Biology subjects

Xie, S.

Publications and source records attributed to Xie, S..

7 recordsLinked to original sources

bioRxiv2018

Synergistic antifungal effect of amphotericin B-loaded PLGA nanoparticles based ultrasound against C. albicans biofilms

C. albicans is human opportunistic pathogens that cause superficial and life-threatening infections. An important reason for the failure of current antifungal drugs is related to biofilm formation mostly associated with implanted medical device. The present study aims to investigate the synergistic antifungal efficacy of low-frequency and low-intensity ultrasound combined with amphotericin B-loaded PLGA nanoparticles (AmB-NPs) on C.albicans biofilms. AmB-NPs were prepared by a double emulsion method and demonstrated the lower toxicity than free AmB, after which biofilms were established and treated with ultrasound and AmB-NPs separately or jointly in vitro and in vivo. The results demonstrated the activity, biomass, and proteinase and phospholipase activities of biofilms were decreased significantly after the combination treatment of AmB-NPs with 42 KHz ultrasound irradiation at an intensity of 0.30 W/cm2 for 15 min compared to the control, the AmB alone or the ultrasound alone treatment (P < 0.01), and the morphology of biofilms was altered remarkably after jointly treatment under CLSM observation and detection, especially thickness thinning and structure loosing. Furthermore, the same synergistic effects were proved in a subcutaneous catheter biofilm rat model. The result of colony forming units of catheter fungus loading exhibited a significant reduction after AmB-NPs and ultrasound jointly treatment for 7 days continuous therapy, and the CLSM images revealed that the biofilm on the catheter surface was substantially eliminated. Our study may provide a new noninvasive, safe and effective application to C.albicans biofilm infection therapy.

microbiology↗

bioRxiv2018

Cyclin D-Cdk4,6 drives cell cycle progression via the retinoblastoma protein’s C-terminal helix

The cyclin-dependent kinases Cdk4 and Cdk6 form complexes with D-type cyclins to drive cell proliferation. A well-known target of cyclin D-Cdk4,6 is the retinoblastoma protein, Rb, which inhibits cell cycle progression until its inactivation by phosphorylation. However, the role of cyclin D-Cdk4,6 phosphorylation of Rb in cell cycle progression is unclear because Rb can be phosphorylated by other cyclin-Cdk complexes and cyclin D-Cdk4,6 complexes have other targets that may drive cell division. Here, we show that cyclin D-Cdk4,6 docks one side of an alpha-helix in the C-terminus of Rb, which is not recognized by cyclins E, A, and B. This helix-based docking mechanism is shared by the p107 and p130 Rb-family members across metazoans. Mutation of the Rb C-terminal helix prevents phosphorylation, promotes G1 arrest, and enhances Rbs tumor suppressive function. Our work conclusively demonstrates that the cyclin D-Rb interaction drives cell division and defines a new class of cyclin-based docking mechanisms.

cell biology↗

bioRxiv2018

A computational strategy for finding novel targets and therapeutic compounds for opioid dependence

Opioids are widely used for treating different types of pains, but overuse and abuse of prescription opioids have led to opioid epidemic in the United States. Besides analgesic effects, chronic use of opioid can also cause tolerance, dependence, and even addiction. Effective treatment of opioid addiction remains a big challenge today. Studies on addictive effects of opioids focus on striatum, a main component in the brain responsible for drug dependence and addiction. Some transcription regulators have been associated with opioid addiction, but relationship between analgesic effects of opioids and dependence behaviors mediated by them at the molecular level has not been thoroughly investigated. In this paper, we developed a new computational strategy that identifies novel targets and potential therapeutic molecular compounds for opioid dependence and addiction. We employed several statistical and machine learning techniques and identified differentially expressed genes over time which were associated with dependence-related behaviors after exposure to either morphine or heroin, as well as potential transcription regulators that regulate these genes, using time course gene expression data from mouse striatum. Moreover, our findings revealed that some of these dependence-associated genes and transcription regulators are known to play key roles in opioid-mediated analgesia and tolerance, suggesting that an intricate relationship between opioid-induce pain-related pathways and dependence may develop at an early stage during opioid exposure. Finally, we determined small compounds that can potentially target the dependence-associated genes and transcription regulators. These compounds may facilitate development of effective therapy for opioid dependence and addiction. We also built a database (http://daportals.org) for all opioid-induced dependence-associated genes and transcription regulators that we discovered, as well as the small compounds that target those genes and transcription regulators.

