Biology subjects

Chen, Z.

Publications and source records attributed to Chen, Z..

At least 451 records · Page 25Linked to original sources

bioRxiv2023

Function-associated scRNA-seq on single lung cancer organoids unravels the immune landscape of tumor parenchyma

In vitro models coupled with multimodal approaches are urgently needed to decipher the local tumor immune microenvironment (TIME) owing to the heterogeneous nature of immune cells and their diverse spatial distributions. Here we generate primary lung cancer organoids (pLCOs) by isolating the tumor cell clusters, including the infiltrated immune cells, from dissected lung cancer samples. A FascRNA-seq platform allowing both phenotypic evaluation and the scRNA-seq of all the single cells in an organoid was developed to dissect the TIME in individual pLCOs. Our analysis on 171 individual pLCOs derived from 7 patients revealed that pLCOs retained the fundamental features as well as intra-tumor heterogeneity of local TIME in the parenchyma of parental tumor tissues, providing a series of models with consistent genetic background but various TIME. Linking the single cell transcriptome data of individual pLCOs with their responses to ICB allowed us to confirm the pivotal role of CD8+ Ts in ICB induced anti-tumor immunity, to identify the potential tumor-reactive T cells with a set of 10 genes, and to unravel the factors regulating T cell activity.

cancer biology↗

bioRxiv2023

uniLIVER: a Human Liver Cell Atlas for Data-Driven Cellular State Mapping

The liver performs several vital functions such as metabolism, toxin removal and glucose storage through the coordination of various cell types. The cell type compositions and cellular states undergo significant changes in abnormal conditions such as fatty liver, cirrhosis and liver cancer. As the recent breakthrough of the single-cell/single-nucleus RNA-seq (sc/snRNA-seq) techniques, there is a great opportunity to establish a reference cell map of liver at single cell resolution with transcriptome-wise features. In this study, we build a unified liver cell atlas uniLIVER by integrative analyzing a large-scale sc/snRNA-seq data collection of normal human liver with 331,125 cells and 79 samples from 6 datasets. Besides the hierarchical cell type annotations, uniLIVER also proposed a novel data-driven strategy to map any query dataset to the normal reference map by developing a machine learning based framework named LiverCT. Applying LiverCT on the datasets from multiple abnormal conditions (1,867,641 cells and 439 samples from 12 datasets), the alterations of cell type compositions and cellular states were systematically investigated in liver cancer.

bioinformatics↗

bioRxiv2023

Spatially resolved molecular and cellular atlas of the mouse brain

A comprehensive atlas of genes, cell types, and their spatial distribution across a whole mammalian brain is fundamental for understanding function of the brain. Here, using snRNA-seq and Stereo-seq techniques, we generated a mouse brain atlas with spatial information for 308 cell clusters with single-cell resolution involving over 6 million cells as well as for 29,655 genes. We have identified new astrocyte clusters, and demonstrated that distinct cell clusters exhibit preference for cortical subregions. In addition, we identified 155 genes exhibiting regional specificity in the brainstem, and 513 long non-coding RNA exhibited regional specificity in the adult brain. Parcellation of brain regions based on spatial transcriptomic information showed large overlap with that by traditional method. Furthermore, we have uncovered 411 transcription factor regulons with spatiotemporal specificity during development. Thus, our study has discovered genes and regulon with spatiotemporal specificity, and provided a high-resolution spatial transcriptomic atlas of the mouse brain.

neuroscience↗

bioRxiv2023

Phenotype Heterogeneity Warrants Trizonal Feedback between Intracellular Contractility and Extracellular stiffening

