Probiotic properties of Lactobacillus isolates originating from porcine intestine and feces

In this study, a total of 94 lactic acid bacterial (LAB) isolates of porcine small intestinal and fecal origin were screened for their probiotic properties. The aim was to evaluate whether their isolation site and putative species identity play a role in these characteristics and whether either of these can be used as a predictive factor for the probiotic potential of bacterial isolates. The isolates were preliminarily identified by partial 16S rRNA gene sequencing and characterized in vitro for their pH and bile tolerance, adhesion capacity towards porcine enterocytes isolated from five intestinal sites and for antimicrobial activity towards five indicator pathogens. The interdependence of these characteristics was statistically evaluated. The isolates tolerated low pH and bile well. Adherence to the enterocytes of different origins did not correlate with the strain isolation site. In general, higher adherence was observed to colon cells in comparison to the small intestinal enterocytes. Culture filtrates of the isolates caused a decrease of up to three orders of magnitude in the intestinal pathogen cell numbers. The inhibition was mostly due to lactic and other organic acids. The predominating phylotypes identified were Lactobacillus reuteri and Lactobacillus salivarius, of which the former generally had the best adhesion capacity, whereas the latter appeared to be the best inhibitor. Based on the results, several strains of the pig Lactobacillus isolates tested may function as promising candidates for use in probiotic products. However, it was not possible to use the isolation site or the species identity of the isolates as reliable preliminary screening factors.     

Two-locus genome-wide linkage scan for prostate cancer susceptibility genes with an interaction effect

Prostate cancer represents a significant worldwide public health burden. Epidemiological and genetic epidemiological studies have consistently provided data supporting the existence of inherited prostate cancer susceptibility genes. Segregation analyses of prostate cancer suggest that a multigene model may best explain familial clustering of this disease. Therefore, modeling gene–gene interactions in linkage analysis may improve the power to detect chromosomal regions harboring these disease susceptibility genes. In this study, we systematically screened for prostate cancer linkage by modeling two-locus gene–gene interactions for all possible pairs of loci across the genome in 426 prostate cancer families from Johns Hopkins Hospital, University of Michigan, University of Umeå, and University of Tampere. We found suggestive evidence for an epistatic interaction for six sets of loci (target chromosome-wide/reference marker-specific P≤0.0001). Evidence for these interactions was found in two independent subsets from within the 426 families. While the validity of these results requires confirmation from independent studies and the identification of the specific genes underlying this linkage evidence, our approach of systematically assessing gene–gene interactions across the entire genome represents a promising alternative approach for gene identification for prostate cancer.     

Markers of Tissue-Specific Insulin Resistance Predict the Worsening of Hyperglycemia,Incident Type 2 Diabetes and Cardiovascular Disease

We investigated the ability of surrogate markers of tissue-specific insulin resistance (IR, Matsuda IR, Adipocyte IR, Liver IR) to predict deterioration of hyperglycemia, incident type 2 diabetes and cardiovascular events in the Metabolic Syndrome in Men (METSIM) Study. The METSIM Study includes 10,197 Finnish men, aged 45–73 years, and examined in 2005–2010. A total of 558 of 8,749 non-diabetic participants at baseline were diagnosed with new-onset diabetes and 239 with a new CVD event during a 5.9-year follow-up of this cohort (2010–2013). Compared to fasting plasma insulin level, Matsuda IR (IR in skeletal muscle) and Adipocyte IR were significantly better predictors of 2-hour plasma glucose and glucose area under the curve after adjustment for confounding factors. Liver IR was the strongest predictor of both incident type 2 diabetes (hazard ratio = 1.83, 95% confidence interval: 1.68–1.98) and cardiovascular events (hazard ratio = 1.31, 95% confidence interval: 1.15–1.48). Hazard ratios for fasting insulin were 1.37 (95% confidence interval: 1.32–1.42) and 1.11 (95% confidence interval: 1.00–1.24), respectively. Tissue-specific markers of IR, Matsuda IR and Adipocyte IR, were superior to fasting plasma insulin level in predicting worsening of hyperglycemia, and Liver IR was superior to fasting insulin level in predicting incident type 2 diabetes and cardiovascular events.     

Comparison of solution-based exome capture methods for next generation sequencing

Background

Techniques enabling targeted re-sequencing of the protein coding sequences of the human genome on next generation sequencing instruments are of great interest. We conducted a systematic comparison of the solution-based exome capture kits provided by Agilent and Roche NimbleGen. A control DNA sample was captured with all four capture methods and prepared for Illumina GAII sequencing. Sequence data from additional samples prepared with the same protocols were also used in the comparison.

Results

We developed a bioinformatics pipeline for quality control, short read alignment, variant identification and annotation of the sequence data. In our analysis, a larger percentage of the high quality reads from the NimbleGen captures than from the Agilent captures aligned to the capture target regions. High GC content of the target sequence was associated with poor capture success in all exome enrichment methods. Comparison of mean allele balances for heterozygous variants indicated a tendency to have more reference bases than variant bases in the heterozygous variant positions within the target regions in all methods. There was virtually no difference in the genotype concordance compared to genotypes derived from SNP arrays. A minimum of 11× coverage was required to make a heterozygote genotype call with 99% accuracy when compared to common SNPs on genome-wide association arrays.

Conclusions

Libraries captured with NimbleGen kits aligned more accurately to the target regions. The updated NimbleGen kit most efficiently covered the exome with a minimum coverage of 20×, yet none of the kits captured all the Consensus Coding Sequence annotated exons.     

