Consensus Gene Network Analysis Identifies the Key Similarities and Differences in Endothelial and Epithelial Cell Dynamics after Candida albicans Infection

Endothelial and epithelial cells are morphologically different and play a critical role in host defense during Candida albicans infection. Both cells respond to C. albicans infection by activating various signaling pathways and gene expression patterns. Their interactions with these pathogens can have beneficial and detrimental effects, and a better understanding of these interactions can help guide the development of new therapies for C. albicans infection. To identify the differences and similarities between human endothelial and oral epithelial cell transcriptomics during C. albicans infection, we performed consensus WGCNA on 32 RNA-seq samples by relating the consensus modules to endothelial-specific modules and analyzing the genes connected. This analysis resulted in the identification of 14 distinct modules. We demonstrated that the magenta module correlates significantly with C. albicans infection in each dataset. In addition, we found that the blue and cyan modules in the two datasets had opposite correlation coefficients with a C. albicans infection. However, the correlation coefficients and p-values between the two datasets were slightly different. Functional analyses of the hub of genes from endothelial cells elucidated the enrichment in TNF, AGE-RAGE, MAPK, and NF-κB signaling. On the other hand, glycolysis, pyruvate metabolism, amino acid, fructose, mannose, and vitamin B6 metabolism were enriched in epithelial cells. However, mitophagy, necroptosis, apoptotic processes, and hypoxia were enriched in both endothelial and epithelial cells. Protein-protein interaction analysis using STRING and CytoHubba revealed STAT3, SNRPE, BIRC2, and NFKB2 as endothelial hub genes, while RRS1, SURF6, HK2, and LDHA genes were identified in epithelial cells. Understanding these similarities and differences may provide new insights into the pathogenesis of C. albicans infections and the development of new therapeutic targets and interventional strategies.

Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines

The Metabolome and Transcriptome are mutually communicating within cancer cells, and this interplay is translated into the existence of quantifiable correlation structures between gene expression and metabolite abundance levels. Studying these correlations could provide a novel venue of understanding cancer and the discovery of novel biomarkers and pharmacological strategies, as well as laying the foundation for the prediction of metabolite quantities by leveraging information from the more widespread transcriptomics data. In the current paper, we investigate the correlation between gene expression and metabolite levels in the Cancer Cell Line Encyclopedia dataset, building a direct correlation network between the two molecular ensembles. We show that a metabolite/transcript correlation network can be used to predict metabolite levels in different samples and datasets, such as the NCI-60 cancer cell line dataset, both on a sample-by-sample basis and in differential contrasts. We also show that metabolite levels can be predicted in principle on any sample and dataset for which transcriptomics data are available, such as the Cancer Genome Atlas (TCGA).

Pediatric Sleep Apnea: The Overnight Electroencephalogram as a Phenotypic Biomarker

Pediatric obstructive sleep apnea (OSA) is a prevalent disorder that disrupts sleep and is associated with neurocognitive and behavioral negative consequences, potentially hampering the development of children for years. However, its relationships with sleep electroencephalogram (EEG) have been scarcely investigated. Here, our main objective was to characterize the overnight EEG of OSA-affected children and its putative relationships with polysomnographic measures and cognitive functions. A two-step analysis involving 294 children (176 controls, 57% males, age range: 5-9 years) was conducted for this purpose. First, the activity and irregularity of overnight EEG spectrum were characterized in the typical frequency bands by means of relative spectral power and spectral entropy, respectively: δ1 (0.1-2 Hz), δ2 (2-4 Hz), θ (4-8 Hz), α (8-13 Hz), σ (10-16 Hz), β1 (13-19 Hz), β2 (19-30 Hz), and γ (30-70 Hz). Then, a correlation network analysis was conducted to evaluate relationships between them, six polysomnography variables (apnea-hypopnea index, respiratory arousal index, spontaneous arousal index, overnight minimum blood oxygen saturation, wake time after sleep onset, and sleep efficiency), and six cognitive scores (differential ability scales, Peabody picture vocabulary test, expressive vocabulary test, design copying, phonological processing, and tower test). We found that as the severity of the disease increases, OSA broadly affects sleep EEG to the point that the information from the different frequency bands becomes more similar, regardless of activity or irregularity. EEG activity and irregularity information from the most severely affected children were significantly associated with polysomnographic variables, which were coherent with both micro and macro sleep disruptions. We hypothesize that the EEG changes caused by OSA could be related to the occurrence of respiratory-related arousals, as well as thalamic inhibition in the slow oscillation generation due to increases in arousal levels aimed at recovery from respiratory events. Furthermore, relationships between sleep EEG and cognitive scores emerged regarding language, visual-spatial processing, and executive function with pronounced associations found with EEG irregularity in δ1 (Peabody picture vocabulary test and expressive vocabulary test maximum absolute correlations 0.61 and 0.54) and β2 (phonological processing, 0.74; design copying, 0.65; and Tow 0.52). Our results show that overnight EEG informs both sleep alterations and cognitive effects of pediatric OSA. Moreover, EEG irregularity provides new information that complements and expands the classic EEG activity analysis. These findings lay the foundation for the use of sleep EEG to assess cognitive changes in pediatric OSA.

