Circular and L50-like leaderless enterocins share a common ABC-transporter immunity gene.

Microbes live within complex communities of interacting populations, either free-living in waters and soils or symbionts of animals and plants. Their interactions include the production of antimicrobial peptides (bacteriocins) to antagonize competitors, and these producers must carry their own immunity gene for self-protection. Whether other coexisting populations are sensitive or resistant to the bacteriocin producer will be key for the population dynamics within the microbial community. The immunity gene frequently consists of an ABC transporter to repel its own bacteriocin but rarely protects against a nonrelated bacteriocin. A case where this cross-resistance occurs mediated by a shared ABC transporter has been shown between enterocins MR10A/B and AS-48. The first is an L50-like leaderless enterocin, while AS-48 is a circular enterocin. In addition, L50-like enterocins such as MR10A/B have been found in E. faecalis and E. faecium, but AS-48 appears only in E. faecalis. Thus, using the ABC transporter of the enterocin MR10A/B gene cluster of Enterococcus faecalis MRR10-3 as a cross-resistance model, we aimed to unravel to what extent a particular ABC transporter can be shared across multiple bacteriocinogenic bacterial populations. To this end, we screened the MR10A/B-ABC transporters in available microbial genomes and analyzed their sequence homologies and distribution. Overall, our main findings are as follows: (i) the MR10A/B-ABC transporter is associated with multiple enterocin gene clusters; (ii) the different enterocins associated with this transporter have a saposin-like fold in common; (iii) the Mr10E component of the transporter is more conserved within its associated enterocin, while the Mr10FGH components are more conserved within the carrying species. This is the least known component of the transporter, but it has shown the greatest specificity to its corresponding enterocin. Bacteriocins are now being investigated as an alternative to antibiotics; hence, the wider or narrower distribution of the particular immunity gene should be taken into account for clinical applications to avoid the selection of resistant strains. Further research will be needed to investigate the mechanistic interactions between the Mr10E transporter component and the bacteriocin as well as the specific ecological and evolutionary mechanisms involved in the spread of the immunity transporter across multiple bacteriocins.

The armadillo-repeat domain of Plakophilin 1 binds to human enzyme PADI4.

Plakophilin 1 (PKP1), a member of the armadillo repeat family of proteins, is a key structural component of cell-cell adhesion scaffolds, although it can also be found in other cell locations, including the cytoplasm and the nucleus. PADI4 (peptidyl-arginine deiminase 4) is one of the human isoforms of a family of enzymes engaged in the conversion of arginine to citrulline, and is present in monocytes, macrophages, granulocytes, and in several types of cancer cells. It is the only family member observed both within the nucleus and the cytoplasm under ordinary conditions. We studied the binding of the armadillo domain of PKP1 (ARM-PKP1) with PADI4, by using several biophysical methods, namely fluorescence, far-ultraviolet (far-UV) circular dichroism (CD), isothermal titration calorimetry (ITC), and molecular simulations; furthermore, binding was also tested by Western-blot (WB) analyses. Our results show that there was binding between the two proteins, with a dissociation constant in the low micromolar range (∼ 1 µM). Molecular modelling provided additional information on the possible structure of the binding complex, and especially on the binding hot-spot predicted for PADI4. This is the first time that the interaction between these two proteins has been described and studied. Our findings could be of importance to understand the development of tumors, where PKP1 and PADI4 are involved. Moreover, our findings pave the way to describe the formation of neutrophil extracellular traps (NETs), whose construction is modulated by PADI4, and which mediate the proteolysis of cell-cell junctions where PKP1 intervenes.

Knowledge4COVID-19: A semantic-based approach for constructing a COVID-19 related knowledge graph from various sources and analyzing treatments' toxicities.

