Evolution of system connectivity to support food production in the Indus Basin in Pakistan.

Sustainability challenges related to food production arise from multiple nature-society interactions occurring over long time periods. Traditional methods of quantitative analysis do not represent long-term changes in the networks of system components, including institutions and knowledge that affect system behavior. Here, we develop an approach to study system structure and evolution by combining a qualitative framework that represents sustainability-relevant human, technological, and environmental components, and their interactions, mediated by knowledge and institutions, with network modeling that enables quantitative metrics. We use this approach to examine the water and food system in the Punjab province of the Indus River Basin in Pakistan, exploring how food production has been sustained, despite high population growth, periodic floods, and frequent political and economic disruptions. Using network models of five periods spanning 75 y (1947 to 2022), we examine how quantitative metrics of network structure relate to observed sustainability-relevant outcomes and how potential interventions in the system affect these quantitative metrics. We find that the persistent centrality of some and evolving centrality of other key nodes, coupled with the increasing number and length of pathways connecting them, are associated with sustaining food production in the system over time. Our assessment of potential interventions shows that regulating groundwater pumping and phasing out fossil fuels alters network pathways, and helps identify potential vulnerabilities for future food production.

Diversity, integration, and variability of intergenerational relationships in old age: New insights from personal network research.

Relationships between family members from different generations have long been described as a source of solidarity and support in aging populations and, more recently, as a potential risk factor for COVID-19 contagion. Personal or egocentric network research offers a powerful kit of conceptual and methodological tools to study these relationships, but this has not yet been employed to its full potential in the literature. We investigate the heterogeneity, social integration, and individual correlates of intergenerational relationships in old age analyzing highly granular data on the personal networks of 230 older adults (2747 social ties) from a local survey in one of the areas of the world at the forefront of global aging trends (northern Italy). Using information on different layers in broad egocentric networks and on the structure of connectivity among the social contacts of aging people, we propose multiple conceptualizations and measures of intergenerational connectedness. Results show that intergenerational relationships are strongly integrated, but also highly diverse and variable, in older adults' social networks. Different types of intergenerational ties exist in different network layers, with various relational roles, degrees of tie strength, and patterns of association with individual and tie characteristics. We discuss how new and existing personal network data can be leveraged to consider novel questions and hypotheses about intergenerational relationships in contemporary aging families.

Genome entropy and network centrality contrast exploration and exploitation in evolution of foodborne pathogens.

Modelling evolution of foodborne pathogens is crucial for mitigation and prevention of outbreaks. We apply network-theoretic and information-theoretic methods to trace evolutionary pathways ofSalmonellaTyphimurium in New South Wales, Australia, by studying whole genome sequencing surveillance data over a five-year period which included several outbreaks. The study derives both undirected and directed genotype networks based on genetic proximity, and relates the network's structural property (centrality) to its functional property (prevalence). The centrality-prevalence space derived for the undirected network reveals a salient exploration-exploitation distinction across the pathogens, further quantified by the normalised Shannon entropy and the Fisher information of the corresponding shell genome. This distinction is also analysed by tracing the probability density along evolutionary paths in the centrality-prevalence space. We quantify the evolutionary pathways, and show that pathogens exploring the evolutionary search-space during the considered period begin to exploit their environment (their prevalence increases resulting in outbreaks), but eventually encounter a bottleneck formed by epidemic containment measures.

Well-being is associated with cortical thickness network topology of human brain.

BACKGROUND: Living a happy and meaningful life is an eternal topic in positive psychology, which is crucial for individuals' physical and mental health as well as social functioning. Well-being can be subdivided into pleasure attainment related hedonic well-being or emotional well-being, and self-actualization related eudaimonic well-being or psychological well-being plus social well-being. Previous studies have mostly focused on human brain morphological and functional mechanisms underlying different dimensions of well-being, but no study explored brain network mechanisms of well-being, especially in terms of topological properties of human brain morphological similarity network. METHODS: Therefore, in the study, we collected 65 datasets including magnetic resonance imaging (MRI) and well-being data, and constructed human brain morphological network based on morphological distribution similarity of cortical thickness to explore the correlations between topological properties including network efficiency and centrality and different dimensions of well-being. RESULTS: We found emotional well-being was negatively correlated with betweenness centrality in the visual network but positively correlated with eigenvector centrality in the precentral sulcus, while the total score of well-being was positively correlated with local efficiency in the posterior cingulate cortex of cortical thickness network. CONCLUSIONS: Our findings demonstrated that different dimensions of well-being corresponded to different cortical hierarchies: hedonic well-being was involved in more preliminary cognitive processing stages including perceptual and attentional information processing, while hedonic and eudaimonic well-being might share common morphological similarity network mechanisms in the subsequent advanced cognitive processing stages.

Network Biology Analyses and Dynamic Modeling of Gene Regulatory Networks under Drought Stress Reveal Major Transcriptional Regulators in Arabidopsis.

Drought is one of the most serious abiotic stressors in the environment, restricting agricultural production by reducing plant growth, development, and productivity. To investigate such a complex and multifaceted stressor and its effects on plants, a systems biology-based approach is necessitated, entailing the generation of co-expression networks, identification of high-priority transcription factors (TFs), dynamic mathematical modeling, and computational simulations. Here, we studied a high-resolution drought transcriptome of Arabidopsis. We identified distinct temporal transcriptional signatures and demonstrated the involvement of specific biological pathways. Generation of a large-scale co-expression network followed by network centrality analyses identified 117 TFs that possess critical properties of hubs, bottlenecks, and high clustering coefficient nodes. Dynamic transcriptional regulatory modeling of integrated TF targets and transcriptome datasets uncovered major transcriptional events during the course of drought stress. Mathematical transcriptional simulations allowed us to ascertain the activation status of major TFs, as well as the transcriptional intensity and amplitude of their target genes. Finally, we validated our predictions by providing experimental evidence of gene expression under drought stress for a set of four TFs and their major target genes using qRT-PCR. Taken together, we provided a systems-level perspective on the dynamic transcriptional regulation during drought stress in Arabidopsis and uncovered numerous novel TFs that could potentially be used in future genetic crop engineering programs.

Hidden effects of habitat restoration on the persistence of pollination networks.

Past and recent studies have focused on the effects of global change drivers such as species invasions on species extinction. However, as we enter the United Nations Decade of Ecosystem Restoration the aim must switch to understanding how invasive-species management affects the persistence of the remaining species in a community. Focusing on plant-pollinator interactions, we test how species persistence is affected by restoration via the removal of invasive plant species. Restoration had a clear positive effect on plant persistence, whereas there was no difference between across treatments for pollinator persistence in the early season, but a clear effect in late season, with higher persistence in unrestored sites. Network structure affected only pollinator persistence, while centrality had a strong positive effect on both plants and pollinators. Our results suggest a hidden effect of invasive plants-although they may compete with native plant species, invasive plants may provide important resources for pollinators, at least in the short term.

Individual Dietary Specialization in a Generalist Bee Varies across Populations but Has No Effect on the Richness of Associated Microbial Communities.

AbstractDespite the increasingly documented occurrence of individual specialization, the relationship between individual consumer interactions and diet-related microbial communities in wild populations is still unclear. Using data from nests of Ceratina australensis from three different wild bee populations, we combine metabarcoding and network approaches to explore the existence of individual variation in resource use within and across populations and whether dietary specialization affects the richness of pollen-associated microbes. We reveal the existence of marked dietary specialization. In the most specialized population, we also show that individuals' diet breadth was positively related to the richness of fungi but not bacteria. Overall, individual specialization appeared to have a weak or negligible effect on the microbial richness of nests, suggesting that different mechanisms beyond environmental transmission may be at play regarding microbial acquisition in wild bees.

Catalytic and binding sites prediction in globular proteins through discrete Markov chains and network centrality measures.

In this work we use a discrete Markov chain approach combined with network centrality measures to identify and predict the location of active sites in globular proteins. To accomplish this, we use a three-dimensional network of proteinCαatoms as nodes connected through weighted edges which represent the varying interaction degree between protein's atoms. We compute the mean first passage time matrixH= {Hji} for this Markov chain and evaluate the averaged number of steps ⟨Hj⟩ to reach single nodenjin order to identify such residues that, on the average, are at the least distant from every other node. We also carry out a graph theory analysis to evaluate closeness centralityCc, betweenness centralityCband eigenvector centralityCemeasures which provide relevant information about the connectivity structure and topology of theCαprotein networks. Finally we also performed an analysis of equivalent random and regular networks of the same sizeNin terms of the average path lengthLand the average clustering coefficient⟨C⟩comparing these with the corresponding values forCαprotein networks. Our results show that the mean-first passage time matrixHand its related quantity ⟨Hj⟩ together withCc,CbandCecan not only predict with relative high accuracy the location of active sites in globular proteins but also exhibit a high feasibility to use them to predict the existence of new regions in protein's structure to identify new potential binding or catalytic activity or, in some cases, the presence of new allosteric pathways.

Haemosporidian taxonomic composition, network centrality and partner fidelity between resident and migratory avian hosts.

Migration can modify interaction dynamics between parasites and their hosts with migrant hosts able to disperse parasites and impact local community transmission. Thus, studying the relationships among migratory hosts and their parasites is fundamental to elucidate how migration shapes host-parasite interactions. Avian haemosporidians are some of the most prevalent and diverse group of wildlife parasites and are also widely studied as models in ecological and evolutionary research. Here, we contrast partner fidelity, network centrality and parasite taxonomic composition among resident and non-resident avian hosts using presence/absence data on haemosporidians parasitic in South American birds as study model. We ran multilevel Bayesian models to assess the role of migration in determining partner fidelity (i.e., normalized degree) and centrality (i.e., weighted closeness) in host-parasite networks of avian hosts and their respective haemosporidian parasites. In addition, to evaluate parasite taxonomic composition, we performed permutational multivariate analyses of variance to quantify dissimilarity in haemosporidian lineages infecting different host migratory categories. We observed similar partner fidelity and parasite taxonomic composition among resident and migratory hosts. Conversely, we demonstrate that migratory hosts play a more central role in host-parasite networks than residents. However, when evaluating partially and fully migratory hosts separately, we observed that only partially migratory species presented higher network centrality when compared to resident birds. Therefore, migration does not lead to differences in both partner fidelity and parasite taxonomic composition. However, migratory behavior is positively associated with network centrality, indicating migratory hosts play more important roles in shaping host-parasite interactions and influence local transmission.

COVID-19 and the Economic Importance of In-Person K-12 Schooling.

The extent to which elementary and secondary (K-12) schools should remain open is at the forefront of discussions on long-term pandemic management. In this context, little mention has been made of the immediate importance of K-12 schooling for the rest of the economy. Eliminating in-person schooling reduces the amount of time parents of school-aged children have available to work and therefore reduces income to those workers and the economy as a whole. We discuss two measures of economic importance and how they can be modified to better reflect the vital role played by K-12 education. The first is its size, as captured by the fraction of gross domestic product produced by that sector. The second is its centrality, reflecting how essential the sector is to the network of economic activity. Using data from Canada's Census of Population and Symmetric Input-Output Tables, we show how accounting for this role dramatically increases the importance of K-12 schooling.

La mesure dans laquelle il conviendrait de garder ouverts les établissements d'enseignement de la maternelle à la 12e année est au premier plan des discussions liées à la gestion à long terme de la pandémie. Dans ce contexte, l'importance immédiate de l'éducation de la maternelle à la 12e année pour le reste de l'économie n'a été que timidement évoquée. La suppression de l'enseignement en classe réduit le temps dont disposent les parents d'enfants d'âge scolaire pour travailler, ce qui a pour effet de réduire le revenu versé à ces travailleurs et d'affaiblir l'économie dans son ensemble. Nous traitons de deux indicateurs de cette importance économique et de la façon dont ces indicateurs peuvent être modifiés de manière à mieux refléter le rôle déterminant que joue l'éducation de la maternelle à la 12e année. Le premier indicateur est la taille du secteur, représentée par la fraction du produit intérieur brut qu'il engendre. Le second est la centralité du secteur, soit la mesure dans laquelle il est essentiel au réseau d'activité économique. À l'aide de données tirées du recensement de la population du Canada et des tableaux d'entrées-sorties symétriques, nous démontrons que la prise en compte de ce rôle crucial accroît considérablement l'importance de l'éducation de la maternelle à la 12e année.

Ensemble disease gene prediction by clinical sample-based networks.