The intricate interplay between actomyosin contractility and extracellular matrix (ECM) strain stiffening is pivotal in cancer invasion. Despite the admitted impact of such feedback, current models are inadequate in predicting the largely overlapping ranges of cell shapes and their corresponding motility levels at intermediate ranges of collagen density. To address this gap, we introduce a free energy-based, trizonal model for cell shape transition under ECM stiffening, which delineates two distinct and one overlapping motility zones entitled with their implications for cancer progression: a low-motility zone with minimal invasiveness, a high-motility zone indicative of significantly invasive cells, and a mesoregion which harbors cells at crossroads of both states. This model integrates critical factors influencing the bidirectional interaction between the cell and ECM, thereby offering a deeper grasp of cancer cell behavior. Our findings reveal that the combined effects of ECM strain stiffening and cellular contractility are key drivers of cell population heterogeneity and invasiveness. This model goes beyond existing paradigms by accurately determining the optimal cell elongation at matrix-driven steady-state equilibrium, factoring in collagen density, contractility density, stress polarization, membrane-cortical tension, and integrin dynamics through the lens of total free energy minimization. The models predictive capability is further validated against measured cell shapes from histological sections. Altogether, this research not only bridges a crucial knowledge gap, but also provides a robust computational framework for predicting and replicating cell shape transitions observed in human functional tissue assays, thereby enhancing our ability to understand and potentially combat cancer invasion. SignificanceECM stiffening is crucial in prompting metastatic phenotypes, with the interaction between cell contractility and ECM stiffening heavily influenced by cell motility level and reflected in distinct cell shapes [1-3]. This research introduces a free-energy-based model that, based on sound physics, not only distinguishes among different cell populations by their motility levels, but also truly replicates the recently observed trizonal cell response to ECM stiffness. This predictive model, validated by experiment, bridges a critical gap in our understanding of cellular dynamics in cancer progression, offering profound insight into the physical concepts driving these complex interactions. Thereupon, this work provides a powerful computational tool, potentially leading to new strategies in diagnosing and treating cancer by targeting specific cell behavioral traits and interactions within the tumor microenvironment.

cell biology↗

bioRxiv2023

Interactions between the gut microbiome, dietary restriction, and aging in genetically diverse mice

The intestinal microbiome changes with age, but the causes and consequences of microbiome aging remain unclear. Furthermore, the gut microbiome has been proposed to mediate the benefit of lifespan- extending interventions such as dietary restriction, but this hypothesis warrants further exploration. Here, by analyzing 2997 metagenomes collected longitudinally from 913 deeply phenotyped, genetically diverse mice, we provide new insights into the interplay between the microbiome, aging, dietary restriction, host genetics, and a wide range of health parameters. First, we find that microbiome uniqueness increases with age across datasets and species. Moreover, age-associated changes are better explained by cumulative exposure to stochastic events (neutral theory) than by the influence of an aging host (selection theory). Second, we unexpectedly find that the majority of microbiome features are significantly heritable and that the amount of variation explained by host genetics is as large as that of aging and dietary restriction. Third, we find that the intensity of dietary restriction parallels the extent of microbiome changes and that dietary restriction does not rejuvenate the microbiome. Lastly, we find that the microbiome is significantly associated with multiple health parameters -- including body composition, immune parameters, and frailty -- but not with lifespan. In summary, this large and multifaceted study sheds light on the factors influencing the microbiome and aspects of host physiology modulated by the microbiome.

microbiology↗

bioRxiv2023

DualNetGO: A Dual Network Model for Protein Function Prediction via Effective Feature Selection

