A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease.

Insulin resistance (IR) contributes to the pathophysiology of diabetes, dementia, viral infection, and cardiovascular disease. Drug repurposing (DR) may identify treatments for IR; however, barriers include uncertainty whether in vitro transcriptomic assays yield quantitative pharmacological data, or how to optimise assay design to best reflect in vivo human disease. We developed a clinical-based human tissue IR signature by combining lifestyle-mediated treatment responses (>500 human adipose and muscle biopsies) with biomarkers of disease status (fasting IR from >1200 biopsies). The assay identified a chemically diverse set of >130 positively acting compounds, highly enriched in true positives, that targeted 73 proteins regulating IR pathways. Our multi-gene RNA assay score reflected the quantitative pharmacological properties of a set of epidermal growth factor receptor-related tyrosine kinase inhibitors, providing insight into drug target specificity; an observation supported by deep learning-based genome-wide predicted pharmacology. Several drugs identified are suitable for evaluation in patients, particularly those with either acute or severe chronic IR.

Developing a new drug that is both safe and effective is a complex and expensive endeavor. An alternative approach is to 'repurpose' existing, safe compounds ­ that is, to establish if they could treat conditions others than the ones they were initially designed for. To achieve this, methods that can predict the activity of thousands of established drugs are necessary. These approaches are particularly important for conditions for which it is hard to find promising treatment. This includes, for instance, heart failure, dementia and other diseases that are linked to the activity of the hormone insulin becoming modified throughout the body, a defect called insulin resistance. Unfortunately, it is difficult to model the complex actions of insulin using cells in the lab, because they involve intricate networks of proteins, tissues and metabolites. Timmons et al. set out to develop a way to better assess whether a drug could be repurposed to treat insulin resistance. The aim was to build a biological signature of the disease in multiple human tissues, as this would help to make the findings more relevant to the clinic. This involved examining which genes were switched on or off in thousands of tissue samples from patients with different degrees of insulin resistance. Importantly, some of the patients had their condition reversed through lifestyle changes, while others did not respond well to treatment. These 'non-responders' provided crucial new clues to screen for active drugs. Carefully piecing the data together revealed the molecules and pathways most related to the severity of insulin resistance. Cross-referencing these results with the way existing drugs act on gene activity, highlighted 138 compounds that directly bind 73 proteins responsible for regulating insulin resistance pathways. Some of the drugs identified are suitable for short-term clinical studies, and it may even be possible to rank similar compounds based on their chemical activity. Beyond giving a glimpse into the complex molecular mechanisms of insulin resistance in humans, Timmons et al. provide a fresh approach to how drugs could be repurposed, which could be adapted to other conditions.

Redressing the interactions between stem cells and immune system in tissue regeneration.

Skeletal muscle has an extraordinary regenerative capacity reflecting the rapid activation and effective differentiation of muscle stem cells (MuSCs). In the course of muscle regeneration, MuSCs are reprogrammed by immune cells. In turn, MuSCs confer immune cells anti-inflammatory properties to resolve inflammation and facilitate tissue repair. Indeed, MuSCs can exert therapeutic effects on various degenerative and inflammatory disorders based on their immunoregulatory ability, including effects primed by interferon-γ (IFN-γ) and tumor necrosis factor-α (TNF-α). At the molecular level, the tryptophan metabolites, kynurenine or kynurenic acid, produced by indoleamine 2,3-dioxygenase (IDO), augment the expression of TNF-stimulated gene 6 (TSG6) through the activation of the aryl hydrocarbon receptor (AHR). In addition, insulin growth factor 2 (IGF2) produced by MuSCs can endow maturing macrophages oxidative phosphorylation (OXPHOS)-dependent anti-inflammatory functions. Herein, we summarize the current understanding of the immunomodulatory characteristics of MuSCs and the issues related to their potential applications in pathological conditions, including COVID-19.

Effect of sleeve gastrectomy on the expression of meteorin-like (METRNL) and Irisin (FNDC5) in muscle and brown adipose tissue and its impact on uncoupling proteins in diet-induced obesity rats.