Highly Sensitive Flow Cytometry Allows Monitoring of Changes in Circulating Immune Cells in Blood After Tdap Booster Vaccination

Antigen-specific serum immunoglobulin (Ag-specific Ig) levels are broadly used as correlates of protection. However, in several disease and vaccination models these fail to predict immunity. In these models, in-depth knowledge of cellular processes associated with protective versus poor responses may bring added value. We applied high-throughput multicolor flow cytometry to track over-time changes in circulating immune cells in 10 individuals following pertussis booster vaccination (Tdap, Boostrix®, GlaxoSmithKline). Next, we applied correlation network analysis to extensively investigate how changes in individual cell populations correlate with each other and with Ag-specific Ig levels. We further determined the most informative cell subsets and analysis time points for future studies. Expansion and maturation of total IgG1 plasma cells, which peaked at day 7 post-vaccination, was the most prominent cellular change. Although these cells preceded the increase in Ag-specific serum Ig levels, they did not correlate with the increase of Ig levels. In contrast, strong correlation was observed between Ag-specific IgGs and maximum expansion of total IgG1 and IgA1 memory B cells at days 7 to 28. Changes in circulating T cells were limited, implying the need for a more sensitive approach. Early changes in innate immune cells, i.e. expansion of neutrophils, and expansion and maturation of monocytes up to day 5, most likely reflected their responses to local damage and adjuvant. Here we show that simultaneous monitoring of multiple circulating immune subsets in blood by flow cytometry is feasible. B cells seem to be the best candidates for vaccine monitoring.

Gene network analysis using SWIM reveals interplay between the transcription factor-encoding genes HMGA1, FOXM1, and MYBL2 in triple-negative breast cancer

Among breast cancer subtypes, triple-negative breast cancer (TNBC) is the most aggressive with the worst prognosis and the highest rates of metastatic disease. To identify TNBC gene signatures, we applied the network-based methodology implemented by the SWIM software to gene expression data of TNBC patients in The Cancer Genome Atlas (TCGA) database. SWIM enables to predict key (switch) genes within the co-expression network, whose perturbations in expression pattern and abundance may contribute to the (patho)biological phenotype. Here, SWIM analysis revealed an interesting interplay between the genes encoding the transcription factors HMGA1, FOXM1, and MYBL2, suggesting a potential cooperation among these three switch genes in TNBC development. The correlative nature of this interplay in TNBC was assessed by in vitro experiments, demonstrating how they may actually modulate the expression of each other.

Network Trees: A Method for Recursively Partitioning Covariance Structures

In many areas of psychology, correlation-based network approaches (i.e., psychometric networks) have become a popular tool. In this paper, we propose an approach that recursively splits the sample based on covariates in order to detect significant differences in the structure of the covariance or correlation matrix. Psychometric networks or other correlation-based models (e.g., factor models) can be subsequently estimated from the resultant splits. We adapt model-based recursive partitioning and conditional inference tree approaches for finding covariate splits in a recursive manner. The empirical power of these approaches is studied in several simulation conditions. Examples are given using real-life data from personality and clinical research.

Marine Microbial Food Web Networks During Phytoplankton Bloom and Non-bloom Periods: Warming Favors Smaller Organism Interactions and Intensifies Trophic Cascade

Microbial food web organisms are at the base of the functioning of pelagic ecosystems and support the whole marine food web. They are very reactive to environmental changes and their interactions are modified in response to different productive periods such as phytoplankton bloom and non-bloom as well as contrasted climatic years. To study ecological associations, identify potential interactions between microorganisms and study the structure of the microbial food web in coastal waters, a weekly monitoring was carried out in the Thau Lagoon on the French Mediterranean coast. The monitoring lasted from winter to late spring during two contrasting climatic years, a typical Mediterranean (2015) and a year with an extreme warm winter (2016). Correlation networks comprising 110 groups/taxa/species were constructed to characterize potential possible interactions between the microorganisms during bloom and non-bloom periods. Complex correlation networks during the bloom and dominated by negative intraguild correlations and positive correlations of phytoplankton with bacteria. Such pattern can be interpreted as a dominance of competition and mutualism. In contrast, correlation networks during the non-bloom period were less complex and mostly dominated by tintinnids associations with bacteria mostly referring to potential feeding on bacteria, which suggests a shift of biomass transfer from phytoplankton-dominated food webs during bloom to more bacterioplankton-based food webs during non-bloom. Inter-annual climatic conditions significantly modified the structure of microbial food webs. The warmer year favored relationships among smaller group/taxa/species at the expense of large phytoplankton and ciliates, possibly due to an intensification of the trophic cascade with a potential shift in energy circulation through microbial food web. Our study compares a typical Mediterranean spring with another mimicking the prospected intensification of global warming; if such consideration holds true, the dominance of future coastal marine ecosystems will be shifted from the highly productive herbivorous food web to the less productive microbial food web.

Network Models to Enhance Automated Cryptocurrency Portfolio Management

The usage of cryptocurrencies, together with that of financial automated consultancy, is widely spreading in the last few years. However, automated consultancy services are not yet exploiting the potentiality of this nascent market, which represents a class of innovative financial products that can be proposed by robo-advisors. For this reason, we propose a novel approach to build efficient portfolio allocation strategies involving volatile financial instruments, such as cryptocurrencies. In other words, we develop an extension of the traditional Markowitz model which combines Random Matrix Theory and network measures, in order to achieve portfolio weights enhancing portfolios' risk-return profiles. The results show that overall our model overperforms several competing alternatives, maintaining a relatively low level of risk.

Leveraging User-Friendly Network Approaches to Extract Knowledge From High-Throughput Omics Datasets

Recent technological advances for the acquisition of multi-omics data have allowed an unprecedented understanding of the complex intricacies of biological systems. In parallel, a myriad of computational analysis techniques and bioinformatics tools have been developed, with many efforts directed towards the creation and interpretation of networks from this data. In this review, we begin by examining key network concepts and terminology. Then, computational tools that allow for their construction and analysis from high-throughput omics datasets are presented. We focus on the study of functional relationships such as co-expression, protein-protein interactions, and regulatory interactions that are particularly amenable to modeling using the framework of networks. We envisage that many potential users of these analytical strategies may not be completely literate in programming languages and code adaptation, and for this reason, emphasis is given to tools' user-friendliness, including plugins for the widely adopted Cytoscape software, an open-source, cross-platform tool for network analysis, visualization, and data integration.

Posttraumatic stress disorder is associated with altered gut microbiota that modulates cognitive performance in veterans with cirrhosis

Posttraumatic stress disorder (PTSD) is associated with cirrhosis in veterans, and therapeutic results are suboptimal. An altered gut-liver-brain axis exists in cirrhosis due to hepatic encephalopathy (HE), but the added impact of PTSD is unclear. The aim of this study was to define linkages between gut microbiota and cognition in cirrhosis with/without PTSD. Cirrhotic veterans (with/without prior HE) underwent cognitive testing [PHES, inhibitory control test (ICT), and block design test (BDT)], serum lipopolysaccharide-binding protein (LBP) and stool collection for 16S rRNA microbiota composition, and predicted function analysis (PiCRUST). PTSD was diagnosed using DSM-V criteria. Correlation networks between microbiota and cognition were created. Patients with/without PTSD and with/without HE were compared. Ninety-three combat-exposed male veterans [ (58 yr, MELD 11, 34% HE, 31% combat-PTSD (42 no-HE/PTSD, 19 PTSD-only, 22 HE-only, 10 PTSD+HE)] were included. PTSD patients had similar demographics, alcohol history, MELD, but worse ICT/BDT, and higher antidepressant use and LBP levels. Microbial diversity was lower in PTSD (2.1 ± 0.5 vs. 2.5 ± 0.5, P = 0.03) but unaffected by alcohol/antidepressant use. PTSD (P = 0.02) and MELD (P < 0.001) predicted diversity on regression. PTSD patients showed higher pathobionts (Enterococcus and Escherichia/Shigella) and lower autochthonous genera belonging to Lachnospiraceaeae and Ruminococcaceae regardless of HE. Enterococcus was correlated with poor cognition, while the opposite was true for autochthonous taxa regardless of PTSD/HE. Escherichia/Shigella was only linked with poor cognition in PTSD patients. Gut-brain axis-associated microbiota functionality was altered in PTSD. In male cirrhotic veterans, combat-related PTSD is associated with cognitive impairment, lower microbial diversity, higher pathobionts, and lower autochthonous taxa composition and altered gut-brain axis functionality compared with non-PTSD combat-exposed patients. Cognition was differentially linked to gut microbiota, which could represent a new therapeutic target.NEW & NOTEWORTHY Posttraumatic stress disorder (PTSD) in veterans with cirrhosis was associated with poor cognitive performance. This was associated with lower gut microbial diversity in PTSD with higher pathobionts belonging to Enterococcus and Escherichia/Shigella and lower beneficial taxa belonging to Lachnospiraceaeae and Ruminococcaceae, with functional alterations despite accounting for prior hepatic encephalopathy, psychoactive drug use, or model for end-stage liver disease score. Given the suboptimal response to current therapies for PTSD, targeting the gut microbiota could benefit the altered gut-brain axis in these patients.

Profiling Cellular Processes in Adipose Tissue during Weight Loss Using Time Series Gene Expression

Obesity is a global epidemic identified as a major risk factor for multiple chronic diseases and, consequently, diet-induced weight loss is used to counter obesity. The adipose tissue is the primary tissue affected in diet-induced weight loss, yet the underlying molecular mechanisms and changes are not completely deciphered. In this study, we present a network biology analysis workflow which enables the profiling of the cellular processes affected by weight loss in the subcutaneous adipose tissue. Time series gene expression data from a dietary intervention dataset with two diets was analysed. Differentially expressed genes were used to generate co-expression networks using a method that capitalises on the repeat measurements in the data and finds correlations between gene expression changes over time. Using the network analysis tool Cytoscape, an overlap network of conserved components in the co-expression networks was constructed, clustered on topology to find densely correlated genes, and analysed using Gene Ontology enrichment analysis. We found five clusters involved in key metabolic processes, but also adipose tissue development and tissue remodelling processes were enriched. In conclusion, we present a flexible network biology workflow for finding important processes and relevant genes associated with weight loss, using a time series co-expression network approach that is robust towards the high inter-individual variation in humans.

Predicted Bacterial Interactions Affect in Vivo Microbial Colonization Dynamics in Nematostella

The maintenance and resilience of host-associated microbiota during development is a fundamental process influencing the fitness of many organisms. Several host properties were identified as influencing factors on bacterial colonization, including the innate immune system, mucus composition, and diet. In contrast, the importance of bacteria–bacteria interactions on host colonization is less understood. Here, we use bacterial abundance data of the marine model organism Nematostella vectensis to reconstruct potential bacteria–bacteria interactions through co-occurrence networks. The analysis indicates that bacteria–bacteria interactions are dynamic during host colonization and change according to the host’s developmental stage. To assess the predictive power of inferred interactions, we tested bacterial isolates with predicted cooperative or competitive behavior for their ability to influence bacterial recolonization dynamics. Within 3 days of recolonization, all tested bacterial isolates affected bacterial community structure, while only competitive bacteria increased bacterial diversity. Only 1 week after recolonization, almost no differences in bacterial community structure could be observed between control and treatments. These results show that predicted competitive bacteria can influence community structure for a short period of time, verifying the in silico predictions. However, within 1 week, the effects of the bacterial isolates are neutralized, indicating a high degree of resilience of the bacterial community.

Tissue-Specific Accumulation of Sulfur Compounds and Saponins in Different Parts of Garlic Cloves from Purple and White Ecotypes

This study set out to determine the distribution of sulfur compounds and saponin metabolites in different parts of garlic cloves. Three fractions from purple and white garlic ecotypes were obtained: the tunic (SS), internal (IS) and external (ES) parts of the clove. Liquid Chromatography coupled to High Resolution Mass spectrometry (LC-HRMS), together with bioinformatics including Principal Component Analysis (PCA), Hierarchical Clustering (HCL) and correlation network analyses were carried out. Results showed that the distribution of these metabolites in the different parts of garlic bulbs was different for the purple and the white ecotypes, with the main difference being a slightly higher number of sulfur compounds in purple garlic. The SS fraction in purple garlic had a higher content of sulfur metabolites, while the ES in white garlic was more enriched by these compounds. The correlation network indicated that diallyl disulfide was the most relevant metabolite with regards to sulfur compound metabolism in garlic. The total number of saponins was almost 40-fold higher in purple garlic than in the white variety, with ES having the highest content. Interestingly, five saponins including desgalactotigonin-rhamnose, proto-desgalactotigonin, proto-desgalactotigonin-rhamnose, voghieroside D1, sativoside B1-rhamnose and sativoside R1 were exclusive to the purple variety. Data obtained from saponin analyses revealed a very different network between white and purple garlic, thus suggesting a very robust and tight coregulation of saponin metabolism in garlic. Findings in this study point to the possibility of using tunics from purple garlic in the food and medical industries, since it contains many functional compounds which can be exploited as ingredients.