Up-regulation of kin17 is essential for proliferation of breast cancer

Background

Kin17 is ubiquitously expressed at low levels in human tissue and participates in DNA replication, DNA repair and cell cycle control. Breast cancer cells are characterized by enabling replicative immortality and accumulated DNA damage. However, whether kin17 contributes to breast carcinogenesis remains unknown.

Methodology/Principal Findings

In this study, we show for the first time that kin17 is an important molecule related to breast cancer. Our results show that kin17 expression was markedly increased in clinical breast tumors and was associated with tumor grade, Ki-67 expression, p53 mutation status and progesterone receptor expression, which were assessed in a clinicopathologic characteristics review. Knockdown of kin17 inhibited DNA replication and repair, blocked cell cycle progression and inhibited anchorage-independent growth, while increasing sensitivity to chemotherapy in breast cancer cells. Moreover, kin17 silencing decreased EGF-stimulated cell growth. Furthermore, overexpression of kin17 promoted DNA replication and cell proliferation in MCF-10A.

Conclusions/Significance

Our findings indicate that up-regulation of kin17 is strongly associated with cellular proliferation, DNA replication, DNA damage response and breast cancer development. The increased level of kin17 was not only a consequence of immortalization but also associated with tumorigenesis. Therefore, kin17 could be a novel therapeutic target for inhibiting cell growth in breast cancer.     

EXAMINE: a computational approach to reconstructing gene regulatory networks

Reverse-engineering of gene networks using linear models often results in an underdetermined system because of excessive unknown parameters. In addition, the practical utility of linear models has remained unclear. We address these problems by developing an improved method, EXpression Array MINing Engine (EXAMINE), to infer gene regulatory networks from time-series gene expression data sets. EXAMINE takes advantage of sparse graph theory to overcome the excessive-parameter problem with an adaptive-connectivity model and fitting algorithm. EXAMINE also guarantees that the most parsimonious network structure will be found with its incremental adaptive fitting process. Compared to previous linear models, where a fully connected model is used, EXAMINE reduces the number of parameters by O(N), thereby increasing the chance of recovering the underlying regulatory network. The fitting algorithm increments the connectivity during the fitting process until a satisfactory fit is obtained. We performed a systematic study to explore the data mining ability of linear models. A guideline for using linear models is provided: If the system is small (3-20 elements), more than 90% of the regulation pathways can be determined correctly. For a large-scale system, either clustering is needed or it is necessary to integrate information in addition to expression profile. Coupled with the clustering method, we applied EXAMINE to rat central nervous system development (CNS) data with 112 genes. We were able to efficiently generate regulatory networks with statistically significant pathways that have been predicted previously.     

Calcitonin gene-related peptide and hypertension

Capsaicin-sensitive sensory nerves participate in the regulation of cardiovascular functions both in the normal state and the pathophysiology of hypertension through the actions of potent vasodilator neuropeptides, including calcitonin gene-related peptide (CGRP). CGRP, a very potent vasodilator, is the predominant neurotransmitter in capsaicin-sensitive sensory nerves, and plays an important role in the initiation, progression and maintenance of hypertension via: (1) the alterations in its synthesis and release and/or in vascular sensitivity response to it; (2) interactions with pro-hypertensive systems, including renin-angiotensin-aldosterone system, sympathetic nervous system and endothelin system; and (3) anti-hypertrophy and anti-proliferation of vascular smooth muscle cells. The decrease in CGRP synthesis and release contributes to the elevated blood pressure, as shown in the spontaneously hypertensive rats, alpha-CGRP knockout mice, Dahl-salt or phenol-induced hypertensive rats. In contrast, the increase in CGRP levels or the enhancement of vascular sensitivity response to CGRP plays a beneficial compensatory depressor role in the development of hypertension, as shown in deoxycorticosterone-salt, sub-total nephrectomy-salt, N(omega)-nitro-L-arginine methyl ester or two-kidney, one-clip models of hypertension in rats. We found that rutaecarpine causes a sustained depressor action by stimulation of CGRP synthesis and release via activation of vanilloid receptor subtype 1 (VR1) in hypertensive rats, which reveals the therapeutic implications of VR1 agonists for treatment of hypertension.     

