ALBERT

Beschreibung: Background: A complete approach for genome-wide selection (GWS) involves reliable statistical genetics models and methods. Reports on this topic are common for additive genetic models but not for additive-dominance models. The objective of this paper was (i) to compare the performance of 10 additive-dominance predictive models (including current models and proposed modifications), fitted using Bayesian, Lasso and Ridge regression approaches; and (ii) to decompose genomic heritability and accuracy in terms of three quantitative genetic information sources, namely, linkage disequilibrium (LD), co-segregation (CS) and pedigree relationships or family structure (PR). The simulation study considered two broad sense heritability levels (0.30 and 0.50, associated with narrow sense heritabilities of 0.20 and 0.35, respectively) and two genetic architectures for traits (the first consisting of small gene effects and the second consisting of a mixed inheritance model with five major genes). Results: G-REML/G-BLUP and a modified Bayesian/Lasso (called BayesA*B* or t-BLASSO) method performed best in the prediction of genomic breeding as well as the total genotypic values of individuals in all four scenarios (two heritabilities x two genetic architectures). The BayesA*B*-type method showed a better ability to recover the dominance variance/additive variance ratio. Decomposition of genomic heritability and accuracy revealed the following descending importance order of information: LD, CS and PR not captured by markers, the last two being very close. Conclusions: Amongst the 10 models/methods evaluated, the G-BLUP, BAYESA*B* (−2,8) and BAYESA*B* (4,6) methods presented the best results and were found to be adequate for accurately predicting genomic breeding and total genotypic values as well as for estimating additive and dominance in additive-dominance genomic models.

Beschreibung: Fractal geometry has been the basis for the development of a diagnosis of preneoplastic and neoplastic cells that clears up the undetermination of the atypical squamous cells of undetermined significance (ASCUS).

Beschreibung: Background: Due to the large amount of data produced by advanced microscopy, automated image analysis is crucial in modern biology. Most applications require reliable cell nuclei segmentation. However, in many biological specimens cell nuclei are densely packed and appear to touch one another in the images. Therefore, a major difficulty of three-dimensional cell nuclei segmentation is the decomposition of cell nuclei that apparently touch each other. Current methods are highly adapted to a certain biological specimen or a specific microscope. They do not ensure similarly accurate segmentation performance, i.e. their robustness for different datasets is not guaranteed. Hence, these methods require elaborate adjustments to each dataset. Results: We present an advanced three-dimensional cell nuclei segmentation algorithm that is accurate and robust. Our approach combines local adaptive pre-processing with decomposition based on Lines-of-Sight (LoS) to separate apparently touching cell nuclei into approximately convex parts. We demonstrate the superior performance of our algorithm using data from different specimens recorded with different microscopes. The three-dimensional images were recorded with confocal and light sheet-based fluorescence microscopes. The specimens are an early mouse embryo and two different cellular spheroids. We compared the segmentation accuracy of our algorithm with ground truth data for the test images and results from state-of-the-art methods. The analysis shows that our method is accurate throughout all test datasets (mean F-measure: 91 %) whereas the other methods each failed for at least one dataset (F-measure ≤ 69 %). Furthermore, nuclei volume measurements are improved for LoS decomposition. The state-of-the-art methods required laborious adjustments of parameter values to achieve these results. Our LoS algorithm did not require parameter value adjustments. The accurate performance was achieved with one fixed set of parameter values. Conclusion: We developed a novel and fully automated three-dimensional cell nuclei segmentation method incorporating LoS decomposition. LoS are easily accessible features that ensure correct splitting of apparently touching cell nuclei independent of their shape, size or intensity. Our method showed superior performance compared to state-of-the-art methods, performing accurately for a variety of test images. Hence, our LoS approach can be readily applied to quantitative evaluation in drug testing, developmental and cell biology.

Beschreibung: Background: Diet is an important environmental factor that interacts with genes to modulate the likelihood of developing disorders in lipid metabolism and the relationship between diet and genes in the presence of other chronic diseases such as obesity. The objective of this study was to analyze the interaction of a high fat diet with the APOA2 (rs3813627 and rs5082), APOA5 (rs662799 and rs3135506) and LEPR (rs8179183 and rs1137101) polymorphisms and its relationship with obesity and dyslipidemia in young subjects. Methods: The study included 200 young subjects aged 18 to 25 years (100 normal-weight and 100 obese subjects). Dietary fat intake was measured using the frequency food consumption questionnaire. Genotyping of polymorphisms was performed by PCR-RFLP. Results: Individuals carrying the APOA5 56 G/G genotype with a high saturated fatty acid consumption (OR = 2.7, p = 0.006) and/or total fat (OR = 2.4, p = 0.018), associated with an increased risk of obesity. We also found that A/G + G/G genotypes of the 668 A/G polymorphism in the LEPR gene with an intake ≥12 g/d of saturated fatty acids, have 2.9 times higher risk of obesity (p = 0.002), 3.8 times higher risk of hypercholesterolemia (p = 0.002) and 2.4 times higher risk of hypertriglyceridemia (p = 0.02), than those with an intake

Beschreibung: Background: There are recent experimental reports on the cross‐regulation between molecules involved in the control of the cell cycle and the differentiation of the vulval precursor cells (VPCs) of Caenorhabditis elegans. Such discoveries provide novel clues on how the molecular mechanisms involved in the cell cycle and cell differentiation processes are coordinated during vulval development. Dynamic computational models are helpful to understand the integrated regulatory mechanisms affecting these cellular processes. Results: Here we propose a simplified model of the regulatory network that includes sufficient molecules involved in the control of both the cell cycle and cell differentiation in the C. elegans vulva to recover their dynamic behavior. We first infer both the topology and the update rules of the cell cycle module from an expected time series. Next, we use a symbolic algorithmic approach to find which interactions must be included in the regulatory network. Finally, we use a continuous‐time version of the update rules for the cell cycle module to validate the cyclic behavior of the network, as well as to rule out the presence of potential artifacts due to the synchronous updating of the discrete model. We analyze the dynamical behavior of the model for the wild type and several mutants, finding that most of the results are consistent with published experimental results. Conclusions: Our model shows that the regulation of Notch signaling by the cell cycle preserves the potential of the VPCs and the three vulval fates to differentiate and de‐differentiate, allowing them to remain completely responsive to the concentration of LIN‐3 and lateral signal in the extracellular microenvironment.

Beschreibung: Background: The action of probiotics has been studied in vitro in cells isolated from both mice and humans, particularly enterocytes (IECs), dendritic cells (DCs) and co-cultures of peripheral DCs and IECs. Peripheral DCs and murine DCs differ from human gut DCs, and to date there are no data on the action of any probiotic on co-cultured human IECs and human intestinal DCs. To address this issue, a novel transwell model was used. Human IECs (Caco-2 cells) grown in the upper chamber of transwell filters were co-cultured with intestinal-like human DCs grown in the basolateral compartment of the transwells. The system was apically exposed for 4 h to live probiotic L. paracasei CNCM I-4034 obtained from the faeces of breastfed infants or to its cell-free culture supernatant (CFS) and challenged with Salmonella typhi. The secretion of pro- and anti-inflammatory cytokines in the basolateral compartment was determined by immunoassay, and the DC expression pattern of 20 TLR signaling pathway genes was analysed by PCR array. Results: The presence of the live probiotic alone significantly increased IL-1β, IL-6, IL-8, TGF-β2, RANTES and IP-10 levels and decreased IL-12p40, IL-10, TGF- β1 and MIP-1α levels. This release was correlated with a significant increase in the expression of almost all TLR signaling genes. By contrast, incubation of the co-culture with CFS increased IL-1β, IL-6, TGF-β2 and IP-10 production only when Salmonella was present. This induction was correlated with an overall decrease in the expression of all TLR genes except TLR9, which was strongly up-regulated. Conclusions: The data presented here clearly indicate that L. paracasei CNCM I-4034 significantly increases the release of pro-inflammatory cytokines, enhances TLR signaling pathway activation and stimulates rather than suppresses the innate immune system. Furthermore, our findings provide evidence that the effects of probiotics in the presence of IECs and DCs differ from the effects of probiotics on cultures of each cell type alone, as reported by us earlier. Thus, co-culture systems such as the one described here are needed to characterise the effects of probiotics in vitro, highlighting the potential utility of such co-cultures as a model system.

Beschreibung: Background: Several studies have revealed a potential role for both small nucleolar RNAs (snoRNAs) and microRNAs (miRNAs) in the physiopathology of relapsing-remitting multiple sclerosis (RRMS). This potential implication has been mainly described through differential expression studies. However, it has been suggested that, in order to extract additional information from large-scale expression experiments, differential expression studies must be complemented with differential network studies. Thus, the present work is aimed at the identification of potential therapeutic ncRNA targets for RRMS through differential network analysis of ncRNA – mRNA coexpression networks. ncRNA – mRNA coexpression networks have been constructed from both selected ncRNA (specifically miRNAs, snoRNAs and sdRNAs) and mRNA large-scale expression data obtained from 22 patients in relapse, the same 22 patients in remission and 22 healthy controls. Condition-specific (relapse, remission and healthy) networks have been built and compared to identify the parts of the system most affected by perturbation and aid the identification of potential therapeutic targets among the ncRNAs. Results: All the coexpression networks we built present a scale-free topology and many snoRNAs are shown to have a prominent role in their architecture. The differential network analysis (relapse vs. remission vs. controls’ networks) has revealed that, although both network topology and the majority of the genes are maintained, few ncRNA – mRNA links appear in more than one network. We have selected as potential therapeutic targets the ncRNAs that appear in the disease-specific network and were found to be differentially expressed in a previous study. Conclusions: Our results suggest that the diseased state of RRMS has a strong impact on the ncRNA – mRNA network of peripheral blood leukocytes, as a massive rewiring of the network happens between conditions. Our findings also indicate that the role snoRNAs have in targeted gene silencing is a widespread phenomenon. Finally, among the potential therapeutic target ncRNAs, SNORA40 seems to be the most promising candidate.

Beschreibung: Background: Thyroid cancer (TC) is the most common malignant cancer of the Endocrine System. Histologically, there are three main subtypes of TC: follicular, papillary and anaplastic. Diagnosing a thyroid tumor subtype with a high level of accuracy and confidence is still a difficult task because genetic, molecular and cellular mechanisms underlying the transition from differentiated to undifferentiated thyroid tumors are not well understood.A genome-wide analysis of these three subtypes of thyroid carcinoma was carried out in order to identify significant differences in expression levels as well as enriched pathways for non-shared molecular and cellular features between subtypes. Results: Inhibition of matrix metalloproteinases pathway is a major event involved in thyroid cancer progression and its dysregulation may result crucial for invasiveness, migration and metastasis. This pathway is drastically altered in ATC while in FTC and PTC, the most important pathways are related to DNA-repair activation or cell to cell signaling cell to cell signaling events. Conclusion: A progression from FTC to PTC and then to ATC was detected and validated on two independent datasets. Moreover, PTX3, COLEC12 and PDGFRA genes were found as possible candidates for biomarkers of ATC while GPR110 could be tested to distinguish PTC over other tumor subtypes. The genome-wide analysis emphasizes the preponderance of pathway-dysregulation mechanisms over simple gene-malfunction as the main mechanism involved in the development of a cancer phenotype.