SFRS10—A Splicing Factor Gene Reduced in Human Obesity?
Authors: Mario Brosch, Witigo von Schönfels, Marcus Ahrens, Michael Nothnagel, Michael Krawczak, Matthias Laudes, Bence Sipos, Thomas Becker, Stefan Schreiber, Christoph Röcken, Clemens Schafmayer, Jochen Hampe
Journal: Cell Metabolism (2012)

In a recent report in Cell Metabolism, Pihlajamaki et al., 2011Pihlajamaki J. Lerin C. Itkonen P. Boes T. Floss T. Schroeder J. Dearie F. Crunkhorn S. Burak F. Jimenez-Chillaron J.C. et al.Cell Metab. 2011; 14: 208-218Abstract Full Text Full Text PDF PubMed Scopus (101) Google Scholar suggested an important role of reduced SFRS10 gene expression in human obesity. A low cellular SFRS10 level was reported to alter splicing of LPIN1 (encoding a key regulator of lipid metabolism) in favor of the lipogenic β isoform. Since such a mechanism would provide an interesting model for the causative link between altered splicing and metabolic disease, we tried to replicate the above findings and measured SFRS10 expression in 47 human liver samples comprising the whole spectrum of liver involvement in obesity. In addition to 8 lean individuals with normal liver histology (median BMI 25.5 kg/m2), we studied 8 obese individuals with normal liver histology (“healthy obese,” median BMI 47.1 kg/m2), 7 obese individuals with hepatic steatosis (median BMI 41.5 kg/m2), 8 obese individuals with nonalcoholic steatohepatitis (median BMI 47.8 kg/m2), and 16 paired samples taken from 8 obese individuals before (median BMI 41.5 kg/m2) and 4–9 months after bariatric surgery (median BMI, 30.4 kg/m2). Samples from individuals with a BMI >30 kg/m2 at the time of sampling were classified as obese (n = 34, median BMI 46.1 kg/m2, mean 44.8 kg/m2, standard deviation 8.7 kg/m2). This group included 16 samples from individuals in the BMI range of 53 ± 7 kg/m2 (46–60 kg/m2) at the time of sampling, which was utilized by Pihlajamaki and coworkers. Samples from individuals with a BMI ≤30 kg/m2 by the time of sampling were classified as “nonobese” (n = 13, median BMI 26.0 kg/m2, mean 25.6 kg/m2, standard deviation 3.0 kg/m2). All samples were obtained following a standardized protocol either directly in the operating theater (open or laparoscopic biopsies) or at the bedside (percutaneous biopsies), ensuring an ex vivo time of less than 60 s until shock freezing in liquid nitrogen. All individuals provided written, informed consent prior to the biopsies, and institutional ethics approval was obtained prior to the initiation of the study (Ethikkommission der Medizinischen Fakultät Kiel, #D425/07, #A110/99). Liver histology was assessed using standard criteria for nonalcoholic fatty liver disease (Kleiner et al., 2005Kleiner D.E. Brunt E.M. Van Natta M. Behling C. Contos M.J. Cummings O.W. Ferrell L.D. Liu Y.C. Torbenson M.S. Unalp-Arida A. et al.Hepatology. 2005; 41: 1313-1321Crossref PubMed Scopus (7112) Google Scholar), and insulin resistance was measured by way of the serum leptin-to-adiponectin ratio (LAR, median 3.9, range 0–19, interquartile range 0.6–6.4). This parameter has been shown to be strongly correlated with hyperinsulinemic euglycemic clamp data (Finucane et al., 2009Finucane F.M. Luan J. Wareham N.J. Sharp S.J. O'Rahilly S. Balkau B. Flyvbjerg A. Walker M. Hojlund K. Nolan J.J. Savage D.B. Diabetologia. 2009; 52: 2345-2349Crossref PubMed Scopus (243) Google Scholar). Expression of SFRS10 was evaluated using semiquantitative RT-PCR and real-time PCR (primers, CAGAACTACGGCGAGCGGGAATC, ATGGCTGTGGCTGTGCCGTC). The relative abundance of LPIN1 splice variants was evaluated by PCR and capillary electrophoresis with laser-induced fluorescence detection (CE-LIF) (Schindler et al., 2009Schindler S. Heiner M. Platzer M. Szafranski K. Electrophoresis. 2009; 30: 3674-3681Crossref PubMed Scopus (11) Google Scholar), employing primers that covered exons 4–7 (GCCGTCAAGCAGCTCTGTA; GCATTTCTGGGTTCTTCTGC). Using PSPower (Dupont and Plummer, 1997Dupont W.D. Plummer W.D. Control. Clin. Trials. 