The Maritime Spatial Planning (MSP) Directive was ratified (2014/89/EU) along the Strategy of the European Union (EU) on the Blue Economy to contribute to the effective management of maritime activities and resources and incorporate the principal elements of Integrated Coastal Zone Management (ICZM) (2002/413/EC) into planning at the land-sea interface. There is a need to develop the ICZM approach throughout Europe to realise the potential for both socio-economic and environmental targets set by the EU and national legislations. In this study, we co-developed different approaches for land-sea interactions in four case areas in Estonia and Finland based on the defined characteristics and key interests derived from local or regional challenges by integrating spatial data on human activities and ecology. Furthermore, four ICZM drafts were co-evaluated by stakeholders and the public using online map-based assessment tools (public participatory GIS). The ICZM approaches of the Estonian cases ranged from the diversification of land use to the enhancement of community-based entrepreneurship. The Finnish cases aimed to define the trends for sustainable marine and coastal tourism and introduce the ecosystem service concept in land use planning. During the project activities, we found that increased communication and exchange of local and regional views and values on the prevailing land-sea interactions were important for the entire process. Thereafter, the ICZM plans were applied to the MSP processes nationally, and they support the sustainable development of coastal areas in Estonia and Finland.

The COVID-19 disease led to an unprecedented health emergency, still ongoing worldwide. Given the lack of a vaccine or a clear therapeutic strategy to counteract the infection as well as its secondary effects, there is currently a pressing need to generate new insights into the SARS-CoV-2 induced host response. Biomedical data can help to investigate new aspects of the COVID-19 pathogenesis, but source heterogeneity represents a major drawback and limitation. In this work, we applied data integration methods to develop a Unified Knowledge Space (UKS) and used it to identify a new set of genes associated with SARS-CoV-2 host response, both in vitro and in vivo. Functional analysis of these genes reveals possible long-term systemic effects of the infection, such as vascular remodelling and fibrosis. Finally, we identified a set of potentially relevant drugs targeting proteins involved in multiple steps of the host response to the virus.

Bacterial microbiota play a critical role in mediating local and systemic immunity, and shifts in these microbial communities have been linked to impaired outcomes in critical illness. Emerging data indicate that other intestinal organisms, including bacteriophages, viruses of eukaryotes, fungi, and protozoa, are closely interlinked with the bacterial microbiota and their host, yet their collective role during antibiotic perturbation and critical illness remains to be elucidated. We employed multi-omics factor analysis (MOFA) to systematically integrate the bacterial (16S rRNA), fungal (intergenic transcribed spacer 1 rRNA), and viral (virus discovery next generation sequencing) components of the intestinal microbiota of 33 critically ill patients with and without sepsis and 13 healthy volunteers. In addition, we quantified the absolute abundances of bacteria and fungi using 16S and 18S rRNA PCRs and characterized the short-chain fatty acids (SCFAs) butyrate, acetate, and propionate using nuclear magnetic resonance spectroscopy. We observe that a loss of the anaerobic intestinal environment is directly correlated with an overgrowth of aerobic pathobionts and their corresponding bacteriophages as well as an absolute enrichment of opportunistic yeasts capable of causing invasive disease. We also observed a strong depletion of SCFAs in both disease states, which was associated with an increased absolute abundance of fungi with respect to bacteria. Therefore, these findings illustrate the complexity of transkingdom changes following disruption of the intestinal bacterial microbiome. IMPORTANCE While numerous studies have characterized antibiotic-induced disruptions of the bacterial microbiome, few studies describe how these disruptions impact the composition of other kingdoms such as viruses, fungi, and protozoa. To address this knowledge gap, we employed MOFA to systematically integrate viral, fungal, and bacterial sequence data from critically ill patients (with and without sepsis) and healthy volunteers, both prior to and following exposure to broad-spectrum antibiotics. In doing so, we show that modulation of the bacterial component of the microbiome has implications extending beyond this kingdom alone, enabling the overgrowth of potentially invasive fungi and viruses. While numerous preclinical studies have described similar findings in vitro, we confirm these observations in humans using an integrative analytic approach. These findings underscore the potential value of multi-omics data integration tools in interrogating how different components of the microbiota contribute to disease states. In addition, our findings suggest that there is value in further studying potential adjunctive therapies using anaerobic bacteria or SCFAs to reduce fungal expansion after antibiotic exposure, which could ultimately lead to improved outcomes in the intensive care unit (ICU).

Epigenetic research involves examining the mitotically heritable processes that regulate gene expression, independent of changes in the DNA sequence. Recent technical advances such as whole-genome bisulfite sequencing and affordable epigenomic array-based technologies, allow researchers to measure epigenetic profiles of large cohorts at a genome-wide level, generating comprehensive high-dimensional datasets that may contain important information for disease development and treatment opportunities. The epigenomic profile for a certain disease is often a result of the complex interplay between multiple genetic and environmental factors, which poses an enormous challenge to visualize and interpret these data. Furthermore, due to the dynamic nature of the epigenome, it is critical to determine causal relationships from the many correlated associations. In this review we provide an overview of recent data analysis approaches to integrate various omics layers to understand epigenetic mechanisms of complex diseases, such as obesity and cancer. We discuss the following topics: (i) advantages and limitations of major epigenetic profiling techniques, (ii) resources for standardization, annotation and harmonization of epigenetic data, and (iii) statistical methods and machine learning methods for establishing data-driven hypotheses of key regulatory mechanisms. Finally, we discuss the future directions for data integration that shall facilitate the discovery of epigenetic-based biomarkers and therapies.

Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics.