Machine learning approaches for the prediction of signal peptides and other protein sorting signals

An insulin-like signalling pathway model for Fasciola gigantica

BACKGROUND

The insulin/insulin-like signalling (IIS) pathway is common in mammals and invertebrates, and the IIS pathway is unknown in Fasciola gigantica. In the present study, the IIS pathway was reconstructed in F. gigantica. We defined the components involved in the IIS pathway and investigated the transcription profiles of these genes for all developmental stages of F. gigantica. In addition, the presence of these components in excretory and secretory products (ESPs) was predicted via signal peptide annotation.

RESULTS

The core components of the IIS pathway were detected in F. gigantica. Among these proteins, one ligand (FgILP) and one insulin-like molecule binding protein (FgIGFBP) were analysed. Interestingly, three receptors (FgIR-1/FgIR-2/FgIR-3) were detected, and a novel receptor, FgIR-3, was screened, suggesting novel functions. Fg14-3-3ζ, Fgirs, and Fgpp2a exhibited increased transcription in 42-day-old juveniles and 70-day-old juveniles, while Fgilp, Fgigfb, Fgsgk-1, Fgakt-1, Fgir-3, Fgpten, and Fgaap-1 exhibited increased transcription in metacercariae. FgILP, FgIGFBP, FgIR-2, FgIR-3, and two transcription factors (FgHSF-1 and FgSKN-1) were predicted to be present in FgESPs, indicating their exogenous roles.

CONCLUSIONS

This study helps to elucidate the signal transduction pathway of IIS in F. gigantica, which will aid in understanding the interaction between flukes and hosts, as well as in understanding fluke developmental regulation, and will also lay a foundation for further characterisation of the IIS pathways of trematodes.

POOE: predicting oomycete effectors based on a pre-trained large protein language model

Oomycetes are fungus-like eukaryotic microorganisms which can cause catastrophic diseases in many plants. Successful infection of oomycetes depends highly on their effector proteins that are secreted into plant cells to subvert plant immunity. Thus, systematic identification of effectors from the oomycete proteomes remains an initial but crucial step in understanding plant-pathogen relationships. However, the number of experimentally identified oomycete effectors is still limited. Currently, only a few bioinformatics predictors exist to detect potential effectors, and their prediction performance needs to be improved. Here, we used the sequence embeddings from a pre-trained large protein language model (ProtTrans) as input and developed a support vector machine-based method called POOE for predicting oomycete effectors. POOE could achieve a highly accurate performance with an area under the precision-recall curve of 0.804 (area under the receiver operating characteristic curve = 0.893, accuracy = 0.874, precision = 0.777, recall = 0.684, and specificity = 0.936) in the fivefold cross-validation, considerably outperforming various combinations of popular machine learning algorithms and other commonly used sequence encoding schemes. A similar prediction performance was also observed in the independent test. Compared with the existing oomycete effector prediction methods, POOE provided very competitive and promising performance, suggesting that ProtTrans effectively captures rich protein semantic information and dramatically improves the prediction task. We anticipate that POOE can accelerate the identification of oomycete effectors and provide new hints to systematically understand the functional roles of effectors in plant-pathogen interactions. The web server of POOE is freely accessible at http://zzdlab.com/pooe/index.php. The corresponding source codes and data sets are also available at https://github.com/zzdlabzm/POOE.IMPORTANCEIn this work, we use the sequence representations from a pre-trained large protein language model (ProtTrans) as input and develop a Support Vector Machine-based method called POOE for predicting oomycete effectors. POOE could achieve a highly accurate performance in the independent test set, considerably outperforming existing oomycete effector prediction methods. We expect that this new bioinformatics tool will accelerate the identification of oomycete effectors and further guide the experimental efforts to interrogate the functional roles of effectors in plant-pathogen interaction.

SignalP: The Evolution of a Web Server

SignalP ( https://services.healthtech.dtu.dk/services/SignalP-6.0/ ) is a very popular prediction method for signal peptides, the intrinsic signals that make proteins secretory. The SignalP web server has existed since 1995 and is now in its sixth major version. In this historical account, we (three authors who have taken part in the entire journey plus the first author of the latest version) describe the differences between the versions and discuss the various decisions taken along the way.

Reconstruction of the TGF-β signaling pathway of Fasciola gigantica

In the present study, we reconstructed the transforming growth factor beta (TGF-β) signaling pathway for Fasciola gigantica, which is a neglected tropical pathogen. We defined the components involved in the TGF-β signaling pathway and investigated the transcription profiles of these genes for all developmental stages of F. gigantica. In addition, the presence of these components in excretory and secretory products (FgESP) was predicted via signal peptide annotation. The core components of the TGF-β signaling pathway have been detected in F. gigantica; classical and nonclassical single transduction pathways were constructed. Four ligands have been detected, which may mediate the TGF-β signaling pathway and BMP signaling pathway. Two ligand-binding type II receptors were detected, and inhibitory Smad7 was not detected. TLP, BMP-3, BMP-1, and ActRIb showed higher transcription in 42-day juvenile and 70-day juvenile, while ActRIIa, Smad1, ActRIIb, Smad8, KAT2B, and PP2A showed higher transcription in egg. TLM, Ski, Smad6, BMPRI, p70S6K, Smad2, Smad3, TgfβRI, Smad4, and p300 showed higher transcription in metacercariae. Four ligands, 2 receptors and 3 Smads are predicted to be present in the FgESP, suggesting their potential extrinsic function. This study should help to understand signal transduction in the TGF-β signaling pathway in F. gigantica. In addition, this study helps to illustrate the complex mechanisms involved in developmental processes and F. gigantica - host interaction and paves the way for further characterization of the signaling pathway in trematodes.

Characterization of a novel polyextremotolerant fungus, Exophiala viscosa, with insights into its melanin regulation and ecological niche

Black yeasts are polyextremotolerant fungi that contain high amounts of melanin in their cell wall and maintain a primar yeast form. These fungi grow in xeric, nutrient depletes environments which implies that they require highly flexible metabolisms and have been suggested to contain the ability to form lichen-like mutualisms with nearby algae and bacteria. However, the exact ecological niche and interactions between these fungi and their surrounding community are not well understood. We have isolated 2 novel black yeasts from the genus Exophiala that were recovered from dryland biological soil crusts. Despite notable differences in colony and cellular morphology, both fungi appear to be members of the same species, which has been named Exophiala viscosa (i.e. E. viscosa JF 03-3 Goopy and E. viscosa JF 03-4F Slimy). A combination of whole genome sequencing, phenotypic experiments, and melanin regulation experiments have been performed on these isolates to fully characterize these fungi and help decipher their fundamental niche within the biological soil crust consortium. Our results reveal that E. viscosa is capable of utilizing a wide variety of carbon and nitrogen sources potentially derived from symbiotic microbes, can withstand many forms of abiotic stresses, and excretes melanin which can potentially provide ultraviolet resistance to the biological soil crust community. Besides the identification of a novel species within the genus Exophiala, our study also provides new insight into the regulation of melanin production in polyextremotolerant fungi.

Blood-feeding adaptations and virome assessment of the poultry red mite Dermanyssus gallinae guided by RNA-seq

Dermanyssus gallinae is a blood-feeding mite that parasitises wild birds and farmed poultry. Its remarkably swift processing of blood, together with the capacity to blood-feed during most developmental stages, makes this mite a highly debilitating pest. To identify specific adaptations to digestion of a haemoglobin-rich diet, we constructed and compared transcriptomes from starved and blood-fed stages of the parasite and identified midgut-enriched transcripts. We noted that midgut transcripts encoding cysteine proteases were upregulated with a blood meal. Mapping the full proteolytic apparatus, we noted a reduction in the suite of cysteine proteases, missing homologues for Cathepsin B and C. We have further identified and phylogenetically analysed three distinct transcripts encoding vitellogenins that facilitate the reproductive capacity of the mites. We also fully mapped transcripts for haem biosynthesis and the ferritin-based system of iron storage and inter-tissue trafficking. Additionally, we identified transcripts encoding proteins implicated in immune signalling (Toll and IMD pathways) and activity (defensins and thioester-containing proteins), RNAi, and ion channelling (with targets for commercial acaricides such as Fluralaner, Fipronil, and Ivermectin). Viral sequences were filtered from the Illumina reads and we described, in part, the RNA-virome of D. gallinae with identification of a novel virus, Red mite quaranjavirus 1.

Rickettsia parkeri hijacks tick hemocytes to manipulate cellular and humoral transcriptional responses

Introduction

Blood-feeding arthropods rely on robust cellular and humoral immunity to control pathogen invasion and replication. Tick hemocytes produce factors that can facilitate or suppress microbial infection and pathogenesis. Despite the importance of hemocytes in regulating microbial infection, understanding of their basic biology and molecular mechanisms remains limited.

Methods

Here we combined histomorphology and functional analysis to identify five distinct phagocytic and non-phagocytic hemocyte populations circulating within the Gulf Coast tick Amblyomma maculatum.

Results and discussion

Depletion of phagocytic hemocytes using clodronate liposomes revealed their function in eliminating bacterial infection. We provide the first direct evidence that an intracellular tick-borne pathogen, Rickettsia parkeri, infects phagocytic hemocytes in Am. maculatum to modify tick cellular immune responses. A hemocyte-specific RNA-seq dataset generated from hemocytes isolated from uninfected and R. parkeri-infected partially blood-fed ticks generated ~40,000 differentially regulated transcripts, >11,000 of which were immune genes. Silencing two differentially regulated phagocytic immune marker genes (nimrod B2 and eater-two Drosophila homologs), significantly reduced hemocyte phagocytosis.

Conclusion

Together, these findings represent a significant step forward in understanding how hemocytes regulate microbial homeostasis and vector competence.

ProteomicsML: An Online Platform for Community-Curated Data sets and Tutorials for Machine Learning in Proteomics

Data set acquisition and curation are often the most difficult and time-consuming parts of a machine learning endeavor. This is especially true for proteomics-based liquid chromatography (LC) coupled to mass spectrometry (MS) data sets, due to the high levels of data reduction that occur between raw data and machine learning-ready data. Since predictive proteomics is an emerging field, when predicting peptide behavior in LC-MS setups, each lab often uses unique and complex data processing pipelines in order to maximize performance, at the cost of accessibility and reproducibility. For this reason we introduce ProteomicsML, an online resource for proteomics-based data sets and tutorials across most of the currently explored physicochemical peptide properties. This community-driven resource makes it simple to access data in easy-to-process formats, and contains easy-to-follow tutorials that allow new users to interact with even the most advanced algorithms in the field. ProteomicsML provides data sets that are useful for comparing state-of-the-art machine learning algorithms, as well as providing introductory material for teachers and newcomers to the field alike. The platform is freely available at https://www.proteomicsml.org/, and we welcome the entire proteomics community to contribute to the project at https://github.com/ProteomicsML/ProteomicsML.

Electron cryo-microscopy reveals the structure of the archaeal thread filament

Pili are filamentous surface extensions that play roles in bacterial and archaeal cellular processes such as adhesion, biofilm formation, motility, cell-cell communication, DNA uptake and horizontal gene transfer. The model archaeaon Sulfolobus acidocaldarius assembles three filaments of the type-IV pilus superfamily (archaella, archaeal adhesion pili and UV-inducible pili), as well as a so-far uncharacterised fourth filament, named "thread". Here, we report on the cryo-EM structure of the archaeal thread. The filament is highly glycosylated and consists of subunits of the protein Saci_0406, arranged in a head-to-tail manner. Saci_0406 displays structural similarity, but low sequence homology, to bacterial type-I pilins. Thread subunits are interconnected via donor strand complementation, a feature reminiscent of bacterial chaperone-usher pili. However, despite these similarities in overall architecture, archaeal threads appear to have evolved independently and are likely assembled by a distinct mechanism.

In silico and in vitro analysis of a rational mutation in gIII signal peptide and its effects on periplasmic expression of rhGH in E. coli

The secretion efficiency of a heterologous protein in E. coli is mainly dictated by the N-terminal signal peptide fused to the desired protein. In this study, we aimed to select and introduce mutations into the - 1, - 2 and - 3 positions of the gIII signal peptide (originated from filamentous phage fd Gene III) fused to the N-terminus of the human growth hormone (hGH), and study its effect on the secretion efficiency of the recombinant hGH into the periplasmic space of E. coli Top10. Bioinformatics software such as SignalP-5.0 and PrediSi were employed to predict the effects of the mutations on the secretion efficiency of the recombinant hGH. Site-directed mutagenesis was applied to introduce the desired mutations into the C-terminus of the gIII signal peptide. The periplasmic expression and the secretion efficiency of the recombinant hGH using the native and mutant gIII signal peptides were compared in E. coli Top10 under the control of araBAD promoter. Our results from bioinformatics analysis indicated that the mutant gIII signal peptide was more potent than the native one for secretion of the recombinant hGH in E. coli. While our experimental results revealed that the mutation had no effect on hGH secretion. This result points to the importance of experimental validation of bioinformatics predictions.

Schistocins: Novel antimicrobial peptides encrypted in the Schistosoma mansoni Kunitz Inhibitor SmKI-1

BACKGROUND

Here we describe a new class of cryptides (peptides encrypted within a larger protein) with antimicrobial properties, named schistocins, derived from SmKI-1, a key protein in Shistosoma mansoni survival. This is a multi-functional protein with biotechnological potential usage as a therapeutic molecule in inflammatory diseases and to control schistosomiasis.

METHODS

We used our algorithm enCrypted, to perform an in silico proteolysis of SmKI-1 and a screening for potential antimicrobial activity. The selected peptides were chemically synthesized, tested in vitro and evaluated by both structural (CD, NMR) and biophysical (ITC) studies to access their structure-function relationship.

RESULTS

EnCrypted was capable of predicting AMPs in SmKI-1. Our biophysical analyses described a membrane-induced conformational change from random coil-to-α-helix and a peptide-membrane equilibrium for all schistocins. Our structural data allowed us to suggest a well-known mode of peptide-membrane interaction in which electrostatic attraction between the cationic peptides and anionic membranes results in the bilayer disordering. Moreover, the NMR exchange H/D data with the higher entropic contribution observed for the peptide-membrane interaction showed that shistocins have different orientations upon the membrane.

CONCLUSIONS

This work demonstrate the robustness for using the physicochemical features of predicted peptides in the identification of new bioactive cryptides besides the relevance of combining these analyses with biophysical methods to understand the peptide-membrane affinity and improve further algorithms.

