From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics

Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is arguably the next major missing piece in a fully predictive theory of evolution. We refer to this generally as the problem of the genotype-phenotype map. Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures, has revealed important facts that deeply affect the dynamical description of evolutionary processes. Empirical evidence supporting the fundamental relevance of features such as phenotypic bias is mounting as well, while the synthesis of conceptual and experimental progress leads to questioning current assumptions on the nature of evolutionary dynamics-cancer progression models or synthetic biology approaches being notable examples. This work delves into a critical and constructive attitude in our current knowledge of how genotypes map onto molecular phenotypes and organismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension. As a final goal, this community should aim at deriving an updated picture of evolutionary processes soundly relying on the structural properties of genotype spaces, as revealed by modern techniques of molecular and functional analysis.Comment: 111 pages, 11 figures uses elsarticle latex clas

Determinants of CRISPR array non-canonical adaptation mechanism

CRISPR-cas systems are incredibly diverse and currently are classified in six major types and over 30 subtypes. Apart from their role in adaptive immunity it has been shown that some of the CRISPR-cas subtypes are also involved in host gene regulation and even in collateral damage leading to bacteriostatic or lethal outcomes for the host. CRISPR array spacers direct and influence canonical and non-canonical functions of the CRISPR-cas system together with subtype Cas proteins. Better understanding of spacer adaptation mechanisms is crucial for uncovering intricacies of evolutionary arms race between prokaryotes and phages. Here we present large-scale analysis of CRISPR array spacers originating from 31845 complete bacterial genomes. All bacterial and 16388 viral genomes were retrieved using NCBI datasets API. CRISPRidentify and CRISPRcasIdentifier tools were used for CRISPR array, Cas genes detection and subtyping. Viral genomes were mapped to their hosts using the latest version of the Virus-Host DB. Mapping was performed on the genus level of the hosts phylogenetic tree. Gumbel extreme value distribution was used to determine statistical significance of each spacer Smith-Waterman alignment score. Differences in melting energy and GC content between identified spacers, origin bacterial genomes and infecting bacteriophages were explored for different CRISPR-cas subtypes and for different bacterial genera. Spacers from the extremes of the GC content distribution were aligned to the origin bacterial and infecting phage genomes in order to determine their origin. GC content of the spacers was lesser than the GC content of the source bacterial genome but greater than infecting viral genome. This observation aligns with the hypothesis that the majority of CRISPR spacers were adapted from the bacteriophage genomes and serve canonical function. Alignments of the spacers from GC rich distribution tail have shown their preferential targeting of host genomes which further supports the hypothesis that GC rich spacers originated from the bacterial genome and have non-canonical function.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202

Analysis of High-Throughput Data - Protein-Protein Interactions, Protein Complexes and RNA Half-life

The development of high-throughput techniques has lead to a paradigm change in biology from the small-scale analysis of individual genes and proteins to a genome-scale analysis of biological systems. Proteins and genes can now be studied in their interaction with each other and the cooperation within multi-subunit protein complexes can be investigated. Moreover, time-dependent dynamics and regulation of these processes and associations can now be explored by monitoring mRNA changes and turnover. The in-depth analysis of these large and complex data sets would not be possible without sophisticated algorithms for integrating different data sources, identifying interesting patterns in the data and addressing the high variability and error rates in biological measurements. In this thesis, we developed such methods for the investigation of protein interactions and complexes and the corresponding regulatory processes. In the first part, we analyze networks of physical protein-protein interactions measured in large-scale experiments. We show that the topology of the complete interactomes can be confidently extrapolated despite high numbers of missing and wrong interactions from only partial measurements of interaction networks. Furthermore, we find that the structure and stability of protein interaction networks is not only influenced by the degree distribution of the network but also considerably by the suppression or propagation of interactions between highly connected proteins. As analysis of network topology is generally focused on large eukaryotic networks, we developed new methods to analyze smaller networks of intraviral and virus-host interactions. By comparing interactomes of related herpesviral species, we could detect a conserved core of protein interactions and could address the low coverage of the yeast two-hybrid system. In addition, common strategies in the interaction of the viruses with the host cell were identified. New affinity purification methods now make it possible to directly study associations of proteins in complexes. Due to experimental errors the individual protein complexes have to be predicted with computational methods from these purification results. As previously published methods relied more or less heavily on existing knowledge on complexes, we developed an unsupervised prediction algorithm which is independent from such additional data. Using this approach, high-quality protein complexes can be identified from the raw purification data alone for any species purification experiments are performed. To identify the direct, physical interactions within these predicted complexes and their subcomponent structure, we describe a new approach to extract the highest scoring subnetwork connecting the complex and interactions not explained by alternative paths of indirect interactions. In this way, important interactions within the complexes can be identified and their substructure can be resolved in a straightforward way. To explore the regulation of proteins and complexes, we analyzed microarray measurements of mRNA abundance, de novo transcription and decay. Based on the relationship between newly transcribed, pre-existing and total RNA, transcript half-life can be estimated for individual genes using a new microarray normalization method and a quality control can be applied. We show that precise measurements of RNA half-life can be obtained from de novo transcription which are of superior accuracy to previously published results from RNA decay. Using such precise measurements, we studied RNA half-lives in human B-cells and mouse fibroblasts to identify conserved patterns governing RNA turnover. Our results show that transcript half-lives are strongly conserved and specifically correlated to gene function. Although transcript half-life is highly similar in protein complexes and \mbox{families}, individual proteins may deviate significantly from the remaining complex subunits or family members to efficiently support the regulation of protein complexes or to create non-redundant roles of functionally similar proteins. These results illustrate several of the many ways in which high-throughput measurements lead to a better understanding of biological systems. By studying large-scale measure\-ments in this thesis, the structure of protein interaction networks and protein complexes could be better characterized, important interactions and conserved strategies for herpes\-viral infection could be identified and interesting insights could be gained into the regulation of important biological processes and protein complexes. This was made possible by the development of novel algorithms and analysis approaches which will also be valuable for further research on these topics

Can we use biobanks to study infectious diseases?

Understanding the molecular and environmental basis of diseases in order to improve diagnosis and treatment represent a top priority for researchers. Much of the progress occurred following the growth of various omics technologies and the IT progress in developing large electronic databases capable of storing huge amounts of data. Biobanks represents the most valuable resource for personalized medicine as these are the large collection of various patient samples with well-annotated clinical data which strive to identify possible links between genetic predisposition and disease. A significant step forward are biobanks that are linked to the electronic health records of each participant enabling up-to-date source of relevant medical information and those “deeply phenotyped” for various other omics data, such as microbiome, epigenome, transcriptome, metabolome and proteome. Since infectious diseases still represent a huge threat to global human health, and host genetic factors have been implied as determining risk factors for observed variations in disease susceptibility, severity, and outcome, during this lecture we will discuss challenges and opportunities of using biobanks as a potential source to study infectious diseases based on the case example of isolated population-based longitudinal biobank “10,001 Dalmatians”. Results of a genome-wide association meta-analyses of 14 different infectious-related phenotypes identified 29 infection-related genetic associations, most belonging to rare variants, all of which have a role in immune response. These findings support the concept that host genetic susceptibility to bacterial and viral infections in adults is polygenic, where common variations have very low explained variance and/or “unfortunate” combinations of numerous rare variants. Expanding our understanding of rare variants may help in the construction of genetic panels which might predict an individual’s lifetime vulnerability to major infectious diseases. Furthermore, longitudinal biobanks are a valuable source of data for discovering host genetic variations involved in infectious disease susceptibility and severity. Because infectious diseases continue to exert selective pressure on our genomes, a global network of biobanks with access to genetic and environmental data is required to further explain complicated mechanisms underlying host-pathogen interactions and infectious disease vulnerability.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202