Biology subjects

Qi, H.

Publications and source records attributed to Qi, H..

60 records · Page 4Linked to original sources

bioRxiv2019

Early post-zygotic mutations contribute to congenital heart disease

BackgroundThe contribution of somatic mosaicism, or genetic mutations arising after oocyte fertilization, to congenital heart disease (CHD) is not well understood. Further, the relationship between mosaicism in blood and cardiovascular tissue has not been determined.\n\nResultsWe developed a computational method, Expectation-Maximization-based detection of Mosaicism (EM-mosaic), to analyze mosaicism in exome sequences of 2530 CHD proband-parent trios. EM-mosaic detected 326 mosaic mutations in blood and/or cardiac tissue DNA. Of the 309 detected in blood DNA, 85/97 (88%) tested were independently confirmed, while 7/17 (41%) candidates of 17 detected in cardiac tissue were confirmed. MosaicHunter detected an additional 64 mosaics, of which 23/46 (50%) among 58 candidates from blood and 4/6 (67%) of 6 candidates from cardiac tissue confirmed. Twenty-five mosaic variants altered CHD-risk genes, affecting 1% of our cohort. Of these 25, 22/22 candidates tested were confirmed. Variants predicted as damaging had higher variant allele fraction than benign variants, suggesting a role in CHD. The frequency of mosaic variants above 10% mosaicism was 0.13/person in blood and 0.14/person in cardiac tissue. Analysis of 66 individuals with matched cardiac tissue available revealed both tissue-specific and shared mosaicism, with shared mosaics generally having higher allele fraction.\n\nConclusionsWe estimate that ~1% of CHD probands have a mosaic variant detectable in blood that could contribute to cardiac malformations, particularly those damaging variants expressed at higher allele fraction compared to benign variants. Although blood is a readily-available DNA source, cardiac tissues analyzed contributed ~5% of somatic mosaic variants identified, indicating the value of tissue mosaicism analyses.

bioinformatics↗

bioRxiv2018

Endoplasmic reticulum stress may activate NLRP3 inflammasomes via TXNIP in preeclampsia

Preeclampsia (PE) development is often associated with placental immune and inflammatory dysregulation, as well as endoplasmic reticulum (ER) stress. However, the mechanisms linking ER stress and inflammatory dysregulation to PE have not been clarified. It has been reported that thioredoxin-interacting protein (TXNIP), which can bind with and activate the NLR family pyrin domain containing 3 (NLRP3) inflammasome, plays a critical role in immune regulation. Recent experimental evidence suggests that activated NLRP3 inflammasomes can activate interleukin-1{beta} (IL-1{beta}) production in the placenta of patients with PE. The objective of the current study was to explore if TXNIP plays a critical signaling role linking ER stress with NLRP3 inflammasome activation in PE. We hypothesised that ER stress would induce TXNIP production, which would bind with NLRP3 inflammasomes to activate IL-1{beta} production. HTR8/SVneo cells were subjected to six hours hypoxia followed by six hours reoxygenation (H/R). These cells showed a higher protein level of NLRP3 and IL-1{beta}, as well as a higher enzymatic activity of caspase-1, indicating enhanced inflammatory dysregulation and ER stress. Cells transfected with TXNIP siRNA showed reduced NLRP3 inflammasome activation. Cells treated with 4-phenylbutyric acid, an inhibitor of ER stress, showed a similar result. In addition, the outgrowth of explant with TXNIP lentivirus in H/R or Tunicamycin (inducers of ER stress) was also measured to verify our hypothesis. These findings demonstrated that TXNIP could influence inflammatory dysregulation by mediating ER stress and NLRP3 inflammasome activation in PE. This novel mechanism may further explain the inflammation observed at the maternal-fetal interface, which leads to placental dysfunction in a patient with PE.

cell biology↗

bioRxiv2018

MVP: predicting pathogenicity of missense variants by deep neural networks

Accurate pathogenicity prediction of missense variants is critical to improve power in genetic studies and accurate interpretation in clinical genetic testing. Here we describe a new prediction method, MVP, which uses a deep learning approach to leverage large training data sets and many correlated predictors. Using cancer mutation hotspots and de novo germline mutations from developmental disorders for benchmarking, MVP achieved better performance in prioritizing pathogenic missense variants than previous methods.

