New Data Analysis and Mining Approaches Identify Unique Proteome and Transcriptome Markers of Susceptibility to Autoimmune Diabetes

MYC: there is more to it than cancer

MYC is a pleiotropic transcription factor involved in multiple cellular processes. While its mechanism of action and targets are not completely elucidated, it has a fundamental role in cellular proliferation, differentiation, metabolism, ribogenesis, and bone and vascular development. Over 4 decades of research and some 10,000 publications linking it to tumorigenesis (by searching PubMed for "MYC oncogene") have led to MYC becoming a most-wanted target for the treatment of cancer, where many of MYC's physiological functions become co-opted for tumour initiation and maintenance. In this context, an abundance of reviews describes strategies for potentially targeting MYC in the oncology field. However, its multiple roles in different aspects of cellular biology suggest that it may also play a role in many additional diseases, and other publications are indeed linking MYC to pathologies beyond cancer. Here, we review these physiological functions and the current literature linking MYC to non-oncological diseases. The intense efforts towards developing MYC inhibitors as a cancer therapy will potentially have huge implications for the treatment of other diseases. In addition, with a complementary approach, we discuss some diseases and conditions where MYC appears to play a protective role and hence its increased expression or activation could be therapeutic.

Machine Learning and Integrative Analysis of Biomedical Big Data

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues.

Early differences in islets from prediabetic NOD mice: combined microarray and proteomic analysis

AIMS/HYPOTHESIS

Type 1 diabetes is an endocrine disease where a long preclinical phase, characterised by immune cell infiltration in the islets of Langerhans, precedes elevated blood glucose levels and disease onset. Although several studies have investigated the role of the immune system in this process of insulitis, the importance of the beta cells themselves in the initiation of type 1 diabetes is less well understood. The aim of this study was to investigate intrinsic differences present in the islets from diabetes-prone NOD mice before the onset of insulitis.

METHODS

The islet transcriptome and proteome of 2-3-week-old mice was investigated by microarray and 2-dimensional difference gel electrophoresis (2D-DIGE), respectively. Subsequent analyses using sophisticated pathway analysis and ranking of differentially expressed genes and proteins based on their relevance in type 1 diabetes were performed.

RESULTS

In the preinsulitic period, alterations in general pathways related to metabolism and cell communication were already present. Additionally, our analyses pointed to an important role for post-translational modifications (PTMs), especially citrullination by PAD2 and protein misfolding due to low expression levels of protein disulphide isomerases (PDIA3, 4 and 6), as causative mechanisms that induce beta cell stress and potential auto-antigen generation.

CONCLUSIONS/INTERPRETATION

We conclude that the pancreatic islets, irrespective of immune differences, may contribute to the initiation of the autoimmune process.

DATA AVAILABILITY

All microarray data are available in the ArrayExpress database ( www.ebi.ac.uk/arrayexpress ) under accession number E-MTAB-5264.

Integrating transcriptome and proteome profiling: Strategies and applications

Discovering the gene expression signature associated with a cellular state is one of the basic quests in majority of biological studies. For most of the clinical and cellular manifestations, these molecular differences may be exhibited across multiple layers of gene regulation like genomic variations, gene expression, protein translation and post-translational modifications. These system wide variations are dynamic in nature and their crosstalk is overwhelmingly complex, thus analyzing them separately may not be very informative. This necessitates the integrative analysis of such multiple layers of information to understand the interplay of the individual components of the biological system. Recent developments in high throughput RNA sequencing and mass spectrometric (MS) technologies to probe transcripts and proteins made these as preferred methods for understanding global gene regulation. Subsequently, improvements in "big-data" analysis techniques enable novel conclusions to be drawn from integrative transcriptomic-proteomic analysis. The unified analyses of both these data types have been rewarding for several biological objectives like improving genome annotation, predicting RNA-protein quantities, deciphering gene regulations, discovering disease markers and drug targets. There are different ways in which transcriptomics and proteomics data can be integrated; each aiming for different research objectives. Here, we review various studies, approaches and computational tools targeted for integrative analysis of these two high-throughput omics methods.

Hyperoxia-Induced Protein Alterations in Renal Rat Tissue: A Quantitative Proteomic Approach to Identify Hyperoxia-Induced Effects in Cellular Signaling Pathways

Introduction. In renal tissue as well as in other organs, supranormal oxygen pressure may lead to deleterious consequences on a cellular level. Additionally, hyperoxia-induced effect in cells and related free radicals may potentially contribute to renal failure. The aim of this study was to analyze time-dependent alterations of rat kidney protein expression after short-term normobaric hyperoxia using proteomics and bioinformatic approaches. Material and Methods. N = 36 Wistar rats were randomized into six different groups: three groups with normobaric hyperoxia (exposure to 100% oxygen for 3 h) and three groups with normobaric normoxia (NN; room air). After hyperoxia exposure, kidneys were removed immediately, after 3 days and after 7 days. Kidney lysates were analyzed by two-dimensional gel electrophoresis followed by peptide mass fingerprinting using tandem mass spectrometry. Statistical analysis was performed with DeCyder 2D software (p < 0.01). Biological functions of differential regulated proteins were studied using functional network analysis (Ingenuity Pathways Analysis and PathwayStudio). Results. Expression of 14 proteins was significantly altered (p < 0.01): eight proteins (MEP1A_RAT, RSSA_RAT, F16P1_RAT, STML2_RAT, BPNT1_RAT, LGMN_RAT, ATPA_RAT, and VDAC1_RAT) were downregulated and six proteins (MTUS1_RAT, F16P1_RAT, ACTG_RAT, ACTB_RAT, 2ABA_RAT, and RAB1A_RAT) were upregulated. Bioinformatic analyses revealed an association of regulated proteins with inflammation. Conclusions. Significant alterations in renal protein expression could be demonstrated for up to 7 days even after short-term hyperoxia. The identified proteins indicate an association with inflammation signaling cascades. MEP1A and VDAC1 could be promising candidates to identify hyperoxic injury in kidney cells.

Molecular pathway alterations in CD4 T-cells of nonobese diabetic (NOD) mice in the preinsulitis phase of autoimmune diabetes

