Accurate deep learning off-target prediction with novel sgRNA-DNA sequence encoding in CRISPR-Cas9 gene editing.

MOTIVATION: Off-target predictions are crucial in gene editing research. Recently, significant progress has been made in the field of prediction of off-target mutations, particularly with CRISPR-Cas9 data, thanks to the use of deep learning. CRISPR-Cas9 is a gene editing technique which allows manipulation of DNA fragments. The sgRNA-DNA (single guide RNA-DNA) sequence encoding for deep neural networks, however, has a strong impact on the prediction accuracy. We propose a novel encoding of sgRNA-DNA sequences that aggregates sequence data with no loss of information. RESULTS: In our experiments, we compare the proposed sgRNA-DNA sequence encoding applied in a deep learning prediction framework with state-of-the-art encoding and prediction methods. We demonstrate the superior accuracy of our approach in a simulation study involving Feedforward Neural Networks (FNNs), Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) as well as the traditional Random Forest (RF), Naive Bayes (NB) and Logistic Regression (LR) classifiers. We highlight the quality of our results by building several FNNs, CNNs and RNNs with various layer depths and performing predictions on two popular gene editing datasets (CRISPOR and GUIDE-seq). In all our experiments, the new encoding led to more accurate off-target prediction results, providing an improvement of the area under the Receiver Operating Characteristic (ROC) curve up to 35%. AVAILABILITY AND IMPLEMENTATION: The code and data used in this study are available at: https://github.com/dagrate/dl-offtarget. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Four erroneous beliefs thwarting more trustworthy research.

A range of problems currently undermines public trust in biomedical research. We discuss four erroneous beliefs that may prevent the biomedical research community from recognizing the need to focus on deserving this trust, and thus which act as powerful barriers to necessary improvements in the research process.

Exact replication: Foundation of science or game of chance?

The need for replication of initial results has been rediscovered only recently in many fields of research. In preclinical biomedical research, it is common practice to conduct exact replications with the same sample sizes as those used in the initial experiments. Such replication attempts, however, have lower probability of replication than is generally appreciated. Indeed, in the common scenario of an effect just reaching statistical significance, the statistical power of the replication experiment assuming the same effect size is approximately 50%-in essence, a coin toss. Accordingly, we use the provocative analogy of "replicating" a neuroprotective drug animal study with a coin flip to highlight the need for larger sample sizes in replication experiments. Additionally, we provide detailed background for the probability of obtaining a significant p value in a replication experiment and discuss the variability of p values as well as pitfalls of simple binary significance testing in both initial preclinical experiments and replication studies with small sample sizes. We conclude that power analysis for determining the sample size for a replication study is obligatory within the currently dominant hypothesis testing framework. Moreover, publications should include effect size point estimates and corresponding measures of precision, e.g., confidence intervals, to allow readers to assess the magnitude and direction of reported effects and to potentially combine the results of initial and replication study later through Bayesian or meta-analytic approaches.

Translational control of depression-like behavior via phosphorylation of eukaryotic translation initiation factor 4E.

Translation of mRNA into protein has a fundamental role in neurodevelopment, plasticity, and memory formation; however, its contribution in the pathophysiology of depressive disorders is not fully understood. We investigated the involvement of MNK1/2 (MAPK-interacting serine/threonine-protein kinase 1 and 2) and their target, eIF4E (eukaryotic initiation factor 4E), in depression-like behavior in mice. Mice carrying a mutation in eIF4E for the MNK1/2 phosphorylation site (Ser209Ala, Eif4e ki/ki), the Mnk1/2 double knockout mice (Mnk1/2-/-), or mice treated with the MNK1/2 inhibitor, cercosporamide, displayed anxiety- and depression-like behaviors, impaired serotonin-induced excitatory synaptic activity in the prefrontal cortex, and diminished firing of the dorsal raphe neurons. In Eif4e ki/ki mice, brain IκBα, was decreased, while the NF-κB target, TNFα was elevated. TNFα inhibition in Eif4e ki/ki mice rescued, whereas TNFα administration to wild-type mice mimicked the depression-like behaviors and 5-HT synaptic deficits. We conclude that eIF4E phosphorylation modulates depression-like behavior through regulation of inflammatory responses.

Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening.

Data generated by high-throughput screening (HTS) technologies are prone to spatial bias. Traditionally, bias correction methods used in HTS assume either a simple additive or, more recently, a simple multiplicative spatial bias model. These models do not, however, always provide an accurate correction of measurements in wells located at the intersection of rows and columns affected by spatial bias. The measurements in these wells depend on the nature of interaction between the involved biases. Here, we propose two novel additive and two novel multiplicative spatial bias models accounting for different types of bias interactions. We describe a statistical procedure that allows for detecting and removing different types of additive and multiplicative spatial biases from multiwell plates. We show how this procedure can be applied by analyzing data generated by the four HTS technologies (homogeneous, microorganism, cell-based, and gene expression HTS), the three high-content screening (HCS) technologies (area, intensity, and cell-count HCS), and the only small-molecule microarray technology available in the ChemBank small-molecule screening database. The proposed methods are included in the AssayCorrector program, implemented in R, and available on CRAN.

Identification and correction of spatial bias are essential for obtaining quality data in high-throughput screening technologies.

Spatial bias continues to be a major challenge in high-throughput screening technologies. Its successful detection and elimination are critical for identifying the most promising drug candidates. Here, we examine experimental small molecule assays from the popular ChemBank database and show that screening data are widely affected by both assay-specific and plate-specific spatial biases. Importantly, the bias affecting screening data can fit an additive or multiplicative model. We show that the use of appropriate statistical methods is essential for improving the quality of experimental screening data. The presented methodology can be recommended for the analysis of current and next-generation screening data.

Detecting and removing multiplicative spatial bias in high-throughput screening technologies.

MOTIVATION: Considerable attention has been paid recently to improve data quality in high-throughput screening (HTS) and high-content screening (HCS) technologies widely used in drug development and chemical toxicity research. However, several environmentally- and procedurally-induced spatial biases in experimental HTS and HCS screens decrease measurement accuracy, leading to increased numbers of false positives and false negatives in hit selection. Although effective bias correction methods and software have been developed over the past decades, almost all of these tools have been designed to reduce the effect of additive bias only. Here, we address the case of multiplicative spatial bias. RESULTS: We introduce three new statistical methods meant to reduce multiplicative spatial bias in screening technologies. We assess the performance of the methods with synthetic and real data affected by multiplicative spatial bias, including comparisons with current bias correction methods. We also describe a wider data correction protocol that integrates methods for removing both assay and plate-specific spatial biases, which can be either additive or multiplicative. CONCLUSIONS: The methods for removing multiplicative spatial bias and the data correction protocol are effective in detecting and cleaning experimental data generated by screening technologies. As our protocol is of a general nature, it can be used by researchers analyzing current or next-generation high-throughput screens. AVAILABILITY AND IMPLEMENTATION: The AssayCorrector program, implemented in R, is available on CRAN. CONTACT: makarenkov.vladimir@uqam.ca. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Control of embryonic stem cell self-renewal and differentiation via coordinated alternative splicing and translation of YY2.

Translational control of gene expression plays a key role during the early phases of embryonic development. Here we describe a transcriptional regulator of mouse embryonic stem cells (mESCs), Yin-yang 2 (YY2), that is controlled by the translation inhibitors, Eukaryotic initiation factor 4E-binding proteins (4E-BPs). YY2 plays a critical role in regulating mESC functions through control of key pluripotency factors, including Octamer-binding protein 4 (Oct4) and Estrogen-related receptor-ß (Esrrb). Importantly, overexpression of YY2 directs the differentiation of mESCs into cardiovascular lineages. We show that the splicing regulator Polypyrimidine tract-binding protein 1 (PTBP1) promotes the retention of an intron in the 5'-UTR of Yy2 mRNA that confers sensitivity to 4E-BP-mediated translational suppression. Thus, we conclude that YY2 is a major regulator of mESC self-renewal and lineage commitment and document a multilayer regulatory mechanism that controls its expression.

Translation control during prolonged mTORC1 inhibition mediated by 4E-BP3.

