Revealing genomic heterogeneity and commonality: A penalized integrative analysis approach accounting for the adjacency structure of measurements.

Advancements in high-throughput genomic technologies have revolutionized the field of disease biomarker identification by providing large-scale genomic data. There is an increasing focus on understanding the relationships among diverse patient groups with distinct disease subtypes and characteristics. Complex diseases exhibit both heterogeneity and shared genomic factors, making it essential to investigate these patterns to accurately detect markers and comprehensively understand the diseases. Integrative analysis has emerged as a promising approach to address this challenge. However, existing studies have been limited by ignoring the adjacency structure of genomic measurements, such as single nucleotide polymorphisms (SNPs) and DNA methylations. In this study, we propose a structured integrative analysis method that incorporates a spline type penalty to accommodate this adjacency structure. We utilize a fused lasso type penalty to identify both heterogeneity and commonality across the groups. Extensive simulations demonstrate its superiority compared to several direct competing methods. The analysis of The Cancer Genome Atlas melanoma data with DNA methylation measurements and GENEVA diabetes data with SNP measurements exhibit that the proposed analysis lead to meaningful findings with better prediction performance and higher selection stability.

Polygenic transcriptome risk scores for COPD and lung function improve cross-ethnic portability of prediction in the NHLBI TOPMed program.

While polygenic risk scores (PRSs) enable early identification of genetic risk for chronic obstructive pulmonary disease (COPD), predictive performance is limited when the discovery and target populations are not well matched. Hypothesizing that the biological mechanisms of disease are shared across ancestry groups, we introduce a PrediXcan-derived polygenic transcriptome risk score (PTRS) to improve cross-ethnic portability of risk prediction. We constructed the PTRS using summary statistics from application of PrediXcan on large-scale GWASs of lung function (forced expiratory volume in 1 s [FEV1] and its ratio to forced vital capacity [FEV1/FVC]) in the UK Biobank. We examined prediction performance and cross-ethnic portability of PTRS through smoking-stratified analyses both on 29,381 multi-ethnic participants from TOPMed population/family-based cohorts and on 11,771 multi-ethnic participants from TOPMed COPD-enriched studies. Analyses were carried out for two dichotomous COPD traits (moderate-to-severe and severe COPD) and two quantitative lung function traits (FEV1 and FEV1/FVC). While the proposed PTRS showed weaker associations with disease than PRS for European ancestry, the PTRS showed stronger association with COPD than PRS for African Americans (e.g., odds ratio [OR] = 1.24 [95% confidence interval [CI]: 1.08-1.43] for PTRS versus 1.10 [0.96-1.26] for PRS among heavy smokers with ≥ 40 pack-years of smoking) for moderate-to-severe COPD. Cross-ethnic portability of the PTRS was significantly higher than the PRS (paired t test p < 2.2 × 10-16 with portability gains ranging from 5% to 28%) for both dichotomous COPD traits and across all smoking strata. Our study demonstrates the value of PTRS for improved cross-ethnic portability compared to PRS in predicting COPD risk.

Contrastively generative self-expression model for single-cell and spatial multimodal data.

Advances in single-cell multi-omics technology provide an unprecedented opportunity to fully understand cellular heterogeneity. However, integrating omics data from multiple modalities is challenging due to the individual characteristics of each measurement. Here, to solve such a problem, we propose a contrastive and generative deep self-expression model, called single-cell multimodal self-expressive integration (scMSI), which integrates the heterogeneous multimodal data into a unified manifold space. Specifically, scMSI first learns each omics-specific latent representation and self-expression relationship to consider the characteristics of different omics data by deep self-expressive generative model. Then, scMSI combines these omics-specific self-expression relations through contrastive learning. In such a way, scMSI provides a paradigm to integrate multiple omics data even with weak relation, which effectively achieves the representation learning and data integration into a unified framework. We demonstrate that scMSI provides a cohesive solution for a variety of analysis tasks, such as integration analysis, data denoising, batch correction and spatial domain detection. We have applied scMSI on various single-cell and spatial multimodal datasets to validate its high effectiveness and robustness in diverse data types and application scenarios.

RefTM: reference-guided topic modeling of single-cell chromatin accessibility data.

