Biological Process Of Interest Research Articles

Regulation of regeneration in Arabidopsis thaliana

We employed several algorithms with high efficacy to analyze the public transcriptomic data, aiming to identify key transcription factors (TFs) that regulate regeneration in Arabidopsis thaliana. Initially, we utilized CollaborativeNet, also known as TF-Cluster, to construct a collaborative network of all TFs, which was subsequently decomposed into many subnetworks using the Triple-Link and Compound Spring Embedder (CoSE) algorithms. Functional analysis of these subnetworks led to the identification of nine subnetworks closely associated with regeneration. We further applied principal component analysis and gene ontology (GO) enrichment analysis to reduce the subnetworks from nine to three, namely subnetworks 1, 12, and 17. Searching for TF-binding sites in the promoters of the co-expressed and co-regulated (CCGs) genes of all TFs in these three subnetworks and Triple-Gene Mutual Interaction analysis of TFs in these three subnetworks with the CCGs involved in regeneration enabled us to rank the TFs in each subnetwork. Finally, six potential candidate TFs—WOX9A, LEC2, PGA37, WIP5, PEI1, and AIL1 from subnetwork 1—were identified, and their roles in somatic embryogenesis (GO:0010262) and regeneration (GO:0031099) were discussed, so were the TFs in Subnetwork 12 and 17 associated with regeneration. The TFs identified were also assessed using the CIS-BP database and Expression Atlas. Our analyses suggest some novel TFs that may have regulatory roles in regeneration and embryogenesis and provide valuable data and insights into the regulatory mechanisms related to regeneration. The tools and the procedures used here are instrumental for analyzing high-throughput transcriptomic data and advancing our understanding of the regulation of various biological processes of interest.

A Reactive Force Field for Molecular Dynamics Simulations of Glucose in Aqueous Solution.

To expand the capabilities of reactive force field (ReaxFF) in simulations of biological processes involving glucose, in this work, using Metropolis Monte Carlo algorithm, new ReaxFF parameters for glucose have been developed to better describe the properties of glucose in water during molecular dynamics (MD) simulations. With the newly trained ReaxFF, the mutarotation of glucose in water can be better described, as suggested by our metadynamics simulations. In addition, the newly trained ReaxFF can better describe the distributions of the three stable conformers along the key dihedral angle of α-anomer and β-anomer. With better descriptions of hydration around glucose, the Raman and Raman optical activity spectra can be more accurately calculated. In addition, the infrared spectra obtained from simulations with the new glucose ReaxFF are more accurate than those obtained with the original ReaxFF. We note that although our trained ReaxFF performs better than the original ReaxFF, it is not generally applicable to all carbohydrates, which require further parametrization. We also find that the absence of explicit water molecules in the training sets may lead to inaccurate descriptions of water-water interactions around the glucose, implicating that it is necessary to optimize the water ReaxFF parameters together with the target molecule. The improved ReaxFF makes it possible to explore interesting biological processes involving glucose more accurately and efficiently.

From genes to modules, from cells to ecosystems.

Twenty years ago, molecular biology transitioned from predominantly studying genes as isolated elements to viewing them as part of complex modules, giving rise to the field of systems biology. This transition was made possible by technological advances that allowed to simultaneously measure the expression levels of thousands of genes in a single experiment and drove a shift toward analyses identifying gene sets, modules, and pathways involved in a biological process of interest. Today we are excitingly facing a similar turning point in cell biology, where single-cell technologies have enabled us to approach cells as cellular modules.

