Research items
I am a broadly trained biologist with 17+ years of experience in ecology, evolution, integrative organismal biology, bioinformatics, and next-gen sequencing analysis. My current research and interests revolve around bioinformatic characterization of genomic, transcriptomic, and epigenetic processes underlying organismal development across a diversity of biological phenomena, including development and disease.
Current institution
Vanderbilt University | Vander Bilt
Center for Quantitative Sciences
Current position
Statistical Geneticist II
Skills and Expertise
Aug 2007 - Aug 2013
The University of Arizona
Ecology and Evolutionary Biology
Aug 2003 - May 2007
DePauw University
Awards & Achievements
Grant · Feb 2016
A Critical Examination of the Model for Insect Body Size Determination: the Mechanisms of Body Size Variation in Bees
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Northern Arizona University
Northern Arizona University
University of Constantine 1
Indiana University School of Medicine
Chongqing Medical University
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Northern Arizona University
Northern Arizona University
University of California, Los Angeles
Indiana University School of Medicine
Shenzhen University
Projects (5)
Transfer learning for scRNA-Seq data integration with applications in human cancer
This project aims to utilize single cell RNA sequencing data in novel ways to study cellular heterogeneity. We propose that integrating single cell data from multiple sources and with other data modalities will provide new insights in the biology home human cellular heterogeneity. We develop feature selection, feature reduction, data substitution, and transfer learning methods to leverage the large amount of single cell data now available.
Developmental mechanisms shaping life history traits in solitary bees and honey bees
1. Characterize physiological regulators of larval and pupal development in multiple species of solitary bees. 2. Understand how environment (nutrition, temperature, stress) alter physiological regulators of development. 3. Connect developmental variation during larval and pupal stages with phenotypic outcomes in adult bees 4. Assess physiological, behavioral, and ecological effects of developmentally-induced variation in phenotypes of bee pollinators.
Dissertation Research: Physiology of Growth and Metamorphosis in Manduca sexta
Research Items (46)
Body size is an important phenotypic trait that correlates with performance and fitness. For determinate growing insects, body size variation is determined by growth rate and the mechanisms that stop growth at the end of juvenile growth. Endocrine mechanisms regulate growth cessation, and their relative timing along development shapes phenotypic variation in body size and development time. Larval insects are generally hypothesized to initiate metamorphosis once they attain a critical weight. However, the mechanisms underlying the critical weight have not been resolved even for well-studied insect species. More importantly, critical weights may or may not be generalizable across species. In this study, we characterized the developmental aspects of size regulation in the solitary bee, Osmia lignaria We demonstrate that starvation cues metamorphosis in O. lignaria and that a critical weight does not exist in this species. Larvae initiated pupation <24 h after food was absent. However, even larvae fed ad libitum eventually underwent metamorphosis, suggesting that some secondary mechanism regulates metamorphosis when provisions are not completely consumed. We show that metamorphosis could be induced by precocene treatment in the presence of food, which suggests that this decision is regulated through juvenile hormone signaling. Removing food at different larval masses produced a 10-fold difference in mass between smallest and largest adults. We discuss the implications of body size variation for insect species that are provided with a fixed quantity of provisions, including many bees which have economic value as pollinators.
In holometabolous insects, larval nutrition affects adult body size, a life history trait with a profound influence on performance and fitness. Individual nutritional components of larval diets are often complex and may interact with one another, necessitating the use of a geometric framework for elucidating nutritional effects. In the honey bee, Apis mellifera, nurse bees provision food to developing larvae, directly moderating growth rates and caste development. However, the eusocial nature of honey bees makes nutritional studies challenging, because diet components cannot be systematically manipulated in the hive. Using in vitro rearing, we investigated the roles and interactions between carbohydrate and protein content on larval survival, growth, and development in A. mellifera We applied a geometric framework to determine how these two nutritional components interact across nine artificial diets. Honey bees successfully completed larval development under a wide range of protein and carbohydrate contents, with the medium protein (∼5%) diet having the highest survival. Protein and carbohydrate both had significant and non-linear effects on growth rate, with the highest growth rates observed on a medium-protein, low-carbohydrate diet. Diet composition did not have a statistically significant effect on development time. These results confirm previous findings that protein and carbohydrate content affect the growth of A. mellifera larvae. However, this study identified an interaction between carbohydrate and protein content that indicates a low-protein, high-carb diet has a negative effect on larval growth and survival. These results imply that worker recruitment in the hive would decline under low protein conditions, even when nectar abundance or honey stores are sufficient.
Life histories, the demographic patterns of the life cycle that make up growth, maturity, reproduction, and survival, are the basis of our understanding of how organisms cope with their environments and how populations and species evolve. This chapter aims to use the Manduca model to build a more general framework for the regulation of body size that is applicable across taxa. Overall, it aims to show that such a framework is possible and that it can generate mechanistic insight into the ecology, evolution and constraints on major life history traits in a broad range of taxa. That is, the framework allows us to step away from system-specific detail (the “trees”) to understand general life history phenomena (the “forest”). The general framework for the regulation of growth and size has four components, which, in ontogenetic order, are: the decision point; the terminal growth period (TGP); the cessation of growth; growth rate.
