Biology subjects

Miao, Z.

Publications and source records attributed to Miao, Z..

At least 55 records · Page 3Linked to original sources

bioRxiv2022

Benchmarking strategies for cross-species integration of single-cell RNA sequencing data

The growing number of available single cell gene expression datasets from different species creates opportunities to explore evolutionary relationships between cell types across species. Cross-species integration of single-cell RNA-sequencing data has been particularly informative in this context. However, in order to do so robustly it is essential to have rigorous benchmarking and appropriate guidelines to ensure that integration results truly reflect biology. We benchmarked 28 combinations of gene homology mapping methods and data integration algorithms in a variety of biological settings. We examined the capability of each strategy to perform species-mixing of known homologous cell types and to preserve biological heterogeneity using 9 established metrics. We also developed a new biology conservation metric to address the maintenance of cell type distinguishability. Overall, scANVI, scVI and SeuratV4 methods achieved a balance between species-mixing and biology conservation. For evolutionarily distant species, including in-paralogs was beneficial. SAMap outperformed when integrating whole-body atlases between species with challenging gene homology annotation. We provided our freely available cross-species integration and assessment pipeline to help analyse new data and develop new algorithms.

bioinformatics↗

bioRxiv2022

Is single nucleus ATAC-seq accessibility a qualitative or quantitative measurement?

Single nucleus ATAC-seq is a key assay for gene regulation analysis. Existing approaches to scoring feature matrices from sequencing reads are inconsistent with each other, creating differences in downstream analysis, and displaying artifacts. We show that even with sparse single cell data, quantitative counts are informative for estimating a cells regulatory state, which calls for consistent treatment. We propose Paired-Insertion-Counting (PIC) as a uniform method for snATAC-seq feature characterization.

bioinformatics↗

bioRxiv2022

Genome-wide CRISPR screens of T cell exhaustion identify chromatin remodeling factors that limit T cell persistence

T cell exhaustion limits anti-tumor immunity, but the molecular determinants of this process remain poorly understood. Using a chronic antigen stimulation assay, we performed genome-wide CRISPR/Cas9 screens to systematically discover genetic regulators of T cell exhaustion, which identified an enrichment of epigenetic factors. In vivo CRISPR screens in murine and human tumor models demonstrated that perturbation of several epigenetic regulators, including members of the INO80 and BAF chromatin remodeling complexes, improved T cell persistence in tumors. In vivo paired CRISPR perturbation and single-cell RNA sequencing revealed distinct transcriptional roles of each complex and that depletion of canonical BAF complex members, including Arid1a, resulted in the maintenance of an effector program and downregulation of terminal exhaustion-related genes in tumor-infiltrating T cells. Finally, Arid1a-depletion limited the global acquisition of chromatin accessibility associated with T cell exhaustion and led to improved anti-tumor immunity after adoptive cell therapy. In summary, we provide a comprehensive atlas of the genetic regulators of T cell exhaustion and demonstrate that modulation of the epigenetic state of T cell exhaustion can improve T cell responses in cancer immunotherapy.

immunology↗

bioRxiv2022

Single-cell atlas of human liver development reveals pathways directing hepatic cell fates

The liver has been studied extensively due to the broad number of diseases affecting its vital functions. However, therapeutic advances, especially in regenerative medicine, are currently hampered by the lack of knowledge concerning human hepatic cell development. Here, we addressed this limitation by describing the developmental trajectories of different cell types comprising the human fetal liver at single-cell resolution. These transcriptomic analyses revealed that sequential cell-to-cell interactions direct functional maturation of hepatocytes, with non-parenchymal cells playing critical, supportive roles during organogenesis. We utilised this information to derive bipotential hepatoblast organoids and then exploited this novel model system to validate the importance of key signalling pathways and developmental cues. Furthermore, these insights into hepatic maturation enabled the identification of stage-specific transcription factors to improve the functionality of hepatocyte-like cells generated from human pluripotent stem cells. Thus, our study establishes a new platform to investigate the basic mechanisms of human liver development and to produce cell types for clinical applications.

