Biology subjects

Zha, Y.

Publications and source records attributed to Zha, Y..

12 recordsLinked to original sources

bioRxiv2026

Marker-specific pixel attribution for single-cell protein quantification with PixAQ

Imaging mass cytometry (IMC) typically quantifies all markers within the same cell mask, although their signals have distinct spatial distributions. This quantification method may omit relevant signals or include signals from neighboring cells. We present PixAQ, which improves single-cell protein quantification in IMC through marker-specific probabilistic pixel attribution. PixAQ combines spatial proximity with anchor-group intensity models to assign pixel-marker observations among neighboring cells and an explicit residual component. Original intensities are averaged over marker-specific supports. Across three cohorts (680 regions), comparisons in 366 patients showed improved agreement with predefined population-marker relationships, with cohort-median patient-paired gains of 1.99 to 4.83 percentage points in expected-marker share over shared-mask quantification. Ablation analyses showed that both spatial and intensity evidence contributed to these gains. Conventional and high-resolution IMC images linked supports to visible local and intracellular signal structures. PixAQ improves single-cell protein quantification by determining which local signals contribute to each marker measurement without changing cell segmentation.

bioinformatics↗

bioRxiv2024

Integrative genomic analysis identifies unique immune environments associated with immunotherapy response in diffuse large B cell lymphoma

Most diffuse large B-cell lymphoma (DLBCL) patients treated with bispecific antibodies (BsAb) or chimeric antigen receptor (CAR) T cells fail to achieve durable treatment responses, underscoring the need for a deeper understanding of mechanisms that regulate the immune environment and response to treatment. Here, an integrative, multi-omic approach was employed to characterize DLBCL immune environments, which effectively segregated DLBCLs into four quadrants - termed DLBCL-immune quadrants (IQ) - defined by cell-of-origin and immune-related gene set expression scores. Recurrent genomic alterations were enriched in each IQ, suggesting that lymphoma cell-intrinsic alterations contribute to orchestrating unique DLBCL immune environments. In relapsed/refractory DLBCL patients, DLBCL-IQ assignment correlated significantly with clinical benefit with the CD20 x CD3 BsAb, mosunetuzumab, but not with CD19-directed CAR T cells. DLBCL-IQ provides a new framework to conceptualize the DLBCL immune landscape and uncovers the differential impact of the endogenous immune environment on outcomes to BsAb and CAR T cell treatment.

cancer biology↗

bioRxiv2023

Deciphering the Biosynthetic Potential of Microbial Genomes Using a BGC Language Processing Neural Network Model

Microbial secondary metabolites are usually synthesized by colocalized genes termed biosynthetic gene clusters (BGCs). A large portion of BGCs remain undiscovered in microbial genomes and metagenomes, representing a pressing challenge in unlocking the full potential of natural product diversity. In this work, we propose BGC-Prophet, a language model based on the transformer encoder that captures the distant location-dependent relationships among biosynthetic genes, allows accurately and efficiently identifies known BGCs and extrapolates novel BGCs among the microbial universe. BGC-Prophet is the first ultrahigh-throughput (UHT) method that is several orders of magnitude faster than existing tools such as DeepBGC, enabling pan-phylogenetic screening and whole-metagenome screening of BGCs. By analyzing 85,203 genomes and 9,428 metagenomes, new insights have been obtained about the diversity of BGCs on genomes from the majority of bacterial and archaeal lineages. The profound enrichment of BGCs in microbes after important geological events have been revealed: Both the Great Oxidation and Cambrian Explosion events led to a surge in BGC diversity and abundance, particularly in polyketides. These findings suggest that it is a general but constantly evolving approach for microbes to produce secondary metabolites for their adaptation in the changing environment. Taken together, BGC-Prophet enables accurate and fast detection of BGCs on a large scale, holds great promise for expanding BGC knowledge, and sheds light on the evolutionary patterns of BGCs for possible applications in synthetic biology. HighlightsO_LIBGC-Prophet shows superior performance to existing tools in terms of accuracy and speed. C_LIO_LIBGC-Prophet is the first ultrahigh-throughput (UHT) method that enables pan-phylogenetic screening and whole-metagenome screening of BGCs. C_LIO_LIBGC-Prophet builds the comprehensive profile of BGCs on 85,203 genomes and 9,428 metagenomes from the majority of bacterial and archaeal lineages. C_LIO_LIBGC-Prophet reveals the profound enrichment pattern of BGCs after important geological events. C_LI

bioinformatics↗

bioRxiv2022

Single-Cell RNA-seq of Heart Reveals Intercellular Communication Drivers of Myocardial Fibrosis in Diabetic Mice

Diabetes-induced cardiomyopathy is characterized by myocardial fibrosis as a main pathology. In-depth study of cardiac heterogeneity and cell-to-cell interactions will help to reveal the pathogenesis of diabetic myocardial fibrosis and provide potential targets for the treatment of this disease. Here, we insighted into the intercellular communication drivers underlying myocardial fibrosis in mouse heart with high-fat-diet (HFD)/streptozotocin (STZ)-induced diabetes at single-cell resolution. Intercellular and protein-protein interaction networks of fibroblasts and macrophages, endothelial cells, as well as fibroblasts and epicardial cells reveal critical changes in ligand-receptor interactions such as Pdgf(s)-Pdgfra and Efemp1-Egfr, which promote the development of a profibrotic microenvironment during diabetes progression and confirm that specific inhibition of Pdgfra axis can significantly improve diabetic myocardial fibrosis. We further identified the phenotypically distinct Hrchi and Postnhi fibroblast subpopulations that are associated with pathological extracellular matrix remodeling, of which Hrchi fibroblasts are the most profibrogenic under diabetic conditions. Finally, we validated the role of Itgb1 hub gene mediated intercellular communication drivers of diabetic myocardial fibrosis in Hrchi fibroblasts, and confirmed the result by AAV9-mediated Itgb1 knockdown in the heart of diabetic mice. In summary, cardiac cell mapping provides novel insights into intercellular communication drivers underlying pathological extracellular matrix remodeling during diabetic myocardial fibrosis.

cell biology↗

bioRxiv2022

Quality Control Framework of TCM Preparations based on Multi-type Fingerprints using a Source Proportion Estimation Model

Traditional Chinese Medicine (TCM) preparations have been widely used in clinical practice for the treatment of various diseases. The quality of TCM preparations is related to clinical efficacy and safety and is highly valued by researchers. The authenticity of TCM preparation can be guaranteed objectively by accurate quality control according to the composition. Here, we proposed a quality control framework of TCM preparations, which is based on multi-type fingerprints using the source proportion estimation model (SPEM). The high-performance liquid chromatography (HPLC) analysis and the high-throughput sequencing analysis are employed to acquire the chemical and taxonomic fingerprints of samples, respectively. The quality of TCM preparations among different manufacturers or batches is evaluated by using SPEM, which is an unsupervised method for source identification of TCM samples. Results showed the good performance of the quality control framework, for example, SPEM achieved a mean accuracy of 0.778 based on the ITS2 taxonomic fingerprint when differentiating manufacturer of BazhenYimu Wan pill. Applications of the quality control framework revealed the batch effect in TCM samples, and environmental factors, such as geography have a profound impact on the consistency of TCM preparations. In summary, this study is an exploration in the field of digital development of TCM preparations and provide a new insight to quantify the batch effect among different batches of TCM samples.

pharmacology and toxicology↗

bioRxiv2022

Microbial dysbiosis and metabolic disorders promote rheumatoid arthritis progression across successive stages

BackgroundRheumatoid arthritis (RA) is a chronic inflammatory disease that typically progresses through four stages, from mild stiffness to severe disability. The critical involvement of gut microbial dysbiosis in promoting RA has been intriguing. The aim of this study was to investigate stage-specific roles of microbial dysbiosis and metabolic disorders in pathogenesis across successive stages of RA. MethodsIn this multi-omics cohort study, fecal metagenomic, plasma metabolomic data were collected from 76 patients grouped into four RA stages, 19 patients with osteoarthritis, and 27 healthy individuals in China. A non-redundant gene catalogue was constructed, functionally annotated, and clustered into metagenomic species (MGS). Leave-one-out analysis was performed to determine the MGS that most drove the microbial dysfunctions with RA across stages. Random forest algorithm was performed to investigate diagnostic potential of microbial or metabolic features for each stage. Additionally, to verify the bacterial invasion of the joint synovial fluid, we conducted 16S rRNA gene sequencing, bacterial isolation, and scanning electron microscopy on joint synovial fluid from another cohort of 271 RA patients grouped into four RA stages. FindingsWe found that microbial dysbiosis and metabolic disorders had stage-specific effects on RA progression. For instance, arginine succinyltransferase pathway was elevated mainly in the second and third stages of RA (p=1{middle dot}4x10-3), which was driven by Escherichia coli, and it was correlated with the increase of the rheumatoid factor (p=1{middle dot}35x10-3). Considerable elevation of methoxyacetic acid (p=1{middle dot}28x10-8) and cysteine-S-sulfate (p=4{middle dot}66x10-12) might inhibit osteoblasts in the second stage and enhance osteoclasts in the third stage, respectively. Moreover, fecal microbes Eggerthella lenta and Bifidobacterium longum that were exclusively elevated in the fourth stage, were also detected in the joint synovial fluid. InterpretationOur findings elucidate for the first time the stage-specific roles of microbial dysbiosis and metabolic disorders across successive stages of RA, which open up new avenues for RA prognosis and therapy. We demonstrate the buildup of these effects might induce microbial invasion of the joint synovial fluid in the fourth stage of RA.

microbiology↗

bioRxiv2021

QTL analysis of vegetative phase change in natural accessions of Arabidopsis thaliana

Shoot development in plants is divided into two phases, a vegetative phase and a reproductive phase. Vegetative growth also has two distinct juvenile and adult phases, the transition between which is termed vegetative phase change. To understand how this developmental transition is regulated in natural populations of plants, we grew a group of 70 accessions of Arabidopsis thaliana and measured the appearance of traits associated with vegetative and reproductive phase change. We found that these transitions were uncorrelated, implying they are regulated by different mechanisms. Furthermore, an analysis of accessions from Central Asia revealed that precocious changes in leaf shape poorly correlated with the timing of abaxial trichome production (an adult trait) and with variation in the level of miR156 (a key regulator of vegetative phase change). This suggests the timing of vegetative phase change is regulated by more than one mechanism. To identify the genes responsible for the precocious vegetative phenotype of these accessions, we used a set of recombinant inbred lines derived from a cross between the standard lab strain, Col-0, and one of these accessions, Shakdara. We identified eight quantitative trait loci involved in the vegetative phase change, some of which regulated different components of leaf development. All of these loci were distinct from those that regulate flowering time. These data provide the foundation for future studies to identify the loci and the regulatory networks responsible for natural variation in the timing of vegetative phase change in A. thaliana.

plant biology↗

bioRxiv2021

Ontology-Aware Deep Learning Enables Novel Antibiotic Resistance Gene Discovery Towards Comprehensive Profiling of ARGs

Antibiotic resistance genes (ARGs) have emerged in pathogens and arousing a worldwide concern, which is estimated to cause millions of deaths each year globally. Accurately identifying and classifying ARGs is a formidable challenge in studying the generation and spread of antibiotic resistance. Current methods could identify close homologous ARGs, have limited utility for discovery of novel ARGs, thus rendering the profiling of ARGs incomprehensive. Here, an ontology-aware neural network (ONN) approach, ONN4ARG, is proposed for comprehensive ARG discovery. Systematic evaluation shows ONN4ARG is advanced than previous methods such as DeepARG in efficiency, accuracy, and comprehensiveness. Experiments using 200 million candidate microbial genes collected from 815 microbial community samples from diverse environments or hosts have resulted in 120,726 candidate ARGs, out of which more than 20% are not yet present in public databases. These comprehensive set of ARGs have clarified the environment-specific and host-specific patterns. The wet-experimental functional validation, together with structural investigation of docking sites, have also validated a novel streptomycin resistance gene from oral microbiome samples, confirming ONN4ARGs ability for novel ARGs identification. In summary, ONN4ARG is superior to existing methods in efficiency, accuracy, and comprehensiveness. It enables comprehensive ARG discovery, which is helpful towards a grand view of ARGs worldwide. ONN4ARG is available at https://github.com/HUST-NingKang-Lab/ONN4ARG, and online web service is available at http://onn4arg.xfcui.com/.

bioinformatics↗

bioRxiv2021

Generative Adversarial Network augmented the gut microbiome-based health index by profoundly improved discrimination power

Gut microbiome-based health index (GMHI) has been applied with success, while the discrimination powers of GMHI varied for different diseases, limiting its utility on a broad-spectrum of diseases. In this work, a generative adversarial network (GAN) model is proposed to improve the discrimination power of GMHI. Built based on the batch corrected data through GAN, GAN-GMHI has largely reduced the batch effects, and profoundly improved the performance for distinguishing healthy individuals and different diseases. GAN-GMHI has provided a solution to unravel the strong association of gut microbiome and diseases, and indicated a more accurate venue toward microbiome-based disease monitoring. The code for GAN-GMHI is available at https://github.com/HUST-NingKang-Lab/GAN-GMHI. ImportanceThe association of gut microbiome and diseases has been proven for many diseases, while the transformation of such association to a robust and universal disease prediction model has remained illusive, largely due to the batch effects presents in multiple microbiome cohorts. Our analyses have indicated a plausible venue, which is based on GAN technique, towards batch effect removal for microbiome datasets. GAN-GMHI is a novel method built based on the batch corrected data through GAN, as well as GMHI for prediction of a broad-spectrum of diseases.

bioinformatics↗

bioRxiv2021

Enabling technology for microbial source tracking based on transfer learning: From ontology-aware general knowledge to context-aware expert systems

Microbial source tracking quantifies the potential origin of microbial communities, facilitates better understanding of how the taxonomic structure and community functions were formed and maintained. However, previous methods involve a tradeoff between speed and accuracy, and have encountered difficulty in source tracking under many context-dependent settings. Here, we present EXPERT for context-aware microbial source tracking, in which we adopted a Transfer Learning approach to profoundly elevate and expand the applicability of source tracking, enabling biologically informed novel microbial knowledge discovery. We demonstrate that EXPERT can predict microbial sources with performance superior to other methods in efficiency and accuracy. More importantly, we demonstrate EXPERTs context-aware ability on several applications, including tracking the progression of infant gut microbiome development and monitoring the changes of gut microbiome for colorectal cancer patients. Broadly, transfer learning enables accurate and context-aware microbial source tracking and has the potential for novel microbial knowledge discovery.

bioinformatics↗

bioRxiv2020

Ontology-Aware Deep Learning Enables Ultrafast, Accurate and Interpretable Source Tracking among Sub-Million Microbial Community Samples from Hundreds of Niches

The taxonomical structure of microbial community sample is highly habitat-specific, making it possible for source tracking niches where samples are originated. Current methods face challenges when the number of samples and niches are magnitudes more than current in use, under which circumstances they are unable to accurately source track samples in a timely manner, rendering them difficult in knowledge discovery from sub-million heterogeneous samples. Here, we introduce a deep learning method based on Ontology-aware Neural Network approach, ONN4MST (https://github.com/HUST-NingKang-Lab/ONN4MST), which takes into consideration the ontology structure of niches and the relationship of samples from these ontologically-organized niches. ONN4MSTs superiority in accuracy, speed and robustness have been proven, for example with an accuracy of 0.99 and AUC of 0.97 in a microbial source tracking experiment that 125,823 samples and 114 niches were involved. Moreover, ONN4MST has been utilized on several source tracking applications, showing that it could provide highly-interpretable results from samples with previously less-studied niches, detect microbial contaminants, and identify similar samples from ontologically-remote niches, with high fidelity.

bioinformatics↗

bioRxiv2020

Optimal protocols for sequence-based characterization of the human vaginal microbiome

The vaginal microbiome has been connected to a wide range of health outcomes. This has led to a thriving research environment, but also to the use of conflicting methodologies to study its microbial composition. Here we systematically assess best practices for the sequencing-based characterization of the human vaginal microbiome. As far as 16S rRNA gene sequencing is concerned, the V1-V3 region has the best theoretical properties, but limitations of current sequencing technologies mean that the V3-V4 region performs equally well. Both of these approaches present very good agreement with qPCR quantification of key taxa, provided an appropriate bioinformatic pipeline is used. Shotgun metagenomic sequencing presents an interesting alternative to 16S amplification and sequencing, but it is not without its challenges. We have assessed different tools for the removal of host reads and the taxonomic annotation of metagenomic reads, including a new, easy-to-build and - use, reference database of vaginal taxa. This strategy performed as well as the best performing previously published strategies. Despite the many advantages of shotgun sequencing none of the shotgun approaches assessed here had as good agreement with the qPCR data as 16S rRNA gene sequencing. ImportanceThe vaginal microbiome has been connected to a wide range of health outcomes, from susceptibility to sexually transmitted infections to gynecological cancers and pregnancy outcomes. This has led to a thriving research environment, but also to conflicting available methodologies, including many studies that do not report their molecular biological and bioinformatic methods in sufficient detail for them to be considered reproducible. This can lead to conflicting messages and delay progress from descriptive to intervention studies. By systematically assessing best practices for the characterization of the human vaginal microbiome, this study will enable past studies to be assessed more critically and assist future studies in the selection of appropriate methods for their specific research questions.

molecular biology↗