Genetic and environmental control of host-gut microbiota interactions

Genetics provides a potentially powerful approach to dissect host-gut microbiota interactions. Toward this end, we profiled gut microbiota using 16s rRNA gene sequencing in a panel of 110 diverse inbred strains of mice. This panel has previously been studied for a wide range of metabolic traits and can be used for high-resolution association mapping. Using a SNP-based approach with a linear mixed model, we estimated the heritability of microbiota composition. We conclude that, in a controlled environment, the genetic background accounts for a substantial fraction of abundance of most common microbiota. The mice were previously studied for response to a high-fat, high-sucrose diet, and we hypothesized that the dietary response was determined in part by gut microbiota composition. We tested this using a cross-fostering strategy in which a strain showing a modest response, SWR, was seeded with microbiota from a strain showing a strong response, A×B19. Consistent with a role of microbiota in dietary response, the cross-fostered SWR pups exhibited a significantly increased response in weight gain. To examine specific microbiota contributing to the response, we identified various genera whose abundance correlated with dietary response. Among these, we chose Akkermansia muciniphila, a common anaerobe previously associated with metabolic effects. When administered to strain A×B19 by gavage, the dietary response was significantly blunted for obesity, plasma lipids, and insulin resistance. In an effort to further understand host-microbiota interactions, we mapped loci controlling microbiota composition and prioritized candidate genes. Our publicly available data provide a resource for future studies.Studies carried out over the last decade have revealed that gut microbiota contribute to a variety of common disorders, including obesity and diabetes (Musso et al. 2011), colitis (Devkota et al. 2012), atherosclerosis (Wang et al. 2011), rheumatoid arthritis (Vaahtovuo et al. 2008), and cancer (Yoshimoto et al. 2013). The evidence for metabolic interactions is particularly strong, as a large body of data now supports the conclusion that gut microbiota influence the energy harvest from dietary components, particularly complex carbohydrates, and that metabolites such as the short-chain fatty acids produced by gut bacteria can perturb metabolic traits, including adiposity and insulin resistance (Turnbaugh et al. 2006, 2009; Backhed et al. 2007; Wen et al. 2008; Ridaura et al. 2013). Gut microbiota communities are assembled each generation, influenced by maternal seeding, environmental factors, host genetics, and age, resulting in substantial variations in composition among individuals in human populations (Eckburg et al. 2005; Costello et al. 2009; Human Microbiome Project Consortium 2012; Goodrich et al. 2014). Most experimental studies of host-gut microbiota interactions have employed large perturbations, such as comparisons of germ-free versus conventional mice, and the significance of common variations in gut microbiota composition for disease susceptibility is still poorly understood. Furthermore, while studies with germ-free mice have clearly implicated microbiota in clinically relevant traits, it has proven difficult to identify the responsible taxa of bacteria.We now report a population-based analysis of host-gut microbiota interactions in the mouse. One of the issues we explore is the role of host genetics. Although some evidence is consistent with significant heritability of gut microbiota composition, the extent to which the host controls microbiota composition under controlled environmental conditions is unclear. We also examined the role of common variations in gut microbiota in metabolic traits such as obesity and insulin resistance and mapped loci contributing to the abundance of certain microbiota. We performed our study using a resource termed the Hybrid Mouse Diversity Panel (HMDP), consisting of about 100 inbred strains of mice that have been either sequenced or subjected to high-density genotyping (Bennett et al. 2010). The resource has several advantages for genetic analysis as compared to traditional genetic crosses. First, it allows high-resolution mapping by association rather than linkage analysis, and it has now been used for the identification of a number of novel genes underlying complex traits (Farber et al. 2011; Lavinsky et al. 2015; Parks et al. 2015; Rau et al. 2015). Second, since the strains are permanent, the data from separate studies can be integrated, allowing the development of large, publicly available databases of physiological and molecular traits relevant to a variety of clinical disorders (systems.genetics.ucla.edu and phenome.jax.org). Third, the panel is ideal for examining gene-by-environment interactions, since it is possible to examine individuals of a particular genotype under a variety of conditions (Orozco et al. 2012; Parks et al. 2013).     

