Biology subjects

Liao, H.

Publications and source records attributed to Liao, H..

45 records · Page 3Linked to original sources

bioRxiv2020

Promoter keyholes enable specific and persistent multi-gene expression programs in primary T cells without genome modification

Human genes are regulated quantitatively, yet the ability to specify the expression level of a native gene accurately and specifically using a defined reagent has remained elusive. Here we show that precise targeting of KRAB repressive domain within regulatory DNA unlocks an endogenous quantitative dial that can be engaged at nucleotide resolution to program expression levels across a wide physiologic range, with single-gene specificity and high reproducibly in primary cells.

synthetic biology↗

bioRxiv2019

Integrated Genomic Analysis Reveals Key Features of Long Undecoded Transcript Isoform (LUTI)-based Gene Repression

Long Undecoded Transcript Isoforms (LUTIs) represent a class of non-canonical mRNAs that downregulate gene expression through the combined act of transcriptional and translational repression. While single gene studies revealed some important aspects of LUTI-based repression, how these features impact gene regulation at a global scale is unknown. By using transcript leader and direct RNA sequencing, here we identify 74 LUTI candidates that are expressed specifically during meiotic prophase. Translational repression of these candidates is ubiquitous and dependent on upstream open reading frames. However, LUTI-based transcriptional repression is highly variable. In only 50% of the cases, LUTI transcription causes downregulation of the protein-coding transcript isoform. Higher LUTI expression, enrichment of histone 3 lysine 36 trimethylation, and changes in nucleosome position are the strongest predictors of LUTI-based transcriptional repression. We conclude that LUTIs downregulate gene expression in a manner that integrates translational repression, chromatin state changes, and the magnitude of LUTI expression.

molecular biology↗

bioRxiv2019

Aurora B-dependent Ndc80 Degradation Regulates Kinetochore Composition in Meiosis

The kinetochore complex is a conserved machinery that connects chromosomes to spindle microtubules. During meiosis, the kinetochore is restructured to accommodate a specialized chromosome segregation pattern. In budding yeast, meiotic kinetochore remodeling is mediated by the temporal changes in the abundance of a single subunit called Ndc80. We have previously described the regulatory events that control the timely synthesis of Ndc80. Here, we report that Ndc80 turnover is also tightly regulated in meiosis: Ndc80 degradation is active in meiotic prophase, but not in metaphase I. Ndc80 degradation depends on the ubiquitin ligase APCAma1 and is mediated by the proteasome. Importantly, Aurora B-dependent Ndc80 phosphorylation, a mark that has been previously implicated in correcting erroneous microtubule-kinetochore attachments, is essential for Ndc80 degradation in a microtubule-independent manner. The N-terminus of Ndc80, including a 27-residue sequence and Aurora B phosphorylation sites, is both necessary and sufficient for kinetochore protein degradation. Finally, defects in Ndc80 turnover predispose meiotic cells to chromosome mis-segregation. Our study elucidates the mechanism by which meiotic cells modulate their kinetochore composition through regulated Ndc80 degradation, and demonstrates that Aurora B-dependent regulation of kinetochores extends beyond altering microtubule attachments.

