The ancient phosphatidylinositol 3-kinase signaling system is a master regulator of energy and carbon metabolism in algae

Algae undergo a complete metabolic transformation under stress by arresting cell growth, inducing autophagy and hyperaccumulating biofuel precursors such as triacylglycerols and starch. However, the regulatory mechanisms behind this stress-induced transformation are still unclear. Here, we use biochemical, mutational, and “omics” approaches to demonstrate that PI3K signaling mediates the homeostasis of energy molecules and influences carbon metabolism in algae. In Chlamydomonas reinhardtii, the inhibition and knockdown (KD) of algal class III PI3K led to significantly decreased cell growth, altered cell morphology, and higher lipid and starch contents. Lipid profiling of wild-type and PI3K KD lines showed significantly reduced membrane lipid breakdown under nitrogen starvation (-N) in the KD. RNA-seq and network analyses showed that under -N conditions, the KD line carried out lipogenesis rather than lipid hydrolysis by initiating de novo fatty acid biosynthesis, which was supported by tricarboxylic acid cycle down-regulation and via acetyl-CoA synthesis from glycolysis. Remarkably, autophagic responses did not have primacy over inositide signaling in algae, unlike in mammals and vascular plants. The mutant displayed a fundamental shift in intracellular energy flux, analogous to that in tumor cells. The high free fatty acid levels and reduced mitochondrial ATP generation led to decreased cell viability. These results indicate that the PI3K signal transduction pathway is the metabolic gatekeeper restraining biofuel yields, thus maintaining fitness and viability under stress in algae. This study demonstrates the existence of homeostasis between starch and lipid synthesis controlled by lipid signaling in algae and expands our understanding of such processes, with biotechnological and evolutionary implications.Ministry of Science, ICT and Future Planning 2015M3A6A2065697Ministry of Oceans and Fisheries 2015018

Renal outcomes and all-cause death associated with sodium-glucose co-transporter-2 inhibitors versus other glucose-lowering drugs (CVD-REAL 3 Korea)

Aims To investigate the effectiveness of sodium-glucose co-transporter-2 (SGLT2) inhibitors on the risk of progression to end-stage renal disease (ESRD) and all-cause mortality in a broad range of patients with type 2 diabetes (T2D) using a Korean nationwide cohort. Materials and Methods Using data from the Korean National Health Insurance Service database from January 2014 to December 2017, a total of 701 674 patients were identified with T2D. We divided these patients into new users of SGLT2 inhibitors and new users of other glucose-lowering drugs (oGLDs). Using propensity scores, patients in the two groups were matched 1:1. We assessed the risk of ESRD and all-cause death. Results There were 45 016 patients in each group, and baseline characteristics were well balanced between the groups. The patients' mean age was 58.1 +/- 10.6 years and mean estimated glomerular filtration rate (eGFR) was 89.2 +/- 27.4 mL/min/1.73m(2), and 8% of patients had proteinuria. We identified 167 incident ESRD cases and 1070 all-cause deaths during follow-up. Use of SGLT2 inhibitors versus oGLDs was associated with a lower risk of ESRD (hazard ratio [HR] 0.47, 95% confidence interval [CI] 0.34 to 0.65) and all-cause death (HR 0.82, 95% CI 0.73 to 0.93). In a subgroup analysis by eGFR, initiation of SGLT2 inhibitor treatment, compared with oGLD treatment, was associated with lower risk of progression to ESRD among patients with eGFR 60 to 90 mL/min/1.73m(2) and those with eGFR = 90 and 60 to 90 mL/min/1.73m(2). Conclusion In this large nationwide study of Korean patients with T2D, initiation of SGLT2 inhibitors versus oGLDs was associated with lower risk of ESRD and all-cause death

Feasibility of Coronary 18F-Sodium Fluoride Positron-Emission Tomography Assessment With the Utilization of Previously Acquired Computed Tomography Angiography

