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Results for “Gene perturbation”

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PubMed2021

Functional impact of cancer-associated cohesin variants on gene expression and cellular identity.

Cohesin is a ring-shaped protein complex that controls dynamic chromosome structure. Cohesin activity is important for a variety of biological processes, including formation of DNA loops that regulate gene expression. The precise mechanisms by which cohesin shapes local chromosome structure and gene expression are not fully understood. Recurrent mutations in cohesin complex members have been reported in various cancers, though it is not clear whether many cohesin sequence variants have phenotypes and contribute to disease. Here, we utilized CRISPR/Cas9 genome editing to introduce a variety of cohesin sequence variants into murine embryonic stem cells and investigate their molecular and cellular consequences. Some of the cohesin variants tested caused changes to transcription, including altered expression of gene encoding lineage-specifying developmental regulators. Altered gene expression was also observed at insulated neighborhoods, where cohesin-mediated DNA loops constrain potential interactions between genes and enhancers. Furthermore, some cohesin variants altered the proliferation rate and differentiation potential of murine embryonic stem cells. This study provides a functional comparison of cohesin variants found in cancer within an isogenic system, revealing the relative roles of various cohesin perturbations on gene expression and maintenance of cellular identity.

Animals↗

PubMed2026

Chemogenomic maps reveal a PRDX1-dependent iron-damage axis in the DNA damage response.

The DNA damage response (DDR) is a sophisticated network of cellular pathways whose perturbation leads to genome instability and is a key hallmark of oncogenesis. Here, we present data from 32 genome-scale loss-of-function CRISPR interference chemical-genetic screens with inhibitors targeting core constituents of the DDR machinery (PARP, ATR, ATM, DNAPK and WEE1), as both single agents and in combination with poly(ADP-ribose) polymerase inhibitors. These experiments identify >1,000 genes whose perturbation modifies the DDR and provides a rich resource to the DDR community. In addition, this compendium of functional genomics data reveals key principles governing the DDR and highlights a strong chemical-genetic interaction between loss of activity of the peroxiredoxin PRDX1 and all tested DDR inhibitors through a mechanism involving iron availability mediated by an MRGBP-PAX7-IREB2 axis. Our data position PRDX1 as a key suppressor of DNA damage accumulation and potential druggable target in combination with DDR inhibitors.

Journal Article↗

PubMed2026

Mendelian disorders of the epigenetic machinery: a decade of insights into the molecular basis.

The Mendelian disorders of the Epigenetic Machinery (MDEMs), or Chromatinopathies, are now understood to be a collectively common cause of childhood neurodevelopmental delays and intellectual disability. In the past decade, the chromatin and gene expression consequences of heterozygous chromatin regulator disruption have been investigated in various disease models, yielding insights into the molecular pathogenesis of MDEMs. In this review, we highlight some of these results - drawing upon studies of representative MDEMs - together with potential unifying concepts. We propose that MDEMs are characterized by distributed, often subtle chromatin and gene expression perturbations, which impact diverse cellular pathways and processes and are frequently shared between distinct disorders. In this sense, they occupy an intermediate space between classical monogenic disorders and complex traits. We propose potential explanations for the variable expressivity in MDEMs and conclude by considering how technological advances can now enable a deeper and more precise mechanistic characterization of this important Mendelian disease group.

Humans↗

PubMed2025

BOGO: A Proteome-Wide Gene Overexpression Platform for Discovering Rational Cancer Combination Therapies.

Cancer drug resistance remains a major barrier to durable treatment success, often leading to relapse despite advances in precision oncology. While combination therapies are being increasingly investigated, such as chemotherapy with small molecule inhibitors, predicting drug response and identifying rational drug combinations based on resistance mechanisms remain major challenges. Therefore, a proteome-wide, single-gene overexpression screening platform is essential for guiding rational therapy selection. Here, we present BOGO (Bxb1-landing pad human ORFeome-integrated system for a proteome-wide Gene Overexpression), a robust, scalable, and reproducible screening platform that enables single-copy, site-specific integration and overexpression of ~19,000 human open across cancer cell models. Using BOGO, we identified drug-specific response drivers for 16 chemotherapeutic agents and integrated clinical datasets to uncover proliferation and resistance-associated genes with prognostic potential. Drug response similarity networks revealed both shared and unique mechanisms, highlighting key pathways such as autophagy, apoptosis, and Wnt signaling, and notable resistance-associated genes including BCL2, POLD2, and TRADD. In particular, we proposed a synergistic combination of the BCL2 family inhibitor ABT-263 (Navitoclax®) and the DNA analog TAS-102 (Lonsurf®), which revealed that lysosomal modulation is a key mechanism driving DNA analog resistance. This combination therapy selectively enhanced cytotoxicity in colorectal and pancreatic cancer cells in vitro, and demonstrated therapeutic benefit in vivo in both cell line-derived xenograft (CDX) and patient-derived xenograft (PDX) models. Together, these findings establish BOGO as a powerful gene overexpression perturbation platform for systematically identifying chemoresistance and chemosensitization drivers, and for discovering rational combination therapies. Its scalability and reproducibility position BOGO as a broadly applicable tool for functional genomics and therapeutic discovery beyond cancer resistance.

Journal Article↗

PubMed2024

Multiome Perturb-seq unlocks scalable discovery of integrated perturbation effects on the transcriptome and epigenome.

Single-cell CRISPR screens link genetic perturbations to transcriptional states, but high-throughput methods connecting these induced changes to their regulatory foundations are limited. Here, we introduce Multiome Perturb-seq, extending single-cell CRISPR screens to simultaneously measure perturbation-induced changes in gene expression and chromatin accessibility. We apply Multiome Perturb-seq in a CRISPRi screen of 13 chromatin remodelers in human RPE-1 cells, achieving efficient assignment of sgRNA identities to single nuclei via an improved method for capturing barcode transcripts from nuclear RNA. We organize expression and accessibility measurements into coherent programs describing the integrated effects of perturbations on cell state, finding that ARID1A and SUZ12 knockdowns induce programs enriched for developmental features. Modeling of perturbation-induced heterogeneity connects accessibility changes to changes in gene expression, highlighting the value of multimodal profiling. Overall, our method provides a scalable and simply implemented system to dissect the regulatory logic underpinning cell state. A record of this paper's transparent peer review process is included in the supplemental information.

Humans↗

PubMed2026

Protocol to identify genes required for cardiomyocyte development using Perturb-Seq.

While Perturb-Seq combines CRISPR-based screening with single-cell RNA sequencing (scRNA-seq), large-scale experiments are costly and its application during development is complicated by differentiation heterogeneity. Here, we present a protocol to identify genes required for cardiomyocyte development using Perturb-Seq. We describe steps for sgRNA (single guide RNA) library cloning and infection, cardiomyocyte differentiation, cell hashing, super loading, and scRNA-seq. We then detail procedures for sequencing, mapping, and data analysis. For complete details on the use and execution of this protocol, please refer to Sivakumar et al.1.

CRISPR↗

PubMed2026

Mapping convergent regulators of melanoma drug resistance by PerturbFate.

High-throughput genomic studies have uncovered associations between diverse genetic alterations and disease phenotypes. However, elucidating how perturbations in functionally disparate genes give rise to convergent cellular states remains challenging. Here we present PerturbFate, a high-throughput, cost-effective, combinatorial-indexing single-cell platform that enables systematic interrogation of massively parallel CRISPR interference1 perturbations across the full spectrum of gene regulation, from chromatin remodelling and nascent transcription to steady-state transcriptomic phenotypes. Using PerturbFate, we profiled more than 300,000 cultured melanoma cells to characterize multimodal phenotypic and gene regulatory responses to perturbations in more than 140 vemurafenib resistance-associated genes. We uncovered a shared dedifferentiated cell state marked by convergent cooperative transcription factor activities across diverse genetic perturbations. We further dissected phenotypic responses to perturbations in Mediator complex components, linking module-specific biochemical properties to convergent transcriptional activations. We identified common regulatory nodes that drive similar phenotypic outcomes across distinct genetic perturbations. We also delineated how perturbations in functionally unrelated genes reshape cell state. Thus, PerturbFate establishes a versatile platform for identifying key molecular regulators by anchoring multimodal regulatory dynamics to disease-relevant phenotypes.

Humans↗

PubMed2024

Transcriptome-Wide Root Causal Inference.

Root causal genes correspond to the first gene expression levels perturbed during pathogenesis by genetic or non-genetic factors. Targeting root causal genes has the potential to alleviate disease entirely by eliminating pathology near its onset. No existing algorithm discovers root causal genes from observational data alone. We therefore propose the Transcriptome-Wide Root Causal Inference (TWRCI) algorithm that identifies root causal genes and their causal graph using a combination of genetic variant and unperturbed bulk RNA sequencing data. TWRCI uses a novel competitive regression procedure to annotate cis and trans-genetic variants to the gene expression levels they directly cause. The algorithm simultaneously recovers a causal ordering of the expression levels to pinpoint the underlying causal graph and estimate root causal effects. TWRCI outperforms alternative approaches across a diverse group of metrics by directly targeting root causal genes while accounting for distal relations, linkage disequilibrium, patient heterogeneity and widespread pleiotropy. We demonstrate the algorithm by uncovering the root causal mechanisms of two complex diseases, which we confirm by replication using independent genome-wide summary statistics.

