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Summary: Adult stem cells play a critical role in tissue repair and maintenance. In tissues with slow turnover, including skeletal muscle, these cells are maintained in a mitotically quiescent state yet remain poised to re-enter the cell cycle to replenish themselves and regenerate the tissue. Using a panomics approach we show that the PAX7/NEDD4L axis acts against muscle stem cell activation in homeostatic skeletal muscle. Our findings suggest that PAX7 transcriptionally activates the E3 ubiquitin ligase Nedd4L and that the conditional genetic deletion of Nedd4L impairs muscle stem cell quiescence, with an upregulation of cell cycle and myogenic differentiation genes. Loss of Nedd4L in muscle stem cells results in the expression of doublecortin (DCX), which is exclusively expressed during their in vivo activation. Together, these data establish that the ubiquitin proteasome system, mediated by Nedd4L, is a key contributor to the muscle stem cell quiescent state in adult mice.

Peptide retention time (RT) prediction algorithms are tools to study and identify the physicochemical properties that drive the peptide-sorbent interaction. Traditional RT algorithms use multiple linear regression with manually curated parameters to determine the degree of direct contribution for each parameter and improvements to RT prediction accuracies relied on superior feature engineering. Deep learning led to a significant increase in RT prediction accuracy and automated feature engineering via chaining multiple learning modules. However, the significance and the identity of these extracted variables are not well understood due to the inherent complexity when interpreting “relationships-of-relationships” found in deep learning variables. To achieve both accuracy and interpretability simultaneously, we isolated individual modules used in deep learning and the isolated modules are the shallow learners employed for RT prediction in this work. Using a shallow convolutional neural network (CNN) and gated recurrent unit (GRU), we find that the spatial features obtained via the CNN correlate with real-world physicochemical properties namely cross-collisional sections (CCS) and variations of assessable surface area (ASA). Furthermore, we determined that the discovered parameters are “micro-coefficients” that contribute to the “macro-coefficient” – hydrophobicity. Manually embedding CCS and the variations of ASA to the GRU model yielded an R2 = 0.981 using only 525 variables and can represent 88% of the ∼110,000 tryptic peptides used in our dataset. This work highlights the feature discovery process of our shallow learners can achieve beyond traditional RT models in performance and have better interpretability when compared with the deep learning RT algorithms found in the literature.

We recently characterized SEC62 as an oncogene in non-small-cell lung cancer (NSCLC). Here we aimed to gain further insight into the molecular mechanisms of the cancer-related functions of this oncogene. We performed 2D-DIGE proteome analysis of tumor material from patients with NSCLC and of HEK293 cells stably overexpressing plasmid-encoded SEC62, combined with investigation of the Sec62 interactome. Furthermore, we analyzed the proteomic effects of siRNA-mediated depletion of the Sec62-interacting protein Sec63. We identified a comprehensive list of differentially regulated proteins, providing new insights into the molecular mechanisms of the cancer-related functions of Sec62 in cell migration, drug resistance, and Ca2+-homeostasis.

More than 450 post-translational modifications (PTMs) are known, however, currently only some of those can be enriched and analyzed from complex samples such as cell lysates. Therefore, we need additional methods and concepts to improve our understanding about the dynamic crosstalk of PTMs and the highly context-dependent regulation of protein function by so-called ‘PTM codes’. The mere focus on affinity-based enrichment techniques may not be sufficient to achieve this ambitious goal. However, the complementary use of two-dimensional chromatography-based strategies such as COFRADIC and ChaFRADIC might open new avenues for enriching a variety of so far inaccessible PTMs for large-scale proteome studies.