β2-adrenergic agonists modulate TNF-α induced astrocytic inflammatory gene expression and brain inflammatory cell populations

Background: The NF-kappa B signaling pathway orchestrates many of the intricate aspects of neuroinflammation. Astrocytic beta(2)-adrenergic receptors have emerged as potential regulators in central nervous system inflammation and are potential targets for pharmacological modulation. The aim of this study was to elucidate the crosstalk between astrocytic beta(2)-adrenergic receptors and the TNF-alpha induced inflammatory gene program. Methods: Proinflammatory conditions were generated by the administration of TNF-alpha. Genes that are susceptible to astrocytic crosstalk between beta(2)-adrenergic receptors (stimulated by clenbuterol) and TNF-alpha were identified by qPCR-macroarray-based gene expression analysis in a human 1321 N1 astrocytoma cell line. Transcriptional patterns of the identified genes in vitro were validated by RT-PCR on the 1321 N1 cell line as well as on primary rat astrocytes. In vivo expression patterns were examined by intracerebroventricular administration of clenbuterol and/or TNF-alpha in rats. To examine the impact on the inflammatory cell content of the brain we performed extensive FACS analysis of rat brain immune cells after intracerebroventricular clenbuterol and/or TNF-alpha administration. Results: Parallel transcriptional patterns in vivo and in vitro confirmed the relevance of astrocytic beta(2)-adrenergic receptors as modulators of brain inflammatory responses. Importantly, we observed pronounced effects of beta(2)-adrenergic receptor agonists and TNF-alpha on IL-6, CXCL2, CXCL3, VCAM1, and ICAM1 expression, suggesting a role in inflammatory brain cell homeostasis. Extensive FACS-analysis of inflammatory cell content in the brain demonstrated that clenbuterol/TNF-alpha co-administration skewed the T cell population towards a double negative phenotype and induced a shift in the myeloid brain cell population towards a neutrophilic predominance. Conclusions: Our results show that astrocytic beta(2)-adrenergic receptors are potent regulators of astrocytic TNF-alpha-activated genes in vitro and in vivo, and ultimately modulate the molecular network involved in the homeostasis of inflammatory cells in the central nervous system. Astrocytic beta(2)-adrenergic receptors and their downstream signaling pathway may serve as potential targets to modulate neuroinflammatory responses

Evaluation of an Immunoturbidimetric Assay for Haemoglobin A1c on a Cobas® Mira S Analyser

Peer Reviewe

Tibial acceleration-based prediction of maximal vertical loading rate during overground running : a machine learning approach

Ground reaction forces are often used by sport scientists and clinicians to analyze the mechanical risk-factors of running related injuries or athletic performance during a running analysis. An interesting ground reaction force-derived variable to track is the maximal vertical instantaneous loading rate (VILR). This impact characteristic is traditionally derived from a fixed force platform, but wearable inertial sensors nowadays might approximate its magnitude while running outside the lab. The time-discrete axial peak tibial acceleration (APTA) has been proposed as a good surrogate that can be measured using wearable accelerometers in the field. This paper explores the hypothesis that applying machine learning to time continuous data (generated from bilateral tri-axial shin mounted accelerometers) would result in a more accurate estimation of the VILR. Therefore, the purpose of this study was to evaluate the performance of accelerometer-based predictions of the VILR with various machine learning models trained on data of 93 rearfoot runners. A subject-dependent gradient boosted regression trees (XGB) model provided the most accurate estimates (mean absolute error: 5.39 +/- 2.04 BW.s(-1), mean absolute percentage error: 6.08%). A similar subject-independent model had a mean absolute error of 12.41 +/- 7.90 BW.s(-1) (mean absolute percentage error: 11.09%). All of our models had a stronger correlation with the VILR than the APTA (p < 0.01), indicating that multiple 3D acceleration features in a learning setting showed the highest accuracy in predicting the lab-based impact loading compared to APTA