Journal Article↗

PubMed2025

Transcriptome-wide root causal inference.

Root causal genes correspond to the first gene expression levels perturbed during pathogenesis by genetic or non-genetic factors. Targeting root causal genes has the potential to alleviate disease entirely by eliminating pathology near its onset. No existing algorithm has been designed to discover root causal genes from observational data alone. We therefore propose the Transcriptome-Wide Root Causal Inference (TWRCI) algorithm that identifies root causal genes and their causal graph using a combination of genetic variant and unperturbed bulk RNA sequencing data. TWRCI uses a novel competitive regression procedure to annotate cis and trans-genetic variants to the gene expression levels they directly cause. The algorithm simultaneously determines the sequence in which gene expression changes propagate through the system to pinpoint the underlying causal graph and estimate root causal effects. TWRCI outperforms alternative approaches across a diverse group of metrics by directly targeting root causal genes while accounting for distal relations, linkage disequilibrium, patient heterogeneity and widespread pleiotropy. We demonstrate the algorithm by uncovering the root causal mechanisms of two complex diseases, which we confirm by replication using independent genome-wide summary statistics.

Algorithms↗

PubMed2025

Genome-wide CRISPR Screening Identifies NFκB and c-MET as Druggable Targets to Sensitize Lenvatinib Treatment in Hepatocellular Carcinoma.

BACKGROUND & AIMS: Hepatocellular carcinoma (HCC), the dominant form of liver cancer, is a leading cause of cancer death worldwide. Sorafenib and lenvatinib have long been the 2 limited options of first-line treatments for patients with unresectable advanced HCC. However, the single-drug treatment strategy only shows modest survival benefit, mostly because of the survival ability of cancer cells to activate alternative pathways for compensation. In this study, we aim to identify druggable targets contributing to lenvatinib resistance and evaluate the efficacy of combining respective inhibitors and lenvatinib on HCC. METHODS: Genome-scale clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 knockout library screening was applied on the vehicle group and lenvatinib treatment group. Identified druggable candidates were validated individually on HCC cell models. Therapeutic effects of the combined treatment of inhibitors of candidate genes and lenvatinib were evaluated in vitro and in vivo. RESULTS: We successfully identified NFKB1 and MET as critical drivers for the development of lenvatinib resistance in HCC cells. By perturbing the 2 genes with either CRISPR knockout or RNA interference approaches, lenvatinib treatments were significantly sensitized. Moreover, using small molecules QNZ and cabozantinib to target NFKB1 and MET, respectively, this together with lenvatinib could synergistically induce apoptosis and suppress HCC growth in vitro and in vivo. CONCLUSION: Our results demonstrated that genome-wide CRISPR/Cas9 screening is a powerful tool for the design of rational combinational cancer therapy and provided candidate genes possible for combined treatments with lenvatinib to improve therapy efficacy.

Carcinoma, Hepatocellular↗

PubMed2026

Transcriptome changes in circulating immune cells of critical COVID-19 patients predict a specific metabolic and epigenetic imprint.

BACKGROUND: The progression to critical COVID-19 arises predominantly from a dysregulated host immune response although the underlying regulatory mechanisms still remain partially elusive. This limits a prompt prediction of the disease progression, reduces the therapeutic options and restrains our understanding of “long COVID”. METHODS: Here, we analyzed the transcriptome of peripheral blood mononuclear cells (PBMCs) collected from COVID-19 patients experiencing different degrees of the disease (mild and critical), and control patients enrolled in the clinical trial COntAGIouS as well as independent bulk RNA-seq, single-cell RNA-seq and proteomic datasets. RESULTS: In critical COVID-19 patients, the integrative analysis of transcriptomic data revealed an altered regulatory network involving microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and coding genes that control mRNA translation-related genes, epigenetics, and metabolism. In parallel, we observed an upregulation of tRNA aminoacylation genes in critical COVID-19 patients by the analysis of either bulk or single-cell RNA-seq data from publicly available independent cohorts. Additionally, we found increased expression of coding genes enriched for the cognate amino acids (glycine, alanine, isoleucine and tyrosine), all related to protein localization, post-translational modifications, and cell metabolism in our cohort. Similar alterations in amino acid frequency were found in an independent proteomic dataset. CONCLUSIONS: Collectively, our findings indicate a broad perturbation of the gene expression landscape that characterizes the aberrant host immune response in critical COVID-19 patients and is potentially coordinated by miRNA and tRNA metabolism alterations. TRIAL REGISTRATION: COntAGIouS, NCT04327570. Registered 26 March 2020, https://clinicaltrials.gov/ct2/show/NCT04327570 .

Female↗

PubMed2026

Interplay between the role of DNA methylation in regulating gene expression and TE-silencing in a reptilian methylome.

DNA methylation is a major component of eukaryotic genomes with an important role in the defence against transposable elements, to transcriptionally silence their activity and prevent transposition. DNA methylation also plays a major role in the regulation of gene expression. This dual role can come into conflict, where DNA methylation in gene regulatory regions becomes perturbed due to transposable element transposition, leading to disruption of gene expression. Here, we describe how this conflict is reflected in DNA methylation patterns in the sand lizard genome where there is recent transposable element activity. Using long-read sequencing technology we show that CpG islands in gene transcriptional start sites are typically hypomethylated and associated with higher gene expression. Outside transcriptional start sites, a majority of CpG islands overlapped transposable elements and were associated with hypermethylation, consistent with a host-defence role in suppressing transposition activity. We identify 605 instances where transcriptional start sites were associated with transposable elements (4.3% of all genes). These instances were far rarer in conjunction with a CpG island, when methylation signatures would be in conflict. Transposable elements were found to be closer to and at higher density the more hypermethylated a transcriptional start site was, suggesting strong selection against selfish genetic elements transposing into hypomethylated transcriptional start sites.

CpG islands↗

PubMed2026

Predicting cellular responses to perturbation across diverse contexts with State.

While machine learning models offer potential for predicting transcriptomic effects of perturbation, they currently struggle to generalize across cellular contexts. Here, we introduce State, a machine learning model that predicts perturbation effects while accounting for cellular heterogeneity within and across experiments. State is trained using single-cell gene expression data to predict perturbation effects across sets of cells. State improved discrimination of effects on large datasets by more than 30% and identified differentially expressed genes across genetic, signaling, and chemical perturbations with significantly improved accuracy compared with baselines. Its cell embeddings trained on observational data from 167 million cells enable the identification of strong perturbations in cellular contexts where no perturbations were observed during training. We further introduce Cell-Eval, a comprehensive evaluation framework that can be used to evaluate future models. Overall, the performance and flexibility of State set the stage for scaling the development of AI models of cell state.

Machine Learning↗

PubMed2026

Disagreement-informed arbitration for gene regulatory network inference: A score-level meta-classifier and a diagnostic typology of inter-method conflict.

Gene regulatory network inference methods routinely disagree about individual edges, and practitioners resolve those conflicts by choosing one method or averaging them all. We ask whether the conflict can instead be arbitrated per edge. A gradient-boosted classifier is trained on the raw scores that ten inference methods-correlation-based, information-theoretic, sparse-regression and tree-ensemble, including GENIE3, GRNBoost2, CLR and ARACNe-assign to each candidate regulator-target pair, so that the weight given to each method varies from edge to edge. Across six single-cell perturbation screens spanning four cell types, arbitration improves on mean ensembling by +0.056 AUROC on Adamson and +0.083 on Shifrut under target-grouped cross-validation. The evaluation protocol turns out to matter more than the model. Edge-level cross-validation, standard in this literature, inflates apparent gains by 0.060 AUROC through target-gene leakage-comparable to the entire honest improvement. The effect is far larger for methods that represent genes implicitly: a supervised graph-attention link predictor trained on identical folds scores AUROC 0.930 under edge-level cross-validation, better than anything else we evaluate, and 0.533 once target genes are held out. Any method that parameterises genes is exposed, which covers most graph- and embedding-based approaches. A five-category typology of inter-method conflict localises where arbitration pays off, with the largest gains on edges where the methods disagree and the smallest where they already agree, while adding nothing as model input; we therefore report it as a diagnostic instrument rather than a modelling contribution. We also characterise what the ground truth measures: most perturbed genes in widely used screens are not transcription factors, and a mediation screen bounds how much of the perturbation response can be direct.

Ensemble methods↗

PubMed2026

An embedding-based framework enables statistical testing of gene-set function hypotheses inferred by large language models.

Emerging large language models (LLMs) can infer gene functions directly from gene lists, enabling hypothesis generation without predefined gene sets. However, these LLM-derived predictions are qualitative, and principled statistical validation is lacking. Here, we develop an embedding-based statistical framework that transforms gene and function descriptions into vector representations, enabling statistical testing of gene-gene and gene-function relationships and quantitative prioritization of de novo functional hypotheses inferred by LLMs. We benchmark seven state-of-the-art embedding models using curated and retrieval-augmented literature-derived gene descriptions across diverse biological contexts. OpenAI's text-embedding-3-large and Google's gemini-embedding-001 perform best, capturing gene-gene functional relationships in 88.7-92.5% of Gene Ontology biological processes and approximately 98.6% of canonical pathways. In gene-function association analyses, these models achieve high sensitivity (95.2-98.4%) and specificity (72.7-84.3%). Through contamination analysis and evaluation using experimentally informed protein assembly gene sets, our framework distinguishes biologically meaningful LLM-inferred hypotheses from noise, outperforming confidence-based inference and conventional enrichment analysis. We further develop the open-source R package DEGEmbedR and demonstrate its utility for interpreting a drug perturbation-derived differentially expressed gene (DEG) signature lacking significant conventional enrichment results. Together, these results establish LLM-derived embeddings as a quantitative foundation for functional genomics and the statistical validation of LLM-based gene function inference.

Large Language Models↗

PubMed2025

UALCAN Mobile, an app for cancer proteogenomic data analysis.

Cancer is a complex disease affecting various organs and is a major cause of death worldwide. During cancer initiation, disease progression, and tumor metastasis, various genomic and proteomic alterations are observed. Recent technological advances have led to the generation of large amounts of molecular data, including genomics and transcriptomics. These large-scale datasets can be utilized to analyze and identify sub-class-specific cancer biomarkers and targets. However, there is a need for the development of user-friendly tools for large-scale data analysis, disseminating the analyzed data in a visualizable format to cancer researchers with no programming skills. We developed UALCAN, a comprehensive platform that allows users to integrate disparate data to better understand the genes, proteins, and pathways perturbed in cancer and make discoveries of potential biomarkers and targets. In the current study, we describe the development of the UALCAN Mobile application (app) that will provide cancer transcriptomic data obtained from The Cancer Genome Atlas (TCGA) project to evaluate protein-coding gene expression based on various stratifications, including stage, grade, race, gender, and molecular-subtypes across over 30 types of cancers. In addition, the UALCAN mobile provides data analysis options for epigenetic changes due to DNA promoter methylation and Clinical Proteomic Tumor Analysis Consortium (CPTAC) cancer proteomic data. The app provides access to large cancer molecular datasets on the go. To find changes in the expression of causative genes and proteins and to identify biomarkers and therapeutic targets, UALCAN mobile app will be extremely valuable. The "UALCAN Mobile" app is free to use and can be downloaded from both the iOS/Apple and the Android Play Store and has been downloaded over 100 times in each of iOS and android app stores.

app↗

PubMed2025

Analog epigenetic memory revealed by targeted chromatin editing.

Cells store information by means of chromatin modifications that persist through cell divisions and can hold gene expression silenced over generations. However, how these modifications may maintain other gene expression states has remained unclear. This study shows that chromatin modifications can maintain a wide range of gene expression levels over time, thus uncovering analog epigenetic memory. By engineering a genomic reporter and epigenetic effectors, we tracked the gene expression dynamics following targeted perturbations to the chromatin state. We found that distinct grades of DNA methylation led to corresponding, persistent gene expression levels. Altering the DNA methylation grade, in turn, resulted in permanent loss of gene expression memory. Consistent with experiments, our chromatin modification model indicates that analog memory arises when the positive feedback between DNA methylation and repressive histone modifications is lacking. This discovery will lead to a deeper understanding of epigenetic memory and to new tools for synthetic biology.

Epigenesis, Genetic↗

PubMed1978

[Enzyme deficiencies of blood cells in bone marrow insufficiency (author's transl].

Numerous enzyme defects-deficiency of pyruvate kinase, phosphofructo-kinase, glocosephosphate isomerase, adenylate kinase, 2,3-diphosphoglycerate mutase and glutathione reductase--in red blood cells have been described to be connected with dyserythropoietic or refractory anemias and panmyelopathies of different origin. These enzyme deficiencies also have been demonstrated in red cells of patients with acute leukemia. Most likely the enzyme deficiencies are acquired and are not important for the origin of anemia or bone marrow insufficiency. Partial derepression of fetal genes, qualitative and quantitative perturbations of genetic expression, and posttranslational variations of the enzyme protein by low molecular factors from plasma, erythrocytes or leukemic cells have been discussed as a reason of enzyme deficiency. The decrease of glutathione reductase deficiency is dependent of FAD deficiency.

Acetylcholinesterase↗