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/.

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.

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.

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.

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.

Cost minimization analysis of an in-house molecular test for cytomegalovirus in relation to a commercial molecular system

ABSTRACT Introduction: Cytomegalovirus may cause severe disease in immunocompromised patients. Nowadays, quantitative polymerase chain reaction is the gold-standard for both diagnosis and monitoring of cytomegalovirus infection. Most of these assays use cytomegalovirus automated molecular kits which are expensive and therefore not an option for small laboratories, particularly in the developing world. Objective: This study aimed to optimize and validate an in-house cytomegalovirus quantitative polymerase chain reaction test calibrated using the World Health Organization Standards, and to perform a cost-minimization analysis, in comparison to a commercial cytomegalovirus quantitative polymerase chain reaction test. Study design: The methodology consisted of determining: optimization, analytical sensitivity, analytical specificity, precision, curve variability analysis, and inter-laboratorial reproducibility. Patients (n = 30) with known results for cytomegalovirus tested with m2000 RealTime System (Abbott Laboratories, BR) were tested with the in-house assay, as well as patients infected with other human herpes virus, in addition to BK virus. A cost-minimization analysis was performed, from a perspective of the laboratory, assuming diagnostic equivalence of the methodologies applied in the study. Results: The in-house assay had a limit of detection and quantification of 60.3 IU/mL, with no cross-reactivity with the other viral agents tested. Moreover, the test was precise and had a R 2 of 0.954 when compared with the m2000 equipment. The cost analysis showed that the assay was economically advantageous costing a median value of 37.8% and 82.2% in comparison to the molecular test in use at the hospital and the m2000 equipment, respectively. Conclusions: These results demonstrated that in-house quantitative polymerase chain reaction testing is an attractive alternative in comparison to automated molecular platforms, being considerably less expensive and as efficacious as the commercial methods.

Cost minimization analysis of an in-house molecular test for cytomegalovirus in relation to a commercial molecular system.

INTRODUCTION: Cytomegalovirus may cause severe disease in immunocompromised patients. Nowadays, quantitative polymerase chain reaction is the gold-standard for both diagnosis and monitoring of cytomegalovirus infection. Most of these assays use cytomegalovirus automated molecular kits which are expensive and therefore not an option for small laboratories, particularly in the developing world. OBJECTIVE: This study aimed to optimize and validate an in-house cytomegalovirus quantitative polymerase chain reaction test calibrated using the World Health Organization Standards, and to perform a cost-minimization analysis, in comparison to a commercial cytomegalovirus quantitative polymerase chain reaction test. STUDY DESIGN: The methodology consisted of determining: optimization, analytical sensitivity, analytical specificity, precision, curve variability analysis, and inter-laboratorial reproducibility. Patients (n=30) with known results for cytomegalovirus tested with m2000 RealTime System (Abbott Laboratories, BR) were tested with the in-house assay, as well as patients infected with other human herpes virus, in addition to BK virus. A cost-minimization analysis was performed, from a perspective of the laboratory, assuming diagnostic equivalence of the methodologies applied in the study. RESULTS: The in-house assay had a limit of detection and quantification of 60.3IU/mL, with no cross-reactivity with the other viral agents tested. Moreover, the test was precise and had a R2 of 0.954 when compared with the m2000 equipment. The cost analysis showed that the assay was economically advantageous costing a median value of 37.8% and 82.2% in comparison to the molecular test in use at the hospital and the m2000 equipment, respectively. CONCLUSIONS: These results demonstrated that in-house quantitative polymerase chain reaction testing is an attractive alternative in comparison to automated molecular platforms, being considerably less expensive and as efficacious as the commercial methods.

Synthesis and biological evaluation of 6-bromo-6-substituted penicillanic acid derivatives as beta-lactamase inhibitors.

The synthesis of a selected set of 6-bromopenicillanic acid derivatives with an additional C6 substituent is reported. All these substances were tested as inhibitors of class A and C beta-lactamase enzymes derived from Escherichia coli (TEM-1) and E. cloacae (P99). As 6-(1-hydroxyethyl) derivatives 4c and 6c were found to be weak beta-lactamase inhibitors, they were further investigated in combination with amoxicillin against a series of beta-lactamase-producing bacterial strains. Some structure-activity relationships are discussed.

Synthesis of new C-6 alkyliden penicillin derivatives as beta-lactamase inhibitors.

New penicillin, penicillin sulfone and sulfoxide derivatives bearing a C-6-alkyliden substituent were prepared. Their chemical synthesis, in vitro antibacterial activity and inhibition properties against two selected enzymes representing Class A and C beta-lactamases are reported. Compounds 3a-c, 7a-c were able to inhibit either TEM-1 (a Class A enzyme, from Escherichia coli) or P-99 (a Class C enzyme, from E. cloacae), or both enzymes, when tested in competition experiments using nitrocefin as the reporter substrate. However, when tested in combination with amoxicillin, the same compounds did not show synergistic effects against E. coli and E. cloacae strains producing TEM-1 and P99 enzymes, respectively. This finding is most likely related to poor penetration through the bacterial cell wall, as shown by using a more permeable isogenic E. coli strain. Interestingly, a synergistic effect against a strain of S. aureus which produces PC1-enzyme (a Class A beta-lactamase) was observed for compound 3a when used in combination with amoxicillin.

Synthesis, pharmacological characterization and QSAR studies on 2-substituted indole melatonin receptor ligands.

A number of 6-methoxy-1-(2-propionylaminoethyl)indoles, carrying properly selected substituents at the C-2 indole position, were prepared and tested as melatonin receptor ligands. Affinities and intrinsic activities for the human cloned mt1 and MT2 receptors were examined and compared with those of some 2-substituted melatonin derivatives recently described by us. A quantitative structure activity relationship (QSAR) study of the sixteen 2-substituted indole compounds, 5a-k, 1, 8-11, using partial least squares (PLS) and multiple regression analysis (MRA) revealed the existence of an optimal range of lipophilicity for the C2 indole substituent. There are also indications that planar, electron-withdrawing substituents contribute to the affinity by establishing additional interactions with the binding pocket. No mt1/MT2 subtype selectivity was observed, with the relevant exception of the 2-phenethyl derivative 5e, which exhibited the highest selectivity for the h-MT2 receptor among all the compounds tested (MT2/mt1 ratio of ca. 50). Conformational analysis and superposition of 5e to other reported selective MT2 ligands revealed structural and conformational similarities that might account for the MT2/mt1 selectivity of 5e.

Ulnar deviation of the thumb: an exceptional finding in Rubinstein-Taybi syndrome.

A 2-year-old girl with Rubinstein-Taybi syndrome and the unusual finding of ulnar deviation of the thumb is described. Possible modes of inheritance are discussed.

A new synthesis of carboxyterfenadine (fexofenadine) and its bioisosteric tetrazole analogs.

A new synthesis of carboxyterfenadine (4), based on the conversion of a alpha-halo-alkylarylketone into the corresponding substituted 2-arylalkanoic ester, is described. The enantioselective synthesis of its two bioisosteric tetrazole analogs together with preliminary biological results are reported.

2-[N-Acylamino(C1-C3)alkyl]indoles as MT1 melatonin receptor partial agonists, antagonists, and putative inverse agonists.

The synthesis of several novel indole melatonin analogues substituted at the 2-position with acylaminomethyl (8-11), acylaminoethyl (5a-k), or acylaminopropyl (13) side chains is reported. On the basis of a novel in vitro functional assay (specific binding of [35S]GTPgammaS), which can discriminate agonist from partial agonist, antagonist, and inverse agonist ligands, 5a,g, h,j and 13 were shown to be partial agonists, 5d,e and 8-11 competitive antagonists, and 5b,c,k putative inverse agonists. Binding and functional assays were performed on cloned human MT1 receptor. Structure-activity relationship considerations indicate that N-[1-aryl-2-(4-methoxy-1H-indol-2-yl)(C1-C2)alkyl]alkanamides represent a lead structure for this type of ligands.

Conformationally restrained melatonin analogues: synthesis, binding affinity for the melatonin receptor, evaluation of the biological activity, and molecular modeling study.

The design, synthesis, and biological profile of several indole melatonin analogues with a conformationally restricted C3 amidoethane side chain are presented. Examination of the accessible conformations of the melatonin side chain led us to explore some of its fully or partially restricted analogues, 2-12, the binding affinity values of which were utilized to gain further insight on the melatonin binding site. Two pharmacophoric models have been devised for melatonin and the active compounds by conformational analysis and superimposition performed using the DISCO program. In these models, the melatonin side chain can adopt a gauche/anti conformation out of the indole plane. Another contribution of this study regards the observation of a possible binding point interaction around the C2 position of the indole, as suggested by the remarkably increased binding affinity observed in the C2-substituted analogues 6 and 9 and especially in the more rigid analogue 5. The biological activity and the efficacy of the new compounds were tested by measuring the inhibition of the forskolin-stimulated cAMP accumulation and the GTP gamma S index. Both analyses demonstrated that all of the compounds were full agonists with the exception of 4 and 9, which showed a slight reduction in efficacy and would seem to be partial agonists.

1-(2-Alkanamidoethyl)-6-methoxyindole derivatives: a new class of potent indole melatonin analogues.

A new series of indole melatonin analogues, bearing the amido ethyl side chain attached at the N-1 position of the indole nucleus, were synthesized and tested for their affinity for the melatonin receptor isolated from quail optic tecta in a series of in vitro ligand-binding experiments using 2-[125I]iodomelatonin as the labeled ligand. The biological activity was evaluated using two models: effects on the forskolin-stimulated cAMP accumulation in explants from quail optic tecta and evaluation of the GTP gamma S index derived from competition experiments performed in the absence or presence of GTP gamma S. Compounds 2a and 2k-n, obtained by shifting the methoxy group and the ethylamido side chain from the C-5 and C-3 positions of melatonin to the C-6 and N-1 positions of the indole nucleus, exhibited an affinity similar to that of melatonin itself, as well as full agonist activity. Optimization of the C-2 substituent by introducing Br, phenyl, or COOCH3 (2b-d) resulted in a significantly enhanced affinity (in the picomolar range) and improved agonist biological activity. Compounds lacking the methoxy group and bearing an N-alicyclic group (2h-j) behaved as partial agonists or antagonists.