| 1. |
- Landegren, Nils, et al.
(författare)
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Transglutaminase 4 as a prostate autoantigen in male subfertility
- 2015
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Ingår i: Science Translational Medicine. - : American Association for the Advancement of Science. - 1946-6234 .- 1946-6242. ; 7:292
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Tidskriftsartikel (refereegranskat)abstract
- Autoimmune polyendocrine syndrome type 1 (APS1), a monogenic disorder caused by AIRE gene mutations, features multiple autoimmune disease components. Infertility is common in both males and females with APS1. Although female infertility can be explained by autoimmune ovarian failure, the mechanisms underlying male infertility have remained poorly understood. We performed a proteome-wide autoantibody screen in APS1 patient sera to assess the autoimmune response against the male reproductive organs. By screening human protein arrays with male and female patient sera and by selecting for gender-imbalanced autoantibody signals, we identified transglutaminase 4 (TGM4) as a male-specific autoantigen. Notably, TGM4 is a prostatic secretory molecule with critical role in male reproduction. TGM4 autoantibodies were detected in most of the adult male APS1 patients but were absent in all the young males. Consecutive serum samples further revealed that TGM4 autoantibodies first presented during pubertal age and subsequent to prostate maturation. We assessed the animal model for APS1, the Aire-deficient mouse, and found spontaneous development of TGM4 autoantibodies specifically in males. Aire-deficient mice failed to present TGM4 in the thymus, consistent with a defect in central tolerance for TGM4. In the mouse, we further link TGM4 immunity with a destructive prostatitis and compromised secretion of TGM4. Collectively, our findings in APS1 patients and Aire-deficient mice reveal prostate autoimmunity as a major manifestation of APS1 with potential role in male subfertility.
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| 2. |
- Badam, Tejaswi V. S., et al.
(författare)
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A validated generally applicable approach using the systematic assessment of disease modules by GWAS reveals a multi-omic module strongly associated with risk factors in multiple sclerosis
- 2021
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Ingår i: BMC Genomics. - : BioMed Central. - 1471-2164. ; 22:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: There exist few, if any, practical guidelines for predictive and falsifiable multi-omic data integration that systematically integrate existing knowledge. Disease modules are popular concepts for interpreting genome-wide studies in medicine but have so far not been systematically evaluated and may lead to corroborating multi-omic modules. Result: We assessed eight module identification methods in 57 previously published expression and methylation studies of 19 diseases using GWAS enrichment analysis. Next, we applied the same strategy for multi-omic integration of 20 datasets of multiple sclerosis (MS), and further validated the resulting module using both GWAS and risk-factor-associated genes from several independent cohorts. Our benchmark of modules showed that in immune-associated diseases modules inferred from clique-based methods were the most enriched for GWAS genes. The multi-omic case study using MS data revealed the robust identification of a module of 220 genes. Strikingly, most genes of the module were differentially methylated upon the action of one or several environmental risk factors in MS (n = 217, P = 10− 47) and were also independently validated for association with five different risk factors of MS, which further stressed the high genetic and epigenetic relevance of the module for MS. Conclusions: We believe our analysis provides a workflow for selecting modules and our benchmark study may help further improvement of disease module methods. Moreover, we also stress that our methodology is generally applicable for combining and assessing the performance of multi-omic approaches for complex diseases.
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| 3. |
- de Weerd, Hendrik A., et al.
