Genomic basis of fishing-associated selection varies with population density.

Fisheries induce one of the strongest anthropogenic selective pressures on natural populations, but the genetic effects of fishing remain unclear. Crucially, we lack knowledge of how capture-associated selection and its interaction with reductions in population density caused by fishing can potentially shift which genes are under selection. Using experimental fish reared at two densities and repeatedly harvested by simulated trawling, we show consistent phenotypic selection on growth, metabolism, and social behavior regardless of density. However, the specific genes under selection-mainly related to brain function and neurogenesis-varied with the population density. This interaction between direct fishing selection and density could fundamentally alter the genomic responses to harvest. The evolutionary consequences of fishing are therefore likely context dependent, possibly varying as exploited populations decline. These results highlight the need to consider environmental factors when predicting effects of human-induced selection and evolution.

The paradise fish, an advanced animal model for behavioral genetics and evolutionary developmental biology.

Paradise fish (Macropodus opercularis) is an air-breathing freshwater fish species with a signature labyrinth organ capable of extracting oxygen from the air that helps these fish to survive in hypoxic environments. The appearance of this evolutionary innovation in anabantoids resulted in a rewired circulatory system, but also in the emergence of species-specific behaviors, such as territorial display, courtship and parental care in the case of the paradise fish. Early zoologists were intrigued by the structure and function of the labyrinth apparatus and a series of detailed descriptive histological studies at the beginning of the 20th century revealed the ontogenesis and function of this specialized system. A few decades later, these fish became the subject of numerous ethological studies, and detailed ethograms of their behavior were constructed. These latter studies also demonstrated a strong genetic component underlying their behavior, but due to lack of adequate molecular tools, the fine genetic dissection of the behavior was not possible at the time. The technological breakthroughs that transformed developmental biology and behavioral genetics in the past decades, however, give us now a unique opportunity to revisit these old questions. Building on the classic descriptive studies, the new methodologies will allow us to follow the development of the labyrinth apparatus at a cellular resolution, reveal the genes involved in this process and also the genetic architecture behind the complex behaviors that we can observe in this species.

Molecular Dynamics Simulations and Diversity Selection by Extended Continuous Similarity Indices.

Molecular dynamics (MD) is a core methodology of molecular modeling and computational design for the study of the dynamics and temporal evolution of molecular systems. MD simulations have particularly benefited from the rapid increase of computational power that has characterized the past decades of computational chemical research, being the first method to be successfully migrated to the GPU infrastructure. While new-generation MD software is capable of delivering simulations on an ever-increasing scale, relatively less effort is invested in developing postprocessing methods that can keep up with the quickly expanding volumes of data that are being generated. Here, we introduce a new idea for sampling frames from large MD trajectories, based on the recently introduced framework of extended similarity indices. Our approach presents a new, linearly scaling alternative to the traditional approach of applying a clustering algorithm that usually scales as a quadratic function of the number of frames. When showcasing its usage on case studies with different system sizes and simulation lengths, we have registered speedups of up to 2 orders of magnitude, as compared to traditional clustering algorithms. The conformational diversity of the selected frames is also noticeably higher, which is a further advantage for certain applications, such as the selection of structural ensembles for ligand docking. The method is available open-source at https://github.com/ramirandaq/MultipleComparisons.

Extended continuous similarity indices: theory and application for QSAR descriptor selection.

Extended (or n-ary) similarity indices have been recently proposed to extend the comparative analysis of binary strings. Going beyond the traditional notion of pairwise comparisons, these novel indices allow comparing any number of objects at the same time. This results in a remarkable efficiency gain with respect to other approaches, since now we can compare N molecules in O(N) instead of the common quadratic O(N2) timescale. This favorable scaling has motivated the application of these indices to diversity selection, clustering, phylogenetic analysis, chemical space visualization, and post-processing of molecular dynamics simulations. However, the current formulation of the n-ary indices is limited to vectors with binary or categorical inputs. Here, we present the further generalization of this formalism so it can be applied to numerical data, i.e. to vectors with continuous components. We discuss several ways to achieve this extension and present their analytical properties. As a practical example, we apply this formalism to the problem of feature selection in QSAR and prove that the extended continuous similarity indices provide a convenient way to discern between several sets of descriptors.

Machine learning models for classification tasks related to drug safety.

