INOM EXAMENSARBETE BIOTEKNIK,AVANCERAD NIVÅ, 30 HP

, STOCKHOLM SVERIGE 2017

Autoantibody profiling in amyotrophic lateral sclerosis plasma

JENNIE OLOFSSON

KTHSKOLAN FÖR BIOTEKNOLOGI

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Autoantibody profiling in amyotrophic lateral sclerosis plasma

Jennie Olofsson Supervisor Julia Remnestål, Examiner Peter Nilsson. Abstract Amyotrophic lateral sclerosis (ALS) is the most common neurodegenerative disease in adults that results in muscular paralysis, followed by death within 2-4 years after onset. The pathogenesis and etiology of ALS is not known, which explains why there is still no cure and the efficiency of the existing treatments is low. Genetic factors have been found to influence rate of progression, neuronal degeneration and increase susceptibility to the disease, while most autoimmune mechanisms remain somewhat unexplored. However, accumulative evidence from biochemical-, morphological-, pharmacological- and physiological studies suggest the existence of autoimmune mechanisms in ALS. However, evidence from larger proteomic studies is lacking. By using a proteomic approach, the aim of this study was to investigate the occurrence of autoantibodies in human plasma samples from ALS patients. Plasma samples from 233 ALS patents and 204 healthy controls were profiled in this study. An untargeted screening on a high density planar protein array was performed followed by a targeted multiplexed bead based suspension array, with 352 protein fragments immobilized on magnetic, color-coded beads. Profiling of autoantibody repertoires in all 437 samples enabled identification of differences in autoantibody frequencies between ALS and control samples. Two proteins, GFAP and SEC14L5, showed a significant difference in reactivity between the groups, with higher number of reactive samples in ALS patients compared to control subjects. Amyotrofisk lateralskleros (ALS) är en av de vanligaste neurodegenerativa sjukdomarna som drabbar vuxna idag. ALS resulterar i förlamning samt död inom 2–4 år efter insjuknande. Mekanismen bakom, samt orsakerna till ALS är fortfarande okända, vilket kan förklara varför det varken finns botemedel eller effektiva behandlingar ännu. Genetiska faktorer som bidrar till ökad framfart, degradering av neuroner samt ökad risk för sjukdomen har identifierats i tidigare studier, medan potentiella autoimmuna mekanismer inom ALS inte är lika utforskade. Emellertid presenterar biokemiska-, morfologiska-, farmaceutiska-, samt fysiologiska studier bevis för att autoimmuna komponenter existerar inom ALS, dock saknas belägg från större studier inom proteomik. Målet med den här studien är att, med hjälp av proteomiska studier, undersöka huruvida det existerar autoantikroppar i plasma hos patienter med ALS. Plasmaprover från 233 patienter med ALS samt prover från 204 friska kontroller profilerades i denna studie som inleddes med en ospecifik screening på en plan protein-array. Därefter gjordes en riktad multiplex screening med hjälp av suspension bead array-teknologi, innehållande 352 antigen immobiliserade på färgkodade magnetiska kulor. Profilering av autoantikropprepertoarer i alla 437 prover gjorde det möjligt att identifiera skillnader i autoantikroppsuttryck mellan grupperna. Två proteiner, GFAP och SEC14L5, visade signifikant skillnad i antalet reaktiva individer mellan de två grupperna. Båda proteinerna visade fler reaktiva ALS patienter jämfört med kontrollerna.

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Introduction Amyotrophic lateral sclerosis (ALS) is a lethal motor neuron disease characterized by degeneration and functional loss of motor neurons. The loss of motor neurons results in progressive muscle atrophy and weakness, followed by muscular paralysis and eventually death within 2-4 years due to respiratory failure. Today, ALS is the most common motor neuron disease with adult onset in the world, effecting 2 in 100.000 yearly.1 Approximately 10% of the patients have a family history of ALS, called familial ALS, while the remaining 90% have no hereditary pattern and is referred to as sporadic ALS.2 Clinical subsets of the disease exist and are distinguished based on the primarily affected regions of the body. Bulbar phenotypes are characterized by loss of function in muscles used for speech, chewing and swallowing. In spinal phenotypes the muscles in the limbs lose their function, and in thoracic/respiratory phenotypes the muscles controlling the torso and/or respiration are affected.3 The average survival differs among the subsets with bulbar and respiratory phenotype having the shortest survival.4 There are diseases showing similar symptoms to ALS and patients are given different diagnoses depending on which type of neurons that lose their function. Patients suffering from loss of both upper and lower motor neurons are diagnosed with ALS. However, some patients only loose function in one type of neuron, for instance the lower motor neurons, and these patients are diagnosed with progressive muscular atrophy (PMA).5 Patients that only suffer from loss of upper motor neurons are diagnosed with primary lateral sclerosis (PLS).6 Both PMA and PLS have a more benign prognosis than ALS, and it has been debated if PMA and PLS are distinct motor neuron diseases or if they belong to ALS. Since the first gene associated to ALS, SOD1, was identified, more than 50 potential genes associated to ALS have been identified and published. Although these genes have primarily been observed in patients with familial ALS, genetic variants have in rare cases been identified in patients with sporadic ALS as well.2 There are different theories regarding what is causing the disease, and it is difficult to determine if these mechanisms contribute to the cause of the

disease or if they are a result of it. For instance, data suggest that malfunctioning nuclear transporter proteins could result in accumulation of both correctly folded and misfolded proteins in the cytoplasm. These accumulations are in turn more prone to form aggregates, which might be toxic to the cell.7 There are also evidence of protein aggregates causing mitochondrial dysfunction, resulting in oxidative stress.8 Neuronal and muscular cells have shown to be more sensitive to oxidative stress due to their high consumption of oxygen that are required to produce energy. Oxidative stress has shown to affect the DNA repair mechanisms resulting in DNA damage. Oxidative damage accumulates over time in the DNA of the neurons, which is thought to play an important role in the pathogenesis of ALS since it may result in cell death.9 Despite numerous studies since the discovery of SOD1, the pathogenesis and etiology of ALS is still not understood and the mechanism of the neuronal death remains unknown. However, accumulative evidence suggests the existence of autoimmune components in the pathogenesis. Glial cells, which are non-neuronal cells in the brain supporting and protecting neuron hemostasis, have shown to be activated as a response to priming events, such as protein aggregation or misfolding. Microglia cells, which are a type of immune cell in the brain, are also activated by these events. They can, amongst other things, trigger a general immune response by expressing pro-inflammatory cytokines which are toxic to surrounding neurons.10 With gathering evidence supporting autoimmune components in ALS, laboratories have looked for typical signs of autoimmune diseases, such as circulating immune complexes, higher frequency of specific histocompatibility types, or association with other autoimmune diseases. For instance, the effect of purified antibodies or sera collected from ALS patients applied on cell cultures have been investigated in several different studies. The studies show varying results, such as motor neuron degeneration11 and evidence for motor nerve terminals as targets for autoimmune responses.12 Using mouse models, evidence of ALS antibodies causing plastic changes at nerve terminals that could result in neuronal death, have been reported.13

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Studies showing evidence of ALS patients having autoantibodies targeting neurofilament subunits14,15, gangliosides16,17, calcium18 and potassium19 channels have also been made. Planar and multiplexed suspension bead array technologies for autoantibody profiling Circulating autoantibodies are key components in diagnosis, prognosis, classification, and monitoring of several diseases. Detection of these could therefore be of crucial interest in the clinics, but also in understanding the pathology and etiology of diseases. Commonly used technologies, such as ELISA, are limited to a priori knowledge of the autoantigens, which limits the chance of finding novel targets. Other limitations to ELISA are the absence of high-throughput and multiplexed applications.20 However, in this study high density planar protein array and multiplexed suspension bead array technologies were used (see figure 1), which enable high throughput, multiplexed screening and profiling of human plasma, and the possibility to find novel targets. A planar array is generated by immobilization of multiple different antigens onto a solid support, such as a glass slide. The antigens used in this study were generated within the Human protein atlas (HPA)21 and have a mean and median length of approximately 80 amino acids. They are designed with no or very low homology to other human proteins in order to avoid unspecific binding of potential autoantibodies.22 Immobilization of up to 42.100 antigens on an array is possible by using a non-contact microarray printer (Arrayjet Marathon, ArrayJet Ltd), with abilities to customize the arrays protein content depending on the study to be made. Applying samples (plasma, serum, or CSF) onto the array allows autoantibodies to bind with affinity to the immobilized antigens. Addition of secondary antibodies conjugated with flourophores enables detection of bound autoantibodies using a two-color microarray-scanner (Agilent G2505C, Agilent Technologies Inc.). This is a multiplexed method due to the ability of screening for up to 42.100 antigens in one experiment, using single samples or small sample pools on the array. High-throughput

applications is possible by designing multiple, smaller arrays with fewer antigens, on the same slide. This enables analysis of up to 84 samples in one experiment. A suspension bead array (SBA) is generated by immobilization of antigens onto color-coded, magnetic beads as solid support instead of a planar

Figure1:Overviewoftheworkflowforplanararraysandsuspensionbeadarrays.Planararray;(1A)Proteinfragmentsareimmobilizedontoaplanarglassslide.(2A)Patientsamplesaredilutedandaddedtotheslideforincubation(RT,1hour).Autoantibodiespresentinthesampleswillbindtotheproteinfragmentsontheslide.(3A)Asecondaryantibodycoupledtoafluorophoreisaddedtoenabledetectionofboundautoantibodies.(4A)Amicroarrayscannerisusedtoscantheslideandgeneratesatwo-colorimage.Suspensionbeadarray;(1B)Proteinfragmentsareimmobilizedontocolor-codedmagneticbeads.EachcolorrepresentsauniquebeadID.(2B)Samplesarerandomizedanddilutedbeforeincubationwiththecoupledbeads(RT,2hours).(3B)AdditionofaR-phycoerythrinconjugateddetectionantibodyenablesdetectionofautoantibodiesboundtothebeads.(4B)AFlexMap3Dinstrument(Luminex)isusedtoreadthebeadIDandthesignalintensityofboundautoantibodies.

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surface. The beads are suspended in solution with each color-coded bead representing a specific bead identity (ID), which makes it possible to link a distinct antigen to one ID and the beads can thus be combined within an array.20 In turn, this allows for multiplex measurements detecting up to 384 antigens in 384 samples in parallel during one experiment. Addition of samples (plasma, serum, or CSF) to be analyzed and detection molecule with a fluorophore, makes it possible to identify relative differences in autoantibody levels between individuals. Using a flow cytometry-like instrument with two lasers (FlexMap 3D, Luminex), the obtained read-out is the median fluorescence intensity (MFI) across each distinct bead ID.20 Project aim The aim of this study was to perform autoantibody profiling of 233 ALS patients and 204 healthy controls to identify profiles and antigens that possibly could contribute to better understanding of the pathology and etiology of ALS. An affinity proteomics approach using planar protein arrays was made for screening and target selection, which was followed by an antigen suspension bead array consisting of 352 protein fragments generated within the HPA.

Materials and method Patients and samples Plasma samples from 233 ALS patients and 204 healthy controls were included in this analysis (table 1). The sample cohort was collected and retrieved from the Brain Center Rudolf Magnus in Utrecht, Netherlands. The ALS patients (mean age 69 years, range 28-90 years, 148 men, 85 women) were well characterized with information such as age, gender, site of onset, diagnosis, ALS Functional Rate score – Revised (ALSFRS-R score), and inheritance (familial or sporadic). Information about presence of ALS associated mutations was available for some of the ALS patients. Pools were created and the samples were randomized before they were distributed into five 96-well plates prior to this study.

Table1:Sampledemographics.(a)NumberanddistributionofageandgenderofthesubjectsintheALSandcontrolgroups.(b)Numberofpatientsanddiseasedurationinmonthsfordiagnosisandsiteofonset.(a) N Agemean Male/FemaleALS 233 69(28-90) 148/85SALS 211 69(28-90) 136/75FALS 13 67(40-82) 4/9Control 204 70(26-90) 131/73

(b) NDiagnosisALS 199PLS 13PMA 22SiteofonsetSpinal 144Bulbar 76Thoracic/respiratory 13Generalized 1

13(14)20(25)35(53)43(-)

16(22)35(36)29(43)

Diseasedurationmean,month(SD)

InformationabouttypeofALS(SALSorFALS)wasmissingfor9ofthepatients.ALS,amyotrophiclateralsclerosis;SALS,sporadicALS;FALS,familialALS;PLS,primarylateralsclerosis;PMA,progressivemuscularatrophy.

Antigen selection Antigens to be included in this study were selected by literature mining, previous internal protein profiling studies in the context of neuroscience, as well as untargeted screenings on planar microarrays described below. Planar array The planar antigen arrays were generated previously within the HPA and contain human protein fragments expressed with a His6-albumin binding protein (His6-ABP) tag.23 Two different planar arrays were performed in this study. First, 84 samples (41 ALS patients, 43 controls) from the studied cohort were screened on 1536 antigens (4 different print batches). Two print batches (PB155 and PB161) were selected to contain antigens previously associated to ALS (KCNK2, NEFH, GFAP, AIF1, PPARG, and VCL) and two print batches (PB170 and PB182) were selected randomly. For this experiment, the plasma samples were diluted 1:250 in assay buffer (PBS-T, 3% bovine serum albumin, 5% nonfat dry milk (Semper) and 160 mg/ml of His6ABP). The diluted samples were stored in -20°C until use. A slide holder (ArraySlide, Gel Company) with a silicon mask containing wells with a capacity of 60-100 µl sample,

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was used as incubation chamber. 60 µl of the diluted samples were incubated on the slides for 1 hour in RT on a shake table before washing 2x5 min in PBS-T. The slides were incubated with Hen anti-His6ABP (diluted 1:40 000 in PBS-T) for 1 hour at RT on shake table before washing 2x5 min with PBS-T. The secondary detection antibodies, Goat anti-chicken Alexa 555 (diluted 1:60 000 in PBS-T) and Alexa Fluor® 647-AffiniPure F(ab')2 Fragment Goat Anti-Human IgG, Fcγ Fragment Specific (109-606-008) (diluted 1:25000 in PBS-T), were incubated on the slides for 1 hour at RT. The slides were washed twice with PBS-T and once in PBS before being spin-dried and scanned using a two-color microarray-scanner (Agilent G2505C, Agilent Technologies Inc.). The second array used in this study was a large antigen micro array, containing 42.100 unique protein fragments representing over 19.000 proteins. Four ALS samples indicating autoreactivity with low background signals in the first, smaller micro array, were selected for screening on the larger array. The selected samples were pooled and diluted 1:10 in assay buffer. Two slides, each containing 21,120 different antigens, were used. 100 µl diluted sample was applied to each slide and incubated 1 hour in RT without shaking. The slides were washed once with PBS-T before incubation with Hen anti-His6ABP (diluted 1:40 000 in PBS-T) for 1 hour at RT on shake table. The slides were washed twice before incubation with the secondary detection antibodies, Goat anti-chicken Alexa 555 (diluted 1:60 000 in PBS-T) and Goat anti-human IgG (H+L) Alexa 647 Life Technology Cat.#A21445 [2mg/ml] (diluted 1:25 000 in PBS-T). The slides were washed twice with PBS-T and once in PBS before being spin-dried and scanned. Suspension bead array Antigens with higher reactivity in ALS patients than controls on the planar array, together with antigens thought to have association to ALS identified in literature search and internal protein profiling studies, were selected and immobilized on magnetic beads. The suspension bead array (SBA) includes five major steps; i) Sample preparation, ii) SBA preparation, iii) Antigen SBA coupling, iv) Quality

control and v) Assay run. This study utilized a 384-plex suspension bead array. Sample preparation. Sample preparations include sample aliquotation, randomization and dilution. The studied cohort was aliquoted and randomized previously and the samples were diluted 1:250 in assay buffer. Diluted samples were stored in -20 °C until use. SBA preparation. SBA preparation involve the dilution and aliquotation of the antigens selected to be used in the bead array. 4 µg of each antigen are required for immobilization onto 40 µl beads and the antigens are diluted in 2-(N-morpholino) ethanesulfonic acid (MES)-buffer. Antigens with a concentration of 0.4 mg/ml were diluted 1:10 for 4 µg, while antigens with a concentration of 0.8 mg/ml were diluted 1:20. The antigen dilution and aliquotation was done using a liquid handling robot (Freedom Evo, Tecan Group Ltd). The positive and negative control beads were diluted manually as follow; rabbit anti-hIgG (1:50), EBNA1 (1:100), and His6ABP (4 µl). Additionally, three empty beads were used as negative controls. Antigen SBA coupling. The antigen SBA coupling involves the coupling of the selected antigens to the beads, and the pooling of all the beads to a complete bead stock. The beads were brought to RT and underwent one cycle of washing with activation buffer (NaH2PO4-buffer) using a plate washer (EL406, Biotek), before incubation with activation solution (Sulfo-N-Hydroxysulfosuccinimide and 1-Ethyl-3-(3-dimethylaminopropyl) carbodiimide solved in NaH2PO4-buffer) for 20 min, dark on a shake table. The beads were washed with MES buffer twice before adding the diluted antigens followed by 2 hours incubation, dark, on shake table. After incubation, the beads where washed twice with PBS-T and incubated overnight in 4°C with storage buffer (milliQ water, ProClin and 10xBRE(blocking Reagent for ELISA)) followed by pooling of the beads to a complete bead stock. Assay run. The diluted samples were thawed and incubated 1 hour at RT for pre-blocking against

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His6ABP, and the bead stock was sonicated. Bead stock and diluted samples were transferred to two Greiner 384 plates (5 µl bead stock, 45 µl diluted sample) and incubated for 2 hour, dark, at RT on shake table. This was followed by three cycles of washing with PBS-T, resuspension with 0.2 % paraform-aldehyde (PFA) and incubation 10 min, at RT on shake table. The plates were washed for three cycles with PBS-T and resuspended in 50 µl R-PE conjugated Goat F(ab’)2 Fragment anti-Human IgG (γ) (H10104, Invitrogen) (1:750) per well and incubated 30 min, at RT on shake table. This was followed by three cycles of washing with PBS-T and resuspension in 60 µl PBS-T. Using a FlexMap 3D instrument (Luminex) for read out, median fluorescence intensities (MFI) per bead ID for each well were obtained, as well as bead count for each bead ID. Image and data analysis Planar array. The resulting two-color images from the scanned slides were analyzed using an image analysis program (GenePix Pro 5.1, Molecular Devices LLC) where too small or morphologically bad spots were flagged to be removed from the data. The output is the MFI for each spot on the array. Processing and visualization of obtained data were done using the statistical computer software R.24 In the first planar array, antigens with more ALS patients showing high MFI values compared to control samples were chosen to be included in the SBA. In the second array, antigens with MFI>10.000 were included. In the SBA data, bead IDs with a bead count <35 were removed prior to data analysis. MFI values were converted to a binary variable and sample specific cutoffs were used indicating whether antigens should be considered reactive or nonreactive. The cutoff was based on the sample median + 50 ´ median absolute deviation (MAD). Statistical significance analysis by Fisher’s exact test was performed to compare the frequency of reactive samples in the tested groups and to determine p-values. P-values < 0.05 were considered significant.

Results Study design To investigate the presence of autoantibodies in ALS plasma, two planar antigen arrays and a suspension bead array were performed. Untargeted screening for detection of potential targets were made using the planar arrays, and autoantibody profiling was done using the suspension bead array technology focusing on protein targets discovered in the screening on planar arrays as well as proteins from literature. The profiling was performed on plasma samples from 233 ALS patients and 204 healthy controls that were randomly divided into two sample plates and screened on 352 protein targets. Target screening and selection With the aim to identify target proteins to use in the suspension bead array, 84 samples were profiled on 1536 proteins using planar arrays. By looking at the differences in measured MFI signals between cases and controls for each protein, 45 proteins were selected to be included in the SBA. Only proteins with ALS samples having a MFI signal >6000 were selected. A second screening on a larger planar array containing 42.100 protein fragments was performed using a pool of 4 ALS samples showing low background noise and reactivity towards at least 2 proteins in the first, smaller, planar array experiment. This revealed 141 proteins with MFI signals >10.000, that were included in the following SBA. Additionally, 166 proteins from literature research and previous in-house projects were added, resulting in a total of 352 proteins included in the SBA. Technical variation and quality control To evaluate the successfulness of the SBA experiment, 15 replicates and 5 empty wells were included in the sample layout, as well as positive and negative control beads in the bead stock. The positive control beads, anti-human IgG and EBNA1, gave high signals. The negative control beads, empty bead and His6ABP, gave low signals. However, the empty bead gave elevated signals in comparison to the His6ABP bead. To assess the technical variation, the coefficients of variations (CV) were calculated for each plate using

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replicates. Three wells with the same replicates were present in all five randomized plates and CV were calculated for each plate, and compared to CV calculated from 5 random samples. The mean CV for the plates in plate order were 22%, 15%, 7%, 7%, and 42%, while the mean CV for 5 random samples were 76%. The technical CV for plate 5 was higher compared to the others, and for 43 of the bead IDs in plate 5, the technical variation was larger than the biological variation. Consequently, plate 5 should be excluded during analysis of these 43 antigens. Correlation plots between the result from the smaller planar array and the SBA for the overlapping antigens were done for the 84 samples used in the planar array. A correlation was observed for most of the antigens. Planar array Analysis of the result from the smaller planar array using 84 samples (41 ALS samples, 43 control samples) revealed two proteins showing a close to

significant difference (P < 0.05) between the two groups (see table 2). Solute carrier family 1 member 7 (SLC1A7) and Sp2 transcription factor (SP2) had both four reactive ALS patients compared to zero controls (See figure 2). Suspension bead array Analysis of the SBA result from all 437 individuals revealed four proteins with significantly more reactive (P < 0.05) ALS patients compared to healthy controls. The four proteins were Glial fibrillary acidic protein (GFAP), SEC14-like protein 5 (SEC14L5), regulation of nuclear pre-mRNA domain containing 1A (RPRD1A), and solute carrier family 45, member 3 (SLC45A3) (see table 2). Of the 4 proteins, GFAP, RPRD1A and SEC14L5 had no or few reactive control samples while SLC45A3 had multiple reactive control samples (see supplementary figure 1). Both GFAP and SEC14L5 are expressed in the brain while RPRD1A is expressed mainly in male tissue. Therefore, further data analysis was only

Table2:TopcandidateproteinsidentifiedusingplanararrayandSBAwithnumberofreactiveindividuals,p-valuefromFisher’sexacttestanddescription.

Case Control

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SP2

MFI

[AU

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p= 0.052

Case Control

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p= 0.052

MFI

[AU

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Figure2:DifferencesinautoantibodylevelsbetweenALSpatients(n=41)andhealthycontrols(n=43).Twooftheantigens,SLC1A7andSP2,showedaclosetosignificantdifferencebetweenthegroups(P=0.052).Onespotrepresentsoneindividual,redspotsindicateindividualsconsideredreactive(cutoff50xMAD+samplemedian).

N(reactive) ALS/Control P-value DescriptionPlanararraySLC1A7 4 4/0 0.052 Solutecarrierfamily1,member7SP2 4 4/0 0.052 Sp2transcriptionfactorSuspensionbeadarrayGFAP 8 8/0 0.008 GlialfibrillaryacidicproteinSEC14L5 9 8/1 0.041 SEC14-likeprotein5RPRD1A 6 6/0 0.032 Regulationofnuclearpre-mRNAdomaincontaining1ASLC45A3 76 50/26 0.016 Solutecarrierfamily45,member3

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made on GFAP and SEC14L5 due to more interesting localization and reactivity profiles in terms of ALS (see figure 3). The technical CV was 20% for GFAP, and 28% for SEC14L5. Relating autoantibody profiles to clinical information The obtained autoantibody profiles for GFAP and SEC14L5 were related to additional available clinical parameters, such as diagnosis, age, age of onset, site of onset, C9orf72 status, and ALS.FRS-R score. For diagnosis, age, age of onset, site of onset, C9orf72 status, and ALS.FRS-R score, no correlation was observed for neither of the proteins (See supplementary figure 2-6). However, a close to significant difference (P=0.053) between site of onset for SEC14L5 was observed, with all 8 reactive ALS patients having a spinal onset (see figure 3). No significant difference between site of onset for GFAP was detected.

Discussion In this study, autoantibody profiling was performed on plasma samples from 233 ALS patients and 204 healthy control subjects. High-throughput and multiplex planar- and suspension bead array technologies were utilized. Autoantibody profiles were generated for all 437 plasma samples using 352 antigens, which revealed two interesting proteins, GFAP and SEC14L5, with higher number of reactive ALS patients compared to controls. GFAP GFAP, which had the highest statistical significance, showed 8 reactive ALS patients, compared to zero controls. No correlation between clinical information and signal intensities or reactivities was observed. GFAP is a type III intermediate filament. It is found mainly in astrocytes, a sub-type of glial cells located in the brain and the central nervous system (CNS)25, but is also expressed in other types of tissues, such as kidneys, pancreas, and lungs.26 The function of this protein is still poorly understood but it is known to be an essential filament component in astrocytes and plays an important role in cell-cell communication and astrocyte-neuron interactions.27 GFAP makes up the central cytoskeletal framework of astrocytes and

is thus considered as a marker for these.28 Studies in transgenic mice revealed that GFAP is not essential for normal function of astrocytes, but is necessary for the process involved in reactivation of astrogliosis and glial scar formation.29 Several studies have displayed an upregulation of this protein as a consequence of CNS injury26,30,31 and degeneration of astrocytes32. A disease with strong association to GFAP is Alexander Disease, which is caused by dominant gain-of-function mutations in the GFAP gene.33 Connections between ALS and the protein GFAP have been identified in several studies. For instance, GFAP-immunoreactive astrocytes have shown to be higher in patients with ALS compared to controls34, as well as elevated expression of GFAP in both mouse models35 and cell cultures36 when exposed to CSF from ALS patients. Evidence of accumulation of highly acetylated GFAP in ALS patients have also been documented, suggesting that heavy acetylation of GFAP prevents degradation and contributes to accumulation of the protein.27 The protein also comprises highly immunogenic epitopes which are more likely to induce an immune response.37 As GFAP is not only expressed in the brain, but also in other tissues, this might increase the risk of immunoreactivity. Since these tissues have a greater connection to the vascular system compared to the brain, immune cells might get in contact with GFAP more frequently and thus initiate an immune response. Among the two potentially interesting proteins, GFAP is the only protein that has been linked to ALS previously, showing elevated protein levels or accumulation in ALS patients. All these factors could result in an increased probability of developing autoantibodies targeting GFAP. However, if the presence of autoantibodies towards GFAP in ALS patients is a result of the elevated protein levels, or the other way around, has not been determined. But as the increased levels of the protein GFAP could be a consequence of astrocyte degeneration which occurs in ALS, it is more likely that the elevated expression of autoantibodies to GFAP is a consequence of the increased protein expression.

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SEC14L5 The second protein with significant differences between ALS patients and controls was SEC14L5. By relating clinical information to autoantibody expression, a close to significant difference (P=0.053) in number of reactive individuals having a spinal onset compared to bulbar onset was observed (see figure 3). SEC14L5 is a subgroup of the SEC14-containing proteins comprising an additional Golgi dynamics (GOLD) domain38. The protein function is not completely known but GOLD domains are thought to be involved in protein-protein interactions

connected to Golgi functions or trafficking. GOLD domains in combination with a lipid binding domain39, such as SEC14, suggest that SEC14L5 is involved in signaling with protein complexes in organelle homeostasis or traffic. Gene ontology (GO) annotations of the protein SEC14L5 include transporter activity (GO:0005215) and intracellular (GO:0005622). No previous studies have linked neither the protein nor gene of SEC14L5 to ALS. However, other studies involving SEC14L5 has been made. For instance, a study investigating single nucleotide polymorphisms (SNPs) in the SEC14L5

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SEC14L5

MFI

[AU

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/ re

spira

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trol

0.004

0.053

Case Control

5010

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0020

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GFAP

MFI

[AU

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p= 0.0082

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GFAP

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0.23 0.005

Case Control

5010

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SEC14L5p= 0.041

MFI

[AU

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A) B)

C) D)

Figure3:A)andB);DifferencesinautoantibodylevelsbetweenALSpatients(n=233)andhealthycontrols(n=204).Twooftheantigens,GFAPandSEC14L5,showedsignificantdifferencesbetweenALSsamplesandcontrols.C)andD);Autoantibodylevelsrelatedtositeofonset.AclosetosignificantdifferencebetweenbulbarandspinalonsetinSEC14L5,whilenocorrelationwasobservedinGFAP(p-valuesshownbelowrespectiveplot).Onespotrepresentsoneindividual,redspotsindicateindividualsconsideredreactive(cutoff50xMAD+samplemedian).

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gene and its association to lung cancer risk have been made40, as well as a study looking at the gene’s role in post-traumatic stress disorders (PTSD)41. No significant link between SNPs in SEC14L5 and lung cancer risk was identified, while the gene seems to be a potential target for PTSD. According to the HPA (www.proteinatlas.org), the protein SEC14L5 is expressed in the cerebral cortex in the brain. This is a particular interesting area of the brain in terms of ALS, since motor neuron pathways originate from the motor cortex, a region of the cerebral cortex, and carry information to the spinal cord and brain stem.42 The fact that the protein might be involved in signaling in organelle homeostasis is also noteworthy since deficiency in calcium homeostasis and protein folding have shown to be an essential feature in neurodegeneration.43 Studies supporting SEC14-proteins involvement in calcium signaling exists, suggesting that SEC14 take part in Ca2+ signaling transduction by promoting calcium release intracellularly.44 Thus, autoantibodies targeting SEC14L5 might impair proper homeostasis regulation resulting in neuron degeneration and/or protein accumulation. This could in turn be toxic to the cells, and could possibly contribute to the development of ALS. Planar array vs. SBA In this study, both planar- and suspension bead array technologies were utilized. Among all 81 overlapping proteins, 25 of the reactive antigens were present in the SBA bead stock. 17 of these antigens showed samples that were reactive in both methods. Correlation was observed for most of the antigens (see supplementary figure 7), however, some of the antigens revealed low or no correlation between the two method. The reasons for the varying result from the two methods is not known, but inconsistency in recoupling of the same protein fragments onto beads have been observed in our lab earlier. Some of this variance may arise due to differences in the fragment alignment during immobilization. Some sites on the protein fragments might be more, or less, available to the autoantibodies in the plasma depending on the coupling. This theory could also explain why findings that are observed in planar arrays cannot be seen in SBAs. Another explanation to the difference

in result might be due to the format of the two methods. Protein fragments and their availability to autoantibodies in a sample might be influenced by their surroundings, which differs a lot between planar arrays and SBAs. For instance, the samples to be analyzed are applied to a dry, planar surface in planar arrays, compared to a solution in the SBA. The SBA format could potentially make the autoantibodies more effective in interacting with the protein fragments on the beads compared to planar arrays and thus detect autoantibodies towards proteins not detectable in planar array format. However, this does not explain reactivities detected in planar arrays but not in SBAs. Hence, another hypothesis could be that the chemistry involved in immobilization of the protein fragments onto its solid support could affect the characteristics of it. The coupling chemistry involves chemicals such as NHS, EDC and carboxyl groups while the printing of antigens to planar arrays involve glycerol and 0.05 M carbonate-bicarbonate. The different immobilization techniques and the chemicals effect on both protein fragments and autoantibodies have not been studied, but could potentially have an impact on how the autoantibodies bind and what protein they bind to in the different methods. Another factor that need to be taken into consideration regarding the difference in result is the storing of the protein fragments on their surface. In planar arrays the protein fragments are dried in micro spots. This might influence the protein fragments characteristics and thus making it harder for the autoantibodies to bind with affinity to the fragment. This could possibly result in making the autoantibodies more prone to bind unspecifically to the protein spots and thus give false positive signals. This could also be the case for the SBA, but in general, the SBA is considered to be the more accurate method since previous findings with SBAs have been validated and confirmed with orthogonal methods (such as mass spectrometry). Future perspectives As ALS is a rapid progressive motor neuron disease with no effective treatment or cure, it is of crucial interest to investigate the disease further and uncover the mysteries of the mechanism behind ALS. Autoimmune components are believed to play a part in the pathology and etiology of the disease, and the

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interest of investigating these components are becoming larger. Still, there is a lot that remains to be discovered when it comes to the pathology of ALS. Even though several different mechanisms, SNPs, malfunctioning proteins, and systems have been found in greater frequencies in ALS patients, one of the major challenges is to identify which of these that cause the disease, and are not only a symptom of it. Future studies will hopefully increase the understanding of the disease and thus provide a means of treating it and give affected patients an increased quality of life. In this study, autoantibody profiling of 347 plasma samples was carried out using both planar array and SBA technologies. The findings presented in this study should be validated by additional experiments with other methods and techniques, as well as profiling of more samples from different cohorts.

Acknowledgements First, I would like to thank Peter Nilsson for giving me the opportunity to do my master thesis in his group, and for supporting me throughout the whole project. I would also like to thank my supervisor, Julia Remnestål, for always being available for discussion, answering my questions and for giving me an extra hand whenever needed. Furthermore, I would like to thank all members of the Biobank Profiling - Affinity Proteomics group for always helping me out, giving me advice and support, and for creating a happy and welcoming environment. Special thanks to Cecilia Hellström, Cecilia Mattson, David Just, and Maria Mikus for answering my questions about the data analysis in autoimmunity profiling.

References: (1) Chiò, A.; Logroscino, G.; Traynor, B. J.;

Collins, J.; Simeone, J. C.; Goldstein, L. A.; White, L. A. Global Epidemiology of Amyotrophic Lateral Sclerosis: A Systematic Review of the Published Literature. Neuroepidemiology 2013, 41 (2), 118–130.

(2) Taylor, J. P.; Brown Jr, R. H.; Cleveland, D.

W. Decoding ALS: From Genes to Mechanism. Nature 2016, 539 (7628), 197–206.

(3) Ravits, J.; Appel, S.; Baloh, R. H.; Barohn, R.; Brooks, B. R.; Elman, L.; Floeter, M. K.; Henderson, C.; Lomen-Hoerth, C.; Macklis, J. D.; Mccluskey, L.; Mitsumoto, H.; Przedborski, S.; Rothstein, J.; Trojanowski, J. Q.; Van Den Berg, L. H.; Ringel, S. Deciphering Amyotrophic Lateral Sclerosis: What Phenotype, Neuropathology and Genetics Are Telling Us about Pathogenesis. Amyotroph. Lateral Scler. Frontotemporal Degener. 2013, 14 (0 1), 5–18.

(4) Haggmark, A.; Mikus, M.; Mohsenchian, A.;

Hong, M.-G.; Forsstrom, B.; Gajewska, B.; Baranczyk-Kuzma, A.; Uhlen, M.; Schwenk, J. M.; Kuzma-Kozakiewicz, M.; Nilsson, P. Plasma Profiling Reveals Three Proteins Associated to Amyotrophic Lateral Sclerosis. Ann. Clin. Transl. Neurol. 2014, 1 (8), 544–553.

(5) Liewluck, T.; Saperstein, D. S. Progressive

Muscular Atrophy. Neurol. Clin. 2015, 33 (4), 761–773.

(6) Statland, J. M.; Barohn, R. J.; Dimachkie, M.

M.; Floeter, M. K.; Mitsumoto, H. Primary Lateral Sclerosis. Neurol. Clin. 2015, 33 (4), 749–760.

(7) Prpar Mihevc, S.; Darovic, S.; Kovanda, A.;

Bajc Česnik, A.; Župunski, V.; Rogelj, B. Nuclear Trafficking in Amyotrophic Lateral Sclerosis and Frontotemporal Lobar Degeneration. Brain 2017, 140 (1), 13–26.

(8) Kaus, A.; Sareen, D. ALS Patient Stem Cells

for Unveiling Disease Signatures of Motoneuron Susceptibility: Perspectives on the Deadly Mitochondria, ER Stress and Calcium Triad. Front. Cell. Neurosci. 2015, 9, 448.

(9) Coppedè, F. An Overview of DNA Repair in

Amyotrophic Lateral Sclerosis. ScientificWorldJournal. 2011, 11, 1679–1691.

(10) Malaspina, A.; Puentes, F.; Amor, S. Disease

Origin and Progression in Amyotrophic Lateral Sclerosis: An Immunology Perspective. Int. Immunol. 2015, 27 (3), 117–129.

(11) Pullen, A. H.; Demestre, M.; Howard, R. S.;

Orrell, R. W. Passive Transfer of Purified IgG

12

from Patients with Amyotrophic Lateral Sclerosis to Mice Results in Degeneration of Motor Neurons Accompanied by Ca2+ Enhancement. Acta Neuropathol. 2004, 107 (1), 35–46.

(12) Pagani, M. R.; Reisin, R. C.; Uchitel, O. D.

Calcium Signaling Pathways Mediating Synaptic Potentiation Triggered by Amyotrophic Lateral Sclerosis IgG in Motor Nerve Terminals. J. Neurosci. 2006, 26 (10), 2661–2672.

(13) Fratantoni, S. A.; Weisz, G.; Pardal, A. M.;

Reisin, R. C.; Uchitel, O. D. Amyotrophic Lateral Sclerosis IgG-Treated Neuromuscular Junctions Develop Sensitivity to L-Type Calcium Channel Blocker. Muscle Nerve 2000, 23 (4), 543–550.

(14) Fialová, L.; Švarcová, J.; Bartos, A.; Ridzoň,

P.; Malbohan, I.; Keller, O.; Rusina, R. Cerebrospinal Fluid and Serum Antibodies against Neurofilaments in Patients with Amyotrophic Lateral Sclerosis. Eur. J. Neurol. 2010, 17 (4), 562–566.

(15) Couratier, P.; Yi, F. H.; Preud’homme, J. L.;

Clavelou, P.; White, A.; Sindou, P.; Vallat, J. M.; Jauberteau, M. O. Serum Autoantibodies to Neurofilament Proteins in Sporadic Amyotrophic Lateral Sclerosis. J. Neurol. Sci. 1998, 154 (2), 137–145.

(16) Mizutani, K.; Oka, N.; Kusunoki, S.; Kaji, R.;

Kanda, M.; Akiguchi, I.; Shibasaki, H. Amyotrophic Lateral Sclerosis with IgM Antibody against Gangliosides GM2 and GD2. Intern. Med. 2003, 42 (3), 277–280.

(17) Niebroj-Dobosz, I.; Jamrozik, Z.; Janik, P.;

Hausmanowa-Petrusewicz, I.; Kwiecinski, H. Anti-Neural Antibodies in Serum and Cerebrospinal Fluid of Amyotrophic Lateral Sclerosis (ALS) Patients. Acta Neurol. Scand. 1999, 100 (4), 238–243.

(18) Smith, R. G.; Hamilton, S.; Hofmann, F.;

Schneider, T.; Nastainczyk, W.; Birnbaumer, L.; Stefani, E.; Appel, S. H. Serum Antibodies to L-Type Calcium Channels in Patients with Amyotrophic Lateral Sclerosis. N. Engl. J. Med. 1992, 327 (24), 1721–1728.

(19) Nwosu, V. K.; Royer, J. A.; Stickler, D. E.

Voltage Gated Potassium Channel Antibodies in Amyotrophic Lateral Sclerosis. Amyotroph. Lateral Scler. 2010, 11 (4), 392–394.

(20) Ayoglu, B.; Schwenk, J. M.; Nilsson, P.

Antigen Arrays for Profiling Autoantibody Repertoires. Bioanalysis 2016, 8 (10), 1105–1126.

(21) Uhlen, M.; Fagerberg, L.; Hallstrom, B. M.;

Lindskog, C.; Oksvold, P.; Mardinoglu, A.; Sivertsson, A.; Kampf, C.; Sjostedt, E.; Asplund, A.; Olsson, I.; Edlund, K.; Lundberg, E.; Navani, S.; Szigyarto, C. A.-K.; Odeberg, J.; Djureinovic, D.; Takanen, J. O.; Hober, S.; Alm, T.; Edqvist, P.-H.; Berling, H.; Tegel, H.; Mulder, J.; Rockberg, J.; Nilsson, P.; Schwenk, J. M.; Hamsten, M.; von Feilitzen, K.; Forsberg, M.; Persson, L.; Johansson, F.; Zwahlen, M.; von Heijne, G.; Nielsen, J.; Ponten, F. Proteomics. Tissue-Based Map of the Human Proteome. Science 2015, 347 (6220), 1260419.

(22) Sjoberg, R.; Mattsson, C.; Andersson, E.;

Hellstrom, C.; Uhlen, M.; Schwenk, J. M.; Ayoglu, B.; Nilsson, P. Exploration of High-Density Protein Microarrays for Antibody Validation and Autoimmunity Profiling. N. Biotechnol. 2016, 33 (5 Pt A), 582–592.

(23) Agaton, C.; Falk, R.; Höidén Guthenberg, I.;

Göstring, L.; Uhlén, M.; Hober, S. Selective Enrichment of Monospecific Polyclonal Antibodies for Antibody-Based Proteomics Efforts. J. Chromatogr. A 2004, 1043 (1), 33–40.

(24) Ihaka, R.; Gentleman, R. R: A Language for

Data Analysis and Graphics. J. Comput. Graph. Stat. 1996, 5 (3), 299–314.

(25) Mondello, S.; Hayes, R. L. Chapter 16 –

Biomarkers. In Handbook of Clinical Neurology; 2015; Vol. 127, pp 245–265.

(26) Sofroniew, M. V; Vinters, H. V. Astrocytes:

Biology and Pathology. Acta Neuropathol. 2010, 119 (1), 7–35.

(27) Liu, D.; Liu, C.; Li, J.; Azadzoi, K.; Yang, Y.;

Fei, Z.; Dou, K.; Kowall, N. W.; Choi, H.-P.; Vieira, F.; Yang, J.-H. Proteomic Analysis Reveals Differentially Regulated Protein Acetylation in Human Amyotrophic Lateral

13

Sclerosis Spinal Cord. PLoS One 2013, 8 (12), e80779.

(28) Crawford, J. D.; Chandley, M. J.; Szebeni, K.;

Szebeni, A.; Waters, B.; Ordway, G. A. Elevated GFAP Protein in Anterior Cingulate Cortical White Matter in Males With Autism Spectrum Disorder. Autism Res. 2015, 8 (6), 649–657.

(29) Herrmann, A. Using Photolabile Protecting

Groups for the Controlled Release of Bioactive Volatiles. Photochem. Photobiol. Sci. 2012, 11 (3), 446.

(30) Bush, T. G.; Puvanachandra, N.; Horner, C.

H.; Polito, A.; Ostenfeld, T.; Svendsen, C. N.; Mucke, L.; Johnson, M. H.; Sofroniew, M. V. Leukocyte Infiltration, Neuronal Degeneration, and Neurite Outgrowth after Ablation of Scar-Forming, Reactive Astrocytes in Adult Transgenic Mice. Neuron 1999, 23 (2), 297–308.

(31) Colangelo, A. M.; Alberghina, L.; Papa, M.

Astrogliosis as a Therapeutic Target for Neurodegenerative Diseases. Neurosci. Lett. 2014, 565, 59–64.

(32) Ishiki, A.; Kamada, M.; Kawamura, Y.;

Terao, C.; Shimoda, F.; Tomita, N.; Arai, H.; Furukawa, K. Glial Fibrillar Acidic Protein in the Cerebrospinal Fluid of Alzheimer’s Disease, Dementia with Lewy Bodies, and Frontotemporal Lobar Degeneration. J. Neurochem. 2016, 136 (2), 258–261.