Targeting mTORC1 is a highly promising strategy in cancer therapy. Suppression of mTORC1 activity leads to rapid dephosphorylation of eIF4E-binding proteins (4E-BP1-3) and subsequent inhibition of mRNA translation. However, how the different 4E-BPs affect translation during prolonged use of mTOR inhibitors is not known. Here we show that the expression of 4E-BP3, but not that of 4E-BP1 or 4E-BP2, is transcriptionally induced during prolonged mTORC1 inhibition in vitro and in vivo. Mechanistically, our data reveal that 4E-BP3 expression is controlled by the transcription factor TFE3 through a cis-regulatory element in the EIF4EBP3 gene promoter. CRISPR/Cas9-mediated EIF4EBP3 gene disruption in human cancer cells mitigated the inhibition of translation and proliferation caused by prolonged treatment with mTOR inhibitors. Our findings show that 4E-BP3 is an important effector of mTORC1 and a robust predictive biomarker of therapeutic response to prolonged treatment with mTOR-targeting drugs in cancer.

Detecting and overcoming systematic bias in high-throughput screening technologies: a comprehensive review of practical issues and methodological solutions.

Significant efforts have been made recently to improve data throughput and data quality in screening technologies related to drug design. The modern pharmaceutical industry relies heavily on high-throughput screening (HTS) and high-content screening (HCS) technologies, which include small molecule, complementary DNA (cDNA) and RNA interference (RNAi) types of screening. Data generated by these screening technologies are subject to several environmental and procedural systematic biases, which introduce errors into the hit identification process. We first review systematic biases typical of HTS and HCS screens. We highlight that study design issues and the way in which data are generated are crucial for providing unbiased screening results. Considering various data sets, including the publicly available ChemBank data, we assess the rates of systematic bias in experimental HTS by using plate-specific and assay-specific error detection tests. We describe main data normalization and correction techniques and introduce a general data preprocessing protocol. This protocol can be recommended for academic and industrial researchers involved in the analysis of current or next-generation HTS data.

Improving detection of rare biological events in high-throughput screens.

The success of high-throughput screening (HTS) strategies depends on the effectiveness of both normalization methods and study design. We report comparisons among normalization methods in two titration series experiments. We also extend the results in a third experiment with two differently designed but otherwise identical screens: compounds in replicate plates were either placed in the same well locations or were randomly assigned to different locations. Best results were obtained when randomization was combined with normalization methods that corrected for within-plate spatial bias. We conclude that potent, reliable, and accurate HTS requires replication, randomization design strategies, and more extensive normalization than is typically done and that formal statistical testing is desirable. The Statistics and dIagnostic Graphs for HTS (SIGHTS) Microsoft Excel Add-In software is available to conduct most analyses reported here.

Estrogen receptor alpha drives proliferation in PTEN-deficient prostate carcinoma by stimulating survival signaling, MYC expression and altering glucose sensitivity.

While high doses of estrogen, in combination with androgens, can initiate prostate cancer (PCa) via activation of the estrogen receptor α (ERα), the role of ERα in PCa cells within established tumors is largely unknown. Here we show that expression of ERα is increased in high grade human PCa. Similarly, ERα is elevated in mouse models of aggressive PCa driven by MYC overexpression or deletion of PTEN. Within the prostate of PTEN-deficient mice, there is a progressive pattern of ERα expression: low in benign glands, moderate in tumors within the dorsal, lateral and ventral lobes, and high in tumors within the anterior prostate. This expression significantly correlates with the proliferation marker Ki67. Furthermore, in vitro knockdown of ERα in cells derived from PTEN-deficient tumors causes a significant and sustained decrease in proliferation. Depletion of ERα also reduces the activity of the PI3K and MAPK pathways, both downstream targets of non-genomic ERα action. Finally, ERα knockdown reduces the levels of the MYC protein and lowers the sensitivity of cellular proliferation to glucose withdrawal, which correlates with decreased expression of the glucose transporter GLUT1. Collectively, these results demonstrate that ERα orchestrates proliferation and metabolism to promote the neoplastic growth of PCa cells.

Novel protein interactions with endoglin and activin receptor-like kinase 1: potential role in vascular networks.

