Exercise preserves physical fitness during aging through AMPK and mitochondrial dynamics.

Exercise is a nonpharmacological intervention that improves health during aging and a valuable tool in the diagnostics of aging-related diseases. In muscle, exercise transiently alters mitochondrial functionality and metabolism. Mitochondrial fission and fusion are critical effectors of mitochondrial plasticity, which allows a fine-tuned regulation of organelle connectiveness, size, and function. Here we have investigated the role of mitochondrial dynamics during exercise in the model organism Caenorhabditis elegans. We show that in body-wall muscle, a single exercise session induces a cycle of mitochondrial fragmentation followed by fusion after a recovery period, and that daily exercise sessions delay the mitochondrial fragmentation and physical fitness decline that occur with aging. Maintenance of proper mitochondrial dynamics is essential for physical fitness, its enhancement by exercise training, and exercise-induced remodeling of the proteome. Surprisingly, among the long-lived genotypes we analyzed (isp-1,nuo-6, daf-2, eat-2, and CA-AAK-2), constitutive activation of AMP-activated protein kinase (AMPK) uniquely preserves physical fitness during aging, a benefit that is abolished by impairment of mitochondrial fission or fusion. AMPK is also required for physical fitness to be enhanced by exercise, with our findings together suggesting that exercise may enhance muscle function through AMPK regulation of mitochondrial dynamics. Our results indicate that mitochondrial connectivity and the mitochondrial dynamics cycle are essential for maintaining physical fitness and exercise responsiveness during aging and suggest that AMPK activation may recapitulate some exercise benefits. Targeting mechanisms to optimize mitochondrial fission and fusion, as well as AMPK activation, may represent promising strategies for promoting muscle function during aging.

Improving annotation propagation on molecular networks through random walks: introducing ChemWalker.

MOTIVATION: Annotation of the mass signals is still the biggest bottleneck for the untargeted mass spectrometry analysis of complex mixtures. Molecular networks are being increasingly adopted by the mass spectrometry community as a tool to annotate large-scale experiments. We have previously shown that the process of propagating annotations from spectral library matches on molecular networks can be automated using Network Annotation Propagation (NAP). One of the limitations of NAP is that the information for the spectral matches is only propagated locally, to the first neighbor of a spectral match. Here, we show that annotation propagation can be expanded to nodes not directly connected to spectral matches using random walks on graphs, introducing the ChemWalker python library. RESULTS: Similarly to NAP, ChemWalker relies on combinatorial in silico fragmentation results, performed by MetFrag, searching biologically relevant databases. Departing from the combination of a spectral network and the structural similarity among candidate structures, we have used MetFusion Scoring function to create a weight function, producing a weighted graph. This graph was subsequently used by the random walk to calculate the probability of 'walking' through a set of candidates, departing from seed nodes (represented by spectral library matches). This approach allowed the information propagation to nodes not directly connected to the spectral library match. Compared with NAP, ChemWalker has a series of improvements, on running time, scalability and maintainability and is available as a standalone python package. AVAILABILITY AND IMPLEMENTATION: ChemWalker is freely available at https://github.com/computational-chemical-biology/ChemWalker. CONTACT: ridasilva@usp.br. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Glutaminolysis dynamics during astrocytoma progression correlates with tumor aggressiveness.

BACKGROUND: Glioblastoma is the most frequent and high-grade adult malignant central nervous system tumor. The prognosis is still poor despite the use of combined therapy involving maximal surgical resection, radiotherapy, and chemotherapy. Metabolic reprogramming currently is recognized as one of the hallmarks of cancer. Glutamine metabolism through glutaminolysis has been associated with tumor cell maintenance and survival, and with antioxidative stress through glutathione (GSH) synthesis. METHODS: In the present study, we analyzed the glutaminolysis-related gene expression levels in our cohort of 153 astrocytomas of different malignant grades and 22 non-neoplastic brain samples through qRT-PCR. Additionally, we investigated the protein expression profile of the key regulator of glutaminolysis (GLS), glutamate dehydrogenase (GLUD1), and glutamate pyruvate transaminase (GPT2) in these samples. We also investigated the glutathione synthase (GS) protein profile and the GSH levels in different grades of astrocytomas. The differential gene expressions were validated in silico on the TCGA database. RESULTS: We found an increase of glutaminase isoform 2 gene (GLSiso2) expression in all grades of astrocytoma compared to non-neoplastic brain tissue, with a gradual expression increment in parallel to malignancy. Genes coding for GLUD1 and GPT2 expression levels varied according to the grade of malignancy, being downregulated in glioblastoma, and upregulated in lower grades of astrocytoma (AGII-AGIII). Significant low GLUD1 and GPT2 protein levels were observed in the mesenchymal subtype of GBM. CONCLUSIONS: In glioblastoma, particularly in the mesenchymal subtype, the downregulation of both genes and proteins (GLUD1 and GPT2) increases the source of glutamate for GSH synthesis and enhances tumor cell fitness due to increased antioxidative capacity. In contrast, in lower-grade astrocytoma, mainly in those harboring the IDH1 mutation, the gene expression profile indicates that tumor cells might be sensitized to oxidative stress due to reduced GSH synthesis. The measurement of GLUD1 and GPT2 metabolic substrates, ammonia, and alanine, by noninvasive MR spectroscopy, may potentially allow the identification of IDH1mut AGII and AGIII progression towards secondary GBM.