Computational Insights into the Selecting Mechanism of α-Amylase Immobilized on Cellulose Nanocrystals: Unveiling the Potential of α-Amylases Immobilized for Efficient Poultry Feed Hydrolysis.

The selection of an appropriate amylase for hydrolysis poultry feed is crucial for achieving improved digestibility and high-quality feed. Cellulose nanocrystals (CNCs), which are known for their high surface area, provide an excellent platform for enzyme immobilization. Immobilization greatly enhances the operational stability of α-amylases and the efficiency of starch bioconversion in poultry feeds. In this study, we immobilized two metagenome-derived α-amylases, PersiAmy2 and PersiAmy3, on CNCs and employed computational methods to characterize and compare the degradation efficiencies of these enzymes for poultry feed hydrolysis. Experimental in vitro bioconversion assessments were performed to validate the computational outcomes. Molecular docking studies revealed the superior hydrolysis performance of PersiAmy3, which displayed stronger electrostatic interactions with CNCs. Experimental characterization demonstrated the improved performance of both α-amylases after immobilization at high temperatures (80 °C). A similar trend was observed under alkaline conditions, with α-amylase activity reaching 88% within a pH range of 8.0 to 9.0. Both immobilized α-amylases exhibited halotolerance at NaCl concentrations up to 3 M and retained over 50% of their initial activity after 13 use cycles. Notably, PersiAmy3 displayed more remarkable improvements than PersiAmy2 following immobilization, including a significant increase in activity from 65 to 80.73% at 80 °C, an increase in activity to 156.48% at a high salinity of 3 M NaCl, and a longer half-life, indicating greater thermal stability within the range of 60 to 80 °C. These findings were substantiated by the in vitro hydrolysis of poultry feed, where PersiAmy3 generated 53.53 g/L reducing sugars. This comprehensive comparison underscores the utility of computational methods as a faster and more efficient approach for selecting optimal enzymes for poultry feed hydrolysis, thereby providing valuable insights into enhancing feed digestibility and quality.

Unlocking the potential of microRNAs: machine learning identifies key biomarkers for myocardial infarction diagnosis.

BACKGROUND: MicroRNAs (miRNAs) play a crucial role in regulating adaptive and maladaptive responses in cardiovascular diseases, making them attractive targets for potential biomarkers. However, their potential as novel biomarkers for diagnosing cardiovascular diseases requires systematic evaluation. METHODS: In this study, we aimed to identify a key set of miRNA biomarkers using integrated bioinformatics and machine learning analysis. We combined and analyzed three gene expression datasets from the Gene Expression Omnibus (GEO) database, which contains peripheral blood mononuclear cell (PBMC) samples from individuals with myocardial infarction (MI), stable coronary artery disease (CAD), and healthy individuals. Additionally, we selected a set of miRNAs based on their area under the receiver operating characteristic curve (AUC-ROC) for separating the CAD and MI samples. We designed a two-layer architecture for sample classification, in which the first layer isolates healthy samples from unhealthy samples, and the second layer classifies stable CAD and MI samples. We trained different machine learning models using both biomarker sets and evaluated their performance on a test set. RESULTS: We identified hsa-miR-21-3p, hsa-miR-186-5p, and hsa-miR-32-3p as the differentially expressed miRNAs, and a set including hsa-miR-186-5p, hsa-miR-21-3p, hsa-miR-197-5p, hsa-miR-29a-5p, and hsa-miR-296-5p as the optimum set of miRNAs selected by their AUC-ROC. Both biomarker sets could distinguish healthy from not-healthy samples with complete accuracy. The best performance for the classification of CAD and MI was achieved with an SVM model trained using the biomarker set selected by AUC-ROC, with an AUC-ROC of 0.96 and an accuracy of 0.94 on the test data. CONCLUSIONS: Our study demonstrated that miRNA signatures derived from PBMCs could serve as valuable novel biomarkers for cardiovascular diseases.

Efficient bioconversion of lignocellulosic waste by a novel computationally screened hyperthermostable enzyme from a specialized microbiota.

A large amount of lignocellulosic waste is generated every day in the world, and their accumulation in the agroecosystems, integration in soil compositions, or incineration for energy production has severe environmental pollution effects. Using enzymes as biocatalysts for the biodegradation of lignocellulosic materials, especially in harsh processing conditions, is a practical step towards green energy and environmental biosafety. Hence, the current study focuses on enzyme computationally screened from camel rumen metagenomics data as specialized microbiota that have the capacity to degrade lignocellulosic-rich and recalcitrant materials. The novel hyperthermostable xylanase named PersiXyn10 with the performance at extreme conditions was proper activity within a broad temperature (30-100 â„ƒ) and pH range (4.0-11.0) but showed the maximum xylanolytic activity in severe alkaline and temperature conditions, pH 8.0 and temperature 90 â„ƒ. Also, the enzyme had highly resistant to metals, surfactants, and organic solvents in optimal conditions. The introduced xylanase had unique properties in terms of thermal stability by maintaining over 82% of its activity after 15 days of incubation at 90 â„ƒ. Considering the crucial role of hyperthermostable xylanases in the paper industry, the PersiXyn10 was subjected to biodegradation of paper pulp. The proper performance of hyperthermostable PersiXyn10 on the paper pulp was confirmed by structural analysis (SEM and FTIR) and produced 31.64 g/L of reducing sugar after 144 h hydrolysis. These results proved the applicability of the hyperthermostable xylanase in biobleaching and saccharification of lignocellulosic biomass for declining the environmental hazards.

A computational learning paradigm to targeted discovery of biocatalysts from metagenomic data: A case study of lipase identification.

The growing adoption of enzymes as biocatalysts in various industries has accentuated the demand for acquiring access to the great natural diversity and, in the meantime, the advent and advancements of metagenomics and high-throughput sequencing technologies have offered an unprecedented opportunity to explore this extensive resource. Lipases, enzymes responsible for the biological turnover of lipids, are among the most commercialized biocatalysts with numerous applications in different domains and therefore are of high industrial value. The relatively costly and time-consuming wet-lab experimental pipelines commonly used for novel enzyme discovery, highlight the necessity of agile in silico approaches to keep pace with the exponential growth of available sequencing data. In the present study, an in-depth analysis of a tannery wastewater metagenome, including taxonomic and enzymatic profiling, was performed. Using sequence homology-based screening methods and supervised machine learning-based regression models aimed at prediction of lipases' pH and temperature optima, the metagenomic data set was screened for lipolytic enzymes, which led to the isolation of alkaline and highly thermophilic novel lipase. Moreover, MeTarEnz (metagenomic targeted enzyme miner) software was developed and made freely accessible (at https://cbb.ut.ac.ir/MeTarEnz) as a part of this study. MeTarEnz offers several functions to automate the process of targeted enzyme mining from high-throughput sequencing data. This study highlights the competence of computational approaches in exploring vast biodiversity within environmental niches, while providing a set of practical in silico tools as well as a generalized methodology to facilitate the sequence-based mining of biocatalysts.

Machine learning-based system for prediction of ascites grades in patients with liver cirrhosis using laboratory and clinical data: design and implementation study.

OBJECTIVES: The aim of the study was to implement a non-invasive model to predict ascites grades among patients with cirrhosis. METHODS: In the present study, we used modern machine learning (ML) methods to develop a scoring system solely based on routine laboratory and clinical data to help physicians accurately diagnose and predict different degrees of ascites. We used ANACONDA3-5.2.0 64 bit, free and open-source platform distribution of Python programming language with numerous modules, packages, and rich libraries that provide various methods for classification problems. Through the 10-fold cross-validation, we employed three common learning models on our dataset, k-nearest neighbors (KNN), support vector machine (SVM), and neural network classification algorithms. RESULTS: According to the data received from the research institute, three types of data analysis have been performed. The algorithms used to predict ascites were KNN, cross-validation (CV), and multilayer perceptron neural networks (MLPNN), which achieved an average accuracy of 94, 91, and 90%, respectively. Also, in the average accuracy of the algorithms, KNN had the highest accuracy of 94%. CONCLUSIONS: We applied well-known ML approaches to predict ascites. The findings showed a strong performance compared to the classical statistical approaches. This ML-based approach can help to avoid unnecessary risks and costs for patients with acute stages of the disease.

Performance of machine learning techniques on prediction of esophageal varices grades among patients with cirrhosis.

OBJECTIVES: All patients with cirrhosis should be periodically examined for esophageal varices (EV), however, a large percentage of patients undergoing screening, do not have EV or have only mild EV and do not have high-risk characteristics. Therefore, developing a non-invasive method to predict the occurrence of EV in patients with liver cirrhosis as a non-invasive method with high accuracy seems useful. In the present research, we compared the performance of several machine learning (ML) methods to predict EV on laboratory and clinical data to choose the best model. METHODS: Four-hundred-and-ninety data from the Liver and Gastroenterology Research Center of Shahid Beheshti University of Medical Sciences in the period 2014-2021, were analyzed applying models including random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression. RESULTS: RF and SVM had the best results in general for all grades of EV. RF showed remarkably better results and the highest area under the curve (AUC). After that, SVM and ANN had the AUC of 98%, for grade 3, the SVM algorithm had the highest AUC after RF (89%). CONCLUSIONS: The findings may help to better predict EV with high precision and accuracy and also can help reduce the burden of frequent visits to endoscopic centers. It can also help practitioners to manage cirrhosis by predicting EV with lower costs.

Identifying predictors of varices grading in patients with cirrhosis using ensemble learning.

OBJECTIVES: The present study was conducted to improve the performance of predictive methods by introducing the most important factors which have the highest effects on the prediction of esophageal varices (EV) grades among patients with cirrhosis. METHODS: In the present study, the ensemble learning methods, including Catboost and XGB classifier, were used to choose the most potent predictors of EV grades solely based on routine laboratory and clinical data, a dataset of 490 patients with cirrhosis gathered. To increase the validity of the results, a five-fold cross-validation method was applied. The model was conducted using python language, Anaconda open-source platform. TRIPOD checklist for prediction model development was completed. RESULTS: The Catboost model predicted all the targets correctly with 100% precision. However, the XGB classifier had the best performance for predicting grades 0 and 1, and totally the accuracy was 91.02%. The most significant variables, according to the best performing model, which was CatBoost, were child score, white blood cell (WBC), vitalism K (K), and international normalized ratio (INR). CONCLUSIONS: Using machine learning models, especially ensemble learning models, can remarkably increase the prediction performance. The models allow practitioners to predict EV risk at any clinical visit and decrease unneeded esophagogastroduodenoscopy (EGD) and consequently reduce morbidity, mortality, and cost of the long-term follow-ups for patients with cirrhosis.

The importance of CDC27 in cancer: molecular pathology and clinical aspects.

BACKGROUND: CDC27 is one of the core components of Anaphase Promoting complex/cyclosome. The main role of this protein is defined at cellular division to control cell cycle transitions. Here we review the molecular aspects that may affect CDC27 regulation from cell cycle and mitosis to cancer pathogenesis and prognosis. MAIN TEXT: It has been suggested that CDC27 may play either like a tumor suppressor gene or oncogene in different neoplasms. Divergent variations in CDC27 DNA sequence and alterations in transcription of CDC27 have been detected in different solid tumors and hematological malignancies. Elevated CDC27 expression level may increase cell proliferation, invasiveness and metastasis in some malignancies. It has been proposed that CDC27 upregulation may increase stemness in cancer stem cells. On the other hand, downregulation of CDC27 may increase the cancer cell survival, decrease radiosensitivity and increase chemoresistancy. In addition, CDC27 downregulation may stimulate efferocytosis and improve tumor microenvironment. CONCLUSION: CDC27 dysregulation, either increased or decreased activity, may aggravate neoplasms. CDC27 may be suggested as a prognostic biomarker in different malignancies.

A generalized machine-learning aided method for targeted identification of industrial enzymes from metagenome: A xylanase temperature dependence case study.

Growing industrial utilization of enzymes and the increasing availability of metagenomic data highlight the demand for effective methods of targeted identification and verification of novel enzymes from various environmental microbiota. Xylanases are a class of enzymes with numerous industrial applications and are involved in the degradation of xylose, a component of lignocellulose. The optimum temperature of enzymes is an essential factor to be considered when choosing appropriate biocatalysts for a particular purpose. Therefore, in silico prediction of this attribute is a significant cost and time-effective step in the effort to characterize novel enzymes. The objective of this study was to develop a computational method to predict the thermal dependence of xylanases. This tool was then implemented for targeted screening of putative xylanases with specific thermal dependencies from metagenomic data and resulted in the identification of three novel xylanases from sheep and cow rumen microbiota. Here we present thermal activity prediction for xylanase, a new sequence-based machine learning method that has been trained using a selected combination of various protein features. This random forest classifier discriminates non-thermophilic, thermophilic, and hyper-thermophilic xylanases. The model's performance was evaluated through multiple iterations of sixfold cross-validations as well as holdout tests, and it is freely accessible as a web-service at arimees.com.

A novel high performance in-silico screened metagenome-derived alkali-thermostable endo-ß-1,4-glucanase for lignocellulosic biomass hydrolysis in the harsh conditions.

BACKGROUND: Lignocellulosic biomass, is a great resource for the production of bio-energy and bio-based material since it is largely abundant, inexpensive and renewable. The requirement of new energy sources has led to a wide search for novel effective enzymes to improve the exploitation of lignocellulose, among which the importance of thermostable and halotolerant cellulase enzymes with high pH performance is significant. RESULTS: The primary aim of this study was to discover a novel alkali-thermostable endo-ß-1,4-glucanase from the sheep rumen metagenome. At first, the multi-step in-silico screening approach was utilized to find primary candidate enzymes with superior properties. Among the computationally selected candidates, PersiCel4 was found and subjected to cloning, expression, and purification followed by functional and structural characterization. The enzymes' kinetic parameters, including Vmax, Km, and specific activity, were calculated. The PersiCel4 demonstrated its optimum activity at pH 8.5 and a temperature of 85 °C and was able to retain more than 70% of its activity after 150 h of storage at 85 °C. Furthermore, this enzyme was able to maintain its catalytic activity in the presence of different concentrations of NaCl and several metal ions contains Mg2+, Mn2+, Cu2+, Fe2+ and Ca2+. Our results showed that treatment with MnCl2 could enhance the enzyme's activity by 78%. PersiCel4 was ultimately used for enzymatic hydrolysis of autoclave pretreated rice straw, the most abundant agricultural waste with rich cellulose content. In autoclave treated rice straw, enzymatic hydrolysis with the PersiCel4 increased the release of reducing sugar up to 260% after 72 h in the harsh condition (T = 85 °C, pH = 8.5). CONCLUSION: Considering the urgent demand for stable cellulases that are operational on extreme temperature and pH conditions and due to several proposed distinctive characteristics of PersiCel4, it can be used in the harsh condition for bioconversion of lignocellulosic biomass.

The Stabilizing Mechanism of Immobilized Metagenomic Xylanases on Bio-Based Hydrogels to Improve Utilization Performance: Computational and Functional Perspectives.

While polysaccharide-based superabsorbent hydrogels (SHs) have attracted increasing interest as proficient carriers in the enzyme immobilization, the nature of the favored interactions between the SHs and enzymes is still unclear. Herein, a combined experimental and computational study was employed to investigate the dominant parameters affecting on the stabilization of two metagenomic xylanases on the SHs. The thermostable enzymes (PersiXyn3 and PersiXyn4) with similar domains were screened, cloned, expressed, and purified from cattle rumen metagenome. Then, the enzymes were immobilized on the carboxymethyl cellulose-g-poly(acrylic acid-co-acrylamide) hydrogel which resulted in increasing their activity and stability. The carboxymethyl cellulose (CMC)-based characteristic of the hydrogel provided high numbers of H-bondings/ionic bridges, causing an improvement in the stability, hydrolysis performance, and reusability of the immobilized enzymes. More specifically, enzyme immobilization resulted in ∼40% increase in the content of the reducing sugars released after treatment of paper pulp. After 16 reuse cycles, the immobilized PersiXyn4 displayed 35.9% activity, but the immobilized PersiXyn3 retained just 8.2% of its initial activity. The comparative investigations illustrated that a higher number of positively charged amino acids in the binding site of the enzyme provided stronger electrostatic attractions between it and negative functionalities of the hydrogel. This was suggested as the main reason for the higher affinity of PersiXyn4 toward hydrogel and explained the better hydrolysis performance and reusability of the immobilized PersiXyn4 on the SH. These findings are essential for designing novel innovative SH carriers and the successful engineering of optimal enzyme assemblies through the prediction of the immobilized enzyme's stabilities.

From indication to decision: A hierarchical approach to model the chemotactic behavior of Escherichia coli.

Reducing the complex behavior of living entities to its underlying physical and chemical processes is a formidable task in biology. Complex behaviors can be characterized as decision making: the ability to process the incoming information via an intracellular network and act upon this information to choose appropriate strategies. Motility is one such behavior that has been the focus many modeling efforts in the past. Our aim is to reduce the chemotactic behavior in Escherichia coli to its molecular constituents in order to paint a comprehensive and end-to-end picture of this intricate behavior. We utilize a hierarchical approach, consisting of three layers, to achieve this goal: at the first level, chemical reactions involved in chemotaxis are simulated. In the second level, the chemical reactions give rise to the mechanical movement of six independent flagella. At the last layer, the two lower layers are combined to allow a digital bacterium to receive information from its environment and swim through it with verve. Our results are in concert with the experimental studies concerning the motility of E.coli cells. In addition, we show that our detailed model of chemotaxis is reducible to a non-homogeneous Markov process.

IAMPE: NMR-Assisted Computational Prediction of Antimicrobial Peptides.

Antimicrobial peptides (AMPs) are at the focus of attention due to their therapeutic importance and developing computational tools for the identification of efficient antibiotics from the primary structure. Here, we utilized the 13CNMR spectral of amino acids and clustered them into various groups. These clusters were used to build feature vectors for the AMP sequences based on the composition, transition, and distribution of cluster members. These features, along with the physicochemical properties of AMPs were exploited to learn computational models to predict active AMPs solely from their sequences. Naïve Bayes (NB), k-nearest neighbors (KNN), support-vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost) were employed to build the classification system using the collected AMP datasets from the CAMP, LAMP, ADAM, and AntiBP databases. Our results were validated and compared with the CAMP and ADAM prediction systems and indicated that the synergistic combination of the 13CNMR features with the physicochemical descriptors enables the proposed ensemble mechanism to improve the prediction performance of active AMP sequences. Our web-based AMP prediction platform, IAMPE, is available at http://cbb1.ut.ac.ir/.

FoPA: identifying perturbed signaling pathways in clinical conditions using formal methods.

BACKGROUND: Accurate identification of perturbed signaling pathways based on differentially expressed genes between sample groups is one of the key factors in the understanding of diseases and druggable targets. Most pathway analysis methods prioritize impacted signaling pathways by incorporating pathway topology using simple graph-based models. Despite their relative success, these models are limited in describing all types of dependencies and interactions that exist in biological pathways. RESULTS: In this work, we propose a new approach based on the formal modeling of signaling pathways. Signaling pathways are formally modeled, and then model checking tools are applied to find the likelihood of perturbation for each pathway in a given condition. By adopting formal methods, various complex interactions among biological parts are modeled, which can contribute to reducing the false-positive rate of the proposed approach. We have developed a tool named Formal model checking based pathway analysis (FoPA) based on this approach. FoPA is compared with three well-known pathway analysis methods: PADOG, CePa, and SPIA on the benchmark of 36 GEO datasets from various diseases by applying the target pathway technique. This validation technique eliminates the need for possibly biased human assessments of results. In the cases that, there is no apriori knowledge of all relevant pathways, simulated false inputs (permuted class labels and decoy pathways) are chosen as a set of negative controls to test the false positive rate of the methods. Finally, to further evaluate the efficiency of FoPA, it is applied to a list of autism-related genes. CONCLUSIONS: The results obtained by the target pathway technique demonstrate that FoPA is able to prioritize target pathways as well as PADOG but better than CePa and SPIA. Also, the false-positive rate of finding significant pathways using FoPA is lower than other compared methods. Also, FoPA can detect more consistent relevant pathways than other methods. The results of FoPA on autism-related genes highlight the role of "Renin-angiotensin system" pathway. This pathway has been supposed to have a pivotal role in some neurodegenerative diseases, while little attention has been paid to its impact on autism development so far.

Genome-wide prediction and prioritization of human aging genes by data fusion: a machine learning approach.

BACKGROUND: Machine learning can effectively nominate novel genes for various research purposes in the laboratory. On a genome-wide scale, we implemented multiple databases and algorithms to predict and prioritize the human aging genes (PPHAGE). RESULTS: We fused data from 11 databases, and used Naïve Bayes classifier and positive unlabeled learning (PUL) methods, NB, Spy, and Rocchio-SVM, to rank human genes in respect with their implication in aging. The PUL methods enabled us to identify a list of negative (non-aging) genes to use alongside the seed (known age-related) genes in the ranking process. Comparison of the PUL algorithms revealed that none of the methods for identifying a negative sample were advantageous over other methods, and their simultaneous use in a form of fusion was critical for obtaining optimal results (PPHAGE is publicly available at https://cbb.ut.ac.ir/pphage). CONCLUSION: We predict and prioritize over 3,000 candidate age-related genes in human, based on significant ranking scores. The identified candidate genes are associated with pathways, ontologies, and diseases that are linked to aging, such as cancer and diabetes. Our data offer a platform for future experimental research on the genetic and biological aspects of aging. Additionally, we demonstrate that fusion of PUL methods and data sources can be successfully used for aging and disease candidate gene prioritization.

Link between short tandem repeats and translation initiation site selection.

BACKGROUND: Despite their vast biological implication, the relevance of short tandem repeats (STRs)/microsatellites to the protein-coding gene translation initiation sites (TISs) remains largely unknown. METHODS: We performed an Ensembl-based comparative genomics study of all annotated orthologous TIS-flanking sequences in human and 46 other species across vertebrates, on the genomic DNA and cDNA platforms (755,956 TISs), aimed at identifying human-specific STRs in this interval. The collected data were used to examine the hypothesis of a link between STRs and TISs. BLAST was used to compare the initial five amino acids (excluding the initial methionine), codons of which were flanked by STRs in human, with the initial five amino acids of all annotated proteins for the orthologous genes in other vertebrates (total of 5,314,979 pair-wise TIS comparisons on the genomic DNA and cDNA platforms) in order to compare the number of events in which human-specific and non-specific STRs occurred with homologous and non-homologous TISs (i.e., ≥ 50% and < 50% similarity of the five amino acids). RESULTS: We detected differential distribution of the human-specific STRs in comparison to the overall distribution of STRs on the genomic DNA and cDNA platforms (Mann Whitney U test p = 1.4 × 10-11 and p < 7.9 × 10-11, respectively). We also found excess occurrence of non-homologous TISs with human-specific STRs and excess occurrence of homologous TISs with non-specific STRs on both platforms (p < 0.00001). CONCLUSION: We propose a link between STRs and TIS selection, based on the differential co-occurrence rate of human-specific STRs with non-homologous TISs and non-specific STRs with homologous TISs.

Information theory in systems biology. Part I: Gene regulatory and metabolic networks.

"A Mathematical Theory of Communication", was published in 1948 by Claude Shannon to establish a framework that is now known as information theory. In recent decades, information theory has gained much attention in the area of systems biology. The aim of this paper is to provide a systematic review of those contributions that have applied information theory in inferring or understanding of biological systems. Based on the type of system components and the interactions between them, we classify the biological systems into 4 main classes: gene regulatory, metabolic, protein-protein interaction and signaling networks. In the first part of this review, we attempt to introduce most of the existing studies on two types of biological networks, including gene regulatory and metabolic networks, which are founded on the concepts of information theory.

Transcriptomic analysis of alternative splicing events for different stages of growth and development in Sistan Yaghooti grape clusters.

Sistan Yaghooti grape variety, despite characteristics such as early ripening, is vulnerable to cluster rot due to small berries and dense clusters. In this regard, AS may serve as a regulatory mechanism during developmental processes and in response to environmental signals. RNA-Seq analysis was performed to measure gene expression and the extent of AS events in the cluster growth and development stages of Sistan Yaghooti grape. The number of AS events increased during stages, suggesting that it contributes to the grapevine's adaptability to various stresses. In addition, DEG and DAS genes showed little overlap in cluster growth stages. Functional analysis of 19,194 DAS -gene sets showed that VIT_06s0004g06670 gene is involved in the activation of calcium channels (Ca2+) through the activation of 5 PLC biosynthetic pathways. Among the 27,229 DEG -sets, VIT_07s0005g05320 gene showed higher expression. Interestingly, this gene is involved in the synthesis of an EF -hand domain-containing protein capable of binding to Ca2+ by activating 4 biochemical pathways. These genes increase cytosolic Ca2+ concentration, enhancing plant stress tolerance and resistance to cracking. These results show that AS can respond independently to different types of stress. Among the other DAS genes, the GA2ox gene (VvGA2ox) showed an increase in AS events during cluster development. This gene is critical for initiating the degradation process of GA and plays a crucial role in different stages of seed development. Therefore, it is very likely that this gene is one of the main factors responsible for the density and seedlessness of Sistan Yaghooti grape.

Precision enzyme discovery through targeted mining of metagenomic data.

Metagenomics has opened new avenues for exploring the genetic potential of uncultured microorganisms, which may serve as promising sources of enzymes and natural products for industrial applications. Identifying enzymes with improved catalytic properties from the vast amount of available metagenomic data poses a significant challenge that demands the development of novel computational and functional screening tools. The catalytic properties of all enzymes are primarily dictated by their structures, which are predominantly determined by their amino acid sequences. However, this aspect has not been fully considered in the enzyme bioprospecting processes. With the accumulating number of available enzyme sequences and the increasing demand for discovering novel biocatalysts, structural and functional modeling can be employed to identify potential enzymes with novel catalytic properties. Recent efforts to discover new polysaccharide-degrading enzymes from rumen metagenome data using homology-based searches and machine learning-based models have shown significant promise. Here, we will explore various computational approaches that can be employed to screen and shortlist metagenome-derived enzymes as potential biocatalyst candidates, in conjunction with the wet lab analytical methods traditionally used for enzyme characterization.

Complex Disease Genes Identification Using a Heterogeneous Network Embedding Approach.

Finding the causal relation between a gene and a disease using experimental approaches is a time-consuming and expensive task. However, computational approaches are cost-efficient methods for identifying candidate genes. This article proposes a new heterogeneous biological network embedding approach, named NetEM, to identify disease-associated genes. To evaluate NetEM, we examine six complex diseases, including peroxisomal disorders, sarcoma, grave's disease, lysosomal storage diseases, blood coagulation disorders, and cardiomyopathy hypertrophic. Our experiments indicate that NetEM outperforms three well-known state-of-the-art algorithms: Cardigan, DIAMOnD and GeneWanderer, in identifying disease genes. We examine TCGA data of Invasive Lobular Breast Cancer and CPTAC data of human glioblastoma as other case studies to evaluate NetEM using real data. This evaluation also indicates the validity of the method. The source codes of NetEM and data are available in the supplementary of this article.