Systems Signatures Reveal Unique Remission-path of Type 2 Diabetes Following Roux-en-Y Gastric Bypass Surgery.

Roux-en-Y Gastric bypass surgery (RYGB) is emerging as a powerful tool for treatment of obesity and may also cause remission of type 2 diabetes. However, the molecular mechanism of RYGB leading to diabetes remission independent of weight loss remains elusive. In this study, we profiled plasma metabolites and proteins of 10 normal glucose-tolerant obese (NO) and 9 diabetic obese (DO) patients before and 1-week, 3-months, 1-year after RYGB. 146 proteins and 128 metabolites from both NO and DO groups at all four stages were selected for further analysis. By analyzing a set of bi-molecular associations among the corresponding network of the subjects with our newly developed computational method, we defined the represented physiological states (called the edge-states that reflect the interactions among the bio-molecules), and the related molecular networks of NO and DO patients, respectively. The principal component analyses (PCA) revealed that the edge states of the post-RYGB NO subjects were significantly different from those of the post-RYGB DO patients. Particularly, the time-dependent changes of the molecular hub-networks differed between DO and NO groups after RYGB. In conclusion, by developing molecular network-based systems signatures, we for the first time reveal that RYGB generates a unique path for diabetes remission independent of weight loss.

De novo identification and quantification of single amino-acid variants in human brain.

The detection of single amino-acid variants (SAVs) usually depends on single-nucleotide polymorphisms (SNPs) database. Here, we describe a novel method that discovers SAVs at proteome level independent of SNPs data. Using mass spectrometry-based de novo sequencing algorithm, peptide-candidates are identified and compared with theoretical protein database to generate SAVs under pairing strategy, which is followed by database re-searching to control false discovery rate. In human brain tissues, we can confidently identify known and novel protein variants with diverse origins. Combined with DNA/RNA sequencing, we verify SAVs derived from DNA mutations, RNA alternative splicing, and unknown post-transcriptional mechanisms. Furthermore, quantitative analysis in human brain tissues reveals several tissue-specific differential expressions of SAVs. This approach provides a novel access to high-throughput detection of protein variants, which may offer the potential for clinical biomarker discovery and mechanistic research.

Quantitative detection of single amino acid polymorphisms by targeted proteomics.

Single-nucleotide polymorphisms (SNPs) are recognized as one kind of major genetic variants in population scale. However, polymorphisms at the proteome level in population scale remain elusive. In the present study, we named amino acid variances derived from SNPs within coding regions as single amino acid polymorphisms (SAPs) at the proteome level, and developed a pipeline of non-targeted and targeted proteomics to identify and quantify SAP peptides in human plasma. The absolute concentrations of three selected SAP-peptide pairs among 290 Asian individuals were measured by selected reaction monitoring (SRM) approach, and their associations with both obesity and diabetes were further analyzed. This work revealed that heterozygotes and homozygotes with various SAPs in a population could have different associations with particular traits. In addition, the SRM approach allows us for the first time to separately measure the absolute concentration of each SAP peptide in the heterozygotes, which also shows different associations with particular traits.