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Tools for semiautomatic generation of genome-scale metabolic models have proved useful for expediting the reconstruction process ( 69 Hemisphere stripe detail jumper Find Great Sale Online OpFhRuyax
). In this study, using the Model SEED pipeline to concurrently generate the two reconstructions aided the manual curation and reconciliation phases since the differences in content and function between the two reconstructions could more easily be tracked and evaluated in parallel. The parallel curation process also ensured that the two reconstructions were of higher detail and quality than if the two reconstructions were developed in isolation, since genetic evidence for reactions were exhaustively cross-checked between the two species. It is unlikely that our reconstructions have incorporated every metabolic function of each species, and further curation will be required as the BCC knowledgebase grows. Growth prediction discrepancies on lysine, tryptophan, and branched-chain amino acids have highlighted areas to be investigated further experimentally.

The size and scope of the reconstructions in addition to high average gene count per reaction likely contributed to the reduced number of predicted essential genes in comparison to other reconstructions. To date, while there are emerging computational efforts to predict essential genes ( Limited Edition For Sale Cheap Sale In China Alexander McQueen jacquard skater dress Free Shipping Newest Hot KtDJouxCV
), no complete transposon mutagenesis library of B. cenocepacia or B. multivorans is available, and our network-driven in silico gene essentiality predictions serve as hypotheses for future work. The essential genes predicted to be unique to each species also offer interesting opportunities for targeted therapeutics. Given the general similarity of the network architectures in terms of the common incorporated reactions, the unique essential gene sets are a novel outcome of our comprehensive formulation and comparison of the GPR associations.

Our comparative analysis enabled system-wide evaluations as well as the evaluation of a single reaction or gene functionality that could be tied to a particular model prediction, which is a strength of genome-scale metabolic modeling. Modeling reaction activity using the reconstructions and constraint-based analysis allowed us to evaluate altered production capacity between models due to specific reaction differences in upstream pathways that limited final production levels. These analyses provide specific, testable hypotheses regarding important metabolic functions that are less intuitive than hypotheses generated through gene essentiality analysis alone.

Ultimately, each comparative analysis performed in this study supports the conclusion that B. cenocepacia has enhanced metabolic capacity over B. multivorans . The predicted growth rate on most in silico media of iBC was equal to or higher than that of iBM and notably higher on SCFM, a medium designed to replicate the environment of the cystic fibrosis lung (see Table S3 in the supplemental material). iBC also showed higher capacity for virulence factor production over iBM on a range of substrates found in CF lung sputum. Our reconstruction process enabled the organization and functional evaluation of the increased genetic redundancy (e.g., isozymes and gene duplications) across a range of pathways that were available to potentially supplement any disrupted gene function in iBC compared to iBM. This added genetic redundancy resulted in both metabolic robustness and increased opportunity for the evolutionary divergence of duplicated genes to perform new functions ( 73 ). We have shown that these reconciled metabolic reconstructions offer a way to develop and investigate intriguing hypotheses for the enhanced virulence of B. cenocepacia —a higher capacity for genetic adaptation in the CF lung in addition to the flexibility provided by a large number of distinct catabolic pathways.

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Assignment of each case to the two types of EATL was determined based on World Health Organization criteria ( Swerdlow et al., 2008 ). The two types of EATL were remarkably similar in terms of overall survival (median survival: 9 mo for type I, 11 mo for type II; Fig. S1 h), age of onset (64 yr for type I, 59 yr for type II), and gender (44% male for type I, 52% male for type II). As expected, the type I patients were primarily Caucasian (93%), whereas the type II patients were split (57% Caucasian and 39% Asian).

History or concurrent diagnosis of celiac disease was observed in 84% of type I EATL cases compared with 16% of type II cases. The DQ2 or DQ8 HLA genotypes were observed in 88% of type I EATL patients, and 43% of type II EATL patients (Table S4). Among patients with type I EATL, adherence to a gluten-free diet appeared to correlate with improved outcomes (P = 0.07; Fig. S1 i).

