Asymmetric Expression of LincGET Biases Cell Fate in Two-Cell Mouse Embryos.

In early mammalian embryos, it remains unclear how the first cell fate bias is initially triggered and amplified toward cell fate segregation. Here, we report that a long noncoding RNA, LincGET, is transiently and asymmetrically expressed in the nucleus of two- to four-cell mouse embryos. Overexpression of LincGET in one of the two-cell blastomeres biases its progeny predominantly toward the inner cell mass (ICM) fate. Mechanistically, LincGET physically binds to CARM1 and promotes the nuclear localization of CARM1, which can further increase the level of H3 methylation at Arginine 26 (H3R26me), activate ICM-specific gene expression, upregulate transposons, and increase global chromatin accessibility. Simultaneous overexpression of LincGET and depletion of Carm1 no longer biased embryonic fate, indicating that the effect of LincGET in directing ICM lineage depends on CARM1. Thus, our data identify LincGET as one of the earliest known lineage regulators to bias cell fate in mammalian 2-cell embryos.

Generation and Application of Mouse-Rat Allodiploid Embryonic Stem Cells.

Mammalian interspecific hybrids provide unique advantages for mechanistic studies of speciation, gene expression regulation, and X chromosome inactivation (XCI) but are constrained by their limited natural resources. Previous artificially generated mammalian interspecific hybrid cells are usually tetraploids with unstable genomes and limited developmental abilities. Here, we report the generation of mouse-rat allodiploid embryonic stem cells (AdESCs) by fusing haploid ESCs of the two species. The AdESCs have a stable allodiploid genome and are capable of differentiating into all three germ layers and early-stage germ cells. Both the mouse and rat alleles have comparable contributions to the expression of most genes. We have proven AdESCs as a powerful tool to study the mechanisms regulating X chromosome inactivation and to identify X inactivation-escaping genes, as well as to efficiently identify genes regulating phenotypic differences between species. A similar method could be used to create hybrid AdESCs of other distantly related species.

Dynamic changes of peripheral inflammatory markers link with disease severity and predict short-term poor outcome of myasthenia gravis.

The relationship between peripheral inflammatory markers, their dynamic changes, and the disease severity of myasthenia gravis (MG) is still not fully understood. Besides, the possibility of using it to predict the short-term poor outcome of MG patients have not been demonstrated. This study aims to investigate the relationship between peripheral inflammatory markers and their dynamic changes with Myasthenia Gravis Foundation of America (MGFA) classification (primary outcome) and predict the short-term poor outcome (secondary outcome) in MG patients. The study retrospectively enrolled 154 MG patients from June 2016 to December 2021. The logistic regression was used to investigate the relationship of inflammatory markers with MGFA classification and determine the factors for model construction presented in a nomogram. Finally, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were utilized to evaluate the incremental capacity. Logistic regression revealed significant associations between neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), aggregate index of systemic inflammation (AISI) and MGFA classification (p = 0.013, p = 0.032, p = 0.017, respectively). Incorporating dynamic changes of inflammatory markers into multivariable models improved their discriminatory capacity of disease severity, with significant improvements observed for NLR, systemic immune-inflammation index (SII) and AISI in NRI and IDI. Additionally, AISI was statistically associated with short-term poor outcome and a prediction model incorporating dynamic changes of inflammatory markers was constructed with the area under curve (AUC) of 0.953, presented in a nomograph. The inflammatory markers demonstrate significant associations with disease severity and AISI could be regarded as a possible and easily available predictive biomarker for short-term poor outcome in MG patients.

ChloroDBPFinder: Machine Learning-Guided Recognition of Chlorinated Disinfection Byproducts from Nontargeted LC-HRMS Analysis.