genetics↗

bioRxiv2019

GmPTF1 Modifies Root Architecture Responses to Phosphate Starvation in Soybean

Though root architecture modifications may be critically important for improving phosphorus (P) efficiency in crops, the regulatory mechanisms triggering these changes remain unclear. In this study, we demonstrate that genotypic variation in GmEXPB2 expression is strongly correlated with root elongation and P acquisition efficiency, and enhancing its transcription significantly improves soybean yield in the field. Promoter deletion analysis was performed using six 5 truncation fragments (P1-P6) of GmEXPB2 fused with the GUS reporter gene in transgenic hairy roots, which revealed that the P1 segment containing 3 E-box elements significantly enhances induction of gene expression in response to phosphate (Pi) starvation. Further experimentation demonstrated that GmPTF1, a bHLH transcription factor, is the regulatory factor responsible for the induction of GmEXPB2 expression in response to Pi starvation. In short, Pi starvation induced expression of GmPTF1, with the GmPTF1 product not only directly binding the E-box motif in the P1 region of the GmEXPB2 promoter, but also activating GUS expression in a dosage dependent manner. Further work with soybean transgenic composite plants showed that, altering GmPTF1 expression significantly impacted GmEXPB2 transcription, and thereby affected root growth, biomass and P uptake. Taken together, this work identifies a novel regulatory factor, GmPTF1, involved in changing soybean root architecture through regulation the expression of GmEXPB2. These findings contribute to understanding the molecular basis of root architecture modifications in response to P deficiency, and, in the process, suggest candidate genes and a promoter region to target for improving soybean yield through molecular breeding of P efficiency. One Sentence SummaryThe bHLH transcription factor GmPTF1 regulates the expression of {beta}-expansin gene GmEXPB2 to modify root architecture, and thus promote phosphate acquisition, and biomass in soybean.

physiology↗

bioRxiv2019

Single Volume Image Generator and Deep Learning-based ASD Classification

Autism spectrum disorder (ASD) is an intricate neuropsychiatric brain disorder characterized by social deficits and repetitive behaviors. Associated ASD biomarkers can be supportive of apprehending the underlying roots of the disease and lead the targeted diagnosis as well as treatment. Although deep learning approaches have been applied in functional magnetic resonance imaging (fMRI) based clinical or behavioral identification of ASD, most erstwhile models are inadequate in their capacity to exploit the data richness. Classification techniques often solely rely on region-based summary and/or functional connectivity analysis of one pipeline or unique site dataset. Besides these, biomedical data modeling to analyze big data related to ASD is still perplexing due to its complexity and heterogeneity. Single volume image consideration has not been previously investigated in classification purposes. By deeming these challenges, in this work, firstly, we design an image generator to generate single volume brain images from the whole-brain image of each subject. Secondly, the single volume images are analyzed by evaluating four deep learning approaches comprising one amended volume base Convolutional Neural Network framework to classify ASD and typical control participants. Thirdly, we propose a novel deep ensemble learning classifier using VGG16 as feature extractor to ensure further classification performance. Then, to evaluate the classifier performance across the inter sites, we apply the proposed method on each site individually and validate our findings by comparing literature reports. We showcase our approaches on large-scale multi-site brain imaging dataset (ABIDE) by considering four preprocessing pipelines, and the outcome demonstrates the state-of-the-art performance compared with the literature findings; hence, which are robust and consistent.

neuroscience↗

bioRxiv2019

Srrm234, but not canonical SR and hnRNP proteins drive inclusion of Dscam exon 9 variable exons

Alternative splicing of pre-mRNA is a major mechanism to diversify protein functionality in metazoans from a limited number of genes. In the Drosophila melanogaster Down Syndrome Cell Adhesion Molecule (Dscam) important for neuronal wiring up to 38,016 isoforms can be generated by mutually exclusive alternative splicing in four clusters of variable exons. However, it is not understood how a specific exon is chosen from the many variables and how variable exons are prevented from being spliced together. A main role in the regulation of Dscam alternative splicing has been attributed to RNA binding proteins, but how they impact on exon selection is not well understood. Serine-arginine-rich (SR) proteins and hnRNP proteins are the two main types of RNA binding proteins with major roles in exon definition and splice site selection. Here, we analyzed the role of SR and hnRNP proteins in Dscam exon 9 alternative splicing in mutant Drosophila melanogaster embryos because of their essential function for development. Strikingly, loss or overexpression of canonical SR and hnRNP proteins even when multiple proteins are depleted together, does not affect Dscam alternative exon selection very dramatically. Conversely, non-canonical SR protein Serine-arginine repetitive matrix 2/3/4 (Srrm234) is a main determinant of exon inclusion in Dscam exon 9 cluster. Since long-range base-pairings are absent in the exon 9 cluster, our data argue for a small complement of regulatory factors as main determinants of exon inclusion in the Dscam exon 9 cluster.

