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

Cell-specific transcriptional control of mitochondrial metabolism by TIF1γ drives erythropoiesis

Transcription and metabolism both influence cell function, but dedicated transcriptional control of metabolic pathways that regulate cell fate has rarely been defined. We discovered, using a chemical suppressor screen, that inhibition of the pyrimidine biosynthesis enzyme dihydroorotate dehydrogenase (DHODH) rescues erythroid differentiation in bloodless zebrafish moonshine (mon) mutant embryos defective for transcriptional intermediary factor 1 gamma (tif1γ). This rescue depends on the functional link of DHODH to mitochondrial respiration. The transcription elongation factor TIF1γ directly controls coenzyme Q (CoQ) synthesis gene expression. Upon tif1γ loss, CoQ levels are reduced, and a high succinate/α-ketoglutarate ratio leads to increased histone methylation. A CoQ analog rescues mon's bloodless phenotype. These results demonstrate that mitochondrial metabolism is a key output of a lineage transcription factor that drives cell fate decisions in the early blood lineage

Early Stages of Golgi Vesicle and Tubule Formation Require Diacylglycerol

We have investigated the role for diacylglycerol (DAG) in membrane bud formation in the Golgi apparatus. Addition of propranolol to specifically inhibit phosphatidate phosphohydrolase (PAP), an enzyme responsible for converting phosphatidic acid into DAG, effectively prevents formation of membrane buds. The effect of PAP inhibition on Golgi membranes is rapid and occurs within 3 min. Removal of the PAP inhibitor then results in a rapid burst of buds, vesicles, and tubules that peaks within 2 min. The inability to form buds in the presence of propranolol does not appear to be correlated with a loss of ARFGAP1 from Golgi membranes, as knockdown of ARFGAP1 by RNA interference has little or no effect on actual bud formation. Rather, knockdown of ARFGAP1 results in an increase in membrane buds and a decrease of vesicles and tubules suggesting it functions in the late stages of scission. How DAG promotes bud formation is discussed

Multiple Hepatic Regulatory Variants at the GALNT2 GWAS Locus Associated with High-Density Lipoprotein Cholesterol

Genome-wide association studies (GWASs) have identified more than 150 loci associated with blood lipid and cholesterol levels; however, the functional and molecular mechanisms for many associations are unknown. We examined the functional regulatory effects of candidate variants at the GALNT2 locus associated with high-density lipoprotein cholesterol (HDL-C). Fine-mapping and conditional analyses in the METSIM study identified a single locus harboring 25 noncoding variants (r(2) > 0.7 with the lead GWAS variants) strongly associated with total cholesterol in medium-sized HDL (e.g., rs17315646, p = 3.5 × 10(−12)). We used luciferase reporter assays in HepG2 cells to test all 25 variants for allelic differences in regulatory enhancer activity. rs2281721 showed allelic differences in transcriptional activity (75-fold [T] versus 27-fold [C] more than the empty-vector control), as did a separate 780-bp segment containing rs4846913, rs2144300, and rs6143660 (49-fold [AT(–) haplotype] versus 16-fold [CC(+) haplotype] more). Using electrophoretic mobility shift assays, we observed differential CEBPB binding to rs4846913, and we confirmed this binding in a native chromatin context by performing chromatin-immunoprecipitation (ChIP) assays in HepG2 and Huh-7 cell lines of differing genotypes. Additionally, sequence reads in HepG2 DNase-I-hypersensitivity and CEBPB ChIP-seq signals spanning rs4846913 showed significant allelic imbalance. Allelic-expression-imbalance assays performed with RNA from primary human hepatocyte samples and expression-quantitative-trait-locus (eQTL) data in human subcutaneous adipose tissue samples confirmed that alleles associated with increased HDL-C are associated with a modest increase in GALNT2 expression. Together, these data suggest that at least rs4846913 and rs2281721 play key roles in influencing GALNT2 expression at this HDL-C locus

Correction: The 5th edition of The World Health Organization Classification of Haematolymphoid Tumours: Lymphoid Neoplasms (vol 36, pg 1720, 2022)

10.1038/s41375-023-01962-5LEUKEMIA3791944-195