Targeted plasma proteomics uncover novel proteins associated with KIF5A -linked SPG10 and ALS spectrum disorders

Targeted plasma proteomics uncover novel proteins associated with KIF5A-linked SPG10 and ALS spectrum disorders | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Targeted plasma proteomics uncover novel proteins associated with KIF5A -linked SPG10 and ALS spectrum disorders Jarosław Dulski , Arun K. Boddapati , Barbara Risi , Pablo Iruzubieta , View ORCID Profile Antonio Orlacchio , Roberto Fernández-Torrón , Tamara Castillo-Triviño , Adolfo López de Munain , Steve Vucic , Laura Donker Kaat , View ORCID Profile Tahsin Stefan Barakat , Leonard Petrucelli , Mercedes Prudencio , John E. Landers , Jochen H. Weishaupt , View ORCID Profile Andreas Prokop , View ORCID Profile Massimiliano Filosto , Zbigniew K. Wszolek , Devesh C. Pant doi: https://doi.org/10.1101/2025.04.29.651203 Jarosław Dulski 1 Division of Neurological and Psychiatric Nursing, Medical University of Gdańsk , Poland 2 Neurology Department, St Adalbert Hospital, Copernicus PL Ltd. , Gdansk, Poland 3 Department of Neurology, Mayo Clinic , Jacksonville, FL, USA M.D., Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Arun K. Boddapati 4 Independent Scholar N Druid , Decatur, GA, USA M.S. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Barbara Risi 5 NeMO-Brescia Clinical Center for Neuromuscular Diseases , Brescia, Italy 6 Department of Molecular and Translational Medicine, University of Brescia , Brescia, Italy M.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pablo Iruzubieta 7 Neurology Department, Donostia University Hospital, Osakidetza, and Biodonostia Health Research Institute-UPV-EHU, San Sebastián , Spain M.D., Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Antonio Orlacchio 8 Department of Medicine and Surgery, University of Perugia , Perugia, Italy 9 Hospital Company of Perugia - “Santa Maria della Misericordia” Hospital , Perugia, Italy M.D., Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Antonio Orlacchio Roberto Fernández-Torrón 7 Neurology Department, Donostia University Hospital, Osakidetza, and Biodonostia Health Research Institute-UPV-EHU, San Sebastián , Spain M.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tamara Castillo-Triviño 7 Neurology Department, Donostia University Hospital, Osakidetza, and Biodonostia Health Research Institute-UPV-EHU, San Sebastián , Spain M.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Adolfo López de Munain 7 Neurology Department, Donostia University Hospital, Osakidetza, and Biodonostia Health Research Institute-UPV-EHU, San Sebastián , Spain M.D., Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Steve Vucic 10 Brain and Nerve Research Centre, Concord Clinical School, University of Sydney, Concord Hospital , Sydney, Australia M.D., Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Laura Donker Kaat 11 Department of Clinical Genetics, Erasmus MC University Medical Center , Rotterdam, Netherlands M.D., Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tahsin Stefan Barakat 11 Department of Clinical Genetics, Erasmus MC University Medical Center , Rotterdam, Netherlands M.D., Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tahsin Stefan Barakat Leonard Petrucelli 12 Department of Neuroscience , Mayo Clinic, Jacksonville, FL, USA Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mercedes Prudencio 12 Department of Neuroscience , Mayo Clinic, Jacksonville, FL, USA Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site John E. Landers 13 Department of Neurology, University of Massachusetts Medical School , Worcester, MA 01605, USA Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jochen H. Weishaupt 14 Department of Neurology, Mannheim Center for Translational Neurosciences, Heidelberg University , Mannheim, Germany M.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andreas Prokop 15 The University of Manchester, Manchester Academic Health Science Centre, Faculty of Biology, Medicine and Health, School of Biology , Manchester, UK Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andreas Prokop Massimiliano Filosto 5 NeMO-Brescia Clinical Center for Neuromuscular Diseases , Brescia, Italy 16 Department of Clinical and Experimental Sciences, University of Brescia , Brescia, Italy M.D., Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Massimiliano Filosto Zbigniew K. Wszolek 3 Department of Neurology, Mayo Clinic , Jacksonville, FL, USA M.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: dcpant{at}emory.edu wszolek.zbigniew{at}mayo.edu Devesh C. Pant 17 Department of Cell Biology, Emory University School of Medicine , Atlanta, GA, USA Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: dcpant{at}emory.edu wszolek.zbigniew{at}mayo.edu Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract KIF5A (Kinesin family member 5A) is a motor protein that functions as a key component of the axonal transport machinery. Variants in KIF5A are linked to several neurodegenerative diseases, mainly spastic paraplegia type 10 (SPG10), Charcot-Marie-Tooth disease type 2 (CMT2), and amyotrophic lateral sclerosis (ALS). These diseases share motor neuron involvement but vary significantly in clinical presentation, severity, and progression. KIF5A variants are mainly categorized into N-terminal variants associated with SPG10/CMT2 and C-terminal variants linked to ALS. This study utilized a novel multiplex NULISA targeted platform to analyze plasma proteome from KIF5A -linked SPG10, ALS patients and compared to healthy controls. Our results revealed distinct proteomic signatures, with significant alterations in proteins related to synaptic function, and inflammation. Notably, neurofilament light polypeptide, a biomarker for neurodegenerative diseases, was elevated in KIF5A ALS but not in SPG10 patients. Moreover, these findings can now be taken forward to gain mechanistic understanding of axonopathies linking to N- vs C-terminal KIF5A variants affecting both central and peripheral nervous systems. Introduction The KIF5A gene encodes a motor protein that is part of the kinesin-1 family, responsible for facilitating the transport of cellular cargoes along microtubules. 1 Several studies have reported variants in KIF5A (OMIM# 602821) as significant contributors to a range of neurodegenerative diseases, particularly spastic paraplegia type 10 (SPG10), Charcot-Marie-Tooth disease type 2 (CMT2), neonatal intractable myoclonus (NEIMY), and recently amyotrophic lateral sclerosis (ALS). 2 - 7 KIF5A is the only motor protein within the KIF5 family (which includes KIF5B and KIF5C) that has been identified as mutated in neurodegenerative disorders with high penetrance. Although KIF5A linked neurodegenerative disorders share some pathophysiological and clinical features, primarily affecting motor neurons and leading to progressive impairment in motor function, they differ substantially in the distribution of symptoms, severity of symptoms and overall prognosis. 2 , 3 , 5 , 6 The clinical spectrum of KIF5A variants has expanded since the first identification of KIF5A -linked to SPG10. A large family pedigree initially identified an autosomal dominant locus for SPG10, which was subsequently mapped to chromosome 12 by Rubinsztein lab (UK). 6 , 8 SPG10 typically presents with progressive spasticity and weakness confined to the lower limbs, resulting from degeneration of the corticospinal tracts. Interestingly, a high degree of intra-familial phenotypic variability was initially reported in a KIF5A family where the index patient had SPG10 and father had axonal CMT with moderate lower limb amyotrophy, distal hypoesthesia and axonal neuropathy but no spasticity. 9 The intra-familial phenotypic heterogeneity was further observed in an Asian KIF5A SPG10 family which was characterized by late-onset, dysarthria and fasciculations. The presence of fasciculations was never reported in SPG10 and the mutation is located at the N-terminal motor domain of KIF5A protein, where the previously reported SPG10, or rare CMT2 variants are clustered. 10 Due to observed fasciculations, the authors suggested that KIF5A may need to be considered in the differential diagnosis of sporadic ALS. Other KIF5A rare phenotypes include adult-onset distal spinal muscular atrophy (SMA), reported in one family with variant in motor domain. 11 KIF5A was previously identified as a potential ALS candidate gene, but the study lacked sufficient cases to make a definitive conclusion. 12 In 2018, the group of Landers (USA) and Weishaupt (Germany) reported KIF5A -linked familial ALS variants in the C-terminal cargo domain of KIF5A protein. 2 , 5 The KIF5A -linked ALS patients display younger age at onset and longer survival. ALS typically involve both upper and lower motor neurons present with rapid-onset muscle weakness and wasting, while KIF5A related ALS tend to show more insidious progression. Additionally, ALS and frontotemporal dementia (FTD) has been reported in one large family with KIF5A cargo domain variant by Traynor lab (USA). 13 Conversely, severe developmental syndrome NEIMY has been linked to de novo variants in the cargo domain of KIF5A . These patients typically presented with myoclonic seizures, accompanied by symptoms such as hypotonia, optic nerve abnormalities, dysphagia, apnea, early developmental stagnation, and progressive leukoencephalopathy. 4 , 7 Axonopathies caused by KIF5A variants have been classically associated with a slow progression of muscle weakness leading to severe disability. Many cell biologists have examined KIF5A mediated axonal transport by expressing the mutant motor proteins and binding partners using cellular models. 14 - 16 These studies are informative but have certain limitations, as they may not fully capture the complexities of axonopathies in its natural context. Gaining a clearer understanding of how KIF5A variants contribute to the diverse clinical outcomes seen in various disorders, including axonopathy, is crucial for advancing our understanding of disease, improving patient counseling, and guiding future therapeutic approaches. Data from in vitro over-expression studies suggest that the majority of KIF5A motor domain variants linked to SPG10/CMT2, display reduce microtubule binding and KIF5A motility, while cargo domain variants linked to ALS, lead to hyperactive kinesin. 17 - 20 Despite knowing those structure-functional correlations, it remains unclear how and why they link to such divergent phenotypes. Potential new understanding might arise from patient proteomics studies comparing potential changes that correlate with the different KIF5A variants. In this study, we collected genetic, phenotypic and blood-plasma from an international cohort of patients with different variants in KIF5A , leveraging on a novel targeted proteomics platform using KIF5A patients’ plasma. Through this approach, we identified a set of novel targets that can now be taken forward to gain mechanistic understanding of axonopathies linking to N- vs C-terminal KIF5A variants. Methods Study participants We reached out to different neuromuscular centers worldwide that had previously reported or following up with KIF5A patients. Plasma samples were collected from total n=19 KIF5A patients and n=18 aged-matched controls ( Table 1 ). Out of nineteen patients, n=7 diagnosed with ALS, n=7 with SPG10, n=2 overlap with SPG10 (CMT2) and n=3 presymptomatic according to the El Escorial criteria. 21 To maximize the potential of a broader KIF5A cohort, we defined two primary groups: one for N-terminal variants (associated with SPG10/CMT2) and another for C-terminal variants (linked to ALS). All participants provided informed consent and the ethics board granted approval (IRB#: 16-009414). View this table: View inline View popup Download powerpoint Table 1. Characterization of KIF5A cohort assessed in the study. Blood collection Standard venipuncture was conducted to collect 10 ml venous blood in an EDTA tube for plasma separation. Blood was centrifuged at 2,465 g for 15 min at 4°C. Samples were processed within 30 min of collection. Plasma was aliquoted into 2 ml screw-cap tubes and stored at −80°C. Proteomics platform The Nucleic Acid Linked Immuno-Sandwich Assay (NULISA) was carried out at Alamar Biosciences (Fremont, CA) as described previously. 22 Briefly, plasma samples were initially stored at −80°C. After thawing on ice, the samples were re-centrifuged at 10,000 g for 10 min. A 10 μL portion of the supernatant was transferred to a 96-well plate and analyzed using Alamar’s NULISASeq™ CNS Disease Panel ( Table 2 ). NULISA’s design features enhance both multiplexing and automation. It creates the reporter DNA molecule via ligation, which simplifies the design of assays with multiple targets. The NULISASeq process was automated, beginning with the formation of immunocomplexes through DNA-barcoded capture and detection antibodies. These immunocomplexes were captured on paramagnetic oligo-dT beads, washed, and then released into a low-salt buffer, followed by capture, and washing on streptavidin beads. The DNA strands at the ends of the immunocomplexes were ligated to form a reporter molecule containing both target-specific and sample-specific barcodes. The capture antibody is attached to a partially double-stranded DNA, which contains a poly-A tail and a target-specific molecular identifier (TMI), while the detection antibody is linked to another partially double-stranded DNA, featuring a biotin group and a complementary TMI. These reporter molecules were then pooled, amplified via PCR, purified, and sequenced using the Illumina NextSeq 2000. View this table: View inline View popup Table 2. List of NULISA protein panel used in the study. Data normalization and quality control The sequencing data were processed using the NULISAseq algorithm developed by Alamar Biosciences (CA). 22 In brief, barcodes corresponding to both the sample and target were quantified. For normalization, the count for each target was divided by the internal control counts for that well. The resulting data were rescaled, incremented by 1, and log 2 transformed to derive the NULISA Protein Quantification (NPQ) arbitrary unit, which was then used for subsequent statistical analysis. Moreover, inter-plate normalization is part of the standard normalization process and is carried out to make data comparable across runs. Statistical Analysis Data analyses were performed using R software (version 4.4.1, the R foundation for statistical analysis). NPQ values from NULISA were already log 2 transformed. Normalized NPQ values have been used to perform differential expression (DE) analysis by fitting linear model (lm, a default package of R package) for each protein using a reduced model representing only two comparisons. The DE is a statistical approach to detect and identify biological targets whose expression varies between groups. We eliminated proteins whose NPQ values were below the NULISA limit of detection, p -value < 0.05 was considered statistically significant. Our study focuses on a predefined set of proteins, p -values are presented