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A genetic Gaussian network of multiple phenotypes, constructed through the inverse matrix of the genetic correlation matrix, is informative for understanding the biological dependencies of the phenotypes. However, its estimation may be challenging because the genetic correlation estimates are biased due to estimation errors and idiosyncratic pleiotropy inherent in GWAS summary statistics. Here, we introduce a novel approach called estimation of genetic graph (EGG), which eliminates the estimation error bias and idiosyncratic pleiotropy bias with the same techniques used in multivariable Mendelian randomization. The genetic network estimated by EGG can be interpreted as shared common biological contributions between phenotypes, conditional on others. We use both simulations and real data to demonstrate the superior efficacy of our novel method in comparison with the traditional network estimators. [ABSTRACT FROM AUTHOR]

Over the past century, environmental changes have significantly impacted wheat spike morphology, crucial for adaptation and grain yield. However, the changes in wheat spike modifications during this period remain largely unknown. This study examines 16 spike morphology traits in 830 accessions released from 1900 to 2020. It finds that spike weight, grain number per spike (GN), and thousand kernel weight have significantly increased, while spike length has no significant change. The increase in fertile spikelets is due to fewer degenerated spikelets, resulting in a higher GN. Genome‐wide association studies identified 49,994 significant SNPs, grouped into 293 genomic regions. The accumulation of favorable alleles in these genomic regions indicates the genetic basis for modification in spike morphology traits. Genetic network analysis of these genomic regions reveals the genetic basis for phenotypic correlations among spike morphology traits. The haplotypes of the identified genomic regions display obvious geographical differentiation in global accessions and environmental adaptation over the past 120 years. In summary, we reveal the genetic basis of adaptive evolution and the interactions of spike morphology, offering valuable resources for the genetic improvement of spike morphology to enhance environmental adaptation. Summary statement: In this study, we investigated the genetic basis of changes in spike morphology during wheat breeding. We identified candidate regions responsible for consistent alterations in spike morphology traits that led to increased yield. Furthermore, we constructed a genetic network of related genomic regions. Our findings unveil the genetic underpinnings of breeding selection and the interplay of spike morphology traits, offering valuable resources for the regulation of spike morphology in wheat. [ABSTRACT FROM AUTHOR]

Psychiatric disorders frequently co-occur and share common symptoms and genetic backgrounds. Previous research has used genome-wide association studies to identify the interrelationships among psychiatric disorders and identify clusters of disorders; however, these methods have limitations in terms of their ability to examine the relationships among disorders as a network structure and their generalizability to the general population. In this study, we explored the network structure of the polygenic risk score (PRS) for 13 psychiatric disorders in a general population (276,249 participants of European ancestry from the UK Biobank) and identified communities and the centrality of the network. In this network, the nodes represented a PRS for each psychiatric disorder and the edges represented the connections between nodes. The psychiatric disorders comprised four robust communities. The first community included attention-deficit hyperactivity disorder, autism spectrum disorder, major depressive disorder, and anxiety disorder. The second community consisted of bipolar I and II disorders, schizophrenia, and anorexia nervosa. The third group included Tourette's syndrome and obsessive–compulsive disorder. Cannabis use disorder, alcohol use disorder, and post-traumatic stress disorder make up the fourth community. The PRS of schizophrenia had the highest values for the three metrics (strength, betweenness, and closeness) in the network. Our findings provide a comprehensive genetic network of psychiatric disorders and biological evidence for the classification of psychiatric disorders. [ABSTRACT FROM AUTHOR]

Palatogenesis is a complex and intricate process involving the formation of the palate through various morphogenetic events highly dependent on the surrounding context. These events comprise outgrowth of palatal shelves from embryonic maxillary prominences, their elevation from a vertical to a horizontal position above the tongue, and their subsequent adhesion and fusion at the midline to separate oral and nasal cavities. Disruptions in any of these processes can result in cleft palate, a common congenital abnormality that significantly affects patient's quality of life, despite surgical intervention. Although many genes involved in palatogenesis have been identified through studies on genetically modified mice and human genetics, the precise roles of these genes and their products in signaling networks that regulate palatogenesis remain elusive. Recent investigations have revealed that palatal shelf growth, patterning, adhesion, and fusion are intricately regulated by numerous transcription factors and signaling pathways, including Sonic hedgehog (Shh), bone morphogenetic protein (Bmp), fibroblast growth factor (Fgf), transforming growth factor beta (Tgf-β), Wnt signaling, and others. These studies have also identified a significant number of genes that are essential for palate development. Integrated information from these studies offers novel insights into gene regulatory networks and dynamic cellular processes underlying palatal shelf elevation, contact, and fusion, deepening our understanding of palatogenesis, and facilitating the development of more efficacious treatments for cleft palate. [ABSTRACT FROM AUTHOR]

Global genetic networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single genes or local networks. The Gaussian graphical model (GGM) is widely applied to learn genetic networks because it defines an undirected graph decoding the conditional dependence between genes. Many algorithms based on the GGM have been proposed for learning genetic network structures. Because the number of gene variables is typically far more than the number of samples collected, and a real genetic network is typically sparse, the graphical lasso implementation of GGM becomes a popular tool for inferring the conditional interdependence among genes. However, graphical lasso, although showing good performance in low dimensional data sets, is computationally expensive and inefficient or even unable to work directly on genome-wide gene expression data sets. In this study, the method of Monte Carlo Gaussian graphical model (MCGGM) was proposed to learn global genetic networks of genes. This method uses a Monte Carlo approach to sample subnetworks from genome-wide gene expression data and graphical lasso to learn the structures of the subnetworks. The learned subnetworks are then integrated to approximate a global genetic network. The proposed method was evaluated with a relatively small real data set of RNA-seq expression levels. The results indicate the proposed method shows a strong ability of decoding the interactions with high conditional dependences among genes. The method was then applied to genome-wide data sets of RNA-seq expression levels. The gene interactions with high interdependence from the estimated global networks show that most of the predicted gene-gene interactions have been reported in the literatures playing important roles in different human cancers. Also, the results validate the ability and reliability of the proposed method to identify high conditional dependences among genes in large-scale data sets. [ABSTRACT FROM AUTHOR]

Global genetic networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single genes or local networks. The Gaussian graphical model (GGM) is widely applied to learn genetic networks because it defines an undirected graph decoding the conditional dependence between genes. Many algorithms based on the GGM have been proposed for learning genetic network structures. Because the number of gene variables is typically far more than the number of samples collected, and a real genetic network is typically sparse, the graphical lasso implementation of GGM becomes a popular tool for inferring the conditional interdependence among genes. However, graphical lasso, although showing good performance in low dimensional data sets, is computationally expensive and inefficient or even unable to work directly on genome-wide gene expression data sets. In this study, the method of Monte Carlo Gaussian graphical model (MCGGM) was proposed to learn global genetic networks of genes. This method uses a Monte Carlo approach to sample subnetworks from genome-wide gene expression data and graphical lasso to learn the structures of the subnetworks. The learned subnetworks are then integrated to approximate a global genetic network. The proposed method was evaluated with a relatively small real data set of RNA-seq expression levels. The results indicate the proposed method shows a strong ability of decoding the interactions with high conditional dependences among genes. The method was then applied to genome-wide data sets of RNA-seq expression levels. The gene interactions with high interdependence from the estimated global networks show that most of the predicted gene-gene interactions have been reported in the literatures playing important roles in different human cancers. Also, the results validate the ability and reliability of the proposed method to identify high conditional dependences among genes in large-scale data sets. [ABSTRACT FROM AUTHOR]

Background: Prenatal exposure to ethanol can cause fetal alcohol spectrum disorder (FASD), a prevalent, preventable pediatric disorder. Identifying genetic risk alleles for FASD is challenging since time, dose, and frequency of exposure are often unknown, and manifestations of FASD are diverse and evident long after exposure. Drosophila melanogaster is an excellent model to study the genetic basis of the effects of developmental alcohol exposure since many individuals of the same genotype can be reared under controlled environmental conditions. Results: We used 96 sequenced, wild-derived inbred lines from the Drosophila melanogaster Genetic Reference Panel (DGRP) to profile genome-wide transcript abundances in young adult flies that developed on ethanol-supplemented medium or standard culture medium. We found substantial genetic variation in gene expression in response to ethanol with extensive sexual dimorphism. We constructed sex-specific genetic networks associated with alcohol-dependent modulation of gene expression that include protein-coding genes, Novel Transcribed Regions (NTRs, postulated to encode long non-coding RNAs) and female-specific coordinated regulation of snoRNAs that regulate pseudouridylation of ribosomal RNA. We reared DGRP lines which showed extreme upregulation or downregulation of snoRNA expression during developmental alcohol exposure on standard or ethanol supplemented medium and demonstrated that developmental exposure to ethanol has genotype-specific effects on adult locomotor activity and sleep. Conclusions: There is significant and sex-specific natural genetic variation in the transcriptional response to developmental exposure to ethanol in Drosophila that comprises networks of genes affecting nervous system development and ethanol metabolism as well as networks of regulatory non-coding RNAs. [ABSTRACT FROM AUTHOR]

Microorganisms often live in complex habitats, where changes in the environment are predictable, providing an opportunity for microorganisms to learn, anticipate the upcoming environmental changes and prepare in advance for better survival and growth. One such environment is the mammalian intestine, where the abundance of different carbon sources is spatially distributed. In this study, we identified seven spatially distributed carbon sources in the mammalian intestine and tested whether Escherichia coli exhibits phenotypes that are consistent with an anticipatory response given their spatial order and abundance within the mammalian intestine. Through RNA-Seq and RT-PCR validation measurements, we found that there was a 67% match in the expression patterns between the measured phenotypes and what would otherwise be expected in the case of anticipatory behavior, while 83% and 0% were in agreement with the homeostatic and random response, respectively. To understand the genetic and phenotypic basis of the discrepancies between the expected and measured anticipatory responses, we thoroughly investigated the discrepancy in D-galactose treatment and the expression of maltose operon in E. coli. Here, the expected anticipatory response, based on the spatial distribution of D-galactose and D-maltose, was that D-galactose should upregulate the maltose operon, but it was the opposite in experimental validation. We performed whole genome random mutagenesis and screening and identified E. coli strains with positive expression of maltose operon in D-galactose. Targeted Sanger sequencing and mutation repair identified that the mutations in the promoter region of malT and in the coding region of the crp gene were the factors responsible for the reversion in the association. Further, to identify why positive association in the D-galactose treatment and the expression of the maltose operon did not evolve naturally, fitness measurements were performed. Fitness experiments demonstrated that the fitness of E. coli strains with a positive association in the D-galactose treatment and the expression of the maltose operon was 12% to 20% lower than that of the wild type strain. [ABSTRACT FROM AUTHOR]

Background: Prenatal exposure to ethanol can cause fetal alcohol spectrum disorder (FASD), a prevalent, preventable pediatric disorder. Identifying genetic risk alleles for FASD is challenging since time, dose, and frequency of exposure are often unknown, and manifestations of FASD are diverse and evident long after exposure. Drosophila melanogaster is an excellent model to study the genetic basis of the effects of developmental alcohol exposure since many individuals of the same genotype can be reared under controlled environmental conditions. Results: We used 96 sequenced, wild-derived inbred lines from the Drosophila melanogaster Genetic Reference Panel (DGRP) to profile genome-wide transcript abundances in young adult flies that developed on ethanol-supplemented medium or standard culture medium. We found substantial genetic variation in gene expression in response to ethanol with extensive sexual dimorphism. We constructed sex-specific genetic networks associated with alcohol-dependent modulation of gene expression that include protein-coding genes, Novel Transcribed Regions (NTRs, postulated to encode long non-coding RNAs) and female-specific coordinated regulation of snoRNAs that regulate pseudouridylation of ribosomal RNA. We reared DGRP lines which showed extreme upregulation or downregulation of snoRNA expression during developmental alcohol exposure on standard or ethanol supplemented medium and demonstrated that developmental exposure to ethanol has genotype-specific effects on adult locomotor activity and sleep. Conclusions: There is significant and sex-specific natural genetic variation in the transcriptional response to developmental exposure to ethanol in Drosophila that comprises networks of genes affecting nervous system development and ethanol metabolism as well as networks of regulatory non-coding RNAs. [ABSTRACT FROM AUTHOR]

Substantial diversity exists for both the size and shape of the leaf, the main photosynthetic organ of flowering plants. The two major forms of leaf are simple leaves, in which the leaf blade is undivided, and compound leaves, which comprise several leaflets. Leaves form at the shoot apical meristem from a group of undifferentiated cells, which first establish polarity, then grow and differentiate. Each of these processes is controlled by a combination of transcriptional regulators, microRNAs and phytohormones. The present review documents recent advances in our understanding of how these various factors modulate the development of both simple leaves (focusing mainly on the model plant Arabidopsis thaliana) and compound leaves (focusing mainly on the model legume species Medicago truncatula). [ABSTRACT FROM AUTHOR]

Background: Refractive eye development is regulated by optical defocus in a process of emmetropization. Excessive exposure to negative optical defocus often leads to the development of myopia. However, it is still largely unknown how optical defocus is detected by the retina. Methods: Here, we used genome-wide RNA-sequencing to conduct analysis of the retinal gene expression network underlying contrast perception and refractive eye development. Results: We report that the genetic network subserving contrast perception plays an important role in optical defocus detection and emmetropization. Our results demonstrate an interaction between contrast perception, the retinal circadian clock pathway and the signaling pathway underlying optical defocus detection. We also observe that the relative majority of genes causing human myopia are involved in the processing of optical defocus. Conclusions: Together, our results support the hypothesis that optical defocus is perceived by the retina using contrast as a proxy and provide new insights into molecular signaling underlying refractive eye development. [ABSTRACT FROM AUTHOR]

