Biology subjects

Luo, R.

Publications and source records attributed to Luo, R..

At least 55 records · Page 3Linked to original sources

bioRxiv2024

Bacterial-tyramine-induced renal-gut countercurrent flow regulates gut immune response by promoting ROS accumulation and gut peristalsis in the fly Bactrocera dorsalis

Insects increase gut peristalsis to purge ingested pathogens. They also rely on the Duox-ROS system to mount an efficient gut response. However, how these two host defense mechanisms are coordinated to clear pathogens remained unclear. We found that gut peristalsis is the major force for bacteria elimination, while Duox is necessary as a signaling molecule to activate gut peristalsis in a Tephritidae fruit fly Bactrocera dorsalis. We found an insect-conserved renal-gut countercurrent flow formed upon recognizing bacteria-derived tyramine counteracts the gut peristalsis forced defecation and drives the accumulation of ROS. Furthermore, we demonstrated that the renal-gut countercurrent flow maintains proper microbiota composition. Our work provided a novel renal-gut interaction that ensures an efficient gut-immune response.

immunology↗

bioRxiv2023

ClusterV-Web: A User-Friendly Tool for Profiling HIV Quasispecies and Generating Drug Resistance Reports from Nanopore Long-Read Data

SummaryThird-generation long-read sequencing is an increasingly utilized technique for profiling HIV quasispecies and detecting drug resistance mutations due to its ability to cover the entire viral genome in individual reads. Recently, the ClusterV tool has demonstrated accurate detection of HIV quasispecies from Nanopore long-read sequencing data. However, the need for scripting skills and a computational environment may act as a barrier for many potential users. To address this issue, we have introduced ClusterV-Web, a user-friendly web-based application that enables easy configuration and execution of ClusterV, both remotely and locally. Our tool provides interactive tables and data visualizations to aid in the interpretation of results. This development is expected to democratize access to long-read sequencing data analysis, enabling a wider range of researchers and clinicians to efficiently profile HIV quasispecies and detect drug resistance mutations. Availability and implementationClusterV-Web is freely available and open source, with detailed documentation accessible at http://www.bio8.cs.hku.hk/ClusterVW/. The standalone Docker image and source code are also available at https://github.com/HKU-BAL/ClusterV-Web. Contactrbluo@cs.hku.hk, Department of Computer Science, The University of Hong Kong, Hong Kong, China; jhsu@cs.hku.hk, Department of Computer Science, The University of Hong Kong, Hong Kong, China Supplementary informationNone

bioinformatics↗

bioRxiv2023

CellContrast: Reconstructing Spatial Relationships in Single-Cell RNA Sequencing Data via Deep Contrastive Learning

A vast amount of single-cell RNA-seq (SC) data has been accumulated via various studies and consortiums, but the lack of spatial information limits its analysis of complex biological activities. To bridge this gap, we introduce cellContrast, a computational method for reconstructing spatial relationships among SC cells from spatial transcriptomics (ST) reference. By adopting a contrastive learning framework and training with ST data, cellContrast projects gene expressions into a hidden space where proximate cells share similar representation values. We performed extensive benchmarking on diverse platforms, including SeqFISH, Stereo-Seq, 10X Visium, and MERSCOPE, on mouse embryo and human breast cells. The results reveal that cellContrast substantially outperforms other related methods, facilitating accurate spatial reconstruction of SC. We further demonstrate cellContrasts utility by applying it to cell-type co-localization and cell-cell communication analysis with real-world SC samples, proving the recovered cell locations empower novel discoveries and mitigate potential false positives.

bioinformatics↗

bioRxiv2023

High-resolution diploid 3D genome reconstruction using Pore-C data

In diploid organisms, spatial variations between homologous chromosomes are essential to many biological phenomena. Currently, it is still challenging to efficiently reconstruct a high-quality diploid 3D human genome. Here, we introduce Dip3D, reconstructing the diploid 3D human genome using Pore-C data of one sample. Dip3D has solved multiple problems in genome-wide SNV calling and haplo-tagging caused by the high sequencing error rates in Pore-C type data. Dip3D capitalizes on the high-order chromosomal interaction characteristics, enabling robust haplotype imputation and intricate haplotype-specific 3D structure discovery. Dip3D outperforms previous methods in data utilization rate, contact matrix resolution, and completeness by one order of magnitude. Moreover, Dip3D allows capturing haplotype high-order interactions that are unseen in Hi-C type data. We demonstrated the identified haplotype substructures such as Topologically Associating Domains (TADs) in the constructed 3D human genome, and unraveled connections between genic haplotype-specific high-order interactions and imbalanced allelic expression.

genomics↗

bioRxiv2023

ClairS: a deep-learning method for long-read somatic small variant calling

Identifying somatic variants in tumor samples is a crucial task, which is often performed using statistical methods and heuristic filters applied to short-read data. However, with the increasing demand for long-read somatic variant calling, existing methods have fallen short. To address this gap, we present ClairS, the first deep-learning-based, long-read somatic small variant caller. ClairS was trained on massive synthetic somatic variants with diverse coverages and variant allele frequencies (VAF), enabling it to accurately detect a wide range of somatic variants from paired tumor and normal samples. We evaluated ClairS using the latest Nanopore Q20+ HCC1395-HCC1395BL dataset. With 50-fold/25-fold tumor/normal, ClairS achieved a 93.01%/86.86% precision/recall rate for Single Nucleotide Variation (SNVs), and 66.54%/66.89% for somatic insertions and deletions (Indels). Applying ClairS to short-read datasets from multiple sources showed comparable or better performance than Strelka2 and Mutect2. Our findings suggest that improved read phasing enabled by long-read sequencing is key to accurate long-read SNV calling, especially for variants with low VAF. Through experiments across various coverage, purity, and contamination settings, we demonstrated that ClairS is a reliable somatic variant caller. ClairS is open-source at https://github.com/HKU-BAL/ClairS.

bioinformatics↗

bioRxiv2023

ChromaFold predicts the 3D contact map from single-cell chromatin accessibility

The identification of cell-type-specific 3D chromatin interactions between regulatory elements can help to decipher gene regulation and to interpret the function of disease-associated non-coding variants. However, current chromosome conformation capture (3C) technologies are unable to resolve interactions at this resolution when only small numbers of cells are available as input. We therefore present ChromaFold, a deep learning model that predicts 3D contact maps and regulatory interactions from single-cell ATAC sequencing (scATAC-seq) data alone. ChromaFold uses pseudobulk chromatin accessibility, co-accessibility profiles across metacells, and predicted CTCF motif tracks as input features and employs a lightweight architecture to enable training on standard GPUs. Once trained on paired scATAC-seq and Hi-C data in human cell lines and tissues, ChromaFold can accurately predict both the 3D contact map and peak-level interactions across diverse human and mouse test cell types. In benchmarking against a recent deep learning method that uses bulk ATAC-seq, DNA sequence, and CTCF ChIP-seq to make cell-type-specific predictions, ChromaFold yields superior prediction performance when including CTCF ChIP-seq data as an input and comparable performance without. Finally, fine-tuning ChromaFold on paired scATAC-seq and Hi-C in a complex tissue enables deconvolution of chromatin interactions across cell subpopulations. ChromaFold thus achieves state-of-the-art prediction of 3D contact maps and regulatory interactions using scATAC-seq alone as input data, enabling accurate inference of cell-type-specific interactions in settings where 3C-based assays are infeasible.

bioinformatics↗

bioRxiv2023

Discovery of Competent Chromatin Regions in Human Embryonic Stem Cells

The mechanisms underlying the ability of embryonic stem cells (ESCs) to rapidly activate lineage-specific genes during differentiation remain largely unknown. Through multiple CRISPR-activation screens, we discovered human ESCs have pre-established transcriptionally competent chromatin regions (CCRs) that support lineage-specific gene expression at levels comparable to differentiated cells. CCRs reside in the same topological domains as their target genes. They lack typical enhancer-associated histone modifications but show enriched occupancy of pluripotent transcription factors, DNA demethylation factors, and histone deacetylases. TET1 and QSER1 protect CCRs from excessive DNA methylation, while HDAC1 family members prevent premature activation. This "push and pull" feature resembles bivalent domains at developmental gene promoters but involves distinct molecular mechanisms. Our study provides new insights into pluripotency regulation and cellular plasticity in development and disease. One sentence summaryWe report a class of distal regulatory regions distinct from enhancers that confer human embryonic stem cells with the competence to rapidly activate the expression of lineage-specific genes.

developmental biology↗

bioRxiv2023

Boosting variant-calling performance with multi-platform sequencing data using Clair3-MP

