Disrupting PIAS3-mediated SUMOylation of MLK3 ameliorates poststroke neuronal damage and deficits in cognitive and sensorimotor behaviors.

Activated small ubiquitin-like modifiers (SUMOs) have been implicated in neuropathological processes following ischemic stroke. However, the target proteins of SUMOylation and their contribution to neuronal injury remain to be elucidated. MLK3 (mixed-lineage kinase 3), a member of the mitogen-activated protein kinase kinase kinase (MAPKKK) family, is a critical regulator of neuronal lesions following cerebral ischemia. Here, we found that SUMOylation of MLK3 increases in both global and focal ischemic rodent models and primary neuronal models of oxygen and glucose deprivation (OGD). SUMO1 conjugation at the Lys401 site of MLK3 promoted its activation, stimulated its downstream p38/c-Jun N-terminal kinase (JNK) cascades, and led to cell apoptosis. The interaction of MLK3 with PIAS3, a SUMO ligase, was elevated following ischemia and reperfusion. The PINIT domain of PIAS3 was involved in direct interactions with MLK3. Overexpression of the PINIT domain of PIAS3 disrupted the MLK3-PIAS3 interaction, inhibited SUMOylation of MLK3, suppressed downstream signaling, and reduced cell apoptosis and neurite damage. In rodent ischemic models, the overexpression of the PINIT domain reduced brain lesions and alleviated deficits in learning, memory, and sensorimotor functions. Our findings demonstrate that brain ischemia-induced MLK3 SUMOylation by PIAS3 is a potential target against poststroke neuronal lesions and behavioral impairments.

Washed microbiota transplantation reduces glycemic variability in unstable diabetes.

BACKGROUND: Dysbiosis of gut microbiota is causally linked to impaired host glucose metabolism. We aimed to study effects of the new method of fecal microbiota transplantation, washed microbiota transplantation (WMT), on reducing glycemic variability (GV) in unstable diabetes. METHODS: Fourteen eligible patients received three allogenic WMTs and were followed up at 1 week, 1 month, and 3 months. Primary outcomes were daily insulin dose, glucose excursions during meal tests, and GV indices calculated from continuous monitoring or self-monitoring glucose values. Secondary outcomes were multiomics data, including 16S rRNA gene sequencing, metagenomics, and metabolomics to explore underlying mechanisms. RESULTS: Daily insulin dose and glucose excursions markedly dropped, whereas GV indices significantly improved up to 1 month. WMT increased gut microbial alpha diversity, beta diversity, and network complexity. Taxonomic changes featured lower abundance of genera Bacteroides and Escherichia-Shigella, and higher abundance of genus Prevotella. Metagenomics functional annotations revealed enrichment of distinct microbial metabolic pathways, including methane biosynthesis, citrate cycle, amino acid degradation, and butyrate production. Derived metabolites correlated significantly with improved GV indices. WMT did not change circulating inflammatory cytokines, enteroendocrine hormones, or C-peptide. CONCLUSIONS: WMT showed strong ameliorating effect on GV, raising the possibility of targeting gut microbiota as an effective regimen to reduce GV in diabetes.

Differences in Insulin Sensitivity, Secretion, and the Metabolic Clearance Rate of Glucose in Newly Diagnosed Type 2 Diabetes Mellitus Patients: The Influences of Body Mass Index and Fatty Liver.

Background: Obesity and nonalcoholic fatty liver disease are strongly associated with type 2 diabetes mellitus (T2DM), affecting insulin sensitivity and ß-cell function. They interact, exacerbating the development of hyperinsulinemia to T2DM. Methods: Through oral glucose tolerance and insulin secretion tests, the relationships between insulin sensitivity and secretion, glucose clearance, body mass index (BMI), and fatty liver were studied in newly diagnosed T2DM patients. The homeostasis model assessment of insulin resistance (HOMA-IR), homeostasis model assessment of ß-cell function (HOMA-ß), insulin sensitivity index (ISI), and metabolic clearance rate (MCR) of glucose were calculated to analyze insulin sensitivity and ß-cell function. Results: There were no differences in HOMA-IR, HOMA-ß, first-phase insulin secretion (1st PH), second-phase insulin secretion (2nd PH), ISI, or MCR between lean fatty liver and lean nonfatty liver patients. Both overweight/obesity (ow/ob) and patients with fatty liver increased HOMA-IR, and decreased ISI and MCR. In the ow/ob subgroup, patients with fatty liver had severe insulin resistance but greater HOMA-ß, 1st PH, and 2nd PH than individuals with nonfatty liver. The difference in MCR between fatty liver and nonfatty liver groups was not significant. Conclusion: BMI and hepatic steatosis are independent determinants of increased insulin resistance and decreased MCR. However, it is steatosis, not BMI, related to the increase in insulin secretion.

