Molecular Datasets Research Articles

Development of methodology to support molecular endotype discovery from synovial fluid of individuals with knee osteoarthritis: The STEpUP OA consortium

Objectives To develop a protocol for largescale analysis of synovial fluid proteins, for the identification of biological networks associated with subtypes of osteoarthritis. Methods Synovial Fluid To detect molecular Endotypes by Unbiased Proteomics in Osteoarthritis (STEpUP OA) is an international consortium utilising clinical data (capturing pain, radiographic severity and demographic features) and knee synovial fluid from 17 participating cohorts. 1746 samples from 1650 individuals comprising OA, joint injury, healthy and inflammatory arthritis controls, divided into discovery (n = 1045) and replication (n = 701) datasets, were analysed by SomaScan Discovery Plex V4.1 (>7000 SOMAmers/proteins). An optimised approach to standardisation was developed. Technical confounders and batch-effects were identified and adjusted for. Poorly performing SOMAmers and samples were excluded. Variance in the data was determined by principal component (PC) analysis. Results A synovial fluid standardised protocol was optimised that had good reliability (<20% co-efficient of variation for >80% of SOMAmers in pooled samples) and overall good correlation with immunoassay. 1720 samples and >6290 SOMAmers met inclusion criteria. 48% of data variance (PC1) was strongly correlated with individual SOMAmer signal intensities, particularly with low abundance proteins (median correlation coefficient 0.70), and was enriched for nuclear and non-secreted proteins. We concluded that this component was predominantly intracellular proteins, and could be adjusted for using an ‘intracellular protein score’ (IPS). PC2 (7% variance) was attributable to processing batch and was batch-corrected by ComBat. Lesser effects were attributed to other technical confounders. Data visualisation revealed clustering of injury and OA cases in overlapping but distinguishable areas of high-dimensional proteomic space. Conclusions We have developed a robust method for analysing synovial fluid protein, creating a molecular and clinical dataset of unprecedented scale to explore potential patient subtypes and the molecular pathogenesis of OA. Such methodology underpins the development of new approaches to tackle this disease which remains a huge societal challenge.

Assessing the effect of model specification and prior sensitivity on Bayesian tests of temporal signal.

Our understanding of the evolution of many microbes has been revolutionised by the molecular clock, a statistical tool to infer evolutionary rates and timescales from analyses of biomolecular sequences. In all molecular clock models, evolutionary rates and times are jointly unidentifiable and 'calibration' information must therefore be used. For many organisms, sequences sampled at different time points can be employed for such calibration. Before attempting to do so, it is recommended to verify that the data carry sufficient information for molecular dating, a practice referred to as evaluation of temporal signal. Recently, a fully Bayesian approach, BETS (Bayesian Evaluation of Temporal Signal), was proposed to overcome known limitations of other commonly used techniques such as root-to-tip regression or date randomisation tests. BETS requires the specification of a full Bayesian phylogenetic model, posing several considerations for untangling the impact of model choice on the detection of temporal signal. Here, we aimed to (i) explore the effect of molecular clock model and tree prior specification on the results of BETS and (ii) provide guidelines for improving our confidence in molecular clock estimates. Using microbial molecular sequence data sets and simulation experiments, we assess the impact of the tree prior and its hyperparameters on the accuracy of temporal signal detection. In particular, highly informative priors that are inconsistent with the data can result in the incorrect detection of temporal signal. In consequence, we recommend: (i) using prior predictive simulations to determine whether the prior generates a reasonable expectation of parameters of interest, such as the evolutionary rate and age of the root node, (ii) conducting prior sensitivity analyses to assess the robustness of the posterior to the choice of prior, and (iii) selecting a molecular clock model that reasonably describes the evolutionary process.

