Multiplatform single-sample estimates of transcriptional activation

Development and validation of a 23-gene expression signature for molecular subtyping of medulloblastoma in a long-term Chinese cohort

PURPOSE

Medulloblastoma is the most common childhood malignant brain tumor and is a leading cause of cancer-related death in children. Recent transcriptional studies have shown that medulloblastomas comprise at least four molecular subgroups, each with distinct demographics, genetics, and clinical outcomes. Medulloblastoma subtyping has become critical for subgroup-specific therapies. The use of gene expression assays to determine the molecular subgroup of clinical specimens is a long-awaited application of molecular biology for this pediatric cancer.

METHODS

In the current study, we established a medulloblastoma transcriptome database of 460 samples retrieved from three published datasets (GSE21140, GSE37382, and GSE37418). With this database, we identified a 23-gene signature that is significantly associated with the medulloblastoma subgroups and achieved a classification accuracy of 95.2%.

RESULTS

The 23-gene signature was further validated in a long-term cohort of 142 Chinese medulloblastoma patients. The 23-gene signature classified 21 patients as WNT (15%), 41 as SHH (29%), 16 as Group 3 (11%), and 64 as Group 4 (45%). For patients of WNT, SHH, Group 3, and Group 4, 5-year overall-survival rate reached 80%, 62%, 27%, and 47%, respectively (p < 0.0001), meanwhile 5-year progression-free survival reached 80%, 52%, 27%, and 45%, respectively (p < 0.0001). Besides, SHH/TP53-mutant tumors were associated with worse prognosis compared with SHH/TP53 wild-type tumors and other subgroups. We demonstrated that subgroup assignments by the 23-gene signature and Northcott's NanoString assay were highly comparable with a concordance rate of 96.4%.

CONCLUSIONS

In conclusion, we present a novel gene signature that is capable of accurately and reliably assigning FFPE medulloblastoma samples to their molecular subgroup, which may serve as an auxiliary tool for medulloblastoma subtyping in the clinic. Future incorporation of this gene signature into prospective clinical trials is warranted to further evaluate its clinical.

Fibronectin Type III Domain Containing 3B as a Potential Prognostic and Therapeutic Biomarker for Glioblastoma

Glioblastoma (GBM) is a representative malignant brain tumor characterized by a dismal prognosis, with survival rates of less than 2 years and high recurrence rates. Despite surgical resection and several alternative treatments, GBM remains a refractory disease due to its aggressive invasiveness and resistance to anticancer therapy. In this report, we explore the role of fibronectin type III domain containing 3B (FNDC3B) and its potential as a prognostic and therapeutic biomarker in GBM. GBM exhibited a significantly higher cancer-to-normal ratio compared to other organs, and patients with high FNDC3B expression had a poor prognosis (p < 0.01). In vitro studies revealed that silencing FNDC3B significantly reduced the expression of Survivin, an apoptosis inhibitor, and also reduced cell migration, invasion, extracellular matrix adhesion ability, and stem cell properties in GBM cells. Furthermore, we identified that FNDC3B regulates PTEN/PI3K/Akt signaling in GBM cells using MetaCore integrated pathway bioinformatics analysis and a proteome profiler phospho-kinase array with sequential western blot analysis. Collectively, our findings suggest FNDC3B as a potential biomarker for predicting GBM patient survival and for the development of treatment strategies for GBM.

Uniformly shaped harmonization combines human transcriptomic data from different platforms while retaining their biological properties and differential gene expression patterns

Introduction: Co-normalization of RNA profiles obtained using different experimental platforms and protocols opens avenue for comprehensive comparison of relevant features like differentially expressed genes associated with disease. Currently, most of bioinformatic tools enable normalization in a flexible format that depends on the individual datasets under analysis. Thus, the output data of such normalizations will be poorly compatible with each other. Recently we proposed a new approach to gene expression data normalization termed Shambhala which returns harmonized data in a uniform shape, where every expression profile is transformed into a pre-defined universal format. We previously showed that following shambhalization of human RNA profiles, overall tissue-specific clustering features are strongly retained while platform-specific clustering is dramatically reduced. Methods: Here, we tested Shambhala performance in retention of fold-change gene expression features and other functional characteristics of gene clusters such as pathway activation levels and predicted cancer drug activity scores. Results: Using 6,793 cancer and 11,135 normal tissue gene expression profiles from the literature and experimental datasets, we applied twelve performance criteria for different versions of Shambhala and other methods of transcriptomic harmonization with flexible output data format. Such criteria dealt with the biological type classifiers, hierarchical clustering, correlation/regression properties, stability of drug efficiency scores, and data quality for using machine learning classifiers. Discussion: Shambhala-2 harmonizer demonstrated the best results with the close to 1 correlation and linear regression coefficients for the comparison of training vs validation datasets and more than two times lesser instability for calculation of drug efficiency scores compared to other methods.

Deciphering Tumour Microenvironment of Liver Cancer through Deconvolution of Bulk RNA-Seq Data with Single-Cell Atlas

Liver cancers give rise to a heavy burden on healthcare worldwide. Understanding the tumour microenvironment (TME) underpins the development of precision therapy. Single-cell RNA sequencing (scRNA-seq) technology has generated high-quality cell atlases of the TME, but its wider application faces enormous costs for various clinical circumstances. Fortunately, a variety of deconvolution algorithms can instead repurpose bulk RNA-seq data, alleviating the need for generating scRNA-seq datasets. In this study, we reviewed major public omics databases for relevance in this study and utilised eight RNA-seqs and one microarray dataset from clinical studies. To decipher the TME of liver cancer, we estimated the fractions of liver cell components by deconvoluting the samples with Cibersortx using three reference scRNA-seq atlases. We also confirmed that Cibersortx can accurately deconvolute cell types/subtypes of interest. Compared with non-tumorous liver, liver cancers showed multiple decreased cell types forming normal liver microarchitecture, as well as elevated cell types involved in fibrogenesis, abnormal angiogenesis, and disturbed immune responses. Survival analysis shows that the fractions of five cell types/subtypes significantly correlated with patient outcomes, indicating potential therapeutic targets. Therefore, deconvolution of bulk RNA-seq data with scRNA-seq atlas references can be a useful tool to help understand the TME.

Differential dependency of human glioblastoma cells on vascular endothelial growth factor‑A signaling via neuropilin‑1

Despite the high expression of neuropilin-1 (NRP-1) in human glioblastoma (GB), the understanding of its function as a co-receptor of vascular endothelial growth factor receptors (VEGFRs) in angiogenesis is currently limited. Therefore, the aim of the present study was to elucidate the non-classical function of NRP-1 expression in human GB. Expression patterns of NRP-1 and VEGF-A were determined by sandwich ELISA, western blot analysis, or immunohistochemistry. Differential dependency of GB cells following ablation of VEGF-A signaling was validated in vitro and in vivo. Cellular mechanism responsible for distinct response to VEGF-A signaling was evaluated by western blotting and immune-precipitation analysis. Prognostic implications were assessed using IHC analysis. GB cells exhibited differing sensitivity to silencing of vascular endothelial growth factor (VEGF)-A signaling, which resulted in a distinct expression pattern of wild-type or chondroitin-sulfated NRP-1. VEGF-A-sensitive GB exhibited the physical interaction between wild-type NRP-1 and FMS related receptor tyrosine kinase 1 (Flt-1) whereas VEGF-A-resistant GB exhibited chondroitin-sulfated NRP-1 without interaction with Flt-1. Eliminating the chondroitin sulfate modification in NRP-1 led to re-sensitization to VEGF-A signaling, and chondroitin sulfate modification was found to be associated with an adverse prognosis in patients with GB. The present study identified the distinct functions of NRP-1 in VEGF-A signaling in accordance with its unique expression type and interaction with Flt-1. The present research is expected to provide a strong basis for targeting VEGF-A signaling in patients with GB, with variable responses.

Transcriptomic Harmonization as the Way for Suppressing Cross-Platform Bias and Batch Effect

(1) Background: Emergence of methods interrogating gene expression at high throughput gave birth to quantitative transcriptomics, but also posed a question of inter-comparison of expression profiles obtained using different equipment and protocols and/or in different series of experiments. Addressing this issue is challenging, because all of the above variables can dramatically influence gene expression signals and, therefore, cause a plethora of peculiar features in the transcriptomic profiles. Millions of transcriptomic profiles were obtained and deposited in public databases of which the usefulness is however strongly limited due to the inter-comparison issues; (2) Methods: Dozens of methods and software packages that can be generally classified as either flexible or predefined format harmonizers have been proposed, but none has become to the date the gold standard for unification of this type of Big Data; (3) Results: However, recent developments evidence that platform/protocol/batch bias can be efficiently reduced not only for the comparisons of limited transcriptomic datasets. Instead, instruments were proposed for transforming gene expression profiles into the universal, uniformly shaped format that can support multiple inter-comparisons for reasonable calculation costs. This forms a basement for universal indexing of all or most of all types of RNA sequencing and microarray hybridization profiles; (4) Conclusions: In this paper, we attempted to overview the landscape of modern approaches and methods in transcriptomic harmonization and focused on the practical aspects of their application.

