Measuring and understanding information storage and transfer in a simulated human gut microbiome.

Considering biological systems as information processing entities and analyzing their organizational structure via information-theoretic measures has become an established approach in life sciences. We transfer this framework to a field of broad general interest, the human gut microbiome. We use BacArena, a software combining agent-based modelling and flux-balance analysis, to simulate a simplified human intestinal microbiome (SIHUMI).

Efficient Detection of Longitudinal Bacteria Fission Using Transfer Learning in Deep Neural Networks.

A very common way to classify bacteria is through microscopic images. Microscopic cell counting is a widely used technique to measure microbial growth. To date, fully automated methodologies are available for accurate and fast measurements; yet for bacteria dividing longitudinally, as in the case of Thiosymbion oneisti, its cell count mainly remains manual.

Gender specific airway gene expression in COPD sub-phenotypes supports a role of mitochondria and of different types of leukocytes.

Chronic obstructive pulmonary disease (COPD) is a destructive inflammatory disease and the genes expressed within the lung are crucial to its pathophysiology. We have determined the RNAseq transcriptome of bronchial brush cells from 312 stringently defined ex-smoker patients. Compared to healthy controls there were for males 40 differentially expressed genes (DEGs) and 73 DEGs for females with only 26 genes shared.

Computing the adaptive cycle.

Gunderson's and Holling's adaptive cycle metaphor provides a qualitative description of the development of a dynamically evolving complex system. According to the metaphor, a complex system alternately passes through phases of stability and predictability and phases of reorganization and stochasticity. So far, there have been no attempts to quantify the underlying notions in a way which is independent of the concrete realization of the system.

Time-Resolved Autoantibody Profiling Facilitates Stratification of Preclinical Type 1 Diabetes in Children.

Progression to clinical type 1 diabetes varies among children who develop β-cell autoantibodies. Differences in autoantibody patterns could relate to disease progression and etiology. Here we modeled complex longitudinal autoantibody profiles by using a novel wavelet-based algorithm.

Sulfonolipids as novel metabolite markers of Alistipes and Odoribacter affected by high-fat diets.

The gut microbiota generates a huge pool of unknown metabolites, and their identification and characterization is a key challenge in metabolomics. However, there are still gaps on the studies of gut microbiota and their chemical structures. In this investigation, an unusual class of bacterial sulfonolipids (SLs) is detected in mouse cecum, which was originally found in environmental microbes.

A novel approach for the analysis of longitudinal profiles reveals delayed progression to type 1 diabetes in a subgroup of multiple-islet-autoantibody-positive children.

Aims/hypothesis: Progression to type 1 diabetes in children and adolescents is not uniform. Based on individual genetic background and environment, islet autoimmunity may develop at variable age, exhibit different autoantibody profiles and progress to clinical diabetes at variable rates. Here, we aimed to quantify the qualitative dynamics of sequential islet autoantibody profiles in order to identify longitudinal patterns that stratify progression rates to type 1 diabetes in multiple-autoantibody-positive children.

Emphysema- and airway-dominant COPD phenotypes defined by standardised quantitative computed tomography.

EvA (Emphysema versus Airway disease) is a multicentre project to study mechanisms and identify biomarkers of emphysema and airway disease in chronic obstructive pulmonary disease (COPD). The objective of this study was to delineate objectively imaging-based emphysema-dominant and airway disease-dominant phenotypes using quantitative computed tomography (QCT) indices, standardised with a novel phantom-based approach.441 subjects with COPD (Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages 1-3) were assessed in terms of clinical and physiological measurements, laboratory testing and standardised QCT indices of emphysema and airway wall geometry.

Gut Immunity and Type 1 Diabetes: a Mélange of Microbes, Diet, and Host Interactions?

Type 1 diabetes (T1D) is a complex autoimmune disease, and first stages of the disease typically develop early in life. Genetic as well as environmental factors are thought to contribute to the risk of developing autoimmunity against pancreatic beta cells. Several environmental factors, such as breastfeeding or early introduction of solid food, have been associated with increased risk for developing T1D.

Towards a functional hypothesis relating anti-islet cell autoimmunity to the dietary impact on microbial communities and butyrate production.

Background: The development of anti-islet cell autoimmunity precedes clinical type 1 diabetes and occurs very early in life. During this early period, dietary factors strongly impact on the composition of the gut microbiome. At the same time, the gut microbiome plays a central role in the development of the infant immune system.

Distinct signatures of host-microbial meta-metabolome and gut microbiome in two C57BL/6 strains under high-fat diet.

A combinatory approach using metabolomics and gut microbiome analysis techniques was performed to unravel the nature and specificity of metabolic profiles related to gut ecology in obesity. This study focused on gut and liver metabolomics of two different mouse strains, the C57BL/6J (C57J) and the C57BL/6N (C57N) fed with high-fat diet (HFD) for 3 weeks, causing diet-induced obesity in C57N, but not in C57J mice. Furthermore, a 16S-ribosomal RNA comparative sequence analysis using 454 pyrosequencing detected significant differences between the microbiome of the two strains on phylum level for Firmicutes, Deferribacteres and Proteobacteria that propose an essential role of the microbiome in obesity susceptibility.

AERS spider: an online interactive tool to mine statistical associations in Adverse Event Reporting System.

Background: Exploration of the Adverse Event Reporting System (AERS) data by a wide scientific community is limited due to several factors. First, AERS data must be intensively preprocessed to be converted into analyzable format. Second, application of the currently accepted disproportional reporting measures results in false positive signals.

Compromised gut microbiota networks in children with anti-islet cell autoimmunity.

The gut microbiome is suggested to play a role in the pathogenesis of autoimmune disorders such as type 1 diabetes. Evidence of anti-islet cell autoimmunity in type 1 diabetes appears in the first years of life; however, little is known regarding the establishment of the gut microbiome in early infancy. Here, we sought to determine whether differences were present in early composition of the gut microbiome in children in whom anti-islet cell autoimmunity developed.

A segmentation procedure using colour features applied to images of Arabidopsis thaliana.

In studies of environmental effects on plant growth, the images of plants are often used for non-destructive measurements in phenotyping. In this work, a computational procedure has been developed to segment images of plants allowing an improved separation of plants and other types of objects in the frame such as moss or soil. The proposed procedure is based on colour analysis and image morphology.

Volatile profiles of fungi--chemotyping of species and ecological functions.

Fungi emit a large spectrum of volatile organic compounds (VOCs). In the present study, we characterized and compared the odor profiles of ectomycorrhizal (EM), pathogenic and saprophytic fungal species with the aim to use these patterns as a chemotyping tool. Volatiles were collected from the headspace of eight fungal species including nine strains (four EM, three pathogens and two saprophytes) using the stir bar sorptive extraction method and analyzed by gas chromatography-mass spectrometry (GC-MS).

Dynamics of glucose and insulin concentration connected to the β-cell cycle: model development and analysis.

Background: Diabetes mellitus is a group of metabolic diseases with increased blood glucose concentration as the main symptom. This can be caused by a relative or a total lack of insulin which is produced by the β-cells in the pancreatic islets of Langerhans. Recent experimental results indicate the relevance of the β-cell cycle for the development of diabetes mellitus.