Physiol. Genomics AJP: Heart and Circulatory Physiology
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

Physiol. Genomics 33: 121-132, 2008. First published January 29, 2008; doi:10.1152/physiolgenomics.00095.2007
1094-8341/08 $8.00
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Supplemental Tables and Figures
Right arrow All Versions of this Article:
33/1/121    most recent
00095.2007v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Kitamura, H.
Right arrow Articles by Ohara, O.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Kitamura, H.
Right arrow Articles by Ohara, O.
Received 27 April 2007; accepted in final form 24 January 2008.
Physiological Genomics 33:121-132 (2008)
1094-8341/08 $8.00 © 2008 American Physiological Society

Genome-wide identification and characterization of transcripts translationally regulated by bacterial lipopolysaccharide in macrophage-like J774.1 cells

Hiroshi Kitamura 1, Masatoshi Ito 1, Tomoko Yuasa 1, Chisato Kikuguchi 1, Atsushi Hijikata 1, Michiyo Takayama 1, Yayoi Kimura 1, Ryo Yokoyama 1, Tomohiro Kaji 2 and Osamu Ohara 1,3

1 Laboratories for Immunogenomics, RIKEN Research Center for Allergy and Immunology, Yokohama
2 Immunological Memory, RIKEN Research Center for Allergy and Immunology, Yokohama
3 Laboratory of Genome Technology, Department of Human Genome Research, Kazusa DNA Research Institute, Kisarazu, Japan


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Although Escherichia coli LPS is known to elicit various proinflammatory responses in macrophages, its effect on the translational states of transcripts has not yet been explored on a genome-wide scale. To address this, we investigated the mRNA profiles in polysomal and free messenger ribonucleoprotein particle (mRNP) fractions of mouse macrophage-like J774.1 cells, using Affymetrix Mouse Genome 430 2.0 GeneChips. Comparison of the mRNA profiles in total cellular, polysomal, and free mRNP fractions enabled us to identify transcripts that were modulated at the translational level by LPS: among 19,791 transcripts, 115 and 418 were up- and downregulated at 1, 2, or 4 h after LPS stimulation (100 ng/ml) in a translation-dependent manner. Interestingly, gene ontology-based analysis suggested that translation-dependent downregulated genes frequently include those encoding proteins in the mitochondrial respiratory chain. In fact, the mRNA levels of some transcripts for complexes I, IV, and V in the mitochondrial respiratory chain were translationally downregulated, eventually contributing to the decline of their protein levels. Moreover, the amount of metabolically labeled cytochrome oxidase subunit Va in complex IV was decreased without any change of its mRNA level in total cellular fraction after LPS stimulation. Consistently, the total amounts and activities of complexes I and IV were attenuated by LPS stimulation, and the attenuation was independent of nitric oxide. These results demonstrated that translational suppression may play a critical role in the LPS-mediated attenuation of mitochondrial oxidative phosphorylation in a nitric oxide-independent manner in J774.1 cells.

macrophage; translational regulation; polysome; mitochondrial respiratory chain


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
INFECTION WITH GRAM-NEGATIVE bacteria including Escherichia coli induces numerous inflammatory responses and occasionally leads to septic shock. The virulence and pathogenicity of this group of bacteria are attributed mainly to LPS, a component of the bacterial outer membrane. LPS affects many immune and inflammatory cells that express Toll-like receptor (TLR) 4 and its accessory protein, MD2 (1, 45). Among TLR4-positive cells, macrophages are one of the most sensitive to LPS stimulation (45). Upon LPS stimulation, they rapidly undergo cytoskeleton remodeling, migrate from circulation to the inflammatory lesion, and phagocytose invading microorganisms (28, 56, 59). They also control both innate and acquired immunity by secreting cytokines, producing arachidonic acid intermediates, and presenting antigens (28, 59). Because the depletion of macrophages causes severe defects in inflammatory responses in animal models (8, 30), it is believed that macrophages play a pivotal role in LPS-induced inflammation.

Dozens of studies have been conducted to elucidate the molecular events in macrophages evoked by LPS. A number of studies have demonstrated that a wide variety of proinflammatory molecules are regulated by transcriptional events mainly mediated by NF-{kappa}B and interferon regulatory factor-3 activation (1, 9, 25, 64). However, accumulating evidence indicates that certain molecules are also regulated by posttranscriptional events. For example, the expression of TNF-{alpha}, IL-1β, IL-8, and cyclooxygenase 2 is controlled by the stability and/or translational activity of their mRNAs, at least in part depending on the AU-rich element in their 3'-untranslated region (3, 4, 21, 26, 34, 69). Moreover, LPS stimulation also changed the stability of transcripts for cytochrome c oxidase subunit I, a component of the mitochondrial respiratory chain, indicating that posttranscriptional modulation also occurs in noninflammatory proteins in response to LPS stimulation (72). Although these studies shed new light on posttranscriptional gene regulation evoked by LPS, they focused on a limited number of genes and thus little is known about the gene regulatory mechanisms induced by LPS stimulation on a genome-wide scale.

Translational regulation is one of the major processes in posttranscriptional events. In the cytoplasm, translation-active transcripts are circularized by attachment of 5' and 3' ends, followed by association with many ribosomes. As this complex named "polysome" has high molecular density, sucrose densityoriented ultracentrifugation is used to separate it from the total cytoplasmic lysate. The combined use of this fractionation method and DNA microarray analysis has provided a wealth of information regarding translational regulation in several organisms including yeast, mouse, and human (2, 23, 37, 54, 65, 75, 76). We have demonstrated that 0.7% of the detectable transcripts of the human myeloid cell line U937 was altered during PMA-induced differentiation to macrophages (37). In addition, Yost et al. (75) investigated the effects of platelet activating factor on the polysomal expression profiles of HL60 human myelocytic cells, and found that retinoic acid receptor-{alpha} was translationally upregulated.

In this study, we comprehensively clarified the translational events in mouse macrophage-like J774.1 cells induced by E. coli LPS using this microarray-based approach. The results indicate that >500 transcripts were translationally regulated by LPS stimulation. Interestingly, translationally downregulated transcripts were found to encode components of mitochondrial respiratory chain complexes, suggesting translational modulation of oxidative phosphorylation in response to LPS stimulation.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Cells and treatment.
Mouse macrophage-like J774.1 cells (RIKEN Bioresource Center, Tsukuba, Japan) were seeded at a density of 2.0 x 105 cells/ml 1 day prior to harvest and were stimulated with LPS (E. coli 055:B5; Sigma, St. Louis, MO). In some experiments, the cells were treated with 2 mM N{omega}-nitro-L-arginine methyl ester (L-NAME; Dojindo Laboratories, Kumamoto, Japan).

Cellular fractionation and RNA preparation.
Preparation of polysomal and free mRNP fractions using a 15–50% sucrose gradient was done as essentially described in previous papers (23, 37, 47). Briefly, 1 x 107 cells were washed with 150 µg/ml cycloheximide in PBS for 5 min and then lysed with buffer containing 10 mM Tris·HCl, pH 8.0, 140 mM NaCl, 1.5 mM MgCl2, 0.5% Nonidet P-40, 150 µg/ml cycloheximide, and 500 U/ml porcine placental RNase inhibitor (Takara Bio, Otsu, Japan). After removal of nuclei by centrifugation at 3,000 g for 2 min, the supernatant was treated with 150 µg/ml cycloheximide, 20 mM DTT, 650 µg/ml heparin, and complete protease inhibitor cocktail (Roche Diagnostics, Basel, Switzerland), and subsequently centrifuged again at 10,000 g for 5 min to remove mitochondria and membrane debris. The postmitochondrial cytoplasmic fractions were immediately applied onto a 15–50% sucrose gradient and ultracentrifuged with a SW41Ti rotor (Beckman Coulter, Palo Alto, CA) at 38,000 rpm for 2 h at 4°C. Twenty fractions were collected from the surface of each gradient. Based on continuous UV profiling at 260 nm using a Bio-Rad EM-1 monitor (Hercules, CA) connected to a Piston Gradient Fractionator (BioComp Instruments, New Brunswick, Canada), fractions 1–5 and 11–20 were pooled as free mRNP and polysomal fractions, respectively. Total RNA was extracted with TRIzol LS reagent (Invitrogen, Carlsbad, CA) and subsequent poly(A)+ RNA isolation was performed with a µMACS mRNA isolation kit (Miltenyi, Bergisch Gladbach, Germany). The quantity and integrity of RNA samples were measured with a RiboGreen RNA quantification kit (Invitrogen) and a 2100 Agilent Bioanalyzer (Waldbronn, Germany), respectively.

