For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or in-silico deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues. Experimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, quantile normalization and use of non-log data are optimal. We demonstrate the feasibility of predicting proportions of constituting cell types from gene expression data of single samples, as a prerequisite for a deconfounding-based classification approach. Classification cross-validation errors with and without using deconfounding results are reported as well as sample-size dependencies. Implementation of the algorithm, simulation and analysis scripts are available. The deconfounding algorithm without decorrelation using quantile normalization on non-log data is proposed for biomarkers that are difficult to detect, and for cases where confounding by varying proportions of cell types is the suspected reason. In this case, a deconfounding ranking approach can be used as a powerful alternative to, or complement of, other statistical learning approaches to define candidate biomarkers for molecular diagnosis and prediction in biomedicine, in realistically noisy conditions and with moderate sample sizes.

Experimentalists are overwhelmed by high-throughput data and there is an urgent need to condense information into simple hypotheses. For example, large amounts of microarray and deep sequencing data are becoming available, describing a variety of experimental conditions such as gene knockout and knockdown, the effect of interventions, and the differences between tissues and cell lines.

Diseases of the digestive system have been found to contribute to a higher symptom burden in older adults. Thus, therapeutic strategies able to treat gastrointestinal discomfort might impact the overall health status and help older adults to increase their overall health status and optimal functionality. The aim of this double-blinded, randomized, placebo-controlled clinical trial was to evaluate the effect of the probiotic strain Lactobacillus reuteri on digestive health and wellbeing in older adults. The study enrolled general older adults (>65 years). After eligibility screening qualified subjects (n = 290) participated in a 2-arm study design, with each arm consisting of 12 weeks of intervention of either active or placebo product. Primary outcome measure was set to changes in gastrointestinal symptoms and secondary outcome measures were changes in level of wellbeing, anxiety and stress. Follow up was performed at 8 and 12 weeks. No persistent significant effects were observed on the primary or secondary outcome parameters of the study. A modest effect was observed in the probiotic arm, were levels of stress decreased at week 8 and 12. Similarly, we found that subjects suffering from indigestion and abdominal pain, respectively, showed a significant decrease of anxiety at week 8 after probiotic treatment, but not at week 12. The RCT failed to show any improvement in digestive health after daily intake of a probiotic supplement containing L. reuteri. Neither was any significant improvement in wellbeing, stress or anxiety observed. Even though the RCT had a negative outcome, the study highlights issues important to take into consideration when designing trials among older adults. Clinicaltrials.gov/ NCT01837940 .

Background: During the past ten years many quantitative trait loci (QTL) affecting mastitis incidence and mastitis related traits like somatic cell score (SCS) were identified in cattle. However, little is known about the molecular architecture of QTL affecting mastitis susceptibility and the underlying physiological mechanisms and genes causing mastitis susceptibility. Here, a genome-wide expression analysis was conducted to analyze molecular mechanisms of mastitis susceptibility that are affected by a specific QTL for SCS on Bos taurus autosome 18 (BTA18). Thereby, some first insights were sought into the genetically determined mechanisms of mammary gland epithelial cells influencing the course of infection. Methods: Primary bovine mammary gland epithelial cells (pbMEC) were sampled from the udder parenchyma of cows selected for high and low mastitis susceptibility by applying a marker-assisted selection strategy considering QTL and molecular marker information of a confirmed QTL for SCS in the telomeric region of BTA18. The cells were cultured and subsequently inoculated with heat-inactivated mastitis pathogens Escherichia coli and Staphylococcus aureus , respectively. After 1, 6 and 24 h, the cells were harvested and analyzed using the microarray expression chip technology to identify differences in mRNA expression profiles attributed to genetic predisposition, inoculation and cell culture. Results: Comparative analysis of co-expression profiles clearly showed a faster and stronger response after pathogen challenge in pbMEC from less susceptible animals that inherited the favorable QTL allele 'Q' than in pbMEC from more susceptible animals that inherited the unfavorable QTL allele 'q'. Furthermore, the results highlighted RELB as a functional and positional candidate gene and related non-canonical Nf-kappaB signaling as a functional mechanism affected by the QTL. However, in both groups, inoculation resulted in up-regulation of genes associated with the Ingenuity pathways 'dendritic cell maturation' and 'acute phase response signaling', whereas cell culture affected biological processes involved in 'cellular development'. Conclusions: The results indicate that the complex expression profiling of pathogen challenged pbMEC sampled from cows inheriting alternative QTL alleles is suitable to study genetically determined molecular mechanisms of mastitis susceptibility in mammary epithelial cells in vitro and to highlight the most likely functional pathways and candidate genes underlying the QTL effect.

Background: Gastrointestinal (GI) health is an important aspect of general health. Gastrointestinal symptoms are of specific importance for the elderly, an increasing group globally. Hence, promoting the elderly’s health and especially gastrointestinal health is important. Gut microbiota can influence gastrointestinal health by modulation of the immune system and the gut–brain axis. Diverse gut microbiota have been shown to be beneficial; however, for the elderly, the gut microbiota is often less diverse. Nutrition and physical activity, in particular, are two components that have been suggested to influence composition or diversity. Materials and Methods: In this study, we compared gut microbiota between two groups of elderly individuals: community-dwelling older adults and physically active senior orienteering athletes, where the latter group has less gastrointestinal symptoms and a reported better well-being. With this approach, we explored if certain gut microbiota were related to healthy ageing. The participant data and faecal samples were collected from these two groups and the microbiota was whole-genome sequenced and taxonomically classified with MetaPhlAn. Results: The physically active senior orienteers had a more homogeneous microbiota within the group and a higher abundance of Faecalibacterium prausnitzii compared to the community-dwelling older adults. Faecalibacterium prausnitzii has previously shown to have beneficial properties. Senior orienteers also had a lower abundance of Parasutterella excrementihominis and Bilophila unclassified, which have been associated with impaired GI health. We could not observe any difference between the groups in terms of Shannon diversity index. Interestingly, a subgroup of community-dwelling older adults showed an atypical microbiota profile as well as the parameters for gastrointestinal symptoms and well-being closer to senior orienteers. Conclusions: Our results suggest specific composition characteristics of healthy microbiota in the elderly, and show that certain components of nutrition as well as psychological distress are not as tightly connected with composition or diversity variation in faecal microbiota samples.

In the field of dairy cattle research, it is of great interest to improve the detection and prevention of diseases (e.g., mastitis and ketosis) and monitor specific traits related to the state of health and management. During the standard milk performance test, traditional milk traits are monitored, and quality and quantity are screened. In addition to the standard test, it is also now possible to analyze milk metabolites in a high-throughput manner and to consider them in connection with milk traits to identify functionally important metabolites that can also serve as biomarker candidates. We present a study in which 190 milk metabolites and 14 milk traits of 1,305 Holstein cows on 18 commercial farms were investigated to characterize interrelations of milk metabolites between each other, to milk traits from the milk standard performance test, and to influencing factors such as farm and sire effect (half-sib structure). The effect of influencing factors (e.g., farm) varied among metabolites and traditional milk traits. The investigations of associations between metabolites and milk traits revealed groups of metabolites that show, for example, positive correlations to protein and casein, and negative correlations to lactose and pH. On the other hand, groups of metabolites jointly associated with the investigated milk traits can be identified and functionally discussed. To enable a multivariate investigation, 2 machine learning methods were applied to detect important metabolites that are highly correlated with the investigated traditional milk traits. For somatic cell score, uracil, lactic acid, and 9 other important metabolites were detected. Lactic acid has already been proposed as a biomarker candidate for mastitis in the recent literature. In conclusion, we found sets of metabolites eligible to predict milk traits, enabling the analysis of milk traits from a metabolic perspective and discussion of the possible functional background for some of the detected associations.