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When grown on agar surfaces, microbes can produce distinct multicellular spatial structures called colonies, which contain characteristic sizes, shapes, edges, textures, and degrees of opacity and color. For over one hundred years, researchers have used these morphology cues to classify bacteria and guide more targeted treatment of pathogens. Advances in genome sequencing technology have revolutionized our ability to classify bacterial isolates and while genomic methods are in the ascendancy, morphological characterization of bacterial species has made a resurgence due to increased computing capacities and widespread application of machine learning tools. In this paper, we revisit the topic of colony morphotype on the within-species scale and apply concepts from image processing, computer vision, and deep learning to a dataset of 69 environmental and clinical Pseudomonas aeruginosa strains. We find that colony morphology and complexity under common laboratory conditions is a robust, repeatable phenotype on the level of individual strains, and therefore forms a potential basis for strain classification. We then use a deep convolutional neural network approach with a combination of data augmentation and transfer learning to overcome the typical data starvation problem in biological applications of deep learning. Using a train/validation/test split, our results achieve an average validation accuracy of 92.9% and an average test accuracy of 90.7% for the classification of individual strains. These results indicate that bacterial strains have characteristic visual 'fingerprints' that can serve as the basis of classification on a sub-species level. Our work illustrates the potential of image-based classification of bacterial pathogens and highlights the potential to use similar approaches to predict medically relevant strain characteristics like antibiotic resistance and virulence from colony data.

Reproductive division of labor (e.g. germ-soma specialization) is a hallmark of the evolution of multicellularity, signifying the emergence of a new type of individual and facilitating the evolution of increased organismal complexity. A large body of work from evolutionary biology, economics, and ecology has shown that specialization is beneficial when further division of labor produces an accelerating increase in absolute productivity (i.e. productivity is a convex function of specialization). Here we show that reproductive specialization is qualitatively different from classical models of resource sharing, and can evolve even when the benefits of specialization are saturating (i.e. productivity is a concave function of specialization). Through analytical theory and evolutionary individual-based simulations, we demonstrate that reproductive specialization is strongly favored in sparse networks of cellular interactions that reflect the morphology of early, simple multicellular organisms, highlighting the importance of restricted social interactions in the evolution of reproductive specialization.

When grown on agar surfaces, microbes can produce distinct multicellular spatial structures called colonies, which contain characteristic sizes, shapes, edges, textures, and degrees of opacity and color. For over one hundred years, researchers have used these morphology cues to classify bacteria and guide more targeted treatment of pathogens. Advances in genome sequencing technology have revolutionized our ability to classify bacterial isolates and while genomic methods are in the ascendancy, morphological characterization of bacterial species has made a resurgence due to increased computing capacities and widespread application of machine learning tools. In this paper, we revisit the topic of colony morphotype on the within-species scale and apply concepts from image processing, computer vision, and deep learning to a dataset of 69 environmental and clinical Pseudomonas aeruginosa strains. We find that colony morphology and complexity under common laboratory conditions is a robust, repeatable phenotype on the level of individual strains, and therefore forms a potential basis for strain classification. We then use a deep convolutional neural network approach with a combination of data augmentation and transfer learning to overcome the typical data starvation problem in biological applications of deep learning. Using a train/validation/test split, our results achieve an average validation accuracy of 92.9% and an average test accuracy of 90.7% for the classification of individual strains. These results indicate that bacterial strains have characteristic visual 'fingerprints' that can serve as the basis of classification on a sub-species level. Our work illustrates the potential of image-based classification of bacterial pathogens and highlights the potential to use similar approaches to predict medically relevant strain characteristics like antibiotic resistance and virulence from colony data., Competing Interests: The authors declare no competing interests., (Copyright: © 2023 Rattray et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The diversity of multicellular organisms is, in large part, due to the fact that multicellularity has independently evolved many times. Nonetheless, multicellular organisms all share a universal biophysical trait: cells are attached to each other. All mechanisms of cellular attachment belong to one of two broad classes; intercellular bonds are either reformable or they are not. Both classes of multicellular assembly are common in nature, having independently evolved dozens of times. In this review, we detail these varied mechanisms as they exist in multicellular organisms. We also discuss the evolutionary implications of different intercellular attachment mechanisms on nascent multicellular organisms. The type of intercellular bond present during early steps in the transition to multicellularity constrains future evolutionary and biophysical dynamics for the lineage, affecting the origin of multicellular life cycles, cell-cell communication, cellular differentiation, and multicellular morphogenesis. The types of intercellular bonds used by multicellular organisms may thus result in some of the most impactful historical constraints on the evolution of multicellularity., (© 2022 Author(s).)

Cellular differentiation is one of the hallmarks of complex multicellularity, allowing individual organisms to capitalize on among-cell functional diversity. The evolution of multicellularity is a major evolutionary transition that allowed for the increase of organismal complexity in multiple lineages, a process that relies on the functional integration of cell-types within an individual. Multiple hypotheses have been proposed to explain the origins of cellular differentiation, but we lack a general understanding of what makes one cell-type distinct from others, and how such differentiation arises. Here, we describe how the use of Boolean networks (BNs) can aid in placing empirical findings into a coherent conceptual framework, and we emphasize some of the standing problems when interpreting data and model behaviors., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

All multicellular organisms develop through one of two basic routes: they either aggregate from free-living cells, creating potentially chimeric multicellular collectives, or they develop clonally via mother-daughter cellular adhesion. Although evolutionary theory makes clear predictions about trade-offs between these developmental modes, these have never been experimentally tested in otherwise genetically identical organisms. We engineered unicellular baker's yeast (Saccharomyces cerevisiae) to develop either clonally ("snowflake"; Δace2) or aggregatively ("floc"; GAL1p::FLO1) and examined their fitness in a fluctuating environment characterized by periods of growth and selection for rapid sedimentation. When cultured independently, aggregation was far superior to clonal development, providing a 35% advantage during growth and a 2.5-fold advantage during settling selection. Yet when competed directly, clonally developing snowflake yeast rapidly displaced aggregative floc. This was due to unexpected social exploitation: snowflake yeast, which do not produce adhesive FLO1, nonetheless become incorporated into flocs at a higher frequency than floc cells themselves. Populations of chimeric clusters settle much faster than floc alone, providing snowflake yeast with a fitness advantage during competition. Mathematical modeling suggests that such developmental cheating may be difficult to circumvent; hypothetical "choosy floc" that avoid exploitation by maintaining clonality pay an ecological cost when rare, often leading to their extinction. Our results highlight the conflict at the heart of aggregative development: non-specific cellular binding provides a strong ecological advantage-the ability to quickly form groups-but this very feature leads to its exploitation., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Reproductive division of labor (e.g. germ-soma specialization) is a hallmark of the evolution of multicellularity, signifying the emergence of a new type of individual and facilitating the evolution of increased organismal complexity. A large body of work from evolutionary biology, economics, and ecology has shown that specialization is beneficial when further division of labor produces an accelerating increase in absolute productivity (i.e. productivity is a convex function of specialization). Here we show that reproductive specialization is qualitatively different from classical models of resource sharing, and can evolve even when the benefits of specialization are saturating (i.e. productivity is a concave function of specialization). Through analytical theory and evolutionary individual-based simulations, we demonstrate that reproductive specialization is strongly favored in sparse networks of cellular interactions that reflect the morphology of early, simple multicellular organisms, highlighting the importance of restricted social interactions in the evolution of reproductive specialization., Competing Interests: DY, SJ, PM, JW, WR, PY No competing interests declared, (© 2020, Yanni et al.)

Microbes are social organisms, interacting primarily through secreted biomolecules. Many traits have evolved based solely on their effects upon other community members, yet even individually beneficial traits often create social side effects that are mediated by spatial population structure. Predicting the evolution of many microbial traits thus requires a comprehensive understanding of their social consequences. In this review, we examine the critical role of population spatial structure in microbial social evolution. We briefly review key mechanisms structuring microbial communities, focusing primarily on the universal roles of cellular death and reproduction. Finally, we explain how spatial assortment can be efficiently calculated in two-dimensional, surface-attached populations., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

The ribosomal RNA (rrn) operon is a key suite of genes related to the production of protein synthesis machinery and thus to bacterial growth physiology. Experimental evidence has suggested an intrinsic relationship between the number of copies of this operon and environmental resource availability, especially the availability of phosphorus (P), because bacteria that live in oligotrophic ecosystems usually have few rrn operons and a slow growth rate. The Cuatro Ciénegas Basin (CCB) is a complex aquatic ecosystem that contains an unusually high microbial diversity that is able to persist under highly oligotrophic conditions. These environmental conditions impose a variety of strong selective pressures that shape the genome dynamics of their inhabitants. The genus Bacillus is one of the most abundant cultivable bacterial groups in the CCB and usually possesses a relatively large number of rrn operon copies (6-15 copies). The main goal of this study was to analyze the variation in the number of rrn operon copies of Bacillus in the CCB and to assess their growth-related properties as well as their stoichiometric balance (N and P content). We defined 18 phylogenetic groups within the Bacilli clade and documented a range of from six to 14 copies of the rrn operon. The growth dynamic of these Bacilli was heterogeneous and did not show a direct relation to the number of operon copies. Physiologically, our results were not consistent with the Growth Rate Hypothesis, since the copies of the rrn operon were decoupled from growth rate. However, we speculate that the diversity of the growth properties of these Bacilli as well as the low P content of their cells in an ample range of rrn copy number is an adaptive response to oligotrophy of the CCB and could represent an ecological mechanism that allows these taxa to coexist. These findings increase the knowledge of the variability in the number of copies of the rrn operon in the genus Bacillus and give insights about the physiology of this bacterial group under extreme oligotrophic conditions.