Search

Antimicrobial resistance (AMR) is arguably one of the major health and economic challenges in our society. A key aspect of tackling AMR is rapid and accurate detection of the emergence and spread of AMR in food animal production, which requires routine AMR surveillance. However, AMR detection can be expensive and time-consuming considering the growth rate of the bacteria and the most commonly used analytical procedures, such as Minimum Inhibitory Concentration (MIC) testing. To mitigate this issue, we utilized machine learning to predict the future AMR burden of bacterial pathogens. We collected pathogen and antimicrobial data from >600 farms in the United States from 2010 to 2021 to generate AMR time series data. Our prediction focused on five bacterial pathogens (Escherichia coli, Streptococcus suis, Salmonella sp., Pasteurella multocida, and Bordetella bronchiseptica). We found that Seasonal Auto-Regressive Integrated Moving Average (SARIMA) outperformed five baselines, including Auto-Regressive Moving Average (ARMA) and Auto-Regressive Integrated Moving Average (ARIMA). We hope this study provides valuable tools to predict the AMR burden not only of the pathogens assessed in this study but also of other bacterial pathogens.

Research on cancer in dogs and cats, among other diseases, finds an important source of information in registry data collected from hospitals. These sources have proved to be decisive in establishing incidences and identifying temporal patterns and risk factors. However, the attendance of patients is not random, so the correct delimitation of the hospital catchment area (CA) as well as the identification of the factors influencing its shape is relevant to prevent possible biases in posterior inferences. Despite this, there is a lack of data-driven approaches in veterinary epidemiology to establish CA. Therefore, our aim here was to apply a Bayesian method to estimate the CA of a hospital. We obtained cancer (n = 27,390) and visit (n = 232,014) registries of dogs and cats attending the Veterinary Medical Teaching Hospital of the University of California, Davis from 2000 to 2019 with 2,707 census tracts (CTs) of 40 neighboring counties. We ran hierarchical Bayesian models with different likelihood distributions to define CA for cancer cases and visits based on the exceedance probabilities for CT random effects, adjusting for species and period (2000–2004, 2005–2009, 2010–2014, and 2015–2019). The identified CAs of cancer cases and visits represented 75.4 and 83.1% of the records, respectively, including only 34.6 and 39.3% of the CT in the study area. The models detected variation by species (higher number of records in dogs) and period. We also found that distance to hospital and average household income were important predictors of the inclusion of a CT in the CA. Our results show that the application of this methodology is useful for obtaining data-driven CA and evaluating the factors that influence and predict data collection. Therefore, this could be useful to improve the accuracy of analysis and inferences based on registry data.

Lignin is identified as a promising candidate in renewable energy and bioproduct manufacturing due to its high abundance, polymeric structure, and biochemical properties of monomers. Thus, emerging opportunities exist in generating high-value small molecules from lignin through depolymerization. This review aims at providing an overview of the major technologies of lignin depolymerization. The feasibility of large-scale implementation of these technologies, including thermal, biological, and chemical depolymerizations, are discussed in relation to potential industrial applications. Lignin as a renewable alternative to petroleum-based chemicals has been well documented. This review attempts to emphasize potential applications of lignin-derived monomers and their derivatives as bioactives in food, natural health product, and pharmaceutical sectors. The critical review of the prospects and challenges of lignin-derived bioproducts reveals that the advancement of research and development is required to explore the applications of depolymerization of lignins to their full potential.

Porcine reproductive and respiratory syndrome is an infectious disease of pigs caused by PRRS virus (PRRSV). A modified live-attenuated vaccine has been widely used to control the spread of PRRSV and the classification of field strains is a key for a successful control and prevention. Restriction fragment length polymorphism targeting the Open reading frame 5 (ORF5) genes is widely used to classify PRRSV strains but showed unstable accuracy. Phylogenetic analysis is a powerful tool for PRRSV classification with consistent accuracy but it demands large computational power as the number of sequences gets increased. Our study aimed to apply four machine learning (ML) algorithms, random forest, k-nearest neighbor, support vector machine and multilayer perceptron, to classify field PRRSV strains into four clades using amino acid scores based on ORF5 gene sequence. Our study used amino acid sequences of ORF5 gene in 1931 field PRRSV strains collected in the US from 2012 to 2020. Phylogenetic analysis was used to labels field PRRSV strains into one of four clades: Lineage 5 or three clades in Linage 1. We measured accuracy and time consumption of classification using four ML approaches by different size of gene sequences. We found that all four ML algorithms classify a large number of field strains in a very short time (0.99 Area under curve of the Receiver of operating characteristics curve). Furthermore, the random forest approach detects a total of 4 key amino acid positions for the classification of field PRRSV strains into four clades. Our finding will provide an insightful idea to develop a rapid and accurate classification model using genetic information, which also enables us to handle large genome datasets in real time or semi-real time for data-driven decision-making and more timely surveillance.

