Fig 1: Successful generation of Fn gene conditional knockout mice.(A, B) Identification and linearization of the Fn1 gene targeting vector, and the results of Hind III digestion, the theoretical band size is 9.8 kb, 5.8 kb and 810bp (810 bp band cannot be displayed because the product is too small). (C-E) Electrophoresis map of 5′ homology arm (5′ arm) and 3′ homology arm (3′ arm) PCR identification of F1 mice (recombinant negative can only clone a single fragment). (F) The PCR product of the genome inserted into LoxP was 1.4 kb, the PCR product of KO was 0.7 kb, and the PCR product of WT mouse was 1.2 kb, electrophoresis grayscale graph showed that knockout efficiency of DNA was 99% in liver. (G) ELISA and (H, I) Western blotting results showing FN levels in the plasma and liver of Fn KO mice at different durations post tamoxifen induction, the knockout efficiency was 95%. (J-M) FN immunohistochemical and (N-Q) immunofluorescence staining of liver and kidney indicated that FN expression was decreased significantly in Fn KO mice.
Fig 2: Target vector plasmid map.Fn1 target vector plasmid map.
Fig 3: FGA and FN1 are increased in the liver of cold-exposed LdlrKO mice. Male LdlrKO mice were fed HFSCD for 16 weeks and then housed at 22 °C or 5 °C for 7 days. a Relative log2 abundance of FGA and FN1 from the BAT proteomics data. Data represent mean values (n = 6) ± SD; *p < 0.05, **p ≤ 0.01. b Western blotting experiment of FGA protein expression in BAT with β-actin (ACTB) as loading control and the c densitometric quantification of FGA relative to ACTB. Data represent mean values (n = 4) ± SD; *p < 0.05, **p ≤ 0.01. d Hepatic protein expression of FGA and f FN1 with β-actin (ACTB) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as loading controls, respectively. Densitometric quantification of e FGA and g FN1 relative to the expression of the respective control. h Log2 protein abundance of FGA and FN1 from the liver proteomics datasets. i mRNA expression of Fga and Fn1 in liver and j sWAT relative to Ppia expression. Data represent mean values (n = 5–6) ± SD; *p < 0.05, **p ≤ 0.01, ***p ≤ 0.001
Fig 4: Genetic variations in FGA and FN1 are associated with cardiometabolic phenotypes in humans. Phenotypes related to genetic variations in aFGA and bFN1 according to the Common Metabolic Diseases Knowledge Portal (CMDKP). Cardiovascular- and metabolic-related phenotypes are shown in red and blue, respectively. Support for genetic evidence is defined as: Moderate: Loge(HuGE score) ≥ 1.09; Strong: Loge(HuGE score) ≥ 2.30; Extreme: Loge(HuGE score) ≥ 4.60; Compelling: Loge(HuGE score) ≥ 5.86. The vertical dotted lines indicate the thresholds between evidence categories
Fig 5: Increased protein abundance of ITGB1 downstream targets in the liver of cold-exposed LdlrKO mice. Male LdlrKO mice were fed HFSCD for 16 weeks and then housed at 22 °C or 5 °C for 7 days. Potential activation of ITGB1 as a common liver receptor for FGA and FN1 shown by log2 protein abundance of ITGB1 downstream targets (vinculin (VCL), paxillin (PAX/PXN), talin (TLN1 &TLN2), cell division cycle 42 (CDC42), activator of heat shock protein ATPase 1 (AHSA1/p38), and catenin beta 1 (CTNNB1)) from the liver proteomics datasets. Data represent mean values (n = 6) ± SD; ***p ≤ 0.001
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