Biology subjects

Hong, X.

Publications and source records attributed to Hong, X..

7 recordsLinked to original sources

bioRxiv2018

Tau accumulation activates STAT1 triggering memory deficits via suppressing NMDA receptor expression

Intracellular tau accumulation forming neurofibrillary tangles is hallmark pathology of Alzheimer's disease (AD), but how tau accumulation induces synapse impairment is elusive. By overexpressing human full-length wildtype tau (termed hTau) to mimic tau abnormality as seen in the brain of sporadic AD patients, we found that hTau accumulation activated JAK2 to phosphorylate STAT1 (Signal Transducer and Activator of Transcription 1) at Tyr701 leading to STAT1 dimerization, nuclear translocation and its activation. STAT1 activation suppressed expression of N-methyl-D-aspartate receptors (NMDARs) through direct binding to the specific GAS element of GluN1, GluN2A and GluN2B promoters, while knockdown STAT1 by AAV-Cre in STAT1flox/flox mice or expressing dominant negative Y701F-STAT1 efficiently rescued hTau-induced suppression of NMDARs expression with amelioration of synaptic functions and memory performance. These findings indicate that hTau accumulation impairs synaptic plasticity through JAK2/STAT1-induced suppression of NMDARs expression, revealing a novel mechanism for hTau-associated synapse and memory deficits.

neuroscience↗

bioRxiv2018

Modular microtissues for the regeneration of functional bone in large defects

Repair of complex fractures with bone loss requires a potent, space-filling intervention to promote regeneration of bone. We present a minimally-invasive strategy combining mesenchymal stromal cells (MSC) with a chitosan-collagen matrix to form modular microtissues designed for delivery through a needle to conformally fill cavital defects. Implantation of microtissues into a calvarial defect in the mouse showed that osteogenically pre-differentiated MSC resulted in complete bridging of the cavity, while undifferentiated MSC produced mineralized tissue only in apposition to native bone. Decreasing the implant volume reduced bone regeneration, while increasing the MSC concentration also attenuated bone formation, suggesting that the cell-matrix ratio is important in achieving a robust response. Conformal filling of the defect with microtissues in a carrier gel resulted in complete healing. Taken together, these results show that modular microtissues can be used to augment the differentiated function of MSC and provide an extracellular environment that potentiates bone repair.

bioengineering↗

bioRxiv2018

EnsembleCNV: An ensemble machine learning algorithm to identify and genotype copy number variation using SNP array data

The associations between diseases/traits and copy number variants (CNVs) have not been systematically investigated in genome-wide association studies (GWASs), primarily due to a lack of robust and accurate tools for CNV genotyping. Herein, we propose a novel ensemble learning framework, ensembleCNV, to detect and genotype CNVs using single nucleotide polymorphism (SNP) array data. EnsembleCNV a) identifies and eliminates batch effects at raw data level; b) assembles individual CNV calls into CNV regions (CNVRs) from multiple existing callers with complementary strengths by a heuristic algorithm; c) re-genotypes each CNVR with local likelihood model adjusted by global information across multiple CNVRs; d) refines CNVR boundaries by local correlation structure in copy number intensities; e) provides direct CNV genotyping accompanied with confidence score, directly accessible for downstream quality control and association analysis. Benchmarked on two large datasets, ensembleCNV outperformed competing methods and achieved a high call rate (93.3%) and reproducibility (98.6%), while concurrently achieving high sensitivity by capturing 85% of common CNVs documented in the 1000 Genomes Project. Given this CNV call rate and accuracy, which are comparable to SNP genotyping, we suggest ensembleCNV holds significant promise for performing genome-wide CNV association studies and investigating how CNVs predispose to human diseases.

bioinformatics↗

bioRxiv2018

ADAR1 A-to-I RNA alters codon usage

BackgroundFully grown mammalian oocytes and eggs are transcriptionally quiescent, and therefore have a unique RNA environment in which cellular processes depend on post-transcriptional regulation. RNA editing of adenosines into inosines (A-to-I) by adenosine deaminases acting on RNA (ADARs) is a common post-transcriptional gene regulatory mechanism, yet it has not been systematically studied in oocytes.\n\nResultsA genome-wide RNA editing analysis of transcriptionally active growing oocytes from postnatal day 12 (PND12) mice, fully grown germinal vesicle (GV) oocytes, and transcriptionally quiescent metaphase II (MII) eggs indicates an abundant amount of A-to-I editing of mRNA transcripts. Editing of mRNA was greatest in GV oocyte and MII eggs compared to the PND12 immature oocytes, this was consistent with ADAR1 levels within these cells. Compared to somatic tissues, oocytes exhibited a different pattern of RNA editing, with a high proportion of RNA edits occurring in the coding regions. These edits resulted in nucleotide substitutions that were enriched at the third nucleotide of the codon (wobble position). Codon usage can affect mRNA stability and translation efficiency.\n\nConclusionsRNA editing in mouse oocytes is distinct from RNA editing in somatic cells due to increased frequencies of coding sequence RNA edits. We provide evidence in support of a previously unreported phenomenon of selective ADAR1 editing of the codon wobble position. Editing of the wobble position has the potential to fine tune post-transcriptional gene regulation through altering codon usage. This important observation advances our current understanding of RNA editing in mammalian cells.

