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Understanding the tissue-specific genetic controls of protein levels is essential to uncover mechanisms of post-transcriptional gene regulation. In this study, we generated a genomic atlas of protein levels in three tissues relevant to neurological disorders (brain, cerebrospinal fluid and plasma) by profiling thousands of proteins from participants with and without Alzheimer’s disease. We identified 274, 127 and 32 protein quantitative trait loci (pQTLs) for cerebrospinal fluid, plasma and brain, respectively. cis-pQTLs were more likely to be tissue shared, but trans-pQTLs tended to be tissue specific. Between 48.0% and 76.6% of pQTLs did not co-localize with expression, splicing, DNA methylation or histone acetylation QTLs. Using Mendelian randomization, we nominated proteins implicated in neurological diseases, including Alzheimer’s disease, Parkinson’s disease and stroke. This first multi-tissue study will be instrumental to map signals from genome-wide association studies onto functional genes, to discover pathways and to identify drug targets for neurological diseases. Yang et al. generated a genomic atlas of protein levels in brain, cerebrospinal fluid and plasma and used human genetics approaches to identify proteins implicated in neurological diseases as well as druggable targets.

Complementary assays are required to comprehensively map complex biological entities such as genomes, proteomes and interactome networks. However, how various assays can be optimally combined to approach completeness while maintaining high precision often remains unclear. Here, we propose a framework for binary protein-protein interaction (PPI) mapping based on optimally combining assays and/or assay versions to maximize detection of true positive interactions, while avoiding detection of random protein pairs. We have engineered a novel NanoLuc two-hybrid (N2H) system that integrates 12 different versions, differing by protein expression systems and tagging configurations. The resulting union of N2H versions recovers as many PPIs as 10 distinct assays combined. Thus, to further improve PPI mapping, developing alternative versions of existing assays might be as productive as designing completely new assays. Our findings should be applicable to systematic mapping of other biological landscapes. Comprehensive mapping of binary protein-protein interactions requires to combine several complementary assays. Here, the authors show that complete coverage could be reached with a minimal number of assays as long as they explore various experimental conditions.

Historically, soil-transmitted helminth (STH) control and prevention strategies have relied on mass drug administration efforts targeting preschool and school-aged children. While these efforts have succeeded in reducing morbidity associated with STH infection, recent modeling efforts have suggested that expanding intervention to treatment of the entire community could achieve transmission interruption in some settings. Testing the feasibility of such an approach requires large-scale clinical trials, such as the DeWorm3 cluster randomized trial. In addition, accurate interpretation of trial outcomes requires diagnostic platforms capable of accurately determining infection prevalence (particularly as infection intensity is reduced) at large population scale and with significant throughput. Here, we describe the development and validation of such a high-throughput molecular testing platform. Through the development, selection, and validation of appropriate controls, we have successfully created and evaluated the performance of a testing platform capable of the semi-automated, high-throughput detection of four species of STH in human stool samples. Comparison of this platform with singleplex reference assays for the detection of these same pathogens has demonstrated comparable performance metrics, with index assay accuracy measuring at or above 99.5% and 98.1% for each target species at the level of the technical replicate and individual extraction respectively. Through the implementation of a rigorous validation program, we have developed a diagnostic platform capable of providing the necessary throughput and performance required to meet the needs of the DeWorm3 cluster randomized trial and other large-scale operational research efforts for STH. Resulting from the rigorous developmental approach taken, the platform we describe here provides the needed confidence in testing outcomes when utilized in conjunction with large-scale efforts such as the DeWorm3 trial. Additionally, the successful development of an evaluation and validation strategy provides a template for the creation of similar diagnostic platforms for other neglected tropical diseases.

There are thousands of known cellular phosphorylation sites, but the paucity of ways to identify kinases for particular phosphorylation events remains a major roadblock for understanding kinase signaling. To address this, we here develop a generally applicable method that exploits the large number of kinase inhibitors that have been profiled on near-kinome-wide panels of protein kinases. The inhibition profile for each kinase provides a fingerprint that allows identification of unknown kinases acting on target phosphosites in cell extracts. We validate the method on diverse known kinase-phosphosite pairs, including histone kinases, EGFR autophosphorylation, and Integrin β1 phosphorylation by Src-family kinases. We also use our approach to identify the previously unknown kinases responsible for phosphorylation of INCENP at a site within a commonly phosphorylated motif in mitosis (a non-canonical target of Cyclin B-Cdk1), and of BCL9L at S915 (PKA). We show that the method has clear advantages over in silico and genetic screening. Identifying kinases responsible for specific phosphorylation events remains challenging. Here, the authors leverage kinase inhibitor profiles for the identification of kinase-substrate site pairs in cell extracts, developing a method that can identify the enzymes responsible for unassigned phosphorylation events.

We compared clinical validity of two non-invasive prenatal screening (NIPS) methods for fetal trisomies 13, 18, 21, and monosomy X. We recruited prospectively 2203 women at high risk of fetal aneuploidy and 1807 at baseline risk. Three-hundred and twenty-nine euploid samples were randomly removed. The remaining 1933 high risk and 1660 baseline-risk plasma aliquots were assigned randomly between four laboratories and tested with two index NIPS tests, blind to maternal variables and pregnancy outcomes. The two index tests used massively parallel shotgun sequencing (semiconductor-based and optical-based). The reference standard for all fetuses was invasive cytogenetic analysis or clinical examination at birth and postnatal follow-up. For each chromosome of interest, chromosomal ratios were calculated (number of reads for chromosome/total number of reads). Euploid samples’ mean chromosomal ratio coefficients of variation were 0.48 (T21), 0.34 (T18), and 0.31 (T13). According to the reference standard, there were 155 cases of T21, 49 T18, 8 T13 and 22 45,X. Using a fetal fraction ≥4% to call results and a chromosomal ratio z-score of ≥3 to report a positive result, detection rates (DR), and false positive rates (FPR) were not statistically different between platforms: mean DR 99% (T21), 100%(T18, T13); 79%(45,X); FPR < 0.3% for T21, T18, T13, and <0.6% for 45,X. Both methods’ negative predictive values in high-risk pregnancies were >99.8%, except for 45,X(>99.6%). Threshold analysis in high-risk pregnancies with different fetal fractions and z-score cut-offs suggested that a z-score cutoff to 3.5 for positive results improved test accuracy. Both sequencing platforms showed equivalent and excellent clinical validity.

PurposeCharacterizing submicron protein particles (approximately 0.1–1μm) is challenging due to a limited number of suitable instruments capable of monitoring a relatively large continuum of particle size and concentration. In this work, we report for the first time the characterization of submicron protein particles using the high size resolution technique of resistive pulse sensing (RPS).MethodsResistive pulse sensing, dynamic light scattering and size-exclusion chromatography with in-line multi-angle light scattering (SEC-MALS) are performed on protein and placebo formulations, polystyrene size standards, placebo formulations spiked with silicone oil, and protein formulations stressed via freeze-thaw cycling, thermal incubation, and acid treatment.ResultsA method is developed for monitoring submicron protein particles using RPS. The suitable particle concentration range for RPS is found to be approximately 4 × 107-1 × 1011 particles/mL using polystyrene size standards. Particle size distributions by RPS are consistent with hydrodynamic diameter distributions from batch DLS and to radius of gyration profiles from SEC-MALS. RPS particle size distributions provide an estimate of particle counts and better size resolution compared to light scattering.ConclusionRPS is applicable for characterizing submicron particles in protein formulations with a high degree of size polydispersity. Data on submicron particle distributions provide insights into particles formation under different stresses encountered during biologics drug development.

Chikungunya virus (CHIKV) is a mosquito-borne arthrogenic alphavirus that causes acute febrile illness in humans accompanied by joint pains and in many cases, persistent arthralgia lasting weeks to years. The re-emergence of CHIKV has resulted in numerous outbreaks in the eastern hemisphere, and threatens to expand in the foreseeable future. Unfortunately, no effective treatment is currently available. The present study reports the use of resazurin in a cell-based high-throughput assay, and an image-based high-content assay to identify and characterize inhibitors of CHIKV-infection in vitro. CHIKV is a highly cytopathic virus that rapidly kills infected cells. Thus, cell viability of HuH-7 cells infected with CHIKV in the presence of compounds was determined by measuring metabolic reduction of resazurin to identify inhibitors of CHIKV-associated cell death. A kinase inhibitor library of 4,000 compounds was screened against CHIKV infection of HuH-7 cells using the resazurin reduction assay, and the cell toxicity was also measured in non-infected cells. Seventy-two compounds showing ≥50% inhibition property against CHIKV at 10 µM were selected as primary hits. Four compounds having a benzofuran core scaffold (CND0335, CND0364, CND0366 and CND0415), one pyrrolopyridine (CND0545) and one thiazol-carboxamide (CND3514) inhibited CHIKV-associated cell death in a dose-dependent manner, with EC50 values between 2.2 µM and 7.1 µM. Based on image analysis, these 6 hit compounds did not inhibit CHIKV replication in the host cell. However, CHIKV-infected cells manifested less prominent apoptotic blebs typical of CHIKV cytopathic effect compared with the control infection. Moreover, treatment with these compounds reduced viral titers in the medium of CHIKV-infected cells by up to 100-fold. In conclusion, this cell-based high-throughput screening assay using resazurin, combined with the image-based high content assay approach identified compounds against CHIKV having a novel antiviral activity--inhibition of virus-induced CPE--likely by targeting kinases involved in apoptosis.

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.

Screening drugs on patient biopsies from solid tumours has immense potential, but is challenging due to the small amount of available material. To address this, we present here a plug-based microfluidics platform for functional screening of drug combinations. Integrated Braille valves allow changing the plug composition on demand and enable collecting >1200 data points (56 different conditions with at least 20 replicates each) per biopsy. After deriving and validating efficient and specific drug combinations for two genetically different pancreatic cancer cell lines and xenograft mouse models, we additionally screen live cells from human solid tumours with no need for ex vivo culturing steps, and obtain highly specific sensitivity profiles. The entire workflow can be completed within 48 h at assay costs of less than US$ 150 per patient. We believe this can pave the way for rapid determination of optimal personalized cancer therapies. Cancer patients exhibit specific sensitivities toward drug combinations that cannot be easily predicted. Here the authors setup a microfluidic platform that allows testing of multiple drug combinations correctly predicting sensitivity in vivo and they use it on patients biopsies to define effective drugs.

Image-based profiling is a maturing strategy by which the rich information present in biological images is reduced to a multidimensional profile, a collection of extracted image-based features. These profiles can be mined for relevant patterns, revealing unexpected biological activity that is useful for many steps in the drug discovery process. Such applications include identifying disease-associated screenable phenotypes, understanding disease mechanisms and predicting a drug’s activity, toxicity or mechanism of action. Several of these applications have been recently validated and have moved into production mode within academia and the pharmaceutical industry. Some of these have yielded disappointing results in practice but are now of renewed interest due to improved machine-learning strategies that better leverage image-based information. Although challenges remain, novel computational technologies such as deep learning and single-cell methods that better capture the biological information in images hold promise for accelerating drug discovery.Image-based profiling is a strategy to mine the rich information in biological images. Carpenter and colleagues discuss how the application of machine learning is renewing interest in image-based profiling for all aspects of the drug discovery process, from understanding disease mechanisms to predicting a drug’s activity or mechanism of action.