==== Front EBioMedicineEBioMedicineEBioMedicine2352-3964Elsevier S2352-3964(18)30222-610.1016/j.ebiom.2018.06.015Research PaperInitial Identification of a Blood-Based Chromosome Conformation Signature for Aiding in the Diagnosis of Amyotrophic Lateral Sclerosis Salter Matthew aCorfield Emily aRamadass Aroul aGrand Francis aGreen Jayne aWestra Jurjen aLim Chun Ren aFarrimond Lucy bFeneberg Emily bScaber Jakub bThompson Alexander bOssher Lynn bTurner Martin bTalbot Kevin bCudkowicz Merit cBerry James cHunter Ewan aAkoulitchev Alexandre alexandre.akoulitchev@oxfordbiodynamics.coma⁎a Oxford BioDynamics, Oxford, UKb Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UKc Neurology Clinical Research Institute, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States⁎ Corresponding author. alexandre.akoulitchev@oxfordbiodynamics.com23 6 2018 7 2018 23 6 2018 33 169 184 3 5 2018 11 6 2018 12 6 2018 © 2018 The Author(s)2018This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Background The identification of blood-based biomarkers specific to the diagnosis of amyotrophic lateral sclerosis (ALS) is an active field of academic and clinical research. While inheritance studies have advanced the field, a majority of patients do not have a known genetic link to the disease, making direct sequence-based genetic testing for ALS difficult. The ability to detect biofluid-based epigenetic changes in ALS would expand the relevance of using genomic information for disease diagnosis. Methods Assessing differences in chromosomal conformations (i.e. how they are positioned in 3-dimensions) represents one approach for assessing epigenetic changes. In this study, we used an industrial platform, EpiSwitch™, to compare the genomic architecture of healthy and diseased patient samples (blood and tissue) to discover a chromosomal conformation signature (CCS) with diagnostic potential in ALS. A three-step biomarker selection process yielded a distinct CCS for ALS, comprised of conformation changes in eight genomic loci and detectable in blood. Findings We applied the ALS CCS to determine a diagnosis for 74 unblinded patient samples and subsequently conducted a blinded diagnostic study of 16 samples. Sensitivity and specificity for ALS detection in the 74 unblinded patient samples were 83∙33% (CI 51∙59 to 97∙91%) and 76∙92% (46∙19 to 94∙96%), respectively. In the blinded cohort, sensitivity reached 87∙50% (CI 47∙35 to 99∙68%) and specificity was 75∙0% (34∙91 to 96∙81%). Interpretations The sensitivity and specificity values achieved using the ALS CCS identified and validated in this study provide an indication that the detection of chromosome conformation signatures is a promising approach to disease diagnosis and can potentially augment current strategies for diagnosing ALS. Fund This research was funded by Oxford BioDynamics and Innovate UK. Work in the Oxford MND Care and Research Centre is supported by grants from the Motor Neurone Disease Association and the Medical Research Council. Additional support was provided by the Northeast ALS Consortium (NEALS). ==== Body Research in context Evidence Before this Study We searched primary research studies done in human ALS subjects in the last ten years and referenced in PubMed by use of the MeSH terms “(amyotrophic lateral sclerosis OR ALS) AND (biomarker OR marker) AND (diagnostic OR diagnosis) AND (blood OR PBMC OR plasma OR serum) AND (sensitivity OR specificity OR AUC)”. There were no language restrictions. We identified 16 studies where diagnostic biomarkers for ALS were determined in a non-invasive, clinically accessible biological sample and statistical power was reported. In these studies, eight used plasma as a biomarker source, five used serum and three studies used PBMCs or whole blood. The biomarkers ranged from mRNA, proteins/peptides, metabolites, metals, or combinations of several molecular modalities combined into a multi-marker panel. Neurofilament light (NfL) was the most extensively studied, and the sensitivity and specificity of the biomarkers in making a diagnosis of ALS ranged between 90 and 93% and 58–91%, respectively. Two studies showed no significant diagnostic power with the biomarkers under investigation. Added Value of this Study The current diagnosis of ALS remains mainly based on clinical parameters. However, the development of molecular tests can be used to exclude other diseases and help to confirm the diagnosis. Currently, there is a shortage of these molecular tests available to physicians. We were able to develop a highly discriminatory chromosome conformation signature that could accurately identify patients with ALS in an independent patient cohort. The test can be performed within a day on standard laboratory equipment, uses a readily accessible biofluid (blood) and requires and requires a minimal amount of volume. Implications of all the Available Evidence Our findings indicate that a simple and rapid blood-based test can distinguish patients with ALS from healthy controls with a high degree of sensitivity and can serve as a complementary tool in helping aid in disease diagnosis. These preliminary findings provide an initial proof of concept that the approach of looking at changes in genomic architecture provide a reliable readout of physiological changes associated with neurological disease. Additional studies that independently validate the biomarker panel identified here and assess the ability of the panel to discriminate ALS from other motor neuron diseases are required before application in pre-clinical and clinical settings. 1 Introduction ALS is a fatal, degenerative neurologic disorder characterized by progressive muscle weakness and eventual paralysis. Disease presentation and rate of progression vary from patient-to-patient and while there are clinical tools to monitor disease progression after diagnosis (e.g. ALSFRS-R, FVC measures), no definitive clinically validated measure currently exists to diagnose the disease. Combined with the insidious nature of symptom onset, lack of recognition of symptoms and signs in non-specialists, and the need to perform multiple investigations to rule out conditions which can mimic ALS, lack of a diagnostic marker significantly contributes to diagnostic delay, which averages 1 year from symptom onset [1]. Given the rapidly progressing nature of ALS, this delay can have significant clinical and lifestyle impact on patients and limits recruitment of patients with early phase disease to clinical trials. With the potential for the approval of new therapies currently in different stages of clinical development over the coming years, there is a pressing need for a validated biomarker for the diagnosis of ALS. While advances in genomic sequencing have provided new insights into the disease [2], gene sequencing is primarily used to assess familial risk and define biological subtypes in the 10% or so of patients carrying mutations once the clinical diagnosis of ALS has been established, and therefore does not in itself provide a way of improving early diagnosis [3]. Another approach that has been used to aid in diagnosis of other neurological disorders is the analysis of non-sequence based alterations in the genome (epigenetics) [4, 5]. This is particularly useful in understanding the pathogenesis of multifactorial neurodegenerative diseases like ALS, where epigenetics can expose an integrated view of cellular function and dysfunction via regulation of gene expression [6]. The exploration of patient-derived samples is an obvious choice in attempting to discover new tools for diagnosing ALS, as biofluid collection is increasingly being done in clinical settings to serve as a data source for potential biomarkers. In light of this, the Northeast ALS Consortium (NEALS) Biofluid Repository was established to provide researches and industry partners with biologic samples collected from the patients using standard operating procedures (SOPs) and linked to clinical information for use in research studies [7]. In this pilot study, samples from the NEALS Biofluid Repository were analyzed using EpiSwitch, a high-resolution technology to identify structural-functional epigenetic changes in genomic architecture associated with pathological phenotypes developed by Oxford BioDynamics [[8], [9], [10], [11], [12], [13]]. As multiple genomic regions contribute to phenotypic differences through changes in genomic architecture, this approach allows for the development of a chromosomal conformation signature (CCS) of alterations in genomic architecture between two states (disease vs. non-diseased, pre-treatment vs. post-treatment). With the aim of identifying novel approaches to the diagnosis of ALS, this study was undertaken to discover and validate a CCS applicable to the diagnosis of ALS. After defining and refining a biomarker panel, we applied the signature to an independent sample cohort for validation. Last, the biological relevance in ALS of the top performing markers was assessed. 