Species identification, distributioin and reproductive interactions of common smoot-hound and blackspotted smooth-hound shark (Genus mustelus) in the Adriatic Sea

Longstanding taxonomic ambiguity and uncertainty exist in the identification of the common (M. mustelus) and blackspotted (M. punctulatus) smooth-hound in the Adriatic Sea. The lack of a clear and accurate method of morphological identification, leading to frequent misidentification, prevents the collation of species-specific landings and survey data for these fishes and hampers the delineation of the distribution ranges and stock boundaries of the species. In this context, adequate species-specific conservation and management strategies can not be applied without risks of population declining and local extinction. In this thesis work I investigated the molecular ecology of the two smooth-hound sharks which are abundant in the demersal trawl surveys carried out in the NC Adriatic Sea to monitor and assess the fishery resources. Ecological and evolutionary relationships were assessed by two molecular tests: a DNA barcoding analysis to improve species identification (and consequently the knowledge of their spatial ecology and taxonomy) and a hybridization assay based on the nuclear codominant marker ITS2 to evaluate reproductive interactions (hybridization or gene introgression). The smooth-hound sharks (N=208) were collected during the MEDITS 2008 and 2010 campaigns along the Italian and Croatian coasts of the Adriatic Sea, in the Sicilian Channel and in the Algerian fisheries. Since the identification based on morphological characters is not strongly reliable, I performed a molecular identification of the specimens producing for each one the cytochrome oxidase subunit 1 (COI) gene sequence (ca. 640 bp long) and compared them with reference sequences from different databases (GenBank and BOLD). From these molecular ID data I inferred the distribution of the two target species in the NC Adriatic Sea. In almost the totality of the MEDITS hauls I found no evidence of species sympatry. The data collected during the MEDITS survey showed an almost different distribution of M. mustelus (confined along the Italian coasts) and M. punctulatus (confined along the Croatian coasts); just one sample (Gulf of Venice, where probably the ranges of the species overlap) was found to have catches of both the species. Despite these data results suggested no interaction occurred between my two target species at least during the summertime (the period in which MEDITS survey is carried out), I still wanted to know if there were inter-species reproductive interactions so I developed a simple molecular genetic method to detect hybridization. This method is based on DNA sequence polymorphism among species in the nuclear ribosomal Internal Transcribed Spacer 2 locus (ITS2). Its application to the 208 specimens collected raised important questions regarding the ecology of this two species in the Adriatic Sea. In fact results showed signs of hybridization and/or gene introgression in two sharks collected during the trawl survey of 2008 and one collected during the 2010 one along the Italian and Croatian coasts. In the case that it will be confirmed the hybrid nature of these individuals, a spatiotemporal overlapping of the mating behaviour and ecology must occur. At the spatial level, the northern part of the Adriatic Sea (an area where the two species occur with high frequency of immature individuals) could likely play the role of a common nursery area for both species.

Modeling and simulation of a synthetic genetic circuit that implements a multicelled behavior in a growing microcolony of E. coli

Synthetic Biology is a relatively new discipline, born at the beginning of the New Millennium, that brings the typical engineering approach (abstraction, modularity and standardization) to biotechnology. These principles aim to tame the extreme complexity of the various components and aid the construction of artificial biological systems with specific functions, usually by means of synthetic genetic circuits implemented in bacteria or simple eukaryotes like yeast. The cell becomes a programmable machine and its low-level programming language is made of strings of DNA. This work was performed in collaboration with researchers of the Department of Electrical Engineering of the University of Washington in Seattle and also with a student of the Corso di Laurea Magistrale in Ingegneria Biomedica at the University of Bologna: Marilisa Cortesi. During the collaboration I contributed to a Synthetic Biology project already started in the Klavins Laboratory. In particular, I modeled and subsequently simulated a synthetic genetic circuit that was ideated for the implementation of a multicelled behavior in a growing bacterial microcolony. In the first chapter the foundations of molecular biology are introduced: structure of the nucleic acids, transcription, translation and methods to regulate gene expression. An introduction to Synthetic Biology completes the section. In the second chapter is described the synthetic genetic circuit that was conceived to make spontaneously emerge, from an isogenic microcolony of bacteria, two different groups of cells, termed leaders and followers. The circuit exploits the intrinsic stochasticity of gene expression and intercellular communication via small molecules to break the symmetry in the phenotype of the microcolony. The four modules of the circuit (coin flipper, sender, receiver and follower) and their interactions are then illustrated. In the third chapter is derived the mathematical representation of the various components of the circuit and the several simplifying assumptions are made explicit. Transcription and translation are modeled as a single step and gene expression is function of the intracellular concentration of the various transcription factors that act on the different promoters of the circuit. A list of the various parameters and a justification for their value closes the chapter. In the fourth chapter are described the main characteristics of the gro simulation environment, developed by the Self Organizing Systems Laboratory of the University of Washington. Then, a sensitivity analysis performed to pinpoint the desirable characteristics of the various genetic components is detailed. The sensitivity analysis makes use of a cost function that is based on the fraction of cells in each one of the different possible states at the end of the simulation and the wanted outcome. Thanks to a particular kind of scatter plot, the parameters are ranked. Starting from an initial condition in which all the parameters assume their nominal value, the ranking suggest which parameter to tune in order to reach the goal. Obtaining a microcolony in which almost all the cells are in the follower state and only a few in the leader state seems to be the most difficult task. A small number of leader cells struggle to produce enough signal to turn the rest of the microcolony in the follower state. It is possible to obtain a microcolony in which the majority of cells are followers by increasing as much as possible the production of signal. Reaching the goal of a microcolony that is split in half between leaders and followers is comparatively easy. The best strategy seems to be increasing slightly the production of the enzyme. To end up with a majority of leaders, instead, it is advisable to increase the basal expression of the coin flipper module. At the end of the chapter, a possible future application of the leader election circuit, the spontaneous formation of spatial patterns in a microcolony, is modeled with the finite state machine formalism. The gro simulations provide insights into the genetic components that are needed to implement the behavior. In particular, since both the examples of pattern formation rely on a local version of Leader Election, a short-range communication system is essential. Moreover, new synthetic components that allow to reliably downregulate the growth rate in specific cells without side effects need to be developed. In the appendix are listed the gro code utilized to simulate the model of the circuit, a script in the Python programming language that was used to split the simulations on a Linux cluster and the Matlab code developed to analyze the data.