Biology subjects

Li, S.

Publications and source records attributed to Li, S..

At least 1,189 records · Page 66Linked to original sources

bioRxiv2020

Various miRNAs are involved in efficient HCV replication

One of the determinants for tissue tropism of hepatitis C virus (HCV) is miR-122, a liver-specific microRNA. Recently, it has been reported that interaction of miR-122 to HCV RNA induces a conformational change of the 5UTR internal ribosome entry site (IRES) structure to form stem-loop II structure (SLII) and hijack of translating 80S ribosome through the binding of SLIII to 40S subunit, which leads to efficient translation. On the other hand, low levels of HCV-RNA replication have also been detected in some non-hepatic cells; however, the details of extrahepatic replication remain unknown. These observations suggest the possibility that miRNAs other than miR-122 can support efficient replication of HCV-RNA in non-hepatic cells. Here, we identified a number of such miRNAs and show that they could be divided into two groups: those that bind HCV-RNA at two locations (miR-122 binding sites I and II), in a manner similar to miR-122 (miR-122-like), and those that target a single site that bridges sites I and II and masking both G28 and C29 in the 5UTR (non-miR-122-like). Although the enhancing activity of these non-hepatic miRNAs were lower than those of miR-122, substantial expression was detected in various normal tissues. Furthermore, structural modeling indicated that both miR-122-like and non-miR-122-like miRNAs not only can facilitate the formation of an HCV IRES SLII but also can stabilize IRES 3D structure in order to facilitate binding of SLIII to the ribosome. Together, these results suggest that HCV facilitates miR-122-independent replication in non-hepatic cells through recruitment of miRNAs other than miR-122. And our findings can provide a more detailed mechanism of miR-122-dependent enhancement of HCV-RNA translation by focusing on IRES tertiary structure. Author summaryOne of the determinants for tissue tropism of hepatitis C virus (HCV) is miR-122, a liver-specific microRNA, which is required for efficient propagation. Recently, it has been reported that interaction of miR-122 with the 5UTR of HCV contributes to the folding of a functional IRES structure that is required for efficient translation of viral RNA. In this study, we examined the minimum motifs in the seed region of miRNAs required for the enhancement of HCV replication. As a result, we found two groups of non-hepatic miRNAs: "miR-122-like miRNAs" that can bind HCV-RNA at two locations in a manner similar to miR-122, and "non-miR-122-like miRNAs" that target a single site that masking both G28 and C29 in the 5UTR. The interaction of these non-hepatic miRNAs with the 5UTR can facilitate not only the folding of active HCV IRES but also the stabilization of IRES 3D structure in order to facilitate binding to the ribosome. These results suggest the possibility of replication of HCV in non-hepatic cells through interaction with miRNAs other than miR-122 and provide insight into the establishment of persistent infection of HCV in non-hepatic tissues that lead to the development of extrahepatic manifestations.

microbiology↗

bioRxiv2020

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular targets, there are currently small-molecules available only for a small proportion of the human proteome. Effective approaches are therefore required to map the massive space of unexplored compound-target interactions for novel and potent activities. Here, we carried out a crowdsourced benchmarking of predictive models for kinase inhibitor potencies across multiple kinase families using unpublished bioactivity data. The top-performing predictions were based on kernel learning, gradient boosting and deep learning, and their ensemble resulted in predictive accuracy exceeding that of kinase activity assays. We then made new experiments based on the model predictions, which further improved the accuracy of experimental mapping efforts and identified unexpected potencies even for under-studied kinases. The open-source algorithms together with the novel bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking new prediction algorithms and for extending the druggable kinome.

biochemistry↗

bioRxiv2020

ZEAMAP, a comprehensive database adapted to the maize multi-omics era

As one of the most extensively cultivated crops, maize (Zea mays L.) has been extensively studied by researchers and breeders for over a century. With advances in high-throughput detection of various omics data, a wealth of multi-dimensional and multi-omics information has been accumulated for maize and its wild relative, teosinte. Integration of this information has the potential to accelerate genetic research and generate improvements in maize agronomic traits. To this end, we constructed ZEAMAP (http://www.zeamap.com), a comprehensive database incorporating multiple reference genomes, annotations, comparative genomics, transcriptomes, open chromatin regions, chromatin interactions, high-quality genetic variants, phenotypes, metabolomics, genetic maps, genetic mapping loci, population structures and domestication selection signals between teosinte and maize. ZEAMAP is user-friendly, with the ability to interactively integrate, visualize and cross-reference multiple different omics datasets.

bioinformatics↗

bioRxiv2019

MONN: a Multi-Objective Neural Network for Predicting Pairwise Non-Covalent Interactions and Binding Affinities between Compounds and Proteins

Computational approaches for inferring the mechanisms of compound-protein interactions (CPIs) can greatly facilitate drug development. Recently, although a number of deep learning based methods have been proposed to predict binding affinities and attempt to capture local interaction sites in compounds and proteins through neural attentions, they still lack a systematic evaluation on the interpretability of the identified local features. In addition, in these previous approaches, the exact matchings between interaction sites from compounds and proteins, which are generally important for understanding drug mechanisms of action, still remain unknown. Here, we compiled the first benchmark dataset containing the inter-molecular non-covalent interactions for more than 10,000 compound-protein pairs, and used it to systematically evaluate the interpretability of neural attentions in existing prediction models. We developed a multi-objective neural network, called MONN, to predict both non-covalent interactions and binding affinity for a given compound-protein pair. MONN uses convolution neural networks on molecular graphs of compounds and primary sequences of proteins to effectively capture the intrinsic features from both inputs, and also takes advantage of the predicted non-covalent interactions to further boost the accuracy of binding affinity prediction. Comprehensive evaluation demonstrated that while the previous neural attention based approaches fail to exhibit satisfactory interpretability results without extra supervision, MONN can successfully predict non-covalent interactions on our benchmark dataset as well as another independent dataset derived from the Protein Data Bank (PDB). Moreover, MONN can outperform other state-of-the-art methods in predicting compound-protein binding affinities. In addition, the pairwise interactions predicted by MONN displayed compatible and accordant patterns in chemical properties, which provided another evidence to support the strong predictive power of MONN. These results suggested that MONN can offer a powerful tool in predicting binding affinities of compound-protein pairs and also provide useful insights into understanding the molecular mechanisms of compound-protein interactions, which thus can greatly advance the drug discovery process. The source code of the MONN model and the dataset creation process can be downloaded from https://github.com/lishuya17/MONN.

bioinformatics↗

bioRxiv2019

Effect of Exogenous Amino Acids on Yield and Quality of Tartary Buckwheat in Non-saline and Saline-alkali Soil

Disclaimer StatementThe authors have withdrawn this manuscript (BIORXIV/2019/887778) entitled "Effect of Exogenous Amino Acids on Yield and Quality of Tartary Buckwheat in Non-saline and Saline-alkali Soil" because it requires adding new data and further modifications. Therefore, the authors do not wish this work to be cited (https://www.biorxiv.org/content/10.1101/2019.12.24.887778v5) as reference for the project. If you have any questions, please contact the corresponding author.

physiology↗

bioRxiv2019

Accurate and simultaneous identification of differential expression and splicing using hierarchical Bayesian analysis

The regulation of mRNA controls both overall gene expression as well as the distribution of mRNA isoforms encoded by the gene. Current algorithmic approaches focus on characterization of significant differential expression or alternative splicing events or isoform distribution without integrating both events. Here, we present Hierarchical Bayesian Analysis of Differential Expression and ALternative SPlicing (HBA-DEALS), which simultaneously characterizes differential expression and splicing in cohorts. HBA-DEALS attains state of the art or better performance for both expression and splicing, and allows genes to be characterized as having differential gene expression (DGE), differential alternative splicing (DAST), both, or neither. Based on an analysis of Genotype-Tissue Expression (GTEx) data we demonstrate the existence of sets of genes that show predominant DGE or DAST across a comparison of 20 tissue types, and show that these sets have pervasive differences with respect to gene structure, function, membership in protein complexes, and promoter architecture.

bioinformatics↗

bioRxiv2019

Noninvasive optical detection of Granzyme B from natural killer cells using enzyme-activated fluorogenic probes

Despite many studies on the cytotoxic protease granzyme B, key aspects of its function remain unexplored due to the lack of selective probes for its activity. In this study, we fully mapped the substrate preferences of GrB using a set of unnatural amino acids, demonstrating previously unknown GrB substrate preferences that we then used to design novel substrate-based inhibitors and a GrB-activatable activity-based probe. We showed that our GrB probes react poorly with caspases, making them ideal for the in-depth analysis of GrB localization and function in cells. With our quenched fluorescence substrate, we determined GrB within the cytotoxic granules of human YT cells. When used as cytotoxic effectors, YT cells loaded with the GrB attack MDA-MB-231 target cells, and active GrB influences its target cell killing efficiency.

biochemistry↗

bioRxiv2019

Ratiometric quorum sensing in bacterial conjugation

Plasmid-mediated horizontal gene transfer of antibiotic-resistance and virulence in pathogenic bacteria underlies a major public health issue. Understanding how, in the absence of antibiotic-mediated selection, plasmid-bearing cells avoid being outnumbered by plasmid-free cells, is key to developing counter strategies. Here we quantified the induction of Enterococcus faecalis plasmidial sex-pheromone pathway to show that the integration of the stimulatory (mate-sensing) and inhibitory (self-sensing) signaling modules from the pCF10 conjugative plasmid, provides a precise measure of the recipient-to-donor ratio, agnostic to variations in population size. Such ratiometric control of conjugation favors vertical plasmid-transfer under low mating likelihood and allows activation of conjugation functions only under high mating likelihood. We further show that this strategy constitutes a cost-effective investment into mating effort, as overstimulation produces unproductive self-aggregation and reductions in the growth rate. A mathematical model suggests that ratiometric control of conjugation limits the spread of antibiotic resistance in absence of antibiotics, predicting a long-term stable co-existence of donors and recipients. Our results demonstrate how population-level parameters can control transfer of antibiotic-resistance in bacteria, opening the door for biotic control strategies. Ratiometric sensing in bacteria mirrors sexual behaviors observed in eukaryotes.

microbiology↗

bioRxiv2019

Regulation of heterotopic ossification through local inflammatory monocytes in a mouse model of aberrant wound healing

Heterotopic ossification (HO) is an aberrant regenerative process with ectopic bone induction in response to musculoskeletal trauma, in which mesenchymal stem cells (MSC) differentiate into osteochondrogenic cells instead of myocytes or tenocytes. Despite frequent cases of hospitalized musculoskeletal trauma, the inflammatory responses and cell population dynamics that regulate subsequent wound healing and tissue regeneration are still unclear. Here we examine, using a mouse model of trauma-induced HO, the local microenvironment of the initial post-injury inflammatory response. Single cell transcriptome analyses identify distinct monocyte/macrophage populations at the injury site, with their dynamic changes over time elucidated using trajectory analyses. Mechanistically, transforming growth factor beta-1 (TGF{beta}1)-producing monocytes/macrophages are associated with HO and aberrant chondrogenic progenitor cell differentiation, while CD47-activating peptides that reduce systemic TGF{beta} levels help ameliorate HO. Our data thus implicate CD47 activation as a novel therapeutic approach for modulating monocyte/macrophage phenotypes, MSC differentiation and HO formation during wound healing.

immunology↗

bioRxiv2019

SpecHap: a fast haplotyping method based on spectral graph theory

Haplotype phasing is essential to study diploid eukaryotic organisms. High-throughput sequencing, including next-generation sequencing and third-generation sequencing from different technologies, brings possibilities for haplotype assembly. Although there exist multiple haplotype phasing algorithms, only a few are portable across sequencing technologies with the premise of efficiency and accuracy. Herein, we proposed SpecHap, a novel haplotype assembly tool that leverages spectral graph theory, transforming haplotype phasing into an algebraic problem. On both in silico and whole-genome-sequencing datasets, SpecHap consumed less memory and required less CPU time, yet achieved comparable accuracy comparing to state-of-art methods across all the test instances of next-generation sequencing, linked-reads, high-throughput chromosome conformation capture sequencing, PacBio single-molecule real-time sequencing and Oxford Nanopore long-reads sequencing data. Furthermore, SpecHap successfully phased an individual Ambystoma mexicanumm, a species with gigantic diploid genomes, within 6 CPU hours and 945MB peak memory usage, while other tools failed to yield results either due to a memory overflow (40GB) or a time limit excess (5 days). Our results demonstrated that SpecHap is scalable, efficient and accurate for diploid phasing, supporting diverse sequencing platforms.

bioinformatics↗

bioRxiv2019

FLS2 is a CDK-like kinase that directly binds IFT70 and is required for proper ciliary disassembly in Chlamydomonas

Intraflagellar transport (IFT) is required for ciliary assembly and maintenance. While disruption of IFT may trigger ciliary disassembly, we show here that IFT mediated transport of a CDK-like kinase ensures proper ciliary disassembly. Mutations in flagellar shortening 2 (FLS2), encoding a CDK-like kinase, lead to retardation of cilia resorption and delay of cell cycle progression. Stimulation for ciliary disassembly induces gradual dephosphorylation of FLS2 accompanied with gradual inactivation. Loss of FLS2 or its kinase activity induces early onset of kinesin13 phosphorylation in cilia. FLS2 is predominantly localized in the cell body, however, it is transported to cilia upon induction of ciliary disassembly. FLS2 directly interacts with IFT70 and loss of this interaction inhibits its ciliary transport, leading to dysregulation of kinesin13 phosphorylation and retardation of ciliary disassembly. Thus, this work demonstrates that IFT plays active roles in controlling proper ciliary disassembly by transporting a protein kinase to cilia to regulate a microtubule depolymerizer. Author SummaryCilia or eukaryotic flagella are cellular surface protrusions that function in cell motility as well as sensing. They are dynamic structures that undergo assembly and disassembly. Cilia are resorbed during cell cycle progression. Dysregulation of cilia resorption may cause delay of cell cycle progression, which underlies aberrant cell differentiation and even cancer. Ciliary resorption requires depolmerization of axonemal microtubules that is mediated by kinesin13. Using the unicellular green alga, Chlamydomonas, we have identified a CDK-like kinase FLS2 that when mutated retards cilia resorption, leading to delay of cell cycle progression. FLS2, a cell body protein, is transported to cilia via intraflagellar transport upon induction of cilia resorption. FLS2 directly interacts with IFT70 and loss of this interaction inhibits transport of FLS2 to cilia and fails to regulate proper phosphorylation of kinesin13 in cilia.

cell biology↗

bioRxiv2019

Quantitative Structure-Mutation-Activity Relationship Tests (QSMART) Model for Protein Kinase Inhibitor Response Prediction

Predicting drug sensitivity profiles from genotypes is a major challenge in personalized medicine. Machine learning and deep neural network methods have shown promise in addressing this challenge, but the "black-box" nature of these methods precludes a mechanistic understanding of how and which genomic and proteomic features contribute to the observed drug sensitivity profiles. Here we provide a combination of statistical and neural network framework that not only estimates drug IC50 in cancer cell lines with high accuracy (R2 = 0.861 and RMSE = 0.818) but also identifies features contributing to the accuracy, thereby enhancing explainability. Our framework, termed QSMART, uses a multi-component approach that includes (1) collecting drug fingerprints, cancer cell lines multi-omics features, and drug responses, (2) testing the statistical significance of interaction terms, (3) selecting features by Lasso with Bayesian information criterion, and (4) using neural networks to predict drug response. We evaluate the contribution of each of these components and use a case study to explain the biological relevance of several selected features to protein kinase inhibitor response in non-small cell lung cancer cells. Specifically, we illustrate how interaction terms that capture associations between drugs and mutant kinases quantitatively contribute to the response of two EGFR inhibitors (afatinib and lapatinib) in non-small cell lung cancer cells. Although we have tested QSMART on protein kinase inhibitors, it can be extended across the proteome to investigate the complex relationships connecting genotypes and drug sensitivity profiles.

bioinformatics↗

bioRxiv2019

LandScape: a web application for interactive genomic summary visualization

SummaryVisualizing integrated-level data from genomic research remains a challenge, as it requires sufficient coding skills and experience. Here, we present LandScapeoviz, a web-based application for interactive and real-time visualization of summarized genetic information. LandScape utilizes a well-designed file format that is capable of handling various data types, and offers a series of built-in functions to customize the appearance, explore results, and export high-quality diagrams that are available for publication. Availability and implementationLandScape is deployed at bio.oviz.org/demo-project/analyses/landscape for online use. Documentation and demo data are freely available on this website and GitHub (github.com/Nobel-Justin/Oviz-Bio-demo). Contactshuaicli@cityu.edu.hk

bioinformatics↗

bioRxiv2019

Trans-acting role of the leader peptide peTrpL in posttranscriptional regulation of multiresistance

Bacterial ribosome-dependent attenuators are widespread posttranscriptional regulators. They harbour small upstream ORFs (uORFs) encoding leader peptides, for which no functions in trans are known yet. In the soil-dwelling plant symbiont Sinorhizobium meliloti, the tryptophan biosynthesis gene trpE(G) is preceded by the uORF trpL and is regulated by transcription attenuation according to tryptophan availability. However, trpLE(G) transcription is initiated independently of the tryptophan level in the cell, thereby ensuring a largely tryptophan-independent production of the leader peptide peTrpL. We provide evidence that peTrpL plays a role in the differential posttranscriptional regulation of the smeABR operon encoding the major multidrug efflux pump SmeAB and the TtgR-type transcription repressor SmeR. We show that peTrpL is involved in a tetracycline-dependent smeR mRNA destabilization and forms an antibiotic-dependent ribonucleoprotein (ARNP) complex with smeR and its antisense RNA (asRNA). Induction of asRNA transcription, ARNP formation and smeR downregulation were promoted by several antibiotics and the flavonoid genistein, and the resistance to these antimicrobial compounds was increased by peTrpL. The role of peTrpL in resistance is conserved in other soil Alphaproteobacteria.

molecular biology↗

bioRxiv2019

Splicing-sensitive fusion transcripts associated with key tumor characteristics occur at high frequency in neuroblastoma

The paucity of recurrent mutations has hampered efforts to understand and treat neuroblastoma. Alternative splicing and splicing-dependent RNA-fusions represent mechanisms able to increase the gene product repertoire but their role in neuroblastoma remains largely unexplored. Through analysis of RNA-sequenced neuroblastoma we here show that elevated expression of splicing factors is a strong predictor of poor clinical outcome. Furthermore, we identified >900 primarily intrachromosomal fusions containing canonical splicing sites. Fusions included transcripts from well-known oncogenes, were enriched for proximal genes and in chromosomal regions commonly gained or lost in neuroblastoma. As a proof-of-principle that these fusions can generate altered gene products, we characterized a ZNF451-BAG2 fusion, generating a truncated BAG2-protein which inhibited retinoic acid-induced differentiation. Spliceosome inhibition impeded neuroblastoma fusion expression, induced apoptosis and inhibited xenograft tumor growth. Our findings elucidate a splicing-dependent mechanism producing altered gene products in neuroblastoma and suggest that the spliceosome is a tractable therapeutical target.

cancer biology↗

bioRxiv2019

Oncologic Therapy Shapes the Fitness Landscape of ClonalHematopoiesis

Clonal hematopoiesis (CH) is frequent in cancer patients and associated with increased risk of therapy related myeloid neoplasms (tMN). To define the relationship between CH, oncologic therapy, and tMN progression, we studied 24,439 cancer patients. We show that previously treated patients have increased rates of CH, with enrichment of mutations in DNA Damage Response (DDR) genes (TP53, PPM1D, CHEK2). Exposure to radiation, platinum and topoisomerase II inhibitors have the strongest association with CH with evidence of dose-dependence and gene-treatment interactions. We validate these associations in serial sampling from 525 patients and show that exposure to cytotoxic and radiation therapy imparts a selective advantage specifically in hematopoietic cells with DDR mutations. In patients who progressed to tMN, the clone at CH demarcated the dominant clone at tMN diagnosis. CH mutational features predict risk of therapy-related myeloid neoplasm in solid tumor patients with clinical implications for early detection and treatment decisions.

genetics↗

bioRxiv2019

LMSM: a modular approach for identifying lncRNA related miRNA sponge modules in breast cancer

Until now, existing methods for identifying lncRNA related miRNA sponge modules mainly rely on lncRNA related miRNA sponge interaction networks, which may not provide a full picture of miRNA sponging activities in biological conditions. Hence there is a strong need of new computational methods to identify lncRNA related miRNA sponge modules. In this work, we propose a framework, LMSM, to identify LncRNA related MiRNA Sponge Modules from heterogeneous data. To understand the miRNA sponging activities in biological conditions, LMSM uses gene expression data to evaluate the influence of the shared miRNAs on the clustered sponge lncRNAs and mRNAs. We have applied LMSM to the human breast cancer (BRCA) dataset from The Cancer Genome Atlas (TCGA). As a result, we have found that the majority of LMSM modules are significantly implicated in BRCA and most of them are BRCA subtype-specific. Most of the mediating miRNAs act as crosslinks across different LMSM modules, and all of LMSM modules are statistically significant. Multi-label classification analysis shows that the performance of LMSM modules is significantly higher than baselines performance, indicating the biological meanings of LMSM modules in classifying BRCA subtypes. The consistent results suggest that LMSM is robust in identifying lncRNA related miRNA sponge modules. Moreover, LMSM can be used to predict miRNA targets. Finally, LMSM outperforms a graph clustering-based strategy in identifying BRCA-related modules. Altogether, our study shows that LMSM is a promising method to investigate modular regulatory mechanism of sponge lncRNAs from heterogeneous data. Author summaryPrevious studies have revealed that long non-coding RNAs (lncRNAs), as microRNA (miRNA) sponges or competing endogenous RNAs (ceRNAs), can regulate the expression levels of messenger RNAs (mRNAs) by decreasing the amount of miRNAs interacting with mRNAs. In this work, we hypothesize that the "tug-of-war" between RNA transcripts for attracting miRNAs is across groups or modules. Based on the hypothesis, we propose a framework called LMSM, to identify LncRNA related MiRNA Sponge Modules. Based on the two miRNA sponge modular competition principles, significant sharing of miRNAs and high canonical correlation between the sponge lncRNAs and mRNAs, LMSM is also capable of predicting miRNA targets. LMSM not only extends the ceRNA hypothesis, but also provides a novel way to investigate the biological functions and modular mechanism of lncRNAs in breast cancer.

bioinformatics↗

bioRxiv2019

Specific gyrA gene mutations correlate with high prevalence of discordant levofloxacin resistance in Mycobacterium tuberculosis isolates from Beijing, China

Although molecular diagnostics are highly sensitive in the diagnosis of fluroquinolone (FQ)-resistant tuberculosis (TB), the discordant results with the sequential phenotypic drug susceptibility testing (DST) invariably occur. To investigate the prevalence and the cause of discordant results between molecular and phenotypic DST for levofloxacin (LFX), the cases who were determined to be LFX-susceptible using the phenotypic DST but LFX-resistant using the MeltPro assay were retrospectively reviewed in Beijing, China. We measured LFX minimal inhibitory concentrations (MICs) using Middlebrook 7H9 broth. Sanger sequencing was used to determine genotypic characteristics of discordant isolates. Between January and December 2018, 126 (22.1%) out of 571 smear-positive TB patients were identified as LFX-resistant TB by MeltPro assay. Among the 126 LFX-resistant TB, there were 34 isolates identified as LFX-susceptible TB by phenotypical DST. This result demonstrated a discordance prevalence of 27.0%. LFX MICs were majorly centered around the critical concentration, and 7 (21.2%) and 13 (39.4%) had MICs of 2.0 mg/l and 4.0 mg/l, respectively. The most prevalent mutations conferring discordant LFX resistance were the amino acid substitutions of Ser to Leu in 90 codon (13, 39.4%) and Asp to Ala in 94 codon of gyrA (11, 33.3mg/l belonged to Ala90Val and Asp94Ala group. In conclusion, our data demonstrate that more than one quarter of LFX-resistant isolates by molecular method are susceptible to LFX using the phenotypic DST. The high prevalence of discordant LFX resistance is majorly due to the isolates with specific mutations within gyrA gene conferring proximity of MICs to the critical concentration.

microbiology↗