G protein-coupled receptors that influence lifespan of human and animal models

Harnessing Genetics to Extend Lifespan and Healthspan: Current Progress and Future Directions

Aging is inevitable, but the lifespan (duration of life) and healthspan (healthy aging) vary greatly among individuals and across species. Unlocking the secrets behind these differences has captivated scientific curiosity for ages. This review presents relevant recent advances in genetics and cell biology that are shedding new light by untangling how subtle changes in conserved genes, pathways, and epigenetic factors influence organismal senescence and associated declines. Biogerontology is a complex and rapidly growing field aimed at elucidating genetic modifications that extend lifespan and healthspan. This review explores gerontogenes, genes influencing lifespan and healthspan across species. Though subtle differences exist, long-lived individuals such as centenarians demonstrate extended healthspans, and numerous studies confirm the heritability of longevity/healthspan genes. Importantly, genes and gerontogenes are directly and indirectly involved in DNA repair, insulin/IGF-1 and mTOR signaling pathways, long non-coding RNAs, sirtuins, and heat shock proteins. The complex interactions between genetics and epigenetics are teased apart. While more research into optimizing healthspan is needed, conserved gerontogenes offer synergistic potential to forestall aging and age-related diseases. Understanding complex longevity genetics brings closer the goal of extending not only lifespan but quality years of life. The primary aim of human Biogerontology is to enhance lifespan and healthspan, but the question remains: are current genetic modifications effectively promoting healthy aging? This article collates the advancements in gerontogenes that enhance lifespan and improve healthspan alongside their potential challenges.

High-content phenotypic analysis of a C. elegans recombinant inbred population identifies genetic and molecular regulators of lifespan

Lifespan is influenced by complex interactions between genetic and environmental factors. Studying those factors in model organisms of a single genetic background limits their translational value for humans. Here, we mapped lifespan determinants in 85 genetically diverse C. elegans recombinant intercross advanced inbred lines (RIAILs). We assessed molecular profiles - transcriptome, proteome, and lipidome - and life-history traits, including lifespan, development, growth dynamics, and reproduction. RIAILs exhibited large variations in lifespan, which positively correlated with developmental time. Among the top candidates obtained from multi-omics data integration and QTL mapping, we validated known and novel longevity modulators, including rict-1, gfm-1 and mltn-1. We translated their relevance to humans using UK Biobank data and showed that variants in RICTOR and GFM1 are associated with an elevated risk of age-related heart disease, dementia, diabetes, kidney, and liver diseases. We organized our dataset as a resource (https://lisp-lms.shinyapps.io/RIAILs/) that allows interactive explorations for new longevity targets.

What matters in aging is signaling for responsiveness

Biological responsiveness refers to the capacity of living organisms to adapt to changes in both their internal and external environments through physiological and behavioral mechanisms. One of the prominent aspects of aging is the decline in this responsiveness, which can lead to a deterioration in the processes required for maintenance, survival, and growth. The vital link between physiological responsiveness and the essential life processes lies within the signaling systems. To devise effective strategies for controlling the aging process, a comprehensive reevaluation of this connecting loop is imperative. This review aims to explore the impact of aging on signaling systems responsible for responsiveness and introduce a novel perspective on intervening in the aging process by restoring the compromised responsiveness. These innovative mechanistic approaches for modulating altered responsiveness hold the potential to illuminate the development of action plans aimed at controlling the aging process and treating age-related disorders.

The million-molecule challenge: a moonshot project to rapidly advance longevity intervention discovery

Targeting aging is the future of twenty-first century preventative medicine. Small molecule interventions that promote healthy longevity are known, but few are well-developed and discovery of novel, robust interventions has stagnated. To accelerate longevity intervention discovery and development, high-throughput systems are needed that can perform unbiased drug screening and directly measure lifespan and healthspan metrics in whole animals. C. elegans is a powerful model system for this type of drug discovery. Combined with automated data capture and analysis technologies, truly high-throughput longevity drug discovery is possible. In this perspective, we propose the "million-molecule challenge", an effort to quantitatively assess 1,000,000 interventions for longevity within five years. The WormBot-AI, our best-in-class robotics and AI data analysis platform, provides a tool to achieve the million-molecule challenge for pennies per animal tested.

