scholarly journals Integrating Cell Morphology with Gene Expression and Chemical Structure to Aid Mitochondrial Toxicity Detection

2022 ◽  
Author(s):  
Srijit Seal ◽  
Jordi Carreras-Puigvert ◽  
Maria-Anna Trapotsi ◽  
Hongbin Yang ◽  
Ola Spjuth ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drug Discovery ◽  
Chemical Structure ◽  
Structural Information ◽  
Chemical Space ◽  
Model Performance ◽  
In Vitro Assays ◽  
Mitochondrial Toxicity ◽  
Mitochondrial Membrane Depolarization

Mitochondrial toxicity is an important safety endpoint in drug discovery. Models based solely on chemical structure for predicting mitochondrial toxicity are currently limited in accuracy and applicability domain to the chemical space of the training compounds. In this work, we aimed to utilize both -omics and chemical data to push beyond the state-of-the-art. We combined Cell Painting and Gene Expression data with chemical structural information from Morgan fingerprints for 382 chemical perturbants tested in the Tox21 mitochondrial membrane depolarization assay. We observed that mitochondrial toxicants significantly differ from non-toxic compounds in morphological space and identified compound clusters having similar mechanisms of mitochondrial toxicity, thereby indicating that morphological space provides biological insights related to mechanisms of action of this endpoint. We further showed that models combining Cell Painting, Gene Expression features and Morgan fingerprints improved model performance on an external test set of 236 compounds by 60% (in terms of F1 score) and improved extrapolation to new chemical space. The performance of our combined models was comparable with dedicated in vitro assays for mitochondrial toxicity; and they were able to detect mitochondrial toxicity where Tox21 assays outcomes were inconclusive because of cytotoxicity. Our results suggest that combining chemical descriptors with different levels of biological readouts enhances the detection of mitochondrial toxicants, with practical implications for use in drug discovery.

scholarly journals Evaluation of Gene Expression and Phenotypic Profiling Data as Quantitative Descriptors for Predicting Drug Targets and Mechanisms of Action

2019 ◽  
Author(s):  
Maris Lapins ◽  
Ola Spjuth
Keyword(s):  
Gene Expression ◽  
Drug Discovery ◽  
Drug Targets ◽  
Chemical Structure ◽  
Predictive Accuracy ◽  
Mechanisms Of Action ◽  
In Vitro Assays ◽  
Using Data ◽  
Drug Leads

AbstractProfiling drug leads by means of in silico and in vitro assays as well as omics is widely used in drug discovery for safety and efficacy predictions. In this study, we evaluate the performance of machine learning models trained on data from gene expression and phenotypic profiling assays, with models trained on chemical structure descriptors, for prediction of various drug mechanisms of action and target proteins. Models for several hundred mechanisms of actions and targets were trained using data on 1484 compounds characterized in both gene expression using L1000 profiles, and phenotypic profiling with cell painting assay. The results indicate that the accuracy of the three profiling technologies varies for different endpoints, and indicate a clear potential synergistic effect if these methods are combined. We also study the effect of predictive accuracy of data from different cell lines for L1000 profiles, showing that the choice of cell line has a non-negligible effect on the predictive accuracy. The results strengthen the idea of integrated approaches for predicting drug targets and mechanisms of action in preclinical drug discovery.

Comparison of Vibrio and Firefly Luciferases as Reporter Gene Systems for Use in Bacteria and Plants

1996 ◽  
Vol 23 (1) ◽  
pp. 75 ◽  
Author(s):  
SR Mudge ◽  
WR Lewis-Henderson ◽  
RG Birch
Keyword(s):  
Gene Expression ◽  
Reporter Gene ◽  
Ccd Camera ◽  
Reporter Gene Expression ◽  
In Vitro Assays ◽  
E Coli ◽  
Cell Extracts ◽  
Cooled Ccd

Luciferase genes from Vibrio harveyi (luxAB) and firefly (luc) were introduced into E. coli, Agrobacteriurn, Arabidopsis and tobacco. Transformed bacteria and plants were quantitatively assayed for luciferase activity using a range of in vitro and in vivo assay conditions. Both lux and luc proved efficient reporter genes in bacteria, although it is important to be aware that the sensitive assays may detect expression due to readthrough from distant promoters. LUX activity was undetectable by liquid nitrogen-cooled CCD camera assays on intact tissues of plants which showed strong luxAB expression by in vitro assays. The decanal substrate for the lux assay was toxic to many plant tissues, and caused chemiluminescence in untransformed Arabidopsis leaves. These are serious limitations to application of the lux system for sensitive, non-toxic assays of reporter gene expression in plants. In contrast, LUC activity was readily detectable in intact tissues of all plants with luc expression detectable by luminometer assays on cell extracts. Image intensities of luc-expressing leaves were commonly two to four orders of magnitude above controls under the CCD camera. Provided adequate penetration of the substrate luciferin is obtained, luc is suitable for applications requiring sensitive, non-toxic assays of reporter gene expression in plants.

