Funct Integr Genomics (2009) 9:335–349 DOI 10.1007/s10142-009-0116-0

ORIGINAL PAPER

Lipopolysaccharide-induced early response in bovine peripheral blood mononuclear cells implicate GLG1/E-selectin as a key ligand–receptor interaction

Cong-jun Li & Robert W. Li & Theodore H. Elsasser & Stanislaw Kahl

Received: 26 September 2008 /Revised: 27 January 2009 /Accepted: 13 February 2009 /Published online: 5 March 2009 # US Government 2009

Abstract This study uses integrated global expression Introduction information and knowledge of the regulatory events in cells to identify transcription networks controlling peripheral Lipopolysaccharide (LPS) is a major component of the blood mononuclear cells’ (PBMCs) immune response to outer membrane of Gram-negative bacteria such as Escher- lipopolysaccharide (LPS) and to identify the molecular and ichia coli, contributing greatly to the structural integrity of cellular pathways’ responses to LPS. We identified that 464 the bacteria. LPS is also an endotoxin that induces a strong genes, including at least 17 transcription factors, are response from normal animal immune systems. In mam- significantly induced by 2-h LPS stimulation using a mals, the innate immune system is the first line of host high-density bovine microarray platform at a very stringent defense involved in detecting a wide variety of invading false discovery rate=0%. The networks show that, in the microbial pathogens. The innate immune system’s receptors LPS-stimulated PBMCs, altered gene expression was are activated by microbial components such as LPS, which transcriptionally regulated via those transcription factors is a key component involved in the initiation of the immune through potential interaction within the pathway networks. response (Schnare et al. 2001). LPS is a highly potent Functional analyses revealed that LPS induces unique activator of the innate immune system and constitutes a pathways, molecular functions, biological processes, and major marker for the host’s recognition of intruding Gram- gene networks. In particular, gene expression data identi- negative pathogens. The interaction of LPS with cells leads fied Golgi complex-localized glycoprotein 1/endothelial- to the formation and release of a large spectrum of selectin as a key ligand–receptor interaction in the early inflammatory mediators that are essential for early innate response of cells. and later adaptive antibacterial defense (Beutler et al. 2003). Under normal conditions, a controlled cellular Keywords Bovine . E-selectin . Gene networks . response to bacterial products protects the host from LPS . PBMC infection. Hyperactivation of the immune response, how- ever, leads to the excessive production of various proin- flammatory cytokines and cellular injuries (Pinsky 2004). Peripheral blood mononuclear cells (PBMCs) are blood Mention of trade names or commercial products in this publication is solely for providing specific information and does not imply cells that have a round nucleus similar to lymphocytes or recommendation or endorsement by the US Department of monocytes. These cells are a critical component in the Agriculture. immune system with regard to fighting infections. PBMCs Electronic supplementary material The online version of this article play a sentinel role, allowing the host to efficiently sense (doi:10.1007/s10142-009-0116-0) contains supplementary material, and adapt to the presence of danger signals such as LPS which is available to authorized users. (Wong et al. 2008). LPS plays a crucial role in the C.-j. Li (*) : R. W. Li : T. H. Elsasser : S. Kahl inflammatory response by inducing the expression of Bovine Functional Genomics Laboratory, cytokine and other immune regulator genes in PBMCs. Animal and Natural Resources Institute, ARS, USDA, Exposure of PBMCs to LPS results in the activation of the 10300 Baltimore Ave, Building 200, Room 209, BARC East, κ Beltsville, MD 20705, USA NF- B transcription factor, which orchestrates the activa- e-mail: [email protected] tion of genes. These changes include the induction or 336 Funct Integr Genomics (2009) 9:335–349 repression of a wide range of genes that regulate inflam- protocol. Fresh blood samples from 10 animals were mation, cell proliferation, migration, and cell survival. This obtained immediately before the experiments and were process is tightly regulated (Sharif et al. 2007), and loss of randomly divided into two groups: control and LPS control is associated with conditions such as septic shock, stimulation. LPS (E. coli, 055:B5) was added to whole inflammatory diseases, and cancer (Van Amersfoort et al. blood (with heparin anticoagulant) to a final concentration 2003). Mechanisms that control the activation of innate of 2.0 μg/ml. Brefeldin-A was added at 1.0 μl/ml to blood immune responses in vivo, however, are poorly understood. cells prepared for Western blot analysis. Brefeldin-A blocks The functions of PMBCs in the innate immune system vesicular transport out of cells (Golgi-Block™,BD require the constitutive expression of a wide range of genes, Scientific, Franklin Lakes, NJ, USA) and results in the including various pattern recognition receptors, as well as accumulation of most cytokine in the endoplasmic the inducible expression of a suite of genes required to reticulum/Golgi complex. This leads to an enhanced ability to initiate inflammation and eliminate pathogens (Tegner et al. detect cytokine such as tumor necrosis factor α (TNF-α)in 2006). Despite extensive investigation over the past 20 years Western blot. All the blood samples were incubated in a cell regarding the pathophysiological effects of LPS, the cellular culture incubator at 37°C and with 5% CO2. After discrete and molecular mechanisms of action are still not completely time periods, red blood cells were eliminated by hypotonic understood. Experimental approaches for understanding the lysis and PBMCs were collected for further analysis. biology of innate immune response to LPS have generally been oversimplified as a result of focusing on one or two Flow cytometric assay of cells (flow cytometry) pathways, or by the examination of a few genomic loci or genesatatime(Lietal.2001)(Qinetal.2005). Flow cytometry analysis is based on the idea that pro- Understanding complex biological phenomena such as liferating cells will be going through phases to increase innate immune response to LPS, however, demands the their DNA content from the original amount (2C) in G1 use of integrated, multitechnological approaches with the phase to twice the amount (4C) in M/G2. DNA content in aim of obtaining comprehensive information. S-phase cells will fall in between the two measurements. To After the major achievements of the DNA sequencing measure the DNA content, cells were stained with a projects, an equally important challenge is to now uncover fluorescent dye (propidium iodide) that is directly bound the functional relationships among genes (i.e., gene net- to the DNA in the nucleus of cells. Measuring the works) (Tegner and Bjorkegren 2007). Systems biology has fluorescence by flow cytometry provided a measurement emerged as an exciting research approach in molecular of the amount of dye taken up by the cells and, indirectly, biology and functional genomics, involving a systematic the amount of DNA content. PBMCs were collected after construction of network-based models of biological pro- the red blood cells were eliminated by hypotonic lysis and cesses using genomic, proteomic, or metabolomic technol- then suspended with ice-cold phosphate-buffered saline ogies (Tegner and Bjorkegren 2007; Tegner et al. 2006). In (PBS, pH 7.4) buffer. Two volumes of ice-cold 100% the present study, the global gene expression after LPS ethanol were added in drops into tubes, and the cells were stimulation in PMBC was evaluated with microarray mixed in suspension through slow vortexing. After ethanol technology. Functional and pathways analysis of gene fixation, the cells were centrifuged (400 g, 5 min) and expression data identified Golgi complex-localized glyco- washed in PBS buffer once. The cells were then resus- protein 1 (GLG1)/endothelial-selectin (E-selectin) as a key pended at 106/ml. Fifty micrograms per milliliter of RNase ligand–receptor interaction in the early response of cells. A (Sigma Chemical, St. Louis, MO, USA) was then added to each sample, and the samples were incubated at 37°C for 30 min. After incubation, 20 μg of propidium iodide Materials and methods (Sigma Chemical) was added to each tube for at least 30 min to provide the nuclear signal for fluorescence- PBMC preparation and stimulation activated cell sorting (flow cytometry). The DNA content of the cells was analyzed using flow cytometry (FC500, All calves used in these studies were born and reared at the Beckman Coulter, Palatine, IL, USA), and the collected US Department of Agriculture Beltsville Agricultural data were analyzed using Cytomics RXP (Beckman Research Center (Beltsville, MD, USA), and their use in Coulter). At least 10,000 cells per sample were analyzed. the study and the associated protocols was preapproved by the US Department of Agriculture Beltsville Animal Care Isolation of Total RNA and Use Committee in regard to animal comfort, safety, and welfare. To rapidly evaluate immune responses in individ- Total RNA was extracted using Trizol by following the ual beef cattle, we optimized an in vitro immune challenge manufacturer’s recommendations (Invitrogen, Carlsbad, Funct Integr Genomics (2009) 9:335–349 337

CA, USA). Trace genomic DNA in the crude total RNA was repeated four times on the array (a total of ∼340,000 samples was removed by incubation with 4–10 units DNase features). These oligonucleotides represented 45,383 I per 100 μg total RNA (Ambion, Austin, TX, USA) at 37° unique bovine sequences/genes, including 40,808 tentative C for 30 min. Total RNA was further purified using an consensus sequences from the TIGR Bos taurus gene index RNeasy Mini kit (Qiagen, Valencia, CA, USA). The (www.tigr.org) and 4,575 singletons. concentration of the total RNA was determined using a Hybridization, image acquisition, and data analysis were NanoDrop ND-1000 spectrophotometer (NanoDrop Tech- described previously (Li and Li 2006). The microarrays nologies, Rockland, DE, USA) and RNA integrity was were scanned using an Axon GenePix 4000B scanner verified using a Bioanalyzer 1000 (Agilent, Palo Alto, CA, (Molecular Devices, Union City, CA, USA) at 5 μM USA). resolution, and the data were extracted from the raw images using NimbleScan software (NimbleGen, Madison, WI, Generation of Biotin-labeled cRNA USA).

Biotin-labeled cRNA was generated using a modified Support trees for hierarchical clustering procedure of the Superscript Choice System (Invitrogen) for double-strand (ds) cDNA synthesis followed by in vitro Hierarchical Clustering has been a common tool used for transcription. Briefly, the first-strand cDNA was synthe- microarray data visualization. In this study, we used the sized from 4.0 μg total RNA by 1.0 unit SuperScript II Support Trees for Hierarchical Clustering method from the reverse transcriptase (Invitrogen) in the presence of TIGR MultiExperiment Viewer v3.0 (www.tigr.org). Brief-

100 pmol T7 promoter Oligo dT primer. After second- ly, relative signal intensities (log2) for each feature were strand synthesis, the DNA was purified with a DNA Clean generated using the Robust Multi-Array Average algorithm & Concentrator-5 kit (Zymo Research, Orange, CA, USA) (Bolstad et al. 2003). The data were processed based on the and eluted with 8 to 16 μl of deionized H2O. The recovered quantile normalization method (Bolstad et al. 2003) and ds cDNA was further concentrated down to 3 μl by a speed using the R package (http://www.bioconductor.org). This vacuum device. The cRNA was synthesized with a MEGA- normalization method aims to make the distribution of script in vitro Transcription kit (Ambion). The in vitro intensities the same for each array in a set of arrays. The transcription reaction was carried out in a total volume of method assumes that a quantile–quantile plot of two data 23.0 μl, consisting of 3.0 μl ds cDNA, 2.3 μl 10× Ambion vectors with the same distribution will have a straight reaction buffer, 2.3 μl 10× Ambion T7 enzyme mix, and diagonal line. The method performed better in dealing with 15.4 μl NTP labeling mix [7.5 mM adenosine triphosphate, bias and reducing variability across arrays compared to 7.5 mM guanosine triphosphate, 5.625 mM uridine triphos- other methods (Bolstad et al. 2003). The background- phate (UTP), 5.625 mM cytidine triphosphate (CTP), and adjusted, normalized, and log-transformed intensity values 1.875 mM biotin-16-UTP and 1.875 mM biotin-11 CTP]. were then analyzed using the Significance Analysis of The in vitro transcription reaction was incubated at 37°C Microarrays method (Tusher et al. 2001) with a two-class for ∼16 h in a thermocycler. The cRNA was purified with unpaired design (SAM version 2.20 from http://www-stat. an RNeasy mini kit (Qiagen). Generally, 40 to 60 μgof stanford.edu/~tibs/clickwrap/sam/academic/). SAM is the cRNA can be obtained from 4.0 μg of input total RNA. The most popular method for microarray analysis, with 635 size range of the cRNA, expected to be between 300 and citations of the original publication as of October 2004 3,000 bp with the maximum intensity centered at least at (Larsson et al. 2005). SAM ranks genes based on a 1,000 bp, was verified using a Bioanalyzer 1000. The modified t-test statistic. The unique features of SAM biotinylated cRNA was fragmented from 50 to 200 bp by include implementing permutation testing and the ability heating the cRNA in a buffer consisting of 40 mM Tris- to estimate a global false discovery rate (an expected acetate, pH 8.0, 100 mM potassium acetate, and 30 mM percentage of false positives among claimed positives) and magnesium acetate at 95°C for 35 min. a gene error chance (q value). A sequence was declared to be significant when it met a stringent median false Oligonucleotide microarray, hybridization, discovery rate cutoff at 0% (Li and Li 2006). A BLAST image acquisition, and data analysis search was conducted for all sequences that met the threshold in order to remove possible redundancies. When The bovine microarray platform that was used in this study a gene was represented by multiple sequences, the fold was described previously (Li and Li 2006). A total of change with a q value of only one sequence was selected to 86,191 unique 60mer oligonucleotides were designed and represent this gene. The selected genes that met this synthesized in situ using photo deprotection chemistry stringent significance threshold, especially those genes (Singh-Gasson et al. 1999). Each unique oligonucleotide homologous to their respective human gene counterparts 338 Funct Integr Genomics (2009) 9:335–349 approved by the HUGO Committee while using p values and z scores as statistical metrics (www.gene.ucl.ac.uk) and/or with known functions and (Shipitsin et al. 2007). Networks of interest were also pathways, were subject to clustering analysis. The param- further built by merging different networks. eters used to generate ST clusters are as follows: for both gene trees and experiment trees, bootstrap resampling with 1,000 iterations was used. The linkage method used to Results calculate cluster-to-cluster distance was complete linkage, meaning that the distances were measured between each PBMC activation after LPS stimulation member of one cluster to each member of another cluster, and the maximum of these distances was considered the To study innate immune responses, it is important to have cluster-to-cluster distance. Euclidean distance was used in vitro model systems that reflect the behavior of the cells with regard to distance metrics. in vivo. We have investigated the LPS response in PBMCs using DNA synthesis as an indication of PBMC activation. Functional and pathways analysis In Fig. 1 (top panel), we show flow cytometry profiles of cell populations as the direct indication that cells are Gene regulatory networks were generated using MetaCore entering into S phase due to stimulation by LPS. Upon analytical suite (version 4.7, GeneGo, St. Joseph, MI, USA). MetaCore is a web-based suite for functional analysis of experimental data in the context of a manually curated database containing the probability of having the protein interactions, protein–DNA interactions, canonical pathways, signaling pathways, and knowledge-base ontol- ogies of cellular processes, diseases, and toxicology. The experimental data in MetaCore can be subjected to enrichment analysis (Subramanian et al. 2005)insix functional ontologies: GeneOntology processes (GO), GeneGo process networks, Diseases, GeneGo Diseases, Canonical pathway maps, and Metabolic processes. Enrichment analysis in GO processes was used in this study. Enrichment analysis consists of matching gene IDs for the common, similar, and unique sets of uploaded files with gene IDs in functional ontologies in MetaCore. The ontologies include canonical pathway maps, GeneGo cellular processes, GO cellular processes, and disease categories. The degree of “relevance” to different categories for the uploaded datasets is defined by p values, so that the lower p value gets higher priority. Both enrichment analysis and the calculation of the statistical significance of net- works are based on p values, which are defined as the probability of a given number of genes from the input list matching a certain number of genes in the ontology folder. The p values can also be defined, as described in the supplementary files of Shipitsin et al. (2007), as the probability of the network's assembly from a random set of nodes (genes) that are the same size as the input list. The whole data set of 411 genes was used to build networks using the Analyze Networks (AN) (transcription regulation) algorithm and the AN (receptor, transcription regulation and transcription factors) networks algorithm, which gen- Fig. 1 a Flow cytometric analysis of PBMCs after LPS stimulation. erate subnetworks centered on transcription factors and One set of representative histogram plots of flow cytometry analysis receptors, respectively. The subnetworks were scored and (DNA content) of PMBCs at 0, 2, 4, 8, and 24 h after LPS stimulation. 2C and 4C: two and four copies of DNA content, respectively. b prioritized based on relative enrichment using the data from Western blot of whole cell extracts from PBMCs at 0, 1, 2, 4, and 8 h the input list and saturation with “canonical pathways” after LPS stimulation Funct Integr Genomics (2009) 9:335–349 339

