Tissue Proteome of 2-Hydroxyacyl-CoA Lyase Deficient Mice Reveals Peroxisome Proliferation and Activation of ω-Oxidation.

Peroxisomal fatty acid α-oxidation is an essential pathway for the degradation of ß-carbon methylated fatty acids such as phytanic acid. One enzyme in this pathway is 2-hydroxyacyl CoA lyase (HACL1), which is responsible for the cleavage of 2-hydroxyphytanoyl-CoA into pristanal and formyl-CoA. Hacl1 deficient mice do not present with a severe phenotype, unlike mice deficient in other α-oxidation enzymes such as phytanoyl-CoA hydroxylase deficiency (Refsum disease) in which neuropathy and ataxia are present. Tissues from wild-type and Hacl1-/- mice fed a high phytol diet were obtained for proteomic and lipidomic analysis. There was no phenotype observed in these mice. Liver, brain, and kidney tissues underwent trypsin digestion for untargeted proteomic liquid chromatography-mass spectrometry analysis, while liver tissues also underwent fatty acid hydrolysis, extraction, and derivatisation for fatty acid gas chromatography-mass spectrometry analysis. The liver fatty acid profile demonstrated an accumulation of phytanic and 2-hydroxyphytanic acid in the Hacl1-/- liver and significant decrease in heptadecanoic acid. The liver proteome showed a significant decrease in the abundance of Hacl1 and a significant increase in the abundance of proteins involved in PPAR signalling, peroxisome proliferation, and omega oxidation, particularly Cyp4a10 and Cyp4a14. In addition, the pathway associated with arachidonic acid metabolism was affected; Cyp2c55 was upregulated and Cyp4f14 and Cyp2b9 were downregulated. The kidney proteome revealed fewer significantly upregulated peroxisomal proteins and the brain proteome was not significantly different in Hacl1-/- mice. This study demonstrates the powerful insight brought by proteomic and metabolomic profiling of Hacl1-/- mice in better understanding disease mechanism in fatty acid α-oxidation disorders.

Circulating CXCL9, monocyte percentage, albumin, and C-reactive protein as a potential, non-invasive, molecular signature of carotid artery disease in 65+ patients with multimorbidity: a pilot study in Age.It.

Background: Carotid endarterectomy (CEA) for the prevention of upcoming vascular and cerebral events is necessary in patients with high-grade stenosis (≥70%). In the framework of the Italian National project Age.It, a pilot study was proposed aiming at the discovery of a molecular signature with predictive potential of carotid stenosis comparing 65+ asymptomatic and symptomatic inpatients. Methods: A total of 42 inpatients have been enrolled, including 26 men and 16 women, with a mean age of 74 ± 6 years. Sixteen symptomatic and 26 asymptomatic inpatients with ≥70% carotid stenosis underwent CEA, according to the recommendations of the European Society for Vascular Surgery and the Society for Vascular Surgeons. Plaque biopsies and peripheral blood samples from the same individuals were obtained. Hematobiochemical analyses were conducted on all inpatients, and plasma cytokines/molecules, such as microRNAs (miRs), IL-6, sIL-6Ralpha, sgp130, myostatin (GDF8), follistatin, activin A, CXCL9, FGF21, and fibronectin, were measured using the ELISA standard technique. MiR profiles were obtained in the discovery phase including four symptomatic and four asymptomatic inpatients (both plasma and plaque samples), testing 734 miRs. MiRs emerging from the profiling comparison were validated through RT-qPCR analysis in the total cohort. Results and conclusion: The two groups of inpatients differ in the expression levels of blood c-miRs-126-5p and -1271-5p (but not in their plaques), which are more expressed in symptomatic subjects. Three cytokines were significant between the two groups: IL-6, GDF8, and CXCL9. Using receiver operating characteristic (ROC) analysis with a machine learning-based approach, the most significant blood molecular signature encompasses albumin, C-reactive protein (CRP), the percentage of monocytes, and CXCL9, allowing for the distinction of the two groups (AUC = 0.83, 95% c.i. [0.85, 0.81], p = 0.0028). The potential of the molecular signature will be tested in a second cohort of monitored patients, allowing the application of a predictive model and the final evaluation of cost/benefit for an assessable screening test.

Biomarkers of aging in frailty and age-associated disorders: State of the art and future perspective.

According to the Geroscience concept that organismal aging and age-associated diseases share the same basic molecular mechanisms, the identification of biomarkers of age that can efficiently classify people as biologically older (or younger) than their chronological (i.e. calendar) age is becoming of paramount importance. These people will be in fact at higher (or lower) risk for many different age-associated diseases, including cardiovascular diseases, neurodegeneration, cancer, etc. In turn, patients suffering from these diseases are biologically older than healthy age-matched individuals. Many biomarkers that correlate with age have been described so far. The aim of the present review is to discuss the usefulness of some of these biomarkers (especially soluble, circulating ones) in order to identify frail patients, possibly before the appearance of clinical symptoms, as well as patients at risk for age-associated diseases. An overview of selected biomarkers will be discussed in this regard, in particular we will focus on biomarkers related to metabolic stress response, inflammation, and cell death (in particular in neurodegeneration), all phenomena connected to inflammaging (chronic, low-grade, age-associated inflammation). In the second part of the review, next-generation markers such as extracellular vesicles and their cargos, epigenetic markers and gut microbiota composition, will be discussed. Since recent progresses in omics techniques have allowed an exponential increase in the production of laboratory data also in the field of biomarkers of age, making it difficult to extract biological meaning from the huge mass of available data, Artificial Intelligence (AI) approaches will be discussed as an increasingly important strategy for extracting knowledge from raw data and providing practitioners with actionable information to treat patients.