Universal prediction of cell-cycle position using transfer learning

BackgroundThe cell cycle is a highly conserved, continuous process which controls faithful replication and division of cells. Single-cell technologies have enabled increasingly precise measurements of the cell cycle both as a biological process of interest and as a possible confounding factor. Despite its importance and conservation, there is no universally applicable approach to infer position in the cell cycle with high-resolution from single-cell RNA-seq data.ResultsHere, we present tricycle, an R/Bioconductor package, to address this challenge by leveraging key features of the biology of the cell cycle, the mathematical properties of principal component analysis of periodic functions, and the use of transfer learning. We estimate a cell-cycle embedding using a fixed reference dataset and project new data into this reference embedding, an approach that overcomes key limitations of learning a dataset-dependent embedding. Tricycle then predicts a cell-specific position in the cell cycle based on the data projection. The accuracy of tricycle compares favorably to gold-standard experimental assays, which generally require specialized measurements in specifically constructed in vitro systems. Using internal controls which are available for any dataset, we show that tricycle predictions generalize to datasets with multiple cell types, across tissues, species, and even sequencing assays.ConclusionsTricycle generalizes across datasets and is highly scalable and applicable to atlas-level single-cell RNA-seq data.

Spatially explicit models for decision‐making in animal conservation and restoration

Models are useful tools for understanding and predicting ecological patterns and processes. Under ongoing climate and biodiversity change, they can greatly facilitate decision‐making in conservation and restoration and help designing adequate management strategies for an uncertain future. Here, we review the use of spatially explicit models for decision support and to identify key gaps in current modelling in conservation and restoration. Of 650 reviewed publications, 217 publications had a clear management application and were included in our quantitative analyses. Overall, modelling studies were biased towards static models (79%), towards the species and population level (80%) and towards conservation (rather than restoration) applications (71%). Correlative niche models were the most widely used model type. Dynamic models as well as the gene‐to‐individual level and the community‐to‐ecosystem level were underrepresented, and explicit cost optimisation approaches were only used in 10% of the studies. We present a new model typology for selecting models for animal conservation and restoration, characterising model types according to organisational levels, biological processes of interest and desired management applications. This typology will help to more closely link models to management goals. Additionally, future efforts need to overcome important challenges related to data integration, model integration and decision‐making. We conclude with five key recommendations, suggesting that wider usage of spatially explicit models for decision support can be achieved by 1) developing a toolbox with multiple, easier‐to‐use methods, 2) improving calibration and validation of dynamic modelling approaches and 3) developing best‐practise guidelines for applying these models. Further, more robust decision‐making can be achieved by 4) combining multiple modelling approaches to assess uncertainty, and 5) placing models at the core of adaptive management. These efforts must be accompanied by long‐term funding for modelling and monitoring, and improved communication between research and practise to ensure optimal conservation and restoration outcomes.

NEM-Tar: A Probabilistic Graphical Model for Cancer Regulatory Network Inference and Prioritization of Potential Therapeutic Targets From Multi-Omics Data.

Targeted therapy has been widely adopted as an effective treatment strategy to battle against cancer. However, cancers are not single disease entities, but comprising multiple molecularly distinct subtypes, and the heterogeneity nature prevents precise selection of patients for optimized therapy. Dissecting cancer subtype-specific signaling pathways is crucial to pinpointing dysregulated genes for the prioritization of novel therapeutic targets. Nested effects models (NEMs) are a group of graphical models that encode subset relations between observed downstream effects under perturbations to upstream signaling genes, providing a prototype for mapping the inner workings of the cell. In this study, we developed NEM-Tar, which extends the original NEMs to predict drug targets by incorporating causal information of (epi)genetic aberrations for signaling pathway inference. An information theory-based score, weighted information gain (WIG), was proposed to assess the impact of signaling genes on a specific downstream biological process of interest. Subsequently, we conducted simulation studies to compare three inference methods and found that the greedy hill-climbing algorithm demonstrated the highest accuracy and robustness to noise. Furthermore, two case studies were conducted using multi-omics data for colorectal cancer (CRC) and gastric cancer (GC) in the TCGA database. Using NEM-Tar, we inferred signaling networks driving the poor-prognosis subtypes of CRC and GC, respectively. Our model prioritized not only potential individual drug targets such as HER2, for which FDA-approved inhibitors are available but also the combinations of multiple targets potentially useful for the design of combination therapies.

A Review on Theory and Modelling of Nanomechanical Sensors for Biological Applications

Over the last decades, nanomechanical sensors have received significant attention from the scientific community, as they find plenty of applications in many different research fields, ranging from fundamental physics to clinical diagnosis. Regarding biological applications, nanomechanical sensors have been used for characterizing biological entities, for detecting their presence, and for characterizing the forces and motion associated with fundamental biological processes, among many others. Thanks to the continuous advancement of micro- and nano-fabrication techniques, nanomechanical sensors have rapidly evolved towards more sensitive devices. At the same time, researchers have extensively worked on the development of theoretical models that enable one to access more, and more precise, information about the biological entities and/or biological processes of interest. This paper reviews the main theoretical models applied in this field. We first focus on the static mode, and then continue on to the dynamic one. Then, we center the attention on the theoretical models used when nanomechanical sensors are applied in liquids, the natural environment of biology. Theory is essential to properly unravel the nanomechanical sensors signals, as well as to optimize their designs. It provides access to the basic principles that govern nanomechanical sensors applications, along with their intrinsic capabilities, sensitivities, and fundamental limits of detection.

MasterPATH: network analysis of functional genomics screening data

BackgroundFunctional genomics employs several experimental approaches to investigate gene functions. High-throughput techniques, such as loss-of-function screening and transcriptome profiling, allow to identify lists of genes potentially involved in biological processes of interest (so called hit list). Several computational methods exist to analyze and interpret such lists, the most widespread of which aim either at investigating of significantly enriched biological processes, or at extracting significantly represented subnetworks.ResultsHere we propose a novel network analysis method and corresponding computational software that employs the shortest path approach and centrality measure to discover members of molecular pathways leading to the studied phenotype, based on functional genomics screening data. The method works on integrated interactomes that consist of both directed and undirected networks – HIPPIE, SIGNOR, SignaLink, TFactS, KEGG, TransmiR, miRTarBase. The method finds nodes and short simple paths with significant high centrality in subnetworks induced by the hit genes and by so-called final implementers – the genes that are involved in molecular events responsible for final phenotypic realization of the biological processes of interest. We present the application of the method to the data from miRNA loss-of-function screen and transcriptome profiling of terminal human muscle differentiation process and to the gene loss-of-function screen exploring the genes that regulates human oxidative DNA damage recognition. The analysis highlighted the possible role of several known myogenesis regulatory miRNAs (miR-1, miR-125b, miR-216a) and their targets (AR, NR3C1, ARRB1, ITSN1, VAV3, TDGF1), as well as linked two major regulatory molecules of skeletal myogenesis, MYOD and SMAD3, to their previously known muscle-related targets (TGFB1, CDC42, CTCF) and also to a number of proteins such as C-KIT that have not been previously studied in the context of muscle differentiation. The analysis also showed the role of the interaction between H3 and SETDB1 proteins for oxidative DNA damage recognition.ConclusionThe current work provides a systematic methodology to discover members of molecular pathways in integrated networks using functional genomics screening data. It also offers a valuable instrument to explain the appearance of a set of genes, previously not associated with the process of interest, in the hit list of each particular functional genomics screening.

Human Ovarian Granulosa Cells Isolated during an IVF Procedure Exhibit Differential Expression of Genes Regulating Cell Division and Mitotic Spindle Formation

