Macrophage inflammatory and regenerative response periodicity is programmed by cell cycle and chromatin state.

Cell cycle (CC) facilitates cell division via robust, cyclical gene expression. Protective immunity requires the expansion of pathogen-responsive cell types, but whether CC confers unique gene expression programs that direct the subsequent immunological response remains unclear. Here, we demonstrate that single macrophages (MFs) adopt different plasticity states in CC, which leads to heterogeneous cytokine-induced polarization, priming, and repolarization programs. Specifically, MF plasticity to interferon gamma (IFNG) is substantially reduced during S-G2/M, whereas interleukin 4 (IL-4) induces S-G2/M-biased gene expression, mediated by CC-biased enhancers. Additionally, IL-4 polarization shifts the CC-phase distribution of MFs toward the G2/M phase, providing a subpopulation-specific mechanism for IL-4-induced, dampened IFNG responsiveness. Finally, we demonstrate CC-dependent MF responses in murine and human disease settings in vivo, including Th2-driven airway inflammation and pulmonary fibrosis, where MFs express an S-G2/M-biased tissue remodeling gene program. Therefore, MF inflammatory and regenerative responses are gated by CC in a cyclical, phase-dependent manner.

Deep learning and CRISPR-Cas13d ortholog discovery for optimized RNA targeting.

Effective and precise mammalian transcriptome engineering technologies are needed to accelerate biological discovery and RNA therapeutics. Despite the promise of programmable CRISPR-Cas13 ribonucleases, their utility has been hampered by an incomplete understanding of guide RNA design rules and cellular toxicity resulting from off-target or collateral RNA cleavage. Here, we quantified the performance of over 127,000 RfxCas13d (CasRx) guide RNAs and systematically evaluated seven machine learning models to build a guide efficiency prediction algorithm orthogonally validated across multiple human cell types. Deep learning model interpretation revealed preferred sequence motifs and secondary features for highly efficient guides. We next identified and screened 46 novel Cas13d orthologs, finding that DjCas13d achieves low cellular toxicity and high specificity-even when targeting abundant transcripts in sensitive cell types, including stem cells and neurons. Our Cas13d guide efficiency model was successfully generalized to DjCas13d, illustrating the power of combining machine learning with ortholog discovery to advance RNA targeting in human cells.

An evolutionary compass for detecting signals of polygenic selection and mutational bias.

Selection and mutation shape the genetic variation underlying human traits, but the specific evolutionary mechanisms driving complex trait variation are largely unknown. We developed a statistical method that uses polarized genome-wide association study (GWAS) summary statistics from a single population to detect signals of mutational bias and selection. We found evidence for nonneutral signals on variation underlying several traits (body mass index [BMI], schizophrenia, Crohn's disease, educational attainment, and height). We then used simulations that incorporate simultaneous negative and positive selection to show that these signals are consistent with mutational bias and shifts in the fitness-phenotype relationship, but not stabilizing selection or mutational bias alone. We additionally replicate two of our top three signals (BMI and educational attainment) in an external cohort, and show that population stratification may have confounded GWAS summary statistics for height in the GIANT cohort. Our results provide a flexible and powerful framework for evolutionary analysis of complex phenotypes in humans and other species, and offer insights into the evolutionary mechanisms driving variation in human polygenic traits.