Cumulative effects of exercise training and consumption of propolis on managing diabetic dyslipidemia in adult women: a single-blind, randomized, controlled trial with pre-post-intervention assessments.

Dyslipidemia is an imbalance of various lipids, and propolis, as a natural resinous viscos mixture made by Apis mellifera L. could improve in this condition. In this single-blind, randomized trial, 60 women with type 2 diabetes and dyslipidemia were divided into four groups: (1) the patients who did not apply the combined training and 500 mg propolis capsules supplement (Control group); (2) subjects performed combined training, including aerobic and resistance training (EXR); (3) subjects received the 500 mg propolis supplement capsules (SUPP); (4) Subjects performed combined training along with receiving the 500 mg propolis supplement capsules (EXR + SUPP). We evaluated the concentration of CTRP12, SFRP5, interleukin-6 (IL6), superoxide dismutase (SOD), malondialdehyde (MDA), adiponectin, and total antioxidant capacity (TAC) before and after the intervention. MDA, TAC, IL6, CTRP12, SFRP5 IL6, adiponectin, and lipid profile levels ameliorated in the EXR + SUPP group. We found that 8 weeks of treatment by combined exercise training and propolis supplement decreased inflammation activity and increased antioxidant defense in women with diabetic dyslipidemia.Trial registration This study was registered in the Iranian Registry of Clinical Trials; IRCT code: IRCT20211229053561N1.

Establishment of a prognostic model related to tregs and natural killer cells infiltration in bladder cancer.

BACKGROUND: Regulatory T cells (Tregs) and natural killer (NK) cells play an essential role in the development of bladder urothelial carcinoma (BUC). AIM: To construct a prognosis-related model to judge the prognosis of patients with bladder cancer, meanwhile, predict the sensitivity of patients to chemotherapy and immunotherapy. METHODS: Bladder cancer information data was obtained from The Cancer Genome Atlas and GSE32894. The CIBERSORT was used to calculate the immune score of each sample. Weighted gene co-expression network analysis was used to find genes that will have the same or similar expression patterns. Subsequently, multivariate cox regression and lasso regression was used to further screen prognosis-related genes. The prrophetic package was used to predict phenotype from gene expression data, drug sensitivity of external cell line and predict clinical data. RESULTS: The stage and risk scores are independent prognostic factors in patients with BUC. Mutations in FGFR3 lead to an increase in Tregs percolation and affect the prognosis of the tumor, and additionally, EMP1, TCHH and CNTNAP3B in the model are mainly positively correlated with the expression of immune checkpoints, while CMTM8, SORT1 and IQSEC1 are negatively correlated with immune checkpoints and the high-risk group had higher sensitivity to chemotherapy drugs. CONCLUSION: Prognosis-related models of bladder tumor patients, based on Treg and NK cell percolation in tumor tissue. In addition to judging the prognosis of patients with bladder cancer, it can also predict the sensitivity of patients to chemotherapy and immunotherapy. At the same time, patients were divided into high and low risk groups based on this model, and differences in genetic mutations were found between the high and low risk groups.

Speeding Up the Structural Analysis of Metabolic Network Models Using the Fredman-Khachiyan Algorithm B.

The problem of computing the Elementary Flux Modes (EFMs) and Minimal Cut Sets (MCSs) of metabolic network is a fundamental one in metabolic networks. A key insight is that they can be understood as a dual pair of monotone Boolean functions (MBFs). Using this insight, this computation reduces to the question of generating from an oracle a dual pair of MBFs. If one of the two sets (functions) is known, then the other can be computed through a process known as dualization. Fredman and Khachiyan provided two algorithms, which they called simply A and B that can serve as an engine for oracle-based generation or dualization of MBFs. We look at efficiencies available in implementing their algorithm B, which we will refer to as FK-B. Like their algorithm A, FK-B certifies whether two given MBFs in the form of Conjunctive Normal Form and Disjunctive Normal Form are dual or not, and in case of not being dual it returns a conflicting assignment (CA), that is, an assignment that makes one of the given Boolean functions True and the other one False. The FK-B algorithm is a recursive algorithm that searches through the tree of assignments to find a CA. If it does not find any CA, it means that the given Boolean functions are dual. In this article, we propose six techniques applicable to the FK-B and hence to the dualization process. Although these techniques do not reduce the time complexity, they considerably reduce the running time in practice. We evaluate the proposed improvements by applying them to compute the MCSs from the EFMs in the 19 small- and medium-sized models from the BioModels database along with 4 models of biomass synthesis in Escherichia coli that were used in an earlier computational survey Haus et al. (2008).

