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Additional File 1: NELA inclusion and exclusion criteria

Additional file 1: Table S1. Names of the bile acids measured in tumor tissues. Table S2. Demographics of colon cancer patients from samples used within this study. Table S4. Comparison of bile acid abundance between RCCs and LCCs by sex and age. Table S5. Pearson correlation of glucuronic acid to bile acid abundances in patients with LCC ≥ 55 years old (n=99). Table S6. Correlations between prognosis-linked bile acid and T regulatory cell abundances in RCC tissues (n=6). Table S7. Multiple reaction monitoring transitions, retention times and collision energy levels for the detection of bile acids in colon tumor tissues. Fig. S1. Bile acid abundances in colon tumors from patients < 55 years old. Bile acid abundance measured in stage I–III tumors combined from left-sided colon cancers (LCCs, n = 14) and right-sided colon cancers (RCCs, n = 17) from patients with age < 55 years old. Data represent median with interquartile range. Nonparametric Wilcoxon Mann–Whitney U test, p values adjusted for false discovery rates (FDR) (Benjamini–Hochberg). ns. = not significant. CA, cholic acid; CDCA, chenodeoxycholic acid; TCA, taurocholic acid; TCDCA, taurochenodeoxycholic acid; GCA, glycocholic acid; GCDCA, glycochenodeoxycholic acid; DCA, deoxycholic acid; LCA, lithocholic acid; UDCA, ursodeoxycholic acid; TDCA, taurodeoxycholic acid; TLCA, taurolithocholic acid; GDCA, glycodeoxycholic acid; GLCA, glycolithocholic acid; GUDCA, glycoursodeoxycholic acid. Fig. S2. Differences in tumor tissue bile acids between left-sided colon cancers (LCCs) and right-sided colon cancers (RCCs) from patients ≥ 55 years old by stage. (A) stages I (LCC n = 25, RCC n = 22), and (B) stage II (LCC n = 42, RCC n = 44), and (C) stage III (LCCs, n = 32, RCCs, n = 32). Data represent median with interquartile range. Nonparametric Wilcoxon Mann–Whitney U test, p values adjusted for false discovery rates (FDR) (Benjamini–Hochberg). ns. = not significant. CA, cholic acid; CDCA, chenodeoxycholic acid; TCA, taurocholic acid; TCDCA, taurochenodeoxycholic acid; GCA, glycocholic acid; GCDCA, glycochenodeoxycholic acid; DCA, deoxycholic acid; LCA, lithocholic acid; UDCA, ursodeoxycholic acid; TDCA, taurodeoxycholic acid; TLCA, taurolithocholic acid; GDCA, glycodeoxycholic acid; GLCA, glycolithocholic acid; GUDCA, glycoursodeoxycholic acid. Fig. S3. Microbiota that differ in abundance between LCC and RCC tumor tissues using data from TCGA COAD. Abundances (log2) of A) Faecalibacterium, B) Coprococcus, C) Dorea, D) Luteibacter, and E) Sorangium, differences determined by t-test *FDR corrected p

Additional file 3. R code for performing survival analysis.

Additional file 1: Table S 1. Corresponding statistical tests and study attributes reported for analyses conducted on KPNA2 expression levels in breast cancer studies selected from cBioportal. Fig S 1. Beeswarm plots of KPNA2 mRNA expression levels as a function of patient age. Fig S 2. Pan cancer evaluation of KPNA2 transcriptional levels in different cancer types. Blue represents lower levels of expression, while red indicates overexpression of KPNA2. The numbers presented represent the number of studies meeting the search criteria for downstream analysis. Fig S 3. Corresponding boxplots of KPNA2 mRNA expression profiles in different breast cancer types (left boxes- 1) versus normal breast tissue (right boxes- 2) as obtained from Oncomine. Data presented for ductal breast carcinoma (DBC) (A), invasive ductal carcinoma (IDC) (B), invasive ductal breast carcinoma (IDBC) (C), invasive lobal breast carcinoma (ILBC) (D), invasive ductal breast carcinoma (IDBC) (E), breast carcinoma (BC) (F), invasive breast carcinoma (IBC) (G) and medullary breast carcinoma (MBC) (H). Table S 2. Breast cancer subtype expected False Discovery Rate (FDR) per prognostic endpoint from KM plotter. Fig S 4. Heatmap of KPNA2 mRNA co-expression with estrogen receptor (ESR1) and progesterone receptors (PGR) across all breast cancers, obtained from Geneminer. Fig S 5. Differences between KPNA2-correlated genes in healthy and a variety of breast cancer tissues. The colour shade indicates the level of confidence that two proteins are functionally associated, given the overall expression data. Co-expression scores are based on mRNA expression levels and protein co-regulation. The black colour indicates the absence of data. Heatmaps of protein-protein correlation matrices in healthy tissue (A), breast cancer tissue (B), ER+ (C), ER- (D), PR+ (E), and PR- breast cancer tissues. Table S 3. Categorizing multiple KPNA2 co-expressed genes according to their biological role and cancer implications obtained from Kyoto Encyclopedia of Genes and Genomes (KEGG) https://doi.org/10.1093/nar/28.1.27 . In association with UniProt and the Human Proteome Atlas, this resource provides a link of gene functions to published literature. Gene subcellular location obtained from EMBL-EBI GO Annotations (QuickGO (ebi.ac.uk)).

The realization of a synthetic biology approach to microbial (1R,2S,5R)-(−)-menthol (1) production relies on the identification of a gene encoding an isopulegone isomerase (IPGI), the only enzyme in the Mentha piperita biosynthetic pathway as yet unidentified. We demonstrate that Δ5-3- ketosteroid isomerase (KSI) from Pseudomonas putida can act as an IPGI, producing (R)-(+)-pulegone ((R)-2) from (+)-cis-isopulegone (3). Using a robotics-driven semirational design strategy, we identified a key KSI variant encoding four active site mutations, which confer a 4.3-fold increase in activity over the wild-type enzyme. This was assisted by the generation of crystal structures of four KSI variants, combined with molecular modeling of 3 binding to identify key active site residue targets. The KSI variant was demonstrated to function efficiently within cascade biocatalytic reactions with downstream Mentha enzymes pulegone reductase and (−)-menthone:(−)-menthol reductase to generate 1 from 3. This study introduces the use of a recombinant IPGI, engineered to function efficiently within a biosynthetic pathway for the production of 1 in microorganisms

Covering: 2000 to 2016 Progress in synthetic biology is enabled by powerful bioinformatics tools allowing the integration of the design, build and test stages of the biological engineering cycle. In this review we illustrate how this integration can be achieved, with a particular focus on natural products discovery and production. Bioinformatics tools for the DESIGN and BUILD stages include tools for the selection, synthesis, assembly and optimization of parts (enzymes and regulatory elements), devices (pathways) and systems (chassis). TEST tools include those for screening, identification and quantification of metabolites for rapid prototyping. The main advantages and limitations of these tools as well as their interoperability capabilities are highlighted.

In this study we have employed metabolomics approaches to understand the metabolic effects of producing enhanced green fluorescent protein (eGFP) as a recombinant protein in Escherichia coli cells. This metabolic burden analysis was performed against a number of recombinant expression systems and control strains and included: (i) standard transcriptional recombinant expression control system BL21(DE3) with the expression plasmid pET-eGFP, (ii) the recently developed dual transcriptional-translational recombinant expression control strain BL21(IL3), with pET-eGFP, (iii) BL21(DE3) with an empty expression plasmid pET, (iv) BL21(IL3) with an empty expression plasmid, and (v) BL21(DE3) without an expression plasmid; all strains were cultured under various induction conditions. The growth profiles of all strains together with the results gathered by the analysis of the Fourier transform infrared (FT-IR) spectroscopy data, identified IPTG-dependent induction as the dominant factor hampering cellular growth and metabolism, which was in general agreement with the findings of GC-MS analysis of cell extracts and media samples. In addition, the exposure of host cells to the synthetic inducer ligand, pyrimido[4,5-d] pyrimidine-2,4-diamine (PPDA), of the orthogonal riboswitch containing expression system (BL21(IL3)) did not display any detrimental effects, and its detected levels in all the samples were at similar levels, emphasising the inability of the cells to metabolise PPDA. The overall results obtained in this study suggested that although the BL21(DE3)-EGFP and BL21(IL3)-EGFP strains produced comparable levels of recombinant eGFP, the presence of the orthogonal riboswitch seemed to be moderating the metabolic burden of eGFP production in the cells enabling higher biomass yield, whilst providing a greater level of control over protein expression.

We have examined the biochemical responses of two sorghum cultivars of differing drought tolerance, Samsorg 17 (more drought tolerant) and Samsorg 40 (less drought tolerant), to sustained drought. Plants were exposed to different degrees of drought and then maintained at that level for five days. Responses were examined in terms of metabolic changes and the expression of drought induced proteins - Heat Shock Proteins (HSPs) and dehydrins (DHNs). Generalised phenotypic changes were studied using Fourier transform infrared (FT-IR) Spectroscopy and non-targeted Gas Chromatography Mass Spectrometry (GC-MS) was employed to detect changes in metabolites, while changes in protein expression were examined using Western blot analysis. Different response profiles of metabolites, HSPs and DHNs were observed in the two cultivars. Metabolic changes involved variation in amino acids, polysaccharides and their derivatives. A total of 188 compounds, with 142 known metabolites and 46 unknown small molecules, were detected in the two sorghum varieties. Under water deficit conditions, Samsorg 17 accumulated sugars and sugar alcohols, while in Samsorg 40 amino acids increased in concentration. This study suggest that the two Sorghum varieties adopt distinct approaches in response to drought, with Samsorg 17 being better able to maintain leaf function under severe drought conditions.

The Manchester Synthetic Biology Research Centre (SYNBIOCHEM) is a foundry for the biosynthesis and sustainable production of fine and speciality chemicals. The Centre's integrated technology platforms provide a unique capability to facilitate predictable engineering of microbial bio-factories for chemicals production. An overview of these capabilities is described.