A Model Integration Pipeline for the Improvement of Human Genome-Scale Metabolic Reconstructions

Abstract

Metabolism has been a major field of study in the last years, mainly due to its importance in understanding cell physiology and certain disease phenotypes due to its deregulation. Genome-scale metabolic models (GSMMs) have been established as important tools to help achieve a better understanding of human metabolism. Towards this aim, advances in systems biology and bioinformatics have allowed the reconstruction of several human GSMMs, although some limitations and challenges remain, such as the lack of external identifiers for both metabolites and reactions. A pipeline was developed to integrate multiple GSMMs, starting by retrieving information from the main human GSMMs and evaluating the presence of external database identifiers and annotations for both metabolites and reactions. Information from metabolites was included into a graph database with omics data repositories, allowing clustering of metabolites through their similarity regarding database cross-referencing. Metabolite annotation of several older GSMMs was enriched, allowing the identification and integration of common entities. Using this information, as well as other metrics, we successfully integrated reactions from these models. These methods can be leveraged towards the creation of a unified consensus model of human metabolism.

Standardization of Human Metabolic Stoichiometric Models: Challenges and Directions

“…Each of the main reconstruction series shown in Figure 1 tends to use its own IDs. The problem with different identifiers between models was also indicated in a recent thorough comparison of the SBML files of Recon and HMR series, HepatoNet and EHMN models (Vieira et al, 2018). In addition, some models include the Ensembl gene IDs of the involved enzymes instead of their Enzyme Commission (EC) number, further hindering their direct comparison, while alignment to more recent genome annotations may be necessary.…”

Emerging computational paradigms to address the complex role of gut microbial metabolism in cardiovascular diseases

“…GEMs for humans (97). Once combined with available highthroughput data, these generic GEMs can be rendered contextspecific for different cell types.…”

“…Rigorous context-specific GEMs can facilitate the development of new therapies and the prevention of metabolic diseases. The efforts for such human models began with the reconstruction of generic genome-scale network reconstructions (Recon) models and human metabolic reaction (HMR), recognized as the two most comprehensive generic GEMs for humans ( 97 ). Once combined with available high-throughput data, these generic GEMs can be rendered context-specific for different cell types.…”

Path to improving the life cycle and quality of genome-scale models of metabolism

“…GEM sequels are usually limited to organisms of high interest. For example, the metabolic reconstruction of E. coli has been updated at least five times (McCloskey et al, 2013;Monk et al, 2017) (iJE660, iJR904, iAF1260, iJO1366, and iML1515), the human metabolic network has been updated over four times (recon 2, recon 2.04, recon 2.2, and recon 3D) (Vieira et al, 2018;Robinson et al, 2020), the S. aureus GEM was updated four times (Seif et al, 2019a(Seif et al, , 2019b, and the S. cerevisiae network has been updated over 17 times (Lopes and Rocha, 2017). Oftentimes, GEM sequel efforts occur contemporaneously across multiple groups, calling for the organization of reconstruction jamborees to produce a consensus model (Herrgå rd et al, 2008;Thiele and Palsson, 2010b).…”