Background: While the genusVariovoraxis known for its aromatic compound metabolism, no detailed study of the peripheral and central pathways of aromatic compound degradation has yet been reported.Variovoraxsp. PAMC26660 is a lichen-associated bacterium isolated from Antarctica. The work presents the genome-based elucidation of peripheral and central catabolic pathways of aromatic compound degradation genes inVariovoraxsp. PAMC26660. Additionally, the accessory, core and unique genes were identified amongVariovoraxspecies using the pan genome analysis tool. A detailed analysis of the genes related to xenobiotic metabolism revealed the potential roles ofVariovoraxsp. PAMC26660 and other species in bioremediation.Results: TYGS analysis, dDDH, phylogenetic placement and average nucleotide identity (ANI) analysis identified the strain asVariovoraxsp. Cell morphology was assessed using scanning electron microscopy (SEM). On analysis of the core, accessory, and unique genes, xenobiotic metabolism accounted only for the accessory and unique genes. On detailed analysis of the aromatic compound catabolic genes, peripheral pathway related to 4-hydroxybenzoate (4-HB) degradation was found among all species while phenylacetate and tyrosine degradation pathways were present in most of the species including PAMC26660. Likewise, central catabolic pathways, like protocatechuate, gentisate, homogentisate, and phenylacetyl-CoA, were also present. The peripheral pathway for 4-HB degradation was functionally tested using PAMC26660, which resulted in the growth using it as a sole source of carbon.Conclusions: Computational tools for genome and pan genome analysis are important to understand the behavior of an organism. Xenobiotic metabolism-related genes, that only account for the accessory and unique genes infer evolution through events like lateral gene transfer, mutation and gene rearrangement. 4-HB, an aromatic compound present among lichen species is utilized by lichen-associatedVariovoraxsp. PAMC26660 as the sole source of carbon. The strain holds genes and pathways for its utilization. Overall, this study outlines the importance ofVariovoraxin bioremediation and presents the genomic information of the species.

Background: The Arthrobacter group is a known set of bacteria from cold regions, the species of which are highly likely to play diverse roles at low temperatures. However, their survival mechanisms in cold regions such as Antarctica are not yet fully understood. In this study, we compared the genomes of 16 strains within the Arthrobacter group, including strain PAMC25564, to identify genomic features that help it to survive in the cold environment. Results: Using 16 S rRNA sequence analysis, we found and identified a species of Arthrobacter isolated from cryoconite. We designated it as strain PAMC25564 and elucidated its complete genome sequence. The genome of PAMC25564 is composed of a circular chromosome of 4,170,970 bp with a GC content of 66.74 % and is predicted to include 3,829 genes of which 3,613 are protein coding, 147 are pseudogenes, 15 are rRNA coding, and 51 are tRNA coding. In addition, we provide insight into the redundancy of the genes using comparative genomics and suggest that PAMC25564 has glycogen and trehalose metabolism pathways (biosynthesis and degradation) associated with carbohydrate active enzyme (CAZymes). We also explain how the PAMC26654 produces energy in an extreme environment, wherein it utilizes polysaccharide or carbohydrate degradation as a source of energy. The genetic pattern analysis of CAZymes in cold-adapted bacteria can help to determine how they adapt and survive in such environments. Conclusions: We have characterized the complete Arthrobacter sp. PAMC25564 genome and used comparative analysis to provide insight into the redundancy of its CAZymes for potential cold adaptation. This provides a foundation to understanding how the Arthrobacter strain produces energy in an extreme environment, which is by way of CAZymes, consistent with reports on the use of these specialized enzymes in cold environments. Knowledge of glycogen metabolism and cold adaptation mechanisms in Arthrobacter species may promote in-depth research and subsequent application in low-temperature biotechnology.

The rapid growth of uncharacterized enzymes and their functional diversity urge accurate and trustworthy computational functional annotation tools. However, current state-of-the-art models lack trustworthiness on the prediction of the multilabel classification problem with thousands of classes. Here, we demonstrate that a novel evidential deep learning model (named ECPICK) makes trustworthy predictions of enzyme commission (EC) numbers with data-driven domain-relevant evidence, which results in significantly enhanced predictive power and the capability to discover potential new motif sites. ECPICK learns complex sequential patterns of amino acids and their hierarchical structures from 20 million enzyme data. ECPICK identifies significant amino acids that contribute to the prediction without multiple sequence alignment. Our intensive assessment showed not only outstanding enhancement of predictive performance on the largest databases of Uniprot, Protein Data Bank (PDB) and Kyoto Encyclopedia of Genes and Genomes (KEGG), but also a capability to discover new motif sites in microorganisms. ECPICK is a reliable EC number prediction tool to identify protein functions of an increasing number of uncharacterized enzymes.

Background: Metabolism including anabolism and catabolism is a prerequisite phenomenon for all living organisms. Anabolism refers to the synthesis of the entire compound needed by a species. Catabolism refers to the breakdown of molecules to obtain energy. Many metabolic pathways are undisclosed and many organism-specific enzymes involved in metabolism are misplaced. When predicting a specific metabolic pathway of a microorganism, the first and foremost steps is to explore available online databases. Among many online databases, KEGG and MetaCyc pathway databases were used to deduce trehalose metabolic network for bacteria Variovorax sp. PAMC28711. Trehalose, a disaccharide, is used by the microorganism as an alternative carbon source. Results: While using KEGG and MetaCyc databases, we found that the KEGG pathway database had one missing enzyme (maltooligosyl-trehalose synthase, EC 5.4.99.15). The MetaCyc pathway database also had some enzymes. However, when we used RAST to annotate the entire genome of Variovorax sp. PAMC28711, we found that all enzymes that were missing in KEGG and MetaCyc databases were involved in the trehalose metabolic pathway. Conclusions: Findings of this study shed light on bioinformatics tools and raise awareness among researchers about the importance of conducting detailed investigation before proceeding with any further work. While such comparison for databases such as KEGG and MetaCyc has been done before, it has never been done with a specific microbial pathway. Such studies are useful for future improvement of bioinformatics tools to reduce limitations.

Hypoxia-inducible factor-1 alpha (HIF-1α) is overexpressed in cancer, leading to a poor prognosis in patients. Diverse cellular factors are able to regulate HIF-1α expression in hypoxia and even in non-hypoxic conditions, affecting its progression and malignant characteristics by regulating the expression of the HIF-1α target genes that are involved in cell survival, angiogenesis, metabolism, therapeutic resistance, et cetera. Numerous studies have exhibited the anti-cancer effect of HIF-1α inhibition itself and the augmentation of anti-cancer treatment efficacy by interfering with HIF-1α-mediated signaling. The anti-cancer effect of plant-derived phytochemicals has been evaluated, and they have been found to possess significant therapeutic potentials against numerous cancer types. A better understanding of phytochemicals is indispensable for establishing advanced strategies for cancer therapy. This article reviews the anti-cancer effect of phytochemicals in connection with HIF-1α regulation.