scholarly journals MicroRNAs in Female Malignancies

2019 ◽  
Vol 18 ◽  
pp. 117693511982874 ◽  
Author(s):  
Themis Liolios ◽  
Stavroula Lila Kastora ◽  
Giorgia Colombo
Keyword(s):  
Gene Expression ◽  
Computational Analysis ◽  
Target Genes ◽  
Vulvar Cancer ◽  
Chemotherapeutic Agents ◽  
In Vitro Assays ◽  
Biological Processes ◽  
Potential Mechanism ◽  
Biological Functions

MicroRNAs (miRNAs) are endogenous 22-nucleotide RNAs that can play a fundamental regulatory role in the gene expression of various organisms. Current research suggests that miRNAs can assume pivotal roles in carcinogenesis. In this article, through bioinformatics mining and computational analysis, we determine a single miRNA commonly involved in the development of breast, cervical, endometrial, ovarian, and vulvar cancer, whereas we underline the existence of 7 more miRNAs common in all examined malignancies with the exception of vulvar cancer. Furthermore, we identify their target genes and encoded biological functions. We also analyze common biological processes on which all of the identified miRNAs act and we suggest a potential mechanism of action. In addition, we analyze exclusive miRNAs among the examined malignancies and bioinformatically explore their functionality. Collectively, our data can be employed in in vitro assays as a stepping stone in the identification of a universal machinery that is derailed in female malignancies, whereas exclusive miRNAs may be employed as putative targets for future chemotherapeutic agents or cancer-specific biomarkers.

Aroyl-Pyrrolyl-Hydroxy-Amides (APHAs), a Novel Family of Synthetic Histone Deacetylases Inhibitors, Are Potent Inducers of Human g-Globin Gene Expression.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 1216-1216
Author(s):  
Antonello Mai ◽  
Silvio Massa ◽  
Antonella Di Noia ◽  
Katija Jelicic ◽  
Elena Alfani ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sickle Cell ◽  
Sickle Cell Anemia ◽  
Histone Deacetylases ◽  
Globin Gene ◽  
Renilla Luciferase ◽  
In Vitro Assays ◽  
Luciferase Reporter ◽  
Globin Gene Expression

Abstract Post-natal pharmacological reactivation of HbF, by restoring the unbalanced α/non-α globin chain production in red cells of patients affected by β-thalassemia or sickle cell anemia, represents a potential cure for these diseases. Many classes of compounds have been identified capable to induce Hb F synthesis in vitro by acting at different levels of the globin gene expression regulatory machinery. One of these classes is represented by inhibitors of a family of enzymes, the histone deacetylases (HDACs), involved in chromatin remodelling and gene transcription regulation. HDACs act in multi-protein complexes that remove acetyl groups from lysine residues on several proteins, including histones and are divided into three distinct structural classes, depending on whether their catalytic activity is zinc (class I/II)- or NAD+ (class III)-dependent. The effects of the HDACs inhibitors identified so far on HbF synthesis is, however, modest and often associated with high toxicity. Therefore, the potential of their clinical use is unclear. We have recently described a new family of synthetic HDACs inhibitors, the Aroyl-pyrrolyl-hydroxy-amides (APHAs), that induce differentiation, growth arrest and/or apoptosis of transformed cell in culture [Mai A et al, J Med Chem2004;47:1098]. In this study, we investigate the capability of 10 different APHA compounds to induce Hb F in two in vitro assays. One assay is based on the ability of APHA compounds to activate either the human Aγ-driven Firefly (Aγ-F) or the β-promoter drives Renilla Luciferase (β-R) reporter in GM979 cells stably transfected with a Dual Luciferase Reporter construct. The second assay is represented by the induction of γ-globin expression (by quantitative RT-PCR) in primary adult erythroblasts obtained in HEMA cultures of mononuclear cells from normal donors. The majority of the compounds tested did not significantly increased the Aγ−F (Aγ−F+β−R) reporter ratio in GM979 cells. However, the compound MC1575 increased by 3-fold (from 0.09 to 0.30) the reporter ratio in GM979 cells at a concentration of 20 μM, with modest effects of the proliferation activity of GM979 cells over the three days of the assay. When MC1575 was added at a concentration of 2–10 μM in cultures of primary adult erythroblasts induced to differentiate in serum-free media for 4 days, it induced a three fold increase of the γ/(γ+β) globin ratio (from 0.04 to 0.12), with no apparent cellular toxicity. Among the HDAC inhibitors tested in this study, MC1575 was not the most potent inhibitor of total enzyme activity. However, it was the compound that most selectively inhibited the activity of the maize homologue of mammalian class IIa HDAC enzymes [Mai et al, J Med Chem2003;46:4826]. These results are consistent with the hypothesis that each class of histone deacetylases might have a specific biological function and indicate that those of class IIa might represent the enzymes most specifically involved in globin gene regulation. We suggest that, by targeting the chemical inhibitors toward the catalytic domain of this class of enzymes, it should be possible to identify more specific, more potent and less toxic compounds for pharmacological treatment of β-thalassemia or sickle cell anemia.