2 Methods High-throughput screening of blood samples and a multi-step selection process was used to define a panel of epigenetic changes that could distinguish ALS patients from healthy patients. 2.1 Sample Collection All samples banked and collected were done under IRB approved protocols (Oxford University samples: NRES Committee South West - Cornwall & Plymouth, reference 15/SW/0224). For the initial biomarker screening and development, the NEALS Biofluid Repository provided 50 ALS (Table 1) and 5 healthy control whole blood samples (Table 2). Oxford BioDynamics sample collection provided a further 37 controls (Table 2). In the validation stages of the project, Oxford University provided an independent sample cohort of eight ALS and eight spousal healthy control samples (Supplementary Table S1). Clinical characteristics for ALS patients were similar between the Discovery and Validation cohorts (Table 3). Whole blood samples were collected by peripheral venipucture using a 22-gauge large bore needle. Blood was collected directly into EDTA filled BD Vacutainer™ The tubes were immediately mixed by inversion and placed into a -80o C freezer. Samples were transported on dry ice and the frozen condition inspected on delivery. 2.2 Application of EpiSwitch and the Stepwise Biomarker Discovery Process EpiSwitch is a high throughput technology platform that pairs high resolution chromosome conformational capture results with regression analysis and machine learning to develop disease classifications [8]. Screening and selection of statistically significant differences in conditional and stable profiles of genome architecture associated with samples from patients suffering from a disease, in comparison to healthy control samples, serves as a way to select epigenetic biomarkers that can diagnose and stratify pathological conditions [[8], [9], [10], [11], [12], [13]]. In this study, EpiSwitch was used on blood samples in a three-step process to identify, evaluate, and validate statistically-significant differences in chromosomal conformations between ALS patients and healthy controls (Fig. 1).Fig. 1 Three-step biomarker discovery workflow. Starting with an initial pool of over 13,000 markers, a series of statistical comparisons between ALS and healthy controls samples refined the final ALS chromosome conformation signature panel into a set of 8 markers that could diagnose ALS patients in a blinded, independent cohort with 87.5% sensitivity. Fig. 1Table 1 NEALS Biofluid Repository samples used in this study. Table 1Study Sample ID Basic Annotation Sample Type Gender Ethnic Category Race Age at Diagnosis Disease Duration (Month from Symptom Onset to Sample Collection) ALSFRS Total Score ALS Type Skin biopsy 701001 ALS Whole blood Male Non-Hispanic or Latino White 67 34.20 38 Sporadic 701002 ALS Whole blood Female Non-Hispanic or Latino White 42 22.51 24 Sporadic 701024 ALS Whole blood Male Non-Hispanic or Latino White 67 15.9 42 Sporadic 701026 ALS Whole blood Male Non-Hispanic or Latino White 35 31.11 41 Sporadic 701028 ALS Whole blood Male Non-Hispanic or Latino Black 48 14.06 43 Sporadic 701032 ALS Whole blood Female Non-Hispanic or Latino White 57 9.66 40 Familial 701035 ALS Whole blood Male Non-Hispanic or Latino White 63 39.26 29 Sporadic 701037 ALS Whole Blood Female Non-Hispanic or Latino White 49 15.28 40 Sporadic 701040 ALS Whole blood Female Non-Hispanic or Latino White 39 19.52 43 Sporadic 701043 ALS Whole blood Male Non-Hispanic or Latino White 50 7.92 29 Sporadic 701054 ALS Whole blood Male Non-Hispanic or Latino White 44 16.99 39 Sporadic 701060 ALS Whole blood Male Non-Hispanic or Latino White 46 3.02 42 Sporadic 701080 ALS Whole blood Female Non-Hispanic or Latino White 66 12.65 36 Sporadic 701021 ALS Whole blood Female Non-Hispanic or Latino White 64 13.96 17 Sporadic ALS Sample repository 701001 ALS Whole blood Female Non-Hispanic or Latino White 57 13.57 N/A Sporadic 701026 ALS Whole blood Female Non-Hispanic or Latino White 47 0 N/A Sporadic 701027 ALS Whole blood Female Non-Hispanic or Latino White 55 0.4 N/A Familial 701038 ALS Whole blood Female Non-Hispanic or Latino White 73 0.92 N/A Sporadic 701042 ALS Whole blood Male Non-Hispanic or Latino White 56 8.76 N/A Sporadic 701043 ALS Whole blood Female Non-Hispanic or Latino White 59 10.61 N/A Sporadic 701044 ALS Whole blood Male Non-Hispanic or Latino White 58 1.5* N/A Sporadic 701045 ALS Whole blood Male Non-Hispanic or Latino White 47 5.9 N/A Sporadic 701048 ALS Whole blood Female Non-Hispanic or Latino White 62 14 N/A Sporadic 701049 ALS Whole blood Female Non-Hispanic or Latino White 47 11 N/A Sporadic 701051 ALS Whole Blood Male Non-Hispanic or Latino White 65 26.23 N/A Sporadic 701052 ALS Whole blood Male Non-Hispanic or Latino White 46 63.26 N/A Sporadic 701055 ALS Whole blood Female Non-Hispanic or Latino White 55 2.1 N/A Sporadic 701056 ALS Whole blood Male Non-Hispanic or Latino White 53 0.43* N/A Sporadic 701064 ALS Whole blood Female Non-Hispanic or Latino White 67 0.74* N/A Sporadic 701066 ALS Whole blood Male Non-Hispanic or Latino White 41 9 N/A Sporadic 701069 ALS Whole blood Male Non-Hispanic or Latino White 51 0.48 N/A Familial 701082 ALS Whole blood Male Non-Hispanic or Latino White 52 11.36* N/A Sporadic 701083 ALS Whole blood Male Hispanic, Latino White 55 50.26* N/A Sporadic 701084 ALS Whole blood Male Non-Hispanic or Latino White 51 1.17 N/A Sporadic 701085 ALS Whole blood Male Non-Hispanic or Latino White 47 38.67* N/A Familial 701101 ALS Whole blood Male Non-Hispanic or Latino Black 43 43.73* N/A Sporadic 701107 ALS Whole blood Female Non-Hispanic or Latino Unknown/Not reported 65 2.83 N/A Familial 701108 ALS Whole blood Male Non-Hispanic or Latino White 70 0 N/A Familiar 701116 ALS Whole blood Male Unknown/Not reported Unknown/Not reported 68 3.89* N/A Sporadic 701122 ALS Whole blood Male Non-Hispanic or Latino White 58 0.45 N/A Sporadic 701132 ALS Whole blood Female Non-Hispanic or Latino White 61 2.86* N/A Sporadic 701136 ALS Whole blood Male Non-Hispanic or Latino White 53 18.86 N/A Sporadic 701139 ALS Whole blood Female Non-Hispanic or Latino White 47 7.03 N/A Familial 701142 ALS Whole blood Male Unknown/Not reported Unknown/Not reported 31 0 N/A Sporadic 701148 ALS Whole blood Male Non-Hispanic or Latino White 44 3.26 N/A Sporadic 701154 ALS Whole blood Male Non-Hispanic or Latino White N/A N/A N/A Disease Control 701158 ALS Whole blood Male Non-Hispanic or Latino White 52 29.48 N/A Sporadic 701161 ALS Whole blood Male Non-Hispanic or Latino White 55 8.5 N/A Sporadic 701163 ALS Whole blood Male Non-Hispanic or Latino White 46 4.54* N/A Sporadic 701185 ALS Whole blood Male Non-Hispanic or Latino White 43 108–120* N/A Sporadic 701151 Healthy control Whole blood Female Non-Hispanic or Latino White N/A N/A N/A Healthy control 701143 Healthy control Whole blood Male Non-Hispanic or Latino White N/A N/A N/A Healthy control 701174 Healthy control Whole blood Female Non-Hispanic or Latino White N/A N/A N/A Healthy control 701172 Healthy control Whole blood Male Non-Hispanic or Latino White N/A N/A N/A Healthy control 701141 Healthy control Whole blood Female Non-Hispanic or Latino Asian N/A N/A N/A Healthy control Table 2 Healthy control blood samples provided by Oxford BioDynamics and the NEALS Consortium used in this study. Table 2Sample type Participant no Date sample collected Baseline diagnosis Healthy control 10088 11/13/2013 NFG 10917 9/20/2013 NFG 14376 9/11/2013 NFG 16345 12/2/2013 NFG 16391 11/13/2013 NFG 16771 11/18/2013 NFG 17139 9/20/2013 NFG 17152 12/6/2013 NFG 17238 10/29/2013 NFG 17265 10/24/2013 NFG 17280 11/8/2013 NFG 17328 11/15/2013 NFG 17410 12/19/2013 NFG 17411 1/17/2014 NFG 17414 1/2/2014 NFG 17415 1/10/2014 NFG 17416 2/18/2014 NFG 17417 2/4/2014 NFG 17419 1/20/2014 NFG 17422 1/6/2014 NFG 17426 12/19/2013 NFG 17427 1/15/2014 NFG 17432 2/4/2014 NFG 17433 1/10/2014 NFG 17434 1/2/2014 NFG 17445 1/2/2014 NFG 17446 1/10/2014 NFG 17447 1/10/2014 NFG 17448 1/17/2014 NFG 17449 1/27/2014 NFG 17451 1/29/2014 NFG 17452 2/4/2014 NFG 17454 1/16/2014 NFG 17456 1/21/2014 NFG 17457 1/31/2014 NFG 17459 2/10/2014 NFG 17467 2/10/2014 NFG 17480 2/11/2014 NFG 17495 2/17/2014 NFG 17508 2/19/2014 NFG 17669 2/24/2014 NFG 701141 12/5/2014 NEALS respository control 701143 12/5/2014 NEALS respository control 701151 12/5/2014 NEALS respository control 701172 12/5/2014 NEALS respository control 701174 12/5/2014 NEALS respository control Abbreviations. NFG: Normal Fasting Glucose. Table 3 ALS clinical characteristics of the Discovery and Validation cohorts. Table 3 Discovery cohort (N = 50) Validation cohort (N = 8) Gender Male (N, (%)) 32 (64) 5 (63) Female (N, (%)) 18 (36) 3 (37) Ethnicity Non-Hispanic or Latino 47 N/A Hispanic, Latino 1 N/A Unknown/not reported 2 N/A Race White (N, (%)) 45 (90) N/A Black (N, (%)) 2 (4) N/A Unknown/not reported (N, (%)) 3 (6) N/A ALS type Sporadic (N, (%)) 42 (84) 6 (75) Familial (N, (%)) 8 (16) 2 (25) Age at diagnosis (Average, (SD)) 53.4 (9.7) 54.9 (11.9) Disease duration (Average, (SD)) 15.6 (20.4) 11.0 (7.9) ALSFRS-R (Average, (SD)) 35.9 (8.1)⁎ 36.4 (7.7) N/A = Not Available. ⁎ ALSFRS-R scores were available for 14 of the 50 patients in the Discovery cohort. An initial customized CGH Agilent microarray (8x60k) was designed to test technical and biological repeats for 13,880 potential chromosome conformations across 308 genetic loci. With the focus on the development of a non-invasive blood based diagnostic test, we first concentrated our attention on potential chromosome conformations in genomic loci specific to ALS's immuno-footprint. A literature search was conducted to identify loci that would be used on the microarray. In an earlier study, we compared and reported unique and common aspects of the ALS immuno-footprint to other Autoimmune Conditions such as Systemic Lupus Erythematosus (SLE), Ulcerative Colitis (UC), Rheumatoid Arthritis (RA), Multiple Sclerosis (MS) and Relapse-Remitting MS (MSRR) [14]. The genetic loci selected for the array were genes primarily involved in immunodeficiency, adaptive and innate immune systems, and cytokine signaling (Table 4).Table 4 List of immune-related genomic loci tested in the initial ALS array. Table 4Gene name Array probe count Gene name Array probe count Gene name Array probe count VCAM1 10 IGHV3-23 13 CD40 7 RAP1A 332 IGHV1-46 2 CXADR 8 NRAS 12 TRAV19 5 IFNAR2 24 CD160 24 TRAC 12 IFNAR1 4 FCGR1A 23 ADCY4 10 IFNGR2 90 RFX5 24 LGALS3 7 DONSON 5 THEM4 88 RASGRP1 9 ICOSLG 12 IL6R 138 B2M 30 ICOSLG;AIRE 3 FCER1A 2 CIITA 4 ITGB2 14 FCER1G 24 PRKCB 415 VPREB1 1 FCGR2A 19 PDPK1 51 IGLV7-43 8 FCGR3A;FCGR2A 4 IL21R 56 IGLC3;IGLC7;IGLC2;IGLC1;IGLC6 2 FCGR3A 42 CD19 10 BCR 100 FCGR2B;FCGR3A 125 LAT 22 IGLL1 10 CD247 254 ITGAL 23 SEC14L3 12 SELL 52 ITGAM 54 CSF2RB 15 DNM3 1121 ADCY9 132 IL2RB 4 PTPRC 154 CDH1 154 MKL1 183 CHI3L1 38 PLCG2 221 TNFRSF13C 4 C4BPB 9 GINS2 27 IRAK2 38 C4BPB;C4BPA 10 TNFRSF13B 7 CBLB 51 C4BPA 35 CPD 61 CD96 159 CD55 33 AP2B1 44 CD200 26 CR2 15 ERBB2 6 CD200R1 9 CR1;CR2 6 PLD2 10 CD80 21 CR1 205 C1QBP 36 CD86 11 CD46 11 MRC2 51 ADCY5 153 CD34 70 CD300LB 5 ITGB5 63 DDOST 11 CD300E 10 NCK1 95 TLR5 53 GRB2 270 TRPC1 21 LYPLA2 30 GUCY2D 13 PLD1 201 AKT3 437 SIGLEC15 2 AP2M1 13 CLIC4 479 MALT1 32 IL1RAP 29 IFI6 116 CD226 15 IL5RA 11 PTAFR 49 ICAM1 9 MYD88 8 ATPIF1;PTAFR 14 ICAM1;ICAM4 1 CCR2 2 ATPIF1 24 DNM2 235 DAPP1 26 LCK 158 PRKCSH 31 NFKB1 27 BCL10 18 ACP5 23 TLR2 5 PIK3CD 191 JAK3 21 CD38 24 CHUK 15 RFXANK 33 TLR10 2 NFKB2 5 HCST 3 TLR1;TLR10 3 PPAPDC1A 19 TYROBP;HCST 5 TLR1 4 FGFR2 95 TYROBP 12 TLR1;TLR6 3 MRC1 23 MATK 27 KLB 27 ITGB1 29 AKT2 34 PDGFRA 69 IL2RA 7 AKT2;PLD3 6 KIT 32 PRKCQ 164 PLD3 23 TICAM2 12 FAS 16 CD79A 6 CD14 55 BLNK 35 MADCAM1 3 PDGFRB 18 PIK3AP1 210 PVR 8 CD74 6 AP2A2 20 PVRL2 43 FGFR4 28 NCAM1 43 PTGIR 42 PRLR 41 AMICA1 16 AP2S1 10 IL7R 14 CD3E 13 AP2A1 22 GHR 114 CD3G 3 SIGLEC16 10 CCNO 7 CD3D 2 SIGLEC14 3 IL6ST 24 CD3G;CD3D 1 LILRB2 2 CD180 1 CXCR5 13 LILRB1 10 PIK3R1 14 CBL 41 LILRB4 13 ADCY2 364 CRTAM 11 KIR2DL4;KIR3DL1;KIR2DL3 13 FYN 332 TIRAP 2 KIR2DL4;KIR3DL1 11 IFNGR1 15 CD81 3 KIR3DL1 4 TAB2 150 IFITM1;IFITM2 4 KIR2DL1;KIR2DL4;KIR3DL1;KIR2DL3 5 ULBP1 14 IFITM3 6 KIR2DS4;KIR3DL1 1 ULBP3 34 CD59 7 KIR2DS4;KIR3DL1;KIR3DL2 2 CCR6 11 CD44 24 C3 28 MICB 28 ART1 1 VAV1;C3 3 CFB 2 RAG1;TRAF6 8 VAV1 49 C4A 3 RAG1 46 IL1R2 5 C4B 1 RAG2;RAG1 10 IL1R1 56 AGER 7 RAG2 8 IL1RN 9 TAP2;TAP1 3 SIGIRR 1 RAPGEF4 195 TAP1 4 HRAS 14 ITGA4 1 TREM2 5 MS4A2 12 ITGAV 1 TREM1 9 SCYL1 9 CD28 2 NCR2 5 PANX1 29 ICOS 10 LY86 54 KLRD1 53 IRS1 6 MAP3K7 2 KLRK1 3 PDCD1 2 EPHB4 30 UNG 18 ADCY3 30 CLEC5A 7 P2RX7 53 RASGRP3 41 CARD11 50 ORAI1 22 SOS1 177 ADCY1 30 KRAS 5 ACTR2 46 EGFR 209 RAPGEF3 15 CD8A 9 LAT2 9 ADCY6 7 CD8A;CD8B 14 CD36 127 ITGB7 6 CD8B 22 ADCY8 121 GLYCAM1 6 IGKV1-5 10 PPAPDC1B 26 ERBB3 2 IGKV3-7 7 FGFR1 7 CD4 57 IGKV3-11 3 IKBKB 18 RAP1B 33 IGKV3-15 10 LYN 179 FRS2 63 IGKV3-20 3 PAG1 73 AICDA 3 IGKV2-30 8 TLR4 10 KLRG1 15 IGKV1D-16 8 ANGPTL2 4 IRS2 1 MAL 3 DNM1 11 ARHGEF7 94 ADAM17 17 SH3GL2 101 KL 23 ZAP70 10 CLTA 110 RFXAP 26 SIRPB1 4 CD274 1 DLEU2 20 GINS1 39 PDCD1LG2 1 AKT1 18 TRIB3 21 SYK 25 CD40LG 3 PLCG1 12 BTK 8 IL3RA;CSF2RA 3 ADA 9 CSF2RA 25 IL3RA 15 IKBKG 2 TAB3 35 IRAK1 5 SH3KBP1 291 Total 13,880 A comparative microarray analysis was conducted using samples from individual ALS patients from the NEALS Biofluid Repository and pooled healthy control samples. Array readouts were analyzed with linear regression modeling to select the 153 chromosomal interactions with the ability to best discriminate ALS from controls (Table 5).Table 5 Top 153 markers produced from the second array. Table 5Probe GeneLocus logFC AveExpr t P·Value adj.P·Val B FC FC_1 Binary ACOXL_2_110997162_111004405_111109898_111117325_RF ACOXL 0.3298921 0.3298921 11.076483 1.09E-09 1.62E-07 12.487822 1.256919 1.2569193 1 ACOXL_2_110704616_110714102_110875631_110879536_RF ACOXL 0.3570591 0.3570591 9.638804 1.03E-08 7.06E-07 10.263069 1.280812 1.2808123 1 ADAMTS20_12_43377477_43383257_43480754_43482402_FR ADAMTS20 −0.246307 −0.246307 −9.267993 1.91E-08 1.10E-06 9.6485602 0.843052 −1.186167 −1 ADAMTS20_12_43377477_43383257_43588948_43590670_FR ADAMTS20 −0.322419 −0.322419 −6.16799 6.55E-06 7.78E-05 3.7882954 0.799728 −1.250425 −1 ALDH1A2_15_58325151_58334051_58485549_58488054_FR ALDH1A2 0.345862 0.345862 13.477242 4.03E-11 2.85E-08 15.710641 1.27091 1.2709101 1 ALDH1A2_15_58325151_58334051_58452555_58457099_FR ALDH1A2 0.3633487 0.3633487 13.188649 5.84E-11 3.35E-08 15.352222 1.286408 1.2864084 1 ALDH1A2_15_58325151_58334051_58538695_58540885_FF ALDH1A2 0.3398615 0.3398615 7.222828 7.78E-07 1.61E-05 5.9304456 1.265635 1.2656351 1 ANKRD29_18_23664553_23665914_23692491_23699757_RR ANKRD29 0.2493527 0.2493527 8.6194055 5.83E-08 2.37E-06 8.5308864 1.188674 1.1886737 1 ATXN7L1_7_105654123_105657510_105741521_105750599_FR ATXN7L1 0.18956 0.18956 6.5975399 2.70E-06 3.98E-05 4.678643 1.140416 1.1404158 1 BANK1_4_101510635_101519668_101619789_101624381_RF BANK1 −0.390246 −0.390246 −10.63806 2.11E-09 2.34E-07 11.83492 0.763 −1.310616 −1 BANK1_4_101476784_101489895_101732279_101733831_FF BANK1 0.3134993 0.3134993 10.813911 1.62E-09 2.03E-07 12.099391 1.242718 1.2427183 1 BTBD11_12_107272037_107279968_107305640_107312061_FF BTBD11 0.3569279 0.3569279 13.088383 6.65E-11 3.57E-08 15.225963 1.280696 1.2806959 1 C2CD2_21_41868909_41872387_41979620_41986582_FR C2CD2 0.3304577 0.3304577 9.7178208 9.05E-09 6.41E-07 10.391775 1.257412 1.2574123 1 C6orf132_6_42104999_42110975_42124471_42126639_RR C6orf132 −0.151906 −0.151906 −10.15532 4.48E-09 3.96E-07 11.090489 0.900061 −1.111036 −1 C6orf58_6_127480771_127483471_127600017_127604343_FR C6orf58 −0.206941 −0.206941 −7.65767 3.38E-07 8.58E-06 6.7690901 0.866372 −1.154238 −1 CAMK1D_10_12516951_12526338_12633776_12637357_FF CAMK1D −0.363678 −0.363678 −8.185719 1.27E-07 4.23E-06 7.7521485 0.777181 −1.286702 −1 CAPN9_1_230738572_230739927_230752057_230757333_RR CAPN9 −0.224589 −0.224589 −8.238209 1.15E-07 3.91E-06 7.8477638 0.855839 −1.168444 −1 CCDC3_10_12924962_12934165_13046815_13049059_FF CCDC3 0.2758308 0.2758308 9.9013068 6.72E-09 5.34E-07 10.687663 1.210691 1.2106911 1 CD1A_1_158226961_158229550_158243613_158252050_FR CD1A 0.179006 0.179006 4.472859 0.000266 0.001399 0.0795021 1.132104 1.1321036 1 CDH12_5_21870983_21872848_21898498_21914941_RF CDH12 0.3146402 0.3146402 9.742694 8.69E-09 6.31E-07 10.432129 1.243701 1.2437014 1 CDK14_7_90936378_90943000_91140859_91158297_FF CDK14 0.3051704 0.3051704 5.623921 2.08E-05 0.000189 2.6276939 1.235565 1.2355646 1 CDK14_7_90855907_90868783_90891759_90898655_FF CDK14 0.3654475 0.3654475 7.6163383 3.65E-07 9.09E-06 6.6905099 1.288281 1.2882812 1 CELSR1_22_46424151_46427901_46457142_46458718_FF CELSR1 0.1775237 0.1775237 7.5038883 4.52E-07 1.07E-05 6.4755148 1.130941 1.130941 1 CHAMP1_13_114265206_114269925_114317775_114320752_RR CHAMP1 −0.230721 −0.230721 −5.754561 1.57E-05 0.000152 2.9095095 0.852209 −1.173421 −1 CHSY3_5_129929941_129937973_130119673_130124677_RF CHSY3 0.2485594 0.2485594 8.4314793 8.14E-08 3.02E-06 8.1965796 1.18802 1.1880203 1 CHSY3_5_130104704_130115924_130186143_130190278_RR CHSY3 0.4537319 0.4537319 14.076616 1.91E-11 1.93E-08 16.432119 1.369578 1.3695785 1 CNTN4_3_2273300_2281776_2314685_2325027_FR CNTN4 0.3355172 0.3355172 15.964666 2.12E-12 1.04E-08 18.519694 1.26183 1.2618297 1 CNTNAP2_7_146728706_146734820_146785878_146792823_RF CNTNAP2 −0.440614 −0.440614 −7.429603 5.21E-07 1.18E-05 6.332521 0.736821 −1.357182 −1 CTNNA3_10_66299269_66302507_66496211_66513003_FF CTNNA3 −0.436585 −0.436585 −14.22858 1.58E-11 1.70E-08 16.610284 0.738882 −1.353396 −1 CTNNA3_10_66496211_66513003_66783614_66787250_RR CTNNA3 −0.309997 −0.309997 −8.536173 6.76E-08 2.64E-06 8.3834114 0.806643 −1.239705 −1 CTNNA3_10_66282876_66290806_66496211_66513003_RR CTNNA3 −0.309827 −0.309827 −5.790351 1.45E-05 0.000143 2.986383 0.806739 −1.239559 −1 CTNND2_5_11851889_11854697_11917286_11928978_FR CTNND2 0.2692826 0.2692826 13.156667 6.09E-11 3.43E-08 15.312047 1.205208 1.2052083 1 DAO_12_108832598_108835352_108845485_108846981_FF DAO 0.2606268 0.2606268 7.576059 3.94E-07 9.68E-06 6.6137018 1.197999 1.197999 1 DBF4B_17_44683672_44686134_44709459_44712660_RR DBF4B −0.269502 −0.269502 −8.674587 5.29E-08 2.23E-06 8.6281464 0.829606 −1.205391 −1 DGKB_7_14197847_14209024_14254130_14267710_FR DGKB −0.43638 −0.43638 −9.791092 8.03E-09 5.98E-07 10.51043 0.738986 −1.353205 −1 DGKB_7_14197847_14209024_14322087_14328928_FF DGKB −0.306194 −0.306194 −7.866591 2.28E-07 6.51E-06 7.162654 0.808772 −1.236442 −1 DIO2_14_80195869_80197807_80255209_80263592_RR DIO2 −0.25104 −0.25104 −8.332113 9.73E-08 3.40E-06 8.0178794 0.84029 −1.190065 −1 DIO2_14_80255209_80263592_80371554_80373780_RR DIO2 −0.307653 −0.307653 −13.98334 2.14E-11 1.99E-08 16.321825 0.807955 −1.237693 −1 DPP10_2_115459376_115465174_115685306_115694818_FR DPP10 −0.327317 −0.327317 −8.420239 8.31E-08 3.06E-06 8.1764331 0.797017 −1.254678 −1 DPP10_2_114901417_114910472_115087678_115094206_FF DPP10 0.3377111 0.3377111 14.406874 1.28E-11 1.51E-08 16.816949 1.26375 1.26375 1 DSCR4_21_37943561_37949090_38013989_38021392_FF DSCR4 −0.429276 −0.429276 −12.81368 9.54E-11 4.43E-08 14.875361 0.742634 −1.346558 −1 ERBB4_2_212097934_212104780_212317329_212325591_RF ERBB4 −0.315689 −0.315689 −4.50592 0.000247 0.001323 0.1537846 0.803467 −1.244606 −1 ERC1_12_1043264_1050801_1096484_1101318_FR ERC1 0.233095 0.233095 13.553981 3.66E-11 2.79E-08 15.804721 1.175354 1.1753537 1 FAM126A_7_22873341_22878517_22945935_22949410_RF FAM126A −0.177613 −0.177613 −4.427145 0.000295 0.001522 −0.023251 0.884165 −1.131011 −1 FARP1_13_98271575_98282700_98346930_98348486_FR FARP1 0.2548222 0.2548222 14.739287 8.58E-12 1.48E-08 17.195539 1.193189 1.1931887 1 FBXO8_4_174254227_174258882_174284851_174288977_RR FBXO8 −0.339902 −0.339902 −6.993433 1.22E-06 2.24E-05 5.4774396 0.790095 −1.265671 −1 FER1L6_8_123963222_123969450_124085753_124093275_FR FER1L6 0.350543 0.350543 5.6694435 1.88E-05 0.000175 2.726108 1.27504 1.2750404 1 FHIT_3_61064178_61073078_61136784_61147623_RR FHIT 0.3193953 0.3193953 4.8524668 0.000113 0.000713 0.9299331 1.247807 1.2478074 1 FRMD3_9_83388882_83396653_83414350_83418756_FR FRMD3 −0.383857 −0.383857 −11.50548 5.83E-10 1.14E-07 13.106219 0.766386 −1.304826 −1 GALNTL6_4_172641518_172647332_172893415_172910203_RF GALNTL6 −0.310946 −0.310946 −14.25721 1.53E-11 1.70E-08 16.643649 0.806113 −1.240521 −1 GFPT1_2_69307499_69311057_69383954_69393165_FR GFPT1 −0.317965 −0.317965 −6.644846 2.45E-06 3.70E-05 4.7752111 0.802201 −1.246571 −1 GFRA1_10_116092148_116100672_116113263_116118675_FR GFRA1 −0.319379 −0.319379 −7.079082 1.03E-06 1.99E-05 5.6474303 0.801415 −1.247793 −1 GLIS3_9_3998831_4010284_4144132_4146272_FF GLIS3 −0.309431 −0.309431 −6.997876 1.21E-06 2.23E-05 5.4862817 0.80696 −1.239219 −1 GMDS_6_2030214_2038438_2217079_2225905_RF GMDS −0.412681 −0.412681 −9.027894 2.87E-08 1.47E-06 9.2412584 0.751226 −1.331157 −1 GPC6_13_93573654_93584189_93748106_93754722_RF GPC6 0.316254 0.316254 11.287633 8.00E-10 1.32E-07 12.794673 1.245093 1.2450934 1 GRIK2_6_101912977_101914543_101980061_101996881_FF GRIK2 −0.412403 −0.412403 −10.64034 2.11E-09 2.34E-07 11.838361 0.751371 −1.330901 −1 GRIP1_12_66761739_66764959_66791577_66801892_RR GRIP1 −0.333991 −0.333991 −10.33187 3.39E-09 3.23E-07 11.365922 0.793339 −1.260496 −1 GRM3_7_86653882_86655924_86695387_86706974_RF GRM3 −0.311021 −0.311021 −9.568226 1.16E-08 7.66E-07 10.147448 0.806071 −1.240585 −1 GRM7_3_6827121_6836161_7047731_7057814_RR GRM7 −0.359722 −0.359722 −7.091136 1.01E-06 1.96E-05 5.6712713 0.779315 −1.283179 −1 HCFC2P1_13_108440737_108444674_108461634_108471282_RR HCFC2P1 0.3137758 0.3137758 11.555559 5.42E-10 1.09E-07 13.177127 1.242957 1.2429565 1 HCFC2P1_13_108440737_108444674_108461634_108471282_FR HCFC2P1 0.329425 0.329425 11.346554 7.34E-10 1.27E-07 12.879437 1.256513 1.2565125 1 HDAC4_2_239204078_239210374_239360246_239368872_FR HDAC4 0.3383322 0.3383322 7.5918458 3.83E-07 9.44E-06 6.6438323 1.264294 1.2642941 1 HPGD_4_174493630_174502964_174542132_174543999_RF HPGD 0.1857242 0.1857242 11.142003 9.91E-10 1.53E-07 12.583558 1.137388 1.1373878 1 HUS1_7_47823192_47830325_47842954_47848362_RF HUS1 −0.174947 −0.174947 −4.911921 9.92E-05 0.000643 1.0624681 0.8858 −1.128923 −1 IL1A_2_112795355_112798834_112816387_112823836_RR IL1A −0.103722 −0.103722 −3.037051 0.006829 0.019309 −3.100991 0.930629 −1.074542 −1 IL1A_2_112765786_112772711_112810765_112813086_RR IL1A 0.1305595 0.1305595 3.6789915 0.001613 0.006001 −1.701299 1.094718 1.0947181 1 INSR_19_7099584_7101451_7138185_7142897_RF INSR 0.1639687 0.1639687 10.303077 3.55E-09 3.32E-07 11.321255 1.120365 1.1203649 1 IQGAP2_5_76698020_76702533_76717099_76725306_FF IQGAP2 −0.310676 −0.310676 −10.6174 2.18E-09 2.39E-07 11.803605 0.806264 −1.240289 −1 IQGAP2_5_76475531_76481400_76717099_76725306_FR IQGAP2 0.2854775 0.2854775 11.70784 4.36E-10 9.61E-08 13.391129 1.218814 1.2188136 1 KCNMA1_10_77411418_77416164_77530837_77543678_RF KCNMA1 0.3281284 0.3281284 8.0962784 1.49E-07 4.77E-06 7.5883505 1.255384 1.2553837 1 KCNN2_5_114353357_114360896_114430044_114433040_RR KCNN2 −0.232264 −0.232264 −7.774872 2.71E-07 7.34E-06 6.9906211 0.851298 −1.174677 −1 KCNS3_2_17835106_17846049_17968712_17974054_FR KCNS3 −0.288729 −0.288729 −11.37806 7.01E-10 1.24E-07 12.924607 0.818623 −1.221564 −1 KIAA0513_16_85000072_85002703_85074194_85077477_RF KIAA0513 0.2060683 0.2060683 5.4679401 2.91E-05 0.000245 2.2888169 1.15354 1.1535402 1 KIFAP3_1_169911687_169919606_169986992_169993331_FR KIFAP3 0.2934999 0.2934999 11.698018 4.42E-10 9.62E-08 13.377399 1.22561 1.22561 1 LINGO2_9_28333777_28339631_28527863_28540481_FR LINGO2 −0.374728 −0.374728 −9.390644 1.55E-08 9.38E-07 9.8537519 0.771251 −1.296595 −1 LINGO2_9_28314155_28333777_28522753_28525112_FR LINGO2 −0.463013 −0.463013 −7.06033 1.07E-06 2.04E-05 5.6102993 0.72547 −1.378417 −1 LINGO2_9_28314155_28333777_28510932_28517950_FF LINGO2 −0.352913 −0.352913 −2.842326 0.010477 0.027372 −3.509391 0.783001 −1.277137 −1 LYPD3_19_43451257_43453307_43494229_43495321_FR LYPD3 −0.108357 −0.108357 −3.699682 0.001539 0.005775 −1.655267 0.927644 −1.078 −1 MACROD2_20_15750414_15763248_15953272_15965024_RF MACROD2 −0.439686 −0.439686 −10.55166 2.41E-09 2.56E-07 11.703674 0.737295 −1.356309 −1 MAGI2_7_78371356_78378740_78502868_78511891_FR MAGI2 −0.309137 −0.309137 −7.781863 2.67E-07 7.28E-06 7.003776 0.807124 −1.238966 −1 MAGI2_7_79009346_79018304_79275810_79284623_RF MAGI2 0.349446 0.349446 10.577281 2.32E-09 2.48E-07 11.742684 1.274071 1.2740713 1 MDGA2_14_47087312_47092151_47301555_47314511_FR MDGA2 −0.376564 −0.376564 −2.53523 0.020267 0.046875 −4.13055 0.77027 −1.298246 −1 MGLL_3_127731924_127736779_127863699_127870283_RF MGLL −0.174384 −0.174384 −4.247058 0.000443 0.002114 −0.428303 0.886146 −1.128482 −1 NAV2_11_19476194_19490077_19749138_19755031_FR NAV2 0.3411694 0.3411694 9.3822413 1.57E-08 9.44E-07 9.839756 1.266783 1.266783 1 NCKAP5_2_133209962_133217496_133394726_133400754_FF NCKAP5 −0.363475 −0.363475 −8.42102 8.30E-08 3.06E-06 8.1778322 0.77729 −1.286521 −1 NCKAP5_2_133084614_133091683_133242680_133249277_RR NCKAP5 0.3800747 0.3800747 11.195242 9.16E-10 1.46E-07 12.661003 1.301409 1.3014092 1 NEFH_22_29442588_29445314_29482081_29484217_RR NEFH 0.1558858 0.1558858 12.070176 2.62E-10 