Reconstruction and inference of the Lactococcus lactis MG1363 gene co-expression network

Abstract

Lactic acid bacteria are Gram-positive bacteria used throughout the world in many industrial applications for their acidification, flavor and texture formation attributes. One of the species, Lactococcus lactis, is employed for the production of fermented milk products like cheese, buttermilk and quark. It ferments lactose to lactic acid and, thus, helps improve the shelf life of the products. Many physiological and transcriptome studies have been performed in L. lactis in order to comprehend and improve its biotechnological assets. Using large amounts of transcriptome data to understand and predict the behavior of biological processes in bacterial or other cell types is a complex task. Gene networks enable predicting gene behavior and function in the context of transcriptionally linked processes. We reconstruct and present the gene co-expression network (GCN) for the most widely studied L. lactis strain, MG1363, using publicly available transcriptome data. Several methods exist to generate and judge the quality of GCNs. Different reconstruction methods lead to networks with varying structural properties, consequently altering gene clusters. We compared the structural properties of the MG1363 GCNs generated by five methods, namely Pearson correlation, Spearman correlation, GeneNet, Weighted Gene Co-expression Network Analysis (WGCNA), and Sparse PArtial Correlation Estimation (SPACE). Using SPACE, we generated an L. lactis MG1363 GCN and assessed its quality using modularity and structural and biological criteria. The L. lactis MG1363 GCN has structural properties similar to those of the gold-standard networks of Escherichia coli K-12 and Bacillus subtilis 168. We showcase that the network can be used to mine for genes with similar expression profiles that are also generally linked to the same biological process.

Fig 1. Workflow of gene co-expression network (GCN) reconstruction using different methods.

Fig 2. Bench-marking L. lactis MG1363 SPACE network to gold-standards.

A: Degree distribution plot for the E. coli K-12 network (black circles). E. coli K-12 (+ edges) represents degree distributions of the network with random edge addition (green triangles). The x-axis shows the log-degree distribution (k); y-axis shows the log-probability of the degree distributions. B: The same as in A for the B. subtilis 168 and B. subtilis 168 (+ edges) plots. The criterion for edge addition is described in S2 File. The degree distribution of the L. lactis MG1363 network is plotted as grey squares in panels A and B. The red dotted lines show the power-law fit to the degree distributions of the L. lactis MG1363 network.

Fig 3. L. lactis MG1363 GCN visualized in Cytoscape v3.2.0.

A: GCN generated using SPACE (ρ = 0.68). Projection of genes (shown in yellow) associated to significantly enriched GO groups in “module 0”, other genes are colored red. The network consists of 1262 genes and 4112 edges. Only genes that satisfied the association threshold levels for inclusion in the adjacency matrix are shown in the network. B: Example network of L. lactis MG1363 generated using GeneNet (ω = 0.90; 2235 genes and 70386 edges). For instance, the GCN obtained using SPACE has enriched gene sets in “module 0”, which are clustered together in the network (enriched gene sets in yellow), while the same genes are spread out in the GeneNet network.

Fig 4. L. lactis MG1363 GCN integrated with literature-predicted operons (visualized in Cytoscape v3.2.0).

The operon IDs are indicated in red, genes predicted to belong to operons are in green, and genes belonging to specific operons based on literature information (http://genome2d.molgenrug.nl) are shown in blue.