Participant Characteristics. Supplementary Table 1 reports demographic and clinical characteristics for the 116 autosomal dominant FTLD mutation carriers and 39 familial controls, representing cognitively normal non-mutation carriers from families with a known C9orf72, GRN, or MAPT mutation. Consistent with the broader ALLFTD cohort10, C9orf72 was the most common mutation (N = 47), followed by MAPT (N = 37) and GRN (N = 32). At baseline, almost half of all mutation carriers were presymptomatic (global CDR®+NACC-FTLD = 0; 54 [47%]; 24 [51%] C9orf72, 12 [39%] GRN, 18 [49%] MAPT). GRN carriers were on average older (mean age = 57.9 years) than C9orf72 (mean age = 49.9 years), MAPT (mean age = 44.5 years), and controls (mean age = 45.8 years). Mutation carriers and controls were comparable in terms of sex (mutation carriers: 49–55% female vs. controls: 59% female) and education (mutation carriers: mean = 15.0 to 15.7 years vs. controls: mean = 15.6 years). The median [IQR] of annual study visits was 3 [1.75, 4] for mutation carriers and 3 [2, 5] for controls.
Genetic FTLD Protein Co-expression Network. The study design is presented in Fig. 1. Using weighted gene correlational network analysis (WGCNA), we built a protein co-expression network from 4,138 proteins across 155 CSF samples to identify protein communities that are dysregulated in FTLD (Fig. 2A). Network analysis revealed 31 protein co-expression modules across controls and mutation carriers. Module size ranged from 360 proteins (M1) to 48 proteins (M31). Protein module membership assignments are provided in Supplementary Table 2. Based on gene ontology and cell type enrichment analyses (Supplementary Table 3; Supplementary Fig. 1), 28 out of the 31 modules had a clear primary ontology that was used for annotation, while 3 modules did not evidence clear ontology (labeled “ambiguous”).
CSF proteomic signatures of FTLD disease severity. Analyses first combined FTLD mutation carriers across all 3 genes (N = 116) to maximize sample size. Module eigenproteins, calculated as the first principal component of module abundance, were compared across symptomatic mutation carriers, presymptomatic mutation carriers, and controls using one-way ANOVA with Tukey’s post-hoc correction, adjusting for age and sex. Module eigenproteins were also correlated with continuous indicators of functional severity (CDR®+NACC-FTLD sum of boxes), adjusting for p-values by false discovery rate (FDR) correction according to the Benjamini-Hochberg method. Seven of the 31 modules showed significant differences between symptomatic carriers and controls (Supplementary Table 4). Four of these modules exhibited particularly strong relationships with functional severity: M26 spliceosome, M2 presynapse, M28 synapse assembly/axon, and M22 autophagy. M26 spliceosome, highly enriched for proteins involved in mRNA splicing and nuclear transport, was markedly increased in symptomatic carriers vs. controls and positively correlated with functional severity (ρ = 0.41, FDR-p = 2.7e-6). M2 presynapse and M28 synapse assembly/axon, enriched for synaptic/neuronal (M2, M28) and oligodendrocyte (M28) cell-type markers, were decreased in symptomatic carriers vs. controls and negatively correlated with functional severity (M2: ρ=-0.33, FDR-p = 2.6e-4; M28: ρ=-0.35, FDR-p = 1.7e-4). M22 autophagy was also decreased in symptomatic carriers vs. controls and negatively correlated with functional severity (ρ=-0.33, FDR-p = 2.6e-4). CSF proteomic alterations were more subtle and less likely to survive post-hoc correction when comparing presymptomatic mutation carriers vs. controls. However, M9 ion transport, enriched for neuronal cell-type markers involved in ion channel activity and transport, was significantly decreased in presymptomatic carriers vs. controls.
We next examined how network modules associated with CSF NfL, an indicator of neuroaxonal degeneration, given that NfL is currently the most validated fluid biomarker for disease monitoring in genetic and sporadic FTLD4,23,24. Six modules associated with CSF NfL concentrations, measured by Simoa, at an unadjusted p < 0.05, with only M26 spliceosome surviving FDR-correction (ρ = 0.32, FDR-p = 9.3e-3). Upon closer examination, we observed that the SomaScan target for NfL (NEFL) was assigned to the M26 spliceosome module. However, NEFL was not a strong driver of M26 eigenprotein co-expression (intramodular kME = 0.32 [87th out of 88 module members]) and the relationship between M26 spliceosome and CSF NfL (Simoa) persisted when recalculating the M26 spliceosome eigenprotein without NEFL (ρ = 0.31, p = 9.1e-3).
Gene-stratified FTLD proteomic signatures. We performed gene-stratified analyses to determine whether proteomic signals observed in the full sample were driven by specific gene groups. For each gene group, we again compared module eigenprotein levels across symptomatic mutation carriers, presymptomatic mutation carriers, and controls using Tukey’s post-hoc correction. We also examined correlations with CDR®+NACC-FTLD sum of boxes and CSF NfL (Supplementary Table 4; Fig. 2B). FDR corrections were not applied to gene-stratified correlational analyses given the reduced sample sizes. Within each gene group, M26 spliceosome levels were increased and M22 autophagy module levels were decreased in symptomatic carriers vs. controls. However, symptomatic C9orf72 and GRN carriers exhibited more pronounced alterations in M26 and M22 (0.85 to 1 difference in z-score vs. controls) than symptomatic MAPT (0.5 to 0.6 difference in z-score vs. controls). Presymptomatic GRN carriers also showed elevations in M26 spliceosome that were not present in presymptomatic C9orf72 or MAPT mutation carriers. M2 presynapse and M28 synapse assembly/axon were decreased in symptomatic GRN and MAPT, with both modules exhibiting particularly strong relationships with CDR®+NACC-FTLD and CSF NfL in MAPT (M2: CDR®+NACC-FTLD ρ=-0.59, p = 1.0e-4; NfL ρ=-0.54, p = 8.0e-4; M28: CDR®+NACC-FTLD ρ=-0.43, p = 7.4e-3; NfL ρ=-0.46, p = 5.0e-3). M9 ion transport, which showed the strongest presymptomatic signal in full sample analyses, was decreased among both presymptomatic C9orf72 and MAPT carriers vs. controls.
Several additional module expression patterns emerged in gene-stratified analyses that were not present when examining modules in all mutation carriers. A cluster of modules adjacent to M26 spliceosome (M24 ubiquitination/translation, M25 protein folding/metabolism, M27 metabolism) were increased and/or positively correlated with disease severity in both symptomatic C9orf72 and GRN. M29 extracellular matrix (ECM), enriched for ECM proteins and microglial cell-type markers, was uniquely elevated in symptomatic MAPT carriers vs. controls. M4 complement/coagulation was closely related to M29 and also selectively elevated in symptomatic MAPT vs. controls. M3 postsynapse/glycosylation did not significantly differ between mutation carrier groups and controls, but was one of the strongest correlates of disease severity measures in MAPT (CDR®+NACC-FTLD ρ=-0.53, p = 7.0e-4; NfL ρ=-0.46, p = 5.9e-3).
Differential Abundance Analysis. To complement network analyses, we examined differential abundance of all 4,138 proteins to identify individual CSF proteins driving module-level differences in gene-stratified comparisons of symptomatic carriers, presymptomatic carriers, and controls (Supplementary Table 5, Extended Data Fig. 1). M26 spliceosome had the highest proportion of individual differentially abundant proteins in symptomatic C9orf72 and GRN carriers (Supplementary Table 6), including nuclear proteins TRA2B, TMPO, and HNRNPAB. Proteins assigned to modules with neuronal enrichment were also differentially abundant across multiple gene groups vs. controls, including established synaptic markers NPTX2 (M2 presynapse), CNTNAP2 (M28 synapse assembly/axon), DLG4 (M3 postsynapse/glycoslyation), and 14-3-3 proteins (e.g., YWHAZ; M19 neuron migration). Symptomatic MAPT exhibited the highest proportion of differentially abundant proteins from these neuronal modules, particularly M28 synapse assembly/axon. M26 spliceosome had the highest proportion of differentially abundant protein members in presymptomatic GRN (e.g., XRCC6) and M9 ion transport had the highest proportion of differentially abundant protein members in presymptomatic C9orf72 and MAPT (e.g., KCNE2, GPR6).
