Introduction
The clinical scenario of a febrile, acutely ill, immunocompromised patient with immunocompromised
lupus remains challenging despite advances in technology and improved understanding
of pathogenic mechanisms. Infection is a major contributor to morbidity and mortality
in systemic lupus erythematosus (SLE); infection-related hospitalisation rates range
from 10% to 35%1–4 and mortality rates range from 29% to 53%.5
6 The disease itself and most treatment strategies are immunosuppressive, rendering
patients with SLE more susceptible to severe infection with common organisms and opportunistic
pathogens. Both infection and lupus flare can incite clinically indistinguishable
inflammatory responses. Consequently, appropriate therapy may be delayed or patients
are treated for both conditions while awaiting results of time-consuming investigations
for bacterial and viral infections. The potential consequences of treating infection
with immunosuppression are obvious and may contribute to the high mortality rates.
Antibiotics given unnecessarily also have potential toxicities. A biomarker that would
accurately and rapidly differentiate between flare and infection would provide an
extremely valuable guide to more directed, precise therapy, likely leading to significantly
decreased morbidity and mortality.
Microarray technology offers an unbiased, systems biology approach to study the expression
level of thousands of genes simultaneously and genome-wide transcriptional studies
have emerged as a powerful investigational tool to study complex diseases as well
as infection.7 The objective of this exploratory study was to use whole blood gene
expression profiling to identify specific RNA expression profiles that would differentiate
systemic inflammation related to SLE disease flare from infection in acutely ill patients
with lupus. We hypothesised that the molecular signature associated with active disease
and no infection in patients with SLE will differ from the molecular signature in
patients with SLE with infection. Comparison groups consisted of SLE subjects with
inactive disease and healthy controls.
Methods
Study design
Blood samples for microarray analysis were obtained from two groups of SLE subjects
(acutely ill SLE, inactive SLE) and healthy controls matched for age, gender and ethnicity.
Blood samples in the acutely ill SLE group were obtained prior to any changes in therapy
and these subjects were followed through the course of their illness to determine
a final outcome of disease flare, infection or both based on clinically indicated
serological tests, cultures and imaging studies.
Subjects
SLE subjects were recruited randomly from three sites; the North Shore University
Hospital in Manhasset, New York, St. Luke's Medical Center in Quezon City, Philippines,
and the Instituto Nacional de Ciencias Medicas y Nutricion in Mexico City, Mexico.
All SLE subjects were aged ≥18 years and fulfilled the American College of Rheumatology
revised criteria for SLE.8 All subjects in the acutely ill SLE group were recruited
from SLE subjects presenting to the hospital with acute illness. Specific signs and
symptoms of acute illness were not specified as there is potential infinite diversity
of signs and symptoms in patients with SLE with an acute change in clinical status
warranting emergency evaluation in a hospital setting. Inclusion criteria for the
acutely ill SLE group required that (1) subjects have signs and symptoms of acute
illness at the time of presentation to the hospital, (2) blood samples for the microarray
analyses were collected prior to any pharmacological intervention for the acute illness,
(3) there was no known history of chronic infection with hepatitis B or C or HIV,
and (4) the final clinical determination for the acute illness was infection, disease
flare or both. SLE subjects that were acutely ill for reasons other than infection
or disease flare (eg, thrombosis, malignancy, cardiovascular events) were excluded
from the study. Patients with SLE with inactive disease (inactive SLE), defined as
a Safety of Estrogens in Lupus Erythematosus National Assessment Systemic Lupus Erythematosus
Disease Activity Index (SELENA-SLEDAI)9–11 score ≤2 (exclusive of serology) with stable
doses of medications for ≥3 months and a maximum prednisone dose of 10 mg per day,
were recruited randomly from the outpatient setting. The ‘inactive SLE’ subjects were
recruited specifically to have little to no clinical disease activity in contrast
to the ‘acutely ill’ SLE group who had signs and symptoms of acute illness; the ‘inactive
SLE’ group was allowed to have abnormal complement and anti-DNA as it is recognised
that serologies do not necessarily reflect disease activity in all patients.12 Patients
with SLE with inactive disease were excluded if they had prior or current evidence
of co-infection with hepatitis B, hepatitis C or HIV. Healthy control subjects (HC)
were recruited by asking for volunteers from friends of patients with SLE and they
were matched to the acutely ill SLE group for gender, age and ethnicity.
