Dravet syndrome patient‐derived neurons suggest a novel epilepsy mechanism

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/99647/1/ana23897.pd

Charge-tagging liquid chromatography–mass spectrometry methodology targeting oxysterol diastereoisomers

The introduction of a hydroxy group to the cholesterol skeleton introduces not only the possibility for positional isomers but also diastereoisomers, where two or more isomers have different configurations at one or more of the stereocentres but are not mirror images. The differentiation of diastereoisomers is important as differing isomers can have differing biochemical properties and are formed via different biochemical pathways. Separation of diasterioisomers is not always easy by chromatographic methods. Here we demonstrate, by application of charge-tagging and derivatisation with the Girard P reagent, the separation and detection of biologically relevant diastereoisomers using liquid chromatography – mass spectrometry with multistage fragmentation

Robust Time Series Forecasting with Non-Heavy-Tailed Gaussian Loss-Weighted Sampler

Forecasting multivariate time series is a computationally intensive task challenged by extreme or redundant samples. Recent resampling methods aim to increase training efficiency by reweighting samples based on their running losses. However, these methods do not solve the problems caused by heavy-tailed distribution losses, such as overfitting to outliers. To tackle these issues, we introduce a novel approach: a Gaussian loss-weighted sampler that multiplies their running losses with a Gaussian distribution weight. It reduces the probability of selecting samples with very low or very high losses while favoring those close to average losses. As it creates a weighted loss distribution that is not heavy-tailed theoretically, there are several advantages to highlight compared to existing methods: 1) it relieves the inefficiency in learning redundant easy samples and overfitting to outliers, 2) It improves training efficiency by preferentially learning samples close to the average loss. Application on real-world time series forecasting datasets demonstrate improvements in prediction quality for 1%-4% using mean square error measurements in channel-independent settings. The code will be available online after 1 the review.Comment: 8 page

Proteomic investigations of adult polyglucosan body disease: insights into the pathobiology of a neurodegenerative disorder

Inadequate glycogen branching enzyme 1 (GBE1) activity results in different forms of glycogen storage disease type IV, including adult polyglucosan body disorder (APBD). APBD is clinically characterized by adult-onset development of progressive spasticity, neuropathy, and neurogenic bladder and is histologically characterized by the accumulation of structurally abnormal glycogen (polyglucosan bodies) in multiple cell types. How insufficient GBE1 activity causes the disease phenotype of APBD is poorly understood. We hypothesized that proteomic analysis of tissue from GBE1-deficient individuals would provide insights into GBE1-mediated pathobiology. In this discovery study, we utilized label-free LC–MS/MS to quantify the proteomes of lymphoblasts from 3 persons with APBD and 15 age- and gender-matched controls, with validation of the findings by targeted MS. There were 531 differentially expressed proteins out of 3,427 detected between APBD subjects vs. controls, including pronounced deficiency of GBE1. Bioinformatic analyses indicated multiple canonical pathways and protein–protein interaction networks to be statistically markedly enriched in APBD subjects, including: RNA processing/transport/translation, cell cycle control/replication, mTOR signaling, protein ubiquitination, unfolded protein and endoplasmic reticulum stress responses, glycolysis and cell death/apoptosis. Dysregulation of these processes, therefore, are primary or secondary factors in APBD pathobiology in this model system. Our findings further suggest that proteomic analysis of GBE1 mutant lymphoblasts can be leveraged as part of the screening for pharmaceutical agents for the treatment of APBD

Statistical measures of transcriptional diversity capture genomic heterogeneity of cancer

International audienceMolecular heterogeneity of tumors suggests the presence of multiple different subclones that may limit response to targeted therapies and contribute to acquisition of drug resistance, but its quantification has remained challenging

Metabolomic analysis of obesity, metabolic syndrome, and type 2 diabetes: amino acid and acylcarnitine levels change along a spectrum of metabolic wellness

Background Metabolic syndrome (MS) is a construct used to separate “healthy” from “unhealthy” obese patients, and is a major risk factor for type 2 diabetes (T2D) and cardiovascular disease. There is controversy over whether obese “metabolically well” persons have a higher morbidity and mortality than lean counterparts, suggesting that MS criteria do not completely describe physiologic risk factors or consequences of obesity. We hypothesized that metabolomic analysis of plasma would distinguish obese individuals with and without MS and T2D along a spectrum of obesity-associated metabolic derangements, supporting metabolomic analysis as a tool for a more detailed assessment of metabolic wellness than currently used MS criteria. Methods Fasting plasma samples from 90 adults were assigned to groups based on BMI and ATP III criteria for MS: (1) lean metabolically well (LMW; n = 24); (2) obese metabolically well (OBMW; n = 26); (3) obese metabolically unwell (OBMUW; n = 20); and (4) obese metabolically unwell with T2D (OBDM; n = 20). Forty-one amino acids/dipeptides, 33 acylcarnitines and 21 ratios were measured. Obesity and T2D effects were analyzed by Wilcoxon rank-sum tests comparing obese nondiabetics vs LMW, and OBDM vs nondiabetics, respectively. Metabolic unwellness was analyzed by Jonckheere-Terpstra trend tests, assuming worsening health from LMW → OBMW → OBMUW. To adjust for multiple comparisons, statistical significance was set at p < 0.005. K-means cluster analysis of aggregated amino acid and acylcarnitine data was also performed. Results Analytes and ratios significantly increasing in obesity, T2D, and with worsening health include: branched-chain amino acids (BCAAs), cystine, alpha-aminoadipic acid, phenylalanine, leucine + lysine, and short-chain acylcarnitines/total carnitines. Tyrosine, alanine and propionylcarnitine increase with obesity and metabolic unwellness. Asparagine and the tryptophan/large neutral amino acid ratio decrease with T2D and metabolic unwellness. Malonylcarnitine decreases in obesity and 3-OHbutyrylcarnitine increases in T2D; neither correlates with unwellness. Cluster analysis did not separate subjects into discreet groups based on metabolic wellness. Discussion Levels of 15 species and metabolite ratios trend significantly with worsening metabolic health; some are newly recognized. BCAAs, aromatic amino acids, lysine, and its metabolite, alpha-aminoadipate, increase with worsening health. The lysine pathway is distinct from BCAA metabolism, indicating that biochemical derangements associated with MS involve pathways besides those affected by BCAAs. Even those considered “obese, metabolically well” had metabolite levels which significantly trended towards those found in obese diabetics. Overall, this analysis yields a more granular view of metabolic wellness than the sole use of cardiometabolic MS parameters. This, in turn, suggests the possible utility of plasma metabolomic analysis for research and public health applications

