2,4-Dichloro-1-[1-(2,4-dichloro­benz­yl­oxy)eth­yl]benzene

In the title compound, C15H12Cl4O, the dihedral angle between the least-squares planes of the two benzene rings is 82.6 (9)°. The dihedral angles between the COC mean plane of the ­oxy group and the two benzene rings are 84.3 (5) and 10.8 (5)°. In the crystal, two weak π–π inter­actions [centroid–centroid distances = 3.9989 (8) and 3.7912 (8) Å] and a C—H⋯π inter­action are observed

Effect of Nitrogen Addition on Selection of Germination Trait in an Alpine Meadow on the Tibet Plateau

Seed germination requirements may determine the kinds of habitat in which plants can survive. We tested the hypothesis that nitrogen (N) addition can change seed germination trait-environmental filter interactions and ultimately redistribute seed germination traits in alpine meadows. We determined the role of N addition on germination trait selection in an alpine meadow after N addition by combining a 3-year N addition experiment in an alpine meadow and laboratory germination experiments. At the species level, germination percentage, germination rate (speed) and breadth of temperature niche for germination (BTN) were positively related to survival of a species in the fertilized community. In addition, community-weighted means of germination percentage, germination rate, germination response to alternating temperature and BTN increased. However, germination response to wet-cold storage (cold stratification) and functional richness of germination traits was lower in alpine meadows with high-nitrogen addition than in those with no, low and medium N addition. Thus, N addition had a significant influence on environmental filter-germination trait interactions and generated a different set of germination traits in the alpine meadow. Further, the effect of N addition on germination trait selection by environmental filters was amount-dependent. Low and medium levels of N addition had less effect on redistribution of germination traits than the high level

Exploring preconception signatures of metabolites in mothers with gestational diabetes mellitus using a non-targeted approach

BackgroundMetabolomic changes during pregnancy have been suggested to underlie the etiology of gestational diabetes mellitus (GDM). However, research on metabolites during preconception is lacking. Therefore, this study aimed to investigate distinctive metabolites during the preconception phase between GDM and non-GDM controls in a nested case-control study in Singapore.MethodsWithin a Singapore preconception cohort, we included 33 Chinese pregnant women diagnosed with GDM according to the IADPSG criteria between 24 and 28 weeks of gestation. We then matched them with 33 non-GDM Chinese women by age and pre-pregnancy body mass index (ppBMI) within the same cohort. We performed a non-targeted metabolomics approach using fasting serum samples collected within 12 months prior to conception. We used generalized linear mixed model to identify metabolites associated with GDM at preconception after adjusting for maternal age and ppBMI. After annotation and multiple testing, we explored the additional predictive value of novel signatures of preconception metabolites in terms of GDM diagnosis.ResultsA total of 57 metabolites were significantly associated with GDM, and eight phosphatidylethanolamines were annotated using HMDB. After multiple testing corrections and sensitivity analysis, phosphatidylethanolamines 36:4 (mean difference beta: 0.07; 95% CI: 0.02, 0.11) and 38:6 (beta: 0.06; 0.004, 0.11) remained significantly higher in GDM subjects, compared with non-GDM controls. With all preconception signals of phosphatidylethanolamines in addition to traditional risk factors (e.g., maternal age and ppBMI), the predictive value measured by area under the curve (AUC) increased from 0.620 to 0.843.ConclusionsOur data identified distinctive signatures of GDM-associated preconception phosphatidylethanolamines, which is of potential value to understand the etiology of GDM as early as in the preconception phase. Future studies with larger sample sizes among alternative populations are warranted to validate the associations of these signatures of metabolites and their predictive value in GDM.Peer reviewe

Analysis and Identification of Aptamer-Compound Interactions with a Maximum Relevance Minimum Redundancy and Nearest Neighbor Algorithm

