A method for effectively comparing gene effects in multiple conditions in RNAi and expression-profiling research

Genetic Interactions in Various Environmental Conditions in Caenorhabditis elegans

Although it is well known that epistasis plays an important role in many evolutionary processes (e.g., speciation, evolution of sex), our knowledge on the frequency and prevalent sign of epistatic interactions is mainly limited to unicellular organisms or cell cultures of multicellular organisms. This is even more pronounced in regard to how the environment can influence genetic interactions. To broaden our knowledge in that respect we studied gene-gene interactions in a whole multicellular organism, Caenorhabditis elegans. We screened over one thousand gene interactions, each one in standard laboratory conditions, and under three different stressors: heat shock, oxidative stress, and genotoxic stress. Depending on the condition, between 7% and 22% of gene pairs showed significant genetic interactions and an overall sign of epistasis changed depending on the condition. Sign epistasis was quite common, but reciprocal sign epistasis was extremally rare. One interaction was common to all conditions, whereas 78% of interactions were specific to only one environment. Although epistatic interactions are quite common, their impact on evolutionary processes will strongly depend on environmental factors.

Identification of Proteins Involved in Cell Membrane Permeabilization by Nanosecond Electric Pulses (nsEP)

The study was aimed at identifying endogenous proteins which assist or impede the permeabilized state in the cell membrane disrupted by nsEP (20 or 40 pulses, 300 ns width, 7 kV/cm). We employed a LentiArray CRISPR library to generate knockouts (KOs) of 316 genes encoding for membrane proteins in U937 human monocytes stably expressing Cas9 nuclease. The extent of membrane permeabilization by nsEP was measured by the uptake of Yo-Pro-1 (YP) dye and compared to sham-exposed KOs and control cells transduced with a non-targeting (scrambled) gRNA. Only two KOs, for SCNN1A and CLCA1 genes, showed a statistically significant reduction in YP uptake. The respective proteins could be part of electropermeabilization lesions or increase their lifespan. In contrast, as many as 39 genes were identified as likely hits for the increased YP uptake, meaning that the respective proteins contributed to membrane stability or repair after nsEP. The expression level of eight genes in different types of human cells showed strong correlation (R > 0.9, p < 0.02) with their LD₅₀ for lethal nsEP treatments, and could potentially be used as a criterion for the selectivity and efficiency of hyperplasia ablations with nsEP.

Development of a High-Throughput Affinity Mass Spectrometry (AMS) Platform Using Laser Diode Thermal Desorption Ionization Coupled to Mass Spectrometry (LDTD-MS)

Affinity selection mass spectrometry (MS) or, simply, affinity mass spectrometry (AMS) is a label-free technology that has been used to identify high-affinity ligands of target proteins of interest by screening against small-molecule compound libraries and identifying molecules that are enriched in the presence of the target protein. We have previously applied Agilent Technology's (Santa Clara, CA) RapidFire solid-phase extraction (SPE)-based high-throughput MS technology to screen small-molecule libraries using AMS. However, SPE-based technologies rely on fluidics for desalting and separation prior to mass analysis with attendant high solvent consumption, relatively high sample volume requirements, risk of sample carryover, and frequent maintenance. To address these challenges, we have established an AMS platform using a laser diode thermal desorption-atmospheric pressure chemical ionization (LDTD-APCI) ionization source (Phytronix, Quebec, Canada) coupled with a SCIEX 5600+ TripleTOF MS (Framingham, MA). We also validated a data-independent acquisition (DIA) Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) method for the robust detection and analysis of small-molecule affinity hits. An informatics platform developed in-house has resulted in a streamlined data analysis workflow for high-throughput AMS screening campaigns and reduced data processing time without compromising data quality. Finally, 68,000 compounds were screened in a single plate and affinity selected hits were confirmed in an orthogonal enzyme activity assay.

Genome-wide identification of microRNAs regulating the human prion protein

The cellular prion protein (PrP^C ) is best known for its misfolded disease-causing conformer, PrP^Sc . Because the availability of PrP^C is often limiting for prion propagation, understanding its regulation may point to possible therapeutic targets. We sought to determine to what extent the human microRNAome is involved in modulating PrP^C levels through direct or indirect pathways. We probed PrP^C protein levels in cells subjected to a genome-wide library encompassing 2019 miRNA mimics using a robust time-resolved fluorescence-resonance screening assay. Screening was performed in three human neuroectodermal cell lines: U-251 MG, CHP-212 and SH-SY5Y. The three screens yielded 17 overlapping high-confidence miRNA mimic hits, 13 of which were found to regulate PrP^C biosynthesis directly via binding to the PRNP 3'UTR, thereby inducing transcript degradation. The four remaining hits (miR-124-3p, 192-3p, 299-5p and 376b-3p) did not bind either the 3'UTR or CDS of PRNP, and were therefore deemed indirect regulators of PrP^C . Our results show that multiple miRNAs regulate PrP^C levels both directly and indirectly. These findings may have profound implications for prion disease pathogenesis and potentially also for their therapy. Furthermore, the possible role of PrP^C as a mediator of Aβ toxicity suggests that its regulation by miRNAs may also impinge on Alzheimer's disease.

Self-renewing Monolayer of Primary Colonic or Rectal Epithelial Cells

BACKGROUND & AIMS

Three-dimensional organoid culture has fundamentally changed the in vitro study of intestinal biology enabling novel assays; however, its use is limited because of an inaccessible luminal compartment and challenges to data gathering in a three-dimensional hydrogel matrix. Long-lived, self-renewing 2-dimensional (2-D) tissue cultured from primary colon cells has not been accomplished.

METHODS

The surface matrix and chemical factors that sustain 2-D mouse colonic and human rectal epithelial cell monolayers with cell repertoires comparable to that in vivo were identified.

RESULTS

The monolayers formed organoids or colonoids when placed in standard Matrigel culture. As with the colonoids, the monolayers exhibited compartmentalization of proliferative and differentiated cells, with proliferative cells located near the peripheral edges of growing monolayers and differentiated cells predominated in the central regions. Screening of 77 dietary compounds and metabolites revealed altered proliferation or differentiation of the murine colonic epithelium. When exposed to a subset of the compound library, murine organoids exhibited similar responses to that of the monolayer but with differences that were likely attributable to the inaccessible organoid lumen. The response of the human primary epithelium to a compound subset was distinct from that of both the murine primary epithelium and human tumor cells.

CONCLUSIONS

This study demonstrates that a self-renewing 2-D murine and human monolayer derived from primary cells can serve as a physiologically relevant assay system for study of stem cell renewal and differentiation and for compound screening. The platform holds transformative potential for personalized and precision medicine and can be applied to emerging areas of disease modeling and microbiome studies.

Nucleotide Excision Repair and Transcription-coupled DNA Repair Abrogate the Impact of DNA Damage on Transcription

DNA adducts derived from carcinogenic polycyclic aromatic hydrocarbons like benzo[a]pyrene (B[a]P) and benzo[c]phenanthrene (B[c]Ph) impede replication and transcription, resulting in aberrant cell division and gene expression. Global nucleotide excision repair (NER) and transcription-coupled DNA repair (TCR) are among the DNA repair pathways that evolved to maintain genome integrity by removing DNA damage. The interplay between global NER and TCR in repairing the polycyclic aromatic hydrocarbon-derived DNA adducts (+)-trans-anti-B[a]P-N(6)-dA, which is subject to NER and blocks transcription in vitro, and (+)-trans-anti-B[c]Ph-N(6)-dA, which is a poor substrate for NER but also blocks transcription in vitro, was tested. The results show that both adducts inhibit transcription in human cells that lack both NER and TCR. The (+)-trans-anti-B[a]P-N(6)-dA lesion exhibited no detectable effect on transcription in cells proficient in NER but lacking TCR, indicating that NER can remove the lesion in the absence of TCR, which is consistent with in vitro data. In primary human cells lacking NER, (+)-trans-anti-B[a]P-N(6)-dA exhibited a deleterious effect on transcription that was less severe than in cells lacking both pathways, suggesting that TCR can repair the adduct but not as effectively as global NER. In contrast, (+)-trans-anti-B[c]Ph-N(6)-dA dramatically reduces transcript production in cells proficient in global NER but lacking TCR, indicating that TCR is necessary for the removal of this adduct, which is consistent with in vitro data showing that it is a poor substrate for NER. Hence, both global NER and TCR enhance the recovery of gene expression following DNA damage, and TCR plays an important role in removing DNA damage that is refractory to NER.

Distribution Analyzer, a methodology for identifying and clustering outlier conditions from single-cell distributions, and its application to a Nanog reporter RNAi screen

Background

Chemical or small interfering (si) RNA screens measure the effects of many independent experimental conditions, each applied to a population of cells (e.g., all of the cells in a well). High-content screens permit a readout (e.g., fluorescence, luminescence, cell morphology) from each cell in the population. Most analysis approaches compare the average effect on each population, precluding identification of outliers that affect the distribution of the reporter in the population but not its average. Other approaches only measure changes to the distribution with a single parameter, precluding accurate distinction and clustering of interesting outlier distributions.

Results

We describe a methodology to identify outlier conditions by considering the cell-level measurements from each condition as a sample of an underlying distribution. With appropriate selection of a distance metric, all effects can be embedded in a fixed-dimensionality Euclidean basis, facilitating identification and clustering of biologically interesting outliers. We demonstrate that measurement of distances with the Hellinger distance metric offers substantial computational efficiencies over alternative metrics. We validate this methodology using an RNA interference (RNAi) screen in mouse embryonic stem cells (ESC) with a Nanog reporter. The methodology clusters effects of multiple control siRNAs into their true identities better than conventional approaches describing the median cell fluorescence or the commonly used Kolmogorov-Smirnov distance between the observed fluorescence distribution and the null distribution. It identifies outlier genes with effects on the reporter distribution that would have been missed by other methods. Among them, siRNA targeting Chek1 leads to a wider Nanog reporter fluorescence distribution. Similarly, siRNA targeting Med14 or Med27 leads to a narrower Nanog reporter fluorescence distribution. We confirm the roles of these three genes in regulating pluripotency by mRNA expression and alkaline phosphatase staining using independent short hairpin (sh) RNAs.

Conclusions

Using our methodology, we describe each experimental condition by a probability distribution. Measuring distances between probability distributions permits a multivariate rather than univariate readout. Clustering points derived from these distances allows us to obtain greater biological insight than methods based solely on single parameters. We find several outliers from a mouse ESC RNAi screen that we confirm to be pluripotency regulators. Many of these outliers would have been missed by other analysis methods.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0636-7) contains supplementary material, which is available to authorized users.

GUItars: a GUI tool for analysis of high-throughput RNA interference screening data

Background

High-throughput RNA interference (RNAi) screening has become a widely used approach to elucidating gene functions. However, analysis and annotation of large data sets generated from these screens has been a challenge for researchers without a programming background. Over the years, numerous data analysis methods were produced for plate quality control and hit selection and implemented by a few open-access software packages. Recently, strictly standardized mean difference (SSMD) has become a widely used method for RNAi screening analysis mainly due to its better control of false negative and false positive rates and its ability to quantify RNAi effects with a statistical basis. We have developed GUItars to enable researchers without a programming background to use SSMD as both a plate quality and a hit selection metric to analyze large data sets.

Results

The software is accompanied by an intuitive graphical user interface for easy and rapid analysis workflow. SSMD analysis methods have been provided to the users along with traditionally-used z-score, normalized percent activity, and t-test methods for hit selection. GUItars is capable of analyzing large-scale data sets from screens with or without replicates. The software is designed to automatically generate and save numerous graphical outputs known to be among the most informative high-throughput data visualization tools capturing plate-wise and screen-wise performances. Graphical outputs are also written in HTML format for easy access, and a comprehensive summary of screening results is written into tab-delimited output files.

Conclusion

With GUItars, we demonstrated robust SSMD-based analysis workflow on a 3840-gene small interfering RNA (siRNA) library and identified 200 siRNAs that increased and 150 siRNAs that decreased the assay activities with moderate to stronger effects. GUItars enables rapid analysis and illustration of data from large- or small-scale RNAi screens using SSMD and other traditional analysis methods. The software is freely available at http://sourceforge.net/projects/guitars/.

cSSMD: assessing collective activity for addressing off-target effects in genome-scale RNA interference screens

MOTIVATION

Off-target activity commonly exists in RNA interference (RNAi) screens and often generates false positives. Existing analytic methods for addressing the off-target effects are demonstrably inadequate in RNAi confirmatory screens.

RESULTS

Here, we present an analytic method assessing the collective activity of multiple short interfering RNAs (siRNAs) targeting a gene. Using this method, we can not only reduce the impact of off-target activities, but also evaluate the specific effect of an siRNA, thus providing information about potential off-target effects. Using in-house RNAi screens, we demonstrate that our method obtains more reasonable and sensible results than current methods such as the redundant siRNA activity (RSA) method, the RNAi gene enrichment ranking (RIGER) method, the frequency approach and the t-test.

CONTACT

xiaohua_zhang@merck.com

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Illustration of SSMD, z score, SSMD*, z* score, and t statistic for hit selection in RNAi high-throughput screens

Hit selection is the ultimate goal in many high-throughput screens. Various analytic methods are available for this purpose. Some commonly used ones are z score, z* score, strictly standardized mean difference (SSMD), SSMD*, and t statistic. It is critical to know how to use them correctly because the misusage of them can readily produce misleading results. Here, the author presents basic concepts, elaborates their commonality and difference, describes some common misusage that people should avoid, and uses simulated simple examples to illustrate how to use them correctly.

An effective method for controlling false discovery and false nondiscovery rates in genome-scale RNAi screens

In most genome-scale RNA interference (RNAi) screens, the ultimate goal is to select siRNAs with a large inhibition or activation effect. The selection of hits typically requires statistical control of 2 errors: false positives and false negatives. Traditional methods of controlling false positives and false negatives do not take into account the important feature in RNAi screens: many small-interfering RNAs (siRNAs) may have very small but real nonzero average effects on the measured response and thus cannot allow us to effectively control false positives and false negatives. To address for deficiencies in the application of traditional approaches in RNAi screening, the author proposes a new method for controlling false positives and false negatives in RNAi high-throughput screens. The false negatives are statistically controlled through a false-negative rate (FNR) or false nondiscovery rate (FNDR). FNR is the proportion of false negatives among all siRNAs examined, whereas FNDR is the proportion of false negatives among declared nonhits. The author also proposes new concepts, q*-value and p*-value, to control FNR and FNDR, respectively. The proposed method should have broad utility for hit selection in which one needs to control both false discovery and false nondiscovery rates in genome-scale RNAi screens in a robust manner.

The use of SSMD-based false discovery and false nondiscovery rates in genome-scale RNAi screens

In genome-scale RNA interference (RNAi) screens, it is critical to control false positives and false negatives statistically. Traditional statistical methods for controlling false discovery and false nondiscovery rates are inappropriate for hit selection in RNAi screens because the major goal in RNAi screens is to control both the proportion of short interfering RNAs (siRNAs) with a small effect among selected hits and the proportion of siRNAs with a large effect among declared nonhits. An effective method based on strictly standardized mean difference (SSMD) has been proposed for statistically controlling false discovery rate (FDR) and false nondiscovery rate (FNDR) appropriate for RNAi screens. In this article, the authors explore the utility of the SSMD-based method for hit selection in RNAi screens. As demonstrated in 2 genome-scale RNAi screens, the SSMD-based method addresses the unmet need of controlling for the proportion of siRNAs with a small effect among selected hits, as well as controlling for the proportion of siRNAs with a large effect among declared nonhits. Furthermore, the SSMD-based method results in reasonably low FDR and FNDR for selecting inhibition or activation hits. This method works effectively and should have a broad utility for hit selection in RNAi screens with replicates.

Assessing the size of gene or RNAi effects in multifactor high-throughput experiments

AIMS

To expand the recently proposed contrast variable and associated concepts to assess the size of gene effects or siRNA effects in multifactor high-throughput experiments, as well as to serve the need to consider both mean and standardized mean of contrast (SMC).

METHODS & RESULTS

The recently proposed concepts of contrast variable and SMC are expanded in the context of multifactor analysis of variance. Based on this expansion, SMC is explored as a tool for analyzing multifactor high-throughput data, a novel plot termed a dual-flashlight plot is proposed, and the incompatibility of false-discovery rates across experiments is demonstrated. The applications show that the results reached using expanded SMC and the dual-flashlight plot are more reasonable than those reached using p-value-based or false-discovery rate-based volcano plot for assessing differential expression, genetic dominance and linear/quadratic time-course changes.

CONCLUSION

Compared with traditional contrast analysis, the expanded contrast variable and SMC may serve as an alternative that can address the real need of assessing the size of gene or siRNA effects in multifactor high-throughput experiments.