High-resolution mass spectrometry (HRMS) is a prominent analytical tool that characterizes chlorinated disinfection byproducts (Cl-DBPs) in an unbiased manner. Due to the diversity of chemicals, complex background signals, and the inherent analytical fluctuations of HRMS, conventional isotopic pattern (37Cl/35Cl), mass defect, and direct molecular formula (MF) prediction are insufficient for accurate recognition of the diverse Cl-DBPs in real environmental samples. This work proposes a novel strategy to recognize Cl-containing chemicals based on machine learning. Our hierarchical machine learning framework has two random forest-based models: the first layer is a binary classifier to recognize Cl-containing chemicals, and the second layer is a multiclass classifier to annotate the number of Cl present. This model was trained using ∼1.4 million distinctive MFs from PubChem. Evaluated on over 14,000 unique MFs from NIST20, this machine learning model achieved 93.3% accuracy in recognizing Cl-containing MFs (Cl-MFs) and 92.9% accuracy in annotating the number of Cl for Cl-MFs. Furthermore, the trained model was integrated into ChloroDBPFinder, a standalone R package for the streamlined processing of LC-HRMS data and annotating both known and unknown Cl-containing compounds. Tested on existing Cl-DBP data sets related to aspartame chlorination in tap water, our ChloroDBPFinder efficiently extracted 159 Cl-containing DBP features and tentatively annotated the structures of 10 Cl-DBPs via molecular networking. In another application of a chlorinated humic substance, ChloroDBPFinder extracted 79 high-quality Cl-DBPs and tentatively annotated six compounds. In summary, our proposed machine learning strategy and the developed ChloroDBPFinder provide an advanced solution to identifying Cl-containing compounds in nontargeted analysis of water samples. It is freely available on GitHub (https://github.com/HuanLab/ChloroDBPFinder).

Self-assembly mechanism, physicochemical analyses and application performance investigations of branched alkyl glycosides with alcohol ether carboxylic acids of varied epoxide numbers.

Green surfactants, specifically alkyl glycosides and fatty alcohol ether carboxylic acids, are known for their biocompatibility, multiresponsiveness, and versatile applications, garnering significant attention in the realms of green and colloid chemistry. This study systematically investigated the mechanism underlying micelle formation within aqueous solutions comprising alcohol ether carboxylic acids featuring diverse EO group chain quantities (AEC-nH, where n equals 5, 7, and 9) and branched alkyl glycosides (IG). The elucidation of these mechanisms sheds light on their prospective application properties. It was observed that the self-assembly of micelles in these hybrid systems is predominantly influenced by hydrogen bonding, electrostatic interactions, and hydrophobic forces. The spherical-rod morphology of the micelles responds to the varying numbers of EO group chains, with an increased number of EO leading to the formation of rod-like micelles, which exhibit relative instability, while a decreased number of EO results in the formation of spherical micelles with relative stability. Additionally, by means of kinetic analysis, it was determined that the micelle formation process of the three hybrid systems is driven by enthalpy, and a mixed diffusion-kinetics adsorption mechanism is involved in the adsorption process. These findings significantly impact their application properties. This report stands as the first exploration of the synergistic mechanisms and application performance of two types of green surfactants in aqueous solutions, considering the influence of different numbers of EO group chains. Not only does it provide fundamental insights into their properties, but it also offers novel perspectives on the applications of green surface activation.

Unveiling the therapeutic promise: exploring Lysophosphatidic Acid (LPA) signaling in malignant bone tumors for novel cancer treatments.

Malignant bone tumors, including primary bone cancer and metastatic bone tumors, are a significant clinical challenge due to their high frequency of presentation, poor prognosis and lack of effective treatments and therapies. Bone tumors are often accompanied by skeletal complications such as bone destruction and cancer-induced bone pain. However, the mechanisms involved in bone cancer progression, bone metastasis and skeletal complications remain unclear. Lysophosphatidic acid (LPA), an intercellular lipid signaling molecule that exerts a wide range of biological effects mainly through specifically binding to LPA receptors (LPARs), has been found to be present at high levels in the ascites of bone tumor patients. Numerous studies have suggested that LPA plays a role in primary malignant bone tumors, bone metastasis, and skeletal complications. In this review, we summarize the role of LPA signaling in primary bone cancer, bone metastasis and skeletal complications. Modulating LPA signaling may represent a novel avenue for future therapeutic treatments for bone cancer, potentially improving patient prognosis and quality of life.

