Periodontal disease is accompanied by alterations to cellular profiles and biological activities of both the subgingival microbiome and host tissues. Although significant progress has been made in describing the molecular basis of the homeostatic balance of host–commensal microbe interactions in health compared to the destructive imbalance in disease, particularly with respect to immune and inflammatory systems, few studies have attempted a comprehensive analysis in diverse host models. Here, we describe the development and application of a metatranscriptomic approach to analysis of host–microbe gene transcription in a murine periodontal disease model, based on oral gavage infection using Porphyromonas gingivalis in C57BL6/J mice. We generated 24 metatranscriptomic libraries from individual mouse oral swabs, representing health and disease. On average, 76% ± 11.7% reads in each sample belonged to the murine host genome and the remainder to the microbes. We found 3,468 (2.4% of the total) murine host transcripts differentially expressed between health and disease, of which 76% were overexpressed in periodontitis. Predictably, there were prominent alterations to genes and pathways linked with the host immune compartment in disease—the CD40 signaling pathway being the top enriched biological process in this data set. However, in addition, we observed significant alterations to other biological processes in disease, particularly cellular/metabolic processes and biological regulation. The number of differentially expressed microbial genes particularly indicated shifts in carbon metabolism pathways in disease with potential consequences for metabolic end-product formation. Together, these metatranscriptome data reveal marked changes between the gene expression patterns in both the murine host and microbiota, which may represent signatures of health and disease, providing the basis for future functional studies of prokaryotic and eukaryotic cellular responses in periodontal disease. In addition, the noninvasive protocol developed in this study will enable further longitudinal and interventionist studies of host–microbe gene expression networks.