Differential expression analysis identifies a prognostically significant extracellular matrix-enriched gene signature in hyaluronan-positive clear cell renal cell carcinoma.

Hyaluronan (HA) accumulation in clear cell renal cell carcinoma (ccRCC) is associated with poor prognosis; however, its biology and role in tumorigenesis are unknown. RNA sequencing of 48 HA-positive and 48 HA-negative formalin-fixed paraffin-embedded (FFPE) samples was performed to identify differentially expressed genes (DEG). The DEGs were subjected to pathway and gene enrichment analyses. The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) data and DEGs were used for the cluster analysis. In total, 129 DEGs were identified. HA-positive tumors exhibited enhanced expression of genes related to extracellular matrix (ECM) organization and ECM receptor interaction pathways. Gene set enrichment analysis showed that epithelial-mesenchymal transition-associated genes were highly enriched in the HA-positive phenotype. A protein-protein interaction network was constructed, and 17 hub genes were discovered. Heatmap analysis of TCGA-KIRC data identified two prognostic clusters corresponding to HA-positive and HA-negative phenotypes. These clusters were used to verify the expression levels and conduct survival analysis of the hub genes, 11 of which were linked to poor prognosis. These findings enhance our understanding of hyaluronan in ccRCC.

Improved NSGA-II algorithms for multi-objective biomarker discovery.

MOTIVATION: In modern translational research, the development of biomarkers heavily relies on use of omics technologies, but implementations with basic data mining algorithms frequently lead to false positives. Non-dominated Sorting Genetic Algorithm II (NSGA2) is an extremely effective algorithm for biomarker discovery but has been rarely evaluated against large-scale datasets. The exploration of the feature search space is the key to NSGA2 success but in specific cases NSGA2 expresses a shallow exploration of the space of possible feature combinations, possibly leading to models with low predictive performances. RESULTS: We propose two improved NSGA2 algorithms for finding subsets of biomarkers exhibiting different trade-offs between accuracy and feature number. The performances are investigated on gene expression data of breast cancer patients. The results are compared with NSGA2 and LASSO. The benchmarking dataset includes internal and external validation sets. The results show that the proposed algorithms generate a better approximation of the optimal trade-offs between accuracy and set size. Moreover, validation and test accuracies are better than those provided by NSGA2 and LASSO. Remarkably, the GA-based methods provide biomarkers that achieve a very high prediction accuracy (>80%) with a small number of features (<10), representing a valid alternative to known biomarker models, such as Pam50 and MammaPrint. AVAILABILITY AND IMPLEMENTATION: The software is publicly available on GitHub at github.com/UEFBiomedicalInformaticsLab/BIODAI/tree/main/MOO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integrated Network Pharmacology Approach for Drug Combination Discovery: A Multi-Cancer Case Study.

Despite remarkable efforts of computational and predictive pharmacology to improve therapeutic strategies for complex diseases, only in a few cases have the predictions been eventually employed in the clinics. One of the reasons behind this drawback is that current predictive approaches are based only on the integration of molecular perturbation of a certain disease with drug sensitivity signatures, neglecting intrinsic properties of the drugs. Here we integrate mechanistic and chemocentric approaches to drug repositioning by developing an innovative network pharmacology strategy. We developed a multilayer network-based computational framework integrating perturbational signatures of the disease as well as intrinsic characteristics of the drugs, such as their mechanism of action and chemical structure. We present five case studies carried out on public data from The Cancer Genome Atlas, including invasive breast cancer, colon adenocarcinoma, lung squamous cell carcinoma, hepatocellular carcinoma and prostate adenocarcinoma. Our results highlight paclitaxel as a suitable drug for combination therapy for many of the considered cancer types. In addition, several non-cancer-related genes representing unusual drug targets were identified as potential candidates for pharmacological treatment of cancer.

Identifying gene expression-based biomarkers in online learning environments.

Motivation: Gene expression-based classifiers are often developed using historical data by training a model on a small set of patients and a large set of features. Models trained in such a way can be afterwards applied for predicting the output for new unseen patient data. However, very often the accuracy of these models starts to decrease as soon as new data is fed into the trained model. This problem, known as concept drift, complicates the task of learning efficient biomarkers from data and requires special approaches, different from commonly used data mining techniques. Results: Here, we propose an online ensemble learning method to continually validate and adjust gene expression-based biomarker panels over increasing volume of data. We also propose a computational solution to the problem of feature drift where gene expression signatures used to train the classifier become less relevant over time. A benchmark study was conducted to classify the breast tumors into known subtypes by using a large-scale transcriptomic dataset (∼3500 patients), which was obtained by combining two datasets: SCAN-B and TCGA-BRCA. Remarkably, the proposed strategy improves the classification performances of gold-standard biomarker panels (e.g. PAM50, OncotypeDX and Endopredict) by adding features that are clinically relevant. Moreover, test results show that newly discovered biomarker models can retain a high classification accuracy rate when changing the source generating the gene expression profiles. Availability and implementation: github.com/UEFBiomedicalInformaticsLab/OnlineLearningBD. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

A systematic comparison of data- and knowledge-driven approaches to disease subtype discovery.

Typical clustering analysis for large-scale genomics data combines two unsupervised learning techniques: dimensionality reduction and clustering (DR-CL) methods. It has been demonstrated that transforming gene expression to pathway-level information can improve the robustness and interpretability of disease grouping results. This approach, referred to as biological knowledge-driven clustering (BK-CL) approach, is often neglected, due to a lack of tools enabling systematic comparisons with more established DR-based methods. Moreover, classic clustering metrics based on group separability tend to favor the DR-CL paradigm, which may increase the risk of identifying less actionable disease subtypes that have ambiguous biological and clinical explanations. Hence, there is a need for developing metrics that assess biological and clinical relevance. To facilitate the systematic analysis of BK-CL methods, we propose a computational protocol for quantitative analysis of clustering results derived from both DR-CL and BK-CL methods. Moreover, we propose a new BK-CL method that combines prior knowledge of disease relevant genes, network diffusion algorithms and gene set enrichment analysis to generate robust pathway-level information. Benchmarking studies were conducted to compare the grouping results from different DR-CL and BK-CL approaches with respect to standard clustering evaluation metrics, concordance with known subtypes, association with clinical outcomes and disease modules in co-expression networks of genes. No single approach dominated every metric, showing the importance multi-objective evaluation in clustering analysis. However, we demonstrated that, on gene expression data sets derived from TCGA samples, the BK-CL approach can find groupings that provide significant prognostic value in both breast and prostate cancers.

Knowledge Generation with Rule Induction in Cancer Omics.

The explosion of omics data availability in cancer research has boosted the knowledge of the molecular basis of cancer, although the strategies for its definitive resolution are still not well established. The complexity of cancer biology, given by the high heterogeneity of cancer cells, leads to the development of pharmacoresistance for many patients, hampering the efficacy of therapeutic approaches. Machine learning techniques have been implemented to extract knowledge from cancer omics data in order to address fundamental issues in cancer research, as well as the classification of clinically relevant sub-groups of patients and for the identification of biomarkers for disease risk and prognosis. Rule induction algorithms are a group of pattern discovery approaches that represents discovered relationships in the form of human readable associative rules. The application of such techniques to the modern plethora of collected cancer omics data can effectively boost our understanding of cancer-related mechanisms. In fact, the capability of these methods to extract a huge amount of human readable knowledge will eventually help to uncover unknown relationships between molecular attributes and the malignant phenotype. In this review, we describe applications and strategies for the usage of rule induction approaches in cancer omics data analysis. In particular, we explore the canonical applications and the future challenges and opportunities posed by multi-omics integration problems.

Prioritizing target-disease associations with novel safety and efficacy scoring methods.

