Biology subjects

Cao, Z.

Publications and source records attributed to Cao, Z..

At least 91 records · Page 5Linked to original sources

bioRxiv2023

Leveraging Machine Learning to Uncover the Hidden Links between Trusting Behavior and Biological Markers

Understanding the decision-making mechanisms underlying trust is essential, particularly for patients with mental disorders who experience difficulties in developing trust. We aimed to explore biomarkers associated with trust-based decision-making by quantitative analysis. However, quantification of decision-making properties is difficult because it cannot be directly observed. Here, we developed a machine learning method based on Bayesian hierarchical model to quantitatively decode the decision-making properties from behavioral data of a trust game. By applying the method to data of patients with MDD and healthy controls, we estimated model parameters regulating trusting decision-making. The estimated model was able to predict behaviors of each participant. Although there is no difference of the estimated parameters between MDD and healthy controls, several biomarkers were associated with the decision-making properties in trusting behavior. Our findings provide valuable insights into the trusting decision-making, offering a basis for developing targeted interventions to improve their social functioning and overall well-being.

bioinformatics↗

bioRxiv2023

Exact solution of a three-stage model of stochastic gene expression model including cell-cycle dynamics

The classical three-stage model of stochastic gene expression predicts the statistics of single cell mRNA and protein number fluctuations as a function of the rates of promoter switching, transcription, translation, degradation and dilution. While this model is easily simulated, its analytical solution remains an unsolved problem. Here we modify this model to explicitly include cell-cycle dynamics and then derive an exact solution for the time-dependent joint distribution of mRNA and protein numbers. We show large differences between this model and the classical model which captures cell-cycle effects implicitly via effective first-order dilution reactions. In particular we find that the Fano factor of protein numbers calculated from a population snapshot measurement are underestimated by the classical model whereas the correlation between mRNA and protein can be either over-or underestimated, depending on the timescales of mRNA degradation and promoter switching relative to the mean cell-cycle duration time.

biophysics↗

bioRxiv2023

Spatial and single-nucleus transcriptomic analysis of genetic and sporadic forms of Alzheimer's Disease

The pathogenesis of Alzheimers disease (AD) depends on environmental and heritable factors, with remarkable differences evident between individuals at the molecular level. Here we present a transcriptomic survey of AD using spatial transcriptomics (ST) and single-nucleus RNA-seq in cortical samples from early-stage AD, late-stage AD, and AD in Down Syndrome (AD in DS) donors. Studying AD in DS provides an opportunity to enhance our understanding of the AD transcriptome, potentially bridging the gap between genetic mouse models and sporadic AD. Our analysis revealed spatial and cell-type specific changes in disease, with broad similarities in these changes between sAD and AD in DS. We performed additional ST experiments in a disease timecourse of 5xFAD and wildtype mice to facilitate cross-species comparisons. Finally, amyloid plaque and fibril imaging in the same tissue samples used for ST enabled us to directly link changes in gene expression with accumulation and spread of pathology.

neuroscience↗

bioRxiv2023

Dietary n-3 PUFA enhances DMI in transition cows by regulating taste transduction gene expression in liver associated with rumen microbial alteration

We hypothesised that the addition of n-3 polyunsaturated fatty acids (PUFAs) in the diet could affect gene expression in the liver and have beneficial effects on the recovery of cows in the transition phase. A total of 30 multiparous non-lactating Holstein dairy cows (35 days before expected calving) were randomly fed a diet with either 1% dry matter (DM) of hydrogenated fatty acid (C16:00 enriched; CON) or 3.5% DM of extruding flaxseed (n-3 enriched; HN3). Parity, body weight (BW), body condition score (BCS) and milk yield were 2.6 {+/-} 1.2, 757.5 {+/-} 65.8 kg, 3.3 {+/-} 0.2 and 10,286.5 {+/-} 1464.8 kg/d (mean {+/-} SD), respectively, at the beginning of the experiment. The relative abundance of Bacteroidota (P = 0.047) and Spirochaetota (P = 0.091) was higher and that of Patescibacteria (P = 0.076) was lower in the HN3 group than in the CON group on prepartum day 4. The DMI of cows was positively correlated with the abundance of bacteria in the rumen (Spirochaetota: r = 0.871, P < 0.001; Bacteroidota: r = 0.896, P < 0.001) and the differential expression of genes involved in taste transduction (ACSL1: r = 0.673, P < 0.001; PLIN4: r = 0.632, P < 0.01; CPT1A: r = 0.694, P < 0.001). These results suggest that dietary n-3 PUFA at an appropriate concentration can promote DMI recovery by upregulating the expression of these genes and maintaining the balance of the rumen microbiota.

zoology↗

bioRxiv2023

Characterization of Tumor-Associated Endothelial Cells and the Development of a Prognostic Model in Pancreatic Ductal Adenocarcinoma

BackgroundPancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy characterized by a complex tumor microenvironment. Angiogenesis is of paramount importance in the proliferation and metastasis of PDAC. However, currently, there are no well-defined biomarkers available to guide the prognosis and treatment of PDAC. ResultsIn this study, we investigated the interactions between tumor-associated endothelial cells (TAECs) and tumor cells in PDAC, and identified a specific subset of TAECs characterized by high expression of COL4A1. COL4A1+ endothelial cells interact with tumor cells through the COLLAGEN signaling pathway to promote tumor cell proliferation, migration, and invasion. We also observed activation of HOXD9 in COL4A1+ endothelial cells. Based on these findings, we developed a prognostic model called TaEMS, which accurately predicts patient prognosis. TaEMS identified high-risk patients enriched in cell cycle-related pathways and low-risk patients enriched in focal adhesions, smooth muscle regulation, and immune pathways. Moreover, high-risk patients displayed a reduced level of immune cell infiltration, indicating the presence of a "cold tumor" phenotype. ConclusionsOverall, our study uncovered an intricate crosstalk between TAECs and tumor cells in PDAC, emphasizing the role of HOXD9 and highlighting the potential of TaEMS as a prognostic biomarker for precise therapies.

cancer biology↗

bioRxiv2023

Personalized connectivity-based network targeting model of TMS for treatment of psychiatric disorders: computational feasibility and reproducibility

Consider the limited clinical efficacy of transcranial magnetic stimulation (TMS) due to heterogeneity in treatment outcomes, the utilization of individual functional connectivity (FC) can enhance the prediction accuracy in the network targeting model. However, the low signal-to-noise ratio (SNR) of FC poses a challenge when utilizing individual resting-state FC (rsFC). To overcome this challenge, proposed solutions include increasing the scan duration and employing clustering approaches to enhance the stability of FC. In this study, we aimed to evaluate the stability of a personalized functional-based network targeting model in individuals with major depressive disorder (MDD) and schizophrenia with auditory verbal hallucinations (AVH). Using resting-state functional magnetic resonance imaging (rsfMRI) data from the Human Connectome Project (HCP), we assessed the models stability and employed longer scan durations (7 minutes, 14 minutes, 21 minutes, 28 minutes) and clustering methodologies to improve the precision of identifying optimal individual sites. Our findings demonstrate that a scan duration of 28 minutes and the utilization of the clustering approach lead to stable identification of individual sites, as evidenced by the intraindividual distance falling below the [~]1cm spatial resolution of TMS. These findings contribute to the understanding of individualized TMS targeting and have implications for improving treatment outcomes in psychiatric disorders.

neuroscience↗

bioRxiv2023

Integration of eQTL and Machine Learning Methods to Dissect Causal Genes with Pleiotropic effects in Genetic Regulation Networks of Seed Cotton Yield

Expression quantitative trait loci (eQTL) provide a powerful means of investigating the biological basis of genome-wide association study (GWAS) results and exploring complex traits or phenotypes. In addition to identifying the causal gene in cis, eQTL analysis also reveals a large number of trans-regulated genes located on different chromosomes, which form a gene regulatory network (GRN) that complements the GWAS locus. However, the dissection of a GRN and the crosstalk underlying multiple agronomical traits, along with prioritizing important genes in eQTL-derived GRNs, remains a major challenge. In this study, we generated 558 transcriptional profiles of lint-bearing ovules at one day post-anthesis (DPA) from a selective core cotton germplasm, from which we identified 12,207 eQTLs. By integrating with a GWAS catalog, we found that 66 out of 187 (35.29%) known phenotypic GWAS loci are colocalized with 1,090 eQTLs, forming 38 major functional GRNs predominantly (30 out of 38) associated with seed size-related phenotypes. Of the eGenes, 34 were shared between at least two functional GRNs, exhibiting pleiotropic effects, such as NF-YB3, GRDP1, and IDD7. Narrow-sense heritability analysis showed that the heritability increased with combining the eQTLs with GRNs compared to those with previous yield trait GWAS loci. The extreme gradient boosting (XGBoost) machine learning approach was then applied to predict seed cotton yield phenotypes based on gene expression. Top-ranking eGenes (NF-YB3, FLA2, and GRDP1) derived by XGBoost with pleiotropic effects on yield traits were validated, along with their potential roles by correlation analysis, domestication selection analysis, and transgenic plants. This study provides insights into the mining of GRNs in relation to the pleiotropy of phenotype. The combination of eQTL and machine learning approaches is efficient in improving the genetic dissection of agricultural traits.

