Ecology

Geochemistry and ecotoxicology of AMDAMD systems are an important source of metal/metalloid pollution to the receiving hydrosphere with devastating consequences on the biological drivers of affected ecosystems. Environmental menaces of AMD have not been exhaustively reported worldwide. Scanty information exists across Africa and many developing economies. The homogenised mixture of detached biofilm and AMD samples from a derelict coal mine at three sampling periods were assayed for geochemical delineation and analysed for pollution intensity against reference background geochemical values. The measured values of the physical properties and contents of selected HMs in drains from a coal mine in Nigeria were as presented in Supplementary Table A.1. Virtually all the measured parameters exceeded the permissible limits of WHO guidelines for potable water. The AMD water was acidic (pH = 3.1 ± 0.265), and contained characteristic anions that are common to AMD including dissolved sulphides (1.37 ± 0.233 mg l−1), sulphates (313.0 ± 15.9 mg l−1), carbonate (253.0 ± 22.4 mg l−1) and nitrate (86.6 ± 41.0 mg l−1) above the allowable limits of WHO. Although the acidic pH of AMD in the present study compares well with those associated with mines in Russia14, more extreme acidic pH values have been reported in other climes. Negative pH values of − 1.56 and − 3.6 were observed in AMD from Iberian Pyrite Belt20 and Richmond Mine at Iron Mountain, USA21, respectively. The values of physicochemical parameters associated with the AMD from Onyeama were similar to data reported for other mine wastewaters in Nigeria22 and elsewhere4. It is known that sulphide minerals, in presence of water and oxygen, oxidise to sulphate as observed in the elevated sulphate concentration (313 ± 15.9 mg l−1) in the present study. The low pH observed in the AMD is due to the formation of sulphuric acid from sulphate in presence of protons (H+). This consequently causes the leaching of metal/metalloid ions into the drains. The concentrations of dissolved organic matter in AMD tends to be relatively low ( Co  > Pb  > As  > Ni  > Cr  > Fe (Table 1). Enrichment of five HMs was exceptionally high (Cd  > Co  > Pb  > As  > Ni), while Cr and Fe were very high and moderately enriched the AMD water, respectively. The astronomically high contamination and enrichment factors of the AMD signified the enrichment potentials the AMD portends on receiving surface waters. The AMD from the Onyeama coal mine has been reportedly impacting the water qualities of rivers within the location25. It is assumed that the extremely high concentrations of toxic metals/metalloids in the AMD dilutes out upon discharges into nearby rivers, contaminating the surface water and raising the bioavailable metals/metalloids beyond safe thresholds. Further reports of toxic metals/metalloids enrichment of surface waters via inflow of AMDs from other mines in Nigeria26 and other climes3,4,27 are worrisome and oblige mitigations.Table 1 Physico-chemistry, pollution and ecological impact determinants of heavy metals and metalloid contained in the AMD from coal mine.Full size tableThe HMs-enriched environments inadvertently exert ecotoxicity unto the drivers of the ecosystems. The level of HMs accumulation to the organic matter in the AMD, through geo-accumulation (Igeo) index of Fe (7.60 ± 0.779) to Cd (20.9 ± 0.075) (Supplementary Table A.2), was very severe and in a similar order to CF. It possibly implies organic matter in the AMD harbours the mobile toxic metal/metalloid concentrations and make them available to the food web28. Thus, biomagnification of the toxic metals/metalloids along the trophic level becomes palpable and a challenge to the biota of any surface water receiving the AMD and to public health21,28. Ecological risk assessments define and categorise the pollution status of ecosystems with the HMs contained in the AMD. Based on the potential ecological risk factor (Er), Cd exerted an extremely high-risk index (36.3 ± 1.96 × 106), and none of the metals/metalloids exercised less than 1000 risk index (Supplementary Table A.2). All the HMs/metalloid contained in the AMD posed very high ecological risks and could be categorised in the order of Cd  > Co  > Pb  > As  > Ni  > Cr  > Fe. The modified potential ecological risk factor (MEr), however, stipulated that five HMs posed a very high risk in the order: Cd  > Co  > Pb  > As  > Ni, whereas Cr and Fe were determined to be of considerate and low risks, respectively. The HMs exerted high risk to the AMD ecosystem as calculated by ecological risk quotient (RQ) in the order: Pb  > Cd  > As  > Ni  > Co  > Fe  > Cr. The ecological risk index of all the HMs as a whole was very high (375,000 ± 22,400) index as stipulated by the modified potential ecological risk index (Table 1). The prodigiously high ecological risks indexes of the HMs/metalloid in the AMD indicated grave danger the AMD would portend on the surface- and ground-waters.Microbial community structure of AMD from Onyeama coal mineA total of 26,160 and 40,403 valid (filtered) sequence reads were obtained for bacteria and eukarya, respectively, after a quality check of biofilm-water amplicon sequence data. The valid sequences were clustered into 2036 and 1002 operational