High Differentiation among Eight Villages in a Secluded Area of Sardinia Revealed by Genome-Wide High Density SNPs Analysis

To better design association studies for complex traits in isolated populations it''s important to understand how history and isolation moulded the genetic features of different communities. Population isolates should not “a priori” be considered homogeneous, even if the communities are not distant and part of a small region. We studied a particular area of Sardinia called Ogliastra, characterized by the presence of several distinct villages that display different history, immigration events and population size. Cultural and geographic isolation characterized the history of these communities. We determined LD parameters in 8 villages and defined population structure through high density SNPs (about 360 K) on 360 unrelated people (45 selected samples from each village). These isolates showed differences in LD values and LD map length. Five of these villages show high LD values probably due to their reduced population size and extreme isolation. High genetic differentiation among villages was detected. Moreover population structure analysis revealed a high correlation between genetic and geographic distances. Our study indicates that history, geography and biodemography have influenced the genetic features of Ogliastra communities producing differences in LD and population structure. All these data demonstrate that we can consider each village an isolate with specific characteristics. We suggest that, in order to optimize the study design of complex traits, a thorough characterization of genetic features is useful to identify the presence of sub-populations and stratification within genetic isolates.     

Generation and Characterization of the First Immortalized Alpaca Cell Line Suitable for Diagnostic and Immunization Studies

Raising of alpacas as exotic livestock for wool and meat production and as companion animals is growing in importance in the United States, Europe and Australia. Furthermore the alpaca, as well as the rest of the camelids, possesses the peculiarity of producing single-chain antibodies from which nanobodies can be generated. Nanobodies, due to their structural simplicity and reduced size, are very versatile in terms of manipulation and bio-therapeutic exploitation. In fact the biotech companies involved in nanobody production and application continue to grow in number and size. Hence, the development of reagents and tools to assist in the further growth of this new scientific and entrepreneurial reality is becoming a necessity. These are needed mainly to address alpaca disease diagnosis and prophylaxis, and to develop alpaca immunization strategies for nanobody generation. For instance an immortalized alpaca cell line would be extremely valuable. In the present work the first stabilized alpaca cell line from alpaca skin stromal cells (ASSCs) was generated and characterized. This cell line was shown to be suitable for replication of viruses bovine herpesvirus-1, bovine viral diarrhea virus and caprine herpesvirus-1 and the endocellular parasite Neospora caninum. Moreover ASSCs were easy to transfect and transduce by several methods. These two latter characteristics are extremely useful when recombinant antigens need to be produced in a host homologous system. This work could be considered as a starting point for the expansion of the biotechnologies linked to alpaca farming and industry.     

Fine mapping of five loci associated with low-density lipoprotein cholesterol detects variants that double the explained heritability

Complex trait genome-wide association studies (GWAS) provide an efficient strategy for evaluating large numbers of common variants in large numbers of individuals and for identifying trait-associated variants. Nevertheless, GWAS often leave much of the trait heritability unexplained. We hypothesized that some of this unexplained heritability might be due to common and rare variants that reside in GWAS identified loci but lack appropriate proxies in modern genotyping arrays. To assess this hypothesis, we re-examined 7 genes (APOE, APOC1, APOC2, SORT1, LDLR, APOB, and PCSK9) in 5 loci associated with low-density lipoprotein cholesterol (LDL-C) in multiple GWAS. For each gene, we first catalogued genetic variation by re-sequencing 256 Sardinian individuals with extreme LDL-C values. Next, we genotyped variants identified by us and by the 1000 Genomes Project (totaling 3,277 SNPs) in 5,524 volunteers. We found that in one locus (PCSK9) the GWAS signal could be explained by a previously described low-frequency variant and that in three loci (PCSK9, APOE, and LDLR) there were additional variants independently associated with LDL-C, including a novel and rare LDLR variant that seems specific to Sardinians. Overall, this more detailed assessment of SNP variation in these loci increased estimates of the heritability of LDL-C accounted for by these genes from 3.1% to 6.5%. All association signals and the heritability estimates were successfully confirmed in a sample of ～10,000 Finnish and Norwegian individuals. Our results thus suggest that focusing on variants accessible via GWAS can lead to clear underestimates of the trait heritability explained by a set of loci. Further, our results suggest that, as prelude to large-scale sequencing efforts, targeted re-sequencing efforts paired with large-scale genotyping will increase estimates of complex trait heritability explained by known loci.     