genetics↗

bioRxiv2023

High-resolution Dynamic Human Brain Neural Activity Recording Using 3T MRI

Despite extensive research over decades, a non-invasive technique capable of capturing neural activities in the human brain with high spatiotemporal resolution is still lacking. The recently proposed direct imaging of neuronal activity (DIANA) using ultrahigh-field magnetic resonance imaging (MRI) has shown promise, but the translation from anesthetized mice to awake humans poses a significant challenge. Here we present Time Resolved ImaGing of Global Electroneurophysiological Record (TRIGGER), a novel technique that enables the direct detection of neural activity in the awake human brain using 3T MRI. In 18 participants, visual responses were captured at 5-mm spatial resolution and 1.4-ms temporal resolution. Importantly, the delay in stimulus presentation reliably corresponded to the latency of neural responses on a millisecond scale. Furthermore, when stimuli were presented to one visual field, the responses in two hemispheres exhibited the expected time difference. This non-invasive mapping approach holds the potential to elucidate neural dynamics underlying human brain function and disorders.

neuroscience↗

bioRxiv2023

Plasma-free samples for transcriptomic analysis: a potential alternative to whole blood samples

RNA sequencing (RNAseq) technology has become increasingly important in precision medicine and clinical diagnostics and emerged as a powerful tool for identifying protein-coding genes, performing differential gene analysis, and inferring immune cell composition. Human peripheral blood samples are widely used for RNAseq, providing valuable insights into individual biomolecular information. Blood samples can be classified as whole blood (WB), plasma, serum, and remaining sediment samples, including plasma-free blood (PFB) and serum-free blood (SFB) samples. However, the feasibility of using PFB and SFB samples for transcriptome analysis remains unclear. In this study, we aimed to assess the viability of employing PFB or SFB samples as substitute RNA sources in transcriptomic analysis and performed a comparative analysis of WB, PFB, and SFB samples for different applications. Our results revealed that PFB samples exhibit greater similarity to WB samples in terms of protein-coding gene expression patterns, differential expression gene profiling, and immunological characterizations, suggesting that PFB can be a viable alternative for transcriptomic analysis. This contributes to the optimization of blood sample utilization and the advancement of precision medicine research.

bioinformatics↗

bioRxiv2023

Efficient and scalable prediction of spatio-temporal stochastic gene expression in cells and tissues using graph neural networks

The simulation of spatial stochastic models is highly computationally expensive, an issue that has severely limited our understanding of the spatial nature of gene expression. Here we devise a graph neural network based method to learn, from stochastic trajectories in a small region of space, an effective master equation for the time-dependent marginal probability distributions of mRNA and protein numbers at sub-cellular resolution for every cell in a tissue. Numerical solution of this equation leads to accurate results in a small fraction of the computation time of standard simulation methods. Moreover its predictions can be extrapolated to a spatial organisation (a cell network topology) and regions of parameter space unseen in its neural network training. The scalability and accuracy of the method suggest it is a promising approach for whole cell modelling and for detailed comparisons of stochastic models with spatial genomics data.

systems biology↗

bioRxiv2022

The structural basis of itch receptor MRGPRX1 activation

Mas-related G-protein-coupled receptors X1-X4 (MRGPRX1-X4) are four primate-specific receptors that are recently reported to be responsible for many biological processes, including itch sensation, pain transmission, and inflammatory reactions. MRGPRX1 is the first identified human MRGPR, and its expression is restricted to primary sensory neurons. Due to its dual roles in itch and pain signaling pathways, MRGPRX1 has been regarded as a promising target for itch remission and pain inhibition. Here, we reported a cryo-electron microscopy (cryo-EM) structure of Gq-coupled MRGPRX1 in complex with a synthetic agonist compound 16 in an active conformation at an overall resolution of 3.0 angstrom via a NanoBiT tethering strategy. Compound 16 is a new pain-relieving compound with high potency and selectivity to MRGPRX1 over opioid receptor. According to the structure analysis, we revealed that MRGPRX1 shares common features of the Gq-mediated receptor activation mechanism of MRGPRX family members. However, the variable residues in orthosteric pocket of MRGPRX1 exhibits the unique agonist recognition pattern, which may facilitate to design MRGPRX1-specific modulators. Together with receptor activation and itch behavior evaluation assays, our study provides a structural snapshot to modify therapeutic molecules for itch relieving and analgesia targeting MRGPRX1.

biochemistry↗

bioRxiv2022

Multi-modal multi-resolution atlas of the human neonatal cerebral cortex based on microstructural similarity

The neonatal period is a critical window for the development of the human brain and may hold implications for the long-term development of cognition and disorders. Multi-modal connectome studies have revealed many important findings underlying the adult brain but related studies were rare in the early human brain. One potential challenge is the lack of an appropriate and unbiased parcellation that combines structural and functional information in this population. Using 348 multi-modal MRI datasets from the developing human connectome project, we found that the information fused from the structural, diffusion, and functional MRI was relatively stable across MRI features and showed high reproducibility at the group level. Therefore, we generated automated multi-resolution parcellations (300 - 500 parcels) based on the similarity across multi-modal features using a gradient-based parcellation algorithm. In addition, to acquire a parcellation with high interpretability, we provided a manually delineated parcellation (210 parcels), which was approximately symmetric, and the adjacent areas around each boundary were statistically different in terms of the integrated similarity metric and at least one kind of original features. Overall, the present study provided multi-resolution and neonate-specific parcellations of the cerebral cortex based on multi-modal MRI properties, which may facilitate future studies of the human connectome in the early development period.

neuroscience↗

bioRxiv2022

Multimodal Imaging-Based Classification of PTSD Using Data-Driven Computational Approaches: A Multisite Big Data Study from the ENIGMA-PGC PTSD Consortium

BackgroundCurrent clinical assessments of Posttraumatic stress disorder (PTSD) rely solely on subjective symptoms and experiences reported by the patient, rather than objective biomarkers of the illness. Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. Here we aimed to classify individuals with PTSD versus controls using heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. MethodsWe analyzed brain MRI data from 3,527 structural-MRI; 2,502 resting state-fMRI; and 1,953 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls (TEHC and HC) using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. ResultsWe found lower performance in classifying PTSD vs. controls with data from over 20 sites (60% test AUC for s-MRI, 59% for rs-fMRI and 56% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history across all three modalities (75% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. ConclusionOur findings highlight the promise offered by machine learning methods for the diagnosis of patients with PTSD. The utility of brain biomarkers across three MRI modalities and the contribution of DVAE models for improving generalizability offers new insights into neural mechanisms involved in PTSD. Significance Classifying PTSD from trauma-unexposed healthy controls (HC) using three imaging modalities performed well ([~]75% AUC), but performance suffered markedly when classifying PTSD from trauma-exposed healthy controls (TEHC) using three imaging modalities ([~]60% AUC). Using deep learning for feature reduction (denoising variational auto-encoder; DVAE) dramatically reduced the number of features with no concomitant performance degradation. Utilizing denoising variational autoencoder (DVAE) models improves generalizability across heterogeneous multi-site data compared with the traditional machine learning frameworks

neuroscience↗

bioRxiv2022

LSR Targets YAP to Modulate Intestinal Paneth Cell Differentiation

Lipolysis-stimulated lipoprotein receptor (LSR) is a multi-functional protein that is best known for its roles in assembly of epithelial tricellular tight junctions and hepatic clearance of lipoproteins. Here, we investigated whether LSR contributes to intestinal epithelium homeostasis and pathogenesis of intestinal disease. By using multiple conditional deletion mouse models and ex vivo cultured organoids, we find that LSR elimination in intestinal stem cells results in disappearance of Paneth cell without affecting the differentiation of other cell lineages. Mechanistic studies reveal that LSR deficiency increases abundance and nuclear localization of YAP by modulating its phosphorylation and proteasomal degradation. Using gain- and loss-of-function studies we show that LSR protects against necrotizing enterocolitis through enhancement of Paneth cell differentiation in small intestinal epithelium. Thus, this study identifies LSR as an upstream negative regulator of YAP activity, an essential factor for Paneth cell differentiation, and a potential novel therapeutic target for inflammatory bowel disease.

cell biology↗

bioRxiv2022

The impact of model misspecification on phylogenetic network inference

1The development of statistical methods to infer species phylogenies with reticulations (species networks) has led to many discoveries of gene flow between distinct species. These methods typically assume only incomplete lineage sorting and introgression. Given that phylogenetic networks can be arbitrarily complex, these methods might compensate for model misspecification by increasing the number of dimensions beyond the true value. Herein, we explore the effect of potential model misspecification, including the negligence of gene tree estimation error (GTEE) and assumption of a single substitution rate for all genomic loci, on the accuracy of phylogenetic network inference using both simulated and biological data. In particular, we assess the accuracy of estimated phylogenetic networks as well as test statistics for determining whether a network is the correct evolutionary history, as opposed to the simpler model that is a tree. We found that while GTEE negatively impacts the performance of test statistics to determine the "tree-ness" of the evolutionary history of a data set, running those tests on triplets of taxa and correcting for multiple testing significantly ameliorates the problem. We also found that accounting for substitution rate heterogeneity improves the reliability of full Bayesian inference methods of phylogenetic networks, whereas summary statistic methods are robust to GTEE and rate heterogeneity, though currently require manual inspection to determine the network complexity.

