Biology subjects

Chen, S.

Publications and source records attributed to Chen, S..

At least 991 records · Page 55Linked to original sources

bioRxiv2018

SIP: An Interchangeable Pipeline for scRNA-seq Data Processing

Multiple steps of bioinformatics processing are needed to convert the raw scRNA-seq data to information that can be used in downstream analyses and in building cell atlases. Dozens of software packages have been developed and different labs tend to have different preferences on choices of the workflow. Such diversity can cause difficulties in future efforts of aggregating data from multiple labs, and also difficulties for new labs to start in this field. A few pipelines have been developed to help integrating multiple steps into a whole, but the fixed software architecture makes it hard for developers to add new features or exchange parts in the pipeline.\n\nWe presented SIP, a Single-cell Interchangeable Pipeline. It is a one-stop platform for the processing of scRNA-seq data from multiple platforms, and will also support for other types of data like scATAC-seq data. SIP utilizes container technology to solve the deployment dilemma when handling multiple packages and provides an easy-to-use interface for users to conduct the complicated multi-step process from raw data to final results with a single command. It also allows advanced users to assemble different versions of the pipeline by interchanging parts or adding new modules. SIP is available at https://github.com/XuegongLab/SIP under the GPL-3.0 license.

bioinformatics↗

bioRxiv2018

Rapid Tilt-Series Acquisition for Electron Cryotomography

Using a new Titan Krios stage equipped with a single-axis holder, we developed two methods to accelerate the collection of tilt-series. We demonstrate a continuous-tilting method that can record a tilt-series in seconds (about 100x faster than current methods), but with loss of details finer than [~]4 nm. We also demonstrate a fast-incremental method that can record a tilt-series about 10x faster than current methods and with similar resolution. We characterize the utility of both methods in real biological electron cryotomography workflows. We identify opportunities for further improvements in hardware and software and speculate on the impact such advances could have on structural biology.

biophysics↗

bioRxiv2018

Universal antibiotic tolerance arising from antibiotic-triggered accumulation of redox metabolites

Pseudomonas aeruginosa is an opportunistic pathogen that often infects open wounds or patients with cystic fibrosis. Once established, P. aeruginosa infections are notoriously difficult to eradicate. This difficulty is in part due to the ability of P. aeruginosa to tolerate antibiotic treatment at the individual-cell level or through collective behaviors. Here we describe a new mechanism by which P. aeruginosa tolerates antibiotic treatment by modulating its global cellular metabolism. In particular, treatment of P. aeruginosa with sublethal concentrations of antibiotics covering all major classes promoted accumulation of the redox-sensitive phenazine - pyocyanin (PYO). PYO in turn conferred general tolerance against diverse antibiotics for both P. aeruginosa and other Gram-negative and Gram-positive bacteria. We show that PYO promotes energy generation to enhance the activity of efflux pumps, leading to enhanced antibiotic tolerance. This property is shared by other redox-active phenazines produced by P. aeruginosa. Our discovery sheds new insights into the physiological functions of phenazines and has implications for designing effective antibiotic treatment protocols.\n\nAuthor SummaryAntibiotic tolerance can facilitate the evolution of resistance, and here we describe a previously unknown mechanism of collective antibiotic tolerance in Pseudomonas aeruginosa. In particular, P. aeruginosa treated with sublethal concentrations of antibiotics covering all major classes promotes accumulation of pyocyanin (PYO), an important virulence factor. In turn, PYO confers general tolerance against diverse antibiotics for both P. aeruginosa and other bacteria. Our discovery is a perfect example of what Nietzsche once said: That which does not kill me makes me stronger.

microbiology↗

bioRxiv2018

The mitochondrial DNA content can not predict the embryo viability

ObjectiveTo investigate whether the mitochondrial DNA content could predict the embryo viability\n\nDesignRetrospective analysis.\n\nSettingReproductive genetics laboratory\n\nPatient(s)A total of 421 biopsied samples obtained from 129 patients\n\nIntervention(s)Embryo biopsies samples underwent whole genome amplification (WGA) and were tested by next generation sequencing (NGS) and array Comparative Genomic Hybridization (aCGH), 30 samples were selected randomly to undergo quantitative real-time polymerase chain reaction (qPCR).\n\nMain Outcome Measure(s)Those embryos which obtained the consistent chromosome status determined both aCGH and NGS platform were further classified. We investigated the relationship of mtDNA content with several factors including female patient age, embryo morphology, chromosome status, and live birth rate of both blastocysts and blastomeres.\n\nResult(s)A total of 386 (110 blastomeres and 276 blastocysts) out of 399 embryos showed consistent chromosome status outcome. We found no statistically difference was observed in aneuploid and euploid blastocysts (p=0.14), the same phenomenon was observed in aneuploid and euploid blastomeres (p=0.89). Similarly, the mtDNA content was independent of female patient age, embryo morphology and live birth rate.\n\nConclusion(s)The mtDNA content did not provide a reliable prediction of the viability of blastocysts to initiate a pregnancy.

ecology↗

bioRxiv2018

The Dynamic Conformational Landscapes of the Protein Methyltransferase SETD8

Elucidating conformational heterogeneity of proteins is essential for understanding protein functions and developing exogenous ligands for chemical perturbation. While structural biology methods can provide atomic details of static protein structures, these approaches cannot in general resolve less populated, functionally relevant conformations and uncover conformational kinetics. Here we demonstrate a new paradigm for illuminating dynamic conformational landscapes of target proteins. SETD8 (Pr-SET7/SET8/KMT5A) is a biologically relevant protein lysine methyltransferase for in vivo monomethylation of histone H4 lysine 20 and nonhistone targets. Utilizing covalent chemical inhibitors and depleting native ligands to trap hidden high-energy conformational states, we obtained diverse novel X-ray structures of SETD8. These structures were used to seed massively distributed molecular simulations that generated six milliseconds of trajectory data of SETD8 in the presence or absence of its cofactor. We used an automated machine learning approach to reveal slow conformational motions and thus distinct conformational states of SETD8, and validated the resulting dynamic conformational landscapes with multiple biophysical methods. The resulting models provide unprecedented mechanistic insight into how protein dynamics plays a role in SAM binding and thus catalysis, and how this function can be modulated by diverse cancer-associated mutants. These findings set up the foundation for revealing enzymatic mechanisms and developing inhibitors in the context of conformational landscapes of target proteins.

biophysics↗

bioRxiv2018

Gating TrkB switch by methylglyoxal enables GLO1 as a target for depression

The endogenous metabolite methylglyoxal (MGO) has recently emerged as a potential mediator of psychiatric disorders, such as anxiety and depression, but its precise mechanism of action remains poorly understood. Here, we find that MGO concentrations are decreased in the prefrontal cortex and hippocampus in rats subjected to chronic stress, and low-dose MGO treatment remarkedly enhances resilience to stress and alleviates depression-like symptoms. This effect is achieved by MGOs promotion on the synaptic plasticity in prefrontal cortex and hippocampus. Both in vitro and in vivo experiments show that MGO provokes the dimerization and autophosphorylation of TrkB and the subsequent activation of downstream Akt/CREB signaling, which leads to a rapid and sustained expression of brain-derived neurotrophic factor (BDNF). We further demonstrate that MGO directly binds to the extracellular domain of TrkB, but not its intracellular domain. In addition, we also identify a natural product luteolin and its derivative lutD as potent inhibitors of Glyoxalase 1 and validate their antidepressant effects in chronic stress rat models. The antidepressant role of endogenous MGO provides a new basis for the understanding and therapeutic intervention design for stress-associated mental disorders.

neuroscience↗

bioRxiv2018

Dissecting heterogeneous cell-populations across signaling and diseaseconditions with PopAlign

Single-cell measurement techniques can now probe gene expression in heterogeneous cell populations from the human body across a range of environmental and physiological conditions. However, new mathematical and computational methods are required to represent and analyze gene expression changes that occur in complex mixtures of single cells as they respond to signals, drugs, or disease states. Here, we introduce a mathematical modeling platform, PopAlign, that automatically identifies subpopulations of cells within a heterogeneous mixture, and tracks gene expression and cell abundance changes across subpopulations by constructing and comparing probabilistic models. Probabilistic models provide a low-error, compressed representation of single cell data that enables efficient large-scale computations. We apply PopAlign to analyze the impact of 40 different immunomodulatory compounds on a heterogeneous population of donor-derived human immune cells as well as patient-specific disease signatures in multiple myeloma. PopAlign scales to comparisons involving tens to hundreds of samples, enabling large-scale studies of natural and engineered cell populations as they respond to drugs, signals or physiological change.

bioinformatics↗

bioRxiv2018

A new statistic for efficient detection of repetitive sequences

Detecting sequences containing repetitive regions is a basic bioinformatics task with many applications. Several methods have been developed for various types of repeat detection tasks. An efficient generic method for detecting all types of repetitive sequences is still desirable.\n\nInspired by the excellent properties and successful applications of the D2 family of statistics in comparative analyses of genomic sequences, we developed a new statistic [Formula] that can efficiently discriminate sequences with or without repetitive regions. Using the statistic, we developed an algorithm of linear complexity in both computation time and memory usage for detecting all types of repetitive sequences in multiple scenarios, including finding candidate CRISPR regions from bacterial genomic or metagenomics sequences. Simulation and real data experiments showed that the method works well on both assembled sequences and unassembled short reads.

bioinformatics↗

bioRxiv2018

Diagnostic Whole Exome Sequencing in Patients with Short Stature

Short stature is among the most common reasons for children being referred to the pediatric endocrinology clinics. The cause of short stature is broad, in which genetic factors play a substantial role, especially in primary growth disorders. However, identifying the molecular causes for short stature remains as a challenge because of the high heterogeneity of the phenotypes. Here, whole exome sequencing (WES) was used to identify the genetic causes of short stature with unknown etiology for 20 patients aged from 1 to 16 years old. The genetic causes of short stature were identified in 9 of the 20 patients, corresponding to a molecular diagnostic rate of 45%. Notably, in 2 of the 9 patients identified with genetic causes, the diagnosed diseases based on WES are different from the original clinical diagnosis. Our results highlight the clinical utility of WES in the diagnosis of rare, high heterogeneity disorders.

genetics↗

bioRxiv2018

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, though having 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 under a variety of disease status. Existing computational methods, though 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 above understanding, we integrate DNA sequences and chromatin accessibility data to accurately predict enhancers in a cell line-specific manner. We proposed DeepCAPE, a deep convolutional neural network to predict enhancers via the integration of DNA sequences and DNase-seq data. We demonstrate that our model not only consistently outperforms existing methods in the classification of enhancers against background sequences, but also accurately predicts enhancers across different cell lines. 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 disease-related enhancers.

bioinformatics↗

bioRxiv2018

TP promotes malignant progression in hepatocellular carcinoma through pentose Warburg effect

Tumor progression is dependent on metabolic reprogramming. Metastasis and vasculogenic mimicry (VM) are typical tumor progression. The relationship of metastasis, VM and metabolic reprogramming is not clear. In this study, we identified the novel role of Twist1, a VM regulator, in the transcriptional regulation of the expression of thymidine phosphorylase (TP). We demonstrated that TP promoted extracellular thymidine metabolization into ATP and amino acids through pentose Warburg effect by coupling the pentose phosphate pathway and glycolysis. Moreover, Twist1 relied on TP-induced metabolic reprogramming to promote hepatocellular carcinoma (HCC) metastasis and VM formation mediated by VE-Cad, VEGFR1, and VEGFR2 in vitro and in vivo. TP inhibitor tipiracil reduced promotion effect of TP enzyme activity on HCC VM formation and metastasis. Our findings demonstrate that TP, transcriptionally activated by Twist1, promotes HCC VM formation and metastasis through pentose Warburg effect, contributing to tumor progression.

cell biology↗

bioRxiv2018

Introduce a New Approach to Detect Genes Associated to Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) represents the most frequent of all oral neoplasms in the world. Genetics plays an important role in the etiopathogenesis of OSCC. However, the investigation of the molecular mechanism of OSCC is still incomplete. In this article, we introduced a new approach to detect OSCC-associated genes, in which we not only compare mean difference, but also variance difference between cases and controls. Based on two OSCC datasets from Gene Expression Omnibus, we identified 456 differentially variable (DV) gene probes, in addition to 2,375 differentially expressed (DE) gene probes. There are 2,193 DE-only probes, 274 DV-only probes, and 182 DE-and-DV probes. DAVID functional analysis showed that genes corresponding to DE-only, DV-only, and DE-and-DV probes were enriched in different KEGG pathways, indicating they play different roles in OSCC. This new approach can be used to investigate the genetic risk factors for other complex human diseases.