LPS stimulation, PBMCs respond with cell-cycle progres- expression was altered by an increasing expression of about sion (Fig. 1) and proliferation. In normal PBMCs (0 h, two- to threefold (Fig. 2b). The moderate increase in gene green line), most cells are in the G0/G1 stage. However, expression may also indicate that the PBMC is in the early when they are incubated with LPS for about 2 h, many cells stages of immune response after only 2 h of interaction with start DNA synthesis as their DNA content increases (2 h, LPS. The control and LPS treatment each had five purple line). After 4 and 8 h of LPS stimulation (4 and 8 h, replicates, and a total of 10 microarrays were used in the blue and red line), more than 50% of the cells showing experiment (GEO Accession GSE12849). DNA content increased or doubled, indicating that cell cycle progression started shortly after LPS stimulation, Analysis of gene regulatory networks indicating that PBMC proliferation is a major response to infection in cattle and that the activation of PBMCs starts To further understand the molecular processes initiated about 2 h after stimulation. Most of the cells were dying following stimulation by LPS, the expression data were after 24 h of stimulation, possibly due to apoptosis. input into MetaCore and analyzed for transcription regula- Apoptosis associated with bacteria infection is primarily tory networks enriched with the expression data. The 464 attributable to the endogenous mediators released during genes altered with regard to expression were used as a list the cells’ response to bacterial LPS. One of the major of input nodes and subjected to the AN Transcription mediators is TNF-α, a cytokine released primarily by regulation algorithm, AN Transcription factors algorithm, monocytes and tissue macrophages. To confirm that TNF- and AN Receptors algorithm. α is the primary response to the LPS stimulation, using Analysis of the gene expression data in MetaCore using whole cell lysis from LPS-stimulated PBMCs, Western the transcription regulation algorithm results in 20 subnet- blotting was performed to detect the cellular TNF-α. After works with a center of various transcription factors. These 2 h of LPS stimulation, the TNF-α accumulated and networks describe functional relationships between gene peaked. TNF-α protein band intensity decreased rapidly products based on the known interaction reported in the thereafter. This result is consistent with the cell population literature. The first 10 networks consisted of transcription profiles, and also consistent with the reported kinetics of factors that directly regulated the expression of downstream TNF-α gene expression after LPS-stimulation in human genes, with a total network size of 74 to 18 genes and p blood cells (DeForge and Remick 1991). values for network significance ranging from 8.81e−160 to 2.41e−36. Transcription factors HNF4-α, c-Myc, ESR1, Microarray SP1, p53, androgen receptor, SRF, CFEB, GCR-α, and PGC1-α are centered as the hubs of the networks. All Gene expression profiling of PBMCs exposed to LPS for networks generated from AN transcription regulation 2 h was analyzed by the bovine oligonucleotide microarray algorithm are listed in Supplemental Table S1. (Li and Li 2006). We identified 464 genes significantly We also analyzed the expression data using the AN stimulated by LPS at a very stringent false discovery rate= Transcription factors algorithm, which generated 30 subnet- 0% (Supplementary Table S1). In all 464 genes that works. Consistent with the AN transcription regulation differentially expressed after LPS stimulation, 463 genes algorithm, all these networks indicated that bovine PBMCs were up-regulated (Supplemental Table S1). This datum, utilize various transcription factors following activation by however, may reflect the fact that, in the early stages of LPS. All networks generated from AN transcription factors immune response, PBMCs are “activated” and are prolifer- algorithm are listed in the Supplement Table S2. The ating. The data also reflect that the immune response to analyses show that, in the LPS-stimulated PBMCs, altered microbial pathogens such as LPS relies on both innate and gene expression was regulated via those transcription acquired immunity. The innate immune system plays a factors through potential interaction within the pathway central role in combating invading microorganisms. With networks. In the most relevant network of SP2-E-selectin, some delay, the adaptive immune system with specific for example, nine transcription factors (ATF-7, c-Jun, antibodies supports the innate immune system mainly to ERR3, HNF4-α, SHP, SP2, TFIIE, α-subunit, TRPS1, facilitate pathogen recognition and elimination (Janeway and ZNF277) are activated. Within the network’s 100 notes, and Medzhitov 2002). Upon activation, the innate and about 67 of the genes are up-regulated. HNF4-alpha, c- acquired immunity up-regulate costimulatory molecules Raf1, NCOA2 (GRIP1/TIF2), and Syk are the hubs of the with regard to antigen-presenting cells. Therefore, LPS network. Canonical pathways activated in this network functions as a potent inducer of gene expression in bovine include MAPKKK cascade (14.5%; p value 6.370e−09), PBMCs (Qin et al. 2005). Hierarchical clustering of intracellular signaling cascade (34.9%; p value 7.068e−09), selected genes that are significantly regulated by LPS in protein kinase cascade (18.1%; p value 8.242e−09), primary bovine PBMCs was generated (Fig. 2a). Most of the gene metabolic process (78.3%; p value 1.428e−07), and protein 340 Funct Integr Genomics (2009) 9:335–349

Fig. 2 a Hierarchical clustering of selected genes significantly regulated by LPS in bovine PBMCs. The clusters were generated using the Support Trees for Hierarchical Clustering from the TIGR MultiExperiment Viewer (v 3.0). The cluster represents those genes signifi- cantly induced by LPS (p<0.05). The top band color density indicates the expression level of the genes (fold). b Signaling distribution of the gene expression and the p value distribution of the dataset

amino acid phosphorylation (21.7%; p value 4.737e−07). All transcription, regulation of cellular process, and cytokine- 30 of the most relevant networks are listed in Supplemental and chemokine-mediated signaling (see details in Supple- Table S2. The network analysis also reveals all the ment Table S3). E-selectin-GLG1 (also known as E-selectin transcription factors activated by LPS stimulation. All of ligand-1, MG-160 and cysteine-rich fibroblast growth these transcription factors are listed in Table 1. factor receptor) served as common ligand-receptor for these The most remarkable character in all of the 30 networks pathway and networks (Table 2 and Supplement Table S4). is that all networks generated from AN transcription factors E-selectin is a cell adhesion molecule and CD antigen that algorithm are involved with E-selectin (also known as mediates neutrophil, monocyte, and memory T-cell adhe- CD62E) (Table 2). Two representative merged networks, in sion to cytokine-activated endothelial cells (Bevilacqua et which E-selectin functions as the key object of the network, al. 1987). E-selectin plays an important part in recruiting are showed in Fig. 3. Networks centered in E2F2 (Blue) leukocytes to the site of injury during inflammation and and NFYb (Green) are activated by NF-κB signaling may function as a tissue-specific homing receptor for T cell pathway (RED). These networks are functioning in some subsets (Shimizu et al. 1991). The local release of cytokines very important GO processes such as regulation of TNF and IL-1 by damaged cells induces the overexpression Funct Integr Genomics (2009) 9:335–349 341

Table 1 LPS activated transcription factors in bovine Transcription factor rR N Mean z Score p Value PBMC BARX2 3 14 18,981 0.2965 5.0200 0.0029 TFIIB 4 45 18,981 0.9531 3.1583 0.0149 E2F3 4 49 18,981 1.0378 2.9428 0.0199 TOPBP1 1 1 18,981 0.0212 6.7983 0.0212 DP1 1 1 18,981 0.0212 6.7983 0.0212 r Number of network objects PERC 1 1 18,981 0.0212 6.7983 0.0212 derived from genes from active NF-AT5 2 12 18,981 0.2541 3.5014 0.0257 experiment that have interac- tions with the given network ZNF143 5 79 18,981 1.6731 2.6050 0.0261 object, R total number of gene- AFX1 2 13 18,981 0.2753 3.3233 0.0299 based network objects in the XBP1 5 83 18,981 1.7579 2.4770 0.0315 complete database that have interactions with the given FOXP2 7 144 18,981 3.0498 2.2950 0.0338 network object, N total number C14orf169 1 2 18,981 0.0424 4.7032 0.0419 of gene-based network objects GMEB1 1 2 18,981 0.0424 4.7032 0.0419 in the complete database, mean NKX2-3 1 2 18,981 0.0424 4.7032 0.0419 mean value for hypergeometric distribution (n*R/N), z score z GMEB2 1 2 18,981 0.0424 4.7032 0.0419 score [(r−mean)/sqrt(variance)], SP1 17 1,202 18,981 25.4572 −1.7505 0.0439 p value probability to have the AP-2A 1 223 18,981 4.7229 −1.7417 0.0483 given value of r or higher of E-selectin on endothelial cells of nearby blood vessels. Pathway analysis Leukocytes in the blood expressing escalated level of ligand GLG1 will bind with low affinity to E-selectin, Enrichment analysis consists of matching gene IDs for the causing the leukocyte to “roll” along the internal surface of common, similar, and unique sets of the uploaded files with the blood vessel as temporary interactions are made (Ryan gene IDs in functional ontologies in MetaCore. The and Worthington 1992). Our experimental data show that ontologies include canonical pathway maps, GeneGo the expression of E-selectin ligand, GLG1, was up cellular processes, GO cellular processes, and disease regulated 2.4 fold by LPS stimulation (Supplement Table categories. The degree of “relevance” to different categories S1). Since E-selectin only expressed on active endothelial for the uploaded datasets is defined by p values, so that the cells, it is not surprising that we did not detect the up- lower p value gets higher priority. regulated expression of E-selcetin in PMBC. E-selectin Canonical pathway maps represent a set of about 500 functions not only as an adhesion receptor but also as a signaling and metabolic maps covering human biology in signaling receptor (Lorenzon et al. 1998). There are various a comprehensive way. Experimental data are visualized on receptors that may play very important roles in the the maps as blue (for down-regulation) and red (for up- activation of PBMCs (see Table 2 and Supplement Table regulation) histograms. The height of the histogram S3). However, when we analyzed the expression data using corresponds to the relative expression value for a the AN Receptor algorithm, only one subnetwork was particular gene/protein. The top 10 most significant generated (Fig. 4). The edge in and out receptor of this pathways, based on the overrepresentation analysis (p< network, E-selectin, is paired with its ligand protein, GLG1 0.05), are shown in Fig. 5. The five most significant (Ahn et al. 2005). STAT1 serves as both the divergence and pathways were (1) cytoskeleton remodeling, (2) transcrip- convergence hub. Three transcription factors, STAT1, tion: ligand-dependent transcription of retinoid-target STAT2, and STAT3, are the active components of tran- genes, (3) development: ligand-dependent activation of scription regulation in the network. Based on the analysis of the ESR1/AP-1 pathway, (4) development: WNT signaling the network, there are five GO processes activated in this pathway: degradation of beta-catenin in the absence WNT network: JAK-STAT cascade (23.1%, p value 1.11e−05), signaling, and (5) apoptosis and survival: DNA damage- protein kinase cascade (38.5%, p value 1.773e−05), induced apoptosis. The most significant canonical path- chemokine-mediated signaling pathway (23.1%, p value ways are shown as (1) transcription: ligand-dependent 5.993e−05), peptidyl-amino modification (23.1%, p value transcription of retinoid-target genes (Table 3), (2) devel- 2.676e−04), and peptidyl–tyrosine phosphorylation (15.4%; opment: ligand-dependent activation of the ESR-AP1 p value 5.8272−04). All transcription factors and receptors pathway (Table 4), and (3) apoptosis and survival: DNA- that have been integrated into the regulatory networks are damage-induced apoptosis (Table 5). These canonical listed in Table 2. pathways represent the second, third, and fifth lowest 342 Funct Integr Genomics (2009) 9:335–349

Table 2 LPS activated transcription factors and receptors integrated in regulatory networks as most relevant objects

