Forced IFIT-2 expression represses LPS induced TNF-alpha expression at posttranscriptional levels

<p>Abstract</p> <p>Background</p> <p>Interferon induced tetratricopeptide repeat protein 2 (IFIT-2, P54) belongs to the type I interferon response genes and is highly induced after stimulation with LPS. The biological function of this protein is so far unclear. Previous studies indicated that IFIT-2 binds to the initiation factor subunit eIF-3c, affects translation initiation and inhibits protein synthesis. The aim of the study was to further characterize the function of IFIT-2.</p> <p>Results</p> <p>Stimulation of RAW264.7 macrophages with LPS or IFN-γ leads to the expression of IFIT-2 in a type I interferon dependent manner. By using stably transfected RAW264.7 macrophages overexpressing IFIT-2 we found that IFIT-2 inhibits selectively LPS induced expression of TNF-α, IL-6, and MIP-2 but not of IFIT-1 or EGR-1. In IFIT-2 overexpressing cells TNF-α mRNA expression was lower after LPS stimulation due to reduced mRNA stability. Further experiments suggest that characteristics of the 3'UTR of transcripts discriminate whether IFIT-2 has a strong impact on protein expression or not.</p> <p>Conclusion</p> <p>Our data suggest that IFIT-2 may affect selectively LPS induced protein expression probably by regulation at different posttranscriptional levels.</p

PSM Peptides From Community-Associated Methicillin-Resistant Staphylococcus aureus Impair the Adaptive Immune Response via Modulation of Dendritic Cell Subsets in vivo

Dendritic cells (DCs) are key players of the immune system and thus a target for immune evasion by pathogens. We recently showed that the virulence factors phenol-soluble-modulins (PSMs) produced by community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) strains induce tolerogenic DCs upon Toll-like receptor activation via the p38-CREB-IL-10 pathway in vitro. Here, we addressed the hypothesis that S. aureus PSMs disturb the adaptive immune response via modulation of DC subsets in vivo. Using a systemic mouse infection model we found that S. aureus reduced the numbers of splenic DC subsets, mainly CD4+ and CD8+ DCs independently of PSM secretion. S. aureus infection induced upregulation of the C-C motif chemokine receptor 7 (CCR7) on the surface of all DC subsets, on CD4+ DCs in a PSM-dependent manner, together with increased expression of MHCII, CD86, CD80, CD40, and the co-inhibitory molecule PD-L2, with only minor effects of PSMs. Moreover, PSMs increased IL-10 production in the spleen and impaired TNF production by CD4+ DCs. Besides, S. aureus PSMs reduced the number of CD4+ T cells in the spleen, whereas CD4+CD25+Foxp3+ regulatory T cells (Tregs) were increased. In contrast, Th1 and Th17 priming and IFN-γ production by CD8+ T cells were impaired by S. aureus PSMs. Thus, PSMs from highly virulent S. aureus strains modulate the adaptive immune response in the direction of tolerance by affecting DC functions

Prime Time Light Exposures Do Not Seem to Improve Maximal Physical Performance in Male Elite Athletes, but Enhance End-Spurt Performance

Many sports competitions take place during television prime time, a time of the day when many athletes have already exceeded their time of peak performance. We assessed the effect of different light exposure modalities on physical performance and melatonin levels in athletes during prime time. Seventy-two young, male elite athletes with a median (interquartile range) age of 23 (21; 29) years and maximum oxygen uptake (VO2max) of 63 (58; 66) ml/kg/min were randomly assigned to three different light exposure groups: bright light (BRIGHT), blue monochromatic light (BLUE), and control light (CONTROL). Each light exposure lasted 60 min and was scheduled to start 17 h after each individual's midpoint of sleep (median time: 9:17 pm). Immediately after light exposure, a 12-min time trial was performed on a bicycle ergometer. The test supervisor and participants were blinded to the light condition each participant was exposed to. The median received light intensities and peak wavelengths (photopic lx/nm) measured at eye level were 1319/545 in BRIGHT, 203/469 in BLUE, and 115/545 in CONTROL. In a multivariate analysis adjusted for individual VO2max, total work performed in 12 min did not significantly differ between the three groups. The amount of exposure to non-image forming light was positively associated with the performance gain during the time trial, defined as the ratio of the work performed in the first and last minute of the time trial, and with stronger melatonin suppression. Specifically, a tenfold increase in the exposure to melanopic light was associated with a performance gain of 8.0% (95% confidence interval: 2.6, 13.3; P = 0.004) and a melatonin decrease of −0.9 pg/ml (95% confidence interval: −1.5, −0.3; P = 0.006). Exposure to bright or blue light did not significantly improve maximum cycling performance in a 12-min all-out time trial. However, it is noteworthy that the estimated difference of 4.1 kJ between BRIGHT and CONTROL might represent an important performance advantage justifying further studies. In conclusion, we report novel evidence that evening light exposure, which strongly impacts the human circadian timing system, enables elite athletes to better maintain performance across a 12-min cycling time trial

