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CRISPR-associated (Cas) DNA-endonucleases are remarkably effective tools for genome engineering, but have limited target ranges due to their protospacer adjacent motif (PAM) requirements. We demonstrate a critical expansion of the targetable sequence space for a type II-A CRISPR-associated enzyme through identification of the natural 5 ′ -NAAN-3 ′ PAM preference of Streptococcus macacae Cas9 (SmacCas9). To achieve efficient editing activity, we graft the PAM-interacting domain of SmacCas9 to its well-established ortholog from Streptococcus pyogenes (SpyCas9), and further engineer an increased efficiency variant (iSpyMac) for robust genome editing activity. We establish that our hybrids can target all adenine dinucleotide PAM sequences and possess robust and accurate editing capabilities in human cells. Protospacer adjacent motif (PAM) requirements limit the target range of CRISPR endonucleases. Here, the authors graft the 5 ′ -NAAN-3 ′ PAM-interacting domain of SmacCas9 onto SpyCas9 to create adenine dinucleotide targeting chimeras.

CRISPR enzymes require a protospacer-adjacent motif (PAM) near the target cleavage site, constraining the sequences accessible for editing. In the present study, we combine protein motifs from several orthologs to engineer two variants of Streptococcus canis Cas9—Sc ++ and a higher-fidelity mutant HiFi-Sc ++ —that have simultaneously broad 5′-NNG-3′ PAM compatibility, robust DNA-cleavage activity and minimal off-target activity. Sc ++ and HiFi-Sc ++ extend the use of CRISPR editing for diverse applications. A hybrid CRISPR–Cas9 variant is able to specifically and efficiently target a larger proportion of the genome.

Fusion oncoproteins, a class of chimeric proteins arising from chromosomal translocations, are major drivers of various pediatric cancers. These proteins are intrinsically disordered and lack druggable pockets, making them highly challenging therapeutic targets for both small molecule-based and structure-based approaches. Protein language models (pLMs) have recently emerged as powerful tools for capturing physicochemical and functional protein features but have yet to be trained on fusion oncoprotein sequences. We introduce FusOn-pLM, a fine-tuned pLM trained on a newly curated, comprehensive set of fusion oncoprotein sequences, FusOn-DB. Employing a unique cosine-scheduled masked language modeling strategy, FusOn-pLM dynamically adjusts masking rates (15%–40%) to optimize feature extraction and representation quality, surpassing baseline embeddings in fusion-specific tasks, including localization, puncta formation, and disorder prediction. FusOn-pLM uniquely predicts drug-resistant mutations, providing insights for therapeutic design that anticipates resistance mechanisms. In total, FusOn-pLM provides biologically relevant representations for advancing therapeutic discovery in fusion-driven cancers. Fusion oncoproteins drive paediatric cancers but are challenging to target due to their intrinsic disorder and lack of druggable pockets. Here, authors present FusOn-pLM, trained on FusOn-DB, which uses dynamic masking to outperform baselines in fusion-specific tasks and predict drug-resistant mutations, advancing therapeutic design.

Alzheimer's Disease (AD) management is challenging due to limitations in detection methods. Currently, cerebrospinal fluid (CSF) biomarkers involve assessing β-amyloid (Aβ) and phosphorylated tau proteins. The lumbar puncture procedure to obtain CSF is invasive and sometimes causes significant anxiety in patients. In contrast, plasma biomarkers would allow rapid, accurate, and cost-effective diagnosis, while minimizing invasiveness and discomfort. Using a dataset involving 120 plasma proteins from clinically diagnosed AD patients versus cognitively normal subjects, we developed classification models by applying various machine learning algorithms (EBlasso, EBEN, XGBoost, LightGBM, TabNet, and TabPFN) to plasma proteomic measurements. Gene ontology and pathway enrichment, and a literature review were used to evaluate the potential relevance of the biomarkers identified in AD-related mechanisms. Biomarkers identified were also evaluated for the enrichment of aging-related biomarkers. The models developed yielded high AUROC and accuracy, mostly >0.9. Proteins selected as predictors by all the models included Angiopoietin-2 (ANG-2), epidermal growth factor (EGF), Interleukin 1α (IL-1α), and platelet growth factor subunit B (PDGF-BB). Ample previous literature supported their relevance in AD. The pool of all the biomarkers identified was significantly enriched with known aging-related biomarkers ( = 0.040). Applying cutting-edge algorithms is expected to be advantageous for developing AD prediction models with plasma proteomic data, and future large studies to externally validate the constructed models in other populations to assess their generalizability is important. The proteins uncovered may represent novel preventative or therapeutic targets.

