Although rheumatoid arthritis (RA) is the most common chronic inflammatory autoimmune disease, diagnosis of RA is currently based on clinical manifestations, and there is no simple, practical assessment tool in the clinical field to assess disease activity and severity. Recently, there has been increasing interest in the discovery of new diagnostic RA biomarkers that can assist in evaluating disease activity, severity, and treatment response. Proteomics, the large-scale study of the proteome, has emerged as a powerful technique for protein identification and characterization. For the past 10 years, proteomic techniques have been applied to different biological samples (synovial tissue/fluid, blood, and urine) from RA patients and experimental animal models. In this review, we summarize the current state of the application of proteomics in RA and its importance in identifying biomarkers and treatment targets.
Arthritis, Rheumatoid* ; Autoimmune Diseases ; Biomarkers ; Diagnosis ; Humans ; Models, Animal ; Proteome ; Proteomics*

Phenotypic characteristics of complex diseases such as rheumatoid arthritis are a consequence of interactions of genetic and environmental factors. Biomolecules closely interact with other molecular components and form functional modules, resulting in significant biologic action capability. While traditional biochemical research focuses on a single disease using narrowly constrained data, systems biology aims to interpret large volumes of highly complex and multilevel data obtained from high-through-put technologies to understand how biological systems function as a whole. Such a systems approach to complex diseases, so-called network medicine, can shape our comprehensive understanding of disease mechanisms by identifying modules temporally and spatially perturbed in the context of health and diseases. Given the unmet needs for diagnosis, monitoring, and treatment in rheumatoid arthritis, systems biology is obviously an emerging powerful tool to gain insight into disease mechanisms, study comorbidities, analyze therapeutic drugs and their targets, and discover novel network-based biomarkers.
Arthritis, Rheumatoid* ; Biomarkers ; Comorbidity ; Diagnosis ; Systems Biology

The advances in mass spectrometry-based proteomics technologies have enabled the generation of global proteome data from tissue or body fluid samples collected from a broad spectrum of human diseases. Comparative proteomic analysis of global proteome data identifies and prioritizes the proteins showing altered abundances, called differentially expressed proteins (DEPs), in disease samples, compared to control samples. Protein biomarker candidates that can serve as indicators of disease states are then selected as key molecules among these proteins. Recently, it has been addressed that cellular pathways can provide better indications of disease states than individual molecules and also network analysis of the DEPs enables effective identification of cellular pathways altered in disease conditions and key molecules representing the altered cellular pathways. Accordingly, a number of network-based approaches to identify disease-related pathways and representative molecules of such pathways have been developed. In this review, we summarize analytical platforms for network-based protein biomarker discovery and key components in the platforms.
Body Fluids ; Humans ; Proteome ; Proteomics ; Biomarkers

Chronic neuroinflammation is an integral pathological feature of major neurodegenerative diseases. The recruitment of microglia to affected brain regions and the activation of these cells are the major events leading to disease-associated neuroinflammation. In a previous study, we showed that neuron-released alpha-synuclein can activate microglia through activating the Toll-like receptor 2 (TLR2) pathway, resulting in proinflammatory responses. However, it is not clear whether other signaling pathways are involved in the migration and activation of microglia in response to neuron-released alpha-synuclein. In the current study, we demonstrated that TLR2 activation is not sufficient for all of the changes manifested by microglia in response to neuron-released alpha-synuclein. Specifically, the migration of and morphological changes in microglia, triggered by neuron-released alpha-synuclein, did not require the activation of TLR2, whereas increased proliferation and production of cytokines were strictly under the control of TLR2. Construction of a hypothetical signaling network using computational tools and experimental validation with various peptide inhibitors showed that beta1-integrin was necessary for both the morphological changes and the migration. However, neither proliferation nor cytokine production by microglia was dependent on the activation of beta1-integrin. These results suggest that beta1-integrin signaling is specifically responsible for the recruitment of microglia to the disease-affected brain regions, where neurons most likely release relatively high levels of alpha-synuclein.
Animals ; Antigens, CD29/genetics/*metabolism ; Cell Line, Tumor ; *Cell Movement ; Cells, Cultured ; Culture Media, Conditioned/*pharmacology ; Gene Regulatory Networks ; Humans ; Mice ; Mice, Inbred C57BL ; Microglia/drug effects/metabolism/*physiology ; Neurons/*metabolism ; Rats ; Rats, Sprague-Dawley ; Signal Transduction ; Toll-Like Receptor 2/metabolism ; alpha-Synuclein/*pharmacology

