Multi-omics regulatory network inference in the presence of missing data

A key problem in systems biology is the discovery of regulatory mechanisms that drive phenotypic behaviour of complex biological systems in the form of multi-level networks. Modern multi-omics profiling techniques probe these fundamental regulatory networks but are often hampered by experimental restrictions leading to missing data or partially measured omics types for subsets of individuals due to cost restrictions. In such scenarios, in which missing data is present, classical computational approaches to infer regulatory networks are limited. In recent years, approaches have been proposed to infer sparse regression models in the presence of missing information. Nevertheless, these methods have not been adopted for regulatory network inference yet. In this study, we integrated regression-based methods that can handle missingness into KiMONo, a Knowledge guided Multi-Omics Network inference approach, and benchmarked their performance on commonly encountered missing data scenarios in single- and multi-omics studies. Overall, two-step approaches that explicitly handle missingness performed best for a wide range of random- and block-missingness scenarios on imbalanced omics-layers dimensions, while methods implicitly handling missingness performed best on balanced omics-layers dimensions. Our results show that robust multi-omics network inference in the presence of missing data with KiMONo is feasible and thus allows users to leverage available multi-omics data to its full extent.

Tolerance of repeated toxic injuries of murine livers is associated with steatosis and inflammation

The human liver has a remarkable capacity to regenerate and thus compensate over decades for fibrosis caused by toxic chemicals, drugs, alcohol, or malnutrition. To date, no protective mechanisms have been identified that help the liver tolerate these repeated injuries. In this study, we revealed dysregulation of lipid metabolism and mild inflammation as protective mechanisms by studying longitudinal multi-omic measurements of liver fibrosis induced by repeated CCl₄ injections in mice (n = 45). Based on comprehensive proteomics, transcriptomics, blood- and tissue-level profiling, we uncovered three phases of early disease development-initiation, progression, and tolerance. Using novel multi-omic network analysis, we identified multi-level mechanisms that are significantly dysregulated in the injury-tolerant response. Public data analysis shows that these profiles are altered in human liver diseases, including fibrosis and early cirrhosis stages. Our findings mark the beginning of the tolerance phase as the critical switching point in liver response to repetitive toxic doses. After fostering extracellular matrix accumulation as an acute response, we observe a deposition of tiny lipid droplets in hepatocytes only in the Tolerant phase. Our comprehensive study shows that lipid metabolism and mild inflammation may serve as biomarkers and are putative functional requirements to resist further disease progression.

Exposure to naphthalene and β-pinene-derived secondary organic aerosol induced divergent changes in transcript levels of BEAS-2B cells

The health effects of exposure to secondary organic aerosols (SOAs) are still limited. Here, we investigated and compared the toxicities of soot particles (SP) coated with β-pinene SOA (SOAβPin-SP) and SP coated with naphthalene SOA (SOANap-SP) in a human bronchial epithelial cell line (BEAS-2B) residing at the air-liquid interface. SOAβPin-SP mostly contained oxygenated aliphatic compounds from β-pinene photooxidation, whereas SOANap-SP contained a significant fraction of oxygenated aromatic products under similar conditions. Following exposure, genome-wide transcriptome responses showed an Nrf2 oxidative stress response, particularly for SOANap-SP. Other signaling pathways, such as redox signaling, inflammatory signaling, and the involvement of matrix metalloproteinase, were identified to have a stronger impact following exposure to SOANap-SP. SOANap-SP also induced a stronger genotoxicity response than that of SOAβPin-SP. This study elucidated the mechanisms that govern SOA toxicity and showed that, compared to SOAs derived from a typical biogenic precursor, SOAs from a typical anthropogenic precursor have higher toxicological potency, which was accompanied with the activation of varied cellular mechanisms, such as aryl hydrocarbon receptor. This can be attributed to the difference in chemical composition; specifically, the aromatic compounds in the naphthalene-derived SOA had higher cytotoxic potential than that of the β-pinene-derived SOA.

Network Embedding Across Multiple Tissues and Data Modalities Elucidates the Context of Host Factors Important for COVID-19 Infection

COVID-19 is a heterogeneous disease caused by SARS-CoV-2. Aside from infections of the lungs, the disease can spread throughout the body and damage many other tissues, leading to multiorgan failure in severe cases. The highly variable symptom severity is influenced by genetic predispositions and preexisting diseases which have not been investigated in a large-scale multimodal manner. We present a holistic analysis framework, setting previously reported COVID-19 genes in context with prepandemic data, such as gene expression patterns across multiple tissues, polygenetic predispositions, and patient diseases, which are putative comorbidities of COVID-19. First, we generate a multimodal network using the prior-based network inference method KiMONo. We then embed the network to generate a meaningful lower-dimensional representation of the data. The input data are obtained via the Genotype-Tissue Expression project (GTEx), containing expression data from a range of tissues with genomic and phenotypic information of over 900 patients and 50 tissues. The generated network consists of nodes, that is, genes and polygenic risk scores (PRS) for several diseases/phenotypes, as well as for COVID-19 severity and hospitalization, and links between them if they are statistically associated in a regularized linear model by feature selection. Applying network embedding on the generated multimodal network allows us to perform efficient network analysis by identifying nodes close by in a lower-dimensional space that correspond to entities which are statistically linked. By determining the similarity between COVID-19 genes and other nodes through embedding, we identify disease associations to tissues, like the brain and gut. We also find strong associations between COVID-19 genes and various diseases such as ischemic heart disease, cerebrovascular disease, and hypertension. Moreover, we find evidence linking PTPN6 to a range of comorbidities along with the genetic predisposition of COVID-19, suggesting that this kinase is a central player in severe cases of COVID-19. In conclusion, our holistic network inference coupled with network embedding of multimodal data enables the contextualization of COVID-19-associated genes with respect to tissues, disease states, and genetic risk factors. Such contextualization can be exploited to further elucidate the biological importance of known and novel genes for severity of the disease in patients.

PathwAX II: Network-based pathway analysis with interactive visualization of network crosstalk

Motivation

Pathway annotation tools are indispensable for the interpretation of a wide range of experiments in life sciences. Network-based algorithms have recently been developed which are more sensitive than traditional overlap-based algorithms, but there is still a lack of good online tools for network-based pathway analysis.

Results

We present PathwAX II—a pathway analysis web tool based on network crosstalk analysis using the BinoX algorithm. It offers several new features compared with the first version, including interactive graphical network visualization of the crosstalk between a query gene set and an enriched pathway, and the addition of Reactome pathways.

Availability and implementation

PathwAX II is available at http://pathwax.sbc.su.se.

Supplementary information

Supplementary data are available at Bioinformatics online.

Effect of Atmospheric Aging on Soot Particle Toxicity in Lung Cell Models at the Air–Liquid Interface: Differential Toxicological Impacts of Biogenic and Anthropogenic Secondary Organic Aerosols (SOAs)

Background:

Secondary organic aerosols (SOAs) formed from anthropogenic or biogenic gaseous precursors in the atmosphere substantially contribute to the ambient fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5)] burden, which has been associated with adverse human health effects. However, there is only limited evidence on their differential toxicological impact.

Objectives:

We aimed to discriminate toxicological effects of aerosols generated by atmospheric aging on combustion soot particles (SPs) of gaseous biogenic (β-pinene) or anthropogenic (naphthalene) precursors in two different lung cell models exposed at the air–liquid interface (ALI).

Methods:

Mono- or cocultures of lung epithelial cells (A549) and endothelial cells (EA.hy926) were exposed at the ALI for 4 h to different aerosol concentrations of a photochemically aged mixture of primary combustion SP and β-pinene (SOAβPIN-SP) or naphthalene (SOANAP-SP). The internally mixed soot/SOA particles were comprehensively characterized in terms of their physical and chemical properties. We conducted toxicity tests to determine cytotoxicity, intracellular oxidative stress, primary and secondary genotoxicity, as well as inflammatory and angiogenic effects.

Results:

We observed considerable toxicity-related outcomes in cells treated with either SOA type. Greater adverse effects were measured for SOANAP-SP compared with SOAβPIN-SP in both cell models, whereas the nano-sized soot cores alone showed only minor effects. At the functional level, we found that SOANAP-SP augmented the secretion of malondialdehyde and interleukin-8 and may have induced the activation of endothelial cells in the coculture system. This activation was confirmed by comet assay, suggesting secondary genotoxicity and greater angiogenic potential. Chemical characterization of PM revealed distinct qualitative differences in the composition of the two secondary aerosol types.

Discussion:

In this study using A549 and EA.hy926 cells exposed at ALI, SOA compounds had greater toxicity than primary SPs. Photochemical aging of naphthalene was associated with the formation of more oxidized, more aromatic SOAs with a higher oxidative potential and toxicity compared with β-pinene. Thus, we conclude that the influence of atmospheric chemistry on the chemical PM composition plays a crucial role for the adverse health outcome of emissions. https://doi.org/10.1289/EHP9413

Epithelial X-Box Binding Protein 1 Coordinates Tumor Protein p53-Driven DNA Damage Responses and Suppression of Intestinal Carcinogenesis

BACKGROUND & AIMS

Throughout life, the intestinal epithelium undergoes constant self-renewal from intestinal stem cells. Together with genotoxic stressors and failing DNA repair, this self-renewal causes susceptibility toward malignant transformation. X-box binding protein 1 (XBP1) is a stress sensor involved in the unfolded protein response (UPR). We hypothesized that XBP1 acts as a signaling hub to regulate epithelial DNA damage responses.

METHODS

Data from The Cancer Genome Atlas were analyzed for association of XBP1 with colorectal cancer (CRC) survival and molecular interactions between XBP1 and p53 pathway activity. The role of XBP1 in orchestrating p53-driven DNA damage response was tested in vitro in mouse models of chronic intestinal epithelial cell (IEC) DNA damage (Xbp1/H2bfl/fl, Xbp1ΔIEC, H2bΔIEC, H2b/Xbp1ΔIEC) and via orthotopic tumor organoid transplantation. Transcriptome analysis of intestinal organoids was performed to identify molecular targets of Xbp1-mediated DNA damage response.

RESULTS

In The Cancer Genome Atlas data set of CRC, low XBP1 expression was significantly associated with poor overall survival and reduced p53 pathway activity. In vivo, H2b/Xbp1ΔIEC mice developed spontaneous intestinal carcinomas. Orthotopic tumor organoid transplantation revealed a metastatic potential of H2b/Xbp1ΔIEC-derived tumors. RNA sequencing of intestinal organoids (H2b/Xbp1fl/fl, H2bΔIEC, H2b/Xbp1ΔIEC, and H2b/p53ΔIEC) identified a transcriptional program downstream of p53, in which XBP1 directs DNA-damage-inducible transcript 4-like (Ddit4l) expression. DDIT4L inhibits mechanistic target of rapamycin-mediated phosphorylation of 4E-binding protein 1. Pharmacologic mechanistic target of rapamycin inhibition suppressed epithelial hyperproliferation via 4E-binding protein 1.

CONCLUSIONS

Our data suggest a crucial role for XBP1 in coordinating epithelial DNA damage responses and stem cell function via a p53-DDIT4L-dependent feedback mechanism.

Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data

Constantly decreasing costs of high-throughput profiling on many molecular levels generate vast amounts of multi-omics data. Studying one biomedical question on two or more omic levels provides deeper insights into underlying molecular processes or disease pathophysiology. For the majority of multi-omics data projects, the data analysis is performed level-wise, followed by a combined interpretation of results. Hence the full potential of integrated data analysis is not leveraged yet, presumably due to the complexity of the data and the lacking toolsets. We propose a versatile approach, to perform a multi-level fully integrated analysis: The Knowledge guIded Multi-Omics Network inference approach, KiMONo (https://github.com/cellmapslab/kimono). KiMONo performs network inference by using statistical models for combining omics measurements coupled to a powerful knowledge-guided strategy exploiting prior information from existing biological sources. Within the resulting multimodal network, nodes represent features of all input types e.g. variants and genes while edges refer to knowledge-supported and statistically derived associations. In a comprehensive evaluation, we show that our method is robust to noise and exemplify the general applicability to the full spectrum of multi-omics data, demonstrating that KiMONo is a powerful approach towards leveraging the full potential of data sets for detecting biomarker candidates.

