BMC Biology最新文献

Background: Evolutionary radiation is widely recognized as a mode of species diversification, but the drivers of the rapid diversification of fungi remain largely unknown. Here, we used Amanitaceae, one of the most diverse families of macro-fungi, to investigate the mechanism underlying its diversification.

Results: The ancestral state of the nutritional modes was assessed based on phylogenies obtained from fragments of 36 single-copy genes and stable isotope analyses of carbon and nitrogen. Moreover, a number of time-, trait-, and paleotemperature-dependent models were employed to investigate if the acquisition of ectomycorrhizal (ECM) symbiosis and climate changes promoted the diversification of Amanitaceae. The results indicate that the evolution of ECM symbiosis has a single evolutionary origin in Amanitaceae. The earliest increase in diversification coincided with the acquisition of the ECM symbiosis with angiosperms in the middle Cretaceous. The recent explosive diversification was primarily triggered by the host-plant switches from angiosperms to the mixed forests dominated by Fagaceae, Salicaceae, and Pinaceae or to Pinaceae.

Conclusions: Our study provides a good example of integrating phylogeny, nutritional mode evolution, and ecological analyses for deciphering the mechanisms underlying fungal evolutionary diversification. This study also provides new insights into how the transition to ECM symbiosis has driven the diversification of fungi.

Background: Accurate and robust drug response prediction is of utmost importance in precision medicine. Although many models have been developed to utilize the representations of drugs and cancer cell lines for predicting cancer drug responses (CDR), their performances can be improved by addressing issues such as insufficient data modality, suboptimal fusion algorithms, and poor generalizability for novel drugs or cell lines.

Results: We introduce TransCDR, which uses transfer learning to learn drug representations and fuses multi-modality features of drugs and cell lines by a self-attention mechanism, to predict the IC₅₀ values or sensitive states of drugs on cell lines. We are the first to systematically evaluate the generalization of the CDR prediction model to novel (i.e., never-before-seen) compound scaffolds and cell line clusters. TransCDR shows better generalizability than 8 state-of-the-art models. TransCDR outperforms its 5 variants that train drug encoders (i.e., RNN and AttentiveFP) from scratch under various scenarios. The most critical contributors among multiple drug notations and omics profiles are Extended Connectivity Fingerprint and genetic mutation. Additionally, the attention-based fusion module further enhances the predictive performance of TransCDR. TransCDR, trained on the GDSC dataset, demonstrates strong predictive performance on the external testing set CCLE. It is also utilized to predict missing CDRs on GDSC. Moreover, we investigate the biological mechanisms underlying drug response by classifying 7675 patients from TCGA into drug-sensitive or drug-resistant groups, followed by a Gene Set Enrichment Analysis.

Conclusions: TransCDR emerges as a potent tool with significant potential in drug response prediction.

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.

Background: The phylum Nematoda is incredibly diverse and includes many parasites of humans, livestock, and plants. Peptide-activated G protein-coupled receptors (GPCRs) are central to the regulation of physiology and numerous behaviors, and they represent appealing pharmacological targets for parasite control. Efforts are ongoing to characterize the functions and define the ligands of nematode GPCRs, with already most peptide GPCRs known or predicted in Caenorhabditis elegans. However, comparative analyses of peptide GPCR conservation between C. elegans and other nematode species are limited, and many nematode GPCRs remain orphan. A phylum-wide perspective on peptide GPCR profiles will benefit functional and applied studies of nematode peptide GPCRs.

Results: We constructed a pan-phylum resource of C. elegans peptide GPCR orthologs in 125 nematode species using a semi-automated pipeline for analysis of predicted proteome datasets. The peptide GPCR profile varies between nematode species of different phylogenetic clades and multiple C. elegans peptide GPCRs have orthologs across the phylum Nematoda. We identified peptide ligands for two highly conserved orphan receptors, NPR-9 and NPR-16, that belong to the bilaterian galanin/allatostatin A (Gal/AstA) and somatostatin/allatostatin C (SST/AstC) receptor families. The AstA-like NLP-1 peptides activate NPR-9 in cultured cells and are cognate ligands of this receptor in vivo. In addition, we discovered an AstC-type peptide, NLP-99, that activates the AstC-type receptor NPR-16. In our pan-phylum resource, the phylum-wide representation of NPR-9 and NPR-16 resembles that of their cognate ligands more than those of allatostatin-like peptides that do not activate these receptors.

