Cell genomics最新文献

N⁶-methyladenosine (m6A) is the most prevalent chemical modification in eukaryotic mRNAs and plays key roles in diverse cellular processes. Precise localization of m6A sites is thus critical for characterizing the functional roles of m6A in various conditions and dissecting the mechanisms governing its deposition. Here, we design a combined framework of Transformer architecture and recurrent neural network, deepSRAMP, to identify m6A sites using sequence-based and genome-derived features. As a result, deepSRAMP achieves a notably enhanced performance compared to its predecessor, SRAMP, the most-used predictor in this field. Moreover, based on multiple benchmark datasets, deepSRAMP greatly outperforms other state-of-the-art m6A predictors, including WHISTLE and DeepPromise, with an average 16.1% and 18.3% increase in AUROC and a 43.9% and 46.4% increase in AUPRC. Finally, deepSRAMP can be successfully exploited on mammalian m6A epitranscriptome mapping under diverse cellular conditions and can potentially reveal differential m6A sites among transcript isoforms of individual genes.

Jayne Hehir-Kwa is based at the Princess Máxima Center for Pediatric Oncology in the Netherlands and is an associate group leader within the Kemmeren group and the Big Data Core. Her work is focused on genomic and transcriptomic sequencing of pediatric cancer and the resulting analysis, storage, and management of this large volume of valuable patient data. In this issue of Cell Genomics, her team presents the research article "Complex structural variation is prevalent and highly pathogenic in pediatric solid tumors," which illustrates complex genomic rearrangements in five pediatric cancer types.

The effects of genetic variation on complex traits act mainly through changes in gene regulation. Although many genetic variants have been linked to target genes in cis, the trans-regulatory cascade mediating their effects remains largely uncharacterized. Mapping trans-regulators based on natural genetic variation has been challenging due to small effects, but experimental perturbations offer a complementary approach. Using CRISPR, we knocked out 84 genes in primary CD4⁺ T cells, targeting inborn error of immunity (IEI) disease transcription factors (TFs) and TFs without immune disease association. We developed a novel gene network inference method called linear latent causal Bayes (LLCB) to estimate the network from perturbation data and observed 211 regulatory connections between genes. We characterized programs affected by the TFs, which we associated with immune genome-wide association study (GWAS) genes, finding that JAK-STAT family members are regulated by KMT2A, an epigenetic regulator. These analyses reveal the trans-regulatory cascades linking GWAS genes to signaling pathways.

Illuminating the precise stepwise genetic programs directing cardiac development provides insights into the mechanisms of congenital heart disease and strategies for cardiac regenerative therapies. Here, we integrate in vitro and in vivo human single-cell multi-omic studies with high-throughput functional genomic screening to reveal dynamic, cardiac-specific gene regulatory networks (GRNs) and transcriptional regulators during human cardiomyocyte development. Interrogating developmental trajectories reconstructed from single-cell data unexpectedly reveal divergent cardiomyocyte lineages with distinct gene programs based on developmental signaling pathways. High-throughput functional genomic screens identify key transcription factors from inferred GRNs that are functionally relevant for cardiomyocyte lineages derived from each pathway. Notably, we discover a critical heat shock transcription factor 1 (HSF1)-mediated cardiometabolic GRN controlling cardiac mitochondrial/metabolic function and cell survival, also observed in fetal human cardiomyocytes. Overall, these multi-modal genomic studies enable the systematic discovery and validation of coordinated GRNs and transcriptional regulators controlling the development of distinct human cardiomyocyte populations.

Latin Americans are underrepresented in genetic studies, increasing disparities in personalized genomic medicine. Despite available genetic data from thousands of Latin Americans, accessing and navigating the bureaucratic hurdles for consent or access remains challenging. To address this, we introduce the Genetics of Latin American Diversity (GLAD) Project, compiling genome-wide information from 53,738 Latin Americans across 39 studies representing 46 geographical regions. Through GLAD, we identified heterogeneous ancestry composition and recent gene flow across the Americas. Additionally, we developed GLAD-match, a simulated annealing-based algorithm, to match the genetic background of external samples to our database, sharing summary statistics (i.e., allele and haplotype frequencies) without transferring individual-level genotypes. Finally, we demonstrate the potential of GLAD as a critical resource for evaluating statistical genetic software in the presence of admixture. By providing this resource, we promote genomic research in Latin Americans and contribute to the promises of personalized medicine to more people.