DNA Methylation Levels in Mononuclear Leukocytes from the Mother and Her Child Are Associated with IgE Sensitization to Allergens in Early Life

DNA methylation changes may predispose becoming IgE-sensitized to allergens. We analyzed whether DNA methylation in peripheral blood mononuclear cells (PBMC) is associated with IgE sensitization at 5 years of age (5Y). DNA methylation was measured in 288 PBMC samples from 74 mother/child pairs from the birth cohort ALADDIN (Assessment of Lifestyle and Allergic Disease During INfancy) using the HumanMethylation450BeadChip (Illumina). PBMCs were obtained from the mothers during pregnancy and from their children in cord blood, at 2 years and 5Y. DNA methylation levels at each time point were compared between children with and without IgE sensitization to allergens at 5Y. For replication, CpG sites associated with IgE sensitization in ALADDIN were evaluated in whole blood DNA of 256 children, 4 years old, from the BAMSE (Swedish abbreviation for Children, Allergy, Milieu, Stockholm, Epidemiology) cohort. We found 34 differentially methylated regions (DMRs) associated with IgE sensitization to airborne allergens and 38 DMRs associated with sensitization to food allergens in children at 5Y (Sidak p ≤ 0.05). Genes associated with airborne sensitization were enriched in the pathway of endocytosis, while genes associated with food sensitization were enriched in focal adhesion, the bacterial invasion of epithelial cells, and leukocyte migration. Furthermore, 25 DMRs in maternal PBMCs were associated with IgE sensitization to airborne allergens in their children at 5Y, which were functionally annotated to the mTOR (mammalian Target of Rapamycin) signaling pathway. This study supports that DNA methylation is associated with IgE sensitization early in life and revealed new candidate genes for atopy. Moreover, our study provides evidence that maternal DNA methylation levels are associated with IgE sensitization in the child supporting early in utero effects on atopy predisposition.

FunCoup 4: new species, data, and visualization

This release of the FunCoup database (http://funcoup.sbc.su.se) is the fourth generation of one of the most comprehensive databases for genome-wide functional association networks. These functional associations are inferred via integrating various data types using a naive Bayesian algorithm and orthology based information transfer across different species. This approach provides high coverage of the included genomes as well as high quality of inferred interactions. In this update of FunCoup we introduce four new eukaryotic species: Schizosaccharomyces pombe, Plasmodium falciparum, Bos taurus, Oryza sativa and open the database to the prokaryotic domain by including networks for Escherichia coli and Bacillus subtilis. The latter allows us to also introduce a new class of functional association between genes - co-occurrence in the same operon. We also supplemented the existing classes of functional association: metabolic, signaling, complex and physical protein interaction with up-to-date information. In this release we switched to InParanoid v8 as the source of orthology and base for calculation of phylogenetic profiles. While populating all other evidence types with new data we introduce a new evidence type based on quantitative mass spectrometry data. Finally, the new JavaScript based network viewer provides the user an intuitive and responsive platform to further evaluate the results.

Genome-wide functional association networks: background, data & state-of-the-art resources

The vast amount of experimental data from recent advances in the field of high-throughput biology begs for integration into more complex data structures such as genome-wide functional association networks. Such networks have been used for elucidation of the interplay of intra-cellular molecules to make advances ranging from the basic science understanding of evolutionary processes to the more translational field of precision medicine. The allure of the field has resulted in rapid growth of the number of available network resources, each with unique attributes exploitable to answer different biological questions. Unfortunately, the high volume of network resources makes it impossible for the intended user to select an appropriate tool for their particular research question. The aim of this paper is to provide an overview of the underlying data and representative network resources as well as to mention methods of integration, allowing a customized approach to resource selection. Additionally, this report will provide a primer for researchers venturing into the field of network integration.

Synthetic lethality between androgen receptor signalling and the PARP pathway in prostate cancer

Emerging data demonstrate homologous recombination (HR) defects in castration-resistant prostate cancers, rendering these tumours sensitive to PARP inhibition. Here we demonstrate a direct requirement for the androgen receptor (AR) to maintain HR gene expression and HR activity in prostate cancer. We show that PARP-mediated repair pathways are upregulated in prostate cancer following androgen-deprivation therapy (ADT). Furthermore, upregulation of PARP activity is essential for the survival of prostate cancer cells and we demonstrate a synthetic lethality between ADT and PARP inhibition in vivo. Our data suggest that ADT can functionally impair HR prior to the development of castration resistance and that, this potentially could be exploited therapeutically using PARP inhibitors in combination with androgen-deprivation therapy upfront in advanced or high-risk prostate cancer.Tumours with homologous recombination (HR) defects become sensitive to PARPi. Here, the authors show that androgen receptor (AR) regulates HR and AR inhibition activates the PARP pathway in vivo, thus inhibition of both AR and PARP is required for effective treatment of high risk prostate cancer.

A novel method for crosstalk analysis of biological networks: improving accuracy of pathway annotation

Analyzing gene expression patterns is a mainstay to gain functional insights of biological systems. A plethora of tools exist to identify significant enrichment of pathways for a set of differentially expressed genes. Most tools analyze gene overlap between gene sets and are therefore severely hampered by the current state of pathway annotation, yet at the same time they run a high risk of false assignments. A way to improve both true positive and false positive rates (FPRs) is to use a functional association network and instead look for enrichment of network connections between gene sets. We present a new network crosstalk analysis method BinoX that determines the statistical significance of network link enrichment or depletion between gene sets, using the binomial distribution. This is a much more appropriate statistical model than previous methods have employed, and as a result BinoX yields substantially better true positive and FPRs than was possible before. A number of benchmarks were performed to assess the accuracy of BinoX and competing methods. We demonstrate examples of how BinoX finds many biologically meaningful pathway annotations for gene sets from cancer and other diseases, which are not found by other methods. BinoX is available at http://sonnhammer.org/BinoX.

PathwAX: a web server for network crosstalk based pathway annotation

Pathway annotation of gene lists is often used to functionally analyse biomolecular data such as gene expression in order to establish which processes are activated in a given experiment. Databases such as KEGG or GO represent collections of how genes are known to be organized in pathways, and the challenge is to compare a given gene list with the known pathways such that all true relations are identified. Most tools apply statistical measures to the gene overlap between the gene list and pathway. It is however problematic to avoid false negatives and false positives when only using the gene overlap. The pathwAX web server (http://pathwAX.sbc.su.se/) applies a different approach which is based on network crosstalk. It uses the comprehensive network FunCoup to analyse network crosstalk between a query gene list and KEGG pathways. PathwAX runs the BinoX algorithm, which employs Monte-Carlo sampling of randomized networks and estimates a binomial distribution, for estimating the statistical significance of the crosstalk. This results in substantially higher accuracy than gene overlap methods. The system was optimized for speed and allows interactive web usage. We illustrate the usage and output of pathwAX.

FunCoup 3.0: database of genome-wide functional coupling networks

We present an update of the FunCoup database (http://FunCoup.sbc.su.se) of functional couplings, or functional associations, between genes and gene products. Identifying these functional couplings is an important step in the understanding of higher level mechanisms performed by complex cellular processes. FunCoup distinguishes between four classes of couplings: participation in the same signaling cascade, participation in the same metabolic process, co-membership in a protein complex and physical interaction. For each of these four classes, several types of experimental and statistical evidence are combined by Bayesian integration to predict genome-wide functional coupling networks. The FunCoup framework has been completely re-implemented to allow for more frequent future updates. It contains many improvements, such as a regularization procedure to automatically downweight redundant evidences and a novel method to incorporate phylogenetic profile similarity. Several datasets have been updated and new data have been added in FunCoup 3.0. Furthermore, we have developed a new Web site, which provides powerful tools to explore the predicted networks and to retrieve detailed information about the data underlying each prediction.