taxonomic units (OTUs) of bacteria and eukarya domains of life, respectively, as presented in Table 2. Microbial community structures are sensitive descriptors of ecological stressors pivotal to understanding ecosystem functions29. The number of clustered high quality, non-chimeric sequences as OTUs based on UCLUST and CD-HIT against the sequence reads was depicted as asymptotic rarefaction curves (Supplementary Fig. A.1). The curves revealed that higher numbers of OTUs were delineated from valid sequence reads of 16S rRNA genes, unlike the lesser number of OTUs obtained from valid sequence reads of ITS2 region located between 5.8S and 28S rRNA genes of eukaryotes. The OTU richness observed in the rarefaction curves established coverage of the majority of species and was further validated with the richness and diversity estimations presented in Table 2. Despite the higher number of valid sequence reads obtained from the amplified ITS2 (40,403) than that of 16S rRNA genes (26,160), the observed OTUs were more in 16S rRNA genes (2036) than those of ITS2 (1002). More than 99.8% and about 98.5% of the microbial community in AMD from the Onyeama coal mine represented eukarya and bacteria OTUs, respectively, based on estimated Good’s library coverage. The coverage degree of the MiSeq sequencing corroborated the rarefaction curves. Furthermore, the estimated OTU richness (based on higher values obtained from ACE, Chao1 and JackKnife indexes) showed that bacterial phylotypes were richer than those of eukarya. Alpha diversity indexes (NPShannon, Shannon, and inverse Simpson) phylogenetic diversity index revealed that bacteria in the AMD were more diverse than eukarya OTUs.Table 2 Alpha diversity of microbiome evenness, richness and varieties of species in the sediments.Full size tableTaxonomy and phylogeny of microbial OTUs in AMD from coal mineThe taxonomic composition and relative abundances of the AMD microbiome, as shown in Fig. 1, revealed that the bacterial community spanned 10 phyla whose sequence reads were at least 1% (Fig. 1a). Whereas the eukarya domain of life (with sequence reads ≥ 1%) found in the AMD include Fungi, Plantae and Animalia kingdoms (Fig. 1b). Ascomycota, unclassified Fungi phylum (Fungi_p), Basidiomycota, and Mucoromycota represented Fungi kingdom, while Ciliophora and Arthropoda phyla were Animalia and Chlorophyta phylum epitomised Plantae kingdom. Association of the domain Eukarya (comprising Alveolates, Chlorophyta and Fungi as observed in this study) with AMD is reported to a lesser extent when compared with Bacteria30. The Fungi, largely represented by Ascomycota and Basidiomycota, are primarily found in sub-surface low-pH biofilms thriving in AMD31. While the Alveolates are suggested to have acted as primary/secondary consumers, the amoebae were secondary grazers in the AMD ecosystem29,32. Fungi taxa must have participated in carbon cycling as the main decomposers in the microbial community of the AMD. The taxonomic composition and relative abundance of phyla regarded as ‘Others’ (sequence reads  50%). Evolutionary analyses were conducted in MEGA6.Full size imageUrease-producing bacteria instigate insoluble metal-carbonate micro-precipitation through urease activity16. The growth-time courses and urease activities of the bacteria consortium in simulated AMD were presented as curves (Fig. 5). It was observed that the impact of high concentrations of HMs cocktails was not pronounced beyond the early 6 h post-inoculation, which was regarded as the lag phase. The bacteria consortium might have activated necessary genes needed to tolerate and sequester the metals/metalloids toxicity during the lag phase without cell multiplications. Afterwards, the bacteria consortium grew steadily with the production of urease, based on increasing measurement of urease activity, as incubation continued. At 30 h post-inoculation, 245.3 (± 23.7) U ml−1 activity of urease was observed in broth without a toxic metal cocktail. However, more urease activity (255 ± 7.6 U ml−1) by the bacteria consortium was observed in medium amended with low concentrations of metal cocktails unlike lesser activities of 235 (± 7.6) U ml−1 and 193.7 (± 10.7) U ml−1 associated with medium and high metal concentrations, respectively. As the growth remains stationary and pH further increased to  > 8.2, urease activities were at least 253 U ml−1 in all the cultures. Although urease activities at acidic pH have been reported in acid-tolerant human pathogens19, the findings in this report were assumedly the first amongst bacterial strains from AMD-polluted environments. The urease activities at acidic pH compared favourably with activities at alkaline pH in previous studies7,16,42,44. Moreover, the pH of the culture system kept increasing, alleviating the acidity condition that initially prevailed in the AMD system.Figure 5Growth kinetics of bacterial consortium via viable counts extrapolated into optical density at 600 nm wavelength (a) and growth-dependent urease activity of bacterial consortium (b) in TGYM broth without heavy metals (HMs) cocktail, and with low, medium, and high concentrations of HMs cocktails. Low HMs concentrations cocktail comprised (per liter) Cd, 27.9 mg; Pb, 118.7 mg; Co, 16.2 mg; Ni, 16.2 mg; and As, 61.5 mg. While