Endoglin and activin receptor-like kinase 1 are specialized transforming growth factor-beta (TGF-ß) superfamily receptors, primarily expressed in endothelial cells. Mutations in the corresponding ENG or ACVRL1 genes lead to hereditary hemorrhagic telangiectasia (HHT1 and HHT2 respectively). To discover proteins interacting with endoglin, ACVRL1 and TGF-ß receptor type 2 and involved in TGF-ß signaling, we applied LUMIER, a high-throughput mammalian interactome mapping technology. Using stringent criteria, we identified 181 novel unique and shared interactions with ACVRL1, TGF-ß receptor type 2, and endoglin, defining potential novel important vascular networks. In particular, the regulatory subunit B-beta of the protein phosphatase PP2A (PPP2R2B) interacted with all three receptors. Interestingly, the PPP2R2B gene lies in an interval in linkage disequilibrium with HHT3, for which the gene remains unidentified. We show that PPP2R2B protein interacts with the ACVRL1/TGFBR2/endoglin complex and recruits PP2A to nitric oxide synthase 3 (NOS3). Endoglin overexpression in endothelial cells inhibits the association of PPP2R2B with NOS3, whereas endoglin-deficient cells show enhanced PP2A-NOS3 interaction and lower levels of endogenous NOS3 Serine 1177 phosphorylation. Our data suggest that endoglin regulates NOS3 activation status by regulating PPP2R2B access to NOS3, and that PPP2R2B might be the HHT3 gene. Furthermore, endoglin and ACVRL1 contribute to several novel networks, including TGF-ß dependent and independent ones, critical for vascular function and potentially defective in HHT.

Single assay-wide variance experimental (SAVE) design for high-throughput screening.

MOTIVATION: Advantages of statistical testing of high-throughput screens include P-values, which provide objective benchmarks of compound activity, and false discovery rate estimation. The cost of replication required for statistical testing, however, may often be prohibitive. We introduce the single assay-wide variance experimental (SAVE) design whereby a small replicated subset of an entire screen is used to derive empirical Bayes random error estimates, which are applied to the remaining majority of unreplicated measurements. RESULTS: The SAVE design is able to generate P-values comparable with those generated with full replication data. It performs almost as well as the random variance model t-test with duplicate data and outperforms the commonly used Z-scores with unreplicated data and the standard t-test. We illustrate the approach with simulated data and with experimental small molecule and small interfering RNA screens. The SAVE design provides substantial performance improvements over unreplicated screens with only slight increases in cost.

Acacetin and chrysin, two polyphenolic compounds, alleviate telomeric position effect in human cells.

We took advantage of the ability of human telomeres to silence neighboring genes (telomere position effect or TPE) to design a high-throughput screening assay for drugs altering telomeres. We identified, for the first time, that two dietary flavones, acacetin and chrysin, are able to specifically alleviate TPE in human cells. We further investigated their influence on telomere integrity and showed that both drugs drastically deprotect telomeres against DNA damage response. However, telomere deprotection triggered by shelterin dysfunction does not affect TPE, indicating that acacetin and chrysin target several functions of telomeres. These results show that TPE-based screening assays represent valuable methods to discover new compounds targeting telomeres.Molecular Therapy-Nucleic Acids (2013) 2, e116; doi:10.1038/mtna.2013.42; published online 20 August 2013.

Re-analysis of genome wide data on mammalian microRNA-mediated suppression of gene expression.

microRNAs are short endogenously expressed RNAs that regulate gene expression post-transcriptionally. Although both mRNA degradation and suppression of mRNA translation can mediate reduced protein levels following microRNA targeting of an mRNA, their relative contributions have remained elusive. A recent genome-wide study in mammals employing RNA-sequencing to measure microRNA effects on mRNA translation and stability concluded that 84-89% of microRNA-induced suppression of gene expression is due to degradation of target mRNAs. We re-analyzed this data set and applied a number of analysis modifications which revealed that the contribution of mRNA translation was likely underestimated for some mRNA subsets. Moreover, in contrast to the original analysis, our analysis indicated that suppression of mRNA translation precedes mRNA degradation upon microRNA targeting. Our findings thereby enhance our understanding of microRNA mediated genome wide suppression of gene expression in mammals.

