# COMPUTATIONAL SYSTEMS BIOLOGY OF PATHOGEN-HOST INTERACTIONS

EDITED BY: Saliha Durmus¸ , Tunahan Çakır and Reinhard Guthke PUBLISHED IN: Frontiers in Microbiology

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ISSN 1664-8714 ISBN 978-2-88919-821-4 DOI 10.3389/978-2-88919-821-4

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# **COMPUTATIONAL SYSTEMS BIOLOGY OF PATHOGEN-HOST INTERACTIONS**

Topic Editors: **Saliha Durmus¸ ,** Gebze Technical University, Turkey **Tunahan Çakır,** Gebze Technical University, Turkey **Reinhard Guthke,** Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knoell-Institute, Germany

Visualization of the 3D cuboid environment of the agent-based model that corresponds to 1µl of the whole-blood infection assay, containing 5000 polymorphonuclear neutrophils, 500 monocytes, and 1000 Candida albicanscells.

Image taken from: Lehnert T, Timme S, Pollmächer J, Hünniger K, Kurzai O and Figge MT (2015) Bottom-up modeling approach for the quantitative estimation of parameters in pathogen-host interactions. Front. Microbiol. 6:608. doi: 10.3389/fmicb.2015.00608

A thorough understanding of pathogenic microorganisms and their interactions with host organisms is crucial to prevent infectious threats due to the fact that Pathogen-Host Interactions (PHIs) have critical roles in initiating and sustaining infections. Therefore, the analysis of infection mechanisms through PHIs is indispensable to identify diagnostic biomarkers and next-generation drug targets and then to develop strategic novel solutions against drug-resistance and for personalized therapy. Traditional approaches are limited in capturing mechanisms of infection since they investigate hosts or pathogens individually. On the other hand, the systems biology approach focuses on the whole PHI system, and is more promising in capturing infection mechanisms. Here, we bring together studies on the below listed sections to present the current picture of the research on Computational Systems Biology of Pathogen-Host Interactions:

# **Computational Inference of PHI Networks using Omics Data**

Gene regulatory, metabolic and protein-protein networks of PHI systems are crucial for a thorough understanding of infection mechanisms. Great advances in molecular biology and biotechnology have allowed the production of related omics data experimentally. Many computational methods are emerging to infer molecular interaction networks of PHI systems from the corresponding omics data.

# **Computational Prediction of PHIs**

Due to the lack of experimentally-found PHI data, many computational methods have been developed for the prediction of pathogen-host protein-protein interactions. Despite being emerging, currently available experimental PHI data are far from complete for a systems view of infection mechanisms through PHIs. Therefore, computational methods are the main tools to predict new PHIs. To this end, the development of new computational methods is of great interest.

# **Text Mining of PHI Data from Literature**

Despite the recent development of many PHI-specific databases, most data relevant to PHIs are still buried in the biomedical literature, which demands for the use of text mining techniques to unravel PHIs hidden in the literature. Only some rare efforts have been performed to achieve this aim. Therefore, the development of novel text mining methods specific for PHI data retrieval is of key importance for efficient use of the available literature.

# **Mathematical Modeling and Bioinformatic Analysis of PHIs**

After the reconstruction of PHI networks experimentally and/or computationally, their mathematical modeling and detailed computational analysis is required using bioinformatics tools to get insights on infection mechanisms. Bioinformatics methods are increasingly applied to analyze the increasing amount of experimentally-found and computationally-predicted PHI data.

# **Acknowledgements**

We, editors of this e-book, acknowledge Emrah Nikerel (Yeditepe University, Turkey) and Arzucan Özgür (Bog˘aziçi University, Turkey) for their contributions during the initiation of the Research Topic.

**Citation:** Durmus¸, S., Çakır, T., Guthke, R., eds. (2016). Computational Systems Biology of Pathogen-Host Interactions. Lausanne: Frontiers Media. doi: 10.3389/978-2-88919-821-4

# Table of Contents

Andreas Dix, Kerstin Hünniger, Michael Weber, Reinhard Guthke, Oliver Kurzai and Jörg Linde

Teresa Lehnert, Sandra Timme, Johannes Pollmächer, Kerstin Hünniger, Oliver Kurzai and Marc Thilo Figge

Kaswara Kraibooj, Hanno Schoeler, Carl-Magnus Svensson, Axel A. Brakhage and Marc Thilo Figge

# Editorial: Computational Systems Biology of Pathogen-Host Interactions

Saliha Durmu ¸s 1 \*, Tunahan Çakır 1 and Reinhard Guthke2

*1 Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey, 2 Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Jena, Germany*

Keywords: pathogen-host interaction, computational systems biology, bioinformatics, omics data, network inference, text mining, constraint-based modeling, image-based systems biology

**The Editorial on the Research Topic**

# **Computational Systems Biology of Pathogen-Host Interactions**

#### Edited by:

*Rustam Aminov, Technical University of Denmark, Denmark*

#### Reviewed by:

*Chuang Ma, Northwest Agricultural and Forestry University, China*

> \*Correspondence: *Saliha Durmu ¸s<EMAIL_ADDRESS>
#### Specialty section:

*This article was submitted to Infectious Diseases, a section of the journal Frontiers in Microbiology*

Received: *10 December 2015* Accepted: *11 January 2016* Published: *04 February 2016*

#### Citation:

*Durmu ¸s S, Çakır T and Guthke R (2016) Editorial: Computational Systems Biology of Pathogen-Host Interactions. Front. Microbiol. 7:21. doi: 10.3389/fmicb.2016.00021* Pathogen-Host Interactions (PHIs) play a significant role in the mechanisms of infections. Therefore, the investigation of infection mechanisms through PHIs is a crucial step to develop novel and more effective solutions against drug-resistance and for personalized therapy. To this aim, systems biology approach considers the whole PHI system instead of focusing hosts or pathogens individually. Computational modeling and analysis has a vital place within the whole systems biology workflow (Cyclic operation of experimental and modeling work). Multi-scale modeling provides the holistic view needed in the investigation of pathogen-host molecular interactions. However, it is usually very difficult to identify the model structure and parameters for complex multi-scale models. On the other hand, focused modeling types require more stringent and advanced feature selection approaches.

This research topic aims to provide examples from the current picture of the research on computational systems biology of PHIs. The papers included here review recent studies or present original research on computational inference of PHI networks, computational prediction of PHIs, text mining of PHI data from the literature, and mathematical modeling and computational analysis of PHI networks. This research topic presents three review papers, 10 original research articles, and one technology report.

Opening this research topic, we provide a comprehensive review of the studies on computational systems biology of PHIs (Durmu¸s et al.). We focus on the computational methods for the inference of molecular interaction networks of PHI systems, bioinformatic analysis of PHI networks, the Web-based PHI databases, and text-mining efforts to extract PHI data hidden in the literature. In this sense, this review provides a systems perspective on which the other articles covered in this research topic are based.

# PHI NETWORK INFERENCE USING OMICS DATA

Schulze et al. deal with the challenge of the inference of inter-species gene regulatory networks from dual transcriptomic data. They use an extended version of NetGenerator, an ordinary differential equations (ODEs)-based tool for network inference that predicts gene regulatory networks from gene expression time series data (Guthke et al., 2005; Tierney et al., 2012).

Budak et al. use a temporal phosphoproteomic dataset of Salmonella-infected human cells (Rogers et al., 2011) to reconstruct the temporal signaling network of the human host by integrating protein-protein interaction (PPI) and the phosphoproteomic data. The Prize-collecting Steiner Forest approach and the Integer Linear Programming based edge inference approach are employed. The complementary use of both methods leads to a network which conserves the information about temporality, direction of interactions, while revealing the hidden entities in the signaling.

# COMPUTATIONAL PREDICTION OF PHIs

Despite the recent advances, the experimentally-found PHI data are still scarce and the computational prediction is a valuable source of PHI data currently. The computational prediction primarily exploits sequence information, protein structure and known interactions. Machine learning techniques are used when there are sufficient known interactions available to be used as training data. On the opposite case, transfer and multitask learning methods are preferred. Nourani et al. provide an overview of these approaches for predicting PHIs.

Experimentally verified data on fungi-host interactions are rare in the literature and in the PHI databases. Remmele et al. reconstruct large-scale PHI networks for the fungal pathogens Aspergillus fumigatus and Candida albicans and their human and mouse hosts. A computational PHI prediction method based on protein orthology, PPI data as well as data on gene functions and cellular localization was developed and used.

# TEXT MINING OF PHI DATA

The emergence of large-scale experimental PHI data has led to the development of PHI databases such as VirusMentha (Calderone et al., 2015), VirhostNet (Guirimand et al., 2015), PATRIC (Wattam et al., 2014), HPIDB (Kumar and Nanduri, 2010), and PHISTO (Durmu¸s Tekir et al., 2013). Nevertheless, most data regarding PHIs are still buried in the articles and they have not been stored in databases. Karadeniz et al. extend text mining tool SciMiner, originally developed for extracting intraspecies molecular interactions, for inter-species PHIs. They use SciMiner to extract host-Brucella gene-gene interactions, which are further analyzed by ontology modeling.

# MATHEMATICAL MODELING AND BIOINFORMATIC ANALYSIS OF PHIs

Few examples of constraint-based PHI models are currently available in the literature. However, there is a lack of definite description of the methodology required for the functional integration of genome scale metabolic models in order to generate PHI models. Jamshidi and Raghunathan outline a systematic procedure to produce functional PHI models, highlighting steps which require debugging and iterative revisions in order to successfully build a functional model. The construction of such models will enable the exploration of PHIs by leveraging the growing wealth of omics data in order to better understand mechanisms of infection and identify novel therapeutic strategies.

Dühring et al. describe the cross-talk between the fungal pathogen C. albicans and the human innate immune system. They review computational systems biology approaches to model and investigate these complex interactions with a special focus on fungal immune evasion and game-theoretical and agent-based models.

Nguyen et al. use ODEs to represent the basic interactions between Ebola virus and wild-type Vero cells, i.e., epithelial cells of green monkeys, in vitro. The parameters in viral kinetics are estimated leading to a first mathematical model for Ebola virus infection.

Dix et al. examine the transcriptional footprint of the host in response to the bacterial pathogens Staphylococcus aureus and Escherichia coli and the fungal pathogens C. albicans and A. fumigatus in a human whole-blood model. Expression data are exploited to build a random forest classifier to classify if a sample contains a bacterial, fungal or mock-infection.

Sinclair et al. develop a method combining in silico prediction of bacterial nucleomodulins, i.e., proteins targeted to the host cell nucleus, and iTRAQ protein profiling (a mass spectrometric technique where two protein expression profiles are compared) to identify potential bacterial-derived nuclear-translocated proteins that could impact transcriptional programming in host cells. This approach was applied to intracellular bacteria such as Anaplasma phagocytophilum, Mycobacterium tuberculosis, and Chlamydia trachomatis.

Finally, the research topic includes articles focusing on image-based systems biology of PHIs. While advances in omics techniques drive the progress of system biology on molecular level, there is also a significant progress on the cellular level based spatio-temporal data, e.g., microscopy images. Lehnert et al. apply non-spatial state-based modeling and agent-based modeling approaches to simulate an experimental assay for C. albicans infection of human blood. They predict cell migration parameters in 3D space where monocytes, granulocytes, and C. albicans cells are treated as migrating and interacting agents. Pollmächer and Figge implement a hybrid agent-based spatio-temporal modeling approach for A. fumigatus infection in human alveoli to decipher chemokine properties. They found by model simulations that the ratio of chemokine secretion rate to the diffusion coefficient is the main indicator for the success of pathogen detection by alveolar macrophages. Kraibooj et al. suggest a novel image analysis algorithm for the automated quantification of the phagocytosis of two wild type A. fumigatus strains. The strains were compared in terms of the phagocytosis process when the fungal conidia interact with alveolar macrophages.

The computational modeling of PHI networks of interacting genes, transcripts, proteins, and metabolites is crucial to enlighten the molecular mechanisms of infection. The experimental detection of levels of biomolecules via omics approaches as well as the detection of PHIs via high-throughput experiments started to generate comprehensive datasets. The modeling of the large-scale data will not only elucidate the mechanisms of infection, but will help in the discovery of biomarkers for novel diagnostic tools and of therapeutic drug targets through identification of essential molecules for the pathogen. Despite the recent efforts, the use of systems biology approaches to investigate PHI systems is still in its infancy, mostly because of data scarcity (Durmu¸s et al.). Ongoing studies in the field will certainly produce more large-scale PHI data in the near future. Heterogeneous data sets (clinical, microbiological, chemical, molecular on different levels such as SNPs, transcriptome, proteome, FACS, microscopic, mass spectrometric, etc.) will be integrated. More complete PHI models will allow the integration of omics-based and imagebased systems biology of infection and will pioneer more complex multi-scale models with different scale in space (from molecules/cells/tissues to organism/population) and time (from

# REFERENCES

seconds to month). These more complex models will improve the PHI-based solutions to infectious diseases.

# AUTHOR CONTRIBUTIONS

SD conceived the content and drafted the manuscript; TC and RG conceived the content and revised the manuscript.

# ACKNOWLEDGMENTS

TC was supported by the Turkish Academy of Sciences - Outstanding Young Scientists Award Program (TÜBA-GEB˙IP). RG was supported by the Deutsche Forschungsgemeinschaft (DFG) in the Collaborative Research Centre/Transregio 124 FungiNet (subprojects B3 and INF).

regulators and SopB-dependent host phosphorylation events. Sci. Signal. 4, rs9. doi: 10.1126/scisignal.2001668

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2016 Durmu¸s, Çakır and Guthke. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# A review on computational systems biology of pathogen–host interactions

# *Saliha Durmu ¸s1\*, Tunahan Çakır1, Arzucan Özgür2 and Reinhard Guthke3*

*1 Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey, 2 Department of Computer Engineering, Bogaziçi University, Istanbul, Turkey, ˇ 3 Leibniz Institute for Natural Product Research and Infection Biology – Hans-Knoell-Institute, Jena, Germany*

Pathogens manipulate the cellular mechanisms of host organisms via pathogen– host interactions (PHIs) in order to take advantage of the capabilities of host cells, leading to infections. The crucial role of these interspecies molecular interactions in initiating and sustaining infections necessitates a thorough understanding of the corresponding mechanisms. Unlike the traditional approach of considering the host or pathogen separately, a systems-level approach, considering the PHI system as a whole is indispensable to elucidate the mechanisms of infection. Following the technological advances in the post-genomic era, PHI data have been produced in large-scale within the last decade. Systems biology-based methods for the inference and analysis of PHI regulatory, metabolic, and protein–protein networks to shed light on infection mechanisms are gaining increasing demand thanks to the availability of omics data. The knowledge derived from the PHIs may largely contribute to the identification of new and more efficient therapeutics to prevent or cure infections. There are recent efforts for the detailed documentation of these experimentally verified PHI data through Web-based databases. Despite these advances in data archiving, there are still large amounts of PHI data in the biomedical literature yet to be discovered, and novel text mining methods are in development to unearth such hidden data. Here, we review a collection of recent studies on computational systems biology of PHIs with a special focus on the methods for the inference and analysis of PHI networks, covering also the Web-based databases and text-mining efforts to unravel the data hidden in the literature.

Keywords: pathogen–host interaction, computational systems biology, bioinformatics, omics data, protein– protein interaction, metabolic interaction, gene regulatory network, drug target

# Introduction

Infectious diseases are one of the preliminary causes of death worldwide each year. Emerging and reemerging diseases and drug resistant pathogens have made the problem more serious for human beings. Therefore, novel therapeutic strategies, called theranostics, are increasingly investigated to fight the biological threats. These strategic solutions require a systems biological approach with a thorough understanding of the underlying mechanisms of infections by focusing on molecular interactions between pathogenic and host organisms (Morens et al., 2004;

#### *Edited by:*

*Anna Norrby-Teglund, Karolinska Institutet, Sweden*

#### *Reviewed by:*

*Marcio Luis Acencio, São Paulo State University, Brazil Peter Schaap, Wageningen University, Netherlands*

#### *\*Correspondence:*

*Saliha Durmu ¸s, Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, 41400 Gebze-Kocaeli, Turkey<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Infectious Diseases, a section of the journal Frontiers in Microbiology*

> *Received: 01 December 2014 Accepted: 10 March 2015 Published: 09 April 2015*

#### *Citation:*

*Durmu ¸s S, Çakır T, Özgür A and Guthke R (2015) A review on computational systems biology of pathogen–host interactions. Front. Microbiol. 6:235. doi: 10.3389/fmicb.2015.00235* Murali et al., 2011; Guthke et al., 2012; Durmu¸s Tekir and Ülgen, 2013). Systems biology is an interdisciplinary research field in life sciences focusing on the study of non-linear interactions among biology entities through the integration and combination of biomolecular and medical sciences with mathematical, computational, and engineering disciplines (Kitano, 2002). By modeling biological phenomena, systems biology uses a more holistic approach based on omics data instead of the traditional reductionism focusing at only a few molecules and interactions. The pathogen–host interactions (PHIs) may be between proteins, nucleotide sequences, metabolites, and small ligands. The protein–protein interactions (PPIs) have been identified as the most important type in the functioning of PHI systems and therefore are the most studied type (Stebbins, 2005; Korkin et al., 2011; Zoraghi and Reiner, 2013). However, non-coding RNAs (ncRNAs) and metabolites have also been reported to have critical functional roles in virus–host and bacteria–host interactions, respectively (Gottwein and Cullen, 2008; Skalsky and Cullen, 2010; Eisenreich et al., 2013; Saayman et al., 2014).

Different levels of omics data collected from pathogens and/or infected cells are crucial components that drive bioinformatic analyses facilitating the construction and analysis of infectionspecific gene-regulatory, metabolic, and protein–protein networks (Westermann et al., 2012; Schulze et al., 2015). Such network-based computational systems biology analyses of PHIbased omics data enable the elucidation of infection mechanisms and their dynamics, the identification of potential drug targets for the next-generation antimicrobial therapeutics, and the development of novel and personalized strategies for the prevention and treatment of infections. With an increasing amount of experimental PHI data, Web-based databases were developed to derive and provide pathogen–host interactome data, usually focusing on specific pathogens or hosts (Wattam et al., 2014; Ako-Adjei et al., 2015; Calderone et al., 2015; Guirimand et al., 2015). Although the available databases are promising in data archiving, a huge amount of PHI data is not stored in any of these databases, since these data are buried in the literature. Therefore, there is an urgent need for novel text mining methods specific for PHI data retrieval. In this paper, the efforts on the collection of PHI-based omics data are reviewed first. Next, a review of the computational systems biology analyses of three major types of PHI networks is provided. Then, the available PHI databases and the current snapshot of the literature on text mining for PHI data are presented.

# Omics Data Reflecting PHI Networks

The systems biology approaches with genome-wide molecular profiling using high-throughput techniques to generate omics data are changing the face of infection biology together with the computational methods for heterogeneous data management and integrative analysis via mathematical modeling (Guthke et al., 2012; Law et al., 2013). New insights in the microbial and viral pathogenesis, in particular in the host's immune response to contact with pathogens, offer opportunities for better diagnostics, therapeutics, and vaccines. Thus, systems biology of infection allows to yield novel therapeutic targets (Sarker et al., 2013) and to establish individualized or personalized medicine. The integrative personal omics profile (iPOP) combines genomics, transcriptomics, proteomics, metabolomics, and autoantibody profiles from a single individual over a 14-month period (Chen et al., 2012; Li-Pook-Than and Snyder, 2013).

There are various platforms for handling of measured data from samples, data storage and exchange, data pre-processing and data analysis. Powerful platforms for data management in systems biology have recently become available and are standardized step by step by the Functional Genomics Data Society1 (FGED, founded in 1999 as MGED; Brazma et al., 2006). Several systems biology projects in Europe including the ones dedicated to PHI research use the SysMO-DB/SEEK system for sharing data, knowledge (including Standard Operating Procedures – SOPs) and mathematical models2 (Wolstencroft et al., 2011). For the management of genomics, transcriptomics, and (2Dgel) proteomics data in infection research, the data warehouse 'OmniFung' was established to support research on fungi–host interactions3 (Albrecht et al., 2011, 2007).

The free, open source and open development software project Bioconductor, which is primarily based on the statistical R programming language, provides 934 software packages, 894 annotation and 224 experimental data sets for the bioinformatic analysis and comprehension of high-throughput genomic data4 (Version 3.0). These packages as well as other R packages not included in the Bioconductor project are useful for the advanced, in particular integrative, analysis of omics data and modeling of PHIs. To identify genes, proteins or metabolites of interest for biomarker discovery or drug target prediction by supervised machine learning methods, there are many data mining tools available. For instance, WEKA5 or RapidMiner6 is used to characterize the response of the host immune system by decision tree analysis of flow cytometric data (Simon et al., 2012). In addition, there are platforms and software tools for the integrative and explorative analysis and visualization of data from the different omics levels of PHIs (Horn et al., 2014).

# PHI-Based Genome and Transcriptome Data

The genomic information from the host and the pathogen represents the basis for all further molecular analyses and bioinformatic investigations of PHI systems. Thus, genome sequencing is fundamental. It helps to improve diagnosis, typing of pathogen, virulence and antibiotic resistance detection, and development of new vaccines and culture media. Single nucleotide polymorphism (SNP) typing is important for both identification and characterization of variants of pathogens (strains, clinical isolates) as well as to study the susceptibility of humans for certain infections. In the last decade, there was, and in the future there will be, an explosion of genome sequence data.

1http://fged.org

2www.sysmo-db.org

3www.omnifung.hki-jena.de

4http://bioconductor.org

5http://www.cs.waikato.ac.nz/ml/weka 6www.rapidminer.de

The new sequencing technologies enable small research units to create huge genome datasets at low cost in short time. As a result, handling, comparing, and extracting useful information from millions of sequences becomes more and more challenging, i.e., increased efforts in computational biology are urgently needed. In particular, sequencing is used for genomic and transcriptomic characterization of new emerging pathogens. Wholegenome sequencing based phylogenetic studies have implications for understanding the evolution of the PHIs as well as tracking and possibly preventing infection diseases as performed for the Enterotoxigenic *Escherichia coli* (ETEC), a major cause of infectious diarrhea (von Mentzer et al., 2014). Metagenomic and metatranscriptomic studies of pathogens revealed how pathogenic microorganisms adapt to hosts, e.g., plants (Guttman et al., 2014).

The first step of genome sequence analysis, the assembling of genome sequence data into a single genomic contig, may be difficult, in particular due to assembling repeated sequences if reference genomes are not available. Then, additional information may be required to resolve the remaining DNA regions. The next step, the functional annotation of virulence-relevant pathogens and focusing on host-interaction genes, is often difficult as the genes of interest for PHIs are frequently speciesspecific and, thus, studies of gene homologies may not be helpful. The situation would be improved by the databases of protein families involved in host interactions, which incorporate the currently used gene names, sequence motifs, gene functions, and experimental results (see section "Web-Based Databases for PHI Systems"). On the other hand, comparative genomics can provide insights into molecular pathogenesis, host specificity, and evolution of pathogens. Next generation sequencing (NGS) has revolutionized the molecular investigation of the diversity of pathogens on the genomic and transcriptomic level. It enables an efficient analysis of complex human microfloras, both commensal and pathological, through metagenomic methods. Genomic sequences and their annotations are provided through several portals, such as the Genomes Online Database7 .

In contrast to the static information from the genome, the transcriptome reflects the dynamics of PHI systems that results in temporal profiles of gene expression with changes in the scale of minutes and hours. More and more, beside the protein-coding mRNAs, also various non-conding small RNAs are investigated. For instance, in *Staphylococcus aureus*, a leading pathogen for animals and humans, about 250 regulatory RNAs were found (Guillet et al., 2013). Repositories for transcriptome data, such as Gene Expression Omnibus8 (GEO) and ArrayExpress9 freely distribute microarray and NGS (RNA-Seq) data as well as other forms of high-throughput functional genomics data. In GEO, data from more than 1600 organisms, both pathogens and hosts, are accessible. For instance, for the pathogens *Mycobacterium tuberculosis*, *S. aureus*, *Candida albicans*, and *Helicobacter pylori* transcriptome data from 1,855,

1,777, 1,627, and 1,284 samples are available, respectively. Other data sets monitor the transcriptome of the host's response, e.g., *Homo sapiens* and *Mus musculus* (GSE56091, GSE56093). Some monitor data from host and pathogen simultaneously, e.g., *S. aureus* and the zebrafish *Danio rerio* (GSE32119). NGS has opened the door for simultaneous transcriptome analysis by the so-called dual RNA-Seq (Tierney et al., 2012a,b; Westermann et al., 2012; Camilios-Neto et al., 2014; Longo et al., 2014; Pittman et al., 2014; Xu et al., 2014; Schulze et al., 2015).