developmental biology↗

bioRxiv2021

Structure prediction of the druggable fragments in SARS-CoV-2 untranslated regions

The outbreak of the COVID-19 pandemic has led to intensive studies of both the structure and replication mechanism of SARS-CoV-2. In spite of some secondary structure experiments being carried out, the 3D structure of the key function regions of the viral RNA has not yet been well understood. At the beginning of COVID-19 breakout, RNA-Puzzles community attempted to envisage the three-dimensional structure of 5'- and 3'-Un-Translated Regions (UTRs) of the SARS-CoV-2 genome. Here, we report the results of this prediction challenge, presenting the methodologies developed by six participating groups and discussing 100 RNA 3D models (60 models of 5'-UTR and 40 of 3'-UTR) predicted through applying both human experts and automated server approaches. We describe the original protocol for the reference-free comparative analysis of RNA 3D structures designed especially for this challenge. We elaborate on the deduced consensus structure and the reliability of the predicted structural motifs. All the computationally simulated models, as well as the development and the testing of computational tools dedicated to 3D structure analysis, are available for further study.

bioinformatics↗

bioRxiv2021

Evaluation of the stereochemical quality of predicted RNA 3D models in the RNA-Puzzles submissions

In silico prediction is a well-established approach to derive a general shape of an RNA molecule based on its sequence or secondary structure. This paper reports an analysis of the stereochemical quality of the RNA three-dimensional models predicted using dedicated computer programs. The stereochemistry of 1,052 RNA 3D structures, including 1,030 models predicted by fully automated and human-guided approaches within 22 RNA-Puzzles challenges and reference structures, is analysed. The evaluation is based on standards of RNA stereochemistry that the Protein Data Bank requires from deposited experimental structures. Deviations from standard bond lengths and angles, planarity or chirality are quantified. A reduction in the number of such deviations should help in the improvement of RNA 3D structure modelling approaches.

bioinformatics↗

bioRxiv2021

Individual Level Differential Expression Analysis for Single Cell RNA-seq data

Bulk RNA-seq data quantify the expression of a gene in an individual by one number (e.g., fragment count). In contrast, single cell RNA-seq (scRNA-seq) data provide much richer information: the distribution of gene expression across many cells. To assess differential expression across individuals using scRNA-seq data, a straightforward solution is to create "pseudo" bulk RNA-seq data by adding up the fragment counts of a gene across cells for each individual, and then apply methods designed for differential expression using bulk RNA-seq data. This pseudo-bulk solution reduces the distribution of gene expression across cells to a single number and thus loses a good amount of information. We propose to assess differential expression using the gene expression distribution measured by cell level data. We find denoising cell level data can substantially improve the power of this approach. We apply our method, named IDEAS (Individual level Differential Expression Analysis for scRNA-seq), to study the gene expression difference between autism subjects and controls. We find neurogranin-expressing neurons harbor a high proportion of differentially expressed genes, and ERBB signals in microglia are associated with autism.

bioinformatics↗

bioRxiv2021

Chromatin profiling reveals genome stability heterogeneity in clinical isolates of the human pathogen Aspergillus fumigatus

Invasive Pulmonary aspergillosis is a life-threatening infection in immunosuppressed patients caused by the filamentous fungus Aspergillus fumigatus. Chromatin structure regulation is important for genome stability maintenance and has the potential to lead to genome rearrangements driving differences in virulence and pathogenesis of different A. fumigatus isolates. Here, we compared the chromatin activities of the most investigated clinical isolates Af293 and CEA17 and uncovered striking differences in the number, locations and expression of transposable elements. We found evidence for higher genome instability in Af293 as compared to CEA17 and identified a spontaneous Af293 variant that exhibits gross chromosomal alterations including the loss of a 320 kb long segment in chromosome VIII and the amplification of a biosynthetic gene cluster. As a consequence of these re-arrangements, the variant shows increased secondary metabolites production, growth and virulence. Our work emphasizes genome stability heterogeneity as an evolutionary driver of A. fumigatus fitness and virulence.

