Rummagene: massive mining of gene sets from supporting materials of biomedical research publications.

Many biomedical research publications contain gene sets in their supporting tables, and these sets are currently not available for search and reuse. By crawling PubMed Central, the Rummagene server provides access to hundreds of thousands of such mammalian gene sets. So far, we scanned 5,448,589 articles to find 121,237 articles that contain 642,389 gene sets. These sets are served for enrichment analysis, free text, and table title search. Investigating statistical patterns within the Rummagene database, we demonstrate that Rummagene can be used for transcription factor and kinase enrichment analyses, and for gene function predictions. By combining gene set similarity with abstract similarity, Rummagene can find surprising relationships between biological processes, concepts, and named entities. Overall, Rummagene brings to surface the ability to search a massive collection of published biomedical datasets that are currently buried and inaccessible. The Rummagene web application is available at https://rummagene.com .

RummaGEO: Automatic Mining of Human and Mouse Gene Sets from GEO.

The Gene Expression Omnibus (GEO) is a major open biomedical research repository for transcriptomics and other omics datasets. It currently contains millions of gene expression samples from tens of thousands of studies collected by many biomedical research laboratories from around the world. While users of the GEO repository can search the metadata describing studies for locating relevant datasets, there are currently no methods or resources that facilitate global search of GEO at the data level. To address this shortcoming, we developed RummaGEO, a webserver application that enables gene expression signature search of a large collection of human and mouse RNA-seq studies deposited into GEO. To develop the search engine, we performed offline automatic identification of sample conditions from the uniformly aligned GEO studies available from ARCHS4. We then computed differential expression signatures to extract gene sets from these studies. In total, RummaGEO currently contains 135,264 human and 158,062 mouse gene sets extracted from 23,395 GEO studies. Next, we analyzed the contents of the RummaGEO database to identify statistical patterns and perform various global analyses. The contents of the RummaGEO database are provided as a web-server search engine with signature search, PubMed search, and metadata search functionalities. Overall, RummaGEO provides an unprecedented resource for the biomedical research community enabling hypothesis generation for many future studies. The RummaGEO search engine is available from: https://rummageo.com/.

Fluid biomarkers in cerebral amyloid angiopathy.

Cerebral amyloid angiopathy (CAA) is a type of cerebrovascular disorder characterised by the accumulation of amyloid within the leptomeninges and small/medium-sized cerebral blood vessels. Typically, cerebral haemorrhages are one of the first clinical manifestations of CAA, posing a considerable challenge to the timely diagnosis of CAA as the bleedings only occur during the later disease stages. Fluid biomarkers may change prior to imaging biomarkers, and therefore, they could be the future of CAA diagnosis. Additionally, they can be used as primary outcome markers in prospective clinical trials. Among fluid biomarkers, blood-based biomarkers offer a distinct advantage over cerebrospinal fluid biomarkers as they do not require a procedure as invasive as a lumbar puncture. This article aimed to provide an overview of the present clinical data concerning fluid biomarkers associated with CAA and point out the direction of future studies. Among all the biomarkers discussed, amyloid ß, neurofilament light chain, matrix metalloproteinases, complement 3, uric acid, and lactadherin demonstrated the most promising evidence. However, the field of fluid biomarkers for CAA is an under-researched area, and in most cases, there are only one or two studies on each of the biomarkers mentioned in this review. Additionally, a small sample size is a common limitation of the discussed studies. Hence, it is hard to reach a solid conclusion on the clinical significance of each biomarker at different stages of the disease or in various subpopulations of CAA. In order to overcome this issue, larger longitudinal and multicentered studies are needed.

Pan-cancer proteogenomics characterization of tumor immunity.

Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.

Electrochemically Switchable Multimode Strong Coupling in Plasmonic Nanocavities.

The strong-coupling interaction between quantum emitters and cavities provides the archetypical platform for fundamental quantum electrodynamics. Here we show that methylene blue (MB) molecules interact coherently with subwavelength plasmonic nanocavity modes at room temperature. Experimental results show that the strong coupling can be switched on and off reversibly when MB molecules undergo redox reactions which transform them to leuco-methylene blue molecules. In simulations we demonstrate the strong coupling between the second excited plasmonic cavity mode and resonant emitters. However, we also show that other detuned modes simultaneously couple efficiently to the molecular transitions, creating unusual cascades of mode spectral shifts and polariton formation. This is possible due to the relatively large plasmonic particle size resulting in reduced mode splittings. The results open significant potential for device applications utilizing active control of strong coupling.

D2H2: diabetes data and hypothesis hub.

Motivation: There is a rapid growth in the production of omics datasets collected by the diabetes research community. However, such published data are underutilized for knowledge discovery. To make bioinformatics tools and published omics datasets from the diabetes field more accessible to biomedical researchers, we developed the Diabetes Data and Hypothesis Hub (D2H2). Results: D2H2 contains hundreds of high-quality curated transcriptomics datasets relevant to diabetes, accessible via a user-friendly web-based portal. The collected and processed datasets are curated from the Gene Expression Omnibus (GEO). Each curated study has a dedicated page that provides data visualization, differential gene expression analysis, and single-gene queries. To enable the investigation of these curated datasets and to provide easy access to bioinformatics tools that serve gene and gene set-related knowledge, we developed the D2H2 chatbot. Utilizing GPT, we prompt users to enter free text about their data analysis needs. Parsing the user prompt, together with specifying information about all D2H2 available tools and workflows, we answer user queries by invoking the most relevant tools via the tools' API. D2H2 also has a hypotheses generation module where gene sets are randomly selected from the bulk RNA-seq precomputed signatures. We then find highly overlapping gene sets extracted from publications listed in PubMed Central with abstract dissimilarity. With the help of GPT, we speculate about a possible explanation of the high overlap between the gene sets. Overall, D2H2 is a platform that provides a suite of bioinformatics tools and curated transcriptomics datasets for hypothesis generation. Availability and implementation: D2H2 is available at: https://d2h2.maayanlab.cloud/ and the source code is available from GitHub at https://github.com/MaayanLab/D2H2-site under the CC BY-NC 4.0 license.

Nanoscale Gap-Plasmon-Enhanced Superconducting Photon Detectors at Single-Photon Level.

With a growing demand for detecting light at the single-photon level in various fields, researchers are focused on optimizing the performance of superconducting single-photon detectors (SSPDs) by using multiple approaches. However, input light coupling for visible light has remained a challenge in the development of efficient SSPDs. To overcome these limitations, we developed a novel system that integrates NbN superconducting microwire photon detectors (SMPDs) with gap-plasmon resonators to improve the photon detection efficiency to 98% while preserving all detector performance features, such as polarization insensitivity. The plasmonic SMPDs exhibit a hot-belt effect that generates a nonlinear photoresponse in the visible range operated at 9 K (∼0.64Tc), resulting in a 233-fold increase in phonon-electron interaction factor (γ) compared to pristine SMPDs at resonance under CW illumination. These findings open up new opportunities for ultrasensitive single-photon detection in areas like quantum information processing, quantum optics, imaging, and sensing at visible wavelengths.

Biologics in peripheral ulcerative keratitis.

Over the past two decades biologic therapies have seen a rapid uptake in the management of ocular inflammation. Peripheral ulcerative keratitis (PUK), once a harbinger of blindness and mortality in refractory rheumatological disease, is now increasingly being treated with these agents. We conducted a review to evaluate the evidence base for this application and to provide a road map for their clinical usage in PUK, including dosage and adverse effects. A literature search across Medline, Embase and Cochrane Database of Systematic Reviews was undertaken to identify all patients with PUK that were treated with a biologic in a peer viewed article. Overall, whilst the evidence base for biologic use in PUK was poor, reported cases demonstrate an increasingly powerful and effective role for biologics in refractory PUK. This was particularly the case for rituximab in PUK secondary to granulomatous with polyangiitis.

Toxicology knowledge graph for structural birth defects.

BACKGROUND: Birth defects are functional and structural abnormalities that impact about 1 in 33 births in the United States. They have been attributed to genetic and other factors such as drugs, cosmetics, food, and environmental pollutants during pregnancy, but for most birth defects there are no known causes. METHODS: To further characterize associations between small molecule compounds and their potential to induce specific birth abnormalities, we gathered knowledge from multiple sources to construct a reproductive toxicity Knowledge Graph (ReproTox-KG) with a focus on associations between birth defects, drugs, and genes. Specifically, we gathered data from drug/birth-defect associations from co-mentions in published abstracts, gene/birth-defect associations from genetic studies, drug- and preclinical-compound-induced gene expression changes in cell lines, known drug targets, genetic burden scores for human genes, and placental crossing scores for small molecules. RESULTS: Using ReproTox-KG and semi-supervised learning (SSL), we scored >30,000 preclinical small molecules for their potential to cross the placenta and induce birth defects, and identified >500 birth-defect/gene/drug cliques that can be used to explain molecular mechanisms for drug-induced birth defects. The ReproTox-KG can be accessed via a web-based user interface available at https://maayanlab.cloud/reprotox-kg . This site enables users to explore the associations between birth defects, approved and preclinical drugs, and all human genes. CONCLUSIONS: ReproTox-KG provides a resource for exploring knowledge about the molecular mechanisms of birth defects with the potential of predicting the likelihood of genes and preclinical small molecules to induce birth defects.

