Biology subjects

Sun, W.

Publications and source records attributed to Sun, W..

14 recordsLinked to original sources

bioRxiv2018

Genome-wide DNA methylation profiling identifies convergent molecular signatures associated with idiopathic and syndromic forms of autism in postmortem human brain tissue.

Autism spectrum disorder (ASD) encompasses a collection of complex neuropsychiatric disorders characterized by deficits in social functioning, communication and repetitive behavior. Building on recent studies supporting a role for developmentally moderated regulatory genomic variation in the molecular etiology of ASD, we quantified genome-wide patterns of DNA methylation in 233 post-mortem tissues samples isolated from three brain regions (prefrontal cortex, temporal cortex and cerebellum) dissected from 43 ASD patients and 38 non-psychiatric control donors. We identified widespread differences in DNA methylation associated with idiopathic ASD (iASD), with consistent signals in both cortical regions that were distinct to those observed in the cerebellum. Individuals carrying a duplication on chromosome 15q (dup15q), representing a genetically-defined subtype of ASD, were characterized by striking differences in DNA methylation across a discrete domain spanning an imprinted gene cluster within the duplicated region. In addition to the dramatic cis-effects on DNA methylation observed in dup15q carriers, we identified convergent methylomic signatures associated with both iASD and dup15q, reflecting the findings from previous studies of gene expression and H3K27ac. Cortical co-methylation network analysis identified a number of co-methylated modules significantly associated with ASD that are enriched for genomic regions annotated to genes involved in the immune system, synaptic signalling and neuronal regulation. Our study represents the first systematic analysis of DNA methylation associated with ASD across multiple brain regions, providing novel evidence for convergent molecular signatures associated with both idiopathic and syndromic autism.

genomics↗

bioRxiv2018

Application of clinical genomic sequencing among Chinese advanced cancer patients to guide precision medicine decisions

PurposeA number of studies have suggested that high-throughput genomic analyses might improve the outcomes of cancer patients. However, whether integrative information about genomic sequencing and related clinical interpretation may benefit Chinese cancer patients with stage IV disease to date has not investigated.\n\nMethodsTargeted gene panel and whole exome of tumor/blood samples in > 1,000 Chinese cancer patients were sequenced. Then we provided patients and their oncologists with the sequencing results and a clinical recommendation roadmap based on evidence-based medicine, defined as CWES. Only patients with stage IV disease who failed the previous treatment upon receiving the CWES reports were included for analyzing the impact of CWES on clinical outcomes in 1-year follow-ups.\n\nResultsWe identified the mutational signatures of 953 Chinese cancer patients, with some being unique. Approximately 88.6% of patients had clinically actionable somatic genomic alterations. We successfully followed up 22 stage IV patients. Of these, 11 patients treatment followed the CWES reports defined as group A. Eleven patients received the next treatment, but did not follow the CWES suggestions, and are defined as group B. The types of therapies before CWES were similar in the two groups. The median PFS of group A was 12 months and 45% patients failed this round of therapy. The median PFS of group B was 4 months and 91% of patients failed the treatment.\n\nConclusionThe current study suggested that CWES has the potential to help explore the clinical benefits in multiple line therapies among advanced stage tumor patients.

cancer biology↗

bioRxiv2018

Comprehensive analysis of potential immunotherapy genomic biomarkers in 1,000 Chinese patients with cancer

BackgroundTumor mutation burden (TMB), DNA mismatch repair deficiency (dMMR), microsatellite instability (MSI), and PD-L1 amplification (PD-L1 AMP) may predict the efficacy of PD-1/PD-L1 blockade. In this study, we aimed to characterize the distributions of these biomarkers in over 1,000 Chinese patients with cancer.\n\nMethodsTMB, MSI, dMMR, and PD-L1 AMP were determined based on whole-exome sequencing of tumor/blood samples from > 1,000 Chinese patients with cancer.\n\nResultsIncidence rates among 953 Chinese patients with cancer showing high TMB (TMB-H), high MSI (MSI-H), dMMR and PD-L1 AMP were 35%, 4%, 0.53% and 3.79%, respectively. We found higher rates of TMB-H among hepatocellular carcinoma, breast cancer, and esophageal cancer patients than was reported for The Cancer Genome Atlas data. Lung cancer patients with EGFR mutations had significantly lower TMB values than those with wild-type EGFR, and increased TMB was significantly associated with dMMR in colorectal cancer (CRC). The frequency of tumors with MSI-H was highest in CRC (14%) and gastric cancer (4%). PD-L1 AMP occurred most frequently in lung squamous cell carcinoma (14.3%) and HER2-positive breast cancer (8.8%). Most MSI-H and dMMR cases exhibited TMB-H, but the overlap among the other biomarkers was low.\n\nConclusionWhile MSI and dMMR are associated with higher mutational loads, correlations between TMB-H and other biomarkers, between MSI-H and dMMR, and between PD-L1 AMP and other biomarkers were low, indicating different underlying causes of the four biomarkers. Thus, it is recommended that all four biomarkers be assessed for certain cancers before administration of PD-1/PD-L1 blockade treatment.

cancer biology↗

bioRxiv2018

Can we obtain in vivo transmural mean hoop stress of the aortic wall without knowing patient-specific material properties and residual deformations?

It is well known that residual deformations/stresses alter the mechanical behavior of arteries, e.g. the pressure-diameter curves. In an effort to enable personalized analysis of the aortic wall stress, approaches have been developed to incorporate experimentally-derived residual deformations into in vivo loaded geometries in finite element simulations using thick-walled models. Solid elements are typically used to account for \"bending-like\" residual deformations. Yet, the difficulty in obtaining patient-specific residual deformations and material properties has become one of the biggest challenges of these thick-walled models. In thin-walled models, fortunately, static determinacy offers an appealing prospect that allows for the calculation of the thin-walled membrane stress without patient-specific material properties. The membrane stress can be computed using forward analysis by enforcing an extremely stiff material property as penalty treatment, which is referred to as the forward penalty approach. However, thin-walled membrane elements, which have zero bending stiffness, are incompatible with the residual deformations, and therefore, it is often stated as a limitation of thin-walled models. In this paper, by comparing the predicted stresses from thin-walled models and thick-walled models, we demonstrate that the transmural mean hoop stress is the same for the two models and can be readily obtained from in vivo clinical images without knowing the patient-specific material properties and residual deformations. Computation of patient-specific mean hoop stress can be greatly simplified by using membrane model and the forward penalty approach, which may be clinically valuable.

bioengineering↗

bioRxiv2018

Estimation of in vivo constitutive parameters of the aortic wall: a machine learning approach

The patient-specific biomechanical analysis of the aorta demands the in vivo mechanical properties of individual patients. Current inverse approaches have shown the feasibility of estimating the nonlinear, anisotropic material parameters from in vivo image data using certain optimization schemes. However, since such inverse methods are dependent on iterative nonlinear optimization, these methods are highly computation-intensive, which may take weeks to complete for only a single patient, inhibiting rapid feedback for clinical use. Recently, machine learning (ML) techniques have led to revolutionary breakthroughs in many applications. A potential paradigm-changing solution to the bottleneck associated with patient-specific computational modeling is to incorporate ML algorithms to expedite the procedure of in vivo material parameter identification. In this paper, we developed a ML-based approach to identify the material parameters from three-dimensional aorta geometries obtained at two different blood pressure levels, namely systolic and diastolic geometries. The nonlinear relationship between the two loaded shapes and the constitutive parameters are established by a ML-model, which was trained and tested using finite element (FE) simulation datasets. Cross-validation was used to adjust the ML-model structure on a training/validation dataset. The accuracy of the ML-model was examined using a testing dataset.

bioengineering↗

bioRxiv2018

Recovered and dead outcome patients caused by influenza A (H7N9) virus infection show different pro-inflammatory cytokine dynamics during disease progress and its application in real-time prognosis

