BMC Biology最新文献

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.

Background: Zoology's dark matter comprises hyperdiverse, poorly known taxa that are numerically dominant but largely unstudied, even in temperate regions where charismatic taxa are well understood. Dark taxa are everywhere, but high diversity, abundance, and small size have historically stymied their study. We demonstrate how entomological dark matter can be elucidated using high-throughput DNA barcoding ("megabarcoding"). We reveal the high abundance and diversity of scuttle flies (Diptera: Phoridae) in Sweden using 31,800 specimens from 37 sites across four seasonal periods. We investigate the number of scuttle fly species in Sweden and the environmental factors driving community changes across time and space.

Results: Swedish scuttle fly diversity is much higher than previously known, with 549 putative specie) detected, compared to 374 previously recorded species. Hierarchical Modelling of Species Communities reveals that scuttle fly communities are highly structured by latitude and strongly driven by climatic factors. Large dissimilarities between sites and seasons are driven by turnover rather than nestedness. Climate change is predicted to significantly affect the 47% of species that show significant responses to mean annual temperature. Results were robust regardless of whether haplotype diversity or species-proxies were used as response variables. Additionally, species-level models of common taxa adequately predict overall species richness.

Conclusions: Understanding the bulk of the diversity around us is imperative during an era of biodiversity change. We show that dark insect taxa can be efficiently characterised and surveyed with megabarcoding. Undersampling of rare taxa and choice of operational taxonomic units do not alter the main ecological inferences, making it an opportune time to tackle zoology's dark matter.

Background: RNA-sequencing technology provides an effective tool for understanding miRNA regulation in complex human diseases, including cancers. A large number of computational methods have been developed to make use of bulk and single-cell RNA-sequencing data to identify miRNA regulations at the resolution of multiple samples (i.e. group of cells or tissues). However, due to the heterogeneity of individual samples, there is a strong need to infer miRNA regulation specific to individual samples to uncover miRNA regulation at the single-sample resolution level.

Results: Here, we develop a framework, Scan, for scanning sample-specific miRNA regulation. Since a single network inference method or strategy cannot perform well for all types of new data, Scan incorporates 27 network inference methods and two strategies to infer tissue-specific or cell-specific miRNA regulation from bulk or single-cell RNA-sequencing data. Results on bulk and single-cell RNA-sequencing data demonstrate the effectiveness of Scan in inferring sample-specific miRNA regulation. Moreover, we have found that incorporating the prior information of miRNA targets can generally improve the accuracy of miRNA target prediction. In addition, Scan can contribute to construct cell/tissue correlation networks and recover aggregate miRNA regulatory networks. Finally, the comparison results have shown that the performance of network inference methods is likely to be data-specific, and selecting optimal network inference methods is required for more accurate prediction of miRNA targets.

Conclusions: Scan provides a useful method to help infer sample-specific miRNA regulation for new data, benchmark new network inference methods and deepen the understanding of miRNA regulation at the resolution of individual samples.

Background: The development of sequence-specific precision treatments like CRISPR gene editing therapies for Duchenne muscular dystrophy (DMD) requires sequence humanized animal models to enable the direct clinical translation of tested strategies. The current available integrated transgenic mouse model containing the full-length human DMD gene, Tg(DMD)72Thoen/J (hDMDTg), has been found to have two copies of the transgene per locus in a tail-to-tail orientation, which does not accurately simulate the true (single) copy number of the DMD gene. This duplication also complicates analysis when testing CRISPR therapy editing outcomes, as large genetic alterations and rearrangements can occur between the cut sites on the two transgenes.

Results: To address this, we performed long read nanopore sequencing on hDMDTg mice to better understand the structure of the duplicated transgenes. Following that, we performed a megabase-scale deletion of one of the transgenes by CRISPR zygotic microinjection to generate a single-copy, full-length, humanized DMD transgenic mouse model (hDMDTgSc). Functional, molecular, and histological characterisation shows that the single remaining human transgene retains its function and rescues the dystrophic phenotype caused by endogenous murine Dmd knockout.

Conclusions: Our unique hDMDTgSc mouse model simulates the true copy number of the DMD gene, and can potentially be used for the further generation of DMD disease models that would be better suited for the pre-clinical assessment and development of sequence specific CRISPR therapies.

Background: Diplomonads are anaerobic flagellates classified within Metamonada. They contain both host-associated commensals and parasites that reside in the intestinal tracts of animals, including humans (e.g., Giardia intestinalis), as well as free-living representatives that inhabit freshwater and marine anoxic sediments (e.g., Hexamita inflata). The evolutionary trajectories within this group are particularly unusual as the free-living taxa appear to be nested within a clade of host-associated species, suggesting a reversal from host-dependence to a secondarily free-living lifestyle. This is thought to be an exceedingly rare event as parasites often lose genes for metabolic pathways that are essential to a free-living life strategy, as they become increasingly reliant on their host for nutrients and metabolites. To revert to a free-living lifestyle would require the reconstruction of numerous metabolic pathways. All previous studies of diplomonad evolution suffered from either low taxon sampling, low gene sampling, or both, especially among free-living diplomonads, which has weakened the phylogenetic resolution and hindered evolutionary insights into this fascinating transition.

Results: We sequenced transcriptomes from 1 host-associated and 13 free-living diplomonad isolates; expanding the genome scale data sampling for diplomonads by roughly threefold. Phylogenomic analyses clearly show that free-living diplomonads form several branches nested within endobiotic species. Moreover, the phylogenetic distribution of genes related to an endobiotic lifestyle suggest their acquisition at the root of diplomonads, while traces of these genes have been identified in free-living diplomonads as well. Based on these results, we propose an evolutionary scenario of ancestral and derived lifestyle transitions across diplomonads.

Conclusions: Free-living taxa form several clades nested within endobiotic taxa in our phylogenomic analyses, implying multiple transitions between free-living and endobiotic lifestyles. The evolutionary history of numerous virulence factors corroborates the inference of an endobiotic ancestry of diplomonads, suggesting that there have been several reversals to a free-living lifestyle. Regaining host independence may have been facilitated by a subset of laterally transferred genes. We conclude that the extant diversity of diplomonads has evolved from a non-specialized endobiont, with some taxa becoming highly specialized parasites, others becoming free-living, and some becoming capable of both free-living and endobiotic lifestyles.

Background: Drug-target interaction (DTI) prediction plays a pivotal role in drug discovery and drug repositioning, enabling the identification of potential drug candidates. However, most previous approaches often do not fully utilize the complementary relationships among multiple biological networks, which limits their ability to learn more consistent representations. Additionally, the selection strategy of negative samples significantly affects the performance of contrastive learning methods.

Results: In this study, we propose CCL-ASPS, a novel deep learning model that incorporates Collaborative Contrastive Learning (CCL) and Adaptive Self-Paced Sampling strategy (ASPS) for drug-target interaction prediction. CCL-ASPS leverages multiple networks to learn the fused embeddings of drugs and targets, ensuring their consistent representations from individual networks. Furthermore, ASPS dynamically selects more informative negative sample pairs for contrastive learning. Experiment results on the established dataset demonstrate that CCL-ASPS achieves significant improvements compared to current state-of-the-art methods. Moreover, ablation experiments confirm the contributions of the proposed CCL and ASPS strategies.

Conclusions: By integrating Collaborative Contrastive Learning and Adaptive Self-Paced Sampling, the proposed CCL-ASPS effectively addresses the limitations of previous methods. This study demonstrates that CCL-ASPS achieves notable improvements in DTI predictive performance compared to current state-of-the-art approaches. The case study and cold start experiments further illustrate the capability of CCL-ASPS to effectively predict previously unknown DTI, potentially facilitating the identification of new drug-target interactions.