Leveraging Cell Painting Images to Expand the Applicability Domain and Actively Improve Deep Learning Quantitative Structure-Activity Relationship Models.

The search for chemical hit material is a lengthy and increasingly expensive drug discovery process. To improve it, ligand-based quantitative structure-activity relationship models have been broadly applied to optimize primary and secondary compound properties. Although these models can be deployed as early as the stage of molecule design, they have a limited applicability domain─if the structures of interest differ substantially from the chemical space on which the model was trained, a reliable prediction will not be possible. Image-informed ligand-based models partly solve this shortcoming by focusing on the phenotype of a cell caused by small molecules, rather than on their structure. While this enables chemical diversity expansion, it limits the application to compounds physically available and imaged. Here, we employ an active learning approach to capitalize on both of these methods' strengths and boost the model performance of a mitochondrial toxicity assay (Glu/Gal). Specifically, we used a phenotypic Cell Painting screen to build a chemistry-independent model and adopted the results as the main factor in selecting compounds for experimental testing. With the additional Glu/Gal annotation for selected compounds we were able to dramatically improve the chemistry-informed ligand-based model with respect to the increased recognition of compounds from a 10% broader chemical space.

Diversity and Clonality of T Cell Receptor Repertoire and Antigen Specificities in Small Joints of Early Rheumatoid Arthritis.

OBJECTIVE: CD4+ T cells are implicated in rheumatoid arthritis (RA) pathology from the strong association between RA and certain HLA class II gene variants. This study was undertaken to examine the synovial T cell receptor (TCR) repertoire, T cell phenotypes, and T cell specificities in small joints of RA patients at time of diagnosis before therapeutic intervention. METHODS: Sixteen patients, of whom 11 patients were anti-citrullinated protein antibody (ACPA)-positive and 5 patients were ACPA-, underwent ultrasound-guided synovial biopsy of a small joint (n = 13) or arthroscopic synovial biopsy of a large joint (n = 3), followed by direct sorting of single T cells for paired sequencing of the αß TCR together with flow cytometry analysis. TCRs from expanded CD4+ T cell clones of 4 patients carrying an HLA-DRB1*04:01 allele were artificially reexpressed to study antigen specificity. RESULTS: T cell analysis demonstrated CD4+ dominance and the presence of peripheral helper T-like cells in both patient groups. We identified >4,000 unique TCR sequences, as well as 225 clonal expansions. Additionally, T cells with double α-chains were a recurring feature. We identified a biased gene usage of the Vß chain segment TRBV20-1 in CD4+ cells from ACPA+ patients. In vitro stimulation of T cell lines expressing selected TCRs with an extensive panel of citrullinated and viral peptides identified several different virus-specific TCRs (e.g., human cytomegalovirus and human herpesvirus 2). Still, the majority of clones remained orphans with unknown specificity. CONCLUSION: Minimally invasive biopsies of the RA synovium allow for single-cell TCR sequencing and phenotyping. Clonally expanded, viral-reactive T cells account for part of the diverse CD4+ T cell repertoire. TRBV20-1 bias in ACPA+ patients suggests recognition of common antigens.

Massive QTL analysis identifies pleiotropic genetic determinants for stress resistance, aroma formation, and ethanol, glycerol and isobutanol production in Saccharomyces cerevisiae.

