Biology subjects

Ma, A.

Publications and source records attributed to Ma, A..

At least 55 records · Page 3Linked to original sources

bioRxiv2024

Deep learning based genomic breeding of pest-resistant grapevine

Crop pests have profoundly deleterious effects on crop yield and food security. However, conventional pest control depends heavily on the utilization of insecticides, which develops strong pesticide resistance and concerns of food safety. Crop and their wild relatives display diverse levels of pest resistance, indicating the feasibility for breeding of pest-resistant crop varieties. In this study, we integrate deep learning (DL)/machine learning (ML) algorithms, plant phenomics and whole genome sequencing (WGS) data to conduct genomic selection (GS) of pest-resistance in grapevine. We employ deep convolutional neural networks (DCNN) to accurately calculate the severity of damage by pests on grape leaves, which achieves a classification accuracy of 95.3% (Visual Geometry Group 16, VGG16, for binary trait) and a correlation coefficient of 0.94 in regression analysis (DCNN with Pest Damage Score, DCNN-PDS, for continuous trait). We apply DL models to predict and integrate phenotype (both binary and continuous) along with WGS data from 231 grape accessions, conducting Genome-Wide Association Studies (GWAS). This analysis detects a total of 69 QTLs, encompassing 139 candidate genes involved in pathways associated with pest resistance, including jasmonic acid (JA), salicylic acid (SA), ethylene, and other related pathways. Furthermore, through the combination with transcriptome data, we identify specific pest-resistant genes, such as ACA12 and CRK3, which play distinct roles in resisting herbivore attacks. Machine learning-based GS demonstrates a high accuracy (95.7%) and a strong correlation (0.90) in predicting the leaf area damaged by pests as binary and continuous traits in grapevine, respectively. In general, our study highlights the power of DL/ML in plant phenomics and GS, facilitating genomic breeding of pest-resistant grapevine.

plant biology↗

bioRxiv2023

ssREAD: A Single-cell and Spatial RNA-seq Database for Alzheimer's Disease

Alzheimers Disease (AD) pathology has been increasingly explored through single-cell and single-nucleus RNA-sequencing (scRNA-seq & snRNA-seq) and spatial transcriptomics (ST). However, the surge in data demands a comprehensive, user-friendly repository. Addressing this, we introduce a single-cell and spatial RNA-seq database for Alzheimers disease (ssREAD). It offers a broader spectrum of AD-related datasets, an optimized analytical pipeline, and improved usability. The database encompasses 1,053 samples (277 integrated datasets) from 67 AD-related scRNA-seq & snRNA-seq studies, totaling 7,332,202 cells. Additionally, it archives 381 ST datasets from 18 human and mouse brain studies. Each dataset is annotated with details such as species, gender, brain region, disease/control status, age, and AD Braak stages. ssREAD also provides an analysis suite for cell clustering, identification of differentially expressed and spatially variable genes, cell-type-specific marker genes and regulons, and spot deconvolution for integrative analysis. ssREAD is freely available at https://bmblx.bmi.osumc.edu/ssread/.

bioinformatics↗

bioRxiv2023

Memorability shapes perceived time (and vice versa)

Visual stimuli are known to vary in their perceived duration. Likewise, some visual stimuli are also known to linger for longer in memory. Yet, whether or not these two features of visual processing are linked is unknown. Despite early assumptions that time is an extracted, or higher-order feature of perception, more recent work over the past two decades has demonstrated that timing may be instantiated within sensory modality circuits. A primary location for many of these studies is the visual system, where duration sensitive responses have been demonstrated. Further, visual stimulus features have been observed to shift perceived duration. These findings suggest that visual circuits mediate or construct perceived time. Here, we present across a series of experiments evidence that perceived time is affected by the image properties of scene size, clutter, and memorability. More specifically, we observe that scene size and memorability dilate time, whereas clutter contracts it. Further, the durations of more memorable images are also perceived more precisely. Conversely, the longer the perceived duration of an image, the more memorable it is. To explain these findings, we applied a recurrent convolutional neural network (rCNN) model of the ventral visual system, in which images are progressively processed over time. We find that more memorable images are processed faster, and that this increase in processing speed predicts both the lengthening and increased precision of perceived durations. These findings thus provide a new avenue in vision research towards the study of perceived image durations as means of explaining visual system responses.

