Fat mass and obesity-related (FTO) shuttles between the nucleus and cytoplasm.

SNPs (single nucleotide polymorphisms) on a chromosome 16 locus encompassing FTO, as well as IRX3, 5, 6, FTM and FTL are robustly associated with human obesity. FTO catalyses the Fe(II)- and 2OG-dependent demethylation of RNA and is an AA (amino acid) sensor that couples AA levels to mTORC1 (mammalian target of rapamycin complex 1) signalling, thereby playing a key role in regulating growth and translation. However, the cellular compartment in which FTO primarily resides to perform its biochemical role is unclear. Here, we undertake live cell imaging of GFP (green fluorescent protein)-FTO, and demonstrate that FTO resides in both the nucleus and cytoplasm. We show using 'FLIP' (fluorescence loss in photobleaching) that a mobile FTO fraction shuttles between both compartments. We performed a proteomic study and identified XPO2 (Exportin 2), one of a family of proteins that mediates the shuttling of proteins between the nucleus and the cytoplasm, as a binding partner of FTO. Finally, using deletion studies, we show that the N-terminus of FTO is required for its ability to shuttle between the nucleus and cytoplasm. In conclusion, FTO is present in both the nucleus and cytoplasm, with a mobile fraction that shuttles between both cellular compartments, possibly by interaction with XPO2.This is the final published version. It first appeared at http://www.bioscirep.org/bsr/034/bsr034e144.htm

FTO is necessary for the induction of leptin resistance by high-fat feeding.

OBJECTIVE: Loss of function FTO mutations significantly impact body composition in humans and mice, with Fto-deficient mice reported to resist the development of obesity in response to a high-fat diet (HFD). We aimed to further explore the interactions between FTO and HFD and determine if FTO can influence the adverse metabolic consequence of HFD. METHODS: We studied mice deficient in FTO in two well validated models of leptin resistance (HFD feeding and central palmitate injection) to determine how Fto genotype may influence the action of leptin. Using transcriptomic analysis of hypothalamic tissue to identify relevant pathways affected by the loss of Fto, we combined data from co-immunoprecipitation, yeast 2-hybrid and luciferase reporter assays to identify mechanisms through which FTO can influence the development of leptin resistant states. RESULTS: Mice deficient in Fto significantly increased their fat mass in response to HFD. Fto (+/-) and Fto (-/-) mice remained sensitive to the anorexigenic effects of leptin, both after exposure to a HFD or after acute central application of palmitate. Genes encoding components of the NFкB signalling pathway were down-regulated in the hypothalami of Fto-deficient mice following a HFD. When this pathway was reactivated in Fto-deficient mice with a single low central dose of TNFα, the mice became less sensitive to the effect of leptin. We identified a transcriptional coactivator of NFкB, TRIP4, as a binding partner of FTO and a molecule that is required for TRIP4 dependent transactivation of NFкB. CONCLUSIONS: Our study demonstrates that, independent of body weight, Fto influences the metabolic outcomes of a HFD through alteration of hypothalamic NFкB signalling. This supports the notion that pharmacological modulation of FTO activity might have the potential for therapeutic benefit in improving leptin sensitivity, in a manner that is influenced by the nutritional environment.The authors thank Roger Cox (MRC Harwell) for kindly providing us with the Fto-deficient mouse strain. This study was supported by the Medical Research Council (MRC) Metabolic Disease Unit (MRC_MC_UU_12012/1), EU FP7- FOOD- 266408 Full4Health and the Helmholtz Alliance ICEMED.This is the final published version. It first appeared at http://www.sciencedirect.com/science/article/pii/S2212877815000241#

Comparative evaluation of equine mesenchymal stem cells derived from amniotic fluid and umbilical cord blood