BACKGROUND: Bariatric surgery is well established as a treatment for obesity and associated complications. This procedure improves metabolic homeostasis through changes in energy expenditure. We hypothesized that sleeve gastrectomy (SG) improves metabolic homeostasis by modulating energy expenditure and enhancing thermogenesis through increasing the expression level of meteorin-like protein (METRNL) and fibronectin type III domain-containing protein 5 (FNDC5/Irisin) through uncoupling proteins 1/2/3 (UCP1, UCP2, and UCP3). OBJECTIVES: To study the effect of SG on the levels of proteins involved in thermogenesis process. SETTING: Laboratory rats at Kuwait University. METHODS: Male Sprague-Dawley rats, aged 4 to 5 weeks, were divided into 2 groups, control (n = 11) and diet-induced obesity (DIO) (n = 22). The control group was fed regular rat chow ad libitum, whereas the DIO group was fed cafeteria diet "high-fat/carbohydrate diet" ad libitum. At 21 weeks, rats in the DIO group that weighed 20% more than the control group animals underwent surgery. These rats were randomly subdivided into Sham and SG operation groups. Gene expression was evaluated, and enzyme-linked immunosorbent assays were employed to assess the changes in gene and protein levels in tissue and circulation. RESULTS: The protein expression data revealed an increase in METRNL levels in the muscles and white adipose tissue of SG animals. METRNL level in circulation in SG animals was reduced compared with control and Sham rats. The level of Irisin increased in the muscle of SG animals compared with the control and Sham group animals; however, a decrease in Irisin level was observed in the white adipose tissue and brown adipose tissue of SG animals compared with controls. Gene expression analysis revealed decreased METRNL levels in muscle tissues in the SG group compared with the control group animals. Increased expression of FNDC5 (Irisin), UCP2, and UCP3 in the muscle tissue of SG animals was also observed. Furthermore, the levels of UCP1, UCP2, UCP3, and METRNL in the brown adipose tissue of SG animals were upregulated. No significant alteration in the gene expression of Irisin was observed in brown adipose tissue. CONCLUSIONS: Sleeve gastrectomy induces weight loss through complex mechanisms that may include browning of fat.

Reduced muscle mass as predictor of intensive care unit hospitalization in COVID-19 patients.

PURPOSE: To evaluate if reduced muscle mass, assessed with Computed Tomography (CT), is a predictor of intensive care unit (ICU) hospitalization in COVID-19 patients. METHODS: In this Institution Review Board approved study, we retrospectively evaluated COVID-19 patients treated in our tertiary center from March to November 2020 who underwent an unenhanced chest CT scan within three weeks from hospitalization.We recorded the mean Hounsfield Unit (Hu) value of the right paravertebral muscle at the level of the 12th thoracic vertebra, the hospitalization unit (ICU and COVID-19 wards), clinical symptoms, Barthel Index, and laboratory findings.Logistic regression analysis was applied to assess if muscle loss (Hu<30) is a predictor of ICU admission and outcome.Fisher's exact and Student's tests were applied to evaluate if differences between patients with and without muscle loss occurred (p<0.05). RESULTS: One-hundred-fifty patients matched the inclusion criteria (46 females; mean age±SD 61.3±15 years-old), 36 treated in ICU. Patients in ICU showed significantly lower Hu values (29±24 vs 39.4±12, p = 0.001). Muscle loss was a predictor of ICU admission (p = 0.004).Patients with muscle loss were significantly older (73.4±10 vs 56.4±14 years), had lower Barthel Index scores (54.4±33 vs 85.1±26), red blood-cell count (3.9±1 vs 4.6±1×1012L-1), and Hb levels (11.5±2 vs 13.2±2g/l) as well as higher white blood-cell count (9.4±7 vs 7.2±4×109L-1), C-reactive protein (71.5±71 vs 44±48U/L), and lactate dehydrogenase levels (335±163 vs 265.8±116U/L) (p<0.05, each). CONCLUSIONS: Muscle loss seems to be a predictor of ICU hospitalization in COVID-19 patients and radiologists reporting chest CT at admission should note this finding in their reports.

Biochemical biomarkers alterations in Coronavirus Disease 2019 (COVID-19).

Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a respiratory disease, which can evolve into multi-organ failure (MOF), leading to death. Several biochemical alterations have been described in COVID-19 patients. To date, many biomarkers reflecting the main pathophysiological characteristics of the disease have been identified and associated with the risk of developing severe disease. Lymphopenia represents the hallmark of the disease, and it can be detected since the early stage of infection. Increased levels of several inflammatory biomarkers, including c-reactive protein, have been found in COVID-19 patients and associated with an increased risk of severe disease, which is characterised by the so-called "cytokine storm". Also, the increase of cardiac and liver dysfunction biomarkers has been associated with poor outcome. In this review, we provide an overview of the main biochemical characteristics of COVID-19 and the associated biomarkers alterations.