Differences in collagen prolyl 4-hydroxylase assembly between two Caenorhabditis nematode species despite high amino acid sequence identity of the enzyme subunits

The collagen prolyl 4-hydroxylases (P4Hs) are essential for proper extracellular matrix formation in multicellular organisms. The vertebrate enzymes are α2β2 tetramers, in which the β subunits are identical to protein disulfide isomerase (PDI). Unique P4H forms have been shown to assemble from the <i>Caenorhabditis</i> <i>elegans</i> catalytic α subunit isoforms PHY-1 and PHY-2 and the β subunit PDI-2. A mixed PHY-1/PHY-2/(PDI-2)<sub>2</sub> tetramer is the major form, while PHY-1/PDI- 2 and PHY-2/PDI-2 dimers are also assembled but less efficiently. Cloning and characterization of the orthologous subunits from the closely related nematode <i>Caenorhabditis</i> <i>briggsae</i> revealed distinct differences in the assembly of active P4H forms in spite of the extremely high amino acid sequence identity (92-97%) between the <i>C. briggsae</i> and <i>C. elegans</i> subunits. In addition to a PHY-1/PHY-2(PDI-2)<sub>2</sub> tetramer and a PHY-1/PDI-2 dimer, an active (PHY- 2)<sub>2</sub>(PDI-2)<sub>2</sub> tetramer was formed in <i>C. briggsae</i> instead of a PHY-2/PDI-2 dimer. Site-directed mutagenesis studies and generation of inter-species hybrid polypeptides showed that the N-terminal halves of the <i>Caenorhabditis</i> PHY-2 polypeptides determine their assembly properties. Genetic disruption of <i>C. briggsae phy-1</i> (<i>Cb-dpy-18</i>) via a <i>Mos1</i> insertion resulted a small (short) phenotype that is less severe than the dumpy (short and fat) phenotype of the corresponding <i>C. elegans</i> mutants (<i>Ce-dpy-18</i>). <i>C. briggsae</i> <i>phy-2</i> RNA interference produced no visible phenotype in the wild type nematodes but produced a severe dumpy phenotype and larval arrest in <i>phy-1</i> mutants. Genetic complementation of the <i>C. briggsae</i> and <i>C. elegans</i> <i>phy-1</i> mutants was achieved by injection of a wild type <i>phy-1</i> gene from either species

Crystal structure of the human, FIC-Domain containing protein HYPE and implications for its functions

Protein AMPylation, the transfer of AMP from ATP to protein targets, has been recognized as a new mechanism of host-cell disruption by some bacterial effectors that typically contain a FIC-domain. Eukaryotic genomes also encode one FIC-domain protein, HYPE, which has remained poorly characterized. Here we describe the structure of human HYPE, solved by X-ray crystallography, representing the first structure of a eukaryotic FIC-domain protein. We demonstrate that HYPE forms stable dimers with structurally and functionally integrated FIC-domains and with TPR-motifs exposed for protein-protein interactions. As HYPE also uniquely possesses a transmembrane helix, dimerization is likely to affect its positioning and function in the membrane vicinity. The low rate of autoAMPylation of the wild-type HYPE could be due to autoinhibition, consistent with the mechanism proposed for a number of putative FIC AMPylators. Our findings also provide a basis to further consider possible alternative cofactors of HYPE and distinct modes of target-recognition

The fitness consequences of inbreeding in natural populations and their implications for species conservation – a systematic map

Background: Threatened species often have small and isolated populations where mating among relatives can result in inbreeding depression increasing extinction risk. Effective management is hampered by a lack of syntheses summarising the magnitude of, and variation in inbreeding depression. Here we describe the nature and scope of the literature examining phenotypic/fitness consequences of inbreeding, to provide a foundation for future syntheses and management. Methods: We searched the literature for articles documenting the impact of inbreeding in natural populations. Article titles, abstracts and full-texts were assessed against a priori defined criteria, and information relating to study design, quality and other factors that may influence inbreeding responses (e.g. population size) was extracted from relevant articles. Results: The searches identified 11457 articles, of which 614 were assessed as relevant and included in the systematic map (corresponding to 703 distinct studies). Most studies (663) assessed within-population inbreeding resulting from self-fertilisation or consanguineous pairings, while 118 studies assessed among-population inbreeding due to drift load. Plants were the most studied taxon (469 studies) followed by insects (52 studies) and birds (43 studies). Most studies investigated the effects of inbreeding on components of fitness (e.g. survival or fecundity; 648 studies) but measurements were typically under laboratory/greenhouse conditions (486 studies). Observations were also often restricted to the first inbred generation (607 studies) and studies frequently lacked contextual information (e.g. population size). Conclusions: Our systematic map describes the scope and quality of the evidence describing the phenotypic consequences of inbreeding. The map reveals substantial evidence relating to inbreeding responses exists, but highlights information is still limited for some aspects, including the effects of multiple generations of inbreeding. The systematic map allowed us to define several conservation-relevant questions, where sufficient data exists to support systematic reviews, e.g. How do inbreeding responses vary with population size? However, we found that such syntheses are likely to be constrained by incomplete reporting of critical contextual information. Our systematic map employed the same rigorous literature assessment methods as systematic review, including a novel survey of study quality and thus provides a robust foundation to guide future research and syntheses seeking to inform conservation decision-making

Packages of Care for Schizophrenia in Low- and Middle-Income Countries

In the third in a series of six articles on packages of care for mental disorders in low- and middle-income countries, Jair Mari and colleagues discuss the treatment of schizophrenia

Accelerated inbreeding depression suggests synergistic epistasis for deleterious mutations in Drosophila melanogaster

Epistasis may have important consequences for a number of issues in quantitative genetics and evolutionary biology. In particular, synergistic epistasis for deleterious alleles is relevant to the mutation load paradox and the evolution of sex and recombination. Some studies have shown evidence of synergistic epistasis for spontaneous or induced deleterious mutations appearing in mutation-accumulation experiments. However, many newly arising mutations may not actually be segregating in natural populations because of the erasing action of natural selection. A demonstration of synergistic epistasis for naturally segregating alleles can be achieved by means of inbreeding depression studies, as deleterious recessive allelic effects are exposed in inbred lines. Nevertheless, evidence of epistasis from these studies is scarce and controversial. In this paper, we report the results of two independent inbreeding experiments carried out with two different populations of Drosophila melanogaster. The results show a consistent accelerated inbreeding depression for fitness, suggesting synergistic epistasis among deleterious alleles. We also performed computer simulations assuming different possible models of epistasis and mutational parameters for fitness, finding some of them to be compatible with the results observed. Our results suggest that synergistic epistasis for deleterious mutations not only occurs among newly arisen spontaneous or induced mutations, but also among segregating alleles in natural populationsWe acknowledge the support by Uvigo Marine Research Centre funded by the “Excellence in Research (INUGA)” Programme from the Regional Council of Culture, Education and Universities, with co-funding from the European Union through the ERDF Operational Programme Galicia 2014-2020. This work was funded by Agencia Estatal de Investigación (AEI) (CGL2016-75904-C2-1-P), Xunta de Galicia (ED431C 2016-037) and Fondos Feder: “Unha maneira de facer Europa.” SD was founded by a predoctoral (FPI) grant from Ministerio de Economía y Competitividad, SpainS

Predicting probable Alzheimer's disease using linguistic deficits and biomarkers

BackgroundThe manual diagnosis of neurodegenerative disorders such as Alzheimer’s disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls.ResultsOur models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM).ConclusionsExperimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD