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Background The PubMed archive contains more than 34 million articles; consequently, it is becoming increasingly difficult for a biomedical researcher to keep up-to-date with different knowledge domains. Computationally efficient and interpretable tools are needed to help researchers find and understand associations between biomedical concepts. The goal of literature-based discovery (LBD) is to connect concepts in isolated literature domains that would normally go undiscovered. This usually takes the form of an A–B–C relationship, where A and C terms are linked through a B term intermediate. Here we describe Serial KinderMiner (SKiM), an LBD algorithm for finding statistically significant links between an A term and one or more C terms through some B term intermediate(s). The development of SKiM is motivated by the observation that there are only a few LBD tools that provide a functional web interface, and that the available tools are limited in one or more of the following ways: (1) they identify a relationship but not the type of relationship, (2) they do not allow the user to provide their own lists of B or C terms, hindering flexibility, (3) they do not allow for querying thousands of C terms (which is crucial if, for instance, the user wants to query connections between a disease and the thousands of available drugs), or (4) they are specific for a particular biomedical domain (such as cancer). We provide an open-source tool and web interface that improves on all of these issues. Results We demonstrate SKiM’s ability to discover useful A–B–C linkages in three control experiments: classic LBD discoveries, drug repurposing, and finding associations related to cancer. Furthermore, we supplement SKiM with a knowledge graph built with transformer machine-learning models to aid in interpreting the relationships between terms found by SKiM. Finally, we provide a simple and intuitive open-source web interface ( https://skim.morgridge.org ) with comprehensive lists of drugs, diseases, phenotypes, and symptoms so that anyone can easily perform SKiM searches. Conclusions SKiM is a simple algorithm that can perform LBD searches to discover relationships between arbitrary user-defined concepts. SKiM is generalized for any domain, can perform searches with many thousands of C term concepts, and moves beyond the simple identification of an existence of a relationship; many relationships are given relationship type labels from our knowledge graph.

Gene edited human pluripotent stem cells are a promising platform for developing reparative cellular therapies that evade immune rejection. Existing first-generation hypoimmune strategies have used CRISPR/Cas9 editing to modulate genes associated with adaptive immune responses, but have largely not addressed the innate immune cells, such as neutrophils, that mediate inflammation and rejection processes occurring early after graft transplantation. We identify the adhesion molecule ICAM-1 as a hypoimmune target that plays multiple critical roles in both adaptive and innate immune responses post-transplantation. In our experiments, we find that ICAM-1 blocking or knockout in human pluripotent stem cell-derived cardiovascular therapies imparts significantly diminished binding of multiple immune cell types. ICAM-1 knockout results in diminished T cell proliferation and activation responses in vitro and in longer in vivo retention/protection of knockout grafts following immune cell encounter in NeoThy humanized mice. We also introduce the ICAM-1 knockout edit into existing first-generation hypoimmune human pluripotent stem cells and prevent immune cell binding. This promising hypoimmune editing strategy has the potential to improve transplantation outcomes for regenerative therapies in the setting of cardiovascular pathologies and several other diseases. Hypoimmune gene editing in human pluripotent stem cells (hPSCs) provides a promising platform for cellular therapies. Here, the authors report that CRISPR mediated deletion of ICAM-1 in hPSC-derived grafts reduces immune cell adhesion, dampens T cell activation, and protects against immune rejection.

Human pluripotent stem cells hold significant promise for regenerative medicine. However, long differentiation protocols and immature characteristics of stem cell-derived cell types remain challenges to the development of many therapeutic applications. In contrast to the slow differentiation of human stem cells in vitro that mirrors a nine-month gestation period, mouse stem cells develop according to a much faster three-week gestation timeline. Here, we tested if co-differentiation with mouse pluripotent stem cells could accelerate the differentiation speed of human embryonic stem cells. Following a six-week RNA-sequencing time course of neural differentiation, we identified 929 human genes that were upregulated earlier and 535 genes that exhibited earlier peaked expression profiles in chimeric cell cultures than in human cell cultures alone. Genes with accelerated upregulation were significantly enriched in Gene Ontology terms associated with neurogenesis, neuron differentiation and maturation, and synapse signaling. Moreover, chimeric mixed samples correlated with in utero human embryonic samples earlier than human cells alone, and acceleration was dose-dependent on human-mouse co-culture ratios. The altered gene expression patterns and developmental rates described in this report have implications for accelerating human stem cell differentiation and the use of interspecies chimeric embryos in developing human organs for transplantation.