Phyllosphere microbial communities inhabit the aerial plant parts, such as leaves and flowers, where they form complex molecular interactions with the host plant. Contrary to the relatively well-studied rhizosphere microbiome, scientists are just starting to understand, and potentially utilize, the phyllosphere microbiome. In this article, we summarize the recent studies that have provided novel insights into the mechanism of the host genotype shaping the phyllosphere microbiome and the possibility to select a stable and well-adapted microbiome. We also discuss the most pressing gaps in our knowledge and identify the most promising research directions and tools for understanding the assembly and function of phyllosphere microbiomes – this understanding is necessary if we are to harness phyllosphere microbiomes for improving plant growth and health in managed systems. Plant immunity networks maintain microbial homeostasis in the phyllosphere, which in turn affects the plant health. Plant exudation and volatiles significantly shape the microbiome structure and composition. Various environmental stresses shape the complex interaction between phyllosphere microbiome and plant immunity. Understanding the molecular basis of plant–microbe and microbe–microbe interactions will help elucidate their impact on plant fitness. Recent advances utilizing synthetic microbial community combined with omics tools (such as metagenomics and metabolomics) provide important insights into the physiology and functionality of the phyllosphere microbiome. An integrated knowledge of multiomics combined with synthetic community approach can help determine the individual as well as community level contribution of phyllosphere microbiome in the host fitness. Microbiome engineering can reshape the microbial composition in the phyllosphere, and holds potential for large-scale microbiome research and reconfiguration of phyllosphere microbiome with desired traits to fight plant stresses. [ABSTRACT FROM AUTHOR]

Purpose: The problem of motif discovery has become a significant challenge in the era of big data where there are hundreds of genomes requiring annotations. The importance of motifs has led many researchers to develop different tools and algorithms for finding them. The purpose of this paper is to propose a new algorithm to increase the speed and accuracy of the motif discovering process, which is the main drawback of motif discovery algorithms. Design/methodology/approach: All motifs are sorted in a tree-based indexing structure where each motif is created from a combination of nucleotides: 'A', 'C', 'T' and 'G'. The full motif can be discovered by extending the search around 4-mer nucleotides in both directions, left and right. Resultant motifs would be identical or degenerated with various lengths. Findings: The developed implementation discovers conserved string motifs in DNA without having prior information about the motifs. Even for a large data set that contains millions of nucleotides and thousands of very long sequences, the entire process is completed in a few seconds. Originality/value: Experimental results demonstrate the efficiency of the proposed implementation; as for a real-sequence of 1,270,000 nucleotides spread into 2,000 samples, it takes 5.9 s to complete the overall discovering process when the code ran on an Intel Core i7-6700 @ 3.4 GHz machine and 26.7 s when running on an Intel Xeon x5670 @ 2.93 GHz machine. In addition, the authors have improved computational performance by parallelizing the implementation to run on multi-core machines using the OpenMP framework. The speedup achieved by parallelizing the implementation is scalable and proportional to the number of processors with a high efficiency that is close to 100%. [ABSTRACT FROM AUTHOR]

Migraine is the most common neurological disorder worldwide and it has been shown to have complex polygenic origins with a heritability of estimated 40-70%. Both common and rare genetic variants are believed to underlie the pathophysiology of the prevalent types of migraine, migraine with typical aura and migraine without aura. However, only common variants have been identified so far. Here we identify for the first time a gene module with rare mutations through a systems genetics approach integrating RNA sequencing data from brain and vascular tissues likely to be involved in migraine pathology in combination with whole genome sequencing of 117 migraine families. We found a gene module in the visual cortex, based on single nuclei RNA sequencing data, that had increased rare mutations in the migraine families and replicated this in a second independent cohort of 1930 patients. This module was mainly expressed by interneurons, pyramidal CA1, and pyramidal SS cells, and pathway analysis showed association with hormonal signalling (thyrotropin-releasing hormone receptor and oxytocin receptor signalling pathways), Alzheimer's disease pathway, serotonin receptor pathway and general heterotrimeric G-protein signalling pathways. Our results demonstrate that rare functional gene variants are strongly implicated in the pathophysiology of migraine. Furthermore, we anticipate that the results can be used to explain the critical mechanisms behind migraine and potentially improving the treatment regime for migraine patients. [ABSTRACT FROM AUTHOR]

SIX homeoproteins were first described in Drosophila, where they participate in the Pax-Six-Eya-Dach (PSED) network with eyeless , eyes absent and dachsund to drive synergistically eye development through genetic and biochemical interactions. The role of the PSED network and SIX proteins in muscle formation in vertebrates was subsequently identified. Evolutionary conserved interactions with EYA and DACH proteins underlie the activity of SIX transcriptional complexes (STC) both during embryogenesis and in adult myofibers. Six genes are expressed throughout muscle development, in embryonic and adult proliferating myogenic stem cells and in fetal and adult post-mitotic myofibers, where SIX proteins regulate the expression of various categories of genes. In vivo, SIX proteins control many steps of muscle development, acting through feedforward mechanisms: in the embryo for myogenic fate acquisition through the direct control of Myogenic Regulatory Factors; in adult myofibers for their contraction/relaxation and fatigability properties through the control of genes involved in metabolism, sarcomeric organization and calcium homeostasis. Furthermore, during development and in the adult, SIX homeoproteins participate in the genesis and the maintenance of myofibers diversity. [ABSTRACT FROM AUTHOR]

Background: The novel approaches to psychiatric classification assume that disorders, contrary to what was previously thought, are not completely separate phenomena. In this regard, in addition to symptom-based criteria, disturbances are also considered on the basis of lower level components. With this viewpoint, identifying common biochemical markers would be beneficial in adopting a comprehensive strategy for prevention, diagnosis and treatment. Main body: One of the problematic areas in clinical settings is the coexistence of both obsessive–compulsive disorder (OCD) and bipolar disorder (BD) that is challenging and difficult to manage. In this study, using a system biologic approach we aimed to assess the interconnectedness of OCD and BD at different levels. Gene Set Enrichment Analysis (GSEA) method was used to identify the shared biological network between the two disorders. The results of the analysis revealed 34 common genes between the two disorders, the most important of which were CACNA1C, GRIA1, DRD2, NOS1, SLC18A1, HTR2A and DRD1. Dopaminergic synapse and cAMP signaling pathway as the pathways, dopamine binding and dopamine neurotransmitter receptor activity as the molecular functions, dendrite and axon part as the cellular component and cortex and striatum as the brain regions were the most significant commonalities. Short conclusion: The results of this study highlight the role of multiple systems, especially the dopaminergic system in linking OCD and BD. The results can be used to estimate the disease course, prognosis, and treatment choice, particularly in the cases of comorbidity. Such perspectives, going beyond symptomatic level, help to identify common endophenotypes between the disorders and provide diagnostic and therapeutic approaches based on biological in addition to the symptomatic level. [ABSTRACT FROM AUTHOR]