In this paper, we present Knowledge4COVID-19, a framework that aims to showcase the power of integrating disparate sources of knowledge to discover adverse drug effects caused by drug-drug interactions among COVID-19 treatments and pre-existing condition drugs. Initially, we focus on constructing the Knowledge4COVID-19 knowledge graph (KG) from the declarative definition of mapping rules using the RDF Mapping Language. Since valuable information about drug treatments, drug-drug interactions, and side effects is present in textual descriptions in scientific databases (e.g., DrugBank) or in scientific literature (e.g., the CORD-19, the Covid-19 Open Research Dataset), the Knowledge4COVID-19 framework implements Natural Language Processing. The Knowledge4COVID-19 framework extracts relevant entities and predicates that enable the fine-grained description of COVID-19 treatments and the potential adverse events that may occur when these treatments are combined with treatments of common comorbidities, e.g., hypertension, diabetes, or asthma. Moreover, on top of the KG, several techniques for the discovery and prediction of interactions and potential adverse effects of drugs have been developed with the aim of suggesting more accurate treatments for treating the virus. We provide services to traverse the KG and visualize the effects that a group of drugs may have on a treatment outcome. Knowledge4COVID-19 was part of the Pan-European hackathon#EUvsVirus in April 2020 and is publicly available as a resource through a GitHub repository and a DOI.

Identifying the presence and severity of dementia by applying interpretable machine learning techniques on structured clinical records.

BACKGROUND: Dementia develops as cognitive abilities deteriorate, and early detection is critical for effective preventive interventions. However, mainstream diagnostic tests and screening tools, such as CAMCOG and MMSE, often fail to detect dementia accurately. Various graph-based or feature-dependent prediction and progression models have been proposed. Whenever these models exploit information in the patients' Electronic Medical Records, they represent promising options to identify the presence and severity of dementia more precisely. METHODS: The methods presented in this paper aim to address two problems related to dementia: (a) Basic diagnosis: identifying the presence of dementia in individuals, and (b) Severity diagnosis: predicting the presence of dementia, as well as the severity of the disease. We formulate these two tasks as classification problems and address them using machine learning models based on random forests and decision tree, analysing structured clinical data from an elderly population cohort. We perform a hybrid data curation strategy in which a dementia expert is involved to verify that curation decisions are meaningful. We then employ the machine learning algorithms that classify individual episodes into a specific dementia class. Decision trees are also used for enhancing the explainability of decisions made by prediction models, allowing medical experts to identify the most crucial patient features and their threshold values for the classification of dementia. RESULTS: Our experiment results prove that baseline arithmetic or cognitive tests, along with demographic features, can predict dementia and its severity with high accuracy. In specific, our prediction models have reached an average f1-score of 0.93 and 0.81 for problems (a) and (b), respectively. Moreover, the decision trees produced for the two issues empower the interpretability of the prediction models. CONCLUSIONS: This study proves that there can be an accurate estimation of the existence and severity of dementia disease by analysing various electronic medical record features and cognitive tests from the episodes of the elderly population. Moreover, a set of decision rules may comprise the building blocks for an efficient patient classification. Relevant clinical and screening test features (e.g. simple arithmetic or animal fluency tasks) represent precise predictors without calculating the scores of mainstream cognitive tests such as MMSE and CAMCOG. Such predictive model can identify not only meaningful features, but also justifications of classification. As a result, the predictive power of machine learning models over curated clinical data is proved, paving the path for a more accurate diagnosis of dementia.

An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study.

BACKGROUND: Artificial intelligence (AI) has contributed substantially in recent years to the resolution of different biomedical problems, including cancer. However, AI tools with significant and widespread impact in oncology remain scarce. The goal of this study is to present an AI-based solution tool for cancer patients data analysis that assists clinicians in identifying the clinical factors associated with poor prognosis, relapse and survival, and to develop a prognostic model that stratifies patients by risk. MATERIALS AND METHODS: We used clinical data from 5275 patients diagnosed with non-small cell lung cancer, breast cancer, and non-Hodgkin lymphoma at Hospital Universitario Puerta de Hierro-Majadahonda. Accessible clinical parameters measured with a wearable device and quality of life questionnaires data were also collected. RESULTS: Using an AI-tool, data from 5275 cancer patients were analyzed, integrating clinical data, questionnaires data, and data collected from wearable devices. Descriptive analyses were performed in order to explore the patients' characteristics, survival probabilities were calculated, and a prognostic model identified low and high-risk profile patients. CONCLUSION: Overall, the reconstruction of the population's risk profile for the cancer-specific predictive model was achieved and proved useful in clinical practice using artificial intelligence. It has potential application in clinical settings to improve risk stratification, early detection, and surveillance management of cancer patients.

Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)-Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study.

BACKGROUND: Eating disorders affect an increasing number of people. Social networks provide information that can help. OBJECTIVE: We aimed to find machine learning models capable of efficiently categorizing tweets about eating disorders domain. METHODS: We collected tweets related to eating disorders, for 3 consecutive months. After preprocessing, a subset of 2000 tweets was labeled: (1) messages written by people suffering from eating disorders or not, (2) messages promoting suffering from eating disorders or not, (3) informative messages or not, and (4) scientific or nonscientific messages. Traditional machine learning and deep learning models were used to classify tweets. We evaluated accuracy, F1 score, and computational time for each model. RESULTS: A total of 1,058,957 tweets related to eating disorders were collected. were obtained in the 4 categorizations, with The bidirectional encoder representations from transformer-based models had the best score among the machine learning and deep learning techniques applied to the 4 categorization tasks (F1 scores 71.1%-86.4%). CONCLUSIONS: Bidirectional encoder representations from transformer-based models have better performance, although their computational cost is significantly higher than those of traditional techniques, in classifying eating disorder-related tweets.

PKP1 and MYC create a feedforward loop linking transcription and translation in squamous cell lung cancer.

PURPOSE: Plakophilin 1 (PKP1) is well-known as an important component of the desmosome, a cell structure specialized in spot-like cell-to-cell adhesion. Although desmosomes have generally been associated with tumor suppressor functions, we recently found that PKP1 is recurrently overexpressed in squamous cell lung cancer (SqCLC) to exert an oncogenic role by enhancing the translation of MYC (c-Myc), a major oncogene. In this study, we aim to further characterize the functional relationship between PKP1 and MYC. METHODS: To determine the functional relationship between PKP1 and MYC, we performed correlation analyses between PKP1 and MYC mRNA expression levels, gain/loss of function models, chromatin immunoprecipitation (ChIP) and promoter mutagenesis followed by luciferase assays. RESULTS: We found a significant correlation between the mRNA levels of MYC and PKP1 in SqCLC primary tumor samples. In addition, we found that MYC is a direct transcription factor of PKP1 and binds to specific sequences within its promoter. In agreement with this, we found that MYC knockdown reduced PKP1 protein expression in different SqCLC models, which may explain the PKP1-MYC correlation that we found. Conversely, we found that PKP1 knockdown reduced MYC protein expression, while PKP1 overexpression enhanced MYC expression in these models. CONCLUSIONS: Based on these results, we propose a feedforward functional relationship in which PKP1 enhances MYC translation in conjunction with the translation initiation complex by binding to the 5'-UTR of MYC mRNA, whereas MYC promotes PKP1 transcription by binding to its promoter. These results suggest that PKP1 may serve as a therapeutic target for SqCLC.

The armadillo-repeat domain of plakophilin 1 binds the C-terminal sterile alpha motif (SAM) of p73.

