Email updates

Keep up to date with the latest news and content from BMC Proceedings and BioMed Central.

This article is part of the supplement: Proceedings of the International Symposium on Animal Genomics for Animal Health (AGAH 2010)

Open Access Proceedings

Jejunal gene expression patterns correlate with severity of systemic infection in chicken

Dirkjan Schokker1*, Mari A Smits1 and Johanna MJ Rebel2

Author Affiliations

1 Wageningen UR Livestock Research, Animal Breeding and Genomics Centre, Lelystad, P.O. Box 65, 8200 AB, The Netherlands

2 Central Veterinary Institute, Wageningen UR, Lelystad, P.O. Box 65, 8200 AB, The Netherlands

For all author emails, please log on.

BMC Proceedings 2011, 5(Suppl 4):S4  doi:10.1186/1753-6561-5-S4-S4

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1753-6561/5/S4/S4


Published:3 June 2011

© 2011 Schokker et al; licensee BioMed Central Ltd.

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

Not much is known about the effect of Salmonella enteritidis on changes in the developmental processes occurring in the intestine of young chicken. Therefore we investigated the correlation of intestinal gene expression patterns with the severity of systemic Salmonella infections.

Methods

The number of Salmonella colony forming units (CFUs) in the liver of infected chicken were plotted against the average intestinal expression profiles of previously identified gene expression clusters. The functional properties of all the genes taken together present in 3 clusters exhibiting positive correlation at early time-points were compared with the functional properties of the genes displaying antagonistic correlations in 1 cluster. The top 5 ranking functional groups were analysed in further detail.

Results

Three clusters showed gene expression profiles which were positively correlated with the severity of systemic disease as measured by the number of Salmonella colony forming units in the liver. In these clusters, genes involved in morphological processes were predominantly present. One cluster had a profile that was negatively correlated with the severity of systemic disease, as measured by numbers of CFUs in the liver. The genes in the latter cluster were mostly involved in cell turn-over and metabolism.

Conclusions

In the developing jejunum of young chicken, both stimulatory and inhibitory gene expression mechanisms are correlated with the severity of systemic Salmonella infections.

Background

Intestinal development is hallmarked by functional, morphological and immunological development. Genes involved in these three categories have different spatial-temporal expression patterns, as observed in earlier studies [1-4]. Developmental studies are mostly performed in healthy chickens and not much is known of the effect of a disturbance on intestinal development. In a previous [5] study we disturbed intestinal development by oral infection with Salmonella and studied the effects of Salmonella on the immunological development of the intestine for 8 hours post infection (pi) until 21 days pi. Besides the immunological pathways we identified clusters of genes whose expression was correlated with the severity of systemic infection (numbers of CFUs in the liver). In this study, we further analyzed this correlation and focused on the functional properties of genes displaying the correlated expression profiles only in the earlier time points until 4 days pi. This study contrasts other studies that use Salmonella infected chicken to study (innate) immune responses or differences in susceptibilities [6-8].

Methods

Design

We used the dataset E-MEXP-2042 from ArrayExpress [9,10], describing whole genome transcriptional profiling of chicken jejunum in a time series (8 hours until 21days pi) after orally infection with Salmonella. The data from Schokker et al.[5] was used to define average expression profiles of 9 clusters of genes and to identify the 4 clusters that showed expression profiles correlated with Salmonella counts in the liver. Here we investigate the differences between the positive and negative correlation to the trait in more depth in the first 4 days pi. We combined the genes of the clusters G, H and I (2169 probes), which were found to possess a positive correlation early in time to severity of systemic disease and compared this group of genes to the genes present in cluster F (791) which showed an opposite correlation pattern to the severity of systemic disease. The Database for Annotation, Visualization and Integrated Discovery (DAVID) 6.7 [11] was used for functional annotation clustering of these two sets of genes (March, 2010) [12]. The inputs were lists with chicken gene symbols or the homologous human symbols. Human has a better annotation, 29% of the probes mapped back to a human gene name compared to 24% to a chicken gene name. Also more databases are available for human and the data is analyzed with a human background. The settings in DAVID were similar to the settings as described by Schokker et al.[13].

Results and discussion

The average gene expression profiles of the genes in clusters F, G, H, and I showed irregularities at 2 days pi compared to the smooth profiles of the corresponding clusters obtained form non-infected chicken. Clusters G, H, and I showed a positive correlation with the amount of Salmonella colony forming units (CFU) in liver, whereas cluster F showed an anti-correlation (Fig. 1). These positive and negative correlations are based on the period between 8 hours pi to 4 days pi. This correlation suggests that the irregularity in gene expression patterns is most probably directly related to the systemic invasion of Salmonella from the intestinal tract.

thumbnailFigure 1. Correlation of average gene expression patterns of genes present in clusters F–I with Salmonella CFU per gram liver tissue in time. Cluster F shows negative correlation with the number of Colony Forming Units (CFUs) in liver from 0.33 – 4 days post infection (dpi), whereas clusters G, H and I have positive correlation in that time period.

