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Abstract Open Science enhances information sharing and makes scientific results of transport research more transparent and accessible at all levels and to everyone allowing integrity and reproducibility. However, what future impacts will Open Science have on the societal, environmental and economic development within the transport sector? Using the Future Wheel methodology, we conducted a workshop with transport experts from both industry and academia to answer this question. The main findings of this study point in the direction of previous studies in other fields, in terms of increased innovation, increased efficiency, economic savings, more equality, and increased participation of citizens. In addition, we found several potential transport specific impacts: lower emission, faster travel times, improved traffic safety, increased awareness for transport policies, artificial intelligence improving mobility services. Several potential negative outcomes of Open Science were also identified by the expert group: job loss, new types of risks, increased cost, increased conflicts, time delays, increased inequality and increased energy consumption. If we know the negative outcomes it is much easier to put in place strategies that are sustainable for a broader stakeholder group, which also increase the probability of taking advantage of all the positive impacts of Open Science.

Abstract This study focuses on the resilience of gut microbiota during a five-month multi-interventional nutrition trial. The modulatory effects of beta-glucan, rye bran and two dietary fiber mixtures on the fecal pH and compositional changes of the microbiome of healthy subjects were studied. To analyze the stability of intestinal microbiota, we collected an extensive dataset of sequential fecal samples (23–29 from each participant) during a week of the base, beta-glucan consumption and wash-out periods accompanied by the collection of daily food diary data. Microbiota analyses were also conducted after the end of each fiber intake and wash-out period, along with measurements of fecal organic acids and pH. Based on the dominant bacterial taxa, two prevalent microbiota types were identified. The Prevotella-type microbiota responded more to the tested dietary fibers, while the Bacteroides-type microbiota was the least affected. Three microbiota types could not be clustered and behaved differently. Although we noted individual effects of definite fibers on participants’ gut microbiota and metabolic profile, relative abundances of bacteria remained stable in the base period (z-scores − 2.2 to 2.3). In most cases, the bacterial abundances of the final samples remained within the normal fluctuation range stressing out the resilience of healthy microbiota. The pH of all fecal samples varied between 6.1 and 8.3 and was associated with the concentration of organic acids and microbial composition. The effect of dietary fibers on the metabolism of fecal microbiota clearly depended on the individual microbiota type. Combining the analysis of gut microbiota with knowledge of the properties of dietary fibers would provide a powerful strategy for nutrition guidance and disease prevention.

Abstract Individual Specific Networks (ISNs) are a tool used in computational biology to infer Individual Specific relationships between biological entities from omics data. ISNs provide insights into how the interactions among these entities affect their respective functions. To address the scarcity of solutions for efficiently computing ISNs on large biological datasets, we present ISN-tractor, a data-agnostic, highly optimized Python library to build and analyse ISNs. ISN-tractor demonstrates superior scalability and efficiency in generating Individual Specific Networks (ISNs) when compared to existing methods such as LionessR, both in terms of time and memory usage, allowing ISNs to be used on large datasets. We show how ISN-tractor can be applied to real-life datasets, including The Cancer Genome Atlas (TCGA) and HapMap, showcasing its versatility. ISN-tractor can be used to build ISNs from various -omics data types, including transcriptomics, proteomics, and genotype arrays, and can detect distinct patterns of gene interactions within and across cancer types. We also show how Filtration Curves provided valuable insights into ISN characteristics, revealing topological distinctions among individuals with different clinical outcomes. Additionally, ISN-tractor can effectively cluster populations based on genetic relationships, as demonstrated with Principal Component Analysis on HapMap data.

