Geroscience-Centric Perspective for Geriatric Psychiatry: Integrating Aging Biology With Geriatric Mental Health Research.

The geroscience hypothesis asserts that physiological aging is caused by a small number of biological pathways. Despite the explosion of geroscience research over the past couple of decades, the research on how serious mental illnesses (SMI) affects the biological aging processes is still in its infancy. In this review, we aim to provide a critical appraisal of the emerging literature focusing on how we measure biological aging systematically, and in the brain and how SMIs affect biological aging measures in older adults. We will also review recent developments in the field of cellular senescence and potential targets for interventions for SMIs in older adults, based on the geroscience hypothesis.

Electrophilic properties of itaconate and derivatives regulate the IκBζ-ATF3 inflammatory axis.

Metabolic regulation has been recognized as a powerful principle guiding immune responses. Inflammatory macrophages undergo extensive metabolic rewiring 1 marked by the production of substantial amounts of itaconate, which has recently been described as an immunoregulatory metabolite 2 . Itaconate and its membrane-permeable derivative dimethyl itaconate (DI) selectively inhibit a subset of cytokines 2 , including IL-6 and IL-12 but not TNF. The major effects of itaconate on cellular metabolism during macrophage activation have been attributed to the inhibition of succinate dehydrogenase2,3, yet this inhibition alone is not sufficient to account for the pronounced immunoregulatory effects observed in the case of DI. Furthermore, the regulatory pathway responsible for such selective effects of itaconate and DI on the inflammatory program has not been defined. Here we show that itaconate and DI induce electrophilic stress, react with glutathione and subsequently induce both Nrf2 (also known as NFE2L2)-dependent and -independent responses. We find that electrophilic stress can selectively regulate secondary, but not primary, transcriptional responses to toll-like receptor stimulation via inhibition of IκBζ protein induction. The regulation of IκBζ is independent of Nrf2, and we identify ATF3 as its key mediator. The inhibitory effect is conserved across species and cell types, and the in vivo administration of DI can ameliorate IL-17-IκBζ-driven skin pathology in a mouse model of psoriasis, highlighting the therapeutic potential of this regulatory pathway. Our results demonstrate that targeting the DI-IκBζ regulatory axis could be an important new strategy for the treatment of IL-17-IκBζ-mediated autoimmune diseases.

Systems Age: A single blood methylation test to quantify aging heterogeneity across 11 physiological systems.

Individuals, organs, tissues, and cells age in diverse ways throughout the lifespan. Epigenetic clocks attempt to quantify differential aging between individuals, but they typically summarize aging as a single measure, ignoring within-person heterogeneity. Our aim was to develop novel systems-based methylation clocks that, when assessed in blood, capture aging in distinct physiological systems. We combined supervised and unsupervised machine learning methods to link DNA methylation, system-specific clinical chemistry and functional measures, and mortality risk. This yielded a panel of 11 system-specific scores- Heart, Lung, Kidney, Liver, Brain, Immune, Inflammatory, Blood, Musculoskeletal, Hormone, and Metabolic. Each system score predicted a wide variety of outcomes, aging phenotypes, and conditions specific to the respective system, and often did so more strongly than existing epigenetic clocks that report single global measures. We also combined the system scores into a composite Systems Age clock that is predictive of aging across physiological systems in an unbiased manner. Finally, we showed that the system scores clustered individuals into unique aging subtypes that had different patterns of age-related disease and decline. Overall, our biological systems based epigenetic framework captures aging in multiple physiological systems using a single blood draw and assay and may inform the development of more personalized clinical approaches for improving age-related quality of life.

Comprehensive Analysis of Metabolic Isozyme Targets in Cancer.

