Ji-Hoon Cho

Direct Transfer of α-Synuclein from Neuron to Astroglia Causes Inflammatory Responses in Synucleinopathies

Abnormal neuronal aggregation of α-synuclein is implicated in the development of many neurological disorders, including Parkinson disease and dementia with Lewy bodies. Glial cells also show extensive α-synuclein pathology and may contribute to disease progression. However, the mechanism that produces the glial α-synuclein pathology and the interaction between neurons and glia in the disease-inflicted microenvironment remain unknown. Here, we show that α-synuclein proteins released from neuronal cells are taken up by astrocytes through endocytosis and form inclusion bodies. The glial accumulation of α-synuclein through the transmission of the neuronal protein was also demonstrated in a transgenic mouse model expressing human α-synuclein. Furthermore, astrocytes that were exposed to neuronal α-synuclein underwent changes in the gene expression profile reflecting an inflammatory response. Induction of pro-inflammatory cytokines and chemokines correlated with the extent of glial accumulation of α-synuclein. Together, these results suggest that astroglial α-synuclein pathology is produced by direct transmission of neuronal α-synuclein aggregates, causing inflammatory responses. This transmission step is thus an important mediator of pathogenic glial responses and could qualify as a new therapeutic target.

Colorectal cancer cell-derived microvesicles are enriched in cell cycle-related mRNAs that promote proliferation of endothelial cells

BackgroundVarious cancer cells, including those of colorectal cancer (CRC), release microvesicles (exosomes) into surrounding tissues and peripheral circulation. These microvesicles can mediate communication between cells and affect various tumor-related processes in their target cells.ResultsWe present potential roles of CRC cell-derived microvesicles in tumor progression via a global comparative microvesicular and cellular transcriptomic analysis of human SW480 CRC cells. We first identified 11,327 microvesicular mRNAs involved in tumorigenesis-related processes that reflect the physiology of donor CRC cells. We then found 241 mRNAs enriched in the microvesicles above donor cell levels, of which 27 were involved in cell cycle-related processes. Network analysis revealed that most of the cell cycle-related microvesicle-enriched mRNAs were associated with M-phase activities. The integration of two mRNA datasets showed that these M-phase-related mRNAs were differentially regulated across CRC patients, suggesting their potential roles in tumor progression. Finally, we experimentally verified the network-driven hypothesis by showing a significant increase in proliferation of endothelial cells treated with the microvesicles.ConclusionOur study demonstrates that CRC cell-derived microvesicles are enriched in cell cycle-related mRNAs that promote proliferation of endothelial cells, suggesting that microvesicles of cancer cells can be involved in tumor growth and metastasis by facilitating angiogenesis-related processes. This information will help elucidate the pathophysiological functions of tumor-derived microvesicles, and aid in the development of cancer diagnostics, including colorectal cancer.

Proteomic analysis of urinary exosomes from patients of early IgA nephropathy and thin basement membrane nephropathy

To identify biomarker candidates associated with early IgA nephropathy (IgAN) and thin basement membrane nephropathy (TBMN), the most common causes presenting isolated hematuria in childhood, a proteomic approach of urinary exosomes from early IgAN and TBMN patients was introduced. The proteomic results from the patients were compared with a normal group to understand the pathophysiological processes associated with these diseases at the protein level. The urinary exosomes, which reflect pathophysiological processes, collected from three groups of young adults (early IgAN, TBMN, and normal) were trypsin‐digested using a gel‐assisted protocol, and quantified by label‐free LC‐MS/MS, using an MSE mode. A total of 1877 urinary exosome proteins, including cytoplasmic, membrane, and vesicle trafficking proteins, were identified. Among the differentially expressed proteins, four proteins (aminopeptidase N, vasorin precursor, α‐1‐antitrypsin, and ceruloplasmin) were selected as biomarker candidates to differentiate early IgAN from TBMN. We confirmed the protein levels of the four biomarker candidates by semi‐quantitative immunoblot analysis in urinary exosomes independently prepared from other patients, including older adult groups. Further clinical studies are needed to investigate the diagnostic and prognostic value of these urinary markers for early IgAN and TBMN. Taken together, this study showed the possibility of identifying biomarker candidates for human urinary diseases using urinary exosomes and might help to understand the pathophysiology of early IgAN and TBMN at the protein level.

