SHORT COMMUNICATION Exploring Biotic Interactions Within Protist Cell Populations Using Network Methods Shu Cheng a,1 , Dana C. Price a,1 , Slim Karkar b & Debashish Bhattacharya a,b a Department of Ecology, Evolution and Natural Resources, Rutgers University, New Brunswick, New Jersey, 08901, USA b Institute of Marine and Coastal Science, Rutgers University, New Brunswick, New Jersey, 08901, USA Keywords Assortativity; Bigelowiella natans; network analysis; Paulinella ovalis; scale-free network; single cell genomics. Correspondence D. Bhattacharya, Rutgers University, 59 Dudley Road, Foran Hall 102, New Brunswick, New Jersey, 08901, USA Telephone number: +1 848-932-6218; FAX number: +1 732-932-8746; e-mail: bhattacharya@aesop.rutgers.edu 1 Equal contribution made by these authors. Received: 4 October 2013; revised 6 January 2014; accepted February 2, 2014. doi:10.1111/jeu.12113 ABSTRACT The study of diseased human cells and of cells isolated from the natural envi- ronment will likely be revolutionized by single cell genomics (SCG). Here, we used protein similarity networks to explore within- and between-cell DNA dif- ferences from SCG data derived from six individual rhizarian cells related to Paulinella ovalis and proteins from the complete genome of another rhizarian, Bigelowiella natans. We identified shared and distinct DNA components within our SCG data and between P. ovalis and B. natans. We show that network properties such as assortativity and degree effectively discriminate genome features between SCG assemblies and that SCG data follow the power law with a small number of protein families dominating networks. NETWORK methods offer rapid and powerful tools for analyzing complex information such as genomic or func- tional genomic data (e.g. Bapteste et al. 2012; Barab asi and Oltvai 2004; Komurov et al. 2012; Zhou et al. 2010). Rather than studying collections of individual genes, it is possible with network methods to interpret the biology of cells and interactions between them as a system com- prised of thousands of interacting components. Genome- wide protein networks can be built using measures of similarity (e.g. with a BLAST cutoff value) to create edges that connect the different nodes (proteins) with groups of related sequences in a network graph referred to as a con- nected component (Beauregard-Racine et al. 2011). The components represent genes and gene families that are shared among the studied genomes. Network analysis does not require predictive gene modeling and can be implemented with freely available tools such as Cytoscape (http://www.cytoscape.org/). This straightforward and inclusive approach has been used to identify ancient con- nections between members of gene families (i.e. when relaxed cutoff values are used), for defining horizontal gene transfer events, and for recognizing gene fusions that may have important functional consequences (e.g. Alvarez-Ponce et al. 2013; Beauregard-Racine et al. 2011). These types of weak or reticulate evolutionary signal are often difficult to assess using phylogenetic methods that rely on simultaneous multiple sequence alignments to generate bifurcating trees. Metagenomic and single cell genome (SCG) data from natural samples (e.g. Halary et al. 2010; Kalisky et al. 2011; Yoon et al. 2011) offer other promising targets for applying network approaches to study gene distribution. Despite the promise, SCG methods still require consid- erable refinement because these genome assemblies may show significant coverage bias introduced by multiple dis- placement amplification (MDA, Rodrigue et al. 2009; Wo- yke et al. 2010 [although the recently developed MALBAC procedure may ameliorate this issue; Zong et al. 2012]). In addition, the challenge remains to assemble and analyze complex DNA mixtures that include the host DNA and potentially, associated nucleic acids from symbionts, pathogens, and prey (Bhattacharya et al. 2012, 2013; Yoon et al. 2011). Given that population-level SCG data will likely become more widely available, here we explore the use of network methods to study the protein comple- ments of a wild-caught sample of microbial eukaryotes. The data are derived from six individual Paulinella ovalis- like cells (phagotrophic rhizarian protists) that form a sister © 2014 The Author(s) Journal of Eukaryotic Microbiology © 2014 International Society of Protistologists Journal of Eukaryotic Microbiology 2014, 61, 399–403 399 Journal of Eukaryotic Microbiology ISSN 1066-5234 Published by the International Society of Protistologists Eukaryotic Microbiology The Journal of