Gene Perturbation Research Articles

Dissecting cell identity via network inference and in silico gene perturbation

Cell identity is governed by the complex regulation of gene expression, represented as gene-regulatory networks1. Here we use gene-regulatory networks inferred from single-cell multi-omics data to perform in silico transcription factor perturbations, simulating the consequent changes in cell identity using only unperturbed wild-type data. We apply this machine-learning-based approach, CellOracle, to well-established paradigms—mouse and human haematopoiesis, and zebrafish embryogenesis—and we correctly model reported changes in phenotype that occur as a result of transcription factor perturbation. Through systematic in silico transcription factor perturbation in the developing zebrafish, we simulate and experimentally validate a previously unreported phenotype that results from the loss of noto, an established notochord regulator. Furthermore, we identify an axial mesoderm regulator, lhx1a. Together, these results show that CellOracle can be used to analyse the regulation of cell identity by transcription factors, and can provide mechanistic insights into development and differentiation.

Improved drug response prediction by drug target data integration via network-based profiling.

Drug response prediction (DRP) is important for precision medicine to predict how a patient would react to a drug before administration. Existing studies take the cell line transcriptome data, and the chemical structure of drugs as input and predict drug response as IC50 or AUC values. Intuitively, use of drug target interaction (DTI) information can be useful for DRP. However, use of DTI is difficult because existing drug response database such as CCLE and GDSC do not have information about transcriptome after drug treatment. Although transcriptome after drug treatment is not available, if we can compute the perturbation effects by the pharmacologic modulation of target gene, we can utilize the DTI information in CCLE and GDSC. In this study, we proposed a framework that can improve existing deep learning-based DRP models by effectively utilizing drug target information. Our framework includes NetGP, a module to compute gene perturbation scores by the network propagation technique on a network. NetGP produces genes in a ranked list in terms of gene perturbation scores and the ranked genes are input to a multi-layer perceptron to generate a fixed dimension vector for the integration with existing DRP models. This integration is done in a model-agnostic way so that any existing DRP tool can be incorporated. As a result, our framework boosts the performance of existing DRP models, in 64 of 72 comparisons. The performance gains are larger especially for test scenarios with samples with unseen drugs by large margins up to 34% in Pearson's correlation coefficient.

Leveraging optogenetics for CRISPRi screens in human iPSC-cardiomyocytes.

New gene modulation methods, e.g., interference or activation CRISPR (CRISPRi/a), are deployable in a highly parallel format and allow selective perturbation of genes of interest in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). When combined with high-throughput (HT) methods for quantitative assessment of cellular responses, they provide new means for functional genomics in human heart cells and engineered tissues for translational studies. Here we demonstrate how all-optical electrophysiology, in part fueled by optogenetics for actuation and/or sensing of functional responses, can be used in conjunction with CRISPRi modulation to yield a HT platform. Our recent studies explore the possibility to use CRISPRi in post-differentiated hiPSC-CMs to selectively perturb key genes underlying cardiac electrophysiology. We validated the use of our all-optical technology for HT analysis as part of a comprehensive characterization pipeline to link electromechanical functional outputs to molecular correlates, identified by CRISPRi perturbation in hiPSC-CMs. The approach entails the short-term genetic engineering of already differentiated cells and the validation of a pipeline for mRNA or protein analysis post-functional assessment, along with correlative and dimension-reduction studies. Our results demonstrate proof of principle utility of optical methods as partner technology to CRISPRi perturbations that yield mild but specific functional changes relevant to cardiac electromechanics and remodeling during disease. This technology can be useful for multiparametric assessment of cardiotoxicity and drug testing in human cells.

Comparison of efficacy and functional consequences of siRNA and CRISPRi gene modulation with different effector domains in post-differentiated human iPSC-CMs.

Current models for cardiotoxicity screening are limited in their ability to only evaluate hERG inhibition and related prediction of QT prolongation and torsadoegnic potential. More comprehensive cardiotoxicity assays are being developed using in vitro platforms with human-induced pluripotent stem-cell-derived cardiomyocytes (iPSC-CMs), which are further aided by experimental and computational approaches. CRISPRi gene perturbation is a powerful source for human functional genomics in the cardiac field as it can link genes to electrophysiological phenotype. We developed a high-throughput system involving all-optical cardiac electrophysiology in 96-well format and CRISPRi gene modulation in post-differentiated iPSC-CMs. Single guide RNAs (sgRNAs) were tested targeting around the transcription start sites of KCNH2, an ion channel responsible for the repolarization of the cardiac action potential. Optogenetic pacing and spectrally-compatible voltage and calcium sensors were used to obtain functional measurements for voltage and calcium responses. The efficacy and functional consequences of gene modulation by siRNA and CRISPRi with two effector domains (dCas9-KRAB and dCas9-Zim3) were compared. We found that adenoviral transduction with a recently developed dCas9-Zim3, induced the most robust APD prolongation (+20%, p<0.0001) in the hiPSC-CMs upon KCNH2 knockdown when compared to using siRNA (+11%, p=0.0002) and transfection of an inducible-dCas9-KRAB (+<10%, p=0.02). Knockdown of hERG in our system yielded mild but specific functional changes which can be combined with computational approaches using dimension reduction approaches to help visualize and quantify electrophysiological changes in the heart. Usage of this platform for CRISPRi mediated knockdown of diseases-associated genes in pre-differentiated cardiomyocytes can improve the assessment of gene function in cellular cardiac electrophysiology.

Single-cell RNA binding protein regulatory network analyses reveal oncogenic HNRNPK-MYC signalling pathway in cancer

RNA-binding proteins (RBPs) are key players of gene expression and perturbations of RBP-RNA regulatory network have been observed in various cancer types. Here, we propose a computational method, RBPreg, to identify the RBP regulators by integration of single cell RNA-Seq (N = 233,591) and RBP binding data. Pan-cancer analyses suggest that RBP regulators exhibit cancer and cell specificity and perturbations of RBP regulatory network are involved in cancer hallmark-related functions. We prioritize an oncogenic RBP-HNRNPK, which is highly expressed in tumors and associated with poor prognosis of patients. Functional assays performed in cancer cells reveal that HNRNPK promotes cancer cell proliferation, migration, and invasion in vitro and in vivo. Mechanistic investigations further demonstrate that HNRNPK promotes tumorigenesis and progression by directly binding to MYC and perturbed the MYC targets pathway in lung cancer. Our results provide a valuable resource for characterizing RBP regulatory networks in cancer, yielding potential biomarkers for precision medicine.

A Suite of New Strain Construction Vectors for Gene Expression Knockdown in Budding Yeast.

Numerous tools for gene expression knockdown have been developed and characterized in the model organism Saccharomyces cerevisiae and extended to facilitate studies in multicellular models. To comparatively evaluate the efficacy of these approaches, we systematically applied seven such published constitutive and inducible knockdown strategies to a panel of essential genes encoding nuclear-localized proteins. In this effort, we created the CEAS (C-SWAT for Essential Allele Strains) collection, a suite of tagging vectors for improved utility and ease of strain construction. Of particular note, we adapted an improved auxin inducible degron (AID) protein degradation strategy previously available only in mammalian tissue culture for one-step strain construction in budding yeast by leveraging both the C-SWAT system and CRISPR/Cas9 editing. Taken together, this work presents a toolbox for endogenous gene expression knockdown and allows us to make recommendations on the efficacy and applicability of these tools for the perturbation of essential genes.

CRISPR-screen identifies ZIP9 and dysregulated Zn2+ homeostasis as a cause of cancer-associated changes in glycosylation.

In epithelial cancers, truncated O-glycans, such as the Thomson-nouveau antigen (Tn) and its sialylated form (STn), are upregulated on the cell surface and associated with poor prognosis and immunological escape. Recent studies have shown that these carbohydrate epitopes facilitate cancer development and can be targeted therapeutically; however, the mechanism underpinning their expression remains unclear. To identify genes directly influencing the expression of cancer-associated O-glycans, we conducted an unbiased, positive-selection, whole-genome CRISPR knockout-screen using monoclonal antibodies against Tn and STn. We show that knockout of the Zn2+-transporter SLC39A9 (ZIP9), alongside the well-described targets C1GALT1 (C1GalT1) and its molecular chaperone, C1GALT1C1 (COSMC), results in surface-expression of cancer-associated O-glycans. No other gene perturbations were found to reliably induce O-glycan truncation. We furthermore show that ZIP9 knockout affects N-linked glycosylation, resulting in upregulation of oligo-mannose, hybrid-type, and α2,6-sialylated structures as well as downregulation of tri- and tetra-antennary structures. Finally, we demonstrate that accumulation of Zn2+ in the secretory pathway coincides with cell-surface presentation of truncated O-glycans in cancer tissue, and that over-expression of COSMC mitigates such changes. Collectively, the findings show that dysregulation of ZIP9 and Zn2+ induces cancer-like glycosylation on the cell surface by affecting the glycosylation machinery.