The persistent circulation of influenza A(H7N9) virus within poultry markets and human society leads to sporadic epidemics of influenza infections. Severe pneumonia and acute respiratory distress syndrome (ARDS) caused by the virus lead to high morbidity and mortality rates in patients. Hyper induction of pro-inflammatory cytokines, which is known as \"cytokine storm\", is closely related to the process of viral infection. However, systemic analyses of H7N9 induced cytokine storm and its relationship with disease progress need further illuminated. In our study we collected 75 samples from 24 clinically confirmed H7N9-infected patients at different time points after hospitalization. Those samples were divided into three groups, which were mild, severe and fatal groups, according to disease severity and final outcome. Human cytokine antibody array was performed to demonstrate the dynamic profile of 80 cytokines and chemokines. By comparison among different prognosis groups and time series, we provide a more comprehensive insight into the hypercytokinemia caused by H7N9 influenza virus infection. Different dynamic changes of cytokines/chemokines were observed in H7N9 infected patients with different severity. Further, 33 cytokines or chemokines were found to be correlated with disease development and 11 of them were identified as potential therapeutic targets. Immuno-modulate the cytokine levels of IL-8, IL-10, BLC, MIP-3a, MCP-1, HGF, OPG, OPN, ENA-78, MDC and TGF-{beta} 3 are supposed to be beneficial in curing H7N9 infected patients. Apart from the identification of 35 independent predictors for H7N9 prognosis, we further established a real-time prediction model with multi-cytokine factors for the first time based on maximal relevance minimal redundancy method, and this model was proved to be powerful in predicting whether the H7N9 infection was severe or fatal. It exhibited promising application in prognosing the outcome of a H7N9 infected patients and thus help doctors take effective treatment strategies accordingly.

immunology↗

bioRxiv2018

ICeD-T Provides Accurate Estimates of Immune Cell Abundance in Tumor Samples by Allowing For Aberrant Gene Expression Patterns

Immunotherapies have achieved phenomenal success in the treatment of cancer and promise even more breakthroughs in the near future. The need to understand the underlying mechanisms of immunotherapies and to develop precision immunotherapy regimens has spurred great interest in characterizing immune cell composition within the tumor microenvironment. Several methods have been developed to estimate immune cell composition using gene expression data from bulk tumor samples. However, these methods are not flexible enough to handle aberrant patterns of gene expression data, e.g., inconsistent cell type-specific gene expression between purified reference samples and this cell type in tumor samples. In this paper, we present a novel statistical model for expression deconvolution called ICeD-T (Immune Cell Deconvolution in Tumor tissues), which models gene expression by a log-normal distribution that is appropriate for both microarray and RNA-seq data. ICeD-T automatically identifies aberrant genes whose expressions are inconsistent with the deconvolution model and down-weights their contributions to cell type abundance estimates. We evaluated the performance of ICeD-T versus existing methods in simulation studies and several real data analyses. ICeD-T displayed comparable or superior performance to these competing methods. Applying these methods to assess the relationship between immunotherapy response and immune cell composition, ICeD-T is able to identify significant associations that are missed by its competitors.

genomics↗

bioRxiv2017

Unconsciously Implanted Visuoauditory Memory in the Presence of Cholecystokinin Retrieved in Behavioral Contexts

We investigated whether visuoauditory association can be artificially implanted in rodents and then retrieved in a behaviorally relevant context. Rats were trained to approach the left or right hole of a behavioral apparatus to retrieve a reward depending on the side of electrical stimulation of the auditory cortex (EAC) they received and mice were fear-conditioned to EAC. Next, an irrelevant visual stimulus (VS) was repeatedly paired with EAC in the presence of cholecystokinin (CCK) or with activation of terminals of entorhinal CCK neurons in the auditory cortex. In subsequent behavioral testing with VS, rats approached the hole associated with reward availability and mice showed a freezing response to the VS. A CCK antagonist blocked the establishment of visuoauditory association, whereas a CCK agonist rescued the deficit of association. Our findings provide a scientific foundation for \"memory implantation\" and indicate that CCK is the switching chemical for formation of visuoauditory association.

neuroscience↗

bioRxiv2017

Cholecystokinin release triggered by presynaptic NMDA receptors produces LTP and sound-sound associative memory formation