A Knowledge-Based Discovery Approach Couples Artificial Neural Networks With Weight Engineering to Uncover Immune-Related Processes Underpinning Clinical Traits of Breast Cancer

Immune-related processes are important in underpinning the properties of clinical traits such as prognosis and drug response in cancer. The possibility to extract knowledge learned by artificial neural networks (ANNs) from omics data to explain cancer clinical traits is a very attractive subject for novel discovery. Recent studies using a version of ANNs called autoencoders revealed their capability to store biologically meaningful information indicating that autoencoders can be utilized as knowledge discovery platforms aside from their initial assigned use for dimensionality reduction. Here, we devise an innovative weight engineering approach and ANN platform called artificial neural network encoder (ANNE) using an autoencoder and apply it to a breast cancer dataset to extract knowledge learned by the autoencoder model that explains clinical traits. Intriguingly, the extracted biological knowledge in the form of gene-gene associations from ANNE shows immune-related components such as chemokines, carbonic anhydrase, and iron metabolism that modulate immune-related processes and the tumor microenvironment play important roles in underpinning breast cancer clinical traits. Our work shows that biological "knowledge" learned by an ANN model is indeed encoded as weights throughout its neuronal connections, and it is possible to extract learned knowledge via a novel weight engineering approach to uncover important biological insights.

Integration analyses of structural variations and differential gene expression associated with beef fatty acid profile in Nellore cattle

This study aimed to integrate analyses of structural variations and differentially expressed genes (DEGs) associated with the beef fatty acid (FA) profile in Nellore cattle. Copy numbers variation (CNV) detection was performed using the penncnv algorithm and CNVRuler software in 3794 genotyped animals through the High-Density Bovine BeadChip. In order to perform the genomic wide association study (GWAS), a total of 963 genotyped animals were selected to obtain the intramuscular lipid concentration and quantify the beef FA profile. A total of 48 animals belonging to the same farm and management lot were extracted from the 963 genotyped and phenotyped animals to carry out the transcriptomic and differentially expressed gene analyses. The GWAS with extreme groups of FA profiles was performed using a logistic model. A total of 43, 42, 66 and 35 significant CNV regions (p < 0.05) for saturated, monounsaturated, polyunsaturated and omega 3 and 6 fatty acids were identified respectively. The paired-end sequencing of 48 samples was performed using the Illumina HiSeq2500 platform. Real-time quantitative PCR was used to validate the DEGs identified by RNA-seq analysis. The results showed several DEGs associated with the FA profile of Longissimus thoracis, such as BSCL2 and SAMD8. Enriched terms as the cellular response to corticosteroid (GO:0071384) and glucocorticoid stimulus (GO:0071385) could be highlighted. The identification of structural variations harboring candidate genes for beef FA must contribute to the elucidation of the genetic basis that determines the beef FA composition of intramuscular fat in Nellore cattle. Our results will contribute to the identification of potential biomarkers for complex phenotypes, such as the FA profile, to improve the reliability of the genomic predictions including pre-selected variants using differentiated weighting in the genomic models.

Shambhala-2: A Protocol for Uniformly Shaped Harmonization of Gene Expression Profiles of Various Formats

Uniformly shaped harmonization of gene expression profiles is central for the simultaneous comparison of multiple gene expression datasets. It is expected to operate with the gene expression data obtained using various experimental methods and equipment, and to return harmonized profiles in a uniform shape. Such uniformly shaped expression profiles from different initial datasets can be further compared directly. However, current harmonization techniques have strong limitations that prevent their broad use for bioinformatic applications. They can either operate with only up to two datasets/platforms or return data in a dynamic format that will be different for every comparison under analysis. This also does not allow for adding new data to the previously harmonized dataset(s), which complicates the analysis and increases calculation costs. We propose here a new method termed Shambhala-2 that can transform multi-platform expression data into a universal format that is identical for all harmonizations made using this technique. Shambhala-2 is based on sample-by-sample cubic conversion of the initial expression dataset into a preselected shape of the reference definitive dataset. Using 8390 samples of 12 healthy human tissue types and 4086 samples of colorectal, kidney, and lung cancer tissues, we verified Shambhala-2's capacity in restoring tissue-specific expression patterns for seven microarray and three RNA sequencing platforms. Shambhala-2 performed well for all tested combinations of RNAseq and microarray profiles, and retained gene-expression ranks, as evidenced by high correlations between different single- or aggregated gene expression metrics in pre- and post-Shambhalized samples, including preserving cancer-specific gene expression and pathway activation features. © 2022 Wiley Periodicals LLC. Basic Protocol: Shambhala-2 harmonizer Alternate Protocol 1: Linear Shambhala/Shambhala-1 Alternate Protocol 2: Alternative (flexible-format and uniformly shaped) normalization methods Support Protocol 1: Watermelon multisection (WM) Support Protocol 2: Calculation of cancer-to-normal log-fold-change (LFC) and pathway activation level (PAL).

CuBlock: a cross-platform normalization method for gene-expression microarrays

Motivation

Cross-(multi)platform normalization of gene-expression microarray data remains an unresolved issue. Despite the existence of several algorithms, they are either constrained by the need to normalize all samples of all platforms together, compromising scalability and reuse, by adherence to the platforms of a specific provider, or simply by poor performance. In addition, many of the methods presented in the literature have not been specifically tested against multi-platform data and/or other methods applicable in this context. Thus, we set out to develop a normalization algorithm appropriate for gene-expression studies based on multiple, potentially large microarray sets collected along multiple platforms and at different times, applicable in systematic studies aimed at extracting knowledge from the wealth of microarray data available in public repositories; for example, for the extraction of Real-World Data to complement data from Randomized Controlled Trials. Our main focus or criterion for performance was on the capacity of the algorithm to properly separate samples from different biological groups.

Results

We present CuBlock, an algorithm addressing this objective, together with a strategy to validate cross-platform normalization methods. To validate the algorithm and benchmark it against existing methods, we used two distinct datasets, one specifically generated for testing and standardization purposes and one from an actual experimental study. Using these datasets, we benchmarked CuBlock against ComBat (Johnson et al., 2007), UPC (Piccolo et al., 2013), YuGene (Lê Cao et al., 2014), DBNorm (Meng et al., 2017), Shambhala (Borisov et al., 2019) and a simple log₂ transform as reference. We note that many other popular normalization methods are not applicable in this context. CuBlock was the only algorithm in this group that could always and clearly differentiate the underlying biological groups after mixing the data, from up to six different platforms in this study.

Availability and implementation

CuBlock can be downloaded from https://www.mathworks.com/matlabcentral/fileexchange/77882-cublock.

Supplementary information

Supplementary data are available at Bioinformatics online.

KDM6B is an androgen regulated gene and plays oncogenic roles by demethylating H3K27me3 at cyclin D1 promoter in prostate cancer

Lysine (K)-specific demethylase 6B (KDM6B), a stress-inducible H3K27me3 demethylase, plays oncogenic or antitumoral roles in malignant tumors depending on the type of tumor cell. However, how this histone modifier affects the progression of prostate cancer (PCa) is still unknown. Here we analyzed sequenced gene expression data and tissue microarray to explore the expression features and prognostic value of KDM6B in PCa. Further, we performed in vitro cell biological experiments and in vivo nude mouse models to reveal the biological function, upstream and downstream regulation mechanism of KDM6B. In addition, we investigated the effects of a KDM6B inhibitor, GSK-J4, on PCa cells. We showed that KDM6B overexpression was observed in PCa, and elevated KDM6B expression was associated with high Gleason Score, low serum prostate-specific antigen level and shorted recurrence-free survival. Moreover, KDM6B prompted proliferation, migration, invasion and cell cycle progression and suppressed apoptosis in PCa cells. GSK-J4 administration could significantly suppress the biological function of KDM6B in PCa cells. KDM6B is involved in the development of castration-resistant prostate cancer (CRPC), and combination of MDV3100 plus GSK-J4 is effective for CRPC and MDV3100-resistant CRPC. Mechanism exploration revealed that androgen receptor can decrease the transcription of KDM6B and that KDM6B demethylates H3K27me3 at the cyclin D1 promoter and cooperates with smad2/3 to prompt the expression of cyclin D1. In conclusion, our study demonstrates that KDM6B is an androgen receptor regulated gene and plays oncogenic roles by promoting cyclin D1 transcription in PCa and GSK-J4 has the potential to be a promising agent for the treatment of PCa.

Extra-domain B of fibronectin as an alternative target for drug delivery and a cancer diagnostic and prognostic biomarker for malignant glioma

Extra-domain B of fibronectin (EDB-FN) is an alternatively spliced form of fibronectin with high expression in the extracellular matrix of neovascularized tissues and malignant cancer cells. In this study, we evaluated the practicality of using EDB-FN as a biomarker and therapeutic target for malignant gliomas (MGs), representative intractable diseases involving brain tumors.