molecular biology↗

bioRxiv2021

Meta-analysis reveals consistent immune response patterns in COVID-19 infected patients at single-cell resolution

A number of single-cell RNA studies looking at the human immune response to the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been recently published. However, the number of samples used in each individual study typically is small, moreover the technologies and protocols used in different studies vary, thus somewhat restricting the range of conclusions that can be made with high confidence. To better capture the cellular gene expression changes upon SARS-CoV-2 infection at different levels and stages of disease severity and to minimise the effect of technical artefacts, we performed meta-analysis of data from 9 previously published studies, together comprising 143 human samples, and a set of 16 healthy control samples (10X). In particular, we used generally accepted immune cell markers to discern specific cell subtypes and to look at the changes of the cell proportion over different disease stages and their consistency across the studies. While half of the observations reported in the individual studies can be confirmed across multiple studies, half of the results seem to be less conclusive. In particular, we show that the differentially expressed genes consistently point to upregulation of type I Interferon signal pathway and downregulation of the mitochondrial genes, alongside several other reproducibly consistent changes. We also confirm the presence of expanded B-cell clones in COVID-19 patients, however, no consistent trend in T-cell clonal expansion was observed.

bioinformatics↗

bioRxiv2020

Mapping the genetic architecture of human traits to cell types in the kidney identifies mechanisms of disease and potential treatments

The functional interpretation of GWAS remains challenging due to cell-type dependent influences of genetic variants. Here, we generated comprehensive maps of expression quantitative trait loci (eQTL) for 659 microdissected human kidney samples and identified cell-type eQTLs by mapping interactions between cell type abundance and genotype. Separately, we generated single cell open chromatin maps (by snATAC-seq) for human kidney samples. We highlight critical enrichment of proximal tubules in kidney function and endothelial cells and distal tubule segments in blood pressure by partitioning heritability using stratified LD-score regression to integrate GWAS with scRNA-seq and snATAC-seq data. Bayesian colocalization analysis nominated more than 200 genes for kidney function and hypertension. Our study clarifies the mechanism of the most commonly used antihypertensive and renal protective drugs and identifies drug repurposing opportunities for kidney disease. One Sentence SummaryWe define causal cell types, genes and mechanism for kidney dysfunction.

genetics↗

bioRxiv2020

Single cell resolution regulatory landscape of the mouse kidney highlights cellular differentiation programs and renal disease targets

Determining the epigenetic program that generates unique cell types in the kidney is critical for understanding cell-type heterogeneity during tissue homeostasis and injury response. Here, we profiled open chromatin and gene expression in developing and adult mouse kidneys at single cell resolution. We show critical reliance of gene expression on distal regulatory elements (enhancers). We define key cell type-specific transcription factors and major gene-regulatory circuits for kidney cells. Dynamic chromatin and expression changes during nephron progenitor differentiation demonstrated that podocyte commitment occurs early and is associated with sustained Foxl1 expression. Renal tubule cells followed a more complex differentiation, where Hfn4a was associated with proximal and Tfap2b with distal fate. Mapping single nucleotide variants associated with human kidney disease identified critical cell types, developmental stages, genes, and regulatory mechanisms. We provide a global single cell resolution view of chromatin accessibility of kidney development. The dataset is available via interactive public websites.

developmental biology↗

bioRxiv2020

Longevity, clonal relationship and transcriptional program of celiac disease-specific plasma cells

Disease-specific plasma cells (PCs) reactive with transglutaminase 2 (TG2) or deamidated gluten peptides (DGP) are abundant in celiac disease (CeD) gut lesions. Their contribution toward CeD pathogenesis is unclear. We assessed expression of markers associated with PC longevity in 15 untreated and 26 treated CeD patients in addition to 13 non-CeD controls, and performed RNA-sequencing with clonal inference and transcriptomic analysis of 3251 single PCs. We observed antigen-dependent V-gene selection and stereotypic antibodies. Generation of recombinant DGP-specific antibodies revealed a key role of a heavy-chain residue that displays polymorphism, suggesting that immunoglobulin gene polymorphisms may influence CeD-specific antibody responses. We identified transcriptional differences between CeD-specific vs non-disease-specific PCs and between short-lived vs long-lived PCs. The short-lived CD19+CD45+ phenotype dominated in untreated and short-term-treated CeD, in particular among disease-specific PCs but also in the general PC population. Thus, the disease lesion of untreated CeD is characterized by massive accumulation of short-lived PCs that are not only directed against disease-specific antigens.

