Identification of key genes and pathways in endometriosis by integrated expression profiles analysis
The purpose of this study was to integrate the existing expression profile data on endometriosis (EM)-related tissues in order to identify the differentially expressed genes. In this study, three series of raw expression data were downloaded from GEO database. Differentially expressed genes (DEGs) in three tissue types were screened. GO, KEGG pathway enrichment analysis, core differential genes (CDGs) protein–protein interaction (PPI) network and weighted gene co-expression network analysis (WGCNA) were performed, finally, the dysregulation of Hippo pathway in ectopic endometrium (EC) was detected by Western blotting. A total of 1,811 DEGs between eutopic (EU) and normal endometrium (NE), 5,947 DEGs between EC and EU, and 3,192 DEGs between EC and NE datasets were identified. After screening, 394 CDGs were obtained, and 5 hub genes identified in the PPI network. CDGs enrichment and WGCNA network analysis revealed cell proliferation, differentiation, migration and other biological processes, Hippo and Wnt signaling pathways, and a variety of tumor-related pathways. Western blotting results showed that YAP/TAZ was upregulated, and MOB1, pMOB1, SAV1, LATS1 and LATS2 were downregulated in EC. Moreover, CDGs, especially the hub genes, are potential biomarkers and therapeutic targets. Finally, the Hippo pathway might play a key role in the development of endometriosis.
Cui et al. (2020) Identification of key genes and pathways in endometriosis by integrated expression profiles analysis. PeerJ 8:e10171 https://doi.org/10.7717/peerj.10171
Influence of montane altitudinal ranges on species distribution models; evidence in Andean blow flies
Background Blow flies are a family of dipterans of medical, veterinary and sanitary importance. We aim to predict the current geographical distribution of six neotropical blowfly species with different altitudinal ranges of distribution (high, medium, and lowlands) and degree of synanthropy (eusynanthropic, hemisynanthropic and asynanthropic) based on their existing fundamental niche (EA) in Northwestern South America. Methods Geographical records were compiled based on data from museum specimens and literature. The accessible area hypothesis (M) was calculated based on three criteria: (1) Altitudinal range, (2) Synanthropy values deducted based on the Human Influence Index (HII) raster dataset, and (3). The mean dispersal capability of flies. The modeling was performed using the Maxent entropy modeling software. The selection of parameters was made with the R Program ENMeval package. Results The models were assessed using the area under the operator-partial receiver curve (ROCp). The high statistical performance was evidenced in every modeling prediction. The modeling allowed identifying possible taxonomic inaccuracies and the lack of exhaustive collection in the field, especially for lowlands species. Geographical distribution predicted by the modeling and empirical data was remarkably coherent in montane species. Discussion The data obtained evidence that montane elevational ranges affect the performance of the distribution models. These models will allow a more precise predicting of medium and high elevation blow flies than lowlands species. Montane species modeling will accurately predict the fly occurrence to use such biological information for medical, legal, veterinary, and conservation purposes.
Spatial and Geographic Information Science
Altamiranda-Saavedra et al. (2020) Influence of montane altitudinal ranges on species distribution models; evidence in Andean blow flies. PeerJ 8:e10370 https://doi.org/10.7717/peerj.10370
Developing a machine learning model to identify protein–protein interaction hotspots to facilitate drug discovery
Throughout the history of drug discovery, an enzymatic-based approach for identifying new drug molecules has been primarily utilized. Recently, protein–protein interfaces that can be disrupted to identify small molecules that could be viable targets for certain diseases, such as cancer and the human immunodeficiency virus, have been identified. Existing studies computationally identify hotspots on these interfaces, with most models attaining accuracies of ~70%. Many studies do not effectively integrate information relating to amino acid chains and other structural information relating to the complex. Herein, (1) a machine learning model has been created and (2) its ability to integrate multiple features, such as those associated with amino-acid chains, has been evaluated to enhance the ability to predict protein–protein interface hotspots. Virtual drug screening analysis of a set of hotspots determined on the EphB2-ephrinB2 complex has also been performed. The predictive capabilities of this model offer an AUROC of 0.842, sensitivity/recall of 0.833, and specificity of 0.850. Virtual screening of a set of hotspots identified by the machine learning model developed in this study has identified potential medications to treat diseases caused by the overexpression of the EphB2-ephrinB2 complex, including prostate, gastric, colorectal and melanoma cancers which are linked to EphB2 mutations. The efficacy of this model has been demonstrated through its successful ability to predict drug-disease associations previously identified in literature, including cimetidine, idarubicin, pralatrexate for these conditions. In addition, nadolol, a beta blocker, has also been identified in this study to bind to the EphB2-ephrinB2 complex, and the possibility of this drug treating multiple cancers is still relatively unexplored.