Combining Algorithms to Find Signatures That Predict Risk in Early-Stage Stomach Cancer.

This study applied two mathematical algorithms, lattice up-stream targeting (LUST) and D -basis, to the identification of prognostic signatures from cancer gene expression data. The LUST algorithm looks for metagenes, which are sets of genes that are either overexpressed or underexpressed in the same patients. Whereas LUST runs unsupervised by clinical data, the D -basis algorithm uses implications and association rules to relate gene expression to clinical outcomes. The D -basis selects a small subset of the metagene (a signature) to predict survival. The two algorithms, LUST and D-basis, were combined and applied to mRNA expression and clinical data from The Cancer Genome Atlas (TCGA) for 203 stage 1 and 2 stomach cancer patients. Two small (four-gene) signatures effectively predict survival in early-stage stomach cancer patients. These signatures could be used as a guide for treatment. The first signature (DU4) consists of genes that are underexpressed on the long-survival/low-risk group: FLRT2, KCNB1, MYOC, and TNXB. The second signature consists of genes that are overexpressed on the short-survival/high-risk group: ASB5, SFRP1, SMYD1, and TACR2. Another nine-gene signature (REC9) predicts recurrence: BNC2, CCDC8, DPYSL3, MOXD1, MXRA8, PRELP, SCARF2, TAGLN, and ZNF423. Each patient is assigned a score that is a linear combination of the expression levels for the genes in the signature. Scores below a selected threshold predict low-risk/long survival, whereas high scores indicate a high risk of short survival. The metagenes associate with TCGA cluster C1. Both our signatures and cluster C1 identify tumors that are genomically silent, and have a low mutation load or mutation count. Furthermore, our signatures identify tumors that are predominantly in the WHO classification of poorly cohesive and the Lauren class of diffuse samples, which have a poor prognosis.

Application of Quantum Computing to Biochemical Systems: A Look to the Future.

Chemistry is considered as one of the more promising applications to science of near-term quantum computing. Recent work in transitioning classical algorithms to a quantum computer has led to great strides in improving quantum algorithms and illustrating their quantum advantage. Because of the limitations of near-term quantum computers, the most effective strategies split the work over classical and quantum computers. There is a proven set of methods in computational chemistry and materials physics that has used this same idea of splitting a complex physical system into parts that are treated at different levels of theory to obtain solutions for the complete physical system for which a brute force solution with a single method is not feasible. These methods are variously known as embedding, multi-scale, and fragment techniques and methods. We review these methods and then propose the embedding approach as a method for describing complex biochemical systems, with the parts not only treated with different levels of theory, but computed with hybrid classical and quantum algorithms. Such strategies are critical if one wants to expand the focus to biochemical molecules that contain active regions that cannot be properly explained with traditional algorithms on classical computers. While we do not solve this problem here, we provide an overview of where the field is going to enable such problems to be tackled in the future.

DNA microarray technology and bioinformatic web services.

