Association of Systemic Collagen Type IV Formation with Survival among Patients Undergoing Hemodialysis

Objective

The 7S domain of collagen type IV (P4NP_7S) assessed in plasma represents systemic collagen type IV formation. The objective of the study was to investigate the association of systemic collagen type IV formation with survival among patients undergoing hemodialysis.

Methods

We performed an observational cohort study of 371 hemodialysis patients. Plasma P4NP_7S was analyzed using a specific enzyme-linked immunosorbent assay detecting the amino-terminal propeptide of type IV procollagen. Association between categories of plasma P4NP_7S concentrations and survival was initially assessed by Kaplan-Meier analysis, then in an adjusted Cox model.

Results

For hemodialysis patients in the highest category of systemic collagen type IV formation, i.e. plasma P4NP_7S concentrations more than 775 pg/L, an increased risk for death was observed (highest P4NP_7S category vs all other categories, hazard ratio, 1.934; 95% confidence interval, 1.139 to 3.285). Survival analysis showed an increased risk of death in the highest P4NP_7S category compared to the other categories (Chi square 6.903; P = 0.032). Median survival was only 105 days in the highest P4NP_7S category whereas it was 629 days in the medium category, and 905 days in the lowest category. Multivariable-adjusted Cox regression showed increased odds for death with higher age and higher P4NP_7S categories. Systemic collagen type IV formation was associated with plasma concentrations of the collagen IV degradation product C4M (Spearman r = 0.764; P<0.0001) confirming extracellular matrix turnover.

Conclusion

Among hemodialysis patients elevated systemic collagen type IV formation suggesting accelerating systemic fibrosis was associated with increased risk of death.     

Snx3 Regulates Recycling of the Transferrin Receptor and Iron Assimilation

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Highlights? Snx3 is highly expressed in vertebrate hematopoietic tissues ? Silencing of Snx3 results in anemia and hemoglobin defects in vertebrates ? Snx3 and Vps35 physically interact with Tfrc ? Snx3 is required for endosomal recycling of Tf-Tfrc complex     

Human Spermatogenic Failure Purges Deleterious Mutation Load from the Autosomes and Both Sex Chromosomes,including the Gene DMRT1

Gonadal failure, along with early pregnancy loss and perinatal death, may be an important filter that limits the propagation of harmful mutations in the human population. We hypothesized that men with spermatogenic impairment, a disease with unknown genetic architecture and a common cause of male infertility, are enriched for rare deleterious mutations compared to men with normal spermatogenesis. After assaying genomewide SNPs and CNVs in 323 Caucasian men with idiopathic spermatogenic impairment and more than 1,100 controls, we estimate that each rare autosomal deletion detected in our study multiplicatively changes a man''s risk of disease by 10% (OR 1.10 [1.04–1.16], p<2×10⁻³), rare X-linked CNVs by 29%, (OR 1.29 [1.11–1.50], p<1×10⁻³), and rare Y-linked duplications by 88% (OR 1.88 [1.13–3.13], p<0.03). By contrasting the properties of our case-specific CNVs with those of CNV callsets from cases of autism, schizophrenia, bipolar disorder, and intellectual disability, we propose that the CNV burden in spermatogenic impairment is distinct from the burden of large, dominant mutations described for neurodevelopmental disorders. We identified two patients with deletions of DMRT1, a gene on chromosome 9p24.3 orthologous to the putative sex determination locus of the avian ZW chromosome system. In an independent sample of Han Chinese men, we identified 3 more DMRT1 deletions in 979 cases of idiopathic azoospermia and none in 1,734 controls, and found none in an additional 4,519 controls from public databases. The combined results indicate that DMRT1 loss-of-function mutations are a risk factor and potential genetic cause of human spermatogenic failure (frequency of 0.38% in 1306 cases and 0% in 7,754 controls, p = 6.2×10⁻⁵). Our study identifies other recurrent CNVs as potential causes of idiopathic azoospermia and generates hypotheses for directing future studies on the genetic basis of male infertility and IVF outcomes.     

Can Abundance of Protists Be Inferred from Sequence Data: A Case Study of Foraminifera

Protists are key players in microbial communities, yet our understanding of their role in ecosystem functioning is seriously impeded by difficulties in identification of protistan species and their quantification. Current microscopy-based methods used for determining the abundance of protists are tedious and often show a low taxonomic resolution. Recent development of next-generation sequencing technologies offered a very powerful tool for studying the richness of protistan communities. Still, the relationship between abundance of species and number of sequences remains subjected to various technical and biological biases. Here, we test the impact of some of these biological biases on sequence abundance of SSU rRNA gene in foraminifera. First, we quantified the rDNA copy number and rRNA expression level of three species of foraminifera by qPCR. Then, we prepared five mock communities with these species, two in equal proportions and three with one species ten times more abundant. The libraries of rDNA and cDNA of the mock communities were constructed, Sanger sequenced and the sequence abundance was calculated. The initial species proportions were compared to the raw sequence proportions as well as to the sequence abundance normalized by rDNA copy number and rRNA expression level per species. Our results showed that without normalization, all sequence data differed significantly from the initial proportions. After normalization, the congruence between the number of sequences and number of specimens was much better. We conclude that without normalization, species abundance determination based on sequence data was not possible because of the effect of biological biases. Nevertheless, by taking into account the variation of rDNA copy number and rRNA expression level we were able to infer species abundance, suggesting that our approach can be successful in controlled conditions.     

Relative Amino Acid Composition Signatures of Organisms and Environments

Background

Identifying organism-environment interactions at the molecular level is crucial to understanding how organisms adapt to and change the chemical and molecular landscape of their habitats. In this work we investigated whether relative amino acid compositions could be used as a molecular signature of an environment and whether such a signature could also be observed at the level of the cellular amino acid composition of the microorganisms that inhabit that environment.

Methodologies/Principal Findings

To address these questions we collected and analyzed environmental amino acid determinations from the literature, and estimated from complete genomic sequences the global relative amino acid abundances of organisms that are cognate to the different types of environment. Environmental relative amino acid abundances clustered into broad groups (ocean waters, host-associated environments, grass land environments, sandy soils and sediments, and forest soils), indicating the presence of amino acid signatures specific for each environment. These signatures correlate to those found in organisms. Nevertheless, relative amino acid abundance of organisms was more influenced by GC content than habitat or phylogeny.

Conclusions

Our results suggest that relative amino acid composition can be used as a signature of an environment. In addition, we observed that the relative amino acid composition of organisms is not highly determined by environment, reinforcing previous studies that find GC content to be the major factor correlating to amino acid composition in living organisms.     

Leading compounds for the validation of animal models of psychopathology

Modelling of complex psychiatric disorders, e.g., depression and schizophrenia, in animals is a major challenge, since they are characterized by certain disturbances in functions that are absolutely unique to humans. Furthermore, we still have not identified the genetic and neurobiological mechanisms, nor do we know precisely the circuits in the brain that function abnormally in mood and psychotic disorders. Consequently, the pharmacological treatments used are mostly variations on a theme that was started more than 50 years ago. Thus, progress in novel drug development with improved therapeutic efficacy would benefit greatly from improved animal models. Here, we review the available animal models of depression and schizophrenia and focus on the way that they respond to various types of potential candidate molecules, such as novel antidepressant or antipsychotic drugs, as an index of predictive validity. We conclude that the generation of convincing and useful animal models of mental illnesses could be a bridge to success in drug discovery.