Nandakumar et al. (2020) Developing a machine learning model to identify protein–protein interaction hotspots to facilitate drug discovery. PeerJ 8:e10381 https://doi.org/10.7717/peerj.10381
Trials in developing a nanoscale material for extravascular contrast-enhanced ultrasound targeting hepatocellular carcinoma
Background Medical imaging is an important approach for the diagnosis of hepatocellular carcinoma (HCC), a common life threaten disease, however, the diagnostic efficiency is still not optimal. Developing a novel method to improve diagnosis is necessary. The aim of this project was to formulate a material that can combine with GPC3 of HCC for targeted enhanced ultrasound. Methods A material of sulfur hexafluoride (SF6) filled liposome microbubbles and conjugated with synthesized peptide (LSPMbs) was prepared and assessed in vitro and vivo. Liposome microbubbles were made of DPPC, DPPG, DSPE-PEG2000,and SF6, using thin film method to form shell, followed filling SF6, and conjugating peptide. A carbodiimide method was used for covalent conjugation of peptide to LSMbs. Results The prepared LSPMbs appeared round shaped, with size of 380.9 ± 176.5 nm, and Zeta potential of −51.4 ± 10.4mV. LSPMbs showed high affinity to Huh-7 cells in vitro, presented good enhanced ultrasound effects, did not show cytotoxicity, and did not exhibit targeted fluorescence and enhanced ultrasound in animal xenograft tumors. Conclusion Extravascular contrast-enhanced ultrasound targeted GPC3 on HCC may not be realized, and the reason may be that targeted contrast agents of microbubbles are hard to access and accumulate in the tumor stroma and matrix.
Wu et al. (2020) Trials in developing a nanoscale material for extravascular contrast-enhanced ultrasound targeting hepatocellular carcinoma. PeerJ 8:e10403 https://doi.org/10.7717/peerj.10403
Neither ant dominance nor abundance explain ant-plant network structure in Mexican temperate forests
Background Ant-plant mutualistic networks tend to have a nested structure that contributes to their stability, but the ecological factors that give rise to this structure are not fully understood. Here, we evaluate whether ant abundance and dominance hierarchy determine the structure of the ant-plant networks in two types of vegetation: oak and grassland, in two temperate environments of Mexico: Flor del Bosque State Park (FBSP) and La Malinche National Park (MNP). We predicted that dominant and abundant ant species make up the core, and submissives, the periphery of the network. We also expected a higher specialization level in the ant trophic level than in plant trophic level due to competition among the ant species for the plant-derived resources. Methods The ant-plant interaction network was obtained from the frequency of ant-plant interactions. We calculated a dominance hierarchy index for the ants using sampling with baits and evaluated their abundance using pitfall traps. Results In MNP, the Formica spp. species complex formed the core of the network (in both the oak forest and the grassland), while in FBSP, the core species were Prenolepis imparis (oak forest) and Camponotus rubrithorax (grassland). Although these core species were dominant in their respective sites, they were not necessarily the most dominant ant species. Three of the four networks (oak forest and grassland in FBSP, and oak forest in MNP) were nested and had a higher number of plant species than ant species. Although greater specialization was observed in the ant trophic level in the two sites and vegetations, possibly due to competition with the more dominant ant species, this was not statistically significant. In three of these networks (grassland and oak forest of MNP and oak forest of FBSP), we found no correlation between the dominance hierarchy and abundance of the ant species and their position within the network. However, a positive correlation was found between the nestedness contribution value and ant dominance hierarchy in the grassland of the site FBSP, which could be due to the richer ant-plant network and higher dominance index of this community. Conclusions Our evidence suggests that ant abundance and dominance hierarchy have little influence on network structure in temperate ecosystems, probably due to the species-poor ant-plant network and a dominance hierarchy formed only by the presence of dominant and submissive species with no intermediate dominant species between them (absence of gradient in hierarchy) in these ecosystems.