Sample collection, RNA extraction and processing
Peripheral blood, 3 mL, was collected directly into Tempus RNA tubes, and frozen and
stored at −20°C. Frozen samples from Mexico City and Quezon were batched and sent
to the Feinstein Institute for Medical Research for RNA extraction and processing.
RNA was extracted with the Tempus Spin RNA Isolation Kit (Ambion) according to the
manufacturer's instructions and assessed for integrity and quantity using the Bioanalyzer
(Agilent) and NanoDrop (NanoDrop Technologies). Total RNA, 50–200 ngs, was processed
using the TotalPrep RNA Labeling Kit (Ambion) that has been optimised for use with
Illumina's whole-genome expression platform. The RNA amplification process uses a
streamlined protocol developed in the laboratory of James Eberwine.13 The procedure
consists of reverse transcription with an oligo(dT) primer bearing a T7 promoter using
a reverse transcriptase (ArrayScript), that catalyses the synthesis of virtually full-length
cDNA. The cDNA then undergoes second strand synthesis and a clean-up step and is used
as a template for in vitro transcription (IVT) (MEGAscript) with T7 RNA polymerase.
Biotinylated-UTP is used in the IVT step to generate hundreds to thousands of biotinylated
antisense RNA copies of each mRNA in a sample. The cRNA is subjected to a clean-up
step, quantitated, labelled, hybridised to an Illumina microarray chip, stained and
scanned.
Clinical assessments
Acutely ill SLE subjects were assessed with the SELENA-SLEDAI9–11 at the time of the
first blood draw, prior to therapeutic intervention. Clinical information including
physical examinations, laboratory results, imaging studies and medications, was collected
throughout the hospitalisation and used to determine the final outcomes of lupus flare
and/or infection. Choice of laboratory investigations and imaging was dictated by
the clinical presentation and the expertise of the treating physicians; specific testing,
other than obtaining blood samples for the microarray analysis, was not specified
for this study. For inactive SLE subjects and HCs, whole blood was collected as above;
disease activity and medications were recorded for the inactive SLE subjects.
End points
Study end points for the acutely ill SLE subjects included:
Infection: This was determined by positive culture results, antiviral antibody titres
or PCR data as indicated. All patients with positive results for infection were included
in this group even if they were also assessed as having a disease flare since there
is no objective ‘gold standard’ biomarker for disease flare.
Lupus flare: There is no gold standard for determination of flare; acutely ill SLE
subjects without evidence of infection or other reason for acute illness were grouped
in the flare group. Investigators also indicated their assessment of flare based on
clinical expertise and a definition of flare similar to that proposed by the Lupus
Foundation of America: ‘A flare is a measurable increase in disease activity in one
or more organ systems involving new or worse clinical signs and symptoms and/or laboratory
measurements. It must be considered clinically significant by the assessor and usually
there would be at least consideration of a change or an increase in treatment’.14
Statistical considerations
Clinical data
Comparisons between groups for clinical characteristics were determined using non-parametrical
Mann-Whitney, Kruskal-Wallis and χ2 analyses as indicated.
Microarray data
Raw data from the Illumina chips were exported from the software Genome Studio and
analysed using the R programming language and a variety of R/Bioconductor packages.
Background correction, variance stabilisation transformation and quantile normalisation
were performed through the Bioconductor package ‘lumi’.15 A threshold p value of 0.01
was used for probe detection, and only probes that were expressed in at least one
sample were retained. This resulted in 29619 probes. Probe annotation was performed
via ‘lumiHumanAll.db’ (http://bioconductor.org/biocLite.R) and for parts of the analysis
only probes that had a known gene symbol were used (20887 probes, corresponding to
15167 unique gene symbols). Differential expression analysis was done using the R/Bioconductor
package; ‘limma’.16 Gene expression results were subsequently analysed using published
data for interferon α (IFNα)-inducible genes from Baechler et al
17 and first generation and second generation modular transcriptional repertoire analyses
developed by Chaussabel et al.18
19 Welsh's two-sample t-test was used to analyse friend leukaemia virus insertion
site 1 (Fli-1) expression in lupus nephritis (LN).