Abstract We present a rigorous validation strategy to evaluate the performance of Ultivue multiplex immunofluorescence panels. We have quantified the accuracy and precision of four different multiplex panels (three human and one mouse) in tumor specimens with varying levels of T cell density. Our results show that Ultivue panels are typically accurate wherein the relative difference in cell proportion between a multiplex image and a 1-plex image is less than 20% for a given biomarker. Ultivue panels exhibited relatively high intra-run precision (CV ≤ 25%) and relatively low inter-run precision (CV >> 25%) which can be remedied by using local intensity thresholding to gate biomarker positivity. We also evaluated the reproducibility of cell–cell distance estimates measured from multiplex images which show high intra- and inter-run precision. We introduce a new metric, multiplex labeling efficiency, which can be used to benchmark the overall fidelity of the multiplex data across multiple batch runs. Taken together our results provide a comprehensive characterization of Ultivue panels and offer practical guidelines for analyzing multiplex images.

Abstract Personalised cancer screening before therapy paves the way toward improving diagnostic accuracy and treatment outcomes. Most approaches are limited to a single data type and do not consider interactions between features, leaving aside the complementary insights that multimodality and systems biology can provide. In this project, we demonstrate the use of graph theory for data integration via individual networks where nodes and edges are individual-specific. We showcase the consequences of early, intermediate, and late graph-based fusion of RNA-Seq data and histopathology whole-slide images for predicting cancer subtypes and severity. The methodology developed is as follows: (1) we create individual networks; (2) we compute the similarity between individuals from these graphs; (3) we train our model on the similarity matrices; (4) we evaluate the performance using the macro F1 score. Pros and cons of elements of the pipeline are evaluated on publicly available real-life datasets. We find that graph-based methods can increase performance over methods that do not study interactions. Additionally, merging multiple data sources often improves classification compared to models based on single data, especially through intermediate fusion. The proposed workflow can easily be adapted to other disease contexts to accelerate and enhance personalized healthcare.

Abstract Individual-specific networks, defined as networks of nodes and connecting edges that are specific to an individual, are promising tools for precision medicine. When such networks are biological, interpretation of functional modules at an individual level becomes possible. An under-investigated problem is relevance or ”significance” assessment of each individual-specific network. This paper proposes novel edge and module significance assessment procedures for weighted and unweighted individual-specific networks. Specifically, we propose a modular Cook’s distance using a method that involves iterative modeling of one edge versus all the others within a module. Two procedures assessing changes between using all individuals and using all individuals but leaving one individual out (LOO) are proposed as well (LOO-ISN, MultiLOO-ISN), relying on empirically derived edges. We compare our proposals to competitors, including adaptions of OPTICS, kNN, and Spoutlier methods, by an extensive simulation study, templated on real-life scenarios for gene co-expression and microbial interaction networks. Results show the advantages of performing modular versus edge-wise significance assessments for individual-specific networks. Furthermore, modular Cook’s distance is among the top performers across all considered simulation settings. Finally, the identification of outlying individuals regarding their individual-specific networks, is meaningful for precision medicine purposes, as confirmed by network analysis of microbiome abundance profiles.

Abstract Pancreatic ductal adenocarcinoma (PDAC) is categorized as the leading cause of cancer mortality worldwide. However, its predictive markers for long-term survival are not well known. It is interesting to delineate individual-specific perturbed genes when comparing long-term (LT) and short-term (ST) PDAC survivors and integrate individual- and group-based transcriptome profiling. Using a discovery cohort of 19 PDAC patients from CHU-Liège (Belgium), we first performed differential gene expression analysis comparing LT to ST survivor. Second, we adopted systems biology approaches to obtain clinically relevant gene modules. Third, we created individual-specific perturbation profiles. Furthermore, we used Degree-Aware disease gene prioritizing (DADA) method to develop PDAC disease modules; Network-based Integration of Multi-omics Data (NetICS) to integrate group-based and individual-specific perturbed genes in relation to PDAC LT survival. We identified 173 differentially expressed genes (DEGs) in ST and LT survivors and five modules (including 38 DEGs) showing associations to clinical traits. Validation of DEGs in the molecular lab suggested a role of REG4 and TSPAN8 in PDAC survival. Via NetICS and DADA, we identified various known oncogenes such as CUL1 and TGFB1. Our proposed analytic workflow shows the advantages of combining clinical and omics data as well as individual- and group-level transcriptome profiling.