Microarray analysis.
Microarray experiments were performed using Mouse Genome 430 2.0 GeneChips (Affymetrix, Santa Clara, CA) as directed in the manufacturer's instructions. All sequence information of probe sets on the GeneChip was downloaded from the NetAffyx Analysis Center (http://www.affymetrix.com/analysis/index.affx). mRNA sequences of RefSeq and GenBank entries were obtained from the FTP site of the National Center for Biotechnology Information (NCBI) (ftp.ncbi.nlm.nih.gov; updated on 10 June, 2007). Since Affymetrix utilized information of mouse genome that was incomplete at the time of GeneChip design, the current GeneChip probe set design could include some discrepancies compared with most recent genomic data (16, 29). Thus, in this study, we used only probe sets in which >9 of 11 probes showed 100% identity with RefSeq and GenBank cDNA entries. These probe sets gave relatively high intensity signals for various types of cells (29). For this analysis, we prepared biological triplicates of each time point/fraction group. The intensity of each probe set was calculated using the MAS5 method of GCOS software package (Affymetrix). We used data of probe sets that were judged to be "present" or "marginal" by the GCOS program in all samples of the either experimental group. Raw data for microarray analysis has been deposited in the Gene Expression Omnibus of the NCBI (series accession no. GSE4288; sample accession nos. GSM93735–93758 and GSM237902–237913). Median normalization was done with GeneSpring GX 7.3 software package (Agilent). Changes of the signal intensity of probe sets and the polysome/free mRNP expression ratio were considered to be significant when they met the following two criteria: 1) more than twofold difference in the intensity and the ratio compared with values of the control group, and 2) statistically significant difference as observed by Welch's T-test P value [cut-off P value was 0.05, and multiple testing correction was performed using Benjamini and Hochberg false discovery rate (FDR)]. Gene lists including transcripts increased and decreased by LPS stimulation are shown in Supplementary Tables S1–S6.1 Gene ontology (GO)-based functional classification of gene groups was conducted using Onto-Express (http://vortex.cs.wayne.edu/projects.htm) (19). GO classifications were considered significant when a term satisfied the following two criteria: 1) P value was <0.05 and the result was confirmed by FDR, and 2) more than three queries matched the term.

Northern blot analysis.
Northern blot analysis was performed as previously described (38). To evaluate the abundance of transcripts per cell, we loaded polysomal, free mRNP, and total RNAs isolated respectively from ~3 x 105, ~1 x 106, and ~6 x 105 cells at each time point. Membranes hybridized with {alpha}32P-dCTP-labeled cDNA fragments were exposed to an IP plate (Fuji Film, Tokyo, Japan) and scanned with a BAS-2000 Bioimage Analyzer (Fuji Film). The intensity of each band was measured with Multi Gauge software package (Fuji Film).

Western blot analysis.
To prepare the total cytoplasmic fraction, cells were treated with lysis buffer containing 10 mM Tris·HCl, pH 7.6, 50 mM NaCl, 5 mM EDTA, 1% Nonidet P-40, protease inhibitor cocktail (Roche Diagnostics), and phosphatase inhibitor (Sigma). Mitochondrial protein was prepared with a Qproteome mitochondria isolation kit (Qiagen, Hilden, Germany).

For SDS-PAGE, 20 µg of heat-denatured sample was loaded onto 10% or 15% polyacrylamide gels. For blue native PAGE, 1 µg of mitochondrial protein was electrophoresed on a NativePAGE Bis-Tris Gel System (Invitrogen). The samples were transferred onto polyvinylidene difluoride membranes and subsequently blocked with Blocking Agent (GE Healthcare, Piscataway, NJ). The membranes were stained with Coomassie brilliant blue (CBB, Invitrogen) or colloidal gold (Bio-Rad, Hercules, CA), and probed in Can Get Signal Immunoreaction Enhancer Solution (Toyobo, Tokyo, Japan) with the following antibodies for cytochrome c oxidase subunit Va (Cox5a) and ATPase inhibitory factor 1 (Atpif1) from Mitoscience (Eugene, OR); superoxide dismutase 1 (Sod1) from Nventa (San Diego, CA); and aldehyde dehydrogenase 2 (Aldh2) from Abnova (Taipei, Taiwan). To detect complexes I, IV, and V in the mitochondrial respiratory chain, we used antibodies for NADH dehydrogenase (ubiquinone) 1 β subcomplex 9, cytochrome c oxidase subunit IV, and complex V subunit {alpha} (Mitoscience). These primary antibodies were visualized with horseradish peroxidase-conjugated secondary antibodies using an ECL plus Western Blotting Detection Kit (GE Healthcare) and scanned with an LAS1000 Bioimage Analyzer (Fuji Film).

Analysis of newly synthesized protein.
To analyze newly synthesized protein, cells were incubated in medium containing 7.1 µCi/ml 35S-methionine (Met) and cysteine (Cys) for 30 min (Fig. 1) or 2 h (Fig. 5) before harvest. To measure the total amount of newly synthesized protein, 100 µg of protein in the cell lysate was precipitated with 5% TCA and absorbed with GC-50 glass-fiber paper (Advantec, Dublin, CA). After extensive wash, radioactivity count was measured with a Wallac 1414 WinSpectral liquid scintillation counter (Perkin Elmer, Wellesley, MA). To detect newly synthesized Cox5a and Sod1, 100 µg of mitochondrial protein and 400 µg of total cellular protein were immunoprecipitated with anti-Cox5a (Mitoscience) and anti-Sod1 (Stressgen, Ann Arbor, MI) antibodies, respectively. The immunoprecipitate was subjected to SDS-PAGE.


Figure 1
View larger version (30K):
[in this window]
[in a new window]

  		Fig. 1. Overview of translational states of J774.1 cells before and after LPS stimulation. A: distribution of polysome/free mRNP expression ratio before and after LPS stimulation. Cells were measured at 0, 1, 2, and 4 h after addition of Escherichia coli LPS (100 ng/ml) to the medium. A total of 18,198 probe sets that were detectable at either time point and either total cellular, polysomal, or free mRNP fraction are shown. B and C: analysis of newly synthesized protein. Cells were incubated in the presence of 35S-Met/Cys for 30 min. B: total radioactivity count in whole cell lysate. Values are means ± SD of 7 independent samples. C: representative SDS-PAGE images of total protein stained with CBB (left) and metabolically labeled protein (right). mRNP, messenger ribonucleoprotein particle; CBB, Coomassie brilliant blue.

 
Measurement of mitochondrial respiratory chain activity and intracellular ATP content.
Enzymatic activities of mitochondrial complexes I and IV were measured as previously described (5). Whole-cell ATP contents were measured with an ATP assay kit (Promega, Madison, WI).

Measurement of intracellular nitric oxide content.
Intracellular nitric oxide (NO) content was detected with a membrane-permeable NO indicator, diaminofluorescein-FM diacetate (DAF-FM-DA) (39). Cells were stained with 50 µM DAF-FM-DA the last 10 min before harvest and subjected to fluorocytometric analysis with FACS Calibur HG (BD Bioscience, San Jose, CA).

Statistics.
All statistical comparisons except microarray analysis were conducted by one-way ANOVA followed by the Bonferroni-type post hoc test or Fisher's protected least significant difference test.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 GRANTS
 REFERENCES
 
Overview of translational states of J774.1 cells before and after E. coli LPS stimulation.
To overview the effect of E. coli LPS stimulation on the translational states of macrophage-like J774.1 cells, we biochemically prepared total cellular, polysomal, and free mRNP fractions at 0, 1, 2, and 4 h after LPS stimulation (100 ng/ml). Then, we examined the mRNA profiles of these fractions using Affymetrix Mouse Genome 430 2.0 GeneChips. No significant changes were noted in the total amounts of RNA in the cellular (n = 7; P = 0.97; 21.80 ± 2.10, 18.48 ± 3.54, 21.96 ± 1.42, and 23.67 ± 1.91 pg/cell at 0, 1, 2, and 4 h, respectively), polysomal (n = 7; P = 0.58; 6.69 ± 1.41, 5.94 ± 1.65, 6.96 ± 1.71, and 6.93 ± 1.32 pg/cell at 0, 1, 2, and 4 h, respectively), and free mRNP fractions (n = 7; P = 0.58; 2.85 ± 0.63, 2.82 ± 0.33, 3.15 ± 0.57, and 2.88 ± 0.42 pg/cell at 0, 1, 2, and 4 h, respectively). Figure 1 shows the distribution of polysome/free mRNP expression ratio during the experimental period. This ratio is a reflection of the translational efficiency of each transcript. Since the mean of the ratio was consistently higher than 1 [i.e., log10 (ratio value) was >0 on the y-axis of Fig. 1], a large portion of transcripts seemed to be preferably incorporated into the polysomes of cells regardless of the stimulation (Fig. 1A). In addition, the ratio distribution pattern was well preserved even after LPS stimulation. Thus, overall translational rate was little affected by LPS stimulation.

To more directly assess the overall effects of LPS on translation, we measured the total amount of newly synthesized protein by conducting pulse-labeling experiments with 35S-Met/Cys. As shown in Fig. 1B, total radioactivity count of labeled protein (per µg protein) was not significantly changed even at 24 h after LPS stimulation. Besides, there were no obvious changes of the major bands in the radioactive\ SDS-PAGE pattern as well as in the CBB-stained one (Fig. 1C). Taken together with the finding that the total amount of protein in the cells was hardly altered by LPS stimulation (n = 14; P = 0.65; 329.54 ± 31.53, 323.16 ± 29.77, 323.13 ± 40.55, 342.17 ± 34.52, and 329.79 ± 48.54 pg/cell at 0, 2, 4, 8, and 24 h, respectively), these results indicate that LPS had little impact on the overall translational rates in J774.1 cells.

Global effect of LPS on translational state of each transcript.
To assess effect of LPS on translational states of each transcript, we next investigated the numbers of transcripts affected by LPS stimulation in total cellular, polysomal and free mRNP fractions (Table 1). In the total cellular fractions, total of 468 and 1,467 transcripts were significantly (P < 0.05, twofold) increased and decreased by LPS stimulation, respectively. On the other hand, more than twofold larger number of transcripts was increased in the both polysome (1,019 transcripts) and free mRNP (1,053 transcripts) fractions, whereas almost equal and smaller number of transcripts were decreased in polysomal (1,400 transcripts) and free mRNP (64 transcripts) fractions, respectively. Thus, LPS has different impacts on transcriptome of the total cellular, polysomal and free mRNP fractions.


View this table:
[in this window]
[in a new window]

  		Table 1. Number of LPS-affected transcripts in total cellular, polysomal, and free mRNP fractions

 
To monitor comprehensively the effect of LPS on the translational state of each transcript, we assessed the change in polysome/free mRNP expression ratio of each transcript after LPS stimulation. As shown in Fig. 2, we employed plots whose x- and y-axes are the log2 value of the signal intensity of total cellular RNA and that of the change in polysome/free mRNP expression ratio, respectively. One-hour LPS stimulation caused significant (more than twofold and P < 0.05) changes in the polysome/free mRNP expression ratio of transcripts, many of which were expressed at relatively low level [log2 (expression level) 2 (expression level) >~1] was extensively affected at 2 h and 4 h. Thus, the translational state of each transcript was markedly influenced by LPS stimulation in a time-dependent manner, although the effect on the overall translation was limited (Fig. 1).


Figure 2
View larger version (32K):
[in this window]
[in a new window]

  		Fig. 2. Overview of changes in polysome/free mRNP expression ratio after LPS stimulation. x-Axis represents hybridization signal intensity of each probe set in total cellular fraction. y-Axis represents log2 (polysome/free mRNP expression ratio at 1, 2, or 4 h) – log2 (polysome/free mRNP expression ratio at 0 h). Each value is the mean of triplicate samples. Blue dots represent probe sets whose polysomal/free mRNP signal ratio was significantly (>2-fold and P < 0.05) changed by LPS stimulation.

 
Characterization of transcripts translationally modulated by LPS.
To further explore translational regulation by LPS in the cells, we extracted four groups of transcripts based on microarray data: 1) transcripts increased in the cells; 2) transcripts decreased in the cells, 3) transcripts translationally upregulated in the cells; and 4) transcripts translationally downregulated in the cells (Fig. 3A). The characteristics of the transcripts are summarized below.


Figure 3
View larger version (32K):
[in this window]
[in a new window]

  		Fig. 3. Classification of transcripts affected by LPS stimulation. A: flow chart showing procedure for classification into transcripts increased and decreased in cells, and translationally up- and downregulated transcripts. Numbers in bottom boxes represent total number of transcripts in each group. B: alteration of polysome/free mRNP expression ratio in the 4 groups.

 
Transcripts increased in J774.1 cells showed more than twofold accumulation in total cellular, polysomal, and free mRNP fractions at the same time points (P < 0.05), and no obvious changes (between 1/1.5- and 1.5-fold) in the polysome/free mRNP expression ratio. This group consisted of 184 nonredundant transcripts recognized by 202 probe sets [Fig. 3, A and B; and Supplementary Table S7]. Of these transcripts, 41, 82, and 125 exhibited increases in all fractions at 1, 2, and 4 h after LPS stimulation, respectively. They seem to be predominantly upregulated by transcriptional activation and/or RNA stabilization.

Conversely, transcripts decreased in J774.1 cells showed significant (P < 0.05) decreases in total cellular, polysomal, and free mRNP fractions and no obvious changes (between 1/1.5- and 1.5-fold) in the polysome/free mRNP expression ratio. A total of 29 transcripts recognized by 30 probe sets were classified into this group, and 3 and 27 transcripts were decreased at 2 and 4 h after LPS stimulation (Fig. 3, A and B; and Supplementary Table S8). They seem to be downregulated by transcriptional suppression and/or RNA destabilization.

Transcripts translationally upregulated in J774.1 cells showed either: 1) more than twofold increase (P < 0.05) in the polysomal fraction despite the <1.5-fold increase in the free mRNP fraction, or 2) <1/2-fold decrease (P < 0.05) in the free mRNP fraction despite the >1/1.5-fold decrease in the polysomal fraction. Their total cellular RNA levels were unchanged (between 1/1.5- and 1.5-fold) during the experimental period. Moreover, their polysome/free mRNP expression ratio showed more than twofold increase with statistically significant difference (P < 0.05) after LPS stimulation. They consisted of 115 transcripts detected by 119 probe sets: 1, 67, and 81 transcripts were affected at 1, 2, and 4 h after LPS stimulation, respectively [Fig. 3, A and B; and Supplementary Table S9].

Transcripts translationally downregulated in J774.1 cells were specifically increased in the free mRNP fraction or specifically decreased in the polysomal fraction with significant (P < 0.05) attenuation of polysome/free mRNP expression ratio. We identified 418 translationally downregulated transcripts by 454 probe sets, and 15, 301, and 286 transcripts were detected at 1, 2, and 4 h after LPS stimulation, respectively [Fig. 3, A and B; and Supplementary Table S10].

To elucidate the functional characteristics of LPS-modulated transcripts, we employed GO-based analysis (Fig. 4). Regarding molecular functions, transcripts increased and decreased in J774.1 cells significantly (P < 0.05) matched terms related to signaling events. For example, increased transcripts matched cytokine activity, chemokine activity, and phosphoinositide binding, while decreased transcripts matched kinase activity. Meanwhile, translationally regulated transcripts significantly (P < 0.05) exhibited enrichment of ATP metabolism (i.e., H+-transporting ATP synthase activity in the downregulated group), protein catabolism (i.e., metallopeptidase activity in the upregulated group and ubiquitin-protein ligase activity in the downregulated group), and RNA processing (i.e., RNA helicase activity and RNA binding in the downregulated group). Regarding biological processes, transcripts increased in cells matched terms associated with immune/inflammatory responses and apoptosis, whereas transcripts decreased in cells showed no significant match to any of the terms. Regarding the translationally upregulated group, GO-based analysis revealed weak condensation of transcripts associated with cell proliferation. In contrast, translationally downregulated transcripts were matched to several terms, such as ATP biosynthesis, isoprenoid biosynthesis, mRNA processing, and autophagy. In terms of cellular components, only translationally downregulated transcripts matched certain terms. Proteins translated from them were preferably distributed in mitochondria. They are also localized in ATPase complex (including mitochondrial ATPase complex) as well as vacuole. Collectively, GO-based analysis indicates that LPS-modulated transcripts showed functional diversity, exhibiting involvement in molecular functions, biological processes, and cellular components.


