Depigmenting potential of lichen extracts evaluated by in vitro and in vivo tests
Melanin is the main pigment of human skin, playing the primary role of protection from ultraviolet radiation. Alteration of the melanin production may lead to hyperpigmentation diseases, with both aesthetic and health consequences. Thus, suppressors of melanogenesis are considered useful tools for medical and cosmetic treatments. A great interest is focused on natural sources, aimed at finding safe and quantitatively available depigmenting substances. Lichens are thought to be possible sources of this kind of compounds, as the occurrence of many phenolic molecules suggests possible effects on phenolase enzymes involved in melanin synthesis, like tyrosinase. In this work, we used four lichen species, Cetraria islandica Ach., Flavoparmelia caperata Hale, Letharia vulpina (L.) Hue, and Parmotrema perlatum (Hudson) M. Choisy, to obtain extracts in solvents of increasing polarity, viz. chloroform, chloroform-methanol, methanol, and water. Cell-free, tyrosinase inhibition experiments showed highest inhibition for L. vulpina methanol extract, followed by C. islandica chloroform-methanol one. Comparable results for depigmenting activities were observed by means of in vitro and in vivo systems, such as MeWo melanoma cells and zebrafish larvae. Our study provides first evidence of depigmenting effects of lichen extracts, from tyrosinase inhibition to cell and in vivo models, suggesting that L. vulpina and C. islandica extracts deserve to be further studied for developing skin-whitening products.
Malaspina et al. (2020) Depigmenting potential of lichen extracts evaluated by in vitro and in vivo tests. PeerJ 8:e9150 https://doi.org/10.7717/peerj.9150
Comparing race, gender, age, and career categories in recognizing and grouping tasks
The purpose of our research was to compare how participants weighed age, gender, race, and career categories in recognizing and grouping tasks. In Study 1, we used a category recognition task to compare participants’ speeds in recognizing information from different categories. The results showed that participants recognized the gender information most quickly, followed by career, race, and age information. In Study 2, a categorization task was used to compare participants’ category preferences. The results showed that the career category had the greatest weight, and the gender category had the lowest weight. Two targets who had different career identities were more possible considered as belonging to different groups than two targets with different gender, race or age identities. Our results have implications in understanding the weight of different categories, with gender and career category are the most important category that affects perception and evaluation.
Song et al. (2020) Comparing race, gender, age, and career categories in recognizing and grouping tasks. PeerJ 8:e9156 https://doi.org/10.7717/peerj.9156
Personalized analysis of breast cancer using sample-specific networks
Breast cancer is a disease with high heterogeneity. Cancer is not usually caused by a single gene, but by multiple genes and their interactions with others and surroundings. Estimating breast cancer-specific gene–gene interaction networks is critical to elucidate the mechanisms of breast cancer from a biological network perspective. In this study, sample-specific gene–gene interaction networks of breast cancer samples were established by using a sample-specific network analysis method based on gene expression profiles. Then, gene–gene interaction networks and pathways related to breast cancer and its subtypes and stages were further identified. The similarity and difference among these subtype-related (and stage-related) networks and pathways were studied, which showed highly specific for subtype Basal-like and Stages IV and V. Finally, gene pairwise interactions associated with breast cancer prognosis were identified by a Cox proportional hazards regression model, and a risk prediction model based on the gene pairs was established, which also performed very well on an independent validation data set. This work will help us to better understand the mechanism underlying the occurrence of breast cancer from the sample-specific network perspective.
