Ovarian Control of Nectar Collection in the Honey Bee (Apis mellifera)

Honey bees are a model system for the study of division of labor. Worker bees demonstrate a foraging division of labor (DOL) by biasing collection towards carbohydrates (nectar) or protein (pollen). The Reproductive ground-plan hypothesis of Amdam et al. proposes that foraging DOL is regulated by the networks that controlled foraging behavior during the reproductive life cycle of honey bee ancestors. Here we test a proposed mechanism through which the ovary of the facultatively sterile worker impacts foraging bias. The proposed mechanism suggests that the ovary has a regulatory effect on sucrose sensitivity, and sucrose sensitivity impacts nectar loading. We tested this mechanism by measuring worker ovary size (ovariole number), sucrose sensitivity, and sucrose solution load size collected from a rate-controlled artificial feeder. We found a significant interaction between ovariole number and sucrose sensitivity on sucrose solution load size when using low concentration nectar. This supports our proposed mechanism. As nectar and pollen loading are not independent, a mechanism impacting nectar load size would also impact pollen load size

A combination of fecal calprotectin and human beta-defensin 2 facilitates diagnosis and monitoring of inflammatory bowel disease

Inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS) show a large overlap in clinical presentation, which presents diagnostic challenges. As a consequence, invasive and burdensome endoscopies are often used to distinguish between IBD and IBS. Here, we aimed to develop a noninvasive fecal test that can distinguish between IBD and IBS and reduce the number of endoscopies. We used shotgun metagenomic sequencing to analyze the composition and function of gut microbiota of 169 IBS patients, 447 IBD patients and 1044 population controls and measured fecal Calprotectin (FCal), human beta defensin 2 (HBD2), and chromogranin A (CgA) in these samples. These measurements were used to construct training sets (75% of data) for logistic regression and machine learning models to differentiate IBS from IBD and inactive from active IBD. The results were replicated on test sets (remaining 25% of the data) and microbiome data obtained using 16S sequencing. Fecal HBD2 showed high sensitivity and specificity for differentiating between IBD and IBS (sensitivity = 0.89, specificity = 0.76), while the inclusion of microbiome data with biomarkers (HBD2 and FCal) showed a potential for improvement in predictive power (optimal sensitivity = 0.87, specificity = 0.93). Shotgun sequencing-based models produced comparable results using 16S-sequencing data. HBD2 and FCal were found to have predictive power for IBD disease activity (AUC approximate to 0.7). HBD2 is a novel biomarker for IBD in patients with gastro-intestinal complaints, especially when used in combination with FCal and potentially in combination with gut microbiome data

Integrated fecal microbiome–metabolome signatures reflect stress and serotonin metabolism in irritable bowel syndrome

To gain insight into the complex microbiome-gut-brain axis in irritable bowel syndrome (IBS) several modalities of biological and clinical data must be combined. We aimed to identify profiles of faecal microbiota and metabolites associated with IBS and to delineate specific phenotypes of IBS that represent potential pathophysiological mechanisms. Faecal metabolites were measured using proton Nuclear Magnetic Resonance (1H-NMR) spectroscopy and gut microbiome using Shotgun Metagenomic Sequencing (MGS) in a combined dataset of 142 IBS patients and 120 healthy controls (HC) with extensive clinical, biological and phenotype information. Data were analysed using support vector classification and regression and kernel t-SNE. Microbiome and metabolome profiles could distinguish IBS and HC with an area-under-the-receiver-operator-curve (AUC) of 77.3% and 79.5%, respectively, but this could be improved by combining microbiota and metabolites to 83.6%. No significant differences in predictive ability of the microbiome-metabolome data were observed between the three classical, stool pattern-based, IBS subtypes. However, unsupervised clustering showed distinct subsets of IBS patients based on faecal microbiome-metabolome data. These clusters could be related plasma levels of serotonin and its metabolite 5-hydroxyindoleacetate, effects of psychological stress on gastrointestinal symptoms, onset of IBS after stressful events, medical history of previous abdominal surgery, dietary caloric intake and IBS symptom duration. Furthermore, pathways in metabolic reaction networks were integrated with microbiota data, that reflect the host-microbiome interactions in IBS. The identified microbiome-metabolome signatures for IBS, associated with altered serotonin metabolism and unfavourable stress-response related to gastrointestinal symptoms, support the microbiota-gut-brain link in the pathogenesis of IBS

Impact of commonly used drugs on the composition and metabolic function of the gut microbiota

The human gut microbiota has now been associated with drug responses and efficacy, while chemical compounds present in these drugs can also impact the gut bacteria. However, drug–microbe interactions are still understudied in the clinical context, where polypharmacy and comorbidities co-occur. Here, we report relations between commonly used drugs and the gut microbiome. We performed metagenomics sequencing of faecal samples from a population cohort and two gastrointestinal disease cohorts. Differences between users and non-users were analysed per cohort, followed by a meta-analysis. While 19 of 41 drugs are found to be associated with microbial features, when controlling for the use of multiple medications, proton-pump inhibitors, metformin, antibiotics and laxatives show the strongest associations with the microbiome. We here provide evidence for extensive changes in taxonomy, metabolic potential and resistome in relation to commonly used drugs. This paves the way for future studies and has implications for current microbiome studies by demonstrating the need to correct for multiple drug use

Effects of Blood Collection Conditions on Ovarian Cancer Serum Markers

Evaluating diagnostic and early detection biomarkers requires comparing serum protein concentrations among biosamples ascertained from subjects with and without cancer. Efforts are generally made to standardize blood processing and storage conditions for cases and controls, but blood sample collection conditions cannot be completely controlled. For example, blood samples from cases are often obtained from persons aware of their diagnoses, and collected after fasting or in surgery, whereas blood samples from some controls may be obtained in different conditions, such as a clinic visit. By measuring the effects of differences in collection conditions on three different markers, we investigated the potential of these effects to bias validation studies.We analyzed serum concentrations of three previously studied putative ovarian cancer serum biomarkers-CA 125, Prolactin and MIF-in healthy women, women with ovarian cancer undergoing gynecologic surgery, women undergoing surgery for benign ovary pathology, and women undergoing surgery with pathologically normal ovaries. For women undergoing surgery, a blood sample was collected either in the clinic 1 to 39 days prior to surgery, or on the day of surgery after anesthesia was administered but prior to the surgical procedure, or both. We found that one marker, prolactin, was dramatically affected by collection conditions, while CA 125 and MIF were unaffected. Prolactin levels were not different between case and control groups after accounting for the conditions of sample collection, suggesting that sample ascertainment could explain some or all of the previously reported results about its potential as a biomarker for ovarian cancer.Biomarker validation studies should use standardized collection conditions, use multiple control groups, and/or collect samples from cases prior to influence of diagnosis whenever feasible to detect and correct for potential biases associated with sample collection