Granulosa cells (GCs) are a population of somatic cells whose role after ovulation is progesterone production. GCs were collected from patients undergoing controlled ovarian stimulation during an in vitro fertilization procedure, and they were maintained for 1, 7, 15, and 30 days of in vitro primary culture before collection for further gene expression analysis. A study of genes involved in the biological processes of interest was carried out using expression microarrays. To validate the obtained results, Reverse Transcription quantitative Polymerase Chain Reaction (RT-qPCR) was performed. The direction of changes in the expression of the selected genes was confirmed in most of the examples. Six ontological groups (“cell cycle arrest”, “cell cycle process”, “mitotic spindle organization”, “mitotic spindle assembly checkpoint”, “mitotic spindle assembly”, and “mitotic spindle checkpoint”) were analyzed in this study. The results of the microarrays obtained by us allowed us to identify two groups of genes whose expressions were the most upregulated (FAM64A, ANLN, TOP2A, CTGF, CEP55, BIRC5, PRC1, DLGAP5, GAS6, and NDRG1) and the most downregulated (EREG, PID1, INHA, RHOU, CXCL8, SEPT6, EPGN, RDX, WNT5A, and EZH2) during the culture. The cellular ultrastructure showed the presence of structures characteristic of mitotic cell division: a centrosome surrounded by a pericentric matrix, a microtubule system, and a mitotic spindle connected to chromosomes. The main goal of the study was to identify the genes involved in mitotic division and to identify the cellular ultrastructure of GCs in a long-term in vitro culture. All of the genes in these groups were subjected to downstream analysis, and their function and relation to the ovarian environment are discussed. The obtained results suggest that long-term in vitro cultivation of GCs may lead to their differentiation toward another cell type, including cells with cancer-like characteristics.

Parameter inference in dynamical systems with co-dimension 1 bifurcations.

Dynamical systems with intricate behaviour are all-pervasive in biology. Many of the most interesting biological processes indicate the presence of bifurcations, i.e. phenomena where a small change in a system parameter causes qualitatively different behaviour. Bifurcation theory has become a rich field of research in its own right and evaluating the bifurcation behaviour of a given dynamical system can be challenging. An even greater challenge, however, is to learn the bifurcation structure of dynamical systems from data, where the precise model structure is not known. Here, we study one aspects of this problem: the practical implications that the presence of bifurcations has on our ability to infer model parameters and initial conditions from empirical data; we focus on the canonical co-dimension 1 bifurcations and provide a comprehensive analysis of how dynamics, and our ability to infer kinetic parameters are linked. The picture thus emerging is surprisingly nuanced and suggests that identification of the qualitative dynamics—the bifurcation diagram—should precede any attempt at inferring kinetic parameters.

Self-assembling manifolds in single-cell RNA sequencing data.

Single-cell RNA sequencing has spurred the development of computational methods that enable researchers to classify cell types, delineate developmental trajectories, and measure molecular responses to external perturbations. Many of these technologies rely on their ability to detect genes whose cell-to-cell variations arise from the biological processes of interest rather than transcriptional or technical noise. However, for datasets in which the biologically relevant differences between cells are subtle, identifying these genes is challenging. We present the self-assembling manifold (SAM) algorithm, an iterative soft feature selection strategy to quantify gene relevance and improve dimensionality reduction. We demonstrate its advantages over other state-of-the-art methods with experimental validation in identifying novel stem cell populations of Schistosoma mansoni, a prevalent parasite that infects hundreds of millions of people. Extending our analysis to a total of 56 datasets, we show that SAM is generalizable and consistently outperforms other methods in a variety of biological and quantitative benchmarks.

4-Hexylresorcinol a New Molecule for Cosmetic Application

4-hexylresorcinol is the most studied and well known alkylresorcinol derivative, known for its pharmacological properties as anesthetic, antiseptic and anthelmintic. It can be both applied topically in creams and included as an active ingredient in throat lozenges. Its interest as a cosmetic ingredient is more recent and is increasing owing to its anti-oxidant, anti-glycation and melanogenic inhibitory properties and that it is safe to use. All those elements make 4-hexylresorcinol a cosmetic ingredient of choice for skin care products. This short review summarizes its mechanism of action and some evidences of its efficacy in certain biological processes of interest in skincare.

MCRiceRepGP: a framework for the identification of genes associated with sexual reproduction in rice.

