Gene Regulatory Cross Networks: Inferring Gene Level Cell-to-Cell Communications of Immune Cells

Abstract

Corresponding authors: altay@lji.org and bpeters@lji.org. Abstract:Background Gene level cell-to-cell communications are crucial part of biology as they may be potential targets of drugs and vaccines against a disease condition of interest. Yet, there are only few studies that propose algorithms on this particularly important research field. ResultsIn this study, we first overview the current literature and define two general terms for the types of approaches in general for gene level cell-to-cell communications: Gene Regulatory Cross Networks (GRCN) and Gene Co-Expression Cross Networks (GCCN). We then propose two algorithms for each type, named as GRCNone and GCCNone. We applied them to reveal communications among 8 different immune cell types and evaluate their performances mainly via membrane protein database. Also, we show the biological relevance of the predicted crossnetworks with pathway enrichment analysis. We then provide an approach that prioritize the targets by ranking them before experimental validations. ConclusionsWe establish two main approaches and propose algorithms for genome-wide scale gene level cellto-cell communications between any two different cell-types. This study aims accelerating this relatively new avenue of research in cross-networks and points out the gap of it with the wellestablished single cell type gene networks. The proposed algorithms have the potential to reveal gene level interactions between normal and disease cell types. For instance, they might reveal the interaction of genes between tumor and normal cells, which are the potential drug-targets and thus can help finding new cures that might prevent the prevailing of tumor cells. Background:Gene network inference (GNI) methods can identify interactions between different genes and the gene products they encode. Gene regulatory networks (GRN), which can be inferred by GNI methods, help in the basic biological understanding of genes and their functions and are well studied within single cell condition [1]. In any GRN inference, the goal is to infer a network that consist of causal interactions between the genes. It is the main difference of GRN from the coexpression networks [2] that infers a network containing both causal and associated interactions.Given the huge number of significantly associated genes, most of the links of a co-expression network are associative. This way it can be considered mostly similar to kind of clustering of interactions among the genes in some more detail than basic clustering. In clustering, the clustered genes are assumed to be fully connected with each other. GRNs are, in theory, more precise to understand the causal mechanism of any interaction but harder to infer accurately. However, different cell types are also interacting with each other, especially immune cell types, to manifest various complex biological functions. So far, GNI methods have not focused on the gene level interactions between different cell types [3]. In fact, it was stated in the very recent publication [3] that gene netw...