evolutionary biology↗

bioRxiv2022

Ratio-based quantitative multiomics profiling using universal reference materials empowers data integration

Multiomics profiling is a powerful tool to characterize the same samples with complementary features orchestrating the genome, epigenome, transcriptome, proteome, and metabolome. However, the lack of ground truth hampers the objective assessment of and subsequent choice from a plethora of measurement and computational methods aiming to integrate diverse and often enigmatically incomparable omics datasets. Here we establish and characterize the first suites of publicly available multiomics reference materials of matched DNA, RNA, proteins, and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters, providing built-in truth defined by family relationship and the central dogma. We demonstrate that the "ratio"-based omics profiling data, i.e., by scaling the absolute feature values of a study sample relative to those of a concurrently measured universal reference sample, were inherently much more reproducible and comparable across batches, labs, platforms, and omics types, thus empower the horizontal (within-omics) and vertical (cross-omics) data integration in multiomics studies. Our study identifies "absolute" feature quantitation as the root cause of irreproducibility in multiomics measurement and data integration, and urges a paradigm shift from "absolute" to "ratio"-based multiomics profiling with universal reference materials.

systems biology↗

bioRxiv2022

Targeting the pathological network: feasibility of network-based optimization of transcranial magnetic stimulation coil placement for treatment of psychiatric disorders

It has been recognized that the efficacy of TMS-based modulation may depend on the network profile of the stimulated regions throughout the brain. However, what profile of this stimulation network optimally benefits treatment outcomes is yet to be addressed. The answer to the question is crucial for informing network-based optimization of stimulation parameters, such as coil placement, in TMS treatments. In this study, we aimed to investigate the feasibility of taking a disease-specific network as the target of stimulation network for guiding individualized coil placement in TMS treatments. We present here a novel network-based model for TMS targeting of the pathological network. First, combining E-field modeling and resting-state functional connectivity, stimulation networks were modeled from locations and orientations of the TMS coil. Second, the spatial anti-correlation between the stimulation network and the pathological network of a given disease was hypothesized to predict the treatment outcome. The proposed model was validated to predict treatment efficacy from the position and orientation of TMS coils in two depression cohorts and one auditory verbal hallucinations cohort. We further demonstrate the utility of the proposed model in guiding individualized TMS treatment for psychiatric disorders. In this proof-of-concept study, we demonstrated the feasibility of the novel network-based targeting strategy that uses the whole-brain, system-level abnormity of a specific psychiatric disease as a target. Results based on empirical data suggest that the strategy may potentially be utilized to identify individualized coil parameters for maximal therapeutic effects. HighlightsO_LIProposed a model of targeting pathological brain networks for pre-treatment TMS coil placement planning in the treatment of psychiatric disorders; C_LIO_LIValidated the network targeting model in three cohorts of patients with depression or auditory verbal hallucinations, via prediction of individual TMS treatment efficacy from the parameters of coil placement; C_LIO_LIDemonstrated the utility of the network targeting model in guiding individualized TMS coil placement. C_LI

neuroscience↗

bioRxiv2022

Correcting batch effects in large-scale multiomic studies using a reference-material-based ratio method

Batch effects are notorious technical variations that are common in multiomic data and may result in misleading outcomes. With the era of big data, tackling batch effects in multiomic integration is urgently needed. As part of the Quartet Project for quality control and data integration of multiomic profiling, we comprehensively assess the performances of seven batch-effect correction algorithms (BECAs) for mitigating the negative impact of batch effects in multiomic datasets, including transcriptomics, proteomics, and metabolomics. Performances are evaluated based on accuracy of identifying differentially expressed features, robustness of predictive models, and the ability of accurately clustering cross-batch samples into their biological sample groups. Ratio-based method is more effective and widely applicable than others, especially in cases when batch effects are highly confounded with biological factors of interests. We further provide practical guidelines for the implementation of ratio-based method using universal reference materials profiled with study samples. Our findings show the promise for eliminating batch effects and enhancing data integration in increasingly large-scale, cross-batch multiomic studies.

systems biology↗