Biology subjects

Hamada, M.

Publications and source records attributed to Hamada, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Systematic Metaproteomics Mapping Reveals Functional and Ecological Landscapes of Human Gut Microbiota Responses to Therapeutic Drugs

Therapeutic compounds exert impacts on gut microbiota; however, how they affect the community functional ecology, especially as reflected at the protein level, remains largely unexplored. In this study, we systematically map metaproteomic responses of ex vivo human gut microbiota to 312 compounds, generating 4.6 million microbial protein responses, available as an interactive resource (https://shiny.imetalab.ca/MPR_Viz/). Protein-level analyses identify significant metaproteomic shifts induced by 47 compounds, with neuropharmaceuticals as the sole drug class significantly enriched among these hits. Further analyses on the community level reveal a tri-stability pattern in microbial composition and the emergence of three distinct functional states, based on a functional beta-diversity metric. Notably, neuropharmaceuticals cause particularly strong effects on the microbiomes, lowering the proteome-level functional redundancy and raising the level of antimicrobial resistance proteins, ultimately pushing the microbiome into an alternative functional state. Preliminary validation suggests that enhancing functional redundancy may contribute to maintaining microbiota resilience against neuropharmaceutical-induced antimicrobial resistance. Overall, this work establishes a comprehensive view of how drugs influence gut microbiome function and ecology at the protein level, proposes a landscape-based framework for interpreting community resilience, and highlights the need to consider protein-level and ecological responses in the evaluation of therapeutic interventions.

microbiology↗

bioRxiv2025

Deep learning generates apo RNA conformations with cryptic ligand binding site

RNA plays vital roles in diverse biological processes and represents an attractive class of therapeutic targets. In particular, cryptic ligand-binding sites--absent in apo structures but formed upon conformational rearrangement--offer high specificity for RNA-ligand recognition, yet remain rare among experimentally-resolved RNA-ligand complex structures and difficult to predict in silico. RNA-targeted structure-based drug design (SBDD) is therefore limited by challenges in sampling cryptic states. Here, we apply Molearn, a hybrid molecular dynamics-deep generative framework, to expand apo RNA conformational ensembles toward cryptic states. Focusing on the paradigmatic HIV-1 TAR-MV2003 system, Molearn was trained exclusively on apo TAR conformations and used to generate a diverse ensemble of TAR structures. Candidate cryptic MV2003-binding conformations were subsequently identified using post-generation geometric analyses. Docking simulations of these conformations with MV2003 yielded binding poses with RNA-ligand interaction scores comparable to those of NMR-derived complexes. Notably, this work provides the first demonstration that a generative model can access cryptic RNA conformations that are ligand-binding competent and have not been recovered in prior molecular dynamics and deep generative modeling studies. Finally, we discuss current limitations in scalability and systematic detection, including application to the Internal Ribosome Entry Site, and outline future directions toward RNA-targeted SBDD.

bioinformatics↗

bioRxiv2024

Multi-objective computational optimization of human 5' UTR sequences

The computational design of mRNA sequences is a critical technology for both scientific research and industrial applications. Recent advances in prediction and optimization models have enabled the automatic scoring and optimization of 5' UTR sequences, key upstream elements of mRNA. However, fully automated design of 5' UTR sequences with more than two objective scores has not yet been explored. In this study, we present a computational pipeline that optimizes human 5' UTR sequences in a multi-objective framework, addressing up to four distinct and conflicting objectives. Our work represents an important advancement in the multi-objective computational design of mRNA sequences, paving the way for more sophisticated mRNA engineering.

bioinformatics↗

bioRxiv2024

Time-dependent computational model of post-traumatic osteoarthritis to estimate how mechanoinflammatory mechanisms impact cartilage aggrecan content