Lipidomics is an emerging technology, which aims at the global characterization and quantification of lipids within biological matrices including biofluids, cells, whole organs and tissues. The changes in individual lipid molecular species in stress treated plant species and different cultivars can indicate the functions of genes affecting lipid metabolism or lipid signaling. Mass spectrometry–based lipid profiling has been used to track the changes of lipid levels and related metabolites in response to salinity stress. We have developed a comprehensive lipidomics platform for the identification and direct qualification and/or quantification of individual lipid species, including oxidized lipids, which enables a more systematic investigation of peroxidation of individual lipid species in barley roots under salinity stress. This new lipidomics approach has improved with an advantage of analyzing the composition of acyl chains at the molecular level, which facilitates to profile precisely the 18:3-containing diacyl-glycerophosphates and allowed individual comparison of lipids across varieties. Our findings revealed a general decrease in most of the galactolipids in plastid membranes, and an increase of glycerophospholipids and acylated steryl glycosides, which indicate that plastidial and extraplastidial membranes in barley roots ubiquitously tend to form a hexagonal II (HII) phase under salinity stress. In addition, salt-tolerant and salt-sensitive cultivars showed contrasting changes in the levels of oxidized membrane lipids. These results support the hypothesis that salt-induced oxidative damage to membrane lipids can be used as an indication of salt stress tolerance in barley.

Seed germination is the essential first step in crop establishment, and can be severely affected by salinity stress which can inhibit essential metabolic processes during the germination process. Salt stress during seed germination can trigger lipid-dependent signalling cascades that activate plant adaptation processes, lead to changes in membrane fluidity to help resist the stress, and cause secondary metabolite responses due to increased oxidative stress. In germinating barley (Hordeum vulgare), knowledge of the changes in spatial distribution of lipids and other small molecules at a cellular level in response to salt stress is limited. In this study, mass spectrometry imaging (MSI), liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QToF-MS), inductively coupled plasma mass spectrometry (ICP-MS), and X-ray fluorescence (XRF) were used to determine the spatial distribution of metabolites, lipids and a range of elements, such as K+ and Na+, in seeds of two barley genotypes with contrasting germination phenology (Australian barley varieties Mundah and Keel). We detected and tentatively identified more than 200 lipid species belonging to seven major lipid classes (fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, prenol lipids, sterol lipids, and polyketides) that differed in their spatial distribution based on genotype (Mundah or Keel), time post-imbibition (0 to 72 h), or treatment (control or salt). We found a tentative flavonoid was discriminant in post-imbibed Mundah embryos under saline conditions, and a delayed flavonoid response in Keel relative to Mundah. We further employed MSI-MS/MS and LC-QToF-MS/MS to explore the identity of the discriminant flavonoid and study the temporal pattern in five additional barley genotypes. ICP-MS was used to quantify the elemental composition of both Mundah and Keel seeds, showing a significant increase in Na+ in salt treated samples. Spatial mapping of elements using µ-XRF localized the elements within the seeds. This study integrates data obtained from three mass spectrometry platforms together with µ-XRF to yield information on the localization of lipids, metabolites and elements improving our understanding of the germination process under salt stress at a molecular level.

Being sessile, plants must regulate energy balance, potentially via source-sink relations, to compromise growth with survival in stressful conditions. Crops are sensitive, possibly because they allocate their energy resources toward growth and yield rather than stress tolerance. In contrast, resurrection plants tightly regulate sugar metabolism and use a series of physiological adaptations to suppress cell death in their vegetative tissue to regain full metabolic capacity from a desiccated state within 72 h of watering. Previously, we showed that shoots of the resurrection plant Tripogon loliiformis, initiate autophagy upon dehydration as one strategy to reinstate homeostasis and suppress cell death. Here, we describe the relationship between energy status, sugar metabolism, trehalose-mediated activation of autophagy pathways and investigate whether shoots and roots utilize similar desiccation tolerance strategies. We show that despite containing high levels of trehalose, dehydrated Tripogon roots do not display elevated activation of autophagy pathways. Using targeted and non-targeted metabolomics, transmission electron microscopy (TEM) and transcriptomics we show that T. loliiformis engages a strategy similar to the long-term drought responses of sensitive plants and continues to use the roots as a sink even during sustained stress. Dehydrating T. loliiformis roots contained more sucrose and trehalose-6-phosphate compared to shoots at an equivalent water content. The increased resources in the roots provides sufficient energy to cope with stress and thus autophagy is not required. These results were confirmed by the absence of autophagosomes in roots by TEM. Upregulation of sweet genes in both shoots and roots show transcriptional regulation of sucrose translocation from leaves to roots and within roots during dehydration. Differences in the cell’s metabolic status caused starkly different cell death responses between shoots and roots. These findings show how shoots and roots utilize different stress response strategies and may provide candidate targets that can be used as tools for the improvement of stress tolerance in crops.

This study evaluated potential antidiabetic and antiobesity properties in vitro of selected medicinal plants. The hot water (WE) and ethanol extracts (EE) of sweet gale (Myrica gale L.), roseroot (Rhodiola rosea L.), sheep sorrel (Rumex acetosa L.), stinging nettles (Utrica dioica L.) and dandelion (Taraxacum officinale L.) were tested for total antioxidant capacity using ferric reducing antioxidant power (FRAP) and DPPH• scavenging capacity assays, followed by α-amylase, α-glucosidase and formation of advanced glycation end products (AGE) inhibition assays in vitro. Myrica gale EE had the highest total phenolic content (12.4 mmol GAE/L), FRAP value (17.4 mmol TE/L) and DPPH• scavenging activity (IC50 = 3.28 mg/L). Similarly, Myrica gale also exhibited significantly lower IC50 values for the percentage inhibition of α-amylase (IC50 = 62.65 mg/L) and α-glucosidase (IC50 = 27.20 mg/L) compared to acarbose (IC50 = 91.71 mg/L; IC50 = 89.50 mg/L, respectively) (p ≤ 0.05). The 3T3-L1 preadipocyte study also revealed that Myrica gale EE (54.8%) and stinging nettles (62.2% EE; 63.2% WE) significantly inhibited the adipogenesis in adipocytes in vitro (p ≤ 0.05). Polyphenols present in these medicinal plants have the potential to use in managing type 2 diabetes and obesity.

Turn-over of RNA and catabolism of nucleotides releases one to four ammonia molecules; the released nutrients being reassimilated into primary metabolism. Preliminary evidence indicates that monocots store high levels of free nucleotides and nucleosides but their potential as a source of internal organic nitrogen for use and remobilization is uncharted. Early tillering wheat plants were therefore starved of N over a 5-day time-course with examination of nucleic acid yields in whole shoots, young and old leaves and roots. Nucleic acids constituted ∼4% of the total N pool of N starved wheat plants, which was comparable with the N available from nitrate (NO3-) and greater than that available from the sum of 20 proteinogenic amino acids. Methods were optimized to detect nucleotide (purine and pyrimidine) metabolites, and wheat orthologs of RNA degradation (TaRNS), nucleoside transport (TaENT1, TaENT3) and salvage (TaADK) were identified. It was found that N starved wheat roots actively catabolised RNA and specific purines but accumulated pyrimidines. Reduced levels of RNA corresponded with induction of TaRNS2, TaENT1, TaENT3, and TaADK in the roots. Reduced levels of GMP, guanine, xanthine, allantoin, allantoate and glyoxylate in N starved roots correlated with accumulation of allantoate and glyoxylate in the oldest leaf, suggesting translocation of allantoin. Furthermore, N starved wheat plants exogenously supplied with N in the form of purine catabolites grew and photosynthesized as well as those plants re-supplied with NO3-. These results support the hypothesis that the nitrogen and carbon recovered from purine metabolism can support wheat growth.

This study evaluated the degradation kinetics of selected anthocyanins and the change in polymeric color, browning index, and haze development of grape juices from “Merlot” and “Ruby” grape cultivars stored at 5, 25, and 35°C for up to 360 days. Five major anthocyanins namely malvidin-3-O-glucoside (M3G), delphinidin-3-O-glucoside (D3G), petunidin-3-O-glucoside (Pt3G), peonidin-3-O-glucoside (Pn3G), and cyanidin-3-O-glucoside (C3G) were identified. Juice from “Merlot” had significantly higher (p < 0.05) content of all individual anthocyanins as compared to “Ruby.” During the long-term storage, total, and individual anthocyanins from both cultivars degraded following first-order reaction kinetics at the rate strongly dependent on temperature. At the end of the storage, noticeably higher loss of anthocyanins (95–99.9%) was observed at 25 and 35°C as compared to storage at 5°C [50–60% (“Merlot”); 74–81% (“Ruby”)]. Considerably lower rate of decay was observed at 5°C (k = 0.01–0.04) as compared to 25 (k = 0.04–0.14) and 35°C (k = 0.05–0.14) storage temperatures. The most temperature sensitive anthocyanin compounds were C3G (Ea = 66.5 kJ/mol) and D3G (Ea = 63.3 kJ/mol). At higher storage temperatures, significant (p < 0.05) and strong negative correlations were observed between anthocyanin concentrations and the levels of haze, polymeric and brown color development during storage. Storing grape juice, at lower temperature conditions could reduce the continuous loss of biologically active anthocyanins as well as the development of haze and brown color.