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Nitrate is an essential nutrient and signaling molecule for plant growth. Plants sense intracellular nitrate to adjust their metabolic and growth responses. Here we identify the primary nitrate sensor in plants. We found that mutation of all seven Arabidopsis NIN-like protein (NLP) transcription factors abolished plants’ primary nitrate responses and developmental programs. Analyses of NIN-NLP7 chimeras and nitrate binding revealed that NLP7 is derepressed upon nitrate perception via its amino terminus. A genetically encoded fluorescent split biosensor, mCitrine-NLP7, enabled visualization of single-cell nitrate dynamics in planta. The nitrate sensor domain of NLP7 resembles the bacterial nitrate sensor NreA. Substitutions of conserved residues in the ligand-binding pocket impaired the ability of nitrate-triggered NLP7 to control transcription, transport, metabolism, development, and biomass. We propose that NLP7 represents a nitrate sensor in land plants.

This study aimed to estimate the prevalence of thyroid nodules (TN) and investigate its correlation with metabolic parameters, especially uric acid (UA) in northwest Chinese population. We conducted a large cross-sectional survey with 67,781 residents (33,020 men, 34,761 women), aged from 18 to 86 years in Shanxi, China, from January 2012 to December 2014. A thyroid ultrasound examination was performed with number and size of nodules being recorded. Metabolic parameters including body mass index (BMI), blood pressure (BP), triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c), high-density lipoprotein cholesterol (HDL-c), fasting glucose (FG), and uric acid (UA) were also examined. Our study revealed that approximately 30.7% of men and 39.9% of women in Northwest China had TN, about half of which were multi-nodularity and a quarter of their TN larger than 1 cm. The prevalence of TN increased with aging and increasing BMI, and metabolic disorders, which also related to the increased incident of multi-nodularity and larger TN. Serum UA appeared to be a protective factor for TN in men older than 30 years, but a risk factor in both men younger than 30 years and women older than 30 years. This phenomenon needs to be further investigated.

Objectives: Lung-protective ventilation for acute respiratory distress syndrome aims for providing sufficient oxygenation and carbon dioxide clearance, while limiting the harmful effects of mechanical ventilation. “Flow-controlled ventilation”, providing a constant expiratory flow, has been suggested as a new lung-protective ventilation strategy. The aim of this study was to test whether flow-controlled ventilation attenuates lung injury in an animal model of acute respiratory distress syndrome. Design: Preclinical, randomized controlled animal study. Setting: Animal research facility. Subjects: Nineteen German landrace hybrid pigs. Intervention: Flow-controlled ventilation (intervention group) or volume-controlled ventilation (control group) with identical tidal volume (7 mL/kg) and positive end-expiratory pressure (9 cm H2O) after inducing acute respiratory distress syndrome with oleic acid. Measurements and Main Results: Pao 2 and Paco 2, minute volume, tracheal pressure, lung aeration measured via CT, alveolar wall thickness, cell infiltration, and surfactant protein A concentration in bronchoalveolar lavage fluid. Five pigs were excluded leaving n equals to 7 for each group. Compared with control, flow-controlled ventilation elevated Pao 2 (154 ± 21 vs 105 ± 9 torr; 20.5 ± 2.8 vs 14.0 ± 1.2 kPa; p = 0.035) and achieved comparable Paco 2 (57 ± 3 vs 54 ± 1 torr; 7.6 ± 0.4 vs 7.1 ± 0.1 kPa; p = 0.37) with a lower minute volume (6.4 ± 0.5 vs 8.7 ± 0.4 L/min; p < 0.001). Inspiratory plateau pressure was comparable in both groups (31 ± 2 vs 34 ± 2 cm H2O; p = 0.16). Flow-controlled ventilation increased normally aerated (24% ± 4% vs 10% ± 2%; p = 0.004) and decreased nonaerated lung volume (23% ± 6% vs 38% ± 5%; p = 0.033) in the dependent lung region. Alveolar walls were thinner (5.5 ± 0.1 vs 7.8 ± 0.2 µm; p < 0.0001), cell infiltration was lower (20 ± 2 vs 32 ± 2 n/field; p < 0.0001), and normalized surfactant protein A concentration was higher with flow-controlled ventilation (1.1 ± 0.04 vs 1.0 ± 0.03; p = 0.039). Conclusions: Flow-controlled ventilation enhances lung aeration in the dependent lung region and consequently improves gas exchange and attenuates lung injury. Control of the expiratory flow may provide a novel option for lung-protective ventilation.