Zhu et al. (2020) Personalized analysis of breast cancer using sample-specific networks. PeerJ 8:e9161 https://doi.org/10.7717/peerj.9161
Application of High Resolution Melt analysis (HRM) for screening haplotype variation in a non-model plant genus: Cyclopia (Honeybush)
Aim This study has three broad aims: to (a) develop genus-specific primers for High Resolution Melt analysis (HRM) of members of Cyclopia Vent., (b) test the haplotype discrimination of HRM compared to Sanger sequencing, and (c) provide an example of using HRM to detect novel haplotype variation in wild C. subternata Vogel. populations. Location The Cape Floristic Region (CFR), located along the southern Cape of South Africa. Methods Polymorphic loci were detected through a screening process of sequencing 12 non-coding chloroplast DNA segments across 14 Cyclopia species. Twelve genus-specific primer combinations were designed around variable cpDNA loci, four of which failed to amplify under PCR; the eight remaining were applied to test the specificity, sensitivity and accuracy of HRM. The three top performing HRM Primer combinations were then applied to detect novel haplotypes in wild C. subternata populations, and phylogeographic patterns of C. subternata were explored. Results We present a framework for applying HRM to non-model systems. HRM accuracy varied across the PCR products screened using the genus-specific primers developed, ranging between 56 and 100%. The nucleotide variation failing to produce distinct melt curves is discussed. The top three performing regions, having 100% specificity (i.e. different haplotypes were never grouped into the same cluster, no false negatives), were able to detect novel haplotypes in wild C. subternata populations with high accuracy (96%). Sensitivity below 100% (i.e. a single haplotype being clustered into multiple unique groups during HRM curve analysis, false positives) was resolved through sequence confirmation of each cluster resulting in a final accuracy of 100%. Phylogeographic analyses revealed that wild C. subternata populations tend to exhibit phylogeographic structuring across mountain ranges (accounting for 73.8% of genetic variation base on an AMOVA), and genetic differentiation between populations increases with distance (p < 0.05 for IBD analyses). Conclusions After screening for regions with high HRM clustering specificity—akin to the screening process associated with most PCR based markers—the technology was found to be a high throughput tool for detecting genetic variation in non-model plants.
Galuszynski et al. (2020) Application of High Resolution Melt analysis (HRM) for screening haplotype variation in a non-model plant genus: Cyclopia (Honeybush). PeerJ 8:e9187 https://doi.org/10.7717/peerj.9187
The prevalence of low back pain among female hospital staff at childbearing age
Background Low back pain (LBP) is considered the most common work-related musculoskeletal disorder among female healthcare workers. The aim of this study is to compare the prevalence of LBP and non-ergonomic risk factors between female nurses, office workers, and patient transporters, and the effect of pain on job performance. Methods Based on semi-structured interviews, we conducted a cross-sectional study on Jordanian female hospital workers between January and July, 2017. Results We included 209 participants with a mean age of 35.57 ± 8 years from four Jordanian medical centers. Nurses have significantly higher frequency of LBP (82.5%; p = 0.05) compared to both office workers (67.5%) and patient transporters (68.6%). The mean difference in pain score using Visual Analogue Scale (VAS) after treatment varied significantly (p = 0.003), since it was 28.2 (±35.4) for office workers, compared to 22.8 (±26.5) for nurses and 6.5 (±33.7) for patient transporters. A higher frequency of nurses reported that LBP affected their job performance (64.9%; p = 0.013), and 43.3% of them reported having previous sick leaves due to LBP (p = 0.008). Conclusions LBP is common among female hospital workers, with significantly higher prevalence among female nurses when compared to other female hospital staff.
Al-Hadidi et al. (2020) The prevalence of low back pain among female hospital staff at childbearing age. PeerJ 8:e9199 https://doi.org/10.7717/peerj.9199
fRNAkenseq: a fully powered-by-CyVerse cloud integrated RNA-sequencing analysis tool
Background Decreasing costs make RNA sequencing technologies increasingly affordable for biologists. However, many researchers who can now afford sequencing lack access to resources necessary for downstream analysis. This means that even as algorithms to process RNA-Seq data improve, many biologists still struggle to manage the sheer volume of data produced by next generation sequencing (NGS) technologies. Scalable bioinformatics tools that exploit multiple platforms are needed to democratize bioinformatics resources in the sequencing era. This is essential for equipping many research groups in the life sciences with the tools to process the increasingly unwieldy datasets they produce. Methods One strategy to address this challenge is to develop a modern generation of sequence analysis tools capable of seamless data sharing and communication. Such tools will provide interoperability through offerings of interlinked resources. Systems of interlinked, scalable resources, which often incorporate cloud data storage, are broadly referred to as cyberinfrastructure. Cyberinfrastructure integrated tools will help researchers to robustly analyze large scale datasets by efficiently sharing data burdens across a distributed architecture. Additionally, interoperability will allow emerging tools to cross-adapt features of existing tools. It is important that these tools are designed to be easy to use for biologists. Results We introduce fRNAkenseq, a powered-by-CyVerse RNA sequencing analysis tool that exhibits interoperability with other resources and meets the needs of biologists for comprehensive, easy to use RNA sequencing analysis. fRNAkenseq leverages a complex set of Application Programming Interfaces (APIs) associated with the NSF-funded cyberinfrastructure project, CyVerse, to execute FASTQ-to-differential expression RNA-Seq analyses. Integrating across bioinformatics platforms, fRNAkenseq also exploits cloud integration and cross-talk with another CyVerse associated tool, CoGe. fRNAkenseq offers novel features for the biologist such as more robust and comprehensive pipelines for enrichment than those currently available by default in a single tool, whether they are cloud-based or local installation. Importantly, cross-talk with CoGe allows fRNAkenseq users to execute RNA-Seq pipelines on an inventory of 47,000 archived genomes stored in CoGe or upload their own draft genome.
