies that used the same technology. We set here to validate and possibly expand the results by profiling the serum proteome of a subset of individuals included in the original study using liquid chromatography tandem mass spectrometry (LC-MS/MS). Following pre-processing, the LC-MS/MS data provided quantification of 398 proteins, with only 266 proteins shared by both platforms. At 1% FDR statistical significance threshold, the analysis of LC-MS/MS data detected 44 proteins associated with extreme old age, including 23 of the original analysis. To identify proteins for which associations between expression and extreme-old age were conserved across platforms, we performed inter-study conservation testing of the 266 proteins quantified by both platforms using a method that accounts for the correlation between the results. From these tests, a total of 80 proteins reached 5% FDR statistical significance, and 26 of these proteins had concordant pattern of gene expression in whole blood. This signature of 80 proteins points to blood coagulation, IGF signaling, extracellular matrix (ECM) organization, and complement cascade as important pathways whose protein level changes provide evidence for age-related adjustments that distinguish centenarians from younger individuals.

Biomarkers selection and mathematical modeling in biological age estimation

Biological age (BA) is important for clinical monitoring and preventing aging-related disorders and disabilities. Clinical and/or cellular biomarkers are measured and integrated in years using mathematical models to display an individual's BA. To date, there is not yet a single or set of biomarker(s) and technique(s) that is validated as providing the BA that reflects the best real aging status of individuals. Herein, a comprehensive overview of aging biomarkers is provided and the potential of genetic variations as proxy indicators of the aging state is highlighted. A comprehensive overview of BA estimation methods is also provided as well as a discussion of their performances, advantages, limitations, and potential approaches to overcome these limitations.

The Complement System, Aging, and Aging-Related Diseases

The complement system is a part of the immune system and consists of multiple complement components with biological functions such as defense against pathogens and immunomodulation. The complement system has three activation pathways: the classical pathway, the lectin pathway, and the alternative pathway. Increasing evidence indicates that the complement system plays a role in aging. Complement plays a role in inflammatory processes, metabolism, apoptosis, mitochondrial function, and Wnt signaling pathways. In addition, the complement system plays a significant role in aging-related diseases, including Alzheimer’s disease, age-related macular degeneration, and osteoarthritis. However, the effect of complement on aging and aging-related diseases is still unclear. Thus, a better understanding of the potential relationship between complement, aging, and aging-related diseases will provide molecular targets for treating aging, while focusing on the balance of complement in during treatment. Inhibition of a single component does not result in a good outcome. In this review, we discussed the research progress and effects of complement in aging and aging-related diseases.

A Machine Learning-Based Aging Measure Among Middle-Aged and Older Chinese Adults: The China Health and Retirement Longitudinal Study

Objective: Biological age (BA) has been accepted as a more accurate proxy of aging than chronological age (CA). This study aimed to use machine learning (ML) algorithms to estimate BA in the Chinese population. Materials and methods: We used data from 9,771 middle-aged and older Chinese adults (≥45 years) in the 2011/2012 wave of the China Health and Retirement Longitudinal Study and followed until 2018. We used several ML algorithms (e.g., Gradient Boosting Regressor, Random Forest, CatBoost Regressor, and Support Vector Machine) to develop new measures of biological aging (ML-BAs) based on physiological biomarkers. R-squared value and mean absolute error (MAE) were used to determine the optimal performance of these ML-BAs. We used logistic regression models to examine the associations of the best ML-BA and a conventional aging measure-Klemera and Doubal method-BA (KDM-BA) we previously developed-with physical disability and mortality, respectively. Results: The Gradient Boosting Regression model performed the best, resulting in an ML-BA with an R-squared value of 0.270 and an MAE of 6.519. This ML-BA was significantly associated with disability in basic activities of daily living, instrumental activities of daily living, lower extremity mobility, and upper extremity mobility, and mortality, with odds ratios ranging from 1 to 7% (per 1-year increment in ML-BA, all P < 0.001), independent of CA. These associations were generally comparable to that of KDM-BA. Conclusion: This study provides a valid ML-based measure of biological aging for middle-aged and older Chinese adults. These findings support the application of ML in geroscience research and may help facilitate preventive and geroprotector intervention studies.

Urinary Proteomics for Noninvasive Prenatal Screening of Trisomy 21: New Biomarker Candidates

Trisomy 21 is a common birth defect in humans. Screening for trisomy 21 is one of the most important tasks in routine prenatal care and robust noninvasive diagnostics are needed in clinical practice. Urinary proteomics offers a new research platform for diagnostics innovation in this context. We report here new biomarker candidates using urinary proteomics profiling. Specifically, we used liquid chromatography-tandem mass spectrometry (LC-MS/MS) to analyze the proteomes of urine samples from 19 pregnant women (aged 28-44 years) carrying fetuses with trisomy 21 and 22 healthy pregnant women (aged 27-42 years) carrying fetuses with normal karyotype. We identified more than 50 differentially expressed proteins between the trisomy 21 group and healthy group, and most of these proteins were associated with the embryonic development. Importantly, tissue inhibitor of metalloproteinases 2 (TIMP2) and lysosomal-associated membrane protein 2 (LAMP2) were further selected as potential urinary protein biomarkers. We found that the combination of TIMP2 and LAMP2 could differentiate fetuses with trisomy 21 from healthy controls with a sensitivity of 74%, a specificity of 82%, and an area under the receiver operating characteristic curves (AUC) value of 0.82 (95% confidence interval, 0.69-0.95). We conclude that TIMP2 and LAMP2 offer promise as biomarker candidates and warrant further clinical research in larger study samples. These findings further our understanding of the pathological processes involved in fetal trisomy 21 and are poised to accelerate the development of new noninvasive potential biomarkers for trisomy 21 prenatal screening.

Mendelian randomization integrating GWAS and eQTL data revealed genes pleiotropically associated with major depressive disorder

Previous genome-wide association studies (GWAS) have identified potential genetic variants associated with the risk of major depressive disorder (MDD), but the underlying biological interpretation remains largely unknown. We aimed to prioritize genes that were pleiotropically or potentially causally associated with MDD. We applied the summary data-based Mendelian randomization (SMR) method integrating GWAS and gene expression quantitative trait loci (eQTL) data in 13 brain regions to identify genes that were pleiotropically associated with MDD. In addition, we repeated the analysis by using the meta-analyzed version of the eQTL summary data in the brain (brain-eMeta). We identified multiple significant genes across different brain regions that may be involved in the pathogenesis of MDD. The prime-specific gene BTN3A2 (corresponding probe: ENSG00000186470.9) was the top hit showing pleiotropic association with MDD in 9 of the 13 brain regions and in brain-eMeta, after correction for multiple testing. Many of the identified genes are located in the human major histocompatibility complex (MHC) region on chromosome 6 and are mainly involved in the immune response. Our SMR analysis indicated that multiple genes showed pleiotropic association with MDD across the brain regions. These findings provided important leads to a better understanding of the mechanism of MDD and revealed potential therapeutic targets for the prevention and effective treatment of MDD.