Long‐distance dispersal (LDD) is a pivotal process for plants determining their range of distribution and promoting gene flow among distant populations. Most fleshy‐fruited species rely on frugivorous vertebrates to disperse their seeds across the landscape. While LDD events are difficult to record, a few ecological studies have shown that birds move a sizeable number of ingested seeds across geographic barriers, such as sea straits. The foraging movements of migrant frugivores across distant populations, including those separated by geographic barriers, creates a constant flow of propagules that in turn shapes the spatial distributions of the genetic variation in populations. Here, we have analysed the genetic diversity and structure of 74 populations of Pistacia lentiscus, a fleshy‐fruited shrub widely distributed in the Mediterranean Basin, to elucidate whether the Mediterranean Sea acts as a geographic barrier or alternatively whether migratory frugivorous birds promote gene flow among populations located on both sides of the sea. Our results show reduced genetic distances among populations, including intercontinental populations, and they show a significant genetic structure across an eastern‐western axis. These findings are consistent with known bird migratory routes that connect the European and African continents following a north‐southwards direction during the fruiting season of many fleshy‐fruited plants. Further, approximate Bayesian analysis failed to explain the observed patterns as a result of historical population migrations at the end of Last Glacial Maximum. Therefore, anthropic and/or climatic changes that would disrupt the migratory routes of frugivorous birds might have genetic consequences for the plant species they feed upon. [ABSTRACT FROM AUTHOR]

Background: Understanding the genetic networks and their role in chronic diseases (e.g., cancer) is one of the important objectives of biological researchers. In this work, we present a text mining system that constructs a gene-gene-interaction network for the entire human genome and then performs network analysis to identify disease-related genes. We recognize the interacting genes based on their co-occurrence frequency within the biomedical literature and by employing linear and non-linear rare-event classification models. We analyze the constructed network of genes by using different network centrality measures to decide on the importance of each gene. Specifically, we apply betweenness, closeness, eigenvector, and degree centrality metrics to rank the central genes of the network and to identify possible cancer-related genes. Results: We evaluated the top 15 ranked genes for different cancer types (i.e., Prostate, Breast, and Lung Cancer). The average precisions for identifying breast, prostate, and lung cancer genes vary between 80-100%. On a prostate case study, the system predicted an average of 80% prostate-related genes. Conclusions: The results show that our system has the potential for improving the prediction accuracy of identifying gene-gene interaction and disease-gene associations. We also conduct a prostate cancer case study by using the threshold property in logistic regression, and we compare our approach with some of the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Background: Sexual precocity is a common biological phenomenon in animal species. A large number of precocity individuals were identified in Chinese mitten crab Eriocheir sinensis, which caused huge economic loss annually. However, the underlying genetic basis of precocity in E. sinensis remains unclear to date. Results: In this study, morphological and histological observation and comparative transcriptome analysis were conducted among different stages of precocious one-year-old and normal two-year-old sexually mature E. sinensis. The expression profiles of the ovary, hepatopancreas, and eyestalk tissues were presented and compared. Genes associated with lipid metabolic process, lipid transport, vitelline membrane formation, vitelline synthesis, and neuropeptide hormone-related genes were upregulated in the ovary, hepatopancreas, and eyestalk of precocious E. sinensis. Our results indicated that the eyestalk was involved in the neuroendocrine system providing neuropeptide hormones that may induce vitellogenesis in the hepatopancreas and further stimulate ovary development. The hepatopancreas is a site for energy storage and vitellogenin synthesis, and it may assist oogenesis through lipid transport in precocious E. sinensis. Conclusion: We provided not only an effective and convenient phenotype measurement method for the identification of potential precocious E. sinensis detection but also valuable genetic resources and novel insights into the molecular mechanism of precocity in E. sinensis. The genetic basis of precocity in E. sinensis is an integrated gene regulatory network of eyestalk, hepatopancreas, and ovary tissues. [ABSTRACT FROM AUTHOR]

We review the basic concepts involved in bioelectrically-coupled multicellular domains, focusing on the role of membrane potentials ( V mem ). In the first model, single-cell V mem is modulated by two generic polarizing and depolarizing ion channels, while intercellular coupling is implemented via voltage-gated gap junctions. Biochemical and bioelectrical signals are integrated via a feedback loop between V mem and the transcription and translation of a protein forming an ion channel. The effective rate constants depend on the single-cell V mem because these potentials modulate the local concentrations of signaling molecules and ions. This electrochemically-based idealization of the complex biophysical problem suggests that the spatio-temporal map of single-cell potentials can influence downstream patterning processes by means of the voltage-gated gap junction interconnectivity, much as in the case of electronic devices where the control of electric potentials and currents allows the local modulation of the circuitry to achieve full functionality. An alternative theoretical approach, the BioElectrical Tissue Simulation Engine (BETSE), is also presented. The BETSE modeling environment utilizes finite volume techniques to simulate bioelectric states from the perspective of ion concentrations and fluxes. This model has been successfully applied to make predictions and explain experimental observations in a variety of embryonic, regenerative, and oncogenic contexts. [ABSTRACT FROM AUTHOR]

Our hypothesis is that changes in gene and protein expression are crucial to the development of late-onset Alzheimer’s disease. Previously we examined how DNA alleles control downstream expression of RNA transcripts and how those relationships are changed in late-onset Alzheimer’s disease. We have now examined how proteins are incorporated into networks in two separate series and evaluated our outputs in two different cell lines. Our pipeline included the following steps: (i) predicting expression quantitative trait loci; (ii) determining differential expression; (iii) analysing networks of transcript and peptide relationships; and (iv) validating effects in two separate cell lines. We performed all our analysis in two separate brain series to validate effects. Our two series included 345 samples in the first set (177 controls, 168 cases; age range 65–105; 58% female; KRONOSII cohort) and 409 samples in the replicate set (153 controls, 141 cases, 115 mild cognitive impairment; age range 66–107; 63% female; RUSH cohort). Our top target is heat shock protein family A member 2 (HSPA2), which was identified as a key driver in our two datasets. HSPA2 was validated in two cell lines, with overexpression driving further elevation of amyloid-β40 and amyloid-β42 levels in APP mutant cells, as well as significant elevation of microtubule associated protein tau and phosphorylated-tau in a modified neuroglioma line. This work further demonstrates that studying changes in gene and protein expression is crucial to understanding late onset disease and further nominates HSPA2 as a specific key regulator of late-onset Alzheimer’s disease processes.10.1093/brain/awy215_video1awy215media15824729224001. [ABSTRACT FROM AUTHOR]

