WikiPathways: capturing the full diversity of pathway knowledge

From SNPs to pathways: Biological interpretation of type 2 diabetes (T2DM) genome wide association study (GWAS) results

Genome-wide association studies (GWAS) have become a common method for discovery of gene-disease relationships, in particular for complex diseases like Type 2 Diabetes Mellitus (T2DM). The experience with GWAS analysis has revealed that the genetic risk for complex diseases involves cumulative, small effects of many genes and only some genes with a moderate effect. In order to explore the complexity of the relationships between T2DM genes and their potential function at the process level as effected by polymorphism effects, a secondary analysis of a GWAS meta-analysis is presented. Network analysis, pathway information and integration of different types of biological information such as eQTLs and gene-environment interactions are used to elucidate the biological context of the genetic variants and to perform an analysis based on data visualization. We selected a T2DM dataset from a GWAS meta-analysis, and extracted 1,971 SNPs associated with T2DM. We mapped 580 SNPs to 360 genes, and then selected 460 pathways containing these genes from the curated collection of WikiPathways. We then created and analyzed SNP-gene and SNP-gene-pathway network modules in Cytoscape. A focus on genes with robust connections to pathways permitted identification of many T2DM pertinent pathways. However, numerous genes lack literature evidence of association with T2DM. We also speculate on the genes in specific network structures obtained in the SNP-gene network, such as gene-SNP-gene modules. Finally, we selected genes relevant to T2DM from our SNP-gene-pathway network, using different sources that reveal gene-environment interactions and eQTLs. We confirmed functions relevant to T2DM for many genes and have identified some-LPL and APOB-that require further validation to clarify their involvement in T2DM.

EZH2 regulates neuroblastoma cell differentiation via NTRK1 promoter epigenetic modifications

The polycomb repressor complex 2 molecule EZH2 is now known to play a role in essential cellular processes, namely, cell fate decisions, cell cycle regulation, senescence, cell differentiation, and cancer development/progression. EZH2 inhibitors have recently been developed; however, their effectiveness and underlying molecular mechanisms in many malignancies have not yet been elucidated in detail. Although the functional role of EZH2 in tumorigenesis in neuroblastoma (NB) has been investigated, mutations of EZH2 have not been reported. A Kaplan–Meier analysis on the event free survival and overall survival of NB patients indicated that the high expression of EZH2 correlated with an unfavorable prognosis. In order to elucidate the functional roles of EZH2 in NB tumorigenesis and its aggressiveness, we knocked down EZH2 in NB cell lines using lentivirus systems. The knockdown of EZH2 significantly induced NB cell differentiation, e.g., neurite extension, and the neuronal differentiation markers, NF68 and GAP43. EZH2 inhibitors also induced NB cell differentiation. We performed a comprehensive transcriptome analysis using Human Gene Expression Microarrays and found that NTRK1 (TrkA) is one of the EZH2-related suppression targets. The depletion of NTRK1 canceled EZH2 knockdown-induced NB cell differentiation. Our integrative methylome, transcriptome, and chromatin immunoprecipitation assays using NB cell lines and clinical samples clarified that the NTRK1 P1 and P2 promoter regions were regulated differently by DNA methylation and EZH2-related histone modifications. The NTRK1 transcript variants 1/2, which were regulated by EZH2-related H3K27me3 modifications at the P1 promoter region, were strongly expressed in favorable, but not unfavorable NB. The depletion and inhibition of EZH2 successfully induced NTRK1 transcripts and functional proteins. Collectively, these results indicate that EZH2 plays important roles in preventing the differentiation of NB cells and also that EZH2-related NTRK1 transcriptional regulation may be the key pathway for NB cell differentiation.

RaMP: A Comprehensive Relational Database of Metabolomics Pathways for Pathway Enrichment Analysis of Genes and Metabolites

The value of metabolomics in translational research is undeniable, and metabolomics data are increasingly generated in large cohorts. The functional interpretation of disease-associated metabolites though is difficult, and the biological mechanisms that underlie cell type or disease-specific metabolomics profiles are oftentimes unknown. To help fully exploit metabolomics data and to aid in its interpretation, analysis of metabolomics data with other complementary omics data, including transcriptomics, is helpful. To facilitate such analyses at a pathway level, we have developed RaMP (Relational database of Metabolomics Pathways), which combines biological pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, WikiPathways, and the Human Metabolome DataBase (HMDB). To the best of our knowledge, an off-the-shelf, public database that maps genes and metabolites to biochemical/disease pathways and can readily be integrated into other existing software is currently lacking. For consistent and comprehensive analysis, RaMP enables batch and complex queries (e.g., list all metabolites involved in glycolysis and lung cancer), can readily be integrated into pathway analysis tools, and supports pathway overrepresentation analysis given a list of genes and/or metabolites of interest. For usability, we have developed a RaMP R package (https://github.com/Mathelab/RaMP-DB), including a user-friendly RShiny web application, that supports basic simple and batch queries, pathway overrepresentation analysis given a list of genes or metabolites of interest, and network visualization of gene-metabolite relationships. The package also includes the raw database file (mysql dump), thereby providing a stand-alone downloadable framework for public use and integration with other tools. In addition, the Python code needed to recreate the database on another system is also publicly available (https://github.com/Mathelab/RaMP-BackEnd). Updates for databases in RaMP will be checked multiple times a year and RaMP will be updated accordingly.

Mathematical modeling of cancer metabolism

Systemic approaches are needed and useful for the study of the very complex issue of cancer. Modeling has a central position in these systemic approaches. Metabolic reprogramming is nowadays acknowledged as an essential hallmark of cancer. Mathematical modeling could contribute to a better understanding of cancer metabolic reprogramming and to identify new potential ways of therapeutic intervention. Herein, I review several alternative approaches to metabolic modeling and their current and future impact in oncology.

Comparison of Whole Body SOD1 Knockout with Muscle-Specific SOD1 Knockout Mice Reveals a Role for Nerve Redox Signaling in Regulation of Degenerative Pathways in Skeletal Muscle

AIMS

Lack of Cu,Zn-superoxide dismutase (CuZnSOD) in homozygous knockout mice (Sod1^-/-) leads to accelerated age-related muscle loss and weakness, but specific deletion of CuZnSOD in skeletal muscle (mSod1KO mice) or neurons (nSod1KO mice) resulted in only mild muscle functional deficits and failed to recapitulate the loss of mass and function observed in Sod1^-/- mice. To dissect any underlying cross-talk between motor neurons and skeletal muscle in the degeneration in Sod1^-/- mice, we characterized neuromuscular changes in the Sod1^-/- model compared with mSod1KO mice and examined degenerative molecular mechanisms and pathways in peripheral nerve and skeletal muscle.

RESULTS

In contrast to mSod1KO mice, myofiber atrophy in Sod1^-/- mice was associated with increased muscle oxidative damage, neuromuscular junction degeneration, denervation, nerve demyelination, and upregulation of proteins involved in maintenance of myelin sheaths. Proteomic analyses confirmed increased proteasomal activity and adaptive stress responses in muscle of Sod1^-/- mice that were absent in mSod1KO mice. Peripheral nerve from neither Sod1^-/- nor mSod1KO mice showed increased oxidative damage or molecular responses to increased oxidation compared with wild type mice. Differential cysteine (Cys) labeling revealed a specific redox shift in the catalytic Cys residue of peroxiredoxin 6 (Cys47) in the peripheral nerve from Sod1^-/- mice. Innovation and Conclusion: These findings demonstrate that neuromuscular integrity, redox mechanisms, and pathways are differentially altered in nerve and muscle of Sod1^-/- and mSod1KO mice. Results support the concept that impaired redox signaling, rather than oxidative damage, in peripheral nerve plays a key role in muscle loss in Sod1^-/- mice and potentially sarcopenia during aging. Antioxid. Redox Signal. 28, 275-295.

Pathway analysis of transcriptomic data shows immunometabolic effects of vitamin D

Unbiased genomic screening analyses have highlighted novel immunomodulatory properties of the active form of vitamin D, 1,25-dihydroxyvitamin D (1,25(OH)₂D). However, clearer interpretation of the resulting gene expression data is limited by cell model specificity. The aim of the current study was to provide a broader perspective on common gene regulatory pathways associated with innate immune responses to 1,25(OH)₂D, through systematic re-interrogation of existing gene expression databases from multiple related monocyte models (the THP-1 monocytic cell line (THP-1), monocyte-derived dendritic cells (DCs) and monocytes). Vitamin D receptor (VDR) expression is common to multiple immune cell types, and thus, pathway analysis of gene expression using data from multiple related models provides an inclusive perspective on the immunomodulatory impact of vitamin D. A bioinformatic workflow incorporating pathway analysis using PathVisio and WikiPathways was utilized to compare each set of gene expression data based on pathway-level context. Using this strategy, pathways related to the TCA cycle, oxidative phosphorylation and ATP synthesis and metabolism were shown to be significantly regulated by 1,25(OH)₂D in each of the repository models (Z-scores 3.52-8.22). Common regulation by 1,25(OH)₂D was also observed for pathways associated with apoptosis and the regulation of apoptosis (Z-scores 2.49-3.81). In contrast to the primary culture DC and monocyte models, the THP-1 myelomonocytic cell line showed strong regulation of pathways associated with cell proliferation and DNA replication (Z-scores 6.1-12.6). In short, data presented here support a fundamental role for active 1,25(OH)₂D as a pivotal regulator of immunometabolism.

Cross-linking BioThings APIs through JSON-LD to facilitate knowledge exploration

BACKGROUND

Application Programming Interfaces (APIs) are now widely used to distribute biological data. And many popular biological APIs developed by many different research teams have adopted Javascript Object Notation (JSON) as their primary data format. While usage of a common data format offers significant advantages, that alone is not sufficient for rich integrative queries across APIs.

RESULTS

Here, we have implemented JSON for Linking Data (JSON-LD) technology on the BioThings APIs that we have developed, MyGene.info , MyVariant.info and MyChem.info . JSON-LD provides a standard way to add semantic context to the existing JSON data structure, for the purpose of enhancing the interoperability between APIs. We demonstrated several use cases that were facilitated by semantic annotations using JSON-LD, including simpler and more precise query capabilities as well as API cross-linking.

CONCLUSIONS

We believe that this pattern offers a generalizable solution for interoperability of APIs in the life sciences.

Semi-supervised identification of cancer subgroups using survival outcomes and overlapping grouping information

Identification of cancer patient subgroups using high throughput genomic data is of critical importance to clinicians and scientists because it can offer opportunities for more personalized treatment and overlapping treatments of cancers. In spite of tremendous efforts, this problem still remains challenging because of low reproducibility and instability of identified cancer subgroups and molecular features. In order to address this challenge, we developed Integrative Genomics Robust iDentification of cancer subgroups (InGRiD), a statistical approach that integrates information from biological pathway databases with high-throughput genomic data to improve the robustness for identification and interpretation of molecularly-defined subgroups of cancer patients. We applied InGRiD to the gene expression data of high-grade serous ovarian cancer from The Cancer Genome Atlas and the Australian Ovarian Cancer Study. The results indicate clear benefits of the pathway-level approaches over the gene-level approaches. In addition, using the proposed InGRiD framework, we also investigate and address the issue of gene sharing among pathways, which often occurs in practice, to further facilitate biological interpretation of key molecular features associated with cancer progression. The R package "InGRiD" implementing the proposed approach is currently available in our research group GitHub webpage ( https://dongjunchung.github.io/INGRID/ ).

B Cell-Intrinsic Role for IRF5 in TLR9/BCR-Induced Human B Cell Activation, Proliferation, and Plasmablast Differentiation

Upon recognition of antigen, B cells undergo rapid proliferation followed by differentiation to specialized antibody secreting cells (ASCs). During this transition, B cells are reliant upon a multilayer transcription factor network to achieve a dramatic remodeling of the B cell transcriptional landscape. Increased levels of ASCs are often seen in autoimmune diseases and it is believed that altered expression of regulatory transcription factors play a role in this imbalance. The transcription factor interferon regulatory factor 5 (IRF5) is one such candidate as polymorphisms in IRF5 associate with risk of numerous autoimmune diseases and correlate with elevated IRF5 expression. IRF5 genetic risk has been widely replicated in systemic lupus erythematosus (SLE), and loss of Irf5 ameliorates disease in murine lupus models, in part, through the lack of pathogenic autoantibody secretion. It remains unclear, however, whether IRF5 is contributing to autoantibody production through a B cell-intrinsic function. To date, IRF5 function in healthy human B cells has not been characterized. Using human primary naive B cells, we define a critical intrinsic role for IRF5 in B cell activation, proliferation, and plasmablast differentiation. Targeted IRF5 knockdown resulted in significant immunoglobulin (Ig) D retention, reduced proliferation, plasmablast differentiation, and IgG secretion. The observed decreases were due to impaired B cell activation and clonal expansion. Distinct from murine studies, we identify and confirm new IRF5 target genes, IRF4, ERK1, and MYC, and pathways that mediate IRF5 B cell-intrinsic function. Together, these results identify IRF5 as an early regulator of human B cell activation and provide the first dataset in human primary B cells to map IRF5 dysfunction in SLE.

A Comparative Proteome Profile of Female Mouse Gonads Suggests a Tight Link between the Electron Transport Chain and Meiosis Initiation

Generation of haploid gametes by meiosis is a unique property of germ cells and is critical for sexual reproduction. Leaving mitosis and entering meiosis is a key step in germ cell development. Several inducers or intrinsic genes are known to be important for meiotic initiation, but the regulation of meiotic initiation, especially at the protein level, is still not well understood. We constructed a comparative proteome profile of female mouse fetal gonads at specific time points (11.5, 12.5, and 13.5 days post coitum), spanning a critical window for initiation of meiosis in female germ cells. We identified 3666 proteins, of which 473 were differentially expressed. Further bioinformatics analysis showed that these differentially expressed proteins were enriched in the mitochondria, especially in the electron transport chain and, notably, 9 proteins in electron transport chain Complex I were differentially expressed. We disrupted the mitochondrial electron transport chain function by adding the complex I inhibitor, rotenone to 11.5 days post coitum female gonads cultured in vitro. This treatment resulted in a decreased proportion of meiotic germ cells, as assessed by staining for histone γH2AX. Rotenone treatment also caused decreased ATP levels, increased reactive oxygen species levels and failure of the germ cells to undergo premeiotic DNA replication. These effects were partially rescued by adding Coenzyme Q10. Taken together, our results suggested that a functional electron transport chain is important for meiosis initiation. Our characterization of the quantitative proteome of female gonads provides an inventory of proteins, useful for understanding the mechanisms of meiosis initiation and female fertility.

Systems Biology Analysis to Understand Regulatory miRNA Networks in Lung Cancer

Lung cancer has currently the highest cancer-related mortality rate worldwide. MicroRNAs (miRNAs) are small noncoding RNAs that play a fundamental role in gene expression and are linked to disease progression of different cancer types such as lung cancer. However, functional characterization is made difficult by the fact that miRNAs generally regulate several mRNA interaction partners, resulting in complex regulatory networks. Thus, analysis of the network biology of miRNAs is essential for comprehensive understanding of their regulatory effects in lung cancer. A deeper understanding of miRNA networks in cancer could finally serve as a basis for the development of new therapeutic interventions. Here, we present a systems biology approach to analyze regulatory miRNA interaction networks to get better insight into their function.

Developing Network Models of Multiscale Host Responses Involved in Infections and Diseases

Complex interactions involved in host response to infections and diseases require advanced analytical tools to infer drivers of the response in order to develop strategies for intervention. This chapter discusses approaches to assemble interactions ranging from molecular to cellular levels and their analysis to investigate the cross talk between immune pathways. Particularly, construction of immune networks by either data-driven or literature-driven methods is explained. Next, graph theoretic approaches for probing static network properties as well as visualization of networks are discussed. Finally, development of Boolean models for simulation of network dynamics to investigate cross talk and emergent properties are considered along with Boolean-like models that may compensate for some of the limitations encountered in Boolean simulations. In conclusion, the chapter will allow readers to construct and analyze multiscale networks involved in immune responses.

Serum levels of miR-320 family members are associated with clinical parameters and diagnosis in prostate cancer patients

We studied the association of the serum levels of the microRNA family members miR-320a/-b/-c with clinico-pathological data to assess their applicability as diagnostic biomarker in prostate cancer (PCa) patients. The levels of miR-320a/-b/-c in 3 groups were evaluated by qRT-PCR (145 patients with PCa, 31 patients with benign prostatic hyperplasia (BPH) and 19 healthy controls). The levels of the three family members of miR-320 were directly correlated within each group (P < 0.001), but they differed significantly among the three groups (P < 0.001). The serum levels of the miR-320 family members were significantly increased in older patients compared to younger patients (≤ 66 years vs. > 66 years, P ≤ 0.001). In addition, the levels of all three miR-320 family members were significantly different in patients with low tumor stage compared with those with high tumor stage (miR-320a: P = 0.034; miR-320b: P = 0.006; miR-320c: P = 0.007) and in patients with low serum PSA compared with those with high serum PSA (≤ 4 ng vs. > 4 ng; miR-320a: P = 0.003; miR-320b: P = 0.003; miR-320c: P = 0.006). The levels of these miRNAs were inversely correlated with serum PSA levels. Detection in the serum samples of PCa patients with or without PSA relapse revealed higher levels of miR-320a/-b/-c in the group without PSA relapse before/after radical prostatectomy than in that with PCa relapse. In summary, the differences among the PCa/BPH/healthy control groups with respect to miR-320a/-b/-c levels in conjunction with higher levels in patients without a PSA relapse than in those with a relapse suggest the diagnostic potential of these miRNA-320 family members in PCa patients.

Effects of cell-cell crosstalk on gene expression patterns in a cell model of renal cell carcinoma lung metastasis

The median survival rate of patients with metastatic renal carcinoma is approximately 10 to 12 months, with up to 50% of patients developing metastases in the lung parenchyma. The molecular basis for metastatic development remains unclear. In the present study, we used renal cell carcinoma (RCC) cells and bronchial epithelial cells, representing metastasis target organ cells, conditioned medium and co-culture models to identify specific gene expression changes responsible for cancer cell viability in a metastatic microenvironment. RCC cell proliferation and migration increased when the culture was supplemented with conditioned medium from lung fibroblasts or pleural epithelial cells. Healthy epithelial cells were, in turn, also stimulated with conditioned medium from RCC cell lines. The mitogen-activated protein kinase (MAPK), interleukin (IL)-6, and phosphatidylinositol 4,5-bisphosphate (PIP2) signaling pathways were identified as deregulated upon cell‑cell interaction. Thus, cell-cell communication may contribute to the development of the metastatic niche. The identified deregulated signaling pathways may be considered as potential therapeutic targets in metastatic renal carcinoma.

Heat Failure Phenotypes Induced by Knockdown of DAPIT in Zebrafish: A New Insight into Mechanism of Dilated Cardiomyopathy

The pathogenesis of heart failure associated with dilated cardiomyopathy (DCM) may result in part from adenosine triphosphate (ATP) dysregulation in the myocardium. Under these conditions, diabetes-associated protein in insulin-sensitive tissue (DAPIT), which is encoded by the upregulated during skeletal muscle growth 5 (USMG5) gene, plays a crucial role in energy production by mitochondrial ATP synthase. To determine whether USMG5 is related to the development of heart failure, we performed clinical and experimental studies. Microarray analysis showed that the expression levels of USMG5 were positively correlated with those of natriuretic peptide precursor A in the human failed myocardium. When endogenous z-usmg5 in zebrafish was disrupted using morpholino (MO) oligonucleotides, the pericardial sac and atrial areas were larger and ventricular fractional shortening was reduced compared to in the control MO group. The expression levels of natriuretic peptides were upregulated in the z-usmg5 MO group compared to in controls. Further, microarray analysis revealed that genes in the calcium signalling pathway were downregulated in the z-usmg5 MO group. These results demonstrate that DAPIT plays a crucial role in the development of heart failure associated with DCM and thus may be a therapeutic target for heart failure.

Dysregulation of miR-155-5p and miR-200-3p and the Anti-Non-Bilayer Phospholipid Arrangement Antibodies Favor the Development of Lupus in Three Novel Murine Lupus Models

Systemic lupus erythematosus (SLE) is characterized by deregulated activation of T and B cells, autoantibody production, and consequent formation of immune complexes. Liposomes with nonbilayer phospholipid arrangements (NPA), induced by chlorpromazine, procainamide, or manganese, provoke a disease resembling human lupus when administered to mice. These mice produce anti-NPA IgM and IgG antibodies and exhibit an increased number of TLR-expressing spleen cells and a modified gene expression associated with TICAM1-dependent TLR-4 signaling (including IFNA1 and IFNA2) and complement activation. Additionally, they showed a diminished gene expression related to apoptosis and NK cell activation. We hypothesized that such gene expression may be affected by miRNAs and so miRNA expression was studied. Twelve deregulated miRNAs were found. Six of them were common to the three lupus-like models. Their validation by qRT-PCR and TaqMan probes, including miR-342-3p, revealed that miR-155-5p and miR-200a-3p expression was statistically significant. Currently described functions for these miRNAs in autoimmune diseases such as SLE reveal their participation in inflammation, interferon production, germinal center responses, and antibody maturation. Taking into account these findings, we propose miR-155-5p and miR-200a-3p, together with the anti-NPA antibodies, as key players in the murine lupus-like models and possible biomarkers of the human SLE.

Functional mapping and annotation of genetic associations with FUMA

A main challenge in genome-wide association studies (GWAS) is to pinpoint possible causal variants. Results from GWAS typically do not directly translate into causal variants because the majority of hits are in non-coding or intergenic regions, and the presence of linkage disequilibrium leads to effects being statistically spread out across multiple variants. Post-GWAS annotation facilitates the selection of most likely causal variant(s). Multiple resources are available for post-GWAS annotation, yet these can be time consuming and do not provide integrated visual aids for data interpretation. We, therefore, develop FUMA: an integrative web-based platform using information from multiple biological resources to facilitate functional annotation of GWAS results, gene prioritization and interactive visualization. FUMA accommodates positional, expression quantitative trait loci (eQTL) and chromatin interaction mappings, and provides gene-based, pathway and tissue enrichment results. FUMA results directly aid in generating hypotheses that are testable in functional experiments aimed at proving causal relations.

Prioritizing genetic variants is a major challenge in genome-wide association studies. Here, the authors develop FUMA, a web-based bioinformatics tool that uses a combination of positional, eQTL and chromatin interaction mapping to prioritize likely causal variants and genes.

Is systems pharmacology ready to impact upon therapy development? A study on the cholesterol biosynthesis pathway

Background and Purpose

An ever‐growing wealth of information on current drugs and their pharmacological effects is available from online databases. As our understanding of systems biology increases, we have the opportunity to predict, model and quantify how drug combinations can be introduced that outperform conventional single‐drug therapies. Here, we explore the feasibility of such systems pharmacology approaches with an analysis of the mevalonate branch of the cholesterol biosynthesis pathway.

Experimental Approach

Using open online resources, we assembled a computational model of the mevalonate pathway and compiled a set of inhibitors directed against targets in this pathway. We used computational optimization to identify combination and dose options that show not only maximal efficacy of inhibition on the cholesterol producing branch but also minimal impact on the geranylation branch, known to mediate the side effects of pharmaceutical treatment.

Key Results

We describe serious impediments to systems pharmacology studies arising from limitations in the data, incomplete coverage and inconsistent reporting. By curating a more complete dataset, we demonstrate the utility of computational optimization for identifying multi‐drug treatments with high efficacy and minimal off‐target effects.

Conclusion and Implications

We suggest solutions that facilitate systems pharmacology studies, based on the introduction of standards for data capture that increase the power of experimental data. We propose a systems pharmacology workflow for the refinement of data and the generation of future therapeutic hypotheses.

About miRNAs, miRNA seeds, target genes and target pathways

miRNAs are typically repressing gene expression by binding to the 3' UTR, leading to degradation of the mRNA. This process is dominated by the eight-base seed region of the miRNA. Further, miRNAs are known not only to target genes but also to target significant parts of pathways. A logical line of thoughts is: miRNAs with similar (seed) sequence target similar sets of genes and thus similar sets of pathways. By calculating similarity scores for all 3.25 million pairs of 2,550 human miRNAs, we found that this pattern frequently holds, while we also observed exceptions. Respective results were obtained for both, predicted target genes as well as experimentally validated targets. We note that miRNAs target gene set similarity follows a bimodal distribution, pointing at a set of 282 miRNAs that seems to target genes with very high specificity. Further, we discuss miRNAs with different (seed) sequences that nonetheless regulate similar gene sets or pathways. Most intriguingly, we found miRNA pairs that regulate different gene sets but similar pathways such as miR-6886-5p and miR-3529-5p. These are jointly targeting different parts of the MAPK signaling cascade. The main goal of this study is to provide a general overview on the results, to highlight a selection of relevant results on miRNAs, miRNA seeds, target genes and target pathways and to raise awareness for artifacts in respective comparisons. The full set of information that allows to infer detailed results on each miRNA has been included in miRPathDB, the miRNA target pathway database (https://mpd.bioinf.uni-sb.de).

A Review of Pathway-Based Analysis Tools That Visualize Genetic Variants

Pathway analysis is a powerful method for data analysis in genomics, most often applied to gene expression analysis. It is also promising for single-nucleotide polymorphism (SNP) data analysis, such as genome-wide association study data, because it allows the interpretation of variants with respect to the biological processes in which the affected genes and proteins are involved. Such analyses support an interactive evaluation of the possible effects of variations on function, regulation or interaction of gene products. Current pathway analysis software often does not support data visualization of variants in pathways as an alternate method to interpret genetic association results, and specific statistical methods for pathway analysis of SNP data are not combined with these visualization features. In this review, we first describe the visualization options of the tools that were identified by a literature review, in order to provide insight for improvements in this developing field. Tool evaluation was performed using a computational epistatic dataset of gene–gene interactions for obesity risk. Next, we report the necessity to include in these tools statistical methods designed for the pathway-based analysis with SNP data, expressly aiming to define features for more comprehensive pathway-based analysis tools. We conclude by recognizing that pathway analysis of genetic variations data requires a sophisticated combination of the most useful and informative visual aspects of the various tools evaluated.

