Molecular evidence for increased regulatory conservation during metamorphosis, and against deleterious cascading effects of hybrid breakdown in Drosophila

<p>Abstract</p> <p>Background</p> <p>Speculation regarding the importance of changes in gene regulation in determining major phylogenetic patterns continues to accrue, despite a lack of broad-scale comparative studies examining how patterns of gene expression vary during development. Comparative transcriptional profiling of adult interspecific hybrids and their parental species has uncovered widespread divergence of the mechanisms controlling gene regulation, revealing incompatibilities that are masked in comparisons between the pure species. However, this has prompted the suggestion that misexpression in adult hybrids results from the downstream cascading effects of a subset of genes improperly regulated in early development.</p> <p>Results</p> <p>We sought to determine how gene expression diverges over development, as well as test the cascade hypothesis, by profiling expression in males of <it>Drosophila melanogaster</it>, <it>D. sechellia</it>, and <it>D. simulans</it>, as well as the <it>D. simulans </it>(♀) × <it>D. sechellia </it>(♂) male F1 hybrids, at four different developmental time points (3rd instar larval, early pupal, late pupal, and newly-emerged adult). Contrary to the cascade model of misexpression, we find that there is considerable stage-specific autonomy of regulatory breakdown in hybrids, with the larval and adult stages showing significantly more hybrid misexpression as compared to the pupal stage. However, comparisons between pure species indicate that genes expressed during earlier stages of development tend to be more conserved in terms of their level of expression than those expressed during later stages, suggesting that while Von Baer's famous law applies at both the level of nucleotide sequence and expression, it may not apply necessarily to the underlying overall regulatory network, which appears to diverge over the course of ontogeny and which can only be ascertained by combining divergent genomes in species hybrids.</p> <p>Conclusion</p> <p>Our results suggest that complex integration of regulatory circuits during morphogenesis may lead to it being more refractory to divergence of underlying gene regulatory mechanisms - more than that suggested by the conservation of gene expression levels between species during earlier stages. This provides support for a 'developmental hourglass' model of divergence of gene expression in <it>Drosophila </it>resulting in a highly conserved pupal stage.</p

Total 18F-dopa PET tumour uptake reflects metabolic endocrine tumour activity in patients with a carcinoid tumour

Positron emission tomography (PET) using 6-[(18)F]fluoro-L-dihydroxyphenylalanine ((18)F-dopa) has an excellent sensitivity to detect carcinoid tumour lesions. (18)F-dopa tumour uptake and the levels of biochemical tumour markers are mediated by tumour endocrine metabolic activity. We evaluated whether total (18)F-dopa tumour uptake on PET, defined as whole-body metabolic tumour burden (WBMTB), reflects tumour load per patient, as measured with tumour markers. Seventy-seven consecutive carcinoid patients who underwent an (18)F-dopa PET scan in two previously published studies were analysed. For all tumour lesions mean standardised uptake values (SUVs) at 40% of the maximal SUV and tumour volume on (18)F-dopa PET were determined and multiplied to calculate a metabolic burden per lesion. WBMTB was the sum of the metabolic burden of all individual lesions per patient. The 24-h urinary serotonin, urine and plasma 5-hydroxindoleacetic acid (5-HIAA), catecholamines (nor)epinephrine, dopamine and their metabolites, measured in urine and plasma, and serum chromogranin A served as tumour markers. All but 1 were evaluable for WBMTB; 74 patients had metastatic disease. (18)F-dopa PET detected 979 lesions. SUV(max) on (18)F-dopa PET varied up to 29-fold between individual lesions within the same patients. WBMTB correlated with urinary serotonin (r = 0.51) and urinary and plasma 5-HIAA (r = 0.78 and 0.66). WBMTB also correlated with urinary norepinephrine, epinephrine, dopamine and plasma dopamine, but not with serum chromogranin A. Tumour load per patient measured with (18)F-dopa PET correlates with tumour markers of the serotonin and catecholamine pathway in urine and plasma in carcinoid patients, reflecting metabolic tumour activity

Mining and state-space modeling and verification of sub-networks from large-scale biomolecular networks

