Gene regulatory network inference in long-lived C. elegans reveals modular properties that are predictive of novel aging genes.
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ABSTRACT:
SUBMITTER: Suriyalaksh M
PROVIDER: S-EPMC8753122 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We design a “wisdom-of-the-crowds” GRN inference pipeline, and couple it to network analyses, to understand the organisational principles governing gene regulation in long-lived glp-1/Notch C. elegans. The GRN has three layers (input, core, output) and is topologically equivalent to bow-tie/hourglass structures prevalent among metabolic networks. To assess functional importance of structural layers, we screened 80% of the regulators and discovered 50 new ageing genes, 86% with human orthologues and 56% further extending the long life of glp-1. Genes essential for longevity—including ones involved in insulin-like signalling (ILS)—are at the core, indicating that GRN’s structure is predictive of functionality. Using in vivo reporters, we found an intricate relationship between fat accumulation, SOD enzymes, and lifespan. We queried 5,497 genetic interactions (https://s-andrews.github.io/wormgrn/qpcr/), and identified modulators that phenocopy ILS genes and share identical targets. We present a framework with predictive power that can accelerate discovery in C. elegans and potentially humans.
2022-01-20 | GSE166512 | GEO
Project description:Gene Regulatory Network inference in long-lived C. elegans reveals modular properties that are predictive of novel ageing genes
| PRJNA701169 | ENA
Project description:Many fundamental questions on aging are still unanswered or are under intense debate. These questions are frequently not addressable by examining a single gene or a single pathway, but can best be addressed at the systems level. Here we examined the modular structure of the protein-protein interaction (PPI) networks during fruitfly and human brain aging. In both networks, there are two modules associated with the cellular proliferation to differentiation temporal switch that display opposite aging-related changes in expression. During fly aging, another couple of modules are associated with the oxidative-reductive metabolic temporal switch. These network modules and their relationships demonstrate (1) that aging is largely associated with a small number, instead of many network modules, (2) that some modular changes might be reversible and (3) that genes connecting different modules through PPIs are more likely to affect aging/longevity, a conclusion that is experimentally validated by Caenorhabditis elegans lifespan analysis. Network simulations further suggest that aging might preferentially attack key regulatory nodes that are important for the network stability, implicating a potential molecular basis for the stochastic nature of aging.
| S-EPMC2174624 | biostudies-literature
Project description:From bacteria to multicellular animals, most organisms exhibit declines in survivorship or reproductive performance with increasing age ("senescence"). Evidence for senescence in clonal plants, however, is scant. During asexual growth, we expect that somatic mutations, which negatively impact sexual fitness, should accumulate and contribute to senescence, especially among long-lived clonal plants. We tested whether older clones of Populus tremuloides (trembling aspen) from natural stands in British Columbia exhibited significantly reduced reproductive performance. Coupling molecular-based estimates of clone age with male fertility data, we observed a significant decline in the average number of viable pollen grains per catkin per ramet with increasing clone age in trembling aspen. We found that mutations reduced relative male fertility in clonal aspen populations by about 5.8 x 10(-5) to 1.6 x 10(-3) per year, leading to an 8% reduction in the number of viable pollen grains, on average, among the clones studied. The probability that an aspen lineage ultimately goes extinct rises as its male sexual fitness declines, suggesting that even long-lived clonal organisms are vulnerable to senescence.
| S-EPMC2923084 | biostudies-literature
Project description:The function of the pharynx, an organ in the model system Caenorhabditis elegans, has been correlated with life span and motility (another measure of health) since 1980. In this study, in order to further understand the relationship between organ function and life span, we measured the age-related decline of the pharynx using an electrophysiological approach. We measured and analyzed electropharyngeograms (EPG) of wild type animals, short-lived hsf-1 mutants, and long-lived animals with genetically decreased insulin signaling or increased heat shock pathway signaling; we recorded a total of 2,478 EPGs from 1,374 individuals. As expected, the long-lived daf-2(e1370) and hsf-1OE(uthIs235) animals maintained pharynx function relatively closer to the youthful state during aging, whereas the hsf-1(sy441) and wild type animals' pharynx function deviated significantly further from the youthful state at advanced age. Measures of the amount of variation in organ function can act as biomarkers of youthful physiology as well. Intriguingly, the long-lived animals had greater variation in the duration of pharynx contraction at older ages.
| S-EPMC6625588 | biostudies-literature
Project description:How does an animal age in natural conditions? Given the multifaceted nature of senescence, identifying the effects of age on physiology and behavior remains challenging. We investigated the effects of age on a broad array of phenotypic traits in a wild, long-lived animal, the wandering albatross. We studied foraging behavior using satellite tracking and activity loggers in males and females (age 6-48+ years), and monitored reproductive performance and nine markers of baseline physiology known to reflect senescence in vertebrates (humoral immunity, oxidative stress, antioxidant defenses, and hormone levels). Age strongly affected foraging behavior and reproductive performance, but not baseline physiology. Consistent with results of mammal and human studies, age affected males and females differently. Overall, our findings demonstrate that age, sex, and foraging ability interact in shaping aging patterns in natural conditions. Specifically, we found an unexpected pattern of spatial segregation by age; old males foraged in remote Antarctica waters, whereas young and middle-aged males never foraged south of the Polar Front. Old males traveled a greater distance but were less active at the sea surface, and returned from sea with elevated levels of stress hormone (corticosterone), mirroring a low foraging efficiency. In contrast to findings in captive animals and short-lived birds, and consistent with disposable soma theory, we found no detectable age-related deterioration of baseline physiology in albatrosses. We propose that foraging efficiency (i.e., the ability of individuals to extract energy from their environment) might play a central role in shaping aging patterns in natural conditions.
| S-EPMC2852007 | biostudies-other
Project description:BackgroundCell survival and development are orchestrated by complex interlocking programs of gene activation and repression. Understanding how this gene regulatory network (GRN) functions in normal states, and is altered in cancers subtypes, offers fundamental insight into oncogenesis and disease progression, and holds great promise for guiding clinical decisions. Inferring a GRN from empirical microarray gene expression data is a challenging task in cancer systems biology. In recent years, module-based approaches for GRN inference have been proposed to address this challenge. Despite the demonstrated success of module-based approaches in uncovering biologically meaningful regulatory interactions, their application remains limited a single condition, without supporting the comparison of multiple disease subtypes/conditions. Also, their use remains unnecessarily restricted to computational biologists, as accurate inference of modules and their regulators requires integration of diverse tools and heterogeneous data sources, which in turn requires scripting skills, data infrastructure and powerful computational facilities. New analytical frameworks are required to make module-based GRN inference approach more generally useful to the research community.ResultsWe present the RMaNI (Regulatory Module Network Inference) framework, which supports cancer subtype-specific or condition specific GRN inference and differential network analysis. It combines both transcriptomic as well as genomic data sources, and integrates heterogeneous knowledge resources and a set of complementary bioinformatic methods for automated inference of modules, their condition specific regulators and facilitates downstream network analyses and data visualization. To demonstrate its utility, we applied RMaNI to a hepatocellular microarray data containing normal and three disease conditions. We demonstrate that how RMaNI can be employed to understand the genetic architecture underlying three disease conditions. RMaNI is freely available at http://inspect.braembl.org.au/bi/inspect/rmaniConclusionRMaNI makes available a workflow with comprehensive set of tools that would otherwise be challenging for non-expert users to install and apply. The framework presented in this paper is flexible and can be easily extended to analyse any dataset with multiple disease conditions.
| S-EPMC3853211 | biostudies-literature
Project description:BackgroundNetwork Component Analysis (NCA) has shown its effectiveness in discovering regulators and inferring transcription factor activities (TFAs) when both microarray data and ChIP-on-chip data are available. However, a NCA scheme is not applicable to many biological studies due to limited topology information available, such as lack of ChIP-on-chip data. We propose a new approach, motif-directed NCA (mNCA), to integrate motif information and gene expression data to infer regulatory networks.ResultsWe develop motif-directed NCA (mNCA) to incorporate motif information into NCA for regulatory network inference. While motif information is readily available from knowledge databases, it is a "noisy" source of network topology information consisting of many false positives. To overcome this problem, we develop a stability analysis procedure embedded in mNCA to resolve the inconsistency between motif information and gene expression data, and to enable the identification of stable TFAs. The mNCA approach has been applied to a time course microarray data set of muscle regeneration. The experimental results show that the inferred TFAs are not only numerically stable but also biologically relevant to muscle differentiation process. In particular, several inferred TFAs like those of MyoD, myogenin and YY1 are well supported by biological experiments.ConclusionA novel computational approach, mNCA, has been developed to integrate motif information and gene expression data for regulatory network reconstruction. Specifically, motif analysis is used to obtain initial network topology, and stability analysis is developed and applied with mNCA to extract stable TFAs. Experimental results on muscle regeneration microarray data have demonstrated that mNCA is a practical and reliable computational method for regulatory network inference and pathway discovery.
| S-EPMC2259422 | biostudies-literature
Project description:The roundworm Caenorhabditis elegans exhibits robust escape behavior in response to rapidly rising temperature. The behavior lasts for a few seconds, shows history dependence, involves both sensory and motor systems, and is too complicated to model mechanistically using currently available knowledge. Instead we model the process phenomenologically, and we use the Sir Isaac dynamical inference platform to infer the model in a fully automated fashion directly from experimental data. The inferred model requires incorporation of an unobserved dynamical variable and is biologically interpretable. The model makes accurate predictions about the dynamics of the worm behavior, and it can be used to characterize the functional logic of the dynamical system underlying the escape response. This work illustrates the power of modern artificial intelligence to aid in discovery of accurate and interpretable models of complex natural systems.
| S-EPMC6462057 | biostudies-literature
Project description:The analysis of disease phenotype data with genetic information indicated that genes associated with clinically similar diseases tend to be functionally related and work together to perform a specific biological function. Therefore, it is of interest to relate disease phenotype data to mirror modular property implied in the association map of disease genes. Hence, we constructed a textbased human disease gene network (HDGN) by using the phenotypic similarity of their associated disease phenotype records in the OMIM database. Analysis shows that the network is highly modular and it is highly correlated with the physiological classification of genetic diseases. Using a graph clustering algorithm, we found 139 gene modules in the network of 1,865 genes and their gene products (proteins) in these gene modules tend to interact with each other via the computation of PPI intensity. Genes in such gene modules are functionally related and may represent the shared genetic basis of their corresponding diseases. These genes, alone or in combination, could be considered as potential therapeutic targets in future clinical therapy.
| S-EPMC4620620 | biostudies-literature