Nephrotoxicity assessment of Esculentoside A using human-induced pluripotent stem cell-derived organoids.

Drug-induced nephrotoxicity is a leading cause of acute kidney injury (AKI). A major obstacle in predicting AKI is the lack of a comprehensive experimental model that mimics stable and physiologically relevant kidney functions and accurately reflects the changes a drug induces. Organoids derived from human-induced pluripotent stem cells (iPSCs) are promising models because of their reproducibility and similarity to the in vivo conditions. In this study, Esculentoside A, the triterpene saponin with the highest concentration isolated from the root of Phytolacca acinose Roxb., was used to induce kidney injury models in vivo and kidney organoids. Esculentoside A induced AKI in mice, together with pathological changes and enhanced apoptosis. Moreover, Esculentoside A damaged podocytes and proximal tubular endothelial cells in kidney organoids in a similar way as in vivo. We also found that treatment with 60 µM Esculentoside A induced the known biomarkers of kidney damage and inflammatory cytokines (such as kidney injury molecule (KIM-1), ß2-microglobulin (ß2-M), and cystatin C (CysC)) in the organoids, in which activation of Cleaved Caspase-3 was involved, possibly due to lowered mitochondrial membrane potential. In summary, this study strongly suggests using kidney organoids as a reliable platform to assess Chinese medicine-induced nephrotoxicity.

Three-dimensional chromatin architecture datasets for aging and Alzheimer's disease.

Recently, increasing studies are indicating a close association between dysregulated enhancers and neurodegenerative diseases, such as Alzheimer's disease (AD). However, their contributions were poorly defined for lacking direct links to disease genes. To bridge this gap, we presented the Hi-C datasets of 4 AD patients, 4 dementia-free aged and 3 young subjects, including 30 billion reads. As applications, we utilized them to link the AD risk SNPs and dysregulated epigenetic marks to the target genes. Combining with epigenetic data, we observed more detailed interactions among regulatory regions and found that many known AD risk genes were under long-distance promoter-enhancer interactions. For future AD and aging studies, our datasets provide a reference landscape to better interpret findings of association and epigenetic studies for AD and aging process.

FL-DTD: an integrated pipeline to predict the drug interacting targets by feedback loop-based network analysis.

Drug target discovery is an essential step to reveal the mechanism of action (MoA) underlying drug therapeutic effects and/or side effects. Most of the approaches are usually labor-intensive while unable to identify the tissue-specific interacting targets, especially the targets with weaker drug binding affinity. In this work, we proposed an integrated pipeline, FL-DTD, to predict the drug interacting targets of novel compounds in a tissue-specific manner. This method was built based on a hypothesis that cells under a status of homeostasis would take responses to drug perturbation by activating feedback loops. Therefore, the drug interacting targets can be predicted by analyzing the network responses after drug perturbation. We evaluated this method using the expression data of estrogen stimulation, gene manipulation and drug perturbation and validated its good performance to identify the annotated drug targets. Using STAT3 as a target protein, we applied this method to drug perturbation data of 500 natural compounds and predicted five compounds with STAT3 interacting activities. Experimental assay validated the STAT3-interacting activities of four compounds. Overall, our evaluation suggests that FL-DTD predicts the drug interacting targets with good accuracy and can be used for drug target discovery.

Circadian nuclear receptor Rev-erbα is expressed by platelets and potentiates platelet activation and thrombus formation.

AIMS: Adverse cardiovascular events have day/night patterns with peaks in the morning, potentially related to endogenous circadian clock control of platelet activation. Circadian nuclear receptor Rev-erbα is an essential and negative component of the circadian clock. To date, the expression profile and biological function of Rev-erbα in platelets have never been reported. METHODS AND RESULTS: Here, we report the presence and functions of circadian nuclear receptor Rev-erbα in human and mouse platelets. Both human and mouse platelet Rev-erbα showed a circadian rhythm that positively correlated with platelet aggregation. Global Rev-erbα knockout and platelet-specific Rev-erbα knockout mice exhibited defective in haemostasis as assessed by prolonged tail-bleeding times. Rev-erbα deletion also reduced ferric chloride-induced carotid arterial occlusive thrombosis, prevented collagen/epinephrine-induced pulmonary thromboembolism, and protected against microvascular microthrombi obstruction and infarct expansion in an acute myocardial infarction model. In vitro thrombus formation assessed by CD41-labelled platelet fluorescence intensity was significantly reduced in Rev-erbα knockout mouse blood. Platelets from Rev-erbα knockout mice exhibited impaired agonist-induced aggregation responses, integrin αIIbß3 activation, and α-granule release. Consistently, pharmacological inhibition of Rev-erbα by specific antagonists decreased platelet activation markers in both mouse and human platelets. Mechanistically, mass spectrometry and co-immunoprecipitation analyses revealed that Rev-erbα potentiated platelet activation via oligophrenin-1-mediated RhoA/ERM (ezrin/radixin/moesin) pathway. CONCLUSION: We provided the first evidence that circadian protein Rev-erbα is functionally expressed in platelets and potentiates platelet activation and thrombus formation. Rev-erbα may serve as a novel therapeutic target for managing thrombosis-based cardiovascular disease.

An Integration of Network Pharmacology and Experimental Verification to Investigate the Mechanism of Guizhi to Treat Nephrotic Syndrome.

Background: Guizhi has the pharmacological activity of anti-inflammatory. However, the effect mechanism of Guizhi against nephrotic syndrome (NS) remains unclear. A network pharmacological approach with experimental verification in vitro and in vivo was performed to investigate the potential mechanisms of Guizhi to treat NS. Methods: Active compounds and potential targets of Guizhi, as well as the related targets of NS were obtained from the public databases. The intersecting targets of Guizhi and NS were obtained through Venny 2.1.0. The key targets and signaling pathways were determined by protein-protein interaction (PPI), genes ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analysis. And the overall network was constructed with Cytoscape. Molecular docking verification was carried out by AutoDock Vina. Finally, in vitro and in vivo experiments were performed to verify the mechanism of Guizhi to treat NS. Results: 63 intersecting targets were obtained, and the top five key targets mainly involed in NF- Kappa B and MAPK signaling pathway. In the overall network, cinnamaldehyde (CA) was the top one active compound with the highest degree value. The molecular docking showed that the top five key targets were of good binding activity with the active components of Guizhi. To in vitro experiment, CA, the main active component of Guizhi, inhibited the secretion of IL-1ß, IL-6, TNF-α in LPS challenged RAW264.7 cells, and down regulated the protein expression of p-NF-κB p65 and p-p38 MAPK in LPS challenged RAW264.7 cells. In vitro experiment showed that, 24 urinary protein and renal function were increased in ADR group. To western blot, CA down regulated the protein expression of p-p38 MAPK in rats of adriamycin-induced nephropathy. Conclusion: CA might be the main active component of Guizhi to treat NS, and the underlying mechanism might mainly be achieved by inhibiting MAPK signaling pathway.

Research on the Mechanism of Guizhi to Treat Nephrotic Syndrome Based on Network Pharmacology and Molecular Docking Technology.

OBJECTIVE: Nephrotic syndrome (NS) is a common glomerular disease caused by a variety of causes and is the second most common kidney disease. Guizhi is the key drug of Wulingsan in the treatment of NS. However, the action mechanism remains unclear. In this study, network pharmacology and molecular docking were used to explore the underlying molecular mechanism of Guizhi in treating NS. METHODS: The active components and targets of Guizhi were screened by the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Hitpick, SEA, and Swiss Target Prediction database. The targets related to NS were obtained from the DisGeNET, GeneCards, and OMIM database, and the intersected targets were obtained by Venny2.1.0. Then, active component-target network was constructed using Cytoscape software. And the protein-protein interaction (PPI) network was drawn through the String database and Cytoscape software. Next, Gene Ontology (GO) and pathway enrichment analyses of Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by DAVID database. And overall network was constructed through Cytoscape. Finally, molecular docking was conducted using Autodock Vina. RESULTS: According to the screening criteria, a total of 8 active compounds and 317 potential targets of Guizhi were chosen. Through the online database, 2125 NS-related targets were identified, and 93 overlapping targets were obtained. In active component-target network, beta-sitosterol, sitosterol, cinnamaldehyde, and peroxyergosterol were the important active components. In PPI network, VEGFA, MAPK3, SRC, PTGS2, and MAPK8 were the core targets. GO and KEGG analyses showed that the main pathways of Guizhi in treating NS involved VEGF, Toll-like receptor, and MAPK signaling pathway. In molecular docking, the active compounds of Guizhi had good affinity with the core targets. CONCLUSIONS: In this study, we preliminarily predicted the main active components, targets, and signaling pathways of Guizhi to treat NS, which could provide new ideas for further research on the protective mechanism and clinical application of Guizhi against NS.

A Comprehensive Investigation to Reveal the Relationship Between Plasmacytoid Dendritic Cells and Breast Cancer by Multiomics Data Analysis.

Plasmacytoid dendritic cells (pDC) are an essential immune microenvironment component. They have been reported for crucial roles in linking the adaptive and immune systems. However, the prognostic role of the pDC in breast cancer (BRCA) was controversial. In this work, we collected large sample cohorts and did a comprehensive investigation to reveal the relationship between pDC and BRCA by multiomics data analysis. Elevated pDC levels were correlated with prolonged survival outcomes in BRCA patients. The distinct mutation landscape and lower burden of somatic copy number alterations (SCNA) and lower intratumoral heterogeneity were observed in the high pDC abundance group. Additionally, a more sensitive immune response and chemotherapies response were observed in the high pDC group, which implicates that patients with high pDC abundance can be benefited from the combination of chemotherapy and immunotherapy. In conclusion, the correlation between pDC abundance and BRCA patients' overall survival (OS) was found to be positive. We identified the molecular profiles of BRCA patients with pDC abundance. Our findings may be beneficial in aiding in the development of immunotherapy and elucidating on the precision treatment for BRCA.

Integrated decoding hematopoiesis and leukemogenesis using single-cell sequencing and its medical implication.

Single-cell RNA sequencing provides exciting opportunities to unbiasedly study hematopoiesis. However, our understanding of leukemogenesis was limited due to the high individual differences. Integrated analyses of hematopoiesis and leukemogenesis potentially provides new insights. Here we analyzed ~200,000 single-cell transcriptomes of bone marrow mononuclear cells (BMMCs) and its subsets from 23 clinical samples. We constructed a comprehensive cell atlas as hematopoietic reference. We developed counterpart composite index (CCI; available at GitHub: https://github.com/pengfeeei/cci) to search for the healthy counterpart of each leukemia cell subpopulation, by integrating multiple statistics to map leukemia cells onto reference hematopoietic cells. Interestingly, we found leukemia cell subpopulations from each patient had different healthy counterparts. Analysis showed the trajectories of leukemia cell subpopulations were similar to that of their healthy counterparts, indicating that developmental termination of leukemia initiating cells at different phases leads to different leukemia cell subpopulations thus explained the origin of leukemia heterogeneity. CCI further predicts leukemia subtypes, cellular heterogeneity, and cellular stemness of each leukemia patient. Analyses of leukemia patient at diagnosis, refractory, remission and relapse vividly presented dynamics of cell population during leukemia treatment. CCI analyses showed the healthy counterparts of relapsed leukemia cells were closer to the root of hematopoietic tree than that of other leukemia cells, although single-cell transcriptomic genetic variants and haplotype tracing analyses showed the relapsed leukemia cell were derived from an early minor leukemia cell population. In summary, this study developed a unified framework for understanding leukemogenesis with hematopoiesis reference, which provided novel biological and medical implication.

Module Analysis Using Single-Patient Differential Expression Signatures Improves the Power of Association Studies for Alzheimer's Disease.

The causal mechanism of Alzheimer's disease is extremely complex. Achieving great statistical power in association studies usually requires a large number of samples. In this work, we illustrated a different strategy to identify AD risk genes by clustering AD patients into modules based on their single-patient differential expression signatures. The evaluation suggested that our method could enrich AD patients with similar clinical manifestations. Applying this to a cohort of only 310 AD patients, we identified 174 AD risk loci at a strict threshold of empirical p < 0.05, while only two loci were identified using all the AD patients. As an evaluation, we collected 23 AD risk genes reported in a recent large-scale meta-analysis and found that 18 of them were rediscovered by association studies using clustered AD patients, while only three of them were rediscovered using all AD patients. Functional annotation suggested that AD-associated genetic variants mainly disturbed neuronal/synaptic function. Our results suggested module analysis helped to enrich AD patients affected by the common risk variants.

Development of Human in vitro Brain-blood Barrier Model from Induced Pluripotent Stem Cell-derived Endothelial Cells to Predict the in vivo Permeability of Drugs.

An in vitro blood-brain barrier (BBB) model is critical for enabling rapid screening of the BBB permeability of the drugs targeting on the central nervous system. Though many models have been developed, their reproducibility and renewability remain a challenge. Furthermore, drug transport data from many of the models do not correlate well with the data for in vivo BBB drug transport. Induced-pluripotent stem cell (iPSC) technology provides reproducible cell resources for in vitro BBB modeling. Here, we generated a human in vitro BBB model by differentiating the human iPSC (hiPSC) line GM25256 into brain endothelial-type cells. The model displayed BBB characteristics including tight junction proteins (ZO-1, claudin-5, and occludin) and endothelial markers (von Willebrand factor and Ulex), as well as high trans-endothelial electrical resistance (TEER) (1560 Ω.cm2 ± 230 Ω.cm2) and γ-GTPase activity. Co-culture with primary rat astrocytes significantly increased the TEER of the model (2970 Ω.cm2 to 4185 Ω.cm2). RNAseq analysis confirmed the expression of key BBB-related genes in the hiPSC-derived endothelial cells in comparison with primary human brain microvascular endothelial cells, including P-glycoprotein (Pgp) and breast cancer resistant protein (BCRP). Drug transport assays for nine CNS compounds showed that the permeability of non-Pgp/BCRP and Pgp/BCRP substrates across the model was strongly correlated with rodent in situ brain perfusion data for these compounds (R2 = 0.982 and R2 = 0.9973, respectively), demonstrating the functionality of the drug transporters in the model. Thus, this model may be used to rapidly screen CNS compounds, to predict the in vivo BBB permeability of these compounds and to study the biology of the BBB.

Transcriptional Dysregulation Study Reveals a Core Network Involving the Progression of Alzheimer's Disease.

Background: The pathogenesis of Alzheimer's disease is associated with dysregulation at different levels from transcriptome to cellular functioning. Such complexity necessitates investigations of disease etiology to be carried out considering multiple aspects of the disease and the use of independent strategies. The established works more emphasized on the structural organization of gene regulatory network while neglecting the internal regulation changes. Methods: Applying a strategy different from popularly used co-expression network analysis, this study investigated the transcriptional dysregulations during the transition from normal to disease states. Results: Ninety- seven genes were predicted as dysregulated genes, which were also associated with clinical outcomes of Alzheimer's disease. Both the co-expression and differential co-expression analysis suggested these genes to be interconnected as a core network and that their regulations were strengthened during the transition to disease states. Functional studies suggested the dysregulated genes to be associated with aging and synaptic function. Further, we checked the conservation of the gene co-expression and found that human and mouse brain might have divergent transcriptional co-regulation even when they had conserved gene expression profiles. Conclusion: Overall, our study reveals a core network of transcriptional dysregulation associated with the progression of Alzheimer's disease by affecting the aging and synaptic functions related genes; the gene regulation is not conserved in the human and mouse brains.

TSD: A Computational Tool To Study the Complex Structural Variants Using PacBio Targeted Sequencing Data.

PacBio sequencing is a powerful approach to study DNA or RNA sequences in a longer scope. It is especially useful in exploring the complex structural variants generated by random integration or multiple rearrangement of endogenous or exogenous sequences. Here, we present a tool, TSD, for complex structural variant discovery using PacBio targeted sequencing data. It allows researchers to identify and visualize the genomic structures of targeted sequences by unlimited splitting, alignment and assembly of long PacBio reads. Application to the sequencing data derived from an HBV integrated human cell line(PLC/PRF/5) indicated that TSD could recover the full profile of HBV integration events, especially for the regions with the complex human-HBV genome integrations and multiple HBV rearrangements. Compared to other long read analysis tools, TSD showed a better performance for detecting complex genomic structural variants. TSD is publicly available at: https://github.com/menggf/tsd.

A six-mRNA prognostic model to predict survival in head and neck squamous cell carcinoma.

BACKGROUND: Transcriptional dysregulation is one of the most important features of cancer genesis and progression. Applying gene expression dysregulation information to predict the development of cancers is useful for cancer diagnosis. However, previous studies mainly focused on the relationship between a single gene and cancer. Prognostic prediction using combined gene models remains limited. MATERIALS AND METHODS: Gene expression profiles were downloaded from The Cancer Genome Atlas and the data sets were randomly divided into training data sets and test data sets. A six-gene signature associated with head and neck squamous cell carcinoma (HNSCC) and overall survival (OS) was identified according to a training cohort by using weighted gene correlation network analysis and least absolute shrinkage and selection operator Cox regression. The test data set and gene expression omnibus (GEO) data set were used to validate this signature. RESULTS: We identified six candidate genes, namely, FOXL2NB, PCOLCE2, SPINK6, ULBP2, KCNJ18, and RFPL1, and, using a six-gene model, predicted the risk of death of head and neck squamous cell carcinoma in The Cancer Genome Atlas. At a selected cutoff, patients were clustered into low- and high-risk groups. The OS curves of the two groups of patients had significant differences, and the time-dependent receiver operating characteristics of OS, disease-specific survival (DSS), and progression-free survival (PFS) were as high as 0.766, 0.731, and 0.623, respectively. Then, the test data set and the GEO data set were used to evaluate our model, and we found that the OS time in the high-risk group was significantly shorter than in the low-risk group in both data sets, and the receiver operating characteristics of test data set were 0.669, 0.675, and 0.614, respectively. Furthermore, univariate and multivariate Cox regression analyses showed that the risk score was independent of clinicopathological features. CONCLUSION: The six-gene model could predict the OS of HNSCC patients and improve therapeutic decision-making.

Applying Expression Profile Similarity for Discovery of Patient-Specific Functional Mutations.

The progress of cancer genome sequencing projects yields unprecedented information of mutations for numerous patients. However, the complexity of mutation profiles of cancer patients hinders the further understanding to mechanisms of oncogenesis. One basic question is how to find mutations with functional impacts. In this work, we introduce a computational method to predict functional somatic mutations of each patient by integrating mutation recurrence with expression profile similarity. With this method, the functional mutations are determined by checking the mutation enrichment among a group of patients with similar expression profiles. We applied this method to three cancer types and identified the functional mutations. Comparison of the predictions for three cancer types suggested that most of the functional mutations were cancer-type-specific with one exception to p53. By checking predicted results, we found that our method effectively filtered non-functional mutations resulting from large protein sizes. In addition, this method can also perform functional annotation to each patient to describe their association with signalling pathways or biological processes. In breast cancer, we predicted "cell adhesion" and other terms to be significantly associated with oncogenesis.

Systemic evaluation of cellular reprogramming processes exploiting a novel R-tool: eegc.

MOTIVATION: Cells derived by cellular engineering, i.e. differentiation of induced pluripotent stem cells and direct lineage reprogramming, carry a tremendous potential for medical applications and in particular for regenerative therapies. These approaches consist in the definition of lineage-specific experimental protocols that, by manipulation of a limited number of biological cues-niche mimicking factors, (in)activation of transcription factors, to name a few-enforce the final expression of cell-specific (marker) molecules. To date, given the intricate complexity of biological pathways, these approaches still present imperfect reprogramming fidelity, with uncertain consequences on the functional properties of the resulting cells. RESULTS: We propose a novel tool eegc to evaluate cellular engineering processes, in a systemic rather than marker-based fashion, by integrating transcriptome profiling and functional analysis. Our method clusters genes into categories representing different states of (trans)differentiation and further performs functional and gene regulatory network analyses for each of the categories of the engineered cells, thus offering practical indications on the potential lack of the reprogramming protocol. AVAILABILITY AND IMPLEMENTATION: eegc R package is released under the GNU General Public License within the Bioconductor project, freely available at https://bioconductor.org/packages/eegc/. CONTACT: christine.nardini.rsrc@gmail.com or hongkang.k.mei@gsk.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BET bromodomain inhibition promotes neurogenesis while inhibiting gliogenesis in neural progenitor cells.

Neural stem cells and progenitor cells (NPCs) are increasingly appreciated to hold great promise for regenerative medicine to treat CNS injuries and neurodegenerative diseases. However, evidence for effective stimulation of neuronal production from endogenous or transplanted NPCs for neuron replacement with small molecules remains limited. To identify novel chemical entities/targets for neurogenesis, we had established a NPC phenotypic screen assay and validated it using known small-molecule neurogenesis inducers. Through screening small molecule libraries with annotated targets, we identified BET bromodomain inhibition as a novel mechanism for enhancing neurogenesis. BET bromodomain proteins, Brd2, Brd3, and Brd4 were found to be downregulated in NPCs upon differentiation, while their levels remain unaltered in proliferating NPCs. Consistent with the pharmacological study using bromodomain selective inhibitor (+)-JQ-1, knockdown of each BET protein resulted in an increase in the number of neurons with simultaneous reduction in both astrocytes and oligodendrocytes. Gene expression profiling analysis demonstrated that BET bromodomain inhibition induced a broad but specific transcription program enhancing directed differentiation of NPCs into neurons while suppressing cell cycle progression and gliogenesis. Together, these results highlight a crucial role of BET proteins as epigenetic regulators in NPC development and suggest a therapeutic potential of BET inhibitors in treating brain injuries and neurodegenerative diseases.

A Systematic Investigation into Aging Related Genes in Brain and Their Relationship with Alzheimer's Disease.

Aging, as a complex biological process, is accompanied by the accumulation of functional loses at different levels, which makes age to be the biggest risk factor to many neurological diseases. Even following decades of investigation, the process of aging is still far from being fully understood, especially at a systematic level. In this study, we identified aging related genes in brain by collecting the ones with sustained and consistent gene expression or DNA methylation changes in the aging process. Functional analysis with Gene Ontology to these genes suggested transcriptional regulators to be the most affected genes in the aging process. Transcription regulation analysis found some transcription factors, especially Specificity Protein 1 (SP1), to play important roles in regulating aging related gene expression. Module-based functional analysis indicated these genes to be associated with many well-known aging related pathways, supporting the validity of our approach to select aging related genes. Finally, we investigated the roles of aging related genes on Alzheimer's Disease (AD). We found that aging and AD related genes both involved some common pathways, which provided a possible explanation why aging made the brain more vulnerable to Alzheimer's Disease.

Reconstruction of gene regulatory network related to photosynthesis in Arabidopsis thaliana.

Photosynthesis is one of the most important biological processes on the earth. So far, though the molecular mechanisms underlying photosynthesis is well understood, however, the regulatory networks of photosynthesis are poorly studied. Given the current interest in improving photosynthetic efficiency for greater crop yield, elucidating the detailed regulatory networks controlling the construction and maintenance of photosynthetic machinery is not only scientifically significant but also holding great potential in agricultural application. In this study, we first identified transcription factors (TFs) related to photosynthesis through the TRAP approach using position weight matrix information. Then, for TFs related to photosynthesis, interactions between them and their targets were also determined by the ARACNE approach. Finally, a gene regulatory network was established by combining TF-targets information generated by these two approaches. Topological analysis of the regulatory network suggested that (a) the regulatory network of photosynthesis has a property of "small world"; (b) there is substantial coordination mediated by transcription factors between different components in photosynthesis.

Condition-specific target prediction from motifs and expression.

MOTIVATION: It is commonplace to predict targets of transcription factors (TFs) by sequence matching with their binding motifs. However, this ignores the particular condition of the cells. Gene expression data can provide condition-specific information, as is, e.g. exploited in Motif Enrichment Analysis. RESULTS: Here, we introduce a novel tool named condition-specific target prediction (CSTP) to predict condition-specific targets for TFs from expression data measured by either microarray or RNA-seq. Based on the philosophy of guilt by association, CSTP infers the regulators of each studied gene by recovering the regulators of its co-expressed genes. In contrast to the currently used methods, CSTP does not insist on binding sites of TFs in the promoter of the target genes. CSTP was applied to three independent biological processes for evaluation purposes. By analyzing the predictions for the same TF in three biological processes, we confirm that predictions with CSTP are condition-specific. Predictions were further compared with true TF binding sites as determined by ChIP-seq/chip. We find that CSTP predictions overlap with true binding sites to a degree comparable with motif-based predictions, although the two target sets do not coincide. AVAILABILITY AND IMPLEMENTATION: CSTP is available via a web-based interface at http://cstp.molgen.mpg.de.

A computational evaluation of over-representation of regulatory motifs in the promoter regions of differentially expressed genes.

BACKGROUND: Observed co-expression of a group of genes is frequently attributed to co-regulation by shared transcription factors. This assumption has led to the hypothesis that promoters of co-expressed genes should share common regulatory motifs, which forms the basis for numerous computational tools that search for these motifs. While frequently explored for yeast, the validity of the underlying hypothesis has not been assessed systematically in mammals. This demonstrates the need for a systematic and quantitative evaluation to what degree co-expressed genes share over-represented motifs for mammals. RESULTS: We identified 33 experiments for human and mouse in the ArrayExpress Database where transcription factors were manipulated and which exhibited a significant number of differentially expressed genes. We checked for over-representation of transcription factor binding sites in up- or down-regulated genes using the over-representation analysis tool oPOSSUM. In 25 out of 33 experiments, this procedure identified the binding matrices of the affected transcription factors. We also carried out de novo prediction of regulatory motifs shared by differentially expressed genes. Again, the detected motifs shared significant similarity with the matrices of the affected transcription factors. CONCLUSIONS: Our results support the claim that functional regulatory motifs are over-represented in sets of differentially expressed genes and that they can be detected with computational methods.