Mesenchymal stem cells (MSCs) are promising therapeutic tools for the treatment of tendon rupture and other musculoskeletal injuries in horses. Although MSCs from bone marrow and adipose tissues are commonly used for therapeutic purpose in equines, umbilical cord blood (UCB) and amniotic fluid (AF) are potential non-invasive sources of MSCs. We collected AF and UCB from twenty mares during foaling for isolation of MSCs and evaluated them for the differences in isolation rates, proliferation capacity, expression of MSC markers and multi-lineage differentiation ability. The plastic adherent colonies were observed in 60% AF and 65% UCB samples. The mean doubling time for AF cells was significantly lower than that of UCB cells. The AF-MSCs proliferated till passage 36 whereas UCB-MSCs till passage 20 only. Both AF and UCB derived cells expressed CD29, CD44, CD73, CD90 and CD105 and were negative for haematopoietic and leukocytic markers (CD14, CD34 and CD45). The CD90 and CD73 expression was significantly higher in AF derived cells as compared to UCB-MSCs. On the other hand, CD29 expression was significantly lower in AF derived cells as compared to UCB derived cells. The UCB-MSCs differentiated poorly to adipogenic lineage compared to AF-MSCs. These results suggested that equine AF yields more MSCs with greater in vitro proliferation and differentiation capacities and is better non-invasive source of MSCs for regenerative therapies in equines

Intestinal Interleukin-17 Receptor Signaling Mediates Reciprocal Control of the Gut Microbiota and Autoimmune Inflammation

Interleukin-17 (IL-17) and IL-17 receptor (IL-17R) signaling are essential for regulating mucosal host defense against many invading pathogens. Commensal bacteria, especially segmented filamentous bacteria (SFB), are a crucial factor that drives T helper 17 (Th17) cell development in the gastrointestinal tract. In this study, we demonstrate that Th17 cells controlled SFB burden. Disruption of IL-17R signaling in the enteric epithelium resulted in SFB dysbiosis due to reduced expression of α-defensins, Pigr and Nox1. When subjected to experimental autoimmune encephalomyelitis, IL-17R signaling deficient mice demonstrated earlier disease onset and worsened severity that was associated with increased intestinal Csf2 expression and elevated systemic GM-CSF cytokine concentrations. Conditional deletion of IL-17R in the enteric epithelium demonstrated that there was a reciprocal relationship between the gut microbiota and enteric IL-17R signaling that controlled dysbiosis, constrained Th17 development, and regulated the susceptibility to autoimmune inflammation

Integrative genomic analysis implicates limited peripheral adipose storage capacity in the pathogenesis of human insulin resistance.

Insulin resistance is a key mediator of obesity-related cardiometabolic disease, yet the mechanisms underlying this link remain obscure. Using an integrative genomic approach, we identify 53 genomic regions associated with insulin resistance phenotypes (higher fasting insulin levels adjusted for BMI, lower HDL cholesterol levels and higher triglyceride levels) and provide evidence that their link with higher cardiometabolic risk is underpinned by an association with lower adipose mass in peripheral compartments. Using these 53 loci, we show a polygenic contribution to familial partial lipodystrophy type 1, a severe form of insulin resistance, and highlight shared molecular mechanisms in common/mild and rare/severe insulin resistance. Population-level genetic analyses combined with experiments in cellular models implicate CCDC92, DNAH10 and L3MBTL3 as previously unrecognized molecules influencing adipocyte differentiation. Our findings support the notion that limited storage capacity of peripheral adipose tissue is an important etiological component in insulin-resistant cardiometabolic disease and highlight genes and mechanisms underpinning this link.This study was funded by the UK Medical Research Council through grants MC_UU_12015/1, MC_PC_13046, MC_PC_13048 and MR/L00002/1. This work was supported by the MRC Metabolic Diseases Unit (MC_UU_12012/5) and the Cambridge NIHR Biomedical Research Centre and EU/EFPIA Innovative Medicines Initiative Joint Undertaking (EMIF grant 115372). Funding for the InterAct project was provided by the EU FP6 program (grant LSHM_CT_2006_037197). This work was funded, in part, through an EFSD Rising Star award to R.A.S. supported by Novo Nordisk. D.B.S. is supported by Wellcome Trust grant 107064. M.I.M. is a Wellcome Trust Senior Investigator and is supported by the following grants from the Wellcome Trust: 090532 and 098381. M.v.d.B. is supported by a Novo Nordisk postdoctoral fellowship run in partnership with the University of Oxford. I.B. is supported by Wellcome Trust grant WT098051. S.O'R. acknowledges funding from the Wellcome Trust (Wellcome Trust Senior Investigator Award 095515/Z/11/Z and Wellcome Trust Strategic Award 100574/Z/12/Z)

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. Methods: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Findings: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Interpretation: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation