CD151 Maintains Endolysosomal Protein Quality to Inhibit Vascular Inflammation.

BACKGROUND: Tetraspanin CD151 is highly expressed in endothelia and reinforces cell adhesion, but its role in vascular inflammation remains largely unknown. METHODS: In vitro molecular and cellular biological analyses on genetically modified endothelial cells, in vivo vascular biological analyses on genetically engineered mouse models, and in silico systems biology and bioinformatics analyses on CD151-related events. RESULTS: Endothelial ablation of Cd151 leads to pulmonary and cardiac inflammation, severe sepsis, and perilous COVID-19, and endothelial CD151 becomes downregulated in inflammation. Mechanistically, CD151 restrains endothelial release of proinflammatory molecules for less leukocyte infiltration. At the subcellular level, CD151 determines the integrity of multivesicular bodies/lysosomes and confines the production of exosomes that carry cytokines such as ANGPT2 (angiopoietin-2) and proteases such as cathepsin-D. At the molecular level, CD151 docks VCP (valosin-containing protein)/p97, which controls protein quality via mediating deubiquitination for proteolytic degradation, onto endolysosomes to facilitate VCP/p97 function. At the endolysosome membrane, CD151 links VCP/p97 to (1) IFITM3 (interferon-induced transmembrane protein 3), which regulates multivesicular body functions, to restrain IFITM3-mediated exosomal sorting, and (2) V-ATPase, which dictates endolysosome pH, to support functional assembly of V-ATPase. CONCLUSIONS: Distinct from its canonical function in strengthening cell adhesion at cell surface, CD151 maintains endolysosome function by sustaining VCP/p97-mediated protein unfolding and turnover. By supporting protein quality control and protein degradation, CD151 prevents proteins from (1) buildup in endolysosomes and (2) discharge through exosomes, to limit vascular inflammation. Also, our study conceptualizes that balance between degradation and discharge of proteins in endothelial cells determines vascular information. Thus, the IFITM3/V-ATPase-tetraspanin-VCP/p97 complexes on endolysosome, as a protein quality control and inflammation-inhibitory machinery, could be beneficial for therapeutic intervention against vascular inflammation.

A Comprehensive Immune Cell Atlas of Cystic Kidney Disease Reveals the Involvement of Adaptive Immune Cells in Injury-Mediated Cyst Progression in Mice.

BACKGROUND: Inducible disruption of cilia-related genes in adult mice results in slowly progressive cystic disease, which can be greatly accelerated by renal injury. METHODS: To identify in an unbiased manner modifier cells that may be influencing the differential rate of cyst growth in injured versus non-injured cilia mutant kidneys at a time of similar cyst severity, we generated a single-cell atlas of cystic kidney disease. We conducted RNA-seq on 79,355 cells from control mice and adult-induced conditional Ift88 mice (hereafter referred to as cilia mutant mice) that were harvested approximately 7 months post-induction or 8 weeks post 30-minute unilateral ischemia reperfusion injury. RESULTS: Analyses of single-cell RNA-seq data of CD45+ immune cells revealed that adaptive immune cells differed more in cluster composition, cell proportion, and gene expression than cells of myeloid origin when comparing cystic models with one another and with non-cystic controls. Surprisingly, genetic deletion of adaptive immune cells significantly reduced injury-accelerated cystic disease but had no effect on cyst growth in non-injured cilia mutant mice, independent of the rate of cyst growth or underlying genetic mutation. Using NicheNet, we identified a list of candidate cell types and ligands that were enriched in injured cilia mutant mice compared with aged cilia mutant mice and non-cystic controls that may be responsible for the observed dependence on adaptive immune cells during injury-accelerated cystic disease. CONCLUSIONS: Collectively, these data highlight the diversity of immune cell involvement in cystic kidney disease.

MNEMONIC: MetageNomic Experiment Mining to create an OTU Network of Inhabitant Correlations.