BACKGROUND: The brewer's yeast Saccharomyces cerevisiae is exploited in several industrial processes, ranging from food and beverage fermentation to the production of biofuels, pharmaceuticals and complex chemicals. The large genetic and phenotypic diversity within this species offers a formidable natural resource to obtain superior strains, hybrids, and variants. However, most industrially relevant traits in S. cerevisiae strains are controlled by multiple genetic loci. Over the past years, several studies have identified some of these QTLs. However, because these studies only focus on a limited set of traits and often use different techniques and starting strains, a global view of industrially relevant QTLs is still missing. RESULTS: Here, we combined the power of 1125 fully sequenced inbred segregants with high-throughput phenotyping methods to identify as many as 678 QTLs across 18 different traits relevant to industrial fermentation processes, including production of ethanol, glycerol, isobutanol, acetic acid, sulfur dioxide, flavor-active esters, as well as resistance to ethanol, acetic acid, sulfite and high osmolarity. We identified and confirmed several variants that are associated with multiple different traits, indicating that many QTLs are pleiotropic. Moreover, we show that both rare and common variants, as well as variants located in coding and non-coding regions all contribute to the phenotypic variation. CONCLUSIONS: Our findings represent an important step in our understanding of the genetic underpinnings of industrially relevant yeast traits and open new routes to study complex genetics and genetic interactions as well as to engineer novel, superior industrial yeasts. Moreover, the major role of rare variants suggests that there is a plethora of different combinations of mutations that can be explored in genome editing.

Tales of 1,008 small molecules: phenomic profiling through live-cell imaging in a panel of reporter cell lines.

Phenomic profiles are high-dimensional sets of readouts that can comprehensively capture the biological impact of chemical and genetic perturbations in cellular assay systems. Phenomic profiling of compound libraries can be used for compound target identification or mechanism of action (MoA) prediction and other applications in drug discovery. To devise an economical set of phenomic profiling assays, we assembled a library of 1,008 approved drugs and well-characterized tool compounds manually annotated to 218 unique MoAs, and we profiled each compound at four concentrations in live-cell, high-content imaging screens against a panel of 15 reporter cell lines, which expressed a diverse set of fluorescent organelle and pathway markers in three distinct cell lineages. For 41 of 83 testable MoAs, phenomic profiles accurately ranked the reference compounds (AUC-ROC ≥ 0.9). MoAs could be better resolved by screening compounds at multiple concentrations than by including replicates at a single concentration. Screening additional cell lineages and fluorescent markers increased the number of distinguishable MoAs but this effect quickly plateaued. There remains a substantial number of MoAs that were hard to distinguish from others under the current study's conditions. We discuss ways to close this gap, which will inform the design of future phenomic profiling efforts.

A genetics-led approach defines the drug target landscape of 30 immune-related traits.

Most candidate drugs currently fail later-stage clinical trials, largely due to poor prediction of efficacy on early target selection1. Drug targets with genetic support are more likely to be therapeutically valid2,3, but the translational use of genome-scale data such as from genome-wide association studies for drug target discovery in complex diseases remains challenging4-6. Here, we show that integration of functional genomic and immune-related annotations, together with knowledge of network connectivity, maximizes the informativeness of genetics for target validation, defining the target prioritization landscape for 30 immune traits at the gene and pathway level. We demonstrate how our genetics-led drug target prioritization approach (the priority index) successfully identifies current therapeutics, predicts activity in high-throughput cellular screens (including L1000, CRISPR, mutagenesis and patient-derived cell assays), enables prioritization of under-explored targets and allows for determination of target-level trait relationships. The priority index is an open-access, scalable system accelerating early-stage drug target selection for immune-mediated disease.

Reproducibility of Molecular Phenotypes after Long-Term Differentiation to Human iPSC-Derived Neurons: A Multi-Site Omics Study.

Reproducibility in molecular and cellular studies is fundamental to scientific discovery. To establish the reproducibility of a well-defined long-term neuronal differentiation protocol, we repeated the cellular and molecular comparison of the same two iPSC lines across five distinct laboratories. Despite uncovering acceptable variability within individual laboratories, we detect poor cross-site reproducibility of the differential gene expression signature between these two lines. Factor analysis identifies the laboratory as the largest source of variation along with several variation-inflating confounders such as passaging effects and progenitor storage. Single-cell transcriptomics shows substantial cellular heterogeneity underlying inter-laboratory variability and being responsible for biases in differential gene expression inference. Factor analysis-based normalization of the combined dataset can remove the nuisance technical effects, enabling the execution of robust hypothesis-generating studies. Our study shows that multi-center collaborations can expose systematic biases and identify critical factors to be standardized when publishing novel protocols, contributing to increased cross-site reproducibility.

Genetically Engineered iPSC-Derived FTDP-17 MAPT Neurons Display Mutation-Specific Neurodegenerative and Neurodevelopmental Phenotypes.

Tauopathies such as frontotemporal dementia (FTD) remain incurable to date, partially due to the lack of translational in vitro disease models. The MAPT gene, encoding the microtubule-associated protein tau, has been shown to play an important role in FTD pathogenesis. Therefore, we used zinc finger nucleases to introduce two MAPT mutations into healthy donor induced pluripotent stem cells (iPSCs). The IVS10+16 mutation increases the expression of 4R tau, while the P301S mutation is pro-aggregant. Whole-transcriptome analysis of MAPT IVS10+16 neurons reveals neuronal subtype differences, reduced neural progenitor proliferation potential, and aberrant WNT/SHH signaling. Notably, these neurodevelopmental phenotypes could be recapitulated in neurons from patients carrying the MAPT IVS10+16 mutation. Moreover, the additional pro-aggregant P301S mutation revealed additional phenotypes, such as an increased calcium burst frequency, reduced lysosomal acidity, tau oligomerization, and neurodegeneration. This series of iPSCs could serve as a platform to unravel a potential link between pathogenic 4R tau and FTD.

Multiplex High-Throughput Targeted Proteomic Assay To Identify Induced Pluripotent Stem Cells.

Induced pluripotent stem cells have great potential as a human model system in regenerative medicine, disease modeling, and drug screening. However, their use in medical research is hampered by laborious reprogramming procedures that yield low numbers of induced pluripotent stem cells. For further applications in research, only the best, competent clones should be used. The standard assays for pluripotency are based on genomic approaches, which take up to 1 week to perform and incur significant cost. Therefore, there is a need for a rapid and cost-effective assay able to distinguish between pluripotent and nonpluripotent cells. Here, we describe a novel multiplexed, high-throughput, and sensitive peptide-based multiple reaction monitoring mass spectrometry assay, allowing for the identification and absolute quantitation of multiple core transcription factors and pluripotency markers. This assay provides simpler and high-throughput classification into either pluripotent or nonpluripotent cells in 7 min analysis while being more cost-effective than conventional genomic tests.

Sustained synchronized neuronal network activity in a human astrocyte co-culture system.

Impaired neuronal network function is a hallmark of neurodevelopmental and neurodegenerative disorders such as autism, schizophrenia, and Alzheimer's disease and is typically studied using genetically modified cellular and animal models. Weak predictive capacity and poor translational value of these models urge for better human derived in vitro models. The implementation of human induced pluripotent stem cells (hiPSCs) allows studying pathologies in differentiated disease-relevant and patient-derived neuronal cells. However, the differentiation process and growth conditions of hiPSC-derived neurons are non-trivial. In order to study neuronal network formation and (mal)function in a fully humanized system, we have established an in vitro co-culture model of hiPSC-derived cortical neurons and human primary astrocytes that recapitulates neuronal network synchronization and connectivity within three to four weeks after final plating. Live cell calcium imaging, electrophysiology and high content image analyses revealed an increased maturation of network functionality and synchronicity over time for co-cultures compared to neuronal monocultures. The cells express GABAergic and glutamatergic markers and respond to inhibitors of both neurotransmitter pathways in a functional assay. The combination of this co-culture model with quantitative imaging of network morphofunction is amenable to high throughput screening for lead discovery and drug optimization for neurological diseases.

Metabolomic biosignature differentiates melancholic depressive patients from healthy controls.

BACKGROUND: Major depressive disorder (MDD) is a heterogeneous disease at the level of clinical symptoms, and this heterogeneity is likely reflected at the level of biology. Two clinical subtypes within MDD that have garnered interest are "melancholic depression" and "anxious depression". Metabolomics enables us to characterize hundreds of small molecules that comprise the metabolome, and recent work suggests the blood metabolome may be able to inform treatment decisions for MDD, however work is at an early stage. Here we examine a metabolomics data set to (1) test whether clinically homogenous MDD subtypes are also more biologically homogeneous, and hence more predictiable, (2) devise a robust machine learning framework that preserves biological meaning, and (3) describe the metabolomic biosignature for melancholic depression. RESULTS: With the proposed computational system we achieves around 80 % classification accuracy, sensitivity and specificity for melancholic depression, but only ~72 % for anxious depression or MDD, suggesting the blood metabolome contains more information about melancholic depression.. We develop an ensemble feature selection framework (EFSF) in which features are first clustered, and learning then takes place on the cluster centroids, retaining information about correlated features during the feature selection process rather than discarding them as most machine learning methods will do. Analysis of the most discriminative feature clusters revealed differences in metabolic classes such as amino acids and lipids as well as pathways studied extensively in MDD such as the activation of cortisol in chronic stress. CONCLUSIONS: We find the greater clinical homogeneity does indeed lead to better prediction based on biological measurements in the case of melancholic depression. Melancholic depression is shown to be associated with changes in amino acids, catecholamines, lipids, stress hormones, and immune-related metabolites. The proposed computational framework can be adapted to analyze data from many other biomedical applications where the data has similar characteristics.

Using Human iPSC-Derived Neurons to Model TAU Aggregation.

Alzheimer's disease and frontotemporal dementia are amongst the most common forms of dementia characterized by the formation and deposition of abnormal TAU in the brain. In order to develop a translational human TAU aggregation model suitable for screening, we transduced TAU harboring the pro-aggregating P301L mutation into control hiPSC-derived neural progenitor cells followed by differentiation into cortical neurons. TAU aggregation and phosphorylation was quantified using AlphaLISA technology. Although no spontaneous aggregation was observed upon expressing TAU-P301L in neurons, seeding with preformed aggregates consisting of the TAU-microtubule binding repeat domain triggered robust TAU aggregation and hyperphosphorylation already after 2 weeks, without affecting general cell health. To validate our model, activity of two autophagy inducers was tested. Both rapamycin and trehalose significantly reduced TAU aggregation levels suggesting that iPSC-derived neurons allow for the generation of a biologically relevant human Tauopathy model, highly suitable to screen for compounds that modulate TAU aggregation.

Association between COMT Val158Met genotype and EEG alpha peak frequency tested in two independent cohorts.

This study could not confirm the association between the Catechol-O-Methyltransferase Val158Met polymorphism (COMT) and electroencephalographic (EEG) alpha peak frequency (APF) in two independent cohorts of 187 (96 depressed and 91 healthy participants) and 413 healthy participants. If COMT and APF play a role in depression or antidepressant treatment response, they do not have a shared pathway. We emphasize the importance of publishing null-findings for obtaining more accurate overall estimates of genetic effects.

EEG alpha power as an intermediate measure between brain-derived neurotrophic factor Val66Met and depression severity in patients with major depressive disorder.

Major depressive disorder has a large impact on patients and society and is projected to be the second greatest global burden of disease by 2020. The brain-derived neurotrophic factor (BDNF) gene is considered to be one of the important factors in the etiology of major depressive disorder. In a recent study, alpha power was found to mediate between BDNF Met and subclinical depressed mood. The current study looked at a population of patients with major depressive disorder (N = 107) to examine the association between the BDNF Val66Met polymorphism, resting state EEG alpha power, and depression severity. For this purpose, repeated-measures analysis of variance, partial correlation, and multiple linear models were used. Results indicated a negative association between parietal-occipital alpha power in the eyes open resting state and depression severity. In addition, Met/Met patients showed lower global absolute alpha power in the eyes closed condition compared with Val-carriers. These findings are in accordance with the previously uncovered pathway between BDNF Val66Met, resting state EEG alpha power, and depression severity. Additional research is needed for the clarification of this tentative pathway and its implication in personalized treatment of major depressive disorder.

