The connectivity map

Identification of nagilactone E as a protein synthesis inhibitor with anticancer activity

Norditerpenoids and dinorditerpenoids represent diterpenoids widely distributed in the genus Podocarpus with notable chemical structures and biological activities. We previously reported that nagilactone E (NLE), a dinorditerpenoid isolated from Podocarpus nagi, possessed anticancer effects against lung cancer cells in vitro. In this study we investigated the in vivo effect of NLE against lung cancer as well as the underlying mechanisms. We administered NLE (10 mg·kg^-1·d^-1, ip) to CB-17/SCID mice bearing human lung cancer cell line A549 xenograft for 3 weeks. We found that NLE administration significantly suppressed the tumor growth without obvious adverse effects. Thereafter, RNA sequencing (RNA-seq) analysis was performed to study the mechanisms of NLE. The effects of NLE on A549 cells have been illustrated by GO and pathway enrichment analyses. CMap dataset analysis supported NLE to be a potential protein synthesis inhibitor. The inhibitory effect of NLE on synthesis of total de novo protein was confirmed in Click-iT assay. Using the pcDNA3-RLUC-POLIRES-FLUC luciferase assay we further demonstrated that NLE inhibited both cap-dependent and cap-independent translation. Finally, molecular docking revealed the low-energy binding conformations of NLE and its potential target RIOK2. In conclusion, NLE is a protein synthesis inhibitor with anticancer activity.

Indirubin, a small molecular deriving from connectivity map (CMAP) screening, ameliorates obesity-induced metabolic dysfunction by enhancing brown adipose thermogenesis and white adipose browning

Background

Obesity occurs when the body’s energy intake is constantly greater than its energy consumption and the pharmacological enhancing the activity of brown adipose tissue (BAT) and (or) browning of white adipose tissue (WAT) has been considered promising strategies to treat obesity.

Methods

In this study, we took a multi-pronged approach to screen UCP1 activators, including in silico predictions, in vitro assays, as well as in vivo experiments.

Results

Base on Connectivity MAP (CMAP) screening, we obtained multiple drugs that possess a remarkably correlating gene expression pattern to that of enhancing activity in BAT and (or) sWAT signature. Particularly, we focused on a previously unreported drug-indirubin, a compound obtained from the Indigo plant, which is now mainly used for the treatment of chronic myelogenous leukemia (CML). In the current study, our results shown that indirubin could enhance the BAT activity, as evidenced by up-regulated Ucp1 expression and enhanced mitochondrial respiratory function in vitro cellular model. Furthermore, indirubin treatment restrained high-fat diet (HFD)-induced body weight gain, improved glucose homeostasis and ameliorated hepatic steatosis which were associated with the increase of energy expenditure in the mice model. Moreover, we revealed that indirubin treatment increased BAT activity by promoting thermogenesis and mitochondrial biogenesis in BAT and induced browning of subcutaneous inguinal white adipose tissue (sWAT) of mice under HFD. Besides, our results indicated that indirubin induced UCP1 expression in brown adipocytes, at least in part, via activation of PKA and p38MAPK signaling pathways.

Conclusions

Our results clearly show that as an effective BAT (as well as beige cells) activator, indirubin may have a protective effect on the prevention and treatment of obesity and its complications.

Consistent Biomarkers and Related Pathogenesis Underlying Asthma Revealed by Systems Biology Approach

Asthma is a common chronic airway disease worldwide. Due to its clinical and genetic heterogeneity, the cellular and molecular processes in asthma are highly complex and relatively unknown. To discover novel biomarkers and the molecular mechanisms underlying asthma, several studies have been conducted by focusing on gene expression patterns in epithelium through microarray analysis. However, few robust specific biomarkers were identified and some inconsistent results were observed. Therefore, it is imperative to conduct a robust analysis to solve these problems. Herein, an integrated gene expression analysis of ten independent, publicly available microarray data of bronchial epithelial cells from 348 asthmatic patients and 208 healthy controls was performed. As a result, 78 up- and 75 down-regulated genes were identified in bronchial epithelium of asthmatics. Comprehensive functional enrichment and pathway analysis revealed that response to chemical stimulus, extracellular region, pathways in cancer, and arachidonic acid metabolism were the four most significantly enriched terms. In the protein-protein interaction network, three main communities associated with cytoskeleton, response to lipid, and regulation of response to stimulus were established, and the most highly ranked 6 hub genes (up-regulated CD44, KRT6A, CEACAM5, SERPINB2, and down-regulated LTF and MUC5B) were identified and should be considered as new biomarkers. Pathway cross-talk analysis highlights that signaling pathways mediated by IL-4/13 and transcription factor HIF-1α and FOXA1 play crucial roles in the pathogenesis of asthma. Interestingly, three chemicals, polyphenol catechin, antibiotic lomefloxacin, and natural alkaloid boldine, were predicted and may be potential drugs for asthma treatment. Taken together, our findings shed new light on the common molecular pathogenesis mechanisms of asthma and provide theoretical support for further clinical therapeutic studies.

