Anastasia Chasapi

SUMMARY: The concept of the pangenome has emerged as the approach of choice for targeted comparative genomics across groups of organisms, from strain or population levels within a species to broader taxonomic or phenotypic categories. Pangenome analyses employ diverse procedures that inevitably introduce reproducibility challenges. Nevertheless, they have already yielded important insights into the structure, function, and evolution of archaeal, bacterial, and eukaryotic genomes across space, time, biogeography, and phylogeny. There is a wide range of applications from vaccine design to synthetic biology that are expected to yield additional technological and biomedical tools for genome biology and biotechnology. We review here the basic concepts underpinning pangenome research, including the estimation of gene and protein family numbers, the modeling of open and closed pangenomes, and key definitions relevant to reporting guidelines. We list and compare computational platforms, algorithms, and data structures, along with their salient features. We present key findings from 249 taxa covering 17,858 genome sequences, subjected to such analyses, and discuss major patterns observed, such as novel families per genome and pangenome size estimates for this validated data set. We propose revised criteria for pangenomes, standard operating procedures, and further targets to enhance the reproducibility of analysis and expand opportunities for experimental and computational comparative genomics. This framework attempts to clarify current methodologies and chart a path forward for integrating pangenome analyses.

Background: The evolutionary rate of disordered protein regions varies greatly due to the lack of structural constraints. So far, few studies have investigated the presence/absence patterns of compositional bias, indicative of disorder, across phylogenies in conjunction with human disease. In this study, we report a genome-wide analysis of compositional bias association with disease in human proteins and their taxonomic distribution. Methods: The human genome protein set provided by the Ensembl database was annotated and analysed with respect to both disease associations and the detection of compositional bias. The Uniprot Reference Proteome dataset, containing 11297 proteomes was used as target dataset for the comparative genomics of a well-defined subset of the Human Genome, including 100 characteristic, compositionally biased proteins, some linked to disease. Results: Cross-evaluation of compositional bias and disease-association in the human genome reveals a significant bias towards biased regions in disease-associated genes, with charged, hydrophilic amino acids appearing as over-represented. The phylogenetic profiling of 17 disease-associated, proteins with compositional bias across 11297 proteomes captures characteristic taxonomic distribution patterns. Conclusions: This is the first time that a combined genome-wide analysis of compositional bias, disease-association and taxonomic distribution of human proteins is reported, covering structural, functional, and evolutionary properties. The reported framework can form the basis for large-scale, follow-up projects, encompassing the entire human genome and all known gene-disease associations.

Background: The evolutionary rate of disordered proteins varies greatly due to the lack of structural constraints. So far, few studies have investigated the presence/absence patterns of intrinsically disordered regions (IDRs) across phylogenies in conjunction with human disease. In this study, we report a genome-wide analysis of compositional bias association with disease in human proteins and their taxonomic distribution. Methods: The human genome protein set provided by the Ensembl database was annotated and analysed with respect to both disease associations and the detection of compositional bias. The Uniprot Reference Proteome dataset, containing 11297 proteomes was used as target dataset for the comparative genomics of a well-defined subset of the Human Genome, including 100 characteristic, compositionally biased proteins, some linked to disease. Results: Cross-evaluation of compositional bias and disease-association in the human genome reveals a significant bias towards low complexity regions in disease-associated genes, with charged, hydrophilic amino acids appearing as over-represented. The phylogenetic profiling of 17 disease-associated, low complexity proteins across 11297 proteomes captures characteristic taxonomic distribution patterns. Conclusions: This is the first time that a combined genome-wide analysis of low complexity, disease-association and taxonomic distribution of human proteins is reported, covering structural, functional, and evolutionary properties. The reported framework can form the basis for large-scale, follow-up projects, encompassing the entire human genome and all known gene-disease associations.

The Database of Intrinsically Disordered Proteins (DisProt, URL: https://disprot.org) is the major repository of manually curated annotations of intrinsically disordered proteins and regions from the literature. We report here recent updates of DisProt version 9, including a restyled web interface, refactored Intrinsically Disordered Proteins Ontology (IDPO), improvements in the curation process and significant content growth of around 30%. Higher quality and consistency of annotations is provided by a newly implemented reviewing process and training of curators. The increased curation capacity is fostered by the integration of DisProt with APICURON, a dedicated resource for the proper attribution and recognition of biocuration efforts. Better interoperability is provided through the adoption of the Minimum Information About Disorder (MIADE) standard, an active collaboration with the Gene Ontology (GO) and Evidence and Conclusion Ontology (ECO) consortia and the support of the ELIXIR infrastructure.

The Covid-19 pandemic has required nonpharmaceutical interventions, primarily physical distancing, personal hygiene and face mask use, to limit community transmission, irrespective of seasons. In fact, the seasonality attributes of this pandemic remain one of its biggest unknowns. Early studies based on past experience from respiratory diseases focused on temperature or humidity, with disappointing results. Our hypothesis that ultraviolet (UV) radiation levels might be a factor and a more appropriate parameter has emerged as an alternative to assess seasonality and exploit it for public health policies. Using geographical, socioeconomic and epidemiological criteria, we selected twelve North-equatorial-South countries with similar characteristics. We then obtained UV levels, mobility and Covid-19 daily incidence rates for nearly the entire 2020. Using machine learning, we demonstrated that UV radiation strongly associated with incidence rates, more so than mobility did, indicating that UV is a key seasonality indicator for Covid-19, irrespective of the initial conditions of the epidemic. Our findings can inform the implementation of public health emergency measures, partly based on seasons in the Northern and Southern Hemispheres, as the pandemic unfolds into 2021.

As genome sequencing efforts are unveiling the genetic diversity of the biosphere with an unprecedented speed, there is a need to accurately describe the structural and functional properties of groups of extant species whose genomes have been sequenced, as well as their inferred ancestors, at any given taxonomic level of their phylogeny. Elaborate approaches for the reconstruction of ancestral states at the sequence level have been developed, subsequently augmented by methods based on gene content. While these approaches of sequence or gene-content reconstruction have been successfully deployed, there has been less progress on the explicit inference of functional properties of ancestral genomes, in terms of metabolic pathways and other cellular processes. Herein, we describe PathTrace, an efficient algorithm for parsimony-based reconstructions of the evolutionary history of individual metabolic pathways, pivotal representations of key functional modules of cellular function. The algorithm is implemented as a five-step process through which pathways are represented as fuzzy vectors, where each enzyme is associated with a taxonomic conservation value derived from the phylogenetic profile of its protein sequence. The method is evaluated with a selected benchmark set of pathways against collections of genome sequences from key data resources. By deploying a pangenome-driven approach for pathway sets, we demonstrate that the inferred patterns are largely insensitive to noise, as opposed to gene-content reconstruction methods. In addition, the resulting reconstructions are closely correlated with the evolutionary distance of the taxa under study, suggesting that a diligent selection of target pangenomes is essential for maintaining cohesiveness of the method and consistency of the inference, serving as an internal control for an arbitrary selection of queries. The PathTrace method is a first step towards the large-scale analysis of metabolic pathway evolution and our deeper understanding of functional relationships reflected in emerging pangenome collections.

The ubiquitin pathway required for most proteins’ targeted degradation involves three classes of enzymes: E1-activating enzyme, E2-conjugating enzyme, and E3-ligases. The human Ark2C is the single known E3 ligase that adopts an alternative, Ub-dependent mechanism for the activation of Ub transfer in the pathway. Its RING domain binds both E2-Ub and free Ub with high affinity, resulting in a catalytic active Ub_R-RING-E2-Ub_D complex formation. We examined potential changes in the conformational plasticity of the Ark2C RING domain and its ligands in their complexed form within the ubiquitin pathway through molecular dynamics (MD). Three molecular mechanics force fields compared to previous NMR relaxation studies of RING domain of Arkadia were used for effective and accurate assessment of MDs. Our results suggest the Ark2C Ub-RING docking site has a substantial impact on maintaining the conformational rigidity of E2-E3 assembly, necessary for the E3’s catalytic activity. In the Ub_R-RING-E2-Ub_D catalytic complex, the Ub_R molecule was found to have greater mobility than the other Ub, bound to E2. Furthermore, network-based bioinformatics helped us identify E3 RING ligase candidates which potentially exhibit similar structural modules as Ark2C, along with predicted substrates targeted by the Ub-binding RING Ark2C. Our findings could trigger a further exploration of related unrevealed functions of various other E3 RING ligases.

The Covid-19 pandemic has spread across the world during early 2020, with unforeseen consequences. Beyond social measures and biomedical research, it is important to assess the seasonality of the epidemic to inform strategies, with limited available data in the short period of time between the March equinox and the June solstice. While the effect of multiple factors is being investigated, little attention has been paid to ultraviolet (UV) radiation, a key parameter of seasonal forcing. We review the effects of UV radiation, proposing it as a potential element of seasonality, and provide evidence from the current literature and scant, yet revealing, observations. Explicit consideration should be given to UV radiation for the seasonality of Covid-19 at high latitudes and altitudes, based on the SARS and MERS epidemics and coronavirus diseases, and not just the ‘warmer days’ of summer.

In bibliometrics, scientific output is typically measured in terms of quantity, e.g. number of publications, or quality, e.g. number of citations ( Almeida et al., 2009). For individual researchers, absolute counts are regarded as sufficient, although it is well-known that these numbers may vary per research field ( Yang et al., 2012). The h-index, the number of N publications that have been cited at least N times ( Hirsch, 2005), has also been shown to vary across scientific disciplines ( Lillquist and Green, 2010). Other, more complex metrics have been devised, yet the h-index is indeed a widely used measure of academic ‘success’ or impact ( Alonso et al., 2009)—despite the fact that the primary metrics on which it depends are the number of publications and citations ( Yong, 2014).

We review the establishment of computational biology in Greece and Cyprus from its inception to date and issue recommendations for future development. We compare output to other countries of similar geography, economy, and size-based on publication counts recorded in the literature-and predict future growth based on those counts as well as national priority areas. Our analysis may be pertinent to wider national or regional communities with challenges and opportunities emerging from the rapid expansion of the field and related industries. Our recommendations suggest a 2-fold growth margin for the 2 countries, as a realistic expectation for further expansion of the field and the development of a credible roadmap of national priorities, both in terms of research and infrastructure funding.