Cerebrospinal Fluid Proteome Research Articles

CSF proteome profiling reveals a protein panel detecting amyloidosis and progression to dementia in cognitively unimpaired individuals

AbstractBackgroundUnderstanding the earliest biochemical changes related to Alzheimer´s disease (AD) pathology is essential for the development of biomarkers and disease‐modifying therapies. Here, we aimed to map the cerebrospinal fluid (CSF) proteomic changes associated with amyloid ß (Aß) pathology in cognitively unimpaired individuals and identify a panel of CSF biomarkers detecting amyloid positivity as well as clinical progression to dementia.MethodProximity extension‐based multiplex immunoassays were used to measure 614 proteins in 297 CSF samples from cognitively unimpaired controls with and without amyloid pathology (232 CON/Aβ‐ (age: 68±8; sex: 37% female) and 65 CON/Aβ+ (age: 65±7; sex: 49% female) from the Amsterdam Dementia cohort. A total of 39 cases (13%) progressed to mild cognitive impairment (MCI; n = 19) or dementia (n = 20). An additional cognitively unimpaired control cohort from the EMIF‐twin60+ study (103 CON/Aβ‐ and 19 CON/Aβ+) was used to validate the panel. Data was analysed using nested linear models adjusted for multiple testing and penalized generalized linear modelling.ResultWe identified 110 CSF proteins with different levels in CON/Aβ+ compared to Aβ‐ (q<0.05). Proteins were enriched in processes related to proteolysis, enzyme activation and immunity. Classification modelling revealed a panel of 12‐CSF markers that detect amyloid positivity with high accuracy in both the discovery (AUC: 0.93) and validation (AUC: 0.89) cohorts. This panel also predicted clinical progression to MCI and dementia with high accuracy (AUC: 0.83). Chow´s test analysis revealed a structural change in the relation between a subset of these proteins and CSF Aβ42 before the amyloid positivity threshold.ConclusionOverall, this study provides novel pathophysiological leads associated to distinct biological processes in individuals at risk of developing AD‐dementia and provides potential biomarker tools for clinical settings or trials.

Multi‐cohort cerebrospinal fluid proteomics identifies robust molecular signatures for asymptomatic and symptomatic Alzheimer’s disease

AbstractBackgroundAlzheimer’s disease (AD), the most common form of dementia, is characterized by the accumulation of Amyloid‐β (Aβ42) and hyperphosphorylated Tau 181 (p‐tau181) proteins in the brain. Changes in these proteins in AD brains and cerebrospinal fluid (CSF) are detected years before AD symptoms. Studying AD proteome in CSF can reflect its diverse underlying pathophysiology and pave the way for reliable diagnostic and therapeutic advancements.MethodWe present one of the largest CSF AD proteomic profiles (7,029 protein analytes) in CSF of 3,065 individuals in three stages. Discovery was performed in 836 samples from the Knight ADRC and 618 samples from the FACE cohorts using the ATN framework (AT‐ = 680 and AT+ = 490). The identified proteins were tested in 832 individuals (AT‐ = 235 and AT+ = 358) from the ADNI and Barcelona‐1 cohorts. The proteins that passed multiple Bonferroni corrections on the meta‐analysis were utilized for AD prediction models and pathway enrichment analysis to gain mechanistic insights into AD pathophysiology.ResultIn discovery, we identified 3,565 proteins to be significantly (FDR < 0.05) altered. Of these, 2,543 passed FDR in replication with a consistent direction. In the meta‐analysis, we identified 2,233 proteins to be significantly (P‐Bonferroni < 0.05) altered in the AD CSF proteome. Some of the highly significant proteins included YWHAG (P‐Bonf < 4.5×10−219), SMOC1 (P‐Bonf < 5.8×10−208), NRGN (P‐Bonf < 3.2×10−119), and NEFL (P‐Bonf < 1.8×10−37). By using lasso, we identified a set of 39 proteins with high predictive power (Discovery AUC = 1.0; Replication AUC = 0.99) representing a robust and precise AD diagnostic biomarker. Enrichment analysis highlighted several neurological disorders (e.g. AD, tauopathy, and synucleinopathy) and neuronal functions (neuron projection morphogenesis, synapse assembly and organization, and neuron differentiation) as significantly enriched in the altered AD CSF proteome.ConclusionOur findings show the promising potential of AD CSF proteomics as a reliable and robust AD prediction model. We identified proteins and biological pathways that are compromised in AD, thereby, increasing our understanding of AD biology. Our findings may accelerate the development of effective intervention therapies that target the earliest molecular triggers of AD.