Receiver operating characteristic (ROC) curves and the associated area under the curves
(AUCs) are used to evaluate the ability of a diagnostic test to distinguish between
clinical states; we used them to assess the predictive value(s) of genes identified
in the differential expression analysis to distinguish disease flare from infection.
A logistic regression analysis was used to determine the predictive value of a composite
measure of differentiating genes to distinguish infection from disease flare.
Results
Clinical data
Twenty-seven acutely ill SLE subjects were recruited. Determinations of outcomes for
these acutely ill SLE subjects were; 16 with infection and 11 with disease flare.
Infections included Mycobacterium tuberculosis (n=4), Mycobacterium bovis (n=1), Candida
albicans urinary tract infection (n=1), H1N1 (n=1), influenza (n=1), Escherichia coli
urinary tract infection (n=3), cellulitis (n=1), viral meningitis (n=1), Streptococcus
pneumoniae (n=1), Mycoplasma pneumoniae (n=1) and E coli sepsis with pulmonary infiltrates
(n=1). Sixteen inactive SLE subjects and 20 HCs, matched for gender, age and race
with the acutely ill SLE group, were enrolled. The HC group was all female with a
mean age of 32.15±8.92 and racial backgrounds similar to the acutely ill SLE cohort
(data not shown).
Four SLE subjects with documented infection were also categorised as having a disease
flare by the investigator; SLEDAI scores for these four subjects were 24 (vasculitis,
arthritis, rash, oral ulcers, pleuritis, low platelets and white blood cells, low
complement, high DNA), 20 (haematuria, pyuria, proteinuria, renal casts, low complement,
high DNA), 11 (arthritis, pleuritis, low platelets, low complement, high DNA) and
5 (fever, low complement, high DNA). Differential gene expression analyses showed
no differences between this group of four with evidence of both infection and clinical
flare and the SLE infection group. In contrast, there were significant differences
in gene expression between the SLE flare group and the group with both infection and
flare except for two genes; IFFI44L and myosin light chain 5 (MYL5) (see below; online
supplementary figure S1). Based on these data, and because disease flare manifestations
are subjective and may be difficult to determine in the context of infection, these
four subjects were included in the infection group for further analyses.
10.1136/lupus-2016-000159.supp1
Supplementary figures
Comparison of the SLE flare and infection groups demonstrated few clinical differences
except for lower C3 levels and higher SLEDAI scores in SLE flare and a higher frequency
of fever at presentation in SLE infection (table 1). There was a non-significant trend
towards a higher frequency of disease modifying drug use in the infection group (68.8%
vs 36.4%; p=0.096). As expected, the inactive SLE subjects had significantly lower
current prednisone doses and SLEDAI scores and had normal complement levels compared
with the acutely ill SLE group. Interestingly, anti-dsDNA antibody titres did not
distinguish between the inactive disease and acutely ill groups; high serum titres
were found in 88.9%, 85.7% and 62.5% of the SLE flare, SLE infection and inactive
SLE groups, respectively. The frequency of SELENA-SLEDAI descriptors in the acutely
ill SLE group stratified by outcome, disease flare or infection, is given in table
2. The SLE flare group had significantly fewer subjects with fever and more subjects
with renal disease compared with SLE infection. On day 1, 63.6% of the flare group
received treatment with increased doses of corticosteroids, 9.1% were treated with
antibiotics and 36% received increased immunosuppressive therapy. Comparatively, 43.8%
of the infection group received initial treatment with increased doses of corticosteroids,
75% received antibiotic therapy and none received increased immunosuppressive therapy.
Four patients with infection were not treated with antibiotics on day 1.
