Research ArticleEndocrinologyGenetics Free access | 10.1172/JCI88648 Lisa C. Burnett,1,2,3 Charles A. LeDuc,2,3,4 Carlos R. Sulsona,5 Daniel Paull,6 Richard Rausch,2,3 Sanaa Eddiry,7 Jayne F. Martin Carli,2,3,8 Michael V. Morabito,2,3 Alicja A. Skowronski,1,2,3 Gabriela Hubner,9 Matthew Zimmer,6 Liheng Wang,2,3 Robert Day,10 Brynn Levy,11 Ilene Fennoy,12 Beatrice Dubern,13 Christine Poitou,13 Karine Clement,13 Merlin G. Butler,14 Michael Rosenbaum,2,3 Jean Pierre Salles,7,15 Maithe Tauber,7,15,16 Daniel J. Driscoll,5,17 Dieter Egli,2,3,6 and Rudolph L. Leibel2,3,4 1Institute of Human Nutrition, 2Department of Pediatrics, Division of Molecular Genetics, and 3Naomi Berrie Diabetes Center, Columbia University, New York, New York, USA. 4New York Obesity Research Center, New York, New York, USA. 5Department of Pediatrics, Division of Genetics and Metabolism, University of Florida College of Medicine Gainesville, Florida, USA. 6The New York Stem Cell Foundation Research Institute, New York, New York, USA. 7Centre de Physiopathologie de Toulouse-Purpan, Université de Toulouse, CNRS UMR 5282, INSERM UMR 1043, Université Paul Sabatier, Toulouse, France. 8Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York, USA. 9Packer Collegiate Institute, New York, New York, USA. 10Institut de pharmacologie de Sherbrooke, Department of Surgery, Division of Urology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Quebec, Canada. 11Department of Pathology and Cell Biology, Columbia University, New York, New York, USA. 12Department of Pediatrics, Division of Pediatric Diabetes, Endocrinology and Metabolism, Columbia University, New York, New York, USA. 13Institute of Cardiometabolism and Nutrition, Assistance Publique Hôpitaux de Paris, Sorbonne University, University Pierre et Marie-Curie, INSERM UMRS 1166, Paris, France. 14Department of Psychiatry and Behavioral Sciences, Division of Research and Genetics, Kansas University Medical Center, Kansas City, Kansas, USA. 15Unité d’Endocrinologie, Hôpital des Enfants, and 16Centre de Référence du Syndrome de Prader-Willi, CHU Toulouse, Toulouse, France. 17Center for Epigenetics, University of Florida College of Medicine, Gainesville, Florida, USA. Address correspondence to: Lisa C. Burnett or Rudolph L. Leibel, 1150 St. Nicholas Avenue Russ Berrie Pavilion Room 620 New York, NY 10033; Phone: 212 851 5315; E-mail: lmc2200@cumc.columbia.edu (L.C. Burnett); rl2332@cumc.columbia.edu (R.L. Leibel). Find articles by Burnett, L. in: JCI | PubMed | Google Scholar 1Institute of Human Nutrition, 2Department of Pediatrics, Division of Molecular Genetics, and 3Naomi Berrie Diabetes Center, Columbia University, New York, New York, USA. 4New York Obesity Research Center, New York, New York, USA. 5Department of Pediatrics, Division of Genetics and Metabolism, University of Florida College of Medicine Gainesville, Florida, USA. 6The New York Stem Cell

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Research Update Jan. 9, 2017 Three recent studies have shown how dietary and other environmental exposures, including those that shape the internal environment created by gut microbes, are critically important during the first few years of life, with implications for a lifetime of good health. These exposures include not only the diet of the mother and child, but also other experiences that have a large impact on the bacterial populations of a child’s gut, such as antibiotic treatment and delivery by vaginal or cesarean modes. More and more, the gut microbial community is being appreciated for its effects on human health, and the first 3 years of life is an important period for maturation of this gut microbial community. For example, by training the developing immune system, gut microbes are thought to play a possible role in guarding against autoimmune diseases such as type 1 diabetes and inflammatory bowel disease, as well as other immune-related diseases, including asthma and allergies. Early disturbances in the gut microbial community from such factors as antibiotics or cesarean delivery have also been linked to an increased risk for metabolic disorders, such as obesity. Studies by three research groups have delved into how great an impact these early exposures can have on infants, potentially affecting their future health. As part of the Healthy Start Study, researchers studied over 1,000 pairs of mothers and infants from multiple ethnic backgrounds to see how different types of foods eaten during pregnancy might affect infant body fat. The mothers were recruited during pregnancy. The researchers collected blood samples and information from the mothers on such subjects as physical activity and diet. Throughout pregnancy, participating mothers also completed several 24-hour dietary recalls online to provide a more complete picture of their diets. After delivery, information was collected in the hospital on the mothers and babies, including measurements of the infants’ length, weight, and skin-fold thickness. The researchers also estimated the infants’ body composition, including fat mass and fat-free mass. The mothers’ diet quality was measured using a scoring system based on the 2010 Dietary Guidelines for Americans. The researchers found that consuming a lower-quality diet (e.g., more fat and sodium, and fewer fruits and vegetables) during pregnancy was associated with a higher percent of fat mass in the newborns, regardless of how much the women had weighed before pregnancy. The researchers plan to continue studying these infants to figure out what effect a larger fat mass at birth has on the risk of developing obesity in childhood and later in life. This study highlights a potential way to improve the health of newborns-eating more healthfully during pregnancy. Another research group followed the gut microbial development of 43 U.S. children during their first 2 years using genetic techniques to characterize the evolving community of

