Biology subjects

Feng, Q.

Publications and source records attributed to Feng, Q..

78 records · Page 5Linked to original sources

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↗