Background:The switch between autophagy and apoptosis is an important and complicated process that is not well understood. Results: Doxorubicin treatment switches protective autophagy in sphingosine-1-phosphate phosphohydrolase-1 (SPP1)-depleted cells to apoptosis, increasing ceramide synthesis that enhances calpain activation and cleavage of pro-autophagic Atg5, generating a pro-apoptotic fragment. Conclusion: Depletion of SPP1 sensitizes cells to doxorubicin-induced apoptosis. Significance: Sphingolipid metabolites are involved in the cross-talk between autophagy and apoptosis.

The pleiotropic sphingolipid mediator, sphingosine-1-phosphate, produced in cells by two sphingosine kinase isoenzymes, SphK1 and SphK2, regulates many cellular and physiological processes important for homeostasis and development and pathophysiology. Many of the actions of S1P are mediated by a family of five specific cell surface receptors that are ubiquitously and specifically expressed, although important direct intracellular targets of S1P have also recently been identified. S1P, SphK1, and or S1P receptors have been linked to onset and progression of numerous diseases, including many types of cancer, and especially inflammatory disorders, such as multiple sclerosis, asthma, rheumatoid arthritis, inflammatory bowel disease, and sepsis. S1P formation and signaling are attractive targets for development of new therapeutics. The effects of a number of inhibitors of SphKs and S1PRs have been examined in animal models of human diseases. The effectiveness of the immunosuppressant FTY720 (known as Fingolomod or Gilenya), recently approved for the treatment of multiple sclerosis, whose actions are mediated by downregulation of S1PR1, has become the gold standard for S1P-centric drugs. Here, we review S1P biology and signaling with an emphasis on potential therapeutic benefits of specific interventions and discuss recent development of small molecule antagonists and agonists that target specific subtypes of S1P receptors as well as inhibitors of SphKs.

BackgroundThere are now a multitude of articles published in a diversity of journals providing information about genes, proteins, pathways, and diseases. Each article investigates subsets of a biological process, but to gain insight into the functioning of a system as a whole, we must integrate information from multiple publications. Particularly, unraveling relationships between extra-cellular inputs and downstream molecular response mechanisms requires integrating conclusions from diverse publications.MethodologyWe present an automated approach to biological knowledge discovery from PubMed abstracts, suitable for “connecting the dots” across the literature. We describe a storytelling algorithm that, given a start and end publication, typically with little or no overlap in content, identifies a chain of intermediate publications from one to the other, such that neighboring publications have significant content similarity. The quality of discovered stories is measured using local criteria such as the size of supporting neighborhoods for each link and the strength of individual links connecting publications, as well as global metrics of dispersion. To ensure that the story stays coherent as it meanders from one publication to another, we demonstrate the design of novel coherence and overlap filters for use as post-processing steps.ConclusionsWe demonstrate the application of our storytelling algorithm to three case studies: i) a many-one study exploring relationships between multiple cellular inputs and a molecule responsible for cell-fate decisions, ii) a many-many study exploring the relationships between multiple cytokines and multiple downstream transcription factors, and iii) a one-to-one study to showcase the ability to recover a cancer related association, viz. the Warburg effect, from past literature. The storytelling pipeline helps narrow down a scientist's focus from several hundreds of thousands of relevant documents to only around a hundred stories. We argue that our approach can serve as a valuable discovery aid for hypothesis generation and connection exploration in large unstructured biological knowledge bases.

Background: Racial and ethnic minority groups have been disproportionately affected by the US coronavirus disease 2019 (COVID-19) pandemic; however, nationwide data on COVID-19 outcomes stratified by race/ethnicity and adjusted for clinical characteristics are sparse. This study analyzed the impacts of race/ethnicity on outcomes among US patients with COVID-19. Methods: This was a retrospective observational study of patients with a confirmed COVID-19 diagnosis in the electronic health record from 01 February 2020 through 14 September 2020. Index encounter site, hospitalization, and mortality were assessed by race/ethnicity (Hispanic, non-Hispanic Black [Black], non-Hispanic White [White], non-Hispanic Asian [Asian], or Other/unknown). Associations between racial/ethnic categories and study outcomes adjusted for patient characteristics were evaluated using logistic regression. Findings: Among 202,908 patients with confirmed COVID-19, patients from racial/ethnic minority groups were more likely than White patients to be hospitalized on initial presentation (Hispanic: adjusted odds ratio 1¢690, 95% CI 1¢620À1¢763; Black: 1¢810, 1¢743À1¢880; Asian: 1¢503, 1¢381À1¢636) and during follow-up (Hispanic: 1¢700, 1¢638À1¢764; Black: 1¢578, 1¢526À1¢633; Asian: 1¢391, 1¢288À1¢501). Among hospitalized patients, adjusted mortality risk was lower for Black patients (0¢881, 0¢809À0¢959) but higher for Asian patients (1¢205, 1¢000À1¢452). Interpretation: Racial/ethnic minority patients with COVID-19 had more severe disease on initial presentation than White patients. Increased mortality risk was attenuated by hospitalization among Black patients but not Asian patients, indicating that outcome disparities may be mediated by distinct factors for different groups. In addition to enacting policies to facilitate equitable access to COVID-19Àrelated care, further analyses of disaggregated population-level COVID-19 data are needed.

Post‐translational protein modification provides real‐time modulation of protein form and function. Poly(ADP‐ribosyl)ation is a post‐translational modification (PTM) that has been implicated in processes as diverse as chromatin remodeling, the DNA damage response, and mammalian cell death. More than 200 proteins have been implicated as targets of poly(ADP‐ribosyl)ation, predominantly through the actions of the nuclear enzyme PARP‐1. However, there is very little data to support the existence of a covalent linkage between modified proteins and poly(ADP‐ribose) polymers, indicating that this association may be non‐covalent. While mass spectrometry offers the ability to directly identify sites of post‐translational modification, no published “ADP‐ribosylated proteome” datasets are presently available. In an effort to provide the means to generate such a dataset and elucidate the nature of the protein linkage in this important PTM, we have embarked upon an effort to utilize mass spectrometry for its evaluation. As an initial series of investigations, both histones and PARP‐1 were poly(ADP‐ribosyl)ated using an in vitro system. The products of these reactions were analyzed by mass spectrometric techniques. The results of these investigations will be reported.