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PubMed2004

Differentially expressed protein markers in human submandibular and sublingual secretions.

Proteome analysis of secretions from individual salivary glands is important for understanding the health of the oral cavity and pathogenesis of certain diseases. However, cross-contamination of submandibular (SM) and sublingual (SL) glandular secretions can occur. The close anatomic relationship of the SM and SL ductal orifices can lead to such contamination. Additionally, these glands may share common ducts. To insure the purity of SM/SL secretions for proteomic analysis, it is important to develop unique biomarkers which could be used to verify the integrity of the individual glandular saliva. In this study, a proteomics approach based on mass spectrometry and gel electrophoresis techniques was utilized to identify and verify a set of proteins (cystatin C, calgranulin B and MUC5B mucin), which are differentially expressed in SM/SL secretions. SM/SL fluids were obtained from nine healthy subjects. Cystatin C was found to be an SM-selective protein as it was found in all SM fluids but not detected in two SL fluids. MUC5B mucin and calgranulin B, on the other hand, were found to be SL-selective proteins. All SL samples contained MUC5B mucin, whereas MUC5B mucin was not detected in four SM samples. Eight of the SL samples contained calgranulin B; however, calgranulin B was absent in eight SM samples. This set of protein markers, especially calgranulin B, can be used to determine the purity of SM/SL samples, and therefore identify potential individuals who do not exhibit cross-contaminated SM/SL secretions, an important requirement for subsequent proteome analysis of pure SM and SL secretions.

Adult↗

PubMed2004

Proteomics in cancer screening and management in gynecologic cancer.

Biomarkers are used routinely for population screening, disease diagnosis and prognosis, monitoring of therapy, and prediction of therapeutic response. Unfortunately, most biomarkers have low sensitivity and specificity and little predictive value. Novel techniques for better screening and early diagnosis of ovarian cancer are urgently needed. Proteomics, the study of the cellular proteins and their activation states, integrates some fundamental techniques, including high-throughput protein purification and profiling, genomic and proteomic databases, and mass spectrometry. In oncology, proteomics will contribute greatly to our understanding of gene functions in tumor development and provide information in clinical applications. This article reviews proteomic techniques and their potential applications in gynecologic cancer screening and management.

Biomarkers, Tumor↗

PubMed2026

Multi-Omics Biomarker Signatures for Precision Diagnosis and Prognosis in Primary Liver Cancer: A Literature Review.

Primary liver cancer (PLC) is a biologically heterogeneous group of malignancies dominated by hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (iCCA), and a smaller subset of combined hepatocellular-cholangiocarcinoma (cHCC-CCA), and its clinical burden remains high because current diagnostic and prognostic tools do not adequately capture molecular diversity. Conventional imaging, serum markers, and histopathological assessment remain insufficient for precise early diagnosis, subtype-resolved classification, and outcome stratification, while tissue and liquid biopsy approaches have expanded the range of analytes available for clinical assessment. Recent studies have identified candidate biomarker signatures across genomic, epigenomic, transcriptomic, proteomic, metabolomic, and circulating layers, suggesting that integrated multi-omics profiling may better represent tumor lineage, clonal evolution, immune context, and therapeutic vulnerability than isolated molecular readouts. However, these layers are not equally mature for clinical use: genomic testing is closest to routine therapeutic application in iCCA, plasma methylation assays are advancing for HCC surveillance augmentation, and many proteomic or metabolomic panels remain validation-stage tools. Their clinical value remains constrained by sampling bias, biospecimen-dependent signal loss, assay standardization, cost, and the need for prospective validation across clinically diverse populations. This narrative review critically synthesizes current evidence on multi-omics biomarker signatures for precision diagnosis and prognosis in primary liver cancer and argues that clinically useful signatures should be question-specific, stage-aware, and specimen-aware rather than universal multi-analyte panels.

Humans↗

PubMed2002

The challenges of developing a sound proteomics strategy.

This paper will review the challenges of developing a proteomics strategy. A key issue is the integration of the two-dimensional (2-D) gel platform with mass spectrometry measurements. The use of both matrix-assisted laser/desorption ionization (on off-line coupling) and electrospray (on-line) ionization are complementary. While the use of one-dimensional and 2-D gels are essential to many aspects of proteomics research (sample preparation, preliminary fractionation and quantitation, storage of protein components), the emergence of shotgun sequencing based on high performance liquid chromatography and tandem mass spectrometry offers a powerful new approach. The latter has particular utility in the characterization of low level samples and complex post-translational modifications. The development of capillary columns, such as 75 to 150 micron, that can be packed in a reproducible manner has been a key step in the development of high sensitivity liquid chromatography/mass spectrometry analysis.

Chromatography↗

PubMed2026

Decoding protein signatures and protein interactions in oral potentially malignant disorders: a systematic review and network analysis.

BACKGROUND: Proteomic profiling offers thorough insights into protein structure and function, as well as it acts as an essential approach for analyzing molecular changes at the tissue level. However, because of the proteome's diversity and dynamic nature, biomarker discovery remains challenging. By combining proteomics with bioinformatics, the level of understanding in relation to molecular interactions and disease processes can be improved. Through an integrative approach, few limitations can be addressed, thereby promoting proteomic profiling for the discovery of new therapeutic targets and novel biomarkers for a variety of disorders. AIM: To identify differentially expressed protein markers and their key molecular pathways associated with Oral Potentially Malignant Disorders. METHODS: Systematic Review was conducted following the PRISMA guidelines and the protocol registered in the International Prospective Register of Systematic Reviews (PROSPERO) with the registration ID number CRD42024557545. A comprehensive literature review was performed using electronic databases, yielding 12,797, studies from which 15 eligible articles were selected. The Newcastle-Ottawa Scale was used to assess the risk of bias. Vote counting was performed to identify proteins reported in more than one study. A bipartite network was constructed using Cytoscape to identify shared and disease-specific protein markers. Lesion-wise protein-protein interaction networks were generated using STRING and analysed in Cytoscape to identify highly interconnected hub proteins, and pathway enrichment analysis for these hubs was performed using Reactome. RESULTS: A total of fifteen studies (Leukoplakia (LK) - n = 1, Proliferative Verrucous Leukoplakia (PVL) - n = 2, Oral Submucous Fibrosis (OSMF) - n = 7, and Oral Lichen Planus (OLP) - n = 5) were included. The Newcastle-Ottawa Scale was used to evaluate methodological quality and the quality of studies included in this systematic review was high for 4 articles and moderate in the remaining 11. The most commonly employed technique was mass spectrometry. A total of 318 candidate proteins (LK - 14, PVL - 82, OSMF - 172, and OLP - 50) were identified across the oral potentially malignant disorders. Key markers identified through vote counting included ERO1A, NUCB1, RHOA, and IL36A for PVL; LUM, KRT1, KRT9, ALB, and VIM for OSMF; and ALB, LYZ, HP, HBB, and AMY1A for OLP. The bipartite network showed that OSMF and OLP shared the highest number of proteins, indicating the strongest overlap among lesions. Network analysis further highlighted distinct hub proteins for each lesion: for LK- AMY1A, AMY1B and APOA1; for PVL- CFL1, RHOA and CDC42; for OSMF- HSP90AA1, ENO1 and SERPINA1; and for OLP- HP, B2M, and ORM1. Lesion-specific pathway enrichment revealed that LK was associated with epithelial differentiation, PVL with oncogenic signaling, OSMF with stress-driven fibrosis, and OLP with immune-mediated inflammation. CONCLUSIONS: Proteomic expression offers insights into disease pathogenesis by identifying important molecular changes across OPMDs. However, the majority of biomarkers are still in the exploratory stage due to the considerable variation in lesion types, sample sources, proteomic techniques, and reporting systems. In order to create reliable and clinically applicable biomarkers, future studies should concentrate on combining multi-omics techniques with large-scale, standardized cohorts.

Humans↗

PubMedDate not supplied

Integrated sampling procedure for metabolome analysis.

Metabolome analysis, the analysis of large sets of intracellular metabolites, has become an important systems analysis method in biotechnological and pharmaceutical research. In metabolic engineering, the integration of metabolome data with fluxome and proteome data into large-scale mathematical models promises to foster rational strategies for strain and cell line improvement. However, the development of reproducible sampling procedures for quantitative analysis of intracellular metabolite concentrations represents a major challenge, accomplishing (i) fast transfer of sample, (ii) efficient quenching of metabolism, (iii) quantitative metabolite extraction, and (iv) optimum sample conditioning for subsequent quantitative analysis. In addressing these requirements, we propose an integrated sampling procedure. Simultaneous quenching and quantitative extraction of intracellular metabolites were realized by short-time exposure of cells to temperatures < or =95 degrees C, where intracellular metabolites are released quantitatively. Based on these findings, we combined principles of heat transfer with knowledge on physiology, for example, turnover rates of energy metabolites, to develop an optimized sampling procedure based on a coiled single tube heat exchanger. As a result, this sampling procedure enables reliable and reproducible measurements through (i) the integration of three unit operations into a one unit operation, (ii) the avoidance of any alteration of the sample due to chemical reagents in quenching and extraction, and (iii) automation. A sampling frequency of 5 s(-)(1) and an overall individual sample processing time faster than 30 s allow observing responses of intracellular metabolite concentrations to extracellular stimuli on a subsecond time scale. Recovery and reliability of the unit operations were analyzed. Impact of sample conditioning on subsequent IC-MS analysis of metabolites was examined as well. The integrated sampling procedure was validated through consistent results from steady-state metabolite analysis of Escherichia coli cultivated in a chemostat at D = 0.1 h(-)(1).

Adenosine Triphosphate↗

PubMed2026

Quantitative Proteomic Profiling of Pinctada fucata Shell Nacre Defines a Solubility-Based Type Classification of Shell Matrix Proteins.

Shell matrix proteins (SMPs) are key organic components of molluscan biominerals, yet previous nacre proteomics have remained largely qualitative, limiting evaluation of the abundance and fraction association of individual SMPs. Here, we established a quantitative proteomic approach for the nacreous layer of the pearl oyster Pinctada fucata by integrating optimized shell preservation, stepwise fractionation, and data-independent acquisition (DIA) proteomics. SMPs were separated into an ethylenediaminetetraacetic acid (EDTA)-soluble matrix (ESM), an EDTA-insoluble but sodium dodecyl sulfate/dithiothreitol (SDS/DTT)-soluble matrix (SSM), and an SDS/DTT-insoluble matrix (ISM). DIA outperformed data-dependent acquisition in proteome coverage and enabled quantification of 327 SMPs across a broad dynamic range. Fraction-resolved abundance profiling showed that each fraction was characterized by distinct SMP compositions. To summarize these distributions, we introduced a solubility-based type classification that grouped SMPs into four types according to their quantitative partitioning among fractions. Well-known SMPs, including nacrein, Pif 80, and MSI60, were assigned to intuitively consistent types, whereas proteases, protease inhibitors, and tyrosinases also showed biased type distributions. These results support a three-compartment model of the nacreous layer consisting of (i) an insoluble interlamellar membrane core, (ii) a relatively extractable interfacial layer, and (iii) a soluble matrix fraction enriched in proteins potentially involved in ionic regulation and protein maturation. This study provides a quantitative framework for understanding coordinated SMP functions during nacre formation and for comparative analyses of molluscan shell proteomes.

Animals↗

PubMed2006

A flexible integration and visualisation system for biomarker discovery.

Biological data have accumulated at an unprecedented pace as a result of improvements in molecular technologies. However, the translation of data into information, and subsequently into knowledge, requires the intricate interplay of data access, visualisation and interpretation. Biological data are complex and are organised either hierarchically or non-hierarchically. For non-hierarchically organised data, it is difficult to view relationships among biological facts. In addition, it is difficult to make changes in underlying data storage without affecting the visualisation interface. Here, we demonstrate a platform where non-hierarchically organised data can be visualised through the application of a customised hierarchy incorporating medical subject headings (MeSH) classifications. This platform gives users flexibility in updating and manipulation. It can also facilitate fresh scientific insight by highlighting biological impacts across different hierarchical branches. An example of the integration of biomarker information from the curated Proteome database using MeSH and the StarTree visualisation tool is presented.

Algorithms↗

PubMed2025

Integrated Multiomics Analyses of the Molecular Landscape of Sarcopenia in Alcohol-Related Liver Disease.

