Expression of the human milk protein β-casein in transgenic potato plants

A 1177 bp cDNA fragment encoding the human milk protein -casein was introduced into Solanum tuberosum cells under control of the auxin-inducible, bidirectional mannopine synthase mas12) promoters using Agrobacterium tumefaciens-mediated leaf disc transformation methods. Antibiotic-resistant plants were regenerated and transformants selected based on luciferase activity carried by the expression vector containing the human -casein cDNA. The presence of human -casein cDNA in the plant genome was detected by PCR and DNA hybridization experiments. Human -casein mRNA was identified in leaf tissues of transgenic plants by RT-PCR analysis. Human - casein was identified in auxin-induced leaf and tuber tissues of transformed potato plants by immunoprecipitation and immunoblot analysis. Human -casein produced in transgenic plants migrated in polyacrylamide gels as a single band with an approximate molecular mass of 30 kDa. Immunoblot experiments identified approximately 0.01% of the total soluble protein of transgenic potato leaf tissue as -casein. The above experiments demonstrate the expression of human milk - casein as part of an edible food plant. These findings open the way for reconstitution of human milk inedible plants for replacement of bovine milk in baby foods for general improvement of infant nutrition, and for prevention of gastric and intestinal diseases in children     

Population genetics and phylogenetics of DNA sequence variation at multiple loci within the Drosophila melanogaster species complex

Two regions of the genome, a 1-kbp portion of the zeste locus and a 1.1- kbp portion of the yolk protein 2 locus, were sequenced in six individuals from each of four species: Drosophila melanogaster, D. simulans, D. mauritiana, and D. sechellia. The species and strains were the same as those of a previous study of a 1.9-kbp region of the period locus. No evidence was found for recent balancing or directional selection or for the accumulation of selected differences between species. Yolk protein 2 has a high level of amino acid replacement variation and a low level of synonymous variation, while zeste has the opposite pattern. This contrast is consistent with information on gene function and patterns of codon bias. Polymorphism levels are consistent with a ranking of effective population sizes, from low to high, in the following order: D. sechellia, D. melanogaster, D.mauritiana, and D. simulans. The apparent species relationships are very similar to those suggested by the period locus study. In particular, D. simulans appears to be a large population that is still segregating variation that arose before the separation of D. mauritiana and D. sechellia. It is estimated that the separation of ancestral D. melanogaster from the other species occurred 2.5-3.4 Mya. The separations of D. sechellia and D. mauritiana from ancestral D. simulans appear to have occurred 0.58- 0.86 Mya, with D. mauritiana having diverged from ancestral D. simulans 0.1 Myr more recently than D. sechellia.      

<Emphasis Type="Italic">Pseudomonas aeruginosa</Emphasis> inhibits <Emphasis Type="Italic">in-vitro Candida</Emphasis> biofilm development

Background  

Elucidation of the communal behavior of microbes in mixed species biofilms may have a major impact on understanding infectious diseases and for the therapeutics. Although, the structure and the properties of monospecies biofilms and their role in disease have been extensively studied during the last decade, the interactions within mixed biofilms consisting of bacteria and fungi such as Candida spp. have not been illustrated in depth. Hence, the aim of this study was to evaluate the interspecies interactions of Pseudomonas aeruginosa and six different species of Candida comprising C. albicans, C. glabrata, C. krusei, C. tropicalis, C. parapsilosis, and C. dubliniensis in dual species biofilm development.     

Photosynthetic Energy Conversion Efficiency: Setting a Baseline for Gauging Future Improvements in Important Food and Biofuel Crops

The conversion efficiency (ε_c) of absorbed radiation into biomass (MJ of dry matter per MJ of absorbed photosynthetically active radiation) is a component of yield potential that has been estimated at less than half the theoretical maximum. Various strategies have been proposed to improve ε_c, but a statistical analysis to establish baseline ε_c levels across different crop functional types is lacking. Data from 164 published ε_c studies conducted in relatively unstressed growth conditions were used to determine the means, greatest contributors to variation, and genetic trends in ε_c across important food and biofuel crop species. ε_c was greatest in biofuel crops (0.049–0.066), followed by C₄ food crops (0.046–0.049), C₃ nonlegumes (0.036–0.041), and finally C₃ legumes (0.028–0.035). Despite confining our analysis to relatively unstressed growth conditions, total incident solar radiation and average growing season temperature most often accounted for the largest portion of ε_c variability. Genetic improvements in ε_c, when present, were less than 0.7% per year, revealing the unrealized potential of improving ε_c as a promising contributing strategy to meet projected future agricultural demand.Substantial increases in yield are needed to feed and fuel the world’s growing human population. With an estimated population of nine billion people by the middle of this century (Lutz and Samir, 2010) and rising affluence resulting in greater consumption of grain-fed animal products (Cirera and Masset, 2010), different studies predict that, by midcentury, global crop production will need to increase 60% to 120% over 2005 levels without the expansion of agricultural land area (Tilman et al., 2011; Alexandratos and Bruinsma, 2012).Doubling yields in major food and fuel crops requires considerable effort, especially as yields are beginning to plateau in many major food crops. Yield increases necessary for doubling productivity by midcentury are estimated at 1.16% to 1.31% each year in all cereals (Hall and Richards, 2013), 1.7% per year in wheat (Triticum aestivum; Rosegrant and Agcaoili, 2010), and 2.4% (noncompounding average per year) across all major grain crops (Ray et al., 2013). However, global mean increases from the past 20 to 30 years suggest that yield gains in rice (Oryza sativa) and wheat are approximately 1% (Lopes et al., 2012; Manès et al, 2012; Ray et al., 2013) and declining in some areas of the world (Cassman et al., 2010; Fischer and Edmeades, 2010; Long and Ort, 2010; Ray et al., 2013). Global yearly increases are estimated at 1.3% in soybean (Glycine max) and 1.6% in maize (Zea mays), with similar concerns that yield trends may also be decreasing in some major growing regions (Lobell and Gourdji, 2012; Ray et al., 2013).Efforts to increase yields in the next few decades must also account for environmental and sustainability goals (Sayer et al., 2013) as well as heightened environmental stresses predicted to occur due to climate change, which are already responsible for some of the stagnation in yield increases. Anthropogenic sources of greenhouse gases have caused an approximately 1°C increase in land surface temperatures since 1900, and global mean surface temperatures are likely to increase by up to 2.4°C to 4.8°C by the end of the century (IPCC, 2013). Drought is also expected to become more frequent and intense in many regions of the world (Dai, 2011; IPCC, 2013). Of the variability present in major food crop yield gains, 30% can be explained by climate change alone (Lobell and Field, 2007), with drastic decreases in barley (Hordeum vulgare), maize, rice, sorghum (Sorghum bicolor), soybean, and wheat yields as average growing season temperatures surpass the temperature optimum for each crop (Lobell and Gourdji, 2012). Current levels of atmospheric CO₂ concentration [CO₂] are the highest they have been in at least 800,000 years (IPCC, 2013). Elevated [CO₂] increases water use efficiency (Ainsworth and Long, 2005, Bernacchi et al., 2007, Leakey et al., 2009), but probably not to an extent that would mitigate the resulting reductions in yield caused by higher temperature and higher vapor pressure deficit (Ort and Long, 2014). Additionally, any fertilization effects on C₃ yields due to elevated [CO₂] would be at least in part negated by drought and temperature stress, leaving yield increases far from optimal (Long et al., 2006a; Lobell and Gourdji, 2012).     

Physiological determinants of three-kilometer running performance in experienced triathletes

The present investigation examined the physiological parameters that contribute to 3-km running performance. Following 2 familiarization sessions, 16 experienced male triathletes (Vo(2)max = 55.7 +/- 4.9 ml.kg(-1).min(-1), age = 31.3 +/- 11.7 years) performed a 3-km time trial (3kmTT) and were assessed for selected physiological and anthropometrical characteristics. Stepwise multiple regression and correlation analysis was used to determine the variables that significantly related to 3kmTT. The analysis revealed that 82.3% of the adjusted variance in 3kmTT performance could be explained by peak treadmill running velocity during a Vo(2)max test (Vmax) alone. The addition of the running velocity at lactate threshold (LT(vel)) and peak lactate concentration ([BLa(-)](peak)) to the prediction equation allowed for 93.6% of the adjusted variance in 3kmTT to be predicted (Y = -13.64 Vmax - 25.61 LT(vel) - 5.40 [BLa(-)](peak) + 1358.5). Correlation analysis revealed that Vmax (r = -0.91), LT(vel) (r = -0.90), and Vo(2)max (r = -0.80) were significantly related to running performance. These results show that Vmax was the single best predictor of 3-km running performance in experienced male triathletes and that both aerobic and anaerobic abilities are related to improved 3kmTT performance. Since the assessment of Vmax is relatively simple to implement, we suggest that determining Vmax may be a practical method for monitoring performance changes in short-term endurance running events.     

The accuracy of several multiple sequence alignment programs for proteins

Background  

There have been many algorithms and software programs implemented for the inference of multiple sequence alignments of protein and DNA sequences. The "true" alignment is usually unknown due to the incomplete knowledge of the evolutionary history of the sequences, making it difficult to gauge the relative accuracy of the programs.