New gene selection method for classification of cancer subtypes considering within-class variation

In this work we propose a new method for finding gene subsets of microarray data that effectively discriminates subtypes of disease. We developed a new criterion for measuring the relevance of individual genes by using mean and standard deviation of distances from each sample to the class centroid in order to treat the well‐known problem of gene selection, large within‐class variation. Also this approach has the advantage that it is applicable not only to binary classification but also to multiple classification problems. We demonstrated the performance of the method by applying it to the publicly available microarray datasets, leukemia (two classes) and small round blue cell tumors (four classes). The proposed method provides a very small number of genes compared with the previous methods without loss of discriminating power and thus it can effectively facilitate further biological and clinical researches.

An Integrative Approach for Identifying a Metabolic Phenotype Predictive of Individualized Pharmacokinetics of Tacrolimus

Individual variation in drug response is influenced by both genes and environment. We evaluated the potential of a metabolic phenotype to predict individual variation in the pharmacokinetics (PK) of tacrolimus. Liquid chromatography–mass spectroscopy (LC‐MS)‐based metabolic profiling was performed on 29 healthy volunteers by measuring the levels of 1,256 metabolite ions in their predose urine samples. After oral administration of tacrolimus, we monitored its plasma concentrations in these volunteers for up to 72 h and calculated the pharmacokinetic parameters. Partial least‐squares (PLS) modeling was conducted with data relating to predose urine metabolites to predict the pharmacokinetic parameters of tacrolimus and to select the metabolites that substantially contributed to such prediction. The selection of these metabolites allowed us to understand their functional role and generate a clinically applicable index to predict individualized PK of tacrolimus. In conclusion, this integrative pharmacometabolomic approach, combining the metabolic profiling of predose urine with PLS modeling, can serve as a useful tool in “individualized drug therapy.”

Gene selection and classification from microarray data using kernel machine

The discrimination of cancer patients (including subtypes) based on gene expression data is a critical problem with clinical ramifications. Central to solving this problem is the issue of how to extract the most relevant genes from the several thousand genes on a typical microarray. Here, we propose a methodology that can effectively select an informative subset of genes and classify the subtypes (or patients) of disease using the selected genes. We employ a kernel machine, kernel Fisher discriminant analysis (KFDA), for discrimination and use the derivatives of the kernel function to perform gene selection. Using a modified form of KFDA in the minimum squared error (MSE) sense and the gradients of the kernel functions, we construct an effective gene selection criterion. We assess the performance of the proposed methodology by applying it to three gene expression datasets: leukemia dataset, breast cancer dataset and colon cancer dataset. Using a few informative genes, the proposed method accurately and reliably classified cancer subtypes (or patients). Also, through a comparison study, we verify the reliability of the gene selection and discrimination results.

Integrative Proteomics and Phosphoproteomics Profiling Reveals Dynamic Signaling Networks and Bioenergetics Pathways Underlying T Cell Activation

SUMMARY The molecular circuits by which antigens activate quiescent T cells remain poorly understood. We combined temporal profiling of the whole proteome and phosphoproteome via multiplexed isobaric labeling proteomics technology, computational pipelines for integrating multi‐omics datasets, and functional perturbation to systemically reconstruct regulatory networks underlying T cell activation. T cell receptors activated the T cell proteome and phosphoproteome with discrete kinetics, marked by early dynamics of phosphorylation and delayed ribosome biogenesis and mitochondrial activation. Systems biology analyses identified multiple functional modules, active kinases, transcription factors and connectivity between them, and mitochondrial pathways including mitoribosomes and complex IV. Genetic perturbation revealed physiological roles for mitochondrial enzyme COX10‐mediated oxidative phosphorylation in T cell quiescence exit. Our multi‐layer proteomics profiling, integrative network analysis, and functional studies define landscapes of the T cell proteome and phosphoproteome and reveal signaling and bioenergetics pathways that mediate lymphocyte exit from quiescence. HIGHLIGHTSProteome and phosphoproteome profiling reveals temporal dynamics of T cell activationTCR activates interconnected functional modules, kinases, and transcription factorsmTORC1 links TCR to mitoribosome biogenesis and complex IV activityCOX10 is crucial for T cell activation in vitro and in vivo &NA; Tan et al. apply multi‐layer proteomic profiling and systems biology approaches to define T cell proteome and phosphoproteome landscapes, and they identify signaling networks and bioenergetics pathways that mediate T cell quiescence exit. These data establish the function and mechanisms of oxidative phosphorylation and mitochondrial activation in antigen‐induced T cell responses.

