Expression of ECM proteins fibulin-1 and -2 in acute and chronic liver disease and in cultured rat liver cells

Fibulin-2 has previously been considered as a marker to distinguish rat liver myofibroblasts from hepatic stellate cells. The function of other fibulins in acute or chronic liver damage has not yet been investigated. The aim of this study has been to evaluate the expression of fibulin-1 and -2 in models of rat liver injury and in human liver cirrhosis. Their cellular sources have also been investigated. In normal rat liver, fibulin-1 and -2 were both mainly present in the portal field. Fibulin-1-coding transcripts were detected in total RNA of normal rat liver, whereas fibulin-2 mRNA was only detected by sensitive, real-time quantitative polymerase chain reaction. In acute liver injury, the expression of fibulin-1 was significantly increased (17.23-fold after 48 h), whereas that of fibulin-2 was not modified. The expression of both fibulin-1 and -2 was increased in experimental rat liver cirrhosis (19.16- and 26.47-fold, respectively). At the cellular level, fibulin-1 was detectable in hepatocytes, “activated” hepatic stellate cells, and liver myofibroblasts (2.71-, 122.65-, and 469.48-fold over the expression in normal rat liver), whereas fibulin-2 was restricted to liver myofibroblasts and was regulated by transforming growth factor beta-1 (TGF-β1) in 2-day-old hepatocyte cultures and in liver myofibroblasts. Thus, fibulin-1 and -2 respond differentially to single and repeated damaging noxae, and their expression is differently present in liver cells. Expression of the fibulin-2 gene is regulated by TGF-β1 in liver myofibroblasts

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Activated SUMOylation restricts MHC class I antigen presentation to confer immune evasion in cancer

Activated SUMOylation is a hallmark of cancer. Starting from a targeted screening for SUMO-regulated immune evasion mechanisms, we identified an evolutionarily conserved function of activated SUMOylation, which attenuated the immunogenicity of tumor cells. Activated SUMOylation allowed cancer cells to evade CD8+ T cell–mediated immunosurveillance by suppressing the MHC class I (MHC-I) antigen-processing and presentation machinery (APM). Loss of the MHC-I APM is a frequent cause of resistance to cancer immunotherapies, and the pharmacological inhibition of SUMOylation (SUMOi) resulted in reduced activity of the transcriptional repressor scaffold attachment factor B (SAFB) and induction of the MHC-I APM. Consequently, SUMOi enhanced the presentation of antigens and the susceptibility of tumor cells to CD8+ T cell–mediated killing. Importantly, SUMOi also triggered the activation of CD8+ T cells and thereby drove a feed-forward loop amplifying the specific antitumor immune response. In summary, we showed that activated SUMOylation allowed tumor cells to evade antitumor immunosurveillance, and we have expanded the understanding of SUMOi as a rational therapeutic strategy for enhancing the efficacy of cancer immunotherapies

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press