Additional file 2: of Modelling overflow metabolism in Escherichia coli with flux balance analysis incorporating differential proteomic efficiencies of energy pathways

Supplementary text and Supplementary Figures S1-S17. (DOCX 229 kb

Additional file 1: of Modelling overflow metabolism in Escherichia coli with flux balance analysis incorporating differential proteomic efficiencies of energy pathways

Supplementary Tables S1-S6. (XLSX 26 kb

Automated pathway reconstruction tools for microbial secondary metabolism.

Automated pathway reconstruction tools for microbial secondary metabolism.</p

Graphical abstract of the workflow of FBA-based metabolic modeling for secondary metabolism.

Orange circle: secondary metabolite; pink circle: primary metabolite. FBA, flux balance analysis; smGSMM, genome-scale metabolic model with secondary metabolic pathway.</p

Summary of different modeling techniques to predict secondary metabolite production.

Summary of different modeling techniques to predict secondary metabolite production.</p

An illustrative example of pH-induced EPS production in lactic acid bacteria, used to explain the constrained proteome allocation model for both primary and secondary metabolism.

(A) The metabolic network of lactic acid bacteria for both primary metabolism (C, R, T sectors) and secondary metabolism (U sector). (B) Simulated metabolic response to pH: the increase of acidity inhibits the growth rate and induces EPS production. (C) Simulated proteome allocation in response to pH: the increase of acidity activates secondary metabolism, and more proteome resources get allocated to the U sector. Note: this “toy” model is for illustration only. EPS, exopolysaccharide.</p

Quantitative assessment of existing FBA-based modeling techniques for predicting secondary metabolite production.

(A) Comparison of predicted and observed growth rates and ACT production fluxes at different observed growth rates in Alam and colleagues [87]. (B) Comparison of 4 different objective functions used in FBA to predict both primary metabolism and clavulanic acid production flux [89]. Correlation scores are computed for predicted and observed fluxes. P-lim: limited phosphorus content. (C) Comparison of predicted and observed production fluxes of ACT and RED in Kim and colleagues [97]. ACT, actinorhodin; FBA, flux balance analysis.</p

Schematic diagram of the combination of metabolic and GRNs to predict the secondary metabolite production.

The stress signal stimulates the expression of RF, resulting in the activation of enzymes catalyzing reactions for secondary metabolite biosynthesis. Orange circle: secondary metabolite; pink circle: primary metabolite. Enz, enzyme; GRN, gene regulatory network; RF, regulatory factor.</p

DataSheet_1_Association of parental HLA-G polymorphisms with soluble HLA-G expressions and their roles on recurrent implantation failure: A systematic review and meta-analysis.docx

IntroductionHLA-G plays a central role in immune tolerance at the maternal-fetal interface. The HLA-G gene is characterized by low allelic polymorphism and restricted tissue expression compared with classical HLA genes. HLA-G polymorphism is associated with HLA-G expression and linked to pregnancy complications. However, the association of parental HLA-G polymorphisms with soluble HLA-G (sHLA-G) expression and their roles in recurrent implantation failure (RIF) is unclear. The study aims to systematically review the association of HLA-G polymorphisms with RIF, the association of sHLA-G expression with RIF, and the association of HLA-G polymorphisms with sHLA-G expressions in patients attending in-vitro fertilization (IVF) treatment.MethodsStudies that evaluated the association of HLA-G polymorphisms with RIF, the association between sHLA-G expression with RIF, and the association between HLA-G polymorphisms with sHLA-G expressions in patients attending IVF treatment were included. Meta-analysis was performed by random-effect models. Sensitivity analysis was performed by excluding one study each time. Subgroup analysis was performed based on ethnicity.ResultsHLA-G 14bp ins variant is associated with a lower expression of sHLA-G in seminal or blood plasma of couples attending IVF treatment. The maternal HLA-G*010101 and paternal HLA-G*010102 alleles are associated with RIF risk compared to other alleles. However, single maternal HLA-G 14bp ins/del polymorphism, HLA-G -725 C>G/T polymorphism, or circulating sHLA-G concentration was not significantly associated with RIF in the general population. HLA-G 14bp ins/ins homozygous genotype or ins variant was associated with a higher risk of RIF in the Caucasian population.DiscussionSpecific HLA-G alleles or HLA-G polymorphisms are associated with sHLA-G expression in couples attending IVF treatment. Several HLA-G polymorphisms may be related to RIF, considering different ethnic backgrounds. A combined genetic effect should be considered in future studies to confirm the association of HLA-G polymorphisms and sHLA-G expressions in relation to RIF.</p

Table_1_Integrated Analysis of Multiple Microarrays Based on Raw Data Identified Novel Gene Signatures in Recurrent Implantation Failure.xlsx

BackgroundRecurrent implantation failure (RIF) is an intricate complication following IVF-ET, which refers to the situation that good-quality embryos repeatedly fail to implant following two or more IVF cycles. Intrinsic molecular mechanisms underlying RIF have not yet been fully elucidated. With enormous improvement in high-throughput technologies, researchers screened biomarkers for RIF using microarray. However, the findings of published studies are inconsistent. An integrated study on the endometrial molecular determinants of implantation will help to improve pregnancy outcomes.ObjectiveTo identify robust differentially expressed genes (DEGs) and hub genes in endometrium associated with RIF, and to investigate the diagnostic role of hub genes in RIF.MethodsRaw data from five GEO microarrays regarding RIF were analyzed. Integrated genetic expression analyses were performed using the Robust Rank Aggregation method to identify robust DEGs. Enrichment analysis and protein-protein interaction (PPI) analysis were further performed with the robust DEGs. Cytohubba was used to screen hub genes based on the PPI network. GSE111974 was used to validate the expression and diagnostic role of hub genes in RIF.Results1532 Robust DEGs were identified by integrating four GEO datasets. Enrichment analysis showed that the robust DEGs were mainly enriched in processes associated with extracellular matrix remodeling, adhesion, coagulation, and immunity. A total of 18 hub genes (HMGCS1, SQLE, ESR1, LAMC1, HOXB4, PIP5K1B, GNG11, GPX3, PAX2, TF, ALDH6A1, IDH1, SALL1, EYA1, TAGLN, TPD52L1, ST6GALNAC1, NNMT) were identified. 10 of the 18 hub genes were significantly differentially expressed in RIF patients as validated by GSE111974. The 10 hub genes (SQLE, LAMC1, HOXB4, PIP5K1B, PAX2, ALDH6A1, SALL1, EYA1, TAGLN, ST6GALNAC1) were effective in predicting RIF with an accuracy rate of 85%, specificity rate of 100%, and sensitivity rate of 88.9%.ConclusionsOur integrated analysis identified novel robust DEGs and hub genes in RIF. The hub genes were effective in predicting RIF and will contribute to the understanding of comprehensive molecular mechanisms in RIF pathogenesis.</p