Shift in the paradigm towards next-generation microbiology

Abstract

In this work, the position of contemporary microbiology is considered from the perspective of scientific success, and a list of historical points and lessons learned from the fields of medical microbiology, microbial ecology and systems biology is presented. In addition, patterns in the development of top-down research topics that emerged over time as well as overlapping ideas and personnel, which are the first signs of trans-domain research activities in the fields of metagenomics, metaproteomics, metatranscriptomics and metabolomics, are explored through analysis of the publication networks of 28 654 papers using the computer programme Pajek. The current state of affairs is defined, and the need for meta-analyses to leverage publication biases in the field of microbiology is put forward as a very important emerging field of microbiology, especially since microbiology is progressively dealing with multi-scale systems. Consequently, the need for cross-fertilisation with other fields/disciplines instead of 'more microbiology' is needed to advance the field of microbiology as such. The reader is directed to consider how novel technologies, the introduction of big data approaches and artificial intelligence have transformed microbiology into a multi-scale field and initiated a shift away from its history of mostly manual work and towards a largely technology-, data- and statistics-driven discipline that is often coupled with automation and modelling.

Keywords: bibliometric analysis; bioinformatics; biology; meta-analyses; metabolomics; metagenomics; metaproteomics; metatranscriptomics; publication bias; statistics; top-down.

Figure 1.

Example of the multilevel and multi-omic layers of information that are being integrated in current multi-scale approaches to the integration of microbiological information into natural systems (reproduced with permission and modified; Hasin, Seldin and Lusis 2017). Circles represent the entire pool of molecules detected in various ‘omic’ data layers. Genetic regulations and environments are embedded within all data layers, except the genome (GWAS) layer, and can affect each individual molecule to a different extent. The potential interactions or correlations between molecules detected within one layer or between different layers are represented by thin red and black arrows, respectively. As an example of the conceptual framework for consolidating multi-omic data to understand the function of the system, the gene in a genome (blue circle) is epigenetically regulated (red circle) and controls multiple transcription targets correlated with multiple proteins that generate metabolites, which can have a greater influence on the microbiome layer as well. The three firsts (i.e. the genome first, the phenotype first and the environment first) imply a starting point: the associated locus versus any other layer versus environmental perturbations (i.e. thermodynamic boundaries within which the system routinely operates). GWAS: genome-wide association studies; B: bacteria; A: archaea; F: fungi; P: protozoa; V: viruses; mE: mobile elements; LPS - Lipopolysaccharides; GlLip - Glycolipids; PrGl - proteoglycans.

Figure 2.

(A) Venn diagram of publications sharing basic keywords. (B) Production rate of papers dealing with one or more sub-disciplines. The major four (top-down) meta-versions of disciplines within microbiology: M.G, M.T, M.P and M.B.

Figure 3.

(A) Papers on select subtopics authored by the same authors, (B) bibliographic coupling (i.e. co-cited papers) and (C) citation of meta-omics papers on select subtopics. Loops in the networks represent connections between papers within a selected subtopic. All the values of ties within the networks are log-transformed and max-normalised. Only ties with values higher than 0.35 are presented. Unconnected subtopics (i.e. those for which ties were cut) are hidden.

Figure 4.

Missing opportunities and balance of the three basic approaches—meta-analysis, the top-down approach and the bottom-up approach—as sources of information to leverage the understanding of systems.