Organisms must accommodate oxygen delivery to developing tissues as body mass increases during growth. In insects, the growth of the respiratory system has been assumed to occur only when it molts, whereas body mass and volume increase during the larval stages between molts. This decouples whole body growth from the growth of the oxygen supply system. This assumption is derived from the observation that the insect respiratory system is an invagination of the exoskeleton, which must be shed during molts for continued growth to occur. Here, we provide evidence that this assumption is incorrect. We found that the respiratory system increases substantially in both mass and volume within the last larval instar of Manduca sexta larvae, and that the growth of the respiratory system changes with diet quality, potentially as a consequence of shifting metabolic demands.
Behavioral Syndromes occur when behaviors are correlated together. Studying behavioral syndromes allows a more integrated view of behavior by recognizing that behaviors often don’t operate independently from one another and considers sources of behavioral variation both within and among individuals. The alfalfa leaf cutter bee, Megachile rotundata, constructs nests that require gathering leaf materials to form a linear series of cells, foraging for provisions, and making reproductive choices including offspring number, provisioning, and sex. Thus, nest construction may be an example of a behavioral syndrome that could be examined by measuring the architecture and composition of each nest. Our aim was to observe within and among-individual variation in multiple, distinct nest-constructing behaviors by examining features of M. rotundata nest architecture. We successfully identify three behavioral modules that constitute a nesting behavioral syndrome: nest protection, leaf foraging, and pollen/nectar provisioning. As an example, we found that the number of leaves invested into each nest cell is uncorrelated to the cell provisioning. This suggests that cell leaf foraging and provision foraging are independent behavioral components. Second, our results indicate that individual differences in nest-constructing behaviors account for 30% of the total phenotypic variation observed in the population and that individual females are tasked with tradeoff choices when expressing different behaviors. Thus, examining the architecture of nests can serve as an enlightening proxy for characterizing behaviors directing nest construction.
Variation in body size has important implications for physical performance and fitness. For insects, adult size and morphology are determined by larval growth and metamorphosis. Female blue orchard bees, Osmia lignaria, (Say) provision a finite quantity of food to their offspring. In this study, we asked how provision-dependent variation in size changes adult morphology. We performed a diet manipulation in which some larvae were starved in the final instar and some were given unlimited food. We examined the consequences on adult morphology in two ways. First, allometric relationships between major body regions (head, thorax, abdomen) and total body mass were measured to determine relative growth of these structures. Second, morphometrics that are critical for flight (wing area, wing loading, and extra flight power index) were quantified. Head and thorax mass had hyperallometric relationships with body size, indicating these parts become disproportionately large in adults when larvae are given copious provisions. However, abdominal mass and wing area increased hypoallometrically with body size. Thus, large adults had disproportionately lighter abdomens and smaller wing areas than smaller adults. Though both males and females followed these general patterns, allometric patterns were affected by sex. For flight metrics, small adults had reduced wing loading and an increased extra flight power index. These results suggest that diet quantity alters development in ways that affect the morphometric trait relationships in adult O. lignaria and may lead to functional differences in performance.
Background and Hypothesis: The objective of this study was to analyze available whole genome sequencing from an adolescent male patient diagnosed with osteosarcoma (OS) in 2014. OS is a primary bone malignancy that most commonly affects the pediatric population. Precision medicine techniques provide new opportunities to improve treatment of OS patients. Pharmaceutical annotation tools such as PharmacoDB and DGIdb can help indicate chemotherapy agents that may benefit patients based on their molecular profiles. We hypothesize that these tools can indicate genome-specific chemotherapy agents for OS after genomic data has been aligned and analyzed. Project Methods: A PDX pipeline and retrospective study were performed that identified and compared pharmaceutical treatment options from software tools with the chemotherapy provided. Gene alignment and variant calling were used to process and analyze DNA sequencing data; germline and somatic mutations were also identified. Ensembl VEP was used for variant annotation. PharmacoDB and DGIdb were then applied to identify potentially beneficial medications. Results: Gene variant annotation indicated 54 potentially high impact mutations. Of these, DGIdb identified 15 drug-gene interactions. PharmacoDB identified no drugs that target any of the genes containing the 54 high impact mutations. For the entire mutated gene list, DGIdb identified 398 drug-gene interactions. After gene set enrichment, DGIdb identified medications targeting genes of pathways such as “O-glycan processing” and “Diseases of glycosylation”. Potentially harmful variants in the NPRL3 gene were identified. Because NPRL3 is a component of the Gator1 complex that serves as a negative regulator of mammalian target of rapamycin complex 1 (mTORC1), the identified variants in NPRL3 could have played a role in the patient’s OS. Potential Impact: This study will foster future collaborations to evaluate the pharmaceutical tool recommendations for this patient’s derived cell lines. These efforts will determine the efficacy of and identify improvements for computational treatment recommendation systems.