Type 1 diabetes (T1D) is a multigenic disease caused by T-cell mediated destruction of the insulin producing pancreatic islet ß-cells. The earliest sign of islet autoimmunity in NOD mice, islet leukocytic infiltration or insulitis, is obvious at around 5 weeks of age. The molecular alterations that occur in T cells prior to insulitis and that may contribute to T1D development are poorly understood. Since CD4 T-cells are essential to T1D development, we tested the hypothesis that multiple genes/molecular pathways are altered in these cells prior to insulitis. We performed a genome-wide transcriptome and pathway analysis of whole, untreated CD4 T-cells from 2, 3, and 4 week-old NOD mice in comparison to two control strains (NOR and C57BL/6). We identified many differentially expressed genes in the NOD mice at each time point. Many of these genes (herein referred to as NOD altered genes) lie within known diabetes susceptibility (insulin-dependent diabetes, Idd) regions, e.g. two diabetes resistant loci, Idd27 (tripartite motif-containing family genes) and Idd13 (several genes), and the CD4 T-cell diabetogenic activity locus, Idd9/11 (2 genes, KH domain containing, RNA binding, signal transduction associated 1 and protein tyrosine phosphatase 4a2). The biological processes associated with these altered genes included, apoptosis/cell proliferation and metabolic pathways (predominant at 2 weeks); inflammation and cell signaling/activation (predominant at 3 weeks); and innate and adaptive immune responses (predominant at 4 weeks). Pathway analysis identified several factors that may regulate these abnormalities: eight, common to all 3 ages (interferon regulatory factor 1, hepatic nuclear factor 4, alpha, transformation related protein 53, BCL2-like 1 (lies within Idd13), interferon gamma, interleukin 4, interleukin 15, and prostaglandin E2); and two each, common to 2 and 4 weeks (androgen receptor and interleukin 6); and to 3 and 4 weeks (interferon alpha and interferon regulatory factor 7). Others were unique to the various ages, e.g. myelocytomatosis oncogene, jun oncogene, and amyloid beta (A4) to 2 weeks; tumor necrosis factor, transforming growth factor, beta 1, NF?B, ERK, and p38MAPK to 3 weeks; and interleukin 12 and signal transducer and activator of transcription 4 to 4 weeks. Thus, our study demonstrated that expression of many genes that lie within several Idds (e.g. Idd27, Idd13 and Idd9/11) was altered in CD4 T-cells in the early induction phase of autoimmune diabetes and identified their associated molecular pathways. These data offer the opportunity to test hypotheses on the roles played by the altered genes/molecular pathways, to understand better the mechanisms of CD4 T-cell diabetogenesis, and to develop new therapeutic strategies for T1D.

Systems biology and proteomic analysis of cerebral cavernous malformation

Cerebral cavernous malformations (CCM) are vascular anomalies caused by mutations in genes encoding KRIT1, OSM and PDCD10 proteins causing hemorrhagic stroke. We examine proteomic change of loss of CCM gene expression. Using human umbilical vein endothelial cells, label-free differential protein expression analysis with multidimensional liquid chromatography/tandem mass spectrometry was applied to three CCM protein knockdown cell lines and two control cell lines: ProteomeXchange identifier PXD000362. Principle component and cluster analyses were used to examine the differentially expressed proteins associated with CCM. The results from the five cell lines revealed 290 and 192 differentially expressed proteins (p < 0.005 and p < 0.001, respectively). Most commonly affected proteins were cytoskeleton-associated proteins, in particular myosin-9. Canonical genetic pathway analysis suggests that CCM may be a result of defective cell-cell interaction through dysregulation of cytoskeletal associated proteins.

CONCLUSION

The work explores signaling pathways that may elucidate early detection and novel therapy for CCM.

Unraveling the equine lymphocyte proteome: differential septin 7 expression associates with immune cells in equine recurrent uveitis

Equine recurrent uveitis is a spontaneous, lymphocyte-driven autoimmune disease. It affects horses worldwide and presents with painful remitting-relapsing inflammatory attacks of inner eye structures eventually leading to blindness. Since lymphocytes are the key players in equine recurrent uveitis, we were interested in potential changes of their protein repertoire which may be involved in disease pathogenesis. To create a reference for differential proteome analysis, we first unraveled the equine lymphocyte proteome by two-dimensional sodium dodecyl sulfate - polyacrylamide gel electrophoresis and subsequently identified 352 protein spots. Next, we compared lymphocytes from ERU cases and healthy horses with a two-dimensional fluorescence difference in gel electrophoresis approach. With this technique, we identified seven differentially expressed proteins between conditions. One of the significantly lower expressed candidates, septin 7, plays a role in regulation of cell shape, motility and migration. Further analyses revealed T cells as the main cell type with decreased septin 7 abundance in equine recurrent uveitis. These findings point to a possible pathogenetic role of septin 7 in this sight-threatening disease.

Gene Expression Profiles of Peripheral Blood Mononuclear Cells Reveal Transcriptional Signatures as Novel Biomarkers for Cardiac Remodeling in Rats with Aldosteronism and Hypertensive Heart Disease

OBJECTIVES

In searching for a noninvasive surrogate tissue having mimicry with the prooxidant/-proinflammatory hypertensive heart disease (HHD) phenotype, we turned to peripheral blood mononuclear cells (PBMC). We tested whether iterations in [Ca²⁺]_i, [Zn²⁺]_i and oxidative stress in cardiomyocytes and PBMC would complement each other eliciting similar shifts in gene expression profiles in these tissues demonstrable during preclinical (wk 1) and pathologic (wk 4) stages of aldosterone/salt treatment (ALDOST).

BACKGROUND

Inappropriate neurohormonal activation contributes to pathologic remodeling of myocardium in HHD associated with aldosteronism. In rats receiving chronic ALDOST, evidence of reparative fibrosis replacing necrotic cardiomyocytes and coronary vasculopathy appears at wk 4 associated with the induction of oxidative stress by mitochondria that overwhelms endogenous, largely Zn²⁺-based, antioxidant defenses. Biomarker-guided prediction of risk prior to the appearance of cardiac pathology would prove invaluable.

METHODS

In PBMC and cardiomyocytes, quantitation of cytoplasmic free Ca²⁺ and Zn²⁺, H₂O₂ and 8-iosprostane levels, as well as isolation of RNA and gene expression, together with statistical and clustering analyses, and confirmation of genes by in situ hybridization and RT-PCR, were performed.

RESULTS

Compared to controls, at wk 1 and 4 ALDOST, we found comparable: increments in [Ca²⁺]_i, [Zn²⁺]_i and 8-isoprotane coupled to increased H₂O₂ production in cardiac mitochondria and PBMC, together with the common networks of expression profiles dominated by genes involved in oxidative stress, inflammation and repair. These included three central Ingenuity pathway-linked genes: p38MAPK, a stress-responsive protein; NFκB, a redox-sensitive transcription factor and a proinflammatory cascade it regulates; and TGF-β₁, a fibrogenic cytokine involved in tissue repair.

CONCLUSIONS

Significant overlapping demonstrated in the molecular mimicry of PBMC and cardiomyocytes during preclinical and pathologic stages of ALDOST implicates that transcriptional signatures of PBMC may serve as early noninvasive and novel sentinels predictive of impending pathologic remodeling in HHD.

Proteome profile of bovine ruminal epithelial tissue based on GeLC-MS/MS

The proteome of rumen epithelial tissue was analysed by SDS-PAGE coupled with LC-MS/MS. 813 non-redundant proteins were identified of which 7.4 % featured membrane-spanning domains and 15.4 % harboured a signal peptide. According to the gene ontology annotation, the most abundant proteins exhibited binding activities related to their molecular functions, were proteins of cellular components or belonged to various metabolic processes. A predominant group of canonical pathways in the rumen epithelial tissue was identified using the IPA software. The GeLC-MS/MS approach was used to characterise the entire protein expression repertoire in rumen tissue, providing a more detailed understanding of the important biological processes in the rumen.

