Biology subjects

Li, S.

Publications and source records attributed to Li, S..

At least 1,261 records · Page 70Linked to original sources

bioRxiv2019

Posttranscriptional regulation of ribosomal and multiresistance genes by the bacterial leader peptide peTrpL

The ribosome-dependent attenuator located upstream of bacterial tryptophan biosynthesis genes harbors a small ORF trpL containing tryptophan codons. When tryptophan is available, efficient trpL translation causes transcription termination and release of the attenuator RNA rnTrpL. In Sinorhizobium meliloti, rnTrpL is a trans-acting sRNA. Here, we identified an evolutionary conserved function for the trpL-encoded 14-aa leader peptide peTrpL. Upon exposure to tetracycline, the cellular peTrpL levels were increased and rnTrpL was generated independently of tryptophan availability. Both peTrpL and rnTrpL were found to be involved in tetracycline-dependent destabilization of rplUrpmA mRNA encoding ribosomal proteins L21 and L27. We provide evidence for redirection of the sRNA rnTrpL from its antibiotic-independent target trpDC to rplUrpmA by formation of an antibiotic-dependent ribonucleoprotein complex (ARNP). ARNPs comprising peTrpL, rnTrpL, rplUrpmA and antisense RNA were also observed for other translation-inhibiting antibiotics, suggesting that bacteria evolved mechanisms to utilize antibiotics for mRNA destabilization.

microbiology↗

bioRxiv2019

Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases

Mendelian randomization (MR) is a widely-used method for causal inference using genetic data. Mendelian randomization studies of unrelated individuals may be susceptible to bias from family structure, for example, through dynastic effects which occur when parental genotypes directly affect offspring phenotypes. Here we describe methods for within-family Mendelian randomization and through simulations show that family-based methods can overcome bias due to dynastic effects. We illustrate these issues empirically using data from 61,008 siblings from the UK Biobank and Nord-Trondelag Health Study. Both within-family and population-based Mendelian randomization analyses reproduced established effects of lower BMI reducing risk of diabetes and high blood pressure. However, while MR estimates from population-based samples of unrelated individuals suggested that taller height and lower BMI increase educational attainment, these effects largely disappeared in within-family MR analyses. We found differences between population-based and within-family based estimates, indicating the importance of controlling for family effects and population structure in Mendelian randomization studies.

genetics↗

bioRxiv2019

Protein structure determination using Riemannian approach

The protein nuclear magnetic resonance (NMR) structure determination is one of the most extensively studied problems due to its increasing importance in biological function analysis. We adopt a novel method, based on one of the matrix completion (MC) techniques-the Riemannian approach, to solve the protein structure determination problem. We formulate the protein structure in terms of low-rank matrix which can be solved by an optimization problem in the Riemannian spectrahedron manifold whose objective function has been delimited with the derived boundary condition. Two efficient algorithms in Riemannian approach-the trust-region (Tr) algorithm and the conjugate gradient (Cg) algorithm are used to reconstruct protein structures. We first use the two algorithms in a toy model and show that the Tr algorithm is more robust. Afterwards, we rebuild the protein structure from the NOE distance information deposited in NMR Restraints Grid (http://restraintsgrid.bmrb.wisc.edu/NRG/MRGridServlet). A dataset with both X-ray crystallographic structure and NMR structure deposited in Protein Data Bank (PDB) is used to statistically evaluate the performance of our method. By comparing both our rebuilt structures and NMR counterparts with the \"standard\" X-ray structures, we conclude that our rebuilt structures have similar (sometimes even smaller) RMSDs relative to \"standard\" X-ray structures in contrast with the reference NMR structures. Besides, we also validate our method by comparing the Z-scores between our rebuilt structures with reference structures using Protein Structure Validation Software suit. All the validation scores indicate that the Riemannian approach in MC techniques is valid in reconstructing the protein structures from NOE distance information. The software based on Riemannian approach is freely available at https://github.com/xubiaopeng/Protein_Recon_MCRiemman.\n\nAuthor summaryMatrix Completion is a technique widely used in many aspects, such as the global positioning in sensor networks, collaborative filtering in recommendation system for many companies and face recognition, etc. In biology, distance geometry used to be a popular method for reconstructing protein structures related to NMR experiment. However, due to the low quality of the reconstructed results, those methods were replaced by other dynamic methods such as ARIA, CYANA and UNIO. Recently, a new MC technique named Riemannian approach is introduced and proved mathematically, which promotes us to apply it in protein structure determination from NMR measurements. In this paper, by combining the Riemannian approach and some post-processing procedures together, we reconstruct the protein structures from the incomplete distance information measured by NMR. By evaluating our results and comparing with the corresponding PDB NMR deposits, we show that the current Riemannian approach method is valid and at least comparable with (if not better than) the state-of-art methods in NMR structure determination.

