Multi-omics blood biomarkers, brain activity, and therapeutic targets for ALS
No biomarker currently exists to facilitate early diagnosis, predict prognosis, stratify patients, identify surrogate endpoints and assess target engagement for amyotrophic lateral sclerosis (ALS), a devastating and fatal neurodegenerative disease characterized by the progressive loss of upper and lower motor neurons. Moreover, research efforts have been unable to develop powerful strategies to prevent, decelerate or stop neuronal death in patients. Most ALS patients are unexplained genetically, and the ALS population shows much variability in age of onset, site of onset, disease duration, upper vs. lower motor neuron involvement, and cognitive/behavioral impairment. This phenotypic heterogeneity reduces not only diagnostic and prognostic accuracy in the clinic, but potentially affects accurate assessment of target engagement during clinical trials. To implement a discovery platform that systematically identifies ALS-specific variants, elucidates their underlying mechanism of action, sheds light on ALS distinct circuitry, and ultimately identifies static and dynamic biomarkers and putative therapeutic targets, we generated high-resolution profiling of genomic, epigenomic and transcriptional alterations in post-mortem brain tissues and antemortem blood from ALS patients and healthy individuals. We mined our generated data to predict driver genes, cell-types, biological pathways, regulatory regions, and single-nucleotide polymorphisms (SNPs) underlying ALS by integrating our data types and publicly available datasets of genetic, epigenetic, and transcriptional variations. Specifically, we aimed to link differentially-expressed genes with their predicted distal regulatory regions, identify causal mediating genes and regions by integrating genetic information (eQTLs), and combine our results with ALS genome-wide association studies (GWAS) and whole-genome sequencing (WGS) data.
Our future studies will prioritize genes and variants by integrating the collected evidence, and select the top ALS-associated loci for validation in a large cohort of ALS patients, disease controls and healthy participants. Using our results and clinical/demographic information, we will carry out a series of statistical comparisons to recognize loci associated with ALS. We will also assess the longitudinal variability of the validated loci in 50 ALS patients and 50 healthy individuals, with each participant undergoing blood collection and standardized testing at baseline, then every four months over a 16-month period, for a total of five repeated measures. Disease progression, cognitive/behavioral functions and other clinical traits will be compared to loci measures, and statistical analysis will be conducted. We will finally develop programmable Cas9/dCas9 constructs to activate, repress or modulate selected loci in neurons and glial cells derived from iPSC lines, and conduct phenotypic dissections to better understand the molecular underpinning of ALS. We are confident that our study will identify ALS-specific loci that may serve as needed biomarkers and putative therapeutic targets. Our findings will greatly facilitate the development of clinically relevant and scalable ALS-specific tests to guide more tailored management of ALS patients and inform the next generation of individualized clinical trials.