br Bioinformatic analyses Standard bioinformatics analyses a

Bioinformatic analyses Standard bioinformatics analyses about this work were reported by Yang et al. [1]. We calculated gene expression values by the read/fragments per kilobase of UM171 per million fragments mapped (RPKM/FPKM) and the False Discovery Rate (FDR). Genes with an FPKM ratio ≥2 and FDR ≤0.05 were considered to be significantly expressed with different treatments. More strict conditions with FDR ≤0.001 and log2 ratio ≥2 to screen more significantly expressed genes. Altogether, 76 genes were identified to be significantly co-expressed in the different treatments using VennDiagram [2]. All these genes were listed in Supplementary Table 1. Compared between CornOf2 and CornJA1, 47 genes were up-regulated and 2 genes were down-regulated (Fig. 1A); compared between CornOf2 and CornJAOf2, 63 genes were up-regulated and 3 genes were down-regulated (Fig. 1B). New isoforms were predicted with Cufflinks (http://cufflinks.cbcb.umd.edu/) [3]. The new transcripts sequences were listed in Supplementary File 1 (fasta format), and the annotation results were in Supplementary File 2 (GTF format). The scatter- and volcano-plots of differently expressed genes are as follows: CornOf2 vs CornJA1 (Fig. 1A) and CornOf2 vs CornJAOf2 (Fig. 1B). The left figure is the scatter-plot of differently expressed genes. The abscissa and ordinate denote the gene/transcript expression level (FPKM value) in one of two treatments. Each point represents a certain gene/transcript. The right figure is the volcano-plot of different genes. The abscissa denotes the fold changes of genes or transcripts between two treatments; the ordinate denotes the p-value.
Acknowledgment Professor Zhigang Zhang at the National Engineering Research Center (Hubei, China) provided technical guidance. The work was partly supported by the Special Fund for Agro-scientific Research in the Public Interest of the People׳s Republic of China (Grant no. 201403075) and the Major Science and Technology Program of Hainan Province (ZDZX2013010).
Data After immunization of two rabbits (A and B) with the peptide antigen, the antiserum harvested from the third bleed of rabbit B that was collected after the seventh booster, was found to be the most reactive compared to pre-immune serum (Fig. 1). Western blots of total protein prepared from human cell lines with this antiserum resulted in the detection of ~52kDa band of GPR30 along with other non-specific proteins, which were also detected by pre-immune serum or secondary antibody alone (Figs. 2 and 3). The affinity purified antibody obtained from the antiserum (third bleed) of rabbit B showed similar reactivity to that of the antiserum (Fig. 4). It produced clean western blotting results, in which, only one ~52kDa band of GPR30 was detected (Fig. 5).
Experimental design, materials and methods
Acknowledgements Funding from Department of Biotechnology, Govt. of India (Sanction order No. BT/506/NE/TBP/2013) is acknowledged. The authors thank Dr. Manish Kumar for sharing his laboratory facilities and Ms. Karukriti Kaushik Ghosh for technical help.
Data Here, we show sub-cellular localizations (Table 1 in supplementary data) and biological processes (Table 2 in supplementary data) GO Terms in which the flor yeast stress related-proteins detected in stressed biofilm formation condition (BFC) and non-biofilm formation condition (NBFC) were sorted. Each type of biofilm formation stresses (lack of fermentable carbon source, ethanol, acetaldehyde and oxidative) were considered separately. Comparison with the Saccharomyces cerevisiae proteome frequency, p-value and the “GO Term frequency BFC/GO Term frequency NBFC” ratio highlighted most relevant cellular components and biological processes in each condition.
Experimental design, materials and methods The effects of two different biofilm formation conditions (BFC and NBFC) on S. cerevisiae G1 flor yeast stress response related-protein expression patterns have been analyzed by using an offgel-based approach. Culture conditions were performed as described in the Process Biochemistry journal paper [1]. Briefly, after growing until the yeast viability reached 90% at the exponential phase, under the two different conditions: BFC with ethanol and glycerol and NBFC with glucose as the main carbon sources; yeasts were collected and proteins extracted. In both conditions, for triplicates, three aliquots for proteomic analysis were carried out. OFFGEL fractionation, LTQ Orbitrap XL mass spectrometer identification, emPAI quantification [2] and SGD filtration were used to obtaining the stress response proteins in each condition. Bioinformatic tool Gene Ontology Slim Mapper from SGD (http://www.yeastgenome.org/), were applied in order to clarify the sub-cellular localization and biological processes of the identified proteins.