GRN mutations are characterized by progranulin protein haploinsufficiency. As expected, CSF progranulin protein (unassigned to a module) exhibited the largest decreased abundance in both pre- and symptomatic GRN carriers vs. controls. C1QTNF1 (unassigned to a module), another lysosomal protein with CSF levels shown to be strongly colinear with GRN levels25, exhibited a similar pattern of decreased abundance in GRN carriers.
Spliceosome, extracellular matrix, and synapse associated proteins predict cognitive trajectories. Module correlations with global cognitive trajectories in the full sample are provided in Supplementary Table 7 and Fig. 2A. Modules that were most strongly associated with cognitive decline in the full sample included M29 ECM (ρ=-0.39, FDR-p = 2.1e-5) and M26 spliceosome (ρ=-0.22, FDR-p = 3.9e-2). Modules that were most strongly associated with cognitive preservation included M28 synapse assembly/axon (ρ = 0.42, FDR-p = 7.8e-6), M2 presynapse (ρ = 0.41, FDR-p = 7.8e-6) and M3 postsynapse/glycoslyation (ρ = 0.33, FDR-p = 5.6e-4; Fig. 3).
To determine whether cognitive findings were driven by specific gene groups, post-hoc analyses examined gene-stratified correlations between modules and cognitive trajectories (Supplementary Table 7). M26 spliceosome exhibited one of the strongest associations with cognitive trajectories in GRN, whereas M29 ECM and synaptic/neuronal modules (M2 presyapse, M3 postsynapse/glycosylation, M28 synapse assembly/axon) were among the strongest contributors to cognitive trajectories in C9orf72 (M29, M2, M28) and MAPT (M29, M2, M3). To determine the relationship between CSF modules and early stage cognitive change, analyses were also conducted within presymptomatic carriers, collapsed across gene group. In these analyses, M29 ECM (ρ=-0.44, p = 1.8e-3) and M2 presynapse (ρ = 0.44, p = 1.7e-3) were most strongly associated with early cognitive change.
In differential correlational analyses, 646 proteins were significantly correlated with global cognitive trajectories in the full sample (FDR-p < .05; Supplementary Table 6). Over half of those differentially expressed proteins were assigned to modules that also strongly associated with cognitive trajectories (M26, M29, M2, M3, M28). To determine whether individual proteins linked to cognitive trajectories were also of high influence (‘hub proteins’) in these target modules, we plotted individual protein correlations with cognitive slope against their intramodular connectivity (intramodular kME). Proteins that were module hubs, defined as top 20th percentile of intramodular kME20, and had FDR-corrected significance with cognitive slope are highlighted in the Fig. 3 inset protein lists. Notable ‘hub’ proteins included the neuronal pentraxins, NPTX2 (M2 presynapse; largest effect on cognitive slope across all proteins) and NPTX1 (M28 synapse assembly/axon), as well as neuroligins NLGN1 and NLGN2 (M3 postsynapse/glycoslyation). Other notable ‘hub’ proteins linked to cognitive trajectories included transmembrane proteins TMEM106B (M3 postsynapse/glycoslyation) and TMEM132B (M2 presynapse), ECM-linked proteins FSTL1 and TIMP1 (M29 ECM), and nuclear proteins HNRNPA1 and RECQL (M26 spliceosome).