medium HMs concentration contained (per liter) Cd, 55.7 mg; Pb, 237.3 mg; Co, 32.4 mg; Ni, 32.3 mg; and As, 123.1 mg. High HMs concentration contained (per liter) Cd, 139.3 mg; Pb, 593.3 mg; Co, 81.1 mg; Ni, 80.7 mg; and As, 307.6 mg. The mean pH at the beginning of experiment was 3.5 and rose to 8.2–8.4 at 48 h post-inoculation. Growth kinetics at exponential growth phase are in the inserts of panel (a), where ‘Td’ represents doubling time and ‘K’ is the growth rate at exponential growth phase. Error bars represent standard error mean (SEM) of triplicate experiments. The culture conditions were as explained in the “Methods” Section (Growth kinetics and urease activity of bacteria consortium; Determination of bacterial growth-dependent HMs/metalloid sequestration in simulated and natural AMD).Full size imageInterestingly, urease activity was observed in low quantity at acidic pH, unlike higher activity when the pH inclined towards alkaline (Fig. 5). It is proposed that urea finds its way into Onyeama coal mine drains through runoff from agricultural soils fortified with urea fertilizers and animal manures, which are common agricultural practices in Nigeria. The products of urea hydrolysis might have equilibrated in water to form bicarbonate, ammonium and hydroxyl ions that serially increased the culture pH. Ultimately, the bicarbonate equilibrium might have shifted to form carbonate ions (HCO3− + H+ + 2NH4+ + 2OH− ↔ CO32− + NH4+  + 2H2O) that enhanced the metal-carbonate micro-precipitation (Me2+  + Cell → Cell-Me2+ + CO32− → Cell-MeCO3). The gradual increase in pH could have further indulged the formation of CO32− from HCO3−, leading to metal-CO3 precipitation around cells and in culture media. Bicarbonates enrichment with inherent ammonia production was thought to have provided additional acid neutralization of the AMD. The growth kinetics after the presumed lag phase in the early 6 h to late exponential phase at 18 h showed that a low concentration of HMs cocktails did not have an impact on the growth of the bacteria consortium. Consequently, the bacteria consortium exhibited excellent sequestration of multi-component toxic HMs in both the simulated toxic metal-rich AMD and the actual AMD obtained from the Onyeama coal mine (Table 3).Table 3 Growth associated sequestration and precipitation of heavy metals/metalloid cocktail and AMD from Onyeama coal mine.Full size tableThe bacteria consortium displayed more than 94% efficiency of Cd and Pb sequestration in natural AMD, while 100% efficiency was observed in all the simulated AMD treatments (Table 3). Low performance was found with Ni and As, but not less than 70% sequestration efficiency was observed in all treatments. Efficient sequestrations of toxic metals, up to 100% removal efficiency of most toxic metals, observed with the bacteria consortium were similar to findings in a previous study13. Mixed-bacterial cultures are known to be able to perform more complex tasks and survive in more unstable environments than a monoculture. Nevertheless, 89.3–98% removal efficiencies of Ni, Pb, Co, and Cd from solution have been reportedly achievable with urease-producing Sporosarcina koreensis45. Similarly, Bacillus sp. KK1 reportedly mitigated lead-contaminated mines tailings containing mobile Pb (1050 mg kg−1) to form insoluble precipitates of PbS and PbSiO334. Growth-dependent sequestration of HMs cocktails by the bacteria consortium was adduced to be via precipitation. The weight of the precipitates was evaluated to be proportional to concentrations of HMs cocktail present. The bacteria consortium was observed to drive the formation of as much as 15.6 (± 0.92) mg ml−1 precipitates (Table 3) that were assumed to be in form of HMs-carbonates in TGYM supplemented with high concentrations of HMs cocktail within 24 h post-inoculation. In natural AMD bio-stimulated with urea and seeded with bacteria consortium for 24 h, 10.5 (± 0.52) mg ml−1 HMs precipitates was observed unlike 8.57 (± 2.52) mg ml−1 precipitates obtained from natural AMD toxic metals sequestration without urea fortification. It appeared that the quantity of toxic metal precipitate was proportional to quantities of available toxic metals, which corresponded to the number of heterogeneous nucleation sites on the surface of the bacterial cells. Omoregie et al.42 reported a relatively similar quantum of precipitation as CaCO3 with species of ureolytic Firmicutes isolated from limestone caves. As such, there was no correlation between urease activity and quantum of toxic metal precipitation since there is a likelihood that other metabolic activities may be linked to urease activities. Nevertheless, the bioremediation strategies demonstrated in the present study exhibited excellent toxic metal sequestrations unlike insignificant (p  > 0.05) natural attenuation process of the autochthonous community without augmentation with bacteria consortium and stimulation with nutrients (as presented in Table 3).In conclusion, AMD from the Onyeama coal mine is a point source of pollution to the surrounding environments because of its richness in anions and toxic metals/metalloids. It has a high potential of enriching the receiving hydrosphere with toxic metals/metalloids and exerts severe ecological risks (Er  > 320) with