The pan-genomic microarray technique is used for environmental and/or clinical studies. Although microarray is an accurate and sharp diagnostic tool, the expertized bioinformaticians were able to minimize the outcome biases and maximize the flexibility and accuracy of the technique. The knowledge of bioinformatics plays a key role in association with probe designing and the utilization of correct probe sets and platforms. This technique is divided into two parts as dry lab (in silico studies) and wet lab (in vitro studies). Each part covers the other and are known as complementary divisions. In the case of microarray probe designing, a wide range of software, tools, and databases are necessary. Obviously, the application of right databases, software, and tools decreases the probable biases in the outcomes. Due to the importance of suitable probe designing, this article has focused its look onto a variety of online/offline databases, software, and tools.

EpiGeNet: A Graph Database of Interdependencies Between Genetic and Epigenetic Events in Colorectal Cancer.

The development of colorectal cancer (CRC)-the third most common cancer type-has been associated with deregulations of cellular mechanisms stimulated by both genetic and epigenetic events. StatEpigen is a manually curated and annotated database, containing information on interdependencies between genetic and epigenetic signals, and specialized currently for CRC research. Although StatEpigen provides a well-developed graphical user interface for information retrieval, advanced queries involving associations between multiple concepts can benefit from more detailed graph representation of the integrated data. This can be achieved by using a graph database (NoSQL) approach. Data were extracted from StatEpigen and imported to our newly developed EpiGeNet, a graph database for storage and querying of conditional relationships between molecular (genetic and epigenetic) events observed at different stages of colorectal oncogenesis. We illustrate the enhanced capability of EpiGeNet for exploration of different queries related to colorectal tumor progression; specifically, we demonstrate the query process for (i) stage-specific molecular events, (ii) most frequently observed genetic and epigenetic interdependencies in colon adenoma, and (iii) paths connecting key genes reported in CRC and associated events. The EpiGeNet framework offers improved capability for management and visualization of data on molecular events specific to CRC initiation and progression.

Bioinformatics Prediction and Experimental Validation of MicroRNAs Involved in Cross-Kingdom Interaction.

MicroRNAs (miRNAs) are a class of small noncoding RNAs that act as efficient post-transcriptional regulators of gene expression. In 2012, the first cross-kingdom miRNA-based interaction had been evidenced, demonstrating that exogenous miRNAs act in a manner of mammalian functional miRNAs. Starting from this evidence, we defined the concept of cross-kingdom functional homology between plant and mammalian miRNAs as a needful requirement for vegetal miRNA to explicit a regulation mechanism into the host mammalian cell, comparable to the endogenous one. Then, we proposed a new dedicated algorithm to compare plant and mammalian miRNAs, searching for functional sequence homologies between them, and we developed a web software called MirCompare. We also predicted human genes regulated by the selected plant miRNAs, and we determined the role of exogenous miRNAs in the perturbation of intracellular interaction networks. Finally, as already performed by Pirrò and coworkers, the ability of MirCompare to select plant miRNAs with functional homologies with mammalian ones has been experimentally confirmed by evaluating the ability of mol-miR168a to downregulate the protein expression of SIRT1, when its mimic is transfected into human hepatoma cell line G2 (HEPG2) cells. This tool is implemented into a user-friendly web interface, and the access is free to public through the website http://160.80.35.140/MirCompare.

The Identification of Discriminating Patterns from 16S rRNA Gene to Generate Signature for Bacillus Genus.

The 16S ribosomal RNA (16S rRNA) gene has been widely used for the taxonomic classification of bacteria. A molecular signature is a set of nucleotide patterns, which constitute a regular expression that is specific to each particular taxon. Our main goal was to identify discriminating nucleotide patterns in 16S rRNA gene and then to generate signatures for taxonomic classification. To demonstrate our approach, we used the phylum Firmicutes as a model using representative taxa Bacilli (class), Bacillales (order), Bacillaceae (family), and Bacillus (genus), according to their dominance at each hierarchical taxonomic level. We applied combined composite vector and multiple sequence alignment approaches to generate gene-specific signatures. Further, we mapped all the patterns into the different hypervariable regions of 16S rRNA gene and confirmed the most appropriate distinguishing region as V3-V4 for targeted taxa. We also examined the evolution in discriminating patterns of signatures across taxonomic levels. We assessed the comparative classification accuracy of signatures with other methods (i.e., RDP Classifier, KNN, and SINA). Results revealed that the signatures for taxa Bacilli, Bacillales, Bacillaceae, and Bacillus could correctly classify isolate sequences with sensitivity of 0.99, 0.97, 0.94, and 0.89, respectively, and specificity close to 0.99. We developed signature-based software DNA Barcode Identification (DNA BarID) for taxonomic classification that is available at website http://www.neeri.res.in/DNA_BarID.htm . This pattern-based study provides a deeper understanding of taxon-specific discriminating patterns in 16S rRNA gene with respect to taxonomic classification.