The QSAR Paradigm in Fragment-Based Drug Discovery: From the Virtual Generation of Target Inhibitors to Multi-Scale Modeling

2020 ◽  
Vol 20 (14) ◽  
pp. 1357-1374 ◽  
Author(s):  
Valeria V. Kleandrova ◽  
Alejandro Speck-Planche
Keyword(s):  
Drug Discovery ◽  
Structural Information ◽  
Biological Data ◽  
Heterogeneous Databases ◽  
Biomedical Sciences ◽  
Multi Scale ◽  
Scale Modeling ◽  
Multi Scale Modeling

Fragment-Based Drug Design (FBDD) has established itself as a promising approach in modern drug discovery, accelerating and improving lead optimization, while playing a crucial role in diminishing the high attrition rates at all stages in the drug development process. On the other hand, FBDD has benefited from the application of computational methodologies, where the models derived from the Quantitative Structure-Activity Relationships (QSAR) have become consolidated tools. This mini-review focuses on the evolution and main applications of the QSAR paradigm in the context of FBDD in the last five years. This report places particular emphasis on the QSAR models derived from fragment-based topological approaches to extract physicochemical and/or structural information, allowing to design potentially novel mono- or multi-target inhibitors from relatively large and heterogeneous databases. Here, we also discuss the need to apply multi-scale modeling, to exemplify how different datasets based on target inhibition can be simultaneously integrated and predicted together with other relevant endpoints such as the biological activity against non-biomolecular targets, as well as in vitro and in vivo toxicity and pharmacokinetic properties. In this context, seminal papers are briefly analyzed. As huge amounts of data continue to accumulate in the domains of the chemical, biological and biomedical sciences, it has become clear that drug discovery must be viewed as a multi-scale optimization process. An ideal multi-scale approach should integrate diverse chemical and biological data and also serve as a knowledge generator, enabling the design of potentially optimal chemicals that may become therapeutic agents.

Association of changes in T regulatory cells (Treg) during nivolumab treatment with melanoma outcome.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 3031-3031 ◽  
Author(s):  
Jeffrey S. Weber ◽  
Rupal Ramakrishnan ◽  
Andressa Laino ◽  
Anders E. Berglund ◽  
David Woods
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Cd4 T Cells ◽  
T Regulatory Cells ◽  
Mononuclear Cells ◽  
In Vitro Assays ◽  
Peripheral Blood Mononuclear ◽  
Suppressive Function ◽  
Blocking Antibodies

3031 Background: PD-1 blocking antibodies have significant efficacy in the treatment of melanoma; however, many patients fail to respond and resistance mechanisms remain unknown. We addressed the role of Tregs, an immunosuppressive T-cell population, in patient outcome after treatment with nivolumab. Methods: Peripheral blood mononuclear cells (PBMC) were obtained from patients on trials with nivolumab as adjuvant therapy for resected disease or as treatment for metastatic melanoma. To measure suppression, Tregs were flow-sorted from PBMC and evaluated in allogeneic mixed lymphocyte reactions. Tregs and conventional CD4 T-cells were evaluated for gene expression changes by RNA-sequencing. Treg percentages and phosphorylated STAT3 (pSTAT3) expression were evaluated by flow cytometry. The effects of PD-1 blockade with nivolumab were evaluated in vitro using T-cells from baseline patient PBMC samples. Results: Tregs from responding patients or adjuvant patients without evidence of disease (NED) had reduced suppressive function post-nivolumab (p < 0.05), but no changes were observed in relapsing/non-responding patients; their Tregs were more suppressive than NED/responding Tregs (p < 0.001). NED Tregs had unique gene expression changes and associated pathways post-nivolumab compared to relapsing patient Tregs and conventional CD4 T-cells, including up-regulation of proliferation pathways (q < 8e-19) and downregulation of oxidative phosphorylation (q < 7e-5). NED Tregs had upregulation of pSTAT3 expression post-nivolumab (p < 0.05), which was not observed in relapsing patients. Evaluation of Tregs from patients with active disease also showed upregulation of pSTAT3 in responders (p < 0.05) but not non-responders. The relative increase in Treg pSTAT3 was associated with increased overall survival (R2= 0.49, p < 0.05). In vitro assays using PD-1 blocking antibodies recapitulated the increase in pSTAT3 (p < 0.05) and Treg percentages (p < 0.001), which were diminished with the addition of a STAT3 inhibitor (p < 0.01). Conclusions: These results demonstrate previously unknown roles of decreased Treg suppressive function and induction of STAT3 as biomarkers of patient’s outcome to nivolumab therapy.

scholarly journals Systematic identification of metabolites controlling gene expression in E. coli

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Martin Lempp ◽  
Niklas Farke ◽  
Michelle Kuntz ◽  
Sven Andreas Freibert ◽  
Roland Lill ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
In Vitro Assays ◽  
E Coli ◽  
A Genome ◽  
Concentration Changes ◽  
Systematic Identification ◽  
Genome Scale

Abstract Metabolism controls gene expression through allosteric interactions between metabolites and transcription factors. These interactions are usually measured with in vitro assays, but there are no methods to identify them at a genome-scale in vivo. Here we show that dynamic transcriptome and metabolome data identify metabolites that control transcription factors in E. coli. By switching an E. coli culture between starvation and growth, we induce strong metabolite concentration changes and gene expression changes. Using Network Component Analysis we calculate the activities of 209 transcriptional regulators and correlate them with metabolites. This approach captures, for instance, the in vivo kinetics of CRP regulation by cyclic-AMP. By testing correlations between all pairs of transcription factors and metabolites, we predict putative effectors of 71 transcription factors, and validate five interactions in vitro. These results show that combining transcriptomics and metabolomics generates hypotheses about metabolism-transcription interactions that drive transitions between physiological states.

scholarly journals An Open Drug Discovery Competition: Experimental Validation of Predictive Models in a Series of Novel Antimalarials

2020 ◽  
Author(s):  
Edwin Tse ◽  
Laksh Aithani ◽  
Mark Anderson ◽  
Jonathan Cardoso-Silva ◽  
Giovanni Cincilla ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Predictive Models ◽  
Structural Information ◽  
Open Data ◽  
Ion Pump ◽  
Learning Methods ◽  
Machine Learning Methods

<p>The discovery of new antimalarial medicines with novel mechanisms of action is key to combating the problem of increasing resistance to our frontline treatments. The Open Source Malaria (OSM) consortium has been developing compounds ("Series 4") that have potent activity against <i>Plasmodium falciparum</i> <i>in vitro</i> and <i>in vivo</i> and that have been suggested to act through the inhibition of <i>Pf</i>ATP4, an essential membrane ion pump that regulates the parasite’s intracellular Na<sup>+</sup> concentration. The structure of <i>Pf</i>ATP4 is yet to be determined. In the absence of structural information about this target, a public competition was created to develop a model that would allow the prediction of anti-<i>Pf</i>ATP4 activity among Series 4 compounds, thereby reducing project costs associated with the unnecessary synthesis of inactive compounds.</p>In the first round, in 2016, six participants used the open data collated by OSM to develop moderately predictive models using diverse methods. Notably, all submitted models were available to all other participants in real time. Since then further bioactivity data have been acquired and machine learning methods have rapidly developed, so a second round of the competition was undertaken, in 2019, again with freely-donated models that other participants could see. The best-performing models from this second round were used to predict novel inhibitory molecules, of which several were synthesised and evaluated against the parasite. One such compound, containing a motif that the human chemists familiar with this series would have dismissed as ill-advised, was active. The project demonstrated the abilities of new machine learning methods in the prediction of active compounds where there is no biological target structure, frequently the central problem in phenotypic drug discovery. Since all data and participant interactions remain in the public domain, this research project “lives” and may be improved by others.

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