MotivationProtein-protein Interaction (PPI) networks are crucial for automatically annotating protein functions. As multiple PPI networks exist for the same set of proteins that capture properties from different aspects, it is a challenging task to effectively utilize these heterogeneous networks. Recently, several deep learning models have combined PPI networks from all evidence, or concatenated all graph embeddings for protein function prediction. However, the lack of a judicious selection procedure prevents the effective harness of information from different PPI networks, as these networks vary in densities, structures, and noise levels. Consequently, combining protein features indiscriminately could increase the noise level, leading to decreased model performance. ResultsWe develop DualNetGO, a dual network model comprised of a classifier and a selector, to predict protein functions by effectively selecting features from different sources including graph embeddings of PPI networks, protein domain and subcellular location information. Evaluation of DualNetGO on human and mouse datasets in comparison with other network-based models show at least 4.5%, 6.2% and 14.2% improvement on Fmax in BP, MF and CC Gene Ontology categories respectively for human, and 3.3%, 10.6% and 7.7% improvement on Fmax for mouse. We demonstrate the generalization capability of our model by training and testing on the CAFA3 data, and show its versatility by incorporating Esm2 embeddings. We further show that our model is insensitive to the choice of graph embedding method and is time- and memory-saving. These results demonstrate that combining a subset of features including PPI networks and protein attributes selected by our model is more effective in utilizing PPI network information than only using one kind of or concatenating graph embeddings from all kinds of PPI networks. Availability and implementationThe source code of DualNetGO and some of the experiment data are available at: https://github.com/georgedashen/DualNetGO. Contactluo@ust.hk, zchen190@connect.hkust-gz.edu.cn Supplementary InformationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2023

Regulators of health and lifespan extension in genetically diverse mice on dietary restriction

Caloric restriction (CR) delays aging and extends healthy lifespan in multiple species. Alternative forms of dietary restriction (DR) such as intermittent fasting (IF) have drawn significant interest as a more sustainable regimen, but the landscape of longevity-promoting dietary interventions remains largely unexplored. Identifying the most robust, efficacious, and experimentally tractable modes of DR is key to better understanding and implementing effective longevity interventions for human healthspan. To that end, we have performed an extensive assessment of DR interventions, investigating the effects of graded levels of CR (20% and 40%) and IF (1 day and 2 days of fasting per week) on the health and survival of 960 genetically diverse female mice. All interventions extended lifespan, although only CR significantly reduced the mortality doubling time. Notably, IF did not extend lifespan in mice with high pre-intervention bodyweight. We carried out extensive phenotyping to determine the health effects of long-term DR and to better understand the mechanisms driving within-diet heterogeneity in lifespan. The top within-diet predictor of lifespan was the ability of mice to maintain bodyweight through periods of handling, an indicator of stress resilience. Additional predictors of long lifespan include specific changes in immune cells, red blood cell distribution width (RDW), and retention of adiposity in late life. We found that lifespan is heritable (h2 = 0.24), and that genetic background has a larger influence on lifespan than dietary interventions. We identified a significant association for lifespan and RDW on chromosome 18 that explained 4.3% of the diet-adjusted variation in lifespan. Diet-induced changes on metabolic traits, although beneficial, were relatively poor predictors of lifespan, arguing against the long-standing notion that DR works by counteracting the negative effects of obesity. These findings indicate that improving health and extending lifespan are not synonymous and that metabolic parameters may be inappropriate endpoints for evaluating aging interventions in preclinical models and clinical trials.

genetics↗

bioRxiv2023

Identification of a novel, tunable interface in the S.pombe HP1 protein, Swi6, that underpins epigenetic inheritance.

HP1 proteins are essential for establishing and maintaining transcriptionally silent heterochromatin. They dimerize, forming a binding interface to recruit diverse chromatin-associated factors. HP1 proteins are specialized and rapidly evolve, but the extent of variation required to achieve functional specialization is unknown. To investigate how changes in amino acid sequence impacts epigenetic inheritance, we performed a targeted mutagenesis screen of the S. pombe HP1 homolog, Swi6. Substitutions within an auxiliary surface adjacent to the HP1 dimerization interface produced Swi6 variants with divergent maintenance properties. Remarkably, substitutions at a single amino acid position led to the persistent gain or loss of epigenetic inheritance. These substitutions increased Swi6 chromatin occupancy in vivo and altered Swi6-protein interactions that reprogram H3K9me maintenance. We show that relatively minor changes in Swi6 amino acid composition can lead to profound changes in epigenetic inheritance which provides a redundant mechanism to evolve novel effector specificity.

molecular biology↗

bioRxiv2023

ProTokens: A Machine-Learned Language for Compact and Informative Encoding of Protein 3D Structures