immunology↗

bioRxiv2020

Efficacy of transoral laser surgery versus linear accelerator radiotherapy for the treatment of T1 glottic carcinoma: a meta-analysis of oncologic outcomes

ObjectiveA meta-analysis was conducted to compare oncologic outcomes for patients of T1 glottic carcinoma who were treated with transoral laser surgery (TLS) or linear accelerator radiotherapy (linac RT). MethodsAll related studies published up to September 2019 were acquired by searching Pubmed, EMBASE, and Cochrane, with the index words: glottic, vocal, laryngeal, radiation, radiotherapy, irradiation, laser, surgery, cordectomy, carcinoma and cancer. Relative studies which compared oncologic outcomes between linac RT and TLS were included. Sensitivity analysis were performed to evaluate heterogeneity. ResultsA total of twelve eligible studies were included for the analysis, which contained three prospective studies and nine retrospective studies. Patients who underwent TLS had increased overall survival (OR = 1.40, 95% CI=1.02-1.94, P=0.04) and laryngeal preservation (OR = 5.37, 95% CI = 3.05-9.44, P < 0.00001) versus who underwent linac RT. No statistical difference was observed between TLS group and linac RT group in terms of local control (OR=0.88, 95% CI = 0.62-1.24, P = 0.47), disease-specific survival (OR = 0.61, 95% CI = 0.26-1.43, P = 0.26), and disease-free survival (OR = 1.63, 95% CI = 0.70-3.81, P = 0.14). ConclusionsThe results of this meta-analysis indicate that there were clinical benefits for patients with glottic carcinoma after TLS compared with linac RT with respect to overall survival and laryngeal preservation. However, more multi-center randomized controlled trials would be urgently needed to prove these differences.

cancer biology↗

bioRxiv2020

Interpretable machine learning framework reveals novel gut microbiome features in predicting type 2 diabetes

Gut microbiome targets for type 2 diabetes (T2D) prevention among human cohorts have been controversial. Using an interpretable machine learning-based analytic framework, we identified robust human gut microbiome features, with their optimal threshold, in predicting T2D. Based on the results, we constructed a microbiome risk score (MRS), which was consistently associated with T2D across 3 independent Chinese cohorts involving 9111 participants (926 T2D cases). The MRS could also predict future glucose increment, and was correlated with a variety of gut microbiota-derived blood metabolites. Faecal microbiota transplantation from humans to germ-free mice demonstrated a causal role of the identified combination of microbes in the T2D development. We further identified adiposity and dietary factors which could prospectively modulate the MRS, and found that body fat distribution may be the key factor modulating the gut microbiome-T2D relationship. Taken together, we proposed a new analytical framework for the investigation of microbiome-disease relationship. The identified microbiota may serve as potential drug targets for T2D in future.

microbiology↗

bioRxiv2020

A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19

The global spread of SARS-CoV-2 requires an urgent need to find effective therapeutics for the treatment of COVID-19. We developed a data-driven drug repositioning framework, which applies both machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph, literature and transcriptome data to discover the potential drug candidates against SARS-CoV-2. The retrospective study using the past SARS-CoV and MERS-CoV data demonstrated that our machine learning based method can successfully predict effective drug candidates against a specific coronavirus. Our in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218, currently in Phase I clinical trial, may be repurposed to treat COVID-19. Our in vitro assays revealed that CVL218 can exhibit effective inhibitory activity against SARS-CoV-2 replication without obvious cytopathic effect. In addition, we showed that CVL218 is able to suppress the CpG-induced IL-6 production in peripheral blood mononuclear cells, suggesting that it may also have anti-inflammatory effect that is highly relevant to the prevention immunopathology induced by SARS-CoV-2 infection. Further pharmacokinetic and toxicokinetic evaluation in rats and monkeys showed a high concentration of CVL218 in lung and observed no apparent signs of toxicity, indicating the appealing potential of this drug for the treatment of the pneumonia caused by SARS-CoV-2 infection. Moreover, molecular docking simulation suggested that CVL218 may bind to the N-terminal domain of nucleocapsid (N) protein of SARS-CoV-2, providing a possible model to explain its antiviral action. We also proposed several possible mechanisms to explain the antiviral activities of PARP1 inhibitors against SARS-CoV-2, based on the data present in this study and previous evidences reported in the literature. In summary, the PARP1 inhibitor CVL218 discovered by our data-driven drug repositioning framework can serve as a potential therapeutic agent for the treatment of COVID-19.

