TSC1/TSC2 signaling in the CNS

AbstractOver the past several years, the study of a hereditary tumor syndrome, tuberous sclerosis complex (TSC), has shed light on the regulation of cellular proliferation and growth. TSC is an autosomal dominant disorder that is due to inactivating mutations in TSC1 or TSC2 and characterized by benign tumors (hamartomas) involving multiple organ systems. The TSC1/2 complex has been found to play a crucial role in an evolutionarily-conserved signaling pathway that regulates cell growth: the mTORC1 pathway. This pathway promotes anabolic processes and inhibits catabolic processes in response to extracellular and intracellular factors. Findings in cancer biology have reinforced the critical role for TSC1/2 in cell growth and proliferation. In contrast to cancer cells, in the CNS, the TSC1/2 complex not only regulates cell growth/proliferation, but also orchestrates an intricate and finely tuned system that has distinctive roles under different conditions, depending on cell type, stage of development, and subcellular localization. Overall, TSC1/2 signaling in the CNS, via its multi-faceted roles, contributes to proper neural connectivity. Here, we will review the TSC signaling in the CNS

Amplification of Xenon NMR and MRI by remote detection

A novel technique is proposed in which a nuclear magneticresonance (NMR) spectrum or magnetic resonance image (MRI) is encoded andstored as spin polarization and is then moved to a different physicallocation to be detected. Remote detection allows the separateoptimization of the encoding and detection steps, permitting theindependent choice of experimental conditions, and excitation anddetection methodologies. In the first experimental demonstration of thistechnique, we show that NMR signal can be amplified by taking diluted129Xe from a porous sample placed inside a large encoding coil, andconcentrating it into a smaller detection coil. In general, the study ofNMR active molecules at low concentration that have low physical fillingfactor is facilitated by remote detection. In the second experiment, MRIinformation encoded in a very low field magnet (4-7mT) is transferred toa high field magnet (4.2 T) in order to be detected under optimizedconditions. Furthermore, remote detection allows the utilization ofultra-sensitive optical or superconducting detection techniques, whichbroadens the horizon of NMR experimentation

A TSC signaling node at the peroxisome regulates mTORC1 and autophagy in response to ROS

Subcellular localization is emerging as an important mechanism for mTORC1 regulation. We report that the tuberous sclerosis complex (TSC) signaling node, TSC1, TSC2 and Rheb, localizes to peroxisomes, where it regulates mTORC1 in response to reactive oxygen species (ROS). TSC1 and TSC2 were bound by PEX19 and PEX5, respectively, and peroxisome-localized TSC functioned as a Rheb GAP to suppress mTORC1 and induce autophagy. Naturally occurring pathogenic mutations in TSC2 decreased PEX5 binding, abrogated peroxisome localization, Rheb GAP activity, and suppression of mTORC1 by ROS. Cells lacking peroxisomes were deficient in mTORC1 repression by ROS and peroxisome-localization deficient TSC2 mutants caused polarity defects and formation of multiple axons in neurons. These data identify a role for TSC in responding to ROS at the peroxisome, and identify the peroxisome as a signaling organelle involved in regulation of mTORC1

Evaluating the performance of malaria genomics for inferring changes in transmission intensity using transmission modelling

AbstractAdvances in genetic sequencing and accompanying methodological approaches have resulted in pathogen genetics being used in the control of infectious diseases. To utilise these methodologies for malaria we first need to extend the methods to capture the complex interactions between parasites, human and vector hosts, and environment. Here we develop an individual-based transmission model to simulate malaria parasite genetics parameterised using estimated relationships between complexity of infection and age from 5 regions in Uganda and Kenya. We predict that cotransmission and superinfection contribute equally to within-host parasite genetic diversity at 11.5% PCR prevalence, above which superinfections dominate. Finally, we characterise the predictive power of six metrics of parasite genetics for detecting changes in transmission intensity, before grouping them in an ensemble statistical model. The best performing model successfully predicted malaria prevalence with mean absolute error of 0.055, suggesting genetic tools could be used for monitoring the impact of malaria interventions.</jats:p

