The Kirkhouse Trust: Successes and Challenges in Twenty Years of Supporting Independent, Contemporary Grain Legume Breeding Projects in India and African Countries.

This manuscript reviews two decades of projects funded by the Kirkhouse Trust (KT), a charity registered in the UK. KT was established to improve the productivity of legume crops important in African countries and in India. KT's requirements for support are: (1) the research must be conducted by national scientists in their home institution, either a publicly funded agricultural research institute or a university; (2) the projects need to include a molecular biology component, which to date has mostly comprised the use of molecular markers for the selection of one or more target traits in a crop improvement programme; (3) the projects funded are included in consortia, to foster the creation of scientific communities and the sharing of knowledge and breeding resources. This account relates to the key achievements and challenges, reflects on the lessons learned and outlines future research priorities.

Three Alternaria Species, Including a New Species, Causing Leaf Spot Disease of Loquat (Eriobotrya japonica) in China.

Loquat (Eriobotrya japonica) is an economically important subtropical fruit crop in China. Field surveys conducted in different loquat orchards located in Chongqing, Sichuan, and Fujian provinces between 2017 and 2020 resulted in a collection of 56 Alternaria-like isolates from trees exhibiting symptoms of loquat leaf spot. Multigene phylogenetic analyses using seven gene regions, namely, ITS, gapdh, RPB2, tef1, Alt a 1, endoPG, and OPA10-2, showed that all the isolates belonged to the genus Alternaria, and supporting morphological analysis identified them as members of species A. alternata, A. gaisen, and A. chongqingensis sp. nov. In vitro and in vivo pathogenicity tests showed all the identified species to be pathogenic and able to cause leaf spot disease on loquat. Moreover, comprehensive phylogenetic analyses employing all combinations of the above seven gene sequences revealed the capability of Alt a 1-tef1-endoPG to provide a well-resolved gene tree for Alternaria spp. at the species level. This study adds to the current knowledge on an unknown species (A. chongqingensis sp. nov.) and is the first report of A. gaisen in loquat worldwide.

A comprehensive trial on PFAS remediation: hemp phytoextraction and PFAS degradation in harvested plants.

Per- and polyfluoroalkyl substances (PFAS) are a class of recalcitrant, highly toxic contaminants, with limited remediation options. Phytoremediation - removal of contaminants using plants - is an inexpensive, community-friendly strategy for reducing PFAS concentrations and exposures. This project is a collaboration between the Mi'kmaq Nation, Upland Grassroots, and researchers at several institutions who conducted phytoremediation field trials using hemp to remove PFAS from soil at the former Loring Air Force base, which has now been returned to the Mi'kmaq Nation. PFAS were analyzed in paired hemp and soil samples using targeted and non-targeted analytical approaches. Additionally, we used hydrothermal liquefaction (HTL) to degrade PFAS in the harvested hemp tissue. We identified 28 PFAS in soil and found hemp uptake of 10 of these PFAS. Consistent with previous studies, hemp exhibited greater bioconcentration for carboxylic acids compared to sulfonic acids, and for shorter-chain compounds compared to longer-chain. In total, approximately 1.4 mg of PFAS was removed from the soil via uptake into hemp stems and leaves, with an approximate maximum of 2% PFAS removed from soil in the most successful area. Degradation of PFAS by HTL was nearly 100% for carboxylic acids, but a portion of sulfonic acids remained. HTL also decreased precursor PFAS and extractable organic fluorine. In conclusion, while hemp phytoremediation does not currently offer a comprehensive solution for PFAS-contaminated soil, this project has effectively reduced PFAS levels at the Loring site and underscores the importance of involving community members in research aimed at remediating their lands.

The Mechanism of Cannabichromene and Cannabidiol Alone Versus in Combination in the Alleviation of Arthritis-Related Inflammation.