1997; 18: 274Abstract Full Text PDF Google Scholar) with standard deviation estimates from our own data, the power to detect the previously reported LPIN1 and SFRS10 expression differences between obese and nonobese individuals (∼0.8 units) in our samples turned out to be virtually 100%. In spite of that, no statistically significant differences in terms of the parameters of interest were observed between samples from obese individuals (mean SFRS10 expression 1.09, LPIN1 ratio [β/α] 0.30) and samples from nonobese individuals (mean SFRS10 expression 1.09, LPIN1 ratio [β/α] 0.30, pSFRS10 = 0.97, pLPIN1 = 0.82, see Figure S1 online). Similarly, no correlation was detected between either liver fat content or LAR on the one hand, and SFRS10 expression or LPIN1 splicing on the other (Figure S1, all p > 0.4). Finally, we were able to assess the intraindividual effects upon SFRS10 expression and LPIN1 ratio associated with a massive change of BMI in our paired samples from eight individuals before and after bariatric surgery (mean change in BMI 15.6 kg/m2, p = 5.6 × 10−6). Such a comparison is particularly informative because it would be inherently corrected for any pertinent genetic variability. Nevertheless, neither SFRS10 expression (mean change 0.004, p = 0.90) nor LPIN1 ratio (mean change 0.03, p = 0.25) was significantly affected by the drastic BMI change seen in this subcohort. In the original report, the specific candidate gene of subsequent analyses, namely SFRS10, was ascertained via a two-step approach. First, transcriptome-wide expression analysis and GO-based pathway analysis served to identify “RNA processing and splicing” as one of the most downregulated pathways in 5 versus 8 liver samples (2 of them were male) and 10 versus 7 muscle biopsies. Notably, in a gene set enrichment analysis (GSEA), a significant downregulation was only seen for the liver, but not for the muscle samples. In the second step, those 13 genes with reduced expression in both liver and muscle were selected for further analysis. The final choice of SFRS10 as the candidate for functional studies was based upon mouse data in which apparently only 2 of the 13 genes showed a similar change of expression as seen in humans. It was not reported whether the other targets showed differential expression, or whether possible changes went in the opposite direction as compared to humans. No exact p value was provided for SFRS10 in humans, only “p < 0.05” was quoted. However, given that ∼14,500 genes are typically evaluated in transcriptome-wide expression analyses, control of either the family-wise error rate or the false discovery rate at the 5% level would have required a much lower nominal p value (e.g., p < 3.5 × 10−6 with Bonferroni correction). After selecting SFRS10 as their functional candidate, the authors moved on to demonstrate an altered expression of lipogenic genes and increased plasma triacylglyceride levels in a series of elegant experiments in heterozygous knockdown mice (Pihlajamaki et al., 2011Pihlajamaki J. Lerin C. Itkonen P. Boes T. Floss T. Schroeder J. Dearie F. Crunkhorn S. Burak F. Jimenez-Chillaron J.C. et al.Cell Metab. 2011; 14: 208-218Abstract Full Text Full Text PDF PubMed Scopus (101) Google Scholar). Concentrating upon genes that were known to regulate lipogenesis via alternatively spliced isoforms, they identified Lpin1 as a likely target gene because it harbors a conserved Sfrs10-binding motif in the alternatively spliced exon 6. Evaluation of LPIN1 splicing in humans was performed in lean (n = 6) and obese (n = 14) individuals, although the relationship between these and the other human samples used in the screening stage was not specified. In conclusion, Pihlajamaki et al., 2011Pihlajamaki J. Lerin C. Itkonen P. Boes T. Floss T. Schroeder J. Dearie F. Crunkhorn S. Burak F. Jimenez-Chillaron J.C. et al.Cell Metab. 2011; 14: 208-218Abstract Full Text Full Text PDF PubMed Scopus (101) Google Scholar did not adhere to the current methodological standards for the analysis and reporting of genome-wide transcriptome data (Storey and Tibshirani, 2003Storey J.D. Tibshirani R. Proc. Natl. Acad. Sci. USA. 2003; 100: 9440-9445Crossref PubMed Scopus (7083) Google Scholar), particularly regarding the allowance for sampling error and multiple statistical testing. In addition to possible effects of sample handling and sample quality, that formal disregard appears to us to be a plausible explanation for our failure to replicate the original findings in our set of human liver samples (which was the tissue showing the more robust effect in humans, according to the original report). This suggests that, although animal experiments provide an excellent means of well-controlled functional analysis, firm data in humans are required before embarking upon such extensive and time-consuming experimental studies of human disease or biology. This study was supported by the German Federal Ministry of Education and Research (BmBF) through the Virtual Liver Project (FK0315763). Download .pdf (.08 MB) Help with pdf files Document S1. Figure S1