Biology subjects

de Magalhaes, J. P.

Publications and source records attributed to de Magalhaes, J. P..

29 records · Page 2Linked to original sources

bioRxiv2021

GeneFriends 2021: Updated co-expression databases and tools for human and mouse genes and transcripts

Gene co-expression analysis has emerged as a powerful method to provide insights into gene function and regulation. The rapid growth of publicly available RNA-sequencing (RNA-seq) data has created opportunities for researchers to employ this abundant data to help decipher the complexity and biology of genomes. Co-expression networks have proven effective for inferring relationship between the genes, for gene prioritization and for assigning function to poorly annotated genes based on their co-expressed partners. To facilitate such analyses we created previously an online co-expression tool for humans and mice entitled GeneFriends. To continue providing a valuable tool to the scientific community, we have updated the GeneFriends database. Here, we present the latest version of GeneFriends, which includes updated gene and transcript co-expression networks based on RNA-seq data from 46,475 human and 34,322 mouse samples. GeneFriends is freely available at http://www.genefriends.org/

genomics↗

bioRxiv2020

An Integrative Analysis of the Age-Associated Genomic, Transcriptomic and Epigenetic Landscape across Cancers

Age is the most important risk factor for cancer, as cancer incidence and mortality increase with age. However, how molecular alterations in tumours differ among patients of different age remains largely unexplored. Here, using data from The Cancer Genome Atlas, we comprehensively characterised genomic, transcriptomic and epigenetic alterations in relation to patients age across cancer types. We showed that tumours from older patients present an overall increase in genomic instability, somatic copy-number alterations (SCNAs) and somatic mutations. Age-associated SCNAs and mutations were identified in several cancer-driver genes across different cancer types. The largest age-related genomic differences were found in gliomas and endometrial cancer. We identified age-related global transcriptomic changes and demonstrated that these genes are controlled by age-associated DNA methylation changes. This study provides a comprehensive view of age-associated alterations in cancer and underscores age as an important factor to consider in cancer research and clinical practice.

cancer biology↗

bioRxiv2020

SynergyAge: a curated database for synergistic and antagonistic interactions of longevity-associated genes

Interventional studies on genetic modulators of longevity have significantly changed gerontology. While available lifespan data is continually accumulating, further understanding of the aging process is still limited by the poor understanding of epistasis and of the non-linear interactions between multiple longevity-associated genes. Unfortunately, based on observations so far, there is no simple method to predict the cumulative impact of genes on lifespan. As a step towards applying predictive methods, but also to provide information for a guided design of epistasis lifespan experiments, we developed SynergyAge - a database containing genetic and lifespan data for animal models obtained through multiple longevity-modulating interventions. The studies included in SynergyAge focus on the lifespan of animal strains which are modified by at least two genetic interventions, with single gene mutants included as reference. SynergyAge, which is publicly available at www.synergyage.info, provides an easy to use web-platform for browsing, searching and filtering through the data, as well as a network-based interactive module for visualization and analysis. Database URL: http://www.synergyage.info/ BACKGROUND & SUMMARYThe aging process can be genetically modulated. This has been previously shown in many studies in which average lifespan, and in some cases even maximum lifespan, has been modified by genetic interventions. It is for example possible to have genetic mutants with an increased lifespan, up to ten times higher compared to wild type in C. elegans 1, and up to 150% and 46% in D. melanogaster and M. musculus, respectively 2,3. Up until now, at least 2,205 genes, whose mutations, downregulation or overexpression results in a long- or short-lived phenotype, have been identified in model organisms. A comprehensive list with these longevity-associated genes (LAGs), including more detailed information about lifespan experiments, can be found in the GenAge database 4. This type and amount of data have made it possible for higher level analyses to be performed 5-7, and the collection of LAGs in public repositories has significantly pushed biogerontology towards more integrative approaches to study longevity. One important aspect observed is that many LAGs seem to act in a cooperative manner 8-10 and are not independent regulators of lifespan. In fact, in most cases when combining two or more genetic interventions, the effect is rarely additive, as genes are generally epistatic and interact in nonlinear ways 11,12. While in most cases combined interventions seem to have lower than expected results in how much they extend lifespan, there are also a minority of cases where genes act synergistically 13,14. Even so, the much more common case is that of studies where partially dependent gene interactions are revealed, making it even more important to understand and predict genetic dependencies. Unfortunately, data on epistasis is much harder to obtain through wide-screen experimental studies, which has been for example the case for the discovery of most LAG interventions in worms. The main impediment comes from the combinatorial explosion of multiple gene groups for which lifespan assays would need to be measured in a "blind" search, through wet-lab experiments. Instead, it would be more efficient to use existing epistasis data to explore predicted synergies in guided lifespan experiments. Luckily, an accumulating number of papers has been published in the last two decades with reported lifespans for double mutants and in some cases even triple or quadruple mutants. As such, it has been now possible for us: (i) to collect the data from existing studies containing lifespan records for strains that have multiple genes modulated, and (ii) to create an intuitive, network-based tool, which allows users to explore in a fast, visual and interactive way the lifespan relationships between these strains. Here, we present SynergyAge, a database containing manually curated data, extracted from experimental studies, regarding gene combinations that affect lifespan. With the creation of SynergyAge, we aim to encourage the investigation of the cumulative effects of different gene interventions on lifespan, by providing the scientific community with a "one-stop" web platform to access, compare and analyze lifespan synergisms or antagonisms. This resource is of particular interest in designing wet-lab experiments in which multi-genetic lifespan interventions are needed. SynergyAge (http://www.synergyage.info) is publicly available and contains data from three animal model organisms: Caenorhabditis elegans, Drosophila melanogaster and Mus musculus.