Effects of bright and blue light on acoustic reaction time and maximum handgrip strength in male athletes: a randomized controlled trial

To assess which type of evening light exposure has the greatest effect on reaction time and maximum handgrip strength. These were pre-specified secondary outcomes in a trial which primarily investigated the influence of light on cycling performance.; Seventy-four male athletes were allocated at random to either bright light (BRIGHT), monochromatic blue light (BLUE), or a control condition (CONTROL). Light exposure lasted for 60 min and started 17 h after the individual midpoint of sleep. Reaction time, handgrip strength, and melatonin levels were measured before and after the light exposure. We used analysis of covariance to compare the groups with respect to the investigated outcomes.; Two participants had to be excluded retrospectively. The remaining 72 participants had a median age of 23 years. The adjusted difference in reaction time was -1 ms [95% confidence interval (CI) -8, 6] for participants in BRIGHT and 2 ms (95% CI -5, 9) for participants in BLUE, both relative to participants in CONTROL. The adjusted difference in handgrip strength was 0.9 kg (95% CI -1.5, 3.3) for participants in BRIGHT and -0.3 kg (95% CI -2.7, 2.0) for participants in BLUE, both relative to participants in CONTROL. After the light exposure, 17% of participants in BRIGHT, 22% in BLUE, and 29% in CONTROL showed melatonin concentrations of 2 pg/ml or higher.; The results suggest that bright light might reduce melatonin levels but neither bright nor blue light exposure in the evening seem to improve reaction time or handgrip strength in athletes

Smart Innovation : Künstliche Intelligenz im Innovationsmanagement

Die vorliegende Studie zeigt, dass das Thema Smart Innovation (der Einsatz von KI-Systemen im Innovationsprozess) von hoher Relevanz ist und Zustimmung für den Einsatz von KI im Innovationsprozess besteht. Sowohl von den Unternehmen als auch von den Studierenden werden Effizienzsteigerung, schnellere Bearbeitung großer Datenmengen, die Steigerung der Wettbewerbsfähigkeit und Kosteneinsparungen als Gründe für den Einsatz von KI im Innovationsprozess gesehen. In Deutschland finden KI-Technologien bereits jetzt punktuell und branchenunabhängig Anwendung im Innovationsprozess. Einflussfaktoren, wie Hochschulkooperationen, Innovationsabteilungen und Open Innovation können den Einsatz fördern. Vor allem KMU aus den frühen Phasen der Industrialisierung sollten davon Gebrauch machen. In einem Zusammenspiel von menschlicher Expertise und der schnellen und präzisen Datenverarbeitung der KI liegt das Erfolgsgeheimnis eines möglichst effizienten Innovationsprozesses. Es wird deutlich, dass verschiedene Einflussfaktoren erforderlich sind, um die Anwendung von Smart Innovation praktikabel zu gestalten. So gilt es zunächst die technischen Voraussetzungen einer funktionierenden IT-Infrastruktur zu erfüllen. Gleichbedeutend sind offene Fragestellungen hinsichtlich der Datenverfügbarkeit, des Dateneigentums und der Datensicherheit. Ohne rechtlichen Rahmen sind kaum Akteure gewillt, ihre Daten zu teilen und zugänglich zu machen. Erschwert wird der Einsatz von KI durch den nationalen IT-Fachkräftemangel. So sehen sowohl Unternehmen als auch die Studierenden das größte Hindernis im Mangel von KI-relevantem Know-how. Dies hemmt einerseits die Forschung, andererseits fehlt es den Unternehmen an erforderlichen Fachkräften für eine Einführung von KI im Unternehmen. Es ist jedoch notwendig, den Unternehmen durch das Aufzeigen von Anwendungsbeispielen, die Potenziale und Chancen von Smart Innovation zu vermitteln. Es gilt, die anwendungsorientierte Forschung zu fördern und einen reibungslosen Transfer in die Wirtschaft sicherzustellen. Dieser Wissensaustausch erfordert zudem eine höhere unternehmerische Risikobereitschaft. Es wächst die Notwendigkeit, unternehmensspezifische KI-Strategien zu entwerfen. Die Technologien entwickeln sich schnell, es gilt daher auch für Unternehmen sich diesem Fortschritt anzupassen, um den Anschluss nicht zu verlieren und die Wettbewerbsfähigkeit zu sichern. So liegt die größte Herausforderung im grundlegenden Wandel der Geschäftsmodelle, denn die Wertschöpfung erfolgreicher Unternehmen basiert zunehmend auf "digitalen assets". Daten gelten generell als die neue Ressource, als Rohstoff, auch für Smarte Innovationen. Die Bedeutung von Smart Innovation wird in Zukunft weiterhin ansteigen. Kurz- und mittelfristig unterstützt die Schwache KI vor allem bei der Datensammlung und -analyse, bei der Prozessautomatisierung sowie bei der Bedürfnis- und Trendidentifikation. Weiter werden sich inkrementelle Veränderungen im Innovationsmanagement mithilfe von Simulationen und der zufälligen Kombination von Technologien erhofft. Langfristig wird eine stärkere KI den Einsatz der Menschen im Innovationsprozess in Teilen ersetzen können. Ob autonomes Innovieren zukünftig möglich sein wird, hängt zunächst von dem Ausmaß der Neuheit einer Innovation, aber vor allem auch von der Möglichkeit einer kreativen KI ab. Es ist davon auszugehen, dass die Fortschritte im Bereich der KI nicht nur radikale Innovationen ermöglichen werden, sondern auch zu einer strukturellen Veränderung unseres heutigen Verständnisses des Innovationsmanagements führen

Smart Innovation – how will artificial intelligence influence innovation management?

Imagine a world in which the search for tomorrow's trends is not subject to a long and laborious data search but is possible with a single mouse click. Through the use of artificial intelligence (AI), this reality is made possible and is to be further advanced through research. The study therefore aims to provide an initial overview of the young research field. Based on research, expert interviews, company and student surveys, current application possibilities of AI in the innovation process (defined as Smart Innovation), existing challenges that slow down the further development are discussed in more detail and future application possibilities are presented. Finally, a recommendation for action is made for business, politics and science to help overcome the current obstacles together and thus drive the future of Smart Innovation

Staphylococcus aureus Phenol-Soluble Modulin Peptides Modulate Dendritic Cell Functions and Increase In Vitro Priming of Regulatory T Cells

The major human pathogen Staphylococcus aureus has very efficient strategies to subvert the human immune system. Virulence of the emerging community-associated methicillin-resistant S. aureus (CA-MRSA) depends on phenol-soluble modulin (PSM) peptide toxins, which are known to attract and lyse neutrophils. However, their influences on other immune cells remain elusive. Here, we analyzed the impact of PSMs on dendritic cells (DCs) playing an essential role in linking innate and adaptive immunity. In human neutrophils, PSMs exert their function by binding to the formyl peptide receptor (FPR) 2. We show that mouse DCs express the FPR2 homologue mFPR2 as well as its paralog mFPR1 and that PSMs are chemoattractants for DCs at non-cytotoxic concentrations. PSMs reduced clathrin-mediated endocytosis and inhibited TLR2 ligand-induced secretion of the proinflammatory cytokines TNF, IL-12 and IL-6 while inducing IL-10 secretion by DCs. As a consequence, treatment with PSMs impaired the capacity of DCs to induce activation and proliferation of CD4(+) T cells, characterized by reduced Th1 but increased frequency of FOXP3(+) regulatory T cells (Tregs). These Tregs secreted high amounts of IL-10 and their suppression capacity was dependent on IL-10 and TGF-β. Interestingly, the induction of tolerogenic DCs by PSMs appeared to be independent of mFPRs as shown by experiments with mice lacking mFPR2 (mFPR2(−/−)) and the cognate G protein (p110γ(−/−)). Thus, PSMs from highly virulent pathogens affect DC functions thereby modulating the adaptive immune response and probably increasing the tolerance towards the pathogen

Smart Innovation – How will Artificial Intelligence influence Innovation Management of (software) products?

Imagine a world in which the search for tomorrow's trends of (software) products is not subject to a long and laborious data search but is possible with a single mouse click. Through the use of artificial intelligence (AI), this reality is made possible and is to be further advanced through research. The study therefore aims to provide an initial overview of the young research field. Based on research, expert interviews, company and student surveys, current application possibilities of AI in the innovation process (defined as Smart Innovation), existing challenges that slow down the further development are discussed in more detail and future application possibilities are presented. Finally, a recommendation for action is made for business, politics and science to help overcome the current obstacles together and thus drive the future of Smart Innovation