7.50E-08 13.89074 1.114105 1.1141054 1 NEFH_22_29467180_29469328_29482081_29484217_FR NEFH 0.3600069 0.3600069 5.6898359 1.80E-05 0.000169 2.7701205 1.283432 1.283432 1 NEFH_22_29434926_29438399_29467180_29469328_RF NEFH 0.3191557 0.3191557 5.4943817 2.75E-05 0.000235 2.3464398 1.2476 1.2476002 1 NEIL3_4_177308770_177311798_177363195_177365833_RR NEIL3 −0.440388 −0.440388 −4.173407 0.000524 0.00242 −0.593968 0.736936 −1.356969 −1 NELL1_11_21419712_21428422_21531472_21542215_RR NELL1 −0.312849 −0.312849 −9.035358 2.83E-08 1.46E-06 9.2540329 0.805051 −1.242158 −1 NFIA_1_60869920_60875614_60989414_60996659_RR NFIA 0.3492172 0.3492172 14.019512 2.04E-11 1.98E-08 16.364679 1.273869 1.2738693 1 NFIA_1_61228266_61235258_61246134_61250763_RF NFIA 0.3172619 0.3172619 12.061499 2.65E-10 7.53E-08 13.87893 1.245964 1.2459636 1 NHSL1_6_138480977_138485998_138514855_138523394_RF NHSL1 0.2467506 0.2467506 7.5585632 4.08E-07 9.94E-06 6.5802688 1.186532 1.1865317 1 NOX4_11_89337353_89346448_89499668_89503122_RF NOX4 −0.321336 −0.321336 −12.36707 1.74E-10 6.04E-08 14.290321 0.800328 −1.249487 −1 NXPH1_7_8466580_8469680_8501926_8510453_FR NXPH1 −0.343359 −0.343359 −9.44196 1.42E-08 8.81E-07 9.9390318 0.788204 −1.268707 −1 OPCML_11_133291292_133302754_133378245_133382756_FR OPCML −0.380902 −0.380902 −12.74907 1.04E-10 4.70E-08 14.791885 0.767957 −1.302156 −1 OSBP2_22_30729329_30730970_30763180_30773593_RR OSBP2 −0.432395 −0.432395 −14.48833 1.16E-11 1.51E-08 16.910523 0.741031 −1.349472 −1 OSBP2_22_30729329_30730970_30817623_30822792_RR OSBP2 −0.366312 −0.366312 −11.45424 6.28E-10 1.19E-07 13.033392 0.775763 −1.289053 −1 PA2G4P4_3_156818917_156822698_156846218_156854773_RR PA2G4P4 −0.174657 −0.174657 −5.663311 1.91E-05 0.000177 2.7128629 0.885978 −1.128696 −1 PACRG_6_163022776_163030350_163324239_163328316_RR PACRG −0.352275 −0.352275 −9.207722 2.11E-08 1.20E-06 9.547019 0.783348 −1.276572 −1 PARVB_22_43989557_43996453_44182513_44187012_FF PARVB −0.170178 −0.170178 −7.603815 3.74E-07 9.26E-06 6.6666544 0.888733 −1.125198 −1 PASD1_X_151600201_151608969_151676020_151678489_RF PASD1 0.3220619 0.3220619 5.8035886 1.41E-05 0.00014 3.0147787 1.250116 1.2501159 1 PASD1_X_151608969_151613880_151648877_151652329_RR PASD1 0.3167815 0.3167815 2.5872149 0.018153 0.042868 −4.027661 1.245549 1.2455488 1 PLCB1_20_8599928_8617739_8698163_8700449_FR PLCB1 −0.364492 −0.364492 −11.16982 9.51E-10 1.49E-07 12.624056 0.776742 −1.287428 −1 PLCB1_20_8599928_8617739_8856413_8858679_FF PLCB1 −0.316603 −0.316603 −8.251608 1.13E-07 3.85E-06 7.8721116 0.802958 −1.245395 −1 PLEKHM3_2_207850576_207856308_208003089_208006543_FF PLEKHM3 −0.385734 −0.385734 −8.300103 1.03E-07 3.58E-06 7.9600253 0.76539 −1.306524 −1 PON2_7_95405100_95420940_95465337_95474032_FR PON2 −0.178367 −0.178367 −5.268811 4.50E-05 0.000344 1.8526765 0.883703 −1.131602 −1 PPP2R5E_14_63423316_63431065_63511598_63515339_FF PPP2R5E 0.3121838 0.3121838 7.9196338 2.07E-07 6.02E-06 7.2616101 1.241586 1.2415857 1 PRKCA_17_66441276_66447067_66475597_66481312_RF PRKCA 0.2425179 0.2425179 7.4515733 5.00E-07 1.15E-05 6.3748921 1.183056 1.1830556 1 PTPRD_9_9798181_9808250_9882262_9891784_RR PTPRD −0.453093 −0.453093 −4.232884 0.000458 0.002168 −0.460189 0.730475 −1.368972 −1 PTPRD_9_9551379_9564487_9756141_9761726_RF PTPRD 0.3282945 0.3282945 10.781263 1.70E-09 2.09E-07 12.050556 1.255528 1.2555283 1 RAP1GAP2_17_2837827_2840795_2974137_2976800_RF RAP1GAP2 −0.145079 −0.145079 −4.529412 0.000234 0.001268 0.2065487 0.90433 −1.105791 −1 RERGL_12_18254365_18255916_18350532_18364514_RF RERGL 0.5204704 0.5204704 17.245127 5.42E-13 8.31E-09 19.793409 1.434423 1.4344229 1 RNF6_13_26220822_26221931_26255215_26259295_RR RNF6 0.3776285 0.3776285 8.7845474 4.37E-08 1.95E-06 8.8207418 1.299204 1.2992044 1 RNU6-1264P_17_6162286_6163870_6195952_6199184_FR RNU6-1264P −0.152642 −0.152642 −9.038985 2.81E-08 1.46E-06 9.2602376 0.899602 −1.111603 −1 RORA_15_60977475_60986445_61019417_61030714_RR RORA 0.3713685 0.3713685 14.275336 1.50E-11 1.70E-08 16.664729 1.293579 1.2935793 1 RPL9P15_3_154664590_154668268_154687949_154695597_RF RPL9P15 0.2007438 0.2007438 5.5395118 2.49E-05 0.000218 2.4446246 1.149291 1.1492908 1 SCNN1B_16_23299279_23301635_23325818_23330880_FF SCNN1B −0.143058 −0.143058 −6.057416 8.25E-06 9.28E-05 3.5552838 0.905598 −1.104243 −1 SETBP1_18_44770146_44772357_44885635_44895879_RF SETBP1 0.355569 0.355569 11.916137 3.25E-10 8.20E-08 13.679971 1.27949 1.2794901 1 SETBP1_18_44885635_44895879_45067644_45082246_FR SETBP1 0.3703696 0.3703696 11.300548 7.85E-10 1.31E-07 12.813285 1.292684 1.2926839 1 SLC9A8_20_49784052_49786161_49876581_49883310_RR SLC9A8 0.1512545 0.1512545 7.2660355 7.15E-07 1.50E-05 6.0149546 1.110535 1.1105347 1 SOD1_21_31645974_31648479_31675373_31681286_RF SOD1 −0.11317 −0.11317 −2.698249 0.014316 0.03529 −3.804687 0.924554 −1.081602 −1 SORCS2_4_7241158_7248673_7449931_7456800_FR SORCS2 0.3595133 0.3595133 10.619505 2.18E-09 2.39E-07 11.806804 1.282993 1.282993 1 SPAG16_2_213413840_213427575_213513566_213522278_FF SPAG16 −0.390916 −0.390916 −12.83763 9.25E-11 4.35E-08 14.906206 0.762645 −1.311225 −1 SPTLC3_20_13133531_13136394_13174631_13182869_FF SPTLC3 0.2023548 0.2023548 12.546312 1.36E-10 5.33E-08 14.527397 1.150575 1.1505748 1 STX7_6_132435332_132445259_132542354_132547678_FR STX7 0.3463849 0.3463849 12.466212 1.52E-10 5.81E-08 14.421833 1.271371 1.2713708 1 STX7_6_132435332_132445259_132511475_132513451_FR STX7 0.3187939 0.3187939 10.825435 1.59E-09 2.01E-07 12.116602 1.247287 1.2472874 1 SYN3_22_32593146_32596862_32844795_32854272_RF SYN3 0.3341746 0.3341746 17.115411 6.20E-13 8.31E-09 19.669129 1.260656 1.2606559 1 SYN3_22_32723620_32730327_32844795_32854272_RF SYN3 0.3345379 0.3345379 11.01189 1.20E-09 1.70E-07 12.392979 1.260973 1.2609735 1 TANGO6_16_68932310_68938132_69047292_69051982_FF TANGO6 0.1825419 0.1825419 6.0822581 7.83E-06 8.95E-05 3.6077649 1.134882 1.1348817 1 TANGO6_16_68852020_68856905_68932310_68938132_RF TANGO6 0.3129309 0.3129309 9.4084105 1.51E-08 9.16E-07 9.8833156 1.242229 1.2422288 1 TARDBP_1_10989562_10991726_11014552_11017016_FF TARDBP −0.096493 −0.096493 −5.268967 4.50E-05 0.000344 1.8530201 0.935304 −1.069171 −1 THSD7A_7_11420798_11426394_11712489_11724603_RF THSD7A 0.3135925 0.3135925 7.2665721 7.14E-07 1.50E-05 6.0160025 1.242799 1.2427986 1 TMTC1_12_29532107_29533497_29647358_29657646_RF TMTC1 0.3355121 0.3355121 11.641723 4.79E-10 1.00E-07 13.298511 1.261825 1.2618252 1 TMTC1_12_29647358_29657646_29765279_29767497_FF TMTC1 0.3639387 0.3639387 3.4118158 0.002954 0.009869 −2.291571 1.286935 1.2869345 1 TP63_3_189677768_189688253_189719534_189721726_FR TP63 −0.179873 −0.179873 −6.360572 4.39E-06 5.73E-05 4.1904365 0.88278 −1.132784 −1 UBQLN2_X_56536168_56538402_56553450_56557221_FR UBQLN2 0.2784451 0.2784451 3.1075781 0.00584 0.016979 −2.950784 1.212887 1.212887 1 UBQLN2_X_56536168_56538402_56570114_56575112_RR UBQLN2 0.3839068 0.3839068 3.3139007 0.003682 0.011761 −2.505503 1.304871 1.3048707 1 UBQLN2_X_56536168_56538402_56570114_56575112_FF UBQLN2 0.3937521 0.3937521 3.2929041 0.00386 0.012196 −2.551177 1.313806 1.3138058 1 UBQLN2_X_56536168_56538402_56570114_56575112_FR UBQLN2 0.3538926 0.3538926 2.693743 0.014455 0.035577 −3.813818 1.278004 1.2780042 1 VSNL1_2_17493823_17506407_17651961_17655968_FR VSNL1 0.3086278 0.3086278 8.9568305 3.24E-08 1.60E-06 9.1192635 1.238529 1.2385291 1 WBSCR17_7_71656523_71666682_71682593_71686909_FR WBSCR17 0.2893541 0.2893541 8.541732 6.69E-08 2.63E-06 8.3932893 1.222093 1.222093 1 WWOX_16_78559412_78566930_78777764_78783921_FF WWOX −0.312268 −0.312268 −7.888672 2.19E-07 6.31E-06 7.203896 0.805375 −1.241658 −1 XRCC1_19_43573185_43575618_43595713_43600345_RF XRCC1 −0.126515 −0.126515 −7.497073 4.58E-07 1.08E-05 6.4624281 0.916042 −1.091653 −1 XRCC1_19_43573185_43575618_43595713_43600345_FR XRCC1 0.1880671 0.1880671 8.9348232 3.36E-08 1.63E-06 9.0813491 1.139236 1.1392364 1 ZBTB20_3_114403907_114406568_114458618_114478185_RR ZBTB20 −0.370005 −0.370005 −11.36464 7.15E-10 1.25E-07 12.90538 0.77378 −1.292358 −1 ZFPM2_8_105632010_105638904_105814873_105824107_FR ZFPM2 −0.425339 −0.425339 −10.49363 2.64E-09 2.70E-07 11.615048 0.744664 −1.342888 −1 ZFPM2_8_105525568_105531254_105736941_105746180_RR ZFPM2 −0.360993 −0.360993 −3.724595 0.001455 0.005518 −1.599789 0.778628 −1.28431 −1 ZFPM2_8_105572686_105580151_105814873_105824107_FR ZFPM2 −0.324519 −0.324519 −3.057007 0.006534 0.018627 −3.058604 0.798565 −1.252247 −1 ZNF804B_7_88937416_88946263_88973548_88984178_RR ZNF804B 0.3913267 0.3913267 16.279963 1.50E-12 1.04E-08 18.843266 1.311599 1.311599 