Genetic modifications during development of paediatric groups 3 and 4 medulloblastoma are responsible for their highly metastatic properties and poor patient survival rates. PRUNE1 is highly expressed in metastatic medulloblastoma group 3, which is characterized by TGF-β signalling activation, c-MYC amplification, and OTX2 expression. We describe the process of activation of the PRUNE1 signalling pathway that includes its binding to NME1, TGF-β activation, OTX2 upregulation, SNAIL (SNAI1) upregulation, and PTEN inhibition. The newly identified small molecule pyrimido-pyrimidine derivative AA7.1 enhances PRUNE1 degradation, inhibits this activation network, and augments PTEN expression. Both AA7.1 and a competitive permeable peptide that impairs PRUNE1/NME1 complex formation, impair tumour growth and metastatic dissemination in orthotopic xenograft models with a metastatic medulloblastoma group 3 cell line (D425-Med cells). Using whole exome sequencing technology in metastatic medulloblastoma primary tumour cells, we also define 23 common 'non-synonymous homozygous' deleterious gene variants as part of the protein molecular network of relevance for metastatic processes. This PRUNE1/TGF-β/OTX2/PTEN axis, together with the medulloblastoma-driver mutations, is of relevance for future rational and targeted therapies for metastatic medulloblastoma group 3.10.1093/brain/awy039_video1awy039media15742053534001. [ABSTRACT FROM AUTHOR]

Background: Available sources indicate that the risk of suicide in people with major depression is higher than other psychiatric disorders. Although it seems that these two conditions may have a shared cause in some cases, no studies have been conducted to identify a common basis for them.Methods: In this study, following an extensive review of literature, we found almost all the genes that are involved in major depression and suicidal behavior, and we isolated genes shared between the two conditions. Then, we found all physical or functional interactions within three mentioned gene sets and reconstructed three genetic interactive networks. All networks were analyzed topologically and enriched functionally. Finally, using a drug repurposing approach, we found the main available drugs that interacted with the most central genes shared between suicidal behavior and depression.Results: The results demonstrated that BDNF, SLC6A4, CREB1, and TNF are the most fundamental shared genes; and generally, disordered dopaminergic, serotonergic, and immunologic pathways in neuronal projections are the main shared deficient pathways. In addition, we found two genes, SLC6A4 and SLC6A2, to be the main therapeutic targets, and Serotonin-Norepinephrine Reuptake Inhibitors (SNRI) and Tricyclic Antidepressants (TCA) to be the most effective drugs for individuals with depression at risk for suicide.Conclusions: Our results, in addition to shedding light on the integrated molecular basis of depression-suicide, offer new therapeutic targets for individuals with depression at high risk for suicide and could pave the way for future preclinical and clinical studies. However, integrative systems biology-based studies highly depend on existing data and related databases, as well as the arrival of new experimental data sources in the future, possibly affecting the current results. [ABSTRACT FROM AUTHOR]

The autosomal dominant cerebellar ataxias, referred to as spinocerebellar ataxias in genetic nomenclature, are a rare group of progressive neurodegenerative disorders characterized by loss of balance and coordination. Despite the identification of numerous disease genes, a substantial number of cases still remain without a genetic diagnosis. Here, we report five novel spinocerebellar ataxia genes, FAT2, PLD3, KIF26B, EP300, and FAT1, identified through a combination of exome sequencing in genetically undiagnosed families and targeted resequencing of exome candidates in a cohort of singletons. We validated almost all genes genetically, assessed damaging effects of the gene variants in cell models and further consolidated a role for several of these genes in the aetiology of spinocerebellar ataxia through network analysis. Our work links spinocerebellar ataxia to alterations in synaptic transmission and transcription regulation, and identifies these as the main shared mechanisms underlying the genetically diverse spinocerebellar ataxia types. [ABSTRACT FROM AUTHOR]

This paper proposes methods for forward and inverse system modeling using Bayesian and least squares regression. These methods are based on both space-filling design criteria for multiple response problems and linear optimality criteria focusing on D-optimality. Modeling with and without the constant term is considered motivated by the case study application of genetic network modeling. We propose extended one-factor-at-a-time experimentation followed by augmentation of next stage design which offers biologists simplicity. Results are illustrated both numerical examples, a test problem from the literature, and a case study motivated by an real world biological research related to genetic network modeling. [ABSTRACT FROM PUBLISHER]

Obsessive–compulsive disorder (OCD) is characterized by recurrent obtrusive and repetitive acts typically occurred following anxiety. In the last two decades, studies done on the gene sequences, large-scale and point mutations and gene–gene, gene–environment and gene–drug interactions have led to the discovery of hundreds of genes associated with OCD. Although each gene in turn is a part of the etiology of this disorder; however, OCD, like other mental disorders is complex and a comprehensive and integrated view is necessary to understand its genetic basis. In this study, through an extensive review of existing published studies, all genes associated with OCD were found. Then, in order to integrate the results, all the interactions between these genes were explored and the achievement was represented as an interactive genetic network. Furthermore, the reconstructed network was analyzed. It was found thatGRIN2A,GRIN2BandGRIA2are the most central nodes in the network. Functional and pathway enrichment analysis showed that glutamate-related pathways are the main deficient systems in patients with OCD. By studying genes shared between OCD and other diseases, it was cleared that OCD, epilepsy and some types of cancer have the most number of shared genes. The results of this study, in addition to reviewing the available results as a comprehensive and integrated manner, provide new hypotheses for future studies. [ABSTRACT FROM AUTHOR]

Sex-specific genetic structure is a commonly observed pattern among vertebrate species. Facing differential selective pressures, individuals may adopt sex-specific life history traits that ultimately shape genetic variation among populations. Although differential dispersal dynamics are commonly detected in the literature, few studies have used genetic structure to investigate sex-specific functional connectivity. The recent use of graph theoretic approaches in landscape genetics has demonstrated network capacities to describe complex system behaviours where network topology represents genetic interaction among subunits. Here, we partition the overall genetic structure into sex-specific graphs, revealing different male and female dispersal dynamics of a fisher ( Pekania [ Martes] pennanti) metapopulation in southern Ontario. Our analyses based on network topologies supported the hypothesis of male-biased dispersal. Furthermore, we demonstrated that the effect of the landscape, identified at the population level, could be partitioned among sex-specific strata. We found that female connectivity was negatively correlated with snow depth, whereas connectivity among males was not. Our findings underscore the potential of conducting sex-specific analysis by identifying landscape elements or configuration that differentially promotes or impedes functional connectivity between sexes, revealing processes that may otherwise remain cryptic. We propose that the sex-specific graph approach would be applicable to other vagile species where differential sex-specific processes are expected to occur. [ABSTRACT FROM AUTHOR]

Providing access to quantitative genomic data is key to ensure large-scale data validation and promote new discoveries. TheCellMap.org serves as a central repository for storing and analyzing quantitative genetic interaction data produced by genome-scale Synthetic Genetic Array (SGA) experiments with the budding yeast Saccharomyces cerevisiae. In particular, TheCellMap.org allows users to easily access, visualize, explore, and functionally annotate genetic interactions, or to extract and reorganize subnetworks, using data-driven network layouts in an intuitive and interactive manner. [ABSTRACT FROM AUTHOR]

• Functional graph (FunGraph) theory emerges to map pharmacogenetic architecture. • The core tenet of FunGraph is to combine functional mapping and evolutionary game theory into a network framework. • Each agent's functional roadmap can be visualized and traced from the networks. • FunGraph can map pharmacogenetics networks for each and every subject. • FunGraph can quantify interindividual and context-specific differences in network architecture. Precision medicine, the utilization of targeted treatments to address an individual's disease, relies on knowledge about the genetic cause of that individual's drug response. Here, we present a functional graph (FunGraph) theory to chart comprehensive pharmacogenetic architecture for each and every patient. FunGraph is the combination of functional mapping – a dynamic model for genetic mapping and evolutionary game theory guiding interactive strategies. It coalesces all pharmacogenetic factors into multilayer and multiplex networks that fully capture bidirectional, signed and weighted epistasis. It can visualize and interrogate how epistasis moves in the cell and how this movement leads to patient- and context-specific genetic architecture in response to organismic physiology. We discuss the future implementation of FunGraph to achieve precision medicine. [ABSTRACT FROM AUTHOR]

Human longevity and diseases are likely influenced by multiple interacting genes within a few biologically conserved pathways. Using long-lived smokers as a phenotype (n = 90)-a group whose survival may signify innate resilience-we conducted a genome-wide association study comparing them to smokers at ages 52-69 (n = 730). These results were used to conduct a functional interaction network and pathway analysis, to identify single nucleotide polymorphisms that collectively related to smokers' longevity. We identified a set of 215 single nucleotide polymorphisms (all of which had p <5×10(-3) in the genome-wide association study) that were located within genes making-up a functional interaction network. These single nucleotide polymorphisms were then used to create a weighted polygenic risk score that, using an independent validation sample of nonsmokers (N = 6,447), was found to be significantly associated with a 22% increase in the likelihood of being aged 90-99 (n = 253) and an over threefold increase in the likelihood of being a centenarian (n = 4), compared with being at ages 52-79 (n = 4,900). Additionally, the polygenic risk score was also associated with an 11% reduction in cancer prevalence over up to 18 years (odds ratio: 0.89, p = .011). Overall, using a unique phenotype and incorporating prior knowledge of biological networks, this study identified a set of single nucleotide polymorphisms that together appear to be important for human aging, stress resistance, cancer, and longevity. [ABSTRACT FROM AUTHOR]

Plants have evolved a series of tolerance mechanisms to saline stress, which perturbs physiological processes throughout the plant. To identify genetic mechanisms associated with salinity tolerance, we performed linkage analysis and genome-wide association study (GWAS) on maintenance of root growth of Arabidopsis thaliana in hydroponic culture with weak and severe NaCl toxicity. The top 200 single-nucleotide polymorphisms (SNPs) determined by GWAS could cumulatively explain approximately 70% of the variation observed at each stress level. The most significant SNPs were linked to the genes of ATP-binding cassette B10 and vacuolar proton ATPase A2. Several known salinity tolerance genes such as potassium channel KAT1 and calcium sensor SOS3 were also linked to SNPs in the top 200. In parallel, we constructed a gene co-expression network to independently verify that particular groups of genes work together to a common purpose. We identify molecular mechanisms to confer salt tolerance from both predictable and novel physiological sources and validate the utility of combined genetic and network analysis. Additionally, our study indicates that the genetic architecture of salt tolerance is responsive to the severity of stress. These gene datasets are a significant information resource for a following exploration of gene function. [ABSTRACT FROM AUTHOR]

At-site environmental conditions can have strong influences on genetic connectivity, and in particular on the immigration and settlement phases of dispersal. However, at-site processes are rarely explored in landscape genetic analyses. Networks can facilitate the study of at-site processes, where network nodes are used to model site-level effects. We used simulated genetic networks to compare and contrast the performance of 7 node-based (as opposed to edge-based) genetic connectivity metrics. We simulated increasing node connectivity by varying migration in two ways: we increased the number of migrants moving between a focal node and a set number of recipient nodes, and we increased the number of recipient nodes receiving a set number of migrants. We found that two metrics in particular, the average edge weight and the average inverse edge weight, varied linearly with simulated connectivity. Conversely, node degree was not a good measure of connectivity. We demonstrated the use of average inverse edge weight to describe the influence of at-site habitat characteristics on genetic connectivity of 653 American martens (Martes americana) in Ontario, Canada. We found that highly connected nodes had high habitat quality for marten (deep snow and high proportions of coniferous and mature forest) and were farther from the range edge. We recommend the use of node-based genetic connectivity metrics, in particular, average edge weight or average inverse edge weight, to model the influences of at-site habitat conditions on the immigration and settlement phases of dispersal. [ABSTRACT FROM AUTHOR]

Stable social organization in a wide variety of organisms has been linked to kinship, which can minimize conflict due to the indirect fitness benefits from cooperating with relatives. In birds, kin selection has been mostly studied in the context of reproduction or in species that are social year round. Many birds however are migratory, and the role of kinship in the winter societies of these species is virtually unexplored. In a previous study, we discovered striking social complexity and stability in a wintering population of migratory golden-crowned sparrows ( Zonotrichia atricapilla) - individuals repeatedly form close associations with the same social partners, including across multiple winters. Here, we test the possibility that kinship might be involved in these close and stable social affiliations. We examine the relationship between kinship and social structure for two of the consecutive wintering seasons from the previous study. We found no evidence that social structure was influenced by kinship. Relatedness between most pairs of individuals was at most that of first cousins (and mostly far lower). Genetic networks based on relatedness do not correspond to the social networks, and Mantel tests revealed no relationship between kinship and pairwise interaction frequency. Kinship also failed to predict social structure in more fine-grained analyses, including analyses of each sex separately (in the event that sex-biased migration might limit kin selection to one sex), and separate analyses for each social community. The complex winter societies of golden-crowned sparrows appear to be based on cooperative benefits unrelated to kin selection. [ABSTRACT FROM AUTHOR]

Endometriosis is a condition in which cells derived from the endometrium grow outside the uterus, e.g. in the peritoneum (external genital endometriosis). As these cells are under the influence of female hormones, major symptoms of endometriosis are pain, especially during the cycle, and infertility. Numerous hypotheses for the formation of endometriosis can be found in the literature, but there is growing evidence of serious genetic contributions to endometriosis susceptibility. The involvement of genes, steroid hormone metabolism, immunological reactions, receptor formation, inflammation, proliferation, apoptosis, intercellular adhesion, cell invasion and angiogenesis as well as genes regulating the activity of aforementioned enzymes have been suggested. Some more recently suggested candidate genes picked up in genome-wide association studies are involved in oncogenesis, metaplasia of endometrium cells and pathways of embryonic development of the female reproductive system. However, gene mutations proven to be causative for endometriosis have not been identified so far, even though the abnormal expression of candidate genes for endometriosis could be provoked by different epigenetic modifications including DNA methylation, heterochromatization or introduction of regulatory miRNA. We hypothesize that endometriosis is induced by a combination of abnormal genetic and/or epigenetic mutations: the latter pave the way for pathological changes which become irreversible, and according to the “epigenetic landscape” theory, this proceeds to the typical clinical manifestations. Two stages in the endometriosis pathway are suggested: (1) induction of primary endometrial cells toward endometriosis, and (2) implantation and progression of these cells into endometriosis lesions. The model favors endometriosis as an outgrowth of primary cells different in their origin, canalization of pathological processes, manifestation diversity provoked by unique genetic background and epigenetic influences, which result in many different clinical forms of the disease. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel cluster approach is conducted a phylogenetic network analysis of viral genomes that provides insights into the dynamic variations for SARS-CoV-2 genomes. • It is elucidated that our genetic network can describe the relationships of viral genomes, modulate the spread tendencies of pandemic, and reflect the fluidity of mutations. • K-mer natural vector has the ability not only to capture the established hotspots of diversities in viral genomes, but also to deal with the cases with recombination and deletions, as well as sequencing errors. Exploring the dynamic variations of viral genomes utilizing with a phylogenetic analysis is vital to control the pandemic and stop its waves. Genetic network can be applied to depict the complicated evolution relationships of viral genomes. However, current phylogenetic methods cannot handle the cases with deletions effectively. Therefore, the k-mer natural vector is employed to characterize the compositions and distribution features of k-mers occurring in a viral genome, and construct a one-to-one relationship between a viral genome and its k-mer natural vector. Utilizing the k-mer natural vector, we proposed a novel genetic network to investigate the variations of viral genomes in transmission among humans. With the assistance of genetic network, we identified the super-spreaders that were responsible for the pandemic outbreaks all over the world and chose the parental strains to evaluate the effectiveness of diagnostics, therapeutics, and vaccines. The obtaining results fully demonstrated that our genetic network can truly describe the relationships of viral genomes, effectively simulate virus spread tendency, and trace the transmission routes precisely. In addition, this work indicated that the k-mer natural vector has the ability to capture established hotspots of diversities existing in the viral genomes and understand how genomic contents change over time. [ABSTRACT FROM AUTHOR]

Absence epilepsy ( AE) is a complex, heritable disease characterized by a brief disruption of normal behavior and accompanying spike-wave discharges ( SWD) on the electroencephalogram. Only a handful of genes has been definitively associated with AE in humans and rodent models. Most studies suggest that genetic interactions play a large role in the etiology and severity of AE, but mapping and understanding their architecture remains a challenge, requiring new computational approaches. Here we use combined analysis of pleiotropy and epistasis ( CAPE) to detect and interpret genetic interactions in a meta-population derived from three C3H × B6J strain crosses, each of which is fixed for a different SWD-causing mutation. Although each mutation causes SWD through a different molecular mechanism, the phenotypes caused by each mutation are exacerbated on the C3H genetic background compared with B6J, suggesting common modifiers. By combining information across two phenotypic measures - SWD duration and frequency - CAPE showed a large, directed genetic network consisting of suppressive and enhancing interactions between loci on 10 chromosomes. These results illustrate the power of CAPE in identifying novel modifier loci and interactions in a complex neurological disease, toward a more comprehensive view of its underlying genetic architecture. [ABSTRACT FROM AUTHOR]

Highlights: [•] Distal–mesial asymmetry in crown–root border formation. [•] Shh-negative cell progeny forms the distal side of the mouse incisor. [•] Barx2, En1, Wnt11 and Runx3 are only expressed at the mesial crown–root border. [Copyright &y& Elsevier]

The sibling disciplines, systems and synthetic biology, are engaged in unraveling the complexity of the biological networks. One is trying to understand the design principle of the existing networks while the other is trying to engineer artificial gene networks with predicted functions. The significant and important role that computational intelligence can play to steer the life engineering discipline towards its ultimate goal, has been acknowledged since its time of birth. However, as the field is facing many challenges in building complex modules/systems from the simpler parts/devices, whether from scratch or through redesign, the role of computational assistance becomes even more crucial. Evolutionary computation, falling under the broader domain of artificial intelligence, is well-acknowledged for its near optimal solution seeking capability for poorly known and partially understood problems. Since the post genome period, these natural-selection simulating algorithms are playing a noteworthy role in identifying, analyzing and optimizing different types of biological networks. This article calls attention to how evolutionary computation can help synthetic biologists in assembling larger network systems from the lego-like parts. [ABSTRACT FROM AUTHOR]

Purpose: In limited samples of valuable biological tissues, univariate ranking methods of microarray analyses often fail to show significant differences among expression profiles. In order to allow for hypothesis generation, novel statistical modeling systems can be greatly beneficial. The authors applied new statistical approaches to solve the issue of limited experimental data to generate new hypotheses in CD14þ cells of patients with HIV-related fatigue (HRF) and healthy controls. Methodology: We compared gene expression profiles of CD14þ cells of nucleoside reverse transcriptase inhibitor (NRTI)-treated HIV patients with low versus high fatigue to healthy controls (n ¼ 5 each). With novel Bayesian modeling procedures, the authors identified 32 genes predictive of low versus high fatigue and 33 genes predictive of healthy versus HIV infection. Sparse association and liquid association networks further elucidated the possible biological pathways in which these genes are involved. Relevance for nursing practice: Genetic networks developed in a comprehensive Bayesian framework from small sample sizes allow nursing researchers to design future research approaches to address such issues as HRF. Implication for practice: The findings from this pilot study may take us one step closer to the development of useful biomarker targets for fatigue status. Specific and reliable tests are needed to diagnosis, monitor and treat fatigue and mitochondrial dysfunction. [ABSTRACT FROM AUTHOR]

The systems genetics is an emerging discipline that integrates high-throughput expression profiling technology and systems biology approaches for revealing the molecular mechanism of complex traits, and will improve our understanding of gene functions in the biochemical pathway and genetic interactions between biological molecules. With the rapid advances of microarray analysis technologies, bioinformatics is extensively used in the studies of gene functions, SNP-SNP genetic interactions, LD block-block interactions, miRNA-mRNA interactions, DNA-protein interactions, protein-protein interactions, and functional mapping for LD blocks. Based on bioinformatics panel, which can integrate '-omics' datasets to extract systems knowledge and useful information for explaining the molecular mechanism of complex traits, systems genetics is all about to enhance our understanding of biological processes. Systems biology has provided systems level recognition of various biological phenomena, and constructed the scientific background for the development of systems genetics. In addition, the next-generation sequencing technology and post-genome wide association studies empower the discovery of new gene and rare variants. The integration of different strategies will help to propose novel hypothesis and perfect the theoretical framework of systems genetics, which will make contribution to the future development of systems genetics, and open up a whole new area of genetics. [ABSTRACT FROM AUTHOR]

Circadian clocks exhibit ‘temperature compensation’, meaning that they show only small changes in period over a broad temperature range. Several clock genes have been implicated in the temperature-dependent control of period in Arabidopsis.We show that blue light is essential for this, suggesting that the effects of light and temperature interact or converge upon common targets in the circadian clock. Our data demonstrate that two cryptochrome photoreceptors differentially control circadian period and sustain rhythmicity across the physiological temperature range. In order to test the hypothesis that the targets of light regulation are sufficient to mediate temperature compensation, we constructed a temperature-compensated clock model by adding passive temperature effects into only the light-sensitive processes in the model. Remarkably, this model was not only capable of full temperature compensation and consistent with mRNA profiles across a temperature range, but also predicted the temperature-dependent change in the level of LATE ELONGATED HYPOCOTYL, a key clock protein. Our analysis provides a systems-level understanding of period control in the plant circadian oscillator. [ABSTRACT FROM AUTHOR]

