A tutorial on Bayesian Networks for psychopathology researchers

Bayesian Networks are probabilistic graphical models that represent conditional independence relationships among variables as a directed acyclic graph (DAG), where edges can be interpreted as causal effects connecting one causal symptom to an effect symptom. These models can help overcome one of the key limitations of partial correlation networks whose edges are undirected. This tutorial aims to introduce Bayesian Networks to identify admissible causal relationships in cross-sectional data, as well as how to estimate these models in R through three algorithm families with an empirical example data set of depressive symptoms. In addition, we discuss common problems and questions related to Bayesian networks. We recommend Bayesian networks be investigated to gain causal insight in psychological data.

Generating weighted and thresholded gene coexpression networks using signed distance correlation

Even within well-studied organisms, many genes lack useful functional annotations. One way to generate such functional information is to infer biological relationships between genes or proteins, using a network of gene coexpression data that includes functional annotations. Signed distance correlation has proved useful for the construction of unweighted gene coexpression networks. However, transforming correlation values into unweighted networks may lead to a loss of important biological information related to the intensity of the correlation. Here introduce a principled method to construct \emph{weighted} gene coexpression networks using signed distance correlation. These networks contain weighted edges only between those pairs of genes whose correlation value is higher than a given threshold. We analyse data from different organisms and find that networks generated with our method based on signed distance correlation are more stable and capture more biological information compared to networks obtained from Pearson correlation. Moreover, we show that signed distance correlation networks capture more biological information than unweighted networks based on the same metric. While we use biological data sets to illustrate the method, the approach is general and can be used to construct networks in other domains.

Defining the Proteomic and Phosphoproteomic Landscape of Circulating Extracellular Vesicles in the Diabetes Spectrum

The purpose of this study was to characterize the proteomic and phosphoproteomic profiles of circulating extracellular vesicles (EVs) from people with normal glucose tolerance (NGT), prediabetes (PDM), and diabetes (T2DM). Archived serum samples from 30 human subjects (N=10 per group, ORIGINS study, ClinicalTrials.gov NCT02226640) were used. EVs were isolated using EVTRAP (Tymora). Mass spectrometry (LC-MS)-based methods were used to detect the global EV proteome and phosphoproteome. Differentially expressed features, correlation networks, enriched pathways, and enriched tissue-specific protein sets were identified using custom R scripts. A total of 2372 unique EV proteins and 716 unique EV phosphoproteins were identified. Unsupervised clustering of the differentially expressed (fold change>2, P<0.05, FDR<0.05) proteins and, particularly, phosphoproteins, showed excellent discrimination among the three groups. Among characteristic changes in the PDM and T2DM EVs, "integrins switching" appeared to be a central feature. Proteins involved in oxidative phosphorylation (OXPHOS), known to be reduced in various tissues in diabetes, were significantly increased in EVs from PDM and T2DM, which suggests that an abnormally elevated EV-mediated secretion of OXPHOS components may underlie development of diabetes. We also detected a highly enriched signature of liver-specific markers among the downregulated EV proteins and phosphoproteins in both PDM and T2DM groups. This suggests that an alteration in liver EV composition and/or secretion may occur early in prediabetes. Levels of signaling molecules involved in cell death pathways were significantly altered in the circulating EVs. Consistent with the fact that patients with T2DM have abnormalities in platelet function, we detected a significant enrichment (FDR<<0.01) for upregulated EV proteins and phosphoproteins that play a role in platelet activation, coagulation, and chemokine signaling pathways in PDM and T2DM. Overall, this pilot study demonstrates the potential of EV proteomic and phosphoproteomic signatures to provide insight into the pathobiology of diabetes and its complications. These insights could lead to the development of new biomarkers of disease risk, classification, progression, and response to interventions that could allow personalization of interventions to improve outcomes.

