Inferring transcriptional compensation interactions in yeast via stepwise structure equation modeling

<p>Abstract</p> <p>Background</p> <p>With the abundant information produced by microarray technology, various approaches have been proposed to infer transcriptional regulatory networks. However, few approaches have studied subtle and indirect interaction such as genetic compensation, the existence of which is widely recognized although its mechanism has yet to be clarified. Furthermore, when inferring gene networks most models include only observed variables whereas latent factors, such as proteins and mRNA degradation that are not measured by microarrays, do participate in networks in reality.</p> <p>Results</p> <p>Motivated by inferring transcriptional compensation (TC) interactions in yeast, a stepwise structural equation modeling algorithm (SSEM) is developed. In addition to observed variables, SSEM also incorporates hidden variables to capture interactions (or regulations) from latent factors. Simulated gene networks are used to determine with which of six possible model selection criteria (MSC) SSEM works best. SSEM with Bayesian information criterion (BIC) results in the highest true positive rates, the largest percentage of correctly predicted interactions from all existing interactions, and the highest true negative (non-existing interactions) rates. Next, we apply SSEM using real microarray data to infer TC interactions among (1) small groups of genes that are synthetic sick or lethal (SSL) to SGS1, and (2) a group of SSL pairs of 51 yeast genes involved in DNA synthesis and repair that are of interest. For (1), SSEM with BIC is shown to outperform three Bayesian network algorithms and a multivariate autoregressive model, checked against the results of qRT-PCR experiments. The predictions for (2) are shown to coincide with several known pathways of Sgs1 and its partners that are involved in DNA replication, recombination and repair. In addition, experimentally testable interactions of Rad27 are predicted.</p> <p>Conclusion</p> <p>SSEM is a useful tool for inferring genetic networks, and the results reinforce the possibility of predicting pathways of protein complexes via genetic interactions.</p

Vitamin D3 Deficiency Differentially Affects Functional and Disease Outcomes in the G93A Mouse Model of Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a neuromuscular disease characterized by motor neuron death in the central nervous system. Vitamin D supplementation increases antioxidant activity, reduces inflammation and improves motor neuron survival. We have previously demonstrated that vitamin D3 supplementation at 10× the adequate intake improves functional outcomes in a mouse model of ALS

hMRE11 deficiency leads to microsatellite instability and defective DNA mismatch repair

DNA mismatch repair (MMR) is essential in the surveillance of accurate transmission of genetic information, and defects in this pathway lead to microsatellite instability and hereditary nonpolyposis colorectal cancer (HNPCC). Our previous study raised the possibility that hMRE11 might be involved in MMR through physical interaction with hMLH1. Here, we show that hMRE11 deficiency leads to significant increase in MSI for both mono- and dinucleotide sequences. Furthermore, RNA-interference-mediated hMRE11-knockdown in HeLa cells results in MMR deficiency. Analysis of seven HNPCC-associated hMLH1 missense mutations located within the hMRE11-interacting domain shows that four mutations (L574P, K618T, R659P and A681T) cause near-complete disruption of the interaction between hMRE11 and hMLH1, and two mutations (Q542L and L582V) cause a 30% reduction of protein interaction. These findings indicate that hMRE11 represents a functional component of the MMR pathway and the disruption of hMLH1-hMRE11 interaction could be an alternative molecular explanation for hMLH1 mutations in a subset of HNPCC tumours

14-3-3 proteins regulate exonuclease 1-dependent processing of stalled replication forks

Replication fork integrity, which is essential for the maintenance of genome stability, is monitored by checkpoint-mediated phosphorylation events. 14-3-3 proteins are able to bind phosphorylated proteins and were shown to play an undefined role under DNA replication stress. Exonuclease 1 (Exo1) processes stalled replication forks in checkpoint-defective yeast cells. We now identify 14-3-3 proteins as in vivo interaction partners of Exo1, both in yeast and mammalian cells. Yeast 14-3-3-deficient cells fail to induce Mec1-dependent Exo1 hyperphosphorylation and accumulate Exo1-dependent ssDNA gaps at stalled forks, as revealed by electron microscopy. This leads to persistent checkpoint activation and exacerbated recovery defects. Moreover, using DNA bi-dimensional electrophoresis, we show that 14-3-3 proteins promote fork progression under limiting nucleotide concentrations. We propose that 14-3-3 proteins assist in controlling the phosphorylation status of Exo1 and additional unknown targets, promoting fork progression, stability, and restart in response to DNA replication stress