# PHI-Based Proteome and Metabolome Data

Proteins are key players in PHIs, in particular in pathogen recognition as well as innate and adaptive immune responses. Pathogen-associated molecular patterns (PAMPs) are molecules or small molecular motifs within a group of pathogens (e.g., the protein flagellin, lipopeptides, lipopolysaccharide – LPS) that are recognized by proteins, the so-called pattern recognition receptors (PRRs), such as Toll-like receptors (TLRs; Qian and Cao, 2013). For instance, TLR4 recognizes bacterial LPS, and TLR5 recognizes bacterial flagellin. The PRRs stimulate signal transduction via pathways, e.g., the tumor necrosis factor alpha (TNFα) signaling or the interferon-gamma (IFNγ)-receptor pathway including the JAK-STAT-pathway. IFNγ is a cytokine that is a key player in innate and adaptive immunity against viral, as well as some microbial and protozoan infections. The nuclear factor NF-κB is a protein, a transcription factor, that is activated by various intra- and extra-cellular stimuli such as bacterial or viral products, for instance via the TLRs signaling and induces the expression of pro-inflammatory cytokines (interleukines, TNFα, Type I interferones). Thus, the application of proteomics is crucial in the investigation of PHI systems and for the above mentioned iPOP, e.g., the immune profiling of patients (Chen et al., 2012).

By dedicated bioinformatic pipelines, a description of pathogen proteomes and their interactions within the context of human host has a strong impact in both diagnostic and clinical treatment of the patient. In the last few years, several advanced proteomic techniques have been established providing individual proteome charts of both pathogens and hosts, including antimicrobial or antimycotic resistance profiling and immune profiling of the patient. Proteome analysis is hampered by the extremely divergent biochemical properties of the individual proteins, making an entire view of the proteome almost impossible (Otto et al., 2014). The coupling of multidimensional separations with mass spectrometry (MS) for protein and peptide analyses via, for instance, the matrix-assisted laser desorption ionization (MALDI) and electrospray ionization (ESI) techniques resulted in powerful MS instrumentations. Many of these MSbased techniques, e.g., MALDI-TOF, have been used in clinical microbiology and research (Del Chierico et al., 2014; Otto et al., 2014). For PHI analyses, the cell wall proteins and the secretomes are of special interest to study the PAMPs and PRRs as well as their interplay (Schmidt and Völker, 2011; Zheng et al., 2011; Heilmann et al., 2012; Di Carli et al., 2012). PHI analysis studies that focus on the host side studying the immune response (Hartlova et al., 2011; Heyl et al., 2014) or on the pathogen side

7https://gold.jgi-psf.org

8http://www.ncbi.nlm.nih.gov/geo

9https://www.ebi.ac.uk/arrayexpress

(Bröker and van Belkum, 2011; Cash, 2011; Ahmad et al., 2012) have also been conducted. The integrative analysis of proteome data with other omics data for both pathogens and hosts is a very challenging task in bioinformatics (Albrecht et al., 2010, 2011).

Stanberry et al. (2013) demonstrated on the host side a strong association between the metabolome profiles, i.e., the metabolite expression levels of differentially expressed pathways, and their temporal patterns at each time point with the disease status of viral infection with a human rhinovirus and a respiratory syncytial virus. For metabolic studies on the pathogen side, there are *in silico* strategies to identify effective targets for anti-infective drugs based on constraint-based modeling of genome-scale metabolic networks (Chavali et al., 2012; see section "PHI Metabolic Network Models"). A prominent type of PHIs is the production of toxins by the pathogens that attack the host. For instance, gliotoxin produced by the human-pathogen fungus *Aspergillus fumigatus* modulates the immune response and induces apoptosis in the host (Gardiner and Howlett, 2005; Scharf et al., 2012). Another type of PHI is due to the pathogens that frequently utilize substrates from the host (Rohmer et al., 2011). The gene regulatory network (GRN) model-assisted studies of the uptake of essential substrates such as iron (Linde et al., 2010, 2012) or nitrogen sources (Ramachandra et al., 2014) by such pathogens address specific but important aspects of PHIs.

# Computational Systems Biology of PHI Networks

A systems biology approach is crucial to model and understand PHIs, in particular interactions between the immune system of humans or animals, and the pathogens (Berglund et al., 2009; Guthke et al., 2012; Horn et al., 2012; Zhou et al., 2013). Systems biology of PHIs aims at describing and analyzing the confrontation of the host with viral, bacterial, and fungal pathogens and parasites by the development of testable computational models of PHIs. The predictive power of such models enables diagnosis and therapy by the prediction of biomarkers and drug targets. Systems biology of PHIs includes an integrative analysis and modeling of genome-wide and/or spatio-temporal data from both the host and the pathogen, or the response of the host or pathogenic cells to defined perturbations that simulate conditions during infection.

At the computational side, systems biology of PHIs comprises:

Both the innate and the adaptive immune system comprise cell-mediated and humoral components. Thus, systems biology of immune defense has to handle multi-scale modeling from molecular to systemic/organ level. The same is required for the pathogen side. The interaction of cellular components is preferentially the area of the agent-based modeling, whereas the humoral immunity can be modeled by ordinary differential equations (ODEs). While the innate immune response is non-specific and acts immediately, the adaptive immune response is pathogen and antigen specific with time lag and immunological memory. Thus, the temporal organization and population dynamics have to be modeled in a different manner for the innate and adaptive immune system in interaction with the pathogen (Perelson, 2002; Gottschalk et al., 2013; Six et al., 2013; Panayidou et al., 2014).

The study of the interplay between pathogens and immune cells remains a challenging task due to its complexity. While the emerging image-systems biology of cellular interaction (Mech et al., 2011; Hünniger et al., 2014; Kraibooj et al., 2014; Pollmächer and Figge, 2014) is here out of the scope, the present review focuses on the molecular, mainly omics data-based level. Here, a difficulty arises to separate host's transcripts, proteins, and metabolites from that in the pathogen and to extract them in a balanced amount for a simultaneous monitoring of these molecules so that the network models of PHIs are inferred. Therefore, most studies focus either on the pathogen or the host side with a defined and controlled change of the respective other side as an external perturbation, i.e., considering an input from the outside of the investigated system. Thus, to simplify the study, the PHIs have been studied mainly in one direction either from pathogen to host or from host to pathogen. Only very recently, the bi-directional interaction of pathogen and host became observable simultaneously using the so-called dual RNA-Seq data generated by NGS of the transcriptome of pathogen and host (see section "PHI-Based Genome and Transcriptome Data").

Understanding the evolutionary dynamics of PHIs by mathematical modeling in terms of both molecular mechanisms and selective forces is important in order to design drugs that will be effective in the long term, i.e., to avoid or to overcome resistance to antibiotics (Guo et al., 2011; Lima et al., 2013; Palmer and Kishony, 2013). Finally, computational systems biology approaches are and will be used to select pathogen-host drug targets and to develop novel anti-infectives and vaccines (Brown et al., 2011; Mooney et al., 2013; Sarker et al., 2013; Rienksma et al., 2014).

# PHI Regulatory Network Models

Biological network models are widely used to improve our understanding of infectious diseases (Mulder et al., 2014). There are many small-scale models (mainly ODE-based), which describe PHIs phenomenologically (Baccam et al., 2009; Saenz et al., 2010; Manchanda et al., 2014). These models without molecular specification are out of the scope of this review, as they usually do not predict PHIs on the molecular level. Here, omics data based PHI models will be reviewed.

Computational modeling of GRNs reveals the molecular logic of adaptation of pathogens to their hosts, the immune evasion of the pathogen as well as the immune response of the host to infection with pathogens. GRNs provide causal explanations for the differentiation, the developmental and effector states, as well as the fate dynamics of immune cells (Singh et al., 2014). Finally, GRNs may also describe the interaction of the two networks, one of the pathogen and the other of the host (see **Figure 1** for example). The inference of GRN models from gene expression data is a problem of great importance for PHI studies. Various reverse engineering methods have been proposed, which include methods based on Boolean networks, Bayesian networks, differential or difference equations, and graphical Gaussian models. In general, due to the high dimensionality (thousands of genes and proteins in both host and pathogen organisms) versus the limited number of samples (not more than hundreds in the case of steady state data from knock-out (KO) mutants; only a few samples in *in vivo* studies of PHI monitored at, e.g., 5–10 time points), the GRN inference is underdetermined implying that there could be many equivalent (indistinguishable) solutions. Motivated by this fundamental limitation, there are various approaches for GRN inference. Again, there are outstanding review articles covering the long-standing problem of gene expression data-driven GRN inference (De Jong, 2002; van Someren et al., 2002; Gardner and Faith, 2005; Bansal et al., 2007; Emmert-Streib et al., 2014; Linde et al., 2015). One of the conclusions from the DREAM initiative10 (Dialog for Reverse Engineering Assessment of Methods; Prill et al., 2010) that performed a comprehensive blind assessment of over 30 network

inference methods was that no single inference method performs optimally across all datasets. Integration of predictions from multiple inference methods shows more robustness and higher performance across diverse datasets (Marbach et al., 2012). For instance, the algorithm TRaCE performs an ensemble inference of GRNs, which takes into account the inherent uncertainty associated with discriminating direct and indirect gene regulations from steady-state data of KO experiments (Ud-Dean and Gunawan, 2014). Another group of GRN inference approaches includes prior knowledge as reviewed by (Hecker et al., 2009; Isci et al., 2014) or further experimental data (Greenfield et al., 2010). A third group of GRN algorithms restricts the GRN to static networks inferred from steady state data (e.g., from KO mutants of the pathogen) or to small-scale networks with a few nodes (genes, proteins), where the pre-selection of them is the critical point (Nakajima and Akutsu, 2014).

The genome-wide GRN model inference, when restricted to the static network models of thousands of genes, requires large gene expression data sets and prior-knowledge in high quality and quantity, which is not the case for most of the pathogens of interest as demonstrated for the human-pathogen *C. albicans* (Altwasser et al., 2012). In contrast to the genome-wide GRN models, the small-scale network models that take into account 5–50 genes or proteins are often used for PHI studies. These models do not represent the holistic view as it is claimed in systems biology, but they generate hypotheses of PHIs that drive further experimental work in infection biology. Afterward, the GRN-based *in silico* predictions have to be validated experimentally. This approach of focused small-scale GRN

10www.the-dream-project.org

inference was reported particularly for human–pathogen fungal infection (Linde et al., 2010, 2012; Ramachandra et al., 2014) by using the ODE-based NetGenerator algorithm. The algorithm was primarily introduced to model the immune response to bacterial infection (Guthke et al., 2005; Weber et al., 2013). This algorithm was also applied for the inference of the PHIs of the human–pathogen fungus *C. albicans* with murine dendritic cells based on dual RNA-Seq data (Tierney et al., 2012b). Here for instance, based on the inferred GRN model shown in **Figure 1**, an inhibition of the expression of the protein HAP3 in the fungus by the murine pentraxin (PTX3) was computationally predicted and, afterward, experimentally validated.

# PHI Metabolic Network Models

Pathogens are dependent on the host environment for the substrates required to maintain a metabolically active state (Chavali et al., 2012; Eisenreich et al., 2013). Therefore, the exchange of several metabolites takes place between pathogens and their host. Besides, the production of virulence factors by the pathogen requires energy, and, hence, an active metabolism, making the nutrients in the host environment crucial for the infection to occur (Milenbachs et al., 1997). The direct functional link between metabolism and virulence is also supported by the finding that metabolic and virulence genes are located on the same pathogenicity island for some pathogens (Rohmer et al., 2011; Heroven and Dersch, 2014). In a different approach, the authors used a network-based computational analysis to elucidate common targeting strategies of bacteria and viruses on human (Durmu¸s Tekir et al., 2012), based on pathogen–host PPIs stored in the PHISTO database (Durmu¸s Tekir et al., 2013). Their results revealed metabolism as a common strategy of both pathogen types to target human cells. The role of metabolism in the pathogenesis was also emphasized by others (Kafsack and Llinás, 2010). Therefore, metabolism is a candidate target for anti-microbial therapies.