BACKGROUND: The number of publicly available metagenomic experiments in various environments has been rapidly growing, empowering the potential to identify similar shifts in species abundance between different experiments. This could be a potentially powerful way to interpret new experiments, by identifying common themes and causes behind changes in species abundance. RESULTS: We propose a novel framework for comparing microbial shifts between conditions. Using data from one of the largest human metagenome projects to date, the American Gut Project (AGP), we obtain differential abundance vectors for microbes using experimental condition information provided with the AGP metadata, such as patient age, dietary habits, or health status. We show it can be used to identify similar and opposing shifts in microbial species, and infer putative interactions between microbes. Our results show that groups of shifts with similar effects on microbiome can be identified and that similar dietary interventions display similar microbial abundance shifts. CONCLUSIONS: Without comparison to prior data, it is difficult for experimentalists to know if their observed changes in species abundance have been observed by others, both in their conditions and in others they would never consider comparable. Yet, this can be a very important contextual factor in interpreting the significance of a shift. We've proposed and tested an algorithmic solution to this problem, which also allows for comparing the metagenomic signature shifts between conditions in the existing body of data.

Use it or lose it: citations predict the continued online availability of published bioinformatics resources.

Scientific Data Analysis Resources (SDARs) such as bioinformatics programs, web servers and databases are integral to modern science, but previous studies have shown that the Uniform Resource Locators (URLs) linking to them decay in a time-dependent manner, with ∼27% decayed to date. Because SDARs are overrepresented among science's most cited papers over the past 20 years, loss of widely used SDARs could be particularly disruptive to scientific research. We identified URLs in MEDLINE abstracts and used crowdsourcing to identify which reported the creation of SDARs. We used the Internet Archive's Wayback Machine to approximate 'death dates' and calculate citations/year over each SDAR's lifespan. At first glance, decayed SDARs did not significantly differ from available SDARs in their average citations per year over their lifespan or journal impact factor (JIF). But the most cited SDARs were 94% likely to be relocated to another URL versus only 34% of uncited ones. Taking relocation into account, we find that citations are the strongest predictors of current online availability after time since publication, and JIF modestly predictive. This suggests that URL decay is a general, persistent phenomenon affecting all URLs, but the most useful/recognized SDARs are more likely to persist.

ALE: automated label extraction from GEO metadata.

BACKGROUND: NCBI's Gene Expression Omnibus (GEO) is a rich community resource containing millions of gene expression experiments from human, mouse, rat, and other model organisms. However, information about each experiment (metadata) is in the format of an open-ended, non-standardized textual description provided by the depositor. Thus, classification of experiments for meta-analysis by factors such as gender, age of the sample donor, and tissue of origin is not feasible without assigning labels to the experiments. Automated approaches are preferable for this, primarily because of the size and volume of the data to be processed, but also because it ensures standardization and consistency. While some of these labels can be extracted directly from the textual metadata, many of the data available do not contain explicit text informing the researcher about the age and gender of the subjects with the study. To bridge this gap, machine-learning methods can be trained to use the gene expression patterns associated with the text-derived labels to refine label-prediction confidence. RESULTS: Our analysis shows only 26% of metadata text contains information about gender and 21% about age. In order to ameliorate the lack of available labels for these data sets, we first extract labels from the textual metadata for each GEO RNA dataset and evaluate the performance against a gold standard of manually curated labels. We then use machine-learning methods to predict labels, based upon gene expression of the samples and compare this to the text-based method. CONCLUSION: Here we present an automated method to extract labels for age, gender, and tissue from textual metadata and GEO data using both a heuristic approach as well as machine learning. We show the two methods together improve accuracy of label assignment to GEO samples.

GenomeRunner web server: regulatory similarity and differences define the functional impact of SNP sets.

MOTIVATION: The growing amount of regulatory data from the ENCODE, Roadmap Epigenomics and other consortia provides a wealth of opportunities to investigate the functional impact of single nucleotide polymorphisms (SNPs). Yet, given the large number of regulatory datasets, researchers are posed with a challenge of how to efficiently utilize them to interpret the functional impact of SNP sets. RESULTS: We developed the GenomeRunner web server to automate systematic statistical analysis of SNP sets within a regulatory context. Besides defining the functional impact of SNP sets, GenomeRunner implements novel regulatory similarity/differential analyses, and cell type-specific regulatory enrichment analysis. Validated against literature- and disease ontology-based approaches, analysis of 39 disease/trait-associated SNP sets demonstrated that the functional impact of SNP sets corresponds to known disease relationships. We identified a group of autoimmune diseases with SNPs distinctly enriched in the enhancers of T helper cell subpopulations, and demonstrated relevant cell type-specificity of the functional impact of other SNP sets. In summary, we show how systematic analysis of genomic data within a regulatory context can help interpreting the functional impact of SNP sets. AVAILABILITY AND IMPLEMENTATION: GenomeRunner web server is freely available at http://www.integrativegenomics.org/ CONTACT: mikhail.dozmorov@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Detrimental effects of duplicate reads and low complexity regions on RNA- and ChIP-seq data.

BACKGROUND: Adapter trimming and removal of duplicate reads are common practices in next-generation sequencing pipelines. Sequencing reads ambiguously mapped to repetitive and low complexity regions can also be problematic for accurate assessment of the biological signal, yet their impact on sequencing data has not received much attention. We investigate how trimming the adapters, removing duplicates, and filtering out reads overlapping low complexity regions influence the significance of biological signal in RNA- and ChIP-seq experiments. METHODS: We assessed the effect of data processing steps on the alignment statistics and the functional enrichment analysis results of RNA- and ChIP-seq data. We compared differentially processed RNA-seq data with matching microarray data on the same patient samples to determine whether changes in pre-processing improved correlation between the two. We have developed a simple tool to remove low complexity regions, RepeatSoaker, available at https://github.com/mdozmorov/RepeatSoaker, and tested its effect on the alignment statistics and the results of the enrichment analyses. RESULTS: Both adapter trimming and duplicate removal moderately improved the strength of biological signals in RNA-seq and ChIP-seq data. Aggressive filtering of reads overlapping with low complexity regions, as defined by RepeatMasker, further improved the strength of biological signals, and the correlation between RNA-seq and microarray gene expression data. CONCLUSIONS: Adapter trimming and duplicates removal, coupled with filtering out reads overlapping low complexity regions, is shown to increase the quality and reliability of detecting biological signals in RNA-seq and ChIP-seq data.

Caloric restriction confers persistent anti-oxidative, pro-angiogenic, and anti-inflammatory effects and promotes anti-aging miRNA expression profile in cerebromicrovascular endothelial cells of aged rats.

In rodents, moderate caloric restriction (CR) without malnutrition exerts significant cerebrovascular protective effects, improving cortical microvascular density and endothelium-dependent vasodilation, but the underlying cellular mechanisms remain elusive. To elucidate the persisting effects of CR on cerebromicrovascular endothelial cells (CMVECs), primary CMVECs were isolated from young (3 mo old) and aged (24 mo old) ad libitum-fed and aged CR F344xBN rats. We found an age-related increase in cellular and mitochondrial oxidative stress, which is prevented by CR. Expression and transcriptional activity of Nrf2 are both significantly reduced in aged CMVECs, whereas CR prevents age-related Nrf2 dysfunction. Expression of miR-144 was upregulated in aged CMVECs, and overexpression of miR-144 significantly decreased expression of Nrf2 in cells derived from both young animals and aged CR rats. Overexpression of a miR-144 antagomir in aged CMVECs significantly decreases expression of miR-144 and upregulates Nrf2. We found that CR prevents age-related impairment of angiogenic processes, including cell proliferation, adhesion to collagen, and formation of capillary-like structures and inhibits apoptosis in CMVECs. CR also exerts significant anti-inflammatory effects, preventing age-related increases in the transcriptional activity of NF-κB and age-associated pro-inflammatory shift in the endothelial secretome. Characterization of CR-induced changes in miRNA expression suggests that they likely affect several critical functions in endothelial cell homeostasis. The predicted regulatory effects of CR-related differentially expressed miRNAs in aged CMVECs are consistent with the anti-aging endothelial effects of CR observed in vivo. Collectively, we find that CR confers persisting anti-oxidative, pro-angiogenic, and anti-inflammatory cellular effects, preserving a youthful phenotype in rat cerebromicrovascular endothelial cells, suggesting that through these effects CR may improve cerebrovascular function and prevent vascular cognitive impairment.

BioVDB: biological vector database for high-throughput gene expression meta-analysis.

High-throughput sequencing has created an exponential increase in the amount of gene expression data, much of which is freely, publicly available in repositories such as NCBI's Gene Expression Omnibus (GEO). Querying this data for patterns such as similarity and distance, however, becomes increasingly challenging as the total amount of data increases. Furthermore, vectorization of the data is commonly required in Artificial Intelligence and Machine Learning (AI/ML) approaches. We present BioVDB, a vector database for storage and analysis of gene expression data, which enhances the potential for integrating biological studies with AI/ML tools. We used a previously developed approach called Automatic Label Extraction (ALE) to extract sample labels from metadata, including age, sex, and tissue/cell-line. BioVDB stores 438,562 samples from eight microarray GEO platforms. We show that it allows for efficient querying of data using similarity search, which can also be useful for identifying and inferring missing labels of samples, and for rapid similarity analysis.

mirCoX: a database of miRNA-mRNA expression correlations derived from RNA-seq meta-analysis.

BACKGROUND: Experimentally validated co-expression correlations between miRNAs and genes are a valuable resource to corroborate observations about miRNA/mRNA changes after experimental perturbations, as well as compare miRNA target predictions with empirical observations. For example, when a given miRNA is transcribed, true targets of that miRNA should tend to have lower expression levels relative to when the miRNA is not expressed. METHODS: We processed publicly available human RNA-seq experiments obtained from NCBI's Sequence Read Archive (SRA) to identify miRNA-mRNA co-expression trends and summarized them in terms of their Pearson's Correlation Coefficient (PCC) and significance. RESULTS: We found that sequence-derived parameters from TargetScan and miRanda were predictive of co-expression, and that TargetScan- and miRanda-derived gene-miRNA pairs tend to have anti-correlated expression patterns in RNA-seq data compared to controls. We provide this data for download and as a web application available at http://wrenlab.org/mirCoX/. CONCLUSION: This database of empirically established miRNA-mRNA transcriptional correlations will help to corroborate experimental observations and could be used to help refine and validate miRNA target predictions.

Systematic classification of non-coding RNAs by epigenomic similarity.

BACKGROUND: Even though only 1.5% of the human genome is translated into proteins, recent reports indicate that most of it is transcribed into non-coding RNAs (ncRNAs), which are becoming the subject of increased scientific interest. We hypothesized that examining how different classes of ncRNAs co-localized with annotated epigenomic elements could help understand the functions, regulatory mechanisms, and relationships among ncRNA families. RESULTS: We examined 15 different ncRNA classes for statistically significant genomic co-localizations with cell type-specific chromatin segmentation states, transcription factor binding sites (TFBSs), and histone modification marks using GenomeRunner (http://www.genomerunner.org). P-values were obtained using a Chi-square test and corrected for multiple testing using the Benjamini-Hochberg procedure. We clustered and visualized the ncRNA classes by the strength of their statistical enrichments and depletions. CONCLUSIONS: Searching for statistically significant associations between ncRNA classes and epigenomic elements permits detection of potential functional and/or regulatory relationships among ncRNA classes, and suggests cell type-specific biological roles of ncRNAs.