Molecular quantum chemical data sets and databases for machine learning potentials

Abstract The field of computational chemistry is increasingly leveraging machine learning (ML) potentials to predict molecular properties with high accuracy and efficiency, providing a viable alternative to traditional quantum mechanical (QM) methods, which are often computationally intensive. Central to the success of ML models is the quality and comprehensiveness of the data sets on which they are trained. Quantum chemistry data sets and databases, comprising extensive information on molecular structures, energies, forces, and other properties derived from QM calculations, are crucial for developing robust and generalizable ML potentials.
In this review, we provide an overview of the current landscape of quantum chemical data sets and databases. We examine key characteristics and functionalities of prominent resources, including the types of information they store, the level of electronic structure theory employed, the diversity of chemical space covered, and the methodologies used for data creation. Additionally, an updatable resource is provided to track new data sets and databases at https://github.com/Arif-PhyChem/datasets_and_databases_4_MLPs. This resource also has the overview in a machine-readable database format with the Jupyter notebook example for analysis.
Looking forward, we discuss the challenges associated with the rapid growth of quantum chemical data sets and databases, emphasizing the need for updatable and accessible resources to ensure the long-term utility of them. We also address the importance of data format standardization and the ongoing efforts to align with the FAIR principles to enhance data interoperability and reusability. Drawing inspiration from established materials databases, we advocate for the development of user-friendly and sustainable platforms for these data sets and databases.

Interaction between subclinical carotid atherosclerosis endotype and biological sex: a study from IMPROVE cohort

Abstract Background Carotid-intima media thickness (c-IMT) is a measure of subclinical carotid atherosclerosis and a predictor of future atherosclerotic cardiovascular events (ASCVD). We have recently generated, using an artificial intelligence approach, four endotypes (E) of subclinical atherosclerosis, i.e. subgroups of individuals predisposed to both c-IMT progression and ASCVD. Each E is defined by a set of demographic, clinical, and molecular data and characterize individuals with a low (E1), intermendiate (E2), high (E3) and very high (E4) cardiovascular risk profile. We observed an unbalanced distribution of males and females across the 4 endotypes, with a higher prevalence of female in the low risk E1. Purpose To estimate the interaction between biological sex and the 4 endotypes on measures of c-IMT progression and ASCVD risk. Methods We performed our study on 3121 individuals from the IMPROVE cohort, which includes participants with at least three ASCVD risk factors. We estimated the interaction between biological sex and the endotypes generated in the IMPROVE on (1) c-IMT and carotid plaque measurements after 30 months of follow-up (c-IMTfastest-progr, Area of plaquesbulb-progr) by linear regression (β, SE) and (2) with the 3-year ASCVD risk by Cox [hazard ratio (HR), 95% confidence interval (CI)] regression model. Crude estimates were adjusted by batch effect for biomarker measurement, geographical region and/or baseline ultrasonographic measures. Adjusted models further included ASCVD common risk factors. Results As shown in Figure 1, we did not observe any significant interaction between endotypes and biological sex on 30-month carotid ultrasound progression measures, and 3-year-ASCVD risk. In both the crude and adjusted models on c-IMTfastest-progr, Area of plaquesbulb-progr, and 3-year ASCVD, the p-value for interaction ranged from 0.18 to 0.98. While E4 was associated with the strongest cardiovascular risk profile in both males and females. Conclusions Our results indicate that the association between endotypes and progression of subclinical atherosclerosis and ASCVD risk mirrors a specific risk profile not entirely explained by differences in biological sex.

Phylogeny, biogeography and morphological evolution of the treehopper‐like leafhoppers (Hemiptera: Cicadellidae) Megophthalminae and Ulopinae

AbstractRecent phylogenetic analyses of anchored‐hybrid, transcriptomic and morphological data have consistently recovered a clade comprising the three previously recognized families of treehoppers (Hemiptera), Aetalionidae, Melizoderidae and Membracidae, as well as two groups traditionally included in the leafhopper family Cicadellidae as subfamilies Megophthalminae and Ulopinae. To reconstruct the phylogeny of these two groups of treehopper‐like leafhoppers, maximum likelihood and multi‐species coalescent analyses were performed on a molecular DNA dataset consisting of ~700 anchored hybrid loci representing 84 terminal taxa. Analyses based on different dataset subsets and approaches yielded largely congruent topologies, although the relationships among Megophthalminae, Ulopinae and treehoppers are still unstable. The monophyly of both subfamilies is strongly supported, but several tribes, including Agalliini, Cephalelini, Megophthalmini and Ulopini, are recovered as non‐monophyletic. The origin of Megophthalminae and Ulopinae was estimated as early Cretaceous (~140 million years ago), and the divergence within each subfamily began in the mid‐Cretaceous. Continental‐scale biogeographic structure is evident in these two groups, with genera occurring on the same continent tending to group together regardless of tribal placement, suggesting that extensive morphological convergence occurred among faunas inhabiting different regions. Ancestral microhabitat reconstruction suggested that megophthalmine and ulopine leafhoppers originally lived on trees or shrubs and later several groups evolved independently to inhabit leaf litter and soil. Convergent modifications of the ocelli, forewings and hindwings accompanied changes in microhabitat preference.

Optimizing GNN Architectures Through Nonlinear Activation Functions for Potent Molecular Property Prediction

Accurate predictions of molecular properties are crucial for advancements in drug discovery and materials science. However, this task is complex and requires effective representations of molecular structures. Recently, Graph Neural Networks (GNNs) have emerged as powerful tools for this purpose, demonstrating significant potential in modeling molecular data. Despite advancements in GNN predictive performance, existing methods lack clarity on how architectural choices, particularly activation functions, affect training dynamics and inference stages in interpreting the predicted results. To address this gap, this paper introduces a novel activation function called the Sine Linear Unit (SLU), aimed at enhancing the predictive capabilities of GNNs in the context of molecular property prediction. To demonstrate the effectiveness of SLU within GNN architecture, we conduct experiments on diverse molecular datasets encompassing various regression and classification tasks. Our findings indicate that SLU consistently outperforms traditional activation functions on hydration free energy (FreeSolv), inhibitory binding of human β secretase (BACE), and blood brain barrier penetration (BBBP), achieving the superior performance in each task, with one exception on the GCN model using the QM9 data set. These results underscore SLU’s potential to significantly improve prediction accuracy, making it a valuable addition to the field of molecular modeling.

CODI: Enhancing machine learning-based molecular profiling through contextual out-of-distribution integration

Abstract Molecular analytics increasingly utilize machine learning (ML) for predictive modeling based on data acquired through molecular profiling technologies. However, developing robust models that accurately capture physiological phenotypes is challenged by the dynamics inherent to biological systems, variability stemming from analytical procedures, and the resource-intensive nature of obtaining sufficiently representative datasets. Here, we propose and evaluate a new method: Contextual Out-of-Distribution Integration (CODI). Based on experimental observations, CODI generates synthetic data that integrate unrepresented sources of variation encountered in real-world applications into a given molecular fingerprint dataset. By augmenting a dataset with out-of-distribution variance, CODI enables an ML model to better generalize to samples beyond the seed training data, reducing the need for extensive experimental data collection. Using three independent longitudinal clinical studies and a case-control study, we demonstrate CODI's application to several classification tasks involving vibrational spectroscopy of human blood. We showcase our approach's ability to enable personalized fingerprinting for multi-year longitudinal molecular monitoring and enhance the robustness of trained ML models for improved disease detection. Our comparative analyses reveal that incorporating CODI into the classification workflow consistently leads to increased robustness against data variability and improved predictive accuracy.

Anomalous latitudinal gradients in parasitoid wasp diversity-Hotspots in regions with larger temperature range.

