Systems Immunology and Infection Microbiology

Systems Immunology and Infection Microbiology
Copyright
Contents
Preface
1 Introduction to systems immunology and infection microbiology
	1.1 Introduction
	1.2 Content and general outline of the book
2 Biological network modeling and system identification in systems immunology and infection microbiology
	2.1 System identification for gene regulatory network
		2.1.1 Least square parameter estimation method
		2.1.2 Maximum likelihood parameter estimation method
	2.2 System identification of protein–protein interaction network
	2.3 System identification of integrated genetic and epigenetic network via high throughput next generation sequencing data
	2.4 Conclusion
3 Identifying the gene regulatory network of systems inflammation in humans by system dynamic model via microarray data and...
	3.1 Introduction
	3.2 Construction of candidate inflammatory gene regulatory network in response to inflammatory stimulus
	3.3 Pruning the candidate gene regulatory network via a dynamic gene regulatory model
	3.4 On the construction of inflammatory gene regulatory network in immune system
	3.5 Biological insight and discussion
	3.6 Conclusion
	3.7 Material and methods
		3.7.1 Dataset selection
		3.7.2 Construction of candidate gene networks of systematic inflammation
		3.7.3 Constructing a dynamic regulatory model for gene regulatory network via microarray data
		3.7.4 Pruning the candidate gene regulatory network
	3.8 Appendix
		3.8.1 Dataset selection
		3.8.2 Gene network construction
		3.8.3 Discussion
4 Dynamic cross-talk analysis among signaling transduction pathways in the vascular endothelial inflammatory response syste...
	4.1 Introduction
	4.2 Methods of constructing cross talks among signaling pathways in inflammation
		4.2.1 Constructing the candidate protein–protein interaction networks
		4.2.2 Pruning the candidate protein–protein interaction network via a dynamic interaction model
		4.2.3 Cross-talk analysis by counting the cross-talk ranking values
	4.3 Signaling transduction, signaling pathways, and their cross talks in inflammatory response
		4.3.1 Construction of protein–protein interaction networks at different time stages of inflammatory system
		4.3.2 Investigation of the tumor necrosis factor α signaling pathway
		4.3.3 Investigation of the interleukin-1 receptor and Toll-like receptor 4 signaling pathways
		4.3.4 Cross-talk analysis of the protein–protein interaction networks
	4.4 Discussion
		4.4.1 Dynamic progression of the protein–protein interaction networks
		4.4.2 Specific architecture in the signaling transduction network
		4.4.3 Possible existence of Toll-like receptor 4 endogenous ligand
		4.4.4 Negative feedback controls of the cross talks among multiple signaling pathways
	4.5 Conclusion
	4.6 Appendix: Supplementary methods
		4.6.1 Identification of the interactive parameters of protein–protein interaction networks
		4.6.2 Determination of significant interaction pairs
5 Prediction of infection-associated genes via a cellular molecular network approach: A Candida albicans infection case study
	5.1 Introduction
	5.2 Methods of constructing cellular molecular networks in Candida albicans infection
		5.2.1 Method overview and data selection
		5.2.2 Constructing cellular molecular network
		5.2.3 Predicting infection-associated genes at different infection stages
	5.3 Infection-associated genes via cellular molecular network approach
		5.3.1 Prediction of Candida albicans infection-associated genes of each infection stage
		5.3.2 Investigation of Candida albicans adhesion associated genes at each infection stage
		5.3.3 Investigation of Candida albicans invasion stage-associated genes
		5.3.4 Investigation of Candida albicans damage stage-associated genes
	5.4 Discussion and conclusion
	5.5 Appendix
		5.5.1 Methods of cellular molecular network construction
6 Global screening of potential Candida albicans biofilm-related transcription factors by network comparison via big databa...
	6.1 Introduction
	6.2 Systems methods of screening biofilm-related transcription factors
		6.2.1 Overview of the proposed systems screening method
		6.2.2 Data used in the proposed systems screening method of biofilm-related transcription factors
		6.2.3 Selection scheme for transcription factors and target genes of biofilm formation and development
		6.2.4 Gene regulatory network reconstruction method
		6.2.5 Comparison scheme between two gene regulatory networks of biofilm and planktonic cells
	6.3 Potential Candida albicans biofilm-related transcription factors
		6.3.1 Screening of potential Candida albicans biofilm-related transcription factors
		6.3.2 The potential biofilm-related transcription factors in the formation and development of biofilm
		6.3.3 Statistical measures of the screening test
	6.4 Discussion
	6.5 Conclusion
	6.6 Appendix
		6.6.1 Appendix 1: Supplementary methods
			6.6.1.1 Identification of the gene regulatory parameters
			6.6.1.2 Determination of significant gene regulations
			6.6.1.3 Statistical measures of the screening test
		6.6.2 Appendix 2: Supplementary
		6.6.3 Appendix 3: Supplementary
		6.6.4 Appendix 4: Supplementary
		6.6.5 Appendix 5: Supplementary
7 Identification of infection- and defense-related genes through dynamic host–pathogen interaction network
	7.1 Introduction
	7.2 Material and methods of constructing host–pathogen interaction network in Candida albicans–zebrafish infection
		7.2.1 Simultaneous time-course microarray experiment of zebrafish and Candida albicans during Candida albicans infection
		7.2.2 Overview of the screening process of infection-related proteins
		7.2.3 Data selection and database mining
		7.2.4 Selection of protein pool for candidate protein–protein interaction networks
		7.2.5 Dynamic system model for the construction of organism protein interaction network during infection
		7.2.6 Determination of significant protein interaction pairings in infection PPI network
		7.2.7 Construction of an interspecies protein–protein interaction network between pathogen and host
	7.3 Pathogenic/offensive mechanism between Candida albicans and zebrafish in infection process
		7.3.1 Construction of the interspecies protein–protein interaction network during infection
		7.3.2 Inspection of the dynamic hyphal growth protein–protein interaction network of Candida albicans
		7.3.3 Utilization of dynamic intracellular protein–protein interaction networks to identify proteins with crucial roles in ...
		7.3.4 Interspecies protein–protein interaction network between Candida albicans and zebrafish during infection
	7.4 Discussion
	7.5 Conclusion
	7.6 Appendix
8 Host–pathogen protein–protein interaction network for Candida albican pathogenesis and zebrafish redox process through dy...
	8.1 Introduction
	8.2 Construction of host/pathogen protein–protein interaction network
		8.2.1 Overview of the host/pathogen protein–protein interaction network construction framework
		8.2.2 Data mining and integration of two-sided microarray data
		8.2.3 Selection of protein pool
		8.2.4 Inference of putative interspecies and intracellular protein–protein interactions
		8.2.5 Multivariate dynamic modeling and identification of host/pathogen protein–protein interaction network during Candida ...
		8.2.6 Identification of interactive abilities and determination of significant interactions of host/pathogen protein–protei...
	8.3 Host/pathogen protein–protein interaction network during the infection process of Candida albicans
		8.3.1 Construction of host/pathogen protein–protein interaction network
		8.3.2 Novel host/pathogen protein–protein interaction network highlights the association between Candida albicans pathogene...
	8.4 Discussion
	8.5 Conclusion
9 Essential functional modules for pathogenic and defensive mechanisms via host/pathogen crosstalk network by database mini...
	9.1 Introduction
	9.2 Material and methods
		9.2.1 Omics data selection and database mining
		9.2.2 Selection of protein pool
		9.2.3 Construction of protein–protein interaction networks
		9.2.4 Network reconfiguration between the early and late stages of the infection process
		9.2.5 Investigation of significant functional modules in the infection process
	9.3 Essential functional modules for pathogenic and defensive mechanisms
		9.3.1 Construction of dynamic protein–protein interaction networks and identification of significant proteins in Candida al...
		9.3.2 Investigation of essential cellular function modules for pathogenic and defensive mechanisms in Candida albicans infe...
			9.3.2.1 Functionally enriched Candida albicans modules for pathogenic mechanism in the infection process of C. albicans wit...
