Molecular Datasets Research Articles

Revealing the cryptic diversity of the widespread and poorly known South American blind snake genusAmerotyphlops(Typhlopidae: Scolecophidia) through integrative taxonomy

AbstractMorphological stasis is generally associated with relative constancy in ecological pressures throughout time, producing strong stabilizing selection that retains similar shared morphology. Although climate and vegetation are commonly the main key factors driving diversity and phenotypic diversification in terrestrial vertebrates, fossorial organisms have their morphology mostly defined by their fossorial lifestyle. Among these secretive fossorial organisms, blind snakes of the South American genus Amerotyphlops are considered poorly studied when compared to other taxa. Here, we evaluate the cryptic diversity of Amerotyphlops using phylogenetic and multivariate approaches. We based our phylogenetic analysis on a molecular dataset composed of 12 gene fragments (eight nuclear and four mitochondrial) for 109 species of Typhlopidae. The multivariate analysis was implemented using 36 morphological variables for 377 specimens of Amerotyphlops. Additionally, we contrast our phylogenetic result with the morphological variation found in cranial, external and hemipenial traits. Our phylogenetic results recovered with strong support the following monophyletic groups within Amerotyphlops: (1) a clade formed by A. tasymicris and A. minuisquamus; (2) a clade composed of A. reticulatus; (3) a north-eastern Brazilian clade including A. yonenagae, A. arenensis, A. paucisquamus and A. amoipira; and (4) a clade composed of A. brongersmianus and a complex of cryptic species. Based on these results we describe four new species of Amerotyphlops from north-eastern and south-eastern Brazil, which can be distinguished from the morphologically similar species, A. brongersmianus and A. arenensis.

Maximizing genetic gain through unlocking genetic variation in different ecotypes of kalmegh (Andrographis paniculata (Burm. f.) Nee).

Andrographis paniculata, commonly known as kalmegh is among the most popular medicinal herbs in Southeast Asia. It is widely cultivated for medicinal purposes. The bioactive molecule, Andrographolide accumulated in herb leaves has immense therapeutic and economic potential. However, comprehensive information regarding genetic diversity is very limited in this species. The present study assessed genetic diversity between and within the six populations (ecotypes) of twenty-four kalmegh accessions using multiple datasets (agro-morphological traits, phytochemical traits, and genic markers). This is the established report where EST-SSR (Expressed sequence tags-Simple Sequence Repeat) markers have been used to unlock genetic variation in kalmegh. Here, we identified and developed ninety-one metabolic pathway-specific EST-SSR markers. Finally, 32 random EST-SSR primer pairs were selected for genetic diversity assessment. Multivariate analysis to unveil the agro-morphological, phytochemical and genotypic variability was helpful in discriminating various germplasms studied in the present study. Among all the morphological discriptors used in present study, days to fifty percent flowering and dry herb yield were found as potential selection index for AP genetic improvement. Hierarchical cluster analysis built with agro-morphological data identified three major groups. However, corresponding analysis with phytochemical and molecular data generated two clear-cut groups among the studied individuals. Moreover, the grouping of individuals into different clusters using multiple datasets was geographically independent, and also showed inconsistency in grouping among agromorphological, phytochemical and molecular dataset based clusters. However, joint analysis using agro-morphological, phytochemical and genotypic information generated two genetic groups, which could be a valuable resource for identifying complementary crossing panels in the kalmegh breeding program. The accessions AP7, AP13, AP5, AP3 belong to cluster I and accessions AP17, AP18 belong to cluster II could be utilized as potential donors for high dry herb yield and andrographolide content, respectively in different selective breeding programs of AP. Thus, our results provided useful information about the overall genetic diversity and variation in economic traits useful for initiating selective breeding programs for contrasting traits of interest and maximizing genetic gain in kalmegh.

Feasibility and outcome of reproducible clinical interpretation of high-dimensional molecular data: a comparison of two molecular tumor boards

BackgroundStructured and harmonized implementation of molecular tumor boards (MTB) for the clinical interpretation of molecular data presents a current challenge for precision oncology. Heterogeneity in the interpretation of molecular data was shown for patients even with a limited number of molecular alterations. Integration of high-dimensional molecular data, including RNA- (RNA-Seq) and whole-exome sequencing (WES), is expected to further complicate clinical application. To analyze challenges for MTB harmonization based on complex molecular datasets, we retrospectively compared clinical interpretation of WES and RNA-Seq data by two independent molecular tumor boards.MethodsHigh-dimensional molecular cancer profiling including WES and RNA-Seq was performed for patients with advanced solid tumors, no available standard therapy, ECOG performance status of 0–1, and available fresh-frozen tissue within the DKTK-MASTER Program from 2016 to 2018. Identical molecular profiling data of 40 patients were independently discussed by two molecular tumor boards (MTB) after prior annotation by specialized physicians, following independent, but similar workflows. Identified biomarkers and resulting treatment options were compared between the MTBs and patients were followed up clinically.ResultsA median of 309 molecular aberrations from WES and RNA-Seq (n = 38) and 82 molecular aberrations from WES only (n = 3) were considered for clinical interpretation for 40 patients (one patient sequenced twice). A median of 3 and 2 targeted treatment options were identified per patient, respectively. Most treatment options were identified for receptor tyrosine kinase, PARP, and mTOR inhibitors, as well as immunotherapy. The mean overlap coefficient between both MTB was 66%. Highest agreement rates were observed with the interpretation of single nucleotide variants, clinical evidence levels 1 and 2, and monotherapy whereas the interpretation of gene expression changes, preclinical evidence levels 3 and 4, and combination therapy yielded lower agreement rates. Patients receiving treatment following concordant MTB recommendations had significantly longer overall survival than patients receiving treatment following discrepant recommendations or physician’s choice.ConclusionsReproducible clinical interpretation of high-dimensional molecular data is feasible and agreement rates are encouraging, when compared to previous reports. The interpretation of molecular aberrations beyond single nucleotide variants and preclinically validated biomarkers as well as combination therapies were identified as additional difficulties for ongoing harmonization efforts.

Improving VAE based molecular representations for compound property prediction

Collecting labeled data for many important tasks in chemoinformatics is time consuming and requires expensive experiments. In recent years, machine learning has been used to learn rich representations of molecules using large scale unlabeled molecular datasets and transfer the knowledge to solve the more challenging tasks with limited datasets. Variational autoencoders are one of the tools that have been proposed to perform the transfer for both chemical property prediction and molecular generation tasks. In this work we propose a simple method to improve chemical property prediction performance of machine learning models by incorporating additional information on correlated molecular descriptors in the representations learned by variational autoencoders. We verify the method on three property prediction tasks. We explore the impact of the number of incorporated descriptors, correlation between the descriptors and the target properties, sizes of the datasets etc. Finally, we show the relation between the performance of property prediction models and the distance between property prediction dataset and the larger unlabeled dataset in the representation space.

Integrative species delimitation reveals fine-scale allopatric speciation in a good-flying insect: a case study on

Alpha taxonomy is fundamental for many biological fields. Delineation of the species boundary, however, can be challenging in a species complex, where different species share a similar morphology and diagnostic characters may not be available. In this context, integrative approaches that incorporate molecular and morphological data sets, and account for speciation history can be helpful to alpha taxonomy. Different approaches to species delimitation based on different assumptions are complementary and by integrating the results from multiple approaches we can generate a more reliable and objective taxonomic decision. In this study, we applied three molecular approaches to species delimitation and inferred the demographic history based on an isolation with migration model to test a morphologically based taxonomic hypothesis for the Cylindera pseudocylindriformis complex. We discuss the association between genetic divergence and microhabitat specialisation, and further corroborate that C. subtilis sp. nov. is a valid new species by integrating the results from model-based species delimitation and the genealogical divergence index. We argue that genetic endemism can occur at a small geographic scale, even in a winged insect like tiger beetles. Our results also indicated that there may still be undocumented species diversity of Taiwanese Cylindera remaining to be discovered. ZooBank LSID: urn:lsid:zoobank.org:pub:9DEC1432-365C-4872-8D06-73B95F30624F

Species Diversity of Gelidium from Southern Madagascar Evaluated by an Integrative Taxonomic Approach

The diversity of red algae is significantly increasing; in the recent interest of climate changes, taxonomic and biogeographic studies are needed in untouched regions such as ecological important islands. We examined a collection of Gelidium specimens gathered during the 2010 Atimo Vatae expedition based on the morphology and DNA sequencing of mitochondrial cox1 and plastid rbcL. Both morphological and molecular datasets demonstrated the presence of three species in Madagascar; G. leptum G.H.Boo, L.Le Gall and H.S.Yoon, sp. nov., described here, and G. sclerophyllum W.R.Taylor and G. usmanghanii Afaq-Husain and Shameel. Gelidium leptum is distinguished by thin, slender, flattened thalli with irregular branches, compactly arranged thick-walled cells in medulla, sparse rhizoidal filaments at both sides of branches, and elongate sori of tetrasporangia. Gelidium sclerophyllum, previously considered as endemic to the Tropical Eastern Pacific, is first reported in the Indian Ocean and Hawai’i; G. usmanghanii, previously reported to occur in Pakistan and Oman, also presents a first record in the Southern Hemisphere. Phylogenies inferred from cox1 and rbcL sequences suggests that each of the three species likely diverged from different ancestors. The Madagascan Gelidium comprises two geographical elements; Madagascan endemic (G. leptum) and widespread species (G. sclerophyllum and G. usmanghanii).

Improving molecular property prediction through a task similarity enhanced transfer learning strategy

SummaryDeeply understanding the properties (e.g., chemical or biological characteristics) of small molecules plays an essential role in drug development. A large number of molecular property datasets have been rapidly accumulated in recent years. However, most of these datasets contain only a limited amount of data, which hinders deep learning methods from making accurate predictions of the corresponding molecular properties. In this work, we propose a transfer learning strategy to alleviate such a data scarcity problem by exploiting the similarity between molecular property prediction tasks. We introduce an effective and interpretable computational framework, named MoTSE (Molecular Tasks Similarity Estimator), to provide an accurate estimation of task similarity. Comprehensive tests demonstrated that the task similarity derived from MoTSE can serve as useful guidance to improve the prediction performance of transfer learning on molecular properties. We also showed that MoTSE can capture the intrinsic relationships between molecular properties and provide meaningful interpretability for the derived similarity.

Transcriptional states of CAR-T infusion relate to neurotoxicity - lessons from high-resolution single-cell SOM expression portraying.

Anti-CD19 CAR-T cell immunotherapy is a hopeful treatment option for patients with B cell lymphomas, however it copes with partly severe adverse effects like neurotoxicity. Single-cell resolved molecular data sets in combination with clinical parametrization allow for comprehensive characterization of cellular subpopulations, their transcriptomic states, and their relation to the adverse effects. We here present a re-analysis of single-cell RNA sequencing data of 24 patients comprising more than 130,000 cells with focus on cellular states and their association to immune cell related neurotoxicity. For this, we developed a single-cell data portraying workflow to disentangle the transcriptional state space with single-cell resolution and its analysis in terms of modularly-composed cellular programs. We demonstrated capabilities of single-cell data portraying to disentangle transcriptional states using intuitive visualization, functional mining, molecular cell stratification, and variability analyses. Our analysis revealed that the T cell composition of the patient’s infusion product as well as the spectrum of their transcriptional states of cells derived from patients with low ICANS grade do not markedly differ from those of cells from high ICANS patients, while the relative abundancies, particularly that of cycling cells, of LAG3-mediated exhaustion and of CAR positive cells, vary. Our study provides molecular details of the transcriptomic landscape with possible impact to overcome neurotoxicity.

NESM: a network embedding method for tumor stratification by integrating multi-omics data

Tumor stratification plays an important role in cancer diagnosis and individualized treatment. Recent developments in high-throughput sequencing technologies have produced huge amounts of multi-omics data, making it possible to stratify cancer types using multiple molecular datasets. We introduce a Network Embedding method for tumor Stratification by integrating Multi-omics data. Network Embedding method for tumor Stratification by integrating Multi-omics pregroup the samples, integrate the gene features and somatic mutation corresponding to cancer types within each group to construct patient features, and then integrate all groups to obtain comprehensive patient information. The gene features contain network topology information, because it is extracted by integrating deoxyribonucleic acid methylation, messenger ribonucleic acid expression data, and protein-protein interactions through network embedding method. On the one hand, a supervised learning method Light Gradient Boosting Machine is used to classify cancer types based on patient features. When compared with other 3 methods, Network Embedding method for tumor Stratification by integrating Multi-omics has the highest AUC in most cancer types. The average AUC for stratifying cancer types is 0.91, indicating that the patient features extracted by Network Embedding method for tumor Stratification by integrating Multi-omics are effective for tumor stratification. On the other hand, an unsupervised clustering algorithm Density-Based Spatial Clustering of Applications with Noise is utilized to divide single cancer subtypes. The vast majority of the subtypes identified by Network Embedding method for tumor Stratification by integrating Multi-omics are significantly associated with patient survival.

Paleogene forest fragmentation and out‐of‐Africa dispersal explain radiation of the Paleotropical dung beetle tribe Epactoidini trib. nov. (Coleoptera: Scarabaeinae)

AbstractPaleotropical clades with largely disjunct distributions are ideal models for biogeographic reconstructions. The dung beetle genera Grebennikovius Mlambo, Scholtz & Deschodt, Epactoides Olsouffief and Ochicanthon Vaz‐de‐Mello are distributed in Tanzania, Madagascar and Réunion, and the Oriental region, respectively. We combine morphology and molecular dataset to reconstruct the phylogenetic relationships between these taxa. Our analyses corroborate previous hypotheses of monophyly of the group, which is here described as new tribe Epactoidini trib. nov. Grebennikovius is recovered as sister to Epactoides, while Ochicanthon emerges as sister to them both. The disjunct distribution of our focal clade is unusual within the subfamily Scarabaeinae. Bayesian divergence time estimates and ancestral range reconstructions indicate an African origin of the crown group of the tribe Epactoidini trib. nov. in the early mid Eocene, ca. 46 Ma. The divergence between Epactoides and its sister is dated to 32.3 Ma, while the crown age for the genus Ochicanthon is dated to 27 Ma. We investigate the factors that may have shaped the current distribution of the tribe Epactoidini trib. nov. The formation of the Gomphotherium landbridge, along with favourable environmental conditions would have allowed dry‐intolerant organisms, such as Ochicanthon, to disperse out of Africa. Remarkable climatic stability of the Eastern Arc Mountains was critical for the retention of the monotypic genus Grebennikovius. We suggest two subsequent overwater dispersal events: the migration of the most recent common ancestor (MRCA) of Epactoides from Africa to Madagascar (32.3–29.5 Ma); the lately dispersal of the MRCA of the today's extinct Epactoides giganteus Rossini, Vaz‐de‐Mello & Montreuil to Réunion island from Madagascar (3.4 Ma). We suggest that the high potential of dispersal of Epactoidini trib. nov. dung beetles and the strict association to forest habitat might have triggered two major radiations, one in Madagascar and one in the Oriental Region.

