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. The gene ontology (GO) term "response to bacterium" was shared, with several different DEGs contributing in males and females. Strongly upregulated genes TCN1 and CYP1B1 were unique to males and females, respectively. For male emphysema (E)-dominant and airway disease (A)-dominant COPD (defined by computed tomography) the term "response to stress" was found for both sub-phenotypes, but this included distinct up-regulated genes for the E-sub-phenotype (neutrophil-related CSF3R, CXCL1, MNDA) and for the A-sub-phenotype (macrophage-related KLF4, F3, CD36). In E-dominant disease, a cluster of mitochondria-encoded (MT) genes forms a signature, able to identify patients with emphysema features in a confirmation cohort. The MT-CO2 gene is upregulated transcriptionally in bronchial epithelial cells with the copy number essentially unchanged. Both MT-CO2 and the neutrophil chemoattractant CXCL1 are induced by reactive oxygen in bronchial epithelial cells. Of the female DEGs unique for E- and A-dominant COPD, 88% were detected in females only. In E-dominant disease we found a pronounced expression of mast cell-associated DEGs TPSB2, TPSAB1 and CPA3. The differential genes discovered in this study point towards involvement of different types of leukocytes in the E- and A-dominant COPD sub-phenotypes in males and females.

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 Candidatus Thiosymbion oneisti, its cell count mainly remains manual. The identification of this type of cell division is important because it helps to detect undergoing cellular division from those which are not dividing once the sample is fixed. Our solution automates the classification of longitudinal division by using a machine learning method called residual network. Using transfer learning, we train a binary classification model in fewer epochs compared to the model trained without it. This potentially eliminates most of the manual labor of classifying the type of bacteria cell division. The approach is useful in automatically labeling a certain bacteria division after detecting and segmenting (extracting) individual bacteria images from microscopic images of colonies.

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

Progression to clinical type 1 diabetes varies among children who develop ß-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. We identified clusters of similar profiles associated with various types of progression among 600 children from The Environmental Determinants of Diabetes in the Young (TEDDY) birth cohort study; these children developed persistent insulin autoantibodies (IAA), GAD autoantibodies (GADA), insulinoma-associated antigen 2 autoantibodies (IA-2A), or a combination of these, and they were followed up prospectively at 3- to 6-month intervals (median follow-up 6.5 years). Children who developed multiple autoantibody types (n = 370) were clustered, and progression from seroconversion to clinical diabetes within 5 years ranged between clusters from 6% (95% CI 0, 17.4) to 84% (59.2, 93.6). Children who seroconverted early in life (median age <2 years) and developed IAA and IA-2A that were stable-positive on follow-up had the highest risk of diabetes, and this risk was unaffected by GADA status. Clusters of children who lacked stable-positive GADA responses contained more boys and lower frequencies of the HLA-DR3 allele. Our novel algorithm allows refined grouping of ß-cell autoantibody-positive children who distinctly progressed to clinical type 1 diabetes, and it provides new opportunities in searching for etiological factors and elucidating complex disease mechanisms.

Influence of lung CT changes in chronic obstructive pulmonary disease (COPD) on the human lung microbiome.

BACKGROUND: Changes in microbial community composition in the lung of patients suffering from moderate to severe COPD have been well documented. However, knowledge about specific microbiome structures in the human lung associated with CT defined abnormalities is limited. METHODS: Bacterial community composition derived from brush samples from lungs of 16 patients suffering from different CT defined subtypes of COPD and 9 healthy subjects was analyzed using a cultivation independent barcoding approach applying 454-pyrosequencing of 16S rRNA gene fragment amplicons. RESULTS: We could show that bacterial community composition in patients with changes in CT (either airway or emphysema type changes, designated as severe subtypes) was different from community composition in lungs of patients without visible changes in CT as well as from healthy subjects (designated as mild COPD subtype and control group) (PC1, Padj = 0.002). Higher abundance of Prevotella in samples from patients with mild COPD subtype and from controls and of Streptococcus in the severe subtype cases mainly contributed to the separation of bacterial communities of subjects. No significant effects of treatment with inhaled glucocorticoids on bacterial community composition were detected within COPD cases with and without abnormalities in CT in PCoA. Co-occurrence analysis suggests the presence of networks of co-occurring bacteria. Four communities of positively correlated bacteria were revealed. The microbial communities can clearly be distinguished by their associations with the CT defined disease phenotype. CONCLUSION: Our findings indicate that CT detectable structural changes in the lung of COPD patients, which we termed severe subtypes, are associated with alterations in bacterial communities, which may induce further changes in the interaction between microbes and host cells. This might result in a changed interplay with the host immune system.

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. METHODS: Qualitative changes in antibody status on follow-up and progression rate to diabetes were analysed in 88 children followed from birth in the prospective BABYDIAB study who developed multiple autoantibodies against insulin (IAA), GAD (GADA), insulinoma-associated antigen-2 (IA-2A) and/or zinc transporter 8 (ZnT8A). An algorithm was developed to define similarities in sequential autoantibody profiles and hierarchical clustering was performed to group children with similar profiles. RESULTS: We defined nine clusters that distinguished children with respect to their sequential profiles of IAA, GADA, IA-2A and ZnT8A. Progression from first autoantibody appearance to clinical diabetes between clusters ranged from 6% (95% CI [0, 16.4]) to 73% (28.4, 89.6) within 5 years. Delayed progression was observed in children who were positive for only two autoantibodies, and for a cluster of 12 children who developed three or four autoantibodies but were IAA-negative in their last samples, nine of whom lost IAA positivity during follow-up. Among all children who first seroconverted to IAA positivity and developed at least two other autoantibodies (n = 57), the 10 year risk of diabetes was 23% (0, 42.9) in those who became IAA-negative during follow-up compared with 76% (58.7, 85.6) in those who remained IAA-positive (p = 0.004). CONCLUSIONS/INTERPRETATION: The novel clustering approach provides a tool for stratification of islet autoantibody-positive individuals that has prognostic relevance, and new opportunities in elucidating disease mechanisms. Our data suggest that losing IAA reactivity is associated with delayed progression to type 1 diabetes in multiple-islet-autoantibody-positive children.

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. During the first years of life, the gut microbial community is shaped by the environment, in particular by dietary factors. Moreover, the gut microbiome has been described for its role in shaping the immune system early in life and early data suggest associations between T1D risk and alterations in gut microbial communities. In this article, we discuss environmental factors influencing the colonization process of the gut microbial community. Furthermore, we review possible interactions between the microbiome and the host that might contribute to the risk of 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. A functional model of the association between diet, microbial communities, and the development of anti-islet cell autoimmunity can provide important new insights regarding the role of the gut microbiome in the pathogenesis of type 1 diabetes. RESULTS: A novel approach was developed to enable the analysis of the microbiome on an aggregation level between a single microbial taxon and classical ecological measures analyzing the whole microbial population. Microbial co-occurrence networks were estimated at age 6 months to identify candidates for functional microbial communities prior to islet autoantibody development. Stratification of children based on these communities revealed functional associations between diet, gut microbiome, and islet autoantibody development. Two communities were strongly associated with breast-feeding and solid food introduction, respectively. The third community revealed a subgroup of children that was dominated by Bacteroides abundances compared to two subgroups with low Bacteroides and increased Akkermansia abundances. The Bacteroides-dominated subgroup was characterized by early introduction of non-milk diet, increased risk for early autoantibody development, and by lower abundances of genes for the production of butyrate via co-fermentation of acetate. By combining our results with information from the literature, we provide a refined functional hypothesis for a protective role of butyrate in the pathogenesis of type 1 diabetes. CONCLUSIONS: Based on functional traits of microbial communities estimated from co-occurrence networks, we provide evidence that alterations in the composition of mucin degrading bacteria associate with early development of anti-islet cell autoimmunity. We hypothesize that lower levels of Bacteroides in favor of increased levels of Akkermansia lead to a competitive advantage of acetogens compared to sulfate reducing bacteria, resulting in increased butyrate production via co-fermentation of acetate. This hypothesis suggests that butyrate has a protective effect on the development of anti-islet cell autoantibodies.

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. METHODS: We proposed a data mining strategy to improve hypothesis generation with respect to potential associations. RESULTS: By numerous examples, we illustrate that our strategy controls the false positive signals. We implemented a free online tool, AERS spider (www.chemoprofiling.org/AERS). CONCLUSIONS: We believe that AERS spider would be a valuable tool for drug safety experts.

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. We investigated the microbiome of 298 stool samples prospectively taken up to age 3 years from 22 case children in whom anti-islet cell autoantibodies developed, and 22 matched control children who remained islet cell autoantibody-negative in follow-up. The microbiome changed markedly during the first year of life, and was further affected by breast-feeding, food introduction, and birth delivery mode. No differences between anti-islet cell autoantibody-positive and -negative children were found in bacterial diversity, microbial composition, or single-genus abundances. However, substantial alterations in microbial interaction networks were observed at age 0.5 and 2 years in the children in whom anti-islet cell autoantibodies developed. The findings underscore a role of the microbiome in the pathogenesis of anti-islet cell autoimmunity and type 1 diabetes.

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). After removal of VOCs released from the growth system, 54 VOCs were detected including 15 novel compounds not reported in fungi before. Principle component and cluster analyses revealed that fungal species differ in their odor profiles, particularly in the pattern of sesquiterpenes. The functional groups and species could be chemotyped by using their specific emission patterns. The different ecological groups could be predicted with probabilities of 90-99%, whereas for the individual species the probabilities varied between 55% and 83%. This study strongly supports the concept that the profiling of volatile compounds can be used for non-invasive identification of different functional fungal groups.

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. The red-green-blue (RGB) values are transformed into a colour space as ratios of R, G and B vs the sum of R, G, and B channels. We introduce an approach to render the training set of pixels on a Microsoft Excel two-dimensional graph and a technique to determine the discriminant regions of pixel classes. Two approaches for the classification based on colour analysis are shown: an automatic method using support vector machines and a procedure based on visual inspection. The segmentation procedure is designed to classify more than two object types utilising flexibly curved boundaries of discriminant regions that can also be non-convex. We propose a machine-vision algorithm to detect plant features - leaf anthocyanin accumulation and trichomes. The procedures of segmentation and feature detection are applied to images of Arabidopsis thaliana (L.) Heynh. that grow under either normal or drought stress conditions.

Dynamics of glucose and insulin concentration connected to the ß-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 ß-cells in the pancreatic islets of Langerhans. Recent experimental results indicate the relevance of the ß-cell cycle for the development of diabetes mellitus. METHODS: This paper introduces a mathematical model that connects the dynamics of glucose and insulin concentration with the ß-cell cycle. The interplay of glucose, insulin, and ß-cell cycle is described with a system of ordinary differential equations. The model and its development will be presented as well as its mathematical analysis. The latter investigates the steady states of the model and their stability. RESULTS: Our model shows the connection of glucose and insulin concentrations to the ß-cell cycle. In this way the important role of glucose as regulator of the cell cycle and the capability of the ß-cell mass to adapt to metabolic demands can be presented. Simulations of the model correspond to the qualitative behavior of the glucose-insulin regulatory system showed in biological experiments. CONCLUSIONS: This work focus on modeling the physiological situation of the glucose-insulin regulatory system with a detailed consideration of the ß-cell cycle. Furthermore, the presented model allows the simulation of pathological scenarios. Modification of different parameters results in simulation of either type 1 or type 2 diabetes.