			9.3.2.2 Functionally enriched zebrafish modules for defensive mechanism during Candida albicans infection
	9.4 Discussion
	9.5 Conclusion
	9.6 Appendix
		9.6.1 Appendix 1: Supplementary method
			9.6.1.1 Details of protein interaction network construction
		9.6.2 Appendix 2: Supplemental figures
10 The role of inflammation and immune response in cerebella wound-healing mechanism after traumatic injury in zebrafish
	10.1 Introduction
	10.2 Materials and methods for constructing protein–protein interaction network of cerebellar wound-healing process in zebr...
		10.2.1 Stab lesion assay and time-course microarray experiments in zebrafish traumatic brain injury model
		10.2.2 Experiments for zebrafish movement index
		10.2.3 Big data mining for candidate protein–protein interaction network
		10.2.4 Dynamic network modeling for constructing cerebellar wound-healing protein–protein interaction network
		10.2.5 Systems biology tools and statistics
	10.3 The role of inflammation and immune response in the cerebellar wound-healing process
		10.3.1 The significant temporal patterns and PPI network for the cerebellar wound-healing process in the zebrafish TBI model
		10.3.2 Significant signaling pathways in the wound-healing process
		10.3.3 Significantly enriched signaling pathways of group P: Having positive correlation with ZMI in the wound-healing process
		10.3.4 Significantly enriched signaling pathways of group N: Having negative correlation with ZMI in the wound-healing process
		10.3.5 Cross talks between the three groups of proteins of protein–protein interaction network in the cerebellar wound-heal...
	10.4 Discussion and conclusion
	10.5 Appendix
		10.5.1 Method A1: Dynamic model of the wound healing–related cellular protein–protein interaction network
		10.5.2 Method A2: Interaction parameter identification using the time profiles microarray data
		10.5.3 Method A3: Determination of realistic interaction pairs
11 Key immune molecular biomarkers in the pathomechanisms of early cardioembolic stroke: Multidatabase mining and systems b...
	11.1 Introduction
	11.2 Immune events in pathomechanisms of early cardioembolic stroke
		11.2.1 Protein–protein interaction networks at different stages of cardioembolic stroke
		11.2.2 Changes in cellular functions and proteins immediately after cardioembolic strokes
		11.2.3 Changes in cellular functions and proteins after tissue plasminogen activator treatment
		11.2.4 Changes in cellular functions and proteins in early tissue plasminogen activator treatment
		11.2.5 Pathomechanisms of early stroke and potential drug targets
	11.3 Material and methods of PPI network construction and principle network projection
		11.3.1 Microarray data for early cardioembolic stroke
		11.3.2 Protein–protein interaction network construction
		11.3.3 Candidate protein–protein interaction network construction via multidatabase mining
		11.3.4 Protein interaction model
		11.3.5 Model order detection and identification
		11.3.6 Core protein–protein interaction network projection
			11.3.6.1 Cellular function networks
			11.3.6.2 Core protein–protein interaction networks
	11.4 Conclusion
	11.5 Appendix
12 Cross-talk network biomarkers of pathogen–host interaction network from innate to adaptive immunity
	12.1 Introduction
	12.2 Material and methods
		12.2.1 Overview of microarray data
		12.2.2 Protein pool selection and database mining
		12.2.3 Construction of pathogen–host protein–protein interaction network
		12.2.4 Interaction variation score calculation
	12.3 Investigating PH-PPINs for cross-talk network markers from innate to adaptive immunity
		12.3.1 The pathogen–host protein–protein interaction networks of innate and adaptive immunity
		12.3.2 Identifying cross-talk network biomarkers in the interaction difference network using interaction variation score
		12.3.3 The cross-talk network biomarkers in the host–host domain
		12.3.4 The cross-talk network biomarkers in the pathogen–pathogen domain
		12.3.5 The cross-talk network biomarkers in the pathogen–host domain
		12.3.6 The interplay among the cross-talk network biomarkers
	12.4 Discussion and conclusion
13 The coordination of defensive and offensive molecular mechanisms in the innate and adaptive host–pathogen interaction ne...
