Biology subjects

Matsumoto, H.

Publications and source records attributed to Matsumoto, H..

4 recordsLinked to original sources

bioRxiv2018

Blocking the Necroptosis Pathway Decreases RPE and Photoreceptor Damage Induced by NaIO3

PurposeSodium iodate (NaIO3) has been extensively used as a retinotoxin to induce RPE cell damage and degeneration of photoreceptors in vitro and in vivo. RIP-Kinase dependent programmed necrosis is an important redundant cell death pathway involved in photoreceptor cell death. We wanted to determine whether these pathways are actively involved in RPE and photoreceptor cell death after NaIO3 insult.\n\nMethodsARPE-19 cells were exposed to different concentrations of NaIO3 in the presence or absence of various concentrations of a RIPK inhibitor (Nec-1) or a pan-caspase inhibitor (Z-VAD), individually or combined. Cell death was determined at different time points by MTT (Sigma-Aldrich), LDH (Promega) and TUNEL (Millipore) assay. C57BL/6 and RIP3-/- mice were treated with a peritoneal injection of NaIO3 and eyes were enucleated at day 3 or 7. TUNEL staining was used to evaluate photoreceptor cell death. Photoreceptor cell loss was evaluated by measuring the thickness of outer nuclear layer (ONL). Microglia in the ONL were quantified in a retinal whole mount with Iba-1 antibody. RPE degeneration was also assessed in a RPE whole mount, with ZO-1 antibody.\n\nResultsNaIO3 resulted in significant cell death of ARPE-19 cells. Treatment with Nec-1 resulted in better protection than treatment with Z-VAD (P<0.01). A synergistic protective effect was observed when co-treating the cells with Nec-1 and Z-VAD. Nec-1 treatment also decreased the ARPE-19 mitochondrial damage caused by NaIO3. In vivo administration of NaIO3 resulted in significant RPE and photoreceptor destruction with substantial inflammatory cell infiltration. RIP3 knockout animals displayed considerably less RPE and photoreceptor cell loss, as well as drastically less inflammation.\n\nConclusionsProgrammed necrosis is an important cell death pathway mediating NaIO3 RPE and photoreceptor cell toxicity. Blocking the necroptosis pathway may serve as a novel therapeutic strategy for various RPE degenerative diseases.

biochemistry↗

bioRxiv2017

An efficient gene regulatory network inference algorithm for early Drosophila melanogaster embryogenesis

The spatial patterns of gene expression in early Drosophila melanogaster embryogenesis have been studied experimentally and theoretically to reveal the molecular basis of morphogenesis. In particular, the gene regulatory network (GRN) of gap genes has been investigated through mathematical modeling and simulation. Although these simulation-based approaches are useful for describing complex dynamics and have revealed several important regulations in spatial patterning, they are computationally intensive because they optimize GRN with iterative simulation. Recently, the advance of experimental technologies is enabling the acquisition of comprehensive spatial expression data, and an efficient algorithm will be necessary to analyze such large-scale data. In this research, we developed an efficient algorithm to infer the GRN based on a linear reaction-diffusion model. First, we qualitatively analyzed the GRNs of gap genes and pair-rule genes based on our algorithm and showed that two mutual repressions are fundamental regulations. Then, we inferred the GRN from gap gene data, and identified asymmetric regulations in addition to the two mutual repressions. We analyzed the effect of these asymmetric regulations on spatial patterns, and showed that they have the potential to adjust peak position. Our algorithm runs in sub-second time, which is significantly smaller than the runtime of simulation-based approaches (between 8 and 160 h, for exmaple). Neverthe-less, our inferred GRN was highly correlated with the simulation-based GRNs. We also analyzed the gap gene network of Clogmia albipunctata and showed that different mutual repression regulations might be important in comparison with those of Drosophila melanogaster. As our algorithm can infer GRNs efficiently and can be applied to several different network analysis, it will be a valuable approach for analyzing large-scale data.

bioinformatics↗

bioRxiv2017

Correction of autoimmune IL2RA mutations in primary human T cells using non-viral genome targeting

Human T cells are central to physiological immune homeostasis, which protects us from pathogens without collateral autoimmune inflammation. They are also the main effectors in most current cancer immunotherapy strategies1. Several decades of work have aimed to genetically reprogram T cells for therapeutic purposes2-5, but as human T cells are resistant to most standard methods of large DNA insertion these approaches have relied on recombinant viral vectors, which do not target transgenes to specific genomic sites6, 7. In addition, the need for viral vectors has slowed down research and clinical use as their manufacturing and testing is lengthy and expensive. Genome editing brought the promise of specific and efficient insertion of large transgenes into target cells through homology-directed repair (HDR), but to date in human T cells this still requires viral transduction8, 9. Here, we developed a non-viral, CRISPR-Cas9 genome targeting system that permits the rapid and efficient insertion of individual or multiplexed large (>1 kilobase) DNA sequences at specific sites in the genomes of primary human T cells while preserving cell viability and function. We successfully tested the potential therapeutic use of this approach in two settings. First, we corrected a pathogenic IL2RA mutation in primary T cells from multiple family members with monogenic autoimmune disease and demonstrated enhanced signalling function. Second, we replaced the endogenous T cell receptor (TCR) locus with a new TCR redirecting T cells to a cancer antigen. The resulting TCR-engineered T cells specifically recognized the tumour antigen, with concomitant cytokine release and tumour cell killing. Taken together, these studies provide preclinical evidence that non-viral genome targeting will enable rapid and flexible experimental manipulation and therapeutic engineering of primary human immune cells.

genetics↗

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↗