An "Observable" horseshoe map.

In this article, a family of diffeomorphisms with growing horseshoes contained in global attracting regions is presented, where the dimension of the unstable direction can be any fixed integer and a growing horseshoe means that the number of the folds of the horseshoe is increasing as a parameter is varied. Moreover, it is demonstrated that the horseshoe-like attractors are observable for certain parameters.

Profiling human pathogenic repeat expansion regions by synergistic and multi-level impacts on molecular connections.

Whilst DNA repeat expansions cause numerous heritable human disorders, their origins and underlying pathological mechanisms are often unclear. We collated a dataset comprising 224 human repeat expansions encompassing 203 different genes, and performed a systematic analysis with respect to key topological features at the DNA, RNA and protein levels. Comparison with controls without known pathogenicity and genomic regions lacking repeats, allowed the construction of the first tool to discriminate repeat regions harboring pathogenic repeat expansions (DPREx). At the DNA level, pathogenic repeat expansions exhibited stronger signals for DNA regulatory factors (e.g. H3K4me3, transcription factor-binding sites) in exons, promoters, 5'UTRs and 5'genes but were not significantly different from controls in introns, 3'UTRs and 3'genes. Additionally, pathogenic repeat expansions were also found to be enriched in non-B DNA structures. At the RNA level, pathogenic repeat expansions were characterized by lower free energy for forming RNA secondary structure and were closer to splice sites in introns, exons, promoters and 5'genes than controls. At the protein level, pathogenic repeat expansions exhibited a preference to form coil rather than other types of secondary structure, and tended to encode surface-located protein domains. Guided by these features, DPREx ( http://biomed.nscc-gz.cn/zhaolab/geneprediction/# ) achieved an Area Under the Curve (AUC) value of 0.88 in a test on an independent dataset. Pathogenic repeat expansions are thus located such that they exert a synergistic influence on the gene expression pathway involving inter-molecular connections at the DNA, RNA and protein levels.

Human embryonic stem cell-derived immunity-and-matrix regulatory cells promote intrahepatic cell renewal to rescue acute liver failure.

Acute liver failure (ALF) is a clinical syndrome characterized by the accelerated development of hepatocyte necrosis and significant mortality. Given that liver transplantation is now the only curative treatment available for ALF, there is an urgent need to explore innovative therapies. Mesenchymal stem cells (MSCs) have been applied in preclinical studies for ALF. It had been demonstrated that human embryonic stem cell-derived immunity-and-matrix regulatory cells (IMRCs) met the properties of MSCs and had been employed in a wide range of conditions. In this study, we conducted a preclinical evaluation of IMRCs in the treatment of ALF and investigated the mechanism involved. ALF was induced in C57BL/6 mice via intraperitoneal administration of 50% CCl4 (6 mL/kg) mixed with corn oil, followed by intravenous injection of IMRCs (3 × 106 cells/each). IMRCs improved histopathological changes in the liver and reduced alanine transaminase (ALT) or aspartate transaminase (AST) levels in serum. IMRCs also promoted cell renewal in the liver and protected it from CCl4 damage. Furthermore, our data indicated that IMRCs protected against CCl4-induced ALF by regulating the IGFBP2-mTOR-PTEN signaling pathway, which is associated with the repopulation of intrahepatic cells. Overall, IMRCs offered protection against CCl4-induced ALF and were capable of preventing apoptosis and necrosis in hepatocytes, which provided a new perspective for treating and improving the prognosis of ALF.

Application of logistic regression and machine learning methods for idiopathic inflammatory myopathies malignancy prediction.

OBJECTIVES: Malignancy is related to idiopathic inflammatory myopathies (IIM) and leads to a poor prognosis. Early prediction of malignancy is thought to improve the prognosis. However, predictive models have rarely been reported in IIM. Herein, we aimed to establish and use a machine learning (ML) algorithm to predict the possible risk factors for malignancy in IIM patients. METHODS: We retrospectively reviewed the medical records of 168 patients diagnosed with IIM in Shantou Central hospital, from 2013 to 2021. We randomly divided patients into two groups, the training sets (70%) for construction of the prediction model, and the validation sets (30%) for evaluation of model performance. We constructed six types of ML algorithms models and the AUC of ROC curves were used to describe the efficacy of the model. Finally, we set up a web version using the best prediction model to make it more generally available. RESULTS: According to the multi-variable regression analysis, three predictors were found to be the risk factors to establish the prediction model, including age, ALT<80U/L, and anti-TIF1-γ, and ILD was found to be a protective factor. Compared with five other ML algorithms models, the traditional algorithm logistic regression (LR) model was as good or better than the other models to predict malignancy in IIM. The AUC of the ROC using LR was 0.900 in the training set and 0.784 in the validation set. We selected the LR model as the final prediction model. Accordingly, a nomogram was constructed using the above four factors. A web version was built and can be visited on the website or acquired by scanning the QR code. CONCLUSIONS: The LR algorithm appears to be a good predictor of malignancy and may help clinicians screen, evaluate and follow up high-risk patients with IIM.

CLDN18.2 expression and its impact on prognosis and the immune microenvironment in gastric cancer.

BACKGROUND: The investigational use of zolbetuximab (IMAB362), a groundbreaking monoclonal antibody medication targeting claudin 18.2 (CLDN18.2), for treatment of advanced gastrointestinal cancers is currently underway. The unclear clinicopathological characteristics and tumour immune microenvironment of CLDN18.2-positive gastric cancer (GC) make it difficult to develop and optimize CLDN18.2-targeted therapies. METHODS: A total of 451 tumour tissues, 342 matched paraneoplastic tissues, and 107 matched metastatic lymph nodes were collected from GC patients. These specimens were stained for CLDN18.2 expression and quantified using immunohistochemistry (IHC). Correlations between CLDN18.2 expression and clinicopathological features as well as immune-related factors were analysed. Survival curves were drawn using the Kaplan‒Meier approach, and independent factors affecting GC prognosis were identified using Cox regression analysis. Information from relevant databases was used for corroboration. RESULTS: Expression of the CLDN18.2 gene was significantly lower in gastric tumour tissues than in normal tissues (p < 0.001) but comparable in metastatic lymph nodes (p = 0.851). CLDN18.2 expression was significantly associated with Borrmann type, degree of differentiation, PD-L1 expression, and survival in GC patients and was identified as an independent risk factor for patient prognosis (HR = 1.57, 95% CI 1.16-2.11, p = 0.003). There was no correlation between CLDN18.2 expression and HER2, Lauren type, tumour size, TNM stage, or any other clinicopathological characteristic. In CLDN18.2-positive tumours, fractions of CD4 + T cells and CD8 + T cells were significantly higher than those in CLDN18.2-negative tumours. Patients with CLDN18.2-negative expression and significant CD4 + T-cell or CD8 + T-cell infiltration had the best prognosis (5-year OS: 61.0%, P = 0.036; 5-year OS: 62.2%, P = 0.034). CONCLUSIONS: CLDN18.2 is expressed at a low level in tumour tissues and serves as an independent prognostic factor for patients with GC. Furthermore, CLDN18.2 correlates with immune infiltrating cells and PD-L1 expression.

Calibrating Laser Three-Dimensional Projection Systems Using Binocular Vision.

A laser three-dimensional (3D) projection system is an auxiliary system in intelligent manufacturing. It works with a positioning system in practical applications. This study proposes a calibration method for laser 3D projection systems based on binocular vision. The significance of the binocular vision positioning function for the calibration process was analyzed. Two calibration methods for laser 3D projection systems based on the binocular vision positioning function were proposed. One method involves simplified calculation models and another used data to solve the conversion relationship. The experimental calibration of the projection system was performed using data to directly solve the conversion relationship. The experiment demonstrated the simplicity of the proposed calibration method. The calculation time was less under the 3D laser projection system based on binocular vision. Moreover, the mean calibration error was 0.38 mm at a working distance of 1.8-2.2 m.

Multi-planar fixation of displaced, multi-fragmentary patella fractures in elderly patients with anterior locking plates and cerclage wires: preliminary results.

PURPOSE: The purpose of this study was to evaluate the outcomes of displaced, multi-fragmentary patella fractures in elderly patients treated with anterior plating and cerclage wires. PATIENTS AND METHODS: Between 2018 and 2020, patients aged at least 60 years old undergoing anterior plating and circumferential wiring for displaced, multi-fragmentary patella fractures with a minimum of 12 months follow-up were included. Fragments were reduced and circumferentially stabilized by loop wires, followed by multi-planar fixation with an anterior locking plate and multi-directional screws. Postoperative outcomes were evaluated with the Böstman scores and range of motion. Patients were also asked about symptomatic hardware as well as satisfaction with postoperative outcomes. RESULTS: Sixteen patients with an average age of 71.8 ± 9.3 years old were followed up for an average follow-up was 25.1 ± 8.2 months. At the latest follow-up, the average Böstman scores were 27.2 ± 3.4, and active knee flexion was 123 ± 14°. Two patients complained pinpoint implant irritation and underwent implant removal. Another underwent implant removal due to cultural reasons. Fifteen patients were satisfied with the operative outcomes, and one was unsatisfied due to intermittent patellofemoral pain. Cerclage wire breakage was noted in nine patients on postoperative radiographs, but none elicited pinpoint pain. No wound complications, infections, nonunion or loss of reduction were observed. CONCLUSIONS: Anterior locking plates and cerclage wires in tandem provide reliable multi-planar fixation for displaced, multi-fragmentary patellar fractures in elderly patients and resulted in favorable clinical outcomes.

Rheumatoid arthritis, systemic lupus erythematosus and primary Sjögren's syndrome shared megakaryocyte expansion in peripheral blood.

OBJECTIVES: Rheumatoid arthritis (RA), systemic lupus erythematosus (SLE) and primary Sjögren's syndrome (pSS) share many clinical manifestations and serological features. The aim of this study was to identify the common transcriptional profiling and composition of immune cells in peripheral blood in these autoimmune diseases (ADs). METHODS: We analysed bulk RNA-seq data for enrichment of biological processes, transcription factors (TFs) and deconvolution-based immune cell types from peripheral blood mononuclear cells (PBMCs) in 119 treatment-naive patients (41 RA, 38 pSS, 28 SLE and 12 polyautoimmunity) and 20 healthy controls. The single-cell RNA-seq (scRNA-seq) and flow cytometry had been performed to further define the immune cell subsets on PBMCs. RESULTS: Similar transcriptional profiles and common gene expression signatures associated with nucleosome assembly and haemostasis were identified across RA, SLE, pSS and polyautoimmunity. Distinct TF ensembles and gene regulatory network were mainly enriched in haematopoiesis. The upregulated cell-lineage-specific TFs PBX1, GATA1, TAL1 and GFI1B demonstrated a strong gene expression signature of megakaryocyte (MK) expansion. Gene expression-based cell type enrichment revealed elevated MK composition, specifically, CD41b+CD42b+ and CD41b+CD61+ MKs were expanded, further confirmed by flow cytometry in these ADs. In scRNA-seq data, MKs were defined by TFs PBX1/GATA1/TAL1 and pre-T-cell antigen receptor gene, PTCRA. Cellular heterogeneity and a distinct immune subpopulation with functional enrichment of antigen presentation were observed in MKs. CONCLUSIONS: The identification of MK expansion provided new insights into the peripheral immune cell atlas across RA, SLE, pSS and polyautoimmunity. Aberrant regulation of the MK expansion might contribute to the pathogenesis of these ADs.