Prediction of the outcome of preoperative chemotherapy in breast cancer using DNA probes that provide information on both complete and incomplete responses

<p>Abstract</p> <p>Background</p> <p>DNA microarray technology has emerged as a major tool for exploring cancer biology and solving clinical issues. Predicting a patient's response to chemotherapy is one such issue; successful prediction would make it possible to give patients the most appropriate chemotherapy regimen. Patient response can be classified as either a pathologic complete response (PCR) or residual disease (NoPCR), and these strongly correlate with patient outcome. Microarrays can be used as multigenic predictors of patient response, but probe selection remains problematic. In this study, each probe set was considered as an elementary predictor of the response and was ranked on its ability to predict a high number of PCR and NoPCR cases in a ratio similar to that seen in the learning set. We defined a valuation function that assigned high values to probe sets according to how different the expression of the genes was and to how closely the relative proportions of PCR and NoPCR predictions to the proportions observed in the learning set was. Multigenic predictors were designed by selecting probe sets highly ranked in their predictions and tested using several validation sets.</p> <p>Results</p> <p>Our method defined three types of probe sets: 71% were mono-informative probe sets (59% predicted only NoPCR, and 12% predicted only PCR), 25% were bi-informative, and 4% were non-informative. Using a valuation function to rank the probe sets allowed us to select those that correctly predicted the response of a high number of patient cases in the training set and that predicted a PCR/NoPCR ratio for validation sets that was similar to that of the whole learning set. Based on DLDA and the nearest centroid method, bi-informative probes proved more successful predictors than probes selected using a t test.</p> <p>Conclusion</p> <p>Prediction of the response to breast cancer preoperative chemotherapy was significantly improved by selecting DNA probe sets that were successful in predicting outcomes for the entire learning set, both in terms of accurately predicting a high number of cases and in correctly predicting the ratio of PCR to NoPCR cases.</p

Kernel-U-Net: Multivariate Time Series Forecasting using Custom Kernels

Time series forecasting task predicts future trends based on historical information. Transformer-based U-Net architectures, despite their success in medical image segmentation, have limitations in both expressiveness and computation efficiency in time series forecasting as evidenced in YFormer. To tackle these challenges, we introduce Kernel-U-Net, a flexible and kernel-customizable U-shape neural network architecture. The kernel-U-Net encoder compresses the input series into latent vectors, and its symmetric decoder subsequently expands these vectors into output series. Specifically, Kernel-U-Net separates the procedure of partitioning input time series into patches from kernel manipulation, thereby providing the convenience of customized executing kernels. Our method offers two primary advantages: 1) Flexibility in kernel customization to adapt to specific datasets; and 2) Enhanced computational efficiency, with the complexity of the Transformer layer reduced to linear. Experiments on seven real-world datasets, demonstrate that Kernel-U-Net\u27s performance either exceeds or meets that of the existing state-of-the-art model in the majority of cases in channel-independent settings. The source code for Kernel-U-Net will be made publicly available for further research and application

Endocytosis of hyaluronidase-1 by the liver

International audienceIt has been suggested that intracellular hyaluronidase-1 (Hyal-1), considered as a lysosomal enzyme, originates from the endocytosis of the serum enzyme. To check this proposal we have investigated the uptake of recombinant human hyaluronidase-1 (rhHyal-1) by mouse liver and its intracellular distribution, making use of centrifugation methods. Experiments were performed on wild type mice injected with 125I-rhHyal-1 and on null mice (Hyal-1 -/-) injected with the unlabelled enzyme. Mice were euthanized at increasing times after injection Activity of the unlabelled enzyme was determined by zymography. Intracellular distribution of the Hyal-1 was investigated by differential and isopycnic centrifugation. Results indicated that rhHyal-1 is endocytosed by the liver, mainly by sinusoidal cells and follows the intracellular pathway described for many endocytosed proteins that find themselves eventually in lysosomes. However, Hyal-1 endocytosis has some particular features. Endocytosed rhHyal-1 is quickly degraded. Its distribution after differential centrifugation differs from the distribution of β-galactosidase, taken as reference enzyme of lysosomes. After isopycnic centrifugation in a sucrose gradient, endocytosed rhHyal-1 behaves like β-galactosidase soon after injection but Hyal-1 distribution is markedly less affected than the distribution of β-galactosidase by a prior injection of Triton WR-1339 to the mice. This agent is a specific density perturbant of lysosomes. Behaviour in centrifugation of endogenous liver Hyal-1, identified by HA zymography exhibits some kinship with the behaviour of the endocytosed enzyme, suggesting that it could originate from an endocytosis of the serum enzyme. Overall, these results could be explained by supposing that active endocytosed Hyal-1 is mainly present in early lysosomes. Although its degradation half-time is short, Hyal-1 could exert its activity owing to a constant supply of active molecules from the blood