Understanding functions of proteins and designing proteins committed to specific functions in silico are highly valuable for science, industry and therapeutics. However, there is a long-standing divergence in how to present function-related protein structures to the machine learning models: Although the 1-dimensional (1D) representation of proteins via Anfinsens tokens (i.e., amino acids) is sufficient in principle and more machine-friendly, it is less successful in structure-oriented protein design compared to symmetry-constrained 3D representation (i.e., atom coordinates). Aiming to bridge the gap between 1D and 3D protein representations and harvest the advantages of the two, we develop probabilistic tokenization theory for metastable protein structures. We present an unsupervised learning strategy, which conjugates inverse folding with structure prediction, to encode protein structures into artificial amino-acid tokens (ProTokens) and decode them back to atom coordinates. We show that tokenizing protein structures variationally can lead to compact and informative representations. Compared to amino acids -- the Anfinsens tokens -- ProTokens are easier to detokenize and more descriptive of finer conformational ensembles. Therefore, protein structures can be efficiently compressed, stored, aligned and compared in the form of ProTokens. By unifying the discrete and continuous representations of protein structures, ProTokens also enable all-atom protein structure design via various generative models without the concern of symmetry or modality mismatch, and allows scalable foundation models to perceive, process and explore the microscopic structures of biomolecules effectively.

bioinformatics↗

bioRxiv2023

A novel, wave-shaped profile of germline selection of pathogenic mtDNA mutations is discovered by bypassing a classical statistical bias.

The shift of the level of disease-causing mtDNA mutations (heteroplasmy) from mother to child is typically negatively correlated with the mothers heteroplasmy (Hm). In other words, mothers with low Hm tend to have children with a higher mutation level (Hch) than their own. In contrast, mothers with high Hm typically see a decrease in heteroplasmy in their children. This trend has been commonly interpreted as a result of a descending germline selection profile, i.e., positive selection at low Hm, gradually turning negative at high Hm. Here we demonstrate, however, that the negative correlation is mostly driven by RTM, or Regression To the Mean, a classical statistical bias. We further show that RTM can be nullified by using the average between the mothers and childs heteroplasmy, as a new variable, instead of the commonly used mothers heteroplasmy in blood. Additionally, we demonstrate that mother/child average is a better approximation of the actual germline heteroplasmy. Moreover, the elimination of RTM revealed a previously hidden wave-shaped HS-profile (positive mother-to-child shift at intermediate average mother-child heteroplasmy, decreasing towards high and low average heteroplasmy). In confirmation of this finding, we show that simulations that involve both wave-shaped HS-profile and RTM, reproduce the observed patterns of inheritance of mtDNA mutations in unprecedented detail. From the health care perspective, the uncovering of the wave-shaped HS-profile (and the removal of the RTM bias) are crucial for families affected by mtDNA disease. From the fundamental perspective, the wave- shaped profile offers a novel understanding of the dynamics of mtDNA in the germline and a novel potential mechanism that prevents the spread of detrimental mtDNA mutations in the population. SignificanceFrom the clinical perspective, the existence of wave-shaped selection may improve predictions and decisions for families affected by mtDNA diseases. From the fundamental perspective, it provides insight into the dynamics of general mtDNA mutations in the germline and in the population, as long as they follow wave-shaped selection profile. In Fig. 1, blue and red arrows represent the direction of expected changes of the heteroplasmy in a lineage with time/generations. With wave- shaped selection (Fig. 1B), a great majority of nascent low- fraction mutations are expected to converge back to zero and vanish. However, due to random intracellular genetic drift, some mutations will, occasionally, expand and enter the range of positive selection. Then they will be expanded by the selection to higher, detrimental levels, and become prone to downstream removal via death of highly mutated germ cells or inability of highly sick individuals to continue their lineage. In this way, the wave-shaped selection may help to prevent the spread of detrimental mutations in the population and in the species. In contrast, if the descending selection profile (Fig. 1A) was in effect, the nascent low heteroplasmy detrimental mutations would have been pushed to intermediate heteroplasmy levels where they will stay longer in hidden disease carriers enabling effective spread of mutation in the population. O_FIG O_LINKSMALLFIG WIDTH=192 HEIGHT=200 SRC="FIGDIR/small/568140v2_fig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@1142cc0org.highwire.dtl.DTLVardef@186002aorg.highwire.dtl.DTLVardef@74d176org.highwire.dtl.DTLVardef@163ca6c_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO (Graphic Summary) Pathogenic mtDNA mutations that cause a host of devastating inherited diseases are usually thought to follow an intriguing inheritance trend: Mothers with low levels of mutation (called mother heteroplasmy, Hm) tend to bear children with higher child heteroplasmy (Hch) then their own which constitutes positive Heteroplasmy Shift (HS=Hch-Hm). In contrast, mothers with high heteroplasmy Hm bear children with lower heteroplasmy Hc (negative HS). C_FIG

