Nod2 and Nod2-regulated microbiota protect BALB/c mice from diet-induced obesity and metabolic dysfunction

Genetics plays a central role in susceptibility to obesity and metabolic diseases. BALB/c mice are known to be resistant to high fat diet (HFD)-induced obesity, however the genetic cause remains unknown. We report that deletion of the innate immunity antibacterial gene Nod2 abolishes this resistance, as Nod2 -/- BALB/c mice developed HFD-dependent obesity and hallmark features of metabolic syndrome. Nod2 -/- HFD mice developed hyperlipidemia, hyperglycemia, glucose intolerance, increased adiposity, and steatosis, with large lipid droplets in their hepatocytes. These changes were accompanied by increased expression of immune genes in adipose tissue and differential expression of genes for lipid metabolism, signaling, stress, transport, cell cycle, and development in both adipose tissue and liver. Nod2 -/- HFD mice exhibited changes in the composition of the gut microbiota and long-term treatment with antibiotics abolished diet-dependent weight gain in Nod2 -/- mice, but not in wild type mice. Furthermore, microbiota from Nod2 -/- HFD mice transferred sensitivity to weight gain, steatosis, and hyperglycemia to wild type germ free mice. In summary, we have identified a novel role for Nod2 in obesity and demonstrate that Nod2 and Nod2-regulated microbiota protect BALB/c mice from diet-induced obesity and metabolic dysfunction

Author Correction: Nod2 and Nod2-regulated microbiota protect BALB/c mice from diet-induced obesity and metabolic dysfunction

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper

Automated microfluidic platform for dynamic and combinatorial drug screening of tumor organoids

The use of organoids in personalized medicine is promising but high throughput platforms are needed. Here the authors develop an automated, high-throughput, microfluidic 3D organoid culture system that allows combinatorial and dynamic drug treatments and real-time analysis of organoids

Intestinal adaptation after ileal interposition surgery increases bile acid recycling and protects against obesity-related comorbidities

Surgical interposition of distal ileum into the proximal jejunum is a bariatric procedure that improves the metabolic syndrome. Changes in intestinal and hepatic physiology after ileal interposition (transposition) surgery (IIS) are not well understood. Our aim was to elucidate the adaptation of the interposed ileum, which we hypothesized, would lead to early bile acid reabsorption in the interposed ileum, thus short circuiting enterohepatic bile acid recycling to more proximal bowel segments. Rats with diet-induced obesity were randomized to IIS, with 10 cm of ileum repositioned distal to the duodenum, or sham surgery. A subgroup of sham rats was pair-fed to IIS rats. Physiological parameters were measured until 6 wk postsurgery. IIS rats ate less and lost more weight for the first 2 wk postsurgery. At study completion, body weights were not different, but IIS rats had reversed components of the metabolic syndrome. The interposed ileal segment adapted to a more jejunum-like villi length, mucosal surface area, and GATA4/ILBP mRNA. The interposed segment retained capacity for bile acid reabsorption and anorectic hormone secretion with the presence of ASBT and glucagon-like-peptide-1-positive cells in the villi. IIS rats had reduced primary bile acid levels in the proximal intestinal tract and higher primary bile acid levels in the serum, suggesting an early and efficient reabsorption of primary bile acids. IIS rats also had increased taurine and glycine-conjugated serum bile acids and reduced fecal bile acid loss. There was decreased hepatic Cyp27A1 mRNA with no changes in hepatic FXR, SHP, or NTCP expression. IIS protects against the metabolic syndrome through short-circuiting enterohepatic bile acid recycling. There is early reabsorption of primary bile acids despite selective “jejunization” of the interposed ileal segment. Changes in serum bile acids or bile acid enterohepatic recycling may mediate the metabolic benefits seen after bariatric surgery

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset