Distinct microbial communities colonize tonsillar squamous cell carcinoma

Squamous cell carcinoma of the tonsil is one of the most frequent cancers of the oropharynx. The escalating rate of tonsil cancer during the last decades is associated with the increase of high risk-human papilloma virus (HR-HPV) infections. While the microbiome in oropharyngeal malignant diseases has been characterized to some extent, the microbial colonization of HR-HPV-associated tonsil cancer remains largely unknown. Using 16S rRNA gene amplicon sequencing, we have characterized the microbiome of human palatine tonsil crypts in patients suffering from HR-HPV-associated tonsil cancer in comparison to a control cohort of adult sleep apnea patients. We found an increased abundance of the phyla Firmicutes and Actinobacteria in tumor patients, whereas the abundance of Spirochetes and Synergistetes was significantly higher in the control cohort. Furthermore, the accumulation of several genera such as Veillonella, Streptococcus and Prevotella_7 in tonsillar crypts was associated with tonsil cancer. In contrast, Fusobacterium, Prevotella and Treponema_2 were enriched in sleep apnea patients. Machine learning-based bacterial species analysis indicated that a particular bacterial composition in tonsillar crypts is tumor-predictive. Species-specific PCR-based validation in extended patient cohorts confirmed that differential abundance of Filifactor alocis and Prevotella melaninogenica is a distinct trait of tonsil cancer. This study shows that tonsil cancer patients harbor a characteristic microbiome in the crypt environment that differs from the microbiome of sleep apnea patients on all phylogenetic levels. Moreover, our analysis indicates that profiling of microbial communities in distinct tonsillar niches provides microbiome-based avenues for the diagnosis of tonsil cancer

Topological Small-World Organization of the Fibroblastic Reticular Cell Network Determines Lymph Node Functionality.

Fibroblastic reticular cells (FRCs) form the cellular scaffold of lymph nodes (LNs) and establish distinct microenvironmental niches to provide key molecules that drive innate and adaptive immune responses and control immune regulatory processes. Here, we have used a graph theory-based systems biology approach to determine topological properties and robustness of the LN FRC network in mice. We found that the FRC network exhibits an imprinted small-world topology that is fully regenerated within 4 wk after complete FRC ablation. Moreover, in silico perturbation analysis and in vivo validation revealed that LNs can tolerate a loss of approximately 50% of their FRCs without substantial impairment of immune cell recruitment, intranodal T cell migration, and dendritic cell-mediated activation of antiviral CD8+ T cells. Overall, our study reveals the high topological robustness of the FRC network and the critical role of the network integrity for the activation of adaptive immune responses

B cell homeostasis and follicle confines are governed by fibroblastic reticular cells

Fibroblastic reticular cells (FRCs) are known to inhabit T cell-rich areas of lymphoid organs where they function to coordinate T cell and dendritic cell interactions. However, in vivo manipulation of FRCs has been limited by a dearth of genetic tools targeting this lineage. Here, using a mouse model to conditionally ablate FRCs, we demonstrate their indispensable role in anti-viral T cell responses. Unexpectedly, FRC loss also attenuated humoral immunity due to impaired B cell viability and follicular organization. Follicle-resident FRCs established a favorable niche for B lymphocytes via production of the cytokine BAFF. Thus, our study indicates that adaptive immunity requires an intact FRC network and illuminates a subset of FRCs that controls B cell homeostasis and follicle identity

Changes in FRC morphology following diphtheria toxin (DT)-mediated ablation.

<p>(A) Three-dimensional single-cell reconstruction of the T cell zone FRC network in <i>Ccl19</i>^{<i>eyfp/idtr</i>} mice at indicated time points after two intraperitoneal (IP) injections of 8 ng/g DT or phosphate-buffered saline (PBS)-treated controls. Scale bars represent 30 µm. (B) Global morphological analysis of the total FRC network volume from the 3-D-reconstructed EYFP channel. (C–G) Single-cell analysis of FRC surface area (C), volume (D), sphericity (E), minimal distance between FRCs (F), and connected protrusions per FRC (G). Each dot represents a measurement for a single FRC. Data represent mean ± standard deviation (SD) (B–F) and median ± interquartile range (IQR) (G) for 3–5 mice per group. * <i>p</i> < 0.05, ** <i>p</i> < 0.01, *** <i>p</i> < 0.001 (one-way ANOVA with Tukey’s post-test [B–F] or Kruskal-Wallis test with Dunn’s post-test [G]). ns, not significant.</p

Graph theory-based analysis of the FRC network topological robustness.

