125 results on '"Fortino, V"'
Search Results
2. Transcriptomic profiling reveals differential cellular response to copper oxide nanoparticles and polystyrene nanoplastics in perfused human placenta
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Chortarea S, Gupta G, Saarimäki LA, Netkueakul W, Manser P, Aengenheister L, Wichser A, Fortino V, Wick P, Greco D, and Buerki-Thurnherr T
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Nanoplastics ,CuO nanoparticles ,Placenta ,Transcriptomic profiling ,Developmental toxicity pathways ,Environmental sciences ,GE1-350 - Abstract
The growing nanoparticulate pollution (e.g. engineered nanoparticles (NPs) or nanoplastics) has been shown to pose potential threats to human health. In particular, sensitive populations such as pregnant women and their unborn children need to be protected from harmful environmental exposures. However, developmental toxicity from prenatal exposure to pollution particles is not yet well studied despite evidence of particle accumulation in human placenta. Our study aimed to investigate how copper oxide NPs (CuO NPs; 10–20 nm) and polystyrene nanoplastics (PS NPs; 70 nm) impact on gene expression in ex vivo perfused human placental tissue. Whole genome microarray analysis revealed changes in global gene expression profile after 6 h of perfusion with sub-cytotoxic concentrations of CuO (10 µg/mL) and PS NPs (25 µg/mL). Pathway and gene ontology enrichment analysis of the differentially expressed genes suggested that CuO and PS NPs trigger distinct cellular response in placental tissue. While CuO NPs induced pathways related to angiogenesis, protein misfolding and heat shock responses, PS NPs affected the expression of genes related to inflammation and iron homeostasis. The observed effects on protein misfolding, cytokine signaling, and hormones were corroborated by western blot (accumulation of polyubiquitinated proteins) or qPCR analysis. Overall, the results of the present study revealed extensive and material-specific interference of CuO and PS NPs with placental gene expression from a single short-term exposure which deserves increasing attention. In addition, the placenta, which is often neglected in developmental toxicity studies, should be a key focus in the future safety assessment of NPs in pregnancy.
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- 2023
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3. Network approaches for modeling the effect of drugs and diseases
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Rintala, T J, primary, Ghosh, Arindam, additional, and Fortino, V, additional
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- 2022
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4. A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity
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Sakhteman, A., primary, Failli, M., additional, Kublbeck, J., additional, Levonen, A.L., additional, and Fortino, V., additional
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- 2021
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5. MaNGA: a novel multi-objective multi-niche genetic algorithm for QSAR modelling
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Serra A, Önlü S, Festa P., Fortino V, Greco D, Serra, A, Önlü, S, Festa, P., Fortino, V, and Greco, D
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QSAR modelling, Combinatorial Optimization, Genetic Algorithms - Abstract
Quantitative structure–activity relationship (QSAR) modelling is currently used in multiple fields to relate structural properties of compounds to their biological activities. This technique is also used for drug design purposes with the aim of predicting parameters that determine drug behaviour. To this end, a sophisticated process, involving various analytical steps concatenated in series, is employed to identify and fine-tune the optimal set of predictors from a large dataset of molecular descriptors (MDs). The search of the optimal model requires to optimize multiple objectives at the same time, as the aim is to obtain the minimal set of features that maximizes the goodness of fit and the applicability domain (AD). Hence, a multi-objective optimization strategy, improving multiple parameters in parallel, can be applied. Here we propose a new multi-niche multi-objective genetic algorithm that simultaneously enables stable feature selection as well as obtaining robust and validated regression models with maximized AD. We benchmarked our method on two simulated datasets. Moreover, we analyzed an aquatic acute toxicity dataset and compared the performances of single- and multi-objective fitness functions on different regression models. Our results show that our multi-objective algorithm is a valid alternative to classical QSAR modelling strategy, for continuous response values, since it automatically finds the model with the best compromise between statistical robustness, predictive performance, widest AD, and the smallest number of MDs
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- 2020
6. Rottlerin inhibits the nuclear factor κB/Cyclin-D1 cascade in MCF-7 breast cancer cells
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Torricelli, C., Fortino, V., Capurro, E., Valacchi, G., Pacini, A., Muscettola, M., Soucek, K., and Maioli, E.
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- 2008
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7. eUTOPIA: SolUTion for Omics data PreprocessIng and Analysis
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Marwah V. S., Scala G., Kinaret P. A. S., Serra A., Alenius H., Fortino V., Greco D., Marwah, V. S., Scala, G., Kinaret, P. A. S., Serra, A., Alenius, H., Fortino, V., and Greco, D.
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Transcriptomics analysis ,R shiny ,Gene expression ,Microarray - Abstract
Application of microarrays in omics technologies enables quantification of many biomolecules simultaneously. It is widely applied to observe the positive or negative effect on biomolecule activity in perturbed versus the steady state by quantitative comparison. Community resources, such as Bioconductor and CRAN, host tools based on R language that have become standard for high-throughput analytics. However, application of these tools is technically challenging for generic users and require specific computational skills. There is a need for intuitive and easy-to-use platform to process omics data, visualize, and interpret results.
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- 2019
8. The EDCMET project:metabolic effects of endocrine disruptors
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Küblbeck, J. (Jenni), Vuorio, T. (Taina), Niskanen, J. (Jonna), Fortino, V. (Vittorio), Braeuning, A. (Albert), Abass, K. (Khaled), Rautio, A. (Arja), Hakkola, J. (Jukka), Honkakoski, P. (Paavo), and Levonen, A.-L. (Anna-Liisa)
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obesity ,endocrine disruptors (EDs) ,risk assessment ,nuclear receptors (NRs) ,human health ,metabolism ,adverse outcome pathway (AOP) ,assay validation ,metabolic syndrome - Abstract
Endocrine disruptors (EDs) are defined as chemicals that mimic, block, or interfere with hormones in the body’s endocrine systems and have been associated with a diverse array of health issues. The concept of endocrine disruption has recently been extended to metabolic alterations that may result in diseases, such as obesity, diabetes, and fatty liver disease, and constitute an increasing health concern worldwide. However, while epidemiological and experimental data on the close association of EDs and adverse metabolic effects are mounting, predictive methods and models to evaluate the detailed mechanisms and pathways behind these observed effects are lacking, thus restricting the regulatory risk assessment of EDs. The EDCMET (Metabolic effects of Endocrine Disrupting Chemicals: novel testing METhods and adverse outcome pathways) project brings together systems toxicologists; experimental biologists with a thorough understanding of the molecular mechanisms of metabolic disease and comprehensive in vitro and in vivo methodological skills; and, ultimately, epidemiologists linking environmental exposure to adverse metabolic outcomes. During its 5-year journey, EDCMET aims to identify novel ED mechanisms of action, to generate (pre)validated test methods to assess the metabolic effects of Eds, and to predict emergent adverse biological phenotypes by following the adverse outcome pathway (AOP) paradigm.
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- 2020
9. Functional interactions of protein kinase A and C in signalling networks: a recapitulation
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Maioli, E., Torricelli, C., and Fortino, V.
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- 2006
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10. The complexity of parathyroid hormone-related proteinsignalling
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Maioli, E. and Fortino, V.
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- 2004
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11. ERKs are the point of divergence of PKA and PKC activation by PTHrP in human skin fibroblasts
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Fortino, V., Torricelli, C., Gardi, C., Valacchi, G., Rossi Paccani, S., and Maioli, E.
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- 2002
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12. Feature set optimization in biomarker discovery from genome-scale data
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Fortino, V, primary, Scala, G, primary, and Greco, D, primary
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- 2020
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13. The dual action of ozone on the skin
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Valacchi, G., Fortino, V., and Bocci, V.
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- 2005
14. The mind-body healing experience (MHE) is associated with gene expression in human leukocytes
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Cozzolino, Mauro, Cicatelli, Angela, Fortino, V., Guarino, F., Tagliaferri, Roberto, Castiglione, Stefano, Luca, De, Napolitano, P., Celia, G, Iannotti, S., Raiconi, S., Rossi, K., and Rossi, E.
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neuroscience ,experimental and clinical hypnosis ,psychotherapy ,psychosocial genomics ,neuroscience, psychosocial genomics, experimental and clinical hypnosis, psychotherapy - Published
- 2015
15. The natural poliphenolic compound Rottlerin overestimates the MTT test because of its mitochondrial uncoupling effect
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Torricelli, Claudia, Maioli, Emanuela, BRANDANI PACINI, A., Fortino, V, Carlucci, F, Valacchi, G, Pecorelli, A, and Muscettola, MARIA MICHELA
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- 2009
16. PTHrP/PTH1R: a complex crosstalk among different signaling pathways
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Maioli, Emanuela, Fortino, V., and Torricelli, Claudia
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- 2007
17. Inverse Analysis of the Laser Forming Process by Analytical Methods and Genetic Algorithms
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Carlone, Pierpaolo, Fortino, V, and Palazzo, Gaetano Salvatore
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- 2006
18. Cutaneous MMPs are differntly modulated by environmental stressors in old and young mice
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Fortino, V, Davis, P, Torricelli, Claudia, Capurro, E, Pacini, A, Maioli, Emanuela, and Valacchi, G.
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- 2006
19. PKC and p21: tumor promoters in MCF-7 cells?
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Fortino, V., Maioli, Emanuela, Torricelli, Claudia, Capurro, E., Ponzo, P., and Pacini, A.
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- 2004
20. The complexity of parathyroid hormone-related peptide (PTHrP) signalling
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Maioli, Emanuela and Fortino, V.
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- 2004
21. Studies for the evaluation of oxygenation and ozonation of blood during extracorporeal circulation
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Bocci, V., DI PAOLO, N., Borrelli, E., Fortino, V., Larini, A., Garosi, G., Aldinucci, C., Carraro, F., Corradeschi, F., and Naldini, A.
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- 2000
22. Novel Pluripotent Adult Stem Cell Source for Neurogenesis
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Fortino, V. R., primary, Pawley, D., additional, Pelaez, D., additional, and Cheung, H. S., additional
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- 2013
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23. PTHrP on MCF-7 breast cancer cells: a growth factor or an antimitogenic peptide?
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Maioli, Emanuela, Fortino, V., Hoey, R. P., Sanderson, C., Iddon, J., Brady, G., Bundred, N. J., and Anderson, N. G.
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Cancer Research ,medicine.medical_specialty ,Intracrine ,Bone Neoplasms ,Breast Neoplasms ,chemistry.chemical_compound ,Cell surface receptor ,Internal medicine ,Cyclic AMP ,Tumor Cells, Cultured ,medicine ,Humans ,Neoplasm Metastasis ,Letter to the Editor ,Protein kinase C ,Receptor, Parathyroid Hormone, Type 1 ,Forskolin ,Reverse Transcriptase Polymerase Chain Reaction ,business.industry ,Cell growth ,Transfection ,Cell biology ,Gene Expression Regulation, Neoplastic ,Autocrine Communication ,Endocrinology ,Oncology ,chemistry ,Receptors, Parathyroid Hormone ,Female ,Mitogen-Activated Protein Kinases ,Signal transduction ,business ,Tyrosine kinase ,Cell Division - Abstract
Sir, We read with interest the publication by Hoey et al (2003), on MCF-7 cell proliferation promoted by the overexpressed PTH/PTHrP receptor, since a study on the effects of PTHrP on MCF-7 cell proliferation is currently in progress in our Lab. We knew that the old and new literature reports contradictory findings in this cancer cell line as far as the proliferative response is concerned; it is surprising, however, that the question is still open in 2003. To our knowledge, the first study on PTHrP and MCF-7 cells is the one by Birch et al (1995), cited in Hoey's paper. However, discordant results had already been presented in 1981 (Cho-Chung et al, 1981). Birch's group found a mitogenic effect of PTH and PTHrP on MCF-7 cells, with a parallel increase in cAMP intracellular levels. Therefore, the work by Hoey et al (2003) appears to support Birch's findings. Conversely, more recent studies, also cited in the paper, show that the cAMP pathway inhibits proliferation in MCF-7 cells (Chen et al, 1998). The recent report by Tovar Sepulveda et al (2002), focused on PTHrP and MCF-7, is particularly instructive here, although it does not meet the question of the different response to PTHrP by the same cell. They clearly demonstrated that PTHrP possesses a double signalling on MCF-7: an antimitogenic pathway mediated by the membrane receptor and a second wave of signalling, pro-proliferative and antiapoptotic, based on nuclear localisation of the peptide. The same dual effect had also been described in vascular smooth muscle cells in 1997 (Massfelder et al, 1997). We believe that this controversial aspect is not discussed enough in Hoey's article. However, we can realise that in May 2002, the date of the first submission, the author could not have read the paper by Tovar Sepulveda et al (2002). On the contrary, Hoey knew and cited the work by Falzon and Du (2000). He suspects that Falzon's results about the intracrine effect of PTHrP in PTHrP-transfected MCF-7 cells is of poor physiological relevance, since it is unlikely that the same phenomenon takes place in parental cells as well. We agree that transfection, in general, may confer cell characteristics completely different from those of the parental one and this is the main risk in transfection experiments. However, the same remark could be made to Hoey's results, too. Our preliminary results, summarised in Figure 1, strongly differ from Hoey's ones. In fact, our MCF-7 cells respond to 640 nM PTHrP, in contrast to Hoey's cells, which are unresponsive to 125 nM PTHrP. Figure 1 Proliferative response of MCF-7 breast cancer cells to 640 nM PTHrp 1–40. Cells were grown in MEM in the presence of 2.5% FCS. Chemical inhibitors and activators were used at the following doses: SQ 22536: 50 μ ... This is the first difference with Hoey's work. On working with parental MCF-7 cells, we needed to increase the PTHrP dose to obtain some effect on proliferation. We also failed to appreciate significant variations below the dose of 640 nM. Thus, MCF-7 cells