Microbial Interactions
What underlies a tangled network?
It has been awhile since my first post on microbial interactions. That is a consequence of two things – I got obsessed with something else for the past few weeks and furthermore I struggled with exactly what to say regarding a path forward to understanding their quantitative importance. The first part laid out some taxonomies of interactions. Neither the classical breakdown nor my own are completely satisfying in that they might suggest all categories are of equal importance in microbial community ecology. I don’t think that likely. Therefore I will focus on just three categories: interspecies competition for resources, interference competition and the unidirectional provision of growth factors. I will also make a few comments on parasitism by viruses.
The primary take home lessons are we don’t really know how quantitatively important any of these mechanisms are because it is very difficult to measure them in authentic microbial communities.
Resource or interference competition? We see the outcome (community composition) but not the process itself playing itself out. Assays of invasion resistance by biofilm communities to introduced organisms generate experimental systems that can be evaluated but are generally less complex than an in situ community.
“Cooperation” via provision of growth factors? It is technically possible to measure concentrations of putative growth factors like vitamins (B12 is present at pM levels in seawater). However it is not the concentration of these molecules that is important – it is their fluxes. The turnover time of an extracellular metabolite could be measure by adding tracer amounts (that is, small enough to not change the concentration) of a radio-labeled form and measuring its disappearance (Hungate, 1970). However, it is difficult to obtain radioisotopes with high enough specific activity to carry out tracer experiments.
The end-members of the literature on microbial interactions are (1) generation of association networks from analysis of multiple DNA sequence-based censuses of microbial community composition and (2) mechanistic analyses of pairwise interactions of cultured microbes (in a number of cases involving mutants of the same species). Network inference has been applied for about 20 years in microbial ecology and has grown in frequency with the decrease in cost of sequencing and the development of new machine learning methods. However, in the end this approach can only identify patterns – further experimental work is necessary to determine the bases for co-existence as multiple mechanisms could result in similar outcomes. Laboratory-based model systems permit delving into the details of an organism-organism interaction, but it is not possible to extrapolate to its quantitative importance in natural communities.
Readers of previous blog posts will not be surprised that I am not a fan of hypothesis-free approaches such as the network analyses that putatively identify interactions. I am intrigued by another top-down approach (Grilli, 2020), which suggests that microbial abundance and diversity across space and time conform to certain statistical distributions:
Abundance fluctuations are gamma-distributed
The variance in the abundance of an organism is related to the mean of its abundance by a power law (Taylor’s Law).
The mean abundance distribution is lognormally distributed.
Grilli (2020) found that datasets from natural communities fit these patterns, and subsequently that data from 100 replicate microcosms derived from a soil community could be fit with a Stochastic Logistic Model, based upon the three rules cited above (Shoemaker et al., 2025).
These observations still beg the question as to the underlying mechanisms that produce the observed distributions. Are they primarily driven by abiotic forces so that interactions among bacteria have only weak effects (Camacho-Mateu et al., 2024) or are interactions between pairs or groups of organisms centrally involved?
Delving into causal mechanisms will require experimental approaches with relatively complex communities, but what system(s) are most likely to be tractable and relevant? Microbial mats have a number of virtues in this regard (Bolhuis et al., 2014). They contain both primary producers as well as aerobic and anaerobic decomposers and can be repeatedly sampled in time. However, accessibility to a laboratory environment to conduct manipulative experiments can be an issue as they are most prominent where fauna cannot graze them: thermophilic and hypersaline environments.
The other system that struck me as tractable is biofilms. Their compact nature makes interactions more direct and experimentally facile to carry out both replications and diverse manipulations. I found myself particularly drawn to the work of Kevin Foster and his colleagues at the University of Oxford. They focus on microbial communities associated with the mammalian gut. Their thinking brings to bear not only experimental approaches but innovative mathematical modeling, with hypotheses generated not only from ecological but also evolutionary theory.
Their publications contain a wealth of insights relevant not only to biofilms but also to many other systems. These include:
1. The availability of limiting nutrients is central to the ecology of microbial communities.
Under resource-limiting conditions, resource competition is of prime importance. For chemoheterotrophs, this generally means the diverse organic carbon and energy sources that are available. For photoautotrophs, phosphorus limitation is most common but nitrogen and iron limitations may also arise. Competition for the limiting resource entails issues raised in my blog post on physiological ecology – in particular, competitors’ specific affinity for limiting substrate(s). This is a function of the number of transporters arrayed in the membrane plus the avidity with which those proteins bind the resource. In the case of chemoheterotrophs, the regulation of simultaneous use of multiple substrates is also important.
2. Negative interactions (competition and exploitation) are much more common than positive ones like cooperation.
Mathematical modeling based upon ecological theory suggests that these interactions will generally be weak rather than strong. Furthermore, in counterpoint to a general view of microbiome scientists, cooperative interactions (i.e., those with a positive feedback loop) are destabilizing whereas competition (negative feedback loops) are stabilizing.
3. Physically-structured environments (like biofilms or soil pore spaces) provide distinct niches where nominal strong competitors can both exist.
Thus, spatial structure can result in a greater diversity (at a macroscopic scale). Priority effects (who colonizes first) become important in such spatially structured habitats as ‘immigrants’ are unable to grow as the extant community can preclude access to the limiting resource. Phenomena such as resistance to invasion by human pathogens in the GI tract are likely driven by such mechanisms (Spragge et al., 2023).
Interference competition
Bacteria have evolved a broad variety of short-range and long-range weapons to disable potential competitors. Short-range systems include Type-VI secretion systems and contact-dependent inhibition. Long range (diffusible) molecules are antibiotics, bacteriocins and tailocins. These mechanisms are most likely to be effective in densely packed habitats, where cells contact each other and diffusion distances are short. Bacteriocins and tailocins are typically directed against phylogenetic close relatives (which are, of course, likely competitors for limiting resource). A recent fascinating article pointed out a nexus between interference competition and resource competition – possession of a Type-VI secretion systems can ease resource competition by liberating nutrients from a lysed neighbor cell (Stubbusch et al., 2025).
“Cooperation” via uni-directional provision of growth factors
There is abundant evidence from culturing that a substantial number of microbes cannot synthesize all of the amino acids, nucleosides, or vitamins required for growth. In addition, Ramoneda et al. (2023) did a very nice analysis of amino acid biosynthetic capability from 26,000 unique Bacterial genomes and concluded that about 20% were not able to synthesize all 20 amino acids. So, how do they get on in nature? My personal (economic) prejudice is that microbes are predatory capitalists rather than socialists (however, note that there are dissenting views – Kost et al. 2023). Has cooperative behavior evolved for provision of growth factors or do they arise from other processes? McKinlay (2023) concludes that cells are not inherently ‘leaky’ but may discharge metabolites by active or passive transporters to maintain intracellular homeostasis. However, it is important to remember that these ‘public goods’ remain available for re-acquistition by the producer as well as any other prototroph in the vicinity. Much more experimental work is required to determine whether the auxotrophies that are common in bacteria are satisfied due to pairwise cooperative relationships or by ‘normal’ physiological and ecological processes such as homeostatic efflux or cell lysis.
Viral ecology
Seawater on average contains 10 million virus particles and 1 million bacterial cells per ml. This would be a respectable multiplicity-of-infection ratio if all particles could absorb to all cells (quite unlikely). But even assuming that, the kinetics of bacteriophage adsorption would be ~100X slower than that found in typical laboratory one-step bacteriophage growth curve experiments (where initial bacterial cell densities are 100X higher). So for me it remains a paradox as to why virus numbers are so high. There are relatively few experiments that bear upon this.
I did find two recent papers that experimentally addressed the issue for cases where a specific bacterium was present at relatively high densities – Prochlorococcus and SAR11 during a phytoplankton bloom (Brüwer et al., 2024; Mruwat et al., 2023). Only about 1% of Prochlorococcus cells were infected and the authors estimated phage accounted for just a few percent of Prochlorococcus mortality. Cyanophages were 2-4X as abundant as Prochlorococcus and the authors conclude that “most encounters did not result in infection.” Given that Prochlorococcus cell densities were 105 per ml, I submit that there just were not that many encounters based on first order kinetics. Brüwer et al. found SAR11 densities exceeding 105 per ml during a bloom and that up to 19% of the cells were phage-infected. In 5 days, SAR11 abundance declined by 90%. These are remarkable results and I hope will be followed up, in light of the relatively low SAR11 cell densities that have yet generated rapid kinetics.
References
Bolhuis, H et al. (2014), Molecular ecology of microbial mats, FEMS Microbiology Ecology, 90: 335–350, https://doi.org/10.1111/1574-6941.12408
Brüwer, J.D. et al. (2024) Globally occurring pelagiphage infections create ribosome-deprived cells. Nat Commun 15, 3715. https://doi.org/10.1038/s41467-024-48172-w
Camacho-Mateu, J et al. (2024) Sparse species interactions reproduce abundance correlation patterns in microbial communities, Proc. Natl. Acad. Sci. U.S.A. 121 (5) e2309575121, https://doi.org/10.1073/pnas.2309575121.
Grilli, J. (2020) Macroecological laws describe variation and diversity in microbial communities. Nat Commun 11, 4743. https://doi.org/10.1038/s41467-020-18529-y
Hungate RE, et al. (1970) Formate as an Intermediate in the Bovine Rumen Fermentation. J Bacteriol 102: 389-397. https://doi.org/10.1128/jb.102.2.389-397.1970
Kost, C. et al. (2023) Metabolic exchanges are ubiquitous in natural microbial communities. Nat Microbiol 8, 2244–2252 (2023). https://doi.org/10.1038/s41564-023-01511-x
Mruwat, N. et al. (2021) A single-cell polony method reveals low levels of infected Prochlorococcus in oligotrophic waters despite high cyanophage abundances. ISME J 15, 41–54. https://doi.org/10.1038/s41396-020-00752-6
Ramoneda, J. et al. (2023) Taxonomic and environmental distribution of bacterial amino acid auxotrophies. Nat Commun 14, 7608. https://doi.org/10.1038/s41467-023-43435-4
Shoemaker WR and Grilli J (2024) Investigating macroecological patterns in coarse-grained microbial communities using the stochastic logistic model of growth. eLife 12:RP89650. https://doi.org/10.7554/eLife.89650.3
Spragge F et al. (2023) Microbiome diversity protects against pathogens by nutrient blocking.Science 382,eadj3502. DOI:10.1126/science.adj3502
Stubbusch AKM et al. (2025) Antagonism as a foraging strategy in microbial communities.Science 388,1214-1217. DOI:10.1126/science.adr8286
Your moment of Zen
I got myself immersed in learning some new approaches for black and white photography — what has been termed “fine art photography.” Not that my efforts are either fine nor art. But here is an effort derived from an image taken in Athens
