scholarly journals Serotonin signaling modulates aging-associated metabolic network integrity in response to nutrient choice in Drosophila melanogaster

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yang Lyu ◽  
Daniel E. L. Promislow ◽  
Scott D. Pletcher
Keyword(s):  
Drosophila Melanogaster ◽  
Metabolic Network ◽  
Biological Networks ◽  
Serotonin Receptor ◽  
Metabolic Networks ◽  
Single Gene ◽  
Complex Interactions ◽  
Serotonin Signaling ◽  
Vinegar Fly ◽  
Increased Susceptibility

AbstractAging arises from complex interactions among multiple biochemical products. Systems-level analyses of biological networks may provide insights into the causes and consequences of aging that evade single-gene studies. We have previously found that dietary choice is sufficient to modulate aging in the vinegar fly, Drosophila melanogaster. Here we show that nutrient choice influenced several measures of metabolic network integrity, including connectivity, community structure, and robustness. Importantly, these effects are mediated by serotonin signaling, as a mutation in serotonin receptor 2A (5-HT2A) eliminated the effects of nutrient choice. Changes in network structure were associated with organism resilience and increased susceptibility to genetic perturbation. Our data suggest that the behavioral or perceptual consequences of exposure to individual macronutrients, involving serotonin signaling through 5-HT2A, qualitatively change the state of metabolic networks throughout the organism from one that is highly connected and robust to one that is fragmented, fragile, and vulnerable to perturbations.

scholarly journals Serotonin signaling modulates aging-associated metabolic network integrity in response to nutrient choice

2021 ◽  
Author(s):  
Yang Lyu ◽  
Daniel EL Promislow ◽  
Scott Pletcher
Keyword(s):  
Metabolic Network ◽  
Biological Networks ◽  
Serotonin Receptor ◽  
Metabolic Networks ◽  
Single Gene ◽  
Complex Interactions ◽  
Dietary Choice ◽  
Metabolic Products ◽  
Serotonin Signaling ◽  
Vinegar Fly

Aging arises from complex interactions among multiple biochemical and metabolic products. Systems-level analyses of biological networks may provide insights into the causes and consequences of aging that evade single-gene or single-pathway studies. We have shown that dietary choice per se is sufficient to modulate aging and metabolic health in the vinegar fly, Drosophila melanogaster. In other words, how each meal is presented, or the way in which it is eaten, is influential, independent of the amount or type of nutrients that are consumed. For example, when major macronutrients were presented separately, male flies exhibited a rapid and significant increase in mortality rate and a reduced overall lifespan relative to those fed a single medium containing both sugar and yeast. These effects are mediated by specific components of serotonin signaling, as a mutation in serotonin receptor 2A (5-HT2A) eliminated the effects of dietary choice. Here we show that dietary choice influenced several measures of metabolic network integrity, including connectivity, average shortest distance, community structure, and robustness, with the effects of the latter two restricted to tissues in the head. These changes in network structure were associated with organism resilience and increased susceptibility to genetic perturbation, as measured by starvation survival. Our data suggest that the behavioral or perceptual consequences of exposure to individual macronutrients, involving serotonin signaling through 5-HT2A, qualitatively change the state of metabolic networks throughout the organism from one that is highly connected and robust to one that is fragmented, fragile, and vulnerable to perturbations.

Systematic Investigation of Mouse Models of Parkinson’s Disease by Transcriptome Mapping on a Brain-Specific Genome-Scale Metabolic Network

2021 ◽  
Author(s):  
Ecehan Abdik ◽  
Tunahan Cakir
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Metabolic Network ◽  
Mouse Models ◽  
Mus Musculus ◽  
Metabolic Networks ◽  
Systematic Investigation ◽  
Omics Data ◽  
Metabolic Alterations ◽  
Genome Scale

Genome-scale metabolic networks enable systemic investigation of metabolic alterations caused by diseases by providing interpretation of omics data. Although Mus musculus (mouse) is one of the most commonly used model...

scholarly journals Molecular Cloning and Functional Characterization of Three 5-HT Receptor Genes (HTR1B, HTR1E and HTR1F) in Chickens

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 891
Author(s):  
Caiyun Sun ◽  
Yang Qiu ◽  
Qin Ren ◽  
Xiao Zhang ◽  
Baolong Cao ◽  
...  
Keyword(s):  
Serotonin Receptor ◽  
Functional Characterization ◽  
Brain Regions ◽  
Luciferase Reporter ◽  
Molecular Characteristics ◽  
Intracellular Camp ◽  
Pituitary Cells ◽  
Dependent Manner ◽  
Serotonin Signaling ◽  
Reporter Assays

The serotonin (5-hydroxytryptamine, 5-HT) signaling system is involved in a variety of physiological functions, including the control of cognition, reward, learning, memory, and vasoconstriction in vertebrates. Contrary to the extensive studies in the mammalian system, little is known about the molecular characteristics of the avian serotonin signaling network. In this study, we cloned and characterized the full-length cDNA of three serotonin receptor genes (HTR1B, HTR1E and HTR1F) in chicken pituitaries. Synteny analyses indicated that HTR1B, HTR1E and HTR1F were highly conserved across vertebrates. Cell-based luciferase reporter assays showed that the three chicken HTRs were functional, capable of binding their natural ligands (5-HT) or selective agonists (CP94253, BRL54443, and LY344864) and inhibiting intracellular cAMP production in a dose-dependent manner. Moreover, activation of these receptors could stimulate the MAPK/ERK signaling cascade. Quantitative real-time PCR analyses revealed that HTR1B, HTR1E and HTR1F were primarily expressed in various brain regions and the pituitary. In cultured chicken pituitary cells, we found that LY344864 could significantly inhibit the secretion of PRL stimulated by vasoactive intestinal peptide (VIP) or forskolin, revealing that HTR1F might be involved in the release of prolactin in chicken. Our findings provide insights into the molecular mechanism and facilitate a better understanding of the serotonergic modulation via HTR1B, HTR1E and HTR1F in avian species.

scholarly journals tarsal-less is expressed as a gap gene but has no gap gene phenotype in the moth midge Clogmia albipunctata

2018 ◽  
Vol 5 (8) ◽  
pp. 180458 ◽  
Author(s):  
Eva Jiménez-Guri ◽  
Karl R. Wotton ◽  
Johannes Jaeger
Keyword(s):  
Gene Expression ◽  
Drosophila Melanogaster ◽  
Rna Interference ◽  
Gap Gene ◽  
Gap Genes ◽  
Subtle Effect ◽  
Bona Fide ◽  
Vinegar Fly ◽  
Clogmia Albipunctata ◽  
Overlapping Domains

Gap genes are involved in segment determination during early development of the vinegar fly Drosophila melanogaster and other dipteran insects (flies, midges and mosquitoes). They are expressed in overlapping domains along the antero-posterior (A–P) axis of the blastoderm embryo. While gap domains cover the entire length of the A–P axis in Drosophila, there is a region in the blastoderm of the moth midge Clogmia albipunctata , which lacks canonical gap gene expression. Is a non-canonical gap gene functioning in this area? Here, we characterize tarsal-less ( tal ) in C. albipunctata . The homologue of tal in the flour beetle Tribolium castaneum (called milles-pattes, mlpt ) is a bona fide gap gene. We find that Ca-tal is expressed in the region previously reported as lacking gap gene expression. Using RNA interference, we study the interaction of Ca-tal with gap genes. We show that Ca-tal is regulated by gap genes, but only has a very subtle effect on tailless (Ca-tll), while not affecting other gap genes at all. Moreover, cuticle phenotypes of Ca-tal depleted embryos do not show any gap phenotype. We conclude that Ca-tal is expressed and regulated like a gap gene, but does not function as a gap gene in C. albipunctata .

Synaptic transmission reversibly conditioned by single-gene mutation in Drosophila melanogaster

Nature ◽  
1976 ◽  
Vol 259 (5543) ◽  
pp. 489-491 ◽  
Author(s):  
KAZUO IKEDA ◽  
SEIJI OZAWA ◽  
SUSUMU HAGIWARA
Keyword(s):  
Drosophila Melanogaster ◽  
Synaptic Transmission ◽  
Gene Mutation ◽  
Single Gene ◽  
Single Gene Mutation

scholarly journals DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks

2020 ◽  
Author(s):  
Pablo Rodríguez-Mier ◽  
Nathalie Poupin ◽  
Carlo de Blasio ◽  
Laurent Le Cam ◽  
Fabien Jourdan
Keyword(s):  
Metabolic Network ◽  
Metabolic Networks ◽  
Relevant Information ◽  
Experimental Information ◽  
Correct Identification ◽  
Good Representation ◽  
Metabolic State ◽  
Whole Space ◽  
Context Specific ◽  
Optimal Networks

AbstractThe correct identification of metabolic activity in tissues or cells under different environmental or genetic conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific organism and condition, containing only the reactions predicted to be active in such context. A major limitation of this approach is that the available information does not generally allow for an unambiguous characterization of the corresponding optimal metabolic sub-network, i.e., there are usually many different sub-network that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic state. In this work, we formalize the problem of enumeration of optimal metabolic networks, we implement a set of techniques that can be used to enumerate optimal networks, and we introduce DEXOM, a novel strategy for diversity-based extraction of optimal metabolic networks. Instead of enumerating the whole space of optimal metabolic networks, which can be computationally intractable, DEXOM samples solutions from the set of optimal metabolic sub-networks maximizing diversity in order to obtain a good representation of the possible metabolic state. We evaluate the solution diversity of the different techniques using simulated and real datasets, and we show how this method can be used to improve in-silico gene essentiality predictions in Saccharomyces Cerevisiae using diversity-based metabolic network ensembles. Both the code and the data used for this research are publicly available on GitHub1.

scholarly journals Genetic analysis of the claret locus of Drosophila melanogaster.

Genetics ◽  
1989 ◽  
Vol 123 (3) ◽  
pp. 511-524 ◽  
Author(s):  
W Sequeira ◽  
C R Nelson ◽  
P Szauter
Keyword(s):  
Drosophila Melanogaster ◽  
Single Gene ◽  
Chromosome Rearrangement ◽  
Overlapping Genes ◽  
Early Cleavage ◽  
Chromosome Behavior ◽  
Eye Color ◽  
Single Target ◽  
Radiation Induced ◽  
New Alleles

Abstract The claret (ca) locus of Drosophila melanogaster comprises two separately mutable domains, one responsible for eye color and one responsible for proper disjunction of chromosomes in meiosis and early cleavage divisions. Previously isolated alleles are of three types: (1) alleles of the claret (ca) type that affect eye color only, (2) alleles of the claret-nondisjunctional (cand) type that affect eye color and chromosome behavior, and (3) a meiotic mutation, non-claret disjunctional (ncd), that affects chromosome behavior only. In order to investigate the genetic structure of the claret locus, we have isolated 19 radiation-induced alleles of claret on the basis of the eye color phenotype. Two of these 19 new alleles are of the cand type, while 17 are of the ca type, demonstrating that the two domains do not often act as a single target for mutagenesis. This suggests that the two separately mutable functions are likely to be encoded by separate or overlapping genes rather than by a single gene. One of the new alleles of the cand type is a chromosome rearrangement with a breakpoint at the position of the claret locus. If this breakpoint is the cause of the mutant phenotype and there are no other mutations associated with the rearrangement, the two functions must be encoded by overlapping genes.

Modules in Biological Networks

2013 ◽  
pp. 637-663
Author(s):  
Bing Zhang ◽  
Zhiao Shi
Keyword(s):  
Biological Networks ◽  
Protein Complex ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Single Gene ◽  
Biological Systems ◽  
Function Prediction ◽  
Diverse Group ◽  
Biological Studies ◽  
Gene Level

One of the most prominent properties of networks representing complex systems is modularity. Network-based module identification has captured the attention of a diverse group of scientists from various domains and a variety of methods have been developed. The ability to decompose complex biological systems into modules allows the use of modules rather than individual genes as units in biological studies. A modular view is shaping research methods in biology. Module-based approaches have found broad applications in protein complex identification, protein function prediction, protein expression prediction, as well as disease studies. Compared to single gene-level analyses, module-level analyses offer higher robustness and sensitivity. More importantly, module-level analyses can lead to a better understanding of the design and organization of complex biological systems.

A Transfer Learning Approach and Selective Integration of Multiple Types of Assays for Biological Network Inference

2013 ◽  
pp. 188-202
Author(s):  
Tsuyoshi Kato ◽  
Kinya Okada ◽  
Hisashi Kashima ◽  
Masashi Sugiyama
Keyword(s):  
Metabolic Network ◽  
Transfer Learning ◽  
Protein Interaction ◽  
Biological Networks ◽  
Protein Interaction Network ◽  
Network Inference ◽  
Interaction Network ◽  
Statistical Test ◽  
Learning Approach ◽  
Selective Integration

The authors’ algorithm was favorably examined on two kinds of biological networks: a metabolic network and a protein interaction network. A statistical test confirmed that the weight that our algorithm assigned to each assay was meaningful.

Finding Minimum Reaction Cuts of Metabolic Networks Under a Boolean Model Using Integer Programming and Feedback Vertex Sets

2012 ◽  
pp. 774-791
Author(s):  
Takeyuki Tamura ◽  
Kazuhiro Takemoto ◽  
Tatsuya Akutsu
Keyword(s):  
Integer Programming ◽  
Metabolic Network ◽  
Drug Targets ◽  
Metabolic Networks ◽  
Boolean Model ◽  
Pentose Phosphate ◽  
Minimum Size ◽  
E Coli ◽  
Potential Applications ◽  
Vertex Set

In this paper, the authors consider the problem of, given a metabolic network, a set of source compounds and a set of target compounds, finding a minimum size reaction cut, where a Boolean model is used as a model of metabolic networks. The problem has potential applications to measurement of structural robustness of metabolic networks and detection of drug targets. They develop an integer programming-based method for this optimization problem. In order to cope with cycles and reversible reactions, they further develop a novel integer programming (IP) formalization method using a feedback vertex set (FVS). When applied to an E. coli metabolic network consisting of Glycolysis/Glyconeogenesis, Citrate cycle and Pentose phosphate pathway obtained from KEGG database, the FVS-based method can find an optimal set of reactions to be inactivated much faster than a naive IP-based method and several times faster than a flux balance-based method. The authors also confirm that our proposed method works even for large networks and discuss the biological meaning of our results.

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