<p>Abstract</p> <p>Background</p> <p>Biomolecular networks dynamically respond to stimuli and implement cellular function. Understanding these dynamic changes is the key challenge for cell biologists. As biomolecular networks grow in size and complexity, the model of a biomolecular network must become more rigorous to keep track of all the components and their interactions. In general this presents the need for computer simulation to manipulate and understand the biomolecular network model.</p> <p>Results</p> <p>In this paper, we present a novel method to model the regulatory system which executes a cellular function and can be represented as a biomolecular network. Our method consists of two steps. First, a novel scale-free network clustering approach is applied to the large-scale biomolecular network to obtain various sub-networks. Second, a state-space model is generated for the sub-networks and simulated to predict their behavior in the cellular context. The modeling results represent <it>hypotheses </it>that are tested against high-throughput data sets (microarrays and/or genetic screens) for both the natural system and perturbations. Notably, the dynamic modeling component of this method depends on the automated network structure generation of the first component and the sub-network clustering, which are both essential to make the solution tractable.</p> <p>Conclusion</p> <p>Experimental results on time series gene expression data for the human cell cycle indicate our approach is promising for sub-network mining and simulation from large-scale biomolecular network.</p

Protein Function Assignment through Mining Cross-Species Protein-Protein Interactions

Background: As we move into the post genome-sequencing era, an immediate challenge is how to make best use of the large amount of high-throughput experimental data to assign functions to currently uncharacterized proteins. We here describe CSIDOP, a new method for protein function assignment based on shared interacting domain patterns extracted from cross-species protein-protein interaction data. Methodology/Principal Findings: The proposed method is assessed both biologically and statistically over the genome of H. sapiens. The CSIDOP method is capable of making protein function prediction with accuracy of 95.42 % using 2,972 gene ontology (GO) functional categories. In addition, we are able to assign novel functional annotations for 181 previously uncharacterized proteins in H. sapiens. Furthermore, we demonstrate that for proteins that are characterized by GO, the CSIDOP may predict extra functions. This is attractive as a protein normally executes a variety of functions in different processes and its current GO annotation may be incomplete. Conclusions/Significance: It can be shown through experimental results that the CSIDOP method is reliable and practical in use. The method will continue to improve as more high quality interaction data becomes available and is readily scalable t

Multiway modeling and analysis in stem cell systems biology

<p>Abstract</p> <p>Background</p> <p>Systems biology refers to multidisciplinary approaches designed to uncover emergent properties of biological systems. Stem cells are an attractive target for this analysis, due to their broad therapeutic potential. A central theme of systems biology is the use of computational modeling to reconstruct complex systems from a wealth of reductionist, molecular data (e.g., gene/protein expression, signal transduction activity, metabolic activity, etc.). A number of deterministic, probabilistic, and statistical learning models are used to understand sophisticated cellular behaviors such as protein expression during cellular differentiation and the activity of signaling networks. However, many of these models are bimodal i.e., they only consider row-column relationships. In contrast, multiway modeling techniques (also known as tensor models) can analyze multimodal data, which capture much more information about complex behaviors such as cell differentiation. In particular, tensors can be very powerful tools for modeling the dynamic activity of biological networks over time. Here, we review the application of systems biology to stem cells and illustrate application of tensor analysis to model collagen-induced osteogenic differentiation of human mesenchymal stem cells.</p> <p>Results</p> <p>We applied Tucker1, Tucker3, and Parallel Factor Analysis (PARAFAC) models to identify protein/gene expression patterns during extracellular matrix-induced osteogenic differentiation of human mesenchymal stem cells. In one case, we organized our data into a tensor of type protein/gene locus link × gene ontology category × osteogenic stimulant, and found that our cells expressed two distinct, stimulus-dependent sets of functionally related genes as they underwent osteogenic differentiation. In a second case, we organized DNA microarray data in a three-way tensor of gene IDs × osteogenic stimulus × replicates, and found that application of tensile strain to a collagen I substrate accelerated the osteogenic differentiation induced by a static collagen I substrate.</p> <p>Conclusion</p> <p>Our results suggest gene- and protein-level models whereby stem cells undergo transdifferentiation to osteoblasts, and lay the foundation for mechanistic, hypothesis-driven studies. Our analysis methods are applicable to a wide range of stem cell differentiation models.</p