are not unresponsive to PTHrP; they simply need a stronger treatment. Our results (still incomplete to be collected in a manuscript) also indicate that exogenous PTHrP triggers two main transduction pathways: the adenylyl-cyclase (AC)/protein kinase (PK) A and the phospholipase (PL) C/PKC cascade. Thus, the native PTH/PTHrP receptor is coupled also to the Ca2+ signalling route in MCF-7 cells. This is the second difference: Hoey's cells have probably lost this transduction pathway. In our MCF-7 cells, both PKA and PKC are antimitogenic and this appears to be the main difference. We cannot tell, at the moment, which is the major player, but the selective block of AC or PLC reduces the antiproliferative effect of PTHrP. We chose to block the membrane enzymes, rather than PKs, because, as reported also by Hoey, chemical inhibitors of PKs are toxic for cells and mask the proliferative effect of a test factor. This is the only experience we have in common with Hoey's group. Hoey describes an effect of PTHrP on cell sensitivity to growth factors, but this is not a novel phenomenon, as it was already described by Linseman et al (1995). Probably, Hoey is dealing with the so-called transactivation of growth factors tyrosine kinase (TK) receptors by PTHrP G-protein-coupled receptor (GPCR) (Linseman et al, 1995; Lowes et al, 2002). We observed a similar phenomenon in 2002 while culturing skin fibroblasts in the presence of serum (then of growth factors) (Maioli et al, 2002). We do not have the presumption to solve the conflict between Hoey's and our results, but in an attempt to reconcile them, we hypothesise that the overexpressed receptor, unable to couple to PLC, thus lacking one of the two antiproliferative pathways, acquired enhanced ability to transactivate TK receptors; the pro-mitogenic effect predominates in the presence of 2% serum. We believe that such a pro-proliferative action of PTHrP, mediated by the membrane receptor, is operative also in parental MCF-7 cells. In fact, in our preliminary study, exogenous 1–40 PTHrP shares pro-mitogenic properties in 2.5% serum-cultured MCF-7 cells, when the membrane AC and PLC are simultaneously blocked. Finally, the main question and the only one that cannot be ascribed to the transfection process is the opposite effect of forskolin (an AC activator) on parental (ours) and transfected MCF-7 cells (Hoey's ones). In fact, it is now clear that cAMP can exert both pro- and antimitogenic effects acting at the level of different Raf isoforms. As a rule, the cAMP/PKA pathway stimulates Ras-independent and Rap-1-dependent extracellular regulated kinase (ERK) phosphorylation and cell proliferation in Raf-B-expressing cells, but it inhibits growth in Raf-B-negative cells (Fujita et al, 2002). Then, is the difference in cAMP levels sufficient to explain the discrepancy? Our previous experience on skin fibroblasts, which express predominantly the Raf-1 isoform (Fortino et al, 2002), indicates that forskolin (then cAMP) is always antiproliferative, at any dose tested (Maioli et al, 2002). Consistently, in our MCF-7 cells, forskolin (as well as phorbol 12-myristyl 13-acetate, PMA) mimics the PTHrP antimitogenic effect. Although a study focused on Raf isoforms in MCF-7 cells is still missing, Raf-1 isoform is surely expressed (Weinstein-Oppenheimer et al, 2001). Clarifying this and the other aspects would add significantly to our present biological and clinical knowledge on PTHrP role and responsibility in cancer.
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- 2004
24. Antiproliferative and Survival Properties of PMA in MCF-7 Breast Cancer Cell
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Fortino, V., primary, Torricelli, C., additional, Capurro, E., additional, Sacchi, G., additional, Valacchi, G., additional, and Maioli, E., additional
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- 2008
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25. Protein kinase C: a target for anticancer drugs?
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Maioli, E, primary and Fortino, V, additional
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- 2004
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26. Multi-omics analysis of ten carbon nanomaterials effects highlights cell type specific patterns of molecular regulation and adaptation
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Giovanni Scala, Veer Singh Marwah, Dario Greco, Jukka Sund, Pia Anneli Sofia Kinaret, Vittorio Fortino, Institute of Biotechnology, Lääketieteen ja biotieteiden tiedekunta - Faculty of Medicine and Life Sciences, University of Tampere, Scala, G., Kinaret, P., Marwah, V., Sund, J., Fortino, V., and Greco, D.
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EXPRESSION ,0301 basic medicine ,Cell type ,BEAS-2B CELLS ,Materials Science (miscellaneous) ,Systems biology ,Cell ,NANOTUBES ,02 engineering and technology ,Computational biology ,Mechanism of action ,SILVER NANOPARTICLES ,Biology ,Article ,Biokemia, solu- ja molekyylibiologia - Biochemistry, cell and molecular biology ,ENDOCRINE DISRUPTORS ,03 medical and health sciences ,Nanoteknologia - Nanotechnology ,Adverse outcome pathway ,ULTRAFINE TIO2 ,microRNA ,medicine ,OXIDATIVE STRESS ,Safety, Risk, Reliability and Quality ,IN-VIVO ,Epigenomics ,Multi-omics ,METHYLATION ,Public Health, Environmental and Occupational Health ,EPITHELIAL-CELLS ,Toxicogenomics ,021001 nanoscience & nanotechnology ,Systems toxicology ,Phenotype ,Carbon nanomaterial ,030104 developmental biology ,medicine.anatomical_structure ,Multi-omic ,DNA methylation ,1182 Biochemistry, cell and molecular biology ,Carbon nanomaterials ,221 Nano-technology ,0210 nano-technology ,Safety Research - Abstract
New strategies to characterize the effects of engineered nanomaterials (ENMs) based on omics technologies are emerging. However, given the intricate interplay of multiple regulatory layers, the study of a single molecular species in exposed biological systems might not allow the needed granularity to successfully identify the pathways of toxicity (PoT) and, hence, portraying adverse outcome pathways (AOPs). Moreover, the intrinsic diversity of different cell types composing the exposed organs and tissues in living organisms poses a problem when transferring in vivo experimentation into cell-based in vitro systems. To overcome these limitations, we have profiled genome-wide DNA methylation, mRNA and microRNA expression in three human cell lines representative of relevant cell types of the respiratory system, A549, BEAS-2B and THP-1, exposed to a low dose of ten carbon nanomaterials (CNMs) for 48 h. We applied advanced data integration and modelling techniques in order to build comprehensive regulatory and functional maps of the CNM effects in each cell type. We observed that different cell types respond differently to the same CNM exposure even at concentrations exerting similar phenotypic effects. Furthermore, we linked patterns of genomic and epigenomic regulation to intrinsic properties of CNM. Interestingly, DNA methylation and microRNA expression only partially explain the mechanism of action (MOA) of CNMs. Taken together, our results strongly support the implementation of approaches based on multi-omics screenings on multiple tissues/cell types, along with systems biology-based multi-variate data modelling, in order to build more accurate AOPs., Graphical abstract Unlabelled Image, Highlights • DNA methylation and microRNA expression partially explain the carbon nanomaterials (CNM) mechanism of action (MOA). • CNM geometrical properties impact several molecular districts of exposed cells. • Same CNM exposure exerts different MOA in different cell types even at concentrations causing similar phenotypic effects. • Multi-omics screenings on multiple cell types, along with multi-variate data modelling, allow building more accurate AOPs.
