Biology subjects

Lai, L.

Publications and source records attributed to Lai, L..

5 recordsLinked to original sources

bioRxiv2018

Sequencing of the MHC region defines HLA-DQA1 as the major independent risk for anti-citrullinated protein antibodies (ACPA)-positive rheumatoid arthritis in Han population

The strong genetic contribution of the major histocompatibility complex (MHC) to rheumatoid arthritis (RA) susceptibility has been generally attributed to HLA-DRB1. However, due to the high linkage disequilibrium in the MHC region, it is difficult to define the real or/and additional independent genetic risks using the conventional HLA genotyping or chip-based microarray technology. By the capture sequencing of entire MHC region for discovery and HLA-typing for validation in 2,773 subjects of Han ancestry, we identified HLA-DQ1:160D as the strongest independent genetic risk for anti-citrullinated protein antibodies (ACPA)-positive RA in Han population (P = 6.16 x 10-36, OR=2.29). Further stepwise conditional analysis revealed that DR{beta}1:37N has an independent protective effect on ACPA-positive RA (P = 5.81 x 10-16, OR=0.49). The DQ1:160 coding allele DQA1*0303 displayed high impact on joint radiographic severity, especially in patients with early disease and smoking (P = 3.02 x 10-5). Interaction analysis by comparative molecular modeling revealed that the negative charge of DQ1:160D stabilizes the dimer of dimers, leading to an increased T cell activation. The electrostatic potential surface analysis indicated that the negative charged DR{beta}1:37N encoding alleles could bind with epitope P9 arginine, thus may result in a decreased RA susceptibility.\n\nIn this study, we provide the first evidence that HLA-DQA1, instead of HLA-DRB1, is the strongest and independent genetic risk for ACPA-positive RA in Chinese Han population. Our study also illustrates the value of MHC deep sequencing for fine mapping disease risk variants in the MHC region.

genetics↗

bioRxiv2018

Genetically modified pigs are protected from classical swine fever virus

Classical swine fever (CSF) caused by classical swine fever virus (CSFV) is among the most detrimental diseases, and leads to significant economic losses in the swine industry. Despite efforts by many government authorities try to stamp out the disease from national pig populations, the disease remains widespread. Here, antiviral small hairpin RNAs (shRNAs) were selected and then inserted at the porcine ROSA26 (pROSA26) locus via a CRISPR/Cas9-mediated knock-in strategy. Finally, anti-CSFV transgenic (TG) pigs were produced by somatic nuclear transfer (SCNT). Importantly, in vitro and in vivo viral challenge assays demonstrated that these TG pigs could effectively limit the growth of CSFV and reduced CSFV-associated clinical signs and mortality, and the disease resistance was stably transmitted to F1-generation. The use of these TG pigs can improve the well-being of livestock and substantially reduce virus-related economic losses. Additionally, this antiviral approach may provide a reference for future antiviral research.\n\nAuthor summaryClassical swine fever (CSF), caused by classical swine fever virus (CSFV), and is a highly contagious, often fatal porcine disease with significant economic losses. Due to its economic importance to the pig industry, the biology and pathogenesis of CSFV have been investigated extensively. Despite efforts by many government authorities to stamp out the disease from national pig populations, the disease remains widespread in some regions and seems to be waiting for the reintroduction and the next round of disease outbreaks. These highlight the necessity and urgency of developing more effective approaches to eradicate the challenging CSFV. In this study, we successfully produced anti-CSFV transgenic pigs and confirmed that these transgenic pigs could effectively limit the growth of CSFV in vivo and in vitro and that the disease resistance traits in the TG founders can be stably transmitted to their F1-generation offspring. This study suggests that these TG pigs can improve the well-being of livestock and contribute to offer potential benefits over commercial vaccination. The use of these TG pigs can improve the well-being of livestock and substantially reduce CSFV-related economic losses.

genomics↗

bioRxiv2018

Verified the effectiveness of AsCpf1 system in a variety of vertebrate species

CRISPR/Cpf1 system is a novel genomic editing tool. Because of its more sophisticated components, and lower off-target rate, it has the potential to become a better gene-editing tool. Previous reports showed that CRISPR/Cpf1 could work effectively in multiple species. But our data show that AsCpf1 activity has a big difference in different vertebrates. Using in vitro experiments, we finally learned that the difference between species is due to temperature.

developmental biology↗

bioRxiv2017

Identification of Cancer-associated Metabolic Vulnerabilities by Modeling Multi-objective Optimality in Metabolism

Computational modeling of the genome-wide metabolic network is essential for designing new therapeutics targeting cancer-associated metabolic disorder, which is a hallmark of human malignancies. However, previous studies generally assumed that metabolic fluxes of cancer cells are subjected to the maximization of biomass production, despite the wide existence of trade-offs among multiple metabolic objectives. To address this issue, we developed a multi-objective model of cancer metabolism with algorithms depicting approximate Pareto surfaces and incorporating multiple omics datasets. To validate this approach, we built individualized models for NCI-60 cancer cell lines, and accurately predicted cell growth rates and other biological consequences of metabolic perturbations in these cells. By analyzing the landscape of approximate Pareto surface, we identified a list of metabolic targets essential for cancer cell proliferation and the Warburg effect, and further demonstrated their close association with cancer patient survival. Finally, metabolic targets predicted to be essential for tumor progression were validated by cell-based experiments, confirming this multi-objective modelling method as a novel and effective strategy to identify cancer-associated metabolic vulnerabilities.

systems biology↗

bioRxiv2016

GSKB: A gene set database for pathway analysis in mouse

Interpretation of high-throughput genomics data based on biological pathways constitutes a constant challenge, partly because of the lack of supporting pathway database. In this study, we created a functional genomics knowledgebase in mouse, which includes 33,261 pathways and gene sets compiled from 40 sources such as Gene Ontology, KEGG, GeneSetDB, PANTHER, microRNA and transcription factor target genes, etc. In addition, we also manually collected and curated 8,747 lists of differentially expressed genes from 2,526 published gene expression studies to enable the detection of similarity to previously reported gene expression signatures. These two types of data constitute a Gene Set Knowledgebase (GSKB), which can be readily used by various pathway analysis software such as gene set enrichment analysis (GSEA). Using our knowledgebase, we were able to detect the correct microRNA (miR-29) pathway that was suppressed using antisense oligonucleotides and confirmed its role in inhibiting fibrogenesis, which might involve upregulation of transcription factor SMAD3. The knowledgebase can be queried as a source of published gene lists for further meta-analysis. Through meta-analysis of 56 published gene lists related to retina cells, we revealed two fundamentally different types of gene expression changes. One is related to stress and inflammatory response blamed for causing blindness in many diseases; the other associated with visual perception by normal retina cells. GSKB is available online at http://ge-lab.org/gs/, and also as a Bioconductor package (gskb, https://bioconductor.org/packages/gskb/). This database enables in-depth interpretation of mouse genomics data both in terms of known pathways and the context of thousands of published expression signatures.

bioinformatics↗