Single-cell RNA sequencing (scRNA-seq) resolves heterogenous cell populations in tissues and helps to reveal single-cell level function and dynamics. In neuroscience, the rarity of brain tissue is the bottleneck for such study. Evidence shows that, mouse and human share similar cell type gene markers. We hypothesized that the scRNA-seq data of mouse brain tissue can be used to complete human data to infer cell type composition in human samples. Here, we supplement cell type information of human scRNA-seq data, with mouse. The resulted data were used to infer the spatial cellular composition of 3702 human brain samples from Allen Human Brain Atlas. We then mapped the cell types back to corresponding brain regions. Most cell types were localized to the correct regions. We also compare the mapping results to those derived from neuronal nuclei locations. They were consistent after accounting for changes in neural connectivity between regions. Furthermore, we applied this approach on Alzheimer’s brain data and successfully captured cell pattern changes in AD brains. We believe this integrative approach can solve the sample rarity issue in the neuroscience.
Background: Recent advances in kernel-based Deep Learning models have introduced a new era in medical research. Originally designed for pattern recognition and image processing, Deep Learning models are now applied to survival prognosis of cancer patients. Specifically, Deep Learning versions of the Cox proportional hazards models are trained with transcriptomic data to predict survival outcomes in cancer patients. Methods: In this study, a broad analysis was performed on TCGA cancers using a variety of Deep Learning-based models, including Cox-nnet, DeepSurv, and a method proposed by our group named AECOX (AutoEncoder with Cox regression network). Concordance index and p-value of the log-rank test are used to evaluate the model performances. Results: All models show competitive results across 12 cancer types. The last hidden layers of the Deep Learning approaches are lower dimensional representations of the input data that can be used for feature reduction and visualization. Furthermore, the prognosis performances reveal a negative correlation between model accuracy, overall survival time statistics, and tumor mutation burden (TMB), suggesting an association among overall survival time, TMB, and prognosis prediction accuracy. Conclusions: Deep Learning based algorithms demonstrate superior performances than traditional machine learning based models. The cancer prognosis results measured in concordance index are indistinguishable across models while are highly variable across cancers. These findings shedding some light into the relationships between patient characteristics and survival learnability on a pan-cancer level.
I recently transitioned from empirical lab-based science into bioinformatics, and I can report that the mathematicians are well ahead of everyone else on the issue. I have been overwhelmed by the number of ways that different mathematical frameworks may be implemented in complex data (and are not necessarily reliant on p-value thresholds to extract meaningful insights--though many do use p-value inference for familiarity). Classic statistics are just one of many tools that may be applied to modern data problems--especially as computing becomes more powerful and easier. For example, I've learned that many questions may be addressed with alternative probability theory (Bayes), graph theory (networks), linear algebra (matrix math), model building for prediction/classification (machine learning), and/or information theory. Sometimes combinations of different analytical frameworks can help build or refute confidence in the trends/patterns. While the very advanced methods (deep machine learning) are necessary for some big data problems, the components/ideas that are built into these tools are often very powerful by themselves--and for smaller datasets! If anything, I'm hoping that these methods are adopted more broadly so that the language with which we ask questions and infer patterns from our data becomes more refined (beyond "different than expected from chance, not different").
Rhabdomyosarcoma is subclassified by the presence or absence of a recurrent chromosome translocation that fuses the FOXO1 and PAX3 or PAX7 genes. The fusion protein (FOXO1-PAX3/7) retains both binding domains and becomes a novel and potent transcriptional regulator in rhabdomyosarcoma subtypes. Many studies have characterized and integrated genomic, transcriptomic, and epigenomic differences among rhabdomyosarcoma subtypes that contain the FOXO1-PAX3/7 gene fusion and those that do not; however, few investigations have investigated how gene co-expression networks are altered by FOXO1-PAX3/7. Although transcriptional data offer insight into one level of functional regulation, gene co-expression networks have the potential to identify biological interactions and pathways that underpin oncogenesis and tumorigenicity. Thus, we examined gene co-expression networks for rhabdomyosarcoma that were FOXO1-PAX3 positive, FOXO1-PAX7 positive, or fusion negative. Gene co-expression networks were mined using local maximum Quasi-Clique Merger (lmQCM) and analyzed for co-expression differences among rhabdomyosarcoma subtypes. This analysis observed 41 co-expression modules that were shared between fusion negative and positive samples, of which 17/41 showed significant up- or down-regulation in respect to fusion status. Fusion positive and negative rhabdomyosarcoma showed differing modularity of co-expression networks with fusion negative (n = 109) having significantly more individual modules than fusion positive (n = 53). Subsequent analysis of gene co-expression networks for PAX3 and PAX7 type fusions observed 17/53 were differentially expressed between the two subtypes. Gene list enrichment analysis found that gene ontology terms were poorly matched with biological processes and molecular function for most co-expression modules identified in this study; however, co-expressed modules were frequently localized to cytobands on chromosomes 8 and 11. Overall, we observed substantial restructuring of co-expression networks relative to fusion status and fusion type in rhabdomyosarcoma and identified previously overlooked genes and pathways that may be targeted in this pernicious disease.
