Fatty acid profile and sensory properties of lamb meat from males of five indigenous breeds

The objective of this study was to determine meat quality characteristics, fatty acid profiles, and sensory characteristics of 50 single-birth male lambs from five breeds: Artli (n = 10), Cepni (n = 10), Hemsin (n = 10), Karayaka (n = 10), and Of (n = 10). At the beginning of the experiment, the average age and weight of the lambs were 120 +/- 5 d and 30.7 +/- 0.68 kg respectively. After 60 d of intensive fattening, the average live weight before slaughter was 40.96 kg +/- 0.76 kg. All evaluations were performed on samples from the longissimus thoracis et lumborum (LTL) muscle. There was no difference between breeds in terms of the pH values of the hot carcasses, whereas the cold carcass pH values were higher (P<0.001) in Hemsin animals than in the other breeds. Meat chemical properties (such as organic matter; dry matter; and fat, measured as the ether extract), physical properties (such as cooking loss; drip loss; and water-holding capacity, WHC), and instrumental values (such as colour, L* and b* values, chewiness, hardness, and resilience) were significantly affected by breed differences. Additionally, the differences between breeds were found to be significant in terms of the fatty acid composition and the evaluation of organoleptic properties, such as sensory characteristics, flavour, and juiciness of cooked (boiled or roasted) meat. The results show that lamb meat's physical, chemical, and sensory properties vary by breed. The differences found in the composition and presence of meat fatty acids between and within breeds can be used as a source of variation for future genetic improvement strategies

Micro-Environment Causes Reversible Changes in DNA Methylation and mRNA Expression Profiles in Patient-Derived Glioma Stem Cells

In vitro and in vivo models are widely used in cancer research. Characterizing the similarities and differences between a patient\u27s tumor and corresponding in vitro and in vivo models is important for understanding the potential clinical relevance of experimental data generated with these models. Towards this aim, we analyzed the genomic aberrations, DNA methylation and transcriptome profiles of five parental tumors and their matched in vitro isolated glioma stem cell (GSC) lines and xenografts generated from these same GSCs using high-resolution platforms. We observed that the methylation and transcriptome profiles of in vitro GSCs were significantly different from their corresponding xenografts, which were actually more similar to their original parental tumors. This points to the potentially critical role of the brain microenvironment in influencing methylation and transcriptional patterns of GSCs. Consistent with this possibility, ex vivo cultured GSCs isolated from xenografts showed a tendency to return to their initial in vitro states even after a short time in culture, supporting a rapid dynamic adaptation to the in vitro microenvironment. These results show that methylation and transcriptome profiles are highly dependent on the microenvironment and growth in orthotopic sites partially reverse the changes caused by in vitro culturing

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

G-cimp status prediction of glioblastoma samples using mRNA expression data.

Glioblastoma Multiforme (GBM) is a tumor with high mortality and no known cure. The dramatic molecular and clinical heterogeneity seen in this tumor has led to attempts to define genetically similar subgroups of GBM with the hope of developing tumor specific therapies targeted to the unique biology within each of these subgroups. Recently, a subset of relatively favorable prognosis GBMs has been identified. These glioma CpG island methylator phenotype, or G-CIMP tumors, have distinct genomic copy number aberrations, DNA methylation patterns, and (mRNA) expression profiles compared to other GBMs. While the standard method for identifying G-CIMP tumors is based on genome-wide DNA methylation data, such data is often not available compared to the more widely available gene expression data. In this study, we have developed and evaluated a method to predict the G-CIMP status of GBM samples based solely on gene expression data

PCA plot of GBM samples with methylation data.

<p>Red: G-CIMP negative, Blue: G-CIMP positive. Methylation sites with std. deviation >0.2 are selected to generate this graph.</p

Number of GBM samples used in this study (downloaded from the TCGA repository on June 29, 2011, Sample IDs are in Table S1).

1<p>Level 2 refers to probeset-level data and level 3 refers to gene-level data for expression and methylation data sets. Level 3 refers to segmented data for copy number and SNP data sets. There is no level number for whole exome sequence data set as we just used the mutations derived from this data set.</p>2<p>Old and Young refer to samples ≥70 and ≤40 years old, respectively.</p

Hierarchical clustering of GBM samples in the REMBRANDT and TCGA data sets.

<p>(A) Clustering of old and young REMBRANDT GBM samples based on the expression profiles of age-specific genes derived from both TCGA Affymetrix U133A and Agilent G4502A data sets. (B) Clustering of old and young REMBRANDT GBM samples based on the expression profiles of all genes in the REMBRANDT data set. (C) Clustering of the old and young TCGA GBM samples based on the expression profiles of age-specific genes derived from both TCGA Affymetrix U133A and Agilent G4502A data sets.</p

Number of differentially expressed genes between Old and Young GBM samples for three transcriptomic platforms.

<p>The last row shows the number of differentially expressed genes found in all three platforms.</p>1<p>In each test, FDR≤0.05 threshold is applied.</p>2<p>Shows the number of differentially expressed genes found in all three platforms.</p

Genome-wide copy number alteration profiles of old and young GBM samples.

<p>Data are from (a) Agilent Human Genome CGH Microarray 244A (Memorial Sloan-Kettering Cancer Center), (b) Affymetrix Genome-Wide Human SNP Array 6.0 (Broad Institute of MIT and Harvard), (c) Illumina 550 K Infinium HumanHap550 SNP Chip (HudsonAlpha Institute for Biotechnology) platforms (chr 1–23). Green bars represent amplification and red bars represent deletion. The height of each bar represents the frequency of the alteration in the group. The differentially amplified genes are in chromosome 7 and differentially deleted genes are in chromosome 10.</p

Source of variation of expression profiles of all genes in the REMBRANDT data set.

<p>The x-axis shows the components of the 3-way ANOVA model and the y-axis shows the median signal to noise ratio. The ANOVA model is built based on the expression profiles of all genes in REMBRANDT data set.</p