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Journal of Perinatal Medicine

Official Journal of the World Association of Perinatal Medicine

Editor-in-Chief: Dudenhausen, Joachim W.

Editorial Board Member: / Bancalari, Eduardo / Milner, Anne / Genc, Mehmet R. / Chervenak, Frank A. / Chappelle, Joseph / Bergmann, Renate L. / Bernardes, J.F. / Bevilacqua, G. / Blickstein, Isaac / Cabero Roura, Luis / Carbonell-Estrany, Xavier / Carrera, Jose M. / D`Addario, Vincenzo / D'Alton, MD, Mary E. / Dimitrou, G. / Grunebaum, Amos / Hentschel, Roland / Köpcke, W. / Kawabata, Ichiro / Keirse, Marc J.N.C. / Kurjak M.D., Asim / Lee, Ben H. / Levene, Malcolm / Lockwood, Charles J. / Marsal, Karel / Makatsariya, Alexander / Nishida, Hiroshi / Ogata, Edward / Papp, Zoltán / Pejaver, Ranjan Kumar / Pooh, Ritsuko K. / Romero, Roberto / Saugstad, Ola D. / Schenker, Joseph G. / Sen, Cihat / Seri, Istvan / Vetter, Klaus / Winn, Hung N. / Young, Bruce K. / Zimmermann, Roland

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The peripheral whole-blood transcriptome of acute pyelonephritis in human pregnancya

Ichchha Madan
  • Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services, Detroit, MI, and Bethesda, MD, USA
  • Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI, USA
/ Nandor Gabor Than
  • Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services, Detroit, MI, and Bethesda, MD, USA
  • Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI, USA
/ Roberto Romero
  • Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services, Detroit, MI, and Bethesda, MD, USA
  • Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI, USA
  • Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA
  • :
/ Piya Chaemsaithong
  • Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services, Detroit, MI, and Bethesda, MD, USA
  • Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI, USA
/ Jezid Miranda
  • Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services, Detroit, MI, and Bethesda, MD, USA
  • Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI, USA
/ Adi L. Tarca
  • Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services, Detroit, MI, and Bethesda, MD, USA
  • Department of Computer Science, Wayne State University, Detroit, MI, USA
/ Gaurav Bhatti
  • Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services, Detroit, MI, and Bethesda, MD, USA
  • Department of Computer Science, Wayne State University, Detroit, MI, USA
/ Sorin Draghici
  • Department of Computer Science, Wayne State University, Detroit, MI, USA
/ Lami Yeo
  • Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services, Detroit, MI, and Bethesda, MD, USA
  • Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI, USA
/ Moshe Mazor
  • Department of Obstetrics and Gynecology, Soroka University Medical Center, School of Medicine, Ben-Gurion University of the Negev, Beer-Sheva, Israel
/ Sonia S. Hassan
  • Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services, Detroit, MI, and Bethesda, MD, USA
  • Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI, USA
/ Tinnakorn Chaiworapongsa
  • Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services, Detroit, MI, and Bethesda, MD, USA
  • Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI, USA
Published Online: 2013-11-30 | DOI: https://doi.org/10.1515/jpm-2013-0085


Objective: Human pregnancy is characterized by activation of the innate immune response and suppression of adaptive immunity. The former is thought to provide protection against infection for the mother, and the latter, tolerance against paternal antigens expressed in fetal cells. Acute pyelonephritis is associated with an increased risk of acute respiratory distress syndrome and sepsis in pregnant (vs. nonpregnant) women. The objective of this study was to describe the gene expression profile (transcriptome) of maternal whole blood in acute pyelonephritis.

Method: A case-control study was conducted to include pregnant women with acute pyelonephritis (n=15) and women with a normal pregnancy (n=34). Affymetrix HG-U133 Plus 2.0 arrays (Affymetrix, Santa Clara, CA, USA) were used for gene expression profiling. A linear model was used to test the association between the presence of pyelonephritis and gene expression levels while controlling for white blood cell count and gestational age. A fold change of 1.5 was considered significant at a false discovery rate of 0.1. A subset of differentially expressed genes (n=56) was tested with real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) (cases, n=19; controls, n=59). Gene ontology and pathway analyses were applied.

Results: A total of 983 genes were differentially expressed in acute pyelonephritis: 457 were upregulated and 526 were downregulated. Significant enrichment of 300 biological processes and 63 molecular functions was found in pyelonephritis. Significantly impacted pathways in pyelonephritis included (a) cytokine-cytokine receptor interaction, (b) T-cell receptor signaling, (c) Jak-STAT signaling, and (d) complement and coagulation cascades. Of 56 genes tested by qRT-PCR, 48 (85.7%) had confirmation of differential expression.

Conclusion: This is the first study of the transcriptomic signature of whole blood in pregnant women with acute pyelonephritis. Acute infection during pregnancy is associated with the increased expression of genes involved in innate immunity and the decreased expression of genes involved in lymphocyte function.

Keywords: Adaptive immunity; high-dimensional biology; infection during pregnancy; innate immunity; mRNA; PAX gene; urinary tract infection


Pregnancy is characterized by the activation of the innate immune response and suppression of adaptive immunity [1, 4, 10, 20, 21, 54, 57, 60, 73, 77–79, 88, 90, 101, 106, 107, 129–131, 133, 134, 137, 144]. This is thought to provide protection against infection for the mother and to promote tolerance of the fetal semi-allograft [1, 4, 10, 13, 20, 21, 54, 57, 60, 73, 77–80, 88, 90, 101, 104–108, 110, 120, 129–134, 137, 144].

The changes in the innate immune response in pregnancy include an increase in the numbers of neutrophils as well as phenotypic and metabolic alterations consistent with leukocyte activation [88, 106, 107]. Despite these physiological changes, pregnant women are more susceptible to the deleterious effects of microbial products than nonpregnant women [46, 81, 84, 101]. Moreover, pregnant animals develop a generalized Schwartzman reaction after a single injection of endotoxin, whereas nonpregnant animals require a priming dose of endotoxin and then a second injection [81–83]. We have attributed this to the physiological activation of innate immunity.

Acute pyelonephritis is a frequent complication of pregnancy [29, 49, 56, 75, 95, 111] and accounts for 12% of all antepartum admissions to an intensive care unit for sepsis [111]. Moreover, acute pyelonephritis during pregnancy can lead to preterm delivery [29, 43, 56, 58, 63, 74, 102, 109] and is more likely to be complicated by acute respiratory distress syndrome (ARDS) [5, 16, 17, 23–27, 33, 45, 49, 56, 66, 97, 116, 136, 147], sepsis [12, 49, 56], septic shock [28, 115], anemia [49, 56], and transient renal dysfunction [40, 49] than acute pyelonephritis in nonpregnant patients. The reasons for this increased susceptibility to microbial products remain unknown. However, acute pyelonephritis during pregnancy has been associated with changes in maternal blood concentrations of soluble cluster of differentiation (CD) 30 (an index of Th2 immune response) [61], adipocytokines (retinol-binding protein 4 [141], adiponectin [70], visfatin [71], and resistin [72]), T-cell chemokines (C-X-C motif chemokine 10, CXCL-10) [41], complement products (C5a [117] and fragment Bb [118]), protein Z [92], and soluble tumor necrosis factor-related apoptosis inducing ligand (TRAIL) [18].

Transcriptome analysis has been used to gain insight into the pathophysiology of disease states and the identification of biomarkers in many disciplines, including obstetrics [37, 42, 50, 56, 67, 76, 93, 99, 100, 113, 121, 135, 142, 145, 148]. The gene expression profiles of peripheral blood have shown promising results in elucidating the mechanisms of other disorders such as multiple sclerosis [2], rheumatoid arthritis [7, 139], sepsis [126, 127], and cancer [9, 62, 69, 138]. The transcriptomic profile of peripheral blood leukocytes after intravenous administration of bacterial endotoxin to healthy human subjects has been characterized [14, 123]. However, the peripheral whole-blood transcriptome of acute infection in human pregnancy has not been studied.

The objective of this study was to characterize the transcriptome of whole blood in pregnant women with acute pyelonephritis.

Materials and methods

Study design and sample collection

A cross-sectional study was conducted by searching our clinical database and bank of biological samples, including patients in the following groups: (1) normal pregnancy (n=34) and (2) acute pyelonephritis (n=15). Patients with multiple gestations and fetal anomalies were excluded. The normal pregnant control group consisted of women who were not in labor, without obstetrical, medical, or surgical complications of pregnancy, and had blood samples collected within the same gestational age window as patients with pyelonephritis. Pyelonephritis was diagnosed in the presence of fever (temperature ≥38°C), clinical signs of an upper urinary tract infection (e.g., flank pain, costovertebral angle tenderness), pyuria, and a positive urine culture for microorganisms.

All patients provided written informed consent for the collection and use of samples for research purposes under the protocols approved by the Institutional Review Boards of Wayne State University and the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services.

RNA preparation

Maternal peripheral venous blood was collected at the time of routine clinical blood draw into PAXgene blood RNA tubes (PreAnalytiX GmbH, distributed by Becton Dickinson Company, Franklin Lakes, NJ, USA). Blood tubes were maintained initially at room temperature for 24 h and then frozen at –70°C until further processing. Blood lysates were reduced to pellets by centrifugation, washed, and resuspended in buffer. Proteins were removed by Proteinase K digestion, and cellular debris were removed by centrifugation through a PAXgene Shredder spin column (PreAnalytiX GmbH). RNA was semi-precipitated with ethanol and selectively bound to the silica membrane of a PAXgene spin column (PreAnalytiX GmbH). The membrane was treated with DNase I to remove any residual DNA and washed, and the purified total RNA was eluted in nuclease-free water. Purified total RNA was quantified by UV spectrophotometry using a DropSense96 Microplate Spectrophotometer (Trinean, Micronic North America LLC, McMurray, PA, USA), and RNA purity was assessed based on the A260/A280 and A260/A230 ratios. An aliquot of the RNA was assessed using the RNA 6000 Nano Assay for the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). The electrophoretogram, RNA integrity number, and the ratio of the 28S/18S RNA bands were examined to determine the overall quality of the RNA.

Microarray analysis

Peripheral blood samples were profiled using Affymetrix GeneChip HG-U133 Plus 2.0 arrays. Briefly, RNA was amplified using the Ovation RNA Amplification System V2 (NuGEN Technologies, San Carlos, CA, USA). cDNA was synthesized using the Ovation buffer mix, first-strand enzyme mix, and first-strand primer mix with 5 μL (∼20 ng) of total RNA in specified thermal cycler protocols according to the manufacturer’s instructions. Amplification and purification of the generated cDNA was performed by combining SPIA Buffer Mix, Enzyme Mix, and nuclease-free water with the products of the second-strand cDNA synthesis reactions in prespecified thermal cycler programs. The optical densities of the amplified cDNA products were obtained to demonstrate product yield and verified purity. Fragmentation and labeling were done using the FL-Ovation cDNA Biotin Module V2 (NuGEN Technologies). In the primary step, a combined chemical and enzymatic fragmentation process was used to produce cDNA products in the 50- to 100-base pair range. Fragmented cDNA products were then biotin-labeled using the Encore Biotin Module (NuGEN Technologies). All reactions were carried out according to the manufacturer’s protocols. Amplified, fragmented, and biotin-labeled cDNAs were used for hybridization cocktail assembly, and then hybridized to the Affymetrix GeneChip HG-U133 Plus 2.0 arrays according to the Affymetrix standard protocol.

Quantitative reverse transcription-polymerase chain reaction

A subset of differentially expressed genes (n=56) was selected for validation in an extended set of samples (pyelonephritis cases, n=19; controls, n=59) using the Biomark high-throughput real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) system (Fluidigm, San Francisco, CA, USA) based on their rank in the list of all differentially expressed genes as well as biological plausibility. Briefly, the Invitrogen Superscript III First-Strand Synthesis System (Invitrogen, Life Technologies, Carlsbad, CA, USA) was used to generate complementary DNA. Pre-amplification procedures included combining 1.25 μL cDNA with 2.5 μL TaqMan PreAMP Mastermix and 1.25 μL pooled assay mix. The reaction was performed with a thermal cycler for one cycle at 95°C for 10 min and 14 cycles at 95°C for 15 s and 60°C for 4 min. After cycling, the reaction was diluted 1:5 by ddH2O to a final volume of 25 μL. The Fluidigm 96.96 Dynamic Array chip was used to perform the next step qRT-PCR assays. The 96.96 array chip was primed in an integrated fluidic circuit (IFC) controller with control fluid. After priming, 2.5-μL 20× TaqMan gene expression assays (Applied Biosystems) was mixed with a 2.5-μL 2× assay loading reagent (Fluidigm) and loaded into the assay inlet on the 96.96 array chip. A total of 2.25 μL preamplified cDNA was mixed with 2.5 μL TaqMan Universal PCR master mix (Applied Biosystems) and 0.25 μL 20× sample loading reagent (Fluidigm) and loaded into the sample inlet on the chip. The chip was returned to the IFC controller for loading. After loading, the chip was placed in the Biomark System to run the reactions. The cycle threshold (Ct) value of each reaction was obtained with the Fluidigm RT-PCR analysis software.

Statistical analysis

Analysis for microarray and real-time quantitative polymerase chain reaction data

A linear model was used to test the association between pyelonephritis and gene expression levels determined by microarray analysis while controlling for white blood cell (WBC) count [32, 35, 146] and gestational age. Moderated t-tests [114] were used to assess the significance of the coefficients in the linear model. Probe sets with false discovery rate-adjusted P-values (q-value) of <0.1 and a fold change of >1.5 were considered significant. Pathway analysis was performed on the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database with an overrepresentation analysis [30] and the signal pathway impact analysis (SPIA) [31, 128]. The SPIA is a systems biology approach that takes into account the gene-gene signaling interactions as well as the magnitude and direction of gene expression changes to determine significantly impacted pathways [128]. Gene ontology analysis was performed using the GOstats package of Bioconductor [34].

The same statistical model was used for qRT-PCR data, and the –ΔCt values were used as a surrogate for log2 gene expression levels. qRT-PCR results were considered significant when P<0.05 with a one tailed t-test, using the direction of expression change obtained from the microarray data.

Mann-Whitney U- and χ2-tests were used to compare the differences in demographics and clinical characteristics between patients with acute pyelonephritis and the control group. SPSS (version 15.0; SPSS, Chicago, IL, USA) was used for the analysis of demographic and clinical characteristic data. A probability value of <0.05 was considered significant.


The demographic and clinical characteristics of the study population are displayed in Table 1. Patients with acute pyelonephritis had a significantly lower median gestational age at venipuncture but higher WBC count in both the microarray and qRT-PCR study (P<0.0001 for each). Therefore, we adjusted gene expression data for these two covariates in the microarray and qRT-PCR data analyses. Patients with pyelonephritis had a significantly higher neutrophil count (P<0.001 for each study) and lower lymphocyte count (microarray, P<0.05; qRT-PCR, P=0.001) than controls. Among patients with acute pyelonephritis (in the qRT-PCR experiment), 12 (63.2%) had a positive urine culture for Escherichia coli, 4 (21.1%) for Klebsiella pneumonia, and the rest were positive for Enterococcus faecalis (n=1), Enterobacter (n=1), and lactose-fermenting Gram-negative bacilli (n=1).

Normal preterm control (n=34)Pyelonephritis (n=15)P-valueNormal preterm control (n=59)Pyelonephritis (n=19)P-value
Maternal age (years)21.7 (16–34)24.3 (19–29)0.18222.9 (16–35)23.4 (18–29)0.935
Nulliparity (%)19 (55.9)4 (26.7)0.07127 (45.8)6 (31.6)0.276
African Americans (%)30 (88.2)13 (86.7)1.049 (83.1)17 (89.5)0.720
Smoking3 (8.8)5 (33.3)0.04710 (16.9)6 (31.6)0.170
Gestational age at venipuncture (weeks)30.9 (20.6–36.0)25.1 (20.4–36.0)0.00431.4 (20–36.9)25.4 (20.4–36.3)0.014
WBC count (×103/μL)9 (4.7–13.8)14.8 (7.3–19.9)<0.00018.9 (2.7–13.8)14.3 (7.3–19.9)<0.0001
Neutrophil count (×103/μL)6.7 (4.9–8.1)12.4 (10.1–13.2)<0.0016.3 (4.8–8.1)12.4 (10.6–13.2)<0.001
Lymphocyte count (×103/μL)1.6 (1.3–2.1)1.2 (1.0–1.4)<0.051.7 (1.3–2.0)1.0 (0.9–1.3)0.001
Birth weight (g)3297.5 (2575–3995)3067.5 (2135–3985)0.118a3310 (2575–4005)3142.5 (2135–3985)0.093b
Table 1

Demographic and clinical characteristics of the study groups.

Microarray analysis

A total of 1309 probe sets corresponding to 983 unique genes demonstrated a differential expression between the two groups (q-value <0.1; fold change >1.5). A total of 457 genes were upregulated and 526 genes were downregulated in acute pyelonephritis. Table 2 shows the top 100 probe sets with differential expression between the study groups ranked by P-value.

RankGene symbolEntrez IDGene nameFold changeAdjusted P-value (q-value)Dysregulated after endotoxin challenge in the same direction [14]
1FAM20A54757Family with sequence similarity 20, member A5.741.80E–10
2FAM20A54757Family with sequence similarity 20, member A3.212.83E–08
3FAM20A54757Family with sequence similarity 20, member A5.782.83E–08
4METTL7B196410Methyltransferase-like 7B10.716.92E–07
5ANKRD22118932Ankyrin repeat domain 227.298.80E–07
7FCGR1A2209Fc fragment of IgG, high-affinity Ia, receptor (CD64)1.972.36E–06
8DUSP31845Dual-specificity phosphatase 32.162.47E–06Yes
9PSTPIP29050Proline-serine-threonine phosphatase interacting protein 21.792.44E–05Yes
10GNS2799Glucosamine (N-acetyl)-6-sulfatase1.692.44E–05Yes
11GBP12633Guanylate-binding protein 1, IFN-inducible, 67 kDa4.332.44E–05Yes
12EPB41L557669Erythrocyte membrane protein band 4.1-like 52.932.44E–05
13FLVCR255640Feline leukemia virus subgroup C cellular receptor family, member 22.332.44E–05
14DUSP31845Dual-specificity phosphatase 31.723.82E–05Yes
15KLHL326249Kelch-like 3 (Drosophila)–2.205.00E–05
16LHFPL210184Lipoma HMGIC fusion partner-like 22.365.75E–05
17SORT16272Sortilin 11.967.22E–05
18MS4A4A51338Membrane-spanning 4-domains, subfamily A, member 43.597.22E–05Yes
19MS4A4A51338Membrane-spanning 4-domains, subfamily A, member 43.867.22E–05Yes
20MAP2K65608MAPK kinase 61.937.22E–05Yes
21MS4A4A51338Membrane-spanning 4-domains, subfamily A, member 42.897.22E–05Yes
22APOL680830Apolipoprotein L, 62.608.22E–05
23CNIH429097Cornichon homologue 4 (Drosophila)2.198.34E–05Yes
24ETV751513ETS variant 710.658.79E–05
25TIFA92610TRAF-interacting protein with forkhead-associated domain2.969.07E–05
26ACOX28309Acyl-coenzyme A oxidase 2, branched chain1.849.07E–05
27FCGR1B2210Fc fragment of IgG, high-affinity Ib, receptor (CD64)1.649.37E–05Yes
28FAM89A375061Family with sequence similarity 89, member A1.919.37E–05
29TP53I39540Tumor protein p53 inducible protein 31.779.48E–05Yes
30PUS383480Pseudo-uridylate synthase 31.650.00011
31BATF2116071Basic leucine zipper transcription factor, ATF-like 25.420.00012
32BATF10538Basic leucine zipper transcription factor, ATF-like1.840.00012Yes
33APOL680830Apolipoprotein L, 62.560.00012
34GBP12633Guanylate-binding protein 1, IFN-inducible, 67 kDa2.890.00012Yes
35DUSP31845Dual-specificity phosphatase 31.830.00013Yes
36ACER355331Alkaline ceramidase 31.940.00013
37LOC344887344887Similar to hcg20412701.640.00013
38C13orf1528984Chromosome 13 open reading frame 15–2.520.00014
39ERLIN110613ER lipid raft-associated 11.650.00015Yes
40KYNU8942Kynureninase (l-kynurenine hydrolase)1.940.00015
41CD27429126CD274 molecule3.220.00015
42GBP12633Guanylate-binding protein 1, IFN-inducible, 67 kDa3.110.00016Yes
43IL23A51561IL-23, α subunit p19–1.760.00017
44MSRB222921Methionine sulfoxide reductase B21.700.00018Yes
45SBK1388228SH3-binding domain kinase 1–1.610.00018
46PLEKHG326030Pleckstrin homology domain containing, family G (with rhogef domain) member 3–1.680.00019
47CCNB1IP157820Cyclin B1 interacting protein 1–1.670.00019
48MARCKSL165108MARCKS-like 1–1.610.00019
49GPR10757720G-protein-coupled receptor 1071.770.00019Yes
50GBP12633Guanylate-binding protein 1, IFN-inducible, 67 kDa2.890.00021Yes
51KYNU8942Kynureninase (l-kynurenine hydrolase)2.110.00022
52FCGBP8857Fc fragment of IgG-binding protein–2.100.00024
53GPR8453831G-protein-coupled receptor 844.130.00024
54ANKRD22118932Ankyrin repeat domain 223.890.00025
56LOC284023284023Hypothetical protein LOC284023–1.590.00025
57CETP1071Cholesteryl ester transfer protein, plasma2.300.00026
58ATPGD157571ATP-grasp domain containing 1–2.060.00026
59TRD@6964T-cell receptor δ locus–2.110.00027
60PSMD69861Proteasome (prosome, macropain) 26S subunit, non-ATPase, 61.590.00027
61CEACAM1634Carcinoembryonic antigen-related cell adhesion molecule 1 (biliary glycoprotein)2.220.00027Yes
62OLAH55301Oleoyl-ACP hydrolase3.470.00028Yes
63GAS78522Growth arrest-specific 71.870.00031Yes
64LRRN354674Leucine-rich repeat neuronal 3–3.490.00033
65PAIP2B400961Poly(A)-binding protein interacting protein 2B–1.620.00033
66LRRN354674Leucine-rich repeat neuronal 3–3.320.00033
67C6orf150115004Chromosome 6 open reading frame 1502.400.00034
68TMEM20479652Transmembrane protein 204–2.090.00036Yes
69MCTP179772Multiple C2 domains, transmembrane 11.700.00036Yes
70KYNU8942Kynureninase (L-kynurenine hydrolase)1.860.00036
71PLXDC157125Plexin domain containing 1–2.290.00036
72PIGL9487Phosphatidylinositol glycan anchor biosynthesis, class L–2.560.00036
73CYP1B11545Cytochrome P450, family 1, subfamily B, polypeptide 12.190.00036Yes
74CD6923CD6 molecule–1.850.00042Yes
75PDE9A5152Phosphodiesterase 9A–2.110.00043
76FLNB2317Filamin B β–1.510.00043
77MTERFD380298MTERF domain containing 3–1.720.00045
78NMT29397N-myristoyltransferase 2–1.950.00045Yes
79RCAN311123RCAN family member 3–2.230.00046Yes
80MARCO8685Macrophage receptor with collagenous structure2.270.00049Yes
81TNIK23043TRAF2- and NCK-interacting kinase–1.640.00049
82KCNH790134Potassium voltage-gated channel, subfamily H (EAG-related), member 71.870.00050
83NCRNA00219114915Non-protein coding RNA 219–1.650.00050
84DHRS910170Dehydrogenase/reductase (SDR family) member 92.360.00051
85LACTB114294Lactamase β1.810.00051
87DHRS910170Dehydrogenase/reductase (SDR family) member 92.310.00054
88FBXO626270F-box protein 62.340.00055
89ZNF63827332Zinc finger protein 638–1.730.00055
90RORA6095RAR-related orphan receptor A–1.580.00055Yes
91CD247919CD247 molecule–1.730.00058Yes
92ETV751513ETS variant 75.640.00059
94MSRB222921Methionine sulfoxide reductase B21.630.00060Yes
95TIFA92610TRAF-interacting protein with forkhead-associated domain1.680.00060
97DRAM155332DNA damage-regulated autophagy modulator 11.950.00060Yes
98SLC16A10117247Solute carrier family 16, member 10 (aromatic amino acid transporter)–2.400.00060
99LCK3932Lymphocyte-specific protein tyrosine kinase–1.820.00061Yes
100WASF18936WAS protein family, member 11.910.00061
Table 2

Top 100 probe sets differentially expressed between acute pyelonephritis and normal controls.

A volcano plot (Figure 1A) displays the differential expression of all the annotated probe sets on the microarray as effect size vs. significance of expression change. An unsupervised principal component analysis (PCA)-based visualization of the microarray data (using all probes on the array, Figure 1B) revealed the between-group differences and no outlier samples.

Figure 1

Volcano plot and three dimensional principal component analysis (PCA) plot.

(A) The volcano plot shows probability values of all probes in the microarray plotted against the fold change. In this figure, the log (base 10) of the false discovery rate-adjusted probability values are plotted against the log (base 2) of fold change between patients with pyelonephritis and normal controls. On the Y-axis, values higher than the gray-line threshold represent significant probes with an adjusted probability value of <0.1. On the X-axis, values outside the red lines represent fold change >1.5. (B) The three-dimensional PCA plot demonstrates a degree of segregation between women with acute pyelonephritis and normal controls. Blue dots indicate individual samples from the normal control group, whereas black dots represent individual samples from the acute pyelonephritis group.

To gain further insight into the biology of the differences in the transcriptome of whole blood between pregnant women with acute pyelonephritis and controls, gene ontology analysis was used. Significant enrichment of 300 biological processes (Table 3) was found in acute pyelonephritis, including the innate immune response, signal transduction, regulation of cytokine production, regulation of adaptive immune response, immunoglobulin (Ig)-mediated immune response, T-cell immunity, B- and T-cell differentiation, positive regulation of leukocyte activation and proliferation, positive regulation of T-cell receptor signaling pathway, and blood coagulation. Moreover, gene ontology analysis revealed that 63 molecular functions were associated with differentially expressed genes in acute pyelonephritis (Table 4).

RankBiological processNumber of genes in the differentially expressed listNumber of genes in the reference arrayP-value
1Regulation of cell activation281430.00000
2Positive regulation of leukocyte activation22920.00000
3Cell surface receptor linked signaling pathway706060.00001
4Positive regulation of developmental process332060.00001
5Positive regulation of calcium-mediated signaling7120.00001
6Multiorganism process665460.00001
7Innate immune response19910.00001
8Response to biotic stimulus402890.00003
9Activation of immune response17800.00004
10Regulation of lymphocyte activation221220.00004
11Regulation of response to stress312060.00005
12Inflammatory response312080.00005
13Signal transduction18119800.00007
14Immune response-regulating signaling pathway14630.00010
15Positive regulation of defense response7180.00013
16Response to virus16810.00014
17Regulation of inflammatory response12500.00015
18Lymphocyte activation181010.00018
19Response to wounding221400.00020
20Cell activation221410.00025
21CD8-positive, α-β T-cell differentiation330.00035
22Positive regulation of T-cell receptor signaling pathway330.00035
23Positive regulation of T-cell activation12550.00036
24Cellular defense response11480.00042
25Immune effector process15820.00049
26Positive regulation of apoptosis443720.00052
27Immune response332690.00055
28Epithelial to mesenchymal transition7220.00058
29Positive regulation of cell death443750.00062
30Leukocyte differentiation231570.00067
31Positive regulation of acute inflammatory response470.00074
32Immune response-activating cell surface receptor signaling pathway10440.00082
33Regulation of cell differentiation413480.00086
34Regulation of MAP kinase activity171030.00086
35Cell recognition9370.00088
36Response to hypoxia16950.00097
37Positive regulation of immune system process15890.00108
38Mechanosensory behavior340.00134
39Regulation of cell-cell adhesion mediated by integrin340.00134
40Regulation of fibroblast growth factor receptor signaling pathway340.00134
42Regulation of anatomical structure morphogenesis231680.00166
43Regulation of body fluid levels161000.00169
44Blood coagulation14820.00172
45System development13515130.00177
46Response to heat9410.00192
47Peptidyl-tyrosine modification14830.00194
48Adaptive immune response based on somatic recombination of immune receptors built from Ig superfamily domains12670.00228
49Immunoglobulin mediated immune response10500.00231
50Response to cytokine stimulus12670.00237
51Regulation of protein amino acid phosphorylation191330.00257
52Regulation of cell-matrix adhesion6210.00268
53Regulation of mononuclear cell proliferation12680.00270
54Signaling process13215410.00296
55Regulation of cytokine production201450.00306
56Cell killing8360.00313
57Tyrosine phosphorylation of Stat1 protein350.00318
58Regulation of adaptive immune response9440.00322
59Positive regulation of lymphocyte proliferation9440.00322
60Response to γ radiation6220.00347
61Positive regulation of leukocyte proliferation9450.00378
62Negative regulation of signaling process231790.00391
63Skin development5160.00399
64T cell selection5160.00399
65Humoral immune response10540.00416
66Cellular component morphogenesis373330.00416
67Lymphocyte mediated immunity8380.00419
68Regulation of α-β T-cell activation7300.00422
69Positive regulation of peptidyl-tyrosine phosphorylation7300.00422
70Signal initiation by diffusible mediator11630.00435
71JAK-STAT cascade9460.00441
72Regulation of immune response13830.00453
73Positive regulation of phosphorylation12730.00493
74Regulation of morphogenesis of a branching structure220.00502
75Macrophage fusion220.00502
76Axon regeneration in the peripheral nervous system220.00502
77Mesenchymal cell differentiation8390.00528
78Calcium-mediated signaling6240.00532
79Activation of MAPK activity10560.00545
80T-cell costimulation4110.00553
81T-cell differentiation9480.00563
82Regulation of transferase activity332940.00566
83G-protein-coupled receptor protein signaling pathway403740.00572
84Positive thymic T cell selection360.00603
85Axonal fasciculation360.00603
86Cgmp-mediated signaling360.00603
88Circulatory system process181340.00636
89Multicellular organismal process13416450.00648
91Intracellular signaling pathway565730.00678
92Regulation of T-cell proliferation9490.00680
93Positive regulation of phosphorus metabolic process12760.00685
94Lymphocyte activation during immune response6250.00687
95Protein kinase cascade413930.00692
96Induction of programmed cell death322870.00703
97Positive regulation of response to stimulus14970.00734
98Cell adhesion464550.00735
99α-β T-cell activation4120.00765
100Programmed cell death808670.00780
Table 3

Gene ontology analysis: top 100 biological processes with enrichment in acute pyelonephritis.

RankMolecular functionNumber of genes in the differentially expressed listNumber of genes in the reference arrayP-value
1Molecular transducer activity13411440.00000
2Collagen binding8210.00005
3Calcium ion binding635570.00011
4Serine-type endopeptidase inhibitor activity10410.00043
5Hyaluronic acid binding480.00137
6C-C chemokine receptor activity5130.00138
7Tropomyosin binding490.00233
8G-protein-coupled receptor activity272190.00316
9Peptidase inhibitor activity12700.00334
10Receptor activity312820.00476
11Hepoxilin-epoxide hydrolase activity220.00497
12Complement receptor activity220.00497
13Methylenetetrahydrofolate dehydrogenase (NADP+) activity220.00497
14MHC class I protein binding4110.00543
15SH3/SH2 adaptor activity9480.00574
16Phosphotyrosine binding360.00595
17Calmodulin binding14950.00657
18Phosphoprotein binding6250.00672
19Nucleotide receptor activity6270.00995
20G-protein chemoattractant receptor activity5200.01098
21Protein dimerization activity424150.01113
22Transmembrane receptor activity242140.01158
23Non-membrane-spanning protein tyrosine kinase activity7360.01167
24Immunoglobulin receptor activity230.01422
25Sodium/amino acid symporter activity230.01422
26Methenyltetrahydrofolate cyclohydrolase activity230.01422
27T-cell receptor binding230.01422
28Chemokine binding5220.01660
29Receptor signaling complex scaffold activity4150.01793
30Growth factor binding10670.01800
31Receptor signaling protein activity161290.01883
32SH2 domain binding5230.02001
33Hydrolase activity, acting on ether bonds390.02131
34Transforming growth factor β binding390.02131
35Endoribonuclease activity, producing 3′-phosphomonoesters390.02131
36Dipeptidyl-peptidase activity390.02131
37Low-density lipoprotein receptor activity390.02131
38Coreceptor activity4160.02260
39Steroid binding7410.02319
40Protein kinase binding161330.02485
41ρ-Guanyl-nucleotide exchange factor activity9610.02631
42NADPH binding240.02711
43Growth hormone receptor binding240.02711
44Lipoxygenase activity240.02711
45Alcohol dehydrogenase (NAD) activity240.02711
46Adenosine receptor activity, G-protein-coupled240.02711
47Scavenger receptor activity5250.02813
48Epidermal growth factor receptor binding3100.02887
49Peptide antigen binding3100.02887
50Cytokine receptor activity7430.02945
51Protein kinase inhibitor activity6340.02978
52Phospholipid binding161360.02987
53Hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds, in cyclic amidines5260.03287
54Receptor binding505490.03509
55Racemase and epimerase activity3110.03766
56Purinergic nucleotide receptor activity, G-protein-coupled4190.04043
57Poly(U) RNA binding250.04309
58RS domain binding250.04309
59Hematopoietin/IFN class (D200-domain) cytokine receptor signal transducer activity250.04309
60Sulfuric ester hydrolase activity3120.04765
61Antigen binding4200.04770
62Single-stranded RNA binding4200.04814
63Small GTPase regulator activity242380.04841
Table 4

Gene ontology analysis: 63 molecular functions associated with differentially expressed genes in acute pyelonephritis.

Pathway analysis of differentially expressed genes was undertaken with an overrepresentation method and the SPIA method. Using the overrepresentation method, three KEGG pathways were significantly impacted (q-value <0.1) in the comparison between the study groups (Table 5): (1) “primary immunodeficiency”, (2) “hematopoietic cell lineage”, and (3) “T-cell receptor signaling pathway”. SPIA identified four pathways that were significantly impacted (Table 6). Three of these pathways had not been identified by the overrepresentation method (the “Jak-STAT signaling pathway”, “cytokine-cytokine receptor interaction”, and “complement and coagulation cascade”) (Table 6, Figure 2).

PathwayKEGG identification numberNumber of genes in the differentially expressed listNumber of genes in the reference arrayOdds ratioAdjusted P-value (q-value)
Primary immunodeficiency534011335.790.00283
Hematopoietic cell lineage464019674.680.00013
T-cell receptor signaling pathway466019952.930.0098
Table 5

Pathway analysis using the overrepresentation method.

PathwayKEGG identification numberNumber of genes in the differentially expressed listNumber of genes in the reference arrayAdjusted P-value (q-value)
T-cell receptor signaling pathway466019950.00315
Jak-STAT signaling pathway4630181030.03792
Complement and coagulation cascade46107360.03792
Cytokine-cytokine receptor interaction4060251720.03792
Table 6

Pathway analysis using the SPIA method.

Figure 2

Two-dimensional plot illustrates the relationship between the two types of evidence considered by SPIA.

The X-axis shows the overrepresentation evidence (–log [P-value]), whereas the Y-axis shows the perturbation evidence (–log [perturbation P-value]). Each pathway is represented by a point. Pathways above the oblique red line (red dots) are significant at 5% after Bonferroni correction; pathways (blue dots and red dots) above the oblique blue line are significant at 5% after false discovery rate correction. The vertical and horizontal thresholds represent the same corrections for the two types of evidence considered individually.

Quantitative real-time reverse transcription-polymerase chain reaction

qRT-PCR was performed on an extended set of samples (normal controls, n=59; acute pyelonephritis, n=19) to validate microarray results. Of the 56 genes selected for testing from the differentially expressed gene list in the microarray, we confirmed differential expression (in terms of both direction and significance) in 48 (85.7%) of the tested genes by qRT-PCR (Table 7).

Gene nameGene symbolMicroarray analysisqRT-PCR analysis
Fold changeAdjusted P-value (q-value)Direction of changeFold changeP-valueDirection of change
Leucine-rich repeat neuronal 3LRRN33.613.22E–054.772.47E–09
Chemokine (C-C motif) receptor 3CCR31.960.0051924.592.68E–08
Hairy/enhancer-of-split related with YRPW motif 1HEY12.460.00178393.951.29E–05
Solute carrier family 2 (facilitated glucose/fructose transporter), member 5SLC2A51.700.09394162.605.20E–05
Defensin, α4, corticostatinDEFA42.240.09943014.920.000112
EF-hand domain (C-terminal) containing 1EFHC11.700.00704061.870.000139
Protease, serine, 33PRSS333.150.01232635.330.000465
EF-hand domain (C-terminal) containing 2EFHC21.740.05836851.980.000869
Carcinoembryonic antigen-related cell adhesion molecule 8CEACAM82.100.07299483.440.002103
Cysteine-rich secretory protein 3CRISP32.270.09991892.370.002528
Cathepsin GCTSG2.500.05334283.510.002572
Ribonuclease, RNase A family, 3RNASE32.650.04606222.720.013675
Contactin-associated protein-like 3BCNTNAP3B2.140.05471547.840.042431
CD24 moleculeCD241.750.02766832.490.047949
Contactin-associated protein-like 3CNTNAP32.200.02692191.76NS
Chromosome 13 open reading frame 15C13orf152.851.15E–051.40NS
Methyltransferase-like 7BMETTL7B11.841.05E–0826.063.08E–18
Family with sequence similarity 20, member AFAM20A5.783.36E–1214.579.11E–18
Feline leukemia virus subgroup C cellular receptor family, member 2FLVCR23.008.41E–052.986.48E–11
Ankyrin repeat domain 22ANKRD228.427.23E–085.191.47E–09
Proline-serine-threonine phosphatase interacting protein 2PSTPIP21.811.89E–063.682.04E–09
Oleoyl-ACP hydrolaseOLAH3.050.00026315.631.06E–08
TRAF-interacting protein with forkhead-associated domainTIFA3.214.10E–062.411.32E–08
Dual-specificity phosphatase 3DUSP32.287.11E–081.961.09E–06
G-protein-coupled receptor 84GPR844.000.00011853.722.08E–06
SLAM family member 8SLAMF82.780.00045442.702.71E–06
Carcinoembryonic antigen-related cell adhesion molecule 1CEACAM12.710.00027813.613.10E–06
Fc fragment of igg, high-affinity Ia, receptor (CD64)FCGR1A2.011.53E–072.803.35E–06
ETS variant 7ETV713.073.11E–066.823.83E–06
CD274 moleculeCD2743.411.35E–053.551.18E–05
Sphingomyelin synthase 2SGMS21.830.05275721.782.12E–05
BMX nonreceptor tyrosine kinaseBMX1.730.01389431.953.22E–05
Guanylate-binding protein 1, IFN-inducible, 67 kDaGBP14.621.61E–063.663.51E–05
Serpin peptidase inhibitor, clade B (ovalbumin), member 2SERPINB23.010.00034192.473.88E–05
Lipoma HMGIC fusion partner-like 2LHFPL22.522.34E–062.765.17E–05
Short stature homeobox 2SHOX22.810.0097524.586.51E–05
Chromosome 15 open reading frame 48C15orf483.860.00056212.878.61E–05
6-Phosphofructo-2-kinase/fructose-2, 6-biphosphatase 2PFKFB22.800.01477682.150.000375
V-set and Ig domain containing 4VSIG41.720.01873042.110.000415
Epithelial stromal interaction 1EPSTI13.380.00027152.790.000553
Basic leucine zipper transcription factor, ATF-like 2BATF26.356.95E–063.600.000685
Serpin peptidase inhibitor, clade G (C1 inhibitor), member 1SERPING14.670.00078514.330.000863
Membrane-spanning 4-domains, subfamily A, member 4MS4A4A4.225.22E–063.460.001822
Ribonuclease, RNase A family, 1RNASE13.020.00106242.410.002062
Transmembrane protein 176BTMEM176B2.860.05981792.780.008158
Guanylate-binding protein 5GBP53.160.00066862.700.013125
Apolipoprotein L, 6APOL62.950.00355362.320.044719
Radical S-adenosyl methionine domain containing 2RSAD22.760.02382952.18NS
Cornichon homologue 4CNIH42.365.41E–061.57NS
Glucosamine (N-acetyl)-6-sulfataseGNS1.684.49E–061.48NS
Erythrocyte membrane protein band 4.1-like 5EPB41L53.051.93E–061.22NS
Purinergic receptor P2Y, G-protein-coupled, 14P2RY143.080.00046661.38NS
Chromosome 1 open reading frame 192C1orf1922.460.00013881.16NS
Table 7

Comparison of microarray gene expression data to qRT-PCR gene expression data of selected genes in an extended sample set.


Principal findings of the study

We report for the first time the transcriptome of maternal whole blood in acute pyelonephritis in pregnancy. The main findings are the following: (1) There was a gene expression signature consistent with a systemic maternal inflammatory response; (2) the transcriptome of peripheral WBCs in pyelonephritis was similar to that reported after intravenous endotoxin administration to nonpregnant individuals [14]. In both conditions, there was upregulation of genes involved in innate immune responses and downregulation of those involved in lymphocyte function. (3) We observed an upregulated expression of genes involved in the induction of apoptosis and downregulation of those with anti-apoptotic properties.

