Oviducal gland transcriptomics of Octopus maya through physiological stages and the negative effects of temperature on fertilization
Background Elevated temperatures reduce fertilization and egg-laying rates in the octopus species. However, the molecular mechanisms that control the onset of fertilization and egg-laying in the octopus’ oviducal gland are still unclear; and the effect of temperature on the expression of key reproductive genes is unknown. This study aims to better understand the molecular bases of octopus fertilization and egg-laying, and how they are affected by elevated temperatures. Method RNA-seq of oviducal glands was performed for samples before, during, and after fertilization and their transcriptomic profiles were compared. Also, at the fertilization stage, the optimal and thermal-stress conditions were contrasted. Expression levels of key reproductive genes were validated via RT-qPCR. Results In mated females before egg-laying, genes required for the synthesis of spermine, spermidine, which may prevent premature fertilization, and the myomodulin neuropeptide were upregulated. Among the genes with higher expression at the fertilization stage, we found those encoding the receptors of serotonin, dopamine, and progesterone; genes involved in the assembly and motility of the sperm flagellum; genes that participate in the interaction between male and female gametes; and genes associated with the synthesis of eggshell mucoproteins. At temperatures above the optimal range for reproduction, mated females reduced the fertilization rate. This response coincided with the upregulation of myomodulin and APGW-amide neuropeptides. Also, genes associated with fertilization like LGALS3, VWC2, and Pcsk1 were downregulated at elevated temperatures. Similarly, in senescent females, genes involved in fertilization were downregulated but those involved in the metabolism of steroid hormones like SRD5A1 were highly expressed.
Juárez et al. (2022) Oviducal gland transcriptomics of Octopus maya through physiological stages and the negative effects of temperature on fertilization. PeerJ 10:e12895 https://doi.org/10.7717/peerj.12895
Heart rate variability, mood and performance: a pilot study on the interrelation of these variables in amateur road cyclists
Objective The present study seeks to explore the relationship between measures of cycling training on a given day and the heart rate variability (HRV) and mood states obtained the following morning. The association between HRV and mood state is also studied, as is the relationship between internal and external measures of training. Methods During a 6-week period, five recreational road cyclists collected 123 recordings of morning HRV and morning mood, and 66 recordings of training power and rate of perceived exertion (RPE). Training power was used as an external measure of performance and RPE as an internal measure of performance. The HRV parameters used in the study were the mean of RR intervals (mean RR) and the standard deviation of all RR intervals (SDNN) as time domain analysis, and the normalized high frequency band (HFnu), normalized low frequency band (LFnu) and the ratio between low and high frequency bands, as frequency domain analysis. Mood was measured using a 10-point cognitive scale. Results It was found that the higher the training power on a given day, the lower the HFnu and the higher LF/HF were on the following morning. At the same time, results showed an inverse relationship between training and mood, so the tougher a training session, the lower the mood the following day. A relationship between morning HRV and mood was also found, so that the higher mean RR and HFnu, the more positive the mood (r = 0.497 and r = 0.420 respectively; p < 0.001). Finally, RPE correlated positively with external power load variables (IF: r = 0.545; p < 0.001). Conclusion Altogether, the results indicate a relationship between training of cyclists on a given day and their morning HRV and mood state on the following day. Mood and HRV also seem positively related. It is argued that developing a monitoring system that considers external and internal training loads, together with morning mood, could help understand the state of the individual, enabling feedback to athletes to facilitate the adaptation to training and to prevent problems associated with overtraining. However, more research is needed to further understand the association between the different variables considered.
Alfonso et al. (2022) Heart rate variability, mood and performance: a pilot study on the interrelation of these variables in amateur road cyclists. PeerJ 10:e13094 https://doi.org/10.7717/peerj.13094
RNA sequencing reveals the emerging role of bronchoalveolar lavage fluid exosome lncRNAs in acute lung injury
Background Bronchoalveolar lavage fluid (BALF) exosomes possess different properties in different diseases, which are mediated through microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), among others. By sequencing the differentially expressed lncRNAs in BALF exosomes, we seek potential targets for the diagnosis and treatment of acute lung injury (ALI). Methods Considering that human and rat genes are about 80% similar, ALI was induced using lipopolysaccharide in six male Wistar rats, with six rats as control (all weighing 200 ± 20 g and aged 6–8 weeks). BALF exosomes were obtained 24 h after ALI. The exosomes in BALF were extracted by ultracentrifugation. The differential expression of BALF exosomal lncRNAs in BALF was analyzed by RNA sequencing. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to predict the functions of differentially expressed lncRNAs, which were confirmed by reverse transcription–polymerase chain reaction. Results Compared with the control group, the ALI group displayed a higher wet/dry ratio, tumor necrosis factor-α levels, and interleukin-6 levels (all P < 0.001). The airway injection of exosomes in rats led to significant infiltration by neutrophils. A total of 2,958 differentially expressed exosomal lncRNAs were identified, including 2,524 upregulated and 434 downregulated ones. Five lncRNAs confirmed the reliability of the sequencing data. The top three GO functions were phagocytic vesicle membrane, regulation of receptor biosynthesis process, and I-SMAD binding. Salmonella infection, Toll-like receptor signaling pathway, and osteoclast differentiation were the most enriched KEGG pathways. The lncRNA–miRNA interaction network of the five confirmed lncRNAs could be predicted using miRDB. Conclusions BALF-derived exosomes play an important role in ALI development and help identify potential therapeutic targets related to ALI.