CRISPR enzymes require a defined protospacer adjacent motif (PAM) flanking a guide RNA-programmed target site, limiting their sequence accessibility for robust genome editing applications. In this study, we recombine the PAM-interacting domain of SpRY, a broad-targeting Cas9 possessing an NRN > NYN (R = A or G, Y = C or T) PAM preference, with the N-terminus of Sc + +, a Cas9 with simultaneously broad, efficient, and accurate NNG editing capabilities, to generate a chimeric enzyme with highly flexible PAM preference: SpRYc. We demonstrate that SpRYc leverages properties of both enzymes to specifically edit diverse PAMs and disease-related loci for potential therapeutic applications. In total, the approaches to generate SpRYc, coupled with its robust flexibility, highlight the power of integrative protein design for Cas9 engineering and motivate downstream editing applications that require precise genomic positioning. CRISPR enzymes require a defined protospacer adjacent motif (PAM) which can be limiting for editing applications. Here the authors recombine the PAM-interacting domain of SpRY with the N-terminus of Sc + + to generate a chimeric enzyme with highly flexible PAM preference: SpRYc.

An in vitro model of human ovarian follicles would greatly benefit the study of female reproduction. Ovarian development requires the combination of germ cells and several types of somatic cells. Among these, granulosa cells play a key role in follicle formation and support for oogenesis. Whereas efficient protocols exist for generating human primordial germ cell-like cells (hPGCLCs) from human induced pluripotent stem cells (hiPSCs), a method of generating granulosa cells has been elusive. Here, we report that simultaneous overexpression of two transcription factors (TFs) can direct the differentiation of hiPSCs to granulosa-like cells. We elucidate the regulatory effects of several granulosa-related TFs and establish that overexpression of NR5A1 and either RUNX1 or RUNX2 is sufficient to generate granulosa-like cells. Our granulosa-like cells have transcriptomes similar to human fetal ovarian cells and recapitulate key ovarian phenotypes including follicle formation and steroidogenesis. When aggregated with hPGCLCs, our cells form ovary-like organoids (ovaroids) and support hPGCLC development from the premigratory to the gonadal stage as measured by induction of DAZL expression. This model system will provide unique opportunities for studying human ovarian biology and may enable the development of therapies for female reproductive health. Ovaries are responsible for forming the eggs humans and other mammals need to reproduce. Once mature, the egg cell is released into the fallopian tube where it can be potentially fertilized by a sperm. Despite their crucial role, how eggs are made in the ovary is poorly understood. This is because ovaries are hard to access, making it difficult to conduct experiments on them. To overcome this, researchers have built artificial ovaries in the laboratory using stem cells from the embryos of mice which can develop into all cell types in the adult body. By culturing these embryonic stem cells under special conditions, researchers can convert them in to the two main cell types of the developing ovary: germ cells which go on to form eggs, and granulosa cells which help eggs grow and mature. The resulting lab-grown ovary can make eggs that produce live mice when fertilized. This approach has also been applied to human induced pluripotent stem cells (iPSCs), adult human cells which have been reprogrammed to a stem-like state. While this has produced human germ cells, generating human granulosa cells has been more challenging. Here, Pierson Smela, Kramme et al. show that activating a specific set of transcription factors (proteins that switch genes on or off) in iPSCs can make them transition to granulosa cells. First, the team tested random combinations of 35 transcription factors which, based on previous literature and genetic data, were likely to play a role in the formation of granulosa cells. This led to the identification of a small number of factors that caused the human iPSCs to develop features and carry out roles seen in mature granulosa cells; this includes producing an important reproductive hormone and supporting the maturation of germ cells. Pierson Smela, Kramme et al. found that growing these granulosa-like cells together with germ cells (also generated via iPSCs) resulted in structures similar to ovarian follicles which help eggs develop. These findings could help researchers build stable systems for studying how granulosa cells behave in human ovaries. This could lead to new insights about reproductive health.

