Curcumol alleviates liver fibrosis by inducing endoplasmic reticulum stress-mediated necroptosis of hepatic stellate cells through Sirt1/NICD pathway
Liver fibrosis is a repair response process after chronic liver injury. During this process, activated hepatic stellate cells (HSCs) will migrate to the injury site and secrete extracellular matrix (ECM) to produce fibrous scars. Clearing activated HSCs may be a major strategy for the treatment of liver fibrosis. Curcumol isolated from plants of the genus Curcuma can effectively induce apoptosis of many cancer cells, but whether it can clear activated HSCs remains to be clarified. In the present study, we found that the effect of curcumol in treating liver fibrosis was to clear activated HSCs by inducing necroptosis of HSCs. Receptor-interacting protein kinase 3 (RIP3) silencing could impair necroptosis induced by curcumol. Interestingly, endoplasmic reticulum (ER) stress-induced cellular dysfunction was associated with curcumol-induced cell death. The ER stress inhibitor 4-PBA prevented curcumol-induced ER stress and necroptosis. We proved that ER stress regulated curcumol-induced necroptosis in HSCs via Sirtuin-1(Sirt1)/Notch signaling pathway. Sirt1-mediated deacetylation of the intracellular domain of Notch (NICD) led to degradation of NICD, thereby inhibiting Notch signalling pathway to alleviate liver fibrosis. Specific knockdown of Sirt1 by HSCs in male ICR mice further exacerbated CCl4-induced liver fibrosis. Overall, our study elucidates the anti-fibrotic effect of curcumol and reveals the underlying mechanism between ER stress and necroptosis.
Sun et al. (2022) Curcumol alleviates liver fibrosis by inducing endoplasmic reticulum stress-mediated necroptosis of hepatic stellate cells through Sirt1/NICD pathway. PeerJ 10:e13376 https://doi.org/10.7717/peerj.13376
Impact of COVID-19 social distancing on medical research from the perspective of postgraduate students: a cross-sectional online survey
Objective To investigate the impact of COVID-19 social distancing on medical research from the perspective of postgraduate students. Methods A cross-sectional study using an online survey was conducted from October 31 to November 1, 2021. A questionnaire was used to assess the impact of COVID-19 social distancing on medical research among postgraduate students. The questionnaire included basic information, medical research information, and information about social distancing measures. Participants also completed the self-made Research Work Affected Scale of Postgraduates (RWAS-P; qualitative evaluation: very mildly 0–10; mildly 11–20; moderately 21–30; severely 31–40; very severely 41–50). Logistic regression was used to identify factors related to the impact of COVID-19 social distancing. Results A total of 468 participants were analyzed; 95.2% of the participants adhered to social distancing measures. The median total RWAS-P score was 22. The median RWAS-P scores for earlier research data, current research projects, future research plans, paper publication, and graduation schedule were 2, 6, 6, 6, and 4, respectively (score range 0–10). The higher grade of students, experimental research, and existence of inappetence or sleeplessness were related to negative attitude towards COVID-19 social distancing (odd ratio = 6.35, 9.80, 2.31, 2.15, 1.95, respectively). Conclusions Participants reported that social distancing had a moderate overall impact on their medical research. Social distancing had the greatest impact on current research projects, future research plans, and paper publications among postgraduate students. Higher grade level, experimental research type, inappetence, and sleeplessness were related to the impact of social distancing on their medical research.
