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Context: Mitochondria are essential for cellular energy homeostasis, yet their role in subcutaneous adipose tissue (SAT) during different types of weight-loss interventions remains unknown.Objective: To investigate how SAT mitochondria change following diet-induced and bariatric surgery-induced weight-loss interventions in 4 independent weight-loss studies.Methods: The DiOGenes study is a European multicenter dietary intervention with an 8-week low caloric diet (LCD; 800 kcal/d; n = 261) and 6-month weight-maintenance (n = 121) period. The Kuopio Obesity Surgery study (KOBS) is a Roux-en-Y gastric bypass (RYGB) surgery study (n = 172) with a 1-year follow-up. We associated weight-loss percentage with global and 2210 mitochondria-related RNA transcripts in linear regression analysis adjusted for age and sex. We repeated these analyses in 2 studies. The Finnish CRYO study has a 6-week LCD (800-1000 kcal/d; n = 19) and a 10.5-month follow-up. The Swedish DEOSH study is a RYGB surgery study with a 2-year (n = 49) and 5-year (n = 37) follow-up.Results: Diet-induced weight loss led to a significant transcriptional downregulation of oxidative phosphorylation (DiOGenes; ingenuity pathway analysis [IPA] z-scores: -8.7 following LCD, -4.4 following weight maintenance; CRYO: IPA z-score: -5.6, all P < 0.001), while upregulation followed surgery-induced weight loss (KOBS: IPA z-score: 1.8, P < 0.001; in DEOSH: IPA z-scores: 4.0 following 2 years, 0.0 following 5 years). We confirmed an upregulated oxidative phosphorylation at the proteomics level following surgery (IPA z-score: 3.2, P < 0.001).Conclusions: Differentially regulated SAT mitochondria-related gene expressions suggest qualitative alterations between weight-loss interventions, providing insights into the potential molecular mechanistic targets for weight-loss success. [ABSTRACT FROM AUTHOR]

Motivation: Mapping bias causes preferential alignment to the reference allele, forming a major obstacle in allele-specific expression (ASE) analysis. The existing methods, such as simulation and SNP-aware alignment, are either inaccurate or relatively slow. To fast and accurately count allelic reads for ASE analysis, we developed a novel approach, ASElux, which utilizes the personal SNP information and counts allelic reads directly from unmapped RNA-sequence (RNA-seq) data. ASElux significantly reduces runtime by disregarding reads outside single nucleotide polymorphisms (SNPs) during the alignment. Results: When compared to other tools on simulated and experimental data, ASElux achieves a higher accuracy on ASE estimation than non-SNP-aware aligners and requires a much shorter time than the benchmark SNP-aware aligner, GSNAP with just a slight loss in performance. ASElux can process 40 million read-pairs from an RNA-sequence (RNA-seq) sample and count allelic reads within 10 min, which is comparable to directly counting the allelic reads from alignments based on other tools. Furthermore, processing an RNA-seq sample using ASElux in conjunction with a general aligner, such as STAR, is more accurate and still ~4x faster than STAR + WASP, and ~33x faster than the lead SNP-aware aligner, GSNAP, making ASElux ideal for ASE analysis of largescale transcriptomic studies. We applied ASElux to 273 lung RNA-seq samples from GTEx and identified a splice-QTL rs11078928 in lung which explains the mechanism underlying an asthma GWAS SNP rs11078927. Thus, our analysis demonstrated ASE as a highly powerful complementary tool to cis-expression quantitative trait locus (eQTL) analysis. [ABSTRACT FROM AUTHOR]