BackgroundWith the continuous advances in third-generation sequencing technology and the increasing affordability of next-generation sequencing technology, sequencing data from different sequencing technology platforms is becoming more common. While numerous benchmarking studies have been conducted to compare variant-calling performance across different platforms and approaches, little attention has been paid to the potential of leveraging the strengths of different platforms to optimize overall performance, especially integrating Oxford Nanopore and Illumina sequencing data. ResultsWe investigated the impact of multi-platform data on the performance of variant calling through carefully designed experiments with a deep learning-based variant caller named Clair3-MP (Multi-Platform). Through our research, we not only demonstrated the capability of ONT-Illumina data for improved variant calling, but also identified the optimal scenarios for utilizing ONT-Illumina data. In addition, we revealed that the improvement in variant calling using ONT-Illumina data comes from an improvement in difficult genomic regions, such as the large low-complexity regions and segmental and collapse duplication regions. Moreover, Clair3-MP can incorporate reference genome stratification information to achieve a small but measurable improvement in variant calling. Clair3-MP is accessible as an open-source project at: https://github.com/HKU-BAL/Clair3-MP. ConclusionsThese insights have important implications for researchers and practitioners alike, providing valuable guidance for improving the reliability and efficiency of genomic analysis in diverse applications.

bioinformatics↗

bioRxiv2023

Parallel genome-scale CRISPR screens distinguish pluripotency and self-renewal

Pluripotent stem cells are defined by their self-renewal capacity, which is the ability of the stem cells to proliferate indefinitely while maintaining the pluripotent identity essential for their ability to differentiate into any somatic cell lineage. However, understanding the mechanisms that control stem cell fitness versus the pluripotent cell identity is challenging. To investigate the interplay between these two aspects of pluripotency, we performed four parallel genome-scale CRISPR-Cas9 loss-of-function screens interrogating stem cell fitness in hPSC self-renewal conditions, and the dissolution of the primed pluripotency identity during early differentiation. Comparative analyses led to the discovery of genes with distinct roles in pluripotency regulation, including mitochondrial and metabolism regulators crucial for stem cell fitness, and chromatin regulators that control pluripotent identity during early differentiation. We further discovered a core set of factors that control both stem cell fitness and pluripotent identity, including a network of chromatin factors that safeguard pluripotency. Our unbiased and systematic screening and comparative analyses disentangle two interconnected aspects of pluripotency, provide rich datasets for exploring pluripotent cell identity versus cell fitness, and offer a valuable model for categorizing gene function in broad biological contexts.

developmental biology↗

bioRxiv2023

Dynamic network-guided CRISPRi screen reveals CTCF loop-constrained nonlinear enhancer-gene regulatory activity in cell state transitions

Comprehensive enhancer discovery is challenging because most enhancers, especially those affected in complex diseases, have weak effects on gene expression. Our network modeling revealed that nonlinear enhancer-gene regulation during cell state transitions can be leveraged to improve the sensitivity of enhancer discovery. Utilizing hESC definitive endoderm differentiation as a dynamic transition system, we conducted a mid-transition CRISPRi-based enhancer screen. The screen discovered a comprehensive set of enhancers (4 to 9 per locus) for each of the core endoderm lineage-specifying transcription factors, and many enhancers had strong effects mid-transition but weak effects post-transition. Through integrating enhancer activity measurements and three-dimensional enhancer-promoter interaction information, we were able to develop a CTCF loop-constrained Interaction Activity (CIA) model that can better predict functional enhancers compared to models that rely on Hi-C-based enhancer-promoter contact frequency. Our study provides generalizable strategies for sensitive and more comprehensive enhancer discovery in both normal and pathological cell state transitions.

genetics↗

bioRxiv2022

Evaluation of Mycobacterium Tuberculosis enrichment in metagenomic samples using ONT adaptive sequencing and amplicon sequencing for identification and variant calling

Sensitive detection of Mycobacterium Tuberculosis (TB) in small percentages in metagenomic samples is essential for microbial classification and drug resistance prediction, and assisting in diagnosis and treatment planning. However, traditional methods, such as bacterial culture and microscopy, are time-consuming and sometimes have limited TB detection sensitivity. Oxford Nanopore Technologies MinION sequencing allows rapid and simple sample preparation for whole genome and amplicon sequencing. Its recently developed adaptive sequencing selects reads from targets, while allowing real-time base-calling during sequencing to achieve sequence enrichment or depletion. Another common enrichment method is PCR amplification of the target TB genes. In this study, we compared both methods with MinION sequencing for TB detection and variant calling in metagenomic samples using both simulation runs and those with synthetic and patient samples. We found that both methods effectively enrich TB reads from a high percentage of human and other microbial DNA. We provide a simple automatic analysis framework, including quality filtering, taxonomic classification, variant calling, and antimicrobial resistance prediction, to support these detection workflows for clinical use, openly available at https://github.com/HKU-BAL/ONT-TB-NF. Depending on the patients medical condition and sample type (commonly including bronchoalveolar lavage fluid, blood samples, sputum, and tissues), we discuss the findings and recommend that users optimize their workflow to improve the detection limit.