An optimized Tet-On system for conditional control of gene expression in sea urchins.

Sea urchins and other echinoderms are important experimental models for studying developmental processes. The lack of approaches for conditional gene perturbation, however, has made it challenging to investigate the late developmental functions of genes that have essential roles during early embryogenesis and genes that have diverse functions in multiple tissues. The doxycycline-controlled Tet-On system is a widely used molecular tool for temporally and spatially regulated transgene expression. Here, we optimized the Tet-On system to conditionally induce gene expression in sea urchin embryos. Using this approach, we explored the roles the MAPK signaling plays in skeletogenesis by expressing genes that perturb the pathway specifically in primary mesenchyme cells during later stages of development. We demonstrated the wide utility of the Tet-On system by applying it to a second sea urchin species and in cell types other than the primary mesenchyme cells. Our work provides a robust and flexible platform for the spatiotemporal regulation of gene expression in sea urchins, which will considerably enhance the utility of this prominent model system.

Inferring Gene Regulatory Networks and Predicting the Effect of Gene Perturbations via IQCELL.

IQCELL is a platform that infers Boolean gene regulatory networks from single-cell RNA sequencing data. Boolean networks can be simulated under normal and perturbed conditions. In this chapter, we provide a detailed guideline for implementing IQCELL from a raw dataset. The steps include processing data, inferring informative genes, inferring gene regulatory network, and simulating the resulted network under normal and perturbed conditions.

An integrated analysis of the cancer genome atlas data discovers a hierarchical association structure across thirty three cancer types.

Cancer cells harbor molecular alterations at all levels of information processing. Genomic/epigenomic and transcriptomic alterations are inter-related between genes, within and across cancer types and may affect clinical phenotypes. Despite the abundant prior studies of integrating cancer multi-omics data, none of them organizes these associations in a hierarchical structure and validates the discoveries in extensive external data. We infer this Integrated Hierarchical Association Structure (IHAS) from the complete data of The Cancer Genome Atlas (TCGA) and compile a compendium of cancer multi-omics associations. Intriguingly, diverse alterations on genomes/epigenomes from multiple cancer types impact transcriptions of 18 Gene Groups. Half of them are further reduced to three Meta Gene Groups enriched with (1) immune and inflammatory responses, (2) embryonic development and neurogenesis, (3) cell cycle process and DNA repair. Over 80% of the clinical/molecular phenotypes reported in TCGA are aligned with the combinatorial expressions of Meta Gene Groups, Gene Groups, and other IHAS subunits. Furthermore, IHAS derived from TCGA is validated in more than 300 external datasets including multi-omics measurements and cellular responses upon drug treatments and gene perturbations in tumors, cancer cell lines, and normal tissues. To sum up, IHAS stratifies patients in terms of molecular signatures of its subunits, selects targeted genes or drugs for precision cancer therapy, and demonstrates that associations between survival times and transcriptional biomarkers may vary with cancer types. These rich information is critical for diagnosis and treatments of cancers.

Type I interferon signaling in SARS-CoV-2 associated neurocognitive disorder (SAND): Mapping host-virus interactions to an etiopathogenesis.