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1. Krol, J., Loedige, I. & Filipowicz, W. The widespread regulation of microRNA biogenesis, function and decay. Nat. Rev. Genet. 11, 597-610 (2010) 2. Sun, L. et al. Mir193b-365 is essential for brown fat differentiation. Nat. Cell Biol. 13, 958-965 (2011) 3. Trajkovski, M. et al. MicroRNAs 103 and 107 regulate insulin sensitivity. Nature 474, 649-653 (2011) 4. Bartel, D. P. MicroRNAs: target recognition and regulatory functions. Cell 136, 215-233 (2009) 5. Ameres, S. L. & Zamore, P. D. Diversifying microRNA sequence and function. Nat. Rev. Mol. Cell Biol. 14, 475-488 (2013) 6. Arroyo, J. D. et al. Argonaute2 complexes carry a population of circulating microRNAs independent of vesicles in human plasma. Proc. Natl Acad. Sci. USA 108, 5003-5008 (2011) 7. Thery, C., Amigorena, S., Raposo, G. & Clayton, A. Curr Protoc Cell Biol Chapter 3, Unit 3 22 (2006) 8. György, B. et al. Membrane vesicles, current state-of-the-art: emerging role of extracellular vesicles. Cell. Mol. Life Sci. 68, 2667-2688 (2011) 9. Hata, A. & Lieberman, J. Dysregulation of microRNA biogenesis and gene silencing in cancer. Sci. Signal. 8, re3 (2015) 10. Dumortier, O., Hinault, C. & Van Obberghen, E. MicroRNAs and metabolism crosstalk in energy homeostasis. Cell Metab. 18, 312-324 (2013) 11. Arner, E. et al. Adipose tissue microRNAs as regulators of CCL2 production in human obesity. Diabetes 61, 1986-1993 (2012) 12. Capobianco, V. et al. miRNA and protein expression profiles of visceral adipose tissue reveal miR-141/YWHAG and miR-520e/RAB11A as two potential miRNA/protein target pairs associated with severe obesity. J. Proteome Res. 11, 3358-3369 (2012) 13. Caroli, A., Cardillo, M. T., Galea, R. & Biasucci, L. M. Potential therapeutic role of microRNAs in ischemic heart disease. J. Cardiol. 61, 315-320 (2013) 14. Guay, C., Roggli, E., Nesca, V., Jacovetti, C. & Regazzi, R. Diabetes mellitus, a microRNA-related disease? Transl. Res. 157, 253-264 (2011) 15. Mori, M. A. et al. Role of microRNA processing in adipose tissue in stress defense and longevity. Cell Metab. 16, 336-347 (2012) 16. Mori, M. A. et al. Altered miRNA processing disrupts brown/white adipocyte determination and associates with lipodystrophy. J. Clin. Invest. 124, 3339-3351 (2014) 17. Skog, J. et al. Glioblastoma microvesicles transport RNA and proteins that promote tumour growth and provide diagnostic biomarkers. Nat. Cell Biol. 10, 1470-1476 (2008) 18. Taylor, D. D., Zacharias, W. & Gercel-Taylor, C. Exosome isolation for proteomic analyses and RNA profiling. Methods Mol. Biol. 728, 235-246 (2011) 19. Escola, J. M. et al. Selective enrichment of tetraspan proteins on the internal vesicles of multivesicular endosomes and on exosomes secreted by human B-lymphocytes. J. Biol. Chem. 273, 20121-20127 (1998) 20. Février, B. & Raposo, G. Exosomes: endosomal-derived vesicles shipping extracellular messages. Curr. Opin. Cell Biol. 16, 415-421 (2004) 21. Ortega, F. J. et al. MiRNA expression profile of human

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Gastric band Surgery In France Intensive blood glucose management for those with type 1 diabetes preserves heart health for decades Research Update Nov. 14, 2016 A long-term NIDDK study reports that keeping blood glucose (sugar) as close to normal as possible for an average of 6.5 years early in the course of type 1 diabetes reduces cardiovascular (heart) disease for up to 30 years. The landmark Diabetes Control and Complications Trial (DCCT) began in 1983. The DCCT randomly assigned half its participants to an intensive blood glucose management regimen designed to keep blood glucose levels as close to normal as safely possible, and half to the less-intensive conventional treatment at the time. When DCCT ended in 1993, it was clear that intensive management had significantly reduced eye, nerve, and kidney complications, but at that time the participants were too young to determine their rates of cardiovascular disease. All DCCT participants were taught the intensive management regimen and invited to join the Epidemiology of Diabetes Interventions and Complications (EDIC) study. EDIC continued to monitor DCCT/EDIC participants’ health, and overall blood glucose management has since been similar in both DCCT treatment groups. To study the long-term effects of the different treatments tested in the DCCT, researchers examined differences in cardiovascular problems, which can take many years to develop, between the former intensive and conventional treatment groups. After an impressive average 30-year follow-up, DCCT/EDIC researchers found that those who practiced intensive blood glucose management during the DCCT still had significantly reduced cardiovascular disease compared to those who did not, despite having similar blood glucose management for 20 years after the DCCT ended. Compared to the former conventional treatment group, the former intensive management group had a 30 percent reduced incidence of cardiovascular disease and 32 percent fewer major cardiovascular events (such as non-fatal heart attack, stroke, or death from cardiovascular disease), after 30 years of follow-up. These results were similar for both men and women who participated in the studies. However, the beneficial effects of intensively managing blood glucose during the DCCT appeared to be wearing off over time. Previously, after 20 years of follow-up, DCCT/EDIC researchers reported that the former intensive treatment group had a 42 percent reduced risk of cardiovascular disease compared to the former conventional treatment group. After 30 years of follow-up, that number had fallen to 30 percent. Even with this reduction in protection, these new data show that a finite period of near-normal blood glucose management early in the course of type 1 diabetes can have substantial beneficial effects on cardiovascular health for up to 30 years. Overall, this finding adds to DCCT/EDIC’s decades of evidence demonstrating how people with type 1 diabetes can dramatically reduce their

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Gastric band Surgery In France Effects of a Gut Pathobiont in a Gnotobiotic Mouse Model of Childhood Undernutrition. Uncultured fecal gut microbiota from an underweight donor confers weight loss on gnotobiotic mice We used anthropometric data collected from members of a birth cohort study (14) of 100 children living in Mirpur thana in Dhaka, Bangladesh, to define whether they were healthy or undernourished (table S1). Those with height-for-age z scores (HAZ) greater than or equal to −2 were classified as “healthy,” whereas those with scores less than or equal to −3 were deemed severely stunted. At 18 months, 30 and 25 children satisfied these criteria for healthy and severely stunted, respectively, whereas at 24 months, 27 and 20 children received these designations; the remaining children were classified as moderately stunted (HAZ between −2 and −3). A PCR-based screen for ETBF targeting all three fragilysin gene subtypes (14) was performed using DNA isolated from fecal samples that had been collected from these children at 18 and 24 months of age. The results revealed that ETBF was variably present between individuals and within a given individual over time, with a total of 25 of 24-month-old children having a positive test (table S1). In this small cohort, ETBF carriage was not significantly correlated with indices of linear or ponderal growth . We combined anthropometric and PCR data to select fecal samples collected at 24 months from two children: (i) a healthy individual (child ID 7114 in table S1) with a HAZ score of −0.71, a WAZ score of −1.49, and a WHZ score of −1.62 who was ETBF-negative at the two time points tested, and (ii) a severely stunted and moderately underweight individual (child ID 7004) with a HAZ score of −3.02, a WAZ score of −2.51, and a WHZ score of −1.34 who was ETBF-positive at both time points. Of the 35 individuals with a positive ETBF test at either time point, only this stunted/underweight child was positive at both 18 and 24 months of age. Fecal samples obtained from members of this singleton birth cohort were screened for parasites using microscopic methods (5); neither of the two donors tested positive (see Materials and Methods for details). To define the effects of diet and these two childrens’ gut microbiota on host biology, we generated three representative versions (embodiments) of the diets consumed by the population represented by the donors. To do so, we determined the relative daily caloric contributions of various selected ingredient types, based on a study by Arsenault and coworkers (16). Selection of specific food items as representative of each ingredient type was based on consumption incidence surveys tabulated by Islam et al. (17), and the results were incorporated into a database consisting of 54 food ingredients. We filtered this database to remove items consumed by

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