Search bioRxiv⌕ Search

Biology subjects

Liu, Y. V.

Publications and source records attributed to Liu, Y. V..

7 recordsLinked to original sources

Antiviral Defence is a Conserved Function of Diverse DNA Glycosylases

Bacteria are frequently attacked by viruses, known as phages, and rely on diverse defence systems like restriction endonucleases and CRISPR-Cas to survive. While phages can evade these defences by covalently modifying their DNA, these non-canonical nucleobases create a strong selective pressure for host proteins that can recognize and exploit them. Here, using a structure-guided discovery approach, we identify widespread families of DNA glycosylases that protect bacteria against phages that incorporate modified guanine bases into their DNA. Despite high sequence variation, these enzymes share a conserved glycosylase fold and occur across bacterial lineages. We also uncover a distinct glycosylase superfamily that defends against phages with thymidine modifications, showing that glycosylases have repeatedly evolved as antiviral defences. Together, these findings reveal DNA glycosylases as versatile effectors of bacterial immunity and underscore structure-guided discovery as a powerful strategy for uncovering hidden layers of antiviral defence.

microbiology↗

Application of machine learning to discriminate photoreceptor cell species in xenotransplanted chimeric retinas

Photoreceptor transplantation is being studied to improve visual function in retinal diseases causing blindness, such as age-related macular degeneration, hereditary eye diseases, and traumatic retinopathy, among others. Preclinical studies often involve the delivery of exogenous human photoreceptor cells into the retinas of animal models. In such experiments, a key readout is the differential frequency of donor cell somatic integration versus artificial labeling secondary to material transfer of cytosolic or nuclear labels from donor to recipient cells. For this analysis, the ability to recognize photoreceptor nuclei as being of donor (human) versus animal is key, but purely immunohistology discrimination can be challenging because of antigenic species overlap or intercellular antigen transfer. To address this challenge, we sought to develop and validate a computational technique to discriminate between photoreceptor cells of different animal species based on machine learning of nuclear morphology. Here, we aimed to evaluate the feasibility of using computer-assisted detection of separate nuclei and employing random forest classification to automate the species differentiation, among DAPI-stained photoreceptors after xeno-transplantation of human photoreceptors into the retinas of mice and pigs. Our models were trained on single-species samples and validated with mixed-species samples. We then transplanted human embryonic stem cell-derived retinal organoid cells into rodent and pig retinal degeneration models. The random forest model accurately determined cell identity post-xenotransplantation, validated by histological assessment using an anti-human nuclear antibody. Our results support the potential efficacy of employing machine learning image analysis and classification techniques that may promote experimental rigor, minimize observer bias, and enable high throughput semi-automated workflows for transplantation outcomes analysis. The methodological framework reported here may enable a more nuanced and precise analysis of the behavior of transplanted photoreceptors for the purposes of human retinal regeneration.

neuroscience↗

Modular vector assembly enables rapid assessment of emerging CRISPR technologies

The diversity of CRISPR systems, coupled with scientific ingenuity, has led to an explosion of applications; however, to test newly-described innovations in their model systems, researchers typically embark on cumbersome, one-off cloning projects to generate custom reagents that are optimized for their biological questions. Here, we leverage Golden Gate cloning to create the Fragmid toolkit, a modular set of CRISPR cassettes and delivery technologies, along with a web portal, resulting in a combinatorial platform that enables scalable vector assembly within days. We further demonstrate that multiple CRISPR technologies can be assessed in parallel in a pooled screening format using this resource, enabling the rapid optimization of both novel technologies and cellular models. These results establish Fragmid as a robust system for the rapid design of CRISPR vectors, and we anticipate that this assembly approach will be broadly useful for systematic development, comparison, and dissemination of CRISPR technologies.

genomics↗

Optimization of Cas12a for multiplexed genome-scale transcriptional activation

Cas12a CRISPR technology, unlike Cas9, allows for facile multiplexing of guide RNAs from a single transcript, simplifying combinatorial perturbations. While Cas12a has been implemented for multiplexed knockout genetic screens, it has yet to be optimized for CRISPR activation (CRISPRa) screens in human cells. Here we develop a new Cas12a-based transactivation domain (TAD) recruitment system using the ALFA nanobody and demonstrate simultaneous activation of up to four genes. We screen a genome-wide library to identify modulators of growth and MEK inhibition and we compare these results to those obtained with open reading frame (ORF) overexpression and Cas9-based CRISPRa. We find that the activity of multiplexed arrays is largely predictable from the best-performing guide and provide criteria for selecting active guides. We anticipate that these results will greatly accelerate the exploration of gene function and combinatorial phenotypes at scale.

