Biology subjects

Ko, M. S. H.

Publications and source records attributed to Ko, M. S. H..

4 recordsLinked to original sources

bioRxiv2023

Joint sequence & chromatin neural networks characterize the differential abilities of Forkhead transcription factors to engage inaccessible chromatin

The DNA-binding activities of transcription factors (TFs) are influenced by both intrinsic sequence preferences and extrinsic interactions with cell-specific chromatin landscapes and other regulatory proteins. Disentangling the roles of these binding determinants remains challenging. For example, the FoxA subfamily of Forkhead domain (Fox) TFs are known pioneer factors that can bind to relatively inaccessible sites during development. Yet FoxA TF binding also varies across cell types, pointing to a combination of intrinsic and extrinsic forces guiding their binding. While other Forkhead domain TFs are often assumed to have pioneering abilities, how sequence and chromatin features influence the binding of related Fox TFs has not been systematically characterized. Here, we present a principled approach to compare the relative contributions of intrinsic DNA sequence preference and cell-specific chromatin environments to a TFs DNA-binding activities. We apply our approach to investigate how a selection of Fox TFs (FoxA1, FoxC1, FoxG1, FoxL2, and FoxP3) vary in their binding specificity. We over-express the selected Fox TFs in mouse embryonic stem cells, which offer a platform to contrast each TFs binding activity within the same preexisting chromatin background. By applying a convolutional neural network to interpret the Fox TF binding patterns, we evaluate how sequence and preexisting chromatin features jointly contribute to induced TF binding. We demonstrate that Fox TFs bind different DNA targets, and drive differential gene expression patterns, even when induced in identical chromatin settings. Despite the association between Forkhead domains and pioneering activities, the selected Fox TFs display a wide range of affinities for preexiting chromatin states. Using sequence and chromatin feature attribution techniques to interpret the neural network predictions, we show that differential sequence preferences combined with differential abilities to engage relatively inaccessible chromatin together explain Fox TF binding patterns at individual sites and genome-wide.

genomics↗

bioRxiv2023

Functional and long-lived melanocytes from human pluripotent stem cells with transient ectopic expression of JMJD3

BackgroundMelanocytes are an essential part of the epidermis, and their regeneration has received much attention because propagation of human adult melanocytes in vitro is too slow for clinical use. Differentiation from human pluripotent stem cells to melanocytes has been reported, but the protocols to produce them require multiple and complex differentiation steps. MethodWe differentiated human embryonic stem cells (hESCs) that transiently express JMJD3 to pigmented cells. We investigated whether the pigmented cells have melanocytic characteristics and functions by qRT-PCR, immunocytochemical analysis and flow cytometry. We also investigated their biocompatibility by injecting the cells into immunodeficient mice for clinical use. ResultWe successfully differentiated and established a pure culture of melanocytes. The melanocytes maintained their growth rate for a long time, approximately 200 days, and were functional. They exhibited melanogenesis and transfer of melanin to peripheral keratinocytes. Moreover, melanocytes simulated the developmental processes from melanoblasts to melanocytes. The melanocytes had high engraftability and biocompatibility in the immunodeficient mice. ConclusionThe robust generation of functional and long-lived melanocytes are key to developing clinical applications for the treatment of pigmentary skin disorders.

cell biology↗

bioRxiv2022

Controllable self-replicating RNA vaccine delivered intradermally elicits predominantly cellular immunity

Intradermal delivery of self-replicating RNA (srRNA) is a promising vaccine platform. Considering that human skin temperature is around 33{degrees}C, lower than core body temperature of 37{degrees}C, we have developed an srRNA that functions optimally at skin temperature and is inactivated at or above 37{degrees}C as a safety switch. This temperature-controllable srRNA (c-srRNA), when tested as an intradermal vaccine against SARS-CoV-2, functions when injected naked without lipid nanoparticles. Unlike most currently available vaccines, c-srRNA vaccines predominantly elicit cellular immunity with little or no antibody production. Interestingly, c-srRNA-vaccinated mice produced antigen-specific antibodies upon subsequent stimulation with antigen protein. Antigen-specific antibodies were also produced when B-cell stimulation using antigen protein was followed by c-srRNA booster vaccination. Using c-srRNA, we have designed a pan-coronavirus booster vaccine that incorporates both spike receptor binding domains as viral surface proteins and evolutionarily conserved nucleoproteins as viral non-surface proteins, from both SARS-CoV-2 and MERS-CoV. It can thereby potentially immunize against SARS-CoV-2, SARS-CoV, MERS-CoV, and their variants. c-srRNA may provide a route to activate cellular immunity against a wide variety of pathogens.

immunology↗

bioRxiv2016

SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation

The analysis of RNA-Seq data from individual differentiating cells enables us to reconstruct the differentiation process and the degree of differentiation (in pseudo-time) of each cell. Such analyses can reveal detailed expression dynamics and functional relationships for differentiation. To further elucidate differentiation processes, more insight into gene regulatory networks is required. The pseudo-time can be regarded as time information and, therefore, single-cell RNA-Seq data are time-course data with high time resolution. Although time-course data are useful for inferring networks, conventional inference algorithms for such data suffer from high time complexity when the number of samples and genes is large. Therefore, a novel algorithm is necessary to infer networks from single-cell RNA-Seq during differentiation.\n\nIn this study, we developed the novel and efficient algorithm SCODE to infer regulatory networks, based on ordinary differential equations. We applied SCODE to three single-cell RNA-Seq datasets and confirmed that SCODE can reconstruct observed expression dynamics. We evaluated SCODE by comparing its inferred networks with use of a DNaseI-footprint based network. The performance of SCODE was best for two of the datasets and nearly best for the remaining dataset. We also compared the runtimes and showed that the runtimes for SCODE are significantly shorter than for alternatives. Thus, our algorithm provides a promising approach for further single-cell differentiation analyses.\n\nThe R source code of SCODE is available at https://github.com/hmatsu1226/SCODE.

bioinformatics↗