Secretion of extracellular vesicles is a general cellular activity that spans the range from simple unicellular organisms (e.g. archaea; Gram-positive and Gram-negative bacteria) to complex multicellular ones, suggesting that this extracellular vesicle-mediated communication is evolutionarily conserved. Extracellular vesicles are spherical bilayered proteolipids with a mean diameter of 20–1,000 nm, which are known to contain various bioactive molecules including proteins, lipids, and nucleic acids. Here, we present EVpedia, which is an integrated database of high-throughput datasets from prokaryotic and eukaryotic extracellular vesicles. EVpedia provides high-throughput datasets of vesicular components (proteins, mRNAs, miRNAs, and lipids) present on prokaryotic, non-mammalian eukaryotic, and mammalian extracellular vesicles. In addition, EVpedia also provides an array of tools, such as the search and browse of vesicular components, Gene Ontology enrichment analysis, network analysis of vesicular proteins and mRNAs, and a comparison of vesicular datasets by ortholog identification. Moreover, publications on extracellular vesicle studies are listed in the database. This free web-based database of EVpedia (http://evpedia.info) might serve as a fundamental repository to stimulate the advancement of extracellular vesicle studies and to elucidate the novel functions of these complex extracellular organelles.

Plant leaves, harvesting light energy and fixing CO 2 , are a major source of foods on the earth. Leaves undergo developmental and physiological shifts during their lifespan, ending with senescence and death. We characterized the key regulatory features of the leaf transcriptome during aging by analyzing total-and small-RNA transcriptomes throughout the lifespan of Arabidopsis (Arabidopsis thaliana) leaves at multidimensions, including age, RNA-type, and organelle. Intriguingly, senescing leaves showed more coordinated temporal changes in transcriptomes than growing leaves, with sophisticated regulatory networks comprising transcription factors and diverse small regulatory RNAs. The chloroplast transcriptome, but not the mitochondrial transcriptome, showed major changes during leaf aging, with a strongly shared expression pattern of nuclear transcripts encoding chloroplast-targeted proteins. Thus, unlike animal aging, leaf senescence proceeds with tight temporal and distinct interorganellar coordination of various transcriptomes that would be critical for the highly regulated degeneration and nutrient recycling contributing to plant fitness and productivity.Most organisms undergo age-dependent developmental changes during their lifespans. The timely decision of developmental changes during the lifespan is a critical evolutionary characteristic that maximizes fitness in a given ecological setting (Leopold, 1961;Fenner, 1998;Samach and Coupland, 2000). Plants use unique developmental strategies throughout their lifespans as opposed to animals. In plants, most organs are formed postnatally from sets of stem cells in the seed. In addition, plants are sessile and cope with encountering environments physiologically, rather than behaviorally. Thus, they have developed highly plastic and interactive developmental programs to incorporate environmental changes into their developmental decisions (Pigliucci, 1998;Sultan, 2000).The leaf is an organ that characterizes the fundamental aspects of plants. Leaves harvest light energy, fix CO 2 to produce carbohydrates, and, as primary producers in our ecosystem, serve as a major food source on the earth. Leaves undergo a series of developmental and physiological shifts during their lifespans. A leaf is initially formed as a leaf primordium derived from the stem cells at the shoot apical meristem and develops into a photosynthetic organ through biogenesis processes involving cell division, differentiation, and expansion (Tsukaya, 2013). In the later stages of their lifespans, leaves undergo organ-level senescence and eventually death. Organlevel senescence in plants involves postmitotic senescence and is a term used similarly as "aging" in animals. During the senescence stage, leaf cells undergo dramatic shifts in physiology from biogenesis to the sequential 1 This research was supported by the Institute for Basic Science (IBS-R013-D1 and IBS-R013-G1), the DGIST R&D Program (2014010043, 2015010004, 2015010011, 20150100012, and 15-01-HRLA-01), Basic Science Research Program (2010-0...

Adipogenesis is a process which induces or represses many genes in a way to drive irreversible changes of cell phenotypes; lipid accumulation, round cell-shape, secreting many adipokines. As a master transcription factor (TF), PPARγ2 induces several target genes to orchestrate these adipogenic changes. Thus induction of Pparg2 gene is tightly regulated by many adipogenic and also anti-adipogenic factors. Four hours after the treatment of adipogenic hormones, more than fifteen TFs including glucocorticoid receptor (GR), C/EBPβ and AP-1 cooperatively bind the promoter of Pparg2 gene covering 400 bps, termed “hotspot”. In this study, we show that TEA domain family transcription factor (TEAD)4 reinforces occupancy of both GR and C/EBPβ on the hotspot of Pparg2 during early adipogenesis. Our findings that TEAD4 requires GR for its expression and for the ability to bind its own promoter and the hotspot region of Pparg2 gene indicate that GR is a common component of two positive circuits, which regulates the expression of both Tead4 and Pparg2. Wnt3a disrupts these mutually related positive circuits by limiting the nuclear location of GR in a β-catenin dependent manner. The antagonistic effects of β-catenin extend to cytoskeletal remodeling during the early phase of adipogenesis. GR is necessary for the rearrangements of both cytoskeleton and chromatin of Pparg2, whereas Wnt3a inhibits both processes in a β-catenin-dependent manner. Our results suggest that hotspot formation during early adipogenesis is related to cytoskeletal remodeling, which is regulated by the antagonistic action of GR and β-catenin, and that Wnt3a reinforces β-catenin function.

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) has been a useful tool to profile secondary ions from the near surface region of specimens with its high molecular specificity and submicrometer spatial resolution. However, the TOF-SIMS analysis of even a moderately large size of samples has been hampered due to the lack of tools for automatically analyzing the huge amount of TOF-SIMS data. Here, we present a computational platform to automatically identify and align peaks, find discriminatory ions, build a classifier, and construct networks describing differential metabolic pathways. To demonstrate the utility of the platform, we analyzed 43 data sets generated from seven gastric cancer and eight normal tissues using TOF-SIMS. A total of 87 138 ions were detected from the 43 data sets by TOF-SIMS. We selected and then aligned 1286 ions. Among them, we found the 66 ions discriminating gastric cancer tissues from normal ones. Using these 66 ions, we then built a partial least square-discriminant analysis (PLS-DA) model resulting in a misclassification error rate of 0.024. Finally, network analysis of the 66 ions showed disregulation of amino acid metabolism in the gastric cancer tissues. The results show that the proposed framework was effective in analyzing TOF-SIMS data from a moderately large size of samples, resulting in discrimination of gastric cancer tissues from normal tissues and identification of biomarker candidates associated with the amino acid metabolism.

Gene expression profiling across various brain areas at the single-cell resolution enables the identification of molecular markers of neuronal subpopulations and comprehensive characterization of their functional roles. Despite the scientific importance and experimental versatility, systematic methods to analyze such data have not been established yet. To this end, we developed a statistical approach based on in situ hybridization data in the Allen Brain Atlas and thereby identified specific genes for each type of neuron in the ventral tegmental area (VTA). This approach also allowed us to demarcate subregions within the VTA comprising specific neuronal subpopulations. We further identified WW domain-containing oxidoreductase as a molecular marker of a population of VTA neurons that co-express tyrosine hydroxylase and vesicular glutamate transporter 2, and confirmed their region-specific distribution by immunohistochemistry. The results demonstrate the utility of our analytical approach for uncovering expression signatures representing specific cell types and neuronal subpopulations enriched in a given brain area.

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) emerges as a promising tool to identify the ions (small molecules) indicative of disease states from the surface of patient tissues. In TOF-SIMS analysis, an enhanced ionization of surface molecules is critical to increase the number of detected ions. Several methods have been developed to enhance ionization capability. However, how these methods improve identification of disease-related ions has not been systematically explored. Here, we present a multi-dimensional SIMS (MD-SIMS) that combines conventional TOF-SIMS and metal-assisted SIMS (MetA-SIMS). Using this approach, we analyzed cancer and adjacent normal tissues first by TOF-SIMS and subsequently by MetA-SIMS. In total, TOF- and MetA-SIMS detected 632 and 959 ions, respectively. Among them, 426 were commonly detected by both methods, while 206 and 533 were detected uniquely by TOF- and MetA-SIMS, respectively. Of the 426 commonly detected ions, 250 increased in their intensities by MetA-SIMS, whereas 176 decreased. The integrated analysis of the ions detected by the two methods resulted in an increased number of discriminatory ions leading to an enhanced separation between cancer and normal tissues. Therefore, the results show that MD-SIMS can be a useful approach to provide a comprehensive list of discriminatory ions indicative of disease states.

Integration 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.

Mammalian cells have cytoplasmic and mitochondrial aminoacyl-tRNA synthetases (ARSs) that catalyze aminoacylation of tRNAs during protein synthesis. Despite their housekeeping functions in protein synthesis, recently, ARSs and ARS-interacting multifunctional proteins (AIMPs) have been shown to play important roles in disease pathogenesis through their interactions with disease-related molecules. However, there are lacks of data resources and analytical tools that can be used to examine disease associations of ARS/AIMPs. Here, we developed an Integrated Database for ARSs (IDA), a resource database including cancer genomic/proteomic and interaction data of ARS/AIMPs. IDA includes mRNA expression, somatic mutation, copy number variation and phosphorylation data of ARS/AIMPs and their interacting proteins in various cancers. IDA further includes an array of analytical tools for exploration of disease association of ARS/AIMPs, identification of disease-associated ARS/AIMP interactors and reconstruction of ARS-dependent disease-perturbed network models. Therefore, IDA provides both comprehensive data resources and analytical tools for understanding potential roles of ARS/AIMPs in cancers.Database URL: http://ida.biocon.re.kr/, http://ars.biocon.re.kr/