Inter-tissue coexpression network analysis reveals DPP4 as an important gene in heart to blood communication

Emerging Technologies and Future Directions in Interorgan Crosstalk Cardiometabolic Research

The heart does not work in isolation, with cardiac health and disease occurring through complex interactions between the heart with multiple organs. Furthermore, the integration of organ-specific lipid metabolism, blood pressure, insulin sensitivity, and inflammation involves a complex network of signaling pathways between many organs. Dysregulation in these communications is now recognized as a key contributor to many manifestations of cardiovascular disease. Mechanistic characterization of specific molecules mediating interorgan signaling has been pivotal in advancing our understanding of cardiovascular disease. The discovery of insulin, glucagon, and other hormones in the early 20th century illustrated the importance of communication between organs in maintaining physiological homeostasis. For example, elegant studies evaluating insulin signaling and its role in regulating glucose metabolism have shed light on its broader impact on cardiovascular health, hypertension, atherosclerosis, and other cardiovascular disease risks. Recent technological advances have revolutionized our understanding of interorgan signaling. Global approaches such as proteomics and metabolomics applications to blood have enabled the simultaneous profiling of thousands of circulating factors, revealing previously unknown signaling molecules and pathways. These large-scale studies have identified biomarkers linked to early stages of heart disease and offered new therapeutic targets. By understanding how specific cells in the heart interact with cells in other organs, such as the kidney or liver, researchers can identify key pathways that, when disrupted, lead to cardiovascular pathology. The ability to capture a more holistic view of the cardiovascular system positions interorgan signaling at the forefront of cardiovascular research. As we continue to refine our tools for mapping these complex networks, the insights gained hold the potential to not only improve early diagnosis but also to develop more targeted and effective treatments for cardiovascular disease. In this review, we discuss current approaches used to enhance our understanding of organ crosstalk with a specific emphasis on cardiac and cardiovascular physiology.

CorrAdjust unveils biologically relevant transcriptomic correlations by efficiently eliminating hidden confounders

Correcting for confounding variables is often overlooked when computing RNA-RNA correlations, even though it can profoundly affect results. We introduce CorrAdjust, a method for identifying and correcting such hidden confounders. CorrAdjust selects a subset of principal components to residualize from expression data by maximizing the enrichment of "reference pairs" among highly correlated RNA-RNA pairs. Unlike traditional machine learning metrics, this novel enrichment-based metric is specifically designed to evaluate correlation data and provides valuable RNA-level interpretability. CorrAdjust outperforms current state-of-the-art methods when evaluated on 25 063 human RNA-seq datasets from The Cancer Genome Atlas, the Genotype-Tissue Expression project, and the Geuvadis collection. In particular, CorrAdjust excels at integrating small RNA and mRNA sequencing data, significantly enhancing the enrichment of experimentally validated miRNA targets among negatively correlated miRNA-mRNA pairs. CorrAdjust, with accompanying documentation and tutorials, is available at https://tju-cmc-org.github.io/CorrAdjust.

RNA-Seq data provide new insights into the molecular regulation of breast muscle glycogen reserves, a key factor in muscle function and meat quality in chickens

Research is needed to better understand the molecular mechanisms that influence muscle glycogen reserves in chickens due to their critical influence on muscle function and meat quality. In this study, breast muscle RNA sequencing data (RNA-Seq) were used to compare the transcriptomic profile of two original chicken lines divergently selected for breast muscle ultimate pH, which is a proxy for glycogen reserves. Weighted gene co-expression network analysis (WGCNA) of muscle and jejunum RNA-Seq data was also performed to highlight biological processes specifically involved in the gut-muscle dialogue that may contribute to the divergence in glycogen reserves between the two lines. Breast muscle RNA-Seq analysis of 4-week-old birds from the 15th generation of selection, in which glycogen reserves in the pHu- line were twice as high as that in the pHu+ line, revealed 2676 differentially expressed genes (Padj ≤ 0.05). Functional analysis of the genes overexpressed in the pHu- line highlighted enrichment in processes related to energy production from a wide range of substrates and pathways, as well as to processes involved in development of blood and lymph tissue. Diverse processes were enriched for genes overexpressed in the pHu+ line: muscle development and remodeling, lipid metabolism, immune response and inflammation, which suggested molecular changes much larger than those for carbohydrate metabolism. WGCNA revealed 64 modules of co-expressed genes. One, which contained 30 % genes expressed in the jejunum and 70 % genes expressed in the muscle, was correlated (P ≤ 0.05) with muscle glycogen reserves and several indicators of intestinal anatomy and health. Functional analysis of it showed an enrichment of processes related to transmission of nerve information and tissue oxygenation that seem to be involved in the gut-muscle dialogue that mediates establishment of breast muscle glycogen reserves. Finally, the study found that transcriptional regulations observed in muscle of the pHu+ line were similar to those in muscle afflicted with "wooden breast", which highlighted a dysfunction of mitochondrial metabolism and suggested several potential gene markers for both conditions.