Cd and Pb wielding a huge critical risk index (38.1 ± 2.18 × 106) on the biological elements of the ecosystems. The dominance of Proteobacteria (50.8%), Bacteroidetes (18.9%), Ascomycota (60.8%), and Ciliophora (12.6%) characterised the microbial community of the AMD, where unclassified OTUs occurred mostly among the species. Enrichment of the AMDs skewed the bacterial community as depicted in the alpha diversity indexes against that of coal AMD leading to the selection of bacteria consortium with an excellent potential of stemming the toxicants in the AMD. The bacteria consortium efficiently removed toxic metals/metalloids ( > 70%) through precipitation and simultaneously neutralised AMD acidity. The bacteria consortium exhibited appreciable urease activity ( > 190 U ml−1), through which the precipitation was assumed possible via the formation of metal/metalloid-carbonates. The bacteria consortium is suggested to be a sustainable biotechnological candidate in designing a bioremediation strategy for decommissioning AMD before discharge into the surrounding environment. More

Convergence is a common feature of evolution and has great effect on the succession of microbial communities. For natural microbial communities such as the microbiome of gut [1], soil [2], sediment [3], rhizosphere [4], and phyllosphere [5], convergence generally means that different communities converge towards a similar species composition, which is accompanied by species loss and acquisition. Such a convergence can be reproduced in simplified synthetic communities [6,7,8], or even in single-species populations, in which convergence can still be achieved at sub-species level [9, 10]. Unlike the convergence of natural microbial community, those experiments carried out in a sterile laboratory environment only involves the loss of species. Specifically, the main manifestation of convergence in the synthetic community containing stably coexisting species lies in that the relative proportion of species tend to become consistent [7, 8]. Nonetheless, synthetic community opens a window for us to investigate the ecological mechanism. Previous studies of synthetic communities have revealed that the convergence of bacterial community can be regulated by pH [11], mortality [12], and particularly nutrient availability [13, 14]. Most existing studies focus on the changes in species proportions, but there is a lack of in-depth understanding of the gene expression changes driven by the community species interaction.In this study, we constructed a synthetic community with two model microorganisms, Escherichia coli K-12 (EC) and Pseudomonas putida KT2440 (PP), and reproduced a convergent community assembly in closed broth-culture system. In monocultures, the growth curves of both E. coli and P. putida fitted well with the bacterial growth model, and fell into a logarithmic phase at the first 4 h of bacterium culture and a stationary phase at subsequent 20 h (after the first 4 h) (Fig. 1a). When same quantities of bacteria were grown in cocultures, their quantities were basically similar to those in monocultures, particularly in the logarithmic phase (Fig. 1b–d). By contrast, the quantities of minority species in cocultures continued to increase, and they were close to the quantities in monocultures at 24 h post co-cultivation (Fig. 1b–d). Besides, statistical analysis showed that the quantities of P. putida in all three cocultures were overall greater than that in monoculture, while E. coli quantities were no more than its monoculture (Fig. 1b–d), suggesting that P. putida has a negative effect on the growth of E. coli, but E. coli promotes that of P. putida.Fig. 1: Convergence of community structure and gene expression.a–d Growth curves of E. coli and P. putida in monoculture (a) and the “1:1000”, “1:1”, “1000:1” cocultures (b–d). In b–d subplots, the growth curves of monocultures were placed on the background layer (dashed lines), and the significant differences in cell quantity between coculture and corresponding monoculture were shown (ns, non-significant; *p  More

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NEWS AND VIEWS
01 September 2021

Policy, drought and fires combine to affect biodiversity in the Amazon basin

Analysis of the ranges of nearly 15,000 plant and vertebrate species in the Amazon basin reveals that, from 2001 to 2019, a majority were affected by fire. Drought and forest policy were the best predictors of fire outcomes.

Thomas W. Gillespie

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Thomas W. Gillespie

Thomas W. Gillespie is in the Department of Geography and at the Institute of the Environment and Sustainability at the University of California, Los Angeles, Los Angeles, California 90095, USA.

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The Amazon basin contains the largest continuous area of tropical rainforests in the world, and has a crucial role in regulating Earth’s climate1. Rates of tropical-rainforest deforestation and the impacts of fire and drought there are well established2,3. Less is known, however, about how these factors might interact to affect biodiversity, and about the role that forest policy and its enforcement have had over time. Writing in Nature, Feng et al.4 address these issues.