genetics↗

bioRxiv2018

Overdosage of balanced protein complexes reduces proliferation rate in aneuploid cells

Cells with complex aneuploidies, such as tumor cells, display a wide range of phenotypic abnormalities. However, molecular basis for this has been mainly studied in trisomic (2n+1) and disomic (n+1) cells. To determine how karyotype affects proliferation rate in cells with complex aneuploidies we generated forty 2n+x yeast strains in which each diploid cell has an extra 5 to 12 chromosomes and found that these strains exhibited abnormal cell-cycle progression. Proliferation rate was negatively correlated with the number of protein complexes in which all subunits were at the 3-copy level, but not with the number of imbalanced complexes made up of a mixture of 2-copy and 3-copy genes. Proteomics revealed that most 3-copy members of imbalanced complexes were expressed at only 2n protein levels whereas members of complexes in which all subunits are stoichiometrically balanced at 3 copies per cell had 3n protein levels. We identified individual protein complexes for which overdosage reduces proliferation rate, and found that deleting one copy of each member partially restored proliferation rate in cells with complex aneuploidies. Lastly, we validated this finding using orthogonal datasets from both yeast and from human cancers. Taken together, our study provides a novel explanation how aneuploidy affects phenotype.

systems biology↗

bioRxiv2018

A robust image registration interface for large volume brain atlas

Mapping the brain structures in three-dimensional accurately is critical for an in-depth understanding of the brain functions. By using the brain atlas as a hub, mapping detected datasets into a standard brain space enables efficiently use of various datasets. However, because of the heterogeneous and non-uniform characteristics of the brain structures at cellular level brought with the recently developed high-resolution whole-brain microscopes, traditional registration methods are difficult to apply to the robust mapping of various large volume datasets. Here, we proposed a robust Brain Spatial Mapping Interface (BrainsMapi) to address the registration of large volume datasets at cellular level by introducing the extract regional features of the anatomically invariant method and a strategy of parameter acquisition and large volume transformation. By performing validation on model data and biological images, BrainsMapi can not only achieve robust registration on sample tearing and streak image datasets, different individual and modality datasets accurately, but also are able to complete the registration of large volume dataset at cellular level which dataset size reaches 20 TB. Besides, it can also complete the registration of historical vectorized dataset. BrainsMapi would facilitate the comparison, reuse and integration of a variety of brain datasets.

neuroscience↗

bioRxiv2018

Targeting a Therapy-Resistant Cancer Cell State Using Masked Electrophiles as GPX4 Inhibitors

We recently discovered that inhibition of the lipid peroxidase GPX4 can selectively kill cancer cells in a therapy-resistant state through induction of ferroptosis. Although GPX4 lacks a conventional druggable pocket, covalent small-molecule inhibitors are able to overcome this challenge by reacting with the GPX4 catalytic selenocysteine residue to eliminate enzymatic activity. Unfortunately, all currently-reported GPX4 inhibitors achieve their activity through reactive chloroacetamide groups. We demonstrate that such chloroacetamide-containing compounds are poor starting points for further advancement given their promiscuity, instability, and low bioavailability. Development of improved GPX4 inhibitors, including those with therapeutic potential, requires the identification of new electrophilic chemotypes and mechanisms of action that do not suffer these shortcomings. Here, we report our discovery that nitrile oxide electrophiles, and a set of remarkable chemical transformations that generates them in cells from masked precursors, provide an effective strategy for selective targeting of GPX4. Our results, which include structural insights, target engagement assays, and diverse GPX4-inhibitor tool compounds, provide critical insights that may galvanize development of improved compounds that illuminate the basic biology of GPX4 and therapeutic potential of ferroptosis induction. In addition, our discovery that nitrile oxide electrophiles engage in highly selective cellular interactions and are bioavailable in their masked forms may be relevant for targeting other currently undruggable proteins, such as those revealed by recent proteome-wide ligandability studies.

biochemistry↗

bioRxiv2018

The Association between Alcohol Consumption and Telomere Length: A Meta-Analysis Focusing on Observational Studies

