Biology subjects

Zou, J.

Publications and source records attributed to Zou, J..

206 records · Page 12Linked to original sources

bioRxiv2018

Hepatitis C virus NS5A inhibitor daclatasvir allosterically impairs NS4B-involved protein-protein interactions within the viral replicase and disrupts the replicase quaternary structure in a replicase assembly surrogate system

Daclatasvir (DCV) is a highly potent direct-acting antiviral that targets the non-structural protein 5A (NS5A) of hepatitis C virus (HCV) and has achieved great clinical successes. Previous studies demonstrate its impact on the viral replication complex assembly. However the precise mechanism by which DCV impairs the replication complex assembly remains elusive. In this study, by using HCV subgenomic replicons and a viral replicase assembly surrogate system that expresses the HCV NS3-5B polyprotein to mimic the viral replicase assembly, we dissected the impacts of DCV on aggregation and tertiary structure of NS5A, the protein-protein interactions within the viral replicase and the quaternary structure of the viral replicase. We found that DCV didnt affect aggregation and tertiary structure of NS5A. DCV induced a quaternary structural change of the viral replicase, evidenced by selectively increasing of the NS4Bs sensitivity to proteinase K digestion. Mechanically, DCV impaired the NS4B-involved protein-protein interactions within the viral replicase. The DCV-resistant mutant Y93H was refractory to the DCV-induced reduction of the NS4B-invoved protein interactions and the quaternary structural change of the viral replicase. In addition, Y93H reduced NS4B-involed protein-protein interactions within the viral replicase and attenuated viral replication. We propose that DCV may induce a position change of NS5A, which allosterically affects the protein interactions within the replicase components and disrupts the replicase assembly.\n\nImportanceThe development of the direct-acting antivirals (DAA) has resulted in great clinical achievements for Hepatitis C Virus (HCV) treatment. Daclatasvir (DCV) is an inhibitor targeting the non-enzymatic NS5A, with the 50% effective concentration values in the picomolar range. Accumulated data suggest that DCV blocks the biogenesis of the HCV replication complex. However the mechanistic actions of DCV are still largely unknown. Insights into the action mechanism of DCV on the viral replication complex assembly of HCV may enlighten the development of next generation of DAAs and new anti-viral strategies for other positive-strand RNA viruses for which there are a scarcity of DAAs. Herein, using HCV subgenomic replicons and a viral replicase assembly surrogate system, we dissected the mechanistic actions of DCV on the viral replicase assembly. We found that DCV allosterically impairs NS4B-involved protein-protein interactions within the viral replicase and disrupts the quaternary structure of the viral replicase.

microbiology↗

bioRxiv2018

The Clinical Imperative for Inclusivity: Race, Ethnicity, and Ancestry (REA) in Genomics

The Clinical Genome Resource (ClinGen) Ancestry and Diversity Working Group highlights the need to develop guidance on race, ethnicity, and ancestry (REA) data collection and use in clinical genomics. We present quantitative and qualitative evidence to characterize: 1) acquisition of REA data via clinical laboratory requisition forms, and 2) information disparity across populations in the Genome Aggregation Database (gnomAD) at clinically relevant sites as determined by variants in ClinVar. Our requisition form analysis showed substantial heterogeneity in clinical laboratory ascertainment of REA, as well as marked incongruity among terms used to define REA categories. There was also striking disparity across REA populations in the amount of information available about variants at clinically relevant sites in gnomAD. European ancestral populations constituted the majority of observations (55.8%), allele counts (59.7%), and private alleles (56.1%) in gnomAD at 550 loci with \"pathogenic\" and \"likely pathogenic\" expert-reviewed variants in ClinVar. Our findings highlight the importance of implementing and supporting programs to increase diversity in genome sequencing and clinical genomics, as well as measuring uncertainty around population-level datasets that are used in variant interpretation. Finally, we suggest the need for a standardized REA data collection framework to be developed and adopted across clinical genomics.

genomics↗

bioRxiv2018

Germline genomic landscapes of breast cancer patients significantly predict clinical outcomes

Germline genetic variants such as BRCA1/2 play an important role in tumorigenesis and clinical outcomes of cancer patients. However, only a small fraction (i.e., 5-10%) of inherited variants has been associated with clinical outcomes (e.g., BRCA1/2, APC, TP53, PTEN and so on). The challenge remains in using these inherited germline variants to predict clinical outcomes of cancer patient population. In an attempt to solve this issue, we applied our recently developed algorithm, eTumorMetastasis, which constructs predictive models, on exome sequencing data to ER+ breast (n=755) cancer patients. Gene signatures derived from the genes containing functionally germline genetic variants significantly distinguished recurred and non-recurred patients in two ER+ breast cancer independent cohorts (n=200 and 295, P=1.4x10-3). Furthermore, we found that recurred patients possessed a higher rate of germline genetic variants. In addition, the inherited germline variants from these gene signatures were predominately enriched in T cell function, antigen presentation and cytokine interactions, likely impairing the adaptive and innate immune response thus favoring a pro-tumorigenic environment. Hence, germline genomic information could be used for developing non-invasive genomic tests for predicting patients outcomes (or drug response) in breast cancer, other cancer types and even other complex diseases.

cancer biology↗

bioRxiv2018

eTumorMetastasis, a network-based algorithm predicts clinical outcomes using whole-exome sequencing data of cancer patients

Continual reduction in sequencing cost is expanding the accessibility of genome sequencing data for routine clinical applications. However, the lack of methods to construct machine learning-based predictive models using these datasets has become a crucial bottleneck for the application of sequencing technology in clinics. Here we developed a new algorithm, eTumorMetastasis, which transforms tumor functional mutations into network-based profiles, and identify network operational gene signatures (NOG signatures) which model the tipping point at which a tumor cell shifts from a state that doesnt favor recurrences to one that does. We showed that NOG signatures derived from genomic mutations of tumor founding clones (i.e., the most recent common ancestor of the cells within a tumor) significantly distinguished recurred and non-recurred breast tumors. These results imply that somatic mutations of tumor founders are association with tumor recurrence and can be used to predict clinical outcomes. Finally, the concepts underlying the eTumorMetastasis pave the way for the application of genome sequencing in predictions for other complex genetic diseases.

bioinformatics↗

bioRxiv2018

The proteome of the malaria plastid organelle, a key anti-parasitic target

Malaria parasites (Plasmodium spp.) and related apicomplexan pathogens contain a non-photosynthetic plastid called the apicoplast. Derived from an unusual secondary eukaryote-eukaryote endosymbiosis, the apicoplast is a fascinating organelle whose function and biogenesis rely on a complex amalgamation of bacterial and algal pathways. Because these pathways are distinct from the human host, the apicoplast is an excellent source of novel antimalarial targets. Despite its biomedical importance and evolutionary significance, the absence of a reliable apicoplast proteome has limited most studies to the handful of pathways identified by homology to bacteria or primary chloroplasts, precluding our ability to study the most novel apicoplast pathways. Here we combine proximity biotinylation-based proteomics (BioID) and a new machine learning algorithm to generate a high-confidence apicoplast proteome consisting of 346 proteins. Critically, the high accuracy of this proteome significantly outperforms previous prediction-based methods and extends beyond other BioID studies of unique parasite compartments. Half of identified proteins have unknown function, and 77% are predicted to be important for normal blood-stage growth. We validate the apicoplast localization of a subset of novel proteins and show that an ATP-binding cassette protein ABCF1 is essential for blood-stage survival and plays a previously unknown role in apicoplast biogenesis. These findings indicate critical organellar functions for newly discovered apicoplast proteins. The apicoplast proteome will be an important resource for elucidating unique pathways derived from secondary endosymbiosis and prioritizing antimalarial drug targets.

microbiology↗

bioRxiv2018

Leveraging allele-specific expression to refine fine-mapping for eQTL studies

Many disease risk loci identified in genome-wide association studies are present in non-coding regions of the genome. It is hypothesized that these variants affect complex traits by acting as expression quantitative trait loci (eQTLs) that influence expression of nearby genes. This indicates that many causal variants for complex traits are likely to be causal variants for gene expression. Hence, identifying causal variants for gene expression is important for elucidating the genetic basis of not only gene expression but also complex traits. However, detecting causal variants is challenging due to complex genetic correlation among variants known as linkage disequilibrium (LD) and the presence of multiple causal variants within a locus. Although several fine-mapping approaches have been developed to overcome these challenges, they may produce large sets of putative causal variants when true causal variants are in high LD with many non-causal variants. In eQTL studies, there is an additional source of information that can be used to improve fine-mapping called allele-specific expression (ASE) that measures imbalance in gene expression due to different alleles. In this work, we develop a novel statistical method that leverages both ASE and eQTL information to detect causal variants that regulate gene expression. We illustrate through simulations and application to the Genotype-Tissue Expression (GTEx) dataset that our method identifies the true causal variants with higher specificity than an approach that uses only eQTL information. In the GTEx dataset, our method achieves the median reduction rate of 11% in the number of putative causal variants.\n\nContactJaeHoonSul@mednet.ucla.edu, eeskin@cs.ucla.edu

genetics↗

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

Architecture of TAF11/TAF13/TBP complex suggests novel regulatory state in General Transcription Factor TFIID function

General transcription factor TFIID is a key component of RNA polymerase II transcription initiation. Human TFIID is a megadalton-sized complex comprising TATA-binding protein (TBP) and 13 TBP-associated factors (TAFs). TBP binds to core promoter DNA, recognizing the TATA-box. We identified a ternary complex formed by TBP and the histone fold (HF) domain-containing TFIID subunits TAF11 and TAF13. We demonstrate that TAF11/TAF13 competes for TBP binding with TATA-box DNA, and also with the N-terminal domain of TAF1 previously implicated in TATA-box mimicry. In an integrative approach combining crystal coordinates, biochemical analyses and data from cross-linking mass-spectrometry (CLMS), we determine the architecture of the TAF11/TAF13/TBP complex, revealing TAF11/TAF13 interaction with the DNA binding surface of TBP. We identify a highly conserved C-terminal TBP-binding domain (CTID) in TAF13 which is essential for supporting cell growth. Our results thus have implications for cellular TFIID assembly and suggest a novel regulatory state for TFIID function.

biochemistry↗