Quantitative model for inferring dynamic regulation of the tumour suppressor gene p53

Background: The availability of various "omics" datasets creates a prospect of performing the study of genome-wide genetic regulatory networks. However, one of the major challenges of using mathematical models to infer genetic regulation from microarray datasets is the lack of information for protein concentrations and activities. Most of the previous researches were based on an assumption that the mRNA levels of a gene are consistent with its protein activities, though it is not always the case. Therefore, a more sophisticated modelling framework together with the corresponding inference methods is needed to accurately estimate genetic regulation from "omics" datasets. Results: This work developed a novel approach, which is based on a nonlinear mathematical model, to infer genetic regulation from microarray gene expression data. By using the p53 network as a test system, we used the nonlinear model to estimate the activities of transcription factor (TF) p53 from the expression levels of its target genes, and to identify the activation/inhibition status of p53 to its target genes. The predicted top 317 putative p53 target genes were supported by DNA sequence analysis. A comparison between our prediction and the other published predictions of p53 targets suggests that most of putative p53 targets may share a common depleted or enriched sequence signal on their upstream non-coding region. Conclusions: The proposed quantitative model can not only be used to infer the regulatory relationship between TF and its down-stream genes, but also be applied to estimate the protein activities of TF from the expression levels of its target genes

Impact of supragingival therapy on subgingival microbial profile in smokers versus non-smokers with severe chronic periodontitis

The aim of this study was to assess subgingival microbiological changes in smokers versus non-smokers presenting severe chronic periodontitis after supragingival periodontal therapy (ST).Non-smokers (n=10) and smokers (n=10) presenting at least nine teeth with probing pocket depth (PPD) (≥5 mm), bleeding on probing (BoP), and no history of periodontal treatment in the last 6 months were selected. Clinical parameters assessed were plaque index (PI), BoP, PPD, relative gingival margin position (rGMP) and relative clinical attachment level (rCAL). Subgingival biofilm was collected before and 21 days after ST. DNA was extracted and the 16S rRNA gene was amplified with the universal primer pair, 27F and 1492R. Amplified genes were cloned, sequenced, and identified by comparison with known 16S rRNA sequences. Statistical analysis was performed by Student's t and Chi-Square tests (α=5%).Clinically, ST promoted a significant reduction in PI and PPD, and gain of rCAL for both groups, with no significant intergroup difference. Microbiologically, at baseline, data analysis demonstrated that smokers harbored a higher proportion of Porphyromonas endodontalis, Bacteroidetes sp., Fusobacterium sp. and Tannerella forsythia and a lower number of cultivated phylotypes (p<0.05). Furthermore, non-smokers featured significant reductions in key phylotypes associated with periodontitis, whereas smokers presented more modest changes.Within the limits of the present study, ST promoted comparable clinical improvements in smokers and non-smokers with severe chronic periodontitis. However, in smokers, ST only slightly affected the subgingival biofilm biodiversity, as compared with non-smokers

Independent S-Locus Mutations Caused Self-Fertility in Arabidopsis thaliana

A common yet poorly understood evolutionary transition among flowering plants is a switch from outbreeding to an inbreeding mode of mating. The model plant Arabidopsis thaliana evolved to an inbreeding state through the loss of self-incompatibility, a pollen-rejection system in which pollen recognition by the stigma is determined by tightly linked and co-evolving alleles of the S-locus receptor kinase (SRK) and its S-locus cysteine-rich ligand (SCR). Transformation of A. thaliana, with a functional AlSRKb-SCRb gene pair from its outcrossing relative A. lyrata, demonstrated that A. thaliana accessions harbor different sets of cryptic self-fertility–promoting mutations, not only in S-locus genes, but also in other loci required for self-incompatibility. However, it is still not known how many times and in what manner the switch to self-fertility occurred in the A. thaliana lineage. Here, we report on our identification of four accessions that are reverted to full self-incompatibility by transformation with AlSRKb-SCRb, bringing to five the number of accessions in which self-fertility is due to, and was likely caused by, S-locus inactivation. Analysis of S-haplotype organization reveals that inter-haplotypic recombination events, rearrangements, and deletions have restructured the S locus and its genes in these accessions. We also perform a Quantitative Trait Loci (QTL) analysis to identify modifier loci associated with self-fertility in the Col-0 reference accession, which cannot be reverted to full self-incompatibility. Our results indicate that the transition to inbreeding occurred by at least two, and possibly more, independent S-locus mutations, and identify a novel unstable modifier locus that contributes to self-fertility in Col-0

Author Correction: Long-term carbon sink in Borneo's forests halted by drought and vulnerable to edges

The original version of this Article contained an error in the third sentence of the abstract and incorrectly read "Here, using long-term plot monitoring records of up to half a century, we find that intact forests in Borneo gained 0.43 Mg C ha-1 year-1 (95% CI 0.14-0.72, mean period 1988-2010) above-ground live biomass", rather than the correct "Here, using long-term plot monitoring records of up to half a century, we find that intact forests in Borneo gained 0.43 Mg C ha-1 year-1 (95% CI 0.14-0.72, mean period 1988-2010) in above-ground live biomass carbon". This has now been corrected in both the PDF and HTML versions of the Article

Author Correction: Long-term carbon sink in Borneo's forests halted by drought and vulnerable to edges

The original version of this Article contained an error in the third sentence of the abstract and incorrectly read "Here, using long-term plot monitoring records of up to half a century, we find that intact forests in Borneo gained 0.43 Mg C ha-1 year-1 (95% CI 0.14-0.72, mean period 1988-2010) above-ground live biomass", rather than the correct "Here, using long-term plot monitoring records of up to half a century, we find that intact forests in Borneo gained 0.43 Mg C ha-1 year-1 (95% CI 0.14-0.72, mean period 1988-2010) in above-ground live biomass carbon". This has now been corrected in both the PDF and HTML versions of the Article

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration

Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To elucidate the biology of sleep-associated adverse lipid profile, we conduct multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identify 49 previously unreported lipid loci, and 10 additional previously unreported lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identify new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The previously unreported lipid loci have a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explain 4.25% of the variance in triglyceride level. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles.</p