In this review, we outline the current trends in the field of machine learning-driven classification studies related to ADME (absorption, distribution, metabolism and excretion) and toxicity endpoints from the past six years (2015-2021). The study focuses only on classification models with large datasets (i.e. more than a thousand compounds). A comprehensive literature search and meta-analysis was carried out for nine different targets: hERG-mediated cardiotoxicity, blood-brain barrier penetration, permeability glycoprotein (P-gp) substrate/inhibitor, cytochrome P450 enzyme family, acute oral toxicity, mutagenicity, carcinogenicity, respiratory toxicity and irritation/corrosion. The comparison of the best classification models was targeted to reveal the differences between machine learning algorithms and modeling types, endpoint-specific performances, dataset sizes and the different validation protocols. Based on the evaluation of the data, we can say that tree-based algorithms are (still) dominating the field, with consensus modeling being an increasing trend in drug safety predictions. Although one can already find classification models with great performances to hERG-mediated cardiotoxicity and the isoenzymes of the cytochrome P450 enzyme family, these targets are still central to ADMET-related research efforts.

Effect of Dataset Size and Train/Test Split Ratios in QSAR/QSPR Multiclass Classification.

Applied datasets can vary from a few hundred to thousands of samples in typical quantitative structure-activity/property (QSAR/QSPR) relationships and classification. However, the size of the datasets and the train/test split ratios can greatly affect the outcome of the models, and thus the classification performance itself. We compared several combinations of dataset sizes and split ratios with five different machine learning algorithms to find the differences or similarities and to select the best parameter settings in nonbinary (multiclass) classification. It is also known that the models are ranked differently according to the performance merit(s) used. Here, 25 performance parameters were calculated for each model, then factorial ANOVA was applied to compare the results. The results clearly show the differences not just between the applied machine learning algorithms but also between the dataset sizes and to a lesser extent the train/test split ratios. The XGBoost algorithm could outperform the others, even in multiclass modeling. The performance parameters reacted differently to the change of the sample set size; some of them were much more sensitive to this factor than the others. Moreover, significant differences could be detected between train/test split ratios as well, exerting a great effect on the test validation of our models.

Large-scale evaluation of cytochrome P450 2C9 mediated drug interaction potential with machine learning-based consensus modeling.

Cytochrome P450 (CYP) enzymes play an important role in the metabolism of xenobiotics. Since they are connected to drug interactions, screening for potential inhibitors is of utmost importance in drug discovery settings. Our study provides an extensive classification model for P450-drug interactions with one of the most prominent members, the 2C9 isoenzyme. Our model involved the largest set of 45,000 molecules ever used for developing prediction models. The models are based on three different types of descriptors, (a) typical one, two and three dimensional molecular descriptors, (b) chemical and pharmacophore fingerprints and (c) interaction fingerprints with docking scores. Two machine learning algorithms, the boosted tree and the multilayer feedforward of resilient backpropagation network were used and compared based on their performances. The models were validated both internally and using external validation sets. The results showed that the consensus voting technique with custom probability thresholds could provide promising results even in large-scale cases without any restrictions on the applicability domain. Our best model was capable to predict the 2C9 inhibitory activity with the area under the receiver operating characteristic curve (AUC) of 0.85 and 0.84 for the internal and the external test sets, respectively. The chemical space covered with the largest available dataset has reached its limit encompassing publicly available bioactivity data for the 2C9 isoenzyme.

Reagent-free total protein quantification of intact extracellular vesicles by attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy.

Extracellular vesicles (EVs) are lipid bilayer-bounded particles that are actively synthesized and released by cells. The main components of EVs are lipids, proteins, and nucleic acids and their composition is characteristic to their type and origin, and it reveals the physiological and pathological conditions of the parent cells. The concentration and protein composition of EVs closely relate to their functions; therefore, total protein determination can assist in EV-based diagnostics and disease prognosis. Here, we present a simple, reagent-free method based on attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy to quantify the protein content of EV samples without any further sample preparation. After calibration with bovine serum albumin, the protein concentration of red blood cell-derived EVs (REVs) were investigated by ATR-FTIR spectroscopy. The integrated area of the amide I band was calculated from the IR spectra of REVs, which was proportional to the protein quantity in the sample' regardless of its secondary structure. A spike test and a dilution test were performed to determine the ability to use ATR-FTIR spectroscopy for protein quantification in EV samples, which resulted in linearity with R2 values as high as 0.992 over the concentration range of 0.08 to 1 mg/mL. Additionally, multivariate calibration with the partial least squares (PLS) regression method was carried out on the bovine serum albumin and EV spectra. R2 values were 0.94 for the calibration and 0.91 for the validation set. The results indicate that ATR-FTIR measurements provide a reliable method for reagent-free protein quantification of EVs. Graphical abstract.

