Explainable machine learning identifies multi-omics signatures of muscle response to spaceflight in mice.

The adverse effects of microgravity exposure on mammalian physiology during spaceflight necessitate a deep understanding of the underlying mechanisms to develop effective countermeasures. One such concern is muscle atrophy, which is partly attributed to the dysregulation of calcium levels due to abnormalities in SERCA pump functioning. To identify potential biomarkers for this condition, multi-omics data and physiological data available on the NASA Open Science Data Repository (osdr.nasa.gov) were used, and machine learning methods were employed. Specifically, we used multi-omics (transcriptomic, proteomic, and DNA methylation) data and calcium reuptake data collected from C57BL/6 J mouse soleus and tibialis anterior tissues during several 30+ day-long missions on the international space station. The QLattice symbolic regression algorithm was introduced to generate highly explainable models that predict either experimental conditions or calcium reuptake levels based on multi-omics features. The list of candidate models established by QLattice was used to identify key features contributing to the predictive capability of these models, with Acyp1 and Rps7 proteins found to be the most predictive biomarkers related to the resilience of the tibialis anterior muscle in space. These findings could serve as targets for future interventions aiming to reduce the extent of muscle atrophy during space travel.

A Novel Method for Creating a Synthetic L-DOPA Proteome and In Vitro Evidence of Incorporation.

Proteinopathies are protein misfolding diseases that have an underlying factor that affects the conformation of proteoforms. A factor hypothesised to play a role in these diseases is the incorporation of non-protein amino acids into proteins, with a key example being the therapeutic drug levodopa. The presence of levodopa as a protein constituent has been explored in several studies, but it has not been examined in a global proteomic manner. This paper provides a proof-of-concept method for enzymatically creating levodopa-containing proteins using the enzyme tyrosinase and provides spectral evidence of in vitro incorporation in addition to the induction of the unfolded protein response due to levodopa.

Elongation factor Tu is a multifunctional and processed moonlighting protein.

Many bacterial moonlighting proteins were originally described in medically, agriculturally, and commercially important members of the low G + C Firmicutes. We show Elongation factor Tu (Ef-Tu) moonlights on the surface of the human pathogens Staphylococcus aureus (SaEf-Tu) and Mycoplasma pneumoniae (MpnEf-Tu), and the porcine pathogen Mycoplasma hyopneumoniae (MhpEf-Tu). Ef-Tu is also a target of multiple processing events on the cell surface and these were characterised using an N-terminomics pipeline. Recombinant MpnEf-Tu bound strongly to a diverse range of host molecules, and when bound to plasminogen, was able to convert plasminogen to plasmin in the presence of plasminogen activators. Fragments of Ef-Tu retain binding capabilities to host proteins. Bioinformatics and structural modelling studies indicate that the accumulation of positively charged amino acids in short linear motifs (SLiMs), and protein processing promote multifunctional behaviour. Codon bias engendered by an A + T rich genome may influence how positively-charged residues accumulate in SLiMs.