Song et al. (2022) RNA sequencing reveals the emerging role of bronchoalveolar lavage fluid exosome lncRNAs in acute lung injury. PeerJ 10:e13159 https://doi.org/10.7717/peerj.13159
ExhauFS: exhaustive search-based feature selection for classification and survival regression
Feature selection is one of the main techniques used to prevent overfitting in machine learning applications. The most straightforward approach for feature selection is an exhaustive search: one can go over all possible feature combinations and pick up the model with the highest accuracy. This method together with its optimizations were actively used in biomedical research, however, publicly available implementation is missing. We present ExhauFS—the user-friendly command-line implementation of the exhaustive search approach for classification and survival regression. Aside from tool description, we included three application examples in the manuscript to comprehensively review the implemented functionality. First, we executed ExhauFS on a toy cervical cancer dataset to illustrate basic concepts. Then, multi-cohort microarray breast cancer datasets were used to construct gene signatures for 5-year recurrence classification. The vast majority of signatures constructed by ExhauFS passed 0.65 threshold of sensitivity and specificity on all datasets, including the validation one. Moreover, a number of gene signatures demonstrated reliable performance on independent RNA-seq dataset without any coefficient re-tuning, i.e., turned out to be cross-platform. Finally, Cox survival regression models were used to fit isomiR signatures for overall survival prediction for patients with colorectal cancer. Similarly to the previous example, the major part of models passed the pre-defined concordance index threshold 0.65 on all datasets. In both real-world scenarios (breast and colorectal cancer datasets), ExhauFS was benchmarked against state-of-the-art feature selection models, including L1-regularized sparse models. In case of breast cancer, we were unable to construct reliable cross-platform classifiers using alternative feature selection approaches. In case of colorectal cancer not a single model passed the same 0.65 threshold. Source codes and documentation of ExhauFS are available on GitHub: https://github.com/s-a-nersisyan/ExhauFS.
Nersisyan et al. (2022) ExhauFS: exhaustive search-based feature selection for classification and survival regression. PeerJ 10:e13200 https://doi.org/10.7717/peerj.13200
Machine learning driven by environmental covariates to estimate high-resolution PM2.5 in data-poor regions
PM2.5, which refers to fine particles with an equivalent aerodynamic diameter of less than or equal to 2.5 µm, can not only affect air quality but also endanger public health. Nevertheless, the spatial distribution of PM2.5 is not well understood in data-poor regions where monitoring stations are scarce. Therefore, we constructed a random forest (RF) model and a bagging algorithm model based on ground-monitored PM2.5 data, aerosol optical depth (AOD) and meteorological data, and auxiliary geographical variables to accurately estimate the spatial distribution of PM2.5 concentrations in Xinjiang during 2015–2020 at a resolution of 1 km. Through 10-fold cross-validation (CV), the RF model and bagging algorithm model were verified and compared. The results showed the following: (1) The RF model achieved better model performance and thus can be used to estimate the PM2.5 concentration at a relatively high resolution. (2) The PM2.5 concentrations were high in southern Xinjiang and low in northern Xinjiang. The high values were concentrated mainly in the Tarim Basin, while most areas of northern Xinjiang maintained low PM2.5 levels year-round. (3) The PM2.5 values in Xinjiang showed significant seasonality, with the seasonally averaged concentrations decreasing as follows: winter (71.95 µg m−3) > spring (64.76 µg m−3) > autumn (46.01 µg m−3) > summer (43.40 µg m−3). Our model provides a way to monitor air quality in data-scarce places, thereby advancing efforts to achieve sustainable development in the future.