Single-cell analysis is a valuable approach for dissecting the cellular heterogeneity, and single-cell chromatin accessibility sequencing (scCAS) can profile the epigenetic landscapes for thousands of individual cells. It is challenging to analyze scCAS data, because of its high dimensionality and a higher degree of sparsity compared with scRNA-seq data. Topic modeling in single-cell data analysis can lead to robust identification of the cell types and it can provide insight into the regulatory mechanisms. Reference-guided approach may facilitate the analysis of scCAS data by utilizing the information in existing datasets. We present RefTM (Reference-guided Topic Modeling of single-cell chromatin accessibility data), which not only utilizes the information in existing bulk chromatin accessibility and annotated scCAS data, but also takes advantage of topic models for single-cell data analysis. RefTM simultaneously models: (1) the shared biological variation among reference data and the target scCAS data; (2) the unique biological variation in scCAS data; (3) other variations from known covariates in scCAS data.

Leveraging trans-ethnic genetic risk scores to improve association power for complex traits in underrepresented populations.

Trans-ethnic genome-wide association studies have revealed that many loci identified in European populations can be reproducible in non-European populations, indicating widespread trans-ethnic genetic similarity. However, how to leverage such shared information more efficiently in association analysis is less investigated for traits in underrepresented populations. We here propose a statistical framework, trans-ethnic genetic risk score informed gene-based association mixed model (GAMM), by hierarchically modeling single-nucleotide polymorphism effects in the target population as a function of effects of the same trait in well-studied populations. GAMM powerfully integrates genetic similarity across distinct ancestral groups to enhance power in understudied populations, as confirmed by extensive simulations. We illustrate the usefulness of GAMM via the application to 13 blood cell traits (i.e. basophil count, eosinophil count, hematocrit, hemoglobin concentration, lymphocyte count, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, mean corpuscular volume, monocyte count, neutrophil count, platelet count, red blood cell count and total white blood cell count) in Africans of the UK Biobank (n = 3204) while utilizing genetic overlap shared in Europeans (n = 746 667) and East Asians (n = 162 255). We discovered multiple new associated genes, which had otherwise been missed by existing methods, and revealed that the trans-ethnic information indirectly contributed much to the phenotypic variance. Overall, GAMM represents a flexible and powerful statistical framework of association analysis for complex traits in underrepresented populations by integrating trans-ethnic genetic similarity across well-studied populations, and helps attenuate health inequities in current genetics research for people of minority populations.

Statistical methods for assessing the effects of de novo variants on birth defects.

With the development of next-generation sequencing technology, de novo variants (DNVs) with deleterious effects can be identified and investigated for their effects on birth defects such as congenital heart disease (CHD). However, statistical power is still limited for such studies because of the small sample size due to the high cost of recruiting and sequencing samples and the low occurrence of DNVs. DNV analysis is further complicated by genetic heterogeneity across diseased individuals. Therefore, it is critical to jointly analyze DNVs with other types of genomic/biological information to improve statistical power to identify genes associated with birth defects. In this review, we discuss the general workflow, recent developments in statistical methods, and future directions for DNV analysis.

Integrative analysis reveals structural basis for transcription activation of Nurr1 and Nurr1-RXRα heterodimer.

Orphan nuclear receptor Nurr1 plays important roles in the progression of various diseases, including Parkinson's disease, neuroinflammation, Alzheimer's disease, and multiple sclerosis. It can recognize DNA as a monomer or heterodimer with retinoid X receptor α (RXRα). But the molecular mechanism of its transcriptional activity regulation is still largely unknown. Here we obtained a crystal structure of monomer Nurr1 (DNA- and ligand-binding domains, DBD and LBD) bound to NGFI-B response element. The structure exhibited two different forms with distinct DBD orientations, unveiling the conformational flexibility of nuclear receptor monomer. We then generated an integrative model of Nurr1-RXRα heterodimer. In the context of heterodimer, the structural flexibility of Nurr1 would contribute to its transcriptional activity modulation. We demonstrated that the DNA sequence may specifically modulate the transcriptional activity of Nurr1 in the absence of RXRα agonist, but the modulation can be superseded when the agonist binds to RXRα. Together, we propose a set of signaling pathways for the constitutive transcriptional activation of Nurr1 and provide molecular mechanisms for therapeutic discovery targeting Nurr1 and Nurr1-RXRα heterodimer.

Interpretable deep learning methods for multiview learning.