BACKGROUND: Skeletal muscle is a major target for ethanol-induced perturbations, leading to sarcopenia in alcohol-related liver disease (ALD). The complex interactions and pathways involved in adaptive and maladaptive responses to ethanol in skeletal muscle are not well understood. Unlike hypothesis-driven experiments, an integrated multiomics-experimental validation approach provides a comprehensive view of these interactions. METHODS: We performed multiomics analyses with experimental validation to identify novel regulatory mechanisms of sarcopenia in ALD. Studies were done in a comprehensive array of models including ethanol-treated (ET) murine and human-induced pluripotent stem cell-derived myotubes (hiPSCm), skeletal muscle from a mouse model of ALD (mALD) and human patients with alcohol-related cirrhosis and controls. We generated 13 untargeted datasets, including chromatin accessibility (assay for transposase accessible chromatin), RNA sequencing, proteomics, phosphoproteomics, acetylomics and metabolomics, and conducted integrated multiomics analyses using UpSet plots and feature extraction. Key findings were validated using immunoblots, redox measurements (NAD+/NADH ratio), imaging and senescence-associated molecular phenotype (SAMP) assays. Mechanistic studies included mitochondrial-targeted Lactobacillus brevis NADH oxidase (MitoLbNOX) to increase redox ratio and MitoTempo as a mitochondrial free radical scavenger. RESULTS: Multiomics analyses revealed enrichment in mitochondrial oxidative function, protein synthesis and senescence pathways consistent with the known effects of hypoxia-inducible factor 1α (HIF1α) during normoxia. Across preclinical and clinical models, HIF1α targets (n = 32 genes) and signalling genes (n > 100 genes) (n = 3 ATACseq, n = 65 phosphoproteomics, n = 10 acetylomics, n = 6 C2C12 proteomics, n = 106 C2C12 RNAseq, n = 64 hiPSC RNAseq, n = 30 hiPSC proteomics, n = 3 mouse proteomics, n = 25 mouse RNAseq, n = 8 human RNAseq, n = 3 human proteomics) were increased. Stabilization of HIF1α (C2C12, 6hEtOH 0.24 ± 0.09; p = 0.043; mALD 0.32 ± 0.074; p = 0.005; data shown as mean difference ± standard error mean) was accompanied by enrichment in the early transient and late change clusters, -log(p-value) = 1.5-3.8, of the HIF1α signalling pathway. Redox ratio was reduced in ET myotubes (C2C12: 15512 ± 872.1, p < 0.001) and mALD muscle, with decreased expression of electron transport chain components (CI-V, p < 0.05) and Sirt3 (C2C12: 0.067 ± 0.023, p = 0.025; mALD: 0.41 ± 0.12, p = 0.013). Acetylation of mitochondrial proteins was increased in both models (C2C12: 107364 ± 4558, p = 0.03; mALD: 40036 ± 18 987, p = 0.049). Ethanol-induced SAMP was observed across models (P16: C2C12: 0.2845 ± 0.1145, p < 0.05; hiPSCm: 0.2591, p = 0.041). MitoLbNOX treatment reversed redox imbalance, HIF1α stabilization, global acetylation and myostatin expression (p < 0.05). CONCLUSIONS: An integrated multiomics approach, combined with experimental validation, identifies HIF1α stabilization and accelerated post-mitotic senescence as novel mechanisms of sarcopenia in ALD. These findings show the complex molecular interactions leading to mitochondrial dysfunction and progressive sarcopenia in ALD.

Sarcopenia↗

PubMed2005

Mass spectrometry-based expression profiling of clinical prostate cancer.

The maturation of MS technologies has provided a rich opportunity to interrogate protein expression patterns in normal and disease states by applying expression protein profiling methods. Major goals of this research strategy include the identification of protein biomarkers that demarcate normal and disease populations, and the identification of therapeutic biomarkers for the treatment of diseases such as cancer (Celis, J. E., and Gromov, P. (2003) Proteomics in translational cancer research: Toward an integrated approach. Cancer Cell 3, 9-151). Prostate cancer is one disease that would greatly benefit from implementing MS-based expression profiling methods because of the need to stratify the disease based on molecular markers. In this review, we will summarize the current MS-based methods to identify and validate biomarkers in human prostate cancer. Lastly, we propose a reverse proteomic approach implementing a quantitative MS research strategy to identify and quantify biomarkers implicated in prostate cancer development. With this approach, the absolute levels of prostate cancer biomarkers will be identified and quantified in normal and diseased samples by measuring the levels of native peptide biomarkers in relation to a chemically identical but isotopically labeled reference peptide. Ultimately, a centralized prostate cancer peptide biomarker expression database could function as a repository for the identification, quantification, and validation of protein biomarker(s) during prostate cancer progression in men.

Biomarkers, Tumor↗

PubMed2002

Protein microarrays and proteomics.