There are well-established bioinformatic methods for metabolic network reconstruction, based on DNA genome sequences and constraint based modeling covered by outstanding review articles (Feist et al., 2008; Oberhardt et al., 2009; Ruppin et al., 2010; Bordbar and Palsson, 2012). The *in silico* methods for metabolic network reconstruction are highly valuable for understanding the physiology of the pathogen, e.g., the biosynthesis of toxins that attack the host or the substrate requirement that shows the dependency of the pathogen on the environment within the host. At the host side, the human metabolic network reconstruction may also have an impact for drug discovery and development (Ma and Goryanin, 2008). A systematic modeling of the metabolic trafficking between pathogens and its hosts first started with the constraint-based modeling of the Gram-negative bacterial pathogen, *Salmonella typhimurium* (Raghunathan et al., 2009). The authors reconstructed a genome-scale metabolic model for the pathogen in question, and then simulated its survival capabilities with the flux-balance approach (Kauffman et al., 2003; Orth et al., 2010). When they used a media mimicking host-cell nutrient environment (e.g., macrophage) rather than laboratory media, their correct predictions considerably increased. They also showed that the use of gene expression data can lead to a better inference of active transport mechanisms, and hence the host cell environment. In another study, the reconstructed metabolic network of the malaria-causing protozoan parasite, *Plasmodium falciparum*, was embedded into its host, erythrocyte, and the combined pathogen-host network was simulated via flux-balance analysis (FBA; Huthmacher et al., 2010). The novelty here was to take also the host network into account to predict metabolite exchanges between the parasite and the host, rather than only considering the host environment to account for pathogen–host metabolic interactions. Such a consideration is important since a pathogen infection causes pathogenspecific or common responses in the host metabolic pathways from central carbon metabolism to fatty acid and amino acid metabolisms (Eisenreich et al., 2013). Their analysis resulted in the prediction of antimalarial drug targets (Huthmacher et al., 2010).

In a more systematic study, genome scale metabolic networks of *Mycobacterium tuberculosis* and its host, alveolar macrophage, were reconstructed in an integrated fashion and the integrated pathogen-host metabolic model was used to analyze infection mechanisms and related different pathological states (Bordbar et al., 2010). The reconstructed joint metabolic network covered 2071 genes (661 for the pathogen, 1410 for the macrophage), controlling a total of 4489 reactions. Integrative analysis of the network with the transcriptome data from the infected macrophage cells enabled the inference of the induced changes in the pathogen. One important issue in the network based drugtarget identification is the selectivity of the identified targets. The candidate target must make no harm to the host. This was taken into consideration by (Bazzani et al., 2012), where they used the integrated pathogen-host metabolic model of *Plasmodium falciparum* and hepatocyte, the first human infection site for malaria parasites. The flux balance approach was combined with 48 experimental antimalarial drug targets to identify the targets which are essential for the parasite but not essential for hepatocyte metabolism. The *in silico* analysis led to the ranking of the identified targets with respect to their reducing effect on the cellular fitness.

One key point in the elucidation of metabolic mechanisms both in the host and in the pathogen is to correctly characterize the nutrient availability for the pathogen in the host environment. This characterization is also important for successful modeling attempts. The available nutrients shape the active parts of the pathogen metabolism, and also the depletion of different metabolites may trigger different responses in the host (Bumann, 2009; Rohmer et al., 2011; Eisenreich et al., 2013; Sasikaran et al., 2014). Therefore, nutritional environment has a crucial role to understand the basis of infection mechanisms (Brown et al., 2008; Gouzy et al., 2014). Systems-level experimental approaches such as lipidomics and metabolomics are getting popular to decipher the pathogen–host nutritional interactions (Wenk, 2006; Olszewski et al., 2009; Antunes et al., 2011). A recent attempt to identify active metabolic routes from the host environment to pathogen inside by using 13C flux spectral analysis (Beste et al., 2013) provided a quantitative measure of interactions between *Mycobacterium tuberculosis* and its host macrophage. The experimental labeling data enabled the identification of substrates used by the pathogen. Another elegant study used 13C-labeling based fluxomics as well as metabolomics and proteomics to shed light on the metabolic interplay between *Shigella flexneri* and HeLa epithelial cells (Kentner et al., 2014). They were able to identify host metabolites that contribute to the growth of Shigella as substrates.

Similar to the use of gene expression data to infer GRNs as discussed in the previous section, metabolome data obtained from the infected cells or PHI systems can be used to infer infection-specific metabolic networks by using reverse engineering approaches. Taking into account several bioinformatics methods proposed for this type of inference as reviewed recently (Cakir and Khatibipour, 2014), we believe the field of infection will witness promising applications in the coming years.

# PHI Protein–Protein Network Models

In the post-genomic era, genes and the corresponding proteins are studied thoroughly, allowing the identification of intra- and interspecies protein interaction networks.  On the other hand, the initial efforts to identify large scale interspecies protein interaction data for PHI systems have been performed since 2007 (**Table 1**). The first large scale PHI examples were for commonly observed and human-threatening viruses and bacteria.  On the other hand, the large scale experimental detection of bacteria-human protein interaction networks was performed for *Bacillus anthracis*, *Francisella tularensis*, and *Yersinia pestis* (Dyer et al., 2010; Yang et al., 2011).

As an initial large scale virus–human PHI network example, protein interactions between the herpesvirus EBV and human were mapped by the yeast two hybrid (Y2H) method, providing 173 PHIs between 40 EBV proteins and 112 human proteins (Calderwood et al., 2007). EBV is the infectious cause of several human diseases such as Burkitt's lymphoma, Hodgkin's disease, and nasopharyngeal carcinoma. This EBV–human protein interaction network enabled the initial observations about EBV strategies (i.e., targeting hub and bottleneck human proteins) for replication and persistence within the host. For the same viral system, 147 human protein interactors for EBV nuclear antigen 5 (EBNA5) were identified with LC-MS/MS in a following study (Forsman et al., 2008). Multifunctional viral protein EBNA5 is already known to be critical in EBV pathogenesis, and these PHI data provided further insights on its molecular mechanisms during infection. The identified interactions between EBNA5 and the human proteins functioning in protein control systems that recognize proteins with abnormal

structures may indicate the roles of the viral protein in this system.

The first proteome-wide PHI map for the flavivirus HCV, a major cause of chronic liver diseases, was deduced by Y2H and then by literature mining of previously found interactions between HCV and human, providing a large network for such a small-genome organism. The resulting network consists of 481 interactions between 11 HCV proteins and 421 human proteins. Pathway enrichment analysis of the targeted cellular proteins indicated focal adhesion as a new function subverted by HCV (De Chassey et al., 2008). Using the same experimental approach, 11 human proteins interacting with HCV Core protein and 45 interacting with NS4B (one of the six HCV non-structural proteins) were found (Tripathi et al., 2010). To further understand the mechanisms of the interactions between HCV and human proteins, two extended PPI networks were constructed. These networks are composed of the Y2H-derived interactions and the secondary interactors of the human proteins that interact with the Core and NS4B proteins. Functional analysis of these networks pointed to the human proteins ENO1, SLC25A5, and PXN as potential antiviral targets. ENO1 and SLC25A5 are interaction partners of HCV Core protein. PXN is the first neighbor of both ENO1 and SLC25A5 within the human PPI network. Observing the effects of small interfering RNA (siRNA) knockdown of these host proteins on HCV propagation and replication validated the computational network analysis results (Tripathi et al., 2010). Another Y2H screen resulted in 112 unique interactions between 7 HCV and 94 human proteins (Dolan et al., 2013). DENV is another member of the flaviviruses family, causing the severe human disease dengue hemorrhagic fever. Using the Y2H method, 139 PHIs were detected between 10 DENV proteins and 105 human proteins (Khadka et al., 2011). These two PHI networks of HCV–human by Dolan et al. (2013) and DENV– human by Khadka et al. (2011) were analyzed comparatively and a large overlap was observed between HCV and DENV targets. To determine if the common cellular targets play crucial roles in infections, siRNA experiments were performed and the results revealed the required cellular proteins (CUL7, PCM1, RILPL2, RNASET2, and TCF7L2) for HCV replication (Dolan et al., 2013). Finally, using protein microarray assays, 103 human proteins were identified as HCV Core-interacting partners. Through these PHI data, the viral modulation of some cellular mechanisms was studied in detail and the cellular MAPKAPK3 was proposed as a potential therapeutic target for HCV infections (Ngo et al., 2013). Prior to these studies, a number of small scale PHI data were produced for the HCV–human interaction system (Matsumoto et al., 1997; Hsieh et al., 1998; Lu et al., 1999; Owsianka and Patel, 1999).

Orthomyxovirus Influenza A virus is the source of all flu pandemics infecting multiple species. For H1N1 A/PR/8/34 strain of influenza virus, 135 PHIs were identified between 10 viral and 87 human proteins, most of which are expressed in primary human bronchial cells. For another strain of influenza A virus, H3N2 A/Udorn/72, a PHI network with 81 interactions between 10 viral and 66 human proteins was constructed. Both of the PHI networks were detected by the Y2H method. Similarities of these two PHI networks highlighted the conserved functions of influenza virus proteins through strains. Observing the topological network properties of these Influenza A virus–human PPI networks allowed to draw crucial conclusions on the multifunctionality of the small number of proteins encoded by RNA viruses, revealing that viral proteins can interact with a significant number of human proteins (Shapira et al., 2009).

AIDS-causing retrovirus HIV, probably the most studied human pathogen, depends largely on human cellular machinery to be replicated, like other RNA-carrying viruses. One large-scale PHI dataset for HIV-1 was produced using affinity chromatography coupled with MS, resulting in 183 human nuclear proteins as interacting partners of HIV-1 Tat (nuclear regulatory protein) which is essential for viral replication within the host nucleus. The following *in silico* analysis of the experimentally verified PHI data provided further insights on the mechanisms of Tat during HIV-1 infection. Firstly, motif composition analysis highlighted that Tattargeted cellular proteins are enriched for domains mediating protein, RNA and DNA interactions, and helicase and ATPase activities. Secondly, functional analysis of Tat-targeted human proteins showed that they are enriched for a wide range of biological processes such as gene expression regulation, RNA biogenesis, chromatin structure, chromosome organization, DNA replication, and nuclear architecture (Gautier et al., 2009). Another large PHI network was constructed for HIV–human protein complexes by affinity tagging and purification MS, resulting in 497 PHIs between 16 HIV-1 proteins and 435 human proteins. In that study, the functional categories of HIV-targeted human proteins were analyzed indicating that the host factors in the found PHI network are enriched for the transcription and the regulation of ubiquitination. Additionally, the domains of the interacting proteins were also investigated, and the enriched domain types (14- 3-3 domains and β-propellers) in targeted human proteins were identified to facilitate future structural modeling studies (Jäger et al., 2012). For HIV-1, several small scale experiments were also carried out to find protein PHI data (Cujec et al., 1997; Le Rouzic et al., 2002; BonHomme et al., 2003; Lusic et al., 2003; Naji et al., 2012) establishing HIV-1 as the pathogenic species having the largest experimentally verified PHI data.

Using the approach of combining modified tandem affinity chromatography and MS analysis, 245 cellular interacting proteins were identified for the viral protein MV-V (one of the virulence factors of paramyxovirus MV). MV-V was found to target known key components of the host antiviral response including STAT1, STAT2, IFIH1, and p53, and also essential components of ribosome, reticulum, and mitochondria. The topological and functional analysis of human proteins targeted by MV-V shows that they have properties within the human interactome similar to the well-known targets of other viruses (Komarova et al., 2011).

As an example for another multi-functional viral protein, HRSV (another member of paramyxoviruses) NS1 can act as an antagonist of host type I and III interferon production and signaling, inhibit apoptosis, suppress dendritic cell maturation, control protein stability, and regulate transcription of host cell mRNAs, among its other functions. A total of 221 PHIs were determined between only one viral protein NS1 and human proteins, reflecting its multifunctional nature. This virus-human PHI network was produced by quantitative proteomics in combination with green fluorescent protein (GFP)-trap immunoprecipitation. It was observed that many of the HRSV-targeted human proteins have roles in transcriptional regulation and cell cycle regulation (Wu et al., 2012).

A study covering several DNA and RNA viruses (Pichlmair et al., 2012) found 1681 PHIs between 70 viral ORFs from 30 species and 579 human proteins. The interacting cellular proteins were isolated by tandem affinity purification (TAP), and the purified proteins were analyzed by one-dimensional gel-free liquid chromatography tandem MS (LC–MS/MS). A comparative interactomics analysis of the produced viral PHI networks (DNA viruses versus RNA viruses) provided crucial insights on the infection strategies of DNA and RNA viruses. It was concluded that RNA viruses target the JAK–STAT and chemokine signaling pathways, as well as pathways associated with intracellular parasitism, whereas DNA viruses target cancer pathways (Pichlmair et al., 2012).