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- 2018
27. Feature set optimization in biomarker discovery from genome-scale data
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Dario Greco, Giovanni Scala, Vittorio Fortino, Fortino, V, Scala, G, and Greco, D
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Statistics and Probability ,DNA Copy Number Variations ,Computer science ,Feature selection ,computer.software_genre ,Biochemistry ,Set (abstract data type) ,03 medical and health sciences ,0302 clinical medicine ,Neoplasms ,medicine ,Humans ,Biomarker discovery ,Precision Medicine ,Molecular Biology ,Selection (genetic algorithm) ,030304 developmental biology ,0303 health sciences ,Cancer ,Precision medicine ,medicine.disease ,3. Good health ,Computer Science Applications ,Random forest ,Computational Mathematics ,Computational Theory and Mathematics ,030220 oncology & carcinogenesis ,Biomarker (medicine) ,Data mining ,computer ,Algorithms ,Biomarkers ,Omics technologies - Abstract
Motivation Omics technologies have the potential to facilitate the discovery of new biomarkers. However, only few omics-derived biomarkers have been successfully translated into clinical applications to date. Feature selection is a crucial step in this process that identifies small sets of features with high predictive power. Models consisting of a limited number of features are not only more robust in analytical terms, but also ensure cost effectiveness and clinical translatability of new biomarker panels. Here we introduce GARBO, a novel multi-island adaptive genetic algorithm to simultaneously optimize accuracy and set size in omics-driven biomarker discovery problems. Results Compared to existing methods, GARBO enables the identification of biomarker sets that best optimize the trade-off between classification accuracy and number of biomarkers. We tested GARBO and six alternative selection methods with two high relevant topics in precision medicine: cancer patient stratification and drug sensitivity prediction. We found multivariate biomarker models from different omics data types such as mRNA, miRNA, copy number variation, mutation and DNA methylation. The top performing models were evaluated by using two different strategies: the Pareto-based selection, and the weighted sum between accuracy and set size (w = 0.5). Pareto-based preferences show the ability of the proposed algorithm to search minimal subsets of relevant features that can be used to model accurate random forest-based classification systems. Moreover, GARBO systematically identified, on larger omics data types, such as gene expression and DNA methylation, biomarker panels exhibiting higher classification accuracy or employing a number of features much lower than those discovered with other methods. These results were confirmed on independent datasets. Availability and implementation github.com/Greco-Lab/GARBO. Contact dario.greco@tuni.fi Supplementary information Supplementary data are available at Bioinformatics online.
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- 2019
28. Knowledge Generation with Rule Induction in Cancer Omics
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Barbara Majello, Dario Greco, Giovanni Scala, Antonio Federico, Vittorio Fortino, Scala, G., Federico, A., Fortino, V., Greco, D., and Majello, B.
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Proteomics ,rule induction ,TCGA (The Cancer Genome Atlas) ,Computer science ,Review ,Catalysis ,Machine Learning ,Inorganic Chemistry ,Omics data ,03 medical and health sciences ,0302 clinical medicine ,Neoplasms ,Databases, Genetic ,medicine ,Humans ,Metabolomics ,cancer ,Physical and Theoretical Chemistry ,patients classification ,Molecular Biology ,Spectroscopy ,030304 developmental biology ,Malignant phenotype ,0303 health sciences ,Rule induction ,Organic Chemistry ,Computational Biology ,Cancer ,Genomics ,General Medicine ,Omics ,medicine.disease ,Data science ,3. Good health ,Computer Science Applications ,Knowledge generation ,ComputingMethodologies_PATTERNRECOGNITION ,omics data ,030220 oncology & carcinogenesis ,Disease risk ,Identification (biology) - Abstract
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.
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- 2019
29. INfORM: Inference of NetwOrk Response Modules
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Veer Singh Marwah, Giovanni Scala, Antti Lauerma, Angela Serra, Dario Greco, Pia Anneli Sofia Kinaret, Vittorio Fortino, Marwah, V. S., Kinaret, P. A. S., Serra, A., Scala, G., Lauerma, A., Fortino, V., Greco, D., Institute of Biotechnology, Clinicum, Department of Dermatology, Allergology and Venereology, HUS Inflammation Center, Lääketieteen ja biotieteiden tiedekunta - Faculty of Medicine and Life Sciences, and University of Tampere
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0301 basic medicine ,Statistics and Probability ,Computer science ,Gene regulatory network ,Inference ,Gene Expression ,Machine learning ,computer.software_genre ,Biochemistry ,Biokemia, solu- ja molekyylibiologia - Biochemistry, cell and molecular biology ,03 medical and health sciences ,0302 clinical medicine ,Gene expression ,Gene Regulatory Networks ,R PACKAGE ,Molecular Biology ,Abstraction (linguistics) ,Gene Regulatory Network ,business.industry ,Computational Biology ,Computer Science Applications ,Algorithm ,Computational Mathematics ,Identification (information) ,Task (computing) ,030104 developmental biology ,Computational Theory and Mathematics ,1182 Biochemistry, cell and molecular biology ,Artificial intelligence ,3111 Biomedicine ,business ,computer ,030217 neurology & neurosurgery ,Algorithms ,Software - Abstract
Summary Detecting and interpreting responsive modules from gene expression data by using network-based approaches is a common but laborious task. It often requires the application of several computational methods implemented in different software packages, forcing biologists to compile complex analytical pipelines. Here we introduce INfORM (Inference of NetwOrk Response Modules), an R shiny application that enables non-expert users to detect, evaluate and select gene modules with high statistical and biological significance. INfORM is a comprehensive tool for the identification of biologically meaningful response modules from consensus gene networks inferred by using multiple algorithms. It is accessible through an intuitive graphical user interface allowing for a level of abstraction from the computational steps. Availability and implementation INfORM is freely available for academic use at https://github.com/Greco-Lab/INfORM. Supplementary information Supplementary data are available at Bioinformatics online.
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- 2018
30. COPS: A novel platform for multi-omic disease subtype discovery via robust multi-objective evaluation of clustering algorithms.
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Rintala TJ and Fortino V
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- Humans, Cluster Analysis, DNA Methylation genetics, MicroRNAs genetics, Genomics methods, Software, Survival Analysis, Prognosis, Male, Female, Gene Expression Profiling methods, DNA Copy Number Variations genetics, Multiomics, Algorithms, Neoplasms genetics, Neoplasms classification, Computational Biology methods
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Recent research on multi-view clustering algorithms for complex disease subtyping often overlooks aspects like clustering stability and critical assessment of prognostic relevance. Furthermore, current frameworks do not allow for a comparison between data-driven and pathway-driven clustering, highlighting a significant gap in the methodology. We present the COPS R-package, tailored for robust evaluation of single and multi-omics clustering results. COPS features advanced methods, including similarity networks, kernel-based approaches, dimensionality reduction, and pathway knowledge integration. Some of these methods are not accessible through R, and some correspond to new approaches proposed with COPS. Our framework was rigorously applied to multi-omics data across seven cancer types, including breast, prostate, and lung, utilizing mRNA, CNV, miRNA, and DNA methylation data. Unlike previous studies, our approach contrasts data- and knowledge-driven multi-view clustering methods and incorporates cross-fold validation for robustness. Clustering outcomes were assessed using the ARI score, survival analysis via Cox regression models including relevant covariates, and the stability of the results. While survival analysis and gold-standard agreement are standard metrics, they vary considerably across methods and datasets. Therefore, it is essential to assess multi-view clustering methods using multiple criteria, from cluster stability to prognostic relevance, and to provide ways of comparing these metrics simultaneously to select the optimal approach for disease subtype discovery in novel datasets. Emphasizing multi-objective evaluation, we applied the Pareto efficiency concept to gauge the equilibrium of evaluation metrics in each cancer case-study. Affinity Network Fusion, Integrative Non-negative Matrix Factorization, and Multiple Kernel K-Means with linear or Pathway Induced Kernels were the most stable and effective in discerning groups with significantly different survival outcomes in several case studies., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Rintala, Fortino. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
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- 2024
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31. Translatome profiling reveals Itih4 as a novel smooth muscle cell-specific gene in atherosclerosis.
