Microarray analysis of peripheral blood lymphocytes from ALS patients and the SAFE detection of the KEGG ALS pathway

  • Jean-Luc C Mougeot1, 2, 3Email author,

    Affiliated with

    • Zhen Li4,

      Affiliated with

      • Andrea E Price1, 2, 3,

        Affiliated with

        • Fred A Wright4 and

          Affiliated with

          • Benjamin R Brooks1, 2, 3

            Affiliated with

            BMC Medical Genomics20114:74

            DOI: 10.1186/1755-8794-4-74

            Received: 24 May 2011

            Accepted: 25 October 2011

            Published: 25 October 2011

            Abstract

            Background

            Sporadic amyotrophic lateral sclerosis (sALS) is a motor neuron disease with poorly understood etiology. Results of gene expression profiling studies of whole blood from ALS patients have not been validated and are difficult to relate to ALS pathogenesis because gene expression profiles depend on the relative abundance of the different cell types present in whole blood. We conducted microarray analyses using Agilent Human Whole Genome 4 × 44k Arrays on a more homogeneous cell population, namely purified peripheral blood lymphocytes (PBLs), from ALS patients and healthy controls to identify molecular signatures possibly relevant to ALS pathogenesis.

            Methods

            Differentially expressed genes were determined by LIMMA (Linear Models for MicroArray) and SAM (Significance Analysis of Microarrays) analyses. The SAFE (Significance Analysis of Function and Expression) procedure was used to identify molecular pathway perturbations. Proteasome inhibition assays were conducted on cultured peripheral blood mononuclear cells (PBMCs) from ALS patients to confirm alteration of the Ubiquitin/Proteasome System (UPS).

            Results

            For the first time, using SAFE in a global gene ontology analysis (gene set size 5-100), we show significant perturbation of the KEGG (Kyoto Encyclopedia of Genes and Genomes) ALS pathway of motor neuron degeneration in PBLs from ALS patients. This was the only KEGG disease pathway significantly upregulated among 25, and contributing genes, including SOD1, represented 54% of the encoded proteins or protein complexes of the KEGG ALS pathway. Further SAFE analysis, including gene set sizes >100, showed that only neurodegenerative diseases (4 out of 34 disease pathways) including ALS were significantly upregulated. Changes in UBR2 expression correlated inversely with time since onset of disease and directly with ALSFRS-R, implying that UBR2 was increased early in the course of ALS. Cultured PBMCs from ALS patients accumulated more ubiquitinated proteins than PBMCs from healthy controls in a serum-dependent manner confirming changes in this pathway.

            Conclusions

            Our study indicates that PBLs from sALS patients are strong responders to systemic signals or local signals acquired by cell trafficking, representing changes in gene expression similar to those present in brain and spinal cord of sALS patients. PBLs may provide a useful means to study ALS pathogenesis.

            Background

            Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease causing muscle weakness and wasting resulting from the loss of motor neurons in brain and spinal cord characterized by ubiquitinated inclusions in brain and spinal cord of post mortem ALS patients [1]. Several genome-wide association studies (GWAS) have shown evidence of genetic heterogeneity underlying disease susceptibility [2]. Single nucleotide polymorphisms were found in the ITPR2 (inositol 1,4,5-triphosphate receptor, type 2) [3], FGGY (FGGY carbohydrate kinase domain containing) [4], DPP6 (dipeptidyl-peptidase 6) [5], with variable strength of association with ALS and limited replication. None of these genes has been proven relevant to the pathogenesis of ALS. More recently, mutations were found in the UNC13A (unc-13 homolog A) gene [6] and in the 9p21 chomosomal locus [7]. To overcome challenges in the interpretation of results from GWAS and data from the world of "omics" in general, ALS researchers are actively engaged in integrative global bioinfomatics and the creation of ALS models for development of new ALS therapies (Euro-MOTOR project) [8]. Despite genetic heterogeneity underlying disease susceptibility, the clinical manifestations of the ALS phenotype are relatively homogeneous; suggesting that at the cellular and molecular levels there may be a convergence of a limited number of pathways that could lead to the ALS phenotype.

            Gene expression profiling studies using microarrays and/or real time quantitative RT-PCR have been conducted on various tissues from rodent models for ALS such as muscle or brain tissues, lumbar spinal anterior horn tissues, spinal cord motor neurons isolated by laser capture microdissection (LCM), whole blood or peripheral blood mononuclear cells (PBMCs). Similar studies were performed on spinal cord tissues or LCM-isolated motor neurons obtained post mortem from ALS patients. Roughly, ~1000 unique genes were found differentially expressed but only ~5% differentially expressed in the same direction in more than one study [9], indicating little reproducibility. Poor reproducibility may be due to the use of different gene expression profiling methods or platforms, tissue of different origin, methods used for biological sample preparation, time of tissue collection at pre-symptomatic or symptomatic stage, and use of a particular batch of rodents or human cohort. Rather, one may find greater commonalities at the pathway alteration level with regard to apoptosis regulation, calcium regulation, oxidative stress and mitochondrial function, ER-stress and unfolded protein response (UPR), UPS and autophagy, RNA processing, DNA metabolism, axonal transport, integrity of the neuromuscular junction, muscle atrophy, and direct/indirect interactions with astrocytes, microglia and T-cells. Within these biological processes, genes of importance are those with mutations or polymorphisms shown to confer susceptibility to or cause ALS; or genes playing a critical role in the pathways that involve susceptibility genes.

            A number of studies have sought blood biomarkers that may be useful to detect early signs of ALS, assess disease progression, monitor treatment effects, or track down the cause(s) of the disease, in a minimally-invasive fashion in ALS patients. Using qRT-PCR, Lin et al. (2009) have shown subtle transcriptional down-regulation of mitochondrial electron-transfer chain genes in whole blood from ALS patients [10]. Saris et al. (2009) have identified co-expressed gene modules (clusters) in total blood from sporadic ALS (sALS) patients [11]. These findings resulted from subtle differential expression of 2300 probe-encoded genes and were related to biological/disease categories such as post-translational modification, infection mechanism, inflammatory disease, neurological disorder, and skeletal and muscular disorder. Gagliardi et al. (2010) showed increased SOD1 mRNA expression in spinal cord, brain stem and lymphocytes of sporadic ALS (sALS) patients [12]. Zhang et al. (2011) identified gene expression profiles of short-term cultured PBMCs from ALS patients, demonstrating the activation of monocytes/macrophages via the LPS/TLR4 neuroinflammatory pathway [13]. Lincecum et al. (2010) demonstrated the activation in ALS pathogenesis of a co-stimulatory pathway bridging the activation of T-cell responses and the amplification of the innate immune response, based on gene expression profiles obtained from whole blood of the G93A SOD1 mouse model and ALS patients [14]. Circulating white blood cells might acquire certain properties from long distance signals mediated by small metabolites or macromolecules circulating in peripheral blood. They might also acquire novel properties from trafficking at sites of neurodegeneration associated with rupture of the blood brain barrier or blood-spinal cord barrier in early and late ALS to a variable degree. Further investigation in this area of ALS research is critically needed [15].

            In the current work, we analyzed RNA extracted from PBLs of ALS patients and control subjects, thereby reducing some of the complexity of mixed expression patterns generated by RNA from reticulocytes, granulocytes, monocytes, thrombocytes and plasma, normally present in whole blood. Indeed, gene expression profiles of blood-derived samples are strongly dependent on the predominant constituent cell type(s) [16, 17]. Analyses of mRNA expression data by LIMMA [18], SAM [19] and SAFE [20], revealed alterations of the ubiquitin/proteasome system (UPS). Using proteasome inhibition assays, parallel changes of UPS activity at the protein level were determined in subcultured PBMCs (mainly composed of lymphocytes) from ALS patients, by Western blot analysis.

            Methods

            Isolation of peripheral blood lymphocytes from ALS patients and controls

            During year 2007 until March 2008, blood samples to be used for microarray analysis were collected at Carolinas Neuromuscular/ALS-MDA Center with approval by the IRB at Carolinas Medical Center. Informed consent was obtained from all participants to this study. ALS diagnosis was determined according to the El Escorial Criteria for "definite" ALS after exclusion of other conditions [21]. Disease onset was defined as time of initial weakness, dysarthria or dysphagia. Blood samples (~18 mL) were drawn from sporadic definite ALS patients and healthy control (HC) subjects by venipuncture into tubes adequate for either serum or lymphocyte isolation. The healthy controls (HCs) consisted of 9 white females (mean age 51.4 ± 11 (standard deviation) years) and 2 white males (64, 65). The sALS patients consisted of one black male (49), one black female (69), 5 white females (mean age 59 ± 20 years), and 4 white males (mean age 47 ± 9 years). Table 1 presents the clinical characteristics of the enrolled patients and healthy controls subjected to microarray analysis. PBMCs were isolated using Histopaque™-1077 density gradient centrifugation method. Using this procedure, yields were generally 1-2 × 106 PBMCs per mL of blood. Lymphocytes were further enriched to over 90% purity from the PBMC fraction by subsequent PERCOLL gradient centrifugation [22]. Blood samples were processed immediately upon reception in the lab within 30 minutes after blood draw.
            Table 1

            Demographic and clinical data for ALS patients (n = 11) and healthy controls (n = 11) enrolled for Agilent Human Whole Genome 4 × 44k Array analysis

            Clinical Data at Time of Collection

            ALS patients

            Healthy controls

            Mean age ± SD

            53.8 ± 13

            52.2 ± 11

            Female

            6

            9

            Male

            5

            2

            Bulbar onset

            2

            -

            Limb onset

            8

            -

            Generalized

            1

            -

            ALSFRS-R <24

            5

            -

            ALSFRS-R >24

            6

            -

            Onset of weakness ≤1 yr

            4

            -

            Onset of weakness 1-5 yrs

            4

            -

            Onset of weakness >5 yrs

            3

            -

            Mean age of onset ± SD

            47.2 ± 18

            -

            Death <3 yrs post-onset

            3

            -

            Death >5 yrs post-onset

            2

            -

            Mean age of death (n = 5) ± SD

            62.2 ± 12

            -

            Three subjects with an ALSFRS-R >24 died within three years (1.5, 2, and 2.5 years) following ALS onset, and two others with an ALSFRS-R <24 died beyond 5 years (7.5 and 9.5 years). Some ALS patients were treated with riluzole (5 out of 11) and taking dietary supplements (7 out of 11) compared to healthy controls which are not matched in this regard. SD is standard deviation.

            RNA extraction, amplification, and dual mode reference design microarrays

            The common reference design [23] was used for sample assignment in the dual color mode of expression assay on the Agilent Human Whole Genome 4 × 44k Microarrays to analyze ~40000 transcripts. Microarray experiments were performed, in which each of the 22 RNA samples (HC and sALS) was co-hybridized with RNA from the HC reference pool that was constituted with equal amounts of each of the 11 RNA samples from healthy controls. Total RNA stored in TRIzol (Invitrogen) at -80°C, was extracted from the lymphocyte samples at Cogenics, Inc. (Morrisville, NC) by standard procedures. The quantity of each of the total RNA samples and determination of the A260/280 nm ratio was determined by spectrophotometry and the size distribution was assessed using an Agilent Bioanalyzer. Fifty nanograms of total RNA was converted into labelled cRNA with nucleotides coupled to a fluorescent dye (either Cy3 or Cy5) using the Quick Amp Kit (Agilent Technologies, Palo Alto, CA) following the manufacturer's protocol. The A260/280 nm ratio and yield of each of the cRNAs were determined and a quality assessment was done using an Agilent Bioanalyzer. Equal amounts of Cy3 and Cy5-labeled cRNA (825 ng) from two different samples were hybridized to Agilent Human Whole Genome 4 × 44k Microarrays. The hybridized array was washed and scanned and data were extracted from the scanned image using Feature Extraction version 10.2 (Agilent Technologies). The non-normalized and normalized microarray datasets have been deposited in the NCBI Gene Expression Omnibus [24] as series GSE28253.

            Statistical analyses and SAFE data mining

            Raw data .txt files in Agilent format were converted to .MEV files using ExpressConverter™ v2.1 of the TM4 Microarray Suite (TIGR Genomics, Rockville, CA). Background-subtracted raw data were normalized using the MIDAS pipeline (TM4, TIGR Genomics, Rockville, MD) according to Sioson et al. (2006) with the following steps: total intensity normalization, LocFit (LOWESS), standard deviation regularization and low intensity trim [25]. Filtering stringencies requiring that the integrated signal intensities (ISI) for each Cy3 and Cy5 channels were more than two standard deviation(s) of the Cy3 and Cy5 background (ISI = 7000), generated the dataset DS7000 [7199 probes, 5540 unique genes]. DS7000 was subjected to LIMMA [18] and SAM [19] analyses using TMeV v4.5.1 program (TM4, TIGR Genomics, Rockville, MD) to determine differentially expressed genes. The false discovery rate (FDR) was 1.17% (delta = 0.90) for DS7000. SAFE analysis was performed with Bioconductor 2.5 according to Barry, Nobel, and Wright (2005) [20] to identify gene sets demonstrating different expression levels between classes of comparison. Default settings for local (t-test) and global (Wilcoxon) statistics were used. Comparisons were based on gene ontology databases for biological processes, molecular functions, cellular components, and the protein families (Pfam) and KEGG databases.

            Total ubiquitination and proteasome inhibition assays with PBMCs from ALS patients and healthy controls

            Freshly isolated PBMCs (composed of ~80% lymphocytes and ~20% of monocytes per flow cytometry analysis) from ALS patients and healthy controls, were subcultured overnight (O.N.) for 18 hr at 37°C in RPMI (Invitrogen) supplemented with 10% FCS (Invitrogen) and supplemented or not with added-back matched autologous serum that had been prepared in parallel. These PBMCs were treated, or not treated, for 1.5 hrs at 37°C with the reversible proteasome inhibitor MG132 (Sigma). For each patient or healthy control, serum was prepared separately from PBMCs from the same blood draw. Matched serum was supplemented at a concentration of 20% to the O.N. cultures. A total of 750000 cells were seeded per well of a 48-well plate. Cells were treated with 10 μM proteasome inhibitor MG132 or DMSO vehicle at 0.09% for 1.5 hrs. Cells were collected and snap frozen until Western blot analysis. Cells were lysed into RIPA buffer (150 mM NaCl, 1.0% IGEPAL® CA-630, 0.5% sodium deoxycholate, 0.1% SDS, 50 mM Tris, pH 8.0) including protease inhibitor cocktails [complete Mini EDTA-free protease inhibitor cocktail tablets (Roche) and Protease Inhibitor cocktail P8340 (Sigma)]. Total protein was quantified using BioRad Dc protein assay. An aliquot (20 μg total protein) was supplemented with 2x Laemmli's sample buffer and boiled for 5 minutes prior to separation by sodium dodecyl sulfate (SDS)-12.5% polyacrylamide gel electrophoresis for 130 min at 100V. Proteins were transferred to polyvinylidene difluoride membranes (Millipore) and quenched with blocking buffer containing 10% non-fat milk in PBS-0.1 Tween 20 for 1 hr at room temperature. The membranes were incubated overnight at 4°C with primary monoclonal anti-ubiquitin sc-8017 antibody (1/1000 dilution; Santa Cruz) diluted in the blocking buffer. Membranes were subsequently incubated with goat anti-mouse human absorbed HRP-secondary sc-2055 antibody (1/10000 dilution; Santa Cruz) for 30 minutes and assayed using the Super Signal Pico chemiluminescence detection system (Thermo Fisher Scientific). Subsequent reprobing with anti-beta-actin antibody sc-81178 (Santa Cruz) was performed by stripping membranes of bound antibodies in stripping buffer (62.5 mM Tris HCL, 2% SDS, and 100 mM 2-mercaptoethanol [pH 6.7]) at 56°C for 20 minutes. ECL films and a LAS3000 imaging system (Fuji) were used for detection of the chemiluminescence. Silver staining was used to confirm loading homogeneity in the PAGEs post-electrotransfer using SilverSNAP stain (Thermo Fisher Scientific), in addition to reprobing of the membranes for beta-actin.

            Semi-quantitative analysis of the Western blot data

            Raw images were processed in ImageJ program (Dr. Wayne Rasband, wayne@codon.nih.gov, National Institute of Mental Health, Bethesda, Maryland, USA). The accumulated HMW ubiquitinated protein forms were delineated by a rectangular area, for which the background subtracted integrated density could be measured. The integrated density could then be measured for same area below the accumulated forms at a level of the blotting membrane demonstrating consistency of staining throughout the lanes, thereby providing a contrast reference area per lane. Calculation of a signal-to-noise (S/N) ratio for the accumulated forms was then determined independently from the detection of beta-actin that was achieved by stripping and reprobing the Western blot membranes.

            Results

            We studied gene expression profiles of lymphocytes isolated from 11 patients diagnosed with definite sporadic ALS (sALS) and 11 healthy control subjects. Clinical characteristics for this cohort are described in Table 1. Figure 1 summarizes results from microarray data normalization and LIMMA, SAM and SAFE analyses.
            http://static-content.springer.com/image/art%3A10.1186%2F1755-8794-4-74/MediaObjects/12920_2011_271_Fig1_HTML.jpg
            Figure 1

            Normalization, filtering, SAFE, LIMMA and SAM analyses of lymphocyte-derived microarray data from ALS patients and healthy controls. Microarray analyses were performed on purified PBLs isolated from patients affected by sporadic amyotrophic lateral sclerosis (sALS) (n = 11) and healthy control subjects (HCs) (n = 11). The dual color mode in the common reference design was used to interrogate the expression of ~40000 transcripts (~30000 unique genes) using Agilent Human Whole Genome 4 × 44k Microarrays. Raw expression data were normalized and filtered using the MIDAS pipeline in the TM4 microarray suite (TIGR Genomics, Rockville, MD) to generate the dataset DS7000. SAFE was used for testing enrichment of functional gene ontology (GO) categories related to biological processes, molecular functions, cellular components, protein families (Pfam) and the KEGG databases. DS7000 was subjected to LIMMA and SAM analyses using TM4/TMeV v4.5.1 to determine differentially expressed (DE) genes. The online tool Data Overlapping and Area-Proportional Venn Diagram (http://​bioinforx.​com/​free/​bxarrays/​overlap.​php) was used to generate the Venn diagram.