Molecular phenotyping of immune cells from young NOD mice reveals abnormal metabolic pathways in the early induction phase of autoimmune diabetes

Islet leukocytic infiltration (insulitis) is first obvious at around 4 weeks of age in the NOD mouse – a model for human type 1 diabetes (T1D). The molecular events that lead to insulitis and initiate autoimmune diabetes are poorly understood. Since TID is caused by numerous genes, we hypothesized that multiple molecular pathways are altered and interact to initiate this disease. We evaluated the molecular phenotype (mRNA and protein expression) and molecular networks of ex vivo unfractionated spleen leukocytes from 2 and 4 week-old NOD mice in comparison to two control strains. Analysis of the global gene expression profiles and hierarchical clustering revealed that the majority (∼90%) of the differentially expressed genes in NOD mice were repressed. Furthermore, analysis using a modern suite of multiple bioinformatics approaches identified abnormal molecular pathways that can be divided broadly into 2 categories: metabolic pathways, which were predominant at 2 weeks, and immune response pathways, which were predominant at 4 weeks. Network analysis by Ingenuity pathway analysis identified key genes/molecules that may play a role in regulating these pathways. These included five that were common to both ages (TNF, HNF4A, IL15, Progesterone, and YWHAZ), and others that were unique to 2 weeks (e.g. MYC/MYCN, TGFB1, and IL2) and to 4 weeks (e.g. IFNG, beta-estradiol, p53, NFKB, AKT, PRKCA, IL12, and HLA-C). Based on the literature, genes that may play a role in regulating metabolic pathways at 2 weeks include Myc and HNF4A, and at 4 weeks, beta-estradiol, p53, Akt, HNF4A and AR. Our data suggest that abnormalities in regulation of metabolic pathways in the immune cells of young NOD mice lead to abnormalities in the immune response pathways and as such may play a role in the initiation of autoimmune diabetes. Thus, targeting metabolism may provide novel approaches to preventing and/or treating autoimmune diabetes.

Proteomics-based investigations of animal models of disease

Cells contain a large yet, constant genome, which contains all the coding information necessary to sustain cellular physiology. However, proteins are the end products of genes, and hence dictate the phenotype of cells and tissues. Therefore, proteomics can provide key information for the elucidation of physiological and pathophysiological mechanisms by identifying the protein profile from cells and tissues. The relatively novel techniques used for the study of proteomics thus have the potential to improve diagnostic, prognostic, as well as therapeutic avenues. In this review, we first discuss the benefits of animal models over the use of human samples for the proteomic analysis of human disease. Next, we aim to demonstrate the potential of proteomics in the elucidation of disease mechanisms that may not be possible by other conventional technologies. Following this, we describe the use of proteomics for the analysis of PTM and protein interactions in animal models and their relevance to the study of human disease. Finally, we discuss the development of clinical biomarkers for the early diagnosis of disease via proteomic analysis of animal models. We also discuss the development of standard proteomes and relate how this data will benefit future proteomic research. A comprehensive review of all animal models used in conjunction with proteomics is beyond the scope of this manuscript. Therefore, we aimed to cover a large breadth of topics, which together, demonstrate the potential of proteomics as a powerful tool in biomedical research.

Data-mining technologies for diabetes: a systematic review

BACKGROUND

The objective of this study is to conduct a systematic review of applications of data-mining techniques in the field of diabetes research.

METHOD

We searched the MEDLINE database through PubMed. We initially identified 31 articles by the search, and selected 17 articles representing various data-mining methods used for diabetes research. Our main interest was to identify research goals, diabetes types, data sets, data-mining methods, data-mining software and technologies, and outcomes.

RESULTS

The applications of data-mining techniques in the selected articles were useful for extracting valuable knowledge and generating new hypothesis for further scientific research/experimentation and improving health care for diabetes patients. The results could be used for both scientific research and real-life practice to improve the quality of health care diabetes patients.

CONCLUSIONS

Data mining has played an important role in diabetes research. Data mining would be a valuable asset for diabetes researchers because it can unearth hidden knowledge from a huge amount of diabetes-related data. We believe that data mining can significantly help diabetes research and ultimately improve the quality of health care for diabetes patients.

Preimplantation factor (PIF) analog prevents type I diabetes mellitus (TIDM) development by preserving pancreatic function in NOD mice

Preimplantation factor (PIF) is a novel embryo-secreted immunomodulatory peptide. Its synthetic analog (sPIF) modulates maternal immunity without suppression. There is an urgent need to develop agents that could prevent the development of type 1 diabetes mellitus (TIDM). Herein, we examine sPIF's preventive effect on TIDM development by using acute adoptive-transfer (ATDM) and spontaneously developing (SDM) in non-obese diabetic (NOD) murine models. Diabetes was evaluated by urinary and plasma glucose, intraperitoneal glucose tolerance test (IPGTT), pancreatic islets insulin staining by immunohistochemistry and by pancreatic proteome evaluation using mass spectrometry, followed by signal pathway analysis. Continuous administration of sPIF for 4-weeks prevents diabetes development in ATDM model in >90% of recipients demonstrated by normal IPGTT, preserved islets architecture, number, and insulin staining. (P < 0.01). sPIF effect was specific; its protective effects are not replicated by scrambled PIF (χ(2) = 0.009) control. sPIF led also to increased circulating Th2 and Th1 cytokines. In SDM model, 4-week continuous sPIF administration prevented onset of diabetes for 21 weeks post-therapy (P < 0.01). Low-dose sPIF administration for 16 weeks prevented diabetes development up to 14 weeks post-therapy, evidenced by preserved islets architecture and insulin staining. In SDM model, pancreatic proteome pathway analysis demonstrated that sPIF regulates protein traffic, prevents protein misfolding and aggregation, and reduces oxidative stress and islets apoptosis, leading to preserved insulin staining. sPIF further increased insulin receptor expression and reduced actin and tubulin proteins, thereby blocking neutrophil invasion and inflammation. Exocrine pancreatic function was also preserved. sPIF administration results in marked prevention of spontaneous and induced adoptive-transfer diabetes suggesting its potential effectiveness in treating early-stage TIDM.

