Challenges and Approaches of Culturing the Unculturable Archaea.

Since Carl Woese's discovery of archaea as a third domain of life, numerous archaeal species have been discovered, yet archaeal diversity is poorly characterized. Culturing archaea is complicated, but several queries about archaeal cell biology, evolution, physiology, and diversity need to be solved by culturing and culture-dependent techniques. Increasing interest in demand for innovative culturing methods has led to various technological and methodological advances. The current review explains frequent hurdles hindering uncultured archaea isolation and discusses features for more archaeal cultivation. This review also discusses successful strategies and available media for archaeal culturing, which might be helpful for future culturing practices.

Biofilms as Battlefield Armor for Bacteria against Antibiotics: Challenges and Combating Strategies.

Bacterial biofilms are formed by communities, which are encased in a matrix of extracellular polymeric substances (EPS). Notably, bacteria in biofilms display a set of 'emergent properties' that vary considerably from free-living bacterial cells. Biofilms help bacteria to survive under multiple stressful conditions such as providing immunity against antibiotics. Apart from the provision of multi-layered defense for enabling poor antibiotic absorption and adaptive persistor cells, biofilms utilize their extracellular components, e.g., extracellular DNA (eDNA), chemical-like catalase, various genes and their regulators to combat antibiotics. The response of biofilms depends on the type of antibiotic that comes into contact with biofilms. For example, excessive production of eDNA exerts resistance against cell wall and DNA targeting antibiotics and the release of antagonist chemicals neutralizes cell membrane inhibitors, whereas the induction of protein and folic acid antibiotics inside cells is lowered by mutating genes and their regulators. Here, we review the current state of knowledge of biofilm-based resistance to various antibiotic classes in bacteria and genes responsible for biofilm development, and the key role of quorum sensing in developing biofilms and antibiotic resistance is also discussed. In this review, we also highlight new and modified techniques such as CRISPR/Cas, nanotechnology and bacteriophage therapy. These technologies might be useful to eliminate pathogens residing in biofilms by combating biofilm-induced antibiotic resistance and making this world free of antibiotic resistance.

Computational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique.

Promotors are those genomic regions on the upstream of genes, which are bound by RNA polymerase for starting gene transcription. Because it is the most critical element of gene expression, the recognition of promoters is crucial to understand the regulation of gene expression. This study aimed to develop a machine learning-based model to predict promotors in Agrobacterium tumefaciens (A. tumefaciens) strain C58. In the model, promotor sequences were encoded by three different kinds of feature descriptors, namely, accumulated nucleotide frequency, k-mer nucleotide composition, and binary encodings. The obtained features were optimized by using correlation and the mRMR-based algorithm. These optimized features were inputted into a random forest (RF) classifier to discriminate promotor sequences from non-promotor sequences in A. tumefaciens strain C58. The examination of 10-fold cross-validation showed that the proposed model could yield an overall accuracy of 0.837. This model will provide help for the study of promoters in A. tumefaciens C58 strain.

Computational identification of N4-methylcytosine sites in the mouse genome with machine-learning method.

N4-methylcytosine (4mC) is a kind of DNA modification which could regulate multiple biological processes. Correctly identifying 4mC sites in genomic sequences can provide precise knowledge about their genetic roles. This study aimed to develop an ensemble model to predict 4mC sites in the mouse genome. In the proposed model, DNA sequences were encoded by k-mer, enhanced nucleic acid composition and composition of k-spaced nucleic acid pairs. Subsequently, these features were optimized by using minimum redundancy maximum relevance (mRMR) with incremental feature selection (IFS) and five-fold cross-validation. The obtained optimal features were inputted into random forest classifier for discriminating 4mC from non-4mC sites in mouse. On the independent dataset, our model could yield the overall accuracy of 85.41%, which was approximately 3.8% -6.3% higher than the two existing models, i4mC-Mouse and 4mCpred-EL respectively. The data and source code of the model can be freely download from https://github.com/linDing-groups/model_4mc.

Ligand-Based Pharmacophore Modeling and Virtual Screening to Discover Novel CYP1A1 Inhibitors.

Backgound: Cytochrome P450, family 1, subfamily A, polypeptide 1 (CYP1A1) is an imperative enzyme due to its immersion in the biotransformation of a wide range of drugs and other xenobiotics. The involvement of enzymes in drug metabolism indicates an effective drug target for the development of novel therapeutics. The discovery of CYP1A1 specific inhibitors would be of particular relevance for the clinical pharmacology. METHODS: In the current work, in silico approaches were utilized to identify the novel potential compounds through a diverse set of reported inhibitors against CYP1A1. A dataset of reported compounds against CYP1 belongs to 10 different classes (alkaloids, coumarins, flavonoids, natural compounds, synthetic inhibitors, drugs, MBI's, PAHs, naphthoquinone and stilbenoids) was retrieved and utilized for the comparative molecular docking analyses followed by pharmacophore modeling. The total eleven novel compounds were scrutinized on the basis of the highest binding affinities and least binding energy values. RESULTS: ZINC08792486 compound attained the highest gold fitness score of 90.11 against CYP1A1 among all the scrutinized molecules. CONCLUSION: It has been elucidated that the residues Phe-224, Gly-316 and Ala-317 were conserved in all ligand-receptor interactions and critical for the development of effective therapies. The ADMET property analyses also predict better absorption and distribution of the selected hits that may be used in the future for in vitro validations and drug development.