Evidence map›Paper›PMID 41096595›Full record

ReviewInternational journal of molecular sciences2025

Bacterial Systematic Genetics and Integrated Multi-Omics: Beyond Static Genomics Toward Predictive Models.

Tatsuya Sakaguchi, Yuta Irifune, Rui Kamada, Kazuyasu Sakaguchi

Abstract readReview
In one paragraph

Review in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Tatsuya SakaguchiDepartment of Chemistry, School of Medicine, Kurume University, Kurume 830-0011, Japan.ORCID 0000-0002-5899-9297
Yuta IrifuneLaboratory of Biological Chemistry, Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo 060-0810, Japan.
Rui KamadaChemistry of Functional Molecules, Graduate School of Biomedical Sciences, Nagasaki University, Nagasaki 852-8521, Japan.ORCID 0000-0002-6118-5091
Kazuyasu SakaguchiLaboratory of Biological Chemistry, Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo 060-0810, Japan.ORCID 0000-0002-8434-4171

Funding

Japan Science and Technology Agency JPMJSP2119Japan Society for the Promotion of Science 23H02098Japan Society for the Promotion of Science 23K17966Japan Society for the Promotion of Science 24K01633Japan Society for the Promotion of Science 25K00273the Photo-excitonix Project at Hokkaido University none
6 · The paper itself

Abstract

The field of bacterial systems biology is rapidly advancing beyond static genomic analyses, and moving toward dynamic, integrative approaches that connect genetic variation with cellular function. This review traces the progression from genome-wide association studies (GWAS) to multi-omics frameworks that incorporate transcriptomics, proteomics, and interactome mapping. We emphasize recent breakthroughs in high-resolution transcriptomics, including single-cell, spatial, and epitranscriptomic technologies, which uncover functional heterogeneity and regulatory complexity in bacterial populations. At the same time, innovations in proteomics, such as data-independent acquisition (DIA) and single-bacterium proteomics, provide quantitative insights into protein-level mechanisms. Experimental and AI-assisted strategies for mapping protein-protein interactions help to clarify the architecture of bacterial molecular networks. The integration of these omics layers through quantitative trait locus (QTL) analysis establishes mechanistic links between single-nucleotide polymorphisms and systems-level phenotypes. Despite persistent challenges such as bacterial clonality and genomic plasticity, emerging tools, including deep mutational scanning, microfluidics, high-throughput genome editing, and machine-learning approaches, are enhancing the resolution and scope of bacterial genetics. By synthesizing these advances, we describe a transformative trajectory toward predictive, systems-level models of bacterial life. This perspective opens new opportunities in antimicrobial discovery, microbial engineering, and ecological research.

Indexed as

Genome, BacterialGenomicsMultiomicsSystems BiologyBacterial ProteinsDrug DiscoveryGene EditingGenome-Wide Association StudyMachine LearningMetabolic EngineeringMicrofluidicsPolymorphism, Single NucleotideProtein Interaction MappingProteomicsQuantitative Trait LociSingle-Cell Gene Expression AnalysisBacterial Proteinsantibiotic resistancegenomeinteractomemachine learningmulti-omicsprotein-protein interactionproteomequantitative trait loci (QTL)transcriptome

Identifiers

PMID41096595
PMCPMC12524605

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.