Memory is stored in neural networks via changes in synaptic strength mediated in part by NMDA-dependent long-term potentiation (LTP). There is evidence that entorhinal cortex enables neocortical neuroplasticity through cholecystokinin (CCK)-containing neocortical projections. Here we show that a CCKB antagonist blocks high-frequency stimulation (HFS)-induced LTP in the auditory cortex, whereas local infusion of CCK induces LTP. CCK-/- mice lacked neocortical LTP and showed deficits in a cue-cue associative learning paradigm; administration of CCK rescued associative learning. HFS of CCK-containing entorhino-neocortical projection neurons in anesthetized mice enabled cue-cue associative learning. Furthermore, when one cue was pre-conditioned to footshock, the mouse showed a freezing response to the other cue, indicating that the mice had formed an association. HFS-induced neocortical LTP was completely blocked by either NMDA antagonist or CCK-BR antagonist, while application of either NMDA or CCK induced LTP after low-frequency stimulation (LFS). Moreover, in the presence of CCK LTP was still induced, even after blockade of NMDA receptors. Local application of NMDA induced CCK release in the neocortex. To identify how NMDA receptor switches LTP, a stimulation protocol of 25 pulse-pairs was adopted to replace HFS; NMDA-dependent LTP was induced with the inter-pulse intervals between 10 and 100 ms, but not with those of 5 and 200 ms. LTP-mediated plasticity was linked to localization of the NMDA receptor subunit NR2a on cortical CCK terminals originating in the entorhinal cortex. These novel findings suggest that presynaptic NMDA receptors on CCK terminals control the release of CCK, which enables neocortical LTP and formation of cue-cue associative memory.\n\nOne Sentence SummaryPresynaptic NMDA receptors switches the release of CCK from entorhinal neurons, which enables neocortical LTP and formation of sound-sound associative memory.

neuroscience↗

bioRxiv2017

A Tetratricopeptide Repeat Protein Regulates Carotenoid Biosynthesis and Chromoplast Development in Monkeyflowers (Mimulus)

The incredible diversity of floral color and pattern in nature is largely determined by the transcriptional regulation of anthocyanin and carotenoid biosynthetic genes. While the transcriptional control of anthocyanin biosynthesis is well understood, little is known about the factors regulating the carotenoid biosynthetic pathway in flowers. Here, we characterize the Reduced Carotenoid Pigmentation 2 (RCP2) locus from two monkeyflower (Mimulus) species, the bumblebee-pollinated M. lewisii and hummingbird-pollinated M. verbenaceus. We show that loss-of-function mutations of RCP2 cause drastic down-regulation of the entire carotenoid biosynthetic pathway in these species. Through bulk segregant analysis and transgenic experiments, we have identified the causal gene underlying RCP2, encoding a tetratricopeptide repeat (TPR) protein that is closely related to the Arabidopsis Reduced Chloroplast Coverage (REC) proteins. RCP2 appears to regulate carotenoid biosynthesis independently of RCP1, a previously identified R2R3-MYB master regulator of carotenoid biosynthesis. We show that RCP2 is required for chromoplast development and suggest that it most likely regulates the expression of carotenoid biosynthetic genes through chromoplast-to-nucleus retrograde signaling. Furthermore, we demonstrate that M. verbenaceus is just as amenable to chemical mutagenesis and in planta transformation as the more extensively studied M. lewisii, making these two species an excellent platform for comparative developmental genetics studies of two closely related species with dramatic phenotypic divergence.

genetics↗

bioRxiv2017

A Deep Learning Approach to Estimate Collagenous Tissue Nonlinear Anisotropic Stress-Strain Responses from Microscopy Images

Biological collagenous tissues comprised of networks of collagen fibers are suitable for a broad spectrum of medical applications owing to their attractive mechanical properties. In this study, we developed a noninvasive approach to estimate collagenous tissue elastic properties directly from microscopy images using Machine Learning (ML) techniques. Glutaraldehyde-treated bovine pericardium (GLBP) tissue, widely used in the fabrication of bioprosthetic heart valves and vascular patches, was chosen as a representative collagenous tissue. A Deep Learning model was designed and trained to process second harmonic generation (SHG) images of collagen networks in GLBP tissue samples, and directly predict the tissue elastic mechanical properties. The trained model is capable of identifying the overall tissue stiffness with a classification accuracy of 84%, and predicting the nonlinear anisotropic stress-strain curves with average regression errors of 0.021 and 0.031. Thus, this study demonstrates the feasibility and great potential of using the Deep Learning approach for fast and noninvasive assessment of collagenous tissue elastic properties from microstructural images.

bioengineering↗

bioRxiv2017

Mapping Tumor-Specific Expression QTLs In Impure Tumor Samples

The study of gene expression quantitative trait loci (eQTL) is an effective approach to illuminate the functional roles of genetic variants. Computational methods have been developed for eQTL mapping using gene expression data from microarray or RNA-seq technology. Application of these methods for eQTL mapping in tumor tissues is problematic because tumor tissues are composed of both tumor and infiltrating normal cells (e.g. immune cells) and eQTL effects may vary between tumor and infiltrating normal cells. To address this challenge, we have developed a new method for eQTL mapping using RNA-seq data from tumor samples. Our method separately estimates the eQTL effects in tumor and infiltrating normal cells using both total expression and allele-specific expression (ASE). We demonstrate that our method controls type I error rate and has higher power than some alternative approaches. We applied our method to study RNA-seq data from The Cancer Genome Atlas and illustrated the similarities and differences of eQTL effects in tumor and normal cells.

genomics↗

bioRxiv2017

mRNA And Long Non-Coding RNA Expression Profiles In Rats Reveal Inflammatory Features In Sepsis-Associated Encephalopathy

BackgroundSepsis-associated encephalopathy (SAE) is related to cognitive sequelae in patients in the intensive care unit (ICU) and can have serious impacts on quality of life after recovery. Although various pathogenic pathways are involved in SAE development, little is known concerning the global role of long non-coding RNAs (lncRNAs) in SAE.\n\nMethodsHerein, we employed transcriptome sequencing approaches to characterize the effects of lipopolysaccharide (LPS) on lncRNA expression patterns in brain tissue isolated from Sprague-Dawley (SD) rats with and without SAE. We performed high-throughput transcriptome sequencing after LPS was intraperitoneally injected and predicted targets and functions using bioinformatics tools. Subsequently, we explored the results in detail according to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses.\n\nResultsLncRNAs were differentially expressed in brain tissue after LPS treatment. After 6 h of LPS exposure, expression of 400 lncRNAs were significantly changed, including an increase in 316 lncRNAs and a decrease in 84 lncRNAs. In addition, 155 mRNAs were differentially expressed, with 84 up-regulated and 71 down-regulated. At 24 h post-treatment, expression of 117 lncRNAs and 57 mRNAs was consistently elevated, while expression of 79 lncRNAs and 21 mRNAs was decreased (change > 1.5-fold; p < 0.05). We demonstrated for the first time that differentially expressed lncRNAs were predicted to be enriched in a post-chaperonin tubulin folding pathway (GO : 007023), which is closely related to the key step in the tubulin folding process.\n\nInterestingly, the predicted pathway (KEGG 04360: axon guidance) was significantly changed under the same conditions. These results reveal that LPS might influence the construction and polarization of microtubules, which exert predominant roles in synaptogenesis and related biofunctions in the rodent central nervous system (CNS).\n\nConclusionsAn inventory of LPS-modulated expression profiles from the rodent CNS is an important step toward understanding the function of mRNAs, including lncRNAs, and suggests that microtubule malformation and dysfunction may be involved in SAE pathogenesis.

genomics↗

bioRxiv2017

Research on Mitochondrial DNA Mutations in Patients with SCA3/MJD

Spinocerebellar ataxia type 3 (SCA3) is a degenerative neurological disorders caused by trinucleotide repeat expansion within the ataxin-3 gene. It is characterized by multi-system involvement and diverse clinical phenotypes, which cannot be fully explained the length of the CAG repeats. One possible explanation for the phenotypic heterogeneity could be the presence of mitochondrial DNA mutations that modify disease severity. To explore the role of Mitochondrial DNA(mtDNA) variations in SCA3 pathogenesis, we analyzed polymorphisms of six mitochondrial genes, MT-LT1, MT-ND1, MT-CO2, MT-TK, MT-ATP8 and MT-ATP6, in 102 unrelated SCA3/MJD patients and 100 healthy controls. The results showed that there were 24 variations of those mtDNA genes in the SCA3 patients and only 10 in the unrelated healthy controls. There was no difference of the relative mtDNA copy number variation between the SCA3 patients and healthy controls (93.20 vs. 89.66, P>0.05). In the group of SCA3 patients, the relative mtDNA copy number showed a negative correlation between the number of CAG repeats (r=-0.210, P < 0.05), but did not correlate with the age at diagnosis, the age of onset, disease duration, ICARS scores and SARA scores. Our research demonstrated that the frequency of mutated mtDNA in SCA3 patients was higher than that in the healthy group. The mtDNA relative copy number in SCA3 patients was not significantly different compared to the healthy group. Thus, the copy number might not be treated as a biomedical indicator when measuring the severity of illness in SCA3 patients.

genetics↗