Biology subjects

Zhong, X.

Publications and source records attributed to Zhong, X..

At least 91 records · Page 5Linked to original sources

bioRxiv2020

Rapid and efficient in vivo astrocyte-to-neuron conversion with regional identity and connectivity?

In vivo reprogramming of glia into functional neurons emerges as potential regeneration-based therapeutics for neural injuries or neurological diseases. Recent studies show that AAV-based manipulation of certain factors can rapidly and highly efficiently convert resident glia into functional neurons with brain region-specificity and precise connectivity. Using NEUROD1 as an example, we here show that the presumed astrocytes-converted neurons are essentially endogenous neurons in the adult mouse brain. AAV-mediated co-expression of NEUROD1 and a reporter indeed specifically, rapidly, and efficiently induces numerous reporter-labeled neurons. However, these neurons cannot be traced back to quiescent or reactive astrocytes by using stringent lineage-mapping strategies. Conversely, reporter-labeled neurons cannot be detected when NEUROD1 is strictly expressed in adult brain astrocytes. Through a retrograde labeling approach, our results rather reveal that endogenous neurons are the cell source for NEUROD1-induced reporter-labeled neurons. These results underline the indispensable value of stringent lineage-tracing strategies and beg for cautious interpretation of the in vivo reprogramming phenomena.

neuroscience↗

bioRxiv2020

Integration of genetic, transcriptomic, and clinical data provides insight into 16p11.2 and 22q11.2 CNV genes

Deletions and duplications of the multigenic 16p11.2 and 22q11.2 copy number variants (CNVs) are associated with brain-related disorders including schizophrenia, intellectual disability, obesity, bipolar disorder, and autism spectrum disorder (ASD). The contribution of individual CNV genes to each of these phenotypes is unknown, as is the contribution of CNV genes to subtler health impacts. Hypothesizing that DNA copy number acts via RNA expression, we attempted a novel in silico fine-mapping approach in non-carriers using both GWAS and biobank data. We first asked whether expression level of a CNV gene impacts risk for a known brain-related phenotype(s). Using transcriptomic imputation, we tested for association within GWAS for schizophrenia, IQ, BMI, bipolar disorder, and ASD. We found individual genes in 16p11.2 associated with schizophrenia, BMI, and IQ (SPN), using conditional analysis to identify INO80E as the driver of schizophrenia, and SPN and INO80E as drivers of BMI. Second, we used a biobank containing electronic health data to compare the medical phenome of CNV carriers to controls within 700,000 individuals to investigate a spectrum of health effects, identifying novel and previously observed traits. Third, we used genotypes for over 48,000 biobank individuals to perform phenome-wide association studies between imputed expressions of 16p11.2 and 22q11.2 genes and over 1,500 health traits, finding seventeen significant gene-trait pairs, including psychosis (NPIPB11, SLX1B) and mood disorders (SCARF2), and overall enrichment of mental traits. Our results demonstrate how integration of genetic and clinical data aids in understanding CNV gene function, and implicate pleiotropy and multigenicity in CNV biology.

genetics↗

bioRxiv2020

Individual cell-based modeling of tumor cell plasticity-induced immune escape after CAR-T therapy

Chimeric antigen receptor (CAR) therapy targeting CD19 is an effective treatment for refractory B cell malignancies, especially B cell acute lymphoblastic leukemia (B-ALL). The majority of patients achieve a complete response following a single infusion of CD19-targeted CAR-modified T cells (CAR-19 T cells); however, many patients suffer relapse after therapy, and the underlying mechanism remains unclear. To better understand the mechanism of tumor relapse, we developed an individual cell based computational model for tumor cell plasticity and the heterogeneous responses to the CAR-T treatment. Model simulations reproduced the process of tumor relapse, and predicted that CAR-T stress-induced cell plasticity can lead to tumor relapse in B-ALL. Model predictions were verified by applying the second-generation CAR-T cells to mice injected with NALM-6-GL leukemic cells, in which 60% of the mice relapse within 3 months, and relapsed tumors retained CD19 expression but exhibited a subpopulation of cells with CD34 transcription. These findings lead to a mechanism of tumor replace by which CAR-T treatment induced tumor cells to transition to hematopoietic stem-like cells (HSLCs) and myeloid-like cells and hence escape of CAR-T targeting. The computational model framework was successfully developed to recapitulate the individual evolutionary dynamics, which could predict clinical survival outcomes in B-ALL patients after CAR-T therapy.

