Role and Function of c-Jun Protein Complex in Cancer Cell Behavior

Transcription factors play a crucial role in the regulation of cell behavior by modulating gene expression profiles. Previous studies have described a dual role for the AP-1 family transcription factor c-Jun in the regulation of cellular fate. In various cell types weak and transient activations of c-Jun N-terminal kinase (JNK) and c-Jun appear to contribute to proliferation and survival, whereas strong and prolonged activation of JNK and c-Jun result in apoptosis. These opposite roles played by c-Jun are cell type specific and the molecular mechanisms defining these antonymous c-Jun-mediated responses remain incompletely understood. c-Jun activity in transformed cells is regulated by signalling cascades downstream of oncoproteins such as Ras and Raf. In addition, the pro-proliferative role and the survival promoting function for c-Jun has been described in various cancer models. Furthermore, c-Jun was described to be overexpressed in different cancer types. However, the molecular mechanisms by which c-Jun exerts these oncogenic functions are not all clearly established. Therefore it is of primary interest to further identify molecular mechanisms and functions for c-Jun in cancer. Regulation of gene expression is tightly dependent on accurate protein-protein interactions. Therefore, co-factors for c-Jun may define the functions for c-Jun in cancer. Identification of protein-protein interactions promoting cancer may provide novel possibilities for cancer treatment. In this study, we show that DNA topoisomerase I (TopoI) is a transcriptional co-factor for c-Jun. Moreover, c-Jun and TopoI together promote expression of epidermal growth factor receptor (EGFR) in cancer cells. We also show that the clinically used TopoI inhibitor topotecan reduces EGFR expression. Importantly, the effect of TopoI on EGFR transcription was shown to depend on c-Jun as Jun-/- cells or cells treated with JNK inhibitor SP600125 are resistant to topotecan treatment both in regulation of EGFR expression and cell proliferation. Moreover, c-Jun regulates the nucleolar localization and the function of the ribonucleic acid (RNA) helicase DDX21, a previously identified member of c-Jun protein complex. In addition, c-Jun stimulates rRNA processing by supporting DDX21 rRNA binding. Finally, this study characterizes a DDX21 dependent expression of cyclin dependent kinase (Cdk) 6, a correlation of DDX21 expression with prostate cancer progression and a substrate binding dependency of DDX21 nucleolar localization in prostate cancer cells. Taken together, the results of this study validate the c-Jun-TopoI interaction and precise the c-Jun-DDX21 interaction. Moreover, these results show the importance for protein-protein interaction in the regulation of their cellular functions in cancer cell behavior. Finally, the results presented here disclose new exciting therapeutic opportunities for cancer treatment.Siirretty Doriast

Machine Learning appliqué au suivi du comportement pour identifier maladies, états reproductifs et perturbations des vaches laitières

Séance : Bien-être animalInternational audienceAnimal behaviour is very sensitive to any external disturbance or change in the animal internal state. We sought to predict and classify a wide range of health, stress and physiological states from daily activity of dairy cow. We had six datasets (total, 122,000 cows*days). The caretakers noted the condition of the cows: illness, oestrus, calving, disturbances (handling, mixing. Cows were equipped with sensors to estimate the duration of activities - eating, resting, in the alleys - from which the activity level was calculated. We modelled the rhythm of activity by the Fourier transform (Harmonics 0 and 1). If the difference between the models obtained during 2 series of 24 h is higher than a certain threshold, there is a strong chance that a particular event has occurred: illness, stress, heat, farrowing. The change takes place before the caretakers spot the event. Next, we applied random forest to attributes describing the 24 h activity series (minimum, maximum, average, autocorrelations...). We correctly classify 99% of the control series (= very rare false positive) and in an episode surrounding a disease or a reproductive event, the probability of correctly classifying at least one series varies from 94 to 100%. Machine learning applied to time series seems therefore a very powerful tool to analyse the behaviour of animals and diagnose its internal state.Le comportement d’un individu est très sensible à toute perturbation extérieure ou modification de l’état interne. Nous avons cherché à prédire et à classer un large éventail d'états de santé, de stress et d'états physiologiques à partir de l'activité quotidienne de vaches laitières. Nous disposions de six jeux de données (au total 122 000 vaches*jours). Les soigneurs notaient l'état des vaches : maladie, oestrus, vêlage, perturbations (manipulations, mélange). Les vaches étaient équipées de capteurs permettant d’estimer la durée des activités - manger, se reposer, dans les couloirs – à partir desquelles était calculé le niveau d’activité. Nous avons modélisé le rythme d’activité par des transformées de Fourier (Harmoniques 0 et 1). Si l’écart entre les modèles obtenus au cours de 2 séries de 24 h est supérieur à un certain seuil, il y a de fortes chances qu’un événement particulier soit survenu : maladie, stress, chaleurs, mise-bas. Le changement s’opère avant que les soigneurs repèrent l’évènement. Ensuite, nous avons appliqué le random forest à des attributs décrivant les séries d’activité de 24 h (minimum, maximum, moyenne, autocorrélations…). Nous classons correctement 99% des séries témoins (= quasiment pas de fausse alertes) et dans un épisode qui entoure une maladie ou un événement reproductif, la probabilité de classer correctement au moins une série varie de 94 à 100%. L'apprentissage automatique appliqué à des séries temporelles semble donc un outil très puissant pour analyser le comportement des animaux et diagnostiquer son état interne

Machine learning to detect behavioural anomalies in dairy cows under subacute ruminal acidosis

Sickness behaviour is characterised by a lethargic state during which the animal reduces its activity, sleeps more and at times when normally awake, reduces its feed and water intake, and interacts less with its environment. Subtle modifications in behaviour can materialise just before clinical signs of a disease. Recent sensor developments enable continuous monitoring of animal behaviour, but the shift to abnormal animal activity remains difficult to detect. We explored the use of Machine Learning (ML) to detect abnormal behaviour from continuous monitoring. We submitted 14 cows (Bos taurus) to Sub-Acute Ruminal Acidosis (SARA), a disease known to induce changes in behaviour. Another 14 control cows were not submitted to SARA. We used a ruminal bolus to monitor pH and detect when a cow experienced SARA. We used a positioning system to infer an animal's activity based on its position in relation to specific elements in the barn (feeder, resting area, and alleys). We tested several ML algorithms: K Nearest Neighbours for Regression (KNNR); Decision Tree for Regression (DTR); MultiLayer Perceptron (MLP); Long Short-Term Memory (LSTM); and an algorithm where activity is assumed to be similar from one day to the next. First, we developed ML models to predict activity on a given day from the previous 24 h, considering all cows together. Then, we calculated the error between observed and predicted values for a given cow. Finally, we compared the error to a threshold chosen to optimise the distinction between normal and abnormal values. KNNR performed best, detecting 83% of SARA cases (true-positives), but it also produced 66% of false-positives, which limits its use in practice. In conclusion, ML can help detect anomalies in behaviour. Further improvements could probably be obtained by applying ML on very large datasets at animal rather than group level

Enzymatic pretreatment of steam-exploded birch wood for increased biogas production and lignin degradation

Lignocellulose is readily available biomass for biogas production; however, due to its rigid structure, it requires pretreatment to obtain a maximum energy extraction. In this study, steam explosion (SE) (220 °C and 10 minute retention time) has been employed to increase the biogas production potential from birch wood. Although the biogas production increased by over two times after SE, the SE of birch wood negatively affects the structure of C5/C6 sugars and doubled the concentration of non-degradable lignin in all the samples. In this work, SE birch wood has been further pretreated by novel lignin-degrading enzymes cocktail to convert lignin into degradable sugars and increase the biogas production rate. The proposed hybrid pretreatment could increase the biogas production by up to 25% (from 450.5 mL/g VS to 566 mL/g VS), and reduced the lignin concentration by up to 48%

DNA Topoisomerase I Is a Cofactor for c-Jun in the Regulation of Epidermal Growth Factor Receptor Expression and Cancer Cell Proliferation

DNA topoisomerase I (Topo I) is a molecular target for the anticancer agent topotecan in the treatment of small cell lung cancer and ovarian carcinomas. However, the molecular mechanisms by which topotecan treatment inhibits cancer cell proliferation are unclear. We describe here the identification of Topo I as a novel endogenous interaction partner for transcription factor c-Jun. Reciprocal coimmunoprecipitation analysis showed that Topo I and c-Jun interact in transformed human cells in a manner that is dependent on JNK activity. c-Jun target gene epidermal growth factor receptor (EGFR) was identified as a novel gene whose expression was specifically inhibited by topotecan. Moreover, Topo I overexpression supported c-Jun-mediated reporter gene activation and both genetic and chemical inhibition of c-Jun converted cells resistant to topotecan-elicited EGFR downregulation. Topotecan-elicited suppression of proliferation was rescued by exogenously expressed EGFR. Furthermore, we demonstrate the cooperation of the JNK-c-Jun pathway, Topo I, and EGFR in the positive regulation of HT-1080 cell proliferation. Together, these results have identified transcriptional coactivator Topo I as a first endogenous cofactor for c-Jun in the regulation of cell proliferation. In addition, the results of the present study strongly suggest that inhibition of EGFR expression is a novel mechanism by which topotecan inhibits cell proliferation in cancer therapy

Detection of early behavioural signs of disease as a way to manage animal health

International audienc

Remodelling of the homeobox gene complement in the tunicate Oikopleura dioica

AbstractHomeodomain transcription factors are involved in many developmental processes [1] and have been intensely studied in a few model organisms, such as mouse, Drosophila and Caenorhabditis elegans. Homeobox genes fall into 10 classes (ANTP, PRD, POU, LIM, TALE, SIX, Cut, ZFH, HNF1, Prox) and 89 different families/groups, all of which are present in vertebrates. Additional groups may be uncovered by further genome annotation, particularly of complex vertebrate genomes. Eight of these groups have been found only in vertebrates, but not in the genome of the tunicate Ciona intestinalis. The other 81 groups of homeobox gene that have been detected in vertebrates so far probably appeared during the early evolution of bilaterians or earlier, as they are also present outside the chordates. How the homeobox genes evolved during and after the main radiation of the bilaterians remains poorly understood, as only a few animal genomes have been sequenced completely. However, drastic changes have occurred at least in the lineage of C. elegans[2], such as loss of several Hox genes and Hox cluster fragmentation [3]. Here we report considerable alterations of the homeobox gene complement in the tunicate lineage

When TCN meet high school students: deciphering western Cévennes landscape evolution (Lozère, France) using TCN on karstic networks

