Noise reduction in genome-wide perturbation screens using linear mixed-effect models

Global analysis of the yeast knockout phenome

Genome-wide phenotypic screens in the budding yeast Saccharomyces cerevisiae, enabled by its knockout collection, have produced the largest, richest, and most systematic phenotypic description of any organism. However, integrative analyses of this rich data source have been virtually impossible because of the lack of a central data repository and consistent metadata annotations. Here, we describe the aggregation, harmonization, and analysis of ~14,500 yeast knockout screens, which we call Yeast Phenome. Using this unique dataset, we characterized two unknown genes (YHR045W and YGL117W) and showed that tryptophan starvation is a by-product of many chemical treatments. Furthermore, we uncovered an exponential relationship between phenotypic similarity and intergenic distance, which suggests that gene positions in both yeast and human genomes are optimized for function.

1,135 ionomes reveal the global pattern of leaf and seed mineral nutrient and trace element diversity in Arabidopsis thaliana

Soil is a heterogeneous reservoir of essential elements needed for plant growth and development. Plants have evolved mechanisms to balance their nutritional needs based on availability of nutrients. This has led to genetically based variation in the elemental composition, the 'ionome', of plants, both within and between species. We explore this natural variation using a panel of wild-collected, geographically widespread Arabidopsis thaliana accessions from the 1001 Genomes Project including over 1,135 accessions, and the 19 parental accessions of the Multi-parent Advanced Generation Inter-Cross (MAGIC) panel, all with full-genome sequences available. We present an experimental design pipeline for high-throughput ionomic screenings and analyses with improved normalisation procedures to account for errors and variability in conditions often encountered in large-scale, high-throughput data collection. We report quantification of the complete leaf and seed ionome of the entire collection using this pipeline and a digital tool, Ion Explorer, to interact with the dataset. We describe the pattern of natural ionomic variation across the A. thaliana species and identify several accessions with extreme ionomic profiles. It forms a valuable resource for exploratory genetic mapping studies to identify genes underlying natural variation in leaf and seed ionome and genetic adaptation of plants to soil conditions.

A targeted RNAi screen identifies factors affecting diverse stages of receptor-mediated transcytosis

Transcytosis plays an important role in establishing cell polarity and in mediating transport of large cargo across epithelial barriers, but its molecular basis is unclear. Nelms et al. present a new dataset of genes involved in receptor-mediated transcytosis and show that the apical and basolateral recycling and transcytotic pathways are genetically separable.

Endosome transport by transcytosis is the primary mechanism by which proteins and other large cargo traverse epithelial barriers in normal tissue. Transcytosis is also essential for establishing and maintaining membrane polarity in epithelia and other polarized cells. To identify novel components of this pathway, we conducted a high-throughput RNA interference screen for factors necessary for the bidirectional transcytosis of IgG by the Fcγ receptor FcRn. This screen identified 23 genes whose suppression resulted in a reproducible decrease in FcRn-mediated transcytosis. Pulse-chase kinetic transport assays on four of the top-ranking genes (EXOC2, EXOC7, PARD6B, and LEPROT) revealed distinct effects on the apical and basolateral recycling and transcytotic pathways, demonstrating that these pathways are genetically separable. We also found a strong dependence on PARD6B for apical, but not basolateral, recycling, implicating this cell polarity gene in assembly or maintenance of the apical endosomal system. This dataset yields insights into how vesicular transport is adapted to the specialized functions of differentiated cell types and opens new research avenues into epithelial trafficking.

Genome-wide RNAi ionomics screen reveals new genes and regulation of human trace element metabolism

Trace elements are essential for human metabolism and dysregulation of their homoeostasis is associated with numerous disorders. Here we characterize mechanisms that regulate trace elements in human cells by designing and performing a genome-wide high-throughput siRNA/ionomics screen, and examining top hits in cellular and biochemical assays. The screen reveals high stability of the ionomes, especially the zinc ionome, and yields known regulators and novel candidates. We further uncover fundamental differences in the regulation of different trace elements. Specifically, selenium levels are controlled through the selenocysteine machinery and expression of abundant selenoproteins; copper balance is affected by lipid metabolism and requires machinery involved in protein trafficking and post-translational modifications; and the iron levels are influenced by iron import and expression of the iron/haeme-containing enzymes. Our approach can be applied to a variety of disease models and/or nutritional conditions, and the generated data set opens new directions for studies of human trace element metabolism.