The CRISPR interference (CRISPRi) system is a powerful tool for selectively and efficiently silencing genes in functional genomics research applications. However, its off-target activity has not been systematically investigated. Here, we utilized a genome-wide CRISPRi-Cas9 single-guide RNA (sgRNA) library to investigate the presence of off-target activity and its effects on gene expression. Our findings suggest that off-target effects in CRISPRi are quite pervasive and have direct and indirect impacts on gene expression. Most of the identified off-targets can be accounted for by complementarity of the protospacer adjacent motif (PAM)-proximal genomic sequence with the 3' half of the sgRNA spacer sequence, the seed sequence. We also report that while the stability of off-target binding is primarily driven by the PAM-proximal seed sequences, variations in the length of these seed sequences and the degree of mismatch tolerance at various positions can differ across different sgRNAs.

Establishing the subcellular distribution of all proteins encoded by the human genome remains a key objective of life science research. This is particularly important in the context of proteins that, through genetic sequencing of patients, have been identified as containing missense mutations. A recent publication in Cell¹ highlights the prominence of protein mislocalization as a hallmark of dysfunctional proteins. The use of high-content subcellular phenotypic screens and allied technology by Lacoste and colleagues has enormous potential to change the landscape of how we approach both diagnostic and therapeutic decisions.

The insufficient availability of comprehensive protein-level perturbation data is impeding the widespread adoption of systems biology. In this perspective, we introduce the rationale, essentiality, and practicality of perturbation proteomics. Biological systems are perturbed with diverse biological, chemical, and/or physical factors, followed by proteomic measurements at various levels, including changes in protein expression and turnover, post-translational modifications, protein interactions, transport, and localization, along with phenotypic data. Computational models, employing traditional machine learning or deep learning, identify or predict perturbation responses, mechanisms of action, and protein functions, aiding in therapy selection, compound design, and efficient experiment design. We propose to outline a generic PMMP (perturbation, measurement, modeling to prediction) pipeline and build foundation models or other suitable mathematical models based on large-scale perturbation proteomic data. Finally, we contrast modeling between artificially and naturally perturbed systems and highlight the importance of perturbation proteomics for advancing our understanding and predictive modeling of biological systems.

Repeat expansions in the C9orf72 gene are the most common genetic cause of (ALS) and frontotemporal dementia (FTD). Like other genetic forms of neurodegeneration, pinpointing the precise mechanism(s) by which this mutation leads to neuronal death remains elusive, and this lack of knowledge hampers the development of therapy for C9orf72-related disease. We used an agnostic approach based on genomic data (n = 41,273 ALS and healthy samples, and n = 1,516 C9orf72 carriers) to overcome these bottlenecks. Our drug-repurposing screen, based on gene- and expression-pattern matching and information about the genetic variants influencing onset age among C9orf72 carriers, identified acamprosate, a γ-aminobutyric acid analog, as a potentially repurposable treatment for patients carrying C9orf72 repeat expansions. We validated its neuroprotective effect in cell models and showed comparable efficacy to riluzole, the current standard of care. Our work highlights the potential value of genomics in repurposing drugs in situations where the underlying pathomechanisms are inherently complex. VIDEO ABSTRACT.

A single gene may have multiple enhancers, but how they work in concert to regulate transcription is poorly understood. To analyze enhancer interactions throughout the genome, we developed a generalized linear modeling framework, GLiMMIRS, for interrogating enhancer effects from single-cell CRISPR experiments. We applied GLiMMIRS to a published dataset and tested for interactions between 46,166 enhancer pairs and corresponding genes, including 264 "high-confidence" enhancer pairs. We found that enhancer effects combine multiplicatively but with limited evidence for further interactions. Only 31 enhancer pairs exhibited significant interactions (false discovery rate <0.1), none of which came from the high-confidence set, and 20 were driven by outlier expression values. Additional analyses of a second CRISPR dataset and in silico enhancer perturbations with Enformer both support a multiplicative model of enhancer effects without interactions. Altogether, our results indicate that enhancer interactions are uncommon or have small effects that are difficult to detect.