Proteomic profiling of S-acylated macrophage proteins identifies a role for palmitoylation in mitochondrial targeting of phospholipid scramblase 3

S-Palmitoylation, the reversible post-translational acylation of specific cysteine residues with the fatty acid palmitate, promotes the membrane tethering and subcellular localization of proteins in several biological pathways. Although inhibiting palmitoylation holds promise as a means for manipulating protein targeting, advances in the field have been hampered by limited understanding of palmitoylation enzymology and consensus motifs. In order to define the complement of S-acylated proteins in the macrophage, we treated RAW 264.7 macrophage membranes with hydroxylamine to cleave acyl thioesters, followed by biotinylation of newly exposed sulfhydryls and streptavidin-agarose affinity chromatography. Among proteins identified by LC-MS/MS, S-acylation status was established by spectral counting to assess enrichment under hydroxylamine versus mock treatment conditions. Of 1183 proteins identified in four independent experiments, 80 proteins were significant for S-acylation at false discovery rate = 0.05, and 101 significant at false discovery rate = 0.10. Candidate S-acylproteins were identified from several functional categories, including membrane trafficking, signaling, transporters, and receptors. Among these were 29 proteins previously biochemically confirmed as palmitoylated, 45 previously reported as putative S-acylproteins in proteomic screens, 24 not previously associated with palmitoylation, and three presumed false-positives. Nearly half of the candidates were previously identified by us in macrophage detergent-resistant membranes, suggesting that palmitoylation promotes lipid raft-localization of proteins in the macrophage. Among the candidate novel S-acylproteins was phospholipid scramblase 3 (Plscr3), a protein that regulates apoptosis through remodeling the mitochondrial membrane. Palmitoylation of Plscr3 was confirmed through (3)H-palmitate labeling. Moreover, site-directed mutagenesis of a cluster of five cysteines (Cys159-161-163-164-166) abolished palmitoylation, caused Plscr3 mislocalization from mitochondrion to nucleus, and reduced macrophage apoptosis in response to etoposide, together suggesting a role for palmitoylation at this site for mitochondrial targeting and pro-apoptotic function of Plscr3. Taken together, we propose that manipulation of protein palmitoylation carries great potential for intervention in macrophage biology via reprogramming of protein localization.

Data mining of mental health issues of non-bone marrow donor siblings

Background

Allogenic hematopoietic stem cell transplantation is a curative treatment for patients with advanced hematologic malignancies. However, the long-term mental health issues of siblings who were not selected as donors (non-donor siblings, NDS) in the transplantation have not been well assessed. Data mining is useful in discovering new findings from a large, multidisciplinary data set and the Scenario Map analysis is a novel approach which allows extracting keywords linking different conditions/events from text data of interviews even when the keywords appeared infrequently. The aim of this study is to assess mental health issues on NDSs and to find helpful keywords for the clinical follow-up using a Scenario Map analysis.

Findings

A 47-year-old woman whose younger sister had undergone allogenic hematopoietic stem cell transplantation 20 years earlier was interviewed as a NDS. The text data from the interview transcriptions was analyzed using Scenario Mapping. Four clusters of words and six keywords were identified. Upon review of the word clusters and keywords, both the subject and researchers noticed that the subject has had mental health issues since the disease onset to date with being a NDS. The issues have been alleviated by her family.

Conclusions

This single subject study suggested the advantages of data mining in clinical follow-up for mental health issues of patients and/or their families.

Systems biology in immunology: a computational modeling perspective

Systems biology is an emerging discipline that combines high-content, multiplexed measurements with informatic and computational modeling methods to better understand biological function at various scales. Here we present a detailed review of the methods used to create computational models and to conduct simulations of immune function. We provide descriptions of the key data-gathering techniques employed to generate the quantitative and qualitative data required for such modeling and simulation and summarize the progress to date in applying these tools and techniques to questions of immunological interest, including infectious disease. We include comments on what insights modeling can provide that complement information obtained from the more familiar experimental discovery methods used by most investigators and the reasons why quantitative methods are needed to eventually produce a better understanding of immune system operation in health and disease.

The effect of gluten-free diet on Th1-Th2-Th3-associated intestinal immune responses in celiac disease

OBJECTIVE

To study T-helper (Th)1-Th2-Th3 gene activation profile in the small intestine and peripheral blood of children with celiac disease (CD) with special interest in the response to the gluten-free diet (GFD) treatment in order to elucidate an immune dysregulation not triggered by gluten.

MATERIAL AND METHODS

Small intestinal biopsies and venous blood were taken from seven children with CD (mean age: 8 years, four girls) at presentation and after 1 year of strict GFD. The Th1-Th2-Th3 gene expression profile was examined by real-time PCR arrays. The findings were compared with the corresponding expressions in peripheral blood and small intestinal biopsies from six reference children without CD (mean age: 6 years, four girls).

RESULTS

The Th1 gene expression profile including interferon (IFN)-γ, signal transducer and activator of transcription (STAT) 1 and interferon regulatory factor (IRF) 1 together with reduced interleukin (IL)-2 expression was pronounced in small intestinal biopsies from children with untreated CD. A downregulation of IFN-γ transcripts was seen after 1 year of GFD, but there was still increased expression of STAT1 and IRF1 in association with low IL-2 expression in spite of eliminated exposure to wheat gluten. By contrast, the decreased intestinal expression of Th2 gene markers observed at presentation was normalized with GFD. The alterations in the mucosal gene expression profile were not reflected in peripheral blood.

CONCLUSION

The GFD did not correct the increased activation of the IFN-γ signaling pathway related markers and reduced IL-2 expression, suggesting that they represent an immune dysregulation not dependent on gluten exposure.

Aberrantly Expressed Genes in HaCaT Keratinocytes Chronically Exposed to Arsenic Trioxide

Inorganic arsenic is a known environmental toxicant and carcinogen of global public health concern. Arsenic is genotoxic and cytotoxic to human keratinocytes. However, the biological pathways perturbed in keratinocytes by low chronic dose inorganic arsenic are not completely understood. The objective of the investigation was to discover the mechanism of arsenic carcinogenicity in human epidermal keratinocytes. We hypothesize that a combined strategy of DNA microarray, qRT-PCR and gene function annotation will identify aberrantly expressed genes in HaCaT keratinocyte cell line after chronic treatment with arsenic trioxide. Microarray data analysis identified 14 up-regulated genes and 21 down-regulated genes in response to arsenic trioxide. The expression of 4 up-regulated genes and 1 down-regulated gene were confirmed by qRT-PCR. The up-regulated genes were AKR1C3 (Aldo-Keto Reductase family 1, member C3), IGFL1 (Insulin Growth Factor-Like family member 1), IL1R2 (Interleukin 1 Receptor, type 2), and TNFSF18 (Tumor Necrosis Factor [ligand] SuperFamily, member 18) and down-regulated gene was RGS2 (Regulator of G-protein Signaling 2). The observed over expression of TNFSF18 (167 fold) coupled with moderate expression of IGFL1 (3.1 fold), IL1R2 (5.9 fold) and AKR1C3 (9.2 fold) with a decreased RGS2 (2.0 fold) suggests that chronic arsenic exposure could produce sustained levels of TNF with modulation by an IL-1 analogue resulting in chronic immunologic insult. A concomitant decrease in growth inhibiting gene (RGS2) and increase in AKR1C3 may contribute to chronic inflammation leading to metaplasia, which may eventually lead to carcinogenicity in the skin keratinocytes. Also, increased expression of IGFL1 may trigger cancer development and progression in HaCaT keratinocytes.

