Omics Studies Research Articles

Genomic and transcriptomic profiling of inflammatory breast cancer reveals distinct molecular characteristics to non-inflammatory breast cancers.

Inflammatory breast cancer (IBC), a rare and highly aggressive form of breast cancer, accounts for 10% of breast cancer-related deaths. Previous omics studies of IBC have focused solely on one of genomics or transcriptomics and did not discover common differences that could distinguish IBC from non-IBC. Seventeen IBC patients and five non-IBC patients as well as additional thirty-three Asian breast cancer samples from TCGA-BRCA were included for the study. We performed whole-exon sequencing (WES) to investigate different somatic genomic alterations, copy number variants, and large structural variants between IBC and non-IBC. Bulk RNA sequencing (RNA-seq) was performed to examine the differentially expressed genes, pathway enrichment, and gene fusions. WES and RNA-seq data were further investigated in combination to discover genes that were dysregulated in both genomics and transcriptomics. Copy number variation analysis identified 10 cytobands that showed higher frequency in IBC. Structural variation analysis showed more frequent deletions in IBC. Pathway enrichment and immune infiltration analysis indicated increased immune activation in IBC samples. Gene fusions including CTSC-RAB38 were found to be more common in IBC. We demonstrated more commonly dysregulated RAS pathway in IBC according to both WES and RNA-seq. Inhibitors targeting RAS signaling and its downstream pathways were predicted to possess promising effects in IBC treatment. We discovered differences unique in Asian women that could potentially explain IBC etiology and presented RAS signaling pathway as a potential therapeutic target in IBC treatment.

Alterations in Hurler-Scheie Syndrome Revealed by Mass Spectrometry-Based Proteomics and Phosphoproteomics Analysis.

Hurler-Scheie syndrome (MPS IH/S), also known as mucopolysaccharidosis type I-H/S (MPS IH/S), is a lysosomal storage disorder caused by deficiency of the enzyme alpha-L-iduronidase (IDUA) leading to the accumulation of glycosaminoglycans (GAGs) in various tissues, resulting in a wide range of symptoms affecting different organ systems. Postgenomic omics technologies offer the promise to understand the changes in proteome, phosphoproteome, and phosphorylation-based signaling in MPS IH/S. Accordingly, we report here a large dataset and the proteomic and phosphoproteomic analyses of fibroblasts derived from patients with MPS IH/S (n = 8) and healthy individuals (n = 8). We found that protein levels of key lysosomal enzymes such as cathepsin D, prosaposin, arylsulfatases (arylsulfatase A and arylsulfatase B), and IDUA were downregulated. We identified 16,693 unique phosphopeptides, corresponding to 4,605 proteins, in patients with MPS IH/S. We found that proteins related to the cell cycle, mitotic spindle assembly, apoptosis, and cytoskeletal organization were differentially phosphorylated in MPS IH/S. We identified 12 kinases that were differentially phosphorylated, including hyperphosphorylation of cyclin-dependent kinases 1 and 2, hypophosphorylation of myosin light chain kinase, and calcium/calmodulin-dependent protein kinases. Taken together, the findings of the present study indicate significant alterations in proteins involved in cytoskeletal changes, cellular dysfunction, and apoptosis. These new observations significantly contribute to the current understanding of the pathophysiology of MPS IH/S specifically, and the molecular mechanisms involved in the storage of GAGs in MPS more generally. Further translational clinical omics studies are called for to pave the way for diagnostics and therapeutics innovation for patients with MPS IH/S.

(Multi-) omics studies of ILC2s in inflammation and metabolic diseases

Type 2 innate lymphoid cells (ILC2s) have emerged as pivotal regulators in the pathogenesis of diseases, with their roles in inflammation, metabolism, and tissue homeostasis becoming increasingly recognized. This review provides an overview of the current understanding of ILC2s in inflammation and metabolic disorders, including their functional contributions. Moreover, we will discuss how these cells adapt their metabolic processes to support their function and survival and how their metabolic requirements change under different physiological and pathological conditions. Lastly, we will review recent omics studies that have provided insights into the molecular and cellular characteristics of ILC2s. This includes transcriptomic, proteomic, and metabolomic analyses that have elucidated the gene expression profiles, protein interactions, and metabolic networks, respectively, associated with ILC2s. These studies have advanced our understanding of the functional diversity of ILC2s and their involvement in metabolic disease.

Exploration of the Sclerotinia sclerotiorum-Brassica pathosystem: advances and perspectives in omics studies.

The polyphagous phytopathogen Sclerotinia sclerotiorum causing Stem rot disease is a major biotic stress in Brassica, and affects the yield and quality in various crops of agricultural significance. It affects the crop at pre-maturity which causes a reduction in the seed yield and deteriorates the oil quality in rapeseeds and Indian mustard globally. The hemibiotrophic nature and long persistence in the soil as sclerotia have made this pathogen difficult to manage through conventional agronomical practices. Hence, for alternative strategies, it is important to understand the basic aspects of the pathogen and the pathogenesis processes in the host. The current developments in technologies for omics studies including whole-genomes, transcriptomes, proteomes, and metabolomes have deciphered various genes, transcription factors, effectors and their target molecules involved in interaction, disease establishment and pathogen progress in the host tissues. The current review encompasses the studies that were conducted to decipher the Brassica-S. sclerotiorum pathosystem and the molecular factors identified through multi-omics studies for their application in building resistance to Sclerotinia stem rot disease in the susceptible cultivars of oilseed Brassica.

A regularized Cox hierarchical model for incorporating annotation information in predictive omic studies

BackgroundAssociated with high-dimensional omics data there are often “meta-features” such as biological pathways and functional annotations, summary statistics from similar studies that can be informative for predicting an outcome of interest. We introduce a regularized hierarchical framework for integrating meta-features, with the goal of improving prediction and feature selection performance with time-to-event outcomes.MethodsA hierarchical framework is deployed to incorporate meta-features. Regularization is applied to the omic features as well as the meta-features so that high-dimensional data can be handled at both levels. The proposed hierarchical Cox model can be efficiently fitted by a combination of iterative reweighted least squares and cyclic coordinate descent.ResultsIn a simulation study we show that when the external meta-features are informative, the regularized hierarchical model can substantially improve prediction performance over standard regularized Cox regression. We illustrate the proposed model with applications to breast cancer and melanoma survival based on gene expression profiles, which show the improvement in prediction performance by applying meta-features, as well as the discovery of important omic feature sets with sparse regularization at meta-feature level.ConclusionsThe proposed hierarchical regularized regression model enables integration of external meta-feature information directly into the modeling process for time-to-event outcomes, improves prediction performance when the external meta-feature data is informative. Importantly, when the external meta-features are uninformative, the prediction performance based on the regularized hierarchical model is on par with standard regularized Cox regression, indicating robustness of the framework. In addition to developing predictive signatures, the model can also be deployed in discovery applications where the main goal is to identify important features associated with the outcome rather than developing a predictive model.

FlyRNAi.org 2025 update-expanded resources for new technologies and species.

The design, analysisand mining of large-scale 'omics studies with the goal of advancing biological and biomedical understanding require use of a range of bioinformatics tools, including approaches tailored to needs specific to a given species and/or technology. The FlyRNAi database at the Drosophila RNAi Screening Center and Transgenic RNAi Project (DRSC/TRiP) Functional Genomics Resources (https://fgr.hms.harvard.edu/tools) supports an increasingly broad group of technologies and species. Recently, for example, we expanded the database to include additional new data-centric resources that facilitate mining and analysis of single-cell transcriptomics. In addition, we have applied our approaches to CRISPR reagent and gene-centric bioinformatics approaches in Drosophila to arthropod vectors of infectious diseases. Building on our previous comprehensive reports on the FlyRNAi database, here we focus on new and updated resources with a primary focus on data-centric tools. Altogether, our suite of online resources supports various stages of functional genomics studies for Drosophila and other arthropods, and facilitate a wide range of reagent design, analysis, data miningand analysis approaches by biologists and biomedical experts studying Drosophila, other common genetic model species, arthropod vectorsand/or human biology.

Deciphering the molecular heterogeneity of intermediate- and (very-)high-risk non–muscle-invasive bladder cancer using multi-layered –omics studies

Bladder cancer (BC) is the most common malignancy of the urinary tract. About 75% of all BC patients present with non-muscle-invasive BC (NMIBC), of which up to 70% will recur, and 15% will progress in stage and grade. As the recurrence and progression rates of NMIBC are strongly associated with some clinical and pathological factors, several risk stratification models have been developed to individually predict the short- and long-term risks of disease recurrence and progression. The NMIBC patients are stratified into four risk groups as low-, intermediate-, high-risk, and very high-risk by the European Association of Urology (EAU). Significant heterogeneity in terms of oncological outcomes and prognosis has been observed among NMIBC patients within the same EAU risk group, which has been partly attributed to the intrinsic heterogeneity of BC at the molecular level. Currently, we have a poor understanding of how to distinguish intermediate- and (very-)high-risk NMIBC with poor outcomes from those with a more benign disease course and lack predictive/prognostic tools that can specifically stratify them according to their pathologic and molecular properties. There is an unmet need for developing a more accurate scoring system that considers the treatment they receive after TURBT to enable their better stratification for further follow-up regimens and treatment selection, based also on a better response prediction to the treatment. Based on these facts, by employing a multi-layered –omics (namely, genomics, epigenetics, transcriptomics, proteomics, lipidomics, metabolomics) and immunohistopathology approach, we hypothesize to decipher molecular heterogeneity of intermediate- and (very-)high-risk NMIBC and to better stratify the patients with this disease. A combination of different –omics will provide a more detailed and multi-dimensional characterization of the tumor and represent the broad spectrum of NMIBC phenotypes, which will help to decipher the molecular heterogeneity of intermediate- and (very-)high-risk NMIBC. We think that this combinatorial multi-omics approach has the potential to improve the prediction of recurrence and progression with higher precision and to develop a molecular feature-based algorithm for stratifying the patients properly and guiding their therapeutic interventions in a personalized manner.

Molecular and cell phenotype programs in oral epithelial cells directed by co-exposure to arsenic and smokeless tobacco.

Chronic arsenic exposure can lead to various health issues, including cancer. Concerns have been mounting about the enhancement of arsenic toxicity through co-exposure to various prevalent lifestyle habits. Smokeless tobacco products are commonly consumed in South Asian countries, where their use frequently co-occurs with exposure to arsenic from contaminated groundwater. To decipher the in vitro molecular and cellular responses to arsenic and/or smokeless tobacco, we performed temporal multi-omics analysis of the transcriptome and DNA methylome remodelling in exposed hTERT-immortalized human normal oral keratinocytes (NOK), as well as arsenic and/or smokeless tobacco genotoxicity and mutagenicity investigations in NOK cells and in human p53 knock-in murine embryonic fibroblasts (Hupki MEF). RNAseq results from acute exposures to arsenic alone and in combination with smokeless tobacco extract revealed upregulation of genes with roles in cell cycle changes, apoptosis and inflammation responses. This was in keeping with global DNA hypomethylation affecting genes involved in the same processes in response to chronic treatment in NOK cells. At the phenotypic level, we observed a dose-dependent decrease in NOK cell viability, induction of DNA damage, cell cycle changes and increased apoptosis, with the most pronounced effects observed under arsenic and SLT co-exposure conditions. Live-cell imaging experiments indicated that the DNA damage likely resulted from induction of apoptosis, an observation validated by a lack of exome-wide mutagenesis in response to chronic exposure to arsenic and/or smokeless tobacco. In sum, our integrative omics study provides novel insights into the acute and chronic responses to arsenic and smokeless tobacco (co-)exposure, with both types of responses converging on several key mechanisms associated with cancer hallmark processes. The generated rich catalogue of molecular programs in oral cells regulated by arsenic and smokeless tobacco (co-)exposure may provide bases for future development of biomarkers for use in molecular epidemiology studies of exposed populations at risk of developing oral cancer.

An emerging multi-omic understanding of the genetics of opioid addiction.

Opioid misuse, addiction, and associated overdose deaths remain global public health crises. Despite the tremendous need for pharmacological treatments, current options are limited in number, use, and effectiveness. Fundamental leaps forward in our understanding of the biology driving opioid addiction are needed to guide development of more effective medication-assisted therapies. This Review focuses on the omics-identified biological features associated with opioid addiction. Recent GWAS have begun to identify robust genetic associations, including variants in OPRM1, FURIN, and the gene cluster SCAI/PPP6C/RABEPK. An increasing number of omics studies of postmortem human brain tissue examining biological features (e.g., histone modification and gene expression) across different brain regions have identified broad gene dysregulation associated with overdose death among opioid misusers. Drawn together by meta-analysis and multi-omic systems biology, and informed by model organism studies, key biological pathways enriched for opioid addiction-associated genes are emerging, which include specific receptors (e.g., GABAB receptors, GPCR, and Trk) linked to signaling pathways (e.g., Trk, ERK/MAPK, orexin) that are associated with synaptic plasticity and neuronal signaling. Studies leveraging the agnostic discovery power of omics and placing it within the context of functional neurobiology will propel us toward much-needed, field-changing breakthroughs, including identification of actionable targets for drug development to treat this devastating brain disease.

Triple and quadruple optimization for feature selection in cancer biomarker discovery

The proliferation of omics data has advanced cancer biomarker discovery but often falls short in external validation, mainly due to a narrow focus on prediction accuracy that neglects clinical utility and validation feasibility. We introduce three- and four-objective optimization strategies based on genetic algorithms to identify clinically actionable biomarkers in omics studies, addressing classification tasks aimed at distinguishing hard-to-differentiate cancer subtypes beyond histological analysis alone. Our hypothesis is that by optimizing more than one characteristic of cancer biomarkers, we may identify biomarkers that will enhance their success in external validation. Our objectives are to: (i) assess the biomarker panel’s accuracy using a machine learning (ML) framework; (ii) ensure the biomarkers exhibit significant fold-changes across subtypes, thereby boosting the success rate of PCR or immunohistochemistry validations; (iii) select a concise set of biomarkers to simplify the validation process and reduce clinical costs; and (iv) identify biomarkers crucial for predicting overall survival, which plays a significant role in determining the prognostic value of cancer subtypes. We implemented and applied triple and quadruple optimization algorithms to renal carcinoma gene expression data from TCGA. The study targets kidney cancer subtypes that are difficult to distinguish through histopathology methods. Selected RNA-seq biomarkers were assessed against the gold standard method, which relies solely on clinical information, and in external microarray-based validation datasets. Notably, these biomarkers achieved over 0.8 of accuracy in external validations and added significant value to survival predictions, outperforming the use of clinical data alone with a superior c-index. The provided tool also helps explore the trade-off between objectives, offering multiple solutions for clinical evaluation before proceeding to costly validation or clinical trials.

Potential of plasma biomarkers for heart failure prediction, management, and prognosis: A multiomics perspective.

Heart failure (HF) remains a major global health challenge, and more effective and comprehensive plasma biomarkers are needed to effectively treat HF patients. Multiomics studies have shown that DNA fragments, noncoding RNAs, proteins, and metabolites may be potential plasma biomarkers for HF. However, comprehensive reviews that focus on research on plasma biomarkers for HF from an omics perspective are lacking. This review summarizes the applications of various omics approaches in the exploration of biomarkers related to the risk assessment, diagnosis, subtype classification, medical management, and prognosis prediction of HF. Moreover, as heart transplantation and left ventricular assistant device (LVAD) implantation are terminal therapies for end-stage HF patients, this review also discusses the role of cell-free DNA as a biomarker for cardiac transplant rejection and omics studies of plasma biomarkers in patients who respond to LVAD therapy. Our findings suggest that future omics research on HF biomarkers should employ integrated multiomics methods and expand the sample size to increase the robustness of the results and that the identified biomarkers should be further validated in large cohorts.

Hypometric genetics: Improved power in genetic discovery by incorporating quality control flags.

Balancing the tradeoff between quantity and quality of phenotypic data is critical in omics studies. Measurements below the limit of quantification (BLQ) are often tagged in quality control fields, but these flags are currently underutilized in human genetics studies. Extreme phenotype sampling is advantageous for mapping rare variant effects. We hypothesize that genetic drivers, along with environmental and technical factors, contribute to the presence of BLQ flags. Here, we introduce "hypometric genetics" (hMG) analysis and uncover a genetic basis for BLQ flags, indicating an additional source of genetic signal for genetic discovery, especially from phenotypic extremes. Applying our hMG approach to n= 227,469UK Biobank individuals with metabolomic profiles, we reveal more than 5% heritability for BLQ flags and report biologically relevant associations, for example, at APOC3, APOA5, and PDE3B loci. For common variants, polygenic scores trained only for BLQ flags predict the corresponding quantitative traits with 91% accuracy, validating the genetic basis. For rare coding variant associations, we find an asymmetric 65.4% higher enrichment of metabolite-lowering associations for BLQ flags, highlighting the impact of putative loss-of-function variants with large effects on phenotypic extremes. Joint analysis of binarized BLQ flags and the corresponding quantitative metabolite measurements improves power in Bayesian rare variant aggregation tests, resulting in an average of 181% more prioritized genes. Our approach is broadly applicable to omics profiling. Overall, our results underscore the benefit of integrating quality control flags and quantitative measurements and highlight the advantage of joint analysis of population-based samples and phenotypic extremes in human genetics studies.

