Section 6 update: XylE as a marker gene for microorganisms

AbstractGlobal oceanographic monitoring initiatives originally measured abiotic essential ocean variables but are currently incorporating biological and metagenomic sampling programs. There is, however, a large knowledge gap on how to infer bacterial functions, the information sought by biogeochemists, ecologists, and modelers, from the bacterial taxonomic information (produced by bacterial marker gene surveys). Here, we provide a correlative understanding of how a bacterial marker gene (16S rRNA) can be used to infer latitudinal trends for metabolic pathways in global monitoring campaigns. From a transect spanning 7000 km in the South Pacific Ocean we infer ten metabolic pathways from 16S rRNA gene sequences and 11 corresponding metagenome samples, which relate to metabolic processes of primary productivity, temperature-regulated thermodynamic effects, coping strategies for nutrient limitation, energy metabolism, and organic matter degradation. This study demonstrates that low-cost, high-throughput bacterial marker gene data, can be used to infer shifts in the metabolic strategies at the community scale.

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Multi-view feature selection for identifying gene markers: a diversified biological data driven approach

BMC Bioinformatics ◽

10.1186/s12859-020-03810-0 ◽

2020 ◽

Vol 21 (S18) ◽

Author(s):

Sudipta Acharya ◽

Laizhong Cui ◽

Yi Pan

Keyword(s):

Gene Expression ◽

Feature Selection ◽

Gene Selection ◽

Marker Gene ◽

Biological Data ◽

Protein Interaction Data ◽

Marker Genes ◽

Data Sets ◽

Gene Markers ◽

Multi Objective

Abstract Background In recent years, to investigate challenging bioinformatics problems, the utilization of multiple genomic and proteomic sources has become immensely popular among researchers. One such issue is feature or gene selection and identifying relevant and non-redundant marker genes from high dimensional gene expression data sets. In that context, designing an efficient feature selection algorithm exploiting knowledge from multiple potential biological resources may be an effective way to understand the spectrum of cancer or other diseases with applications in specific epidemiology for a particular population. Results In the current article, we design the feature selection and marker gene detection as a multi-view multi-objective clustering problem. Regarding that, we propose an Unsupervised Multi-View Multi-Objective clustering-based gene selection approach called UMVMO-select. Three important resources of biological data (gene ontology, protein interaction data, protein sequence) along with gene expression values are collectively utilized to design two different views. UMVMO-select aims to reduce gene space without/minimally compromising the sample classification efficiency and determines relevant and non-redundant gene markers from three cancer gene expression benchmark data sets. Conclusion A thorough comparative analysis has been performed with five clustering and nine existing feature selection methods with respect to several internal and external validity metrics. Obtained results reveal the supremacy of the proposed method. Reported results are also validated through a proper biological significance test and heatmap plotting.

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Signaling in the Tomato Immunity against Fusarium oxysporum

Molecules ◽

10.3390/molecules26071818 ◽

2021 ◽

Vol 26 (7) ◽

pp. 1818

Author(s):

Francisco Hernández-Aparicio ◽

Purificación Lisón ◽

Ismael Rodrigo ◽

José María Bellés ◽

M. Pilar López-Gresa

Keyword(s):

Fusarium Oxysporum ◽

Environmental Sustainability ◽

Control Strategies ◽

Marker Gene ◽

Crop Protection ◽

Incompatible Interaction ◽

Tomato Plants ◽

Fatty Acid Derivatives ◽

Resistance Inducers ◽

Plant Pathogen Interactions

New strategies of control need to be developed with the aim of economic and environmental sustainability in plant and crop protection. Metabolomics is an excellent platform for both understanding the complex plant–pathogen interactions and unraveling new chemical control strategies. GC-MS-based metabolomics, along with a phytohormone analysis of a compatible and incompatible interaction between tomato plants and Fusarium oxysporum f. sp. lycopersici, revealed the specific volatile chemical composition and the plant signals associated with them. The susceptible tomato plants were characterized by the over-emission of methyl- and ethyl-salicylate as well as some fatty acid derivatives, along with an activation of salicylic acid and abscisic acid signaling. In contrast, terpenoids, benzenoids, and 2-ethylhexanoic acid were differentially emitted by plants undergoing an incompatible interaction, together with the activation of the jasmonic acid (JA) pathway. In accordance with this response, a higher expression of several genes participating in the biosynthesis of these volatiles, such as MTS1, TomloxC,TomloxD, and AOS, as well as JAZ7, a JA marker gene, was found to be induced by the fungus in these resistant plants. The characterized metabolome of the immune tomato plants could lead to the development of new resistance inducers against Fusarium wilt treatment.

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