Spatial and Geographic Information Science
Jin et al. (2022) Machine learning driven by environmental covariates to estimate high-resolution PM2.5 in data-poor regions. PeerJ 10:e13203 https://doi.org/10.7717/peerj.13203
Bioinformatical analysis identifies PDLIM3 as a potential biomarker associated with immune infiltration in patients with endometriosis
Background Endometriosis is a chronic systemic disease, whose classic symptoms are pelvic pain and infertility. This disease seriously reduces the life quality of patients. The pathogenesis, recognition and treatment of endometriosis is still unclear, and cannot be over emphasized. The aim of our study was to investigate the potential biomarker of endometriosis for the mechanism and treatment. Methods Using GSE11691, GSE23339 and GSE5108 datasets, differentially expressed genes (DEGs) were identified between endometriosis and normal samples. The functions of DEGs were reflected by the analysis of gene ontology (GO), pathway enrichment and gene set enrichment analysis (GSEA). The LASSO regression model was performed to identify candidate biomarkers. The receiver operating characteristic curve (ROC) was used to evaluate discriminatory ability of candidate biomarkers. The predictive value of the markers in endometriosis were further validated in the GSE120103 dataset. Then, the expression level of biomarkers was detected by qRT-PCR and Western blot. Finally, the relationship between candidate biomarker expression and immune infiltration was estimated using CIBERSORT. Results A total of 42 genes were identified, which were mainly involved in cytokine–cytokine receptor interaction, systemic lupus erythematosus and chemokine signaling pathway. We confirmed PDLIM3 was a specific biomarker in endometriosis (AUC = 0.955) and validated in the GSE120103 dataset (AUC = 0.836). The mRNA and protein expression level of PDLIM3 in endometriosis tissue was significantly higher than normal. Immune cell infiltration analysis revealed that PDLIM3 was correlated with M2 macrophages, neutrophils, CD4+ memory resting T cells, gamma delta T cells, M1 Macrophages, resting mast cells, follicular helper T cells, activated NK cells, CD8+ T cells, regulatory T cells (Tregs), naive B cells, plasma cells and resting NK cells.
Gan et al. (2022) Bioinformatical analysis identifies PDLIM3 as a potential biomarker associated with immune infiltration in patients with endometriosis. PeerJ 10:e13218 https://doi.org/10.7717/peerj.13218
Long non-coding RNA SPRY4-IT1 promotes proliferation and metastasis in nasopharyngeal carcinoma cell
Background Long non-coding RNA SPRY4 intronic transcript 1 (Lnc RNA SPRY4-IT1) was aberrant-expressed in various kinds of cancer. Increasing evidence demonstrated that lnc RNAs involved in tumorigenesis and metastasis. In this study, we aimed to explore the biological role of SPRY4-IT1 on the phenotype of nasopharyngeal carcinoma (NPC) in vitro and in vivo. Methods The expression level of SPRY4-IT1 in NPC cell lines were measured by quantitative real-time polymerase chain reaction (qRT-PCR). Cell Counting Kit-8 (CCK-8) and colony formation assay were used to detect cell proliferation. Wound-healing assay, transwell assay and animal experiment were performed to evaluate the ability of cell migration and metastasis. Cell cycle distribution and apoptosis were determined by flow cytometry. Western blotting and immunofluorescence were employed to identify protein expression. Results SPRY4-IT1 was significantly up-regulated in several NPC cell lines (6-10B, CNE-2, and HONE-1) compared with human immortalized nasopharyngeal epithelial cell (NP69). Silencing of SPRY4-IT1 inhibited proliferation, migration, and metastasis, and induced significant G2/M phase arrest and apoptosis. Western blotting showed that the expression levels of cell cycle-related proteins (cyclin B1, cdc2 and p-cdc2) were down-regulated and apoptosis-associated proteins (PARP, cleaved PARP and cleaved caspase-3) were up-regulated after knockdown of SPRY4-IT1. The expression level of E-cadherin was increased and the expression of Vimentin, Snail and Twist1 were decreased after the SPRY4-IT1 knockdown. Conclusion lncRNA SPRY4-IT1 played a significant role in NPC proliferation, migration and metastasis, suggesting that SPRY4-IT1 might be a potential therapeutic target for the treatment of NPC.
Li et al. (2022) Long non-coding RNA SPRY4-IT1 promotes proliferation and metastasis in nasopharyngeal carcinoma cell. PeerJ 10:e13221 https://doi.org/10.7717/peerj.13221
Classifying the difficulty levels of working memory tasks by using pupillary response
Knowing the difficulty of a given task is crucial for improving the learning outcomes. This paper studies the difficulty level classification of memorization tasks from pupillary response data. Developing a difficulty level classifier from pupil size features is challenging because of the inter-subject variability of pupil responses. Eye-tracking data used in this study was collected while students solved different memorization tasks divided as low-, medium-, and high-level. Statistical analysis shows that values of pupillometric features (as peak dilation, pupil diameter change, and suchlike) differ significantly for different difficulty levels. We used a wrapper method to select the pupillometric features that work the best for the most common classifiers; Support Vector Machine (SVM), Decision Tree (DT), Linear Discriminant Analysis (LDA), and Random Forest (RF). Despite the statistical difference, experiments showed that a random forest classifier trained with five features obtained the best F1-score (82%). This result is essential because it describes a method to evaluate the cognitive load of a subject performing a task using only pupil size features.
