Identifying pathway modules of tuberculosis in children by analyzing multiple different networks

Cheng, Lu; Han, Yanxiao; Zhao, Xiuxia; Xu, Xiaoli; Wang, Jing

doi:10.3892/etm.2017.5434

Cited by 2 publications

(2 citation statements)

References 28 publications

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“…CFHR5, ILF2, and LTN are novel protein candidate biomarkers for TB identified by the discovery phase and all were successfully verified. Consistent with our findings, a recent report identified ILF2 as a potential biomarker in pediatric TB by bioinformatic mining of gene expression data sets (61).…”

Section: L I N I C a L M E D I C I N Esupporting

confidence: 92%

Comprehensive plasma proteomic profiling reveals biomarkers for active tuberculosis

Garay‐Baquero¹,

White²,

Walker³

et al. 2020

JCI Insight

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BACKGROUND. Tuberculosis (TB) kills more people than any other infection, and new diagnostic tests to identify active cases are required. We aimed to discover and verify novel markers for TB in nondepleted plasma. METHODS. We applied an optimized quantitative proteomics discovery methodology based on multidimensional and orthogonal liquid chromatographic separation combined with high-resolution mass spectrometry to study nondepleted plasma of 11 patients with active TB compared with 10 healthy controls. Prioritized candidates were verified in independent UK (n = 118) and South African cohorts (n = 203). RESULTS. We generated the most comprehensive TB plasma proteome to date, profiling 5022 proteins spanning 11 orders-of-magnitude concentration range with diverse biochemical and molecular properties. We analyzed the predominantly low-molecular weight subproteome, identifying 46 proteins with significantly increased and 90 with decreased abundance (peptide FDR ≤ 1%, q ≤ 0.05). Verification was performed for novel candidate biomarkers (CFHR5, ILF2) in 2 independent cohorts. Receiver operating characteristics analyses using a 5-protein panel (CFHR5, LRG1, CRP, LBP, and SAA1) exhibited discriminatory power in distinguishing TB from other respiratory diseases (AUC = 0.81). CONCLUSION. We report the most comprehensive TB plasma proteome to date, identifying novel markers with verification in 2 independent cohorts, leading to a 5-protein biosignature with potential to improve TB diagnosis. With further development, these biomarkers have potential as a diagnostic triage test.

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Section: L I N I C a L M E D I C I N Esupporting

confidence: 92%

Comprehensive plasma proteomic profiling reveals biomarkers for active tuberculosis

Garay‐Baquero¹,

White²,

Walker³

et al. 2020

JCI Insight

View full text Add to dashboard Cite

show abstract

“…DINGO examined genes belonging to biological pathways in giloblastoma and inferred differential correlation-based GRNs by decomposing them to global and group-specific components (Ha et al, 2015). Differential coexpression networks were constructed from gene expression data and protein-protein interaction datasets (Cheng et al, 2018). A Two Dimensional Joint Graphical Lasso (TDJGL) model enhanced the performance of coexpressed networks with gene expression profiles collected across multiple databases (Zhang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

DiffGRN: differential gene regulatory network analysis

Youngsoon

Hao

Gautam

et al. 2018

IJDMB

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Identification of differential gene regulators with significant changes under disparate conditions is essential to understand complex biological mechanism in a disease. Differential Network Analysis (DiNA) examines different biological processes based on gene regulatory networks that represent regulatory interactions between genes with a graph model. While most studies in DiNA have considered correlation-based inference to construct gene regulatory networks from gene expression data due to its intuitive representation and simple implementation, the approach lacks in the representation of causal effects and multivariate effects between genes. In this paper, we propose an approach named Differential Gene Regulatory Network (DiffGRN) that infers differential gene regulation between two groups. We infer gene regulatory networks of two groups using Random LASSO, and then we identify differential gene regulations by the proposed significance test. The advantages of DiffGRN are to capture multivariate effects of genes that regulate a gene simultaneously, to identify causality of gene regulations, and to discover differential gene regulators between regression-based gene regulatory networks. We assessed DiffGRN by simulation experiments and showed its outstanding performance than the current state-of-the-art correlation-based method, DINGO. DiffGRN is applied to gene expression data in asthma. The DiNA with asthma data showed a number of gene regulations, such as ADAM12 and RELB, reported in biological literature.

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Identifying pathway modules of tuberculosis in children by analyzing multiple different networks

Cited by 2 publications

References 28 publications

Comprehensive plasma proteomic profiling reveals biomarkers for active tuberculosis

Comprehensive plasma proteomic profiling reveals biomarkers for active tuberculosis

DiffGRN: differential gene regulatory network analysis

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