Biophysical controls on cluster dynamics and architectural differentiation of microbial biofilms in contrasting flow environments

Ecology, with a traditional focus on plants and animals, seeks to understand the mechanisms underlying structure and dynamics of communities. In microbial ecology, the focus is changing from planktonic communities to attached biofilms that dominate microbial life in numerous systems. Therefore, interest in the structure and function of biofilms is on the rise. Biofilms can form reproducible physical structures (i.e. architecture) at the millimetre-scale, which are central to their functioning. However, the spatial dynamics of the clusters conferring physical structure to biofilms remains often elusive. By experimenting with complex microbial communities forming biofilms in contrasting hydrodynamic microenvironments in stream mesocosms, we show that morphogenesis results in 'ripple-like' and 'star-like' architectures - as they have also been reported from monospecies bacterial biofilms, for instance. To explore the potential contribution of demographic processes to these architectures, we propose a size-structured population model to simulate the dynamics of biofilm growth and cluster size distribution. Our findings establish that basic physical and demographic processes are key forces that shape apparently universal biofilm architectures as they occur in diverse microbial but also in single-species bacterial biofilm

Fluvial network organization imprints on microbial co-occurrence networks

Recent studies highlight linkages among the architecture of ecological networks, their persistence facing environmental disturbance, and the related patterns of biodiversity. A hitherto unresolved question is whether the structure of the landscape inhabited by organisms leaves an imprint on their ecological networks. We analyzed, based on pyrosequencing profiling of the biofilm communities in 114 streams, how features inherent to fluvial networks affect the co-occurrence networks that the microorganisms form in these biofilms. Our findings suggest that hydrology and metacommunity dynamics, both changing predictably across fluvial networks, affect the fragmentation of the microbial co-occurrence networks throughout the fluvial network. The loss of taxa from co-occurrence networks demonstrates that the removal of gatekeepers disproportionately contributed to network fragmentation, which has potential implications for the functions biofilms fulfill in stream ecosystems. Our findings are critical because of increased anthropogenic pressures deteriorating stream ecosystem integrity and biodiversity

Gene prediction in metagenomic fragments: A large scale machine learning approach

<p>Abstract</p> <p>Background</p> <p>Metagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positive predictions.</p> <p>Results</p> <p>We introduce a novel gene prediction algorithm for metagenomic fragments based on a two-stage machine learning approach. In the first stage, we use linear discriminants for monocodon usage, dicodon usage and translation initiation sites to extract features from DNA sequences. In the second stage, an artificial neural network combines these features with open reading frame length and fragment GC-content to compute the probability that this open reading frame encodes a protein. This probability is used for the classification and scoring of gene candidates. With large scale training, our method provides fast single fragment predictions with good sensitivity and specificity on artificially fragmented genomic DNA. Additionally, this method is able to predict translation initiation sites accurately and distinguishes complete from incomplete genes with high reliability.</p> <p>Conclusion</p> <p>Large scale machine learning methods are well-suited for gene prediction in metagenomic DNA fragments. In particular, the combination of linear discriminants and neural networks is promising and should be considered for integration into metagenomic analysis pipelines. The data sets can be downloaded from the URL provided (see Availability and requirements section).</p

Voronoi Tessellation Captures Very Early Clustering of Single Primary Cells as Induced by Interactions in Nascent Biofilms

