Genetic Engineering

Leading scientists from different countries around the world contributed valuable essays on the basic applications and safety, as well as the ethical and moral considerations, of the powerful genetic engineering tools now available for modifying the molecules, pathways, and phenotypes of species of agricultural, industrial and even medical importance. After three decades of perfecting such tools, we now see a refined technology, surprisingly unexpected applications, and matured guidelines to avoid unintentional damage to our and other species, as well as the environment, while trying to contribute to solve the biological, medical and technical challenges of society and industry. Chapters on thermo-stabilization of luciferase, engineering of the phenylpropanoid pathway in a species of high demand for the paper industry, more efficient regeneration of transgenic soybean, viral resistant plants, and a novel approach for rapidly screening properties of newly discovered animal growth hormones, illustrate the state-of-the-art science and technology of genetic engineering, but also serve to raise public awareness of the pros and cons that this young scientific discipline has to offer to mankind

Variable selection in gamma regression model using chaotic firefly algorithm with application in chemometrics

Variable selection is a very helpful procedure for improving computational speed and prediction accuracy by identifying the most important variables that related to the response variable. Regression modeling has received much attention in several science fields. Firefly algorithm is one of the recently efficient proposed nature-inspired algorithms that can efficiently be employed for variable selection. In this work, chaotic firefly algorithm is proposed to perform variable selection for gamma regression model.  A real data application related to the chemometrics is conducted to evaluate the performance of the proposed method in terms of prediction accuracy and variable selection criteria. Further, its performance is compared with other methods. The results proved the efficiency of our proposed methods and it outperforms other popular methods

Coordination between growth and stress responses by DELLA in the liverwort Marchantia polymorpha

Plant survival depends on the optimal use of resources under variable environmental conditions. Among the mechanisms that mediate the balance between growth, differentiation, and stress responses, the regulation of transcriptional activity by DELLA proteins stands out. In angiosperms, DELLA accumulation promotes defense against biotic and abiotic stress and represses cell division and expansion, while the loss of DELLA function is associated with increased plant size and sensitivity toward stress.1 Given that DELLA protein stability is dependent on gibberellin (GA) levels2 and GA metabolism is influenced by the environment,3 this pathway is proposed to relay environmental information to the transcriptional programs that regulate growth and stress responses in angiosperms.4,5 However, DELLA genes are also found in bryophytes, whereas canonical GA receptors have been identified only in vascular plants.6, 7, 8, 9, 10 Thus, it is not clear whether these regulatory functions of DELLA predated or emerged with typical GA signaling. Here, we show that, as in vascular plants, the only DELLA in the liverwort Marchantia polymorpha also participates in the regulation of growth and key developmental processes and promotes oxidative stress tolerance. Moreover, part of these effects is likely caused by the conserved physical interaction with the MpPIF transcription factor. Therefore, we suggest that the role in the coordination of growth and stress responses was already encoded in the DELLA protein of the common ancestor of land plants, and the importance of this function is underscored by its conservation over the past 450 million years

Development and Application of Next-Generation Sequencing Methods to Profile Cellular Translational Dynamics

The transmission of genetic information from the transcription of DNA to RNA and the subsequent translation of RNA into protein is often abstracted into a linear process. However, as methods and technologies to measure the genomic, transcriptomic, and proteomic content of cells have advanced, so too has our understanding that the transmission of genetic information does not always flow in a lossless manner. For instance, changes observed in messenger RNA (mRNA) abundance are not always retained at the proteomic level. Indeed, a diverse array of mechanisms have been identified that exert regulatory control over this transmission of information. Next-generation short read sequencing has driven many of these insights and provided increasingly nuanced understanding of these regulatory mechanisms. However, the continued development and application of sequencing methodologies and analytics are required to properly contextualize many of these insights on a more global scale. Ribosome profiling is one such recent advancement which enriches for ribosome-protected fragments of mRNA; sequencing and analysis of these ribosome-protected mRNA fragments enables profiling of the translational content of a sample. The aim of this dissertation is to address the need for the development and application of statistical and analytical algorithms to profile the regulatory factors that contribute to the translational dynamics in cells. In the first chapter, I survey the development and application of next-generation sequencing methods for the profiling and computational analysis of translation and translational dynamics. In the second chapter of this thesis, I present SPECtre, a software package that identifies regions of active translation through measurement of the translational engagement of ribosomes over a transcript. SPECtre achieves high sensitivity and specificity in its classification of regions undergoing translation by leveraging the codon-dependent elongation of peptides; this tri-nucleotide periodicity is evident in the alignment of ribosome profiling sequence reads to a reference transcriptome. SPECtre classifies actively translated transcripts according to their coherence in read coverage over a region to an optimal tri-nucleotide signal. In the third chapter, I describe the application of SPECtre to identify the translation of upstream-initiated open-reading frames that may regulate differentiation in a neuron-like cell model. uORFs are transcripts that result from the initiation of translation from AUG, and under certain biological constraints, from non-AUG sequences localized in the 5’ untranslated regions of annotated protein-coding genes. Subsets of these uORFs have been implicated in the regulation of their downstream protein-coding genes in yeast, mice and humans. In this chapter, I provide further evidence for this regulation as well as the spatial context for the functional consequences of uORF translation on downstream protein-coding genes in a neuron-like cell line model of differentiation. Finally, in the fourth chapter, I outline a strategy using our coherence-based translational scoring algorithm to profile ribosomal engagement over chimeric gene fusion breakpoints in prostate cancer. Here, known breakpoints from current annotation databases are integrated with novel junctions nominated by existing whole genome and transcriptomic gene fusion detection algorithms, and the translational profile over these chimeric junctions using SPECtre is measured. This provides an additional layer of translational evidence to known and novel gene fusion breakpoints in prostate cancer. Ongoing development of a database and visualization platform based on these results will enable integrative insights into the transcriptional and translational topology of these breakpoints.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144106/1/stonyc_1.pd

