Simultaneous non-negative matrix factorization for multiple large scale gene expression datasets in toxicology

Non-negative matrix factorization is a useful tool for reducing the dimension of large datasets. This work considers simultaneous non-negative matrix factorization of multiple sources of data. In particular, we perform the first study that involves more than two datasets. We discuss the algorithmic issues required to convert the approach into a practical computational tool and apply the technique to new gene expression data quantifying the molecular changes in four tissue types due to different dosages of an experimental panPPAR agonist in mouse. This study is of interest in toxicology because, whilst PPARs form potential therapeutic targets for diabetes, it is known that they can induce serious side-effects. Our results show that the practical simultaneous non-negative matrix factorization developed here can add value to the data analysis. In particular, we find that factorizing the data as a single object allows us to distinguish between the four tissue types, but does not correctly reproduce the known dosage level groups. Applying our new approach, which treats the four tissue types as providing distinct, but related, datasets, we find that the dosage level groups are respected. The new algorithm then provides separate gene list orderings that can be studied for each tissue type, and compared with the ordering arising from the single factorization. We find that many of our conclusions can be corroborated with known biological behaviour, and others offer new insights into the toxicological effects. Overall, the algorithm shows promise for early detection of toxicity in the drug discovery process

The Transcriptomic Landscape of Prostate Cancer Development and Progression: An Integrative Analysis

Next-generation sequencing of primary tumors is now standard for transcriptomic studies, but microarray-based data still constitute the majority of available information on other clinically valuable samples, including archive material. Using prostate cancer (PC) as a model, we developed a robust analytical framework to integrate data across different technical platforms and disease subtypes to connect distinct disease stages and reveal potentially relevant genes not identifiable from single studies alone. We reconstructed the molecular profile of PC to yield the first comprehensive insight into its development, by tracking changes in mRNA levels from normal prostate to high-grade prostatic intraepithelial neoplasia, and metastatic disease. A total of nine previously unreported stage-specific candidate genes with prognostic significance were also found. Here, we integrate gene expression data from disparate sample types, disease stages and technical platforms into one coherent whole, to give a global view of the expression changes associated with the development and progression of PC from normal tissue through to metastatic disease. Summary and individual data are available online at the Prostate Integrative Expression Database (PIXdb), a user-friendly interface designed for clinicians and laboratory researchers to facilitate translational research

The Transcriptomic Landscape of Prostate Cancer Development and Progression: An Integrative Analysis

Next-generation sequencing of primary tumors is now standard for transcriptomic studies, but microarray-based data still constitute the majority of available information on other clinically valuable samples, including archive material. Using prostate cancer (PC) as a model, we developed a robust analytical framework to integrate data across different technical platforms and disease subtypes to connect distinct disease stages and reveal potentially relevant genes not identifiable from single studies alone. We reconstructed the molecular profile of PC to yield the first comprehensive insight into its development, by tracking changes in mRNA levels from normal prostate to high-grade prostatic intraepithelial neoplasia, and metastatic disease. A total of nine previously unreported stage-specific candidate genes with prognostic significance were also found. Here, we integrate gene expression data from disparate sample types, disease stages and technical platforms into one coherent whole, to give a global view of the expression changes associated with the development and progression of PC from normal tissue through to metastatic disease. Summary and individual data are available online at the Prostate Integrative Expression Database (PIXdb), a user-friendly interface designed for clinicians and laboratory researchers to facilitate translational research

Characterization of four subtypes in morphologically normal tissue excised proximal and distal to breast cancer

Widespread mammographic screening programs and improved self-monitoring allow for breast cancer to be detected earlier than ever before. Breast-conserving surgery is a successful treatment for select women. However, up to 40% of women develop local recurrence after surgery despite apparently tumor-free margins. This suggests that morphologically normal breast may harbor early alterations that contribute to increased risk of cancer recurrence. We conducted a comprehensive transcriptomic and proteomic analysis to characterize 57 fresh-frozen tissues from breast cancers and matched histologically normal tissues resected proximal to (<2 cm) and distant from (5–10 cm) the primary tumor, using tissues from cosmetic reduction mammoplasties as baseline. Four distinct transcriptomic subtypes are identified within matched normal tissues: metabolic; immune; matrisome/epithelial–mesenchymal transition, and non-coding enriched. Key components of the subtypes are supported by proteomic and tissue composition analyses. We find that the metabolic subtype is associated with poor prognosis (p < 0.001, HR6.1). Examination of genes representing the metabolic signature identifies several genes able to prognosticate outcome from histologically normal tissues. A subset of these have been reported for their predictive ability in cancer but, to the best of our knowledge, these have not been reported altered in matched normal tissues. This study takes an important first step toward characterizing matched normal tissues resected at pre-defined margins from the primary tumor. Unlocking the predictive potential of unexcised tissue could prove key to driving the realization of personalized medicine for breast cancer patients, allowing for more biologically-driven analyses of tissue margins than morphology alone

