P-447 Enhancing oxygen availability in the dynamic culture of bovine ovarian cortical tissue improves the yield of secondary follicles

Study question

Does enhancing oxygen availability during dynamic in vitro culture of bovine ovarian cortical tissue (BOCT) improve follicle growth and health?

Summary answer

Enhancing oxygen availability during dynamic in vitro culture of BOCT in perifusion bioreactors (PB) does improve follicle health and yield to secondary follicles

What is known already

Oxygen availability has been demonstrated to represent a key factor in follicle health and growth during in vitro culture of bovine and human ovarian cortical tissue (HOCT) under static culture conditions. Disruption of solutes gradients and application of physiological fluid mechanical stress, through in vitro dynamic culture of HOCT in a newly designed perifusion bioreactor have been shown to further enhance follicle growth and health. As it shows striking similarities with human, bovine folliculogenesis is considered a valuable model to study follicle growth in vitro

Study design, size, duration

Bovine ovaries from animals aged 8-24 months were collected at slaughterhouse. In each experiment (n = 3), BOCT strips from the same ovary were cultured for 6 days in perifusion bioreactors (PB, dynamic culture) and conventional dishes (CD, static culture). Culture outcome in static culture was analysed and compared to two bioreactor configurations in which medium oxygenation was kept low by using a standard tube reservoir (StPB) or was enhanced by using a gas-permeable dish reservoir (PB+O2).

Participants/materials, setting, methods

Slices of BOCT 0.5mm thick were cut with a tissue slicer and chopped into 1x1mm strips. In each experiment, fresh (D0) and cultured tissue (groups of ten strips) were analyzed. Follicle stages and health were assessed by histology (hematoxylin-eosin staining). Follicle viability was estimated by labelling with live-dead far-red and propidium iodide followed by clearing before analysis at the confocal laser scanning microscope.

Main results and the role of chance

Overall, 2417 follicles were analyzed (histology, 1476; viability, 941). At day 0 most follicles were primordial (primordial, 88.7%; primary, 10.6%; secondary, 0.7%), and had good quality (grade 1-2, 92.2%; grade 3, 7.8%), and high viability (91.8%). At day 6, follicle growth and health in StPB was superior than in CD (StPB vs CD - staging: primordial, 6.8 vs 16.3, P < 0.01; primary, 70.7 vs 74.1, NS; secondary, 22.5 vs 9.6%, P < 0.01; grading: grade 1 + 2, 71.4 vs 44.8, P < 0.01; grade 3, 28.6 vs 55.2%, P < 0.01). Dynamic culture in StPB better-preserved follicle viability compared to static culture in CD (StPB vs CD: 77.75 vs 64.9%, P < 0.01). Enhancing oxygen availability during dynamic culture increased follicle progression and viability (PB+O2 vs StPB - staging: primordial, 5.1 vs 6.8, NS; primary, 65.4 vs 70.7, NS; secondary, 29.5 vs 22.5%, P < 0.05; viability - 92 vs 77.75, P < 0.01). Overall, the obtained results demonstrate that i) disruption of stagnant layers of medium and application of shear stress to BOCT through dynamic culture improves follicle activation, growth and health; ii) enhancing oxygen availability by means of a gas-permeable medium reservoir further increases follicle progression and viability.

Limitations, reasons for caution

Although the bovine is considered a reliable model for human folliculogenesis, the study should be validated on human ovarian tissue.

Wider implications of the findings

A limiting step in the in vitro production of mature oocytes starting from primordial follicles is the low yield of secondary follicles after organ culture. The adoption of a newly designed dynamic bioreactor and modulation of oxygen availability could represent a valuable tool for multistep in vitro folliculogenesis.

Trial registration number

none

P-446 Influence of strip thickness and FSH supplementation on follicle growth in the in vitro culture of strips of bovine ovarian cortical tissue in gas-permeable dishes

Study question

Do tissue thickness and FSH supplementation affect follicle growth and health in the vitro culture of bovine ovarian cortical strips (BOCS) in gas-permeable dishes?

Summary answer

Culture of 0.5mm thin BOCS with 5ng/ml FSH does improve follicle health and yield to secondary follicles compared with 1mm thick BOCS

What is known already

Oxygen availability inside tissue has been demonstrated to represent a key factor in follicle health and growth during in vitro culture of bovine and human ovarian cortical strips (HOCS). Although, strip thickness can limit nutrients and gases diffusion in and out of the innermost tissue zone, the presence of the outer medulla in thick strips could positively affect follicle growth. The role of FSH on the progression of primordial to secondary follicles in ovarian organ culture is still debated.

Study design, size, duration

Bovine ovaries from animals aged 8-24 months were collected at a slaughterhouse. In each experiment (n = 3), BOCS of varying thickness collected from the same ovary were cultured with or without FSH in gas-permeable dishes for 10 and 15 days.

Participants/materials, setting, methods

Slices either 0.5mm or 1mm thick were cut with a tissue-slicer and were chopped into 1x1mm2 strips. BOCS were cultured for 10 or 15 days at the same tissue/medium volume ratio (groups of either five 1mm strips or ten 0.5mm strips in 5ml medium) with 0, 1, or 5ng/ml FSH. Follicle stages were assessed by histology. Follicle viability was estimated by labeling with live-dead far red and propidium iodide at the confocal laser scanning microscope.

Main results and the role of chance

Overall, 2314 follicles were analyzed (histology, 998; viability, 1316). At day 0 most follicles were primordial (primordial, 89.4%; primary, 8.7%; secondary, 1.9%), and had a high viability (94.69%). The best follicle growth and viability was observed in 0.5mm thin BOCS cultured with 5ng/ml FSH. In particular, when compared to 1mm thick BOCS cultured with 5ng/ml FSH, 0.5mm thin BOCS cultured with 5ng/ml FSH showed a higher and significant proportion of secondary follicles at day 10 (0.5 vs 1mm, % secondary follicles: 26.5 vs 10, P < 0.05) and a significantly higher proportion of viable follicles at day 15 (0.5 vs 1mm, % viable follicles: 89.4 vs 60.7, P < 0.01).

These results demonstrate that smaller BOCS thickness and 5ng/ml FSH supplementation significantly improve the growth and health of secondary follicles.

Limitations, reasons for caution

Although the bovine is considered a reliable model for human folliculogenesis, the study should be validated on human ovarian tissue.

Wider implications of the findings

A limiting step in the production of mature oocytes starting from primordial follicles is the low yield to secondary follicles at the end of organ culture. Given the similarities between bovine and human folliculogenesis, the best culture conditions herein identified could contribute to the refinement of human in vitro folliculogenesis.

Trial registration number

Not applicable

Orione, a web-based framework for NGS analysis in microbiology

Summary: End-to-end next-generation sequencing microbiology data analysis requires a diversity of tools covering bacterial resequencing, de novo assembly, scaffolding, bacterial RNA-Seq, gene annotation and metagenomics. However, the construction of computational pipelines that use different software packages is difficult owing to a lack of interoperability, reproducibility and transparency. To overcome these limitations we present Orione, a Galaxy-based framework consisting of publicly available research software and specifically designed pipelines to build complex, reproducible workflows for next-generation sequencing microbiology data analysis. Enabling microbiology researchers to conduct their own custom analysis and data manipulation without software installation or programming, Orione provides new opportunities for data-intensive computational analyses in microbiology and metagenomics.

Availability and implementation: Orione is available online at http://orione.crs4.it.

Contact:gianmauro.cuccuru@crs4.it

Supplementary information:Supplementary data are available at Bioinformatics online.

Cancer markers: integratively annotated classification

Translational cancer genomics research aims to ensure that experimental knowledge is subject to computational analysis, and integrated with a variety of records from omics and clinical sources. The data retrieval from such sources is not trivial, due to their redundancy and heterogeneity, and the presence of false evidence. In silico marker identification, therefore, remains a complex task that is mainly motivated by the impact that target identification from the elucidation of gene co-expression dynamics and regulation mechanisms, combined with the discovery of genotype-phenotype associations, may have for clinical validation. Based on the reuse of publicly available gene expression data, our aim is to propose cancer marker classification by integrating the prediction power of multiple annotation sources. In particular, with reference to the functional annotation for colorectal markers, we indicate a classification of markers into diagnostic and prognostic classes combined with susceptibility and risk factors.

Warehousing re-annotated cancer genes for biomarker meta-analysis

Translational research in cancer genomics assigns a fundamental role to bioinformatics in support of candidate gene prioritization with regard to both biomarker discovery and target identification for drug development. Efforts in both such directions rely on the existence and constant update of large repositories of gene expression data and omics records obtained from a variety of experiments. Users who interactively interrogate such repositories may have problems in retrieving sample fields that present limited associated information, due for instance to incomplete entries or sometimes unusable files. Cancer-specific data sources present similar problems. Given that source integration usually improves data quality, one of the objectives is keeping the computational complexity sufficiently low to allow an optimal assimilation and mining of all the information. In particular, the scope of integrating intraomics data can be to improve the exploration of gene co-expression landscapes, while the scope of integrating interomics sources can be that of establishing genotype-phenotype associations. Both integrations are relevant to cancer biomarker meta-analysis, as the proposed study demonstrates. Our approach is based on re-annotating cancer-specific data available at the EBI's ArrayExpress repository and building a data warehouse aimed to biomarker discovery and validation studies. Cancer genes are organized by tissue with biomedical and clinical evidences combined to increase reproducibility and consistency of results. For better comparative evaluation, multiple queries have been designed to efficiently address all types of experiments and platforms, and allow for retrieval of sample-related information, such as cell line, disease state and clinical aspects.

Manifold learning in protein interactomes

Many studies and applications in the post-genomic era have been devoted to analyze complex biological systems by computational inference methods. We propose to apply manifold learning methods to protein-protein interaction networks (PPIN). Despite their popularity in data-intensive applications, these methods have received limited attention in the context of biological networks. We show that there is both utility and unexplored potential in adopting manifold learning for network inference purposes. In particular, the following advantages are highlighted: (a) fusion with diagnostic statistical tools designed to assign significance to protein interactions based on pre-selected topological features; (b) dissection into components of the interactome in order to elucidate global and local connectivity organization; (c) relevance of embedding the interactome in reduced dimensions for biological validation purposes. We have compared the performances of three well-known techniques--kernel-PCA, RADICAL ICA, and ISOMAP--relatively to their power of mapping the interactome onto new coordinate dimensions where important associations among proteins can be detected, and then back projected such that the corresponding sub-interactomes are reconstructed. This recovery has been done selectively, by using significant information according to a robust statistical procedure, and then standard biological annotation has been provided to validate the results. We expect that a byproduct of using subspace analysis by the proposed techniques is a possible calibration of interactome modularity studies. Supplementary Material is available online at www.libertonlinec.com.

Inferring modules from human protein interactome classes

Background

The integration of protein-protein interaction networks derived from high-throughput screening approaches and complementary sources is a key topic in systems biology. Although integration of protein interaction data is conventionally performed, the effects of this procedure on the result of network analyses has not been examined yet. In particular, in order to optimize the fusion of heterogeneous interaction datasets, it is crucial to consider not only their degree of coverage and accuracy, but also their mutual dependencies and additional salient features.

Results

We examined this issue based on the analysis of modules detected by network clustering methods applied to both integrated and individual (disaggregated) data sources, which we call interactome classes. Due to class diversity, we deal with variable dependencies of data features arising from structural specificities and biases, but also from possible overlaps. Since highly connected regions of the human interactome may point to potential protein complexes, we have focused on the concept of modularity, and elucidated the detection power of module extraction algorithms by independent validations based on GO, MIPS and KEGG. From the combination of protein interactions with gene expressions, a confidence scoring scheme has been proposed before proceeding via GO with further classification in permanent and transient modules.

Conclusions

Disaggregated interactomes are shown to be informative for inferring modularity, thus contributing to perform an effective integrative analysis. Validation of the extracted modules by multiple annotation allows for the assessment of confidence measures assigned to the modules in a protein pathway context. Notably, the proposed multilayer confidence scheme can be used for network calibration by enabling a transition from unweighted to weighted interactomes based on biological evidence.