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.

The miR-142 Suppresses U-87 Glioblastoma Cell Growth by Targeting EGFR Oncogenic Signaling Pathway.

Glioblastoma is the most lethal malignancy of the brain and is resistant to conventional cancer treatments. Gene-therapy approaches like using tumor suppressor miRNAs are promising in the treatment of glioblastoma. They control the expression of oncogenes and influence tumor features and behaviors. Therefore, in the present study, it was predicted that miR-142 regulates oncogenic epidermal growth factor receptor (EGFR) signaling pathway via TargetScan and miRWalk online tools. Its differential expression level was reduced in glioblastoma according to the previous microarray results, and its predicted target genes were upregulated, as shown by the Expression Atlas. The miR-142 was overexpressed in U-87 glioblastoma cells via lentiviral transduction, and the way it influences proliferation and migration of cells was investigated through MTT assay and wound healing assay. Apoptosis rate was also measured via the Annexin V assay, and cell-cycle analysis was done. Then, real-time polymerase chain reaction (real-time PCR) and western blotting were performed to assess fold changes in mRNA and protein levels of the miR-142 predicted targets. Direct target genes of miR-142 were confirmed through a dual-luciferase reporter assay. The miR-142 significantly suppressed cell proliferation and migration and induced apoptosis and cell-cycle arrest in U-87 glioblastoma cells. This was accompanied by a decrease in expression of SHC adaptor protein 4 (SHC4), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), v-akt murine thymoma viral oncogene homolog 1 (AKT1), Kirsten rat sarcoma viral oncogene homolog (KRAS), and mitogen-activated protein kinase 8 (MAPK8) oncogenes at mRNA and protein levels in glioblastoma cells. Also, AKT1 was demonstrated as a direct target of miR-142. Overall, miR-424 acts as tumor suppressor miRNA in glioblastoma cells.

MCIC: Automated Identification of Cellulases From Metagenomic Data and Characterization Based on Temperature and pH Dependence.

As the availability of high-throughput metagenomic data is increasing, agile and accurate tools are required to analyze and exploit this valuable and plentiful resource. Cellulose-degrading enzymes have various applications, and finding appropriate cellulases for different purposes is becoming increasingly challenging. An in silico screening method for high-throughput data can be of great assistance when combined with the characterization of thermal and pH dependence. By this means, various metagenomic sources with high cellulolytic potentials can be explored. Using a sequence similarity-based annotation and an ensemble of supervised learning algorithms, this study aims to identify and characterize cellulolytic enzymes from a given high-throughput metagenomic data based on optimum temperature and pH. The prediction performance of MCIC (metagenome cellulase identification and characterization) was evaluated through multiple iterations of sixfold cross-validation tests. This tool was also implemented for a comparative analysis of four metagenomic sources to estimate their cellulolytic profile and capabilities. For experimental validation of MCIC's screening and prediction abilities, two identified enzymes from cattle rumen were subjected to cloning, expression, and characterization. To the best of our knowledge, this is the first time that a sequence-similarity based method is used alongside an ensemble machine learning model to identify and characterize cellulase enzymes from extensive metagenomic data. This study highlights the strength of machine learning techniques to predict enzymatic properties solely based on their sequence. MCIC is freely available as a python package and standalone toolkit for Windows and Linux-based operating systems with several functions to facilitate the screening and thermal and pH dependence prediction of cellulases.

BIPEP: Sequence-based Prediction of Biofilm Inhibitory Peptides Using a Combination of NMR and Physicochemical Descriptors.

Biofilms are biological systems that are formed by a community of microorganisms in which microbial cells are connected on a surface within a self-produced matrix of an extracellular polymeric substance. On some occasions, microorganisms use biofilms to protect themselves against the harmful effects of the host body immune system and the surrounding environment, hence increasing their chances of survival against the various anti-microbial agents. Biofilms play a crucial role in medicine and industry because of the problems they cause. Designing agents that inhibit bacterial biofilm formation is very costly and takes too much time in the laboratory to be discovered and validated. Therefore, developing computational tools for the prediction of biofilm inhibitor peptides is inevitable and important. Here, we present a computational prediction tool to screen the vast number of peptide sequences and select potential candidate peptides for further lab experiments and validation. In this learning model, different feature vectors, extracted from the peptide primary structure, are exploited to learn patterns from the sequence of biofilm inhibitory peptides. Various classification algorithms including SVM, random forest, and k-nearest neighbor have been examined to evaluate their performance. Overall, our approach showed better prediction in comparison with other prediction methods. In this study, for the first time, we applied features extracted from NMR spectra of amino acids along with physicochemical features. Although each group of features showed good discrimination potential alone, we used a combination of features to enhance the performance of our method. Our prediction tool is freely available.

Co-abundance analysis reveals hidden players associated with high methane yield phenotype in sheep rumen microbiome.

Rumen microbial environment hosts a variety of microorganisms that interact with each other to carry out the feed digestion and generation of several by-products especially methane, which plays an essential role in global warming as a greenhouse gas. However, due to its multi-factorial nature, the exact cause of methane production in the rumen has not yet been fully determined. The current study is an attempt to use system modeling to analyze the relationship between interacting components of rumen microbiome and its role in methane production. Metagenomic data of sheep rumen, with equal numbers of high methane yield (HMY) and low methane yield (LMY) samples, were used. As a well-known approach for the systematic comparative study of complex traits, the co-abundance networks were constructed in both operational taxonomic unit (OTU) and gene levels. A gene-catalog of 1,444 different rumen microbial strains was developed as a reference to measure gene abundances. The results from both types of co-abundance networks showed that methanogens, which are the main ruminal source for methanogenesis, need other microbial species to accomplish the task of methane production through producing the main precursor molecules like H2 and acetate for methanogenesis pathway as their byproducts. KEGG Orthology(KO) analysis of the current study shows that the metabolism and growth rate of methanogens will be increased due to the higher rate of the metabolism and carbohydrate/fiber digestion pathways in the hidden elements. This finding proposes that any ruminant methane yield alteration strategy should consider complex interactions of rumen microbiome components as one tightly integrated unit rather than several separate parts.

miR-548x and miR-4698 controlled cell proliferation by affecting the PI3K/AKT signaling pathway in Glioblastoma cell lines.

Glioblastoma multiforme (GBM) is the most aggressive and prevalent form of brain tumor cancers that originate from glial cells. This study proposed to investigate the effect of miR-548x and miR-4698 on the proliferation and the PI3K/AKT signaling pathway in glioblastoma cell lines. The molecular features of glioblastoma were studied using KEGG and TCGA sites. Next, by using miRwalk 2.0 and TargetScan version 7.1, the microRNAs that target critical genes in the PI3k/AKT pathway were selected according to score. The pre-miR-548x and pre-miR-4698 were cloned in a pCDH plasmid to produced lentiviral vector. The expression levels of miR-548x, miR-4698 and target genes were detected by qRT-PCR. The MTT, cell cycle, annexin and colony formation assay was used to detect the cell proliferation. MiR-548x and miR-4698 predicted target genes (Rheb, AKT1, mTOR, PDK1) were also evaluated by luciferase assay. The expression of AKT was detected by western blotting. Our results described that overexpression of miR-548x and miR-4698 could inhibit proliferation of A-172 and U251 cells. Also, miR-548x promoted the cell cycle arrest of GBM cell lines. The luciferase reporter assay results showed the 3' UTR of PDK1, RHEB, and mTOR are direct targets of the miR-548x and miR-4698. Too, the western blot analysis revealed that miR-548x and miR-4698 could downregulate the AKT1 protein expression. Overall, our findings suggest that miR-548x and miR-4698 could function as tumor suppressor genes in glioblastoma by controlling the PI3K/AKT signaling pathway and may act as gene therapy for clinical treatment of glioblastoma multiforme.