Functional modelling of an equine bronchoalveolar lavage fluid proteome provides experimental confirmation and functional annotation of equine genome sequences

The equine genome sequence enables the use of high-throughput genomic technologies in equine research, but accurate identification of expressed gene products and interpreting their biological relevance require additional structural and functional genome annotation. Here, we employ the equine genome sequence to identify predicted and known proteins using proteomics and model these proteins into biological pathways, identifying 582 proteins in normal cell-free equine bronchoalveolar lavage fluid (BALF). We improved structural and functional annotation by directly confirming the in vivo expression of 558 (96%) proteins, which were computationally predicted previously, and adding Gene Ontology (GO) annotations for 174 proteins, 108 of which lacked functional annotation. Bronchoalveolar lavage is commonly used to investigate equine respiratory disease, leading us to model the associated proteome and its biological functions. Modelling of protein functions using Ingenuity Pathway Analysis identified carbohydrate metabolism, cell-to-cell signalling, cellular function, inflammatory response, organ morphology, lipid metabolism and cellular movement as key biological processes in normal equine BALF. Comparative modelling of protein functions in normal cell-free bronchoalveolar lavage proteomes from horse, human, and mouse, performed by grouping GO terms sharing common ancestor terms, confirms conservation of functions across species. Ninety-one of 92 human GO categories and 105 of 109 mouse GO categories were conserved in the horse. Our approach confirms the utility of the equine genome sequence to characterize protein networks without antibodies or mRNA quantification, highlights the need for continued structural and functional annotation of the equine genome and provides a framework for equine researchers to aid in the annotation effort.

Analysis of Bovine Viral Diarrhea Viruses-infected monocytes: identification of cytopathic and non-cytopathic biotype differences

Background

Bovine Viral Diarrhea Virus (BVDV) infection is widespread in cattle worldwide, causing important economic losses. Pathogenesis of the disease caused by BVDV is complex, as each BVDV strain has two biotypes: non-cytopathic (ncp) and cytopathic (cp). BVDV can cause a persistent latent infection and immune suppression if animals are infected with an ncp biotype during early gestation, followed by a subsequent infection of the cp biotype. The molecular mechanisms that underscore the complex disease etiology leading to immune suppression in cattle caused by BVDV are not well understood.

Results

Using proteomics, we evaluated the effect of cp and ncp BVDV infection of bovine monocytes to determine their role in viral immune suppression and uncontrolled inflammation. Proteins were isolated by differential detergent fractionation and identified by 2D-LC ESI MS/MS. We identified 137 and 228 significantly altered bovine proteins due to ncp and cp BVDV infection, respectively. Functional analysis of these proteins using the Gene Ontology (GO) showed multiple under- and over- represented GO functions in molecular function, biological process and cellular component between the two BVDV biotypes. Analysis of the top immunological pathways affected by BVDV infection revealed that pathways representing macropinocytosis signalling, virus entry via endocytic pathway, integrin signalling and primary immunodeficiency signalling were identified only in ncp BVDV-infected monocytes. In contrast, pathways like actin cytoskeleton signalling, RhoA signalling, clathrin-mediated endocytosis signalling and interferon signalling were identified only in cp BDVD-infected cells. Of the six common pathways involved in cp and ncp BVDV infection, acute phase response signalling was the most significant for both BVDV biotypes. Although, most shared altered host proteins between both BVDV biotypes showed the same type of change, integrin alpha 2b (ITGA2B) and integrin beta 3 (ITGB3) were down- regulated by ncp BVDV and up- regulated by cp BVDV infection.

Conclusions

This study shows that, as we expected, there are significant functional differences in the host proteins that respond to cp or ncp BVDV infection. The combined use of GO and systems biology network modelling facilitated a better understanding of host-pathogen interactions.

Proteomics-based systems biology modeling of bovine germinal vesicle stage oocyte and cumulus cell interaction

Background

Oocytes are the female gametes which establish the program of life after fertilization. Interactions between oocyte and the surrounding cumulus cells at germinal vesicle (GV) stage are considered essential for proper maturation or ‘programming’ of oocytes, which is crucial for normal fertilization and embryonic development. However, despite its importance, little is known about the molecular events and pathways involved in this bidirectional communication.

Methodology/Principal Findings

We used differential detergent fractionation multidimensional protein identification technology (DDF-Mud PIT) on bovine GV oocyte and cumulus cells and identified 811 and 1247 proteins in GV oocyte and cumulus cells, respectively; 371 proteins were significantly differentially expressed between each cell type. Systems biology modeling, which included Gene Ontology (GO) and canonical genetic pathway analysis, showed that cumulus cells have higher expression of proteins involved in cell communication, generation of precursor metabolites and energy, as well as transport than GV oocytes. Our data also suggests a hypothesis that oocytes may depend on the presence of cumulus cells to generate specific cellular signals to coordinate their growth and maturation.

Conclusions/Significance

Systems biology modeling of bovine oocytes and cumulus cells in the context of GO and protein interaction networks identified the signaling pathways associated with the proteins involved in cell-to-cell signaling biological process that may have implications in oocyte competence and maturation. This first comprehensive systems biology modeling of bovine oocytes and cumulus cell proteomes not only provides a foundation for signaling and cell physiology at the GV stage of oocyte development, but are also valuable for comparative studies of other stages of oocyte development at the molecular level.

Integrated network analysis of transcriptomic and proteomic data in psoriasis

BACKGROUND

Psoriasis is complex inflammatory skin pathology of autoimmune origin. Several cell types are perturbed in this pathology, and underlying signaling events are complex and still poorly understood.

RESULTS

In order to gain insight into molecular machinery underlying the disease, we conducted a comprehensive meta-analysis of proteomics and transcriptomics of psoriatic lesions from independent studies. Network-based analysis revealed similarities in regulation at both proteomics and transcriptomics level. We identified a group of transcription factors responsible for overexpression of psoriasis genes and a number of previously unknown signaling pathways that may play a role in this process. We also evaluated functional synergy between transcriptomics and proteomics results.

CONCLUSIONS

We developed network-based methodology for integrative analysis of high throughput data sets of different types. Investigation of proteomics and transcriptomics data sets on psoriasis revealed versatility in regulatory machinery underlying pathology and showed complementarities between two levels of cellular organization.

Proteomic analysis of liver tissue from HBx-transgenic mice at early stages of hepatocarcinogenesis

The hepatitis B virus X-protein (HBx), a multifunctional viral regulator, participates in the viral life cycle and in the development of hepatocellular carcinoma (HCC). We previously reported a high incidence of HCC in transgenic mice expressing HBx. In this study, proteomic analysis was performed to identify proteins that may be involved in hepatocarcinogenesis and/or that could be utilized as early detection biomarkers for HCC. Proteins from the liver tissue of HBx-transgenic mice at early stages of carcinogenesis (dysplasia and hepatocellular adenoma) were separated by 2-DE, and quantitative changes were analyzed. A total of 22 spots displaying significant quantitative changes were identified using LC-MS/MS. In particular, several proteins involved in glucose and fatty acid metabolism, such as mitochondrial 3-ketoacyl-CoA thiolase, intestinal fatty acid-binding protein 2 and cytoplasmic malate dehydrogenase, were differentially expressed, implying that significant metabolic alterations occurred during the early stages of hepatocarcinogenesis. The results of this proteomic analysis provide insights into the mechanism of HBx-mediated hepatocarcinogenesis. Additionally, this study identifies possible therapeutic targets for HCC diagnosis and novel drug development for treatment of the disease.

