Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes

BACKGROUND: A cluster analysis is the most commonly performed procedure (often regarded as a first step) on a set of gene expression profiles. In most cases, a post hoc analysis is done to see if the genes in the same clusters can be functionally correlated. While past successes of such analyses have often been reported in a number of microarray studies (most of which used the standard hierarchical clustering, UPGMA, with one minus the Pearson's correlation coefficient as a measure of dissimilarity), often times such groupings could be misleading. More importantly, a systematic evaluation of the entire set of clusters produced by such unsupervised procedures is necessary since they also contain genes that are seemingly unrelated or may have more than one common function. Here we quantify the performance of a given unsupervised clustering algorithm applied to a given microarray study in terms of its ability to produce biologically meaningful clusters using a reference set of functional classes. Such a reference set may come from prior biological knowledge specific to a microarray study or may be formed using the growing databases of gene ontologies (GO) for the annotated genes of the relevant species. RESULTS: In this paper, we introduce two performance measures for evaluating the results of a clustering algorithm in its ability to produce biologically meaningful clusters. The first measure is a biological homogeneity index (BHI). As the name suggests, it is a measure of how biologically homogeneous the clusters are. This can be used to quantify the performance of a given clustering algorithm such as UPGMA in grouping genes for a particular data set and also for comparing the performance of a number of competing clustering algorithms applied to the same data set. The second performance measure is called a biological stability index (BSI). For a given clustering algorithm and an expression data set, it measures the consistency of the clustering algorithm's ability to produce biologically meaningful clusters when applied repeatedly to similar data sets. A good clustering algorithm should have high BHI and moderate to high BSI. We evaluated the performance of ten well known clustering algorithms on two gene expression data sets and identified the optimal algorithm in each case. The first data set deals with SAGE profiles of differentially expressed tags between normal and ductal carcinoma in situ samples of breast cancer patients. The second data set contains the expression profiles over time of positively expressed genes (ORF's) during sporulation of budding yeast. Two separate choices of the functional classes were used for this data set and the results were compared for consistency. CONCLUSION: Functional information of annotated genes available from various GO databases mined using ontology tools can be used to systematically judge the results of an unsupervised clustering algorithm as applied to a gene expression data set in clustering genes. This information could be used to select the right algorithm from a class of clustering algorithms for the given data set

Design, Synthesis and Characterization of a Highly Effective Inhibitor for Analog-Sensitive (as) Kinases

Highly selective, cell-permeable and fast-acting inhibitors of individual kinases are sought-after as tools for studying the cellular function of kinases in real time. A combination of small molecule synthesis and protein mutagenesis, identified a highly potent inhibitor (1-Isopropyl-3-(phenylethynyl)-1H-pyrazolo[3,4-d]pyrimidin-4-amine) of a rationally engineered Hog1 serine/threonine kinase (Hog1T100G). This inhibitor has been successfully used to study various aspects of Hog1 signaling, including a transient cell cycle arrest and gene expression changes mediated by Hog1 in response to stress. This study also underscores that the general applicability of this approach depends, in part, on the selectivity of the designed the inhibitor with respect to activity versus the engineered and wild type kinases. To explore this specificity in detail, we used a validated chemogenetic assay to assess the effect of this inhibitor on all gene products in yeast in parallel. The results from this screen emphasize the need for caution and for case-by-case assessment when using the Analog-Sensitive Kinase Allele technology to assess the physiological roles of kinases

Cultural Aspects of Attachment Anxiety, Avoidance, and Life Satisfaction: Comparing the US and Turkey

Attachment insecurity can interfere with the experience, expression, and benefits of positive emotions, including happiness and life satisfaction (LS). However, both the pattern and effects of insecure attachment orientations on LS vary across cultures. Considering that attachment anxiety is higher in collectivist cultures and attachment avoidance is relatively high in individualistic cultures, the present chapter elaborates on the idea that anxious and avoidant attachment would have varying effects on LS in individualistic and collectivistic cultural contexts. Study 1 (N = 2456) involved a community sample of married couples in Turkey and demonstrated that attachment avoidance was a stronger predictor of LS than attachment anxiety in Turkish collectivist context. Study 2 tested the hypothesis that the roles of attachment anxiety and avoidance in predicting LS would vary between collectivistic and individualistic cultures. Mothers’ adult attachment dimensions and LS in Turkey (N = 89) and the United States (N = 91) were measured. As expected, results indicated that LS was predicted only by attachment avoidance in Turkey and by attachment anxiety in the United States. These findings are in line with the cultural fit hypothesis, suggesting that culturally incongruent attachment orientations have a stronger negative impact on individuals’ LS

Shear Stress Modulation of Smooth Muscle Cell Marker Genes in 2-D and 3-D Depends on Mechanotransduction by Heparan Sulfate Proteoglycans and ERK1/2