Juárez-Juárez et al. (2020) Neither ant dominance nor abundance explain ant-plant network structure in Mexican temperate forests. PeerJ 8:e10435 https://doi.org/10.7717/peerj.10435
Association between parenting and non-suicidal self-injury among adolescents in Yunnan, China: a cross-sectional survey
Background Non-suicidal self-injury (NSSI) among adolescents is prevalent and its rate has increased in recent years worldwide. Previous studies had investigated the association between parenting and childhood NSSI, but little is known about the relationship between parental rearing and repetition and severity of NSSI. The aim of this study was to investigate associations of parenting with NSSI and its repetition and severity in a representative adolescent sample from southwestern China. Methods In this cross-sectional study, a sample of 2,705 adolescents (F/M: 1,245/1,460; mean age: 13.4 ± 2.2 years) was recruited from 14 randomly selected schools in Lincang municipality, Yunnan province, China. A self-report questionnaire was used to collect data. The Adolescent Non-Suicidal Self-Injury Function Assessment Scale and the short Chinese Egna Minnen av Barndoms Uppfostran (s-EMBU-C) were used to evaluate NSSI behaviors and parenting style, respectively. Univariate and multivariate logistic regression models were adopted to examine association between parenting and NSSI. Results Overall lifetime prevalence of NSSI was 47.1% (95% CI [36.2–58.0]), with self-cutting being the most common form (23.5% (95% CI [19.3–27.7])), followed by hitting hard objects (23.4% (95% CI [20.2–26.7])) and pulling hairs (20.9% (95% CI [18.8–22.6])). In multiple logistic regression analyses, NSSI was positively associated with high level of father’s rejection (OR: 1.32 (95% CI [1.01–1.72])), high level of mother’s rejection (OR: 1.76 (95% CI [1.46–2.13])), low level of mother’s emotional warmth (OR: 1.42 (95% CI [1.15–1.75])), and high level of mother’s overprotection (OR: 1.74 (95% CI [1.49–2.03])), repeated NSSI was positively associated with low level of father’s emotional warmth (OR: 1.39 (95% CI [1.10–1.75])) and high level of mother’s overprotection (OR: 1.79 (95% CI [1.33–2.41])), and severe NSSI was positively associated with low level of father’s emotional warmth (OR: 1.64 (95% CI [1.11–2.43])) and high level of mother’s rejection (OR: 2.16 (95% CI [1.71–2.71])). Conclusion NSSI is common among adolescents in southwestern China. Negative parenting styles are associated with NSSI, repeated NSSI, and severe NSSI. The development of intervention measures for preventing or reducing NSSI among Chinese adolescents in school settings should consider parenting styles.
Liu et al. (2020) Association between parenting and non-suicidal self-injury among adolescents in Yunnan, China: a cross-sectional survey. PeerJ 8:e10493 https://doi.org/10.7717/peerj.10493
Recalibration of mapping quality scores in Illumina short-read alignments improves SNP detection results in low-coverage sequencing data
Background Low-coverage sequencing is a cost-effective way to obtain reads spanning an entire genome. However, read depth at each locus is low, making sequencing error difficult to separate from actual variation. Prior to variant calling, sequencer reads are aligned to a reference genome, with alignments stored in Sequence Alignment/Map (SAM) files. Each alignment has a mapping quality (MAPQ) score indicating the probability a read is incorrectly aligned. This study investigated the recalibration of probability estimates used to compute MAPQ scores for improving variant calling performance in single-sample, low-coverage settings. Materials and Methods Simulated tomato, hot pepper and rice genomes were implanted with known variants. From these, simulated paired-end reads were generated at low coverage and aligned to the original reference genomes. Features extracted from the SAM formatted alignment files for tomato were used to train machine learning models to detect incorrectly aligned reads and output estimates of the probability of misalignment for each read in all three data sets. MAPQ scores were then re-computed from these estimates. Next, the SAM files were updated with new MAPQ scores. Finally, Variant calling was performed on the original and recalibrated alignments and the results compared. Results Incorrectly aligned reads comprised only 0.16% of the reads in the training set. This severe class imbalance required special consideration for model training. The F1 score for detecting misaligned reads ranged from 0.76 to 0.82. The best performing model was used to compute new MAPQ scores. Single Nucleotide Polymorphism (SNP) detection was improved after mapping score recalibration. In rice, recall for called SNPs increased by 5.2%, while for tomato and pepper it increased by 3.1% and 1.5%, respectively. For all three data sets the precision of SNP calls ranged from 0.91 to 0.95, and was largely unchanged both before and after mapping score recalibration. Conclusion Recalibrating MAPQ scores delivers modest improvements in single-sample variant calling results. Some variant callers operate on multiple samples simultaneously. They exploit every sample’s reads to compensate for the low read-depth of individual samples. This improves polymorphism detection and genotype inference. It may be that small improvements in single-sample settings translate to larger gains in a multi-sample experiment. A study to investigate this is ongoing.
