Two of Them Do It Better: Novel Serum Biomarkers Improve Autoimmune Hepatitis Diagnosis

Amino acid sequence homology between thyroid autoantigens and central nervous system proteins: Implications for the steroid-responsive encephalopathy associated with autoimmune thyroiditis

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Alpha-enolase, aldehyde reductase-I and dimethylargininase-I are SREAT autoantigens.

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Molecular mimicry between thyroid and CNS autoantigens is hypothesized in SREAT.

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Homology with TSH-R, Tg and TPO exists for 6, 27 and 47 of 46,809 CNS-proteins.

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The above homologies are often in epitope-containing parts of thyroid autoantigens.

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Most of the above proteins are expressed in CNS regions which are altered in SREAT.

A few patients with Hashimoto’s thyroiditis or Graves’ disease develop a multiform syndrome of the central nervous system (CNS) termed Hashimoto’s encephalopathy or steroid-responsive encephalopathy associated with autoimmune thyroid disease (HE/SREAT). They have high levels of thyroid autoantibodies (TgAb, TPOAb and/or TSH-R-Ab) in blood and cerebrospinal fluid. Autoantibodies against alpha-enolase, aldehyde reductase-I (AKRIA) and/or dimethylargininase-I (DDAHI), proteins expressed in the CNS among other tissues, were detected in the blood and, when searched, in the cerebrospinal fluid of HE/SREAT patients. Recently, we reported that alpha-enolase, AKRIA and DDAHI share local sequence homology with each of the three autoantigens (TgAb, TPOAb, TSH-R-Ab), often in epitope-containing segments of the thyroid autoantigens. We hypothesized that there might be additional CNS-expressed proteins homologous to thyroid autoantigens, possibly overlapping known epitopes of the thyroid autoantigens. We used bioinformatic methods to address this hypothesis.

Six, 27 and 47 of 46,809 CNS-expressed proteins share homology with TSH-R, Tg and TPO, respectively. The homologous regions often contain epitopes, and some match regions of thyroid autoantigens which have homology with alpha-enolase, AKRIA and/or DDAHI. Several of the aforementioned proteins are present in CNS areas that show abnormalities at neuroimaging in HE/SREAT patients. Furthermore, autoantibodies against some of the said six, 27 and 47 proteins were reported to be associated with a number of autoimmune diseases.

Not only we validated our hypothesis, but we think that such a variety of potential CNS targets for thyroid Ab against epitopes contained in regions that have local homology with CNS proteins may explain the polymorphic phenotypes of HE/SREAT. Only when elevated amounts of these Ab are synthesized and trespass the blood-brain barrier, HE/SREAT appears. This might explain why HE/SREAT is so relatively rare.

Applications of Protein Microarrays in Biomarker Discovery for Autoimmune Diseases

Dysregulated autoantibodies and cytokines were deemed to provide important cues for potential illnesses, such as various carcinomas and autoimmune diseases. Increasing biotechnological approaches have been applied to screen and identify the specific alterations of these biomolecules as distinctive biomarkers in diseases, especially autoimmune diseases. As a versatile and robust platform, protein microarray technology allows researchers to easily profile dysregulated autoantibodies and cytokines associated with autoimmune diseases using various biological specimens, mainly serum samples. Here, we summarize the applications of protein microarrays in biomarker discovery for autoimmune diseases. In addition, the key issues in the process of using this approach are presented for improving future studies.

CombiROC: an interactive web tool for selecting accurate marker combinations of omics data

Diagnostic accuracy can be improved considerably by combining multiple markers, whose performance in identifying diseased subjects is usually assessed via receiver operating characteristic (ROC) curves. The selection of multimarker signatures is a complicated process that requires integration of data signatures with sophisticated statistical methods. We developed a user-friendly tool, called CombiROC, to help researchers accurately determine optimal markers combinations from diverse omics methods. With CombiROC data from different domains, such as proteomics and transcriptomics, can be analyzed using sensitivity/specificity filters: the number of candidate marker panels rising from combinatorial analysis is easily optimized bypassing limitations imposed by the nature of different experimental approaches. Leaving to the user full control on initial selection stringency, CombiROC computes sensitivity and specificity for all markers combinations, performances of best combinations and ROC curves for automatic comparisons, all visualized in a graphic interface. CombiROC was designed without hard-coded thresholds, allowing a custom fit to each specific data: this dramatically reduces the computational burden and lowers the false negative rates given by fixed thresholds. The application was validated with published data, confirming the marker combination already originally described or even finding new ones. CombiROC is a novel tool for the scientific community freely available at http://CombiROC.eu.

Untargeted LC-HRMS-Based Metabolomics for Searching New Biomarkers of Pancreatic Ductal Adenocarcinoma: A Pilot Study

Pancreatic ductal adenocarcinoma is one of the most lethal tumors since it is usually detected at an advanced stage in which surgery and/or current chemotherapy have limited efficacy. The lack of sensitive and specific markers for diagnosis leads to a dismal prognosis. The purpose of this study is to identify metabolites in serum of pancreatic ductal adenocarcinoma patients that could be used as diagnostic biomarkers of this pathology. We used liquid chromatography-high-resolution mass spectrometry for a nontargeted metabolomics approach with serum samples from 28 individuals, including 16 patients with pancreatic ductal adenocarcinoma and 12 healthy controls. Multivariate statistical analysis, which included principal component analysis and partial least squares, revealed clear separation between the patient and control groups analyzed by liquid chromatography-high-resolution mass spectrometry using a nontargeted metabolomics approach. The metabolic analysis showed significantly lower levels of phospholipids in the serum from patients with pancreatic ductal adenocarcinoma compared with serum from controls. Our results suggest that the liquid chromatography-high-resolution mass spectrometry-based metabolomics approach provides a potent and promising tool for the diagnosis of pancreatic ductal adenocarcinoma patients using the specific metabolites identified as novel biomarkers that could be used for an earlier detection and treatment of these patients.

Protein microarray applications: Autoantibody detection and posttranslational modification

The discovery of DNA microarrays was a major milestone in genomics; however, it could not adequately predict the structure or dynamics of underlying protein entities, which are the ultimate effector molecules in a cell. Protein microarrays allow simultaneous study of thousands of proteins/peptides, and various advancements in array technologies have made this platform suitable for several diagnostic and functional studies. Antibody arrays enable researchers to quantify the abundance of target proteins in biological fluids and assess PTMs by using the antibodies. Protein microarrays have been used to assess protein-protein interactions, protein-ligand interactions, and autoantibody profiling in various disease conditions. Here, we summarize different microarray platforms with focus on its biological and clinical applications in autoantibody profiling and PTM studies. We also enumerate the potential of tissue microarrays to validate findings from protein arrays as well as other approaches, highlighting their significance in proteomics.

Accurate Quantification of Disease Markers in Human Serum Using Iron Oxide Nanoparticle-linked Immunosorbent Assay

Accurate and reliable quantification of biomarkers in the blood is essential in disease screening and diagnosis. Here we describe an iron oxide nanoparticle (IONP)-linked immunosorbent assay (ILISA) for detecting biomolecules in human serum. Sandwich ILISA was optimized for the detection of four important serological markers, IgA, IgG, IgM, and C-reactive protein (CRP), and assessed with normal sera, simulated disease-state sera and the serum samples from patients infected with West Nile virus (WNV) or human herpes virus (HHV). Our study shows that using the detection assay formulated with 18.8 nm wüstite nanocrystals, ILISA can achieve sub-picomolar detection sensitivity, and all four markers can be accurately quantified over a large dynamic range. In addition, ILISA is not susceptible to variations in operating procedures and shows better linearity and higher stability compared with ELISA, which facilitates its integration into detection methods suitable for point of care. Our results demonstrate that ILISA is a simple and versatile nanoplatform for highly sensitive and reliable detection of serological biomarkers in biomedical research and clinical applications.