Study of Lcn2 in inflammation and characterization of its RNA aptamer

Lipocalin-2 (Lcn2), a member of the lipocalin superfamily, has been implicated in diverse physiological and pathological processes, such as apoptosis, cell differentiation, inflammation, iron metabolism and wound injury healing. However, most of these reports are of correlations and no cause-effect relationship has been established for Lcn2\u27s role in inflammation and wound healing. Using Lcn2 gene knockout mice (Lcn2-/-), we investigated the role of Lcn2 in both lipopolysaccharide (LPS)-induced acute lung inflammation response and dextran sulfate sodium (DSS)-induced colitis. Under all the treatment conditions, no significant differences were observed in proinflammatory cytokines expression level between the LCN2-/- and wild-type mice for the lung inflammation model. For both of animal models, the histological studies and the observed disease severity also indicated no significant difference between these two types of mice. We conclude from the results of this study that Lcn2 has no causal role in the induction of inflammation nor does it play a protective role against inflammation. In addition to the functional/mechanism studies, Lcn2 has been re-evaluated as a standard biomarker for many diseases. To develop a new detection tool for Lcn2, in the current study we selected and characterized a mouse Lcn2 (mLcn2) RNA aptamer. Binding affinity analysis demonstrated this RNA aptamer has a dissociation constant for mLcn2 of 0.34 ± 0.07 μM, but does not bind the human and chicken forms of Lcn2. RNA foot printing and mutational assay indicated that the nucleic acids to protein contact regions are mainly located on the loops of the aptamer, which was predicted to fold in a 3-way junction secondary structure. Further analysis from site mutagenesis of mLcn2 revealed that the aptamer-protein interaction involves the amino acids in the pocket of mLcn2 that normally bind its native ligand, iron-siderophore, but does not involve the mLcn2 surface polar amino acids. Application of the mLcn2 aptamer as a detection probe in a homogenous assay was also demonstrated by using micro-cantilever system

An RNA Aptamer-Based Microcantilever Sensor To Detect the Inflammatory Marker, Mouse Lipocalin-2

Lipocalin-2 (Lcn2) is a biomarker for many inflammatory-based diseases, including acute kidney injury, cardiovascular stress, diabetes, and various cancers. Inflammatory transitions occur rapidly in kidney and cardiovascular disease, for which an in-line monitor could be beneficial. Microcantilever devices with aptamers as recognition elements can be effective and rapidly responsive sensors. Here, we have selected and characterized an RNA aptamer that specifically binds mouse Lcn2 (mLcn2) with a dissociation constant of 340 ± 70 nM in solution and 38 ± 22 nM when immobilized on a surface. The higher apparent affinity of the immobilized aptamer may result from its effective multivalency that decreases the off-rate. The aptamer competes with a catechol iron-siderophore, the natural ligand of mLcn2. This and the results of studies with mLcn2 mutants demonstrate that the aptamer binds to the siderophore binding pocket of the protein. A differential interferometer-based microcantilever sensor was developed with the aptamer as the recognition element in which the differential response between two adjacent cantilevers (a sensing/reference pair) is utilized to detect the binding between mLcn2 and the aptamer, ensuring that sensor response is independent of environmental influences, distance between sensing surface and detector and nonspecific binding. The system showed a detection limit of 4 nM. This novel microcantilever aptasensor has potential for development as an in-line monitoring system for mLcn2 in studies of animal models of acute diseases such as kidney and cardiac failure

Artificial Intelligence in the Radiomic Analysis of Glioblastomas: A Review, Taxonomy, and Perspective

Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications in neuro-oncological radiomic analysis, such as lack of large accessible standardized real patient radiomic brain tumor data of all kinds and reliable predictions on tumor response upon various treatments. Therefore, understanding ML-based AI technologies is critically important to help us address the skyrocketing demands of neuro-oncology clinical deployments. Here, we provide an overview on the latest advancements in ML techniques for brain tumor radiomic analysis, emphasizing proprietary and public dataset preparation and state-of-the-art ML models for brain tumor diagnosis, classifications (e.g., primary and secondary tumors), discriminations between treatment effects (pseudoprogression, radiation necrosis) and true progression, survival prediction, inflammation, and identification of brain tumor biomarkers. We also compare the key features of ML models in the realm of neuroradiology with ML models employed in other medical imaging fields and discuss open research challenges and directions for future work in this nascent precision medicine area

Artificial Intelligence in the Radiomic Analysis of Glioblastomas: A Review, Taxonomy, and Perspective

Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications in neuro-oncological radiomic analysis, such as lack of large accessible standardized real patient radiomic brain tumor data of all kinds and reliable predictions on tumor response upon various treatments. Therefore, understanding ML-based AI technologies is critically important to help us address the skyrocketing demands of neuro-oncology clinical deployments. Here, we provide an overview on the latest advancements in ML techniques for brain tumor radiomic analysis, emphasizing proprietary and public dataset preparation and state-of-the-art ML models for brain tumor diagnosis, classifications (e.g., primary and secondary tumors), discriminations between treatment effects (pseudoprogression, radiation necrosis) and true progression, survival prediction, inflammation, and identification of brain tumor biomarkers. We also compare the key features of ML models in the realm of neuroradiology with ML models employed in other medical imaging fields and discuss open research challenges and directions for future work in this nascent precision medicine area

