Biology subjects

Gong, C.

Publications and source records attributed to Gong, C..

32 records · Page 2Linked to original sources

bioRxiv2023

Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes

Disease variant annotation in the context of biological reactions and pathways can provide a standardized overview of molecular phenotypes of pathogenic mutations that is amenable to computational mining and mathematical modeling. Reactome, an open source, manually curated, peer-reviewed database of human biological pathways, provides annotations for over 4000 disease variants of close to 400 genes in the context of [~]800 disease reactions constituting [~]400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics (ACMG). Reactomes pathway-based, reaction-specific disease variant dataset and data model provide a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity.

systems biology↗

bioRxiv2023

Variants in the DDX6-CXCR5 autoimmune disease risk locus influence the regulatory network in immune cells and salivary gland

Fine mapping and bioinformatic analysis of the DDX6-CXCR5 genetic risk association in Sjogrens Disease (SjD) and Systemic Lupus Erythematosus (SLE) identified five common SNPs with functional evidence in immune cell types: rs4938573, rs57494551, rs4938572, rs4936443, rs7117261. Functional interrogation of nuclear protein binding affinity, enhancer/promoter regulatory activity, and chromatin-chromatin interactions in immune, salivary gland epithelial, and kidney epithelial cells revealed cell type-specific allelic effects for all five SNPs that expanded regulation beyond effects on DDX6 and CXCR5 expression. Mapping the local chromatin regulatory network revealed several additional genes of interest, including lnc-PHLDB1-1. Collectively, functional characterization implicated the risk alleles of these SNPs as modulators of promoter and/or enhancer activities that regulate cell type-specific expression of DDX6, CXCR5, and lnc-PHLDB1-1, among others. Further, these findings emphasize the importance of exploring the functional significance of SNPs in the context of complex chromatin architecture in disease-relevant cell types and tissues.

genetics↗

bioRxiv2023

SAW: An efficient and accurate data analysis workflow for Stereo-seq spatial transcriptomics

The basic analysis steps of spatial transcriptomics involve obtaining gene expression information from both space and cells. This process requires a set of tools to be completed, and existing tools face performance issues when dealing with large data sets. These issues include computationally intensive spatial localization, RNA genome alignment, and excessive memory usage in large chip scenarios. These problems affect the applicability and efficiency of the process. To address these issues, a high-performance and accurate spatial transcriptomics data analysis workflow called Stereo-Seq Analysis Workflow (SAW) has been developed for the Stereo-Seq technology developed by BGI. This workflow includes mRNA spatial position reconstruction, genome alignment, gene expression matrix generation and clustering, and generate results files in a universal format for subsequent personalized analysis. The excutation time for the entire analysis process is [~]148 minutes on 1G reads 1*1 cm chip test data, 1.8 times faster than unoptimized workflow.

bioinformatics↗

bioRxiv2023

Scalable projected Light Sheet Microscopy for high-resolution imaging of large samples

Light sheet fluorescence microscopy (LSFM) is a widely used imaging technique for living and large cleared samples. However, high-performance LSFM systems are often prohibitively expensive and not easily scalable for high-throughput applications. Here, we introduce a cost-effective, scalable, and versatile high-resolution imaging framework, called projected Light Sheet Microscopy (pLSM), which repurposes readily available off-the-shelf consumer-grade components and an over-the-network control architecture to achieve high-resolution imaging of living and cleared samples. We extensively characterize the pLSM framework and showcase its capabilities through high-resolution, multi-color imaging and quantitative analysis of mouse and post-mortem human brain samples cleared using various techniques. Moreover, we show the applicability of pLSM for high-throughput molecular phenotyping of human induced pluripotent cells (iPSC)-derived brain and vessel organoids. Additionally, we utilized pLSM for comprehensive live imaging of bacterial pellicle biofilms at the air-liquid interface, uncovering their intricate layered architecture and diverse cellular dynamics across different depths. Overall, the pLSM framework has the potential to further democratize LSFM by making high-resolution light sheet microscopy more accessible and scalable.

neuroscience↗

bioRxiv2023

Stability Oracle: A Structure-Based Graph-Transformer for Identifying Stabilizing Mutations

Stabilizing proteins is a fundamental challenge in protein engineering and is almost always a prerequisite for the development of industrial and pharmaceutical biotechnologies. Here we present Stability Oracle: a structure-based graph-transformer framework that achieves state-of-the-art performance on predicting the effect of a point mutation on a proteins thermodynamic stability ({Delta}{Delta}G). A strength of our model is its ability to identify stabilizing mutations, which often make up a small fraction of a proteins mutational landscape. Our framework introduces several data and machine learning innovations to overcome well-known challenges in data scarcity and bias, generalization, and computation time. Stability Oracle is first pretrained on over 2M masked microenvironments and then fine-tuned using a novel data augmentation technique, Thermodynamic Permutations (TP), applied to a [~]120K curated subset of the mega-scale cDNA display proteolysis dataset. This technique increases the original 120K mutations to over 2M thermodynamically valid {Delta}{Delta}G measurements to generate the first structure training set that samples and balances all 380 mutation types. By using the masked microenvironment paradigm, Stability Oracle does not require a second mutant structure and instead uses amino acid structural embeddings to represent a mutation. This architectural design accelerates training and inference times: we can both train on 2M instances with just 119 structures and generate deep mutational scan (DMS) predictions from only the wildtype structure. We benchmark Stability Oracle with both experimental and AlphaFold structures of all proteins on T2837, a test set that aggregates the common test sets (SSym, S669, p53, and Myoglobin) with all additional experimental data from proteins with over a 30% sequence similarity overlap. We used TP augmented T2837 to evaluate performance for engineering protein stability: Stability Oracle correctly identifies 48% of stabilizing mutations ({Delta}{Delta}G < -0.5 kcal/mol) and 74% of its stabilizing predictions are indeed stabilizing (18% and 8% of predictions were neutral and destabilizing, respectively). For a fair comparison between sequence and structure-based fine-tuned deep learning models, we build on the Prostata framework and fine-tune the sequence embeddings of ESM2 on our training set (Prostata-IFML). A head-to-head comparison demonstrates that Stability Oracle outperforms Prostata-IFML on regression and classification even though the model is 548 times smaller and is pretrained with 4000 times fewer proteins, highlighting the advantages of learning from structures.

biochemistry↗

bioRxiv2023

Whole-brain mapping reveals the divergent impact of ketamine on the dopamine system

Ketamine is a multifunctional drug with clinical applications as an anesthetic, as a pain management medication and as a transformative fast-acting antidepressant. It is also abused as a recreational drug due to its dissociative property. Recent studies in rodents are revealing the neuronal mechanisms that mediate the complex actions of ketamine, however, its long-term impact due to prolonged exposure remains much less understood with profound scientific and clinical implications. Here, we develop and utilize a high-resolution whole-brain phenotyping approach to show that repeated ketamine administration leads to a dosage-dependent decrease of dopamine (DA) neurons in the behavior state-related midbrain regions and, conversely, an increase within the hypothalamus. Congruently, we show divergently altered innervations of prefrontal cortex, striatum, and sensory areas. Further, we present supporting data for the post-transcriptional regulation of ketamine-induced structural plasticity. Overall, through an unbiased whole-brain analysis, we reveal the divergent brain-wide impact of chronic ketamine exposure on the association and sensory pathways.

neuroscience↗

bioRxiv2023

A novel chimeric RNA originating from BmCPV S4 and Bombyx mori HDAC11 transcripts regulates virus proliferation

Polymerases encoded by segmented negative-strand RNA viruses cleave 5-m7G-capped host transcripts to prime viral mRNA synthesis ( cap-snatching) to generate chimeric RNA, and trans-splicing occurs between viral and cellular transcripts. Bombyx mori cytoplasmic polyhedrosis virus (BmCPV), an RNA virus belonging to Reoviridae, is a major pathogen of silkworm (B. mori). The genome of BmCPV consists of 10 segmented double-stranded RNAs (S1-S10) from which viral RNAs encoding a protein are transcribed. In this study, chimeric silkworm-BmCPV RNAs, in which the sequence derived from the silkworm transcript could fuse with both the 5 end and the 3 end of viral RNA, were identified in the midgut of BmCPV-infected silkworms by RNA_seq and further confirmed by PCR and Sanger sequencing. A novel chimeric RNA, HDAC11-S4 RNA 4, derived from silkworm histone deacetylase 11 (HDAC11) and the BmCPV S4 transcript encoding viral structural protein 4 (VP4), was selected for validation by in situ hybridization and Northern blotting. Interestingly, our results indicated that HDAC11-S4 RNA 4 was generated in a BmCPV RNA-dependent RNA polymerase (RdRp)-independent manner and could be translated into a truncated BmCPV VP4 with a silkworm HDAC11-derived N-terminal extension. Moreover, it was confirmed that HDAC11-S4 RNA 4 inhibited BmCPV proliferation, decreased the level of H3K9me3 and increased the level of H3K9ac. These results indicated that during infection with BmCPV, a novel mechanism, different from that described in previous reports, allows the genesis of chimeric silkworm-BmCPV RNAs with biological functions. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/527451v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@1b3b949org.highwire.dtl.DTLVardef@4972c0org.highwire.dtl.DTLVardef@cc15aorg.highwire.dtl.DTLVardef@16a0a19_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2022