Network Transcription factors Receptors

Edges IN Edges OUT Edges IN Edges OUT

AP-2A, E-selectin AP-2A, SP1 AP-2A, SP1 E-selectin, GLVR1, TfR1 E-selectin ATF-2, E-selectin ATF-2 ATF-2 E-selectin, GLVR1, TNF-R1 E-selectin, TNF-R1 BRG1, E-selectin SMAD4 SMAD4 E-selectin, TNF-R1 E-selectin, TNF-R1 C/EBPalpha, C/EBPalpha C/EBPalpha E-selectin E-selectin E-selectin E2F2, E-selectin E2F2, SP1 E2F2, SP1 E-selectin, GLVR1, TfR1 E-selectin EGR1, E-selectin EGR1 EGR1 E-selectin E-selectin ELF1, E-selectin ELF1 ELF1 E-selectin E-selectin ERR3, E-selectin c-Jun, ERR3 c-Jun, ERR3 E-selectin, FasR(CD95), E-selectin, FasR(CD95), IL-2 RECEPTOR IL-2 RECEPTOR ESR1 (nuclear), ESR1 (nuclear) ESR1 (nuclear) E-selectin, PTPRF (LAR) E-selectin E-selectin FAST-1/2, E-selectin FAST-1/2, GSC, MEF2C, FAST-1/2, GSC, E-selectin, TNF-R1 E-selectin, TNF-R1 SMAD4 SMAD4 GABP alpha, ATF-7, c-Myc, GABP c-Myc, gabp alpha, E-selectin, PTPRF (LAR), E-selectin E-selectin alpha, UBF UBP TfR1 GATA-1, E-selectin GATA-1 GATA-1 E-selectin E-selectin GLI-1, E-selectin GLI-1, SP1 GLI-1, SP1 E-selectin, GLVR1, PTCH1, E-selectin TfR1 MAD, E-selectin MAD, UBF MAD E-selectin E-selectin NF-Y, E-selectin FKHR, NF-Y, p53 FKHR, NF-Y, p53 E-selectin, TNF-R1 E-selectin, TNF-R1 NF-kB, E-selectin NF-kB NF-kB E-selectin E-selectin NFYB, E-selectin MEF2C, NFYB, SRF MEF2C, NFYB, SRF E-selectin E-selectin PRDM5, E-selectin ATF-7, , cJun, HNF4-alpha, c-Jun, hnf4-ALPHA E-selectin, FasR(CD95), E-selectin, FasR(CD95), TFIIE, alpha subunit, IL-2 receptor, IP3R2 IL-2 receptor, IP3R2 TRPS1, ZNF277 PU.1, E-selectin PU.1, STAT1, STAT3 PU.1, STAT1, STAT3 E-selectin E-selectin RARalpha, E-selectin RARalpha, WDR9 RARalpha E-selectin E-selectin SOX9, E-selectin MEF2C E-selectin E-selectin SP2, E-selectin ATF-7, c-Jun, ERR3, c-Jun, ERR3, HNF4- E-selectin, FasR(CD95), E-selectin, FasR(CD95), HNF4-alpha, SHP, SP2, alpha, SHP, SP2 IL-2 receptor, IP3R2 IL-2 receptor, IP3R2 TFIIE, alpha subunit, TRPS1, ZNF277 SRF, E-selectin MEF2C, SRF MEF2C, SRF E-selectin E-selectin STAT3, E-selectin STAT3 STAT3 E-selectin E-selectin Sry, E-selectin SP1, Sry SP1, Sry E-selectin, GLVR1, TfR1 E-selectin TCF7L2 (TCF4), TCF7L2 (TCF4) TCF7L2 (TCF4) E-selectin E-selectin E-selectin TCF8, E-selectin MEF2C, TCF8 TCF8 E-selectin E-selectin ZNF217, E-selectin ESR1 (nuclear), ZNF217 ESR1 (nuclear), E-selectin, PTPRF (LAR) E-selectin ZNF217 c-Jun/c-Fos, E-selectin c-Jun/c-Fos, SMAD4 c-Jun/c-Fos, SMAD4 E-selectin, TNF-R1 E-selectin, TNF-R1 p63, E-selectin MEF2C, p63, TCF8 p63, TCF8 E-selectin E-selectin

p values based on the distribution sorted by statistically GeneGo process networks significant maps. Many of these pathways identified are involved in cell cycle progression, transcriptional regula- In order to explore the biological significance of LPS- tion, apoptosis, and cell proliferation regulation (Tables 3, induced gene expression in bovine PBMCs, the GO 4,and5). classification was analyzed using MetaCore. There are Funct Integr Genomics (2009) 9:335–349 343

Fig. 3 Biological networks generated using the AN Tran- scription Factors Algorithm indicates utilization of various transcription factors in LPS induced immune response. The figure shows two merged networks (blue and green). E-selectin-GLG1 function as the key object of both networks. Both networks are activated by NF-κB signaling pathway (red). Triple-colored notes are shared components. Up-regulated genes are marked with red circles. The large ellipse highlights the GLG1-E-selectin interaction

about 117 cellular and molecular processes whose content is defined and annotated by GeneGo. Each process represents a preset network of protein interactions charac- teristic to the process. The most highly represented process networks (Fig. 6), sorted by statistical significance, include cell cycle mitosis, transcription—by RNA polymerase II, transcription—nuclear receptors’ transcription regulation, signal transduction—androgen receptor nuclear signaling, and apoptosis—apoptotic nucleus.

Discussion

Large-scale expression profiling using oligonucleotide or cDNA arrays has become one of the most fruitful methods for characterization of physiological and pathological processes. In this study, we conducted a pathway and regulatory network analysis to understand the mechanisms of activation in PBMCs induced by LPS. The results highlight the ability of microarray technology to define the Fig. 4 Biological network generated using the AN Receptor immune response genes induced by LPS in bovine PBMCs. Algorithm. Expression dataset is used as the input list for generation Gene expression profiles are very informative, but they are of biological network. This is a variant of the shortest-paths algorithm limited with regard to describing the complexity of the with relative enrichment and relative saturation of networks with PBMC activation (Ravasi et al. 2002). To reveal the canonical pathways. Key network objects include GLG1, E-selectin, STAT1, STAT2, and STAT3. Up-regulated genes are marked with red underlying regulatory networks requires extensive data circles. The large ellipse highlights the GLG1–E-selectin interaction mining. Web-based software and knowledge databases such 344 Funct Integr Genomics (2009) 9:335–349