Post-weaning epiphysiolysis causes distal femur dysplasia and foreshortened hindlimbs in fetuin-A-deficient mice

Fetuin-A / α2-Heremans-Schmid-glycoprotein (gene name Ahsg) is a systemic inhibitor of ectopic calcification. Due to its high affinity for calcium phosphate, fetuin-A is highly abundant in mineralized bone matrix. Foreshortened femora in fetuin-A-deficient Ahsg-/- mice indicated a role for fetuin-A in bone formation. We studied early postnatal bone development in fetuin-A-deficient mice and discovered that femora from Ahsg-/- mice exhibited severely displaced distal epiphyses and deformed growth plates, similar to the human disease slipped capital femoral epiphysis (SCFE). The growth plate slippage occurred in 70% of Ahsg-/- mice of both sexes around three weeks postnatal. At this time point, mice weaned and rapidly gained weight and mobility. Epiphysis slippage never occurred in wildtype and heterozygous Ahsg+/- mice. Homozygous fetuin-A-deficient Ahsg-/- mice and, to a lesser degree, heterozygous Ahsg+/- mice showed lesions separating the proliferative zone from the hypertrophic zone of the growth plate. The hypertrophic growth plate cartilage in long bones from Ahsg-/- mice was significantly elongated and V-shaped until three weeks of age and thus prior to the slippage. Genome-wide transcriptome analysis of laser-dissected distal femoral growth plates from 13-day-old Ahsg-/- mice revealed a JAK-STAT-mediated inflammatory response including a 550-fold induction of the chemokine Cxcl9. At this stage, vascularization of the elongated growth plates was impaired, which was visualized by immunofluorescence staining. Thus, fetuin-A-deficient mice may serve as a rodent model of growth plate pathologies including SCFE and inflammatory cartilage degradation.

LiverWiki: a wiki-based database for human liver

BACKGROUND

Recent advances in omics technology have produced a large amount of liver-related data. A comprehensive and up-to-date source of liver-related data is needed to allow biologists to access the latest data. However, current liver-related data sources each cover only a specific part of the liver. It is difficult for them to keep pace with the rapid increase of liver-related data available at those data resources. Integrating diverse liver-related data is a critical yet formidable challenge, as it requires sustained human effort.

RESULTS

We present LiverWiki, a first wiki-based database that integrates liver-related genes, homolog genes, gene expressions in microarray datasets and RNA-Seq datasets, proteins, protein interactions, post-translational modifications, associated pathways, diseases, metabolites identified in the metabolomics datasets, and literatures into an easily accessible and searchable resource for community-driven sharing. LiverWiki houses information in a total of 141,897 content pages, including 19,787 liver-related gene pages, 17,077 homolog gene pages, 50,251 liver-related protein pages, 36,122 gene expression pages, 2067 metabolites identified in the metabolomics datasets, 16,366 disease-related molecules, and 227 liver disease pages. Other than assisting users in searching, browsing, reviewing, refining the contents on LiverWiki, the most important contribution of LiverWiki is to allow the community to create and update biological data of liver in visible and editable tables. This integrates newly produced data with existing knowledge. Implemented in mediawiki, LiverWiki provides powerful extensions to support community contributions.

CONCLUSIONS

The main goal of LiverWiki is to provide the research community with comprehensive liver-related data, as well as to allow the research community to share their liver-related data flexibly and efficiently. It also enables rapid sharing new discoveries by allowing the discoveries to be integrated and shared immediately, rather than relying on expert curators. The database is available online at http://liverwiki.hupo.org.cn /.

Molecular mechanisms underlying gliomas and glioblastoma pathogenesis revealed by bioinformatics analysis of microarray data

The aim of this study was to identify key genes associated with gliomas and glioblastoma and to explore the related signaling pathways. Gene expression profiles of three glioma stem cell line samples, three normal astrocyte samples, three astrocyte overexpressing 4 iPSC-inducing and oncogenic factors (myc(T58A), OCT-4, p53DD, and H-Ras(G12V)) samples, three astrocyte overexpressing 7 iPSC-inducing and oncogenic factors (OCT4, H-Ras(G12V), myc(T58A), p53DD, cyclin D1, CDK4(RC24) and hTERT) samples and three glioblastoma cell line samples were downloaded from the ArrayExpress database (accession: E-MTAB-4771). The differentially expressed genes (DEGs) in gliomas and glioblastoma were identified using FDR and t tests, and protein-protein interaction (PPI) networks for these DEGs were constructed using the protein interaction network analysis. The GeneTrail2 1.5 tool was used to identify potentially enriched biological processes among the DEGs using gene ontology (GO) terms and to identify the related pathways using the Kyoto Encyclopedia of Genes and Genomes, Reactome and WikiPathways pathway database. In addition, crucial modules of the constructed PPI networks were identified using the PEWCC1 plug-in, and their topological properties were analyzed using NetworkAnalyzer, both available from Cytoscape. We also constructed microRNA-target gene regulatory network and transcription factor-target gene regulatory network for these DEGs were constructed using the miRNet and binding and expression target analysis. We identified 200 genes that could potentially be involved in the gliomas and glioblastoma. Among them, bioinformatics analysis identified 137 up-regulated and 63 down-regulated DEGs in gliomas and glioblastoma. The significant enriched pathway (PI3K-Akt) for up-regulated genes such as COL4A1, COL4A2, EGFR, FGFR1, LAPR6, MYC, PDGFA, SPP1 were selected as well as significant GO term (ear development) for up-regulated genes such as CELSR1, CHRNA9, DDR1, FGFR1, GLI2, LGR5, SOX2, TSHR were selected, while the significant enriched pathway (amebiasis) for down-regulated gene such as COL3A1, COL5A2, LAMA2 were selected as well as significant GO term (RNA polymerase II core promoter proximal region sequence-specific binding (5) such as MEIS2, MEOX2, NR2E1, PITX2, TFAP2B, ZFPM2 were selected. Importantly, MYC and SOX2 were hub proteins in the up-regulated PPI network, while MET and CDKN2A were hub proteins in the down-regulated PPI network. After network module analysis, MYC, FGFR1 and HOXA10 were selected as the up-regulated coexpressed genes in the gliomas and glioblastoma, while SH3GL3 and SNRPN were selected as the down-regulated coexpressed genes in the gliomas and glioblastoma. MicroRNA hsa-mir-22-3p had a regulatory effect on the most up DEGs, including VSNL1, while hsa-mir-103a-3p had a regulatory effect on the most down DEGs, including DAPK1. Transcription factor EZH2 had a regulatory effect on the both up and down DEGs, including CD9, CHI3L1, MEIS2 and NR2E1. The DEGs, such as MYC, FGFR1, CDKN2A, HOXA10 and MET, may be used for targeted diagnosis and treatment of gliomas and glioblastoma.

A genomic screen for angiosuppressor genes in the tumor endothelium identifies a multifaceted angiostatic role for bromodomain containing 7 (BRD7)

Tumor angiogenesis is characterized by deregulated gene expression in endothelial cells (EC). While studies until now have mainly focused on overexpressed genes in tumor endothelium, we here describe the identification of transcripts that are repressed in tumor endothelium and thus have potential suppressive effects on angiogenesis. We identified nineteen putative angiosuppressor genes, one of them being bromodomain containing 7 (BRD7), a gene that has been assigned tumor suppressor properties. BRD7 was studied in more detail, and we demonstrate that BRD7 expression is inversely related to EC activation. Ectopic expression of BRD7 resulted in a dramatic reduction of EC proliferation and viability. Furthermore, overexpression of BRD7 resulted in a bromodomain-dependent induction of NFκB-activity and NFκB-dependent gene expression, including ICAM1, enabling leukocyte–endothelial interactions. In silico functional annotation analysis of genome-wide expression data on BRD7 knockdown and overexpression revealed that the transcriptional signature of low BRD7 expressing cells is associated with increased angiogenesis (a.o. upregulation of angiopoietin-2, VEGF receptor-1 and neuropilin-1), cytokine activity (a.o. upregulation of CXCL1 and CXCL6), and a reduction of immune surveillance (TNF-α, NFκB, ICAM1). Thus, combining in silico and in vitro data reveals multiple pathways of angiosuppressor and anti-tumor activities of BRD7.

Systematic integration of biomedical knowledge prioritizes drugs for repurposing

The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data were integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then, we predicted the probability of treatment for 209,168 compound-disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.

Determinants and Regulation of Protein Turnover in Yeast

Protein turnover maintains the recycling needs of the proteome, and its malfunction has been linked to aging and age-related diseases. However, not all proteins turnover equally, and the factors that contribute to accelerate or slow down turnover are mostly unknown. We measured turnover rates for 3,160 proteins in exponentially growing yeast and analyzed their dependence on physical, functional, and genetic properties. We found that functional characteristics, including protein localization, complex membership, and connectivity, have greater effect on turnover than sequence elements. We also found that protein turnover and mRNA turnover are correlated. Analysis under nutrient perturbation and osmotic stress revealed that protein turnover highly depends on cellular state and is faster when proteins are being actively used. Finally, stress-induced changes in protein and transcript abundance correlated with changes in protein turnover. This study provides a resource of protein turnover rates and principles to understand the recycling needs of the proteome under basal conditions and perturbation.

Mapping the STK4/Hippo signaling network in prostate cancer cell

Dysregulation of MST1/STK4, a key kinase component of the Hippo-YAP pathway, is linked to the etiology of many cancers with poor prognosis. However, how STK4 restricts the emergence of aggressive cancer remains elusive. Here, we investigated the effects of STK4, primarily localized in the cytoplasm, lipid raft, and nucleus, on cell growth and gene expression in aggressive prostate cancer. We demonstrated that lipid raft and nuclear STK4 had superior suppressive effects on cell growth in vitro and in vivo compared with cytoplasmic STK4. Using RNA sequencing and bioinformatics analysis, we identified several differentially expressed (DE) genes that responded to ectopic STK4 in all three subcellular compartments. We noted that the number of DE genes observed in lipid raft and nuclear STK4 cells were much greater than cytoplasmic STK4. Our functional annotation clustering showed that these DE genes were commonly associated with oncogenic pathways such as AR, PI3K/AKT, BMP/SMAD, GPCR, WNT, and RAS as well as unique pathways such as JAK/STAT, which emerged only in nuclear STK4 cells. These findings indicate that MST1/STK4/Hippo signaling restricts aggressive tumor cell growth by intersecting with multiple molecular pathways, suggesting that targeting of the STK4/Hippo pathway may have important therapeutic implications for cancer.

The future of metabolomics in ELIXIR

Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the "Future of metabolomics in ELIXIR" was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community. The workshop established metabolite identification as the critical area, where a maximal impact of computational metabolomics and data management on other fields could be achieved. In particular, the existing four ELIXIR Use Cases, where the metabolomics community - both industry and academia - would benefit most, and which could be exhaustively mapped onto the current five ELIXIR Platforms were discussed. This opinion article is a call for support for a new ELIXIR metabolomics Use Case, which aligns with and complements the existing and planned ELIXIR Platforms and Use Cases.

A new semisynthetic cardenolide analog 3β-[2-(1-amantadine)- 1-on-ethylamine]-digitoxigenin (AMANTADIG) affects G2/M cell cycle arrest and miRNA expression profiles and enhances proapoptotic survivin-2B expression in renal cell carcinoma cell lines

Cardiac glycosides are well known in the treatment of cardiovascular diseases; however, their application as treatment option for cancer patients is under discussion. We showed that the cardiac glycoside digitoxin and its analog AMANTADIG can inhibit the growth of renal cell carcinoma (RCC) cell lines and increase G2/M cell cycle arrest. To identify the signaling pathways and molecular basis of this G2/M arrest, microRNAs were profiled using microRNA arrays. Cardiac glycoside treatment significantly deregulated two microRNAs, miR-2278 and miR-670-5p. Pathway enrichment analysis showed that all cardiac glycoside treatments affected the MAPK and the axon guidance pathway. Within these pathways, three genes, MAPK1, NRAS and RAC2, were identified as in silico targets of the deregulated miRNAs. MAPK1 and NRAS are known regulators of G2/M cell cycle arrest. AMANTADIG treatment enhanced the expression of phosphorylated MAPK1 in 786-O cells. Secondly, we studied the expression of survivin known to be affected by cardiac glycosides and to regulate the G2/M cell phase. AMANTADIG treatment upregulated the expression of the pro-apoptotic survivin-2B variant in Caki-1 and 786-O cells. Moreover, treatment with AMANTADIG resulted in significantly lower survivin protein expression compared to 786-O control cells. Summarizing, treatment with all cardiac glycosides induced G2/M cell cycle arrest and downregulated the miR-2278 and miR-670-5p in microarray analysis. All cardiac glycosides affected the MAPK-pathway and survivin expression, both associated with the G2/M phase. Because cells in the G2/M phase are radio- and chemotherapy sensitive, cardiac glycosides like AMANTADIG could potentially improve the efficacy of radio- and/or chemotherapy in RCCs.

