Structural and functional investigation of underexplored carbohydrate-active enzyme families

The known consequences of the current fossil-based economy require a transition towards a bio-based economy. Development of biorefineries in which plant biomass can be utilized as a renewable source of energy and building blocks to produce both commodities and high-value products, is a key step in this transition. Lignocellulosic biomass has, however, evolved a highly complex architecture to be recalcitrant to degradation, and this represents a major challenge in its utilization. In nature, a wide variety of microorganisms has evolved to exploit lignocellulose as carbon source. They produce carbohydrate-active enzymes (CAZymes) to degrade lignocellulose polymers into components that can be utilized for their growth. CAZymes therefore represent powerful tools that could be utilized in industrial settings for the degradation of plant biomass.\ua0In this thesis, I investigated different CAZymes belonging to relatively unexplored families. The aim was to expand our yet limited knowledge and to gain further insights into their physiological roles. Bacterial enzymes belonging to the carbohydrate esterase family 15 (CE15) were identified in putative pectin-targeting polysaccharide utilization loci (PULs) – clusters of co-regulated genes coding for proteins involved in the degradation of specific polysaccharide motifs. These CE15 enzymes showed comparable activities on model substrates mimicking pectin-esters and on canonical model substrates. This result led to study their activity also on extracted pectins and pectin-rich biomass, although no new activities were revealed. X-ray protein crystallography was used to obtain structures of PvCE15, also in complex with the sugar moiety of the model substrates, to gain insight into its likely specificity. A broader selection of CE15 enzymes of both fungal and bacterial origin was characterized on an additional, non-conventional, model substrate to define their substrate specificity in regards of the position of the ester substituents in the targeted bond. Furthermore, one of the first bacterial copper radical oxidases, belonging to an unexplored clade of the Auxiliary Activity family 5 (AA5), was heterologously produced and characterized on a wide range of alcohol substrates. Finally, I determined the structure of a previously characterized AA9 lytic polysaccharide monooxygenase with broad substrate specificity, indicating certain structural features as possible determinants of the described specificity

The Revival of the Notes Field: Leveraging the Unstructured Content in Electronic Health Records

Problem: Clinical practice requires the production of a time- and resource-consuming great amount of notes. They contain relevant information, but their secondary use is almost impossible, due to their unstructured nature. Researchers are trying to address this problems, with traditional and promising novel techniques. Application in real hospital settings seems not to be possible yet, though, both because of relatively small and dirty dataset, and for the lack of language-specific pre-trained models.Aim: Our aim is to demonstrate the potential of the above techniques, but also raise awareness of the still open challenges that the scientific communities of IT and medical practitioners must jointly address to realize the full potential of unstructured content that is daily produced and digitized in hospital settings, both to improve its data quality and leverage the insights from data-driven predictive models.Methods: To this extent, we present a narrative literature review of the most recent and relevant contributions to leverage the application of Natural Language Processing techniques to the free-text content electronic patient records. In particular, we focused on four selected application domains, namely: data quality, information extraction, sentiment analysis and predictive models, and automated patient cohort selection. Then, we will present a few empirical studies that we undertook at a major teaching hospital specializing in musculoskeletal diseases.Results: We provide the reader with some simple and affordable pipelines, which demonstrate the feasibility of reaching literature performance levels with a single institution non-English dataset. In such a way, we bridged literature and real world needs, performing a step further toward the revival of notes fields

Structure of a C1/C4-oxidizing AA9 lytic polysaccharide monooxygenase from the thermophilic fungus Malbranchea cinnamomea

The thermophilic fungus Malbranchea cinnamomea contains a host of enzymes that enable its ability as an efficient degrader of plant biomass and that could be mined for industrial applications. This thermophilic fungus has been studied and found to encode eight lytic polysaccharide monooxygenases (LPMOs) from auxiliary activity family 9 (AA9), which collectively possess different substrate specificities for a range of plant cell-wall-related polysaccharides and oligosaccharides. To gain greater insight into the molecular determinants defining the different specificities, structural studies were pursued and the structure of McAA9F was determined. The enzyme contains the immunoglobulin-like fold typical of previously solved AA9 LPMO structures, but contains prominent differences in the loop regions found on the surface of the substrate-binding site. Most significantly, McAA9F has a broad substrate specificity, with activity on both crystalline and soluble polysaccharides. Moreover, it contains a small loop in a region where a large loop has been proposed to govern specificity towards oligosaccharides. The presence of the small loop leads to a considerably flatter and more open surface that is likely to enable the broad specificity of the enzyme. The enzyme contains a succinimide residue substitution, arising from intramolecular cyclization of Asp10, at a position where several homologous members contain an equivalent residue but cyclization has not previously been observed. This first structure of an AA9 LPMO from M. cinnamomea aids both the understanding of this family of enzymes and the exploration of the repertoire of industrially relevant lignocellulolytic enzymes from this fungus

Anales del III Congreso Internacional de Vivienda y Ciudad "Debate en torno a la nueva agenda urbana"

Acta de congresoEl III Congreso Internacional de Vivienda y Ciudad “Debates en torno a la NUEVa Agenda Urbana”, ha sido una apuesta de alto compromiso por acercar los debates centrales y urgentes que tensionan el pleno ejercicio del derecho a la ciudad. Para ello las instituciones organizadoras (INVIHAB –Instituto de Investigación de Vivienda y Hábitat y MGyDH-Maestría en Gestión y Desarrollo Habitacional-1), hemos convidado un espacio que se concretó con potencia en un debate transdisciplinario. Convocó a intelectuales de prestigio internacional, investigadores, académicos y gestores estatales, y en una metodología de innovación articuló las voces académicas con las de las organizaciones sociales y/o barriales en el Foro de las Organizaciones Sociales que tuvo su espacio propio para dar voz a quienes están trabajando en los desafíos para garantizar los derechos a la vivienda y los bienes urbanos en nuestras ciudades del Siglo XXI

Enzyme discovery and structure-function investigation of carbohydrate-active enzymes

The plant cell wall is the main component of wood, and it has a highly complex structure. In nature, microorganisms can essentially break down all of its parts, and their enzymes can be used for catalyzing specific reaction to remodel and modify wood-derived individual polysaccharides and complex fibers. There are still significant knowledge gaps regarding the interaction with the complex cell wall matrix on the molecular level. Further research on carbohydrate-active enzymes is hence needed, with a particular focus on enzyme active on hemicelluloses and on covalent bonds between lignin and polysaccharides. Investigating proteins of unknown function is also interesting, as they may be enzymes possessing novel activities towards the polymers of the cell wall

Enzymes targeting lignin-carbohydrate complexes

The plant cell wall is the main component of wood, and it has a highly complex structure that confers limited accessibility to the polymers that compose it, lowering the possibility of extracting them efficiently. Lignocellulose in particular is highly recalcitrant to enzymatic hydrolysis due to the presence of lignin-carbohydrate complexes (LCCs), in which the polysaccharides of the cell wall are linked to lignin with covalent bonds. In this context, the utilization of enzymes targeting specific linkages between the polymers is of crucial importance, but still little is known about the enzyme activity on lignocellulosic materials.CE15 enzymes are glucuronoyl esterases that have been suggested to play a key role in reducing lignocellulose recalcitrance by cleaving the ester bond between glucuronoxylan and lignin in LCCs

A unique AA5 alcohol oxidase fused with a catalytically inactive CE3 domain from the bacterium Burkholderia pseudomallei

Copper radical oxidases (CROs) are redox enzymes able to oxidize alcohols or aldehydes, while only requiring a single copper atom as cofactor. Studied CROs are found in one of two subfamilies within the Auxiliary Activities family 5 (AA5) in the carbohydrate-active enzymes database. We here characterize an AA5 enzyme outside the subfamily classification from the opportunistic bacterial pathogen Burkholderia pseudomallei, which curiously was fused to a carbohydrate esterase family 3 domain. The enzyme was shown to be a promiscuous primary alcohol oxidase, with an activity profile similar to enzymes from subfamily 2. The esterase domain was inactive on all tested substrates, and structural predictions revealed this being an effect of crippling substitutions in the expected active site residues

