Biology subjects

Li, R.

Publications and source records attributed to Li, R..

At least 541 records · Page 30Linked to original sources

bioRxiv2018

Calcium nanodomains in spindles

The role of calcium signaling in specific events of animal cell meiosis or mitosis (M-phase) is a subject of enduring controversy. Early efforts suggested that increases in intracellular free calcium ([Ca2+]i) promote spindle disassembly 1, 2 while subsequent work suggested that global [Ca2+]i increases trigger nuclear envelope breakdown, spindle assembly, the metaphase-anaphase transition, and cytokinesis 3-6. However, further studies led to the conclusion that elevation of [Ca2+]i either has no role in these events, plays a permissive role in these events, or functions as an auxiliary signaling pathway that supplements other mechanisms 7. One potential explanation of the controversy is that specific M-phase events might depend on highly localized increases in [Ca2+]i, variously referred to as microdomains 8 or nanodomains 9, as proposed recently 10. Such domains are hypothesized to arise from rapid shuttling of calcium between closely positioned sources and sinks, rendering them potentially difficult to detect with traditional dyes and largely insensitive to slow chelators such as EGTA 9. Here a novel microtubule-binding calcium sensor--TubeCamp--was used to test the hypothesis 10 that spindles are associated with calcium nanodomains. TubeCamp imaging revealed that spindles in Xenopus eggs, Xenopus embryos, and HeLa cells were all associated with calcium nanodomains at the spindle poles. Calcium nanodomains also formed in spindles assembled in cell extracts and at the center of monopolar spindles, suggesting that they are a basic feature of spindle self-assembly. Disruption of calcium nanodomains via perturbation of inositol-1,4,5-trisphosphate signaling or rapid chelation of [Ca2+]i resulted in spindle disassembly in vivo and vitro. The results demonstrate the existence of spindle-associated calcium nanodomains and indicate that such domains are an essential and common feature of spindles in vertebrates.

cell biology↗

bioRxiv2018

Inference of Chromosome-length Haplotypes Using Genomic Data of Three to Five Single Gametes

Knowledge of chromosome-length haplotypes will not only advance our understanding of the relationship between DNA and phenotypes, but also promote a variety of genetic applications. Here we present Hapi, an innovative method for chromosomal haplotype inference using only 3 to 5 gametes. Hapi outperformed all existing haploid-based phasing methods in terms of accuracy, reliability, and cost efficiency in both simulated and real gamete datasets. This highly cost-effective phasing method will make large-scale haplotype studies feasible to facilitate human disease studies and plant/animal breeding. In addition, Hapi can detect meiotic crossovers in gametes, which has promise in the diagnosis of abnormal recombination activity in human reproductive cells.

bioinformatics↗

bioRxiv2018

Feedback Regulation between Initiation and Maturation Networks Orchestrates the Chromatin Dynamics of Epidermal Lineage Commitment

Tissue development results from lineage-specific transcription factors (TF) programming a dynamic chromatin landscape through progressive cell fate transitions. Here, we interrogate the epigenomic landscape during epidermal differentiation and create an inference network that ranks the coordinate effects of TF-accessible regulatory element-target gene expression triplets on lineage commitment. We discover two critical transition periods: surface ectoderm initiation and keratinocyte maturation, and identify TFAP2C and p63 as lineage initiation and maturation factors, respectively. Surprisingly, we find that TFAP2C, and not p63, is sufficient to initiate surface ectoderm differentiation, with TFAP2C-initiated progenitor cells capable of maturing into functional keratinocytes. Mechanistically, TFAP2C primes the surface ectoderm chromatin landscape and induces p63 expression and binding sites, thus allowing maturation factor p63 to positively auto-regulate its expression and close a subset of the TFAP2C-initiated early program. Our work provides a general framework to infer TF networks controlling chromatin transitions that will facilitate future regenerative medicine advances.

cell biology↗

bioRxiv2018

Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity

Integrative analysis of multi-omics layers at single cell level is critical for accurate dissection of cell-to-cell variation within certain cell populations. Here we report scCAT-seq, a technique for simultaneously assaying chromatin accessibility and the transcriptome within the same single cell. We show that the combined single cell signatures enable accurate construction of regulatory relationships between cis-regulatory elements and the target genes at single-cell resolution, providing a new dimension of features that helps direct discovery of regulatory patterns specific to distinct cell identities. Moreover, we generated the first single cell integrated maps of chromatin accessibility and transcriptome in human pre-implantation embryos and demonstrated the robustness of scCAT-seq in the precise dissection of master transcription factors in cells of distinct states during embryo development. The ability to obtain these two layers of omics data will help provide more accurate definitions of \"single cell state\" and enable the deconvolution of regulatory heterogeneity from complex cell populations.

genomics↗

bioRxiv2018

Inhibition of EV71 replication by L3HYPDH, a newly identified interferon-stimulated gene product

Enterovirus 71 (EV71) is the common causative agent of hand-foot-mouth disease (HFMD). Despite evidence in mice model suggested that the interferon (IFN) signaling pathways play a role in defending against this virus, knowledge on the IFN-mediated antiviral response is still limited. Here we identified an IFN-stimulated gene (ISG) called L3HYPHD, whose expression inhibits EV71 replication. Mapping assay indicated that amino acids 61-120 and 295-354 are critical for its optimal antiviral activity. Mechanismly, L3HYPDH specifically inhibits protein translation mediated by EV71 internal ribosome entry site (IRES). Our data thus uncovered a new mechanism utilized by the host cell to restrict EV71 replication.

microbiology↗

bioRxiv2018

Development and validation of a phenotypic high-content imaging assay for assessing the antiviral activity of small-molecule inhibitors targeting the Zika virus

Zika virus (ZIKV) has been linked to the development of microcephaly in newborns, as well as Guillain-Barre syndrome. There are currently no drugs available to treat infection, and accordingly there is an unmet medical need for discovery of new therapies. High-throughput drug screening efforts focusing on indirect readouts of cell viability are prone to a higher frequency of false positives in cases where the virus is viable in the cell but the cytopathic effect is reduced or delayed. Here, we describe a fast and label-free phenotypic high-content imaging assay used to detect cells affected by the viral-induced cytopathic effect (CPE) using automated imaging and analysis. Protection from CPE correlates with a decrease in viral antigen production as observed by immunofluorescence. We trained our assay using a collection of nucleoside analogues against ZIKV; the previously reported antiviral activities of 2-C-methylribonucleosides and ribavirin against the Zika virus in Vero cells were confirmed using our developed method. Profiling of a novel library of 24 natural product derivatives using our assay revealed compound 1 as an inhibitor of ZIKV-induced cytopathic effect; activity of the compound was confirmed in human fetal neural stem cells (NSCs). The described technique can be easily leveraged as a primary screening assay for profiling large compound libraries against ZIKV, and can be expanded to other ZIKV strains and other cell lines displaying morphological changes upon ZIKV infection.

microbiology↗

bioRxiv2018

GNE: A deep learning framework for gene network inference by aggregating biological information

The topological landscape of gene interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, it is still a challenging task to aggregate heterogeneous biological information such as gene expression and gene interactions to achieve more accurate inference for prediction and discovery of new gene interactions. In particular, how to generate a unified vector representation to integrate diverse input data is a key challenge addressed here. We propose a scalable and robust deep learning framework to learn embedded representations to unify known gene interactions and gene expression for gene interaction predictions. These low-dimensional embeddings derive deeper insights into the structure of rapidly accumulating and diverse gene interaction networks and greatly simplify downstream modeling. We compare the predictive power of our deep embeddings to the strong baselines. The results suggest that our deep embeddings achieve significantly more accurate predictions. Moreover, a set of novel gene interaction predictions are validated by up-to-date literature-based database entries. GNE is freely available under the GNU General Public License and can be downloaded from Github (https://github.com/kckishan/GNE)

systems biology↗

bioRxiv2018

Collective feature selection to identify crucial epistatic variants

BackgroundMachine learning methods have gained popularity and practicality in identifying linear and non-linear effects of variants associated with complex disease/traits. Detection of epistatic interactions still remains a challenge due to the large number of features and relatively small sample size as input, thus leading to the so-called \"short fat data\" problem. The efficiency of machine learning methods can be increased by limiting the number of input features. Thus, it is very important to perform variable selection before searching for epistasis. Many methods have been evaluated and proposed to perform feature selection, but no single method works best in all scenarios. We demonstrate this by conducting two separate simulation analyses to evaluate the proposed collective feature selection approach.\n\nResultsThrough our simulation study we propose a collective feature selection approach to select features that are in the \"union\" of the best performing methods. We explored various parametric, non-parametric, and data mining approaches to perform feature selection. We choose our top performing methods to select the union of the resulting variables based on a user-defined percentage of variants selected from each method to take to downstream analysis. Our simulation analysis shows that non-parametric data mining approaches, such as MDR, may work best under one simulation criteria for the high effect size (penetrance) datasets, while non-parametric methods designed for feature selection, such as Ranger and Gradient boosting, work best under other simulation criteria. Thus, using a collective approach proves to be more beneficial for selecting variables with epistatic effects also in low effect size datasets and different genetic architectures. Following this, we applied our proposed collective feature selection approach to select the top 1% of variables to identify potential interacting variables associated with Body Mass Index (BMI) in ~44,000 samples obtained from Geisingers MyCode Community Health Initiative (on behalf of DiscovEHR collaboration).\n\nConclusionsIn this study, we were able to show that selecting variables using a collective feature selection approach could help in selecting true positive epistatic variables more frequently than applying any single method for feature selection via simulation studies. We were able to demonstrate the effectiveness of collective feature selection along with a comparison of many methods in our simulation analysis. We also applied our method to identify non-linear networks associated with obesity.

bioinformatics↗

bioRxiv2018

Transcriptome Landscape of Human Oocytes and Granulosa Cells Throughout Folliculogenesis

Folliculogenesis is a highly regulated process that involves bidirectional interactions of the oocytes and surrounding granulosa cells (GCs). Little is unknown, however, about the transcriptomic profiles of human oocytes and GCs throughout folliculogenesis. Here we performed a high resolution RNA-Seq of human oocytes and GCs at each follicular stage, which revealed unique transcriptional profiles, stage-specific signature genes, oocyte- and GC-derived genes that reflect ovarian reserve. We identified reciprocal cell-to-cell interactions between oocytes and GCs, including NOTCH, TGF-{beta} signaling and gap junctions and determined the expression patterns of maternal-effect genes involved in folliculogenesis and early embryogenesis. Finally, we demonstrated robust differences between human and mice oocyte transcriptomes. This is the first comprehensive overview of the transcriptomic signatures governing the stepwise human folliculogenesis in-vivo that provides a valuable resource for basic and translational research in human reproductive biology.

cell biology↗

bioRxiv2018

Multi-hierarchical Profiling the Structure-Activity Relationships of Engineered Nanomaterials at Nano-Bio Interfaces

Increasingly raised concerns (nanotoxicity, clinical translation, etc) on nanotechnology require breakthroughs in structure-activity relationship (SAR) analyses of engineered nanomaterials (ENMs) at nano-bio interfaces. However, current nano-SAR assessments failed to disclosure sufficient information to understand ENM-induced bio-effects. Here we developed a multi-hierarchical nano-SAR assessment for a representative ENM, Fe2O3 by systematically examining cellular metabolite and protein changes. This nano-SAR profile allows visualizing the contributions of 7 basal properties of Fe2O3 to their diverse bio-effects. For instance, while surface reactivity is responsible for Fe2O3-induced cell migration, the inflammatory effects of Fe2O3 nanorods and nanoplates are determined by their aspect ratio and surface reactivity, respectively. We further discovered the detailed mechanisms, including NLRP3 inflammasome pathway and monocyte chemoattractant protein-1 involved signaling. Both effects were further validated in animal lungs. Our findings provide substantial new insights at nano-bio interfaces, which may facilitate the tailored design of ENMs to endow them with desired bio-effects.

pharmacology and toxicology↗

bioRxiv2018

Tumour purity as a prognostic factor in colon cancer

Tumour purity is defined as the proportion of cancer cells in the tumour tissue. The impact of tumour purity on colon cancer (CC) prognosis, genetic profile and microenvironment has not been thoroughly accessed. Therefore, clinical and transcriptomic data from three public datasets, GSE17536/17537, GSE39582, and TCGA were retrospectively collected (n = 1248). Tumour purity of each sample was inferred by a computational method based on transcriptomic data. Stage III and MMR-deficient (dMMR) CC patients showed a significantly lower tumour purity. Low purity CC conferred worse survival and tumour purity was identified as an independent prognostic factor. Moreover, high tumour purity CC patients benefited more from adjuvant chemotherapy. Subsequent genomic analysis found that the mutation burden was negatively associated with tumour purity with only APC and KRAS significantly more mutated in high purity CC. However, no somatic copy number alteration event was correlated with tumour purity. Furthermore, immune-related pathways and immunotherapy-associated markers (PD-1, PD-L1, CTLA-4, LAG-3, and TIM-3) were highly enriched in low purity samples. Notably, the relative proportion of M2 macrophages and neutrophils, which indicated worse survival in CC, was negatively associated with tumour purity. Therefore, tumour purity exhibited potential value for CC prognostic stratification as well as adjuvant chemotherapy benefit prediction. The relative worse survival in low purity CC may attribute to higher mutation frequency in key pathways and purity related microenvironmental changing.\n\nSummaryLow purity colon cancer patients conferred worse survival and benefited less from adjuvant chemotherapy. The mutation burden was negatively associated with tumour purity. Low purity samples exhibited intense immune phenotype with more M2 macrophages and neutrophils infiltration.

cancer biology↗

bioRxiv2018

Optimizing Trait Predictability in Hybrid Rice Using Superior Prediction Models and Selective Omic Datasets

Hybrid breeding has dramatically boosted yield and its stability in rice. Genomic prediction further benefits rice breeding by increasing selection intensity and accelerating breeding cycles. With the rapid advancement of technology, other omic data, such as metabolomic data and transcriptomic data, are readily available for predicting genetic values (or breeding values) for agronomically important traits. In the current study, we searched for the best prediction strategy for four traits (yield, 1000 grain weight, number of grains per panicle and number of tillers per plant) of hybrid rice by evaluating all possible combinations of omic datasets with different prediction methods. We conclude that, in rice, the predictions using the combination of genomic and metabolomic data generally produce better results than single-omics predictions or predictions based on other combined omic data. Inclusion of transcriptomic data does not improve predictability possibly because transcriptome does not provide more information for the trait than the sum of genome and metabolome; rather, the computational complexity is substantially increased if transcriptomic data is included in the models. Best linear unbiased prediction (BLUP) appears to be the most efficient prediction method compared to the other commonly used approaches, including LASSO, SSVS, SVM-RBF, SVP-POLY and PLS. Our study has provided a guideline for selection of hybrid rice in terms of which types of omic datasets and which method should be used to achieve higher trait predictability.

genetics↗

bioRxiv2018

First Efficient Transfection in Choanoflagellates using Cell-Penetrating Peptides

Only recently, based on phylogenetic studies choanoflagellates have been confirmed to form the sister group to metazoan. The mechanisms and genes behind the step from single to multicellular organisation and as a consequence the evolution of metazoan multicellularity could not be verified yet, as no reliable and efficient method for transfection of choanoflagellates was available. Here we present cell-penetrating peptides (CPPs) as an alternative to conventional transfection methods. In a series of experiments with the choanoflagellate Diaphanoeca grandis we proof for the first time that the use of CPPs is a reliable and highly efficient method for the transfection of choanoflagellates. We were able to silence the silicon transporter gene (SIT) by siRNA, and hence, to suppress the lorica (characteristic siliceous basket) formation. High gene silencing efficiency was determined and measured by light microscope and RT-qPCR. In addition, only low cytotoxic effects of CPP were detected. Our new method allows the reliable and efficient transfection of choanoflagellates, finally enabling us to verify the function of genes, thought to be involved in cell adhesion or cell signaling by silencing them via siRNA. This is a step stone for the research on the origin of multicellularity in metazoans.

molecular biology↗

bioRxiv2018

New insights on human essential genes based on integrated multi-omics analysis

Essential genes are those whose functions govern critical processes that sustain life in the organism. Recent gene-editing technologies have provided new opportunities to characterize essential genes. Here, we present an integrated analysis for comprehensively and systematically elucidating the genetic and regulatory characteristics of human essential genes. First, essential genes act as \"hubs\" in protein-protein interactions networks, in chromatin structure, and in epigenetic modifications, thus are essential for cell growth. Second, essential genes represent the conserved biological processes across species although gene essentiality changes itself. Third, essential genes are import for cell development due to its discriminate transcription activity in both embryo development and oncogenesis. In addition, we develop an interactive web server, the Human Essential Genes Interactive Analysis Platform (HEGIAP) (http://sysomics.com/HEGIAP/), which integrates abundant analytical tools to give a global, multidimensional interpretation of gene essentiality. Our study provides a new view for understanding human essential genes.

genomics↗

bioRxiv2018

TrackSig: reconstructing evolutionary trajectories of mutation signature exposure

We present a new method, TrackSig, to estimate the evolutionary trajectories of signatures of different somatic mutational processes from DNA sequencing data from a single, bulk tumour sample. TrackSig uses probability distributions over mutation types, called mutational signatures, to represent different mutational processes and detects the changes in the signature activity using an optimal segmentation algorithm that groups somatic mutations based on their estimated cancer cellular fraction (CCF) and their mutation type (e.g. CAG->CTG). We use two different simulation frameworks to assess both TrackSigs reconstruction accuracy and its robustness to violations of its assumptions, as well as to compare it to a baseline approach. We find 2-4% median error in reconstructing the signature activities on simulations with varying difficulty with one to three subclones at an average depth of 30x. The size and the direction of the activity change is consistent in 83% and 95% of cases respectively. There were an average of 0.02 missed and 0.12 false positive subclones per sample. In our simulations, grouping mutations by mutation type (TrackSig), rather than by clustering CCF (baseline strategy), performs better at estimating signature activities and at identifying subclonal populations in the complex scenarios like branching, CNA gain, violation of infinite site assumption, and the inclusion of neutrally evolving mutations. TrackSig is open source software, freely available at https://github.com/morrislab/TrackSig.

bioinformatics↗

bioRxiv2017

Mechanism of Membrane Recovery in Intra-Cytoplasmic Sperm Injection

ICSI (Intra-cytoplasmic sperm injection) is a broadly utilized technique for artificial fertilization. This approach has been successfully performed in human oocytes as well as others such as mouse and bovine. The piercing through the zona layer and the membrane needs to be achieved with a minimal biological damage to facilitate a rapid healing. Since the injection methodology serves as a crucial factor to success rate of ICSI, a significant amount of research efforts has been devoted to the development of injections. In this paper, we conduct comparative study among the major milestones for injection techniques in ICSI. Technical details are provided for each milestone and each technique is evaluated from engineering perspective. Later, we present a mechanism for healing process of membrane after drilling, which could potentially provide guidance for improvement of injection method. More importantly, we perform coarse-grained molecular dynamics simulation to reveal the mechanism of membrane recovery in intra-cytoplasmic sperm injection.

bioengineering↗

bioRxiv2017

GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, miRNA, and mRNA data in GDC

The large-scale multidimensional omics data in the Genomic Data Commons (GDC) provides opportunities to investigate the crosstalk among different RNA species and their regulatory mechanisms in cancers. Easy-to-use bioinformatics pipelines are needed to facilitate such studies. We have developed a user-friendly R/Bioconductor package, named GDCRNATools, to facilitate downloading, organizing, and analyzing RNA data in GDC with an emphasis on deciphering the lncRNA-mRNA related competing endogenous RNAs (ceRNAs) regulatory network in cancers. Many widely used bioinformatics tools and databases are utilized in our package. Users can easily pack preferred downstream analysis pipelines or integrate their own pipelines into the workflow. Interactive shiny web apps built in GDCRNATools greatly improve visualization of results from the analysis.\n\nAvailabilityGDCRNATools is an R/Bioconductor package that is freely available at https://github.com/Jialab-UCR/GDCRNATools

bioinformatics↗

bioRxiv2017

TahcoRoll: An Efficient Approach for Signature Profiling in Genomic Data through Variable-Length k-mers

k-mer profiling has been one of the trending approaches to analyze read data generated by high-throughput sequencing technologies. The tasks of k-mer profiling include, but are not limited to, counting the frequencies and determining the occurrences of short sequences in a dataset. The notion of k-mer has been extensively used to build de Bruijn graphs in genome or transcriptome assembly, which requires examining all possible k-mers presented in the dataset. Recently, an alternative way of profiling has been proposed, which constructs a set of representative k-mers as genomic markers and profiles their occurrences in the sequencing data. This technique has been applied in both transcript quantification through RNA-Seq and taxonomic classification of metagenomic reads. Most of these applications use a set of fixed-size k-mers since the majority of existing k-mer counters are inadequate to process genomic sequences with variable-length k-mers. However, choosing the appropriate k is challenging, as it varies for different applications. As a pioneer work to profile a set of variable-length k-mers, we propose TahcoRoll in order to enhance the Aho-Corasick algorithm. More specifically, we use one bit to represent each nucleotide, and integrate the rolling hash technique to construct an efficient in-memory data structure for this task. Using both synthetic and real datasets, results show that TahcoRoll outperforms existing approaches in either or both time and memory efficiency without using any disk space. In addition, compared to the most efficient state-of-the-art k-mer counters, such as KMC and MSBWT, TahcoRoll is the only approach that can process long read data from both PacBio and Oxford Nanopore on a commodity desktop computer. The source code of TahcoRoll is implemented in C++14, and available at https://github.com/chelseaju/TahcoRoll.git.

bioinformatics↗