Serum tryptophan metabolites are associated with erosive hand osteoarthritis and pain: results from the DIGICOD cohort.

OBJECTIVE: To investigate host and gut-microbiota related Tryptophan metabolism in hand osteoarthritis (HOA). METHODS: The baseline serum concentration of 20 Tryptophan metabolites was measured in 416 HOA patients in a cross-sectional analysis of the DIGICOD cohort. Tryptophan metabolites levels, metabolite-ratios and metabolism pathway activation were compared between erosive (N = 141) and non-erosive HOA (N = 275) by multiple logistic regressions adjusted on age, BMI and sex. The association between Tryptophan metabolite levels and HOA symptoms was investigated by a Spearman's rank correlation analysis. RESULTS: Four serum Tryptophan metabolites, eight metabolite ratios and one metabolism pathway were associated with erosive HOA. Erosive HOA was negatively associated with Tryptophan (odds ratio (OR) = 0.41, 95% confidence interval [0.24-0.70]), indole-3-aldehyde (OR = 0.67 [0.51-0.90]) and 3-OH-anthranilic acid (OR = 1.32 [1.13-1.54]) and positively with 5-OH-Tryptophan levels (OR = 1.41 [1.13-1.77]). The pro-inflammatory kynurenine-indoleamine 2,3-dioxygenase pathway was upregulated in erosive HOA (OR = 1.60 [1.11-2.29]). Eleven metabolites were correlated with HOA symptoms and were mostly pain-related. Serotonin and N-acetyl serotonin levels were negatively correlated with number of tender joints. Indole-3-aldehyde level was negatively correlated and 3-OH-anthranilic acid, 3-OH-kynurenine and 5-OH-Tryptophan levels were positively correlated with number of patients-reported painful joints. Quinolinic acid and 3-OH-kynurenine levels correlated positively with AUSCAN pain. CONCLUSIONS: Tryptophan metabolites disturbance is associated with erosive HOA and pain and emphasize the role of low-grade inflammation and gut dysbiosis in HOA.

Comprehensive transcriptomic analysis of cell lines as models of primary tumors across 22 tumor types.

Cancer cell lines are a cornerstone of cancer research but previous studies have shown that not all cell lines are equal in their ability to model primary tumors. Here we present a comprehensive pan-cancer analysis utilizing transcriptomic profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia to evaluate cell lines as models of primary tumors across 22 tumor types. We perform correlation analysis and gene set enrichment analysis to understand the differences between cell lines and primary tumors. Additionally, we classify cell lines into tumor subtypes in 9 tumor types. We present our pancreatic cancer results as a case study and find that the commonly used cell line MIA PaCa-2 is transcriptionally unrepresentative of primary pancreatic adenocarcinomas. Lastly, we propose a new cell line panel, the TCGA-110-CL, for pan-cancer studies. This study provides a resource to help researchers select more representative cell line models.

Integrating Clinical Phenotype and Gene Expression Data to Prioritize Novel Drug Uses.

Drug repositioning has been based largely on genomic signatures of drugs and diseases. One challenge in these efforts lies in connecting the molecular signatures of drugs into clinical responses, including therapeutic and side effects, to the repurpose of drugs. We addressed this challenge by evaluating drug-drug relationships using a phenotypic and molecular-based approach that integrates therapeutic indications, side effects, and gene expression profiles induced by each drug. Using cosine similarity, relationships between 445 drugs were evaluated based on high-dimensional spaces consisting of phenotypic terms of drugs and genomic signatures, respectively. One hundred fifty-one of 445 drugs comprising 450 drug pairs displayed significant similarities in both phenotypic and genomic signatures (P value < 0.05). We also found that similar gene expressions of drugs do indeed yield similar clinical phenotypes. We generated similarity matrixes of drugs using the expression profiles they induce in a cell line and phenotypic effects.

Identification of matrix metalloproteinase-12 as a candidate molecule for prevention and treatment of cardiometabolic disease.

Obesity is strongly associated with metabolic syndrome, a combination of risk factors that predispose to the development of the cardiometabolic diseases: atherosclerotic cardiovascular disease and type 2 diabetes mellitus. Prevention of metabolic syndrome requires novel interventions to address this health challenge. The objective of this study was the identification of candidate molecules for the prevention and treatment of insulin resistance and atherosclerosis, conditions that underlie type 2 diabetes mellitus and cardiovascular disease, respectively. We used an unbiased bioinformatics approach to identify molecules that are upregulated in both conditions by combining murine and human data from a microarray experiment and meta-analyses. We obtained a pool of eight genes that were upregulated in all the databases analysed. This included well known and novel molecules involved in the pathophysiology of type 2 diabetes mellitus and cardiovascular disease. Notably, matrix metalloproteinase 12 (MMP12) was highly ranked in all analyses and was therefore chosen for further investigation. Analyses of visceral and subcutaneous white adipose tissue from obese compared to lean mice and humans convincingly confirmed the up-regulation of MMP12 in obesity at mRNA, protein and activity levels. In conclusion, using this unbiased approach an interesting pool of candidate molecules was identified, all of which have potential as targets in the treatment and prevention of cardiometabolic diseases.

A patient-level data meta-analysis of standard-of-care treatments from eight prostate cancer clinical trials.

Open clinical trial data offer many opportunities for the scientific community to independently verify published results, evaluate new hypotheses and conduct meta-analyses. These data provide valuable opportunities for scientific advances in medical research. Herein we present the comparative meta-analysis of different standard of care treatments from newly available comparator arm data from several prostate cancer clinical trials. Comparison of survival rates following treatment with mitoxantrone or docetaxel in combination with prednisone as well as prednisone alone, validated the previously demonstrated superiority of treatment with docetaxel. Additionally, comparison of four testosterone suppression treatments in hormone-refractory prostate cancer revealed that subjects who had undergone surgical castration had significantly lower survival rates than those treated with LHRH, anti-androgen or LHRH plus anti-androgen, suggesting that this treatment option is less optimal. This study illustrates how the use of patient-level clinical trial data enables meta-analyses that can provide new insights into clinical outcomes of standard of care treatments and thus, once validated, has the potential to help optimize healthcare delivery.

Leveraging big data to transform target selection and drug discovery.

The advances of genomics, sequencing, and high throughput technologies have led to the creation of large volumes of diverse datasets for drug discovery. Analyzing these datasets to better understand disease and discover new drugs is becoming more common. Recent open data initiatives in basic and clinical research have dramatically increased the types of data available to the public. The past few years have witnessed successful use of big data in many sectors across the whole drug discovery pipeline. In this review, we will highlight the state of the art in leveraging big data to identify new targets, drug indications, and drug response biomarkers in this era of precision medicine.

Relating Chemical Structure to Cellular Response: An Integrative Analysis of Gene Expression, Bioactivity, and Structural Data Across 11,000 Compounds.

A central premise in systems pharmacology is that structurally similar compounds have similar cellular responses; however, this principle often does not hold. One of the most widely used measures of cellular response is gene expression. By integrating gene expression data from Library of Integrated Network-based Cellular Signatures (LINCS) with chemical structure and bioactivity data from PubChem, we performed a large-scale correlation analysis of chemical structures and gene expression profiles of over 11,000 compounds taking into account confounding factors such as biological conditions (e.g., cell line, dose) and bioactivities. We found that structurally similar compounds do indeed yield similar gene expression profiles. There is an ∼20% chance that two structurally similar compounds (Tanimoto Coefficient ≥ 0.85) share significantly similar gene expression profiles. Regardless of structural similarity, two compounds tend to share similar gene expression profiles in a cell line when they are administrated at a higher dose or when the cell line is sensitive to both compounds.

Network medicine in disease analysis and therapeutics.

Two parallel trends are occurring in drug discovery. The first is that we are moving away from a symptom-based disease classification system to a system based on molecules and molecular states. The second is that we are shifting from targeting a single molecule toward targeting multiple molecules, pathways, or networks. Network medicine is an approach to understanding disease and discovering therapeutics looking at many molecules and how they interrelate, and it may play a critical role in the adoption of both trends.

A peripheral blood diagnostic test for acute rejection in renal transplantation.

Monitoring of renal graft status through peripheral blood (PB) rather than invasive biopsy is important as it will lessen the risk of infection and other stresses, while reducing the costs of rejection diagnosis. Blood gene biomarker panels were discovered by microarrays at a single center and subsequently validated and cross-validated by QPCR in the NIH SNSO1 randomized study from 12 US pediatric transplant programs. A total of 367 unique human PB samples, each paired with a graft biopsy for centralized, blinded phenotype classification, were analyzed (115 acute rejection (AR), 180 stable and 72 other causes of graft injury). Of the differentially expressed genes by microarray, Q-PCR analysis of a five gene-set (DUSP1, PBEF1, PSEN1, MAPK9 and NKTR) classified AR with high accuracy. A logistic regression model was built on independent training-set (n = 47) and validated on independent test-set (n = 198)samples, discriminating AR from STA with 91% sensitivity and 94% specificity and AR from all other non-AR phenotypes with 91% sensitivity and 90% specificity. The 5-gene set can diagnose AR potentially avoiding the need for invasive renal biopsy. These data support the conduct of a prospective study to validate the clinical predictive utility of this diagnostic tool.

Integration of disease-specific single nucleotide polymorphisms, expression quantitative trait loci and coexpression networks reveal novel candidate genes for type 2 diabetes.

AIMS/HYPOTHESIS: While genome-wide association studies (GWASs) have been successful in identifying novel variants associated with various diseases, it has been much more difficult to determine the biological mechanisms underlying these associations. Expression quantitative trait loci (eQTL) provide another dimension to these data by associating single nucleotide polymorphisms (SNPs) with gene expression. We hypothesised that integrating SNPs known to be associated with type 2 diabetes with eQTLs and coexpression networks would enable the discovery of novel candidate genes for type 2 diabetes. METHODS: We selected 32 SNPs associated with type 2 diabetes in two or more independent GWASs. We used previously described eQTLs mapped from genotype and gene expression data collected from 1,008 morbidly obese patients to find genes with expression associated with these SNPs. We linked these genes to coexpression modules, and ranked the other genes in these modules using an inverse sum score. RESULTS: We found 62 genes with expression associated with type 2 diabetes SNPs. We validated our method by linking highly ranked genes in the coexpression modules back to SNPs through a combined eQTL dataset. We showed that the eQTLs highlighted by this method are significantly enriched for association with type 2 diabetes in data from the Wellcome Trust Case Control Consortium (WTCCC, p = 0.026) and the Gene Environment Association Studies (GENEVA, p = 0.042), validating our approach. Many of the highly ranked genes are also involved in the regulation or metabolism of insulin, glucose or lipids. CONCLUSIONS/INTERPRETATION: We have devised a novel method, involving the integration of datasets of different modalities, to discover novel candidate genes for type 2 diabetes.

Translational bioinformatics: data-driven drug discovery and development.

Internet-accessible computing power and data-sharing mandates now enable researchers to interrogate thousands of publicly available databases containing molecular, clinical, and epidemiological data. With emerging new approaches, translational bioinformatics can now provide answers to previously untouchable questions, ranging from detecting population signals of adverse drug reactions to clinical interpretation of the whole genome. There are challenges, including lack of access to some data sources and software, but there are also overwhelming doses of hopes and expectations.

Predicting adverse drug reactions using publicly available PubChem BioAssay data.

Adverse drug reactions (ADRs) can have severe consequences, and therefore the ability to predict ADRs prior to market introduction of a drug is desirable. Computational approaches applied to preclinical data could be one way to inform drug labeling and marketing with respect to potential ADRs. Based on the premise that some of the molecular actors of ADRs involve interactions that are detectable in large, and increasingly public, compound screening campaigns, we generated logistic regression models that correlate postmarketing ADRs with screening data from the PubChem BioAssay database. These models analyze ADRs at the level of organ systems, using the system organ classes (SOCs). Of the 19 SOCs under consideration, nine were found to be significantly correlated with preclinical screening data. With regard to six of the eight established drugs for which we could retropredict SOC-specific ADRs, prior knowledge was found that supports these predictions. We conclude this paper by predicting that SOC-specific ADRs will be associated with three unapproved or recently introduced drugs.

Systematic evaluation of drug-disease relationships to identify leads for novel drug uses.