<p>(A) In silico perturbation analysis of a representative FRC network from PBS-treated control mice by random node removal for one simulation. Each image denotes the FRC network in a real coordinate system of the LN T cell zone at indicated fractions of nodes randomly removed. The number of nodes remaining and the starting number of nodes are indicated in the top right of each image. Green nodes represent the largest connected cluster, and blue nodes represent fragmented clusters. See <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002515#pbio.1002515.s008" target="_blank">S2 Video</a> for the full simulation. (B) Average shortest path length versus fraction of nodes removed. The dashed line represents fraction of nodes removed for the maximal value of average shortest path length, i.e., the network threshold point. (C) Relative size of the largest cluster compared to the size of the starting network at 0% versus fraction of nodes removed. The indicated value in the top right denotes estimated network robustness R. The dashed line represents the minimal damage line. Data represent mean ± SD over 1,000 simulations of random node removals for a representative FRC network (<i>n</i> = 6 mice from two independent experiments). (D) Network robustness R values for FRC networks at indicated doses of DT. Data represent mean ± SD for 3–6 mice per group from two independent experiments. * <i>p</i> < 0.05, ** <i>p</i> < 0.01, *** <i>p</i> < 0.001 (one-way ANOVA with Tukey’s post-test). ns, not significant; na, not applicable.</p

Assessing the topology of the T cell zone FRC network.

<p>(A) Overview 2-D image of an inguinal LN section from a naive adult <i>Ccl19</i>^<i>eyfp</i> mouse stained with antibodies against the indicated markers. Rectangles indicate representative T cell zones acquired with high-resolution confocal microscopy. (B) Representative 3-D Z-stack indicating the T cell zone FRCs (left panel), merged with FRC network (middle panel) and the network representation (right panel) with nodes (FRCs) and edges (physical connections). Size of T cell zone image: 304 x 304 x 32 μm. (C) Zoom-in area of single FRCs from (B, left panel) with signals for EYFP, PDPN, merged, and network representation, respectively. (D) Representative FRC network from (B, right panel). The equivalent random network was constructed using the Erdos-Renyi model, and the regular ring lattice network was constructed with eight edges for every node (FRC network median). Lattice and random networks are shown in Kamada-Kawai representation, while the FRC network is arranged in the real coordinate system of the LN T cell zone. N denotes the number of nodes, and E denotes the number of edges for each network. Small-world parameters σ and ω are shown below. The color legend represents number of edges per node. Data are representative of six mice from two independent experiments. Scale bars represent 300 μm (A), 30 μm (B, D), and 10 μm (C).</p

Impairment of LN functionality following FRC ablation.

<p>LN cellularity as determined by flow cytometry with total numbers of CD45⁺ hematopoietic cells (A), CD8⁺ T cells (B), and CD11c⁺ DCs (C) in <i>Ccl19</i>^<i>idtr</i> mice injected twice IP with the indicated doses of DT. (D) Correlation between CD45⁺ cells and FRCs remaining in the LN for indicated doses of DT with Pearson correlation coefficient r² = 0.9448, <i>p</i> = 0.00117 (Fisher’s F test). (E–G) Two-photon microscopy analysis of adoptively transferred CD8⁺ T cells into <i>Ccl19</i>^<i>idtr</i> mice injected IP with indicated doses of DT. The migration parameters analyzed include average cell speed (E), cell arrest coefficient (F), and motility coefficient (G). (H) Total numbers of transferred TCR-transgenic Thy1.1⁺CD8⁺ T cells in <i>Ccl19</i>^<i>idtr</i> LNs at indicated doses of DT. (I) Flow cytometric analysis of CD8⁺ T cell activation in <i>Ccl19</i>^<i>idtr</i> LNs on day 3 post immunization with DC-targeting viral particles. Numbers indicate mean percentage ± standard error of the mean (SEM) of proliferating Thy1.1⁺ cells of the whole Thy1.1⁺ population. Indicated <i>p</i>-values represent comparison to the 0 ng/g group. Controls indicate PBS-treated mice without viral particles. Representative experiment for 3–6 mice per group from three independent experiments. Data represent mean ± SEM for 3–20 mice per group from three independent experiments (A–D, H). Data represent mean ± SD (E–F) or median ± range (G) for 5–10 datasets from 2–3 mice per group from two independent experiments. Plus “+” indicates mean. * <i>p</i> < 0.05, ** <i>p</i> < 0.01, *** <i>p</i> < 0.001 (one-way ANOVA with Tukey’s post-test [A–C, H–I] and Benferroni’s post-test [G] or Kruskal-Wallis test with Dunn’s post-test [E–F]). ns, not significant.</p

B cell zone reticular cell microenvironments shape CXCL13 gradient formation

Through the formation of concentration gradients, morphogens drive graded responses to extracellular signals, thereby fine-tuning cell behaviors in complex tissues. Here we show that the chemokine CXCL13 forms both soluble and immobilized gradients. Specifically, CXCL13+ follicular reticular cells form a small-world network of guidance structures, with computer simulations and optimization analysis predicting that immobilized gradients created by this network promote B cell trafficking. Consistent with this prediction, imaging analysis show that CXCL13 binds to extracellular matrix components in situ, constraining its diffusion. CXCL13 solubilization requires the protease cathepsin B that cleaves CXCL13 into a stable product. Mice lacking cathepsin B display aberrant follicular architecture, a phenotype associated with effective B cell homing to but not within lymph nodes. Our data thus suggest that reticular cells of the B cell zone generate microenvironments that shape both immobilized and soluble CXCL13 gradients