Tue Bjerg Bennike

Biomarkers in inflammatory bowel diseases: current status and proteomics identification strategies

Unambiguous diagnosis of the two main forms of inflammatory bowel diseases (IBD): Ulcerative colitis (UC) and Crohns disease (CD), represents a challenge in the early stages of the diseases. The diagnosis may be established several years after the debut of symptoms. Hence, protein biomarkers for early and accurate diagnostic could help clinicians improve treatment of the individual patients. Moreover, the biomarkers could aid physicians to predict disease courses and in this way, identify patients in need of intensive treatment. Patients with low risk of disease flares may avoid treatment with medications with the concomitant risk of adverse events. In addition, identification of disease and course specific biomarker profiles can be used to identify biological pathways involved in the disease development and treatment. Knowledge of disease mechanisms in general can lead to improved future development of preventive and treatment strategies. Thus, the clinical use of a panel of biomarkers represents a diagnostic and prognostic tool of potentially great value. The technological development in recent years within proteomic research (determination and quantification of the complete protein content) has made the discovery of novel biomarkers feasible. Several IBD-associated protein biomarkers are known, but none have been successfully implemented in daily use to distinguish CD and UC patients. The intestinal tissue remains an obvious place to search for novel biomarkers, which blood, urine or stool later can be screened for. When considering the protein complexity encountered in intestinal biopsy-samples and the recent development within the field of mass spectrometry driven quantitative proteomics, a more thorough and accurate biomarker discovery endeavor could today be performed than ever before. In this review, we report the current status of the proteomics IBD biomarkers and discuss various emerging proteomic strategies for identifying and characterizing novel biomarkers, as well as suggesting future targets for analysis.

Neutrophil Extracellular Traps in Ulcerative Colitis: A Proteome Analysis of Intestinal Biopsies

Background:The etiology of the inflammatory bowel diseases, including ulcerative colitis (UC), remains incompletely explained. We hypothesized that an analysis of the UC colon proteome could reveal novel insights into the disease etiology. Methods:Mucosal colon biopsies were taken by endoscopy from noninflamed tissue of 10 patients with UC and 10 controls. The biopsies were either snap-frozen for protein analysis or prepared for histology. The protein content of the biopsies was characterized by high-throughput gel-free quantitative proteomics, and biopsy histology was analyzed by light microscopy and confocal microscopy. Results:We identified and quantified 5711 different proteins with proteomics. The abundance of the proteins calprotectin and lactotransferrin in the tissue correlated with the degree of tissue inflammation as determined by histology. However, fecal calprotectin did not correlate. Forty-six proteins were measured with a statistically significant differences in abundances between the UC colon tissue and controls. Eleven of the proteins with increased abundances in the UC biopsies were associated with neutrophils and neutrophil extracellular traps. The findings were validated by microscopy, where an increased abundance of neutrophils and the presence of neutrophil extracellular traps by extracellular DNA present in the UC colon tissue were confirmed. Conclusions:Neutrophils, induced neutrophil extracellular traps, and several proteins that play a part in innate immunity are all increased in abundance in the morphologically normal colon mucosa from patients with UC. The increased abundance of these antimicrobial compounds points to the stimulation of the innate immune system in the etiology of UC.

The Pig PeptideAtlas: A resource for systems biology in animal production and biomedicine.

Biological research of Sus scrofa, the domestic pig, is of immediate relevance for food production sciences, and for developing pig as a model organism for human biomedical research. Publicly available data repositories play a fundamental role for all biological sciences, and protein data repositories are in particular essential for the successful development of new proteomic methods. Cumulative proteome data repositories, including the PeptideAtlas, provide the means for targeted proteomics, system‐wide observations, and cross‐species observational studies, but pigs have so far been underrepresented in existing repositories. We here present a significantly improved build of the Pig PeptideAtlas, which includes pig proteome data from 25 tissues and three body fluid types mapped to 7139 canonical proteins. The content of the Pig PeptideAtlas reflects actively ongoing research within the veterinary proteomics domain, and this article demonstrates how the expression of isoform‐unique peptides can be observed across distinct tissues and body fluids. The Pig PeptideAtlas is a unique resource for use in animal proteome research, particularly biomarker discovery and for preliminary design of SRM assays, which are equally important for progress in research that supports farm animal production and veterinary health, as for developing pig models with relevance to human health research.

A Normative Study of the Synovial Fluid Proteome from Healthy Porcine Knee Joints

Synovial fluid in an articulating joint contains proteins derived from the blood plasma and proteins that are produced by cells within the joint tissues, such as synovium, cartilage, ligament, and meniscus. The proteome composition of healthy synovial fluid and the cellular origins of many synovial fluid components are not fully understood. Here, we present a normative proteomics study using porcine synovial fluid. Using our optimized method, we identified 267 proteins with high confidence in healthy synovial fluid. We also evaluated mRNA expression data from tissues that can contribute to the synovial fluid proteome, including synovium, cartilage, blood, and liver, to better estimate the relative contributions from these sources to specific synovial fluid components. We identified 113 proteins in healthy synovial fluid that appear to be primarily derived from plasma transudates, 37 proteins primarily derived from synovium, and 11 proteins primarily derived from cartilage. Finally, we compared the identified synovial fluid proteome to the proteome of human plasma, and we found that the two body fluids share many similarities, underlining the detected plasma derived nature of many synovial fluid components. Knowing the synovial fluid proteome of a healthy joint will help to identify mechanisms that cause joint disease and pathways involved in disease progression.

Comparing the proteome of snap frozen, RNAlater preserved, and formalin-fixed paraffin-embedded human tissue samples

Graphical abstract

Proteome stability analysis of snap frozen, RNAlater preserved, and formalin-fixed paraffin-embedded human colon mucosal biopsies

Large repositories of well characterized RNAlater preserved samples and formalin-fixed, paraffin-embedded samples have been generated worldwide. However, the impact on the proteome of the preservation methods remain poorly described. Therefore, we analyzed the impact on the proteome of preserving samples in RNAlater, and by formalin-fixation, paraffin-embedding on human soft tissue, using directly frozen samples as a control (“Comparing the proteome of snap frozen, RNAlater preserved, and formalin-fixed paraffin-embedded human tissue samples” [1]). We here report the data from the analysis. The comparative analysis was performed on 24 colon mucosa biopsies, extracted from the sigmoideum of two gastroenterologically healthy participants for the purpose of this study. A set of biopsies were additionally stored for 30 min at room temperature prior to formalin-fixation. The samples were analyzed by high throughput gel free quantitative proteomics. The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PRIDE: PXD002029.

Proteome analysis of rheumatoid arthritis gut mucosa

Rheumatoid arthritis (RA) is an inflammatory joint disease leading to cartilage damage and ultimately impaired joint function. To gain new insight into the systemic immune manifestations of RA, we characterized the colon mucosa proteome from 11 RA-patients and 10 healthy controls. The biopsies were extracted by colonoscopy and analyzed by label-free quantitative proteomics, enabling the quantitation of 5366 proteins. The abundance of dihydrofolate reductase (DHFR) was statistically significantly increased in RA-patient biopsies compared with controls and correlated with the administered dosage of methotrexate (MTX), the most frequently prescribed immunosuppressive drug for RA. Additionally, our data suggest that treatment with Leflunomide, a common alternative to MTX, increases DHFR. The findings were supported by immunohistochemistry with confocal microscopy, which furthermore demonstrated that DHFR was located in the cytosol of the intestinal epithelial and interstitial cells. Finally, we identified 223 citrullinated peptides from 121 proteins. Three of the peptides were unique to RA. The list of citrullinated proteins was enriched in extracellular and membrane proteins and included known targets of anticitrullinated protein antibodies (ACPAs). Our findings support that the colon mucosa could trigger the production of ACPAs, which could contribute to the onset of RA. The MS data have been deposited to ProteomeXchange with identifiers PXD001608 and PXD003082.

Condenser: a statistical aggregation tool for multi-sample quantitative proteomic data from Matrix Science Mascot Distiller™

We describe Condenser, a freely available, comprehensive open-source tool for merging multidimensional quantitative proteomics data from the Matrix Science Mascot Distiller Quantitation Toolbox into a common format ready for subsequent bioinformatic analysis. A number of different relative quantitation technologies, such as metabolic (15)N and amino acid stable isotope incorporation, label-free and chemical-label quantitation are supported. The program features multiple options for curative filtering of the quantified peptides, allowing the user to choose data quality thresholds appropriate for the current dataset, and ensure the quality of the calculated relative protein abundances. Condenser also features optional global normalization, peptide outlier removal, multiple testing and calculation of t-test statistics for highlighting and evaluating proteins with significantly altered relative protein abundances. Condenser provides an attractive addition to the gold-standard quantitative workflow of Mascot Distiller, allowing easy handling of larger multi-dimensional experiments. Source code, binaries, test data set and documentation are available at http://condenser.googlecode.com/.

High-Throughput Parallel Proteomic Sample Preparation Using 96-Well Polyvinylidene Fluoride (PVDF) Membranes and C18 Purification Plates

Meaningful proteomic-based biomarker discovery projects using primary human-derived specimens require the analysis of hundreds of samples in order to address the issue of interpersonal variability. Thus, robust high-throughput methods for the digestion of plasma samples are a prerequisite for such large clinical proteomic studies with hundreds of samples. Commonly used sample preparation methods are often difficult to parallelize and/or automate. Herein we describe a method for parallel 96-well plate-based sample preparation. Protein digestion is performed in 96-well polyvinylidene fluoride (PVDF) membrane plates and the subsequent purification in 96-well reversed phase C18 purification plates, enabling the usage of multichannel pipettes in all steps. The protocol can be applied using neat or depleted plasma/serum samples, but has also proven effective with other sample types.

Time-course investigation of Phytophthora infestans infection of potato leaf from three cultivars by quantitative proteomics

Potato late blight is one the most important crop diseases worldwide. Even though potato has been studied for many years, the potato disease late blight still has a vast negative effect on the potato production [1], [2], [3]. Late blight is caused by the pathogen Phytophthora infestans (P. infestans), which initiates infection through leaves. However, the biological activities during different stages of infection are poorly described, and could enable novel or improved ways of defeating late blight infection [4]. Therefore, we investigated the interactions between P. infestans (mixed strain culture) and potato (Solanum tuberosum). Three commercially available field potato cultivars of different resistance to late blight infection; Kuras (moderate), Sarpo Mira (highly resistant) and Bintje (very susceptable) were grown under controlled green house conditions and inoculated with a diversity of P. infestans populations. We used label-free quantitative proteomics to investigate the infection with P. infestans in a time-course study over 258 h. Several key issues limits proteome analysis of potato leaf tissue [5], [6], [7]. Firstly, the immense complexity of the plant proteome, which is further complicated by the presence of highly abundant proteins, such as ribulose bisphosphate carboxylase/oxygenase (RuBisCO). Secondly, plant leaf and potato, in particular, contain abundant levels amounts of phenols and polyphenols, which hinder or completely prevent a successful protein extraction. Hitherto, protein profiling of potato leaf tissues have been limited to few proteome studies and only 1484 proteins have been extracted and comprehensively described [5], [8], [9]. We here present the detailed methods and raw data by optimized gel-enhanced label free quantitative approach. The methodology enabled us to detect and quantify between 3248 and 3529 unique proteins from each cultivar, and up to 758 P. infestans derived proteins. The complete dataset is available via ProteomeXchange, with the identifier PXD002767.