Knowledge of global patterns of genetic diversity is essential for biodiversity conservation as this parameter describes the ability of a species to respond to environmental changes. Ichneumonoids parasitoid wasps are among the few taxa showing an anomalous latitudinal diversity gradient. Using the largest georeferenced molecular dataset for this group, we used a macrogenetics approach to examine latitudinal patterns and predictors of intraspecific genetic diversity. We calculated the mean nucleotide diversity of mitochondrial DNA barcode sequences at three geographic levels: grid cells, latitudinal bands and climatic zones. Nucleotide diversity values were consistently higher at northern temperate latitudes, peaking at 50°. We found a positive but weak relationship between intraspecific diversity and the latitude, between intra- and interspecific diversity, and a positive effect of the temperature range. Examining the spatial relationship between different levels of biodiversity and its drivers is particularly relevant considering climate change and its impact on species distribution. Yet, in insects, it has been challenging to integrate ecological, evolutionary and geographical components when analysing the processes leading to species richness gradients.

Phylogenetic and Morphological Perspectives on Crepidotus subg. Dochmiopus: Exploratively Unveiling Hidden Diversity in China.

Crepidotus subg. Dochmiopus contributes to more than half of Crepidotus species and exhibits highly hidden diversity. However, C. subg. Dochmiopus is challenging to study because the basidiomata of C. subg. Dochmiopus species are usually small and white, inconspicuous interspecific distinctions, and possess a familiar complex. In this study, we utilized a variety of characteristics for species identification, including habitat, presence or absence of a stipe in mature specimens, pileipellis and cheilocystidia patterns, whether the lamellae edges are fimbriated, and other characteristics. Above all, cheilocystidia and pileipellis patterns will be important in C. subg. Dochmiopus research. Based on the present specimens, we constructed a multigene phylogenetic tree (ITS + LSU) and recognized four new species: C. lamellomaculatus sp. nov., C. capitatocystidiatus sp. nov., C. succineus sp. nov., C. clavocystidiatustustus sp. nov. Detailed morphological descriptions, photographs, line drawings and comparisons with closely related taxa for the new species are provided. The current phylogenetic analysis does not support the previously classifications, indicating that the classification of Crepidotus requires re-evaluation. But the existing molecular datasets and species' descriptions are insufficient to fully resolve the classification. Further integration of new gene segments and a comprehensive review of morphological characteristics will reveal a natural classification for Crepidotus.

DrugReAlign: a multisource prompt framework for drug repurposing based on large language models.

Drug repurposing is a promising approach in the field of drug discovery owing to its efficiency and cost-effectiveness. Most current drug repurposing models rely on specific datasets for training, which limits their predictive accuracy and scope. The number of both market-approved and experimental drugs is vast, forming an extensive molecular space. Due to limitations in parameter size and data volume, traditional drug-target interaction (DTI) prediction models struggle to generalize well within such a broad space. In contrast, large language models (LLMs), with their vast parameter sizes and extensive training data, demonstrate certain advantages in drug repurposing tasks. In our research, we introduce a novel drug repurposing framework, DrugReAlign, based on LLMs and multi-source prompt techniques, designed to fully exploit the potential of existing drugs efficiently. Leveraging LLMs, the DrugReAlign framework acquires general knowledge about targets and drugs from extensive human knowledge bases, overcoming the data availability limitations of traditional approaches. Furthermore, we collected target summaries and target-drug space interaction data from databases as multi-source prompts, substantially improving LLM performance in drug repurposing. We validated the efficiency and reliability of the proposed framework through molecular docking and DTI datasets. Significantly, our findings suggest a direct correlation between the accuracy of LLMs' target analysis and the quality of prediction outcomes. These findings signify that the proposed framework holds the promise of inaugurating a new paradigm in drug repurposing.

Response Matching for Generating Materials and Molecules.

Diffusion models have recently emerged as powerful tools for the generation of new molecular and material structures. The key insight is that the noise in these models is related to the response of the atoms to displacement, and the denoising step is thus analogous to the geometry relaxation of atomistic systems starting from a random structure. Building on this, we present a generative method called Response Matching (RM), which leverages the fact that each stable material or molecule exists at the minimum of its potential energy surface. Any perturbation induces a response in energy and stress, driving the structure back to equilibrium. Matching this response is closely related to score matching in diffusion models. Another important aspect of state-of-the-art diffusion models is the incorporation of physical symmetries such as translation, rotation, and periodicity. RM employs a machine learning interatomic potential and random structure search as the denoising model, inherently respecting these symmetries and exploiting the locality of atomic interactions. RM handles both molecules and bulk materials under the same framework. Its efficiency and generalization are demonstrated on three systems: a small organic molecular data set, stable crystals from the Materials Project, and one-shot learning on a single diamond configuration.