The first extensive bacterial PHI networks were identified for important human pathogens, *B. anthracis*, *F. tularensis*, and *Y. pestis* (Dyer et al., 2010; Yang et al., 2011). Gram-positive bacteria *B. anthracis* and *Y. pestis* and Gram-negative bacterium *F. tularensis* are respiratory pathogens causing anthrax, bubonic plague, and acute pneumonic disease, respectively. Using the Y2H method, large-scale interaction data were generated between these bacteria and human, leading to 3073 PHIs between 943 *B. anthracis* proteins and 1748 human proteins, 4059 PHIs between 1218 *Y. pestis* proteins and 2108 human proteins, and 1383 PHIs between 349 *F. tularensis* proteins and 999 human proteins. Bioinformatic analysis of these experimentally found bacteria–human interaction data revealed that bacterial proteins preferentially interact with human proteins that are hubs and bottlenecks in the human PPI network, as previously observed for viral PHIs. The modules of bacterial PHIs that are conserved amongst the three networks were computed. The found conserved modules may reveal commonalities among how different bacterial pathogens interact with crucial host pathways involved in inflammation and immunity (Dyer et al., 2010). A different Y2H strategy was used for *Y. pestis* by choosing only potential virulence factors as bait proteins. 204 PHIs were identified between 66 *Y. pestis* proteins and 109 human proteins, and then 23 previously reported PHIs were integrated to construct a comprehensive network between *Y. pestis* and human (Yang et al., 2011).

The increase in the amount of experimentally verified pathogen–human PPI data allowed a number of bioinformatic studies to investigate infection mechanisms at the level of PHIs for different pathogen types (Dyer et al., 2008; Singh et al., 2010; Durmu¸s Tekir et al., 2012). The first global analysis of more than 10,000 PHI data revealed important observations (Dyer et al., 2008). Firstly, targeting hub and bottleneck proteins were concluded as a common behavior for all pathogens. Targeting human transcription factors and key proteins that control the cell cycle and regulate apoptosis and transport of genetic material

across the nuclear membrane were found to be common infection strategies of viruses. On the other hand, targeting human proteins that function in the immune response was observed as a common bacterial infection strategy (Dyer et al., 2008). In a following study, investigation of more than 20,000 experimental PHI data revealed that the preference of interacting with hub and bottleneck proteins is more pronounced in viruses than bacteria. The analysis of the human proteins targeted by both bacteria and viruses indicated that attacking human metabolic processes is a common strategy used by both pathogens (Durmu¸s Tekir et al., 2012). In addition to these comparative interactomics studies for bacterial and viral PHI networks, a comparative analysis of virus interactions with human signal transduction pathways revealed that different viruses tend to target the same cellular pathways, not necessarily via interacting with the same cellular proteins (Singh et al., 2010).

# Web-Based Databases for PHI Systems

In parallel with the first large-scale experimentally verified PHI data, the initial efforts on the development of PHI-specific databases were performed toward the end of the first decade of this century (**Table 2**). Currently, a number of Web-based resources aim to integrate pathogen–host molecular interactions and related data available in the literature. Some of them store data on only one specific pathogen species as in the case of HCVpro (Kwofie et al., 2011), HIV-1 Human Interaction Database at NCBI (Ako-Adjei et al., 2015), HoPaCI-DB (Bleves et al., 2014) for *Pseudomonas aeruginosa* and *Coxiella burnetii*, and Proteopathogen (Vialás et al., 2009) for *C. albicans*. The resources based on a wider range of specific pathogens are VirHostNet (Guirimand et al., 2015), VirusMentha (Calderone et al., 2015) and ViRBase (Li et al., 2015) for viruses, PATRIC (Wattam et al., 2014) for bacteria and PHI-base (Urban et al., 2015) for bacterial, fungal, and oomycete pathogens. Finally, PHIDIAS (Xiang et al., 2007), HPIDB (Kumar and Nanduri, 2010), and PHISTO (Durmu¸s Tekir et al., 2013) are PHI databases for all pathogen types with known interaction data.

HCVPro (HCV interaction database) is dedicated to only HCV, cataloging the characterized protein interactions for intraviral and virus–human systems. Additionally, it includes information on the structure and functions of HCV proteins (Kwofie et al., 2011). The HIV-1 Human Protein Interaction Database at NCBI includes the interactions between HIV-1 and human proteins. In its content, the majority of the protein interaction data are indirect (e.g., upregulation, modification) whereas the rest are direct (e.g., binding; Ako-Adjei et al., 2015). HoPaCl-DB (Host–Pseudomonas and Coxiella interaction database) provides information on interactions between molecules, bioprocesses, and cellular structures for the bacterial pathogens *Pseudomonas aeruginosa* and *C. burnetti* and their host organisms. The graphical representation of these interaction systems is also available in HoPaCl-DB (Bleves et al., 2014). The other pathogen-specific data resource, Proteopathogen is a protein database for studying *C. albicans*–host interactions.

TABLE 2 | Contents of Web-based PHI databases.

Although the focus of the database is on *C. albicans* and its interactions with macrophages, the database also includes data for different fungal pathogens and other mammalian cells. Proteopathogen provides additional information about the interacting proteins such as Gene Ontology (GO) and pathway annotations, and protein structures (Vialás et al., 2009).

PATRIC (The PathoSytems Resource Integration Center) is a dedicated resource for bacterial systems including comprehensive data on genomics, transcriptomics, PPIs, 3D protein structures, and sequence typing. However, its focus is on the genomic data, currently covering more than 10,000 bacterial genome sequences. PATRIC provides a private workspace for each user where they can store their own data. In their workspaces, users can perform comparative genomics and transcriptomics via the corresponding analysis tools. PATRIC provides bacteria–host PPI data through its tool Pathogen Integration Gateway (PIG; Wattam et al., 2014). PHI-base (Pathogen–Host Interactions Database) is a Web-accessible PHI database specific for bacterial, fungal, and oomycete pathogens, which are medically and agronomically important. PHI-base serves options to facilitate the discovery of genes that may be potential targets for chemical intervention, containing information on the pathogenicity/virulence genes functioning in the PHI systems. As a genomic data focused resource, PHI-base has the functionalities allowing functional annotations of the genes and comparative genomics analysis (Urban et al., 2015). On the other hand, there are databases developed specifically for viral PHI systems such as VirHostNet (Guirimand et al., 2015), VirusMentha (Calderone et al., 2015) and ViRBase (Li et al., 2015). VirHostNet (Virus–Host Network) is one of the earliest PHI resources specialized in the management and analysis of integrated virus–virus, virus–host, and host–host protein interaction networks coupled to their functional annotations. The host organism in the VirHostNet is only human. Its Web interface provides both table-based and graph-based visualizations of the PHI networks (Guirimand et al., 2015). The recently developed tool, VirusMentha is another virus-virus and virus–host protein interaction resource. VirusMentha is an extension of a previous tool VirusMINT (Chatr-Aryamontri et al., 2009). VirusMentha is the most comprehensive viral PHI data source without limitation with respect to virus species or host organisms. The tool offers a graphical representation option for viral PHI networks (Calderone et al., 2015). On the other hand, ViRBase is a resource for virus–host ncRNA-associated interactions. It provides browsing and visualization of viral and cellular ncRNA-associated virus–virus, host–virus, and host–host interactions (Li et al., 2015).

Finally, the Web-based PHI databases comprising all pathogen types with known interactions are PHIDIAS (Xiang et al., 2007), HPIDB (Kumar and Nanduri, 2010), and PHISTO (Durmu¸s Tekir et al., 2013). PHIDIAS (Pathogen–Host Interaction Data Integration and Analysis System) stores data on genome sequences, conserved domains, and gene expression data related to PHIs. In addition to data storage, PHIDIAS offers the analysis of these data (Xiang et al., 2007). HPIDB (Host–Pathogen Interaction Database) is not limited to any pathogen or host regarding pathogen–host PPI data. HPIDB offers the BLASTP search option that allows searching for homologous PHI data for pathogens without experimental PHI data (Kumar and Nanduri, 2010). Currently, PHISTO (Pathogen-Host Interaction Search Tool) is the most comprehensive PHI database on the Web including data for all pathogenic microorganisms for which experimental protein interactions with human are available. Bioinformatic analysis tools in PHISTO allow users to visualize and analyze PHI networks to get insights on infection mechanisms (Durmu¸s Tekir et al., 2013). Using the tools in the current version of PHISTO, users can access the functional and topological properties of pathogen-targeted human proteins within the human intranetwork. Furthermore, a comparative analysis tool is provided to perform these analyses comparatively for different pathogens to observe the similarities and differences in their infection strategies.

Pathogen–host protein interaction data in the above PHI databases are integrated mainly from other PPI databases using automatic integration tools such as PSICQUIC (Aranda et al., 2011) and by manual curation from the literature.

There are other informative databases for pathogens, providing useful information for studying infection mechanisms. For instance, ARDB (Antibiotic Resistance Genes Database) unifies most of the publicly available information on antibiotic resistance. The information can be used as a compendium of antibiotic resistance genes of newly sequenced genomes (Liu and Pop, 2009). IVDB (Influenza Virus Database) is an integrated information resource and analysis platform for influenza virus research focusing on the genetic, genomic, and phylogenetic studies. IVDB provides complete genome sequences of the virus to facilitate the analysis of global viral transmission and evolution (Chang et al., 2007). MPIDB (Microbial Protein Interaction Database) aims to collect all known physical interactions among the bacterial proteins (Goll et al., 2008). MvirDB is a microbial database of protein toxins, virulence factors, and antibiotic resistance genes for biodefense applications (Zhou et al., 2007). VFDB (Virulence Factor Database) is a comprehensive repository for bacterial virulence factors (Chen et al., 2011). VIDA is a virus database system for open reading frames (ORFs) of animal viruses (Albà et al., 2001). Finally, ViPR (Virus Pathogen Database and Analysis Resource) is an open bioinformatic resource for virology research. ViPR captures various types of information, including sequence data, gene, and protein annotations, 3D protein structures, clinical and surveillance metadata, and novel data derived from comparative genomics analyses (Pickett et al., 2012).

# Text Mining of PHI Data from the Literature

Scientific publications are the main media through which researchers report their new findings. The huge amount and the continuing rapid growth of the number of published articles in biomedicine has made it particularly difficult for researchers to access and utilize the knowledge contained in them. Currently, there are over 24 million publications indexed in PubMed11, which is the main system that provides access to the biomedical literature.

To address the challenge of information overload in the biomedical literature, a number of manually curated databases have been developed to store biologically important information such as protein interactions, gene–disease associations, or PHIs. However, given the current amount and the continuing rapid growth of the biomedical literature, it usually takes a lot of time and effort before new discoveries are included in these databases. Human database curation cannot keep up with literature production (Baumgartner et al., 2007). As a consequence, most of the knowledge remains hidden in the unstructured text of theh publised articles. Therefore, developing text mining techniques to

uncover this knowledge has become an important research area. Several text mining approaches have been proposed for identifying articles relevant to a particular topic, detecting biomedical entities such as genes, proteins, and diseases in text, as well as extracting the relations among them. A number of shared tasks such as the BioCreative Challenges (Krallinger et al., 2008; Arighi et al., 2011) and the BioNLP Shared Tasks (Kim et al., 2009, 2011; Nédellec et al., 2013) have been conducted, which have further boosted research in this area. However, text mining for the pathogen-host interactions domain has not been well studied yet, although it has its own peculiarities and challenges. Only a handful of studies, which are discussed in the subsections below, have been conducted so far in this domain. One thread of research focuses on identifying the articles that contain PHIrelevant information (Yin et al., 2010; Korkin et al., 2011; Thieu et al., 2012) and another thread of research addresses performing more detailed semantic analysis of the text and extracting more fine-grained information such as the specific proteins that interact and the associated pathogen and host organisms (Korkin et al., 2011; Thieu et al., 2012).

# PHI-Relevant Abstract Detection

Identifying and ranking articles that contain PHI-relevant information can be used for selecting and prioritizing articles for manual curation. It can also be an initial step for filtering the relevant articles before performing more fine-grained semantic analysis for identifying the biomedical entities and the relations among them. The task for detecting articles describing PPI information has been addressed in the BioCreative II, II.5, and III challenges (Krallinger et al., 2008; Leitner et al., 2010; Arighi et al., 2011). However, the focus has not been on PHI relevant articles. The first study that focused on detecting PHI-relevant abstracts, i.e., abstracts that describe pathogen host PPI, was conducted by (Yin et al., 2010). Similarly to most systems that participated in the BioCreative Challenges Article Classification Task, the problem was formulated as a supervised machine learning based classification task. Support Vector Machines (SVM) was used as the classification algorithm (Cortes and Vapnik, 1995). Feature selection methods including Information Gain, Mutual Information, and Chi-square were evaluated using a data set of 1360 manually labeled abstracts. The results showed that Information Gain and Chi-square perform better than Mutual Information as the number of features used decreases. Although the focus of the study was on PHI-relevant abstract classification, no any PHI specific features were used. Only the word unigrams and bigrams were used as features.

