Philippa Kaur

Oceanographic context of the sampling locationAll Trichodesmium colonies used in this study were collected from the same phytoplankton net which sampled a surface-ocean Southern Caribbean Sea community (Fig. 1a). At the sampling station the phosphate concentration was low (0.13 μM at 100 m) as is typical in an oligotrophic environment, while the surface dissolved iron concentration was relatively high (2.02 nM at 100 m), consistent with coastal or atmospheric inputs being mobilized in this region (Fig. 1a). By far the most abundant Trichodesmium species at this location was an uncharacterized Trichodesmium thiebautii species, as determined by Trichodesmium-specific metagenome-assembled-genome recruiting (see Table S1).Thirty individual colonies of mixed morphology were separated by hand-picking, immediately examined, and photographed by fluorescent microscopy (385 excitation, >420 nm emission), then frozen individually for particle characterization and metaproteomic analysis (Fig. S1). All colonies used in this study presented as healthy with reddish-orange pigmentation and well-defined shape. When the particles were present they auto-fluoresced in the visual light range, appearing as yellow, red, or blue dots. In general, the particles were concentrated in the center of puff-type colonies, though they were also present in tufts but in smaller numbers. Strikingly, colonies either had many such particles or none at all. Based on prior experimental evidence demonstrating that Trichodesmium colonies can capture mineral particles and access iron from them [14,15,16, 21], we hypothesized that these particles were terrestrially derived minerals (Fig. 1c–h). Therefore, we embarked to understand the morphological heterogeneity by characterizing the particles and the colony’s molecular response to them.Mineralogical characterization of the colony-associated particlesTo find out whether these natural colonies of Trichodesmium had captured iron-rich mineral particles, we performed synchrotron-based micro-X-ray fluorescence (μ-XRF) element mapping of representative colonies with the observed particle associations. Prior evidence of Trichodesmium–particle associations has been based mainly on experimental “feeding” of dust to cultured or captured colonies [15, 16, 20, 22,23,24,25], and it was therefore important to establish these specific Trichodesmium–particle relationships, which developed in nature. We examined one tuft- and two puff-type colonies, all of which had particles associated with them. The element maps were consistent with the hypothesis that there were mineral particles enriched in iron (Fe), copper (Cu), zinc (Zn), titanium/barium (Ti/Ba, which cannot be distinguished by this method), manganese (Mn) and cobalt (Co), though the concentrations approached the limit of detection for the latter two elements (Fig. 2, Figs. S2 and S3). Iron concentrations were particularly high in the particles. Micro-X-ray absorption near-edge structure (μ-XANES) spectra for iron were collected on six particles—three each from the two puffs (Fig. 2 and Fig. S4). The particles contained mineral-bound iron with average oxidation states of 2.6, 2.7, two of oxidation state 2.9, and two of oxidation state 3.0 (Table S2, Fig. S5). While the mineralogy of these particles could not be definitively resolved using μ-XANES, the structure of the absorption edge and post-edge region provided insight into broad mineral groups. Both Fe(III) (oxy/hydro)oxides and mixed-valence iron-bearing minerals consistent with iron silicates were present, suggesting heterogeneous mineral character. While we could not positively identify the silicate mineral phases based on XANES, the spectroscopic similarity of some samples to iron-smectite and the geologic context suggest iron-bearing clays were present (Fig. S5). In this geographic region, iron oxides and clays could be sourced from atmospheric dust deposition, which is common in this region [27, 28] and/or from riverine sources such as the Orinoco and/or Amazon rivers [29, 30].Fig. 2: μ-XRF-based element maps of a Trichodesmium tuft (left) and puff (right) colony (beamsize 3 ×3 μm).White/gray contours, based on the sulfur panel, which is indicative of biomass, have been provided (white = high [S] threshold, gray = lower [S] threshold). The color scale is the same for each image, with the maximum concentration for each element indicated in parentheses; iron is displayed using two scales. Iron oxidation states were determined via μ XANES for three particles in the puff colony, and these are annotated in yellow. The corresponding XANES spectra