Hubbard et al. (2020) fRNAkenseq: a fully powered-by-CyVerse cloud integrated RNA-sequencing analysis tool. PeerJ 8:e8592 https://doi.org/10.7717/peerj.8592
Transcriptomic changes across the life cycle of Trypanosoma cruzi II
Trypanosoma cruzi is a flagellated protozoan that causes Chagas disease; it presents a complex life cycle comprising four morphological stages: epimastigote (EP), metacyclic trypomastigote (MT), cell-derived trypomastigote (CDT) and amastigote (AM). Previous transcriptomic studies on three stages (EPs, CDTs and AMs) have demonstrated differences in gene expressions among them; however, to the best of our knowledge, no studies have reported on gene expressions in MTs. Therefore, the present study compared differentially expressed genes (DEGs), and signaling pathway reconstruction in EPs, MTs, AMs and CDTs. The results revealed differences in gene expressions in the stages evaluated; these differences were greater between MTs and AMs-PTs. The signaling pathway that presented the highest number of DEGs in all the stages was associated with ribosomes protein profiles, whereas the other related pathways activated were processes related to energy metabolism from glucose, amino acid metabolism, or RNA regulation. However, the role of autophagy in the entire life cycle of T. cruzi and the presence of processes such as meiosis and homologous recombination in MTs (where the expressions of SPO11 and Rad51 plays a role) are crucial. These findings represent an important step towards the full understanding of the molecular basis during the life cycle of T. cruzi.
Cruz-Saavedra et al. (2020) Transcriptomic changes across the life cycle of Trypanosoma cruzi II. PeerJ 8:e8947 https://doi.org/10.7717/peerj.8947
An interaction mechanism for the maintenance of fission–fusion dynamics under different individual densities
Animals often show high consistency in their social organisation despite facing changing environmental conditions. Especially in shoaling fish, fission–fusion dynamics that describe for which periods individuals are solitary or social have been found to remain unaltered even when density changed. This compensatory ability is assumed to be an adaptation towards constant predation pressure, but the mechanism through which individuals can actively compensate for density changes is yet unknown. The aim of the current study is to identify behavioural patterns that enable this active compensation. We compared the fission–fusion dynamics of two populations of the live-bearing Atlantic molly (Poecilia mexicana) that live in adjacent habitats with very different predator regimes: cave mollies that inhabit a low-predation environment inside a sulfidic cave with a low density of predatory water bugs (Belostoma sp.), and mollies that live directly outside the cave (henceforth called “surface” mollies) in a high-predation environment. We analysed their fission–fusion dynamics under two different fish densities of 12 and 6 fish per 0.36 m2. As expected, surface mollies spent more time being social than cave mollies, and this difference in social time was a result of surface mollies being less likely to discontinue social contact (once they had a social partner) and being more likely to resume social contact (once alone) than cave mollies. Interestingly, surface mollies were also less likely to switch among social partners than cave mollies. A random walk simulation predicted each population to show reduced social encounters in the low density treatment. While cave mollies largely followed this prediction, surface mollies maintained their interaction probabilities even at low density. Surface mollies achieved this by a reduction in the size of a convex polygon formed by the group as density decreased. This may allow them to largely maintain their fission–fusion dynamics while still being able to visit large parts of the available area as a group. A slight reduction (21%) in the area visited at low densities was also observed but insufficient to explain how the fish maintained their fission–fusion dynamics. Finally, we discuss potential movement rules that could account for the reduction of polygon size and test their performance.