bioinformatics↗

bioRxiv2019

Extensive adaptive immune response of AAVs and Cas proteins in non-human primates

The CRISPR-mediated Cas system is the most widely used tool in gene editing and gene therapy for its convenience and efficiency. Delivery of the CRISPR system by adeno-associated viruses (AAVs) is currently the most promising approach to gene therapy. However, pre-existing adaptive immune responses against CRISPR nuclease (PAIR-C) and AAVs has been found in human serum, indicating that immune response is a problem that cannot be ignored, especially for in vivo gene correction. Non-human primates (NHPs) share many genetic and physiological traits with human, and are considered as the bridge for translational medicine. However, whether NHPs have same PAIR-C status with human is still unknown. Here, macaques (rhesus and cynomolgus), including normal housed and CRISPR-SpCas9 or TALENs edited individuals, were used to detect PAIR-C which covered SaCas9, SpCas9, AsCas12a and LbCas12a. Dogs and mice were also detected to expand the range of species. In addition, pre-existing adaptive antibodies to AAV8 and AAV9 were performed against macaques of different ages. The results showed that adaptive immunity was pre-existing in the macaques regardless of Cas proteins and AAVs. These findings indicate that the pre-existing adaptive immune of AAV-delivered CRISPR construction and correction system should be concerned for in vivo experiments.

immunology↗

bioRxiv2019

Augmentation of T-cell activation by oscillatory forces and engineered antigen-presenting cells

Activation of T cells by antigen presenting cells allows them to proliferate, produce cytokines, and kill infected or cancerous cells. We and others have shown that T cell receptors receive and in fact require mechanical forces from their own movements and the movements of antigen presenting cells. Emulation of T cell activation in vitro allows for the massive expansion of T cells necessary for clinical applications. In this paper, we studied the impact of augmenting novel artificial antigen presenting cells of various sizes and antigenic signal strength with mechanical, oscillatory movement. We showed that dynamic culture roughly doubles signal strength as compared to conventional, static culture. We demonstrated that tuning the strength of signal to a \"sweet spot\" allows for robust expansion of induced regulatory T cells, which is impeded by approaches that simply maximize activation.

immunology↗

bioRxiv2019

On triangular inequalities of correlation-based distances for gene expression profiles

Various distance functions for evaluating the differences be- tween gene expression profiles have been proposed in the past. Such a function would output a low value if the profiles are strongly correlated--either negatively or positively--and vice versa. One popular distance function is the absolute correlation distance, da = 1 - |{rho}|, where{rho} is some similarity measures, such as Pearson or Spearman correlation. How- ever, absolute correlation distance fails to fulfill the triangular inequality, which would have guaranteed better performance at vector quantization, allowed fast data localization, as well as sped up data clustering. In this work, we propose [Formula] as an alternative. We prove that dr satisfies the triangular equality when{rho} represents Pearson correlation, Spearman correlation, or Cosine similarity. We empirically compared dr with da in gene clustering and sample clustering experiment, using real biological data. The two distances performed similarly in both gene cluster and sample cluster in hierarchical cluster and PAM cluster. However, dr demonstrated more robust clustering. According to bootstrap experiment, the number of times where dr generated more robust sample pair partition is significantly (p-value < 0.05) larger. This advantage in robustness is also supported by the class \"dissolved\" event.

bioinformatics↗

bioRxiv2019

CytoGPS: A Web-Enabled Karyotype Analysis Tool for Cytogenetics

Karyotype data are the most common form of genetic data that is regularly used clinically. They are collected as part of the standard of care in many diseases, particularly in pediatric and cancer medicine contexts. Karyotypes are represented in a unique text-based format, with a syntax defined by the International System for human Cytogenetic Nomenclature (ISCN). While human-readable, ISCN is not intrinsically machine-readable. This limitation has prevented the full use of complex karyotype data in discovery science use cases. To enhance the utility and value of karyotype data, we developed a tool named CytoGPS. CytoGPS first parses ISCN karyotypes into a machine-readable format. It then converts the ISCN karyotype into a binary Loss-Gain-Fusion (LGF) model, which represents all cytogenetic abnormalities as combinations of loss, gain, or fusion events, in a format that is analyzable using modern computational methods. Such data is then made available for comprehensive \"downstream\" analyses that previously were not feasible.\n\nAvailability and ImplementationFreely available at https://cytogps.org\n\nContactprpayne@wustl.edu\n\nSupplementary informationNot applicable

bioinformatics↗

bioRxiv2019

Comprehensive Classification of the RNaseH-like domain-containing Proteins in Plants

BackgroundR-loop is a nucleic acid structure containing an RNA-DNA hybrid and a displaced single-stranded DNA. Recently, accumulated evidence showed that R-loops widely present in various organisms genomes and are involved in many physiological processes, including DNA replication, RNA transcription, and DNA repair. RNase H-like superfamily (RNHLS) domain-containing proteins, such as RNase H enzymes, are essential in restricting R-loop levels. However, little is known about the function and relationship of other RNHLS proteins on R-loop regulation, especially in plants. ResultsIn this study, we characterized 6193 RNHLS proteins from 13 representative plant species and clustered these proteins into 27 clusters, among which reverse transcriptases and exonucleases are the two largest groups. Moreover, we found 691 RNHLS proteins in Arabidopsis with a conserved catalytic alpha-helix and beta-sheet motif. Interestingly, each of the Arabidopsis RNHLS proteins is composed of not only an RNHLS domain but also another different protein domain. Additionally, the RNHLS genes are highly expressed in different meristems and metabolic tissues, which indicate that the RNHLS proteins might play important roles in the development and maintenance of these tissues. ConclusionsIn summary, we systematically analyzed RNHL proteins in plants and found that there are mainly 27 subclusters of them. Most of these proteins might be implicated in DNA replication, RNA transcription, and nucleic acid degradation. We classified and characterized the RNHLS proteins in plants, which may afford new insights into the investigation of novel regulatory mechanisms and functions of R-loops.

plant biology↗

bioRxiv2019

Fertility intentions and influential factors in Dalian urban city ---A cross-sectional study based on Universal Two-child policy in China

BackgroundIn October, 2015, Chinese government announced that one-child policy had finally been replaced by a universal two-child policy. However, the effects of new policy may be far less than expected. So we conducted this research to explore potential influential factors of fertility intention.\n\nMethodsThis cross-sectional study was conducted and a self-administered questionnaire was designed for collecting socio-demographic information, future fertility intention and influential factors of individual reproductive behavior. The analyses were performed using the SPSS 19.0 statistical software package.\n\nResultsA total of 1370 respondents were interviewed. Our research indicated that the mean ideal number of children was only 1.73 and urban respondents sex preference was symmetrical preference. 79.1% (884) married people had the first child already, only 7.6% (71) respondents had two children. Among 1370 participants, 30.4% respondents stated that they would have a second child; while 69.6% respondents refused to have two children in future (just wanted only a child). Binary logistic regression analysis (model 1) showed that female, older age, lower education lever, birth place was Dalian, lower family income, the ideal number of children were associated with having 1 child in the future. Model 2 (only respondents with childbearing experience) showed that female, lower family income, couldnt get additional financial support from parents were more likely refused to have two children; in additional, the ideal number of children and childbearing experience were significantly influences on future fertility intention.\n\nConclusionFertility intention and reproductive behavior still below replacement in Dalian city. Our results suggest that several factors (including socioeconomic characteristics, economic factors, desired number of child, childbearing experience) have distinctive effects on fertility intention.

