Investigation of temporal and spatial heterogeneities of the immune responses to Bordetella pertussis infection in the lung and spleen of mice via analysis and modeling of dynamic microarray gene expression data

Bordetella pertussis(B.pertussis)is the causative agent of pertussis,also referenced as whooping cough.Although pertussis has been appropriately controlled by routine immunization of infants,it has experienced a resurgence since the beginning of the 21st century.Given that elucidating the immune response to pertussis is a crucial factor to improve therapeutic and preventive treatments,we re-analyzed a time course microarray dataset of B.pertussis infection by applying a newly developed dynamic data analysis pipeline.Our results indicate that the immune response to B.pertussis is highly dynamic and heterologous across different organs during infection.Th1 and Th17 cells,which are two critical types of T helper cell populations in the immune response to B.pertussis,and follicular T helper cells(TFHs),which are also essential for generating antibodies,might be generated at different time points and distinct locations after infection.This phenomenon may indicate that different lymphoid organs may have their unique functions during infection.These findings provide a better understanding of the basic immunology of bacterial infection,which may provide valuable insights for the improvement of pertussis vaccine design in the future.

Correlation-based iterative clustering methods for time course data:The identification of temporal gene response modules for influenza infection in humans

Many pragmatic clustering methods have been developed to group data vectors or objects into clusters so that the objects in one cluster are very similar and objects in different clusters are distinct based on some similarity measure.The availability of time course data has motivated researchers to develop methods,such as mixture and mixed-effects modelling approaches,that incorporate the temporal information contained in the shape of the trajectory of the data.However,there is still a need for the development of time-course clustering methods that can adequately deal with inhomogeneous clusters(some clusters are quite large and others are quite small).Here we propose two such methods,hierarchical clustering(IHC)and iterative pairwise-correlation clustering(IPC).We evaluate and compare the proposed methods to the Markov Cluster Algorithm(MCL)and the generalised mixed-effects model(GMM)using simulation studies and an application to a time course gene expression data set from a study containing human subjects who were challenged by a live influenza virus.We identify four types of temporal gene response modules to influenza infection in humans,i.e.,single-gene modules(SGM),small-size modules(SSM),mediumsize modules(MSM)and large-size modules(LSM).The LSM contain genes that perform various fundamental biological functions that are consistent across subjects.The SSM and SGM contain genes that perform either different or similar biological functions that have complex temporal responses to the virus and are unique to each subject.We show that the temporal response of the genes in the LSM have either simple patterns with a single peak or trough a consequence of the transient stimuli sustained or state-transitioning patterns pertaining to developmental cues and that these modules can differentiate the severity of disease outcomes.Additionally,the size of gene response modules follows a power-law distribution with a consistent exponent across all subjects,which reveals the presence of universality in the underlying biological principles that generated these modules.