Interleukin-1 as innate mediator of T cell immunity

The three-signal paradigm tries to capture how the innate immune system instructs adaptive immune responses in three well-defined actions: (1) presentation of antigenic peptides in the context of MHC molecules, which allows for a specific T cell response; (2) T cell co-stimulation, which breaks T cell tolerance; and (3) secretion of polarizing cytokines in the priming environment, thereby specializing T cell immunity. The three-signal model provides an empirical framework for innate instruction of adaptive immunity, but mainly discusses STAT-dependent cytokines in T cell activation and differentiation, while the multi-faceted roles of type I IFNs and IL-1 cytokine superfamily members are often neglected. IL-1 alpha and IL-1 beta are pro-inflammatory cytokines, produced following damage to the host (release of DAMPs) or upon innate recognition of PAMPs. IL-1 activity on both DCs and T cells can further shape the adaptive immune response with variable outcomes. IL-1 signaling in DCs promotes their ability to induce T cell activation, but also direct action of IL-1 on both CD4(+) and CD8(+) T cells, either alone or in synergy with prototypical polarizing cytokines, influences T cell differentiation under different conditions. The activities of IL-1 form a direct bridge between innate and adaptive immunity and could therefore be clinically translatable in the context of prophylactic and therapeutic strategies to empower the formation of T cell immunity. Understanding the modalities of IL-1 activity during T cell activation thus could hold major implications for rational development of the next generation of vaccine adjuvants

On the whereabouts of SARS-CoV-2 in the human body : a systematic review

Author summary Since the beginning of 2020, SARS-CoV-2 quickly spread throughout the human population and caused a pandemic with devastating consequences at a global scale. The scientific community is challenged to find good strategies for the containment and treatment of this virus. In this context, an important step is charting the viral presence in the human body to improve diagnostics, prevention or treatment. Here, we bring together the current scientific knowledge on SARS-CoV-2 detection in the human body and body fluids. We observe that SARS-CoV-2 impacts the human body well beyond the lungs and shows a complex interplay with the human host that is not always correlated with its entry receptor (ACE2) expression levels. Many studies identified viral components (RNA, proteins) of SARS-CoV-2 in multiple organs (pharynx, trachea, lungs, blood, heart, vessels, intestines, brain, male genitals and kidneys) and body fluids (mucus, saliva, urine, cerebrospinal fluid, semen and breast milk). However, besides the lungs, researchers were only able to detect infectious virus in stool and urine in a limited set of SARS-CoV-2 patients. By combining these studies, our study provides an eagle's view on the current status of SARS-CoV-2 pathogenesis and lays the foundation for better diagnosis and treatment of COVID-19 patients. Since SARS-CoV-2 appeared in the human population, the scientific community has scrambled to gather as much information as possible to find good strategies for the containment and treatment of this pandemic virus. Here, we performed a systematic review of the current (pre)published SARS-CoV-2 literature with a focus on the evidence concerning SARS-CoV-2 distribution in human tissues and viral shedding in body fluids. In addition, this evidence is aligned with published ACE2 entry-receptor (single cell) expression data across the human body to construct a viral distribution and ACE2 receptor body map. We highlight the broad organotropism of SARS-CoV-2, as many studies identified viral components (RNA, proteins) in multiple organs, including the pharynx, trachea, lungs, blood, heart, vessels, intestines, brain, male genitals and kidneys. This also implicates the presence of viral components in various body fluids such as mucus, saliva, urine, cerebrospinal fluid, semen and breast milk. The main SARS-CoV-2 entry receptor, ACE2, is expressed at different levels in multiple tissues throughout the human body, but its expression levels do not always correspond with SARS-CoV-2 detection, indicating that there is a complex interplay between virus and host. Together, these data shed new light on the current view of SARS-CoV-2 pathogenesis and lay the foundation for better diagnosis and treatment of COVID-19 patients

Hunting for Serine 276-Phosphorylated p65

The transcription factor nuclear factor kappaB (NF-κB) is one of the central mediators of inflammatory gene expression. Several posttranslational modifications of NF-κB, regulating its transactivation ability, have been described. Especially phosphorylation of the NF-κB subunit p65 has been investigated in depth and several commercial phosphospecific antibodies, targeting selected p65 residues, are available. One of the p65 residues, that is subject to phosphorylation by protein kinase A (PKA) as well as by mitogen-stimulated kinase-1 (MSK-1), is the serine at position 276. Here, we have performed a detailed analysis of the performance of the most commonly used commercial anti-P-p65 Ser276 antibodies. Our findings indicate that at least three widely used anti-P-p65 Ser276 antibodies do not detect p65 in vivo via Western Blot, but instead crossreact with PKA-regulated proteins. As PKA is one of the main kinases responsible for phosphorylation of p65 at Ser276, this observation warrants cautious interpretation of data generated using the tested antibodies

Differential chemosensitization of P-glycoprotein overexpressing K562/Adr cells by withaferin A and Siamois polyphenols

Background: Multidrug resistance (MDR) is a major obstacle in cancer treatment and is often the result of overexpression of the drug efflux protein, P-glycoprotein (P-gp), as a consequence of hyperactivation of NF-kappa B, AP1 and Nrf2 transcription factors. In addition to effluxing chemotherapeutic drugs, P-gp also plays a specific role in blocking caspase-dependent apoptotic pathways. One feature that cytotoxic treatments of cancer have in common is activation of the transcription factor NF-kappa B, which regulates inflammation, cell survival and P-gp expression and suppresses the apoptotic potential of chemotherapeutic agents. As such, NF-kappa B inhibitors may promote apoptosis in cancer cells and could be used to overcome resistance to chemotherapeutic agents. Results: Although the natural withanolide withaferin A and polyphenol quercetin, show comparable inhibition of NF-kappa B target genes (involved in inflammation, angiogenesis, cell cycle, metastasis, anti-apoptosis and multidrug resistance) in doxorubicin-sensitive K562 and -resistant K562/Adr cells, only withaferin A can overcome attenuated caspase activation and apoptosis in K562/Adr cells, whereas quercetin-dependent caspase activation and apoptosis is delayed only. Interestingly, although withaferin A and quercetin treatments both decrease intracellular protein levels of Bcl2, Bim and P-Bad, only withaferin A decreases protein levels of cytoskeletal tubulin, concomitantly with potent PARP cleavage, caspase 3 activation and apoptosis, at least in part via a direct thiol oxidation mechanism. Conclusions: This demonstrates that different classes of natural NF kappa B inhibitors can show different chemosensitizing effects in P-gp overexpressing cancer cells with impaired caspase activation and attenuated apoptosis

How the venom from the ectoparasitoid wasp Nasonia vitripennis exhibits anti-inflammatory properties on mammalian cell lines

With more than 150,000 species, parasitoids are a large group of hymenopteran insects that inject venom into and then lay their eggs in or on other insects, eventually killing the hosts. Their venoms have evolved into different mechanisms for manipulating host immunity, physiology and behavior in such a way that enhance development of the parasitoid young. The venom from the ectoparasitoid Nasonia vitripennis inhibits the immune system in its host organism in order to protect their offspring from elimination. Since the major innate immune pathways in insects, the Toll and Imd pathways, are homologous to the NF-kappa B pathway in mammals, we were interested in whether a similar immune suppression seen in insects could be elicited in a mammalian cell system. A well characterized NF-kappa B reporter gene assay in fibrosarcoma cells showed a dose-dependent inhibition of NF-kappa B signaling caused by the venom. In line with this NF-kappa B inhibitory action, N. vitripennis venom dampened the expression of IL-6, a prototypical proinflammatory cytokine, from LPS-treated macrophages. The venom also inhibited the expression of two NF-kappa B target genes, I kappa B alpha and A20, that act in a negative feedback loop to prevent excessive NF-kappa B activity. Surprisingly, we did not detect any effect of the venom on the early events in the canonical NF-kappa B activation pathway, leading to NF-kappa B nuclear translocation, which was unaltered in venom-treated cells. The MAP kinases ERK, p38 and JNK are other crucial regulators of immune responses. We observed that venom treatment did not affect p38 and ERK activation, but induced a prolonged JNK activation. In summary, our data indicate that venom from N. vitripennis inhibits NF-kappa B signaling in mammalian cells. We identify venom-induced up regulation of the glucocorticoid receptor-regulated GILZ as a most likely molecular mediator for this inhibition

Predicting gait events from tibial acceleration in rearfoot running: a structured machine learning approach

Gait event detection of the initial contact and toe off is essential for running gait analysis, allowing the derivation of parameters such as stance time. Heuristic-based methods exist to estimate these key gait events from tibial accelerometry. However, these methods are tailored to very specific acceleration profiles, which may offer complications when dealing with larger data sets and inherent biological variability. Therefore, this paper investigates whether a structured machine learning approach can achieve a more accurate prediction of running gait event timings from tibial accelerometry. Force-based event detection acted as the criterion measure in order to assess the accuracy, repeatability and sensitivity of the predicted gait events. A heuristic method and two structured machine learning methods were employed to derive initial contact, toe off and stance time from tibial acceleration signals. Both a structured perceptron model (median absolute error of stance time estimation: 10.00 $\pm$ 8.73 ms) and a structured recurrent neural network model (median absolute error of stance time estimation: 6.50 $\pm$ 5.74 ms) significantly outperformed the existing heuristic approach (median absolute error of stance time estimation: 11.25 $\pm$ 9.52 ms) on data from 93 rearfoot runners. Thus, results indicate that a structured recurrent neural network machine learning model offers the most accurate and consistent estimation of the gait events and its derived stance time during level overground running. The machine learning methods seem less affected by intra- and inter-subject variation within the data, allowing for accurate and efficient automated data output during rearfoot overground running. Furthermore offering possibilities for real-time monitoring and biofeedback during prolonged measurements, even outside the laboratory