genomics↗

bioRxiv2023

Transcriptional inhibition after irradiation occurs preferentially at highly expressed genes in a manner dependent on cell cycle progression

In response to DNA double strand damage, ongoing transcription is inhibited to facilitate accurate DNA repair while transcriptional recovery occurs after DNA repair is complete. However, the mechanisms at play and identity of the transcripts being regulated in this manner are unclear. In contrast to the situation following UV damage, we found that transcriptional recovery after ionizing radiation (IR) occurs in a manner independent of the HIRA histone chaperone. Sequencing of the nascent transcripts identified a programmed transcriptional response, where certain transcripts and pathways are rapidly downregulated after IR, while other transcripts and pathways are upregulated. Specifically, most of the loss of nascent transcripts occurring after IR is due to inhibition of transcriptional initiation of the highly transcribed histone genes and the rDNA. To identify factors responsible for transcriptional inhibition after IR in an unbiased manner, we performed a whole genome gRNA library CRISPR / Cas9 screen. Many of the top hits in our screen were factors required for protein neddylation. However, at short times after inhibition of neddylation, transcriptional inhibition still occurred after IR, even though neddylation was effectively inhibited. Persistent inhibition of neddylation blocked transcriptional inhibition after IR, and it also leads to cell cycle arrest. Indeed, we uncovered that many inhibitors and conditions that lead to cell cycle arrest in G1 or G2 phase also prevent transcriptional inhibition after IR. As such, it appears that transcriptional inhibition after IR occurs preferentially at highly expressed genes in cycling cells.

molecular biology↗

bioRxiv2023

Abnormal white matter development during early childhood in autism and developmental disability

The abnormal maturation of white matter microstructure in autism spectrum disorder (ASD) compared with typical developing (TD) and developmental delay/intellectual disorder (DD/ID) during early childhood and their associations with clinical implications remain unclear. Here, we characterized spatial and temporal patterns of white matter development in 364 children (including ASD, DD/ID and TD) under 8 years old using neurite orientation dispersion and density imaging. Using neurite density index (NDI), we consistently detected three white matter clusters across all the groups through clustering analysis, indicating the conserved spatial layout in atypical development. Further, three developmental stages with successively lower growth rates were identified in ASD, similar to those in TD but delayed overall. Meanwhile, DD/ID children showed the slowest growth rates and lacked staging features. Finally, for ASD children, the NDI was correlated with cognitive impairments at the fast developmental stage but associated with social deficits during the moderate-to-slow stage.

neuroscience↗

bioRxiv2023

Universal protection against SARS-CoV-2 viruses by multivalent mRNA vaccine in mice