Mitre-Hernandez et al. (2022) Classifying the difficulty levels of working memory tasks by using pupillary response. PeerJ 10:e12864 https://doi.org/10.7717/peerj.12864
Responsiveness of domesticated goats towards various stressors following long-term cognitive test exposure
Current evidence suggests that frequent exposure to situations in which captive animals can solve cognitive tasks may have positive effects on stress responsiveness and thus on welfare. However, confounding factors often hamper the interpretation of study results. In this study, we used human-presented object-choice tests (in form of visual discrimination and reversal learning tests and a cognitive test battery), to assess the effect of long-term cognitive stimulation (44 sessions over 4–5 months) on behavioural and cardiac responses of female domestic goats in subsequent stress tests. To disentangle whether cognitive stimulation per se or the reward associated with the human–animal interaction required for testing was affecting the stress responsiveness, we conditioned three treatment groups: goats that were isolated for participation in human-presented cognitive tests and rewarded with food (‘Cognitive’, COG treatment), goats that were isolated as for the test exposure and rewarded with food by the experimenter without being administered the object-choice tests (‘Positive’, POS treatment), and goats that were isolated in the same test room but neither received a reward nor were administered the tests (‘Isolation’, ISO treatment). All treatment groups were subsequently tested in four stress tests: a novel arena test, a novel object test, a novel human test, and a weighing test in which goats had to enter and exit a scale cage. All treatment groups weretested at the same two research sites, each using two selection lines, namely dwarf goats, not selected for production traits, and dairy goats, selected for high productivity. Analysing the data with principal component analysis and linear mixed-effects models, we did not find evidence that cognitive testing per se (COG–POS contrast) reduces stress responsiveness of goats in subsequent stress tests. However, for dwarf goats but not for dairy goats, we found support for an effect of reward-associated human–animal interactions (POS–ISO contrast) at least for some stress test measures. Our results highlight the need to consider ontogenetic and genetic variation when assessing stress responsiveness and when interacting with goats.
Rosenberger et al. (2022) Responsiveness of domesticated goats towards various stressors following long-term cognitive test exposure. PeerJ 10:e12893 https://doi.org/10.7717/peerj.12893
Faecal DNA metabarcoding reveals novel bacterial community patterns of critically endangered Southern River Terrapin, Batagur affinis
Southern River Terrapin, Batagur affinis, is a freshwater turtle listed as critically endangered on the IUCN Red List since 2000. Many studies suggest that faecal DNA metabarcoding can shield light on the host-associated microbial communities that play important roles in host health. Thus, this study aimed to characterise and compare the faecal bacterial community between captive and wild B. affinis using metabarcoding approaches. A total of seven faeces samples were collected from captive (N = 5) and wild (N = 2) adult B. affinis aseptically, crossing the East and West coast of peninsular Malaysia. The DNA was extracted from the faeces samples, and the 16S rRNA gene (V3–V4 region) was amplified using polymerase chain reaction (PCR). The amplicon was further analysed using SILVA and DADA2 pipelines. In total, 297 bacterial communities taxonomic profile (phylum to genus) were determined. Three phyla were found in high abundance in all faeces samples, namely Firmicutes (38.69%), Bacteroidetes (24.52%), and Fusobacteria (6.95%). Proteobacteria were detected in all faeces samples (39.63%), except the wild sample, KBW3. Under genus level, Cetobacteriumwas found as the most abundant genus (67.79%), followed by Bacteroides (24.56%) and Parabacteroides (21.78%). The uncultured genus had the highest abundance (88.51%) even though not detected in the BK31 and KBW2 samples. The potential probiotic genera (75.00%) were discovered to be more dominant in B. affinis faeces samples. Results demonstrated that the captive B. affinis faeces samples have a greater bacterial variety and richness than wild B. affinis faeces samples. This study has established a starting point for future investigation of the gut microbiota of B. affinis.
Mohd Salleh et al. (2022) Faecal DNA metabarcoding reveals novel bacterial community patterns of critically endangered Southern River Terrapin, Batagur affinis. PeerJ 10:e12970 https://doi.org/10.7717/peerj.12970