Abstract The coronavirus disease-19 (COVID-19) pandemic has already claimed millions of lives and remains one of the major catastrophes in the recorded history. While mitigation and control strategies provide short term solutions, vaccines play critical roles in long term control of the disease. Recent emergence of potentially vaccine-resistant and novel variants necessitated testing and deployment of novel technologies that are safe, effective, stable, easy to administer, and inexpensive to produce. Here we developed three recombinant Newcastle disease virus (rNDV) vectored vaccines and assessed their immunogenicity, safety, and protective efficacy against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in mice and hamsters. Intranasal administration of rNDV-based vaccine candidates elicited high levels of neutralizing antibodies. Importantly, the nasally administrated vaccine prevented lung damage, and significantly reduced viral load in the respiratory tract of vaccinated animal which was compounded by profound humoral immune responses. Taken together, the presented NDV-based vaccine candidates fully protected animals against SARS-CoV-2 challenge and warrants evaluation in a Phase I human clinical trial as a promising tool in the fight against COVID-19.

Abstract The complete understanding of the genomic contribution to complex traits, diseases, and response to treatments, as well as genomic medicine application to the well-being of all humans will be achieved through the global variome that encompasses fine-scale genetic diversity. Despite significant efforts in recent years, uneven representation still characterizes genomic resources and among the underrepresented European populations are the Western Balkans including the Serbian population. Our research addresses this gap and presents the first ever targeted sequencing dataset of variants in clinically relevant genes. By measuring population differentiation and applying the Principal Component and Admixture analysis we demonstrated that the Serbian population differs little from other European populations, yet we identified several novel and more frequent variants that appear as its unique genetic determinants. We explored thoroughly the functional impact of frequent variants and its correlation with the health burden of the population of Serbia based on a sample of 144 individuals. Our variants catalogue improves the understanding of genetics of modern Serbia, contributes to research on ancestry, and aids in improvements of well-being and health equity. In addition, this resource may also be applicable in neighboring regions and valuable in worldwide functional analyses of genetic variants in individuals of European descent.

Abstract Previous research identified potential prebiotic substrates for oral health like the structural analogues N-acetyl-d-mannosamine (NADM) and N-acetyl-d-glucosamine (NADG). The main hypothesis of the current study was twofold. Firstly, it was hypothesized that the modulatory effects of NADM are not limited to changes in multi-species oral biofilm composition, but also include effects on metabolism, virulence, and inflammatory potential. Secondly, the presence and orientation of their N-acetyl group could play a role. Therefore, a comparison was made between the effects of NADM, NADG and d-(+)-mannose on multi-species oral biofilms. Besides a beneficial compositional shift, NADM-treated biofilms also showed an altered metabolism, a reduced virulence and a decreased inflammatory potential. At a substrate concentration of 1 M, these effects were pronounced for all biofilm aspects, whereas at ~ 0.05 M (1%(w/v)) only the effects on virulence were pronounced. When comparing between substrates, both the presence and orientation of the N-acetyl group played a role. However, this was generally only at 1 M and dependent on the biofilm aspect. Overall, NADM was found to have different effects at two concentrations that beneficially modulate in vitro multi-species oral biofilm composition, metabolism, virulence and inflammatory potential. The presence and orientation of the N-acetyl group influenced these effects.

Abstract The oral use of antimicrobial agents embedded in toothpastes and mouth rinses results in an oral microbial massacre with high amounts of dead bacteria in close proximity to few surviving bacteria. It was hypothesized that this provides the surviving pathogenic bacteria a large amount of dead microbial biomass as a nutritional source for growth (necrotrophy). This study demonstrated the necrotrophic growth of periodontal pathogens in the presence of different dead oral species. In addition, the presence of dead bacteria resulted in an outgrowth of several periodontal pathogens in complex multi-species biofilms. Additionally, upon contact with dead oral bacteria, virulence genes of P. intermedia and P. gingivalis were up-regulated (necrovirulence). This resulted in a more pronounced epithelial cytotoxicity (necrotoxicity). These findings indicate that presence of dead bacteria induce necrotrophy, necrovirulence and necrotoxicity in several oral pathogens.