GENERAL SIGNIFICANCE

Bioprospecting cryptides can be conducted through data mining of protein databases demonstrating the success of our strategy. The peptides-based agents derived from SmKI-1 might have high impact for system-biology and biotechnology.

Machine learning classifiers aid virtual screening for efficient design of mini-protein therapeutics

De novo design of mini-proteins (4-12 kDa) has recently been shown to produce new candidates for protein therapeutics. They are temperature stable molecules that bind to the drug target with high affinity for inhibiting its interactions. The development of mini-protein binders requires laboratory screening of tens of thousands of molecules for effective target binding. In this study we trained machine learning classifiers which can distinguish, with 90% accuracy and 80% precision, mini-protein binders from non-binding molecules designed for a particular target; this significantly reduces the number of mini protein candidates for experimental screening. Further, on the basis of our results we propose a multi-stage protocol where a small dataset (few hundred experimentally verified target-specific mini-proteins) can be used to train classifiers for improving the efficiency of mini-protein design for any specific target.

Analysis of Adaptive Evolution and Coevolution of rbcL Gene in the Genus Hildenbrandia (Rhodophyta)

The adaptive evolution and coevolution of the ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit (rbcL) gene in the genus Hildenbrandia were studied based on phylogenetic tree construction and the physicochemical properties and the secondary structures of protein encoded by rbcL (Rubisco large subunit) were analyzed. The amino acids compositions and grand average of hydropathicity of freshwater H. rivularis and marine H. rubra were similar. Rubisco large subunit of Hildenbrandia was hydrophilic and the secondary structure was primarily composed of α-helixes and β-sheets, revealing the relatively stable structure of this protein. The predicted phosphorylation sites in H. rivularis and H. rubra were 33 and 36, respectively. No positive selection sites were detected in the genus Hildenbrandia, implying that rbcL gene evolved either neutrally or under purifying selection. A total of 41 coevolutionary groups were detected in the Rubisco large subunit of Hildenbrandia and the coevolving sites are in closer proximity in 3-dimensional structure of the protein. Despite the long evolutionary history, rbcL gene in genus Hildenbrandia under different environments is rather conservative.

The Central Role of Salivary Metalloproteases in Host Acquired Resistance to Tick Feeding

During feeding on vertebrate hosts, ticks secrete saliva composed of a rich cocktail of bioactive molecules modulating host immune responses. Although most of the proteinaceous fraction of tick saliva is of little immunogenicity, repeated feeding of ticks on mammalian hosts may lead to impairment of tick feeding, preventing full engorgement. Here, we challenged rabbits with repeated feeding of both Ixodes ricinus nymphs and adults and observed the formation of specific antibodies against several tick salivary proteins. Repeated feeding of both I. ricinus stages led to a gradual decrease in engorged weights. To identify the salivary antigens, isolated immunoglobulins from repeatedly infested rabbits were utilized for a protein pull-down from the saliva of pilocarpine-treated ticks. Eluted antigens were first identified by peptide mass fingerprinting with the aid of available I. ricinus salivary gland transcriptomes originating from early phases of tick feeding. To increase the authenticity of immunogens identified, we also performed, for the first time, de novo assembly of the sialome from I. ricinus females fed for six days, a timepoint used for pilocarpine-salivation. The most dominant I. ricinus salivary immunogens identified in our study were zinc-dependent metalloproteases of three different families. To corroborate the role of metalloproteases at the tick/host interface, we fed ticks micro-injected with a zinc metalloprotease inhibitor, phosphoramidon, on a rabbit. These ticks clearly failed to initiate feeding and to engorge. However, neither feeding to ticks immune blood of repeatedly infested rabbits, nor phosphoramidon injection into ticks, prevented their engorgement when fed in vitro on an artificial membrane system. These data show that Zn metalloproteases play a decisive role in the success of tick feeding, mediated by complex molecular interactions between the host immune, inflammatory, and hemostatic processes, which are absent in in vitro feeding. This basic concept warrants further investigation and reconsideration of the current strategies towards the development of an effective “anti-tick” vaccine.

A physiologic overview of the organ-specific transcriptome of the cattle tick Rhipicephalus microplus

To further obtain insights into the Rhipicephalus microplus transcriptome, we used RNA-seq to carry out a study of expression in (i) embryos; (ii) ovaries from partially and fully engorged females; (iii) salivary glands from partially engorged females; (iv) fat body from partially and fully engorged females; and (v) digestive cells from partially, and (vi) fully engorged females. We obtained > 500 million Illumina reads which were assembled de novo, producing > 190,000 contigs, identifying 18,857 coding sequences (CDS). Reads from each library were mapped back into the assembled transcriptome giving a view of gene expression in different tissues. Transcriptomic expression and pathway analysis showed that several genes related in blood digestion and host-parasite interaction were overexpressed in digestive cells compared with other tissues. Furthermore, essential genes for the cell development and embryogenesis were overexpressed in ovaries. Taken altogether, these data offer novel insights into the physiology of production and role of saliva, blood digestion, energy metabolism, and development with submission of 10,932 novel tissue/cell specific CDS to the NCBI database for this important tick species.

A physiologic overview of the organ-specific transcriptome of the cattle tick Rhipicephalus microplus

To further obtain insights into the Rhipicephalus microplus transcriptome, we used RNA-seq to carry out a study of expression in (i) embryos; (ii) ovaries from partially and fully engorged females; (iii) salivary glands from partially engorged females; (iv) fat body from partially and fully engorged females; and (v) digestive cells from partially, and (vi) fully engorged females. We obtained > 500 million Illumina reads which were assembled de novo, producing > 190,000 contigs, identifying 18,857 coding sequences (CDS). Reads from each library were mapped back into the assembled transcriptome giving a view of gene expression in different tissues. Transcriptomic expression and pathway analysis showed that several genes related in blood digestion and host-parasite interaction were overexpressed in digestive cells compared with other tissues. Furthermore, essential genes for the cell development and embryogenesis were overexpressed in ovaries. Taken altogether, these data offer novel insights into the physiology of production and role of saliva, blood digestion, energy metabolism, and development with submission of 10,932 novel tissue/cell specific CDS to the NCBI database for this important tick species.

The sialotranscriptome of the gopher-tortoise tick, Amblyomma tuberculatum

The gopher tortoise tick, Amblyomma tuberculatum, is known to parasitize keystone ectotherm reptile species. The biological success of ticks requires precise mechanisms to evade host hemostatic and immune responses. Acquisition of a full blood meal requires attachment, establishment of the blood pool, and engorgement of the tick. Tick saliva contains molecules which counter the host responses to allow uninterrupted feeding on the host. RNASeq of the salivary glands of Amblyomma tuberculatum ticks were sequenced resulting in 138,030 pyrosequencing reads which were assembled into 29,991 contigs. A total of 1875 coding sequences were deduced from the transcriptome assembly, including 602 putative secretory and 982 putative housekeeping proteins. The annotated data sets are available as a hyperlinked spreadsheet. The sialotranscriptome assembled for this tick species made available a valuable resource for mining novel pharmacological activities and comparative analysis.

High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases

It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.

TickSialoFam (TSFam): A Database That Helps to Classify Tick Salivary Proteins, a Review on Tick Salivary Protein Function and Evolution, With Considerations on the Tick Sialome Switching Phenomenon

Tick saliva contains a complex mixture of peptides and non-peptides that counteract their hosts' hemostasis, immunity, and tissue-repair reactions. Recent transcriptomic studies have revealed over one thousand different transcripts coding for secreted polypeptides in a single tick species. Not only do these gene products belong to many expanded families, such as the lipocalins, metalloproteases, Antigen-5, cystatins, and apyrases, but also families that are found exclusively in ticks, such as the evasins, Isac, DAP36, and many others. Phylogenetic analysis of the deduced protein sequences indicate that the salivary genes exhibit an increased rate of evolution due to a lower evolutionary constraint and/or positive selection, allowing for a large diversity of tick salivary proteins. Thus, for each new tick species that has its salivary transcriptome sequenced and assembled, a formidable task of annotation of these transcripts awaits. Currently, as of November 2019, there are over 287 thousand coding sequences deposited at the National Center for Biotechnology Information (NCBI) that are derived from tick salivary gland mRNA. Here, from these 287 thousand sequences we identified 45,264 potential secretory proteins which possess a signal peptide and no transmembrane domains on the mature peptide. By using the psiblast tools, position-specific matrices were constructed and assembled into the TickSialoFam (TSF) database. The TSF is a rpsblastable database that can help with the annotation of tick sialotranscriptomes. The TSA database identified 136 tick salivary secreted protein families, as well as 80 families of endosomal-related products, mostly having a protein modification function. As the number of sequences increases, and new annotation details become available, new releases of the TSF database may become available.

Signal-3L 3.0: Improving Signal Peptide Prediction through Combining Attention Deep Learning with Window-Based Scoring

Signal peptides play an important role in guiding and transferring transmembrane proteins and secreted proteins. In recent years, with the explosive growth of protein sequences, computationally predicting signal peptides and their cleavage sites from protein sequences is highly desired. In this work, we present an improved approach, Signal-3L 3.0, for signal peptide recognition and cleavage-site prediction using a 3-layer hybrid method of integrating deep learning algorithms and window-based scoring. There are three main components in the Signal-3L 3.0 prediction engine: (1) a deep bidirectional long short-term memory (Bi-LSTM) network with a soft self-attention learns abstract features from sequences to determine whether a query protein contains a signal peptide; (2) the statistics propensity window-based cleavage site screening method is applied to generate the set of candidate cleavage sites; (3) the prediction of a conditional random field with a hybrid convolutional neural network (CNN) and Bi-LSTM is fused with the window-based score for identifying the final unique cleavage site. Experimental results on the benchmark datasets show that the new deep learning-driven Signal-3L 3.0 yields promising performance. The online server of Signal-3L 3.0 is available at http://www.csbio.sjtu.edu.cn/bioinf/Signal-3L/.

The use of non-adapted anaerobic consortium in batch reactors enable to couple polychlorinated biphenyl degradation and community adaptation

The removal of polychlorinated biphenyls (PCBs) and PCB biosorption was investigated in anaerobic batch reactors with non-adapted sludge fed with 1.5 mg L^-1 of six PCB congener (PCB 10, 28, 52, 153, 138 and 180), mineral medium and co-substrates. PCBs were analyzed by gas chromatography using headspace solid-phase microextraction (HS-SPME). In the methanogenic reactor the methane production, COD (Carbon Organic Demand) removal (90% of initial 2292.60 mg L^-1) and consumption of volatile organic acids were verified. Nevertheless, anaerobic activity was not observed in the reactor with inactivated biomass and biosorption range of 38% to 89% was measured for distinct PCB congeners in this reactor. The PCB removal was calculated from the PCB bioavailable (not biosorbed) and reached 76% of total PCBs. The selection of some representatives of the Thermotogaceae family, Sedimentibacter and Pseudomonas at 101 days of operation in the methanogenic reactor was correlated with PCB degradation. In addition, the various removal rates for each PCB congener indicate that the removal depends on bioavailability. The selection of the former non-adapted microbiota in the methanogenic reactor combined with PCB degradation occurred at 101 days. These results allow to assert that it is possible to simultaneously couple PCB degradation and community selection, without the previous adaptation step, which is a time-consuming stage.

CNN-Peaks: ChIP-Seq peak detection pipeline using convolutional neural networks that imitate human visual inspection

ChIP-seq is one of the core experimental resources available to understand genome-wide epigenetic interactions and identify the functional elements associated with diseases. The analysis of ChIP-seq data is important but poses a difficult computational challenge, due to the presence of irregular noise and bias on various levels. Although many peak-calling methods have been developed, the current computational tools still require, in some cases, human manual inspection using data visualization. However, the huge volumes of ChIP-seq data make it almost impossible for human researchers to manually uncover all the peaks. Recently developed convolutional neural networks (CNN), which are capable of achieving human-like classification accuracy, can be applied to this challenging problem. In this study, we design a novel supervised learning approach for identifying ChIP-seq peaks using CNNs, and integrate it into a software pipeline called CNN-Peaks. We use data labeled by human researchers who annotate the presence or absence of peaks in some genomic segments, as training data for our model. The trained model is then applied to predict peaks in previously unseen genomic segments from multiple ChIP-seq datasets including benchmark datasets commonly used for validation of peak calling methods. We observe a performance superior to that of previous methods.

Biochemical characterization and structural insights into the high substrate affinity of a dimeric and Ca2+ independent Bacillus subtilis α-amylase

An extracellular amylase (AmyKS) produced by a newly isolated Bacillus subtilis strain US572 was purified and characterized. AmyKS showed maximal activity at pH 6 and 60°C with a half-life of 10 min at 70°C. It is a Ca²⁺ independent enzyme and able to hydrolyze soluble starch into oligosaccharides consisting mainly of maltose and maltotriose. When compared to the studied α-amylases, AmyKS presents a high affinity toward soluble starch with a K_m value of 0.252 mg ml^-1 . Coupled with the size-exclusion chromatography data, MALDI-TOF/MS analysis indicated that the purified amylase is a dimer with a molecular mass of 136,938.18 Da. It is an unusual feature of a non-maltogenic α-amylase. A 3D model and a dimeric model of AmyKS were generated showing the presence of an additional domain suspected to be involved in the dimerization process. This dimer arrangement could explain the high substrate affinity and catalytic efficiency of this enzyme.

Gap Junction Channels of Innexins and Connexins: Relations and Computational Perspectives

Gap junction (GJ) channels in invertebrates have been used to understand cell-to-cell communication in vertebrates. GJs are a common form of intercellular communication channels which connect the cytoplasm of adjacent cells. Dysregulation and structural alteration of the gap junction-mediated communication have been proven to be associated with a myriad of symptoms and tissue-specific pathologies. Animal models relying on the invertebrate nervous system have exposed a relationship between GJs and the formation of electrical synapses during embryogenesis and adulthood. The modulation of GJs as a therapeutic and clinical tool may eventually provide an alternative for treating tissue formation-related diseases and cell propagation. This review concerns the similarities between Hirudo medicinalis innexins and human connexins from nucleotide and protein sequence level perspectives. It also sets forth evidence of computational techniques applied to the study of proteins, sequences, and molecular dynamics. Furthermore, we propose machine learning techniques as a method that could be used to study protein structure, gap junction inhibition, metabolism, and drug development.

Accelerating Biologics Manufacturing by Modeling or: Is Approval under the QbD and PAT Approaches Demanded by Authorities Acceptable Without a Digital-Twin?

Innovative biologics, including cell therapeutics, virus-like particles, exosomes,recombinant proteins, and peptides, seem likely to substitute monoclonal antibodies as the maintherapeutic entities in manufacturing over the next decades. This molecular variety causes agrowing need for a general change of methods as well as mindset in the process development stage,as there are no platform processes available such as those for monoclonal antibodies. Moreover,market competitiveness demands hyper-intensified processes, including accelerated decisionstoward batch or continuous operation of dedicated modular plant concepts. This indicates gaps inprocess comprehension, when operation windows need to be run at the edges of optimization. Inthis editorial, the authors review and assess potential methods and begin discussing possiblesolutions throughout the workflow, from process development through piloting to manufacturingoperation from their point of view and experience. Especially, the state-of-the-art for modeling inred biotechnology is assessed, clarifying differences and applications of statistical, rigorousphysical-chemical based models as well as cost modeling. “Digital-twins” are described and effortsvs. benefits for new applications exemplified, including the regulation-demanded QbD (quality bydesign) and PAT (process analytical technology) approaches towards digitalization or industry 4.0based on advanced process control strategies. Finally, an analysis of the obstacles and possiblesolutions for any successful and efficient industrialization of innovative methods from processdevelopment, through piloting to manufacturing, results in some recommendations. A centralquestion therefore requires attention: Considering that QbD and PAT have been required byauthorities since 2004, can any biologic manufacturing process be approved by the regulatoryagencies without being modeled by a “digital-twin” as part of the filing documentation?

Sialome diversity of ticks revealed by RNAseq of single tick salivary glands

Ticks salivate while feeding on their hosts. Saliva helps blood feeding through host anti-hemostatic and immunomodulatory components. Previous transcriptomic and proteomic studies revealed the complexity of tick saliva, comprising hundreds of polypeptides grouped in several multi-genic families such as lipocalins, Kunitz-domain containing peptides, metalloproteases, basic tail secreted proteins, and several other families uniquely found in ticks. These studies also revealed that the composition of saliva changes with time; expression of transcripts from the same family wax and wane as a function of feeding time. Here, we examined whether host immune factors could influence sialome switching by comparing sialomes of ticks fed naturally on a rabbit, to ticks artificially fed on defibrinated blood depleted of immune components. Previous studies were based on transcriptomes derived from pools of several individuals. To get an insight into the uniqueness of tick sialomes, we performed transcriptomic analyses of single salivary glands dissected from individual adult female I. ricinus ticks. Multivariate analysis identified 1,279 contigs differentially expressed as a function of time and/or feeding mode. Cluster analysis of these contigs revealed nine clusters of differentially expressed genes, four of which appeared consistently across several replicates, but five clusters were idiosyncratic, pointing to the uniqueness of sialomes in individual ticks. The disclosure of tick quantum sialomes reveals the unique salivary composition produced by individual ticks as they switch their sialomes throughout the blood meal, a possible mechanism of immune evasion.

In this work, we confirm previous reports that the repertoire of tick salivary gland transcripts changes as a function of time, but in addition, we now identify transcripts that change their levels according to the mode of feeding of ticks. Implementation of membrane feeding allowed us to feed ticks on an immune-deficient diet and identify transcripts that are subject to immunity-stimulated expression. Such identification may help to prioritise selection of salivary gland transcripts for further investigation. One novelty of this work was creating cDNA libraries from a single pair of salivary glands, which helped to gain insight into sialomic diversity at the single tick level. We observed that ticks express a battery of genes in defined clusters as feeding progresses (over tested replicates), but also individual ticks were found to express idiosyncratic clusters of genes. Such a biological phenomenon may imply novel tick mechanisms for evading host-mediated recognition of tick antigens.

Recognition of protein allosteric states and residues: Machine learning approaches

Allostery is a process by which proteins transmit the effect of perturbation at one site to a distal functional site upon certain perturbation. As an intrinsically global effect of protein dynamics, it is difficult to associate protein allostery with individual residues, hindering effective selection of key residues for mutagenesis studies. The machine learning models including decision tree (DT) and artificial neural network (ANN) models were applied to develop classification model for a cell signaling allosteric protein with two states showing extremely similar tertiary structures in both crystallographic structures and molecular dynamics simulations. Both DT and ANN models were developed with 75% and 80% of predicting accuracy, respectively. Good agreement between machine learning models and previous experimental as well as computational studies of the same protein validates this approach as an alternative way to analyze protein dynamics simulations and allostery. In addition, the difference of distributions of key features in two allosteric states also underlies the population shift hypothesis of dynamics-driven allostery model. © 2018 Wiley Periodicals, Inc.

An insight into the salivary gland and fat body transcriptome of Panstrongylus lignarius (Hemiptera: Heteroptera), the main vector of Chagas disease in Peru

Triatomines are hematophagous arthropod vectors of Trypanosoma cruzi, the causative agent of Chagas Disease. Panstrongylus lignarius, also known as Panstrongylus herreri, is considered one of the most versatile triatomines because it can parasitize different hosts, it is found in different habitats and countries, it has sylvatic, peridomestic and domestic behavior and it is a very important vector of Chagas disease, especially in Peru. Molecules produced and secreted by salivary glands and fat body are considered of important adaptational value for triatomines because, among other functions, they subvert the host haemostatic, inflammatory and immune systems and detoxify or protect them against environmental aggressors. In this context, the elucidation of the molecules produced by these tissues is highly valuable to understanding the ability of this species to adapt and transmit pathogens. Here, we use high-throughput sequencing techniques to assemble and describe the coding sequences resulting from the transcriptome of the fat body and salivary glands of P. lignarius. The final assembly of both transcriptomes together resulted in a total of 11,507 coding sequences (CDS), which were mapped from a total of 164,676,091 reads. The CDS were subdivided according to their 10 folds overexpression on salivary glands (513 CDS) or fat body (2073 CDS). Among the families of proteins found in the salivary glands, lipocalins were the most abundant. Other ubiquitous families of proteins present in other sialomes were also present in P. lignarius, including serine protease inhibitors, apyrase and antigen-5. The unique transcriptome of fat body showed proteins related to the metabolic function of this organ. Remarkably, nearly 20% of all reads mapped to transcripts coded by Triatoma virus. The data presented in this study improve the understanding on triatomines' salivary glands and fat body function and reveal important molecules used in the interplay between vectors and vertebrate hosts.

Exploring the molecular complexity of Triatoma dimidiata sialome

Triatoma dimidiata, a Chagas disease vector widely distributed along Central America, has great capability for domestic adaptation as the majority of specimens caught inside human dwellings or in peridomestic areas fed human blood. Exploring the salivary compounds that overcome host haemostatic and immune responses is of great scientific interest. Here, we provide a deeper insight into its salivary gland molecules. We used high-throughput RNA sequencing to examine in depth the T. dimidiata salivary gland transcriptome. From >51 million reads assembled, 92.21% are related to putative secreted proteins. Lipocalin is the most abundant gene family, confirming it is an expanded family in Triatoma genus salivary repertoire. Other putatively secreted members include phosphatases, odorant binding protein, hemolysin, proteases, protease inhibitors, antigen-5 and antimicrobial peptides. This work expands the previous set of functionally annotated sequences from T. dimidiata salivary glands available in NCBI from 388 to 3815. Additionally, we complemented the salivary analysis through proteomics (available data via ProteomeXchange with identifier PXD008510), disclosing the set complexity of 119 secreted proteins and validating the transcriptomic results. Our large-scale approach enriches the pharmacologically active molecules database and improves our knowledge about the complexity of salivary compounds from haematophagous vectors and their biological interactions.

SIGNIFICANCE

Several haematophagous triatomine species can transmit Trypanosoma cruzi, the etiological agent of Chagas disease. Due to the reemergence of this disease, new drugs for its prevention and treatment are considered priorities. For this reason, the knowledge of vector saliva emerges as relevant biological finding, contributing to the design of different strategies for vector control and disease transmission. Here we report the transcriptomic and proteomic compositions of the salivary glands (sialome) of the reduviid bug Triatoma dimidiata, a relevant Chagas disease vector in Central America. Our results are robust and disclosed unprecedented insights into the notable diversity of its salivary glands content, revealing relevant anti-haemostatic salivary gene families. Our work expands almost ten times the previous set of functionally annotated sequences from T. dimidiata salivary glands available in NCBI. Moreover, using an integrated transcriptomic and proteomic approach, we showed a correlation pattern of transcription and translation processes for the main gene families found, an important contribution to the research of triatomine sialomes. Furthermore, data generated here reinforces the secreted proteins encountered can greatly contribute for haematophagic habit, Trypanosoma cruzi transmission and development of therapeutic agent studies.

Intracellular metabolic pathway distribution in diatoms and tools for genome-enabled experimental diatom research

Diatoms are important primary producers in the oceans and can also dominate other aquatic habitats. One reason for the success of this phylogenetically relatively young group of unicellular organisms could be the impressive redundancy and diversity of metabolic isoenzymes in diatoms. This redundancy is a result of the evolutionary origin of diatom plastids by a eukaryote-eukaryote endosymbiosis, a process that implies temporary redundancy of functionally complete eukaryotic genomes. During the establishment of the plastids, this redundancy was partially reduced via gene losses, and was partially retained via gene transfer to the nucleus of the respective host cell. These gene transfers required re-assignment of intracellular targeting signals, a process that simultaneously altered the intracellular distribution of metabolic enzymes compared with the ancestral cells. Genome annotation, the correct assignment of the gene products and the prediction of putative function, strongly depends on the correct prediction of the intracellular targeting of a gene product. Here again diatoms are very peculiar, because the targeting systems for organelle import are partially different to those in land plants. In this review, we describe methods of predicting intracellular enzyme locations, highlight findings of metabolic peculiarities in diatoms and present genome-enabled approaches to study their metabolism.This article is part of the themed issue 'The peculiar carbon metabolism in diatoms'.

Construction of a versatile expression library for all human single-pass transmembrane proteins for receptor pairings by high throughput screening

Interactions between protein ligands and receptors play crucial roles in cell-cell signalling. Most of the human cell surface receptors have been identified in the post-Human Genome Project era but many of their corresponding ligands remain unknown. To facilitate the pairing of orphan receptors, 2762 sequences encoding all human single-pass transmembrane proteins were selected for inclusion into a mammalian-cell expression library. This expression library, consisting of all the individual extracellular domains (ECDs), was constructed as a Fab fusion for each protein. In this format, individual ECD can be produced as a soluble protein or displayed on cell surface, depending on the applied heavy-chain Fab configuration. The unique design of the Fab fusion concept used in the library led to not only superior success rate of protein production, but also versatile applications in various high-throughput screening paradigms including protein-protein binding assays as well as cell binding assays, which were not possible for any other existing expression libraries. The protein library was screened against human coagulation factor VIIa (FVIIa), an approved therapeutic for the treatment of hemophilia, for binding partners by AlphaScreen and ForteBio assays. Two previously known physiological ligands of FVIIa, tissue factor (TF) and endothelial protein C receptor (EPCR) were identified by both assays. The cell surface displayed library was screened against V-domain Ig suppressor of T-cell activation (VISTA), an important immune-checkpoint regulator. Immunoglobulin superfamily member 11 (IgSF11), a potential target for cancer immunotherapy, was identified as a new and previously undescribed binding partner for VISTA. The specificity of the binding was confirmed and validated by both fluorescence-activated cell sorting (FACS) and surface plasmon resonance (SPR) assays in different experimental setups.

Anopheline salivary protein genes and gene families: an evolutionary overview after the whole genome sequence of sixteen Anopheles species

BACKGROUND

Mosquito saliva is a complex cocktail whose pharmacological properties play an essential role in blood feeding by counteracting host physiological response to tissue injury. Moreover, vector borne pathogens are transmitted to vertebrates and exposed to their immune system in the context of mosquito saliva which, in virtue of its immunomodulatory properties, can modify the local environment at the feeding site and eventually affect pathogen transmission. In addition, the host antibody response to salivary proteins may be used to assess human exposure to mosquito vectors. Even though the role of quite a few mosquito salivary proteins has been clarified in the last decade, we still completely ignore the physiological role of many of them as well as the extent of their involvement in the complex interactions taking place between the mosquito vectors, the pathogens they transmit and the vertebrate host. The recent release of the genomes of 16 Anopheles species offered the opportunity to get insights into function and evolution of salivary protein families in anopheline mosquitoes.

RESULTS

Orthologues of fifty three Anopheles gambiae salivary proteins were retrieved and annotated from 18 additional anopheline species belonging to the three subgenera Cellia, Anopheles, and Nyssorhynchus. Our analysis included 824 full-length salivary proteins from 24 different families and allowed the identification of 79 novel salivary genes and re-annotation of 379 wrong predictions. The comparative, structural and phylogenetic analyses yielded an unprecedented view of the anopheline salivary repertoires and of their evolution over 100 million years of anopheline radiation shedding light on mechanisms and evolutionary forces that contributed shaping the anopheline sialomes.

CONCLUSIONS

We provide here a comprehensive description, classification and evolutionary overview of the main anopheline salivary protein families and identify two novel candidate markers of human exposure to malaria vectors worldwide. This anopheline sialome catalogue, which is easily accessible as hyperlinked spreadsheet, is expected to be useful to the vector biology community and to improve the capacity to gain a deeper understanding of mosquito salivary proteins facilitating their possible exploitation for epidemiological and/or pathogen-vector-host interaction studies.

An insight into the sialome of Hyalomma excavatum

Tick saliva contains hundreds or thousands of proteins that help blood feeding by impairing their hosts' hemostasis, inflammation and immunity. Salivary gland transcriptomes allow the disclosure of this pharmacologically active potion that consists of several multi-gene families, many of which are tick-specific. We here report the "de novo" assembly of ∼138 million reads deriving from a cDNA library from salivary glands of adult male and female Hyalomma excavatum leading to the public deposition of 5337 coding sequences to GenBank. Among the deducted putative secreted proteins, metalloproteases, glycine rich proteins, mucins, anticoagulants of the madanin family and lipocalins were the most expressed. Novel protein families were identified. These sequences will permit proteomic studies aiming at identification of target antigens, epidemiological markers or salivary pharmaceuticals of interest, and contribute to our understanding of the fast evolution of the tick sialome.

Use of machine learning approaches for novel drug discovery

INTRODUCTION

The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning (ML) approaches have been of special interest, since they can be applied in several steps of the drug discovery methodology, such as prediction of target structure, prediction of biological activity of new ligands through model construction, discovery or optimization of hits, and construction of models that predict the pharmacokinetic and toxicological (ADMET) profile of compounds.