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View this table:
Table 1. Summary of EATL clinical characteristics

Given the paucity of our understanding of the molecular distinction between the two described types of EATL, we further investigated the mutation frequencies in type I ( n = 41) and type II ( n = 23) EATLs ( Fig. 2 a ). Overall, we found that the overwhelming majority of the genes (90%) to be mutated in both types of EATL. A few selected genes appeared to be mutated more frequently in one type or the other. SOCS1 and DAPK3 had higher frequencies in type I, whereas KRAS , STAT5B (>50% in type II), and SETD2 (>60% in type II) had higher frequencies in type II EATL. McQ Alexander McQueen laceup detail hoodie Sale Inexpensive RJFiLc
shows similar findings when we examined copy-number alterations separately in type I and II cases. Chromosome 9q gains were the most common alteration in both types. Diagnostic guidelines have attributed 1q gains primarily to type I EATL ( Mr amp; Mrs Italy tropical print parka coat Clean And Classic NdxSoyZ
); however, we found chromosome 1q gains to be present in at least 20% of cases in both types of EATL. Chromosome 8 alterations, particularly gains in chromosome 8q, which includes MYC , were significantly more frequent in type II EATL but also occurred in type I.

Figure 2.

Differences between type I and type II EATL: mutations, copy number, and gene expression. (a) Bar graph indicating frequency of mutation of each gene within the type I ( = 41) and type II ( = 23) EATL cohorts separately: type I frequency (purple; left); type II frequency (orange; right). (b) Bar graph indicating frequency of alteration of each arm-level copy-number alteration within the type I ( = 41) and type II ( = 23) EATL cohorts separately: type I frequency (purple; left); type II frequency (orange; right). (c) Principal-component (PC) analysis of EATL RNA-sequencing gene expression data ( = 29). The second principal component is on the x axis, and the third principal component is on the y-axis. One point is plotted for each sample in the analysis in the principal-component space, labeled by the clinical assignment of type I or type II. (d) Volcano plot showing the selection of differentially expressed genes between EATL type I ( = 16) and type II ( = 13). Every point is one gene in the analysis (number of significant genes is 578). The x axis shows the log2 transformed ratio of mean expression in the type I samples over the mean expression in the type II samples. The y axis shows the log10 transformed q-value (after Benjamini–Hochberg correction) of a Student’s test between EATL type I and type II. Genes that pass the thresholds for significance (dotted lines) are colored in orange and purple (FDR <0.1 and fold change >1.5× in either direction). Notable genes are labeled with text. (e) Heat map showing the genes that are differentially expressed between EATL type I and type II (type I: = 16; type II: = 13; number of genes shown is 578). Genes are median centered per row. Red indicates higher expression; blue indicates lower expression. The color bar shows the twofold range of expression depicted. Genes shown have FDR <0.1 and fold change >1.5× in either direction. Bars above the heat map indicate the clinical subtype, HLA type, and history of celiac disease. (f) Differentially expressed genes in the interferon-γ signaling pathway, showing higher expression in the EATL type I samples (type I: = 16; type II: = 13). (g) Gene set enrichment plot showing the enrichment of interferon-γ genes at the top of the ranked list, with genes ordered by difference in type I versus type II (type I: = 16; type II: = 13). Gene set enrichment analysis KS-test FDR <0.001. (h) Differentially expressed genes in the natural killer (NK)–like cytotoxicity pathway, showing higher expression in the EATL type II samples (type I: = 16; type II: = 13). (i) Gene set enrichment plot showing the enrichment of natural killer–like cytotoxicity genes at the top of the ranked list, with genes ordered by difference in type II versus type I (type I: = 16; type II: = 13). Gene set enrichment analysis KS-test FDR = 0.02.

These data indicate that type I and type II EATL cases have strongly overlapping genetic alterations and likely arise from shared genetic mechanisms.

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