BACKGROUND: Technological advances have enabled the generation of unique and complementary types of data or views (e.g. genomics, proteomics, metabolomics) and opened up a new era in multiview learning research with the potential to lead to new biomedical discoveries. RESULTS: We propose iDeepViewLearn (Interpretable Deep Learning Method for Multiview Learning) to learn nonlinear relationships in data from multiple views while achieving feature selection. iDeepViewLearn combines deep learning flexibility with the statistical benefits of data and knowledge-driven feature selection, giving interpretable results. Deep neural networks are used to learn view-independent low-dimensional embedding through an optimization problem that minimizes the difference between observed and reconstructed data, while imposing a regularization penalty on the reconstructed data. The normalized Laplacian of a graph is used to model bilateral relationships between variables in each view, therefore, encouraging selection of related variables. iDeepViewLearn is tested on simulated and three real-world data for classification, clustering, and reconstruction tasks. For the classification tasks, iDeepViewLearn had competitive classification results with state-of-the-art methods in various settings. For the clustering task, we detected molecular clusters that differed in their 10-year survival rates for breast cancer. For the reconstruction task, we were able to reconstruct handwritten images using a few pixels while achieving competitive classification accuracy. The results of our real data application and simulations with small to moderate sample sizes suggest that iDeepViewLearn may be a useful method for small-sample-size problems compared to other deep learning methods for multiview learning. CONCLUSION: iDeepViewLearn is an innovative deep learning model capable of capturing nonlinear relationships between data from multiple views while achieving feature selection. It is fully open source and is freely available at https://github.com/lasandrall/iDeepViewLearn .

HyperGCN: an effective deep representation learning framework for the integrative analysis of spatial transcriptomics data.

BACKGROUND: Advances of spatial transcriptomics technologies enabled simultaneously profiling gene expression and spatial locations of cells from the same tissue. Computational tools and approaches for integration of transcriptomics data and spatial context information are urgently needed to comprehensively explore the underlying structure patterns. In this manuscript, we propose HyperGCN for the integrative analysis of gene expression and spatial information profiled from the same tissue. HyperGCN enables data visualization and clustering, and facilitates downstream analysis, including domain segmentation, the characterization of marker genes for the specific domain structure and GO enrichment analysis. RESULTS: Extensive experiments are implemented on four real datasets from different tissues (including human dorsolateral prefrontal cortex, human positive breast tumors, mouse brain, mouse olfactory bulb tissue and Zabrafish melanoma) and technologies (including 10X visium, osmFISH, seqFISH+, 10X Xenium and Stereo-seq) with different spatial resolutions. The results show that HyperGCN achieves superior clustering performance and produces good domain segmentation effects while identifies biologically meaningful spatial expression patterns. This study provides a flexible framework to analyze spatial transcriptomics data with high geometric complexity. CONCLUSIONS: HyperGCN is an unsupervised method based on hypergraph induced graph convolutional network, where it assumes that there existed disjoint tissues with high geometric complexity, and models the semantic relationship of cells through hypergraph, which better tackles the high-order interactions of cells and levels of noise in spatial transcriptomics data.

Genomic dissection and mutation-specific target discovery for breast cancer PIK3CA hotspot mutations.

BACKGROUND: Recent advancements in high-throughput genomics and targeted therapies have provided tremendous potential to identify and therapeutically target distinct mutations associated with cancers. However, to date the majority of targeted therapies are used to treat all functional mutations within the same gene, regardless of affected codon or phenotype. RESULTS: In this study, we developed a functional genomic analysis workflow with a unique isogenic cell line panel bearing two distinct hotspot PIK3CA mutations, E545K and H1047R, to accurately identify targetable differences between mutations within the same gene. We performed RNA-seq and ATAC-seq and identified distinct transcriptomic and epigenomic differences associated with each PIK3CA hotspot mutation. We used this data to curate a select CRISPR knock out screen to identify mutation-specific gene pathway vulnerabilities. These data revealed AREG as a E545K-preferential target that was further validated through in vitro analysis and publicly available patient databases. CONCLUSIONS: Using our multi-modal genomics framework, we discover distinct differences in genomic regulation between PIK3CA hotspot mutations, suggesting the PIK3CA mutations have different regulatory effects on the function and downstream signaling of the PI3K complex. Our results demonstrate the potential to rapidly uncover mutation specific molecular targets, specifically AREG and a proximal gene regulatory region, that may provide clinically relevant therapeutic targets. The methods outlined provide investigators with an integrative strategy to identify mutation-specific targets for the treatment of other oncogenic mutations in an isogenic system.

Integrative transcriptome analysis uncovers common components containing CPS2 regulated by maize lncRNA GARR2 in gibberellin response.

MAIN CONCLUSION: The combined transcriptome outcome provides an important clue to the regulatory cascade centering on lncRNA GARR2 and CPS2 gene in GA response. Long non-coding RNAs (lncRNAs) serve as regulatory components in transcriptional hierarchy governing multiple aspects of biological processes. Dissecting regulatory mechanisms underpinning tetracyclic diterpenoid gibberellin (GA) cascade holds both theoretical and applied significance. However, roles of lncRNAs in transcriptional modulation of GA pathway remain largely elusive. Gypsy retrotransposon-derived GIBBERELLIN RESPONSIVE lncRNA2 (GARR2) has been reported as GA-responsive maize lncRNA. Here a novel GARR2-edited line garr2-1 was identified, characteristic of GA-induced phenotype of increased seedling height and elongated leaf sheath. Transcriptome analysis indicated that transcriptional abundance of five genes [ent-copalyl diphosphate synthase2 (CPS2), ent-kaurene synthase4 (KS4), ent-kaurene synthase6 (KS6), ent-kaurene oxidase2 (KO2), and ent-kaurenoic acid oxidase1/Dwarf3 (KAO1/D3)] was elevated in garr2-1 for early steps of GA biosynthesis. Five GA biosynthetic genes as hub regulators were interlaced to shape regulatory network of GA response. Different transcriptome resources were integrated to discover common differentially expressed genes (DEGs) in the independent GARR2-edited lines GARR2KO and garr2-1. A total of 320 common DEGs were retrieved. These common DEGs were enriched in diterpenoid biosynthetic pathway. Integrative transcriptome analysis revealed the common CPS2 encoding the CPS enzyme that catalyzes the conversion of the precursor trans-geranylgeranyl diphosphate to ent-copalyl diphosphate. The up-regulated CPS2 supported the GA-induced phenotype of slender seedlings observed in the independent GARR2-edited lines GARR2KO and garr2-1. Our integrative transcriptome analysis uncovers common components of the GA pathway regulated by lncRNA GARR2. These common components, especially for the GA biosynthetic gene CPS2, provide a valuable resource for further delineating the underlying mechanisms of lncRNA GARR2 in GA response.

SYSMut: decoding the functional significance of rare somatic mutations in cancer.

Current tailored-therapy efforts in cancer are largely focused on a small number of highly recurrently mutated driver genes but therapeutic targeting of these oncogenes remains challenging. However, the vast number of genes mutated infrequently across cancers has received less attention, in part, due to a lack of understanding of their biological significance. We present SYSMut, an extendable systems biology platform that can robustly infer the biologic consequences of somatic mutations by integrating routine multiomics profiles in primary tumors. We establish SYSMut's improved performance vis-à-vis state-of-the-art driver gene identification methodologies by recapitulating the functional impact of known driver genes, while additionally identifying novel functionally impactful mutated genes across 29 cancers. Subsequent application of SYSMut on low-frequency gene mutations in head and neck squamous cell (HNSC) cancers, followed by molecular and pharmacogenetic validation, revealed the lipidogenic network as a novel therapeutic vulnerability in aggressive HNSC cancers. SYSMut is thus a robust scalable framework that enables the discovery of new targetable avenues in cancer.

Network-based integrative analysis of single-cell transcriptomic and epigenomic data for cell types.

Advances in single-cell biotechnologies simultaneously generate the transcriptomic and epigenomic profiles at cell levels, providing an opportunity for investigating cell fates. Although great efforts have been devoted to either of them, the integrative analysis of single-cell multi-omics data is really limited because of the heterogeneity, noises and sparsity of single-cell profiles. In this study, a network-based integrative clustering algorithm (aka NIC) is present for the identification of cell types by fusing the parallel single-cell transcriptomic (scRNA-seq) and epigenomic profiles (scATAC-seq or DNA methylation). To avoid heterogeneity of multi-omics data, NIC automatically learns the cell-cell similarity graphs, which transforms the fusion of multi-omics data into the analysis of multiple networks. Then, NIC employs joint non-negative matrix factorization to learn the shared features of cells by exploiting the structure of learned cell-cell similarity networks, providing a better way to characterize the features of cells. The graph learning and integrative analysis procedures are jointly formulated as an optimization problem, and then the update rules are derived. Thirteen single-cell multi-omics datasets from various tissues and organisms are adopted to validate the performance of NIC, and the experimental results demonstrate that the proposed algorithm significantly outperforms the state-of-the-art methods in terms of various measurements. The proposed algorithm provides an effective strategy for the integrative analysis of single-cell multi-omics data (The software is coded using Matlab, and is freely available for academic https://github.com/xkmaxidian/NIC ).