eXDiL: A Tool for Classifying and eXplaining Hospital Discharge Letters

International audienceDischarge letters (DiL) are used within any hospital Information Systems to track diseases of patients during their hospitalisation. Such records are commonly classified over the standard taxonomy made by the World Health Organization, that is the International Statistical Classification of Diseases and Related Health Problems (ICD-10). Particularly, classifying DiLs on the right code is crucial to allow hospitals to be refunded by Public Administrations on the basis of the health service provided. In many practical cases the classification task is carried out by hospital operators, that often have to cope under pressure, making this task an error-prone and time-consuming activity. This process might be improved by applying machine learning techniques to empower the clinical staff. In this paper, we present a system, namely eXDiL, that uses a two-stage Machine Learning and XAI-based approach for classifying DiL data on the ICD-10 taxonomy. To skim the common cases, we first classify automatically the most frequent codes. The codes that are not automatically discovered will be classified into the appropriate chapter and given to an operator to assess the correct code, in addition to an extensive explanation to help the evaluation, comprising of an explainable local surrogate model and a word similarity task. We also show how our approach will be beneficial to healthcare operators, and in particular how it will speed up the process and potentially reduce human errors

The Revival of the Notes Field: Leveraging the Unstructured Content in Electronic Health Records

Problem: Clinical practice requires the production of a time- and resource-consuming great amount of notes. They contain relevant information, but their secondary use is almost impossible, due to their unstructured nature. Researchers are trying to address this problems, with traditional and promising novel techniques. Application in real hospital settings seems not to be possible yet, though, both because of relatively small and dirty dataset, and for the lack of language-specific pre-trained models.Aim: Our aim is to demonstrate the potential of the above techniques, but also raise awareness of the still open challenges that the scientific communities of IT and medical practitioners must jointly address to realize the full potential of unstructured content that is daily produced and digitized in hospital settings, both to improve its data quality and leverage the insights from data-driven predictive models.Methods: To this extent, we present a narrative literature review of the most recent and relevant contributions to leverage the application of Natural Language Processing techniques to the free-text content electronic patient records. In particular, we focused on four selected application domains, namely: data quality, information extraction, sentiment analysis and predictive models, and automated patient cohort selection. Then, we will present a few empirical studies that we undertook at a major teaching hospital specializing in musculoskeletal diseases.Results: We provide the reader with some simple and affordable pipelines, which demonstrate the feasibility of reaching literature performance levels with a single institution non-English dataset. In such a way, we bridged literature and real world needs, performing a step further toward the revival of notes fields

Detecting the Effect Size of Weather Conditions on Patient-Reported Outcome Measures (PROMs)

One of the next frontiers in medical research, particularly in orthopaedic surgery, is personalized treatment outcome prediction. In personalized medicine, treatment choices are adjusted for the patient based on the individual’s and their disease’s distinct features. A high-value and patient-centered health care system requires evaluating results that integrate the patient’s viewpoint. Patient-reported outcome measures (PROMs) are widely used to shed light on patients’ perceptions of their health status after an intervention by using validated questionnaires. The aim of this study is to examine whether meteorological or light (night vs. day) conditions affect PROM scores and hence indirectly affect health-related outcomes. We collected scores for PROMs from questionnaires completed by patients (N = 2326) who had undergone hip and knee interventions between June 2017 and May 2020 at the IRCCS Orthopaedic Institute Galeazzi (IOG), Milan, Italy. Nearest neighbour propensity score (PS) matching was applied to ensure the similarity of the groups tested under the different weather-related conditions. The exposure PS was derived through logistic regression. The data were analysed using statistical tests (Student’s t-test and Mann−Whitney U test). According to Cohen’s effect size, weather conditions may affect the scores for PROMs and, indirectly, health-related outcomes via influencing the relative humidity and weather-related conditions. The findings suggest avoiding PROMs’ collection in certain conditions if the odds of outcome-based underperformance are to be minimized. This would ensure a balance between costs for PROMs’ collection and data availability