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.

Longitudinal characterization of the metabolome of dairy cows transitioning from one lactation to the next one: Investigations in the liver.

This study aimed to investigate the metabolic changes in the livers of dairy cows from 1 wk before dry-off to 1 wk after calving. Twelve high-yielding Holstein cows were included in a longitudinal study and housed in a tie-stall barn. The cows were dried off at 6 wk before the expected calving date (dry period length = 42 d). During the entire lactation, the cows were milked twice daily at 0600 and 1700 h. Liver biopsies were taken from each cow at 4 different times: wk -7 (before drying off), -5 (after drying off), -1 and +1 relative to calving. A targeted metabolomics approach was performed by liquid chromatography and flow injection with electrospray ionization triple quadrupole mass spectrometry using the MxP® Quant 500 kit. A total of 185 metabolites in the liver were used for the final data analysis. Principal component analysis (PCA) revealed a clear separation by days of sampling, indicating a notable shift in metabolic phenotype from late lactation to dry period and further changes after calving. Changes were observed in several classes of compounds, including amino acids and biogenic amines. In particular, the changes in acylcarnitines (AcylCN), phosphatidylcholines (PC), sphingomyelins (SM) and bile acids (BA) indicated extensive remodeling of the hepatic lipidome. The changes in AcylCN concentrations in early lactation suggest incomplete fatty acid oxidation in the liver, possibly indicating mitochondrial dysfunction or enzymatic imbalance. In addition, the changes in PC and SM species in early lactation indicate altered cell membrane composition, which may affect cell signaling and functionality. In addition, changes in BA concentrations and profiles indicate dynamic adaptations in BA synthesis, lipid digestion and absorption during the observation period. In particular, PCA analysis showed an overlapping distribution of liver metabolites in primiparous and multiparous cows, indicating no significant difference between these groups. In addition, Volcano plots showed similar liver metabolism between primiparous and multiparous cows, with no significant fold changes (>1.5) in any metabolite at significant P-values (false discovery rate <0.05). These results provide valuable insight into the physiological ranges of liver metabolites during dry period and calving in healthy dairy cows and should contribute to the design and interpretation of future metabolite-based studies of the transition dairy cow.

Longitudinal characterization of the metabolome of dairy cows transitioning from one lactation to the next: Investigations in blood serum.

The objective of this study was to characterize changes in the serum metabolome and various indicators of oxidative balance in dairy cows starting 2 wk before dry-off and continuing until wk 16 of lactation. Twelve Holstein dairy cows (body weight 745 ± 71 kg, body condition score 3.43 ± 0.66; mean ± SD) were housed in a tiestall barn from 10 wk before to 16 wk after parturition. Cows were dried off 6 wk before the expected calving date (mean dry period length = 42 d). From 8 wk before calving to 16 wk after calving, blood samples were taken weekly to study redox metabolism by determining antioxidant capacity, measured as the ferric-reducing ability of plasma, reactive oxidative metabolites, oxidative stress index, oxidative damage of lipids, measured as thiobarbituric acid reactive substances, and glutathione peroxidase activity. According to these results, dairy cows had the lowest serum antioxidant capacity and greater levels of oxidative stress during the dry-off period and the early postpartum period. For metabolomics, a subset of serum samples including wk -7 (before dry-off), -5 (after dry-off), -1, 1, 5, 10, and 15 relative to calving were used. A targeted metabolomics approach was performed using liquid chromatography and flow injection with electrospray ionization triple quadrupole mass spectrometry using the MxP Quant 500 kit (Biocrates Life Sciences AG). A total of 240 metabolites in serum were used in the final data analysis. Principal component analysis revealed a clear separation by days of sampling, indicating a remarkable shift in metabolic phenotype between the dry period and late and early lactation. Changes in many non-lipid metabolites associated with one-carbon metabolism, the tricarboxylic acid cycle, the urea cycle, and AA catabolism were observed in the study, with changes in AA serum concentrations likely related to factors such as energy and nitrogen balance, digestive efficiency, and changing diets. The study confirmed an extensive remodeling of the serum lipidome in peripartum dairy cows, highlighting the importance of changes in acylcarnitine (acylCN), phosphatidylcholines (PC), and triacylglycerols (TG), as they play a crucial role in lipid metabolism. Results showed that short-chain acylCN increased after dry-off and decreased thereafter, whereas lipid-derived acylCN increased around parturition, suggesting that more fatty acids could enter mitochondria. Phospholipids and sphingolipids in serum showed changes during lactation. In particular, concentrations of sphingomyelins, PC, and lysoPC decreased around calving but increased in mid- and late lactation. In contrast, concentrations of TG remained consistently low after parturition. The serum concentrations of bile acids fluctuated during the dry period and lactation, with glycocholic acid, cholic acid, glycodeoxycholic acid, and taurocholic acid showing the greatest concentrations. These changes are likely due to the interplay of diet, liver function, and the ability of the gut microbiota to convert primary to secondary bile acids. Overall, these descriptive results may aid in hypothesis generation and in the design and interpretation of future metabolite-based studies in dairy cows. Furthermore, they contribute to our understanding of the physiological ranges in serum metabolites relative to the lactation cycle of the dairy cow.

Water, mineral, and blood acid-base balance in calves with naturally occurring diarrhea receiving two alternative oral rehydration solutions or a placebo.

Quantifying the water and mineral losses in feces is essential to determine the optimal composition of oral rehydration solutions (ORS) for diarrheic animals. In a randomized complete block design, this study evaluated water, mineral, and blood acid-base balance of calves with naturally occurring diarrhea receiving ORS or a placebo. On d 0, 45 calves (age: 18 ± 3.2 d; mean ± SD) were selected based on the presence of visual signs of diarrhea, such as dirty tail or wet feces, along with clinical symptoms evaluated by measuring the skin turgor and the degree of enophthalmos. On d 1, calves were divided into blocks of 3 animals based on blood base excess (BE) measured at 0900 h, and within each block, calves were randomly assigned to 1 of 3 treatments (15 calves per treatment) including (1) a hypertonic ORS (HYPER; Na+ = 110 mmol/L; 370 mOsm/kg; strong ion difference [SID] = 60 mEq/L), (2) a hypotonic ORS with low Na+ (HYPO; Na+ = 77 mmol/L; 278 mOsm/kg; SID = 71 mEq/L), and (3) a placebo consisting of lukewarm water with 5 g/L of whey powder (CON). Milk replacer (MR) was fed through teat buckets twice daily at 0630 h and 1700 h in 2 equally sized meals of 2.5 L from d 1 to 3 and of 3.0 L on d 4 and 5. Treatments consisting of 2.0 L lukewarm solutions were administered between milk meals from d 1 to 3 at 1200 h and 2030 h through teat buckets. Refusals of MR and treatments were recorded daily, and blood samples were collected from the jugular vein once daily at arrival in the afternoon of d 0 and at 0900 h from d 1 to 5 after arrival. Urine and feces were collected quantitatively over a 48-h period from 1200 h on d 1 to 1200 h on d 3, and a representative sample of each 24-h period was stored. In addition, the volume of extracellular fluid was evaluated on d 2 by postprandial sampling over a 4-h period relative to the injection of sodium thiosulfate at 1300 h. Total daily fluid intake (MR, treatment, and water) from d 1 to 3 was greater in HYPER (LSM ± SEM; 8.9 ± 0.36 L/d) and HYPO (7.8 ± 0.34 L/d) than in CON (6.6 ± 0.34 L/d). This resulted in a greater water balance (water intake - fluid output in urine and feces) in calves receiving ORS (59.6 ± 6.28 g/kg BW per 24 h vs. 39.6 ± 6.08 g/kg BW per 24 h). Fecal Na+ losses were greater in HYPER than in the other treatments (81 ± 12.0 mg/kg BW per 24 h vs. 24 ± 11.8 mg/kg BW per 24 h). Blood pH was higher in HYPO (7.41 ± 0.016) than CON (7.35 ± 0.016) over the 5 monitoring days, whereas HYPER (7.37 ± 0.017) did not differ with other treatments. In this experimental model, diarrheic calves were likely unable to absorb the high Na+ load from HYPER, resulting in greater Na+ losses in feces, which might have impaired the alkalinizing capacity of HYPER. In contrast, HYPO significantly sustained blood acid-base balance compared with CON, whereas HYPER did not. This suggests that low tonicity ORS with a high SID are more suitable for diarrheic calves.

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.

Evaluating lactation performance of multiparous dairy cattle to prepartum and/or postpartum supplementation of fat-embedded calcium gluconate.

Prebiotic compounds may be supplemented in the diet to improve animal health and performance in a variety of ways. In dairy cattle, the transition from pregnancy through parturition and lactation represents a critical life stage with many concurrent stressors. The objectives of this study were to evaluate responses to the provision of a hindgut-targeted prebiotic compound (calcium gluconate; HFCG) when supplemented prepartum and/or postpartum in a 2 × 2 factorial design. One hundred and sixty-four multiparous Holstein cattle were enrolled and followed from approximately 21 d prior to calving until 100 d of lactation. Treatments were administered as a pelleted compound feed offered in the rotary milking parlor once daily prepartum and thrice daily postpartum. Information pertaining to milk production and body weight were automatically recorded by the milking equipment, and information pertaining to reproductive and health performance was recorded by farm staff. Cattle that received HFCG prepartum were confirmed pregnant approximately 21 d earlier (P = 0.024). Cattle that received HFCG both pre- and postpartum had 9% to 10% higher yields of milk protein, fat, and energy-corrected milk (P ≤ 0.037) from weeks 4 to 9 of lactation relative to those that received HFCG exclusively prepartum. Conversely, cattle that received HFCG exclusively postpartum had 9% to 10% higher yields of milk protein, fat, and energy-corrected milk (P ≤ 0.037) from weeks 9 to 14 of lactation relative to those that received exclusively the negative control in both periods. The mechanism underlying these responses remains unclear, however, we hypothesize that these responses are due to localized reductions in inflammation in the gut and/or signaling to extragastrointestinal tissues altering energy partitioning and balance.

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.

A randomised, double-blind, placebo-controlled study of the LAG-3-depleting monoclonal antibody GSK2831781 in patients with active ulcerative colitis.

BACKGROUND: Selective depletion of T cells expressing LAG-3, an immune checkpoint receptor that is upregulated on activated T cells, has been investigated in pre-clinical models as a potential therapeutic approach in inflammatory and autoimmune diseases where activated T cells are implicated. AIMS: GSK2831781, a depleting monoclonal antibody that specifically binds LAG-3 proteins, may deplete activated LAG-3+ cells in ulcerative colitis (UC). METHODS: Patients with moderate to severe UC were randomised to GSK2831781 or placebo. Safety, tolerability, efficacy, pharmacokinetics and pharmacodynamics of GSK2831781 were evaluated. RESULTS: One hundred four participants across all dose levels were randomised prior to an interim analysis indicating efficacy futility criteria had been met. Efficacy results focus on the double-blind induction phase of the study (GSK2831781 450 mg intravenously [IV], N = 48; placebo, N = 27). Median change from baseline (95% credible interval [CrI]) in complete Mayo score was similar between groups (GSK2831781 450 mg IV: -1.4 [-2.2, -0.7]; placebo: -1.4 [-2.4, -0.5]). Response rates for endoscopic improvement favoured placebo. Clinical remission rates were similar between groups. In the 450-mg IV group, 14 (29%) participants had an adverse event of UC versus 1 (4%) with placebo. LAG-3+ cells were depleted to 51% of baseline in blood; however, there was no reduction in LAG-3+ cells in the colonic mucosa. Transcriptomic analysis of colon biopsies showed no difference between groups. CONCLUSION: Despite evidence of target cell depletion in blood, GSK2831781 failed to reduce inflammation in the colonic mucosa suggesting no pharmacological effect. The study was terminated early (NCT03893565).

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.

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.

A qualitative study to explore the symptoms and impacts of Crohn's disease and to develop the Crohn's Disease Diary.

PURPOSE: To explore symptoms and disease impacts of Crohn's disease and to develop a new patient-reported outcomes (PRO) measure according to industry best practices. METHODS: A conceptual model of relevant symptoms experienced by patients with Crohn's disease was developed following a literature review. Three rounds of combined qualitative semi-structured concept elicitation and cognitive debriefing interviews with 36 patients (≥ 16 years) with Crohn's disease and 4 clinicians were conducted to further explore the most commonly reported and most bothersome symptoms to patients. Interview results were used to update the conceptual model as well as items and response options included in The Crohn's Disease Diary, a new PRO measure. RESULTS: All patients (N = 36) reported abdominal pain, loose or liquid bowel movements, and high or increased frequency of bowel movements, with most reporting these symptoms spontaneously (100%, 92%, and 75%, respectively). All patients reported bowel movement urgency, but 61% reported this symptom only when probed. Most also reported that symptoms impacted activities of daily living, work/school, and emotional, social, and physical functioning (overall, 78%-100%; spontaneously, 79% - 92%). Data regarding core symptoms of Crohn's disease from clinician concept elicitation interviews supported patient data. The 17-item Crohn's Disease Diary assesses core symptoms and impacts of Crohn's disease over 24 h, and extraintestinal manifestations over 7 days. The content validity of the diary was confirmed during cognitive debriefing interviews. CONCLUSION: The Crohn's Disease Diary is a new PRO measure for the assessment of Crohn's disease symptoms and impacts, developed according to industry best practices.

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.

Transforming L1000 profiles to RNA-seq-like profiles with deep learning.

The L1000 technology, a cost-effective high-throughput transcriptomics technology, has been applied to profile a collection of human cell lines for their gene expression response to > 30,000 chemical and genetic perturbations. In total, there are currently over 3 million available L1000 profiles. Such a dataset is invaluable for the discovery of drug and target candidates and for inferring mechanisms of action for small molecules. The L1000 assay only measures the mRNA expression of 978 landmark genes while 11,350 additional genes are computationally reliably inferred. The lack of full genome coverage limits knowledge discovery for half of the human protein coding genes, and the potential for integration with other transcriptomics profiling data. Here we present a Deep Learning two-step model that transforms L1000 profiles to RNA-seq-like profiles. The input to the model are the measured 978 landmark genes while the output is a vector of 23,614 RNA-seq-like gene expression profiles. The model first transforms the landmark genes into RNA-seq-like 978 gene profiles using a modified CycleGAN model applied to unpaired data. The transformed 978 RNA-seq-like landmark genes are then extrapolated into the full genome space with a fully connected neural network model. The two-step model achieves 0.914 Pearson's correlation coefficients and 1.167 root mean square errors when tested on a published paired L1000/RNA-seq dataset produced by the LINCS and GTEx programs. The processed RNA-seq-like profiles are made available for download, signature search, and gene centric reverse search with unique case studies.