BACKGROUND: Patients suffering from arthritis have limited treatment options for nonoperative management. In search of pain relief, patients have been taking over-the-counter cannabinoids. Cannabidiol (CBD) and cannabichromene (CBC) are minor cannabinoids with reported analgesic and anti-inflammatory properties and have been implicated as potential therapeutics for arthritis-related pain. To this end, we utilized a murine model to investigate the effectiveness of and mechanism by which CBC alone, CBD alone, or CBD and CBC in combination may provide a reduction in arthritis-associated inflammation. METHODS: Forty-eight mice were included in the study, which were separated into 4 groups: control group (n = 12), treatment with CBD alone (n = 12), treatment with CBC alone (n = 12), and treatment with CBD + CBC (n = 12). We induced inflammation in each mouse utilizing the collagen-induced arthritis model. At scheduled timepoints, mice were clinically assessed for weight gain, swelling, and arthritis severity. In addition, inflammation-associated serum cytokine levels were analyzed for each animal. RESULTS: Thirty-five of 48 mice survived the duration of the study resulting in the following group numbers: control group (n = 8), treatment with CBD alone (n = 9), treatment with CBC alone (n = 9), and treatment with CBD + CBC (n = 9). Animals treated with CBC and CBD + CBC showed significant weight gain between 3 and 5 weeks. Irrespective of treatment, regression analysis comparing all cytokine measurement and physical outcomes found a significant positive correlation between levels of 5 individual cytokines and both arthritis scores and swelling. Animals treated with CBD + CBC showed a significant decrease in swelling between 3 and 5 weeks compared with the control group. Cannabinoid treatment selectively affected the gene expression of eotaxin and lipopolysaccharide-induced CXC chemokine with combined treatment of CBC + CBD. CONCLUSION: Treatment with cannabinoids resulted in decreased clinical markers of inflammation. Further, the anti-inflammatory effect of CBC and CBD in conjunction was associated with a greater anti-inflammatory effect than either minor cannabinoid alone. Future work will elucidate the possibility of synergistic or entourage effects of minor cannabinoids used in combination for the treatment of arthritis-related pain and inflammation.

Dynamic associations between the respiratory tract and gut antibiotic resistome of patients with COVID-19 and its prediction power for disease severity.

The antibiotic resistome is the collection of all antibiotic resistance genes (ARGs) present in an individual. Whether an individual's susceptibility to infection and the eventual severity of coronavirus disease 2019 (COVID-19) is influenced by their respiratory tract antibiotic resistome is unknown. Additionally, whether a relationship exists between the respiratory tract and gut ARGs composition has not been fully explored. We recruited 66 patients with COVID-19 at three disease stages (admission, progression, and recovery) and conducted a metagenome sequencing analysis of 143 sputum and 97 fecal samples obtained from them. Respiratory tract, gut metagenomes, and peripheral blood mononuclear cell (PBMC) transcriptomes are analyzed to compare the gut and respiratory tract ARGs of intensive care unit (ICU) and non-ICU (nICU) patients and determine relationships between ARGs and immune response. Among the respiratory tract ARGs, we found that Aminoglycoside, Multidrug, and Vancomycin are increased in ICU patients compared with nICU patients. In the gut, we found that Multidrug, Vancomycin, and Fosmidomycin were increased in ICU patients. We discovered that the relative abundances of Multidrug were significantly correlated with clinical indices, and there was a significantly positive correlation between ARGs and microbiota in the respiratory tract and gut. We found that immune-related pathways in PBMC were enhanced, and they were correlated with Multidrug, Vancomycin, and Tetracycline ARGs. Based on the ARG types, we built a respiratory tract-gut ARG combined random-forest classifier to distinguish ICU COVID-19 patients from nICU patients with an AUC of 0.969. Cumulatively, our findings provide some of the first insights into the dynamic alterations of respiratory tract and gut antibiotic resistome in the progression of COVID-19 and disease severity. They also provide a better understanding of how this disease affects different cohorts of patients. As such, these findings should contribute to better diagnosis and treatment scenarios.

Interaction of oxalate with ß-glucan: Implications for the fungal extracellular matrix, and metabolite transport.

ß-glucan is the major component of the extracellular matrix (ECM) of many fungi, including wood degrading fungi. Many of these species also secrete oxalate into the ECM. Our research demonstrates that ß-glucan forms a novel, previously unreported, hydrogel at room temperature with oxalate. Oxalate was found to alter the rheometric properties of the ß-glucan hydrogels, and modeling showed that ß-glucan hydrogen bonds with oxalate in a non-covalent matrix. Change of oxalate concentration also impacted the diffusion of a high-molecular-weight protein through the gels. This finding has relevance to the diffusion of extracellular enzymes into substrates and helps to explain why some types of wood-decay fungi rely on non-enzymatic degradation schemes for carbon cycling. Further, this research has potential impact on the diffusion of metabolites in association with pathogenic/biomedical fungi.

Machine Learning Predictions of Oil Yields Obtained by Plastic Pyrolysis and Application to Thermodynamic Analysis.