Degenerative musculoskeletal diseases like osteoarthritis can be initiated by joint injury. Injurious overloading-induced mechanical straining of articular cartilage and subsequent biological responses may trigger cartilage degradation. One early sign of degradation is loss of aggrecan content which is potentially accelerated near chondral lesions under physiological loading. Yet, the mechanoinflammatory mechanisms explaining time-dependent degradation in regions with disparate mechanical loading are unclear and challenging to assess with experiments alone. Here, we developed computational models unraveling potential mechanisms behind aggrecan content adaptation. Incorporating mechanical strain-driven cell damage and downstream proteolytic enzyme release, fluid flow-driven aggrecan depletion, and fluid pressure-stimulated regulation of aggrecan biosynthesis, the models agreed with experiments and exhibited 14%-points greater near-lesion aggrecan loss after 12 days of physiological loading compared to without loading. This significant advancement in mechanistic understanding incorporated into cartilage adaptation model can help in development and guidance of personalized therapies, such as rehabilitation protocols and tissue- engineered constructs.

bioengineering↗

bioRxiv2024

Tunicate-specific protein Epi-1 is essential for conferring hydrophilicity to the larval tunic in the ascidian Ciona

Animals must avoid adhesion to objects in the environment to maintain their mobility and independence. The marine invertebrate chordate ascidians are characterized by an acellular matrix tunic enveloping their entire body for protection and swimming. The tunic of ascidian larvae consists of a surface cuticle layer and inner matrix layer. Hydrophilic substances coat the cuticle; this modification is thought to be for preventing adhesion. However, the molecule responsible for regulating this modification has not been clarified. We here found that the tunicate-specific protein Epi-1 is responsible for preventing adhesiveness of the tunic in the ascidian Ciona intestinalis Type A. Ciona mutants with homozygous knockouts of Epi-1 exhibited adhesion to plastic plates and to other individuals. The cuticle of the Epi-1 mutants was fragile, and it lost the glycosaminoglycans supplied by test cells, the accessory cells that normally attach to the tunic surface. Although it has an apparent signal peptide for membrane trafficking, we showed that the Epi-1 protein is localized to the cytosol of the epidermal cells. Our study demonstrated that the emergence of the tunicate-specific protein Epi-1 accelerated the immediate ancestor of tunicates to evolve a tunic by providing a way to avoid the adhesiveness of this structure. HighlightsO_LIThe ascidian Ciona tunic has a glycosaminoglycan (GAG)-coated hydrophilic cuticle. C_LIO_LIEpi-1 is a protein expressed in the epidermis of tunicates during embryogenesis. C_LIO_LIEpi-1 Ciona mutants have GAG-free, hydrophobic and therefore sticky cuticles. C_LIO_LIEpi-1 acquisition may have prevented adhesion of tunicates to environmental objects. C_LI

developmental biology↗

bioRxiv2024

The Lomb-Scargle periodogram-based differentially expressed gene detection along pseudotime

MotivationIn recent years, single-cell RNA sequencing (scRNA-seq) has provided high-resolution snapshots of biological processes and has contributed to the understanding of cell dynamics. Trajectory inference has the potential to provide a quantitative representation of cell dynamics, and several trajectory inference algorithms have been developed. However, the downstream analysis of trajectory inference, such as the analysis of differentially expressed genes (DEG), remains challenging. ResultsIn this study, we introduce a Lomb-Scargle (LS) periodogram-based algorithm for identifying DEGs associated with pseudotime in a trajectory analysis. The algorithm is capable of analyzing any inferred trajectory, including tree structures with multiple branching points, leading to diverse cell types. We validated this approach using simulated data and real datasets, and our results showed that our approach was superior when performing DEG analysis on complex structured trajectories. Our approach will contribute to gene characterization in trajectory analysis and help gain deeper biological insights. AvailabilityAll code used in our proposed method can be found at https://github.com/hiuchi/LS. Contacthitoshi.iuchi@hamadalab.com Supplementary informationSupplementary data are available at Journal Name online.

