1,721,267 research outputs found
Functional characterization of EI24-induced autophagy in the degradation of RING-domain E3 ligases
No full textHistorically, the ubiquitin-proteasome system (UPS) and autophagy pathways were believed to be independent; however, recent data indicate that these pathways engage in crosstalk. To date, the players mediating this crosstalk have been elusive. Here, we show experimentally that EI24 (EI24, autophagy associated transmembrane protein), a key component of basal macroautophagy/autophagy, degrades 14 physiologically important E3 ligases with a RING (really interesting new gene) domain, whereas 5 other ligases were not degraded. Based on the degradation results, we built a statistical model that predicts the RING E3 ligases targeted by EI24 using partial least squares discriminant analysis. Of 381 RING E3 ligases examined computationally, our model predicted 161 EI24 targets. Those targets are primarily involved in transcription, proteolysis, cellular bioenergetics, and apoptosis and regulated by TP53 and MTOR signaling. Collectively, our work demonstrates that EI24 is an essential player in UPS-autophagy crosstalk via degradation of RING E3 ligases. These results indicate a paradigm shift regarding the fate of E3 ligases. © 2016 Sushil Devkota, Hyobin Jeong, Yunmi Kim, Muhammad Ali, Jae-il Roh, Daehee Hwang, and Han-Woong Lee. Published with license by Taylor & Francis.1661sciescopu
Identification of novel urinary biomarkers for assessing disease activity and prognosis of rheumatoid arthritis
Full text linkTo 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.
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Modelling cytokine signalling networks
No full textAlthough an imbalance between proinflammatory and anti-inflammatory cytokines has been implicated in rheumatoid arthritis (RA), the identification of specific cytokines regulating RA pathophysiotogy is still challenging. Modelling cytokine signalling networks and evaluating associated pathway activities might facilitate the effective identification of cytokines for treatment and prevention of RA. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.4
Single-cell multiomics: technologies and data analysis methods
No full textSingle-cell profiling: understanding disease at the cellular level The expansion of single-cell profiling technologies will provide unprecedented insights into the molecular mechanisms inherent in disease. Novel technologies known collectively as ‘single-cell multiomics’ enable systematic, high-resolution profiling of DNA, RNA and proteins in individual cells. This provides valuable data about gene regulation and molecular populations, and cellular processes during disease development and progression. Daehee Hwang and co-workers at Seoul National University, Seoul, South Korea, reviewed existing single-cell multiomics technologies and highlighted ways to integrate the data generated. Analytical features of multiomics allow scientists to isolate, sequence and label (or ‘barcode’) multiple molecules in single cells. Different sequencing techniques can be used for different purposes, such as exploring gene mutation coverage or measuring RNA transcripts. Combining these sequencing data will help identify links between significant features during disease
ADP-ribosylation factor 6 (ARF6) bidirectionally regulates dendritic spine formation depending on neuronal maturation and activity
No full textRecent studies have reported conflicting results regarding the role of ARF6 in dendritic spine development, but no clear answer for the controversy has been suggested. We found that ADP-ribosylation factor 6 (ARF6) either positively or negatively regulates dendritic spine formation depending on neuronal maturation and activity. ARF6 activation increased the spine formation in developing neurons, whereas it decreased spine density in mature neurons. Genome-wide microarray analysis revealed that ARF6 activation in each stage leads to opposite patterns of expression of a subset of genes that are involved in neuronal morphology. ARF6-mediated Rac1 activation via the phospholipase D pathway is the coincident factor in both stages, but the antagonistic RhoA pathway becomes involved in the mature stage. Furthermore, blocking neuronal activity in developing neurons using tetrodotoxin or enhancing the activity in mature neurons using picrotoxin or chemical long term potentiation reversed the effect of ARF6 on each stage. Thus, activity-dependent dynamic changes in ARF6-mediated spine structures may play a role in structural plasticity of mature neurons. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc9911sciescopu
GREM1 Is a Key Regulator of Synoviocyte Hyperplasia and Invasiveness
No full textObjective. To investigate the expression of Gremlin 1 (GREM1), an antagonist of bone morphogenetic protein, in rheumatoid arthritis (RA) synovia and its involvement in the hyperplasia and invasiveness of fibroblast-like synoviocytes of RA (RA-FLS). Methods. Computational analysis was introduced to identify FLS-predominant regulators. GREM1 expression was examined by immunohistochemistry, real-time PCR, and ELISA. FLS proliferation and apoptosis were determined using tetrazolium-based colorimetric assay and APO Percentage assay, respectively. FLS migration and invasion were evaluated by wound migration and Matrigel invasion assay, respectively. Expressions of Bax, Bcl2, pErk1/2, and pAkt were detected by Western blot analysis. Results. Through global transcriptome profiling, we identified a GREM1 gene predominantly expressed in RA-FLS. Indeed, the GREM1 expression was higher in synovia, synovial fluids, and FLS of patients with RA than in those of patients with osteoarthritis, and its levels correlated well with proinflammatory cytokine concentrations. Knockdown of GREM1 transcripts using short interfering RNA (siRNA) reduced the proliferation and survival of RA-FLS along with downregulation of pErk1/2, pAkt, and Bcl2 expressions, whereas it induced Bax expression. Conversely, the addition of recombinant GREM1 to RA-FLS showed the opposite results. Moreover, GREM1 siRNA decreased the migratory and invasive capacity of RA-FLS, whereas exogenous GREM1 increased it. The GREM1-induced FLS survival, migration, and invasion were completely blocked by neutralizing antibodies to alpha(nu)beta(3) integrin on RA-FLS, suggesting that alpha(nu)beta(3) integrin mediates the antiapoptotic and promigratory effects of GREM1. Conclusion. GREM1 is highly expressed in RA joints, and functions as a regulator of survival, proliferation, migration, and invasion of RA-FLS. The Journal of Rheumatology Copyright © 2016. All rights reserved.8711sciescopu