Fig. 5 Representation of the canonical pathway maps sorted by statistically significant maps with the lowest p value and ordered by −log10 of the p value of the hypergeometric distribution

as MetaCore are the perfect tools for this purpose. In the stimulated immune response. Most of the studies are present study, the global gene expression data (global concentrated on signal gene/protein or a part of a signaling expression profiling) generated with microarray technology pathway. To date, there are only a limited number of studies were evaluated systemically using MetaCore (GeneGo, in which gene expression data are integrated with other version 4.7) in the context of GO classification and gene genome-wide databases in order to gain extensive insights regulatory networks. To our knowledge, this is the first into the regulatory mechanisms of the immune response of study of its kind that has been done in bovine PBMCs. PBMCs induced by LPS (Tegner and Bjorkegren 2007; LPS, a glycolipid known as endotoxin, is a principal Tegner et al. 2006). PBMCs play a sentinel role in that they constituent of Gram-negative bacteria recognized by the enable the host to efficiently sense and adapt to the innate immune system, and it often causes endotoxin shock. presence of infection. It has been suggested that the LPS is a complex glycolipid composed of a hydrophilic initiation of adaptive immune responses is controlled by polysaccharide region and a hydrophobic domain known as innate immune recognition. Mammalian Toll-like receptors lipid A, which is responsible for most of the LPS-induced play an essential role in innate immunity by recognizing biological effects (Schletter et al. 1995). LPS stimulates conserved pathogen-associated molecular patterns and host cells such as monocytes, macrophages, and B cells initiating the activation of NF-κB and other signaling through the activation of transcription factors and protein pathways (Janeway and Medzhitov 2002). We did not kinases. Some of the transcription factors and protein detect upregulated NF-κB gene expression in our micro- kinases such as NF-κB (Doyle and O'Neill 2006; Vincenti array. However, it is consistent with the regulating function et al. 1992), AP-1, extracellular signal-regulated kinases, c- of NF-κB. Part of NF-κB's function in regulating cellular Jun N-terminal kinases, and p38 kinases (Brand et al. 1991; responses is that it belongs to the category of “rapid-acting” Guha and Mackman 2001; Mackman et al. 1991) have been primary transcription factors, i.e., transcription factors that extensively studied in relation to their roles in the LPS- are present in cells in an inactive state and do not require Funct Integr Genomics (2009) 9:335–349 345

Table 3 Biological processes in canonical pathway map reveal Process Percent p Value transcription regulatory pathways 1 Regulation of transcription, DNA-dependent 49.45 1.23E-24 2 Regulation of transcription 28.57 2.53E-20 3 Transcription 41.76 6.76E-20 4 Steroid hormone receptor signaling pathway 6.59 9.37E-14 5 Positive regulation of transcription from RNA polymerase II promoter 20.88 1.76E-12 6 Ventricular cardiac muscle cell differentiation 6.59 4.21E-11 7 Negative regulation of transcription 12.09 1.30E-09 8 N-terminal peptidyl-lysine acetylation 4.4 1.07E-08 9 Negative regulation of transcription from RNA polymerase II promoter 12.09 4.32E-08 10 Chromatin modification 8.79 1.33E-07 11 Positive regulation of transcription factor activity 4.4 2.65E-07 12 Vitamin metabolic process 3.3 3.22E-07 13 Histone methylation 3.3 3.22E-07 14 Transcription from RNA polymerase II promoter 10.99 6.38E-07 15 Positive regulation of transcription, DNA-dependent 9.89 9.19E-07 16 Histone acetylation 4.4 1.47E-06 17 Transcription initiation from RNA polymerase II promoter 4.4 3.68E-06 18 mRNA transcription from RNA polymerase II promoter 3.3 6.34E-06 19 Thyroid hormone generation 3.3 6.34E-06 20 Telencephalon development 3.3 6.34E-06 new protein synthesis to be activated (other members of this 2006). Cytokines exert autocrine and paracrine effects on family include transcription factors such as c-Jun, STATs, the tissue in response to challenges to the immune system. and nuclear hormone receptors). This allows NF-κB to act Characterization of induced cytokine proteins and intracel- as a “first responder” to harmful cellular stimuli (Gilmore lular response mediators can be used to identify specific

Table 4 Biological processes in development—ligand-dependent Process Percent p Value activation of the ESR1/AP1 pathway 1 Regulation of transcription, DNA-dependent 76.92 5.13E-25 2 Positive regulation of transcription from RNA polymerase II promoter 46.15 8.20E-19 3 Transcription 61.54 4.56E-18 4 negative regulation of transcription from RNA polymerase II promoter 30.77 8.61E-14 5 Histone deacetylation 15.38 3.30E-11 6 N-terminal peptidyl-lysine acetylation 10.26 3.31E-10 7 Regulation of transcription 28.21 2.95E-09 8 Positive regulation of transcription factor activity 10.26 8.28E-09 9 Ovarian follicle rupture 7.69 2.42E-08 10 Leading edge cell differentiation 7.69 2.42E-08 11 Negative regulation of protein amino acid autophosphorylation 7.69 2.42E-08 12 Negative regulation of mitosis 7.69 2.42E-08 13 Histone acetylation 10.26 4.66E-08 14 Chromatin modification 15.38 1.81E-07 15 Steroid hormone receptor signaling pathway 7.69 4.81E-07 16 Estrogen receptor signaling pathway 7.69 4.81E-07 17 Ovulation 7.69 8.41E-07 18 Homeostatic process 7.69 3.93E-06 19 Positive regulation of transcription 15.38 4.27E-06 20 Positive regulation of transcription, DNA-dependent 15.38 4.80E-06 346 Funct Integr Genomics (2009) 9:335–349

Table 5 Biological processes in canonical pathways involved in Process Percent p Value apoptosis and survival 1 DNA repair 66.67 6.66E-42 2 Response to DNA damage stimulus 64.44 4.22E-41 3 DNA damage checkpoint 35.56 5.08E-33 4 Cell cycle 62.22 4.44E-27 5 Gamete generation 20 3.50E-17 6 Response to gamma radiation 20 3.77E-16 7 Telomere maintenance 20 1.37E-15 8 DNA damage response, signal transduction resulting in induction of 17.78 2.08E-14 apoptosis 9 Cell cycle checkpoint 17.78 4.01E-14 10 Meiotic recombination 15.56 2.55E-13 11 DNA recombination 15.56 1.65E-10 12 Brain development 20 3.94E-10 13 Negative regulation of cell cycle 20 1.20E-09 14 Regulation of cell proliferation 20 1.53E-09 15 Positive regulation of transcription, DNA-dependent 20 1.62E-09 16 Eiosis 13.33 4.71E-09 17 DNA damage response, signal transduction by p53 class mediator 8.89 8.33E-09 18 ds break repair 13.33 8.44E-09 19 Somitogenesis 13.33 8.44E-09 20 RNA-protein covalent cross-linking 6.67 3.76E-08 aspects of host responsiveness or resistance to disease There are several recent reports that use genome-wide challenges. LPS induces rapid formation of proinflamma- expression patterns to study the immune response to LPS tory cytokines such as TNF-α and many TNF actions. stimulation in PBMCs (Chen et al. 2007; Jeyaseelan et al. TNF-α protein can be detected in cells as early as 2 h after 2004; Wong et al. 2008). Most of these studies use cultured LPS is introduced. human cell lines and much more genes are altered at the

