Multivariate indicators of disease severity in COVID-19.

The novel coronavirus pandemic continues to cause significant morbidity and mortality around the world. Diverse clinical presentations prompted numerous attempts to predict disease severity to improve care and patient outcomes. Equally important is understanding the mechanisms underlying such divergent disease outcomes. Multivariate modeling was used here to define the most distinctive features that separate COVID-19 from healthy controls and severe from moderate disease. Using discriminant analysis and binary logistic regression models we could distinguish between severe disease, moderate disease, and control with rates of correct classifications ranging from 71 to 100%. The distinction of severe and moderate disease was most reliant on the depletion of natural killer cells and activated class-switched memory B cells, increased frequency of neutrophils, and decreased expression of the activation marker HLA-DR on monocytes in patients with severe disease. An increased frequency of activated class-switched memory B cells and activated neutrophils was seen in moderate compared to severe disease and control. Our results suggest that natural killer cells, activated class-switched memory B cells, and activated neutrophils are important for protection against severe disease. We show that binary logistic regression was superior to discriminant analysis by attaining higher rates of correct classification based on immune profiles. We discuss the utility of these multivariate techniques in biomedical sciences, contrast their mathematical basis and limitations, and propose strategies to overcome such limitations.

A Unified Model of Age-Related Cardiovascular Disease.

Despite progress in biomedical technologies, cardiovascular disease remains the main cause of mortality. This is at least in part because current clinical interventions do not adequately take into account aging as a driver and are hence aimed at suboptimal targets. To achieve progress, consideration needs to be given to the role of cell aging in disease pathogenesis. We propose a model unifying the fundamental processes underlying most age-associated cardiovascular pathologies. According to this model, cell aging, leading to cell senescence, is responsible for tissue changes leading to age-related cardiovascular disease. This process, occurring due to telomerase inactivation and telomere attrition, affects all components of the cardiovascular system, including cardiomyocytes, vascular endothelial cells, smooth muscle cells, cardiac fibroblasts, and immune cells. The unified model offers insights into the relationship between upstream risk factors and downstream clinical outcomes and explains why interventions aimed at either of these components have limited success. Potential therapeutic approaches are considered based on this model. Because telomerase activity can prevent and reverse cell senescence, telomerase gene therapy is discussed as a promising intervention. Telomerase gene therapy and similar systems interventions based on the unified model are expected to be transformational in cardiovascular medicine.

The Molecular Basis of Spinocerebellar Ataxia Type 7.

Spinocerebellar ataxia (SCA) type 7 (SCA7) is caused by a CAG trinucleotide repeat expansion in the ataxin 7 (ATXN7) gene, which results in polyglutamine expansion at the amino terminus of the ATXN7 protein. Although ATXN7 is expressed widely, the best characterized symptoms of SCA7 are remarkably tissue specific, including blindness and degeneration of the brain and spinal cord. While it is well established that ATXN7 functions as a subunit of the Spt Ada Gcn5 acetyltransferase (SAGA) chromatin modifying complex, the mechanisms underlying SCA7 remain elusive. Here, we review the symptoms of SCA7 and examine functions of ATXN7 that may provide further insights into its pathogenesis. We also examine phenotypes associated with polyglutamine expanded ATXN7 that are not considered symptoms of SCA7.