molecular biology↗

bioRxiv2018

Large-scale neuroimaging and genetic study reveals genetic architecture of brain white matter microstructure

Microstructural changes of white matter (WM) tracts are known to be associated with various neuropsychiatric disorders/diseases. Heritability of structural changes of WM tracts has been examined using diffusion tensor imaging (DTI) in family-based studies for different age groups. The availability of genetic and DTI data from recent large population-based studies offers opportunity to further improve our understanding of genetic contributions. Here, we analyzed the genetic architecture of WM tracts using DTI and single-nucleotide polymorphism (SNP) data of unrelated individuals in the UK Biobank (n [~] 8000). The DTI parameters were generated using the ENIGMA-DTI pipeline. We found that DTI parameters are substantially heritable on most WM tracts. We observed a highly polygenic or omnigenic architecture of genetic influence across the genome as well as the enrichment of SNPs in active chromatin regions. Our bivariate analyses showed strong genetic correlations for several pairs of WM tracts as well as pairs of DTI parameters. We performed voxel-based analysis to illustrate the pattern of genetic effects on selected parts of the tract-based spatial statistics skeleton. Comparing the estimates from the UK Biobank to those from small population-based studies, we illustrated that sufficiently large sample size is essential for genetic architecture discovery in imaging genetics. We confirmed this finding with a simulation study.

genetics↗

bioRxiv2017

Heritability of regional brain volumes in large-scale neuroimaging and genetic studies

Brain genetics is an active research area. The degree to which genetic variants impact variations in brain structure and function remains largely unknown. We examined the heritability of regional brain volumes (p ~ 100) captured by single-nucleotide polymorphisms (SNPs) in UK Biobank (n ~ 9000). We found that regional brain volumes are highly heritable in this study population. We observed omni-genic impact across the genome as well as enrichment of SNPs in active chromatin regions. Principal components derived from regional volume data are also highly heritable, but the amount of variance in brain volume explained by the component did not seem to be related to its heritability. Heritability estimates vary substantially across large-scale functional networks and brain regions. The variation in heritability across regions was not related to measurement reliability. Heritability estimates exhibit a symmetric pattern across left and right hemispheres and are consistent in females and males. Our main findings in UK Biobank are consistent with those in Alzheimers Disease Neuroimaging Initiative (n ~ 1100), Philadelphia Neurodevelopmental Cohort (n ~ 600), and Pediatric Imaging, Neurocognition, and Genetics (n ~ 500) datasets, with more stable estimates in UK Biobank.

genetics↗

bioRxiv2017

Transcriptome analysis of the quantitative distribution of the Cyrtotrachelus buqueti population in two cities in China

Background: Cyrtotrachelus buqueti is a forest pest that severely damages bamboo shoots. Reducing the population of this insect involves complex mechanisms and is dependent on diverse gene expression influenced by environmental factors.\n\nMethods: In this study, samples from two regions of China, Muchuan in Sichuan Province and Chishui in Guizhou Province, were investigated through RNA-seq to explore the causes and molecular mechanisms underlying the population reduction of this species. Environmental factors, such as temperature, heavy metal content, and pH, may affect the reduced population of C. buqueti in Chishui.\n\nResults: Approximately 44 million high-quality reads were generated, and 94.2% of the data were mapped to the transcriptome. A total of 15,641 out of the 29,406 identified genes were predicted. Moreover, 348 genes were differentially expressed between the two groups of imagoes and included 77 upregulated and 271 downregulated UniGenes. The functional analysis showed that these genes were significantly enriched in ribosome and metabolic pathway categories. The candidate genes, which contributed to C. Buqueti reduction, included 41 genes involved in the ribosome constitution category, five genes in the one-carbon pool pathway by folate category, and five heat shock protein genes.\n\nConclusions: Downregulation of these candidate genes seems to have impaired metabolic processes, such as protein, DNA, RNA, and purine synthesis, as well as carbon and folate metabolism, and finally resulted in the observed reduced population of C. buqueti. Furthermore, temperature, heavy metal content, and pH might influence the population by altering the expressions of genes involved in these metabolic processes.

genomics↗