The system-wide study of proteins presents an exciting challenge in this information-rich age of whole-genome biology. Although traditional investigations have yielded abundant information about individual proteins, they have been less successful at providing us with an integrated understanding of biological systems. The promise of proteomics is that, by studying many components simultaneously, we will learn how proteins interact with each other, as well as with non-proteinaceous molecules, to control complex processes in cells, tissues and even whole organisms. Here, I discuss the role of microarray technology in this burgeoning area.

Forecasting↗

PubMedDate not supplied

Proteomics by FTICR mass spectrometry: top down and bottom up.

This review provides a broad overview of recent Fourier transform ion cyclotron resonance (FTICR) applications and technological developments relevant to the field of proteomics. Both the "bottom up" (peptide level) and "top down" (intact protein level) approaches are discussed and illustrated with examples. "Bottom up" topics include peptide fragmentation, the accurate mass and time (AMT) tag approach and dynamic range extension technology, aspects of quantitative proteomics measurements, post-translational modifications, and developments in FTICR operation software focused on peptide and protein identification. Topics related to the "top down" approach include various aspects of high mass measurements, protein tandem mass spectrometry, methods for the study of protein conformations, and protein complexes as well as advanced technologies that may become of practical utility in the coming years. Finally, early examples of the integration of both FTICR approaches to biomedical proteomics applications are presented, along with an outlook for future directions.

Mass Spectrometry↗

PubMed2002

Proteomic tools for biomedicine.

Proteomic tools measure gene expression, protein activity and interactions of biological events at the protein level. Proteins are the major catalysts of biological functions and contain several dimensions of information that collectively indicate the actual rather than the potential functional state as indicated by mRNA analysis. Measurements can be made in terms of protein quantity, location, and time-point. For the future we see a further integration of existing and new technologies for proteomics from a wide range of areas of biochemistry, chemistry, physics, computing science and molecular biology. This will further advance our knowledge of how biological systems are built up and what mechanisms control these systems. However, the potential of proteomics to comprehensively answer all biological questions is limited as only protein activity is measured. A unification of genomics, proteomics, and other technologies is needed if we are to start to understand the complexity of biological function in the context of disease and health.

Proteome↗

PubMed2001

The human proteomics initiative (HPI).

The availability of the human genome sequence has enabled the exploration and exploitation of the human genome and proteome to begin. Research has now focussed on the annotation of the genome and in particular of the proteome. With expert annotation extracted from the literature by biologists as the foundation, it has been possible to expand into the areas of data mining and automatic annotation. With further development and integration of pattern recognition methods and the application of alignments clustering, proteome analysis can now be provided in a meaningful way. These various approaches have been integrated to attach, extract and combine as much relevant information as possible to the proteome. This resource should be valuable to users from both research and industry.

Algorithms↗

PubMed2002

Integration of capillary isoelectric focusing with capillary reversed-phase liquid chromatography for two-dimensional proteomics separation.

On-line combination of capillary isoelectric focusing (CIEF) with capillary reversed-phase liquid chromatography (CRPLC) is developed using a microinjector as the interface for performing two-dimensional (2-D) protein/peptide separations of complex protein mixtures. The focusing effect of CIEF not only contributes to a high-resolution protein/peptide separation, but also may permit the analysis of low-abundance proteins with a typical concentration factor of 50-100 times. The preparative capabilities of CIEF are much larger than most of capillary-based electrokinetic separation techniques since the entire capillary is initially filled with a solution containing proteins/peptides and carrier ampholytes for the creation of a pH gradient inside the capillary. The focused peptides which have a similar pI are coinjected into the second separation dimension and further resolved by their differences in hydrophobicity. The resolving power of combined CIEF-CRPLC system is demonstrated using the soluble fraction of Drosophila salivary glands taken from a period beginning before steroid-triggered programmed cell death and extending to its completion. The separation mechanisms of CIEF and CRPLC are completely orthogonal and the overall peak capacity is estimated to be around approximately 1800 over a run time of less than 8 h. Significant enhancement in the separation peak capacity can be realized by further increasing the number of CIEF fractions and/or slowing the solvent gradient in CRPLC, however, at the expense of overall analysis time. The results of our preliminary studies display significant differences in the separation profiles of peptide samples obtained from salivary glands of animals staged at the 6 and 12 h following puparium formation.

Animals↗

PubMed2006

Integration of multidimensional chromatographic protein separations with a combined "top-down" and "bottom-up" proteomic strategy.