The systematics of Tachymenini (Serpentes, Dipsadidae): An updated classification based on molecular and morphological evidence

AbstractDespite the recent advances in the systematics of snakes, the diversity of several Neotropical groups of species remains poorly understood. The lack of studies focused on the phylogenetic relationship within most of the 20 tribes of Dipsadidae precludes a better understanding of the evolution of this diverse family. Here, we present a comprehensive phylogenetic analysis of Tachymenini, a heterogeneous tribe that comprises 36 viviparous species of dipsadids, grouped in seven genera based on morphological similarities. The tribe is widely distributed throughout South America presenting very distinctive phenotypes, habitats, and behaviours. The phylogenetic relationship among tachymenins is a well‐recognized challenge regarding the systematics of the South American dipsadids. The similar morphotype of some generalist species, combined with the very derived morphology of some strict specialists, creates a complexity of traits that has prevented the comprehension of the systematics of the group. To address such a challenge, we combine molecular (six loci) and morphological (70 characters) datasets in an integrative phylogenetic approach. The resultant phylogenetic trees indicate, with strong support, that three of the seven current recognized genera (Tachymenis, Tomodon, and Thamnodynastes) are non‐monophyletic and, consequently, we propose a new systematic arrangement for Tachymenini. We revalidate two genera, Dryophylax and Mesotes, and we describe three additional monotypic genera, Apographon gen. n., Tachymenoides gen. n., and Zonateres gen. n. to accommodate Tomodon orestes, Tachymenis affinis, and Thamnodynastes lanei, respectively. We also include Tomodon ocellatus and Pseudotomodon trigonatus in Tachymenis and describe a new genus, Galvarinus gen. n., to accommodate the Tachymenis chilensis species group. Furthermore, we also provide an evolutionary scenario for the speciation events based on a time‐calibrated tree, commenting on the diversification and origin of the tribe, and on the probable existence of undescribed species of Mesotes and Dryophylax.

Phylogenetics, integrative taxonomy and systematics of the Sesamia cretica species group (Lepidoptera: Noctuidae: Apameini: Sesamiina), with the description of 21 new species from the Afrotropical region

Summary In this study, 31 species of noctuid stemborers belonging to the genus Sesamia Guenée, 1852 (Lepidoptera: Noctuidae: Noctuinae: Apameini: Sesamiina) are reviewed. All these species are assigned to the Sesamia cretica group sensu Tams & Bowden (1953). Based on genitalic characters, several subgroups are hereby defined. Nine species belong to a species complex defined as the Sesamia albivena Hampson, 1902 subgroup; it consists of S. albivena, S. mocoensis Tams & Bowden, 1953, n. stat., S. sudanensis Tams & Bowden, 1953, n. stat. S. taenioleuca (Wallengren, 1863), and five new species that are described (S. aethiopica Le Ru n. sp. from Ethiopia, S. kafulo Le Ru n. sp. from Botswana and Zambia, S. kavirondo Le Ru n. sp. from Kenya and Uganda, S. maloukou Le Ru n. sp. from Republic of Congo, and S. soyema Le Ru n. sp. from Ethiopia). Four species belong to a species complex defined as the Sesamia cretica subgroup; this encompasses S. cretica, S. rufescens Hampson, 1910, and two new species that are described (S. ihambane Le Ru n. sp. from Mozambique and Tanzania and S. kikuyuensis Le Ru n. sp. from Kenya); two new synonyms are introduced for Sesamia cretica: Nonagria uniformis Dudgeon, 1905 n. syn. and Sesamia griselda Warren, 1913, n. syn. Ten species belong to a species complex defined as the Sesamia fuscifrontia Hampson, 1914 subgroup; this includes S. fuscifrontia, S. geyri (Strand, 1915) and eight new species that are described (S. babati Le Ru n. sp. from Tanzania, S. babessi Le Ru n. sp. from Cameroon and Zambia, S. mabira Le Ru n. sp. from Uganda, S. nangaensis Le Ru n. sp. from Cameroon and Republic of Congo, S. rungwa Le Ru n. sp. from Tanzania, S. simillima Le Ru n. sp. from Benin, Cameroon, Kenya and Uganda, S. taveta Le Ru n. sp. from Kenya and S. ulaukae Le Ru n. sp. from Ethiopia). One species belongs to a species complex defined as the Sesamia salama Le Ru n. sp. subgroup; this consists of S. salama Le Ru n. sp. from Kenya and another undescribed Sesamia species from South Africa. One species belongs to a species complex defined as the Sesamia viettei Rungs, 1954 subgroup. Six species belong to a species complex defined as the Sesamia wiltshirei Rungs, 1963 subgroup; this groups S. wiltshirei and five new species that are described (S. djenoensis Le Ru n. sp. from Republic of Congo, S. inexpectata Le Ru n. sp. from South Africa and Zambia, S. lefini Le Ru n. sp. from Republic of Congo, S. echinochloa Le Ru n. sp. from Botswana, Kenya, Mozambique, South Africa, Tanzania and Zambia and S. rindini Le Ru n. sp. from Tanzania). A supplemental description of the previously described species is also provided. Novel host plant records are also provided for 11 species of the S. cretica group. To complement the morphological study, both phylogenetic and molecular species delimitation analyses were carried out on a multimarker (four mitochondrial and two nuclear genes) molecular dataset encompassing 144 specimens representing 35 species (including 25 species from the S. cretica group). Molecular analyses provide a well-supported phylogenetic framework for the species of interest, which are all recovered monophyletic. Molecular species delimitation analyses also support the species status of almost all sampled species. Interestingly, the inferred tree indicates that the S. cretica group and the S. fuscifrontia subgroup are both paraphyletic; this indicates that, while highly informative, the chosen genitalic characters in Sesamia are not all synapomorphies.

Identification of potential core genes and miRNAs in pediatric ACC via bioinformatics analysis.

Pediatric adrenocortical carcinomas (ACC) are rare aggressive neoplasms with heterogeneous prognosis, and often produce a most lethal malignant tumor, whereas its aetiology is still unclear. The aim of the present study was to identify the factors responsible for the development of pediatric ACC, a better understanding of the disease, and investigate new molecular biomarkers and therapeutic targets. To identify the key genes and miRNAs linked to pediatric ACC, as well as their potential molecular mechanisms, the GSEGSE75415 and GSE169253 microarray datasets were analyzed. A total of 329 differentially produced genes (DEGs) and 187 differentially produced miRNAs (DEMs) were obtained after analyzing the GSEGSE75415 and GSE169253 datasets, respectively. Next, 3,359 genes were obtained by overlapping the target mRNAs of DEMs. Following protein-protein interaction network and Gene Ontology analysis, the ten nodes with the highest degrees were screened as hub genes. Among them, the highly expressed hub genes, MAPK1 and EP300, were associated with a worse overall survival. Additionally, hsa-miR-376, hsa-miR-148, hsa-miR-139, and hsa-miR-1305 were strongly associated with poorer survival. We proposed that the hub genes (MAPK1, EP300, hsa-miR-376, hsa-miR-148, hsa-miR-139, and hsa-miR-1305) may have a definite impact on cellular proliferation and migration in adrenocortical tumors. The roles of these hub genes in adrenocortical tumors may provide novel insight to improve the diagnosis and treatment of patients with pediatric ACC.

Drivers of intraspecific differentiation of an alpine cold‐tolerant herb, Notopterygium oviforme: Roles of isolation by distance and ecological factors

AbstractDetermining the mechanisms that drive phenotypic and genetic divergence by species has been a central topic in evolutionary biology for many years. However, the relative contributions of isolation by distance (IBD) and environment factors to species divergence are largely unknown in perennial herbs in East Asia. In this study, we collected population genetic samples of the cold‐tolerant perennial herb Notopterygium oviforme R. H. Shan in central China. We integrated the population transcriptomes, whole chloroplast genomes, genotyping‐by‐sequencing, microsatellite markers, and ecological factors to determine the relative contributions of geography, climate, and soil factors to genetic, chemical, and phenotypic divergence. A clear genetic distinction was identified between the West Qinling and East Qinling Mountains geographical groups of N. oviforme based on various molecular datasets. Interestingly, obvious niche conservation might have been responsible for the similarity of most of the leaf functional traits in the two groups. Multiple matrix regression with randomization analysis showed that the spatial intraspecific divergence pattern was mainly due to IBD rather than isolation by environment. In addition, transcriptomic expression divergence and some ecological factors have played key roles in intraspecific lineage differentiation. Approximate Bayesian Computation showed that both lineages have experienced historical population expansion and recent range contraction. Molecular dating suggested that intraspecific divergence was closely associated with dramatic uplifts of the Qinling Mountains in East Asia. These results demonstrate that geographical, geological, and environmental factors together shaped cryptic intraspecific diversification and shifts in the population dynamics of the perennial cold‐tolerant herb N. oviforme.

Becoming urban: How city life shapes the social structure and genetics of ants.

Cities and urban environments can do peculiar things to biodiversity that shares them with us. How cities affect their invited and uninvited inhabitants has become an increasingly important question. More than half of the world's population dwells in urban areas, and these environments will keep expanding considerably. Understanding how this relatively recent, rapid, and pervasive form of landscape modification influences the ecology and evolution of organisms that cannot escape, or may benefit from it, is an emerging field of biology. Although we are aware of how some birds, mammals or plants respond to urban environments, less is known about insects and invertebrates in general. In this issue of Molecular Ecology, Blumenfeld et al. (2022) bring new remarkable insights into how a common ant species adjusts to urban settings across the United States by changing its social structure and behaviour. Using a large‐scale molecular, chemical and behavioural dataset, they document how the odorous house ant Tapinoma sessile differs in its colony organisation and dispersal strategy between rural and urban habitats. In each of the study regions and continent‐wide, rural and urban colonies are genetically and chemically differentiated, suggesting that urban settings act as potent agents of selection and isolation. The novelty and importance of this study are that it documents multiple independent transitions toward the same social organisation and the apparent effect of habitat on the life history of a eusocial insect species.

Late Cenozoic environmental changes drove the diversification of a weevil genus endemic to the Cape Floristic Region

AbstractThe Cape Floristic Region in the Republic of South Africa is a well‐recognized hotspot of biodiversity. Although this region is mostly known for its high level of plant diversity and endemicity, it also hosts an understudied and likely diverse arthropod fauna. Here we investigate the evolutionary history and timing of diversification of the apterous weevil genus Phlyctinus (Curculionidae: Entiminae), which is endemic to the coastal area and adjacent mountain ranges of the Cape floristic region and generally associated with sunflower plants (Asteraceae). We use a diverse array of molecular analyses (phylogenetic inference, molecular species delimitation and dating analyses) to analyse a novel molecular dataset of 202 weevil specimens (including 170 Phlyctinus sampled in 60 sites), and sequenced for two mitochondrial and four nuclear gene fragments. Phylogenetic and dating analyses indicate that the genus started diversifying in the late Miocene, with contrasting diversification dynamics for the three inferred clades, which present disjunct distributions. Host plant records and the lack of relatedness of species living in sympatry indicate that the diversification of Phlyctinus was predominantly driven by allopatric (geographic) speciation. We hypothesize that the interplay between topography and recurring cycles of coastline‐habitat fragmentation resulting from sea level oscillations spurred the diversification of the most speciose clade, whereas in the two remaining clades populations likely remained connected thus hampering allopatric speciation. Interestingly, this pattern echoes with the role of sea level oscillations as an important driver of the radiation of several lineages in the coastline ecosystems of the Cape Floristic Region.

The evolving role of morphology in endometrial cancer diagnostics: From histopathology and molecular testing towards integrative data analysis by deep learning