Spermatogonial stem cells (SSCs) are germline stem cells located along the basement membrane of seminiferous tubules in testes. Recently, SSCs were shown to be reprogrammed into multipotent SSCs (mSSCs). However, both the key factors and biological networks underlying this reprogramming remain elusive. Here, we present transcriptional regulatory networks (TRNs) that control cellular processes related to the SSC-to-mSSC reprogramming. Previously, we established intermediate SSCs (iSSCs) undergoing the transition to mSSCs and generated gene expression profiles of SSCs, iSSCs and mSSCs. By comparing these profiles, we identified 2643 genes that were up-regulated during the reprogramming process and 15 key transcription factors (TFs) that regulate these genes. Using the TF-target relationships, we developed TRNs describing how these TFs regulate three pluripotency-related processes (cell proliferation, stem cell maintenance and epigenetic regulation) during the reprogramming. The TRNs showed that 4 of the 15 TFs (Oct4/Pou5f1, Cux1, Zfp143 and E2f4) regulated cell proliferation during the early stages of reprogramming, whereas 11 TFs (Oct4/Pou5f1, Foxm1, Cux1, Zfp143, Trp53, E2f4, Esrrb, Nfyb, Nanog, Sox2 and Klf4) regulated the three pluripotency-related processes during the late stages of reprogramming. Our TRNs provide a model for the temporally coordinated transcriptional regulation of pluripotency-related processes during the SSC-to-mSSC reprogramming, which can be further tested in detailed functional studies.
Basement Membrane ; Cell Proliferation ; Epigenomics ; Multipotent Stem Cells* ; Seminiferous Tubules ; Stem Cells* ; Testis ; Transcription Factors ; Transcriptome

To optimize treatment for rheumatoid arthritis (RA), it is ideal to monitor the disease activity on a daily basis because RA activity fluctuates over time. Urine can be collected routinely at home by patients. Recently, we identified four urinary biomarker candidates—gelsolin (GSN), orosomucoid (ORM)1, ORM2 and soluble CD14 (sCD14)—in RA patients through transcriptomic and proteomic studies. Here, we investigated the clinical significance of the aforementioned urinary biomarker candidates in a prospective manner. For the first time, we found that urinary ORM1, ORM2 and sCD14 levels, but not GSN, were elevated in RA patients and had a positive correlation with the status of the disease activity. In particular, urine tests for ORM 1, ORM 2 and sCD14 efficiently represented the presence of high RA activity without the need for measuring blood markers. In a parallel study, a more rapid radiographic progression over 3 years was observed in patients with higher ORM2 levels. Combined measurements of urinary ORM2 and serum C-reactive protein synergistically increased the predictability of the radiographic progression of RA (odds ratio: 46.5). Collectively, our data provide evidence that blood-free, urinary biomarkers are promising surrogates for assessing disease activity and prognosis of RA. We anticipate that our urinary biomarkers will provide novel candidates for patient-driven measurements of RA activity at home and can shift the paradigm from blood to urine testing in the assessment of RA activity and prognosis in hospitals.
Arthritis, Rheumatoid* ; Biomarkers* ; C-Reactive Protein ; Humans ; Orosomucoid ; Prognosis* ; Prospective Studies

A primary characteristic of autism, which is a neurodevelopmental disorder, is impaired social interaction and communication. Furthermore, patients with autism frequently show abnormal social recognition. In mouse models of autism, social recognition is usually assessed by examining same-sex social behavior using various tests, such as the three-chamber test. However, no studies have examined the ability of male mice with autism to recognize the estrous cycle of female partners. In this study, we investigated the sexual behaviors, especially mounting and ultrasonic vocal communication (USV), of BTBR T+ tf/J (BTBR) mice, which are used as a well-known mouse model of autism, when they encountered estrus or diestrus female mice. As expected, C57BL/6 mice mounted more female mice in the estrus stage compared with the diestrus stage. We found that BTBR mice also mounted more female mice in the estrus stage than female mice in the diestrus stage. Although the USV emission of male mice was not different between estrus and diestrus female mice in both strains, the mounting result implies that BTBR mice distinguish sexual receptivity of females.
Animals ; Autistic Disorder ; Diestrus ; Estrous Cycle* ; Estrus ; Female* ; Humans ; Interpersonal Relations ; Male* ; Mice* ; Neurodevelopmental Disorders ; Sexual Behavior* ; Social Behavior ; Ultrasonics*

