Epigenetic changes enable genomes to respond to changes in the environment, such as altered nutrition, activity, or social setting. Epigenetic modifications, thereby, provide a source of phenotypic plasticity in many species. The honey bee (Apis mellifera) uses nutritionally sensitive epigenetic control mechanisms in the development of the royal caste (queens) and the workers. The workers are functionally sterile females that can take on a range of distinct physiological and/or behavioral phenotypes in response to environmental changes. Honey bees have a wide repertoire of epigenetic mechanisms which, as in mammals, include cytosine methylation, hydroxymethylated cytosines, together with the enzymatic machinery responsible for these cytosine modifications. Current data suggests that honey bees provide an excellent system for studying the “social repertoire” of the epigenome. In this review, we elucidate what is known so far about the honey bee epigenome and its mechanisms. Our discussion includes what may distinguish honey bees from other model animals, how the epigenome can influence worker behavioral task separation, and how future studies can answer central questions about the role of the epigenome in social behavior.

Alzheimer’s disease (AD) involves a gradual breakdown of brain connectivity, and network analyses offer a promising new approach to track and understand disease progression. Even so, our ability to detect degenerative changes in brain networks depends on the methods used. Here we compared several tractography and feature extraction methods to see which ones gave best diagnostic classification for 202 people with AD, mild cognitive impairment or normal cognition, scanned with 41-gradient diffusion-weighted magnetic resonance imaging as part of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project. We computed brain networks based on whole brain tractography with nine different methods – four of them tensor-based deterministic (FACT, RK2, SL, and TL), two orientation distribution function (ODF)-based deterministic (FACT, RK2), two ODF-based probabilistic approaches (Hough and PICo), and one “ball-and-stick” approach (Probtrackx). Brain networks derived from different tractography algorithms did not differ in terms of classification performance on ADNI, but performing principal components analysis on networks helped classification in some cases. Small differences may still be detectable in a truly vast cohort, but these experiments help assess the relative advantages of different tractography algorithms, and different post-processing choices, when used for classification.

Alzheimer's disease (AD) is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI), are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages of Alzheimer's disease. Here, we focused on brain structural networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying different stages of Alzheimer's disease.

Understanding the extent to which vascular disease and its risk factors are associated with prodromal dementia, notably Alzheimer's disease (AD), may enhance predictive accuracy as well as guide early interventions. One promising avenue to determine this relationship consists of looking for reliable and sensitive in-vivo imaging methods capable of characterizing the subtle brain alterations before the clinical manifestations. However, little is known from the imaging perspective about how risk factors such as vascular disease influence AD progression. Here, for the first time, we apply an innovative T1 and DTI fusion analysis of 3D corpus callosum (CC) on mild cognitive impairment (MCI) populations with different levels of vascular profile, aiming to de-couple the vascular factor in the prodromal AD stage. Our new fusion method successfully increases the detection power for differentiating MCI subjects with high from low vascular risk profiles, as well as from healthy controls. MCI subjects with high and low vascular risk profiles showed differed alteration patterns in the anterior CC, which may help to elucidate the inter-wired relationship between MCI and vascular risk factors.

Recent neuroimaging findings have highlighted the impact of premature birth on subcortical development and morphological changes in the deep grey nuclei and ventricular system. To help characterize subcortical microstructural changes in preterm neonates, we recently implemented a multivariate tensor-based method (mTBM). This method allows to precisely measure local surface deformation of brain structures in infants. Here, we investigated ventricular abnormalities and their spatial relationships with surrounding subcortical structures in preterm neonates. We performed regional group comparisons on the surface morphometry and relative position of the lateral ventricles between 19 full-term and 17 preterm born neonates at term-equivalent age. Furthermore, a relative pose analysis was used to detect individual differences in translation, rotation, and scale of a given brain structure with respect to an average. Our mTBM results revealed broad areas of alterations on the frontal horn and body of the left ventricle, and narrower areas of differences on the temporal horn of the right ventricle. A significant shift in the rotation of the left ventricle was also found in preterm neonates. Furthermore, we located significant correlations between morphology and pose parameters of the lateral ventricles and that of the putamen and thalamus. These results show that regional abnormalities on the surface and pose of the ventricles are also associated with alterations on the putamen and thalamus. The complementarity of the information provided by the surface and pose analysis may help to identify abnormal white and grey matter growth, hinting toward a pattern of neural and cellular dysmaturation.

In this paper, we develop a new automated surface registration system based on surface conformal parameterization by holomorphic 1-forms, inverse consistent surface fluid registration, and multivariate tensor-based morphometty (mTBM). First, we conformally map a surface onto a planar rectangle space with holomorphic 1-forms. Second, we compute surface conformal representation by combining its local conformal factor and mean curvature and linearly scale the dynamic range of the conformal representation to form the feature image of the surface. Third, we align the feature image with a chosen template image via the fluid image registration algorithm, which has been extended into the curvilinear coordinates to adjust for the distortion introduced by surface parameterization. The inverse consistent image registration algorithm is also incorporated in the system to jointly estimate the forward and inverse transformations between the study and template images. This alignment induces a corresponding deformation on the surface. We tested the system on Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset to study AD symptoms on hippocampus. In our system, by modeling a hippocampus as a 3D parametric surface, we nonlinearly registered each surface with a selected template surface. Then we used mTBM to analyze the morphometry difference between diagnostic groups. Experimental results show that the new system has better performance than two publicly available subcortical surface registration tools: FIRST and SPHARM. We also analyzed the genetic influence of the Apolipoprotein E(is an element of)4 allele (ApoE4), which is considered as the most prevalent risk factor for AD. Our work successfully detected statistically significant difference between ApoE4 carriers and non-carriers in both patients of mild cognitive impairment (MCI) and healthy control subjects. The results show evidence that the ApoE genotype may be associated with accelerated brain atrophy so that our work provides a new MRI analysis tool that may help presymptomatic AD research.

