Biology subjects

Feng, Q.

Publications and source records attributed to Feng, Q..

9 recordsLinked to original sources

bioRxiv2018

Bacterial strain displacement in inflammatory bowel diseases after fecal microbiota transplantation

Fecal microbiota transplantation (FMT), which is thought to have the potential to correct dysbiosis of gut microbiota, has recently been used to treat inflammatory bowel disease (IBD). To elucidate the extent and principles of microbiota engraftment in IBD patients after FMT treatment, we conducted an interventional prospective cohort study. The cohort included two categories of patients: (1) patients with moderate to severe Crohns disease (CD) (Harvey-Bradshaw Index [≥] 7, n = 11, and (2) patients with ulcerative colitis (UC) (Montreal classification, S2 and S3, n = 4). All patients were treated with a single FMT (via mid-gut, from healthy donors) and follow-up visits were performed at baseline, 3 days, one week, and one month after FMT (missing time points included). At each follow-up time point, fecal samples of the participants were collected along with their clinical metadata. For comparative analysis, 10 fecal samples from 10 healthy people were included to represent the diversity level of normal gut microbiota. Additionally, the metagenomic data of 25 fecal samples from 5 individuals with metabolic syndrome who underwent autologous FMT treatment were downloaded from a previous published paper to represent natural microbiota shifts during FMT. All fecal samples underwent shotgun metagenomic sequencing. We found that 3 days after FMT, 11 out of 15 recipients were in remission (3 out of 4 UC recipients; 8 out of 11 CD recipients). Generally, bacterial colonization was observed to be lower in CD recipients than in UC recipients at both species and strain levels. Furthermore, across species, different strains displayed disease-specific displacement advantages under two-disease status. Finally, most post-FMT species (> 80%) could be properly predicted (AUC > 85%) using a random forest classification model, with the gut microbiota composition and clinical parameters of pre-FMT recipients acting as the most contributive factors for prediction accuracy.

clinical trials↗

bioRxiv2018

Tau accumulation activates STAT1 triggering memory deficits via suppressing NMDA receptor expression

Intracellular tau accumulation forming neurofibrillary tangles is hallmark pathology of Alzheimer's disease (AD), but how tau accumulation induces synapse impairment is elusive. By overexpressing human full-length wildtype tau (termed hTau) to mimic tau abnormality as seen in the brain of sporadic AD patients, we found that hTau accumulation activated JAK2 to phosphorylate STAT1 (Signal Transducer and Activator of Transcription 1) at Tyr701 leading to STAT1 dimerization, nuclear translocation and its activation. STAT1 activation suppressed expression of N-methyl-D-aspartate receptors (NMDARs) through direct binding to the specific GAS element of GluN1, GluN2A and GluN2B promoters, while knockdown STAT1 by AAV-Cre in STAT1flox/flox mice or expressing dominant negative Y701F-STAT1 efficiently rescued hTau-induced suppression of NMDARs expression with amelioration of synaptic functions and memory performance. These findings indicate that hTau accumulation impairs synaptic plasticity through JAK2/STAT1-induced suppression of NMDARs expression, revealing a novel mechanism for hTau-associated synapse and memory deficits.

neuroscience↗

bioRxiv2018

Learning from Longitudinal Data in Electronic Health Record and Genetic Data to Improve Cardiovascular Event Prediction

BackgroundCurrent approaches to predicting Cardiovascular disease rely on conventional risk factors and cross-sectional data. In this study, we asked whether: i) machine learning and deep learning models with longitudinal EHR information can improve the prediction of 10-year CVD risk, and ii) incorporating genetic data can add values to predictability.\n\nMethodsWe conducted two experiments. In the first experiment, we modeled longitudinal EHR data with aggregated features and temporal features. We applied logistic regression (LR), random forests (RF) and gradient boosting trees (GBT) and Convolutional Neural Networks (CNN) and Recurrent Neural Networks, using Long Short-Term Memory (LSTM) units. In the second experiment, we proposed a late-fusion framework to incorporate genetic features.\n\nResultsOur study cohort included 109, 490 individuals (9,824 were cases and 99, 666 were controls) from Vanderbilt University Medical Centers (VUMC) de-identified EHRs. American College of Cardiology and the American Heart Association (ACC/AHA) Pooled Cohort Risk Equations had areas under receiver operating characteristic curves (AUROC) of 0.732 and areas under receiver under precision and recall curves (AUPRC) of 0.187. LSTM, CNN and GBT with temporal features achieved best results, which had AUROC of 0.789, 0.790, and 0.791, and AUPRC of 0.282, 0.280 and 0.285, respectively. The late fusion approach achieved a significant improvement for the prediction performance.\n\nConclusionsMachine learning and deep learning with longitudinal features improved the 10-year CVD risk prediction. Incorporating genetic features further enhanced 10-year CVD prediction performance, underscoring the importance of integrating relevant genetic data whenever available in the context of routine care.

epidemiology↗

bioRxiv2018

Isolation and Purification of Active Antimicrobial Peptides from Hermetia illucens L., and Its Effects on CNE2 Cells

Active antimicrobial peptide HI-3 was isolated and purified from the 5th instar larvae of Hermetia illucens L., and its effects on proliferation, apoptosis and migration of nasopharyngeal carcinoma (CNE2) cells were investigated. The expressions of telomerase reverse transcriptase (hTERT) in CNE2 cells were also studied in vitro to elucidate the mechanism involved in the action of HI-3 on CNE2 cells. Results showed that three fractions (HI-1, HI-2, HI-3) were isolated from the hemolymph of H. illucens larvae. After purified by RP-HPLC, only HI-3 showed the inhibitory activities to four strains of bacteria. It was also showed that HI-3 could effectively inhibit the proliferation of CNE2 cells in a dose- and time-dependent manner. Apoptosis of CNE2 cells was observed in the treatment with 160 g/ml HI-3, and the early apoptosis rate up to 27.59 {+/-} 1.14%. However, no significantly inhibitory effects and apoptosis were found on human umbilical vein endothelial cells (HUV-C). Moreover, HI-3 could significantly reduce the migration ability of CNE2 cells when compared with that of the control. On the other hand, the levels of mRNA and protein of hTERT in the HI-3 treatment were all significantly lower than that of the control. Results indicated that HI-3 could inhibit the proliferation of CNE2 cells and induce the apoptosis of CNE2 cells by down-regulating the telomerase activity in CNE2 cells, while no obvious effect was occurred on HUV-C. It inferred that HI-3 is a potential anti-tumor drug with low toxicity to normal cells.\n\nSummary StatementO_LIAn active antimicrobial peptide HI-3 was isolated and purified.\nC_LIO_LIInhibitory proliferation of CNE2 cells, but no effect on normal cells.\nC_LIO_LIA potential antitumoral drug.\nC_LI

immunology↗

bioRxiv2018

Using Topic Modeling via Non-negative Matrix Factorization to Identify Relationships between Genetic Variants and Disease Phenotypes: A Case Study of Lipoprotein(a) (LPA)

Genome-wide and phenome-wide association studies are commonly used to identify important relationships between genetic variants and phenotypes. Most of these studies have treated diseases as independent variables and suffered from heavy multiple adjustment burdens due to the large number of genetic variants and disease phenotypes. In this study, we propose using topic modeling via non-negative matrix factorization (NMF) for identifying associations between disease phenotypes and genetic variants. Topic modeling is an unsupervised machine learning approach that can be used to learn the semantic patterns from electronic health record data. We chose rs10455872 in LPA as the predictor since it has been shown to be associated with increased risk of hyperlipidemia and cardiovascular diseases (CVD). Using data of 12,759 individuals from the biobank at Vanderbilt University Medical Center, we trained a topic model using NMF from 1,853 distinct phecodes extracted from the cohorts electronic health records and generated six topics. We quantified their associations with rs10455872 in LPA. Topics indicating CVD had positive correlations with rs10455872 (P < 0.001), replicating a previous finding. We also identified a negative correlation between LPA and a topic representing lung cancer (P < 0.001). Our results demonstrate the applicability of topic modeling in exploring the relationship between the genome and clinical diseases.\n\nAuthor summaryIdentifying the clinical associations of genetic variants remains crucial in understanding how the human genome modulates disease risk. Traditional phenome-wide association studies consider each disease phenotype as an independent variable, however, diseases often present as complex clusters of comorbid conditions. In this study, we propose using topic modeling to model electronic health record data as a mixture of topics (e.g., disease clusters or relevant comorbidities) and testing associations between topics and genetic variants. Our results demonstrated the feasibility of using topic modeling to replicate and discover novel associations between the human genome and clinical diseases.

genetics↗

bioRxiv2018

Sapropterin Treatment Prevents Congenital Heart Defects Induced by Pregestational Diabetes in Mice

AimsTetrahydrobiopterin (BH4) is a co-factor of endothelial nitric oxide synthase (eNOS), which is critical to embryonic heart development. We aimed to study the effects of sapropterin (Kuvan(R)), an orally active synthetic form of BH4 on eNOS uncoupling and congenital heart defects (CHDs) induced by pregestational diabetes in mice.\n\nMethodsAdult female mice were induced to pregestational diabetes by streptozotocin and bred with normal males to produce offspring. Pregnant mice were treated with sapropterin or vehicle during gestation. CHDs were identified by histological analysis. Cell proliferation, eNOS dimerization and reactive oxygen species (ROS) production were assessed in the fetal heart.\n\nResultsPregestational diabetes results in a spectrum of CHDs in their offspring. Oral treatment with sapropterin in the diabetic dams significantly decreased the incidence of CHDs from 59% to 27% and major abnormalities, such as atrioventricular septal defect and double outlet right ventricle were absent in the sapropterin treated group. Lineage tracing reveals that pregestational diabetes results in decreased commitment of second heart field progenitors to the outflow tract, endocardial cushions, and ventricular myocardium of the fetal heart. Notably, decreased cell proliferation and cardiac transcription factor expression induced by maternal diabetes were normalized with sapropterin treatment. Furthermore, sapropterin administration in the diabetic dams increased eNOS dimerization and lowered ROS levels in the fetal heart.\n\nConclusionsSapropterin treatment in the diabetic mothers improves eNOS coupling, increases cell proliferation and prevents the development of CHDs in the offspring. Thus, sapropterin may have therapeutic potential in preventing CHDs in pregestational diabetes.

pharmacology and toxicology↗

bioRxiv2018

S-Nitrosylation of STIM1 by neuronal nitric oxide synthase inhibits store-operated Ca2+ entry

Store-operated Ca2+ entry (SOCE) mediated by stromal interacting molecule-1 (STIM1) and Orai1 represents a major route of Ca2+ entry in mammalian cells and is initiated by STIM1 oligomerization in the endoplasmic or sarcoplasmic reticulum (ER/SR). However, the effects of nitric oxide (NO) on STIM1 function are unknown. Neuronal NO synthase (nNOS) is located in the SR of cardiomyocytes. Here, we show that STIM1 is susceptible to S-nitrosylation. nNOS deficiency or inhibition enhanced Ca2+ release-activated Ca2+ channel current (ICRAC) and SOCE in cardiomyocytes. Consistently, NO donor S-nitrosoglutathione (GSNO) inhibited STIM1 puncta formation and ICRAC in HEK293 cells, but this effect was absent in cells expressing the Cys49Ser/Cys56Ser STIM1 double mutant. Furthermore, NO donors caused Cys49 and Cys56-specific structural changes associated with reduced protein backbone mobility, increased thermal stability and suppressed Ca2+-depletion-dependent oligomerization of the luminal Ca2+-sensing region of STIM1. Collectively, our data show that S-nitrosylation of STIM1 suppresses oligomerization via enhanced luminal domain stability and rigidity, and inhibits SOCE in cardiomyocytes.

physiology↗

bioRxiv2017

Motor reattachment kinetics play a dominant role in multimotor-driven cargo transport

Kinesin-based cargo transport in cells frequently involves the coordinated activity of multiple motors, including kinesins from different families that move at different speeds. However, compared to the progress at the single-molecule level, mechanisms by which multiple kinesins coordinate their activity during cargo transport are poorly understood. To understand these multi-motor coordination mechanisms, defined pairs of kinesin-1 and kinesin-2 motors were assembled on DNA scaffolds and their motility examined in vitro. Although less processive than kinesin-1 at the single-molecule level, addition of kinesin-2 motors more effectively amplified cargo run lengths. By applying the law of total expectation to cargo binding durations in ADP, the kinesin-2 microtubule reattachment rate was shown to be 4-fold faster than that of kinesin-1. This difference in microtubule binding rates was also observed in solution by stopped-flow. High-resolution tracking of gold-nanoparticle-labeled cargo with 1 ms and 2 nm precision revealed that kinesin-2 motors detach and rebind to the microtubule much more frequently than do kinesin-1. Finally, cargo transported by kinesin-2 motors more effectively navigated roadblocks on the microtubule track. These results highlight the importance of motor reattachment kinetics during multi-motor transport and suggest a coordinated transport model in which kinesin-1 motors step effectively against loads while kinesin-2 motors rapidly unbind and rebind to the microtubule. This dynamic tethering by kinesin-2 maintains the cargo near the microtubule and enables effective navigation along crowded microtubules.

biophysics↗

bioRxiv2016

Inference of Multiple-wave Admixtures by Length Distribution of Ancestral Tracks

The ancestral tracks in admixed genomes are of valuable information for population history inference. A few methods have been developed to infer admixture history based on ancestral tracks. Nonetheless, these methods suffered the same flaw that only population admixture history under some specific models can be inferred. In addition, the inference of history might be biased or even unreliable if the specific model is deviated from the real situation. To address this problem, we firstly proposed a general discrete admixture model to describe the admixture history with multiple ancestral populations and multiple-wave admixtures. We next deduced the length distribution of ancestral tracks under the general discrete admixture model. We further developed a new method, MultiWaver, to explore the multiple-wave admixture histories. Our method could automatically determine an optimal admixture model based on the length distribution of ancestral tracks, and estimate the corresponding parameters under this optimal model. Specifically, we used a likelihood ratio test (LRT) to determine the number of admixture waves, and implemented an expectation??maximization (EM) algorithm to estimate parameters. We used simulation studies to validate the reliability and effectiveness of our method. Finally, good performance was observed when our method was applied to real datasets of African Americans, Mexicans, Uyghurs, and Hazaras.

genetics↗