Introducing a new breed of wine yeast: interspecific hybridisation between a commercial Saccharomyces cerevisiae wine yeast and Saccharomyces mikatae

Interspecific hybrids are commonplace in agriculture and horticulture; bread wheat and grapefruit are but two examples. The benefits derived from interspecific hybridisation include the potential of generating advantageous transgressive phenotypes. This paper describes the generation of a new breed of wine yeast by interspecific hybridisation between a commercial Saccharomyces cerevisiae wine yeast strain and Saccharomyces mikatae, a species hitherto not associated with industrial fermentation environs. While commercially available wine yeast strains provide consistent and reliable fermentations, wines produced using single inocula are thought to lack the sensory complexity and rounded palate structure obtained from spontaneous fermentations. In contrast, interspecific yeast hybrids have the potential to deliver increased complexity to wine sensory properties and alternative wine styles through the formation of novel, and wider ranging, yeast volatile fermentation metabolite profiles, whilst maintaining the robustness of the wine yeast parent. Screening of newly generated hybrids from a cross between a S. cerevisiae wine yeast and S. mikatae (closely-related but ecologically distant members of the Saccharomyces sensu stricto clade), has identified progeny with robust fermentation properties and winemaking potential. Chemical analysis showed that, relative to the S. cerevisiae wine yeast parent, hybrids produced wines with different concentrations of volatile metabolites that are known to contribute to wine flavour and aroma, including flavour compounds associated with non-Saccharomyces species. The new S. cerevisiae x S. mikatae hybrids have the potential to produce complex wines akin to products of spontaneous fermentation while giving winemakers the safeguard of an inoculated ferment.Jennifer R. Bellon, Frank Schmid, Dimitra L. Capone, Barbara L. Dunn, Paul J. Chamber

Publishing perishing? Towards tomorrow's information architecture

Scientific articles are tailored to present information in human-readable aliquots. Although the Internet has revolutionized the way our society thinks about information, the traditional text-based framework of the scientific article remains largely unchanged. This format imposes sharp constraints upon the type and quantity of biological information published today. Academic journals alone cannot capture the findings of modern genome-scale inquiry. Like many other disciplines, molecular biology is a science of facts: information inherently suited to database storage. In the past decade, a proliferation of public and private databases has emerged to house genome sequence, protein structure information, functional genomics data and more; these digital repositories are now a vital component of scientific communication. The next challenge is to integrate this vast and ever-growing body of information with academic journals and other media. To truly integrate scientific information we must modernize academic publishing to exploit the power of the Internet. This means more than online access to articles, hyperlinked references and web-based supplemental data; it means making articles fully computer-readable with intelligent markup and Structured Digital Abstracts. Here, we examine the changing roles of scholarly journals and databases. We present our vision of the optimal information architecture for the biosciences, and close with tangible steps to improve our handling of scientific information today while paving the way for an expansive central index in the future

Dipoid-Specific Genome Stability Genes of S. cerevisiae: Genomic Screen Reveals Haploidization as an Escape from Persisting DNA Rearrangement Stress

Maintaining a stable genome is one of the most important tasks of every living cell and the mechanisms ensuring it are similar in all of them. The events leading to changes in DNA sequence (mutations) in diploid cells occur one to two orders of magnitude more frequently than in haploid cells. The majority of those events lead to loss of heterozygosity at the mutagenesis marker, thus diploid-specific genome stability mechanisms can be anticipated. In a new global screen for spontaneous loss of function at heterozygous forward mutagenesis marker locus, employing three different mutagenesis markers, we selected genes whose deletion causes genetic instability in diploid Saccharomyces cerevisiae cells. We have found numerous genes connected with DNA replication and repair, remodeling of chromatin, cell cycle control, stress response, and in particular the structural maintenance of chromosome complexes. We have also identified 59 uncharacterized or dubious ORFs, which show the genome instability phenotype when deleted. For one of the strongest mutators revealed in our screen, ctf18Δ/ctf18Δ the genome instability manifests as a tendency to lose the whole set of chromosomes. We postulate that this phenomenon might diminish the devastating effects of DNA rearrangements, thereby increasing the cell's chances of surviving stressful conditions. We believe that numerous new genes implicated in genome maintenance, together with newly discovered phenomenon of ploidy reduction, will help revealing novel molecular processes involved in the genome stability of diploid cells. They also provide the clues in the quest for new therapeutic targets to cure human genome instability-related diseases

Ploidy of Cell-Sorted Trophic and Cystic Forms of Pneumocystis carinii

Once regarded as an AIDS-defining illness, Pneumocystis pneumonia (PcP) is nowadays prevailing in immunocompromised HIV-negative individuals such as patients receiving immunosuppressive therapies or affected by primary immunodeficiency. Moreover, Pneumocystis clinical spectrum is broadening to non-severely-immunocompromised subjects who could be colonized by the fungus while remaining asymptomatic for PcP, thus being able to transmit the infection by airborne route to susceptible hosts. Although the taxonomical position of the Pneumocystis genus has been clarified, several aspects of its life cycle remain elusive such as its mode of proliferation within the alveolus or its ploidy level. As no long-term culture model exists to grow Pneumocystis organisms in vitro, an option was to use a model of immunosuppressed rat infected with Pneumocystis carinii and sort life cycle stage fractions using a high-through-put cytometer. Subsequently, ploidy levels of the P. carinii trophic and cystic form fractions were measured by flow cytometry. In the cystic form, eight contents of DNA were measured thus strengthening the fact that each mature cyst contains eight haploid spores. Following release, each spore evolves into a trophic form. The majority of the trophic form fraction was haploid in our study. Some less abundant trophic forms displayed two contents of DNA indicating that they could undergo (i) mating/fusion leading to a diploid status or (ii) asexual mitotic division or (iii) both. Even less abundant trophic forms with four contents of DNA were suggestive of mitotic divisions occurring following mating in diploid trophic forms. Of interest, was the presence of trophic forms with three contents of DNA, an unusual finding that could be related to asymmetrical mitotic divisions occurring in other fungal species to create genetic diversity at lower energetic expenses than mating. Overall, ploidy data of P. carinii life cycle stages shed new light on the complexity of its modes of proliferation

Identifying older diabetic patients at risk of poor glycemic control

BACKGROUND: Optimal glycemic control prevents the onset of diabetes complications. Identifying diabetic patients at risk of poor glycemic control could help promoting dedicated interventions. The purpose of this study was to identify predictors of poor short-term and long-term glycemic control in older diabetic in-patients. METHODS: A total of 1354 older diabetic in-patients consecutively enrolled in a multicenter study formed the training population (retrospective arm); 264 patients consecutively admitted to a ward of general medicine formed the testing population (prospective arm). Glycated hemoglobin (HbA1c) was measured on admission and one year after the discharge in the testing population. Independent correlates of a discharge glycemia ≥ 140 mg/dl in the training population were assessed by logistic regression analysis and a clinical prediction rule was developed. The ability of the prediction rule and that of admission HbA1c to predict discharge glycemia ≥ 140 mg/dl and HbA1c > 7% one year after discharge was assessed in the testing population. RESULTS: Selected admission variables (diastolic arterial pressure < 80 mmHg, glycemia = 143–218 mg/dl, glycemia > 218 mg/dl, history of insulinic or combined hypoglycemic therapy, Charlson's index > 2) were combined to obtain a score predicting a discharge fasting glycemia ≥ 140 mg/dl in the training population. A modified score was obtained by adding 1 if admission HbA1c exceeded 7.8%. The modified score was the best predictor of both discharge glycemia ≥ 140 mg/dl (sensitivity = 79%, specificity = 63%) and 1 year HbA1c > 7% (sensitivity = 72%, specificity = 71%) in the testing population. CONCLUSION: A simple clinical prediction rule might help identify older diabetic in-patients at risk of both short and long term poor glycemic control

State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data

Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods to neural spike data simultaneously recorded from the motor cortex of an awake monkey and demonstrate that the higher-order spike correlation organizes dynamically in relation to a behavioral demand

Kernel bandwidth optimization in spike rate estimation

Kernel smoother and a time-histogram are classical tools for estimating an instantaneous rate of spike occurrences. We recently established a method for selecting the bin width of the time-histogram, based on the principle of minimizing the mean integrated square error (MISE) between the estimated rate and unknown underlying rate. Here we apply the same optimization principle to the kernel density estimation in selecting the width or “bandwidth” of the kernel, and further extend the algorithm to allow a variable bandwidth, in conformity with data. The variable kernel has the potential to accurately grasp non-stationary phenomena, such as abrupt changes in the firing rate, which we often encounter in neuroscience. In order to avoid possible overfitting that may take place due to excessive freedom, we introduced a stiffness constant for bandwidth variability. Our method automatically adjusts the stiffness constant, thereby adapting to the entire set of spike data. It is revealed that the classical kernel smoother may exhibit goodness-of-fit comparable to, or even better than, that of modern sophisticated rate estimation methods, provided that the bandwidth is selected properly for a given set of spike data, according to the optimization methods presented here