Biology subjects

Qiao, D.

Publications and source records attributed to Qiao, D..

9 recordsLinked to original sources

bioRxiv2025

Hippocampal single-cell RNA Atlas of chronic methamphetamine abuse-induced cognitive decline in mice

BackgroundChronic methamphetamine abuse leads to cognitive decline, posing a significant threat to human health and contributing to loss of productivity. However, the intricate and multifaceted mechanisms underlying methamphetamine-induced neurotoxicity have impeded the development of effective therapeutic interventions. MethodsTo establish a mouse model of cognitive decline induced by chronic methamphetamine exposure, we employed a large sample size and conducted two behavioral tests (Y-maze and novel object recognition test) at 2 and 4 weeks post-exposure. Subsequently, single-cell RNA sequencing was utilized to delineate the mRNA expression profiles of individual cells within the hippocampus. Comprehensive bioinformatics analyses, including cell clustering and identification, differential gene expression analysis, cellular communication analysis, pseudotemporal trajectory analysis, and transcription factor regulation analysis, were performed to elucidate the cellular-level changes in mRNA profiles caused by chronic methamphetamine exposure. ResultsOur findings demonstrated impairments in working memory, spatial cognition, learning, and cognitive memory. After 4 weeks of behavioral testing, we identified diverse cell types in the hippocampi of METH- and saline-treated mice through scRNA-seq, including glial cells, stromal cells, vascular cells, and immune cells. We observed that methamphetamine exerts cell-specific effects on gene expression changes associated with neuroinflammation, blood-brain barrier disruption, neuronal support dysfunction, and immune dysregulation. Furthermore, cross-talk analysis revealed extensive alterations in cellular communication patterns and signal changes within the hippocampal microenvironment induced by methamphetamine exposure. Pseudotime analysis predicted hippocampal neurogenesis disorders and identified key regulatory genes implicated in chronic methamphetamine abuse. Transcription factor analysis uncovered regulators and pathways linked to astrocyte-mediated neuroinflammation, endothelial junction integrity, microglial synaptic remodeling, and oligodendrocyte-supported neuronal cell bodies and axons. Additionally, it highlighted the role of neural precursor cells in various forms of neurodegeneration. ConclusionsThis study establishes a robust mouse model of cognitive impairment induced by chronic methamphetamine exposure. It provides valuable biological insights, characterizes the single-cell atlas of the hippocampus, and offers novel directions for investigating neurological damage associated with chronic methamphetamine-induced cognitive decline.

pharmacology and toxicology↗

bioRxiv2024

Phosphate starvation stops bacteria digesting algal fucan that sequesters carbon

Algae provide a solar powered pathway to capture and sequester carbon by injecting stable fucan made from carbon dioxide into the ocean 1-4. Stability of the pathway is at odds with the presence of marine bacteria with genes of enzymes that can digest fucan and release the carbon dioxide 5. Biochemical explanations for stable fucan remain hypothetical 6. We assembled a biological carbon cycle model and found phosphate limitation enhanced fucan synthesis by algae, stopped digestion by bacteria and thereby stabilized the fucan carbon sequestration pathway. Marine microalgae Glossomastix sp. PLY432 increased synthesis of fucan, a part of its extracellular matrix, under nutrient-growth limiting conditions. Rate and extent of fucan digestion by a marine, isolated bacterium of the Akkermansiaceae family decreased with decreasing phosphate concentration. Phosphate starvation restricted bacterial growth rate, biomass yield and in turn increased the amount of stable fucan. Phosphate is universally required for growth but rare relative to glycan carbon in photosynthesis-derived ecosystems. The fact that phosphate is required for replication, transcription and translation explains why bacteria can digest gigatons of laminarin with a few enzymes, but not fucan during nutrient limited algal blooms. We conclude phosphate starvation constrains the ability of bacteria to digest fucan, which evolves to maintain stability around algal cells and consequentially also to keep carbon dioxide in the ocean.

