Natural products modulate phthalate-associated miRNAs and targets.

Phthalates are widespread and commonly used plasticizers that lead to adverse health effects. Several natural products provide a protective effect against phthalates. Moreover, microRNAs (miRNAs) are regulated by natural products and phthalates. Therefore, miRNAs' impacts and potential targets may underlie the mechanism of phthalates. However, the relationship between phthalate-modulated miRNAs and phthalate protectors derived from natural products is poorly understood and requires further supporting information. In this paper, we review the adverse effects and potential targets of phthalates on reproductive systems as well as cancer and non-cancer responses. Information on natural products that attenuate the adverse effects of phthalates is retrieved through a search of Google Scholar and the miRDB database. Moreover, information on miRNAs that are upregulated or downregulated in response to phthalates is collected, along with their potential targets. The interplay between phthalate-modulated miRNAs and natural products is established. Overall, this review proposes a straightforward pathway showing how phthalates modulate different miRNAs and targets and cause adverse effects, which are partly attenuated by several natural products, thereby providing a direction for investigating the natural product-miRNA-target axis against phthalate-induced effects.

Marine anticancer drugs in modulating miRNAs and antioxidant signaling.

Several marine drugs exert anticancer effects by inducing oxidative stress, which becomes overloaded and kills cancer cells when redox homeostasis is imbalanced. The downregulation of antioxidant signaling induces oxidative stress, while its upregulation attenuates oxidative stress. Marine drugs have miRNA-modulating effects against cancer cells. However, the potential antioxidant targets of such drugs have been rarely explored. This review aims to categorize the marine-drug-modulated miRNAs that downregulate their antioxidant targets, causing oxidative stress in anticancer treatments. We also categorize the downregulation of oxidative-stress-inducing miRNAs in antioxidant protection among non-cancer cells. We summarize the putative antioxidant targets of miRNA-modulating marine drugs by introducing a bioinformatics tool (miRDB). Finally, the marine drugs affecting antioxidant targets are surveyed. In this way, the connections between marine drugs and their modulating miRNA and antioxidant targets are innovatively categorized to provide a precise network for exploring their potential anticancer functions and protective effects on non-cancer cells.

Ferroptosis-Regulated Natural Products and miRNAs and Their Potential Targeting to Ferroptosis and Exosome Biogenesis.

Ferroptosis, which comprises iron-dependent cell death, is crucial in cancer and non-cancer treatments. Exosomes, the extracellular vesicles, may deliver biomolecules to regulate disease progression. The interplay between ferroptosis and exosomes may modulate cancer development but is rarely investigated in natural product treatments and their modulating miRNAs. This review focuses on the ferroptosis-modulating effects of natural products and miRNAs concerning their participation in ferroptosis and exosome biogenesis (secretion and assembly)-related targets in cancer and non-cancer cells. Natural products and miRNAs with ferroptosis-modulating effects were retrieved and organized. Next, a literature search established the connection of a panel of ferroptosis-modulating genes to these ferroptosis-associated natural products. Moreover, ferroptosis-associated miRNAs were inputted into the miRNA database (miRDB) to bioinformatically search the potential targets for the modulation of ferroptosis and exosome biogenesis. Finally, the literature search provided a connection between ferroptosis-modulating miRNAs and natural products. Consequently, the connections from ferroptosis-miRNA-exosome biogenesis to natural product-based anticancer treatments are well-organized. This review sheds light on the research directions for integrating miRNAs and exosome biogenesis into the ferroptosis-modulating therapeutic effects of natural products on cancer and non-cancer diseases.

Protein phosphatase 2A modulation and connection with miRNAs and natural products.

Protein phosphatase 2A (PP2A), a heterotrimeric holoenzyme (scaffolding, catalytic, and regulatory subunits), regulates dephosphorylation for more than half of serine/threonine phosphosites and exhibits diverse cellular functions. Although several studies on natural products and miRNAs have emphasized their impacts on PP2A regulation, their connections lack systemic organization. Moreover, only part of the PP2A family has been investigated. This review focuses on the PP2A-modulating effects of natural products and miRNAs' interactions with potential PP2A targets in cancer and non-cancer cells. PP2A-modulating natural products and miRNAs were retrieved through a literature search. Utilizing the miRDB database, potential PP2A targets of these PP2A-modulating miRNAs for the whole set (17 members) of the PP2A family were retrieved. Finally, PP2A-modulating natural products and miRNAs were linked via a literature search. This review provides systemic directions for assessing natural products and miRNAs relating to the PP2A-modulating functions in cancer and disease treatments.

