Dose-related effects of alcohol on cognitive functioning

We assessed the suitability of six applied tests of cognitive functioning to provide a single marker for dose-related alcohol intoxication. Numerous studies have demonstrated that alcohol has a deleterious effect on specific areas of cognitive processing but few have compared the effects of alcohol across a wide range of different cognitive processes. Adult participants (N = 56, 32 males, 24 females aged 18–45 years) were randomized to control or alcohol treatments within a mixed design experiment involving multiple-dosages at approximately one hour intervals (attained mean blood alcohol concentrations (BACs) of 0.00, 0.048, 0.082 and 0.10%), employing a battery of six psychometric tests; the Useful Field of View test (UFOV; processing speed together with directed attention); the Self-Ordered Pointing Task (SOPT; working memory); Inspection Time (IT; speed of processing independent from motor responding); the Traveling Salesperson Problem (TSP; strategic optimization); the Sustained Attention to Response Task (SART; vigilance, response inhibition and psychomotor function); and the Trail-Making Test(TMT; cognitive flexibility and psychomotor function). Results demonstrated that impairment is not uniform across different domains of cognitive processing and that both the size of the alcohol effect and the magnitude of effect change across different dose levels are quantitatively different for different cognitive processes. Only IT met the criteria for a marker for wide-spread application: reliable dose-related decline in a basic process as a function of rising BAC level and easy to use non-invasive task properties.Mathew J. Dry, Nicholas R. Burns, Ted Nettelbeck, Aaron L. Farquharson and Jason M. Whit

Vision, challenges and opportunities for a Plant Cell Atlas

With growing populations and pressing environmental problems, future economies will be increasingly plant-based. Now is the time to reimagine plant science as a critical component of fundamental science, agriculture, environmental stewardship, energy, technology and healthcare. This effort requires a conceptual and technological framework to identify and map all cell types, and to comprehensively annotate the localization and organization of molecules at cellular and tissue levels. This framework, called the Plant Cell Atlas (PCA), will be critical for understanding and engineering plant development, physiology and environmental responses. A workshop was convened to discuss the purpose and utility of such an initiative, resulting in a roadmap that acknowledges the current knowledge gaps and technical challenges, and underscores how the PCA initiative can help to overcome them.</jats:p

Phloem Exudate Protein Profiles during Drought and Recovery Reveal Abiotic Stress Responses in Tomato Vasculature

Drought is the leading cause of agricultural yield loss among all abiotic stresses, and the link between water deficit and phloem protein contents is relatively unexplored. Here we collected phloem exudates from Solanum lycopersicum leaves during periods of drought stress and recovery. Our analysis identified 2558 proteins, the most abundant of which were previously localized to the phloem. Independent of drought, enrichment analysis of the total phloem exudate protein profiles from all samples suggests that the protein content of phloem sap is complex, and includes proteins that function in chaperone systems, branched-chain amino acid synthesis, trehalose metabolism, and RNA silencing. We observed 169 proteins whose abundance changed significantly within the phloem sap, either during drought or recovery. Proteins that became significantly more abundant during drought include members of lipid metabolism, chaperone-mediated protein folding, carboxylic acid metabolism, abscisic acid signaling, cytokinin biosynthesis, and amino acid metabolism. Conversely, proteins involved in lipid signaling, sphingolipid metabolism, cell wall organization, carbohydrate metabolism, and a mitogen-activated protein kinase are decreased during drought. Our experiment has achieved an in-depth profiling of phloem sap protein contents during drought stress and recovery that supports previous findings and provides new evidence that multiple biological processes are involved in drought adaptation

Distinct Preflowering Drought Tolerance Strategies of Sorghum bicolor Genotype RTx430 Revealed by Subcellular Protein Profiling

Drought is the largest stress affecting agricultural crops, resulting in substantial reductions in yield. Plant adaptation to water stress is a complex trait involving changes in hormone signaling, physiology, and morphology. Sorghum (Sorghum bicolor (L.) Moench) is a C4 cereal grass; it is an agricultural staple, and it is particularly drought-tolerant. To better understand drought adaptation strategies, we compared the cytosolic- and organelle-enriched protein profiles of leaves from two Sorghum bicolor genotypes, RTx430 and BTx642, with differing preflowering drought tolerances after 8 weeks of growth under water limitation in the field. In agreement with previous findings, we observed significant drought-induced changes in the abundance of multiple heat shock proteins and dehydrins in both genotypes. Interestingly, our data suggest a larger genotype-specific drought response in protein profiles of organelles, while cytosolic responses are largely similar between genotypes. Organelle-enriched proteins whose abundance significantly changed exclusively in the preflowering drought-tolerant genotype RTx430 upon drought stress suggest multiple mechanisms of drought tolerance. These include an RTx430-specific change in proteins associated with ABA metabolism and signal transduction, Rubisco activation, reactive oxygen species scavenging, flowering time regulation, and epicuticular wax production. We discuss the current understanding of these processes in relation to drought tolerance and their potential implications