as independent results without adjustments. Additionally, we check for individual protein performance evaluation via bootstrapped AUC calculation on the p < 0.05 significant proteins across all the different comparisons ( Table S1, Figure S2 ). For each protein and comparison group, we performed 1000 bootstrap iterations. In each iteration, we sampled with replacement from the original dataset, maintaining the same sample size. Results The main goal of our study was to identify proteins that exhibit alterations in individuals carrying autosomal dominant KIF5A variants, primarily associated with SPG10 or ALS, with a view to improve our understanding of the mechanisms that determine such different disease outcomes. Blood is frequently utilized in liquid biopsy for diagnostic purposes. However, the blood proteome, includes proteins actively secreted into the bloodstream, as well as the proteomes from other tissues holds even greater potential in providing a real-time insight into disease. To do this, we used multiplexed NULISA with next-generation sequencing readouts for detecting central nervous system targets. We analyzed 109 unique proteins which passed the NULISA limit of detection, from human KIF5A mutation carriers and control plasma samples ( Figure 1 ). We identified 109 proteins in the plasma of control and disease groups to pinpoint potential significant differences. Participants in the control group were sex and age matched to the patient group, thus further enhancing the power of our study to detect reliable proteomic alterations. Download figure Open in new tab Figure 1. Design and outcome of the KIF5A linked SPG10, ALS plasma proteomics study. ( A ) Schematic representation of the KIF5A isoform 202 (ENST00000455537.7, 5779bp, 1032aa), with functional domains colour-coded as indicated, angularly numbered stippled vertical lines showing residue position of exon borders (number of every other exon provided); point mutations with identified residue changes are indicated by arrow heads, data taken from 2 , 5 , 34 , 35 , 58 - 60 (and this study; see Table 1 ): green arrow heads represent the SPG10/CMT2-linked mutations: V12A, Y63C, D73N, Q87E, R111P, K132R R162W/P, S189P, R191H, A194P, T196N, M198T, S202N, S203C, R204Q/W/P , V231L D232N/W, G235E, L249V, E251K, K253N, L255M, N256Δ/S, K257N, S258L, L259Q, L262P A268T, Y276C, P278L, R280H /C/L, D290H, R323W , Q341R, K444Efs*38 , E351Δ , A361V, K362N, A391V, S458F, R468W, L494M, L558P, G568R, K595N*27 , R606W, A669T, R716W, R718W, E758K, Q764*, K781E , R864*, K907M, A980V; blue arrow heads represent the ALS-linked mutations: K29R, P46S, R297Q, E413G, R423H, Q474H, L488R, H542N, S577G, A579T, T585A, R588Q, R716Q, D853N, E881K, K920fs, V922I , T976E, Q952fs, C975fs, S978L/fs, P986L, D996fs, N997fs, N999fs/Δ, T1001Qfs, D1002G, R1007G/K, F1023C, E1028D; C-terminal ALS-linked intronic and exonic point mutations occurring only at DNA level are listed in the blue box; underlined mutations indicate patients of this study, also represented as circles where each circle indicates an individual patient. ( B ) Experimental flow of the blood analysis: antibodies detecting 109 biomarker proteins (α1-α109) are DNA-barcoded either to indicate the antibody identity (‘1’ in yellow circle) or the patient’s identity (‘2’ in yellow circle); readily formed immuno-complexes with both sets of antibodies from each blood sample are combined and subjected to NULISAseq analysis quantifying the abundance of each barcode pair. ( C ) Heatmap displaying the log 2 -normalized read counts (z score: from -4, blue, lower expression to 4, red, higher expression). Rows represent individual proteins indicated on the right (for abbreviations see Tab.2) clustered based on similarity, while columns represent individual patient samples clustered by clinical diagnosis (color-coded on top). The dendrogram on the left indicates hierarchical clustering of proteins (5 distinct clusters; indicated by curly brackets on the right), revealing distinct patterns of expression across clinical diagnoses. Plasma analyses of KIF5A -linked ALS patients showed a significant increase in a range of proteins that could be grouped under the term: amyloidopathy (amyloid-beta 40 [Aβ38]), microtubule stabilization (pTau-181, or pTau-231), synaptic function (contactin-2 [CNTN2], presenilin-1 [PSEN1]), fatty acid metabolism regulation (fatty acid binding protein 3 [FABP3]), cell cycle regulation (RuvB-like 2 [RUVBL2]), inflammation regulation (C X-C Motif Chemokine Ligand 1 [CXCL1], and pulmonary surfactant-associated protein D [SFTPD]). Neuronal pentraxin-1 (NPTX1), interleukin-15 [IL15] were the only two proteins that displayed significant downregulation in KIF5A ALS ( Figure 2 ). The most notable finding in the plasma of KIF5A -linked ALS patients was neurofilament light polypeptide (NEFL), a well-known biomarker for neurodegenerative diseases that has been extensively studied in the context of ALS. 23 - 25 Further analysis conducted on three presymptomatic individuals carrying a KIF5A splice site mutation (c.3020G>A) known to have strong links to ALS 5 , 26 revealed significant deviations from KIF5A ALS patients: the NEFL and Aβ40 proteins that were found significantly increased in the main analyses were found to be significantly reduced in presymptomatic carriers ( Figure S1 ). Download figure Open in new tab Figure 2. Comparison of NULISAseq biomarkers between control and KIF5A -linked ALS. Box plots illustrating the distributions of targets in plasma of controls (n=18) and KIF5A ALS (n=7) participants with significant or marginally significant differences for proteins (Aβ38, CNTN2, FABP3, NEFL, NPTX1, pTau-181, pTau-231, and SFTPD). The y-axis represents log 2 -tranformation of normalized read counts (NULISA Protein Quantification, NPQ). On each box, the central mark indicates the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. The whiskers extend to the most extreme data points not considered outliers, and the outliers are plotted individually using the grey dot symbol. The p values were determined using linear model. Significance determination was based on p -value < 0.05. Plasma analyses of KIF5A -linked SPG10 patients revealed significant decrease in three unique proteins which include the actin-cytoskeleton protein BASP1 (brain acid soluble protein 1), the synaptic function and plasticity regulator NPTXR (neuronal pentraxin receptor), the synaptic protein SNAP25 (synaptosomal-associated protein 25), whereas the common vascular inflammatory target SFTPD was significantly decreased ( Figure 3 ). Download figure Open in new tab Figure 3. Comparison of NULISAseq biomarkers between control and KIF5A -linked SPG10. Box plots illustrating the distributions of targets in plasma of controls (n=18) and KIF5A SPG (n=9) participants with significant or marginally significant differences for proteins (BASP1, NPTXR, SNAP25 and SFTPD). The y-axis represents log 2 -tranformation of normalized read counts (NULISA Protein Quantification, NPQ). On each box, the central mark indicates the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. The whiskers extend to the most extreme data points not considered outliers, and the outliers are plotted individually using the grey dot symbol. The p values were determined using linear model. Significance determination was based on p -value < 0.05. In the last step, we investigated the differentially expressed proteins in the ALS and SPG10 groups which could distinguish and possibly mirror the pathogenic mechanisms underlying axonopathy. We found significant changes in the BASP1, CNTN2 and NEFL proteins ( Figure 4 ). In summary, the results highlighted significant proteins changes within the KIF5A ALS and SPG10 clinical spectrum. By identifying proteins that were differentially expressed under these conditions, the study offers valuable insights into physiological and pathological changes associated with KIF5A linked axonopathies. The differential expression of proteins identified in presymptomatic vs ALS suggests a dynamic relationship between neurodegeneration and early-stage alterations ( Figure S1 ), providing new avenues to gain understanding how axon damage develops. Download figure Open in new tab Figure 4. Comparison of NULISAseq biomarkers between KIF5A ALS and SPG10. Box plots illustrating the distributions of targets in plasma of KIF5A ALS (n=7) and KIF5A SPG (n=9) participants with significant or marginally significant differences for proteins (BASP1, CNTN2 and NEFL). The y-axis represents log 2 -tranformation of normalized read counts (NULISA Protein Quantification, NPQ). On each box, the central mark indicates the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. The whiskers extend to the most extreme data points not considered outliers, and the outliers are plotted individually using the grey dot symbol. The p values were determined using linear model. Significance determination was based on p -value < 0.05. Discussion Multiplex NULISA™ platform offers a promising approach for the study of neurodegenerative disorders So far, blood-based biomarkers were performed using non-targeted liquid chromatography-tandem mass spectrometry techniques (LC-MS/MS). However, they are limited by the broad dynamic ranges of protein concentrations in plasma or serum where the detection of subtle variations is impeded by more abundant proteins, such as albumins and immunoglobulins. 27 , 28 Here we adopted a pioneered technology, a far more sensitive multiplex NULISA™ platform and focused on a defined set of central nervous system proteins in plasma samples that can be detected with attomolar sensitivity. 22 Notably, neurodegenerative diseases (ALS, SPG, AD, PD) that affect axons often exhibit common phenotypic features, such as impairments in axonal transport or a “dying-back” process that begins at the distal synaptic terminals. In contrast, mutations in one gene can lead to different neurodegenerative disorders. To better understand these phenomena, it is crucial to examine axonopathies from both disease-specific and broader cell type perspectives. The type 1 kinesin (KIF5A) is a prime candidate for such an approach. KIF5A is a very prominent motor protein mediating anterograde microtubule-based transport in neurons, partly redundant with two KIF5 paralogues, various type 3 kinesin motors and potentially some other kinesin classes, such as type 2 kinesins. 29 - 31 The loss of function of KIF5A in neurons would be expected to negatively impact on the provision with life-sustaining materials and organelles resulting in loss of axonal homeostasis and gradual degeneration; but it remains poorly explained as to why this affects primarily certain sub-types of neurons which then lead to disease-specific symptoms. 32 , 33 Similarly, we lack clear explanations for why different mutant alleles in the same motor protein, KIF5A, give rise to distinct neurodegenerative disease ( Figure 1A ). Some hypotheses arise from molecular analyses of KIF5A C-terminal ALS-linked high penetrant variants suggest these variants cause a gain of function rather than a loss. However, this does not clarify the specific pathological outcomes associated with these variants. 17 , 19 , 34 - 36 We believe that new insights may emerge from blood plasma proteomics studies aimed at identifying possible molecular features that differ between SPG10- and ALS-linked KIF5A variants. A few key measures were implemented to further refine our study: Firstly, the study was largely driven by international collaboration, which enabled the collection of plasma and critical clinical data from an exceptionally diverse cohort of recruited patients. Secondly, we ensured that KIF5A mutation carriers were compared to with age- and sex-matched healthy controls. Thirdly, the use of DNA barcodes on the NULISA™ platform facilitated multiplex proteomics, enabling us to run plasma samples from both patient and control groups together to minimize experimental variation in protein level differences. The data we obtained with the NULISA™ platform clearly revealed robust differences in protein expression between the compared groups, suggesting the platform’s future potential as diagnostic tool for KIF5A-linked SPG10, ALS patients and beyond. Moreover, we firmly believe that these findings could offer valuable mechanistic insights into neurodegenerative diseases and their progression. A critical question in this context is why proteins appear in the blood at varying levels, as it may reflect cell type-specific degrees of deterioration and/or the strength of target protein expression. For instance, we observed high levels of NEFL in one patient group and not the other, although axons affected in both groups are partly overlapping and all of them are enriched with neurofilaments. 37 We believe that striving to understand such unfathomable observations like these opens up important new avenues of enquiry for the investigation of these diseases. ALS-linked KIF5A mutations correlate with a distinct plasma proteome Our use of targeted multiplex plasma proteomics to identify brain-derived proteins revealed consistent protein signatures across multiple independent samples as clearly illustrated by the comparisons of plasma from KIF5A ALS patients to age-matched healthy controls ( Figure 2 ). Strongest differences were observed for a number of genes with a diverse range of functions: upregulation of the APP fragment Aβ38 has previously been correlated with slowed cognitive decline in AD; 38 , 39 and the γ-secretase PSEN1 is linked to AD (OMIM #607822), FTD (#600274) and Pick disease (#172700); proteins with cytoskeletal links include the actin regulator CNTN2 (linked to epilepsy; #615400), the neurofilament subunit NEFL (linked to CMT; #617882, #607734, #607684) and the tau fragments pTau-181 and pTau-231 (predictors of AD and cognitive decline; 40 four proteins are known inflammation factors including the chemokine CXCL1, the cytokine IL15, vascular inflammatory SFTPD and the fatty acid transporter FABP3. Further proteins comprise the synaptic transmembrane protein NPTX1 (linked to ataxia; #620158); and the ATP-dependent DNA regulator RUVBL2. 41 - 45 Notably, the presence of various factors linked to other neural disorders in the blood of ALS patients may reflect the heterogeneity in clinical symptoms and disease progression. The NEFL results are in line with previous report showing no or modest increase in SPG10 patients 46 , whereas phosphorylated neurofilament heavy chain was prominently elevated in serum and CSF of sporadic ALS patients compared with different SPG cases in a previous study. 47 This further confirms the reproductivity of the NULISA assay. One possible explanation for the differences in NEFL could be the involvement of both upper and lower motor neurons, resulting in more widespread axonal damage with the release of larger amounts of NEFL, as occurs in typical ALS. 48 , 49 SPG10-linked KIF5A mutations show a different signature from ALS patients The plasma proteome of the SPG10 cohort identified four proteins that were significantly elevated compared to controls ( Figure 3 ). Of these, only SFTPD was shared with the ALS cohort whereas the other proteins clearly deviated. These are all proteins localized to axonal terminals or synapses; they comprise the nerve growth and plasticity regulator BASP1 50 , the synaptic transmembrane protein NPTXR which is related to NPTX1 and considered a potential biomarker of synaptic dysfunction, 51 and the t-SNARE SNAP25 pivotal for synaptic release and linked to neuromuscular myasthenic syndrome (#616330) 52 , 53 . The upregulation of these factors might reflect the slow progression, resilience, and regenerative capacity in SPG10 patients. For example, the presence of BASP1 might be due to increases sprouting of regenerative collaterals 50 , 54 - 57 , and enrichment of synaptic proteins might indicate synaptic loss or dysfunction involved in slow disease progression. Major conclusions and future perspectives Our study is the first and largest comparative analysis of the KIF5A spectrum with patients from multiple continents and reveals promising correlations between KIF5A variants and underlying disease manifestations. To confirm results obtained here, longitudinal studies tracking proteomic changes over time would be a valuable next step, although such studies will face practical challenges given the slow disease progression reported in KIF5A patients, which can span 40 to 60 years. Nonetheless, the importance of such studies is underscored by our finding that presymptomatic individuals with known ALS-linked mutations exhibited down-regulation of several proteins (Aβ38, Aβ40, CCL11, GFAP, NEFL, SMOC1, VCAM1, VGF) compared to the symptomatic phase of the disease where elevated levels of Aβ38 and NEFL were prominent, likely indicators of axon degeneration ( Figure S1 ). Therefore, to enable reliable early diagnoses, we need better descriptions of such dynamic changes over time. It is also important to note that identifying KIF5A patients for our study was a major effort, requiring outreach to numerous neuromuscular units worldwide. An international registry for patients with variants in the KIF5A gene would significantly facilitate more systematic studies, which could be complemented by repeated neuroimaging to establish closer correlations between observed morphological pathologies and blood proteins. We fully recognize the challenges of cohort size and validation, particularly given the rarity of KIF5A -related disorders. However, rather than a limitation, this can be viewed as an opportunity—our dataset provides the first insights into plasma proteomic changes in this population, establishing a foundation for future basic cell biology and translational studies. While an independent validation cohort was not feasible, our work aligns with a well-recognized precedent in rare neurodegenerative research, where numerous published studies, including those utilizing N=1 design, have made significant contributions. Such underpowered studies are increasingly valued in the field, particularly in motor neuron diseases (ALS, SPG10, CMT), as they support the growing emphasis on personalized medicine. Furthermore, the diagnostic accuracy of the applied method was assessed through a bootstrap procedure. The AUC calculations were performed for proteins with p < 0.05 across all comparisons. The resulting analysis identified targets that consistently showed significance, providing evidence that these targets are robust and not likely due to chance. In the context of rare diseases or complex comorbid conditions like KIF5A spectrum disorders, large-scale trials are often impractical or even inappropriate, highlighting the importance of smaller, well-characterized datasets in driving research and clinical progress. Our findings could further play a critical role in shaping natural history studies and guiding the design of clinical trials and therapies for KIF5A -linked multifactorial disorders. Data availability Data used in this study are available within the article. Additional data supporting the information may be available upon reasonable request. Funding This work is partially supported by Department of Neurology, Mayo Clinic, Florida to ZKW. JD is partially supported by the Haworth Family Professorship in Neurodegenerative Diseases fund (90052067). ZKW is partially supported by the NIH/NIA and NIH/NINDS (1U19AG063911, FAIN: U19AG063911), the Haworth Family Professorship in Neurodegenerative Diseases fund, The Albertson Parkinson’s Research Foundation, PPND Family Foundation, and the gift from Margaret N. and John Wilchek Family. PI, TC and ALM were partially supported by SEED-ALS Spain Project and CIBER. TSB was supported by the Netherlands Organisation for Scientific Research (ZonMw Vidi, grant 09150172110002). We thank Stichting 12q ( https://stichting12q.nl/ ) for supporting our work related to disorders on chromosome 12q. DCP is supported MDA DG and LLL CDA. AO is supported by the Department of Medicine and Surgery of the University of Perugia (Fondo Ricerca di Base, Grants no. DSCH_BASE19_ORLACCHIO