ScanFold 2.0: a rapid approach for identifying potential structured RNA targets in genomes and transcriptomes
A major limiting factor in target discovery for both basic research and therapeutic intervention is the identification of structural and/or functional RNA elements in genomes and transcriptomes. This was the impetus for the original ScanFold algorithm, which provides maps of local RNA structural stability, evidence of sequence-ordered (potentially evolved) structure, and unique model structures comprised of recurring base pairs with the greatest structural bias. A key step in quantifying this propensity for ordered structure is the prediction of secondary structural stability for randomized sequences which, in the original implementation of ScanFold, is explicitly evaluated. This slow process has limited the rapid identification of ordered structures in large genomes/transcriptomes, which we seek to overcome in this current work introducing ScanFold 2.0. In this revised version of ScanFold, we no longer explicitly evaluate randomized sequence folding energy, but rather estimate it using a machine learning approach. For high randomization numbers, this can increase prediction speeds over 100-fold compared to ScanFold 1.0, allowing for the analysis of large sequences, as well as the use of additional folding algorithms that may be computationally expensive. In the testing of ScanFold 2.0, we re-evaluate the Zika, HIV, and SARS-CoV-2 genomes and compare both the consistency of results and the time of each run to ScanFold 1.0. We also re-evaluate the SARS-CoV-2 genome to assess the quality of ScanFold 2.0 predictions vs several biochemical structure probing datasets and compare the results to those of the original ScanFold program.
Andrews et al. (2022) ScanFold 2.0: a rapid approach for identifying potential structured RNA targets in genomes and transcriptomes. PeerJ 10:e14361 https://doi.org/10.7717/peerj.14361
Identification and expression analysis of maize NF-YA subunit genes
NF-YAs encode subunits of the nuclear factor-Y (NF-Y) gene family. NF-YAs represent a kind of conservative transcription factor in plants and are involved in plant growth and development, as well as resistance to biotic and abiotic stress. In this study, 16 maize (Zea mays) NF-YA subunit genes were identified using bioinformatics methods, and they were divided into three categories by a phylogenetic analysis. A conserved domain analysis showed that most contained a CCAAT-binding transcription factor (CBFB) _NF-YA domain. Maize NF-YA subunit genes showed very obvious tissue expression characteristics. The expression level of the NF-YA subunit genes significantly changed under different abiotic stresses, including Fusarium graminearum infection and salicylic acid (SA) or jasmonic acid (JA) treatments. After inoculation with Setosphaeria turcica and Cochliobolus heterostrophus, the lesion areas of nfya01 and nfya06 were significantly larger than that of B73, indicating that ZmNFYA01 and ZmNFYA06 positively regulated maize disease resistance. ZmNFYA01 and ZmNFYA06 may regulated maize disease resistance by affecting the transcription levels of ZmPRs. Thus, NF-YA subunit genes played important roles in promoting maize growth and development and resistance to stress. The results laid a foundation for clarifying the functions and regulatory mechanisms of NF-YA subunit genes in maize.
Lv et al. (2022) Identification and expression analysis of maize NF-YA subunit genes. PeerJ 10:e14306 https://doi.org/10.7717/peerj.14306
Assessment of the association between periodontal disease and total cancer incidence and mortality: a meta-analysis
Background Periodontal disease (PD) is a chronic inflammatory disease that leads to alveolar bone resorption and tooth loss. Many studies have reported the association between periodontal disease and various cancers including oral cancer, lung cancer, breast cancer and so on. However, there is still no specialized meta-analysis that assesses the association between periodontal disease and cancer incidence and mortality in-deepth. Thus, we conducted this meta-analysis. Methods This meta-analysis was registered with PROSPERO: CRD42020183497. We searched five online databases for observational studies about the association between periodontal disease and breast, prostate, lung and bronchial, colorectal, and total cancers by July 2020. Then we evaluated quality of the included studies by the Newcastle-Ottawa scale. Risk ratios (HRs) and their 95% confidence intervals (CIs) were pooled to evaluate the strength of the association between periodontal disease and four cancers, total cancer incidence and mortality. In addition, we analyzed heterogeneity by subgroup analysis and sensitivity analysis. Finally, we inspected publication bias by Begg’s and Egger’s tests. Results None of the studies included in this meta-analysis were of poor quality. PD is not only related to breast cancer incidence (HR = 1.26,95%CI [1.11–1.43], I2 = 75.8%, P = 0.000), but also connected with total cancer mortality (HR = 1.40,95%CI [1.24–1.58], I2 = 0.0%, P = 0.718). Subgroup analyses showed that study population, study design, dental status, follow-up period, adjustment for smoking partially explained the heterogeneity between studies. The results of Begg’s test and Egger’s test were consistent and indicated that there is no publication bias in this study. Conclusion In conclusion, this meta-analysis revealed a positive relationship between periodontal disease and breast cancer incidence and total cancer mortality. Further well-designed studies with specific inclusion and exclusion criteria are required to strengthen the conclusion of this meta-analysis. However, longer follow-up period, multi-center trials and even multinational studies are required to corroborate the results.
Wang et al. (2022) Assessment of the association between periodontal disease and total cancer incidence and mortality: a meta-analysis. PeerJ 10:e14320 https://doi.org/10.7717/peerj.14320
AAA+ proteases: the first line of defense against mitochondrial damage
Mitochondria play essential cellular roles in Adenosine triphosphate (ATP) synthesis, calcium homeostasis, and metabolism, but these vital processes have potentially deadly side effects. The production of the reactive oxygen species (ROS) and the aggregation of misfolded mitochondrial proteins can lead to severe mitochondrial damage and even cell death. The accumulation of mitochondrial damage is strongly implicated in aging and several incurable diseases, including neurodegenerative disorders and cancer. To oppose this, metazoans utilize a variety of quality control strategies, including the degradation of the damaged mitochondrial proteins by the mitochondrial-resident proteases of the ATPase Associated with the diverse cellular Activities (AAA+) family. This mini-review focuses on the quality control mediated by the mitochondrial-resident proteases of the AAA+ family used to combat the accumulation of damaged mitochondria and on how the failure of this mitochondrial quality control contributes to diseases.
Pareek (2022) AAA+ proteases: the first line of defense against mitochondrial damage. PeerJ 10:e14350 https://doi.org/10.7717/peerj.14350
The seeker R package: simplified fetching and processing of transcriptome data
Transcriptome data have become invaluable for interrogating biological systems. Preparing a transcriptome dataset for analysis, particularly an RNA-seq dataset, entails multiple steps and software programs, each with its own command-line interface (CLI). Although these CLIs are powerful, they often require shell scripting for automation and parallelization, which can have a high learning curve, especially when the details of the CLIs vary from one tool to another. However, many individuals working with transcriptome data are already familiar with R due to the plethora and popularity of R-based tools for analyzing biological data. Thus, we developed an R package called seeker for simplified fetching and processing of RNA-seq and microarray data. Seeker is a wrapper around various existing tools, and provides a standard interface, simple parallelization, and detailed logging. Seeker’s primary output—sample metadata and gene expression values based on Entrez or Ensembl Gene IDs—can be directly plugged into a differential expression analysis. To maximize reproducibility, seeker is available as a standalone R package and in a Docker image that includes all dependencies, both of which are accessible at https://seeker.hugheylab.org.
Schoenbachler et al. (2022) The seeker R package: simplified fetching and processing of transcriptome data. PeerJ 10:e14372 https://doi.org/10.7717/peerj.14372
APPINetwork: an R package for building and computational analysis of protein–protein interaction networks
Background Protein–protein interactions (PPIs) are essential to almost every process in a cell. Analysis of PPI networks gives insights into the functional relationships among proteins and may reveal important hub proteins and sub-networks corresponding to functional modules. Several good tools have been developed for PPI network analysis but they have certain limitations. Most tools are suited for studying PPI in only a small number of model species, and do not allow second-order networks to be built, or offer relevant functions for their analysis. To overcome these limitations, we have developed APPINetwork (Analysis of Protein–protein Interaction Networks). The aim was to produce a generic and user-friendly package for building and analyzing a PPI network involving proteins of interest from any species as long they are stored in a database. Methods APPINetwork is an open-source R package. It can be downloaded and installed on the collaborative development platform GitLab (https://forgemia.inra.fr/GNet/appinetwork). A graphical user interface facilitates its use. Graphical windows, buttons, and scroll bars allow the user to select or enter an organism name, choose data files and network parameters or methods dedicated to network analysis. All functions are implemented in R, except for the script identifying all proteins involved in the same biological process (developed in C) and the scripts formatting the BioGRID data file and generating the IDs correspondence file (implemented in Python 3). PPI information comes from private resources or different public databases (such as IntAct, BioGRID, and iRefIndex). The package can be deployed on Linux and macOS operating systems (OS). Deployment on Windows is possible but it requires the prior installation of Rtools and Python 3. Results APPINetwork allows the user to build a PPI network from selected public databases and add their own PPI data. In this network, the proteins have unique identifiers resulting from the standardization of the different identifiers specific to each database. In addition to the construction of the first-order network, APPINetwork offers the possibility of building a second-order network centered on the proteins of interest (proteins known for their role in the biological process studied or subunits of a complex protein) and provides the number and type of experiments that have highlighted each PPI, as well as references to articles containing experimental evidence. Conclusion More than a tool for PPI network building, APPINetwork enables the analysis of the resultant network, by searching either for the community of proteins involved in the same biological process or for the assembly intermediates of a protein complex. Results of these analyses are provided in easily exportable files. Examples files and a user manual describing each step of the process come with the package.