BACKGROUND: We assessed the feasibility of utilizing previously acquired computed tomography angiography (CTA) with subsequent positron-emission tomography (PET)-only scan for the quantitative evaluation of 18F-NaF PET coronary uptake. METHODS AND RESULTS: Forty-five patients (age 67.1±6.9 years; 76% males) underwent CTA (CTA1) and combined 18F-NaF PET/CTA (CTA2) imaging within 14 [10, 21] days. We fused CTA1 from visit 1 with 18F-NaF PET (PET) from visit 2 and compared visual pattern of activity, maximal standard uptake (SUVmax) values, and target to background ratio (TBR) measurements on (PET/CTA1) fused versus hybrid (PET/CTA2). On PET/CTA2, 226 coronary plaques were identified. Fifty-eight coronary segments from 28 (62%) patients had high 18F-NaF uptake (TBR >1.25), whereas 168 segments had lesions with 18F-NaF TBR ≤1.25. Uptake in all lesions was categorized identically on coregistered PET/CTA1. There was no significant difference in 18F-NaF uptake values between PET/CTA1 and PET/CTA2 (SUVmax, 1.16±0.40 versus 1.15±0.39; P=0.53; TBR, 1.10±0.45 versus 1.09±0.46; P=0.55). The intraclass correlation coefficient for SUVmax and TBR was 0.987 (95% CI, 0.983-0.991) and 0.986 (95% CI, 0.981-0.992). There was no fixed or proportional bias between PET/CTA1 and PET/CTA2 for SUVmax and TBR. Cardiac motion correction of PET scans improved reproducibility with tighter 95% limits of agreement (±0.14 for SUVmax and ±0.15 for TBR versus ±0.20 and ±0.20 on diastolic imaging; P<0.001). CONCLUSIONS: Coronary CTA/PET protocol with CTA first followed by PET-only allows for reliable and reproducible quantification of 18F-NaF coronary uptake. This approach may facilitate selection of high-risk patients for PET-only imaging based on results from prior CTA, providing a practical workflow for clinical application.ope

STK295900, a Dual Inhibitor of Topoisomerase 1 and 2, Induces G<inf>2</inf> Arrest in the Absence of DNA Damage

STK295900, a small synthetic molecule belonging to a class of symmetric bibenzimidazoles, exhibits antiproliferative activity against various human cancer cell lines from different origins. Examining the effect of STK295900 in HeLa cells indicates that it induces G2 phase arrest without invoking DNA damage. Further analysis shows that STK295900 inhibits DNA relaxation that is mediated by topoisomerase 1 (Top 1) and topoisomerase 2 (Top 2) in vitro. In addition, STK295900 also exhibits protective effect against DNA damage induced by camptothecin. However, STK295900 does not affect etoposide-induced DNA damage. Moreover, STK295900 preferentially exerts cytotoxic effect on cancer cell lines while camptothecin, etoposide, and Hoechst 33342 affected both cancer and normal cells. Therefore, STK295900 has a potential to be developed as an anticancer chemotherapeutic agent. © 2013 Kim et al

Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning.

OBJECTIVES:To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. BACKGROUND:Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. METHODS:Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. RESULTS:The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups. CONCLUSIONS:An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level

Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry.

Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume ≥1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all P&lt;0.001; statistical model, 0.81 [0.75-0.87], P=0.128). Conclusions Based on a ML framework, quantitative atherosclerosis characterization has been shown to be the most important feature when compared with clinical, laboratory, and qualitative measures in identifying patients at risk of RPP

Independent effect of body mass index variation on amyloid-β positivity

ObjectivesThe relationship of body mass index (BMI) changes and variability with amyloid-β (Aβ) deposition remained unclear, although there were growing evidence that BMI is associated with the risk of developing cognitive impairment or AD dementia. To determine whether BMI changes and BMI variability affected Aβ positivity, we investigated the association of BMI changes and BMI variability with Aβ positivity, as assessed by PET in a non-demented population.MethodsWe retrospectively recruited 1,035 non-demented participants ≥50 years of age who underwent Aβ PET and had at least three BMI measurements in the memory clinic at Samsung Medical Center. To investigate the association between BMI change and variability with Aβ deposition, we performed multivariable logistic regression. Further distinctive underlying features of BMI subgroups were examined by employing a cluster analysis model.ResultsDecreased (odds ratio [OR] = 1.68, 95% confidence interval [CI] 1.16–2.42) or increased BMI (OR = 1.60, 95% CI 1.11–2.32) was associated with a greater risk of Aβ positivity after controlling for age, sex, APOE e4 genotype, years of education, hypertension, diabetes, baseline BMI, and BMI variability. A greater BMI variability (OR = 1.73, 95% CI 1.07–2.80) was associated with a greater risk of Aβ positivity after controlling for age, sex, APOE e4 genotype, years of education, hypertension, diabetes, baseline BMI, and BMI change. We also identified BMI subgroups showing a greater risk of Aβ positivity.ConclusionOur findings suggest that participants with BMI change, especially those with greater BMI variability, are more vulnerable to Aβ deposition regardless of baseline BMI. Furthermore, our results may contribute to the design of strategies to prevent Aβ deposition with respect to weight control