Hox genes

Hox geni se neprestano javljaju kao ključni čimbenici u embrionalnom razvitku i evoluciji. Zajedno s drugim genima čine signalne mreže, djelujući kao modulatori ili pak sudjeluju u formiranju novih struktura. Specifičnost i funkcija hox gena ovisi o njihovoj regulaciji koja je višeslojna i kompleksna. Glavni cilj ovoga rada bio je predstaviti uloge hox gena kod embriogeneze, te ukazati na osnovne aspekte regulacije. Za potpuno razumijevanje uloga i specifičnosti hox gena potrebno je još mnogo istraživanja posebice kod dešifriranja mreža hox gena i njihove regulacije, što bi olakšalo liječenje bolesti povezanih s hox genima, i omogućilo bolje razumijevanje uloge hox gena u evoluciji.Hox genes keep on occuring as key elements in embryogenesis and evolution. Together with other genes they form signaling networks, acting as modulators or participating in the formation of new structures. Specificity and function of hox genes depend on their regulation which is multilayered and complex. The main goal of this review was to present the roles of hox genes during embryogenesis, and to point out the basic aspects of regulation. More research is needed to completely understand the roles and specificity of hox genes, especially in deciphering hox networks and their regulation, which would make it easier to cure hox-related diseases, and allow better understanding of the roles of hox genes in evolution

Hox genes

Hox geni se neprestano javljaju kao ključni čimbenici u embrionalnom razvitku i evoluciji. Zajedno s drugim genima čine signalne mreže, djelujući kao modulatori ili pak sudjeluju u formiranju novih struktura. Specifičnost i funkcija hox gena ovisi o njihovoj regulaciji koja je višeslojna i kompleksna. Glavni cilj ovoga rada bio je predstaviti uloge hox gena kod embriogeneze, te ukazati na osnovne aspekte regulacije. Za potpuno razumijevanje uloga i specifičnosti hox gena potrebno je još mnogo istraživanja posebice kod dešifriranja mreža hox gena i njihove regulacije, što bi olakšalo liječenje bolesti povezanih s hox genima, i omogućilo bolje razumijevanje uloge hox gena u evoluciji.Hox genes keep on occuring as key elements in embryogenesis and evolution. Together with other genes they form signaling networks, acting as modulators or participating in the formation of new structures. Specificity and function of hox genes depend on their regulation which is multilayered and complex. The main goal of this review was to present the roles of hox genes during embryogenesis, and to point out the basic aspects of regulation. More research is needed to completely understand the roles and specificity of hox genes, especially in deciphering hox networks and their regulation, which would make it easier to cure hox-related diseases, and allow better understanding of the roles of hox genes in evolution

Hox genes

Hox geni se neprestano javljaju kao ključni čimbenici u embrionalnom razvitku i evoluciji. Zajedno s drugim genima čine signalne mreže, djelujući kao modulatori ili pak sudjeluju u formiranju novih struktura. Specifičnost i funkcija hox gena ovisi o njihovoj regulaciji koja je višeslojna i kompleksna. Glavni cilj ovoga rada bio je predstaviti uloge hox gena kod embriogeneze, te ukazati na osnovne aspekte regulacije. Za potpuno razumijevanje uloga i specifičnosti hox gena potrebno je još mnogo istraživanja posebice kod dešifriranja mreža hox gena i njihove regulacije, što bi olakšalo liječenje bolesti povezanih s hox genima, i omogućilo bolje razumijevanje uloge hox gena u evoluciji.Hox genes keep on occuring as key elements in embryogenesis and evolution. Together with other genes they form signaling networks, acting as modulators or participating in the formation of new structures. Specificity and function of hox genes depend on their regulation which is multilayered and complex. The main goal of this review was to present the roles of hox genes during embryogenesis, and to point out the basic aspects of regulation. More research is needed to completely understand the roles and specificity of hox genes, especially in deciphering hox networks and their regulation, which would make it easier to cure hox-related diseases, and allow better understanding of the roles of hox genes in evolution

Dominantna epistaza između dva lokusa kvantitativnog svojstva učinkovitosti sporulacije kvasca Saccharomyces cerevisiae

Sporulation efficiency in the yeast Saccharomyces cerevisiae is a well-established model for studying quantitative traits. A variety of genes and nucleotides causing different sporulation efficiencies in laboratory, as well as in wild strains, has already been extensively characterised (mainly by reciprocal hemizygosity analysis and nucleotide exchange methods). We applied a different strategy in order to analyze the variation in sporulation efficiency of laboratory yeast strains. Coupling classical quantitative genetic analysis with simulations of phenotypic distributions (a method we call phenotype modelling) enabled us to obtain a detailed picture of the quantitative trait loci (QTLs) relationships underlying the phenotypic variation of this trait. Using this approach, we were able to uncover a dominant epistatic inheritance of loci governing the phenotype. Moreover, a molecular analysis of known causative quantitative trait genes and nucleotides allowed for the detection of novel alleles, potentially responsible for the observed phenotypic variation. Based on the molecular data, we hypothesise that the observed dominant epistatic relationship could be caused by the interaction of multiple quantitative trait nucleotides distributed across a 60-kb QTL region located on chromosome XIV and the RME1 locus on chromosome VII. Furthermore, we propose a model of molecular pathways which possibly underlie the phenotypic variation of this trait.Učinkovitost sporulacije često se koristi za proučavanje kvantitativnih svojstava kvasca Saccharomyces cerevisiae. Velik broj gena i nukleotida koji utječu na učinkovitost sporulacije kvasca u laboratorijskim te divljim sojevima temeljito je okarakteriziran (uglavnom pomoću tehnike recipročne hemizigotnosti i ciljanom izmjenom nukleotida). U ovom smo radu primijenili drukčiju strategiju analize učinkovitosti sporulacije laboratorijskih sojeva kvasca. Povezivanjem klasičnih analiza kvantitativne genetike sa simulacijama fenotipskih distribucija (metoda modeliranja fenotipova) omogućena je detaljna analiza genetičkih odnosa između lokusa kvantitativnog svojstva učinkovitosti sporulacije. Na taj smo način otkrili dominantno epistatski odnos između dva lokusa koji pridonose učinkovitosti sporulacije. Štoviše, molekularna analiza poznatih gena i nukleotida što utječu na sporulaciju omogućila je pronalazak novih alela, koji su vjerojatno odgovorni za fenotipsku varijaciju. Pretpostavljamo da je dominantno epistatski način nasljeđivanja učinkovitosti sporulacije rezultat interakcije regije DNA na kromosomu XIV, duge 60 kb, te lokusa RME1 na kromosomu VII. Nadalje, predlažemo model pomoću kojeg se mogu opisati signalni putevi što reguliraju učinkovitost sporulacije

Inference in population genetics using forward and backward, discrete and continuous time processes

DS and CK were partially funded by FWF-P24551-B25. CK has been partially funded by the Vienna Science and Technology Fund (WWTF) through project MA16-061.A central aim of population genetics is the inference of the evolutionary history of a population. To this end, the underlying process can be represented by a model of the evolution of allele frequencies parametrized by e.g., the population size, mutation rates and selection coefficients. A large class of models use forward-in-time models, such as the discrete Wright-Fisher and Moran models and the continuous forward diffusion, to obtain distributions of population allele frequencies, conditional on an ancestral initial allele frequency distribution. Backward-in-time diffusion processes have been rarely used in the context of parameter inference. Here, we demonstrate how forward and backward diffusion processes can be combined to efficiently calculate the exact joint probability distribution of sample and population allele frequencies at all times in the past, for both discrete and continuous population genetics models. This procedure is analogous to the forward-backward algorithm of hidden Markov models. While the efficiency of discrete models is limited by the population size, for continuous models it suffices to expand the transition density in orthogonal polynomials of the order of the sample size to infer marginal likelihoods of population genetic parameters. Additionally, conditional allele trajectories and marginal likelihoods of samples from single populations or from multiple populations that split in the past can be obtained. The described approaches allow for efficient maximum likelihood inference of population genetic parameters in a wide variety of demographic scenarios.PostprintPeer reviewe

Inference of genomic landscapes using ordered Hidden Markov Models with emission densities (oHMMed)

CV and BY were supported by the the Austrian Science Fund (FWF; DK W1225-B20); MK and HK were supported by the the Austrian Science Fund (FWF; SFB F6101 and F6106). This work was also partially funded by the Vienna Science and Technology Fund (WWTF) (10.47379/MA16061 to CK). LCM’s research was funded by the School of Biology at the University of StAndrews.Genomes are inherently inhomogeneous, with features such as base composition, recombination, gene density, and gene expression varying along chromosomes. Evolutionary, biological, and biomedical analyses aim to quantify this variation, account for it during inference procedures, and ultimately determine the causal processes behind it. Since sequential observations along chromosomes are not independent, it is unsurprising that autocorrelation patterns have been observed e.g., in human base composition. In this article, we develop a class of Hidden Markov Models (HMMs) called oHMMed (ordered HMM with emission densities, the corresponding R package of the same name is available on CRAN): They identify the number of comparably homogeneous regions within autocorrelated observed sequences. These are modelled as discrete hidden states; the observed data points are realisations of continuous probability distributions with state-specific means that enable ordering of these distributions. The observed sequence is labelled according to the hidden states, permitting only neighbouring states that are also neighbours within the ordering of their associated distributions. The parameters that characterise these state-specific distributions are inferred.Peer reviewe