Application of a new method for GWAS in a related case/control sample with known pedigree structure: identification of new loci for nephrolithiasis

In contrast to large GWA studies based on thousands of individuals and large meta-analyses combining GWAS results, we analyzed a small case/control sample for uric acid nephrolithiasis. Our cohort of closely related individuals is derived from a small, genetically isolated village in Sardinia, with well-characterized genealogical data linking the extant population up to the 16(th) century. It is expected that the number of risk alleles involved in complex disorders is smaller in isolated founder populations than in more diverse populations, and the power to detect association with complex traits may be increased when related, homogeneous affected individuals are selected, as they are more likely to be enriched with and share specific risk variants than are unrelated, affected individuals from the general population. When related individuals are included in an association study, correlations among relatives must be accurately taken into account to ensure validity of the results. A recently proposed association method uses an empirical genotypic covariance matrix estimated from genome-screen data to allow for additional population structure and cryptic relatedness that may not be captured by the genealogical data. We apply the method to our data, and we also investigate the properties of the method, as well as other association methods, in our highly inbred population, as previous applications were to outbred samples. The more promising regions identified in our initial study in the genetic isolate were then further investigated in an independent sample collected from the Italian population. Among the loci that showed association in this study, we observed evidence of a possible involvement of the region encompassing the gene LRRC16A, already associated to serum uric acid levels in a large meta-analysis of 14 GWAS, suggesting that this locus might lead a pathway for uric acid metabolism that may be involved in gout as well as in nephrolithiasis.     

Metabolic response of yellow mealworm larvae to two alternative rearing substrates

Introduction

Bitter melon (Momordica charantia, Cucurbitaceae) is a popular edible medicinal plant, which has been used as a botanical dietary supplement for the treatment of diabetes and obesity in Chinese folk medicine. Previously, our team has proved that cucurbitanes triterpenoid were involved in bitter melon’s anti-diabetic effects as well as on increasing energy expenditure. The triterpenoids composition can however be influenced by changes of varieties or habitats.

Objectives

To clarify the significance of bioactive metabolites diversity among different bitter melons and to provide a guideline for selection of bitter melon varieties, an exploratory study was carried out using a UHPLC-HRMS based metabolomic study to identify chemotypes.

Methods

Metabolites of 55 seed samples of bitter melon collected in different parts of China were profiled by UHPLC-HRMS. The profiling data were analysed with multivariate (MVA) statistical methods. Principle component analysis (PCA) and hierarchical cluster analysis (HCA) were applied for sample differentiation. Marker compounds were identified by comparing spectroscopic data with isolated compounds, and additional triterpenes were putatively identified by propagating annotations through a molecular network (MN) generated from UHPLC-HRMS & MS/MS metabolite profiling.

Results

PCA and HCA provided a good discrimination between bitter melon samples from various origins in China. This study revealed for the first time the existence of two chemotypes of bitter melon. Marker compounds of those two chemotypes were identified at different MSI levels. The combined results of MN and MVA demonstrated that the two chemotypes mainly differ in their richness in cucurbitane versus oleanane triterpenoid glycosides (CTGs vs. OTGs).

Conclusion

Our finding revealed a clear chemotype distribution of bioactive components across bitter melon varieties. While bioactivities of individual CTGs and OTGs still need to be investigated in more depth, our results could help in future the selection of bitter melon varieties with optimised metabolites profile for an improved management of diabetes with this popular edible Chinese folk medicine.