Prediction of human protein function from post-translational modifications and localization features

Effects of different protein phosphorylation levels on the tenderness of different ultimate pH beef

This study investigated the relationship between tenderness and protein phosphorylation levels of normal ultimate pH (pHu, 5.4-5.8, NpHu), intermediate pHu (5.8-6.2, IpHu) and high pHu (≥6.2, HpHu) Longissimus lumborum from beef. During 21 d of ageing, the HpHu group had the lowest Warner-Bratzler shear force (WBSF) values, while the IpHu group showed the highest and even after 21 days of ageing still had high levels. In the late stage of the 24 h post-mortem period the faster degradation rate of troponin T and earlier activation of caspase 9 in the HpHu group were the key reasons for the lower WBSF compared with the NpHu and IpHu groups. The activity of caspase 3 cannot explain the tenderness differences between IpHu and HpHu groups, since their activities did not show any difference. At 24 h post-mortem, 17 common differential phosphorylated peptides were detected among pHu groups, of which nine were associated with pHu and WBSF. The higher phosphorylation level of glycogen synthase may have caused the delay of meat tenderization in the IpHu group.

Effects of In Utero EtOH Exposure on 18S Ribosomal RNA Processing: Contribution to Fetal Alcohol Spectrum Disorder

Fetal alcohol spectrum disorders (FASD) are leading causes of neurodevelopmental disability. The mechanisms by which alcohol (EtOH) disrupts fetal brain development are incompletely understood, as are the genetic factors that modify individual vulnerability. Because the phenotype abnormalities of FASD are so varied and widespread, we investigated whether fetal exposure to EtOH disrupts ribosome biogenesis and the processing of pre-ribosomal RNAs and ribosome assembly, by determining the effect of exposure to EtOH on the developmental expression of 18S rRNA and its cleaved forms, members of a novel class of short non-coding RNAs (srRNAs). In vitro neuronal cultures and fetal brains (11-22 weeks) were collected according to an IRB-approved protocol. Twenty EtOH-exposed brains from the first and second trimester were compared with ten unexposed controls matched for gestational age and fetal gender. Twenty fetal-brain-derived exosomes (FB-Es) were isolated from matching maternal blood. RNA was isolated using Qiagen RNA isolation kits. Fetal brain srRNA expression was quantified by ddPCR. srRNAs were expressed in the human brain and FB-Es during fetal development. EtOH exposure slightly decreased srRNA expression (1.1-fold; p = 0.03). Addition of srRNAs to in vitro neuronal cultures inhibited EtOH-induced caspase-3 activation (1.6-fold, p = 0.002) and increased cell survival (4.7%, p = 0.034). The addition of exogenous srRNAs reversed the EtOH-mediated downregulation of srRNAs (2-fold, p = 0.002). EtOH exposure suppressed expression of srRNAs in the developing brain, increased activity of caspase-3, and inhibited neuronal survival. Exogenous srRNAs reversed this effect, possibly by stabilizing endogenous srRNAs, or by increasing the association of cellular proteins with srRNAs, modifying gene transcription. Finally, the reduction in 18S rRNA levels correlated closely with the reduction in fetal eye diameter, an anatomical hallmark of FASD. The findings suggest a potential mechanism for EtOH-mediated neurotoxicity via alterations in 18S rRNA processing and the use of FB-Es for early diagnosis of FASD. Ribosome biogenesis may be a novel target to ameliorate FASD in utero or after birth. These findings are consistent with observations that gene-environment interactions contribute to FASD vulnerability.

Enzyme Substrate Prediction from Three-Dimensional Feature Representations Using Space-Filling Curves

Compact and interpretable structural feature representations are required for accurately predicting properties and function of proteins. In this work, we construct and evaluate three-dimensional feature representations of protein structures based on space-filling curves (SFCs). We focus on the problem of enzyme substrate prediction, using two ubiquitous enzyme families as case studies: the short-chain dehydrogenase/reductases (SDRs) and the S-adenosylmethionine-dependent methyltransferases (SAM-MTases). Space-filling curves such as the Hilbert curve and the Morton curve generate a reversible mapping from discretized three-dimensional to one-dimensional representations and thus help to encode three-dimensional molecular structures in a system-independent way and with only a few adjustable parameters. Using three-dimensional structures of SDRs and SAM-MTases generated using AlphaFold2, we assess the performance of the SFC-based feature representations in predictions on a new benchmark database of enzyme classification tasks including their cofactor and substrate selectivity. Gradient-boosted tree classifiers yield binary prediction accuracy of 0.77-0.91 and area under curve (AUC) characteristics of 0.83-0.92 for the classification tasks. We investigate the effects of amino acid encoding, spatial orientation, and (the few) parameters of SFC-based encodings on the accuracy of the predictions. Our results suggest that geometry-based approaches such as SFCs are promising for generating protein structural representations and are complementary to the existing protein feature representations such as evolutionary scale modeling (ESM) sequence embeddings.

Contrastive learning on protein embeddings enlightens midnight zone

Experimental structures are leveraged through multiple sequence alignments, or more generally through homology-based inference (HBI), facilitating the transfer of information from a protein with known annotation to a query without any annotation. A recent alternative expands the concept of HBI from sequence-distance lookup to embedding-based annotation transfer (EAT). These embeddings are derived from protein Language Models (pLMs). Here, we introduce using single protein representations from pLMs for contrastive learning. This learning procedure creates a new set of embeddings that optimizes constraints captured by hierarchical classifications of protein 3D structures defined by the CATH resource. The approach, dubbed ProtTucker, has an improved ability to recognize distant homologous relationships than more traditional techniques such as threading or fold recognition. Thus, these embeddings have allowed sequence comparison to step into the 'midnight zone' of protein similarity, i.e. the region in which distantly related sequences have a seemingly random pairwise sequence similarity. The novelty of this work is in the particular combination of tools and sampling techniques that ascertained good performance comparable or better to existing state-of-the-art sequence comparison methods. Additionally, since this method does not need to generate alignments it is also orders of magnitudes faster. The code is available at https://github.com/Rostlab/EAT.

Seq2Enz: An application of mask BLAST methodology with a new chemical logic of amino acids for improved enzyme function prediction

Seq2Enz method is a new way to identify whether a query protein sequence is an enzyme and to assign an enzyme class to the protein sequence. The method is based on mask BLAST fortified with some novel structural-chemical properties (NCL) of the building blocks of proteins. All available reviewed enyme sequences (267,276 in number) in Uniprot/SwissProt and most recent depositions (7062) not used for training in ECPred, a state of the art software for enzyme class prediction, are taken for assessment and the results are compared with those from conventional BLAST and ECPred respectively. Seq2Enz is seen to perform consistently better for all the enzyme classes to all the four levels. Seq2Enz methodology is converted into an easy to use web-server and made freely accessible at http://www.scfbio-iitd.res.in Seq2Enz/.