Table 1
Subject clinical characteristics
SLE flare* n=11
Infection*n=16
pFlare versusinfection
Inactive SLEn=16
pFlare/inf versus inactive
Age
34.4±13.1
34.04±12.84
0.942
38.5±12.8
Gender: female
100%
100%
100%
Ethnicity
Latino/Hispanic
6 (54.5%)
12 (75%)
0.179
37.5%
0.035
Asian
4 (36.4%)
2 (12.5%)
12.5%
African-American
0%
2 (12.5%)
18.8%
Caucasian
1 (9.1%)
0%
31.2%
Disease duration (years)
6.18±5.23
8.44±9.25
0.472
11.44±8.8
0.137
Fever at presentation
6 (54.5%)
15 (93.8%)
0.016
NA
History CNS disease
1 (9.1%)
1 (6.3%)
0.782
History renal disease
7 (63.6%)
9 (56.3%)
0.701
Comorbid states
NA
Diabetes
0%
0%
1.0
HTN
2 (18.2%)
5 (31.3%)
0.446
Smoking
0
3 (18.8%)
0.199
Medications
Current prednisone (mg/day)
18.18±20.13
19.06±18.20
0.909
1.64±2.98
0.001
Current DMARD†
4 (36.4%)
11 (68.8%)
0.096
75%
0.202
WBC (×109/L′) day 1
5.19±3.31
6.23±4.16
0.499
5.58±3.06
0.843
% Neutrophils day 1
78.36±12.44
74.48±23.58
0.622
% Lymphocytes day 1
16.31±9.82
15.98±13.09
0.943
22.78±8.33
0.054
Creatine day 1
0.91±0.58
0.92±.41
0.987
NA
low C3‡
7/10 (70%)
4/15 (26.7%)
0.032
1/16 (6.3%)
0.001
low C4‡
5/7 (71.4%)
4/13 (30.8%
0.081
0%
0.002
High titre anti-dsDNA‡
8/9 (88.9%)
12/14 (85.7%)
0.825
10/16 (62.5%)
0.075
SLEDAI (mean score) §
13±6.02
6.31±6.94
0.016
2.13±1.82
0.001
SLEDAI ≤6
18.2%
75%
0.006
NA
SLEDAI ≥12
72.7%
12.5%
0.001
NA
*SLE flare/infection: A final diagnosis of infection was determined by positive culture
results, antiviral antibody titres and/or PCR data. Others were grouped as flare.
†Current use of azathioprine, methotrexate, cyclophosphamide or mycophenolate mofetil.
‡High or low determination of C3, C4 and anti-dsDNA antibody titres was based on normal
ranges provided by local labs. Serological testing was not done on all subjects; results
are given for the subjects with available data.
§SLEDAI scores were determined at presentation on day 1and include points for complement
and anti-dsDNA antibody titres.
CNS, central nervous system; DMARD, disease modifying anti-rheumatic drug; HTN, hypertension;
SLE, systemic lupus erythematosus; SLEDAI, Systemic Lupus Erythematosus Disease Activity
Index; WBC, white blood cell.
Table 2
Frequency of Safety of Estrogens in Lupus Erythematosus National Assessment Systemic
Lupus Erythematosus Disease Activity Index (SELENA-SLEDAI) descriptors in the acutely
ill SLE group stratified by outcome, disease flare or infection
SELENA-SLEDAI descriptor*
Disease flare(n=11)
Infection(n=16)
p Value
Vasculitis
9% (1)
6% (1)
0.782
Arthritis
9% (1)
13% (2)
0.782
Urinary casts
18% (2)
6% (1)
0.332
Haematuria
55% (6)
19% (3)
0.053
Proteinuria
55% (6)
6% (1)
0.005
Pyuria
36% (4)
13% (2)
0.143
Rash
36% (4)
19% (3)
0.305
Alopecia
9% (1)
0
0.219
Mucosal ulcers
18% (2)
6% (1)
0.332
Pleuritis
18% (2)
13% (2)
0.683
Pericarditis
0
6% (1)
0.52
Fever
55% (6)
94% (15)
0.016
Platelets <100 000
9% (1)
19% (3)
0.488
WBC<3000
18% (2)
13% (2)
0.683
Mean SLEDAI±SD (range)
13±6 (2–21)
6.3±6.9 (0–24)
0.016
Seizure, psychosis, organic brain syndrome, visual disturbance, cranial neuropathy,
headache, cerebrovascular accidents and myositis did not occur in this cohort and
are not represented in this table. Serologies are reported in table 1.
SLE, systemic lupus erythematosus; WBC, white blood cell.