1 PDE4B_1_66194325_66201588_66342345_66350066_RF PDE4B −0.283024 −0.283024 −6.650398 2.43E-06 3.67E-05 4.7865237 0.821866 −1.216743 −1 Abbreviations. logFC: logarithm of the fold change; AveExpr: Average expression; adj.P·Val: Adjusted p-value; B: B-statistic (log-odds that that gene is differentially expressed); FC: Fold change; FC_1: Fold change centered around 1; Binary: Binary call for loop presence/absence. For the second step, the evaluation stage, the 153 biomarkers selected from the array analysis were translated into EpiSwitch™ PCR based-detection probes and used in multiple rounds of biomarker evaluation on an increasing number of patient samples. PCR primers were selected according to their ability to distinguish between ALS and healthy controls. Exact Fisher's P-value, GLMNET (alpha 0.5, penalized score) and standard logistic modeling scores including Coef, SE, Wald S and P-value were used to select the top eight biomarkers (Table 6). This selected chromosomal-conformation signature-biomarker set was then tested on a known (n = 74) and a blinded cohort (n = 16). Principal component analysis was also used to determine abundance levels and to identify potential outliers (Fig. 2). With EpiSwitch, initial screens identify significant markers using a small subset of patient samples, while the larger sample cohort sizes in later screens provide the statistical power to allow for the results to more closely approximate real-world populations.Fig. 2 Principal component analysis for the 8 markers applied to 74 known samples (ALS samples in red and healthy controls (HC) in green) and 16 unknown blinded samples (black). The blinded samples appear as a mixture of ALS and control samples. Fig. 2Table 6 The genomic loci contained in the chromosome conformation signature (CCS) used to inform an ALS diagnosis. Table 6Gene Fisher's P value Coefficient SE CD36 0.003 −1.7788 0.8 TAB2 0.098 −0.8568 0.94 GLYCAM1 0.213 −0.9582 0.94 GRB2 0.055 −0.811 0.91 FYN 0.276 −2.0869 0.93 PTPRC 0.027 −1.7059 1.42 DNM3 0.142 −2.0227 0.92 IKBKB 0.117 −1.395 1.32 Abbreviations. SE: Standard error. The sample cohort sizes in this study were progressively increased to enable selection of the optimal markers for discriminating ALS samples from healthy controls. Cohort sizes were statistically powered to a level of sensitivity and specificity needed for clinical application. More specifically, in the first screening series, six ALS and six healthy control samples were used. In the subsequent screening step, 24 ALS and 24 controls samples were used. For the final screening, a panel consisting of the eight top biomarkers (Table 6) was applied to a separate cohort of 74 samples from the NEALS Biofluid Repository and Oxford University. Statistical analysis was carried out on the final screen of the binary data results. (See Results, (Table 7)).Table 7 Sensitivity and specificity of the ALS chromosome conformation signature (CCS) when used to classify a set of clinical samples (n = 74) taken from two ALS clinical trials. Table 7Statistic Value 95% Cl Sensitivity 0.8333 51.59% to 97.91% Specificity 0.7692 46.19% to 94.96% Positive likelihood ratio 3.61 1.30 to 10.06 Negative likelihood ratio 0.22 0.06 to 0.79 Disease prevalence 48% (*) 27.80% to 68.69% Positive predictive value 76.92% (*) 46.19% to 94.96% Negative predictive value 83.33% (*) 51.59% to 97.91% Abbreviations. 95% CI: 95% confidence interval. To further validate the ALS CCS, the panel was tested on a blinded, independent (n = 16) cohort of blood samples supplied by Oxford University. The results were analyzed using Bayesian Logistic modeling, p-value null hypothesis (Pr(>|z|) analysis, Fisher-Exact P test and Glmnet (Table 8).Table 8 Results from classification of blinded Oxford University samples (N = 16). Table 8Statistic Value 95% Cl Sensitivity 0.875 47.35% to 99.68% Specificity 0.75 34.91% to 96.81% Positive likelihood ratio 3.5 1.02 to 11.96 Negative likelihood ratio 0.17 0.03 to 1.09 Disease prevalence 50% (*) 24.65% to 75.35% Positive predictive value 77.78% (*) 39.99% to 97.19% Negative predictive value 85.71% (*) 42.13% to 99.64% Abbreviations. 95% CI: 95% confidence interval. Last, we explored the biological relevance of the biomarkers by using a second array with 171,408 potential chromosome conformations across 467 loci that were functionally related to ALS [15] (Table 9). The list of 150 top performing loci from this screen were uploaded to the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database containing over 9 million known and predicted protein-protein interactions (https://string-db.org) to create a network of ALS regulation.Table 9 Loci that are functionally related to ALS used for the second array. Table 9Gene name DPP6 AGBL1 DNAH2 KCNN3 RAMP3 ZMYND8 C9orf72 AGPAT5 DNAH9 KCNQ1 RAP1GAP2 ZNF407 ITPR2 AK8 DOCK8 KDM4B RAPGEF4 ZNF423 UNC13A AKAP6 DOCK9 KDR RAPGEF5 ZNF485 MOB3B AKAP7 DPF3 KIAA0040 RBFOX3 ZNF519 ALS2 AMD1 DPP10 KIAA0513 RDH14 ZNF710 CDH13 ANG DSC3 KIAA1644 RERGL ZNF804B SOD1 ANKDD1A DSCR4 KLHL29 RETSAT ALK ANKRD29 DSTNP5 KLHL38 RGS17 BARD1 ANKS1B E2F7 KRT18P3 RNA5SP142 BTBD11 ANO1 ELK3 KRT18P64 RNA5SP158 FAM19A5 ANXA5 ELMSAN1 KSR2 RNF14 GRIP1 AP2A2 ELP3 LAMA3 RNF17 MACROD2 APBB2 EN1 LHFP RNF6 RBFOX1 APP EPB41L3 LINGO2 RNGTT SHROOM3 ARHGEF3 ERG LIPC RNU6-1264P TARDBP ARMS2 ERGIC1 LMO2 RNU6-242P AGAP1 ARNT2 ERMARD LOX RNU6ATAC32P AGBL4 ARSG ESRRG LRIG1 RORA ALDH1A2 ASIC2 EVC2 LRP1B RPF2 ATP2C2 ATP2A3 EXO1 LSAMP RPL9P15 ATXN2 ATXN1 EXOC2 LYPD3 RSPO4 BANK1 ATXN7L1 EYA1 MAPK1 SAMD5 BTBD16 AVEN FAM126A MAST4 SARM1 CACNA2D3 B4GALT6 FAM13B MDGA2 SCN8A CALN1 BCL6 FAM149A MED13L SCNN1B CDC42BPA BEND7 FAM155A MGLL SETBP1 CHSY3 BMPR2 FAM169B MICAL3 SEZ6L CNTN6 BTLA FAM189A1 MICB SGMS1 CNTNAP2 C15orf32 FAM194B MKL2 SGSM1 CSMD1 C2CD2 FBXO32 MKLN1 SHISA3 DAB1 C4orf19 FER MORN5 SIGLEC12 DIO2 C6orf132 FER1L6 MTMR7 SIRPG DNM1L C6orf58 FGF1 MTUS2 SLC24A2 DOCK1 C7orf57 FGF12 MUC6 SLC35A3 DSCAM C8orf47 FGGY MYH11 SLC35F3 ERBB4 C9orf170 FHDC1 MYO10 SLC41A1 ERC1 CABIN1 FHIT MYO18B SLC9A8 FARP1 CADM2 FHOD3 MYO1D SLIT3 FBXO8 CAMK1D FIG4 NAALADL2 SMIM13 FMN2 CAPN9 FMN1 NAV2 SNORD113–2 FTO CAPZA1 FNDC3B NAV3 SNRPD3 FUS CASC10 FRMD3 NCKAP5 SNTG2 GLI2 CAST GABBR2 NEIL3 SORBS2 HIATL1 CCDC3 GALNT2 NELL1 SORCS2 IL18RAP CCDC81 GALNTL6 NFIA SOX5 IQGAP2 CD1A GAS6 NLRP7 SOX7 KALRN CD1B GCH1 NOTCH4 SPAG16 KCNS3 CD1E GFPT1 NOX4 SPATA13 KIFAP3 CDH12 GFRA1 NPR3 SPG11 KRT18P55 CDH23 GLIS3 NPTXR SPTLC3 LARGE CDH4 GLT8D2 NRXN1 SRGAP3 LOXHD1 CDH8 GMDS NRXN3 SRRM4 LRPPRC CDK14 GNG7 NUDT12 STAC MAGI2 CDRT4 GNPDA1 NXPH1 STK36 NEFH CELF2 GPC6 OPTN STX7 NHSL1 CELF4 GPR176 ORC5 SUN3 NKAIN2 CELSR1 GRB14 OSBP2 SYN3 NSMAF CEP44 GRIK2 OTP SYNJ2 OPCML CGNL1 GRIK4 PA2G4P4 SYT9 PCDH15 CHAF1A GRIN2B PACRG SYTL3 PCSK6 CHAMP1 GRM3 PALLD TACR2 PDE4B CHGB GRM7 PARK2 TANGO6 PLXDC2 CHRM5 GRM8 PARVB TANK PON2 CHST1 GRN PASD1 TCEA1 PTPRN2 CNTN3 HABP2 PAX7 TCF7L1 PTPRT CNTN4 HCFC2P1 PBX1 TCL1B RGS6 COL14A1 HDAC4 PCDH12 TIAM2 ROBO2 COL1A1 HFE PDCL3P1 TMEM132C SLC25A26 COL27A1 HHAT PDE7B TMEM135 SNX29 COL28A1 HLA-DOA PDZD2 TMEM91 SPATA22 COLGALT2 HLA-DPB2 PEPD TMPRSS13 SPTLC1P2 CREB3L2 HLA-DRB9 PFKP TMTC1 SUSD1 CREB5 HMCN2 PHC1 TNPO3 THSD7A CRHBP HNF1B PIGL TP63 TIAM1 CRYGGP HNRNPA1P32 PLA2G12B TRAK2 TLR1 CSRP1 HPGD PLCB1 TUFT1 TLR10 CST5 HS3ST4 PLEKHM3 TULP4 TMEM132D CTNNA3 HSCB PLGRKT TXNRD1 TMEM163 CTNND2 HUS1 PON1 UBQLN2 TMTC2 CTSC IFI44L PPP1R14C VAPB UNC13C CX3CR1 IFT74 PPP2R5E VRK2 UPF2 CXCL12 IL1A PRDM16 VSNL1 VEGFA DAO IL20RA PRKAG2 WBSCR17 ZFP64 DBF4B INSIG2 PRKCA WDFY3 ACOXL DCC INSR PRKCQ WNT9A ADAMTS20 DCLK1 IQCJ-SCHIP1 PRPH WWOX ADARB2 DCTN1 JARID2 PRR9 XRCC1 ADCY1 DGKB KCNIP1 PSD3 YPEL1 ADH7 DIAPH3 KCNMA1 PTPRD ZBTB20 ADRBK2 DIRC3 KCNMB3 PXDNL ZFP36L1 AFTPH DISC1 KCNN2 RAB3C ZFPM2 3 Results 3.1 Patient Clinical Characteristics In order to develop, test and validate the CCS biomarker panel, we used blood samples from two main cohorts. The first cohort (Discovery) consisted of 50 ALS samples and 42 healthy controls provided by NEALS and Oxford BioDynamics. The second cohort (Validation) consisted of 16 samples (8 ALS and 8 healthy familial controls) provided by the University of Oxford. For the ALS patients, samples in both cohorts were sex matched (approximately 2/3 male and 1/3 female), with the majority of cases being sporadic ALS (84% in the Discovery Cohort and 75% in the Validation cohort) (Table 3). Average ALSFRS-R scores (35.9 in Discovery and 36.4 in Validation), average age at diagnosis (53.4 years in Discovery and 54.9 years in Validation) and disease duration (53.4 in Discovery and 54.9 in Validation) were similar in both cohorts (Table 3). Although ethnicity and race were not available for the Validation cohort, the vast majority of patients in the Discovery (90%) were non-Hispanic or Latino Whites. 