Molecular footprint of Medawar's mutation accumulation process in mammalian aging

Medawar's mutation accumulation hypothesis explains aging by the declining force of natural selection with age: Slightly deleterious germline mutations expressed in old age can drift to fixation and thereby lead to aging-related phenotypes. Although widely cited, empirical evidence for this hypothesis has remained limited. Here, we test one of its predictions that genes relatively highly expressed in old adults should be under weaker purifying selection than genes relatively highly expressed in young adults. Combining 66 transcriptome datasets (including 16 tissues from five mammalian species) with sequence conservation estimates across mammals, here we report that the overall conservation level of expressed genes is lower at old age compared to young adulthood. This age-related decrease in transcriptome conservation (ADICT) is systematically observed in diverse mammalian tissues, including the brain, liver, lung, and artery, but not in others, most notably in the muscle and heart. Where observed, ADICT is driven partly by poorly conserved genes being up-regulated during aging. In general, the more often a gene is found up-regulated with age among tissues and species, the lower its evolutionary conservation. Poorly conserved and up-regulated genes have overlapping functional properties that include responses to age-associated tissue damage, such as apoptosis and inflammation. Meanwhile, these genes do not appear to be under positive selection. Hence, genes contributing to old age phenotypes are found to harbor an excess of slightly deleterious alleles, at least in certain tissues. This supports the notion that genetic drift shapes aging in multicellular organisms, consistent with Medawar's mutation accumulation hypothesis

Global network of computational biology communities: ISCB's regional student groups breaking barriers [version 1; peer review: Not peer reviewed]

Regional Student Groups (RSGs) of the International Society for Computational Biology Student Council (ISCB-SC) have been instrumental to connect computational biologists globally and to create more awareness about bioinformatics education. This article highlights the initiatives carried out by the RSGs both nationally and internationally to strengthen the present and future of the bioinformatics community. Moreover, we discuss the future directions the organization will take and the challenges to advance further in the ISCB-SC main mission: “Nurture the new generation of computational biologists”.Fil: Shome, Sayane. University of Iowa; Estados UnidosFil: Parra, Rodrigo Gonzalo. European Molecular Biology Laboratory; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fatima, Nazeefa. Uppsala Universitet; SueciaFil: Monzon, Alexander Miguel. Università di Padova; ItaliaFil: Cuypers, Bart. Universiteit Antwerp; BélgicaFil: Moosa, Yumna. University of KwaZulu Natal; SudáfricaFil: Da Rocha Coimbra, Nilson. Universidade Federal de Minas Gerais; BrasilFil: Assis, Juliana. Universidade Federal de Minas Gerais; BrasilFil: Giner Delgado, Carla. Universitat Autònoma de Barcelona; EspañaFil: Dönertaş, Handan Melike. European Molecular Biology Laboratory. European Bioinformatics Institute; Reino UnidoFil: Cuesta Astroz, Yesid. Universidad de Antioquia; Colombia. Universidad Ces. Facultad de Medicina.; ColombiaFil: Saarunya, Geetha. University of South Carolina; Estados UnidosFil: Allali, Imane. Universite Mohammed V. Rabat; Otros paises de África. University of Cape Town; SudáfricaFil: Gupta, Shruti. Jawaharlal Nehru University; IndiaFil: Srivastava, Ambuj. Indian Institute of Technology Madras; IndiaFil: Kalsan, Manisha. Jawaharlal Nehru University; IndiaFil: Valdivia, Catalina. Universidad Andrés Bello; ChileFil: Olguín Orellana, Gabriel José. Universidad de Talca; ChileFil: Papadimitriou, Sofia. Vrije Unviversiteit Brussel; Bélgica. Université Libre de Bruxelles; BélgicaFil: Parisi, Daniele. Katholikie Universiteit Leuven; BélgicaFil: Kristensen, Nikolaj Pagh. Technical University of Denmark; DinamarcaFil: Rib, Leonor. Universidad de Copenhagen; DinamarcaFil: Guebila, Marouen Ben. University of Luxembourg; LuxemburgoFil: Bauer, Eugen. University of Luxembourg; LuxemburgoFil: Zaffaroni, Gaia. University of Luxembourg; LuxemburgoFil: Bekkar, Amel. Universite de Lausanne; SuizaFil: Ashano, Efejiro. APIN Public Health Initiatives; NigeriaFil: Paladin, Lisanna. Università di Padova; ItaliaFil: Necci, Marco. Università di Padova; ItaliaFil: Moreyra, Nicolás Nahuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Ecología, Genética y Evolución de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Ecología, Genética y Evolución de Buenos Aires; Argentin

Gen anlatım geri dönüşlerinin yaşlanan beyinde meta-analizi.

Brain ageing is characterised by disruptive changes in cognitive abilities, histology, and anatomy. The underlying molecular nature of brain ageing, on the other hand, is little understood, partly due to the stochastic and heterogeneous nature of ageing process. In this study, using published microarray studies spanning 22 brain regions with 1,015 samples, gene expression changes in ageing are analysed in comparison to those in postnatal development. A previous observation that mRNA abundance of a large number of genes in the ageing prefrontal cortex reverses toward pre-adolescent levels, is shown to be a widespread phenomenon across different brain regions. Furthermore, functional analysis reveals that gene expression reversals are consistently associated with decline in neuronal / synaptic gene expression across all studied brain regions, and thus may be linked to ageing-related phenotypes such as decline in cognitive functions. Regulatory analysis show that the genes increasing in expression in development and decreasing in expression in ageing are associated with several trans-regulators, whereas there is no consistent association with any potential trans-regulator for the genes decreasing in expression in development and increasing in expression in ageing. Overall, the results show that meta-analysis is crucial for ageing studies due to the stochastic nature of ageing and that studying gene expression change in ageing in the context of changes in development is a promising approach to discover the molecular mechanisms of ageing.M.S. - Master of Scienc

Inter-tissue convergence of gene expression during ageing suggests age-related loss of tissue and cellular identity

Developmental trajectories of gene expression may reverse in their direction during ageing, a phenomenon previously linked to cellular identity loss. Our analysis of cerebral cortex, lung, liver, and muscle transcriptomes of 16 mice, covering development and ageing intervals, revealed widespread but tissue-specific ageing-associated expression reversals. Cumulatively, these reversals create a unique phenomenon: mammalian tissue transcriptomes diverge from each other during postnatal development, but during ageing, they tend to converge towards similar expression levels, a process we term Divergence followed by Convergence (DiCo). We found that DiCo was most prevalent among tissue-specific genes and associated with loss of tissue identity, which is confirmed using data from independent mouse and human datasets. Further, using publicly available single-cell transcriptome data, we showed that DiCo could be driven both by alterations in tissue cell-type composition and also by cell-autonomous expression changes within particular cell types

Characterising Complex Enzyme Reaction Data

The relationship between enzyme-catalysed reactions and the Enzyme Commission (EC) number, the widely accepted classification scheme used to characterise enzyme activity, is complex and with the rapid increase in our knowledge of the reactions catalysed by enzymes needs revisiting. We present a manual and computational analysis to investigate this complexity and found that almost one-third of all known EC numbers are linked to more than one reaction in the secondary reaction databases (e.g., KEGG). Although this complexity is often resolved by defining generic, alternative and partial reactions, we have also found individual EC numbers with more than one reaction catalysing different types of bond changes. This analysis adds a new dimension to our understanding of enzyme function and might be useful for the accurate annotation of the function of enzymes and to study the changes in enzyme function during evolution.Publisher's Versio

An overview of reaction diversity in the EC classification.

<p>(a) Frequency of reaction diversity group assignments. (b) Total number of multi-reaction EC numbers by EC class for each group of reaction diversity. The colour code is identical to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0147952#pone.0147952.g002" target="_blank">Fig 2A</a>.</p

Examples of the collective and specific approaches.

<p>(a) The <i>different</i> reactants of arginine racemase (EC 5.1.1.9) are combined into a single-reaction EC number using R-group. (b) The two <i>different</i> types of reaction catalysed by 4-chlorobenzoyl-CoA dehalogenase (EC 3.8.1.7) are split and re-defined into two single-reaction EC numbers.</p

Survey of EC numbers associated with more than one enzyme reaction.

<p>(a) Overall distribution. Coloured slices indicate single and multi-reaction EC numbers. “R-group” represents EC numbers containing a Markush label in at least one reaction (see <i>Generic</i> reactions in main text) (b) Distribution by EC class (c) Distribution of EC numbers according to the number of reactions.</p

Examples of isomerase EC numbers associated with more than one enzyme reaction.

<p>(a) A schematic diagram summarising the groups of reaction diversity. (b) Arginine racemase (EC 5.1.1.9) is an isomerase acting on <i>different</i> reactants. The variability in chemical substituents is highlighted in green and the common scaffold in black. (c) Amino acid racemase (EC 5.1.1.10) is an example of <i>generic</i> reaction on the basis of R-group. Same colouring as in (b). (d) 2-acetolactate mutase (EC 5.4.99.3) is an example of <i>generic</i> reaction based on stereochemistry. The stereochemistry of C2 in acetolactate is represented as straight (undefined), up and down (defined) bonds and highlighted in green. (e) UDP-N-acetyl-D-glucosamine 2-epimerase (EC 5.1.3.14) belongs to <i>partial</i> reaction, (i) <i>overall</i> reaction–epimerisation of UDP-N-acetyl-α-D-glucosamine (green) and UDP-N-acetyl-α-D-mannosamine (blue), (ii) first <i>partial</i> reaction–hydrolysis and epimerisation of UDP-N-acetyl-α-D-glucosamine and (iii) second <i>partial</i> reaction–addition of UDP to N-acetyl-α-D-mannosamine. Intermediate compounds are highlighted in red. (f) Dichloromuconate cycloisomerase (EC 5.5.1.11) and 4-chlorobenzoyl-CoA dehalogenase (EC 3.8.1.7) catalyse <i>different</i> types of reactions. Shared bond changes are coloured in black, whereas different bond changes in green.</p