Study of Heuristic IR Constraints Under Function Discovery Framework

Cold-loving microorganisms of all three domains of life have unique and special abilities that allow them to live in harsh environments. They have acquired structural and molecular mechanisms of adaptation to the cold that include the production of anti-freeze proteins, carbohydrate-based extracellular polymeric substances and lipids which serve as cryo- and osmoprotectants by maintaining the fluidity of their membranes. They also produce a wide diversity of pigmented molecules to obtain energy, carry out photosynthesis, increase their resistance to stress and provide them with ultraviolet light protection. Recently developed analytical techniques have been applied as high-throughoutput technologies for function discovery and for reconstructing functional networks in psychrophiles. Among them, omics deserve special mention, such as genomics, transcriptomics, proteomics, glycomics, lipidomics and metabolomics. These techniques have allowed the identification of microorganisms and the study of their biogeochemical activities. They have also made it possible to infer their metabolic capacities and identify the biomolecules that are parts of their structures or that they secrete into the environment, which can be useful in various fields of biotechnology. This Review summarizes current knowledge on psychrophiles as sources of biomolecules and the metabolic pathways for their production. New strategies and next-generation approaches are needed to increase the chances of discovering new biomolecules.

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Functionality and genomics of selenium and vitamin E supplementation in ruminants

Animal Production Science ◽

10.1071/an15263 ◽

2016 ◽

Vol 56 (8) ◽

pp. 1285 ◽

Cited By ~ 6

Author(s):

S. S. Chauhan ◽

F. Liu ◽

B. J. Leury ◽

J. J. Cottrell ◽

P. Celi ◽

...

Keyword(s):

Vitamin E ◽

Functional Foods ◽

Transition Period ◽

Animal Health ◽

Antioxidant Properties ◽

Meat Products ◽

Lipid Hydroperoxides ◽

Great Opportunity ◽

Multiple Functions ◽

Function Discovery

Selenium (Se) and vitamin E are essential micronutrients for animal health and production. The major function of both Se and vitamin E is to prevent the oxidative damage of biological membranes and they can influence growth, reproduction, immune function, health, and product quality in ruminants. Both Se and vitamin E are important for maintaining low cellular and systemic concentrations of reactive oxygen species and lipid hydroperoxides, to ensure optimum cellular function. Discovery of various selenoproteins and vitamin E-responsive genes has contributed significantly to improving our understanding about multiple functions of Se and vitamin E. There is evidence that these functions extend beyond the classical antioxidant properties to immunomodulation and intracellular cell signalling and gene regulation. Research in recent years has also shown that supranutritional supplementation of Se and vitamin E is required to improve the performance of ruminants under certain stressful conditions such as heat stress and during transition period. Considering the growing awareness among consumers of the benefits of antioxidant-rich food, there is a great opportunity for the livestock industries to focus on producing antioxidant-enriched milk and meat products or functional foods. The present review focuses on the recent developments in understanding multiple functions of Se and vitamin E at the cellular and molecular level and the effects of supranutritional supplementation on ruminant performance. In addition, the paper also articulates the potential opportunities to produce functional foods enriched with antioxidants, and underlines the need for optimum supplementation of these micronutrients for efficient ruminant production.

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Metabo-Endotypes of Asthma Reveal Clinically Important Differences in Lung Function: Discovery and validation in two TOPMed Cohorts

10.21203/rs.3.rs-358819/v1 ◽

2021 ◽

Author(s):

Rachel Kelly ◽

Kevin Mendez ◽

Mengna Huang ◽

Brian Hobbs ◽

Clary Clish ◽

...

Keyword(s):

Fatty Acids ◽

Pulmonary Surfactant ◽

Vital Capacity ◽

Molecular Classification ◽

Forced Expiratory Volume ◽

Metabolomic Profiling ◽

Metabolomic Data ◽

Heterogeneous Nature ◽

Function Discovery ◽

Current Guidelines

Abstract Current guidelines do not sufficiently capture the heterogeneous nature of asthma; a detailed molecular classification is needed. Metabolomics represents a novel and compelling approach to derive asthma endotypes, i.e., subtypes defined by functional/pathobiological mechanisms. In two cohorts of asthmatics, untargeted metabolomic profiling and Similarity Network Fusion was used to derive and validate five “metabo-endotypes” of asthma, which displayed significant differences in asthma-relevant phenotypes including pre-bronchodilator and post-bronchodilator forced expiratory volume/forced vital capacity (FEV1/FVC). The “most-severe” asthma metabo-endotype was defined by the lowest FEV1/FVC and characterized by altered levels of phospholipids and polyunsaturated fatty acids, suggesting dysregulation of pulmonary surfactant homeostasis. This was supported by genetic analyses as members of this endotype were more likely to carry variants in key pulmonary surfactant regulation genes including BMPR1B (meta-analyzed p=2.8x10-4) and BMP3 (meta-analyzed p=5.23x10-4). These findings suggest clinically meaningful endotypes can be derived and validated using metabolomic data. Interrogating the drivers of these metabo-endotypes can help understand their pathophysiology.

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Colocality to Cofunctionality: Eukaryotic Gene Neighborhoods as a Resource for Function Discovery

Molecular Biology and Evolution ◽

10.1093/molbev/msaa221 ◽

2020 ◽

Cited By ~ 1

Author(s):

Fatima Foflonker ◽

Crysten E Blaby-Haas

Keyword(s):

Green Algae ◽

Sequence Similarity ◽

Phosphoglycerate Kinase ◽

Orthologous Gene ◽

Model Organisms ◽

Comparative Genomic ◽

Evolutionary Origins ◽

Eukaryotic Gene ◽

Arsenic Detoxification ◽

Function Discovery

Abstract Diverging from the classic paradigm of random gene order in eukaryotes, gene proximity can be leveraged to systematically identify functionally related gene neighborhoods in eukaryotes, utilizing techniques pioneered in bacteria. Current methods of identifying gene neighborhoods typically rely on sequence similarity to characterized gene products. However, this approach is not robust for nonmodel organisms like algae, which are evolutionarily distant from well-characterized model organisms. Here, we utilize a comparative genomic approach to identify evolutionarily conserved proximal orthologous gene pairs conserved across at least two taxonomic classes of green algae. A total of 317 gene neighborhoods were identified. In some cases, gene proximity appears to have been conserved since before the streptophyte–chlorophyte split, 1,000 Ma. Using functional inferences derived from reconstructed evolutionary relationships, we identified several novel functional clusters. A putative mycosporine-like amino acid, “sunscreen,” neighborhood contains genes similar to either vertebrate or cyanobacterial pathways, suggesting a novel mosaic biosynthetic pathway in green algae. One of two putative arsenic-detoxification neighborhoods includes an organoarsenical transporter (ArsJ), a glyceraldehyde 3-phosphate dehydrogenase-like gene, homologs of which are involved in arsenic detoxification in bacteria, and a novel algal-specific phosphoglycerate kinase-like gene. Mutants of the ArsJ-like transporter and phosphoglycerate kinase-like genes in Chlamydomonas reinhardtii were found to be sensitive to arsenate, providing experimental support for the role of these identified neighbors in resistance to arsenate. Potential evolutionary origins of neighborhoods are discussed, and updated annotations for formerly poorly annotated genes are presented, highlighting the potential of this strategy for functional annotation.

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The Escherichia coli transcriptome mostly consists of independently regulated modules

Nature Communications ◽

10.1038/s41467-019-13483-w ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 29

Author(s):

Anand V. Sastry ◽

Ye Gao ◽

Richard Szubin ◽

Ying Hefner ◽

Sibei Xu ◽

...

Keyword(s):

Escherichia Coli ◽

Transcriptional Regulatory Network ◽

Transcriptional Regulators ◽

Specific Gene ◽

E Coli ◽

Gene Sets ◽

Transcriptional Regulatory ◽

Genetic Perturbations ◽

Function Discovery ◽

Multiple Strains

AbstractUnderlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome.

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