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      In Vitro and In Vivo Pipeline for Validation of Disease-Modifying Effects of Systems Biology-Derived Network Treatments for Traumatic Brain Injury—Lessons Learned

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          Abstract

          We developed a pipeline for the discovery of transcriptomics-derived disease-modifying therapies and used it to validate treatments in vitro and in vivo that could be repurposed for TBI treatment. Desmethylclomipramine, ionomycin, sirolimus and trimipramine, identified by in silico LINCS analysis as candidate treatments modulating the TBI-induced transcriptomics networks, were tested in neuron-BV2 microglial co-cultures, using tumour necrosis factor α as a monitoring biomarker for neuroinflammation, nitrite for nitric oxide-mediated neurotoxicity and microtubule associated protein 2-based immunostaining for neuronal survival. Based on (a) therapeutic time window in silico, (b) blood-brain barrier penetration and water solubility, (c) anti-inflammatory and neuroprotective effects in vitro ( p < 0.05) and (d) target engagement of Nrf2 target genes ( p < 0.05), desmethylclomipramine was validated in a lateral fluid-percussion model of TBI in rats. Despite the favourable in silico and in vitro outcomes, in vivo assessment of clomipramine, which metabolizes to desmethylclomipramine, failed to demonstrate favourable effects on motor and memory tests. In fact, clomipramine treatment worsened the composite neuroscore ( p < 0.05). Weight loss ( p < 0.05) and prolonged upregulation of plasma cytokines ( p < 0.05) may have contributed to the worsened somatomotor outcome. Our pipeline provides a rational stepwise procedure for evaluating favourable and unfavourable effects of systems-biology discovered compounds that modulate post-TBI transcriptomics.

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          Scikit‐learn: machine learning in python

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            Selection bias in gene extraction on the basis of microarray gene-expression data.

            In the context of cancer diagnosis and treatment, we consider the problem of constructing an accurate prediction rule on the basis of a relatively small number of tumor tissue samples of known type containing the expression data on very many (possibly thousands) genes. Recently, results have been presented in the literature suggesting that it is possible to construct a prediction rule from only a few genes such that it has a negligible prediction error rate. However, in these results the test error or the leave-one-out cross-validated error is calculated without allowance for the selection bias. There is no allowance because the rule is either tested on tissue samples that were used in the first instance to select the genes being used in the rule or because the cross-validation of the rule is not external to the selection process; that is, gene selection is not performed in training the rule at each stage of the cross-validation process. We describe how in practice the selection bias can be assessed and corrected for by either performing a cross-validation or applying the bootstrap external to the selection process. We recommend using 10-fold rather than leave-one-out cross-validation, and concerning the bootstrap, we suggest using the so-called .632+ bootstrap error estimate designed to handle overfitted prediction rules. Using two published data sets, we demonstrate that when correction is made for the selection bias, the cross-validated error is no longer zero for a subset of only a few genes.
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              Molecular mechanisms activating the Nrf2-Keap1 pathway of antioxidant gene regulation.

              Several years have passed since NF-E2-related factor 2 (Nrf2) was demonstrated to regulate the induction of genes encoding antioxidant proteins and phase 2 detoxifying enzymes. Following a number of studies, it was realized that Nrf2 is a key factor for cytoprotection in various aspects, such as anticarcinogenicity, neuroprotection, antiinflammatory response, and so forth. These widespread functions of Nrf2 spring from the coordinated actions of various categories of target genes. The activation mechanism of Nrf2 has been studied extensively. Under normal conditions, Nrf2 localizes in the cytoplasm where it interacts with the actin binding protein, Kelch-like ECH associating protein 1 (Keap1), and is rapidly degraded by the ubiquitin-proteasome pathway. Signals from reactive oxygen species or electrophilic insults target the Nrf2-Keap1 complex, dissociating Nrf2 from Keap1. Stabilized Nrf2 then translocates to the nuclei and transactivates its target genes. Interestingly, Keap1 is now assumed to be a substrate-specific adaptor of Cul3-based E3 ubiquitin ligase. Direct participation of Keap1 in the ubiquitination and degradation of Nrf2 is plausible. The Nrf2-Keap1 system is present not only in mammals, but in fish, suggesting that its roles in cellular defense are conserved throughout evolution among vertebrates. This review article recounts recent knowledge of the Nrf2-Keap1 system, focusing especially on the molecular mechanism of Nrf2 regulation.
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                Author and article information

                Journal
                Int J Mol Sci
                Int J Mol Sci
                ijms
                International Journal of Molecular Sciences
                MDPI
                1422-0067
                29 October 2019
                November 2019
                : 20
                : 21
                : 5395
                Affiliations
                [1 ]A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, PO Box 1627, FIN-70211 Kuopio, Finland; anssi.lipponen@ 123456uef.fi (A.L.); robert.ciszek@ 123456uef.fi (R.C.); elina.hamalainen@ 123456uef.fi (E.H.); jussi.tohka@ 123456uef.fi (J.T.); emilia.kansanen@ 123456uef.fi (E.K.); xavier.ekollendode-ekane@ 123456uef.fi (X.E.N.-E.); anna-liisa.levonen@ 123456uef.fi (A.-L.L.)
                [2 ]Institute of Biomedicine, University of Eastern Finland, PO Box 1627, FIN-70211 Kuopio, Finland; teemu.natunen@ 123456uef.fi (T.N.); mikko.hiltunen@ 123456uef.fi (M.H.); jussi.paananen@ 123456uef.fi (J.P.)
                [3 ]School of Computing, University of Eastern Finland, PO Box 1627, FIN-70211 Kuopio, Finland; mika.hujo@ 123456uef.fi
                [4 ]Bioinformatics Center, University of Eastern Finland, PO Box 1627, FIN-70211 Kuopio, Finland
                [5 ]Jacobs School of Medicine and Biomedical Sciences, University of Buffalo, 875 Ellicott St, 6071 CTRC, Buffalo, NY 14203, USA; davidpou@ 123456buffalo.edu
                Author notes
                [* ]Correspondence: asla.pitkanen@ 123456uef.fi ; Tel.: +358-50-517-2091
                Author information
                https://orcid.org/0000-0002-4403-9084
                https://orcid.org/0000-0002-1048-5860
                Article
                ijms-20-05395
                10.3390/ijms20215395
                6861918
                31671916
                cf7b0354-fed8-425b-a2e6-5059833a89f6
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 15 August 2019
                : 22 October 2019
                Categories
                Article

                Molecular biology
                adverse event,common data element,lincs analysis,machine-learning,traumatic brain injury,neuroinflammation,neuroprotection

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