epidemiology↗

bioRxiv2019

EPIC: MHC-I epitope prediction integrating mass spectrometry derived motifs and tissue-specific expression profiles

BackgroundAccurate prediction of epitopes presented by human leukocyte antigen (HLA) is crucial for personalized cancer immunotherapies targeting T cell epitopes. Mass spectrometry (MS) profiling of eluted HLA ligands, which provides high-throughput measurements of HLA associated peptides in vivo, can be used to faithfully model the presentation of epitopes on the cell surface. In addition, gene expression profiles measured by RNA-seq data in a specific cell/tissue type can significantly improve the performance of epitope presentation prediction. However, although large amount of high-quality MS data of HLA-bound peptides is being generated in recent years, few provide matching RNA-seq data, which makes incorporating gene expression into epitope prediction difficult. MethodsWe collected publicly available HLA peptidome and matching RNA-seq data of 34 cell lines derived from various sources. We built position score specific matrixes (PSSMs) for 21 HLA-I alleles based on these MS data, then used logistic regression (LR) to model the relationship among PSSM score, gene expression and peptide length to predict whether a peptide could be presented in each of the cell line. We further built a universal LR model, termed Epitope Presentation Integrated Prediction (EPIP), based on more than 180,000 unique HLA ligands collected from public sources and ~3,000 HLA ligands generated by ourselves, to predict epitope presentation for 66 common HLA-I alleles. ResultsWhen evaluating EPIP on large, independent HLA eluted ligand datasets, it performed substantially better than other popular methods, including MixMHCpred (v2.0), NetMHCpan (v4.0), and MHCflurry (v1.2.2), with an average 0.1% positive predictive value (PPV) of 52.01%, compared to 37.24%, 36.96%, 24.90% and 23.76% achieved by MixMHCpred, NetMHCpan-4.0 (EL), NetMHCpan-4.0 (BA) and MHCflurry, respectively. It is also comparable to EDGE, a recent deep learning-based model that is not publicly available, on predicting epitope presentation and selecting immunogenic cancer neoantigens. However, the simplicity and flexibility of EPIP makes it easier to be applied in diverse situations, and we demonstrated this by generating MS data for the HCC4006 cell line and adding the support of HLA-A*33:03 to EPIP. EPIP is publicly available as a web tool < http://epip.genomics.cn/>. Conclusionswe have developed an easy to use, publicly available epitope prediction tool, EPIP, that incorporates information from both MS and RNA-seq data, and demonstrated its superior performance over existing public methods.

bioinformatics↗

bioRxiv2019

Unsupervised Toxoplasma gondii Recognition by Fuzzy Cycle Generative Adversarial Network

Toxoplasma gondii, one of the worlds most common parasites, can infect all types of warm-blooded animals, including one-third of the worlds human population. Most current routine diagnostic methods are costly, time-consuming, and labor-intensive. Although T.gondii can be directly observed under the microscope in tissue or spinal fluid samples, this form of identification is difficult and requires well trained professionals. Nevertheless, the traditional identification of parasites under the microscope is still performed by a large number of laboratories. Novel, efficient and reliable methods of T.gondii identification are therefore needed, particularly in developing countries. To this end, we developed a novel transfer learning based microscopic image recognition method for T.gondii identification. This approach employs Fuzzy Cycle Generative Adversarial Network (FCGAN) with transfer learning utilizing knowledge gained by the parasitologists that Toxoplasma is in banana or crescent shaped form. Our approach aims to build connection between micro and macro associated objects by embedding fuzzy C-means cluster algorithm into Cycle Generative Adversarial Network (Cycle GAN). Our approach achieves 93.1% and 94.0% detection accuracy for 400X and 1000X Toxoplasma microscopic images respectively. We show the high accuracy and effectiveness of our approach in the newly collected unlabeled Toxoplasma microscopic images, comparing to other current available deep learning methods. This novel method for Toxoplasma microscopic image recognition will open a new window for developing cost-effective and scalable deep learning based diagnostic solution, potentially enabling broader clinical access in developing countries.