Time-dependent alterations of cerebral proteins following short-term normobaric hyperoxia

Sufficient oxygenation is indispensable for cognitive performance in mammals. In order to assure adequate oxygenation and to prevent hypoxia in medicine or aviation, different approaches of oxygen delivery are realized. With regard to hyperoxia, it is well known that it increases the risk of tissue toxicity and inflammation by generating radical oxygen species. However, this impact of hyperoxia on the expression of specific brain proteins has not been evaluated in detail yet. The present study analyzes time-dependent changes in protein expression in rat brain after a short-term exposure to normobaric hyperoxia. Thirty-six Wistar rats were randomly assigned to six different groups, three normobaric hyperoxia (NH) groups or three normobaric normoxia (NN) groups, each consisting of n = 6 animals. NH animals were exposed to 100% oxygen, NN rats to 21% oxygen, each group for 3 h. One group of NH and one group of NN were killed immediately after the 3 h, one group each after 3 days and one group each after 7 days. Rat brains were removed for analysis and whole brain detergent protein lysates were separated via two-dimensional gel electrophoresis followed by subsequent identification of protein expression alterations by peptide mass fingerprinting using mass spectrometry. Also, a functional network mapping and molecular pathway analysis were carried out. Statistical analysis was performed using analysis of variance (ANOVA) with Bonferroni correction using P < 0.01. Physiological parameters of the animals did not differ significantly between the two groups except for partial oxygen pressure (580 vs. 89 mmHg; P < 0.05). The expression of nine proteins was found to be significantly altered (five up-regulated: GOT1, CCT2, TCP1, G6PD, and ALB; four down-regulated: PEBP1, PRDX2, ENO1, and MDH1). IPA generated a network with eight focus proteins associated with pathways in "cell death, cancer, and signalling". Although hyperoxia was normobaric and induced for only 3 h, significant changes in brain protein expression were detectable immediately after the 3 h, after 3 days, as well as after 7 days. This may indicate effects on brain protein expression take place in the rat brain following a relatively short period of hyperoxia.

Temporal responses to intrinsically coupled calcium and zinc dyshomeostasis in cardiac myocytes and mitochondria during aldosteronism

Intracellular Ca(2+) overloading, coupled to induction of oxidative stress, is present at 4-wk aldosterone/salt treatment (ALDOST). This prooxidant reaction in cardiac myocytes and mitochondria accounts for necrotic cell death and subsequent myocardial scarring. It is intrinsically linked to increased intracellular zinc concentration ([Zn(2+)](i)) serving as an antioxidant. Herein, we addressed the temporal responses in coupled Ca(2+) and Zn(2+) dyshomeostasis, reflecting the prooxidant-antioxidant equilibrium, by examining preclinical (week 1) and pathological (week 4) stages of ALDOST to determine whether endogenous antioxidant defenses would be ultimately overwhelmed to account for this delay in cardiac remodeling. We compared responses in cardiomyocyte free [Ca(2+)](i) and [Zn(2+)](i) and mitochondrial total [Ca(2+)](m) and [Zn(2+)](m), together with biomarkers of oxidative stress and antioxidant defenses, during 1- and 4-wk ALDOST. At week 1 and compared with controls, we found: 1) elevations in [Ca(2+)](i) and [Ca(2+)](m) were coupled with [Zn(2+)](i) and [Zn(2+)](m); 2) increased mitochondrial H(2)O(2) production, cardiomyocyte xanthine oxidase activity, and cardiac and mitochondrial 8-isoprostane levels, counterbalanced by increased activity of antioxidant proteins, enzymes, and the nonenzymatic antioxidants that can be considered as cumulative antioxidant capacity; some of these enzymes and proteins (e.g., metallothionein-1, Cu/Zn-superoxide, glutathione synthase) are regulated by metal-responsive transcription factor-1; and 3) although these augmented antioxidant defenses were sustained at week 4, they fell short in combating the persistent intracellular Ca(2+) overloading and marked rise in cardiac tissue 8-isoprostane and mitochondrial transition pore opening. Thus a coupled Ca(2+) and Zn(2+) dyshomeostasis occurs early during ALDOST in cardiac myocytes and mitochondria that regulate redox equilibrium until week 4 when ongoing intracellular Ca(2+) overloading and prooxidants overwhelm antioxidant defenses.

Tumor-Associated Antigens and Biomarkers in Cancer and Immune Therapy

Cancer has currently overtaken heart disease as the major cause of mortality in the United States. The Human Genome Project, advances in informatics, miniaturization of sample collection, and increased knowledge of cell signaling pathways has revolutionized the study of disease. Genomics, proteomics, and metabolomics are currently being used to develop molecular signatures for disease diagnosis, prognosis, and therapeutic efficacy. Tumor-associated antigens discovered by these methods are being used to develop passive (humoral) as well as active immunotherapy strategies to stimulate the immune system. Development and validation of biomarkers on a parallel track with therapeutics can speed development times by accurate screening of patient populations and substituting surrogate markers that correlate well with clinical outcomes.

Bioinformatics for study of autoimmunity

Recent years have witnessed an explosive growth in available biological data pertaining to autoimmunity research. This includes a tremendous quantity of sequence data (biological structures, genetic and physical maps, pathways, etc.) generated by genome and proteome projects plus extensive clinical and epidemiological data. Autoimmunity research stands to greatly benefit from this data so long as appropriate strategies are available to enable full access to and utilization of this data. The quantity and complexity of this biological data necessitates use of advanced bioinformatics strategies for its efficient retrieval, analysis and interpretation. Major progress has been made in development of specialized tools for storage, analysis and modeling of immunological data, and this has led to development of a whole new field know as immunoinformatics. With advances in novel high-throughput immunology technologies immunoinformatics is transforming understanding of how the immune system functions. This paper reviews advances in the field of immunoinformatics pertinent to autoimmunity research including databases, tools in genomics and proteomics, tools for study of B- and T-cell epitopes, integrative approaches, and web servers.

Proteomic analysis of the retina: removal of RPE alters outer segment assembly and retinal protein expression

The mechanisms that regulate the complex physiological task of photoreceptor outer segment assembly remain an enigma. One limiting factor in revealing the mechanism(s) by which this process is modulated is that not all of the role players who participate in this process are known. The purpose of this study was to determine some of the retinal proteins that likely play a critical role in regulating photoreceptor outer segment assembly. To do so, we analyzed and compared the proteome map of tadpole Xenopus laevis retinal pigment epithelium (RPE)-supported retinas containing organized outer segments with that of RPE-deprived retinas containing disorganized outer segments. Solubilized proteins were labeled with CyDye fluors followed by multiplexed two-dimensional separation. The intensity of protein spots and comparison of proteome maps was performed using DeCyder software. Identification of differentially regulated proteins was determined using nanoLC-ESI-MS/MS analysis. We found a total of 27 protein spots, 21 of which were unique proteins, which were differentially expressed in retinas with disorganized outer segments. We predict that in the absence of the RPE, oxidative stress initiates an unfolded protein response. Subsequently, downregulation of several candidate Müller glial cell proteins may explain the inability of photoreceptors to properly fold their outer segment membranes. In this study, we have used identification and bioinformatics assessment of proteins that are differentially expressed in retinas with disorganized outer segments as a first step in determining probable key molecules involved in regulating photoreceptor outer segment assembly.

