A comparative analysis of heart microRNAs in vertebrates brings novel insights into the evolution of genetic regulatory networks.

BACKGROUND: During vertebrate evolution, the heart has undergone remarkable changes that lead to morphophysiological differences in the fully formed heart of these species, such as chamber septation, heart rate frequency, blood pressure, and cardiac output volume. Despite these differences, the heart developmental process is guided by a core gene set conserved across vertebrates. Nonetheless, the regulatory mechanisms controlling the expression of genes involved in heart development and maintenance are largely uncharted. MicroRNAs (miRNAs) have been described as important regulatory elements in several biological processes, including heart biology. These small RNA molecules are broadly conserved in sequence and genomic context in metazoans. Mutations may occur in miRNAs and/or genes that contribute to the establishment of distinct repertoires of miRNA-target interactions, thereby favoring the differential control of gene expression and, consequently, the origin of novel phenotypes. In fact, several studies showed that miRNAs are integrated into genetic regulatory networks (GRNs) governing specific developmental programs and diseases. However, studies integrating miRNAs in vertebrate heart GRNs under an evolutionary perspective are still scarce. RESULTS: We comprehensively examined and compared the heart miRNome of 20 species representatives of the five major vertebrate groups. We found 54 miRNA families with conserved expression and a variable number of miRNA families with group-specific expression in fishes, amphibians, reptiles, birds, and mammals. We also detected that conserved miRNAs present higher expression levels and a higher number of targets, whereas the group-specific miRNAs present lower expression levels and few targets. CONCLUSIONS: Both the conserved and group-specific miRNAs can be considered modulators orchestrating the core and peripheral genes of heart GRNs of vertebrates, which can be related to the morphophysiological differences and similarities existing in the heart of distinct vertebrate groups. We propose a hypothesis to explain evolutionary differences in the putative functional roles of miRNAs in the heart GRNs analyzed. Furthermore, we present new insights into the molecular mechanisms that could be helping modulate the diversity of morphophysiology in the heart organ of vertebrate species.

A comparative analysis of heart microRNAs in vertebrates brings novel insights into the evolution of genetic regulatory networks

Background: During vertebrate evolution, the heart has undergone remarkable changes that lead to morphophysiological differences in the fully formed heart of these species, such as chamber septation, heart rate frequency, blood pressure, and cardiac output volume. Despite these differences, the heart developmental process is guided by a core gene set conserved across vertebrates. Nonetheless, the regulatory mechanisms controlling the expression of genes involved in heart development and maintenance are largely uncharted. MicroRNAs (miRNAs) have been described as important regulatory elements in several biological processes, including heart biology. These small RNA molecules are broadly conserved in sequence and genomic context in metazoans. Mutations may occur in miRNAs and/or genes that contribute to the establishment of distinct repertoires of miRNA-target interactions, thereby favoring the differential control of gene expression and, consequently, the origin of novel phenotypes. In fact, several studies showed that miRNAs are integrated into genetic regulatory networks (GRNs) governing specific developmental programs and diseases. However, studies integrating miRNAs in vertebrate heart GRNs under an evolutionary perspective are still scarce. Results: We comprehensively examined and compared the heart miRNome of 20 species representatives of the five major vertebrate groups. We found 54 miRNA families with conserved expression and a variable number of miRNA families with group-specific expression in fishes, amphibians, reptiles, birds, and mammals. We also detected that conserved miRNAs present higher expression levels and a higher number of targets, whereas the group-specific miRNAs present lower expression levels and few targets. Conclusions: Both the conserved and group-specific miRNAs can be considered modulators orchestrating the core and peripheral genes of heart GRNs of vertebrates, which can be related to the morphophysiological differences and similarities existing in the heart of distinct vertebrate groups. We propose a hypothesis to explain evolutionary differences in the putative functional roles of miRNAs in the heart GRNs analyzed. Furthermore, we present new insights into the molecular mechanisms that could be helping modulate the diversity of morphophysiology in the heart organ of vertebrate species.

miRTil: An Extensive Repository for Nile Tilapia microRNA Next Generation Sequencing Data.

Nile tilapia is the third most cultivated fish worldwide and a novel model species for evolutionary studies. Aiming to improve productivity and contribute to the selection of traits of economic impact, biotechnological approaches have been intensively applied to species enhancement. In this sense, recent studies have focused on the multiple roles played by microRNAs (miRNAs) in the post-transcriptional regulation of protein-coding genes involved in the emergence of phenotypes with relevance for aquaculture. However, there is still a growing demand for a reference resource dedicated to integrating Nile Tilapia miRNA information, obtained from both experimental and in silico approaches, and facilitating the analysis and interpretation of RNA sequencing data. Here, we present an open repository dedicated to Nile Tilapia miRNAs: the "miRTil database". The database stores data on 734 mature miRNAs identified in 11 distinct tissues and five key developmental stages. The database provides detailed information about miRNA structure, genomic context, predicted targets, expression profiles, and relative 5p/3p arm usage. Additionally, miRTil also includes a comprehensive pre-computed miRNA-target interaction network containing 4936 targets and 19,580 interactions.

MicrobioLink: An Integrated Computational Pipeline to Infer Functional Effects of Microbiome-Host Interactions.

Microbiome-host interactions play significant roles in health and in various diseases including autoimmune disorders. Uncovering these inter-kingdom cross-talks propels our understanding of disease pathogenesis and provides useful leads on potential therapeutic targets. Despite the biological significance of microbe-host interactions, there is a big gap in understanding the downstream effects of these interactions on host processes. Computational methods are expected to fill this gap by generating, integrating, and prioritizing predictions-as experimental detection remains challenging due to feasibility issues. Here, we present MicrobioLink, a computational pipeline to integrate predicted interactions between microbial and host proteins together with host molecular networks. Using the concept of network diffusion, MicrobioLink can analyse how microbial proteins in a certain context are influencing cellular processes by modulating gene or protein expression. We demonstrated the applicability of the pipeline using a case study. We used gut metaproteomic data from Crohn's disease patients and healthy controls to uncover the mechanisms by which the microbial proteins can modulate host genes which belong to biological processes implicated in disease pathogenesis. MicrobioLink, which is agnostic of the microbial protein sources (bacterial, viral, etc.), is freely available on GitHub.

Cooperative and sequence-dependent model for RNAP dynamics: Application to ribosomal gene transcription.

Escherichia coli ribosomal genes are a well-established experimental model used to investigate the transcription process. These genes are essential to cell physiology and are therefore strongly expressed. Multiple transcription units collaborate in rrn expression. Experiments involving electron microscopy have shown the non-uniform density of the RNA polymerases transcribing these ribosomal operons. Here, we investigate RNAP collaborative transcription in E. coli ribosomal genes using a stochastic sequence-dependent model that included interactions among the RNAPs. We achieved results consistent with experimental data using a model with variable parametrization for genic and intergenic regions, compared with previous attempts that used uniform parameters for genic and intergenic regions. Our model also showed that cooperative behaviour reduced the dwell times in pause sites predicted by the single-round approach but induced a new pausing event at an upstream position. This work may stimulate new experimental research and provide other scenarios to test our model predictions.

Understanding the Modus Operandi of MicroRNA Regulatory Clusters.

MicroRNAs (miRNAs) are non-coding RNAs that regulate a wide range of biological pathways by post-transcriptionally modulating gene expression levels. Given that even a single miRNA may simultaneously control several genes enrolled in multiple biological functions, one would expect that these tiny RNAs have the ability to properly sort among distinctive cellular processes to drive protein production. To test this hypothesis, we scrutinized previously published microarray datasets and clustered protein-coding gene expression profiles according to the intensity of fold-change levels caused by the exogenous transfection of 10 miRNAs (miR-1, miR-7, miR-9, miR-124, miR-128a, miR-132, miR-133a, miR-142, miR-148b, miR-181a) in a human cell line. Through an in silico functional enrichment analysis, we discovered non-randomic regulatory patterns, proper of each cluster identified. We demonstrated that miRNAs are capable of equivalently modulate the expression signatures of target genes in regulatory clusters according to the biological function they are assigned to. Moreover, target prediction analysis applied to ten vertebrate species, suggest that such miRNA regulatory modus operandi is evolutionarily conserved within vertebrates. Overall, we discovered a complex regulatory cluster-module strategy driven by miRNAs, which relies on the controlled intensity of the repression over distinct targets under specific biological contexts. Our discovery helps to clarify the mechanisms underlying the functional activity of miRNAs and makes it easier to take the fastest and most accurate path in the search for the functions of miRNAs in any distinct biological process of interest.

Genome-wide microRNA screening in Nile tilapia reveals pervasive isomiRs' transcription, sex-biased arm switching and increasing complexity of expression throughout development.

MicroRNAs (miRNAs) are key regulators of gene expression in multicellular organisms. The elucidation of miRNA function and evolution depends on the identification and characterization of miRNA repertoire of strategic organisms, as the fast-evolving cichlid fishes. Using RNA-seq and comparative genomics we carried out an in-depth report of miRNAs in Nile tilapia (Oreochromis niloticus), an emergent model organism to investigate evo-devo mechanisms. Five hundred known miRNAs and almost one hundred putative novel vertebrate miRNAs have been identified, many of which seem to be teleost-specific, cichlid-specific or tilapia-specific. Abundant miRNA isoforms (isomiRs) were identified with modifications in both 5p and 3p miRNA transcripts. Changes in arm usage (arm switching) of nine miRNAs were detected in early development, adult stage and even between male and female samples. We found an increasing complexity of miRNA expression during ontogenetic development, revealing a remarkable synchronism between the rate of new miRNAs recruitment and morphological changes. Overall, our results enlarge vertebrate miRNA collection and reveal a notable differential ratio of miRNA arms and isoforms influenced by sex and developmental life stage, providing a better picture of the evolutionary and spatiotemporal dynamics of miRNAs.

PLA2-like proteins myotoxic mechanism: a dynamic model description.

Phospholipase A2-like (PLA2-like) proteins contribute to the development of muscle necrosis in Viperidae snake bites and are not efficiently neutralized by current antivenom treatments. The toxic mechanisms of PLA2-like proteins are devoid of catalytic activity and not yet fully understood, although structural and functional experiments suggest a dimeric assembly and that the C-terminal residues are essential to myotoxicity. Herein, we characterized the functional mechanism of bothropic PLA2-like structures related to global and local measurements using the available models in the Protein Data Bank and normal mode molecular dynamics (NM-MD). Those measurements include: (i) new geometric descriptions between their monomers, based on Euler angles; (ii) characterizations of canonical and non-canonical conformations of the C-terminal residues; (iii) accessibility of the hydrophobic channel; (iv) inspection of ligands; and (v) distance of clustered residues to toxin interface of interaction. Thus, we described the allosteric activation of PLA2-like proteins and hypothesized that the natural movement between monomers, calculated from NM-MD, is related to their membrane disruption mechanism, which is important for future studies of the inhibition process. These methods and strategies can be applied to other proteins to help understand their mechanisms of action.

Combining Results from Distinct MicroRNA Target Prediction Tools Enhances the Performance of Analyses.

Target prediction is generally the first step toward recognition of bona fide microRNA (miRNA)-target interactions in living cells. Several target prediction tools are now available, which use distinct criteria and stringency to provide the best set of candidate targets for a single miRNA or a subset of miRNAs. However, there are many false-negative predictions, and consensus about the optimum strategy to select and use the output information provided by the target prediction tools is lacking. We compared the performance of four tools cited in literature-TargetScan (TS), miRanda-mirSVR (MR), Pita, and RNA22 (R22), and we determined the most effective approach for analyzing target prediction data (individual, union, or intersection). For this purpose, we calculated the sensitivity, specificity, precision, and correlation of these approaches using 10 miRNAs (miR-1-3p, miR-17-5p, miR-21-5p, miR-24-3p, miR-29a-3p, miR-34a-5p, miR-124-3p, miR-125b-5p, miR-145-5p, and miR-155-5p) and 1,400 genes (700 validated and 700 non-validated) as targets of these miRNAs. The four tools provided a subset of high-quality predictions and returned few false-positive predictions; however, they could not identify several known true targets. We demonstrate that union of TS/MR and TS/MR/R22 enhanced the quality of in silico prediction analysis of miRNA targets. We conclude that the union rather than the intersection of the aforementioned tools is the best strategy for maximizing performance while minimizing the loss of time and resources in subsequent in vivo and in vitro experiments for functional validation of miRNA-target interactions.

Corrigendum: Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review.

[This corrects the article on p. 75 in vol. 7, PMID: 27014079.].

An ensemble framework for identifying essential proteins.

BACKGROUND: Many centrality measures have been proposed to mine and characterize the correlations between network topological properties and protein essentiality. However, most of them show limited prediction accuracy, and the number of common predicted essential proteins by different methods is very small. RESULTS: In this paper, an ensemble framework is proposed which integrates gene expression data and protein-protein interaction networks (PINs). It aims to improve the prediction accuracy of basic centrality measures. The idea behind this ensemble framework is that different protein-protein interactions (PPIs) may show different contributions to protein essentiality. Five standard centrality measures (degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and subgraph centrality) are integrated into the ensemble framework respectively. We evaluated the performance of the proposed ensemble framework using yeast PINs and gene expression data. The results show that it can considerably improve the prediction accuracy of the five centrality measures individually. It can also remarkably increase the number of common predicted essential proteins among those predicted by each centrality measure individually and enable each centrality measure to find more low-degree essential proteins. CONCLUSIONS: This paper demonstrates that it is valuable to differentiate the contributions of different PPIs for identifying essential proteins based on network topological characteristics. The proposed ensemble framework is a successful paradigm to this end.

Gene polymorphisms as a predictor of body weight loss after Roux-en-Y gastric bypass surgery among obese women.

This study aimed to investigate the association between twelve gene polymorphisms and body weight loss, 12 months after Roux-en-Y gastric bypass (RYGB) surgery. Three hundred and fifty-one obese women participated in this study. The statistical software WEKA was used to identify which gene polymorphisms were potential predictors of postoperative percentage of excess weight loss (%EWL). Our results indicate that the only gene polymorphism that predicted %EWL was rs3813929, which is related to the serotonin receptor gene (5-HT2C). Therefore, the 5-HT2C gene polymorphism rs3813929 (more specifically, the TT genotype) predicted greater %EWL 12 months after RYGB surgery among female patients.

Bending-Twisting Motions and Main Interactions in Nucleoplasmin Nuclear Import.

Alpha solenoid proteins play a key role in regulating the classical nuclear import pathway, recognizing a target protein and transporting it into the nucleus. Importin-α (Impα) is the solenoid responsible for cargo protein recognition, and it has been extensively studied by X-ray crystallography to understand the binding specificity. To comprehend the main motions of Impα and to extend the information about the critical interactions during carrier-cargo recognition, we surveyed different conformational states based on molecular dynamics (MD) and normal mode (NM) analyses. Our model of study was a crystallographic structure of Impα complexed with the classical nuclear localization sequence (cNLS) from nucleoplasmin (Npl), which was submitted to multiple 100 ns of MD simulations. Representative conformations were selected for calculating the 87 lowest frequencies NMs of vibration, and a displacement approach was applied along each NM. Based on geometric criteria, using the radius of curvature and inter-repeat angles as the reference metrics, the main motions of Impα were described. Moreover, we determined the salt bridges, hydrogen bonds and hydrophobic interactions in the Impα-NplNLS interface. Our results show the bending and twisting motions participating in the recognition of nuclear proteins, allowing the accommodation and adjustment of a classical bipartite NLS sequence. The essential contacts for the nuclear import were also described and were mostly in agreement with previous studies, suggesting that the residues in the cNLS linker region establish important contacts with Impα adjusting the cNLS backbone. The MD simulations combined with NM analysis can be applied to the Impα-NLS system to help understand interactions between Impα and cNLSs and the analysis of non-classic NLSs.

Synchronization and Propagation of Global Sleep Spindles.

Sleep spindles occur thousands of times during normal sleep and can be easily detected by visual inspection of EEG signals. These characteristics make spindles one of the most studied EEG structures in mammalian sleep. In this work we considered global spindles, which are spindles that are observed simultaneously in all EEG channels. We propose a methodology that investigates both the signal envelope and phase/frequency of each global spindle. By analysing the global spindle phase we showed that 90% of spindles synchronize with an average latency time of 0.1 s. We also measured the frequency modulation (chirp) of global spindles and found that global spindle chirp and synchronization are not correlated. By investigating the signal envelopes and implementing a homogeneous and isotropic propagation model, we could estimate both the signal origin and velocity in global spindles. Our results indicate that this simple and non-invasive approach could determine with reasonable precision the spindle origin, and allowed us to estimate a signal speed of 0.12 m/s. Finally, we consider whether synchronization might be useful as a non-invasive diagnostic tool.

Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review.

Essential proteins/genes are indispensable to the survival or reproduction of an organism, and the deletion of such essential proteins will result in lethality or infertility. The identification of essential genes is very important not only for understanding the minimal requirements for survival of an organism, but also for finding human disease genes and new drug targets. Experimental methods for identifying essential genes are costly, time-consuming, and laborious. With the accumulation of sequenced genomes data and high-throughput experimental data, many computational methods for identifying essential proteins are proposed, which are useful complements to experimental methods. In this review, we show the state-of-the-art methods for identifying essential genes and proteins based on machine learning and network topological features, point out the progress and limitations of current methods, and discuss the challenges and directions for further research.

A novel synthetic quinolinone inhibitor presents proteolytic and hemorrhagic inhibitory activities against snake venom metalloproteases.

Metalloproteases play a fundamental role in snake venom envenomation inducing hemorrhagic, fibrigen(ogen)olytic and myotoxic effects in their victims. Several snake venoms, such as those from the Bothrops genus, present important local effects which are not efficiently neutralized by conventional serum therapy. Consequently, these accidents may result in permanent sequelae and disability, creating economic and social problems, especially in developing countries, leading the attention of the World Health Organization that considered ophidic envenomations a neglected tropical disease. Aiming to produce an efficient inhibitor against bothropic venoms, we synthesized different molecules classified as quinolinones - a group of low-toxic chemical compounds widely used as antibacterial and antimycobacterial drugs - and tested their inhibitory properties against hemorrhage caused by bothropic venoms. The results from this initial screening indicated the molecule 2-hydroxymethyl-6-methoxy-1,4-dihydro-4-quinolinone (Q8) was the most effective antihemorrhagic compound among all of the assayed synthetic quinolinones. Other in vitro and in vivo experiments showed this novel compound was able to inhibit significantly the hemorrhagic and/or proteolytic activities of bothropic crude venoms and isolated snake venom metalloproteases (SVMPs) even at lower concentrations. Docking and molecular dynamic simulations were also performed to get insights into the structural basis of Q8 inhibitory mechanism against proteolytic and hemorrhagic SVMPs. These structural studies demonstrated that Q8 may form a stable complex with SVMPs, impairing the access of substrates to the active sites of these toxins. Therefore, both experimental and structural data indicate that Q8 compound is an interesting candidate for antiophidic therapy, particularly for the treatment of the hemorrhagic and necrotic effects induced by bothropic venoms.

Prediction of Druggable Proteins Using Machine Learning and Systems Biology: A Mini-Review.

The emergence of -omics technologies has allowed the collection of vast amounts of data on biological systems. Although, the pace of such collection has been exponential, the impact of these data remains small on many critical biomedical applications such as drug development. Limited resources, high costs, and low hit-to-lead ratio have led researchers to search for more cost effective methodologies. A possible alternative is to incorporate computational methods of potential drug target prediction early during drug discovery workflow. Computational methods based on systems approaches have the advantage of taking into account the global properties of a molecule not limited to its sequence, structure or function. Machine learning techniques are powerful tools that can extract relevant information from massive and noisy data sets. In recent years the scientific community has explored the combined power of these fields to propose increasingly accurate and low cost methods to propose interesting drug targets. In this mini-review, we describe promising approaches based on the simultaneous use of systems biology and machine learning to access gene and protein druggability. Moreover, we discuss the state-of-the-art of this emerging and interdisciplinary field, discussing data sources, algorithms and the performance of the different methodologies. Finally, we indicate interesting avenues of research and some remaining open challenges.

Transcriptome response signatures associated with the overexpression of a mitochondrial uncoupling protein (AtUCP1) in tobacco.

