Evolutionary history of dimethylsulfoniopropionate (DMSP) demethylation enzyme DmdA in marine bacteria
Dimethylsulfoniopropionate (DMSP), an osmolyte produced by oceanic phytoplankton and bacteria, is primarily degraded by bacteria belonging to the Roseobacter lineage and other marine Alphaproteobacteria via DMSP-dependent demethylase A protein (DmdA). To date, the evolutionary history of DmdA gene family is unclear. Some studies indicate a common ancestry between DmdA and GcvT gene families and a co-evolution between Roseobacter and the DMSP-producing-phytoplankton around 250 million years ago (Mya). In this work, we analyzed the evolution of DmdA under three possible evolutionary scenarios: (1) a recent common ancestor of DmdA and GcvT, (2) a coevolution between Roseobacter and the DMSP-producing-phytoplankton, and (3) an enzymatic adaptation for utilizing DMSP in marine bacteria prior to Roseobacter origin. Our analyses indicate that DmdA is a new gene family originated from GcvT genes by duplication and functional divergence driven by positive selection before a coevolution between Roseobacter and phytoplankton. Our data suggest that Roseobacter acquired dmdA by horizontal gene transfer prior to an environment with higher DMSP. Here, we propose that the ancestor that carried the DMSP demethylation pathway genes evolved in the Archean, and was exposed to a higher concentration of DMSP in a sulfur-rich atmosphere and anoxic ocean, compared to recent Roseobacter eco-orthologs (orthologs performing the same function under different conditions), which should be adapted to lower concentrations of DMSP.
Hernández et al. (2020) Evolutionary history of dimethylsulfoniopropionate (DMSP) demethylation enzyme DmdA in marine bacteria. PeerJ 8:e9861 https://doi.org/10.7717/peerj.9861
Consistent administration of cetuximab is associated with favorable outcomes in recurrent/metastatic head and neck squamous cell carcinoma in an endemic carcinogen exposure area: a retrospective observational study
Background This study aimed to analyze the clinical outcomes associated with patients with recurrent/metastatic head and neck squamous cell carcinoma (RM HNSCC) who received cetuximab-based chemotherapy in a real-world clinical setting. Methods Clinical data were extracted from RM HNSCC patients diagnosed between 2016 and 2019. Kaplan–Meier survival estimates and Cox proportional hazards model were used for survival analyses. Results Of 106 RM HNSCC patients (mean age = 55.1 years), 38.7% exhibited recurrent disease and 61.3% had metastatic disease. The majority of patients showed a habit of addictive substance use, including alcohol (67.0%), betel nuts (71.7%), or tobacco (74.5%). The primary tumor sites included the oral cavity (64.1%), hypopharynx (19.8%), and oropharynx (16.0%). The median number of cetuximab cycles for the 106 patients was 11 (2–24). The disease control rate (DCR) was 48.1%, and the overall response rate (ORR) was 28.3%. The median progression-free survival (PFS) and overall survival (OS) were 5.0 and 9.23 months, respectively. Patients treated with more than 11 cycles of cetuximab exhibited a longer median PFS and median OS than did patients treated with less than 11 cycles (median PFS: 7.0 vs. 3.0 months, p < 0.001; OS: 12.43 vs. 4.46 months, p = 0.001). Patients without previous concurrent chemoradiotherapy (CRT) had a better median PFS than did those with previous CRT (6.0 vs. 4.0 months, p = 0.046). Multivariable analysis revealed that perineural invasion and fewer cycles of cetuximab (<11 cycles) were independent risk factors associated with disease progression. In addition, the reduction in treatment cycles of cetuximab and advanced lymph node metastasis were independent prognostic factors predicting poorer overall survival. Conclusion Our study provides important real-world data regarding cetuximab-containing treatment in RM HNSCC. Consistent administration of cetuximab could be associated with more favorable outcomes in RM HNSCC in endemic carcinogen exposure areas.
Wang et al. (2020) Consistent administration of cetuximab is associated with favorable outcomes in recurrent/metastatic head and neck squamous cell carcinoma in an endemic carcinogen exposure area: a retrospective observational study. PeerJ 8:e9862 https://doi.org/10.7717/peerj.9862
A survey of RNA secondary structural propensity encoded within human herpesvirus genomes: global comparisons and local motifs
There are nine herpesviruses known to infect humans, of which Epstein–Barr virus (EBV) is the most widely distributed (>90% of adults infected). This ubiquitous virus is implicated in a variety of cancers and autoimmune diseases. Previous analyses of the EBV genome revealed numerous regions with evidence of generating unusually stable and conserved RNA secondary structures and led to the discovery of a novel class of EBV non-coding (nc)RNAs: the stable intronic sequence (sis)RNAs. To gain a better understanding of the roles of RNA structure in EBV biology and pathogenicity, we revisit EBV using recently developed tools for genome-wide motif discovery and RNA structural characterization. This corroborated previous results and revealed novel motifs with potential functionality; one of which has been experimentally validated. Additionally, since many herpesviruses increasingly rival the seroprevalence of EBV (VZV, HHV-6 and HHV-7 being the most notable), analyses were expanded to include all sequenced human Herpesvirus RefSeq genomes, allowing for genomic comparisons. In total 10 genomes were analyzed, for EBV (types 1 and 2), HCMV, HHV-6A, HHV-6B, HHV-7, HSV-1, HSV-2, KSHV, and VZV. All resulting data were archived in the RNAStructuromeDB (https://structurome.bb.iastate.edu/herpesvirus) to make them available to a wide array of researchers.
Andrews et al. (2020) A survey of RNA secondary structural propensity encoded within human herpesvirus genomes: global comparisons and local motifs. PeerJ 8:e9882 https://doi.org/10.7717/peerj.9882
Evaluation of different culture media to support in vitro growth and biofilm formation of bacterial vaginosis-associated anaerobes
Background Bacterial vaginosis (BV) is one of the most common vaginal infections worldwide. It is associated with the presence of a dense polymicrobial biofilm on the vaginal epithelium, formed mainly by Gardnerella species. The biofilm also contains other anaerobic species, but little is known about their role in BV development. Aim To evaluate the influence of different culture media on the planktonic and biofilm growth of six cultivable anaerobes frequently associated with BV, namely Gardnerella sp., Atopobium vaginae, Lactobacillus iners, Mobiluncus curtisii, Peptostreptococcus anaerobius and Prevotella bivia. Methods A total of nine different culture media compositions, including commercially available and chemically defined media simulating genital tract secretions, were tested in this study. Planktonic cultures and biofilms were grown under anaerobic conditions (10% carbon dioxide, 10% helium and 80% nitrogen). Planktonic growth was assessed by optical density measurements, and biofilm formation was quantified by crystal violet staining. Results Significant planktonic growth was observed for Gardnerella sp., A. vaginae and L. iners in New York City III broth, with or without ascorbic acid supplementation. Biofilm quantification showed high in vitro biofilm growth for Gardnerella sp., P. anaerobius and P. bivia in almost all culture media excluding Brucella broth. Contrary, only New York City III broth was able to promote biofilm formation for A. vaginae, L. iners and M. curtisii. Conclusions Our data demonstrate that New York City III broth relative to the other tested media is the most conducive for future studies addressing polymicrobial biofilms development as this culture medium allowed the formation of significant levels of single-species biofilms.
Rosca et al. (2020) Evaluation of different culture media to support in vitro growth and biofilm formation of bacterial vaginosis-associated anaerobes. PeerJ 8:e9917 https://doi.org/10.7717/peerj.9917
A multi-class classification model for supporting the diagnosis of type II diabetes mellitus
Background Numerous studies have utilized machine-learning techniques to predict the early onset of type 2 diabetes mellitus. However, fewer studies have been conducted to predict an appropriate diagnosis code for the type 2 diabetes mellitus condition. Further, ensemble techniques such as bagging and boosting have likewise been utilized to an even lesser extent. The present study aims to identify appropriate diagnosis codes for type 2 diabetes mellitus patients by means of building a multi-class prediction model which is both parsimonious and possessing minimum features. In addition, the importance of features for predicting diagnose code is provided. Methods This study included 149 patients who have contracted type 2 diabetes mellitus. The sample was collected from a large hospital in Taiwan from November, 2017 to May, 2018. Machine learning algorithms including instance-based, decision trees, deep neural network, and ensemble algorithms were all used to build the predictive models utilized in this study. Average accuracy, area under receiver operating characteristic curve, Matthew correlation coefficient, macro-precision, recall, weighted average of precision and recall, and model process time were subsequently used to assess the performance of the built models. Information gain and gain ratio were used in order to demonstrate feature importance. Results The results showed that most algorithms, except for deep neural network, performed well in terms of all performance indices regardless of either the training or testing dataset that were used. Ten features and their importance to determine the diagnosis code of type 2 diabetes mellitus were identified. Our proposed predictive model can be further developed into a clinical diagnosis support system or integrated into existing healthcare information systems. Both methods of application can effectively support physicians whenever they are diagnosing type 2 diabetes mellitus patients in order to foster better patient-care planning.
Kuo et al. (2020) A multi-class classification model for supporting the diagnosis of type II diabetes mellitus. PeerJ 8:e9920 https://doi.org/10.7717/peerj.9920
KEYLINK: towards a more integrative soil representation for inclusion in ecosystem scale models. I. review and model concept
The relatively poor simulation of the below-ground processes is a severe drawback for many ecosystem models, especially when predicting responses to climate change and management. For a meaningful estimation of ecosystem production and the cycling of water, energy, nutrients and carbon, the integration of soil processes and the exchanges at the surface is crucial. It is increasingly recognized that soil biota play an important role in soil organic carbon and nutrient cycling, shaping soil structure and hydrological properties through their activity, and in water and nutrient uptake by plants through mycorrhizal processes. In this article, we review the main soil biological actors (microbiota, fauna and roots) and their effects on soil functioning. We review to what extent they have been included in soil models and propose which of them could be included in ecosystem models. We show that the model representation of the soil food web, the impact of soil ecosystem engineers on soil structure and the related effects on hydrology and soil organic matter (SOM) stabilization are key issues in improving ecosystem-scale soil representation in models. Finally, we describe a new core model concept (KEYLINK) that integrates insights from SOM models, structural models and food web models to simulate the living soil at an ecosystem scale.
Deckmyn et al. (2020) KEYLINK: towards a more integrative soil representation for inclusion in ecosystem scale models. I. review and model concept. PeerJ 8:e9750 https://doi.org/10.7717/peerj.9750
Omics approaches in Allium research: Progress and way ahead
Background The genus Allium (Family: Amaryllidaceae) is an economically important group of crops cultivated worldwide for their use as a vegetable and spices. Alliums are also well known for their nutraceutical properties. Among alliums, onion, garlic, leek, and chives cultivated worldwide. Despite their substantial economic and medicinal importance, the genome sequence of any of the Allium is not available, probably due to their large genome sizes. Recently evolved omics technologies are highly efficient and robust in elucidating molecular mechanisms of several complex life processes in plants. Omics technologies, such as genomics, transcriptomics, proteomics, metabolomics, metagenomics, etc. have the potential to open new avenues in research and improvement of allium crops where genome sequence information is limited. A significant amount of data has been generated using these technologies for various Allium species; it will help in understanding the key traits in Allium crops such as flowering, bulb development, flavonoid biosynthesis, male sterility and stress tolerance at molecular and metabolite level. This information will ultimately assist us in speeding up the breeding in Allium crops. Method In the present review, major omics approaches, and their progress, as well as potential applications in Allium crops, could be discussed in detail. Results Here, we have discussed the recent progress made in Allium research using omics technologies such as genomics, transcriptomics, micro RNAs, proteomics, metabolomics, and metagenomics. These omics interventions have been used in alliums for marker discovery, the study of the biotic and abiotic stress response, male sterility, organ development, flavonoid and bulb color, micro RNA discovery, and microbiome associated with Allium crops. Further, we also emphasized the integrated use of these omics platforms for a better understanding of the complex molecular mechanisms to speed up the breeding programs for better cultivars. Conclusion All the information and literature provided in the present review throws light on the progress and potential of omics platforms in the research of Allium crops. We also mentioned a few research areas in Allium crops that need to be explored using omics technologies to get more insight. Overall, alliums are an under-studied group of plants, and thus, there is tremendous scope and need for research in Allium species.
Khandagale et al. (2020) Omics approaches in Allium research: Progress and way ahead. PeerJ 8:e9824 https://doi.org/10.7717/peerj.9824
Assessment of postoperative health functioning after knee arthroplasty in relation to pain catastrophizing: a 6-month follow-up cohort study
Background Knee arthroplasty (KA) is a typically successful surgical procedure commonly performed to alleviate painin participants with end-stage knee osteoarthritis. Despite its beneficial effects, a significant proportion of individuals with KA continue experiencing persistent pain and functional limitations. The purpose of this study was to assess the postoperative outcomes after KA in relation to postoperative pain catastrophizing. Methods Participants were recruited at a domiciliary physiotherapy service, using a prospective, observational, hypothesis-generating cohort design. Participants were divided into two groups based on their Pain Catastrophizing Scale (PCS) total score (50th percentile), which resulted in high and low PCS groups. The primary outcome measure was the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). In addition, quality of life, walking speed, physical performance, range of motion, and pain were measured. Outcome measures were collected at baseline (1 week postoperatively) and at follow-up (1, 3, and 6 months postoperatively). Results A total of 60 participants (21 total KA and 39 unicompartmental KA) were recruited. Individuals with a higher degree of pain catastrophizing showed significantly higher WOMAC total scores at every follow-up, indicating poorer health functioning (p < 0.01). Similarly, the high PCS group showed higher WOMAC pain, stiffness and disability subscale scores (p < 0.05), poorer quality of life (p < 0.01), and poorer physical performance (p < 0.05) at every follow-up. In addition, the high PCS group achieved a slower walking speed at baseline and at 3 months follow-up (p < 0.05), and a higher degree of pain at rest, on walking and on knee flexion at every follow-up (p < 0.01, p < 0.05 and p < 0.05, respectively) except for walking pain at 3 months follow-up. No significant differences were observed between groups in range of motion, except for active knee extension at the 6-month follow-up (p < 0.05). Effect size was large at 1 month follow-up in WOMAC total score (r = 0.578) and pain intensity during knee flexion (r = 0.529). Longitudinal analyses revealed different improvement trends during the rehabilitation process between groups, with a lack of significant improvements in the high PCS group between the 3- and 6-month follow-up in WOMAC total score, WOMAC pain, WOMAC disability, quality of life, physical performance, active knee extension and resting pain (p > 0.05). Conclusion The results of the present study suggest that participants with high postoperative pain catastrophizing might have poorer outcomes during the rehabilitation process after KA. Future work should seek to clarify if this relationship is causal.