Microarray data
Differential expression analysis
Differential expression analysis with Benjamin-Hochberg multiple testing correction
yielded eight genes that differentiated the SLE flare and SLE infection groups (table
3). Seven of these are upregulated in SLE flare and two of these, IFIT1 and IFI44L,
are IFN-inducible. Fli-1 has been implicated in LN due to its effects on renal expression
of chemokines and recruitment of inflammatory cells;20 seven of the SLE subjects (six
in the SLE flare group and one in the SLE infection group) had new or recurrent LN
and these subjects all demonstrated increased expression of Fli-1 compared with SLE
subjects without active LN (p=0.022, see online supplementary figure S2). MYL5 is
the only differentiating gene significantly upregulated in SLE infection. ROC curves
were generated for each gene to assess individual predictive values for distinguishing
flare from infection (figure 1). Each ROC curve has a value for AUC that summarises
the overall accuracy of each gene as a diagnostic parameter. While the AUC values
for the differentiating genes are all in the moderate to high accuracy range, they
are highest for chromosome X open reading frame 21 (CXorf21) (0.93), FLI-1 (0.91),
IFIT1 (0.0.88) and MYL5 (0.86), indicating excellent predictive values of these genes
for differentiating disease flare from infection. Logistic regression using these
four genes provides perfect prediction of membership in the SLE flare or SLE infection
group.
Table 3
Genes distinguishing SLE flare from SLE infection; results of differential expression
analysis following Benjami-Hochberg multiple testing comparison
Gene symbol
Gene name
Log fold change(FC)
p Value
Adj p*
IFIT1
IFN-induced protein with tetricopeptide repeats 1
0.75
1.62E-05
0.04
OTOF
Otoferlin
0.69
1.52E-05
0.04
Fli-1
Friend leukaemia virus integration 1
0.40
7.96E-06
0.04
PRKAG2
Protein kinase, AMP-activated, gamma 2 non-catalytic subunit
0.30
1.54E-05
0.04
CNOT8
CCR4-NOT transcription complex, subunit 8
0.28
1.5E-05
0.04
MYL5
Myosin, light chain 5, regulatory
−0.21
1.19E-05
0.04
CXorf21
Chromosome X, open reading frame 21
0.18
1.1E-06
0.033
IFI44L
Interferon-induced protein-44 like
1.11
1.52E-05
0.04
Note that 7/8 genes are upregulated in SLE flare; MYL5 is the only gene significantly
expressed in SLE infection compared with SLE flare.
SLE, systemic lupus erythematosus.
Figure 1
Receiver operating characteristic (ROC) curves and associated area under the curve
(AUC). ROC curves were generated to assess the ability of the eight genes identified
by differential expression analysis that differentiate systemic lupus erythematosus
(SLE) flare from infection. The ROC curves plot sensitivity (true positive rate) on
the y axis against 1−specificity (false positive rate) on the x axis; the upper left
hand corner of the graph corresponds to perfect prediction, that is, where sensitivity=1
and specificity=1. The AUC summarises the overall accuracy of each gene to predict
the outcome. Two of the genes, CXorf21 and FLi-1, have AUCs in the highly accurate
range (>0.9) and the rest fall in the moderate range (>0.7 to 0.9).
If significant genes (unadjusted p value of 0.05 and a fold change of >1.5) are considered
without the stringent multiple testing corrections, a larger list of 84 distinguishing
genes was identified (see online supplementary table S1); 60 with increased expression
in flare and 24 with increased expression in infection. Of the 60 genes upregulated
in SLE flare, 36 (60%) are IFN-inducible genes listed by Baechler et al.
20 In contrast, none of the 24 genes preferentially upregulated in SLE infection are
IFN-inducible and three (CD177, CD64, SIGLEC14) are associated with a neutrophil signal
for bacterial infection.21–23
10.1136/lupus-2016-000159.supp2
Supplementary table
Modular analyses
As reported by Chaussabel et al, and others, modular analysis of gene expression can
be useful in identifying characteristic changes among disease groups.18
19
24 In order to explore whether modules can be useful in distinguishing between infection
and SLE flare, we chose to compare the modular patterns in these two patient groups
with baseline patterns observed in inactive SLE. As shown in figure 2, there was some
evidence of significant upregulation of the IFN-inducible module (M 3.1) in the SLE
flare group compared with inactive SLE but not in SLE infection compared with inactive
SLE. Not surprisingly, both SLE flare and SLE infection were distinguished from inactive
SLE by increased gene expression in the myeloid (M2.6), inflammation 1 (M3.2) and
inflammation 2 (M3.3) modules (p=0.0005). Overall there was not a striking difference
in the pattern of modular changes between SLE flare or SLE infection compared with
inactive SLE. Direct comparison demonstrated increased gene expression in SLE flare
in two modules compared with SLE infection; the IFN-inducible (M3.1, p=.005) and plasma
cell modules (M1.1, p=0.005) (data not shown). However, the lack of differences in
plasma cell module gene expression in SLE flare or SLE infection compared with inactive
SLE (figure 2), challenges the significance of the findings in the direct comparison
between SLE flare and SLE infection. It is, however, possible that all SLE has a signature
and that some aspects of that signature are diminished during infection.
Figure 2
First-generation modular analysis of acutely ill systemic lupus erythematosus (SLE)
(flare or infection) versus inactive SLE. The modules are numbered; each contains
22–325 gene probes and is described in terms of the known functions of the transcripts
as reported by Chaussabel et al.18 Under each module group, the bar on the left represents
gene expression in SLE flare compared with quiescent SLE and the bar on the right
represents gene expression in SLE infection compared with inactive SLE. A indicated
by tick marks on the right, bars above the horizontal line indicate fractional increased
expression and bars below represent decreased expression of transcript within each
module. Bar colour codes for significance: black indicates p=0.0005, dark grey indicates
p=0.005, light grey indicates p=0.05 and white indicates a non-significant p value.
Compared with inactive SLE, both SLE flare and infection upregulate genes in modules
M1.2, 2.6, 3.2 and downregulate genes in modules 1.3, 1.7, 1.8, 2.1, 2.4, 2.8 and
2.9. The interferon module (M 3.1) is more significantly increased in SLE flare compared
with inactive SLE than SLE infection.
Analysis of IFN-related modules
To further explore whether IFN-inducible gene expression can differentiate disease
flare from infection, the microarray raw data were applied to a recently published
‘second generation’ modular transcriptional repertoire that identified three separate
IFN modules with distinct activation thresholds. These modules exhibit an ordered
appearance such that expression in module 1.2 (M1.2) precedes expression in M3.4,
which in turn precedes expression in M 5.12.19 Using our data set, an expression score
was calculated for each patient for each of these second-generation IFN modules. The
expression score represents the per cent difference between upregulated and downregulated
probes, in this case compared with the average of all healthy controls for that module.
As expected, compared with normal controls, 100% of SLE flare demonstrated significantly
increased expression in all three of these IFN modules and none of the IFN-related
genes were downregulated in this group. Increased gene expression in modules M1.2
and M3.4 was present in 81% of SLE infection and 75% of inactive SLE. Increased gene
expression in M5.12 was present in 88% of SLE infection compared with 25% of inactive
SLE. These data support the previously reported observation of a coordinated gradient
of IFN gene expression that associates with disease activity.19 Using a groupwise
fractional analysis of genes expressed in each of the modules, there was some evidence
that overall the probes in each of these IFN modules were more highly expressed in
the SLE flare group compared with the SLE infection group. Thus, 89% of all probes
in M1.2, 38% of all probes in M3.4 and 32% of all probes in M5.12 are more highly
expressed (p<0.05 uncorrected, data not shown) in the SLE flare group than the SLE
infection group. However, these differences are modest in comparison to the overall
increase in these modules compared with healthy controls, and they are not a robust
measure for distinguishing between the flare and infection groups.
IFN-inducible genes and the IFN score
Finally, as an alternative method of analysis, we chose to examine one of the early
IFN ‘score’ measures originally identified by Baechler et al;17 67 out of the 73 IFN-inducible
genes used by this group were contained in our data set. A row-scaled heat map with
samples grouped by clinical status demonstrates a gradient of IFN-inducible gene expression
ranging from virtually none in the HC group to high in the SLE flare group (see online
supplementary figure S3). An ‘IFN score’ was computed for each subject; expression
values for each IFN-inducible gene were transformed to be in a range from 0 to 1,
thereby allowing each gene to contribute equally to the score, irrespective of whether
it generally has high or low expression. An average of all 67 IFN-inducible genes
was then calculated for each subject and plotted according to clinical status (figure
3). As expected, the heat map and plot of individual IFN scores demonstrate that SLE
flare had the highest IFN scores and IFN-inducible gene expression, while HC had the
lowest. Additionally, mean IFN scores (p=0.006) and mean SLEDAI scores (p=0.014) were
significantly higher in SLE flare compared with SLE infection by a Welsh two-sample
t-test analysis. However, IFN scores in the SLE infection and inactive SLE groups
were variable and these two groups are not well differentiated by mean IFN scores
(p=0.08). Thus, as shown in figure 3, the simple presence of a high IFN score in an
individual is not a good predictor of disease flare or a good differentiator between
disease flare, infection or inactive SLE.
