Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance

Beyond the Nucleus: Plastic Chemicals Activate G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) are central mediators of cell signaling and physiological function. Despite their biological significance, GPCRs have not been widely studied in the field of toxicology. Herein, we investigated these receptors as novel targets of plastic chemicals using a high-throughput drug screening assay with 126 human non-olfactory GPCRs. In a first-pass screen, we tested the activity of triphenol phosphate, bisphenol A, and diethyl phthalate, as well as three real-world mixtures of chemicals extracted from plastic food packaging covering all major polymer types. We found 11 GPCR-chemical interactions, of which the chemical mixtures exhibited the most robust activity at adenosine receptor 1 (ADORA1) and melatonin receptor 1 (MTNR1A). We further confirm that polyvinyl chloride and polyurethane products contain ADORA1 or MTNRA1 agonists using a confirmatory secondary screen and pharmacological knockdown experiments. Finally, an analysis of the associated gene ontology terms suggests that ADORA1 and MTNR1A activation may be linked to downstream effects on circadian and metabolic processes. This work highlights that signaling disruption caused by plastic chemicals is broader than that previously believed and demonstrates the relevance of nongenomic pathways, which have, thus far, remained unexplored.

A combination of conserved and diverged responses underlies Theobroma cacao's defense response to Phytophthora palmivora

BACKGROUND

Plants have complex and dynamic immune systems that have evolved to resist pathogens. Humans have worked to enhance these defenses in crops through breeding. However, many crops harbor only a fraction of the genetic diversity present in wild relatives. Increased utilization of diverse germplasm to search for desirable traits, such as disease resistance, is therefore a valuable step towards breeding crops that are adapted to both current and emerging threats. Here, we examine diversity of defense responses across four populations of the long-generation tree crop Theobroma cacao L., as well as four non-cacao Theobroma species, with the goal of identifying genetic elements essential for protection against the oomycete pathogen Phytophthora palmivora.

RESULTS

We began by creating a new, highly contiguous genome assembly for the P. palmivora-resistant genotype SCA 6 (Additional file 1: Tables S1-S5), deposited in GenBank under accessions CP139290-CP139299. We then used this high-quality assembly to combine RNA and whole-genome sequencing data to discover several genes and pathways associated with resistance. Many of these are unique, i.e., differentially regulated in only one of the four populations (diverged 40 k-900 k generations). Among the pathways shared across all populations is phenylpropanoid biosynthesis, a metabolic pathway with well-documented roles in plant defense. One gene in this pathway, caffeoyl shikimate esterase (CSE), was upregulated across all four populations following pathogen treatment, indicating its broad importance for cacao's defense response. Further experimental evidence suggests this gene hydrolyzes caffeoyl shikimate to create caffeic acid, an antimicrobial compound and known inhibitor of Phytophthora spp.

CONCLUSIONS

Our results indicate most expression variation associated with resistance is unique to populations. Moreover, our findings demonstrate the value of using a broad sample of evolutionarily diverged populations for revealing the genetic bases of cacao resistance to P. palmivora. This approach has promise for further revealing and harnessing valuable genetic resources in this and other long-generation plants.

Transcriptomic Analyses of Neurotoxic Astrocytes Derived from Adult Triple Transgenic Alzheimer's Disease Mice

Neurodegenerative diseases such as Alzheimer's disease have been classically studied from a purely neuronocentric point of view. More recent evidences support the notion that other cell populations are involved in disease progression. In this sense, the possible pathogenic role of glial cells like astrocytes is increasingly being recognized. Once faced with tissue damage signals and other stimuli present in disease environments, astrocytes suffer many morphological and functional changes, a process referred as reactive astrogliosis. Studies from murine models and humans suggest that these complex and heterogeneous responses could manifest as disease-specific astrocyte phenotypes. Clear understanding of disease-associated astrocytes is a necessary step to fully disclose neurodegenerative processes, aiding in the design of new therapeutic and diagnostic strategies. In this work, we present the transcriptomics characterization of neurotoxic astrocytic cultures isolated from adult symptomatic animals of the triple transgenic mouse model of Alzheimer's disease (3xTg-AD). According to the observed profile, 3xTg-AD neurotoxic astrocytes show various reactivity features including alteration of the extracellular matrix and release of pro-inflammatory and proliferative factors that could result in harmful effects to neurons. Moreover, these alterations could be a consequence of stress responses at the endoplasmic reticulum and mitochondria as well as of concomitant metabolic adaptations. Present results support the hypothesis that adaptive changes of astrocytic function induced by a stressed microenvironment could later promote harmful astrocyte phenotypes and further accelerate or induce neurodegenerative processes.

Parallel and convergent genomic changes underlie independent subterranean colonization across beetles

Adaptation to life in caves is often accompanied by dramatically convergent changes across distantly related taxa, epitomized by the loss or reduction of eyes and pigmentation. Nevertheless, the genomic underpinnings underlying cave-related phenotypes are largely unexplored from a macroevolutionary perspective. Here we investigate genome-wide gene evolutionary dynamics in three distantly related beetle tribes with at least six instances of independent colonization of subterranean habitats, inhabiting both aquatic and terrestrial underground systems. Our results indicate that remarkable gene repertoire changes mainly driven by gene family expansions occurred prior to underground colonization in the three tribes, suggesting that genomic exaptation may have facilitated a strict subterranean lifestyle parallelly across beetle lineages. The three tribes experienced both parallel and convergent changes in the evolutionary dynamics of their gene repertoires. These findings pave the way towards a deeper understanding of the evolution of the genomic toolkit in hypogean fauna.

Risperidone response in patients with schizophrenia drives DNA methylation changes in immune and neuronal systems

Background: The choice of efficient antipsychotic therapy for schizophrenia relies on a time-consuming trial-and-error approach, whereas the social and economic burdens of the disease call for faster alternatives. Material & methods: In a search for predictive biomarkers of antipsychotic response, blood methylomes of 28 patients were analyzed before and 4 weeks into risperidone therapy. Results: Several CpGs exhibiting response-specific temporal dynamics were identified in otherwise temporally stable methylomes and noticeable global response-related differences were observed between good and bad responders. These were associated with genes involved in immunity, neurotransmission and neuronal development. Polymorphisms in many of these genes were previously linked with schizophrenia etiology and antipsychotic response. Conclusion: Antipsychotic response seems to be shaped by both stable and medication-induced methylation differences.

The Foundational Data Initiative for Parkinson Disease: Enabling efficient translation from genetic maps to mechanism

The Foundational Data Initiative for Parkinson Disease (FOUNDIN-PD) is an international collaboration producing fundamental resources for Parkinson disease (PD). FOUNDIN-PD generated a multi-layered molecular dataset in a cohort of induced pluripotent stem cell (iPSC) lines differentiated to dopaminergic (DA) neurons, a major affected cell type in PD. The lines were derived from the Parkinson's Progression Markers Initiative study, which included participants with PD carrying monogenic PD variants, variants with intermediate effects, and variants identified by genome-wide association studies and unaffected individuals. We generated genetic, epigenetic, regulatory, transcriptomic, and longitudinal cellular imaging data from iPSC-derived DA neurons to understand molecular relationships between disease-associated genetic variation and proximate molecular events. These data reveal that iPSC-derived DA neurons provide a valuable cellular context and foundational atlas for modeling PD genetic risk. We have integrated these data into a FOUNDIN-PD data browser as a resource for understanding the molecular pathogenesis of PD.

HPV-induced host epigenetic reprogramming is lost upon progression to high-grade cervical intraepithelial neoplasia

The impact of a pathogen on host disease can only be studied in samples covering the entire spectrum of pathogenesis. Persistent oncogenic human papilloma virus (HPV) infection is the most common cause for cervical cancer. Here, we investigate HPV-induced host epigenome-wide changes prior to development of cytological abnormalities. Using cervical sample methylation array data from disease-free women with or without an oncogenic HPV infection, we develop the WID (Women's cancer risk identification)-HPV, a signature reflective of changes in the healthy host epigenome related to high-risk HPV strains (AUC = 0.78, 95% CI: 0.72-0.85, in nondiseased women). Looking at HPV-associated changes across disease development, HPV-infected women with minor cytological alterations (cervical intraepithelial neoplasia grade 1/2, CIN1/2), but surprisingly not those with precancerous changes or invasive cervical cancer (CIN3+), show an increased WID-HPV index, indicating the WID-HPV may reflect a successful viral clearance response absent in progression to cancer. Further investigation revealed the WID-HPV is positively associated with apoptosis (ρ = 0.48; P < .001) and negatively associated with epigenetic replicative age (ρ = -0.43; P < .001). Taken together, our data suggest the WID-HPV captures a clearance response associated with apoptosis of HPV-infected cells. This response may be dampened or lost with increased underlying replicative age of infected cells, resulting in progression to cancer.

EventPointer 3.0: flexible and accurate splicing analysis that includes studying the differential usage of protein-domains

Alternative splicing (AS) plays a key role in cancer: all its hallmarks have been associated with different mechanisms of abnormal AS. The improvement of the human transcriptome annotation and the availability of fast and accurate software to estimate isoform concentrations has boosted the analysis of transcriptome profiling from RNA-seq. The statistical analysis of AS is a challenging problem not yet fully solved. We have included in EventPointer (EP), a Bioconductor package, a novel statistical method that can use the bootstrap of the pseudoaligners. We compared it with other state-of-the-art algorithms to analyze AS. Its performance is outstanding for shallow sequencing conditions. The statistical framework is very flexible since it is based on design and contrast matrices. EP now includes a convenient tool to find the primers to validate the discoveries using PCR. We also added a statistical module to study alteration in protein domain related to AS. Applying it to 9514 patients from TCGA and TARGET in 19 different tumor types resulted in two conclusions: i) aberrant alternative splicing alters the relative presence of Protein domains and, ii) the number of enriched domains is strongly correlated with the age of the patients.

Testing biological network motif significance with exponential random graph models

Analysis of the structure of biological networks often uses statistical tests to establish the over-representation of motifs, which are thought to be important building blocks of such networks, related to their biological functions. However, there is disagreement as to the statistical significance of these motifs, and there are potential problems with standard methods for estimating this significance. Exponential random graph models (ERGMs) are a class of statistical model that can overcome some of the shortcomings of commonly used methods for testing the statistical significance of motifs. ERGMs were first introduced into the bioinformatics literature over 10 years ago but have had limited application to biological networks, possibly due to the practical difficulty of estimating model parameters. Advances in estimation algorithms now afford analysis of much larger networks in practical time. We illustrate the application of ERGM to both an undirected protein-protein interaction (PPI) network and directed gene regulatory networks. ERGM models indicate over-representation of triangles in the PPI network, and confirm results from previous research as to over-representation of transitive triangles (feed-forward loop) in an E. coli and a yeast regulatory network. We also confirm, using ERGMs, previous research showing that under-representation of the cyclic triangle (feedback loop) can be explained as a consequence of other topological features.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s41109-021-00434-y.

RNA sequencing of whole blood reveals early alterations in immune cells and gene expression in Parkinson's disease

Changes in the blood-based RNA transcriptome have the potential to inform biomarkers of Parkinson's disease (PD) progression. Here we sequenced a discovery set of whole-blood RNA species in 4,871 longitudinally collected samples from 1,570 clinically phenotyped individuals from the Parkinson's Progression Marker Initiative (PPMI) cohort. Samples were sequenced to an average of 100 million read pairs to create a high-quality transcriptome. Participants with PD in the PPMI had significantly altered RNA expression (>2,000 differentially expressed genes), including an early and persistent increase in neutrophil gene expression, with a concomitant decrease in lymphocyte cell counts. This was validated in a cohort from the Parkinson's Disease Biomarkers Program (PDBP) consisting of 1,599 participants and by alterations in immune cell subtypes. This publicly available transcriptomic dataset, coupled with available detailed clinical data, provides new insights into PD biological processes impacting whole blood and new paths for developing diagnostic and prognostic PD biomarkers.

Limitations of Explainability for Established Prognostic Biomarkers of Prostate Cancer

High-throughput technologies do not only provide novel means for basic biological research but also for clinical applications in hospitals. For instance, the usage of gene expression profiles as prognostic biomarkers for predicting cancer progression has found widespread interest. Aside from predicting the progression of patients, it is generally believed that such prognostic biomarkers also provide valuable information about disease mechanisms and the underlying molecular processes that are causal for a disorder. However, the latter assumption has been challenged. In this paper, we study this problem for prostate cancer. Specifically, we investigate a large number of previously published prognostic signatures of prostate cancer based on gene expression profiles and show that none of these can provide unique information about the underlying disease etiology of prostate cancer. Hence, our analysis reveals that none of the studied signatures has a sensible biological meaning. Overall, this shows that all studied prognostic signatures are merely black-box models allowing sensible predictions of prostate cancer outcome but are not capable of providing causal explanations to enhance the understanding of prostate cancer.

Data-Driven Computational Social Network Science: Predictive and Inferential Models for Web-Enabled Scientific Discoveries

The ultimate goal of the social sciences is to find a general social theory encompassing all aspects of social and collective phenomena. The traditional approach to this is very stringent by trying to find causal explanations and models. However, this approach has been recently criticized for preventing progress due to neglecting prediction abilities of models that support more problem-oriented approaches. The latter models would be enabled by the surge of big Web-data currently available. Interestingly, this problem cannot be overcome with methods from computational social science (CSS) alone because this field is dominated by simulation-based approaches and descriptive models. In this article, we address this issue and argue that the combination of big social data with social networks is needed for creating prediction models. We will argue that this alliance has the potential for gradually establishing a causal social theory. In order to emphasize the importance of integrating big social data with social networks, we call this approach data-driven computational social network science (DD-CSNS).

Prognostic gene expression signatures of breast cancer are lacking a sensible biological meaning

The identification of prognostic biomarkers for predicting cancer progression is an important problem for two reasons. First, such biomarkers find practical application in a clinical context for the treatment of patients. Second, interrogation of the biomarkers themselves is assumed to lead to novel insights of disease mechanisms and the underlying molecular processes that cause the pathological behavior. For breast cancer, many signatures based on gene expression values have been reported to be associated with overall survival. Consequently, such signatures have been used for suggesting biological explanations of breast cancer and drug mechanisms. In this paper, we demonstrate for a large number of breast cancer signatures that such an implication is not justified. Our approach eliminates systematically all traces of biological meaning of signature genes and shows that among the remaining genes, surrogate gene sets can be formed with indistinguishable prognostic prediction capabilities and opposite biological meaning. Hence, our results demonstrate that none of the studied signatures has a sensible biological interpretation or meaning with respect to disease etiology. Overall, this shows that prognostic signatures are black-box models with sensible predictions of breast cancer outcome but no value for revealing causal connections. Furthermore, we show that the number of such surrogate gene sets is not small but very large.