(författare)
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MODifieR : an ensemble R package for inference of disease modules from transcriptomics networks
- 2020
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Ingår i: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811 .- 1460-2059. ; 36:12, s. 3918-3919
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Tidskriftsartikel (refereegranskat)abstract
- MOTIVATION: Complex diseases are due to the dense interactions of many disease-associated factors that dysregulate genes that in turn form so-called disease modules, which have shown to be a powerful concept for understanding pathological mechanisms. There exist many disease module inference methods that rely on somewhat different assumptions, but there is still no gold standard or best performing method. Hence, there is a need for combining these methods to generate robust disease modules.RESULTS: We developed MODule IdentiFIER (MODifieR), an ensemble R package of nine disease module inference methods from transcriptomics networks. MODifieR uses standardized input and output allowing the possibility to combine individual modules generated from these methods into more robust disease-specific modules, contributing to a better understanding of complex diseases.AVAILABILITY: MODifieR is available under the GNU GPL license and can be freely downloaded from https://gitlab.com/Gustafsson-lab/MODifieR and as a Docker image from https://hub.docker.com/r/ddeweerd/modifier.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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| 4. |
- Gustafsson, Erik, et al.
(författare)
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Characterizing the dynamics of the quorum-sensing system in Staphylococcus aureus
- 2004
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Ingår i: Journal of molecular microbiology and biotechnology. - : S. Karger AG. - 1464-1801. ; 8:4, s. 232-242
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Tidskriftsartikel (refereegranskat)abstract
- The virulence determinants of <i>Staphylococcus aureus</i> are expressed in a growth phase-dependent manner governed by the autoinducible quorum-sensing system <i>agr</i>. Activation of the <i>agr</i> system results in a rapid increase in the regulator RNAIII and occurs in response to accumulation of AIP. In order to activate the <i>agr</i> system, a basal transcription of <i>agr</i> components must be assumed. This basal transcription of <i>agr</i> components seems to be stimulated by <i>sarA</i>. To better understand how SarA would affect activation of the <i>agr</i> system by modulating the basal <i>agr</i> activity, a mathematical model for autoactivation of the <i>agr</i> system was set up. The model predicted that the <i>agr</i> system is hysteretic, meaning that the <i>agr</i> system is activated at a specific concentration of autoinducing peptide (AIP), whereas it is inactivated at a specific lower concentration of AIP. According to the model, changing the basal <i>agr</i> activity only had a marginal effect on steady-state levels of RNAIII but changed the sensitivity of the cells to AIP. This was supported by Northern blot analysis of RNAIII in <i>S. aureus</i> mutants with different levels of SarA expression. Since natural antagonistic AIPs have been demonstrated, the effect of adding inhibitors to the system was analyzed.
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| 5. |
- Lövfors, William, 1991-, et al.
(författare)
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A comprehensive mechanistic model of adipocyte signaling with layers of confidence
- 2023
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Ingår i: npj Systems Biology and Applications. - : Springer Nature. - 2056-7189. ; 9:1
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Tidskriftsartikel (refereegranskat)abstract
- Adipocyte signaling, normally and in type 2 diabetes, is far from fully understood. We have earlier developed detailed dynamic mathematical models for several well-studied, partially overlapping, signaling pathways in adipocytes. Still, these models only cover a fraction of the total cellular response. For a broader coverage of the response, large-scale phosphoproteomic data and systems level knowledge on protein interactions are key. However, methods to combine detailed dynamic models with large-scale data, using information about the confidence of included interactions, are lacking. We have developed a method to first establish a core model by connecting existing models of adipocyte cellular signaling for: (1) lipolysis and fatty acid release, (2) glucose uptake, and (3) the release of adiponectin. Next, we use publicly available phosphoproteome data for the insulin response in adipocytes together with prior knowledge on protein interactions, to identify phosphosites downstream of the core model. In a parallel pairwise approach with low computation time, we test whether identified phosphosites can be added to the model. We iteratively collect accepted additions into layers and continue the search for phosphosites downstream of these added layers. For the first 30 layers with the highest confidence (311 added phosphosites), the model predicts independent data well (70–90% correct), and the predictive capability gradually decreases when we add layers of decreasing confidence. In total, 57 layers (3059 phosphosites) can be added to the model with predictive ability kept. Finally, our large-scale, layered model enables dynamic simulations of systems-wide alterations in adipocytes in type 2 diabetes.
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