Role of ADAM17 in invasion and migration of CD133-expressing liver cancer stem cells after irradiation
Full text linkWe investigated the biological role of CD133-expressing liver cancer stem cells (CSCs) enriched after irradiation of Huh7 cells in cell invasion and migration. We also explored whether a disintegrin and metalloproteinase-17 (ADAM17) influences the metastatic potential of CSC-enriched hepatocellular carcinoma (HCC) cells after irradiation. A CD133-expressing Huh7 cell subpopulation showed greater resistance to sublethal irradiation and specifically enhanced cell invasion and migration capabilities. We also demonstrated that the radiation-induced MMP-2 and MMP-9 enzyme activities as well as the secretion of vascular endothelial growth factor were increased more predominantly in Huh7CD133+ cell subpopulations than Huh7CD133- cell subpopulations. Furthermore, we showed that silencing ADAM17 significantly inhibited the migration and invasiveness of enriched Huh7CD133+ cells after irradiation; moreover, Notch signaling was significantly reduced in irradiated CD133-expressing liver CSCs following stable knockdown of the ADAM17 gene. In conclusion, our findings indicate that CD133-expressing liver CSCs have considerable metastatic capabilities after irradiation of HCC cells, and their metastatic capabilities might be maintained by ADAM17. Therefore, suppression of ADAM17 shows promise for improving the efficiency of current radiotherapies and reducing the metastatic potential of liver CSCs during HCC treatment.18151sciescopu
TEMPI: probabilistic modeling time-evolving differential PPI networks with multiPle information
Full text linkMotivation: Time-evolving differential protein-protein interaction (PPI) networks are essential to understand serial activation of differentially regulated (up- or downregulated) cellular processes (DRPs) and their interplays over time. Despite developments in the network inference, current methods are still limited in identifying temporal transition of structures of PPI networks, DRPs associated with the structural transition and the interplays among the DRPs over time. Results: Here, we present a probabilistic model for estimating Time-Evolving differential PPI networks with MultiPle Information (TEMPI). This model describes probabilistic relationships among network structures, time-course gene expression data and Gene Ontology biological processes (GOBPs). By maximizing the likelihood of the probabilistic model, TEMPI estimates jointly the time-evolving differential PPI networks (TDNs) describing temporal transition of PPI network structures together with serial activation of DRPs associated with transiting networks. This joint estimation enables us to interpret the TDNs in terms of temporal transition of the DRPs. To demonstrate the utility of TEMPI, we applied it to two time-course datasets. TEMPI identified the TDNs that correctly delineated temporal transition of DRPs and time-dependent associations between the DRPs. These TDNs provide hypotheses for mechanisms underlying serial activation of key DRPs and their temporal associations.X1122Ysciescopu
The level of autoantibodies targeting eukaryote translation elongation factor 1 α1 and ubiquitin-conjugating enzyme 2L3 in nondiabetic young adults
Full text linkBackground: 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 cutoffpoint 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 cutoffpoints 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 cutoffderived 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 cutoffreference range for the diagnosis of T1DM. © 2016 Korean Diabetes Association11Nscopuskc
iNID: An analytical framework for identifying Network models for Interplays among Developmental Signaling in Arabidopsis
No full textIntegration of internal and external cues into developmental programs is indispensable for growth and
development of plants, which involve complex interplays among signaling pathways activated by the internal and external
factors (IEFs). However, decoding these complex interplays is still challenging. Here, we present a web-based platform
that identifies key regulators and Network models delineating Interplays among Developmental signaling (iNID)
in Arabidopsis. iNID provides a comprehensive resource of (1) transcriptomes previously collected under the conditions
treated with a broad spectrum of IEFs and (2) protein and genetic interactome data in Arabidopsis. In addition, iNID
provides an array of tools for identifying key regulators and network models related to interplays among IEFs using
transcriptome and interactome data. To demonstrate the utility of iNID, we investigated the interplays of (1) phytohormones
and light and (2) phytohormones and biotic stresses. The results revealed 34 potential regulators of the interplays,
some of which have not been reported in association with the interplays, and also network models that delineate the
involvement of the 34 regulators in the interplays, providing novel insights into the interplays collectively defined by
phytohormones, light, and biotic stresses. We then experimentally verified that BME3 and TEM1, among the selected
regulators, are involved in the auxin–brassinosteroid (BR)–blue light interplay. Therefore, iNID serves as a useful tool to
provide a basis for understanding interplays among IEFs.4411sciescopu
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