cancer biology↗

bioRxiv2020

Local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits

Local genetic correlation quantifies the genetic similarity of complex traits in specific genomic regions, which could shed unique light on etiologic sharing and provide additional mechanistic insights into the genetic basis of complex traits compared to global genetic correlation. However, accurate estimation of local genetic correlation remains challenging, in part due to extensive linkage disequilibrium in local genomic regions and pervasive sample overlap across studies. We introduce SUPERGNOVA, a unified framework to estimate both global and local genetic correlations using summary statistics from genome-wide association studies. Through extensive simulations and analyses of 30 complex traits, we demonstrate that SUPERGNOVA substantially outperforms existing methods and identifies 150 trait pairs with significant local genetic correlations. In particular, we show that the positive, consistently-identified, yet paradoxical genetic correlation between autism spectrum disorder and cognitive performance could be explained by two etiologically-distinct genetic signatures with bidirectional local genetic correlations. We believe that statistically-rigorous local genetic correlation analysis could accelerate progress in complex trait genetics research.

genetics↗

bioRxiv2020

Substrate deformation regulates DRM2-mediated DNA methylation in plants

DNA methylation is an important epigenetic mechanism that critically regulates gene expression and genomic stability. In plants, Domains Rearranged Methyltransferase 2 (DRM2) preferentially mediates CHH methylation (H=C, T, A), a substrate specificity distinct from that of mammalian DNA methyltransferases. However, the underlying mechanism is unknown. Here, we report structure-function characterizations of DRM2-mediated methylation. An arginine finger from the catalytic loop intercalates into DNA minor groove, inducing large DNA deformation that impacts the substrate specificity of DRM2. To accommodate the substrate deformation, the target recognition domain of DRM2 embraces the enlarged DNA major groove via shape complementarity, disruption of which via C397R mutation shifts the substrate specificity of DRM2 toward CHG DNA. This study uncovers DNA deformation as a mechanism in regulating the substrate specificity of DRM2, implicative of transposon-specific repression in plants.

biochemistry↗

bioRxiv2020

Genetic Variants and Functional Pathways Associated with Resilience to Alzheimer's Disease

Approximately 30% of older adults exhibit the neuropathologic features of Alzheimers disease (AD) without signs of cognitive impairment. Yet, little is known about the genetic factors that allow these potentially resilient individuals to remain cognitively normal in the face of substantial neuropathology. We performed a large, genome-wide association study (GWAS) of two previously validated metrics of cognitive resilience quantified using a latent variable modeling approach and representing better-than-predicted cognitive performance for a given level of neuropathology. Data were harmonized across 5,108 participants from a clinical trial of AD and three longitudinal cohort studies of cognitive aging. All analyses were run across all participants and repeated restricting the sample to individuals with normal cognition to identify variants at the earliest stages of disease. As expected, all resilience metrics were genetically correlated with cognitive performance and education attainment traits (p-values<2.5x10-20), and we observed novel correlations with neuropsychiatric conditions (p-values<7.9x10-4). Notably, neither resilience metric was genetically correlated with clinical AD (p-values>0.42) nor associated with APOE (p-values>0.13). In single variant analyses, we observed a genome-wide significant locus among participants with normal cognition on chromosome 18 upstream of ATP8B1 (index SNP rs2571244, MAF=0.08, p=2.3x10-8). The top variant at this locus (rs2571244) was significantly associated with methylation in prefrontal cortex tissue at multiple CpG sites, including one just upstream of ATPB81 (cg19596477; p=2x10-13). Overall, this comprehensive genetic analysis of resilience implicates a putative role of vascular risk, metabolism, and mental health in protection from the cognitive consequences of neuropathology, while also providing evidence for a novel resilience gene along the bile acid metabolism pathway. Furthermore, the genetic architecture of resilience appears to be distinct from that of clinical AD, suggesting that a shift in focus to molecular contributors to resilience may identify novel pathways for therapeutic targets.

