High Prevalence of Prolonged QT Interval in Obese Hypogonadal Males

Obese subjects show several electrocardiographic alterations, including prolonged QT interval, a marker for fatal cardiac arrhythmias. Prolonged QT interval has recently been linked to low testosterone levels, a frequent occurrence in male obese patients but no study has yet assessed whether hypoandrogenism contributes to QT interval prolongation in this population. Aim of this study was to evaluate whether prolonged QT interval is linked to hypogonadism in male obese subjects. QT interval corrected for heart rate (QTc) was measured from standard electrocardiogram recordings in 136 obese men (BMI 30 >kg/m², range 30.1–75.4 kg/m²). Obese men were classified as eugonadal or hypogonadal according to serum total testosterone levels (i.e., greater or less than 9.9 nmol/l). Our study showed that QTc measurements corrected by either Bazett (419 ± 3.2 vs. 408 ± 3.4 ms, P < 0.05), Fridericia (406.3 ± 3.39 vs. 396.4 ± 3.03 ms, P < 0.05) or Hodges (407.0 ± 3.12 vs. 397.3 ± 2.84 ms, P < 0.05) were longer in hypogonadal compared with eugonadal obese men; further, prolonged QTc interval (i.e., >440 ms) was more frequent among hypogonadal compared with eugonadal obese men (23% vs. 10%, P < 0.05). The degree of weight excess, diabetes, sleep apnoea and potassium levels were not associated with prolonged QTc. In conclusion, obese hypogonadal men show a greater prevalence of prolonged QT interval compared with their eugonadal counterparts. It appears therefore that low levels of testosterone in obese men may contribute to the arrhythmogenic profile of these patients, a heretofore unknown link which warrants further clinical attention.     

Assessment of Heavy Metals Contamination and Potential Ecological Risk in Soils Affected by a Former Mn Mining Activity,Drama District,Northern Greece

Manganese mining activities in the Drama district, northern Greece, have resulted in a legacy of abandoned mine wastes at the “25 km Mn-mine” site. Current research was focused on the western Drama plain (WDP), constituting the recipient of the effluents from Xiropotamos stream, which passes through the “25 km Mn-mine” place. A total of 148 top soil samples were collected and their heavy metals (HMs) concentrations (Mn, Pb, Zn, Cu, Cd, and As) were determined using inductively coupled plasma mass spectrometry. Enrichment factor (EF), geoaccumulation index (Igeo), and pollution load index (PLI) were calculated as an effort to assess metal accumulation, distribution, and pollution status of the soils due to the former mining activity. The overall potential ecological risk of HMs to the environment was also evaluated using the potential toxicity response index (RI). Results showed that peak values of the elements (13 wt% for Mn, 0.2 wt% for Pb, 0.2 wt% for Zn, 0.1 wt% for As, 153 mg/kg for Cu, and 27.5 mg/kg for Cd) were found in soils from sites close to and along both sides of the Xiropotamos stream. In this sector of WDP, values of EF, Igeo, and PLI classify the soils as moderately to highly polluted with Mn, Pb, Zn, Cd, and As. Based on RI values, soils in this part of WDP display considerable to very great potential ecological risk and, therefore, a remediation has to be applied. The main cause of soil contamination is considered the Xiropotamos downstream transfer and dispersion of Mn mine wastes via flooding episodes.     

Coupled global and targeted proteomics of human embryonic stem cells during induced differentiation

Elucidating the complex combinations of growth factors and signaling molecules that maintain pluripotency or, alternatively, promote the controlled differentiation of human embryonic stem cells (hESCs) has important implications for the fundamental understanding of human development, devising cell replacement therapies, and cancer cell biology. hESCs are commonly grown on irradiated mouse embryonic fibroblasts (MEFs) or in conditioned medium from MEFs. These culture conditions interfere with many experimental conclusions and limit the ability to perform conclusive proteomics studies. The current investigation avoided the use of MEFs or MEF-conditioned medium for hESC culture, allowing global proteomics analysis without these confounding conditions, and elucidated neural cell-specific signaling pathways involved in noggin-induced hESC differentiation. Based on these analyses, we propose the following early markers of hESC neural differentiation: collapsin response mediator proteins 2 and 4 and the nuclear autoantigenic sperm protein as a marker of pluripotent hESCs. We then developed a directed mass spectrometry assay using multiple reaction monitoring (MRM) to identify and quantify these markers and in addition the epidermal ectoderm marker cytokeratin-8. Analysis of global proteomics, quantitative RT-PCR, and MRM data led to testing the isoform interference hypothesis where redundant peptides dilute quantification measurements of homologous proteins. These results show that targeted MRM analysis on non-redundant peptides provides more exact quantification of homologous proteins. This study describes the facile transition from discovery proteomics to targeted MRM analysis and allowed us to identify and verify several potential biomarkers for hESCs during noggin-induced neural and BMP4-induced epidermal ectoderm differentiation.     

Novel mutations in the adipose triglyceride lipase gene causing neutral lipid storage disease with myopathy

A subgroup of neutral lipid storage disease has been recently associated with myopathy (NLSDM) and attributed to mutations in the gene (PNPLA2) encoding an adipose triglyceride lipase involved in the degradation of intracellular triglycerides. Five NLSDM patients have been described thus far and we reported three additional patients. A 44-year old Iranian woman and two Italian brothers, aged 40 and 35, presented with exercise intolerance and proximal limb weakness, elevated CK levels, and Jordan’s anomaly. Muscle biopsies showed marked neutral lipid accumulation in all patients. The 10 exons and the intron-exon junctions of the PNPLA2 gene were sequenced. Two novel homozygous mutations in exon 5 of PNPLA2 gene were found (c.695delT and c.542delAC). Both mutations resulted in frameshifts leading to premature stop codons (p.L255X and p.I212X, respectively). These mutations predict a truncated PNPLA2 protein lacking the C-terminal hydrophobic domain. These findings indicate that NLSDM is rare, but genetically heterogeneous.     

Analysis of xyloglucan endotransglycosylase/hydrolase (XTH) genes from allotetraploid (Gossypium hirsutum) cotton and its diploid progenitors expressed during fiber elongation

Multiple cellular pathways have been shown to be involved during fiber initiation and elongation stages in the cultivated allotetraploid cotton (Gossypium hirsutum). The cell wall enzymes xyloglucan endotransglycosylase/hydrolases (XTH) have been reported to be associated with the biosynthesis of the cell wall and the growth of cotton fibers, probably regulating the plasticity of the primary cell wall. Among various cotton fiber cDNAs found to be preferentially expressed in cotton fibers, a xyloglucan endotransglycosylase (XTH) cDNA was significantly up-regulated during the elongation stage of cotton fiber development. In the present study, we isolated and characterized genomic clones encoding cotton XTH from cultivated cotton (Gossypium hirsutum) and its diploid progenitors (Gossypium arboreum and Gossypium raimondii), designated GhXTH1-1, GhXTH1-2, GaXTH1 and GrXTH, respectively. In addition, we isolated and characterized, by in silico methods, the putative promoter of XTH1 from Gossypium hirsutum. Sequence analysis revealed more than 50% homology to XTH's at the protein level. DNA gel blot hybridization indicated that at least two copies of GhXTH1 are present in Gossypium hirsutum whereas the diploid progenitor species Gossypium arboreum and Gossypium raimondii has only a single copy. Quantitative real-time PCR and high-resolution melting experiments indicated that in Gossypium hirsutum cultivars, in cotton fibers during early stages of fiber elongation specifically expressing only the GhXTH1-1 gene and expression levels of GhXTH1-1 in fibers varies among cultivars differing in fiber percentage and fiber length.     

Pairwise Variable Selection for High‐Dimensional Model‐Based Clustering

Summary Variable selection for clustering is an important and challenging problem in high‐dimensional data analysis. Existing variable selection methods for model‐based clustering select informative variables in a “one‐in‐all‐out” manner; that is, a variable is selected if at least one pair of clusters is separable by this variable and removed if it cannot separate any of the clusters. In many applications, however, it is of interest to further establish exactly which clusters are separable by each informative variable. To address this question, we propose a pairwise variable selection method for high‐dimensional model‐based clustering. The method is based on a new pairwise penalty. Results on simulated and real data show that the new method performs better than alternative approaches that use ?₁ and ?_∞ penalties and offers better interpretation.     

Genome-Scale Identification of SARS-CoV-2 and Pan-coronavirus Host Factor Networks

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The application of rule-based methods to class prediction problems in genomics.

We propose a method for constructing classifiers using logical combinations of elementary rules. The method is a form of rule-based classification, which has been widely discussed in the literature. In this work we focus specifically on issues that arise in the context of classifying cell samples based on RNA or protein expression measurements. The basic idea is to specify elementary rules that exhibit a locally strong pattern in favor of a single class. Strict admissibility criteria are imposed to produce a manageable universe of elementary rules. Then the elementary rules are combined using a set covering algorithm to form a composite rule that achieves a perfect fit to the training data. The user has explicit control over a parameter that determines the composite rule's level of redundancy and parsimony. This built-in control, along with the simplicity of interpreting the rules, makes the method particularly useful for classification problems in genomics. We demonstrate the new method using several microarray datasets and examine its generalization performance. We also draw comparisons to other machine-learning strategies such as CART, ID3, and C4.5.