Mitochondria Profoundly Influence Apolipoprotein E Biology

BACKGROUND

Mitochondria can trigger Alzheimer's disease (AD)-associated molecular phenomena, but how mitochondria impact apolipoprotein E (APOE; apoE) is not well known.

OBJECTIVE

Consider whether and how mitochondrial biology influences APOE and apoE biology.

METHODS

We measured APOE expression in human SH-SY5Y neuronal cells with different forms of mitochondrial dysfunction including total, chronic mitochondrial DNA (mtDNA) depletion (ρ0 cells); acute, partial mtDNA depletion; and toxin-induced mitochondrial dysfunction. We further assessed intracellular and secreted apoE protein levels in the ρ0 cells and interrogated the impact of transcription factors and stress signaling pathways known to influence APOE expression.

RESULTS

SH-SY5Y ρ0 cells exhibited a 65-fold increase in APOE mRNA, an 8-fold increase in secreted apoE protein, and increased intracellular apoE protein. Other models of primary mitochondrial dysfunction including partial mtDNA-depletion, toxin-induced respiratory chain inhibition, and chemical-induced manipulations of the mitochondrial membrane potential similarly increased SH-SY5Y cell APOE mRNA. We explored potential mediators and found in the ρ0 cells knock-down of the C/EBPα and NFE2L2 (Nrf2) transcription factors reduced APOE mRNA. The activity of two mitogen-activated protein kinases, JNK and ERK, also strongly influenced ρ0 cell APOE mRNA levels.

CONCLUSION

Primary mitochondrial dysfunction either directly or indirectly activates APOE expression in a neuronal cell model by altering transcription factors and stress signaling pathways. These studies demonstrate mitochondrial biology can influence the biology of the APOE gene and apoE protein, which are implicated in AD.

Pharmacologic enrichment of exosome yields and mitochondrial cargo

In studies with human participants, exosome-based biospecimens can facilitate unique biomarker assessments. As exosome cargos can include mitochondrial components, there is interest in using exosomes to inform the status of an individual's mitochondria. Here, we evaluated whether targeted pharmacologic manipulations could influence the quantity of exosomes shed by cells, and whether these manipulations could impact their mitochondrial cargos. We treated human SH-SY5Y cells with bafilomycin A1, which interferes with general autophagy and mitophagy by inhibiting lysosome acidification and lysosome-autophagosome fusion; deferiprone (DFP), which enhances receptor-mediated mitophagy; or both. Exosome fractions from treated cells were harvested from the cell medium and analyzed for content including mitochondria-derived components. We found bafilomycin increased particle yields, and a combination of bafilomycin plus DFP consistently increased particle yields and mitochondria-associated content. Specifically, the exosome fractions from the bafilomycin plus DFP-treated cells contained more mitochondrial DNA (mtDNA), mtDNA-derived mRNA transcripts, and citrate synthase protein. Our data suggest pharmacologic manipulations that enhance mitophagy initiation, while inhibiting the lysosomal digestion of autophagosomes and multivesicular bodies, could potentially enhance the sensitivity of exosome-based biomarker assays intended to inform the status of an individual's mitochondria.

GeneAnalytics Pathways and Profiling of Shared Autism and Cancer Genes

Recent research revealed that autism spectrum disorders (ASD) and cancer may share common genetic architecture, with evidence first reported with the PTEN gene. There are approximately 800 autism genes and 3500 genes associated with cancer. The VarElect phenotype program was chosen to identify genes jointly associated with both conditions based on genomic information stored in GeneCards. In total, 138 overlapping genes were then profiled with GeneAnalytics, an analysis pathway enrichment tool utilizing existing gene datasets to identify shared pathways, mechanisms, and phenotypes. Profiling the shared gene data identified seven significantly associated diseases of 2310 matched disease entities with factors implicated in shared pathology of ASD and cancer. These included 371 super-pathways of 455 matched entities reflecting major cell-signaling pathways and metabolic disturbances (e.g., CREB, AKT, GPCR); 153 gene ontology (GO) biological processes of 226 matched processes; 41 GO molecular functions of 78 matched functions; and 145 phenotypes of 232 matched phenotypes. The entries were scored and ranked using a matching algorithm that takes into consideration genomic expression, sequencing, and microarray datasets with cell or tissue specificity. Shared mechanisms may lead to the identification of a common pathology and a better understanding of causation with potential treatment options to lessen the severity of ASD-related symptoms in those affected.

Exploring genetic susceptibility to obesity through genome functional pathway analysis

OBJECTIVE

Obesity has been reaching epidemic levels in recent decades, with a growing body of research identifying predisposing genetic components. To explore the relationship of genetic factors contributing to obesity, an analytical computer-based gene-profiling approach utilizing an updated list of clinically relevant and known obesity-related genes was undertaken.

METHODS

An updated list of 494 genes reportedly associated with obesity was compiled, and the GeneAnalytics profiling software was utilized to interrogate genomic databases from GeneCards® to cross-reference obesity gene sets against tissues and cells, diseases, genetic pathways, gene ontology (GO)-biological processes and GO-molecular functions, phenotypes, and compounds.

RESULTS

Obesity-related fields identified by GeneAnalytics algorithms included 8 diseases, 46 pathways, 62 biological processes, 22 molecular functions, 148 phenotypes, and 286 compounds impacting adipogenesis, signal transduction by G-protein coupled receptors, and lipid metabolism involving insulin-related genes (IGF1, INS, IRS1). GO-biological processes identified feeding behavior, cholesterol metabolic process, and glucose and cholesterol homeostasis pathways, while GO-molecular processes pertained to receptor binding, affecting glucose homeostasis, body weight, and circulating insulin and triglyceride levels.

CONCLUSIONS

The gene-profiling model suggests that pathogenesis of obesity relates to the coordination of biological responses to glucose and intracellular lipids possibly through a disruption of biochemical cascades and cellular signaling arising from affected receptors.