Potential Roles of the GRF Transcription Factors in Sorghum Internodes during Post-Reproductive Stages.

Growth-regulating factor (GRF) is a plant-specific family of transcription factors crucial for meristem development and plant growth. Sorghum (Sorghum bicolor L. Moench) is a cereal species widely used for food, feed and fuel. While sorghum stems are important biomass components, the regulation of stem development and the carbohydrate composition of the stem tissues remain largely unknown. Here, we identified 11 SbGRF-encoding genes and found the SbGRF expansion driven by whole-genome duplication events. By comparative analyses of GRFs between rice and sorghum, we demonstrated the divergence of whole-genome duplication (WGD)-derived OsGRFs and SbGRFs. A comparison of SbGRFs' expression profiles supports that the WGD-duplicated OsGRFs and SbGRFs experienced distinct evolutionary trajectories, possibly leading to diverged functions. RNA-seq analysis of the internode tissues identified several SbGRFs involved in internode elongation, maturation and cell wall metabolism. We constructed co-expression networks with the RNA-seq data of sorghum internodes. Network analysis discovered that SbGRF1, 5 and 7 could be involved in the down-regulation of the biosynthesis of cell wall components, while SbGRF4, 6, 8 and 9 could be associated with the regulation of cell wall loosening, reassembly and/or starch biosynthesis. In summary, our genome-wide analysis of SbGRFs reveals the distinct evolutionary trajectories of WGD-derived SbGRF pairs. Importantly, expression analyses highlight previously unknown functions of several SbGRFs in internode elongation, maturation and the potential involvement in the metabolism of the cell wall and starch during post-anthesis stages.

Characterization and Transcriptomic Analysis of Sorghum EIN/EIL Family and Identification of Their Roles in Internode Maturation.

Ethylene-insensitive 3/Ethylene-insensitive3-like proteins (EIN3/EIL) represent a group of transcription factors critical for the ethylene signaling transduction that manipulate downstream ethylene-responsive genes, thereby regulating plant growth, development, and stress responses. However, the identification, evolution, and divergence of the EIL family remain to be studied in Sorghum bicolor. Here, we identified eight SbEILs, which were expanded due to whole-genome-duplication (WGD) events. Characterization of the protein sequences and expression atlas demonstrates that the WGD-duplicated SbEILs could become divergent due to the differential expression patterns, rather than domain and motif architectures. Comparative expression analysis was performed between the RNA-seq data sets of internodes from several sorghum cultivars to understand the potential roles of SbEIL members in internode elongation and maturation. Our results identified SbEIL3 and 7 (the latter as a homolog of OsEIL7/OsEIL1) to be the highly expressed SbEIL genes in sorghum internodes and revealed a potential functional link between SbEIL7 and internode maturation. The co-expression analysis and comparative expression analysis with ethylene-regulated gene sets found that SbEIL7 was co-regulated with a set of ubiquitin-related protein degradation genes, suggesting possible involvement of SbEIL7 in protein degradation and processing during the post-anthesis stages. Altogether, our findings lay a foundation for future functional studies of ethylene signaling-mediated gene regulation and improvement of sorghum internode development.

Divergent Roles of the Auxin Response Factors in Lemongrass (Cymbopogon flexuosus (Nees ex Steud.) W. Watson) during Plant Growth.