Epidemiological, clinical, and radiological studies have provided insights into the phenomenology and biological basis of cognitive impairment in COVID-19 survivors. Furthermore, its association with biomarkers associated with neuroinflammation and neurodegeneration supports the notion that it is a distinct aspect of LongCOVID syndrome with specific underlying biology. Accounting for the latter, translational studies on SARS-CoV-2's interactions with its hosts have provided evidence on type I interferon dysregulation, which is seen in neuroinflammatory and neurodegenerative diseases. To date, studies attempting to describe this overlap have only described common mechanisms. In this manuscript, we attempt to propose a mechanistic model based on the host-virus interaction hypothesis. We discuss the molecular basis for a SARS-CoV-2-associated neurocognitive disorder (SAND) focusing on specific genes and pathways with potential mechanistic implications, several of which have been predicted by Vavougios and their research group. Furthermore, our hypothesis links translational evidence on interferon-responsive gene perturbations introduced by SARS-CoV-2 and known dysregulated pathways in dementia. Discussion emphasizes the crosstalk between central and peripheral immunity via danger-associated molecular patterns in inducing SAND's emergence in the absence of neuroinfection. Finally, we outline approaches to identifying targets that are both testable and druggable, and could serve in the design of future clinical and translational studies.

Pan-cancer illumination of TRIM gene family reveals immunology regulation and potential therapeutic implications

BackgroundThe tripartite motif (TRIM) proteins function as important regulators in innate immunity, tumorigenesis, cell differentiation and ontogenetic development. However, we still lack knowledge about the genetic and transcriptome alterations landscape of TRIM proteins across cancer types.MethodsWe comprehensively reviewed and characterized the perturbations of TRIM genes across > 10,000 samples across 33 cancer types. Genetic mutations and transcriptome of TRIM genes were analyzed by diverse computational methods. A TRIMs score index was calculated based on the expression of TRIM genes. The correlation between TRIMs scores and clinical associations, immune cell infiltrations and immunotherapy response were analyzed by correlation coefficients and gene set enrichment analysis.ResultsAlterations in TRIM genes and protein levels frequently emerge in a wide range of tumors and affect expression of TRIM genes. In particular, mutations located in domains are likely to be deleterious mutations. Perturbations of TRIM genes are correlated with expressions of immune checkpoints and immune cell infiltrations, which further regulated the cancer- and immune-related pathways. Moreover, we proposed a TRIMs score index, which can accurately predict the clinical outcome of cancer patients. TRIMs scores of patients are correlated with clinical survival and immune therapy response across cancer types. Identifying the TRIM genes with genetic and transcriptome alterations will directly contribute to cancer therapy in the context of predictive, preventive, and personalized medicine.ConclusionsOur study provided a comprehensive analysis and resource for guiding both mechanistic and therapeutic analyses of the roles of TRIM genes in cancer.

Comparison of apical and basolateral Cu treatment for iron-related gene regulation during deferoxamine induced iron deficiency.

Intestinal copper transporter (Atp7a) mutant-brindled mice with systemic Cu deficiency had elevated Cu levels in enterocyte cells without any perturbation of iron-regulating genes, suggesting that blood Cu level might be important for intestinal iron homeostasis during iron deficiency (ID). We hypothesized that the blood Cu level and polarization (apical and basolateral) of enterocyte cells might be important regulators for the compensatory response on the regulation of genes in enterocyte cells during iron deficiency. We grew Caco-2 cells on a bicameral cell culture plate to mimic the human intestine system and on a regular tissue culture plate. Iron deficiency was induced by deferoxamine (DFO). The cells were treated with Cu and Cu with Fe following mRNA expressions of DMT1, FPN, TFR, and ANKRD37 were analyzed. Our main finding was that basolateral treatment of Cu significantly reduced mRNA expressions of iron-regulated genes, including DMT1, FPN, TFR, and ANKRD37, compared to DFO-treated and DFO with apical Cu-treated groups in both bicameral and regular tissue culture plates. Cu level in the basolateral side of Caco-2 cells significantly influenced the intracellular gene regulation in DFO-induced iron-deficient condition, and polarization of the cells might be important factor gene regulation in enterocyte cells.

A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer

Cancer is a heterogeneous disease mainly driven by abnormal gene perturbations in regulatory networks. Therefore, it is appealing to identify the common and specific perturbed genes from multiple cancer networks. We developed an integrative network medicine approach to identify novel biomarkers and investigate drug repurposing across cancer types. We used a network-based method to prioritize genes in cancer-specific networks reconstructed using human transcriptome and interactome data. The prioritized genes show extensive perturbation and strong regulatory interaction with other highly perturbed genes, suggesting their vital contribution to tumorigenesis and tumor progression, and are therefore regarded as cancer genes. The cancer genes detected show remarkable performances in discriminating tumors from normal tissues and predicting survival times of cancer patients. Finally, we developed a network proximity approach to systematically screen drugs and identified dozens of candidates with repurposable potential in several cancer types. Taken together, we demonstrated the power of the network medicine approach to identify novel biomarkers and repurposable drugs in multiple cancer types. We have also made the data and code freely accessible to ensure reproducibility and reusability of the developed computational workflow.

TEMPO enables sequential genetic labeling and manipulation of vertebrate cell lineages

During development, regulatory factors appear in a precise order to determine cell fates over time. Consequently, to investigate complex tissue development, it is necessary to visualize and manipulate cell lineages with temporal control. Current strategies for tracing vertebrate cell lineages lack genetic access to sequentially produced cells. Here, we present TEMPO (Temporal Encoding and Manipulation in a Predefined Order), an imaging-readable genetic tool allowing differential labeling and manipulation of consecutive cell generations in vertebrates. TEMPO is based on CRISPR and powered by a cascade of gRNAs that drive orderly activation and inactivation of reporters and/or effectors. Using TEMPO to visualize zebrafish and mouse neurogenesis, we recapitulated birth-order-dependent neuronal fates. Temporally manipulating cell-cycle regulators in mouse cortex progenitors altered the proportion and distribution of neurons and glia, revealing the effects of temporal gene perturbation on serial cell fates. Thus, TEMPO enables sequential manipulation of molecular factors, crucial to study cell-type specification.

Efficient Gene Knockout in Salivary Gland Epithelial Explant Cultures

We have developed methods to achieve efficient CRISPR-Cas9–mediated gene knockout in ex vivo mouse embryonic salivary epithelial explants. Salivary epithelial explants provide a valuable model for characterizing cell signaling, differentiation, and epithelial morphogenesis, but research has been limited by a paucity of efficient gene perturbation methods. Here, we demonstrate highly efficient gene perturbation by transient transduction of guide RNA–expressing lentiviruses into Cas9-expressing salivary epithelial buds isolated from Cas9 transgenic mice. We first show that salivary epithelial explants can be cultured in low-concentration, nonsolidified Matrigel suspensions in 96-well plates, which greatly increases sample throughput compared to conventional cultures embedded in solidified Matrigel. We further show that salivary epithelial explants can grow and branch with FGF7 alone, while supplementing with insulin, transferrin, and selenium (ITS) enhances growth and branching. We then describe an efficient workflow to produce experiment-ready, high-titer lentiviruses within 1 wk after molecular cloning. To track transduced cells, we designed the lentiviral vector to coexpress a nuclear fluorescent reporter with the guide RNA. We routinely achieved 80% transduction efficiency when antibiotic selection was used. Importantly, we detected robust loss of targeted protein products when testing 9 guide RNAs for 3 different genes. Moreover, targeting the β1 integrin gene (Itgb1) inhibited branching morphogenesis, which supports the importance of cell–matrix adhesion in driving branching morphogenesis. In summary, we have established a lentivirus-based method that can efficiently perturb genes of interest in salivary epithelial explants, which will greatly facilitate studies of specific gene functions using this system.

Spatially resolved gene regulatory and disease-related vulnerability map of the adult Macaque cortex

Single cell approaches have increased our knowledge about the cell type composition of the non-human primate (NHP), but a detailed characterization of area-specific regulatory features remains outstanding. We generated single-cell transcriptomic and chromatin accessibility (single-cell ATAC) data of 358,237 cells from prefrontal cortex (PFC), primary motor cortex (M1) and primary visual cortex (V1) of adult female cynomolgus monkey brain, and integrated this dataset with Stereo-seq (spatial enhanced resolution omics-sequencing) of the corresponding cortical areas to assign topographic information to molecular states. We identified area-specific chromatin accessible sites and their targeted genes, including the cell type-specific transcriptional regulatory network associated with excitatory neurons heterogeneity. We reveal calcium ion transport and axon guidance genes related to specialized functions of PFC and M1, identified the similarities and differences between adult macaque and human oligodendrocyte trajectories, and mapped the genetic variants and gene perturbations of human diseases to NHP cortical cells. This resource establishes a transcriptomic and chromatin accessibility combinatory regulatory landscape at a single-cell and spatially resolved resolution in NHP cortex.