GenomeRunner: automating genome exploration.

MOTIVATION: One of the challenges in interpreting high-throughput genomic studies such as a genome-wide associations, microarray or ChIP-seq is their open-ended nature-once a set of experimentally identified regions is identified as statistically significant, at least two questions arise: (i) besides P-value, do any of these significant regions stand out in terms of biological implications? (ii) Does the set of significant regions, as a whole, have anything in common genome wide? These issues are difficult to address because of the growing number of annotated genomic features (e.g. single nucleotide polymorphisms, transcription factor binding sites, methylation peaks, etc.), and it is difficult to know a priori which features would be most fruitful to analyze. Our goal is to provide partial automation of this process to begin examining associations between experimental features and annotated genomic regions in a hypothesis-free, data-driven manner. RESULTS: We created GenomeRunner-a tool for automating annotation and enrichment of genomic features of interest (FOI) with annotated genomic features (GFs), in different organisms. Besides simple association of FOIs with known GFs GenomeRunner tests whether the enriched FOIs, as a group, are statistically associated with a large and growing set of genomic features. AVAILABILITY: GenomeRunner setup files and source code are freely available at http://sourceforge.net/projects/genomerunner. CONTACT: mikhail-dozmorov@omrf.org; Jonathan-Wren@omrf.org; jdwren@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Methylation Array Signals are Predictive of Chronological Age Without Bisulfite Conversion.

DNA methylation data has been used to make "epigenetic clocks" which attempt to measure chronological and biological aging. These models rely on data derived from bisulfite-based measurements, which exploit a semi-selective deamination and a genomic reference to determine methylation states. Here, we demonstrate how another hallmark of aging, genomic instability, influences methylation measurements in both bisulfite sequencing and methylation arrays. We found that non-methylation factors lead to "pseudomethylation" signals that are both confounding of epigenetic clocks and uniquely age predictive. Quantifying these covariates in aging studies will be critical to building better clocks and designing appropriate studies of epigenetic aging.

Predicting gene ontology from a global meta-analysis of 1-color microarray experiments.

BACKGROUND: Global meta-analysis (GMA) of microarray data to identify genes with highly similar co-expression profiles is emerging as an accurate method to predict gene function and phenotype, even in the absence of published data on the gene(s) being analyzed. With a third of human genes still uncharacterized, this approach is a promising way to direct experiments and rapidly understand the biological roles of genes. To predict function for genes of interest, GMA relies on a guilt-by-association approach to identify sets of genes with known functions that are consistently co-expressed with it across different experimental conditions, suggesting coordinated regulation for a specific biological purpose. Our goal here is to define how sample, dataset size and ranking parameters affect prediction performance. RESULTS: 13,000 human 1-color microarrays were downloaded from GEO for GMA analysis. Prediction performance was benchmarked by calculating the distance within the Gene Ontology (GO) tree between predicted function and annotated function for sets of 100 randomly selected genes. We find the number of new predicted functions rises as more datasets are added, but begins to saturate at a sample size of approximately 2,000 experiments. For the gene set used to predict function, we find precision to be higher with smaller set sizes, yet with correspondingly poor recall and, as set size is increased, recall and F-measure also tend to increase but at the cost of precision. CONCLUSIONS: Of the 20,813 genes expressed in 50 or more experiments, at least one predicted GO category was found for 72.5% of them. Of the 5,720 genes without GO annotation, 4,189 had at least one predicted ontology using top 40 co-expressed genes for prediction analysis. For the remaining 1,531 genes without GO predictions or annotations, ~17% (257 genes) had sufficient co-expression data yet no statistically significantly overrepresented ontologies, suggesting their regulation may be more complex.

Many chronological aging clocks can be found throughout the epigenome: Implications for quantifying biological aging.

Epigenetic alterations are a hallmark of aging and age-related diseases. Computational models using DNA methylation data can create "epigenetic clocks" which are proposed to reflect "biological" aging. Thus, it is important to understand the relationship between predictive clock sites and aging biology. To do this, we examined over 450,000 methylation sites from 9,699 samples. We found ~20% of the measured genomic cytosines can be used to make many different epigenetic clocks whose age prediction performance surpasses that of telomere length. Of these predictive sites, the average methylation change over a lifetime was small (~1.5%) and these sites were under-represented in canonical regions of epigenetic regulation. There was only a weak association between "accelerated" epigenetic aging and disease. We also compare tissue-specific and pan-tissue clock performance. This is critical to applying clocks both to new sample sets in basic research, as well as understanding if clinically available tissues will be feasible samples to evaluate "epigenetic aging" in unavailable tissues (e.g., brain). Despite the reproducible and accurate age predictions from DNA methylation data, these findings suggest they may have limited utility as currently designed in understanding the molecular biology of aging and may not be suitable as surrogate endpoints in studies of anti-aging interventions. Purpose-built clocks for specific tissues age ranges or phenotypes may perform better for their specific purpose. However, if purpose-built clocks are necessary for meaningful predictions, then the utility of clocks and their application in the field needs to be considered in that context.

Calorie restriction prevents age-related changes in the intestinal microbiota.

The effect of calorie restriction (CR) on the microbiome, fecal metabolome, and colon transcriptome of adult and old male mice was compared. Life-long CR increased microbial diversity and the Bacteroidetes/Firmicutes ratio and prevented the age-related changes in the microbiota, shifting it to a younger microbial and fecal metabolite profile in both C57BL/6JN and B6D2F1 mice. Old mice fed CR were enriched in the Rikenellaceae, S24-7 and Bacteroides families. The changes in the microbiome that occur with age and CR were initiated in the cecum and further modified in the colon. Short-term CR in adult mice had a minor effect on the microbiome but a major effect on the transcriptome of the colon mucosa. These data suggest that CR has a major impact on the physiological status of the gastrointestinal system, maintaining it in a more youthful state, which in turn could result in a more diverse and youthful microbiome.

Nicotinamide mononucleotide (NMN) supplementation promotes anti-aging miRNA expression profile in the aorta of aged mice, predicting epigenetic rejuvenation and anti-atherogenic effects.

Understanding molecular mechanisms involved in vascular aging is essential to develop novel interventional strategies for treatment and prevention of age-related vascular pathologies. Recent studies provide critical evidence that vascular aging is characterized by NAD+ depletion. Importantly, in aged mice, restoration of cellular NAD+ levels by treatment with the NAD+ booster nicotinamide mononucleotide (NMN) exerts significant vasoprotective effects, improving endothelium-dependent vasodilation, attenuating oxidative stress, and rescuing age-related changes in gene expression. Strong experimental evidence shows that dysregulation of microRNAs (miRNAs) has a role in vascular aging. The present study was designed to test the hypothesis that age-related NAD+ depletion is causally linked to dysregulation of vascular miRNA expression. A corollary hypothesis is that functional vascular rejuvenation in NMN-treated aged mice is also associated with restoration of a youthful vascular miRNA expression profile. To test these hypotheses, aged (24-month-old) mice were treated with NMN for 2 weeks and miRNA signatures in the aortas were compared to those in aortas obtained from untreated young and aged control mice. We found that protective effects of NMN treatment on vascular function are associated with anti-aging changes in the miRNA expression profile in the aged mouse aorta. The predicted regulatory effects of NMN-induced differentially expressed miRNAs in aged vessels include anti-atherogenic effects and epigenetic rejuvenation. Future studies will uncover the mechanistic role of miRNA gene expression regulatory networks in the anti-aging effects of NAD+ booster treatments and determine the links between miRNAs regulated by NMN and sirtuin activators and miRNAs known to act in the conserved pathways of aging and major aging-related vascular diseases.