BACKGROUND: Plakophilin 1 (PKP1) is a component of desmosomes, which are key structural components for cell-cell adhesion, and can also be found in other cell locations. The p53, p63 and p73 proteins belong to the p53 family of transcription factors, playing crucial roles in tumour suppression. The α-splice variant of p73 (p73α) has at its C terminus a sterile alpha motif (SAM); such domain, SAMp73, is involved in the interaction with other macromolecules. METHODS: We studied the binding of SAMp73 with the armadillo domain of PKP1 (ARM-PKP1) in the absence and the presence of 100 mM NaCl, by using several biophysical techniques, namely fluorescence, far-ultraviolet circular dichroism (CD), nuclear magnetic resonance (NMR), isothermal titration calorimetry (ITC), and molecular docking and simulations. RESULTS: Association was observed between the two proteins, with a dissociation constant of ~5 µM measured by ITC and fluorescence in the absence of NaCl. The binding region of SAMp73 involved residues of the so-called "middle-loop-end-helix" binding region (i.e., comprising the third helix, together with the C terminus of the second one, and the N-cap of the fourth), as shown by 15N, 1H- HSQC-NMR spectra. Molecular modelling provided additional information on the possible structure of the binding complex. CONCLUSIONS: This newly-observed interaction could have potential therapeutic relevance in the tumour pathways where PKP1 is involved, and under conditions when there is a possible inactivation of p53. GENERAL SIGNIFICANCE: The discovery of the binding between SAMp73 and ARM-PKP1 suggests a functional role for their interaction, including the possibility that SAMp73 could assist PKP1 in signalling pathways.

Intrinsically disordered protein NUPR1 binds to the armadillo-repeat domain of Plakophilin 1.

Plakophilin 1 (PKP1), a member of the armadillo repeat family of proteins, is a scaffold component of desmosomes, which are key structural components for cell-cell adhesion. However, PKP1 can be also found in the nucleus of several cells. NUPR1 is an intrinsically disordered protein (IDP) that localizes throughout the whole cell, and intervenes in the development and progression of several cancers. In this work, we studied the binding between PKP1 and NUPR1 by using several in vitro biophysical techniques and in cellulo approaches. The interaction occurred with an affinity in the low micromolar range (~10 µM), and involved the participation of at least one of the tryptophan residues of PKP1 (as shown by fluorescence and molecular docking). The binding region of NUPR1, mapped by NMR and molecular modelling, was a polypeptide patch at the 30s region of its sequence. The association between PKP1 and NUPR1 also occurred in cellulo and was localized in the nucleus, as tested by protein ligation assays (PLAs). We hypothesize that NUPR1 plays an active role in carcinogenesis modulating the function of PKP1.

Correction: Plakophilin 1 enhances MYC translation, promoting squamous cell lung cancer.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Plakophilin 1 enhances MYC translation, promoting squamous cell lung cancer.

Plakophilin 1 (PKP1) is a member of the arm-repeat (armadillo) and plakophilin gene families and it is an essential component of the desmosomes. Although desmosomes have generally been associated with tumor suppressor functions, we have consistently observed that PKP1 is among the top overexpressed proteins in squamous cell lung cancer. To explore this paradox, we developed in vivo and in vitro functional models of PKP1 gain/loss in squamous cell lung cancer. CRISPR-Cas9 PKP1 knockout severely impaired cell proliferation, but it increased cell dissemination. In addition, PKP1 overexpression increased cell proliferation, cell survival, and in vivo xenograft engraftment. We further investigated the molecular mechanism of the mainly oncogenic function of PKP1 by combining transcriptomics, proteomics, and protein-nucleic acid interaction assays. Interestingly, we found that PKP1 enhances MYC translation in collaboration with the translation initiation complex by binding to the 5'-UTR of MYC mRNA. We propose PKP1 as an oncogene in SqCLC and a novel posttranscriptional regulator of MYC. PKP1 may be a valuable diagnostic biomarker and potential therapeutic target for SqCLC. Importantly, PKP1 inhibition may indirectly target MYC, a primary anticancer target.

The value of desmosomal plaque-related markers to distinguish squamous cell carcinoma and adenocarcinoma of the lung.

Background: An antibody panel is needed to definitively differentiate between adenocarcinoma (AC) and squamous cell carcinoma (SCC) in order to meet more stringent requirements for the histologic classification of lung cancers. Staining of desmosomal plaque-related proteins may be useful in the diagnosis of lung SCC.Materials and methods: We compared the usefulness of six conventional (CK5/6, p40, p63, CK7, TTF1, and Napsin A) and three novel (PKP1, KRT15, and DSG3) markers to distinguish between lung SCC and AC in 85 small biopsy specimens (41 ACs and 44 SCCs). Correlations were examined between expression of the markers and patients' histologic and clinical data.Results: The specificity for SCC of membrane staining for PKP1, KRT15, and DSG3 was 97.4%, 94.6%, and 100%, respectively, and it was 100% when the markers were used together and in combination with the conventional markers (AUCs of 0.7619 for Panel 1 SCC, 0.7375 for Panel 2 SCC, 0.8552 for Panel 1 AC, and 0.8088 for Panel 2 AC). In a stepwise multivariate logistic regression model, the combination of CK5/6, p63, and PKP1 in membrane was the optimal panel to differentiate between SCC and AC, with a percentage correct classification of 96.2% overall (94.6% of ACs and 97.6% of SCCs). PKP1 and DSG3 are related to the prognosis.Conclusions: PKP1, KRT15, and DSG3 are highly specific for SCC, but they were more useful to differentiate between SCC and AC when used together and in combination with conventional markers. PKP1 and DSG3 expressions may have prognostic value.

Progressive changes in composition of lymphocytes in lung tissues from patients with non-small-cell lung cancer.

Immune cell infiltration is a common feature of many human solid tumors. Innate and adaptative immune systems contribute to tumor immunosurveillance. We investigated whether tumors evade immune surveillance by inducing states of tolerance and/or through the inability of some immune subpopulations to effectively penetrate tumor nests. Immunohistochemistry and flow cytometry analysis were used to study the composition and distribution of immune subpopulations in samples of peripheral blood, tumor tissue (TT), adjacent tumor tissue (ATT), distant non-tumor tissue (DNTT), cancer nests, cancer stroma, and invasive margin in 61 non-small-cell lung cancer (NSCLC) patients. A significantly higher percentage of T and B cells and significantly lower percentage of NK cells were detected in TT than in DNTT. Memory T cells (CD4+CD45RO+, CD8+CD45RO+) and activated T cells (CD8+DR+) were more prevalent in TT. Alongside this immune activation, the percentage of T cells with immunosuppressive activity was higher in TT than in DNTT. B- cells were practically non-existent in tumor nests and were preferentially located in the invasive margin. The dominant NK cell phenotype in peripheral blood and DNTT was the cytotoxic phenotype (CD56+ CD16+), while the presence of these cells was significantly decreased in ATT and further decreased in TT. Finally, the immunologic response differed between adenocarcinoma and squamous cell carcinoma and according to the tumor differentiation grade. These findings on the infiltration of innate and adaptative immune cells into tumors contribute to a more complete picture of the immune reaction in NSCLC.

Mobile teleradiology system suitable for m-health services supporting content and semantic based image retrieval on a grid infrastructure.

Teleradiology systems tackle the problem of transferring radiological images between medical image workstations for facilitating different medical activities, e.g., diagnosis, treatment and follow up a patient, medical training, or consulting second opinion. Nowadays, m-Health (aka mobile health) is becoming popular because of high quality of mobile displays, although remains a work in progress. In this paper a mobile teleradiology system is reported, which main contribution is the development of a platform: (1) supported by a Grid infrastructure, (2) using biomedical ontologies for adding semantic annotations on medical images, and (3) supporting semantic and content-based image retrieval. Images are located physically in different repositories like; hospitals and diagnostic imaging centers. All these features make the system ubiquitous, portable, and suitable for m-Health services.

Determining similarity of scientific entities in annotation datasets.

Linked Open Data initiatives have made available a diversity of scientific collections where scientists have annotated entities in the datasets with controlled vocabulary terms from ontologies. Annotations encode scientific knowledge, which is captured in annotation datasets. Determining relatedness between annotated entities becomes a building block for pattern mining, e.g. identifying drug-drug relationships may depend on the similarity of the targets that interact with each drug. A diversity of similarity measures has been proposed in the literature to compute relatedness between a pair of entities. Each measure exploits some knowledge including the name, function, relationships with other entities, taxonomic neighborhood and semantic knowledge. We propose a novel general-purpose annotation similarity measure called 'AnnSim' that measures the relatedness between two entities based on the similarity of their annotations. We model AnnSim as a 1-1 maximum weight bipartite match and exploit properties of existing solvers to provide an efficient solution. We empirically study the performance of AnnSim on real-world datasets of drugs and disease associations from clinical trials and relationships between drugs and (genomic) targets. Using baselines that include a variety of measures, we identify where AnnSim can provide a deeper understanding of the semantics underlying the relatedness of a pair of entities or where it could lead to predicting new links or identifying potential novel patterns. Although AnnSim does not exploit knowledge or properties of a particular domain, its performance compares well with a variety of state-of-the-art domain-specific measures. Database URL: http://www.yeastgenome.org/

An automatic method for the enrichment of DICOM metadata using biomedical ontologies.

This work is a novel contribution for enriching medical images using semantic annotations with a strategy for unifying different ontologies and instances of DICOM medical files. We present the L-MOM library (Library for Mapping of Ontological Metadata) as a tool for making an automatic mapping between instances of DICOM medical files and different medical ontologies (e.g., FMA, RadLex, MeSH). The main contributions are: i) the domain independent L-MOM library which is able to integrate DICOM metadata with ontologies from different domains; ii) a strategy to automatically annotate DICOM data with universally accepted medical ontologies, and provide values of similarity between ontologies and DICOM metadata; and iii) a framework to traverse ontological concepts that characterized clinical studies of patients registered in the framework catalog.

Differential immunohistochemical localization of desmosomal plaque-related proteins in non-small-cell lung cancer.

AIMS: Immunohistochemistry is a highly valuable and widely used tool in the subtyping of lung carcinomas. The aim of this study was to identify markers for the differential diagnosis of non-small-cell carcinomas. METHODS AND RESULTS: We report on the immunohistochemical localization of plakophilin-1 (PKP1), keratin-15 (KRT15) and desmoglein-3 (DSG3) intercellular adhesion proteins in samples from 75 primary non-small-cell lung cancers in non-treated patients. The staining pattern of these proteins differed between squamous cell carcinomas and adenocarcinomas, with no membrane staining in the latter. Membrane staining for all three proteins was characteristic of squamous cell carcinomas. We observed a relationship between the presence/absence of these proteins in the membranes of squamous cell carcinomas and the differentiation grade, with more intense staining in better differentiated areas. CONCLUSIONS: Staining for these proteins marked intercellular junctions that are characteristic of stratified squamous epithelium and of neoplasias with this type of differentiation, and can be useful in the diagnosis of patients with squamous cell carcinoma of the lung. The high specificity of membrane staining for PKP1 and DSG3 and high sensitivity of cytoplasmic and membrane staining for KRT15 for the diagnosis of squamous cell carcinoma may be useful for the differential diagnosis of non-small-cell carcinomas.

Path-based systems to guide scientists in the maze of biological data sources.

Fueled by novel technologies capable of producing massive amounts of data for a single experiment, scientists are faced with an explosion of information which must be rapidly analyzed and combined with other data to form hypotheses and create knowledge. Today, numerous biological questions can be answered without entering a wet lab. Scientific protocols designed to answer these questions can be run entirely on a computer. Biological resources are often complementary, focused on different objects and reflecting various experts' points of view. Exploiting the richness and diversity of these resources is crucial for scientists. However, with the increase of resources, scientists have to face the problem of selecting sources and tools when interpreting their data. In this paper, we analyze the way in which biologists express and implement scientific protocols, and we identify the requirements for a system which can guide scientists in constructing protocols to answer new biological questions. We present two such systems, BioNavigation and BioGuide dedicated to help scientists select resources by following suitable paths within the growing network of interconnected biological resources.