Downregulation of Complement C3 and C3aR Expression in Subcutaneous Adipose Tissue in Obese Women

Background

The central component of the complement system, C3, is associated with obesity, metabolic syndrome and cardiovascular disease however the underlying reasons are unknown. In the present study we evaluated gene expression of C3, the cleavage product C3a/C3adesArg and its cognate receptor C3aR in subcutaneous and omental adipose tissue in women.

Methods

Women (n = 140, 21–69 years, BMI 19.5–79 kg/m²) were evaluated for anthropometric and blood parameters, and adipose tissue gene expression.

Results

Subjects were separated into groups (n = 34–36) according to obesity: normal/overweight (≤30 kg/m²), obese I (≤45 kg/m²), obese II (≤51 kg/m²), and obese III (≤80 kg/m²). Overall, while omental expression remained unchanged, subcutaneous C3 and C3aR gene expression decreased with increasing adiposity (2-way ANOVA, p<0.01), with a concomitant decrease in SC/OM ratio (p<0.001). In subcutaneous adipose, both C3 and C3aR expression correlated with apoB, and apoA1 and inversely with waist circumference and blood pressure, while C3aR also correlated with glucose (p<0.05–0.0001). While omental C3aR expression did not correlate with any factor, omental C3 correlated with waist circumference, glucose and apoB (all p<0.05). Further, while plasma C3a/C3adesArg increased and adiponectin decreased with increasing BMI, both correlated (C3a negatively and adiponectin positively) with subcutaneous C3 and C3aR expression (p<0.05–0.001) or less).

Conclusions

The obesity-induced down-regulation of complement C3 and C3aR which is specific to subcutaneous adipose tissue, coupled to the strong correlations with multiple anthropometric, plasma and adipokine variables support a potential role for complement in immunometabolism.     

An ensemble approach to accurately detect somatic mutations using SomaticSeq

SomaticSeq is an accurate somatic mutation detection pipeline implementing a stochastic boosting algorithm to produce highly accurate somatic mutation calls for both single nucleotide variants and small insertions and deletions. The workflow currently incorporates five state-of-the-art somatic mutation callers, and extracts over 70 individual genomic and sequencing features for each candidate site. A training set is provided to an adaptively boosted decision tree learner to create a classifier for predicting mutation statuses. We validate our results with both synthetic and real data. We report that SomaticSeq is able to achieve better overall accuracy than any individual tool incorporated.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0758-2) contains supplementary material, which is available to authorized users.     

Light producing reporter plasmids for Escherichia coli K-12 that can be integrated into the chromosome

Plasmid vectors using the Photorhabdus luminescenslux operon can be used for real time measurements of promoter activity. We have generated a series of lux vectors that have a conditional origin of replication, different selectable markers and the attP sequence from λ. Single copies of these plasmids can be integrated into the λ attachment site in the Escherichia coli chromosome. We constructed reporter derivatives and compared light production when the plasmids were present in multiple copies and in single copies. We also demonstrated that expression could be induced under the appropriate conditions.     

A hidden Markov model reliably characterizes ketamine-induced spectral dynamics in macaque local field potentials and human electroencephalograms

Ketamine is an NMDA receptor antagonist commonly used to maintain general anesthesia. At anesthetic doses, ketamine causes high power gamma (25-50 Hz) oscillations alternating with slow-delta (0.1-4 Hz) oscillations. These dynamics are readily observed in local field potentials (LFPs) of non-human primates (NHPs) and electroencephalogram (EEG) recordings from human subjects. However, a detailed statistical analysis of these dynamics has not been reported. We characterize ketamine’s neural dynamics using a hidden Markov model (HMM). The HMM observations are sequences of spectral power in seven canonical frequency bands between 0 to 50 Hz, where power is averaged within each band and scaled between 0 and 1. We model the observations as realizations of multivariate beta probability distributions that depend on a discrete-valued latent state process whose state transitions obey Markov dynamics. Using an expectation-maximization algorithm, we fit this beta-HMM to LFP recordings from 2 NHPs, and separately, to EEG recordings from 9 human subjects who received anesthetic doses of ketamine. Our beta-HMM framework provides a useful tool for experimental data analysis. Together, the estimated beta-HMM parameters and optimal state trajectory revealed an alternating pattern of states characterized primarily by gamma and slow-delta activities. The mean duration of the gamma activity was 2.2s([1.7,2.8]s) and 1.2s([0.9,1.5]s) for the two NHPs, and 2.5s([1.7,3.6]s) for the human subjects. The mean duration of the slow-delta activity was 1.6s([1.2,2.0]s) and 1.0s([0.8,1.2]s) for the two NHPs, and 1.8s([1.3,2.4]s) for the human subjects. Our characterizations of the alternating gamma slow-delta activities revealed five sub-states that show regular sequential transitions. These quantitative insights can inform the development of rhythm-generating neuronal circuit models that give mechanistic insights into this phenomenon and how ketamine produces altered states of arousal.     

The clinical significance of miR-335, miR-124, miR-218 and miR-484 downregulation in gastric cancer

Gastric cancer (GC) is one of the leading types of malignancy worldwide, particularly in Asian populations. Although the exact molecular mechanism of GC development remains unknown, microRNA (miRNA) has recently been shown to be involved. The current study aims to investigate the expression levels of bioinformatically ranked miRNAs in gastric tissues. Using bioinformatics tools, we prioritized miRNAs thought to be implicated in GC. Furthermore, polyA-qPCR was used to validate bioinformatics findings in 40 GC, 31 normal gastric tissue (NG) and 45 gastric dysplasia (GD) samples. As identified by bioinformatics analysis, miR-335 was shown to be the top-ranked miRNA implicated in GC. Moreover, a significant downregulation of miR-335, miR-124, miR-218 and miR-484 was found in GC and GD compared to NG samples. We found bioinformatics to be an efficient approach to finding candidate miRNAs relevant to GC development. Finally, the findings show that downregulation of miRNAs such as miR-124 and miR-218 in gastric tissue can be a significant indicator for neoplastic transformation.