Deciphering the nuclear bile acid receptor FXR paradigm

Originally called retinoid X receptor interacting protein 14 (RIP14), the farnesoid X receptor (FXR) was renamed after the ability of its rat form to bind supra-physiological concentrations of farnesol. In 1999 FXR was de-orphanized since primary bile acids were identified as natural ligands. Strongly expressed in the liver and intestine, FXR has been shown to be the master transcriptional regulator of several entero-hepatic metabolic pathways with relevance to the pathophysiology of conditions such as cholestasis, fatty liver disease, cholesterol gallstone disease, intestinal inflammation and tumors. Furthermore, given the importance of FXR in the gut-liver axis feedbacks regulating lipid and glucose homeostasis, FXR modulation appears to have great input in diseases such as metabolic syndrome and diabetes. Exciting results from several cellular and animal models have provided the impetus to develop synthetic FXR ligands as novel pharmacological agents. Fourteen years from its discovery, FXR has gone from bench to bedside; a novel nuclear receptor ligand is going into clinical use

Mineral maturity and crystallinity index are distinct characteristics of bone mineral

The purpose of this study was to test the hypothesis that mineral maturity and crystallinity index are two different characteristics of bone mineral. To this end, Fourier transform infrared microspectroscopy (FTIRM) was used. To test our hypothesis, synthetic apatites and human bone samples were used for the validation of the two parameters using FTIRM. Iliac crest samples from seven human controls and two with skeletal fluorosis were analyzed at the bone structural unit (BSU) level by FTIRM on sections 2–4 lm thick. Mineral maturity and crystallinity index were highly correlated in synthetic apatites but poorly correlated in normal human bone. In skeletal fluorosis, crystallinity index was increased and maturity decreased, supporting the fact of separate measurement of these two parameters. Moreover, results obtained in fluorosis suggested that mineral characteristics can be modified independently of bone remodeling. In conclusion, mineral maturity and crystallinity index are two different parameters measured separately by FTIRM and offering new perspectives to assess bone mineral traits in osteoporosis

A statistical learning strategy for closed-loop control of fluid flows

This work discusses a closed-loop control strategy for complex systems utilizing scarce and streaming data. A discrete embedding space is first built using hash functions applied to the sensor measurements from which a Markov process model is derived, approximating the complex system’s dynamics. A control strategy is then learned using reinforcement learning once rewards relevant with respect to the control objective are identified. This method is designed for experimental configurations, requiring no computations nor prior knowledge of the system, and enjoys intrinsic robustness. It is illustrated on two systems: the control of the transitions of a Lorenz’63 dynamical system, and the control of the drag of a cylinder flow. The method is shown to perform well

BioMart Central Portal: an open database network for the biological community

BioMart Central Portal is a first of its kind, community-driven effort to provide unified access to dozens of biological databases spanning genomics, proteomics, model organisms, cancer data, ontology information and more. Anybody can contribute an independently maintained resource to the Central Portal, allowing it to be exposed to and shared with the research community, and linking it with the other resources in the portal. Users can take advantage of the common interface to quickly utilize different sources without learning a new system for each. The system also simplifies cross-database searches that might otherwise require several complicated steps. Several integrated tools streamline common tasks, such as converting between ID formats and retrieving sequences. The combination of a wide variety of databases, an easy-to-use interface, robust programmatic access and the array of tools make Central Portal a one-stop shop for biological data querying. Here, we describe the structure of Central Portal and show example queries to demonstrate its capabilities

A HIF-LIMD1 negative feedback mechanism mitigates the pro-tumorigenic effects of hypoxia

The adaptive cellular response to low oxygen tensions is mediated by the hypoxia inducible factors (HIFs), a family of heterodimeric transcription factors composed of HIF-α and β subunits. Prolonged HIF expression is a key contributor to cellular transformation, tumourigenesis and metastasis. As such, HIF degradation under hypoxic conditions is an essential homeostatic and tumour suppressive mechanism. LIMD1 complexes with PHD2 and VHL in physiological oxygen levels (normoxia) to facilitate proteasomal degradation of the HIF-α subunit. Here, we identify LIMD1 as a HIF-1 target gene, which mediates a previously uncharacterised, negative regulatory feedback mechanism for hypoxic HIF-α degradation by modulating PHD2-LIMD1- VHL complex formation. Hypoxic induction of LIMD1 expression results in increased HIF-α protein degradation, inhibiting HIF-1 target-gene expression, tumour growth and vascularisation. Furthermore, we report that copy number variation at the LIMD1 locus occurs in 47.1% of lung adenocarcinoma patients, correlates with enhanced expression of a HIF target gene signature and is a negative prognostic indicator. Taken together, our data open a new field of research into the aetiology, diagnosis and prognosis of LIMD1-negative lung cancers