AREAS COVERED

This article presents an overview on some applications of ML techniques in drug design. These techniques can be employed in ligand-based drug design (LBDD) and structure-based drug design (SBDD) studies, such as similarity searches, construction of classification and/or prediction models of biological activity, prediction of secondary structures and binding sites docking and virtual screening.

EXPERT OPINION

Successful cases have been reported in the literature, demonstrating the efficiency of ML techniques combined with traditional approaches to study medicinal chemistry problems. Some ML techniques used in drug design are: support vector machine, random forest, decision trees and artificial neural networks. Currently, an important application of ML techniques is related to the calculation of scoring functions used in docking and virtual screening assays from a consensus, combining traditional and ML techniques in order to improve the prediction of binding sites and docking solutions.

The parameter sensitivity of random forests

Background

The Random Forest (RF) algorithm for supervised machine learning is an ensemble learning method widely used in science and many other fields. Its popularity has been increasing, but relatively few studies address the parameter selection process: a critical step in model fitting. Due to numerous assertions regarding the performance reliability of the default parameters, many RF models are fit using these values. However there has not yet been a thorough examination of the parameter-sensitivity of RFs in computational genomic studies. We address this gap here.

Results

We examined the effects of parameter selection on classification performance using the RF machine learning algorithm on two biological datasets with distinct p/n ratios: sequencing summary statistics (low p/n) and microarray-derived data (high p/n). Here, p, refers to the number of variables and, n, the number of samples. Our findings demonstrate that parameterization is highly correlated with prediction accuracy and variable importance measures (VIMs). Further, we demonstrate that different parameters are critical in tuning different datasets, and that parameter-optimization significantly enhances upon the default parameters.

Conclusions

Parameter performance demonstrated wide variability on both low and high p/n data. Therefore, there is significant benefit to be gained by model tuning RFs away from their default parameter settings.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1228-x) contains supplementary material, which is available to authorized users.

A Deep Insight into the Sialome of Rhodnius neglectus, a Vector of Chagas Disease

BACKGROUND

Triatomines are hematophagous insects that act as vectors of Chagas disease. Rhodnius neglectus is one of these kissing bugs found, contributing to the transmission of this American trypanosomiasis. The saliva of hematophagous arthropods contains bioactive molecules responsible for counteracting host haemostatic, inflammatory, and immune responses.

METHODS/PRINCIPAL FINDINGS

Next generation sequencing and mass spectrometry-based protein identification were performed to investigate the content of triatomine R. neglectus saliva. We deposited 4,230 coding DNA sequences (CDS) in GenBank. A set of 636 CDS of proteins of putative secretory nature was extracted from the assembled reads, 73 of them confirmed by proteomic analysis. The sialome of R. neglectus was characterized and serine protease transcripts detected. The presence of ubiquitous protein families was revealed, including lipocalins, serine protease inhibitors, and antigen-5. Metalloproteases, disintegrins, and odorant binding protein families were less abundant.

CONCLUSIONS/SIGNIFICANCE

The data presented improve our understanding of hematophagous arthropod sialomes, and aid in understanding hematophagy and the complex interplay among vectors and their vertebrate hosts.

Recurring patterns in bacterioplankton dynamics during coastal spring algae blooms

A process of global importance in carbon cycling is the remineralization of algae biomass by heterotrophic bacteria, most notably during massive marine algae blooms. Such blooms can trigger secondary blooms of planktonic bacteria that consist of swift successions of distinct bacterial clades, most prominently members of the Flavobacteriia, Gammaproteobacteria and the alphaproteobacterial Roseobacter clade. We investigated such successions during spring phytoplankton blooms in the southern North Sea (German Bight) for four consecutive years. Dense sampling and high-resolution taxonomic analyses allowed the detection of recurring patterns down to the genus level. Metagenome analyses also revealed recurrent patterns at the functional level, in particular with respect to algal polysaccharide degradation genes. We, therefore, hypothesize that even though there is substantial inter-annual variation between spring phytoplankton blooms, the accompanying succession of bacterial clades is largely governed by deterministic principles such as substrate-induced forcing.

DOI:http://dx.doi.org/10.7554/eLife.11888.001

Small algae in the world's oceans remove about as much carbon dioxide from the atmosphere as land plants. These algae do not grow continuously, but often surge in numbers during temporary blooms. Such blooms can be large enough to be seen from space by satellites. The lifespan of algae within such blooms is short, and when they die, marine bacteria feed on the remnants, which releases much of the stored carbon dioxide.

Much of an algal cell consists of different types of polysaccharides. These large molecules are essentially made from sugars linked together. Polysaccharides are varied molecules and can contain many different sugars that can be linked in a number of different ways. During algae blooms bacteria proliferate that are specialized in the degradation of these polysaccharides. In 2012, researchers reported how over the progression of an algae bloom different groups of marine bacteria bloomed in rapid succession. However, it remained unknown whether the same or different groups of bacteria respond to algae blooms at the same place from year to year, and whether or not these bacteria use the same enzymes to degrade the polysaccharides.

Teeling, Fuchs et al. – who include many of the researchers from the 2012 study – now report on the analysis of a series of algae blooms that occurred in the southern North Sea between 2009 and 2012. The analysis is based on samples collected every week during the spring seasons, and shows that certain groups of related bacteria, known as clades, became common during each bloom. Teeling, Fuchs et al. also found indications that the clades that repeatedly occurred had similar sets of genes for degrading algal polysaccharides, but that the sets were different between the clades.

These data suggest that there is a specialized bacterial community that together can degrade the complex mixture of algal polysaccharides during blooms. This community reappears each year with an unexpectedly low level of variation. Since different species of algae made up the blooms in each year, this finding suggests that the major polysaccharides in these algae are similar or even identical.

Future work will focus on the specific activities of bacterial enzymes that are needed to degrade polysaccharides during algae blooms. Study of these enzymes in the laboratory will help to resolve, which polysaccharides are attacked in which manner, and to ultimately help to identify the most abundant algal polysaccharides. This will improve our current understanding of the carbon cycle in the world’s oceans.

DOI:http://dx.doi.org/10.7554/eLife.11888.002

Predicting bacteriophage proteins located in host cell with feature selection technique

A bacteriophage is a virus that can infect a bacterium. The fate of an infected bacterium is determined by the bacteriophage proteins located in the host cell. Thus, reliably identifying bacteriophage proteins located in the host cell is extremely important to understand their functions and discover potential anti-bacterial drugs. Thus, in this paper, a computational method was developed to recognize bacteriophage proteins located in host cells based only on their amino acid sequences. The analysis of variance (ANOVA) combined with incremental feature selection (IFS) was proposed to optimize the feature set. Using a jackknife cross-validation, our method can discriminate between bacteriophage proteins located in a host cell and the bacteriophage proteins not located in a host cell with a maximum overall accuracy of 84.2%, and can further classify bacteriophage proteins located in host cell cytoplasm and in host cell membranes with a maximum overall accuracy of 92.4%. To enhance the value of the practical applications of the method, we built a web server called PHPred (〈http://lin.uestc.edu.cn/server/PHPred〉). We believe that the PHPred will become a powerful tool to study bacteriophage proteins located in host cells and to guide related drug discovery.