Gosset et al. (2022) APPINetwork: an R package for building and computational analysis of protein–protein interaction networks. PeerJ 10:e14204 https://doi.org/10.7717/peerj.14204
Role of neuroinflammation mediated potential alterations in adult neurogenesis as a factor for neuropsychiatric symptoms in Post-Acute COVID-19 syndrome—A narrative review
Persistence of symptoms beyond the initial 3 to 4 weeks after infection is defined as post-acute COVID-19 syndrome (PACS). A wide range of neuropsychiatric symptoms like anxiety, depression, post-traumatic stress disorder, sleep disorders and cognitive disturbances have been observed in PACS. The review was conducted based on PRISMA-S guidelines for literature search strategy for systematic reviews. A cytokine storm in COVID-19 may cause a breach in the blood brain barrier leading to cytokine and SARS-CoV-2 entry into the brain. This triggers an immune response in the brain by activating microglia, astrocytes, and other immune cells leading to neuroinflammation. Various inflammatory biomarkers like inflammatory cytokines, chemokines, acute phase proteins and adhesion molecules have been implicated in psychiatric disorders and play a major role in the precipitation of neuropsychiatric symptoms. Impaired adult neurogenesis has been linked with a variety of disorders like depression, anxiety, cognitive decline, and dementia. Persistence of neuroinflammation was observed in COVID-19 survivors 3 months after recovery. Chronic neuroinflammation alters adult neurogenesis with pro-inflammatory cytokines supressing anti-inflammatory cytokines and chemokines favouring adult neurogenesis. Based on the prevalence of neuropsychiatric symptoms/disorders in PACS, there is more possibility for a potential impairment in adult neurogenesis in COVID-19 survivors. This narrative review aims to discuss the various neuroinflammatory processes during PACS and its effect on adult neurogenesis.
Saikarthik et al. (2022) Role of neuroinflammation mediated potential alterations in adult neurogenesis as a factor for neuropsychiatric symptoms in Post-Acute COVID-19 syndrome—A narrative review. PeerJ 10:e14227 https://doi.org/10.7717/peerj.14227
Machine learning based estimation of field-scale daily, high resolution, multi-depth soil moisture for the Western and Midwestern United States
Background High-resolution soil moisture estimates are critical for planning water management and assessing environmental quality. In-situ measurements alone are too costly to support the spatial and temporal resolutions needed for water management. Recent efforts have combined calibration data with machine learning algorithms to fill the gap where high resolution moisture estimates are lacking at the field scale. This study aimed to provide calibrated soil moisture models and methodology for generating gridded estimates of soil moisture at multiple depths, according to user-defined temporal periods, spatial resolution and extent. Methods We applied nearly one million national library soil moisture records from over 100 sites, spanning the U.S. Midwest and West, to build Quantile Random Forest (QRF) calibration models. The QRF models were built on covariates including soil moisture estimates from North American Land Data Assimilation System (NLDAS), soil properties, climate variables, digital elevation models, and remote sensing-derived indices. We also explored an alternative approach that adopted a regionalized calibration dataset for the Western U.S. The broad-scale QRF models were independently validated according to sampling depths, land cover type, and observation period. We then explored the model performance improved with local samples used for spiking. Finally, the QRF models were applied to estimate soil moisture at the field scale where evaluation was carried out to check estimated temporal and spatial patterns. Results The broad-scale QRF model showed moderate performance (R2 = 0.53, RMSE = 0.078 m3/m3) when data points from all depth layers (up to 100 cm) were considered for an independent validation. Elevation, NLDAS-derived moisture, soil properties, and sampling depth were ranked as the most important covariates. The best model performance was observed for forest and pasture sites (R2 > 0.5; RMSE < 0.09 m3/m3), followed by grassland and cropland (R2 > 0.4; RMSE < 0.11 m3/m3). Model performance decreased with sampling depths and was slightly lower during the winter months. Spiking the national QRF model with local samples improved model performance by reducing the RMSE to less than 0.05 m3/m3 for grassland sites. At the field scale, model estimates illustrated more accurate temporal trends for surface than subsurface soil layers. Model estimated spatial patterns need to be further improved and validated with management data. Conclusions The model accuracy for top 0–20 cm soil depth (R2 > 0.5, RMSE < 0.08 m3/m3) showed promise for adopting the methodology for soil moisture monitoring. The success of spiking the national model with local samples showed the need to collect multi-year high frequency (e.g., hourly) sensor-based field measurements to improve estimates of soil moisture for a longer time period. Future work should improve model performance for deeper depths with additional hydraulic properties and use of locally-selected calibration datasets.
