Necrotizing enterocolitis is a devastating disease of prematurity characterized by gram-negative bacteria colonization and enterocyte death. We identify a role of Toll-like receptor 4-mediated necroptosis of the intestinal epithelium as a precursor to necrotizing enterocolitis development. BACKGROUND & AIMS: Necrotizing enterocolitis (NEC) is a devastating disease of premature infants characterized by Toll-like receptor 4 (TLR4)-dependent intestinal inflammation and enterocyte death. Given that necroptosis is a proinflammatory cell death process that is linked to bacterial signaling, we investigated its potential role in NEC, and the mechanisms involved. METHODS: Human and mouse NEC intestine were analyzed for necroptosis gene expression (ie, RIPK1, RIPK3, and MLKL), and protein activation (phosphorylated RIPK3). To evaluate a potential role for necroptosis in NEC, the effects of genetic (ie, Ripk3 knockout or Mlkl knockout) or pharmacologic (ie, Nec1s) inhibition of intestinal inflammation were assessed in a mouse NEC model, and a possible upstream role of TLR4 was assessed in Tlr4-deficient mice. The NEC-protective effects of human breast milk and its constituent milk oligosaccharides on necroptosis were assessed in a NEC-in-a-dish model, in which mouse intestinal organoids were cultured as either undifferentiated or differentiated epithelium in the presence of NEC bacteria and hypoxia. RESULTS: Necroptosis was activated in the intestines of human and mouse NEC in a TLR4-dependent manner, and was upregulated specifically in differentiated epithelium of the immature ileum. Inhibition of necroptosis genetically and pharmacologically reduced intestinal-epithelial cell death and mucosal inflammation in experimental NEC, and ex vivo in the NEC-in-a-dish system. Strikingly, the addition of human breast milk, or the human milk oligosaccharide 2 fucosyllactose in the ex vivo system, reduced necroptosis and inflammation. CONCLUSIONS: Necroptosis is activated in the intestinal epithelium upon TLR4 signaling and is required for NEC development, and explains in part the protective effects of breast milk.

The data science of networks is a rapidly developing field with myriad applications. In neuroscience, the brain is commonly modeled as a connectome, a network of nodes connected by edges. While there have been thousands of papers on connectomics, the statistics of networks remains limited and poorly understood. Here, we provide an overview from the perspective of statistical network science of the kinds of models, assumptions, problems, and applications that are theoretically and empirically justified for analysis of connectome data. We hope this review spurs further development and application of statistically grounded methods in connectomics.

The data science of networks is a rapidly developing field with myriad applications. In neuroscience, the brain is commonly modeled as a connectome, a network of nodes connected by edges. While there have been thousands of papers on connectomics, the statistics of networks remains limited and poorly understood. Here, we provide an overview from the perspective of statistical network science of the kinds of models, assumptions, problems, and applications that are theoretically and empirically justified for analysis of connectome data. We hope this review spurs further development and application of statistically grounded methods in connectomics.

The graph matching problem seeks to find an alignment between the nodes of two graphs that minimizes the number of adjacency disagreements. Solving the graph matching is increasingly important due to it's applications in operations research, computer vision, neuroscience, and more. However, current stateof-the-art algorithms are inefficient in matching very large graphs, though they produce good accuracy. The main computational bottleneck of these algorithms is the linear assignment problem, which must be solved at each iteration. In this paper, we leverage the recent advances in the field of optimal transport to replace the accepted use of linear assignment algorithms. We present GOAT, a modification to the state-of-the-art graph matching approximation algorithm "FAQ" (Vogelstein, 2015), replacing its linear sum assignment step with the "Lightspeed Optimal Transport" method of Cuturi (2013). The modification provides improvements to both speed and empirical matching accuracy. The effectiveness of the approach is demonstrated in matching graphs in simulated and real data examples.