CSF proteome profiling identifies novel biomarkers for Frontotemporal Dementia and its pathological subtypes

AbstractBackgroundFrontotemporal dementia (FTD) is caused by frontotemporal lobar degeneration (FTLD) and the most common forms are characterized by either tau (FTLD‐Tau) or TDP43 (FTLD‐TDP) brain aggregates. However, FTD‐specific fluid biomarkers are lacking. Furthermore, the pathological subtypes are not distinct in their presentation, hampering accurate subtyping at clinical diagnosis. Therefore, there is a strong need to identify fluid biomarkers that could aid in FTD diagnosis and to discriminate the pathological subtypes.MethodWe employed an antibody‐based proteomic technology to analyze >600 proteins in a large multicenter cohort including cerebrospinal fluid (CSF) samples from FTD (n = 189), AD (n = 235) and cognitively unimpaired individuals (n = 196). For a subset of cases the underlying neuropathology was known or could be predicted (FTLD‐Tau = 85 and FTLD‐TDP = 57). Differences in protein expression profiles were analyzed by nested linear models. Penalized generalized linear modeling was used to identify classification protein panels. Protein panels were then validated in independent clinical cohorts (cohort 1: n = 157; cohort 2: n = 165) and a neuropathology cohort (n = 100) using customized assays.ResultWe observed 65 differentially regulated proteins in FTD versus controls and AD patients, associated with axonogenesis, synapse assembly, or locomotory behavior pathways. We identified panels of 14 and 13 proteins that could discriminate FTD from controls (AUC = 0.96, 95%CI:0.91‐0.99) and AD patients (AUC = 0.91, 95%CI:0.85‐0.96), respectively. Most of these proteins (21 out of 27) were translated into customized panels, which discriminated between groups with high accuracy for all three cohorts (FTDvsCon: AUCs > 0.96, FTDvsAD: AUCs > 0.88). When comparing the FTLD‐Tau and FTLD‐TDP subtypes, we observed that 86 proteins were increased in FTLD‐Tau, and associated with developmental and cellular processes and locomotion pathways. A panel of 8 proteins could discriminate between the pathological subtypes (AUC = 0.82, 95%CI:0.66‐0.95), which was however not stable during cross‐validation.ConclusionWe identified and validated CSF panels to discriminate FTD from controls and AD with high accuracy. The identification of a biomarker panel to discriminate between the FTLD pathological subtypes likely requires larger and more homogeneous groups. The panels developed within this study might be useful for diagnosis and trial inclusion of FTD patients.

GRP94 in cerebrospinal fluid may contribute to a potential biomarker of depression: Based on proteomics

The present study was designed to investigate potential biomarkers of depression and targets of antidepressants from the perspective of hippocampal endoplasmic reticulum stress (ERS) based on cerebrospinal fluid (CSF) proteomics. Firstly, a six-week depression model was established and treated with fluoxetine (FLX). We found antidepressant-FLX could ameliorate depression-like behaviors and cognition in depressed rats caused by chronic unpredictable mild stress (CUMS). FLX significantly increased neuronal numbers in dentate gyrus (DG) and CA3 regions of hippocampus. CSF proteome data revealed thirty-seven differentially expressed proteins (DEPs) co-regulated by CUMS and FLX, including GRP94 and EIF2α. Results of Gene Oncology (GO) annotation and KEGG pathway enrichment for DEPs mainly included PERK-mediated unfolded protein response, endoplasmic reticulum, and translational initiation. The expression levels of GRP94, p-PERK, p-EIF2α, CHOP and Caspase-12 were increased in hippocampus of CUMS rats, and FLX worked the opposite way. FLX had strong affinity and binding activity with GRP94 protein, and four key proteins on the PERK pathway (PERK, EIF2α, p-EIF2α, CHOP). We proposed that FLX may exert antidepressant effects and neuroprotective action by alleviating excessive activation of the hippocampal PERK pathway and reducing neuronal deficits in depressed rats. PERK, EIF2α, p-EIF2α, and CHOP may be potential targets for antidepressant-FLX. GRP94 in CSF may be a potential biomarker of depression and the therapeutic effects of antidepressants.

Promising therapeutic targets for ischemic stroke identified from plasma and cerebrospinal fluid proteomes: a multicenter Mendelian randomization study.

Ischemic stroke (IS) is more common every year, the condition is serious, and have a poor prognosis. New, efficient, and safe therapeutic targets are desperately needed as early treatment especially prevention and reperfusion is the key to lowering the occurrence of poorer prognosis. Generally circulating proteins are attractive therapeutic targets, this study aims to identify potential pharmacological targets among plasma and cerebrospinal fluid (CSF) proteins for the prevention and treatment of IS using a multicenter Mendelian randomization (MR) approach. First, the genetic instruments of 734 plasma and 151 CSF proteins were assessed for causative connections with IS from MEGASTROKE consortium by MR to identify prospective therapeutic targets. Then, for additional validation, plasma proteins from the deCODE consortium and the Fenland consortium, as well as IS GWAS data from the FinnGen cohort, the ISGC consortium and UK biobank, were employed. A thorough evaluation of the aforementioned possible pharmacological targets was carried out using meta-analysis. The robustness of MR results was then confirmed through sensitivity analysis using several techniques, such as bidirectional MR analysis, Steiger filtering, and Bayesian colocalization. Finally, methods like Protein-Protein Interaction (PPI) Networking were utilized to investigate the relationship between putative drug targets and therapeutic agents. The authors discovered three proteins that may function as promising therapeutic targets for IS and meet the Bonferroni correction ( P <0.05/885=5.65×10 -5 ). Prekallikrein (OR=0.41, 95% CI: 0.27-0.63, P =3.61×10 -5 ), a protein found in CSF, has a 10-fold protective impact in IS, while the plasma proteins SWAP70 (OR=0.85, 95% CI: 0.80-0.91, P =1.64×10 -6 ) and MMP-12 (OR=0.92, 95% CI: 0.89-0.95, P =4.49×10 -6 ) of each SD play a protective role in IS. Prekallikrein, MMP-12, SWAP70 was replicated in the FinnGen cohort and ISGC database. MMP-12 (OR=0.93, 95% CI: 0.91-0.94, P <0.001), SWAP70 (OR=0.92, 95% CI: 0.90-0.94, P <0.001), and prekallikrein (OR=0.53, 95% CI: 0.33-0.72, P <0.001) may all be viable targets for IS, according to the combined meta-analysis results. Additionally, no evidence of reverse causality was identified, and Bayesian colocalization revealed MMP-12 (PPH 4 =0.995), SWAP70 (PPH 4 =0.987), and prekallikrein (PPH 4 =0.894) shared the same variant with IS, supporting the robustness of the aforementioned causation. Prekallikrein and MMP-12 were associated with the target protein of the current treatment of IS. Among them, Lanadelumab, a new drug whose target protein is a prekallikrein, may be a promising new drug for the treatment of IS. The prekallikrein, MMP-12, and SWAP70 are causally associated with the risk of IS. Moreover, MMP-12 and prekallikrein may be treated as promising therapeutic targets for medical intervention of IS.

Cerebrospinal Fluid Proteomic Profiles in Patients with Postherpetic Neuralgia.

The intrinsic mechanism of postherpetic neuralgia (PHN) remains unclear. Herein, we aimed to seek the hub proteins in the cerebrospinal fluid (CSF), which display significant changes between the PHN and nonpainful patients (Control). First, the proteomic results showed that compared with the Control-CSF, there were 100 upregulated and 50 downregulated differentially expressed proteins (DEPs) in the PHN-CSF. Besides, functional analyses including gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) revealed that biological processes and pathways including complement activation, infection, coagulation, and lipid metabolism were activated, while synaptic organization was suppressed. Next, the protein-protein interaction (PPI) analysis indicated that increased PLG, F2, APOA1, APOA2, SERPINC1, and KNG1 and reduced APOE, which were all enriched in the top pathways according to the KEGG analysis, were defined as hub proteins. Finally, three of the hub proteins, such as PLG, APOA1, and APOE, were reconfirmed in a larger cohort using both enzyme-linked immunosorbent assay (ELISA) and Western blotting methods. Above all, the results indicated that PLG, APOA1, and APOE and their involved processes such as infection, inflammation, cholesterol metabolism, and coagulation shall be potential therapeutic approaches. (The raw mass spectrometry proteome data and search results have been deposited to the iProx-integrated Proteome Resources (http://www.iprox.cn) with the data set identifier IPX0007372000.).

