No abstract

Aging occurs due to a combination of several factors, such as telomere attrition, cellular senescence, and stem cell exhaustion. The telomere attrition-dependent cellular senescence is regulated by increased levels of SMAD specific E3 ubiquitin protein ligase 2 (smurf2). With age smurf2 expression increases and Smurf2 protein interacts with several regulatory proteins including, Smad7, Ep300, Yy1, Sirt1, Mdm2, and Tp53, likely affecting its function related to cellular aging. The current study aimed at analyzing smurf2 expression in the aged brain because of its potential regulatory roles in the cellular aging process. Zebrafish were used because like humans they age gradually and their genome has 70% similarity. In the current study, we demonstrated that smurf2 gene and protein expression levels altered in a region-specific manner during the aging process. Also, in both young and old brains, Smurf2 protein was enriched in the cytosol. These results imply that during aging Smurf2 is regulated by several mechanisms including post-translational modifications (PTMs) and complex formation. Also, the expression levels of its interacting partners defined by the STRING database, tp53, mdm2, ep300a, yy1a, smad7, and sirt1, were analyzed. Multivariate analysis indicated that smurf2, ep300a, and sirt1, whose proteins regulate ubiquitination, acetylation, and deacetylation of target proteins including Smad7 and Tp53, showed age-and brain region-dependent patterns. Our data suggest a likely balance between Smurf2-and Mdm2mediated ubiquitination, and Ep300a-mediated acetylation/Sirt1-mediated deacetylation, which most possibly affects the functionality of other interacting partners in regulating cellular and synaptic aging and ultimately cognitive dysfunction.

No abstract

For decades, molecular biologists have been uncovering the mechanics of biological systems. Efforts to bring their findings together have led to the development of multiple databases and information systems that capture and present pathway information in a computable network format. Concurrently, the advent of modern omics technologies has empowered researchers to systematically profile cellular processes across different modalities. Numerous algorithms, methodologies, and tools have been developed to use prior knowledge networks in the analysis of omics datasets. Interestingly, it has been repeatedly demonstrated that the source of prior knowledge can greatly impact the results of a given analysis. For these methods to be successful it is paramount that their selection of prior knowledge networks is amenable to the data type and the computational task they aim to accomplish. Here we present a five-level framework that broadly describes network models in terms of their scope, level of detail, and ability to inform causal predictions. To contextualize this framework, we review a handful of network-based omics analysis methods at each level, while also describing the computational tasks they aim to accomplish.

Interactions among tumor, immune and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood. Here, we characterize functional and clinical relevance of genes encoding CMPs in GBM at bulk, single cell, and spatial anatomical resolution. We identify a matrix code for genes encoding CMPs whose expression levels categorize GBM tumors into matrisome-high and matrisome-low groups that correlate with worse and better survival, respectively, of patients. The matrisome enrichment is associated with specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells and immune checkpoint gene expression. Anatomical and single cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative anatomic structures that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene matrisome signature that retains and further refines the prognostic value of genes encoding CMPs and, importantly, potentially predicts responses to PD1 blockade in clinical trials for GBM. The matrisome gene expression profiles may provide biomarkers of functionally relevant GBM niches that contribute to mesenchymal-immune cross talk and patient stratification to optimize treatment responses.