Abstract To fully utilize the power of single-cell RNA sequencing (scRNA-seq) technologies for identifying cell lineages and bona fide transcriptional signals, it is necessary to combine data from multiple experiments. We present BERMUDA (Batch Effect ReMoval Using Deep Autoencoders), a novel transfer-learning-based method for batch effect correction in scRNA-seq data. BERMUDA effectively combines different batches of scRNA-seq data with vastly different cell population compositions and amplifies biological signals by transferring information among batches. We demonstrate that BERMUDA outperforms existing methods for removing batch effects and distinguishing cell types in multiple simulated and real scRNA-seq datasets.
Tumors are heterogeneous tissues with different types of cells such as cancer cells, fibroblasts, and lymphocytes. Although the morphological features of tumors are critical for cancer diagnosis and prognosis, the underlying molecular events and genes for tumor morphology are far from being clear. With the advancement in computational pathology and accumulation of large amount of cancer samples with matched molecular and histopathology data, researchers can carry out integrative analysis to investigate this issue. In this study, we systematically examine the relationships between morphological features and various molecular data in breast cancers. Specifically, we identified 73 breast cancer patients from the TCGA and CPTAC projects matched whole slide images, RNA-seq, and proteomic data. By calculating 100 different morphological features and correlating them with the transcriptomic and proteomic data, we inferred four major biological processes associated with various interpretable morphological features. These processes include metabolism, cell cycle, immune response, and extracellular matrix development, which are all hallmarks of cancers and the associated morphological features are related to area, density, and shapes of epithelial cells, fibroblasts, and lymphocytes. In addition, protein specific biological processes were inferred solely from proteomic data, suggesting the importance of proteomic data in obtaining a holistic understanding of the molecular basis for tumor tissue morphology. Furthermore, survival analysis yielded specific morphological features related to patient prognosis, which have a strong association with important molecular events based on our analysis. Overall, our study demonstrated the power for integrating multiple types of biological data for cancer samples in generating new hypothesis as well as identifying potential biomarkers predicting patient outcome. Future work includes causal analysis to identify key regulators for cancer tissue development and validating the findings using more independent data sets.
Co-expression analysis of FOXO1-PAX3/7 fusion positive and fusion negative alveolar rhabdomyosarcomas
Improved cancer prognosis is a central goal for precision health medicine. Though many models can predict differential survival from data, there is a strong need for sophisticated algorithms that can aggregate and filter relevant predictors from increasingly complex data inputs. In turn, these models should provide deeper insight into which types of data are most relevant to improve prognosis. Deep Learning-based neural networks offer a potential solution for both problems because they are highly flexible and account for data complexity in a non-linear fashion. In this study, we implement Deep Learning-based networks to determine how gene expression data predicts Cox regression survival in breast cancer. We accomplish this through an algorithm called SALMON (Survival Analysis Learning with Multi-Omics Neural Networks), which aggregates and simplifies gene expression data and cancer biomarkers to enable prognosis prediction. The results revealed improved performance when more omics data were used in model construction. Rather than use raw gene expression values as model inputs, we innovatively use eigengene modules from the result of gene co-expression network analysis. The corresponding high impact co-expression modules and other omics data are identified by feature selection technique, then examined by conducting enrichment analysis and exploiting biological functions, escalated the interpretation of input feature from gene level to co-expression modules level. Our study shows the feasibility of discovering breast cancer related co-expression modules, sketch a blueprint of future endeavors on Deep Learning-based survival analysis. SALMON source code is available at https://github.com/huangzhii/SALMON/.
Technical advances have enabled the identification of high-resolution cell types within tissues based on single-cell transcriptomics. However, such analyses are restricted in human brain tissue due to the limited number of brain donors. In this study, we integrate mouse and human data to predict cell-type proportions in human brain tissue, and spatially map the resulting cellular composition. By applying feature selection and linear modeling, combinations of human and mouse brain single-cell transcriptomics profiles can be integrated to "fill in" missing information. These combined "in silico chimeric" datasets are used to model the composition of nine cell types in 3,702 human brain samples in six Allen Human Brain Atlas (AHBA) donors. Cell types were spatially consistent regardless of the scRNA-Seq dataset (91% significantly correlated) or AHBA donor (p-value = 4.43E-20 by t-test) used in the model. Importantly, neuron nuclei location and neuron mRNA location were correlated only after accounting for neural connectivity (p-value = 1.26E-10), which supports the notion that gene expression is a better indicator than nuclei location of cellular localization for cells with large and irregularly shaped cell bodies, such as neurons. These results advocate for the integration of mouse and human data in models of brain tissue heterogeneity.
Insect metamorphosis involves a complex change in form and function. In this study, we examined development of the solitary bee, Megachile rotundata, using micro-computed tomography (μCT) and volume analysis. We describe volumetric changes of brain, tracheae, flight muscles, gut, and fat bodies in prepupal, pupal, and adult M. rotundata. We observed that individual organ systems have distinct patterns of developmental progression, which vary in their timing and duration. This has important implications for commercial management of this agriculturally relevant pollinator.
Structures such as nests and burrows are an essential component of many organisms’ life-cycle and requires a complex sequence of behaviors. Because behaviors can vary consistently among individuals and be correlated with one another, we hypothesized that these structures would 1) show evidence of among-individual variation, 2) be organized into distinct functional modules, and 3) show evidence of trade-offs among functional modules due to limits on energy budgets. We tested these hypotheses using the alfalfa leafcutting bee, Megachile rotundata, a solitary bee and important crop pollinator. M. rotundata constructs complex nests by gathering leaf materials to form a linear series of cells in pre-existing cavities. In this study, we examined variation in the following nest construction traits: reproduction (number of cells per nest and nest length), nest protection (cap length and number of leaves per cap), cell construction (cell size and number of leaves per cell), and cell provisioning (cell mass) from 60 nests. We found a general decline in investment in cell construction and provisioning with each new cell built. In addition, we found evidence for both repeatability and plasticity in cell provisioning with little evidence for trade-offs among traits. Instead, most traits were positively, albeit weakly, correlated (r ~ 0.15), and traits were loosely organized into covarying modules. Our results show that individual differences in nest construction are detectable at a level similar to that of other behavioral traits and that these traits are only weakly integrated. This suggests that nest components are capable of independent evolutionary trajectories.
Insects—especially holometabolous—undergo a complex metamorphosis in form and function from the immature to mature stage of their life cycle. Physiologically, metamorphosis is regulated by hormones, primarily juvenile hormone and ecdysone, which control different aspects of the metamorphic processes. However, much of our understanding of metamorphosis is based upon studies focusing on just a few model organisms, and connections between the physiological dynamics and their underlying molecular mechanisms remain poorly described. Here, we simultaneously characterize the developmental physiology and corresponding molecular mechanisms of larval to adult metamorphosis in the alfalfa leaf cutter bee, Megachile rotundata. We measured the hemolymph titer of juvenile hormone III (JHIII) using a recently established HPLC-MSMS protocol. From these same individuals, we quantified the expression of genes that regulate JHIII synthesis, degradation, and reception in target tissues. While we did not directly assay ecdysone quantities in hemolymph for this study, we quantified expression of genes that regulate its synthesis and reception. This research integrates molecular mechanisms with overarching patterns in hormones controlling insect metamorphosis.
Species-specific biochemistry, morphology, and function of the Dufour’s gland have been investigated for social bees and some non-social bee families. Most of the solitary bees previously examined are ground-nesting bees that use Dufour’s gland secretions to line brood chambers. This study examines the chemistry of the cuticle and Dufour’s gland of cavity-nesting Megachile rotundata and Osmia lignaria, which are species managed for crop pollination. Glandular and cuticular lipid compositions were characterized and compared to each other and according to the nesting experience of adult females. Major lipid classes found were hydrocarbons, free fatty acids, and wax esters. Many components were common to the cuticle and Dufour’s glands of each species, yet not identical in number or relative composition. Wax esters and fatty acids were more prevalent in Dufour’s glands of M. rotundata than on cuticles. Wax esters were more abundant on cuticles of O. lignaria than in Dufour’s glands. In both species, fatty acids were more prevalent in glands of field-collected females compared to any other sample type. Chemical profiles of cuticles and glands were distinct from each other, and, for O. lignaria, profiles of laboratory-maintained bees could be distinguished from those of field-collected bees. Comparison of percentiles of individual components of cuticular and glandular profiles of the same bee showed that the proportions of some cuticular components were predictive of the proportion of the same glandular components, especially for nesting females. Lastly, evidence suggested that Dufour’s gland is the major source of nest-marking substances in M. rotundata, but evidence for this role in O. lignaria was less conclusive. Electronic supplementary material The online version of this article (doi:10.1007/s10886-017-0844-x) contains supplementary material, which is available to authorized users.
Body size is an important trait because it strongly correlates with morphology, performance, and fitness. In insects, the body size model argues that adult size is determined during the larval stage of life—by the mechanisms regulating growth rate and the duration of growth. Though explicit links have been drawn between larval growth and adult size variation, few studies have examined how changes in size affect adult morphology and performance. In this study, we asked how altering larval growth impacts adult morphology and performance in the solitary bee pollinator, Osmia lignaria. We manipulated the duration of larval growth by the altering food provisions during larval development. This induced twice the variation in body size as is observed in natural populations with more than a 10-fold difference between smallest and largest adult bees. The impact of altered larval development on adult morphology was evaluated by examining allometric relationships between body size and head, thorax, and abdominal masses. We also examined how flight morphometrics varied with body size by evaluating wing loading and a flight power index. O. lignaria thoraces increased hypermetrically with body mass, but head and abdomens increased isometrically. Wing loading decreased with increasing adult mass, suggesting that flight was more energetically demanding in larger bees; however, the flight power index remained similar across different body sizes, indicating that increased thoracic investment offset challenges in flight for larger bees. These scaling relationships are consistent with the ecology of O. lignaria because reproductive performance is limited provisioning of nests rather than egg production.
The insect body size model posits that adult size is determined by growth rate and the duration of growth during the larval stage of development. Within the model, growth rate is regulated by many mechanistic elements that are influenced by both internal and external factors. However, the duration of growth is regulated by the physiological processes underlying metamorphosis—attainment of a critical weight, a terminal growth period, and finally cessation of growth itself. While the hormonal dynamics that regulate the terminal growth period and cessation of growth are well-understood, the mechanistic basis of the critical weight has remained elusive. More importantly, the body size model is based almost entirely on a few insect species, and its applicability for other insects is still an open question. In this study, we aimed to characterize the critical weight in the solitary bee, Osmia lignaria. In doing so, we found that this species does not have a “critical weight” per se, but rather uses food provisioning and its absence as a strong cue for metamorphic commitment. Individuals that are provisioned with an excess of larval provisions still eventually undergo metamorphosis—although after considerable delay—suggesting that there may be multiple cues that can trigger a critical weight decision. Finally, our study showed that nearly all (~90%) of variation in adult size for O. lignaria was determined by the relative timing of growth cessation during larval development. Thus, the duration of larval growth had profound impacts on adult size.
Honey should be relatively simple, depending on which nutritional elements you are interested in assessing, and there are many approaches/assays that could quantify carbohydrate contents. Many papers have quantified nutrients and minerals in honey that may have good methodological references. A quick google search brought up many papers like this: http://www.aqa.org.ar/pdf9612/9612art4.pdf General methodologies for quantifying nutrients and minerals for honey would probably also work for bee bread. Oyster tissues seem like a really strange reference. None of the honey nutrient papers I have read used anything in reference to oyster. Are you referring to the standards against which you will test your unknowns in a chemical assay? 
Nutrition is of central importance for the overall health and physiology of developing organisms. Insects are particularly responsive to nutritional status during development because all growth occurs during the larval stage of life, and more importantly nutritional reserves are acquired that will be used during metamorphosis. As a consequence larval nutrition can have direct positive or negative effects on adult phenotypes. Dietary provisions in larval honey bees, Apis mellifera, are quite unique because workers provision brood entirely with secretions, called jelly. As a consequence it is difficult to ascertain the effects of larval nutritive components on development performance and adult phenotypes. In this study, we implemented an in vitro rearing method to ascertain the effects of nutrition on larval growth rate, development rate and survival. Guided by the geometric framework for nutrition, we systematically varied the relative jelly, carbohydrate, and water contents of larval diet while monitoring growth and development. In summary, we observed survival and growth rate differences among our altered larval diets; however, we did not observe differences in development time. In greater detail, our results show the direct implications different nutritive components on the physiology of developing larval honeybees.
The contents of the Dufour’s gland of the solitary bees Megachile rotundata and Osmia lignaria are suspected to be the source of individual nest recognition cues applied to the inner walls of nest cavities. Furthermore, in bumble bees, the chemistry of the Dufour’s gland has been shown to match that of the cuticle. Field- and lab-reared female bees were freezer-killed. For each bee, we solvent-extracted first the cuticular components and next the Dufour’s gland contents for analysis with gas chromatography coupled with flame ionization detection and also with mass spectrometry. Therefore, we obtained the chemical profile (percent composition) of each individual bees’ cuticle and Dufour’s gland. Using principal components analyses for each species, we searched for factors that discriminate between the chemical compositions of 1) field and lab-reared bees and of 2) cuticles and glands. We also explored the level of unique matching of an individual bee cuticle with her Dufour’s gland.
Pupal development is particularly sensitive to environmental perturbations, because the insect is immobile while physiological systems essential for the adult are constructed from larval and imaginal tissues. Severe environmental fluctuations during the pupal period could disrupt development, resulting in a negative impact on adult performance. In the alfalfa leaf-cutting bee, Megachile rotundata, exposure to cold temperatures during pupal development causes sub-lethal effects on adult performance, including flight deficiencies, altered metabolic rates, behavioral changes, and decreased longevity; however, the mechanisms underlying these effects are not known. Disruption of one or more developing systems may be responsible for the deleterious effects seen in adults after pupae were exposed to cold temperatures; however the internal changes associated with pupation are a “black box.” We used μCT to investigate the development of tracheae, flight structures, metabolic reserves, and the digestive tract during M. rotundatapupal development. Pupal development occurs over three weeks, during which time we were able to clearly see development of these structures. Gut and flight structures appear to change early during the pupal period. However metabolic reserves may decrease only slightly throughout pupal development, and tracheae supplying these tissues remain fairly constant in form. These data will allow us to better understand what happens during metamorphosis, and to make comparisons about how these systems are altered after cold exposure.
The scaling laws governing metabolism suggest that we can predict metabolic rates across taxonomic scales that span large differences in mass. Yet, scaling relationships can vary with development, body region, and environment. Within species, there is variation in metabolic rate that is independent of mass and which may be explained by genetic variation, the environment or their interaction (i.e., metabolic plasticity). Additionally, some structures, such as the insect tracheal respiratory system, change throughout development and in response to the environment to match the changing functional requirements of the organism. We discuss how study of the development of respiratory function meets multiple challenges set forth by the NSF Grand Challenges Workshop. Development of the structure and function of respiratory and metabolic systems (1) is inherently stable and yet can respond dynamically to change, (2) is plastic and exhibits sensitivity to environments, and (3) can be examined across multiple scales in time and space. Predicting respiratory performance and plasticity requires quantitative models that integrate information across scales of function from the expression of metabolic genes and mitochondrial biogenesis to the building of respiratory structures. We present insect models where data are available on the development of the tracheal respiratory system and of metabolic physiology and suggest what is needed to develop predictive models. Incorporating quantitative genetic data will enable mapping of genetic and genetic-by-environment variation onto phenotypes, which is necessary to understand the evolution of respiratory and metabolic systems and their ability to enable respiratory homeostasis as organisms walk the tightrope between stability and change.