systems biology↗

bioRxiv2020

Shift of maternal gut microbiome of Tibetan antelope (Pantholops hodgsonii) during the perinatal period

The maternal gut microbiome can influence and be affected by the substantial physiological changes taking place during the perinatal period. However, little information is known about the changes in the maternal gut microbiome during this period. Tibetan antelope (Pantholops hodgsonii) provide a unique system to address this issue because their summer migration cycle is synchronized with the perinatal period. We used 16S rRNA gene sequencing to generate gut microbiome profiles using fecal samples collected from female migratory Tibetan antelope. We then correlated microbiome diversity with fecal hormone metabolite concentrations of glucocorticoids (GCs) and triiodothyronine (T3) extracted from the same fecal samples. The maternal gut microbiome of Tibetan antelope was dominated by Firmicutes and Bacteroidetes. There was a clear separation in gut microbial composition by female reproductive states based on both hierarchical clustering and PCoA analyses. The shift in the maternal gut microbiome likely reflects the metabolic and immune system dynamics during the perinatal period. Overall, the microbiome diversity was higher in the late pregnancy compared to the postpartum period. The negative association between T3 and microbiome diversity may be moderated by the shift of reproductive states since the correlations disappeared when considering each reproductive state separately. Integrating the microbiome dimension, migration pattern and reproduction may have direct conservation implications as by establishing a baseline of the physiological changes during the migration/perinatal period, we can have a better understanding of the impacts of increasing human activities on the Tibetan Plateau on the reproductive health of Tibetan antelope.

zoology↗

bioRxiv2019

The interplay between host genetics and the gut microbiome reveals common and distinct microbiome features for human complex diseases

There is increasing interest about the interplay between host genetics and gut microbiome on human complex diseases, with prior evidence mainly derived from animal models. In addition, the shared and distinct microbiome features among human complex diseases remain largely unclear. We performed a microbiome genome-wide association study to identify host genetic variants associated with gut microbiome in a Chinese population with 1475 participants. We then conducted bi-directional Mendelian randomization analyses to examine the potential causal associations between gut microbiome and human complex diseases. We found that Saccharibacteria (also known as TM7 phylum) could potentially improve renal function by affecting renal function biomarkers (i.e., creatinine and estimated glomerular filtration rate). In contrast, atrial fibrillation, chronic kidney disease and prostate cancer, as predicted by the host genetics, had potential causal effect on gut microbiome. Further disease-microbiome feature analysis suggested that gut microbiome features revealed novel relationship among human complex diseases. These results suggest that different human complex diseases share common and distinct gut microbiome features, which may help re-shape our understanding about the disease etiology in humans.

microbiology↗

bioRxiv2019

Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

Single-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. Contrary to single-cell RNA seq (scRNA-seq), we observe that snRNA-seq is commonly subject to contamination by high amounts of extranuclear background RNA, which can lead to identification of spurious cell types in downstream clustering analyses if overlooked. We present a novel approach to remove debris-contaminated droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: 1) human differentiating preadipocytes in vitro, 2) fresh mouse brain tissue, and 3) human frozen adipose tissue (AT) from six individuals. All three data sets showed various degrees of extranuclear RNA contamination. We observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq data, we also successfully applied DIEM to single-cell data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.

bioinformatics↗