Gaussian graphical models have been widely used as an effective method for studying the conditional independency structure among genes and for constructing genetic networks. However, gene expression data typically have heavier tails or more outlying observations than the standard Gaussian distribution. Such outliers in gene expression data can lead to wrong inference on the dependency structure among the genes. We propose a l1 penalized estimation procedure for the sparse Gaussian graphical models that is robustified against possible outliers. The likelihood function is weighted according to how the observation is deviated, where the deviation of the observation is measured based on its own likelihood. An efficient computational algorithm based on the coordinate gradient descent method is developed to obtain the minimizer of the negative penalized robustified-likelihood, where nonzero elements of the concentration matrix represents the graphical links among the genes. After the graphical structure is obtained, we re-estimate the positive definite concentration matrix using an iterative proportional fitting algorithm. Through simulations, we demonstrate that the proposed robust method performs much better than the graphical Lasso for the Gaussian graphical models in terms of both graph structure selection and estimation when outliers are present. We apply the robust estimation procedure to an analysis of yeast gene expression data and show that the resulting graph has better biological interpretation than that obtained from the graphical Lasso. [ABSTRACT FROM AUTHOR]

Abstract: The aim of this paper is to provide an introduction to the rapidly developing field of genetic programming (GP). Particular emphasis is placed on the application of GP to genomics. First, the basic methodology of GP is introduced. This is followed by a review of applications in the areas of gene network inference, gene expression data analysis, SNP analysis, epistasis analysis and gene annotation. Finally this paper concluded by suggesting potential avenues of possible future research on genetic programming, opportunities to extend the technique, and areas for possible practical applications. [Copyright &y& Elsevier]

Abstract: Dugesia japonica has become the suitable model system for studying the visual system molecular developmental processes because of their simple structure and high regenerative capacity. To further dissect the molecular events of genetic network controlling the visual system regeneration in D. japonica, we investigated the morphogenesis of regenerating eyes under the stereomicroscope and the transcripts expression levels of eight genes involved in this process by quantitative real-time PCR. The eight genes were Djeya, Djsix-1, Eye53, DjotxA, Djpax6, Djopsin, Djnetrin and 1020HH. The results showed that each gene was of different expression pattern at distinct regeneration stage and these eight genes could be divided into three groups according to the expression levels at different time points and the morphogenesis during eye reconstruction: (1) the early expression group, including Djeya, Djsix-1, Eye53, and DjotxA, which expression levels were significant increase from 1 to 3days after amputation; (2) the medium-term expression group, only including one gene, Djpax6, which expression level reached the peak on day 5; and (3) the late expression genes, including Djopsin, Djnetrin and 1020HH, which gradually increase transcription with the eye regeneration. Our data suggested that eye reconstruction was the results of polygenic services and the genes in the same group might have similar role or function in symphony. [Copyright &y& Elsevier]

MicroRNAs (miRs), the 17- to 25-nucleotide-long non-coding RNAs, regulate expression of approximately 30% of the protein-coding genes at the post-transcriptional level and have emerged as critical components of the complex functional pathway networks controlling important cellular processes, such as proliferation, development, differentiation, stress response' and apoptosis. Abnormal expression levels of miRs, regulating critical cancer-associated pathways, have been implicated to play important roles in the oncogenic processes, functioning both as oncogenes and as tumour suppressor genes. Elucidation of the genetic networks regulated by the abnormally expressing miRs in cancer cells is proving to be extremely significant in understanding the role of these miRs in the induction of malignant-transformation-associated phenotypic changes. As a result, the miRs involved in the oncogenic transformation process are being investigated as novel biomarkers of disease detection and prognosis as well as potential therapeutic targets for human cancers. In this \article, we review the existing literature in the field documenting the significance of aberrantly expressed miRs in human pancreatic cancer and discuss how the oncogenic miRs may be involved in the genetic networks regulating functional pathways deregulated in this malignancy. [ABSTRACT FROM AUTHOR]

Developmental control mechanisms often use multimeric complexes containing transcription factors, coregulators, and additional non-DNA binding components. It is challenging'to ascertain how such components contribute to complex function at endogenous loci. We analyzed the function of components of a complex containing master regulators of hematopoiesis (GATA-l and Sd/TALl) and the non-DNA binding components ETO2, the LIM domain protein LMO2, and the chrornatin looping factor LDB1. Surprisingly, we discovered that ETO2 and LMO2 regulate distinct target-gene ensembles in erythroid cells. ETO2 commonly repressed GATA-1 function via suppressing histone H3 acetylation, although it also regulated methylation of histone H3 at lysine 27 at select loci. Prior studies defined multiple modes by which GAlA-i regulates target genes with or without the coregulator Friend of GAlA-i (FOG-i). LMO2 selectively repressed genes that GAlA-i represses in a FOG-I-independent manner. As LMO2 controls hematopoiesis, its dysregulation is leukemogenic, and its influence on GATA factor function is unknown, this mechanistic link has important biological and pathophysiological implications. The demonstration that ETO2 and LMO2 exert qualitatively distinct functions at endogenous loci illustrates how components of complexes containing master developmental regulators can impart the capacity to regulate unique cohorts of target genes, thereby diversifying complex function. [ABSTRACT FROM AUTHOR]

Abstract: We investigate a two-component gene network model, originally used to describe the spatiotemporal patterning of the gene products in early Drosophila development. By considering a particular mode of interaction between the two gene products, denoted proteins and , we find both stable stationary and time-oscillatory fronts can occur in the reaction–diffusion system. We reduce the system by replacing with its spatial average (shadow system) and assume an abrupt “on-and-off” switch for the genes. In doing so, explicit formula are obtained for all steady-state solutions and their linear eigenvalues. Using the diffusion of , and the basal production rate, , as bifurcation parameters, we explore ranges in which a monotone, stationary front is stable, and show it can lose stability through a Hopf bifurcation, giving rise to oscillatory fronts. We also discuss the existence and stability of steady-state and time-oscillatory solutions with multiple extrema. An intuitive explanation for the occurrence of stable stationary and oscillatory front solutions is provided based on the behavior of in the absence of and the opposite regulation between and . Such behavior is also interpreted in terms of the biological parameters in the model, including those governing the connection of the gene network. [Copyright &y& Elsevier]

Abstract: The study of the stability of genetic network is an important motif for the understanding of the living organism at both molecular and cellular levels. In this Letter, we provide a theoretical method for analyzing the asymptotic stability of a genetic network under impulsive control. And the sufficient conditions of its asymptotic stability under impulsive control are obtained. Finally, an example is given to illustrate the effectiveness of the obtained method. [Copyright &y& Elsevier]