Over expression of mTOR gene predicts overall survival in myelodysplastic syndromes.

Myelodysplastic syndromes (MDS) is defined as heterogenous disease, it contains heterogenous leukemic stem cells with various degree of cell differentiation. The perturbation of genes involved in myeloid progenitor cell growth, differentiation and proliferation lead to morphologic dysplasia, maturation arrest, ineffective hematopoiesis hence the cytopenias and propensity to develop into acute myeloid leukemia (AML). Heterogeneous subsets of MDS patients have been defined by their clinical and biologic abnormalities. These different features lead to the development of different prognostic system; however, these approaches are limited in predicting clinical course, and management of patients remains challenging given the uncertainty of the time course of disease progression. It is of importance to identify transcriptomic marker causing maturational and differentiation arrest which could help in understanding the pathogenesis of disease. We have studied differential gene expression profiles (GEPs) in CD34 + marrow cells from myelodysplastic syndrome (MDS) patients (n = 14) and control CD34 + cells using Affymetrix Human Clariom S microarray with 20,000 well annotated genes. We found 4165 genes significantly (p < 0.05) differentially expressed in MDS. Using stringent bioinformatics analyses, we were able to identify few genes (MAPK8, JUNB, mTOR) which were differentially upregulated i.e. 5.39, 73.61 and 2.7 fold change observed in MDS than control and also validated (n = 60) these genes by RT - qPCR. Kaplan - Meier survival analysis indicated that MAPK8 and JUNB could be poor prognostic marker as patients with increased expression showed poor survival, whereas surprisingly mTOR increased expression proved to be good prognostic marker. The correlation analysis showed that the level of gene (MAPK8, JUNB, mTOR) expression was significantly (p ≤ 0.05) associated with frequency of genetic lesions. Interestingly the increased expression of MAPK8 was significantly accompanied with ASXL1 gene mutation. Our study showed an elevation of TNF and AMPK signalling pathways in MDS. TNF signalling might be mediating the proliferative advantage to myeloid clonal cells (mutation carrying cells) over normal cells, whereas, AMPK signalling could be acting as protector against it (favouring normal cells). Hence it would be interesting to explore the functions and pathways associated with mTOR, AMPK, MAPK8 and JUNB in myelopoiesis related diseases like MDS.

A comprehensive Bioconductor ecosystem for the design of CRISPR guide RNAs across nucleases and technologies

The success of CRISPR-mediated gene perturbation studies is highly dependent on the quality of gRNAs, and several tools have been developed to enable optimal gRNA design. However, these tools are not all adaptable to the latest CRISPR modalities or nucleases, nor do they offer comprehensive annotation methods for advanced CRISPR applications. Here, we present a new ecosystem of R packages, called crisprVerse, that enables efficient gRNA design and annotation for a multitude of CRISPR technologies. This includes CRISPR knockout (CRISPRko), CRISPR activation (CRISPRa), CRISPR interference (CRISPRi), CRISPR base editing (CRISPRbe) and CRISPR knockdown (CRISPRkd). The core package, crisprDesign, offers a user-friendly and unified interface to add off-target annotations, rich gene and SNP annotations, and on- and off-target activity scores. These functionalities are enabled for any RNA- or DNA-targeting nucleases, including Cas9, Cas12, and Cas13. The crisprVerse ecosystem is open-source and deployed through the Bioconductor project (https://github.com/crisprVerse).

Systematic single-cell pathway analysis to characterize early Tcell activation.

Pathway analysis is a key analytical stage in the interpretation of omics data, providing a powerful method for detecting alterations in cellular processes. We recently developed a sensitive and distribution-free statistical framework for multisample distribution testing, which we implement here in the open-source R package single-cell pathway analysis (SCPA). We demonstrate the effectiveness of SCPA over commonly used methods, generate a scRNA-seq Tcell dataset, and characterize pathway activity over early cellular activation. This reveals regulatory pathways in Tcells, including an intrinsic type I interferon system regulating Tcell survival and a reliance on arachidonic acid metabolism throughout Tcell activation. A systems-level characterization of pathway activity in Tcells across multiple tissues also identifies alpha-defensin expression as a hallmark of bone-marrow-derived Tcells. Overall, this work provides a widely applicable tool for single-cell pathway analysis and highlights regulatory mechanisms of Tcells.