Methods: The microarray- and sequence-based patient transcriptomic database 'Oncopression' and tissue microarray of MG patient tissue samples were analyzed. EDB-FN data were extracted and evaluated from 23,344 patient samples of 17 types of cancer to assess its effectiveness and selectivity as a molecular target. To strengthen the results of the patient data analysis, the utility of EDB-FN as a molecular marker and target for MG was verified using active EDB-FN-targeting ultrasmall lipidic micellar nanoparticles (~12 nm), which had a high drug-loading capacity and were efficiently internalized by MG cells in vitro and in vivo.

Results: Brain tumors had a 1.42-fold cancer-to-normal ratio (p < 0.0001), the second highest among 17 cancers after head and neck cancer. Patient tissue microarray analysis showed that the EDB-FN high-expression group had a 5.5-fold higher risk of progression than the EDB-FN low-expression group (p < 0.03). By labeling docetaxel-containing ultrasmall micelles with a bipodal aptide targeting EDB-FN (termed APT_EDB-DSPE-DTX), we generated micelles that could specifically bind to MG cells, leading to superior antitumor efficacy of EDB-FN-targeting nanoparticles compared to nontargeting controls.

Conclusions: Taken together, these results show that EDB-FN can be an effective drug delivery target and biomarker for MG.

A hierarchical Bayesian mixture model for inferring the expression state of genes in transcriptomes

How do the cells of an organism—each with an identical genome—give rise to tissues of incredible phenotypic diversity? Key to answering this question is the transcriptome: the set of genes expressed in a given tissue. We would clearly benefit from the ability to identify qualitative differences in expression (whether a gene is active or inactive in a given tissue/species). Inferring the expression state of genes is surprisingly difficult, owing to the complex biological processes that give rise to transcriptomes and to the vagaries of techniques used to generate transcriptomic datasets. We develop a hierarchical Bayesian mixture model that—by describing those biological and technical processes—allows us to infer the expression state of genes from replicate transcriptomic datasets.

Transcriptomes are key to understanding the relationship between genotype and phenotype. The ability to infer the expression state (active or inactive) of genes in the transcriptome offers unique benefits for addressing this issue. For example, qualitative changes in gene expression may underly the origin of novel phenotypes, and expression states are readily comparable between tissues and species. However, inferring the expression state of genes is a surprisingly difficult problem, owing to the complex biological and technical processes that give rise to observed transcriptomic datasets. Here, we develop a hierarchical Bayesian mixture model that describes this complex process and allows us to infer expression state of genes from replicate transcriptomic libraries. We explore the statistical behavior of this method with analyses of simulated datasets—where we demonstrate its ability to correctly infer true (known) expression states—and empirical-benchmark datasets, where we demonstrate that the expression states inferred from RNA-sequencing (RNA-seq) datasets using our method are consistent with those based on independent evidence. The power of our method to correctly infer expression states is generally high and remarkably, approaches the maximum possible power for this inference problem. We present an empirical analysis of primate-brain transcriptomes, which identifies genes that have a unique expression state in humans. Our method is implemented in the freely available R package zigzag.

Cell-of-Origin Subtyping of Diffuse Large B-Cell Lymphoma by Using a qPCR-based Gene Expression Assay on Formalin-Fixed Paraffin-Embedded Tissues

The well-established cell-of-origin (COO) algorithm categorizes diffuse large B-cell lymphoma (DLBCL) into activated B-cell-like (ABC) and germinal center B-cell-like (GCB) subgroups through gene expression profiling. We aimed to develop and validate a qPCR-based gene expression assay to determine the COO subgroups of DLBCL with formalin-fixed paraffin-embedded (FFPE) tissue. We first established a DLBCL transcriptome database of 1,016 samples retrieved from three published datasets (GSE10846, GSE22470, and GSE31312). With this database, we identified a qPCR-based 32-gene expression signature (DLBCL-COO assay) that is significantly associated with the COO subgroups. The DLBCL-COO assay was further validated in a cohort of 160 Chinese DLBCL patients. Biopsy samples from DLBCL patients with paired FFPE and fresh frozen tissue were collected to assign COO subtypes based on the immunohistochemistry (IHC) algorithm (Han's algorithm), DLBCL-COO assay, and global gene expression profiling with RNA-seq. For 111 paired FFPE and fresh DLBCL samples, the concordance between the IHC, qPCR, and RNA-seq methods was 77.5% and 91.9%, respectively. The DLBCL-COO assay demonstrated a significantly superior concordance of COO determination with the “gold standard” RNA-seq compared with the IHC assignment with Han's algorithm (P = 0.005). Furthermore, the overall survival of GCB patients defined by the DLBCL-COO assay was significantly superior to that of ABC patients (P = 0.023). This effect was not seen when the tumors were classified by the IHC algorithm. The DLBCL-COO assay provides flexibility and accuracy in DLBCL subtype characterization. These findings demonstrated that the DLBCL-COO assay might serve as a useful tool for guiding prognostic and therapeutic options for DLBCL patients.

MyoMiner: explore gene co-expression in normal and pathological muscle

BACKGROUND

High-throughput transcriptomics measures mRNA levels for thousands of genes in a biological sample. Most gene expression studies aim to identify genes that are differentially expressed between different biological conditions, such as between healthy and diseased states. However, these data can also be used to identify genes that are co-expressed within a biological condition. Gene co-expression is used in a guilt-by-association approach to prioritize candidate genes that could be involved in disease, and to gain insights into the functions of genes, protein relations, and signaling pathways. Most existing gene co-expression databases are generic, amalgamating data for a given organism regardless of tissue-type.

METHODS

To study muscle-specific gene co-expression in both normal and pathological states, publicly available gene expression data were acquired for 2376 mouse and 2228 human striated muscle samples, and separated into 142 categories based on species (human or mouse), tissue origin, age, gender, anatomic part, and experimental condition. Co-expression values were calculated for each category to create the MyoMiner database.

RESULTS

Within each category, users can select a gene of interest, and the MyoMiner web interface will return all correlated genes. For each co-expressed gene pair, adjusted p-value and confidence intervals are provided as measures of expression correlation strength. A standardized expression-level scatterplot is available for every gene pair r-value. MyoMiner has two extra functions: (a) a network interface for creating a 2-shell correlation network, based either on the most highly correlated genes or from a list of genes provided by the user with the option to include linked genes from the database and (b) a comparison tool from which the users can test whether any two correlation coefficients from different conditions are significantly different.

CONCLUSIONS

These co-expression analyses will help investigators to delineate the tissue-, cell-, and pathology-specific elements of muscle protein interactions, cell signaling and gene regulation. Changes in co-expression between pathologic and healthy tissue may suggest new disease mechanisms and help define novel therapeutic targets. Thus, MyoMiner is a powerful muscle-specific database for the discovery of genes that are associated with related functions based on their co-expression. MyoMiner is freely available at https://www.sys-myo.com/myominer.

Scattered DUSP28 is a novel biomarker responsible for aggravating malignancy via the autocrine and paracrine signaling in metastatic pancreatic cancer

Pancreatic cancer remains one of the most dangerous cancers with a grave prognosis. We have reported that dual specificity phosphatise 28 (DUSP28) could be secreted in pancreatic cancer cells. However, its biological function is poorly understood. Here, we distinguish the function of scattered DUSP28 in human pancreatic cancer. DUSP28 was specifically secreted to cultured medium in metastatic pancreatic cancer cells. Treatment with recombinant DUSP28 significantly increased the migration, invasion, and viability of metastatic pancreatic cancer cells through the activation of CREB, AKT, and ERK1/2 signaling pathways. In addition, administration of recombinant DUSP28 elicited pro-angiogenic effects in human umbilical vein endothelial cells. Injection of recombinant DUSP28 also produced tumor growth in vivo. Of interest, DUSP28 formed an autocrine loop with integrin α1 (ITGα1) by transcriptional regulation and recombinant DUSP28 acted as an oncogenic reagent through the interaction with ITGα1. Notably, scattered DUSP28 could be detected in whole blood samples of pancreatic cancer patients by accessible immunoassay. These results provide the basis for DUSP28 as a promising therapeutic target and a biomarker for metastatic pancreatic cancer.

L1TD1 - a prognostic marker for colon cancer

BACKGROUND

Prognostic markers specific to a particular cancer type can assist in the evaluation of survival probability of patients and help clinicians to assess the available treatment modalities.

METHODS

Gene expression data was analyzed from three independent colon cancer microarray gene expression data sets (N = 1052). Survival analysis was performed for the three data sets, stratified by the expression level of the LINE-1 type transposase domain containing 1 (L1TD1). Correlation analysis was performed to investigate the role of the interactome of L1TD1 in colon cancer patients.

RESULTS

We found L1TD1 as a novel positive prognostic marker for colon cancer. Increased expression of L1TD1 associated with longer disease-free survival in all the three data sets. Our results were in contrast to a previous study on medulloblastoma, where high expression of L1TD1 was linked with poor prognosis. Notably, in medulloblastoma L1TD1 was co-expressed with its interaction partners, whereas our analysis revealed lack of co-expression of L1TD1 with its interaction partners in colon cancer.

CONCLUSIONS

Our results identify increased expression of L1TD1 as a prognostic marker predicting longer disease-free survival in colon cancer patients.

African American Specific Gene Panel Predictive of Poor Prostate Cancer Outcome

PURPOSE

Most prostate cancer in African American men lacks the ETS (E26 transforming specific) family fusion event (ETS-). We aimed to establish clinically relevant biomarkers in African American men by studying ETS dependent gene expression patterns to identified race specific genes predictive of outcomes.

MATERIALS AND METHODS

Two multicenter cohorts of a total of 1,427 men were used for the discovery and validation (635 and 792 men, respectively) of race specific predictive biomarkers. We used false discovery rate adjusted q values to identify race and ETS dependent genes which were differentially expressed in African American men who experienced biochemical recurrence within 5 years. Principal component modeling along with survival analysis was done to assess the accuracy of the gene panel in predicting recurrence.

RESULTS

We identified 3,047 genes which were differentially expressed based on ETS status. Of these genes 362 were differentially expressed in a race specific manner (false discovery rate 0.025 or less). A total of 81 genes were race specific and over expressed in African American men who experienced biochemical recurrence. The final gene panel included APOD, BCL6, EMP1, MYADM, SRGN and TIMP3. These genes were associated with 5-year biochemical recurrence (HR 1.97, 95% CI 1.27-3.06, p = 0.002) and they improved the predictive accuracy of clinicopathological variables only in African American men (60-month time dependent AUC 0.72).

CONCLUSIONS

In an effort to elucidate biological features associated with prostate cancer aggressiveness in African American men we identified ETS dependent biomarkers predicting early onset biochemical recurrence only in African American men. Thus, these ETS dependent biomarkers representing ideal candidates for biomarkers of aggressive disease in this patient population.

Gene expression signature of atypical breast hyperplasia and regulation by SFRP1

Background

Atypical breast hyperplasias (AH) have a 10-year risk of progression to invasive cancer estimated at 4–7%, with the overall risk of developing breast cancer increased by ~ 4-fold. AH lesions are estrogen receptor alpha positive (ERα+) and represent risk indicators and/or precursor lesions to low grade ERα+ tumors. Therefore, molecular profiles of AH lesions offer insights into the earliest changes in the breast epithelium, rendering it susceptible to oncogenic transformation.

Methods

In this study, women were selected who were diagnosed with ductal or lobular AH, but no breast cancer prior to or within the 2-year follow-up. Paired AH and histologically normal benign (HNB) tissues from patients were microdissected. RNA was isolated, amplified linearly, labeled, and hybridized to whole transcriptome microarrays to determine gene expression profiles. Genes that were differentially expressed between AH and HNB were identified using a paired analysis. Gene expression signatures distinguishing AH and HNB were defined using AGNES and PAM methods. Regulation of gene networks was investigated using breast epithelial cell lines, explant cultures of normal breast tissue and mouse tissues.

Results

A 99-gene signature discriminated the histologically normal and AH tissues in 81% of the cases. Network analysis identified coordinated alterations in signaling through ERα, epidermal growth factor receptors, and androgen receptor which were associated with the development of both lobular and ductal AH. Decreased expression of SFRP1 was also consistently lower in AH. Knockdown of SFRP1 in 76N-Tert cells resulted altered expression of 13 genes similarly to that observed in AH. An SFRP1-regulated network was also observed in tissues from mice lacking Sfrp1. Re-expression of SFRP1 in MCF7 cells provided further support for the SFRP1-regulated network. Treatment of breast explant cultures with rSFRP1 dampened estrogen-induced progesterone receptor levels.

Conclusions

The alterations in gene expression were observed in both ductal and lobular AH suggesting shared underlying mechanisms predisposing to AH. Loss of SFRP1 expression is a significant regulator of AH transcriptional profiles driving previously unidentified changes affecting responses to estrogen and possibly other pathways. The gene signature and pathways provide insights into alterations contributing to AH breast lesions.

Electronic supplementary material

The online version of this article (10.1186/s13058-019-1157-5) contains supplementary material, which is available to authorized users.

In vitro assessment of mechanistic events induced by structurally related chemical rubber sensitizers

Allergic contact dermatitis (ACD) is one of the most common forms of immunotoxicity, and increased understanding of how chemicals trigger these adverse reactions is needed in order to treat or design testing strategies to identify and subsequently avoid exposure to such substances. In this study, we investigated the cellular response induced by rubber chemicals in a dendritic cell (DC) model, focusing on the structurally similar chemicals diethylthiocarbamylbenzothiazole sulfide and dimethylthiocarbamylbenzothiazole sulfide, with regard to regulation of microRNA, and messenger RNA expression. Only a few miRNAs were found to be commonly regulated by both rubber chemicals, among them miR1973, while the overall miRNA expression profiles were diverse. Similarly, out of approximately 500 differentially regulated transcripts for each chemical, about 60% overlapped, while remaining were unique. The pathways predicted to be enriched in the cell model by stimulation with the rubber chemicals were linked to immunological events, relevant in the context of ACD. These results suggest that small structural differences can trigger specific activation of the immune system in response to chemicals. The here presented mechanistic data can be valuable in explaining the immunotoxicological events in DC activation after exposure to skin sensitizing chemicals, and can contribute to understanding, preventing and treating ACD.

Blockade of integrin α3 attenuates human pancreatic cancer via inhibition of EGFR signalling

The prognosis of pancreatic cancer remains dismal despite continuous and considerable efforts. Integrins (ITGs) are highly expressed in various malignant cancers. However, very few studies investigated the role of integrin α3 (ITGα3) in malignant cancers. Here, we determined the functional role of ITGα3 in pancreatic cancer. Analysis of public microarray databases and Western blot analysis indicated a unique expression of ITGα3 in human pancreatic cancer. Silencing ITGα3 expression significantly inhibited the viability and migration of human pancreatic cancer cells. Notably, ablation of ITGα3 expression resulted in a significant decrease of epidermal growth factor receptor (EGFR) expression compared with transfection of control-siRNA through an increased number of leucine-rich repeats and immunoglobulin-like domain protein 1 (LRIG1) expression. In addition, ablating ITGα3 inhibited tumour growth via blockade of EGFR signalling in vivo. Furthermore, the highly expressed ITGα3 led to a poor prognosis of pancreatic cancer patients. Our results provide novel insights into ITGα3-induced aggressive pancreatic cancer.

Ambient Pollution-related Reprogramming of the Human Small Airway Epithelial Transcriptome

RATIONALE

Epidemiologic studies have demonstrated that exposure to particulate matter ambient pollution has adverse effects on lung health, exacerbated by cigarette smoking. Particulate matter less than or equal to 2.5 μm in aerodynamic diameter (PM2.5) is among the most harmful urban pollutants and is closely linked to respiratory disease.

OBJECTIVES

Based on the knowledge that the small airway epithelium (SAE) plays a central role in the pathogenesis of smoking-related lung disease, we hypothesized that elevated PM2.5 levels are associated with dysregulation of SAE gene expression, which may contribute to the development of respiratory disease.

METHODS

From 2009 to 2012, healthy nonsmoker (n = 29) and smoker (n = 129) residents of New York City underwent bronchoscopy with SAE brushing (2.6 ± 1.3 samples/subject; total of 405 samples). SAE gene expression was assessed by Affymetrix HG-U133 Plus 2.0 microarray. New York City PM2.5 levels (Environmental Protection Agency data) were averaged for the 30 days before bronchoscopy. A linear mixed model was used to assess PM2.5-related gene dysregulation accounting for multiple clinical and methodologic variables.

MEASUREMENTS AND MAIN RESULTS

Thirty-day mean PM2.5 levels varied from 6.2 to 18 μg/m3. In nonsmokers, there was no dysregulation of SAE gene expression associated with ambient PM2.5 levels. In marked contrast, n = 219 genes were significantly dysregulated in association with PM2.5 levels in the SAE of smokers. Many of these genes relate to cell growth and transcription regulation. Interestingly, 11% of genes were mitochondria associated.

CONCLUSIONS

PM2.5 exposure contributes to significant dysregulation of the SAE transcriptome of smokers, linking pollution and airway epithelial biology in the risk of development of respiratory disease in susceptible individuals.

microRNA 193a-5p Regulates Levels of Nucleolar- and Spindle-Associated Protein 1 to Suppress Hepatocarcinogenesis

BACKGROUND & AIMS

We performed an integrated analysis to identify microRNAs (miRNAs) and messenger RNAs (mRNAs) with altered expression in liver tumors from 3 mouse models of hepatocellular carcinoma (HCC) and human tumor tissues.