Obesity in Aging Exacerbates Neuroinflammation, Dysregulating Synaptic Function-Related Genes and Altering Eicosanoid Synthesis in the Mouse Hippocampus: Potential Role in Impaired Synaptic Plasticity and Cognitive Decline.

There is strong evidence that obesity has deleterious effects on cognitive function of older adults. Previous preclinical studies demonstrate that obesity in aging is associated with a heightened state of systemic inflammation, which exacerbates blood-brain barrier disruption, promoting neuroinflammation and oxidative stress. To test the hypothesis that synergistic effects of obesity and aging on inflammatory processes exert deleterious effects on hippocampal function, young and aged C57BL/6 mice were rendered obese by chronic feeding of a high-fat diet followed by assessment of learning and memory function, measurement of hippocampal long-term potentiation (LTP), assessment of changes in hippocampal expression of genes relevant for synaptic function and determination of synaptic density. Because there is increasing evidence that altered production of lipid mediators modulate LTP, neuroinflammation and neurovascular coupling responses, the effects of obesity on hippocampal levels of relevant eicosanoid mediators were also assessed. We found that aging exacerbates obesity-induced microglia activation, which is associated with deficits in hippocampal-dependent learning and memory tests, impaired LTP, decreased synaptic density, and dysregulation of genes involved in regulation of synaptic plasticity. Obesity in aging also resulted in an altered hippocampal eicosanoid profile, including decreases in vasodilator and pro-LTP epoxy-eicosatrienoic acids (EETs). Collectively, our results taken together with previous findings suggest that obesity in aging promotes hippocampal inflammation, which in turn may contribute to synaptic dysfunction and cognitive impairment.

Large-scale directional relationship extraction and resolution.

BACKGROUND: Relationships between entities such as genes, chemicals, metabolites, phenotypes and diseases in MEDLINE are often directional. That is, one may affect the other in a positive or negative manner. Detection of causality and direction is key in piecing pathways together and in examining possible implications of experimental results. Because of the size and growth of biomedical literature, it is increasingly important to be able to automate this process as much as possible. RESULTS: Here we present a method of relation extraction using dependency graph parsing with SVM classification. We tested the SVM classifier first on gold standard corpora from GENIA and find it achieved 82% precision and 94.8% recall (F-measure of 87.9) on these standardized test sets. We then applied the entire system to all available MEDLINE abstracts for two target interactions with known effects. We find that while some directional relations are extracted with low ambiguity, others are apparently contradictory, at least when considered in an isolated context. When examined, it is apparent some are dependent upon the surrounding context (e.g. whether the relationship referred to short-term or long-term effects, or whether the focus was extracellular versus intracellular). CONCLUSION: Thesaurus-based directional relation extraction can be done with reasonable accuracy, but is prone to false-positives on larger corpora due to noun modifiers. Furthermore, methods of resolving or disambiguating relationship context and contingencies are important for large-scale corpora.

Endothelial dysfunction and angiogenesis impairment in the ageing vasculature.

Ageing is the main risk factor for the development of cardiovascular diseases. A central mechanism by which ageing promotes vascular pathologies is compromising endothelial health. The age-related attenuation of endothelium-dependent dilator responses (endothelial dysfunction) associated with impairment of angiogenic processes and the subsequent pathological remodelling of the microcirculation contribute to compromised tissue perfusion and exacerbate functional decline in older individuals. This Review focuses on cellular, molecular, and functional changes that occur in the endothelium during ageing. We explore the links between oxidative and nitrative stress and the conserved molecular pathways affecting endothelial dysfunction and impaired angiogenesis during ageing. We also speculate on how these pathological processes could be therapeutically targeted. An improved understanding of endothelial biology in older patients is crucial for all cardiologists because maintenance of a competently functioning endothelium is critical for adequate tissue perfusion and long-term cardiac health.