Microbes in the gut microbiome form sub-communities based on shared niche specialisations and specific interactions between individual taxa. The inter-microbial relationships that define these communities can be inferred from the co-occurrence of taxa across multiple samples. Here, we present an approach to identify comparable communities within different gut microbiota co-occurrence networks, and demonstrate its use by comparing the gut microbiota community structures of three geographically diverse populations. We combine gut microbiota profiles from 2,764 British, 1,023 Dutch, and 639 Israeli individuals, derive co-occurrence networks between their operational taxonomic units, and detect comparable communities within them. Comparing populations we find that community structure is significantly more similar between datasets than expected by chance. Mapping communities across the datasets, we also show that communities can have similar associations to host phenotypes in different populations. This study shows that the community structure within the gut microbiota is stable across populations, and describes a novel approach that facilitates comparative community-centric microbiome analyses.

Abstract-Multiplex networks, a special type of multilayer networks, are increasingly applied in many domains ranging from social media analytics to biology. A common task in these applications concerns the detection of community structures. Many existing algorithms for community detection in multiplexes attempt to detect communities which are shared by all layers. In this article we propose a community detection algorithm, LART (Locally Adaptive Random Transitions), for the detection of communities that are shared by either some or all the layers in the multiplex. The algorithm is based on a random walk on the multiplex, and the transition probabilities defining the random walk are allowed to depend on the local topological similarity between layers at any given node so as to facilitate the exploration of communities across layers. Based on this random walk, a node dissimilarity measure is derived and nodes are clustered based on this distance in a hierarchical fashion. We present experimental results using networks simulated under various scenarios to showcase the performance of LART in comparison to related community detection algorithms.

The circulating proteome offers insights into the biological pathways that underlie disease. Here, we test relationships between 1,468 Olink protein levels and the incidence of 23 age-related diseases and mortality, ascertained over 16 years of electronic health linkage in the UK Biobank (N=49,234). We report 3,123 associations between 1,052 protein levels and incident diseases (PBonferroni < 5.4x10-6). Forty four proteins are indicators of eight or more morbidities. Next, protein-based scores (ProteinScores) are developed using penalised Cox regression. When applied to test sets, eight ProteinScores improve Area Under the Curve (AUC) estimates for the 10-year onset of incident outcomes (PBonferroni < 0.0025) beyond age, sex and additional health and lifestyle covariates. The type 2 diabetes ProteinScore outperforms HbA1c (P = 5.7x10-12), a clinical marker used to monitor and diagnose type 2 diabetes. A maximal type 2 diabetes model including the ProteinScore, HbA1c and a polygenic risk score has AUC = 0.90 and Precision-Recall AUC = 0.76. These data characterise early proteomic contributions to major age-related disease.

Characterizing the transcriptome architecture of the human brain is fundamental in gaining an understanding of brain function and disease. A number of recent studies have investigated patterns of brain gene expression obtained from an extensive anatomical coverage across the entire human brain using experimental data generated by the Allen Human Brain Atlas (AHBA) project. In this paper, we propose a new representation of a gene's transcription activity that explicitly captures the pattern of spatial co-expression across different anatomical brain regions. For each gene, we define a Spatial Expression Network (SEN), a network quantifying co-expression patterns amongst several anatomical locations. Network similarity measures are then employed to quantify the topological resemblance between pairs of SENs and identify naturally occurring clusters. Using network-theoretical measures, three large clusters have been detected featuring distinct topological properties. We then evaluate whether topological diversity of the SENs reects significant differences in biological function through a gene ontology analysis. We report on evidence suggesting that one of the three SEN clusters consists of genes specifically involved in the nervous system, including genes related to brain disorders, while the remaining two clusters are representative of immunity, transcription and translation. These findings are consistent with previous studies showing that brain gene clusters are generally associated with one of these three major biological processes.

We develop a novel TVBG-SEIR spline model for analysis of the coronavirus infection (COVID-19). It aims to analyze the long-term global evolution of the epidemics "controlled" by the introduction of lockdown/open up measures by the authorities. The incorporation of different "lockdown scenarios" varying in time permits to analyze not only the primary epidemic wave but also the arising secondary wave and any further waves.The model is supplied by a web-based Scenario Building Tool for COVID-19 (called shortly SBT-COVID19) which may be used as a decision support software by (health) policy makers to explore various scenarios. This can be achieved by controlling/changing the scale of the containment measures (home and social isolation/quarantine, travel restrictions and other) and to assess their effectiveness. In particular, the SBT-COVID19 Tool permits to assess how long the lockdown measures should be maintained.