Diel investments in metabolite production and consumption in a model microbial system

Organic carbon transfer between surface ocean photosynthetic and heterotrophic microbes is a central but poorly understood process in the global carbon cycle. In a model community in which diatom extracellular release of organic molecules sustained growth of a co-cultured bacterium, we determined quantitative changes in the diatom endometabolome and the bacterial uptake transcriptome over two diel cycles. Of the nuclear magnetic resonance (NMR) peaks in the diatom endometabolites, 38% had diel patterns with noon or mid-afternoon maxima; the remaining either increased (36%) or decreased (26%) through time. Of the genes in the bacterial uptake transcriptome, 94% had a diel pattern with a noon maximum; the remaining decreased over time (6%). Eight diatom endometabolites identified with high confidence were matched to the bacterial genes mediating their utilization. Modeling of these coupled inventories with only diffusion-based phytoplankton extracellular release could not reproduce all the patterns. Addition of active release mechanisms for physiological balance and bacterial recognition significantly improved model performance. Estimates of phytoplankton extracellular release range from only a few percent to nearly half of annual net primary production. Improved understanding of the factors that influence metabolite release and consumption by surface ocean microbes will better constrain this globally significant carbon flux.


Supplemental Modeling Methods
The model consists of three metabolite pools, the phytoplankton endometabolome (P), the medium exometabolome (E), and the bacterial endometabolome (B). The time evolution of these pools was calculated using the following differential equations.
Where N is the metabolite biosynthesis rate, R is release rate from the phytoplankton, and T is rate at which endometabolites are allocated for biomass and energy generation by phytoplankton cells. U represents bacterial uptake rate of the exometabolome and C represents catabolism rate of the metabolite within the bacterial metabolome. These differential equations were solved at times steps of 0.1 h for 10 d using the variable definitions provided below. To simulate the experimental conditions the bacterial term was set to zero until inoculation on day 6. In the base model N is represented by the following cosine equation, gated such that negative values were counted as zeros.
= * cos 1 ( + 0.5) * 120 < + 1 3 where A represents the amplitude of N, t represents the independent variable time in tenths of an hour. T was calculated as where P0 is the endometabolite concentration at the previous time point and TF is the fraction of the endometabolite consumed by internal cellular processes during each time step.
where Vmax is the maximum uptake velocity and km is the half saturation constant. Finally, C is represented as where B0 is the magnitude of the bacterial endometabolome at the previous time step and CF is the fraction of the bacterial endometabolite that is consumed in cellular processes each time step.
In addition to the base model three 'active' terms were added to the model in order to better explain the experimental observations. These active terms include the fixationirradiation oscillation function (o), a cellular homeostasis mechanism (h), and the bacterial recognition response term (b). The term o accounts for asymmetric carbon fixation as light intensity approaches and falls from its peak (48). When o is enabled the equation to calculate N is modified to Where A represents the amplitude of N, t represents the independent variable time in tenths of an hour, and H influences the intensity of this oscillation as the length of time, in tenths of an hour, it takes for N to fall to 30% efficiency. To align with the experiment's light-dark cycle the modeled cycle is: dark from 0:00 hours to 08:00 hours, and light from 08:00 hours to 24:00 hours, with peak light intensity at 16:00 hours. The coefficients a and b were solved for as = tan(−2 + 1.2) − tan (−2 " + 1.2) − " = − tan(−2 + 1.2) + " so that the function N is continuous, where N0 is the initial value of N for each time range and t0 is the initial time.
The term h increases release during periods of high light intensity. When h is active the term R in Eq. 1 and 2 is multiplied by p calculated as ℎ = cos 1 ( + 0.5) * 120 + 80 * 120 < + 1 Finally, the bacterial recognition response term (b) increases the production in response The model was fit to each metabolite/gene pair by an iterative method. Value ranges were selected for each parameter based on preliminary testing of the model. The model was run with all possible combinations of these parameters (806,400 total parameter combinations). For each parameter combination, model output of P and B was tested for correlation with experimental endometabolite and transcript data, respectively. If both metabolite and gene correlations were positive and significant, the mean of both correlation coefficients (r) was calculated. The parameter set that yielded the highest mean r value was used as the best fit model for each metabolite.
Parameter values used in iterative model fitting are given below. Type indicates whether each parameter is part of the base model or an optional active term. n indicates the total number of values for each parameter Analysis of the contribution of bacterial metabolites to the 2.0-µm fraction.
In the co-culture treatments (diatom plus bacteria), we collected diatom endometabolites using 2.0-µm filters. Here we describe an experimental check for potential contamination by metabolites originating from bacteria that were trapped on the filters.
Three sample types (diatom alone, bacteria alone, and diatom plus bacteria; n=3) were filtered onto 2.0-µm filters. Diatom and bacterial strains, cell number (10 5 and 10 6 cells ml -1 for diatom and bacteria, respectively), media, filters, and the extraction protocol used were identical to those in the original experiment. Analysis of metabolites from cells caught on the filters using a 1-dimensional proton NMR experiment (panel a) shows little difference between bacteria-alone treatment and the blank (panel b, blue and purple lines at the baseline), indicating bacterial metabolites captured by 2.0-µm filters are minimal. In addition, the spectra of the diatom+bacteria treatment overlapped with diatom-alone (panel b, orange and red lines), further indicating that metabolites extracted from the 2.0-µm filters are dominated by those originating from the diatom endometabolome with a negligible contribution by trapped bacterial cells .  Figure S1. Diatom endometabolome annotation methods. Representative peak(s) for compounds indicated on HSQC-TOCSY spectra (a and b). Additional structural validation (e.g., for polysaccharide β-1,3-glucan) by HSQC-TOCSY (c) and HMBC experiments (d). Peaks from HSQC experiments are overlaid and colored in red. A complete compound list is provided in Table 1, and chemical shift information used for annotation is provided in Table S1. 3-Hydroxybutyrate, 4-hydroxyphenylacetate, and uridine are not visible in a and b due to relatively low intensities. AA, amino acid alpha carbon.  Figure S2. Temporal patterns in 21 diatom endometabolites annotated with high confidence and having significant membership values in a temporal cluster (M-1 through M-4). Metabolite abundance cell -1 is shown as Z-scores for one representative non-overlapping NMR peak (see also Table S1). Error bars indicate standard deviation; n = 3.  log 2 (fold-difference) log 2 (fold-difference) log 2 (fold-difference) Figure S4. Effects of light exposure on bacterial gene expression as percent of transcriptome. Three light levels corresponding to those at the sample times in the co-culture experiment were examined: 100%, light level at noon; 50%, light level at mid-morning and mid-afternoon; dark, light level at night. Differentially expressed genes with fold-difference of >2 and DESeq2 adjusted-p < 0.05 are shown as black symbols.  Figure S5. Direct effects of light on gene expression by R. pomeroyi. Filled circles indicate differentially expressed genes (fold-difference >2 and DESeq2 adjusted-p < 0.05) at 100% light level relative to dark (red symbols), 50% light level relative to dark (blue symbols), and 100% light level relative to 50% light level (black symbols). Open circles with the same color codes represent comparisons that were not significantly different. All 61 genes enriched in the presence of light are included, and detailed gene information is given in ---Metabolite ---Transcript Figure S6. Temporal concentration changes in the eight diatom endometabolites (green) plotted with bacterial transcript inventories (transcript cell -1 ) for all identified genes encoding uptake or catabolism of the same compound (blue shades). Error bars indicate standard deviation (n = 3, except for genes in the first night where n = 2).