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We retrieved 684 Lokiarchaeota and 31 Heimdallarchaeota near-full-length 16S rRNA sequences from sequence libraries generated from sediment sampled at 27 m water depth in 5 cm intervals between 0 and 40 cm below seafloor (cm.b.s.f) in Aarhus Bay (Supplementary Information). The maximum relative read abundance of Lokiarchaeota was 1.6% at 15–20 cm.b.s.f. and 0.1% for Heimdallarchaeota at 10–15 cm.b.s.f. (Fig. 1). The sequences were grouped into 58 Loki- and 3 Heimdallarchaeota operational taxonomic units (OTUs) using a 98% sequence identity threshold and formed three distinct Lokiarchaeota clades and one monophyletic Heimdallarchaeota cluster (Fig. 1). The primer-free sequencing of RNA extracts enabled us to broadly sample the Asgard archaeal diversity in Aarhus Bay sediments and provided a solid database to design oligonucleotide probes for their visualization.Fig. 1: Phylogenetic analysis and depth distribution of Loki- and Heimdallarchaeota 16S rRNA sequences from Aarhus Bay sediments.A Maximum likelihood phylogeny of Loki- and Heimdallarchaeota operational taxonomic units (OTUs) and related sequences selected from the SILVA database (v. 132). Specificities of FISH probes and the number of sequences constituting each OTU are also depicted. TACK archaea were selected as outgroup. Bar: 0.1 substitutions per nucleotide position. B Heatmap and relative abundances of Loki- and Heimdallarchaeota sequences at different sediment depths.Full size imageBased on the newly retrieved full-length sequences, we designed four novel oligonucleotide probes specifically targeting Loki- and Heimdallarchaeota 16S rRNA with high coverage (Fig. 1, Supplementary Table 1). Probe LOK1183 targets almost all sequences in Lokiarchaeota Clade A, which contains 92% of the retrieved Lokiarchaeota sequences from Aarhus Bay sediments, while probe LOK1378 targets 85% of the sequences in all three Lokiarchaeota clades. Probe HEIM329 and HEIM529 each target >97% of the retrieved Heimdallarchaeota sequences. All designed probes cover >89% sequences in their target groups in the SILVA database (v. 132). The two Lokiarchaeota probes match 5 and 10 different non-target sequences in the SILVA database (v. 132), respectively, while the Heimdallarchaeota probes have no match outside their target group. The broad coverage and high specificity suggest that our probes can also be used to detect Loki- and Heimdallarchaeota in other habitats. Furthermore, designing two probes for each phylum enabled us to identify Lokiarchaeota clade A and Heimdallarchaeota cells via double hybridizations with two distinct dyes and thus confidently distinguish true- and false-positive signals (Supplementary Fig. 1). The general archaeal probe ARC915 also targets Lokiarchaeota and thereby provided yet another control for specific hybridization of the two Lokiarchaeota-specific probes, while the non-sense probe NON338 served as the negative control. We also designed competitor probes to minimize the theoretical false-positive hybridizations with the most frequent one and two mismatches [11] in the SILVA database (v. 132) and helper probes to facilitate probe binding [12]. This comprehensive experimental design with appropriate controls enabled reliable detection of low-abundant Loki- and Heimdallarchaeota cells in Aarhus Bay sediments.We used both confocal laser scanning microscopy (CLSM) and three-dimensional super-resolution structured illumination (SR-SIM) microscopy for detailed imaging of dual-labeled Loki- and Heimdallarchaeota signals. Loki- and Heimdallarchaeota cells featured coccoid shapes and often formed clusters (Fig. 2) (Supplementary Fig. 2). Based on SR-SIM imaging, Lokiarchaeota cells (n = 18) were 1.27 ± 0.24 µm in diameter and 1.43 ± 0.25 µm in length, while the width and the length of Heimdallarchaeota cells (n = 11) were 1.30 ± 0.20 µm and 1.37 ± 0.21 µm, respectively (Supplementary Table 2). In addition, we observed a few large ( >3 µm) ovoid and filamentous cells, resembling some of the Lokiarchaeota morphotypes reported from lake sediment [9]; however, we never detected these cell types in double hybridizations with two probes (Supplementary Fig. 1P–R), and therefore consider them false-positives.Fig. 2: Visualization of Loki- and Heimdallarchaeota cells in Aarhus Bay sediments by CARD-FISH.Probe names and the dyes are indicated for each panel. Representative cell morphotypes were imaged in a super-resolution structured illumination microscope (SR-SIM; panels (A), (B), (D), (E)) and confocal laser scanning microscope (CLSM; panels (C) and (F)). For SR-SIM images, single slices from the center of the focal plane are shown. For CLSM images, three-dimensional (3D) surface reconstructions are depicted. All z-stack images taken in CLSM are included in Supplementary Fig. 2. 360° rotation of 3D reconstructed images are also provided in Supplementary Video. Negative and positive controls are shown in Supplementary Fig. 1 together with large ovoid and filamentous false-positive signals. Images are representative of dual labeled Lokiarchaeota (n = 72) and Heimdallarchaeota (n = 70) cells in five individual experiments using two different sediment cores taken from the same sampling site. The scale bar is 1 µm.Full size imageThe DNA stain (4′,6-diamidino-2-phenylindole; DAPI) in the FISH-identified Loki- and Heimdallarchaeota cells was consistently confined to a single spherical central or lateral position (Fig. 2), corroborating the signal pattern suggested for some of the Asgard archaeal cells in lake sediments [9]. Using SR-SIM, we could image a clear gap, which separated the DNA from the ribosome-originated FISH signals with an average width of 0.18 ± 0.07 µm in Heimdallarchaeota and 0.16 ± 0.13 µm in Lokiarchaeota cells (Supplementary Table 2). The spatial separation of DNA and ribosomes in Loki- and Heimdallarchaeota cells represents an unusual observation since DAPI and FISH signals generally overlap partially or completely in prokaryotic cells [13]. Accordingly, SR-SIM imaging of benthic bacteria in Aarhus Bay sediments demonstrated the prevalence of this overlapping signal pattern (Supplementary Fig. 3). Also, the separated DNA signal observed in Loki- and Heimdallarchaeota cells appeared different from the condensed DNA formation previously described, for example, in Escherichia coli cells [14] and the Thaumarcheota Cenarcheum symbiosum [15] and Nitrosopumilus maritimus [16]. To corroborate this, we performed SR-SIM imaging of CARD-FISH-labeled E.coli and N. maritimus cells. Although their DNA was condensed in particular cellular locations, their FISH and DAPI signals always overlapped, indicating that their DNA and ribosomes are partially co-localized and not fully separated (Supplementary Fig. 4).To assess whether the gap between DAPI and FISH signals was indicative of an internal membrane, we tried various dyes to stain membranes of the CARD-FISH-labeled Asgard archaeal cells (Supplementary Information). However, none of these stainings was successful, not even for the outer cell membrane, most likely because cell membranes were disintegrated during the CARD-FISH protocol. We then used wheat germ agglutinin (WGA), a lectin primarily binding to N-acetyl-D-glucosamine but also other glycoconjugates and oligosaccharides [17] to at least be able to visualize the surfaces of Loki- and Heimdallarchaeota cells. WGA consistently decorated a cell surface that enclosed the proximal FISH and DAPI signals, suggesting that both signals originated from the same single cell (Supplementary Fig. 5). The WGA staining also demonstrated extracellular structures connected to some Heimdallarchaeota cells (Supplementary Fig. 5). These structures appear different than the membrane protrusions in the first cultured Lokiarchaeon “Ca. P. syntrophicum”, which has a considerably smaller cell size (550 nm in diameter) and does not possess the separated DNA and ribosome signals [5]. Our observations therefore indicate diverse cellular organizations and morphotypes within Asgard archaea superphylum.Our combined results suggest that genomic material is condensed and spatially distinct from the riboplasm within the detected Loki- and Heimdallarchaeota cells. Considering the anticipated role of Asgard archaea in eukaryogenesis, in particular the presence of ESPs potentially involved in dynamic cytoskeleton formation [18] and membrane remodeling [4, 19], the separation of DNA- and ribosome-derived signals might be indicative of cellular compartmentalization. Alternatively, the observed pattern could be the result of a membrane invagination to form a nucleoid region, similar to membrane organizations for example in Planctomycetes cells [20] or Atribacter laminatus [21].Our study demonstrates the first visualization of diverse Loki- and Heimdallarchaeota cells in the marine environment and provides a protocol for reliable in situ imaging of rare microorganisms in environmental samples. Future research should address the ultrastructure of Asgard archaeal cells using electron microscopy. This would help to elucidate the cell biology of Asgard archaea and provide insights into the emergence of subcellular complexity of the eukaryotic cell. More

The following experimental workflow was implemented to address the main questions raised in our study (Fig. 1).Fig. 1: The scheme of experimental pipeline used in this study to examine the impact of lytic phage infection on the P. aeruginosa population and the development of phage-resistance.Experiments were conducted as follows: culture preparation (1); biofilm formation (2); phage infection with single or cocktail preparations (3); incubation (4); biofilm and planktonic populations sampling (5); culture plating on TSA agar and isolation of discrete colonies (6); phage typing determination (7); to select isolates with unique patterns (8) for further phenotypic (9) and genome sequencing analyses (10).Full size imageThe P. aeruginosa PAO1 reference strain and four other clinical representatives were infected with distinct lytic phages in a single or different cocktail combination. Randomly picked colonies from the surviving cultures were then tested in terms of susceptibility to inoculated phages as well as to the others from the Pseudomonas phages panel (Table 1). We were interested in exploring the broadest clonal variability developed in phage infected Pseudomonas population. Therefore, the first phase of the study was focused on examining the phenotypic heterogeneity of PAO1 reference mutants (phage typing) within planktonic and biofilm populations. Since the consequences of introducing lytic phages into the bacterial population are difficult to predict, a representative pool of bacterial clones that have survived infection was sampled. A total of 780 P. aeruginosa PAO1 clones were typed with phages (planktonic (320), biofilm populations (400) and 60 control clones). No resistance to phages was observed among the control clones taken from untreated biofilm or plankton. Therefore, three biofilm and three planktonic representatives and the wild-type PAO1 were selected for further genetic and fitness analyses (Table S1). Finally, a pool of 95 isolates has been filtered, representing seventeen different phage susceptibility patterns (Tables S1, 2). This selection was based on the maximum variety of phage-type profiles, without accounting for the origin of the isolate (biofilm/plankton), as the infected planktonic bacteria turned out to be less diverse and all phage types were also present in the biofilm population.Since we did not aim to analyse the differences of planktonic versus sessile cells response to phage infection but rather look for maximum population heterogeneity, we decided to focus the investigation on the biofilm population for the other clinical strains during the second stage of this research. Accordingly, 880 (30 clones from every condition plus 10 control clones for each strain) isolated colonies from A5803, AA43, CHA, and PA biofilm populations were first subjected to phage typing. No phage resistance was observed among clones taken from phage-untreated samples compared to the wild-type strain. Ultimately, 35 phage-treated colonies, three controls, and the wild-type from each strain were selected for further investigation, resulting in a pool of 156 clones in total (39 × 4 strains) representing over twenty different phage susceptibility patterns (Table S1 and S3).Do phages always select for cross-resistance to other phages recognising the same bacterial receptor?The application of monovalent phage against reference PAO1 population generally led to the selection of cross-resistance against phages that recognise the same receptor as the applied one (Table S2). This was observed for 12/17 and 23/24 PAO1 clones isolated after LPS- and T4P-dependent phages treatment, respectively. Similar relation (15/20) was only observed for other clinical cultures infected with phiKZ phage (T4P-dependent) (Table S3). The resistance to both groups of phages was less frequent in monovalent infections (14.5% in PAO1 and 32.5% for other clinical strains) compared to polyvalent infections (61.1%; 33/54) and 51.6% (31/60) for PAO1 and clinical strains, respectively. The use of a cocktail of two phages recognising LPS selected for PAO1 clones resistant only to LPS-dependent phages. In contrast, LPS-dependent phages application was mostly accompanied by the emergence of resistance to phages recognising alternative receptors in clinical strains (28/60 cases).The introduction of a particular phage into the population did not guarantee the isolation of clones resistant to this phage. This event was recorded in the case of single phages, as well as for polyvalent combinations (23 PAO1 mutants). However, the cross-resistance to other phages recognising the same or both receptors did also occur. Interestingly, LUZ7 and KTN6 phages could still infect surviving clinical populations with a frequency of 23/60 and 44/80, respectively. Indicating that the resistance to LPS-dependent phages in clinical strains was more difficult to develop compared to those impaired by giant viruses, with 11/60 and 1/20 still sensitive to phiKZ and PA5oct phages, respectively. Almost all PAO1 (80/95) and clinical (127/140) clones treated with phages developed resistance to phage PA5oct, whereas the resistance to the entire phage panel emerged regardless of the single or cocktails application.To conclude, the selection of cross-resistance to other phages recognising the same bacterial receptor was mostly valid in the PAO1 model, whereas the other clinical strains primarily developed the cross-resistance to T4P-dependent phages.Do phages from different taxonomy groups recognising the same receptor cause the emergence of the same type of resistant mutants? Are the defence response and genome changes correlated with the receptor specificity of infecting phage?To assess the genetic basis of the resistance selected by phages, we performed single nucleotide polymorphisms (SNPs) and mapping analyses of 102 reference PAO1 clones and 156 clones derived from clinical strains (Figs. 2, 3, Table S2–S4). The wild-type P. aeruginosa strains were also re-sequenced with Illumina and PacBio technologies to ascertain their complete genomic background. Missense, nonsense, and frameshift mutation variants were taken into account in the analyses. Mutations that also occurred in control isolates were excluded from further consideration. The remaining mutations were divided into six groups: LPS-related genes, mucoidity-associated genes (EPS production, biofilm formation), T4P-related genes, global regulatory genes, and others (hypothetical or undefined function genes). The comparative analysis showed the presence of point mutations in 64 out of 95 examined PAO1 mutants. The frequency of mutations in PAO1 clones isolated after treatment with single or multiple phages was similar (73% vs 61%, respectively). In most of those isolates, only one gene mutation event was recorded (43%). However, in 23 cases SNPs occurred in two or three genes belonging to different metabolic gene groups. Five PAO1 isolates showed the presence of mutations in two genes from one gene group. The 33 cases of SNPs related to LPS synthesis were found in 29 mutants selected with single LPS-dependent phage preparations or in polyvalent combinations. Among these, the most frequent mutation (21/33 cases) was observed within the wzy gene, encoding the B-band O-antigen polymerase [30]. These frequent mutations in the LPS-biosynthesis cluster confirmed the phage resistance results emerging after LUZ7, KT28, and KTN6 phages propagation. In some cases, the LPS gene modification was accompanied by changes in EPS-related genes, leading to a mucoid phenotype. The T4P-dependent phage treatment also led to the selection of specific mutations in genes responsible for T4P expression, but also alterations in flagella-related genes (flgH, fliN, fliP, flhA). The mutations in global regulatory genes (most frequent yqjG and vfr) and “others” gene groups did not show any correlation to the type of phages used.Fig. 2: Graphical presentation of genetic changes occurring in the population of P. aeruginosa as a result of the infection by selected phages.The colour dots refer to particular gene groups where the point mutations (accumulated results) were recorded within the genomes of examined mutant clones. The lower line contains information on the maximum and minimum size of large deletions (grey bands) and the presence of intact prophages (light blue bands). * means mutation in promoter region of the gene.Full size imageFig. 3: The frequency of genetic changes per clone detected in P. aeruginosa strains.Panel (A) represents the PAO1 clones, and panel (B) represents the clinical strains populations. Populations were selected by specific phages targeting LPS (red dots) or T4P (blue dots) as a single treatment or in cocktails. The colour bars refer to particular gene groups where the point mutations were recorded within the genomes of examined mutant clones. N means the number of analysed clones for each strain.Full size imageApart from point mutations, 23% of phage-resistant PAO1 isolates contained large genomic deletions (23,983 bp–544,729 bp) appeared regardless of the phage-type and cocktail composition used as selective pressure agents. All deletions were located in the same region and despite different starting/ending points, they hold a core element of 19,038 bp. This core element carries the galU gene (responsible for LPS biosynthesis), as well as the hmgA gene, which causes the accumulation of brown pigment in bacterial cells when absent. Besides, the cumulated deletion range contained a total of 706,374 bp, including many key genes involved in the bacterial metabolism.Mutations detected in other clinical phage-resistant clones were classified according to the same criteria as in PAO1 (Figs. 2, 3, Table S3, S4). The genome-driven response to phage infection of A5803 was primarily located in global (71%, cpdA) and other genes (34%, PA2911); of AA43 in other genes (31%, PA2911); of CHA in T4P (34%) and global genes (34%, morA); and of PAK in T4P (25%) and other genes (23%, PA2911). Most of the mutations selected by LPS-dependent phage exposition were found in the global regulatory genes (9–11–25–54%) or “other” genes (17–23–31%), rather than in the LPS biosynthesis locus (0–3–6–17%) depending on the impacted strain (Table S3). That confirmed the phage-typing results where LUZ7 and KTN6 phages remained lytic towards surviving clones. In contrast, the application of phiKZ selected for the cross-resistance to T4P-dependent phages as well as for the genetic modifications in pili-related genes. Mutations in global regulatory and “others” genes show no correlation to the receptor specificity of phages used. Interestingly, a portion of phenotypically phage-resistant clones in each clinical P. aeruginosa population (5-9/35 clones) did not reveal any distinguishable genetic modifications. Consistent with PAO1, large genomic deletions were observed in A5803, AA43, and PAK strains ranging between 92,207 bp and 383,693 bp in size, encompassing the galU region. The MEME analysis of the regions flanking the deletions did not reveal specific motifs that would indicate recombination events. Interestingly, the unique large deletion found in CHA strain (15,126 bp) turned out to be the induced ssDNA filamentous Pf1-like phage.Summarising the analyses one might say that phages from different taxonomy groups recognising the same receptor generally cause the emergence of a similar type of resistance within a particular strain. However, the defence response and genome changes correlated with the receptor specificity of infecting phage differ in a strain-dependent manner.Do different strains of P. aeruginosa react similarly to a specific phage infection?The next step aimed to assess the impact of gaining phage resistance in terms of population growth efficiency as an indicator for bacterial fitness. Three of the examined wild-type strains (PAO1, A5803, and CHA) have a naturally rapid growth rate, while the other two (AA43 and PAK) display moderate growth rates. For this reason, the final results are expressed as the cumulated OD600 (Fig. 4, Table S2, S3). Overall, the majority of PAO1 mutants (61/95; 64%, p  0.001) for the clones resistant to 6–7 phages but only in the PAO1 group. Moreover, only the selection done by phage cocktails gave a statistically significant reduction of bacterial growth (p  > 0.001), while no differences were observed regarding groups treated with single LPS- or T4P- dependent phages. In contrast to the PAO1 reference strain, the statistical analyses conducted in the A5803, AA43, CHA, and PAK strains did not show any differences in terms of phage-typing profile nor phage-type selection pressure versus the population fitness reduction (growth rate).Fig. 4: The population growth efficiency as an indicator for bacterial fitness expressed as the cumulative OD600 values of 18 h culture at 37 °C measured at 20-minute intervals.Dots represent the growth of particular clones: the wild-type and control clones (green dots); mutants selected by LPS-dependent phage (red dots); mutants selected by T4P-dependent phage (blue dots); mutants selected by LPS/T4P-dependent PA5oct phage (orange dots); mutants selected by phage cocktail (black dots). * statistically different cumulative OD value compared to phage-untreated pool (p  More

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