While birth defects are common, for most birth defects there are no known causes. During pregnancy, developing babies are exposed to drugs, cosmetics, food, and environmental pollutants that may cause birth defects. However, exactly how these environmental factors are involved in producing birth defects is difficult to discern. Also, birth defects can be a consequence of the genes inherited from the parents. We combined general data about human genes and drugs with specific data previously implicating genes and drugs in inducing birth defects to create a knowledge graph representation that connects genes, drugs, and birth defects. This knowledge graph can be used to explore new links that may explain why birth defects occur, particularly those that result from a combination of inherited and environmental influences.

GeneRanger and TargetRanger: processed gene and protein expression levels across cells and tissues for target discovery.

Several atlasing efforts aim to profile human gene and protein expression across tissues, cell types and cell lines in normal physiology, development and disease. One utility of these resources is to examine the expression of a single gene across all cell types, tissues and cell lines in each atlas. However, there is currently no centralized place that integrates data from several atlases to provide this type of data in a uniform format for visualization, analysis and download, and via an application programming interface. To address this need, GeneRanger is a web server that provides access to processed data about gene and protein expression across normal human cell types, tissues and cell lines from several atlases. At the same time, TargetRanger is a related web server that takes as input RNA-seq data from profiled human cells and tissues, and then compares the uploaded input data to expression levels across the atlases to identify genes that are highly expressed in the input and lowly expressed across normal human cell types and tissues. Identified targets can be filtered by transmembrane or secreted proteins. The results from GeneRanger and TargetRanger are visualized as box and scatter plots, and as interactive tables. GeneRanger and TargetRanger are available from https://generanger.maayanlab.cloud and https://targetranger.maayanlab.cloud, respectively.

Enrichr-KG: bridging enrichment analysis across multiple libraries.

Gene and protein set enrichment analysis is a critical step in the analysis of data collected from omics experiments. Enrichr is a popular gene set enrichment analysis web-server search engine that contains hundreds of thousands of annotated gene sets. While Enrichr has been useful in providing enrichment analysis with many gene set libraries from different categories, integrating enrichment results across libraries and domains of knowledge can further hypothesis generation. To this end, Enrichr-KG is a knowledge graph database and a web-server application that combines selected gene set libraries from Enrichr for integrative enrichment analysis and visualization. The enrichment results are presented as subgraphs made of nodes and links that connect genes to their enriched terms. In addition, users of Enrichr-KG can add gene-gene links, as well as predicted genes to the subgraphs. This graphical representation of cross-library results with enriched and predicted genes can illuminate hidden associations between genes and annotated enriched terms from across datasets and resources. Enrichr-KG currently serves 26 gene set libraries from different categories that include transcription, pathways, ontologies, diseases/drugs, and cell types. To demonstrate the utility of Enrichr-KG we provide several case studies. Enrichr-KG is freely available at: https://maayanlab.cloud/enrichr-kg.

Resistance exercise acutely elevates dynamic cerebral autoregulation gain.

Dynamic cerebral autoregulation (dCA) describes the regulation of cerebral blood flow (CBF) in response to fluctuations in systemic blood pressure (BP). Heavy resistance exercise is known to induce large transient elevations in BP, which are translated into perturbations of CBF, and may alter dCA in the immediate aftermath. This study aimed to better quantify the time course of any acute alterations in dCA after resistance exercise. Following familiarisation to all procedures, 22 (14 male) healthy young adults (22 ± 2 years) completed an experimental trial and resting control trial, in a counterbalanced order. Repeated squat-stand manoeuvres (SSM) at 0.05 and 0.10 Hz were used to quantify dCA before, and 10 and 45 min after four sets of ten repetition back squats at 70% of one repetition maximum, or time matched seated rest (control). Diastolic, mean and systolic dCA were quantified by transfer function analysis of BP (finger plethysmography) and middle cerebral artery blood velocity (transcranial Doppler ultrasound). Mean gain (p = 0.02; d = 0.36) systolic gain (p = 0.01; d = 0.55), mean normalised gain (p = 0.02; d = 0.28) and systolic normalised gain (p = 0.01; d = 0.67) were significantly elevated above baseline during 0.10 Hz SSM 10-min post resistance exercise. This alteration was not present 45 min post-exercise, and dCA indices were never altered during SSM at 0.05 Hz. dCA metrics were acutely altered 10 min post resistance exercise at the 0.10 Hz frequency only, which indicate changes in the sympathetic regulation of CBF. These alterations recovered 45 min post-exercise.