microbiology↗

bioRxiv2022

A Statistical Physics Approach for Disease Module Detection

Extensive evidence indicates that the pathobiological processes of a complex disease are associated with perturbation within specific disease neighborhoods of the human protein-protein interaction (PPI) network (a.k.a. the interactome), often referred to as the disease module. Many computational methods have been developed to integrate the interactome and omics profiles to extract context-dependent disease modules. Yet, existing methods all have fundamental limitations in terms of rigor and/or efficiency. Here, we developed a statistical physics approach based on the random-field Ising model (RFIM) for disease module detection, which is both mathematically rigorous and computationally efficient. We applied our RFIM approach with genome-wide association studies (GWAS) of six complex diseases to examine its performance for disease module detection. We found that our RFIM approach outperforms existing methods in terms of computational efficiency, connectivity of disease modules, and robustness to the interactome incompleteness.

systems biology↗

bioRxiv2021

Clonal hematopoiesis is driven by aberrant activation of TCL1A

A diverse set of driver genes, such as regulators of DNA methylation, RNA splicing, and chromatin remodeling, have been associated with pre-malignant clonal expansion of hematopoietic stem cells (HSCs). The factors mediating expansion of these mutant clones remain largely unknown, partially due to a paucity of large cohorts with longitudinal blood sampling. To circumvent this limitation, we developed and validated a method to infer clonal expansion rate from single timepoint data called PACER (passenger-approximated clonal expansion rate). Applying PACER to 5,071 persons with clonal hematopoiesis accurately recapitulated the known fitness effects due to different driver mutations. A genome-wide association study of PACER revealed that a common inherited polymorphism in the TCL1A promoter was associated with slower clonal expansion. Those carrying two copies of this protective allele had up to 80% reduced odds of having driver mutations in TET2, ASXL1, SF3B1, SRSF2, and JAK2, but not DNMT3A. TCL1A was not expressed in normal or DNMT3A-mutated HSCs, but the introduction of mutations in TET2 or ASXL1 by CRISPR editing led to aberrant expression of TCL1A and expansion of HSCs in vitro. These effects were abrogated in HSCs from donors carrying the protective TCL1A allele. Our results indicate that the fitness advantage of multiple common driver genes in clonal hematopoiesis is mediated through TCL1A activation. PACER is an approach that can be widely applied to uncover genetic and environmental determinants of pre-malignant clonal expansion in blood and other tissues.

genetics↗

bioRxiv2021

A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables

The identification and understanding of gene-environment interactions can provide insights into the pathways and mechanisms underlying complex diseases. However, testing for gene-environment interaction remains a challenge since statistical power is often limited, the specification of environmental effects is nontrivial, and such misspecifications can lead to false positive findings. To address the lack of statistical power, recent methods aim to identify interactions on an aggregated level using, for example, polygenic risk scores. While this strategy increases power to detect interactions, identifying contributing key genes and pathways is difficult based on these global results. Here, we propose RITSS (Robust Interaction Testing using Sample Splitting), a gene-environment interaction testing framework for quantitative traits that is based on sample splitting and robust test statistics. RITSS can incorporate multiple genetic variants and/or multiple environmental factors. Using sample splitting, a screening step enables the selection and combination of potential interactions into scores with improved interpretability, based on the users unrestricted choices for statistical/machine learning approaches. In the testing step, the application of robust test statistics minimizes the susceptibility of the results to main effect misspecifications. Using extensive simulation studies, we demonstrate that RITSS controls the type 1 error rate in a wide range of scenarios. In an application to lung function phenotypes and human height in the UK Biobank, RITSS identified genome-wide significant interactions with subcomponents of genetic risk scores. While the contributing single variant interactions are moderate, our analysis results indicate interesting interaction patterns that result in strong aggregated signals that provide further insights into gene-environment interaction mechanisms.

bioinformatics↗

bioRxiv2021

MS2 RNA aptamer enhances prime editing in rice

Prime editing is a universal and very promising precise genome editing technology. However, optimization of prime editor (PE) from different aspects remains vital for its use as a routine tool in plant basic research and crop molecular breeding. In this report, we tested MS2-based prime editor (MS2PE). We fused the M-MLV reverse transcriptase (RT) gene variant to the MS2 RNA binding protein gene, MCP, and allowed the MCP-RT fusion gene to co-express with the SpCas9 nickase gene, SpCas9H840A, and various engineered pegRNAs harboring MS2 RNA (MS2pegR). Compared with control PEs, MS2PEs significantly enhanced editing efficiency at four of six targets in rice protoplasts, and achieved 1.2[~]10.1-fold increase in editing efficiency at five of six targets in transgenic rice lines. Furthermore, we tested total 22 different MS2pegR scaffolds, 3 RT variants or genes, 2 MCP variants, and various combinations of the Cas9 nickase, RT, and MCP modules. Our results demonstrated an alternative strategy for enhancing prime editing.