HLA-DPB1 and HLA Class I Confer Risk of and Protection from Narcolepsy

Correction: AMERICAN JOURNAL OF HUMAN GENETICS Volume: 96 Issue: 5 Pages: 852-852 DOI: 10.1016/j.ajhg.2015.04.001 Published:MAY 7 2015Peer reviewe

Racial, Ethnic, and Rural Disparities in US Veteran COVID-19 Vaccine Rates

Background: Race, ethnicity, and rurality-related disparities in coronavirus disease 2019 (COVID-19) vaccine uptake have been documented in the United States (US). Objective: We determined whether these disparities existed among patients at the Department of Veterans Affairs (VA), the largest healthcare system in the US. Design, Settings, Participants, Measurements: Using VA Corporate Data Warehouse data, we included 5,871,438 patients (9.4% women) with at least one primary care visit in 2019 in a retrospective cohort study. Each patient was assigned a single race/ethnicity, which were mutually exclusive, self-reported categories. Rurality was based on 2019 home address at the zip code level. Our primary outcome was time-to-first COVID-19 vaccination between December 15, 2020-June 15, 2021. Additional covariates included age (in years), sex, geographic region (North Atlantic, Midwest, Southeast, Pacific, Continental), smoking status (current, former, never), Charlson Comorbidity Index (based on ≥1 inpatient or two outpatient ICD codes), service connection (any/none, using standardized VA-cutoffs for disability compensation), and influenza vaccination in 2019-2020 (yes/no). Results: Compared with unvaccinated patients, those vaccinated (n=3,238,532; 55.2%) were older (mean age in years vaccinated=66.3, (standard deviation=14.4) vs. unvaccinated=57.7, (18.0), p<.0001)). They were more likely to identify as Black (18.2% vs. 16.1%, p<.0001), Hispanic (7.0% vs. 6.6% p<.0001), or Asian American/Pacific Islander (AA/PI) (2.0% vs. 1.7%, P<.0001). In addition, they were more likely to reside in urban settings (68.0% vs. 62.8, p<.0001). Relative to non-Hispanic White urban Veterans, the reference group for race/ethnicity-urban/rural hazard ratios reported, all urban race/ethnicity groups were associated with increased likelihood for vaccination except American Indian/Alaskan Native (AI/AN) groups. Urban Black groups were 12% more likely (Hazard Ratio (HR)=1.12 [CI 1.12-1.13]) and rural Black groups were 6% more likely to receive a first vaccination (HR=1.06 [1.05-1.06]) relative to white urban groups. Urban Hispanic, AA/PI and Mixed groups were more likely to receive vaccination while rural members of these groups were less likely (Hispanic: Urban HR=1.17 [1.16-1.18], Rural HR=0.98 [0.97-0.99]; AA/PI: Urban HR=1.22 [1.21-1.23], Rural HR=0.86 [0.84-0.88]). Rural White Veterans were 21% less likely to receive an initial vaccine compared with urban White Veterans (HR=0.79 [0.78-0.79]). AI/AN groups were less likely to receive vaccination regardless of rurality: Urban HR=0.93 [0.91-0.95]; AI/AN-Rural HR=0.76 [0.74-0.78]. Conclusions: Urban Black, Hispanic, and AA/PI Veterans were more likely than their urban White counterparts to receive a first vaccination; all rural race/ethnicity groups except Black patients had lower likelihood for vaccination compared with urban White patients. A better understanding of disparities and rural outreach will inform equitable vaccine distribution

Classification of Protein Kinases on the Basis of Both Kinase and Non-Kinase Regions