Predictive Modeling of MAFLD Based on Hsp90α and the Therapeutic Application of Teprenone in a Diet-Induced Mouse Model.

Background and Aims: The heat shock protein (Hsp) 90α is induced by stress and regulates inflammation through multiple pathways. Elevated serum Hsp90α had been found in nonalcoholic steatohepatitis (NASH). Geranylgeranylacetone (GGA, also called teprenone) is a terpenoid derivative. It was reported to induce Hsp and alleviate insulin resistance. We aimed to evaluate the Hsp90α as a biomarker in predicting metabolic-associated fatty liver disease (MAFLD) and define the therapeutic effects of geranylgeranylacetone for the disease. Methods: A clinical study was conducted to analyze the elements associated with Hsp90α, and a predictive model of MAFLD was developed based on Hsp90α. The histopathological correlation between Hsp90α and MAFLD was investigated through a diet-induced mouse model. Furthermore, GGA was applied to the mouse model. Results: Serum Hsp90α was increased in patients with MAFLD. A positive linear relationship was found between age, glycosylated hemoglobin (HbA1c), MAFLD, and serum Hsp90α. Meanwhile, a negative linear relationship with body mass index (BMI) was found. A model using Hsp90α, BMI, HbA1c, and ALT was established for predicting MAFLD. The area under the receiver operating characteristic (ROC) curves was 0.94 (95% CI 0.909-0.971, p = 0.000). The sensitivity was 84.1%, and the specificity was 93.1%. In vitro experiments, GGA induced Hsp90α in steatosis cells. In the mice model, Hsp90α decreased in the GGA treatment group. Hepatic steatosis, inflammation, insulin resistance, and glucose intolerance were improved in the GGA-treated group. Serum Hsp90α was positively correlated with steatohepatitis activity according to hepatic histopathology. Conclusions: Serum Hsp90α was elevated in MAFLD, and a positive correlation between serum Hsp90α and the grade of activity of steatohepatitis was observed. The model using BMI, HbA1c, and alanine aminotransferase (ALT) had a good value to predict MAFLD. The findings also revealed the effectiveness of GGA in the treatment of MAFLD.

Dysbiosis of the Saliva Microbiome in Patients With Polycystic Ovary Syndrome.

Significant differences in salivary microbiota communities between polycystic ovary syndrome (PCOS) patients and healthy controls have been reported, and interestingly, some salivary microbiota exhibit diurnal oscillation in healthy people. However, whether the diurnal oscillation of salivary microbiota is present in PCOS patients is unknown. In this study, we describe the differences in the saliva microbiome between the PCOS group and the control group at different time points over 24 h. 16S rRNA gene amplicon sequencing was performed on salivary and fecal samples from 10 PCOS patients and 10 healthy controls, and salivary samples were collected at 6-h intervals over 24 h (Zeitgeber (ZT)0, ZT6, ZT12, and ZT18). Among the salivary samples, those from the PCOS group showed significant differences from those of the control group at each time point. Differences were evident in taxa level and metabolic pathways. Interestingly, we found that PCOS disrupted the diurnal rhythm of the salivary microbiota abundance, as determined in the group of healthy women. In addition, no similar changes were found in PCOS patients and controls between the oral and fecal microbiota, including differential microbiota at the phylum level. In this study, significant differences in the composition of the salivary microbiota between PCOS and healthy women were detected at different time points. We also showed that the diurnal rhythm of relative abundance of the salivary microbiota was disrupted in patients with PCOS, which might be related to development of oral-related diseases and systematic metabolic disorders.