Rice is an important cereal crop, being a staple food for over half of the world's population, and sexual reproduction resulting in grain formation underpins global food security. However, despite considerable research efforts, many of the genes, especially long intergenic non-coding RNA (lincRNA) genes, involved in sexual reproduction in rice remain uncharacterized. With an increasing number of public resources becoming available, information from different sources can be combined to perform gene functional annotation. We report the development of MCRiceRepGP, a machine learning framework which integrates heterogeneous evidence and employs multicriteria decision analysis and machine learning to predict coding and lincRNA genes involved in sexual reproduction in rice. The rice genome was reannotated using deep-sequencing transcriptomic data from reproduction-associated tissue/cell types identifying previously unannotated putative protein-coding genes and lincRNAs. MCRiceRepGP was used for genome-wide discovery of sexual reproduction associated coding and lincRNA genes. The protein-coding and lincRNA genes identified have distinct expression profiles, with a large proportion of lincRNAs reaching maximum expression levels in the sperm cells. Some of the genes are potentially linked to male- and female-specific fertility and heat stress tolerance during the reproductive stage. MCRiceRepGP can be used in combination with other genome-wide studies, such as genome-wide association studies, giving greater confidence that the genes identified are associated with the biological process of interest. As more data, especially about mutant plant phenotypes, become available, the power of MCRiceRepGP will grow, providing researchers with a tool to identify candidate genes for future experiments. MCRiceRepGP is available as a web application (http://mcgplannotator.com/MCRiceRepGP/).

Breaking the Rules

We have known for decades that proteins consist of tens to thousands of amino acids strung together, but it has proven difficult to predict the function of a given protein based on its amino acid sequence. This difficulty rests on the overarching principle that the 3D structure of a protein determines its functionality by specifying with which other proteins and molecules it can interact. Conventional structural biology approaches, like X-ray crystallography, have enabled us to figure out the shapes and interactions of many rigidly folded proteins or their constituent domains. However, sometimes invisible to crystallographers are so-called low-complexity and intrinsically disordered regions of proteins, which often do not form stable and predictable 3D structures in physiological conditions or in the absence of their binding partners. Despite this conformational heterogeneity and flexibility, intrinsically disordered proteins (IDPs) and unstructured stretches of amino acids participate in many essential processes and provide new paradigms for protein-protein interactions. IDPs are generally thought to either conditionally establish secondary structure elements upon binding to their target or remain unfolded and participate in low-affinity, high-avidity dynamic interactions with other proteins. The former case can be exemplified by the formation of an α-helical region in the disordered domain of the transcription factor CREB upon binding to its transcriptional coactivator, CBP/p300 (Sugase et al., 2007Sugase K. Dyson H.J. Wright P.E. Mechanism of coupled folding and binding of an intrinsically disordered protein.Nature. 2007; 447: 1021-1025Crossref PubMed Scopus (852) Google Scholar). Transient and multivalent interactions are perhaps best illustrated by the core selectivity filter of nuclear pore complexes, where proteins with low-complexity sequence regions dynamically bind and unbind at many sites on the surface of nuclear import or export receptors, which is thought to drive fast directional transport. Indeed, intrinsically disordered regions of proteins mediate many of the multivalent interactions that form the basis of phase-separated liquid droplets, which are now widely appreciated in myriad cellular processes like RNA processing (Schmidt and Gorlich, 2016Schmidt H.B. Gorlich D. Transport Selectivity of Nuclear Pores, Phase Separation, and Membraneless Organelles.Trends Biochem. Sci. 2016; 41: 46-61Abstract Full Text Full Text PDF PubMed Scopus (257) Google Scholar). Technological innovation in protein structural biology, biophysics, molecular dynamics simulations, and single-molecule studies have enabled many advances in our understanding of how proteins or protein segments without defined structure uniquely contribute to important cellular processes. Several recent studies are challenging the long-held paradigms of how unstructured protein regions and peptides interact with their cognate partners to achieve specificity, affinity, and mechanostability—and to do what canonical interactions between globular domains simply cannot do. A recent study published in Nature uncovered an extremely high-affinity interaction between two IDPs, the linker histone H1 and its nuclear chaperone prothymosin-α. Surprisingly, the authors did not detect the formation of any transient secondary structure elements upon binding of these two unstructured proteins, despite the formation of a complex with incredibly strong binding and picomolar affinity. A variety of structural, biochemical, and modeling techniques led to the conclusion that the two proteins form an extremely dynamic complex wherein long-range electrostatic interactions lead to rapid interconversion between countless different combinations of oppositely charged residues and patches (Borgia et al., 2018Borgia A. Borgia M.B. Bugge K. Kissling V.M. Heidarsson P.O. Fernandes C.B. Sottini A. Soranno A. Bulhozer K.J. Nettels D. et al.Extreme disorder in an ultrahigh-affinity protein complex.Nature. 2018; 555: 61-66Crossref PubMed Scopus (369) Google Scholar). This striking new example expands the known universe of protein-protein interaction determinants and may defy conventional thinking about how protein structure determines function. While low-complexity protein domains have been implicated in liquid-liquid phase separation and the formation of membraneless organelles, like stress granules and P-bodies in eukaryotic cells, the precise molecular determinants governing these condensates have, in most cases, remained obscure. In a study published in Science, scientists determined the atomic structures of segments from several low-complexity domains implicated in phase separation and hydrogel formation and found that the short peptides consistently formed kinked beta sheet structures. While highly stable beta strands are core features of amyloid-forming proteins, the kinked nature of these LARKS (low-complexity, aromatic-rich, kinked segments) prevent cross-beta strand interactions among amino acid side chains that give amyloid fibrils their characteristic strength and irreversible nature. These molecular determinants lead to reversible protein meshworks among LARKS-containing proteins, forming the basis for phase-separated droplets. Strikingly, the authors found that hundreds of human proteins have the sequence propensity to form similar kinked beta strands and therefore may take part in multivalent interaction networks and phase separation (Hughes et al., 2018Hughes M.P. Sawaya M.R. Boyer D.R. Goldschmidt L. Rodriguez J.A. Cascio D. Chong L. Gonen T. Eisenberg D.S. Atomic structures of low-complexity protein segments reveal kinked B sheets that assemble networks.Science. 2018; 359: 698-701Crossref PubMed Scopus (242) Google Scholar). With more and more cellular processes relying on phase separation continually being uncovered, this list of proteins may lead to a treasure trove of new biology. In another extreme example of protein acrobatics, a paper recently published in Science showed that an adhesin protein from Staphylococcus species defies canonical interaction paradigms to establish the strongest known non-covalent protein association with its target—a staggering 10-fold higher mechanostability than biotin and streptavidin. The strength of this interaction with a segment of human fibrinogen is important for pathogenic staphylococci to form recalcitrant biofilms. The authors found that this remarkable stability is largely independent of amino acid side chains on the fibrinogen peptide, since mutating many residues and even replacing all the amino acids with glycine in a simulation did not impact the stability of the complex once it was bound. By bending the fibrinogen peptide into an elongated, corkscrew-like coiled shape that is buried within the adhesin protein and locked into place, the adhesin can achieve multivalent hydrogen bonding with the peptide backbone in a direction perpendicular to the shear force, thereby dissipating the mechanical stress over many individually weak bonds (Milles et al., 2018Milles L.F. Schulten K. Gaub H.E. Bernardi R.C. Molecular mechanism of extreme mechanostability in a pathogen adhesion.Science. 2018; 359: 1527-1533Crossref PubMed Scopus (124) Google Scholar). The evolutionary pressures facing pathogenic bacteria could have favored the development of this unconventional mechanism for extreme mechanostability. In addition to these new examples of proteins breaking the traditional rules, scientists are also finding IDPs and low-complexity domains popping up in all kinds of interesting biological processes. From keeping tardigrades alive in extreme dessication (Boothby et al., 2017Boothby T.C. Tapia H. Brozena A.H. Piszkiewicz S. Smith A.E. Giovannini I. Rebecchi L. Pielak G.J. Koshland D. Goldstein B. Tardigrades Use Intrinsically Disordered Proteins to Survive Desiccation.Mol. Cell. 2017; 65: 975-984Abstract Full Text Full Text PDF PubMed Scopus (183) Google Scholar) to the formation of tooth enamel (Wald et al., 2017Wald T. Spoutil F. Osickova A. Prochazkova M. Benada O. Kasparek P. Bumba L. Klein O.D. Sedlacek R. Sebo P. et al.Intrinsically disordered proteins drive enamel formation via an evolutionarily conserved self-assembly motif.Proc. Natl. Acad. Sci. USA. 2017; 114: E1641-E1650Crossref PubMed Scopus (38) Google Scholar), it is clear that biology is not constrained by neatly folded protein domains. At the same time, protein engineers are closer than ever to being able to design IDPs to assemble in programmable ways (Simon et al., 2017Simon J.R. Carroll N.J. Rubinstein M. Chilkoti A. López G.P. Programming molecular self-assembly of intrinsically disordered proteins containing sequences of low complexity.Nat. Chem. 2017; 9: 509-515Crossref PubMed Scopus (172) Google Scholar). As many labs around the world take a deep dive into the biophysics of protein-protein interactions with new tools at their disposal, who knows what they might find (or create) next? After all, rules are made to be broken.