            LIMMA and SAM analyses

            Differentially expressed genes between ALS patients and healthy controls were determined using LIMMA (pLIMMA < 0.001, qLIMMA ≤ 10%) and SAM (qSAM ≤ 1%) in TM4/TMeV v4.5.1 program [25] for the dataset DS7000 (Figure 1). A significant overlap was found by comparing LIMMA and SAM results (Figure 1, Additional File 1). Table 2 presents the 24 most differentially expressed genes (qSAM = 0%, pLIMMA < 0.0005, qLIMMA ≤ 10%). Five genes (C12orf35, DYNLT1, IRS2, SKIV2L2, and TARDBP) were significant at high stringency (qSAM = 0%, pLIMMA < 0.001, qLIMMA < 5%). C12orf35, DYNLT1, SKIV2L2, and TARDBP were upregulated 1.5-1.8 fold change (FC), while IRS2 was downregulated by two fold. DYNLT1 (dynein, light chain, Tctex-type 1) encodes a component of the dynein-dynactin complex composed of dynactin (DCTN1), those mutations have been involved in ALS [26], while TARDBP encodes TAR DNA binding protein 43 (TDP-43), those mutations may cause ALS [27]. TIA-1, a marker of stress granules that colocalizes with TDP-43 inclusions in frontotemporal lobar degeneration (FTLD-U) and ALS [28], is also upregulated (FC = 1.4, qSAM = 0%, pLIMMA < 0.005, qLIMMA~10%; Additional File 1. SKIV2L2 encodes a DEAD-box RNA helicase which is part of the exosome and spliceosome complexes [29, 30] and it is known, for example, that some DEAD-box RNA helicases interact with FUS/TLS to control pre-mRNA splicing [31]. Defects or deregulation in RNA processing are a hallmark in the pathogenesis of motor neuron diseases, since motor neurons may be uniquely sensitive to perturbations in RNA processing pathways [32]. We also note that IL7R (interleukin-7 receptor subunit alpha), polymorphisms of which have been associated with risk in multiple sclerosis [33], is upregulated (FC = 1.7, qSAM = 0%, p < 0.005, Additional File 1). In addition, we identified upregulation (FC = 1.4, qSAM~0.5%, pLIMMA < 0.005, qLIMMA~10%) of RTN4IP1 (reticulon 4 interacting protein alias NOGO-interacting mitochondrial protein), a mitochondrial protein that interacts with reticulon 4, a potent inhibitor of regeneration following spinal cord injury [34]. We also identified upregulation of SOD1 (FC = 1.3, q~1%, pLIMMA<0.05, qLIMMA~10%), confirming the work by Gagliardi et al. (2010) [12].
            Table 2

            Differentially expressed genes in peripheral blood lymphocytes from ALS patients by SAM (q = 0) ranked by independent LIMMA (p < 0.0005)

            αProbe

            βSymbol

            γGene Description

            δp value

            εq value

            χFC

            A23P8185

            DYNLT1

            dynein, light chain, Tctex-type 1

            0.000004

            0.028

            1.5

            A24P154037

            IRS2

            insulin receptor substrate 2

            0.000015

            0.048

            0.5

            A32P234935

            TARDBP

            TAR DNA binding protein

            0.000024

            0.048

            1.6

            A23P98930

            C12orf35

            chromosome 12 open reading frame 35

            0.000031

            0.048

            1.6

            A23P110661

            SKIV2L2

            superkiller viralicidic activity 2-like 2

            0.000033

            0.048

            1.8

            A23P104624

            ENDOD1

            endonuclease domain containing 1

            0.000069

            0.083

            1.5

            A23P317800

            ANAPC4

            anaphase promoting complex subunit 4

            0.000106

            0.100

            1.4

            A23P21673

            KIAA1797

            KIAA1797

            0.000150

            0.100

            1.5

            A23P145437

            PHIP

            pleckstrin homology domain interacting protein

            0.000185

            0.100

            1.4

            A23P15714

            NSF

            N-ethylmaleimide-sensitive factor

            0.000210

            0.100

            1.6

            A23P82588

            C7orf55

            chromosome 7 open reading frame 55

            0.000234

            0.100

            1.6

            A23P120153

            RNF149

            ring finger protein 149

            0.000290

            0.100

            1.7

            A23P70998

            tcag7.903

            full-length cDNA clone CS0DF028YG12

            0.000308

            0.100

            1.5

            A23P145874

            SAMD9L

            sterile alpha motif domain containing 9-like

            0.000310

            0.100

            2.1

            A24P172481

            TRIM22

            tripartite motif-containing 22

            0.000319

            0.100

            1.5

            A23P42664

            SHFM1

            split hand/foot malformation (ectrodactyly) type 1

            0.000328

            0.100

            1.6

            A23P156355

            TMEM161B

            transmembrane protein 161B

            0.000340

            0.100

            1.5

            A23P129925

            SLFN11

            schlafen family member 11

            0.000364

            0.100

            1.5

            A24P209455

            GIMAP4

            GTPase, IMAP family member 4

            0.000383

            0.100

            1.8

            A23P84775

            PLRG1

            pleiotropic regulator 1

            0.000386

            0.100

            1.5

            A23P213255

            SMARCAD1

            SWI/SNF-related, matrix-associated actin-dependent regulator of chromatin, subfamily a, containing 1 DEAD/H box 1

            0.000445

            0.100

            1.6

            A23P19565

            ASCC3

            activating signal cointegrator 1 complex subunit 3

            0.000457

            0.100

            1.4

            A23P61854

            KIAA0372

            tetratricopeptide repeat protein 37

            0.000461

            0.100

            1.4

            A23P91891

            COPB2

            coatomer protein complex, subunit beta 2

            0.000471

            0.100

            1.9

            The list presents the 24 most discriminatory genes distinguishing the definite sALS patients (n = 11) from the healthy control subjects (n = 11). αAgilent Array 4 × 44K probe ID, βgene symbol, γdescription, δLIMMA significance p value, εLIMMA FDR (q value), and χfold change (FC) in expression are indicated. Using the TM4-MIDAS/TMeV pipeline, all genes had a local FDR of q = 0 according to SAM performed on DS7000 [7199 probes, 5540 unique genes]. Five genes had a LIMMA q value ≤0.05: DYNLT1, IRS2, TARDBP, C12orf35, and SKIV2L2.

            SAFE identification of molecular signatures in lymphocytes from ALS patients

            SAFE [20] is a resampling-based procedure that is similar to GSEA (Gene Set Enrichment Analysis) [35], but with more flexible choices of test statistics. SAFE was used to obtain information on unifying biological themes from databases specific for (i) gene ontology (GO) pathways/categories (biological process, cellular component and molecular function), (ii) pathways/categories defined by the KEGG (Kyoto Encyclopedia of Genes and Genomes) and (iii) Pfam (protein families). Such resampling procedures have been shown to provide more accurate control of false positives than simpler enrichment-test methods using only lists of p-values [36].

            Following determination of local (t-test) and global (Wilcoxon test) statistics using SAFE default settings, the significance for each gene set category was determined by bootstrap re-sampling and multiple test correction (for the multiple categories examined) by an FDR procedure with qSAFE < 25% considered significant (similarly to GSEA). This relatively liberal threshold was intended to avoid false negatives, although many of the findings presented here achieve more striking significance. For SAFE gene ontology category analysis, gene sets of 5-100 genes were examined, similar to restrictions used by others (e.g., Barry et al., 2005) [20]. This approach ensures that gene sets were not so small as to call into question a "pathway" interpretation, and not so large as to defy biological interpretation. In addition, the approach helps to manage the multiple testing penalties across numerous categories. To simplify overall interpretation, we only reported "upregulated" categories to highlight pathway activations caused by the disease rather than pathway inhibitions.

            Significant upregulated categories representing gene sets associated with "biological processes", "cellular components", and "KEGG pathways" are shown in Tables 3, 4 and 5. We used same gene set size restriction (5-100 genes) for SAFE analysis of KEGG gene ontology groups including the KEGG Human Disease pathways, of which 25 were annotated in Bioconductor 2.5 for the Agilent platform. We identified that the KEGG ALS pathway was significant (qSAFE = 18%). The KEGG ALS pathway was the only significantly upregulated disease pathway among the 25 disease pathways (Additional File 2). In addition, we considered all 34 KEGG Human Disease pathways annotated in Bioconductor 2.5 for the Agilent platform among 55 total represented in the KEGG database (http://​www.​genome.​jp/​kegg-bin/​get_​htext?​htext=​br08901&​query=​%22Human%20​Diseases%22&​option=​-s) [37] by including gene set sizes >100 in a secondary analysis.
            Table 3

            SAFE gene ontology pathways related to Biological Processes affected in peripheral blood lymphocytes from ALS patients

            αPathway

            βSizes

            δq-value

            εGO Term

            DNA Metabolism

            GO:0006310

            54/71

            0

            DNA recombination

            GO:0006302

            27/50

            0.003

            double-strand break repair

            GO:0000718

            12/21

            0.0414

            nucleotide-excision repair DNA damage removal

            ER and Golgi

            GO:0006895

            6/7

            0.0071

            Golgi to endosome transport

            GO:0006888

            26/43

            0.1127

            ER to Golgi vesicle-mediated transport

            GO:0006904

            10/19

            0.0071

            vesicle docking during exocytosis

            GO:0006892

            11/41

            0.0471

            post-Golgi vesicle-mediated transport

            GO:0007041

            7/6

            0.1885

            lysosomal transport

            Mitochondrial Function

            GO:0022904

            53/98

            0.0558

            mitochondrial ATP synthesis coupled electron transport

               

            respiratory electron transport chain

            GO:0006120

            44/43

            0.0523

            mitochondrial electron transport NADH to ubiquinone

            GO:0006626

            17/25

            0.0972

            protein targeting to mitochondrion

            GO:0006119

            81/8

            0.1

            oxidative phosphorylation

            Neurological Function

            GO:0010001

            8/13

            0.1

            glial cell differentiation

            GO:0042552

            8/30

            0.1138

            myelination

            Oxidation

            GO:0019395

            19/6

            0.2252

            fatty acid oxidation

            RNA Metabolism

            GO:0000387

            25/26

            0.003

            spliceosomal snRNP biogenesis

            GO:0033119

            10/3

            0.0071

            negative regulation of RNA splicing

            GO:0051028

            81/62

            0.0644

            mRNA transport

            PTM

            GO:0070206

            7/7

            0

            protein trimerization

            GO:0018279

            8/47

            0.0071

            protein amino acid N-linked glycosylation via asparagine

            GO:0051262

            10/12

            0.0644

            protein tetramerization

            GO:0006465

            7/9

            0.0835

            signal peptide processing

            GO:0045116

            8/7

            0.0644

            protein neddylation

            GO:0016925

            15/11

            0.0627

            protein sumoylation

            UPS

            GO:0051443

            62/6

            0.0644

            positive regulation of ubiquitin-protein ligase activity

            GO:0051444

            59/4

            0.0644

            negative regulation of ubiquitin-protein ligase activity

            GO:0043161

            79/43

            0.0644

            proteasomal ubiquitin-dependent protein catabolic process

            Viral Infection

            GO:0019047

            11/8

            0.044

            provirus integration

            GO:0019059

            18/12

            0.0852

            initiation of viral infection

            GO:0019058

            41/92

            0.0644

            viral infectious cycle

            αGene ontology (GO) pathway identities for biological processes and βnumber of probes represented on the 4 × 44K human genome array per GO group of directly associated proteins is shown (note: one gene may be represented by different or redundant probes on the array). γFDR (q-value) at significance level p < 0.25 (25%) was determined by bootstrapping following SAFE analysis. δThe table shows partial listing of representative significant εGO categories associated with biological processes (30 among 99). UPS is ubiquitin/proteasome system, PTM is post-translational modification, and ER is endoplasmic reticulum.

            Table 4

            SAFE gene ontology pathways related to Cellular Components affected in peripheral blood lymphocytes from ALS patients

            αPathway

            βSizes

            δq-value

            εGO Term

            Cytoskeleton

            GO:0005868

            6/10

            0.001

            cytoplasmic dynein complex

            GO:0005885

            8/7

            0.0322

            Arp2/3 protein complex

            ER and Golgi

            GO:0030130

            9/9

            0.0001

            clathrin coat of trans-Golgi network vesicle

            GO:0008250

            8/10

            0.0001

            oligosaccharyltransferase complex

            GO:0030660

            23/6

            0.0001

            Golgi-associated vesicle membrane

            GO:0030134

            7/3

            0.0057

            ER to Golgi transport vesicle

            GO:0005791

            12/20

            0.0613

            rough endoplasmic reticulum

            GO:0030131

            18/19

            0.024

            clathrin adaptor complex

            GO:0030119

            18/5

            0.024

            AP-type membrane coat adaptor complex

            GO:0001669

            8/34

            0.2372

            acrosomal vesicle

            GO:0030127

            6/8

            0.0122

            COPII vesicle coat

            GO:0030126

            8/12

            0.003

            COPI vesicle coat

            Mitochondria

            GO:0005758

            22/30

            0.0004

            mitochondrial intermembrane space

            GO:0005763

            20/18

            0.0002

            mitochondrial small ribosomal subunit

            GO:0000276

            7/8

            0.0006

            mitochondrial proton-transporting ATP synthase complex coupling factor F(o)

            GO:0005747

            44/44

            0.0126

            mitochondrial respiratory chain complex I

            GO:0005742

            6/6

            0.024

            mitochondrial outer membrane translocase complex

            Motor Neuron

            GO:0031594

            8/25

            0.028

            neuromuscular junction

            GO:0030424

            21/133

            0.1525

            axon

            Nucleus

            GO:0005680

            12/24

            0.0167

            anaphase-promoting complex

            GO:0000777

            18/61

            0.1854

            condensed chromosome kinetochore

            GO:0000779

            20/4

            0.1989

            condensed chromosome centromeric region

            GO:0000783

            9/9

            0.1197

            nuclear telomere cap complex

            GO:0005643

            51/67

            0.2084

            nuclear pore

            GO:0005637

            10/28

            0.1318

            nuclear inner membrane

            Transcriptional Complexes

             

            GO:0016591

            43/6

            0.001

            DNA-directed RNA polymerase II holoenzyme

            GO:0000178

            11/9

            0.0025

            exosome (RNase complex)

            GO:0016580

            10/8

            0.0712

            sin3 complex

            GO:0005669

            11/20

            0.1817

            transcription factor TFIID complex

            UPS

            GO:0005832

            5/7

            0.0001

            chaperonin-containing T-complex

            GO:0000151

            57/57

            0.0003

            ubiquitin ligase complex

            GO:0000152

            14/2

            0.024

            nuclear ubiquitin ligase complex

            GO:0031461

            10/8

            0.0012

            cullin-RING ubiquitin ligase complex

            GO:0005839

            20/14

            0.0167

            proteasome core complex

            GO:0016272

            8/10

            0.0402

            prefoldin complex

            GO:0008180

            8/10

            0.003

            signalosome

            αGene ontology (GO) pathway identities for cellular components and βnumber of probes on the 4 × 44K human genome array per GO group of directly associated proteins is shown (note: one gene may be represented by different or redundant probes on the array). γFDR (q-value) at significance level q < 0.25 (25%) was determined by bootstrapping following SAFE analysis. δThe table shows partial listing of representative significant εGO categories associated with cellular components (36 among 83). UPS stands for the ubiquitin/proteasome system and ER for endoplasmic reticulum.

            Table 5

            SAFE analysis of the KEGG pathway database

            αPathway (UP)

            βSize

            δq-value

            εGO Term

            Disease

            KEGG:05014

            92/54

            0.1831

            Amyotrophic lateral sclerosis

            DNA Metabolism

            KEGG:03030

            35/36

            0.2048

            DNA replication

            KEGG:03410

            46/34

            0.0003

            Base excision repair

            KEGG:03420

            42/44

            0.0758

            Nucleotide excision repair

            KEGG:03430

            36/23

            0.1367

            Mismatch repair

            KEGG:03440

            13/28

            0.0003

            Homologous recombination

            RNA Metabolism

            KEGG:03018

            55/54

            0.0007

            RNA degradation

            KEGG:03020

            16/29

            0.0296

            RNA polymerase

            KEGG:00970

            18/63

            0.005

            Aminoacyl-tRNA biosynthesis

            Mitochondrial Function

            KEGG:00062

            5/8

            0.0381

            Fatty acid elongation in mitochondria

            KEGG:00071

            17/44

            0.1726

            Fatty acid metabolism

            Amino acid Metabolism

            KEGG:00270

            22/36

            0.0261

            Cysteine and methionine metabolism

            KEGG:00280

            24/44

            0.025

            Valine leucine and isoleucine degradation

            KEGG:00290

            7/11

            0.0072

            Valine leucine and isoleucine biosynthesis

            KEGG:00310

            24/44

            0.0385

            Lysine degradation

            KEGG:00450

            13/26

            0.2048

            Selenoamino acid metabolism

            Carbohydrate Metabolism

             

            KEGG:00632

            8/33

            0.0003

            Benzoate degradation via CoA ligation

            KEGG:00640

            18/32

            0.0381

            Propanoate metabolism

            KEGG:00650

            13/30

            0.0003

            Butanoate metabolism

            KEGG:00670

            8/18

            0.0177

            One carbon pool by folate

            KEGG:00903

            7/32

            0.0425

            Limonene and pinene degradation

            KEGG:00510

            31/49

            0.2155

            N-Glycan biosynthesis

            Cell-Cell Communication

              

            KEGG:04330

            35/47

            0.2288

            Notch signaling pathway

            Chaperone-dependent protein transport

            KEGG:03060

            13/23

            0.0573

            Protein export

            Neuromuscular

            KEGG:04260

            46/77

            0.1493

            Cardiac muscle contraction

            UPS

            KEGG:03050

            42/45

            0.032

            Proteasome

            KEGG:04120

            93/138

            0.032

            Ubiquitin mediated proteolysis

            αKEGG pathway identities and βnumber of probes of the array mapping to the pathway per number of unique genes representing the pathway is shown. FDR (q-value) at significance level p < 0.25 (25%) was determined by bootstrapping following SAFE analysis. εThe table shows listing of the 27 significant KEGG pathway categories. UPS stands for the ubiquitin/proteasome system.

            We found that only neurodegenerative disease pathways (4 in total) were significantly upregulated (Additional File 2). In this latter analysis, ALS was less significant than the other three neurodegenerative diseases (Huntington's disease, Parkinson's disease, Alzheimer's disease), suggesting that neurodegeneration affects lymphocytes to a greater extent than ALS-specific biological processes. Nevertheless, such interpretation has to be taken with caution. Indeed, some genes represented on the pathway maps of these other three neurodegenerative diseases are related to the UPS, cytoskeleton or dynein-dynactin complex, and therefore should be represented on the KEGG ALS pathway. However, ALS, Alzheimer's, Huntington's and Parkinson's are all neurodegenerative diseases related to aging and/or associated with mitochondrial dysfunction. For this reason, results are in alignment with the results of Saris et al. (2009) [11]. Prion disease, generally thought to be less related to the other neurodegenerative disorders, was found not significant (Additional File 2).

            A total of 54 unique gene IDs (including the pseudogene caspase 12) constitute the KEGG ALS pathway (hsa05014) [37] and correspond to 36 protein entities defining unique proteins or protein complexes (Figure 2). A total of 35 protein entities corresponding to 53 genes represented on the KEGG ALS pathway map (pseudogene CASP12 excluded) include membrane receptors, cytosolic or secreted proteins, kinases, phosphatases, proteases, and protein channels, which are likely to play a direct/indirect role in ALS pathogenesis to a variable degree at different stages that lead to motor neuron degeneration. Some protein entities may correspond to different isoforms represented by unique gene IDs. For example, calcineurin (CaN entity) may be composed by three catalytic isoforms (α,β,γ) encoded by three different chromosomes and many types of glutamate receptors may represent the GluR entity (Figure 2).
            http://static-content.springer.com/image/art%3A10.1186%2F1755-8794-4-74/MediaObjects/12920_2011_271_Fig2_HTML.jpg
            Figure 2

            Genes from lymphocytes of ALS patients contributing to the perturbation of the KEGG ALS pathway per SAFE analysis. The KEGG (Kyoto Encyclopedia of Genes and Genomes) ALS pathway map relates to motor neuron degeneration in the context of a microenvironment represented by glial cells and can be found online at http://​www.​genome.​jp/​kegg/​pathway/​hsa/​hsa05014.​html. There are 54 unique gene entries (including CASP12 pseudogene) defined by ENTREZ identities. There are 36 protein entities represented on the map that are not all designated by official HUGO gene symbols. Red dashed areas represent subpathway modules affected by differentially expressed genes. Up or down-regulations determined following SAFE for DS7000 are shown by (↑) or (↓), unchanged is shown by (=) (fold changes in expression and HUGO gene symbols are reported in Table 6). HUGO aliases for protein entities represented on the map are as follows: ALS2 [ALS2], Apaf1 [APAF1], ASK1 [MAP3K5], Bad [BAD], Bax [BAX], Bcl2 [BCL2], Bcl-XL [BCL2L1], Bid [BID], CaN [CHP, CHP2, PPP3CA, PPP3CB, PPP3CC, PPP3R1, PPP3R2], CASP1 [CASP1], CASP3 [CASP3], CASP9 [CASP9], CASP12 [CASP12], CAT [CAT], CCS [CCS], CytC [CYCS], Daxx [DAXX], Derlin-1 [DERL1], EAAT2 [SLC1A2], GPX1 [GPX1], GluR [GRIA1, GRIA2, GRIN1, GRIN2A, GRIN2B, GRIN2C, GRIN2D], MKK3 [MAP2K3], MKK6 [MAP2K6], p38 [MAPK11, MAPK12, MAPK13, MAPK14], NEFH [NEFH], NEFL [NEFL], NEFM [NEFM], NOS1 [NOS1], p53 [TP53], PRPH [PRPH, PRPH2], Rab5 [RAB5A], Rac1 [RAC1], SOD1 [SOD1], TNF-α [TNF], TNFR [TNFRSF1A, TNFRSF1B], and Tom [TOMM40, TOMM40L].

            There were 23 unique genes (43%, 81 probes), the aggregate expression pattern of which contributes to the perturbation of the KEGG ALS pathway (Table 6). The number of protein entities defined by these genes and represented on the KEGG ALS pathway map was 19 out of 35 (54%) (Table 6, Figure 2). The dynamics of up- and down-regulations, assuming they are functionally effective, may be interpreted as responses to signals originating from serum or cell-cell interactions. For example upregulation of ASK1 (alias MAP3K5) may be associated with an ER-stress response that correlates with ALS progression (Figure 2). Also, assuming that transcriptional regulation produces more or less active protein with appropriate subcellular localization and in a timely manner, about half of the genes would have a negative effect on motor neuron survival while the rest would have a positive effect according to current ALS literature. This said contribution of aggregate expression to a pathway does not necessarily signify differential expression of each participant gene in terms of differences between the mean expression levels in lymphocytes from ALS vs. healthy controls. One limitation is that individual protein entities of the UPS, the dynein-dynactin complex, and TARDBP/TDP43 pathway and other elements of ALS pathogenesis are not represented on the KEGG ALS pathway, so that other potential aggregate effects relevant of ALS pathogenesis cannot be determined using the current gene set. However, pathway perturbations were determined by SAFE for genes belonging to the UPS both in terms of "biological process" and "cellular components" affected (Tables 3 and 4). Gene ontology categories corresponding to gene sets related to the UPS (AmiGO database) [38] included the following: positive or negative regulation of ubiquitin-protein ligase, proteasomal ubiquitin-dependent proteins [GO:0051443, 6 proteins; GO:0051444, 4 proteins; GO:0043161, 43 proteins] (Table 3); chaperonin-containing T-complex, ubiquitin ligase complex, nuclear ubiquitin ligase complex, cullin-RING ubiquitin ligase complex, proteasome core complex, prefoldin complex, and signalosome [GO:0005832, 7 proteins; GO:0000151, 57 proteins; GO:0000152, 2 proteins; GO:0031461, 8 proteins; GO:0005839, 14 proteins, GO:0016272, 10 proteins; GO:0008180, 10 proteins] (Table 4). Considering gene listing overlaps, UPS GO categories for biological processes and cellular components found significant by SAFE, represent altogether 234 unique proteins defined by official HUGO (Human Genome Organization) gene nomenclature. Among 220 unique genes that were differentially expressed, as determined by SAM and LIMMA, (Figure 1, Additional File 1), nine are related to the UPS, including four E3 ubiquitin ligases (RNF149, TRIM22, UBR1, and UBR2) (Table 7). Also, ANAPC4, SHFM1, SUGT1, UBR1 and UBR2 were represented by the UPS GO groups found significant by SAFE.
            Table 6

            Genes differentially expressed in lymphocytes from ALS patients compared to healthy controls and contributing to the KEGG ALS pathway as determined by SAFE

            αEntrez Gene ID

            βSymbol

            γDescription

            δSAFE

            εPS/PT

            λFC

            κEffect in ALS

            τLR

            57679

            ALS2

            amyotrophic lateral sclerosis 2 (juvenile)

            -

            n.d.

            <

            -

            [11586298]

            n.d.

            317

            APAF1

            apoptotic peptidase activating factor 1

            -

            n.d.

            <

            -

            [16046141]

            n.d.

            572

            BAD

            BCL2-associated agonist of cell death

            +

            1/1

            1.38 ↓

            -

            [19043451]

            +

            581

            BAX

            BCL2-associated X protein

            +

            10/11

            1.11 ↓

            -

            [17171827]

            +

            596

            BCL2

            apoptosis regulator B-cell CLL/lymphoma 2

            +

            10/11

            1.07 ↑

            +

            [20460269]

            +

            598

            BCL2L1

            inhibitor of cell death BCL2-like 1

            -

            n.d.

            <

            +

            [12097494]

            n.d.

            637

            BID

            BH3 interacting domain death agonist

            +

            1/2

            <

            -

            [12213439]

            n.d.

            834

            CASP1

            caspase 1, apoptosis-related cysteine peptidase

            +

            1/1

            1.54 ↑

            -

            [10764647]

            -

            836

            CASP3

            caspase 3, apoptosis-related cysteine peptidase

            +

            10/10

            1.20 ↑

            -

            [10764647]

            -

            842

            CASP9

            caspase 9, apoptosis-related cysteine peptidase

            -

            2/2

            1.07 ↑

            -

            [14657037]

            -

            847

            CAT

            catalase (heme containing)

            +

            1/1

            1.46 ↑

            +

            [8731383]

            +

            9973

            CCS

            copper chaperone for superoxide dismutase

            -

            1/1

            1.08 ↓

            -

            [17389365]

            -

            11261

            CHP

            calcineurin B homolog

            +

            1/1

            1.16 ↑

            +

            [11350981]

            +

            63928

            CHP2

            calcineurin B homologous protein 2

            -

            n.d.

            <

            +

            [12226101]

            n.d.

            54205

            CYCS

            cytochrome c, somatic

            +

            2/3

            1.10 ↑

            -

            [17454840]

            +

            1616

            DAXX

            death-domain associated protein

            -

            n.d.

            <

            -

            [12354397]

            n.d.

            79139

            DERL1

            degradation in endoplasmic reticulum protein 1

            +

            1/1

            <

            -

            [18519638]

            n.d.

            2876

            GPX1

            glutathione peroxidase 1

            +

            1/1

            1.22 ↓

            -

            [9335008]

            -

            2890

            GRIA1

            glutamate receptor, ionotropic, AMPA 1

            -

            n.d.

            <

            -

            [8981413]

            n.d.

            2891

            GRIA2

            glutamate receptor, ionotropic, AMPA 2

            -

            n.d.

            <

            -

            [8981413]

            n.d.

            2902

            GRIN1

            glutamate receptor, ionotropic, N-methyl D-aspartate 1

            +

            2/2

            1.44 ↓

            -

            [1320444]

            -

            2903

            GRIN2A

            glutamate receptor, ionotropic, N-methyl D-aspartate 2A

            -

            n.d.

            <

            -

            [8842405]

            n.d.

            2904

            GRIN2B

            glutamate receptor, ionotropic, N-methyl D-aspartate 2B

            -

            n.d.

            <

            +

            [16490316]

            n.d.

            2905

            GRIN2C

            glutamate receptor, ionotropic, N-methyl D-aspartate 2C

            -

            n.d.

            <

            +

            [11717388]

            n.d.

            2906

            GRIN2D

            glutamate receptor, ionotropic, N-methyl D-aspartate 2D

            +

            1/1

            1.58 ↓

            +

            [15152019]

            -

            5606

            MAP2K3

            mitogen-activated protein kinase kinase 3

            +

            1/1

            1.34 ↓

            -

            [17686961]

            +

            5608

            MAP2K6

            mitogen-activated protein kinase kinase 6

            -

            1/1

            1.32 ↑

            -

            [16219474]

            -

            4217

            MAP3K5

            mitogen-activated protein kinase kinase kinase 5

            +

            1/1

            1.10 ↑

            -

            [15910777]

            -

            5600

            MAPK11

            mitogen-activated protein kinase 11

            -

            n.d.

            <

            -

            [9218798]

            n.d.

            6300

            MAPK12

            mitogen-activated protein kinase 12

            -

            n.d.

            <

            -

            [9169156]

            n.d.

            5603

            MAPK13

            mitogen-activated protein kinase 13

            -

            n.d.

            <

            -

            [9218798]

            n.d.

            1432

            MAPK14

            mitogen-activated protein kinase 14

            +

            10/10

            1.26 ↑

            -

            [15910777]

            -

            4744

            NEFH

            neurofilament, heavy polypeptide

            -

            n.d.

            <

            -

            [7849698]

            n.d.

            4747

            NEFL

            neurofilament, light polypeptide

            -

            n.d.

            <

            -

            [15207859]

            n.d.

            4741

            NEFM

            neurofilament, medium polypeptide

            -

            n.d.

            <

            -

            [11732278]

            n.d.

            4842

            NOS1

            nitric oxide synthase 1 (neuronal)

            -

            n.d.

            <

            -

            [15033415]

            n.d.

            5530

            PPP3CA

            protein phosphatase 3, catalytic subunit, alpha isozyme

            +

            3/3

            1.06 ↑

            -

            [11701756]

            +

            5532

            PPP3CB

            protein phosphatase 3, catalytic subunit, beta isozyme

            +

            1/1

            1.28 ↑

            -

            [15312178]

            +

            5533

            PPP3CC

            protein phosphatase 3, catalytic subunit, gamma isozyme

            +

            1/1

            <

            -

            [15312178]

            n.d.

            5534

            PPP3R1

            protein phosphatase 3, regulatory subunit B, alpha

            -

            n.d.

            <

            -

            [11754729]

            n.d.

            5535

            PPP3R2

            protein phosphatase 3, regulatory subunit B, beta

            -

            n.d.

            <

            -

            [11754729]

            n.d.

            5630

            PRPH

            peripherin

            -

            n.d.

            <

            -

            [20363051]

            n.d.

            5961

            PRPH2

            peripherin 2

            -

            n.d.

            <

            -

            [8125718]

            n.d.

            5868

            RAB5A

            ras-related protein Rab-5A

            +

            1/1

            1.11 ↓

            +

            [11316809]

            +

            5879

            RAC1

            ras-related C3 botulinum toxin substrate 1

            +

            12/12

            1.08 ↓

            -

            [18219391]

            -

            6506

            SLC1A2

            sodium-dependent glutamate/aspartate transporter 2

            -

            n.d.

            <

            +

            [14530974]

            n.d.

            6647

            SOD1

            superoxide dismutase 1, soluble

            +

            11/11

            1.24 ↑

            -

            [20644736]

            +

            7124

            TNF

            tumor necrosis factor alpha

            +

            1/1

            1.15 ↑

            +

            [18823372]

            +

            7132

            TNFRSF1A

            tumor necrosis factor receptor superfamily, member 1A

            -

            n.d.

            <

            -

            [11917000]

            n.d.

            7133

            TNFRSF1B

            tumor necrosis factor receptor superfamily, member 1B

            -

            n.d.

            <

            +

            [11917000]

            n.d.

            10452

            TOMM40

            mitochondrial import receptor subunit TOM40 homolog

            -

            n.d.

            <

            -

            [20797528]

            n.d.

            84134

            TOMM40L

            mitochondrial import receptor subunit TOM40B

            -

            1/1

            1.23 ↓

            -

            [20797528]

            -

            7157

            TP53

            tumor protein p53

            -

            n.d.

            <

            -

            [8609941]

            n.d.

            Genes (53 in total, pseudogene CASP12 excluded) belonging to the KEGG ALS pathway (hsa05014) that describe pathogenic effects in motor neurons are defined by their official HUGO symbol. αEntrez Gene accession number and βHUGO symbol and γdescription are provided. δGenes that contribute to the KEGG ALS pathway through SAFE analysis are marked with a (+) sign and if not with a (-) sign. Number of probes significant (PS) per total number of probe signal intensity values per gene on the Agilent 4 × 44K array (PT) is shown. λFC is average fold change ALS (n = 11) compared to healthy controls (n = 11) considering one or more probe signal intensity values per gene, with upregulated genes indicated by (↑) and downregulated genes by (↓). Less than 1.05 fold changes are indicated by (<). κPMID referenced positive (+) or negative (-) effect on motor neuron survival if wild type protein activity or function is increased (↑) or normal function or activity is altered (↓). Genes that were not determined (n.d.) to contribute to the KEGG ALS pathway or with an FC < 1.05, are also referenced for known effects. τLymphocyte response (LR) represented by the genes contributing to the KEGG ALS pathway is shown.

            Table 7

            Differentially expressed genes related to the UPS, as determined by SAM and LIMMA

            αProbe

            βSymbol

            γAccession

            δFC

            εFunctional Description

            A_23_P317800

            ANAPC4

            NM_013367

            1.40

            anaphase promoting complex subunit 4: Component of the anaphase promoting complex/cyclosome (APC/C), a cell cycle-regulated E3 ubiquitin ligase that controls progression through mitosis and the G1 phase of the cell cycle. The APC/C complex acts by mediating ubiquitination and subsequent degradation of target proteins: it mainly mediates the formation of 'Lys-11'-linked polyubiquitin chains and, to a lower extent, the formation of 'Lys-48'- and 'Lys-63'-linked polyubiquitin chains

            A_23_P106741

            PSMD7

            NM_002811

            1.41

            proteasome (prosome, macropain) 26S subunit, non-ATPase, 7: Acts as a regulatory subunit of the 26S proteasome which is involved in the ATP-dependent degradation of ubiquitinated proteins

            A_23_P120153

            RNF149

            NM_173647

            1.67

            E3 ubiquitin-protein ligase RNF149: Unknown

            A_23_P42664

            SHFM1

            NM_006304

            1.63

            split hand/foot malformation (ectrodactyly) type 1: Subunit of the 26S proteasome which plays a role in ubiquitin-dependent proteolysis

            A_24_P172481

            TRIM22

            NM_006074

            1.55

            E3 ubiquitin-protein ligase TRIM22: Interferon-induced antiviral protein involved in cell innate immunity. The antiviral activity could in part be mediated by TRIM22-dependent ubiquitination of viral proteins. Plays a role in restricting the replication of HIV-1, encephalomyocarditis virus (EMCV) and hepatitis B virus (HBV). Acts as a transcriptional repressor of HBV corepromoter. May have E3 ubiquitin-protein ligase activity.