Simultaneous test and estimation of total genetic effect in eQTL integrative analysis through mixed models.

Integration of expression quantitative trait loci (eQTL) into genome-wide association studies (GWASs) is a promising manner to reveal functional roles of associated single-nucleotide polymorphisms (SNPs) in complex phenotypes and has become an active research field in post-GWAS era. However, how to efficiently incorporate eQTL mapping study into GWAS for prioritization of causal genes remains elusive. We herein proposed a novel method termed as Mixed transcriptome-wide association studies (TWAS) and mediated Variance estimation (MTV) by modeling the effects of cis-SNPs of a gene as a function of eQTL. MTV formulates the integrative method and TWAS within a unified framework via mixed models and therefore includes many prior methods/tests as special cases. We further justified MTV from another two statistical perspectives of mediation analysis and two-stage Mendelian randomization. Relative to existing methods, MTV is superior for pronounced features including the processing of direct effects of cis-SNPs on phenotypes, the powerful likelihood ratio test for assessment of joint effects of cis-SNPs and genetically regulated gene expression (GReX), two useful quantities to measure relative genetic contributions of GReX and cis-SNPs to phenotypic variance, and the computationally efferent parameter expansion expectation maximum algorithm. With extensive simulations, we identified that MTV correctly controlled the type I error in joint evaluation of the total genetic effect and proved more powerful to discover true association signals across various scenarios compared to existing methods. We finally applied MTV to 41 complex traits/diseases available from three GWASs and discovered many new associated genes that had otherwise been missed by existing methods. We also revealed that a small but substantial fraction of phenotypic variation was mediated by GReX. Overall, MTV constructs a robust and realistic modeling foundation for integrative omics analysis and has the advantage of offering more attractive biological interpretations of GWAS results.

Multiomics analyses reveal the central role of the nucleolus and its machinery during heat stress acclimation in Pinus radiata.

Global warming is causing rapid changes in mean annual temperature and more severe drought periods. These are major contributors of forest dieback, which is becoming more frequent and widespread. In this work, we investigated how the transcriptome of Pinus radiata changed during initial heat stress response and acclimation. To this end, we generated a high-density dataset employing Illumina technology. This approach allowed us to reconstruct a needle transcriptome, defining 12 164 and 13 590 transcripts as down- and up-regulated, respectively, during a time course stress acclimation experiment. Additionally, the combination of transcriptome data with other available omics layers allowed us to determine the complex inter-related processes involved in the heat stress response from the molecular to the physiological level. Nucleolus and nucleoid activities seem to be a central core in the acclimating process, producing specific RNA isoforms and other essential elements for anterograde-retrograde stress signaling such as NAC proteins (Pra_vml_051671_1 and Pra_vml_055001_5) or helicase RVB. These mechanisms are connected by elements already known in heat stress response (redox, heat-shock proteins, or abscisic acid-related) and with others whose involvement is not so well defined such as shikimate-related, brassinosteriods, or proline proteases together with their potential regulatory elements. This work provides a first in-depth overview about molecular mechanisms underlying the heat stress response and acclimation in P. radiata.

Global and precise identification of functional miRNA targets in mESCs by integrative analysis.

MicroRNA (miRNA) loaded Argonaute (AGO) complexes regulate gene expression via direct base pairing with their mRNA targets. Previous works suggest that up to 60% of mammalian transcripts might be subject to miRNA-mediated regulation, but it remains largely unknown which fraction of these interactions are functional in a specific cellular context. Here, we integrate transcriptome data from a set of miRNA-depleted mouse embryonic stem cell (mESC) lines with published miRNA interaction predictions and AGO-binding profiles. Using this integrative approach, combined with molecular validation data, we present evidence that < 10% of expressed genes are functionally and directly regulated by miRNAs in mESCs. In addition, analyses of the stem cell-specific miR-290-295 cluster target genes identify TFAP4 as an important transcription factor for early development. The extensive datasets developed in this study will support the development of improved predictive models for miRNA-mRNA functional interactions.

Incorporating graph information in Bayesian factor analysis with robust and adaptive shrinkage priors.