Combining RNA-seq and proteomic profiling to identify seminal fluid proteins in the migratory grasshopper Melanoplus sanguinipes (F)

BACKGROUND

Seminal fluid proteins control many aspects of fertilization and in turn, they play a key role in post-mating sexual selection and possibly reproductive isolation. Because effective proteome profiling relies on the availability of high-quality DNA reference databases, our knowledge of these proteins is still largely limited to model organisms with ample genetic resources. New advances in sequencing technology allow for the rapid characterization of transcriptomes at low cost. By combining high throughput RNA-seq and shotgun proteomic profiling, we have characterized the seminal fluid proteins secreted by the primary male accessory gland of the migratory grasshopper (Melanoplus sanguinipes), one of the main agricultural pests in central North America.

RESULTS

Using RNA sequencing, we characterized the transcripts of ~ 8,100 genes expressed in the long hyaline tubules (LHT) of the accessory glands. Proteomic profiling identified 353 proteins expressed in the long hyaline tubules (LHT). Of special interest are seminal fluid proteins (SFPs), such as EJAC-SP, ACE and prostaglandin synthetases, which are known to regulate female oviposition in insects.

CONCLUSIONS

Our study provides new insights into the proteomic components of male ejaculate in Orthopterans, and highlights several important patterns. First, the presence of proteins that lack predicted classical secretory tags in accessory gland proteomes is common in male accessory glands. Second, the products of a few highly expressed genes dominate the accessory gland secretions. Third, accessory gland transcriptomes are enriched for novel transcripts. Fourth, there is conservation of SFPs' functional classes across distantly related taxonomic groups with very different life histories, mating systems and sperm transferring mechanisms. The identified SFPs may serve as targets of future efforts to develop species- specific genetic control strategies.

A small multigene hydroxyproline-O-galactosyltransferase family functions in arabinogalactan-protein glycosylation, growth and development in Arabidopsis

BACKGROUND

Arabinogalactan-proteins (AGPs) are ubiquitous components of cell walls throughout the plant kingdom and are extensively post translationally modified by conversion of proline to hydroxyproline (Hyp) and by addition of arabinogalactan polysaccharides (AG) to Hyp residues. AGPs are implicated to function in various aspects of plant growth and development, but the functional contributions of AGP glycans remain to be elucidated. Hyp glycosylation is initiated by the action of a set of Hyp-O-galactosyltransferase (Hyp-O-GALT) enzymes that remain to be fully characterized.

RESULTS

Three members of the GT31 family (GALT3-At3g06440, GALT4-At1g27120, and GALT6-At5g62620) were identified as Hyp-O-GALT genes by heterologous expression in tobacco leaf epidermal cells and examined along with two previously characterized Hyp-O-GALT genes, GALT2 and GALT5. Transcript profiling by real-time PCR of these five Hyp-O-GALTs revealed overlapping but distinct expression patterns. Transiently expressed GALT3, GALT4 and GALT6 fluorescent protein fusions were localized within Golgi vesicles. Biochemical analysis of knock-out mutants for the five Hyp-O-GALT genes revealed significant reductions in both AGP-specific Hyp-O-GALT activity and β-Gal-Yariv precipitable AGPs. Further phenotypic analysis of these mutants demonstrated reduced root hair growth, reduced seed coat mucilage, reduced seed set, and accelerated leaf senescence. The mutants also displayed several conditional phenotypes, including impaired root growth, and defective anisotropic growth of root tips under salt stress, as well as less sensitivity to the growth inhibitory effects of β-Gal-Yariv reagent in roots and pollen tubes.

CONCLUSIONS

This study provides evidence that all five Hyp-O-GALT genes encode enzymes that catalyze the initial steps of AGP galactosylation and that AGP glycans play essential roles in both vegetative and reproductive plant growth.

An insight into the sialome of the horse fly, Tabanus bromius

Blood feeding animals face their host's defenses against tissue injury and blood loss while attempting to feed. One adaptation to surmount these barriers involves the evolution of a salivary potion that disarms their host's inflammatory and anti-hemostatic processes. The composition of the peptide moiety of this potion, or sialome (from the Greek sialo = saliva), can be deducted in part by proper interpretation of the blood feeder' sialotranscriptome. In this work we disclose the sialome of the blood feeding adult female Tabanus bromius. Following assembly of over 75 million Illumina reads (101 nt long) 16,683 contigs were obtained from which 4078 coding sequences were extracted. From these, 320 were assigned as coding for putative secreted proteins. These 320 contigs mapped 85% of the reads. The antigen-5 proteins family was studied in detail, indicating three Tabanus specific clades with and without disintegrin domains, as well as with and without leukotriene binding domains. Defensins were also detailed; a clade of salivary tabanid peptides was found lacking the propeptide domain ending in the KR dipeptide signaling furin cleavage. Novel protein families were also disclosed. Viral transcripts were identified closely matching the Kotonkan virus capsid proteins. Full length Mariner transposases were also identified. A total of 3043 coding sequences and their protein products were deposited in Genbank. Hyperlinked excel spreadsheets containing the coding sequences and their annotation are available at http://exon.niaid.nih.gov/transcriptome/T_bromius/Tbromius-web.xlsx (hyperlinked excel spreadsheet, 11 MB) and http://exon.niaid.nih.gov/transcriptome/T_bromius/Tbromius-SA.zip (Standalone excel with all local links, 360 MB). These sequences provide for a platform from which further proteomic studies may be designed to identify salivary proteins from T. bromius that are of pharmacological interest or used as immunological markers of host exposure.

Multi-location gram-positive and gram-negative bacterial protein subcellular localization using gene ontology and multi-label classifier ensemble

Background

It has become a very important and full of challenge task to predict bacterial protein subcellular locations using computational methods. Although there exist a lot of prediction methods for bacterial proteins, the majority of these methods can only deal with single-location proteins. But unfortunately many multi-location proteins are located in the bacterial cells. Moreover, multi-location proteins have special biological functions capable of helping the development of new drugs. So it is necessary to develop new computational methods for accurately predicting subcellular locations of multi-location bacterial proteins.

Results

In this article, two efficient multi-label predictors, Gpos-ECC-mPLoc and Gneg-ECC-mPLoc, are developed to predict the subcellular locations of multi-label gram-positive and gram-negative bacterial proteins respectively. The two multi-label predictors construct the GO vectors by using the GO terms of homologous proteins of query proteins and then adopt a powerful multi-label ensemble classifier to make the final multi-label prediction. The two multi-label predictors have the following advantages: (1) they improve the prediction performance of multi-label proteins by taking the correlations among different labels into account; (2) they ensemble multiple CC classifiers and further generate better prediction results by ensemble learning; and (3) they construct the GO vectors by using the frequency of occurrences of GO terms in the typical homologous set instead of using 0/1 values. Experimental results show that Gpos-ECC-mPLoc and Gneg-ECC-mPLoc can efficiently predict the subcellular locations of multi-label gram-positive and gram-negative bacterial proteins respectively.

Conclusions

Gpos-ECC-mPLoc and Gneg-ECC-mPLoc can efficiently improve prediction accuracy of subcellular localization of multi-location gram-positive and gram-negative bacterial proteins respectively. The online web servers for Gpos-ECC-mPLoc and Gneg-ECC-mPLoc predictors are freely accessible at http://biomed.zzuli.edu.cn/bioinfo/gpos-ecc-mploc/ and http://biomed.zzuli.edu.cn/bioinfo/gneg-ecc-mploc/ respectively.