Caveolin-1 Phosphorylation Is Essential for Axonal Growth of Human Neurons Derived From iPSCs.

Proper axonal growth and guidance is essential for neuron differentiation and development. Abnormal neuronal development due to genetic or epigenetic influences can contribute to neurological and mental disorders such as Down syndrome, Rett syndrome, and autism. Identification of the molecular targets that promote proper neuronal growth and differentiation may restore structural and functional neuroplasticity, thus improving functional performance in neurodevelopmental disorders. Using differentiated human neuronal progenitor cells (NPCs) derived from induced pluripotent stem cells (iPSCs), the present study demonstrates that during early stage differentiation of human NPCs, neuron-targeted overexpression constitutively active Rac1 (Rac1CA) and constitutively active Cdc42 (Cdc42CA) enhance expression of P-Cav-1, T-Cav-1, and P-cofilin and increases axonal growth. Similarly, neuron-targeted over-expression of Cav-1 (termed SynCav1) increases axonal development by increasing both axon length and volume. Moreover, inhibition of Cav-1(Y14A) phosphorylation blunts Rac1/Cdc42-mediated both axonal growth and differentiation of human NPCs and SynCav1(Y14A)-treated NPCs exhibited blunted axonal growth. These results suggest that: (1) SynCav1-mediated dendritic and axonal growth in human NPCs is dependent upon P-Cav-1, (2) P-Cav-1 is necessary for proper axonal growth during early stages of neuronal differentiation, and (3) Rac1/Cdc42CA-mediated neuronal growth is in part dependent upon P-Cav-1. In conclusion, Cav-1 phosphorylation is essential for human neuronal axonal growth during early stages of neuronal differentiation

The effects of graded levels of calorie restriction : VIII. impact of short term calorie and protein restriction on basal metabolic rate in the C57BL/6 mouse

We are grateful to the animal house staff for looking after the animals. The work was supported by the UK Biotechnology and Biological Sciences Research Council BBSRC (grants BB/G009953/1 and BB/J020028/1) to JRS and SEM. DD was supported by a studentship from the Centre for Genome Enabled Biology and Medicine, Aberdeen, UK, and CG was supported by a BBSRC EastBio studentship. Joint meetings were funded by a BBSRC China partnering award (BB/JO20028/1).Peer reviewedPublisher PD

Predicting Hydration Status Using Machine Learning Models From Physiological and Sweat Biomarkers During Endurance Exercise: A Single Case Study

Improper hydration routines can reduce athletic performance. Recent studies show that data from noninvasive biomarker recordings can help to evaluate the hydration status of subjects during endurance exercise. These studies are usually carried out on multiple subjects. In this work, we present the first study on predicting hydration status using machine learning models from single-subject experiments, which involve 32 exercise sessions of constant moderate intensity performed with and without fluid intake. During exercise, we measured four noninvasive physiological and sweat biomarkers including heart rate, core temperature, sweat sodium concentration, and whole-body sweat rate. Sweat sodium concentration was measured from six body regions using absorbent patches. We used three machine learning models to determine the percentage of body weight loss as an indicator of dehydration with these biomarkers and compared the prediction accuracy. The results on this single subject show that these models gave similar mean absolute errors, while in general the nonlinear models slightly outperformed the linear model in most of the experiments. The prediction accuracy of using the whole-body sweat rate or heart rate was higher than using core temperature or sweat sodium concentration. In addition, the model trained on the sweat sodium concentration collected from the arms gave slightly better accuracy than from the other five body regions. This exploratory work paves the way for the use of these machine learning models to develop personalized health monitoring together with emerging, noninvasive wearable sensor devices

The effects of graded levels of calorie restriction : I. impact of short term calorie and protein restriction on body composition in the C57BL/6 mouse

We acknowledge the BSU staff for their invaluable help with caring for the animals and anonymous referees for their inputs. The work was supported by the Biotechnology and Biological Sciences Research Council (BBSRC) of the UK (Standard grant BB/G009953/1 and China partnering award BB/JO20028/1). The authors declare no competing interests.Peer reviewedPublisher PD

Identification and characterization of an irreversible inhibitor of CDK2

Irreversible inhibitors that modify cysteine or lysine residues within a protein kinase ATP binding site offer, through their distinctive mode of action, an alternative to ATP-competitive agents. 4-((6-(Cyclohexylmethoxy)- 9H-purin-2-yl)amino)benzenesulfonamide (NU6102) is a potent and selective ATP-competitive inhibitor of CDK2 in which the sulfonamide moiety is positioned close to a pair of lysine residues. Guided by the CDK2/NU6102 structure, we designed 6-(cyclohexylmethoxy)-N-(4-(vinylsulfonyl)phenyl)-9H-purin-2-amine (NU6300), which binds covalently to CDK2 as shown by a co-complex crystal structure. Acute incubation with NU6300 produced a durable inhibition of Rb phosphorylation in SKUT-1B cells, consistent with it acting as an irreversible CDK2 inhibitor. NU6300 is the first covalent CDK2 inhibitor to be described, and illustrates the potential of vinyl sulfones for the design of more potent and selective compounds

Association of comorbidity with healthcare utilization in people living With dementia, 2010–2019: a population-based cohort study

Evidence on the healthcare utilization associated with comorbidity in people with dementia is lacking in Chinese societies. This study aimed to quantify healthcare utilization associated with comorbidity that is common in people living with dementia. We conducted a cohort study using population-based data from Hong Kong public hospitals. Individuals aged 35+ with a dementia diagnosis between 2010 and 2019 were included. Among 88,151 participants, people with at least two comorbidities accounted for 81.2%. Estimates from negative binomial regressions showed that compared to those with one or no comorbid condition other than dementia, adjusted rate ratios of hospitalizations among individuals with six or seven and eight or more conditions were 1.97 [98.75% CI, 1.89–2.05] and 2.74 [2.63–2.86], respectively; adjusted rate ratios of Accident and Emergency department visits among individuals with six or seven and eight or more conditions were 1.53 [1.44–1.63] and 1.92 [1.80–2.05], respectively. Comorbid chronic kidney diseases were associated with the highest adjusted rate ratios of hospitalizations (1.81 [1.74–1.89]), whereas comorbid chronic ulcer of the skin was associated with the highest adjusted rate ratios of Accident and Emergency department visits (1.73 [1.61–1.85]). Healthcare utilization for individuals with dementia differed substantially by both the number of comorbid chronic conditions and the presence of some specific comorbid conditions. These findings further highlight the importance of taking account of multiple long-term conditions in tailoring the care approach and developing healthcare plans for people with dementia

A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas

The computational identification of oncogenic lesions is still a key open problem in cancer biology. Although several methods have been proposed, they fail to model how such events are mediated by the network of molecular interactions in the cell. In this paper, we introduce a systems biology approach, based on the analysis of molecular interactions that become dysregulated in specific tumor phenotypes. Such a strategy provides important insights into tumorigenesis, effectively extending and complementing existing methods. Furthermore, we show that the same approach is highly effective in identifying the targets of molecular perturbations in a human cellular context, a task virtually unaddressed by existing computational methods. To identify interactions that are dysregulated in three distinct non-Hodgkin's lymphomas and in samples perturbed with CD40 ligand, we use the B-cell interactome (BCI), a genome-wide compendium of human B-cell molecular interactions, in combination with a large set of microarray expression profiles. The method consistently ranked the known gene in the top 20 (0.3%), outperforming conventional approaches in 3 of 4 cases