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The selection of yeast strains adapted to fermentation stresses in their winegrowing area is a key factor to produce quality wines. Twelve non-Saccharomyces native strains from Denomination of Origin (D.O.) “Vinos de Madrid” (Spain), a warm climate winegrowing region, were tested under osmotic pressure, ethanol, and acidic pH stresses. In addition, mixed combinations between non-Saccharomyces and a native Saccharomyces cerevisiae strain were practised. Phenotypic microarray technology has been employed to study the metabolic output of yeasts under the different stress situations. The yeast strains, Lachancea fermentati, Lachancea thermotolerans, and Schizosaccharomyces pombe showed the best adaptation to three stress conditions examined. The use of mixed cultures improved the tolerance to osmotic pressure by Torulaspora delbrueckii, S. pombe, and Zygosaccharomyces bailii strains and to high ethanol content by Candida stellata, S. pombe, and Z. bailii strains regarding the control. In general, the good adaptation of the native non-Saccharomyces strains to fermentative stress conditions makes them great candidates for wine elaboration in warm climate areas.

Background In recent years the Illumina HumanMethylation450 (HM450) BeadChip has provided a user-friendly platform to profile DNA methylation in human samples. However, HM450 lacked coverage of distal regulatory elements. Illumina have now released the MethylationEPIC (EPIC) BeadChip, with new content specifically designed to target these regions. We have used HM450 and whole-genome bisulphite sequencing (WGBS) to perform a critical evaluation of the new EPIC array platform. Results EPIC covers over 850,000 CpG sites, including >90 % of the CpGs from the HM450 and an additional 413,743 CpGs. Even though the additional probes improve the coverage of regulatory elements, including 58 % of FANTOM5 enhancers, only 7 % distal and 27 % proximal ENCODE regulatory elements are represented. Detailed comparisons of regulatory elements from EPIC and WGBS show that a single EPIC probe is not always informative for those distal regulatory elements showing variable methylation across the region. However, overall data from the EPIC array at single loci are highly reproducible across technical and biological replicates and demonstrate high correlation with HM450 and WGBS data. We show that the HM450 and EPIC arrays distinguish differentially methylated probes, but the absolute agreement depends on the threshold set for each platform. Finally, we provide an annotated list of probes whose signal could be affected by cross-hybridisation or underlying genetic variation. Conclusion The EPIC array is a significant improvement over the HM450 array, with increased genome coverage of regulatory regions and high reproducibility and reliability, providing a valuable tool for high-throughput human methylome analyses from diverse clinical samples.

Pharmacological treatment of several diseases, such as attention-deficit hyperactivity disorder (ADHD), presents marked variability in efficiency and its adverse effects. The genotyping of specific single nucleotide polymorphisms (SNPs) can support the prediction of responses to drugs and the genetic risk of presenting comorbidities associated with ADHD. This study presents two rapid and affordable microarray-based strategies to discriminate three clinically important SNPs in genes ADRA2A , SL6CA2 , and OPRM1 (rs1800544, rs5569, and rs1799971, respectively). These approaches are allele-specific oligonucleotide hybridization (ASO) and a combination of allele-specific amplification (ASA) and solid-phase hybridization. Buccal swab and blood samples taken from ADHD patients and controls were analyzed by ASO, ASA, and a gold-reference method. The results indicated that ASA is superior in genotyping capability and analytical performance. Graphical abstract Scheme of microarray chip to discriminate three SNPs realated to drug treatment of attention-deficit hyperactivity disorder (ADHD). Format: 6 samples per chip

We sought to define whether there are intrinsic molecular subtypes of high-grade bladder cancer. Consensus clustering performed on gene expression data from a meta-dataset of high-grade, muscle-invasive bladder tumors identified two intrinsic, molecular subsets of high-grade bladder cancer, termed \"luminal\" and \"basal-like,\" which have characteristics of different stages of urothelial differentiation, reflect the luminal and basal-like molecular subtypes of breast cancer, and have clinically meaningful differences in outcome. A gene set predictor, bladder cancer analysis of subtypes by gene expression (BASE47) was defined by prediction analysis of microarrays (PAM) and accurately classifies the subtypes. Our data demonstrate that there are at least two molecularly and clinically distinct subtypes of high-grade bladder cancer and validate the BASE47 as a subtype predictor. Future studies exploring the predictive value of the BASE47 subtypes for standard of care bladder cancer therapies, as well as novel subtype-specific therapies, will be of interest.

Summary Biological assay has been based on analysis of all individuals collected from sample populations. Bulked sample analysis (BSA), which works with selected and pooled individuals, has been extensively used in gene mapping through bulked segregant analysis with biparental populations, mapping by sequencing with major gene mutants and pooled genomewide association study using extreme variants. Compared to conventional entire population analysis, BSA significantly reduces the scale and cost by simplifying the procedure. The bulks can be built by selection of extremes or representative samples from any populations and all types of segregants and variants that represent wide ranges of phenotypic variation for the target trait. Methods and procedures for sampling, bulking and multiplexing are described. The samples can be analysed using individual markers, microarrays and high‐throughput sequencing at all levels of DNA, RNA and protein. The power of BSA is affected by population size, selection of extreme individuals, sequencing strategies, genetic architecture of the trait and marker density. BSA will facilitate plant breeding through development of diagnostic and constitutive markers, agronomic genomics, marker‐assisted selection and selective phenotyping. Applications of BSA in genetics, genomics and crop improvement are discussed with their future perspectives.

Key messageDuring abiotic stress low abundant amino acids are not synthesized but they accumulate due to increased protein turnover under conditions inducing carbohydrate starvation (dehydration, salt stress, darkness) and are degraded.Metabolic adaptation is crucial for abiotic stress resistance in plants, and accumulation of specific amino acids as well as secondary metabolites derived from amino acid metabolism has been implicated in increased tolerance to adverse environmental conditions. The role of proline, which is synthesized during Arabidopsis stress response to act as a compatible osmolyte, has been well established. However, conclusions drawn about potential functions of other amino acids such as leucine, valine, and isoleucine are not entirely consistent. This study reevaluates published datasets with a special emphasis on changes in the free amino acid pool and transcriptional regulation of the associated anabolic and catabolic pathways. In order to gain a comprehensive overview about the general direction of amino acid metabolism under abiotic stress conditions a complete map of all currently known enzymatic steps involved in amino acid synthesis and degradation was assembled including also the initial steps leading to the synthesis of secondary metabolites. Microarray datasets and amino acid profiles of Arabidopsis plants exposed to dehydration, high salinity, extended darkness, cold, and heat were systematically analyzed to identify trends in fluxes of amino acid metabolism. Some high abundant amino acids such as proline, arginine, asparagine, glutamine, and GABA are synthesized during abiotic stress to act as compatible osmolytes, precursors for secondary metabolites, or storage forms of organic nitrogen. In contrast, most of the low abundant amino acids are not synthesized but they accumulate due to increased protein turnover under conditions inducing carbohydrate starvation (dehydration, salt stress, extended darkness) and are degraded.

The recently discovered lytic polysaccharide monooxygenases (LPMOs) are known to carry out oxidative cleavage of glycoside bonds in chitin and cellulose, thus boosting the activity of well-known hydrolytic depolymerizing enzymes. Because biomass-degrading microorganisms tend to produce a plethora of LPMOs, and considering the complexity and copolymeric nature of the plant cell wall, it has been speculated that some LPMOs may act on other substrates, in particular the hemicelluloses that tether to cellulose microfibrils. We demonstrate that an LPMO from Neurospora crassa , Nc LPMO9C, indeed degrades various hemicelluloses, in particular xyloglucan. This activity was discovered using a glycan microarray-based screening method for detection of substrate specificities of carbohydrate-active enzymes, and further explored using defined oligomeric hemicelluloses, isolated polymeric hemicelluloses and cell walls. Products generated by Nc LPMO9C were analyzed using high performance anion exchange chromatography and multidimensional mass spectrometry. We show that Nc LPMO9C generates oxidized products from a variety of substrates and that its product profile differs from those of hydrolytic enzymes acting on the same substrates. The enzyme particularly acts on the glucose backbone of xyloglucan, accepting various substitutions (xylose, galactose) in almost all positions. Because the attachment of xyloglucan to cellulose hampers depolymerization of the latter, it is possible that the beneficial effect of the LPMOs that are present in current commercial cellulase mixtures in part is due to hitherto undetected LPMO activities on recalcitrant hemicellulose structures.

This report provides direct evidence that strigolactone (SL) positively regulates drought and high salinity responses in Arabidopsis . Both SL-deficient and SL-response [ more axillary growth (max)] mutants exhibited hypersensitivity to drought and salt stress, which was associated with shoot- rather than root-related traits. Exogenous SL treatment rescued the drought-sensitive phenotype of the SL-deficient mutants but not of the SL-response mutant, and enhanced drought tolerance of WT plants, confirming the role of SL as a positive regulator in stress response. In agreement with the drought-sensitive phenotype, max mutants exhibited increased leaf stomatal density relative to WT and slower abscisic acid (ABA)-induced stomatal closure. Compared with WT, the max mutants exhibited increased leaf water loss rate during dehydration and decreased ABA responsiveness during germination and postgermination. Collectively, these results indicate that cross-talk between SL and ABA plays an important role in integrating stress signals to regulate stomatal development and function. Additionally, a comparative microarray analysis of the leaves of the SL-response max2 mutant and WT plants under normal and dehydrative conditions revealed an SL-mediated network controlling plant responses to stress via many stress- and/or ABA-responsive and cytokinin metabolism-related genes. Our results demonstrate that plants integrate multiple hormone-response pathways for adaptation to environmental stress. Based on our results, genetic modulation of SL content/response could be applied as a potential approach to reduce the negative impact of abiotic stress on crop productivity.

Technologies capable of cell separation based on cell images provide powerful tools enabling cell selection criteria that rely on spatially or temporally varying properties. Image-based cell sorting (IBCS) systems utilize microfluidic or microarray platforms, each having unique characteristics and applications. The advent of IBCS marks a new paradigm in which cell phenotype and behavior can be explored with high resolution and tied to cellular physiological and omics data, providing a deeper understanding of single-cell physiology and the creation of cell lines with unique properties. Cell sorting guided by high-content image information has far-reaching implications in biomedical research, clinical medicine, and pharmaceutical development. Advancements in imaging and computational decision-making have given rise to a new method for classifying and isolating single cells from samples, which addresses major limitations of commonly used technologies, for example, fluorescence activated cell sorting (FACS).Microfluidic flow, microfluidic containment, and microarray-based systems have been utilized to separate cells utilizing high-spatial resolution and real-time kinetic information.From inexpensive and flexible in-laboratory devices to precise clinical tools, image-based cell sorting (IBCS) systems have broad potential.Machine learning and deep neural networks can be combined with IBCS for intelligent cell selection and isolation.Microfluidics and microarrays can be combined in single IBCS platforms, taking advantage of both technologies.

Background Constructing gene coexpression networks is a powerful approach for analyzing high-throughput gene expression data towards module identification, gene function prediction, and disease-gene prioritization. While optimal workflows for constructing coexpression networks, including good choices for data pre-processing, normalization, and network transformation, have been developed for microarray-based expression data, such well-tested choices do not exist for RNA-seq data. Almost all studies that compare data processing and normalization methods for RNA-seq focus on the end goal of determining differential gene expression. Results Here, we present a comprehensive benchmarking and analysis of 36 different workflows, each with a unique set of normalization and network transformation methods, for constructing coexpression networks from RNA-seq datasets. We test these workflows on both large, homogenous datasets and small, heterogeneous datasets from various labs. We analyze the workflows in terms of aggregate performance, individual method choices, and the impact of multiple dataset experimental factors. Our results demonstrate that between-sample normalization has the biggest impact, with counts adjusted by size factors producing networks that most accurately recapitulate known tissue-naive and tissue-aware gene functional relationships. Conclusions Based on this work, we provide concrete recommendations on robust procedures for building an accurate coexpression network from an RNA-seq dataset. In addition, researchers can examine all the results in great detail at https://krishnanlab.github.io/RNAseq_coexpression to make appropriate choices for coexpression analysis based on the experimental factors of their RNA-seq dataset.