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

Abstract

ObjectiveAmyotrophic 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.MethodsWe 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.ResultsWe 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.InterpretationWe 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.

Unveiling the double-edged sword: SOD1 trimers possess tissue-selective toxicity and bind septin-7 in motor neuron-like cells

“…We performed Mann-Whitney statistical testing with adjusted p-value cutoffs (at the false positive rate α = 0.05 level of significance) within each tissue using the Benjamini-Hochberg procedure, and found that SOD1 trimers selectively and significantly bound one protein in the brain (septin-7; p = 0.00045), five proteins in the spinal cord (cytochrome c oxidase; p = 0.0012, creatine kinase; p = 0.00012, brain acid soluble protein 1; p = 0.0019, microtubule-associated protein 1; p = 0.018, and hemoglobin sub unit alpha; p = 0.018) and one protein in skeletal muscle (fructose-bisphosphate aldolase A; p-adjusted <0.0001) (Table 1). Notably, septin-7 32–34 , brain acid soluble protein 1 35,36 , and fructose-bisphosphate aldolase A 37,38 are tissue-specific protein hits and have been previously linked to neurodegenerative processes. Overall, SOD1 trimers feature tissue-selective interactomes compared to that of native SOD1 dimers.…”

State-transition modeling of blood transcriptome predicts disease evolution and treatment response in chronic myeloid leukemia

“…Constructing a state-space to model biological transitions can capture changes produced by genetic, epigenetic, or microenvironmental perturbations on the transcriptome, which simultaneously deregulate multiple biological processes in order to generate a phenotype such as the leukemic transformation observed here [ 26 – 29 ]. We have previously shown that micro-RNA and protein abundance (proteomic) data can also be used to create a state-space and characterize state-transitions [ 10 , 30 ]. Using a transgenic CP CML mouse model that recapitulated the human disease, we report here the application of state-transition theory to interpret the temporal transcriptomic changes involved both in CML development and in treatment (where CML is “reversed”) to accurately predict outcome and treatment response.…”

Unraveling the Heterogeneity of ALS—A Call to Redefine Patient Stratification for Better Outcomes in Clinical Trials

“…In another study from Vu et al, the authors identified a list of proteins differentially expressed between fast and slow progressors. Interestingly, proteins associated with fast progressors were part of the immune response pathway, while pathways related to synaptogenesis and glycolysis/gluconeogenesis were downregulated [ 44 ]. These two studies again highlight the importance of monitoring the immune response pathway for ALS clustering.…”

“…These two studies again highlight the importance of monitoring the immune response pathway for ALS clustering. Interestingly, while transcriptomic clustering did not succeed in explaining patient clinical profile, proteomic clustering demonstrated some correlation with some clinical features (concomitant cognitive impairment, progression rate) [ 43 , 44 ]. Thus, studying the proteomic profile of ALS patients might give access to more pertinent information.…”