Mining Trends of COVID-19 Vaccine Beliefs on Twitter with Lexical Embeddings (Preprint)

BACKGROUND Social media plays a pivotal role in disseminating news globally and acts as a platform for people to express their opinions on various topics. A wide variety of views accompanies COVID-19 vaccination drives across the globe, often colored by emotions, which change along with rising cases, approval of vaccines, and multiple factors discussed online. OBJECTIVE This study aims at analyzing the temporal evolution of different emotions and the related influencing factors in tweets belonging to five countries with vital vaccine roll-out programs, namely, India, United States of America(USA), Brazil, United Kingdom(UK), and Australia. METHODS We extracted a corpus of nearly 1.8 million Twitter posts related to COVID-19 vaccination and created two classes of lexical categories – Emotions and Influencing factors. Using cosine distance from selected seed words’ embeddings, we expanded the vocabulary of each category and tracked the longitudinal change in their strength from June 2020 to April 2021 in each country. Community detection algorithms were used to find modules in positive correlation networks. RESULTS Our findings indicated the varying relationship among Emotions and Influencing Factors across countries. Tweets expressing hesitancy towards vaccines contained the highest mentions of health-related effects in all countries. We also observed a significant change in the linear trends of categories like hesitation and contentment before and after approval of vaccines. Negative emotions like rage and sorrow gained the highest importance in the alluvial diagram and formed a significant module with all the influencing factors in April 2021, when India observed the second wave of COVID-19 cases. CONCLUSIONS By extracting and visualizing these, we propose that such a framework may help guide the design of effective vaccine campaigns and be used by policymakers to model vaccine uptake and targeted interventions.

CorDiffViz: an R package for visualizing multi-omics differential correlation networks

Background Differential correlation networks are increasingly used to delineate changes in interactions among biomolecules. They characterize differences between omics networks under two different conditions, and can be used to delineate mechanisms of disease initiation and progression. Results We present a new R package, , that facilitates the estimation and visualization of differential correlation networks using multiple correlation measures and inference methods. The software is implemented in , and , and is available at https://github.com/sqyu/CorDiffViz. Visualization has been tested for the Chrome and Firefox web browsers. A demo is available at https://diffcornet.github.io/CorDiffViz/demo.html. Conclusions Our software offers considerable flexibility by allowing the user to interact with the visualization and choose from different estimation methods and visualizations. It also allows the user to easily toggle between correlation networks for samples under one condition and differential correlations between samples under two conditions. Moreover, the software facilitates integrative analysis of cross-correlation networks between two omics data sets.

Appendectomy Is Associated With Alteration of Human Gut Bacterial and Fungal Communities

Recent research has revealed the importance of the appendix in regulating the intestinal microbiota and mucosal immunity. However, the changes that occur in human gut microbial communities after appendectomy have never been analyzed. We assessed the alterations in gut bacterial and fungal populations associated with a history of appendectomy. In this cross-sectional study, we investigated the association between appendectomy and the gut microbiome using 16S and ITS2 sequencing on fecal samples from 30 healthy individuals with prior appendectomy (HwA) and 30 healthy individuals without appendectomy (HwoA). Analysis showed that the gut bacterial composition of samples from HwA was less diverse than that of samples from HwoA and had a lower abundance of Roseburia, Barnesiella, Butyricicoccus, Odoribacter, and Butyricimonas species, most of which were short-chain fatty acids-producing microbes. The HwA subgroup analysis indicated a trend toward restoration of the HwoA bacterial microbiome over time after appendectomy. HwA had higher gut fungi composition and diversity than HwoA, even 5 years after appendectomy. Compared with those in samples from HwoA, the abundance correlation networks in samples from HwA displayed more complex fungal–fungal and fungal–bacterial community interactions. This study revealed a marked impact of appendectomy on gut bacteria and fungi, which was particularly durable for fungi.

Novel interferon-sensitive genes unveiled by correlation-driven gene selection and systems biology

Interferons (IFNs) are key cytokines involved in alerting the immune system to viral infection. After IFN stimulation, cellular transcriptional profile critically changes, leading to the expression of several IFN stimulated genes (ISGs) that exert a wide variety of antiviral activities. Despite many ISGs have been already identified, a comprehensive network of coding and non-coding genes with a central role in IFN-response still needs to be elucidated. We performed a global RNA-Seq transcriptome profile of the HCV permissive human hepatoma cell line Huh7.5 and its parental cell line Huh7, upon IFN treatment, to define a network of genes whose coordinated modulation plays a central role in IFN-response. Our study adds molecular actors, coding and non-coding genes, to the complex molecular network underlying IFN-response and shows how systems biology approaches, such as correlation networks, network’s topology and gene ontology analyses can be leveraged to this aim.