METHODS

We analyzed miRNA and mRNA expression profiles of liver tissues from mice with diethylnitrosamine-induced hepatocarcinogenesis, conditional expression of lymphotoxin alpha and lymphotoxin beta, or inducible expression of a Myc transgene (Tet-O-Myc mice), as well as male C57BL/6 mice (controls). miRNA mimics were expressed and miRNAs and mRNAs were knocked down in human (Huh7, Hep3B, JHH2) hepatoma cell lines; cells were analyzed for viability, proliferation, apoptosis, migration, and invasion. Cells were grown as xenograft tumors in nude mice and analyzed. We combined in silico target gene prediction with mRNA profiles from all 3 mouse models. We quantified miRNA levels in 146 fresh-frozen tissues from patients (125 HCCs, 17 matched nontumor tissues, and 4 liver samples from patients without cancer) and published human data sets and tested correlations with patient survival times using Kaplan-Meier curves and the log-rank test. Levels of NUSAP1 mRNA were quantified in 237 HCCs and 5 nontumor liver samples using the TaqMan assay.

RESULTS

Levels of the miRNA 193a-5p (MIR193A-5p) were reduced in liver tumors from all 3 mouse tumor models and in human HCC samples, compared with nontumor liver tissues. Expression of a MIR193A-5p mimic in hepatoma cells reduced proliferation, survival, migration, and invasion and their growth as xenograft tumors in nude mice. We found nucleolar and spindle-associated protein 1 (NUSAP1) to be a target of MIR193A-5p; HCC cells and tissues with low levels of MIR193A-5p had increased expression of NUSAP1. Increased levels of NUSAP1 in HCC samples correlated with shorter survival times of patients. Knockdown of NUSAP1 in Huh7 cells reduced proliferation, survival, migration, and growth as xenograft tumors in nude mice. Hydrodynamic tail-vein injections of a small hairpin RNA against NUSAP1 reduced growth of Akt1-Myc-induced tumors in mice.

CONCLUSIONS

MIR193A-5p appears to prevent liver tumorigenesis by reducing levels of NUSAP1. Levels of MIR193A-5p are reduced in mouse and human HCC cells and tissues, leading to increased levels of NUSAP1, associated with shorter survival times of patients. Integrated analyses of miRNAs and mRNAs in tumors from mouse models can lead to identification of therapeutic targets in humans. The currently reported miRNA and mRNA profiling data have been submitted to the Gene Expression Omnibus (super-series accession number GSE102418).

Genome-Wide Transcriptional and Functional Analysis of Human T Lymphocytes Treated with Benzo[α]pyrene

Polycyclic aromatic hydrocarbons (PAHs) are widely distributed environmental contaminants, known to affect T lymphocytes. However, the molecular targets and pathways involved in their immunotoxic effects in human T lymphocytes remain unknown. Here, we analyzed the gene expression profile of primary human T lymphocytes treated with the prototypical PAH, benzo[α]pyrene (B[α]P), using a microarray-based transcriptome analysis. After a 48 h exposure to B[α]P, we identified 158 genes differentially expressed in T lymphocytes, including not only genes well-known to be affected by PAHs such as the cytochromes P450 (CYP) 1A1 and 1B1, but also others not previously shown to be targeted by B[α]P such as genes encoding the gap junction beta (GJB)-2 and 6 proteins. Functional enrichment analysis revealed that these candidates were significantly associated with the aryl hydrocarbon (AhR) and interferon (IFN) signaling pathways; a marked alteration in T lymphocyte recruitment was also observed. Using functional tests in transwell migration experiments, B[α]P was then shown to significantly decrease the chemokine (C-X-C motif) ligand 12-induced chemotaxis and transendothelial migration of T lymphocytes. In total, this study opens the way to unsuspected responsive pathway of interest, i.e., T lymphocyte migration, thus providing a more thorough understanding of the molecular basis of the immunotoxicity of PAHs.

Distinct transcriptional repertoire of the androgen receptor in ETS fusion-negative prostate cancer

Background

Prostate cancer (PCa) tumors harboring translocations of ETS family genes with the androgen responsive TMPRSS2 gene (ETS+ tumors) provide a robust biomarker for detecting PCa in approximately 70% of patients. ETS+ PCa express high levels of the androgen receptor (AR), yet PCa tumors lacking ETS fusions (ETS−) also express AR and demonstrate androgen-regulated growth. In this study, we evaluate the differences in the AR-regulated transcriptomes between ETS+ and ETS− PCa tumors.

Methods

10,608 patient tumors from three independent PCa datasets classified as ETS+ (samples overexpressing ERG or other ETS family members) or ETS− (all other PCa) were analyzed for differential gene expression using false-discovery-rate adjusted methods and gene-set enrichment analysis (GSEA).

Results

Based on the expression of AR-dependent genes and an unsupervised Principal Component Analysis (PCA) model, AR-regulated gene expression alone was able to separate PCa samples into groups based on ETS status in all PCa databases. ETS status distinguished several differentially expressed genes in both TCGA (6.9%) and GRID (6.6%) databases, with 413 genes overlapping in both databases. Importantly, GSEA showed enrichment of distinct androgen-responsive genes in both ETS− and ETS+ tumors, and AR ChIP-seq data identified 131 direct AR-target genes that are regulated in an ETS-specific fashion. Notably, dysregulation of ETS-dependent AR-target genes within the metabolic and non-canonical WNT pathways was associated with clinical outcomes.

Conclusions

ETS status influences the transcriptional repertoire of the AR, and ETS− PCa tumors appear to rely on distinctly different AR-dependent transcriptional programs to drive and sustain tumorigenesis.

Soy supplementation: Impact on gene expression in different tissues of ovariectomized rats and evaluation of the rat model to predict (post)menopausal health effect

•

The usefulness of PBMC gene expressions as a surrogate tissue for risk assessment is questionable.

•

SIF in a dose of 2 mg/kg b.w/day is not able to influence ERGs in target tissues such as breast and uterus.

•

Plasma concentrations of SIF after 8 weeks oral exposure similar as the recommended dose for humans do not proliferate cells in in vitro cellular models.

•

The ovariectomized rat is probably not a good model to predict human risk or benefit assessment of SIF in human.

This toxicogenomic study was conducted to predict (post)menopausal human health effects of commercial soy supplementation using ovariectomized rats as a model. Different target tissues (i.e. breast, uterus and sternum) and non-target tissues (i.e. peripheral blood mononuclear cells (PBMC), adipose and liver) of ovariectomized F344 rats exposed to a commercially available soy supplement for eight weeks, were investigated. Changes in gene expression in these tissues were analysed using whole-genome microarray analysis. No correlation in changes in gene expression were observed among different tissues, indicating tissue specific effects of soy isoflavone supplementation. Out of 87 well-established estrogen responsive genes (ERGs), only 19 were found to be significantly regulated (p < 0.05) in different tissues, particularly in liver, adipose and uterus tissues. Surprisingly, no ERGs were significantly regulated in estrogen sensitive breast and sternum tissues. The changes in gene expression in PBMC and adipose tissue in rats were compared with those in (post)menopausal female volunteers who received the same supplement in a similar oral dose and exposure duration in human intervention studies. No correlation in changes in gene expression between rats and humans was observed. Although receiving a similar dose, in humans the plasma levels expressed as total free aglycones were several folds higher than in the rat. Therefore, the overall results in young ovariectomized female F344 rats indicated that using rat transcriptomic data does not provide a suitable model for human risk or benefit analysis of soy isoflavone supplementation.

Comparative transcriptome analysis of the wild-type model apomict Hieracium praealtum and its loss of parthenogenesis (lop) mutant

BACKGROUND

Asexual seed formation (apomixis) has been observed in diverse plant families but is rare in crop plants. The generation of apomictic crops would revolutionize agriculture, as clonal seed production provides a low cost and efficient way to produce hybrid seed. Hieracium (Asteraceae) is a model system for studying the molecular components of gametophytic apomixis (asexual seed reproduction).

RESULTS

In this study, a reference transcriptome was produced from apomictic Hieracium undergoing the key apomictic events of apomeiosis, parthenogenesis and autonomous endosperm development. In addition, transcriptome sequences from pre-pollination and post-pollination stages were generated from a loss of parthenogenesis (lop) mutant accession that exhibits loss of parthenogenesis and autonomous endosperm development. The transcriptome is composed of 147,632 contigs, 50% of which were annotated with orthologous genes and their probable function. The transcriptome was used to identify transcripts differentially expressed during apomictic and pollination dependent (lop) seed development. Gene Ontology enrichment analysis of differentially expressed transcripts showed that an important difference between apomictic and pollination dependent seed development was the expression of genes relating to epigenetic gene regulation. Genes that mark key developmental stages, i.e. aposporous embryo sac development and seed development, were also identified through their enhanced expression at those stages.

CONCLUSION

The production of a comprehensive floral reference transcriptome for Hieracium provides a valuable resource for research into the molecular basis of apomixis and the identification of the genes underlying the LOP locus.

Lithocholic Acid Improves the Survival of Drosophila Melanogaster

SCOPE

Primary bile acids are produced in the liver, whereas secondary bile acids, such as lithocholic acid (LCA), are generated by gut bacteria from primary bile acids that escape the ileal absorption. Besides their well-known function as detergents in lipid digestion, bile acids are important signaling molecules mediating effects on the host's metabolism.

METHODS AND RESULTS

Fruit flies (Drosophila melanogaster) are supplemented with 50 μmol L^-1 LCA either for 30 days or throughout their lifetime. LCA supplementation results in a significant induction of the mean (+12 days), median (+10 days), and maximum lifespan (+ 11 days) in comparison to untreated control flies. This lifespan extension is accompanied by an induction of spargel (srl), the fly homolog of mammalian PPAR-γ co-activator 1α (PGC1α). In wild-type flies, the administration of antibiotics abrogates both the LCA-mediated lifespan induction as well as the upregulation of srl.

CONCLUSION

It is shown that the secondary bile acid LCA significantly induces the mean, the median, and the maximum survival in D. melanogaster. Our data suggest that besides an upregulation of the PGC1α-homolog srl, unidentified alterations in the structure or metabolism of the gut microbiota contribute to the longevity effect mediated by LCA.

Tributyltin induces a transcriptional response without a brite adipocyte signature in adipocyte models

Tributyltin (TBT), a peroxisome proliferator-activated receptor γ (PPARγ)/retinoid X receptor (RXR) ligand and founding member of the environmental obesogen chemical class, induces adipocyte differentiation and suppresses bone formation. A growing number of environmental PPARγ ligands are being identified. However, the potential for environmental PPARγ ligands to induce adverse metabolic effects has been questioned because PPARγ is a therapeutic target in treatment of type II diabetes. We evaluated the molecular consequences of TBT exposure during bone marrow multipotent mesenchymal stromal cell (BM-MSC) differentiation in comparison to rosiglitazone, a therapeutic PPARγ ligand, and LG100268, a synthetic RXR ligand. Mouse primary BM-MSCs (female, C57BL/6J) undergoing bone differentiation were exposed to maximally efficacious and human relevant concentrations of rosiglitazone (100 nM), LG100268 (100 nM) or TBT (80 nM) for 4 days. Gene expression was assessed using microarrays, and in silico functional annotation was performed using pathway enrichment analysis approaches. Pathways related to osteogenesis were downregulated by all three ligands, while pathways related to adipogenesis were upregulated by rosiglitazone and TBT. However, pathways related to mitochondrial biogenesis and brown-in-white (brite) adipocyte differentiation were more significantly upregulated in rosiglitazone-treated than TBT-treated cells. The lack of induction of genes involved in adipocyte energy dissipation by TBT was confirmed by an independent gene expression analysis in BM-MSCs undergoing adipocyte differentiation and by analysis of a publically available 3T3 L1 data set. Furthermore, rosiglitazone, but not TBT, induced mitochondrial biogenesis and respiration. This study is the first to show that an environmental PPARγ ligand has a limited capacity to induce health-promoting activities of PPARγ.

A merged lung cancer transcriptome dataset for clinical predictive modeling

The Gene Expression Omnibus (GEO) database is an excellent public source of whole transcriptomic profiles of multiple cancers. The main challenge is the limited accessibility of such large-scale genomic data to people without a background in bioinformatics or computer science. This presents difficulties in data analysis, sharing and visualization. Here, we present an integrated bioinformatics pipeline and a normalized dataset that has been preprocessed using a robust statistical methodology; allowing others to perform large-scale meta-analysis, without having to conduct time-consuming data mining and statistical correction. Comprising 1,118 patient-derived samples, the normalized dataset includes primary non-small cell lung cancer (NSCLC) tumors and paired normal lung tissues from ten independent GEO datasets, facilitating differential expression analysis. The data has been merged, normalized, batch effect-corrected and filtered for genes with low variance via multiple open source R packages integrated into our workflow. Overall this dataset (with associated clinical metadata) better represents the diseased population and serves as a powerful tool for early predictive biomarker discovery.

Tributyltin induces a transcriptional response without a brite adipocyte signature in adipocyte models

Tributyltin (TBT), a peroxisome proliferator-activated receptor γ (PPARγ)/retinoid X receptor (RXR) ligand and founding member of the environmental obesogen chemical class, induces adipocyte differentiation and suppresses bone formation. A growing number of environmental PPARγ ligands are being identified. However, the potential for environmental PPARγ ligands to induce adverse metabolic effects has been questioned because PPARγ is a therapeutic target in treatment of type II diabetes. We evaluated the molecular consequences of TBT exposure during bone marrow multipotent mesenchymal stromal cell (BM-MSC) differentiation in comparison to rosiglitazone, a therapeutic PPARγ ligand, and LG100268, a synthetic RXR ligand. Mouse primary BM-MSCs (female, C57BL/6J) undergoing bone differentiation were exposed to maximally efficacious and human relevant concentrations of rosiglitazone (100 nM), LG100268 (100 nM) or TBT (80 nM) for 4 days. Gene expression was assessed using microarrays, and in silico functional annotation was performed using pathway enrichment analysis approaches. Pathways related to osteogenesis were downregulated by all three ligands, while pathways related to adipogenesis were upregulated by rosiglitazone and TBT. However, pathways related to mitochondrial biogenesis and brown-in-white (brite) adipocyte differentiation were more significantly upregulated in rosiglitazone-treated than TBT-treated cells. The lack of induction of genes involved in adipocyte energy dissipation by TBT was confirmed by an independent gene expression analysis in BM-MSCs undergoing adipocyte differentiation and by analysis of a publically available 3T3 L1 data set. Furthermore, rosiglitazone, but not TBT, induced mitochondrial biogenesis and respiration. This study is the first to show that an environmental PPARγ ligand has a limited capacity to induce health-promoting activities of PPARγ.

Lactobacillus acidophilus Attenuates Salmonella-Induced Stress of Epithelial Cells by Modulating Tight-Junction Genes and Cytokine Responses

Scope: Salmonellosis is a prevalent food-borne illness that causes diarrhea in over 130 million humans yearly and can lead to death. There is an urgent need to find alternatives to antibiotics as many salmonellae are now multidrug resistant. As such, specific beneficial bacteria and dietary fibers can be an alternative as they may prevent Salmonella Typhimurium (STM) infection and spreading by strengthening intestinal barrier function. Methods and Results: We tested whether immune active long-chain inulin-type fructans and/or L. acidophilus W37, L. brevis W63, and L. casei W56 can strengthen barrier integrity of intestinal Caco-2 cells in the presence and absence of a STM. Effects of the ingredients on intestinal barrier function were first evaluated by quantifying trans-epithelial electric resistance (TEER) and regulation of gene expression by microarray. Only L. acidophilus had effects on TEER and modulated a group of 26 genes related to tight-junctions. Inulin-type fructans, L. brevis W63 and L. casei W56 regulated other genes, unrelated to tight-junctions. L. acidophilus also had unique effects on a group of six genes regulating epithelial phenotype toward follicle-associated epithelium. L. acidophilus W37 was therefore selected for a challenge with STM and prevented STM-induced barrier disruption and decreased secretion of IL-8. Conclusion:L. acidophilus W37 increases TEER and can protect against STM induced disruption of gut epithelial cells integrity in vitro. Our results suggest that selection of specific bacterial strains for enforcing barrier function may be a promising strategy to reduce or prevent STM infections.

Integrity, standards, and QC-related issues with big data in pre-clinical drug discovery

The tremendous expansion of data analytics and public and private big datasets presents an important opportunity for pre-clinical drug discovery and development. In the field of life sciences, the growth of genetic, genomic, transcriptomic and proteomic data is partly driven by a rapid decline in experimental costs as biotechnology improves throughput, scalability, and speed. Yet far too many researchers tend to underestimate the challenges and consequences involving data integrity and quality standards. Given the effect of data integrity on scientific interpretation, these issues have significant implications during preclinical drug development. We describe standardized approaches for maximizing the utility of publicly available or privately generated biological data and address some of the common pitfalls. We also discuss the increasing interest to integrate and interpret cross-platform data. Principles outlined here should serve as a useful broad guide for existing analytical practices and pipelines and as a tool for developing additional insights into therapeutics using big data.

Development of a primary human Small Intestine-on-a-Chip using biopsy-derived organoids

Here we describe a method for fabricating a primary human Small Intestine-on-a-Chip (Intestine Chip) containing epithelial cells isolated from healthy regions of intestinal biopsies. The primary epithelial cells are expanded as 3D organoids, dissociated, and cultured on a porous membrane within a microfluidic device with human intestinal microvascular endothelium cultured in a parallel microchannel under flow and cyclic deformation. In the Intestine Chip, the epithelium forms villi-like projections lined by polarized epithelial cells that undergo multi-lineage differentiation similar to that of intestinal organoids, however, these cells expose their apical surfaces to an open lumen and interface with endothelium. Transcriptomic analysis also indicates that the Intestine Chip more closely mimics whole human duodenum in vivo when compared to the duodenal organoids used to create the chips. Because fluids flowing through the lumen of the Intestine Chip can be collected continuously, sequential analysis of fluid samples can be used to quantify nutrient digestion, mucus secretion and establishment of intestinal barrier function over a period of multiple days in vitro. The Intestine Chip therefore may be useful as a research tool for applications where normal intestinal function is crucial, including studies of metabolism, nutrition, infection, and drug pharmacokinetics, as well as personalized medicine.

