The gut microbiota contributes to maintaining human health and regulating immune responses. Severe COVID-19 illness is associated with a dysregulated pro-inflammatory immune response. The effect of SARS-CoV-2 on altering the gut microbiome and the relevance of the gut microbiome on COVID-19 severity needs to be clarified. In this prospective study, we analyzed the gut microbiome of 212 patients of a tertiary care hospital (117 patients infected with SARS-CoV-2 and 95 SARS-CoV-2 negative patients) using 16S rRNA gene sequencing of the V3-V4 region. Inflammatory markers and immune cells were quantified from blood. The gut microbiome in SARS-CoV-2 infected patients was characterized by a lower bacterial richness and distinct differences in the gut microbiome composition, including an enrichment of the phyla Proteobacteria and Bacteroidetes and a decrease of Actinobacteria compared to SARS-CoV-2 negative patients. The relative abundance of several genera including Bifidobacterium, Streptococcus and Collinsella was lower in SARS-CoV-2 positive patients while the abundance of Bacteroides and Enterobacteriaceae was increased. Higher pro-inflammatory blood markers and a lower CD8+ T cell number characterized patients with severe COVID-19 illness. The gut microbiome of patients with severe/critical COVID-19 exhibited a lower abundance of butyrate-producing genera Faecalibacterium and Roseburia and a reduction in the connectivity of a distinct network of anti-inflammatory genera that was observed in patients with mild COVID-19 illness and in SARS-CoV-2 negative patients. Dysbiosis of the gut microbiome associated with a pro-inflammatory signature may contribute to the hyperinflammatory immune response characterizing severe COVID-19 illness.

Purpose: Merkel cell carcinoma (MCC) is an aggressive neuroendocrine skin cancer, which can be effectively controlled by immunotherapy with PD-1/PD-L1 checkpoint inhibitors. However, a significant proportion of patients are characterized by primary therapy resistance. Predictive biomarkers for response to immunotherapy are lacking.Experimental Design: We applied Bayesian inference analyses on 41 patients with MCC testing various clinical and biomolecular characteristics to predict treatment response. Further, we performed a comprehensive analysis of tumor tissue-based immunologic parameters including multiplexed immunofluorescence for T-cell activation and differentiation markers, expression of immune-related genes and T-cell receptor (TCR) repertoire analyses in 18 patients, seven objective responders, and 11 nonresponders.Results: Bayesian inference analyses demonstrated that among currently discussed biomarkers only unimpaired overall perfor-mance status and absence of immunosuppression were associated with response to therapy. However, in responders, a predominance of central memory T cells and expression of genes associated with lymphocyte attraction and activation was evident. In addition, TCR repertoire usage of tumor-infiltrating lymphocytes (TILs) demonstrated low T-cell clonality, but high TCR diversity in responding patients. In nonresponders, terminally differentiated effector T cells with a constrained TCR repertoire prevailed. Sequential analyses of tumor tissue obtained during immunotherapy revealed a more pronounced and diverse clonal expansion of TILs in responders indicating an impaired proliferative capacity among TILs of nonresponders upon checkpoint blockade.Conclusions: Our explorative study identified new tumor tissue-based molecular characteristics associated with response to anti-PD-1/PD-L1 therapy in MCC. These observations warrant further investigations in larger patient cohorts to confirm their potential value as predictive markers.

We introduce an Interaction- and Trade-off-based Eco-Evolutionary Model (ITEEM), in which species are competing in a well-mixed system, and their evolution in interaction trait space is subject to a life-history trade-off between replication rate and competitive ability. We demonstrate that the shape of the trade-off has a fundamental impact on eco-evolutionary dynamics, as it imposes four phases of diversity, including a sharp phase transition. Despite its minimalism, ITEEM produces a remarkable range of patterns of eco-evolutionary dynamics that are observed in experimental and natural systems. Most notably we find self-organization towards structured communities with high and sustained diversity, in which competing species form interaction cycles similar to rock-paper-scissors games.

Community structures of gut microbiota rather than individual species might facilitate risk assessment of CHD in HIV-infected individuals. Sexual behaviour appears as an important factor affecting gut microbiota beta diversity and should be considered in future studies.

The community, the assemblage of organisms co-existing in a given space and time, has the potential to become one of the unifying concepts of biology, especially with the advent of high-throughput sequencing experiments that reveal genetic diversity exhaustively. In this spirit we show that a tool from community ecology, the Rank Abundance Distribution (RAD), can be turned by the new MaxRank normalization method into a generic, expressive descriptor for quantitative comparison of communities in many areas of biology. To illustrate the versatility of the method, we analyze RADs from various generalized communities, i.e. assemblages of genetically diverse cells or organisms, including human B cells, gut microbiomes under antibiotic treatment and of different ages and countries of origin, and other human and environmental microbial communities. We show that normalized RADs enable novel quantitative approaches that help to understand structures and dynamics of complex generalized communities.