Figure 3
Individual ‘IFN scores’ modified from Baechler et al
16 grouped by clinical status (healthy control, inactive systemic lupus erythematosus
(SLE), SLE infection, SLE flare). Red and blue lines indicate the mean of the healthy
controls±1 SD.
Impact of organism type on IFN-regulated gene expression and disease activity
Impact of organism type on IFN-regulated gene expression and disease activity. Recognising
that some viral, fungal and mycobacterial infections have been shown to induce upregulation
of IFN-related genes,25–27 we examined the molecular gene expression associated with
the subjects that had mycobacterial and fungal infections. The IFN scores in this
small subset (n=6) were extremely variable (data not shown) indicating no increased
expression of IFN-regulated genes. Additionally, there were no significant correlations
between SLEDAI scores and infection type grouped as mycobacterium or viral. Of interest,
the one subject with a candida infection also had the highest SLEDAI score of 24.
Discussion
We have carried out group differential expression and modular transcriptional analyses
of microarray data from whole blood samples of acutely ill patients with SLE prior
to any pharmacological intervention. The data suggest that expression profiling can
provide clinically useful information in the evaluation of an acutely ill patient
with lupus whose clinical symptoms may be attributable to either infection or disease
flare.
Our approach was provoked in part by preclinical studies in mice which have shown
that peripheral blood gene expression profiles distinguish between sterile and infectious
sources of inflammation with 94% accuracy.28 These data were extended by the detection
of a ‘sepsis signature’ in the peripheral blood leucocytes in paediatric patients
with pneumonia and adult trauma patients with early sepsis29
30 and subsequently multiple studies have demonstrated that genomic profiling of circulating
cells can identify distinctive transcriptional signatures that distinguish sepsis
from other causes of systemic inflammation (reviewed in31). In SLE, peripheral blood
gene expression profiling has identified the ‘IFN signature’, an overexpression of
IFNα-inducible genes, a granulopoiesis signature in paediatric SLE and a plasma cell
signature as biomarkers for lupus that may also correlate with disease activity or
predict clinical flare.17
32–35
In our data set, differential gene expression analysis using a stringent Benjamini-Hochberg
correction identified seven genes preferentially expressed in SLE flare and one in
SLE infection (table 2). In particular, upregulation of CXorf21, FLi-1 or IFIT1 and
downregulation of MYL5 are candidate predictors of flare in acutely ill patients.
While there is little available information on the function or clinical associations
of the CXorf21 and MYL5 genes, the Fli-1 gene encodes for a Fli-1 transcription factor
that is a member of the Ets family and has been implicated in SLE pathogenesis. Overexpression
of Fli-1 in transgenically altered non-autoimmune mice and Fli-1 knockout mice both
result in a lupus-like phenotype including renal disease.36–38 Reducing Fli-1 expression
improves disease and survival in the murine models39
40 and in human SLE a specific microsatellite length of the Fli-1 promotor has been
reported to be significantly more prevalent in patients with SLE without nephritis.41
In our cohort, Fli-1 was preferentially expressed in subjects with active renal disease.
IFIT1 is one of the IFN-inducible genes whose role in SLE pathogenesis has now been
firmly established.42 Using logistic regression, the combination of CXorf-21, IFIT1,
FLi-1, MYL5 actually show a perfect correlation with SLE flare versus infection in
our data set, and provide an initial hypothesis for subsequent replication. Beyond
this core set of highly discriminatory transcripts, differential gene expression analysis
with an unadjusted p value of 0.05 yielded larger lists of genes preferentially expressed
in flare (n=60) or infection (n=24) (see online supplementary table S1). Of interest,
and again corroborating previously published data, many of the genes associated with
flare are IFN-inducible (61%) but none of those associated with infection were IFN-inducible.