Testosterone levels are negatively associated with childlessness in males, but positively related to offspring count in fathers

Variation in testosterone (T) is thought to affect the allocation of effort between reproductive and parenting strategies. Here, using a large sample of elderly American men (n = 754) and women (n = 669) we examined the relationship between T and self-reported parenthood, as well as the relationship between T and number of reported children. Results supported previous findings from the literature, showing that fathers had lower T levels than men who report no children. Furthermore, we found that among fathers T levels were positively associated with the number of children a man reports close to the end of his lifespan. Results were maintained when controlling for a number of relevant factors such as time of T sampling, participant age, educational attainment, BMI, marital status and reported number of sex partners. In contrast, T was not associated with either motherhood or the number of children women had, suggesting that, at least in this sample, T does not influence the allocation of effort between reproductive and parenting strategies among women. Findings from this study contribute to the growing body of literature suggesting that, among men, pair bonding and paternal care are associated with lower T levels, while searching and acquiring sex partners is associated with higher T levels.27 Jun 2013: Pollet TV, Cobey KD, van der Meij L (2013) Correction: Testosterone Levels Are Negatively Associated with Childlessness in Males, but Positively Related to Offspring Count in Fathers. PLoS ONE 8(6): 10.1371/annotation/bccccb7e-48a7-4594-b3e6-ce8c9d2489a2

On-line mass spectrometry: membrane inlet sampling

Significant insights into plant photosynthesis and respiration have been achieved using membrane inlet mass spectrometry (MIMS) for the analysis of stable isotope distribution of gases. The MIMS approach is based on using a gas permeable membrane to enable the entry of gas molecules into the mass spectrometer source. This is a simple yet durable approach for the analysis of volatile gases, particularly atmospheric gases. The MIMS technique strongly lends itself to the study of reaction flux where isotopic labeling is employed to differentiate two competing processes; i.e., O2 evolution versus O2 uptake reactions from PSII or terminal oxidase/rubisco reactions. Such investigations have been used for in vitro studies of whole leaves and isolated cells. The MIMS approach is also able to follow rates of isotopic exchange, which is useful for obtaining chemical exchange rates. These types of measurements have been employed for oxygen ligand exchange in PSII and to discern reaction rates of the carbonic anhydrase reactions. Recent developments have also engaged MIMS for online isotopic fractionation and for the study of reactions in inorganic systems that are capable of water splitting or H2 generation. The simplicity of the sampling approach coupled to the high sensitivity of modern instrumentation is a reason for the growing applicability of this technique for a range of problems in plant photosynthesis and respiration. This review offers some insights into the sampling approaches and the experiments that have been conducted with MIMS

Epistasis: Obstacle or Advantage for Mapping Complex Traits?

Identification of genetic loci in complex traits has focused largely on one-dimensional genome scans to search for associations between single markers and the phenotype. There is mounting evidence that locus interactions, or epistasis, are a crucial component of the genetic architecture of biologically relevant traits. However, epistasis is often viewed as a nuisance factor that reduces power for locus detection. Counter to expectations, recent work shows that fitting full models, instead of testing marker main effect and interaction components separately, in exhaustive multi-locus genome scans can have higher power to detect loci when epistasis is present than single-locus scans, and improvement that comes despite a much larger multiple testing alpha-adjustment in such searches. We demonstrate, both theoretically and via simulation, that the expected power to detect loci when fitting full models is often larger when these loci act epistatically than when they act additively. Additionally, we show that the power for single locus detection may be improved in cases of epistasis compared to the additive model. Our exploration of a two step model selection procedure shows that identifying the true model is difficult. However, this difficulty is certainly not exacerbated by the presence of epistasis, on the contrary, in some cases the presence of epistasis can aid in model selection. The impact of allele frequencies on both power and model selection is dramatic