Bioinformatics Applications Based On Machine Learning

The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems

Computational Intelligence in Healthcare

This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic

Computational Intelligence in Healthcare

The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications

Ancestral Functions of DELLA Proteins

[ES] Las plantas necesitan acomodar su crecimiento a las condiciones ambientales. Con el objetivo de ajustar su desarrollo a las señales externas, usan una serie de mecanismos moleculares. Uno de estos son las rutas de señalización hormonal, que participan en integrar la información externa con programas de desarrollo propios. Una de las hormonas más relevantes en la biología vegetal son las giberelinas (GAs). La señalización por GAs se inicia con la percepción de la hormona a través del receptor GID1, y continúa por la degradación de las reguladoras transcripcionales DELLA. Sin embargo, solo las plantas vasculares tienen un sistema de percepción de GAs completo. Entender la relevancia de la señalización por GAs requiere estudiar cómo se ensambló la ruta y qué funciones atribuidas a las GAs estaban ya codificadas en las proteínas DELLA ancestrales. Aquí mostramos mediante análisis filogenéticos y bioquímicos que las proteínas DELLA emergieron inequívocamente en un ancestro común de las plantas terrestres, y que el reclutamiento de las DELLAs al módulo de percepción de GAs depende de la presencia de un dominio de transactivación conservado que fue co-optado por el receptor GID1 ancestral para actuar como un degrón dependiente de GAs. Este dominio de transactivación parece regular la co-activación transcripcional de genes concretos por las DELLAs en todas las plantas terrestres mediante el reclutamiento de complejos Mediator a través de su subunidad MED15. Por último, nos hemos centrado en entender las funciones de las proteínas DELLA en briófitas, un clado sin señalización por GAs. Hemos descubierto el rol de la DELLA de Marchantia polymorpha como coordinadora entre las respuestas de crecimiento y estrés, sugiriendo que dicha función estaba ya codificada en proteínas DELLA del ancestro común de plantas terrestres y se ha mantenido durante más de 450 millones de años.[CA] Les plantes necessiten acomodar el seu creixement a les condicions ambientals. Amb l'objectiu d'ajustar el seu desenvolupament als senyals externs, usen una sèrie de mecanismes moleculars. Un d'aquests són les rutes de senyalització hormonal, que participen en integrar la informació externa amb programes de desenvolupament propis. Una de les hormones més rellevants en la biologia vegetal són les giberel·lines (GAs). La senyalització per GAs s'inicia amb la percepció de l'hormona a través del receptor GID1, i continua per la degradació de les reguladores transcripcionals DELLA. No obstant això, només les plantes vasculars tenen un sistema complet de percepció de GAs. Entendre la rellevància de la senyalització per GAs requereix estudiar com es va assemblar la ruta i quines funcions atribuïdes a les GAs estaven ja codificades en les proteïnes DELLA ancestrals. Ací mostrem mitjançant anàlisis filogenètiques i bioquímiques que les proteïnes DELLA van emergir inequívocament en un ancestre comú de les plantes terrestres, i que el reclutament de les DELLAs al mòdul de percepció de GAs depén de la presència d'un domini de transactivació conservat que va ser co-optat pel receptor GID1 ancestral per a actuar com un degró dependent de GAs. Aquest domini de transactivació sembla regular la co-activació transcripcional de gens concrets per les DELLAs en totes les plantes terrestres mitjançant el reclutament de complexos Mediator a través de la seua subunitat MED15. Finalment, ens hem centrat en entendre les funcions de les proteïnes DELLA en briòfites, un clade sense senyalització per GAs. Hem descobert el rol de la DELLA de Marchantia polymorpha com a coordinadora entre les respostes de creixement i estrés, suggerint que aquesta funció estava ja codificada en proteïnes DELLA de l'ancestre comú de plantes terrestres i s'ha mantingut durant més de 450 milions d'anys.[EN] Plants need to accommodate their growth habits to environmental conditions. For this aim, several mechanisms are used to adjust developmental responses to exogenous signals. Among them, hormonal signalling pathways participate by integrating external information with endogenous programs. One of the most relevant hormones in plant biology are gibberellins (GAs). GA signalling involves perception of the hormone by the GA receptor GID1 and subsequent degradation of the DELLA transcriptional regulators. However, only vascular plants possess a full GA perception system. Understanding the relevance of GA signalling requires elucidating how this pathway was assembled and which of the functions attributed to GAs were encoded in the ancestral DELLA proteins. Here we show by phylogenetic and biochemical analyses that DELLA proteins emerged unequivocally in a land plant common ancestor and that their recruitment into the GA-perception module relies in the presence of a conserved transactivation domain co-opted by an ancestral GID1 receptor to act as a GA-dependent degron. Moreover, this transactivation domain seems to regulate DELLA-dependent transcriptional co-activation of selected target genes by recruitment of Mediator complexes through the MED15 subunit in all land plants. Finally, we have focused on understanding the functions of DELLA proteins in bryophytes, a clade with no GA signalling. We have uncovered the role of Marchantia polymorpha DELLA protein as a coordinator between growth and stress responses, suggesting that this function was already present in the DELLA protein of a land plant common ancestor and has been maintained for over 450 millions of years.La realización de esta tesis doctoral ha sido posible gracias a una ayuda para contratos predoctorales FPU (FPU15/01756), dos Ayudas para Estancias Breves FPU (EST17/00237, IPS2, París; EST18/00400, WUR, Wageningen), una ayuda EMBO Short-Term (ASTF 8239, WUR, Wageningen), y la financiación MSCA H2020 RISE para desplazamientos en el contexto del proyecto SIGNAT (RISE Action 644435, PUC, Santiago). Así mismo, el grueso del trabajo experimental incluido ha sido financiado por el proyecto HUBFUN del MINECO (BFU2016-80621-P)Hernández García, J. (2021). Ancestral Functions of DELLA Proteins [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/169370TESI

Engineering mono- and multi-valent inhibitors on a modular repeat-protein scaffold to target oncogenic Tankyrase

Tandem-repeat proteins are a class of proteins ubiquitous in nature and exploited in recent years in biotechnological and pharmaceutical applications due to their favourable biophysical properties. One such repeat motif, the tetratricopeptide repeat (TPR), has already been exploited for biotechnological applications and here the consensus-designed TPR (CTPR) sequence was used as a scaffold to create novel arrays of binding molecules. The platform allows us to display single and multiple functions with diverse geometrical arrangements by grafting short binding sequences onto the loops between adjacent repeats or at the terminal alpha-helices. As proof of concept, proteins were designed to bind to and inhibit the human tankyrase (TNKS), a key regulatory protein involved in Wnt signalling and overexpressed in cancer and other disorders. For this purpose, a tankyrase-binding peptide (TBP) was grafted between two adjacent repeats to create a solvent-exposed loop. A series of mono- and multi- valent TNKS binders, named TBP-CTPR, was assembled by repeating the TNKS-binding unit in tandem in both a monomeric format and also in a trimeric arrangement. The folding and thermodynamic stability of these TBP-CTPR proteins were characterised and the interaction with TNKS was measured using a range of biophysical approaches. Both the engineered TBP- CTPR proteins and TNKS are multivalent, and the effects of multivalency were explored both in the test tube and in the cell. The results show that the proteins interact to form large assemblies. Moreover, the TBP-CTPR proteins were found to have exceptional activity in inhibiting the Wnt signaling pathway upon delivery by encapsulation in fusogenic liposomes. Lastly, hetero-bifunctional constructs were generated by grafting two different binding sequences onto the CTPR scaffold, and a preliminary analysis of their activities was performed. In conclusion, these results point to the tremendous potential of the CTPR scaffold as a platform to build synthetic protein binders, with a particular focus on multivalent interactions