Diabetic kidney disease is a major cause of renal failure that urgently necessitates a breakthrough in disease management. Here we show using untargeted metabolomics that levels of phenyl sulfate, a gut microbiota-derived metabolite, increase with the progression of diabetes in rats overexpressing human uremic toxin transporter SLCO4C1 in the kidney, and are decreased in rats with limited proteinuria. In experimental models of diabetes, phenyl sulfate administration induces albuminuria and podocyte damage. In a diabetic patient cohort, phenyl sulfate levels significantly correlate with basal and predicted 2-year progression of albuminuria in patients with microalbuminuria. Inhibition of tyrosine phenol-lyase, a bacterial enzyme responsible for the synthesis of phenol from dietary tyrosine before it is metabolized into phenyl sulfate in the liver, reduces albuminuria in diabetic mice. Together, our results suggest that phenyl sulfate contributes to albuminuria and could be used as a disease marker and future therapeutic target in diabetic kidney disease.

Sarcopenia is associated with increased morbidity and mortality in chronic kidney disease (CKD). Pathogenic mechanism of skeletal muscle loss in CKD, which is defined as uremic sarcopenia, remains unclear. We found that causative pathological mechanism of uremic sarcopenia is metabolic alterations by uremic toxin indoxyl sulfate. Imaging mass spectrometry revealed indoxyl sulfate accumulated in muscle tissue of a mouse model of CKD. Comprehensive metabolomics revealed that indoxyl sulfate induces metabolic alterations such as upregulation of glycolysis, including pentose phosphate pathway acceleration as antioxidative stress response, via nuclear factor (erythroid-2-related factor)-2. The altered metabolic flow to excess antioxidative response resulted in downregulation of TCA cycle and its effected mitochondrial dysfunction and ATP shortage in muscle cells. In clinical research, a significant inverse association between plasma indoxyl sulfate and skeletal muscle mass in CKD patients was observed. Our results indicate that indoxyl sulfate is a pathogenic factor for sarcopenia in CKD.

The mass extinction of life 66 million years ago at the Cretaceous/Paleogene boundary, marked by the extinctions of dinosaurs and shallow marine organisms, is important because it led to the macroevolution of mammals and appearance of humans. The current hypothesis for the extinction is that an asteroid impact in present-day Mexico formed condensed aerosols in the stratosphere, which caused the cessation of photosynthesis and global near-freezing conditions. Here, we show that the stratospheric aerosols did not induce darkness that resulted in milder cooling than previously thought. We propose a new hypothesis that latitude-dependent climate changes caused by massive stratospheric soot explain the known mortality and survival on land and in oceans at the Cretaceous/Paleogene boundary. The stratospheric soot was ejected from the oil-rich area by the asteroid impact and was spread globally. The soot aerosols caused sufficiently colder climates at mid–high latitudes and drought with milder cooling at low latitudes on land, in addition to causing limited cessation of photosynthesis in global oceans within a few months to two years after the impact, followed by surface-water cooling in global oceans in a few years. The rapid climate change induced terrestrial extinctions followed by marine extinctions over several years.

Purpose: We aimed to develop an ovarian cancer-specific predictive framework for clinical stage, histotype, residual tumor burden, and prognosis using machine learning methods based on multiple biomarkers.Experimental Design: Overall, 334 patients with epithelial ovarian cancer (EOC) and 101 patients with benign ovarian tumors were randomly assigned to "training" and "test" cohorts. Seven supervised machine learning classifiers, including Gradient Boosting Machine (GBM), Support Vector Machine, Random Forest (RF), Conditional RF (CRF), Na€ ve Bayes, Neural Network, and Elastic Net, were used to derive diagnostic and prognostic information from 32 parameters commonly available from pretreatment peripheral blood tests and age.Results: Machine learning techniques were superior to conventional regression-based analyses in predicting multiple clinical parameters pertaining to EOC. Ensemble meth-ods combining weak decision trees, such as GBM, RF, and CRF, showed the best performance in EOC prediction. The values for the highest accuracy and area under the ROC curve (AUC) for segregating EOC from benign ovarian tumors with RF were 92.4% and 0.968, respectively. The highest accuracy and AUC for predicting clinical stages with RF were 69.0% and 0.760, respectively. High-grade serous and mucinous histotypes of EOC could be preoperatively predicted with RF. An ordinal RF classifier could distinguish complete resection from others. Unsupervised clustering analysis identified subgroups among early-stage EOC patients with significantly worse survival.Conclusions: Machine learning systems can provide critical diagnostic and prognostic prediction for patients with EOC before initial intervention, and the use of predictive algorithms may facilitate personalized treatment options through pretreatment stratification of patients.NOTE: There were too few early-stage EOC patients with residual tumor. A definition for the significance of bold is P value of < 0.05.