Dynamic rewiring of biological activity across genotype and lineage revealed by context-dependent functional interactions

On knowing a gene: A distributional hypothesis of gene function

As words can have multiple meanings that depend on sentence context, genes can have various functions that depend on the surrounding biological system. This pleiotropic nature of gene function is limited by ontologies, which annotate gene functions without considering biological contexts. We contend that the gene function problem in genetics may be informed by recent technological leaps in natural language processing, in which representations of word semantics can be automatically learned from diverse language contexts. In contrast to efforts to model semantics as "is-a" relationships in the 1990s, modern distributional semantics represents words as vectors in a learned semantic space and fuels current advances in transformer-based models such as large language models and generative pre-trained transformers. A similar shift in thinking of gene functions as distributions over cellular contexts may enable a similar breakthrough in data-driven learning from large biological datasets to inform gene function.

Cofitness network connectivity determines a fuzzy essential zone in open bacterial pangenome

Most in silico evolutionary studies commonly assumed that core genes are essential for cellular function, while accessory genes are dispensable, particularly in nutrient-rich environments. However, this assumption is seldom tested genetically within the pangenome context. In this study, we conducted a robust pangenomic Tn-seq analysis of fitness genes in a nutrient-rich medium for Sinorhizobium strains with a canonical open pangenome. To evaluate the robustness of fitness category assignment, Tn-seq data for three independent mutant libraries per strain were analyzed by three methods, which indicates that the Hidden Markov Model (HMM)-based method is most robust to variations between mutant libraries and not sensitive to data size, outperforming the Bayesian and Monte Carlo simulation-based methods. Consequently, the HMM method was used to classify the fitness category. Fitness genes, categorized as essential (ES), advantage (GA), and disadvantage (GD) genes for growth, are enriched in core genes, while nonessential genes (NE) are over-represented in accessory genes. Accessory ES/GA genes showed a lower fitness effect than core ES/GA genes. Connectivity degrees in the cofitness network decrease in the order of ES, GD, and GA/NE. In addition to accessory genes, 1599 out of 3284 core genes display differential essentiality across test strains. Within the pangenome core, both shared quasi-essential (ES and GA) and strain-dependent fitness genes are enriched in similar functional categories. Our analysis demonstrates a considerable fuzzy essential zone determined by cofitness connectivity degrees in Sinorhizobium pangenome and highlights the power of the cofitness network in understanding the genetic basis of ever-increasing prokaryotic pangenome data.

CancerGATE: Prediction of cancer-driver genes using graph attention autoencoders

Discovery of the cancer type specific-driver genes is important for understanding the molecular mechanisms of each cancer type and for providing proper treatment. Recently, graph deep learning methods became widely used in finding cancer-driver genes. However, previous methods had limited performance in individual cancer types due to a small number of cancer-driver genes used in training and biases toward the cancer-driver genes used in training the models. Here, we introduce a novel pipeline, CancerGATE that predicts the cancer-driver genes using graph attention autoencoder (GATE) to learn in a self-supervised manner and can be applied to each of the cancer types. CancerGATE utilizes biological network topology and multi-omics data from 15 types of cancer of 20,079 samples from the cancer genome atlas (TCGA). Attention coefficients calculated in the model are used to prioritize cancer-driver genes by comparing coefficients of cancer and normal contexts. CancerGATE shows a higher AUPRC with a difference ranging from 1.5 % to 36.5 % compared to the previous graph deep learning models in each cancer type. We also show that CancerGATE is free from the bias toward cancer-driver genes used in training, revealing mechanisms of the cancer-driver genes in specific cancer types. Finally, we propose novel cancer-driver gene candidates that could be therapeutic targets for specific cancer types.

A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization

Genetic screens in cancer cell lines inform gene function and drug discovery. More comprehensive screen datasets with multi-omics data are needed to enhance opportunities to functionally map genetic vulnerabilities. Here, we construct a second-generation map of cancer dependencies by annotating 930 cancer cell lines with multi-omic data and analyze relationships between molecular markers and cancer dependencies derived from CRISPR-Cas9 screens. We identify dependency-associated gene expression markers beyond driver genes, and observe many gene addiction relationships driven by gain of function rather than synthetic lethal effects. By combining clinically informed dependency-marker associations with protein-protein interaction networks, we identify 370 anti-cancer priority targets for 27 cancer types, many of which have network-based evidence of a functional link with a marker in a cancer type. Mapping these targets to sequenced tumor cohorts identifies tractable targets in different cancer types. This target prioritization map enhances understanding of gene dependencies and identifies candidate anti-cancer targets for drug development.

Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks

The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.