genetics↗

bioRxiv2020

Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations

Advances in technology have allowed for the study of metabolomics in the context of disease, enabling the discovery of new potential risk factors, diagnostic markers, and drug targets. For neurological and psychiatric phenotypes, the cerebrospinal fluid (CSF) is of particular biomedical importance as it is in direct contact with the brain and spinal cord. However, the CSF metabolome is difficult to study on a large scale due to the relative complexity of the procedure needed to collect the fluid compared to blood or urine studies. Here, we present a metabolome-wide association study (MWAS), an analysis using individual-level genetic and metabolomic data from two cohorts to impute metabolites into large samples with genome-wide association summary statistics. We conducted a metabolome-wide genome-wide association analysis with 338 CSF metabolites, identifying 16 genotype-metabolite associations, 6 of which were novel. Using these results, we then built prediction models for all available CSF metabolites and tested for associations with 27 neurological and psychiatric phenotypes in large cohorts, identifying 19 significant CSF metabolite-phenotype associations. Our results demonstrate the potential of MWAS to overcome the logistic challenges inherent in cerebrospinal fluid research to study the role of metabolomics in brain-related phenotypes and the feasibility of this framework for similar studies of omic data in scarce sample types.

genomics↗

bioRxiv2020

An extension to: Systematic assessment of commercially available low-input miRNA library preparation kits

High-throughput sequencing has emerged as the favoured method to study microRNA (miRNA) expression, but biases introduced during library preparation have been reported. To assist researchers choose the most appropriate library preparation kit, we recently compared the performance of six commercially-available kits on synthetic miRNAs and human RNA, where library preparation was performed by the vendors. We hereby supplement this study with data from two further commonly used kits (NEBNext, NEXTflex) whose manufacturers initially declined to participate. As before, performance was assessed with respect to sensitivity, reliability, titration response and differential expression. Despite NEXTflex employing partially-randomised adapter sequences to minimise bias, we reaffirm that biases in miRNA abundance are kit-specific, complicating the comparison of miRNA datasets generated using different kits.

molecular biology↗

bioRxiv2019

Fine-tuning Polygenic Risk Scores with GWAS Summary Statistics

Polygenic risk scores (PRSs) have wide applications in human genetics research. Notably, most PRS models include tuning parameters which improve predictive performance when properly selected. However, existing model-tuning methods require individual-level genetic data as the training dataset or as a validation dataset independent from both training and testing samples. These data rarely exist in practice, creating a significant gap between PRS methodology and applications. Here, we introduce PUMAS (Parameter-tuning Using Marginal Association Statistics), a novel method to fine-tune PRS models using summary statistics from genome-wide association studies (GWASs). Through extensive simulations, external validations, and analysis of 65 traits, we demonstrate that PUMAS can perform a variety of model-tuning procedures (e.g. cross-validation) using GWAS summary statistics and can effectively benchmark and optimize PRS models under diverse genetic architecture. On average, PUMAS improves the predictive R2 by 205.6% and 62.5% compared to PRSs with arbitrary p-value cutoffs of 0.01 and 1, respectively. Applied to 211 neuroimaging traits and Alzheimers disease, we show that fine-tuned PRSs will significantly improve statistical power in downstream association analysis. We believe our method resolves a fundamental problem without a current solution and will greatly benefit genetic prediction applications.

genetics↗

bioRxiv2019

Deletion of M-opsin prevents \"M cone\" degeneration in a mouse model of Leber congenital amaurosis

