Biology subjects

Yang, J. J.

Publications and source records attributed to Yang, J. J..

29 records · Page 2Linked to original sources

bioRxiv2021

A Developmentally Prometastatic Niche to Hepatoblastoma in Neonatal Liver mediated by the Cxcl1/Cxcr2 Axis

Background and RationaleHepatoblastoma (HB) is the most common pediatric liver cancer. Its predominant occurrence in very young children led us to investigating whether the neonatal liver provides a protumorigenic niche to HB development. MethodsHB development was compared between orthotopic transplantation models established in postnatal day 5 and 60 mice (P5Tx and P60Tx models). Single-cell RNA-sequencing was performed using tumor and liver tissues from both models and the top candidate cell types and genes identified are investigated for their roles in HB cell growth, migration, and survival. ResultsWe found that various HB cell lines including HepG2 cells were consistently and considerably more tumorigenic and metastatic in the P5Tx model than in the P60Tx models. Sc-RNAseq of the P5Tx and P60Tx HepG2 models revealed that the P5Tx tumor was more hypoxic and had a larger number of activated hepatic stellate cells (aHSCs) in the tumor-surrounding liver which express significantly higher levels of Cxcl1 than those from the P60Tx model. We found these differences were developmentally present in normal P5 and P60 liver. We showed that the Cxcl1/Cxcr2 axis mediated HB cell migration and was critical to HB cell survival under hypoxia. Treating HepG2 P60Tx model with recombinant CXCL1 protein induced intrahepatic and pulmonary metastasis and CXCR2 knockout in HepG2 cells abolished their metastatic potential in the P5Tx model. Lastly, we showed that in metastatic HB patient tumors there was a similar larger population of aHSCs in the tumor-surrounding liver than in localized tumors, and tumor hypoxia was uniquely associated with HB patient prognosis among pediatric cancers. ConclusionWe demonstrated that the neonatal liver provides a prometastatic niche to HB development via the Cxcl1/Cxcr2 axis.

cancer biology↗

bioRxiv2021

Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination

BackgroundLINCS, "Library of Integrated Network-based Cellular Signatures", and IDG, "Illuminating the Druggable Genome", are both NIH projects and consortia that have generated rich datasets for the study of the molecular basis of human health and disease. LINCS L1000 expression signatures provide unbiased systems/omics experimental evidence. IDG provides compiled and curated knowledge for illumination and prioritization of novel drug target hypotheses. Together, these resources can support a powerful new approach to identifying novel drug targets for complex diseases, such as Parkinsons disease (PD), which continues to inflict severe harm on human health, and resist traditional research approaches. ResultsIntegrating LINCS and IDG, we built the Knowledge Graph Analytics Platform (KGAP) to support an important use case: identification and prioritization of drug target hypotheses for associated diseases. The KGAP approach includes strong semantics interpretable by domain scientists and a robust, high performance implementation of a graph database and related analytical methods. Illustrating the value of our approach, we investigated results from queries relevant to PD. Approved PD drug indications from IDGs resource DrugCentral were used as starting points for evidence paths exploring chemogenomic space via LINCS expression signatures for associated genes, evaluated as target hypotheses by integration with IDG. The KG-analytic scoring function was validated against a gold standard dataset of genes associated with PD as elucidated, published mechanism-of-action drug targets, also from DrugCentral. IDGs resource TIN-X was used to rank and filter KGAP results for novel PD targets, and one, SYNGR3 (Synaptogyrin-3), was manually investigated further as a case study and plausible new drug target for PD. ConclusionsThe synergy of LINCS and IDG, via KG methods, empowers graph analytics methods for the investigation of the molecular basis of complex diseases, and specifically for identification and prioritization of novel drug targets. The KGAP approach enables downstream applications via integration with resources similarly aligned with modern KG methodology. The generality of the approach indicates that KGAP is applicable to many disease areas, in addition to PD, the focus of this paper.

bioinformatics↗

bioRxiv2020

TIGA: Target illumination GWAS analytics

Genome wide association studies (GWAS) can reveal important genotype-phenotype associations, however, data quality and interpretability issues must be addressed. For drug discovery scientists seeking to prioritize targets based on the available evidence, these issues go beyond the single study. Here, we describe rational ranking, filtering and interpretation of inferred gene-trait associations and data aggregation across studies by leveraging existing curation and harmonization efforts. Each gene-trait association is evaluated for confidence, with scores derived solely from aggregated statistics, linking a protein-coding gene and phenotype. We propose a method for assessing confidence in gene-trait associations from evidence aggregated across studies, including a bibliometric assessment of scientific consensus based on the iCite Relative Citation Ratio, and meanRank scores, to aggregate multivariate evidence. This method, intended for drug target hypothesis generation, scoring and ranking, has been implemented as an analytical pipeline, available as open source, with public datasets of results, and a web application designed for usability by drug discovery scientists, at https://unmtid-shinyapps.net/tiga/.

bioinformatics↗

bioRxiv2020

Effects of NT5C2 germline variants on 6-mecaptopurine metabolism in children with acute lymphoblastic leukemia

6-mercaptopurine (6-MP) is widely used in the treatment of acute lymphoblastic leukemia (ALL), and its cytotoxicity is primarily mediated by thioguanine nucleotide metabolites (TGN). Recent genome-wide association study has identified germline polymorphisms (e.g., rs72846714) in the NT5C2 gene associated with 6-MP metabolism in patients with ALL. However, the full spectrum of genetic variation in NT5C2 is unclear and its impact on 6-MP drug activation has not been comprehensively examined. To this end, we performed targeted sequencing of NT5C2 in 588 children with ALL and identified 121 single nucleotide polymorphisms (SNPs) nominally associated with erythrocyte TGN during 6-MP treatment (P < 0.05). Of these, 61 variants were validated in a replication cohort of 372 children with ALL. After considering linkage disequilibrium and multivariate analysis, we confirmed two clusters of variants, represented by rs72846714 and rs58700372, that independently affected 6-MP metabolism. Functional studies showed that rs58700372 directly altered the activity of an intronic enhancer, with the variant allele linked to higher transcription activity and reduced 6-MP metabolism (lower TGN). By contrast, rs72846714 was not located in a regulatory element and instead its association signal was explained by linkage disequilibrium with a proximal functional variant rs12256506 that activated NT5C2 transcription in-cis. Our results indicated that NT5C2 germline variation significantly contributes to inter-patient variability in thiopurine drug disposition.

genetics↗

bioRxiv2020

Understanding the origins of loss of protein function by analyzing the effects of thousands of variants on activity and abundance

Understanding and predicting how amino acid substitutions affect proteins is key to our basic understanding of protein function and evolution. Amino acid changes may affect protein function in a number of ways including direct perturbations of activity or indirect effects on protein folding and stability. We have analysed 6749 experimentally determined variant effects from multiplexed assays on abundance and activity in two proteins (NUDT15 and PTEN) to quantify these effects, and find that a third of the variants cause loss of function, and about half of loss-of-function variants also have low cellular abundance. We analyse the structural and mechanistic origins of loss of function, and use the experimental data to find residues important for enzymatic activity. We performed computational analyses of protein stability and evolutionary conservation and show how we may predict positions where variants cause loss of activity or abundance. In this way, our results link thermodynamic stability and evolutionary conservation to experimental studies of different properties of protein fitness landscapes.

biophysics↗

bioRxiv2020

Non-coding germline GATA3 variants alter chromatin topology and contribute to pathogenesis of acute lymphoblastic leukemia

Inherited non-coding genetic variants confer significant disease susceptibility in many cancers. However, the molecular processes of by which germline variants contribute to somatic lesions are poorly understood. We performed targeted sequencing in 5,008 patients and identified a key regulatory germline variant in GATA3 strongly associated with Philadelphia chromosome-like acute lymphoblastic leukemia (Ph-like ALL). By creating an isogenic cellular model with CRISPR-Cas9 system, we showed that this variant activated a strong enhancer that significantly upregulated GATA3 transcription, which in turn reshaped the global chromatin accessibility and 3D genome organization. Remarkably, this genotype switch induced a chromatin loop between the CRLF2 oncogene and a distal enhancer, similar to the somatically acquired super-enhancer hijacking event in patients. GATA3 genotype-related alterations in transcriptional control and 3D chromatin organization were further validated in Ph-like ALL patients. Finally, we showed that GATA3 directly regulates CRLF2 and potentiates the oncogenic effects of JAK-STAT signaling in leukemogenesis. Altogether, our results provide evidence for a novel mechanism by which a germline non-coding variant contributes to oncogene activation epigenetic regulation and 3D genome reprogramming.

cancer biology↗

bioRxiv2019

Massive parallel variant characterization identifies NUDT15 alleles associated with thiopurine toxicity

As a prototype of genomics-guided precision medicine, individualized thiopurine dosing based on pharmacogenetics is a highly effective way to mitigate hematopoietic toxicity of this class of drugs. Recently, NUDT15 deficiency was identified as a novel genetic cause of thiopurine toxicity, and NUDT15-informed preemptive dose reduction is quickly adopted in clinical settings. To exhaustively identify pharmacogenetic variants in this gene, we developed massively parallel NUDT15 function assays to determine variants effect on protein abundance and thiopurine cytotoxicity. Of the 3,097 possible missense variants, we characterized the abundance of 2,922 variants and found 54 hotspot residues at which variants resulted in complete loss of protein stability. Analyzing 2,935 variants in the thiopurine cytotoxicity-based assay, we identified 17 additional residues where variants altered NUDT15 activity without affecting protein stability. We identified structural elements key to NUDT15 stability and/or catalytical activity with single amino-acid resolution. Functional effects for NUDT15 variants accurately predicted toxicity risk alleles in 2,398 patients treated with thiopurines, with 100% sensitivity and specificity, in contrast with poor performance of bioinformatic prediction algorithms. In conclusion, our massively parallel variant function assays identified 1,103 deleterious NUDT15 variants, providing a comprehensive reference of variant function and vastly improving the ability to implement pharmacogenetics-guided thiopurine treatment individualization.

genomics↗

bioRxiv2019

Relationship between birth weight and chronic kidney disease: an integrative analysis of observational studies and causal inference through genetic approaches

ObjectiveAlthough many observational studies have shown that there was an inverse association between birth weight and chronic kidney disease (CKD) in adults, whether such association is causal remains largely unclear.\n\nMethodsWe first conducted a systematic review and meta-analysis to investigate the association between birth weight and CKD. Then using a set of valid instrumental variables for birth weight, we performed a two-sample Mendelian randomization (MR) to evaluate its causal effect on CKD based on summary association statistics available from large scale genome-wide association study (GWAS) (up to 143,677 individuals for birth weight and 118,147 individuals for CKD). We further validated the MR results with extensive sensitive analyses.\n\nResultsThe results of meta-analysis showed that individuals with low birth weight have about 76% (95% CI 36[~]126%) higher risk of CKD in late life compared with those with normal birth weight. Depending on 26 instrumental variables, the inverse variance weighted MR showed that the odds ratio per one SD increase of birth weight on CKD was estimated to be 0.91 (95% CI 0.72[~]1.14, p=0.396). The similar null association between birth weight and CKD is also observed using the weighted median method and maximum likelihood method as well as the Egger regression. Such non-significant association is robust against potential instrumental outliers and pleiotropic effects.\n\nConclusionOur study identifies an inverse association between birth weight and adult CKD in observational studies, while it is not supportive of the causal role of birth weight on CKD based on our MR analysis.

genetics↗

bioRxiv2019

Autophagy dark genes: Can we find them with machine learning?

Identifying novel genes associated with autophagy (ATG) in man remains an important task for gaining complete understanding on this fundamental physiological process. A machine-learning guided approach can highlight potentially \"missing pieces\" linking core autophagy genes with understudied, \"dark\" genes that can help us gain deeper insight into these processes. In this study, we used a set of 103 (out of 288 genes from the Autophagy Database, ATGdb), based on the presence of ATG-associated terms annotated from 3 secondary sources: GO (gene ontology), KEGG pathway and UniProt keywords, respectively. We regarded these as additional confirmation for their importance in ATG. As negative labels, we used the OMIM list of genes associated with monogenic diseases (after excluding the 288 ATG-associated genes). Data associated with these genes from 17 different public sources were compiled and used to derive a Meta Path/XGBoost (MPxgb) machine learning model trained to distinguish ATG and non-ATG genes (10-fold cross-validated, 100-times randomized models, median AUC = 0.994 +/- 0.0084). Sixteen ATG-relevant variables explain 64% of the total model gain, and 23% of the top 251 predicted genes are annotated in ATGdb. Another 15 genes have potential ATG associations, whereas 193 do not. We suggest that some of these 193 genes may represent \"autophagy dark genes\", and argue that machine learning can be used to guide autophagy research in order to gain a more complete functional and pathway annotation of this complex process.

bioinformatics↗

bioRxiv2018

Interactome Analysis Reveals Regulator of G Protein Signaling 14 (RGS14) is a Novel Calmodulin (CaM) Effector in Mouse Brain

Regulator of G Protein Signaling 14 (RGS14) is a complex scaffolding protein with an unusual domain structure that allows it to integrate G protein and mitogen-activated protein kinase (MAPK) signaling pathways. RGS14 mRNA and protein are enriched in brain tissue of rodents and primates. In the adult mouse brain, RGS14 is predominantly expressed in postsynaptic dendrites and spines of hippocampal CA2 pyramidal neurons where it naturally inhibits synaptic plasticity and hippocampus-dependent learning and memory. However, the signaling proteins that RGS14 natively interacts with in neurons to regulate plasticity are unknown. Here, we show that RGS14 exists as a component of a high molecular weight protein complex in brain. To identify RGS14 neuronal interacting partners, endogenous RGS14 immunoprecipitated from mouse brain was subjected to mass spectrometry and proteomic analysis. We find that RGS14 interacts with key postsynaptic proteins that regulate neuronal plasticity. Gene ontology analysis reveals that the most enriched RGS14 interacting proteins have functional roles in actin-binding, calmodulin(CaM)-binding, and CaM-dependent protein kinase (CaMK) activity. We validate these proteomics findings using biochemical assays that identify interactions between RGS14 and two previously unknown binding partners: CaM and CaMKII. We report that RGS14 directly interacts with CaM in a calcium-dependent manner and is phosphorylated by CaMKII in vitro. Lastly, we detect that RGS14 associates with CaMKII and with CaM in hippocampal CA2 neurons by proximity ligation assays in mouse brain sections. Taken together, these findings demonstrate that RGS14 is a novel CaM effector and CaMKII phosphorylation substrate thereby providing new insight into cellular mechanisms by which RGS14 controls plasticity in CA2 neurons.

biochemistry↗

bioRxiv2017

Drug Target Ontology to Classify and Integrate Drug Discovery Data

BackgroundOne of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome.\n\nResultsAs part of that effort, we have been developing a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while also supporting manual knowledge acquisition. A modular, hierarchical implementation facilitates development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships.\n\nConclusionsDTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/, Github (https://github.com/DrugTargetOntology/DTO), and the NCBO Bioportal (https://bioportal.bioontology.org/ontologies/DTO). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

bioinformatics↗