CTIM-33. EFFICACY AND SAFETY STUDY OF NEOADJUVANT EFINEPTAKIN ALFA (NT-I7) AND PEMBROLIZUMAB IN RECURRENT GLIOBLASTOMA

Abstract Glioblastoma (GBM) is the most common and aggressive form of brain tumor in adults. Despite maximal surgical resection, irradiation, and chemotherapy, median overall survival (OS) for recurrent GBM (rGBM) remains at only 30 weeks. GBM tumors have an immune-suppressive microenvironment with low numbers of tumor-infiltrating lymphocytes (TILs). Interleukin 7 (IL-7) plays a key role in T cell homeostasis and survival, with IL-7 inducing T cell expansion in lymphopenic conditions. Efineptakin alfa (NT-I7), a long-acting IL-7, has been shown to increase effector CD8 T cells and reduce suppressor T-regulatory cells in murine GBM models, with subsequently improved OS. We recently completed a Phase I study in newly diagnosed GBM patients which showed that NT-I7 is safe, well-tolerated, and increased absolute lymphocyte counts (ALCs), and determined maximum tolerated dose for Phase II. We hypothesized that NT-I7 may not only mitigate GBM treatment-associated immune suppression, but also potentiate the effect of immune checkpoint blockade, leading to an increase in TILs and improved survival. The purpose of this study is to evaluate the safety and efficacy of combining NT-I7 and pembrolizumab in rGBM patients. This is an open-label, single arm one-stage Phase II study of NT-I7 and pembrolizumab as neoadjuvant and adjuvant therapy with surgery for adult patients with rGBM. We plan to enroll 30 evaluable patients, including a 6-patient safety run-in. The primary endpoint is the 9-month OS rate. Secondary endpoints include progression-free survival, objective response rate, changes in ALC, and adverse events profile. The correlative endpoints include studies to assess the anti-glioma immune response, changes in T cell subsets and functionality, peripheral cytokine quantification, and immunohistochemical analysis of GBM tissue. Longitudinal cerebrospinal fluid proteomics is optional. This study opened in Jan 2023. Six (6) of the planned thirty (30) patients had been enrolled by time of submission.

Cerebrospinal Fluid Proteomic Changes after Nusinersen in Patients with Spinal Muscular Atrophy.

Disease-modifying treatments have transformed the natural history of spinal muscular atrophy (SMA), but the cellular pathways altered by SMN restoration remain undefined and biomarkers cannot yet precisely predict treatment response. We performed an exploratory cerebrospinal fluid (CSF) proteomic study in a diverse sample of SMA patients treated with nusinersen to elucidate therapeutic pathways and identify predictors of motor improvement. Proteomic analyses were performed on CSF samples collected before treatment (T0) and at 6 months (T6) using an Olink panel to quantify 1113 peptides. A supervised machine learning approach was used to identify proteins that discriminated patients who improved functionally from those who did not after 2 years of treatment. A total of 49 SMA patients were included (10 type 1, 18 type 2, and 21 type 3), ranging in age from 3 months to 65 years. Most proteins showed a decrease in CSF concentration at T6. The machine learning algorithm identified ARSB, ENTPD2, NEFL, and IFI30 as the proteins most predictive of improvement. The machine learning model was able to predict motor improvement at 2 years with 79.6% accuracy. The results highlight the potential application of CSF biomarkers to predict motor improvement following SMA treatment. Validation in larger datasets is needed.

Cerebrospinal fluid proteomics in meningitis patients with reactivated varicella zoster virus.

This study investigated the proteomic characteristics of cerebrospinal fluid (CSF) in patients with varicella zoster virus (VZV) meningitis to understanding the pathogenesis of central nervous system (CNS) infection by reactivated VZV. We used data-independent acquisition model to analyze the CSF proteomic differences of 28 patients with VZV meningitis and 11 herpes zoster (HZ) patients. According to the clinical manifestations at discharge, 28 VZV meningitis patients were divided into favorable outcome group and unfavorable outcome (UO) group and their differences in CSF proteome were also analyzed. Compared with the HZ group, the proteins (CXCL10, ELANE, IL-1RN, MPO, PRTN3, etc.) related to inflammation and immune cell activation were significantly upregulated in the VZV meningitis group (p < .01). The protein related to the nerve function and energy metabolism (CKMT1B, SLITRK3, Synaptotagmin-3, KIF5B, etc.) were significantly downregulated (p < .05). The levels of a pro-inflammatory factor, IL-18, in CSF were significantly higher in patients in the UO group as compared to patients with favorable prognosis (p < .05). Inflammatory immune response is an important pathophysiological mechanism of CNS infection by VZV, and the CSF IL-18 levels might be a potential prognostic indicator of the outcomes of VZV meningitis.

