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Pleiotropic genes as “module connectors”. The extensive existence of pleiotropic genes suggests that gene modules are overlapping rather than separate from one another. Genes assigned to the same functional class are represented as a module and pleiotropic genes correspond to the intersections of modules. In the illustrated example, the most pleiotropic kinases, including dom-6, mpk-1, plk-1 and air-1, connect most of the modules in early embryogenesis into a “module network”.
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In a biological system, some genes play single roles while others perform multiple functions. How can we determine which genes are multi-functional? An informative way for probing gene functions is to eliminate the expression of a given gene and observe the phenotypic consequences. RNAi techniques have enabled the generation of genom...
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... Bottlenecks control the flow of information in a network and improve network efficiency. It has shown that proteins with high betweenness are essential and tend to be highly pleiotropic (Ahmed et al., 2018;Estrada and Ross, 2018;Zou et al., 2008). ...
The recent advancements of machine learning and deep learning (DL) methods are making it possible to create systems that automatically mine patterns and learn from data. Applications of those methods in chemistry, in particular QSAR and drug discovery, are already available. While DL can be applied as a conventional way of learning from chemical descriptors, the potentialities of the method are far more. In particular the capabilities of DL to autonomously extract, through multiple transformations, the structural elements that are correlated with the property under investigation can help in discovering the link between a chemical and its biological/physical effects. After presenting the principal DL methods developed for chemical problems, the focus is on a study case in mutagenicity prediction that uses directly the chemical graph, either as SMILES, graphs, or images, and applies convolutional and recurrent networks. The knowledge extracted from the networks is analyzed and compared with the accepted structural alerts for mutagenicity. The next challenges and the future of DL for QSAR are finally discussed.
... Bottlenecks control the flow of information in a network and improve network efficiency. It has shown that proteins with high betweenness are essential and tend to be highly pleiotropic (Ahmed et al., 2018;Estrada and Ross, 2018;Zou et al., 2008). ...
In this chapter, we have discussed a relatively advanced and successful hybrid machine learning workflow that may help to unravel causative agents of disease from high throughput RNA-Seq datasets. The method is then applied to a breast cancer dataset taken from the Gene Expression Omnibus repository, and disease genes associated with breast cancer are identified. Finally, using the PPI network analysis approach, we observed the significance that the detected disease genes possess a role in the causal mechanism of disease. This method discussed here is universal and can be applied to any RNA-Seq data independent of disease.
... According to the statistics of the number of IRGs contained in modules, it was found that only 1.06% of modules enriched with IRGs ( Figure 2H), which further proved that IRGs were involved in diverse biological processes. Since the network centrality (75) and tissue expression patterns at mRNA expression level (76) can reflect the acquirement of pleiotropic functions, here we found immune-related genes, including IRGs and IRG&VTGs, gradually evolved into a broader expression patterns ( Figure 4C), indicating that immune-related genes gradually acquired pleiotropic functions. ...
The immune system is highly networked and complex, which is continuously changing as encountering old and new pathogens. However, reductionism-based researches do not give a systematic understanding of the molecular mechanism of the immune response and viral pathogenesis. Here, we present HUMPPI-2022, a high-quality human protein-protein interaction (PPI) network, containing > 11,000 protein-coding genes with > 78,000 interactions. The network topology and functional characteristics analyses of the immune-related genes (IRGs) reveal that IRGs are mostly located in the center of the network and link genes of diverse biological processes, which may reflect the gene pleiotropy phenomenon. Moreover, the virus-human interactions reveal that pan-viral targets are mostly hubs, located in the center of the network and enriched in fundamental biological processes, but not for coronavirus. Finally, gene age effect was analyzed from the view of the host network for IRGs and virally-targeted genes (VTGs) during evolution, with IRGs gradually became hubs and integrated into host network through bridging functionally differentiated modules. Briefly, HUMPPI-2022 serves as a valuable resource for gaining a better understanding of the composition and evolution of human immune system, as well as the pathogenesis of viruses.
... Even though all kinds of artificial perturbation (e.g. RNA interference, laser ablation, eggshell removal, external compression) and subsequent comparative analysis with wildtype embryo have been applied to C. elegans embryogenesis research [22][23][24][25], those operations sometimes cause pleiotropic defects and induce numerous potential influence factors, possibly making the conclusions obtained from comparative analysis less convincing [2,26]. To overcome this shortcoming, some closely related nematode species have been introduced (e.g. ...