Fibroblast-like synoviocytes (FLSs) constitute a major cell subset of rheumatoid arthritis (RA) synovia. Dysregulation of microRNAs (miRNAs) has been implicated in activation and proliferation of RA-FLSs. However, the functional association of various miRNAs with their targets that are characteristic of the RA-FLS phenotype has not been globally elucidated. In this study, we performed microarray analyses of miRNAs and mRNAs in RA-FLSs and osteoarthritis FLSs (OA-FLSs), simultaneously, to validate how dysregulated miRNAs may be associated with the RA-FLS phenotype. Global miRNA profiling revealed that miR-143 and miR-145 were differentially upregulated in RA-FLSs compared to OA-FLSs. miR-143 and miR-145 were highly expressed in independent RA-FLSs. The miRNA-target prediction and network model of the predicted targets identified insulin-like growth factor binding protein 5 (IGFBP5) and semaphorin 3A (SEMA3A) as potential target genes downregulated by miR-143 and miR-145, respectively. IGFBP5 level was inversely correlated with miR-143 expression, and its deficiency rendered RA-FLSs more sensitive to TNFα stimulation, promoting IL-6 production and NF-κB activity. Moreover, SEMA3A was a direct target of miR-145, as determined by a luciferase reporter assay, antagonizing VEGF165-induced increases in the survival, migration and invasion of RA-FLSs. Taken together, our data suggest that enhanced expression of miR-143 and miR-145 renders RA-FLSs susceptible to TNFα and VEGF165 stimuli by downregulating IGFBP5 and SEMA3A, respectively, and that these miRNAs could be therapeutic targets.
Arthritis, Rheumatoid* ; Fibroblasts* ; Insulin-Like Growth Factor Binding Protein 5 ; Interleukin-6 ; Luciferases ; MicroRNAs ; Osteoarthritis ; Phenotype* ; RNA, Messenger ; Semaphorin-3A ; Synovial Fluid

BACKGROUND: The prevalence of novel type 1 diabetes mellitus (T1DM) antibodies targeting eukaryote translation elongation factor 1 alpha 1 autoantibody (EEF1A1-AAb) and ubiquitin-conjugating enzyme 2L3 autoantibody (UBE2L3-AAb) has been shown to be negatively correlated with age in T1DM subjects. Therefore, we aimed to investigate whether age affects the levels of these two antibodies in nondiabetic subjects. METHODS: EEF1A1-AAb and UBE2L3-AAb levels in nondiabetic control subjects (n=150) and T1DM subjects (n=101) in various ranges of age (18 to 69 years) were measured using an enzyme-linked immunosorbent assay. The cutoff point for the presence of each autoantibody was determined based on control subjects using the formula: [mean absorbance+3×standard deviation]. RESULTS: In nondiabetic subjects, there were no significant correlations between age and EEF1A1-AAb and UBE2L3-AAb levels. However, there was wide variation in EEF1A1-AAb and UBE2L3-AAb levels among control subjects <40 years old; the prevalence of both EEF1A1-AAb and UBE2L3-AAb in these subjects was 4.4%. When using cutoff points determined from the control subjects <40 years old, the prevalence of both autoantibodies in T1DM subjects was decreased (EEFA1-AAb, 15.8% to 8.9%; UBE2L3-AAb, 10.9% to 7.9%) when compared to the prevalence using the cutoff derived from the totals for control subjects. CONCLUSION: There was no association between age and EEF1A1-AAb or UBE2L3-AAb levels in nondiabetic subjects. However, the wide variation in EEF1A1-AAb and UBE2L3-AAb levels apparent among the control subjects <40 years old should be taken into consideration when determining the cutoff reference range for the diagnosis of T1DM.
Antibodies ; Autoantibodies* ; Diabetes Mellitus, Type 1 ; Diagnosis ; Enzyme-Linked Immunosorbent Assay ; Eukaryota* ; Humans ; Peptide Elongation Factor 1* ; Peptide Elongation Factors* ; Prevalence ; Reference Values ; Young Adult*

PURPOSE: We wanted to improve the quality of subsequent screening by reviewing the previous screening of breast cancer patients. MATERIALS AND METHODS: Twenty-four breast cancer patients who underwent previous screening were enrolled. All 24 took mammograms and 15 patients also took sonograms. We reviewed the screening retrospectively according to the BI-RADS criteria and we categorized the results into false negative, true negative, true positive and occult cancers. We also categorized the causes of false negative cancers into misperception, misinterpretation and technical factors and then we analyzed the attributing factors. RESULTS: Review of the previous screening revealed 66.7% (16/24) false negative, 25.0% (6/24) true negative, and 8.3% (2/24) true positive cancers. False negative cancers were caused by the mammogram in 56.3% (9/16) and by the sonogram in 43.7% (7/16). For the false negative cases, all of misperception were related with mammograms and this was attributed to dense breast, a lesion located at the edge of glandular tissue or the image, and findings seen on one view only. Almost all misinterpretations were related with sonograms and attributed to loose application of the final assessment. CONCLUSION: To improve the quality of breast screening, it is essential to overcome the main causes of false negative examinations, including misperception and misinterpretation. We need systematic education and strict application of final assessment categories of BI-RADS. For effective communication among physicians, it is also necessary to properly educate them about BI-RADS.
Breast Neoplasms* ; Breast* ; Early Detection of Cancer ; Education ; Humans ; Mass Screening* ; Retrospective Studies