Predicting the murine enterocyte metabolic response to diets that differ in lipid and carbohydrate composition

The small intestine serves as gatekeeper at the interface between body and diet and is thought to play an important role in the etiology of obesity and associated metabolic disorders. A computational modelling approach was used to improve our understanding of the metabolic responses of epithelial cells to different diets. A constraint based, mouse-specific enterocyte metabolic model (named mmu_ENT717) was constructed to describe the impact of four fully characterized semi-purified diets, that differed in lipid and carbohydrate composition, on uptake, metabolism, as well as secretion of carbohydrates and lipids. Our simulation results predicted luminal sodium as a limiting factor for active glucose absorption; necessity of apical localization of glucose transporter GLUT2 for absorption of all glucose in the postprandial state; potential for gluconeogenesis in enterocytes; and the requirement of oxygen for the formation of endogenous cholesterol needed for chylomicron formation under luminal cholesterol-free conditions. In addition, for a number of enzymopathies related to intestinal carbohydrate and lipid metabolism it was found that their effects might be ameliorated through dietary interventions. In conclusion, our improved enterocyte-specific model was shown to be a suitable platform to study effects of dietary interventions on enterocyte metabolism, and provided novel and deeper insights into enterocyte metabolism.

Benchmarking Water Quality from Wastewater to Drinking Waters Using Reduced Transcriptome of Human Cells

One of the major challenges in environmental science is monitoring and assessing the risk of complex environmental mixtures. In vitro bioassays with limited key toxicological end points have been shown to be suitable to evaluate mixtures of organic pollutants in wastewater and recycled water. Omics approaches such as transcriptomics can monitor biological effects at the genome scale. However, few studies have applied omics approach in the assessment of mixtures of organic micropollutants. Here, an omics approach was developed for profiling bioactivity of 10 water samples ranging from wastewater to drinking water in human cells by a reduced human transcriptome (RHT) approach and dose-response modeling. Transcriptional expression of 1200 selected genes were measured by an Ampliseq technology in two cell lines, HepG2 and MCF7, that were exposed to eight serial dilutions of each sample. Concentration-effect models were used to identify differentially expressed genes (DEGs) and to calculate effect concentrations (ECs) of DEGs, which could be ranked to investigate low dose response. Furthermore, molecular pathways disrupted by different samples were evaluated by Gene Ontology (GO) enrichment analysis. The ability of RHT for representing bioactivity utilizing both HepG2 and MCF7 was shown to be comparable to the results of previous in vitro bioassays. Finally, the relative potencies of the mixtures indicated by RHT analysis were consistent with the chemical profiles of the samples. RHT analysis with human cells provides an efficient and cost-effective approach to benchmarking mixture of micropollutants and may offer novel insight into the assessment of mixture toxicity in water.

Review of emerging metabolomic tools and resources: 2015-2016

Data processing and analysis are major bottlenecks in high-throughput metabolomic experiments. Recent advancements in data acquisition platforms are driving trends toward increasing data size (e.g., petabyte scale) and complexity (multiple omic platforms). Improvements in data analysis software and in silico methods are similarly required to effectively utilize these advancements and link the acquired data with biological interpretations. Herein, we provide an overview of recently developed and freely available metabolomic tools, algorithms, databases, and data analysis frameworks. This overview of popular tools for MS and NMR-based metabolomics is organized into the following sections: data processing, annotation, analysis, and visualization. The following overview of newly developed tools helps to better inform researchers to support the emergence of metabolomics as an integral tool for the study of biochemistry, systems biology, environmental analysis, health, and personalized medicine.

Logic Modeling in Quantitative Systems Pharmacology

Here we present logic modeling as an approach to understand deregulation of signal transduction in disease and to characterize a drug's mode of action. We discuss how to build a logic model from the literature and experimental data and how to analyze the resulting model to obtain insights of relevance for systems pharmacology. Our workflow uses the free tools OmniPath (network reconstruction from the literature), CellNOpt (model fit to experimental data), MaBoSS (model analysis), and Cytoscape (visualization).

Gene Expression Profiling in Human Lung Cells Exposed to Isoprene-Derived Secondary Organic Aerosol

Secondary organic aerosol (SOA) derived from the photochemical oxidation of isoprene contributes a substantial mass fraction to atmospheric fine particulate matter (PM2.5). The formation of isoprene SOA is influenced largely by anthropogenic emissions through multiphase chemistry of its multigenerational oxidation products. Considering the abundance of isoprene SOA in the troposphere, understanding mechanisms of adverse health effects through inhalation exposure is critical to mitigating its potential impact on public health. In this study, we assessed the effects of isoprene SOA on gene expression in human airway epithelial cells (BEAS-2B) through an air-liquid interface exposure. Gene expression profiling of 84 oxidative stress and 249 inflammation-associated human genes was performed. Our results show that the expression levels of 29 genes were significantly altered upon isoprene SOA exposure under noncytotoxic conditions (p < 0.05), with the majority (22/29) of genes passing a false discovery rate threshold of 0.3. The most significantly affected genes belong to the nuclear factor (erythroid-derived 2)-like 2 (Nrf2) transcription factor network. The Nrf2 function is confirmed through a reporter cell line. Together with detailed characterization of SOA constituents, this study reveals the impact of isoprene SOA exposure on lung responses and highlights the importance of further understanding its potential health outcomes.

