Human hypothalamic hamartoma (HH) is a rare developmental malformation often characterized by gelastic seizures, which are refractory to medical therapy. Ictal EEG recordings from the HH have demonstrated that the epileptic source of gelastic seizures lies within the HH lesion itself. Recent advances in surgical techniques targeting HH have led to dramatic improvements in seizure control, which further supports the hypothesis that gelastic seizures originate within the HH. However, the basic cellular and molecular mechanisms of epileptogenesis in this subcortical lesion are poorly understood. Since 2003, Barrow Neurological Institute has maintained a multidisciplinary clinical program to evaluate and treat patients with HH. This program has provided a unique opportunity to investigate the basic mechanisms of epileptogenesis using surgically resected HH tissue. The first report on the electrophysiological properties of HH neurons was published in 2005. Since then, ongoing research has provided additional insights into the mechanisms by which HH generate seizure activity. In this review, we summarize this progress and propose a cellular model that suggests that GABA-mediated excitation contributes to epileptogenesis in HH lesions.

Background/Aims: Chronic kidney disease (CKD) is a worldwide public health problem. Regardless of the underlying primary disease, CKD tends to progress to end-stage kidney disease, resulting in unsatisfactory and costly treatment. Its common pathogenesis, however, remains unclear. The aim of this study was to provide an unbiased catalog of common gene-expression changes of CKD and reveal the underlying molecular mechanism using an integrative bioinformatics approach. Methods: We systematically collected over 250 Affymetrix microarray datasets from the glomerular and tubulointerstitial compartments of healthy renal tissues and those with various types of established CKD (diabetic kidney disease, hypertensive nephropathy, and glomerular nephropathy). Then, using stringent bioinformatics analysis, shared differentially expressed genes (DEGs) of CKD were obtained. These shared DEGs were further analyzed by the gene ontology (GO) and pathway enrichment analysis. Finally, the protein-protein interaction networks(PINs) were constructed to further refine our results. Results: Our analysis identified 176 and 50 shared DEGs in diseased glomeruli and tubules, respectively, including many transcripts that have not been previously reported to be involved in kidney disease. Enrichment analysis also showed that the glomerular and tubulointerstitial compartments underwent a wide range of unique pathological changes during chronic injury. As revealed by the GO enrichment analysis, shared DEGs in glomeruli were significantly enriched in exosomes. By constructing PINs, we identified several hub genes (e.g. OAS1, JUN, and FOS) and clusters that might play key roles in regulating the development of CKD. Conclusion: Our study not only further reveals the unifying molecular mechanism of CKD pathogenesis but also provides a valuable resource of potential biomarkers and therapeutic targets.

Our results suggest significant alterations in hippocampal dendritic structure and synaptic function in Apoe4 mice, even at an early age.

Background Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease (ESKD) in the world. Emerging evidence has shown that urinary mRNAs may serve as early diagnostic and prognostic biomarkers of DKD. In this article, we aimed to first establish a novel bioinformatics-based methodology for analyzing the “urinary kidney-specific mRNAs” and verify their potential clinical utility in DKD. Methods To select candidate mRNAs, a total of 127 Affymetrix microarray datasets of diabetic kidney tissues and other tissues from humans were compiled and analyzed using an integrative bioinformatics approach. Then, the urinary expression of candidate mRNAs in stage 1 study (n = 82) was verified, and the one with best performance moved on to stage 2 study (n = 80) for validation. To avoid potential detection bias, a one-step Taqman PCR assay was developed for quantification of the interested mRNA in stage 2 study. Lastly, the in situ expression of the selected mRNA was further confirmed using fluorescent in situ hybridization (FISH) assay and bioinformatics analysis. Results Our bioinformatics analysis identified sixteen mRNAs as candidates, of which urinary BBOX1 (uBBOX1) levels were significantly upregulated in the urine of patients with DKD. The expression of uBBOX1 was also increased in normoalbuminuric diabetes subjects, while remained unchanged in patients with urinary tract infection or bladder cancer. Besides, uBBOX1 levels correlated with glycemic control, albuminuria and urinary tubular injury marker levels. Similar results were obtained in stage 2 study. FISH assay further demonstrated that BBOX1 mRNA was predominantly located in renal tubular epithelial cells, while its expression in podocytes and urothelium was weak. Further bioinformatics analysis also suggested that tubular BBOX1 mRNA expression was quite stable in various types of kidney diseases. Conclusions Our study provided a novel methodology to identify and analyze urinary kidney-specific mRNAs. uBBOX1 might serve as a promising biomarker of DKD. The performance of the selected urinary mRNAs in monitoring disease progression needs further validation. Electronic supplementary material The online version of this article (10.1186/s12967-019-1818-2) contains supplementary material, which is available to authorized users.

