Construction and Analysis of the Model of Energy Metabolism in <em>E. coli</em>

<div><p>Genome-scale models of metabolism have only been analyzed with the constraint-based modelling philosophy and there have been several genome-scale gene-protein-reaction models. But research on the modelling for energy metabolism of organisms just began in recent years and research on metabolic weighted complex network are rare in literature. We have made three research based on the complete model of <em>E. coli</em>’s energy metabolism. We first constructed a metabolic weighted network using the rates of free energy consumption within metabolic reactions as the weights. We then analyzed some structural characters of the metabolic weighted network that we constructed. We found that the distribution of the weight values was uneven, that most of the weight values were zero while reactions with abstract large weight values were rare and that the relationship between <em>w</em> (weight values) and <em>v</em> (flux values) was not of linear correlation. At last, we have done some research on the equilibrium of free energy for the energy metabolism system of <em>E. coli</em>. We found that (free energy rate input from the environment) can meet the demand of (free energy rate dissipated by chemical process) and that chemical process plays a great role in the dissipation of free energy in cells. By these research and to a certain extend, we can understand more about the energy metabolism of <em>E. coli</em>.</p> </div

Equilibrium of free energy.

<p>Equilibrium of free energy.</p

Reaction names (RM) and their corresponding related genes.

<p>Reaction names (RM) and their corresponding related genes.</p

<i>w</i> scopes, number of reactions (NR) and their percentages.

<p><i>w</i> scopes, number of reactions (NR) and their percentages.</p

<i>w</i> scopes, number of reactions (NR) and reaction names (RM).

<p><i>w</i> scopes, number of reactions (NR) and reaction names (RM).</p

Weight value distribution of the metabolic network of <i>E. coli</i>_iAF1260.

<p><i>X</i>-axis indicates every reaction in the reconstructed reactions (the order is as the same as in <b>rxns</b>, total 2077) and <i>y</i>-axis indicates the value of its corresponding weight. <b>rxns</b> is the reaction set in the model.</p

DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues

<div><p>DNA-binding proteins are fundamentally important in cellular processes. Several computational-based methods have been developed to improve the prediction of DNA-binding proteins in previous years. However, insufficient work has been done on the prediction of DNA-binding proteins from protein sequence information. In this paper, a novel predictor, DNABP (DNA-binding proteins), was designed to predict DNA-binding proteins using the random forest (RF) classifier with a hybrid feature. The hybrid feature contains two types of novel sequence features, which reflect information about the conservation of physicochemical properties of the amino acids, and the binding propensity of DNA-binding residues and non-binding propensities of non-binding residues. The comparisons with each feature demonstrated that these two novel features contributed most to the improvement in predictive ability. Furthermore, to improve the prediction performance of the DNABP model, feature selection using the minimum redundancy maximum relevance (mRMR) method combined with incremental feature selection (IFS) was carried out during the model construction. The results showed that the DNABP model could achieve 86.90% accuracy, 83.76% sensitivity, 90.03% specificity and a Matthews correlation coefficient of 0.727. High prediction accuracy and performance comparisons with previous research suggested that DNABP could be a useful approach to identify DNA-binding proteins from sequence information. The DNABP web server system is freely available at <a href="http://www.cbi.seu.edu.cn/DNABP/" target="_blank">http://www.cbi.seu.edu.cn/DNABP/</a>.</p></div

The Patterns of Histone Modifications in the Vicinity of Transcription Factor Binding Sites in Human Lymphoblastoid Cell Lines

<div><p>Transcription factor (TF) binding at specific DNA sequences is the fundamental step in transcriptional regulation and is highly dependent on the chromatin structure context, which may be affected by specific histone modifications and variants, known as histone marks. The lack of a global binding map for hundreds of TFs means that previous studies have focused mainly on histone marks at binding sites for several specific TFs. We therefore studied 11 histone marks around computationally-inferred and experimentally-determined TF binding sites (TFBSs), based on 164 and 34 TFs, respectively, in human lymphoblastoid cell lines. For H2A.Z, methylation of H3K4, and acetylation of H3K27 and H3K9, the mark patterns exhibited bimodal distributions and strong pairwise correlations in the 600-bp region around enriched TFBSs, suggesting that these marks mainly coexist within the two nucleosomes proximal to the TF sites. TFs competing with nucleosomes to access DNA at most binding sites, contributes to the bimodal distribution, which is a common feature of histone marks for TF binding. Mark H3K79me2 showed a unimodal distribution on one side of TFBSs and the signals extended up to 4000 bp, indicating a longer-distance pattern. Interestingly, H4K20me1, H3K27me3, H3K36me3 and H3K9me3, which were more diffuse and less enriched surrounding TFBSs, showed unimodal distributions around the enriched TFBSs, suggesting that some TFs may bind to nucleosomal DNA. Besides, asymmetrical distributions of H3K36me3 and H3K9me3 indicated that repressors might establish a repressive chromatin structure in one direction to repress gene expression. In conclusion, this study demonstrated the ranges of histone marks associated with TF binding, and the common features of these marks around the binding sites. These findings have epigenetic implications for future analysis of regulatory elements.</p> </div

sj-docx-1-onc-10.1177_11795549221123620 – Supplemental material for Prognostic Values From Integrated Analysis of the Nomogram Based on RNA-Binding Proteins and Clinical Factors in Endometrial Cancer

Supplemental material, sj-docx-1-onc-10.1177_11795549221123620 for Prognostic Values From Integrated Analysis of the Nomogram Based on RNA-Binding Proteins and Clinical Factors in Endometrial Cancer by Shuang Yuan, Xiao Sun and Lihua Wang in Clinical Medicine Insights: Oncology</p

Proportion of CENTIPEDE binding sites enriched with histone marks in different windows.

<p>Histone marks showed different tendencies with increasing window size, indicating that the marks were associated with TF binding within different regions around binding sites. Enrichment was determined by comparing the number of tags mapping in the window region surrounding the sites with the threshold set for the mark. Sites with more tags than the threshold were considered as enriched sites.</p