Multi-Level Comparison of Machine Learning Classifiers and Their Performance Metrics.

Machine learning classification algorithms are widely used for the prediction and classification of the different properties of molecules such as toxicity or biological activity. the prediction of toxic vs. non-toxic molecules is important due to testing on living animals, which has ethical and cost drawbacks as well. The quality of classification models can be determined with several performance parameters. which often give conflicting results. In this study, we performed a multi-level comparison with the use of different performance metrics and machine learning classification methods. Well-established and standardized protocols for the machine learning tasks were used in each case. The comparison was applied to three datasets (acute and aquatic toxicities) and the robust, yet sensitive, sum of ranking differences (SRD) and analysis of variance (ANOVA) were applied for evaluation. The effect of dataset composition (balanced vs. imbalanced) and 2-class vs. multiclass classification scenarios was also studied. Most of the performance metrics are sensitive to dataset composition, especially in 2-class classification problems. The optimal machine learning algorithm also depends significantly on the composition of the dataset.

Comparison of Data Fusion Methods as Consensus Scores for Ensemble Docking.

Ensemble docking is a widely applied concept in structure-based virtual screening-to at least partly account for protein flexibility-usually granting a significant performance gain at a modest cost of speed. From the individual, single-structure docking scores, a consensus score needs to be produced by data fusion: this is usually done by taking the best docking score from the available pool (in most cases- and in this study as well-this is the minimum score). Nonetheless, there are a number of other fusion rules that can be applied. We report here the results of a detailed statistical comparison of seven fusion rules for ensemble docking, on five case studies of current drug targets, based on four performance metrics. Sevenfold cross-validation and variance analysis (ANOVA) allowed us to highlight the best fusion rules. The results are presented in bubble plots, to unite the four performance metrics into a single, comprehensive image. Notably, we suggest the use of the geometric and harmonic means as better alternatives to the generally applied minimum fusion rule.

Synthesis and Biochemical Evaluation of Lid-Open D-Amino Acid Oxidase Inhibitors.

Most of the known inhibitors of D-amino acid oxidase (DAAO) are small polar molecules recognized by the active site of the enzyme. More recently a new class of DAAO inhibitors has been disclosed that interacts with loop 218-224 at the top of the binding pocket. These compounds have a significantly larger size and more beneficial physicochemical properties than most reported DAAO inhibitors, however, their structure-activity relationship is poorly explored. Here we report the synthesis and evaluation of this type of DAAO inhibitors that open the lid over the active site of DAAO. In order to collect relevant SAR data we varied two distinct parts of the inhibitors. A systematic variation of the pendant aromatic substituents according to the Topliss scheme resulted in DAAO inhibitors with low nanomolar activity. The activity showed low sensitivity to the substituents investigated. The variation of the linker connecting the pendant aromatic moiety and the acidic headgroup revealed that the interactions of the linker with the enzyme were crucial for achieving significant inhibitory activity. Structures and activities were analyzed based on available X-ray structures of the complexes. Our findings might support the design of drug-like DAAO inhibitors with advantageous physicochemical properties and ADME profile.

Discovery of d-amino acid oxidase inhibitors based on virtual screening against the lid-open enzyme conformation.

d-Amino acid oxidase (DAAO) inhibitors are typically small polar compounds with often suboptimal pharmacokinetic properties. Features of the native binding site limit the operational freedom of further medicinal chemistry efforts. We therefore initiated a structure based virtual screening campaign based on the X-ray structures of DAAO complexes where larger ligands shifted the loop (lid opening) covering the native binding site. The virtual screening of our in-house collection followed by the in vitro test of the best ranked compounds led to the identification of a new scaffold with micromolar IC50. Subsequent SAR explorations enabled us to identify submicromolar inhibitors. Docking studies supported by in vitro activity measurements suggest that compounds bind to the active site with a salt-bridge characteristic to DAAO inhibitor binding. In addition, displacement of and interaction with the loop covering the active site contributes significantly to the activity of the most potent compounds.