bioinformatics↗

bioRxiv2019

Comparing Time Series Transcriptome Data Between Plants Using A Network Module Finding Algorithm

Comparative transcriptome analysis is the comparison of expression patterns between homologous genes in different species. Since most molecular mechanistic studies in plants have been performed in model species including Arabidopsis and rice, comparative transcriptome analysis is particularly important for functional annotation of genes in other plant species. Many biological processes, such as embryo development, are highly conserved between different plant species. The challenge is to establish one-to-one mapping of the developmental stages between two species. In this protocol, we solve this problem by converting the gene expression patterns into a co-expression network and then apply network module-finding algorithms to the cross-species co-expression network. We describe how to perform such analysis using bash scripts for preliminary data processing and R programming language, which implemented simulated annealing method for module finding. We also provide instructions on how to visualize the resulting co-expression networks across species.

bioinformatics↗

bioRxiv2019

Prediction of condition-specific regulatory maps in Arabidopsis using integrated genomic data

Recent advances in genomic technologies have generated large-scale protein-DNA interaction data and open chromatic regions for multiple plant species. To predict condition specific gene regulatory networks using these data, we developed the Condition Specific Regulatory network inference engine (ConSReg), which combines heterogeneous genomic data using sparse linear model followed by feature selection and stability selection to select key regulatory genes. Using Arabidopsis as a model system, we constructed maps of gene regulation under more than 50 experimental conditions including abiotic stresses, cell type-specific expression, and stress responses in individual cell types. Our results show that ConSReg accurately predicted gene expressions (average auROC of 0.84) across multiple testing datasets. We found that, (1) including open chromatin information from ATAC-seq data significantly improves the performance of ConSReg across all tested datasets; (2) choice of negative training samples and length of promoter regions are two key factors that affect model performance. We applied ConSReg to Arabidopsis single cell RNA-seq data of two root cell types (endodermis and cortex) and identified five regulators in two root cell types. Four out of the five regulators have additional experimental evidence to support their roles in regulating gene expression in Arabidopsis roots. By comparing regulatory maps in abiotic stress responses and cell type-specific experiments, we revealed that transcription factors that regulate tissue levels abiotic stresses tend to also regulate stress responses in individual cell types in plants.

bioinformatics↗

bioRxiv2019

Haplotype-Based Noninvasive Prenatal Diagnosis for Duchenne Muscular Dystrophy: A pilot study in South China

ObjectiveTo explore the accuracy and feasibility of noninvasive prenatal diagnosis (NIPD) for Duchenne Muscular Dystrophy (DMD) based on the haplotype approach. MethodsWe recruited singleton pregnancies at-risk of DMD at 12-25 weeks of gestation from 17 families who all had a proband children affected by DMD. We have identified the pathogenic mutations in probands and their mothers by multiplex ligation-dependent probe amplification (MLPA). To construct parental haplotypes, we performed captured sequencing on genomic DNA from parents and probands. The integration analysis of parental haplotypes and targeted sequencing results of maternal plasma DNA were used to infer the fetal haplotype and genotypes in DMD gene. Fetal DMD genotypes were further confirmed by invasive prenatal diagnosis. ResultsWe have successfully performed the haplotype-based NIPD in all recruited families. Ten fetuses were identified as normal, including four female and six male fetuses. Four female fetuses were carriers and the other three male fetuses were affected by DMD with exons 49-52 deletion, exons 8-37 deletion and c.628G > T mutation, respectively. The results of NIPD were consistent with those of invasive diagnosis. ConclusionHaplotype-based NIPD for DMD by targeted sequencing is promising and has potential for clinical application.

genetics↗

bioRxiv2019

Gene pathogenicity prediction of Mendelian diseases via the Random Forest algorithm

