Polysaccharide utilization loci from Bacteroidota encode CE15 enzymes with possible roles in cleaving pectin-lignin bonds.

Lignocellulose is a renewable but complex material exhibiting high recalcitrance to enzymatic hydrolysis, which is attributed, in part, to the presence of covalent linkages between lignin and polysaccharides in the plant cell wall. Glucuronoyl esterases from carbohydrate esterase family 15 (CE15) have been proposed as an aid in reducing this recalcitrance by cleaving ester bonds found between lignin and glucuronoxylan. In the Bacteroidota phylum, some species organize genes related to carbohydrate metabolism in polysaccharide utilization loci (PULs) which encode all necessary proteins to bind, deconstruct, and respond to a target glycan. Bioinformatic analyses identified CE15 members in some PULs that appear to not target the expected glucuronoxylan. Here, five CE15 members from such PULs were investigated with the aim of gaining insights on their biological roles. The selected targets were characterized using glucuronoyl esterase model substrates and with a new synthetic molecule mimicking a putative ester linkage between pectin and lignin. The CE15 enzyme from Phocaeicola vulgatus was structurally determined by X-ray crystallography both with and without carbohydrate ligands with galacturonate binding in a distinct conformation than that of glucuronate. We further explored whether these CE15 enzymes could act akin to pectin methylesterases on pectin-rich biomass but did not find evidence to support the proposed activity. Based on the evidence gathered, the CE15 enzymes in the PULs expected to degrade pectin could be involved in cleavage of uronic acid esters in rhamnogalacturonans.IMPORTANCEThe plant cell wall is a highly complex matrix, and while most of its polymers interact non-covalently, there are also covalent bonds between lignin and carbohydrates. Bonds between xylan and lignin are known, such as the glucuronoyl ester bonds that are cleavable by CE15 enzymes. Our work here indicates that enzymes from CE15 may also have other activities, as we have discovered enzymes in PULs proposed to target other polysaccharides, including pectin. Our study represents the first investigation of such enzymes. Our first hypothesis that the enzymes would act as pectin methylesterases was shown to be false, and we instead propose that they may cleave other esters on complex pectins such as rhamnogalacturonan II. The work presents both the characterization of five novel enzymes and can also provide indirect information about the components of the cell wall itself, which is a highly challenging material to chemically analyze in fine detail.

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.

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.

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.

Role of time-normalized laboratory findings in predicting COVID-19 outcome.

Objectives The pandemic COVID-19 currently reached 213 countries worldwide with nearly 9 million infected people and more than 460,000 deaths. Although several Chinese studies, describing the laboratory findings characteristics of this illness have been reported, European data are still scarce. Furthermore, previous studies often analyzed the averaged laboratory findings collected during the entire hospitalization period, whereas monitoring their time-dependent variations should give more reliable prognostic information. Methods We analyzed the time-dependent variations of 14 laboratory parameters in two groups of COVID-19 patients with, respectively, a positive (40 patients) or a poor (42 patients) outcome, admitted to the San Raffaele Hospital (Milan, Italy). We focused mainly on laboratory parameters that are routinely tested, thus, prognostic information would be readily available even in low-resource settings. Results Statistically significant differences between the two groups were observed for most of the laboratory findings analyzed. We showed that some parameters can be considered as early prognostic indicators whereas others exhibit statistically significant differences only at a later stage of the disease. Among them, earliest indicators were: platelets, lymphocytes, lactate dehydrogenase, creatinine, alanine aminotransferase, C-reactive protein, white blood cells and neutrophils. Conclusions This longitudinal study represents, to the best of our knowledge, the first study describing the laboratory characteristics of Italian COVID-19 patients on a normalized time-scale. The time-dependent prognostic value of the laboratory parameters analyzed in this study can be used by clinicians for the effective treatment of the patients and for the proper management of intensive care beds, which becomes a critical issue during the pandemic peaks.

Ordinal labels in machine learning: a user-centered approach to improve data validity in medical settings.

BACKGROUND: Despite the vagueness and uncertainty that is intrinsic in any medical act, interpretation and decision (including acts of data reporting and representation of relevant medical conditions), still little research has focused on how to explicitly take this uncertainty into account. In this paper, we focus on the representation of a general and wide-spread medical terminology, which is grounded on a traditional and well-established convention, to represent severity of health conditions (for instance, pain, visible signs), ranging from Absent to Extreme. Specifically, we will study how both potential patients and doctors perceive the different levels of the terminology in both quantitative and qualitative terms, and if the embedded user knowledge could improve the representation of ordinal values in the construction of machine learning models. METHODS: To this aim, we conducted a questionnaire-based research study involving a relatively large sample of 1,152 potential patients and 31 clinicians to represent numerically the perceived meaning of standard and widely-applied labels to describe health conditions. Using these collected values, we then present and discuss different possible fuzzy-set based representations that address the vagueness of medical interpretation by taking into account the perceptions of domain experts. We also apply the findings of this user study to evaluate the impact of different encodings on the predictive performance of common machine learning models in regard to a real-world medical prognostic task. RESULTS: We found significant differences in the perception of pain levels between the two user groups. We also show that the proposed encodings can improve the performances of specific classes of models, and discuss when this is the case. CONCLUSIONS: In perspective, our hope is that the proposed techniques for ordinal scale representation and ordinal encoding may be useful to the research community, and also that our methodology will be applied to other widely used ordinal scales for improving validity of datasets and bettering the results of machine learning tasks.

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.