bioinformatics↗

bioRxiv2024

Landscape of Evolutionary Arms Races between Transposable Elements and KRAB-ZFP Family

Transposable elements (TEs) are mobile parasitic sequences that have expanded within the host genome. It has been hypothesized that host organisms have expanded the Kruppel-associated box-containing zinc finger proteins (KRAB-ZFPs), which epigenetically suppress TEs, to counteract disorderly TE transpositions. This process is referred to as the evolutionary arms race. However, the extent to which this evolutionary arms race occurred across various TE families remains unclear. In the present study, we systematically explored the evolutionary arms race between TE families and KRAB-ZFPs using public ChIP-seq data. We discovered and characterized new instances of evolutionary arms races with KRAB-ZFPs in endogenous retroviruses. Furthermore, we found that the regulatory landscape shaped by this arms race contributed to the gene regulatory network. In summary, our results provide insight into the impact of the evolutionary arms race on TE families, the KRAB-ZFP family, and host gene regulatory networks.

evolutionary biology↗

bioRxiv2024

Intraplacental injection of human iPSC-derived PDX1+ pancreatic progenitors prolongs Pdx1-deficient mice survival

Interspecies chimeras comprising human tissues have potential for use in disease modeling and regenerative medicine. Here, we successfully transplanted human induced pluripotent stem cell (iPSC)-derived PDX1+ pancreatic progenitor cells into Pdx1-deficient mouse embryos via intraplacental injection. The engrafted human cells predominantly localized to the duodenum, produced insulin, and extended the lifespan of Pdx1-/- mice by up to 10 days after birth. Transcriptomic analyses confirmed human pancreatic gene expression in human cells engrafted into the mouse duodenum. Our findings demonstrated the feasibility of generating interspecies chimeras with functional human pancreatic cells through in utero transplantation of lineage-committed progenitors. This approach circumvents developmental barriers while minimizing ethical concerns associated with PSCs. However, the incomplete rescue of the Pdx1-/- phenotype highlights the need for further research to enhance human cell engraftment and tissue integration. Overall, this study provides a foundation for developing human-animal chimera models for studying human development and regenerative therapies.

cell biology↗

bioRxiv2024

Prediction of antibiotic resistance mechanisms using a protein language model

MotivationAntibiotic resistance has emerged as a major global health threat, with an increasing number of bacterial infections becoming difficult to treat. Predicting the underlying resistance mechanisms of antibiotic resistance genes (ARGs) is crucial for understanding and combating this problem. However, existing methods struggle to accurately predict resistance mechanisms for ARGs with low similarity to known sequences and lack sufficient interpretability of the prediction models. ResultsIn this study, we present a novel approach for predicting ARG resistance mechanisms using Protein-BERT, a protein language model based on deep learning. Our method outperforms state-of-the-art techniques on diverse ARG datasets, including those with low homology to the training data, highlighting its potential for predicting the resistance mechanisms of unknown ARGs. Attention analysis of the model reveals that it considers biologically relevant features, such as conserved amino acid residues and antibiotic target binding sites, when making predictions. These findings provide valuable insights into the molecular basis of antibiotic resistance and demonstrate the interpretability of protein language models, offering a new perspective on their application in bioinformatics. AvailabilityThe source code is available for free at https://github.com/hmdlab/ARG-BERT. The output results of the model are published at https://waseda.box.com/v/ARG-BERT-suppl. Contactmhamada@waseda.jp

bioinformatics↗

bioRxiv2024

Hidden Challenges in Evaluating Spillover Risk of Zoonotic Viruses using Machine Learning Models

Machine learning models have been deployed to assess the zoonotic spillover risk of viruses by identifying their human infectivity potential. However, the scarcity of comprehensive datasets poses a major challenge, limiting the predictable range of viruses. Our study addressed this limitation through two key strategies: constructing expansive datasets across 26 viral families and developing new models leveraging large language models pre-trained on extensive nucleotide sequences. Our approaches substantially boosted our model performance. This enhancement was particularly notable in segmented RNA viruses, which are involved with severe zoonoses but have been overlooked due to limited data availability. Furthermore, models trained on data up to 2018 displayed strong generalization capability for viruses emerging post-2018. Nonetheless, we also found remaining challenges in alerting the zoonotic potential of specific viral lineages, including SARS-CoV-2. Our study elaborates on the models and datasets for predicting viral infectivity and highlights the unresolved issues to fully exploit machine learning in preparing for future zoonotic threats.

microbiology↗

bioRxiv2024

Stimulatory and inhibitory G-protein signaling relays drive cAMP accumulation for timely metamorphosis in the chordate Ciona

Larvae of the ascidian Ciona initiate metamorphosis tens of minutes after adhesion to a substratum via their adhesive organ. The gap between adhesion and metamorphosis initiation is suggested to ensure the rigidity of adhesion, allowing Ciona to maintain settlement after losing locomotive activity through metamorphosis. The mechanism producing the gap is unknown. Here, by combining gene functional analyses, pharmacological analyses, and live imaging, we propose that the gap represents the time required for sufficient cAMP accumulation to trigger metamorphosis. Not only the Gs pathway but also the Gi and Gq pathways are involved in the initiation of metamorphosis in the downstream signaling cascade of the neurotransmitter GABA, the known initiator of Ciona metamorphosis. The mutual crosstalk of stimulatory and inhibitory G-proteins functions as the accelerator and brake for cAMP production, ensuring the faithful initiation of metamorphosis at an appropriate time and in the right situation.

developmental biology↗

bioRxiv2024

REPrise: de novo interspersed repeat detection using inexact seeding

MotivationInterspersed repeats occupy a large part of many eukaryotic genomes, and thus their accurate annotation is essential for various genome analyses. Database-free de novo repeat detection approaches are powerful for annotating genomes that lack well-curated repeat databases. However, existing tools do not yet have sufficient repeat detection performance. ResultsIn this study, we developed REPrise, a de novo interspersed repeat detection software program based on a seed-and-extension method. Although the algorithm of REPrise is similar to that of RepeatScout, which is currently the de facto standard tool, we incorporated three unique techniques into REPrise: inexact seeding, affine gap scoring and loose masking. Analyses of rice and simulation genome datasets showed that REPrise outperformed RepeatScout in terms of sensitivity, especially when the repeat sequences contained many mutations. Furthermore, when applied to the complete human genome dataset T2T-CHM13, REPrise demonstrated the potential to detect novel repeat sequence families. AvailabilityThe source code of REPrise is freely available at https://github.com/hmdlab/REPrise. Repeat annotations predicted for the T2T genome using REPrise are also available at https://waseda.box.com/v/REPrise-data. Contactfukunaga@aoni.waseda.jp and mhamada@waseda.jp

bioinformatics↗

bioRxiv2024

Discovery of regulatory motifs associated with human insulator sites reveals the role of directional bias in insulator function

Insulator proteins function as barriers to enhancer-promoter interactions (EPIs), thereby regulating gene expression. The primary insulator protein in vertebrates is CTCF, a DNA-binding protein (DBP); however, the roles of other DBPs in EPI insulation are not fully understood. To address this, we developed a systematic and comprehensive deep learning-based approach to identify DNA motifs of DBPs associated with insulator function. Applying this method to human fibroblast cells, we identified 97 directional motifs and a smaller number of non-directional motifs. These motifs were mapped to 23 DBPs previously linked to insulator activity, CTCF, and/or other forms of chromosomal transcriptional regulation. We found that the estimated orientation bias of CTCF was consistently proportional to the orientation bias observed in chromatin interaction data. Furthermore, these motifs showed significant enrichment at insulator sites that separate repressive and active chromatin regions, at chromatin interaction-defined boundaries, and at splice sites, compared to motifs of other DBPs. For instance, we observed that the key regulator MyoD-binding site is located at an insulator site near a gene involved in skeletal muscle differentiation and function. Importantly, our findings support the previously proposed insulator-pairing model, which suggests that insulator-insulator interactions are orientation-dependent, and highlight the involvement of multiple DNA-binding proteins beyond CTCF. Together, these results provide new insights into transcriptional regulatory mechanisms mediated by insulator-associated DBPs.

