CyanoPATH: a knowledgebase of genome-scale functional repertoire for toxic cyanobacterial blooms.

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.

DOOR: a prokaryotic operon database for genome analyses and functional inference.

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.

Structural and functional analyses of microbial metabolic networks reveal novel insights into genome-scale metabolic fluxes.

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 ∼56, ∼31 and ∼13% 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.

CapsNet-SSP: multilane capsule network for predicting human saliva-secretory proteins.

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.

The fitness effects of a point mutation in Escherichia coli change with founding population density.

Although intraspecific competition plays a seminal role in organismal evolution, little is known about the fitness effects of mutations at different population densities. We identified a point mutation in the cyclic AMP receptor protein (CRP) gene in Escherichia coli that confers significantly higher fitness than the wildtype at low founding population density, but significantly lower fitness at high founding density. Because CRP is a transcription factor that regulates the expression of nearly 500 genes, we compared global gene expression profiles of the mutant and wildtype strains. This mutation (S63F) does not affect expression of crp itself, but it does significantly affect expression of 170 and 157 genes at high and low founding density, respectively. Interestingly, acid resistance genes, some of which are known to exhibit density-dependent effects in E. coli, were consistently differentially expressed at high but not low density. As such, these genes may play a key role in reducing the crp mutant's fitness at high density, although other differentially expressed genes almost certainly also contribute to the fluctuating fitness differences we observed. Whatever the causes, we suspect that many mutations may exhibit density-dependent fitness effects in natural populations, so the fate of new mutations may frequently depend on the effective population size when they originate.

Bibliometric Screening of Helicobacter Pylori Pathogenic Genes Through Pathway Enrichment and Operon Analysis.

BACKGROUND: Helicobacter pylori is one of the most common pathogenic bacteria that causes chronic gastritis, peptic ulcers, and gastric cancer. It is very difficult to treat H. pylori bacterial infections, in part due to its polymorphisms. The objective of this study was to identify critical contributors to the pathogenicity of H. pylori bacteria through bibliometric screening of pathogenic genes. METHODS: The pathogenic genes of H. pylori strain 26695 were collected from major publication databases using the bibliometric approach. Information about the pathways, operons, and functions of these genes was obtained from the KEGG and DOOR databases. RESULTS: We inferred that HP0067, HP0069, HP1109, HP1036, and HP1037 are key pathogenic genes in H. pylori pathogenicity. These genes provided new targets for the clinical diagnosis and prognosis judgment of H. pylori. CONCLUSIONS: Finally, this study may serve as a model for other genomic level studies when more H. pylori genomes become available. These results lay a theoretical foundation for further studies on the detection of novel pathogenic genes and relevant clinical analyses.

A survey of plant and algal genomes and transcriptomes reveals new insights into the evolution and function of the cellulose synthase superfamily.

BACKGROUND: Enzymes of the cellulose synthase (CesA) family and CesA-like (Csl) families are responsible for the synthesis of celluloses and hemicelluloses, and thus are of great interest to bioenergy research. We studied the occurrences and phylogenies of CesA/Csl families in diverse plants and algae by comprehensive data mining of 82 genomes and transcriptomes. RESULTS: We found that 1) charophytic green algae (CGA) have orthologous genes in CesA, CslC and CslD families; 2) liverwort genes are found in the CesA, CslA, CslC and CslD families; 3) The fern Pteridium aquilinum not only has orthologs in these conserved families but also in the CslB, CslH and CslE families; 4) basal angiosperms, e.g. Aristolochia fimbriata, have orthologs in these families too; 5) gymnosperms have genes forming clusters ancestral to CslB/H and to CslE/J/G respectively; 6) CslG is found in switchgrass and basal angiosperms; 7) CslJ is widely present in dicots and monocots; 8) CesA subfamilies have already diversified in ferns. CONCLUSIONS: We speculate that: (i) ferns and horsetails might both have CslH enzymes, responsible for the synthesis of mixed-linkage glucans and (ii) CslD and similar genes might be responsible for the synthesis of mannans in CGA. Our findings led to a more detailed model of cell wall evolution and suggested that gene loss played an important role in the evolution of Csl families. We also demonstrated the usefulness of transcriptome data in the study of plant cell wall evolution and diversity.

ß-cyclocitral induced rapid cell death of Microcystis aeruginosa.

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%∼100% decreases for 100-300 mg/L BCC) and significantly down-regulate photosynthetic system Ⅰ-related genes. Also, chlorophyll a (by 30%∼90%) and ATP (by ∼80%) 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.

Optimizing UVA and UVC synergy for effective control of harmful cyanobacterial blooms.

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.

The Multiomics Response of Bacillus subtilis to Simultaneous Genetic and Environmental Perturbations.

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.

Local flux coordination and global gene expression regulation in metabolic modeling.

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.

Protoplast Preparation for Algal Single-Cell Omics Sequencing.

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.

Draft Genome Sequence of an Epibiotic Bacterium, Bacillus cereus, Isolated from Cyanobacterial Blooms in Lake Taihu, China.

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.

The Special and General Mechanism of Cyanobacterial Harmful Algal Blooms.

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.

Cyanobacterial Blooms Are Not a Result of Positive Selection by Freshwater Eutrophication.

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.

Determination of oligosaccharide product distributions of PL7 alginate lyases by their structural elements.

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.

Relaxed natural selection alone does not permit transposable element expansion within 4,000 generations in Escherichia coli.

Insertion sequences (ISs) are transposable genetic elements in bacterial genomes. IS elements are common among bacteria but are generally rare within free-living species, probably because of the negative fitness effects they have on their hosts. Conversely, ISs frequently proliferate in intracellular symbionts and pathogens that recently transitioned from a free-living lifestyle. IS elements can profoundly influence the genomic evolution of their bacterial hosts, although it is unknown why they often expand in intracellular bacteria. We designed a laboratory evolution experiment with Escherichia coli K-12 to test the hypotheses that IS elements often expand in intracellular bacteria because of relaxed natural selection due to (1) their generally small effective population sizes (N (e)) and thus enhanced genetic drift, and (2) their nutrient rich environment, which makes many biosynthetic genes unnecessary and thus selectively neutral territory for IS insertion. We propagated 12 populations under four experimental conditions: large N (e) versus small N (e), and nutrient rich medium versus minimal medium. We found that relaxed selection over 4,000 generations was not sufficient to permit IS element expansion in any experimental population, thus leading us to hypothesize that IS expansion in intracellular symbionts may often be spurred by enhanced transposition rates, possibly due to environmental stress, coupled with relaxed natural selection.

Co-occurring microorganisms regulate the succession of cyanobacterial harmful algal blooms.

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.

DEEPSMP: A deep learning model for predicting the ectodomain shedding events of membrane proteins.

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/.

Suppression of water-bloom cyanobacterium Microcystis aeruginosa by algaecide hydrogen peroxide maximized through programmed cell death.

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 1-15 mg 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 10 mg 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.