A microglial activity state biomarker panel differentiates FTD-granulin and Alzheimer’s disease patients from controls

BackgroundWith the emergence of microglia-modulating therapies there is an urgent need for reliable biomarkers to evaluate microglial activation states.MethodsUsing mouse models and human induced pluripotent stem cell-derived microglia (hiMGL), genetically modified to yield the most opposite homeostatic (TREM2-knockout) and disease-associated (GRN-knockout) states, we identified microglia activity-dependent markers. Non-targeted mass spectrometry was used to identify proteomic changes in microglia and cerebrospinal fluid (CSF) of Grn- and Trem2-knockout mice. Additionally, we analyzed the proteome of GRN- and TREM2-knockout hiMGL and their conditioned media. Candidate marker proteins were tested in two independent patient cohorts, the ALLFTD cohort (GRN mutation carriers versus non-carriers), as well as the proteomic data set available from the EMIF-AD MBD study.ResultsWe identified proteomic changes between the opposite activation states in mouse microglia and CSF, as well as in hiMGL cell lysates and conditioned media. For further verification, we analyzed the CSF proteome of heterozygous GRN mutation carriers suffering from frontotemporal dementia (FTD). We identified a panel of six proteins (FABP3, MDH1, GDI1, CAPG, CD44, GPNMB) as potential indicators for microglial activation. Moreover, we confirmed three of these proteins (FABP3, GDI1, MDH1) to be significantly elevated in the CSF of Alzheimer’s (AD) patients. Remarkably, each of these markers differentiated amyloid-positive cases with mild cognitive impairment (MCI) from amyloid-negative individuals.ConclusionsThe identified candidate proteins reflect microglia activity and may be relevant for monitoring the microglial response in clinical practice and clinical trials modulating microglial activity and amyloid deposition. Moreover, the finding that three of these markers differentiate amyloid-positive from amyloid-negative MCI cases in the AD cohort suggests that these proteins associate with a very early immune response to seeded amyloid. This is consistent with our previous findings in the Dominantly Inherited Alzheimer’s Disease Network (DIAN) cohort, where soluble TREM2 increases as early as 21 years before symptom onset. Moreover, in mouse models for amyloidogenesis, seeding of amyloid is limited by physiologically active microglia further supporting their early protective role. The biological functions of some of our main candidates (FABP3, CD44, GPNMB) also further emphasize that lipid dysmetabolism may be a common feature of neurodegenerative disorders.

Myalgic encephalomyelitis/chronic fatigue syndrome and fibromyalgia are indistinguishable by their cerebrospinal fluid proteomes

Background Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and fibromyalgia have overlapping neurologic symptoms particularly disabling fatigue. This has given rise to the question whether they are distinct central nervous system (CNS) entities or is one an extension of the other. Material and methods To investigate this, we used unbiased quantitative mass spectrometry-based proteomics to examine the most proximal fluid to the brain, cerebrospinal fluid (CSF). This was to ascertain if the proteome profile of one was the same or different from the other. We examined two separate groups of ME/CFS, one with (n = 15) and one without (n = 15) fibromyalgia. Results We quantified a total of 2083 proteins using immunoaffinity depletion, tandem mass tag isobaric labelling and offline two-dimensional liquid chromatography coupled to tandem mass spectrometry, including 1789 that were quantified in all the CSF samples. ANOVA analysis did not yield any proteins with an adjusted p value <.05. Conclusion This supports the notion that ME/CFS and fibromyalgia as currently defined are not distinct entities. Key message ME/CFS and fibromyalgia as currently defined are not distinct entities. Unbiased quantitative mass spectrometry-based proteomics can be used to discover cerebrospinal fluid proteins that are biomarkers for a condition such as we are studying.

Identification of Candidate Biomarkers of Alzheimer's Disease via Multiplex Cerebrospinal Fluid and Serum Proteomics.