Auxin Response Factors (ARFs) make up a plant-specific transcription factor family that mainly couples perception of the phytohormone, auxin, and gene expression programs and plays an important and multi-faceted role during plant growth and development. Lemongrass (Cymbopogon flexuosus) is a representative Cymbopogon species widely used in gardening, beverages, fragrances, traditional medicine, and heavy metal phytoremediation. Biomass yield is an important trait for several agro-economic purposes of lemongrass, such as landscaping, essential oil production, and phytoremediation. Therefore, we performed gene mining of CfARFs and identified 26 and 27 CfARF-encoding genes in each of the haplotype genomes of lemongrass, respectively. Phylogenetic and domain architecture analyses showed that CfARFs can be divided into four groups, among which groups 1, 2, and 3 correspond to activator, repressor, and ETTN-like ARFs, respectively. To identify the CfARFs that may play major roles during the growth of lemongrass plants, RNA-seq was performed on three tissues (leaf, stem, and root) and four developmental stages (3-leaf, 4-leaf, 5-leaf. and mature stages). The expression profiling of CfARFs identified several highly expressed activator and repressor CfARFs and three CfARFs (CfARF3, 18, and 35) with gradually increased levels during leaf growth. Haplotype-resolved transcriptome analysis revealed that biallelic expression dominance is frequent among CfARFs and contributes to their gene expression patterns. In addition, co-expression network analysis identified the modules enriched with CfARFs. By establishing orthologous relationships among CfARFs, sorghum ARFs, and maize ARFs, we showed that CfARFs were mainly expanded by whole-genome duplications, and that the duplicated CfARFs might have been divergent due to differential expression and variations in domains and motifs. Our work provides a detailed catalog of CfARFs in lemongrass, representing a first step toward characterizing CfARF functions, and may be useful in molecular breeding to enhance lemongrass plant growth.

Harnessing the Genetic Basis of Sorghum Biomass-Related Traits to Facilitate Bioenergy Applications.

The extensive use of fossil fuels and global climate change have raised ever-increasing attention to sustainable development, global food security and the replacement of fossil fuels by renewable energy. Several C4 monocot grasses have excellent photosynthetic ability, stress tolerance and may rapidly produce biomass in marginal lands with low agronomic inputs, thus representing an important source of bioenergy. Among these grasses, Sorghum bicolor has been recognized as not only a promising bioenergy crop but also a research model due to its diploidy, simple genome, genetic diversity and clear orthologous relationship with other grass genomes, allowing sorghum research to be easily translated to other grasses. Although sorghum molecular genetic studies have lagged far behind those of major crops (e.g., rice and maize), recent advances have been made in a number of biomass-related traits to dissect the genetic loci and candidate genes, and to discover the functions of key genes. However, molecular and/or targeted breeding toward biomass-related traits in sorghum have not fully benefited from these pieces of genetic knowledge. Thus, to facilitate the breeding and bioenergy applications of sorghum, this perspective summarizes the bioenergy applications of different types of sorghum and outlines the genetic control of the biomass-related traits, ranging from flowering/maturity, plant height, internode morphological traits and metabolic compositions. In particular, we describe the dynamic changes of carbohydrate metabolism in sorghum internodes and highlight the molecular regulators involved in the different stages of internode carbohydrate metabolism, which affects the bioenergy utilization of sorghum biomass. We argue the way forward is to further enhance our understanding of the genetic mechanisms of these biomass-related traits with new technologies, which will lead to future directions toward tailored designing sorghum biomass traits suitable for different bioenergy applications.

Modeling and insights into the structural characteristics of endocrine-disrupting chemicals.

Endocrine-disrupting chemicals (EDCs) can cause serious harm to human health and the environment; therefore, it is important to rapidly and correctly identify EDCs. Different computational models have been proposed for the prediction of EDCs over the past few decades, but the reported models are not always easily available, and few studies have investigated the structural characteristics of EDCs. In the present study, we have developed a series of artificial intelligence models targeting EDC receptors: the androgen receptor (AR); estrogen receptor (ER); and pregnane X receptor (PXR). The consensus models achieved good predictive results for validation sets with balanced accuracy values of 87.37%, 90.13%, and 79.21% for AR, ER, and PXR binding assays, respectively. Analysis of the physical-chemical properties suggested that several chemical properties were significantly (p < 0.05) different between EDCs and non-EDCs. We also identified structural alerts that can indicate an EDC, which were integrated into the web server SApredictor. These models and structural characteristics can provide useful tools and information in the discrimination and mechanistic understanding of EDCs in drug discovery and environmental risk assessment.

Association of proton pump inhibitors with gastric and colorectal cancer risk: A systematic review and meta-analysis.