Mutations in RPE65 or lecithin-retinol acyltransferase (LRAT) disrupt 11-cis-retinal synthesis and cause Leber congenital amaurosis (LCA). In Lrat-/- mouse model, mislocalized medium (M)-wavelength sensitive opsin was degraded whereas mislocalized short (S)-wavelength sensitive opsin accumulated before the onset of cone degeneration. The mechanism for the foveal medium (M)/long (L)-wavelength-sensitive cone degeneration in LCA is unknown. By crossing Lrat-/- mice with a proteasome reporter mouse line, we showed that M-opsin enriched dorsal cones in Lrat-/- mice exhibit proteasome stress due to the degradation of large amounts of M-opsin. Deletion of M-opsin relieves the proteasome stress and completely prevents \"M cone\" degeneration in Lrat-/-Opn1sw-/- mice (a pure \"M cone\" LCA model, Opn1sw-/- encoding S-opsin) for at least 12 months. Our results suggest that M-opsin degradation associated proteasome stress plays a major role in \"M cone\" degeneration in Lrat-/- model. This finding may represent a general mechanism for \"M cone\" degeneration for multiple forms of cone degeneration due to M-opsin mislocalization and degradation. Our results have important implications for the current gene therapy strategy for LCA that emphasizes the need for a combinatorial therapy to both improve vision and slow photoreceptor degeneration.

neuroscience↗

bioRxiv2019

LncRNA MIR4435-2HG mediates cisplatin resistance in HCT116 cells by regulating Nrf2 and HO-1

PURPOSECisplatin resistance is still a serious problem in clinic. However, the underlying mechanism remains unclear. In this study investigated the drug resistance of cisplatin by the cisplatin resistance cell line HCT116R.\n\nRESULTSIn this study, we found that LncRNA MIR4435-2HG level dramatically increased in the cisplatin resistance cell line HCT116R. Knockdown of MIR4435-2HG in HCR116R significantly restored the sensitivity to cisplatin, inhibited cell proliferation and promoted cell apoptosis. Furthermore, Nrf2 and HO-1 mRNA level, as the critical molecular of the oxidative stress pathway, was inhibited by the siRNA targeting to MIR4435-2HG, displaying MIR4435-2HG-mediated cisplatin resistance through the Nrf2/HO-1 pathway.\n\nCONCLUSIONOur findings demonstrated that LncRNA MIR4435-2HG as a main factor could drive the cisplatin resistance of HCT116.

cancer biology↗

bioRxiv2019

Urbilaterian origin and evolution of sNPF-type neuropeptide signalling

Physiology and behaviour are controlled by neuropeptide signalling systems comprising peptide ligands and cognate receptors. Molecular phylogenetics combined with experimental identification of neuropeptide-receptor pairs has revealed that many neuropeptide signalling systems originated in the urbilaterian common ancestor of protostomes and deuterostomes. Neuropeptide-Y/neuropeptide-F (NPY/NPF)-type signalling is one such example, whereas NPY/NPF-related short-NPF (sNPF)-type signalling has hitherto only been identified in protostomes. Here we report the discovery of a neuropeptide (pQDRSKAMQAERTGQLRRLNPRF-NH2) that is the ligand for an sNPF-type receptor in a deuterostome, the starfish Asterias rubens (Phylum Echinodermata). Informed by phylogenetic analysis of sequence data, we conclude that the paralogous NPY/NPF-type and sNPF-type signalling systems originated in Urbilateria but NPY/NPF-type signalling was lost in echinoderms. Furthermore, we present evidence that sNPF-type peptides are orthologs of vertebrate prolactin-releasing peptides. Our findings demonstrate the importance of experimental studies on echinoderms for reconstructing the evolutionary history of neuropeptide signalling systems.

neuroscience↗

bioRxiv2019

Systematic assessment of commercially available low-input miRNA library preparation kits

High-throughput sequencing is increasingly favoured to assay the presence and abundance of micro RNAs (miRNAs) in biological samples, even from low RNA amounts, and a number of commercial vendors now offer kits that allow miRNA sequencing from sub-nanogram (ng) inputs. However, although biases introduced during library preparation have been documented, the relative performance of current reagent kits has not been investigated in detail. Here, six commercial kits capable of handling <100ng total RNA input were used for library preparation, performed by kit manufactures, on synthetic miRNAs of known quantities and human biological total RNA samples. We compared the performance of miRNA detection sensitivity, reliability, titration response and the ability to detect differentially expressed miRNAs. In addition, we assessed the use of unique molecular identifiers sequence (UMI) tags in one kit. We observed differences in detection sensitivity and ability to identify differentially expressed miRNAs between the kits, but none were able to detect the full repertoire of expected miRNAs. The reliability within the replicates of all kits was good, while larger differences were observed between the kits, although none could accurately quantify the majority of miRNAs. UMI tags, at least within the input ranges tested, offered little advantage to improve data utility. In conclusion, biases in miRNA abundance are heavily influenced by the kit used for library preparation, suggesting that comparisons of datasets prepared by different procedures should be made with caution. This article is intended to assist researchers select the most appropriate kit for their experimental conditions.

