New insights about host response to smallpox using microarray data

Abstract Background Smallpox is a lethal disease that was endemic in many parts of the world until eradicated by massive immunization. Due to its lethality, there are serious concerns about its use as a bioweapon. Here we analyze publicly available microarray data to further understand survival of smallpox infected macaques, using systems biology approaches. Our goal is to improve the knowledge about the progression of this disease. Results We used KEGG pathways annotations to define groups of genes (or modules), and subsequently compared them to macaque survival times. This technique provided additional insights about the host response to this disease, such as increased expression of the cytokines and ECM receptors in the individuals with higher survival times. These results could indicate that these gene groups could influence an effective response from the host to smallpox. Conclusion Macaques with higher survival times clearly express some specific pathways previously unidentified using regular gene-by-gene approaches. Our work also shows how third party analysis of public datasets can be important to support new hypotheses to relevant biological problems.

Special thanks to Lucas Perin for the English revision and the three reviewers for critical and insightful suggestions. The authors would like also to thank Dr. Eduardo Jordão Neves for discussions about the statistical models and for reading the manuscript; Dr. Luiz Fernando Reis, Dra. Erna Kroon, Dr. Ricardo Vêncio, Tobias Mann, Leonardo Varuzza, Elier Cristo and Dr. Roberto Hirata Jr. for helpful discussions. GHE is supported by CAPES grant; ES is supported by CNPq grant; ACQS, TMV, RAD and RO are supported by FAPESP grants. GHE, ACQS and TMV are PhD students in Bioinformatics at Universidade de São Paulo; ES and RO are PhD students in Statistics at Instituto de Matemática e Estatística, Universidade de São Paulo; RAD is PhD student in Computer Science at Instituto de Matemática e Estatística, Universidade de São Paulo.

Special thanks to Lucas Perin for the English revision and the three reviewers for critical and insightful suggestions. The authors would like also to thank Dr. Eduardo Jordão Neves for discussions about the statistical models and for reading the manuscript; Dr. Luiz Fernando Reis, Dra. Erna Kroon, Dr. Ricardo Vêncio, Tobias Mann, Leonardo Varuzza, Elier Cristo and Dr. Roberto Hirata Jr. for helpful discussions. GHE is supported by CAPES grant; ES is supported by CNPq grant; ACQS, TMV, RAD and RO are supported by FAPESP grants. GHE, ACQS and TMV are PhD students in Bioinformatics at Universidade de São Paulo; ES and RO are PhD students in Statistics at Instituto de Matemática e Estatística, Universidade de São Paulo; RAD is PhD student in Computer Science at Instituto de Matemática e Estatística, Universidade de São Paulo.