	13.1 Introduction
	13.2 Materials and methods to coordinate defensive molecular mechanisms in innate and adaptive host–pathogen networks
		13.2.1 Transcriptome datasets
		13.2.2 Dynamic protein–protein interaction model
		13.2.3 Candidate protein–protein interaction network
		13.2.4 Dynamic interaction model and model order detection method
	13.3 Defensive and offensive molecular mechanisms based on the innate and adaptive HP-PPINs
		13.3.1 Overview of dynamic innate and adaptive host–pathogen protein–protein interaction networks
		13.3.2 Interspecies cross talk between host immune–related molecular mechanisms and their pathogen counterparts
		13.3.3 Interspecies cross talk of pathogen resource competition–related molecular mechanisms and their host counterparts
		13.3.4 Impacts of immunological memory on host systems
	13.4 Discussion
	13.5 Appendix
14 The significant signaling pathways and their cellular functions in innate and adaptive immune responses during infection...
	14.1 Introduction of innate and adaptive immune systems
	14.2 Materials and methods
		14.2.1 Zebrafish strain and maintenance
		14.2.2 Candida albicans strain and growth conditions
		14.2.3 Infection and survival assay
		14.2.4 Purification of Candida albicans and zebrafish RNA
		14.2.5 Microarray experiments
	14.3 Investigating the defense/offensive strategies of innate and adaptive immunity
		14.3.1 Method for strategies of innate and adaptive immunity
		14.3.2 Dataset selection and target protein pool determination
		14.3.3 Construction of zebrafish intracellular protein–protein interaction networks
		14.3.4 Investigating the zebrafish intracellular protein–protein interaction networks for primary and secondary infection
		14.3.5 Centrality analysis of the zebrafish intracellular protein–protein interaction networks for primary and secondary in...
		14.3.6 Investigation of proteins common to protein–protein interaction networks for primary and secondary infection
	14.4 The roles of significant signaling pathways in the innate and adaptive immune responses
		14.4.1 The TGF-β pathway is involved in the control of the primary and secondary immune response
		14.4.2 The role of proteasome in controlling the adaptive immune response
		14.4.3 The regulation of apoptosis in the primary and secondary infection
		14.4.4 The identification of Ncstn for relationship between bacteria- and fungus-induced immune responses
	14.5 Conclusion
	14.6 Appendix
15 Genetic-and-epigenetic host/pathogen networks for cross-talk mechanisms in human macrophages and dendritic cells during ...
	15.1 Introduction to tuberculosis infected by Mycobacterium tuberculosis
	15.2 Materials and methods for constructing cross-talk GWGEINs and their core networks
		15.2.1 Overview of the construction processes of cross-talk GWGEINs in Mφs and DCs infected with Mtb
		15.2.2 Big data mining and data preprocessing
		15.2.3 Dynamic models of the cross-talk GWGEIN for Mφs, DCs, and Mtb during the early infection process
		15.2.4 System identification method of the dynamic models of GWGEIN
		15.2.5 System order detection scheme of the dynamic system models of GWGEIN
		15.2.6 Core network extraction from the real cross-talk GWGEIN by applying the PNP method
	15.3 Investigating pathogenic/host defense mechanism to identify drug targets
		15.3.1 GWGEINs of Mφs and DCs infected with Mtb
		15.3.2 HPCNs in Mφs and DCs infected with Mtb
			15.3.2.1 The biological processes of the host core networks in both cell types
			15.3.2.2 Host–pathogen cross-talk interactions in both cell types
			15.3.2.3 Host responses in Mnterrupt the antigen pMtb infection
			15.3.2.4 The defense mechanisms of Mtb in Min chanism
			15.3.2.5 Overview of the defense mechanisms of the host and pathogen and the dysfunctions of the host in Mction of s
		15.3.3 Drug targets, drug mining, and multimolecule drug design
	15.4 Conclusion
16 Investigating the host/pathogen cross-talk mechanism during Clostridium difficile infection for drug targets by construc...
	16.1 Introduction
	16.2 Materials and methods
		16.2.1 Overview of the construction of GEINs and HPCNs in Caco-2 cells during the early and late stages of CDI
		16.2.2 Big data mining and data preprocessing of host/pathogen gene/miRNA microarray data
		16.2.3 Construction of a candidate genetic-and-epigenetic interspecies network
		16.2.4 Dynamic models of GEINs for Caco-2 cells and Clostridium difficile during infection
		16.2.5 Parameter estimation of the dynamic models of candidate GEIN via the system identification method
		16.2.6 Pruning false positives in candidate GEIN for real GEIN via system order detection scheme
		16.2.7 Extracting core network structures from real GEINs via principal network projection method
	16.3 Investigating the cross-talk mechanism by constructing the genetic-and-epigenetic interspecies network
		16.3.1 The identified GEINs at the early and late stages of Clostridium difficile infection
			16.3.1.1 The host–pathogen core networks (HPCNs) during the infection of C. difficile
				16.3.1.1.1 Construction of HPCNs to investigate the epigenetic activities in host core networks of CDI
				16.3.1.1.2 Comparison of the pathogen core networks with previously predicted core genes in Clostridium difficile
				16.3.1.1.3 Cross-talk mechanisms among host–pathogen interactions and their validations
			16.3.1.2 A precise view of pathogenic effects and host responses at the early stage of C. difficile invasion
				16.3.1.2.1 Pathogenic factors utilized by C. difficile and the resulting pathogenesis in Caco-2 cells
				16.3.1.2.2 Caco-2 cells adopt autophagy, DNA damage response, and the activation of PAK1 and GRB2 as remedial schemes in re...
				16.3.1.2.3 The offensive mechanisms of Caco-2 cells and the defense mechanisms of C. difficile at the early stage of CDI
			16.3.1.3 The strong cellular activities of Caco-2 cells and the infection results of host and pathogen at the late stage of...
				16.3.1.3.1 The emphasized ROS production and stress accumulation of host cells, and the failure of antioxidative defense me...
				16.3.1.3.2 The apoptosis process triggered by severe inflammation, accumulated cellular stresses of Caco-2 cells, and the l...
			16.3.1.4 Drug targets prediction and multimolecule drug design for treating Clostridium difficile infection
	16.4 Discussion and conclusion
17 Investigating the common pathogenic mechanism for drug design between different strains of Candida albicans infection in...
	17.1 Introduction
	17.2 Materials and methods
		17.2.1 Overview of the construction of genetic and epigenetic interspecies networks and host–pathogen core cross-talk netwo...
		17.2.2 Data preprocessing of microarray data for human and pathogen
	17.3 Investigating pathogenic mechanism of C. albicans infection by comparing genetic and epigenetic interspecies networks
		17.3.1 The identified interspecies genetic and epigenetic interspecies networks under the infection of Candida albicans SC5...
		17.3.2 The host–pathogen core cross-talk networks during the infection of C. albicans SC5314 and C. albicans WO-1
		17.3.3 Analysis of core interspecies pathways to investigate host/pathogen cross-talk and common and specific pathogenic pr...
		17.3.4 Analysis of core interspecies pathways to investigate host/pathogen cross-talk and common and specific pathogenic me...
	17.4 Discussion
		17.4.1 Defense mechanism of OKF6/TERT-2 cell and the offense mechanism of different strains of C. albicans at host cell surface
		17.4.2 OKF6/TERT-2 cell confronts different strains of C. albicans by strong reactive oxygen species and microenvironment r...
		17.4.3 Released pathogenic factor and accumulated cellular response result in apoptosis and inflammatory response further l...
		17.4.4 Prediction of drug target proteins and multiple-molecules drug design for the infection of different strains of C. a...
	17.5 Conclusion
	17.6 Appendix
		17.6.1 Construction of candidate interspecies genetic and epigenetic interspecies networks via big data mining
		17.6.2 Dynamic models of candidate interspecies genetic and epigenetic interspecies networks for OKF6/TERT-2 cells and C. a...
		17.6.3 Parameter estimation of the dynamic models of candidate interspecies genetic and epigenetic interspecies network by ...