Computational screen to identify potential targets for immunotherapeutic identification and removal of senescence cells.

To prioritize gene and protein candidates that may enable the selective identification and removal of senescent cells, we compared gene expression signatures from replicative senescent cells to transcriptomics and proteomics atlases of normal human tissues and cell types. RNA-seq samples from in vitro senescent cells (6 studies, 13 conditions) were analyzed for identifying targets at the gene and transcript levels that are highly expressed in senescent cells compared to their expression in normal human tissues and cell types. A gene set made of 301 genes called SenoRanger was established based on consensus analysis across studies and backgrounds. Of the identified senescence-associated targets, 29% of the genes in SenoRanger are also highly differentially expressed in aged tissues from GTEx. The SenoRanger gene set includes previously known as well as novel senescence-associated genes. Pathway analysis that connected the SenoRanger genes to their functional annotations confirms their potential role in several aging and senescence-related processes. Overall, SenoRanger provides solid hypotheses about potentially useful targets for identifying and removing senescence cells.

lncHUB2: aggregated and inferred knowledge about human and mouse lncRNAs.

Long non-coding ribonucleic acids (lncRNAs) account for the largest group of non-coding RNAs. However, knowledge about their function and regulation is limited. lncHUB2 is a web server database that provides known and inferred knowledge about the function of 18 705 human and 11 274 mouse lncRNAs. lncHUB2 produces reports that contain the secondary structure fold of the lncRNA, related publications, the most correlated coding genes, the most correlated lncRNAs, a network that visualizes the most correlated genes, predicted mouse phenotypes, predicted membership in biological processes and pathways, predicted upstream transcription factor regulators, and predicted disease associations. In addition, the reports include subcellular localization information; expression across tissues, cell types, and cell lines, and predicted small molecules and CRISPR knockout (CRISPR-KO) genes prioritized based on their likelihood to up- or downregulate the expression of the lncRNA. Overall, lncHUB2 is a database with rich information about human and mouse lncRNAs and as such it can facilitate hypothesis generation for many future studies. The lncHUB2 database is available at https://maayanlab.cloud/lncHUB2. Database URL: https://maayanlab.cloud/lncHUB2.

PrismEXP: gene annotation prediction from stratified gene-gene co-expression matrices.

Background: Gene-gene co-expression correlations measured by mRNA-sequencing (RNA-seq) can be used to predict gene annotations based on the co-variance structure within these data. In our prior work, we showed that uniformly aligned RNA-seq co-expression data from thousands of diverse studies is highly predictive of both gene annotations and protein-protein interactions. However, the performance of the predictions varies depending on whether the gene annotations and interactions are cell type and tissue specific or agnostic. Tissue and cell type-specific gene-gene co-expression data can be useful for making more accurate predictions because many genes perform their functions in unique ways in different cellular contexts. However, identifying the optimal tissues and cell types to partition the global gene-gene co-expression matrix is challenging. Results: Here we introduce and validate an approach called PRediction of gene Insights from Stratified Mammalian gene co-EXPression (PrismEXP) for improved gene annotation predictions based on RNA-seq gene-gene co-expression data. Using uniformly aligned data from ARCHS4, we apply PrismEXP to predict a wide variety of gene annotations including pathway membership, Gene Ontology terms, as well as human and mouse phenotypes. Predictions made with PrismEXP outperform predictions made with the global cross-tissue co-expression correlation matrix approach on all tested domains, and training using one annotation domain can be used to predict annotations in other domains. Conclusions: By demonstrating the utility of PrismEXP predictions in multiple use cases we show how PrismEXP can be used to enhance unsupervised machine learning methods to better understand the roles of understudied genes and proteins. To make PrismEXP accessible, it is provided via a user-friendly web interface, a Python package, and an Appyter. AVAILABILITY. The PrismEXP web-based application, with pre-computed PrismEXP predictions, is available from: https://maayanlab.cloud/prismexp; PrismEXP is also available as an Appyter: https://appyters.maayanlab.cloud/PrismEXP/; and as Python package: https://github.com/maayanlab/prismexp.

Prophylactic intranasal administration of lipid nanoparticle formulated siRNAs reduce SARS-CoV-2 and RSV lung infection.

RNA interference (RNAi) is an emerging and promising therapy for a wide range of respiratory viral infections. This highly specific suppression can be achieved by the introduction of short-interfering RNA (siRNA) into mammalian systems, resulting in the effective reduction of viral load. Unfortunately, this has been hindered by the lack of a good delivery system, especially via the intranasal (IN) route. Here, we have developed an IN siRNA encapsulated lipid nanoparticle (LNP) in vivo delivery system that is highly efficient at targeting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and respiratory syncytial virus (RSV) lung infection in vivo. Importantly, IN siRNA delivery without the aid of LNPs abolishes anti-SARS-CoV-2 activity in vivo. Our approach using LNPs as the delivery vehicle overcomes the significant barriers seen with IN delivery of siRNA therapeutics and is a significant advancement in our ability to delivery siRNAs. The study presented here demonstrates an attractive alternate delivery strategy for the prophylactic treatment of both future and emerging respiratory viral diseases.

Substrate engineering of plasmonic nanocavity antenna modes.

Plasmonic nanocavities have emerged as a promising platform for next-generation spectroscopy, sensing and photonic quantum information processing technologies, benefiting from a unique confluence of nanoscale compactness and integrability, ultrafast functionality and room-temperature viability. Harnessing their unprecedented optical field confinement and enhancement properties for such diverse application domains, however, demands continued innovation in cavity design and robust strategies for engineering their plasmonic mode characteristics, with the aim of optimizing spatial and spectral matching conditions for strong light-matter interaction involving embedded quantum emitters. Adopting the canonical gold bowtie nanoantenna, we show that the complex refractive index, n + ik, of the substrate material provides additional design flexibility in tailoring the properties of plasmonic nanocavity modes, including their resonance wavelengths, hotspot locations, intracavity field polarization and radiative decay rates. In particular, we predict that highly refractive (n ≥ 4) or highly absorptive (k ≥ 4) substrates provide two complementary approaches to engineering nanocavity modes that are especially desirable for coupling two-dimensional quantum materials, featuring namely an elevated hotspot with a dominantly in-plane polarized near-field, as well as a strongly radiative character. Our study elucidates the benefits and intricacies of a largely unexplored facet of nanocavity mode manipulation, beyond the widely practiced synthetic control over the cavity topology or physical dimensions, and paves the way for plasmonic cavity quantum electrodynamics with two-dimensional excitonic matter.

Making Common Fund data more findable: catalyzing a data ecosystem.

The Common Fund Data Ecosystem (CFDE) has created a flexible system of data federation that enables researchers to discover datasets from across the US National Institutes of Health Common Fund without requiring that data owners move, reformat, or rehost those data. This system is centered on a catalog that integrates detailed descriptions of biomedical datasets from individual Common Fund Programs' Data Coordination Centers (DCCs) into a uniform metadata model that can then be indexed and searched from a centralized portal. This Crosscut Metadata Model (C2M2) supports the wide variety of data types and metadata terms used by individual DCCs and can readily describe nearly all forms of biomedical research data. We detail its use to ingest and index data from 11 DCCs.

Gene set predictor for post-treatment Lyme disease.

Lyme disease (LD) is tick-borne disease whose post-treatment sequelae are not well understood. For this study, we enrolled 152 individuals with symptoms of post-treatment LD (PTLD) to profile their peripheral blood mononuclear cells (PBMCs) with RNA sequencing (RNA-seq). Combined with RNA-seq data from 72 individuals with acute LD and 44 uninfected controls, we investigated differences in differential gene expression. We observe that most individuals with PTLD have an inflammatory signature that is distinguished from the acute LD group. By distilling gene sets from this study with gene sets from other sources, we identify a subset of genes that are highly expressed in the cohorts but are not already established as biomarkers for inflammatory response or other viral or bacterial infections. We further reduce this gene set by feature importance to establish an mRNA biomarker set capable of distinguishing healthy individuals from those with acute LD or PTLD as a candidate for translation into an LD diagnostic.

A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses.

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods ( synapse.org/LINCS_MCF10A ). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes. Beyond these analyses, this dataset will serve as a resource for the broader scientific community to mine for biological insights, to compare signals carried across distinct molecular modalities, and to develop new computational methods for integrative data analysis.