ABSTRACT
Harmful cyanobacterial blooms (HCBs) pose a global ecological threat. Ultraviolet C (UVC) irradiation at 254 nm is a promising method for controlling cyanobacterial proliferation, but the growth suppression is temporary. Resuscitation remains a challenge with UVC application, necessitating alternative strategies for lethal effects. Here, we show synergistic inhibition of Microcystis aeruginosa using ultraviolet A (UVA) pre-irradiation before UVC. We find that low-dosage UVA pre-irradiation (1.5 J cm-2) combined with UVC (0.085 J cm-2) reduces 85% more cell densities compared to UVC alone (0.085 J cm-2) and triggers mazEF-mediated regulated cell death (RCD), which led to cell lysis, while high-dosage UVA pre-irradiations (7.5 and 14.7 J cm-2) increase cell densities by 75-155%. Our oxygen evolution tests and transcriptomic analysis indicate that UVA pre-irradiation damages photosystem I (PSI) and, when combined with UVC-induced PSII damage, synergistically inhibits photosynthesis. However, higher UVA dosages activate the SOS response, facilitating the repair of UVC-induced DNA damage. This study highlights the impact of UVA pre-irradiation on UVC suppression of cyanobacteria and proposes a practical strategy for improved HCBs control.
ABSTRACT
ß-cyclocitral (BCC) is an odorous compound that can be produced by bloom-forming cyanobacteria, for example, Microcystis aeruginosa. BCC has been proposed to explain the rapid decline of cyanobacterial blooms in natural water bodies due to its lytic effects on cyanobacteria cells. However, few insights have been gained regarding the mechanisms of its lethality on cyanobacteria. In this study, M. aeruginosa was exposed to 0-300 mg/L BCC, and the physiological responses were comprehensively studied at the cellular, molecular, and transcriptomic levels. The result indicated that the lethal effect was concentration-dependent; 100 mg/L BCC only caused recoverable stress, while 150-300 mg/L BCC caused rapid rupture of cyanobacterial cells. Scanning electron microscope images suggested two typical morphological changes exposed to above 150 mg/LBCC: wrinkled/shrank with limited holes on the surface at 150 and 200 mg/L BCC exposure; no apparent shrinkage at the surface but with cell perforation at 250 and 300 mg/L BCC exposure. BCC can rapidly inhibit the photosynthetic activity of M. aeruginosa cells (40%â¼100% decreases for 100-300 mg/L BCC) and significantly down-regulate photosynthetic system â -related genes. Also, chlorophyll a (by 30%â¼90%) and ATP (by â¼80%) contents severely decreased, suggesting overwhelming pressure on the energy metabolism in cells. Glutathione levels increased significantly, and stress response-related genes were upregulated, indicating the perturbation of intracellular redox homeostasis. Two cell death pathways were proposed to explain the lethal effect: apoptosis-like death as revealed by the upregulation of SOS response genes when exposed to 200 mg/L BCC and mazEF-mediated death as revealed by the upregulation of mazEF system genes when exposed to 300 mg/L BCC. Results of the current work not only provide insights into the potential role of BCC in inducing programmed cell death during bloom demise but also indicate the potential of using BCC for harmful algal control.
Subject(s)
Aldehydes , Cyanobacteria , Diterpenes , Microcystis , Chlorophyll A/metabolism , Cyanobacteria/metabolism , ApoptosisABSTRACT
Genome-scale metabolic networks (GSMs) are fundamental systems biology representations of a cell's entire set of stoichiometrically balanced reactions. However, such static GSMs do not incorporate the functional organization of metabolic genes and their dynamic regulation (e.g., operons and regulons). Specifically, there are numerous topologically coupled local reactions through which fluxes are coordinated; the global growth state often dynamically regulates many gene expression of metabolic reactions via global transcription factor regulators. Here, we develop a GSM reconstruction method, Decrem, by integrating locally coupled reactions and global transcriptional regulation of metabolism by cell state. Decrem produces predictions of flux and growth rates, which are highly correlated with those experimentally measured in both wild-type and mutants of three model microorganisms Escherichia coli, Saccharomyces cerevisiae, and Bacillus subtilis under various conditions. More importantly, Decrem can also explain the observed growth rates by capturing the experimentally measured flux changes between wild-types and mutants. Overall, by identifying and incorporating locally organized and regulated functional modules into GSMs, Decrem achieves accurate predictions of phenotypes and has broad applications in bioengineering, synthetic biology, and microbial pathology.
Subject(s)
Gene Expression Regulation , Transcription Factors , Bacillus subtilis/genetics , Bioengineering , Escherichia coli/genetics , Saccharomyces cerevisiae/geneticsABSTRACT
How bacteria respond at the systems level to both genetic and environmental perturbations imposed at the same time is one fundamental yet open question in biology. Bioengineering or synthetic biology provides an ideal system for studying such responses, as engineered strains always have genetic changes as opposed to wildtypes and are grown in conditions which often change during growth for maximal yield of desired products. So, engineered strains were used to address the outstanding question. Two Bacillus subtilis strains (MT1 and MT2) were created previously for the overproduction of N-acetylglucosamine (GlcNAc), which were grown in an environment with a carbon shift from glucose to glucose and xylose in the same culture system. We had four groups: (1) a wildtype (WT) grown with glucose at t1; (2) a WT with glucose and xylose at t2; (3) a mutant (MT1) grown with glucose at t1; and (4) MT1 with glucose and xylose at t2. By measuring transcriptomes and metabolomes, we found that GlcNAc-producing mutants, particularly MT2, had a higher yield of N-acetylglucosamine than WT but displayed a smaller maximum growth rate than the wildtype, despite MT1 reaching higher carrying capacity. Underlying the observed growth, the engineered pathways leading to N-acetylglucosamine had both higher gene expression and associated metabolite concentrations in MT1 than WT at both t1 and t2; in bioenergetics, there was higher energy supply in terms of ATP and GTP, with the energy state metric higher in MT1 than WT at both timepoints. Additionally, most top key precursor metabolites were equally abundant in MT1 and WT at either timepoints. Besides that, one prominent feature was the high consistency between transcriptomics and metabolomics in revealing the response. First, both metabolomes and transcriptomes revealed the same PCA clusters of the four groups. Second, we found that the important functions enriched both by metabolomes and transcriptomes overlapped, such as amino acid metabolism and ABC transport. Strikingly, these functions overlapped those enriched by the genes showing a high (positive or negative) correlation with metabolites. Furthermore, these functions also overlapped the enriched KEGG pathways identified using weighted gene coexpression network analysis. All these findings suggest that the responses to simultaneous genetic and environmental perturbations are well coordinated at the metabolic and transcriptional levels: they rely heavily on bioenergetics, but core metabolism does not differ much, while amino acid metabolism and ABC transport are important. This serves as a design guide for bioengineering, synthetic biology, and systems biology.
ABSTRACT
Cyanobacterial harmful algal blooms (CyanoHABs) are longstanding aquatic hazards worldwide, of which the mechanism is not yet fully understood, i.e., the process in which cyanobacteria establish dominance over coexisting algae in the same eutrophic waters. The dominance of CyanoHABs represents a deviation from their low abundance under conventional evolution in the oligotrophic state, which has been the case since the origin of cyanobacteria on early Earth. To piece together a comprehensive mechanism of CyanoHABs, we revisit the origin and adaptive radiation of cyanobacteria in oligotrophic Earth, demonstrating ubiquitous adaptive radiation enabled by corresponding biological functions under various oligotrophic conditions. Next, we summarize the biological functions (ecophysiology) which drive CyanoHABs and ecological evidence to synthesize a working mechanism at the population level (the special mechanism) for CyanoHABs: CyanoHABs are the consequence of the synergistic interaction between superior cyanobacterial ecophysiology and elevated nutrients. Interestingly, these biological functions are not a result of positive selection by water eutrophication, but an adaptation to a longstanding oligotrophic state as all the genes in cyanobacteria are under strong negative selection. Last, to address the relative dominance of cyanobacteria over coexisting algae, we postulate a "general" mechanism of CyanoHABs at the community level from an energy and matter perspective: cyanobacteria are simpler life forms and thus have lower per capita nutrient demand for growth than coexisting eukaryotic algae. We prove this by comparing cyanobacteria and eukaryotic algae in cell size and structure, genome size, size of genome-scale metabolic networks, cell content, and finally the golden standard-field studies with nutrient supplementation in the same waters. To sum up, the comprehensive mechanism of CyanoHABs comprises a necessary condition, which is the general mechanism, and a sufficient condition, which is the special mechanism. One prominent prediction based on this tentative comprehensive mechanism is that eukaryotic algal blooms will coexist with or replace CyanoHABs if eutrophication continues and goes over the threshold nutrient levels for eukaryotic algae. This two-fold comprehensive mechanism awaits further theoretic and experimental testing and provides an important guide to control blooms of all algal species.
ABSTRACT
Single-cell sequencing (SCS) is an evolutionary technique for conducting life science research, providing the highest genome-sale throughput and single-cell resolution and unprecedented capabilities in addressing mechanistic and operational questions. Unfortunately, the current SCS pipeline cannot be directly applied to algal research as algal cells have cell walls, which makes RNA extraction hard for the current SCS platforms. Fortunately, effective methods are available for producing algal protoplasts (cells without cell walls), which can be directly fed into current SCS pipelines. In this review, we first summarize the cell wall structure and chemical composition of algal cell walls, particularly in Chlorophyta, then summarize the advances made in preparing algal protoplasts using physical, chemical, and biological methods, followed by specific cases of algal protoplast production in some commonly used eukaryotic algae. This review provides a timely primer to those interested in applying SCS in eukaryotic algal research.
ABSTRACT
Here, we report the draft genome sequence of Bacillus cereus strain THSB-6-2, which was isolated from cyanobacterial blooms in Lake Taihu, China. The 5,496,658-bp genome assembly of Bacillus cereus consists of 28 contigs, with a GC content of 35% and with 5,587 protein-coding sequences and 58 RNA genes.
ABSTRACT
Long-standing cyanobacterial harmful algal blooms (CyanoHABs) are known to result from synergistic interaction between elevated nutrients and superior ecophysiology of cyanobacteria. However, it remains to be determined whether CyanoHABs are a result of positive selection by eutrophic waters. To address this, we conducted molecular evolutionary analyses on the genomes of 9 bloom-forming cyanobacteria, combined with pangenomics and metatranscriptomics. The results showed no positive selection by water eutrophication. Instead, all homologous genes in the species are under strong purifying selection based on the ratio of divergence at nonsynonymous and synonymous sites (dN/dS) and phylogeny. The dN/dS < 0.85 (median = 0.3) for all homologous genes are similar between the genes in the pathways driving CyanoHABs and housekeeping functions. Phylogenetic support for non-positive selection comes from the mixed clustering of strains: strains of the same species from diverse geographic origins form the same clusters, while strains from the same origins form different clusters. Further support lies in the codon adaptation index (CAI) and single nucleotide polymorphism (SNP). The CAI ranged from 0.42 to 0.9 (mean = 0.75), which indicates high-level codon usage bias; the pathways for CyanoHABs and housekeeping functions showed a similar CAI. Interestingly, CAI was negatively correlated with gene expression in 3 metatranscriptomes. The numbers of SNPs were concentrated around 5 to 50. As the SNP number increases, the gene expression level decreases. These negative correlations agree with the population-level dN/dS and phylogeny in supporting purifying selection in bloom-forming cyanobacteria. In summary, superior ecophysiology appears to be acquired prior to water eutrophication. IMPORTANCE CyanoHABs are global environmental hazards, and their mechanisms of action are being intensively investigated. On an ecological scale, CyanoHABs are consequences of synergistic interactions between biological functions and elevated nutrients in eutrophic waters. On an evolutionary scale, one important question is how bloom-forming cyanobacteria acquire these superior biological functions. There are several possibilities, including adaptive evolution and horizontal gene transfer. Here, we explored the possibility of positive selection. We reasoned that there are two possible periods for cyanobacteria to acquire these functions: before the onset of water eutrophication or during water eutrophication. Either way, there should be molecular signatures in protein sequences for positive selection. Interestingly, we found no positive selection by water eutrophication, but strong purifying selection instead on nearly all the genes, suggesting these superior functions aiding CyanoHABs are acquired prior to water eutrophication.
Subject(s)
Cyanobacteria , Lakes , Phylogeny , Lakes/microbiology , Cyanobacteria/genetics , Harmful Algal Bloom , WaterABSTRACT
Alginate lyases can be used to produce well-defined alginate oligosaccharides (AOSs) because of their specificities for AOS products. A large number of alginate lyases have been recorded in the CAZy database; however, the majority are annotated-only alginate lyases that include little information on their products, thus limiting their applications. Here, we establish a simple and experiment-saving approach to predict product distributions for PL7 alginate lyases through extensive structural biology, bioinformatics and biochemical studies. Structural study on several PL7 alginate lyases reveals that two loops around the substrate binding cleft determine product distribution. Furthermore, a database containing the loop information of all annotated-only single-domain PL7 alginate lyases is constructed, enabling systematic exploration of the association between loop and product distribution. Based on these results, a simplified loop/product distribution relationship is proposed, giving us information on product distribution directly from the amino acid sequence.
Subject(s)
Alginates , Oligosaccharides , Amino Acid Sequence , Oligosaccharides/metabolism , Substrate SpecificityABSTRACT
Cyanobacterial harmful algal blooms (CyanoHABs) have been found to transmit from N2 fixer-dominated to non-N2 fixer-dominated in many freshwater environments when the supply of N decreases. To elucidate the mechanisms underlying such "counter-intuitive" CyanoHAB species succession, metatranscriptomes (biotic data) and water quality-related variables (abiotic data) were analyzed weekly during a bloom season in Harsha Lake, a multipurpose lake that serves as a drinking water source and recreational ground. Our results showed that CyanoHABs in Harsha Lake started with N2-fixing Anabaena in June (ANA stage) when N was high, and transitioned to non-N2-fixing Microcystis- and Planktothrix-dominated in July (MIC-PLA stage) when N became limited (low TN/TP). Meanwhile, the concentrations of cyanotoxins, i.e., microcystins were significantly higher in the MIC-PLA stage. Water quality results revealed that N species (i.e., TN, TN/TP) and water temperature were significantly correlated with cyanobacterial biomass. Expression levels of several C- and N-processing-related cyanobacterial genes were highly predictive of the biomass of their species. More importantly, the biomasses of Microcystis and Planktothrix were also significantly associated with expressions of microbial genes (mostly from heterotrophic bacteria) related to processing organic substrates (alkaline phosphatase, peptidase, carbohydrate-active enzymes) and cyanophage genes. Collectively, our results suggest that besides environmental conditions and inherent traits of specific cyanobacterial species, the development and succession of CyanoHABs are regulated by co-occurring microorganisms. Specifically, the co-occurring microorganisms can alleviate the nutrient limitation of cyanobacteria by remineralizing organic compounds.
Subject(s)
Cyanobacteria , Microcystis , Biomass , Harmful Algal Bloom , LakesABSTRACT
CyanoPATH is a database that curates and analyzes the common genomic functional repertoire for cyanobacteria harmful algal blooms (CyanoHABs) in eutrophic waters. Based on the literature of empirical studies and genome/protein databases, it summarizes four types of information: common biological functions (pathways) driving CyanoHABs, customized pathway maps, classification of blooming type based on databases and the genomes of cyanobacteria. A total of 19 pathways are reconstructed, which are involved in the utilization of macronutrients (e.g. carbon, nitrogen, phosphorus and sulfur), micronutrients (e.g. zinc, magnesium, iron, etc.) and other resources (e.g. light and vitamins) and in stress resistance (e.g. lead and copper). These pathways, comprised of both transport and biochemical reactions, are reconstructed with proteins from NCBI and reactions from KEGG and visualized with self-created transport/reaction maps. The pathways are hierarchical and consist of subpathways, protein/enzyme complexes and constituent proteins. New cyanobacterial genomes can be annotated and visualized for these pathways and compared with existing species. This set of genomic functional repertoire is useful in analyzing aquatic metagenomes and metatranscriptomes in CyanoHAB research. Most importantly, it establishes a link between genome and ecology. All these reference proteins, pathways and maps and genomes are free to download at http://www.csbg-jlu.info/CyanoPATH.
Subject(s)
Cyanobacteria , Databases, Genetic , Genome, Bacterial , Harmful Algal Bloom , Knowledge Bases , Cyanobacteria/genetics , Cyanobacteria/metabolismABSTRACT
Water bloom development due to eutrophication constitutes a case of niche specialization among planktonic cyanobacteria, but the genomic repertoire allowing bloom formation in only some species has not been fully characterized. We posited that the habitat relevance of a trait begets its underlying genomic complexity, so that traits within the repertoire would be differentially more complex in species successfully thriving in that habitat than in close species that cannot. To test this for the case of bloom-forming cyanobacteria, we curated 17 potentially relevant query metabolic pathways and five core pathways selected according to existing ecophysiological literature. The available 113 genomes were split into those of blooming (45) or nonblooming (68) strains, and an index of genomic complexity for each strain's version of each pathway was derived. We show that strain versions of all query pathways were significantly more complex in bloomers, with complexity in fact correlating positively with strain blooming incidence in 14 of those pathways. Five core pathways, relevant everywhere, showed no differential complexity or correlations. Gas vesicle, toxin and fatty acid synthesis, amino acid uptake, and C, N, and S acquisition systems were most strikingly relevant in the blooming repertoire. Further, we validated our findings using metagenomic gene expression analyses of blooming and nonblooming cyanobacteria in natural settings, where pathways in the repertoire were differentially overexpressed according to their relative complexity in bloomers, but not in nonbloomers. We expect that this approach may find applications to other habitats and organismal groups.IMPORTANCE We pragmatically delineate the trait repertoire that enables organismal niche specialization. We based our approach on the tenet, derived from evolutionary and complex-system considerations, that genomic units that can significantly contribute to fitness in a certain habitat will be comparatively more complex in organisms specialized to that habitat than their genomic homologs found in organisms from other habitats. We tested this in cyanobacteria forming harmful water blooms, for which decades-long efforts in ecological physiology and genomics exist. Our results essentially confirm that genomics and ecology can be linked through comparative complexity analyses, providing a tool that should be of general applicability for any group of organisms and any habitat, and enabling the posing of grounded hypotheses regarding the ecogenomic basis for diversification.
Subject(s)
Cyanobacteria/genetics , Eutrophication , Gene Expression Profiling , Genome, Bacterial , Lakes/microbiology , Cyanobacteria/physiology , Ecosystem , Genomics , Metabolic Networks and Pathways , Metagenomics , PhenotypeABSTRACT
Membrane proteins play essential roles in modern medicine. In recent studies, some membrane proteins involved in ectodomain shedding events have been reported as the potential drug targets and biomarkers of some serious diseases. However, there are few effective tools for identifying the shedding event of membrane proteins. So, it is necessary to design an effective tool for predicting shedding event of membrane proteins. In this study, we design an end-to-end prediction model using deep neural networks with long short-term memory (LSTM) units and attention mechanism, to predict the ectodomain shedding events of membrane proteins only by sequence information. Firstly, the evolutional profiles are encoded from original sequences of these proteins by Position-Specific Iterated BLAST (PSI-BLAST) on Uniref50 database. Then, the LSTM units which contain memory cells are used to hold information from past inputs to the network and the attention mechanism is applied to detect sorting signals in proteins regardless of their position in the sequence. Finally, a fully connected dense layer and a softmax layer are used to obtain the final prediction results. Additionally, we also try to reduce overfitting of the model by using dropout, L2 regularization, and bagging ensemble learning in the model training process. In order to ensure the fairness of performance comparison, firstly we use cross validation process on training dataset obtained from an existing paper. The average accuracy and area under a receiver operating characteristic curve (AUC) of five-fold cross-validation are 81.19% and 0.835 using our proposed model, compared to 75% and 0.78 by a previously published tool, respectively. To better validate the performance of the proposed model, we also evaluate the performance of the proposed model on independent test dataset. The accuracy, sensitivity, and specificity are 83.14%, 84.08%, and 81.63% using our proposed model, compared to 70.20%, 71.97%, and 67.35% by the existing model. The experimental results validate that the proposed model can be regarded as a general tool for predicting ectodomain shedding events of membrane proteins. The pipeline of the model and prediction results can be accessed at the following URL: http://www.csbg-jlu.info/DeepSMP/.
Subject(s)
Deep Learning , Membrane Proteins/chemistry , Models, Molecular , Area Under Curve , Databases, Protein , Membrane Proteins/metabolism , Neural Networks, Computer , Protein Domains , Reproducibility of Results , Sensitivity and SpecificityABSTRACT
BACKGROUND: Compared with disease biomarkers in blood and urine, biomarkers in saliva have distinct advantages in clinical tests, as they can be conveniently examined through noninvasive sample collection. Therefore, identifying human saliva-secretory proteins and further detecting protein biomarkers in saliva have significant value in clinical medicine. There are only a few methods for predicting saliva-secretory proteins based on conventional machine learning algorithms, and all are highly dependent on annotated protein features. Unlike conventional machine learning algorithms, deep learning algorithms can automatically learn feature representations from input data and thus hold promise for predicting saliva-secretory proteins. RESULTS: We present a novel end-to-end deep learning model based on multilane capsule network (CapsNet) with differently sized convolution kernels to identify saliva-secretory proteins only from sequence information. The proposed model CapsNet-SSP outperforms existing methods based on conventional machine learning algorithms. Furthermore, the model performs better than other state-of-the-art deep learning architectures mostly used to analyze biological sequences. In addition, we further validate the effectiveness of CapsNet-SSP by comparison with human saliva-secretory proteins from existing studies and known salivary protein biomarkers of cancer. CONCLUSIONS: The main contributions of this study are as follows: (1) an end-to-end model based on CapsNet is proposed to identify saliva-secretory proteins from the sequence information; (2) the proposed model achieves better performance and outperforms existing models; and (3) the saliva-secretory proteins predicted by our model are statistically significant compared with existing cancer biomarkers in saliva. In addition, a web server of CapsNet-SSP is developed for saliva-secretory protein identification, and it can be accessed at the following URL: http://www.csbg-jlu.info/CapsNet-SSP/. We believe that our model and web server will be useful for biomedical researchers who are interested in finding salivary protein biomarkers, especially when they have identified candidate proteins for analyzing diseased tissues near or distal to salivary glands using transcriptome or proteomics.
Subject(s)
Proteins/chemistry , Saliva/chemistry , HumansABSTRACT
The global expansion and intensification of toxic cyanobacterial blooms require effective algaecides. Algaecides should be selective, effective, fast-acting, and ideally suppress cyanotoxin production. In this study, whether both maximum growth suppression and minimal toxin production can be simultaneously achieved was tested with a selective algaecide H2O2, through its ability to induce apoptosis-like programmed cell death (AL PCD) in a common bloom species Microcystis aeruginosa. Under doses of 1-15â¯mgâ¯L-1, non-monotonic dose-response suppression of H2O2 on M. aeruginosa were observed, where maximal cell death and minimal microcystin production both occurred at a moderate dose of 10â¯mgâ¯L-1 H2O2. Maximal cell death was indeed achieved through AL PCD, as revealed by integrated biochemical, structural, physiological and transcriptional evidence; transcriptional profile suggested AL PCD was mediated by mazEF and lexA systems. Higher H2O2 doses directly led to necrosis in M. aeruginosa, while lower doses only caused recoverable stress. The integrated data showed the choice between the two modes of cell death is determined by the intracellular energy state under stress. A model was proposed for suppressing M. aeruginosa with AL PCD or necrosis. H2O2 was demonstrated to simultaneously maximize the suppression of both growth and microcystin production through triggering AL PCD.
Subject(s)
Herbicides/pharmacology , Hydrogen Peroxide/pharmacology , Microcystis/drug effects , Adenosine Triphosphate/metabolism , Apoptosis/drug effects , Eutrophication , Marine Toxins/biosynthesis , Microcystins/biosynthesis , Microcystis/growth & development , Microcystis/metabolism , Photosynthesis/drug effectsABSTRACT
Lakes of meltwater in the Artic have become one of the transforming landscape changes under global warming. We herein compared microbial communities between sediments and bank soils at an arctic lake post land submergence using geochemistry, 16S rRNA amplicons, and metagenomes. The results obtained showed that each sample had approximately 2,609 OTUs on average and shared 1,716 OTUs based on the 16S rRNA gene V3-V4 region. Dominant phyla in sediments and soils included Proteobacteria, Acidobacteria, Actinobacteria, Gemmatimonadetes, and Nitrospirae; sediments contained a unique phylum, Euryarchaeota, with the phylum Thaumarchaeota being primarily present in bank soils. Among the top 35 genera across all sites, 17 were more abundant in sediments, while the remaining 18 were more abundant in bank soils; seven out of the top ten genera across all sites were only from sediments. A redundancy analysis separated sediment samples from soil samples based on the components of nitrite and ammonium. Metagenome results supported the role of nitrite because most of the genes for denitrification and methane metabolic genes were more abundant in sediments than in soils, while the abundance of phosphorus-utilizing genes was similar and, thus, was not a significant explanatory factor. We identified several modules from the global networks of OTUs that were closely related to some geochemical factors, such as pH and nitrite. Collectively, the present results showing consistent changes in geochemistry, microbiome compositions, and functional genes suggest an ecological mechanism across molecular and community levels that structures microbiomes post land submergence.
Subject(s)
Geologic Sediments/microbiology , Metagenomics , Microbiota , Soil Microbiology , Archaea/classification , Archaea/genetics , Archaeal Proteins/genetics , Archaeal Proteins/metabolism , Arctic Regions , Bacteria/classification , Bacteria/genetics , Bacterial Proteins/genetics , Bacterial Proteins/metabolism , Biodiversity , Geologic Sediments/chemistry , Lakes , Methane/metabolism , Microbiota/genetics , Nitrogen/metabolism , RNA, Ribosomal, 16S/genetics , Soil/chemistryABSTRACT
Cyanobacterial blooms are intensifying global ecological hazards. The fine structure and dynamics of bloom community are critical to understanding bloom development but little understood. Here, the questions whether dominant bloomers have high diversity and whether dominant OTUs (operational taxonomical units) compete with one another were addressed. 16S rRNA gene amplicons from an annual bloom at five locations in Harsha Lake (Ohio, USA) showed cyanobacteria were the dominant phylum, and co-existing major bacterial phyla included Proteobacteria, Bacteroidetes, Actinoacteria, and Verrucomicrobia. On the genus level, the initial dominance by Dolichospermum in June yielded to Planktothrix in July, which were replaced by Microcystis and Cylindrospermopsis in August throughout the bloom. Based on the number of verified unique OTUs (a within-genus biodiversity metric), dominant genera tended to have high within-genus diversity. For example, Dolichospermum had 57 unique OTUs, Planktothrix had 36, Microcystis had 12, and Cylindrospermopsis had 4 unique OTUs. Interestingly, these different OTUs showed different dynamics and association with other OTUs. First, no between-OTU competitions were observed during the bloom cycle, and dominant OTUs were abundant throughout the bloom. Such biodiversity of OTUs and their dynamics were verified in Microcystis aeruginosa with two microcystin synthetase genes (mcyA and mcyG): the relative abundance of both genes varied during the bloom based on quantitative PCR. Two Dolichospermum circinale OTUs and one P. rubescens OTU were most abundant and persistently present throughout the entire bloom. Second, these OTUs differed in the OTUs they were associated with. Third, these OTUs tended to have different levels of association with the environmental factors, even they belonged to the same genera. These findings suggest the structure and dynamics of a cyanobacterial bloom community is complex, with only few OTUs dominating the bloom. Thus, high-resolution molecular characterization will be necessary to understand bloom development.
Subject(s)
Cyanobacteria , Lakes , Biodiversity , Ohio , RNA, Ribosomal, 16SABSTRACT
We present here an integrated analysis of structures and functions of genome-scale metabolic networks of 17 microorganisms. Our structural analyses of these networks revealed that the node degree of each network, represented as a (simplified) reaction network, follows a power-law distribution, and the clustering coefficient of each network has a positive correlation with the corresponding node degree. Together, these properties imply that each network has exactly one large and densely connected subnetwork or core. Further analyses revealed that each network consists of three functionally distinct subnetworks: (i) a core, consisting of a large number of directed reaction cycles of enzymes for interconversions among intermediate metabolites; (ii) a catabolic module, with a largely layered structure consisting of mostly catabolic enzymes; (iii) an anabolic module with a similar structure consisting of virtually all anabolic genes; and (iv) the three subnetworks cover on average â¼56, â¼31 and â¼13% of a network's nodes across the 17 networks, respectively. Functional analyses suggest: (1) cellular metabolic fluxes generally go from the catabolic module to the core for substantial interconversions, then the flux directions to anabolic module appear to be determined by input nutrient levels as well as a set of precursors needed for macromolecule syntheses; and (2) enzymes in each subnetwork have characteristic ranges of kinetic parameters, suggesting optimized metabolic and regulatory relationships among the three subnetworks.
Subject(s)
Metabolic Networks and Pathways , Microbiological Phenomena , Bacteria/genetics , Bacteria/metabolism , Cluster Analysis , Computational Biology , Escherichia coli K12/genetics , Escherichia coli K12/metabolism , Gene Expression Profiling , Genome, Microbial , Kinetics , Metabolic Networks and Pathways/genetics , Models, BiologicalABSTRACT
The rapid accumulation of fully sequenced prokaryotic genomes provides unprecedented information for biological studies of bacterial and archaeal organisms in a systematic manner. Operons are the basic functional units for conducting such studies. Here, we review an operon database DOOR (the Database of prOkaryotic OpeRons) that we have previously developed and continue to update. Currently, the database contains 6 975 454 computationally predicted operons in 2072 complete genomes. In addition, the database also contains the following information: (i) transcriptional units for 24 genomes derived using publicly available transcriptomic data; (ii) orthologous gene mapping across genomes; (iii) 6408 cis-regulatory motifs for transcriptional factors of some operons for 203 genomes; (iv) 3 456 718 Rho-independent terminators for 2072 genomes; as well as (v) a suite of tools in support of applications of the predicted operons. In this review, we will explain how such data are computationally derived and demonstrate how they can be used to derive a wide range of higher-level information needed for systems biology studies to tackle complex and fundamental biology questions.
Subject(s)
Databases, Genetic , Genomics/statistics & numerical data , Operon , Algorithms , Computational Biology , Evolution, Molecular , Gene Expression Profiling/statistics & numerical data , Genome, Bacterial , Prokaryotic CellsABSTRACT
H2O2 has been suggested and applied as effective algaecide for harmful cyanobacterial bloom control, however, the transport of exogenous H2O2 into microalgal cells, the subsequent intracellular damage pathway and dose-response variations were little studied. We addressed these questions in a bloom-forming cyanobacterium Microcystis aeruginosa with H2O2 at 0.1-1.5â¯mM. The results showed that H2O2 at 0.4â¯mM and above significantly suppressed M. aeruginosa growth for over two weeks, and induced apoptosis-like death in terms of membrane potential dissipation, caspase-3 activation, chromatin condensation, and lysis induction. However, the dose-response effects were not monotonic. H2O2 at 0.7â¯mM resulted in the severest growth suppression among 0.1-1.5â¯mM treatments, including the lowest biomass for 74% loss, the highest cell lysis ratio for 79%, and the highest utilization rate of H2O2 for 0.101â¯mMâ¯d-1. Moreover, several evidence point to severer apoptosis-like cell death in 0.7â¯mM treatments, involving fastest and severest cell lysis, smallest cell size and wrinkled surface and lowest membrane potential. Therefore, the apoptosis-like cell death induced by H2O2 at moderate dosages should be a crucial cause for the non-monotonic dose-response effects on growth suppression. Additionally, intracellular H2O2 level increased rapidly within 20â¯min after exposure at 0.4â¯mM and above, directly confirming the transport of exogenous H2O2 into M. aeruginosa cells and the intracellular damages due to subsequent elevation in intracellular oxidative stress. The study demonstrates that H2O2 at moderate dosages could be a promising method for the biomass control, in a fast and efficient way, on M. aeruginosa blooms.