3.2 Identity of the Markers in the Signature The EpiSwitch three-step biomarker selection process yielded a distinct chromosome conformational disease classification signature for ALS comprised of chromosomal interactions in eight genomic loci. The loci contained in the signature are CD36, TAB2, GLYCAM1, GRB2, FYN, PTPRC, DNM3 and IKBKB (Table 6). The final ALS CCS was derived from high-throughput analysis using the EpiSwitch discovery platform initially identifying 153 potential biomarker interactions. Statistical analysis results from the binary data analysis are shown in Table 5. 3.3 Sensitivity and Specificity Analysis The discriminating power of the ALS CCS are shown in Table 6, Table 7. Sensitivity and specificity for ALS detection in the 74 unblinded-tissue samples using the ALS CCS was 83∙33% (CI 51∙59 to 97∙91%) and 76∙92 (46∙19 to 94∙96%), respectively. In an independent, blinded cohort, sensitivity of the ALS CCS reached 87∙50% (CI 47∙35 to 99∙68%) and specificity was 75∙0% (34∙91 to 96∙81%).Fig. 3 Toll-like receptor signaling cascade showing the biological involvement of three of the eight chromosome conformation signature loci; CD36, TAB2 and IKKB (red thermometers) in the regulation of the inflammatory response. Image generated using Metacore™. Fig. 3Fig. 4 Protein STRING network of ALS regulation. The gene loci for the top 150 chromosome conformations that could best discriminate between ALS samples and healthy controls in the second array screen were uploaded as proteins to the STRING database and a resulting interaction network was generated. The two main networks are shown. Network nodes represent proteins and edges represent protein-protein associations. All nodes shown are query proteins and the first shell of interactors. Nodes are colored according to their association with the top gene ontology (GO) terms for Biological Process and Cellular Component. Edges are colored according to their interactions, either known, predicted or other. Fig. 4 3.4 Biological Relevance in ALS When we mapped the markers in the ALS CCS to the Metacore™ signaling pathway database, the TLR2 and 4 signaling pathways showed significant enrichment with three genomic loci (CD36, TAB2 and IKBKB) mapping to this signaling cascade (Fig. 3). To acquire additional insights into how the loci identified in this study contribute to the pathophysiology of ALS, we expanded our analysis to include an array-based comparison of ALS patients versus healthy controls using a set of loci that have been previously associated with ALS as an initial screen. Based on comparison of 16 ALS patients and 16 controls, 150 statistically disseminating markers were identified (Table 9). Genetic loci enriched with significant epigenetic deregulation were used to build a protein regulatory network using the STRING database (Fig. 4). When analyzing the resulting network (Additional File 1, Additional File 2), key hubs included proteins with known links to the pathophysiology of ALS including SOD1, TARDBP (TDP-43), NEFH, and UBQLN2 [[16], [17], [18]] (Fig. 4). In addition to the well-studied loci with known links to ALS, the network analysis confirmed the involvement of emerging and lesser-studied genomic loci in the development of ALS including KIFAP3 which has been recently identified as a potential risk locus for ALS and GRIP1 which was shown to be altered in ALS2 deficient spinal motor neurons leading to neuronal degeneration [[19], [20], [21]]. This indicates that consistent epigenetic deregulation is observed in key genetic loci in the largely sporadic ALS patient population used in this study. 4 Discussion For the majority of patients with ALS, the etiology of the disorder is unknown. Mutations in a number of genes such as: C9orf72, Cu, Zn superoxide dismutase 1 (SOD1) and TAR-DNA binding protein (TDP-43), found in familial ALS occur in only about 10% of the patient population [[22], [23], [24], [25]]. Other studies suggest common pathogenic mechanisms for both the non-genetic and the genetic forms of ALS, as well as, similar clinical courses and dysfunctional features [2, 25]. A hexanucleotide expansion in the C9orf72 gene is the most common genetic mutation [26, 27]. The discovery of the repeat expansion of the C9orf72 hexanucleotide provides bridge between familial ALS and sporadic ALS [[27], [28], [29]]. The mutation is detectable in about 40% of familial ALS patients and 8–10% of sporadic ALS. The rapidly progressive nature of ALS means any improvement in diagnosis would be of great value to patients, their families and the professionals who treat them. Epigenetics is the conduit for interactions between the environment and the genome. Epigenetic regulation of the genome can be used to explain complex diseases especially in the absence of any genetic mutations or patterns to explain pathology [14]. The foundation for diagnostic biomarker discovery using epigenetic frameworks rests with the detection and validation of conditional chromosome conformational changes at genetic loci of interest. Discovery is feasible and possible because chromosomal conformation comprises the smallest unit of regulated genome-linked to phenotype and asserts a high-level of regulation [14]. The binary quality (either the change is there or not), high biochemical stability and other characteristics of conditional chromosomal conformations make these genomic interactions highly advantageous as a source for potential diagnostic biomarkers [14]. OBD's platform technology and methodology, EpiSwitch, employs chromosome conformation capture and algorithmic analysis to detect and define a panel of epigenetic differences capable of discerning between diseased tissue samples and healthy controls. In this study, we identified, defined and evaluated chromosome conformations as biologically-distinguishing markers that comprise the first example of a non-invasive blood-based epigenetic signature for ALS. The study also provides the first indication that chromosomal conformational biomarker discovery may also provide a way to explore pathogenic pathways and mechanisms. The top performing EpiSwitch chromosome conformation biomarker maps to CD36, which encodes a fatty acid transport protein and has been shown to be linked to mitochondrial function. CD36 is also involved in the Toll Like Receptor (TLR) 2 and TLR4 signaling pathways. These toll-like receptors are activated in microglia in response to damage-associated molecular patterns. Interestingly, microglia have been shown to have a protective effect in early stage motor neuron degeneration [30]. Four biomarkers (i.e., Fyn, GRB2, IKBKB and CD45) appear in the pathways that map to the Major Histocompatibility Complex (MHC). Fyn plays a key role in initiating myelination by myelin-forming glial cells [31]. Three biomarker proteins (CD36, TAB2, IKBKB) are involved in pathways associated with the innate immune system. The IKK kinase complex is found in the regulation of gene expression in response to neurotransmission [32]. The location of these biomarkers in key pathways regulating the immune response point to the involvement of neuroinflammatory mechanisms in the pathogenesis of the disease [33]. The findings reported here add an additional clinical tool to aid in ALS diagnosis and further increase understanding of the disease Support for those concepts can be found in the panel of biomarkers discovered during this investigation. The gene loci related to the biomarkers in the ALS CCS, and their protein products, feature in cellular pathways linked to the phenotypic manifestations of ALS including: fatty acid transport, mitochondrial dysfunction, and alterations in both immune function and glial activation. Future studies using this approach and assessing blood samples collected longitudinally can also be applied as a new approach to monitor disease progression. In fact, recent studies indicate that the prediction of progression in ALS is possible using a set of patient clinical data (e.g. ALSFRS-R, ALSFRS slope, Trunk sub-score, time since diagnosis, systolic blood pressure, predicted survival) [34, 35]. Although the clinical annotations available for many of the samples used in this study were limited, future analysis of samples from longitudinal studies using EpiSwitch in combination with clinical metadata analysis and predictive algorithms are