International audienceThe rates and chronologies of valley incision are closely modulated by both the tectonic uplift of active mountain ranges and repeated climate changes during the Quaternary. The Neogene evolution of the Cévennes, the southernmost part of the French Massif Central, is poorly constrained. According to Séranne et al. (2002), most of its incision is due to a topographic uplift between the Langhian (16 Ma) and the Messinian (5.32 Ma) due to Mediterranean geodynamics as well as eustatic variations. Studies performed at the Montagne Noire and east of the Massif Central (e.g., Olivetti et al., 2016) suggest in addition a marked Pliocene incision. Finally, the Mediterranean facade (Ardèche) records a marked incision during the Messinian and the Pliocene controlled by eustatic fluctuations (Tassy et al., 2013).With the aim of quantifying the incision rates in the western Cévennes area since the Miocene, alluvium-filled horizontal epiphreatic passages in limestone karstic networks were studied. Such landforms are used as substitutes of fluvial terraces because they record the transient positions of former local base levels during the process of valley deepening. In the study area, the Jonte, Tarn and Lot valleys contain stepped cavities particularly well-suited for such purpose.As part of the Erasmus+ “Live on the karst” project, 4 high school students and the research team firstly performed morphological and petrographic observations. Then, the burial durations of alluvial sediments from 13 caves located in the Jonte and Tarn valleys were determined using cosmogenic 26Al/10Be and 10Be/21Ne ratios. The results obtained allow us to document the incision processes since the Tortonian (~ 11-8 Ma) in the Tarn gorges, and the Zanclean (~4 Ma) in the Jonte gorges. In both valleys, the estimated incision rates range from 40 to 120 m/Ma, also giving an estimate of the uplift rates. The digging would then be posterior to the Messinian envisioned by Séranne et al. (2002) for the Jonte gorges and could result from changes in drainage systems or even closure of the valley. Concerning the Tarn valley, the incision of the Causse de Sauveterre and the Causse Méjean would have started at least 8.39 ± 1.04 Ma ago, in agreement with the scenario envisaged by Séranne et al. (2002). This work still in progress provides new and original constrains on incision, paleo-denudation and related uplift rates in the study area. This may help to better understand the late evolution of this area, particularly its relations with the French Massif Central volcanism and the synchronous post-orogenic evolution of the French Alps and the Pyrenees.Furthermore, the “Live on the karst” project allows high school students, as part of an advanced examination of the French A levels, to study the biodiversity and geodiversity of the Grands Causses karsts (southern Cévennes), and to compare them to other European karsts in interactions with Italian and Slovenian high school students. In this project, most of the cosmogenic nuclide concentrations were acquired by high school students supervised by members of the CEREGE team

Identification of nucleolar effects in JNK-deficient cells

AbstractThe c-Jun N-terminal kinase (JNK) signalling pathway has an established role in cellular stress signalling, cell survival and tumorigenesis. Here, we demonstrate that inhibition of JNK signalling results in partial delocalization of the RNA helicase DDX21 from the nucleolus to the nucleoplasm, increased nucleolar mobility of DDX21 and inhibition of rRNA processing. Furthermore, our results show that JNK signalling regulates DDX21 phosphorylation and protein expression. In conclusion, the results presented in this study reveal a previously unidentified cellular role for JNK signalling in the regulation of nucleolar functions. Based on these results, we propose that JNK-mediated effects on nucleolar homeostasis and rRNA processing should be considered when interpreting cellular phenotypes observed in JNK-deficient cell and animal models

c-Jun supports ribosomal RNA processing and nucleolar localization of RNA helicase DDX21

The molecular mechanisms by which the AP-1 transcription factor c-Jun exerts its biological functions are not clearly understood. In addition to its well established role in transcriptional regulation of gene expression, several reports have suggested that c-Jun may also regulate cell behavior by non-transcriptional mechanisms. Here, we report that small interfering RNA-mediated depletion of c-Jun from mammalian cells results in inhibition of 28 S and 18 S rRNA accumulation. Moreover, we show that c-Jun depletion results in partial translocation of RNA helicase DDX21, implicated in rRNA processing, from the nucleolus to the nucleoplasm. We demonstrate that DDX21 translocation is rescued by exogenous c-Jun expression and that c-Jun depletion inhibits rRNA binding of DDX21. Furthermore, the direct interaction between c-Jun and DDX21 regulates nucleolar localization of DDX21. These results demonstrate that in addition to its transcriptional effects, c-Jun regulates rRNA processing and nucleolar compartmentalization of the rRNA processing protein DDX21. Thus, our results demonstrate a nucleolar mechanism through which c-Jun can regulate cell behavior. Moreover, these results suggest that the phenotypes observed previously in c-Jun-depleted mouse models and cell lines could be partly due to the effects of c-Jun on rRNA processing