neuroscience↗

bioRxiv2023

MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer

Rare cell populations are key in neoplastic progression and therapeutic response, offering potential intervention targets. However, their computational identification and analysis often lag behind major cell types. To fill this gap, we introduced MarsGT: Multi-omics Analysis for Rare population inference using Single-cell Graph Transformer. It identifies rare cell populations using a probability-based heterogeneous graph transformer on single-cell multi-omics data. MarsGT outperformed existing tools in identifying rare cells across 400 simulated and four real human datasets. In mouse retina data, it revealed unique subpopulations of rare bipolar cells and a Muller glia cell subpopulation. In human lymph node data, MarsGT detected an intermediate B cell population potentially acting as lymphoma precursors. In human melanoma data, it identified a rare MAIT-like population impacted by a high IFN-I response and revealed the mechanism of immunotherapy. Hence, MarsGT offers biological insights and suggests potential strategies for early detection and therapeutic intervention of disease.

bioinformatics↗

bioRxiv2023

Identification of HPV oncogene and host cell differentiation associated cellular heterogeneity in cervical cancer via single-cell transcriptomic analysis

Human Papillomaviruses (HPVs) are associated with around 5-10% of human cancer, notably nearly 99% of cervical cancer. The mechanisms HPV interacts with stratified epithelium (differentiated layers) during the viral life cycle, and oncogenesis remain unclear. In this study, we used single-cell transcriptome analysis to study viral gene and host cell differentiation-associated heterogeneity of HPV-positive cervical cancer tissue. We examined the HPV16 genes - E1, E6, and E7, and found they expressed differently across nine epithelial clusters. We found that three epithelial clusters had the highest proportion of HPV-positive cells (33.6%, 37.5%, and 32.4%, respectively), while two exhibited the lowest proportions (7.21% and 5.63%, respectively). Notably, the cluster with the most HPV-positive cells deviated significantly from normal epithelial layer markers, exhibiting functional heterogeneity and altered epithelial structuring, indicating that significant molecular heterogeneity existed in cancer tissues and that these cells exhibited unique/different gene signatures compared with normal epithelial cells. These HPV-positive cells, compared to HPV-negative, showed different gene expressions related to the extracellular matrix, cell adhesion, proliferation, and apoptosis. Further, the viral oncogenes E6 and E7 appeared to modify epithelial function via distinct pathways, thus contributing to cervical cancer progression. We investigated the HPV and host transcripts from a novel viewpoint focusing on layer heterogeneity. Our results indicated varied HPV expression across epithelial clusters and epithelial heterogeneity associated with viral oncogenes, contributing biological insights to this critical field of study.

bioinformatics↗

bioRxiv2023

A bioinformatics tool for identifying intratumoral microbes from the ORIEN dataset

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10-20% of human cancers, emphasizing the importance of further investigating these complex relationships. However, the implications and significance of tumor-related microbes remain largely unknown. Studies have demonstrated the critical roles of host microbes in cancer prevention and treatment responses. Understanding interactions between host microbes and cancer can drive cancer diagnosis and microbial therapeutics (bugs as drugs). Computational identification of cancer-specific microbes and their associations is still challenging due to the high dimensionality and high sparsity of intratumoral microbiome data, which requires large datasets containing sufficient event observations to identify relationships, and the interactions within microbial communities, the heterogeneity in microbial composition, and other confounding effects that can lead to spurious associations. To solve these issues, we present a bioinformatics tool, MEGA, to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of 9 cancer centers in the Oncology Research Information Exchange Network (ORIEN). This package has 3 unique features: species-sample relations are represented in a heterogeneous graph and learned by a graph attention network; it incorporates metabolic and phylogenetic information to reflect intricate relationships within microbial communities; and it provides multiple functionalities for association interpretations and visualizations. We analyzed 2704 tumor RNA-seq samples and MEGA interpreted the tissue-resident microbial signatures of each of 12 cancer types. MEGA can effectively identify cancer-associated microbial signatures and refine their interactions with tumors. SIGNIFICANCEStudying the tumor microbiome in high-throughput sequencing data is challenging because of the extremely sparse data matrices, heterogeneity, and high likelihood of contamination. We present a new deep-learning tool, microbial graph attention (MEGA), to refine the organisms that interact with tumors.

bioinformatics↗

bioRxiv2023

An explainable graph neural framework to identify cancer-associated intratumoral microbial communities

Microbes are extensively present among various cancer tissues and play a vital role in cancer prevention and treatment responses. However, the underlying relationships between intratumoral microbes and tumors are still not well understood. Here, we developed a MIcrobial Cancer-association Analysis using a Heterogeneous graph transformer (MICAH) to identify intratumoral cancer-associated microbial communities. MICAH integrates metabolic and phylogenetic relationships among microbes into a heterogeneous graph representation. It uses a graph attention transformer to holistically capture the relationships between intratumoral microbes and cancer tissues, which improves the explainability of the association between identified microbial communities and cancer. We applied MICAH to intratumoral microbiome data across five cancer types and demonstrated its good generalizability and reproducibility. We believe this graph neural network framework can provide novel insights into cancer pathogenesis associated with the intratumoral microbiome.

bioinformatics↗

bioRxiv2023

A Weighted Two-stage Sequence Alignment Framework to Identify DNA Motifs from ChIP-exo Data

Identifying precise transcription factor binding sites (TFBS) or regulatory DNA motifs plays a fundamental role in researching transcriptional regulatory mechanisms in cells and in helping construct regulatory networks. Current algorithms developed for motif searching focus on the analysis of ChIP-enriched peaks but are not able to integrate the ChIP signal in nucleotide resolution. We present a weighted two-stage alignment tool (TESA). Our framework implements an analysis workflow from experimental datasets to TFBS prediction results. It employs a binomial distribution model and graph searching model with ChIP-exonuclease (ChIP-exo) reads depth and sequence data. TESA can effectively measure the possibility for each position to be an actual TFBS in a given promoter sequence and predict statistically significant TFBS sequence segments. The algorithm substantially improves prediction accuracy and extends the scope of applicability of existing approaches. We apply the framework to a collection of 20 ChIP-exo datasets of E. coli from proChIPdb and evaluate the prediction performance through comparison with three existing programs. The performance evaluation against the compared programs indicates that TESA is more accurate for identifying regulatory motifs in prokaryotic genomes.

bioinformatics↗

bioRxiv2023

Tumor Explants Elucidate a Cascade of Paracrine SHH, WNT, and VEGF Signals Driving Pancreatic Cancer Angiosuppression

The sparse vascularity of Pancreatic Ductal Adenocarcinoma (PDAC) presents a mystery: what prevents this aggressive malignancy from undergoing neoangiogenesis to counteract hypoxia and better support growth? An incidental finding from prior work on paracrine communication between malignant PDAC cells and fibroblasts revealed that inhibition of the Hedgehog (HH) pathway partially relieved angiosuppression, increasing tumor vascularity through unknown mechanisms. Initial efforts to study this phenotype were hindered by difficulties replicating the complex interactions of multiple cell types in vitro. Here we identify a cascade of paracrine signals between multiple cell types that act sequentially to suppress angiogenesis in PDAC. Malignant epithelial cells promote HH signaling in fibroblasts, leading to inhibition of WNT signaling in fibroblasts and epithelial cells, thereby limiting VEGFR2-dependent activation of endothelial hypersprouting. This cascade was elucidated using human and murine PDAC explant models, which effectively retain the complex cellular interactions of native tumor tissues.

cancer biology↗

bioRxiv2023

On the money and right on target: How robust are reward and task success effects on implicit motor adaptation?

While implicit motor adaptation is driven by sensory-prediction errors (SPEs), recent work has shown that task success modulates this process. Task success has typically been defined as hitting a target, which signifies the goal of the movement. Visuomotor adaptation tasks are uniquely situated to experimentally manipulate task success independently from SPE by changing the target size or the location of the target. These two, distinct manipulations may influence implicit motor adaptation in different ways, so, over four experiments, we sought to probe the efficacy of each manipulation. We found that changes in target size which caused the target to fully envelop the cursor only affected implicit adaptation for a narrow range of SPE sizes, while jumping the target to overlap with the cursor more reliably and robustly affected implicit adaptation. Taken together, our data indicate that, while task success exerts a small effect on implicit adaptation, these effects are susceptible to methodological variations. Future investigations of the effect of task success on implicit adaptation could benefit from employing target jump manipulations instead of target size manipulations.

neuroscience↗

bioRxiv2022

Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data

Deciphering the intricate relationships between transcription factors (TFs), enhancers, and genes through the inference of enhancer-driven gene regulatory networks is crucial in understanding gene regulatory programs in a complex biological system. This study introduces STREAM, a novel method that leverages a Steiner Forest Problem model, a hybrid biclustering pipeline, and submodular optimization to infer enhancer-driven gene regulatory networks from jointly profiled single-cell transcriptome and chromatin accessibility data. Compared to existing methods, STREAM demonstrates enhanced performance in terms of TF recovery, TF-enhancer relation prediction, and enhancer-gene discovery. Application of STREAM to an Alzheimers disease dataset and a diffuse small lymphocytic lymphoma dataset reveals its ability to identify TF-enhancer-gene relationships associated with pseudotime, as well as key TF-enhancer-gene relationships and TF cooperation underlying tumor cells.

