Statistical integration of multi-omics and drug screening data from cell lines

Data integration methods are used to obtain a unified summary of multiple datasets. For multi-modal data, we propose a computational workflow to jointly analyze datasets from cell lines. The workflow comprises a novel probabilistic data integration method, named POPLS-DA, for multi-omics data.The workflow is motivated by a study on synucleinopathies where transcriptomics, proteomics, and drug screening data are measured in affected LUHMES cell lines and controls. The aim is to highlight potentially druggable pathways and genes involved in synucleinopathies. First, POPLS-DA is used to prioritize genes and proteins that best distinguish cases and controls. For these genes, an integrated interaction network is constructed where the drug screen data is incorporated to highlight druggable genes and pathways in the network. Finally, sfunctional enrichment analyses are performed to identify clusters of synaptic and lysosome-related genes and proteins targeted by the protective drugs. POPLS-DA is compared to other single- and multi-omics approaches.We found that HSPA5, a member of the heat shock protein 70 family, was one of the most targeted genes by the validated drugs, in particular by AT1-blockers. HSPA5 and AT1-blockers have been previously linked to alpha-synuclein pathology and Parkinson's disease, showing the relevance of our findings.Our computational workflow identified new directions for therapeutic targets for synucleinopathies. POPLS-DA provided a larger interpretable gene set than other single- and multi-omic approaches. An implementation based on R and markdown is freely available online. We present a computational workflow that combines the analysis of different types of data measured in cell line studies with non-overlapping samples. We apply the workflow to measurements of gene expression, protein abundances, and a screening of a wide range of FDA-approved drugs. These different types of data are obtained from LUHMES brain cells and jointly analyzed to discover new treatment options in synucleinopathies, such as Parkinson's disease. Our workflow includes a new probabilistic method, named POPLS-DA. POPLS-DA combines the analysis of the genes and proteins to pinpoint a set of relevant genes and proteins that can distinguish affected and non-affected cells. Compared to other approaches, POPLS-DA found a larger set of genes relevant to the disease. Further, we constructed a network that connects the relevant genes and proteins that interact with each other. We incorporate the drug screening data to highlight which part of the network is relevant to the disease and druggable. Through additional analysis of the functionality, we discovered that the genes and proteins that are targeted by protective drugs share relevant properties, namely they are synaptic and lysosome-related genes. Notably, we found that specific types of drugs, namely AT1-blockers such as Telmisartan, are protective and target the network of relevant genes and proteins. These drugs are approved by the FDA and readily available to further investigate their potential in treating synucleinopathies. We further found that a gene named HSPA5, a member of the heat shock protein 70 family, is highly targeted by the protective drugs. This gene has been linked to Parkinson's disease in previous scientific literature. Our computational workflow and the implementation in R and markdown are freely available online

Statistical integration of multi-omics and drug screening data from cell lines

Data integration methods are used to obtain a unified summary of multiple datasets. For multi-modal data, we propose a computational workflow to jointly analyze datasets from cell lines. The workflow comprises a novel probabilistic data integration method, named POPLS-DA, for multi-omics data. The workflow is motivated by a study on synucleinopathies where transcriptomics, proteomics, and drug screening data are measured in affected LUHMES cell lines and controls. The aim is to highlight potentially druggable pathways and genes involved in synucleinopathies. First, POPLS-DA is used to prioritize genes and proteins that best distinguish cases and controls. For these genes, an integrated interaction network is constructed where the drug screen data is incorporated to highlight druggable genes and pathways in the network. Finally, sfunctional enrichment analyses are performed to identify clusters of synaptic and lysosome-related genes and proteins targeted by the protective drugs. POPLS-DA is compared to other single- and multi-omics approaches. We found that HSPA5, a member of the heat shock protein 70 family, was one of the most targeted genes by the validated drugs, in particular by AT1-blockers. HSPA5 and AT1-blockers have been previously linked to α-synuclein pathology and Parkinson's disease, showing the relevance of our findings. Our computational workflow identified new directions for therapeutic targets for synucleinopathies. POPLS-DA provided a larger interpretable gene set than other single- and multi-omic approaches. An implementation based on R and markdown is freely available online

Late Maastrichtian carbon isotope stratigraphy and cyclostratigraphy of the Newfoundland Margin (Site U1403, IODP Expedition 342)

Earth’s climate during the Maastrichtian (latest Cretaceous) was punctuated by brief warming and cooling episodes, accompanied by perturbations of the global carbon cycle. Superimposed on a long-term cooling trend, the middle Maastrichtian is characterized by deep-sea warming and relatively high values of stable carbon-isotope ratios, followed by strong climatic variability towards the end of the Cretaceous. A lack of knowledge on the timing of climatic change inhibits our understanding of underlying causal mechanisms. We present an integrated stratigraphy from Integrated Ocean Drilling Program (IODP) Site U1403, providing an expanded deep ocean record from the North Atlantic (Expedition 342, Newfoundland Margin). Distinct sedimentary cyclicity suggests that orbital forcing played a major role in depositional processes, which is confirmed by statistical analyses of high resolution elemental data obtained by X-ray fluorescence (XRF) core scanning. Astronomical calibration reveals that the investigated interval encompasses seven 405-kyr cycles (Ma4051 to Ma4057) and spans the 2.8 Myr directly preceding the Cretaceous/Paleocene (K/Pg) boundary. A high-resolution carbon-isotope record from bulk carbonates allows us to identify global trends in the late Maastrichtian carbon cycle. Low-amplitude variations (up to 0.4‰) in carbon isotopes at Site U1403 match similar scale variability in records from Tethyan and Pacific open-ocean sites. Comparison between Site U1403 and the hemipelagic restricted basin of the Zumaia section (northern Spain), with its own well-established independent cyclostratigraphic framework, is more complex. Whereas the pre-K/Pg oscillations and the negative values of the Mid-Maastrichtian Event (MME) can be readily discerned in both the Zumaia and U1403 records, patterns diverge during a ~ 1 Myr period in the late Maastrichtian (67.8–66.8 Ma), with Site U1403 more reliably reflecting global carbon cycling. Our new carbon isotope record and cyclostratigraphy offer promise for Site U1403 to serve as a future reference section for high-resolution studies of late Maastrichtian paleoclimatic change

Scalable production of large quantities of defect-free few-layer graphene by shear exfoliation in liquids

To progress from the laboratory to commercial applications, it will be necessary to develop industrially scalable methods to produce large quantities of defect-free graphene. Here we show that high-shear mixing of graphite in suitable stabilizing liquids results in large-scale exfoliation to give dispersions of graphene nanosheets. X-ray photoelectron spectroscopy and Raman spectroscopy show the exfoliated flakes to be unoxidized and free of basal-plane defects. We have developed a simple model that shows exfoliation to occur once the local shear rate exceeds 10(4) s(-1). By fully characterizing the scaling behaviour of the graphene production rate, we show that exfoliation can be achieved in liquid volumes from hundreds of millilitres up to hundreds of litres and beyond. The graphene produced by this method performs well in applications from composites to conductive coatings. This method can be applied to exfoliate BN, MoS2 and a range of other layered crystals

Statistical integration of multi-omics and drug screening data from cell lines.

Data integration methods are used to obtain a unified summary of multiple datasets. For multi-modal data, we propose a computational workflow to jointly analyze datasets from cell lines. The workflow comprises a novel probabilistic data integration method, named POPLS-DA, for multi-omics data. The workflow is motivated by a study on synucleinopathies where transcriptomics, proteomics, and drug screening data are measured in affected LUHMES cell lines and controls. The aim is to highlight potentially druggable pathways and genes involved in synucleinopathies. First, POPLS-DA is used to prioritize genes and proteins that best distinguish cases and controls. For these genes, an integrated interaction network is constructed where the drug screen data is incorporated to highlight druggable genes and pathways in the network. Finally, functional enrichment analyses are performed to identify clusters of synaptic and lysosome-related genes and proteins targeted by the protective drugs. POPLS-DA is compared to other single- and multi-omics approaches. We found that HSPA5, a member of the heat shock protein 70 family, was one of the most targeted genes by the validated drugs, in particular by AT1-blockers. HSPA5 and AT1-blockers have been previously linked to α-synuclein pathology and Parkinson's disease, showing the relevance of our findings. Our computational workflow identified new directions for therapeutic targets for synucleinopathies. POPLS-DA provided a larger interpretable gene set than other single- and multi-omic approaches. An implementation based on R and markdown is freely available online

Protective efficacy of phosphodiesterase-1 inhibition against alpha-synuclein toxicity revealed by compound screening in LUHMES cells

Abstract α-synuclein-induced neurotoxicity is a core pathogenic event in neurodegenerative synucleinopathies such as Parkinson’s disease, dementia with Lewy bodies, or multiple system atrophy. There is currently no disease-modifying therapy available for these diseases. We screened 1,600 FDA-approved drugs for their efficacy to protect LUHMES cells from degeneration induced by wild-type α-synuclein and identified dipyridamole, a non-selective phosphodiesterase inhibitor, as top hit. Systematic analysis of other phosphodiesterase inhibitors identified a specific phosphodiesterase 1 inhibitor as most potent to rescue from α-synuclein toxicity. Protection was mediated by an increase of cGMP and associated with the reduction of a specific α-synuclein oligomeric species. RNA interference experiments confirmed PDE1A and to a smaller extent PDE1C as molecular targets accounting for the protective efficacy. PDE1 inhibition also rescued dopaminergic neurons from wild-type α-synuclein induced degeneration in the substantia nigra of mice. In conclusion, this work identifies inhibition of PDE1A in particular as promising target for neuroprotective treatment of synucleinopathies