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Principal Investigator: Karen SACHS
Organization: MODULO BIO, INC.
Fiscal Year: 2024
Award: $1,428,644
Funding agency: National Institute of Mental Health
Project Summary/Abstract: ALS and FTD are fatal neurodegenerative diseases that presently have no cure.
To date, one focus area in ALS research has been developing model systems to characterize the condition,
with over 20 different ALS mouse models, and more recently, numerous iPSC based models, each gradually
contributing to our overall knowledge of the mechanisms behind neurodegeneration, and the contribution of the
neuro-immune interface. Despite the multitude of disease models, there is no overarching, computational
modeling framework for integrating disparate datasets, towards the goal of characterizing disease networks,
and identifying therapeutic targets. Moreover, while standard ML models for target prediction have become
ubiquitous in the biomedical sciences, they fail to learn causality, shedding little insight into underlying disease
etiology and failing to make effective target predictions. Our proposal’s long-term goal is to create a flexible
pipeline, applicable to ND diseases, to characterize the neuro-immune interface and its contribution to ND
etiology, to enable therapeutic intervention by creating an integrated workflow to identify ND microglial disease
networks in health, disease, and disease subsets. We will capitalize on existing experimental data as well as
internal iPSC based in vitro models, paired with a causal ML model. Each component of this workflow can work
independently, or can be linked to the other in a powerful ‘active learning’ framework, in which the ML model
makes predictions, the co-culture system validates or disproves the prediction, and in each such round the in
silico model is refined by integrating the new experimental data. Our causal machine learning model
characterizes ND neuro-immune networks from analysis of combined molecular, clinical, and functional data in
a multi-layered format with individual layers for ND disease state, data platform, and cell state analyzed
simultaneously to bolster confidence for inferences shared among numerous layers and identify unique, and
therapeutically relevant, network elements. We will focus initially on therapeutic interventions for ALS, followed
by related ND diseases also characterized in the network model. The objectives of this proposal are: (1) to
refine an in silico framework for data integration across NDs, microglial subsets, and heterogeneous
datasets/data platforms enabling a robust model for therapeutic target prediction and (2) to validate predicted
targets in our iPSC microglia and neuron co-culture system using in vitro perturbations (including
antisense-oligonucleotides and small molecules) and high-content imaging analysis. The central
hypothesis is that comprehensively integrating available data across public datasets and databases, ND
diseases, model species, data platforms, and tissue types, with data from our co-culture screening platform, in
a powerful mechanistic model, will enable elucidation of causal disease pathways, comparative analysis across
conditions, and the identification of therapeutic targets. Ultimately, characterization of individuals can even
enable personalized therapy approaches as well as identification of disease subtypes.
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Terms: <Acceleration><Active Learning><Address><Affect><Amyotrophic lateral sclerosis and frontotemporal degeneration><Amyotrophic lateral sclerosis and frontotemporal dementia><Antisense Agent><Antisense Oligonucleotides><Area><Assay><Bioassay><Biologic Models><Biological><Biological Assay><Biological Models><Biology><Body Tissues><Causality><Cell Body><Cells><Classification><Clinical><Co-culture><Cocultivation><Coculture><Coculture Techniques><Computer Models><Computerized Models><Cooperative Learning><Coupled><Data><Data Bases><Data Set><Databases><Degenerative Neurologic Disorders><Disease><Disease Pathway><Disorder><Disparate><Drug Targeting><Elements><Etiology><Experiential Learning><FTD/ALS><FTLD/ALS><Frontotemporal Lobar Degeneration/Amyotrophic lateral sclerosis><Genes><Goals><Health><Hortega cell><Image><In Vitro><Individual><Induced Neurons><Knowledge><Learning><Link><Methods><Mice><Mice Mammals><Microglia><Model System><Modeling><Molecular><Murine><Mus><Nerve Cells><Nerve Degeneration><Nerve Unit><Nervous System Degenerative Diseases><Network Analysis><Neural Cell><Neural Degenerative Diseases><Neural degenerative Disorders><Neurocyte><Neurodegenerative Diseases><Neurodegenerative Disorders><Neuroimmune><Neurologic Degenerative Conditions><Neuron Degeneration><Neurons><Organism><Pathogenesis><Pathway Analysis><Pathway interactions><Patients><Persons><Physiologic><Physiological><Research><Risk-associated variant><Science><System><Systematics><Testing><Therapeutic><Therapeutic Intervention><Tissues><Validation><Work><amyotrophic lateral sclerosis with frontotemporal dementia><amyotrophic lateral sclerosis/FTLD><amyotrophic lateral sclerosis/frontotemporal dementia><amyotrophic lateral sclerosis/ftd><antisense oligo><biologic><causal allele><causal diagram><causal gene><causal model><causal mutation><causal variant><causation><causative mutation><causative variant><cell type><comparative><computational modeling><computational models><computer based models><computer based prediction><computerized modeling><data base><data driven platform><data framework><data heterogeneity><data integration><data platform><data set heterogeneity><dataset heterogeneity><degenerative diseases of motor and sensory neurons><degenerative neurological diseases><design><designing><disease causation><disease model><disease subgroups><disease subtype><disorder model><disorder subtype><drug development><effective therapy><effective treatment><flexibility><flexible><frontotemporal dementia-amyotrophic lateral sclerosis><frontotemporal lobar dementia amyotrophic lateral sclerosis><gitter cell><heterogeneous data><heterogeneous data sets><heterogeneous datasets><heterogenous data><heterogenous data sets><heterogenous datasets><high dimensional data><iNeuron><iPS><iPSC><iPSCs><imaging><in silico><in vitro Model><in vivo><individual patient><induced pluripotent cell><induced pluripotent stem cell><inducible pluripotent stem cell><insight><intervention therapy><large scale data><large scale data sets><large scale datasets><learning network><living system><machine learning based framework><machine learning based model><machine learning framework><machine learning model><mesoglia><microglial cell><microgliocyte><mouse model><multidimensional data><multidimensional datasets><multiple data sets><multiple datasets><murine model><network models><neural degeneration><neurodegeneration><neurodegenerative><neurodegenerative illness><neuroimmunologic disease><neurological degeneration><neuronal><neuronal degeneration><neuroprotection><neuroprotective><new drug target><new druggable target><new pharmacotherapy target><new therapeutic target><new therapy target><novel drug target><novel druggable target><novel pharmacotherapy target><novel therapeutic target><novel therapy target><pathway><perivascular glial cell><personalization of treatment><personalized medicine><personalized therapy><personalized treatment><predictive modeling><progenitor cell model><progenitor model><risk allele><risk gene><risk genotype><risk loci><risk locus><risk variant><scRNA-seq><screening><screenings><single cell RNA-seq><single cell RNAseq><single cell expression profiling><single cell transcriptomic profiling><single-cell RNA sequencing><small molecule><stem and progenitor cell model><stem cell based model><stem cell derived model><stem cell model><therapeutic target><tool><validations>