Update on acute myeloid leukemia stem cells: New discoveries and therapeutic opportunities

The existence of cancer stem cells has been well established in acute myeloid leukemia. Initial proof of the existence of leukemia stem cells (LSCs) was accomplished by functional studies in xenograft models making use of the key features shared with normal hematopoietic stem cells (HSCs) such as the capacity of self-renewal and the ability to initiate and sustain growth of progenitors in vivo. Significant progress has also been made in identifying the phenotype and signaling pathways specific for LSCs. Therapeutically, a multitude of drugs targeting LSCs are in different phases of preclinical and clinical development. This review focuses on recent discoveries which have advanced our understanding of LSC biology and provided rational targets for development of novel therapeutic agents. One of the major challenges is how to target the self-renewal pathways of LSCs without affecting normal HSCs significantly therefore providing an acceptable therapeutic window. Important issues pertinent to the successful design and conduct of clinical trials evaluating drugs targeting LSCs will be discussed as well.

An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era

BACKGROUND

Gene expression microarray has been the primary biomarker platform ubiquitously applied in biomedical research, resulting in enormous data, predictive models, and biomarkers accrued. Recently, RNA-seq has looked likely to replace microarrays, but there will be a period where both technologies co-exist. This raises two important questions: Can microarray-based models and biomarkers be directly applied to RNA-seq data? Can future RNA-seq-based predictive models and biomarkers be applied to microarray data to leverage past investment?

RESULTS

We systematically evaluated the transferability of predictive models and signature genes between microarray and RNA-seq using two large clinical data sets. The complexity of cross-platform sequence correspondence was considered in the analysis and examined using three human and two rat data sets, and three levels of mapping complexity were revealed. Three algorithms representing different modeling complexity were applied to the three levels of mappings for each of the eight binary endpoints and Cox regression was used to model survival times with expression data. In total, 240,096 predictive models were examined.

CONCLUSIONS

Signature genes of predictive models are reciprocally transferable between microarray and RNA-seq data for model development, and microarray-based models can accurately predict RNA-seq-profiled samples; while RNA-seq-based models are less accurate in predicting microarray-profiled samples and are affected both by the choice of modeling algorithm and the gene mapping complexity. The results suggest continued usefulness of legacy microarray data and established microarray biomarkers and predictive models in the forthcoming RNA-seq era.

6-Shogaol inhibits breast and colon cancer cell proliferation through activation of peroxisomal proliferator activated receptor γ (PPARγ)

6-Shogaol has been shown to possess many antitumor properties including inhibition of cancer cell growth, inhibition of cancer metastasis, induction of apoptosis in cancer cells and induction of cancer cell differentiation. Despite its prominent antitumor effects, the direct molecular target of 6-shogaol has remained elusive. To identify the direct targets of 6-shogaol, a comprehensive antitumor profile of 6-shogaol (NSC752389) was tested in the NCI-60 cell line in an in vitro screen. The results show that 6-shogaol is COMPARE negative suggesting that it functions via a mechanism of action distinct from existing classes of therapeutic agents. Further analysis using microarray gene profiling and Connectivity Map analysis showed that MCF-7 cells treated with 6-shogaol display gene expression signatures characteristic of peroxisome proliferator activated receptor γ (PPARγ) agonists, suggesting that 6-shogaol may activate the PPARγ signaling pathway for its antitumor effects. Indeed, treatment of MCF-7 and HT29 cells with 6-shogaol induced PPARγ transcriptional activity, suppressed NFκB activity, and induced apoptosis in breast and colon cancer cells in a PPARγ-dependent manner. Furthermore, 6-shogaol is capable of binding to PPARγ with a binding affinity comparable to 15-delta prostaglandin J2, a natural ligand for PPARγ. Together, our findings suggest that the antitumor effects of 6-shogaol are mediated through activation of PPARγ and imply that activation of PPARγ might be beneficial for breast and colon cancer treatment.

Bioinformatic and experimental fishing for artemisinin-interacting proteins from human nasopharyngeal cancer cells

Determining interacting cellular partners of drugs by chemical proteomic techniques is complex and tedious. Most approaches rely on activity-based probe profiling and compound-centric chemical proteomics. The anti-malarial artemisinin also exerts profound anti-cancer activity, but the mechanisms of action are incompletely understood. In the present investigation, we present a novel approach to identify artemisinin-interacting target proteins. Our approach overcomes usual problems in traditional fishing procedures, because the drug was attached to a surface without further chemical modification. The proteins identified effect among others, cell cycle arrest, apoptosis, inhibition of angiogenesis, disruption of cell migration, and modulation of nuclear receptor responsiveness. Furthermore, a bioinformatic approach confirmed experimentally identified proteins and suggested a large number of other interacting proteins. Theoretically predicted interaction partners may serve as a starting point to complete the whole set of proteins binding artemisinin.

Connectivity map analysis of nonsense-mediated decay-positive BMPR2-related hereditary pulmonary arterial hypertension provides insights into disease penetrance

The molecular mechanisms underlying the reduced penetrance seen in the nonsense-mediated decay-positive (NMD+) BMPR2 mutation-associated hereditary pulmonary arterial hypertension (HPAH) remain unknown. We reasoned that the cellular and genetic mechanisms behind this phenomenon could be uncovered by combining expression profiling with Connectivity Map (cMap) analysis. Cultured lymphocytes from 10 patients with HPAH and 10 matched familial control subjects, all with NMD+ BMPR2 mutations, were subjected to expression analysis. For each group, the expression data were combined before analysis. This generated a signature of 23 up-regulated and 12 down-regulated genes in patients with HPAH compared with control subjects (the "PAH penetrance signature"). Although gene set enrichment analysis of this signature was not uniquely informative, cMap analysis identified drugs with expression signatures similar to the PAH penetrance signature. Several of these drugs were predicted to influence reactive oxygen species (ROS) formation. This hypothesis was tested and confirmed in the same cells initially subjected to the expression analysis using quantitative biochemical detection of ROS concentration. We conclude that expression of the PAH penetrance signature represents an increased risk of developing clinical HPAH and that ROS formation may play a role in pathogenesis of HPAH. These results provide the first molecular insights into NMD+ BMPR2 related HPAH penetrance and highlight the potential utility of cMap analyses in pulmonary research.

Alignment of gene expression profiles from test samples against a reference database: New method for context-specific interpretation of microarray data

Background

Gene expression microarray data have been organized and made available as public databases, but the utilization of such highly heterogeneous reference datasets in the interpretation of data from individual test samples is not as developed as e.g. in the field of nucleotide sequence comparisons. We have created a rapid and powerful approach for the alignment of microarray gene expression profiles (AGEP) from test samples with those contained in a large annotated public reference database and demonstrate here how this can facilitate interpretation of microarray data from individual samples.

Methods

AGEP is based on the calculation of kernel density distributions for the levels of expression of each gene in each reference tissue type and provides a quantitation of the similarity between the test sample and the reference tissue types as well as the identity of the typical and atypical genes in each comparison. As a reference database, we used 1654 samples from 44 normal tissues (extracted from the Genesapiens database).

Results

Using leave-one-out validation, AGEP correctly defined the tissue of origin for 1521 (93.6%) of all the 1654 samples in the original database. Independent validation of 195 external normal tissue samples resulted in 87% accuracy for the exact tissue type and 97% accuracy with related tissue types. AGEP analysis of 10 Duchenne muscular dystrophy (DMD) samples provided quantitative description of the key pathogenetic events, such as the extent of inflammation, in individual samples and pinpointed tissue-specific genes whose expression changed (SAMD4A) in DMD. AGEP analysis of microarray data from adipocytic differentiation of mesenchymal stem cells and from normal myeloid cell types and leukemias provided quantitative characterization of the transcriptomic changes during normal and abnormal cell differentiation.

Conclusions

The AGEP method is a widely applicable method for the rapid comprehensive interpretation of microarray data, as proven here by the definition of tissue- and disease-specific changes in gene expression as well as during cellular differentiation. The capability to quantitatively compare data from individual samples against a large-scale annotated reference database represents a widely applicable paradigm for the analysis of all types of high-throughput data. AGEP enables systematic and quantitative comparison of gene expression data from test samples against a comprehensive collection of different cell/tissue types previously studied by the entire research community.