Using ChEMBL web services for building applications and data processing workflows relevant to drug discovery

INTRODUCTION

ChEMBL is a manually curated database of bioactivity data on small drug-like molecules, used by drug discovery scientists. Among many access methods, a REST API provides programmatic access, allowing the remote retrieval of ChEMBL data and its integration into other applications. This approach allows scientists to move from a world where they go to the ChEMBL web site to search for relevant data, to one where ChEMBL data can be simply integrated into their everyday tools and work environment. Areas covered: This review highlights some of the audiences who may benefit from using the ChEMBL API, and the goals they can address, through the description of several use cases. The examples cover a team communication tool (Slack), a data analytics platform (KNIME), batch job management software (Luigi) and Rich Internet Applications. Expert opinion: The advent of web technologies, cloud computing and micro services oriented architectures have made REST APIs an essential ingredient of modern software development models. The widespread availability of tools consuming RESTful resources have made them useful for many groups of users. The ChEMBL API is a valuable resource of drug discovery bioactivity data for professional chemists, chemistry students, data scientists, scientific and web developers.

p21 maintains senescent cell viability under persistent DNA damage response by restraining JNK and caspase signaling

Cellular senescence is a permanent state of cell cycle arrest that protects the organism from tumorigenesis and regulates tissue integrity upon damage and during tissue remodeling. However, accumulation of senescent cells in tissues during aging contributes to age‐related pathologies. A deeper understanding of the mechanisms regulating the viability of senescent cells is therefore required. Here, we show that the CDK inhibitor p21 (CDKN1A) maintains the viability of DNA damage‐induced senescent cells. Upon p21 knockdown, senescent cells acquired multiple DNA lesions that activated ataxia telangiectasia mutated (ATM) and nuclear factor (NF)‐κB kinase, leading to decreased cell survival. NF‐κB activation induced TNF‐α secretion and JNK activation to mediate death of senescent cells in a caspase‐ and JNK‐dependent manner. Notably, p21 knockout in mice eliminated liver senescent stellate cells and alleviated liver fibrosis and collagen production. These findings define a novel pathway that regulates senescent cell viability and fibrosis.

Deconstructing Olfactory Stem Cell Trajectories at Single-Cell Resolution

A detailed understanding of the paths that stem cells traverse to generate mature progeny is vital for elucidating the mechanisms governing cell fate decisions and tissue homeostasis. Adult stem cells maintain and regenerate multiple mature cell lineages in the olfactory epithelium. Here we integrate single-cell RNA sequencing and robust statistical analyses with in vivo lineage tracing to define a detailed map of the postnatal olfactory epithelium, revealing cell fate potentials and branchpoints in olfactory stem cell lineage trajectories. Olfactory stem cells produce support cells via direct fate conversion in the absence of cell division, and their multipotency at the population level reflects collective unipotent cell fate decisions by single stem cells. We further demonstrate that Wnt signaling regulates stem cell fate by promoting neuronal fate choices. This integrated approach reveals the mechanisms guiding olfactory lineage trajectories and provides a model for deconstructing similar hierarchies in other stem cell niches.

CD36 Is a Marker of Human Adipocyte Progenitors with Pronounced Adipogenic and Triglyceride Accumulation Potential

White adipose tissue (WAT) expands in part through adipogenesis, a process involving fat cell generation and fatty acid (FA) storage into triglycerides (TGs). Several findings suggest that inter-individual and regional variations in adipogenesis are linked to metabolic complications. We aimed to identify cellular markers that define human adipocyte progenitors (APs) with pronounced adipogenic/TG storage ability. Using an unbiased single cell screen of passaged human adipose-derived stromal cells (hADSCs), we identified cell clones with similar proliferation rates but discordant capabilities to undergo adipogenic differentiation. Transcriptomic analyses prior to induction of differentiation showed that adipogenic clones displayed a significantly higher expression of CD36, encoding the scavenger receptor CD36. CD36+ hADSCs, in comparison with CD36-cells, displayed almost complete adipogenic differentiation while CD36 RNAi attenuated lipid accumulation. Similar findings were observed in primary CD45-/CD34+/CD31-APs isolated from human WAT where the subpopulation of MSCA1+/CD36+ cells displayed a significantly higher differentiation degree/TG storage capacity than MSCA1+/CD36-cells. Functional analyses in vitro and ex vivo confirmed that CD36 conferred APs an increased capacity to take up FAs thereby facilitating terminal differentiation. Among primary APs from subcutaneous femoral, abdominal and visceral human WAT, the fraction of CD36+ cells was significantly higher in depots associated with higher adipogenesis and reduced metabolic risk (i.e., femoral WAT). We conclude that CD36 marks APs with pronounced adipogenic potential, most probably by facilitating lipid uptake. This may be of value in developing human adipocyte cell clones and possibly in linking regional variations in adipogenesis to metabolic phenotype. Stem Cells 2017;35:1799-1814.

Crowdsourcing biomedical research: leveraging communities as innovation engines

The generation of large-scale biomedical data is creating unprecedented opportunities for basic and translational science. Typically, the data producers perform initial analyses, but it is very likely that the most informative methods may reside with other groups. Crowdsourcing the analysis of complex and massive data has emerged as a framework to find robust methodologies. When the crowdsourcing is done in the form of collaborative scientific competitions, known as Challenges, the validation of the methods is inherently addressed. Challenges also encourage open innovation, create collaborative communities to solve diverse and important biomedical problems, and foster the creation and dissemination of well-curated data repositories.

Interferon Receptor Signaling Pathways Regulating PD-L1 and PD-L2 Expression

PD-L1 and PD-L2 are ligands for the PD-1 immune inhibiting checkpoint that can be induced in tumors by interferon exposure, leading to immune evasion. This process is important for immunotherapy based on PD-1 blockade. We examined the specific molecules involved in interferon-induced signaling that regulates PD-L1 and PD-L2 expression in melanoma cells. These studies revealed that the interferon-gamma-JAK1/JAK2-STAT1/STAT2/STAT3-IRF1 axis primarily regulates PD-L1 expression, with IRF1 binding to its promoter. PD-L2 responded equally to interferon beta and gamma and is regulated through both IRF1 and STAT3, which bind to the PD-L2 promoter. Analysis of biopsy specimens from patients with melanoma confirmed interferon signature enrichment and upregulation of gene targets for STAT1/STAT2/STAT3 and IRF1 in anti-PD-1-responding tumors. Therefore, these studies map the signaling pathway of interferon-gamma-inducible PD-1 ligand expression.

Interferon Receptor Signaling Pathways Regulating PD-L1 and PD-L2 Expression

PD-L1 and PD-L2 are ligands for the PD-1 immune inhibiting checkpoint that can be induced in tumors by interferon exposure, leading to immune evasion. This process is important for immunotherapy based on PD-1 blockade. We examined the specific molecules involved in interferon-induced signaling that regulates PD-L1 and PD-L2 expression in melanoma cells. These studies revealed that the interferon-gamma-JAK1/JAK2-STAT1/STAT2/STAT3-IRF1 axis primarily regulates PD-L1 expression, with IRF1 binding to its promoter. PD-L2 responded equally to interferon beta and gamma and is regulated through both IRF1 and STAT3, which bind to the PD-L2 promoter. Analysis of biopsy specimens from patients with melanoma confirmed interferon signature enrichment and upregulation of gene targets for STAT1/STAT2/STAT3 and IRF1 in anti-PD-1-responding tumors. Therefore, these studies map the signaling pathway of interferon-gamma-inducible PD-1 ligand expression.