Pathogen–host interaction-relevant abstract classification was also tackled by (Thieu et al., 2012). Similarly to (Yin et al., 2010), the task was addressed as a supervised machine learning classification problem and SVM was used as the classification algorithm. However, unlike (Yin et al., 2010), the authors defined and used PHI specific features including the identified host and pathogen protein and gene names in the text, the host and pathogen organism names, the interaction signaling keywords, the experimental method keywords, and PHI-specific keywords such as virulence and effector. In order to account for the abstracts that report the absence of an interaction between a host and pathogen

11http://www.ncbi.nlm.nih.gov/pubmed

protein, features that make use of the negation signaling keywords were also designed. The protein and gene names, as well as the corresponding organisms were tagged by using the NLProt software (Mika and Rost, 2004). A set of dictionaries for interaction keywords, experimental keywords, negation keywords, PHI-keywords, host names, pathogen names, and uncertainty keywords was manually compiled. A data set of 175 PHI-relevant (positive set) and 175 PHI non-relevant (negative) abstracts was manually annotated and used for evaluation. The results showed that using PHI specific features is a promising approach for identifying PHI-relevant articles. However, it is not possible to compare the results with the results of (Yin et al., 2010), since a different data set was used for evaluation.

In order to be able to assess the performances of the proposed methods a larger and publicly available benchmark data set should be created. Such a data set should in fact contain three types of abstracts: (1) Abstracts that do not contain any PPI information (negative class 1); (2) Abstracts that contain PPI information which are not pathogen–host PPIs (negative class 2); and (3) Abstracts that contain pathogen–host PPI information (positive class). Distinguishing the positive class from negative class 2 is probably more difficult, since they both contain PPI information. The only difference is that the PPIs in negative class 2 are not PHIs. To distinguish these two classes from each other, PHI specific features should be utilized. On the other hand, distinguishing the positive class from negative class 1 is probably easier and generic PPI relevant features might be sufficient. It is not clear whether the data sets annotated and used in Yin et al. (2010) and Thieu et al. (2012) contain these three classes, or contain only two of them (i.e., the positive class and negative class 1). Therefore, it is difficult to assess and compare the reported results.

# PHI-Relevant Relation Extraction

One of the most important opportunities for text mining in biomedicine is the identification of the relations among the biomolecules, which can help elucidate their roles in important biological processes, as well as in diseases. In order to extract the relations among biomedical entities from text, first the sequences of characters that correspond to entities should be tagged in text. This task is called Named Entity Recognition (NER) and has been an active research topic in the biomedical text mining domain.

While the earliest systems for biomedical NER were usually based on rule-based approaches (Fukuda et al., 1998), as annotated corpora became available, machine-learning based methods gained popularity (McDonald and Pereira, 2005; Tsai et al., 2006; Hsu et al., 2008). State-of-the-art gene and protein NER systems achieve a practically applicable level of performance (e.g., 87% *F*-score performance was obtained at the second BioCreative shared task on gene mention tagging (Smith et al., 2008)). Genia Tagger (Tsuruoka et al., 2005), ABNER (Settles, 2005), and BANNER (Leaman and Gonzalez, 2008) are some of the publicly available biomedical NER tools. LINNAEUS (Gerner et al., 2010) and OrganismTagger (Naderi et al., 2011) are tools developed for recognizing species names in biomedical text. Both achieve *F*-score performances of over 94%. Although the usability of these NER tools for the PHI domain has not been well addressed yet, in principle they can also be used for PHI text mining to identify the entity names such as gene, protein, and species names in text.

One of the first studies on using text mining for pathogen– host relationship extraction was conducted by (Anthony et al., 2010). As a case-study, the authors targeted the extraction of genotype, pathogen, and syndrome relations. A corpus consisting of 43 abstracts from PubMed was manually annotated. The available technologies for the automatic recognition of host–pathogen named entities and the relations among them were discussed. However, they have not been evaluated over the annotated corpus, which makes it difficult to draw conclusions about their usability for the PHI text mining domain.

Thieu et al. (2012) addressed the problem of extracting pathogen–host PPIs from text. The authors proposed a linguistically motivated approach that makes use of the link grammar representations of the sentences (Sleator and Temperley, 1995). Thieu et al. (2012) generated additional rules to map the protein names to the corresponding pathogen and host organism names. For instance, if an organism name occurs before a protein name (e.g., *Arabidopsis* RIN4 protein) the protein is mapped to the preceding organism. In addition, Thieu et al. (2012) incorporated an anaphora resolution module that resolves the pronouns such as "it," "they," etc. in the sentences with their corresponding protein/gene or organism names, which makes possible extracting relations that span multiple sentences. This module is based on the RelEx anaphora resolution method that uses the Hobbs' pronoun resolution algorithm (Hobbs, 1978). The proposed approach was evaluated by using the 350 annotated abstracts described in the section "PHI-Relevant Abstract Detection." The results of (Thieu et al., 2012) showed that the proposed approach significantly outperformed a naïve approach based on using one of the state-of-the-art generic PPI extraction tools Protein Interaction information Extraction (PIE) system (Kim et al., 2008). This motivates the development of methods that specifically address pathogen–host PPI extraction. The 24% *F*-score obtained by the proposed system suggests that there is room for improvement and further research in this domain is necessary. An error analysis suggested that an important source of error was the incorrect identification of protein names and incorrect assignment of species to the corresponding proteins. While the first one is a NER problem, which is an active research topic in biomedical text mining, the second one has not been tackled much by the researches. The results of the current studies suggest that it is a crucial research direction for PHI text mining studies.

Pathogen–host interaction-specific PPI extraction is a similar problem to the general problem of mining PPI relevant information from text (Ono et al., 2001; Blaschke and Valencia, 2002; Temkin and Gilder, 2003; Daraselia et al., 2004; Jelier et al., 2005; Erkan et al., 2007; Fundel et al., 2007; Airola et al., 2008; Tikk et al., 2010). However, it has its own peculiarities that require the development of methods specialized for PHI text mining. In order for a PPI to qualify as a PHI, the interaction should be intra-species. In other words, one of the proteins should be a host protein and the other one should be a pathogen protein. Therefore, besides tackling the problem of extracting the pair of proteins that interact, the problems of identifying the species associated with them, as well as the classification of the species as host or pathogen should also be addressed. These additional requirements render the PHI text mining task more difficult than the already challenging PPI text mining task. Most PPI extraction systems operate on a sentence-level to extract the interactions. The underlying assumption is that the majority of the relations are contained within a single sentence. Analysis of the Genia event corpus (Kim et al., 2009) supports this assumption, since only 5% of the relations in the corpus span multiple sentences (Björne et al., 2009). However, this assumption does not in general hold for the PHI extraction task, since in many cases the species of the associated entities do not occur in the same sentence where the interaction is described (Thieu et al., 2012). Therefore, in order to extract PHIs from text, wider scope than a sentence should be considered and methods to merge information contained in multiple sentences should be developed. Nevertheless, the current findings from the generic PPI text mining domain can be utilized. For instance, recent studies have demonstrated the utility of integrating machine learning methods with similarity functions (or kernels) defined using the syntactic and semantic analysis of text (Tikk et al., 2010). Some of these approaches can be adapted to the PHI text mining domain by performing anaphora resolution as a prior step and extending the methods to operate on scopes wider than a sentence. In addition, novel methods should be developed to address the problem of assigning the species to their corresponding entities (e.g., proteins and genes). Sentence-level processing will probably not be sufficient to develop solutions to this problem, since species names do not necessarily occur in the same sentences or even in the same paragraphs as the entity names. Another challenge is that a species can be a host in one context, while it is a pathogen in another context. Therefore, methods for determining which species are pathogens and which are host in the given context should be designed.

The PHI information extracted using text mining can be utilized in at least two ways. First, such information can be used to populate PHI databases, either directly or indirectly by facilitating manual curation. This will make the data buried in the literature easily accessible to the researchers in this domain. Second, further analysis of the uncovered information can be integrated into a systems biology approach to generate new scientific hypothesis such as predicting currently unknown interactions among pathogen and host proteins.

# Conclusion and Future Directions

Conventional therapeutics aim to kill pathogenic microorganisms directly usually by targeting the pathogen only. However, the drug resistance of pathogens demands alternative solutions for infectious threats, i.e., targeting host proteins required by pathogens for replication and persistence within the host organism or targeting PHIs (Murali et al., 2011; Zoraghi and Reiner, 2013). If these host proteins are indispensable for pathogens during infections, but not essential for host cells, they may serve as antimicrobial therapeutic targets to fight drug

resistance.  The detailed knowledge about mechanisms of the relationships between these host factors and their targeting pathogens is required urgently to develop new and more effective antimicrobial therapeutics, necessitating a computational systems biology approach to PHIs.

The computational modeling of networks of interacting genes, transcripts, proteins, and metabolites is of great importance in biomedical research to understand molecular mechanisms of PHIs. The high-throughput experimental detection of levels of biomolecules (gene transcripts, proteins, and metabolites) via omics approaches as well as the detection of PHIs via highthroughput experiments has generated comprehensive datasets. The presented review has provided a snapshot of recent developments in this area and a survey about databases that store such infection-specific data. Using text mining is necessary to extract the PHI-relevant data that are only available in the text of the huge amount of scientific literature. Although biomedical text mining is an active research area, there are only a limited number of studies focusing on extracting PHI information. The lack of a publicly available data set ('gold standard') makes it difficult to evaluate and compare the current approaches. Besides reviewing the current studies, we have also provided future directions for research including analyzing the usability of the already available biomedical text mining methods for the PHI text mining task, developing novel approaches addressing the peculiarities and challenges of the PHI domain, and creating publicly available benchmark data sets in order to provide a better assessment of the different methods. We have also covered studies on the bioinformatic analysis of three types (protein-based, regulatory, and metabolic) of PHI networks. The integrative analysis of the high-throughput omics experiments using modeling approaches will not only elucidate the mechanisms of infection, but will help in the discovery of potential therapeutic targets and drugs through selective identification of essential genes, proteins, and metabolites for the pathogen. Despite the recent efforts reviewed above, the use of systems biology approaches to investigate PHI systems is still in its infancy, mostly because of data scarcity. Ongoing studies in the field will lead to more complete PHI networks in the coming decade, improving the PHI-based solutions to infectious diseases.

# Acknowledgments

AÖ was supported by Marie Curie FP7-Reintegration-Grants within the 7th European Community Framework Programme. RG was supported by the Deutsche Forschungsgemeinschaft (DFG) in the Collaborative Research Centre/Transregio 124 FungiNet (subprojects B3 and INF).

# References

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retrieving biological relationships between genes. *Bioinformatics* 21, 2049–2058. doi: 10.1093/bioinformatics/bti268

*Candida albicans* invading innate immune cells. *Front. Microbiol.* 3:85. doi: 10.3389/fmicb.2012.00085

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2015 Durmu¸s, Çakır, Özgür and Guthke. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Computational prediction of molecular pathogen-host interactions based on dual transcriptome data

#### *Sylvie Schulze1, Sebastian G. Henkel 2, Dominik Driesch2, Reinhard Guthke1 and Jörg Linde1 \**

*1 Department of Systems Biology and Bioinformatics, Leibniz-Institute for Natural Product Research and Infection Biology – Hans-Knoell-Institute, Jena, Germany 2 BioControl Jena GmbH, Jena, Germany*

#### *Edited by:*

*Saliha Durmus, Gebze Technical University, Turkey*

#### *Reviewed by:*

*Ikbal Agah Ince, Wageningen University and Research Centrum, Netherlands Kazim Yalcin Arga, Marmara University, Turkey*

#### *\*Correspondence:*

*Jörg Linde, Department of Systems Biology and Bioinformatics, Leibniz-Institute for Natural Product Research and Infection Biology – Hans-Knoell-Institute, Beutenbergstr. 11a, 07745 Jena, Germany e-mail<EMAIL_ADDRESS>
Inference of inter-species gene regulatory networks based on gene expression data is an important computational method to predict pathogen-host interactions (PHIs). Both the experimental setup and the nature of PHIs exhibit certain characteristics. First, besides an environmental change, the battle between pathogen and host leads to a constantly changing environment and thus complex gene expression patterns. Second, there might be a delay until one of the organisms reacts. Third, toward later time points only one organism may survive leading to missing gene expression data of the other organism. Here, we account for PHI characteristics by extending NetGenerator, a network inference tool that predicts gene regulatory networks from gene expression time series data. We tested multiple modeling scenarios regarding the stimuli functions of the interaction network based on a benchmark example. We show that modeling perturbation of a PHI network by multiple stimuli better represents the underlying biological phenomena. Furthermore, we utilized the benchmark example to test the influence of missing data points on the inference performance. Our results suggest that PHI network inference with missing data is possible, but we recommend to provide complete time series data. Finally, we extended the NetGenerator tool to incorporate gene- and time point specific variances, because complex PHIs may lead to high variance in expression data. Sample variances are directly considered in the objective function of NetGenerator and indirectly by testing the robustness of interactions based on variance dependent disturbance of gene expression values. We evaluated the method of variance incorporation on dual RNA sequencing (RNA-Seq) data of *Mus musculus* dendritic cells incubated with *Candida albicans* and proofed our method by predicting previously verified PHIs as robust interactions.

**Keywords: network inference, NetGenerator, transcriptomics, dual RNA-Seq, microarrays, gene regulatory networks, inter-species interactions**

# **1. INTRODUCTION**

Organisms need to constantly adapt to environmental changes. On a molecular level, this is mediated by complex signaling cascades, which transmit the signal to cell nuclei. Transcription factors bind to their target genes, which consequently leads to a change in gene expression. This way, biological systems adapt to new environmental conditions.

In most cases underlying networks are unknown. This is especially interesting for interacting organisms, such as pathogens and host. Both the experimental setup and the nature of PHIs exhibit certain characteristics: (i) pathogen and host are in a battle leading to constantly changing conditions, (ii) a change in gene expression is triggered by new environmental conditions and the response of one organism might initiate faster or persist longer than the response of the other organism and (iii) two different organisms interact and eventually one survives which can lead to missing data time points.

The immune system of the host is permanently active to recognize and eliminate infectious microorganisms. As a first line of defense, components of the innate immune system such as the complement system, immune cells, and antimicrobial peptides recognize pathogen-associated molecular patterns (PAMPs). In contrast, pathogens developed many strategies to evade these mechanisms. They can shield microbe-associated cell surface proteins, mimic host surfaces or secrete proteases degrading host immune proteins (Zipfel et al., 2011). Nevertheless, the interaction with host cells is also important for pathogens, e.g., to acquire nutrients and to replicate (Casadevall and Pirofski, 2000).

The transcriptome of pathogen and host can be measured by physical separation of pathogen and host cells before RNA extraction. This enables RNA extraction from pathogen and host at different time points. For example, Oosthuizen et al. (2011) used separate pathogen and host microarrays to measure the transcriptome of *Aspergillus fumigatus* and human epithelial cells. The advantage of microarrays is, that they are cheap, processing of raw data is fast and well-established (Zhao et al., 2014). On the other hand, the recently developed RNA-Seq technology (Nagalakshmi et al., 2008) opened up the opportunity to study transcriptomes at a high level of accuracy and depth, also of nonmodel organisms. With the advent of dual RNA-Seq it became possible to measure transcriptomes of multiple species simultaneously without physical separation of cells. A promising research field for application are infection processes of mammalian cells by pathogens (Westermann et al., 2012).

Network inference is a systems biology approach which aims to reverse engineer underlying interaction networks based on gene expression data (Hecker et al., 2009). To account for dynamics in the change of gene expression, some tools reconstruct gene regulatory networks (GRNs) based on gene expression time series data (Gustafsson et al., 2005; Guthke et al., 2005; Gupta et al., 2011; Vlaic et al., 2012). Predicted networks suggest interactions for experimental validation, but can also put experimental findings in a bigger context (Smet and Marchal, 2010). While numerous tools are applied to predict single-species networks, e.g., (Bansal et al., 2006; Bonneau et al., 2006; Linde et al., 2010; Altwasser et al., 2012), few inter-species approaches have been published.

NetGenerator, a tool to infer small scale GRNs (Guthke et al., 2005; Toepfer et al., 2007; Weber et al., 2013), has been successfully applied to predict single-species GRNs (Linde et al., 2012; Ramachandra et al., 2014). NetGenerator infers gene-regulatory networks from gene expression time series data. The interactions and their strength are identified by a heuristic structure search and parameter optimization. The resulting model is described by ordinary differential equations and can be displayed as a directed network graph as well as simulated. In a pioneering study, the applicability of NetGenerator to predict PHI networks has been demonstrated (Tierney et al., 2012). However, this publication focused on the specific biological example while the requirements for data processing and for the algorithm to a broader class of PHI experiments are not discussed extensively.

Hereafter, we discuss a variety of aspects for dual RNA-Seq data acquisition and processing. Furthermore, we describe the application of the extended NetGenerator version to infer an inter-species GRN based on dual RNA-Seq data. Even though we focus on the novel technique RNA-Seq, most parts of the described workflow can be applied to microarray data. We evaluate the impact of multiple input stimuli on the inference accuracy with NetGenerator based on a benchmark example. The extended NetGenerator version handles missing data values, which we demonstrate with the same benchmark example. We further extended the algorithm and its application to consider variances in replicated measurement data. This is directly embedded in the inference process and indirectly through a robustness analysis. We applied this method to a real dual RNA-Seq data set of murine dendritic cells infected with *C. albicans* published by Tierney et al. (2012).

#### **2. RESULTS**

#### **2.1. DUAL RNA-SEQ DATA**

#### *2.1.1. Data acquisition*

RNA-Seq requires a certain amount of input RNA often in a microgram range, which is practically difficult to extract. Furthermore, mRNA should be enriched to avoid sequencing data being dominated by structural RNAs (Tariq et al., 2011). Additionally, the experimental setup needs to ensure that enough mRNA of both organisms can be extracted to obtain an appropriate sequencing depth (**Figure 1A**). Westermann et al. (2012) discuss various important limitations for dual RNA-Seq. One aspect is that different genome sizes of pathogen and host lead to different amounts of cellular RNA. It is estimated that for instance only 1.5% of the human genome encodes proteins (International Human Genome Sequencing Consortium, 2001). For that reason, we suggest to estimate an appropriate sequencing depth for both organisms based on their transcriptome sizes and recommend a genome coverage of at least 10. Tools like featureCounts return transcriptome sizes based on given annotation files as side products (Liao et al., 2014).

Furthermore, the pathogen-host cell ratio of the experimental setup, also known as multiplicity of infection (MOI), has to be considered. A high MOI results in more pathogenic RNA, but may also lead to a faster and stronger host response and less clinical relevance.

The number of reads required to achieve a good genome coverage in both species has to be estimated in advance. The number of reads needs to be calculated for the least abundant species based on the intended fold coverage, transcriptome size and read length. The total number of reads can be estimated through the ratio of the amount of extracted pathogen and host RNA.

Furthermore, sequencing parameters need to be set taking into account transcriptome sizes and how closely related studied species are. Number of reads, read length, strand-specificity and single / paired end sequencing have a great impact on the number of ambiguously mapped reads. For instance, Yazawa et al. (2013) sequenced 100-base-pair single-end reads of the grass *Sorghum bicolor* and the pathogenic fungus *Bipolaris sorghicola*. Pittman et al. (2014) sequenced 100-base-pair paired-end reads of *M. musculus* and the parasite *Toxoplasma gondii*.

Finally, data time points have to be determined. A change of the transcriptional program triggered by a stimulus is usually strong at the start of the response. Thus, in best case the organism adapts and the degree of transcriptional change decreases. The temporal onset and duration of transcriptional response of pathogen and host can be very different. To detect both responses, RNA extraction time points need to be chosen carefully. Smallscale experiments should be carried out in advance to determine good data time points.

#### *2.1.2. Dual RNA-Seq data processing*

Preprocessing and analysis of sequencing data and the selection of candidate genes is an important step in advance of network inference (**Figure 1B**). The output of RNA-Seq are raw reads, of which low quality bases need to be trimmed [e.g., with trimmomatic (Bolger et al., 2014), btrim (Kong, 2011)]. Pathogen and host read data is separated *in silico* by aligning reads to the reference genomes (mapping). Engström et al. (2013) compare various available mapping tools and evaluate the conservative MapSplice, TopHat and STAR with comparatively low run time as favorable. From this point on, pathogen and host data are processed separately.

Tools like featureCounts (Liao et al., 2014) and htseq-Counts (Anders et al., 2014) calculate the number of reads mapped to a feature, e.g., an exon or gene, to determine gene expression levels. Subsequently, differential gene expression can be tested. Various tools [e.g., edgeR (Robinson et al., 2010), DESeq2 (Love

et al., 2014)] are available for that purpose and were reviewed recently (Soneson and Delorenzi, 2013; Zhang et al., 2014). The SEQC/MAQC-III Consortium recommends to apply pipeline dependent filters for *p*-value, fold change and expression-level to decrease estimated false discovery rates. Thereby, the outputs from different differential expression analysis pipelines yield a greater agreement (SEQC/MAQC-III Consortium, 2014).

Typically, hundreds of DEGs are found, of which a subset of candidate genes has to be selected. This number can be reduced, for instance by clustering gene expression kinetics (Bezdek, 1992) and choosing one representative for each cluster. This is advantageous, because it results in a set of candidate genes representing the major expression kinetics of the system. Furthermore, gene enrichment analysis can be carried out to select functional relevant candidate genes. FungiFun2 is one of the few enrichment tools for fungi and includes 298 strains from 240 species (Priebe et al., 2015). On the other hand, many enrichment tools exist for vertebrates. The underlying algorithms can be divided into three classes of which each shows certain advantages and drawbacks. It is also recommended to apply multiple tools (Huang et al., 2009; Tipney and Hunter, 2010).

#### **2.2. MODELING PHI DATA**

We extended the heuristic network inference tool NetGenerator (see Data and Methods) and its application to predict PHI networks. NetGenerator requires logarithmic fold changes (logFCs) of gene expression time series data that can be obtained by various technologies, such as RNA-Seq or microarrays. Furthermore, the user of NetGenerator has to provide at least one input stimulus representing the external signal leading to a change in gene expression. Also, prior knowledge can be provided by the user to support the inference process (**Figure 1C**). It can be integrated in a compulsory ("fix") or soft ("flexible") way.

We generated a benchmark example to evaluate the influence of different stimuli and missing data on the inference performance (see Data and Methods). The benchmark comprised six data points of seven genes and two stimuli (**Figure 2A**). Prior knowledge data sets of two, four, six or eight interactions were randomly generated. We applied the extended NetGenerator version to infer GRNs based on the benchmark data set and each prior knowledge data set (soft integration). For small networks as the benchmark example the number of possible solutions was already very high. On sum, 63 edges (49 gene to gene interactions and 14 stimulus to gene interactions) and 263 network topologies were possible not even including the interaction sign.

#### *2.2.1. Multiple stimuli improve network inference*

Multiple stimuli trigger responses in both pathogen and host during infection, such as the mutual stimulation of pathogen and host. This can be translated into at least two stimuli—the host stimulating the pathogen and *vice versa*. Weber et al. (2013) published the previous NetGenerator version V2.0 which can integrate multiple stimuli. We tested the influence of one or two stimuli on the performance of NetGenerator based on the benchmark example and each prior knowledge data set (**Figure 2B**).

First, only one constant stimulus (Test-1) set to a value of 1 was given. In a second test, an additional stimulus set to 0 until 30 min and set to 1 afterwards (Test-2) was given (Supplementary Table S1). We calculated mean values of F-measure (**Figure 2C**), sensitivity and specificity for every prior knowledge data set to

**FIGURE 2 | Testing PHI data characteristics. (A)** Benchmark example of an inter-species GRN with 3 pathogen candidate genes (orange nodes), four host candidate genes (green nodes) and two stimuli (gray nodes). Edges represent interactions. **(B)** Test setup. **(C)** F-measures calculated from predicted network topologies and the known network topology

given different stimuli functions. Two stimuli increase F-measures (Test-2). **(D)** F-measures calculated from predicted network topologies and the known network topology based on missing data values. Carefully selected time points covering both the host and pathogen response increase F-measures (Test-3).

determine the accuracy of predicted GRNs in comparison to the known topology (Supplementary Table S2) (see Data and Methods).

We always observed noticeable larger F-measures given two stimuli in comparison to only one given stimulus. The difference in F-measure of Test-1 and Test-2 was up to 1.36 fold (**Figure 2C**). The less prior knowledge was given, the larger were the differences in F-measures between Test-1 and Test-2. We found the biggest performance difference between Test-1 and Test-2 when no or only two prior knowledge interactions were given. In these cases, 15 of 21 possible true positive edges were predicted when two stimuli were given, but only 11 true positive edges given one stimulus (Supplementary Table S2). In general, we observed increasing F-measures for more given prior knowledge independent of the number of stimuli.

## *2.2.2. Avoid missing data values*

It is conceivable that time series experiments of pathogen and host were carried out independently under comparable experimental conditions. In this case, it is possible to utilize the pathogen and host data sets to predict PHI networks. Thus, data time points might differ which leads to missing values at intermediate time points or at the end of the time series. In case of dual RNA-Seq, pathogen and host are collectively processed. This may lead to a reduced amount of sample RNA of either of the species resulting in missing gene expression data. This is a problem especially for later time points when one species may dye. We extended the NetGenerator algorithm to handle missing data values at intermediate time points (see Data and Methods). We evaluated the influence of missing data on the performance based on the benchmark example, prior knowledge data sets and two given stimuli as in Test-2 (**Figure 2B**). Again, we calculated Fmeasure (**Figure 2D**), sensitivity and specificity (Supplementary Table S2).

We included data of one additional time point (165 min) for host genes, but additional data for pathogen genes were not given (Test-3). Thereby, we demonstrated the applicability of the extended NetGenerator version to data with missing values. We set the time point in such a way, that an additional data point covering the onset of the host response was provided and observed a noticeable increase of F-measure (**Figure 2D**). The difference in F-measure is greatest with 0.12 for eight given prior knowledge interactions. In this case, a mean number of 16.7 (Test-2) and 19.2 (Test-3) out of 21 possible true positive edges were predicted representing an improvement of 11.9%. This pointed out the importance of good time point selection covering both the pathogen and host response in a dual transcriptome data set.

NetGenerator requires complete data for the last time point. In case of missing measurements at the end of the time range for a subset of candidate genes, their values must be obtained in a different way and provided by the user. Here, we set the last time point to its preceding value (Test-4). We found slightly greater F-measures for Test-2 in comparison to Test-4 independent of the number of given prior knowledge. We observed a maximal difference between the F-measures between Test-2 and Test-4 (0.02) given four, six and eight prior knowledge interactions (**Figure 2D**).

### **2.3. INCORPORATION OF MEASUREMENT VARIANCES**

Various differential expression analysis tools are available that calculate fold changes from multiple replicates. However, fold changes alone cannot reflect the degree of gene- and time point specific variances. This variance might be high especially regarding complex biological systems such as PHIs where cells from two species constantly interact and change the environment. However, biological variances can be considered in the network inference process to obtain robust predictions. For this purpose, we extended and applied NetGenerator to incorporate variances within the algorithm and in an outer robustness analysis. The extended NetGenerator algorithm was applied to one of the first published dual RNA-Seq data sets (Tierney et al., 2012) (see Data and Methods).

## *2.3.1. Extended NetGenerator algorithm incorporates measurement variances*

Variances from replicated measurements were incorporated in the objective function of NetGenerator and need to be provided by the user. We calculated variances of the dual RNA-Seq data set of Tierney et al. (2012) as described (see Data and Methods).

We predicted a GRN (Supplementary Figure S1) with the extended NetGenerator based on logFCs and prior knowledge that were used as inputs for the previous NetGenerator in Tierney et al. (2012). Calculated gene- and time point specific variances were provided as input. Measured and simulated time courses of the GRN were plotted showing the standard deviations of measurements as shaded areas (**Figure 3A**). We observed that simulated data reproduced the measured data very well and were mostly within the shaded areas. Furthermore, simulated time courses were closer to data points with smaller standard deviation (e.g., *Hap3* at 30 min) than to data points with higher standard deviation (e.g., *Mta2* at 30 min).

## *2.3.2. Variance incorporation by an outer robustness analysis*

Furthermore, variances were considered in an outer robustness analysis which we carried out based on the data of Tierney et al. (2012). The mean standard deviation was 1.24 with a minimum of 0.27 (*Sod5* at 120 min) and a maximum of 3.49 (*Mta2* at 30 min) (Supplementary Table S3). We scaled the standard deviations to a value of σmax = 0.1 (Supplementary Table S4). We calculated Gaussian distributed logFCs for every gene and time point (mean = measured logFC, σ = scaled standard deviation of replicates) (see Data and Methods). Thus, we generated 500 noisy data sets and applied the extended NetGenerator (**Figure 3B**). The robustness scores of the edges in the resulting 500 GRNs were illustrated in the bubble map (**Figure 3C**).

Tierney et al. (2012) experimentally verified the predicted inter-species interactions of *Ptx3* inhibiting *Hap3* and *Hap3* inhibiting *Mta2*. We predicted these verified interactions again as robust with the extended NetGenerator version (**Figure 3C**, Supplementary Table S5). Inhibition of *Hap3* by *Ptx3* was present in 71 % of predicted GRNs with a robustness score of 0.76. Inhibition of *Mta2* by *Hap3* was present in 72 % of predicted GRNs with a robustness score of 0.78. This also demonstrated the applicability of the presented robustness test.

**FIGURE 3 | GRN robustness analysis and visualization. (A)** Fitting plots for each gene are generated showing measured time points (dots), simulated time courses (solid lines), interpolated time courses (dashed lines), and standard deviations from replicated measurements (shaded areas). **(B)** Outer robustness analysis. Noise is added to time series data with variances

calculated from replicates of genes and time points. This is repeated *n* times to predict *n* GRNs. **(C)** The bubble map visualizes the robustness of a predicted edge from column gene to row gene. Bubble sizes illustrate the robustness score assigned to an edge. Orange and blue pies illustrate the fraction of activating and inhibiting edges, respectively.

# **3. DISCUSSION**

In this study, we propose a workflow for dual RNA-Seq data acquisition, data processing and inter-species network inference. Furthermore, we describe how to handle a different temporal onset of transcriptional changes, missing data and how to integrate variances from replicated measurements based on the extended NetGenerator algorithm.

# *3.0.3. Delayed host response in PHI data*

In a dual transcriptome data set we expect the onset of the pathogen and host transcriptional response at different time points. So far, several infection-related transcriptome studies of fungi were carried out. Transcriptome data was generated already at two to three time points within 60 min after infection (Linde et al., 2012; Ramachandra et al., 2014) suggesting an early onset of the pathogen's transcriptional response. This is further supported by a mechanism called adaptive prediction, that some pathogens have evolved. Based on cues from the current environment, pathogens predict a coming change in conditions and adapt their transcriptome in advance. An appropriate adaptation of the pathogen increases its survival chances (Brunke and Hube, 2014).

On the other hand, it takes some time until the host recognizes a pathogen. Moyes et al. (2010) showed that host epithelial cells initiate a response when a certain amount of pathogens exceeding a threshold is recognized.  Furthermore, the assumption of a later onset of the host transcriptional response is supported by various studies monitoring the host transcriptome from 1 h onwards (Banchereau et al., 2014; Favila et al., 2014).

However, we do not see a delayed transcriptional response of host DEGs in comparison to pathogen DEGs in the data set of Tierney et al. (2012) possibly because of the high MOI. Experimentalists keep improving their procedures to achieve realistic experimental setups, e.g., they decrease the MOI as much as possible still allowing them to extract the required amount of RNA for sequencing. Therefore, we expect to see a delayed host response in upcoming dual RNA-Seq data sets. To test the performance of the extended NetGenerator regarding different stimuli functions and missing data values, we generated a benchmark example showing a delayed onset of the host transcriptional response.

### *3.0.4. Gene expression time series data*

NetGenerator requires time series gene expression data, at least one stimulus function and optionally prior knowledge. LogFCs are passed to NetGenerator in form of a data matrix, where columns correspond to candidate genes and rows to measured time points.

PHIs are very complex systems, but available data is limited regarding the number of time points and replicates. Furthermore, transcriptome data do not provide any information about processes taking place as for instance on protein level and in the extracellular space. Therefore, it has to be considered that predicted PHIs are indirect, when they are interpreted.

### *3.0.5. Modeling PHI stimuli*

A GRN can be understood as a biological system that adapts to external, environmental stimuli yielding changes in gene expression. NetGenerator can integrate multiple stimuli and requires one function per stimulus representing it.

Many biological processes can be interpreted as external stimuli triggering responses in both pathogen and host cells during infection. In a typical experimental setup the host is incubated with the pathogen stimulating both organisms. The host recognizes PAMPs on pathogen cell surfaces by pathogen recognition receptors (PRRs). This initiates an information flow through signaling cascades (Akira et al., 2006). Nevertheless, the process of pathogen recognition resulting in a transcriptional response requires some time. Besides the molecular interaction with the host, the pathogen is also stimulated by different environmental factors, e.g., a change of temperature, pH and ion concentrations (Linde et al., 2010).

We found that multiple stimuli functions improve network inference results significantly. Therefore, we recommended to provide two or more stimuli functions for inter-species network inference. One option to model the stimulus representing the influence of the host on the pathogen is a constant function. Therewith, the stimulus is active from time point zero onwards and models an early pathogen transcriptional response. *Vice versa*, a second stimulus can represent the stimulation of the host by the pathogen. We predicted GRNs providing an additional input signal as a delayed step function (Test-2) aiming to model a later onset of the host transcriptional response. Another possible scenario would be to provide a stimulus function representing a slow increase of the influence.

More options for stimuli functions are possible when real experiments are carried out. For example, the number of differentially expressed host and pathogen genes can be determined for every time point and translated into stimuli functions. This can be done by scaling the number of DEGs to a range from zero to one. Additional measurements, e.g., cytokine release or cell contacts, can also be used as a basis for stimuli functions. Of particular interest is the growth curve of the pathogen, which we recommend to measure and integrate in the stimuli functions. Nevertheless, many biological events trigger responses, of which not all can be integrated in the network inference.

#### *3.0.6. Prior knowledge sources*

Optionally, the user of NetGenerator can provide prior knowledge about interactions of candidate genes. This is strongly recommended to reduce the search space resulting from the large number of possible interactions (Hecker et al., 2009). Prior knowledge can be softly integrated by assigning a score between zero and one that reflects its reliability. A score smaller than one allows prior knowledge to be rejected if it does not fit the data.

Prior knowledge about interactions in GRNs originates from published results that were transferred to databases. PHI databases like PHISTO (Tekir et al., 2013), PHI-base (Winnenburg et al., 2006), and HPIDB (Kumar and Nanduri, 2010) have been established. Mukherjee et al. (2013)listed various web sources of interaction data.

Host specific prior knowledge can be extracted manually from literature or automatically with text mining tools. Pathway Studio is a text mining tool specific for mammals (Nikitin et al., 2003). Further gene information is provided by organism specific websites, e.g., the human gene database GeneCards1 .

As well, organism specific websites exist for pathogens, e.g., Aspergillus Genome Database (Cerqueira et al., 2014) and Candida Genome Database (Inglis et al., 2012). To our knowledge, no fungi specific text mining tool is available. More general tools like GeneView—a semantic search engine for PubMed can be applied (Thomas et al., 2012). Little is known about some pathogenic species. In this case, prior knowledge can be generated by searching orthologous genes in closely related and better studied organisms.

For both host and pathogen transcription factor binding motifs and binding sites can be obtained from databases, e.g., TRANSFAC (Matys et al., 2006), or predicted with bioinformatic tools as SiTaR (Fazius et al., 2011).

#### *3.0.7. Robustness analysis*

We extended the NetGenerator algorithm and its application to incorporate variances from replicated measurements in the inference method and in a robustness analysis. The output provides guidance for experimental validation of predicted interactions.

Inference methods should take into account the variance of replicates, because this additional information improves the parameter estimation. Under the assumption of independent Gaussian distributed noise the minimization of the objective function (Equation 4) corresponds to a Maximum Likelihood

1www.genecards.org

Estimator (MLE) (see e.g., Klipp et al., 2009, p. 155). Here, we assume that the variances of each gene and time point exhibit those statistical properties sufficiently. The extended NetGenerator version incorporates available measurement variances thus providing more reliable inference results. Nevertheless, the option to predict GRNs without providing variances is still available.

In previous publications a similar robustness analysis was carried out with the same standard deviation for each gene and time point set to a fixed value (Linde et al., 2010, 2012). Biological replicates can show high variance, that is gene- and time point specific and has a great influence on the estimated fold changes as well as their significance. Both the extended objective function (Equation 4) and the robustness analysis incorporate variances. They should be determined based on the available data to account for differences between genes and time points. One possibility is the rather simple approach to calculate the total variance of a logFC from sample variances as proposed (Equation 6). Another possibility is to derive the variances from software packages that take into account the statistical nature of the measurement method (including both biological and technical variances), perform processing steps, test for significant changes and determine logFCs. For instance, the R-package DESeq2 calculates standard errors for estimated logFCs (Love et al., 2014). Since those methods adjust the variances based on a statistical foundation, the inference results can be expected to further improve.