are shown in Fig. S4 and tabulated data in Table S2.Full size imageThese colony-associated mineral particles likely serve as a simultaneous source of nutritional (Fe, Ni, Co, Mn) and toxic (Cu) metals to the colonies. The elemental composition of the particles is similar to a recent characterization of Trichodesmium-particle associations in the South Atlantic [30]. Release of metals from the particles likely vary over time, with copper, nickel, zinc, and cobalt continually leaching and iron leaching initially, then re-adsorbing back onto particles unless organic chelates assist in solubilization [31].Proteome composition is altered by particle presenceTo understand the impact of the particles on colony diversity and function, we performed comparative metaproteomic analysis of the individual Trichodesmium colonies and their microbiota. Seven puffs without particles, 14 puffs with particles, and 4 tufts with particles were analyzed by a new single-colony metaproteomic method. This approach allowed for the first time the molecular profiles of heterogeneous Trichodesmium colonies to be examined individually. Compared to bulk population-level metaproteomes from this location, which achieved deeper resolution of low-abundance proteins by integrating biomass from 50 to 100 colonies (4478 proteins identified) [32], proteome coverage for the low-biomass single colonies was lower yet sufficient for characterizing colony function (2078 proteins identified, Fig. S6) [32]. In total, 1591 Trichodesmium and 487 epibiont proteins were identified across the 25 single-colony metaproteomes versus 2944 Trichodesmium and 1534 epibiont proteins across triplicate population-level metaproteomes (Tables S3 and S4). Phylogenetic exclusivity was checked such that peptides used to identify epibiont proteins were not present in the Trichodesmium genome (Table S5 and Fig. S7) [33, 34].Trichodesmium’s epibiont community plays crucial roles in colony health and physiology, and together the single-colony proteomes demonstrated a diverse and functionally active microbiome associated with the colonies (Fig. 1b and Fig. S8). The proteomic analysis generally reflected the more abundant, “core” members of the epibiont community as was expected given their low-biomass proportion relative to Trichodesmium cells. Many commonly identified epibiont groups were present including Alphaproteobacteria, Microscilla, and non-Trichodesmium cyanobacteria [12, 35, 36]. In general, epibiont abundance was unaffected by particle presence, with one exception: Firmicute proteins were more abundant in tufts and puffs with particles, suggesting enhanced, possibly anaerobic, metabolism. Greater differences were identified between the puff and tuft morphologies, independent of particle presence and consistent with prior characterizations finding that puffs and tufts harbor distinct epibiont communities [12]. Specifically, eukaryotic proteins were more abundant in puffs compared to tufts. These proteins likely represent copepods due to sequence similarity to the model organism Calanus finmarchicus, and this result is consistent with observed associations between copepods and puffs at this location (Fig. S8B). Notably, proteins from the PVC superphylum, particularly an uncharacterized eukaryote pathogen species related to Chlamydia, were also more abundant in puffs. Eukaryotes are often observed in association with Trichodesmium colonies, but are not always identified due to differences in sampling protocols that could wash them away [12], as well as due to biases in analytical methods, for instance in studies with a focus on bacterial 16S or metagenomic analyses. Overall, the differences in the epibiont community were small, suggesting that these do not explain the observed morphological heterogeneity. We therefore turn our attention to describing how the particles impacted the proteome of Trichodesmium specifically.Mineral presence was associated with significant differences in the Trichodesmium proteome. In total, 131 proteins were differently abundant in puffs with particles versus without particles (p  More

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Soil diversity, taxonomic classification and binning of BGCsNonpareil analysis estimated an abundance-weighted coverage of 85.3% for the 44.4 Gb used in the long-read assembly. To achieve 95% and 99% coverage, respectively, 250 Gb and 1.6 Tb of sequencing were predicted to be necessary. Alpha diversity was estimated at Nd = 21.6. Contigs were binned using CONCOCT, MaxBin2 and MetaBAT2, consensus bins were generated using metaWRAP refine and classified using GTDB-Tk. This yielded 114 bacterial bins with CheckM completeness  > 50% and contamination  More

If an endpoint of continued circulation (endemicity rather than eradication) seems likely, this still leaves us with questions about the range of outbreak sizes, their intensity and seasonality. Surprisingly, some basic epidemiological parameters for predicting these dynamical features are still uncertain. For example, R0, the reproductive number, which captures the infectiousness of the pathogen, is typically measured from the growth of the epidemic and is harder to estimate once non-pharmaceutical interventions (NPIs) are in place. Similarly, changes to R0 for evolved SARS- CoV-2 variants are difficult to ascertain given simultaneous changes to behaviour and interventions. It is not yet clear whether there is an evolutionary limit to strain infectiousness. To date, structural changes to the SARS-CoV-2 spike furin cleavage site7 as well as enhanced binding of the receptor binding domain to the human ACE2 receptor8 have been associated with enhanced transmissibility in variant strains, but in the longer term, transmission increases may saturate and viral evolution may modulate other aspects of disease transmission including host susceptibility. Nevertheless, any present or future changes to R0 will affect long-term epidemic dynamics, including the intensity of outbreaks and the age-structure of infections.The transmission of many respiratory pathogens varies seasonally, driven either by climatic factors or seasonal changes in behaviour such as schooling. The role of climate in driving transmission of SARS-CoV-2 is currently unclear: high susceptibility during the early pandemic likely limited any climate effect9, and statistical analyses of the climate-SARS-CoV-2 link have been confounded by trends in the data and regional differences in reporting and control measures. This has not been helped by the relatively short case time series (that is, just over a year’s worth of data) compared to typical climate–disease studies that look for climate links over many seasons. An alternative line of evidence comes from the four endemic coronaviruses, which exhibit seasonal wintertime outbreaks. It is possible that SARS-CoV-2 will follow suit. Disentangling the climate drivers of SARS-CoV-2 will become easier over time as both longer time series are available, and susceptibility declines9.A further question is the extent to which SARS-CoV-2 endemic dynamics will be affected by interactions with other circulating pathogens, including the endemic coronaviruses. Both modelling and laboratory work implies a degree of cross-immunity between coronaviruses10,11,12. The NPIs put in place to limit the spread of SARS-CoV-2 have also limited the circulation of many other pathogens, such that infection interactions have not been observed in current case trajectories13. However, as NPIs are relaxed, signatures of cross-species interactions will likely become increasingly visible.Beyond cross-immunity with other pathogens, the longitudinal trajectory of immunity, as depicted in Fig. 1, will play a crucial role in determining SARS-CoV-2 endemic dynamics14. For immunizing infections, susceptibility is driven by birth rates, and infections may be concentrated in younger age groups. For infections with waning immunity or antigenic evolution, susceptibility is driven by the rate at which immunity wanes or the rate the pathogen evolves as well as characteristics of secondary infections. The disease dynamics of pathogens with high rates of antigenic evolution are particularly hard to predict: evolved strains may have variable transmission rates and manifest variable immune responses. An analogy can be made with influenza, where the size and intensity of the seasonal influenza peak is typically very difficult to forecast15.The future course of SARS-CoV-2 remains uncertain. The next few months to a year represents a critical time where we will begin to develop an understanding of key parameters, such as the strength and duration of vaccinal and natural immunity, the seasonality of transmission and the possible interaction of SARS-CoV-2 with other circulating pathogens. In combination, these parameters will allow improved prediction of both long-term SARS-CoV-2 epidemic dynamics, as well as the likelihood of elimination and eradication. An area of particular focus will be the rate of antigenic evolution and the extent to which vaccines remain protective against evolved strains. In all scenarios, rapid and equitable distribution of vaccines presents the greatest hope for minimizing future severe outbreaks. More