Pluripotent stem cells retain the developmental timing of their species of origin in vitro, an observation that suggests the existence of a cell-intrinsic developmental clock, yet the nature and machinery of the clock remain a mystery. We hypothesize that one possible component may lie in species-specific differences in the kinetics of transcriptional responses to differentiation signals. Using a liquid-handling robot, mouse and human pluripotent stem cells were exposed to identical neural differentiation conditions and sampled for RNA-sequencing at high frequency, every 4 or 10 minutes, for the first 10 hours of differentiation to test for differences in transcriptomic response rates. The majority of initial transcriptional responses occurred within a rapid window in the first minutes of differentiation for both human and mouse stem cells. Despite similarly early onsets of gene expression changes, we observed shortened and condensed gene expression patterns in mouse pluripotent stem cells compared to protracted trends in human pluripotent stem cells. Moreover, the speed at which individual genes were upregulated, as measured by the slopes of gene expression changes over time, was significantly faster in mouse compared to human cells. These results suggest that downstream transcriptomic response kinetics to signaling cues are faster in mouse versus human cells, and may offer a partial account for the vast differences in developmental rates across species.

Many important scientific discoveries require lengthy experimental processes of trial and error and could benefit from intelligent prioritization based on deep domain understanding. While exponential growth in the scientific literature makes it difficult to keep current in even a single domain, that same rapid growth in literature also presents an opportunity for automated extraction of knowledge via text mining. We have developed a web application implementation of the KinderMiner algorithm for proposing ranked associations between a list of target terms and a key phrase. Any key phrase and target term list can be used for biomedical inquiry. We built the web application around a text index derived from PubMed. It is the first publicly available implementation of the algorithm, is fast and easy to use, and includes an interactive analysis tool. The KinderMiner web application is a public resource offering scientists a cohesive summary of what is currently known about a particular topic within the literature, and helping them to prioritize experiments around that topic. It performs comparably or better to similar state-of-the-art text mining tools, is more flexible, and can be applied to any biomedical topic of interest. It is also continually improving with quarterly updates to the underlying text index and through response to suggestions from the community. The web application is available at https://www.kinderminer.org.

Abstract The blue whale, Balaenoptera musculus, is the largest animal known to have ever existed, making it an important case study in longevity and resistance to cancer. To further this and other blue whale-related research, we report a reference-quality, long-read-based genome assembly of this fascinating species. We assembled the genome from PacBio long reads and utilized Illumina/10×, optical maps, and Hi-C data for scaffolding, polishing, and manual curation. We also provided long read RNA-seq data to facilitate the annotation of the assembly by NCBI and Ensembl. Additionally, we annotated both haplotypes using TOGA and measured the genome size by flow cytometry. We then compared the blue whale genome with other cetaceans and artiodactyls, including vaquita (Phocoena sinus), the world's smallest cetacean, to investigate blue whale's unique biological traits. We found a dramatic amplification of several genes in the blue whale genome resulting from a recent burst in segmental duplications, though the possible connection between this amplification and giant body size requires further study. We also discovered sites in the insulin-like growth factor-1 gene correlated with body size in cetaceans. Finally, using our assembly to examine the heterozygosity and historical demography of Pacific and Atlantic blue whale populations, we found that the genomes of both populations are highly heterozygous and that their genetic isolation dates to the last interglacial period. Taken together, these results indicate how a high-quality, annotated blue whale genome will serve as an important resource for biology, evolution, and conservation research.

Many important scientific discoveries require lengthy experimental processes of trial and error and could benefit from intelligent prioritization based on deep domain understanding. While exponential growth in the scientific literature makes it difficult to keep current in even a single domain, that same rapid growth in literature also presents an opportunity for automated extraction of knowledge via text mining. We have developed a web application implementation of the KinderMiner algorithm for proposing ranked associations between a list of target terms and a key phrase. Any key phrase and target term list can be used for biomedical inquiry. We built the web application around a text index derived from PubMed. It is the first publicly available implementation of the algorithm, is fast and easy to use, and includes an interactive analysis tool. The KinderMiner web application is a public resource offering scientists a cohesive summary of what is currently known about a particular topic within the literature, and helping them to prioritize experiments around that topic. It performs comparably or better to similar state-of-the-art text mining tools, is more flexible, and can be applied to any biomedical topic of interest. It is also continually improving with quarterly updates to the underlying text index and through response to suggestions from the community. The web application is available at https://www.kinderminer.org.