Xia et al. (2022) Machine learning based estimation of field-scale daily, high resolution, multi-depth soil moisture for the Western and Midwestern United States. PeerJ 10:e14275 https://doi.org/10.7717/peerj.14275
Examining abnormal Silurian trilobites from the Llandovery of Australia
Abnormal trilobites present insight into how arthropods with fully biomineralised exoskeletons recovered from injuries, genetic malfunctions, and pathologies. Records of abnormal Silurian trilobites in particular show an abundance of specimens with teratologies and a limited record of injuries. Here we expand the record of abnormal Silurian trilobites by presenting seven new abnormal specimens of Odontopleura (Sinespinaspis) markhami from the early Silurian (Llandovery, Telychian) Cotton Formation, New South Wales. We use these specimens to illustrate novel evidence for asymmetric distribution of pleural thoracic spine bases. These abnormal bases likely reflect genetic complications, resulting in morphologies that would unlikely have aided the fitness of abnormal individuals. In considering records of malformed Silurian trilobites more broadly, we propose that the largest trilobites may have been prey at this time. This indicates a possible change in the trophic position of trilobites when compared to Cambrian and Ordovician palaeoecosystems.
Bicknell et al. (2022) Examining abnormal Silurian trilobites from the Llandovery of Australia. PeerJ 10:e14308 https://doi.org/10.7717/peerj.14308
Comparison of iCOR and Rayleigh atmospheric correction methods on Sentinel-3 OLCI images for a shallow eutrophic reservoir
Remote sensing of inland waters is challenging, but also important, due to the need to monitor the ever-increasing harmful algal blooms (HABs), which have serious effects on water quality. The Ocean and Land Color Instrument (OLCI) of the Sentinel-3 satellites program is capable of providing images for the monitoring of such waters. Atmospheric correction is a necessary process in order to retrieve the desired surface-leaving radiance signal and several atmospheric correction methods have been developed through the years. However, many of these correction methods require programming language skills, or function as commercial software plugins, limiting their possibility of use by end users. Accordingly, in this study, the free SNAP software provided by the European Space Agency (ESA) was used to evaluate the possible differences between a partial atmospheric correction method accounting for Rayleigh scattering and a full atmospheric correction method (iCOR), applied on Sentinel-3 OLCI images of a shallow, highly eutrophic water reservoir. For the complete evaluation of the two methods, in addition to the comparison of the band reflectance values, chlorophyll (CHL) and cyanobacteria (CI) indices were also calculated and their values were intercompared. The results showed, that although the absolute values between the two correction methods did not coincide, there was a very good correlation between the two methods for both bands’ reflectance (r > 0.73) and the CHL and CI indices values (r > 0.95). Therefore, since iCOR correction image processing time is 25 times longer than Rayleigh correction, it is proposed that the Rayleigh partial correction method may be alternatively used for seasonal water monitoring, especially in cases of long time-series, enhancing time and resources use efficiency. Further comparisons of the two methods in other inland water bodies and evaluation with in situ chlorophyll and cyanobacteria measurements will enhance the applicability of the methodology.
Spatial and Geographic Information Science
Katsoulis-Dimitriou et al. (2022) Comparison of iCOR and Rayleigh atmospheric correction methods on Sentinel-3 OLCI images for a shallow eutrophic reservoir. PeerJ 10:e14311 https://doi.org/10.7717/peerj.14311