Background: Proton pump inhibitors (PPI) are generally considered to be one of the well-established prescription drug classes and are commonly used to treat most acid-related diseases. However, a growing body of literature showing an association between gastric and colorectal cancer risk and PPI use continues to raise concerns about the safety of PPI use. Therefore, we aimed to investigate the association between proton pump inhibitor use and risk of gastric and colorectal cancer. Methods: We collected relevant articles using PubMed, Embase, Web of Science and Cochrane library from 1 January 1990 to 21 March 2022. The pooled effect sizes were calculated based on the random-effects model. The study was registered with PROSPERO (CRD42022351332). Results: A total of 24 studies (n = 8,066,349) were included in the final analysis in the screening articles. Compared with non-PPI users, PPI users had a significantly higher risk of gastric cancer (RR = 1.82, 95% CI: 1.46-2.29), but not colorectal cancer (RR = 1.22, 95% CI: 0.95-1.55). Subgroup analysis showed that there was a significant positive correlation between the use of PPI and the risk of non-cardiac cancer (RR = 2.75, 95% CI: 2.09-3.62). There was a significant trend between the duration dependent effect of PPI use and the risk of gastric cancer (<1 year RR = 1.56, 95% CI: 1.30-1.86; 1-3 years RR = 1.75, 95% CI: 1.28-2.37; >3 years RR = 2.32, 95% CI: 1.15-4.66), but not colorectal cancer (≤1 year RR = 1.00, 95% CI: 0.78-1.28; >1 year RR = 1.18, 95% CI: 0.91-1.54; ≥5 years RR = 1.06, 95% CI: 0.95-1.17). Conclusion: We found that PPI use increased gastric cancer risk, but not colorectal cancer risk. This result may be biased due to confounding factors. More prospective studies are needed to further validate and support our findings. Systematic Review Registration: [https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022351332], identifier [CRD42022351332].

Machine Learning Modeling and Insights into the Structural Characteristics of Drug-Induced Neurotoxicity.

Neurotoxicity can be resulted from many diverse clinical drugs, which has been a cause of concern to human populations across the world. The detection of drug-induced neurotoxicity (DINeurot) potential with biological experimental methods always required a lot of budget and time. In addition, few studies have addressed the structural characteristics of neurotoxic chemicals. In this study, we focused on the computational modeling for drug-induced neurotoxicity with machine learning methods and the insights into the structural characteristics of neurotoxic chemicals. Based on the clinical drug data with neurotoxicity effects, we developed 35 different classifiers by combining five different machine learning methods and seven fingerprint packages. The best-performing model achieved good results on both 5-fold cross-validation (balanced accuracy of 76.51%, AUC value of 0.83, and MCC value of 0.52) and external validation (balanced accuracy of 83.63%, AUC value of 0.87, and MCC value of 0.67). The model can be freely accessed on the web server DINeuroTpredictor (http://dineurot.sapredictor.cn/). We also analyzed the distribution of several key molecular properties between neurotoxic and non-neurotoxic structures. The results indicated that several physicochemical properties were significantly different between the neurotoxic and non-neurotoxic compounds, including molecular polar surface area (MPSA), AlogP, the number of hydrogen bond acceptors (nHAcc) and donors (nHDon), the number of rotatable bonds (nRotB), and the number of aromatic rings (nAR). In addition, 18 structural alerts responsible for chemical neurotoxicity were identified. The structural alerts have been integrated with our web server SApredictor (http://www.sapredictor.cn). The results of this study could provide useful information for the understanding of the structural characteristics and computational prediction for chemical neurotoxicity.

Modeling and insights into the structural characteristics of drug-induced autoimmune diseases.

The incidence and complexity of drug-induced autoimmune diseases (DIAD) have been on the rise in recent years, which may lead to serious or fatal consequences. Besides, many environmental and industrial chemicals can also cause DIAD. However, there are few effective approaches to estimate the DIAD potential of drugs and other chemicals currently, and the structural characteristics and mechanism of action of DIAD compounds have not been clarified. In this study, we developed the in silico models for chemical DIAD prediction and investigated the structural characteristics of DIAD chemicals based on the reliable drug data on human autoimmune diseases. We collected 148 medications which were reported can cause DIAD clinically and 450 medications that clearly do not cause DIAD. Several different machine learning algorithms and molecular fingerprints were combined to develop the in silico models. The best performed model provided the good overall accuracy on validation set with 76.26%. The model was made freely available on the website http://diad.sapredictor.cn/. To further investigate the differences in structural characteristics between DIAD chemicals and non-DIAD chemicals, several key physicochemical properties were analyzed. The results showed that AlogP, molecular polar surface area (MPSA), and the number of hydrogen bond donors (nHDon) were significantly different between the DIAD and non-DIAD structures. They may be related to the DIAD toxicity of chemicals. In addition, 14 structural alerts (SA) for DIAD toxicity were detected from predefined substructures. The SAs may be helpful to explain the mechanism of action of drug induced autoimmune disease, and can used to identify the chemicals with potential DIAD toxicity. The structural alerts have been integrated in a structural alert-based web server SApredictor (http://www.sapredictor.cn). We hope the results could provide useful information for the recognition of DIAD chemicals and the insights of structural characteristics for chemical DIAD toxicity.

SApredictor: An Expert System for Screening Chemicals Against Structural Alerts.

The rapid and accurate evaluation of chemical toxicity is of great significance for estimation of chemical safety. In the past decades, a great number of excellent computational models have been developed for chemical toxicity prediction. But most machine learning models tend to be "black box", which bring about poor interpretability. In the present study, we focused on the identification and collection of structural alerts (SAs) responsible for a series of important toxicity endpoints. Then, we carried out effective storage of these structural alerts and developed a web-server named SApredictor (www.sapredictor.cn) for screening chemicals against structural alerts. People can quickly estimate the toxicity of chemicals with SApredictor, and the specific key substructures which cause the chemical toxicity will be intuitively displayed to provide valuable information for the structural optimization by medicinal chemists.

Modeling and insights into the structural basis of chemical acute aquatic toxicity.

It has become a top global regulatory priority to prevent and control pollution from the release of synthetic chemicals, which continues to affect the aquatic communities. In the past decades, computational tools were largely used to significantly reduce the budget and time cost of chemical acute aquatic toxicity assessment. But the structural basis of toxic compounds was rarely analyzed. In the present study, we collected 1438, 485 and 961 chemicals with acute toxicity data records for three representative aquatic species, including Tetrahymena pyriformis, Daphnia magna, and Fathead minnow, respectively. A series of artificial intelligence models were developed using OCHEM tools. For each aquatic toxicity endpoint, a consensus model was developed based on the top performed individual models. The consensus models provided good performance on external validation sets with total accuracy values 96.88 %, 90.63 %, and 84.90 % for Tetrahymena pyriformis toxicity (TPT), Daphnia magna toxicity (DMT), and Fathead minnow toxicity (FMT), respectively. The models can be freely accessed via https://ochem.eu/article/146910. Moreover, the analysis of physical-chemical properties suggested that several key molecular properties of aquatic toxic compounds were significantly different with those of non-toxic compounds. Thus, these descriptors may be associated to chemical acute aquatic toxicity, and may be useful for the understand of chemical aquatic toxicity. Besides, in this study, the structural alerts for aquatic toxicity were detected using f-score and frequency ratio analysis of predefined substructures. A total of 112, 58 and 33 structural alerts were identified responsible for TPT, DMT, and FMT, respectively. These structural alerts could provide useful information for the mechanisms of chemical aquatic toxicity and visual alerts for environmental assessment. All the structural alerts were integrated in the web-server SApredictor (www.sapredictor.cn).

In silico prediction of chemical-induced hematotoxicity with machine learning and deep learning methods.

Chemical-induced hematotoxicity is an important concern in the drug discovery, since it can often be fatal when it happens. It is quite useful for us to give special attention to chemicals which can cause hematotoxicity. In the present study, we focused on in silico prediction of chemical-induced hematotoxicity with machine learning (ML) and deep learning (DL) methods. We collected a large data set contained 632 hematotoxic chemicals and 1525 approved drugs without hematotoxicity. Computational models were built using several different machine learning and deep learning algorithms integrated on the Online Chemical Modeling Environment (OCHEM). Based on the three best individual models, a consensus model was developed. It yielded the prediction accuracy of 0.83 and balanced accuracy of 0.77 on external validation. The consensus model and the best individual model developed with random forest regression and classification algorithm (RFR) and QNPR descriptors were made available at https://ochem.eu/article/135149 , respectively. The relevance of 8 commonly used molecular properties and chemical-induced hematotoxicity was also investigated. Several molecular properties have an obvious differentiating effect on chemical-induced hematotoxicity. Besides, 12 structural alerts responsible for chemical hematotoxicity were identified using frequency analysis of substructures from Klekota-Roth fingerprint. These results should provide meaningful knowledge and useful tools for hematotoxicity evaluation in drug discovery and environmental risk assessment.

In silico prediction of drug-induced ototoxicity using machine learning and deep learning methods.

Drug-induced ototoxicity has become a serious global problem, because of leading to deafness in hundreds of thousands of people every year. It always results from exposure to drugs or environmental chemicals that cause the impairment and degeneration of the inner ear. Herein, we focused on the in silico modeling of drug-induced ototoxicity of chemicals. We collected 1,102 ototoxic medications and 1,705 non-ototoxic drugs. Based on the data set, a series of computational models were developed with different traditional machine learning and deep learning algorithms implemented on an online chemical database and modeling environment. Six ML models performed best on 5-fold cross-validation and test set. A consensus model was developed with the best individual models. These models were further validated with an external validation. The consensus model showed best predictive ability, with high accuracy of 0.95 on test set and 0.90 on validation set. The consensus model and the data sets used for model development are available at https://ochem.eu/model/46566321. Besides, 16 structural alerts responsible for drug-induced ototoxicity were identified. We hope the results could provide meaningful knowledge and useful tools for ototoxicity evaluation in drug discovery and environmental risk assessment.

In Silico Prediction and Insights Into the Structural Basis of Drug Induced Nephrotoxicity.

Drug induced nephrotoxicity is a major clinical challenge, and it is always associated with higher costs for the pharmaceutical industry and due to detection during the late stages of drug development. It is desirable for improving the health outcomes for patients to distinguish nephrotoxic structures at an early stage of drug development. In this study, we focused on in silico prediction and insights into the structural basis of drug induced nephrotoxicity, based on reliable data on human nephrotoxicity. We collected 565 diverse chemical structures, including 287 nephrotoxic drugs on humans in the real world, and 278 non-nephrotoxic approved drugs. Several different machine learning and deep learning algorithms were employed for in silico model building. Then, a consensus model was developed based on three best individual models (RFR_QNPR, XGBOOST_QNPR, and CNF). The consensus model performed much better than individual models on internal validation and it achieved prediction accuracy of 86.24% external validation. The results of analysis of molecular properties differences between nephrotoxic and non-nephrotoxic structures indicated that several key molecular properties differ significantly, including molecular weight (MW), molecular polar surface area (MPSA), AlogP, number of hydrogen bond acceptors (nHBA), molecular solubility (LogS), the number of rotatable bonds (nRotB), and the number of aromatic rings (nAR). These molecular properties may be able to play an important part in the identification of nephrotoxic chemicals. Finally, 87 structural alerts for chemical nephrotoxicity were mined with f-score and positive rate analysis of substructures from Klekota-Roth fingerprint (KRFP). These structural alerts can well identify nephrotoxic drug structures in the data set. The in silico models and the structural alerts could be freely accessed via https://ochem.eu/article/140251 and http://www.sapredictor.cn, respectively. We hope the results should provide useful tools for early nephrotoxicity estimation in drug development.