Metabolism of Oat Leaves during Senescence: V. Senescence in Light

A comparison has been made of the progress of senescence in the first leaf of 7-day-old oat plants (Avena sativa cv. Victory) in darkness and in white light. Light delays the senescence, and intensities not over 100 to 200 ft-c (1000-2000 lux) suffice for the maximum effect. In such intensities, chlorophyll loss and amino acid liberation still go on in detached leaves at one-third to one-half the rate observed in darkness; however, when the leaves are attached to the plant, the loss of chlorophyll in 5 days is barely detectable. Transfer of the leaves from 1 or 2 days in the low intensity light to darkness, or vice versa, shows no carryover of the effects of the preceding exposure, so that such treatment affords no evidence for the photoproduction of a stable substance, such as cytokinin, inhibiting senescence. Light causes a large increase in invertaselabile sugar and a smaller increase in glucose, and application of 100 to 300 mm glucose or sucrose in the dark maintains the chlorophyll, at least partially. Correspondingly, short exposure to high light intensity, which increased the sugar content, had a moderate effect in maintaining the chlorophyll. However, 3-(3,4-dichlorphenyl)-1,1-dimethylurea (DCMU) completely prevents the increases in sugars and yet does not prevent the effect of light on senescence, whether determined by chlorophyll loss or by protein hydrolysis. Light causes a 300% increase in the respiration of detached oat leaves, and kinetin lowers that only partly, but unlike the increased respiration associated with senescence in the dark, the increase in the light is fully sensitive to dinitrophenol, and therefore cannot be ascribed to respiratory uncoupling. The increased respiration in light is prevented by DCMU, parallel with the prevention of sugar formation. It is therefore ascribed to the accumulation of soluble sugars, acting as respirable substrate. Also, l-serine does not antagonize the light effect. For all of these reasons, it is concluded that the action of light is not mediated by photosynthetic sugar formation, nor by photoproduction of a cytokinin. Instead, we propose that light exerts its effect by photoproduction of ATP. The action of sugars is ascribed to the same mechanism but by way of respiratory ATP. This hypothesis unifies most of the observed phenomena of the senescence process in oat leaves, and helps to explain some of the divergent findings of earlier workers.     

Acid-sensing ion channel 3 mediates peripheral anti-hyperalgesia effects of acupuncture in mice inflammatory pain

Background  

Peripheral tissue inflammation initiates hyperalgesia accompanied by tissue acidosis, nociceptor activation, and inflammation mediators. Recent studies have suggested a significantly increased expression of acid-sensing ion channel 3 (ASIC3) in both carrageenan- and complete Freund's adjuvant (CFA)-induced inflammation. This study tested the hypothesis that acupuncture is curative for mechanical hyperalgesia induced by peripheral inflammation.     

Protein structure similarity from principle component correlation analysis

Background  

Owing to rapid expansion of protein structure databases in recent years, methods of structure comparison are becoming increasingly effective and important in revealing novel information on functional properties of proteins and their roles in the grand scheme of evolutionary biology. Currently, the structural similarity between two proteins is measured by the root-mean-square-deviation (RMSD) in their best-superimposed atomic coordinates. RMSD is the golden rule of measuring structural similarity when the structures are nearly identical; it, however, fails to detect the higher order topological similarities in proteins evolved into different shapes. We propose new algorithms for extracting geometrical invariants of proteins that can be effectively used to identify homologous protein structures or topologies in order to quantify both close and remote structural similarities.     

Conditional random pattern model for copy number aberration detection

Background  

DNA copy number aberration (CNA) is very important in the pathogenesis of tumors and other diseases. For example, CNAs may result in suppression of anti-oncogenes and activation of oncogenes, which would cause certain types of cancers. High density single nucleotide polymorphism (SNP) array data is widely used for the CNA detection. However, it is nontrivial to detect the CNA automatically because the signals obtained from high density SNP arrays often have low signal-to-noise ratio (SNR), which might be caused by whole genome amplification, mixtures of normal and tumor cells, experimental noise or other technical limitations. With the reduction in SNR, many false CNA regions are often detected and the true CNA regions are missed. Thus, more sophisticated statistical models are needed to make the CNAs detection, using the low SNR signals, more robust and reliable.     

Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces

Background

Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data.

Results

Using the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG.

Conclusions

We report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.

     

Comparative genome-wide DNA methylation analysis of colorectal tumor and matched normal tissues

Aberrant DNA methylation often occurs in colorectal cancer (CRC). In our study we applied a genome-wide DNA methylation analysis approach, MethylCap-seq, to map the differentially methylated regions (DMRs) in 24 tumors and matched normal colon samples. In total, 2687 frequently hypermethylated and 468 frequently hypomethylated regions were identified, which include potential biomarkers for CRC diagnosis. Hypermethylation in the tumor samples was enriched at CpG islands and gene promoters, while hypomethylation was distributed throughout the genome. Using epigenetic data from human embryonic stem cells, we show that frequently hypermethylated regions coincide with bivalent loci in human embryonic stem cells. DNA methylation is commonly thought to lead to gene silencing; however, integration of publically available gene expression data indicates that 75% of the frequently hypermethylated genes were most likely already lowly or not expressed in normal tissue. Collectively, our study provides genome-wide DNA methylation maps of CRC, comprehensive lists of DMRs, and gives insights into the role of aberrant DNA methylation in CRC formation.