Figure 4
View larger version (28K):
[in this window]
[in a new window]

  		Fig. 4. Gene ontology (GO)-based classification of LPS-modulated transcripts. GO terms consist of molecular function, biological process, and cellular component. Terms that matched >3 queries and are significantly (P < 0.05) observed in either group are shown.

 
Translational repression of mitochondrial transcripts followed by suppression of respiratory chain.
One of the remarkable observations of GO-based analysis was that a significant number of translationally downregulated transcripts encoded mitochondrial proteins. Thus, we randomly chose seven translationally downregulated transcripts encoding mitochondrial proteins and further investigated translational repression of their encoded proteins. First, we measured their amounts in polysomal and free mRNP fractions from the same number of cells (~3 x 105 cells and ~1 x 106 cells in respective fractions) by Northern blotting to confirm whether or not those transcripts actually behaved as translationally downregulated transcripts. As shown in Fig. 5A, mRNA levels of Cox5a, NADH dehydrogenase (ubiquinone) Fe-S protein 8 (Ndufs8), mitochondrial carrier homolog 2 (Mtch2), proton transporting ATP synthase mitochondrial F1 complex {delta} subunit (Atp5d), Atpif1, Sod1, and Aldh2 were increased in the free mRNP fraction after LPS stimulation, while their levels were attenuated in the polysomal fraction. On the other hand, the level of 18S ribosomal RNA, a major constituent of both fractions, was not changed in the fractions after LPS stimulation. Moreover, no apparent changes were noted in ribosomal protein S6 (Rps6) mRNA level, similar to the result of microarray analysis. In agreement with the mRNA levels in polysomal and free mRNP fractions, the protein levels of Cox5a, Atpif1, Sod1, and Aldh2 were also decreased after LPS stimulation (Fig. 5B).


Figure 5
View larger version (26K):
[in this window]
[in a new window]

  		Fig. 5. Translational repression of transcripts encoding mitochondrial proteins after LPS stimulation. A: Northern blot analysis of translationally downregulated transcripts encoding mitochondrial proteins in polysomal, free mRNP, and total cellular fractions before and after LPS stimulation (100 ng/ml). RNA corresponding to the same number of cells (polysome, ~3 x 105 cells; free mRNP, ~1 x 106 cells; total cell, ~6 x 105 cells) was applied on a gel. Values are means ± SD of 4 independent samples and are expressed relative to intact cells (0 h). We also detected 18SrRNA and Rps6 mRNA in the same sample as loading controls. *Significant difference (P < 0.05) compared with intact cells. B: Western blot images of translationally downregulated proteins in the mitochondrial fraction. CBB staining pattern is shown as reference. C: newly synthesized Cox5a and Sod1 levels. Cells were incubated with 35S-Met/Cys for 2 h before harvest and subjected to immunoprecipitation-Western blot analysis. Values are means ± SD of 3 independent samples and are expressed relative to intact cells. *Statistically significant difference (P < 0.05) compared with intact cells.

 
LPS might also decrease protein levels in mitochondria by not only translational repression but also decreasing the absolute amount of transcripts within the cells. To this end, we measured the total RNA levels of translationally repressed transcripts (Fig. 5A). Similar to the levels of 18S rRNA and Rps6 mRNA, Cox5a, Ndufs8, and Mtch2, mRNA levels did not show any significant changes after LPS stimulation. In contrast, the mRNA levels of Atp5d, Atpif1, Sod1, and Aldh2 were slightly decreased, although the decreases were less than those observed in the polysomal fraction. These results indicate that the protein expression of Cox5a, Ndufs8, and Mtch2 was regulated dominantly at the translational level, while that of Atp5d, Atpif1, Sod1, and Aldh2 was probably regulated at multiple levels including translation.

To more directly evaluate the synthesis of mitochondrial proteins, we performed pulse-labeling experiment of proteins encoded by translationally repressed transcripts. For this analysis, we examined Cox5a and Sod1 simply because their antibodies were commercially available for immunoprecipitation. As shown in Fig. 5C, the amount of 35S-Met/Cys-labeled Cox5a protein was significantly decreased after LPS stimulation in the precipitate although the total RNA level was unchanged (Fig. 5A). Similarly, the amount of labeled newly synthesized Sod1 protein was rapidly reduced at 4 h after stimulation (Fig. 5C), while the total RNA level was decreased more slowly (Fig. 5A). These results indicate that LPS likely attenuated the expression of Cox5a and Sod1 proteins by altering their translational states.

Ndufs8, Cox5a, Atp5d, Atpif1, and other proteins predicted to be translationally regulated in the microarray analysis [NADH dehydrogenase (ubiquinone) 1 β subcomplex 5 (Ndufb5), NADH dehydrogenase (ubiquinone) 1 β subcomplex 6 (Ndufb6), NADH dehydrogenase (ubiquinone) 1, subcomplex unknown 2 (Ndufc2), cytochrome c oxidase, subunit VIc (Cox6c), COX11 homolog (Cox11), proton transporting ATP synthase mitochondrial F0 complex, subunit b, isoform 1 (Atp5f1); Supplementary Table S10] are components of mitochondrial respiratory chain complexes I (Ndufb5, Ndufb6, Ndufc2, and Ndufs8), IV (Cox5a, Cox6c, and Cox11), and V (Atp5d, Atp5f1, and Atpif1). Thus, we next examined the relevance of the decrease of these proteins to mitochondrial function. The total amounts of complexes I and IV were apparently decreased by treatment with both low and high doses (100 ng/ml and 10 µg/ml, respectively) of LPS for 24 h, while the total amount of complex V was little affected by the treatments (Fig. 6A). In addition, LPS at both doses attenuated the activities of complexes I and IV (Fig. 6B), as well as intracellular ATP content (Fig. 6C). Taken together, these results indicate that LPS suppressed the translation of some components in the respiratory chain, and this may lead to energy depletion.


Figure 6
View larger version (18K):
[in this window]
[in a new window]

  		Fig. 6. Suppression of respiratory chain after LPS stimulation. Cells were cultured in the presence or absence of LPS (100 ng/ml and 10 µg/ml) for 24 h. A: native PAGE analysis of respiratory chain complexes I, IV, and V. Activities of complexes I and IV (B) and intracellular ATP content (C). Values are means ± SD of 13 independent samples and are expressed as ratio to intact cells (0 h). *Statistically significant difference (P < 0.05) compared with intact cells.

 
In the mouse macrophage-like cell line ANA-1, NO contributes to LPS-induced suppression of the mitochondrial respiratory chain through destabilization of the mRNAs of respiratory components (72). We thus evaluate whether NO also participates in translational repression of the complexes after LPS stimulation in J774.1 cells. First, we assessed NO production in the cells after LPS stimulation. Figure 7A shows fluorocytometric detection of intracellular NO with a membrane-permeable NO indicator, DAF-FM-DA. No increase in intracellular NO level was observed after LPS stimulation for 4 h. However, the signal was increased at 24 h. In agreement with this finding, the accumulation of NO2– in the medium was not observed at 8 h while it was apparent at 24 h (Supplementary Fig. S1A). Thus, the accumulation of NO does not occur prior to changes in the abundance of transcripts in polysomal and free mRNP fractions (Fig. 6A). To evaluate more directly the involvement of NO in the response, we investigated the effect of L-NAME, an NO inhibitor, on mRNA abundance in polysomal and free mRNP fractions after LPS stimulation. We employed L-NAME at 2 mM because L-NAME at this dose almost completely abolished NO production even at 24 h after LPS stimulation (Supplementary Fig. S1B). As shown in Fig. 7B, L-NAME at this dose failed to attenuate LPS-induced increment and decrement of transcripts for Cox5a, Ndufs8, Atp5d, and Atpif1 in both free mRNP and polysomal fractions. Consistently, L-NAME did not attenuate the LPS-induced decrement of both amount and activity of complexes I and IV (Fig. 7, C and D). Therefore, NO is likely to be involved in neither the acute translational repression of mitochondrial respiratory components nor the subsequent suppression of respiratory chain complexes I and IV in J774.1 cells after LPS stimulation.