There has been an increasing interest in decomposing high-dimensional multi-omics data into a product of low-rank and sparse matrices for the purpose of dimension reduction and feature engineering. Bayesian factor models achieve such low-dimensional representation of the original data through different sparsity-inducing priors. However, few of these models can efficiently incorporate the information encoded by the biological graphs, which has been already proven to be useful in many analysis tasks. In this work, we propose a Bayesian factor model with novel hierarchical priors, which incorporate the biological graph knowledge as a tool of identifying a group of genes functioning collaboratively. The proposed model therefore enables sparsity within networks by allowing each factor loading to be shrunk adaptively and by considering additional layers to relate individual shrinkage parameters to the underlying graph information, both of which yield a more accurate structure recovery of factor loadings. Further, this new priors overcome the phase transition phenomenon, in contrast to existing graph-incorporated approaches, so that it is robust to noisy edges that are inconsistent with the actual sparsity structure of the factor loadings. Finally, our model can handle both continuous and discrete data types. The proposed method is shown to outperform several existing factor analysis methods through simulation experiments and real data analyses.

Integrative deep learning with prior assisted feature selection.

Integrative analysis has emerged as a prominent tool in biomedical research, offering a solution to the "small n $$ n $$ and large p $$ p $$ " challenge. Leveraging the powerful capabilities of deep learning in extracting complex relationship between genes and diseases, our objective in this study is to incorporate deep learning into the framework of integrative analysis. Recognizing the redundancy within candidate features, we introduce a dedicated feature selection layer in the proposed integrative deep learning method. To further improve the performance of feature selection, the rich previous researches are utilized by an ensemble learning method to identify "prior information". This leads to the proposed prior assisted integrative deep learning (PANDA) method. We demonstrate the superiority of the PANDA method through a series of simulation studies, showing its clear advantages over competing approaches in both feature selection and outcome prediction. Finally, a skin cutaneous melanoma (SKCM) dataset is extensively analyzed by the PANDA method to show its practical application.

Effects of ambient UVB light on Pacific oyster Crassostrea gigas mantle tissue based on multivariate data.

Ambient ultraviolet radiation (UVB) from solar and artificial light presents serious environmental risks to aquatic ecosystems. The Pacific oyster, Crassostrea gigas, perceives changes in the external environment primarily through its mantle tissue, which contains many nerve fibers and tentacles. Changes within the mantles can typically illustrate the injury of ambient UVB. In this study, a comprehensive analysis of phenotypic, behavioral, and physiological changes demonstrated that extreme UVB radiation (10 W/m²) directly suppressed the behavioral activities of C. gigas. Conversely, under ambient UVB radiation (5 W/m²), various physiological processes exhibited significant alterations in C. gigas, despite the behavior remaining relatively unaffected. Using mathematical model analysis, the integrated analysis of the full-length transcriptome, proteome, and metabolome showed that ambient UVB significantly affected the metabolic processes (saccharide, lipid, and protein metabolism) and cellular biology processes (autophagy, apoptosis, oxidative stress) of the C. gigas mantle. Subsequently, using Procrustes analysis and Pearson correlation analysis, the association between multi-omics data and physiological changes, as well as their biomarkers, revealed the effect of UVB on three crucial biological processes: activation of autophagy signaling (key factors: Ca2+, LC3B, BECN1, caspase-7), response to oxidative stress (reactive oxygen species, heat shock 70, cytochrome c oxidase), and recalibration of energy metabolism (saccharide, succinic acid, translation initiation factor IF-2). These findings offer a fresh perspective on the integration of multi-data from non-model animals in ambient UVB risk assessment.

Liver transcriptomic and proteomic analyses provide new insight into the pathogenesis of liver fibrosis in mice.

BACKGROUND: Liver fibrosis (LF) is a kind of progressive liver injury reaction. The goal of this study was to achieve a more detailed understanding of the molecular changes in response to CCl4-induced LF through the identification of a differentially expressed liver transcriptomic and proteomic. RESULTS: A total of 1224 differentially expressed genes (DEGs) and 302 differentially expressed proteins (DEPs) were significantly identified at the transcriptomic and proteomic level, respectively, and 69 genes (hereafter "cor-DEGs-DEPs" genes) were detected at both levels. Pathway enrichment analysis showed that these cor-DEGs-DEPs genes were significantly enriched in 133 pathways. Importantly, among the cor-DEGs-DEPs genes, Gstm1, Gstm3, Ephx1 and Gstp1 were shown to be associated with metabolic pathways, and confirmed by RT-qPCR and parallel reaction monitoring (PRM) verification. CONCLUSIONS: Through the combined analysis of transcriptomic and proteomic data, this study provides valuable insights into the potential mechanism of the pathogenesis of LF, and lays a theoretical foundation for the further development of targeted therapy for LF.