bioinformatics↗

bioRxiv2017

Cell Lysate Microarray for Mapping the Network of Genetic Regulators for Histone Marks

Protein, as the major executer for cell progresses and functions, its abundance and the level of post-translational modifications, are tightly monitored by regulators. Genetic perturbation could help us to understand the relationships between genes and protein functions. Herein, we developed a cell lysate microarray on kilo-conditions (CLICK) from 4,837 yeast knockout (YKO) strains and 322 temperature-sensitive mutant strains to explore the impact of the genome-wide interruption on certain protein. Taking histone marks as examples, a general workflow was established for the global identification of upstream regulators. Through a single CLICK array test, we obtained a series of regulators for H3K4me3 which covers most of the known regulators in Saccharomyces cerevisiae. We also noted that several group of proteins that are linked to negatively regulation of H3K4me3. Further, we discovered that Cab4p and Cab5p, two key enzymes of CoA biosynthesis, play central roles in histone acylation. Because of its general applicability, CLICK array could be easily adopted to rapid and global identification of upstream protein/enzyme(s) that regulate/modify the level of a protein or the posttranslational modification of a non-histone protein.

systems biology↗

bioRxiv2017

Genetic analysis of de novo variants reveals sex differences in complex and isolated congenital diaphragmatic hernia and indicates MYRF as a candidate gene

Congenital diaphragmatic hernia (CDH) is one of the most common and lethal birth defects. Previous studies using exome sequencing support a significant contribution of coding de novo variants in complex CDH cases with additional anomalies and likely gene-disrupting (LGD) variants in isolated CDH cases. To further investigate the genetic architecture of CDH, we performed exome or genome sequencing in 283 proband-parent trios. Combined with data from previous studies, we analyzed a total of 357 trios, including 148 complex and 209 isolated cases. Complex and isolated cases both have a significant burden of deleterious de novo coding variants (1.7~fold, p= 1.2x10-5 for complex, 1.5~fold, p= 9.0x10-5 for isolated). Strikingly, in isolated CDH, almost all of the burden is carried by female cases (2.1~fold, p=0.004 for likely gene disrupting and 1.8~fold, p= 0.0008 for damaging missense variants); whereas in complex CDH, the burden is similar in females and males. Additionally, de novo LGD variants in complex cases are mostly enriched in genes highly expressed in developing diaphragm, but distributed in genes with a broad range of expression levels in isolated cases. Finally, we identified a new candidate risk gene MYRF (4 de novo variants, p-value=2x10-10), a transcription factor intolerant of mutations. Patients with MYRF mutations have additional anomalies including congenital heart disease and genitourinary defects, likely representing a novel syndrome.

genomics↗

bioRxiv2017

Contrasting determinants of mutation rates in germline and soma

Recent studies of somatic and germline mutations have led to the identification of a number of factors that influence point mutation rates, including CpG methylation, expression levels, replication timing and GC content. Intriguingly, some of the effects appear to differ between soma and germline: in particular, whereas mutation rates have been reported to decrease with expression levels in tumors, no clear effect has been detected in the germline. Distinct approaches were taken to analyze the data, however, so it is hard to know whether these apparent differences are real. To enable a cleaner comparison, we considered a statistical model in which the mutation rate of a coding region is predicted by GC content, expression levels, replication timing, and two histone repressive marks. We applied this model to both a set of germline mutations identified in exomes and to exonic somatic mutations in four types of tumors. Germline and soma share most determinants of mutations; notably, we detected an effect of expression levels on germline mutations as well as on somatic ones. However, whereas in somatic tissues, increased expression levels are associated with greater strand asymmetry and decreased mutation rates, in ovaries and testes, increased expression leads to greater strand asymmetry but increased mutation rates. This contrast points to differences in damage or repair rates during transcription in soma and germline.

evolutionary biology↗