Functional genomics of abiotic environmental adaptation in lacertid lizards and other vertebrates

Understanding the genomic basis of adaptation to different abiotic environments is important in the context of climate change and resulting short-term environmental fluctuations. Using functional and comparative genomics approaches, we here investigated whether signatures of genomic adaptation to a set of environmental parameters are concentrated in specific subsets of genes and functions in lacertid lizards and other vertebrates. We first identify 200 genes with signatures of positive diversifying selection from transcriptomes of 24 species of lacertid lizards and demonstrate their involvement in physiological and morphological adaptations to climate. To understand how functionally similar these genes are to previously predicted candidate functions for climate adaptation and to compare them with other vertebrate species, we then performed a meta-analysis of 1,100 genes under selection obtained from -omics studies in vertebrate species adapted to different abiotic factors. We found that the vertebrate gene set formed a tightly connected interactome, which was to 23% enriched in previously predicted functions of adaptation to climate, and to a large part (18%) involved in organismal stress response. We found a much higher degree of identical genes being repeatedly selected among different animal groups (43.6%), and of functional similarity and post-translational modifications than expected by chance, and no clear functional division between genes used for ectotherm and endotherm physiological strategies. In total, 171 out of 200 genes of Lacertidae were part of this network. These results highlight an important role of a comparatively small set of genes and their functions in environmental adaptation and narrow the set of candidate pathways and markers to be used in future research on adaptation and stress response related to climate change.

An integrated deep learning and dynamic programming method for predicting tumor suppressor genes, oncogenes, and fusion from PDB structures

Mutations in proto-oncogenes (ONGO) and the loss of regulatory function of tumor suppression genes (TSG) are the common underlying mechanism for uncontrolled tumor growth. While cancer is a heterogeneous complex of distinct diseases, finding the potentiality of the genes related functionality to ONGO or TSG through computational studies can help develop drugs that target the disease. This paper proposes a classification method that starts with a preprocessing stage to extract the feature map sets from the input 3D protein structural information. The next stage is a deep convolutional neural network stage (DCNN) that outputs the probability of functional classification of genes. We explored and tested two approaches: in Approach 1, all filtered and cleaned 3D-protein-structures (PDB) are pooled together, whereas in Approach 2, the primary structures and their corresponding PDBs are separated according to the genes' primary structural information. Following the DCNN stage, a dynamic programming-based method is used to determine the final prediction of the primary structures' functionality. We validated our proposed method using the COSMIC online database. For the ONGO vs TSG classification problem the AUROC of the DCNN stage for Approach 1 and Approach 2 DCNN are 0.978 and 0.765, respectively. The AUROCs of the final genes' primary structure functionality classification for Approach 1 and Approach 2 are 0.989, and 0.879, respectively. For comparison, the current state-of-the-art reported AUROC is 0.924. Our results warrant further study to apply the deep learning models to humans' (GRCh38) genes, for predicting their corresponding probabilities of functionality in the cancer drivers.

Rapid discovery of novel prophages using biological feature engineering and machine learning

Prophages are phages that are integrated into bacterial genomes and which are key to understanding many aspects of bacterial biology. Their extreme diversity means they are challenging to detect using sequence similarity, yet this remains the paradigm and thus many phages remain unidentified. We present a novel, fast and generalizing machine learning method based on feature space to facilitate novel prophage discovery. To validate the approach, we reanalyzed publicly available marine viromes and single-cell genomes using our feature-based approaches and found consistently more phages than were detected using current state-of-the-art tools while being notably faster. This demonstrates that our approach significantly enhances bacteriophage discovery and thus provides a new starting point for exploring new biologies.

Identification of potential drug targets in human pathogen Bacillus cereus and insight for finding inhibitor through subtractive proteome and molecular docking studies

Bacillus cereus is a gram-positive, anaerobic, spore-forming bacterium related to food poisoning in humans. Vomit and diarrhea are the symptoms of foodborne B. cereus infection caused by emetic toxins and three enterotoxins, respectively. This bacterium is broadly present in soil and foods such as vegetables, spices, milk, and meat. The antibiotics impenem, vancomycin, chloramphenicol, gentamicin, and ciprofloxacin are used for all susceptible strains of B. cereus. But these antibiotics cause side effects in the host due to the drug-host interaction; because the targeted proteins by the drugs are not pathogen specific proteins, they are similar to human proteins also. To overcome this problem, this study focused on identifying putative drug targets in the pathogen B. cereus and finding new drugs to inhibit the function of the pathogen. The identification of drug targets is a pipeline process, starting with the identification of targets non-homologous to human and gutmicrobiota proteins, finding essential proteins, finding other proteins that highly interact with these essential proteins that are also highly important for protein network stability, finding cytoplasmic proteins with a clear pathway and known molecular function, and finding non-druggable proteins. Through this process, two novel drug targets were identified in B. cereus. Among the various antibiotics, Gentamicin had showed good binding affinity with the identified novel targets through molecular modeling and docking studies using Prime and GLIDE module of Schrödinger. Hence, this study suggest that the identified novel drug targets may very useful in drug therapeutic field for finding inhibitors which are similar to Gentamicin and designing new formulation of drug molecules to control the function of the foodborne illness causing pathogen B. cereus.

Effects of protein posttranslational modifications on meat quality: A review

Meat quality plays an important role in the purchase decision of consumers, affecting producers and retailers. The formation mechanisms determining meat quality are intricate, as several endogenous and exogenous factors contribute during antemortem and postmortem periods. Abundant research has been performed on meat quality; however, unexpected variation in meat quality remains an issue in the meat industry. Protein posttranslational modifications (PTMs) regulate structures and functions of proteins in living tissues, and recent reports confirmed their importance in meat quality. The objective of this review was to provide a summary of the research on the effects of PTMs on meat quality. The effects of four common PTMs, namely, protein phosphorylation, acetylation, S-nitrosylation, and ubiquitination, on meat quality were discussed, with emphasis on the effects of protein phosphorylation on meat tenderness, color, and water holding capacity. The mechanisms and factors that may affect the function of protein phosphorylation are also discussed. The current research confirms that meat quality traits are regulated by multiple PTMs. Cross talk between different PTMs and interactions of PTMs with postmortem biochemical processes need to be explored to improve our understanding on factors affecting meat quality.

Pseudo2GO: A Graph-Based Deep Learning Method for Pseudogene Function Prediction by Borrowing Information From Coding Genes

Pseudogenes are indicating more and more functional potentials recently, though historically were regarded as relics of evolution. Computational methods for predicting pseudogene functions on Gene Ontology is important for directing experimental discovery. However, no pseudogene-specific computational methods have been proposed to directly predict their Gene Ontology (GO) terms. The biggest challenge for pseudogene function prediction is the lack of enough features and functional annotations, making training a predictive model difficult. Considering the close functional similarity between pseudogenes and their parent coding genes that share great amount of DNA sequence, as well as that coding genes have rich annotations, we aim to predict pseudogene functions by borrowing information from coding genes in a graph-based way. Here we propose Pseudo2GO, a graph-based deep learning semi-supervised model for pseudogene function prediction. A sequence similarity graph is first constructed to connect pseudogenes and coding genes. Multiple features are incorporated into the model as the node attributes to enable the graph an attributed graph, including expression profiles, interactions with microRNAs, protein-protein interactions (PPIs), and genetic interactions. Graph convolutional networks are used to propagate node attributes across the graph to make classifications on pseudogenes. Comparing Pseudo2GO with other frameworks adapted from popular protein function prediction methods, we demonstrated that our method has achieved state-of-the-art performance, significantly outperforming other methods in terms of the M-AUPR metric.

Graph2GO: a multi-modal attributed network embedding method for inferring protein functions

BACKGROUND

Identifying protein functions is important for many biological applications. Since experimental functional characterization of proteins is time-consuming and costly, accurate and efficient computational methods for predicting protein functions are in great demand for generating the testable hypotheses guiding large-scale experiments."

RESULTS

Here, we propose Graph2GO, a multi-modal graph-based representation learning model that can integrate heterogeneous information, including multiple types of interaction networks (sequence similarity network and protein-protein interaction network) and protein features (amino acid sequence, subcellular location, and protein domains) to predict protein functions on gene ontology. Comparing Graph2GO to BLAST, as a baseline model, and to two popular protein function prediction methods (Mashup and deepNF), we demonstrated that our model can achieve state-of-the-art performance. We show the robustness of our model by testing on multiple species. We also provide a web server supporting function query and downstream analysis on-the-fly.

CONCLUSIONS

Graph2GO is the first model that has utilized attributed network representation learning methods to model both interaction networks and protein features for predicting protein functions, and achieved promising performance. Our model can be easily extended to include more protein features to further improve the performance. Besides, Graph2GO is also applicable to other application scenarios involving biological networks, and the learned latent representations can be used as feature inputs for machine learning tasks in various downstream analyses.

Proteomics of Select Neglected Tropical Diseases

Technological advances in mass spectrometry have enabled the extensive identification, characterization, and quantification of proteins in any biological system. In disease processes proteins are often altered in response to external stimuli; therefore, proteomics, the large-scale study of proteins and their functions, represents an invaluable tool for understanding the molecular basis of disease. This review highlights the use of mass spectrometry-based proteomics to study the pathogenesis, etiology, and pathology of several neglected tropical diseases (NTDs), a diverse group of disabling diseases primarily associated with poverty in tropical and subtropical regions of the world. While numerous NTDs have been the subject of proteomic studies, this review focuses on Buruli ulcer, dengue, leishmaniasis, and snakebite envenoming. The proteomic studies highlighted provide substantial information on the pathogenic mechanisms driving these diseases; they also identify molecular targets for drug discovery and development and uncover promising biomarkers that can assist in early diagnosis.

Defeating the trypanosomatid trio: proteomics of the protozoan parasites causing neglected tropical diseases

Mass spectrometry-based proteomics enables accurate measurement of the modulations of proteins on a large scale upon perturbation and facilitates the understanding of the functional roles of proteins in biological systems. It is a particularly relevant methodology for studying Leishmania spp., Trypanosoma cruzi and Trypanosoma brucei, as the gene expression in these parasites is primarily regulated by posttranscriptional mechanisms. Large-scale proteomics studies have revealed a plethora of information regarding modulated proteins and their molecular interactions during various life processes of the protozoans, including stress adaptation, life cycle changes and interactions with the host. Important molecular processes within the parasite that regulate the activity and subcellular localisation of its proteins, including several co- and post-translational modifications, are also accurately captured by modern proteomics mass spectrometry techniques. Finally, in combination with synthetic chemistry, proteomic techniques facilitate unbiased profiling of targets and off-targets of pharmacologically active compounds in the parasites. This provides important data sets for their mechanism of action studies, thereby aiding drug development programmes.

Genome-wide identification, characterization and expression analysis of lineage-specific genes within Hanseniaspora yeasts

Lineage-specific genes (LSGs) are defined as genes with sequences that are not significantly similar to those in any other lineage. LSGs have been proposed, and sometimes shown, to have significant effects in the evolution of biological function. In this study, two sets of Hanseniaspora spp. LSGs were identified by comparing the sequences of the Kloeckera apiculata genome and of 80 other yeast genomes. This study identified 344 Hanseniaspora-specific genes (HSGs) and 109 genes ('orphan genes') specific to K. apiculata. Three thousand three hundred thirty-one K. apiculata genes that showed significant similarity to at least one sequence outside the Hanseniaspora were classified into evolutionarily conserved genes. We analyzed their sequence features, functional categories, gene origin, gene structure and gene expression. We also investigated the predicted cellular roles and Gene Ontology categories of the LSGs using functional inference. The patterns of the functions of LSGs do not deviate significantly from genome-wide average. The results showed that a few LSGs were formed by gene duplication, followed by rapid sequence divergence. Many of the HSGs and orphan genes exhibited altered expression in response to abiotic stress. Studying these LSGs might be helpful for understanding the molecular mechanism of yeast adaption.

Towards region-specific propagation of protein functions

MOTIVATION

Due to the nature of experimental annotation, most protein function prediction methods operate at the protein-level, where functions are assigned to full-length proteins based on overall similarities. However, most proteins function by interacting with other proteins or molecules, and many functional associations should be limited to specific regions rather than the entire protein length. Most domain-centric function prediction methods depend on accurate domain family assignments to infer relationships between domains and functions, with regions that are unassigned to a known domain-family left out of functional evaluation. Given the abundance of residue-level annotations currently available, we present a function prediction methodology that automatically infers function labels of specific protein regions using protein-level annotations and multiple types of region-specific features.

RESULTS

We apply this method to local features obtained from InterPro, UniProtKB and amino acid sequences and show that this method improves both the accuracy and region-specificity of protein function transfer and prediction. We compare region-level predictive performance of our method against that of a whole-protein baseline method using proteins with structurally verified binding sites and also compare protein-level temporal holdout predictive performances to expand the variety and specificity of GO terms we could evaluate. Our results can also serve as a starting point to categorize GO terms into region-specific and whole-protein terms and select prediction methods for different classes of GO terms.

AVAILABILITY AND IMPLEMENTATION

The code and features are freely available at: https://github.com/ek1203/rsfp.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Immune transcriptome analysis in predatory beetles reveals two cecropin genes overexpressed in mandibles

The great complexity and variety of the innate immune system and the production of antimicrobial peptides in insects is correlated with their evolutionary success and adaptation to different environments. Tiger beetles are an example of non-pest species with a cosmopolitan distribution, but the immune system is barely known and its study could provide useful information about the humoral immunity of predatory insects. Suppression subtractive hybridization (SSH) was performed in Calomera littoralis beetles to obtain a screening of those genes that were overexpressed after an injection with Escherichia coli lipopolysaccharide (LPS). Several genes were identified to be related to immune defense. Among those genes, two members of the cecropin antimicrobial peptides were characterized and identified as CliCec-A and CliCec-B2. Both protein sequences showed cecropin characteristics including 37 and 38 residue mature peptides, composed by two α-helices structures with amphipathic and hydrophobic nature, as shown in their predicted three-dimensional structure. Chemically synthesized CliCec-B2 confirmed cecropin antimicrobial activity against some Gram (+) and Gram (-) bacteria, but not against yeast. Expression of both cecropin genes was assessed by qPCR and showed increases after a LPS injection and highlighted their overexpression in adult beetle mandibles, which could be related to their alimentary habits.

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

BACKGROUND

The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.

RESULTS

Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory.

CONCLUSION

We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

Alignment-Free Method to Predict Enzyme Classes and Subclasses

The Enzyme Classification (EC) number is a numerical classification scheme for enzymes, established using the chemical reactions they catalyze. This classification is based on the recommendation of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology. Six enzyme classes were recognised in the first Enzyme Classification and Nomenclature List, reported by the International Union of Biochemistry in 1961. However, a new enzyme group was recently added as the six existing EC classes could not describe enzymes involved in the movement of ions or molecules across membranes. Such enzymes are now classified in the new EC class of translocases (EC 7). Several computational methods have been developed in order to predict the EC number. However, due to this new change, all such methods are now outdated and need updating. In this work, we developed a new multi-task quantitative structure-activity relationship (QSAR) method aimed at predicting all 7 EC classes and subclasses. In so doing, we developed an alignment-free model based on artificial neural networks that proved to be very successful.

Isolation and expression of four Megalobrama amblycephala toll-like receptor genes in response to a bacterial infection

Toll-like receptors (TLRs) are a category of pattern recognition receptors (PRRs), which recognize pathogen associated molecular patterns (PAMPs) and participate in the immune responses. We identified tlr5a, tlr5b, tlr9 and tlr21 from the genome of blunt snout bream (Megalobrama amblycephala). All four tlrs were constitutively expressed in all examined tissues. After an immune bacterial challenge with Aeromonas hydrophila, their expressionwas up-regulated in lymphoid organs and tissues. Recombinant eukaryotic plasmid pEGFP-N1 was transfected into the common carp (Cyprinus carpio) EPC (epithelioma papulosum cyprini) cells for the purpose of subcellular localization. pcDNA3.1(+) recombinant eukaryotic plasmid was used to investigate the effects of overexpression of tlrs on the expression of downstream interferon-associated immune factors. The four Tlrs were distributed in the cytoplasm of transfected cells and appeared as filamentous or reticular. The expression of irf3, irf7, isg15, mx1, pkr and viperin at 0, 6, 12, 18, 24, 36, 48 and 72 h post-transfection in transfected EPC cells was quantified by qPCR. Overexpression of tlrs upregulated the expression of viperin, isg15, irf3, irf7, mx1 and pkr (in that order of magnitude). We also cloned the following promoters of irfs: Irf1-p, irf2-p, irf6-p, irf7-p, irf8-p and irf9-p. Results of the dual luciferase reporter assay suggested that tlr5a, tlr5b and tlr9 enhanced the activities of irf7-p, while tlr5b enhanced the activities of irf1-p and irf7-p. This suggests that they all play a role in the innate immunity. The experiments also indicated that TLRs activate irf3 or irf7 signaling to induce IFN secretion and subsequent upregulation of IFN-stimulated genes. These results indicate that tlrs and irfs play an important immune role in response to A. hydrophila infection in blunt snout bream, and pave the way for further studies of immune mechanisms mediated by TLRs in fish.

Cloning, Expression, and Bioinformatics Analysis of Heavy Metal Resistance Gene afe_1862 from Acidithiobacillus ferrooxidans L1 in Escherichia coli

Molecular studies of copper and cadmium resistances in acidophilic bacteria are significant in biomining. In this study, afe_1862, which encodes a heavy metal-binding protein in Acidithiobacillus ferrooxidans L1, was amplified using PCR, cloned into the pET32a plasmid, and sequenced. Following SDS-PAGE analysis, optimization of the expression conditions and heterologous overexpression of afe_1862 in Escherichia coli BL21 in the presence of Cu²⁺ and Cd²⁺ were studied as well. The results indicated that AFE_1862 has higher resistance to Cu²⁺ than Cd²⁺. Bioinformatics analysis illustrated that AFE_1862 has a conserved HMA domain containing heavy metal-binding sites, which may play a role in transporting or detoxifying heavy metals.

Genetic Analysis of Rare Human Variants of Regulators of G Protein Signaling Proteins and Their Role in Human Physiology and Disease

Regulators of G protein signaling (RGS) proteins modulate the physiologic actions of many neurotransmitters, hormones, and other signaling molecules. Human RGS proteins comprise a family of 20 canonical proteins that bind directly to G protein-coupled receptors/G protein complexes to limit the lifetime of their signaling events, which regulate all aspects of cell and organ physiology. Genetic variations account for diverse human traits and individual predispositions to disease. RGS proteins contribute to many complex polygenic human traits and pathologies such as hypertension, atherosclerosis, schizophrenia, depression, addiction, cancers, and many others. Recent analysis indicates that most human diseases are due to extremely rare genetic variants. In this study, we summarize physiologic roles for RGS proteins and links to human diseases/traits and report rare variants found within each human RGS protein exome sequence derived from global population studies. Each RGS sequence is analyzed using recently described bioinformatics and proteomic tools for measures of missense tolerance ratio paired with combined annotation-dependent depletion scores, and protein post-translational modification (PTM) alignment cluster analysis. We highlight selected variants within the well-studied RGS domain that likely disrupt RGS protein functions and provide comprehensive variant and PTM data for each RGS protein for future study. We propose that rare variants in functionally sensitive regions of RGS proteins confer profound change-of-function phenotypes that may contribute, in newly appreciated ways, to complex human diseases and/or traits. This information provides investigators with a valuable database to explore variation in RGS protein function, and for targeting RGS proteins as future therapeutic targets.

ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature

BACKGROUND

The automated prediction of the enzymatic functions of uncharacterized proteins is a crucial topic in bioinformatics. Although several methods and tools have been proposed to classify enzymes, most of these studies are limited to specific functional classes and levels of the Enzyme Commission (EC) number hierarchy. Besides, most of the previous methods incorporated only a single input feature type, which limits the applicability to the wide functional space. Here, we proposed a novel enzymatic function prediction tool, ECPred, based on ensemble of machine learning classifiers.

RESULTS

In ECPred, each EC number constituted an individual class and therefore, had an independent learning model. Enzyme vs. non-enzyme classification is incorporated into ECPred along with a hierarchical prediction approach exploiting the tree structure of the EC nomenclature. ECPred provides predictions for 858 EC numbers in total including 6 main classes, 55 subclass classes, 163 sub-subclass classes and 634 substrate classes. The proposed method is tested and compared with the state-of-the-art enzyme function prediction tools by using independent temporal hold-out and no-Pfam datasets constructed during this study.

CONCLUSIONS

ECPred is presented both as a stand-alone and a web based tool to provide probabilistic enzymatic function predictions (at all five levels of EC) for uncharacterized protein sequences. Also, the datasets of this study will be a valuable resource for future benchmarking studies. ECPred is available for download, together with all of the datasets used in this study, at: https://github.com/cansyl/ECPred . ECPred webserver can be accessed through http://cansyl.metu.edu.tr/ECPred.html .

Deep Learning in Biomedical Data Science

Since the 1980s, deep learning and biomedical data have been coevolving and feeding each other. The breadth, complexity, and rapidly expanding size of biomedical data have stimulated the development of novel deep learning methods, and application of these methods to biomedical data have led to scientific discoveries and practical solutions. This overview provides technical and historical pointers to the field, and surveys current applications of deep learning to biomedical data organized around five subareas, roughly of increasing spatial scale: chemoinformatics, proteomics, genomics and transcriptomics, biomedical imaging, and health care. The black box problem of deep learning methods is also briefly discussed.

A mini review: Proteomics approaches to understand disused vs. exercised human skeletal muscle

Immobilization, bed rest, or denervation leads to muscle disuse and subsequent skeletal muscle atrophy. Muscle atrophy can also occur as a component of various chronic diseases such as cancer, AIDS, sepsis, diabetes, and chronic heart failure or as a direct result of genetic muscle disorders. In addition to this atrophic loss of muscle mass, metabolic deregulation of muscle also occurs. In contrast, physical exercise plays a beneficial role in counteracting disuse-induced atrophy by increasing muscle mass and strength. Along with this, exercise can also reduce mitochondrial dysfunction and metabolic deregulation. Still, while exercise causes valuable metabolic and functional adaptations in skeletal muscle, the mechanisms and effectors that lead to these changes such as increased mitochondria content or enhanced protein synthesis are not fully understood. Therefore, mechanistic insights may ultimately provide novel ways to treat disuse induced atrophy and metabolic deregulation. Mass spectrometry (MS)-based proteomics offers enormous promise for investigating the molecular mechanisms underlying disuse and exercise-induced changes in skeletal muscle. This review will focus on initial findings uncovered by using proteomics approaches with human skeletal muscle specimens and discuss their potential for the future study.

A targeted in situ hybridization screen identifies putative seminal fluid proteins in a simultaneously hermaphroditic flatworm

BACKGROUND

Along with sperm, in many taxa ejaculates also contain large numbers of seminal fluid proteins (SFPs). SFPs and sperm are transferred to the mating partner, where they are thought to play key roles in mediating post-mating sexual selection. They modulate the partner's behavior and physiology in ways that influence the reproductive success of both partners, thus potentially leading to sexual conflict. Despite the presumed general functional and evolutionary significance of SFPs, their identification and characterization has to date focused on just a few animal groups, predominantly insects and mammals. Moreover, until now seminal fluid profiling has mainly focused on species with separate sexes. Here we report a comprehensive screen for putative SFPs in the simultaneously hermaphroditic flatworm Macrostomum lignano.

RESULTS

Based on existing transcriptomic data, we selected 150 transcripts known to be (a) predominantly expressed in the tail region of the worms, where the seminal fluid-producing prostate gland cells are located, and (b) differentially expressed in social environments differing in sperm competition level, strongly implying that they represent a phenotypically plastic aspect of male reproductive allocation in this species. For these SFP candidates, we then performed whole-mount in situ hybridization (ISH) experiments to characterize tissue-specific expression. In total, we identified 98 transcripts that exhibited prostate-specific expression, 76 of which we found to be expressed exclusively in the prostate gland cells; additional sites of expression for the remaining 22 included the testis or other gland cells. Bioinformatics analyses of the prostate-limited candidates revealed that at least 64 are predicted to be secretory proteins, making these especially strong candidates to be SFPs that are transferred during copulation.

CONCLUSIONS

Our study represents a first comprehensive analysis using a combination of transcriptomic and ISH screen data to identify SFPs based on transcript expression in seminal fluid-producing tissues. We thereby extend the range of taxa for which seminal fluid has been characterized to a flatworm species with a sequenced genome and for which several methods such as antibody staining, transgenesis and RNA interference have been established. Our data provide a basis for testing the functional and evolutionary significance of SFPs.

Diet-dependent changes in the intestinal DNA methylome after introduction of enteral feeding in preterm pigs

AIM

To examine how enteral feeding affects the intestinal epigenome and gene expression just after preterm birth.

MATERIALS & METHODS

Intestinal tissue from preterm pigs, modeling preterm infants, was collected at birth and 5 days after gradual introduction of infant formula or bovine colostrum. The intestinal tissue was analyzed by reduced representation bisulfite sequencing and real-time qPCR.

RESULTS

Relative to colostrum, formula increased bacterial epithelial adherence and lipopolysaccharide binding protein (LBP) expression, which was regulated by promoter methylation. Diet-dependent changes in DNA methylation and/or mRNA expression were related to innate immune response, hypoxia, angiogenesis and epithelial-mesenchymal transition pathways (e.g., TTC38, IL8, C3, HIF1A and VEGFR1).

CONCLUSION

Epigenetic changes may mediate important effects of the first feeding on intestinal development in preterm neonates.

Glutathione S-Transferase Pi Prevents Sepsis-Related High Mobility Group Box-1 Protein Translocation and Release

Glutathione S-transferase Pi (GSTP) was originally identified as one of cytosolic phase II detoxification enzymes and also was considered to function via its non-catalytic, ligand-binding activity. We have reported that GSTP played an anti-inflammatory role in macrophages, suggesting that GSTP may have a protective role in inflammation. In this study, we deleted the murine Gstp gene cluster and found that GSTP significantly decreased the mortality of experimental sepsis and reduced related serum level of high mobility group box-1 protein (HMGB1). As HMGB1 is the key cytokine involved in septic death, we further studied the effect of GSTP on HMGB1 release. The results demonstrated that a classic protein kinase C (cPKC) dependent phosphorylation of cytoplasmic GSTP at Ser184 occurred in macrophages in response to lipopolysaccharide (LPS) stimulation. Phosphorylated GSTP was then translocated to the nucleus. In the nucleus, GSTP bound to HMGB1 and suppressed LPS-triggered and cPKC-mediated HMGB1 phosphorylation. Consequently, GSTP prevented the translocation of HMGB1 to cytoplasm and release. Our findings provide the new evidence that GSTP inhibited HMGB1 release via binding to HMGB1 in the nucleus independent of its transferase activity. cPKC-mediated GSTP phosphorylation was essential for GSTP to translocate from cytoplasm to nucleus. To our knowledge, we are the first to report that nuclear GSTP functions as a negative regulator to control HMGB1 release from macrophages and decreases the mortality of sepsis.

Immunomodulatory Effects of Amblyomma variegatum Saliva on Bovine Cells: Characterization of Cellular Responses and Identification of Molecular Determinants

The tropical bont tick, Amblyomma variegatum, is a tick species of veterinary importance and is considered as one of major pest of ruminants in Africa and in the Caribbean. It causes direct skin lesions, transmits heartwater, and reactivates bovine dermatophilosis. Tick saliva is reported to affect overall host responses through immunomodulatory and anti-inflammatory molecules, among other bioactive molecules. The general objective of this study was to better understand the role of saliva in interaction between the Amblyomma tick and the host using cellular biology approaches and proteomics, and to discuss its impact on disease transmission and/or activation. We first focused on the immuno-modulating effects of semi-fed A. variegatum female saliva on bovine peripheral blood mononuclear cells (PBMC) and monocyte-derived macrophages in vitro. We analyzed its immuno-suppressive properties by measuring the effect of saliva on PBMC proliferation, and observed a significant decrease in ConA-stimulated PBMC lymphoproliferation. We then studied the effect of saliva on bovine macrophages using flow cytometry to analyze the expression of MHC-II and co-stimulation molecules (CD40, CD80, and CD86) and by measuring the production of nitric oxide (NO) and pro- or anti-inflammatory cytokines. We observed a significant decrease in the expression of MHC-II, CD40, and CD80 molecules, associated with decreased levels of IL-12-p40 and TNF-α and increased level of IL-10, which could explain the saliva-induced modulation of NO. To elucidate these immunomodulatory effects, crude saliva proteins were analyzed using proteomics with an Orbitrap Elite mass spectrometer. Among the 336 proteins identified in A. variegatum saliva, we evidenced bioactive molecules exhibiting anti-inflammatory, immuno-modulatory, and anti-oxidant properties (e.g., serpins, phospholipases A2, heme lipoprotein). We also characterized an intriguing ubiquitination complex that could be involved in saliva-induced immune modulation of the host. We propose a model for the interaction between A. variegatum saliva and host immune cells that could have an effect during tick feeding by favoring pathogen dissemination or activation by reducing the efficiency of host immune response to the corresponding tick-borne diseases.

Confronting the catalytic dark matter encoded by sequenced genomes

The post-genomic era has provided researchers with a deluge of protein sequences. However, a significant fraction of the proteins encoded by sequenced genomes remains without an identified function. Here, we aim at determining how many enzymes of uncertain or unknown function are still present in the Saccharomyces cerevisiae and human proteomes. Using information available in the Swiss-Prot, BRENDA and KEGG databases in combination with a Hidden Markov Model-based method, we estimate that >600 yeast and 2000 human proteins (>30% of their proteins of unknown function) are enzymes whose precise function(s) remain(s) to be determined. This illustrates the impressive scale of the ‘unknown enzyme problem’. We extensively review classical biochemical as well as more recent systematic experimental and computational approaches that can be used to support enzyme function discovery research. Finally, we discuss the possible roles of the elusive catalysts in light of recent developments in the fields of enzymology and metabolism as well as the significance of the unknown enzyme problem in the context of metabolic modeling, metabolic engineering and rare disease research.

Online Hydrogen-Deuterium Exchange Traveling Wave Ion Mobility Mass Spectrometry (HDX-IM-MS): a Systematic Evaluation

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) is an important tool for measuring and monitoring protein structure. A bottom-up approach to HDX-MS provides peptide level deuterium uptake values and a more refined localization of deuterium incorporation compared with global HDX-MS measurements. The degree of localization provided by HDX-MS is proportional to the number of peptides that can be identified and monitored across an exchange experiment. Ion mobility spectrometry (IMS) has been shown to improve MS-based peptide analysis of biological samples through increased separation capacity. The integration of IMS within HDX-MS workflows has been commercialized but presently its adoption has not been widespread. The potential benefits of IMS, therefore, have not yet been fully explored. We herein describe a comprehensive evaluation of traveling wave ion mobility integrated within an online-HDX-MS system and present the first reported example of UDMS^E acquisition for HDX analysis. Instrument settings required for optimal peptide identifications are described and the effects of detector saturation due to peak compression are discussed. A model system is utilized to confirm the comparability of HDX-IM-MS and HDX-MS uptake values prior to an evaluation of the benefits of IMS at increasing sample complexity. Interestingly, MS and IM-MS acquisitions were found to identify distinct populations of peptides that were unique to the respective methods, a property that can be utilized to increase the spatial resolution of HDX-MS experiments by >60%. Graphical Abstract ᅟ.

From Protein Sequence to Protein Function via Multi-Label Linear Discriminant Analysis

Sequence describes the primary structure of a protein, which contains important structural, characteristic, and genetic information and thereby motivates many sequence-based computational approaches to infer protein function. Among them, feature-base approaches attract increased attention because they make prediction from a set of transformed and more biologically meaningful sequence features. However, original features extracted from sequence are usually of high dimensionality and often compromised by irrelevant patterns, therefore dimension reduction is necessary prior to classification for efficient and effective protein function prediction. A protein usually performs several different functions within an organism, which makes protein function prediction a multi-label classification problem. In machine learning, multi-label classification deals with problems where each object may belong to more than one class. As a well-known feature reduction method, linear discriminant analysis (LDA) has been successfully applied in many practical applications. It, however, by nature is designed for single-label classification, in which each object can belong to exactly one class. Because directly applying LDA in multi-label classification causes ambiguity when computing scatters matrices, we apply a new Multi-label Linear Discriminant Analysis (MLDA) approach to address this problem and meanwhile preserve powerful classification capability inherited from classical LDA. We further extend MLDA by l₁-normalization to overcome the problem of over-counting data points with multiple labels. In addition, we incorporate biological network data using Laplacian embedding into our method, and assess the reliability of predicted putative functions. Extensive empirical evaluations demonstrate promising results of our methods.

Computational Methods for Annotation Transfers from Sequence

Surveys of public sequence resources show that experimentally supported functional information is still completely missing for a considerable fraction of known proteins and is clearly incomplete for an even larger portion. Bioinformatics methods have long made use of very diverse data sources alone or in combination to predict protein function, with the understanding that different data types help elucidate complementary biological roles. This chapter focuses on methods accepting amino acid sequences as input and producing GO term assignments directly as outputs; the relevant biological and computational concepts are presented along with the advantages and limitations of individual approaches.

Protein Function Prediction

Protein function is a concept that can have different interpretations in different biological contexts, and the number and diversity of novel proteins identified by large-scale "omics" technologies poses increasingly new challenges. In this review we explore current strategies used to predict protein function focused on high-throughput sequence analysis, as for example, inference based on sequence similarity, sequence composition, structure, and protein-protein interaction. Various prediction strategies are discussed together with illustrative workflows highlighting the use of some benchmark tools and knowledge bases in the field.

Novel extracellular and nuclear caspase-1 and inflammasomes propagate inflammation and regulate gene expression: a comprehensive database mining study

Background

Caspase-1 is present in the cytosol as an inactive zymogen and requires the protein complexes named “inflammasomes” for proteolytic activation. However, it remains unclear whether the proteolytic activity of caspase-1 is confined only to the cytosol where inflammasomes are assembled to convert inactive pro-caspase-1 to active caspase-1.

Methods

We conducted meticulous data analysis methods on proteomic, protein interaction, protein intracellular localization, and gene expressions of 114 experimentally identified caspase-1 substrates and 38 caspase-1 interaction proteins in normal physiological conditions and in various pathologies.

Results

We made the following important findings: (1) Caspase-1 substrates and interaction proteins are localized in various intracellular organelles including nucleus and secreted extracellularly; (2) Caspase-1 may get activated in situ in the nucleus in response to intra-nuclear danger signals; (3) Caspase-1 cleaves its substrates in exocytotic secretory pathways including exosomes to propagate inflammation to neighboring and remote cells; (4) Most of caspase-1 substrates are upregulated in coronary artery disease regardless of their subcellular localization but the majority of metabolic diseases cause no significant expression changes in caspase-1 nuclear substrates; and (5) In coronary artery disease, majority of upregulated caspase-1 extracellular substrate-related pathways are involved in induction of inflammation; and in contrast, upregulated caspase-1 nuclear substrate-related pathways are more involved in regulating cell death and chromatin regulation.

Conclusions

Our identification of novel caspase-1 trafficking sites, nuclear and extracellular inflammasomes, and extracellular caspase-1-based inflammation propagation model provides a list of targets for the future development of new therapeutics to treat cardiovascular diseases, inflammatory diseases, and inflammatory cancers.

Electronic supplementary material

The online version of this article (doi:10.1186/s13045-016-0351-5) contains supplementary material, which is available to authorized users.

The Membrane Protein LasM Promotes the Culturability of Legionella pneumophila in Water

The water-borne pathogen Legionella pneumophila (Lp) strongly expresses the lpg1659 gene in water. This gene encodes a hypothetical protein predicted to be a membrane protein using in silico analysis. While no conserved domains were identified in Lpg1659, similar proteins are found in many Legionella species and other aquatic bacteria. RT-qPCR showed that lpg1659 is positively regulated by the alternative sigma factor RpoS, which is essential for Lp to survive in water. These observations suggest an important role of this novel protein in the survival of Lp in water. Deletion of lpg1659 did not affect cell morphology, membrane integrity or tolerance to high temperature. Moreover, lpg1659 was dispensable for growth of Lp in rich medium, and during infection of the amoeba Acanthamoeba castellanii and of THP-1 human macrophages. However, deletion of lpg1659 resulted in an early loss of culturability in water, while over-expression of this gene promoted the culturability of Lp. Therefore, these results suggest that lpg1659 is required for Lp to maintain culturability, and possibly long-term survival, in water. Since the loss of culturability observed in the absence of Lpg1659 was complemented by the addition of trace metals into water, this membrane protein is likely a transporter for acquiring essential trace metal for maintaining culturability in water and potentially in other metal-deprived conditions. Given its role in the survival of Lp in water, Lpg1659 was named LasM for Legionella aquatic survival membrane protein.

Characterization of a Novel Endoplasmic Reticulum Protein Involved in Tubercidin Resistance in Leishmania major

BACKGROUND

Tubercidin (TUB) is a toxic adenosine analog with potential antiparasitic activity against Leishmania, with mechanism of action and resistance that are not completely understood. For understanding the mechanisms of action and identifying the potential metabolic pathways affected by this drug, we employed in this study an overexpression/selection approach using TUB for the identification of potential targets, as well as, drug resistance genes in L. major. Although, TUB is toxic to the mammalian host, these findings can provide evidences for a rational drug design based on purine pathway against leishmaniasis.

METHODOLOGY/PRINCIPAL FINDINGS

After transfection of a cosmid genomic library into L. major Friedlin (LmjF) parasites and application of the overexpression/selection method, we identified two cosmids (cosTUB1 and cosTU2) containing two different loci capable of conferring significant levels of TUB resistance. In the cosTUB1 contained a gene encoding NUPM1-like protein, which has been previously described as associated with TUB resistance in L. amazonensis. In the cosTUB2 we identified and characterized a gene encoding a 63 kDa protein that we denoted as tubercidin-resistance protein (TRP). Functional analysis revealed that the transfectants were less susceptible to TUB than LmjF parasites or those transfected with the control vector. In addition, the trp mRNA and protein levels in cosTUB2 transfectants were higher than LmjF. TRP immunolocalization revealed that it was co-localized to the endoplasmic reticulum (ER), a cellular compartment with many functions. In silico predictions indicated that TRP contains only a hypothetical transmembrane domain. Thus, it is likely that TRP is a lumen protein involved in multidrug efflux transport that may be involved in the purine metabolic pathway.

CONCLUSIONS/SIGNIFICANCE

This study demonstrated for the first time that TRP is associated with TUB resistance in Leishmania. The next challenge is to determine how TRP mediates TUB resistance and whether purine metabolism is affected by this protein in the parasite. Finally, these findings may be helpful for the development of alternative anti-leishmanial drugs that target purine pathway.

FFPred 3: feature-based function prediction for all Gene Ontology domains

Predicting protein function has been a major goal of bioinformatics for several decades, and it has gained fresh momentum thanks to recent community-wide blind tests aimed at benchmarking available tools on a genomic scale. Sequence-based predictors, especially those performing homology-based transfers, remain the most popular but increasing understanding of their limitations has stimulated the development of complementary approaches, which mostly exploit machine learning. Here we present FFPred 3, which is intended for assigning Gene Ontology terms to human protein chains, when homology with characterized proteins can provide little aid. Predictions are made by scanning the input sequences against an array of Support Vector Machines (SVMs), each examining the relationship between protein function and biophysical attributes describing secondary structure, transmembrane helices, intrinsically disordered regions, signal peptides and other motifs. This update features a larger SVM library that extends its coverage to the cellular component sub-ontology for the first time, prompted by the establishment of a dedicated evaluation category within the Critical Assessment of Functional Annotation. The effectiveness of this approach is demonstrated through benchmarking experiments, and its usefulness is illustrated by analysing the potential functional consequences of alternative splicing in human and their relationship to patterns of biological features.

Computational characterization of Iron metabolism in the Tsetse disease vector, Glossina morsitans: IRE stem-loops

Background

Iron metabolism and regulation is an indispensable part of species survival, most importantly for blood feeding insects. Iron regulatory proteins are central regulators of iron homeostasis, whose binding to iron response element (IRE) stem-loop structures within the UTRs of genes regulate expression at the post-transcriptional level. Despite the extensive literature on the mechanism of iron regulation in human, less attention has been given to insect and more specifically the blood feeding insects, where research has mainly focused on the characterization of ferritin and transferrin. We thus, examined the mechanism of iron homeostasis through a genome-wide computational identification of IREs and other enriched motifs in the UTRs of Glossina morsitans with the view to identify new IRE-regulated genes.

Results

We identified 150 genes, of which two are known to contain IREs, namely the ferritin heavy chain and the MRCK-alpha. The remainder of the identified genes is considered novel including 20 hypothetical proteins, for which an iron-regulatory mechanism of action was inferred. Forty-three genes were found with IRE-signatures of regulation in two or more insects, while 46 were only found to be IRE-regulated in two species. Notably 39 % of the identified genes exclusively shared IRE-signatures in other Glossina species, which are potentially Glossina-specific adaptive measures in addressing its unique reproductive biology and blood meal-induced iron overload. In line with previous findings, we found no evidence pertaining to an IRE regulation of Transferrin, which highlight the importance of ferritin heavy chain and the other proposed transporters in the tsetse fly. In the context of iron-sequestration, key players of tsetse immune defence against trypanosomes have been introduced namely 14 stress and immune response genes, while 28 cell-envelop, transport, and binding genes were assigned a putative role in iron trafficking. Additionally, we identified and annotated enriched motifs in the UTRs of the putative IRE-regulated genes to derive at a co-regulatory network that maintains iron homeostasis in tsetse flies. Three putative microRNA-binding sites namely Gy-box, Brd-box and K-box motifs were identified among the regulatory motifs, enriched in the UTRs of the putative IRE-regulated genes.

Conclusion

Beyond our current view of iron metabolism in insects, with ferritin and transferrin as its key players, this study provides a comprehensive catalogue of genes with possible roles in the acquisition; transport and storage of iron hence iron homeostasis in the tsetse fly.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2932-7) contains supplementary material, which is available to authorized users.

Discovery of Azurin-Like Anticancer Bacteriocins from Human Gut Microbiome through Homology Modeling and Molecular Docking against the Tumor Suppressor p53

Azurin from Pseudomonas aeruginosa is known anticancer bacteriocin, which can specifically penetrate human cancer cells and induce apoptosis. We hypothesized that pathogenic and commensal bacteria with long term residence in human body can produce azurin-like bacteriocins as a weapon against the invasion of cancers. In our previous work, putative bacteriocins have been screened from complete genomes of 66 dominant bacteria species in human gut microbiota and subsequently characterized by subjecting them as functional annotation algorithms with azurin as control. We have qualitatively predicted 14 putative bacteriocins that possessed functional properties very similar to those of azurin. In this work, we perform a number of quantitative and structure-based analyses including hydrophobic percentage calculation, structural modeling, and molecular docking study of bacteriocins of interest against protein p53, a cancer target. Finally, we have identified 8 putative bacteriocins that bind p53 in a same manner as p28-azurin and azurin, in which 3 peptides (p1seq16, p2seq20, and p3seq24) shared with our previous study and 5 novel ones (p1seq09, p2seq05, p2seq08, p3seq02, and p3seq17) discovered in the first time. These bacteriocins are suggested for further in vitro tests in different neoplastic line cells.

Transcriptomics-based identification of WRKY genes and characterization of a salt and hormone-responsive PgWRKY1 gene in Panax ginseng

WRKY proteins belong to a transcription factor (TF) family and play dynamic roles in many plant processes, including plant responses to abiotic and biotic stresses, as well as secondary metabolism. However, no WRKY gene in Panax ginseng C.A. Meyer has been reported to date. In this study, a number of WRKY unigenes from methyl jasmonate (MeJA)-treated adventitious root transcriptome of this species were identified using next-generation sequencing technology. A total of 48 promising WRKY unigenes encoding WRKY proteins were obtained by eliminating wrong and incomplete open reading frame (ORF). Phylogenetic analysis reveals 48 WRKY TFs, including 11 Group I, 36 Group II, and 1 Group III. Moreover, one MeJA-responsive unigene designated as PgWRKY1 was cloned and characterized. It contains an entire ORF of 1077 bp and encodes a polypeptide of 358 amino acid residues. The PgWRKY1 protein contains a single WRKY domain consisting of a conserved amino acid sequence motif WRKYGQK and a C2H2-type zinc-finger motif belonging to WRKY subgroup II-d. Subcellular localization of PgWRKY1-GFP fusion protein in onion and tobacco epidermis cells revealed that PgWRKY1 was exclusively present in the nucleus. Quantitative real-time polymerase chain reaction analysis demonstrated that the expression of PgWRKY1 was relatively higher in roots and lateral roots compared with leaves, stems, and seeds. Importantly, PgWRKY1 expression was significantly induced by salicylic acid, abscisic acid, and NaCl, but downregulated by MeJA treatment. These results suggested that PgWRKY1 might be a multiple stress-inducible gene responding to hormones and salt stresses.