Many insects are tolerant of hypoxic conditions, but survival may come at a cost to long-term health. The alfalfa leaf-cutting bee, Megachile rotundata, develops in brood cells inside natural cavities, and may be exposed to hypoxic conditions for extended periods of time. Whether M. rotundata is tolerant of hypoxia, and whether exposure results in sub-lethal effects, has never been investigated. Overwintering M. rotundata prepupae were exposed to 10%, 13%, 17%, 21% and 24% O2 for 11 months. Once adults emerged, five indicators of quality — emergence weight, body size, feeding activity, flight performance, and adult longevity, — were measured to determine whether adult bees that survived past exposure to hypoxia were competent pollinators. M. rotundata prepupae are tolerant of hypoxic condition and have higher survival rates in hypoxia, than in normoxia. Under hypoxia, adult emergence rates did not decrease over the 11 months of the experiment. In contrast, bees reared in normoxia had decreased emergence rates by 8 months, and were dead by 11 months. M. rotundata prepupae exposed to extended hypoxic conditions had similar emergence weight, head width, and cross-thorax distance compared to bees reared in standard 21% oxygen. Despite no significant morphological differences, hypoxia-exposed bees had lower feeding rates and shorter adult lifespans. Hypoxia may play a role in post-diapause physiology of M. rotundata, with prepupae showing better survival under hypoxic conditions. Extended exposure to hypoxia, while not fatal, causes sub-lethal effects in feeding rates and longevity in the adults, indicating that hypoxia tolerance comes at a cost.
Juvenile organisms must invest incoming nutritional resources among growth, storage and maintenance, and tradeoffs among these traits will occur because incoming resources are limited. As a consequence, resources must be allocated so that survival and future reproduction are optimized both within a single growth trajectory and in light of environmental conditions experienced. In this study, we asked how the relationships among these traits change during ontogeny and in response to different environmental conditions. We examined growth rate, storage rate, and resting metabolic rate relative to increasing body size in last instar larvae of the tobacco hornworm, Manduca sexta. We measured these traits in individuals that had been reared at 30C, 25C, or 20C and on either a standard high quality or reduced low quality artificial diet. We found that the tradeoffs among these traits varied along ontogeny as the organism grew. During early growth of the last instar, growth rate, storage rate and metabolic rate were positively correlated. However, growth rate declined approximately half-way through the instar and became negatively correlated with increasing storage and metabolic rates. Environmental conditions affected growth, storage and metabolic rate, but the relationships among traits shared a common pattern across environmental conditions. We conclude that dynamic changes in the allocation scheme between these traits reflect evolved strategies for optimizing survival and future reproduction based on both developmental status and varying environmental conditions.
We use a common currency, calories, to examine relative investment among body parts (head, thorax, legs, wings, abdomen) within individuals as a function of body size, diet quality, and sex, in the hawkmoth Manduca sexta. 50-90 moths per sex, per diet (478 total), were disarticulated and each body part dry weighed. Separate calibration curves for the caloric content of each body part, diet, and sex were generated using a bomb calorimeter. These calibration curves were used to calculate the predicted caloric content of each of the 5 body parts in the 478 moths. As expected, moths invested more into individual body parts as diet quality increased. Contrary to our prediction, however, males and females allocated resources very differently when diet quality varied. For example, mass-specific (cal/g) allocation to the thorax in females increased with thorax size on high quality diet but decreased on low quality diets. This trend was opposite in males: as thoraces increased in size, cal/g decreased on high quality diet but increased on low. To our surprise, tradeoffs in caloric investment were seen only between the abdomen and other parts. The head, thorax, wings and legs did not trade off with each other in caloric content. These patterns of tradeoffs held for both sexes and all diets. Together these results show that males and females have different rules of mass-specific caloric investment into individual body parts when diet quality varies. These results help explain differences in flight performance between males and females, but further implications of these different allocation rules require further study.
The scaling laws governing metabolism suggest that we can predict metabolic traits across taxonomic scales that span large differences in mass. Yet, scaling relationships can vary with development, body region, and environment. Within species, there is variation in metabolic rate that is independent of mass and which may be explained by genetic variation, the environment or their interaction (i.e., metabolic plasticity). Additionally, some structures, such as the insect tracheal respiratory system, change throughout development and in response to the environment to match the changing functional requirements of the organism. We discuss how study of the development of respiratory function meets multiple challenges set forth by the NSF Grand Challenges Workshop. Development of respiratory system structure and function 1) is inherently stable and yet can respond dynamically to change, 2) is plastic and exhibits sensitivity to environments, and 3) can be examined across multiple scales in time and space. Predicting respiratory performance and plasticity requires quantitative models that integrate information across scales of function from metabolic gene expression and mitochondrial biogenesis to the building of respiratory structures. We present insect models where data are available on the development of the tracheal respiratory system and of metabolic physiology and suggest what is needed to develop predictive models. Incorporating quantitative genetic data will enable mapping of genetic and genetic-by-environment variation onto phenotypes, which is necessary to understand the evolution of respiratory systems and their ability to enable respiratory homeostasis as organisms walk the tightrope between stability and change.