bioinformatics↗

bioRxiv2019

SCINA: Semi-Supervised Analysis of Single Cells in silico

Advances in single-cell RNA sequencing (scRNA-Seq) have allowed for comprehensive analyses of single cell data. However, current analyses of scRNA-Seq data usually start from unsupervised clustering or visualization. These methods ignore the prior knowledge of transcriptomes and of the probable structures of the data. Moreover, cell identification heavily relies on subjective and inaccurate human inspection afterwards. We reversed this paradigm and developed SCINA, a semi-supervised model, for analyses of scRNA-Seq and flow cytometry/CyTOF data, and other data of similar format, by automatically exploiting previously established gene signatures using an expectation-maximization (EM) algorithm. We applied SCINA on a wide range of datasets, and showed its accuracy, stableness and efficiency exceeded most popular unsupervised approaches. Notably, SCINA discovered an intermediate stage of oligodendrocyte from mouse brain scRNA-Seq data. SCINA also detected immune cell population shifting in Stk4 knock-out mouse cytometry data. Finally, SCINA identified a new kidney tumor clade with similarity to FH-deficient tumors from bulk tumor data. Overall, SCINA provides both methodological advances and biological insights from perspectives different from traditional analytical methods.

bioinformatics↗

bioRxiv2019

Functional dissection of Alzheimer’s disease brain gene expression signatures in humans and mouse models

Human brain transcriptomes can highlight biological pathways associated with Alzheimers disease (AD); however, challenges remain to link expression changes with causal triggers. We have examined 30 AD-associated, gene coexpression modules from human brains for overlap with 251 differentially-expressed gene sets from mouse brain RNA-sequencing experiments, including from models of AD and other neurodegenerative disorders. Human-mouse overlaps highlight responses to amyloid versus neurofibrillary tangle pathology and further reveal age- and sex-dependent expression signatures for AD progression. Human coexpression modules enriched for neuronal and/or microglial genes overlap broadly with signatures from mouse models of AD, Huntingtons disease, Amyotrophic Lateral Sclerosis, and also aging. Several human AD coexpression modules, including those implicated in the unfolded protein response and oxidative phosphorylation, were not activated in AD models, but instead were detected following other, unexpected mouse genetic manipulations. Our results comprise a powerful, cross-species resource and pinpoint experimental models for diverse features of AD pathophysiology from human brain transcriptomes.

systems biology↗

bioRxiv2018

Long Non-Coding RNA LncKdm2b Regulates Cortical Neuronal Differentiation by Cis-Activating Kdm2b

The mechanisms underlying spatial and temporal control of cortical neurogenesis of the brain are largely elusive. Long non-coding RNAs (lncRNAs) have emerged as essential cell fate regulators. Here we found LncKdm2b (also known as Kancr), a lncRNA divergently transcribed from a bidirectional promoter of Kdm2b, is transiently expressed during early differentiation of cortical projection neurons. Interestingly, Kdm2bs transcription is positively regulated in cis by LncKdm2b, which has intrinsic-activating function and facilitates a permissive chromatin environment at the Kdm2bs promoter by associating with hnRNPAB. Lineage tracing experiments and phenotypic analyses indicated LncKdm2b and Kdm2b are crucial in proper differentiation and migration of cortical projection neurons. Moreover, KDM2B exerts its role relying on its leucine-rich repeats (LRR) but independent of its PRC1-related function. These observations unveiled a lncRNA-dependent machinery in regulating cortical neuronal differentiation.

molecular biology↗

bioRxiv2018

The systemic activin response to pancreatic cancer: Implications for effective cancer cachexia therapy

Pancreatic ductal adenocarcinoma (PDAC) is a particularly lethal malignancy with high rates of cachexia. Serum activin correlates with PDAC cachexia and mortality, while activin administration causes cachexia in mice. We studied activin in human tumors and in mice with orthotopic or genetic PDAC. Cachexia severity correlated with activin expression in tumor lines. Activins were expressed in both cancer and tumor stromal cells, but also in organs in murine PDAC cachexia. Tumor cells expressed activin-{beta}A, or Inhba, while organs expressed both activin-{beta}A and activin-{beta}B, or Inhbb. PDAC elicits activin expression; PDAC conditioned medium induced activin and atrophy of myotubes. Treatment with the activin trap, ACVR2B/Fc, reduced cachexia and prolonged survival in mice with activin-low tumors, and reduced cachexia in activin-high tumors, without affecting activin expression in organs. Mice expressing dominant negative ACVR2B in muscle were protected for weight loss but not survival. Overall our results indicate that PDAC induces a systemic activin response, leading to cachexia, and that activin targets might include organs beyond muscle. Targeting of both tumor-derived and host-derived activins could improve cachexia therapy.

cancer biology↗

bioRxiv2017

miRAW: A deep learning approach to predict miRNA targets by analyzing whole miRNA transcripts

AbstractMicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by binding to partially complementary regions within the UTR of their target genes. Computational methods play an important role in target prediction and assume that the miRNA \"seed region\" (nt 2 to 8) is required for functional targeting, but typically only identify {bsim}80% of known bindings. Recent studies have highlighted a role for the entire miRNA, suggesting that a more flexible methodology is needed.\n\nWe present a novel approach for miRNA target prediction based on Deep Learning (DL) which, rather than incorporating any knowledge (such as seed regions), investigates the entire miRNA and 3UTR mRNA nucleotides to learn a uninhibited set of feature descriptors related to the targeting process.\n\nWe collected more than 150,000 experimentally validated homo sapiens miRNA:gene targets and cross referenced them with different CLIP-Seq, CLASH and iPAR-CLIP datasets to obtain {bsim}20,000 validated miRNA:gene exact target sites. Using this data, we implemented and trained a deep neural network - composed of autoencoders and a feed-forward network - able to automatically learn features describing miRNA-mRNA interactions and assess functionality. Predictions were then refined using information such as site location or site accessibility energy.\n\nIn a comparison using independent datasets, our DL approach consistently outperformed existing prediction methods, recognizing the seed region as a common feature in the targeting process, but also identifying the role of pairings outside this region. Thermodynamic analysis also suggests that site accessibility plays a role in targeting but that it cannot be used as a sole indicator for functionality. Predictions were then refined using information such as site location or site accessibility energy.\n\nIn a comparison using independent datasets, our DL approach consistently outperformed existing prediction methods, recognizing the seed region as a common feature in the targeting process, but also identifying the role of pairings outside this region. Thermodynamic analysis also suggests that site accessibility plays a role in targeting but that it cannot be used as a sole indicator for functionality.\n\nData and source code available at: https://bitbucket.org/account/user/bipous/projects/MIRAW\n\nAuthor summarymicroRNAs are small RNA molecules that regulate biological processes by binding to the 3'UTR of a gene and their dysregulation is associated with several diseases. Computationally predicting these targets remains a challenge as they only partially match their target and so there can be hundreds of targets for a single microRNA. Current tools assume that most of the knowledge defining a microRNA-gene interaction can be captured by analysing the binding produced in the seed region ({approx} the first 8nt in the miRNA). However, recent studies show that the whole microRNA can be important and form non-canonical targets. Here, we use a target prediction methodology that relies on deep neural networks to automatically learn the relevant features describing microRNA-gene interactions for predicting microRNA targets. This means we make no assumptions about what is important, leaving the task to the deep neural network. A key part of the work is obtaining a suitable dataset. Thus, we collected and curated more than 150,000 experimentally verified microRNA targets and used them to train the network. Using this approach, we are able to gain a better understanding of non-canonical targets and to improve the accuracy of state-of-the-art prediction tools.

bioinformatics↗