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Gao, Yuqian (ORCID:0000000316464515)

Publications and source records attributed to Gao, Yuqian (ORCID:0000000316464515).

Circadian immunometabolic states impart a temporal response to SARS-CoV-2 spike proteins in mammalian macrophages

Circadian rhythms, the 24-hour cycles that tune organismal physiology to the daily rhythms of light and dark, optimally organize cellular processes such as metabolism and mitochondrial function. In mammals, macrophage functions are regulated by these 24-hour circadian rhythms such that the immunometabolic response is coordinated across the day, consolidating macrophage physiology into temporally distinct phases to time the cellular immune response. However, while it is known that there are time-of-day specific responses to stress in a macrophage, little has been done to determine if circadian regulation coordinates the response of a macrophage to real-world pathogens. Importantly, key proteins in the response to viral infection have been found to be under circadian control, and time of day of application is known to affect the efficacy of vaccinations, including in the case of the COVID-19 virus. Therefore, to investigate if the circadian regulation of macrophage physiology imparted a time-of-day response to viral exposure, we exposed primary mouse and human macrophages to the SARS-CoV-1 and CoV-2 spike proteins at different times over the circadian day. To establish a time-of-day effect, we performed a multi-omics analysis and in vitro tissue culture assays examining macrophage responses over circadian time. We found that, conserved across the species, the timing of spike protein exposure dictated two distinct temporal responses which were characterized by hallmarks of immunometabolic suppression and modest inflammatory activation. However, these responses were primarily influenced by central metabolic and mitochondrial changes and not by classical immune activation.

Circadian Biology↗

The anaerobic fungus Caecomyces churrovis produces H2 via a non-3 bifurcating NADH-dependent enzyme complex

Anaerobic fungi (AF) decompose lignocellulose-based biomass into fermentable sugars through the production of powerful biomass-degrading enzymes. AF are unusual among fungi in that they generate energy via hydrogenosomes, which are also associated with the release of H2 though yet unknown metabolic mechanisms. In particular, it remains unclear how NAD(P)+ is regenerated within hydrogenosomes and how H2 is formed. Here, we reveal the molecular mechanism for hydrogenosomal H2 production in the AF strain C. churrovis by combining genomic search, proteomic analysis, and enzymology. Our enzyme assays on the large organelle fraction of C. churrovis revealed the activity of H2:NAD+ oxidoreductase but not pyruvate:ferredoxin oxidoreductase activity. We identified genes encoding [FeFe] hydrogenase (Hyd) and NADH dehydrogenase subunits E and F (NuoE, NuoF) in C. churrovis, and confirmed their expression in the isolated hydrogenosomal fractions by proteomic analysis. Combining the individually purified proteins, we found that the assay system consisting of Hyd-Strep and NuoEF-Strep reduced NAD+ with H2. Furthermore, this system formed H2 directly from NADH independent of ferredoxin, functioning as a non-bifurcating NADH-dependent enzyme rather than an electron-bifurcating enzyme. We identified homologs of hydrogenosomal NuoE, NuoF, and Hyd in many other AF, indicating this pathway is widely conserved among the early-branching AF. This work demonstrates the existence of a non-bifurcating NADH-dependent enzyme complex in eukaryotes. Moreover, this complex could be a target for controlling AF H2 production and altering fungal metabolism.

fungi↗

DAISY Complement Protein ML-Ready Data

A total of 172 children from the DAISY study with multiple plasma samples collected over time, with up to 23 years of follow-up, were characterized via proteomics analysis. Of the children there were 40 controls and 132 cases. All 132 cases had measurements across time relative to IA. Sampling was not consistent for all children. There were 47 of the children who had samples taken and evaluated prior to IA (Pre-IA), and 131 children had measurements at or after IA, but prior to diagnosis of clinical T1D (Post-IA). The control children were frequency matched on HLA genotypes and age and sex with an observed lower frequency of first degree relatives within the control group versus the cases For machine learning the children that will develop islet autoantibodies the 40 control and 47 Pre-IA children were down-selected to a single sample time point. For the 40 control children this was the earliest sample collected and for the 47 Pre-IA children it was a random selection of the first or second time point prior to the detection of autoantibodies to assure the age distributions were not significantly different.

Webb-Robertson, Bobbie-Jo M↗