... A previous study showed that a number of maternally loaded RNA transcripts in unfertilized eggs were linked to male function in Tribolium castaneum, suggesting a pleiotropic function of maternal transcripts [53]. Moreover, high-through RNA interference data from Caenorhabditis elegans has shown that pleiotropic genes were involved in an important role in early embryological life and functioned as a potential bridge between different protein complexes and pathways [54]. In this respect, we believed that function of maternal transcripts can be deduced from their tissue-specific coexpressed transcripts of known function [55,56], and therefore, we further classified and characterized transcripts that were coexpressed in the unfertilized eggs with other tissues using the TissueEnrich tool [57]. ...
Maternal genes are important in directing early development and determining egg quality in fish. We here report the de novo transcriptome from four tissue libraries of the cyprinid loach, Misgurnus anguillicaudatus, and for the first time identified maternal gene transcripts in unfertilized eggs and suggest their immune system involvement. Expression profiles and functional enrichment revealed a total 24,116 transcripts were expressed as maternal transcripts in unfertilized eggs, which were involved in a wide range of biological functions and pathways. Comparison expression profiles and analysis of tissue specificity revealed that the large numbers of maternal transcripts were stored in unfertilized eggs near the late phase of ovarian maturation and before ovulation. Functional classification showed a total of 279 maternal immune-related transcripts classified with immune system process GO term and immune system KEGG pathway. qPCR analysis showed that transcript levels of identified maternal immune-related candidate genes were dynamically modulated during development and early ontogeny of M. anguillicaudatus. Taken together, this study could not only provide knowledge on the protective roles of maternal immune-related genes during early life stage of M. anguillicaudatus but could also be a valuable transcriptomic/genomic resource for further analysis of maternally provisioned genes in M. anguillicaudatus and other related teleost fishes.
... In the late 1970s, researchers started to learn the mechanisms of pleiotropy at a molecular level in model organisms 11 . For instance, fundamental questions have been addressed in model organisms, such as the number of traits that can be influenced by pleiotropy and the multiple functions of certain genes [12][13][14][15][16][17] . Research on pleiotropy in humans is only at the beginning of its era. ...
Pleiotropy is a phenomenon which describes a gene or a genetic variant that affects more than one phenotype. This fundamental concept has been thought to play a critical role in genetics, medicine, evolutionary biology, molecular biology, and clinical research. With the recent development in sequencing technologies and statistical methods, pleiotropy can be characterized systematically in human genome. Circulatory system diseases and nervous system disorders have a significant impact on mortality rates worldwide and frequently co-occur in patients. Thus, the field would benefit greatly from the knowledge of the underlying genetic relationship between multiple diseases in these disease categories. In this dissertation, we aim to identify pleiotropy across a wide range of circulatory system diseases and nervous system disorders using large-scale electronic health record-linked biobank datasets. For common genetic variants, we applied an ensemble of methods including univariate, multivariate, and sequential multivariate association methods to characterize pleiotropy in the UK Biobank and the eMERGE network. Our results implicated five pleiotropic regions that help to explain the disease relationships across these disease categories. For rare variants, we performed univariate burden and dispersion tests using whole-exome sequencing data from the UK Biobank and characterized 143 Bonferroni significant pleiotropic genes. Our analytical framework on both common and rare genetic variants offer novel insights into biology and provide a new perspective for studying pleiotropy in large-scale biobank datasets. Besides the application of statistical methods on natural biomedical datasets, we also conducted simulation projects investigating the impact of sample size imbalance on the performance of the proposed statistical methods. Our simulation results can serve as a reference guideline to assist sample size design for association studies.
... According to the definition of betweenness, we can find that bottlenecks would control most of the information flow in the network, and deleting the bottlenecks will reduce the network efficiency significantly. Studies on the protein-protein network indicate that proteins with high betweenness are more likely to be highly pleiotropic [161] and tend to be essential [162,163] or disease-related genes [164]. Just as with the hub protein, the evolutionary age of proteins has a positive correlation with betweenness [159], and the hubs vary significantly more slowly than bottlenecks [165]. ...
Biological entities are involved in intricate and complex interactions, in which uncovering the biological information from the network concepts are of great significance. Benefiting from the advances of network science and high-throughput biomedical technologies, studying the biological systems from network biology has attracted much attention in recent years, and networks have long been central to our understanding of biological systems, in the form of linkage maps among genotypes, phenotypes, and the corresponding environmental factors. In this review, we summarize the recent developments of computational network biology, first introducing various types of biological networks and network structural properties. We then review the network-based approaches, ranging from some network metrics to the complicated machine-learning methods, and emphasize how to use these algorithms to gain new biological insights. Furthermore, we highlight the application in neuroscience, human disease, and drug developments from the perspectives of network science, and we discuss some major challenges and future directions. We hope that this review will draw increasing interdisciplinary attention from physicists, computer scientists, and biologists.
... Although the phenomenon of pleiotropy has been known for over 100 years [Stearns 2010], the mechanisms by which a single gene can affect multiple traits is still far from being fully understood. Analysis of the properties of pleiotropic protein interaction networks associated with human disease has shown that, similarly to essential proteins, pleiotropic proteins are central in protein-protein interaction networks and have more interactors compared with non-pleiotropic proteins [Zou et al., 2008;Chavali et al., 2010]. At molecular level, several mechanisms have been proposed to explain the pleiotropic effect of a gene and its protein product [Hodgkin 1998]. ...
... This is consistent with previous studies in humans and yeast [Chavali et al., 2010] and supports the hypothesis that pleiotropic proteins have a tendency to have a different biological role compared with non-pleiotropic proteins. Studies in Caenorhabditis elegans have shown that pleiotropic proteins have an important role in early embryological life and potentially bridge different protein pathways [Zou et al., 2008]. Enrichment in essential proteins in our pleiotropic dataset suggests that pleiotropic proteins could also play a similar role in human embryonic life. ...
Pleiotropy is the phenomenon by which the same gene can result in multiple phenotypes. Pleiotropic proteins are emerging as important contributors to rare and common disorders. Nevertheless, little is known on the mechanisms underlying pleiotropy and the characteristic of pleiotropic proteins. We analysed disease-causing proteins reported in UniProt and observed that 12% are pleiotropic (variants in the same protein cause more than one disease). Pleiotropic proteins were enriched in deleterious and rare variants, but not in common variants. Pleiotropic proteins were more likely to be involved in the pathogenesis of neoplasms, neurological and circulatory diseases, and congenital malformations, whereas non-pleiotropic proteins in endocrine and metabolic disorders. Pleiotropic proteins were more essential and had a higher number of interacting partners compared to non-pleiotropic proteins. Significantly more pleiotropic than non-pleiotropic proteins contained at least one intrinsically long disordered region (p<0.001). Deleterious variants occurring in structurally disordered regions were more commonly found in pleiotropic, rather than non-pleiotropic proteins. In conclusion, pleiotropic proteins are an important contributor to human disease. They represent a biologically different class of proteins compared to non-pleiotropic proteins and a better understanding of their characteristics and genetic variants, can greatly aid in the interpretation of genetic studies and drug design. This article is protected by copyright. All rights reserved
... Using Tukey HSD tests on U133A and U133B DNA microarray data, the study [11] identified nearly 4,000 genes that are each significantly preferentially expressed in six or less tissue types out of 97. (A finding supportive of this methodology is Zou et al. (Figure S2, ref. [13]), which associates high pleiotropy with low expression levels in C. elegans genes, so high expression suggests a role in a narrower set of traits.) The count of high-contrast tissue-selective genes for each tissue on each chromosome was compiled (e.g., 98 brain tissue genes on chromosome 1); see in Additional file 1:Table S3. ...
Chromosomes have territories, or preferred locales, in the cell nucleus. When these sites are taken into account, some large-scale structure of the human genome emerges.
The synoptic picture is that genes highly expressed in particular topologically compact tissues are not randomly distributed on the genome. Rather, such tissue-specific genes tend to map somatotopically onto the complete chromosome set. They seem to form a "genome homunculus": a multi-dimensional, genome-wide body representation extending across chromosome territories of the entire spermcell nucleus. The antero-posterior axis of the body significantly corresponds to the head-tail axis of the nucleus, and the dorso-ventral body axis to the central-peripheral nucleus axis.
This large-scale genomic structure includes thousands of genes. One rationale for a homuncular genome structure would be to minimize connection costs in genetic networks. Somatotopic maps in cerebral cortex have been reported for over a century.
... In the systematic analysis of pleiotropy [15], the average gene was associated with 4.6 defects, which was described as consistent with low pleiotropy (Figure 3c). However, another group examined the same data and found that the average gene induced seven defects, and concluded that pleiotropy 'occurs extensively among genes' [24]. Clearly, there is room for interpretation not only in what is 'low' or 'high,' but also in how to do the counting. ...
