Peter H. Hagedorn

Functional immunomics: Microarray analysis of IgG autoantibody repertoires predicts the future response of mice to induced diabetes

Ones present repertoire of antibodies encodes the history of ones past immunological experience. Can the present autoantibody repertoire be consulted to predict resistance or susceptibility to the future development of an autoimmune disease? Here, we developed an antigen microarray chip and used bioinformatic analysis to study a model of type 1 diabetes developing in nonobese diabetic male mice in which the disease was accelerated and synchronized by exposing the mice to cyclophosphamide at 4 weeks of age. We obtained sera from 19 individual mice, treated the mice to induce cyclophosphamide-accelerated diabetes (CAD), and found, as expected, that 9 mice became severely diabetic, whereas 10 mice permanently resisted diabetes. We again obtained serum from each mouse after CAD induction. We then analyzed, by using rank-order and superparamagnetic clustering, the patterns of antibodies in individual mice to 266 different antigens spotted on the chip. A selected panel of 27 different antigens (10% of the array) revealed a pattern of IgG antibody reactivity in the pre-CAD sera that discriminated between the mice resistant or susceptible to CAD with 100% sensitivity and 82% specificity (P = 0.017). Surprisingly, the set of IgG antibodies that was informative before CAD induction did not separate the resistant and susceptible groups after the onset of CAD; new antigens became critical for post-CAD repertoire discrimination. Thus, at least for a model disease, present antibody repertoires can predict future disease, predictive and diagnostic repertoires can differ, and decisive information about immune system behavior can be mined by bioinformatic technology. Repertoires matter.

Transcriptional regulation by an NAC (NAM–ATAF1,2–CUC2) transcription factor attenuates ABA signalling for efficient basal defence towards Blumeria graminis f. sp. hordei in Arabidopsis

ATAF1 is a member of a largely uncharacterized plant-specific gene family encoding NAC transcription factors, and is induced in response to various abiotic and biotic stimuli in Arabidopsis thaliana. Previously, we showed that a mutant allele of ATAF1 compromises penetration resistance in Arabidopsis with respect to the non-host biotrophic pathogen Blumeria graminis f. sp. hordei (Bgh). In this study, we have used genome-wide transcript profiling to characterize signalling perturbations in ataf1 plants following Bgh inoculation. Comparative transcriptomic analyses identified an over-representation of abscisic acid (ABA)-responsive genes, including the ABA biosynthesis gene AAO3, which is significantly induced in ataf1 plants compared to wild-type plants following inoculation with Bgh. Additionally, we show that Bgh inoculation results in decreased endogenous ABA levels in an ATAF1-dependent manner, and that the ABA biosynthetic mutant aao3 showed increased penetration resistance to Bgh compared to wild-type plants. Furthermore, we show that ataf1 plants show ABA-hyposensitive phenotypes during seedling development and germination. Our data support a negative correlation between ABA levels and penetration resistance, and identify ATAF1 as a new stimuli-dependent attenuator of ABA signalling for the mediation of efficient penetration resistance in Arabidopsis upon Bgh attack.

Functional immunomics: Microarray analysis of IgG autoantibody repertoires predicts the future response of NOD mice to an inducer of accelerated diabetes

Ones present repertoire of antibodies encodes the history of ones past immunological experience. Can the present autoantibody repertoire be consulted to predict resistance or susceptibility to the future development of an autoimmune disease? Here, we developed an antigen microarray chip and used bioinformatic analysis to study a model of type 1 diabetes developing in nonobese diabetic male mice in which the disease was accelerated and synchronized by exposing the mice to cyclophosphamide at 4 weeks of age. We obtained sera from 19 individual mice, treated the mice to induce cyclophosphamide-accelerated diabetes (CAD), and found, as expected, that 9 mice became severely diabetic, whereas 10 mice permanently resisted diabetes. We again obtained serum from each mouse after CAD induction. We then analyzed, by using rank-order and superparamagnetic clustering, the patterns of antibodies in individual mice to 266 different antigens spotted on the chip. A selected panel of 27 different antigens (10% of the array) revealed a pattern of IgG antibody reactivity in the pre-CAD sera that discriminated between the mice resistant or susceptible to CAD with 100% sensitivity and 82% specificity (P = 0.017). Surprisingly, the set of IgG antibodies that was informative before CAD induction did not separate the resistant and susceptible groups after the onset of CAD; new antigens became critical for post-CAD repertoire discrimination. Thus, at least for a model disease, present antibody repertoires can predict future disease, predictive and diagnostic repertoires can differ, and decisive information about immune system behavior can be mined by bioinformatic technology. Repertoires matter.