Mitochondrial inner membrane uncoupling proteins (UCP) dissipate the proton electrochemical gradient established by the respiratory chain, thus affecting the yield of ATP synthesis. UCP overexpression in plants has been correlated with oxidative stress tolerance, improved photosynthetic efficiency and increased mitochondrial biogenesis. This study reports the main transcriptomic responses associated with the overexpression of an UCP (AtUCP1) in tobacco seedlings. Compared to wild-type (WT), AtUCP1 transgenic seedlings showed unaltered ATP levels and higher accumulation of serine. By using RNA-sequencing, a total of 816 differentially expressed genes between the investigated overexpressor lines and the untransformed WT control were identified. Among them, 239 were up-regulated and 577 were down-regulated. As a general response to AtUCP1 overexpression, noticeable changes in the expression of genes involved in energy metabolism and redox homeostasis were detected. A substantial set of differentially expressed genes code for products targeted to the chloroplast and mainly involved in photosynthesis. The overall results demonstrate that the alterations in mitochondrial function provoked by AtUCP1 overexpression require important transcriptomic adjustments to maintain cell homeostasis. Moreover, the occurrence of an important cross-talk between chloroplast and mitochondria, which culminates in the transcriptional regulation of several genes involved in different pathways, was evidenced.

An improved interolog mapping-based computational prediction of protein-protein interactions with increased network coverage.

Automated and efficient methods that map ortholog interactions from several organisms and public databases (pDB) are needed to identify new interactions in an organism of interest (interolog mapping). When computational methods are applied to predict interactions, it is important that these methods be validated and their efficiency proven. In this study, we compare six Blast+ metrics over three datasets to identify the best metric for protein-protein interaction predictions. Using Blast+ to align the protein pairs, the ortholog interactions from DIP were mapped to String, Intact and Psibase pDBs. For each interaction mapped to each pDBs, we retrieved the alignment score, e-value, bitscore, similarity, identity and coverage. We evaluated these Blast+ values, and combinations thereof, with the Receiver Operating Characteristic (ROC) curves and computed the Area Under Curve (AUC). To validate these predictions, we used a subset of the Database of Interacting Proteins (DIP) composed of experimental interactions curated by the International Molecular Exchange (IMEx). The cut-off point for each metric/pDB was computed aiming to identify the best one that separates the true and false predicted interactions. In contrast to other methods that only compute the first Blast hit, we considered the first 20 hits, thus increasing the number of predicted interaction pairs. In addition, we identified the contribution of each individual pDB, as well as their combined contribution to the prediction. The best metric had an AUC of 0.96 for a single pDB and AUC of 0.93 for combined pDBs. Compared to other studies, with a cut-off point of 0.70 representing a specificity of 0.95 and a sensitivity of 0.90 for individual pDB, our method efficiently predicts protein-protein interactions.

Loss of sleep spindle frequency deceleration in Obstructive Sleep Apnea.

OBJECTIVE: Sleep spindles have been suggested as surrogates of thalamo-cortical activity. Internal frequency modulation within a spindle's time frame has been demonstrated in healthy subjects, showing that spindles tend to decelerate their frequency before termination. We investigated internal frequency modulation of slow and fast spindles according to Obstructive Sleep Apnea (OSA) severity and brain topography. METHODS: Seven non-OSA subjects and 21 patients with OSA contributed with 30min of Non-REM sleep stage 2, subjected to a Matching pursuit procedure with Gabor chirplet functions for automatic detection of sleep spindles and quantification of sleep spindle internal frequency modulation (chirp rate). RESULTS: Moderate OSA patients showed an inferior percentage of slow spindles with deceleration when compared to Mild and Non-OSA groups in frontal and parietal regions. In parietal regions, the percentage of slow spindles with deceleration was negatively correlated with global apnea-hypopnea index (rs=-0.519, p=0.005). DISCUSSION: Loss of physiological sleep spindle deceleration may either represent a disruption of thalamo-cortical loops generating spindle oscillations or some compensatory mechanism, an interesting venue for future research in the context of cognitive dysfunction in OSA. SIGNIFICANCE: Quantification of internal frequency modulation (chirp rate) is proposed as a promising approach to advance description of sleep spindle dynamics in brain pathology.