A computational framework for detecting inter-tissue gene-expression coordination changes with aging

Aging is a complex and systematic biological process that involves multiple genes and biological pathways across different tissues. While existing studies focus on tissue-specific aging factors, the inter-tissue interplay between molecular pathways during aging remains insufficiently explored. To bridge this gap, we propose a novel computational framework to identify the effect of aging on the coordinated patterns of gene-expression across multiple tissues. Our framework includes (1) an adjusted multi-tissue weighted gene co-expression network analysis, (2) differential network connectivity analysis between age groups and (3) machine learning models, XGBoost and Random Forest (RF) fed by gene expression levels and lower-dimensional pathway score space, to identify unique key inter-tissue genes and biological pathways for classifying aging. We applied our approach to three representative tissues: Adipose-Subcutaneous, Muscle-Skeletal and Brain-Cortex. The RF model demonstrated the best performance in predicting age group (AUC < 88%) highlighting key genes involved in inter-tissue coordination processes in aging. We also identified the inter-tissue involvement of lipid metabolism, immune system, and cell communication pathways during aging and detected distinct aging pathways manifested between tissues. The proposed framework highlights the importance of inter-tissue coordination processes underlying aging and provides valuable insights into aging mechanisms which can further assist in the development of therapeutic strategies promoting healthy aging.

Alzheimer's disease rewires gene coexpression networks coupling different brain regions

Connectome studies have shown how Alzheimer's disease (AD) disrupts functional and structural connectivity among brain regions. But the molecular basis of such disruptions is less studied, with most genomic/transcriptomic studies performing within-brain-region analyses. To inspect how AD rewires the correlation structure among genes in different brain regions, we performed an Inter-brain-region Differential Correlation (Inter-DC) analysis of RNA-seq data from Mount Sinai Brain Bank on four brain regions (frontal pole, superior temporal gyrus, parahippocampal gyrus and inferior frontal gyrus, comprising 264 AD and 372 control human post-mortem samples). An Inter-DC network was assembled from all pairs of genes across two brain regions that gained (or lost) correlation strength in the AD group relative to controls at FDR 1%. The differentially correlated (DC) genes in this network complemented known differentially expressed genes in AD, and likely reflects cell-intrinsic changes since we adjusted for cell compositional effects. Each brain region used a distinctive set of DC genes when coupling with other regions, with parahippocampal gyrus showing the most rewiring, consistent with its known vulnerability to AD. The Inter-DC network revealed master dysregulation hubs in AD (at genes ZKSCAN1, SLC5A3, RCC1, IL17RB, PLK4, etc.), inter-region gene modules enriched for known AD pathways (synaptic signaling, endocytosis, etc.), and candidate signaling molecules that could mediate region-region communication. The Inter-DC network generated in this study is a valuable resource of gene pairs, pathways and signaling molecules whose inter-brain-region functional coupling is disrupted in AD, thereby offering a new perspective of AD etiology.

IntiCom-DB: A Manually Curated Database of Inter-Tissue Communication Molecules and Their Communication Routes

Inter-tissue communication (ITC) is critical for maintaining the physiological functions of multiple tissues and is closely related to the onset and development of various complex diseases. Nevertheless, there is no well-organized data resource for known ITC molecules with explicit ITC routes from source tissues to target tissues. To address this issue, in this work, we manually reviewed nearly 190,000 publications and identified 1408 experimentally supported ITC entries in which the ITC molecules, their communication routes, and their functional annotations were included. To facilitate our work, these curated ITC entries were incorporated into a user-friendly database named IntiCom-DB. This database also enables visualization of the expression abundances of ITC proteins and their interaction partners. Finally, bioinformatics analyses on these data revealed common biological characteristics of the ITC molecules. For example, tissue specificity scores of ITC molecules at the protein level are often higher than those at the mRNA level in the target tissues. Moreover, the ITC molecules and their interaction partners are more abundant in both the source tissues and the target tissues. IntiCom-DB is freely available as an online database. As the first comprehensive database of ITC molecules with explicit ITC routes to the best of our knowledge, we hope that IntiCom-DB will benefit future ITC-related studies.

