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Most candidate drugs currently fail later-stage clinical trials, largely due to poor prediction of efficacy on early target selection 1 . Drug targets with genetic support are more likely to be therapeutically valid 2 , 3 , but the translational use of genome-scale data such as from genome-wide association studies for drug target discovery in complex diseases remains challenging 4 – 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. A genetics-led translational approach integrating functional genomic predictors, knowledge of network connectivity and immune ontologies defines the drug target prioritization landscape for 30 immune traits at the gene and pathway level.

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.

ObjectivesSmoking has been associated with an increased risk of developing rheumatoid arthritis (RA) in individuals carrying shared epitope (SE) HLA-DRB1 alleles. Yet, little is known about the regional and systemic T cell dynamics of smoking and a potential link to T cell infiltration in inflamed synovia. In this study, we, therefore, sought to study T cell features in lung and inflamed joints in smoking versus non-smoking patients.MethodsWe set up a framework to monitor T cells in paired bronchoalveolar lavage fluid, blood and inflamed synovium tissue samples from 17 new-onset treatment naïve anticitrullinated protein antibody+RA patients. T cell receptor (TCR) repertoire of index-sorted tissue residing in T cells was determined by single-cell TCR sequencing coupled with deep immunophenotyping.ResultsA significant enrichment of CD4+ and CD8+ T cells was seen in synovial samples from smoking versus non-smoking patients, along with an increase in expanded T cell clonotypes. This was particularly pronounced among SE+smokers, suggestive of a synergic gene-smoke effect. Strikingly, identical TCR clonalities were present in matched lung and joint samples of RA smokers, the majority being also detectable in circulation. This was mirrored by an increased clustering of lung and synovium TCRs across patients, suggesting a shared specificity by conserved motifs. The lung-joint shared T cell clonotypes showed a restricted TCR gene usage and exhibited a particular 4-1BB+CD57 hi effector profile within the inflamed synovium.ConclusionThe data indicate a profound interplay between a strong MHC predisposition, smoking and induction of autoimmunity by shaping the TCR repertoire.

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.

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.

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 ≥ 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.

We selected HTR6 (serotonin receptor 6) as a candidate gene to test for associations with obesity since earlier studies have shown that mice with a disrupted serotonin receptor are less prone to become obese on a high-fat diet. We genotyped three tagSNPs (rs6658108, rs6699866 and rs9659997) and included one multimarker prediction test to cover the genetic information of the entire gene in our Belgian study population (1089 obese cases and 308 lean controls). Statistical analysis revealed no signicant associations with obesity for all variants that were tested. Our data therefore indicate that common HTR6 variants do not contribute to obesity in the tested population.

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.

This study aimed to determine, quantify and explain regional differences in the relaxant response to the selective 5‐HT1 and 5‐HT7 receptor agonist 5‐carboxamidotryptamine (5‐CT) throughout the canine stomach. Longitudinal muscle strips from eight gastric corpus regions and six antrum regions were mounted for isotonic measurement. The 5‐CT‐induced relaxation was examined on a prostaglandin F2α‐induced submaximal response, expressed as percentage of this response and fitted to the operational model of agonism (OMOA). 5‐HT7 receptor messenger RNA (mRNA) expression was compared by means of quantitative PCR. 5‐CT inhibited PGF2α‐induced tonic contraction (corpus) and increase of phasic contraction amplitude (antrum). The consistent antagonism produced by the selective 5‐HT7 receptor antagonist SB‐269970 (10 nM, pA2 estimates 8.2–8.9) confirmed that in every region, the inhibition by 5‐CT was 5‐HT7 receptor mediated. However, variation in the maximum effect (61–108%) and pEC50 (6.4–8.6) was observed throughout the different regions. The OMOA explained these differences as differences in the efficacy parameter τ (ratio of receptor density and coupling efficiency; log τ estimates ranging from 0.1 to 2.1). The log τ gradient decreases going from the lesser to the greater curvature. A proportional difference (68%) in the relative expression of 5‐HT7 receptor mRNA between the lesser and the greater curvature indicates that differences in receptor density contribute to the observed functional differences. This study illustrates that 5‐HT7 receptors are present throughout the ventral wall of the canine stomach, but the efficacy (expressed as log τ) is clearly greater close to the lesser curvature. Differences in 5‐HT7 receptor expression at least partially explain the functional differences. British Journal of Pharmacology (2004) 143, 331–342. doi:10.1038/sj.bjp.0705922

Image-based profiling has emerged as a powerful technology for various steps in basic biological and pharmaceutical discovery, but the community has lacked a large, public reference set of data from chemical and genetic perturbations. Here we present data generated by the Joint Undertaking for Morphological Profiling (JUMP)-Cell Painting Consortium, a collaboration between 10 pharmaceutical companies, six supporting technology companies, and two non-profit partners. When completed, the dataset will contain images and profiles from the Cell Painting assay for over 116,750 unique compounds, over-expression of 12,602 genes, and knockout of 7,975 genes using CRISPR-Cas9, all in human osteosarcoma cells (U2OS). The dataset is estimated to be 115 TB in size and capturing 1.6 billion cells and their single-cell profiles. File quality control and upload is underway and will be completed over the coming months at the Cell Painting Gallery: https://registry.opendata.aws/cellpainting-gallery. A portal to visualize a subset of the data is available at https://phenaid.ardigen.com/jumpcpexplorer/.