Endogenous FGF21-signaling controls paradoxical obesity resistance of UCP1-deficient mice

Uncoupling protein 1 (UCP1) executes thermogenesis in brown adipose tissue, which is a major focus of human obesity research. Although the UCP1-knockout (UCP1 KO) mouse represents the most frequently applied animal model to judge the anti-obesity effects of UCP1, the assessment is confounded by unknown anti-obesity factors causing paradoxical obesity resistance below thermoneutral temperatures. Here we identify the enigmatic factor as endogenous FGF21, which is primarily mediating obesity resistance. The generation of UCP1/FGF21 double-knockout mice (dKO) fully reverses obesity resistance. Within mild differences in energy metabolism, urine metabolomics uncover increased secretion of acyl-carnitines in UCP1 KOs, suggesting metabolic reprogramming. Strikingly, transcriptomics of metabolically important organs reveal enhanced lipid and oxidative metabolism in specifically white adipose tissue that is fully reversed in dKO mice. Collectively, this study characterizes the effects of endogenous FGF21 that acts as master regulator to protect from diet-induced obesity in the absence of UCP1.


Statistics
For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section.
n/a Confirmed The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly The statistical test(s) used AND whether they are one-or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section.
A description of all covariates tested A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals) For null hypothesis testing, the test statistic (e.g. F, t, r) with confidence intervals, effect sizes, degrees of freedom and P value noted Give P values as exact values whenever suitable.

For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings
For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes Estimates of effect sizes (e.g. Cohen's d, Pearson's r), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above.

Software and code
Policy information about availability of computer code Data collection of metabolic cage analysis was performed using TSE PhenoMaster Software (TSE Systems GmbH, Bad Homburg, Germany). Data collection of qPCR were collected using the ViiA™ 7 Real-Time PCR System (Applied Biosystems). The energy content of the dry urine and feces residues were automatically calculated by the software of the IKA C7000 bomb calorimeter, Staufen, Germany. For ELISA/Serum analysis, the Pherastar spectrophotometer-fluorescence reader system (BMG Labtech) was used. For RNA seq analysis, The quality of the RNA was determined with the Agilent 2100 BioAnalyzer (RNA 6000 Nano Kit, Agilent). All samples had a RNA integrity number (RIN) value greater than 8. RNA libraries were assessed for quality and quantity with the Agilent 2100 BioAnalyzer and the Quant-iT PicoGreen dsDNA Assay Kit (Life Technologies). Strand specific RNA libraries were sequenced as 100 bp paired-end runs on an Illumina HiSeq4000 platform. Bacterial DNA was profiled by sequencing of the V4 region of the 16S rRNA gene on an Illumina MiSeq (llumina RTA v1.17.28; MCS v2.5) using 515F and 806R primers designed for dual indexing 60 and the V2 kit (2x250 bp paired-end reads).
Following codes were used to analyze data: Transcriptomic Analysis: The STAR aligner* (v 2.4.2a) (Anders, S., et al.. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166-169, 2015) with modified parameter settings (--twopassMode=Basic) was used for split-read alignment against the mouse genome assembly mm10 (GRCm38) and UCSC known Gene annotation. To quantify the number of reads mapping to annotated genes we used HTseq-count°(v0.6.0). Raw read counts were count files were normalized and DEG were estimated using R package DESeq2. All calculations were done using R version 3.4 and Matlab R2018a.
DNA extraction and 16S rRNA gene sequencing: IIlumina paired-end reads were merged using PEAR (Zhang, J.,et al. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30, 614-620, 2014), and quality filtered to remove reads that had at least one base with a q-score lower than 20 and that were shorter than 220 nucleotides or longer than 350 nucleotides. Quality filtered reads were analyzed with the software package QIIME 62 (version 1.9.1). Sequences were clustered into operational taxonomic units (OTUs) at a 97% identity threshold using an open-reference OTU picking approach with UCLUST (Edgar, 2010) against the Greengenes reference database (DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Applied and environmental microbiology 72, 5069-5072, 2006) (13_8 release). All sequences that failed to cluster when tested against the Greengenes database were used as input for picking OTUs de novo. Representative sequences for the OTUs were Greengenes reference nature research | reporting summary

October 2018
For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors/reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Research guidelines for submitting code & software for further information.

Data
Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability Field-specific reporting Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection.

Life sciences Behavioural & social sciences Ecological, evolutionary & environmental sciences
For a reference copy of the document with all sections, see nature.com/documents/nr-reporting-summary-flat.pdf

Life sciences study design
All studies must disclose on these points even when the disclosure is negative.

Data exclusions
Replication Randomization Blinding Reporting for specific materials, systems and methods We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.
sequences or cluster seeds, and were taxonomically assigned using the Greengenes taxonomy and the Ribosomal Database Project Classifier (Wang, Q.et al. Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and environmental microbiology 73, [5261][5262][5263][5264][5265][5266][5267]2007 , 494-504, 2011). and excluded from all downstream analyses. Similarly, sequences that could not be aligned with PyNAST, singletons, sequences present in the blank extraction control and very low abundant sequences (relative abundance <0.005%) were also excluded. To correct for differences in sequencing depth, the same amount of sequences was randomly sub-sampled for each group of samples (rarefaction; maximum depth depending on sample group). A bootstrap version of Mann-Whitney-U test was used to compare the genotype-dependent abundance of OTUs at different taxonomical levels; significant differences were identified after correction for false discovery rate. Abundances higher than 1% are displayed on the genus level. QIIME was used to compute alpha diversity from rarefied OTU tables and to determine statistical significance at maximum rarefaction level by using a two-sample t-test and 999 Monte-Carlo permutations. Beta-diversity and weighted unifrac distance matrix were computed with QIIME and statistical significance of sample groupings was determined by adonis method and 999 permutations.
Data availability statement has been provided.
Sample size estimation was based upon our own previous results in comparable studies, assuming to achieve 90% power at a significance level of 0.05.
Of metabolic cage analysis, data were excluded from obvious technical error that would result in false data collection. Specifically, the foodhopper did not registrate data in one case, and water bottle leakage occured in two other cases. Non-responders during GTT's glucose admission were not included. Otherwise, no data were excluded from the analysis unless Grubbs test for outlier justified the exclusion of a significant outlier Reproducibility of data was ensured by using independent mouse cohorts, showing reproducibility of genotype-dependent body weight progression. Each mouse was treated as independent biological sample.
All experimental mice were randomized during data assessment.
Complete blinding of the investigators was not possible with regard to diet and genotypes of the mice. Blinded data collection and/or analysis were performed in following experiments: all metabolic data of living mice and histological stainings. RNA sequencing data were collected blinded by using alphanumeric coding, post-analysis was performed non-blinded.