Eva Csosz

A single conformational transglutaminase 2 epitope contributed by three domains is critical for celiac antibody binding and effects

The multifunctional, protein cross-linking transglutaminase 2 (TG2) is the main autoantigen in celiac disease, an autoimmune disorder with defined etiology. Glutamine-rich gliadin peptides from ingested cereals, after their deamidation by TG2, induce T-lymphocyte activation accompanied by autoantibody production against TG2 in 1–2% of the population. The pathogenic role and exact binding properties of these antibodies to TG2 are still unclear. Here we show that antibodies from different celiac patients target the same conformational TG2 epitope formed by spatially close amino acids of adjacent domains. Glu153 and 154 on the first alpha-helix of the core domain and Arg19 on first alpha-helix of the N-terminal domain determine the celiac epitope that is accessible both in the closed and open conformation of TG2 and dependent on the relative position of these helices. Met659 on the C-terminal domain also can cooperate in antibody binding. This composite epitope is disease-specific, recognized by antibodies derived from celiac tissues and associated with biological effects when passively transferred from celiac mothers into their newborns. These findings suggest that celiac antibodies are produced in a surface-specific way for which certain homology of the central glutamic acid residues of the TG2 epitope with deamidated gliadin peptides could be a structural basis. Monoclonal mouse antibodies with partially overlapping epitope specificity released celiac antibodies from patient tissues and antagonized their harmful effects in cell culture experiments. Such antibodies or similar specific competitors will be useful in further functional studies and in exploring whether interference with celiac antibody actions leads to therapeutic benefits.

Phage display selection of efficient glutamine-donor substrate peptides for transglutaminase 2

Understanding substrate specificity and identification of natural targets of transglutaminase 2 (TG2), the ubiquitous multifunctional cross‐linking enzyme, which forms isopeptide bonds between protein‐linked glutamine and lysine residues, is crucial in the elucidation of its physiological role. As a novel means of specificity analysis, we adapted the phage display technique to select glutamine‐donor substrates from a random heptapeptide library via binding to recombinant TG2 and elution with a synthetic amine‐donor substrate. Twenty‐six Gln‐containing sequences from the second and third biopanning rounds were susceptible for TG2‐mediated incorporation of 5‐(biotinamido)penthylamine, and the peptides GQQQTPY, GLQQASV, and WQTPMNS were modified most efficiently. A consensus around glutamines was established as pQX(P,T,S)l, which is consistent with identified substrates listed in the TRANSDAB database. Database searches showed that several proteins contain peptides similar to the phage‐selected sequences, and the N‐terminal glutamine‐rich domain of SWI1/SNF1‐related chromatin remodeling proteins was chosen for detailed analysis. MALDI/TOF and tandem mass spectrometry‐based studies of a representative part of the domain, SGYGQQGQTPYYNQQSPHPQQQQPPYS (SnQ1), revealed that Q6, Q8, and Q22 are modified by TG2. Kinetic parameters of SnQ1 transamidation (KMapp = 250 μM, kcat = 18.3 sec−1, and kcat/KMapp = 73,200) classify it as an efficient TG2 substrate. Circular dichroism spectra indicated that SnQ1 has a random coil conformation, supporting its accessibility in the full‐length parental protein. Added together, here we report a novel use of the phage display technology with great potential in transglutaminase research.

Substrate Preference of Transglutaminase 2 Revealed by Logistic Regression Analysis and Intrinsic Disorder Examination

Tissue transglutaminase (TG2) catalyzes the Ca(2+)-dependent posttranslational modification of proteins via formation of isopeptide bonds between their glutamine and lysine residues. Although substrate specificity of TG2 has been studied repeatedly at the sequence level, no clear consensus sequences have been determined so far. With the use of the extensive structural information on TG2 substrate proteins listed in TRANSDAB Wiki database, a slight preference of TG2 for glutamine and lysine residues situated in turns could be observed. When the spatial environment of the favored glutamine and lysine residues was analyzed with logistic regression, the presence of specific amino acid patterns was identified. By using the occurrence of the predictor amino acids as selection criteria, several polypeptides were predicted and later identified as novel in vitro substrates for TG2. By studying the sequence of TG2 substrate proteins lacking available crystal structure, the strong favorable influence on substrate selection of the presence of substrate glutamine and lysine residues in intrinsically disordered regions could also be revealed. The collected structural data have provided novel understanding of how this versatile enzyme selects its substrates in various cell compartments and tissues.

Tear fluid proteomics multimarkers for diabetic retinopathy screening

BackgroundThe aim of the project was to develop a novel method for diabetic retinopathy screening based on the examination of tear fluid biomarker changes. In order to evaluate the usability of protein biomarkers for pre-screening purposes several different approaches were used, including machine learning algorithms.MethodsAll persons involved in the study had diabetes. Diabetic retinopathy (DR) was diagnosed by capturing 7-field fundus images, evaluated by two independent ophthalmologists. 165 eyes were examined (from 119 patients), 55 were diagnosed healthy and 110 images showed signs of DR. Tear samples were taken from all eyes and state-of-the-art nano-HPLC coupled ESI-MS/MS mass spectrometry protein identification was performed on all samples. Applicability of protein biomarkers was evaluated by six different optimally parameterized machine learning algorithms: Support Vector Machine, Recursive Partitioning, Random Forest, Naive Bayes, Logistic Regression, K-Nearest Neighbor.ResultsOut of the six investigated machine learning algorithms the result of Recursive Partitioning proved to be the most accurate. The performance of the system realizing the above algorithm reached 74% sensitivity and 48% specificity.ConclusionsProtein biomarkers selected and classified with machine learning algorithms alone are at present not recommended for screening purposes because of low specificity and sensitivity values. This tool can be potentially used to improve the results of image processing methods as a complementary tool in automatic or semiautomatic systems.

Combined Methods for Diabetic Retinopathy Screening, Using Retina Photographs and Tear Fluid Proteomics Biomarkers

Background. It is estimated that 347 million people suffer from diabetes mellitus (DM), and almost 5 million are blind due to diabetic retinopathy (DR). The progression of DR can be slowed down with early diagnosis and treatment. Therefore our aim was to develop a novel automated method for DR screening. Methods. 52 patients with diabetes mellitus were enrolled into the project. Of all patients, 39 had signs of DR. Digital retina images and tear fluid samples were taken from each eye. The results from the tear fluid proteomics analysis and from digital microaneurysm (MA) detection on fundus images were used as the input of a machine learning system. Results. MA detection method alone resulted in 0.84 sensitivity and 0.81 specificity. Using the proteomics data for analysis 0.87 sensitivity and 0.68 specificity values were achieved. The combined data analysis integrated the features of the proteomics data along with the number of detected MAs in the associated image and achieved sensitivity/specificity values of 0.93/0.78. Conclusions. As the two different types of data represent independent and complementary information on the outcome, the combined model resulted in a reliable screening method that is comparable to the requirements of DR screening programs applied in clinical routine.

The application of a microfluidic reactor including spontaneously adsorbed trypsin for rapid protein digestion of human tear samples

The application of a newly developed microfluidic immobilized enzymatic reactor (IMER) designed to accelerate protein digestion in clinical samples is presented.