Enterovirus-associated changes in blood transcriptomic profiles of children with genetic susceptibility to type 1 diabetes

AIMS/HYPOTHESIS

Enterovirus infections have been associated with the development of type 1 diabetes in multiple studies, but little is known about enterovirus-induced responses in children at risk for developing type 1 diabetes. Our aim was to use genome-wide transcriptomics data to characterise enterovirus-associated changes in whole-blood samples from children with genetic susceptibility to type 1 diabetes.

METHODS

Longitudinal whole-blood samples (356 samples in total) collected from 28 pairs of children at increased risk for developing type 1 diabetes were screened for the presence of enterovirus RNA. Seven of these samples were detected as enterovirus-positive, each of them collected from a different child, and transcriptomics data from these children were analysed to understand the individual-level responses associated with enterovirus infections. Transcript clusters with peaking or dropping expression at the time of enterovirus positivity were selected as the enterovirus-associated signals.

RESULTS

Strong signs of activation of an interferon response were detected in four children at enterovirus positivity, while transcriptomic changes in the other three children indicated activation of adaptive immune responses. Additionally, a large proportion of the enterovirus-associated changes were specific to individuals. An enterovirus-induced signature was built using 339 genes peaking at enterovirus positivity in four of the children, and 77 of these genes were also upregulated in human peripheral blood mononuclear cells infected in vitro with different enteroviruses. These genes separated the four enterovirus-positive samples clearly from the remaining 352 blood samples analysed.

CONCLUSIONS/INTERPRETATION

We have, for the first time, identified enterovirus-associated transcriptomic profiles in whole-blood samples from children with genetic susceptibility to type 1 diabetes. Our results provide a starting point for understanding the individual responses to enterovirus infections in blood and their potential connection to the development of type 1 diabetes.

DATA AVAILABILITY

The datasets analysed during the current study are included in this published article and its supplementary information files ( www.btk.fi/research/computational-biomedicine/1234-2 ) or are available from the Gene Expression Omnibus (GEO) repository (accession GSE30211).

Single subject transcriptome analysis to identify functionally signed gene set or pathway activity

Analysis of single-subject transcriptome response data is an unmet need of precision medicine, made challenging by the high dimension, dynamic nature and difficulty in extracting meaningful signals from biological or stochastic noise. We have proposed a method for single subject analysis that uses a mixture model for transcript fold-change clustering from isogenically paired samples, followed by integration of these distributions with Gene Ontology Biological Processes (GO-BP) to reduce dimension and identify functional attributes. We then extended these methods to develop functional signing metrics for gene set process regulation by incorporating biological repressor relationships encoded in GO-BP as negatively_regulates edges. Results revealed reproducible and biologically meaningful signals from analysis of a single subject's response, opening the door to future transcriptomic studies where subject and resource availability are currently limiting. We used inbred mouse strains fed different diets to provide isogenic biological replicates, permitting rigorous validation of our method. We compared significant genotype-specific GO-BP term results for overlap and rank order across three replicate pairs per genotype, and cross-methods to reference standards (limma+FET, SAM+FET, and GSEA). All single-subject analytics findings were robust and highly reproducible (median area under the ROC curve=0.96, n=24 genotypes × 3 replicates), providing confidence and validation of this approach for analyses in single subjects. R code is available online at http://www.lussiergroup.org/publications/PathwayActivity.

Identification and validation of a 44-gene expression signature for the classification of renal cell carcinomas

BACKGROUND

Renal cancers account for more than 3% of all adult malignancies and cause more than 23,400 deaths per year in China alone. The four most common types of kidney tumours include clear cell, papillary, chromophobe and benign oncocytoma. These histological subtypes vary in their clinical course and prognosis, and different clinical strategies have been developed for their management. Some kidney tumours can be very difficult to distinguish based on the pathological assessment of morphology and immunohistochemistry.

METHODS

Six renal cell carcinoma microarray data sets, including 106 clear cell, 66 papillary, 42 chromophobe, 46 oncocytoma and 35 adjacent normal tissue samples, were subjected to integrative analysis. These data were combined and used as a training set for candidate gene expression signature identification. In addition, two independent cohorts of 1020 RNA-Seq samples from The Cancer Genome Atlas database and 129 qRT-PCR samples from Fudan University Shanghai Cancer Center (FUSCC) were analysed to validate the selected gene expression signature.

RESULTS

A 44-gene expression signature derived from microarray analysis was strongly associated with the histological differentiation of renal tumours and could be used for tumour subtype classification. The signature performance was further validated in 1020 RNA-Seq samples and 129 qRT-PCR samples with overall accuracies of 93.4 and 93.0%, respectively.

CONCLUSIONS

A 44-gene expression signature that could accurately discriminate renal tumour subtypes was identified in this study. Our results may prompt further development of this gene expression signature into a molecular assay amenable to routine clinical practice.

Triangular Alignment (TAME): A Tensor-Based Approach for Higher-Order Network Alignment

Network alignment has extensive applications in comparative interactomics. Traditional approaches aim to simultaneously maximize the number of conserved edges and the underlying similarity of aligned entities. We propose a novel formulation of the network alignment problem that extends topological similarity to higher-order structures and provides a new objective function that maximizes the number of aligned substructures. This objective function corresponds to an integer programming problem, which is NP-hard. Consequently, we identify a closely related surrogate function whose maximization results in a tensor eigenvector problem. Based on this formulation, we present an algorithm called Triangular AlignMEnt (TAME), which attempts to maximize the number of aligned triangles across networks. Using a case study on the NAPAbench dataset, we show that triangular alignment is capable of producing mappings with high node correctness. We further evaluate our method by aligning yeast and human interactomes. Our results indicate that TAME outperforms the state-of-art alignment methods in terms of conserved triangles. In addition, we show that the number of conserved triangles is more significantly correlated, compared to the conserved edge, with node correctness and co-expression of edges. Our formulation and resulting algorithms can be easily extended to arbitrary motifs.

Open source machine-learning algorithms for the prediction of optimal cancer drug therapies

Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal of precision cancer medicine is the accurate prediction of optimal drug therapies from the genomic profiles of individual patient tumors. We introduce here an open source software platform that employs a highly versatile support vector machine (SVM) algorithm combined with a standard recursive feature elimination (RFE) approach to predict personalized drug responses from gene expression profiles. Drug specific models were built using gene expression and drug response data from the National Cancer Institute panel of 60 human cancer cell lines (NCI-60). The models are highly accurate in predicting the drug responsiveness of a variety of cancer cell lines including those comprising the recent NCI-DREAM Challenge. We demonstrate that predictive accuracy is optimized when the learning dataset utilizes all probe-set expression values from a diversity of cancer cell types without pre-filtering for genes generally considered to be “drivers” of cancer onset/progression. Application of our models to publically available ovarian cancer (OC) patient gene expression datasets generated predictions consistent with observed responses previously reported in the literature. By making our algorithm “open source”, we hope to facilitate its testing in a variety of cancer types and contexts leading to community-driven improvements and refinements in subsequent applications.

An alternative biomarker-based approach for the prediction of proteins known to sensitize the respiratory tract

Many natural and industrial proteins are known to have properties that can result in type I hypersensitivity, however, to date, no validated test system exists that can predict the sensitizing potential of these allergens. Thus, the objective of this study was to develop a protocol based on the myeloid cell-based Genomic Allergen Rapid Detection (GARD) assay that can be used to assess and predict the capacity of protein allergens known to induce sensitization in the respiratory tract. Cellular responses induced by eight selected proteins were assessed using transcriptional profiling, flow cytometry and multiplex cytokine analysis. 391 potential biomarkers were identified as a predictive signature and a series of cross-validations supported the validity of the model. These results together with biological pathway analysis of the transcriptomic data indicate that the investigated cell system is able to capture relevant events linked to type I hypersensitization.

A Dual Role of Caspase-8 in Triggering and Sensing Proliferation-Associated DNA Damage, a Key Determinant of Liver Cancer Development

Concomitant hepatocyte apoptosis and regeneration is a hallmark of chronic liver diseases (CLDs) predisposing to hepatocellular carcinoma (HCC). Here, we mechanistically link caspase-8-dependent apoptosis to HCC development via proliferation- and replication-associated DNA damage. Proliferation-associated replication stress, DNA damage, and genetic instability are detectable in CLDs before any neoplastic changes occur. Accumulated levels of hepatocyte apoptosis determine and predict subsequent hepatocarcinogenesis. Proliferation-associated DNA damage is sensed by a complex comprising caspase-8, FADD, c-FLIP, and a kinase-dependent function of RIPK1. This platform requires a non-apoptotic function of caspase-8, but no caspase-3 or caspase-8 cleavage. It may represent a DNA damage-sensing mechanism in hepatocytes that can act via JNK and subsequent phosphorylation of the histone variant H2AX.