Data-driven batch detection enhances single-cell omics data analysis

In single-cell omics studies, data are typically collected across multiple batches, resulting in batch effects: technical confounders that introduce noise and distort data distribution. Correcting these effects is challenging due to their unknown sources, nonlinear distortions, and the difficulty of accurately assigning data to batches that are optimal for integration methods.

Omics Studies of Specialized Cells and Stem Cells under Microgravity Conditions.

The primary objective of omics in space with focus on the human organism is to characterize and quantify biological factors that alter structure, morphology, function, and dynamics of human cells exposed to microgravity. This review discusses exciting data regarding genomics, transcriptomics, epigenomics, metabolomics, and proteomics of human cells and individuals in space, as well as cells cultured under simulated microgravity. The NASA Twins Study significantly heightened interest in applying omics technologies and bioinformatics in space and terrestrial environments. Here, we present the available publications in this field with a focus on specialized cells and stem cells exposed to real and simulated microgravity conditions. We summarize current knowledge of the following topics: (i) omics studies on stem cells, (ii) omics studies on benign specialized different cell types of the human organism, (iii) discussing the advantages of this knowledge for space commercialization and exploration, and (iv) summarizing the emerging opportunities for translational regenerative medicine for space travelers and human patients on Earth.

Graph pooling in graph neural networks: methods and their applications in omics studies

Graph neural networks (GNNs) process the graph-structured data using neural networks and have proven successful in various graph processing tasks. Currently, graph pooling operators have emerged as crucial components that bridge the gap between node representation learning and diverse graph-level tasks by transforming node representations into graph representations. Given the rapid growth and widespread adoption of graph pooling, this review aims to summarize the existing graph pooling operators for GNNs and their representative applications in omics. Specifically, we first present a comprehensive taxonomy of existing graph pooling algorithms, expanding the categorization for both global and hierarchical pooling operators, and for the first time reviewing the inverse operation of graph pooling, named unpooling. Next, we describe the general evaluation framework for graph pooling operators, encompassing three fundamental aspects: experimental setup, ablation analysis, and model interpretation. We also discuss open issues that significantly influence the design of graph pooling operators, including complexity, connectivity, adaptability, additional loss, and attention mechanisms. Finally, we summarize bioinformatics applications of graph pooling operators in omics, including graphs of gene interaction, medical images, and protein structures for drug discovery and disease diagnosis. Furthermore, we showcase the impact of graph pooling operators on research in specific real-world domains, with a focus on prediction performance and model interpretability. This review provides methodological insights in machine learning based graph modeling and related omics research, as well as an ongoing resource by gathering related papers and code in a dedicated GitHub repository (https://github.com/Hou-WJ/Graph-Pooling-Operators-and-Bioinformatics-Applications).

Cytotoxicity and mechanisms of perfluorobutane sulfonate (PFBS) in umbilical cord fibroblast cells of Yangtze finless porpoise

Yangtze finless porpoises (YFP) accumulate high levels of per- and polyfluoroalkyl substances (PFASs). However, the health impacts of PFASs to YFP are still unknown because it is technically and ethically unfeasible to use the critically endangered YFP in toxicological exposures. To uncover the potential toxicities of PFASs to YFP, this study exposed a YFP umbilical cord fibroblast cell line to perfluorobutane sulfonate (PFBS), an emerging PFASs pollutant in the aquatic environments. After exposure, the cytotoxicity and mechanisms of PFBS were explored. Our preliminary experiments found that PFBS compromised the cell viability in a concentration and duration dependent manner. In an exposure of 48-h duration, the maximum no observed effect concentration (NOEC) of PFBS was determined to be 400 µM. High-throughput proteomics were then conducted to identify the differentially expressed proteins in YFP cells exposed to 400 µM PFBS for 48 h. The results found that PFBS exposure significantly perturbed the proteome fingerprints of YFP umbilical cord fibroblast cells. Functional annotation of differential proteins showed that PFBS had the potential to impair a variety of biological processes associated with the immunity, oxidative stress, metabolism, and proteolysis. Consistently, the intracellular levels of reactive oxygen species (ROS) and proinflammatory cytokine IL-1β were significantly increased by PFBS in YFP umbilical cord fibroblast cells. Overall, this study highlights the toxic effects of emerging PFASs on YFP and provides reference data to evaluate the health risks of aquatic pollution under the context of national YFP protection. To our knowledge, this is the first omics study using YFP umbilical cord fibroblast cells in ecotoxicology of PFASs, which is applicable to various cetacean species and pollutants.

103 Integration of omics studies identifies molecular signatures associated with uterine function in beef cattle

103 Integration of omics studies identifies molecular signatures associated with uterine function in beef cattle

Characterization of α-hairpinin in the genome of lima bean (Phaseolus lunatus)

Antimicrobial α-Hairpinin peptides are sources of bioactive molecules with significant structural diversity and various biological activities. Phaseolus lunatus, known as lima bean, is a plant from the Fabaceae family, native to Central and South America. It is the second most important legume agronomically and economically in tropical regions, after the common bean. Due to its importance, several omics studies have been recently conducted on this species. The research aimed to characterize the antimicrobial peptides of the α-hairpinin family in the lima bean genome by analyzing their physicochemical properties, in silico biological activities, and three-dimensional structure. To this end, α-hairpinin sequences were identified according to the cysteine pattern characteristic of this protein family. After target identification, a search for conserved domains, signal peptides, and subcellular localization was conducted. Molecular mass, isoelectric point, grand average of hydropathicity (GRAVY), and biological activities were also analyzed, and a three-dimensional model of the candidate was generated. Following the mining process, an α-hairpinin sequence from P. lunatus (PlHrp1) was identified. PlHrp1 consisted of 41 amino acids, was predicted to be in the extracellular region, had an isoelectric point of 6.28, a mass of 4.81 kDa, and a gravy of -1.078. The alignment revealed that PlHrp1 contained the 4 conserved cysteine residues. In silico activity analyses demonstrated that PlHrp1 exhibited antifungal and antibacterial activities, with no antiviral activity. The secondary structure displayed two antiparallel α-helices connected by a loop. The model described 94.4% of amino acid residues in favorable positions. This study provides a comprehensive analysis of α-hairpinin peptides in the Phaseolus lunatus genome, highlighting the presence of this peptide family in the lima bean species and offering insights into their potential.

Population Power Curves in ASCA With Permutation Testing

ABSTRACTIn this paper, we revisit the power curves in ANOVA simultaneous component analysis (ASCA) based on permutation testing and introduce the population curves derived from population parameters describing the relative effect among factors and interactions. The relative effect has important practical implications: The statistical power of a given factor depends on the design of other factors in the experiment and not only of the sample size. Thus, understanding the relative power in a specific experimental design can be extremely useful to maximize our capability of success when planning the experiment. In the paper, we derive relative and absolute population curves, where the former represent statistical power in terms of the normalized effect size between structure and noise, and the latter in terms of the sample size. Both types of population curves allow us to make decisions regarding the number and nature (fixed/random) of factors, their relationships (crossed/nested), and the number of levels and replicates, among others, in an multivariate experimental design (e.g., an omics study) during the planning phase of the experiment. We illustrate both types of curves through simulation.

Immunometabolic interplay in Edwardsiella tarda-infected crucian carp (Carassius auratus) and in vitro identification of the antimicrobial activity of apolipoprotein D (ApoD) by utilization of multiomics analyses

Edwardsiella tarda is an intracellular pathogenic bacteria that can imperil the health of farmed fish. However, the interactive networks of immune regulation and metabolic response in E. tarda-infected fish are still unclear. In this investigation, we aimed to explore immunometabolic interplay in crucian carp after E. tarda infection by utilizing multiomics analyses. Crucian carp (Carassius auratus) receiving E. tarda infection showed increased levels of tissue damage and oxidative injury in liver. Multiomics analyses suggested that carbon and amino acid metabolism may be considered as crucial metabolic pathways in liver of crucian carp following E. tarda infection, while spaglumic acid, isocitric acid and tetrahydrocortisone were the crucial liver biomarkers. After that, a potential antimicrobial peptide (AMP) sequence called apolipoprotein D (ApoD) was identified from omics study. Then, tissue-specific analysis indicated that liver CaApoD showed the highest expression among isolated tissues. After Aeromonas hydrophila stimulated, CaApoD expressions increased sharply in immune-related tissues. Moreover, CaApoD fusion protein could mediate the in vitro binding to A. hydrophila and E. tarda, attenuate bacterial growth as well as diminish bacterial biofilm forming activity. These findings may have a comprehensive implication for understanding immunometabolic response in crucian carp upon infection.