The correlations further suggest that these associated genes encode for functional properties related to the severity of systemic disease. To investigate the functional properties, clusters G, H and I were grouped, because of their similar expression pattern and similar association to the number of CFUs in the liver. Subsequently, the genes residing in positively or negatively correlated clusters were used as input for the functional clustering analysis by DAVID. The resulting top 5 of the functional annotation clustering is depicted in Table 1.

Table 1. DAVID Functional Annotation Clustering Top 5

Most functional groups in the positively correlated expression profiles are related to morphological processes, like ‘fibronectin’, ‘contractile fiber’ and ‘cell morphogenesis’. Another top 5 group is ‘Calmodulin (IQ-domain)’, which is involved in multiple processes, like metabolism, inflammation and intracellular movement. Thus this positively correlated group is characterized by major processes involved in both morphological and immunological functions. Apparently, due to transmigration of Salmonella many genes involved in morphological related processes are regulated. Increased transmigration from the gut correlates with increased expression of a number of genes involved in morphological processes in the jejunum, whereas decreased transmigration of Salmonella leads to lower expression of such genes.

Also a specific immune related process ‘immunoglobulin I-set’ is present among the top 5 list. However, the genes contained in this domain are mainly involved in cell adhesion processes. For example vascular (VCAM), intercellular (ICAM), neural (NCAM) and mucosal addressin (MADCAM) cell adhesion molecules, as well as junction adhesion molecules (JAM) [14]. Some of these genes are also involved in immune cell adhesion, for example ICAM1 and VCAM1 are involved in monocyte-endothelial adhesion [15]. Moreover JAM genes are known to be involved in lymphocyte homing [16]. The expression of these adhesion genes is directly and positively related to Salmonella transmigration and the severity of systemic disease.

In the negatively correlated group high expression is observed at 8 hours pi, followed by stable expression around zero until 21 days pi. Compared to the corresponding cluster of non-infected chicken, in the Salmonella disturbed chicken this cluster showed an irregularity in gene expression profile opposite to the Salmonella load in liver. This peak in gene expression may reflect a feedback mechanism of the jejunum. In this cluster, functional groups like ‘thrombospondin’, homeostasis related processes and ‘programmed cell death’ are observed, which are mainly involved in cell turn-over processes. The thrombospondin family is related to adhesive glycoproteins, and is involved in various processes like adhesion/migration, cytoskeletal organization, proliferation, phagocytosis, apoptosis and platelet aggregations [17,18]. The functional annotation clustering results furthermore indicate that in this cluster, genes are also involved in programmed cell death. After infection with Salmonella at day zero different processes are initiated and also the Salmonella load increases in liver (Fig. 1). Many cells will die or loose functionality due to the infection, therefore it necessary for the host to respond by replacing infected and affected cells. Thus the majority of genes residing in the negatively correlated group are associated to cell turn-over processes suggesting that due to the transmigration of Salmonella across the intestinal mucosa, processes for tissue repair are induced almost immediately. However the data suggest that the genes involved in such processes have a delayed response to the Salmonella infection, therefore a peak in gene expression is observed after the peak of Salmonella load in the liver.

In addition to the turn-over processes, also processes involved in ‘homeostasis’, maintenance of an internal steady-state at the level of the cell are observed. These latter processes can be characterized as metabolic.

Conclusions

Severity of systemic Salmonella infections can be associated to gene expression patterns in the jejunum. Negatively correlated gene expression patterns correspond to processes involved in metabolism, cell turn-over and tissue repair. Positively correlated gene expression patterns are associated with morphological and immunological related processes.

Competing interests

The authors declare that they have no competing interests.

Acknowledgements

These results are obtained through the EC-funded FP6 Integrated Project SABRE – Cutting Edge Genomics for Farm Animal Breeding (contract no. FOOD-CT-2006-016250), EADGENE (contract no. FOOD-CT-2004-506416) and IP/OP: Systems Biology financially supported by the Ministry of Agriculture, Nature and Food Quality (BAS no. 4434660800). The text represents the authors’ views and does not necessarily represent a position of the Commission who will not be liable for the use made of such information.

This article has been published as part of BMC Proceedings Volume 5 Supplement 4, 2011: Proceedings of the International Symposium on Animal Genomics for Animal Health (AGAH 2010). The full contents of the supplement are available online at http://www.biomedcentral.com/1753-6561/5?issue=S4.

References

  1. Bar Shira E, Sklan D, Friedman A: Impaired immune responses in broiler hatchling hindgut following delayed access to feed.

    Veterinary Immunology and Immunopathology 2005, 105(1-2):33-45. PubMed Abstract | Publisher Full Text OpenURL

  2. Bar-Shira E, Sklan D, Friedman A: Establishment of immune competence in the avian GALT during the immediate post-hatch period.

    Developmental and Comparative Immunology 2003, 27(2):147-157. PubMed Abstract | Publisher Full Text OpenURL

  3. Sklan D: Development of the digestive tract of poultry.

    World's Poultry Science Journal 2001, 57:415-458. PubMed Abstract | Publisher Full Text OpenURL

  4. Uni Z, Geyra A, Ben-Hur H, Sklan D: Small intestinal development in the young chick: crypt formation and enterocyte proliferation and migration.

    British poultry science 2000, 41(5):544-551. PubMed Abstract | Publisher Full Text OpenURL

  5. Schokker D, Smits MA, Hoekman AJ, Parmentier HK, Rebel JM: Effects of Salmonella on spatial-temporal processes of jejunal development in chickens.

    Dev Comp Immunol 2010, 34(10):1090-1100. PubMed Abstract | Publisher Full Text OpenURL

  6. Kogut MH: Cytokines and prevention of infectious diseases in poultry: a review.

    Avian Pathol 2000, 29(5):395-404. PubMed Abstract | Publisher Full Text OpenURL

  7. Muir WI, Bryden WL, Husband AJ: Immunity, vaccination and the avian intestinal tract.

    Dev Comp Immunol 2000, 24(2-3):325-342. PubMed Abstract | Publisher Full Text OpenURL

  8. Nurmi E, Rantala M: New aspects of Salmonella infection in broiler production.

    Nature 1973, 241(5386):210-211. PubMed Abstract | Publisher Full Text OpenURL

  9. Parkinson H, Kapushesky M, Kolesnikov N, Rustici G, Shojatalab M, Abeygunawardena N, Berube H, Dylag M, Emam I, Farne A, et al.: ArrayExpress update--from an archive of functional genomics experiments to the atlas of gene expression.

    Nucleic acids research 2009, 37(Database issue):D868-872. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  10. Rustici G, Kapushesky M, Kolesnikov N, Parkinson H, Sarkans U, Brazma A: Data storage and analysis in ArrayExpress and Expression Profiler.

    Curr Protoc Bioinformatics 2008.

    Chapter 7:Unit 7 13

    PubMed Abstract | Publisher Full Text OpenURL

  11. Dennis G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA: DAVID: Database for annotation, visualization, and integrated discovery.

    Genome Biol 2003, 4(5):P3. PubMed Abstract | BioMed Central Full Text OpenURL

  12. Huang DW, Sherman BT, Tan Q, Collins JR, Alvord WG, Roayaei J, Stephens R, Baseler MW, Lane HC, Lempicki RA: The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists.

    Genome Biol 2007, 8(9):R183. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  13. Schokker D, Hoekman AJ, Smits MA, Rebel JM: Gene expression patterns associated with chicken jejunal development.

    Dev Comp Immunol 2009, 33(11):1156-1164. PubMed Abstract | Publisher Full Text OpenURL

  14. Harpaz Y, Chothia C: Many of the Immunoglobulin Superfamily Domains in Cell-Adhesion Molecules and Surface-Receptors Belong to a New Structural Set Which Is Close to That Containing Variable Domains.

    Journal of Molecular Biology 1994, 238(4):528-539. PubMed Abstract | Publisher Full Text OpenURL

  15. Apostolov EO, Shah SV, Ok E, Basnakian AG: Carbamylated low-density lipoprotein induces monocyte adhesion to endothelial cells through intercellular adhesion molecule-1 and vascular cell adhesion molecule-1.

    Arterioscler Thromb Vasc Biol 2007, 27(4):826-832. PubMed Abstract | Publisher Full Text OpenURL

  16. Liang TW, Chiu HH, Gurney A, Sidle A, Tumas DB, Schow P, Foster J, Klassen T, Dennis K, DeMarco RA, et al.: Vascular endothelial-junctional adhesion molecule (VE-JAM)/JAM 2 interacts with T, NK, and dendritic cells through JAM 3.

    J Immunol 2002, 168(4):1618-1626. PubMed Abstract | Publisher Full Text OpenURL

  17. Adams JC, Lawler J: The thrombospondins.

    Int J Biochem Cell Biol 2004, 36(6):961-968. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  18. Lawler J: The functions of thrombospondin-1 and-2.

    Current opinion in cell biology 2000, 12(5):634-640. PubMed Abstract | Publisher Full Text OpenURL