Developing antibiotics-based strategies to efficiently enrich ammonia-oxidizing archaea from wastewater treatment plants.

The effects of five antibiotics (i.e., ampicillin, streptomycin, carbenicillin, kanamycin and tetracycline) on ammonia-oxidizing archaea (AOA) enrichment from anoxic activated sludge were investigated. The combined use of five antibiotics during 90-day cultivation could selectively inhibit nitrite-oxidizing bacteria (NOB) and ammonia-oxidizing bacteria (AOB) with AOA unaffected, as evidenced by the nitrite accumulation ratio of 100 % and the proportion of AOA in ammonia-oxidizing microbes over 91 %. The alternative use of five antibiotics was the optimal approach to screening for AOA during 348-day cultivation, which inhibited AOB growth at a level equivalent to the combined use of five antibiotics (the AOB-amoA gene decreased by over 99.90 %), further promoted AOA abundance (the much higher AOA-amoA to AOB-amoA gene copy number ratio (1453.30) than that in the groups with the combined use of five antibiotics (192.94)), eliminated bacterial adaptation to antibiotics and reduced antibiotic-resistant bacteria to form Nitrocosmicus-dominant community (42.35 % in abundance).

Identification of co-expression modules and potential biomarkers of breast cancer by WGCNA.

Breast cancer is a very serious disease that threatens human health. The identification of co-expression modules is conducive to revealing the interaction mechanism between genes. The potential biomarkers identified from the co-expression modules have profound implications for the diagnosis and treatment of breast cancer. According to the clinical staging information, the gene expression data of breast cancer was divided into different stages and analyzed separately. The co-expression modules for each stage were identified by WGCNA. The pathways involved in the co-expression modules of each stage were revealed by KEGG enrichment analysis. Combined with clinical information, 81 core genes were screened from the co-expression modules of each stage. By constructing a support vector machine, it was confirmed that these core genes can effectively distinguish breast cancer samples. The biological functions involved in these core genes are revealed by GO enrichment analysis. Survival analysis showed that the expression of 11 genes had significant effects on the survival of breast cancer patients. These results may provide a reference for the mechanism study of breast cancer.

Divalent cations modulate TMEM16A calcium-activated chloride channels by a common mechanism.

The gating of Ca²âº-activated Cl⁻ channels is controlled by a complex interplay among [Ca²âº](i), membrane potential and permeant anions. Besides Ca²âº, Ba²âº also can activate both TMEM16A and TMEM16B. This study reports the effects of several divalent cations as regulators of TMEM16A channels stably expressed in HEK293T cells. Among the divalent cations that activate TMEM16A, Ca²âº is most effective, followed by Sr²âº and Ni²âº, which have similar affinity, while Mg²âº is ineffective. Zn²âº does not activate TMEM16A but inhibits the Ca²âº-activated chloride currents. Maximally effective concentrations of Sr²âº and Ni²âº occluded activation of the TMEM16A current by Ca²âº, which suggests that Ca²âº, Sr²âº and Ni²âº all regulate the channel by the same mechanism.

Combining fragment homology modeling with molecular dynamics aims at prediction of Ca²âº binding sites in CaBPs.

The family of calcium-binding proteins (CaBPs) consists of dozens of members and contributes to all aspects of the cell's function, from homeostasis to learning and memory. However, the Ca²âº-binding mechanism is still unclear for most of CaBPs. To identify the Ca²âº-binding sites of CaBPs, this study presented a computational approach which combined the fragment homology modeling with molecular dynamics simulation. For validation, we performed a two-step strategy as follows: first, the approach is used to identify the Ca²âº-binding sites of CaBPs, which have the EF-hand Ca²âº-binding site and the detailed binding mechanism. To accomplish this, eighteen crystal structures of CaBPs with 49 Ca²âº-binding sites are selected to be analyzed including calmodulin. The computational method identified 43 from 49 Ca²âº-binding sites. Second, we performed the approach to large-conductance Ca²âº-activated K⁺ (BK) channels which don't have clear Ca²âº-binding mechanism. The simulated results are consistent with the experimental data. The computational approach may shed some light on the identification of Ca²âº-binding sites in CaBPs.

The relation between mRNA folding and protein structure.

About 200 mRNA sequences of Escherichia coli and human with matching protein secondary structure data were studied. The mRNA folding for each native sequence and for corresponding randomized sequences was calculated through free energy minimization. We have found that the folding energy of mRNA segments in different protein secondary structures is significantly different. The average Z score is more negative for regular secondary structure (alpha-helix and beta-strand) than that for coil. This suggests that the codon choice in native mRNA sequence coding for protein regular structure contributes more to the mRNA folding stability.

The relationship among gene expression, folding free energy and codon usage bias in Escherichia coli.

Taking advantage of microarray data in Escherichia coli genome, the relationship among mRNA expression levels, folding free energy and codon usage bias are investigated. Our results indicate that mRNA expression is correlated to the stability of mRNA secondary structure and the codon usage bias. The decrease of the stability of mRNA structure contributes to the increase of mRNA expression. There is a negative correlation between codon adaptation index (CAI) and mRNA expression in genes with less stable structure. The relationship between the stability of mRNA structure and mRNA half-life indicates the stability of mRNA structure is different from mRNA half-life.

Protein structure preference, tRNA copy number, and mRNA stem/loop content.

From statistical analyses of protein sequences for humans and Escherichia coli we found that the messenger RNA segment of m-codons (for m=2 to 6) with average high tRNA copy number (TCN) (larger than approximately 10.5 for humans or approximately 1.95 for E. coli) preferably code for the alpha helix and that with low TCN (smaller than approximately 7.5 for humans or approximately 1.7 for E. coli) preferably code for coil. Between them there is an intermediate region without correlation to structure preference. For the beta strand the preference/ avoidance tendency is not obvious. All strong preference-modes of TCN for protein secondary structures have been deduced. The mutual interaction between two factors--protein secondary structural type and codon TCN--is tested by F distribution. A phenomenological model on the relation between structure preference and translational efficiency or accuracy is proposed. It is pointed out that the structure preference of codons is related to the distribution of mRNA stem/loop content in three TCN regions.

Statistical correlation between protein secondary structure and messenger RNA stem-loop structure.

A new integrated sequence-structure database, called IADE (Integrated ASTRAL-DSSP-EMBL), incorporating matching mRNA sequence, amino acid sequence, and protein secondary structural data, is constructed. It includes 648 protein domains. Based on the IADE database, we studied the relation between RNA stem-loop frequencies and protein secondary structure. It was found that the alpha-helices and beta-strands on proteins tend to be preferably "coded" by mRNA stem region, while the coils on proteins tend to be preferably "coded" by mRNA loop region. These tendencies are more obvious if we observe the structural words (SWs). An SW is defined by a four-amino-acid-fragment that shows the pronounced secondary structural (alpha-helix or beta-strand) propensity. It is demonstrated that the deduced correlation between protein and mRNA structure can hardly be explained as the stochastic fluctuation effect.