Science behind herbarium and its importance in recent years

The interest in and significance of herbarium collections for the study of plant biodiversity and evolution has significantly expanded in recent decades due to the introduction of new technologies for large‐scale, automated digitization and the availability of new techniques for DNA sequencing. These innovative methods gave rise to new initiatives with the goal of compiling a sizable molecular and phenological data set. In light of the various national projects that are currently underway and driving the study of herbarium specimens for the purpose of understanding biodiversity loss and habitat shifts as a result of climate change and habitat destruction due to human activity. Here, we showcased a number of cutting‐edge research, mini‐reviews, and technical comments that demonstrate the ever‐growing range of applications for herbarium specimens. It examines the variety of applications for which herbarium specimens are employed, the primary users' profiles of herbarium collections, patterns and systematics of biodiversity, and the evolution of lineages and traits.

A novel method for clustering cellular data to improve classification.

Many fields, such as neuroscience, are experiencing the vast proliferation of cellular data, underscoring the need for organizing and interpreting large datasets. A popular approach partitions data into manageable subsets via hierarchical clustering, but objective methods to determine the appropriate classification granularity are missing. We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters. Here we present the corresponding protocol to classify cellular datasets by combining data-driven unsupervised hierarchical clustering with statistical testing. These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values, including molecular, physiological, and anatomical datasets. We demonstrate the protocol using cellular data from the Janelia MouseLight project to characterize morphological aspects of neurons.

Linking transcriptome and morphology in bone cells at cellular resolution with generative AI

Abstract Recent advancements in deep learning (DL) have revolutionized the capability of artificial intelligence (AI) by enabling the analysis of large-scale, complex datasets that are difficult for humans to interpret. However, large amounts of high-quality data are required to train such generative AI models successfully. With the rapid commercialization of single-cell sequencing and spatial transcriptomics platforms, the field is increasingly producing large-scale datasets such as histological images, single-cell molecular data, and spatial transcriptomic data. These molecular and morphological datasets parallel the multimodal text and image data used to train highly successful generative AI models for natural language processing and computer vision. Thus, these emerging data types offer great potential to train generative AI models that uncover intricate biological processes of bone cells at a cellular level. In this Perspective, we summarize the progress and prospects of generative AI applied to these datasets and their potential applications to bone research. In particular, we highlight three AI applications: predicting cell differentiation dynamics, linking molecular and morphological features, and predicting cellular responses to perturbations. To make generative AI models beneficial for bone research, important issues, such as technical biases in bone single-cell datasets, lack of profiling of important bone cell types, and lack of spatial information, need to be addressed. Realizing the potential of generative AI for bone biology will also likely require generating large-scale, high-quality cellular-resolution spatial transcriptomics datasets, improving the sensitivity of current spatial transcriptomics datasets, and thorough experimental validation of model predictions.

Supervised machine learning on Galactic filaments. II. Encoding the position to optimize the detection of filaments over a wide range of column density and contrast