BACKGROUND: Protein phosphorylation is a generic way to regulate signal transduction pathways in all kingdoms of life. In many organisms, it is achieved by the large family of Ser/Thr/Tyr protein kinases which are traditionally classified into groups and subfamilies on the basis of the amino acid sequence of their catalytic domains. Many protein kinases are multi-domain in nature but the diversity of the accessory domains and their organization are usually not taken into account while classifying kinases into groups or subfamilies. METHODOLOGY: Here, we present an approach which considers amino acid sequences of complete gene products, in order to suggest refinements in sets of pre-classified sequences. The strategy is based on alignment-free similarity scores and iterative Area Under the Curve (AUC) computation. Similarity scores are computed by detecting common patterns between two sequences and scoring them using a substitution matrix, with a consistent normalization scheme. This allows us to handle full-length sequences, and implicitly takes into account domain diversity and domain shuffling. We quantitatively validate our approach on a subset of 212 human protein kinases. We then employ it on the complete repertoire of human protein kinases and suggest few qualitative refinements in the subfamily assignment stored in the KinG database, which is based on catalytic domains only. Based on our new measure, we delineate 37 cases of potential hybrid kinases: sequences for which classical classification based entirely on catalytic domains is inconsistent with the full-length similarity scores computed here, which implicitly consider multi-domain nature and regions outside the catalytic kinase domain. We also provide some examples of hybrid kinases of the protozoan parasite Entamoeba histolytica. CONCLUSIONS: The implicit consideration of multi-domain architectures is a valuable inclusion to complement other classification schemes. The proposed algorithm may also be employed to classify other families of enzymes with multi-domain architecture

Evaluating the Performance of Malaria Genetics for Inferring Changes in Transmission Intensity Using Transmission Modeling.

Substantial progress has been made globally to control malaria, however there is a growing need for innovative new tools to ensure continued progress. One approach is to harness genetic sequencing and accompanying methodological approaches as have been used in the control of other infectious diseases. However, to utilize these methodologies for malaria, we first need to extend the methods to capture the complex interactions between parasites, human and vector hosts, and environment, which all impact the level of genetic diversity and relatedness of malaria parasites. We develop an individual-based transmission model to simulate malaria parasite genetics parameterized using estimated relationships between complexity of infection and age from five regions in Uganda and Kenya. We predict that cotransmission and superinfection contribute equally to within-host parasite genetic diversity at 11.5% PCR prevalence, above which superinfections dominate. Finally, we characterize the predictive power of six metrics of parasite genetics for detecting changes in transmission intensity, before grouping them in an ensemble statistical model. The model predicted malaria prevalence with a mean absolute error of 0.055. Different assumptions about the availability of sample metadata were considered, with the most accurate predictions of malaria prevalence made when the clinical status and age of sampled individuals is known. Parasite genetics may provide a novel surveillance tool for estimating the prevalence of malaria in areas in which prevalence surveys are not feasible. However, the findings presented here reinforce the need for patient metadata to be recorded and made available within all future attempts to use parasite genetics for surveillance

Reconstructing extreme AMOC events through nudging of the ocean surface: a perfect model approach

While the Atlantic Meridional Overturning Circulation (AMOC) is thought to be a crucial component of the North Atlantic climate, past changes in its strength are challenging to quantify, and only limited information is available. In this study, we use a perfect model approach with the IPSL-CM5A-LR model to assess the performance of several surface nudging techniques in reconstructing the variability of the AMOC. Special attention is given to the reproducibility of an extreme positive AMOC peak from a preindustrial control simulation. Nudging includes standard relaxation techniques towards the sea surface temperature and salinity anomalies of this target control simulation, and/or the prescription of the wind-stress fields. Surface nudging approaches using standard fixed restoring terms succeed in reproducing most of the target AMOC variability, including the timing of the extreme event, but systematically underestimate its amplitude. A detailed analysis of the AMOC variability mechanisms reveals that the underestimation of the extreme AMOC maximum comes from a deficit in the formation of the dense water masses in the main convection region, located south of Iceland in the model. This issue is largely corrected after introducing a novel surface nudging approach, which uses a varying restoring coefficient that is proportional to the simulated mixed layer depth, which, in essence, keeps the restoring time scale constant. This new technique substantially improves water mass transformation in the regions of convection, and in particular, the formation of the densest waters, which are key for the representation of the AMOC extreme. It is therefore a promising strategy that may help to better constrain the AMOC variability and other ocean features in the models. As this restoring technique only uses surface data, for which better and longer observations are available, it opens up opportunities for improved reconstructions of the AMOC over the last few decades