            A_23_P203137

            UBE4A

            NM_004788

            1.34

            ubiquitin conjugation factor E4 A: Binds to the ubiquitin moieties of preformed conjugates and catalyzes ubiquitin chain assembly in conjunction with E1, E2, and E3.

            A_23_P152066

            UBR1

            NM_174916

            1.33

            ubiquitin protein ligase E3 component n-recognin 1: E3 ubiquitin-protein ligase which is a component of the N-end rule pathway. Recognizes and binds to proteins bearing specific N-terminal residues that are destabilizing according to the N-end rule, leading to their ubiquitination and subsequent degradation. Plays a critical role in chromatin inactivation and chromosome-wide transcriptional silencing during meiosis via ubiquitination of histone H2A. Binds leucine and is a negative regulator of the leucine-mTOR signaling pathway, thereby controlling cell growth.

            A_23_P362637

            UBR2

            NM_015255

            1.25

            ubiquitin protein ligase E3 component n-recognin 2: Same as for UBR1

            A_32_P178945

            YOD1

            NM_018566

            1.52

            YOD1 OTU deubiquinating enzyme 1 homolog alias HIV-1-induced protease: Hydrolase that can remove conjugated ubiquitin from proteins and participates in endoplasmic reticulum-associated degradation (ERAD) for misfolded lumenal proteins. May act by triming the ubiquitin chain on the associated substrate to facilitate their threading through the VCP/p97 pore. Ubiquitin moieties on substrates may present a steric impediment to the threading process when the substrate is transferred to the VCP pore and threaded through VCP's axial channel. Mediates deubiquitination of both 'Lys-48'- and 'Lys-63'-linked polyubiquitin chains. Able to cleave both polyubiquitin and di-ubiquitin.

            A total of nine genes among 206 related to the ubiquitin/proteasome system (UPS) were significantly upregulated in lymphocytes from ALS patients compared to controls, as determined by SAM (q < 1%) and LIMMA analyses (p < 0.001). Among them, four genes encode E3 ubiquitin ligases (RNF149, TRIM22, UBR1, and UBR2) and one gene a deubiquitinase (YOD1). αAgilent Array 4 × 44K probe IDs, βgene symbol, γNCBI GenBank accession number, δfold change in expression and εGeneCard functional description (http://​www.​genecards.​org) are provided.

            Assessment of alteration of UPS-related gene expression in lymphocytes from ALS patients based on microarray data

            Correlation of ANAPC4, SHFM, SUGT1, UBR1 and UBR2 with demographic and disease parameters was determined. Among these five genes, UBR2 (Ubiquitin-protein ligase E3-alpha-2) encoded protein is known to act in conjunction with UBR1 in a quality control pathway for degradation of unfolded cytosolic proteins [39]. We calculated Spearman correlation between expression data and length of the disease from symptom onset and the ALS Functional Rating Scale-Revised score (ALSFRS-R) at the time of peripheral blood sampling. Significant correlation was found between UBR2 increased gene expression and time of disease from onset to time of lymphocyte sampling (r = -0.8091, p = 0.0039), as well as ALSFRS-R (r = 0.6333, p = 0.0402) (Figure 3, Table 8). Similar to Saris et al. (2009) [11], we found no correlation between the expression of these genes with gender, age at onset, age at collection, and site of onset. However, unlike Saris et al. (2009) [11] but similar to Zhang et al. (2006) [13] we present correlations of individual genes with disease duration and ALSFRS-R.
            http://static-content.springer.com/image/art%3A10.1186%2F1755-8794-4-74/MediaObjects/12920_2011_271_Fig3_HTML.jpg
            Figure 3

            Relationship betweenUBR2transcriptional expression in lymphocytes from ALS patients and progression of the disease. Expression of UBR2 varies inversely with the length of the disease from onset to lymphocyte gene expression testing, and varies directly with the ALS-FRS-R score. Duration of the disease from onset to sampling (i.e. Disease Duration) (a) (*three close values) or the ALSFRS-R score at the time of sampling (b) are indicated on the x-axis. Log2 ratios of expression obtained from the dual mode reference design are represented on the y axis. Dot plot (c) shows that with a cut-off of 0.15, discrimination between ALS patients [ALS] and healthy controls [HC] for UBR2 expression is achieved with p = 0.000953 (Fisher's exact test).

            Table 8

            Correlation between expression data of differentially expressed UPS genes and ALSFRS-R or disease duration

              

            Spearman Correlation

            Gene

            Probe

            Disease Duration

            ALSFRS-R

            ANAPC4

            A_23_P317800

            r = -0.0364

            r = -0.2642

              

            p = 0.9241

            p = 0.4348

            SHFM1

            A_23_P42664

            r = 0.3909

            r = -0.3144

              

            p = 0.2366

            p = 0.3415

            SUGT1

            A_23_P162787

            r = 0.3636

            r = -0.3736

              

            p = 0.2731

            p = 0.2608

            UBR1

            A_23_P152066

            r = 0.0636

            r = 0.2323

              

            p = 0.8603

            p = 0.4854

            UBR2

            A_23_P362637

            r = -0.8091

            r = 0.6333

              

            p = 0.0039*

            p = 0.0402*

            *p < 0.05

            Assessment of alteration of the UPS in PBMCs from ALS patients using proteasome inhibition assays

            We employed the MG132 proteasome inhibition assay to test whether the UPS transcriptional alterations described above are accompanied by ubiquitination changes at the protein level. MG132 blocks the proteolytic activity of the 26S proteasome complex reversibly, which inhibits the degradation of ubiquitin-conjugated proteins and has multiple effects including, for instance, reducing muscle atrophy associated with disuse [40] or increasing caspase-mediated generation of TDP-43 C-terminal fragments [41]. We prepared peripheral blood mononuclear cell (PBMC) short-term cultures from ALS patients (n = 6) and healthy control subjects (n = 5). High molecular weight (HMW) poly-ubiquitinated protein forms were detected in protein lysates of these PBMCs by Western blot analysis using monoclonal anti-ubiquitin antibody similarly to Jury et al. (2003) [42]. For PBMCs from healthy control subjects, cultured in RPMI [10% FCS] medium, accumulation of HMW poly-ubiquitinated proteins was induced by MG132 treatment, but this accumulation was partially mitigated by the supplementation of the RPMI [10% FCS] medium with matched autologous human serum at a final concentration of 20% (Figure 4). For PBMCs from ALS patients, cultured in RPMI [10% FCS] medium, accumulation of HMW poly-ubiquinated proteins was induced by MG132 treatment, and this accumulation was further increased by addition of autologous human serum from each ALS patient (Figure 4).
            http://static-content.springer.com/image/art%3A10.1186%2F1755-8794-4-74/MediaObjects/12920_2011_271_Fig4_HTML.jpg
            Figure 4

            Total ubiquitination Western blot (WB) analysis of cultured PBMCs from ALS patients and controls in the presence or absence of added-back serum for 16 hours and treated or not with proteasome inhibitor MG132 for 1.5 hr. Comparison of PBMCs from one healthy control and one ALS patient incubated or not in the presence of added-back matched autologous serum is shown in (a). Semi-quantitative Western blot analysis was performed to measure the accumulation of high molecular weight (HMW) ubiquitinated protein species in PBMCs that were prepared the same day from one healthy control and one ALS patient (WB1). A signal (S) to noise (N) ratio (S/N) was determined with ImageJ program by comparing the integrated density of two areas consistently stained throughout the membrane and visually contrasting the accumulation of HMW ubiquitinated protein species (WB1). ALS patient serum exacerbates the effects of MG132 on total ubiquitination and accumulation of HMW ubiquitinated species, while serum from healthy control mitigates these effects. Comparison of PBMCs from ALS patients (n = 5) and healthy controls (n = 4) incubated in the presence of added-back matched autologous serum is shown in (b). PBMCs obtained at different times from additional ALS patients (n = 5) and healthy controls (n = 4) show similar result (WB2 and WB3).

            Discussion

            We report, for the first time, genome-wide expression profiling of purified lymphocytes from patients with amyotrophic lateral sclerosis. This study, performed with the long oligonucleotide Agilent Human Whole Genome 44 × 4K Array, demonstrates that ALS relevant differential gene expression and pathway perturbations can be identified in peripheral blood lymphocytes by a functional enrichment method such as SAFE [20] and not only in brain or spinal cord that are directly affected by the disease. In the search for blood biomarkers in neurological disorders, determination of molecular signatures or pathway alterations becomes critical in the analysis of microarray data generated from the blood compartment. This is due to the fact that at the genome-wide scale of gene expression, relevant biological differences may be modest or even negligible relative to the noise. The expression profiling studies on whole blood from ALS patients by Saris et al. (2009) [11] and Lin et al. (2009) [10] clearly illustrate the challenge for data interpretation when variations in gene expression are minimal and performed by different methods. In the first case, 2300 probe-encoded genes were differentially expressed with fold changes in expression (ALS vs. controls) varying from 1.015 to 1.588 (mean value ± SD = 1.097 ± 0.073). In the second case, Lin et al. (2009) also reported small fold changes in expression of four mitochondrial genes of the electron transport chain (FLAD1, RFK, CYCS, and SDHB). A broader range of fold changes in expression for subcultured PBMCs was reported in the work by Zhang et al. (2011) [13] which could be due to subculture conditions and/or the method chosen for normalization of the microarray data [43, 44]. In our study, fold changes in expression for the genes found significant by SAM and LIMMA (i.e. DS3500), varied from 1.244 to 3.422 (mean value ± SD = 1.556 ± 0.28). Therefore, one may not expect to correlate differential expression by qRT-PCR for many genes due to the large sample size required to eventually confirm small changes in expression. This problem is partially circumvented by global pathway analysis methods. Many differentially expressed genes identified by SAM and LIMMA may be subjectively placed in the context of ALS pathogenesis. In addition, there was little overlap with 166 genes that were found associated with ALS to a variable degree in several single-gene and genome-wide association studies (GWAS). For instance, TARDBP, SOD1, KIFAP3 and COX7C were differentially expressed in our study. TARDBP and SOD1, clearly associated with ALS pathogenesis, have also been identified by various genetic analyses for their association with ALS. The fact that SAFE, LIMMA, and SAM identified SOD1 mRNA upregulation to be significant confirms findings by Gagliardi et al. (2010). Gagliardi et al. showed that SOD1 mRNA levels were increased in spinal cord, brain stem, and lymphocytes of sporadic ALS patients, but did not correlate with gender, age or duration of the disease [12].

            For the first time, gene expression data from the blood compartment from sporadic ALS patients could be associated with the KEGG ALS disease pathway and KEGG disease pathways of neurodegenerative disorders such as Alzheimer's, Parkinson's and Huntington's diseases. Considering that global genome-wide subtle changes in gene expression were used for this determination, this result is rather unexpected. Protein activity changes that are caused by the presence of the disease are generally not expected to consistently correspond to transcriptional regulations. Our use of purified lymphocytes has likely provided a better dataset to study ALS-specific signature in the blood compartment as opposed to total blood.

            However, because of the small sample size of our study (n = 22) and because ALS is a heterogeneous disease, it is not possible to capture the breadth of the disease process occurring during onset and progression of the disease. In addition, disease responses in lymphocytes may not mirror many of the disease processes occurring in brain, which depend on the alteration of the blood brain barrier and the microenvironment represented by glia and microglia. Furthermore, assuming that transcriptional regulation produces more or less active protein with appropriate subcellular localization and in a timely manner, about half of the genes based on their expression would have a negative effect on motor neuron survival while the rest would have a positive effect according to current ALS literature (Table 6). This clearly indicates very limited replication of processes occurring in brain or spinal cord of ALS patients. Thus, while similar pathways are affected in motor neurons and lymphocytes due to a possible systemic common cause(s), it is expected that some responses may differ in their details possibly reflecting differential susceptibility.

            In our pathway analysis of the dataset DS7000 generated with Agilent Human Whole Genome 4 × 44K Array, SAFE identified alteration of gene expression pertaining to gene ontology (GO) categories relevant to ALS pathogenesis (and/or other neurological diseases), such as DNA metabolism, RNA splicing, mitochondrial function, oxidation, ER and Golgi functions, UPS, neurological function, post-translational modification and viral infection. These results are consistent with findings by Saris et al. (2009) that were determined by whole blood RNA profiling [11]. However, following pathway analysis using SAFE, we went further in the analysis of transcriptional alterations of the UPS by identifying a correlation between the expression of differentially expressed individual UPS-related genes and the time of presence of the disease or the ALSFRS-R. Indeed, whole exome sequencing identified mutations in the gene encoding valosin-containing protein (VCP), a key component of the UPS, as a cause of familial ALS, demonstrating that disturbances of UPS function may be closely linked to ALS pathogenesis [45]. A total of nine differentially expressed genes, were related to the UPS including four ubiquitin ligases representative of UPS GO groups identified by SAFE (ANAPC4, SHFM1, UBR1, and UBR2). Differential expression of the "N-end rule" ubiquitin ligase UBR2 gene [46] in lymphocytes from ALS patients was found to correlate with disease duration and ALSFRS-R at the time of sampling. Although, overall UBR2 mRNA expression is upregulated in ALS patients compared to healthy controls, a decrease in expression correlated with more advanced stage or severity. This apparent paradox can be explained by the possibility that an initial disease process to which healthy controls are never exposed, causes an initial upregulation of UBR2 mRNA expression which then declines as the disease progresses with increasing impairment of the UPS machinery. One possible mechanism of action of E3 ubiquitin ligases UBR1 and UBR2 could be to facilitate targeting of foldable conformers to the proteasome [39] and to provide protection against toxicity of (unknown) misfolded proteins that accumulate during the disease course in lymphocytes from ALS patients. This mechanism is similar to the E3 ubiquitin ligase dorfin (encoded by RNF19A) that prevents mutant SOD1-mediated neurotoxicity and improves symptoms in the transgenic G93A SOD1 mouse model [47, 48]. Indeed, the presence of some cellular toxicity in PBMCs was shown by De Marco et al. (2010) [49] who determined that the cytoplasmic fraction of TDP-43 in circulating PBMCs of sporadic and familial ALS patients was increased. In addition, by analogy with mutant SOD1-mediated toxicity, human wild-type TDP-43-mediated neurotoxicity might be partially alleviated by co-expression with ubiquilin 1 (encoded by UBQLN1) involved in autophagy and proteasome targeting [50, 51]. Moreover, mutations in ubiquilin 2 (encoded by UBQLN2) have been associated with X-linked juvenile ALS and adult sporadic ALS [52]. Ubiquilins bind to both ubiquitin ligases and the proteasome, providing a connector function within the UPS [53].

            Our proteasome inhibition assays also indicate that lymphocytes from ALS patients exposed to serum factors and metabolites in vivo have acquired new properties with regard to the UPS and other pathways that are normally perturbed in degenerating motor neurons. In this respect, the study by Watanabe et al. (2010) [54], showing that metabolic alterations of the UPS may take place in the skin of ALS patients, follows the same paradigm. In addition, using short-term PBMC cultures Zhang et al. (2011) [13] showed that monocytes in ALS patients have acquired unique properties that relate to neuroinflammation and innate immunity.

            Conclusions

            Our approach demonstrates that subtle changes in gene expression measured by Agilent Human Whole Genome 4 × 44K Array may be interpreted objectively. Without underestimating the complexity of ALS pathogenesis, our analyses with these arrays identify multiple new directions worth further investigation, including systemic UPS pathway alterations, in the search of biomarkers associated with the cause(s) or the progression of ALS. Overall, it remains to be determined which properties the circulating lymphocytes acquire by long distance signaling in the peripheral blood system, and which properties they acquire by local signaling or local cell-cell contact due to trafficking of the lymphocytes at the sites of neurodegeneration in brain or spinal cord.

            Declarations

            Acknowledgements and Funding

            We would like to thank Drs. Herbert L Bonkovsky and Farah K Mougeot for critical review of the manuscript. We also thank the Carolinas Neuromuscular/ALS-MDA Center staff for their help with consenting patients and acquisition of patients' samples and data. This work was supported by grants from the Carolinas ALS Research Fund of the Carolinas Healthcare Foundation, the Charlotte-Mecklenburg Health Services Foundation (HSF), and a grant from the North Carolina Translational and Clinical Sciences Institute (Award 10KR40936).

            Authors’ Affiliations

            (1)
            Department of Neurology, ALS Biomarker Laboratory - James G Cannon Research Center, Carolinas Medical Center
            (2)
            Department of Neurology, Carolinas Neuromuscular/ALS-MDA Center, Carolinas Medical Center
            (3)
            Department of Neurology, University of North Carolina School of Medicine-Charlotte Campus, Carolinas Medical Center
            (4)
            Department of Biostatistics, University of North Carolina at Chapel Hill

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            55. Pre-publication history

              1. The pre-publication history for this paper can be accessed here:http://​www.​biomedcentral.​com/​1755-8794/​4/​74/​prepub

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