Defining the Roles of Cardiokines in Human Aging and Age-Associated Diseases

In recent years an expanding collection of heart-secreted signaling proteins have been discovered that play cellular communication roles in diverse pathophysiological processes. This minireview briefly discusses current evidence for the roles of cardiokines in systemic regulation of aging and age-associated diseases. An analysis of human transcriptome and secretome data suggests the possibility that many other cardiokines remain to be discovered that may function in long-range physiological regulations. We discuss the ongoing challenges and emerging technologies for elucidating the identity and function of cardiokines in endocrine regulations.

Integrating Multimorbidity into a Whole-Body Understanding of Disease Using Spatial Genomics

Multimorbidity is characterized by multidimensional complexity emerging from interactions between multiple diseases across levels of biological (including genetic) and environmental determinants and the complex array of interactions between and within cells, tissues and organ systems. Advances in spatial genomic research have led to an unprecedented expansion in our ability to link alterations in genome folding with changes that are associated with human disease. Studying disease-associated genetic variants in the context of the spatial genome has enabled the discovery of transcriptional regulatory programmes that potentially link dysregulated genes to disease development. However, the approaches that have been used have typically been applied to uncover pathological molecular mechanisms occurring in a specific disease-relevant tissue. These forms of reductionist, targeted investigations are not appropriate for the molecular dissection of multimorbidity that typically involves contributions from multiple tissues. In this perspective, we emphasize the importance of a whole-body understanding of multimorbidity and discuss how spatial genomics, when integrated with additional omic datasets, could provide novel insights into the molecular underpinnings of multimorbidity.

Coordinated expression of the renin-angiotensin genes in the lumbar spinal cord: Lateralization and effects of unilateral brain injury

In spite of its apparent symmetry, the spinal cord is asymmetric in its reflexes and gene expression patterns including leftward expression bias of the opioid and glutamate genes. To examine whether this is a general phenomenon for neurotransmitter and neurohormonal genes, we here characterized expression and co-expression (transcriptionally coordinated) patterns of genes of the renin-angiotensin system (RAS) that is involved in neuroprotection and pathological neuroplasticity in the left and right lumbar spinal cord. We also tested whether the RAS expression patterns were affected by unilateral brain injury (UBI) that rewired lumbar spinal neurocircuits. The left and right halves of the lumbar spinal cord were analysed in intact rats, and rats with left- or right-sided unilateral cortical injury, and left- or right-sided sham surgery. The findings were (i) lateralized expression of the RAS genes Ace, Agtr2 and Ren with higher levels on the left side; (ii) the asymmetry in coordination of the RAS gene expression that was stronger on the right side; (iii) the decay in coordination of co-expression of the RAS and neuroplasticity-related genes induced by the right-side but not left-side sham surgery and UBI; and (iv) the UBI-induced shift to negative regulatory interactions between RAS and neuroplasticity-related genes on the contralesional spinal side. Thus, the RAS genes may be a part of lateralized gene co-expression networks and have a role in a side-specific regulation of spinal neurocircuits.

CD14, CD163, and CCR1 are involved in heart and blood communication in ischemic cardiac diseases

Objective

Cardiac diseases lead to heart failure (HF), but the progression can take several years. Using blood samples to monitor changes in the heart before clinical symptoms begin may help to improve patient management.

Methods

Microarray data GSE42955 and GSE9128 were used as study datasets and GSE16499, GSE57338, and GSE59867 were used as validation groups. The “limma” package from R Language was used to identify differentially expressed genes. Functional enrichment analyses of gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways were performed using Database for Annotation, Visualization and Integrated Discovery. We also investigated the correlation between the heart and blood using the mRNA expression level.

Results

Three hub genes, CD14, CD163, and CCR1, were identified. Functional enrichment analyses showed their involvement in the immune response and in the inflammatory response, which are the critical biochemical processes in ischemic HF. The mRNA expression level further demonstrated that a special model may exist to help to predict the mRNA level in the heart based on that in blood.

Conclusions

Our study identified three biomarkers that can connect the heart and blood in ischemic heart diseases, which may be a new approach to help better manage ischemic cardiac disease patients.