Dong et al. (2022) Impact of COVID-19 social distancing on medical research from the perspective of postgraduate students: a cross-sectional online survey. PeerJ 10:e13384 https://doi.org/10.7717/peerj.13384
Quantifying phenological diversity: a framework based on Hill numbers theory
Background Despite the great concern triggered by the environmental crisis worldwide, the loss of temporal key functions and processes involved in biodiversity maintenance has received little attention. Species are restricted in their life cycles by environmental variables because of their physiological and behavioral properties; thus, the timing and duration of species’ presence and their activities vary greatly between species within a community. Despite the ecological relevance of such variation, there is currently no measure that summarizes the key temporal aspects of biological diversity and allows comparisons of community phenological patterns. Here, we propose a measure that synthesizes variability of phenological patterns using the Hill numbers-based attribute diversity framework. Methods We constructed a new phenological diversity measure based on the aforementioned framework through pairwise overlapping distances, which was supplemented with wavelet analysis. The Hill numbers approach was chosen as an adequate way to define a set of diversity values of different order q, a parameter that determines the sensitivity of the diversity measure to abundance. Wavelet transform analysis was used to model continuous variables from incomplete data sets for different phenophases. The new measure, which we call Phenological Hill numbers (PD), considers the decouplings of phenophases through an overlapping area value between pairs of species within the community. PD was first tested through simulations with varying overlap in phenophase magnitude and intensity and varying number of species, and then by using one real data set. Results PD maintains the diversity patterns of order q as in any other diversity measure encompassed by the Hill numbers framework. Minimum PD values in the simulated data sets reflect a lack of differentiation in the phenological curves of the community over time; by contrast, the maximum PD values reflected the most diverse simulations in which phenological curves were equally distributed over time. PD values were consistent with the homogeneous distribution of the intensity and concurrence of phenophases over time, both in the simulated and the real data set. Discussion PD provides an efficient, readily interpretable and comparable measure that summarizes the variety of phenological patterns observed in ecological communities. PD retains the diversity patterns of order q characteristic of all diversity measures encompassed by the distance-based Hill numbers framework. In addition, wavelet transform analysis proved useful for constructing a continuous phenological curve. This methodological approach to quantify phenological diversity produces simple and intuitive values for the examination of phenological diversity and can be widely applied to any taxon or community’s phenological traits.
Sánchez-Ochoa et al. (2022) Quantifying phenological diversity: a framework based on Hill numbers theory. PeerJ 10:e13412 https://doi.org/10.7717/peerj.13412
Genome-wide identification of CBF genes and their responses to cold acclimation in Taraxacum kok-saghyz
C-repeat binding factors (CBFs) are transcription factors that are known to play important roles in plant cold acclimation. They are highly conserved in most higher plants. Taraxacum kok-saghyz (TKS) is an herb native to China and Kazakhstan and is well-known for its production of rubber silk with industrial and economic value. To understand cold acclimation mechanisms, we conducted a genome-wide discovery of the CBF family genes in TKS and revealed ten CBF genes. A bioinformatic analysis of the CBF genes was carried out to analyze the phylogenetic relationship, protein conservative motifs, protein physicochemical properties, gene structure, promoter cis-acting elements, and the gene expression patterns under cold acclimation and control conditions. It was found that most of these genes were highly responsive at the late stage of cold acclimation, indicating that they play important roles in the cold acclimation processes of TKS. This study provides a theoretical basis for the study of the molecular functions of the CBF gene family in TKS, and a useful guidance for the genetic improvement of the cold tolerance traits of TKS and other plants, including crops.
Zhang et al. (2022) Genome-wide identification of CBF genes and their responses to cold acclimation in Taraxacum kok-saghyz. PeerJ 10:e13429 https://doi.org/10.7717/peerj.13429
Dental pathologies in lamniform and carcharhiniform sharks with comments on the classification and homology of double tooth pathologies in vertebrates
Double tooth pathologies are important indicators of trauma, disease, diet, and feeding biomechanics, and are widely documented in mammals. However, diagnosis of double tooth pathologies in extinct non-mammalian vertebrates is complicated by several compounding factors including: a lack of shared terminology reflecting shared etiology, inconsistencies in definitions and key features within and outside of mammals (e.g., gemination, fusion, twinning, concrescence); differences in tooth morphology, heterodonty, regeneration, and implantation between mammals and non-mammalian vertebrates; and the unmet need for diagnostic criteria that can be applied to isolated teeth, which are common in the fossil record. Here we report on double tooth pathologies in the lamniform and carcharhiniform Cenozoic sharks Otodus megalodon (NCSM 33639) and Carcharhinus leucas (NCSM 33640, 33641). All three teeth bear a singular bifid crown with mirrored halves and abnormal internal microstructure—a single, bifurcating pulp cavity in C. leucas and a more than tripling of vessels in O. megalodon (from two to seven main ascending canals). We identify these abnormalities as likely examples of gemination due to their symmetry, which rules out fusion of tooth buds in one tooth file in different developmental stages in polyphyodont taxa; however, we note that incomplete forms of mesiodistal tooth fusion can be morphologically indistinguishable from gemination, and thus fusion cannot be rejected. We further compile and recategorize, when possible, the diversity of tooth pathologies in sharks. The identification of double tooth pathologies in O. megalodon and C. leucas has paleobiological implications. Such pathologies in sharks are largely hypothesized to stem from trauma to developing tooth buds. Carcharhinus leucas is known to feed on prey documented to cause feeding-related oral traumas (e.g., rays, sawfish, spiny fish, and sea urchins). However, O. megalodon, is considered to have largely fed on marine mammals, and perhaps turtles and/or fish, raising the possibility that the dietary diversity of this species is, as of yet, underappreciated. The genetic underpinnings of tooth morphogenesis and regeneration is highly conserved throughout vertebrate evolution, suggesting a homologous framework can be established. However, more research is needed to link developmental, paleobiological, and/or paleoenvironmental factors to gemination/fusion in polyphyodont taxa. We argue that the definitions and diagnostic criteria for dental pathologies in vertebrates require standardization in order to advance macroevolutionary studies of feeding trauma in deep time.