molecular biology↗

bioRxiv2020

Mutations in SARS-CoV-2 spike protein and RNA polymerase complex are associated with COVID-19 mortality risk

BackgroundSARS-CoV-2 mortality has been extensively studied in relation to host susceptibility. How sequence variations in the SARS-CoV-2 genome affect pathogenicity is poorly understood. Whole-genome sequencing (WGS) of the virus with death in SARS-CoV-2 patients is one potential method of early identification of highly pathogenic strains to target for containment. MethodsWe analyzed 7,548 single stranded RNA-genomes of SARS-CoV-2 patients in the GISAID database (Elbe and Buckland-Merrett, 2017; Shu and McCauley, 2017) and associated variants with reported patients health status from COVID-19, i.e. deceased versus non-deceased. We probed each locus of the single stranded RNA of the SARS-CoV-2 virus for direct association with host/patient mortality using a logistic regression. ResultsIn total, evaluating 29,891 loci of the viral genome for association with patient/host mortality, two loci, at 12,053bp and 25,088bp, achieved genome-wide significance (p-values of 4.09e-09 and 4.41e-23, respectively). ConclusionsMutations at 25,088bp occur in the S2 subunit of the SARS-CoV-2 spike protein, which plays a key role in viral entry of target host cells. Additionally, mutations at 12,053bp are within the ORF1ab gene, in a region encoding for the protein nsp7, which is necessary to form the RNA polymerase complex responsible for viral replication and transcription. Both mutations altered amino acid coding sequences, potentially imposing structural changes that could enhance viral infectivity and symptom severity, and may be important to consider as targets for therapeutic development. Identification of these highly significant associations, unlikely to occur by chance, may assist with COVID-19 early containment of strains that are potentially highly pathogenic.

genomics↗

bioRxiv2020

Robust, flexible, and scalable tests for Hardy-Weinberg Equilibrium across diverse ancestries

Traditional Hardy-Weinberg equilibrium (HWE) tests (the {chi}2 test and the exact test) have long been used as a metric for evaluating genotype quality, as technical artifacts leading to incorrect genotype calls often can be identified as deviations from HWE. However, in datasets comprised of individuals from diverse ancestries, HWE can be violated even without genotyping error, complicating the use of HWE testing to assess genotype data quality. In this manuscript, we present the Robust Unified Test for HWE (RUTH) to test for HWE while accounting for population structure and genotype uncertainty, and evaluate the impact of population heterogeneity and genotype uncertainty on the standard HWE tests and alternative methods using simulated and real sequence datasets. Our results demonstrate that ignoring population structure or genotype uncertainty in HWE tests can inflate false positive rates by many orders of magnitude. Our evaluations demonstrate different tradeoffs between false positives and statistical power across the methods, with RUTH consistently amongst the best across all evaluations. RUTH is implemented as a practical and scalable software tool to rapidly perform HWE tests across millions of markers and hundreds of thousands of individuals while supporting standard VCF/BCF formats. RUTH is publicly available at https://www.github.com/statgen/ruth.

bioinformatics↗

bioRxiv2018

Expanded genetic landscape of chronic obstructive pulmonary disease reveals heterogeneous cell type and phenotype associations

Chronic obstructive pulmonary disease (COPD) is the leading cause of respiratory mortality worldwide. Genetic risk loci provide novel insights into disease pathogenesis. To broaden COPD genetic risk loci discovery and identify cell type and phenotype associations we performed a genome-wide association study in 35,735 cases and 222,076 controls from the UK Biobank and additional studies from the International COPD Genetics Consortium. We identified 82 loci with P value < 5x10-8; 47 were previously described in association with either COPD or population-based lung function. Of the remaining 35 novel loci, 13 were associated with lung function in 79,055 individuals from the SpiroMeta consortium. Using gene expression and regulation data, we identified enrichment for loci in lung tissue, smooth muscle and alveolar type II cells. We found 9 shared genomic regions between COPD and asthma and 5 between COPD and pulmonary fibrosis. COPD genetic risk loci clustered into groups of quantitative imaging features and comorbidity associations. Our analyses provide further support to the genetic susceptibility and heterogeneity of COPD.

genetics↗