developmental biology↗

bioRxiv2018

RMalign: an RNA structural alignment tool based on a size independent scoring function

RNA-protein 3D complex structure prediction is still challenging. Recently, a template-based approach PRIME is proposed in our team to build RNA-protein complex 3D structure models with a higher success rate than computational docking software. However, scoring function of RNA alignment algorithm SARA in PRIME is size-dependent, which limits its ability to detect templates in some cases. Herein, we developed a novel RNA 3D structural alignment approach RMalign, which is based on a size-independent scoring function RMscore. The parameter in RMscore is then optimized in randomly selected RNA pairs and phase transition points (from dissimilar to similar) are determined in another randomly selected RNA pairs. In tRNA benchmarking, the precision of RMscore is higher than that of SARAscore (0.8771 and 0.7766, respectively) with phase transition points. In balance-FSCOR benchmarking, RMalign performed as good as ESA-RNA with a non-normalized score measuring RNA structure similarity. In balance-x-FSCOR benchmarking, RMalign achieves much better than a state-of-the-art RNA 3D structural alignment approach SARA due to a size-independent scoring function. Taking the advantage of RMalign, we update our RNA-protein modeling approach PRIME to version 2.0. The PRIME2.0 significantly improves about 10% success rate than PRIME.\n\nAuthor summaryRNA structures are important for RNA functions. With the increasing of RNA structures in PDB, RNA 3D structure alignment approaches have been developed. However, the scoring function which is used for measuring RNA structural similarity is still length dependent. This shortcoming limits its ability to detect RNA structure templates in modeling RNA structure or RNA-protein 3D complex structure. Thus, we developed a length independent scoring function RMscore to enhance the ability to detect RNA structure homologs. The benchmarking data shows that RMscore can distinct the similar and dissimilar RNA structure effectively. RMscore should be a useful scoring function in modeling RNA structures for the biological community. Based on RMscore, we develop an RNA 3D structure alignment RMalign. In both RNA structure and function classification benchmarking, RMalign obtains as good as or even better performance than the state-of-the-art approaches. With a length independent scoring function RMscore, RMalign should be useful for the modeling RNA structures. Based on above results, we update PRIME to PRIME2.0. We provide a more accurate RNA-protein 3D complex structure modeling tool PRIME2.0 which should be useful for the biological community.

bioinformatics↗

bioRxiv2017

Deep-RBPPred: Predicting RNA binding proteins in the proteome scale based on deep learning

RNA binding protein (RBP) plays an important role in cell processes. Identifying RBPs by computation and experiment are both essential. Recently, RBPPred is proposed in our group to predict RBP with a high performance. However, RBPPred is too slow for that it will generate PSSM matrix as its feature. Herein, we develop a deep learning model called Deep-RBPPred. The model has three advantages comparing to previous models. 1. Deep-RBPPred only needs few physicochemical properties. 2. Deep-RBPPred runs much faster. 3. Deep-RBPPred has a good generalization ability. In the meantime, the performance is still as good as the stats-of-the-art method. In the testing in A. thaliana, S. cerevisiae and H. sapiens proteomics, MCC (AUC) are 0.6077 (0.9421), 0.573 (0.9034) and 0.8141(0.9515) respectively when the score cutoff is set to 0.5. In the verifying in Gerstberger-1538, the SN of our model is 90.38%. The running times are 9s, 7s, 8s and 10s, respectively, for H.sapiens, A.thaliana, S.cerevisiae and Gerstberger-1538 when it is tested in GPU. Deep-RBPPred forecasts 94.65% of 299 new RBP and about 8% higher sensitivity than RBPPred. We also apply deep-RBPPred in 19 eukaryotes proteomics and 11 bacteria proteomics downloaded from Uniprot. The result shows that rate of RBPs in eukaryotes proteome are much higher than bacteria proteome. Testing in 6 proteomics shows the many RBPs may be still undiscovered so far.

bioinformatics↗

bioRxiv2017

Targeting posttranslational modifications of RioK1 inhibits the progression of colorectal and gastric cancers

RioK1 has recently been shown to play important roles in cancers, but its posttranslational regulation is largely unknown. Here we report that RioK1 is methylated at K411 by SETD7 methyltransferase, and that lysine-specific demethylase 1 (LSD1) reverses its methylation. The mutated RioK1 (K411R) that cannot be methylated exhibits a longer half-life than does the methylated RioK1. FBXO6 specifically interacts with K411-methylated RioK1 through its FBA domain to induce RioK1 ubiquitination. Casein kinase 2 (CK2) phosphorylates RioK1 at T410, which stabilizes RioK1 by antagonizing K411 methylation and impeding the recruitment of FBXO6 to RioK1. Functional experiments demonstrate the RioK1 methylation reduces the tumor growth and metastasis in CRC and GC. Importantly, the protein levels of CK2 and LSD1 show an inverse correlation with FBXO6 and SETD7 expression in human CRC tissues. Therefore, this study highlights the importance of a RioK1 methylation-phosphorylation switch in determining CRC and GC development.

cancer biology↗