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Ravindran A, Holappa L, Niskanen H, Skovorodkin I, Kaisto S, Beter M, Kiema M, Selvarajan I, Nurminen V, Aavik E, Aherrahrou R, Pasonen-Seppänen S, Fortino V, Laakkonen JP, Ylä-Herttuala S, Vainio S, Örd T, and Kaikkonen MU
- Subjects
- Animals, Female, Humans, Male, Mice, Aorta metabolism, Aorta pathology, Apolipoprotein B-100 genetics, Apolipoprotein B-100 metabolism, Gene Expression Profiling, Gene Expression Regulation, Green Fluorescent Proteins genetics, Green Fluorescent Proteins metabolism, Mice, Inbred C57BL, Mice, Knockout, Mice, Transgenic, Phenotype, Receptors, LDL genetics, Receptors, LDL metabolism, Transcriptome, Aortic Diseases genetics, Aortic Diseases pathology, Aortic Diseases metabolism, Atherosclerosis genetics, Atherosclerosis metabolism, Atherosclerosis pathology, Disease Models, Animal, Muscle, Smooth, Vascular metabolism, Muscle, Smooth, Vascular pathology, Myocytes, Smooth Muscle metabolism, Myocytes, Smooth Muscle pathology, Plaque, Atherosclerotic, Ribosomal Proteins genetics, Ribosomal Proteins metabolism
- Abstract
Aims: Vascular smooth muscle cells (SMCs) and their derivatives are key contributors to the development of atherosclerosis. However, studying changes in SMC gene expression in heterogeneous vascular tissues is challenging due to the technical limitations and high cost associated with current approaches. In this paper, we apply translating ribosome affinity purification sequencing to profile SMC-specific gene expression directly from tissue., Methods and Results: To facilitate SMC-specific translatome analysis, we generated SMCTRAP mice, a transgenic mouse line expressing enhanced green fluorescent protein (EGFP)-tagged ribosomal protein L10a (EGFP-L10a) under the control of the SMC-specific αSMA promoter. These mice were further crossed with the atherosclerosis model Ldlr-/-, ApoB100/100 to generate SMCTRAP-AS mice and used to profile atherosclerosis-associated SMCs in thoracic aorta samples of 15-month-old SMCTRAP and SMCTRAP-AS mice. Our analysis of SMCTRAP-AS mice showed that EGFP-L10a expression was localized to SMCs in various tissues, including the aortic wall and plaque. The TRAP fraction demonstrated high enrichment of known SMC-specific genes, confirming the specificity of our approach. We identified several genes, including Cemip, Lum, Mfge8, Spp1, and Serpina3, which are known to be involved in atherosclerosis-induced gene expression. Moreover, we identified several novel genes not previously linked to SMCs in atherosclerosis, such as Anxa4, Cd276, inter-alpha-trypsin inhibitor-4 (Itih4), Myof, Pcdh11x, Rab31, Serpinb6b, Slc35e4, Slc8a3, and Spink5. Among them, we confirmed the SMC-specific expression of Itih4 in atherosclerotic lesions using immunofluorescence staining of mouse aortic roots and spatial transcriptomics of human carotid arteries. Furthermore, our more detailed analysis of Itih4 showed its link to coronary artery disease through the colocalization of genome-wide association studies, splice quantitative trait loci (QTL), and protein QTL signals., Conclusion: We generated a SMC-specific TRAP mouse line to study atherosclerosis and identified Itih4 as a novel SMC-expressed gene in atherosclerotic plaques, warranting further investigation of its putative function in extracellular matrix stability and genetic evidence of causality., Competing Interests: Conflict of interest: All authors confirm that there are no conflicts of interest related to this manuscript., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)
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- 2024
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32. Differential expression analysis identifies a prognostically significant extracellular matrix-enriched gene signature in hyaluronan-positive clear cell renal cell carcinoma.
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Jokelainen O, Rintala TJ, Fortino V, Pasonen-Seppänen S, Sironen R, and Nykopp TK
- Subjects
- Humans, Prognosis, Gene Expression Profiling, Protein Interaction Maps genetics, Transcriptome, Male, Female, Biomarkers, Tumor genetics, Biomarkers, Tumor metabolism, Epithelial-Mesenchymal Transition genetics, Gene Regulatory Networks, Carcinoma, Renal Cell genetics, Carcinoma, Renal Cell pathology, Carcinoma, Renal Cell metabolism, Carcinoma, Renal Cell mortality, Hyaluronic Acid metabolism, Kidney Neoplasms genetics, Kidney Neoplasms pathology, Kidney Neoplasms metabolism, Kidney Neoplasms mortality, Gene Expression Regulation, Neoplastic, Extracellular Matrix metabolism, Extracellular Matrix genetics
- Abstract
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., (© 2024. The Author(s).)
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- 2024
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33. Integrative network analysis suggests prioritised drugs for atopic dermatitis.
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Federico A, Möbus L, Al-Abdulraheem Z, Pavel A, Fortino V, Del Giudice G, Alenius H, Fyhrquist N, and Greco D
- Subjects
- Humans, Skin, Gene Expression Profiling, Phenotype, Biomarkers, Dermatitis, Atopic drug therapy, Dermatitis, Atopic genetics
- Abstract
Background: Atopic dermatitis (AD) is a prevalent chronic inflammatory skin disease whose pathophysiology involves the interplay between genetic and environmental factors, ultimately leading to dysfunction of the epidermis. While several treatments are effective in symptom management, many existing therapies offer only temporary relief and often come with side effects. For this reason, the formulation of an effective therapeutic plan is challenging and there is a need for more effective and targeted treatments that address the root causes of the condition. Here, we hypothesise that modelling the complexity of the molecular buildup of the atopic dermatitis can be a concrete means to drive drug discovery., Methods: We preprocessed, harmonised and integrated publicly available transcriptomics datasets of lesional and non-lesional skin from AD patients. We inferred co-expression network models of both AD lesional and non-lesional skin and exploited their interactional properties by integrating them with a priori knowledge in order to extrapolate a robust AD disease module. Pharmacophore-based virtual screening was then utilised to build a tailored library of compounds potentially active for AD., Results: In this study, we identified a core disease module for AD, pinpointing known and unknown molecular determinants underlying the skin lesions. We identified skin- and immune-cell type signatures expressed by the disease module, and characterised the impaired cellular functions underlying the complex phenotype of atopic dermatitis. Therefore, by investigating the connectivity of genes belonging to the AD module, we prioritised novel putative biomarkers of the disease. Finally, we defined a tailored compound library by characterising the therapeutic potential of drugs targeting genes within the disease module to facilitate and tailor future drug discovery efforts towards novel pharmacological strategies for AD., Conclusions: Overall, our study reveals a core disease module providing unprecedented information about genetic, transcriptional and pharmacological relationships that foster drug discovery in atopic dermatitis., (© 2024. The Author(s).)