Chemical recycling via thermal processes such as pyrolysis is a potentially viable way to convert mixed streams of waste plastics into usable fuels and chemicals. Unfortunately, experimentally measuring product yields for real waste streams can be time- and cost-prohibitive, and the yields are very sensitive to feed composition, especially for certain types of plastics like poly(ethylene terephthalate) (PET) and polyvinyl chloride (PVC). Models capable of predicting yields and conversion from feed composition and reaction conditions have potential as tools to prioritize resources to the most promising plastic streams and to evaluate potential preseparation strategies to improve yields. In this study, a data set consisting of 325 data points for pyrolysis of plastic feeds was collected from the open literature. The data set was divided into training and test sub data sets; the training data were used to optimize the seven different machine learning regression methods, and the testing data were used to evaluate the accuracy of the resulting models. Of the seven types of models, eXtreme Gradient Boosting (XGBoost) predicted the oil yield of the test set with the highest accuracy, corresponding to a mean absolute error (MAE) value of 9.1%. The optimized XGBoost model was then used to predict the oil yields from real waste compositions found in Municipal Recycling Facilities (MRFs) and the Rhine River. The dependence of oil yields on composition was evaluated, and strategies for removing PET and PVC were assessed as examples of how to use the model. Thermodynamic analysis of a pyrolysis system capable of achieving oil yields predicted using the machine-learned model showed that pyrolysis of Rhine River plastics should be net exergy producing under most reasonable conditions.

Proteomic and single-cell landscape reveals novel pathogenic mechanisms of HBV-infected intrahepatic cholangiocarcinoma.

Despite the epidemiological association between intrahepatic cholangiocarcinoma (ICC) and hepatitis B virus (HBV) infection, little is known about the relevant oncogenic effects. A cohort of 32 HBV-infected ICC and 89 non-HBV-ICC patients were characterized using whole-exome sequencing, proteomic analysis, and single-cell RNA sequencing. Proteomic analysis revealed decreased cell-cell junction levels in HBV-ICC patients. The cell-cell junction level had an inverse relationship with the epithelial-mesenchymal transition (EMT) program in ICC patients. Analysis of the immune landscape found that more CD8 T cells and Th2 cells were present in HBV-ICC patients. Single-cell analysis indicated that transforming growth factor beta signaling-related EMT program changes increased in tumor cells of HBV-ICC patients. Moreover, ICAM1+ tumor-associated macrophages are correlated with a poor prognosis and contributed to the EMT in HBV-ICC patients. Our findings provide new insights into the behavior of HBV-infected ICC driven by various pathogenic mechanisms involving decreased cell junction levels and increased progression of the EMT program.

Dimensionally reduced machine learning model for predicting single component octanol-water partition coefficients.

MF-LOGP, a new method for determining a single component octanol-water partition coefficients ([Formula: see text]) is presented which uses molecular formula as the only input. Octanol-water partition coefficients are useful in many applications, ranging from environmental fate and drug delivery. Currently, partition coefficients are either experimentally measured or predicted as a function of structural fragments, topological descriptors, or thermodynamic properties known or calculated from precise molecular structures. The MF-LOGP method presented here differs from classical methods as it does not require any structural information and uses molecular formula as the sole model input. MF-LOGP is therefore useful for situations in which the structure is unknown or where the use of a low dimensional, easily automatable, and computationally inexpensive calculations is required. MF-LOGP is a random forest algorithm that is trained and tested on 15,377 data points, using 10 features derived from the molecular formula to make [Formula: see text] predictions. Using an independent validation set of 2713 data points, MF-LOGP was found to have an average [Formula: see text] = 0.77 ± 0.007, [Formula: see text] = 0.52 ± 0.003, and [Formula: see text] = 0.83 ± 0.003. This performance fell within the spectrum of performances reported in the published literature for conventional higher dimensional models ([Formula: see text] = 0.42-1.54, [Formula: see text] = 0.09-1.07, and [Formula: see text] = 0.32-0.95). Compared with existing models, MF-LOGP requires a maximum of ten features and no structural information, thereby providing a practical and yet predictive tool. The development of MF-LOGP provides the groundwork for development of more physical prediction models leveraging big data analytical methods or complex multicomponent mixtures.

EMLI-ICC: an ensemble machine learning-based integration algorithm for metastasis prediction and risk stratification in intrahepatic cholangiocarcinoma.

Robust strategies to identify patients at high risk for tumor metastasis, such as those frequently observed in intrahepatic cholangiocarcinoma (ICC), remain limited. While gene/protein expression profiling holds great potential as an approach to cancer diagnosis and prognosis, previously developed protocols using multiple diagnostic signatures for expression-based metastasis prediction have not been widely applied successfully because batch effects and different data types greatly decreased the predictive performance of gene/protein expression profile-based signatures in interlaboratory and data type dependent validation. To address this problem and assist in more precise diagnosis, we performed a genome-wide integrative proteome and transcriptome analysis and developed an ensemble machine learning-based integration algorithm for metastasis prediction (EMLI-Metastasis) and risk stratification (EMLI-Prognosis) in ICC. Based on massive proteome (216) and transcriptome (244) data sets, 132 feature (biomarker) genes were selected and used to train the EMLI-Metastasis algorithm. To accurately detect the metastasis of ICC patients, we developed a weighted ensemble machine learning method based on k-Top Scoring Pairs (k-TSP) method. This approach generates a metastasis classifier for each bootstrap aggregating training data set. Ten binary expression rank-based classifiers were generated for detection of metastasis separately. To further improve the accuracy of the method, the 10 binary metastasis classifiers were combined by weighted voting based on the score from the prediction results of each classifier. The prediction accuracy of the EMLI-Metastasis algorithm achieved 97.1% and 85.0% in proteome and transcriptome datasets, respectively. Among the 132 feature genes, 21 gene-pair signatures were developed to establish a metastasis-related prognosis risk-stratification model in ICC (EMLI-Prognosis). Based on EMLI-Prognosis algorithm, patients in the high-risk group had significantly dismal overall survival relative to the low-risk group in the clinical cohort (P-value < 0.05). Taken together, the EMLI-ICC algorithm provides a powerful and robust means for accurate metastasis prediction and risk stratification across proteome and transcriptome data types that is superior to currently used clinicopathological features in patients with ICC. Our developed algorithm could have profound implications not just in improved clinical care in cancer metastasis risk prediction, but also more broadly in machine-learning-based multi-cohort diagnosis method development. To make the EMLI-ICC algorithm easily accessible for clinical application, we established a web-based server for metastasis risk prediction (http://ibi.zju.edu.cn/EMLI/).

Hydroxyapatite catalyzed hydrothermal liquefaction transforms food waste from an environmental liability to renewable fuel.

Food waste is an abundant and inexpensive resource for the production of renewable fuels. Biocrude yields obtained from hydrothermal liquefaction (HTL) of food waste can be boosted using hydroxyapatite (HAP) as an inexpensive and abundant catalyst. Combining HAP with an inexpensive homogeneous base increased biocrude yield from 14 ± 1 to 37 ± 3%, resulting in the recovery of 49 ± 2% of the energy contained in the food waste feed. Detailed product analysis revealed the importance of fatty-acid oligomerization during biocrude formation, highlighting the role of acid-base catalysts in promoting condensation reactions. Economic and environmental analysis found that the new technology has the potential to reduce US greenhouse gas emissions by 2.6% while producing renewable diesel with a minimum fuel selling price of $1.06/GGE. HAP can play a role in transforming food waste from a liability to a renewable fuel.

Multi-omics molecular biomarkers and database of osteoarthritis.

Osteoarthritis (OA) is the most common form of arthritis in the adult population and is a leading cause of disability. OA-related genetic loci may play an important role in clinical diagnosis and disease progression. With the rapid development of diverse technologies and omics methods, many OA-related public data sets have been accumulated. Here, we retrieved a diverse set of omics experimental results from 159 publications, including genome-wide association study, differentially expressed genes and differential methylation regions, and 2405 classified OA-related gene markers. Meanwhile, based on recent single-cell RNA-seq data from different joints, 5459 cell-type gene markers of joints were collected. The information has been integrated into an online database named OAomics and molecular biomarkers (OAOB). The database (http://ibi.zju.edu.cn/oaobdb/) provides a web server for OA marker genes, omics features and so on. To our knowledge, this is the first database of molecular biomarkers for OA.

Dynamic Alterations in the Respiratory Tract Microbiota of Patients with COVID-19 and its Association with Microbiota in the Gut.

The role of respiratory tract microbes and the relationship between respiratory tract and gut microbiomes in coronavirus disease 2019 (COVID-19) remain uncertain. Here, the metagenomes of sputum and fecal samples from 66 patients with COVID-19 at three stages of disease progression are sequenced. Respiratory tract, gut microbiome, and peripheral blood mononuclear cell (PBMC) samples are analyzed to compare the gut and respiratory tract microbiota of intensive care unit (ICU) and non-ICU (nICU) patients and determine relationships between respiratory tract microbiome and immune response. In the respiratory tract, significantly fewer Streptococcus, Actinomyces, Atopobium, and Bacteroides are found in ICU than in nICU patients, while Enterococcus and Candida increase. In the gut, significantly fewer Bacteroides are found in ICU patients, while Enterococcus increases. Significant positive correlations exist between relative microbiota abundances in the respiratory tract and gut. Defensin-related pathways in PBMCs are enhanced, and respiratory tract Streptococcus is reduced in patients with COVID-19. A respiratory tract-gut microbiota model identifies respiratory tract Streptococcus and Atopobium as the most prominent biomarkers distinguishing between ICU and nICU patients. The findings provide insight into the respiratory tract and gut microbial dynamics during COVID-19 progression, considering disease severity, potentially contributing to diagnosis, and treatment strategies.

A Randomized Controlled Trial of Topical Cannabidiol for the Treatment of Thumb Basal Joint Arthritis.

PURPOSE: Since the passage of the Agricultural Improvement Act of 2018, hand surgeons have increasingly encountered patients seeking counseling on over-the-counter, topical cannabidiol (CBD) for the treatment of pain. To this end, we designed a human clinical trial to investigate the therapeutic potential of CBD for the treatment of pain associated with thumb basal joint arthritis. METHODS: Following Food and Drug Administration and institutional approval, a phase 1 skin test was completed with 10 healthy participants monitored for 1 week after twice-daily application of 1 mL of topical CBD (6.2 mg/mL) with shea butter. After no adverse events were identified, we proceeded with a phase 2, double-blinded, randomized controlled trial. Eighteen participants with symptomatic thumb basal joint arthritis were randomized to 2 weeks of twice-daily treatment with CBD (6.2 mg/mL CBD with shea butter) or shea butter alone, followed by a 1-week washout period and then crossover for 2 weeks with the other treatment. Safety data and physical examination measurements were obtained at baseline and after completion of each treatment arm. RESULTS: Cannabidiol treatment resulted in improvements from baseline among patient-reported outcome measures, including Visual Analog Scale pain; Disabilities of the Arm, Shoulder, and Hand; and Single Assessment Numeric Evaluation scores, compared to the control arm during the study period. There were similar physical parameters identified with range of motion, grip, and pinch strength. CONCLUSIONS: In this single-center, randomized controlled trial, topical CBD treatment demonstrated significant improvements in thumb basal joint arthritis-related pain and disability without adverse events. TYPE OF STUDY/LEVEL OF EVIDENCE: Therapeutic II.

Horizontal transfer and evolution of the biosynthetic gene cluster for benzoxazinoids in plants.

Benzoxazinoids are a class of protective and allelopathic plant secondary metabolites that have been identified in multiple grass species and are encoded by the Bx biosynthetic gene cluster (BGC) in maize. Data mining of 41 high-quality grass genomes identified complete Bx clusters (containing genes Bx1-Bx5 and Bx8) in three genera (Zea, Echinochloa, and Dichanthelium) of Panicoideae and partial clusters in Triticeae. The Bx cluster probably originated from gene duplication and chromosomal translocation of native homologs of Bx genes. An ancient Bx cluster that included additional Bx genes (e.g., Bx6) is presumed to have been present in ancestral Panicoideae. The ancient Bx cluster was putatively gained by the Triticeae ancestor via horizontal transfer (HT) from the ancestral Panicoideae and later separated into multiple segments on different chromosomes. Bx6 appears to have been under less constrained selection compared with the Bx cluster during the evolution of Panicoideae, as evidenced by the fact that it was translocated away from the Bx cluster in Zea mays, moved to other chromosomes in Echinochloa, and even lost in Dichanthelium. Further investigations indicate that purifying selection and polyploidization have shaped the evolutionary trajectory of Bx clusters in the grass family. This study provides the first candidate case of HT of a BGC between plants and sheds new light on the evolution of BGCs.

Cannabinoids and Pain for the Plastic Surgeon: What Is the Evidence?

BACKGROUND: Since the passage of the 2018 Farm Bill, practitioners have encountered more patients self-treating pain with over-the-counter topical cannabidiol (CBD) derived from hemp-Cannabis sativa with less than 0.3% delta-9-tetrahydrocannabinol-with reported improvements in pain control and activities of daily living. Cannabidiol has been touted for its capacity to improve inflammatory, arthritic, and neuropathic pain conditions, and increasing numbers of patients are exploring its use as potential replacement for opioids. However, limited rigorous clinical trials have been performed evaluating the safety and efficacy of cannabinoids for the treatment of pain. METHODS: A systematic search of PubMed was performed using the Medical Subject Headings (MeSH) terms "cannabinoid" or "CBD" or "cannabidiol" or "cannabis" or "medical marijuana" and "pain." It yielded 340 article titles. Twelve full-text primary studies of oral or topical CBD for chronic pain were selected for review, including 6 animal (2 randomized clinical trial and 4 prospective trials) and 6 human (4 randomized clinical trial and 2 prospective trials) studies. RESULTS: With respect to the safety and efficacy of oral and topical CBD for treating pain, animal and human studies have shown early positive results with limited minor side effects. However, all human studies may be underpowered with small sample sizes. CONCLUSIONS: With respect to the safety and efficacy of oral and topical CBD for treating pain, the evidence remains inconclusive in that we have a paucity of data to share with our patients who are considering the use of these products, which may be associated with significant costs.

The WRKY transcription factor family in cowpea: Genomic characterization and transcriptomic profiling under root dehydration.

Cowpea [Vigna unguiculata (L.) Walp.] is one of the most tolerant legume crops to drought and salt stresses. WRKY transcription factor (TF) family members stand out among plant transcriptional regulators related to abiotic stress tolerance. However, little information is currently available on the expression of the cowpea WRKY gene family (VuWRKY) in response to water deficit. Thus, we analyzed genomic and transcriptomic data from cowpea to identify VuWRKY members and characterize their structure and transcriptional response under root dehydration stress. Ninety-two complete VuWRKY genes were found in the cowpea genome based on their domain characteristics. They were clustered into three groups: I (15 members), II (58), and III (16), while three genes were unclassified. Domain analysis of the encoded proteins identified four major variants of the conserved heptapeptide motif WRKYGQK. In silico analysis of VuWRKY gene promoters identified eight candidate binding motifs of cis-regulatory elements, regulated mainly by six TF families associated with abiotic stress responses. Ninety-seven VuWRKY modulated splicing variants associated with 55 VuWRKY genes were identified via RNA-Seq analysis available at the Cowpea Genomics Consortium (CpGC) database. qPCR analyses showed that 22 genes are induced under root dehydration, with VuWRKY18, 21, and 75 exhibiting the most significant induction levels. Given their central role in activating signal transduction cascades in abiotic stress response, the data provide a foundation for the targeted modification of specific VuWRKY family members to improve drought tolerance in this important climate-resilient legume in the developing world and beyond.

Genomic insights into the evolution of Echinochloa species as weed and orphan crop.

As one of the great survivors of the plant kingdom, barnyard grasses (Echinochloa spp.) are the most noxious and common weeds in paddy ecosystems. Meanwhile, at least two Echinochloa species have been domesticated and cultivated as millets. In order to better understand the genomic forces driving the evolution of Echinochloa species toward weed and crop characteristics, we assemble genomes of three Echinochloa species (allohexaploid E. crus-galli and E. colona, and allotetraploid E. oryzicola) and re-sequence 737 accessions of barnyard grasses and millets from 16 rice-producing countries. Phylogenomic and comparative genomic analyses reveal the complex and reticulate evolution in the speciation of Echinochloa polyploids and provide evidence of constrained disease-related gene copy numbers in Echinochloa. A population-level investigation uncovers deep population differentiation for local adaptation, multiple target-site herbicide resistance mutations of barnyard grasses, and limited domestication of barnyard millets. Our results provide genomic insights into the dual roles of Echinochloa species as weeds and crops as well as essential resources for studying plant polyploidization, adaptation, precision weed control and millet improvements.

Improving Protein Quantity and Quality-The Next Level of Plant Molecular Farming.

Plants offer several unique advantages in the production of recombinant pharmaceuticals for humans and animals. Although numerous recombinant proteins have been expressed in plants, only a small fraction have been successfully put into use. The hugely distinct expression systems between plant and animal cells frequently cause insufficient yield of the recombinant proteins with poor or undesired activity. To overcome the issues that greatly constrain the development of plant-produced pharmaceuticals, great efforts have been made to improve expression systems and develop alternative strategies to increase both the quantity and quality of the recombinant proteins. Recent technological revolutions, such as targeted genome editing, deconstructed vectors, virus-like particles, and humanized glycosylation, have led to great advances in plant molecular farming to meet the industrial manufacturing and clinical application standards. In this review, we discuss the technological advances made in various plant expression platforms, with special focus on the upstream designs and milestone achievements in improving the yield and glycosylation of the plant-produced pharmaceutical proteins.