bioRxiv Science⌕ Search

bioRxiv · 10.1101/2023.02.09.527847

Regulated bacterial interaction networks: A mathematical framework to describe competitive growth under inclusion of metabolite cross-feeding

Abstract

When bacterial species with the same resource preferences share the same growth environment, it is commonly believed that direct competition will arise. A large variety of competition and more general interaction models have been formulated, but what is currently lacking are models that link mono-culture growth kinetics and community growth under inclusion of emerging biological interactions, such as metabolite cross-feeding. In order to understand and mathematically describe the nature of potential cross-feeding interactions, we design experiments where two bacterial species Pseudomonas putida and Pseudomonas veronii grow in liquid medium either in mono- or as co-culture in a resource-limited environment. We measure population growth under single substrate competition or with double species-specific substrates (substrate indifference), and starting from varying cell ratios of either species. Using experimental data as input, we first consider a mean-field model of resource-based competition, which captures well the empirically observed growth rates for mono-cultures, but fails to correctly predict growth rates in co-culture mixtures, in particular for skewed starting species ratios. Based on this, we extend the model by cross-feeding interactions where the consumption of substrate by one consumer produces metabolites that in turn are resources for the other consumer, thus leading to positive feedback loops in the species system. Two different cross-feeding options were considered, which either lead to constant metabolite cross-feeding, or to a regulated form, where metabolite utilization is activated with rates according to either a threshold or a Hill function, dependent on metabolite concentration. Both mathematical proof and experimental data indicate regulated cross-feeding to be the preferred model over constant metabolite utilization, with best co-culture growth predictions in case of high Hill coefficients, close to binary (on/off) activation states. This suggests that species use the appearing metabolite concentrations only when they are becoming high enough; possibly as a consequence of their lower energetic content than the primary substrate. Metabolite sharing was particularly relevant at unbalanced starting cell ratios, causing the minority partner to proliferate more than expected from the competitive substrate because of metabolite release from the majority partner. This effect thus likely quells immediate substrate competition and may be important in natural communities with typical very skewed relative taxa abundances and slower-growing taxa. In conclusion, the regulated bacterial interaction network correctly describes species substrate growth reactions in mixtures with few kinetic parameters that can be obtained from mono-culture growth experiments. 1 Author summaryCorrectly predicting growth of communities of diverse bacterial taxa remains a challenge, because of the very different growth properties of individual members and their myriads of interactions that can influence growth. Here we tried to improve and empirically validate mathematical models that combine theory of bacterial growth kinetics (i.e., Monod models) with mathematical definition of interaction parameters. We focused in particular on common cases of shared primary substrates (i.e., competition) and independent substrates (i.e., indifference) in an experimental system consisting of one fast-growing and one slower growing Pseudomonas species. Growth kinetic parameters derived from mono-culture experiments included in a Monod-type consumer-resource model explained some 75% of biomass formation of either species in co-culture, but underestimated the observed growth improvement when either of the species started as a minority compared to the other. This suggested an in important role of cross-feeding, whereby released metabolites from one of the partners is utilized by the other. Inclusion of cross-feeding feedback in the two-species Monod growth model largely explained empirical data at all species-starting ratios, in particular when cross-feeding is activated in almost binary manner as a function of metabolite concentration. Our results also indicate the importance of cross-feeding for minority taxa, which can explain their survival despite being poorly competitive.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Guex, I., Mazza, C., Batsch, M., Dubey, M., Li, R., van der Meer, J. R.. 2023-02-09. Regulated bacterial interaction networks: A mathematical framework to describe competitive growth under inclusion of metabolite cross-feeding. https://doi.org/10.1101/2023.02.09.527847

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Reverse Transcription Loop-Mediated Isothermal Amplification Combined with Lateral Flow Assay (RT-LAMP-LFA) for Rapid Detection of the 2026 Bundibugyo Virus (BDBV) Outbreak Strain

Making accurate diagnosis is a critical first step in patient management and control for emerging and re-emerging disease outbreaks. Bundibugyo virus (BDBV) is a filovirus that causes Ebola disease, and the 2026 outbreak highlights the need for rapid diagnostic approaches that can be deployed in settings with limited laboratory infrastructure. Here, we developed a reverse transcription loop-mediated isothermal amplification-lateral flow assay (RT-LAMP-LFA) for rapid detection of the 2026 BDBV outbreak strain. LAMP primers were designed based on the genome sequence of the 2026 outbreak strain, and the conservation of the primer binding sites among all available 2026 outbreak genomes was confirmed in silico. LAMP primers were evaluated for analytical specificity and sensitivity using in vitro-transcribed RNA (ivtRNA) from BDBV and other filoviruses, as well as RNA derived from arboviruses and mammalian cells. Amplification products were detected using a Chromatography-Printed Array Strip (C-PAS) lateral flow platform. The newly designed primers specifically detected the presence of BDBV RNA without nonspecific amplification of the other viral or cellular RNA samples tested. Consistent amplification was observed down to 103 copies per reaction. In addition, BDBV viral RNA was successfully amplified, and the amplification products were detected using C-PAS, with the total workflow being completed within 25 min. These findings demonstrate the analytical specificity and sensitivity of the developed RT-LAMP-LFA assay and its compatibility with rapid visual lateral flow detection, supporting its potential application as a point-of-care approach for BDBV detection.

microbiology↗

Divergent methane production and microbial community responses to carbon enrichment in freshwater wetland sediments

Methane (CH4) accumulation in wetland sediments reflects interactions among organic matter, sediment chemistry, and microbial communities. We combined depth-resolved field characterization across four wetland habitats in northern Israel with complementary incubations to examine methane accumulation and responses to labile-carbon enrichment. Sediment cores were characterized using Rock-Eval analysis, porewater chemistry, oxygen profiling at three sites, and mcrA amplicon sequencing. High CH4 concentrations occurred under contrasting conditions: Ein Nimfit (EN) contained approximately 12% total organic carbon and reached 2.1 mM CH4, whereas Ein Afek site EA2 contained approximately 5% carbon and accumulated 0.7-1.7 mM CH4. Both showed sulfate depletion, but EN exhibited pronounced sulfide accumulation, whereas EA2 showed greater dissolved iron and nutrient enrichment. Their classified methanogenic assemblages also differed. EA1 remained methane poor despite increasing carbon content with depth, while EA3 combined low carbon content and CH4 accumulation with detectable methanogens and lower methanogenic diversity. In a 36-day bottle experiments using sediments from two of the four wetland habitats, sucrose-amended EA3 exhibited a transient maximum of 18.8 mmol collected CH4 kg-1 wet sediment during Days 22-29, whereas EN reached its highest collected amount during Days 29-36. Methane proportions increased before the large output increase in EA3 but remained comparatively stable and high in EN. Soluble chemical oxygen demand trajectories and microbial community changes also differed. These findings identify distinct biogeochemical settings supporting methane accumulation and demonstrate methanogenic potential in low-CH4 sediment, emphasizing the complementary information provided by field profiles, microbial characterization, and exploratory enrichment experiments.

microbiology↗

The absence of microbiome in germ-free mice exacerbates the pathogenicity of mouse-adapted SARS-CoV-2 MA10

Germ-free mice, which are devoid of microbiota, provide a powerful system to define the contribution of the microbiome to host responses during infection. Severe COVID-19 outcomes are associated with multiple comorbidities that are themselves characterized by microbiome dysbiosis; however, the causal role of the microbiota in SARS-CoV-2 pathogenesis remains poorly understood. Here, we use germ-free mouse models to evaluate the impact of the microbiome on disease severity following infection with mouse-adapted SARS-CoV-2 MA10. Comparison with conventionally housed mice revealed that the absence of the microbiome markedly exacerbates SARS-CoV-2-induced disease in both C57BL/6 and Swiss Webster backgrounds. Germ-free mice exhibited greater weight loss, impaired lung function, worsened pulmonary pathology, and pronounced alterations in circulating cytokine and chemokine profiles compared with conventional controls. These findings show that resident microbiota limit SARS-CoV-2 pathogenicity and establish germ-free COVID-19 models as a powerful platform to identify protective microbial taxa and microbiota-based interventions to reduce severe disease.

microbiology↗