Project description:BackgroundWhile global breast cancer gene expression data sets have considerable commonality in terms of their data content, the populations that they represent and the data collection methods utilized can be quite disparate. We sought to assess the extent and consequence of these systematic differences with respect to identifying clinically significant prognostic groups.MethodsWe ascertained how effectively unsupervised clustering employing randomly generated sets of genes could segregate tumors into prognostic groups using four well-characterized breast cancer data sets.ResultsUsing a common set of 5,000 randomly generated lists (70 genes/list), the percentages of clusters with significant differences in metastasis latencies (HR p-value<0.01) was 62%, 15%, 21% and 0% in the NKI2 (Netherlands Cancer Institute), Wang, TRANSBIG and KJX64/KJ125 data sets, respectively. Among ER positive tumors, the percentages were 38%, 11%, 4% and 0%, respectively. Few random lists were predictive among ER negative tumors in any data set. Clustering was associated with ER status and, after globally adjusting for the effects of ER-alpha gene expression, the percentages were 25%, 33%, 1% and 0%, respectively. The impact of adjusting for ER status depended on the extent of confounding between ER-alpha gene expression and markers of proliferation.ConclusionIt is highly probable to identify a statistically significant association between a given gene list and prognosis in the NKI2 dataset due to its large sample size and the interrelationship between ER-alpha expression and markers of proliferation. In most respects, the TRANSBIG data set generated similar outcomes as the NKI2 data set, although its smaller sample size led to fewer statistically significant results.
Project description:G protein-coupled receptors (GPCRs) are typically characterized by their seven transmembrane (7TM) architecture, and interaction with two universal signal-transducers namely, the heterotrimeric G-proteins and β-arrestins (βarrs). Synthetic ligands and receptor mutants have been designed to elicit transducer-coupling preferences and distinct downstream signaling outcomes for many GPCRs. This raises the question if some naturally-occurring 7TMRs may selectively engage one of these two signal-transducers, even in response to their endogenous agonists. Although there are scattered hints in the literature that some 7TMRs lack G-protein coupling but interact with βarrs, an in-depth understanding of their transducer-coupling preference, GRK-engagement, downstream signaling and structural mechanism remains elusive. Here, we use an array of cellular, biochemical and structural approaches to comprehensively characterize two non-canonical 7TMRs namely, the human decoy D6 receptor (D6R) and the human complement C5a receptor (C5aR2), in parallel with their canonical GPCR counterparts, CCR2 and C5aR1, respectively. We discover that D6R and C5aR2 couple exclusively to βarrs, exhibit distinct GRK-preference, and activate non-canonical downstream signaling partners. We also observe that βarrs, in complex with these receptors, adopt distinct conformations compared to their canonical GPCR counterparts despite being activated by a common natural agonist. Our study therefore establishes D6R and C5aR2 as bona-fide arrestin-coupled receptors (ACRs), and provides important insights into their regulation by GRKs and downstream signaling with direct implications for biased agonism.
Project description:Predictive cues induce large changes in people's choices by biasing responses towards the expected stimulus category. At the same time, even in the absence of predictive cues, humans often exhibit substantial intrinsic response biases. Despite the ubiquity of both of these biasing effects, it remains unclear how predictive cues interact with intrinsic bias. To understand the nature of this interaction, we examined data across three previous experiments that featured a combination of neutral cues (revealing intrinsic biases) and predictive cues. We found that predictive cues decreased the intrinsic bias to about half of its original size. This result held both when bias was quantified as the criterion location estimated using signal detection theory and as the probability of choosing a particular stimulus category. Our findings demonstrate that predictive cues reduce but do not eliminate intrinsic response bias, testifying to both the malleability and rigidity of intrinsic biases.
Project description:MotivationThe rank distance model represents genome rearrangements in multi-chromosomal genomes as matrix operations, which allows the reconstruction of parsimonious histories of evolution by rearrangements. We seek to generalize this model by allowing for genomes with different gene content, to accommodate a broader range of biological contexts. We approach this generalization by using a matrix representation of genomes. This leads to simple distance formulas and sorting algorithms for genomes with different gene contents, but without duplications.ResultsWe generalize the rank distance to genomes with different gene content in two different ways. The first approach adds insertions, deletions and the substitution of a single extremity to the basic operations. We show how to efficiently compute this distance. To avoid genomes with incomplete markers, our alternative distance, the rank-indel distance, only uses insertions and deletions of entire chromosomes. We construct phylogenetic trees with our distances and the DCJ-Indel distance for simulated data and real prokaryotic genomes, and compare them against reference trees. For simulated data, our distances outperform the DCJ-Indel distance using the Quartet metric as baseline. This suggests that rank distances are more robust for comparing distantly related species. For real prokaryotic genomes, all rearrangement-based distances yield phylogenetic trees that are topologically distant from the reference (65% similarity with Quartet metric), but are able to cluster related species within their respective clades and distinguish the Shigella strains as the farthest relative of the Escherichia coli strains, a feature not seen in the reference tree.Availability and implementationCode and instructions are available at https://github.com/meidanis-lab/rank-indel.Supplementary informationSupplementary data are available at Bioinformatics online.
Project description:Several statistical methods have been developed for adjusting the Odds Ratio of the relation between two dichotomous variables X and Y for some confounders Z. With the exception of the Mantel-Haenszel method, commonly used methods, notably binary logistic regression, are not symmetrical in X and Y. The classical Mantel-Haenszel method however only works for confounders with a limited number of discrete strata, which limits its utility, and appears to have no basis in statistical models. Here we revisit the Mantel-Haenszel method and propose an extension to continuous and vector valued Z. The idea is to replace the observed cell entries in strata of the Mantel-Haenszel procedure by subject specific classification probabilities for the four possible values of (X,Y) predicted by a suitable statistical model. For situations where X and Y can be treated symmetrically we propose and explore the multinomial logistic model. Under the homogeneity hypothesis, which states that the odds ratio does not depend on Z, the logarithm of the odds ratio estimator can be expressed as a simple linear combination of three parameters of this model. Methods for testing the homogeneity hypothesis are proposed. The relationship between this method and binary logistic regression is explored. A numerical example using survey data is presented.
Project description:Despite recent advances in GPCR structure and pharmacology, the regulation of GPCR activation, signaling, and functioning by diverse post-translational modifications (PTMs) has been largely unexplored. Furthermore, for a special category of self-activating orphan GPCRs, it is completely unknown whether and how specific PTMs control their unique signaling profiles and cellular functions. In this study of GPR52, an orphan GPCR with exceedingly high constitutive G protein signaling activity and emerging as a neurotherapeutic target, we discovered its disproportionately low arrestin recruitment activity. Through profiling the N-glycosylation and phosphorylation patterns of the receptor and clarifying their roles in G protein versus arrestin coupling, we found these two types of PTMs in specific motifs differentially shape the intrinsic signaling bias of GPR52. While N-terminal N-glycosylation promotes constitutive Gs signaling possibly through favoring the self-activating conformation, phosphorylation in helix 8, to our great surprise, suppresses arrestin recruitment and thus attenuates receptor internalization. In addition, we uncovered the counteracting roles of N-glycosylation and phosphorylation in modulating GPR52-dependent accumulation of the huntingtin protein (HTT) in brain striatal cells. Thus, our study provides novel insights into the regulation of intrinsic signaling bias and cellular function of an orphan GPCR via distinct PTMs in different motifs.
Project description:Many geodynamo models predict an inverse relationship between geomagnetic reversal frequency and field strength. However, most of the absolute paleointensity data, obtained predominantly by the Thellier method from bulk volcanic rocks, fail to confirm this relationship. Although low paleointensities are commonly observed during periods of high reversal rate (notably, in the late Jurassic), higher than present-day intensity values are rare during periods of no or few reversals (superchrons). We have identified a fundamental mechanism that results in a pervasive and previously unrecognized low-field bias that affects most paleointensity data in the global database. Our results provide an explanation for the discordance between the experimental data and numerical models, and lend additional support to an inverse relationship between the reversal rate and field strength as a fundamental property of the geodynamo. We demonstrate that the accuracy of future paleointensity analyses can be improved by integration of the Thellier protocol with low-temperature demagnetizations.
Project description:G-protein-coupled receptors (GPCRs), also known as seven transmembrane receptors (7TMRs), typically interact with two distinct signal-transducers, i.e., G proteins and β-arrestins (βarrs). Interestingly, there are some non-canonical 7TMRs that lack G protein coupling but interact with βarrs, although an understanding of their transducer coupling preference, downstream signaling, and structural mechanism remains elusive. Here, we characterize two such non-canonical 7TMRs, namely, the decoy D6 receptor (D6R) and the complement C5a receptor subtype 2 (C5aR2), in parallel with their canonical GPCR counterparts. We discover that D6R and C5aR2 efficiently couple to βarrs, exhibit distinct engagement of GPCR kinases (GRKs), and activate non-canonical downstream signaling pathways. We also observe that βarrs adopt distinct conformations for D6R and C5aR2, compared to their canonical GPCR counterparts, in response to common natural agonists. Our study establishes D6R and C5aR2 as βarr-coupled 7TMRs and provides key insights into their regulation and signaling with direct implication for biased agonism.
Project description:Chemokine receptors constitute an important subfamily of G protein-coupled receptors (GPCRs), and they are critically involved in a broad range of immune response mechanisms. Ligand promiscuity among these receptors makes them an interesting target to explore multiple aspects of biased agonism. Here, we comprehensively characterize two chemokine receptors namely, CXCR4 and CXCR7, in terms of their transducer-coupling and downstream signaling upon their stimulation by a common chemokine agonist, CXCL12, and a small molecule agonist, VUF11207. We observe that CXCR7 lacks G-protein-coupling while maintaining robust βarr recruitment with a major contribution of GRK5/6. On the other hand, CXCR4 displays robust G-protein activation as expected but exhibits significantly reduced βarr-coupling compared to CXCR7. These two receptors induce distinct βarr conformations even when activated by the same agonist, and CXCR7, unlike CXCR4, fails to activate ERK1/2 MAP kinase. We also identify a key contribution of a single phosphorylation site in CXCR7 for βarr recruitment and endosomal localization. Our study provides molecular insights into intrinsic-bias encoded in the CXCR4-CXCR7 system with broad implications for drug discovery.
Project description:Weight stigma typically focuses on suggestions that people with overweight and obesity are incompetent and immoral. Integrating so far unconnected lines of research, the current research presents two studies that examine the motivational relevance of these aspects of weight stigma. Specifically, we tested the proposition that people with overweight and obesity respond differently to the public viewing them as incompetent compared to immoral, as these aspects of weight stigma differ in reparability. We expect that threats to competence are more acceptable and thus related to a constructive response that is more effective in losing weight in the long-run. By contrast, we propose that threats to morality elicit an acute urge to defend one's moral image, thereby prompting responses that are more visible to the social environment, but potentially less effective for losing weight. Study 1 experimentally compared exposure to weight stigma focused on morality vs. weight stigma focused on competence in a sample of adults with overweight and obesity (N = 122; M BMI = 31.89, SD BMI = 4.39). We found that when exposed to weight stigma focused on morality, people with overweight and obesity respond by defending their moral social-image but that this is less effective for encouraging weight loss, while exposure to weight stigma focused on competence led to an increased likelihood of engagement in weight loss behaviors. Complementing and extending the findings, Study 2 (N = 348, M BMI = 26.78, SD BMI = 6.78) tested the notion that internalized weight bias predominantly revolves around moral concerns, and thus will lead to less self-determined behavioral regulation. We found strong support for the moral core of weight bias internalization. In line with our predictions, greater weight bias internalization was associated less self-determined and more other-determined regulation of dieting and exercising. This suggests that weight bias internalization operates as a facilitator of maladaptive behavioral regulation following weight stigma, contributing to lower psychological functioning and well-being of people with overweight and obesity. The current research presents novel findings about the underlying mechanisms of weight stigma and weight bias internalization and identifies strategies to avoid maladaptive and facilitate adaptive health behaviors.