Radhakrishnan Nagarajan

Thrombospondin-1 Is an Adipokine Associated With Obesity, Adipose Inflammation, and Insulin Resistance

OBJECTIVE—We examined the relationship between the expression of thrombospondin (TSP)1, an antiangiogenic factor and regulator of transforming growth factor-β activity, obesity, adipose inflammation, and insulin resistance. RESEARCH DESIGN AND METHODS—TSP1 gene expression was quantified in subcutaneous adipose tissue (SAT) of 86 nondiabetic subjects covering a wide range of BMI and insulin sensitivity, from visceral adipose (VAT) and SAT from 14 surgical patients and from 38 subjects with impaired glucose tolerance randomized to receive either pioglitazone or metformin for 10 weeks. An adipocyte culture system was also used to assess the effects of pioglitazone and coculture with macrophages on TSP1 gene expression. RESULTS—TSP1 mRNA was significantly associated with obesity (BMI) and insulin resistance (low insulin sensitivity index). Relatively strong positive associations were seen with markers of inflammation, including CD68, macrophage chemoattractant protein-1, and plasminogen activator inhibitor (PAI)-1 mRNA (r ≥ 0.46, P = 0.001 for each), that remained significant after controlling for BMI and Si. However, TSP1 mRNA was preferentially expressed in adipocyte fraction, whereas inflammatory markers predominated in stromal vascular fraction. Coculture of adipocytes and macrophages augmented TSP1 gene expression and secretion from both cell types. Pioglitazone (not metformin) treatment resulted in a 54% decrease (P < 0.04) in adipose TSP gene expression, as did in vitro pioglitazone treatment of adipocytes. CONCLUSIONS—TSP1 is a true adipokine that is highly expressed in obese, insulin-resistant subjects; is highly correlated with adipose inflammation; and is decreased by pioglitazone. TSP1 is an important link between adipocytes and macrophage-driven adipose tissue inflammation and may mediate the elevation of PAI-1 that promotes a prothrombotic state.

Microarray analysis of gene expression during early adipocyte differentiation

The molecular mechanisms that regulate cellular differentiation during development and throughout life are complex. It is now recognized that precise patterns of differentially expressed genes ultimately direct a particular cell toward a given lineage and many of these are regulated during the earliest stages of differentiation. Using a microarray-based expression analysis, we have examined gene expression profiles during the first 24 h of 3T3-L1 adipocyte differentiation. RNA was isolated at times 0, 2, 8, 16, and 24 h following stimulation of differentiation and hybridized in duplicate to high density Affymetrix microarray gene chips containing a series of 13,179 cDNA/expressed sequence tag (EST) probe sets. Two hundred and eighty-five cDNA/ESTs were shown to have at least a fivefold change in expression levels during this time course and both hierarchical and self-organizing map clustering analysis was performed to categorize them by expression profiles. Several genes known to be regulated during this time period were confirmed and Western blot analysis of the proteins encoded by some of the identified genes revealed expression profiles similar to their mRNA counterparts. As expected, many of the genes identified have not been examined in such a critical time period during adipogenesis and may well represent novel adipogenic mediators.

Quantifying physiological data with Lempel-Ziv complexity-certain issues

The oscillations observed in physiological data can be attributed largely to the presence of either a nonlinear deterministic or a nondeterministic component. The Lempel-Ziv [1976] complexity and its variants have been used successfully to quantify the regularity of these oscillations. The decrease in the complexity can be observed in the case of nontrivial deterministic patterns as well as correlated noise. Thus, any conclusion on the nature of the pattern based solely on the value of the Lempel-Ziv complexity is incomplete. In this paper, the use of the surrogate data technique is suggested to avoid spurious interpretation of this measure of complexity. The data sets considered include the uterine contraction obtained during active labor. The surrogates are generated using the amplitude adjusted Fourier transform and the iterated amplitude adjusted Fourier transform. The approximate entropy [S.M. Pincus, 1991] is used as an alternate measure to verify the results obtained.

A multifractal description of wind speed records

In this paper, a systematic analysis of hourly wind speed data obtained from four potential wind generation sites in North Dakota is conducted. The power spectra of the data exhibited a power law decay characteristic of 1/fα processes with possible long range correlations. The temporal scaling properties of the records were studied using the sophisticated multifractal detrended fluctuation analysis MFDFA. It is seen that the records at all four locations exhibit similar scaling behavior which is also reflected in the multifractal spectrum determined under the assumption of a binomial multiplicative cascade model.

Alterations in the TGFβ signaling pathway in myogenic progenitors with age

Myogenic progenitors in adult muscle are necessary for the repair, maintenance and hypertrophy of post‐mitotic muscle fibers. With age, fat deposition and fibrosis contribute to the decline in the integrity and functional capacity of muscles. In a previous study we reported increased accumulation of lipid in myogenic progenitors obtained from aged mice, accompanied by an up‐regulation of genes involved in adipogenic differentiation. The present study was designed to extend our understanding of how aging affects the fate and gene expression profile of myogenic progenitors. Affymetrix murine U74 Genechip analysis was performed using RNA extracted from myogenic progenitors isolated from adult (8‐month‐old) and aged (24‐month‐old) DBA/2JNIA mice. The cells from the aged animals exhibited major alterations in the expression level of many genes directly or indirectly involved with the TGFβ signaling pathway. Our data indicate that with age, myogenic progenitors acquire the paradoxical phenotype of being both TGFβ activated based on overexpression of TGFβ‐inducible genes, but resistant to the differentiation‐inhibiting effects of exogenous TGFβ. The overexpression of TGFβ‐regulated genes, such as connective tissue growth factor, may play a role in increasing fibrosis in aging muscle.

Intensity-based segmentation of microarray images

The underlying principle in microarray image analysis is that the spot intensity is a measure of the gene expression. This implicitly assumes the gene expression of a spot to be governed entirely by the distribution of the pixel intensities. Thus, a segmentation technique based on the distribution of the pixel intensities is appropriate for the current problem. In this paper, clustering-based segmentation is described to extract the target intensity of the spots. The approximate boundaries of the spots in the microarray are determined by manual adjustment of rectilinear grids. The distribution of the pixel intensity in a grid containing a spot is assumed to be the superposition of the foreground and the local background. The k-means clustering technique and the partitioning around medoids (PAM) were used to generate a binary partition of the pixel intensity distribution. The median (k-means) and the medoid (PAM) of the cluster members are chosen as the cluster representatives. The effectiveness of the clustering-based segmentation techniques was tested on publicly available arrays generated in a lipid metabolism experiment (Callow et al., 2000). The results are compared against those obtained using the region-growing approach (SPOT) (Yang et al., 2001). The effect of additive white Gaussian noise is also investigated.

Evidence of crossover phenomena in wind-speed data

In this paper, a systematic analysis of hourly wind-speed data obtained from three potential wind-generation sites (in North Dakota) is analyzed. The power spectra of the data exhibited a power-law decay characteristic of 1/f/sup /spl alpha// processes with possible long-range correlations. Conventional analysis using Hurst exponent estimators proved to be inconclusive. Subsequent analysis using detrended fluctuation analysis revealed a crossover in the scaling exponent (/spl alpha/). At short time scales, a scaling exponent of /spl alpha//spl sim/1.4 indicated that the data resembled Brownian noise, whereas for larger time scales the data exhibited long-range correlations (/spl alpha//spl sim/0.7). The scaling exponents obtained were similar across the three locations. Our findings suggest the possibility of multiple scaling exponents characteristic of multifractal signals.

Interleukin-1 polymorphisms are associated with the inflammatory response in human muscle to acute resistance exercise.

Inflammation appears to play an important role in the repair and regeneration of skeletal muscle after damage. We tested the hypothesis that the severity of the inflammatory response in muscle after an acute bout of resistance exercise is associated with single nucleotide polymorphisms (SNPs) previously shown to alter interleukin‐1 (IL‐1) activity. Using a double‐blind prospective design, sedentary young men were screened (n= 100) for enrolment (n= 24) based upon having 1 of 4 haplotype patterns composed of five polymorphic sites in the IL‐1 gene cluster: IL‐1A (+4845), IL‐1B (+3954), IL‐1B (−511), IL‐1B (−3737) and IL‐1RN (+2018). Subjects performed a standard bout of resistance leg exercise and vastus lateralis biopsies were obtained pre‐, and at 24, and 72 h post‐exercise. Inflammatory marker mRNAs (IL‐1β, IL‐6 and tumor necrosis factor‐α (TNF‐α)) and the number of CD68+ macrophages were quantified. Considerable variation was observed in the expression of these gene products between subjects. At 72 h post‐exercise, IL‐1β had increased in a number of subjects (n= 10) and decreased (n= 4) or did not change (n= 10) in others. Inflammatory responses were significantly associated with specific haplotype patterns and were also influenced by individual SNPs. Subjects with genotypes 1.1 at IL‐1B (+3954) or 2.2 at IL‐1B (−3737) had approximately a 2‐fold higher median induction of several markers, but no increase in macrophages, suggesting that cytokine gene expression is elevated per macrophage. The IL‐1RN (+2018) SNP maximized the response specifically within these groups and was associated with increased macrophage recruitment. This is the first report that IL‐1 genotype is associated with the inflammation of skeletal muscle following acute resistance exercise that may potentially affect the adaptations to chronic resistance exercise.

Identifying spots in microarray images

Microarray technology has provided a way to quantitate the simultaneous expression of a large number of genes. This approach is dependent on reproducible, accurate identification and quantitation of spot intensities. In this paper, clustering-based image segmentation is described to extract the target intensity of the microarray spots. While the technique is generic, its effectiveness on extracting spot intensities on arrays obtained from a two-color (Cy3/Cy5) experiment is discussed. The approximate boundaries of the spots are determined initially by manual alignment of rectangular grids. The pixel intensities of the image (I) inside a grid, is mapped onto a one-dimensional vector (v). The k-means clustering technique is applied to generate a binary partition of v. The median value of the pixel intensities inside each of the clusters for a given spot determines its foreground and the local background intensity. The difference in the median value of the foreground and the background intensity is the desired target intensity of the spot. The results are compared against those obtained using a region growing approach.

Identifying significant edges in graphical models of molecular networks

Objective Modelling the associations from high-throughput experimental molecular data has provided unprecedented insights into biological pathways and signalling mechanisms. Graphical models and networks have especially proven to be useful abstractions in this regard. Ad hoc thresholds are often used in conjunction with structure learning algorithms to determine significant associations. The present study overcomes this limitation by proposing a statistically motivated approach for identifying significant associations in a network. Methods and materials A new method that identifies significant associations in graphical models by estimating the threshold minimising the L1 norm between the cumulative distribution function (CDF) of the observed edge confidences and those of its asymptotic counterpart is proposed. The effectiveness of the proposed method is demonstrated on popular synthetic data sets as well as publicly available experimental molecular data corresponding to gene and protein expression profiles. Results The improved performance of the proposed approach is demonstrated across the synthetic data sets using sensitivity, specificity and accuracy as performance metrics. The results are also demonstrated across varying sample sizes and three different structure learning algorithms with widely varying assumptions. In all cases, the proposed approach has specificity and accuracy close to 1, while sensitivity increases linearly in the logarithm of the sample size. The estimated threshold systematically outperforms common ad hoc ones in terms of sensitivity while maintaining comparable levels of specificity and accuracy. Networks from experimental data sets are reconstructed accurately with respect to the results from the original papers. Conclusion Current studies use structure learning algorithms in conjunction with ad hoc thresholds for identifying significant associations in graphical abstractions of biological pathways and signalling mechanisms. Such an ad hoc choice can have pronounced effect on attributing biological significance to the associations in the resulting network and possible downstream analysis. The statistically motivated approach presented in this study has been shown to outperform ad hoc thresholds and is expected to alleviate spurious conclusions of significant associations in such graphical abstractions.