and RICERCABASE_2020_ORLACCHIO), and the University of Perugia (Progetto di Ricerca di Ateneo, Grant n. RICERCA_ATENEO_ALIMENTAZIONE). Funding bodies did not have any influence on study design, results, and data interpretation or final manuscript. Competing interests JD is partially supported by the Polish Ministry of Science (Scholarship for Outstanding Young Scientists, SMN/19/1279/2023). He serves as an editorial board member of ‘Neurologia i Neurochirurgia Polska’. He has received speakers’ bureau honoraria from VM Media Ltd., Radosław Lipiński 90 Consulting, and Ipsen. He has intellectual property rights for ‘Application of Hydrogen Peroxide and 17β -Estradiol and its Metabolites as Biomarkers in a Method of Diagnosing Neurodegenerative Diseases In Vitro’ (WO/2023/234790). ZKW serves as PI or Co-PI on Biohaven Pharmaceuticals, Inc. (BHV4157-206) and Vigil Neuroscience, Inc. (VGL101-01.002, VGL101-01.201, and ultra-high field MRI in the diagnosis and management of CSF1R-related adult-onset leukoencephalopathy with axonal spheroids and pigmented glia), ONO-2808-03, and Amylyx AMX0035-009 projects/grants. He serves as Co-PI of the Mayo Clinic APDA Center for Advanced Research and as an external advisory board member for the Vigil Neuroscience, Inc., and as a consultant on neurodegenerative medical research for Eli Lilli & Company, and NovoGlia, Inc. Supplementary Material Table S1. Comparison of target performance across groups using AUC metrics from bootstrap analysis . Download figure Open in new tab Figure S1. Comparison of NULISAseq biomarkers between presymptomatic and KIF5A ALS. Box plots illustrating the distributions of targets in plasma of KIF5A ALS (n=7) and presymptom (presymptomatic, n=3) participants with significant or marginally significant differences for proteins (Aβ38, Aβ40, CCL11, VCAM1, GFAP, NEFL, SMOC1, and VGF). The y-axis represents log 2 -tranformation of normalized read counts (NULISA Protein Quantification, NPQ). On each box, the central mark indicates the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. The whiskers extend to the most extreme data points not considered outliers, and the outliers are plotted individually using the grey dot symbol. The p values were determined using linear model. Significance determination was based on p -value < 0.05. Download figure Open in new tab Figure S2. Box plot comparing accuracy assessment statistic area under the receiver operating characteristic curve (AUC) for different KIF5A groups. Box plots of the AUC values showing median, inter-quartile ranges and total ranges from the 1000 bootstrap iterations. The red dashed line indicates the mean AUC derived from random distribution. Acknowledgements We thank patients for their donation of biofluids. We would like to thank members of Jiang, Faundez laboratories at Emory, as well as the colleagues at Emory ALS center and the Emory proteomics core for helpful discussions. We would like to extend our gratitude to the Alamar Technology Access Program team for their valuable inputs. 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Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Targeted plasma proteomics uncover novel proteins associated with KIF5A -linked SPG10 and ALS spectrum disorders Jarosław Dulski , Arun K. Boddapati , Barbara Risi , Pablo Iruzubieta , Antonio Orlacchio , Roberto Fernández-Torrón , Tamara Castillo-Triviño , Adolfo López de Munain , Steve Vucic , Laura Donker Kaat , Tahsin Stefan Barakat , Leonard Petrucelli , Mercedes Prudencio , John E. Landers , Jochen H. Weishaupt , Andreas Prokop , Massimiliano Filosto , Zbigniew K. Wszolek , Devesh C. Pant bioRxiv 2025.04.29.651203; doi: https://doi.org/10.1101/2025.04.29.651203 Share This Article: Copy Citation Tools Targeted plasma proteomics uncover novel proteins associated with KIF5A -linked SPG10 and ALS spectrum disorders Jarosław Dulski , Arun K. Boddapati , Barbara Risi , Pablo Iruzubieta , Antonio Orlacchio , Roberto Fernández-Torrón , Tamara Castillo-Triviño , Adolfo López de Munain , Steve Vucic , Laura Donker Kaat , Tahsin Stefan Barakat , Leonard Petrucelli , Mercedes Prudencio , John E. Landers , Jochen H. Weishaupt , Andreas Prokop , Massimiliano Filosto , Zbigniew K. Wszolek , Devesh C. Pant bioRxiv 2025.04.29.651203; doi: https://doi.org/10.1101/2025.04.29.651203 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neuroscience Subject Areas All Articles Animal Behavior and Cognition (7626) Biochemistry (17653) Bioengineering (13876) Bioinformatics (41893) Biophysics (21430) Cancer Biology (18568) Cell Biology (25469) Clinical Trials (138) Developmental Biology (13366) Ecology (19875) Epidemiology (2067) Evolutionary Biology (24294) Genetics (15591) Genomics (22478) Immunology (17717) Microbiology (40331) Molecular Biology (17154) Neuroscience (88497) Paleontology (666) Pathology (2829) Pharmacology and Toxicology (4817) Physiology (7635) Plant Biology (15116) Scientific Communication and Education (2044) Synthetic Biology (4286) Systems Biology (9817) Zoology (2268)