Biofilms dominate microbial life in numerous aquatic ecosystems, and in engineered and medical systems, as well. The formation of biofilms is initiated by single primary cells colonizing surfaces from the bulk liquid. The next steps from primary cells towards the first cell clusters as the initial step of biofilm formation remain relatively poorly studied. Clonal growth and random migration of primary cells are traditionally considered as the dominant processes leading to organized microcolonies in laboratory grown monocultures. Using Voronoi tessellation, we show that the spatial distribution of primary cells colonizing initially sterile surfaces from natural streamwater community deviates from uniform randomness already during the very early colonisation. The deviation from uniform randomness increased with colonisation — despite the absence of cell reproduction — and was even more pronounced when the flow of water above biofilms was multidirectional and shear stress elevated. We propose a simple mechanistic model that captures interactions, such as cell-to-cell signalling or chemical surface conditioning, to simulate the observed distribution patterns. Model predictions match empirical observations reasonably well, highlighting the role of biotic interactions even already during very early biofilm formation despite few and distant cells. The transition from single primary cells to clustering accelerated by biotic interactions rather than by reproduction may be particularly advantageous in harsh environments — the rule rather than the exception outside the laboratory

Shotgun sequencing of Yersinia enterocolitica strain W22703 (biotype 2, serotype O:9): genomic evidence for oscillation between invertebrates and mammals

<p>Abstract</p> <p>Background</p> <p><it>Yersinia enterocolitica </it>strains responsible for mild gastroenteritis in humans are very diverse with respect to their metabolic and virulence properties. Strain W22703 (biotype 2, serotype O:9) was recently identified to possess nematocidal and insecticidal activity. To better understand the relationship between pathogenicity towards insects and humans, we compared the W22703 genome with that of the highly pathogenic strain 8081 (biotype1B; serotype O:8), the only <it>Y. enterocolitica </it>strain sequenced so far.</p> <p>Results</p> <p>We used whole-genome shotgun data to assemble, annotate and analyse the sequence of strain W22703. Numerous factors assumed to contribute to enteric survival and pathogenesis, among them osmoregulated periplasmic glucan, hydrogenases, cobalamin-dependent pathways, iron uptake systems and the <it>Yersinia </it>genome island 1 (YGI-1) involved in tight adherence were identified to be common to the 8081 and W22703 genomes. However, sets of ~550 genes revealed to be specific for each of them in comparison to the other strain. The plasticity zone (PZ) of 142 kb in the W22703 genome carries an ancient flagellar cluster Flg-2 of ~40 kb, but it lacks the pathogenicity island YAPI_Ye, the secretion system <it>ysa </it>and <it>yts1</it>, and other virulence determinants of the 8081 PZ. Its composition underlines the prominent variability of this genome region and demonstrates its contribution to the higher pathogenicity of biotype 1B strains with respect to W22703. A novel type three secretion system of mosaic structure was found in the genome of W22703 that is absent in the sequenced strains of the human pathogenic <it>Yersinia </it>species, but conserved in the genomes of the apathogenic species. We identified several regions of differences in W22703 that mainly code for transporters, regulators, metabolic pathways, and defence factors.</p> <p>Conclusion</p> <p>The W22703 sequence analysis revealed a genome composition distinct from other pathogenic <it>Yersinia enterocolitica </it>strains, thus contributing novel data to the <it>Y. enterocolitica </it>pan-genome. This study also sheds further light on the strategies of this pathogen to cope with its environments.</p

Ecological strategies and metabolic trade-offs of complex environmental biofilms

Microorganisms aggregated into matrix-enclosed biofilms dominate microbial life in most natural, engineered, and medical systems. Despite this, the ecological adaptations and metabolic trade-offs of the formation of complex biofilms are currently poorly understood. Here, exploring the dynamics of bacterial ribosomal RNA operon (rrn) copy numbers, we unravel the genomic underpinning of the formation and success of stream biofilms that contain hundreds of bacterial taxa. Experimenting with stream biofilms, we found that nascent biofilms in eutrophic systems had reduced lag phases and higher growth rates, and more taxa with higher rrn copy number than biofilms from oligotrophic systems. Based on these growth-related traits, our findings suggest that biofilm succession was dominated by slow-but-efficient bacteria likely with leaky functions, such as the production of extracellular polymeric substances at the cost of rapid growth. Expanding our experimental findings to biofilms from 140 streams, we found that rrn copy number distribution reflects functional trait allocation and ecological strategies of biofilms to be able to thrive in fluctuating environments. These findings suggest that alternative trade-offs dominating over rate-yield trade-offs contribute to the evolutionary success of stream biofilms

Bacterial Community Composition of Stream Biofilms in Spatially Variable-Flow Environments▿ †

Streams are highly heterogeneous ecosystems, in terms of both geomorphology and hydrodynamics. While flow is recognized to shape the physical architecture of benthic biofilms, we do not yet understand what drives community assembly and biodiversity of benthic biofilms in the heterogeneous flow landscapes of streams. Within a metacommunity ecology framework, we experimented with streambed landscapes constructed from bedforms in large-scale flumes to illuminate the role of spatial flow heterogeneity in biofilm community composition and biodiversity in streams. Our results show that the spatial variation of hydrodynamics explained a remarkable percentage (up to 47%) of the variation in community composition along bedforms. This suggests species sorting as a model of metacommunity dynamics in stream biofilms, though natural biofilm communities will clearly not conform to a single model offered by metacommunity ecology. The spatial variation induced by the hydrodynamics along the bedforms resulted in a gradient of bacterial beta diversity, measured by a range of diversity and similarity indices, that increased with bedform height and hence with spatial flow heterogeneity at the flume level. Our results underscore the necessity to maintain small-scale physical heterogeneity for community composition and biodiversity of biofilms in stream ecosystems

Bacterial community composition and function along spatiotemporal connectivity gradients in the Danube floodplain (Vienna, Austria)

It is well recognized that river-floodplain systems contribute significantly to riverine ecosystem metabolism, and that bacteria are key players in the aquatic organic carbon cycle, but surprisingly few studies have linked bacterial community composition (BCC), function and carbon quality in these hydrologically highly dynamic habitats. We investigated aquatic BCC and extracellular enzymatic activity (EEA) related to dissolved organic carbon quality and algae composition, including the impact of a major flood event in one of the last remaining European semi-natural floodplain-systems. We found that surface connectivity of floodplain pools homogenizes BCC and EEA, whereas low connectivity led to increased BCC and EEA heterogeneity, supported by their relationship to electrical conductivity, an excellent indicator for surface connection strength. Hydrogeochemical parameters best explained variation of both BCC and EEA, while the algal community and chromophoric DOM properties explained only minor fractions of BCC variation. We conclude that intermittent surface connectivity and especially permanent isolation of floodplain pools from the main river channel may severely alter BCC and EEA, with potential consequences for nutrient cycling, ecological services and greenhouse gas emissions. Disentangling microbial structure-function coupling is therefore crucial, if we are to understand and predict the consequences of human alterations on these dynamic systems.Funding Agencies|Austrian Science FundAustrian Science Fund (FWF) [P24604]; Linkoping University</p

Altitudinal patterns of diversity and functional traits of metabolically active microorganisms in stream biofilms

Resources structure ecological communities and potentially link biodiversity to energy flow. It is commonly believed that functional traits (generalists versus specialists) involved in the exploitation of resources depend on resource availability and environmental fluctuations. The longitudinal nature of stream ecosystems provides changing resources to stream biota with yet unknown effects on microbial functional traits and community structure. We investigated the impact of autochthonous (algal extract) and allochthonous (spruce extract) resources, as they change along alpine streams from above to below the treeline, on microbial diversity, community composition and functions of benthic biofilms. Combining bromodeoxyuridine labelling and 454 pyrosequencing, we showed that diversity was lower upstream than downstream of the treeline and that community composition changed along the altitudinal gradient. We also found that, especially for allochthonous resources, specialisation by biofilm bacteria increased along that same gradient. Our results suggest that in streams below the treeline biofilm diversity, specialisation and functioning are associated with increasing niche differentiation as potentially modulated by divers allochthonous and autochthonous constituents contributing to resources. These findings expand our current understanding on biofilm structure and function in alpine streams