Abstract
Anti-TNF agents have been in the first line of treatment of various inflammatory diseases such as Rheumatoid Arthritis and Crohn’s Disease, with a number of different biologics being currently in use. A detailed analysis of their effect at transcriptome level has nevertheless been lacking. We herein present a concise analysis of an extended transcriptomics profiling of four different anti-TNF biologics upon treatment of the established hTNFTg (Tg197) mouse model of spontaneous inflammatory polyarthritis. We implement a series of computational analyses that include clustering of differentially expressed genes, functional analysis and random forest classification. Taking advantage of our detailed sample structure, we devise metrics of treatment efficiency that take into account changes in gene expression compared to both the healthy and the diseased state. Our results suggest considerable variability in the capacity of different biologics to modulate gene expression that can be attributed to treatment-specific functional pathways and differential preferences to restore over- or under-expressed genes. Early intervention appears to manage inflammation in a more efficient way but is accompanied by increased effects on a number of genes that are seemingly unrelated to the disease. Administration at an early stage is also lacking in capacity to restore healthy expression levels of under-expressed genes. We record quantifiable differences among anti-TNF biologics in their efficiency to modulate over-expressed genes related to immune and inflammatory pathways. More importantly, we find a subset of the tested substances to have quantitative advantages in addressing deregulation of under-expressed genes involved in pathways related to known RA comorbidities. Our study shows the potential of transcriptomic analyses to identify comprehensive and distinct treatment-specific gene signatures combining disease-related and unrelated genes and proposes a generalized framework for the assessment of drug efficacy, the search of biosimilars and the evaluation of the efficacy of TNF small molecule inhibitors.
Highlights
In an era of unprecedented accumulation of biomedical data, our understanding of the mechanisms of the development of complex diseases is greatly enabled by the performance of highthroughput experiments and their subsequent analyses at various levels that range from single genes to biological pathways, modules and networks [1, 2]
We applied a series of bioinformatics analyses in order to define the sets of genes, biological pathways and functions that are affected in the diseased animals and modulated by each of the different treatments
We found that the majority of differentially expressed genes in disease are under-expressed and that they are associated with functions related to Rheumatoid Arthritis comorbidities such as cardiovascular disease
Summary
In an era of unprecedented accumulation of biomedical data, our understanding of the mechanisms of the development of complex diseases is greatly enabled by the performance of highthroughput experiments and their subsequent analyses at various levels that range from single genes to biological pathways, modules and networks [1, 2]. Inflammatory diseases such as Rheumatoid Arthritis (RA) present great challenges in the understanding of the process through which an initial trigger may lead to generalized and highly variable changes at molecular, cellular and eventually organ level [8]. In this respect, animal models have proven invaluable in the detailed study of these intricate mechanisms and have been the choice of preference in many studies due to particular advantages such as accessibility of material, robustness and standardization [9,10]. Among the various therapeutic agents used in the treatment of RA, anti-TNF antibodies have been the primary
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