On entropy and information in gene interaction networks

Decreasing Intracellular Entropy by Increasing Mitochondrial Efficiency and Reducing ROS Formation-The Effect on the Ageing Process and Age-Related Damage

A hypothesis is presented to explain how the ageing process might be influenced by optimizing mitochondrial efficiency to reduce intracellular entropy. Research-based quantifications of entropy are scarce. Non-equilibrium metabolic reactions and compartmentalization were found to contribute most to lowering entropy in the cells. Like the cells, mitochondria are thermodynamically open systems exchanging matter and energy with their surroundings-the rest of the cell. Based on the calculations from cancer cells, glycolysis was reported to produce less entropy than mitochondrial oxidative phosphorylation. However, these estimations depended on the CO2 concentration so that at slightly increased CO2, it was oxidative phosphorylation that produced less entropy. Also, the thermodynamic efficiency of mitochondrial respiratory complexes varies depending on the respiratory state and oxidant/antioxidant balance. Therefore, in spite of long-standing theoretical and practical efforts, more measurements, also in isolated mitochondria, with intact and suboptimal respiration, are needed to resolve the issue. Entropy increases in ageing while mitochondrial efficiency of energy conversion, quality control, and turnover mechanisms deteriorate. Optimally functioning mitochondria are necessary to meet energy demands for cellular defence and repair processes to attenuate ageing. The intuitive approach of simply supplying more metabolic fuels (more nutrients) often has the opposite effect, namely a decrease in energy production in the case of nutrient overload. Excessive nutrient intake and obesity accelerate ageing, while calorie restriction without malnutrition can prolong life. Balanced nutrient intake adapted to needs/activity-based high ATP requirement increases mitochondrial respiratory efficiency and leads to multiple alterations in gene expression and metabolic adaptations. Therefore, rather than overfeeding, it is necessary to fine-tune energy production by optimizing mitochondrial function and reducing oxidative stress; the evidence is discussed in this paper.

Comparative analysis of tissue-specific genes in maize based on machine learning models: CNN performs technically best, LightGBM performs biologically soundest

Introduction: With the advancement of RNA-seq technology and machine learning, training large-scale RNA-seq data from databases with machine learning models can generally identify genes with important regulatory roles that were previously missed by standard linear analytic methodologies. Finding tissue-specific genes could improve our comprehension of the relationship between tissues and genes. However, few machine learning models for transcriptome data have been deployed and compared to identify tissue-specific genes, particularly for plants. Methods: In this study, an expression matrix was processed with linear models (Limma), machine learning models (LightGBM), and deep learning models (CNN) with information gain and the SHAP strategy based on 1,548 maize multi-tissue RNA-seq data obtained from a public database to identify tissue-specific genes. In terms of validation, V-measure values were computed based on k-means clustering of the gene sets to evaluate their technical complementarity. Furthermore, GO analysis and literature retrieval were used to validate the functions and research status of these genes. Results: Based on clustering validation, the convolutional neural network outperformed others with higher V-measure values as 0.647, indicating that its gene set could cover as many specific properties of various tissues as possible, whereas LightGBM discovered key transcription factors. The combination of three gene sets produced 78 core tissue-specific genes that had previously been shown in the literature to be biologically significant. Discussion: Different tissue-specific gene sets were identified due to the distinct interpretation strategy for machine learning models and researchers may use multiple methodologies and strategies for tissue-specific gene sets based on their goals, types of data, and computational resources. This study provided comparative insight for large-scale data mining of transcriptome datasets, shedding light on resolving high dimensions and bias difficulties in bioinformatics data processing.

RSNET: inferring gene regulatory networks by a redundancy silencing and network enhancement technique

Background

Current gene regulatory network (GRN) inference methods are notorious for a great number of indirect interactions hidden in the predictions. Filtering out the indirect interactions from direct ones remains an important challenge in the reconstruction of GRNs. To address this issue, we developed a redundancy silencing and network enhancement technique (RSNET) for inferring GRNs.

Results

To assess the performance of RSNET method, we implemented the experiments on several gold-standard networks by using simulation study, DREAM challenge dataset and Escherichia coli network. The results show that RSNET method performed better than the compared methods in sensitivity and accuracy. As a case of study, we used RSNET to construct functional GRN for apple fruit ripening from gene expression data.

Conclusions

In the proposed method, the redundant interactions including weak and indirect connections are silenced by recursive optimization adaptively, and the highly dependent nodes are constrained in the model to keep the real interactions. This study provides a useful tool for inferring clean networks.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04696-w.