Local and systemic immune responses in infection of the urinary tract during pregnancy

The innate limb of the immune response represents the first line of defense against bacterial invasion of the urinary tract [91, 119]. Urinary epithelial cells are the first to enter into contact with microorganisms. Bacterial attachment can trigger exfoliation of bacteria-laden epithelial cells, reconstitution of the urothelium, and an inflammatory response [47, 85–87, 143]. Microorganisms and their products are recognized by pattern recognition receptors, and this leads to the production of chemokines, cytokines, and antimicrobial peptides, and the generation of an acute inflammatory response [47, 85–87, 91, 119, 143]. Neutrophils play an important role in host defense in the urinary tract, and they appear in the bladder and kidney within hours of transurethral inoculation with uropathogenic E. coli [38, 48]. Disruption of neutrophil chemotaxis in animals with a gene deletion for the interleukin (IL) 8 receptor homologue [38] or depletion of neutrophils with a granulocyte-specific antibody [48] can lead to an increased bacterial burden in the bladder and kidney [38, 48] as well as bacteremia [38].

In addition to the local host defense in the urinary tract, acute pyelonephritis is also associated with a systemic inflammatory response that is characterized by fever, increased serum concentrations of cytokines (IL-6, IL-8) and acute phase reactant proteins, and an elevated WBC and neutrophil count [68]. Consistent with this, we found in the current study that the median WBC count of patients with pyelonephritis was higher than that of pregnant women with a normal pregnancy outcome (Table 1). We had previously reported that the maternal serum concentrations of a panel of chemokines and cytokines are also higher in this condition than in normal pregnant women [19]. Indeed, the median maternal serum concentrations of IL-8, TNF-α, IL-6, IL-7, IL-10, and interferon (IFN) γ were higher in pregnant women with acute pyelonephritis than in gestational age-matched normal pregnant women [19]. Moreover, acute pyelonephritis in pregnancy was found to be associated with higher median maternal serum concentrations of the pro-inflammatory chemokine CXCL-10 (also known as IP-10) [41] and higher median maternal plasma concentrations of the pro-inflammatory adipokine resistin [72] than in normal pregnant women. Resistin concentrations are considered an index of the severity of sepsis and a prognostic factor for survival in critically ill nonpregnant patients [55, 72, 122]. Of interest, median maternal plasma concentrations of the anti-inflammatory adipokine adiponectin [70] were lower in patients with pyelonephritis than in normal pregnant women. We have also reported the activation of the complement system, a component of the innate immune system, in acute pyelonephritis in pregnancy, as the median plasma concentrations of complement fragment Bb [118] and complement C5a [117] are higher in pregnant patients with acute pyelonephritis than in normal pregnant women. Complement C5a is a potent chemoattractant for neutrophils, and can upregulate the activating IgG Fc receptors and downregulate the inhibitory IgG Fc receptors on leukocytes, linking the complement system and IgG Fc receptor effector pathways [112]. This is consistent with the observations made in the present study, in which we observed an upregulation of FCGR1A and FCGR1B expression in pregnant women with acute pyelonephritis. These genes encode for the high-affinity IgG Fc receptor (CD64), which is expressed on neutrophils and other myeloid cells and is involved in the binding of IgG1 and IgG3 [94]. Consistent with this finding, Naccasha et al. [88] reported higher expression of CD64 on granulocyte and monocyte surfaces (determined by flow cytometry as median mean channel brightness) in pregnant women with pyelonephritis than in normal pregnant women. Neutrophil CD64 has emerged as a biomarker for the diagnosis of bacterial infection [22, 64, 94], because resting neutrophils express very low levels of CD64, whereas the expression of CD64 is upregulated in the context of acute bacterial infections [94]. Using flow cytometry analysis of whole blood, a CD64 index (ratio of mean fluorescent intensity of the cell to the beads) of >1.66 in hospitalized patients had a 100% sensitivity and a 95% specificity in the identification of sepsis, defined as the combination of bacteremia and clinical signs of infection [39]. Meta-analysis of 13 studies showed a pooled sensitivity of 79% [95% confidence interval (CI), 70%–86%)] and a pooled specificity of 91% (95% CI, 85%–95%) in the diagnosis of bacterial infection [22]. No studies have addressed the value of this marker in the assessment of pyelonephritis during pregnancy.

Changes in the transcriptome of whole-blood leukocytes in acute pyelonephritis in pregnancy

This is the first report of the transcriptome of whole-blood cells in pregnant women with pyelonephritis. This snapshot of the global mRNA expression of peripheral blood leukocytes and subsequent pathway analyses revealed interesting features of the systemic inflammatory response to bacterial infection in human pregnancy. We found 1309 probe sets corresponding to 983 unique genes differentially expressed in pregnant women with pyelonephritis, of which 457 genes were upregulated and 526 were downregulated. Gene ontology analysis indicated that these findings were associated with 63 molecular functions enriched in leukocytes, many of them strongly related to the innate and adaptive immune responses (e.g., “C-C chemokine receptor activity”, “complement receptor activity”, MHC class I protein binding”, “immunoglobulin receptor activity”, “T-cell receptor binding”, “chemokine binding”, “scavenger receptor activity”, “peptide antigen binding”, “cytokine receptor binding”) (Table 4). In accordance, several of the most enriched biological processes in pyelonephritis during pregnancy are related to immune responses (e.g., “positive regulation of leukocyte activation”, “innate immune response”, “activation of immune response” “regulation of lymphocyte activation”, “inflammatory response”). Of interest, biological processes related to apoptosis (i.e., “positive regulation of apoptosis”, “positive regulation of cell death”) are also among the most enriched processes (Table 3).

To identify pathways significantly impacted in pyelonephritis during pregnancy, we applied two pathway analysis methods. The overrepresentation analysis method identified three KEGG pathways significantly impacted in pyelonephritis (“primary immunodeficiency”, “hematopoietic cell lineage”, “T-cell receptor signaling pathway”) (Table 5). The SPIA method, which takes also into account the gene-gene signaling interactions as well as the magnitude and direction of gene expression changes besides differential expression [31, 128], identified one pathway in common with the overrepresentation method (“T-cell receptor signaling pathway”) and three pathways that the overrepresentation method could not identify (the “Jak-STAT signaling pathway”, “cytokine-cytokine receptor interaction”, and “complement and coagulation cascade”) (Table 6). Importantly, all of these impacted pathways are related to immune responses, suggesting that the systemic inflammatory response elicited in pyelonephritis in pregnancy has a characteristic gene expression signature in peripheral blood leukocytes.

To get further insight into the cellular pathways of systemic inflammation in pyelonephritis in pregnancy, we compared transcriptomic changes in our study to those documented in systemic inflammation in response to bacterial endotoxin in nonpregnant individuals (see below).

Changes in the transcriptome of whole-blood leukocytes from nonpregnant individuals after treatment with bacterial endotoxin

The transcriptome of peripheral blood leukocytes of nonpregnant volunteers has been studied after the administration of a single dose of bacterial endotoxin [14, 123]. Calvano et al. [14] reported dysregulation of 3714 genes in whole-blood cells at 2, 4, and 9 h after endotoxin administration and noted that gene expression returned to baseline by 24 h after endotoxin injection. The endotoxin quickly and transiently activated genes involved in the innate immune response, and after an initial pro-inflammatory phase, a self-limiting counter-regulatory response followed, with eventual resolution of gene expression changes within a day of endotoxin administration. Specifically, there was an increased expression of pro-inflammatory cytokines and chemokines (e.g., IL1A, IL1B, IL8, TNF) and NFκB family transcription factors within 2–4 h of endotoxin treatment [14]. There was upregulation of transcription factors critical in both the initiation and the containment of an innate immune response [e.g., signal transducer and activators of transcription (STAT) genes, suppressor of cytokine signaling 3, SOCS3], which was observed within 4–6 h of endotoxin administration. There was also increased expression of genes encoding membrane-bound and secreted proteins that limit the inflammatory response (e.g., IL1R2, IL1RAP, IL10) [14].

Similar observations were made by Talwar et al. [123], who investigated temporal gene expression changes in peripheral blood mononuclear cells and whole-blood cells from nonpregnant volunteers after a single dose of endotoxin. An upregulation of genes associated with pattern recognition receptors, intracellular signaling, cell mobility, and defense function was reported. The largest change in gene expression occurred 6 h after endotoxin treatment, with changes returning to baseline within 24 h [123]. Collectively, these results suggest that leukocyte response to bacterial products include a short pro-inflammatory phase followed by a counter-regulatory phase and resolution of inflammation [14, 123].