Miller et al. (2022) Dental pathologies in lamniform and carcharhiniform sharks with comments on the classification and homology of double tooth pathologies in vertebrates. PeerJ 10:e12775 https://doi.org/10.7717/peerj.12775
DeepNC: a framework for drug-target interaction prediction with graph neural networks
The exploration of drug-target interactions (DTI) is an essential stage in the drug development pipeline. Thanks to the assistance of computational models, notably in the deep learning approach, scientists have been able to shorten the time spent on this stage. Widely practiced deep learning algorithms such as convolutional neural networks and recurrent neural networks are commonly employed in DTI prediction projects. However, they can hardly utilize the natural graph structure of molecular inputs. For that reason, a graph neural network (GNN) is an applicable choice for learning the chemical and structural characteristics of molecules when it represents molecular compounds as graphs and learns the compound features from those graphs. In an effort to construct an advanced deep learning-based model for DTI prediction, we propose Deep Neural Computation (DeepNC), which is a framework utilizing three GNN algorithms: Generalized Aggregation Networks (GENConv), Graph Convolutional Networks (GCNConv), and Hypergraph Convolution-Hypergraph Attention (HypergraphConv). In short, our framework learns the features of drugs and targets by the layers of GNN and 1-D convolution network, respectively. Then, representations of the drugs and targets are fed into fully-connected layers to predict the binding affinity values. The models of DeepNC were evaluated on two benchmarked datasets (Davis, Kiba) and one independently proposed dataset (Allergy) to confirm that they are suitable for predicting the binding affinity of drugs and targets. Moreover, compared to the results of baseline methods that worked on the same problem, DeepNC proves to improve the performance in terms of mean square error and concordance index.
Tran et al. (2022) DeepNC: a framework for drug-target interaction prediction with graph neural networks. PeerJ 10:e13163 https://doi.org/10.7717/peerj.13163
Comparative proteomic analysis between mature and germinating seeds in Paris polyphylla var. yunnanensis
The long dormancy period of Paris polyphylla var. yunnanensis seeds affects the supply of this scarce plant, which is used as an important traditional Chinese medicine. Mature seeds with a globular embryo and germinating seeds with developed embryo were used to explore the mechanisms of seed germination in this species. The protein profiles between the mature and germinating seeds were compared using the isobaric tags for relative and absolute quantification (iTRAQ) approach. Of the 4,488 proteins identified, a total of 1,305 differentially expressed proteins (DEPs) were detected. A Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of these DEPs indicated that metabolic pathways and the biosynthesis of secondary metabolites were the two top pathways. Additionally, phytohormone quantification shows that the abscisic acid (ABA) level significantly decreased, whereas the GA3 level dramatically increased among nine endogenous gibberellins (GAs), resulting in a significant increase of the GA3/ABA ratio in germinating seeds. The biosynthesis pathways of carotenoid as a precursor for ABA production and GA were further analyzed, and showed that proteinic expressions of the candidate genes in the two pathways did not correlate with the transcriptional abundances. However, 9-cis-epoxycarotenoid dioxygenase (NCED), a rate limited enzyme for ABA biosynthesis, was significantly decreased in mRNA levels in germinating seeds. By contrast, gibberellin 20-oxidase (GA20ox), a key enzyme GA biosynthesis, exhibited the major increase in one copy and a slight decrease in three others at the protentional level in germinating seeds. Gibberellin 2-oxidase (GA2ox), an inactivate enzyme in bioactive GAs, has the tendency to down-regulate in mRNA or at the proteinic level in germinating seeds. Altogether, these results suggested that the analyses of ABA and GA levels, the GA3/ABA ratio, and the expressional patterns of their regulatory genes may provide a novel mechanistic understanding of how phytohormones regulate seed germination in P. polyphylla var. yunnanensis.
