Bugs in the system

Abstract

Immunity to respiratory virus infection is governed by complex biological networks that influence disease progression and pathogenesis. Systems biology provides an opportunity to explore and understand these multifaceted interactions based on integration and modeling of multiple biological parameters. In this review, we describe new and refined systems-based approaches used to model, identify, and validate novel targets within complex networks following influenza and coronavirus infection. In addition, we propose avenues for extension and expansion that can revolutionize our understanding of infectious disease processes. Together, we hope to provide a window into the unique and expansive opportunity presented by systems biology to understand complex disease processes within the context of infectious diseases.

Keywords: H1N1; H5N1; MERS-CoV; SARS-CoV; coronavirus; influenza; systems biology.

Figure 1

Systems biology requires integration across numerous components and disciplines. The experimental, modeling, and validation components utilized within our systems biology program divided into six functional categories. Each color within the border of the component signifies the various laboratory groups contributing to this component generation or analysis. Green – Baric RS (UNC); Blue – Heise MT (UNC); Red – Kawaoka Y(Wisconsin); Teal – Messer W(OHSU); Maroon – De Silva A(UNC); Pink – Gale (Univ. Washington); Purple – Pardo Manuel de Villena (UNC); Black – UNC Cystic Fibrosis Center; White – Katze (Univ. Washington); Yellow – Metz (PNNL); Fuchsia – Waters (PNNL); Orange – McWeeney (OHSU).

Figure 2

A refined systems biology paradigm layers in targeting and expansion. The standard systems biology paradigm includes an iterative cycle consisting of experimental design, data integration, predictive modeling, confirmation/validation, and model refinement (Blue circle). The new approach adds a targeting module that incorporates specific contrasts and biological knowledge to refine and improve the modeling outputs (Orange circle). Similarly, an expansion module adds the opportunity to explore the insights with further analysis using both systems‐ and reductionist‐based approaches to derive mechanistic insights (Green circle).

Figure 3

Context likelihood of relatedness method reveals functional modules that impact SARS ‐CoV or influenza infection in vivo . RNA expression data from in vivo infection of SARS‐CoV and H5N1‐VN1203 were utilized to infer coexpression networks between genes and help identify functional modules important during respiratory virus infection. Topological analysis permits identification of bottlenecks of network communication and provides potential target areas for knockdown studies. Data representative of RNA expression 4‐day post‐SARS‐CoV infection (100 pfu dose) with red representing upregulation and blue representing downregulation relative to mock.

Figure 4

Multidimensional scaling analysis illuminates variation between wildtype and mutant viruses. Multidimensional scaling analysis provides a statistical method to evaluate ISG expression of a virus relative to the expression from other mutants. Analysis completed on Calu3 cells following mock (Black), type I interferon treatment (Green), or infection with (A) wildtype (WT) SARS‐CoV (Orange), ΔORF6 (Red) and (B) WT H5N1‐VN1203 (Purple), NS1‐trunc124 (Teal), PB1‐F2del (Red), PB2‐627E (Maroon), and WT H1N1‐CA04 (Blue).

Figure 5

The collaborative cross offers the opportunity to examine allelic variation in systems‐based gene targets. (A) A schematic of three approaches used to evaluate how allelic diversity in a target gene may alter viral pathogenesis. Allele variation holds the background relatively constant and shifts the possible gene alleles. Background variation assesses a single allele in multiple backgrounds. Finally, paired combinations examine the relationship between allele combinations in multiple systems or genome‐wide association studies‐based targets. (B) Phylogenetic tree of mouse Serpine1 within the collaborative cross. (C) Consequences of single nucleotide polymorphisms within various CC strains. A. single nucleotide polymorphisms consequences include untranslated region (Blue); synonymous coding (Green); and non‐synonymous coding (Yellow). (D) Structure of human Serpine1 78.

Figure 6

Novel experimental conditions, modeling approaches, and validation methods will continue to expand the power of systems biology and provide further insights into complex biological interactions. Depictions of current and future data types are divided into the three faces on the systems biology cube. The right face includes experimental approaches that have and will be utilized for high‐throughput data generation. The left face illustrates modeling approaches and outputs from current and future studies. Finally, the top face shows results for validation and expansion of systems‐based targets.