Alzheimer's disease (AD) is the most prevalent form of dementia among elderly people worldwide. Cerebrospinal fluid (CSF) is the optimal fluid source for AD biomarkers, while serum biomarkers are much more achievable. To search for novel diagnostic AD biomarkers, we performed a quantitative proteomic analysis of CSF and serum samples from AD and normal cognitive controls (NC). CSF and serum proteomes were analyzed via data-independent acquisition quantitative mass spectrometry. Our bioinformatic analysis was based on Gene Ontology (GO) functional annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. In comparison to the controls, 8 proteins were more abundant in AD CSF, and 60 were less abundant in AD CSF, whereas 55 proteins were more and 10 were less abundant in the serum samples. ATPase-associated activity for CSF and mitochondrial functions for CSF and serum were the most enriched GO terms of the DEPs. KEGG enrichment analysis showed that the most significant pathways for the differentially expressed proteins were the N-glycan biosynthesis pathways. The area under the curve (AUC) values for CSF sodium-/potassium-transporting ATPase subunit beta-1 (AT1B1), serglycin (SRGN), and thioredoxin-dependent peroxide reductase, mitochondrial (PRDX3) were 0.867 (p = 0.004), 0.833 (p = 0.008), and 0.783 (p = 0.025), respectively. A panel of the above three CSF proteins accurately differentiated AD (AUC = 0.933, p = 0.001) from NC. The AUC values for serum probable phospholipid-transporting ATPase IM (AT8B4) and SRGN were moderate. The AUC of the CSF SRGN + serum SRGN was 0.842 (p = 0.007). These novel AD biomarker candidates are mainly associated with inflammation, ATPase activity, oxidative stress, and mitochondrial dysfunction. Further studies are needed to investigate the molecular mechanisms by which these potential biomarkers are involved in AD.

CSF proteome profiling reveals biomarkers to discriminate dementia with Lewy bodies from Alzheimer´s disease

Diagnosis of dementia with Lewy bodies (DLB) is challenging and specific biofluid biomarkers are highly needed. We employed proximity extension-based assays to measure 665 proteins in the cerebrospinal fluid (CSF) from patients with DLB (n = 109), Alzheimer´s disease (AD, n = 235) and cognitively unimpaired controls (n = 190). We identified over 50 CSF proteins dysregulated in DLB, enriched in myelination processes among others. The dopamine biosynthesis enzyme DDC was the strongest dysregulated protein, and could efficiently discriminate DLB from controls and AD (AUC:0.91 and 0.81 respectively). Classification modeling unveiled a 7-CSF biomarker panel that better discriminate DLB from AD (AUC:0.93). A custom multiplex panel for six of these markers (DDC, CRH, MMP-3, ABL1, MMP-10, THOP1) was developed and validated in independent cohorts, including an AD and DLB autopsy cohort. This DLB CSF proteome study identifies DLB-specific protein changes and translates these findings to a practicable biomarker panel that accurately identifies DLB patients, providing promising diagnostic and clinical trial testing opportunities.

A protein panel in cerebrospinal fluid for diagnostic and predictive assessment of Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disease with heterogenous pathophysiological changes that develop years before the onset of clinical symptoms. These preclinical changes have generated considerable interest in identifying markers for the pathophysiological mechanisms linked to AD and AD-related disorders (ADRD). On the basis of our prior work integrating cerebrospinal fluid (CSF) and brain proteome networks, we developed a reliable and high-throughput mass spectrometry-selected reaction monitoring assay that targets 48 key proteins altered in CSF. To test the diagnostic utility of these proteins and compare them with existing AD biomarkers, CSF collected at baseline visits was assayed from 706 participants recruited from the Alzheimer's Disease Neuroimaging Initiative. We found that the targeted CSF panel of 48 proteins (CSF 48 panel) performed at least as well as existing AD CSF biomarkers (Aβ42, tTau, and pTau181) for predicting clinical diagnosis, FDG PET, hippocampal volume, and measures of cognitive and dementia severity. In addition, for each of those outcomes, the CSF 48 panel plus the existing AD CSF biomarkers significantly improved diagnostic performance. Furthermore, the CSF 48 panel plus existing AD CSF biomarkers significantly improved predictions for changes in FDG PET, hippocampal volume, and measures of cognitive decline and dementia severity compared with either measure alone. A potential reason for these improvements is that the CSF 48 panel reflects a range of altered biology observed in AD/ADRD. In conclusion, we show that the CSF 48 panel complements existing AD CSF biomarkers to improve diagnosis and predict future cognitive decline and dementia severity.

Proteomics and mathematical modeling of longitudinal CSF differentiates fast versus slow ALS progression.

Amyotrophic lateral sclerosis (ALS) is a heterogeneous disease with a complex etiology that lacks biomarkers predicting disease progression. The objective of this study was to use longitudinal cerebrospinal fluid (CSF) samples to identify biomarkers that distinguish fast progression (FP) from slow progression (SP) and assess their temporal response. We utilized mass spectrometry (MS)-based proteomics to identify candidate biomarkers using longitudinal CSF from a discovery cohort of SP and FP ALS patients. Immunoassays were used to quantify and validate levels of the top biomarkers. A state-transition mathematical model was created using the longitudinal MS data that also predicted FP versus SP. We identified a total of 1148 proteins in the CSF of all ALS patients. Pathway analysis determined enrichment of pathways related to complement and coagulation cascades in FPs and synaptogenesis and glucose metabolism in SPs. Longitudinal analysis revealed a panel of 59 candidate markers that could segregate FP and SP ALS. Based on multivariate analysis, we identified three biomarkers (F12, RBP4, and SERPINA4) as top candidates that segregate ALS based on rate of disease progression. These proteins were validated in the discovery and a separate validation cohort. Our state-transition model determined that the overall variance of the proteome over time was predictive of the disease progression rate. We identified pathways and protein biomarkers that distinguish rate of ALS disease progression. A mathematical model of the CSF proteome determined that the change in entropy of the proteome over time was predictive of FP versus SP.

Genome- and Exome-Wide Association Studies Revealed Candidate Genes Associated with DaTscan Imaging Features.

Despite remarkable progress in identifying Parkinson's disease (PD) genetic risk loci, the genetic basis of PD remains largely unknown. With the help of the endophenotype approach and using data from dopamine transporter single-photon emission computerized tomography (DaTscan), we identified potentially involved genes in PD. We conducted an imaging genetic study by performing exome-wide association study (EWAS) and genome-wide association study (GWAS) on the specific binding ratio (SBR) of six DaTscan anatomical areas between 489 and 559 subjects of Parkinson's progression markers initiative (PPMI) cohort and 83,623 and 36,845 single-nucleotide polymorphisms (SNPs)/insertion-deletion mutations (INDELs). We also investigated the association of cerebrospinal fluid (CSF) protein concentration of our significant genes with PD progression using PPMI CSF proteome data. Among 83,623 SNPs/INDELs in EWAS, one SNP (rs201465075) on 1 q32.1 locus was significantly (P value = 4.03 × 10-7) associated with left caudate DaTscan SBR, and 33 SNPs were suggestive. Among 36,845 SNPs in GWAS, one SNP (rs12450112) on 17 p.12 locus was significantly (P value = 1.34 × 10-6) associated with right anterior putamen DaTscan SBR, and 39 SNPs were suggestive among which 8 SNPs were intergenic. We found that rs201465075 and rs12450112 are most likely related to IGFN1 and MAP2K4 genes. The protein level of MAP2K4 in the CSF was significantly associated with PD progression in the PPMI cohort; however, proteomic data were not available for the IGFN1 gene. We have shown that particular variants of IGFN1 and MAP2K4 genes may be associated with PD. Since DaTscan imaging could be positive in other Parkinsonian syndromes, caution should be taken when interpreting our results. Future experimental studies are also needed to verify these findings.

Deciphering Protein Secretion from the Brain to Cerebrospinal Fluid for Biomarker Discovery.

Cerebrospinal fluid (CSF) is an essential matrix for the discovery of neurological disease biomarkers. However, the high dynamic range of protein concentrations in CSF hinders the detection of the least abundant protein biomarkers by untargeted mass spectrometry. It is thus beneficial to gain a deeper understanding of the secretion processes within the brain. Here, we aim to explore if and how the secretion of brain proteins to the CSF can be predicted. By combining a curated CSF proteome and the brain elevated proteome of the Human Protein Atlas, brain proteins were classified as CSF or non-CSF secreted. A machine learning model was trained on a range of sequence-based features to differentiate between CSF and non-CSF groups and effectively predict the brain origin of proteins. The classification model achieves an area under the curve of 0.89 if using high confidence CSF proteins. The most important prediction features include the subcellular localization, signal peptides, and transmembrane regions. The classifier generalized well to the larger brain detected proteome and is able to correctly predict novel CSF proteins identified by affinity proteomics. In addition to elucidating the underlying mechanisms of protein secretion, the trained classification model can support biomarker candidate selection.

TIPS-07 LONGITUDINAL CSF PROTEOMICS FOR MONITORING AND PHARMACODYNAMIC BIOMARKER DISCOVERY

Abstract Rapid feedback is needed to understand the individualized biological impacts of novel glioma therapies. We are performing glioma biomarker discovery by serial cerebrospinal fluid (CSF) sampling from Ommaya reservoirs or lumbar punctures to determine how the CSF proteome can reveal early longitudinal intelligence regarding glioma status, biology, and therapeutic response. We aimed to leverage access to each patient’s glioma via CSF to identify candidate biomarkers of tumor burden, biology, and therapeutic challenge. Global proteomic CSF analysis was performed via Somalogic– an aptamer-based technology for highly sensitive and specific analysis of over 7,000 proteins. Discovery analysis comprised of the top 500 ranked proteins in the CSF from seven patients with high-grade gliomas versus non-glioma (normal pressure hydrocephalus) controls. The top HGG proteins were then filtered to include only proteins that met two additional criteria of 1) decrease with resection, and 2) increase with recurrence in individual paired patient samples. Additionally, in individual patients for whom serial CSF samples were available, protein fold change lists were created and ranked to determine the most or least differentially abundant proteins between various time points. Proteomic enrichment analysis revealed a conserved HGG CSF proteomic signature defined by 79 proteins, including ones known to be over-expressed in solid tumor malignancies, such as retinoblastoma binding protein 4, heat shock protein 90, and sorcin. The HGG proteomic signature was consistently enriched in an independent validation cohort consisting of 13 gliomas diverse in primary versus recurrent status, subtype, and grade, when compared to independent control CSF samples. Encouragingly, proteins in the HGG signature decreased in two patients following decreased tumor burden from surgical resection. Our preliminary data demonstrate the ability to gain detailed, individualized insights regarding glioma biology, tumor burden, and evolution through global CSF proteomics acquired from longitudinal neurosurgical access to each patient’s unique glioma.

BSLD-05 ESTABLISHMENT AND CHARACTERIZATION OF MOUSE MODELS OF LEPTOMENINGEAL METASTASIS

Abstract There is critical need to study leptomeningeal metastasis (LM), a fatal complication of cancer accompanied by severe neurological deficits. LM occurs when cancer cells enter the leptomeninges, float in the cerebrospinal fluid (CSF), and adhere to the surface of the brain and spinal cord. Understanding the molecular basis of LM requires reliable models of the disease that can recapitulate the complex microenvironment that supports LM cell growth. Here, we define a protocol for iterative in vivo selection of cancer cells competent to colonize the leptomeninges (LeptoM cells) from a larger population of primary tumor cells that stably express GFP and luciferase. We use these LeptoM cells to establish tractable models of leptomeningeal disease in both syngeneic and xenograft mice. With these models, we modulate disease severity and capture snapshots of hallmark LM characteristics over time. We demonstrate the fidelity of these mouse models to human disease in several dimensions: 1. Biochemically, through CSF proteomics and metabolomics; 2. Immunologically, via flow cytometry and cytokine profiling; 3. Transcriptionally through RNA sequencing; and 4. Neurologically by accumulation of neurological signs. They are freely available to the academic community. These models provide robust, reproducible tools for discovery and mechanistic dissection. They will enable identification of critical disease mechanisms, and assays of novel treatment modalities against LM.

Multi-omics profiling of CSF from spinal muscular atrophy type 3 patients after nusinersen treatment: a 2-year follow-up multicenter retrospective study

Spinal muscular atrophy (SMA) is a neurodegenerative disorder caused by mutations in the SMN1 gene resulting in reduced levels of the SMN protein. Nusinersen, the first antisense oligonucleotide (ASO) approved for SMA treatment, binds to the SMN2 gene, paralogue to SMN1, and mediates the translation of a functional SMN protein. Here, we used longitudinal high-resolution mass spectrometry (MS) to assess both global proteome and metabolome in cerebrospinal fluid (CSF) from ten SMA type 3 patients, with the aim of identifying novel readouts of pharmacodynamic/response to treatment and predictive markers of treatment response. Patients had a median age of 33.5 [29.5; 38.25] years, and 80% of them were ambulant at time of the enrolment, with a median HFMSE score of 37.5 [25.75; 50.75]. Untargeted CSF proteome and metabolome were measured using high-resolution MS (nLC-HRMS) on CSF samples obtained before treatment (T0) and after 2 years of follow-up (T22). A total of 26 proteins were found to be differentially expressed between T0 and T22 upon VSN normalization and LIMMA differential analysis, accounting for paired replica. Notably, key markers of the insulin-growth factor signaling pathway were upregulated after treatment together with selective modulation of key transcription regulators. Using CombiROC multimarker signature analysis, we suggest that detecting a reduction of SEMA6A and an increase of COL1A2 and GRIA4 might reflect therapeutic efficacy of nusinersen. Longitudinal metabolome profiling, analyzed with paired t-Test, showed a significant shift for some aminoacid utilization induced by treatment, whereas other metabolites were largely unchanged. Together, these data suggest perturbation upon nusinersen treatment still sustained after 22 months of follow-up and confirm the utility of CSF multi-omic profiling as pharmacodynamic biomarker for SMA type 3. Nonetheless, validation studies are needed to confirm this evidence in a larger sample size and to further dissect combined markers of response to treatment.