Correlation and predictive ability of sensory characteristics and social interaction in children with autism spectrum disorder

BackgroundIndividuals with autism spectrum disorder (ASD) often have different social characteristics and particular sensory processing patterns, and these sensory behaviors may affect their social functioning. The objective of our study is to investigate the sensory profiles of children with ASD and their association with social behavior. Specifically, we aim to identify the predictive role of sensory processing in social functioning.MethodsThe Short Sensory Profile (SSP) was utilized to analyze sensory differences between ASD children and their peers. The Social Responsiveness Scale (SRS) and other clinical scales were employed to assess the social functioning of children with ASD. Additionally, the predictive ability of sensory perception on social performance was discussed using random forest and support vector machine (SVM) models.ResultsThe SSP scores of ASD children were lower than those of the control group, and there was a significant negative correlation between SSP scores and clinical scale scores (P < 0.05). The random forest and SVM models, using all the features, showed higher sensitivity, while the random forest model with 7-feature factors had the highest specificity. The area under the receiver operating characteristic (ROC) curve (AUC) for all the models was higher than 0.8.ConclusionAutistic children in our study have different patterns of sensory processing than their peers, which are significantly related to their patterns of social functioning. Sensory features can serve as a good predictor of social functioning in individuals with ASD

The Coincidence Between Increasing Age, Immunosuppression, and the Incidence of Patients With Glioblastoma

Background: Glioblastoma (GBM) is the most aggressive primary brain tumor in adults and is associated with a median overall survival (mOS) of 16–21 months. Our previous work found a negative association between advanced aging and the survival benefit after treatment with immunotherapy in an experimental brain tumor model. Given the recent phase III clinical success of immunotherapy in patients with many types of cancer, but not for patients with GBM, we hypothesize that aging enhances immunosuppression in the brain and contributes to the lack of efficacy for immunotherapy to improve mOS in patients with malignant glioma. Herein, we compare epidemiological data for the incidence and mortality of patients with central nervous system (CNS) cancers, in addition to immune-related gene expression in the normal human brain, as well as peripheral blood immunological changes across the adult lifespan.Methods: Data were extracted from the National Cancer Institute’s surveillance, epidemiology, and end results (SEER)-, the Broad Institute’s Genotype Tissue Expression project (GTEx)-, and the University of California San Francisco’s 10k Immunomes-databases and analyzed for associations with aging.Results: The proportion of elderly individuals, defined as ≥65 years of age, has predominantly increased for more than 100 years in the United States. Over time, the rise in elderly United States citizens has correlated with an increased incidence and mortality rate associated with primary brain and other CNS cancer. With advanced aging, human mRNA expression for factors associated with immunoregulation including immunosuppressive indoleamine 2,3 dioxygenase 1 (IDO) and programmed death-ligand 1 (PD-L1), as well as the dendritic cell surface marker, CD11c, increase in the brain of normal human subjects, coincident with increased circulating immunosuppressive Tregs and decreased cytolytic CD8+ T cells in the peripheral blood. Strikingly, these changes are maximally pronounced in the 60–69 year old group; consistent with the median age of a diagnosis for GBM.Conclusion: These data demonstrate a significant association between normal human aging and increased immunosuppression in the circulation and CNS; particularly late in life. Our data raise several hypotheses including that, aging: (i) progressively suppresses normal immunosurveillance and thereby contributes to GBM cell initiation and/or outgrowth; (ii) decreases immunotherapeutic efficacy against malignant glioma

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Study of Lcn2 in inflammation and characterization of its RNA aptamer

Lipocalin-2 (Lcn2), a member of the lipocalin superfamily, has been implicated in diverse physiological and pathological processes, such as apoptosis, cell differentiation, inflammation, iron metabolism and wound injury healing. However, most of these reports are of correlations and no cause-effect relationship has been established for Lcn2's role in inflammation and wound healing. Using Lcn2 gene knockout mice (Lcn2-/-), we investigated the role of Lcn2 in both lipopolysaccharide (LPS)-induced acute lung inflammation response and dextran sulfate sodium (DSS)-induced colitis. Under all the treatment conditions, no significant differences were observed in proinflammatory cytokines expression level between the LCN2-/- and wild-type mice for the lung inflammation model. For both of animal models, the histological studies and the observed disease severity also indicated no significant difference between these two types of mice. We conclude from the results of this study that Lcn2 has no causal role in the induction of inflammation nor does it play a protective role against inflammation. In addition to the functional/mechanism studies, Lcn2 has been re-evaluated as a standard biomarker for many diseases. To develop a new detection tool for Lcn2, in the current study we selected and characterized a mouse Lcn2 (mLcn2) RNA aptamer. Binding affinity analysis demonstrated this RNA aptamer has a dissociation constant for mLcn2 of 0.34 ± 0.07 μM, but does not bind the human and chicken forms of Lcn2. RNA foot printing and mutational assay indicated that the nucleic acids to protein contact regions are mainly located on the loops of the aptamer, which was predicted to fold in a 3-way junction secondary structure. Further analysis from site mutagenesis of mLcn2 revealed that the aptamer-protein interaction involves the amino acids in the pocket of mLcn2 that normally bind its native ligand, iron-siderophore, but does not involve the mLcn2 surface polar amino acids. Application of the mLcn2 aptamer as a detection probe in a homogenous assay was also demonstrated by using micro-cantilever system.</p