		17.6.4 Trimming false positives in candidate genetic and epigenetic interspecies networks by system order detection scheme
		17.6.5 Extracting core network structures from real interspecies genetic and epigenetic interspecies networks by using prin...
18 Constructing host/pathogen genetic-and-epigenetic networks for investigating molecular mechanisms to identify drug targe...
	18.1 Introduction
	18.2 Materials and methods
		18.2.1 Overview of the construction for interspecies GIGENs in human B cells infected with EBV during the lytic production ...
		18.2.2 Big data mining and data preprocessing of NGS data for human and EBV and methylation data for human
		18.2.3 Dynamic models of the interspecies GIGENs for human B cells and EBV during the lytic infection process
		18.2.4 System identification approach of the dynamic models of GIGENs
		18.2.5 System order detection scheme of the dynamic models of GIGENs
		18.2.6 Extracting core network from the real interspecies GIGEN by using the PNP method
	18.3 Investigating interspecies molecular mechanisms for human B lymphocytes infected with Epstein–Barr virus
		18.3.1 GIGENs of the first and the second infection stage in the lytic phase of B cells infected with EBV
	18.4 HVCNs at the first and second infection stage in the lytic phase of B cells infected with EBV
		18.4.1 The significant cellular processes of the HVCNs in the lytic replication cycle
		18.4.2 The intracellular signaling pathways in HVCNs modified by the epigenetic regulation during the lytic infection
	18.5 HVCPs at the first and second infection stage during the lytic replication cycle
		18.5.1 The new virion production through host–virus cross-talk interactions at the first infection stage
	18.6 The transportation process of viral particles through host–virus cross-talk interactions at the second infection stage
	18.7 Overview of the lytic infection molecular mechanism from the first to second infection stage in human B cells infected...
	18.8 Drug target proteins and multimolecule drug design
	18.9 Discussion
	18.10 Conclusion
19 Human immunodeficiency virus–human interaction networks investigating pathogenic mechanism via for drug discovery: A sys...
	19.1 Introduction
	19.2 Investigate pathogenic mechanisms at different stages of human immunodeficiency virus infection
		19.2.1 Functional analysis of the core PPMI networks at three infection stages
	19.3 HIV/human interaction networks for multiple drug designs at three infection stages
		19.3.1 Functional analysis of the common core PPMI network at the three infection stages
		19.3.2 Functional analysis of host–pathogen interaction networks
		19.3.3 Functional analysis of the specific PPMI networks at the three infection stages
		19.3.4 Network-based pathway enrichment analysis
		19.3.5 Multiple drug combinations for treatment at each stage of HIV-1 infection based on the specific PPMI networks
	19.4 Methods
		19.4.1 Protein pool selection
		19.4.2 Reconstruction of the candidate PPMI network through data mining
		19.4.3 Dynamic system models of the host–pathogen interspecies PPMI network
		19.4.4 Use of an infection score to Identify the core proteins involved in the changes of the biological processes between ...
		19.4.5 Extraction of the common and specific PPMI networks during the HIV-1 infection
		19.4.6 Design strategy for determining multiple molecule drug combinations for treating patients with HIV-1 infection in CD...
	19.5 Conclusion
	19.6 Abbreviations
	19.7 Appendix
		19.7.1 Identification of the real PPMI network by pruning false-positive interactions through system identification and sys...
20 Systems multiple-molecule drug design in infectious diseases: Drug-design specifications approach
	20.1 Introduction
	20.2 Systems drug-design method in infectious diseases
		20.2.1 Systems multiple drug design of infectious diseases with less side effects: Drug-design specification approach
		20.2.2 Identification of multiple drug targets for infectious diseases via systems biology method
		20.2.3 Multiple-molecule drug design of infectious diseases with less side effects based on drug-design specifications
		20.2.4 Multiple-molecule drug design with less side effects in bacterial infection diseases
		20.2.5 Multiple-molecule drug design with less side effects in viral infection disease
	20.3 Discussion
	20.4 Conclusion
References
Index