The onset of metamorphosis in insects occurs in close correlation with the attainment of a critical weight late in larval growth. At this size, time to metamorphosis becomes fixed irrespective of continued feeding and growth because the hormonal signaling that commits the larva to metamorphosis is irreversibly initiated. Larval resource acquisition and storage are an important component of successful metamorphosis and reproductive provisioning in the adult stage of life. This suggests that resource accumulation during the larval phase should be a critical factor in determining when, and at what size, commitment to metamorphosis occurs. Our study addresses the question, “What role does larval resource storage play in attainment of the critical weight?” We examine resource accumulation by quantifying the growth, chemical composition, and caloric content of the fat body in growth-phase 5th instar Manduca sexta larvae reared on five environmental treatments of diet quality and temperature. We summarize our findings within the context of attainment of the critical weight.
Within the life history of a single organism, the onset of maturation marks a significant shift from the juvenile stage of life to the reproductive adult form. For a wide taxonomic sampling of organisms, this transition has been found to occur upon the attainment of a critical threshhold size near the end of larval growth after which hormonal and developmental mechanisms control maturation to the adult form. The internal physiological conditions that are assessed at the critical weight are, however, unresolved. An implicit assumption of life history theory is that the role of the juvenile stage acquire resources for growth and development. This pattern and theory lead to the hypothesis that the critical weight is triggered by the attainment of a resource threshold late in larval development. In this study, we use the model organism Manduca sexta to test two key predictions derived from this hypothesis: first, experimental augmentation of resources should move the critical weight forward in ontogeny, and 2.) environmentally-induced plasticity of the critical weight can be explained by differential rates of resource allocation to storage.
In some group-living organisms, labor is divided among individuals. This allocation to particular tasks is frequently stable and predicted by individual physiology. Social insects are excellent model organisms in which to investigate the interplay between physiology and individual behavior, as division of labor is an important feature within colonies, and individual physiology varies among the highly related individuals of the colony. Previous studies have investigated what factors are important in determining how likely an individual is, compared to nestmates, to perform certain tasks. One such task is foraging. Corpulence (i.e., percent lipid) has been shown to determine foraging propensity in honey bees and ants, with leaner individuals being more likely to be foragers. Is this a general trend across all social insects? Here we report data analyzing the individual physiology, specifically the percent lipid, of worker bumble bees (Bombus impatiens) from whom we also analyze behavioral task data. Bumble bees are also unusual among the social bees in that workers may vary widely in size. Surprisingly we find that, unlike other social insects, percent lipid is not associated with task propensity. Rather, body size closely predicts individual relative lipid stores, with smaller worker bees being allometrically fatter than larger worker bees.
In insects, oxygen is supplied directly to tissues through a network of branching trachea and tracheoles: tubules that branch inward from openings along the side of the body. The tracheal system is lined with the same hardened cuticle as the insect exoskeleton. Larval structures composed of cuticle grow when hardened cuticle is shed during molts. As such, it has long been assumed that tracheal growth can only occur during molts. However, some larval insects undergo ten-fold increases in size within a single larval instar, potentially stunting larval growth and development because of an inadequate oxygen supply. An unresolved question is how are these quickly growing larval insects able to adequately supply their tissues with oxygen, despite a tracheal system that presumably must be molted to grow larger? In this study, we demonstrate that tracheal systems grow an order of magnitude during the last larval instar in the tobacco hornworm, Manduca sexta, and that the tracheal mass relative to body size increases with reduced diet quality.
In holometabolous insects, adult body size is determined by exponential growth that occurs during the larval stage. As a result, 90% of growth occurs during the last larval instar. Because growth rates are exponential, slight variation in the timing of the cessation of growth results in large differences in body size, making this a primary determinant of body size. In the tobacco hornworm, Manduca sexta, the cascade of physiological events leading to the cessation of growth is well established. The first of these events, the critical weight, defines when the corpora allata, the glands that synthesize and secrete juvenile hormone, switch off. Once it reaches the critical weight, the larva is committed to the cessation of feeding and pupation. Despite our understanding of the physiological events that regulate body size, the ultimate causes for the cessation of growth and determination of body size remain a mystery. In particular, little is known about what signals attainment of the critical weight to the organism. We investigated the physiological basis underlying critical weight by testing the hypothesis that nutrient accumulation in the the fat body signals attainment of the critical weight. We are surgically implanting fat body into caterpillars that have not yet reached their the critical weight, expecting to see a decrease in the time to pupation. Understanding the mechanism by which an organism ceases growth can ultimately provide an understanding of the physiological regulation of life histories as well and their response to selection.