Left-Right Side-Specific Neuropeptide Mechanism Mediates Contralateral Responses to a Unilateral Brain Injury

Neuropeptides are implicated in control of lateralized processes in the brain. A unilateral brain injury (UBI) causes the contralesional sensorimotor deficits. To examine whether opioid neuropeptides mediate UBI induced asymmetric processes we compared effects of opioid antagonists on the contralesional and ipsilesional hindlimb responses to the left-sided and right-sided injury in rats. UBI induced hindlimb postural asymmetry (HL-PA) with the contralesional hindlimb flexion, and activated contralesional withdrawal reflex of extensor digitorum longus (EDL) evoked by electrical stimulation and recorded with EMG technique. No effects on the interossei (Int) and peroneaus longus (PL) were evident. The general opioid antagonist naloxone blocked postural effects, did not change EDL asymmetry while uncovered cryptic asymmetry in the PL and Int reflexes induced by UBI. Thus, the spinal opioid system may either mediate or counteract the injury effects. Strikingly, effects of selective opioid antagonists were the injury side-specific. The μ-antagonist β-funaltrexamine (FNA) and κ-antagonist nor-binaltorphimine (BNI) reduced postural asymmetry after the right but not left UBI. In contrast, the δ-antagonist naltrindole (NTI) inhibited HL-PA after the left but not right-side brain injury. The opioid gene expression and opioid peptides were lateralized in the lumbar spinal cord, and coordination between expression of the opioid and neuroplasticity-related genes was impaired by UBI that together may underlie the side-specific effects of the antagonists. We suggest that mirror-symmetric neural circuits that mediate effects of left and right brain injury on the contralesional hindlimbs are differentially controlled by the lateralized opioid system.

Processing and Analysis of RNA-seq Data from Public Resources

Advances in next generation sequencing (NGS) technologies resulted in a broad array of large-scale gene expression studies and an unprecedented volume of whole messenger RNA (mRNA) sequencing data, or the transcriptome (also known as RNA sequencing, or RNA-seq). These include the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA), among others. Here we cover some of the commonly used datasets, provide an overview on how to begin the analysis pipeline, and how to explore and interpret the data provided by these publicly available resources.

Hindlimb motor responses to unilateral brain injury: spinal cord encoding and left-right asymmetry

Mechanisms of motor deficits (e.g. hemiparesis and hemiplegia) secondary to stroke and traumatic brain injury remain poorly understood. In early animal studies, a unilateral lesion to the cerebellum produced postural asymmetry with ipsilateral hindlimb flexion that was retained after complete spinal cord transection. Here we demonstrate that hindlimb postural asymmetry in rats is induced by a unilateral injury of the hindlimb sensorimotor cortex, and characterize this phenomenon as a model of spinal neuroplasticity underlying asymmetric motor deficits. After cortical lesion, the asymmetry was developed due to the contralesional hindlimb flexion and persisted after decerebration and complete spinal cord transection. The asymmetry induced by the left-side brain injury was eliminated by bilateral lumbar dorsal rhizotomy, but surprisingly, the asymmetry after the right-side brain lesion was resistant to deafferentation. Pancuronium, a curare-mimetic muscle relaxant, abolished the asymmetry after the right-side lesion suggesting its dependence on the efferent drive. The contra- and ipsilesional hindlimbs displayed different musculo-articular resistance to stretch after the left but not right-side injury. The nociceptive withdrawal reflexes evoked by electrical stimulation and recorded with EMG technique were different between the left and right hindlimbs in the spinalized decerebrate rats. On this asymmetric background, a brain injury resulted in greater reflex activation on the contra- versus ipsilesional side; the difference between the limbs was higher after the right-side brain lesion. The unilateral brain injury modified expression of neuroplasticity genes analysed as readout of plastic changes, as well as robustly impaired coordination of their expression within and between the ipsi- and contralesional halves of lumbar spinal cord; the effects were more pronounced after the left side compared to the right-side injury. Our data suggest that changes in the hindlimb posture, resistance to stretch and nociceptive withdrawal reflexes are encoded by neuroplastic processes in lumbar spinal circuits induced by a unilateral brain injury. Two mechanisms, one dependent on and one independent of afferent input may mediate asymmetric hindlimb motor responses. The latter, deafferentation resistant mechanism may be based on sustained muscle contractions which often occur in patients with central lesions and which are not evoked by afferent stimulation. The unusual feature of these mechanisms is their lateralization in the spinal cord.