Meta-Analysis in Gene Expression Studies.

This chapter introduces methods to synthesize experimental results from independent high-throughput genomic experiments, with a focus on adaptation of traditional methods from systematic review of clinical trials and epidemiological studies. First, it reviews methods for identifying, acquiring, and preparing individual patient data for meta-analysis. It then reviews methodology for synthesizing results across studies and assessing heterogeneity, first through outlining of methods and then through a step-by-step case study in identifying genes associated with survival in high-grade serous ovarian cancer.

Elucidating the Functional Roles of Spatial Organization in Cross-Membrane Signal Transduction by a Hybrid Simulation Method.

The ligand-binding of membrane receptors on cell surfaces initiates the dynamic process of cross-membrane signal transduction. It is an indispensable part of the signaling network for cells to communicate with external environments. Recent experiments revealed that molecular components in signal transduction are not randomly mixed, but spatially organized into distinctive patterns. These patterns, such as receptor clustering and ligand oligomerization, lead to very different gene expression profiles. However, little is understood about the molecular mechanisms and functional impacts of this spatial-temporal regulation in cross-membrane signal transduction. In order to tackle this problem, we developed a hybrid computational method that decomposes a model of signaling network into two simulation modules. The physical process of binding between receptors and ligands on cell surfaces are simulated by a diffusion-reaction algorithm, while the downstream biochemical reactions are modeled by stochastic simulation of Gillespie algorithm. These two processes are coupled together by a synchronization framework. Using this method, we tested the dynamics of a simple signaling network in which the ligand binding of cell surface receptors triggers the phosphorylation of protein kinases, and in turn regulates the expression of target genes. We found that spatial aggregation of membrane receptors at cellular interfaces is able to either amplify or inhibit downstream signaling outputs, depending on the details of clustering mechanism. Moreover, by providing higher binding avidity, the co-localization of ligands into multi-valence complex modulates signaling in very different ways that are closely related to the binding affinity between ligand and receptor. We also found that the temporal oscillation of the signaling pathway that is derived from genetic feedback loops can be modified by the spatial clustering of membrane receptors. In summary, our method demonstrates the functional importance of spatial organization in cross-membrane signal transduction. The method can be applied to any specific signaling pathway in cells.

Probing RNA Native Conformational Ensembles with Structural Constraints.

Noncoding ribonucleic acids (RNA) play a critical role in a wide variety of cellular processes, ranging from regulating gene expression to post-translational modification and protein synthesis. Their activity is modulated by highly dynamic exchanges between three-dimensional conformational substates, which are difficult to characterize experimentally and computationally. Here, we present an innovative, entirely kinematic computational procedure to efficiently explore the native ensemble of RNA molecules. Our procedure projects degrees of freedom onto a subspace of conformation space defined by distance constraints in the tertiary structure. The dimensionality reduction enables efficient exploration of conformational space. We show that the conformational distributions obtained with our method broadly sample the conformational landscape observed in NMR experiments. Compared to normal mode analysis-based exploration, our procedure diffuses faster through the experimental ensemble while also accessing conformational substates to greater precision. Our results suggest that conformational sampling with a highly reduced but fully atomistic representation of noncoding RNA expresses key features of their dynamic nature.