bioinformatics↗

bioRxiv2022

SpaGFT is a graph Fourier transform for tissue module identification from spatially resolved transcriptomics

Tissue module (TM) is a spatially organized tissue region and executes specialized biological functions, recurring and varying at different tissue sites. However, the computational identification of TMs poses challenges due to their convoluted biological functions, poorly-defined molecular features, and varying spatially organized patterns. Here, we present a hypothesis-free graph Fourier transform model, SpaGFT, to represent spatially organized features using the Fourier coefficients, leading to an accurate representation of spatially variable genes and proteins and the characterization of TM at a fast computational speed. We implemented sequencing-based and imaging-based spatial transcriptomics, spatial-CITE-seq, and spatial proteomics to identify spatially variable genes and proteins, define TM identities, and infer convoluted functions among TMs in mouse brains and human lymph nodes. We collected a human tonsil sample and performed CODEX to accurately demonstrate molecular and cellular variability within the secondary follicle structure. The superior accuracy, scalability, and interpretability of SpaGFT indicate that it is an effective representation of spatially-resolved omics data and an essential tool for bringing new insights into molecular tissue biology.

bioinformatics↗

bioRxiv2022

Characterization of the cytopathic effect of monkeypox virus isolated from clinical specimens and differentiation from common viral exanthems

While the practice of viral culture has largely been replaced by nucleic acid amplification tests, circumstances still exist in which the availability of viral culture will allow for the diagnosis of infections not included in a providers differential diagnosis. Here, we examine the cytopathic effect (CPE) and clinical data associated with eighteen cases of monkeypox virus (MPXV) isolated from nineteen clinical samples submitted for viral culture. During the study period a total of 3,468 viral cultures were performed with herpes simplex virus most commonly isolated (646/3,468; 18.6%), followed by monkeypox virus (19/3,468; 0.6%) and varicella zoster virus (12/3,468; 0.4%). Most MPXV-positive samples were obtained from males (14/19) and taken from genital (7/19) or rectal lesions (5/19). Cycle threshold values of tested samples ranged from 15.3 to 29.0. Growth of MPXV in cell culture was rapid, yielding detectable CPE at a median of 2 days (range: 1-4) often with >50% of the monolayer affected in RMK, BGM, A549, and MRC-5 cell lines. As clinical features of MPXV, HSV, and VZV lesions may overlap, CPE patterns were comparted between viruses. HSV CPE developed in a similar time frame (median: 2 days, range: 1-7) but was more often negative in RMK cells relative to MPXV. VZV grew more slowly (median: 9 days, range: 5-11) and demonstrated CPE affecting [≤]25% of cell monolayers when positive. Viral culture remains an important tool for the detection of rare or emerging viral pathogens, particularly when high viral load specimens are easily obtained.

microbiology↗

bioRxiv2022

Aspartate reduces liver inflammation and fibrosis by suppressing the NLRP3 inflammasome pathway via upregulating NS3TP1 expression

Aspartate (Asp) can act on liver Kupffer cells, inhibit NOD-like receptor-P 3 (NLRP3) inflammatory bodies, and improve liver inflammation in acute hepatitis. However, the effect of Asp on the role of hepatic stellate cells (HSCs) in the pathogenesis of liver fibrosis in chronic liver injury remains unexplored. This study aimed to investigate the effects of Asp on CCl4-induced liver fibrosis in mice and HSCs via the NF-{kappa}B/ NLRP3 signaling pathway. Liver fibrosis was induced in C57BL/6J mice by intraperitoneally (IP) injecting 0.5 mL/kg 2% CCl4 three times weekly for 8 weeks. Asp was administered to mice by gavage once every morning for 4 weeks. Massons trichrome staining, Sirius red staining and hematoxylin and eosin staining were used to detect and analyze the pathological changes in liver tissues. Western blot analysis and immunohistochemistry were applied to determine the protein expression levels of -smooth muscle actin (-SMA), collagen III (COL III), NLRP3, and IL-1{beta}. Also, reverse transcription-quantitative PCR was performed to detect the mRNA expression levels. In the liver fibrosis model, the pathological changes in liver tissues improved following treatment with Asp. A marked decrease was observed in protein and mRNA expression levels of -SMA, COL III, NLRP3, and IL-1{beta}. In addition, HSCs were treated with Asp. The expression levels of -SMA, COL III, NLRP3, and IL-1{beta} reduced in dose- and time-dependent manners. Furthermore, Asp upregulated the expression of NS3TP1 in vivo and in vitro, and NS3TP1 had a significant inhibitory effect on liver fibrosis. Asp attenuated liver fibrosis and reduced collagen production by suppressing the NF-{kappa}B/ NLRP3 signaling pathway via upregulating the expression of NS3TP1.