systems biology↗

bioRxiv2020

Positive selection and fast turnover rate in tumor suppressor genes reveal how cetaceans resist cancer

Cetaceans are the longest-living species of mammals and the largest in the history of the planet. They have developed mechanisms against diseases such cancer, although the underlying molecular bases of these remain unknown. The goal of this study was to investigate the role of natural selection in the evolution of 1077 tumour suppressor genes (TSGs) in cetaceans. We used a comparative genomic approach to analyse two sources of molecular variation in the form of dN/dS rates and gene copy number variation. We found a signal of positive selection in the ancestor of cetaceans within the CXCR2 gene, an important regulator of DNA-damage, tumour dissemination, and immune system. Further, in the ancestor of baleen whales, we found six genes exhibiting positive selection relating to such diseases as breast carcinoma, lung neoplasm (ADAMTS8) and leukaemia (ANXA1). The TSG turnover rate (gene gain and loss) was almost 2.4-fold higher in cetaceans as compared to other mammals, and noticeably even faster in baleen whales. The molecular variants in TSGs found in baleen whales, combined with the faster gene turnover rate, could have favoured the evolution of their particular traits of anti-cancer resistance, gigantism and longevity. Additionally, we report 71 genes with duplications, of which 11 genes are linked to longevity (e.g. NOTCH3 and SIK1) and are important regulators of senescence, cell proliferation and metabolism. Overall, these results provide evolutionary evidence that natural selection in tumour suppressor genes could act on species with large body sizes and extended life span, providing novel insights into the genetic basis of disease resistance.

evolutionary biology↗

bioRxiv2020

Gene size matters: What determines gene length in the human genome?

While it is expected for gene length to be influenced by factors such as intron number and evolutionary conservation, we have yet to fully understand the connection between gene length and function in the human genome. In this study, we show that, as expected, there is a strong positive correlation between gene length and the number of SNPs, introns and protein size. Amongst tissue specific genes, we find that the longest genes are expressed in blood vessels, nerve, thyroid, cervix uteri and brain, while the smallest genes are expressed within the pancreas, skin, stomach, vagina and testis. We report, as shown previously, that natural selection suppresses changes for genes with longer lengths and promotes changes for smaller genes. We also observed that longer genes have a significantly higher number of co-expressed genes and protein-protein interactions. In the functional analysis, we show that bigger genes are often associated with neuronal development, while smaller genes tend to play roles in skin development and in the immune system. Furthermore, pathways related to cancer, neurons and heart diseases tend to have longer genes, with smaller genes being present in pathways related to immune response and neurodegenerative diseases. We hypothesise that longer genes tend to be associated with functions that are important early in life, while smaller genes play a role in functions that are important throughout the organisms whole life, like the immune system which require fast responses. Author SummaryEven though the human genome has been fully sequenced, we still do not fully grasp all of its nuances. One such nuance is the length of the genes themselves. Why are certain genes longer than others? Is there a common function shared by longer/smaller genes? What exactly makes gene longer? We tried answering these questions using a variety of analysis. We found that, while there was not a particular strong factor in genes that influenced their size, there could be an influence of several gene characteristics in determining the length of a gene. We also found that longer genes are linked with the development of neurons, cancer, heart diseases and muscle cells, while smaller genes seem to be mostly related with the immune system and the development of the skin. This led us to believe that, whether the gene has an important function early in our life, or throughout our whole lives, or even if the function requires a rapid response, that its gene size will be influenced accordingly.

genomics↗

bioRxiv2019

Ageing Transcriptome Meta-Analysis Reveals Similarities Between Key Mammalian Tissues

1Understanding the expression changes that come with age is an important step in understanding the ageing process as a whole. By combining such transcriptomic data with other sources of information, for instance protein-protein interaction (PPI) data, it is possible to make inferences about the functional changes that occur with age. To address this, we conducted a meta-analysis on 127 publicly available microarray and RNA-Seq datasets from mice, rats and humans, to identify genes that are commonly differentially expressed with age in mammals. We also conducted analyses on subsets of these datasets, to produce transcriptomic signatures for brain, heart and muscle tissues, all of which are important tissues in the pathophysiology of ageing. This approach identified the transcriptomic signatures of the ageing system, as well as brain, heart and muscle tissues. We then applied enrichment analysis and machine learning to functionally describe those signatures. This revealed a typical ageing signature including the overexpression of immune and stress response genes and the underexpression of metabolic and developmental genes. Further analysis of the ageing expression signatures revealed that genes differentially expressed with age tend to be broadly expressed across tissues, rather than be tissue-specific, and that the ageing expression signatures (particularly the overexpressed signatures) of the whole system, brain and muscle tend to include genes that are central in PPI networks. We also show that genes underexpressed in the brain are highly central in a co-expression map, suggesting that underexpression of these genes may play a part in cognitive ageing. In sum, we show numerous functional similarities between the ageing transcriptomes of these important tissues, a broad non-specific expression pattern in genes differentially expressed with age, along with altered network properties of these genes in both a PPI and co-expression network.

bioinformatics↗

bioRxiv2019

A Multidimensional Systems Biology Analysis of Cellular Senescence in Ageing and Disease

Cellular senescence, a permanent state of replicative arrest in otherwise proliferating cells, is a hallmark of ageing and has been linked to ageing-related diseases like cancer. Senescent cells have been shown to accumulate in tissues of aged organisms which in turn can lead to chronic inflammation. Many genes have been associated with cell senescence, yet a comprehensive understanding of cell senescence pathways is still lacking. To this end, we created CellAge (http://genomics.senescence.info/cells), a manually curated database of 279 human genes associated with cellular senescence, and performed various integrative and functional analyses. We observed that genes promoting cell senescence tend to be overexpressed with age in human tissues and are also significantly overrepresented in anti-longevity and tumour-suppressor gene databases. By contrast, genes inhibiting cell senescence overlapped with pro-longevity genes and oncogenes. Furthermore, an evolutionary analysis revealed a strong conservation of senescence-associated genes in mammals, but not in invertebrates. Using the CellAge genes as seed nodes, we also built protein-protein interaction and co-expression networks. Clusters in the networks were enriched for cell cycle and immunological processes. Network topological parameters also revealed novel potential senescence-associated regulators. We then used siRNAs and observed that of 26 candidates tested, 19 induced markers of senescence. Overall, our work provides a new resource for researchers to study cell senescence and our systems biology analyses provide new insights and novel genes regarding cell senescence.

systems biology↗

bioRxiv2019

PICKER-HG: a web server using random forests for classifying human genes into categories

MotivationOne of the main challenges faced by biologists is how to extract valuable knowledge from the data produced by high-throughput genomic experiments. Although machine learning can be used for this, in general, machine learning tools on the web were not designed for biologist users. They require users to create suitable biological datasets and often produce results that are hard to interpret.\n\nObjectiveOur aim is to develop a freely available web server, named PerformIng Classification and Knowledge Extraction via Rules using random forests on Human Genes (PICKER-HG), aimed at biologists looking for a straightforward application of a powerful machine learning technique (random forests) to their data.\n\nResultsWe have developed the first web server that, as far as we know, dynamically constructs a classification dataset, given a list of human genes with annotations entered by the user, and outputs classification rules extracted of a Random Forest model. The web server can also classify a list of genes whose class labels are unknown, potentially assisting biologists investigating the association between class labels of interest and human genes.\n\nAvailabilityhttp://machine-learning-genomics.com/

bioinformatics↗

bioRxiv2019

A Human Tissue-Specific Transcriptomic Analysis Reveals that Ageing Hinders Cancer and Boosts Cellular Senescence

Ageing is the biggest risk factor for cancer, but the mechanisms linking these two processes remain unclear. We compared genes differentially expressed with age and genes differentially expressed in cancer among nine human tissues. In most tissues, ageing and cancer gene expression surprisingly changed in the opposite direction. These overlapping gene sets were related to several processes, mainly cell cycle and the immune system. Moreover, cellular senescence signatures derived from a meta-analysis changed in the same direction as ageing and in the opposite direction of cancer signatures. Therefore, transcriptomic changes in ageing and cellular senescence might relate to a decrease in cell proliferation, while cancer transcriptomic changes shift towards an increase in cell division. Our results highlight the complex relationship between ageing, cancer and cellular senescence and suggest that in most human tissues ageing processes and senescence act in tandem while being detrimental to cancer. Our work challenges the traditional view concerning the relationship between cancer and ageing and suggests that ageing processes may hinder cancer development.

cancer biology↗

bioRxiv2019

The fog of genetics: Known unknowns and unknown unknowns in the genetics of complex traits and diseases

Associating genetic variants with phenotypes is not only important to understand the underlying biology but also to identify potential drug targets for treating diseases. It is widely accepted that for most complex traits many associations remain to be discovered, the so-called "missing heritability." Yet missing heritability can be estimated, it is a known unknown, and we argue is only a fraction of the unknowns in genetics. The majority of possible genetic variants in the genome space are either too rare to be detected or even entirely absent from populations, and therefore do not contribute to estimates of phenotypic or genetic variability. We call these unknown unknowns in genetics the "fog of genetics." Using data from the 1000 Genomes Project we then show that larger genes with greater genetic diversity are more likely to be associated with human traits, demonstrating that genetic associations are biased towards particular types of genes and that the genetic information we are lacking about traits and diseases is potentially immense. Our results and model have multiple implications for how genetic variability is perceived to influence complex traits, provide insights on molecular mechanisms of disease and for drug discovery efforts based on genetic information.

genetics↗

bioRxiv2017

Human Ageing Genomic Resources: 2018 Update

In spite of a growing body of research and data, human ageing remains a poorly understood process. To facilitate studies of ageing, over 10 years ago we developed the Human Ageing Genomic Resources (HAGR), which are now the leading online resource for biogerontologists. In this update, we present HAGRs main functionalities, including new additions and improvements to HAGR. HAGR consists of five databases: 1) the GenAge database of ageing-related genes, in turn composed of a dataset of >300 human ageing-related genes and a dataset with >2000 genes associated with ageing or longevity in model organisms; 2) the AnAge database of animal ageing and longevity, featuring >4000 species; 3) the GenDR database with >200 genes associated with the life-extending effects of dietary restriction; 4) the LongevityMap database of human genetic association studies of longevity with >500 entries; 5) the DrugAge database with >400 ageing or longevity-associated drugs or compounds; 6) the CellAge database with >200 genes associated with cell senescence. All our databases are manually curated by experts to ensure a high quality data and presented in an intuitive and clear interface that includes cross-links across our databases and to external resources. HAGR is freely available online (http://genomics.senescence.info/).

genomics↗