Biological target (commonly genes or proteins) identification is still largely a manual process, where experts manually try to collect and combine information from hundreds of data sources, ranging from scientific publications to omics databases. Targeting the wrong gene or protein will lead to failure of the drug development process, as well as incur delays and costs. To improve this process, different software platforms are being developed. These platforms rely strongly on efficacy estimates based on target-disease association scores created by computational methods for drug target prioritization. Here novel computational methods are presented to more accurately evaluate the efficacy and safety of potential drug targets. The proposed efficacy scores utilize existing gene expression data and tissue/disease specific networks to improve the inference of target-disease associations. Conversely, safety scores enable the identification of genes that are essential, potentially susceptible to adverse effects or carcinogenic. Benchmark results demonstrate that our transcriptome-based methods for drug target prioritization can increase the true positive rate of target-disease associations. Additionally, the proposed safety evaluation system enables accurate predictions of targets of withdrawn drugs and targets of drug trials prematurely discontinued.

Nano-sized zinc oxide and silver, but not titanium dioxide, induce innate and adaptive immunity and antiviral response in differentiated THP-1 cells.

Nano-sized metal oxides are currently the most manufactured nanomaterials (NMs), and are increasingly used in consumer products. Recent exposure data reveal a genuine potential for adverse health outcomes for a vast array of NMs, however the underlying mechanisms are not fully understood. To elucidate size-related molecular effects, differentiated THP-1 cells were exposed to nano-sized materials (n-TiO2, n-ZnO and n-Ag), or their bulk-sized (b-ZnO and b-TiO2) or ionic (i-Ag) counterparts, and genome-wide gene expression changes were studied at low-toxic concentrations (<15% cytotoxicity). TiO2 materials were nontoxic in MTT assay, inducing only minor transcriptional changes. ZnO and Ag elicited dose-dependent cytotoxicity, wherein ionic and particulate effects were synergistic with respect to n-ZnO-induced cytotoxicity. In gene expression analyzes, 6 h and 24 h samples formed two separate hierarchical clusters. N-ZnO and n-Ag shared only 3.1% and 24.6% differentially expressed genes (DEGs) when compared to corresponding control. All particles, except TiO2, activated various metallothioneins. At 6 h, n-Zn, b-Zn and n-Ag induced various immunity related genes associating to pattern recognition (including toll-like receptor), macrophage maturation, inflammatory response (TNF and IL-1beta), chemotaxis (CXCL8) and leucocyte migration (CXCL2-3 and CXCL14). After 24 h exposure, especially n-Ag induced the expression of genes related to virus recognition and type I interferon responses. These results strongly suggest that in addition to ionic effects mediated by metallothioneins, n-Zn and n-Ag induce expression of genes involved in several innate and adaptive immunity associated pathways, which are known to play crucial role in immuno-regulation. This raises the concern of safe use of metal oxide and metal nanoparticle products, and their biological effects.

Inhalation and Oropharyngeal Aspiration Exposure to Rod-Like Carbon Nanotubes Induce Similar Airway Inflammation and Biological Responses in Mouse Lungs.

Carbon nanotubes (CNTs) have the potential to impact technological and industrial progress, but their production and use may, in some cases, cause serious health problems. Certain rod-shaped multiwalled CNTs (rCNTs) can, in fact, induce severe asbestos-like pathogenicity in mice, including granuloma formation, fibrosis, and even cancer. Evaluating the comparability between alternative hazard assessment methods is needed to ensure fast and reliable evaluation of the potentially adverse effects of these materials. To compare two alternative airway exposure methods, C57BL/6 mice were exposed to rCNTs by a state-of-the-art but laborious and expensive inhalation method (6.2-8.2 mg/m3, 4 h/day for 4 days) or by oropharyngeal aspiration (10 or 40 µg/day for 4 days), which is cheaper and easier to perform. In addition to histological and cytological studies, transcriptome analysis was also carried out on the lung tissue samples. Both inhalation and low-dose (10 µg/day) aspiration exposure to rCNTs promoted strong accumulation of eosinophils in the lungs and recruited also a few neutrophils and lymphocytes. In contrast, the aspiration of a high-dose (40 µg/day) rCNT caused only a mild pulmonary eosinophilia but enhanced accumulation of neutrophils in the airways. Inhalation and low-dose aspiration exposure promoted comparable giant cell formation, mucus production, and IL-13 expression in the lungs. Both exposure methods also exacerbated similar expression alterations with 154 (56.4%) differentially expressed, overlapping genes in microarray analyses. Of all differentially expressed genes, up to 80% of the activated biological functions were shared according to pathway enrichment analyses. Inhalation and low-dose aspiration elicited very similar pulmonary inflammation providing evidence that oropharyngeal aspiration is a valid approach and a convenient alternative to the inhalation exposure for the hazard assessment of nanomaterials.