systems biology↗

bioRxiv2018

Unbiased Precision Estimation under Separate Sampling

MotivationPrecision and recall have become very popular classification accuracy metrics in the statistical learning literature. These metrics are ordinarily defined under the assumption that the data are sampled randomly from the mixture of the populations. However, observational case-control studies for biomarker discovery often collect data that are sampled separately from the case and control populations, particularly in the case of rare diseases. This discrepancy may introduce severe bias in classifier accuracy estimation.\n\nResultsWe demonstrate, using both analytical and numerical methods, that classifier precision estimates can display strong bias under separating sampling, with the bias magnitude depending on the difference between the case prevalences in the data and in the actual population. We show that this bias is systematic in the sense that it cannot be reduced by increasing sample size. If information about the true case prevalence is available from public health records, then a modified precision estimator is proposed that displays smaller bias, which can in fact be reduced to zero as sample size increases under regularity conditions on the classification algorithm. The accuracy of the theoretical analysis and the performance of the proposed precision estimator under separate sampling are investigated using synthetic and real data from observational case-control studies. The results confirmed that the proposed precision estimator indeed becomes unbiased as sample size increases, while the ordinary precision estimator may display large bias, particularly in the case of rare diseases.\n\nAvailabilityExtra plots are available as Supplementary Materials.\n\nAuthor summaryBiomedical data are often sampled separately from the case and control populations, particularly in the case of rare diseases. Precision is a popular classification accuracy metric in the statistical learning literature, which implicitly assumes that the data are sampled randomly from the mixture of the populations. In this paper we study the bias of precision under separate sampling using theoretical and numerical methods. We also propose a precision estimator for separate sampling in the case when the prevalence is known from public health records. The results confirmed that the proposed precision estimator becomes unbiased as sample size increases, while the ordinary precision estimator may display large bias, particularly in the case of rare diseases. In the absence of any knowledge about disease prevalence, precision estimates should be avoided under separate sampling.

bioinformatics↗

bioRxiv2018

Tissue-specific Gene Expression Prediction Associates Vitiligo with SUOX through an Active Enhancer

Vitiligo is an autoimmune disease featuring destruction of melanocytes, which results in patchy depigemtation of skin and hair; two vitiligo GWAS studies identified multiple significant associations, including SNPs in 12q13.2 region. But one study ascribed the association to IKZF4 because it encodes a regulator of T cell activation and is associated with two autoimmune diseases; while the other study ascribed the association to PMEL because it encodes melanocyte protein and has the strongest differential expression between vitiligo lesions and perilesional normal skins. Here we show that vitiligo associated gene in 12q13.2 region is SUOX. Reanalyzing one GWAS dataset, we predicted tissue-specific gene-expression by leveraging Genotype-Tissue Expression (GTEx) datasets, and performed association mapping between the predicted gene-expressions and vitiligo status. SUOX expression is significantly associated with vitiligo in both Nerve (tibia) and Skin (sun exposed) tissues. Epigenetic marks encompass the most significant eQTL of SUOX in both nerve and skin tissues suggest a putative enhancer 3Kb downstream of SUOX. We silenced the putative enhancer using the CRISPR interference system and observed 50% decrease in SUOX expression in K562 cells, a cell line that has similar DNase hypersensitive sites and gene expression pattern to the skin tissue at SUOX locus. Our work provided an example to make sense GWAS hits through examining factors that affect gene expression both computationally and experimentally.

genetics↗

bioRxiv2018

Frequent sgRNA-barcode Recombination in Single-cell Perturbation Assays

Simultaneously detecting CRISPR-based perturbations and induced transcriptional changes in the same cell is a powerful approach to unraveling genome function. Several lentiviral approaches have been developed, some of which rely on the detection of distally located genetic barcodes as an indirect proxy of sgRNA identity. Since barcodes are often several kilobases from their corresponding sgRNAs, viral recombination-mediated swapping of barcodes and sgRNAs is feasible. Using a self-circularization-based sgRNA-barcode library preparation protocol, we estimate the recombination rate to be ~50% and we trace this phenomenon to the pooled viral packaging step. Recombination is random, and decreases the signal-to-noise ratio of the assay. Our results suggest that alternative approaches can increase the throughput and sensitivity of single-cell perturbation assays.

genomics↗

bioRxiv2018

Cell type-specific CLIP reveals that NOVA regulates cytoskeleton interactions in motoneurons

BackgroundAlternative RNA processing plays an essential role in shaping cell identity and connectivity in the central nervous system (CNS). This is believed to involve differential regulation of RNA processing in various cell types. However, in vivo study of cell-type specific post-transcriptional regulation has been a challenge. Here, we developed a sensitive and stringent method combining genetics and CLIP (crosslinking and immunoprecipitation) to globally identify regulatory interactions between NOVA and RNA in the mouse spinal cord motoneurons (MNs).\n\nResultsWe developed a means of undertaking MN-specific CLIP to explore MN-specific protein-RNA interactions relative to studies of the whole spinal cord. This allowed us to pinpoint differential RNA regulation specific to MNs, revealing major role for NOVA in regulating cytoskeleton interactions in MNs. In particular, NOVA specifically promotes the palmitoylated isoform of a cytoskeleton protein Septin 8 in MNs, which enhances dendritic arborization.\n\nConclusionsOur study demonstrates that cell type-specific RNA regulation is important for fine-tuning motoneuron physiology, and highlights the value of defining RNA processing regulation at single cell type resolution.

neuroscience↗