BMC Biology最新文献

Background: Pangolins are the only mammals that have overlapping scales covering most of their bodies, and they play a crucial role in the ecosystem, biological research, and human health and disease. Previous studies indicated pangolin scale might provide an important mechanical defense to themselves. The origin and exact functions of this unique trait remain a mystery. Using a multi-omics analysis approach, we report a novel functional explanation for how mammalian scales can provide host-pathogen defense.

Results: Our data suggest that pangolin scales have a sophisticated structure that could potentially trap pathogens. We identified numerous proteins and metabolites exhibiting antimicrobial activity, which could suggest a role for scales in pathogen defense. Notably, we found evidence suggesting the presence of exosomes derived from diverse cellular origins, including mesenchymal stem cells, immune cells, and keratinocytes. This observation suggests a complex interplay where various cell types may contribute to the release of exosomes and antimicrobial compounds at the interface between scales and viable tissue. These findings indicate that pangolin scales may serve as a multifaceted defense system, potentially contributing to innate immunity. Comparisons with human nail and hair revealed pangolin-specific proteins that were enriched in functions relating to sensing, immune responses, neutrophil degranulation, and stress responses. We demonstrated the antimicrobial activity of key pangolin scale components on pathogenic bacteria by antimicrobial assays.

Conclusions: This study identifies a potential role of pangolin scales and implicates scales, as possible determinants of pathogen defense due to their structure and contents. We indicate for the first time the presence of exosomes in pangolin scales and propose the new functions of scales and their mechanisms. This new mechanism could have implications for multiple fields, including providing interesting new research directions and important insights that can be useful for synthesizing and implementing new biomimetic antimicrobial approaches.

Background: Juvenile hormone (JH) is an insect-exclusive hormone involved in regulating diverse aspects of insect physiology, and the evolution of its diverse function is widely interesting. Studying embryogenesis in basal wingless insects is important to understand the functional evolution of JH; however, experimental studies in this regard are scarce. In this study, we conducted CRISPR/Cas9-mediated knockout (KO) of genes involved in JH biosynthesis and signaling cascades in the ametabolous firebrat, Thermobia domestica. Additionally, we investigated whether the primitive action of JH is conserved in the hemimetabolous cricket, Gryllus bimaculatus.

Results: We observed that KO of JHAMT, CYP15A1, Met, and Kr-h1 resulted in embryonic lethality in T. domestica. Deprivation of JH or JH signaling arrested the progression of extraembryonic fluid resorption after dorsal closure and hatching, which is consistent with the gene expression pattern showing high Kr-h1 expression in the late embryos of T. domestica. The embryos deficient in JH signaling displayed wrinkled and weak legs. Comparative transcriptome analysis revealed that JH signaling promotes embryonic leg maturation through inducing energy supply and muscle activity, as validated by transmission electron microscopy (TEM). In addition, JH signaling exhibited similar embryonic effects in G. bimaculatus.

Conclusions: This study reveals the indispensable role of JH signaling in facilitating the maturation of terminal tissues during late embryogenesis, as demonstrated by the regulation of leg development, in ametabolous and hemimetabolous insects. These findings further indicate that the embryonic functions of JH evolved earlier than its anti-metamorphic functions during postembryonic development.

Background: Organisms have evolved a range of phenotypic and genetic adaptations to live in different environments along an altitudinal gradient. Herein, we studied the widely distributed Chinese toad, Bufo gargarizans, as a model and used an integrated phenotype-genotype approach to assess adaptations to different altitudinal environments.

Results: Comparison of populations from four altitudes (50 m, 1200 m, 2300 m, and 3400 m) showed more effective defenses among high-altitude toads. These included thickened epidermis, more epidermal capillaries and granular glands, greater gland size in skin, and higher antioxidant enzyme activities in plasma. High-altitude toads also showed increased erythrocytes and hematocrit and elevated hemoglobin concentration, potentially improving oxygen delivery. Elevated altitude led to a metabolic shift from aerobic to anaerobic metabolism, and high-altitude populations favored carbohydrates over fatty acids to fuel for energy metabolism. Differentially expressed genes were associated with adaptive phenotypic changes. For instance, expression of genes associated with fatty acid metabolism showed greater suppression at high altitude (3400 m), consistent with decreased flux of β-hydroxybutyric acid and lower free fatty acids levels. Moreover, down-regulation of genes involved in carbon metabolism processes at high altitude (3400 m) were coincident with reduced TCA cycle flux. These results suggest that high-altitude toads adopt a metabolic suppression strategy for survival under harsh environmental conditions. Moreover, the hypoxia-inducible factor signaling cascade was activated at high altitude.

Conclusions: Collectively, these results advance our comprehension of adaptation to high-altitude environments by revealing physiological and genetic mechanisms at work in Chinese toads living along altitudinal gradients.

Background: Mitochondrial genes and nuclear genes cooperate closely to maintain the functions of mitochondria, especially in the oxidative phosphorylation (OXPHOS) pathway. However, mitochondrial genes among arthropod lineages have dramatic evolutionary rate differences. Haplodiploid arthropods often show fast-evolving mitochondrial genes. One hypothesis predicts that the small effective population size of haplodiploid species could enhance the effect of genetic drift leading to higher substitution rates in mitochondrial and nuclear genes. Alternatively, positive selection or compensatory changes in nuclear OXPHOS genes could lead to the fast-evolving mitochondrial genes. However, due to the limited number of arthropod genomes, the rates of evolution for nuclear genes in haplodiploid species, besides hymenopterans, are largely unknown. To test these hypotheses, we used data from 76 arthropod genomes, including 5 independently evolved haplodiploid lineages, to estimate the evolutionary rates and patterns of gene family turnover of mitochondrial and nuclear genes.

Results: We show that five haplodiploid lineages tested here have fast-evolving mitochondrial genes and fast-evolving nuclear genes related to mitochondrial functions, while nuclear genes not related to mitochondrion showed no significant evolutionary rate differences. Among hymenopterans, bees and ants show faster rates of molecular evolution in mitochondrial genes and mitochondrion-related nuclear genes than sawflies and wasps. With genome data, we also find gene family expansions and contractions in mitochondrion-related genes of bees and ants.

Conclusions: Our results reject the small population size hypothesis in haplodiploid species. A combination of positive selection and compensatory changes could lead to the observed patterns in haplodiploid species. The elevated evolutionary rates in OXPHOS complex 2 genes of bees and ants suggest a unique evolutionary history of social hymenopterans.

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.