Similarities in the expression of innate immune genes in pregnant women with acute pyelonephritis and nonpregnant individuals after endotoxin administration

As the microarray data set of whole-blood leukocytes after endotoxin administration was available online from the study of Calvano et al. [14], we compared such data set with our findings (this comparison included only those genes from the study of Calvano et al. [14] that were differentially expressed at three to five time points after endotoxin administration). We found that 296 of the 983 genes in our study changed in the same direction as that of the Calvano et al. study [14] (Table 1).

Differentially expressed genes involved in the innate immune response in both studies were mainly upregulated (Table 8). Among the functions of the proteins encoded by these genes, the following groups emerged: (a) cell adhesion and cell-cell signaling (CD44, CLEC4D, CLEC4E, CLEC5A, ICAM1), (b) activation and/or differentiation of macrophages (CEBPD), (c) inflammasome priming (CASP1, CASP4, CASP5), (d) activation of the nuclear factor (NF) κB and mitogen-activated protein kinase (MAPK) pathways (IL18R1, IL18RAP, IRAK2, IRAK3), (e) cellular binding to particles and immune complexes that have activated complement (CR1), (f) phagocytosis and antibody-dependent cell-mediated cytotoxicity (FCAR, FCGR1A, FCGR1B, MARCO), and (g) breakdown of extracellular matrix and type IV and V collagens (MMP9). These results suggest that acute pyelonephritis during pregnancy elicits a host response similar to that induced by intravenous bacterial endotoxin in nonpregnant volunteers.

Gene symbolEntrez IDGene nameDirection of change
CASP1834Caspase 1, apoptosis-related cysteine peptidaseUp
CASP4837Caspase 4, apoptosis-related cysteine peptidaseUp
CASP5838Caspase 5, apoptosis-related cysteine peptidaseUp
CD44960CD44 molecule (Indian blood group)Up
CD59966CD59 molecule, complement regulatory proteinUp
CEBPD1052CCAAT/enhancer-binding protein (C/EBP) δUp
CLEC4E26253C-type lectin domain family 4, member EUp
CLEC5A23601C-type lectin domain family 5, member AUp
CR11378Complement component (3b/4b) receptor 1 (Knops blood group)Up
FCAR2204Receptor for Fc fragment of IgAUp
FCGR1B2210Fc fragment of IgG, high-affinity Ib, receptor (CD64)sUp
ICAM13383Intercellular adhesion molecule 1Up
IL18R18809IL-18 receptor 1Up
IL18RAP8807IL-18 receptor accessory proteinUp
IRAK311213IL-1 receptor-associated kinase 3Up
MARCO8685Macrophage receptor with collagenous structureUp
MMP94318Matrix metallopeptidase 9 (gelatinase B, 92 kDa gelatinase, 92 kDa type IV collagenase)Up
FCER1A2205Fc fragment of IgE, high-affinity I, receptor for; α polypeptideDown
IL11RA3590IL-11 receptor αDown
KLRB13820Killer cell lectin-like receptor subfamily B, member 1Down
KLRK122914Killer cell lectin-like receptor subfamily K, member 1Down
NLRC3197358NLR family, CARD domain containing 3Down
Table 8

Differentially expressed innate immune genes common in acute pyelonephritis during pregnancy and in bacterial endotoxin administration in nonpregnant individuals.

Similarities in the expression of genes involved in lymphocyte functions in pregnant women with acute pyelonephritis and nonpregnant individuals after endotoxin administration

SPIA pathway analysis of the commonly differentially expressed genes in pyelonephritis and in the endotoxin-induced model of acute bacterial infection revealed five impacted pathways in both conditions: (1) “T-cell receptor signaling pathway”, (2) “natural killer cell mediated cytotoxicity”, (3) “cytokine-cytokine receptor interaction”, (4) “RIG-I-like receptor signaling pathway”, and (5) “complement and coagulation cascades”. We observed that the pathways inhibited in pyelonephritis included those generally implicated in adaptive immune responses. This is consistent with the findings of Talwar et al. [123], who described the downregulation of T lymphocyte-associated genes after endotoxin administration. Moreover, differentially expressed genes involved in lymphocyte functions common in our study and in the study reported by Calvano et al. [14] were mainly downregulated (Table 9).

Gene symbolEntrez IDGene nameDirection of change
CCR71236Chemokine (C-C motif) receptor 7Down
CD24100133941CD24 moleculeDown
CD247919CD247 moleculeDown
CD27939CD27 moleculeDown
CD28940CD28 moleculeDown
CD3D915CD3d molecule, δ (CD3-TCR complex)Down
CD3E916CD3e molecule, ε (CD3-TCR complex)Down
CD5921CD5 moleculeDown
CD6923CD6 moleculeDown
CD8A925CD8a moleculeDown
CD8B926CD8b moleculeDown
CXCR32833Chemokine (C-X-C motif) receptor 3Down
DPP41803Dipeptidyl-peptidase 4Down
GATA32625GATA-binding protein 3Down
GPR1831880G-protein-coupled receptor 183Down
GZMA3001Granzyme A (granzyme 1, cytotoxic T-lymphocyte-associated serine esterase 3)Down
IL2RB3560IL-2 receptor βDown
IL7R3575IL-7 receptorDown
ITK3702IL2-inducible T-cell kinaseDown
LCK3932Lymphocyte-specific protein tyrosine kinaseDown
PRKCQ5588Protein kinase C θDown
STAT46775Signal transducer and activator of transcription 4Down
TBX2130009T-box 21Down
TCF76932Transcription factor 7 (T-cell-specific, HMG-box)Down
TRAC28755T-cell receptor α constantDown
ZAP707535ζ-chain (TCR)-associated protein kinase 70 kDaDown
IL1RN3557IL-1 receptor antagonistUp
SOCS39021Suppressor of cytokine signaling 3Up
Table 9

Differentially expressed genes associated with lymphocyte function common in acute pyelonephritis during pregnancy and in bacterial endotoxin administration in nonpregnant individuals.

The decreased expression of these genes and the encoded proteins may result in impairment of (a) T-cell recognition of antigens displayed by antigen presenting cells (CD3D, CD3E, CD8A, CD8B, CD247), (b) T-cell chemotaxis and migration to inflamed tissues (CXCR3), (c) T-helper lineage development (GATA3, STAT4, TBX21), (d) T-cell activation, proliferation, development, signal transduction, survival, and cytokine production (CCR7, CD6, CD28, DPP4, IL23A, IL23R, IL2RB, IL5RA, IL9R, ITK, LCK, PRKCQ, TCF7, ZAP70), (e) generation and long-term maintenance of T cell immunity (CD27), (f) T-cell-mediated cytotoxicity (GNLY, GZMA), and (g) regulation of B-cell activation, V(D)J recombination, and Ig synthesis (CCR7, IL7R).

These observations are consistent with the findings derived from transcriptome analysis of patients with sepsis [65, 125]. By analyzing multiple microarray data sets, Lindig et al. [65] found that the upregulated gene ontology categories in patients with sepsis include those related to innate immune responses, whereas the downregulated categories include those related to adaptive immune responses. The systematic review of the transcriptomic data of 12 studies by Tang et al. [125] revealed reduced expression of genes associated with immune response in lymphocytes (e.g., CCR7, CD28, CXCR3, IL2RB, IL7R) and upregulation of genes limiting inflammatory responses (e.g., SOCS1, SOCS3), which also changed in the same direction in our study.

Immunosuppression in sepsis

Contrary to what was originally believed, sepsis does not represent a steady and uncontrolled systemic pro-inflammatory response [6, 51]. Some patients have a state consistent with immune suppression [51, 53, 140], which has been termed “immunoparalysis” [3, 53], characterized by a decreased Th1-like response [3, 51]. The initial pro-inflammatory state in sepsis is followed by a compensatory anti-inflammatory state, or in some cases, both pro-inflammatory and anti-inflammatory responses can occur at the onset of sepsis [51, 140]. The composition and direction of this complex systemic host response to infection depends on the load and virulence of the pathogen, the genetic characteristics of the host, and coexisting illnesses [6, 140].

Of importance, the majority of deaths occur in patients with sepsis who are immunosuppressed [53], and the prevention of immunosuppression improves the survival rate in animal models of sepsis [51]. The occurrence of the immunosuppression state has been attributed to the following[3, 11, 44, 51, 53, 98, 103, 140]: (1) an adaptive compensatory response characterized by increased expression of anti-inflammatory mediators (e.g., IL1RN, SOCS1, SOCS3) and the activation of T regulatory cells and myeloid derived suppressor cells, (2) apoptosis of T and B lymphocytes due to activation of Fas-Fas-ligand system and caspases as well as decreased expression and/or function of anti-apoptotic molecules (e.g., Bcl-2), (3) anti-inflammatory responses in phagocytic cells induced by the uptake of apoptotic immune cells, and (4) activation of a neuroinflammatory reflex through vagal nerve stimulation, which leads to acetylcholine secretion by a subset of T-helper lymphocytes and the subsequent suppression of proinflammatory cytokine release from acetylcholine receptor expressing macrophages.

Patients with sepsis may have a complex set of immunologic defects attributable to the cross-talk between specialized cells in the immune system that coordinate microbial eradication. For example, T lymphocytes play a pivotal role in the initial response to microbial infection because they produce IFN-γ, which activates macrophages [52, 53], and reduced Th1 function due to apoptotic cell death of T lymphocytes in sepsis leads to dampened cytokine production [52]. Indeed, the anti-inflammatory responses in sepsis lead to enhanced susceptibility to secondary infections [3, 6].

Differential expression of genes implicated in immunosuppression and apoptosis

We found upregulation of IL1RN (IL-1 receptor antagonist) and suppressors of cytokine signaling (SOCS1, SOCS3) in women with pyelonephritis in pregnancy – these findings are consistent with a compensatory anti-inflammatory response. Moreover, we found upregulation of FAS (CD95/Fas cell surface death receptor), which plays a central role in the apoptosis of lymphocytes in sepsis and in the pathogenesis of ARDS [36], a severe complication of pyelonephritis in pregnancy [5, 16, 17, 23–27, 33, 45, 49, 56, 66, 97, 116, 136, 147].

Consistent with the observation of increased apoptosis of lymphocytes in sepsis [3, 44, 51, 53, 98], we found upregulation of pro-apoptotic genes (CFLAR, PDCD1LG2) as well as downregulation of anti-apoptotic genes (BCL2, FAIM3) in cases of acute pyelonephritis (Table 10). Moreover, the expression of CD36 (thrombospondin receptor), which is involved in the phagocytosis of apoptotic cells, was increased in pyelonephritis during pregnancy. We also found that the median absolute lymphocyte count of patients with pyelonephritis was significantly lower than that of in controls in both the microarray (P<0.05) and qRT-PCR (P=0.001) populations in the current report (Table 1).

Gene symbolEntrez IDGene nameDirection of change
CFLAR8837CASP8 and FADD-like apoptosis regulatorUp
FAS355Fas (TNF receptor superfamily, member 6)Up
BCL2596B-cell CLL/lymphoma 2Down
FAIM39214Fas apoptotic inhibitory molecule 3Down
Table 10

Differentially expressed genes involved in apoptosis common in acute pyelonephritis during pregnancy and in bacterial endotoxin administration in nonpregnant individuals.

In contrast, the median absolute neutrophil count was higher in patients with pyelonephritis than in controls (P<0.001, Table 1). This finding is consistent with the well-known phenomenon of delayed apoptosis of neutrophils in sepsis [8, 96, 124]. Previous studies also reported a higher median maternal plasma concentration of visfatin [71], a pro-inflammatory adipokine that promotes delayed neutrophil apoptosis as well as a lower median maternal plasma concentration of TRAIL [18], one of the mediators responsible for neutrophil apoptosis, in patients with pyelonephritis in pregnancy than in controls.

Differences in gene expression patterns between pregnant women with acute pyelonephritis and nonpregnant individuals after endotoxin administration

There were 22 differentially expressed genes in our study that changed in the opposite direction from that reported by Calvano et al. [14]. In addition, there were 665 differentially expressed genes in our study that did not change in expression after bacterial endotoxin administration [14]. Enrichment analyses showed that these 665 genes play a role in “immune response”, “immune effector process”, “inflammatory response”, “lymphocyte activation”, and “response to viruses”, among other biological processes. These results suggest that, although the pathways fundamentally impacted in pyelonephritis during pregnancy and endotoxin challenge in nonpregnant women are similar, the increased susceptibility of pregnant women to microbial products [46, 81, 84, 101] may have a well-defined molecular basis, and such differences (qualitative and quantitative) may be responsible for the difference in nature of the immune response to microbial products or infection in pregnant women. This requires further study of nonpregnant patients with pyelonephritis.

The administration of a single intravenous dose of endotoxin has been used as a model to study the systemic effects of a microbial product (endotoxin) and assumed by many to represent a state similar to systemic infection. However, this experimental approach cannot be equated with intravenous administration of live bacteria or actual systemic infection in humans. Therefore, the comparison of our results in pregnant women with pyelonephritis to those reported by Calvano et al. [14] should be interpreted with caution. The findings reported herein can be considered to reflect real bacterial infection in pregnant women.

There are technical differences between our study and that of Calvano et al. [14]. (1) methods used in blood collection, leukocyte lysis, storage, and RNA isolation were not the same. Whole-blood samples were collected into PAX gene tubes in our study, whereas leukocyte separation by centrifugation was undertaken by Calvano et al. before RNA isolation. (2) Our study was cross-sectional, whereas blood was collected at several time points [before (0 h) and at 2, 4, 6, 9, and 24 h after endotoxin infusion] in the study of Calvano et al. [14]. (3) Our population had several bacteria identified in urine and/or blood, whereas nonpregnant volunteers in the study of Calvano et al. received a single dose of endotoxin [14]. (4) We adjusted our gene expression results as a function of WBC count, but this was not performed by Calvano et al. [14].

Differential expression of FAM20A and ETV7

The top three significantly upregulated probe sets in our study correspond to transcripts encoded by the FAM20A (family with sequence similarity 20, member A) gene. FAM20A belongs to an evolutionarily conserved family of three proteins (FAM20A, FAM20B, and FAM20C) secreted by hematopoietic cells [89]. FAM20A is a glycoprotein with high expression levels in hematopoietic tissues [89], especially in those cells committed to the granulocytic lineage [89]. Another gene involved in hematopoiesis and upregulated in pyelonephritis was ETV7 (encoding for ETS variant 7). The expression of ETV7 in normal and leukemic hematopoietic cells suggests an important role for this gene in normal hematopoietic development as well as in oncogenesis [15, 59]. As gene expression in our data was adjusted for WBC count, the results are not simply the reflection of a higher WBC count in patients with pyelonephritis but probably reflect enhanced hematopoiesis that involves the upregulation of these genes in response to acute microbial infection.

Strengths and limitations

This is the first study to characterize the transcriptome of whole blood in pregnant women with an acute episode of infection. A limitation of this study was that the differentially expressed genes identified reflect the changes in the total intracellular mRNA in whole blood. However, it is not possible to attribute these changes to a particular population of leukocytes or reticulocytes. Meanwhile, if we had attempted to separate WBCs before RNA isolation, artifacts derived from cell separation procedures may have been introduced.


This is the first report of the transcriptome of whole-blood cells in pregnant women with acute pyelonephritis. We found increased expression of genes involved in innate immunity and decreased expression of genes that participate in lymphocyte function. These findings are similar to the transcriptional changes reported in nonpregnant individuals exposed to bacterial endotoxin. However, we identified a set of differentially expressed genes that were unique in pyelonephritis during pregnancy. Our study provides necessary information to characterize the nature of a systemic inflammatory response in pregnant women. A major reason for this study is the interest in comparing conditions in which there is acute intravascular inflammation (such as preeclampsia) with that induced by microorganisms (such as pyelonephritis).

This research was supported, in part, by the Perinatology Research Branch, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services (NICHD/NIH), and, in part, with federal funds from NICHD/NIH under contract no. HHSN275201300006C.

Presented at the 58th Annual Meeting of the Society for Gynecologic Investigation, March 16–19, 2011, Miami, FL, USA


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The authors stated that there are no conflicts of interest regarding the publication of this article.

Corresponding author: Roberto Romero, MD, DMedSci, Perinatology Research Branch, NICHD, NIH, DHHS, Wayne State University/Hutzel Women’s Hospital, 3990 John R, Box 4, Detroit, MI 48201, USA, Tel.: +1-313-993-2700, Fax: +1-313-993-2694, E-mail:

Published Online: 2013-11-30

Published in Print: 2014-01-01

Citation Information: Journal of Perinatal Medicine. Volume 42, Issue 1, Pages 31–53, ISSN (Online) 1619-3997, ISSN (Print) 0300-5577, DOI: https://doi.org/10.1515/jpm-2013-0085, November 2013

©2014 by Walter de Gruyter Berlin Boston. This content is open access.

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