Expression of DAI by an oncolytic vaccinia virus boosts the immunogenicity of the virus and enhances antitumor immunity.

In oncolytic virotherapy, the ability of the virus to activate the immune system is a key attribute with regard to long-term antitumor effects. Vaccinia viruses bear one of the strongest oncolytic activities among all oncolytic viruses. However, its capacity for stimulation of antitumor immunity is not optimal, mainly due to its immunosuppressive nature. To overcome this problem, we developed an oncolytic VV that expresses intracellular pattern recognition receptor DNA-dependent activator of IFN-regulatory factors (DAI) to boost the innate immune system and to activate adaptive immune cells in the tumor. We showed that infection with DAI-expressing VV increases expression of several genes related to important immunological pathways. Treatment with DAI-armed VV resulted in significant reduction in the size of syngeneic melanoma tumors in mice. When the mice were rechallenged with the same tumor, DAI-VV-treated mice completely rejected growth of the new tumor, which indicates immunity established against the tumor. We also showed enhanced control of growth of human melanoma tumors and elevated levels of human T-cells in DAI-VV-treated mice humanized with human peripheral blood mononuclear cells. We conclude that expression of DAI by an oncolytic VV is a promising way to amplify the vaccine potency of an oncolytic vaccinia virus to trigger the innate-and eventually the long-lasting adaptive immunity against cancer.

Gremlin-1 Overexpression in Mouse Lung Reduces Silica-Induced Lymphocyte Recruitment - A Link to Idiopathic Pulmonary Fibrosis through Negative Correlation with CXCL10 Chemokine.

Idiopathic pulmonary fibrosis (IPF) is characterized by activation and injury of epithelial cells, the accumulation of connective tissue and changes in the inflammatory microenvironment. The bone morphogenetic protein (BMP) inhibitor protein gremlin-1 is associated with the progression of fibrosis both in human and mouse lung. We generated a transgenic mouse model expressing gremlin-1 in type II lung epithelial cells using the surfactant protein C (SPC) promoter and the Cre-LoxP system. Gremlin-1 protein expression was detected specifically in the lung after birth and did not result in any signs of respiratory insufficiency. Exposure to silicon dioxide resulted in reduced amounts of lymphocyte aggregates in transgenic lungs while no alteration in the fibrotic response was observed. Microarray gene expression profiling and analyses of bronchoalveolar lavage fluid cytokines indicated a reduced lymphocytic response and a downregulation of interferon-induced gene program. Consistent with reduced Th1 response, there was a downregulation of the mRNA and protein expression of the anti-fibrotic chemokine CXCL10, which has been linked to IPF. In human IPF patient samples we also established a strong negative correlation in the mRNA expression levels of gremlin-1 and CXCL10. Our results suggest that in addition to regulation of epithelial-mesenchymal crosstalk during tissue injury, gremlin-1 modulates inflammatory cell recruitment and anti-fibrotic chemokine production in the lung.

MVDA: a multi-view genomic data integration methodology.

BACKGROUND: Multiple high-throughput molecular profiling by omics technologies can be collected for the same individuals. Combining these data, rather than exploiting them separately, can significantly increase the power of clinically relevant patients subclassifications. RESULTS: We propose a multi-view approach in which the information from different data layers (views) is integrated at the levels of the results of each single view clustering iterations. It works by factorizing the membership matrices in a late integration manner. We evaluated the effectiveness and the performance of our method on six multi-view cancer datasets. In all the cases, we found patient sub-classes with statistical significance, identifying novel sub-groups previously not emphasized in literature. Our method performed better as compared to other multi-view clustering algorithms and, unlike other existing methods, it is able to quantify the contribution of single views on the final results. CONCLUSION: Our observations suggest that integration of prior information with genomic features in the subtyping analysis is an effective strategy in identifying disease subgroups. The methodology is implemented in R and the source code is available online at http://neuronelab.unisa.it/a-multi-view-genomic-data-integration-methodology/ .

Inhalation of rod-like carbon nanotubes causes unconventional allergic airway inflammation.

BACKGROUND: Carbon nanotubes (CNT) represent a great promise for technological and industrial development but serious concerns on their health effects have also emerged. Rod-shaped CNT are, in fact, able to induce asbestos-like pathogenicity in mice including granuloma formation in abdominal cavity and sub-pleural fibrosis. Exposure to CNT, especially in the occupational context, happens mainly by inhalation. However, little is known about the possible effects of CNT on pulmonary allergic diseases, such as asthma. METHODS: We exposed mice by inhalation to two types of multi-walled CNT, rigid rod-like and flexible tangled CNT, for four hours a day once or on four consecutive days. Early events were monitored immediately and 24 hours after the single inhalation exposure and the four day exposure mimicked an occupational work week. Mast cell deficient mice were used to evaluate the role of mast cells in the occurring inflammation. RESULTS: Here we show that even a short-term inhalation of the rod-like CNT induces novel innate immunity-mediated allergic-like airway inflammation in healthy mice. Marked eosinophilia was accompanied by mucus hypersecretion, AHR and the expression of Th2-type cytokines. Exploration of the early events by transcriptomics analysis reveals that a single 4-h exposure to rod-shaped CNT, but not to tangled CNT, causes a radical up-regulation of genes involved in innate immunity and cytokine/chemokine pathways. Mast cells were found to partially regulate the inflammation caused by rod-like CNT, but also alveaolar macrophages play an important role in the early stages. CONCLUSIONS: These observations emphasize the diverse abilities of CNT to impact the immune system, and they should be taken into account for hazard assessment.

Cloning and characterization of a Δ9-desaturase gene of the Antarctic fish Chionodraco hamatus and Trematomus bernacchii.

Chionodraco hamatus and Trematomus bernacchii are perciforms, members of the fish suborder Notothenioidei that live in the Antarctic Ocean and experience very cold and persistent environmental temperature. These fish have biochemical and molecular features that allow them to live at these extreme cold temperatures. Fine tuning of the level of unsaturated fatty acids content in membrane is a key mechanism of living organisms to adapt to cold and high temperatures. Desaturases are key enzymes that synthesize unsaturated fatty acyl-CoAs from saturated fatty acids. We cloned and sequenced a Δ(9)-desaturase gene and its cDNA of C. hamatus, and the cDNA of T. bernacchii. The coded proteins are virtually identical and share homology to other Δ(9)-desaturase fish sequences. These proteins contain, in the first trans-membrane domain, two cysteine residues that may form a disulfur bond present in the corresponding membrane region of Δ(9)-desaturase proteins of other Antarctic fish but not in Eleginops maclovinus that experiences higher environmental temperatures and in all other Δ(9)-desaturase genes of mammals present in data bases. C. hamatus Δ(9)-desaturase gene complements a Saccharomyces cerevisiae mutant lacking Δ(9)-desaturase (Ole1) gene. Analysis of sequence homology of the trans-membrane domains of Δ(9)-desaturase and the cytoplasmic region of the same proteins of Antarctic fish, non-Antarctic fish and mammals suggest that the significant differences found in the homologous sequences of the first trans-membrane domain may be due to the specific lipid content of their membrane.