In this paper, we present a combined top-down/bottom-up proteomic analysis workflow for the characterization of proteomic samples. This workflow combines protein fractionation (multidimensional chromatographic separation) with parallel online ESI-TOF-MS intact protein analysis, and fraction collection. Collected fractions were digested and protein identifications were produced using MALDI Q-TOF-MS analysis. These identifications were then linked with corresponding ESI-TOF-MS intact protein mass data to permit full protein characterization. This methodology was applied to an E. coli cytosolic protein fraction, and enabled the identification and characterization of proteins exhibiting co-translational processing, post-translational modification, and proteolytic processing events. The approach also provided the ability to distinguish between closely related protein isoforms. The summary of results from this study indicated that roughly one-third of all detected components generated corresponding data from both top-down and bottom-up analyses, and that significant and novel information can be derived from this application of the hybrid analytical methodology.

Chemical Fractionation↗

PubMed2001

SPINE: an integrated tracking database and data mining approach for identifying feasible targets in high-throughput structural proteomics.

High-throughput structural proteomics is expected to generate considerable amounts of data on the progress of structure determination for many proteins. For each protein this includes information about cloning, expression, purification, biophysical characterization and structure determination via NMR spectroscopy or X-ray crystallography. It will be essential to develop specifications and ontologies for standardizing this information to make it amenable to retrospective analysis. To this end we created the SPINE database and analysis system for the Northeast Structural Genomics Consortium. SPINE, which is available at bioinfo.mbb.yale.edu/nesg or nesg.org, is specifically designed to enable distributed scientific collaboration via the Internet. It was designed not just as an information repository but as an active vehicle to standardize proteomics data in a form that would enable systematic data mining. The system features an intuitive user interface for interactive retrieval and modification of expression construct data, query forms designed to track global project progress and external links to many other resources. Currently the database contains experimental data on 985 constructs, of which 740 are drawn from Methanobacterium thermoautotrophicum, 123 from Saccharomyces cerevisiae, 93 from Caenorhabditis elegans and the remainder from other organisms. We developed a comprehensive set of data mining features for each protein, including several related to experimental progress (e.g. expression level, solubility and crystallization) and 42 based on the underlying protein sequence (e.g. amino acid composition, secondary structure and occurrence of low complexity regions). We demonstrate in detail the application of a particular machine learning approach, decision trees, to the tasks of predicting a protein's solubility and propensity to crystallize based on sequence features. We are able to extract a number of key rules from our trees, in particular that soluble proteins tend to have significantly more acidic residues and fewer hydrophobic stretches than insoluble ones. One of the characteristics of proteomics data sets, currently and in the foreseeable future, is their intermediate size ( approximately 500-5000 data points). This creates a number of issues in relation to error estimation. Initially we estimate the overall error in our trees based on standard cross-validation. However, this leaves out a significant fraction of the data in model construction and does not give error estimates on individual rules. Therefore, we present alternative methods to estimate the error in particular rules.

Animals↗

PubMed2006

Integrated molecular signature of disease: analysis of influenza virus-infected macaques through functional genomics and proteomics.

Recent outbreaks of avian influenza in humans have stressed the need for an improved nonhuman primate model of influenza pathogenesis. In order to further develop a macaque model, we expanded our previous in vivo genomics experiments with influenza virus-infected macaques by focusing on the innate immune response at day 2 postinoculation and on gene expression in affected lung tissue with viral genetic material present. Finally, we sought to identify signature genes for early infection in whole blood. For these purposes, we infected six pigtailed macaques (Macaca nemestrina) with reconstructed influenza A/Texas/36/91 virus and three control animals with a sham inoculate. We sacrificed one control and two experimental animals at days 2, 4, and 7 postinfection. Lung tissue was harvested for pathology, gene expression profiling, and proteomics. Blood was collected for genomics every other day from each animal until the experimental endpoint. Gross and microscopic pathology, immunohistochemistry, viral gene expression by arrays, and/or quantitative real-time reverse transcription-PCR confirmed successful yet mild infections in all experimental animals. Genomic experiments were performed using macaque-specific oligonucleotide arrays, and high-throughput proteomics revealed the host response to infection at the mRNA and protein levels. Our data showed dramatic differences in gene expression within regions in influenza virus-induced lesions based on the presence or absence of viral mRNA. We also identified genes tightly coregulated in peripheral white blood cells and in lung tissue at day 2 postinoculation. This latter finding opens the possibility of using gene expression arrays on whole blood to detect infection after exposure but prior to onset of symptoms or shedding.

Animals↗