The Free NADH Concentration Is Kept Constant in Plant Mitochondria under Different Metabolic Conditions

The reduced coenzyme NADH plays a central role in mitochondrial respiratory metabolism. However, reports on the amount of free NADH in mitochondria are sparse and contradictory. We first determined the emission spectrum of NADH bound to proteins using isothermal titration calorimetry combined with fluorescence spectroscopy. The NADH content of actively respiring mitochondria (from potato tubers [Solanum tuberosum cv Bintje]) in different metabolic states was then measured by spectral decomposition analysis of fluorescence emission spectra. Most of the mitochondrial NADH is bound to proteins, and the amount is low in state 3 (substrate + ADP present) and high in state 2 (only substrate present) and state 4 (substrate + ATP). By contrast, the amount of free NADH is low but relatively constant, even increasing a little in state 3. Using modeling, we show that these results can be explained by a 2.5- to 3-fold weaker average binding of NADH to mitochondrial protein in state 3 compared with state 4. This indicates that there is a specific mechanism for free NADH homeostasis and that the concentration of free NADH in the mitochondrial matrix per se does not play a regulatory role in mitochondrial metabolism. These findings have far-reaching consequences for the interpretation of cellular metabolism.

Independent component and pathway-based analysis of miRNA-regulated gene expression in a model of type 1 diabetes

BackgroundSeveral approaches have been developed for miRNA target prediction, including methods that incorporate expression profiling. However the methods are still in need of improvements due to a high false discovery rate. So far, none of the methods have used independent component analysis (ICA). Here, we developed a novel target prediction method based on ICA that incorporates both seed matching and expression profiling of miRNA and mRNA expressions. The method was applied on a cellular model of type 1 diabetes.ResultsMicrorray profiling identified eight miRNAs (miR-124/128/192/194/204/375/672/708) with differential expression. Applying ICA on the mRNA profiling data revealed five significant independent components (ICs) correlating to the experimental conditions. The five ICs also captured the miRNA expressions by explaining >97% of their variance. By using ICA, seven of the eight miRNAs showed significant enrichment of sequence predicted targets, compared to only four miRNAs when using simple negative correlation. The ICs were enriched for miRNA targets that function in diabetes-relevant pathways e.g. type 1 and type 2 diabetes and maturity onset diabetes of the young (MODY).ConclusionsIn this study, ICA was applied as an attempt to separate the various factors that influence the mRNA expression in order to identify miRNA targets. The results suggest that ICA is better at identifying miRNA targets than negative correlation. Additionally, combining ICA and pathway analysis constitutes a means for prioritizing between the predicted miRNA targets. Applying the method on a model of type 1 diabetes resulted in identification of eight miRNAs that appear to affect pathways of relevance to disease mechanisms in diabetes.

Chronic rejection of a lung transplant is characterized by a profile of specific autoantibodies

Obliterative bronchiolitis (OB) continues to be the major limitation to long‐term survival after lung transplantation. The specific aetiology and pathogenesis of OB are not well understood. To explore the role of autoreactivity in OB, we spotted 751 different self molecules onto glass slides, and used these antigen microarrays to profile 48 human serum samples for immunoglobulin G (IgG) and IgM autoantibodies; 27 patients showed no or mild bronchiolitis obliterans syndrome (BOS; a clinical correlate of OB) and 15 patients showed medium to severe BOS. We now report that these BOS grades could be differentiated by a profile of autoantibodies binding to 28 proteins or their peptides. The informative autoantibody profile included down‐regulation as well as up‐regulation of both IgM and IgG specific reactivities. This profile was evaluated for robustness using a panel of six independent test patients. Analysis of the functions of the 28 informative self antigens showed that eight of them are connected in an interaction network involved in apoptosis and protein metabolism. Thus, a profile of autoantibodies may reflect pathological processes in the lung allograft, suggesting a role for autoimmunity in chronic rejection leading to OB.

Integrative analysis correlates donor transcripts to recipient autoantibodies in primary graft dysfunction after lung transplantation.