Some of the upregulated gene transcripts in the infection group were neutrophil signals
for bacterial infection and SIGLEC genes that transcribe cell surface adhesion molecules.
Clinically, the SLE flare group was differentiated from those with infection by increased
frequency of low C3 levels, higher SLEDAI scores and absence of fever. It is therefore
possible that a composite measure comprised of an increased SLEDAI score, low C3 and
expression of selected gene transcripts may improve the ability to rapidly distinguish
SLE flare from infection in a clinical setting but this will rely on a larger validation
study. Discordance between serum levels of C reactive protein (CRP) and erythrocyte
sedimentation rate in disease flare is well recognised and elevated CRP has been reported
as a surrogate marker for infection in patients with SLE in several studies (reviewed
in43). However, elevated CRP levels have been reported in patients with SLE with active
arthritis, serositis and African heritage.44–46 CRP levels were not measured in our
study; it therefore remains possible that CRP remains an important discriminator between
infection and disease flare and perhaps should be considered as part of a composite
measure for infection in combination with gene expression, SLEDAI, C3 and fever.
The modular analyses provide additional support for the known importance of α IFN
in disease pathogenesis. The first-generation modular analyses demonstrated modestly
increased IFN-inducible gene expression in the SLE flare group compared with SLE infection
and inactive SLE. This is further illustrated by three recently defined IFN modules
(M1.2, M3.4, M5.12) in a ‘second generation’ modular transcriptional repertoire that
have sequential and distinct activation thresholds. It has been previously reported
that gene expression in M1.2 is stable over time and unrelated to disease activity
whereas expression in modules M3.4 and M5.12 is more variable and is related to disease
activity.19 Accordingly, all of the SLE groups in our cohort demonstrated significant
expression in M1.2. In contrast, increased gene expression in M5.12 was seen in only
25% of inactive SLE compared with 100% of SLE flare. These results corroborate the
findings of Chiche et al, suggesting that intensity of IFN-inducible gene expression
(demonstrated by gene expression in M5.12) correlates with disease activity whereas
gene expression in M1.2 alone is a marker for SLE. However, 88% of the SLE infection
group also had increased gene expression in M5.12. Additionally, analysis of ‘IFN
scores’ derived from 67 IFN-inducible genes identified by Baechler et al, suggests
that the presence of increased IFN-inducible gene expression is not a reliable predictor
of flare that is exclusive of infection. Of note, the differential expression analysis
identified eight genes that had excellent predictive value for differentiating SLE
flare from SLE infection and only two of those are IFN-inducible.
Four subjects with infection also had high SLEDAI scores suggesting that concomitant
clinical evidence of disease flare likely alters expression of IFN-inducible genes.
Additionally, host responses to some infections, including fungal and mycobacterial
infections, have been characterised by type I IFN-mediated signalling.25–27 Moreover,
gene expression in the second-generation IFN modules was not exclusively associated
with IFNα; IFNβ was shown to contribute to M1.2 gene expression and IFNγ is a contributor
to expression in M3.4 and M5.12.19 In our analyses, those subjects with fungal and
mycobacterial infections did not have the highest IFN scores, however, given the small
sample size, microbial influences on IFN gene expression cannot be ruled out. It is
possible that an SLE infection cohort with a larger viral infection group may in fact
demonstrate different results.
Although a granulopoiesis signature has been associated with paediatric SLE,18
32 upregulation of the neutrophil module in our cohort was only associated with SLE
infection and not disease flare. Whether this reflects the effects of immunosuppression
on the granulopoiesis signature (the paediatric findings were in newly diagnosed,
untreated subjects) or an age- related difference is not clear.
Our pilot study has several limitations aside from the relatively small sample size.
Microarray experiments portray a moment in time and do not address possible changes
in transcription. Additionally, whole blood transcriptomes can be influenced by alterations
in numbers of peripheral blood cells and cellular subsets due to tissue migration
and haematopoiesis. Cellular subsets may also be influenced by medication, disease
activity and race or ethnicity. Therefore, while these results suggest that whole
blood gene expression profiling may prove to be useful in differentiating disease
flare from infection, future studies should aim at replicating these results in a
larger cohort and the data reported here may be used to power this study. The long-term
goal will be to validate use of individual genes or composite measures as a bedside
diagnostic test to provide more directed medical care resulting in lower morbidity
and mortality.