ATGL and HSL are not coordinately regulated in response to fuel partitioning in fasted rats

Prolonged fasting is characterized by lipid mobilization (Phase 2), followed by protein breakdown (Phase 3). Knowing that body lipids are not exhausted in Phase 3, we investigated whether changes in the metabolic status of prolonged fasted rats are associated with differences in the expression of epididymal adipose tissue proteins involved in lipid mobilization. The final body mass, body lipid content, locomotor activity and metabolite and hormone plasma levels differed between groups. Compared with fed rats, adiposity and epididymal fat mass decreased in Phase 2 (approximately two- to threefold) and Phase 3 (∼4.5-14-fold). Plasma nonesterified fatty acids (NEFA) concentrations were increased in Phase 2 (approximately twofold) and decreased in Phase 3 (approximately twofold). Daily locomotor activity was markedly increased in Phase 3 (∼11-fold). Compared with the fed state, expressions of adipose triglyceride lipase (ATGL; mRNA and protein), hormone-sensitive lipase (HSL; mRNA) and phosphorylated HSL at residue Ser660 (HSL Ser⁶⁶⁰) were increased during Phase 2 (∼1.5-2-fold). HSL (mRNA and protein) and HSL Ser⁶⁶⁰ levels were lowered during Phase 3 (∼3-12-fold). Unlike HSL and HSL Ser⁶⁶⁰, ATGL expression did not correlate with circulating NEFA, mostly due to data from animals in Phase 3. At this stage, ATGL could play an essential role for maintaining a low mobilization rate of NEFA, possibly to sustain muscle performance and hence increased locomotor activity. We conclude that ATGL and HSL are not coordinately regulated in response to changes in fuel partitioning during prolonged food deprivation, ATGL appearing as the major lipase in late fasting.     

Up-regulation of the novel proinflammatory adipokines lipocalin-2, chitinase-3 like-1 and osteopontin as well as angiogenic-related factors in visceral adipose tissue of patients with colon cancer

Background

Obesity is widely recognised as an important risk factor for colorectal cancer (CC).

Aim

The study aimed to evaluate the effect of CC on circulating concentrations and gene expression levels of inflammatory and angiogenesis-related factors in human visceral adipose tissue (VAT).

Methods

VAT biopsies were obtained from 18 healthy individuals and 11 patients with CC. Real-time polymerase chain reactions were performed to quantify gene expression levels and zymographic analyses were used to determine the activity of matrix metalloproteinases (MMPs).

Results

Patients with CC exhibited increased mRNA expression levels of lipocalin-2 (P=.014), osteopontin (P=.027), tumor necrosis factor-α (TNF-α) (P=.016) and chitinase-3 like-1 (P=.006) compared to control subjects in VAT. Gene expression levels of hypoxia-inducible factor-1 α, vascular endothelial growth factor and MMP-2 were significantly higher (P<.05) in VAT of patients with CC. The expression of insulin-like growth factor I, insulin growth factor binding protein 3 and MMP-9 followed the same trend, although no significant differences were reached. The enzymatic activity of MMP-9 was increased (P<.001) in patients with CC. Furthermore, individuals with CC showed increased (P<.05) circulating concentrations of the inflammatory markers interleukin-6, tumour necrosis factor α and hepatocyte growth factor, whereas levels of the anti-inflammatory adipokine adiponectin were decreased (P<.01).

Conclusion

These findings represent the first observation that mRNA levels of the novel inflammatory factors lipocalin-2, chitinase-3 like-1 and osteopontin are increased in human VAT of subjects with CC. This observation together with the up-regulation of angiogenic factors suggests that adipokines secreted by VAT may be involved in the development of colon cancer.     

High efficient expression of cellobiase gene from Aspergillus niger in the cells of Trichoderma reesei

The cellobiase gene from Aspergillus niger was cloned and connected with the strong promoter Pcbh1 from Trichoderma reesei to construct a recombinant plasmid pHB9 with the hygromycin B resistance marker. The plasmid was transformed into conidia of T. reesei using the modified PEG-CaCl₂ method. Main factors effecting the transformation were discussed and about 99-113 transformants/μg DNA could be obtained under optimal conditions. It was found that the molecular mass of the recombinant cellobiase was about 120 kDa by SDS-PAGE analysis. The activity of cellobiase could reach 5.3 IU/ml after 48 h fermentation, which was as high as 106 times compared with that of the host strain. Meanwhile, the filter paper activity of recombinant T. reesei was 1.44-fold of the host strain. Saccharification of corncob residue with the crude enzyme showed that the hydrolysis yield (84.2%) of recombinant T. reesei was 21% higher than that (69.5%) of the host strain.     

phiGENOME: an integrative navigation throughout bacteriophage genomes

phiGENOME is a web-based genome browser generating dynamic and interactive graphical representation of phage genomes stored in the phiSITE, database of gene regulation in bacteriophages. phiGENOME is an integral part of the phiSITE web portal (http://www.phisite.org/phigenome) and it was optimised for visualisation of phage genomes with the emphasis on the gene regulatory elements. phiGENOME consists of three components: (i) genome map viewer built using Adobe Flash technology, providing dynamic and interactive graphical display of phage genomes; (ii) sequence browser based on precisely formatted HTML tags, providing detailed exploration of genome features on the sequence level and (iii) regulation illustrator, based on Scalable Vector Graphics (SVG) and designed for graphical representation of gene regulations. Bringing 542 complete genome sequences accompanied with their rich annotations and references, makes phiGENOME a unique information resource in the field of phage genomics.     

Polymorphism in exon 3 of follicle stimulating hormone beta (FSHB) subunit gene and its association with litter traits and superovulation in the goat

The polymorphism in follicle stimulating hormone beta (FSHB) subunit gene was detected by PCR-SSCP and PCR-RFLP methods in 780 does from four goat breeds including Boer, Matou, Black and Boer-Matou crossbred (BM). The associations of FSHB genotypes with litter size (LS), litter weight at birth (LWB) and gestation length (GL) as well as superovulation performances were analyzed in relatively lower-prolific Boer and higher-prolific Matou breeds. A 1514 bp linear DNA sequence of goat FSHB gene covering the complete 3 exons was cloned by four pairs of primer. A new mutation (A2645G, GenBank Accession no: S64745) locating in exon 3 causing an amino acid change from glutamine (Gln) to arginine (Arg) at residue 115 was identified, which resulted in three genotypes named AA, AB and BB. The three genotypes were detected in the four studied goat breeds. The higher-prolific Matou breed had the highest frequency of genotype AA. Association analysis showed that Boer and Matou does with AA genotype have the largest LS both in average and in parities from first to fourth (P < 0.05). Furthermore, Boer does with AA genotype had the heaviest LWB compared to other two genotypes (P < 0.05). Matou does with AA genotype have more numbers of ova harvested, large follicles and corpus lutea on ovaries after superovulation than those with BB genotype (P < 0.05). Therefore, these results suggest that the FSHB gene is a candidate gene that affects reproduction traits in goats.     

A new disease-specific machine learning approach for the prediction of cancer-causing missense variants

High-throughput genotyping and sequencing techniques are rapidly and inexpensively providing large amounts of human genetic variation data. Single Nucleotide Polymorphisms (SNPs) are an important source of human genome variability and have been implicated in several human diseases, including cancer. Amino acid mutations resulting from non-synonymous SNPs in coding regions may generate protein functional changes that affect cell proliferation. In this study, we developed a machine learning approach to predict cancer-causing missense variants. We present a Support Vector Machine (SVM) classifier trained on a set of 3163 cancer-causing variants and an equal number of neutral polymorphisms. The method achieve 93% overall accuracy, a correlation coefficient of 0.86, and area under ROC curve of 0.98. When compared with other previously developed algorithms such as SIFT and CHASM our method results in higher prediction accuracy and correlation coefficient in identifying cancer-causing variants.