Institution
Kyoto University
Education•Kyoto, Japan•
About: Kyoto University is a education organization based out in Kyoto, Japan. It is known for research contribution in the topics: Catalysis & Population. The organization has 85837 authors who have published 217215 publications receiving 6526826 citations. The organization is also known as: Kyōto University & Kyōto daigaku.
Topics: Catalysis, Population, Gene, Transplantation, Ion
Papers published on a yearly basis
Papers
More filters
••
TL;DR: In this paper, the authors present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macro-autophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes.
Abstract: In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes.
For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure flux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy.
Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation, it is imperative to target by gene knockout or RNA interference more than one autophagy-related protein. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways implying that not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular assays, we hope to encourage technical innovation in the field.
5,187 citations
••
TL;DR: The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015) as discussed by the authors was used to estimate the incidence, prevalence, and years lived with disability for diseases and injuries at the global, regional, and national scale over the period of 1990 to 2015.
5,050 citations
••
TL;DR: The KEGG GENES database now includes viruses, plasmids, and the addendum category for functionally characterized proteins that are not represented in complete genomes, and new automatic annotation servers, BlastKOalA and GhostKOALA, are made available utilizing the non-redundant pangenome data set generated from theGENES database.
Abstract: KEGG (http://www.kegg.jp/ or http://www.genome.jp/kegg/) is an integrated database resource for biological interpretation of genome sequences and other high-throughput data. Molecular functions of genes and proteins are associated with ortholog groups and stored in the KEGG Orthology (KO) database. The KEGG pathway maps, BRITE hierarchies and KEGG modules are developed as networks of KO nodes, representing high-level functions of the cell and the organism. Currently, more than 4000 complete genomes are annotated with KOs in the KEGG GENES database, which can be used as a reference data set for KO assignment and subsequent reconstruction of KEGG pathways and other molecular networks. As an annotation resource, the following improvements have been made. First, each KO record is re-examined and associated with protein sequence data used in experiments of functional characterization. Second, the GENES database now includes viruses, plasmids, and the addendum category for functionally characterized proteins that are not represented in complete genomes. Third, new automatic annotation servers, BlastKOALA and GhostKOALA, are made available utilizing the non-redundant pangenome data set generated from the GENES database. As a resource for translational bioinformatics, various data sets are created for antimicrobial resistance and drug interaction networks.
4,847 citations
••
TL;DR: The Global Burden of Disease 2015 Study provides a comprehensive assessment of all-cause and cause-specific mortality for 249 causes in 195 countries and territories from 1980 to 2015, finding several countries in sub-Saharan Africa had very large gains in life expectancy, rebounding from an era of exceedingly high loss of life due to HIV/AIDS.
4,804 citations
••
TL;DR: It is reported here that the ligand of PD-1 (PD-L1), an immunoinhibitory receptor expressed by activated T cells, B cells, and myeloid cells, is a member of the B7 gene family.
Abstract: PD-1 is an immunoinhibitory receptor expressed by activated T cells, B cells, and myeloid cells. Mice deficient in PD-1 exhibit a breakdown of peripheral tolerance and demonstrate multiple autoimmune features. We report here that the ligand of PD-1 (PD-L1) is a member of the B7 gene family. Engagement of PD-1 by PD-L1 leads to the inhibition of T cell receptor-mediated lymphocyte proliferation and cytokine secretion. In addition, PD-1 signaling can inhibit at least suboptimal levels of CD28-mediated costimulation. PD-L1 is expressed by antigen-presenting cells, including human peripheral blood monocytes stimulated with interferon gamma, and activated human and murine dendritic cells. In addition, PD-L1 is expressed in nonlymphoid tissues such as heart and lung. The relative levels of inhibitory PD-L1 and costimulatory B7-1/B7-2 signals on antigen-presenting cells may determine the extent of T cell activation and consequently the threshold between tolerance and autoimmunity. PD-L1 expression on nonlymphoid tissues and its potential interaction with PD-1 may subsequently determine the extent of immune responses at sites of inflammation.
4,633 citations
Authors
Showing all 86225 results
Name | H-index | Papers | Citations |
---|---|---|---|
Kari Alitalo | 174 | 817 | 114231 |
Ralph M. Steinman | 171 | 453 | 121518 |
Masayuki Yamamoto | 171 | 1576 | 123028 |
Karl Deisseroth | 160 | 556 | 101487 |
Kenji Kangawa | 153 | 1117 | 110059 |
Takashi Taniguchi | 152 | 2141 | 110658 |
Ben Zhong Tang | 149 | 2007 | 116294 |
Takeo Kanade | 147 | 799 | 103237 |
Yuji Matsuzawa | 143 | 836 | 116711 |
Tasuku Honjo | 141 | 712 | 88428 |
Kenneth M. Yamada | 139 | 446 | 72136 |
Y. B. Hsiung | 138 | 1258 | 94278 |
Shuh Narumiya | 137 | 595 | 70183 |
Kevin P. Campbell | 137 | 521 | 60854 |
Junji Tojo | 135 | 878 | 84615 |