DeepBarcoding: Deep Learning for Species Classification Using DNA Barcoding.

DNA barcodes with short sequence fragments are used for species identification. Because of advances in sequencing technologies, DNA barcodes have gradually been emphasized. DNA sequences from different organisms are easily and rapidly acquired. Therefore, DNA sequence analysis tools play an increasingly crucial role in species identification. This study proposed deep barcoding, a deep learning framework for species classification by using DNA barcodes. Deep barcoding uses raw sequence data as the input to represent one-hot encoding as a one-dimensional image and uses a deep convolutional neural network with a fully connected deep neural network for sequence analysis. It can achieve an average accuracy of >90 percent for both simulation and real datasets. Although deep learning yields outstanding performance for species classification with DNA sequences, its application remains a challenge. The deep barcoding model can be a potential tool for species classification and can elucidate DNA barcode-based species identification.

Application of simulation-based CYP26 SNP-environment barcodes for evaluating the occurrence of oral malignant disorders by odds ratio-based binary particle swarm optimization: A case-control study in the Taiwanese population.

INTRODUCTION: Genetic polymorphisms and social factors (alcohol consumption, betel quid (BQ) usage, and cigarette consumption), both separately or jointly, play a crucial role in the occurrence of oral malignant disorders such as oral and pharyngeal cancers and oral potentially malignant disorders (OPMD). MATERIAL AND METHODS: Simultaneous analyses of multiple single nucleotide polymorphisms (SNPs) and environmental effects on oral malignant disorders are essential to examine, albeit challenging. Thus, we conducted a case-control study (N = 576) to analyze the risk of occurrence of oral malignant disorders by using binary particle swarm optimization (BPSO) with an odds ratio (OR)-based method. RESULTS: We demonstrated that a combination of SNPs (CYP26B1 rs887844 and CYP26C1 rs12256889) and socio-demographic factors (age, ethnicity, and BQ chewing), referred to as the combined effects of SNP-environment, correlated with maximal risk diversity of occurrence observed between the oral malignant disorder group and the control group. The risks were more prominent in the oral and pharyngeal cancers group (OR = 10.30; 95% confidence interval (CI) = 4.58-23.15) than in the OPMD group (OR = 5.42; 95% CI = 1.94-15.12). CONCLUSIONS: Simulation-based "SNP-environment barcodes" may be used to predict the risk of occurrence of oral malignant disorders. Applying simulation-based "SNP-environment barcodes" may provide insight into the importance of screening tests in preventing oral and pharyngeal cancers and OPMD.

Decision Theory-Based COI-SNP Tagging Approach for 126 Scombriformes Species Tagging.

The mitochondrial gene cytochrome c oxidase I (COI) is commonly used for DNA barcoding in animals. However, most of the COI barcode nucleotides are conserved and sequences longer than about 650 base pairs increase the computational burden for species identification. To solve this problem, we propose a decision theory-based COI SNP tagging (DCST) approach that focuses on the discrimination of species using single nucleotide polymorphisms (SNPs) as the variable nucleotides of the sequences of a group of species. Using the example of 126 teleost mackerel fish species (order: Scombriformes), we identified 281 SNPs by alignment and trimming of their COI sequences. After decision rule making, 49 SNPs in 126 fish species were determined using the scoring system of the DCST approach. These COI-SNP barcodes were finally transformed into one-dimensional barcode images. Our proposed DCST approach simplifies the computational complexity and identifies the most effective and fewest SNPs to resolve or discriminate species for species tagging.

Protein folding prediction in the HP model using ions motion optimization with a greedy algorithm.

BACKGROUND: The function of a protein is determined by its native protein structure. Among many protein prediction methods, the Hydrophobic-Polar (HP) model, an ab initio method, simplifies the protein folding prediction process in order to reduce the prediction complexity. RESULTS: In this study, the ions motion optimization (IMO) algorithm was combined with the greedy algorithm (namely IMOG) and implemented to the HP model for the protein folding prediction based on the 2D-triangular-lattice model. Prediction results showed that the integration method IMOG provided a better prediction efficiency in a HP model. Compared to others, our proposed method turned out as superior in its prediction ability and resilience for most of the test sequences. The efficiency of the proposed method was verified by the prediction results. The global search capability and the ability to escape from the local best solution of IMO combined with a local search (greedy algorithm) to the new algorithm IMOG greatly improve the search for the best solution with reliable protein folding prediction. CONCLUSION: Overall, the HP model integrated with IMO and a greedy algorithm as IMOG provides an improved way of protein structure prediction of high stability, high efficiency, and outstanding performance.

Decision Tree Algorithm-Generated Single-Nucleotide Polymorphism Barcodes of rbcL Genes for 38 Brassicaceae Species Tagging.

DNA barcode sequences are accumulating in large data sets. A barcode is generally a sequence larger than 1000 base pairs and generates a computational burden. Although the DNA barcode was originally envisioned as straightforward species tags, the identification usage of barcode sequences is rarely emphasized currently. Single-nucleotide polymorphism (SNP) association studies provide us an idea that the SNPs may be the ideal target of feature selection to discriminate between different species. We hypothesize that SNP-based barcodes may be more effective than the full length of DNA barcode sequences for species discrimination. To address this issue, we tested a ribulose diphosphate carboxylase (rbcL) SNP barcoding (RSB) strategy using a decision tree algorithm. After alignment and trimming, 31 SNPs were discovered in the rbcL sequences from 38 Brassicaceae plant species. In the decision tree construction, these SNPs were computed to set up the decision rule to assign the sequences into 2 groups level by level. After algorithm processing, 37 nodes and 31 loci were required for discriminating 38 species. Finally, the sequence tags consisting of 31 rbcL SNP barcodes were identified for discriminating 38 Brassicaceae species based on the decision tree-selected SNP pattern using RSB method. Taken together, this study provides the rational that the SNP aspect of DNA barcode for rbcL gene is a useful and effective sequence for tagging 38 Brassicaceae species.

Single nucleotide polymorphism barcoding of cytochrome c oxidase I sequences for discriminating 17 species of Columbidae by decision tree algorithm.

DNA barcodes are widely used in taxonomy, systematics, species identification, food safety, and forensic science. Most of the conventional DNA barcode sequences contain the whole information of a given barcoding gene. Most of the sequence information does not vary and is uninformative for a given group of taxa within a monophylum. We suggest here a method that reduces the amount of noninformative nucleotides in a given barcoding sequence of a major taxon, like the prokaryotes, or eukaryotic animals, plants, or fungi. The actual differences in genetic sequences, called single nucleotide polymorphism (SNP) genotyping, provide a tool for developing a rapid, reliable, and high-throughput assay for the discrimination between known species. Here, we investigated SNPs as robust markers of genetic variation for identifying different pigeon species based on available cytochrome c oxidase I (COI) data. We propose here a decision tree-based SNP barcoding (DTSB) algorithm where SNP patterns are selected from the DNA barcoding sequence of several evolutionarily related species in order to identify a single species with pigeons as an example. This approach can make use of any established barcoding system. We here firstly used as an example the mitochondrial gene COI information of 17 pigeon species (Columbidae, Aves) using DTSB after sequence trimming and alignment. SNPs were chosen which followed the rule of decision tree and species-specific SNP barcodes. The shortest barcode of about 11 bp was then generated for discriminating 17 pigeon species using the DTSB method. This method provides a sequence alignment and tree decision approach to parsimoniously assign a unique and shortest SNP barcode for any known species of a chosen monophyletic taxon where a barcoding sequence is available.

A hybrid feature selection method for DNA microarray data.

Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis tool. In cancer classification, available training data sets are generally of a fairly small sample size compared to the number of genes involved. Along with training data limitations, this constitutes a challenge to certain classification methods. Feature (gene) selection can be used to successfully extract those genes that directly influence classification accuracy and to eliminate genes which have no influence on it. This significantly improves calculation performance and classification accuracy. In this paper, correlation-based feature selection (CFS) and the Taguchi-genetic algorithm (TGA) method were combined into a hybrid method, and the K-nearest neighbor (KNN) with the leave-one-out cross-validation (LOOCV) method served as a classifier for eleven classification profiles to calculate the classification accuracy. Experimental results show that the proposed method reduced redundant features effectively and achieved superior classification accuracy. The classification accuracy obtained by the proposed method was higher in ten out of the eleven gene expression data set test problems when compared to other classification methods from the literature.