The study of Mendelian diseases and the identification of their causative genes are of great significance in the field of genetics. The evaluation of the pathogenicity of genes and the total number of Mendelian disease genes are both important questions worth studying. However, very few studies have addressed these issues to date, so we attempt to answer them in this study. We calculated gene pathogenicity prediction (GPP) score by a machine learning approach (random forest algorithm) to evaluate the pathogenicity of genes. When we applied the GPP score to the testing gene set, we obtained accuracy of 80%, recall of 93% and area under the curve (AUC) of 0.87. Our results estimated that a total of 10,399 protein-coding genes were Mendelian disease genes. Furthermore, we found the GPP score was positively correlated with the severity of disease. Our results indicate that GPP score may provide a robust and reliable guideline to predict the pathogenicity of protein-coding genes. To our knowledge, this is the first trial to estimate the total number of Mendelian disease genes.

genetics↗

bioRxiv2019

Silicone oil-induced ocular hypertension in mouse models glaucomatous neurodegeneration and neuroprotection

Understanding the molecular mechanism of glaucoma and development of neuroprotectants are significantly hindered by the lack of a reliable animal model that accurately recapitulates human glaucoma. Here we sought to develop a mouse model for the secondary glaucoma that is often observed in humans after silicone oil (SO) blocks the pupil or migrates into the anterior chamber following vitreoretinal surgery. We observed similar intraocular pressure (IOP) elevation after intracameral injection into mouse eyes of SO, and removing the SO allows the IOP level to quickly return to normal. This simple, inducible and reversible mouse model showed dynamic changes of visual function that correlate with progressive RGC loss and axon degeneration. We also used a single AAV vector for the first time to co-express miRNA-based shRNA and a neuroprotective transgene and further validated this model as an effective in vivo means to test neuroprotective therapies by targeting neuronal endoplasmic reticulum stress.

neuroscience↗

bioRxiv2019

Gut microbiota influence B cell function in a TLR5-dependent manner

Toll-like receptor (TLR) 5-deficient mice display aberrantly low levels of flagellin-specific antibodies (Flic-IgA) secreted into the gut, combined with excess bacterial flagellin in the gut, and together these attributes define microbiome dysbiosis (T5-dysbiosis). How TLR5 signaling deficiency results in T5-dysbiosis is unclear. Here, we address the role of B cells in T-dysbiosis. We observed that B cells do not express TLR5, and that B cell transplantation from TLR5-/- mouse donors into B-cell deficient mice resulted in a slight reduction in Flic-IgA levels compared to B-cells from WT donors. Bone marrow transplants from WT and TLR5-/- donors into recipients of both genotypes confirmed that TLR5 signaling by non-hematopoietic cells is required for T5-dysbiosis. We observed TLR5 deficiency was associated with an expanded population of IgA+ B cells. TLR5-/- mice tended to have higher richness for the IgA gene hypervariable region (CDR3 gene) variants. Transplantation of microbiomes from TLR5-/- and WT microbiomes donors into germfree mice resulted in a higher proportion of IgA-secreting B cells, and higher overall fecal IgA and anti-Flic IgA for TLR5-/- microbiome recipients. This observation indicated that the TLR5-/- mouse microbiome elicits an anti-flagellin antibody response that requires TLR5 signaling. Together these results indicate that TLR5 signaling on epithelial cells influences B cell populations and antibody repertoire.

immunology↗

bioRxiv2019

Cell type composition and circuit organization of neocortical radial clones

SummaryExcitatory neurons arising from a common progenitor establish radially-oriented clonal units in the neocortex which have been proposed to serve as elementary information processing modules. To characterize the cell types and circuit diagram within these clonal units, we performed single-cell RNA-sequencing and multi-cell patch clamp recordings of neurons derived from Nestin-positive progenitors. We found that radial clones do not appear to be fate-restricted, but instead individual clones are composed of a random sampling of the transcriptomic cell types present in a particular cortical area. The effect of lineage on synaptic connectivity depends on the type of connection tested: pairs of clonally related neurons were more likely to be connected vertically, across cortical layers, but not laterally within the same layer, compared to unrelated pairs. We propose that integration of vertical input from related neurons with lateral input from unrelated neurons may represent a developmentally programmed motif for assembling neocortical circuits.

neuroscience↗