Re-evaluating frontopolar and temporoparietal contributions to detection and discrimination confidence

Previously, we identified a subset of regions where the relation between decision confidence and univariate fMRI activity was quadratic, with stronger activation for both high and low compared to intermediate levels of confidence. We further showed that, in a subset of these regions, this quadratic modulation appeared only for confidence in detection decisions about the presence or absence of a stimulus, and not for confidence in discrimination decisions about stimulus identity (Mazor, Friston & Fleming, 2021). Here, in a pre-registered follow-up experiment, we sought to replicate our original findings and identify the origins of putative detection-specific confidence signals by introducing a novel asymmetric-discrimination condition: a discrimination task with the signal-detection properties of a detection task. This task required discriminating two alternatives (two different grating tilts) but was engineered such that the distribution of perceptual evidence was asymmetric, just as in yes/no detection. We successfully replicated the quadratic modulation of subjective confidence in prefrontal, parietal and temporal cortices. However, in contrast to our original report, this quadratic effect was similar in detection and discrimination responses, but stronger in the novel asymmetric-discrimination condition. We interpret our findings as weighing against the detection-specificity of confidence signatures and speculate about possible alternative origins of a quadratic modulation of decision confidence.

neuroscience↗

bioRxiv2022

A point mutation in Chl biosynthesis gene Mg-chelatase I subunit influences on leaf color and metabolism in strawberry

Magnesium chelatase catalysis the insertion of magnesium into protoporphyrin IX is a vital step in chlorophyll biogenesis. It consists of three subunits, CHLI, CHLD and CHLH. The CHLI subunit is an ATPase and hydrolysis ATP in the catalysis. However, its key point on influencing flavonoid biosynthesis and chlorophyll accumulation under different light density was still unknown. In this study, we identified an N-Ethyl-N-nitrosourea (ENU) mutant, p240 from strawberry Fragaria pentaphylla that has yellow-green leaves and lower chlorophyll level. We verified the mutation occurs in the 186th amino acid of CHLI subunit, which is conserved in most photosynthetic organism. Mutants generated from RNAi and CRISPR/Cas9 gene editing confirmed this phenotype. In addition, we found that FpCHLI was localized in chloroplast and its subcellular location have not been changed by mutation. Further study showed that the interaction between FpCHLI and FpCHLD were not affected by mutagenesis. In contrast, all types of mutants showed reduced ATPase and magnesium chelatase activity indicating mutagenesis decreased enzymatic activities. Furthermore, mutagenesis suppressed the biosynthesis of Mg-proto IX. Metabolites analysis of gene knock-out mutant and WT revealed that CHLI may help to keep stabilizing the flavonoid level in leaves. Furthermore, both p240 and chli mutants are light sensitive, which has yellow leaves under high light but pale green leaves under poor light. The photosynthesis ability of mutants was also increased under shade. Moreover, stomatal apertures of mutants were wider than WT. Taken together, these results suggest that CHLI plays an important role in both leaf coloration and metabolism.

plant biology↗

bioRxiv2022

Global Spatial Transcriptome of Macaque Brain at Single-Cell Resolution

Global profile of gene expression at single-cell resolution remains to be determined for primates. Using a recently developed technology ("Stereo-seq"), we have obtained a comprehensive single-cell spatial transcriptome map at the whole-brain level for cynomolgus monkeys, with [~]600 genes per cell for 10 m-thick coronal sections (up to 15 cm2 in size). Large-scale single-nucleus RNA-seq analysis for [~]1 million cells helped to identify cell types corresponding to Stereo-seq gene expression profiles, providing a 3-D cell type atlas of the monkey brain. Quantitative analysis of Stereo-seq data revealed molecular fingerprints that mark distinct neocortical layers and subregions, as well as domains within subcortical structures including hippocampus, thalamus, striatum, cerebellum, hypothalamus and claustrum. Striking whole-brain topography and coordinated patterns were found in the expression of genes encoding receptors and transporters for neurotransmitters and neuromodulators. These results pave the way for cellular and molecular understanding of organizing principles of the primate brain.

cell biology↗

bioRxiv2020

HBV pgRNA can generate a circRNA with two junction sites

To verify whether Hepatitis B virus (HBV) can form circRNA, the circRNA sequencing results of the HBV-positive HepG 2.2.15 hepatocarcinoma cell line were compared with the HBV genome. A novel circRNA, named as HBV_circ_1, was mapped to the 489-2985 nt region of the HBV genome (GenBank accession No. KU668446.1), which was derived from HBV pgRNA and has two junction sites. A partial fragment of the pgRNA (corresponding to HBV DNA genome 489-3,182-2,985nt) was resected and then connected to form a junction site of HBV_circ_1 (corresponding to HBV DNA genome 489/2985nt). The 5-terminal region (corresponding to HBV DNA genome 1,820-1,932nt) of pgRNA was repeated with the region of pgRNA at the 3-terminal region. Another junction site of HBV_circ_1 (corresponding to HBV DNA genome 1820nt) was formed in a manner similar to DNA homologous recombination mediated by repeats of pgRNA. Moreover, this RNA homologous recombination mediated by the repeats of pgRNA does not have tissue specificity and genus specificity. Reverse transcription PCR, northern blotting, and tissue in situ hybridization confirmed the existence of HBV_circ_1 in HepG2.2.15 cells and HBV related hepatocellular carcinoma (HCC) tissue. This study expands our understanding of circRNA generation mechanisms, and provide a new perspective for understanding the molecular pathogenesis of the virus.

molecular biology↗

bioRxiv2020

Diversified Learning Environment Alleviates Learning Impairment Caused by Contextual Stress

Environment is capable of affecting our learning ability. Existing studies reported that learning ability can be enhanced by enriched environment and impaired by stressful context. However, it is still unclear whether diversified environment can reverse or ameliorate the learning difficulty caused by stressful context. In this study, several behavioral tasks were designed to test the role of diversified environment in active avoidance learning. In the present study, sound-cued active avoidance (two-way shuttle box) acted as learning paradigm. Multiple shuttle boxes with the identical size but different designs were employed to mimic diversified environment in learning tasks. Mild but inevitable foot shocking was adopted to increase animals stress to certain context. To quantify the depression/anxiety level of animals, open field test, forced swimming test, light-dark box test, and elevated plus maze were performed. The following findings were reported. First, diversified learning environment could improve learning ability in active avoidance, as manifested by higher successful rate and sharper learning curve. Second, elevating the stress level of animal to a certain context could noticeably reduce its performance in active avoidance learning. Third, the learning impairment attributed to stressful context can be improved by training in diversified environments. Thus, as revealed from the results, learning impairment caused by stressful context can be alleviated by diversified learning environment which may facilitate further medical and education applications.

animal behavior and cognition↗

bioRxiv2019

Development of SSR markers related to seed storability traits in maize subjected to artificial seed aging conditions

Seed storability is an important and complex agronomic trait in maize because annual seed production considerably exceeds consumption. The viability of seeds decreases over time, even when stored at low temperature, until seeds finally lose viability. In our previous study, two inbred lines with significantly different storability, Dong156 with high storage tolerance and Dong237 with low storage tolerance, were selected over six years using a natural seed aging test. In the present study, an F2:3 population and a RIL (recombinant inbred line) population were constructed from these two inbred lines and used to map QTL (quantitative trait loci) with SSR (simple sequence repeat) markers. A phenotypic index of traits related to seed storability that includes germination rate, germination potential, a germination index, a vigor index, seedling weight, and seedling length was generated using the results of an artificial aging treatment. Two consistent regions, cQTL-7 on chromosome 7 and cQTL-10 on chromosome 10, were identified by comparing QTL analysis results from these two populations. After genotyping SSR markers in these two regions, cQTL-7 was remapped to between umc1671 and phi328175 in a 7.97-Mb region, and cQTL-10 was remapped to between umc1648 and phi050 in a 39.15-Mb region. Four SSR markers linked to cQTL-7 and cQTL-10, including umc1671, phi328175, umc1648, and phi050, were identified using a Chi-squared test. The combined selection efficiency of these four markers was 83.94% in 85 RIL lines with high storability, and marker umc1648 exhibited the highest efficiency value of 88.89%. These results indicated that the four SSR markers developed in this study could be used for selection of maize germplasm with high seed storability.

plant biology↗

bioRxiv2019

Reconstructing lost BOLD signal in individual participants using deep machine learning

The blood oxygen level-dependent (BOLD) signal in functional neuroimaging suffers from magnetic susceptibility artifacts and interference from metal implants. The resulting signal loss hampers functional neuroimaging studies and can lead to misinterpretation of findings. Here, we reconstructed compromised BOLD signal using deep machine learning. We trained a deep learning model to learn principles governing BOLD activity in one dataset and reconstructed artificially-compromised regions in another dataset, frame by frame. Strikingly, BOLD time series extracted from reconstructed frames were correlated with the original time series, even though the frames did not independently carry information about BOLD fluctuations through time. Moreover, reconstructed functional connectivity (FC) maps exhibited good correspondence with the original FC maps, indicating that the deep learning model recovered functional relationships among brain regions. We replicated this result in patients whose scans suffered signal loss due to intracortical electrodes. Critically, the reconstructions captured individual-specific information rather than group information learned during training. Deep machine learning thus presents a unique opportunity to reconstruct compromised BOLD signal while capturing features of an individuals own functional brain organization.

neuroscience↗