A convex optimization approach for identification of human tissue-specific interactomes

Motivation: Analysis of organism-specific interactomes has yielded novel insights into cellular function and coordination, understanding of pathology, and identification of markers and drug targets. Genes, however, can exhibit varying levels of cell type specificity in their expression, and their coordinated expression manifests in tissue-specific function and pathology. Tissue-specific/tissue-selective interaction mechanisms have significant applications in drug discovery, as they are more likely to reveal drug targets. Furthermore, tissue-specific transcription factors (tsTFs) are significantly implicated in human disease, including cancers. Finally, disease genes and protein complexes have the tendency to be differentially expressed in tissues in which defects cause pathology. These observations motivate the construction of refined tissue-specific interactomes from organism-specific interactomes.

Results: We present a novel technique for constructing human tissue-specific interactomes. Using a variety of validation tests (Edge Set Enrichment Analysis, Gene Ontology Enrichment, Disease-Gene Subnetwork Compactness), we show that our proposed approach significantly outperforms state-of-the-art techniques. Finally, using case studies of Alzheimer’s and Parkinson’s diseases, we show that tissue-specific interactomes derived from our study can be used to construct pathways implicated in pathology and demonstrate the use of these pathways in identifying novel targets.

Availability and implementation:http://www.cs.purdue.edu/homes/mohammas/projects/ActPro.html

Contact:mohammadi@purdue.edu

Use of Microarray Datasets to generate Caco-2-dedicated Networks and to identify Reporter Genes of Specific Pathway Activity

Intestinal epithelial cells, like Caco-2, are commonly used to study the interaction between food, other luminal factors and the host, often supported by microarray analysis to study the changes in gene expression as a result of the exposure. However, no compiled dataset for Caco-2 has ever been initiated and Caco-2-dedicated gene expression networks are barely available. Here, 341 Caco-2-specific microarray samples were collected from public databases and from in-house experiments pertaining to Caco-2 cells exposed to pathogens, probiotics and several food compounds. Using these datasets, a gene functional association network specific for Caco-2 was generated containing 8937 nodes 129711 edges. Two in silico methods, a modified version of biclustering and the new Differential Expression Correlation Analysis, were developed to identify Caco-2-specific gene targets within a pathway of interest. These methods were subsequently applied to the AhR and Nrf2 signalling pathways and altered expression of the predicted target genes was validated by qPCR in Caco-2 cells exposed to coffee extracts, known to activate both AhR and Nrf2 pathways. The datasets and in silico method(s) to identify and predict responsive target genes can be used to more efficiently design experiments to study Caco-2/intestinal epithelial-relevant biological processes.

Metabolic modeling of energy balances in Mycoplasma hyopneumoniae shows that pyruvate addition increases growth rate

Mycoplasma hyopneumoniae is cultured on large-scale to produce antigen for inactivated whole-cell vaccines against respiratory disease in pigs. However, the fastidious nutrient requirements of this minimal bacterium and the low growth rate make it challenging to reach sufficient biomass yield for antigen production. In this study, we sequenced the genome of M. hyopneumoniae strain 11 and constructed a high quality constraint-based genome-scale metabolic model of 284 chemical reactions and 298 metabolites. We validated the model with time-series data of duplicate fermentation cultures to aim for an integrated model describing the dynamic profiles measured in fermentations. The model predicted that 84% of cellular energy in a standard M. hyopneumoniae cultivation was used for non-growth associated maintenance and only 16% of cellular energy was used for growth and growth associated maintenance. Following a cycle of model-driven experimentation in dedicated fermentation experiments, we were able to increase the fraction of cellular energy used for growth through pyruvate addition to the medium. This increase in turn led to an increase in growth rate and a 2.3 times increase in the total biomass concentration reached after 3-4 days of fermentation, enhancing the productivity of the overall process. The model presented provides a solid basis to understand and further improve M. hyopneumoniae fermentation processes. Biotechnol. Bioeng. 2017;114: 2339-2347. © 2017 Wiley Periodicals, Inc.

Whole-Genome Sequence of the Metastatic PC3 and LNCaP Human Prostate Cancer Cell Lines

The bone metastasis-derived PC3 and the lymph node metastasis-derived LNCaP prostate cancer cell lines are widely studied, having been described in thousands of publications over the last four decades. Here, we report short-read whole-genome sequencing (WGS) and de novo assembly of PC3 (ATCC CRL-1435) and LNCaP (clone FGC; ATCC CRL-1740) at ∼70 × coverage. A known homozygous mutation in TP53 and homozygous loss of PTEN were robustly identified in the PC3 cell line, whereas the LNCaP cell line exhibited a larger number of putative inactivating somatic point and indel mutations (and in particular a loss of stop codon events). This study also provides preliminary evidence that loss of one or both copies of the tumor suppressor Capicua (CIC) contributes to primary tumor relapse and metastatic progression, potentially offering a treatment target for castration-resistant prostate cancer (CRPC). Our work provides a resource for genetic, genomic, and biological studies employing two commonly-used prostate cancer cell lines.

N-of-1-pathways MixEnrich: advancing precision medicine via single-subject analysis in discovering dynamic changes of transcriptomes

Background

Transcriptome analytic tools are commonly used across patient cohorts to develop drugs and predict clinical outcomes. However, as precision medicine pursues more accurate and individualized treatment decisions, these methods are not designed to address single-patient transcriptome analyses. We previously developed and validated the N-of-1-pathways framework using two methods, Wilcoxon and Mahalanobis Distance (MD), for personal transcriptome analysis derived from a pair of samples of a single patient. Although, both methods uncover concordantly dysregulated pathways, they are not designed to detect dysregulated pathways with up- and down-regulated genes (bidirectional dysregulation) that are ubiquitous in biological systems.

Results

We developed N-of-1-pathways MixEnrich, a mixture model followed by a gene set enrichment test, to uncover bidirectional and concordantly dysregulated pathways one patient at a time. We assess its accuracy in a comprehensive simulation study and in a RNA-Seq data analysis of head and neck squamous cell carcinomas (HNSCCs). In presence of bidirectionally dysregulated genes in the pathway or in presence of high background noise, MixEnrich substantially outperforms previous single-subject transcriptome analysis methods, both in the simulation study and the HNSCCs data analysis (ROC Curves; higher true positive rates; lower false positive rates). Bidirectional and concordant dysregulated pathways uncovered by MixEnrich in each patient largely overlapped with the quasi-gold standard compared to other single-subject and cohort-based transcriptome analyses.

Conclusion

The greater performance of MixEnrich presents an advantage over previous methods to meet the promise of providing accurate personal transcriptome analysis to support precision medicine at point of care.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-017-0263-4) contains supplementary material, which is available to authorized users.

Feeding Angptl4-/- mice trans fat promotes foam cell formation in mesenteric lymph nodes without leading to ascites

Angiopoietin-like 4 (ANGPTL4) regulates plasma triglyceride levels by inhibiting LPL. Inactivation of ANGPTL4 decreases plasma triglycerides and reduces the risk of coronary artery disease. Unfortunately, targeting ANGPTL4 for the therapeutic management of dyslipidemia and atherosclerosis is hampered by the observation that mice and monkeys in which ANGPTL4 is inactivated exhibit lipid accumulation in the mesenteric lymph nodes (MLNs). In mice these pathological events exclusively unfold upon feeding a high saturated FA diet and are followed by an ultimately lethal pro-inflammatory response and chylous ascites. Here, we show that Angptl4^-/- mice fed a diet rich in trans FAs develop numerous lipid-filled giant cells in their MLNs, yet do not have elevated serum amyloid and haptoglobin, do not exhibit ascites, and survive, unlike Angptl4^-/- mice fed a saturated FA-rich diet. In RAW264.7 macrophages, the saturated FA, palmitate, markedly increased markers of inflammation and the unfolded protein response, whereas the trans-unsaturated elaidate and the cis-unsaturated oleate had the opposite effect. In conclusion, trans and saturated FAs have very distinct biological effects in macrophages. Furthermore, lipid accumulation in MLNs is uncoupled from activation of an acute-phase response and chylous ascites, suggesting that ANGPTL4 should not be fully dismissed as target for dyslipidemia.

Evolutionary and Functional Features of Copy Number Variation in the Cattle Genome

Genomic structural variations are an important source of genetic diversity. Copy number variations (CNVs), gains and losses of large regions of genomic sequence between individuals of a species, have been associated with a wide variety of phenotypic traits. However, in cattle, as well as many other species, relatively little is understood about CNV, including frequency of CNVs in the genome, sizes, and locations, chromosomal properties, and evolutionary processes acting to shape CNV. In this work, we focused on copy number variation in the bovine genome, with the aim to detect CNVs in Bos taurus coding sequence and explore potential evolutionary mechanisms shaping these CNV. We identified and characterized CNV regions by utilizing exome sequence from 175 influential sires used in the Germplasm Evaluation project, representing 10 breeds. We examined various evolutionary and functional aspects of these CNVs, including selective constraint on CNV-overlapped genes, centrality of CNV genes in protein-protein interaction networks, and tissue-specific expression of CNV genes. Patterns of CNV in the Bos taurus genome reveal that reduced functional constraint and mutational bias may play a prominent role in shaping this type of structural variation.