Translation of a tumor microenvironment mimicking 3D tumor growth co-culture assay platform to high-content screening.

For drug discovery, cell-based assays are becoming increasingly complex to mimic more realistically the nature of biological processes and their diversifications in diseases. Multicellular co-cultures embedded in a three-dimensional (3D) matrix have been explored in oncology to more closely approximate the physiology of the human tumor microenvironment. High-content analysis is the ideal technology to characterize these complex biological systems, although running such complex assays at higher throughput is a major endeavor. Here, we report on adapting a 3D tumor co-culture growth assay to automated microscopy, and we compare various imaging platforms (confocal vs. nonconfocal) with correlating automated image analysis solutions to identify optimal conditions and settings for future larger scaled screening campaigns. The optimized protocol has been validated in repeated runs where established anticancer drugs have been evaluated for performance in this innovative assay.

The application of selective reaction monitoring confirms dysregulation of glycolysis in a preclinical model of schizophrenia.

BACKGROUND: Establishing preclinical models is essential for novel drug discovery in schizophrenia. Most existing models are characterized by abnormalities in behavioral readouts, which are informative, but do not necessarily translate to the symptoms of the human disease. Therefore, there is a necessity of characterizing the preclinical models from a molecular point of view. Selective reaction monitoring (SRM) has already shown promise in preclinical and clinical studies for multiplex measurement of diagnostic, prognostic and treatment-related biomarkers. METHODS: We have established an SRM assay for multiplex analysis of 7 enzymes of the glycolysis pathway which is already known to be affected in human schizophrenia and in the widely-used acute PCP rat model of schizophrenia. The selected enzymes were hexokinase 1 (Hk1), aldolase C (Aldoc), triosephosphate isomerase (Tpi1), glyceraldehyde-3-phosphate dehydrogenase (Gapdh), phosphoglycerate mutase 1 (Pgam1), phosphoglycerate kinase 1 (Pgk1) and enolase 2 (Eno2). The levels of these enzymes were analyzed using SRM in frontal cortex from brain tissue of PCP treated rats. RESULTS: Univariate analyses showed statistically significant altered levels of Tpi1 and alteration of Hk1, Aldoc, Pgam1 and Gapdh with borderline significance in PCP rats compared to controls. Most interestingly, multivariate analysis which considered the levels of all 7 enzymes simultaneously resulted in generation of a bi-dimensional chart that can distinguish the PCP rats from the controls. CONCLUSIONS: This study not only supports PCP treated rats as a useful preclinical model of schizophrenia, but it also establishes that SRM mass spectrometry could be used in the development of multiplex classification tools for complex psychiatric disorders such as schizophrenia.

Corticotropin releasing factor-induced ERK phosphorylation in AtT20 cells occurs via a cAMP-dependent mechanism requiring EPAC2.

CRF-induced ERK phosphorylation has been shown to be an important mechanism underlying expression of pro-opiomelanocortin, a key precursor molecule in the hypothalamic pituitary adrenal axis. In AtT20 cells, CRF signalling has been investigated but the mechanism behind CRF-induced ERK activity is not fully understood. This paper elucidates the signalling cascade involved in this phenomenon. Involvement of CRF(1) receptor on ERK phosphorylation was shown by using CRF and urocortin 1. The lack of inhibitory effect of pertussis toxin and BAPTA-AM excluded involvement of G(i)-coupling and calcium mobilization respectively. In contrast, the process is suggested to be driven by cAMP since treatment of AtT20 cells with forskolin triggered strong ERK phosphorylation. Treatment with PKA inhibitors had a minor effect on CRF-induced ERK signalling while phosphorylation of CREB was completely abolished. This ruled out involvement of PKA and suggested a role for exchange protein directly activated by cAMP (EPAC). Moreover, an activator of EPACs 8-(4-methoxyphenylthio)-2'-O-methyladenosine-3',5'-cyclic monophosphate mimicked CRF-induced ERK phosphorylation. Gene expression analysis showed high levels of EPAC2 mRNA and protein but low levels of EPAC1. Knockdown of EPAC2 expression by the use of specific siRNAs abolished CRF- and forskolin-induced ERK phosphorylation. The current study demonstrates a clear cAMP-dependent but PKA-independent mechanism underlying CRF-induced ERK activity that proceeds via EPAC2 signalling. Further research will provide more insight in the role of EPAC2 in CRF signalling.

Mammalian Navigators are microtubule plus-end tracking proteins that can reorganize the cytoskeleton to induce neurite-like extensions.

Mammalian microtubule plus-end tracking proteins (+TIPs) specifically associate with the ends of growing microtubules. +TIPs are involved in many cellular processes, including mitosis, cell migration and neurite extension. Navigators are mammalian homologues of the C. elegans unc-53 protein, an ATPase that has been linked to the migration and outgrowth of muscles, axons and excretory canals. Here we show that all three mammalian Navigators are +TIPs, consistent with a previous study on Navigator 1 (NAV1) (Martinez-Lopez et al., Mol Cell Neurosci 2005;28:599-612). Overexpression of GFP-tagged Navigators causes displacement of CAP_GLY-motif containing +TIPs, such as CLIP-170, from microtubule ends, suggesting that the Navigator-binding sites on microtubule ends overlap with those of the CAP_GLY-motif proteins. In interphase cells, mammalian Navigators also prominently localize to centrosomes, a localization that does not depend on an intact microtubule network. Fluorescence recovery after photobleaching (FRAP) experiments indicate that NAV1 associates with intracellular structures other than microtubules or centrosomes. Expression of GFP-tagged Navigators induces the formation of neurite-like extensions in non-neuronal cells, showing that Navigators can dominantly alter cytoskeletal behavior. For NAV1 this function depends on its ATPase activity; it is not achieved by a classical type of MT bundling and stabilization. Combined our data suggest that Navigators are +TIPs that can reorganize the cytoskeleton to guide cell shape changes. Our data are consistent with a role for Navigators in neurite outgrowth.

Association of SIRT1 gene variation with visceral obesity.

The sirtuin SIRT1 is an important regulator of energy metabolism through its impact on glucose and lipid metabolism and therefore we tested the hypothesis that genetic variation in SIRT1 may have an effect on adiposity in a Belgian case/control association study. This study included 1,068 obese patients (BMI > or = 30 kg/m(2)) from the outpatient obesity clinic and 313 lean controls (BMI between 18.5 and 25 kg/m(2)). Anthropometrics were assessed by classical methods and visceral (VFA), subcutaneous (SFA) and total abdominal (TFA) fat areas were determined by a CT scan. The extent of linkage disequilibrium in SIRT1 allowed us to reduce the number of SNPs to two, sufficient to cover the entire gene. The two tagSNPs (rs7069102 and rs3818292) were analyzed by LightSNiP assays in all subjects. Rs3818292 genotypes were similarly distributed in cases and controls, whereas rs7069102 was different for the additive (P = 0.007) and dominant (P = 0.01) model. The variant C-allele of rs7069102 reduced obesity risk with an OR of 0.74 (P = 0.025; 95% CI 0.57-0.96) under a dominant model. In obese male subjects, this variant allele was associated with increased waist circumference (P = 0.04), WHR (P = 0.02), TFA (P = 0.03) and VFA (P = 0.005) (dominant model; adjusted for age and BMI). Rs3818292 was related to VFA (P = 0.005; adjusted for age and BMI) in obese males while in obese women, no significant associations were detected. Our data suggest that genetic variation in SIRT1 increases the risk for obesity, and that SIRT1 genotype correlates with visceral obesity parameters in obese men.