Honey bees as other insects rely on the innate immune system for protection against diseases. The innate immune system includes the circulating hemocytes (immune cells) that clear pathogens from hemolymph (blood) by phagocytosis, nodulation or encapsulation. Honey bee hemocyte numbers have been linked to hemolymph levels of vitellogenin. Vitellogenin is a multifunctional protein with immune-supportive functions identified in a range of species, including the honey bee. Hemocyte numbers can increase via mitosis, and this recruitment process can be important for immune system function and maintenance. Here, we tested if hemocyte mediated phagocytosis differs among the physiologically different honey bee worker castes (nurses, foragers and winter bees), and study possible interactions with vitellogenin and hemocyte recruitment. To this end, we adapted phagocytosis assays, which—together with confocal microscopy and flow cytometry—allow qualitative and quantitative assessment of hemocyte performance. We found that nurses are more efficient in phagocytic uptake than both foragers and winter bees. We detected vitellogenin within the hemocytes, and found that winter bees have the highest numbers of vitellogenin-positive hemocytes. Connections between phagocytosis, hemocyte-vitellogenin and mitosis were worker caste dependent. Our results demonstrate that the phagocytic performance of immune cells differs significantly between honey bee worker castes, and support increased immune competence in nurses as compared to forager bees. Our data, moreover, provides support for roles of vitellogenin in hemocyte activity.

Honey bees (Apis mellifera) provide a system for studying social and food-related behavior. A caste of workers performs age-related tasks: young bees (nurses) usually feed the brood and other adult bees inside the nest, while older bees (foragers) forage outside for pollen, a protein/lipid source, or nectar, a carbohydrate source. The workers' transition from nursing to foraging and their foraging preferences correlate with differences in gustatory perception, metabolic gene expression, and endocrine physiology including the endocrine factors vitellogenin (Vg) and juvenile hormone (JH). However, the understanding of connections among social behavior, energy metabolism, and endocrine factors is incomplete. We used RNA interference (RNAi) to perturb the gene network of Vg and JH to learn more about these connections through effects on gustation, gene transcripts, and physiology. The RNAi perturbation was achieved by single and double knockdown of the genes ultraspiracle (usp) and vg, which encode a putative JH receptor and Vg, respectively. The double knockdown enhanced gustatory perception and elevated hemolymph glucose, trehalose, and JH. We also observed transcriptional responses in insulin like peptide 1 (ilp1), the adipokinetic hormone receptor (AKHR), and cGMP-dependent protein kinase (PKG, or “foraging gene” Amfor). Our study demonstrates that the Vg–JH regulatory module controls changes in carbohydrate metabolism, but not lipid metabolism, when worker bees shift from nursing to foraging. The module is also placed upstream of ilp1, AKHR, and PKG for the first time. As insulin, adipokinetic hormone (AKH), and PKG pathways influence metabolism and gustation in many animals, we propose that honey bees have conserved pathways in carbohydrate metabolism and conserved connections between energy metabolism and gustatory perception. Thus, perhaps the bee can make general contributions to the understanding of food-related behavior and metabolic disorders.

Insect immune systems can recognize specific pathogens and prime offspring immunity. High specificity of immune priming can be achieved when insect females transfer immune elicitors into developing oocytes. The molecular mechanism behind this transfer has been a mystery. Here, we establish that the egg-yolk protein vitellogenin is the carrier of immune elicitors. Using the honey bee, Apis mellifera, model system, we demonstrate with microscopy and western blotting that vitellogenin binds to bacteria, both Paenibacillus larvae – the gram-positive bacterium causing American foulbrood disease – and to Escherichia coli that represents gram-negative bacteria. Next, we verify that vitellogenin binds to pathogen-associated molecular patterns; lipopolysaccharide, peptidoglycan and zymosan, using surface plasmon resonance. We document that vitellogenin is required for transport of cell-wall pieces of E. coli into eggs by imaging tissue sections. These experiments identify vitellogenin, which is distributed widely in oviparous species, as the carrier of immune-priming signals. This work reveals a molecular explanation for trans-generational immunity in insects and a previously undescribed role for vitellogenin.

The early indications of vitamin C deficiency are unremarkable (fatigue, malaise, depression) and may manifest as a reduced desire to be physically active; moreover, hypovitaminosis C may be associated with increased cold duration and severity. This study examined the impact of vitamin C on physical activity and respiratory tract infections during the peak of the cold season. Healthy non-smoking adult men (18–35 years; BMI <34 kg/m²; plasma vitamin C<45 µmol/L) received either 1000 mg of vitamin C daily (n = 15) or placebo (n = 13) in a randomized, double-blind, eight-week trial. All participants completed the Wisconsin Upper Respiratory Symptom Survey-21 daily and the Godin Leisure-Time Exercise Questionnaire weekly. In the final two weeks of the trial, the physical activity score rose modestly for the vitamin C group vs. placebo after adjusting for baseline values: +39.6% (95% CI [−4.5,83.7]; p = 0.10). The number of participants reporting cold episodes was 7 and 11 for the vitamin C and placebo groups respectively during the eight-week trial (RR = 0.55; 95% CI [0.33,0.94]; p = 0.04) and cold duration was reduced 59% in the vitamin C versus placebo groups (−3.2 days; 95% CI [−7.0,0.6]; p = 0.06). These data suggest measurable health advantages associated with vitamin C supplementation in a population with adequate-to-low vitamin C status.