Naoki Konno

All Publications

• Host range and antibiotic resistance dissemination are shaped by distinct survival strategies of conjugative plasmids. Nucleic acids research Ono, R., Konno, N., Nishimura, Y., Furusawa, C. 2026; 54 (2)

  Abstract

  Horizontal gene transfer is a major driver of bacterial evolution and the global dissemination of antibiotic resistance genes (ARGs). Conjugative plasmids play a crucial role in ARG spread across hosts within their host range, yet the genetic and functional determinants shaping plasmid host range remain poorly understood. Here, we systematically analyzed the gene content of conjugative/mobilizable plasmids derived from Enterobacterales from public databases and found that two distinct survival strategies were enriched in different host-range groups: a "stealth" strategy, which actively represses its own transcription by employing a global regulator hns, was particularly enriched in broad-host-range plasmids, whereas a "manipulative" strategy, which promotes its establishment by manipulating host machineries including SOS response and defense systems, was more common in narrow-host-range plasmids. Plasmids employing either strategy constituted the majority of conjugative plasmids analyzed, and accumulated significantly more ARGs than plasmids with neither strategy. Our data further suggested that stealth plasmids facilitate the acquisition of emerging ARGs, while manipulative plasmids amplify the copy number of established ARGs. This "stealth-first" model successfully recapitulated historical ARG dissemination patterns. These findings provide critical insights into the relationship between plasmid survival strategies and host range, advancing our understanding of the global patterns underlying plasmid-mediated ARG transmission.

  View details for DOI 10.1093/nar/gkaf1479

  View details for PubMedID 41587753

  View details for PubMedCentralID PMC12826799

• Repeatability of protein structural evolution following convergent gene fusions. Nature communications Konno, N., Miyake, K., Nishino, S., Omae, K., Yanagisawa, H., Tsuru, S., Nishimura, Y., Kikkawa, M., Furusawa, C., Iwasaki, W. 2025; 16 (1): 8278

  Abstract

  Convergent evolution of proteins provides insights into repeatability of genetic adaptation. While local convergence of proteins at residue or domain level has been characterized, global structural convergence by inter-domain/molecular interactions remains largely unknown. Here we present structural convergent evolution on fusion enzymes of aldehyde dehydrogenases (ALDHs) and alcohol dehydrogenases (ADHs). We discover BdhE (bifunctional dehydrogenase E), an enzyme clade that emerged independently from the previously known AdhE family through distinct gene fusion events. AdhE and BdhE show shared enzymatic activities and non-overlapping phylogenetic distribution, suggesting common functions in different species. Cryo-electron microscopy reveals BdhEs form donut-like homotetramers, contrasting AdhE's helical homopolymers. Intriguingly, despite distinct quaternary structures and < 30% amino acid sequence identity, both enzymes forms resemble dimeric structure units by ALDH-ADH interactions via convergently elongated loop structures. These findings suggest convergent gene fusions recurrently led to substrate channeling evolution to enhance two-step reaction efficiency. Our study unveils structural convergence at inter-domain/molecular level, expanding our knowledges on patterns behind molecular evolution exploring protein structural universe.

  View details for DOI 10.1038/s41467-025-63898-x

  View details for PubMedID 40983608

  View details for PubMedCentralID PMC12454647

• Meeting Report: The First Evo-Devo Young Researchers Meeting. Development, growth & differentiation Uchida, Y., Ishida, T., Ono, S., Kuroda, S., Kobayashi, A., Konno, N., Tanaka, Y., Nishizawa, T., Yoshida, J., Yoshimoto, K. I., Yoneda, M. 2025; 67 (6): 331-335

  Abstract

  The official poster for the First Evo-Devo Young Researchers Meeting. It features the meeting title, themes ("Evo-Devo so far" and "Evo-Devo in the future"), invited speakers, program highlights, and logistical details. The content is written in Japanese.

  View details for DOI 10.1111/dgd.70016

  View details for PubMedID 40603246

• Evolutionary paths toward multi-level convergence of lactic acid bacteria in fructose-rich environments. Communications biology Konno, N., Maeno, S., Tanizawa, Y., Arita, M., Endo, A., Iwasaki, W. 2024; 7 (1): 902

  Abstract

  Convergence provides clues to unveil the non-random nature of evolution. Intermediate paths toward convergence inform us of the stochasticity and the constraint of evolutionary processes. Although previous studies have suggested that substantial constraints exist in microevolutionary paths, it remains unclear whether macroevolutionary convergence follows stochastic or constrained paths. Here, we performed comparative genomics for hundreds of lactic acid bacteria (LAB) species, including clades showing a convergent gene repertoire and sharing fructose-rich habitats. By adopting phylogenetic comparative methods we showed that the genomic convergence of distinct fructophilic LAB (FLAB) lineages was caused by parallel losses of more than a hundred orthologs and the gene losses followed significantly similar orders. Our results further suggested that the loss of adhE, a key gene for phenotypic convergence to FLAB, follows a specific evolutionary path of domain architecture decay and amino acid substitutions in multiple LAB lineages sharing fructose-rich habitats. These findings unveiled the constrained evolutionary paths toward the convergence of free-living bacterial clades at the genomic and molecular levels.

  View details for DOI 10.1038/s42003-024-06580-0

  View details for PubMedID 39048718

  View details for PubMedCentralID PMC11269746

• Machine learning enables prediction of metabolic system evolution in bacteria. Science advances Konno, N., Iwasaki, W. 2023; 9 (2): eadc9130

  Abstract

  Evolution prediction is a long-standing goal in evolutionary biology, with potential impacts on strategic pathogen control, genome engineering, and synthetic biology. While laboratory evolution studies have shown the predictability of short-term and sequence-level evolution, that of long-term and system-level evolution has not been systematically examined. Here, we show that the gene content evolution of metabolic systems is generally predictable by applying ancestral gene content reconstruction and machine learning techniques to ~3000 bacterial genomes. Our framework, Evodictor, successfully predicted gene gain and loss evolution at the branches of the reference phylogenetic tree, suggesting that evolutionary pressures and constraints on metabolic systems are universally shared. Investigation of pathway architectures and meta-analysis of metagenomic datasets confirmed that these evolutionary patterns have physiological and ecological bases as functional dependencies among metabolic reactions and bacterial habitat changes. Last, pan-genomic analysis of intraspecies gene content variations proved that even "ongoing" evolution in extant bacterial species is predictable in our framework.

  View details for DOI 10.1126/sciadv.adc9130

  View details for PubMedID 36630500

  View details for PubMedCentralID PMC9833677

• Molecular recorders to track cellular events. Science (New York, N.Y.) Masuyama, N., Konno, N., Yachie, N. 2022; 377 (6605): 469-470

  Abstract

  DNA tapes could be used to record dynamic molecular and cellular events in animals.

  View details for DOI 10.1126/science.abo3471

  View details for PubMedID 35901151

• Deep distributed computing to reconstruct extremely large lineage trees. Nature biotechnology Konno, N., Kijima, Y., Watano, K., Ishiguro, S., Ono, K., Tanaka, M., Mori, H., Masuyama, N., Pratt, D., Ideker, T., Iwasaki, W., Yachie, N. 2022; 40 (4): 566-575

  Abstract

  Phylogeny estimation (the reconstruction of evolutionary trees) has recently been applied to CRISPR-based cell lineage tracing, allowing the developmental history of an individual tissue or organism to be inferred from a large number of mutated sequences in somatic cells. However, current computational methods are not able to construct phylogenetic trees from extremely large numbers of input sequences. Here, we present a deep distributed computing framework to comprehensively trace accurate large lineages (FRACTAL) that substantially enhances the scalability of current lineage estimation software tools. FRACTAL first reconstructs only an upstream lineage of the input sequences and recursively iterates the same produce for its downstream lineages using independent computing nodes. We demonstrate the utility of FRACTAL by reconstructing lineages from >235 million simulated sequences and from >16 million cells from a simulated experiment with a CRISPR system that accumulates mutations during cell proliferation. We also successfully applied FRACTAL to evolutionary tree reconstructions and to an experiment using error-prone PCR (EP-PCR) for large-scale sequence diversification.

  View details for DOI 10.1038/s41587-021-01111-2

  View details for PubMedID 34992246

  View details for PubMedCentralID PMC9934975