BackgroundBoth telomere length and alcohol consumption play important roles in carcinogenesis and biological age. Many efforts have been made to investigate the association between alcohol consumption and telomere length. However, no consensus has been reached yet.\n\nMethodsIn this article, we performed a meta-analysis to integrate the investigation results in the literature about the association between alcohol consumption and telomere length. After searching articles published between 2000 and 2016, 21 articles (including 27 analyses, total sample size 35,891) met our eligibility criteria.\n\nResultsWe found a significant association between alcohol consumption and telomere length (Fishers combined p-value = 3.52E-8 and Liptaks weighted p-value = 8.24E-3). We also found that the significance of the association between alcohol consumption and telomere length varies with study type (cohort, case-control, or cross-sectional) and study population (Europe, Asia, American, or Australia).\n\nConclusionsCombined evidence showed that alcohol consumption is associated with telomere length. The consistent quantifications of alcohol consumption and telomere length would benefit the future aggregation of the evidence from different studies.

cancer biology↗

bioRxiv2018

On the impact of competing intra- and intermolecular triplet-state quenching on photobleaching and photoswitching kinetics of organic fluorophores

While buffer cocktails remain the gold-standard for photostabilization and photoswitching of fluorescent markers, intramolecular triplet-state quenchers emerge as an alternative strategy to impart fluorophores with self-healing or even functional properties such as photoswitching. In this contribution, we evaluated various combinations of both approaches and show that inter- and intramolecular triplet-state quenching processes compete with each other rather than being additive or even synergistic. Often intramolecular processes dominate the photophysical situation for combinations of covalently-linked and solution-based photostabilizers and photoswitching agents. In this context we identified a new function of intramolecular photostabilizers, i.e., protection of fluorophores from reversible off-switching events caused by solution-additives, which were previously misinterpreted as photobleaching. Our studies also provide practical guidance for usage of photostabilizer-dye conjugates for STORM-type super-resolution microscopy permitting the exploitation of their improved photophysics for increased spatio-temporal resolution. Finally, we provide evidence that the biochemical environment, e.g., proximity of aromatic amino-acids such as tryptophan, reduces the photostabilization efficiency of commonly used buffer cocktails. Not only have our results important implications for a deeper mechanistic understanding of self-healing dyes, but they will provide a general framework to select label positions for optimal and reproducible photostability or photoswitching kinetics.

biophysics↗

bioRxiv2018

Insight into relationship between micro-consortia, nitrogen source and petroleum degradation at low temperature anaerobic condition

Biostimulation by addition nutrients has been proved to be an effective bioremediation strategies. Revealing response law of nitrogen source and structure characteristics of anaerobic petroleum degrading microorganisms microbial population will help us optimize nutrient to promote oil degradation. Anaerobic micro-consortia characteristics in the enrichment marine sediment samples with different nitrogen source, combining with analysis of the oil degradation rates were studied in this paper, as well as functional genes involved in petroleum degradation were also analyzed. On the basis of optimizing the best inorganic nitrogen sources and organic nitrogen sources, an effective medium was designed by response surface methodology that used for enriching petroleum degradation micro-consortia. Amplicon sequencing analysis showed that the population of microorganisms migrated obviously when enriched with different nitrogen sources. With the increase of oil degradation rate, the microbial diversity was significantly decreased, and concentrated on a limited number of genera. The reasonable proportions of GammaProteobacteria, Bacteroidetes and Fusobacteria made the greatest contribution to petroleum degradation. Metagenomic analysis unveiled the mixed nitrogen source promoted the expression of functional genes related to petroleum degradation such as the transfer of succinyl-CoA, synthesis of acetyl CoA and {beta}-oxidation cycle, and was beneficial to degradation of petroleum at low temperature anaerobic condition.\n\nOriginality Significance StatementAddition of nutrients can promote growth of indigenous petroleum degradation-related bacteria and be helpful to the rapid degradation of petroleum. Previous studies accurately characterized aerobic microorganisms on petroleum degradation. However, we still known little about anaerobic microorganisms in marine environment. Most biostimulation methods use inorganic salt as the main nutritional supplement to improve the efficiency of petroleum degradation, but effects of different nitrogen sources on diversity of microorganisms and distribution of functional genes related to petroleum degradation at anaerobic conditions are still unknown. In this research, the effects of nitrogen on petroleum biodegradation, anaerobic microconsortium structure and distribution of genes related to petroleum degradation were unveiled by using amplicon sequencing and metagenomic analysis.

microbiology↗