Ling et al. (2022) Comparative proteomic analysis between mature and germinating seeds in Paris polyphylla var. yunnanensis. PeerJ 10:e13304 https://doi.org/10.7717/peerj.13304
QTL mapping and candidate gene analysis of low temperature germination in rice (Oryza sativa L.) using a genome wide association study
Low temperature germination (LTG) is a key agronomic trait in rice (Oryza sativa L.). However, the genetic basis of natural variation for LTG is largely unknown. Here, a genome-wide association study (GWAS) was performed using 276 accessions from the 3,000 Rice Genomes (3K-RG) project with 497 k single nucleotide polymorphisms (SNPs) to uncover potential genes for LTG in rice. In total, 37 quantitative trait loci (QTLs) from the 6th day (D6) to the 10th day (D10) were detected in the full population, overlapping with 12 previously reported QTLs for LTG. One novel QTL, namely qLTG1-2, was found stably on D7 in both 2019 and 2020. Based on two germination-specific transcriptome datasets, 13 seed-expressed genes were isolated within a 200 kb interval of qLTG1-2. Combining with haplotype analysis, a functional uncharacterized gene, LOC_Os01g23580, and a seed germination-associated gene, LOC_Os01g23620 (OsSar1a), as promising candidate genes, both of which were significantly differentially expressed between high and low LTG accessions. Collectively, the candidate genes with favorable alleles may be useful for the future characterization of the LTG mechanism and the improvement of the LTG trait in rice breeding.
Mao et al. (2022) QTL mapping and candidate gene analysis of low temperature germination in rice (Oryza sativa L.) using a genome wide association study. PeerJ 10:e13407 https://doi.org/10.7717/peerj.13407
Combinations of action observation and motor imagery on golf putting’s performance
Motor imagery (MI) and action observation (AO) have been found to enhance motor performance, but recent research found that a combination of action observation and motor imagery (AOMI) together is even better. Despite this initial finding, the most effective way to combine them is unknown. The present study examined the effects of synchronized (i e., concurrently doing AO and MI), asynchronised (i.e., first doing AO then MI), and progressive (first asynchronised approach, then doing synchronized approach) AOMI on golf putting performance and learning. We recruited 45 university students (Mage = 20.18 + 1.32 years; males = 23, females = 22) and randomly assigned them into the following four groups: synchronized group (S-AOMI), asynchronised group (A-AOMI), progressive group (A-S-AOMI), and a control group with a pre-post research design. Participants engaged in a 6-week (three times/per-week) intervention, plus two retention tests. A two-way (group × time) mixed ANOVA statistical analysis found that the three experimental groups performed better than the control group after intervention. However, we found progressive and asynchronised had better golf putting scores than synchronized group and the control group on the retention tests. Our results advance knowledge in AOMI research, but it needs more research to reveal the best way of combining AOMI in the future. Theoretical implications, limitations, applications, and future suggestions are also discussed.
Lin et al. (2022) Combinations of action observation and motor imagery on golf putting’s performance. PeerJ 10:e13432 https://doi.org/10.7717/peerj.13432
Base water potential but not hydrotime predicts seedling emergence of Medicago sativa under water stress conditions
We determined the hydrotime model parameters of 10 alfalfa seed lots by incubating seeds at various water potentials in the laboratory. Meanwhile, seedling emergence under drought and salinity conditions in a greenhouse pot experiment, and seedling establishment in the field were determined. Correlation analysis was utilized to detect the relationship between hydrotime model parameters and seedling emergence under water stress conditions. The germination percentage did not differ significantly among seed lots when seeds were incubated at −0.1 MPa, while it differed significantly among seed lots at water potentials of −0.3 and −0.6 MPa. Compared to control conditions, drought and salinity decreased seedling emergence to different extents, depending on seed lots. Seedling emergence in the field differed significantly among seed lots and ranged from 30% to 80%. Ψb(50) showed a significant correlation with seedling emergence under various conditions and with seedling establishment in the field, while no correlation was observed between θH, σφb and seedling emergence and establishment. These results suggest that Ψb(50) can be used to rank the vigor of alfalfa seed lots and thus predict seedling emergence and establishment under water stress conditions.
Chen et al. (2022) Base water potential but not hydrotime predicts seedling emergence of Medicago sativa under water stress conditions. PeerJ 10:e13206 https://doi.org/10.7717/peerj.13206