Whole-genome-based phylogenetic analyses provide new insights into the evolution of springtails (Hexapoda: Collembola).

Springtails (Collembola) stand as one of the most abundant, widespread, and ancient terrestrial arthropods on earth. However, their evolutionary history and deep phylogenetic relationships remain elusive. In this study, we employed phylogenomic approaches to elucidate the basal relationships among Collembola. We sampled whole-genome data representing all major collembolan lineages in proportion to their known diversity. To account for potential phylogenomic biases, we implemented various data extraction, locus sampling, and signal filtering strategies to generate matrices. Subsequently, we applied a diverse array of tree-searching and rate-modelling methods to reconstruct the phylogeny. Our analyses, utilizing different matrices and methods, converged on the same unrooted relationships among collembolan ingroups, supporting the current ordinal classification and challenging the monophyly of Arthropleona and Symphypleona s.l. However, discrepancies across analyses existed in the root of Collembola. Among various root positions, those based on more informative matrices and biologically realistic models, favoring a basal topology of Entomobryomorpha + (Symphypleona s.s. + (Neelipleona + Poduromorpha)), were supported by subsequent methodological assessment, topology tests, and rooting analyses. This optimal topology suggests multiple independent reduction of the pronotum in non-poduromorph orders and aligns with the plesiomorphic status of neuroendocrine organs and epicuticular structure of Entomobryomorpha. Fossil-calibrated dating analyses based on the optimal topology indicated late-Paleozoic to mid-Mesozoic origins of the crown Collembola and four orders. In addition, our results questioned the monophyly of Isotomidae and Neanuridae, underscoring the need for further attention to the systematics of these families. Overall, this study provides novel insights into the phylogenetic backbone of Collembola, which will inform future studies on the systematics, ecology, and evolution of this significant arthropod lineage.

Optimal control of bioproduction in the presence of population heterogeneity.

Cell-to-cell variability, born of stochastic chemical kinetics, persists even in large isogenic populations. In the study of single-cell dynamics this is typically accounted for. However, on the population level this source of heterogeneity is often sidelined to avoid the inevitable complexity it introduces. The homogeneous models used instead are more tractable but risk disagreeing with their heterogeneous counterparts and may thus lead to severely suboptimal control of bioproduction. In this work, we introduce a comprehensive mathematical framework for solving bioproduction optimal control problems in the presence of heterogeneity. We study population-level models in which such heterogeneity is retained, and propose order-reduction approximation techniques. The reduced-order models take forms typical of homogeneous bioproduction models, making them a useful benchmark by which to study the importance of heterogeneity. Moreover, the derivation from the heterogeneous setting sheds light on parameter selection in ways a direct homogeneous outlook cannot, and reveals the source of approximation error. With view to optimally controlling bioproduction in microbial communities, we ask the question: when does optimising the reduced-order models produce strategies that work well in the presence of population heterogeneity? We show that, in some cases, homogeneous approximations provide remarkably accurate surrogate models. Nevertheless, we also demonstrate that this is not uniformly true: overlooking the heterogeneity can lead to significantly suboptimal control strategies. In these cases, the heterogeneous tools and perspective are crucial to optimise bioproduction.

Fusing Sequence and Structural Knowledge by Heterogeneous Models to Accurately and Interpretively Predict Drug-Target Affinity.

Drug-target affinity (DTA) prediction is crucial for understanding molecular interactions and aiding drug discovery and development. While various computational methods have been proposed for DTA prediction, their predictive accuracy remains limited, failing to delve into the structural nuances of interactions. With increasingly accurate and accessible structure prediction of targets, we developed a novel deep learning model, named S2DTA, to accurately predict DTA by fusing sequence features of drug SMILES, targets, and pockets and their corresponding graph structural features using heterogeneous models based on graph and semantic networks. Experimental findings underscored that complex feature representations imparted negligible enhancements to the model's performance. However, the integration of heterogeneous models demonstrably bolstered predictive accuracy. In comparison to three state-of-the-art methodologies, such as DeepDTA, GraphDTA, and DeepDTAF, S2DTA's performance became more evident. It exhibited a 25.2% reduction in mean absolute error (MAE) and a 20.1% decrease in root mean square error (RMSE). Additionally, S2DTA showed some improvements in other crucial metrics, including Pearson Correlation Coefficient (PCC), Spearman, Concordance Index (CI), and R2, with these metrics experiencing increases of 19.6%, 17.5%, 8.1%, and 49.4%, respectively. Finally, we conducted an interpretability analysis on the effectiveness of S2DTA by bidirectional self-attention mechanism. The analysis results supported that S2DTA was an effective and accurate tool for predicting DTA.

