Indirect inference of synergistic and alternative signalling of intracellular pathways

In this work we introduce Boolean Nested Effects Models (B-NEM). B-NEM combines the indirect inference on signalling pathways of the original Nested Effects Models (NEM) with the logical modelling of the CellNet Optimizer. The pathway molecules are called signalling genes (S-genes). These S-genes can be perturbed, e.g. stimulated or inhibited. These perturbations effect the transcription and production of mRNA (gene expression) in the cell nucleus. The mRNA products respectively the effected genes are called E-genes. From a subset relationship between sets of E-genes we can make inference on the signalling pathway. For example if the E-genes reacting to the inhibition of S-gene B are a noisy subset of the E-genes reacting to the inhibition of A, we conclude, that A is upstream of B. We extend NEM by replacing the upstream-downstream relationship of S-genes with boolean functions. For example if S-genes A and B are both needed for the signal to be propagated to C, we call that an AND-gate. E-genes, whcih react to the inhibition of C also reacto to the inhibition of A or the inhibition of B. If the signal can independently be propagated to C via A or B, we call that an OR-gate. This time the E-genes do not react to the single inhibitions of A or B, but to the double inhibition of both. We successfully apply B-NEM to simulated data and three different cancer datasets

Single cell network analysis with a mixture of Nested Effects Models

Abstract Motivation New technologies allow for the elaborate measurement of different traits of single cells under genetic perturbations. These interventional data promise to elucidate intra-cellular networks in unprecedented detail and further help to improve treatment of diseases like cancer. However, cell populations can be very heterogeneous. Results We developed a mixture of Nested Effects Models (M&NEM) for single-cell data to simultaneously identify different cellular subpopulations and their corresponding causal networks to explain the heterogeneity in a cell population. For inference, we assign each cell to a network with a certain probability and iteratively update the optimal networks and cell probabilities in an Expectation Maximization scheme. We validate our method in the controlled setting of a simulation study and apply it to three data sets of pooled CRISPR screens generated previously by two novel experimental techniques, namely Crop-Seq and Perturb-Seq. Availability and implementation The mixture Nested Effects Model (M&NEM) is available as the R-package mnem at https://github.com/cbg-ethz/mnem/. Supplementary information Supplementary data are available at Bioinformatics online

Inferring perturbation profiles of cancer samples

Motivation Cancer is one of the most prevalent diseases in the world. Tumors arise due to important genes changing their activity, e.g. when inhibited or over-expressed. But these gene perturbations are difficult to observe directly. Molecular profiles of tumors can provide indirect evidence of gene perturbations. However, inferring perturbation profiles from molecular alterations is challenging due to error-prone molecular measurements and incomplete coverage of all possible molecular causes of gene perturbations. Results We have developed a novel mathematical method to analyze cancer driver genes and their patient-specific perturbation profiles. We combine genetic aberrations with gene expression data in a causal network derived across patients to infer unobserved perturbations. We show that our method can predict perturbations in simulations, CRISPR perturbation screens and breast cancer samples from The Cancer Genome Atlas.ISSN:1367-4803ISSN:1460-205

Single cell network analysis with a mixture of Nested Effects Models

Motivation New technologies allow for the elaborate measurement of different traits of single cells under genetic perturbations. These interventional data promise to elucidate intra-cellular networks in unprecedented detail and further help to improve treatment of diseases like cancer. However, cell populations can be very heterogeneous. Results We developed a mixture of Nested Effects Models (M&NEM) for single-cell data to simultaneously identify different cellular subpopulations and their corresponding causal networks to explain the heterogeneity in a cell population. For inference, we assign each cell to a network with a certain probability and iteratively update the optimal networks and cell probabilities in an Expectation Maximization scheme. We validate our method in the controlled setting of a simulation study and apply it to three data sets of pooled CRISPR screens generated previously by two novel experimental techniques, namely Crop-Seq and Perturb-Seq. Availability and implementation The mixture Nested Effects Model (M&NEM) is available as the R-package mnem at https://github.com/cbg-ethz/mnemISSN:1367-4803ISSN:1460-205

Analyzing synergistic and non-synergistic interactions in signalling pathways using Boolean Nested Effect Models

Motivation: Understanding the structure and interplay of cellular signalling pathways is one of the great challenges in molecular biology. Boolean Networks can infer signalling networks from observations of protein activation. In situations where it is difficult to assess protein activation directly, Nested Effect Models are an alternative. They derive the network structure indirectly from downstream effects of pathway perturbations. To date, Nested Effect Models cannot resolve signalling details like the formation of signalling complexes or the activation of proteins by multiple alternative input signals. Here we introduce Boolean Nested Effect Models (B-NEM). B-NEMs combine the use of downstream effects with the higher resolution of signalling pathway structures in Boolean Networks. Results: We show that B-NEMs accurately reconstruct signal flows in simulated data. Using B-NEM we then resolve BCR signalling via PI3K and TAK1 kinases in BL2 lymphoma cell lines

Identifying cancer pathway dysregulations using differential causal effects

Motivation Signaling pathways control cellular behavior. Dysregulated pathways, for example, due to mutations that cause genes and proteins to be expressed abnormally, can lead to diseases, such as cancer. Results We introduce a novel computational approach, called Differential Causal Effects (dce), which compares normal to cancerous cells using the statistical framework of causality. The method allows to detect individual edges in a signaling pathway that are dysregulated in cancer cells, while accounting for confounding. Hence, technical artifacts have less influence on the results and dce is more likely to detect the true biological signals. We extend the approach to handle unobserved dense confounding, where each latent variable, such as, for example, batch effects or cell cycle states, affects many covariates. We show that dce outperforms competing methods on synthetic datasets and on CRISPR knockout screens. We validate its latent confounding adjustment properties on a GTEx (Genotype–Tissue Expression) dataset. Finally, in an exploratory analysis on breast cancer data from TCGA (The Cancer Genome Atlas), we recover known and discover new genes involved in breast cancer progression. Availability and implementation The method dce is freely available as an R package on Bioconductor (https://bioconductor.org/packages/release/bioc/html/dce.html) as well as on https://github.com/cbg-ethz/dce. The GitHub repository also contains the Snakemake workflows needed to reproduce all results presented here.ISSN:1367-4803ISSN:1460-205

One Hand Clapping: detection of condition-specific transcription factor interactions from genome-wide gene activity data

We present One Hand Clapping (OHC), a method for the detection of condition-specific interactions between transcription factors (TFs) from genome-wide gene activity measurements. OHC is based on a mapping between transcription factors and their target genes. Given a single case-control experiment, it uses a linear regression model to assess whether the common targets of two arbitrary TFs behave differently than expected from the genes targeted by only one of the TFs. When applied to osmotic stress data in S. cerevisiae, OHC produces consistent results across three types of expression measurements: gene expression microarray data, RNA Polymerase II ChIP-chip binding data and messenger RNA synthesis rates. Among the eight novel, condition-specific TF pairs, we validate the interaction between Gcn4p and Arr1p experimentally. We apply OHC to a large gene activity dataset in S. cerevisiae and provide a compendium of condition-specific TF interactions

dCas9-mediated dysregulation of gene expression in human induced pluripotent stem cells during primitive streak differentiation

CRISPR-based systems have fundamentally transformed our ability to study and manipulate stem cells. We explored the possibility of using catalytically dead Cas9 (dCas9) from S. pyogenes as a platform for targeted epigenetic editing in stem cells to enhance the expression of the eomesodermin gene (EOMES) during differentiation. We observed, however, that the dCas9 protein itself exerts a potential non-specific effect in hiPSCs, affecting the cell's phenotype and gene expression patterns during subsequent directed differentiation. We show that this effect is specific to the condition when cells are cultured in medium that does not actively maintain the pluripotency network, and that the sgRNA-free apo-dCas9 protein itself influences endogenous gene expression. Transcriptomics analysis revealed that a significant number of genes involved in developmental processes and various other genes with non-overlapping biological functions are affected by dCas9 overexpression. This suggests a potential adverse phenotypic effect of dCas9 itself in hiPSCs, which could have implications for when and how CRISPR/Cas9-based tools can be used reliably and safely in pluripotent stem cells.ISSN:1096-7176ISSN:1096-718

Viral Load Dynamics in SARS-CoV-2 Omicron Breakthrough Infections

To determine viral dynamics in Omicron breakthrough infections, we measured severe acute respiratory syndrome coronavirus 2 RNA in 206 double-vaccinated or boostered individuals. During the first 3 days following the onset of symptoms, viral loads were significantly higher (cycle threshold [Ct], 21.76) in vaccinated compared to boostered (Ct, 23.14) individuals (P = .029). However, by performing a longitudinal analysis on 32 individuals over 14 days, no difference in the viral load trajectory was observed between double-vaccinated and boostered patients. Our results indicate that booster immunization results in a reduction in detectable viral loads without significantly changing viral load dynamics over time. This study provides longitudinal data upon Omicron breakthrough infections in double-vaccinated and boostered individuals. While peak viral loads were reduced in boostered individuals, no difference was observed for the duration of SARS-CoV-2 detection between both groups