Proteome-wide prediction of protein-protein interactions from high-throughput data

In this paper,we present a brief review of the existing computational methods for predicting proteome-wide protein-protein interaction networks from high-throughput data.The availability of various types of omics data provides great opportunity and also un-precedented challenge to infer the interactome in cells.Reconstructing the interactome or interaction network is a crucial step for studying the functional relationship among proteins and the involved biological processes.The protein interaction network will provide valuable resources and alternatives to decipher the mechanisms of these functionally interacting elements as well as the running system of cellular operations.In this paper,we describe the main steps of predicting protein-protein interaction networks and categorize the available ap-proaches to couple the physical and functional linkages.The future topics and the analyses beyond prediction are also discussed and concluded.

Dynamical network biomarkers for identifying critical transitions and their driving networks of biologic processes

Non-smooth or even abrupt state changes exist during many biological processes, e.g., cell differentiation processes, proliferation processes, or even disease deterioration processes. Such dynamics generally signals the emergence of critical transition phenomena, which result in drastic changes of system states or eventually qualitative changes of phenotypes. Hence, it is of great importance to detect such transitions and further reveal their molecular mechanisms at network level. Here, we review the recent advances on dynamical network biomarkers （DNBs） as well as the related theoretical foundation, which can identify not only early signals of the critical transitions but also their leading networks, which drive the whole system to initiate such transitions. In order to demonstrate the effectiveness of this novel approach, examples of complex diseases are also provided to detect pre-disease stage, for which traditional methods or biomarkers failed.

THE EFFECT OF COUPLED FEEDBACK ON NOISE FILTERING IN SIGNAL TRANSDUCTION NETWORKS

Many interacting biomolecular components in cells form different positive or negative feedback loops. When biological signals transduce through cascades consisting of various loops they will be affected or even distorted. Especially, how to process various signals buried in various intrinsic and extrinsic noises is an important issue. This paper analyzes how the response time influences noise filtering ability and how to enhance the ability by coupling different feedback loops. A parameter to measure the response time of the signal transduction, i.e., τ0.9, and its relationship between the response time and noise filtering will be discussed. The authors show clearly that the longer the response time is, the better the ability to filter noises will be. Therefore, to enhance the ability to filter noises, the authors can prolong the response time by coupling different positive or negative feedback loops. The results provide a possible approach to enhance the ability to filter noises in larger biomolecular networks.

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 nontargeted 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.

From gut changes to type 2 diabetes remission after gastric bypass surgeries

Increasing evidence suggests that the gut may influence the host’s metabolism and ultimately change the outcomes of type 2 diabetes mellitus(T2DM).We review the evidence on the relationship between the gut and T2DM remission after gastric bypass surgery,and discuss the potential mechanisms underlying the above relationship:gut anatomical rearrangement,microbial composition changes,altered gut cells,and gut hormone modulation.However,the exact changes and their relative importance in the metabolic improvements after gastric bypass surgery remain to be further clarified.Elucidating the precise metabolic mechanisms of T2DM resolution after bypass surgery will help to reveal the molecular mechanisms of pathogenesis,and facilitate the development of novel diagnoses and preventative interventions for this common disease.