Histone Variants and Their Post-Translational Modifications in Primary Human Fat Cells

Epigenetic changes related to human disease cannot be fully addressed by studies of cells from cultures or from other mammals. We isolated human fat cells from subcutaneous abdominal fat tissue of female subjects and extracted histones from either purified nuclei or intact cells. Direct acid extraction of whole adipocytes was more efficient, yielding about 100 µg of protein with histone content of 60% –70% from 10 mL of fat cells. Differential proteolysis of the protein extracts by trypsin or ArgC-protease followed by nanoLC/MS/MS with alternating CID/ETD peptide sequencing identified 19 histone variants. Four variants were found at the protein level for the first time; particularly HIST2H4B was identified besides the only H4 isoform earlier known to be expressed in humans. Three of the found H2A potentially organize small nucleosomes in transcriptionally active chromatin, while two H2AFY variants inactivate X chromosome in female cells. HIST1H2BA and three of the identified H1 variants had earlier been described only as oocyte or testis specific histones. H2AFX and H2AFY revealed differential and variable N-terminal processing. Out of 78 histone modifications by acetylation/trimethylation, methylation, dimethylation, phosphorylation and ubiquitination, identified from six subjects, 68 were found for the first time. Only 23 of these modifications were detected in two or more subjects, while all the others were individual specific. The direct acid extraction of adipocytes allows for personal epigenetic analyses of human fat tissue, for profiling of histone modifications related to obesity, diabetes and metabolic syndrome, as well as for selection of individual medical treatments

MEANS: Moving Effective Assonances for Novice Students

Vocabulary acquisition constitutes a crucial but difficult and time consuming step of learning a foreign language. There exist several teaching methods which aim to facilitate this step by providing learners with various verbal and visual tips. However, building systems based on these methods is generally very costly since it requires so many resources such as time, money and human labor. In this paper, we introduce a fully automatized vocabulary teaching system which uses state-of-the-art natural language processing (NLP) and information retrieval (IR) techniques. For each foreign word the user is willing to learn, the system is capable of automatically producing memorization tips including key- words, sentences, colors, textual animations and images