The continual emergence of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants challenges available SARS-CoV-2 vaccines for adequate control of outbreaks. Currently, universal vaccines capable of obviating the need for exact strain matching between mRNA vaccines and circulating viruses are absent. In this study, we designed, manufactured, and evaluated a nucleoside-modified lipid nanoparticle mRNA vaccine, aimed for offering broad-spectrum protection against recent SARS-CoV-2 variants. Additionally, the protection efficiency of monovalent, bivalent, quadrivalent, and XBB.1.5 mRNA vaccines was compared with the proposed universal vaccine. The neutralizing antibody activity against wuhan-1, BA.4/5, XBB.1.5, B.1.1.529, BQ.1.1, EG.5.1 and JN.1 was assessed using enzyme-linked immunosorbent assay, rapid fiber-optic biolayer interferometry-based biosensor, and pseudovirus neutralization test. Our results reveal that the proposed multivalent vaccine affords comprehensive protection against previously circulating, current and previously unidentified SARS-CoV-2 strains.

immunology↗

bioRxiv2023

Transcriptomics and chromatin accessibility in multiple African population samples

Mapping the functional human genome and impact of genetic variants is often limited to European-descendent population samples. To aid in overcoming this limitation, we measured gene expression using RNA sequencing in lymphoblastoid cell lines (LCLs) from 599 individuals from six African populations to identify novel transcripts including those not represented in the hg38 reference genome. We used whole genomes from the 1000 Genomes Project and 164 Maasai individuals to identify 8,881 expression and 6,949 splicing quantitative trait loci (eQTLs/sQTLs), and 2,611 structural variants associated with gene expression (SV-eQTLs). We further profiled chromatin accessibility using ATAC-Seq in a subset of 100 representative individuals, to identity chromatin accessibility quantitative trait loci (caQTLs) and allele-specific chromatin accessibility, and provide predictions for the functional effect of 78.9 million variants on chromatin accessibility. Using this map of eQTLs and caQTLs we fine-mapped GWAS signals for a range of complex diseases. Combined, this work expands global functional genomic data to identify novel transcripts, functional elements and variants, understand population genetic history of molecular quantitative trait loci, and further resolve the genetic basis of multiple human traits and disease.

genomics↗

bioRxiv2023

Transcranial cortex-wide imaging of murine ischemic perfusion with large-field multifocal illumination fluorescence microscopy

Ischemic stroke is a common cause of death worldwide and a main cause of morbidity. Presently, computed tomography and magnetic resonance imaging are the mainstay for stroke diagnosis and therapeutic monitoring. These modalities are often limited in terms of accessibility as well as their ability to map brain perfusion with sufficient spatial and temporal resolution, particularly in the context of preclinical studies, thus calling for development of new brain perfusion techniques featuring rapid imaging speed, cost-effectiveness, and ease of use. Herein, we report on cortex-wide perfusion imaging in murine ischemic stroke with large-field multi-focal illumination fluorescence microscopy (LMI). We attained quantitative readings of hemodynamic and structural changes in cerebral vascular network and pial vessels at capillary level resolution and 80 Hz frame rate fully transcranially. The in vivo perfusion maps accurately delineated the ischemic core and penumbra, further exhibiting strong correlation with ex vivo triphenyl tetrazolium chloride staining. Interestingly, monitoring of therapeutic effects of thrombolysis in stroke has revealed that early recanalization could effectively save the penumbra whilst reducing the infarct area. Furthermore, cross-strain comparison of perfusion dynamics affirmed that C57BL/6 mice, benefiting from more extensive pial collateral recruitment, feature a larger penumbra and smaller infarct core as compared to BALB/c mice which have few or no collaterals. Finally, we apply LMI to show that sensory stimulation-based treatment enhances blood flow and abolish perfusion deficit in the ischemic core and penumbra regions. The simple, cost-effective and minimally invasive nature of the proposed approach offers new venues for brain perfusion research under various disease conditions such as stroke, neurodegeneration or epileptic seizures.

neuroscience↗

bioRxiv2023

One-carbon unit supplementation fuels tumor-infiltrating T cells and augments checkpoint blockade

Nucleotides perform important metabolic functions, carrying energy and feeding nucleic acid synthesis. Here, we use isotope tracing-mass spectrometry to quantitate the contributions to purine nucleotides of salvage versus de novo synthesis. We further explore the impact of augmenting a key precursor for purine synthesis, one-carbon (1C) units. We show that tumors and tumor-infiltrating T cells (relative to splenic T cells) synthesize purines de novo. Purine synthesis requires two 1C units, which come from serine catabolism and circulating formate. Shortage of 1C units is a potential bottleneck for anti-tumor immunity. Elevating circulating formate drives its usage by tumor-infiltrating T cells. Orally administered methanol functions as a formate pro-drug, with deuteration enabling control of formate-production kinetics. In MC38 tumors, safe doses of methanol raise formate levels and augment anti-PD-1 checkpoint blockade, tripling durable regressions. Thus, 1C deficiency can gate antitumor immunity and this metabolic checkpoint can be overcome with pharmacological 1C supplementation. Statement of significanceCheckpoint blockade has revolutionized cancer therapy. Durable tumor control, however, is achieved in only a minority of patients. We show that the efficacy of anti-PD-1 blockade can be enhanced by metabolic supplementation with one-carbon donors. Such donors support nucleotide synthesis in tumor-infiltrating T cells and merit future clinical evaluation.

immunology↗

bioRxiv2023

MiceVAPORDot: A novel automated approach for high-throughput behavioral characterization during E-cigarette exposure in mice

The prosperity of electronic nicotine delivery systems (ENDS) or e-cigarette use has been regarded to lead an increasing risk of nicotine addiction, especially among youth. Understanding and evaluating the behaviors induced by ENDS are fundamental to the study of neuropsychiatric effects of e-cigarettes. However, little is known regarding the behavioral features during e-cigarette exposure in mice. Current behavioral assessments for nicotine addiction are based on nicotine withdrawal-induced anxiety which can be only performed after ENDS exposures. Here we developed MiceVAPORDot, a novel high-throughput tool for automated in situ behavioral characterization during e-cigarette exposure. The integration of a deep learning-based animal pose tracking method by MiceVAPORDot allows precise characterization on behavioral phenotypes of e-vapor exposed mice, which were unable revealed by traditional evaluation methodology such as conditioned place preference and elevated plus maze tests. The behavioral fingerprints recognized by MiceVAPORDot can be used for high-throughput screening on incentive nature of e-cigarette flavors as well as medications for smoking cessation.

animal behavior and cognition↗

bioRxiv2023

Identification and functional analysis of eRNA markers for hepatocellular carcinoma based on high-throughput data

Hepatocellular carcinoma (HCC) is a common type of liver cancer with a high mortality rate. enhancer RNA (eRNA) has been proved to play an important role in cancer progress and development. However, the eRNA studies in HCC are still limited. In this study, we attempted to identify some eRNA biomarkers for HCC diagnosis and analyzed their biological function. First, we identified three eRNA biomarkers (CAP2e, COLEC10e, and MARCOe), which were significant differentially expressed between tumor and normal tissues in 115 HCC patients across three datasets. CAP2e demonstrated upregulation in tumors while COLEC10e and MARCOe were downregulated. These results could be validated in TCGA-LIHC data. There were significant positive correlations between the expression of these eRNAs and their host genes. Then, functional enrichment analysis of protein-coding genes associated with the eRNA biomarkers revealed their involvement in cancer-related pathways. MARCOe was suggested to be a potential target for therapeutic drugs in HCC by a drug related study. The next, survival analysis demonstrated significant prognostic values of these eRNAs in prediction of overall survival. Immune infiltration analysis revealed a positive correlation between MARCOe expression and immune cell infiltration level. Finally, we found similar expression patterns of these eRNA biomarkers in other cancers, such as cholangiocarcinoma, through a pan-cancer comparison. CAP2e and COLEC10e in HCC were validated by other studies. However, the studies about MARCOe in HCC were limited. In conclusion, as best as our knowledge, it is the first time to identify three eRNA biomarkers for HCC diagnosis. These biomarkers are proved to be involved in HCC progress and development, have prognosis prediction values, and are potential to be therapeutic targets.

cancer biology↗