Heterotrimeric G Protein-Mediated Signaling Is Involved in Stress-Mediated Growth Inhibition in Arabidopsis thaliana

Heterotrimeric G protein-mediated signaling plays a vital role in physiological and developmental processes in eukaryotes. On the other hand, because of the absence of a G protein-coupled receptor and self-activating mechanism of the Gα subunit, plants appear to have different regulatory mechanisms, which remain to be elucidated, compared to canonical G protein signaling established in animals. Here we report that Arabidopsis heterotrimeric G protein subunits, such as Gα (GPA1) and Gβ (AGB1), regulate plant growth under stress conditions through the analysis of heterotrimeric G protein mutants. Flg22-mediated growth inhibition in wild-type roots was found to be caused by a defect in the elongation zone, which was partially blocked in agb1-2 but not gpa1-4. These results suggest that AGB1 may negatively regulate plant growth under biotic stress conditions. In addition, GPA1 and AGB1 exhibited genetically opposite effects on FCA-mediated growth inhibition under heat stress conditions. Therefore, these results suggest that plant G protein signaling is probably related to stress-mediated growth regulation for developmental plasticity in response to biotic and abiotic stress conditions.

Correlated evolution of social organization and lifespan in mammals

Discerning the relationship between sociality and longevity would permit a deeper understanding of how animal life history evolved. Here, we perform a phylogenetic comparative analysis of ~1000 mammalian species on three states of social organization (solitary, pair-living, and group-living) and longevity. We show that group-living species generally live longer than solitary species, and that the transition rate from a short-lived state to a long-lived state is higher in group-living than non-group-living species, altogether supporting the correlated evolution of social organization and longevity. The comparative brain transcriptomes of 94 mammalian species identify 31 genes, hormones and immunity-related pathways broadly involved in the association between social organization and longevity. Further selection features reveal twenty overlapping pathways under selection for both social organization and longevity. These results underscore a molecular basis for the influence of the social organization on longevity.

SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19

Background and Objective

The global population has been heavily impacted by the COVID-19 pandemic of coronavirus. Infections are spreading quickly around the world, and new spikes (Delta, Delta Plus, and Omicron) are still being made. The real-time reverse transcription-polymerase chain reaction (RT-PCR) is the method most often used to find viral RNA in a nasopharyngeal swab. However, these diagnostic approaches require human involvement and consume more time per prediction. Moreover, the existing conventional test mainly suffers from false negatives, so there is a chance for the virus to spread quickly. Therefore, a rapid and early diagnosis of COVID-19 patients is needed to overcome these problems.

Methods

Existing approaches based on deep learning for COVID detection are suffering from unbalanced datasets, poor performance, and gradient vanishing problems. A customized skip connection-based network with a feature union approach has been developed in this work to overcome some of the issues mentioned above. Gradient information from chest X-ray (CXR) images to subsequent layers is bypassed through skip connections. In the script's title, "SCovNet" refers to a skip-connection-based feature union network for detecting COVID-19 in a short notation. The performance of the proposed model was tested with two publicly available CXR image databases, including balanced and unbalanced datasets.

Results

A modified skip connection-based CNN model was suggested for a small unbalanced dataset (Kaggle) and achieved remarkable performance. In addition, the proposed model was also tested with a large GitHub database of CXR images and obtained an overall best accuracy of 98.67% with an impressive low false-negative rate of 0.0074.

Conclusions

The results of the experiments show that the proposed method works better than current methods at finding early signs of COVID-19. As an additional point of interest, we must mention the innovative hierarchical classification strategy provided for this work, which considered both balanced and unbalanced datasets to get the best COVID-19 identification rate.

Regular Late Bedtime Significantly Affects the Skin Physiological Characteristics and Skin Bacterial Microbiome

Background

Late bedtime is a common form of unhealthy sleep pattern in adulthood, which influences circadian rhythm, and negatively affects health. However, little is known about the effect of regular late bedtime on skin characteristics, particularly on skin microbiome.

Objective

To investigate the changes and effects of the regular late bedtime on skin physiological parameters and facial bacterial microbiome of 219 cases of Chinese women aged 18-38 years living in Shanghai.

Methods

Based on the Self-Evaluation Questionnaire, bedtime was categorized as 11:00 PM; thus, the volunteers were divided into early bedtime group (S0) and late bedtime group (S1). The physiological parameters of facial skin were measured by non-invasive instrumental methods, and the skin microbiome was analyzed by 16S rRNA high-throughput sequencing.

Results

The skin physiological parameters of the late bedtime group exhibited significant decrease in skin hydration content, skin firmness (F4) and elasticity (R2), while TEWL, sebum and wrinkle significantly increased. The result indicated that late bedtime significantly impaired the integrity of skin barrier, damaged skin structure, and disrupted water-oil balance. Furthermore, the analysis of α-diversity, Sobs, Ace and Chao index were found to significantly decrease (P < 0.05) in the late bedtime group, suggesting that late bedtime reduced both the abundance and the diversity of facial bacterial microbiota. Moreover, the abundance of Pseudomonas increased significantly, while Streptococcus, Stenotrophomonas, Acinetobacter, Haemophilus, Actinomyces and Neisseria decreased significantly. In addition, Spearman correlation analysis revealed strong correlations between the microbiota and the physiological parameters. Notably, the abundance of Pseudomonas significantly positively correlated with skin firmness and elasticity, but significantly negatively correlated with skin hemoglobin content, melanin content and skin hydration.

Conclusion

Bedtime is an important factor in maintaining skin health. Regular late bedtime not only damages the skin barrier and skin structure but also reduces the diversity and composition of facial bacterial microbiome.

SIRT7 in the aging process

Aging is the result of the accumulation of a wide variety of molecular and cellular damage over time. This has been associated with a number of features termed hallmarks of aging, including genomic instability, loss of proteostasis, telomere attrition, dysregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and impaired intercellular communication. On the other hand, sirtuins are enzymes with an important role in aging and life extension, of which humans have seven paralogs (SIRT1 to SIRT7). SIRT7 is the least studied sirtuin to date, but it has been reported to serve important functions, such as promoting ribosomal RNA expression, aiding in DNA damage repair, and regulating chromatin compaction. Several studies have established a close relationship between SIRT7 and age-related processes, but knowledge in this area is still scarce. Therefore, the purpose of this review was to analyze how SIRT7 is associated with each of the hallmarks of aging, as well as with some of age-associated diseases, such as cardiovascular diseases, obesity, osteoporosis, and cancer.

Role of the Synergistic Interactions of Environmental Pollutants in the Development of Cancer

There is a growing awareness that the large number of environmental pollutants we are exposed to on a daily basis are causing major health problems. Compared to traditional studies that focus on individual pollutants, there are relatively few studies on how pollutants mixtures interact. Several studies have reported a relationship between environmental pollutants and the development of cancer, even when pollutant levels are below toxicity reference values. The possibility of synergistic interactions between different pollutants could explain how even low concentrations can cause major health problems. These intricate that molecular interactions can occur through a wide variety of mechanisms, and our understanding of the physiological effects of mixtures is still limited. The purpose of this paper is to discuss recent reports that address possible synergistic interactions between different types of environmental pollutants that could promote cancer development. Our literature studies suggest that key biological pathways are frequently implicated in such processes. These include increased production of reactive oxygen species, activation by cytochrome P450, and aryl hydrocarbon receptor signaling, among others. We discuss the need to understand individual pathological vulnerability not only in relation to basic genetics and gene expression, but also in terms of measurable exposure to contaminants. We also mention the need for significant improvements in future studies using a multitude of disciplines, such as the development of high-throughput study models, better tools for quantifying pollutants in cancer patients, innovative pharmacological and toxicological studies, and high-efficiency computer analysis, which allow us to analyze the molecular mechanisms of mixtures.