Currently, there is no adequate, sensitive, reproducible, specific and noninvasive biomarker that can reliably be used to detect renal cell carcinoma (RCC). Previous studies have elucidated the urinary non-volatile metabolic profile of RCC. However, whether urinary volatile organic compound (VOC) profiles are able to identify RCC remains to be elucidated. In the present study, urine was collected from 22 patients with RCC and 25 healthy subjects. Principal component analysis and orthogonal partial least square discriminant analysis were used to compare the data of patients and healthy subjects, and preoperative and postoperative patients undergoing radical nephrectomy. In total, 11 VOC biomarkers were elevated in the RCC patients compared to the healthy subjects, which were phenol; decanal; 1,6-dioxacyclododecane-7,12-dione; 1-bromo-1-(3-methyl-1-pentenylidene)-2,2,3,3-tetramethyl-cyclopropane; nonanal; 3-ethyl-3-methylheptane; isolongifolene-5-ol; 2,5-cyclohexadiene-1,4-dione, 2,6-bis(1,1-dimethylethyl); tetradecane; aniline; and 2,6,10,14-tetramethyl-pentadecane. Three biomarkers were decreased in RCC patients: styrene, 4-heptanone and dimethylsilanediol. In preoperative patients, 2-ethyl-1-hexanol and cyclohexanone were elevated, while 6-t-butyl-2,2,9,9-tetramethyl-3,5-decadien-7-yne were decreased when compared to postoperative patients. Compared with the healthy subjects, RCC has a unique VOC profile, suggesting that VOC profiles may be a useful diagnostic assay for RCC.

Type 2 diabetes (T2D) is characterized by β-cell dedifferentiation, but underlying mechanisms remain unclear. The purpose of the current study was to explore the mechanisms of β-cell dedifferentiation with and without long-term control of calorie intake. We used a diabetes mouse model (db/db) to analyze the changes in the expression levels of β-cell-specific transcription factors (TFs) and functional factors with long-term caloric restriction (CR). Our results showed that chronic euglycemia was maintained in the db/db mice with long-term CR intervention, and β-cell dedifferentiation was significantly reduced. The expression of Glut2, Pdx1, and Nkx6.1 was reversed, while MafA expression was significantly increased with long-term CR. GLP-1 pathway was reactivated with long-term CR. Our work showed that the course of β-cell dedifferentiation can intervene by long-term control of calorie intake. Key β-cell-specific TFs and functional factors play important roles in maintaining β-cell differentiation. Targeting these factors could optimize T2D therapies.

Background and objectiveClinical characteristics of obesity are heterogenous, but current classification for diagnosis is simply based on BMI or metabolic healthiness. The purpose of this study was to use machine learning to explore a more precise classification of obesity subgroups towards informing individualized therapy.Subjects and MethodsIn a multi-center study (n=2495), we used unsupervised machine learning to cluster patients with obesity from Shanghai Tenth People’s hospital (n=882, main cohort) based on three clinical variables (AUCs of glucose and of insulin during OGTT, and uric acid). Verification of the clustering was performed in three independent cohorts from external hospitals in China (n = 130, 137, and 289, respectively). Statistics of a healthy normal-weight cohort (n=1057) were measured as controls.ResultsMachine learning revealed four stable metabolic different obese clusters on each cohort. Metabolic healthy obesity (MHO, 44% patients) was characterized by a relatively healthy-metabolic status with lowest incidents of comorbidities. Hypermetabolic obesity-hyperuricemia (HMO-U, 33% patients) was characterized by extremely high uric acid and a large increased incidence of hyperuricemia (adjusted odds ratio [AOR] 73.67 to MHO, 95%CI 35.46-153.06). Hypermetabolic obesity-hyperinsulinemia (HMO-I, 8% patients) was distinguished by overcompensated insulin secretion and a large increased incidence of polycystic ovary syndrome (AOR 14.44 to MHO, 95%CI 1.75-118.99). Hypometabolic obesity (LMO, 15% patients) was characterized by extremely high glucose, decompensated insulin secretion, and the worst glucolipid metabolism (diabetes: AOR 105.85 to MHO, 95%CI 42.00-266.74; metabolic syndrome: AOR 13.50 to MHO, 95%CI 7.34-24.83). The assignment of patients in the verification cohorts to the main model showed a mean accuracy of 0.941 in all clusters.ConclusionMachine learning automatically identified four subtypes of obesity in terms of clinical characteristics on four independent patient cohorts. This proof-of-concept study provided evidence that precise diagnosis of obesity is feasible to potentially guide therapeutic planning and decisions for different subtypes of obesity.Clinical Trial Registrationwww.ClinicalTrials.gov, NCT04282837.

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