Biology subjects

Kameda, T.

Publications and source records attributed to Kameda, T..

9 recordsLinked to original sources

bioRxiv2026

FDXR Upregulation by p63/p73 is a Prognostic and Therapeutic Marker of HTLV-1-Associated Adult T Cell Leukemia/Lymphoma

Infection with human T-lymphotropic virus 1 (HTLV-1) can lead to severe diseases, including adult T-cell leukemia/lymphoma (ATL), for which current therapies are inadequate. HTLV-1 persists chronically through integration of its genome into host DNA and expansion of infected clones via cellular proliferation. Selective elimination of infected cells is the ideal treatment goal but requires identifying cellular markers dysregulated by HTLV-1 that can be exploited therapeutically. Combining metabolomics and proteomics analyses, we identified increased expression of the mitochondrial enzyme ferredoxin reductase (FDXR) as a hallmark of HTLV-1-infected cells. In patient cells, FDXR upregulation mirrored disease severity, was enriched in proliferating tumour cells and predicted worse ATL prognosis. Mechanistically, FDXR upregulation was directly driven by the transactivating isoforms of two p53 family members, TAp63 and TAp73. In silico drug susceptibility predictions, followed by targeted cytotoxicity, proliferation and Drug-Seq assays demonstrated that FDXR upregulation is associated with increased sensitivity of ATL cells to repurposed chemotherapeutic agents, particularly fluorouracil. Overall, our findings establish FDXR as a novel biomarker of HTLV-1-associated disease progression and ATL prognosis, elucidate its upstream molecular regulation, and highlight its predictive potential for chemotherapeutic sensitivity of ATL cells.

microbiology↗

bioRxiv2021

Machine-learning-guided library design cycle for directed evolution of enzymes: the effects of training data composition on sequence space exploration

Machine learning (ML) is becoming an attractive tool in mutagenesis-based protein engineering because of its ability to design a variant library containing proteins with a desired function. However, it remains unclear how ML guides directed evolution in sequence space depending on the composition of training data. Here, we present a ML-guided directed evolution study of an enzyme to investigate the effects of a known "highly positive" variant (i.e., variant known to have high enzyme activity) in training data. We performed two separate series of ML-guided directed evolution of Sortase A with and without a known highly positive variant called 5M in training data. In each series, two rounds of ML were conducted: variants predicted by the first round were experimentally evaluated, and used as additional training data for the second-round prediction. The improvements in enzyme activity were comparable between the two series, both achieving enzyme activity 2.2-2.5 times higher than 5M. Intriguingly, the sequences of the improved variants were largely different between the two series, indicating that ML guided the directed evolution to the distinct regions of sequence space depending on the presence/absence of the highly positive variant in the training data. This suggests that the sequence diversity of improved variants can be expanded not only by conventional ML using the whole training data, but also by ML using a subset of the training data even when it lacks highly positive variants. In summary, this study demonstrates the importance of regulating the composition of training data in ML-guided directed evolution.

bioengineering↗

bioRxiv2021

Free Energy Landscape of RNA Binding Dynamics in Start Codon Recognition by Eukaryotic Ribosomal Pre-Initiation Complex

Specific interaction between the start codon, 5-AUG-3, and the anticodon, 5-CAU-3, ensures accurate initiation of translation. Recent studies show that several near-cognate start codons (e.g. GUG and CUG) can play a role in initiating translation in eukaryotes. However, the mechanism allowing initiation through mismatched base-pairs at the ribosomal decoding site is still unclear at an atomic level. In this work, we propose an extended simulation-based method to evaluate free energy profiles, through computing the distance between each base-pair of the triplet interactions (d1, d2 and d3) involved in recognition of start codons in eukaryotic translation pre-initiation complex. Our method provides not only the free energy penalty ({Delta}{Delta}G) for mismatched start codons relative to the AUG start codon, but also the preferred pathways of transitions between bound and unbound states, which has not been described by previous studies. To verify the method, the binding dynamics of cognate (AUG) and near-cognate start codons (CUG and GUG) were simulated. Evaluated free energy profiles agree with experimentally observed changes in initiation frequencies from respective codons. This work proposes for the first time how a G:U mismatch at the first position of codon (GUG)-anticodon base-pairs destabilizes the accommodation in the initiating eukaryotic ribosome and how initiation at a CUG codon is nearly as strong as, or sometimes stronger than, that at a GUG codon. Our method is expected to be applied to study the affinity changes for various mismatched base-pairs.

biophysics↗

bioRxiv2020

Structural Dynamics of DNA Depending on Methylation Patterns

DNA methylation is associated with a number of biological phenomena, and plays crucial roles in epigenetic regulation of eukaryotic gene expression. It is also suggested that DNA methylation alters the mechanical properties of DNA molecules, which is likely to affect epigenetic regulation. However, it has not been systematically investigated how methylation changes the structural and dynamic features of DNA. In this research, to elucidate the effects of methylation on DNA mechanics, a fully atomic molecular dynamics simulation of double-stranded DNA with several methylation patterns was performed. Through the analysis of the relative positioning of the nucleotides (base-step variables), characteristic changes in terms of local flexibility were observed, which further affected the overall DNA geometry and stiffness. These findings may serve as a basis for a discussion on methylation-dependent DNA dynamics in physiological conditions.

biophysics↗

bioRxiv2019

The local state of chromatin compaction at transcription start sites controls transcription levels

A defined amount of transcript is produced from transcription start sites (TSSs) of each gene, suggesting that the binding frequency of RNA polymerase varies among genes. However, what structure in chromatin controls this frequency remains elusive. We established a method to fractionate chromatin according to its degree of three-dimensional compaction. Nucleosomes were evenly detected through all of the fractions, but histone H1 was more highly enriched in the more compact chromatin fractions. Similarly, HP1 and MBD2b were more abundant in more compact chromatin, while the levels of tri-methylated H3 (Lys9) and 5-methyl cytosine subtly increased. Via genome-wide analyses, nearly the entire genome was found to exist in compact chromatin without variations between repeat and non-repeat sequences; however, active TSSs were rarely found in compact chromatin. Based on a correlation between weak compaction and RNA polymerase binding at TSSs, it appears that local states of chromatin compaction determine transcription levels.

molecular biology↗

bioRxiv2019

Histone Tail Dynamics in Partially Disassembled Nucleosomes During Chromatin Remodeling

Nucleosomes are structural units of the chromosome consisting of DNA wrapped around histone proteins, and play important roles in compaction and regulation of the chromatin structure. While the structure and dynamics of canonical nucleosomes have been studied extensively, those of nucleosomes in intermediate states, that occur when their structure or positioning is modulated, have been less understood. In particular, the dynamic features of partially disassembled nucleosomes have not been discussed in previous studies. Using all-atom molecular dynamics simulations, in this study, we investigated the dynamics and stability of nucleosome structures lacking a histone-dimer. DNA in nucleosomes lacking a histone H2A/H2B dimer was drastically deformed due to loss of local interactions between DNA and histones. In contrast, conformation of DNA in nucleosomes lacking H3/H4 was similar to the canonical nucleosome, as the H2A C-terminal domain infiltrated the space originally occupied by the dissociated H3/H4 histones and stabilized DNA in close proximity. Our results suggest that, besides histone chaperones, the intrinsic dynamics of nucleosomes support the exchange of H2A/H2B, which is significantly more frequent than that of H3/H4.\n\nStatement of SignificanceEukaryotic chromosomes are composed of nucleosomes, in which the DNA wraps around the core histone proteins. To enable transcription and replication of DNA, or to modulate these functions by exchange of histones, nucleosomes should be at least partially disassembled, as evidenced by the observation of nucleosome structures lacking an H2A/H2B histone dimer by crystallography. The dynamic behavior of nucleosomes in such intermediate states may affect gene expression and repair, however it has not been completely elucidated so far. In this study, we adopted molecular dynamics simulations to analyze the conformational changes in partially disassembled nucleosomes. Enhanced structural fluctuations of DNA were observed in these nucleosomes, which may, as well as specific histone chaperones, support the exchange of H2A/H2B.

biophysics↗

bioRxiv2018

Nearest-neighbor parameter for inosine-cytosine pairs through a combined experimental and computational approach

In RNA secondary structure prediction, nearest-neighbor parameters are used to determine the stability of a given structure. We derived the nearest-neighbor parameters for RNAs containing inosine-cytosine pairs. For parameter derivation, we developed a method that combines UV adsorption measurement experiments with free-energy calculations using molecular dynamics simulations. The method provides fast drop-in parameters for modified bases. Derived parameters were compared and found to be consistent with existing parameters for canonical RNAs. A duplex with an internal inosine-cytosine pair is 0.9 kcal/mol more unstable than the same duplex with an internal guanine-cytosine pair, and is as stable as the one with an internal adenine-uracil pair (only 0.1 kcal/mol more stable) on average.

bioinformatics↗

bioRxiv2018

Antibacterial effects of nanoimprinted moth-eye film in practical settings

Recent studies report that surfaces displaying micrometer-or nanometer-sized undulating structures exhibit antibacterial effects. In previous work, we described the use of an advanced nanofabrication technique to generate an artificial biomimetic moth-eye film by coating a polyethylene terephthalate (PET) film with nanoscale moth-eye protrusions made from a hydrophilic resin. This moth-eye film exhibited enhanced antibacterial effects in in vitro experiments. The aim of the present study was to verify the antibacterial efficacy of the Moth-eye film in practical environments. Three types of films (Moth-eye film, Flat film, and PET film) were used to compare antibacterial effects. Sample films were pasted onto hand-wash sinks at the testing locations. After several hours of elapsed time, bacteria from the surface of sample films were collected using one of three kinds of culture media stampers (to permit identification of bacterial species). The stampers were incubated for 48 hours at 35 {degrees}C, and the numbers of colonies were counted.\n\nThe number of common bacteria including E. coli and S. aureus from the Moth-eye film was significantly lower than that from the PET film (p<0.05) and that from the Flat film at 1 hour (p<0.05). This study found that the Moth-eye film had durability of antibacterial effect and the Moth-eye structure (PET coated with nanoscale cone-shaped pillars) had a physical antibacterial effect from the earlier time points. Therefore, the Moth-eye film might be useful for general-purpose applications in practical environments.

microbiology↗

bioRxiv2018

A machine-learning-guided mutagenesis platform for accelerated discovery of novel functional proteins

Molecular evolution based on mutagenesis is widely used in protein engineering. However, optimal proteins are often difficult to obtain due to a large sequence space that requires high costs for screening experiments. Here, we propose a novel approach that combines molecular evolution with machine learning. In this approach, we conduct two rounds of mutagenesis where an initial library of protein variants is used to train a machine-learning model to guide mutagenesis for the second-round library. This enables to prepare a small library suited for screening experiments with high enrichment of functional proteins. We demonstrated a proof-of-concept of our approach by altering the reference green fluorescent protein (GFP) so that its fluorescence is changed to yellow while improving its fluorescence intensity. Using 155 and 78 variants for the initial and the second-round libraries, respectively, we successfully obtained a number of proteins showing yellow fluorescence, 12 of which had better fluorescence performance than the reference yellow fluorescent protein (YFP). These results show the potential of our approach as a powerful platform for accelerated discovery of functional proteins.

bioengineering↗