Robust, scalable, and informative clustering for diverse biological networks

Clustering molecular data into informative groups is a primary step in extracting robust conclusions from big data. However, due to foundational issues in how they are defined and detected, such clusters are not always reliable, leading to unstable conclusions. We compare popular clustering algorithms across thousands of synthetic and real biological datasets, including a new consensus clustering algorithm-SpeakEasy2: Champagne. These tests identify trends in performance, show no single method is universally optimal, and allow us to examine factors behind variation in performance. Multiple metrics indicate SpeakEasy2 generally provides robust, scalable, and informative clusters for a range of applications.

Comparative landscape of genetic dependencies in human and chimpanzee stem cells

Comparative studies of great apes provide a window into our evolutionary past, but the extent and identity of cellular differences that emerged during hominin evolution remain largely unexplored. We established a comparative loss-of-function approach to evaluate whether human cells exhibit distinct genetic dependencies. By performing genome-wide CRISPR interference screens in human and chimpanzee pluripotent stem cells, we identified 75 genes with species-specific effects on cellular proliferation. These genes comprised coherent processes, including cell-cycle progression and lysosomal signaling, which we determined to be human-derived by comparison with orangutan cells. Human-specific robustness to CDK2 and CCNE1 depletion persisted in neural progenitor cells and cerebral organoids, supporting the G1-phase length hypothesis as a potential evolutionary mechanism in human brain expansion. Our findings demonstrate that evolutionary changes in human cells reshaped the landscape of essential genes and establish a platform for systematically uncovering latent cellular and molecular differences between species.

Optimized whole-genome CRISPR interference screens identify ARID1A-dependent growth regulators in human induced pluripotent stem cells

Perturbing expression is a powerful way to understand the role of individual genes, but can be challenging in important models. CRISPR-Cas screens in human induced pluripotent stem cells (iPSCs) are of limited efficiency due to DNA break-induced stress, while the less stressful silencing with an inactive Cas9 has been considered less effective so far. Here, we developed the dCas9-KRAB-MeCP2 fusion protein for screening in iPSCs from multiple donors. We found silencing in a 200 bp window around the transcription start site in polyclonal pools to be as effective as using wild-type Cas9 for identifying essential genes, but with much reduced cell numbers. Whole-genome screens to identify ARID1A-dependent dosage sensitivity revealed the PSMB2 gene, and enrichment of proteasome genes among the hits. This selective dependency was replicated with a proteasome inhibitor, indicating a targetable drug-gene interaction. Many more plausible targets in challenging cell models can be efficiently identified with our approach.

Comparative landscape of genetic dependencies in human and chimpanzee stem cells

Comparative studies of great apes provide a window into our evolutionary past, but the extent and identity of cellular differences that emerged during hominin evolution remain largely unexplored. We established a comparative loss-of-function approach to evaluate whether changes in human cells alter requirements for essential genes. By performing genome-wide CRISPR interference screens in human and chimpanzee pluripotent stem cells, we identified 75 genes with species-specific effects on cellular proliferation. These genes comprised coherent processes, including cell cycle progression and lysosomal signaling, which we determined to be human-derived by comparison with orangutan cells. Human-specific robustness to CDK2 and CCNE1 depletion persisted in neural progenitor cells, providing support for the G1-phase length hypothesis as a potential evolutionary mechanism in human brain expansion. Our findings demonstrate that evolutionary changes in human cells can reshape the landscape of essential genes and establish a platform for systematically uncovering latent cellular and molecular differences between species.

Optimal construction of a functional interaction network from pooled library CRISPR fitness screens

BACKGROUND

Functional interaction networks, where edges connect genes likely to operate in the same biological process or pathway, can be inferred from CRISPR knockout screens in cancer cell lines. Genes with similar knockout fitness profiles across a sufficiently diverse set of cell line screens are likely to be co-functional, and these "coessentiality" networks are increasingly powerful predictors of gene function and biological modularity. While several such networks have been published, most use different algorithms for each step of the network construction process.

RESULTS

In this study, we identify an optimal measure of functional interaction and test all combinations of options at each step-essentiality scoring, sample variance and covariance normalization, and similarity measurement-to identify best practices for generating a functional interaction network from CRISPR knockout data. We show that Bayes Factor and Ceres scores give the best results, that Ceres outperforms the newer Chronos scoring scheme, and that covariance normalization is a critical step in network construction. We further show that Pearson correlation, mathematically identical to ordinary least squares after covariance normalization, can be extended by using partial correlation to detect and amplify signals from "moonlighting" proteins which show context-dependent interaction with different partners.

CONCLUSIONS

We describe a systematic survey of methods for generating coessentiality networks from the Cancer Dependency Map data and provide a partial correlation-based approach for exploring context-dependent interactions.