(33) Messing, A.; Brenner, M.; Feany, M. B.;

Nedergaard, M.; Goldman, J. E. Alexander Disease. J. Neurosci. 2012, 32 (15), 5017 LP-5023.

(34) Sugiyama, M.; Takao, M.; Hatsuta, H.;

Funabe, S.; Ito, S.; Obi, T.; Tanaka, F.; Kuroiwa, Y.; Murayama, S. Increased Number of Astrocytes and Macrophages/microglial Cells in the Corpus Callosum in Amyotrophic Lateral Sclerosis. Neuropathology 2013, 33 (6), 591–599.

(35) Keller, A. F.; Gravel, M.; Kriz, J. Live

Imaging of Amyotrophic Lateral Sclerosis Pathogenesis: Disease Onset Is Characterized by Marked Induction of GFAP in Schwann Cells. Glia 2009, 57 (10), 1130–1142.

(36) Shobha, K.; Alladi, P. A.; Nalini, A.;

Sathyaprabha, T. N.; Raju, T. R. Exposure to CSF from Sporadic Amyotrophic Lateral Sclerosis Patients Induces Morphological Transformation of Astroglia and Enhances GFAP and S100beta Expression. Neurosci. Lett. 2010, 473 (1), 56–61.

(37) Eng, L. F.; Shibura, R. A. Chapter 14 - Glial

Fibrillary Acidic Protein: A Review of Structure, Function, and Clinical Application - Marangos, Paul J.; Campbell, I. C., Cohen, R. M. B. T.-N. and G. P., Eds.; Academic Press, 1988; pp 339–359.

(38) Saito, K.; Tautz, L.; Mustelin, T. The Lipid-

Binding SEC14 Domain. Biochim. Biophys. Acta - Mol. Cell Biol. Lipids 2007, 1771 (6), 719–726.

(39) Anantharaman, V.; Aravind, L. The GOLD

Domain, a Novel Protein Module Involved in Golgi Function and Secretion. Genome Biol. 2002, 3 (5), research0023.

(40) Zhang, S.; Thakur, A.; Liang, Y.; Wang, T.;

Gao, L.; Yang, T.; Li, Y.; Geng, T.; Jin, T.; Chen, T.; Liu, J. J.; Chen, M. Polymorphisms in C-Reactive Protein and Glypican-5 Are Associated with Lung Cancer Risk and Gartrokine-1 Influences Cisplatin-Based Chemotherapy Response in a Chinese Han Population. Disease Markers. 2015.

(41) Chitrala, K. N.; Nagarkatti, P.; Nagarkatti, M.

Prediction of Possible Biomarkers and Novel Pathways Conferring Risk to Post-Traumatic Stress Disorder. PLoS ONE. San Francisco, CA USA 2016.

(42) Dale, P.; George J, A.; David, F.; Lawrence

C, K.; Anthony-Samuel, L.; James O, M.; S Mark, W. Damage to Descending Motor Pathways: The Upper Motor Neuron Syndrome. In Neuroscience; Dale, P., George J, A., David, F., Lawrence C, K., Anthony-Samuel, L., James O, M., S Mark, W., Eds.; Sinauer Associates: Sunderland (MA), 2001.

(43) Prell, T.; Lautenschlager, J.; Grosskreutz, J.

Calcium-Dependent Protein Folding in Amyotrophic Lateral Sclerosis. Cell Calcium 2013, 54 (2), 132–143.

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(44) Gong, B.; Shen, W.; Xiao, W.; Meng, Y.; Meng, A.; Jia, S. The Sec14-like Phosphatidylinositol Transfer Proteins Sec14l3/SEC14L2 Act as GTPase Proteins to Mediate Wnt/Ca(2+) Signaling. Elife 2017, 6, e26362.

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Supplementary material

Supplementaryfigure1:DifferencesinautoantibodylevelsbetweenALSpatients(n=233)andhealthycontrols(n=204).Theothertwoantigens,RPRD1AandSLC45A3,showingsignificantdifferencesbetweenthegroups.Onespotrepresentsoneindividual,redspotsindicateindividualsconsideredreactive(cutoff50xMAD+samplemedian).

Supplementaryfigure2:Diagnosisdistributionforthetwopotentiallyinterestingproteins,GFAPandSEC14L5.Nosignificantdifferencesinreactivityorsignalintensitybetweenthegroupswereobserved(p<0.05).

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Supplementaryfigure3:C9orf72mutationdistributionforthetwopotentiallyinterestingproteins,GFAPandSEC14L5.Nosignificantdifferencesinreactivityorsignalintensitybetweenthegroupswereobserved(p<0.05).

Supplementaryfigure4:CorrelationplotswithMFIsignalandageoftheALSpatientsforthetwopotentiallyinterestingproteins,GFAPandSEC14L5.Nocorrelationwasobserved.

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Supplementaryfigure5:CorrelationplotswithMFIsignalandageofonsetoftheALSpatientsforthetwopotentiallyinterestingproteins,GFAPandSEC14L5.Nocorrelationwasobserved.

Supplementaryfigure6:CorrelationplotswithMFIsignalandALSFRS-RscoreoftheALSpatientsforthetwopotentiallyinterestingproteins,GFAPandSEC14L5.Nocorrelationwasobserved.

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Supplementaryfigure7:Correlationplotsforfourofthe81overlappingantigens.Correlationwasobservedformostoftheantigensthatwerereactiveinanyofthemethods.

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●●

●

●

●

●●●●●●●

●

● ●●●●●● ●

●

●●

●●●● ●

●

●

●

200 500 1000 5000

2050

100

500

HDGFL1

PA Intensity

SBA

Inte

nsity

●

●

●

● ●

●●

●

●

●● ●●● ●

●

●

●

●●

●●● ● ●● ●

●

● ●●●

● ●●●

●●●

●

●●

●●●

●

● ●●

●●

●● ● ●●

●●●

●

●●●●●●

●

●●

●

● ●

● ●● ●●● ●

● ●

●

●

200 500 1000

2050

100

200

500

ADRB1

PA Intensity

SBA

Inte

nsity ●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●●

●

●●

●

●

●

●

●

●

●

●●

●

●

●

●

● ●

●

●●

●

●

●

●

●●

●●

●

●

●●

●

●●

●

●

200 500 2000 10000

5020

010

0050

00

ATF3

PA Intensity

SBA

Inte

nsity