Although it is well known that metabolic control plays a crucial role in regulating the health span and life span of various organisms, little is known for the systems metabolic profile of centenarians, the paradigm of human healthy aging and longevity. Meanwhile, how to well characterize the system‐level metabolic states in an organism of interest remains to be a major challenge in systems metabolism research. To address this challenge and better understand the metabolic mechanisms of healthy aging, we developed a method of genome‐wide precision metabolic modeling (GPMM) which is able to quantitatively integrate transcriptome, proteome and kinetome data in predictive modeling of metabolic networks. Benchmarking analysis showed that GPMM successfully characterized metabolic reprogramming in the NCI‐60 cancer cell lines; it dramatically improved the performance of the modeling with an R 2 of 0.86 between the predicted and experimental measurements over the performance of existing methods. Using this approach, we examined the metabolic networks of a Chinese centenarian cohort and identified the elevated fatty acid oxidation (FAO) as the most significant metabolic feature in these long‐lived individuals. Evidence from serum metabolomics supports this observation. Given that FAO declines with normal aging and is impaired in many age‐related diseases, our study suggests that the elevated FAO has potential to be a novel signature of healthy aging of humans.

Colon adenocarcinoma (COAD) represents a major public health issue due to its high incidence and mortality. As different histological subtypes of COAD are related to various survival outcomes and different therapies, finding specific targets and treatments for different subtypes is one of the major demands of individual disease therapy. Interestingly, as these different subtypes show distinct metabolic profiles, it may be possible to find specific targets related to histological typing by targeting COAD metabolism. In this study, the differential expression patterns of metabolism-related genes between COAD (n = 289) and adjacent normal tissue (n = 41) were analyzed by one-way ANOVA. We then used weighted gene co-expression network analysis (WGCNA) to further identify metabolism-related gene connections. To determine the critical genes related to COAD metabolism, we obtained 2,114 significantly differentially expressed genes (DEGs) and 12 modules. Among them, we found the hub module to be significantly associated with histological typing, including non-mucin-producing colon adenocarcinoma and mucin-producing colon adenocarcinoma. Combining survival analysis, we identified glycerophosphodiester phosphodiesterase 1 (GDE1) as the most significant gene associated with histological typing and prognosis. This gene displayed significantly lower expression in COAD compared with normal tissues and was significantly correlated with the prognosis of non-mucin-producing colon adenocarcinoma (p = 0.0017). Taken together, our study showed that GDE1 exhibits considerable potential as a novel therapeutic target for non-mucin-producing colon adenocarcinoma.

Glioblastoma (GBM) is one of the most aggressive forms of cancer. Although IDH1 mutation indicates a good prognosis and a potential target for treatment, most GBMs are IDH1 wild-type. Identifying additional molecular markers would help to generate personalized therapies and improve patient outcomes. Here, we used our recently developed metabolic modeling method (genome-wide precision metabolic modeling, GPMM) to investigate the metabolic profiles of GBM, aiming to identify additional novel molecular markers for this disease. We systematically analyzed the metabolic reaction profiles of 149 GBM samples lacking IDH1 mutation. Forty-eight reactions showing significant association with prognosis were identified. Further analysis indicated that the purine recycling, nucleotide interconversion, and folate metabolism pathways were the most robust modules related to prognosis. Considering the three pathways, we then identified the most significant GBM type for a better prognosis, namely N+P-. This type presented high nucleotide interconversion (N+) and low purine recycling (P-). N+P--type exhibited a significantly better outcome (log-rank p = 4.7 × 10-7) than that of N-P+. GBM patients with the N+P−-type had a median survival time of 19.6 months and lived 65% longer than other GBM patients. Our results highlighted a novel molecular type of GBM, which showed relatively high frequency (26%) in GBM patients lacking the IDH1 mutation, and therefore exhibits potential in GBM prognostic assessment and personalized therapy.

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