Human umbilical cord-derived mesenchymal stem cells promote repair of neonatal brain injury caused by hypoxia/ischemia in rats

Administration of human umbilical cord-derived mesenchymal stem cells(hUC-MSCs)is believed to be an effective method for treating neurodevelopmental disorde rs.In this study,we investigated the possibility of hUC-MSCs treatment of neonatal hypoxic/ischemic brain injury associated with maternal immune activation and the underlying mechanism.We established neonatal rat models of hypoxic/ischemic brain injury by exposing pregnant rats to lipopolysaccharide on day 16 or 17 of pregnancy.Rat offspring were intranasally administe red hUC-MSCs on postnatal day 14.We found that polypyrimidine tract-binding protein-1(PTBP-1)participated in the regulation of lipopolysaccharide-induced maternal immune activation,which led to neonatal hypoxic/ischemic brain injury.Intranasal delive ry of hUC-MSCs inhibited PTBP-1 expression,alleviated neonatal brain injury-related inflammation,and regulated the number and function of glial fibrillary acidic protein-positive astrocytes,there by promoting plastic regeneration of neurons and im p roving brain function.These findings suggest that hUC-MSCs can effectively promote the repair of neonatal hypoxic/ischemic brain injury related to maternal immune activation through inhibition of PTBP-1 expression and astrocyte activation.

Value of predictive bioinformatics in inherited metabolic diseases

Typically,inherited metabolic diseases arise from point mutations in genes encoding metabolic enzymes. Although some of these mutations directly affect amino acid residues in the active sites of these enzymes,the majority do not. It is now well accepted that the majority of these disease-associated mutations exert their effects through alteration of protein stability,which causes a reduction in enzymatic activity. This finding suggests a way to predict the severity of newly discovered mutations. In silico prediction of the effects of amino acid sequence alterations on protein stability often correlates with disease severity. However,no stability prediction tool is perfect and,in general,better results are obtained if the predictions from a variety of tools are combined and then interpreted. In addition to predicted alterations to stability,the degree of conservation of a particular residue can also be a factor which needs to be taken into account: alterations to highly conserved residues are more likely to be associated with severe forms of the disease. The approach has been successfully applied in a variety of inherited metabolic diseases,but further improvements are necessary to enable robust translation into clinically useful tools.

DeepCAPE: A Deep Convolutional Neural Network for the Accurate Prediction of Enhancers

The establishment of a landscape of enhancers across human cells is crucial to deciphering the mechanism of gene regulation,cell differentiation,and disease development.High-throughput experimental approaches,which contain successfully reported enhancers in typical cell lines,are still too costly and time-consuming to perform systematic identification of enhancers specific to different cell lines.Existing computational methods,capable of predicting regulatory elements purely relying on DNA sequences,lack the power of cell line-specific screening.Recent studies have suggested that chromatin accessibility of a DNA segment is closely related to its potential function in regulation,and thus may provide useful information in identifying regulatory elements.Motivated by the aforementioned understanding,we integrate DNA sequences and chromatin accessibility data to accurately predict enhancers in a cell line-specific manner.We proposed Deep CAPE,a deep convolutional neural network to predict enhancers via the integration of DNA sequences and DNase-seq data.Benefitting from the well-designed feature extraction mechanism and skip connection strategy,our model not only consistently outperforms existing methods in the imbalanced classification of cell line-specific enhancers against background sequences,but also has the ability to self-adapt to different sizes of datasets.Besides,with the adoption of autoencoder,our model is capable of making cross-cell line predictions.We further visualize kernels of the first convolutional layer and show the match of identified sequence signatures and known motifs.We finally demonstrate the potential ability of our model to explain functional implications of putative disease-associated genetic variants and discriminate diseaserelated enhancers.The source code and detailed tutorial of Deep CAPE are freely available at https://github.com/Shengquan Chen/DeepCAPE.

Recombination and mutation shape variations in the major histocompatibility complex

The major histocompatibility complex(MHC)is closely associated with numerous diseases,but its high degree of polymorphism complicates the discovery of disease-associated variants.In principle,recombination and de novo mutations are two critical factors responsible for MHC polymorphisms.However,direct evidence for this hypothesis is lacking.Here,we report the generation of fine-scale MHC recombination and de novo mutation maps of~5 Mb by deep sequencing(>100×)of the MHC genome for 17 MHC recombination and 30 non-recombination Han Chinese families(a total of 190 individuals).Recombination hotspots and Han-specific breakpoints are located in close proximity at haplotype block boundaries.The average MHC de novo mutation rate is higher than the genome-wide de novo mutation rate,particularly in MHC recombinant individuals.Notably,mutation and recombination generated polymorphisms are located within and outside linkage disequilibrium regions of the MHC,respectively,and evolution of the MHC locus was mainly controlled by positive selection.These findings provide insights on the evolutionary causes of the MHC diversity and may facilitate the identification of disease-associated genetic variants.

Deletion of Gab2 in mice protects against hepatic steatosis and steatohepatitis:a novel therapeutic target for fatty liver disease

Fatty liver disease is a serious health problem worldwide and is the most common cause for chronic liver disease and metabolic disorders.The major challenge in the prevention and intervention of this disease is the incomplete understanding of the underlying mechanism and thus lack of potent therapeutic targets due to multifaceted and interdependent disease factors.In this study,we investigated the role of a signaling adaptor protein,GRB2-associated-binding protein 2(Gab2),in fatty liver using an animal disease model.Gab2 expression in hepatocytes responded to various disease factor stimulations,and Gab2 knockout mice exhibited resistance to fat-induced obesity,fat-or alcohol-stimulated hepatic steatosis,as well as methionine and choline deficiency-induced steatohepatitis.Concordantly,the forced expression or knockdown of Gab2 enhanced or diminished oleic acid(OA)-or ethanol-induced lipid production in hepatocytes in vitro,respectively.During lipid accumulation in hepatocytes,both fat and alcohol induced the recruitment of PI3K or Socs3 by Gab2 and the activation of their downstream signaling proteins AKT,ERK,and Stat3.Therefore,Gab2 may be a disease-associated protein that is induced by pathogenic factors to amplify and coordinate multifactor-induced signals to govern disease development in the liver.Our research provides a novel potential target for the prevention and intervention of fatty liver disease.

Functional characterization of disease/comorbidity-associated IncRNA

Background:Functional characterization of the long noncoding RNAs(IncRNAs)in disease attracts great attention,which results in a limited number of experimentally characterized IncRNAs.The major problems underlying the lack of experimental verifications are considered to come from the significant false-positive assignments and extensive genetic-heterogeneity of disease.These problems are even worse when it comes to the functional characterization in comorbidity(simultaneous/sequential presence of multiple diseases in a patient,and showing much wider prevalence,poorer treatment-response and longer illness-course than a single disease).Methods:Herein,FCCLnc was developed to characterize IncRNA function by(1)integrating diverse SNPs that were associated with 193 diseases standardized by International Classification of Diseases(ICD-11),(2)condition-specific expression of IncRNAs,(3)weighted correlation network of IncRNAs and protein-coding neighboring genes.Results:FCCLnc can characterize IncRNA function in both disease and comorbidity by not only controlling false discovery but also tolerating their disease heterogeneity.Moreover,FCCLnc can provide interactive visualization and full download of IncRNA-centered co-expression network.Conclusion:In summary,FCCLnc is unique in characterizing IncRNA function in diverse diseases and comorbidities and is highly expected to emerge to be an indispensable complement to other available tools.FCCLnc is accessible at https://idrblab.org/fcclnc/.