bioinformatics↗

bioRxiv2024

Systemic Dosing of Virus-derived Serpin Improves Survival and Immunothrombotic Damage in Murine Colitis

Inflammatory bowel disease (IBD) is potentially life-threatening, with risk of bleeding, clotting, infection, sepsis, cancer and toxic megacolon. Systemic and local immune and coagulation dysfunction increase IBD severity. Current treatments are partially effective, but there is no definitive cure. Serine protease cascades activate thrombotic, thrombolytic and complement pathways and are regulated by inhibitors, serpins. Viruses encode proteins evolved from endogenous central regulatory pathways. A purified secreted Myxomavirus-derived serpin, Serp-1, dosed as a systemic anti-inflammatory drug, has proven efficacy in vascular and inflammatory disorders. PEGylated Serp-1 protein (PEGSerp-1) has improved efficacy in lupus and SARS-CoV-2 models. We examined PEGSerp-1 treatment in a mouse Dextran Sodium Sulfate (DSS) colitis model. Prophylactic PEGSerp-1 significantly improved survival in acute severe 4-5% DSS colitis, reducing inflammation and crypt damage in acute 4-5% DSS induced colitis and when dosed as a chronic delayed treatment for recurrent 2% DSS colitis. PEGSerp-1 reduced iNOS+ M1 macrophage invasion, damage to crypt architecture and vascular inflammation with decreased uPAR, fXa, fibrinogen and complement activation. This work supports PEGSerp-1 as a tissue targeting serpin therapeutic.

molecular biology↗

bioRxiv2023

Inflammation primes the kidney for recovery by activating AZIN1 A-to-I editing

The progression of kidney disease varies among individuals, but a general methodology to quantify disease timelines is lacking. Particularly challenging is the task of determining the potential for recovery from acute kidney injury following various insults. Here, we report that quantitation of post-transcriptional adenosine-to-inosine (A-to-I) RNA editing offers a distinct genome-wide signature, enabling the delineation of disease trajectories in the kidney. A well-defined murine model of endotoxemia permitted the identification of the origin and extent of A-to-I editing, along with temporally discrete signatures of double-stranded RNA stress and Adenosine Deaminase isoform switching. We found that A-to-I editing of Antizyme Inhibitor 1 (AZIN1), a positive regulator of polyamine biosynthesis, serves as a particularly useful temporal landmark during endotoxemia. Our data indicate that AZIN1 A-to-I editing, triggered by preceding inflammation, primes the kidney and activates endogenous recovery mechanisms. By comparing genetically modified human cell lines and mice locked in either A-to-I edited or uneditable states, we uncovered that AZIN1 A-to-I editing not only enhances polyamine biosynthesis but also engages glycolysis and nicotinamide biosynthesis to drive the recovery phenotype. Our findings implicate that quantifying AZIN1 A-to-I editing could potentially identify individuals who have transitioned to an endogenous recovery phase. This phase would reflect their past inflammation and indicate their potential for future recovery.

molecular biology↗

bioRxiv2023

Willingness of Japanese people in their 20s, 30s and 40s to pay for genetically modified foods

The application of genetically modified (GM) technology to food products has increased worldwide. The adaptation has extended to conventional grains and animal products, such as salmon. However, in Japan, the publics acceptance of GM foods is low and experts and policymakers need to know the publics preference for various types of GM foods. Therefore, this study aims to clarify and compare the preferences for various GM foods among Japanese people in their 20s, 30s, and 40s, using the Willingness-to-pay (WTP) indicator. An online survey with 1122 valid responses from people in their 20s-40s was used for analysis. The results showed that the percentages of willingness to purchase various items were as follows - GM blue roses (n = 628; 56%), tomatoes fertilized with GM oil cake (n = 519; 46.3%), potato chips made from GM potatoes (n = 489; 43.6%), chicken thighs fed on GM corn (n = 472; 42.1%), corn flakes made from GM corn (n = 471; 42.0%), GM apples (n = 420; 37.4%), wine brewed with GM yeast (n = 416; 37.1%), GM tomatoes (n = 408; 36.4%), GM chicken thighs (n = 360; 32.1%), GM salmon (n = 349; 31.1%). Comparing the WTP discount rates for animal and plant foods, it was around 20% for animal products (GM chicken thighs = 17.8% and GM salmon = 19.5%), and 15-35% for plant foods (GM corn flakes = 24.1%, GM tomatoes = 17.2%, GM potato chips = 16.4%, GM yeast wine = 36.7%, GM apples = 25.3%). The WTP discount rates were 11.9% for tomatoes fertilized with GM oil cake compared with 17.2% for GM tomatoes, and 21.5% for GM-fed animal products compared with 17.8% for chicken thighs fed with GM corn. Therefore, the WTP values of GM animal foods were lower than GM plant foods and ornamental products.

scientific communication and education↗

bioRxiv2023

DeepRaccess: High-speed RNA accessibility prediction using deep learning

RNA accessibility is a useful RNA secondary structural feature for predicting RNA-RNA interactions and translation efficiency in prokaryotes. However, conventional accessibility calculation tools, such as Raccess, are computationally expensive and require considerable computational time to perform transcriptome-scale analyses. In this study, we developed DeepRaccess, which predicts RNA accessibility based on deep learning methods. DeepRaccess was trained to take artificial RNA sequences as input and to predict the accessibility of these sequences as calculated by Raccess. Simulation and empirical dataset analyses showed that the accessibility predicted by DeepRaccess was highly correlated with the accessibility calculated by Raccess. In addition, we confirmed that DeepRaccess can predict protein abundance in E.coli with moderate accuracy from the sequences around the start codon. We also demonstrated that DeepRaccess achieved tens to hundreds of times software speed-up in a GPU environment. The source codes and the trained models of DeepRaccess are freely available at https://github.com/hmdlab/DeepRaccess.

bioinformatics↗

bioRxiv2023

The evolution of RNA interference among Metazoa

In animals, three main RNA interference mechanisms have been described so far, which respectively maturate three types of small noncoding RNAs (sncRNAs): miRNAs, piRNAs and endo-siRNAs. The diversification of these mechanisms is deeply linked with the evolution of the Argonaute gene superfamily since each type of sncRNA is loaded by a specific Argonaute homolog protein. Moreover, other protein families play pivotal roles in the maturation of sncRNAs, like the DICER ribonuclease family, whose DICER1 and DICER2 paralogs maturate respectively miRNAs and endo-siRNAs. Among Metazoa, the distribution of these families has been only studied in major groups, and there are very few data for clades like Lophotrochozoa. Thus, we here inferred the evolutionary history of the animal Argonaute and DICER families including 43 lophotrochozoan species. Phylogenetic analyses along with newly sequenced sncRNA libraries depicted a loss of the endo-siRNA pathway along the Lophotrochozoa evolution, with the absence of DICER2 in Nematoda and Polyzoa, and with the absence of DICER2 and the Argonaute homolog in the rest of Trochozoa phyla. On the contrary, early diverging phyla, Platyhelminthes and Syndermata, showed a complete endo-siRNA pathway. On the other hand, miRNAs were revealed the most conserved and ubiquitous mechanism of the metazoan RNA interference machinery, confirming their pivotal role in animal cell regulation.

evolutionary biology↗