A novel estimator of the interaction matrix in Graphical Gaussian Model of omics data using the entropy of non-equilibrium systems

MOTIVATION

Inferring the direct relationships between biomolecules from omics datasets is essential for the understanding of biological and disease mechanisms. Gaussian Graphical Model (GGM) provides a fairly simple and accurate representation of these interactions. However, estimation of the associated interaction matrix using data is challenging due to a high number of measured molecules and a low number of samples.

RESULTS

In this article, we use the thermodynamic entropy of the non-equilibrium system of molecules and the data-driven constraints among their expressions to derive an analytic formula for the interaction matrix of Gaussian models. Through a data simulation, we show that our method returns an improved estimation of the interaction matrix. Also, using the developed method, we estimate the interaction matrix associated with plasma proteome and construct the corresponding GGM and show that known NAFLD-related proteins like ADIPOQ, APOC, APOE, DPP4, CAT, GC, HP, CETP, SERPINA1, COLA1, PIGR, IGHD, SAA1 and FCGBP are among the top 15% most interacting proteins of the dataset.

AVAILABILITY AND IMPLEMENTATION

The supplementary materials can be found in the following URL: http://dynamic-proteome.utmb.edu/PrecisionMatrixEstimater/PrecisionMatrixEstimater.aspx.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A novel essential protein identification method based on PPI networks and gene expression data

Background

Some proposed methods for identifying essential proteins have better results by using biological information. Gene expression data is generally used to identify essential proteins. However, gene expression data is prone to fluctuations, which may affect the accuracy of essential protein identification. Therefore, we propose an essential protein identification method based on gene expression and the PPI network data to calculate the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network. Our experiments show that the method can improve the accuracy in predicting essential proteins.

Results

In this paper, we propose a new measure named JDC, which is based on the PPI network data and gene expression data. The JDC method offers a dynamic threshold method to binarize gene expression data. After that, it combines the degree centrality and Jaccard similarity index to calculate the JDC score for each protein in the PPI network. We benchmark the JDC method on four organisms respectively, and evaluate our method by using ROC analysis, modular analysis, jackknife analysis, overlapping analysis, top analysis, and accuracy analysis. The results show that the performance of JDC is better than DC, IC, EC, SC, BC, CC, NC, PeC, and WDC. We compare JDC with both NF-PIN and TS-PIN methods, which predict essential proteins through active PPI networks constructed from dynamic gene expression.

Conclusions

We demonstrate that the new centrality measure, JDC, is more efficient than state-of-the-art prediction methods with same input. The main ideas behind JDC are as follows: (1) Essential proteins are generally densely connected clusters in the PPI network. (2) Binarizing gene expression data can screen out fluctuations in gene expression profiles. (3) The essentiality of the protein depends on the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network.

Network Patterns of Herbal Combinations in Traditional Chinese Clinical Prescriptions

As a well-established multidrug combinations schema, traditional Chinese medicine (herbal prescription) has been used for thousands of years in real-world clinical settings. This paper uses a complex network approach to investigate the regularities underlying multidrug combinations in herbal prescriptions. Using five collected large-scale real-world clinical herbal prescription datasets, we construct five weighted herbal combination networks with herb as nodes and herbal combinational use in herbal prescription as links. We found that the weight distribution of herbal combinations displays a clear power law, which means that most herb pairs were used in low frequency and some herb pairs were used in very high frequency. Furthermore, we found that it displays a clear linear negative correlation between the clustering coefficients and the degree of nodes in the herbal combination network (HCNet). This indicates that hierarchical properties exist in the HCNet. Finally, we investigate the molecular network interaction patterns between herb related target modules (i.e., subnetworks) in herbal prescriptions using a network-based approach and further explore the correlation between the distribution of herb combinations and prescriptions. We found that the more the hierarchical prescription, the better the corresponding effect. The results also reflected a well-recognized principle called “Jun-Chen-Zuo-Shi” in TCM formula theories. This also gives references for multidrug combination development in the field of network pharmacology and provides the guideline for the clinical use of combination therapy for chronic diseases.

The Inhibitory Effects of Recombinant Hespintor Combined with Sorafenib on Transplanted Human Hepatoma in Nude Mice, and Transcriptional Regulation of Hespintor Based on RNA-Seq

Objective: As targeted drugs, exogenous serpins could be introduced to patients to restore body balance. This study aimed to observe further the inhibitory effects of recombinant Hespintor (a Kazal-type serpin) combined with Sorafenib on transplanted human hepatoma tumors in nude mice specimens and to explore the possible transcriptional regulation by Hespintor. Methods: A model of human hepatoma tumors transplanted in nude mice was established, and the medication was administrated to observe the growth of the tumors. Four weeks after the drug administration, the tumors were removed to evaluate the inhibition effects of Hespintor on in-situ tumor growth and liver metastasis. The expression levels of MMP2, MMP9, Bax, Bcl-2, and caspase-3 in the tumor organizations were detected with Western blot. The target genes of the Hespintor were screened based on tissue RNA-Seq, and the regulatory network was constructed. Results: It was found that the recombinant Hespintor displayed a significant antitumor effect on the subcutaneous growth of MHCC97-H cells. Moreover, the therapeutic effects of the combination therapy were significantly better than those of single therapy. 10 target genes with significantly different expression by Hespintoron tumor tissue were identified. Finally, a visual regulatory networkwas constructed for target mRNA-pathway. Conclusions: The antitumor effect of Hespintor combined with Sorafenib in treating the subcutaneously implanted hepatocellular carcinoma tumors in nude mice was significant. The possible transcriptional regulation by Hespintor involved multiple signaling pathways, and it was not just the antitumor effect of uPA via its extracellular inhibitions.

Nucleosome movement analysis based on second-order information entropy and density functional theory

Dynamics of +1 and -1 nucleosomes near TSS of yeast chromosome 2 were analyzed by using second-order information entropy and density functional theory method. Second-order information entropy can measure the interaction intensity between nucleosome sequences and nucleosome histones based on the intensity of base association. In addition, density functional theory method can be used to obtain the global interaction intensity between nucleosome sequences and nucleosome histones based on energy state size and active or non-active state of binucleoside pairs. Our results showed asymmetry of interaction intensity on both sides of the nucleosome central site, and that +1 nucleosomes tend to move toward the 5'-end and -1 nucleosomes tend to move toward the 3'-end. Under the dynamic balance of nucleosome movement, in roder to shut down gene transcription, +1 and -1 nucleosomes will cover TSS. If the dynamic balance is destroyed, +1 and -1 nucleosomes stay away from each other to expose TSS to restart gene transcription. The movement trend of +1 and -1 nucleosomes coincides with the biological mechanism of gene transcription and non-transcription, and the nucleosome sequences contain the dynamic information of nucleosome movement, which provides effective technical support for the study of gene transcription regulation mechanism.

PL-search: a profile-link-based search method for protein remote homology detection

Protein remote homology detection is a fundamental and important task for protein structure and function analysis. Several search methods have been proposed to improve the detection performance of the remote homologues and the accuracy of ranking lists. The position-specific scoring matrix (PSSM) profile and hidden Markov model (HMM) profile can contribute to improving the performance of the state-of-the-art search methods. In this paper, we improved the profile-link (PL) information for constructing PSSM or HMM profiles, and proposed a PL-based search method (PL-search). In PL-search, more robust PLs are constructed through the double-link and iterative extending strategies, and an accurate similarity score of sequence pairs is calculated from the two-level Jaccard distance for remote homologues. We tested our method on two widely used benchmark datasets. Our results show that whether HHblits, JackHMMER or position-specific iterated-BLAST is used, PL-search obviously improves the search performance in terms of ranking quality as well as the number of detected remote homologues. For ease of use of PL-search, both its stand-alone tool and the web server are constructed, which can be accessed at http://bliulab.net/PL-search/.

An in vivo genome-wide CRISPR screen identifies the RNA-binding protein Staufen2 as a key regulator of myeloid leukemia

Aggressive myeloid leukemias such as blast crisis chronic myeloid leukemia and acute myeloid leukemia remain highly lethal. Here we report a genome-wide in vivo CRISPR screen to identify new dependencies in this disease. Among these, RNA-binding proteins (RBPs) in general, and the double-stranded RBP Staufen2 (Stau2) in particular, emerged as critical regulators of myeloid leukemia. In a newly developed knockout mouse, loss of Stau2 led to a profound decrease in leukemia growth and improved survival in mouse models of the disease. Further, Stau2 was required for growth of primary human blast crisis chronic myeloid leukemia and acute myeloid leukemia. Finally, integrated analysis of CRISPR, eCLIP and RNA-sequencing identified Stau2 as a regulator of chromatin-binding factors, driving global alterations in histone methylation. Collectively, these data show that in vivo CRISPR screening is an effective tool for defining new regulators of myeloid leukemia progression and identify the double-stranded RBP Stau2 as a critical dependency of myeloid malignancies.