Drug repositioning refers to the discovery of alternative uses for drugs--uses that are different from that for which the drugs were originally intended. One challenge in this effort lies in choosing the indication for which a drug of interest could be prospectively tested. We systematically evaluated a drug treatment-based view of diseases in order to address this challenge. Suggestions for novel drug uses were generated using a "guilt by association" approach. When compared with a control group of drug uses, the suggested novel drug uses generated by this approach were significantly enriched with respect to previous and ongoing clinical trials.

Data-driven methods to discover molecular determinants of serious adverse drug events.

The dangers of serious adverse drug reactions (SADRs) are well known to clinicians, pharmacologists, and the lay public. Efforts to elucidate the molecular mechanisms behind SADRs have made significant progress through genetics and gene expression measurements. However, as the field of pharmacology adopts the same novel higher-density measurement modalities that have proven successful in other areas of biology, one wonders whether there can be more ways to benefit from the explosion of data created by these tools. The development of analytic tools and algorithms to interpret these biological data to create tools for medicine is central to the field of translational bioinformatics. In this review we introduce some of the types of SADR predictors that are required, and we discuss several databases that are publicly available for the study of SADRs, ranging from clinical to molecular measurements. We also describe recent examples of how bioinformatics methods coupled with data repositories can advance the science of SADRs.

Strict interpretation of vaccination guidelines with computerized algorithms and improper timing of administered doses.

BACKGROUND: Frequently changing immunization recommendations may lead to incorrectly administered doses. OBJECTIVE: To determine the incidence and characteristics of inappropriately timed vaccinations. METHODS: Prospectively collected immunization histories of patients <5 years old from well-child care encounters with pediatric residents in a large urban clinic during a 3-month study period. New patients or those with no immunization history in the medical record were excluded. Paper records were verified before each visit and served as the immunization history. Immunization records were entered into and analyzed by the Massachusetts Immunization Information System with strict interpretation of minimum spacing and age guidelines to identify invalid vaccine doses. Reasons for invalidity were determined by manual review. Invalid doses were cross-referenced with clinic schedule to determine who delivered doses. RESULTS: Inclusion criteria were met by 690 encounters. Charts were available for review before the encounter for 580, containing 6983 total immunizations. Of these 289 (4.1%) administered doses were invalid; 206 of 580 (35.5%) patients had at least one invalid dose. Common invalid doses given were unnecessary poliovirus vaccine around 18 months (n = 66) and second hepatitis B vaccine given too soon after the first (n = 53). All types of providers gave invalid doses; pediatric residents and fellows delivered significantly more (P < 0.01). CONCLUSIONS: By strict interpretation of immunization guidelines, many patients were immunized incorrectly. Clinicians should be aware of common errors in vaccine dosing and national guidelines should be simplified.

Determining significant fold differences in gene expression analysis.

A typical use for RNA expression microarrays is comparing the measurement of gene expression of two groups. There has not been a study reproducing an entire experiment and modeling the distribution of reproducibility of fold differences. Our goal was to create a model of significance for fold differences, then maximize the number of ESTs above that threshold. Multiple strategies were tested to filter out those ESTs contributing to noise, thus decreasing the requirements of what was needed for significance. We found that even though RNA expression levels appears consistent in duplicate measurements, when entire experiments are duplicated, the calculated fold differences are not as consistent. Thus, it is critically important to repeat as many data points as possible, to ensure that genes and ESTs labeled as significant are truly so. We were successfully able to use duplicated expression measurements to model the duplicated fold differences, and to calculate the levels of fold difference needed to reach significance. This approach can be applied to many other experiments to ascertain significance without a priori assumptions.

Comparing the similarity of time-series gene expression using signal processing metrics.

Many algorithms have been used to cluster genes measured by microarray across a time series. Instead of clustering, our goal was to compare all pairs of genes to determine whether there was evidence of a phase shift between them. We describe a technique where gene expression is treated as a discrete time-invariant signal, allowing the use of digital signal-processing tools, including power spectral density, coherence, and transfer gain and phase shift. We used these on a public RNA expression set of 2467 genes measured every 7 min for 119 min and found 18 putative associations. Two of these were known in the biomedical literature and may have been missed using correlation coefficients. Digital signal processing tools can be embedded and enhance existing clustering algorithms.