The development of biochemistry and molecular biology has revealed an increasingly important role of compounds in several biological processes. Like the aptamer-protein interaction, aptamer-compound interaction attracts increasing attention. However, it is time-consuming to select proper aptamers against compounds using traditional methods, such as exponential enrichment. Thus, there is an urgent need to design effective computational methods for searching effective aptamers against compounds. This study attempted to extract important features for aptamer-compound interactions using feature selection methods, such as Maximum Relevance Minimum Redundancy, as well as incremental feature selection. Each aptamer-compound pair was represented by properties derived from the aptamer and compound, including frequencies of single nucleotides and dinucleotides for the aptamer, as well as the constitutional, electrostatic, quantum-chemical, and space conformational descriptors of the compounds. As a result, some important features were obtained. To confirm the importance of the obtained features, we further discussed the associations between them and aptamer-compound interactions. Simultaneously, an optimal prediction model based on the nearest neighbor algorithm was built to identify aptamer-compound interactions, which has the potential to be a useful tool for the identification of novel aptamer-compound interactions. The program is available upon the request

Knockout and transgenic mice of Trp53: what have we learned about p53 in breast cancer?

The human p53 tumor suppressor gene TP53 is mutated at a high frequency in sporadic breast cancer, and Li-Fraumeni syndrome patients who carry germline mutations in one TP53 allele have a high incidence of breast cancer. In the 10 years since the first knockout of the mouse p53 tumor suppressor gene (designated Trp53) was published, much has been learned about the contribution of p53 to biology and tumor suppression in the breast through the use of p53 transgenic and knockout mice. The original mice deficient in p53 showed no mammary gland phenotype. However, studies using BALB/c-Trp53-deficient mice have demonstrated a delayed involution phenotype and a mammary tumor phenotype. Together with other studies of mutant p53 transgenes and p53 bitransgenics, a greater understanding has been gained of the role of p53 in involution, of the regulation of p53 activity by hormones, of the effect of mouse strain and modifier genes on tumor phenotype, and of the cooperation between p53 and other oncogenic pathways, chemical carcinogens and hormonal stimulation in mammary tumorigenesis. Both p53 transgenic and knockout mice are important in vivo tools for understanding breast cancer, and are yet to be exploited for developing therapeutic strategies in breast cancer

Deficiency in Nucleotide Excision Repair Family Gene Activity, Especially ERCC3, Is Associated with Non-Pigmented Hair Fiber Growth

We conducted a microarray study to discover gene expression patterns associated with a lack of melanogenesis in non-pigmented hair follicles (HF) by microarray. Pigmented and non-pigmented HFs were collected and micro-dissected into the hair bulb (HB) and the upper hair sheaths (HS) including the bulge region. In comparison to pigmented HS and HBs, nucleotide excision repair (NER) family genes ERCC1, ERCC2, ERCC3, ERCC4, ERCC5, ERCC6, XPA, NTPBP, HCNP, DDB2 and POLH exhibited statistically significantly lower expression in non- pigmented HS and HBs. Quantitative PCR verified microarray data and identified ERCC3 as highly differentially expressed. Immunohistochemistry confirmed ERCC3 expression in HF melanocytes. A reduction in ERCC3 by siRNA interference in human melanocytes in vitro reduced their tyrosinase production ability. Our results suggest that loss of NER gene function is associated with a loss of melanin production capacity. This may be due to reduced gene transcription and/or reduced DNA repair in melanocytes which may eventually lead to cell death. These results provide novel information with regard to melanogenesis and its regulation

The contribution of dynamic stromal remodeling during mammary development to breast carcinogenesis

Breast cancer is a heterogeneous disease whose prognosis varies depending upon the developmental stage of the breast tissue at diagnosis. Notably, breast cancers associated with pregnancy exhibit increased rates of metastasis and poorer long-term survival compared to those diagnosed after menopause. However, postmenopausal breast cancers associated with obesity exhibit a more aggressive behavior and confer decreased overall patient survival compared to those diagnosed in non-obese individuals. Since the mammary gland is a dynamic tissue that undergoes significant changes throughout a woman's lifetime, especially during pregnancy and following menopause, we present evidence to support the notion that changes occurring throughout development within the mammary stromal compartment may account for some of the biological differences in breast cancer subtypes and behaviors

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p