Optimal approach for classification of acute leukemia subtypes based on gene expression data.

The classification of cancer subtypes, which is critical for successful treatment, has been studied extensively with the use of gene expression profiles from oligonucleotide chips or cDNA microarrays. Various pattern recognition methods have been successfully applied to gene expression data. However, these methods are not optimal, rather they are high‐performance classifiers that emphasize only classification accuracy. In this paper, we propose an approach for the construction of the optimal linear classifier using gene expression data. Two linear classification methods, linear discriminant analysis (LDA) and discriminant partial least‐squares (DPLS), are applied to distinguish acute leukemia subtypes. These methods are shown to give satisfactory accuracy. Moreover, we determined optimally the number of genes participating in the classification (a remarkably small number compared to previous results) on the basis of the statistical significance test. Thus, the proposed method constructs the optimal classifier that is composed of a small size predictor and provides high accuracy.

Process system engineering in wastewater treatment process

This paper reviews the research and development of process system engineering (PSE) in the wastewater treatment process (WWTP). A diverse range of PSE applications have evolved in the wastewater treatment process, such as modeling, control, estimation, expert system, fault detection and monitoring system. This article describes several types of PSE that have proven to be effective in WWTP. The merits and shortcoming of PSE and its detailed applications are presented. Since its development is the forefront in WWTP, a reasonable review of the research progress in this field is addressed.

‘Two-Stage Double-Technique Hybrid (TSDTH)’ Identification Strategy for the Analysis of BMP2-Induced Transdifferentiation of Premyoblast C2C12 Cells to Osteoblast

Transdifferentiation offers new opportunities in the area of cell replacement therapy; however, the molecular mechanism by which transdifferentiation occurs is not fully understood. Our understanding about the sophisticated regulations of transdifferentiation is limited yet since their comprehensive proteome regulations have not been fully elucidated. Studies on bone morphogenic protein-2 (BMP2)-induced transdifferentiation of murine C2C12 cells, a myogenic lineage committed premyoblast, to osteogenic cells can provide a full picture of the dynamic events that occur at the level of protein activity and/or expression. Here, we investigated the overall dynamic regulatory proteome associated with BMP2-induced osteoblast transdifferentiation in premyoblast C2C12 cells using a novel Two-Stage Double-Technique Hybrid (TSDTH) strategy for proteomic analysis. Here, we took the approach of a TSDTH involving phosphoproteomic analysis after a short-term treatment (stage one, 30 min) and a long-term treatment (stage two, 3 days); SILAC (Stable isotope labeling with amino acids in cell culture)-proteomics was used to map the proteins. In these experiments, a total of 1321 potential phosphoproteins were identified in stage one analysis and 433 proteins were quantified in stage two analysis. Among them, 374 BMP2-specific phosphoproteins and 54 up- or down-regulated proteins were selected. In first stage analysis, several deubiquitination enzymes including Uch-l3 as well as ubiquitination related proteins were newly identified, and its inhibitor reduced the stability of phosphorylated Smad1, and the BMP2-induced ALP levels of C2C12 cells were detected. In second stage analysis, Thrombospondin1 was identified as the highest up-regulated protein by BMP2-long time stimulation and this was confirmed with immunoblot analysis. Furthermore, pathway enrichment and network analyses revealed that insulin-like growth factor (IGF) and calcium signaling pathways as well as TGFbeta/BMP signaling proteins are found to be potentially involved in the early and long-term actions of BMP2. Collectively, our TSDTH is a useful simple strategy to obtain comprehensive molecular mechanism of cellular processes such as transdifferentiation.