Phylotranscriptomics suggests the jawed vertebrate ancestor could generate diverse helper and regulatory T cell subsets.

BACKGROUND: The cartilaginous fishes diverged from other jawed vertebrates ~ 450 million years ago (mya). Despite this key evolutionary position, the only high-quality cartilaginous fish genome available is for the elephant shark (Callorhinchus milii), a chimaera whose ancestors split from the elasmobranch lineage ~ 420 mya. Initial analysis of this resource led to proposals that key components of the cartilaginous fish adaptive immune system, most notably their array of T cell subsets, was primitive compared to mammals. This proposal is at odds with the robust, antigen-specific antibody responses reported in elasmobranchs following immunization. To explore this discrepancy, we generated a multi-tissue transcriptome for small-spotted catshark (Scyliorhinus canicula), a tractable elasmobranch model for functional studies. We searched this, and other newly available sequence datasets, for CD4+ T cell subset-defining genes, aiming to confirm the presence or absence of each subset in cartilaginous fishes. RESULTS: We generated a new transcriptome based on a normalised, multi-tissue RNA pool, aiming to maximise representation of tissue-specific and lowly expressed genes. We utilized multiple transcriptomic datasets and assembly variants in phylogenetic reconstructions to unambiguously identify several T cell subset-specific molecules in cartilaginous fishes for the first time, including interleukins, interleukin receptors, and key transcription factors. Our results reveal the inability of standard phylogenetic reconstruction approaches to capture the site-specific evolutionary processes of fast-evolving immune genes but show that site-heterogeneous mixture models can adequately do so. CONCLUSIONS: Our analyses reveal that cartilaginous fishes are capable of producing a range of CD4+ T cell subsets comparable to that of mammals. Further, that the key molecules required for the differentiation and functioning of these subsets existed in the jawed vertebrate ancestor. Additionally, we highlight the importance of considering phylogenetic diversity and, where possible, utilizing multiple datasets for individual species, to accurately infer gene presence or absence at higher taxonomic levels.

Improving phylogenetic inference of core Chlorophyta using chloroplast sequences with strong phylogenetic signals and heterogeneous models.

Phylogenetic relationships within the green algal phylum Chlorophyta have proven difficult to resolve. The core Chlorophyta include Chlorophyceae, Ulvophyceae, Trebouxiophyceae, Pedinophyceae and Chlorodendrophyceae, but the relationships among these classes remain unresolved and the monophyly of Ulvophyceae and Trebouxiophyceae are highly controversial. We analyzed a dataset of 101 green algal species and 73 protein-coding genes sampled from complete and partial chloroplast genomes, including six newly sequenced ulvophyte genomes (Blidingia minima NIES-1837, Ulothrix zonata, Halochlorococcum sp. NIES-1838, Scotinosphaera sp. NIES-154, Caulerpa brownii and Cephaleuros sp. HZ-2017). We applied the Tree Certainty (TC) score to quantify the level of incongruence between phylogenetic trees in chloroplast genomic datasets, and show that the conflicting phylogenetic trees of core Chlorophyta stem from the most GC-heterogeneous sites. With removing the most GC-heterogeneous sites, our chloroplast phylogenomic analyses using heterogeneous models consistently support monophyly of the Chlorophyceae and of the Trebouxiophyceae, but the Ulvophyceae was resolved as polyphyletic. Our analytical framework provides an efficient approach to reconstruct the optimal phylogenetic relationships by minimizing conflicting signals.

Streptophyte algae and the origin of land plants revisited using heterogeneous models with three new algal chloroplast genomes.

The phylogenetic branching order of the green algal groups that gave rise to land plants remains uncertain despite its fundamental importance to understanding plant evolution. Previous studies have demonstrated that land plants evolved from streptophyte algae, but different lineages of streptophytes have been suggested to be the sister group of land plants. To better understand the evolutionary history of land plants and to determine the potential effects of "long-branch attraction" in phylogenetic reconstruction, we analyzed a chloroplast genome data set including three new chloroplast genomes from streptophyte algae: Coleochaetae orbicularis (Coleochaetales), Nitella hookeri (Charales), and Spirogyra communis (Zygnematales). We further applied a site pattern sorting method together with site- and time-heterogeneous models to investigate the branching order among streptophytes and land plants. Our chloroplast phylogenomic analyses support previous hypotheses based on nuclear data in placing Zygnematales alone, or a clade consisting of Coleochaetales plus Zygnematales, as the closest living relatives of land plants.

Revisiting moment-closure methods with heterogeneous multiscale population models.

Stochastic chemical kinetics at the single-cell level give rise to heterogeneous populations of cells even when all individuals are genetically identical. This heterogeneity can lead to nonuniform behaviour within populations, including different growth characteristics, cell-fate dynamics, and response to stimuli. Ultimately, these diverse behaviours lead to intricate population dynamics that are inherently multiscale: the population composition evolves based on population-level processes that interact with stochastically distributed single-cell states. Therefore, descriptions that account for this heterogeneity are essential to accurately model and control chemical processes. However, for real-world systems such models are computationally expensive to simulate, which can make optimisation problems, such as optimal control or parameter inference, prohibitively challenging. Here, we consider a class of multiscale population models that incorporate population-level mechanisms while remaining faithful to the underlying stochasticity at the single-cell level and the interplay between these two scales. To address the complexity, we study an order-reduction approximations based on the distribution moments. Since previous moment-closure work has focused on the single-cell kinetics, extending these techniques to populations models prompts us to revisit old observations as well as tackle new challenges. In this extended multiscale context, we encounter the previously established observation that the simplest closure techniques can lead to non-physical system trajectories. Despite their poor performance in some systems, we provide an example where these simple closures outperform more sophisticated closure methods in accurately, efficiently, and robustly solving the problem of optimal control of bioproduction in a microbial consortium model.

Phylogenomic analyses of echinoid diversification prompt a re-evaluation of their fossil record.

Echinoids are key components of modern marine ecosystems. Despite a remarkable fossil record, the emergence of their crown group is documented by few specimens of unclear affinities, rendering their early history uncertain. The origin of sand dollars, one of its most distinctive clades, is also unclear due to an unstable phylogenetic context. We employ 18 novel genomes and transcriptomes to build a phylogenomic dataset with a near-complete sampling of major lineages. With it, we revise the phylogeny and divergence times of echinoids, and place their history within the broader context of echinoderm evolution. We also introduce the concept of a chronospace - a multidimensional representation of node ages - and use it to explore methodological decisions involved in time calibrating phylogenies. We find the choice of clock model to have the strongest impact on divergence times, while the use of site-heterogeneous models and alternative node prior distributions show minimal effects. The choice of loci has an intermediate impact, affecting mostly deep Paleozoic nodes, for which clock-like genes recover dates more congruent with fossil evidence. Our results reveal that crown group echinoids originated in the Permian and diversified rapidly in the Triassic, despite the relative lack of fossil evidence for this early diversification. We also clarify the relationships between sand dollars and their close relatives and confidently date their origins to the Cretaceous, implying ghost ranges spanning approximately 50 million years, a remarkable discrepancy with their rich fossil record.

Phylogenomics Places Orphan Protistan Lineages in a Novel Eukaryotic Super-Group.

Recent phylogenetic analyses position certain "orphan" protist lineages deep in the tree of eukaryotic life, but their exact placements are poorly resolved. We conducted phylogenomic analyses that incorporate deeply sequenced transcriptomes from representatives of collodictyonids (diphylleids), rigifilids, Mantamonas, and ancyromonads (planomonads). Analyses of 351 genes, using site-heterogeneous mixture models, strongly support a novel super-group-level clade that includes collodictyonids, rigifilids, and Mantamonas, which we name "CRuMs". Further, they robustly place CRuMs as the closest branch to Amorphea (including animals and fungi). Ancyromonads are strongly inferred to be more distantly related to Amorphea than are CRuMs. They emerge either as sister to malawimonads, or as a separate deeper branch. CRuMs and ancyromonads represent two distinct major groups that branch deeply on the lineage that includes animals, near the most commonly inferred root of the eukaryote tree. This makes both groups crucial in examinations of the deepest-level history of extant eukaryotes.

Toward the Broad Adoption of 3D Tumor Models in the Cancer Drug Pipeline.

Despite a cost of approximately $1 billion to develop a new cancer drug, about 90% of drugs that enter clinical trials fail. A tremendous opportunity exists to streamline the drug selection and testing process, and innovative approaches promise to reduce the burdensome cost of health care for those suffering from cancer. There is great potential for 3D models of human tumors to complement more traditional testing methods; however, the shift from 2D to 3D assays at early stages of the drug discovery and development process is far from widely accepted. 3D platforms range from simple tumor spheroids to more complex microfluidic hydrogels that better mimic the tumor microenvironment. While several companies have developed and patented advanced high-throughput 3D platforms for drug screening, their cost and complexity have limited their adoption as an industry standard. In this review, we will highlight the various tumor platforms that have been developed, emphasizing the approaches that have successfully led to commercial products. We will then consider potential directions toward more relevant tumor models, advantages of the adoption of such platforms within the drug development and screening process, and new opportunities in personalized medicine that such platforms will uniquely enable.