During vascular injury, vascular smooth muscle cells (SMCs) and fibroblasts/myofibroblasts (FBs/MFBs) are exposed to altered luminal blood flow or transmural interstitial flow. We investigate the effects of these two types of fluid flows on the phenotypes of SMCs and MFBs and the underlying mechanotransduction mechanisms.Exposure to 8 dyn/cm(2) laminar flow shear stress (2-dimensional, 2-D) for 15 h significantly reduced expression of alpha-smooth muscle actin (alpha-SMA), smooth muscle protein 22 (SM22), SM myosin heavy chain (SM-MHC), smoothelin, and calponin. Cells suspended in collagen gels were exposed to interstitial flow (1 cmH(2)O, approximately 0.05 dyn/cm(2), 3-D), and after 6 h of exposure, expression of SM-MHC, smoothelin, and calponin were significantly reduced, while expression of alpha-SMA and SM22 were markedly enhanced. PD98059 (an ERK1/2 inhibitor) and heparinase III (an enzyme to cleave heparan sulfate) significantly blocked the effects of laminar flow on gene expression, and also reversed the effects of interstitial flow on SM-MHC, smoothelin, and calponin, but enhanced interstitial flow-induced expression of alpha-SMA and SM22. SMCs and MFBs have similar responses to fluid flow. Silencing ERK1/2 completely blocked the effects of both laminar flow and interstitial flow on SMC marker gene expression. Western blotting showed that both types of flows induced ERK1/2 activation that was inhibited by disruption of heparan sulfate proteoglycans (HSPGs).The results suggest that HSPG-mediated ERK1/2 activation is an important mechanotransduction pathway modulating SMC marker gene expression when SMCs and MFBs are exposed to flow. Fluid flow may be involved in vascular remodeling and lesion formation by affecting phenotypes of vascular wall cells. This study has implications in understanding the flow-related mechanobiology in vascular lesion formation, tumor cell invasion, and stem cell differentiation

Sensing the fuels: glucose and lipid signaling in the CNS controlling energy homeostasis

The central nervous system (CNS) is capable of gathering information on the body’s nutritional state and it implements appropriate behavioral and metabolic responses to changes in fuel availability. This feedback signaling of peripheral tissues ensures the maintenance of energy homeostasis. The hypothalamus is a primary site of convergence and integration for these nutrient-related feedback signals, which include central and peripheral neuronal inputs as well as hormonal signals. Increasing evidence indicates that glucose and lipids are detected by specialized fuel-sensing neurons that are integrated in these hypothalamic neuronal circuits. The purpose of this review is to outline the current understanding of fuel-sensing mechanisms in the hypothalamus, to integrate the recent findings in this field, and to address the potential role of dysregulation in these pathways in the development of obesity and type 2 diabetes mellitus

Deconstructing transcriptional heterogeneity in pluripotent stem cells

SUMMARY Pluripotent stem cells (PSCs) are capable of dynamic interconversion between distinct substates, but the regulatory circuits specifying these states and enabling transitions between them are not well understood. We set out to characterize transcriptional heterogeneity in PSCs by single-cell expression profiling under different chemical and genetic perturbations. Signaling factors and developmental regulators show highly variable expression, with expression states for some variable genes heritable through multiple cell divisions. Expression variability and population heterogeneity can be influenced by perturbation of signaling pathways and chromatin regulators. Strikingly, either removal of mature miRNAs or pharmacologic blockage of signaling pathways drives PSCs into a low-noise ground state characterized by a reconfigured pluripotency network, enhanced self-renewal, and a distinct chromatin state, an effect mediated by opposing miRNA families acting on the c-myc / Lin28 / let-7 axis. These data illuminate the nature of transcriptional heterogeneity in PSCs

Exploring attachment to the "homeland" and its association with heritage culture identification

Conceptualisations of attachment to one’s nation of origin reflecting a symbolic caregiver can be found cross-culturally in literature, art, and language. Despite its prevalence, the relationship with one’s nation has not been investigated empirically in terms of an attachment theory framework. Two studies employed an attachment theory approach to investigate the construct validity of symbolic attachment to one’s nation of origin, and its association with acculturation (operationalized as heritage and mainstream culture identification). Results for Study 1 indicated a three-factor structure of nation attachment; the factors were labelled secure-preoccupied, fearful, and dismissive nation attachment. Hierarchical linear modelling was employed to control for differing cultures across participants. Secure-preoccupied nation attachment was a significant predictor of increased heritage culture identification for participants residing in their country of birth, whilst dismissive nation attachment was a significant predictor of decreased heritage culture identification for international migrants. Securepreoccupied nation attachment was also associated with higher levels of subjective-wellbeing. Study 2 further confirmed the validity of the nation attachment construct through confirmatory factor analysis; the three-factor model adequately fit the data. Similar to the results of Study 1, secure-preoccupied nation attachment was associated with increased levels of heritage culture identification and psychological well-being. Implications of the tripartite model of nation attachment for identity and well-being will be discussed