Comparison of brain connectomes by MRI and genomics and its implication in Alzheimer's disease

BACKGROUND

The human brain is complex and interconnected structurally. Brain connectome change is associated with Alzheimer's disease (AD) and other neurodegenerative diseases. Genetics and genomics studies have identified molecular changes in AD; however, the results are often limited to isolated brain regions and are difficult to interpret its findings in respect to brain connectome. The mechanisms of how one brain region impacts the molecular pathways in other regions have not been systematically studied. And how the brain regions susceptible to AD pathology interact with each other at the transcriptome level and how these interactions relate to brain connectome change are unclear.

METHODS

Here, we compared structural brain connectomes defined by probabilistic tracts using diffusion magnetic resonance imaging data in Alzheimer's Disease Neuroimaging Initiative database and a brain transcriptome dataset covering 17 brain regions.

RESULTS

We observed that the changes in diffusion measures associated with AD diagnosis status and the associations were replicated in an independent cohort. The result suggests that disease associated white matter changes are focal. Analysis of the brain connectome by genomic data, tissue-tissue transcriptional synchronization between 17 brain regions, indicates that the regions connected by AD-associated tracts were likely connected at the transcriptome level with high number of tissue-to-tissue correlated (TTC) gene pairs (P = 0.03). And genes involved in TTC gene pairs between white matter tract connected brain regions were enriched in signaling pathways (P = 6.08 × 10-9). Further pathway interaction analysis identified ionotropic glutamate receptor pathway and Toll receptor signaling pathways to be important for tissue-tissue synchronization at the transcriptome level. Transcript profile entailing Toll receptor signaling in the blood was significantly associated with diffusion properties of white matter tracts, notable association between fractional anisotropy and bilateral cingulum angular bundles (Ppermutation = 1.0 × 10-2 and 4.9 × 10-4 for left and right respectively).

CONCLUSIONS

In summary, our study suggests that brain connectomes defined by MRI and transcriptome data overlap with each other.

Using Network Pharmacology to Explore Potential Treatment Mechanism for Coronary Heart Disease Using Chuanxiong and Jiangxiang Essential Oils in Jingzhi Guanxin Prescriptions

Background

To predict the active components and potential targets of traditional Chinese medicine and to determine the mechanism behind the curative effect of traditional Chinese medicine, a multitargeted method was used. Jingzhi Guanxin prescriptions expressed a high efficacy for coronary heart disease (CHD) patients of which essential oils from Chuanxiong and Jiangxiang were confirmed to be the most important effective substance. However, the interaction between the active components and the targets for the treatment of CHD has not been clearly explained in previous studies.

Materials and Methods

Genes associated with the disease and the treatment strategy were searched from the electronic database and analyzed by Cytoscape (version 3.2.1). Protein-protein interaction network diagram of CHD with Jiangxiang and Chuanxiong essential oils was constructed by Cytoscape. Pathway functional enrichment analysis was executed by clusterProfiler package in R platform.

Results

121 ingredients of Chuanxiong and Jiangxiang essential oils were analyzed, and 393 target genes of the compositions and 912 CHD-related genes were retrieved. 15 coexpression genes were selected, including UGT1A1, DPP4, RXRA, ADH1A, RXRG, UGT1A3, PPARA, TRPC3, CYP1A1, ABCC2, AHR, and ADRA2A. The crucial pathways of occurrence and treatment molecular mechanism of CHD were analyzed, including retinoic acid metabolic process, flavonoid metabolic process, response to xenobiotic stimulus, cellular response to xenobiotic stimulus, cellular response to steroid hormone stimulus, retinoid binding, retinoic acid binding, and monocarboxylic acid binding. Finally, we elucidate the underlying role and mechanism behind these genes in the pathogenesis and treatment of CHD.

Conclusions

Generally speaking, the nodes in subnetwork affect the pathological process of CHD, thus indicating the mechanism of Jingzhi Guanxin prescriptions containing Chuanxiong and Jiangxiang essential oils in the treatment of CHD.

Batch correction evaluation framework using a-priori gene-gene associations: applied to the GTEx dataset

BACKGROUND

Correcting a heterogeneous dataset that presents artefacts from several confounders is often an essential bioinformatics task. Attempting to remove these batch effects will result in some biologically meaningful signals being lost. Thus, a central challenge is assessing if the removal of unwanted technical variation harms the biological signal that is of interest to the researcher.