Target discovery from data mining approaches

Data mining of available biomedical data and information has greatly boosted target discovery in the 'omics' era. Target discovery is the key step in the biomarker and drug discovery pipeline to diagnose and fight human diseases. In biomedical science, the 'target' is a broad concept ranging from molecular entities (such as genes, proteins and miRNAs) to biological phenomena (such as molecular functions, pathways and phenotypes). Within the context of biomedical science, data mining refers to a bioinformatics approach that combines biological concepts with computer tools or statistical methods that are mainly used to discover, select and prioritize targets. In response to the huge demand of data mining for target discovery in the 'omics' era, this review explicates various data mining approaches and their applications to target discovery with emphasis on text and microarray data analysis. Two emerging data mining approaches, chemogenomic data mining and proteomic data mining, are briefly introduced. Also discussed are the limitations of various data mining approaches found in the level of database integration, the quality of data annotation, sample heterogeneity and the performance of analytical and mining tools. Tentative strategies of integrating different data sources for target discovery, such as integrated text mining with high-throughput data analysis and integrated mining with pathway databases, are introduced.

Approaches to reduce false positives and false negatives in the analysis of microarray data: applications in type 1 diabetes research

Background

As studies of molecular biology system attempt to achieve a comprehensive understanding of a particular system, Type 1 errors may be a significant problem. However, few investigators are inclined to accept the increase in Type 2 errors (false positives) that may result when less stringent statistical cut-off values are used. To address this dilemma, we developed an analysis strategy that used a stringent statistical analysis to create a list of differentially expressed genes that served as "bait" to "fish out" other genes with similar patterns of expression.

Results

Comparing two strains of mice (NOD and C57Bl/6), we identified 93 genes with statistically significant differences in their patterns of expression. Hierarchical clustering identified an additional 39 genes with similar patterns of expression differences between the two strains. Pathway analysis was then employed: 1) identify the central genes and define biological processes that may be regulated by the genes identified, and 2) identify genes on the lists that could not be connected to each other in pathways (potential false positives). For networks created by both gene lists, the most connected (central) genes were interferon gamma (IFN-γ) and tumor necrosis factor alpha (TNF-α). These two cytokines are relevant to the biological differences between the two strains of mice. Furthermore, the network created by the list of 39 genes also suggested other biological differences between the strains.

Conclusion

Taken together, these data demonstrate how stringent statistical analysis, combined with hierarchical clustering and pathway analysis may offer deeper insight into the biological processes reflected from a set of expression array data. This approach allows us to 'recapture" false negative genes that otherwise would have been missed by the statistical analysis.

Transcriptome and proteome expressions involved in insulin resistance in muscle and activated T-lymphocytes of patients with type 2 diabetes

We analyzed the genes expressed (transcriptomes) and the proteins translated (pro- teomes) in muscle tissues and activated CD4(+) and CD8(+) T-lymphocytes (T-cells) of five Type 2 diabetes (T2DM) subjects using Affymetrix microarrays and mass spectrometry, and compared them with matched non-diabetic controls. Gene expressions of insulin receptor (INSR), vitamin D receptor, insulin degrading enzyme, Akt, insulin receptor substrate-1 (IRS-1), IRS-2, glucose transporter 4 (GLUT4), and enzymes of the glycolytic pathway were decreased at least 50% in T2DM than in controls. However, there was greater than two-fold gene upregulation of plasma cell glycoprotein-1, tumor necrosis factor alpha (TNFalpha, and gluconeogenic enzymes in T2DM than in controls. The gene silencing for INSR or TNFalpha resulted in the inhibition or stimulation of GLUT4, respectively. Proteome profiles corresponding to molecular weights of the above translated transcriptomes showed different patterns of changes between T2DM and controls. Meanwhile, changes in transcriptomes and proteomes between muscle and activated T-cells of T2DM were comparable. Activated T-cells, analogous to muscle cells, expressed insulin signaling and glucose metabolism genes and gene products. In conclusion, T-cells and muscle in T2DM exhibited differences in expression of certain genes and gene products relative to non-diabetic controls. These alterations in transcriptomes and proteomes in T2DM may be involved in insulin resistance.

Proteomic studies in animal models of diabetes

The aim of this review is to provide an overview of proteomic studies in animal models of diabetes and to give some insight into the different methods available today in the rapidly developing field of proteomics. A summary of 31 papers published between 1997 and 2007 is presented. For instance, proteomics has been used to study the development of both type 1 and type 2 diabetes, diabetic complications in tissues like heart, kidney and retina and changes after treatment with anti-diabetic drugs like peroxisome proliferator-activated receptors agonists. Together, these studies give a good overview of a number of experimental approaches. Proteomics holds the promise of providing major contributions to the field of diabetes research. However, to achieve this, a number of issues need to be resolved. Appropriate data representation to facilitate data comparison, exchange, and verification is required, as well as improved statistical assessment of proteomic experiments. In addition, it is important to follow up the results with functional studies to be able to make biologically relevant conclusions. The potential of proteomics to dissect complex human disorders is now beginning to be realized. In the future, this will result in new important information concerning diabetes.

Comprehensive proteomic analysis of bovine spermatozoa of varying fertility rates and identification of biomarkers associated with fertility

BACKGROUND

Male infertility is a major problem for mammalian reproduction. However, molecular details including the underlying mechanisms of male fertility are still not known. A thorough understanding of these mechanisms is essential for obtaining consistently high reproductive efficiency and to ensure lower cost and time-loss by breeder.

RESULTS

Using high and low fertility bull spermatozoa, here we employed differential detergent fractionation multidimensional protein identification technology (DDF-Mud PIT) and identified 125 putative biomarkers of fertility. We next used quantitative Systems Biology modeling and canonical protein interaction pathways and networks to show that high fertility spermatozoa differ from low fertility spermatozoa in four main ways. Compared to sperm from low fertility bulls, sperm from high fertility bulls have higher expression of proteins involved in: energy metabolism, cell communication, spermatogenesis, and cell motility. Our data also suggests a hypothesis that low fertility sperm DNA integrity may be compromised because cell cycle: G2/M DNA damage checkpoint regulation was most significant signaling pathway identified in low fertility spermatozoa.

CONCLUSION

This is the first comprehensive description of the bovine spermatozoa proteome. Comparative proteomic analysis of high fertility and low fertility bulls, in the context of protein interaction networks identified putative molecular markers associated with high fertility phenotype.