Metabolic reprogramming is a hallmark of malignant transformation, and loss of isozyme diversity (LID) contributes to this process. Isozymes are distinct proteins that catalyze the same enzymatic reaction but can have different kinetic characteristics, subcellular localization, and tissue specificity. Cancer-dominant isozymes that catalyze rate-limiting reactions in critical metabolic processes represent potential therapeutic targets. Here, we examined the isozyme expression patterns of 1,319 enzymatic reactions in 14 cancer types and their matching normal tissues using The Cancer Genome Atlas mRNA expression data to identify isozymes that become cancer-dominant. Of the reactions analyzed, 357 demonstrated LID in at least one cancer type. Assessment of the expression patterns in over 600 cell lines in the Cancer Cell Line Encyclopedia showed that these reactions reflect cellular changes instead of differences in tissue composition; 50% of the LID-affected isozymes showed cancer-dominant expression in the corresponding cell lines. The functional importance of the cancer-dominant isozymes was assessed in genome-wide CRISPR and RNAi loss-of-function screens: 17% were critical for cell proliferation, indicating their potential as therapeutic targets. Lists of prioritized novel metabolic targets were developed for 14 cancer types; the most broadly shared and functionally validated target was acetyl-CoA carboxylase 1 (ACC1). Small molecule inhibition of ACC reduced breast cancer viability in vitro and suppressed tumor growth in cell line- and patient-derived xenografts in vivo. Evaluation of the effects of drug treatment revealed significant metabolic and transcriptional perturbations. Overall, this systematic analysis of isozyme expression patterns elucidates an important aspect of cancer metabolic plasticity and reveals putative metabolic vulnerabilities. SIGNIFICANCE: This study exploits the loss of metabolic isozyme diversity common in cancer and reveals a rich pool of potential therapeutic targets that will allow the repurposing of existing inhibitors for anticancer therapy. See related commentary by Kehinde and Parker, p. 1695.

Pyruvate Kinase M1 Suppresses Development and Progression of Prostate Adenocarcinoma.

SIGNIFICANCE: Differential expression of PKM1 and PKM2 impacts prostate tumorigenesis and suggests a potential therapeutic vulnerability in prostate cancer.

Multi-Tissue Acceleration of the Mitochondrial Phosphoenolpyruvate Cycle Improves Whole-Body Metabolic Health.

The mitochondrial GTP (mtGTP)-dependent phosphoenolpyruvate (PEP) cycle couples mitochondrial PEPCK (PCK2) to pyruvate kinase (PK) in the liver and pancreatic islets to regulate glucose homeostasis. Here, small molecule PK activators accelerated the PEP cycle to improve islet function, as well as metabolic homeostasis, in preclinical rodent models of diabetes. In contrast, treatment with a PK activator did not improve insulin secretion in pck2-/- mice. Unlike other clinical secretagogues, PK activation enhanced insulin secretion but also had higher insulin content and markers of differentiation. In addition to improving insulin secretion, acute PK activation short-circuited gluconeogenesis to reduce endogenous glucose production while accelerating red blood cell glucose turnover. Four-week delivery of a PK activator in vivo remodeled PK phosphorylation, reduced liver fat, and improved hepatic and peripheral insulin sensitivity in HFD-fed rats. These data provide a preclinical rationale for PK activation to accelerate the PEP cycle to improve metabolic homeostasis and insulin sensitivity.

El-MAVEN: A Fast, Robust, and User-Friendly Mass Spectrometry Data Processing Engine for Metabolomics.

Analysis of large metabolomic datasets is becoming commonplace with the increased realization of the role that metabolites play in biology and pathophysiology. While there are many open-source analysis tools to extract peaks from liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and tandem mass spectrometry (LC-MS/MS) data, these tools are not very interactive and are suboptimal when a large number of samples are to be analyzed. El-MAVEN is an open-source analysis platform that extends MAVEN and provides fast, powerful, and interactive analysis capabilities especially for datasets containing over 100 samples. The El-MAVEN workflow is easy to use with just four steps from loading data to exporting of the results. Advanced analysis and software techniques such as multiprocessing, machine learning, and reduction of memory leaks are implemented so as to provide a seamless and interactive user experience. Results from El-MAVEN can be exported in a range of formats allowing continued analysis on other platforms. Additionally, El-MAVEN is also fully integrated with Polly™, a cloud-based analysis platform that provides a range of tools for flux analysis and integrative-omics analysis. El-MAVEN is a powerful tool that enables fast and efficient analysis of large metabolomic datasets to accelerate the process of gaining insight from raw data.