Discovery of a fluorescent probe with HDAC6 selective inhibition

There is increasing interest in discovering HDAC6 selective inhibitors as chemical probes to elucidate the biological functions of HDAC6 and ultimately as new therapeutic agents. Small-molecular fluorescent probes are widely used to detect target protein location and function, identify protein complex composition in biological processes of interest. In the present study, structural modification of the previously reported compound 4MS leads to two novel fluorescent HDAC inhibitors, 6a and 6b. Determination of IC50 values against the panel of Zn2+ dependent HDACs (HDAC1-11) reveals that 6b is a HDAC6 selective inhibitor, which can induce hyperacetylation of tubulin but not histone H4. Importantly, fluorescent and immunofluorescent analyses of cells treated with the proteasome inhibitor MG132 demonstrates that 6b can selectively target and image HDAC6 within the inclusion body, the aggresome. These results identify 6b not only as a HDAC6 selective inhibitor but also as a fluorescent probe for imaging HDAC6 and investigating the roles of HDAC6 in various physiological and pathological contexts.

Boolean regulatory network reconstruction using literature based knowledge with a genetic algorithm optimization method.

BackgroundPrior knowledge networks (PKNs) provide a framework for the development of computational biological models, including Boolean models of regulatory networks which are the focus of this work. PKNs are created by a painstaking process of literature curation, and generally describe all relevant regulatory interactions identified using a variety of experimental conditions and systems, such as specific cell types or tissues. Certain of these regulatory interactions may not occur in all biological contexts of interest, and their presence may dramatically change the dynamical behaviour of the resulting computational model, hindering the elucidation of the underlying mechanisms and reducing the usefulness of model predictions. Methods are therefore required to generate optimized contextual network models from generic PKNs.ResultsWe developed a new approach to generate and optimize Boolean networks, based on a given PKN. Using a genetic algorithm, a model network is built as a sub-network of the PKN and trained against experimental data to reproduce the experimentally observed behaviour in terms of attractors and the transitions that occur between them under specific perturbations. The resulting model network is therefore contextualized to the experimental conditions and constitutes a dynamical Boolean model closer to the observed biological process used to train the model than the original PKN. Such a model can then be interrogated to simulate response under perturbation, to detect stable states and their properties, to get insights into the underlying mechanisms and to generate new testable hypotheses.ConclusionsGeneric PKNs attempt to synthesize knowledge of all interactions occurring in a biological process of interest, irrespective of the specific biological context. This limits their usefulness as a basis for the development of context-specific, predictive dynamical Boolean models. The optimization method presented in this article produces specific, contextualized models from generic PKNs. These contextualized models have improved utility for hypothesis generation and experimental design. The general applicability of this methodological approach makes it suitable for a variety of biological systems and of general interest for biological and medical research. Our method was implemented in the software optimusqual, available online at http://www.vital-it.ch/software/optimusqual/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1287-z) contains supplementary material, which is available to authorized users.