Expression and characterization of an endo-1,4-β-galactanase from Emericella nidulans in Pichia pastoris for enzymatic design of potentially prebiotic oligosaccharides from potato galactans

Potato pulp is a high-volume side-stream from industrial potato starch manufacturing. Enzymatically solubilized β-1,4-galactan-rich potato pulp polysaccharides of molecular weights >100 kDa (SPPP) are highly bifidogenic in human fecal sample fermentations in vitro. The objective of the present study was to use potato β-1,4-galactan and the SPPP as substrates for enzymatic production of potentially prebiotic compounds of lower and narrower molecular weight. A novel endo-1,4-β-galactanase from Emericella nidulans (anamorph Aspergillus nidulans), GH family 53, was produced in a recombinant Pichia pastoris strain. The enzyme was purified by Cu(2+) affinity chromatography and its optimal reaction conditions were determined to pH 5 and 49°C via a statistical experimental design. The specific activity of the E. nidulans enzyme expressed in P. pastoris was similar to that of an endo-1,4-β-galactanase from Aspergillus niger used as benchmark. The E. nidulans enzyme expressed in P. pastoris generated a spectrum poly- and oligo-saccharides which were fractionated by membrane filtration. The potential growth promoting properties of each fraction were evaluated by growth of beneficial gut microbes and pathogenic bacteria. All the galactan- and SPPP-derived products promoted the growth of probiotic strains of Bifidobacterium longum and Lactobacillus acidophilus and generally did not support the propagation of Clostridium perfringens in single culture fermentations. Notably the growth of B. longum was significantly higher (p<0.05) or at least as good on galactan- and SPPP-derived products as fructooligosaccharides (FOS). Except in one case these products did not support the growth of the pathogen Cl. perfringens to any significant extent.     

Contribution of genetic and dietary insulin resistance to Alzheimer phenotype in APP/PS1 transgenic mice

According to epidemiological studies, type‐2 diabetes increases the risk of Alzheimer’s disease. Here, we induced hyperglycaemia in mice overexpressing mutant amyloid precursor protein and presenilin‐1 (APdE9) either by cross‐breeding them with pancreatic insulin‐like growth factor 2 (IGF‐2) overexpressing mice or by feeding them with high‐fat diet. Glucose and insulin tolerance tests revealed significant hyperglycaemia in mice overexpressing IGF‐2, which was exacerbated by high‐fat diet. However, sustained hyperinsulinaemia and insulin resistance were observed only in mice co‐expressing IGF‐2 and APdE9 without correlation to insulin levels in brain. In behavioural tests in aged mice, APdE9 was associated with poor spatial learning and the combination of IGF‐2 and high‐fat diet further impaired learning. Neither high‐fat diet nor IGF‐2 increased β‐amyloid burden in the brain. In male mice, IGF‐2 increased β‐amyloid 42/40 ratio, which correlated with poor spatial learning. In contrast, inhibitory phosphorylation of glycogen synthase kinase 3β, which correlated with good spatial learning, was increased in APdE9 and IGF‐2 female mice on standard diet, but not on high‐fat diet. Interestingly, high‐fat diet altered τ isoform expression and increased phosphorylation of τ at Ser202 site in female mice regardless of genotype. These findings provide evidence for new regulatory mechanisms that link type‐2 diabetes and Alzheimer pathology.     

Computational Methods for Estimation of Cell Cycle Phase Distributions of Yeast Cells

Two computational methods for estimating the cell cycle phase distribution of a budding yeast (Saccharomyces cerevisiae) cell population are presented. The first one is a nonparametric method that is based on the analysis of DNA content in the individual cells of the population. The DNA content is measured with a fluorescence-activated cell sorter (FACS). The second method is based on budding index analysis. An automated image analysis method is presented for the task of detecting the cells and buds. The proposed methods can be used to obtain quantitative information on the cell cycle phase distribution of a budding yeast S. cerevisiae population. They therefore provide a solid basis for obtaining the complementary information needed in deconvolution of gene expression data. As a case study, both methods are tested with data that were obtained in a time series experiment with S. cerevisiae. The details of the time series experiment as well as the image and FACS data obtained in the experiment can be found in the online additional material at http://www.cs.tut.fi/sgn/csb/yeastdistrib/.     

Identification of novel p53 pathway activating small-molecule compounds reveals unexpected similarities with known therapeutic agents

Manipulation of the activity of the p53 tumor suppressor pathway has demonstrated potential benefit in preclinical mouse tumor models and has entered human clinical trials. We describe here an improved, extensive small-molecule chemical compound library screen for p53 pathway activation in a human cancer cell line devised to identify hits with potent antitumor activity. We uncover six novel small-molecule lead compounds, which activate p53 and repress the growth of human cancer cells. Two tested compounds suppress in vivo tumor growth in an orthotopic mouse model of human B-cell lymphoma. All compounds interact with DNA, and two activate p53 pathway in a DNA damage signaling-dependent manner. A further screen of a drug library of approved drugs for medicinal uses and analysis of gene-expression signatures of the novel compounds revealed similarities to known DNA intercalating and topoisomerase interfering agents and unexpected connectivities to known drugs without previously demonstrated anticancer activities. These included several neuroleptics, glycosides, antihistamines and adrenoreceptor antagonists. This unbiased screen pinpoints interference with the DNA topology as the predominant mean of pharmacological activation of the p53 pathway and identifies potential novel antitumor agents.