Conclusions: The repertoire of C. elegans peptide GPCR orthologs varies across phylogenetic clades and several peptide GPCRs show broad conservation in the phylum Nematoda. Our work functionally characterizes the conserved receptors NPR-9 and NPR-16 as the respective GPCRs for the AstA-like NLP-1 peptides and the AstC-related peptide NLP-99. NLP-1 and NLP-99 are widely conserved in nematodes and their representation matches that of their receptor in most species. These findings demonstrate the conservation of a functional Gal/AstA and SST/AstC signaling system in nematodes. Our dataset of C. elegans peptide GPCR orthologs also lays a foundation for further functional studies of peptide GPCRs in the widely diverse nematode phylum.

Background: Homologous recombination deficiency (HRD) is recognized as a pan-cancer predictive biomarker that potentially indicates who could benefit from treatment with PARP inhibitors (PARPi). Despite its clinical significance, HRD testing is highly complex. Here, we investigated in a proof-of-concept study whether Deep Learning (DL) can predict HRD status solely based on routine hematoxylin & eosin (H&E) histology images across nine different cancer types.

Methods: We developed a deep learning pipeline with attention-weighted multiple instance learning (attMIL) to predict HRD status from histology images. As part of our approach, we calculated a genomic scar HRD score by combining loss of heterozygosity (LOH), telomeric allelic imbalance (TAI), and large-scale state transitions (LST) from whole genome sequencing (WGS) data of n = 5209 patients across two independent cohorts. The model's effectiveness was evaluated using the area under the receiver operating characteristic curve (AUROC), focusing on its accuracy in predicting genomic HRD against a clinically recognized cutoff value.

Results: Our study demonstrated the predictability of genomic HRD status in endometrial, pancreatic, and lung cancers reaching cross-validated AUROCs of 0.79, 0.58, and 0.66, respectively. These predictions generalized well to an external cohort, with AUROCs of 0.93, 0.81, and 0.73. Moreover, a breast cancer-trained image-based HRD classifier yielded an AUROC of 0.78 in the internal validation cohort and was able to predict HRD in endometrial, prostate, and pancreatic cancer with AUROCs of 0.87, 0.84, and 0.67, indicating that a shared HRD-like phenotype occurs across these tumor entities.

Conclusions: This study establishes that HRD can be directly predicted from H&E slides using attMIL, demonstrating its applicability across nine different tumor types.

Background: Nematodes are the most abundant metazoans in marine sediments, many of which are bacterivores; however, how habitat bacteria affect physiological outcomes in marine nematodes remains largely unknown.  RESULTS: Here, we used a Litoditis marina inbred line to assess how native bacteria modulate host nematode physiology. We characterized seasonal dynamic bacterial compositions in L. marina habitats and examined the impacts of 448 habitat bacteria isolates on L. marina development, then focused on HQbiome with 73 native bacteria, of which we generated 72 whole genomes sequences. Unexpectedly, we found that the effects of marine native bacteria on the development of L. marina and its terrestrial relative Caenorhabditis elegans were significantly positively correlated. Next, we reconstructed bacterial metabolic networks and identified several bacterial metabolic pathways positively correlated with L. marina development (e.g., ubiquinol and heme b biosynthesis), while pyridoxal 5'-phosphate biosynthesis pathway was negatively associated. Through single metabolite supplementation, we verified CoQ₁₀, heme b, acetyl-CoA, and acetaldehyde promoted L. marina development, while vitamin B6 attenuated growth. Notably, we found that only four development correlated metabolic pathways were shared between L. marina and C. elegans. Furthermore, we identified two bacterial metabolic pathways correlated with L. marina lifespan, while a distinct one in C. elegans. Strikingly, we found that glycerol supplementation significantly extended L. marina but not C. elegans longevity. Moreover, we comparatively demonstrated the distinct gut microbiota characteristics and their effects on L. marina and C. elegans physiology.

Conclusions: Given that both bacteria and marine nematodes are dominant taxa in sedimentary ecosystems, the resource presented here will provide novel insights to identify mechanisms underpinning how habitat bacteria affect nematode biology in a more natural context. Our integrative approach will provide a microbe-nematodes framework for microbiome mediated effects on host animal fitness.

Background: Abnormalities of in utero testis development are strongly associated with reproductive health conditions, including male infertility and testis cancer. In mouse testes, SOX9 and FGF9 support Sertoli cell development, while VEGF signalling is essential for the establishment of vasculature. The mitogen-activated protein kinase (MAPK) pathway is a major signalling cascade, essential for cell proliferation, differentiation and activation of Sry during primary sex-determination, but little is known about its function during fetal testis morphogenesis. We explored potential functions of MAPK signalling immediately after the establishment of testis cords in embryonic day (E)12.5 Oct4-eGFP transgenic mouse testes cultured using a MEK1/2 inhibitor.

Results: RNA sequencing in isolated gonadal somatic cells identified 116 and 114 differentially expressed genes after 24 and 72 h of MEK1/2 inhibition, respectively. Ingenuity Pathway Analysis revealed an association of MEK1/2 signalling with biological functions such as angiogenesis, vasculogenesis and cell migration. This included a failure to upregulate the master transcriptional regulators of vascular development, Sox7 and Sox17, VEGF receptor genes, the cell adhesion factor gene Cd31 and a range of other endothelial cell markers such as Cdh5 (encoding VE-cadherin) and gap junction genes Gja4 and Gja5. In contrast, only a small number of Sertoli cell enriched genes were affected. Immunofluorescent analyses of control testes revealed that the MEK1/2 downstream target, ERK1/2 was phosphorylated in endothelial cells and Sertoli cells. Inhibition of MEK1/2 eliminated pERK1/2 in fetal testes, and CD31, VE-cadherin, SOX7 and SOX17 and endothelial cells were lost. Consistent with a role for VEGF in driving endothelial cell development in the testis, inhibition of VEGFR also abrogated pERK1/2 and SOX7 and SOX17 expressing endothelial cells. Moreover, while Sertoli cell proliferation and localisation to the testis cord basement membrane was disrupted by inhibition of MEK1/2, it was unaffected by VEGFR inhibition. Instead, inhibition of FGF signalling compromised Sertoli cell proliferation and localisation to the testis cord basement membrane.

Conclusions: Together, our data highlight an essential role for VEGF-dependent MEK1/2 signalling in promoting vasculature and indicate that FGF signalling through MEK1/2 regulates Sertoli cell organisation in the developing mouse testis.

Background: Eukaryotic cells are highly compartmentalized by a variety of organelles that carry out specific cellular processes. The position of these organelles within the cell is elaborately regulated and vital for their function. For instance, the position of lysosomes relative to the nucleus controls their degradative capacity and is altered in pathophysiological conditions. The molecular components orchestrating the precise localization of organelles remain incompletely understood. A confounding factor in these studies is the fact that organelle positioning is surprisingly non-trivial to address e.g., perturbations that affect the localization of organelles often lead to secondary phenotypes such as changes in cell or organelle size. These phenotypes could potentially mask effects or lead to the identification of false positive hits. To uncover and test potential molecular components at scale, accurate and easy-to-use analysis tools are required that allow robust measurements of organelle positioning.

Results: Here, we present an analysis workflow for the faithful, robust, and quantitative analysis of organelle positioning phenotypes. Our workflow consists of an easy-to-use Fiji plugin and an R Shiny App. These tools enable users without background in image or data analysis to (1) segment single cells and nuclei and to detect organelles, (2) to measure cell size and the distance between detected organelles and the nucleus, (3) to measure intensities in the organelle channel plus one additional channel, (4) to measure radial intensity profiles of organellar markers, and (5) to plot the results in informative graphs. Using simulated data and immunofluorescent images of cells in which the function of known factors for lysosome positioning has been perturbed, we show that the workflow is robust against common problems for the accurate assessment of organelle positioning such as changes of cell shape and size, organelle size and background.

Conclusions: OrgaMapper is a versatile, robust, and easy-to-use automated image analysis workflow that can be utilized in microscopy-based hypothesis testing and screens. It effectively allows for the mapping of the intracellular space and enables the discovery of novel regulators of organelle positioning.

Background: The growth factor receptor bound protein 7 (Grb7) family of signalling adaptor proteins comprises Grb7, Grb10 and Grb14. Each can interact with the insulin receptor and other receptor tyrosine kinases, where Grb10 and Grb14 inhibit insulin receptor activity. In cell culture studies they mediate functions including cell survival, proliferation, and migration. Mouse knockout (KO) studies have revealed physiological roles for Grb10 and Grb14 in glucose-regulated energy homeostasis. Both Grb10 KO and Grb14 KO mice exhibit increased insulin signalling in peripheral tissues, with increased glucose and insulin sensitivity and a modestly increased ability to clear a glucose load. In addition, Grb10 strongly inhibits fetal growth such that at birth Grb10 KO mice are 30% larger by weight than wild type littermates.

Results: Here, we generate a Grb7 KO mouse model. We show that during fetal development the expression patterns of Grb7 and Grb14 each overlap with that of Grb10. Despite this, Grb7 and Grb14 did not have a major role in influencing fetal growth, either alone or in combination with Grb10. At birth, in most respects both Grb7 KO and Grb14 KO single mutants were indistinguishable from wild type, while Grb7:Grb10 double knockout (DKO) were near identical to Grb10 KO single mutants and Grb10:Grb14 DKO mutants were slightly smaller than Grb10 KO single mutants. In the developing kidney Grb7 had a subtle positive influence on growth. An initial characterisation of Grb7 KO adult mice revealed sexually dimorphic effects on energy homeostasis, with females having a significantly smaller renal white adipose tissue depot and an enhanced ability to clear glucose from the circulation, compared to wild type littermates. Males had elevated fasted glucose levels with a trend towards smaller white adipose depots, without improved glucose clearance.

Conclusions: Grb7 and Grb14 do not have significant roles as inhibitors of fetal growth, unlike Grb10, and instead Grb7 may promote growth of the developing kidney. In adulthood, Grb7 contributes subtly to glucose mediated energy homeostasis, raising the possibility of redundancy between all three adaptors in physiological regulation of insulin signalling and glucose handling.

Background: Small RNA (sRNAs)- mediated RNA silencing is emerging as a key player in host-microbe interactions. However, its role in fungus-plant interactions relevant to biocontrol of plant diseases is yet to be explored. This study aimed to investigate Dicer (DCL)-mediated endogenous and cross-kingdom gene expression regulation in the biocontrol fungus Clonostachys rosea and wheat roots during interactions.

Results: C. rosea Δdcl2 strain exhibited significantly higher root colonization than the WT, whereas no significant differences were observed for Δdcl1 strains. Dual RNA-seq revealed the upregulation of CAZymes, membrane transporters, and effector coding genes in C. rosea, whereas wheat roots responded with the upregulation of stress-related genes and the downregulation of growth-related genes. The expression of many of these genes was downregulated in wheat during the interaction with DCL deletion strains, underscoring the influence of fungal DCL genes on wheat defense response. sRNA sequencing identified 18 wheat miRNAs responsive to C. rosea, and three were predicted to target the C. rosea polyketide synthase gene pks29. Two of these miRNAs (mir_17532_x1 and mir_12061_x13) were observed to enter C. rosea from wheat roots with fluorescence analyses and to downregulate the expression of pks29, showing plausible cross-kingdom RNA silencing of the C. rosea gene by wheat miRNAs.

Conclusions: We provide insights into the mechanisms underlying the interaction between biocontrol fungi and plant roots. Moreover, the study sheds light on the role of sRNA-mediated gene expression regulation in C. rosea-wheat interactions and provides preliminary evidence of cross-kingdom RNA silencing between plants and biocontrol fungi.