molecular biology↗

bioRxiv2022

PHAT: interpretable prediction of peptide secondary structures using hypergraph multi-head attention network and transfer learning

Accurately predicting peptide secondary structures remains a challenging task due to the lack of discriminative information in short peptides. In this study, we propose PHAT, a deep graph learning framework for the prediction of peptide secondary structures. The framework includes a novel interpretable deep hypergraph multi-head attention network that uses residue-based reasoning for structure prediction. Our algorithm can incorporate sequential semantic information from large-scale biological corpus and structural semantic information from multi-scale structural segmentation, leading to better accuracy and interpretability even with extremely short peptides. Our interpretable models are able to highlight the reasoning of structural feature representations and the classification of secondary substructures. We further demonstrate the importance of secondary structures in peptide tertiary structure reconstruction and downstream functional analysis, highlighting the versatility of our models. To facilitate the use of our model, we establish an online server which is accessible via http://inner.wei-group.net/PHAT/. We expect our work to assist in the design of functional peptides and contribute to the advancement of structural biology research.

bioinformatics↗

bioRxiv2022

Origin of Protein Quake: Energy Waves Conducted by a Precise Mechanical Machine

A long-standing challenge in protein biophysics is to understand protein quake in myoglobin--the structural dynamics responsible for redistributing the excess heme energy after photolysis. Despite extensive efforts, the molecular mechanism of this process remains elusive. Using the energy flow theory, we uncovered a fundamental new phenomenon: the heme energy is redistributed by energy waves with a ubiquitous fundamental frequency and two overtones. Energy waves emanate from the heme into the myoglobin backbone via a conduit of five consecutive dihedrals of the proximal histidine, then travel quickly along the backbone to reach sidechains across the protein. This mechanism is far more effective than the diffusion-based mechanism from previous studies because waves are systematic while diffusion is random. To propagate energy waves, coordinates must cooperate, resulting in collective modes that are singular vectors of the generalized work functional. These modes show task partitioning: a handful of high-energy modes generate large-scale breathing motion, which loosens up the protein matrix to enable hundreds of low-energy vibrational modes for energy transduction.

biophysics↗

bioRxiv2022

Uncovering One-Dimensional Reaction Coordinate that Underlies Structure-Function Relationship of Proteins

Understanding the mechanism of functional protein dynamics is critical to understanding protein functions. Reaction coordinates is a central topic in protein dynamics and the grail is to find the one-dimensional reaction coordinate that can fully determine the value of committor (i.e. the reaction probability in configuration space) for any protein configuration. We present a powerful new method that can, for the first time, identify the rigorous one-dimensional reaction coordinate in complex molecules. This one-dimensional reaction coordinate is determined by a fundamental mechanical operator--the generalized work functional. This method only requires modest computational cost and can be readily applied to large molecules. Most importantly, the generalized work functional is the physical origin of the collectivity in functional protein dynamics and provides a tentative roadmap that connects the structure of a protein to its function.

biophysics↗

bioRxiv2021

Microbial signals and lymphotoxin drive TNF-independent death of A20 and ABIN-1 deficient epithelium

Anti-TNF antibodies are effective for treating patients with inflammatory bowel disease (IBD), but many patients fail to respond to anti-TNF therapy, highlighting the importance of TNF-independent disease. We previously demonstrated that acute deletion of two IBD susceptibility genes, A20 (Tnfaip3) and Abin-1 (Tnip1), in intestinal epithelial cells (IECs) sensitizes mice to both TNF-dependent and TNF-independent death. Here we show that TNF-independent IEC death after A20 and Abin-1 deletion is rescued by germ-free derivation or deletion of MyD88, while deletion of Trif provides only partial protection. Combined deletion of Ripk3 and Casp8, which inhibits both apoptotic and necroptotic death, completely protects against death after acute deletion of A20 and Abin-1 in IECs. A20 and Abin-1-deficient IECs are sensitized to TNF-independent, TNFR-1-mediated death in response to lymphotoxin alpha (LT) homotrimers. Blockade of LT in vivo reduces weight loss and improves survival when combined with partial deletion of MyD88. These data show that microbial signals, MyD88, and LT all contribute to TNF-independent intestinal injury. SUMMARYHere we show that germ-free derivation, MyD88 deletion, combined Ripk3 and Casp8 deletion, or anti-LT, all reduce TNF-independent intestinal injury after A20 and Abin-1 deletion.

immunology↗