Intensity quantile estimation and mapping--a novel algorithm for the correction of image non-uniformity bias in HCS data.

MOTIVATION: Image non-uniformity (NU) refers to systematic, slowly varying spatial gradients in images that result in a bias that can affect all downstream image processing, quantification and statistical analysis steps. Image NU is poorly modeled in the field of high-content screening (HCS), however, such that current conventional correction algorithms may be either inappropriate for HCS or fail to take advantage of the information available in HCS image data. RESULTS: A novel image NU bias correction algorithm, termed intensity quantile estimation and mapping (IQEM), is described. The algorithm estimates the full non-linear form of the image NU bias by mapping pixel intensities to a reference intensity quantile function. IQEM accounts for the variation in NU bias over broad cell intensity ranges and data acquisition times, both of which are characteristic of HCS image datasets. Validation of the method, using simulated and HCS microtubule polymerization screen images, is presented. Two requirements of IQEM are that the dataset consists of large numbers of images acquired under identical conditions and that cells are distributed with no within-image spatial preference. AVAILABILITY AND IMPLEMENTATION: MATLAB function files are available at http://nadon-mugqic.mcgill.ca/.

Two effective methods for correcting experimental high-throughput screening data.

MOTIVATION: Rapid advances in biomedical sciences and genetics have increased the pressure on drug development companies to promptly translate new knowledge into treatments for disease. Impelled by the demand and facilitated by technological progress, the number of compounds evaluated during the initial high-throughput screening (HTS) step of drug discovery process has steadily increased. As a highly automated large-scale process, HTS is prone to systematic error caused by various technological and environmental factors. A number of error correction methods have been designed to reduce the effect of systematic error in experimental HTS (Brideau et al., 2003; Carralot et al., 2012; Kevorkov and Makarenkov, 2005; Makarenkov et al., 2007; Malo et al., 2010). Despite their power to correct systematic error when it is present, the applicability of those methods in practice is limited by the fact that they can potentially introduce a bias when applied to unbiased data. We describe two new methods for eliminating systematic error from HTS data based on a prior knowledge of the error location. This information can be obtained using a specific version of the t-test or of the χ(2) goodness-of-fit test as discussed in Dragiev et al. (2011). We will show that both new methods constitute an important improvement over the standard practice of not correcting for systematic error at all as well as over the B-score correction procedure (Brideau et al., 2003) which is widely used in the modern HTS. We will also suggest a more general data preprocessing framework where the new methods can be applied in combination with the Well Correction procedure (Makarenkov et al., 2007). Such a framework will allow for removing systematic biases affecting all plates of a given screen as well as those relative to some of its individual plates.

Vesicoureteral reflux and other urinary tract malformations in mice compound heterozygous for Pax2 and Emx2.

Congenital anomalies of the kidney and urinary tract (CAKUT) are the most common cause of chronic kidney disease in children. This disease group includes a spectrum of urinary tract defects including vesicoureteral reflux, duplex kidneys and other developmental defects that can be found alone or in combination. To identify new regulators of CAKUT, we tested the genetic cooperativity between several key regulators of urogenital system development in mice. We found a high incidence of urinary tract anomalies in Pax2;Emx2 compound heterozygous mice that are not found in single heterozygous mice. Pax2⁺/⁻;Emx2⁺/⁻ mice harbor duplex systems associated with urinary tract obstruction, bifid ureter and a high penetrance of vesicoureteral reflux. Remarkably, most compound heterozygous mice refluxed at low intravesical pressure. Early analysis of Pax2⁺/⁻;Emx2⁺/⁻ embryos point to ureter budding defects as the primary cause of urinary tract anomalies. We additionally establish Pax2 as a direct regulator of Emx2 expression in the Wolffian duct. Together, these results identify a haploinsufficient genetic combination resulting in CAKUT-like phenotype, including a high sensitivity to vesicoureteral reflux. As both genes are located on human chromosome 10q, which is lost in a proportion of VUR patients, these findings may help understand VUR and CAKUT in humans.