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.

Large-scale plant ionomics

Large-scale phenotyping methods are at the heart of efficiently deciphering the functions of genes and gene networks in the postgenomic era. In order to obtain meaningful results when comparing natural variants, and mutants and wild-types during large-scale quantitative analyses, necessary precautions must be employed throughout the whole process. Here, we describe large-scale elemental profiling in Arabidopsis thaliana and other genetic model organisms using high-throughput analytical methodologies. We also include a description of workflow management and data storage systems.

High-resolution genome-wide scan of genes, gene-networks and cellular systems impacting the yeast ionome

Background

To balance the demand for uptake of essential elements with their potential toxicity living cells have complex regulatory mechanisms. Here, we describe a genome-wide screen to identify genes that impact the elemental composition (‘ionome’) of yeast Saccharomyces cerevisiae. Using inductively coupled plasma – mass spectrometry (ICP-MS) we quantify Ca, Cd, Co, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, P, S and Zn in 11890 mutant strains, including 4940 haploid and 1127 diploid deletion strains, and 5798 over expression strains.

Results

We identified 1065 strains with an altered ionome, including 584 haploid and 35 diploid deletion strains, and 446 over expression strains. Disruption of protein metabolism or trafficking has the highest likelihood of causing large ionomic changes, with gene dosage also being important. Gene over expression produced more extreme ionomic changes, but over expression and loss of function phenotypes are generally not related. Ionomic clustering revealed the existence of only a small number of possible ionomic profiles suggesting fitness tradeoffs that constrain the ionome. Clustering also identified important roles for the mitochondria, vacuole and ESCRT pathway in regulation of the ionome. Network analysis identified hub genes such as PMR1 in Mn homeostasis, novel members of ionomic networks such as SMF3 in vacuolar retrieval of Mn, and cross-talk between the mitochondria and the vacuole. All yeast ionomic data can be searched and downloaded at http://www.ionomicshub.org.

Conclusions

Here, we demonstrate the power of high-throughput ICP-MS analysis to functionally dissect the ionome on a genome-wide scale. The information this reveals has the potential to benefit both human health and agriculture.

Normalization and statistical analysis of multiplexed bead-based immunoassay data using mixed-effects modeling

Multiplexed bead-based flow cytometric immunoassays are a powerful experimental tool for investigating cellular communication networks, yet their widespread adoption is limited in part by challenges in robust quantitative analysis of the measurements. Here we report our application of mixed-effects modeling for the normalization and statistical analysis of bead-based immunoassay data. Our data set consisted of bead-based immunoassay measurements of 16 phospho-proteins in lysates of HepG2 cells treated with ligands that regulate acute-phase protein secretion. Mixed-effects modeling provided estimates for the effects of both the technical and biological sources of variance, and normalization was achieved by subtracting the technical effects from the measured values. This approach allowed us to detect ligand effects on signaling with greater precision and sensitivity and to more accurately characterize the HepG2 cell signaling network using constrained fuzzy logic. Mixed-effects modeling analysis of our data was vital for ascertaining that IL-1α and TGF-α treatment increased the activities of more pathways than IL-6 and TNF-α and that TGF-α and TNF-α increased p38 MAPK and c-Jun N-terminal kinase (JNK) phospho-protein levels in a synergistic manner. Moreover, we used mixed-effects modeling-based technical effect estimates to reveal the substantial variance contributed by batch effects along with the absence of loading order and assay plate position effects. We conclude that mixed-effects modeling enabled additional insights to be gained from our data than would otherwise be possible and we discuss how this methodology can play an important role in enhancing the value of experiments employing multiplexed bead-based immunoassays.