Genetic FTLD modules are preserved in sporadic PSP-RS. We applied the same WGCNA methods to CSF SomaScan data from an independent cohort of individuals with sporadic PSP-RS26 to determine whether protein co-expression patterns identified in genetic FTLD could be reproduced in sporadic FTLD, specifically a sporadic FTLD tauopathy (Supplementary Table 8B). All 31 modules from the genetic FTLD cohort were highly preserved in the sporadic PSP-RS network (all Zsummary scores > 10 [> q = 1 x 10− 23]), indicating strong consistency of the CSF proteomic correlational architecture across genetic and sporadic disease (Fig. 4A). Next, we reconstructed the genetic FTLD network modules in PSP-RS using synthetic eigenproteins to determine whether modules derived from the genetic network could also differentiate PSP-RS from controls. Of the 31 synthetic eigenproteins derived from the genetic FTLD protein module assignments, five were significantly altered in sporadic PSP-RS vs. controls after FDR-correction (Supplementary Table 8A; Fig. 4B; decreased in PSP-RS: M28 synapse assembly/axon, M2 presynapse, M3 postsynapse/glycoslyation, M19 neuron migration; increased in PSP-RS: M29 ECM). Notably, four of these five modules were those most strongly related to MAPT disease severity (M2, M3, M28, M29), with consistency in directionality of effects (Fig. 4C). These results suggest that, despite differences in initial pathogenesis, genetic and sporadic forms of FTLD-tau exhibit shared proteomic signatures in CSF, characterized by decreases in neuronal cell-type specific proteins and increases in ECM proteins.
Genetic FTLD modules differentiate FTLD from AD and controls. To assess whether proteomic signatures from the FTLD network were specific to FTLD or more broadly reflective of neurodegeneration, irrespective of molecular etiology, we constructed synthetic eigenproteins in a second replication cohort (BioFINDER 2; Supplementary Table 9b) comprised of 29 patients with frontotemporal dementia clinical syndromes, 87 AD patients matched on demographics and disease severity, and 248 AD biomarker-negative controls. BioFINDER 2 CSF samples were analyzed using a proximity extension assay platform (Olink). Synthetic eigenproteins were computed based on protein measurements that overlapped between Olink and SomaScan, offering the opportunity to validate proteomic signatures across platforms. CSF proteomic alterations were more pronounced in FTLD than AD. Thirteen synthetic eigenproteins differed between FTLD and controls, 6 differed between FTLD and AD, and only 2 differed between AD and controls (all FDR-p < .05; Supplementary Table 9a; Fig. 4B). Most synthetic eigenproteins that differentiated FTLD from AD and/or controls represented modules that also differed between FTLD mutation carriers and familial controls from ALLFTD (Fig. 4D). These included increased M26 spliceosome (FTLD > AD > control) and decreased M2 presynapse (FTLD < AD, control), M28 synapse assembly/axon (FTLD, AD < control), and M22 autophagy (FTLD < AD, control). These results support the cross-platform and cross-cohort reproducibility of genetic FTLD network-derived modules, which are affected to a greater extent in FTLD than AD.
Module Overlap with CSF AD Networks. The protein co-expression patterns and pathway enrichment present in the CSF genetic FTLD network partially resembled CSF networks previously identified in AD21,27. We employed module overrepresentation analyses to empirically compare the genetic FTLD network to previously constructed CSF networks in sporadic AD and thus identify CSF modules that overlap across neurodegenerative conditions. These AD networks were built using multi-platform (SomaScan + Olink + TMT-MS; Dammer et al.21) and TMT-MS (Modeste et. al27) proteomic approaches, which also allowed us to probe the influence of platform on FTLD and AD network overlap (Fig. 5). Of the 31 genetic FTLD network modules, 14 had significant overrepresentation of protein members in at least one corresponding module from the multi-platform AD network and 11 had significant overrepresentation in at least one module from the TMT-MS AD network (10 of 14 modules). M4 complement/coagulation was the most strongly overlapping FTLD network module in both AD networks, which also exhibited the strongest preservation across brain tissue, CSF and plasma in the multi-platform study21. M29 ECM and neuron/oligodendrocyte-enriched FTLD network modules (M2, M3, M28, M31) also exhibited strong overlap with modules from both CSF AD networks. M26 spliceosome overlapped with a module from the multi-platform network, M30 ribonucleoprotein complex, which did not differ between AD and controls. M26 spliceosome did not overlap with any module from the TMT-MS network, which broadly lacked modules with primary enrichment for RNA binding/splicing pathways. Overall, we observed strong conservation of modules linked to neuronal, oligodendrocyte, ECM, and immune processes in both FTLD and AD CSF samples.