BACKGROUND: Disease gene prediction is a critical and challenging task. Many computational methods have been developed to predict disease genes, which can reduce the money and time used in the experimental validation. Since proteins (products of genes) usually work together to achieve a specific function, biomolecular networks, such as the protein-protein interaction (PPI) network and gene co-expression networks, are widely used to predict disease genes by analyzing the relationships between known disease genes and other genes in the networks. However, existing methods commonly use a universal static PPI network, which ignore the fact that PPIs are dynamic, and PPIs in various patients should also be different. RESULTS: To address these issues, we develop an ensemble algorithm to predict disease genes from clinical sample-based networks (EdgCSN). The algorithm first constructs single sample-based networks for each case sample of the disease under study. Then, these single sample-based networks are merged to several fused networks based on the clustering results of the samples. After that, logistic models are trained with centrality features extracted from the fused networks, and an ensemble strategy is used to predict the finial probability of each gene being disease-associated. EdgCSN is evaluated on breast cancer (BC), thyroid cancer (TC) and Alzheimer's disease (AD) and obtains AUC values of 0.970, 0.971 and 0.966, respectively, which are much better than the competing algorithms. Subsequent de novo validations also demonstrate the ability of EdgCSN in predicting new disease genes. CONCLUSIONS: In this study, we propose EdgCSN, which is an ensemble learning algorithm for predicting disease genes with models trained by centrality features extracted from clinical sample-based networks. Results of the leave-one-out cross validation show that our EdgCSN performs much better than the competing algorithms in predicting BC-associated, TC-associated and AD-associated genes. de novo validations also show that EdgCSN is valuable for identifying new disease genes.

Plant-pollinator network structural properties differentially affect pollen transfer dynamics and pollination success.

Plant-pollinator network studies have uncovered important generalities in the structure of these communities, rapidly advancing our understanding of the underlying drivers of such a structure. In spite of this, however, it is still unclear how changes in structural network properties influence overall plant pollination success. One key limitation is the lack of information on the relationship between network structural properties and aspects of pollination and plant reproductive success. Here, we estimate four plant species network structural metrics (interaction strength, weighted degree, closeness centrality, and specialization level), commonly used to describe their importance within plant-pollinator networks, at two different sites, and evaluate their effects on pollen deposition and pollen tube success. We found a positive effect of plant-pollinator specialization and a negative effect of closeness centrality on heterospecific pollen load size. We also found a marginal negative effect of closeness centrality on pollen tube success. Our results suggest that increasing plant-pollinator specialization within nested communities (pollinated by one or very few generalist insect species) may result in high levels of heterospecific pollen transfer. Furthermore, the differential effects of plant-pollinator network metrics on pollination success (pollen receipt and pollen tube success), highlight the need to integrate quantity (e.g. visitation rate) and quality (e.g. pollen delivery) aspects of pollination to achieve a more mechanistic understanding of the relationship between plant-pollinator network structure and function. Such knowledge is key to evaluate the resilience and stability of plant-pollinator communities and the services they provide in the face of increasing human disturbances.

A network approach to quantifying radiotherapy effect on cancer: Radiosensitive gene group centrality.

Radiotherapy plays a vital role in cancer treatment, for which accurate prognosis is important for guiding sequential treatment and improving the curative effect for patients. An issue of great significance in radiotherapy is to assess tumor radiosensitivity for devising the optimal treatment strategy. Previous studies focused on gene expression in cells closely associated with radiosensitivity, but factors such as the response of a cancer patient to irradiation and the patient survival time are largely ignored. For clinical cancer treatment, a specific pre-treatment indicator taking into account cancer cell type and patient radiosensitivity is of great value but it has been missing. Here, we propose an effective indicator for radiosensitivity: radiosensitive gene group centrality (RSGGC), which characterizes the importance of the group of genes that are radiosensitive in the whole gene correlation network. We demonstrate, using both clinical patient data and experimental cancer cell lines, which RSGGC can provide a quantitative estimate of the effect of radiotherapy, with factors such as the patient survival time and the survived fraction of cancer cell lines under radiotherapy fully taken into account. Our main finding is that, for patients with a higher RSGGC score before radiotherapy, cancer treatment tends to be more effective. The RSGGC can have significant applications in clinical prognosis, serving as a key measure to classifying radiosensitive and radioresistant patients.

Network analysis of a stakeholder community combatting illegal wildlife trade.

The illegal wildlife trade has emerged as a growing and urgent environmental issue. Stakeholders involved in the efforts to curb wildlife trafficking include nongovernmental organizations (NGOs), academia, and state government and enforcement bodies. The extent to which these stakeholders work and communicate among each other is fundamental to effectively combatting illicit trade. Using the United Kingdom as a case study, we used a social network analysis and semistructured interviews of stakeholders to assess communication relationships in the counter wildlife trafficking community. The NGOs consistently occupied 4 of the 5 most central positions in the generated networks, whereas academic institutions routinely occupied 4 of the 5 most peripheral positions. However, NGOs were the least diverse in their communication practices compared with the other stakeholder groups. Stakeholders identified personal relationships as the most important aspect of functioning communication. Participant insights also showed that stakeholder-specific variables (e.g., ethical and confidentiality concerns), competition, and fundraising can have a confounding effect on intercommunication. Evaluating communication networks and intrastakeholder communication trends is essential to creating cohesive, productive, and efficient responses to the challenges of combatting illegal wildlife trade. Article impact statement: Communication among those combatting illegal wildlife trade is confounded by stakeholder variables (ethics, confidentiality), competition, and fundraising.

Análisis de las Redes de una Comunidad de Activistas que Combate al Mercado Ilegal de Fauna Resumen El mercado ilegal de fauna ha emergido como un tema ambiental creciente y urgente. Los actores involucrados en los esfuerzos por detener el tráfico de fauna incluyen a las organizaciones no gubernamentales (ONGs), los académicos y a los cuerpos estatales de gobierno y aplicación de la ley. El grado hasta el que estos accionistas trabajen y se comuniquen entre sí es fundamental para combatir efectivamente el mercado ilícito. Con el Reino Unido como estudio de caso, usamos un análisis de redes sociales y entrevistas semiestructuradas con los actores para evaluar las relaciones de comunicación dentro de la comunidad que combate el tráfico de fauna. Las ONGs ocuparon sistemáticamente cuatro de las cinco posiciones más centrales en las redes generadas, mientras que las instituciones académicas ocuparon rutinariamente cuatro de las cinco posiciones más periféricas. Sin embargo, las ONGs fueron las menos diversas con sus prácticas de comunicación en comparación con los otros grupos de actores. Los actores identificaron a las relaciones personales como el aspecto más importante de la comunicación funcional. La percepción de los participantes también mostró que las variables específicas por accionista (p. ej.: preocupaciones éticas y de confidencialidad), la competencia y la captación de fondos pueden tener un efecto confuso sobre la intercomunicación. La evaluación de las tendencias en las redes de comunicación y la comunicación entre accionistas es esencial para la creación de respuestas cohesivas, productivas y eficientes ante los obstáculos que presenta el combate al comercio ilegal de fauna.

Brain structural covariance network centrality in maltreated youth with PTSD and in maltreated youth resilient to PTSD.

Child maltreatment is a major cause of pediatric posttraumatic stress disorder (PTSD). Previous studies have not investigated potential differences in network architecture in maltreated youth with PTSD and those resilient to PTSD. High-resolution magnetic resonance imaging brain scans at 3 T were completed in maltreated youth with PTSD (n = 31), without PTSD (n = 32), and nonmaltreated controls (n = 57). Structural covariance network architecture was derived from between-subject intraregional correlations in measures of cortical thickness in 148 cortical regions (nodes). Interregional positive partial correlations controlling for demographic variables were assessed, and those correlations that exceeded specified thresholds constituted connections in cortical brain networks. Four measures of network centrality characterized topology, and the importance of cortical regions (nodes) within the network architecture were calculated for each group. Permutation testing and principle component analysis method were employed to calculate between-group differences. Principle component analysis is a methodological improvement to methods used in previous brain structural covariance network studies. Differences in centrality were observed between groups. Larger centrality was found in maltreated youth with PTSD in the right posterior cingulate cortex; smaller centrality was detected in the right inferior frontal cortex compared to youth resilient to PTSD and controls, demonstrating network characteristics unique to pediatric maltreatment-related PTSD. Larger centrality was detected in right frontal pole in maltreated youth resilient to PTSD compared to youth with PTSD and controls, demonstrating structural covariance network differences in youth resilience to PTSD following maltreatment. Smaller centrality was found in the left posterior cingulate cortex and in the right inferior frontal cortex in maltreated youth compared to controls, demonstrating attributes of structural covariance network topology that is unique to experiencing maltreatment. This work is the first to identify cortical thickness-based structural covariance network differences between maltreated youth with and without PTSD. We demonstrated network differences in both networks unique to maltreated youth with PTSD and those resilient to PTSD. The networks identified are important for the successful attainment of age-appropriate social cognition, attention, emotional processing, and inhibitory control. Our findings in maltreated youth with PTSD versus those without PTSD suggest vulnerability mechanisms for developing PTSD.

