Skeletal Muscle Fibre-Specific Knockout of p53 Does Not Reduce Mitochondrial Content or Enzyme Activity

Tumour protein 53 (p53) has been implicated in the regulation of mitochondrial biogenesis in skeletal muscle, with whole-body p53 knockout mice displaying impairments in basal mitochondrial content, respiratory capacity, and enzyme activity. This study aimed to determine the effect of skeletal muscle-specific loss of p53 on mitochondrial content and enzyme activity. Mitochondrial protein content, enzyme activity and mRNA profiles were assessed in skeletal muscle of 8-week-old male muscle fibre-specific p53 knockout mice (p53 mKO) and floxed littermate controls (WT) under basal conditions. p53 mKO and WT mice displayed similar content of electron transport chain proteins I-V and citrate synthase enzyme activity in skeletal muscle. In addition, the content of proteins regulating mitochondrial morphology (MFN2, mitofillin, OPA1, DRP1, FIS1), fatty acid metabolism (β-HAD, ACADM, ACADL, ACADVL), carbohydrate metabolism (HKII, PDH), energy sensing (AMPKα2, AMPKβ2), and gene transcription (NRF1, PGC-1α, and TFAM) were comparable in p53 mKO and WT mice (p > 0.05). Furthermore, p53 mKO mice exhibited normal mRNA profiles of targeted mitochondrial, metabolic and transcriptional proteins (p > 0.05). Thus, it appears that p53 expression in skeletal muscle fibres is not required to develop or maintain mitochondrial protein content or enzyme function in skeletal muscle under basal conditions

Muscle-specific knockout of general control of amino acid synthesis 5 (GCN5) does not enhance basal or endurance exercise-induced mitochondrial adaptation

Objective  Lysine acetylation is an important post-translational modification that regulates metabolic function in skeletal muscle. The acetyltransferase, general control of amino acid synthesis 5 (GCN5), has been proposed as a regulator of mitochondrial biogenesis via its inhibitory action on peroxisome proliferator activated receptor-γ coactivator-1α (PGC-1α). However, the specific contribution of GCN5 to skeletal muscle metabolism and mitochondrial adaptations to endurance exercise in vivo remain to be defined. We aimed to determine whether loss of GCN5 in skeletal muscle enhances mitochondrial density and function, and the adaptive response to endurance exercise training.  Methods  We used Cre-LoxP methodology to generate mice with muscle-specific knockout of GCN5 (mKO) and floxed, wildtype (WT) littermates. We measured whole-body energy expenditure, as well as markers of mitochondrial density, biogenesis, and function in skeletal muscle from sedentary mice, and mice that performed 20 days of voluntary endurance exercise training.  Results  Despite successful knockdown of GCN5 activity in skeletal muscle of mKO mice, whole-body energy expenditure as well as skeletal muscle mitochondrial abundance and maximal respiratory capacity were comparable between mKO and WT mice. Further, there were no genotype differences in endurance exercise-mediated mitochondrial biogenesis or increases in PGC-1α protein content.  Conclusion  These results demonstrate that loss of GCN5 in vivo does not promote metabolic remodeling in mouse skeletal muscle

Resistance exercise initiates mechanistic target of rapamycin (mTOR) translocation and protein complex co-localisation in human skeletal muscle

The mechanistic target of rapamycin (mTOR) is a central mediator of protein synthesis in skeletal muscle. We utilized immunofluorescence approaches to study mTOR cellular distribution and protein-protein co-localisation in human skeletal muscle in the basal state as well as immediately, 1 and 3 h after an acute bout of resistance exercise in a fed (FED; 20 g Protein/40 g carbohydrate/1 g fat) or energy-free control (CON) state. mTOR and the lysosomal protein LAMP2 were highly co-localised in basal samples. Resistance exercise resulted in rapid translocation of mTOR/LAMP2 towards the cell membrane. Concurrently, resistance exercise led to the dissociation of TSC2 from Rheb and increased in the co-localisation of mTOR and Rheb post exercise in both FED and CON. In addition, mTOR co-localised with Eukaryotic translation initiation factor 3 subunit F (eIF3F) at the cell membrane post-exercise in both groups, with the response significantly greater at 1 h of recovery in the FED compared to CON. Collectively our data demonstrate that cellular trafficking of mTOR occurs in human muscle in response to an anabolic stimulus, events that appear to be primarily influenced by muscle contraction. The translocation and association of mTOR with positive regulators (i.e. Rheb and eIF3F) is consistent with an enhanced mRNA translational capacity after resistance exercise

Obesity and Gastroesophageal Reflux: Quantifying the Association Between Body Mass Index, Esophageal Acid Exposure, and Lower Esophageal Sphincter Status in a Large Series of Patients with Reflux Symptoms

Obesity and gastroesophageal reflux disease (GERD) are increasingly important health problems. Previous studies of the relationship between obesity and GERD focus on indirect manifestations of GERD. Little is known about the association between obesity and objectively measured esophageal acid exposure. The aim of this study is to quantify the relationship between body mass index (BMI) and 24-h esophageal pH measurements and the status of the lower esophageal sphincter (LES) in patients with reflux symptoms. Data of 1,659 patients (50% male, mean age 51 ± 14) referred for assessment of GERD symptoms between 1998 and 2008 were analyzed. These subjects underwent 24-h pH monitoring off medication and esophageal manometry. The relationship of BMI to 24-h esophageal pH measurements and LES status was studied using linear regression and multiple regression analysis. The difference of each acid exposure component was also assessed among four BMI subgroups (underweight, normal weight, overweight, and obese) using analysis of variance and covariance. Increasing BMI was positively correlated with increasing esophageal acid exposure (adjusted R 2 = 0.13 for the composite pH score). The prevalence of a defective LES was higher in patients with higher BMI (p < 0.0001). Compared to patients with normal weight, obese patients are more than twice as likely to have a mechanically defective LES [OR = 2.12(1.63–2.75)]. An increase in body mass index is associated with an increase in esophageal acid exposure, whether BMI was examined as a continuous or as a categorical variable; 13% of the variation in esophageal acid exposure may be attributable to variation in BMI

N-Myc and GCN5 Regulate Significantly Overlapping Transcriptional Programs in Neural Stem Cells

Here we examine the functions of the Myc cofactor and histone acetyltransferase, GCN5/KAT2A, in neural stem and precursor cells (NSC) using a conditional knockout approach driven by nestin-cre. Mice with GCN5-deficient NSC exhibit a 25% reduction in brain mass with a microcephaly phenotype similar to that observed in nestin-cre driven knockouts of c- or N-myc. In addition, the loss of GCN5 inhibits precursor cell proliferation and reduces their populations in vivo, as does loss of N-myc. Gene expression analysis indicates that about one-sixth of genes whose expression is affected by loss of GCN5 are also affected in the same manner by loss of N-myc. These findings strongly support the notion that GCN5 protein is a key N-Myc transcriptional cofactor in NSC, but are also consistent with recruitment of GCN5 by other transcription factors and the use by N-Myc of other histone acetyltransferases. Putative N-Myc/GCN5 coregulated transcriptional pathways include cell metabolism, cell cycle, chromatin, and neuron projection morphogenesis genes. GCN5 is also required for maintenance of histone acetylation both at its putative specific target genes and at Myc targets. Thus, we have defined an important role for GCN5 in NSC and provided evidence that GCN5 is an important Myc transcriptional cofactor in vivo

Antidepressants and Breast and Ovarian Cancer Risk: A Review of the Literature and Researchers' Financial Associations with Industry

BACKGROUND: Antidepressant (AD) use has been purported to increase the risk of breast and ovarian cancer, although both epidemiological and pre-clinical studies have reported mixed results. Previous studies in a variety of biomedical fields have found that financial ties to drug companies are associated with favorable study conclusions. METHODS AND FINDINGS: We searched English-language articles in MEDLINE, PsychINFO, the Science Citations Index and the Cochrane Central Register of Controlled Clinical Trials (through November 2010). A total of 61 articles that assessed the relationship between breast and ovarian cancer and AD use and articles that examined the effect of ADs on cell growth were included. Multi-modal screening techniques were used to investigate researchers' financial ties with industry. A random effects meta-analysis was used to pool the findings from the epidemiological literature. Thirty-three percent (20/61) of the studies reported a positive association between ADs and cancer. Sixty-seven percent (41/61) of the studies reported no association or antiproliferative effect. The pooled odds ratio for the association between AD use and breast/ovarian cancer in the epidemiologic studies was 1.11 (95% CI, 1.03-1.20). Researchers with industry affiliations were significantly less likely than researchers without those ties to conclude that ADs increase the risk of breast or ovarian cancer. (0/15 [0%] vs 20/46 [43.5%] (Fisher's Exact test P = 0.0012). CONCLUSIONS: Both the pre-clinical and clinical data are mixed in terms of showing an association between AD use and breast and ovarian cancer. The possibility that ADs may exhibit a bi-phasic effect, whereby short-term use and/or low dose antidepressants may increase the risk of breast and ovarian cancer, warrants further investigation. Industry affiliations were significantly associated with negative conclusions regarding cancer risk. The findings have implications in light of the 2009 USPSTF guidelines for breast cancer screening and for the informed consent process

Challenges of COVID-19 Case Forecasting in the US, 2020–2021

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)

Basic science232. Certolizumab pegol prevents pro-inflammatory alterations in endothelial cell function

Background: Cardiovascular disease is a major comorbidity of rheumatoid arthritis (RA) and a leading cause of death. Chronic systemic inflammation involving tumour necrosis factor alpha (TNF) could contribute to endothelial activation and atherogenesis. A number of anti-TNF therapies are in current use for the treatment of RA, including certolizumab pegol (CZP), (Cimzia ®; UCB, Belgium). Anti-TNF therapy has been associated with reduced clinical cardiovascular disease risk and ameliorated vascular function in RA patients. However, the specific effects of TNF inhibitors on endothelial cell function are largely unknown. Our aim was to investigate the mechanisms underpinning CZP effects on TNF-activated human endothelial cells. Methods: Human aortic endothelial cells (HAoECs) were cultured in vitro and exposed to a) TNF alone, b) TNF plus CZP, or c) neither agent. Microarray analysis was used to examine the transcriptional profile of cells treated for 6 hrs and quantitative polymerase chain reaction (qPCR) analysed gene expression at 1, 3, 6 and 24 hrs. NF-κB localization and IκB degradation were investigated using immunocytochemistry, high content analysis and western blotting. Flow cytometry was conducted to detect microparticle release from HAoECs. Results: Transcriptional profiling revealed that while TNF alone had strong effects on endothelial gene expression, TNF and CZP in combination produced a global gene expression pattern similar to untreated control. The two most highly up-regulated genes in response to TNF treatment were adhesion molecules E-selectin and VCAM-1 (q 0.2 compared to control; p > 0.05 compared to TNF alone). The NF-κB pathway was confirmed as a downstream target of TNF-induced HAoEC activation, via nuclear translocation of NF-κB and degradation of IκB, effects which were abolished by treatment with CZP. In addition, flow cytometry detected an increased production of endothelial microparticles in TNF-activated HAoECs, which was prevented by treatment with CZP. Conclusions: We have found at a cellular level that a clinically available TNF inhibitor, CZP reduces the expression of adhesion molecule expression, and prevents TNF-induced activation of the NF-κB pathway. Furthermore, CZP prevents the production of microparticles by activated endothelial cells. This could be central to the prevention of inflammatory environments underlying these conditions and measurement of microparticles has potential as a novel prognostic marker for future cardiovascular events in this patient group. Disclosure statement: Y.A. received a research grant from UCB. I.B. received a research grant from UCB. S.H. received a research grant from UCB. All other authors have declared no conflicts of interes

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks