Coevolutionary Landscapes Research Articles

Residue coevolution and mutational landscape for OmpR and NarL response regulator subfamilies

DNA-binding response regulators (DBRRs) are a broad class of proteins that operate in tandem with their partner kinase proteins to form two-component signal transduction systems in bacteria. Typical DBRRs are composed of two domains where the conserved N-terminal domain accepts transduced signals and the evolutionarily diverse C-terminal domain binds to DNA. These domains are assumed to be functionally independent, and hence recombination of the two domains should yield novel DBRRs of arbitrary input/output response, which can be used as biosensors. This idea has been proved to be successful in some cases; yet, the error rate is not trivial. Improvement of the success rate of this technique requires a deeper understanding of the linker-domain and inter-domain residue interactions, which have not yet been thoroughly examined. Here, we studied residue coevolution of DBRRs of the two main subfamilies (OmpR and NarL) using large collections of bacterial amino acid sequences to extensively investigate the evolutionary signatures of linker-domain and inter-domain residue interactions. Coevolutionary analysis uncovered evolutionarily selected linker-domain and inter-domain residue interactions of known experimental structures, as well as previously unknown inter-domain residue interactions. We examined the possibility of these inter-domain residue interactions as contacts that stabilize an inactive conformation of the DBRR where DNA binding is inhibited for both subfamilies. The newly gained insights on linker-domain/inter-domain residue interactions and shared inactivation mechanisms improve the understanding of the functional mechanism of DBRRs, providing clues to efficiently create functional DBRR-based biosensors. Additionally, we show the feasibility of applying coevolutionary landscape models to predict the functionality of domain-swapped DBRR proteins. The presented result demonstrates that sequence information can be used to filter out bioengineered DBRR proteins that are predicted to be nonfunctional due to a high negative predictive value.

Agrevolutions. Esempi di coevoluzione nel paesaggio agrario tra Sardegna e Portogallo

The paper refers to research and projects shared between Sardinian and Portuguese agricultural landscapes which, starting from the continuous investigation of this common cultural matrix, try to trace an evolutionary line of the historicized ways of co-evolutionary landscape construction and foreshadow some possible scenarios of continuity.In particular, the paper will focus on two projects by the Global Arquitectura Pajsagista studio in Alentejo, in which the approach to the study of the place can refer to a multi-scale methodology that links the study of the historical uses of agricultural landscapes, with the understanding of physical structures and evolution of vegetation cover foreshadowing a new idea of space starting from the interaction between man and these two dominant ones. With respect to these two topics declined in the “man-soil” and “man-living beings” relationship, it can be said that agricultural projects - and this is historically true, but even more so today - argued exactly the necessary meeting between utilitarian practices and symbolic of the primary productive activities of man with the “self poietic” and “ecological” regenerative dynamics of a specific way of organizing the terrestrial space. The presented cases, in fact, try to show the landscape design ability, into the rural Mediterranean areas, to activate co-evolution processes between the regeneration of agricultural soils and the human needs of inhabiting linked to leisure and refreshment.

Coupling a Live Cell Directed Evolution Assay with Coevolutionary Landscapes to Engineer an Improved Fluorescent Rhodopsin Chloride Sensor.

Our understanding of chloride in biology has been accelerated through the application of fluorescent protein-based sensors in living cells. These sensors can be generated and diversified to have a range of properties using laboratory-guided evolution. Recently, we established that the fluorescent proton-pumping rhodopsin wtGR from Gloeobacter violaceus can be converted into a fluorescent sensor for chloride. To unlock this non-natural function, a single point mutation at the Schiff counterion position (D121V) was introduced into wtGR fused to cyan fluorescent protein (CFP) resulting in GR1-CFP. Here, we have integrated coevolutionary analysis with directed evolution to understand how the rhodopsin sequence space can be explored and engineered to improve this starting point. We first show how evolutionary couplings are predictive of functional sites in the rhodopsin family and how a fitness metric based on a sequence can be used to quantify the known proton-pumping activities of GR-CFP variants. Then, we couple this ability to predict potential functional outcomes with a screening and selection assay in live Escherichia coli to reduce the mutational search space of five residues along the proton-pumping pathway in GR1-CFP. This iterative selection process results in GR2-CFP with four additional mutations: E132K, A84K, T125C, and V245I. Finally, bulk and single fluorescence measurements in live E.coli reveal that GR2-CFP is a reversible, ratiometric fluorescent sensor for extracellular chloride with an improved dynamic range. We anticipate that our framework will be applicable to other systems, providing a more efficient methodology to engineer fluorescent protein-based sensors with desired properties.

Co-evolutionary landscape at the interface and non-interface regions of protein-protein interaction complexes

Proteins involved in interactions throughout the course of evolution tend to co-evolve and compensatory changes may occur in interacting proteins to maintain or refine such interactions. However, certain residue pair alterations may prove to be detrimental for functional interactions. Hence, determining co-evolutionary pairings that could be structurally or functionally relevant for maintaining the conservation of an inter-protein interaction is important. Inter-protein co-evolution analysis in several complexes utilizing multiple existing methodologies suggested that co-evolutionary pairings can occur in spatially proximal and distant regions in inter-protein interactions. Subsequently, the Co-Var (Correlated Variation) method based on mutual information and Bhattacharyya coefficient was developed, validated, and found to perform relatively better than CAPS and EV-complex. Interestingly, while applying the Co-Var measure and EV-complex program on a set of protein–protein interaction complexes, co-evolutionary pairings were obtained in interface and non-interface regions in protein complexes. The Co-Var approach involves determining high degree co-evolutionary pairings that include multiple co-evolutionary connections between particular co-evolved residue positions in one protein with multiple residue positions in the binding partner. Detailed analyses of high degree co-evolutionary pairings in protein–protein complexes involved in cancer metastasis suggested that most of the residue positions forming such co-evolutionary connections mainly occurred within functional domains of constituent proteins and substitution mutations were also common among these positions. The physiological relevance of these predictions suggested that Co-Var can predict residues that could be crucial for preserving functional protein–protein interactions. Finally, Co-Var web server (http://www.hpppi.iicb.res.in/ishi/covar/index.html) that implements this methodology identifies co-evolutionary pairings in intra and inter-protein interactions.

Abstract CT261: METAMECH -A Master Observational Trial empowering mechanobiology translational research and mechanobased proof of concept trials in breast cancer

Abstract Background: Breast cancer (BC) is the most frequent tumor in women worldwide. BC lethality is caused by aggressive, therapy-resistant metastases (mBC). Preliminary data have shown that mBC lesions are invariably embedded into a densely packed network of fibrous extracellular matrix, making the metastatic microenvironment a potent inducer of mechanical inputs, ultimately leading to the activation of the transcription factors YAP/TAZ. Aberrant mechano-signaling could thus represent a vulnerability of metastasis, which can be exploited to develop new therapeutic strategies. To investigate how metastatic outgrowth is regulated by the physical properties of the microenvironment, and how the altered mechano-transduction of human BC metastasis can be exploited to define new unconventional therapies, we designed and activated METAMECH, a Master Observational Trial (MOT). MOTs are trial constructs hybridizing a canonical observational trial with a multiplex sample-collection platform, aimed to empower the bi-directional collaboration between pre-clinical and clinical research, an essential prerequisite to feed and implement precision oncology. Methods: METAMECH (IFOM-CPO007/2019/PO006) is a MOT that will follow a stage-mixed cohort of at least 500 BC patients throughout their course of treatments, until death or a minimum of 5 years. METAMECH has been designed to streamline the study of the co-evolutionary landscape between tumor and host cells across all BC subtypes. METAMECH is a resource for integrative clinical and imaging data and fresh and archival sample collection which will allow to auction mechanotransduction mechanisms supporting the outgrowth of human metastases, mine for new potentially actionable targets and permit the selection of appropriate patients for mechano-biology experimentally-driven trials. METAMECH is a multi-tiers research scaffold that will allow to integrate the clinical and basic aspects of the project, to optimize patients enrollment and the logistic of longitudinal collection of theirs data/samples. A customized data lake is being designed for data storage, analysis and retrieval. To facilitate patients access, METAMECH has been designed as a flexible infrastructure organized in TIERS, which allow to pursue different objectives: TIER0/1, Retrieving/Recording: to retrospectively and prospectively access to selected cohorts of clinically annotated BC samples to validate/discover mechanotransduction-linked biomarkers and create a mechano-classifier; TIER2, Modelling: to develop pertinent experimental models to study the aberrant mechanisms underlying the metastatic outgrowth and define mechanotransduction-targeting therapeutic strategies; TIER3, Linking: to access data and samples of patients enrolled in Proof-of-Concept trials to prove the efficacy and study/understand resistance mechanisms of mechanotransduction-targeting therapies. Citation Format: Paolo Luraghi, Luca Lazzari, Maria Vittoria Dieci, Serena Bellani, Chiara Soffientini, Rosario Rizzuto, Antonio Rosato, Vincenzo Costanzo, Giorgio Scita, Giannino Del Sal, Massimiliano Pagani, Marco Foiani, Claudio Tripodo, Silvio Bicciato, Giovanni Blandino, Alberto Zambelli, Valter Torri, Matteo Fassan, PierFranco Conte, Silvia Marsoni, Stefano Piccolo. METAMECH -A Master Observational Trial empowering mechanobiology translational research and mechanobased proof of concept trials in breast cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr CT261.

Abstract CT214: AlfaOmega- a master protocol empowering precision research in colorectal cancer

Abstract Background The overall survival of mCRC patients has been increased by the availability of new cytotoxic and targeted agents and today potentially by the advent of immunotherapies. However, the impact of these advances has been incremental rather than transformative, and a number of unmet medical needs still await rational solutions. In order to navigate the co-evolutionary pathways of CRC tumors during their natural history and under the Darwinian pressure of therapies, we need to feed the experimental laboratories with “the right sample, at the right time, for the right experiment”. This can be accomplished through the design of master protocols that represent a new subset of observational trials aimed to empower the bi-directional collaboration between pre-clinical and clinical research, an essential prerequisite to feed and implement precision oncology. Methods AlfaΩmega (protocol number IFOM-CPO003/2018/PO002) has been designed to streamline the study of the co-evolutionary landscape between tumor and host cells in a stage-mixed cohort of at least 500 CRC patients, with the aim of understanding how their outcomes can be significantly improved. This resource for integrative clinical data and sample collection will allow the molecular story-telling of CRC metastatic spread along time and space and the selection of appropriate patients for experimentally-driven trials. To achieve the required level of ‘experimental precision’, patients can enter AlfaΩmega at two different ‘therapeutic checkpoints’: i) prior to a surgical event or ii) prior to a systemic treatment. Moreover, to optimize the enrollment of patients, the longitudinal collection of data/samples and their logistic management, AlfaΩmega has been designed as a flexible infrastructure organized in TIERs, each of which represents a building block for the stepwise comprehension of the biological processes that drive tumor evolution and that will have an independent informed consensus process: § TIER1, Monitoring: the ability to follow CRC evolution under standard of care treatments and to define new evolution-linked biomarkers: access to clinical & imaging data, FFPE, plasma and PBMCs. TIER1 informed consent is mandatory for the enrolment. § TIER2, Modelling: the ability to develop pertinent experimental models to study evolutionary mechanisms and define evolution-targeting therapeutic strategies: access to fresh tissue, blood, stools, buccal swabs and other fluids. Sample collection in TIER2 is discretionary, i.e. may not be applied to all patients entering TIER1. § TIER3, Linking: the ability to access data and samples of patients enrolled in proof-of-concept trials to prove the efficacy and study/understand resistance mechanisms of evolution-targeting therapies: linking with AlfaΩmega logistic backbone and IT architecture. Citation Format: Luca Lazzari, Paolo Luraghi, Nabil Amirouchene-Angelozzi, Andrea Bertotti, Enzo Medico, Federica Di Nicolantonio, Fabrizio d'Adda di Fagagna, Sergio Abrignani, Daniele Regge, Anna Sapino, Emanuela Bonoldi, Angelo Vanzulli, Vittorina Zagonel, Filippo de Braud, Livio Trusolino, Alberto Bardelli, Salvatore Siena, Silvia Marsoni. AlfaOmega- a master protocol empowering precision research in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr CT214.

Coevolutionary Landscapes of Kinase Family Proteins

The co-variation of pairs of mutations in multiple sequence alignments (MSAs) of protein families can be used to build statistical “Potts” models of the sequence patterns. These models have been found to accurately predict contacts in protein structures, illustrating how evolutionary fitness landscapes of protein families can be connected to corresponding conformational free energy landscapes. We show how a Potts model, parametrized on the protein-kinase family MSA, can further be used to predict the propensity of particular kinase sequences to assume a “DFG-out” conformation, implicated in the susceptibility of some kinases to type-II inhibitors. We also investigate the model's ability to describe kinase mutational statistics. We show how the pairwise (residue-residue) interaction terms of the model are necessary and sufficient to capture the higher-than-pairwise mutation patterns of natural kinase sequences, and can indicate which physical interactions contribute to the fitness and conformational preference of individual sequences, and discuss the number of sequences necessary for model inference.

Designing bacterial signaling interactions with coevolutionary landscapes.

Selecting amino acids to design novel protein-protein interactions that facilitate catalysis is a daunting challenge. We propose that a computational coevolutionary landscape based on sequence analysis alone offers a major advantage over expensive, time-consuming brute-force approaches currently employed. Our coevolutionary landscape allows prediction of single amino acid substitutions that produce functional interactions between non-cognate, interspecies signaling partners. In addition, it can also predict mutations that maintain segregation of signaling pathways across species. Specifically, predictions of phosphotransfer activity between the Escherichia coli histidine kinase EnvZ to the non-cognate receiver Spo0F from Bacillus subtilis were compiled. Twelve mutations designed to enhance, suppress, or have a neutral effect on kinase phosphotransfer activity to a non-cognate partner were selected. We experimentally tested the ability of the kinase to relay phosphate to the respective designed Spo0F receiver proteins against the theoretical predictions. Our key finding is that the coevolutionary landscape theory, with limited structural data, can significantly reduce the search-space for successful prediction of single amino acid substitutions that modulate phosphotransfer between the two-component His-Asp relay partners in a predicted fashion. This combined approach offers significant improvements over large-scale mutations studies currently used for protein engineering and design.

Coevolutionary Landscape of Kinase Family Proteins: Sequence Probabilities and Functional Motifs

The protein kinase catalytic domain is one of the most abundant domains across all branches of life. Although kinases share a common core function of phosphoryl-transfer, they also have wide functional diversity and play varied roles in cell signaling networks, and for this reason are implicated in a number of human diseases. This functional diversity is primarily achieved through sequence variation, and uncovering the sequence-function relationships for the kinase family is a major challenge. In this study we use a statistical inference technique inspired by statistical physics, which builds a coevolutionary “Potts” Hamiltonian model of sequence variation in a protein family. We show how this model has sufficient power to predict the probability of specific subsequences in the highly diverged kinase family, which we verify by comparing the model’s predictions with experimental observations in the Uniprot database. We show that the pairwise (residue-residue) interaction terms of the statistical model are necessary and sufficient to capture higher-than-pairwise mutation patterns of natural kinase sequences. We observe that previously identified functional sets of residues have much stronger correlated interaction scores than are typical.

Connecting the Sequence-Space of Bacterial Signaling Proteins to Phenotypes using Coevolutionary Landscapes

Two-component signaling (TCS) is the primary means by which bacteria sense and respond to the environment. TCS involves two partner proteins working in tandem, which interact to perform cellular functions while limiting interactions with non-partners (i.e., “cross-talk”). We construct a Potts model for TCS that can quantitatively predict how mutating amino acid identities affect the interaction between TCS partners and non-partners. The parameters of this model are inferred directly from protein sequence data. This approach drastically reduces the computational complexity of exploring the sequence-space of TCS proteins. As a stringent test, we compare its predictions to a recent comprehensive mutational study, which characterized the functionality of 204 mutational variants of the PhoQ kinase in Escherichia coli. We find that our best predictions accurately reproduce the amino acid combinations found in experiment, which enable functional signaling with its partner PhoP. These predictions demonstrate the evolutionary pressure to preserve the interaction between TCS partners as well as prevent unwanted “cross-talk”. Further, we calculate the mutational change in the binding affinity between PhoQ and PhoP, providing an estimate to the amount of destabilization needed to disrupt TCS.

Connecting the Sequence-Space of Bacterial Signaling Proteins to Phenotypes Using Coevolutionary Landscapes.

Two-component signaling (TCS) is the primary means by which bacteria sense and respond to the environment. TCS involves two partner proteins working in tandem, which interact to perform cellular functions whereas limiting interactions with non-partners (i.e., cross-talk). We construct a Potts model for TCS that can quantitatively predict how mutating amino acid identities affect the interaction between TCS partners and non-partners. The parameters of this model are inferred directly from protein sequence data. This approach drastically reduces the computational complexity of exploring the sequence-space of TCS proteins. As a stringent test, we compare its predictions to a recent comprehensive mutational study, which characterized the functionality of 204 mutational variants of the PhoQ kinase in Escherichia coli. We find that our best predictions accurately reproduce the amino acid combinations found in experiment, which enable functional signaling with its partner PhoP. These predictions demonstrate the evolutionary pressure to preserve the interaction between TCS partners as well as prevent unwanted cross-talk. Further, we calculate the mutational change in the binding affinity between PhoQ and PhoP, providing an estimate to the amount of destabilization needed to disrupt TCS.

Socio-technical Transitions and Varieties of Capitalism: Green Regional Innovation and Distinctive Market Niches

The aim of this paper is to examine strategic niche management theory applied to green technology industries in a number of different geographical and economic settings. This is aided by a further quest, to establish firm groundwork for a neo-Schumpeterian theory of economic geography based upon his theorisation of the key roles of innovation and entrepreneurship in regional development. These two perspectives fit together rather seamlessly conceptually and in the empirical case material drawn from the USA and Europe. The results point to the centrality of geographical space in the formation not only of paradigms of regime change in industrial organisation but also of more thoroughgoing transitions from one socio-technical landscape, within which many technological regimes may have been subject to ‘creative destruction’, to another coevolutionary landscape that may be the setting for the next set of, in this case, successive post-hydrocarbon technological regimes. Observation of potential landscape change of the kind in focus in the paper is rare, and the conclusions do not go much beyond the identification of the first key elements from which the grander regime and landscape transitions may be consequent.