A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses.

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods ( synapse.org/LINCS_MCF10A ). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes. Beyond these analyses, this dataset will serve as a resource for the broader scientific community to mine for biological insights, to compare signals carried across distinct molecular modalities, and to develop new computational methods for integrative data analysis.

Answer ALS, a large-scale resource for sporadic and familial ALS combining clinical and multi-omics data from induced pluripotent cell lines.

Answer ALS is a biological and clinical resource of patient-derived, induced pluripotent stem (iPS) cell lines, multi-omic data derived from iPS neurons and longitudinal clinical and smartphone data from over 1,000 patients with ALS. This resource provides population-level biological and clinical data that may be employed to identify clinical-molecular-biochemical subtypes of amyotrophic lateral sclerosis (ALS). A unique smartphone-based system was employed to collect deep clinical data, including fine motor activity, speech, breathing and linguistics/cognition. The iPS spinal neurons were blood derived from each patient and these cells underwent multi-omic analytics including whole-genome sequencing, RNA transcriptomics, ATAC-sequencing and proteomics. The intent of these data is for the generation of integrated clinical and biological signatures using bioinformatics, statistics and computational biology to establish patterns that may lead to a better understanding of the underlying mechanisms of disease, including subgroup identification. A web portal for open-source sharing of all data was developed for widespread community-based data analytics.

An integrated multi-omic analysis of iPSC-derived motor neurons from C9ORF72 ALS patients.

Neurodegenerative diseases are challenging for systems biology because of the lack of reliable animal models or patient samples at early disease stages. Induced pluripotent stem cells (iPSCs) could address these challenges. We investigated DNA, RNA, epigenetics, and proteins in iPSC-derived motor neurons from patients with ALS carrying hexanucleotide expansions in C9ORF72. Using integrative computational methods combining all omics datasets, we identified novel and known dysregulated pathways. We used a C9ORF72 Drosophila model to distinguish pathways contributing to disease phenotypes from compensatory ones and confirmed alterations in some pathways in postmortem spinal cord tissue of patients with ALS. A different differentiation protocol was used to derive a separate set of C9ORF72 and control motor neurons. Many individual -omics differed by protocol, but some core dysregulated pathways were consistent. This strategy of analyzing patient-specific neurons provides disease-related outcomes with small numbers of heterogeneous lines and reduces variation from single-omics to elucidate network-based signatures.

Aberrant Development Corrected in Adult-Onset Huntington's Disease iPSC-Derived Neuronal Cultures via WNT Signaling Modulation.

Aberrant neuronal development and the persistence of mitotic cellular populations have been implicated in a multitude of neurological disorders, including Huntington's disease (HD). However, the mechanism underlying this potential pathology remains unclear. We used a modified protocol to differentiate induced pluripotent stem cells (iPSCs) from HD patients and unaffected controls into neuronal cultures enriched for medium spiny neurons, the cell type most affected in HD. We performed single-cell and bulk transcriptomic and epigenomic analyses and demonstrated that a persistent cyclin D1+ neural stem cell (NSC) population is observed selectively in adult-onset HD iPSCs during differentiation. Treatment with a WNT inhibitor abrogates this NSC population while preserving neurons. Taken together, our findings identify a mechanism that may promote aberrant neurodevelopment and adult neurogenesis in adult-onset HD striatal neurons with the potential for therapeutic compensation.

A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules.

High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resulting uncertainties can lead to the failure of clinical trials. We developed an approach for uncovering MoAs through an interpretable machine learning model of transcriptomics, epigenomics, metabolomics, and proteomics. Examining compounds with beneficial effects in models of Huntington's Disease, we found common MoAs for compounds with unrelated structures, connectivity scores, and binding targets. The approach also predicted highly divergent MoAs for two FDA-approved antihistamines. We experimentally validated these effects, demonstrating that one antihistamine activates autophagy, while the other targets bioenergetics. The use of multiple omics was essential, as some MoAs were virtually undetectable in specific assays. Our approach does not require reference compounds or large databases of experimental data in related systems and thus can be applied to the study of agents with uncharacterized MoAs and to rare or understudied diseases.

Treatment with JQ1, a BET bromodomain inhibitor, is selectively detrimental to R6/2 Huntington's disease mice.

Transcriptional and epigenetic alterations occur early in Huntington's disease (HD), and treatment with epigenetic modulators is beneficial in several HD animal models. The drug JQ1, which inhibits histone acetyl-lysine reader bromodomains, has shown promise for multiple cancers and neurodegenerative disease. We tested whether JQ1 could improve behavioral phenotypes in the R6/2 mouse model of HD and modulate HD-associated changes in transcription and epigenomics. R6/2 and non-transgenic (NT) mice were treated with JQ1 daily from 5 to 11 weeks of age and behavioral phenotypes evaluated over this period. Following the trial, cortex and striatum were isolated and subjected to mRNA-seq and ChIP-seq for the histone marks H3K4me3 and H3K27ac. Initially, JQ1 enhanced motor performance in NT mice. In R6/2 mice, however, JQ1 had no effect on rotarod or grip strength but exacerbated weight loss and worsened performance on the pole test. JQ1-induced gene expression changes in NT mice were distinct from those in R6/2 and primarily involved protein translation and bioenergetics pathways. Dysregulation of HD-related pathways in striatum was exacerbated by JQ1 in R6/2 mice, but not in NTs, and JQ1 caused a corresponding increase in the formation of a mutant huntingtin protein-dependent high molecular weight species associated with pathogenesis. This study suggests that drugs predicted to be beneficial based on their mode of action and effects in wild-type or in other neurodegenerative disease models may have an altered impact in the HD context. These observations have important implications in the development of epigenetic modulators as therapies for HD.

QTY code enables design of detergent-free chemokine receptors that retain ligand-binding activities.

Structure and function studies of membrane proteins, particularly G protein-coupled receptors and multipass transmembrane proteins, require detergents. We have devised a simple tool, the QTY code (glutamine, threonine, and tyrosine), for designing hydrophobic domains to become water soluble without detergents. Here we report using the QTY code to systematically replace the hydrophobic amino acids leucine, valine, isoleucine, and phenylalanine in the seven transmembrane α-helices of CCR5, CXCR4, CCR10, and CXCR7. We show that QTY code-designed chemokine receptor variants retain their thermostabilities, α-helical structures, and ligand-binding activities in buffer and 50% human serum. CCR5QTY, CXCR4QTY, and CXCR7QTY also bind to HIV coat protein gp41-120. Despite substantial transmembrane domain changes, the detergent-free QTY variants maintain stable structures and retain their ligand-binding activities. We believe the QTY code will be useful for designing water-soluble variants of membrane proteins and other water-insoluble aggregated proteins.

Identifying therapeutic targets by combining transcriptional data with ordinal clinical measurements.

The immense and growing repositories of transcriptional data may contain critical insights for developing new therapies. Current approaches to mining these data largely rely on binary classifications of disease vs. control, and are not able to incorporate measures of disease severity. We report an analytical approach to integrate ordinal clinical information with transcriptomics. We apply this method to public data for a large cohort of Huntington's disease patients and controls, identifying and prioritizing phenotype-associated genes. We verify the role of a high-ranked gene in dysregulation of sphingolipid metabolism in the disease and demonstrate that inhibiting the enzyme, sphingosine-1-phosphate lyase 1 (SPL), has neuroprotective effects in Huntington's disease models. Finally, we show that one consequence of inhibiting SPL is intracellular inhibition of histone deacetylases, thus linking our observations in sphingolipid metabolism to a well-characterized Huntington's disease pathway. Our approach is easily applied to any data with ordinal clinical measurements, and may deepen our understanding of disease processes.Identifying gene subsets affecting disease phenotypes from transcriptome data is challenge. Here, the authors develop a method that combines transcriptional data with disease ordinal clinical measurements to discover a sphingolipid metabolism regulator involving in Huntington's disease progression.