RESULTS

We describe a novel framework, B-CeF, to evaluate the effectiveness of batch correction methods and their tendency toward over or under correction. The approach is based on comparing co-expression of adjusted gene-gene pairs to a-priori knowledge of highly confident gene-gene associations based on thousands of unrelated experiments derived from an external reference. Our framework includes three steps: (1) data adjustment with the desired methods (2) calculating gene-gene co-expression measurements for adjusted datasets (3) evaluating the performance of the co-expression measurements against a gold standard. Using the framework, we evaluated five batch correction methods applied to RNA-seq data of six representative tissue datasets derived from the GTEx project.

CONCLUSIONS

Our framework enables the evaluation of batch correction methods to better preserve the original biological signal. We show that using a multiple linear regression model to correct for known confounders outperforms factor analysis-based methods that estimate hidden confounders. The code is publicly available as an R package.

Systems-based approaches for investigation of inter-tissue communication

Secreted proteins serve as crucial mediators of many physiology processes, and beginning with the discovery of insulin, studies have revealed numerous context-specific regulatory networks across various cell types. Here, we review “omics” approaches to deconvolute the complex milieu of proteins that are released from the cell. We emphasize a novel “systems genetics” approach our laboratory has developed to investigate mechanisms of tissue-tissue communication using population-based datasets. Finally, we highlight potential future directions for these studies, discuss several caveats, and propose new ways to investigate modes of endocrine communication.

A Strategy for Discovery of Endocrine Interactions with Application to Whole-Body Metabolism

Inter-tissue communication via secreted proteins has been established as a vital mechanism for proper physiologic homeostasis. Here, we report a bioinformatics framework using a mouse reference population, the Hybrid Mouse Diversity Panel (HMDP), which integrates global multi-tissue expression data and publicly available resources to identify and functionally annotate novel circuits of tissue-tissue communication. We validate this method by showing that we can identify known as well as novel endocrine factors responsible for communication between tissues. We further show the utility of this approach by identification and mechanistic characterization of two new endocrine factors. Adipose-derived Lipocalin-5 is shown to enhance skeletal muscle mitochondrial function, and liver-secreted Notum promotes browning of white adipose tissue, also known as "beiging." We demonstrate the general applicability of the method by providing in vivo evidence for three additional novel molecules mediating tissue-tissue interactions.

THD-Module Extractor: An Application for CEN Module Extraction and Interesting Gene Identification for Alzheimer's Disease

There exist many tools and methods for construction of co-expression network from gene expression data and for extraction of densely connected gene modules. In this paper, a method is introduced to construct co-expression network and to extract co-expressed modules having high biological significance. The proposed method has been validated on several well known microarray datasets extracted from a diverse set of species, using statistical measures, such as p and q values. The modules obtained in these studies are found to be biologically significant based on Gene Ontology enrichment analysis, pathway analysis, and KEGG enrichment analysis. Further, the method was applied on an Alzheimer’s disease dataset and some interesting genes are found, which have high semantic similarity among them, but are not significantly correlated in terms of expression similarity. Some of these interesting genes, such as MAPT, CASP2, and PSEN2, are linked with important aspects of Alzheimer’s disease, such as dementia, increase cell death, and deposition of amyloid-beta proteins in Alzheimer’s disease brains. The biological pathways associated with Alzheimer’s disease, such as, Wnt signaling, Apoptosis, p53 signaling, and Notch signaling, incorporate these interesting genes. The proposed method is evaluated in regard to existing literature.

Disease-specific classification using deconvoluted whole blood gene expression

Blood-based biomarker assays have an advantage in being minimally invasive. Diagnostic and prognostic models built on peripheral blood gene expression have been reported for various types of disease. However, most of these studies focused on only one disease type, and failed to address whether the identified gene expression signature is disease-specific or more widely applicable across diseases. We conducted a meta-analysis of 46 whole blood gene expression datasets covering a wide range of diseases and physiological conditions. Our analysis uncovered a striking overlap of signature genes shared by multiple diseases, driven by an underlying common pattern of cell component change, specifically an increase in myeloid cells and decrease in lymphocytes. These observations reveal the necessity of building disease-specific classifiers that can distinguish different disease types as well as normal controls, and highlight the importance of cell component change in deriving blood gene expression based models. We developed a new strategy to develop blood-based disease-specific models by leveraging both cell component changes and cell molecular state changes, and demonstrate its superiority using independent datasets.