Figure 7
View larger version (23K):
[in this window]
[in a new window]

  		Fig. 7. Role of nitric oxide (NO) in LPS-induced suppression of respiratory chain. A: fluorocytometric analysis of diaminofluorescein-FM diacetate (DAF-FM-DA)-stained cells. Cells were cultured in the presence of LPS (100 ng/ml) for 0, 1, 2, 4, and 24 h and stained with DAF-FM-DA (50 µM) the last 10 min before harvest. The area shaded gray represents the distribution pattern at 0 h. B: Northern blot analysis of Cox5a, Ndufs8, Atp5d, and Atpif1 in polysomal and free mRNP fractions of J774.1 cells cultured in the presence and absence of LPS (100 ng/ml) and/or N{omega}-nitro-L-arginine methyl ester (L-NAME, 2 mM). All signal intensities were normalized to those of 18S rRNA. Values are means ± SD of 6 independent samples and are expressed relative to intact cells. *Statistically significant (P < 0.05) difference compared with LPS (–) group. Note that there was no significant difference between L-NAME (+) and (–) groups. Measurement of total amount (C) and enzymatic activity (D) of respiratory complexes I and IV. Cells were incubated in the presence or absence of LPS and L-NAME for 24 h. As reference, we show a representative colloidal gold staining pattern of samples. Values are means ± SD of 8 independent samples and are expressed relative to intact cells. *Statistically significant difference (P < 0.05) compared with LPS (–) group. Note that there was no significant difference between L-NAME (+) and (–) groups.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
GRANTS
 REFERENCES
 
The fractionation of transcripts into polysomes and free mRNPs has been used as a conventional method to monitor the translational activity of transcripts. As a logical extension of this approach, previous studies have coupled this method with DNA microarray analysis to obtain a genome-wide overview of the translational regulation of gene expression (2, 23, 37, 54, 65, 76). In this study, we utilized this technique to investigate the impact of translational regulation on the response of macrophage-like cells to E. coli LPS stimulation and characterized transcripts translationally regulated by the stimulation.

In this study, we employed the mouse macrophage-like cell line J774.1 established from reticulum cell sarcoma of BALB/c mice (55). Although this cell line showed some functional diversities compared with primary isolated macrophages (49, 67), it conserved various macrophage-like properties, such as high phagocytic activity, C5a-induced chemotaxis, and antibody-dependent cellular toxicity (48, 66). The cells expressed TLR4, CD14, and MD2 (Supplementary Fig. S2 and Refs. 58 and 61) and could produced intestinal collagenase after LPS stimulation by the monocyte/macrophage-specific transcriptional mechanism (53). Moreover, LPS offered robust induction of iron transporters (70), reactive oxygen species (52), eicosanoids and their receptors (35, 43), proinflammatory cytokines (32, 33, 60, 71), and transcriptional modulators (33, 36), most of which are also evinced to be produced in isolated macrophages after LPS stimulation (10, 17, 18, 31, 36, 52, 62, 68, 70). Thus, our results provide important knowledge to help us understand translational events in macrophages in response to LPS stimulation.

After LPS stimulation, a total of 184 transcripts were concomitantly increased in total cellular, polysomal, and free mRNP fractions at the same extent, whereas 29 transcripts were decreased at the same time points. It is very likely that those transcripts were predominantly under the control of transcriptional regulation and/or RNA stabilization. On the other hand, 115 and 418 transcripts showed respectively increases and decreases in polysome/free mRNP expression ratio without any apparent changes in total cellular RNA level. Moreover, they were specifically increased or decreased in either polysomal or free mRNP fraction, suggesting that they were translationally regulated transcripts. It should be noted that we might underestimate the total number of transcripts translationally regulated by LPS stimulation, owing to the relatively stringent classification procedure. For example, TNF-{alpha}, a cytokine known to be translationally regulated by LPS stimulation (26, 34), was not classified as translationally up-regulated transcripts in this study. Although remarkable increase was noted in its polysome/free mRNP expression ratio (5.3-, 6.6-, and 5.9-fold at 1, 2, and 4 h, respectively), concurrent increases were observed in polysomal (30.3-, 33.5-, and 39.8-fold at 1, 2, and 4 h), free mRNP (5.8-, 5.0-, and 6.7-fold at 1, 2, and 4 h), and total cellular fractions (10.6-, 7.8-, and 7.9-fold at 1, 2, and 4 h) after LPS stimulation. Thus, translational modulation seems to occur globally in J774.1 cells after LPS stimulation, compared with our estimation.

In this study, we found that the four groups of transcripts encoded proteins with characteristic functions according to GO terms. In particular, transcripts increased or decreased in cells well matched GO terms related to signaling events. For example, increased transcripts encode cytokines (e.g., IL-1β, PDGF, and colony stimulating factor 3), chemokines [e.g., chemokine (C-C motif) ligands 2, 3, 4, 9, and 12], and phosphoinositide binding proteins (e.g., neutrophil cytosolic factor 1), while decreased transcripts encode kinases (e.g., urokinase and microtubule associated serine/threonine kinase 3). Furthermore, particularly in the increased group, a significant number of proteins encoded by the transcripts played a regulatory role in LPS-induced cellular responses, such as inflammatory response (e.g., TLR2), immune response (e.g., CD40 and interferon regulatory factor 7), and apoptosis (e.g., caspase 4 and receptor-interacting serine-threonine kinase 2). As many of them are known to be regulated by LPS stimulation and to participate in cellular responses elicited by LPS stimulation (6, 56, 64), major proinflammatory modulators are likely to be regulated in a manner dependent on cytoplasmic mRNA levels rather than on their translational states. Although most of the translationally regulated transcripts did not match terms associated with inflammatory responses, they also might be responsible for inflammatory responses. For example, carboxypeptidase D, a metallopeptidase, is included in translationally upregulated group and participates in NO production by cleaving the precursor of NO synthase in macrophage-like RAW264 cells (24). On the other hand, poly(rC) binding protein/{alpha}CP-1, which is an RNA binding protein mediating STAT3-induced suppression of NF-{kappa}B activation (50), is included in the translationally downregulated group. These findings support the assumption that translationally regulated transcripts also play a pivotal role in immune/inflammatory modulation.

One of the striking results of the GO overrepresentation analysis was that the translationally downregulated group highly includes transcripts for mitochondrial proteins participating in metabolic responses, such as ATP biosynthesis and isoprenoid biosynthesis. Regarding ATP biosynthesis, previous reports have demonstrated that the combinatory treatment of LPS with IFN-{gamma} remarkably reduced enzymatic activity of mitochondrial respiratory chain complexes in J774.1 cells as well as glial cells (7, 46). Although an early study had surmised that LPS inhibited mitochondrial respiration through nitrosylation of the complexes (11), LPS is also known to repress production of the complexes at the mRNA level: intraperitoneal injection of LPS slightly decreased transcript levels of some components of the respiratory chain in rat whole blood cells (20) and decreased cytochrome c oxidase subunit I in complex IV by destabilizing its mRNA in murine macrophage-like ANA1 cells (72). The present study shows that an additional regulatory process, translational repression, participates in the attenuation of the respiratory chain, as follows: 1) a prominent decrease of transcript levels of the respiratory chain complexes in the polysomal fraction rather than the total cellular fraction, 2) a significant increase of transcript levels in the free mRNP fraction, and 3) a decrement of metabolically labeled Cox5a. It is also possible that other transcripts encoding respiratory components were translationally repressed because transcripts showing more than twofold (P < 0.05) decrease in polysome/free mRNP expression ratio by LPS stimulation encode various components, such as NADH dehydrogenase (ubiquinone) 1 {alpha} subcomplex 3, cytochrome c oxidase subunit VIb-2, and proton transporting ATP synthase mitochondrial F1F0 complex subunit e (data not shown). Hence, LPS may extensively attenuate the mitochondrial respiratory chain at least in part via the translational process.

Molecular events underlying translational attenuation of the respiratory chain are still unclear. Although NO contributed to repression of mitochondrial complexes I and IV in J774.1 cells after combinatory treatment of LPS with IFN-{gamma} (46), our results indicate that NO does not seem to be involved in the translational attenuation of the complexes by LPS alone in the cells, as follows: 1) NO induction did not occur prior to changes in the transcripts of respiratory components in polysomal and free mRNP fractions; 2) the effective dose of L-NAME (2 mM) did not restore LPS-induced changes in the transcripts; and 3) the effective dose of L-NAME also failed to restore attenuation of both amounts and activities of complexes I and IV. Discrepancies between previous results and ours might be attributed to the cotreatment of IFN-{gamma}. IFN-{gamma} strongly enhanced induction of NO by LPS stimulation in macrophages (14, 41, 51). Thus, LPS with IFN-{gamma} led to the production of excess NO, resulting in nitrosylation of the complexes, while NO induced by LPS alone might not be enough to repress complexes in the cells. Consistently, L-guanidino monomethyl L-arginine, another NO synthase inhibitor, could largely but not completely restore suppression of the mitochondrial respiratory chain by LPS and IFN-{gamma} in J774.1 cells, suggesting that NO-independent mechanisms also participate in the suppression of the respiratory chain (46).

One study demonstrated that treatment with LPS alone (100 ng/ml) attenuated the mitochondrial respiratory chain in an NO-dependent manner in another mouse macrophage-like cell line, ANA-1 (72). The difference in NO production ability of the cells could be the reason for the discrepancy. J774.1 cells were originally isolated from BALB/c mice (55), while ANA-1 cells were isolated from C57BL/6 mice (13). Previous reports have indicated that macrophages from BALB/c mice produced small amounts of NO and large amounts of TGF-β, while C57BL/6-derived macrophages produced large amounts of NO and small amounts of TGF-β (44). Similarly, compared with macrophages from C57BL/6 mice, BALB/c-derived macrophages produced a large amount of PGE2 (40), which inhibited the expression of inducible NO synthase by LPS stimulation (15, 27, 42). In agreement with those reports, our data indicated that J774.1 cells required 100 ng/ml of E. coli LPS to produce ~300 pmol of NO2– from 1 x 105 cells (Supplementary Fig. S1), while ANA-1 cells produced a comparable level (~500 pmol) of NO2– with 1/10 lower dose from the same number of cells (14). Future studies are warranted to clarify the molecular mechanisms underlying NO-independent translational repression of respiratory complexes in J774.1 cells.

Biological relevance of the LPS-induced translational repression of mitochondrial respiratory chains in J774.1 cells is still unclear. Since energy depletion was proposed to be one of the major causes of necrosis (22), the repression of respiratory chain may contribute to necrosis of macrophages in lesions. In the acute phase of inflammation, activation of macrophages is one of the first defense systems against invaded microorganisms. However, prolonged activation of the cells brings about remarkable local tissue damages (12, 57). Thus, removal of excess activated macrophages could be beneficial for adequate control of local tissue environment. So far, several papers demonstrated that LPS and IFN-{gamma} induced apoptosis in macrophages by NO-dependent and -independent pathways (63, 73). Moreover, recent studies also showed that LPS causes autophagy in RAW264 cells and human isolated macrophages (74). Current study added possibility that ATP deficiency-induced necrosis might be also responsible for death of macrophages in LPS response. Indeed, our preliminary study confirmed that necrotic cells stained with both propidium iodide and annexin-V accumulated at 48 h after LPS stimulation. Thus, although the functional relationship between apoptosis, autophagy, and necrosis of the cells is still obscure, several alternative self-killing systems might be intrinsically equipped in macrophages.

In conclusion, to our knowledge, this study provides the first genome-wide view of the translational states of transcripts in macrophage-like cells before and after LPS stimulation. Our data-driven exploration of translationally regulated transcripts following LPS stimulation enabled us to identify >500 genes as candidates, which were mainly modulated at the translational step in response to LPS stimulation. In particular, through the identification of translationally downregulated transcripts, we were able to demonstrate that oxidative phosphorylation was controlled after LPS stimulation, at least in part, at the translational step. Thus, this study sheds new light on the mechanisms of LPS response in macrophages. Based on these genome-wide data of translational control of transcripts in macrophages, it may be possible to develop new approaches to manipulate the expression of certain functional subsets crucial to the response to LPS stimulation. In the future, such approaches may contribute to the design of drugs for macrophage-mediated inflammatory diseases, such as inflammatory bowel disease and septicemia.


   GRANTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
REFERENCES
 
This work was supported in part by a Grant-in Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science, and Technology of Japan (17780223).


   ACKNOWLEDGMENTS
 
The authors thank Drs. Hiroyuki Takatsu and David Coombs for technical assistance. We are grateful to Drs. Bob Meek and Jun Okabe for critically reading the manuscript.


   FOOTNOTES
 
Address for reprint requests and other correspondence: H. Kitamura, Laboratory for Immunogenomics, RIKEN Research Center for Allergy and Immunology, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan (e-mail: ktmr{at}rcai.riken.jp).

Article published online before print. See web site for date of publication (http://physiolgenomics.physiology.org).

1 The online version of this article contains supplemental material. Back


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 

  1. Akira S, Takeda K. Toll-like receptor signalling. Nat Rev Immunol 4: 499–511, 2004.
  2. Arava Y, Wang Y, Storey JD, Liu CL, Brown PO, Herschlag D. Genome-wide analysis of mRNA translation profiles in Saccharomyces cerevisiae. Proc Natl Acad Sci USA 100: 3889–3894, 2003.
  3. Barreau C, Paillard L, Osborne HB. AU-rich elements and associated factors: are there unifying principles? Nucleic Acids Res 33: 7138–7150, 2006.
  4. Barrios-Rodiles M, Tiraloche G, Chadee K. Lipopolysaccharide modulates cyclooxygenase-2 transcriptionally and posttranscriptionally in human macrophages independently from endogenous IL-1β and TNF-{alpha}. J Immunol 163: 963–969, 1999.
  5. Bates TE, Almeida A, Heales SJ, Clark JB. Postnatal development of the complexes of the electron transport chain in isolated rat brain mitochondria. Dev Neurosci 16: 321–327, 1994.
  6. Björkbacka H, Fitzgerald KA, Huet F, Li X, Gregory JA, Lee MA, Ordija CM, Dowley NE, Golenbock DT, Freeman MW. The induction of macrophage gene expression by LPS predominantly utilizes Myd88-independent signaling cascades. Physiol Genomics 19: 319–330, 2004.
  7. Bolanos JP, Peuchen S, Heales SJR, Land JM, Clark JB. Nitric oxide-mediated inhibition of the mitochondrial respiratory chain in cultured astrocytes. J Neurochem 63: 910–914, 1994.
  8. Cailhier JF, Partolina M, Vuthoori S, Wu S, Ko K, Watson S, Savill J, Hughes J, Lang RA. Conditional macrophage ablation demonstrates that resident macrophages initiate acute peritoneal inflammation. J Immunol 174: 2336–2342, 2005.
  9. Carayol N, Chen J, Yang F, Jin T, Jin L, States D, Wang CY. A dominant function of IKK/NF-{kappa}B signaling in global lipopolysaccharide-induced gene expression. J Biol Chem 281: 31142–31151, 2006.
  10. Chen G, Li J, Ochani M, Rendon-Mitchell B, Qiang X, Susarla S, Ulloa L, Yang H, Fan S, Goyert SM, Wang P, Tracey KJ, Sama AE, Wang H. Bacterial endotoxin stimulates macrophages to release HMGB1 partly through CD14- and TNF-dependent mechanisms. J Leukoc Biol 76:994–1001, 2004.
  11. Clementi E, Brown GC, Feelisch M, Moncada S. Persistent inhibition of cell respiration by nitric oxide: crucial role of S-nitrosylation of mitochondrial complex I and protective action of glutathione. Proc Natl Acad Sci USA 95: 7631–7636, 1998.
  12. Cohen J. The immunopathogenesis of sepsis. Nature 420: 885–891, 2002.
  13. Cox GW, Mathieson BJ, Gandino L, Blasi E, Radzioch D, Varesio L. Heterogeneity of hematopoietic cells immortalized by v-myc/v-raf recombinant retrovirus infection of bone marrow or fetal liver. J Natl Cancer Inst 81: 1492–1496, 1987.
  14. Cox GW, Melillo G, Chattopadhyay U, Mullet D, Fertel RH, Varesio L. Tumor necrosis factor-{alpha}-dependent production of reactive nitrogen intermediates mediates IFN-{gamma} plus IL-2-induced murine macrophage tumoricidal activity. J Immunol 149: 3290–3296, 1992.
  15. D'Acquisto F, Sautebin L, Iuvone T, Di Rosa M, Carnuccio R. Prostaglandins prevent inducible nitric oxide synthase protein expression by inhibiting nuclear factor-kappaB activation in J774 macrophages. FEBS Lett 440: 76–80, 1998.
  16. Dai M, Wang P, Boyd AD, Kostov G, Athey B, Jones EG, Bunney WE, Myers RM, Speed TP, Akil H, Watson SJ, Meng F. Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res 33: e175, 2005.
  17. Dieter P, Kolada A, Kamionka S, Schadow A, Kaszkin M. Lipopolysaccharide-induced release of arachidonic acid and prostaglandins in liver macrophages: regulation by Group IV cytosolic phospholipase A2, but not by Group V and Group IIA secretory phospholipase A2. Cell Signal 14: 199–204, 2002.
  18. Doherty TM, Seder RA, Sher A. Induction and regulation of IL-15 expression in murine macrophages. J Immunol 156: 735–741, 1996.
  19. Draghici S, Khatri P, Bhavsar P, Shah A, Krawetz SA, Tainsky MA. Onto-Tools, the toolkit of the modern biologist: Onto-Express, Onto-Compare, Onto-Design and Onto-Translate. Nucleic Acids Res 31: 3775–3781, 2003.
  20. Fannin RD, Auman JT, Bruno ME, Sieber SO, Ward SM, Tucker CJ, Merrick BA, Paules RS. Differential gene expression profiling in whole blood during acute systemic inflammation in lipopolysaccharide-treated rats. Physiol Genomics 21: 92–104, 2005.
  21. Gewert K, Svensson U, Andersson K, Holst E, Sundler R. Dexamethasone differentially regulates cytokine transcription and translation in macrophages responding to bacteria or okadaic acid. Cell Signal 11: 665–670, 1999.
  22. Golstein P, Kroemer G. Cell death by necrosis: towards a molecular definition. Trends Biochem Sci 32: 37–43, 2007.
  23. Grolleau A, Bowman J, Pradet-Balade B, Puravs E, Hanash S, Garcia-Sanz JA, Beretta L. Global and specific translational control by rapamycin in T cells uncovered by microarrays and proteomics. J Biol Chem 277: 22175–22184, 2002.
  24. Hadkar V, Skidgel RA. Carboxypeptidase D is up-regulated in RAW 264.7 macrophages and stimulates nitric oxide synthesis by cells in arginine-free medium. Mol Pharmacol 59: 1324–1332, 2001.
  25. Hamilton TA. Molecular basis of macrophage activation: from gene expression to phenotypic diversity. In: The Macrophages (2nd ed.), edited by Burke B, Lewis C. Oxford, UK: Oxford University Press, 2002, p. 73–102.
  26. Han J, Brown T, Beutler B. Endotoxin-responsive sequences control cachectin/tumor necrosis factor biosynthesis at the translational level. J Exp Med 171: 465–475, 1990.
  27. Harbrecht BG, Kim YM, Wirant EA, Simmons RL, Billiar TR. Timing of prostaglandin exposure is critical for the inhibition of LPS- or IFN-{gamma}-induced macrophage NO synthesis by PGE2. J Leukoc Biol 61: 712–720, 1997.
  28. Heale JP, Speert DP. Macrophages in bacterial infection. In: The Macrophages (2nd ed.), edited by Burke B, Lewis C. Oxford, UK: Oxford University Press, 2002, p. 210–252.
  29. Hijikata A, Kitamura H, Kimura Y, Yokoyama R, Aiba Y, Bao Y, Fujita S, Hase K, Hori S, Ishii Y, Kanagawa O, Kawamoto H, Kawano K, Koseki H, Kubo M, Kurita-Miki A, Kurosaki T, Masuda K, Nakata M, Oboki K, Ohno H, Okamoto M, Okayama Y, O-Wang J, Saito H, Saito T, Sakuma M, Sato K, Sato K, Seino K, Setoguchi R, Tamura Y, Tanaka M, Taniguchi M, Taniuchi I, Teng A, Watanabe T, Watarai H, Yamasaki S, Ohara O. Construction of an open-access database that integrates cross-reference information from the transcriptome and proteome of immune cells. Bioinformatics 23: 2934–2941, 2007.
  30. Hildebrand F, Hubbard WJ, Choudhry MA, Frink M, Pape HC, Kunkel SL, Chaudry IH. Kupffer cells and their mediators: the culprits in producing distant organ damage after trauma-hemorrhage. Am J Pathol 169: 784–794, 2006.
  31. Ikegami R, Sugimoto Y, Segi E, Katsuyama M, Karahashi H, Amano F, Maruyama T, Yamane H, Tsuchiya S, Ichikawa A. The expression of prostaglandin E receptors EP2 and EP4 and their different regulation by lipopolysaccharide in C3H/HeN peritoneal macrophages. J Immunol 166: 4689–4696, 2001.
  32. Ishii T, Masuzaki H, Tanaka T, Arai N, Yasue S, Kobayashi N, Tomita T, Noguchi M, Fujikura J, Ebihara K, Hosoda K, Nakao K. Augmentation of 11beta-hydroxysteroid dehydrogenase type 1 in LPS-activated J774.1 macrophages–role of 11beta-HSD1 in pro-inflammatory properties in macrophages. FEBS Lett 581: 349–354, 2007.
  33. Jiang W, Li J, Gallowitsch-Puerta M, Tracey KJ, Pisetsky DS. The effects of CpG DNA on HMGB1 release by murine macrophage cell lines. J Leukoc Biol 78: 930–936, 2005.
  34. Katsanou V, Papadaki O, Milatos S, Blackshear PJ, Anderson P, Kollias G, Kontoyiannis DL. HuR as a negative posttranscriptional modulator in inflammation. Mol Cell 19: 777–789, 2005.
  35. Katsuyama M, Ikegami R, Karahashi H, Amano F, Sugimoto Y, Ichikawa A. Characterization of the LPS-stimulated expression of EP2 and EP4 prostaglandin E receptors in mouse macrophage-like cell line, J774.1. Biochem Biophys Res Commun 251: 727–731, 1998.
  36. Kitamura H, Matsushita Y, Iwanaga T, Mori K, Kanehira K, Fujikura D, Morimatsu M, Saito M. Bacterial lipopolysaccharide-induced expression of the I{kappa}B protein MAIL in B-lymphocytes and macrophages. Arch Histol Cytol 66: 53–62, 2003.