Terradas-Monllor et al. (2020) Assessment of postoperative health functioning after knee arthroplasty in relation to pain catastrophizing: a 6-month follow-up cohort study. PeerJ 8:e9903 https://doi.org/10.7717/peerj.9903
Effect of nest age and habitat variables on nest survival in Marsh Harrier (Circus aeruginosus) in a fishpond habitat
Background One important anti‐predator strategy adopted by birds involves nest site selection and timing of breeding. Nest-site selection by marsh-nesting birds often involves nest concealment and water depth as key features influencing nest survival. Marsh Harrier (Circus aeruginosus) is an obligate ground nester, which sets it apart from other raptors. The aim of the present study was to identify for the first time possible temporal and habitat factors affecting nest survival in Marsh Harrier. Understanding features which affect nest survival are essential for assessing relevant conservation strategies. Methods To understand the relative contributions of different temporal and habitat variables to brood losses, it is useful to determine the daily survival rate (DSR). We examined 82 Marsh Harrier nests located on fishponds in eastern Poland, where predation is the main cause of nest loss. Six habitat variables were measured for each active nest. DSR was calculated using known-fate models with the RMark package. Results The best-supported model predicted that DSR decreased with nest age and was positively affected by the water depth and the diameter of reed stems, but not by the height or density of vegetation at the nest site. The distances of nests to the fishpond dyke and to open water received no support in the models. The chances of nest survival were lower if a neighbouring nest had been depredated. This result suggests that the Marsh Harrier is more susceptible to mammalian than avian predation and confirms the high level of predator pressure in fishpond habitats.
Zaremba et al. (2020) Effect of nest age and habitat variables on nest survival in Marsh Harrier (Circus aeruginosus) in a fishpond habitat. PeerJ 8:e9929 https://doi.org/10.7717/peerj.9929
A descriptive study of random forest algorithm for predicting COVID-19 patients outcome
Background The outbreak of coronavirus disease 2019 (COVID-19) that occurred in Wuhan, China, has become a global public health threat. It is necessary to identify indicators that can be used as optimal predictors for clinical outcomes of COVID-19 patients. Methods The clinical information from 126 patients diagnosed with COVID-19 were collected from Wuhan Fourth Hospital. Specific clinical characteristics, laboratory findings, treatments and clinical outcomes were analyzed from patients hospitalized for treatment from 1 February to 15 March 2020, and subsequently died or were discharged. A random forest (RF) algorithm was used to predict the prognoses of COVID-19 patients and identify the optimal diagnostic predictors for patients’ clinical prognoses. Results Seven of the 126 patients were excluded for losing endpoints, 103 of the remaining 119 patients were discharged (alive) and 16 died in the hospital. A synthetic minority over-sampling technique (SMOTE) was used to correct the imbalanced distribution of clinical patients. Recursive feature elimination (RFE) was used to select the optimal subset for analysis. Eleven clinical parameters, Myo, CD8, age, LDH, LMR, CD45, Th/Ts, dyspnea, NLR, D-Dimer and CK were chosen with AUC approximately 0.9905. The RF algorithm was built to predict the prognoses of COVID-19 patients based on the best subset, and the area under the ROC curve (AUC) of the test data was 100%. Moreover, two optimal clinical risk predictors, lactate dehydrogenase (LDH) and Myoglobin (Myo), were selected based on the Gini index. The univariable logistic analysis revealed a substantial increase in the risk for in-hospital mortality when Myo was higher than 80 ng/ml (OR = 7.54, 95% CI [3.42–16.63]) and LDH was higher than 500 U/L (OR = 4.90, 95% CI [2.13–11.25]). Conclusion We applied an RF algorithm to predict the mortality of COVID-19 patients with high accuracy and identified LDH higher than 500 U/L and Myo higher than 80 ng/ml to be potential risk factors for the prognoses of COVID-19 patients in the early stage of the disease.
Wang et al. (2020) A descriptive study of random forest algorithm for predicting COVID-19 patients outcome. PeerJ 8:e9945 https://doi.org/10.7717/peerj.9945