Discovery of isatin and 1H-indazol-3-ol derivatives as d-amino acid oxidase (DAAO) inhibitors.

d-Amino acid oxidase (DAAO) is a potential target in the treatment of schizophrenia as its inhibition increases brain d-serine level and thus contributes to NMDA receptor activation. Inhibitors of DAAO were sought testing [6+5] type heterocycles and identified isatin derivatives as micromolar DAAO inhibitors. A pharmacophore and structure-activity relationship analysis of isatins and reported DAAO inhibitors led us to investigate 1H-indazol-3-ol derivatives and nanomolar inhibitors were identified. The series was further characterized by pKa and isothermal titration calorimetry measurements. Representative compounds exhibited beneficial properties in in vitro metabolic stability and PAMPA assays. 6-fluoro-1H-indazol-3-ol (37) significantly increased plasma d-serine level in an in vivo study on mice. These results show that the 1H-indazol-3-ol series represents a novel class of DAAO inhibitors with the potential to develop drug candidates.

How does school size affect tail beat frequency in turbulent water?

The energy savings experienced by fish swimming in a school have so far been investigated in an near-idealised experimental context including a relatively laminar water flow. The effects of explicitly turbulent flows and different group sizes are yet to be considered. Our repeated-measures study is a first step in addressing both of these issues: whether schooling is more energetically economical for fish when swimming in a quantified non-laminar flow and how this might be moderated by group size. We measured tail beat frequency (tbf) in sea bass swimming in a group of 3 or 6, or singly. Video data enabled us to approximately track the movements of the fish during the experiments and in turn ascertain the water flow rates and turbulence levels experienced for each target individual. Although the fish exhibited reductions in tbf during group swimming, which may indicate some energy savings, these savings appear to be attenuated, presumably due to the water turbulence and the movement of the fish relative to each other. Surprisingly, tbf was unrelated to flow rate when the fish were swimming singly or in a group of three, and decreased with increasing flow rates when swimming in a group of six. However, the fish increased tbf in greater turbulence at all group sizes. Our study demonstrates that under the challenging and complex conditions of turbulent flow and short-term changes in school structure, group size can moderate the influences of water flow on a fish's swimming kinematics, and in turn perhaps their energy costs. SUMMARY STATEMENT: The energy savings that sea bass experience from schooling are affected by flow speed or turbulence, moderated by group size.

Quantitative determination and classification of energy drinks using near-infrared spectroscopy.

Almost a hundred commercially available energy drink samples from Hungary, Slovakia, and Greece were collected for the quantitative determination of their caffeine and sugar content with FT-NIR spectroscopy and high-performance liquid chromatography (HPLC). Calibration models were built with partial least-squares regression (PLSR). An HPLC-UV method was used to measure the reference values for caffeine content, while sugar contents were measured with the Schoorl method. Both the nominal sugar content (as indicated on the cans) and the measured sugar concentration were used as references. Although the Schoorl method has larger error and bias, appropriate models could be developed using both references. The validation of the models was based on sevenfold cross-validation and external validation. FT-NIR analysis is a good candidate to replace the HPLC-UV method, because it is much cheaper than any chromatographic method, while it is also more time-efficient. The combination of FT-NIR with multidimensional chemometric techniques like PLSR can be a good option for the detection of low caffeine concentrations in energy drinks. Moreover, three types of energy drinks that contain (i) taurine, (ii) arginine, and (iii) none of these two components were classified correctly using principal component analysis and linear discriminant analysis. Such classifications are important for the detection of adulterated samples and for quality control, as well. In this case, more than a hundred samples were used for the evaluation. The classification was validated with cross-validation and several randomization tests (X-scrambling). Graphical Abstract The way of energy drinks from cans to appropriate chemometric models.

[Regulation of vasomotor tone of small skeletal muscle veins by intrinsic mechanisms]. / Vázizomkisvénák vazomotortónusának intrinszik szabályozómechanizmusai.

In many developed countries the prevalence of venous disorders and its consequences are higher than that of arterial diseases. Thus it is very important to understand the exact physiological and pathophysiological function of small veins and their control mechanisms. Small veins and venules have an important role in the regulation of capillary fluid exchange, as well as return of the venous blood into the heart. However, there is only limited knowledge available regarding the role of local mechanisms controlling the vasomotor tone and diameter of small veins. In the last decade the authors focused on the elucidation of these mechanisms in isolated skeletal muscle venules of rats. Their results suggest that the tone of small veins is controlled by the integration of several mechanisms, activated by the intraluminal pressure and flow/wall shear stress, in addition to numerous local mediators synthesized and released from the smooth muscle and endothelium. These mechanisms are involved - in a complex manner - in the control of postcapillary resistance, thus regulation of tissue blood supply, venous return and consequently in the modulation of the cardiac output, as well.