Characterization of Local and Systemic Impact of Whitefly (Bemisia tabaci) Feeding and Whitefly-Transmitted Tomato Mottle Virus Infection on Tomato Leaves by Comprehensive Proteomics

Tomato mottle virus (ToMoV) is a single-stranded DNA (ssDNA) begomovirus transmitted to solanaceous crops by the whitefly species complex (Bemisia tabaci), causing stunted growth, leaf mottling, and reduced yield. Using a genetic repertoire of seven genes, ToMoV pathogenesis includes the manipulation of multiple plant biological processes to circumvent antiviral defenses. To further understand the effects of whitefly feeding and whitefly-transmitted ToMoV infection on tomato plants (Solanum lycopersicum &lsquo;Florida Lanai&rsquo;), we generated comprehensive protein profiles of leaves subjected to feeding by either viruliferous whiteflies harboring ToMoV, or non-viruliferous whiteflies, or a no-feeding control. The effects of whitefly feeding and ToMoV infection were measured both locally and systemically by sampling either a mature leaf directly from the site of clip-cage confined whitefly feeding, or from a newly formed leaf 10 days post feeding (dpf). At 3 dpf, tomato&rsquo;s response to ToMoV included proteins associated with translation initiation and elongation as well as plasmodesmata dynamics. In contrast, systemic impacts of ToMoV on younger leaves 10 dpf were more pronounced and included a virus-specific change in plant proteins associated with mRNA maturation and export, RNA-dependent DNA methylation, and other antiviral plant processes. Our analysis supports previous findings and provides novel insight into tomato&rsquo;s local and systemic response to whitefly feeding and ToMoV infection

Summary of protein and metabolite profile data.

<p>(a)-(c) Number of proteins or metabolites showing significant intensity differences in pair wise tissue comparisons (<i>p-</i>value < 0.05). Light grey bars indicate the number of features with greater abundance in distal tissues, while dark grey bars indicate the number of proteins with greater abundance in proximal tissues. (d) Total proteins identified by ontology. Light grey and dark grey bars correspond to <i>S</i>. <i>medicae</i> and <i>M</i>. <i>truncatula</i> proteins, respectively. FI, Fraction I; FII, Fraction II; FIII, Fraction III; TCA, Tricarboxylic Acid Cycle; ETC, Electron Transport Chain; EFs, Elongation Factors; TFs, Transcription Factors; NSPs, Nodule specific proteins.</p

Multivariate analysis.

<p>(a) Scoreplot results from PCA using protein profiles of all tissues sampled. (b) PLS-DA scoreplot of fraction-specific protein profiles. (c) PLS-DA scoreplot of fraction-specific metabolite profiles. In all cases dashed circles indicate a 95% confidence region. FI, Fraction I; FII, Fraction II; FIII, Fraction III.</p

Comprehensive model of coordinated metabolism of carbohydrates, central carbon, fatty acids, amino acids, and others.

<p>6PFK, 6 phosphofructokinase; ACC1, Acetyl-CoA carboxylase 1; ACC2, Acetyl-CoA carboxylase 2; AcpP, Acyl carrier protein; AKGDH, Alpha ketoglutarate dehydrogenase; Asn, Asparagine; ASNS, Asparagine synthase; Asp, Aspartate; AST, Aspartate transaminase; Cit, Citrate; ENO, Enolase; F1,6BP, Fructose 1,6-bisphosphate; F6P, Fructose 6-phosphate; FabB, 3-oxoacyl-(acyl carrier protein) synthase I; FbpA, Fructose-bisphosphate aldolase; FI, Fraction I; FII, Fraction II; FIII, Fraction III; G2P, Glyceraldehyde 2-phosphate; G3P, Glyceraldehyde 3-phosphate; GAPDH, Glyceraldehyde phosphate dehydrogenase; Glc, Glucose; Glc1P, Glucose 1-phosphate; Glc6P, Glucose 6-phosphate; Gln, Glutamine; Glu, Glutamate; GS, Glutamine synthetase; GSI, Glutamine synthetase I; ICDH, Isocitrate dehydrogenase; Mal, Malate; MDH, Malate dehydrogenase; NifD, Nitrogenase molybdenum-iron protein alpha chain; NifH, Nitrogenase reductase; NifK, Nitrogenase molybdenum-iron protein beta chain; NifN, Nitrogenase molybdenum-cofactor biosynthetic protein; OAA, Oxaloacetate; OGD, Oxoglutarate dehydrognease; PC, Pyruvate carboxylase; PDH, Pyruvate dehydrogenase; PEP, Phosphoenolpyruvate; PEPC, Phosphoenolpyruvate carboxylase; PFK, Phosphofructokinase; PGM, Phosphoglucomutase; PK, Pyruvate kinase; SDH, Succinate dehydrogenase; Suc, Sucrose; TCA, Tricarboxylic acid cycle; TIM, Triose phosphate isomerase; α-KG, alpha ketoglutarate.</p