Candidate gene prioritization with Endeavour.

Genomic studies and high-throughput experiments often produce large lists of candidate genes among which only a small fraction are truly relevant to the disease, phenotype or biological process of interest. Gene prioritization tackles this problem by ranking candidate genes by profiling candidates across multiple genomic data sources and integrating this heterogeneous information into a global ranking. We describe an extended version of our gene prioritization method, Endeavour, now available for six species and integrating 75 data sources. The performance (Area Under the Curve) of Endeavour on cross-validation benchmarks using ‘gold standard’ gene sets varies from 88% (for human phenotypes) to 95% (for worm gene function). In addition, we have also validated our approach using a time-stamped benchmark derived from the Human Phenotype Ontology, which provides a setting close to prospective validation. With this benchmark, using 3854 novel gene–phenotype associations, we observe a performance of 82%. Altogether, our results indicate that this extended version of Endeavour efficiently prioritizes candidate genes. The Endeavour web server is freely available at https://endeavour.esat.kuleuven.be/.

Computational studies of transport in ion channels using metadynamics

Molecular dynamics simulations have played a fundamental role in numerous fields of science by providing insights into the structure and dynamics of complex systems at the atomistic level. However, exhaustive sampling by standard molecular dynamics is in most cases computationally prohibitive, and the time scales accessible remain significantly shorter than many biological processes of interest. In particular, in the study of ion channels, realistic models to describe permeation and gating require accounting for large numbers of particles and accurate interaction potentials, which severely limits the length of the simulations. To overcome such limitations, several advanced methods have been proposed among which is metadynamics. In this algorithm, an external bias potential to accelerate sampling along selected collective variables is introduced. This bias potential discourages visiting regions of the configurational space already explored. In addition, the bias potential provides an estimate of the free energy as a function of the collective variables chosen once the simulation has converged. In this review, recent contributions of metadynamics to the field of ion channels are discussed, including how metadynamics has been used to search for transition states, predict permeation pathways, treat conformational flexibility that underlies the coupling between gating and permeation, or compute free energy of permeation profiles. This article is part of a Special Issue entitled: Membrane Proteins edited by J.C. Gumbart and Sergei Noskov.

Large scale gene regulatory network inference with a multi-level strategy.

Transcriptional regulation is a basis of many crucial molecular processes and an accurate inference of the gene regulatory network is a helpful and essential task to understand cell functions and gain insights into biological processes of interest in systems biology. Inspired by the Dialogue for Reverse Engineering Assessments and Methods (DREAM) projects, many excellent gene regulatory network inference algorithms have been proposed. However, it is still a challenging problem to infer a gene regulatory network from gene expression data on a large scale. In this paper, we propose a gene regulatory network inference method based on a multi-level strategy (GENIMS), which can give results that are more accurate and robust than the state-of-the-art methods. The proposed method mainly consists of three levels, which are an original feature selection step based on guided regularized random forest, normalization of individual feature selection and the final refinement step according to the topological property of the gene regulatory network. To prove the accuracy and robustness of our method, we compare our method with the state-of-the-art methods on the DREAM4 and DREAM5 benchmark networks and the results indicate that the proposed method can significantly improve the performance of gene regulatory network inference. Additionally, we also discuss the influence of the selection of different parameters in our method.

Morphology and Gene Expression Screening with Morpholinos in Zebrafish Embryos.

High-throughput screening with a loss-of-function strategy is a logical and efficient way to identify novel genes involved in biological processes of interest. In zebrafish, morpholinos have been developed as a convenient tool to knock down gene expression. Here, we describe procedures for systematic screening using morpholinos in zebrafish to identify novel deubiquitylases involved in convergent extension during gastrulation. In this example, we examine candidates based on embryonic morphology and molecular signals of whole mount in situ hybridization assay.