Multispecies biofilm behavior and host interaction support the association of Tannerella serpentiformis with periodontal health

The recently identified bacterium Tannerella serpentiformis is the closest phylogenetic relative of Tannerella forsythia, whose presence in oral biofilms is associated with periodontitis. Conversely, T. serpentiformis is considered health-associated. This discrepancy was investigated in a comparative study of the two Tannerella species. The biofilm behavior was analyzed upon their addition and of Porphyromonas gingivalis-each bacterium separately or in combinations-to an in vitro five-species oral model biofilm. Biofilm composition and architecture was analyzed quantitatively using real-time PCR and qualitatively by fluorescence in situ hybridization/confocal laser scanning microscopy, and by scanning electron microscopy. The presence of T. serpentiformis led to a decrease of the total cell number of biofilm bacteria, while P. gingivalis was growth-promoting. This effect was mitigated by T. serpentiformis when added to the biofilm together with P. gingivalis. Notably, T. serpentiformis outcompeted T. forsythia numbers when the two species were simultaneously added to the biofilm compared to biofilms containing T. forsythia alone. Tannerella serpentiformis appeared evenly distributed throughout the multispecies biofilm, while T. forsythia was surface-located. Adhesion and invasion assays revealed that T. serpentiformis was significantly less effective in invading human gingival epithelial cells than T. forsythia. Furthermore, compared to T. forsythia, a higher immunostimulatory potential of human gingival fibroblasts and macrophages was revealed for T. serpentiformis, based on mRNA expression levels of the inflammatory mediators interleukin 6 (IL-6), IL-8, monocyte chemoattractant protein-1 and tumor necrosis factor α, and production of the corresponding proteins. Collectively, these data support the potential of T. serpentiformis to interfere with biological processes relevant to the establishment of periodontitis

Guidelines for the use of flow cytometry and cell sorting in immunological studies (third edition)

The third edition of Flow Cytometry Guidelines provides the key aspects to consider when performing flow cytometry experiments and includes comprehensive sections describing phenotypes and functional assays of all major human and murine immune cell subsets. Notably, the Guidelines contain helpful tables highlighting phenotypes and key differences between human and murine cells. Another useful feature of this edition is the flow cytometry analysis of clinical samples with examples of flow cytometry applications in the context of autoimmune diseases, cancers as well as acute and chronic infectious diseases. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid. All sections are written and peer‐reviewed by leading flow cytometry experts and immunologists, making this edition an essential and state‐of‐the‐art handbook for basic and clinical researchers.DFG, 389687267, Kompartimentalisierung, Aufrechterhaltung und Reaktivierung humaner Gedächtnis-T-Lymphozyten aus Knochenmark und peripherem BlutDFG, 80750187, SFB 841: Leberentzündungen: Infektion, Immunregulation und KonsequenzenEC/H2020/800924/EU/International Cancer Research Fellowships - 2/iCARE-2DFG, 252623821, Die Rolle von follikulären T-Helferzellen in T-Helferzell-Differenzierung, Funktion und PlastizitätDFG, 390873048, EXC 2151: ImmunoSensation2 - the immune sensory syste

Small differences matter : How regional distinctions in educational and labour market policy account for heterogeneity in NEET rates

Labour market and education policy makers and researchers are increasingly focusing on the NEET indicator as a supplement to the youth unemployment rate. Analyses of factors influencing NEET have concentrated primarily on individual characteristics such as gender and migration background on one hand, and on macro-level factors of nations such as economic growth and minimum wage regulations on the other. However, nations are not homogenous, especially when a country is divided into several federal states, as is the case with Austria. This article aims to analyse regional differences within Austria. In order to explain the differences, we define a multilevel model that contains four contextual factors: the importance of upper secondary education; the importance of dual education; vacant jobs; and expenditures for active labour market policy. Because the institutional level addresses different age groups, the analysis was split into two age groups: 15–19 and 20–24 years. The results have shown that, besides the social structure of the population, contextual factors like the upper secondary education, the dual education, vacant jobs, and expenditures for active labour market policy are also relevant for explaining regional differences in the NEET rates. But one main insight was that the impact of the contextual factors varies between different social groups