Cell genomics最新文献

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.

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.

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.

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.

The Long-Read Personalized OncoGenomics (POG) dataset comprises a cohort of 189 patient tumors and 41 matched normal samples sequenced using the Oxford Nanopore Technologies PromethION platform. This dataset from the POG program and the Marathon of Hope Cancer Centres Network includes DNA and RNA short-read sequence data, analytics, and clinical information. We show the potential of long-read sequencing for resolving complex cancer-related structural variants, viral integrations, and extrachromosomal circular DNA. Long-range phasing facilitates the discovery of allelically differentially methylated regions (aDMRs) and allele-specific expression, including recurrent aDMRs in the cancer genes RET and CDKN2A. Germline promoter methylation in MLH1 can be directly observed in Lynch syndrome. Promoter methylation in BRCA1 and RAD51C is a likely driver behind homologous recombination deficiency where no coding driver mutation was found. This dataset demonstrates applications for long-read sequencing in precision medicine and is available as a resource for developing analytical approaches using this technology.

The biodiversity crisis is a global phenomenon, and measures to monitor, stop, and revert the impacts on species' extinction risk are urgently needed. Megadiverse countries, especially in the Global South, are responsible for managing and protecting Earth's biodiversity. Various initiatives have started to sequence reference-level genomes or perform large-scale species detection and monitoring through environmental DNA. Here, we outline the Genomics of the Brazilian Biodiversity (GBB) consortium that is contributing to public policies on the conservation and management of Brazilian species. We describe our unique public-private governance and lessons in setting up a genomic consortium in a megadiverse country of continental scale. We explore the challenges while sharing knowledge for similar initiatives in the Global South. Ultimately, we aim to encourage Brazilian institutions and other megadiverse countries to invest and participate in large-scale genomic initiatives, demonstrating their commitment to preserving and monitoring their exceptional natural heritage while contributing to global biodiversity conservation.

In pediatric cancer, structural variants (SVs) and copy-number alterations contribute to cancer initiation as well as progression, thereby aiding diagnosis and treatment stratification. Although suggested to be of importance, the prevalence and biological relevance of complex genomic rearrangements (CGRs) across pediatric solid tumors is largely unexplored. In a cohort of 120 primary tumors, we systematically characterized patterns of extrachromosomal DNA, chromoplexy, and chromothripsis across five pediatric solid cancer types. CGRs were identified in 56 tumors (47%), and in 42 of these tumors, CGRs affect cancer driver genes or result in unfavorable chromosomal alterations. This demonstrates that CGRs are prevalent and pathogenic in pediatric solid tumors and suggests that selection likely contributes to the structural variation landscape. Moreover, carrying CGRs is associated with more adverse clinical events. Our study highlights the potential for CGRs to be incorporated in risk stratification or exploited for targeted treatments.

Type I interferon (IFN-I) plays an important role in the innate immune response through inducing IFN-I-stimulated genes (ISGs). However, how alternative splicing (AS) events, especially over time, affect their function remains poorly understood. We generated an annotation (113,843 transcripts) for IFN-I-stimulated human B cells called isoISG using high-accuracy long-read sequencing data from PacBio Sequel II/IIe. Transcript isoform profiling using isoISG revealed that isoform switching occurred in the early response to IFN-I so that ISGs would gain functional domains (e.g., C4B) or higher protein production (e.g., IRF3). Conversely, isoforms lacking functional domains increased during the late phase of IFN-I response, mainly due to intron retention events. This suggests that isoform switching both triggers and terminates IFN-I responses at the translation and protein levels. Furthermore, genetic variants influencing the isoform ratio of ISGs were associated with immunological and infectious diseases. AS has essential roles in regulating innate immune response and associated diseases.

The emerging field of induced proximity therapeutics, which involves designing molecules to bring together an effector and target protein-typically to induce target degradation-is rapidly advancing. However, its progress is constrained by the lack of scalable and unbiased tools to explore effector-target protein interactions. We combine pooled endogenous gene tagging using a ligand-binding domain with generic small-molecule-based recruitment to screen for induction of protein proximity. We apply this methodology to identify effectors for degradation in two orthogonal screens: using fluorescence to monitor target levels and a cellular growth that depends on the degradation of an essential protein. Our screens revealed new effector proteins for degradation, including previously established examples, and converged on members of the C-terminal-to-LisH (CTLH) complex. We introduce a platform for pooled induction of endogenous protein-protein interactions to expand our toolset of effector proteins for protein degradation and other forms of induced proximity.