Bierbach et al. (2020) An interaction mechanism for the maintenance of fission–fusion dynamics under different individual densities. PeerJ 8:e8974 https://doi.org/10.7717/peerj.8974
Full-length transcriptome and targeted metabolome analyses provide insights into defense mechanisms of Malus sieversii against Agrilus mali
Malus sieversii is the wild progenitor for many cultivars of domesticated apple and an important germplasm resource for breeding. However, this valuable species faces a significant threat in the areas north of the Tianshan Mountains in China, by the invasion of Agrilus mali, a destructive pest of apple trees belonging to the family Buprestidae. Our preliminary study has has shown that there may be resistance to this insect in M. sieversii plants in the field, but the corresponding molecular mechanisms remain unclear. In this study, we compared the response of insect-resistant and insect-susceptible plants of M. sieversii to insect feeding using full-length transcriptome and targeted metabolome. 112,103 non-chimeric full-length reads (FLNC) totaling 10.52 Gb of data were generating with Pacific Biosciences SingleMolecule, Real-Time (PacBio SMRT) sequencing. A total of 130.06 Gb data of long reads were acquired with an Illumina HiSeq. Function annotation indicated that the different expressed genes (DEGs) were mainly involved in signal transduction pathway of plant hormones and in the synthesis of compounds such as terpenes, quinones, flavonoids, and jasmonic acid. Through targeted metabolome analysis resistant strains showed higher levels of trans-cinnamic acid, caffeine and ferulic acid after pest infestation. This study helps to decipher the transcriptional changes and related signaling paths in M. sieversii after an insect feeding, which lays a foundation for further research on molecular mechanisms of insect resistance in apples.
Mei et al. (2020) Full-length transcriptome and targeted metabolome analyses provide insights into defense mechanisms of Malus sieversii against Agrilus mali. PeerJ 8:e8992 https://doi.org/10.7717/peerj.8992
Individual behavioral type captured by a Bayesian model comparison of cap making by sponge crabs
‘Animal personality’ is considered to be developed through complex interactions of an individual with its surrounding environment. How can we quantify the ‘personality’ of an individual? Quantifying intra- and inter-individual variability of behavior, or individual behavioral type, appears to be a prerequisite in the study of animal personality. We propose a statistical method from a predictive point of view to measure the appropriateness of our assumption of ‘individual’ behavior in repeatedly measured behavioral data from several individuals. For a model case, we studied the sponge crab Lauridromia dehaani known to make and carry a ‘cap’ from a natural sponge for camouflage. Because a cap is most likely to be rebuilt and replaced repeatedly, we hypothesized that each individual crab would grow a unique behavioral type and it would be observed under an experimentally controlled environmental condition. To test the hypothesis, we conducted behavioral experiments and employed a new Bayesian model-based comparison method to examine whether crabs have individual behavioral types in the cap making behavior. Crabs were given behavioral choices by using artificial sponges of three different sizes. We modeled the choice of sponges, size of the trimmed part of a cap, size of the cavity of a cap, and the latency to produce a cap, as random variables in 26 models, including hierarchical models specifying the behavioral types. In addition, we calculated the marginal-level widely applicable information criterion (mWAIC) values for hierarchical models to evaluate and compared them with the non-hierarchical models from the predictive point of view. As a result, the crabs of less than about 9 cm in size were found to make caps from the sponges. The body size explained the behavioral variables namely, choice, trimmed cap characteristics, and cavity size, but not latency. Furthermore, we captured the behavioral type as a probabilistic distribution structure of the behavioral data by comparing WAIC. Our statistical approach is not limited to behavioral data but is also applicable to physiological or morphological data when examining whether some group structure exists behind fluctuating empirical data.
Harada et al. (2020) Individual behavioral type captured by a Bayesian model comparison of cap making by sponge crabs. PeerJ 8:e9036 https://doi.org/10.7717/peerj.9036