warranted. Additionally, the CCS identified during this investigation aligns with more recent evidence that points to the concept of non-cell-autonomous disease pathogenesis and the contribution of microglia in ALS [36]. A number of investigations have indicated that the cells lose their surveillance and neuro-protective capacity by switching from an activated neuroprotective to a neurodegenerative phenotype as the disease progresses. Under normal conditions microglia activation results in upregulation of MHC class 2 proteins involved in presentation of antigens to T lymphocytes. Microglia also express a diverse set of pattern recognition receptors, including TLRs, for sensing pathogen-associated molecular patterns and endogenous ligands derived from cellular injury. In addition to the eight loci that make up the CCS, expanded array analysis and overlay of a STRING protein network with genetic loci enriched in epigenetic deregulation shows evidence for strong biological concordance with genetic cases based on familial SOD1, TDP-43 and UBQLN2 (ALS subtype 15) genetic variants [37]. Epigenetic deregulation of SOD1, TDP-43, ERBB4 (ALS19), UBQLN2, INSR– all present themselves as significant events related to ALS etiology in sporadic cases investigated in this study. In addition to the STRING network identifying interconnected nodes with known link to disease, the analysis is also useful in the identification of novel disease mechanisms for target discovery. For example, the most interconnected node in the network was the alpha subunit of Protein Kinase C (PRKCA) (Fig. 4). While a role for Protein Kinase C has been known for decades in ALS ([38, 39]), our results implicate a supportive role for the glutamate metabotropic receptors GRM3 and GRM7 as potential intermediaries that can serve as novel disease targets [40]. Some of the caveats associated with our findings are the relatively modest sample size and the clinical homogeneity of the samples. According to a recently published global epidemiology analysis of published studies, ALS affects approximately 100,000 people worldwide [41]. In this study, we used 50 ALS patient samples and 42 healthy controls to develop the CCS and validated the signature on an additional 16 samples (8 ALS and 8 healthy controls), which represents a very small fraction of global cases. It is important to note however, that historical studies seeking to identify fluid-based biomarkers of ALS diagnosis have been developed on the analysis of between 28 and 103 ALS patients and between 12 and 43 healthy controls [42], sample numbers that are within the range of this study. Perhaps more importantly is the relative homogeneity of the samples in this study. The patients in the discovery cohort were overwhelmingly (>90%) Non-Hispanic or Latino Whites. While in the United States it has been recognized that there is a higher rate of ALS among Whites, a recent analysis of worldwide ALS incidence confirms the observation that the disease can affect people from a wide range of racial and ethnic backgrounds [43, 44]. Future studies looking at larger patient sets and the inclusion of a greater diversity of ethnic representation are warranted. Last, it is important to acknowledge that the benefits and limitations of using any surrogate non-invasive readout in a liquid biopsy, including this study, remain an important point of inquiry. Interestingly, recent studies have demonstrated that while distinct differences in gene expression profiles are observed in different tissue and cellular types; epigenetic differences, from DNA methylation to histone modifications to high order chromatin structures, certain markers could show local synchronization across cellular types. This includes macrophages and dendritic cells involved in immune surveillance which show concordant epigenetic signals between the primary site of pathology and in surrogate blood-based readouts [8, 45]. The current understanding of this phenomenon involves the exosome-mediated resetting of selective targeted cellular populations, described as a horizontal transfer [46, 47]. This is particularly relevant to exosome-based transfer of non-coding RNA, such as miRNA, long implicated in epigenetic resetting of secondary cellular targets in peripheral blood and distant tissues and directly associated with resetting of specific chromosome conformations in individual cells. Further epigenetic-based biomarker studies and extended validation on independent cohorts will help to better understand the features of these observed systemic epigenetic sub-signatures. Finally, we believe that epigenetic insights may help to provide biomarkers directly related to ALS. EMG and conductivity tests have increased the sensitivity of ALS diagnosis by providing the first biological information to inform ALS diagnosis. The sensitivity of our assay using patient samples approaches the sensitivity results reported using Awaji Criteria and those reported for CSF neurofilaments, another biomarker in development for diagnosing ALS [48, 49]. Ultimately, complex and heterogeneous diseases like ALS will require an integrative multi-omics approach to better characterize disease onset and progression and the results presented here offer an additional molecular approach to understand disease pathology. One of the major challenges in the current clinical practice of diagnosing ALS is the time is takes to make a definitive diagnosis. Under the current “rule-out” paradigm, it can take months to confirm a diagnosis of ALS. In addition, many of the current tools for aiding a diagnosis are expensive and can themselves take days to weeks to interpret. The ALS CCS described here is based on simple, inexpensive and well-accepted molecular biology techniques and technical readouts are available within 24 h, offering a substantial time and cost savings to physicians and payors. While the application of the ALS CCS in the clinical setting will require further investigation, the potential use of a chromosome conformation signature to diagnose ALS from a simple to collect, non-invasive biofluid and simple, rapidly available clinical readouts available to physicians and caregivers promise to help fill the gap in the current methods for diagnosing ALS. The following are the supplementary data related to this article.Additional File 1 ALS STRING network in .csv format. Additional File 1 Additional File 2 ALS STRING network in XML format. Additional File 2 Supplementary Table S1 Clinical annotations for the blind independent sample cohort provided by Oxford University. Supplementary Table S1 Acknowledgements Oxford BioDynamics would like to thank Barbara Nasto for her assistance in the preparation of this manuscript, which was funded by Oxford BioDynamics. Oxford BioDynamics holds ethical approval information for all patient samples. Oxford University holds written consents for individual patient samples. Consents for NEALS Biofluid Repository samples are held at NEALS Biofluid Repository. Sources of Funding This research was funded by Oxford BioDynamics as well as Innovate UK. Work in the Oxford Motor Neurone Disease Care and Research Centre is supported by grants from the Motor Neurone Disease Association and the Medical Research Council. MRT is funded by the Medical Research Council & Motor Neurone Disease Association Lady Edith Wolfson Senior Clinical Fellowship (MR/K01014X/1). Author Contributions The study was conceived and designed by M. Cudkowicz, J. Berry, E. Hunter and A. Akoulitchev. The manuscript was prepared by M. Salter (main author matthew.salter@oxfordbiodynamics.com), J. Green, J. Westra, A. Akoulitchev, M. Cudkowicz, L. Ossher and K. Talbot. Methodology development and data acquisition was done by M. Salter, J. Green, E. Corfield, L. Ossher and K. Talbot. Analysis and data interpretation were done by M. Salter, J. Green, J. Westra, E. Hunter, A. Ramadass, A. Akoulitchev and F. Grand. Declaration of Interests All authors (1) are employees of Oxford BioDynamics and are shareholders within the company. A. Akoulitchev and A. Ramadass are company directors. Oxford BioDynamics holds patents on the EpiSwitch™ technology. Authors (2) are employees of the University of Oxford, Nuffield Department of Clinical Sciences and Authors (3) is from Massachusetts General Hospital, Amyotrophic Lateral Sclerosis clinic and the Neurological Clinical Research Institute. ==== Refs References 1 Mitchell J.D. Callagher P. Gardham J. Mitchell C. Dixon M. Addison-Jones R. 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