Up to one in four lung‐transplanted patients develop pulmonary infiltrates and impaired oxygenation within the first days after lung transplantation. Known as primary graft dysfunction (PGD), this condition increases mortality significantly. Complex interactions between donor lung and recipient immune system are the suspected cause. We took an integrative, systems‐level approach by first exploring whether the recipient’s immune response to PGD includes the development of long‐lasting autoreactivity. We next explored whether proteins displaying such differential autoreactivity also display differential gene expression in donor lungs that later develop PGD compared with those that did not. We evaluated 39 patients from whom autoantibody profiles were already available for PGD based on chest radiographs and oxygenation data. An additional nine patients were evaluated for PGD based on their medical records and set aside for validation. From two recent donor lung gene expression studies, we reanalysed and paired gene profiles with autoantibody profiles. Primary graft dysfunction can be distinguished by a profile of differentially reactive autoantibodies binding to 17 proteins. Functional analysis showed that 12 of these proteins are part of a protein–protein interaction network (P = 3 × 10−6) involved in proliferative processes. A nearest centroid classifier assigned correct PGD grades to eight out of the nine patients in the validation cohort (P = 0·048). We observed significant positive correlation (r = 0·63, P = 0·011) between differences in IgM reactivity and differences in gene expression levels. This connection between donor lung gene expression and long‐lasting recipient IgM autoantibodies towards a specific set of proteins suggests a mechanism for the development of autoimmunity in PGD.

Transcriptional profiling of type 1 diabetes genes on chromosome 21 in a rat beta-cell line and human pancreatic islets

We recently finemapped a type 1 diabetes (T1D)-linked region on chromosome 21, indicating that one or more T1D-linked genes exist in this region with 33 annotated genes. In the current study, we have taken a novel approach using transcriptional profiling in predicting and prioritizing the most likely candidate genes influencing beta-cell function in this region. Two array-based approaches were used, a rat insulinoma cell line (INS-1αβ) overexpressing pancreatic duodenum homeobox 1 (pdx-1) and treated with interleukin 1β (IL-1β) as well as human pancreatic islets stimulated with a mixture of cytokines. Several candidate genes with likely functional significance in T1D were identified. Genes showing differential expression in the two approaches were highly similar, supporting the role of these specific gene products in cytokine-induced beta-cell damage. These were genes involved in cytokine signaling, oxidative phosphorylation, defense responses and apoptosis. The analyses, furthermore, revealed several transcription factor binding sites shared by the differentially expressed genes and by genes demonstrating highly similar expression profiles with these genes. Comparable findings in the rat beta-cell line and human islets support the validity of the methods used and support this as a valuable approach for gene mapping and identification of genes with potential functional significance in T1D, within a region of linkage.

Brownian Motion after Einstein: Some New Applications and New Experiments

The first half of this chapter describes the development in mathematical models of Brownian motion after Einsteins seminal papers and current applications to optical tweezers. This instrument of choice among single-molecule biophysicists is also an instrument of precision that requires an understanding of Brownian motion beyond Einsteins. This is illustrated with some applications, current and potential, and it is shown how addition of a controlled forced motion on the nano-scale of the tweezed objects thermal motion can improve the calibration of the instrument in general, and make it possible also in complex surroundings. The second half of the present chapter, starting with Sect. 9, describes the co-evolution of biological motility models with models of Brownian motion, including very recent results for how to derive cell-type-specific motility models from experimental cell trajectories.

Quantitative distinction between bound and free NADH in biological systems

Without exceptions, in all living cells NADH is a key metabolite linking a large number of metabolic pathways. Flux rates through such pathways are an essential component in the understanding of the functioning of living cells. Knowledge about the way these fluxes depend on the concentrations of the metabolites involved (including NADH/NAD+) allows calculation of these fluxes. Therefore, a method to determine the concentration of free NADH is necessary. A distinction between the free and protein-bound NADH can be made on the basis of fluorescence emission spectra and fluorescence lifetimes. A method for such measurements using a microscopic set-up for time-gated fluorescence spectroscopy has been introduced by Schneckenburger and co-workers (Paul RJ, Schneckenburger H. Naturwissenschaften 83, pp. 32-35, 1996). We further improve this method by first characterizing NADH binding to model proteins by isothermal titration calorimetry and fluorescence. This allows a precise calculation of bound and free NADH and their respective spectra. An analysis of experimental data is advanced by applying two-component deconvolution and subsequent fitting. Using this method we can detect a significant proportion of free NADH in isolated potato tuber mitochondria respiring malate. Taken together these improvements allow a more accurate characterization of the NADH turnover in biological systems.