Fig. 6 GeneGo process networks: representation of the most significant biological process networks induced by LPS, sorted by statistically significant networks, ordered by −log10 of the p value of the hypergeometric distribution Funct Integr Genomics (2009) 9:335–349 347 expression level after LPS stimulation. It has been well metaphase-to-anaphase transition (Garcia-Higuera et al. established that the exposure of macrophages to bacterial 2008; Skaar and Pagano 2008). The Skp–Cullin–F-box products such as LPS results in activation of the NF-κB (SCF) complex, on the other hand, is a multiprotein E3 transcription factor, which orchestrates a gene expression ligase complex that catalyzes the ubiquitination of proteins program that underpins the macrophage-dependent immune destined for proteasomal degradation. The SCF complex response. These changes include the induction or repression plays an important role in the ubiquitination of proteins of a wide range of genes that regulate inflammation, cell involved in the cell cycle (Chen et al. 2008; Nakayama and proliferation, migration, and cell survival. This process is Nakayama 2005; Ren et al. 2008). The APC and SCF are tightly regulated, and loss of control is associated with both main players in the regulation of protein destruction conditions such as septic shock, inflammatory diseases, and and mediation of the transition of cells from the metaphase cancer (Sharif et al. 2007). These early response genes may to anaphase (Peters 2002; Vodermaier 2004). Extensive reflect the activation of the innate immune system, serving induced transcription factors and cell cycle-related genes as the first line of defense in protecting the host from such as TFIIB, SP1, AP-2A, BARX2, and E2F3 may be invading pathogens such as bacteria. However, an interest- closely associated with the activation of PBMCs and cell ing question arises: what are the earliest response genes to proliferation induced by LPS. bacterial infection or the LPS challenge? This is the As an important part of this report, the global gene question that we are trying to answer in this study. expression data (global expression profiling) generated with It has been shown that a dynamic and diverse transcrip- microarray technology were evaluated systemically using tional response to LPS in macrophages (e.g., the gene MetaCore (GeneGo, version 4.7) in the context of GO expression) changes in a time-dependent manner (Sharif et classification and gene regulatory networks. MetaCore is a al. 2007). In the present study, unlike in other reports, we very powerful tool with which we systematically interro- observed that 464 genes altered expression, and almost all gate, model, and iteratively refine our knowledge of the of the genes were up-regulated. The likely explanation for regulatory events inside the cells. These analyses revealed this discrepancy is that we are looking at the relatively early that various transcription factor genes are induced by LPS stages of the immune response of PBMCs. In a recent in bovine PBMCs. These analyses also present current report (Carayol et al. 2006), after 1 h of LPS stimulation, a knowledge on signaling cascades that integrate activation of total of only 88 early response genes were found to be various transcription factors into functional gene networks. induced by LPS in a human THP.1 monocytic cell line. The With MetaCore, we not only defined the specific subnet- differences between these two sets of data may be due to works, but we also identified highly connected transcription different LPS used in two experiments. LPS from E. coli factors (hubs of the network). Pathway analysis on the LPS- used in our experiment is more agonistic than LPS from induced genes in bovine PBMCs using MetaCore success- Porphyromonas gingivalis, which was used in the report fully identified remarkable changes in the genetic networks mentioned above. They may also activate different signal- related to the nucleotide and nucleic acid metabolic process, ing pathways (Netea et al. 2002; Seydel et al. 2000). gene expression, transcription, regulation of transcription, Nevertheless, analysis indicates that two sets and apoptosis. One of the important and novel findings of of data have some very important consistencies. For this study is the analysis results obtained from using the AN example, both studies show that genes related to apoptosis, Receptor algorithm. The data and regulatory network cell cycle regulation, inflammatory response, MAPK, and analysis indicate that E-selectin, paired with its ligand cytokine- and chemokine-mediated signaling, as well as protein, GLG1 (Ahn et al. 2005), may serve as an important NF-κB signaling pathways, such as ATF, MAPK, MAPKK, signaling pathway for the LPS-induced innate immune AP, JAK1, and Btk, are among the early response genes response. This signaling pathway may directly target the after LPS stimulation. The gene expression data in the regulatory gene network centered on STAT1, which plays present study are also consistent with the observation that, an important role as both the divergence and convergence after 2 h of LPS stimulation, PBMC activation was induced hub. Three transcription factors, STAT1, STAT2, and and cells transit from G1/G0, or quiescent stages, into STAT3, are the active components of transcription regula- active proliferation and DNA synthesis. Many cell cycle tion in the network. These important findings certainly are regulatory genes and transcription factors (Tables 1 and 2) the leading direction for our future study to biologically that are required for cell proliferation are up-regulated. The confirm those findings and to understand the mechanism(s) genes that we identified (Supplementary Table S1) fall of how these genes were regulated during the early stage of within a broad range of functional categories. For example, immune response to LPS challenge. the anaphase-promoting complex (APC), as one of the In conclusion, global gene expression profiling and significantly up-regulated genes in our experiment, controls computational pathway analysis provide detailed knowl- the G0 and G1 phases of the cell cycle and regulates the edge of changes in gene expression induced by LPS in 348 Funct Integr Genomics (2009) 9:335–349

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