Quantitative determination of coenzyme Q10 from dietary supplements by FT-NIR spectroscopy and statistical analysis.

A novel, time- and money-sparing method has been developed and validated for the quantitative determination of coenzyme Q10 (CoQ10) from several dietary supplements. FT-NIR spectroscopy was applied for the examination, and a calibration model was built by partial least-square regression (PLS-R) using 50 dietary supplements. The combination of FT-NIRS and multivariate calibration methods is a very fast and simple way to replace the commonly used HPLC-UV method; because in contrast with the traditional techniques, sample pretreatment and reagents are not required and no wastes are produced. The calibration models could be improved by different variable selection techniques (for instance interval PLS, interval selectivity ratio, genetic algorithm), which are very fast and user-friendly. The R(2) (goodness of calibration) and Q(2) (goodness of validation) of the variable selected models are highly increased, the R(2) values being over 0.90 and the Q(2) values being over 0.86 in every case. Fivefold cross-validation and external validation were applied. The developed method(s) could be used by quality assurance laboratories for routine measurement of coenzyme Q10 products.

The reference genome of Macropodus opercularis (the paradise fish).

Amongst fishes, zebrafish (Danio rerio) has gained popularity as a model system over most other species and while their value as a model is well documented, their usefulness is limited in certain fields of research such as behavior. By embracing other, less conventional experimental organisms, opportunities arise to gain broader insights into evolution and development, as well as studying behavioral aspects not available in current popular model systems. The anabantoid paradise fish (Macropodus opercularis), an "air-breather" species has a highly complex behavioral repertoire and has been the subject of many ethological investigations but lacks genomic resources. Here we report the reference genome assembly of M. opercularis using long-read sequences at 150-fold coverage. The final assembly consisted of 483,077,705 base pairs (~483 Mb) on 152 contigs. Within the assembled genome we identified and annotated 20,157 protein coding genes and assigned ~90% of them to orthogroups.

Extending the limitations in the prediction of PAMPA permeability with machine learning algorithms.

Gastrointestinal absorption is a key factor amongst the ADME-related (absorption, distribution, metabolism and excretion) pharmacokinetic properties; therefore, it has a major role in drug discovery and drug safety determinations. The Parallel Artificial Membrane Permeability Assay (PAMPA) can be considered as the most popular and well-known screening assay for the measurement of gastrointestinal absorption. Our study provides quantitative structure-property relationship (QSPR) models based on experimental PAMPA permeability data for almost four hundred diverse molecules, which is a great extension of the applicability of the models in the chemical space. Two- and three-dimensional molecular descriptors were applied for the model building in every case. We have compared the performance of a classical partial least squares regression (PLS) model with two major machine learning algorithms: artificial neural networks (ANN) and support vector machine (SVM). Due to the applied gradient pH in the experiments, we have calculated the descriptors for the model building at pH values of 7.4 and 6.5, and compared the effect of pH on the performance of the models. After a complex validation protocol, the best model had an R2=0.91 for the training set, and R2= 0.84 for the external test set. The developed models are capable for the robust and fast prediction of new compounds with an excellent accuracy compared to the previous QSPR models.

The reference genome of the paradise fish (Macropodus opercularis).

Over the decades, a small number of model species, each representative of a larger taxa, have dominated the field of biological research. Amongst fishes, zebrafish (Danio rerio) has gained popularity over most other species and while their value as a model is well documented, their usefulness is limited in certain fields of research such as behavior. By embracing other, less conventional experimental organisms, opportunities arise to gain broader insights into evolution and development, as well as studying behavioral aspects not available in current popular model systems. The anabantoid paradise fish (Macropodus opercularis), an "air-breather" species from Southeast Asia, has a highly complex behavioral repertoire and has been the subject of many ethological investigations, but lacks genomic resources. Here we report the reference genome assembly of Macropodus opercularis using long-read sequences at 150-fold coverage. The final assembly consisted of ≈483 Mb on 152 contigs. Within the assembled genome we identified and annotated 20,157 protein coding genes and assigned ≈90% of them to orthogroups. Completeness analysis showed that 98.5% of the Actinopterygii core gene set (ODB10) was present as a complete ortholog in our reference genome with a further 1.2 % being present in a fragmented form. Additionally, we cloned multiple genes important during early development and using newly developed in situ hybridization protocols, we showed that they have conserved expression patterns.