Effect of incubation temperature on green sturgeon embryos, <Emphasis Type="Italic">Acipenser medirostris</Emphasis>

Regulation of river flow and the amount of winter rainfall are the major factors affecting the water temperature of the spawning grounds, for green sturgeon in the Klamath River. During the primary spawning period of green sturgeon, mid-April to June, the water temperature may vary from 8 to 21°C. To estimate the potential implications of this modified thermal regime, we examined the survival and development in three progeny groups of green sturgeon embryos from zygote to hatch, at constant incubation temperatures (11–26°C). Temperatures 23–26°C affected cleavage and gastrulation and all died before hatch. Temperatures 17.5–22°C were suboptimal as an increasing number of embryos developed abnormally and hatching success decreased at 20.5–22°C, although the tolerance to these temperatures varied between progenies. The lower temperature limit was not evident from this study, although hatching rate decreased at 11°C and hatched embryos were shorter, compared to 14°C. The mean total length of hatched embryos decreased with increasing temperature, although their wet and dry weight remained relatively constant. We concluded that temperatures 17–18°C may be the upper limit of the thermal optima for green sturgeon embryos, and that the river thermal regime during dry years may affect green sturgeon reproduction.     

Phylogeny of Prokaryotes and Chloroplasts Revealed by a Simple Composition Approach on All Protein Sequences from Complete Genomes Without Sequence Alignment

The complete genomes of living organisms have provided much information on their phylogenetic relationships. Similarly, the complete genomes of chloroplasts have helped to resolve the evolution of this organelle in photosynthetic eukaryotes. In this paper we propose an alternative method of phylogenetic analysis using compositional statistics for all protein sequences from complete genomes. This new method is conceptually simpler than and computationally as fast as the one proposed by Qi et al. (2004b) and Chu et al. (2004). The same data sets used in Qi et al. (2004b) and Chu et al. (2004) are analyzed using the new method. Our distance-based phylogenic tree of the 109 prokaryotes and eukaryotes agrees with the biologists tree of life based on 16S rRNA comparison in a predominant majority of basic branching and most lower taxa. Our phylogenetic analysis also shows that the chloroplast genomes are separated to two major clades corresponding to chlorophytes s.l. and rhodophytes s.l. The interrelationships among the chloroplasts are largely in agreement with the current understanding on chloroplast evolution.Reviewing Editor: Dr. John Oakeshott     

Detecting differentially expressed genes by relative entropy

DNA microarray experiments have generated large amount of gene expression measurements across different conditions. One crucial step in the analysis of these data is to detect differentially expressed genes. Some parametric methods, including the two-sample t-test (T-test) and variations of it, have been used. Alternatively, a class of non-parametric algorithms, such as the Wilcoxon rank sum test (WRST), significance analysis of microarrays (SAM) of Tusher et al. (2001), the empirical Bayesian (EB) method of Efron et al. (2001), etc., have been proposed. Most available popular methods are based on t-statistic. Due to the quality of the statistic that they used to describe the difference between groups of data, there are situations when these methods are inefficient, especially when the data follows multi-modal distributions. For example, some genes may display different expression patterns in the same cell type, say, tumor or normal, to form some subtypes. Most available methods are likely to miss these genes. We developed a new non-parametric method for selecting differentially expressed genes by relative entropy, called SDEGRE, to detect differentially expressed genes by combining relative entropy and kernel density estimation, which can detect all types of differences between two groups of samples. The significance of whether a gene is differentially expressed or not can be estimated by resampling-based permutations. We illustrate our method on two data sets from Golub et al. (1999) and Alon et al. (1999). Comparing the results with those of the T-test, the WRST and the SAM, we identified novel differentially expressed genes which are of biological significance through previous biological studies while they were not detected by the other three methods. The results also show that the genes selected by SDEGRE have a better capability to distinguish the two cell types.     

Colorimetric approach to high-throughput mutation analysis

High-throughput genomic mutation screening for primary tumors has characteristically been expensive, labor-intensive, and inadequate to detect low levels of mutation in a background of wild-type signal. We present a new, combined PCR and colorimetric approach that is inexpensive, simple, and can detect the presence of 1% mutation in a background of wild-type. We compared manual dideoxy sequencing of p53 for eight lung cancer samples to a novel assay combining a primer extension step and an enzymatic colorimetric step in a 96-well plate with covalently attached oligonucleotide sequences. For every sample, we were able to detect the presence or absence of the specific mutation with a statistically significant difference between the sample optical density (OD) and the background OD, with a sensitivity and specificity of 100%. This assay is straightforward, accurate, inexpensive, and allows for rapid, high-throughput analysis of samples, making it ideal for genomic mutation or polymorphism screening studies in both clinical and research settings.