Filaments host star formation and are fundamental structures of galaxies. Their diversity, as observed in the interstellar medium, from very low-density structures to very dense hubs, and their complex life cycles make their complete detection challenging over this large diversity range. Using 2D H$_2$ column density images obtained as part of the Herschel Hi-GAL survey of the Galactic plane (Gp), we want to detect, simultaneously and using a single model, filaments over a large range of column density and contrast over the whole Gp. In particular, we target low-contrast and low-density structures that are particularly difficult to detect with classical algorithms. The whole H$_2$ column density image of the Gp was subdivided into individual patches of 32times 32 pixels. Following our proof of concept study aimed at exploring the potential of supervised learning for the detection of filaments, we propose an innovative supervised learning method based on adding information by encoding the position of these patches in the Gp. To allow the segmentation of the whole Gp, we introduced a random procedure that preserves the balance within the model training and testing datasets over the Gp plane. Four architectures and six models were tested and compared using different metrics. For the first time, a segmentation of the whole Gp has been obtained using supervised deep learning. A comparison of the models based on metrics and astrophysical results shows that one of the architectures (PE-UNet-Latent), where the position encoding was done in the latent space gives the best performance to detect filaments over the whole range of density and contrast observed in the Gp. A normalized map of the whole Gp was also produced and reveals the highly filamentary structure of the Gp in all density regimes. We successfully tested the generalization of our best model by applying it to the 2D 12CO COHRS molecular data obtained on a 52 portion (in longitude) of the plane. We demonstrate the interest of position encoding to allow the detection of filaments over the wide range of density and contrast observed in the Gp. The produced maps (both normalized and segmented) offer a unique opportunity for follow-up studies of the life cycle of Galactic filaments. The promising generalization possibility tested on a molecular dataset of the Gp opens new opportunities for systematic detection of filamentary structures in the big data context available for the Gp.

ResGAT: Residual Graph Attention Networks for molecular property prediction

Molecular property prediction is an important step in the drug discovery pipeline. Numerous computational methods have been developed to predict a wide range of molecular properties. While recent approaches have shown promising results, no single architecture can comprehensively address all tasks, making this area persistently challenging and requiring substantial time and effort. Beyond traditional machine learning and deep learning architectures for regular data, several deep learning architectures have been designed for graph-structured data to overcome the limitations of conventional methods. Utilizing graph-structured data in quantitative structure–activity relationship (QSAR) modeling allows models to effectively extract unique features, especially where connectivity information is crucial. In our study, we developed residual graph attention networks (ResGAT), a deep learning architecture for molecular graph-structured data. This architecture is a combination of graph attention networks and shortcut connections to address both regression and classification problems. It is also customizable to adapt to various dataset sizes, enhancing the learning process based on molecular patterns. When tested multiple times with both random and scaffold sampling strategies on nine benchmark molecular datasets, QSAR models developed using ResGAT demonstrated stability and competitive performance compared to state-of-the-art methods.

Conformational Space Profiling Enhances Generic Molecular Representation for AI-Powered Ligand-Based Drug Discovery.

The molecular representation model is a neural network that converts molecular representations (SMILES, Graph) into feature vectors, and is an essential module applied across a wide range of artificial intelligence-driven drug discovery scenarios. However, current molecular representation models rarely consider the three-dimensional conformational space of molecules, losing sight of the dynamic nature of small molecules as well as the essence of molecular conformational space that covers the heterogeneity of molecule properties, such as the multi-target mechanism of action, recognition of different biomolecules, dynamics in cytoplasm and membrane. In this study, a new model named GeminiMol is proposed to incorporate conformational space profiles into molecular representation learning, which extracts the feature of capturing the complicated interplay between the molecular structure and the conformational space. Although GeminiMol is pre-trained on a relatively small-scale molecular dataset (39290 molecules), it shows balanced and superior performance not only on 67 molecular properties predictions but also on 73 cellular activity predictions and 171 zero-shot tasks (including virtual screening and target identification). By capturing the molecular conformational space profile, the strategy paves the way for rapid exploration of chemical space and facilitates changing paradigms for drug design.

Unlocking comprehensive molecular design across all scenarios with large language model and unordered chemical language.

Molecular generation stands at the forefront of AI-driven technologies, playing a crucial role in accelerating the development of small molecule drugs. The intricate nature of practical drug discovery necessitates the development of a versatile molecular generation framework that can tackle diverse drug design challenges. However, existing methodologies often struggle to encompass all aspects of small molecule drug design, particularly those rooted in language models, especially in tasks like linker design, due to the autoregressive nature of large language model-based approaches. To empower a language model for a wider range of molecular design tasks, we introduce an unordered simplified molecular-input line-entry system based on fragments (FU-SMILES). Building upon this foundation, we propose FragGPT, a universal fragment-based molecular generation model. Initially pretrained on extensive molecular datasets, FragGPT utilizes FU-SMILES to facilitate efficient generation across various practical applications, such as de novo molecule design, linker design, R-group exploration, scaffold hopping, and side chain optimization. Furthermore, we integrate conditional generation and reinforcement learning (RL) methodologies to ensure that the generated molecules possess multiple desired biological and physicochemical properties. Experimental results across diverse scenarios validate FragGPT's superiority in generating molecules with enhanced properties and novel structures, outperforming existing state-of-the-art models. Moreover, its robust drug design capability is further corroborated through real-world drug design cases.

From coral reefs into the abyss: the evolution of corallivory in the Coralliophilinae (Neogastropoda, Muricidae)

In this study, we delved into the interaction between corallivorous marine gastropods, the muricid Coralliophilinae Chenu, 1859, and their cnidarian food targets. Coralliophilinae is a subfamily of specialised corallivorous caenogastropods that feed by browsing on octocorals or hexacorals. Only sparse information is available on the phylogenetic relationships and the degree of specificity of the trophic relationships within this corallivorous lineage. To address these gaps, we generated the largest molecular dataset to date, comprising two mitochondrial (cox1 and 16S rDNA) and one nuclear gene (ITS2 rDNA) from 586 specimens collected worldwide. The coral hosts of coralliophilines were identified through an integrative approach, combining literature data with new records, employing morphological and/or molecular markers, and incorporating data from DNA barcoding of the snail stomach content. Our comprehensive approach unveiled the existence of numerous cryptic species in Coralliophilinae, while the phylogeny showed that most of the currently accepted genera are not monophyletic. The molecular dating confirmed the origin of the Coralliophilinae in Middle Eocene, with diversification of most lineages during the Miocene. Our results indicate that the subfamily’s ancestor evolved in shallow waters in association with Scleractinia. Through the evolutionary history of Coralliophilinae, multiple host shifts to other cnidarian orders were observed, not correlated with changes in the depth range. The results of diversification analyses within the subfamily further suggest that the association with the host has influenced the evolutionary patterns of Coralliophilinae, but not vice versa.

Molecular Phylogeny and Historical Biogeography of Byrsonima (Malpighiaceae) Corroborates the Mid-Miocene Origins of Neotropical Savannas

We present a dated and calibrated molecular phylogeny for one of the most characteristic genera of Neotropical savannas, Byrsonima (Malpighiaceae), based on the ETS, ITS, and psbA-trnH markers. We sampled 33 species of Byrsonima and four species of the outgroups Blepharandra, Diacidia, and Pterandra to test the monophyly of the infrageneric classification of the genus. Bayesian inference (BI) analysis was performed for the combined molecular dataset. Seven morphological characters were optimized on the obtained tree. Calibration points derived from a published chronogram for Malpighiaceae were used alongside a relaxed, uncorrelated molecular clock on Beast 1.8.4. Ancestral range reconstructions focusing on four main Neotropical biomes (Cerrado, Atlantic rainforest, Amazon rainforest, and Caatinga dry forests) were performed on BioGeoBEARS. Our phylogenetic results corroborated the monophyly of Byrsonima, but all of its subgenera and sections were polyphyletic, with all morphological characters circumscribing these infrageneric ranks being highly homoplastic. The most recent common ancestor of Byrsonima was widespread in South American biomes at 11.41 Ma, posteriorly diversifying in the Amazon rainforests up to 7.72 Ma, when it started massively diversifying in Neotropical savannas. A few re-colonization events from savannas to rain or dry forests occurred from 2.95–0.53 Ma. These results corroborate the mid-Miocene origins of Neotropical savannas, and future studies should aim to sample Mesoamerican species of Byrsonima.