Predictive model of risk and severity of enteritis in systemic lupus erythematosus.

INTRODUCTION: To describe the clinical and laboratory features of systemic lupus erythematosus (SLE) enteritis and to establish a predictive model of risk and severity of lupus enteritis (LE). METHODS: Records of patients with SLE complaining about acute digestive symptoms were reviewed. The predictive nomogram for the diagnosis of LE was constructed by using R. The accuracy of the model was tested with correction curves. The receiver operating characteristic curve (ROC curve) program and a Decision curve analysis (DCA) were used for the verification of LE model. Receiver operating characteristic curve was also employed for evaluation of factors in the prediction of severity of LE. RESULTS: During the eight year period, 46 patients were in the LE group, while 32 were in the non-LE group. Abdominal pain, emesis, D-dimer >5 µg/mL, hypo-C3, and anti-SSA positive remained statistically significant and were included into the prediction model. Area under the curve (AUC) of ROC curve in this model was 0.909. Correction curve indicated consistency between the predicted rate and actual diagnostic rates. The DCA showed that the LE model was of benefit. Forty-four patients were included in developing the prediction model of LE severity. Infection, SLE disease activity index (SLEDAI), CT score, and new CT score were validated as risk factors for LE severity. The AUC of the combined SLEDAI, infection and new CT score were 0.870. CONCLUSION: The LE model exhibits good predictive ability to assess LE risk in SLE patients with acute digestive symptoms. The combination of SLEDAI, infection, and new CT score could improve the assessment of LE severity.

Polydopamine-based molecularly imprinted electrochemical sensor for the highly selective determination of ecstasy components.

3,4-Methylenedioxyamphetamine (MDA) and 3,4-methylenedioxymethamphetamine (MDMA) are the main components of illicit stimulant drugs, also known as "ecstasy", which belong to psychoactive medicine and tend to be increasingly abused among drug addicts worldwide. Herein, an electrochemical sensor based on molecularly imprinted polydopamine (MIP@PDA) was developed to detect MDA and MDMA using differential pulse voltammetry (DPV). An MIP film on a Au electrode was synthesized via electrochemical polymerization with the safe chemical DA as the polymerization monomer and the uncontrolled pharmaceutical intermediate 3,4-methylenedioxyphenethylamine (MDPEA) as the template molecule, which can provide a great quantity of specific binding sites and expand the practical application of the sensor. Due to the superior affinity of MIP@PDA to the target, the proposed sensor displayed excellent analytical performance, with LODs of 37 nM and 54 nM for the determination of MDA and MDMA, respectively. Additionally, this sensor presented suitable selectivity, stability, reproducibility and detection ability in practical urine samples, which suggested that it is a promising candidate as a rapid diagnostic method in drug investigations.

Sleep duration and efficiency are associated with plasma amyloid-ß in non-demented older people.

STUDY OBJECTIVES: This study aims to investigate the extent to which sleep duration and efficiency are associated with plasma amyloid-ß (Aß) levels in non-demented older people. METHODS: This study is a cross-sectional analysis of 305 non-demented older people. Sleep duration and efficiency were assessed used the Pittsburgh Sleep Quality Index. Levels of plasma Aß were determined by sandwich enzyme-linked immunosorbent assay technique. Associations between sleep variables and plasma Aß levels were evaluated with multivariable linear regression analysis. RESULTS: Compared to those with sleep duration > 7 h, participants with sleep duration < 6 h had a higher plasma Aß42 level (ß = 0.495, 95% CI 0.077~0.913, p = 0.021) and Aß42/Aß40 ratio (ß = 0.101, 95% CI 0.058~0.144, p < 0.001). Compared to those with sleep efficiency ≥ 85%, participants with lower sleep efficiency (65~74%, <65%) had a higher level of plasma Aß42 (<65%: ß = 0.627, 95% CI 0.147~1.108, p = 0.011) and Aß42/Aß40 ratio (65~74%: ß = 0.052, 95% CI 0.007~0.097, p = 0.026; <65%: ß = 0.117, 95% CI 0.067~0.168, p < 0.001). CONCLUSIONS: These findings indicated that short sleep duration and low sleep efficiency were associated with a high level of Aß42. A better comprehending of the link between sleep and plasma Aß levels may lead to effective sleep-based intervention to reduce the risk of Alzheimer's disease.

Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators.

This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger-Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions.

miR-19a/b-3p promotes inflammation during cerebral ischemia/reperfusion injury via SIRT1/FoxO3/SPHK1 pathway.

BACKGROUND: Stroke affects 3-4% of adults and kills numerous people each year. Recovering blood flow with minimal reperfusion-induced injury is crucial. However, the mechanisms underlying reperfusion-induced injury, particularly inflammation, are not well understood. Here, we investigated the function of miR-19a/b-3p/SIRT1/FoxO3/SPHK1 axis in ischemia/reperfusion (I/R). METHODS: MCAO (middle cerebral artery occlusion) reperfusion rat model was used as the in vivo model of I/R. Cultured neuronal cells subjected to OGD/R (oxygen glucose deprivation/reperfusion) were used as the in vitro model of I/R. MTT assay was used to assess cell viability and TUNEL staining was used to measure cell apoptosis. H&E staining was employed to examine cell morphology. qRT-PCR and western blot were performed to determine levels of miR-19a/b-3p, SIRT1, FoxO3, SPHK1, NF-κB p65, and cytokines like TNF-α, IL-6, and IL-1ß. EMSA and ChIP were performed to validate the interaction of FoxO3 with SPHK1 promoter. Dual luciferase assay and RIP were used to verify the binding of miR-19a/b-3p with SIRT1 mRNA. RESULTS: miR-19a/b-3p, FoxO3, SPHK1, NF-κB p65, and cytokines were elevated while SIRT1 was reduced in brain tissues following MCAO/reperfusion or in cells upon OGD/R. Knockdown of SPHK1 or FoxO3 suppressed I/R-induced inflammation and cell death. Furthermore, knockdown of FoxO3 reversed the effects of SIRT1 knockdown. Inhibition of the miR-19a/b-3p suppressed inflammation and this suppression was blocked by SIRT1 knockdown. FoxO3 bound SPHK1 promoter and activated its transcription. miR-19a/b-3p directly targeted SIRT1 mRNA. CONCLUSION: miR-19a/b-3p promotes inflammatory responses during I/R via targeting SIRT1/FoxO3/SPHK1 axis.

PhenoPro: a novel toolkit for assisting in the diagnosis of Mendelian disease.

MOTIVATION: Whole-exome sequencing (WES) is now being used in clinical practice for the diagnosis of the causal genes of Mendelian diseases. In order to make the diagnosis, however, the clinical phenotypes [e.g. Human Phenotype Ontology (HPO) terms] of a patient are needed for prioritizing the variants called from the WES data of the patient. Computational tools are therefore needed to standardize and accelerate this process. RESULTS: Here, we introduce a tool named PhenoPro for prioritizing the causal gene of Mendelian disease given both the HPO terms assigned to and the variants called from the WES data of a patient. PhenoPro has been benchmarked using both simulated patients and 287 real diagnosed patients of Chinese ancestry, and shows significant improvements over five previous tools. Moreover, the addition of an internal variant data of Chinese ancestry and the variant data from the patients' parents can further improve PhenoPro's performance. To make PhenoPro a fully automated tool, we also include a natural language processing component for automated HPO term assignment from clinical reports, and demonstrate that the natural language processing is as effective as manual HPO assignment using real clinical reports. In conclusion, PhenoPro can be used as a pre-screening tool to assist in the diagnosis of Mendelian disease genes. AVAILABILITY AND IMPLEMENTATION: The web server of PhenoPro is freely available at http://app.tianlab.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.