Nutrigenomic approaches for benefit-risk analysis of foods and food components: defining markers of health

To be able to perform a comprehensive and rigorous benefit-risk analysis of individual food components, and of foods, a number of fundamental questions need to be addressed first. These include whether it is feasible to detect all relevant biological effects of foods and individual food components, how such effects can confidently be categorised into benefits and risks in relation to health and, for that matter, how health can be quantified. This article examines the last of these issues, focusing upon concepts for the development of new biomarkers of health. Clearly, there is scope for refinement of classical biomarkers so that they may be used to detect even earlier signs of disease, but this approach defines health solely as the absence of detectable disease or disease risk. We suggest that the health of a biological system may better be reflected by its ability to withstand and manage relevant physiological challenges so that homeostasis is maintained. We discuss the potential for expanding the range of current challenge tests for use in conjunction with functional genomic technologies to develop new types of biomarkers of health.

Gene expression in peripheral blood mononuclear cells from children with diabetes

OBJECTIVE

We hypothesized that type 1 diabetes (T1D) is accompanied by changes in gene expression in peripheral blood mononuclear cells due to dysregulation of adaptive and innate immunity, counterregulatory responses to immune dysregulation, insulin deficiency, and hyperglycemia.

RESEARCH DESIGN AND METHODS

Microarray analysis was performed on peripheral blood mononuclear cells from 43 patients with newly diagnosed T1D, 12 patients with newly diagnosed type 2 diabetes (T2D), and 24 healthy controls. One- and 4-month follow-up samples were obtained from 20 of the T1D patients.

RESULTS

Microarray analysis identified 282 genes differing in expression between newly diagnosed T1D patients and controls at a false discovery rate of 0.05. Changes in expression of IL1B, early growth response gene 3, and prostaglandin-endoperoxide synthase 2 resolved within 4 months of insulin therapy and were also observed in T2D, suggesting that they resulted from hyperglycemia. With use of a knowledge base, 81 of 282 genes could be placed within a network of interrelated genes with predicted functions including apoptosis and cell proliferation. IL1B and the MYC oncogene were the most highly connected genes in the network. IL1B was highly overexpressed in both T1D and T2D, whereas MYC was dysregulated only in T1D.

CONCLUSION

T1D and T2D likely share a final common pathway for beta-cell dysfunction that includes secretion of IL-1beta and prostaglandins by immune effector cells, exacerbating existing beta-cell dysfunction, and causing further hyperglycemia. The results identify several targets for disease-modifying therapy of diabetes and potential biomarkers for monitoring treatment efficacy.

Multivariate analysis of 2-DE protein patterns--practical approaches

Practical approaches to the use of multivariate data analysis of 2-DE protein patterns are demonstrated by three independent strategies for the image analysis and the multivariate analysis on the same set of 2-DE data. Four wheat varieties were selected on the basis of their baking quality. Two of the varieties were of strong baking quality and hard wheat kernel and two were of weak baking quality and soft kernel. Gliadins at different stages of grain development were analyzed by the application of multivariate data analysis on images of 2-DEs. Patterns related to the wheat varieties, harvest times and quality were detected on images of 2-DE protein patterns for all the three strategies. The use of the multivariate methods was evaluated in the alignment and matching procedures of 2-DE gels. All the three strategies were able to discriminate the samples according to quality, harvest time and variety, although different subsets of protein spots were selected. The explorative approach of using multivariate data analysis and variable selection in the analyses of 2-DEs seems to be promising as a fast, reliable and convenient way of screening and transforming many gel images into spot quantities.

Halothane binding proteome in human brain cortex

Inhaled anesthetics bind specifically to a wide variety of proteins in the brain. This set of proteins must include those that contribute to the physiological and behavioral phenotypes of anesthesia and the related side effects. To identify the anesthetic-binding targets and functional pathways associated with these targets in human brain, halothane photolabeling and two-dimensional (2D) gel electrophoresis were used. Both membrane and soluble proteins from human temporal cortex were prepared. More than 300 membrane and 400 soluble protein spots were detected on the stained blots, of which 23 membrane and 34 soluble proteins were labeled by halothane and identified by mass spectroscopy. Their functional classification reveals five groups, including carbohydrate metabolism, protein folding, oxidative phosphorylation, nucleoside triphosphatase, and dimer/kinase activity with different correlative stringency. When network analysis of the interaction between these protein molecules is used, the weighted interaction accentuates the cellular protein components important in cell growth and proliferation, cell cycle and cell death, and cell-cell signaling and interactions, although no pathway was specific. This study provides evidence for multiple anesthetic binding targets and suggests potential pathways involved in their actions.

Surviving sepsis: bcl-2 overexpression modulates splenocyte transcriptional responses in vivo

We hypothesized that spleen microarray gene expression profiles analyzed with contemporary pathway analysis software would provide molecular pathways of interest and target genes that might help explain the effect of bcl-2 on improving survival during sepsis. Two mouse models of sepsis, cecal ligation and puncture and tracheal instillation of Pseudomonas aeruginosa, were tested in both wild-type mice and mice that overexpress bcl-2. Whole spleens were obtained 6 h after septic injury. DNA microarray transcriptional profiles were obtained using the Affymetrix 430A GeneChip, containing 22,690 elements. Ingenuity Pathway Analysis software was used to construct hypothetical transcriptional networks that changed in response to sepsis and expression of the bcl-2 transgene. A conservative approach was used wherein only changes induced by both abdominal and pulmonary sepsis were studied. At 6 h, sepsis induced alterations in the abundance of hundreds of spleen genes, including a number of proinflammatory mediators (e.g., interleukin-6). These sepsis-induced alterations were blocked by expression of the bcl-2 transgene. Network analysis implicated a number of bcl-2-related apoptosis genes, including bcl2L11 (bim), bcl-2L2 (bcl-w), bmf, and mcl-1. Sepsis in bcl-2 transgenic animals resulted in alteration of RNA abundance for only a single gene, ceacam1. These findings are consistent with sepsis-induced alterations in the balance of pro- and anti-apoptotic transcriptional networks. In addition, our data suggest that the ability of bcl-2 overexpression to improve survival in sepsis in this model is related in part to prevention of sepsis-induced alterations in spleen transcriptional responses.

Proteomic biomarkers for autoimmune disease

Autoimmune diseases affect 3% of the world population, yet the diagnosis and classification of autoimmune diseases remain based on clinical examination combined with traditional laboratory tests and imaging studies. The development of genomic and proteomic technologies provides an unprecedented ability to identify novel biosignatures to diagnose, classify, and guide therapeutic decision making in patients with autoimmune disease. In this article, we review recent advances in proteomics technologies and their application to autoimmune disease.

Proteomics for diabetes research: an update and future perspectives

Diabetes is a common disease worldwide and can cause several complications, leading to systemic derangements and end-organ damage. Despite blood sugar control and adequate therapy with currently available drugs, diabetic complications remain a serious issue in clinical practice, indicating that our knowledge of diabetes and its complications is only at the tip of the iceberg. Better understanding of its pathogenesis and pathophysiology is crucial to achieve better therapeutic outcomes and to prevent its complications. This review provides an overview of proteomics and introduces proteomic technologies commonly used for diabetes research. Recent proteomic studies for the investigation of diabetes and its complications are summarized. Finally, the future perspectives for the field of proteomics in diabetes research are discussed.