HMQC and HSQC experiments with water flip-back optimized for large proteins

An HMQC experiment is proposed, dubbed FHMQC, where water flip-back is achieved by a single water-selective pulse preceding the basic HMQC pulse sequence. The scheme is demonstrated with a ¹⁵N, ¹H-HMQC spectrum of uniformly ¹⁵N/²H-labelled S. aureus DNA gyrase B with a molecular weight of 45 kDa for the unlabelled protein. The sensitivity of the experiment is improved compared to that of an FHSQC spectrum. It is further shown that the original FHSQC experiment can be shortened by the use of bipolar gradients. Relaxation times of different ¹⁵N magnetizations and coherences were measured. The new FHMQC scheme is implemented in 3D NOESY-¹⁵N-HMQC and 3D¹⁵ N-HMQC-NOESY-¹⁵N-HMQC pulse sequences which are demonstrated with a 24 kDa fragment of uniformly ¹⁵N/¹³C/²H-labelled S. aureus DNA gyrase B.     

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.     

GENOTYPE‐BY‐TEMPERATURE INTERACTIONS MAY HELP TO MAINTAIN CLONAL DIVERSITY IN ASTERIONELLA FORMOSA (BACILLARIOPHYCEAE)

Marine and freshwater phytoplankton populations often show large clonal diversity, which is in disagreement with clonal selection of the most vigorous genotype(s). Temporal fluctuation in selection pressures in variable environments is a leading explanation for maintenance of such genetic diversity. To test the influence of temperature as a selection force in continually (seasonally) changing aquatic systems we carried out reaction norms experiments on co‐occurring clonal genotypes of a ubiquitous diatom species, Asterionella formosa Hassall, across an environmentally relevant range of temperatures. We report within population genetic diversity and extensive diversity in genotype‐specific reaction norms in growth rates and cell size traits. Our results showed genotype by environment interactions, indicating that no genotype could outgrow all others across all temperature environments. Subsequently, we constructed a model to simulate the relative proportion of each genotype in a hypothetical population based on genotype and temperature‐specific population growth rates. This model was run with different seasonal temperature patterns. Our modeling exercise showed a succession of two to several genotypes becoming numerically dominant depending on the underlying temperature pattern. The results suggest that (temperature) context dependent fitness may contribute to the maintenance of genetic diversity in isolated populations of clonally reproducing microorganisms in temporally variable environments.     

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.     

Winter severity determines functional trait composition of phytoplankton in seasonally ice‐covered lakes

How climate change will affect the community dynamics and functionality of lake ecosystems during winter is still little understood. This is also true for phytoplankton in seasonally ice‐covered temperate lakes which are particularly vulnerable to the presence or absence of ice. We examined changes in pelagic phytoplankton winter community structure in a north temperate lake (Müggelsee, Germany), covering 18 winters between 1995 and 2013. We tested how phytoplankton taxa composition varied along a winter‐severity gradient and to what extent winter severity shaped the functional trait composition of overwintering phytoplankton communities using multivariate statistical analyses and a functional trait‐based approach. We hypothesized that overwintering phytoplankton communities are dominated by taxa with trait combinations corresponding to the prevailing winter water column conditions, using ice thickness measurements as a winter‐severity indicator. Winter severity had little effect on univariate diversity indicators (taxon richness and evenness), but a strong relationship was found between the phytoplankton community structure and winter severity when taxon trait identity was taken into account. Species responses to winter severity were mediated by the key functional traits: motility, nutritional mode, and the ability to form resting stages. Accordingly, one or the other of two functional groups dominated the phytoplankton biomass during mild winters (i.e., thin or no ice cover; phototrophic taxa) or severe winters (i.e., thick ice cover; exclusively motile taxa). Based on predicted milder winters for temperate regions and a reduction in ice‐cover durations, phytoplankton communities during winter can be expected to comprise taxa that have a relative advantage when the water column is well mixed (i.e., need not be motile) and light is less limiting (i.e., need not be mixotrophic). A potential implication of this result is that winter severity promotes different communities at the vernal equinox, which may have different nutritional quality for the next trophic level and ecosystem‐scale effects.     

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.