Endogenous aggregates of amyloidogenic cystatin C variant are removed by THP-1 cells in vitro and induce differentiation and a proinflammatory response

A mutation in the human cystatin C gene leads to familial cerebral amyloid angiopathy. This disease is known as “hereditary cerebral hemorrhage with amyloidosis-Icelandic type” or “hereditary cystatin C amyloid angiopathy.” The mutant cystatin C protein forms aggregates and amyloid, within the central nervous system almost exclusively in connection with the vascular system. It was not known whether immune cells could remove mutant cystatin C protein aggregates. Ex vivo mutant cystatin C protein aggregates, both in solution and dried onto a glass surface, induced adhesion to the substrate, differentiated the THP-1 monocyte cell line and led to a proinflammatory response. Aggregates were also taken up by both THP-1 cells and THP-1 derived macrophages. These are the same responses induced by other amyloidogenic protein species, such as amyloid β protein and amylin, supporting the model of all amyloidogenic proteins being toxic due to common structural motifs. Proinflammatory response induced by the ex vivo mutant cystatin C protein aggregates suggests that vascular inflammation plays an important role in hereditary cerebral hemorrhage with amyloidosis-Icelandic type. Ex vivo protein aggregates of cystatin C might better model cellular behavior than in vitro-generated aggregates or supplement in vitro material.     

Granzyme D Is a Novel Murine Mast Cell Protease That Is Highly Induced by Multiple Pathways of Mast Cell Activation

Granzymes are serine proteases known mostly for their role in the induction of apoptosis. Granzymes A and B have been extensively studied, but relatively little is known about granzymes C to G and K to M. T cells, lymphohematopoietic stromal cells, and granulated metrial gland cells express granzyme D, but the function of granzyme D is unknown. Here we show that granzyme D is expressed by murine mast cells and that its level of expression correlates positively with the extent of mast cell maturation. Coculture of mast cells with live, Gram-positive bacteria caused a profound, Toll-like receptor 2 (TLR2)-dependent induction of granzyme D expression. Granzyme D expression was also induced by isolated bacterial cell wall components, including lipopolysaccharide (LPS) and peptidoglycan, and by stem cell factor, IgE receptor cross-linking, and calcium ionophore stimulation. Granzyme D was released into the medium in response to mast cell activation. Granzyme D induction was dependent on protein kinase C and nuclear factor of activated T cells (NFAT). Together, these findings identify granzyme D as a novel murine mast cell protease and implicate granzyme D in settings where mast cells are activated, such as bacterial infection and allergy.     

To keep or reject,that is the question - A survey on radiologists and radiographers’ assessments of plain radiography images

IntroductionThis study aimed to survey radiographers and radiologists' assessment of plain radiographs to identify the imaging clinicians’ differences in acceptance of image quality.MethodAn online, questionnaire was distributed among radiographers (n = 116) and radiologists (n = 76) in a hospital trust in Norway, including 30 clinical cases (one image and a short referral text) that were divided into 3 categories; keep, could keep and reject, based on European guidelines. When rejecting, the respondents identified the main reason by ticking a list (positioning, collimation, centering, artifact or exposure error). Group differences were explored using 2-tailed chi-squared test. Inter-subjectivity was measured using Cohen's kappa for multi-rater sample.ResultsIn total, 36% of the radiographers (n = 42) and 14% of the radiologists (n = 14) responded to the survey. Total response rate was 30% (56/192). Analysis showed significant difference between radiographers and radiologists in the categories of Reject (χ² = 6.3, df = 1, p = 0.01), and Could keep (χ² = 6.3, df = 1, p = 0.01), identifying radiologists as keeping more images compared to radiographers. Agreement among radiographers (Cohen's κ: 0,39; 95% CI: 0.30–0.48; p < 0.001) and radiologists (Cohen's κ: 0,23; 95% CI: 0.09–0.37; p < 0.001) respectively, is fair. The most common reason for rejecting an image is suboptimal positioning. Suboptimal collimation constituted 15% of the rejected images among radiographers, compared to 5% among radiologists. Centering, artifacts and exposure error showed quite similar rates as reasons for rejection.ConclusionRadiographers and radiologists seem to agree on the assessment of good quality images, however, radiographers seem more reluctant to accept images of lower quality than radiologists.Implications for practiceFurther research on reasons for differences in image quality assessment between radiographers and radiologists is needed. This could enable reduction in reject rates and increase image quality in conventional X-ray examinations.