Hypoimmune gene edited human pluripotent stem cells (hPSCs) are a promising platform for developing reparative cellular therapies that evade immune rejection. Existing first-generation hypoimmune strategies have used CRISPR/Cas9 editing to modulate genes associated with adaptive (e.g., T cell) immune responses, but have largely not addressed the innate immune cells (e.g., monocytes, neutrophils) that mediate inflammation and rejection processes occurring early after graft transplantation. We identified the adhesion molecule ICAM-1 as a novel hypoimmune target that plays multiple critical roles in both adaptive and innate immune responses post-transplantation. In a series of studies, we found that ICAM-1 blocking or knock-out (KO) in hPSC-derived cardiovascular therapies imparted significantly diminished binding of multiple immune cell types. ICAM-1 KO resulted in diminished T cell proliferation responses and in longer retention/protection of KO grafts following immune cell encounter in NeoThy humanized mice. The ICAM-1 KO edit was also introduced into existing first-generation hypoimmune hPSCs and prevented immune cell binding, thereby enhancing the overall hypoimmune capacity of the cells. This novel hypoimmune editing strategy has the potential to improve the long-term efficacy and safety profiles of regenerative therapies for cardiovascular pathologies and a number of other diseases.

The PubMed database contains more than 34 million articles; consequently, it is becoming increasingly difficult for a biomedical researcher to keep up-to-date with different knowledge domains. Computationally efficient and interpretable tools are needed to help researchers find and understand associations between biomedical concepts. The goal of literature-based discovery (LBD) is to connect concepts in isolated literature domains that would normally go undiscovered. This usually takes the form of an A-B-C relationship, where A and C terms are linked through a B term intermediate. Here we describe Serial KinderMiner (SKiM), an LBD algorithm for finding statistically significant links between an A term and one or more C terms through some B term intermediate(s). The development of SKiM is motivated by the the observation that there are only a few LBD tools that provide a functional web interface, and that the available tools are limited in one or more of the following ways: 1) they identify a relationship but not the type of relationship, 2) they do not allow the user to provide their own lists of B or C terms, hindering flexibility, 3) they do not allow for querying thousands of C terms (which is crucial if, for instance, the user wants to query connections between a disease and the thousands of available drugs), or 4) they are specific for a particular biomedical domain (such as cancer). We provide an open-source tool and web interface that improves on all of these issues. We demonstrate SKiM's ability to discover useful A-B-C linkages in three control experiments: classic LBD discoveries, drug repurposing, and finding associations related to cancer. Furthermore, we supplement SKiM with a knowledge graph built with transformer machine-learning models to aid in interpreting the relationships between terms found by SKiM. Finally, we provide a simple and intuitive open-source web interface ( https://skim.morgridge.org ) with comprehensive lists of drugs, diseases, phenotypes, and symptoms so that anyone can easily perform SKiM searches. SKiM is a simple algorithm that can perform LBD searches to discover relationships between arbitrary user-defined concepts. SKiM is generalized for any domain, can perform searches with many thousands of C term concepts, and moves beyond the simple identification of an existence of a relationship; many relationships are given relationship type labels from our knowledge graph.

During an immune response, macrophages systematically rewire their metabolism in specific ways to support their diversve functions. However, current knowledge of macrophage metabolism is largely concentrated on central carbon metabolism. Using multi-omics analysis, we identified nucleotide metabolism as one of the most significantly rewired pathways upon classical activation. Further isotopic tracing studies revealed several major changes underlying the substantial metabolomic alterations: 1) synthesis of both purines and pyrimidines is shut down at several specific steps; 2) nucleotide degradation activity to nitrogenous bases is increased but complete oxidation of bases is reduced, causing a great accumulation of nucleosides and bases; and 3) cells gradually switch to primarily relying on salvaging the nucleosides and bases for maintaining most nucleotide pools. Mechanistically, the inhibition of purine nucleotide synthesis is mainly caused by nitric oxide (NO)-driven inhibition of the IMP synthesis enzyme ATIC, with NO-independent transcriptional downregulation of purine synthesis genes augmenting the effect. The inhibition of pyrimidine nucleotide synthesis is driven by NO-driven inhibition of CTP synthetase (CTPS) and transcriptional downregulation of thymidylate synthase (TYMS). For the rewiring of degradation, purine nucleoside phosphorylase (PNP) and uridine phosphorylase (UPP) are transcriptionally upregulated, increasing nucleoside degradation activity. However, complete degradation of purine bases by xanthine oxidoreductase (XOR) is inhibited by NO, diverting flux into nucleotide salvage. Inhibiting the activation-induced switch from nucleotide synthesis to salvage by knocking out the purine salvage enzyme hypoxanthine-guanine phosporibosyl transferase ( ) significantly alters the expression of genes important for activated macrophage functions, suppresses macrophage migration, and increases pyroptosis. Furthermore, knocking out or increases proliferation of the intracellular parasite in macrophages. Together, these studies comprehensively reveal the characteristics, the key regulatory mechanisms, and the functional importance of the dynamic rewiring of nucleotide metabolism in classically activated macrophages.