Endometrial cancer (EC) diagnostics is evolving into a system in which molecular aspects are increasingly important. The traditional histological subtype-driven classification has shifted to a molecular-based classification that stratifies EC into DNA polymerase epsilon mutated (POLEmut), mismatch repair deficient (MMRd), and p53 abnormal (p53abn), and the remaining EC as no specific molecular profile (NSMP). The molecular EC classification has been implemented in the World Health Organization 2020 classification and the 2021 European treatment guidelines, as it serves as a better basis for patient management. As a result, the integration of the molecular class with histopathological variables has become a critical focus of recent EC research. Pathologists have observed and described several morphological characteristics in association with specific genomic alterations, but these appear insufficient to accurately classify patients according to molecular subgroups. This requires pathologists to rely on molecular ancillary tests in routine workup. In this new era, it has become increasingly challenging to assign clinically relevant weights to histological and molecular features on an individual patient basis. Deep learning (DL) technology opens new options for the integrative analysis of multi-modal image and molecular datasets with clinical outcomes. Proof-of-concept studies in other cancers showed promising accuracy in predicting molecular alterations from H&E-stained tumor slide images. This suggests that some morphological characteristics that are associated with molecular alterations could be identified in EC, too, expanding the current understanding of the molecular-driven EC classification. Here in this review, we report the morphological characteristics of the molecular EC classification currently identified in the literature. Given the new challenges in EC diagnostics, this review discusses, therefore, the potential supportive role that DL could have, by providing an outlook on all relevant studies using DL on histopathology images in various cancer types with a focus on EC. Finally, we touch upon how DL might shape the management of future EC patients.

Identification and characterization of in vitro expanded hematopoietic stem cells

Hematopoietic stem cells (HSCs) cultured outside the body are the fundamental component of a wide range of cellular and gene therapies. Recent efforts have achieved > 200‐fold expansion of functional HSCs, but their molecular characterization has not been possible since the majority of cells are non‐HSCs and single cell‐initiated cultures have substantial clone‐to‐clone variability. Using the Fgd5 reporter mouse in combination with the EPCR surface marker, we report exclusive identification of HSCs from non‐HSCs in expansion cultures. By directly linking single‐clone functional transplantation data with single‐clone gene expression profiling, we show that the molecular profile of expanded HSCs is similar to proliferating fetal HSCs and reveals a gene expression signature, including Esam, Prdm16, Fstl1, and Palld, that can identify functional HSCs from multiple cellular states. This “repopulation signature” (RepopSig) also enriches for HSCs in human datasets. Together, these findings demonstrate the power of integrating functional and molecular datasets to better derive meaningful gene signatures and opens the opportunity for a wide range of functional screening and molecular experiments previously not possible due to limited HSC numbers.

Rainforest pelican spiders (Archaeidae: Austrarchaea) of south-eastern Queensland, Australia: two new species and a distributional reassessment of regional endemic clades

Two new species of pelican spiders (family Archaeidae) are described from rainforest habitats in south-eastern Queensland, Australia – Austrarchaea davidi Rix, sp. nov. and A. laidlawae Rix, sp. nov. – and the female of A. clyneae Rix & Harvey, 2011 is described for the first time. Phylogenetic analysis of a mitochondrial molecular dataset for the genes cytochrome c oxidase subunits I and II reveals that these two new species are the closest relatives of A. judyae Rix & Harvey, 2011 and A. clyneae, respectively, with mitochondrial sequencing also used to identify newly-collected specimens of Austrarchaea from populations throughout south-eastern Queensland. These recent collections were largely made during dedicated survey work conducted after the devastating 2019–20 summer bushfires in eastern Australia. We further provide a synopsis of the archaeid fauna of south-eastern Queensland, which is comprised of 11 species in two monophyletic regional-endemic clades. A revised key to species is presented for both clades, along with updated distributional information, and live habitus images for nine of the 11 species.

Pan-cancer proteomic map of 949 human cell lines.

SummaryThe proteome provides unique insights into disease biology beyond the genome and transcriptome. A lack of large proteomic datasets has restricted the identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types are analyzed by mass spectrometry. Deploying a workflow to quantify 8,498 proteins, these data capture evidence of cell-type and post-transcriptional modifications. Integrating multi-omics, drug response, and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline reveals thousands of protein biomarkers of cancer vulnerabilities that are not significant at the transcript level. The power of the proteome to predict drug response is very similar to that of the transcriptome. Further, random downsampling to only 1,500 proteins has limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger) is a comprehensive resource available at https://cellmodelpassports.sanger.ac.uk.