bioinformatics↗

bioRxiv2022

VStrains: De Novo Reconstruction of Viral Strains via Iterative Path Extraction From Assembly Graphs

With the high mutation rate in viruses, a mixture of closely related viral strains (called viral quasispecies) often co-infect an individual host. Reconstructing individual strains from viral quasispecies is a key step to characterizing the viral population, revealing strain-level genetic variability, and providing insights into biomedical and clinical studies. Reference-based approaches of reconstructing viral strains suffer from the lack of high-quality references due to high mutation rates and biased variant calling introduced by a selected reference. De novo methods require no references but face challenges due to errors in reads, the high similarity of quasispecies, and uneven abundance of strains. In this paper, we propose VStrains, a de novo approach for reconstructing strains from viral quasispecies. VStrains incorporates contigs, paired-end reads, and coverage information to iteratively extract the strain-specific paths from assembly graphs. We benchmark VStrains against multiple state-of-the-art de novo and reference-based approaches on both simulated and real datasets. Experimental results demonstrate that VStrains achieves the best overall performance on both simulated and real datasets under a comprehensive set of metrics such as genome fraction, duplication ratio, NGA50, error rate, etc. AvailabilityVStrains is freely available at https://github.com/MetaGenTools/VStrains.

bioinformatics↗

bioRxiv2022

Gut bacteria-derived serotonin promotes immune tolerance in early life

The gut microbiome promotes immune system development in early life, but the neonatal gut metabolome remains undefined. Here, we demonstrate that, distinct from adults, the neonatal mouse gut is enriched with neurotransmitters, and specific bacteria produce serotonin directly while downregulating monoamine oxidase A to limit serotonin breakdown. Serotonin inhibits mTOR activation to promote regulatory T cells and suppress T cell responses both ex vivo and in vivo in the neonatal intestine. Oral gavage of serotonin into neonatal mice leads to long-term immune tolerance toward both dietary antigens and commensal bacteria as well as alterations of the gut microbiome. Together, our study has uncovered unique microbiome-dependent mechanisms to maximize serotonin in the neonatal gut and a novel role for intestinal serotonin to promote immune tolerance in early life.

immunology↗

bioRxiv2022

Analysis of the mechanism of castor tolerance to Cd metal based on proteomics and metabolomics

The pollution of soil with heavy metals is an increasingly serious worldwide problem, and cadmium (Cd) has attracted attention because of its high toxicity to almost all plants. Since castor tolerates the accumulation of heavy metals, it has the potential for heavy metal soil remediation. We studied the mechanism of the tolerance of castor to Cd stress treatments at three doses: 300 mg/L, 700 mg/L, and 1,000 mg/L. Our research provides new ideas for revealing the defense and detoxification mechanisms of Cd-stressed castor. By combining the results of physiology, differential proteomics and comparative metabolomics, we conducted a comprehensive analysis of the networks that regulate the response of castor to Cd stress. The physiological results mainly emphasize the super-sensitive responses of castor plant roots to Cd stress and the effects of Cd stress on plants antioxidant system, ATP synthesis and ion homeostasis. We confirmed these results at the protein and metabolite levels. At the same time, proteomics and metabolomics show that castor plants mainly block the root systems absorption of Cd2+ by enhancing the strength of the cell wall, and inducing programmed cell death in response to the three different doses of Cd stress. In addition, the plasma membrane ATPase encoding gene (RcHA4), which was significantly upregulated in our differential proteomics and RT-qPCR studies, was transgenically overexpressed in wild type Arabidopsis thaliana for functional verification. The results indicated that this gene plays an important role in improving plant Cd tolerance.

molecular biology↗

bioRxiv2022

Duet: SNP-Assisted Structural Variant Calling and Phasing Using Oxford Nanopore Sequencing

BackgroundWhole genome sequencing (WGS) using the long-read Oxford Nanopore Technologies (ONT) MinION sequencer provides a cost-effective option for structural variant (SV) detection in clinical applications. Despite the advantage of using long reads, however, accurate SV calling and phasing are still challenging. ResultsWe introduce Duet, an SV detection tool optimized for SV calling and phasing using ONT data. The tool uses novel features integrated from both SV signatures and single-nucleotide polymorphism (SNP) signatures, which can accurately distinguish SV haplotype from a false signal. Duet was benchmarked against state-of-the-art tools on multiple ONT sequencing datasets of sequencing coverage ranging from 8X to 40X. At low sequencing coverage of 8X, Duet performs better than all other tools in SV calling, SV genotyping and SV phasing. When the sequencing coverage is higher (20X to 40X), the F1-score for SV phasing is further improved in comparison to the performance of other tools, while its performance of SV genotyping and SV calling remains comparable or higher than other tools. ConclusionDuet can perform accurate SV calling, SV genotyping and SV phasing using low-coverage ONT data, making it very useful for low-coverage genomes. It has great performance when scaled to high-coverage genomes, which is adaptable to various clinical applications. Duet is open source and is available at https://github.com/yekaizhou/duet.

bioinformatics↗

bioRxiv2022

DNA packaging via hierarchical chromatin structures revealed by live-cell 3D imaging

Single-molecule localization microscopy is a powerful superresolution imaging technique to study biological questions by visualizing subcellular fine structures with nanometer-scale precision. However, its application in live-cell imaging studies has been impeded by the paucity of self-blinking organic fluorophores that enable high spatiotemporal resolution and labeling/localization density at a moderate laser intensity. Herein, we report a self-blinking Si-rhodamine dye 6-HESiR with a suitably increased "ON" fraction and a fluorogenic self-blinking dsDNA probe 6-HoeHESiR as a powerful tool for 3D superresolution imaging of native chromatin in eukaryotes without the use of photoswitching buffer and high laser intensity. With the probe 6-HoeHESiR, 3D superresolution imaging of in vitro reconstituted nucleosomal arrays and chromatin fibers yielded results consistent with EM analysis. Similar euchromatin and heterochromatin structures were visualized in fixed and live cells with high spatiotemporal resolution and labeling density, providing the first live-cell evidence for a hierarchical model of chromatin organization. 3D imaging results obtained in the presence of selective inhibitors of histone deacetylases also corroborate chromatin fiber decompaction upon hyperacetylation of histones.

cell biology↗

bioRxiv2022

Assembly-free discovery of human novel sequences using long reads

DNA sequences that are absent in the human reference genome are classified as novel sequences. The discovery of these missed sequences is crucial for exploring the genomic diversity of populations and understanding the genetic basis of human diseases. However, various DNA lengths of reads generated from different sequencing technologies can significantly affect the results of novel sequences. In this work, we designed an Assembly-Free Novel Sequence (AF-NS) approach to identify novel sequences from Oxford Nanopore Technology long reads. Among the newly detected sequences using AF-NS, more than 95% were omitted from those using long-read assemblers, and 85% were not present in short reads of Illumina. We identified the common novel sequences among all the samples and revealed their association with the binding motifs of transcription factors. Regarding the placements of the novel sequences, we found about 70% enriched in repeat regions and generated 430 for one specific subpopulation that might be related to their evolution. Our study demonstrates the advance of the Assembly-Free approach to capture more novel sequences over other assembler based methods. Combining the long-read data with powerful analytical methods can be a robust way to improve the completeness of novel sequences.

bioinformatics↗

bioRxiv2022

Clair3-Trio: high-performance Nanopore long-read variant calling in family trios with Trio-to-Trio deep neural networks

Accurate identification of genetic variants from family child-mother-father trio sequencing data is important in genomics. However, state-of-the-art approaches treat variant calling from trios as three independent tasks, which limits their calling accuracy for Nanopore long-read sequencing data. For better trio variant calling, we introduce Clair3-Trio, the first variant caller tailored for family trio data from Nanopore long-reads. Clair3-Trio employs a Trio-to-Trio deep neural network model, which allows it to input the trio sequencing information and output all of the trios predicted variants within a single model to improve variant calling. We also present MCVLoss, a novel loss function tailor-made for variant calling in trios, leveraging the explicit encoding of the Mendelian inheritance. Clair3-Trio showed comprehensive improvement in experiments. It predicted far fewer Mendelian inheritance violation variations than current state-of-the-art methods. We also demonstrated that our Trio-to-Trio model is more accurate than competing architectures. Clair3-Trio is accessible as a free, open-source project at https://github.com/HKU-BAL/Clair3-Trio.

bioinformatics↗