Rank-rank hypergeometric overlap: identification of statistically significant overlap between gene-expression signatures

Comparing independent high-throughput gene-expression experiments can generate hypotheses about which gene-expression programs are shared between particular biological processes. Current techniques to compare expression profiles typically involve choosing a fixed differential expression threshold to summarize results, potentially reducing sensitivity to small but concordant changes. We present a threshold-free algorithm called Rank–rank Hypergeometric Overlap (RRHO). This algorithm steps through two gene lists ranked by the degree of differential expression observed in two profiling experiments, successively measuring the statistical significance of the number of overlapping genes. The output is a graphical map that shows the strength, pattern and bounds of correlation between two expression profiles. To demonstrate RRHO sensitivity and dynamic range, we identified shared expression networks in cancer microarray profiles driving tumor progression, stem cell properties and response to targeted kinase inhibition. We demonstrate how RRHO can be used to determine which model system or drug treatment best reflects a particular biological or disease response. The threshold-free and graphical aspects of RRHO complement other rank-based approaches such as Gene Set Enrichment Analysis (GSEA), for which RRHO is a 2D analog. Rank–rank overlap analysis is a sensitive, robust and web-accessible method for detecting and visualizing overlap trends between two complete, continuous gene-expression profiles. A web-based implementation of RRHO can be accessed at http://systems.crump.ucla.edu/rankrank/.

Human lymphoblastoid cell line panels: novel tools for assessing shared drug pathways

Aims: While powerful in silico tools are emerging for predicting drug targets and pathways, general in vitro tools for assessing such predictions are lacking. We present a novel in vitro method for distinguishing shared versus distinct drug pathways based on comparative cell growth inhibition profiles across a small panel of human lymphoblastoid cell lines (LCLs) from individual donors. Materials & methods: LCLs from unrelated healthy donors were examined in parallel for growth inhibition profiles of various drugs, including antidepressants (paroxetine, fluoxetine, fluvoxamine, citalopram, amitriptyline and imipramine); anticancer drugs (5-fluorouracil, 6-mercaptopurine, azathioprine, methotrexate and resveratrol); steroid drugs (dexamethasone, beclomethasone and prednisolone); and antipsychotic drugs (haloperidol and clozapine). Cell growth was assessed by the colorimetric 2,3-bis(2-methoxy-4-nitro-5-sulfophenly)-5-[(phenylamino) carbonyl]-2H-tetrazolium hydroxide method following 72 h of drug exposure. Results: LCLs from unrelated individuals exhibited a wide range of sensitivities to growth inhibition by a given drug, which were independent of basal cell replication rates. Yet, each individual cell line demonstrated a consistent sensitivity to multiple drugs from the same family. High goodness-of-fit values (R2 > 0.6) were consistently observed for plots comparing the growth-inhibition profiles for paired drugs sharing a similar pathway, for example antidepressants, steroid drugs, antipsychotics, or 6-mercaptopurine compared with azathioprine, but not for drugs with different pathways. The method’s utility is demonstrated by the observation that chlorpheniramine, an antihistamine drug long suspected to also possess antidepressant-like properties, exhibits a growth-inhibition profile very similar to antidepressants. Conclusion: Comparing the growth-inhibition profiles of drugs (or compounds) of interest with the profiles of drugs with known pathways may assist in drug pathway classification. The method is useful for in vitro assessment of in silico-generated drug pathway predictions and for distinguishing shared versus distinct pathways for compounds of interest. Comparative transcriptomics analysis of human lymphoblastoid cell lines exhibiting ‘edge’ sensitivities can subsequently be utilized in the search for drug response biomarkers for personalized pharmacotherapy. The limitations and advantages of the method are discussed.

Identification of novel hair-growth inducers by means of connectivity mapping

The aim of this study was to identify novel inducers of hair growth using gene expression profiling at various stages of hair-growth induction. First, we analyzed gene expression at the onset of hair growth in mice induced by cyclosporin A (CsA), a well-known hair-growth inducer, using DNA microarray analysis. The results unveiled genes involved in the step-by-step progression of hair growth, including increases in melanin biosynthesis and decreases in immune response at d 2 and the subsequent stimulation of cell proliferation at d 4, followed by the up-regulation of hair specific keratins at d 7 after CsA treatment. With the use of the connectivity map (Cmap), agents that had a similar "gene signature" to that of the profiles of CsA-treated mice were identified. Several agents, including CsA, were identified by the Cmap and were evaluated for hair induction activity in vivo. One of the proposed agents, fluphenazine (from the d 2 signature) actually induced hair growth in vivo (ED(50): 2 mM for single application), and the subsequent application of 5 mM iloprost (from the d 4 signature) significantly enhanced the hair-growth effect of fluphenazine. From these results, Cmap analysis was proven to be a useful method that connects gene expression profiles of complicated biological processes, such as hair-growth induction, to effective agents.