Cline et al. (2020) Recalibration of mapping quality scores in Illumina short-read alignments improves SNP detection results in low-coverage sequencing data. PeerJ 8:e10501 https://doi.org/10.7717/peerj.10501
Characterization of a profilin-like protein from Fasciola hepatica
Fasciola hepatica is the causative agent of fasciolosis, an important disease of humans and livestock around the world. There is an urgent requirement for novel treatments for F. hepatica due to increasing reports of drug resistance appearing around the world. The outer body covering of F. hepatica is referred to as the tegument membrane which is of crucial importance for the modulation of the host response and parasite survival; therefore, tegument proteins may represent novel drug or vaccine targets. Previous studies have identified a profilin-like protein in the tegument of F. hepatica. Profilin is a regulatory component of the actin cytoskeleton in all eukaryotic cells, and in some protozoan parasites, profilin has been shown to drive a potent IL-12 response. This study characterized the identified profilin form F. hepatica (termed FhProfilin) for the first time. Recombinant expression of FhProfilin resulted in a protein approximately 14 kDa in size which was determined to be dimeric like other profilins isolated from a range of eukaryotic organisms. FhProfilin was shown to bind poly-L-proline (pLp) and sequester actin monomers which is characteristic of the profilin family; however, there was no binding of FhProfilin to phosphatidylinositol lipids. Despite FhProfilin being a component of the tegument, it was shown not to generate an immune response in experimentally infected sheep or cattle.
Wilkie et al. (2020) Characterization of a profilin-like protein from Fasciola hepatica. PeerJ 8:e10503 https://doi.org/10.7717/peerj.10503
Description of a new genus and species for a common and widespread Amazonian satyrine butterfly (Lepidoptera: Nymphalidae: Satyrinae: Satyrini)
We here propose a new monotypic butterfly genus Scriptor Nakahara & Espeland, n. gen. to accommodate a new species, S. sphenophorus Lamas & Nakahara, n. sp., described and named herein. Scriptor sphenophorus n. gen. and n. sp.is a relatively common and widespread butterfly species which is recovered as a member of the so-called “Splendeuptychia clade” in the nymphalid subtribe Euptychiina, based on our molecular phylogenetic analysis using a maximum likelihood approach. Nevertheless, its sister group is not confidently resolved in any analysis, supporting a relatively distant relationship to any described genus as well as our decision to establish a new monotypic genus. We further discuss the proposed taxonomy in the light of frequent criticism of the description of monotypic taxa, as well as emphasize the importance of incorporating multiple evidence when describing new genera, illustrated by reference to several recent generic descriptions in this subtribe.
Nakahara et al. (2020) Description of a new genus and species for a common and widespread Amazonian satyrine butterfly (Lepidoptera: Nymphalidae: Satyrinae: Satyrini). PeerJ 8:e10324 https://doi.org/10.7717/peerj.10324
Comparing multiple comparisons: practical guidance for choosing the best multiple comparisons test
Multiple comparisons tests (MCTs) include the statistical tests used to compare groups (treatments) often following a significant effect reported in one of many types of linear models. Due to a variety of data and statistical considerations, several dozen MCTs have been developed over the decades, with tests ranging from very similar to each other to very different from each other. Many scientific disciplines use MCTs, including >40,000 reports of their use in ecological journals in the last 60 years. Despite the ubiquity and utility of MCTs, several issues remain in terms of their correct use and reporting. In this study, we evaluated 17 different MCTs. We first reviewed the published literature for recommendations on their correct use. Second, we created a simulation that evaluated the performance of nine common MCTs. The tests examined in the simulation were those that often overlapped in usage, meaning the selection of the test based on fit to the data is not unique and that the simulations could inform the selection of one or more tests when a researcher has choices. Based on the literature review and recommendations: planned comparisons are overwhelmingly recommended over unplanned comparisons, for planned non-parametric comparisons the Mann-Whitney-Wilcoxon U test is recommended, Scheffé’s S test is recommended for any linear combination of (unplanned) means, Tukey’s HSD and the Bonferroni or the Dunn-Sidak tests are recommended for pairwise comparisons of groups, and that many other tests exist for particular types of data. All code and data used to generate this paper are available at: https://github.com/stevemidway/MultipleComparisons.
Midway et al. (2020) Comparing multiple comparisons: practical guidance for choosing the best multiple comparisons test. PeerJ 8:e10387 https://doi.org/10.7717/peerj.10387