  37. Kitamura H, Nakagawa T, Takayama M, Hijikata A, Kimura Y, Ohara O. Post-transcriptional effects of phorbol 12-myristate 13-acetate on transcriptome of U937 cells. FEBS Lett 578: 180–184, 2004.
  38. Kitamura H, Okita K, Fujikura D, Mori K, Iwanaga T, Saito M. Induction of Src-suppressed C kinase substrate (SSeCKS) in vascular endothelial cells by bacterial lipopolysaccharide. J Histochem Cytochem 50: 245–255, 2002.
  39. Kojima H, Urano Y, Kikuchi K, Higuchi T, Hirata Y, Nagano T. Fluorescent indicators for imaging nitric oxide production. Angew Chem Int Ed 38: 3209–3212, 1999.
  40. Kuroda E, Yamashita U. Mechanisms of enhanced macrophage-mediated prostaglandin E2 production and its suppressive role in Th1 activation in Th2-dominant BALB/c mice. J Immunol 170: 757–764, 2003.
  41. Lorsbach RB, Murphy WJ, Lowenstein CJ, Snyder SH, Russell SW. Expression of the nitric oxide synthase gene in mouse macrophages activated for tumor cell killing. Molecular basis for the synergy between interferon-gamma and lipopolysaccharide. J Biol Chem 268: 1908–1913, 1993.
  42. Marotta P, Sautebin L, Di Rosa M. Modulation of the induction of nitric oxide synthase by eicosanoids in the murine macrophage cell line J774. Br J Pharmacol 107: 640–641, 1992.
  43. McGuire JC, Richard KA, Sun FF, Tracey DE. Production of prostaglandin D2 by murine macrophage cell lines. Prostaglandins 30: 949–967, 1985.
  44. Mills CD, Kincaid K, Alt JM, Heilman MJ, Hill AM. M-1/M-2 macrophages and the Th1/Th2 paradigm. J Immunol 164: 6166–6173, 2000.
  45. Miyake K. Innate recognition of lipopolysaccharide by Toll-like receptor 4-MD-2. Trends Microbiol 12: 186–192, 2004.
  46. Moss DW, Bates TE. Activation of murine microglial cell lines by lipopolysaccharide and interferon-gamma causes NO-mediated decreases in mitochondrial and cellular function. Eur J Neurosci 13: 529–538, 2001.
  47. Mullner EW, Garcia-Sanz J. Polysome gradients In: Manual of Immunological Methods, edited by Lefkovits I. San Diego, CA: Academic, 1997, p. 457–462.
  48. Muno D, Kominami E, Mizuochi T. Generation of both MHC class I- and class II-restricted antigenic peptides from exogenously added ovalbumin in murine phagosomes. FEBS Lett 478: 178–182, 2000.
  49. Nibbering PH, van Furth R. Quantitative immunocytochemical characterization of four murine macrophage-like cell lines. Immunobiology 176: 432–439, 1988.
  50. Nishinakamura H, Minoda Y, Saeki K, Koga K, Takaesu G, Onodera M, Yoshimura A, Kobayashi T. An RNA-binding protein {alpha}CP-1 is involved in the STAT3-mediated suppression of NF-{kappa}B transcriptional activity. Int Immunol 19: 609–619, 2007.
  51. Ohshima H, Tsuda M, Adachi H, Ogura T, Sugimura T, Esumi H. L-arginine-dependent formation of N-nitrosamines by the cytosol of macrophages activated with lipopolysaccharide and interferon-{gamma}. Carcinogenesis 12: 1217–1220, 1991.
  52. Pabst MJ, Johnston RB Jr. Increased production of superoxide anion by macrophages exposed in vitro to muramyl dipeptide or lipopolysaccharide. J Exp Med 151: 101–114, 1980.
  53. Pierce RA, Sandefur S, Doyle GA, Welgus HG. Monocytic cell type-specific transcriptional induction of collagenase. J Clin Invest 97: 1890–1899, 1996.
  54. Rajasekhar VK, Viale A, Socci ND, Wiedmann M, Hu X, Holland EC. Oncogenic Ras and Akt signaling contribute to glioblastoma formation by differential recruitment of existing mRNAs to polysomes. Mol Cell 12: 889–901, 2003.
  55. Ralph P, Prichard J, Cohn M. Reticulum cell sarcoma: an effector cell in antibody-dependent cell-mediated immunity. J Immunol 114: 898–905, 1975.
  56. Ricciardi-Castagnoli P, Granucci F. Opinion: Interpretation of the complexity of innate immune responses by functional genomics. Nat Rev Immunol 2: 881–889, 2002.
  57. Ricevuti G. Host tissue damage by phagocytes. Ann NY Acad Sci 832: 426–448, 1997.
  58. Roger T, Miconnet I, Schiesser AL, Kai H, Miyake K, Calandra T. Critical role for Ets, AP-1 and GATA-like transcription factors in regulating mouse Toll-like receptor 4 (Tlr4) gene expression. Biochem J 387: 355–365, 2005.
  59. Ross JA, Auger MJ. The biology of the macrophage. In: The Macrophages (2nd ed.), edited by Burke B, Lewis C. Oxford, UK: Oxford University Press, 2002, p. 1–72.
  60. Saito S, Matsuura M, Hirai Y. Regulation of lipopolysaccharide-induced interleukin-12 production by activation of repressor element GA-12 through hyperactivation of the ERK pathway. Clin Vaccine Immunol 13: 876–883, 2006.
  61. Saito S, Matsuura M, Tominaga K, Kirikae T, Nakano M. Important role of membrane-associated CD14 in the induction of IFN-β and subsequent nitric oxide production by murine macrophages in response to bacterial lipopolysaccharide. Eur J Biochem 267: 37–45, 2000.
  62. Salkowski CA, Detore GR, Vogel SN. Lipopolysaccharide and monophosphoryl lipid A differentially regulate interleukin-12, gamma interferon, and interleukin-10 mRNA production in murine macrophages. Infect Immun 65: 3239–3247, 1997.
  63. Seminara AR, Ruvolo PP, Murad F. LPS/IFNgamma-induced RAW 264.7 apoptosis is regulated by both nitric oxide-dependent and -independent pathways involving JNK and the Bcl-2 family. Cell Cycle 6: 1772–1778, 2007.
  64. Sharif O, Bolshakov VN, Raines S, Newham P, Perkins ND. Transcriptional profiling of the LPS induced NF-{kappa}B response in macrophages. BMC Immunol 8: 1, 2007.
  65. Shenton D, Smirnova JB, Selley JN, Carroll K, Hubbard SJ, Pavitt GD, Ashe MP, Grant CM. Global translational responses to oxidative stress impact upon multiple levels of protein synthesis. J Biol Chem 281: 29011–29021, 2006.
  66. Snyderman R, Pike MC, Fischer DG, Koren HS. Biological and biochemical activities of continuous macrophage cell lines P388D1 and J774.1. J Immunol 119: 2060–2066, 1977.
  67. Tabas I, Weiland DA, Tall AR. Unmodified low density lipoprotein causes cholesteryl ester accumulation in J774 macrophages. Proc Natl Acad Sci USA 82: 416–420, 1985.
  68. Thorley AJ, Ford PA, Giembycz MA, Goldstraw P, Young A, Tetley TD. Differential regulation of cytokine release and leukocyte migration by lipopolysaccharide-stimulated primary human lung alveolar type II epithelial cells and macrophages. J Immunol 178: 463–473, 2007.
  69. Villarete LH, Remick DG. Transcriptional and post-transcriptional regulation of interleukin-8. Am J Pathol 149: 1685–1693, 1996.
  70. Wardrop SL, Richardson DR. Interferon-{gamma} and lipopolysaccharide regulate the expression of Nramp2 and increase the uptake of iron from low relative molecular mass complexes by macrophages. Eur J Biochem 267: 6586–6593, 2000.
  71. Washizu J, Nishimura H, Nakamura N, Nimura Y, Yoshikai Y. The NF-{kappa}B binding site is essential for transcriptional activation of the IL-15 gene. Immunogenetics 48: 1–7, 1998.
  72. Wei J, Guo H, Kuo PC. Endotoxin-stimulated nitric oxide production inhibits expression of cytochrome c oxidase in ANA-1 murine macrophages. J Immunol 168: 4721–4727, 2001.
  73. Xaus J, Comalada M, Valledor AF, Lloberas J, López-Soriano F, Argilés JM, Bogdan C, Celada A. LPS induces apoptosis in macrophages mostly through the autocrine production of TNF-{alpha}. Blood 95: 3823–3831, 2000.
  74. Xu Y, Jagannath C, Liu XD, Sharafkhaneh A, Kolodziejska KE, Eissa NT. Toll-like receptor 4 is a sensor for autophagy associated with innate immunity. Immunity 27: 135–144, 2007.
  75. Yost CC, Denis MM, Lindemann S, Rubner FJ, Marathe GK, Buerke M, McIntyre TM, Weyrich AS, Zimmerman GA. Activated polymorphonuclear leukocytes rapidly synthesize retinoic acid receptor-alpha: a mechanism for translational control of transcriptional events. J Exp Med 200: 671–680, 2004.
  76. Zong Q, Schummer M, Hood L, Morris DR. Messenger RNA translation state: the second dimension of high-throughput expression screening. Proc Natl Acad Sci USA 96: 10632–10636, 1999.




This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Supplemental Tables and Figures
Right arrow All Versions of this Article:
33/1/121    most recent
00095.2007v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Kitamura, H.
Right arrow Articles by Ohara, O.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Kitamura, H.
Right arrow Articles by Ohara, O.

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Visit Other APS Journals Online
Copyright © 2008 by the American Physiological Society.