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- 2024
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34. The integration of large-scale public data and network analysis uncovers molecular characteristics of psoriasis.
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Federico A, Pavel A, Möbus L, McKean D, Del Giudice G, Fortino V, Niehues H, Rastrick J, Eyerich K, Eyerich S, van den Bogaard E, Smith C, Weidinger S, de Rinaldis E, and Greco D
- Subjects
- Humans, Skin metabolism, Gene Regulatory Networks genetics, Transcriptome genetics, Psoriasis genetics
- Abstract
In recent years, a growing interest in the characterization of the molecular basis of psoriasis has been observed. However, despite the availability of a large amount of molecular data, many pathogenic mechanisms of psoriasis are still poorly understood. In this study, we performed an integrated analysis of 23 public transcriptomic datasets encompassing both lesional and uninvolved skin samples from psoriasis patients. We defined comprehensive gene co-expression network models of psoriatic lesions and uninvolved skin. Moreover, we curated and exploited a wide range of functional information from multiple public sources in order to systematically annotate the inferred networks. The integrated analysis of transcriptomics data and co-expression networks highlighted genes that are frequently dysregulated and show aberrant patterns of connectivity in the psoriatic lesion compared with the unaffected skin. Our approach allowed us to also identify plausible, previously unknown, actors in the expression of the psoriasis phenotype. Finally, we characterized communities of co-expressed genes associated with relevant molecular functions and expression signatures of specific immune cell types associated with the psoriasis lesion. Overall, integrating experimental driven results with curated functional information from public repositories represents an efficient approach to empower knowledge generation about psoriasis and may be applicable to other complex diseases., (© 2022. The Author(s).)
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- 2022
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35. Identifying gene expression-based biomarkers in online learning environments.
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Cattelani L and Fortino V
- Abstract
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., (© The Author(s) 2022. Published by Oxford University Press.)
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- 2022
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36. Improved NSGA-II algorithms for multi-objective biomarker discovery.
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Cattelani L and Fortino V
- Subjects
- Humans, Software, Algorithms, Biomedical Research
- Abstract
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., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)
- Published
- 2022
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37. Biomarkers of nanomaterials hazard from multi-layer data.
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Fortino V, Kinaret PAS, Fratello M, Serra A, Saarimäki LA, Gallud A, Gupta G, Vales G, Correia M, Rasool O, Ytterberg J, Monopoli M, Skoog T, Ritchie P, Moya S, Vázquez-Campos S, Handy R, Grafström R, Tran L, Zubarev R, Lahesmaa R, Dawson K, Loeschner K, Larsen EH, Krombach F, Norppa H, Kere J, Savolainen K, Alenius H, Fadeel B, and Greco D
- Subjects
- Biomarkers, RNA, Messenger genetics, Nanostructures toxicity
- Abstract
There is an urgent need to apply effective, data-driven approaches to reliably predict engineered nanomaterial (ENM) toxicity. Here we introduce a predictive computational framework based on the molecular and phenotypic effects of a large panel of ENMs across multiple in vitro and in vivo models. Our methodology allows for the grouping of ENMs based on multi-omics approaches combined with robust toxicity tests. Importantly, we identify mRNA-based toxicity markers and extensively replicate them in multiple independent datasets. We find that models based on combinations of omics-derived features and material intrinsic properties display significantly improved predictive accuracy as compared to physicochemical properties alone., (© 2022. The Author(s).)
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- 2022
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38. Characterization of ENM Dynamic Dose-Dependent MOA in Lung with Respect to Immune Cells Infiltration.
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Serra A, Del Giudice G, Kinaret PAS, Saarimäki LA, Poulsen SS, Fortino V, Halappanavar S, Vogel U, and Greco D
- Abstract
The molecular effects of exposures to engineered nanomaterials (ENMs) are still largely unknown. In classical inhalation toxicology, cell composition of bronchoalveolar lavage (BAL) is a toxicity indicator at the lung tissue level that can aid in interpreting pulmonary histological changes. Toxicogenomic approaches help characterize the mechanism of action (MOA) of ENMs by investigating the differentially expressed genes (DEG). However, dissecting which molecular mechanisms and events are directly induced by the exposure is not straightforward. It is now generally accepted that direct effects follow a monotonic dose-dependent pattern. Here, we applied an integrated modeling approach to study the MOA of four ENMs by retrieving the DEGs that also show a dynamic dose-dependent profile (dddtMOA). We further combined the information of the dddtMOA with the dose dependency of four immune cell populations derived from BAL counts. The dddtMOA analysis highlighted the specific adaptation pattern to each ENM. Furthermore, it revealed the distinct effect of the ENM physicochemical properties on the induced immune response. Finally, we report three genes dose-dependent in all the exposures and correlated with immune deregulation in the lung. The characterization of dddtMOA for ENM exposures, both for apical endpoints and molecular responses, can further promote toxicogenomic approaches in a regulatory context.
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- 2022
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39. Integrated Network Pharmacology Approach for Drug Combination Discovery: A Multi-Cancer Case Study.
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Federico A, Fratello M, Scala G, Möbus L, Pavel A, Del Giudice G, Ceccarelli M, Costa V, Ciccodicola A, Fortino V, Serra A, and Greco D
- Abstract
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.
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- 2022
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40. EDTox: an R Shiny application to predict the endocrine disruption potential of compounds.
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Sakhteman A, Ghosh A, and Fortino V
- Subjects
- Software, Toxicogenetics
- Abstract
Purpose: Endocrine disruptors are a rising concern due to the wide array of health issues that it can cause. Although there are tools for mode of action (MoA)-based prediction of endocrine disruption (e.g. QSAR Toolbox and iSafeRat), none of them is based on toxicogenomics data. Here, we present EDTox, an R Shiny application enabling users to explore and use a computational method that we have recently published to identify and prioritize endocrine disrupting (ED) chemicals based on toxicogenomic data. The EDTox pipeline utilizes previously trained toxicogenomic-driven classifiers to make predictions on new untested compounds by using their molecular initiating events. Furthermore, the proposed R Shiny app allows users to extend the prediction systems by training and adding new classifiers based on new available toxicogenomic data. This functionality helps users to explore the ED potential of chemicals in new, untested exposure scenarios., Availability and Implementation: This tool is available as web application (www.edtox.fi) and stand-alone software on GitHub and Zenodo (https://doi.org/10.5281/zenodo.5817093)., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press.)
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- 2022
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41. Nextcast: A software suite to analyse and model toxicogenomics data.
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Serra A, Saarimäki LA, Pavel A, Del Giudice G, Fratello M, Cattelani L, Federico A, Laurino O, Marwah VS, Fortino V, Scala G, Sofia Kinaret PA, and Greco D
- Abstract
The recent advancements in toxicogenomics have led to the availability of large omics data sets, representing the starting point for studying the exposure mechanism of action and identifying candidate biomarkers for toxicity prediction. The current lack of standard methods in data generation and analysis hampers the full exploitation of toxicogenomics-based evidence in regulatory risk assessment. Moreover, the pipelines for the preprocessing and downstream analyses of toxicogenomic data sets can be quite challenging to implement. During the years, we have developed a number of software packages to address specific questions related to multiple steps of toxicogenomics data analysis and modelling. In this review we present the Nextcast software collection and discuss how its individual tools can be combined into efficient pipelines to answer specific biological questions. Nextcast components are of great support to the scientific community for analysing and interpreting large data sets for the toxicity evaluation of compounds in an unbiased, straightforward, and reliable manner. The Nextcast software suite is available at: ( https://github.com/fhaive/nextcast)., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)
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- 2022
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42. Supervised Methods for Biomarker Detection from Microarray Experiments.
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Serra A, Cattelani L, Fratello M, Fortino V, Kinaret PAS, and Greco D
- Subjects
- Biomarkers, Biomedical Research, Microarray Analysis
- Abstract
Biomarkers are valuable indicators of the state of a biological system. Microarray technology has been extensively used to identify biomarkers and build computational predictive models for disease prognosis, drug sensitivity and toxicity evaluations. Activation biomarkers can be used to understand the underlying signaling cascades, mechanisms of action and biological cross talk. Biomarker detection from microarray data requires several considerations both from the biological and computational points of view. In this chapter, we describe the main methodology used in biomarkers discovery and predictive modeling and we address some of the related challenges. Moreover, we discuss biomarker validation and give some insights into multiomics strategies for biomarker detection., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)
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- 2022
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43. A systematic comparison of data- and knowledge-driven approaches to disease subtype discovery.
- Author
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Rintala TJ, Federico A, Latonen L, Greco D, and Fortino V
- Subjects
- Algorithms, Cluster Analysis, Databases, Genetic, Gene Expression Profiling methods, Gene Regulatory Networks, Genetic Predisposition to Disease, Genomics methods, Humans, Prognosis, Signal Transduction, Survival Analysis, Workflow, Biomarkers, Computational Biology methods, Data Mining, Disease Susceptibility
- Abstract
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., (© The Author(s) 2021. Published by Oxford University Press.)
- Published
- 2021
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44. Machine-learning-driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis.
- Author
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Fortino V, Wisgrill L, Werner P, Suomela S, Linder N, Jalonen E, Suomalainen A, Marwah V, Kero M, Pesonen M, Lundin J, Lauerma A, Aalto-Korte K, Greco D, Alenius H, and Fyhrquist N
- Subjects
- Adult, Algorithms, Allergens, Databases, Genetic, Dermatitis, Allergic Contact genetics, Dermatitis, Irritant genetics, Diagnosis, Differential, Female, Gene Expression Regulation, Gene Regulatory Networks, Humans, Irritants, Leukocytes metabolism, Male, Patch Tests, Reproducibility of Results, Severity of Illness Index, Skin pathology, Transcriptome genetics, Biomarkers metabolism, Dermatitis, Allergic Contact diagnosis, Dermatitis, Irritant diagnosis, Machine Learning
- Abstract
Contact dermatitis tremendously impacts the quality of life of suffering patients. Currently, diagnostic regimes rely on allergy testing, exposure specification, and follow-up visits; however, distinguishing the clinical phenotype of irritant and allergic contact dermatitis remains challenging. Employing integrative transcriptomic analysis and machine-learning approaches, we aimed to decipher disease-related signature genes to find suitable sets of biomarkers. A total of 89 positive patch-test reaction biopsies against four contact allergens and two irritants were analyzed via microarray. Coexpression network analysis and Random Forest classification were used to discover potential biomarkers and selected biomarker models were validated in an independent patient group. Differential gene-expression analysis identified major gene-expression changes depending on the stimulus. Random Forest classification identified CD47 , BATF , FASLG , RGS16 , SYNPO , SELE , PTPN7 , WARS , PRC1 , EXO1 , RRM2 , PBK , RAD54L , KIFC1 , SPC25 , PKMYT , HISTH1A , TPX2 , DLGAP5 , TPX2 , CH25H , and IL37 as potential biomarkers to distinguish allergic and irritant contact dermatitis in human skin. Validation experiments and prediction performances on external testing datasets demonstrated potential applicability of the identified biomarker models in the clinic. Capitalizing on this knowledge, novel diagnostic tools can be developed to guide clinical diagnosis of contact allergies., Competing Interests: The authors declare no competing interest., (Copyright © 2020 the Author(s). Published by PNAS.)
- Published
- 2020
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45. An omics perspective on drug target discovery platforms.
- Author
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Paananen J and Fortino V
- Subjects
- Databases, Factual, Drug Delivery Systems, Pharmaceutical Preparations, Proteomics, Computational Biology, Drug Discovery, Genomics, Knowledge Bases
- Abstract
The drug discovery process starts with identification of a disease-modifying target. This critical step traditionally begins with manual investigation of scientific literature and biomedical databases to gather evidence linking molecular target to disease, and to evaluate the efficacy, safety and commercial potential of the target. The high-throughput and affordability of current omics technologies, allowing quantitative measurements of many putative targets (e.g. DNA, RNA, protein, metabolite), has exponentially increased the volume of scientific data available for this arduous task. Therefore, computational platforms identifying and ranking disease-relevant targets from existing biomedical data sources, including omics databases, are needed. To date, more than 30 drug target discovery (DTD) platforms exist. They provide information-rich databases and graphical user interfaces to help scientists identify putative targets and pre-evaluate their therapeutic efficacy and potential side effects. Here we survey and compare a set of popular DTD platforms that utilize multiple data sources and omics-driven knowledge bases (either directly or indirectly) for identifying drug targets. We also provide a description of omics technologies and related data repositories which are important for DTD tasks., (© The Author(s) 2019. Published by Oxford University Press.)
- Published
- 2020
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46. Multiparametric Profiling of Engineered Nanomaterials: Unmasking the Surface Coating Effect.
- Author
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Gallud A, Delaval M, Kinaret P, Marwah VS, Fortino V, Ytterberg J, Zubarev R, Skoog T, Kere J, Correia M, Loeschner K, Al-Ahmady Z, Kostarelos K, Ruiz J, Astruc D, Monopoli M, Handy R, Moya S, Savolainen K, Alenius H, Greco D, and Fadeel B
- Abstract
Despite considerable efforts, the properties that drive the cytotoxicity of engineered nanomaterials (ENMs) remain poorly understood. Here, the authors inverstigate a panel of 31 ENMs with different core chemistries and a variety of surface modifications using conventional in vitro assays coupled with omics-based approaches. Cytotoxicity screening and multiplex-based cytokine profiling reveals a good concordance between primary human monocyte-derived macrophages and the human monocyte-like cell line THP-1. Proteomics analysis following a low-dose exposure of cells suggests a nonspecific stress response to ENMs, while microarray-based profiling reveals significant changes in gene expression as a function of both surface modification and core chemistry. Pathway analysis highlights that the ENMs with cationic surfaces that are shown to elicit cytotoxicity downregulated DNA replication and cell cycle responses, while inflammatory responses are upregulated. These findings are validated using cell-based assays. Notably, certain small, PEGylated ENMs are found to be noncytotoxic yet they induce transcriptional responses reminiscent of viruses. In sum, using a multiparametric approach, it is shown that surface chemistry is a key determinant of cellular responses to ENMs. The data also reveal that cytotoxicity, determined by conventional in vitro assays, does not necessarily correlate with transcriptional effects of ENMs., Competing Interests: The authors declare no conflict of interest., (© 2020 The Authors. Published by Wiley‐VCH GmbH.)
- Published
- 2020
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47. Immune-microbiota interaction in Finnish and Russian Karelia young people with high and low allergy prevalence.
- Author
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Ruokolainen L, Fyhrquist N, Laatikainen T, Auvinen P, Fortino V, Scala G, Jousilahti P, Karisola P, Vendelin J, Karkman A, Markelova O, Mäkelä MJ, Lehtimäki S, Ndika J, Ottman N, Paalanen L, Paulin L, Vartiainen E, von Hertzen L, Greco D, Haahtela T, and Alenius H
- Subjects
- Adolescent, Age Factors, Female, Finland epidemiology, Gene Regulatory Networks, Genome-Wide Association Study, Host Microbial Interactions, Humans, Hypersensitivity immunology, Hypersensitivity microbiology, Hypersensitivity virology, Immunoglobulin E blood, Leukocytes, Mononuclear immunology, Leukocytes, Mononuclear microbiology, Leukocytes, Mononuclear virology, Male, Nasal Mucosa immunology, Nasal Mucosa microbiology, Nasal Mucosa virology, Polymorphism, Single Nucleotide, Prevalence, Russia epidemiology, Skin immunology, Skin microbiology, Skin virology, Transcriptome, Young Adult, Health Status Disparities, Hypersensitivity epidemiology, Immunity, Innate genetics, Microbiota immunology
- Abstract
Background: After the Second World War, the population living in the Karelian region was strictly divided by the "iron curtain" between Finland and Russia. This resulted in different lifestyle, standard of living, and exposure to the environment. Allergic manifestations and sensitization to common allergens have been much more common on the Finnish compared to the Russian side., Objective: The remarkable allergy disparity in the Finnish and Russian Karelia calls for immunological explanations., Methods: Young people, aged 15-20 years, in the Finnish (n = 69) and Russian (n = 75) Karelia were studied. The impact of genetic variation on the phenotype was studied by a genome-wide association analysis. Differences in gene expression (transcriptome) were explored from the blood mononuclear cells (PBMC) and related to skin and nasal epithelium microbiota and sensitization., Results: The genotype differences between the Finnish and Russian populations did not explain the allergy gap. The network of gene expression and skin and nasal microbiota was richer and more diverse in the Russian subjects. When the function of 261 differentially expressed genes was explored, innate immunity pathways were suppressed among Russians compared to Finns. Differences in the gene expression paralleled the microbiota disparity. High Acinetobacter abundance in Russians correlated with suppression of innate immune response. High-total IgE was associated with enhanced anti-viral response in the Finnish but not in the Russian subjects., Conclusions and Clinical Relevance: Young populations living in the Finnish and Russian Karelia show marked differences in genome-wide gene expression and host contrasting skin and nasal epithelium microbiota. The rich gene-microbe network in Russians seems to result in a better-balanced innate immunity and associates with low allergy prevalence., (© 2020 The Authors. Clinical & Experimental Allergy published by John Wiley & Sons Ltd.)
- Published
- 2020
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48. ThETA: transcriptome-driven efficacy estimates for gene-based TArget discovery.
- Author
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Failli M, Paananen J, and Fortino V
- Subjects
- Drug Discovery, Gene Regulatory Networks, Software, Transcriptome
- Abstract
Summary: Estimating efficacy of gene-target-disease associations is a fundamental step in drug discovery. An important data source for this laborious task is RNA expression, which can provide gene-disease associations on the basis of expression fold change and statistical significance. However, the simply use of the log-fold change can lead to numerous false-positive associations. On the other hand, more sophisticated methods that utilize gene co-expression networks do not consider tissue specificity. Here, we introduce Transcriptome-driven Efficacy estimates for gene-based TArget discovery (ThETA), an R package that enables non-expert users to use novel efficacy scoring methods for drug-target discovery. In particular, ThETA allows users to search for gene perturbation (therapeutics) that reverse disease-gene expression and genes that are closely related to disease-genes in tissue-specific networks. ThETA also provides functions to integrate efficacy evaluations obtained with different approaches and to build an overall efficacy score, which can be used to identify and prioritize gene(target)-disease associations. Finally, ThETA implements visualizations to show tissue-specific interconnections between target and disease-genes, and to indicate biological annotations associated with the top selected genes., Availability and Implementation: ThETA is freely available for academic use at https://github.com/vittoriofortino84/ThETA., Contact: vittorio.fortino@uef.fi., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press.)
- Published
- 2020
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49. The EDCMET Project: Metabolic Effects of Endocrine Disruptors.
- Author
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Küblbeck J, Vuorio T, Niskanen J, Fortino V, Braeuning A, Abass K, Rautio A, Hakkola J, Honkakoski P, and Levonen AL
- Subjects
- Animals, Biomarkers, Disease Susceptibility, Endocrine System drug effects, Endocrine System metabolism, Environmental Exposure, Environmental Pollutants, Epigenesis, Genetic, Humans, Metabolic Diseases etiology, Metabolic Diseases metabolism, Mitochondria genetics, Mitochondria metabolism, Receptors, Cytoplasmic and Nuclear genetics, Receptors, Cytoplasmic and Nuclear metabolism, Endocrine Disruptors adverse effects, Energy Metabolism drug effects
- Abstract
Endocrine disruptors (EDs) are defined as chemicals that mimic, block, or interfere with hormones in the body's endocrine systems and have been associated with a diverse array of health issues. The concept of endocrine disruption has recently been extended to metabolic alterations that may result in diseases, such as obesity, diabetes, and fatty liver disease, and constitute an increasing health concern worldwide. However, while epidemiological and experimental data on the close association of EDs and adverse metabolic effects are mounting, predictive methods and models to evaluate the detailed mechanisms and pathways behind these observed effects are lacking, thus restricting the regulatory risk assessment of EDs. The EDCMET (Metabolic effects of Endocrine Disrupting Chemicals: novel testing METhods and adverse outcome pathways) project brings together systems toxicologists; experimental biologists with a thorough understanding of the molecular mechanisms of metabolic disease and comprehensive in vitro and in vivo methodological skills; and, ultimately, epidemiologists linking environmental exposure to adverse metabolic outcomes. During its 5-year journey, EDCMET aims to identify novel ED mechanisms of action, to generate (pre)validated test methods to assess the metabolic effects of Eds, and to predict emergent adverse biological phenotypes by following the adverse outcome pathway (AOP) paradigm.
- Published
- 2020
- Full Text
- View/download PDF
50. Publisher Correction: Prioritizing target-disease associations with novel safety and efficacy scoring methods.
- Author
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Failli M, Paananen J, and Fortino V
- Abstract
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
- Published
- 2020
- Full Text
- View/download PDF
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