Adaptive changes in the neuronal proteome: mitochondrial energy production, endoplasmic reticulum stress, and ribosomal dysfunction in the cellular response to metabolic stress

Impaired energy metabolism in neurons is integral to a range of neurodegenerative diseases, from Alzheimer’s disease to stroke. To investigate the complex molecular changes underpinning cellular adaptation to metabolic stress, we have defined the proteomic response of the SH-SY5Y human neuroblastoma cell line after exposure to a metabolic challenge of oxygen glucose deprivation (OGD) in vitro. A total of 958 proteins across multiple subcellular compartments were detected and quantified by label-free liquid chromatography mass spectrometry. The levels of 130 proteins were significantly increased (P<0.01) after OGD and the levels of 63 proteins were significantly decreased (P<0.01) while expression of the majority of proteins (765) was not altered. Network analysis identified novel protein–protein interactomes involved with mitochondrial energy production, protein folding, and protein degradation, indicative of coherent and integrated proteomic responses to the metabolic challenge. Approximately one third (61) of the differentially expressed proteins was associated with the endoplasmic reticulum and mitochondria. Electron microscopic analysis of these subcellular structures showed morphologic changes consistent with the identified proteomic alterations. Our investigation of the global cellular response to a metabolic challenge clearly shows the considerable adaptive capacity of the proteome to a slowly evolving metabolic challenge

Systematic inference of the long-range dependence and heavy-tail distribution parameters of ARFIMA models

Long-Range Dependence (LRD) and heavy-tailed distributions are ubiquitous in natural and socio-economic data. Such data can be self-similar whereby both LRD and heavy-tailed distributions contribute to the self-similarity as measured by the Hurst exponent. Some methods widely used in the physical sciences separately estimate these two parameters, which can lead to estimation bias. Those which do simultaneous estimation are based on frequentist methods such as Whittle’s approximate maximum likelihood estimator. Here we present a new and systematic Bayesian framework for the simultaneous inference of the LRD and heavy-tailed distribution parameters of a parametric ARFIMA model with non-Gaussian innovations. As innovations we use the α-stable and t-distributions which have power law tails. Our algorithm also provides parameter uncertainty estimates. We test our algorithm using synthetic data, and also data from the Geostationary Operational Environmental Satellite system (GOES) solar X-ray time series. These tests show that our algorithm is able to accurately and robustly estimate the LRD and heavy-tailed distribution parameters

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease