Institute of Chartered Accountants of Nigeria

About: Institute of Chartered Accountants of Nigeria is a based out in . It is known for research contribution in the topics: Population & Adipose tissue. The organization has 528 authors who have published 579 publications receiving 18688 citations.

Genetically defined elevated homocysteine levels do not result in widespread changes of DNA methylation in leukocytes

Abstract: Background: DNA methylation is affected by the activities of the key enzymes and intermediate metabolites of the one-carbon pathway, one of which involves homocysteine. We investigated the effect of the well-known genetic variant associated with mildly elevated homocysteine: MTHFR 677C>T independently and in combination with other homocysteine-associated variants, on genome-wide leukocyte DNA-methylation. Methods: Methylation levels were assessed using Illumina 450k arrays on 9,894 individuals of European ancestry from 12 cohort studies. Linear-mixed-models were used to study the association of additive MTHFR 677C>T and genetic-risk score (GRS) based on 18 homocysteine-associated SNPs, with genome-wide methylation. Results: Meta-analysis revealed that the MTHFR 677C>T variant was associated with 35 CpG sites in cis, and the GRS showed association with 113 CpG sites near the homocysteine-associated variants. Genome-wide analysis revealed that the MTHFR 677C>T variant was associated with 1 trans-CpG (nearest gene ZNF184), while the GRS model showed association with 5 significant trans-CpGs annotated to nearest genes PTF1A, MRPL55, CTDSP2, CRYM and FKBP5. Conclusions: Our results do not show widespread changes in DNA-methylation across the genome, and therefore do not support the hypothesis that mildly elevated homocysteine is associated with widespread methylation changes in leukocytes.

Data on Determinants Are Needed to Curb the Sedentary Epidemic in Europe. Lessons Learnt from the DEDIPAC European Knowledge Hub.

Abstract: Societal and technological changes have resulted in sitting being the dominant posture during most activities of daily living, such as learning, working, travelling and leisure time. Too much time spent in seated activities, referred to as sedentary behaviour, is a novel concern for public health as it is one of the key lifestyle causes of poor health. The European DEDIPAC (Determinants of Diet and Physical Activity) Knowledge Hub coordinated the work of 35 institutions across 12 European member states to investigate the determinants of sedentary behaviour. DEDIPAC reviewed current evidence, set a theoretical framework and harmonised the available epidemiological data. The main results are summarised. The conclusion is that there is a dire lack of data that is exploitable across Europe to inform policy and intervention. There is an urgent need to develop international data collection compliant with FAIR (Findable, Accessible, Interoperable, Re-usable) and standardised surveillance systems for sedentary behaviour.

Spectral consensus strategy for accurate reconstruction of large biological networks

Abstract: The last decades witnessed an explosion of large-scale biological datasets whose analyses require the continuous development of innovative algorithms. Many of these high-dimensional datasets are related to large biological networks with few or no experimentally proven interactions. A striking example lies in the recent gut bacterial studies that provided researchers with a plethora of information sources. Despite a deeper knowledge of microbiome composition, inferring bacterial interactions remains a critical step that encounters significant issues, due in particular to high-dimensional settings, unknown gut bacterial taxa and unavoidable noise in sparse datasets. Such data type make any a priori choice of a learning method particularly difficult and urge the need for the development of new scalable approaches. We propose a consensus method based on spectral decomposition, named Spectral Consensus Strategy, to reconstruct large networks from high-dimensional datasets. This novel unsupervised approach can be applied to a broad range of biological networks and the associated spectral framework provides scalability to diverse reconstruction methods. The results obtained on benchmark datasets demonstrate the interest of our approach for high-dimensional cases. As a suitable example, we considered the human gut microbiome co-presence network. For this application, our method successfully retrieves biologically relevant relationships and gives new insights into the topology of this complex ecosystem. The Spectral Consensus Strategy improves prediction precision and allows scalability of various reconstruction methods to large networks. The integration of multiple reconstruction algorithms turns our approach into a robust learning method. All together, this strategy increases the confidence of predicted interactions from high-dimensional datasets without demanding computations.