The impact of cytokine responses in the intra- and extracellular signaling network of a traumatic injury.

Investigations of cellular responses involved in injury and repair processes have generated valuable information contributing to the advancement of wound healing and treatments. Intra- and extracellular regulators of healing mechanisms, such as cytokines, signaling proteins, and growth factors, have been described to possess significant roles in facilitating optimal recovery. This study explored a collection of 30 spatiotemporal responses comprised of cytokines (IL-1α, IL-1ß, IL-2, IL-6, TNF-α, MIP-1α), intracellular proteins (Akt, c-Jun, CREB, ERK1/2, JNK, MEK1, p38, p53, p90RSK), phosphorylated proteins (p-Akt, p-c-Jun, p-CREB, p-ERK1/2, p-GSK-3α/ß, p-HSP27, p-IκBα, p-JNK, p-MEK1, p-p38, p-p70S6K, p-p90RSK, p-STAT2, p-STAT3), and a protease (Caspase-3), measured in skeletal muscle tissue following a traumatic injury (rodent Gustilo IIIB fracture). To optimize the analysis of context-specific data sets, a network centrality parameter approach was used to assess the impact of each response in relation to all other measured responses. This approach identified proteins that were substantially amplified and potentially central in the wound healing network by evaluation of their corresponding centrality parameter, radiality. Network analysis allowed us to distinguish the progression of healing that occurred at certain time points and regions of injury. Notably, new tissue formation was proposed to occur by 168 h post-injury in severely injured tissue, while tissue 1-cm away from the site of injury that experienced relatively minor injury appeared to exhibit signs of new tissue formation as early as 24 h post-injury. In particular, hallmarks of inflammation, cytokines IL-1ß, IL-6, and IL-2, appear to have a pronounced impact at earlier time points (0-24 h post-injury), while intracellular proteins involved in cell proliferation, differentiation, or proteolysis (c-Jun, CREB, JNK, p38, p-c-Jun; p-MEK1, p-p38, p-STAT3) are more significant at later times (24-168 h). Overall, this study demonstrates the feasibility of a network analysis approach to extract significant information and also offers a spatiotemporal visualization of the intra- and extracellular signaling responses that regulate healing mechanisms.

MIDAS: Mining differentially activated subpaths of KEGG pathways from multi-class RNA-seq data.

Pathway based analysis of high throughput transcriptome data is a widely used approach to investigate biological mechanisms. Since a pathway consists of multiple functions, the recent approach is to determine condition specific sub-pathways or subpaths. However, there are several challenges. First, few existing methods utilize explicit gene expression information from RNA-seq. More importantly, subpath activity is usually an average of statistical scores, e.g., correlations, of edges in a candidate subpath, which fails to reflect gene expression quantity information. In addition, none of existing methods can handle multiple phenotypes. To address these technical problems, we designed and implemented an algorithm, MIDAS, that determines condition specific subpaths, each of which has different activities across multiple phenotypes. MIDAS utilizes gene expression quantity information fully and the network centrality information to determine condition specific subpaths. To test performance of our tool, we used TCGA breast cancer RNA-seq gene expression profiles with five molecular subtypes. 36 differentially activate subpaths were determined. The utility of our method, MIDAS, was demonstrated in four ways. All 36 subpaths are well supported by the literature information. Subsequently, we showed that these subpaths had a good discriminant power for five cancer subtype classification and also had a prognostic power in terms of survival analysis. Finally, in a performance comparison of MIDAS to a recent subpath prediction method, PATHOME, our method identified more subpaths and much more genes that are well supported by the literature information. AVAILABILITY: http://biohealth.snu.ac.kr/software/MIDAS/.

Gene alterations in monocytes are pathogenic factors for immunoglobulin a nephropathy by bioinformatics analysis of microarray data.

BACKGROUND: Immunoglobulin A nephropathy (IgAN) is the most frequent primary glomerulopathy worldwide. The study aimed to provide potential molecular biomarkers for IgAN management. METHODS: The public gene expression profiling GSE58539 was utilized, which contained 17 monocytes samples (8 monocytes samples isolated from IgAN patients and 9 monocytes samples isolated from healthy blood donors). Firstly, differentially expressed genes (DEGs) between the two kinds of samples were identified by limma package. Afterwards, pathway enrichment analysis was implemented. Thereafter, protein-protein interaction (PPI) network was constructed and key nodes in PPI network were predicted using four network centrality analyses. Ultimately, gene functional interaction (FI) was constructed according to expressions in each sample, and then module network was extracted from FI network. RESULTS: A total of 678 DEGs were screened out, of these, 72 DEGs were identified as crucial nodes in PPI network that could well distinguish IgAN and healthy samples. In particular, IL6, TNF, IL1B, PRKACA and CCL20 were closely related to pathways such as hematopoietic cell lineage, apoptosis and Toll-like receptor (TLR) signaling pathway. Moreover, 12 genes in the FI network belonged to the 72 identified key nodes, such as CCL20, HDAC10, FPR2 and PRKACA, which were also key genes in 4 module networks. CONCLUSIONS: Several crucial genes were identified in monocytes of IgAN patients, such as IL6, TNF, IL1B, CCL20, PRKACA, FPR2 and HDAC10. These genes might co-involve in pathways such as TLR and apoptosis signaling during IgAN progression.

Genome-wide profiling of 24 hr diel rhythmicity in the water flea, Daphnia pulex: network analysis reveals rhythmic gene expression and enhances functional gene annotation.

BACKGROUND: Marine and freshwater zooplankton exhibit daily rhythmic patterns of behavior and physiology which may be regulated directly by the light:dark (LD) cycle and/or a molecular circadian clock. One of the best-studied zooplankton taxa, the freshwater crustacean Daphnia, has a 24 h diel vertical migration (DVM) behavior whereby the organism travels up and down through the water column daily. DVM plays a critical role in resource tracking and the behavioral avoidance of predators and damaging ultraviolet radiation. However, there is little information at the transcriptional level linking the expression patterns of genes to the rhythmic physiology/behavior of Daphnia. RESULTS: Here we analyzed genome-wide temporal transcriptional patterns from Daphnia pulex collected over a 44 h time period under a 12:12 LD cycle (diel) conditions using a cosine-fitting algorithm. We used a comprehensive network modeling and analysis approach to identify novel co-regulated rhythmic genes that have similar network topological properties and functional annotations as rhythmic genes identified by the cosine-fitting analyses. Furthermore, we used the network approach to predict with high accuracy novel gene-function associations, thus enhancing current functional annotations available for genes in this ecologically relevant model species. Our results reveal that genes in many functional groupings exhibit 24 h rhythms in their expression patterns under diel conditions. We highlight the rhythmic expression of immunity, oxidative detoxification, and sensory process genes. We discuss differences in the chronobiology of D. pulex from other well-characterized terrestrial arthropods. CONCLUSIONS: This research adds to a growing body of literature suggesting the genetic mechanisms governing rhythmicity in crustaceans may be divergent from other arthropod lineages including insects. Lastly, these results highlight the power of using a network analysis approach to identify differential gene expression and provide novel functional annotation.

Gene expression patterns in transgenic mouse models of hypertrophic cardiomyopathy caused by mutations in myosin regulatory light chain.

Using microarray and bioinformatics, we examined the gene expression profiles in transgenic mouse hearts expressing mutations in the myosin regulatory light chain shown to cause hypertrophic cardiomyopathy (HCM). We focused on two malignant RLC-mutations, Arginine 58→Glutamine (R58Q) and Aspartic Acid 166 â†’ Valine (D166V), and one benign, Lysine 104 â†’ Glutamic Acid (K104E)-mutation. Datasets of differentially expressed genes for each of three mutants were compared to those observed in wild-type (WT) hearts. The changes in the mutant vs. WT samples were shown as fold-change (FC), with stringency FC ≥ 2. Based on the gene profiles, we have identified the major signaling pathways that underlie the R58Q-, D166V- and K104E-HCM phenotypes. The correlations between different genotypes were also studied using network-based algorithms. Genes with strong correlations were clustered into one group and the central gene networks were identified for each HCM mutant. The overall gene expression patterns in all mutants were distinct from the WT profiles. Both malignant mutations shared certain classes of genes that were up or downregulated, but most similarities were noted between D166V and K104E mice, with R58Q hearts showing a distinct gene expression pattern. Our data suggest that all three HCM mice lead to cardiomyopathy in a mutation-specific manner and thus develop HCM through diverse mechanisms.