The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has elicited a global health crisis of catastrophic proportions. With only a few vaccines approved for early or limited use, there is a critical need for effective antiviral strategies. In this study, we report a unique antiviral platform, through computational design of ACE2-derived peptides which both target the viral spike protein receptor binding domain (RBD) and recruit E3 ubiquitin ligases for subsequent intracellular degradation of SARS-CoV-2 in the proteasome. Our engineered peptide fusions demonstrate robust RBD degradation capabilities in human cells and are capable of inhibiting infection-competent viral production, thus prompting their further experimental characterization and therapeutic development. Pranam Chatterjee et al. present a novel computational platform for engineering peptide fusions that bind to the SARS-CoV-2 spike protein and tag it for proteasomal degradation. They experimentally validate an optimal variant in human cells, showing that it inhibits production of infection-competent virus.

Dysregulated protein degradation via the ubiquitin-proteasomal pathway can induce numerous disease phenotypes, including cancer, neurodegeneration, and diabetes. While small molecule-based targeted protein degradation (TPD) and targeted protein stabilization (TPS) platforms can address this dysregulation, they rely on structured and stable binding pockets, which do not exist to classically “undruggable” targets. Here, we expand the TPS target space by engineering “deubiquibodies” (duAbs) via fusion of computationally-designed peptide binders to the catalytic domain of the potent OTUB1 deubiquitinase. In human cells, duAbs effectively stabilize exogenous and endogenous proteins in a DUB-dependent manner. Using protein language models to generate target-binding peptides, we engineer duAbs to conformationally diverse target proteins, including key tumor suppressor proteins p53 and WEE1, and heavily-disordered fusion oncoproteins, such as PAX3::FOXO1. We further encapsulate p53-targeting duAbs as mRNA in lipid nanoparticles and demonstrate effective intracellular delivery, p53 stabilization, and apoptosis activation, motivating further in vivo translation. Dysregulated protein degradation drives diseases like cancer. Here, authors use protein language models to design target-binding peptides, which are subsequently attached to the catalytic domain of the OTUB1 deubiquitinase, generating “deubiquibodies” (duAbs). duAbs restore tumor suppressors and fusion oncoproteins, offering a programmable strategy for protein stabilization.

Tumor cells proliferate in cellular environments characterized by a lack of optimal tissue organization resulting oftentimes in compromised cellular metabolism affecting nutrition, respiration, and energetics. The response of tumor cells to adverse environmental conditions is a key feature affecting their pathogenicity. We found that inhibitor of DNA binding 2 ( ID2 ) expression levels significantly correlate with the ability of glioblastoma (GBM)-derived cell lines to survive glucose deprivation. ID2 suppressed mitochondrial oxidative respiration and mitochondrial ATP production by regulating the function of mitochondrial electron transport chain (mETC) complexes, resulting in reduced superoxide and reactive oxygen species (ROS) production from mitochondria. ID2 suppression of ROS production reduced mitochondrial damage and enhanced tumor cell survival during glucose deprivation. Bioinformatics analysis of GBM gene expression data from The Cancer Genome Atlas (TCGA) database revealed that expression of ID2 mRNA is unique among ID gene family members in correlating with the expression of nuclear genes involved in mitochondrial energy metabolism and assembly of mETC. Our data indicate that the expression level of ID2 in GBM cells can predict the sensitivity of GBM-derived tumor cells to decreased glucose levels. Low levels of ID2 expression in human GBM tissues may identify a clinical group in which metabolic targeting of glycolytic pathways can be expected to have the greatest therapeutic efficacy.