genomics↗

Targeted intragenic demethylation initiates chromatin rewiring for gene activation

Aberrant DNA methylation in the region surrounding the transcription start site is a hallmark of gene silencing in cancer. Currently approved demethylating agents lack specificity and exhibit high toxicity. Herein we show, using the p16 gene as an example, that targeted demethylation of the promoter-exon 1-intron 1 (PrExI) region initiates an epigenetic wave of local chromatin remodeling and distal long-range interactions, culminating in gene-locus specific activation. Through development of CRISPR-DiR (DNMT1-interacting RNA), in which ad hoc edited guides block methyltransferase activity in a locus-specific fashion, we demonstrate that demethylation is coupled to epigenetic and topological changes. These results suggest the existence of a specialized "demethylation firing center (DFC)" which can be switched on by an adaptable and selective RNA-mediated approach for locus-specific transcriptional activation. One Sentence SummaryLocus demethylation via CRISPR-DiR reshapes chromatin structure and specifically reactivates its cognate gene.

genetics↗

Pseudogene-mediated DNA demethylation leads to oncogene activation

Despite being one of the leading causes of cancer-related deaths, there is an unmet clinical need for hepatocellular carcinoma (HCC) patients. The lack of effective treatment is, at least in part, due to our lack of understanding of the molecular pathogenesis of this disease. Oncofetal protein SALL4 is re-activated in patients with aggressive HCC along with other solid tumors and hematologic malignancies. This study identifies a previously unrecognized mechanism of SALL4 reactivation which is mediated by pseudogene-induced demethylation. Using a locus-specific demethylating technology, we identified the critical CpG region for SALL4 expression. We showed that SALL4 pseudogene 5 hypomethylates this region through interaction with DNMT1, resulting in SALL4 upregulation. Intriguingly, pseudogene 5 is significantly upregulated in a hepatitis B virus (HBV) model prior to SALL4 induction, and both are increased in HBV-HCC patients. Our results suggest that pseudogene-mediated demethylation represents a unique mechanism of oncogene activation in cancer. SignificanceOur study provides a mechanistic link between HBV infection, activation of the oncogene SALL4, and HCC. We reveal a previously undescribed capability of a pseudogene to epigenetically activate an oncogene by demethylation in a locus-specific manner.

cancer biology↗

Quantifiable In Vivo Imaging Biomarkers of Retinal Regeneration by Photoreceptor Cell Transplantation

PurposeShort-term improvements in retinal anatomy are known to occur in preclinical models of photoreceptor transplantation. However, correlative changes over the long term are poorly understood. We aimed to develop a quantifiable imaging biomarker grading scheme, using non-invasive multimodal confocal scanning laser ophthalmoscopy (cSLO) imaging, to enable serial evaluation of photoreceptor transplantation over the long term. MethodsYellow-green fluorescent microspheres were transplanted into the vitreous cavity and/or subretinal space of C57/BL6J mice. Photoreceptor cell suspensions or sheets from rhodopsin-green fluorescent protein mice were transplanted subretinally, into either NOD.CB17-Prkdcscid/J or C3H/HeJ-Pde6brd1 mice. Multimodal cSLO imaging was performed serially for up to three months after transplantation. Imaging biomarkers were scored, and a grade was defined for each eye by integrating the scores. Image grades were correlated with immunohistochemistry (IHC) data. ResultsMultimodal imaging enabled the extraction of quantitative imaging biomarkers including graft size, GFP intensity, graft length, on-target graft placement, intra-graft lamination, hemorrhage, retinal atrophy, and peri-retinal proliferation. Migration of transplanted material was observed. Changes in biomarker scores and grades were detected in 13/16 and 7/16 eyes, respectively. A high correlation was found between image grades and IHC parameters. ConclusionsSerial evaluation of multiple imaging biomarkers, when integrated into a per-eye grading scheme, enabled comprehensive tracking of longitudinal changes in photoreceptor cell grafts over time. The application of systematic multimodal in vivo imaging could be useful in increasing the efficiency of preclinical retinal cell transplantation studies in rodents and other animal models.

neuroscience↗