Algorithms for Regular Tree Grammar Network Search and Their Application to Mining Human-viral Infection Patterns.

Network querying is a powerful approach to mine molecular interaction networks. Most state-of-the-art network querying tools either confine the search to a prespecified topology in the form of some template subnetwork, or do not specify any topological constraints at all. Another approach is grammar-based queries, which are more flexible and expressive as they allow for expressing the topology of the sought pattern according to some grammar-based logic. Previous grammar-based network querying tools were confined to the identification of paths. In this article, we extend the patterns identified by grammar-based query approaches from paths to trees. For this, we adopt a higher order query descriptor in the form of a regular tree grammar (RTG). We introduce a novel problem and propose an algorithm to search a given graph for the k highest scoring subgraphs matching a tree accepted by an RTG. Our algorithm is based on the combination of dynamic programming with color coding, and includes an extension of previous k-best parsing optimization approaches to avoid isomorphic trees in the output. We implement the new algorithm and exemplify its application to mining viral infection patterns within molecular interaction networks. Our code is available online.

Error Tree: A Tree Structure for Hamming and Edit Distances and Wildcards Matching.

Approximate pattern matching is a fundamental problem in the bioinformatics and information retrieval applications. The problem involves different matching relations such as Hamming distance, edit distances, and the wildcards matching problem. The input is usually a text of length n over a fixed alphabet of length Σ, a pattern of length m, and an integer k. The output is to find all positions that have ≤ k Hamming distance, edit distance, or wildcards matching with P. Many algorithms and indexes have been proposed to solve the problems more efficiently, but due to the space and time complexities of the problems, most tools adopted heuristics approaches based on, for instance, suffix tree, suffix array, or Burrows Wheeler Transform to reach practical implementations. Error Tree is a novel tree structure that is mainly oriented to solve the approximate pattern matching problems, using less space and faster computation time. The algorithm proposes for Hamming distance and wildcards matching a tree structure that needs [Formula: see text] words and takes [Formula: see text] in the average case) of query time for any online/offline pattern, where occ is the number of outputs. In addition, a tree structure of [Formula: see text] words and [Formula: see text] in the average case) query time for edit distance for any online/offline pattern.

Reconstructing breakage fusion bridge architectures using noisy copy numbers.

The Breakage Fusion Bridge (BFB) process is a key marker for genomic instability, producing highly rearranged genomes in relatively small numbers of cell cycles. While the process itself was observed during the late 1930s, little is known about the extent of BFB in tumor genome evolution. Moreover, BFB can dramatically increase copy numbers of chromosomal segments, which in turn hardens the tasks of both reference-assisted and ab initio genome assembly. Based on available data such as Next Generation Sequencing (NGS) and Array Comparative Genomic Hybridization (aCGH) data, we show here how BFB evidence may be identified, and how to enumerate all possible evolutions of the process with respect to observed data. Specifically, we describe practical algorithms that, given a chromosomal arm segmentation and noisy segment copy number estimates, produce all segment count vectors supported by the data that can be produced by BFB, and all corresponding BFB architectures. This extends the scope of analyses described in our previous work, which produced a single count vector and architecture per instance. We apply these analyses to a comprehensive human cancer dataset, demonstrate the effectiveness and efficiency of the computation, and suggest methods for further assertions of candidate BFB samples. Source code of our tool can be found online.

cDREM: inferring dynamic combinatorial gene regulation.

Genes are often combinatorially regulated by multiple transcription factors (TFs). Such combinatorial regulation plays an important role in development and facilitates the ability of cells to respond to different stresses. While a number of approaches have utilized sequence and ChIP-based datasets to study combinational regulation, these have often ignored the combinational logic and the dynamics associated with such regulation. Here we present cDREM, a new method for reconstructing dynamic models of combinatorial regulation. cDREM integrates time series gene expression data with (static) protein interaction data. The method is based on a hidden Markov model and utilizes the sparse group Lasso to identify small subsets of combinatorially active TFs, their time of activation, and the logical function they implement. We tested cDREM on yeast and human data sets. Using yeast we show that the predicted combinatorial sets agree with other high throughput genomic datasets and improve upon prior methods developed to infer combinatorial regulation. Applying cDREM to study human response to flu, we were able to identify several combinatorial TF sets, some of which were known to regulate immune response while others represent novel combinations of important TFs.

Fast, approximate kinetics of RNA folding.

In this article, we introduce the software suite Hermes, which provides fast, novel algorithms for RNA secondary structure kinetics. Using the fast Fourier transform to efficiently compute the Boltzmann probability that a secondary structure S of a given RNA sequence has base pair distance x (resp. y) from reference structure A (resp. B), Hermes computes the exact kinetics of folding from A to B in this coarse-grained model. In particular, Hermes computes the mean first passage time from the transition probability matrix by using matrix inversion, and also computes the equilibrium time from the rate matrix by using spectral decomposition. Due to the model granularity and the speed of Hermes, it is capable of determining secondary structure refolding kinetics for large RNA sequences, beyond the range of other methods. Comparative benchmarking of Hermes with other methods indicates that Hermes provides refolding kinetics of accuracy suitable for use in the computational design of RNA, an important area of synthetic biology. Source code and documentation for Hermes are available.

A Novel Algorithm for Enhanced Structural Motif Matching in Proteins.

As widely discussed in literature, spatial patterns of amino acids, so-called structural motifs, play an important role in protein function. The functionally responsible part of proteins often lies in an evolutionarily highly conserved spatial arrangement of only a few amino acids, which are held in place tightly by the rest of the structure. Those recurring amino acid arrangements can be seen as patterns in the three-dimensional space and are known as structural motifs. In general, these motifs can mediate various functional interactions, such as DNA/RNA targeting and binding, ligand interactions, substrate catalysis, and stabilization of the protein structure. Hence, characterizing and identifying such conserved structural motifs can contribute to the understanding of structure-function relationships. Therefore, and because of the rapidly increasing number of solved protein structures, it is highly desirable to identify, understand, and moreover to search for structurally scattered amino acid motifs. This work aims at the development and the implementation of a novel and robust matching algorithm to detect structural motifs in large sets of target structures. The proposed methods were combined and implemented to a feature-rich and easy-to-use command line software tool written in Java.

Building a pan-genome reference for a population.

A reference genome is a high quality individual genome that is used as a coordinate system for the genomes of a population, or genomes of closely related subspecies. Given a set of genomes partitioned by homology into alignment blocks we formalize the problem of ordering and orienting the blocks such that the resulting ordering maximally agrees with the underlying genomes' ordering and orientation, creating a pan-genome reference ordering. We show this problem is NP-hard, but also demonstrate, empirically and within simulations, the performance of heuristic algorithms based upon a cactus graph decomposition to find locally maximal solutions. We describe an extension of our Cactus software to create a pan-genome reference for whole genome alignments, and demonstrate how it can be used to create novel genome browser visualizations using human variation data as a test. In addition, we test the use of a pan-genome for describing variations and as a reference for read mapping.

An approximative approach for single cell spatial modeling of quorum sensing.

Quorum sensing, a special kind of cell-cell communication, has originally been described for well-mixed homogeneous bacterial cultures. However, recent perception supports its ecological relevance for spatially heterogeneous distributed cells, like colonies and biofilms. New experimental techniques allow for single cell analysis under these conditions, which is crucial to understanding the effect of chemical gradients and intercell variations. Based on a reaction-diffusion system, we develop a method that drastically reduces the computational complexity of the model. In comparison to similar former approaches, handling and scaling is much easier. Via a suitable scaling, this approach leads to approximative algebraic equations for the stationary case. This approach can be easily used for numerical situations.