98%
921
2 minutes
20
Genetic interaction networks can help identify functional connections between genes and pathways, which can be leveraged to establish (new) gene function, drug targets, and fill pathway gaps. Since there is no optimal tool that can map genetic interactions across many different bacterial strains and species, we develop CRISPRi-TnSeq, a genome-wide tool that maps genetic interactions between essential genes and nonessential genes through the knockdown of a targeted essential gene (CRISPRi) and the simultaneous knockout of individual nonessential genes (Tn-Seq). CRISPRi-TnSeq thereby identifies, on a genome-wide scale, synthetic and suppressor-type relationships between essential and nonessential genes, enabling the construction of essential-nonessential genetic interaction networks. To develop and optimize CRISPRi-TnSeq, CRISPRi strains were obtained for 13 essential genes in involved in different biological processes including metabolism, DNA replication, transcription, cell division and cell envelope synthesis. Transposon-mutant libraries were constructed in each strain enabling screening of ∼24,000 gene-gene pairs, which led to the identification of 1,334 genetic interactions, including 754 negative and 580 positive genetic interactions. Through extensive network analyses and validation experiments we identify a set of 17 pleiotropic genes, of which a subset tentatively functions as genetic capacitors, dampening phenotypic outcomes and protecting against perturbations. Furthermore, we focus on the relationships between cell wall synthesis, integrity and cell division and highlight: 1) how essential gene knockdown can be compensated by rerouting flux through nonessential genes in a pathway; 2) the existence of a delicate balance between Z-ring formation and localization, and septal and peripheral peptidoglycan (PG) synthesis to successfully accomplish cell division; 3) the control of c-di-AMP over intracellular K and turgor, and thereby modulation of the cell wall synthesis machinery; 4) the dynamic nature of cell wall protein CozEb and its effect on PG synthesis, cell shape morphology and envelope integrity; 5) functional dependency between chromosome decatenation and segregation, and the critical link with cell division, and cell wall synthesis. Overall, we show that CRISPRi-TnSeq uncovers genetic interactions between closely functionally linked genes and pathways, as well as disparate genes and pathways, highlighting pathway dependencies and valuable leads for gene function. Importantly, since both CRISPRi and Tn-Seq are widely used tools, CRISPRi-TnSeq should be relatively easy to implement to construct genetic interaction networks across many different microbial strains and species.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312587 | PMC |
http://dx.doi.org/10.1101/2023.05.31.543074 | DOI Listing |
Plant Cell
September 2025
Department of Plant Sciences, College of Biological Sciences, State Key Laboratory of Plant Environmental Resilience, China Agricultural University, Beijing 100193, China.
Plant thermomorphogenesis is a critical adaptive response to elevated ambient temperatures. The transcription factor PHYTOCHROME-INTERACTING FACTOR 4 (PIF4) integrates diverse environmental and phytohormone signals to coordinate thermoresponsive growth. However, the cellular mechanisms underlying plant thermomorphogenic growth remain poorly understood.
View Article and Find Full Text PDFPlant J
September 2025
College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310058, China.
Genome imbalance, resulting from varying the dosage of individual chromosomes (aneuploidy), has a more detrimental effect than changes in complete sets of chromosomes (haploidy/polyploidy). This imbalance is likely due to disruptions in stoichiometry and interactions among macromolecular assemblies. Previous research has shown that aneuploidy causes global modulation of protein-coding genes (PCGs), microRNAs, and transposable elements (TEs), affecting both the varied chromosome (cis-located) and unvaried genome regions (trans-located) across various taxa.
View Article and Find Full Text PDFSci Signal
September 2025
Department of Pathology, Microbiology and Immunology, New York Medical College, Valhalla, NY, USA.
Replication of HIV-1 requires the coordinated action of host and viral transcription factors, most critically the viral transactivator Tat and the host nuclear factor κB (NF-κB). This activity is disrupted in infected cells that are cultured with extracellular vesicles (EVs) present in human semen, suggesting that they contain factors that could inform the development of new therapeutics. Here, we explored the contents of semen-derived EVs (SEVs) from uninfected donors and individuals with HIV-1 and identified host proteins that interacted with HIV Tat and the NF-κB subunit p65.
View Article and Find Full Text PDFPLoS One
September 2025
Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China.
MicroRNAs (miRNAs) are critical regulators of gene expression in cancer biology, yet their spatial dynamics within tumor microenvironments (TMEs) remain underexplored due to technical limitations in current spatial transcriptomics (ST) technologies. To address this gap, we present STmiR, a novel XGBoost-based framework for spatially resolved miRNA activity prediction. STmiR integrates bulk RNA-seq data (TCGA and CCLE) with spatial transcriptomics profiles to model nonlinear miRNA-mRNA interactions, achieving high predictive accuracy (Spearman's ρ > 0.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
Objective: This study employs integrated network toxicology and molecular docking to investigate the molecular basis underlying 4-nonylphenol (4-NP)-mediated enhancement of breast cancer susceptibility.
Methods: We integrated data from multiple databases, including ChEMBL, STITCH, Swiss Target Prediction, GeneCards, OMIM and TTD. Core compound-disease-associated target genes were identified through Protein-Protein Interaction (PPI) network analysis.