User Centered Rare Disease Clinical Trial Knowledge Graph (RCTKG).

Drug development in rare diseases is challenging due to the limited availability of subjects with the diseases and recruiting from a small patient population. The high cost and low success rate of clinical trials motivate deliberate analysis of existing clinical trials to understand status of clinical development of orphan drugs and discover new insight for new trial. In this project, we aim to develop a user centered Rare disease based Clinical Trial Knowledge Graph (RCTKG) to integrate publicly available clinical trial data with rare diseases from the Genetic and Rare Disease (GARD) program in a semantic and standardized form for public use. To better serve and represent the interests of rare disease users, user stories were defined for three types of users, patients, healthcare providers and informaticians, to guide the RCTKG design in supporting the GARD program at NCATS/NIH and the broad clinical/research community in rare diseases.

Exploring NCATS in-house biomedical data for evidence-based drug repurposing.

Drug repurposing is a strategy for identifying new uses of approved or investigational drugs that are outside the scope of the original medical indication. Even though many repurposed drugs have been found serendipitously in the past, the increasing availability of large volumes of biomedical data has enabled more systemic, data-driven approaches for drug candidate identification. At National Center of Advancing Translational Sciences (NCATS), we invent new methods to generate new data and information publicly available to spur innovation and scientific discovery. In this study, we aimed to explore and demonstrate biomedical data generated and collected via two NCATS research programs, the Toxicology in the 21st Century program (Tox21) and the Biomedical Data Translator (Translator) for the application of drug repurposing. These two programs provide complementary types of biomedical data from uncovering underlying biological mechanisms with bioassay screening data from Tox21 for chemical clustering, to enrich clustered chemicals with scientific evidence mined from the Translator towards drug repurposing. 129 chemical clusters have been generated and three of them have been further investigated for drug repurposing candidate identification, which is detailed as case studies.

Exploring NCATS In-House Biomedical Data for Evidence-based Drug Repurposing.

Drug repurposing is a strategy for identifying new uses of approved or investigational drugs that are outside the scope of the original medical indication. Even though many repurposed drugs have been found serendipitously in the past, the increasing availability of large volumes of biomedical data has enabled more systemic, data-driven approaches for drug candidate identification. At National Center of Advancing Translational Sciences (NCATS), we invent new methods to generate new data and information publicly available to spur innovation and scientific discovery. In this study, we aimed to explore and demonstrate biomedical data generated and collected via two NCATS research programs, the Toxicology in the 21st Century program (Tox21) and the Biomedical Data Translator (Translator) for the application of drug repurposing. These two programs provide complementary types of biomedical data from uncovering underlying biological mechanisms with bioassay screening data from Tox21 for chemical clustering, to enrich clustered chemicals with scientific evidence mined from the Translator towards drug repurposing. 129 chemical clusters have been generated and three of them have been further investigated for drug repurposing candidate identification, which is detailed as case studies.

Precision information extraction for rare disease epidemiology at scale.

BACKGROUND: The United Nations recently made a call to address the challenges of an estimated 300 million persons worldwide living with a rare disease through the collection, analysis, and dissemination of disaggregated data. Epidemiologic Information (EI) regarding prevalence and incidence data of rare diseases is sparse and current paradigms of identifying, extracting, and curating EI rely upon time-intensive, error-prone manual processes. With these limitations, a clear understanding of the variation in epidemiology and outcomes for rare disease patients is hampered. This challenges the public health of rare diseases patients through a lack of information necessary to prioritize research, policy decisions, therapeutic development, and health system allocations. METHODS: In this study, we developed a newly curated epidemiology corpus for Named Entity Recognition (NER), a deep learning framework, and a novel rare disease epidemiologic information pipeline named EpiPipeline4RD consisting of a web interface and Restful API. For the corpus creation, we programmatically gathered a representative sample of rare disease epidemiologic abstracts, utilized weakly-supervised machine learning techniques to label the dataset, and manually validated the labeled dataset. For the deep learning framework development, we fine-tuned our dataset and adapted the BioBERT model for NER. We measured the performance of our BioBERT model for epidemiology entity recognition quantitatively with precision, recall, and F1 and qualitatively through a comparison with Orphanet. We demonstrated the ability for our pipeline to gather, identify, and extract epidemiology information from rare disease abstracts through three case studies. RESULTS: We developed a deep learning model to extract EI with overall F1 scores of 0.817 and 0.878, evaluated at the entity-level and token-level respectively, and which achieved comparable qualitative results to Orphanet's collection paradigm. Additionally, case studies of the rare diseases Classic homocystinuria, GRACILE syndrome, Phenylketonuria demonstrated the adequate recall of abstracts with epidemiology information, high precision of epidemiology information extraction through our deep learning model, and the increased efficiency of EpiPipeline4RD compared to a manual curation paradigm. CONCLUSIONS: EpiPipeline4RD demonstrated high performance of EI extraction from rare disease literature to augment manual curation processes. This automated information curation paradigm will not only effectively empower development of the NIH Genetic and Rare Diseases Information Center (GARD), but also support the public health of the rare disease community.

A Hydrazine-Hydrazone Adamantine Compound Shows Antimycobacterial Activity and Is a Probable Inhibitor of MmpL3.

Tuberculosis remains an important cause of morbidity and mortality throughout the world. Notably, an important number of multi drug resistant cases is an increasing concern. This problem points to an urgent need for novel compounds with antimycobacterial properties and to improve existing therapies. Whole-cell-based screening for compounds with activity against Mycobacterium tuberculosis complex strains in the presence of linezolid was performed in this study. A set of 15 bioactive compounds with antimycobacterial activity in vitro were identified with a minimal inhibitory concentration of less than 2 µg/mL. Among them, compound 1 is a small molecule with a chemical structure consisting of an adamantane moiety and a hydrazide-hydrazone moiety. Whole genome sequencing of spontaneous mutants resistant to the compounds suggested compound 1 to be a new inhibitor of MmpL3. This compound binds to the same pocket as other already published MmpL3 inhibitors, without disturbing the proton motive force of M. bovis BCG and M. smegmatis. Compound 1 showed a strong activity against a panel ofclinical strains of M. tuberculosis in vitro. This compound showed no toxicity against mammalian cells and protected Galleria mellonella larvae against M. bovis BCG infection. These results suggest that compound 1 is a promising anti-TB agent with the potential to improve TB treatment in combination with standard TB therapies.

Experiments with LDA and Top2Vec for embedded topic discovery on social media data-A case study of cystic fibrosis.

Social media has become an important resource for discussing, sharing, and seeking information pertinent to rare diseases by patients and their families, given the low prevalence in the extraordinarily sparse populations. In our previous study, we identified prevalent topics from Reddit via topic modeling for cystic fibrosis (CF). While we were able to derive/access concerns/needs/questions of patients with CF, we observed challenges and issues with the traditional techniques of topic modeling, e.g., Latent Dirichlet Allocation (LDA), for fulfilling the task of topic extraction. Thus, here we present our experiments to extend the previous study with an aim of improving the performance of topic modeling, by experimenting with LDA model optimization and examination of the Top2Vec model with different embedding models. With the demonstrated results with higher coherence and qualitatively higher human readability of derived topics, we implemented the Top2Vec model with doc2vec as the embedding model as our final model to extract topics from a subreddit of CF ("r/CysticFibrosis") and proposed to expand its use with other types of social media data for other rare diseases for better assessing patients' needs with social media data.

Rare disease-based scientific annotation knowledge graph.

Rare diseases (RDs) are naturally associated with a low prevalence rate, which raises a big challenge due to there being less data available for supporting preclinical and clinical studies. There has been a vast improvement in our understanding of RD, largely owing to advanced big data analytic approaches in genetics/genomics. Consequently, a large volume of RD-related publications has been accumulated in recent years, which offers opportunities to utilize these publications for accessing the full spectrum of the scientific research and supporting further investigation in RD. In this study, we systematically analyzed, semantically annotated, and scientifically categorized RD-related PubMed articles, and integrated those semantic annotations in a knowledge graph (KG), which is hosted in Neo4j based on a predefined data model. With the successful demonstration of scientific contribution in RD via the case studies performed by exploring this KG, we propose to extend the current effort by expanding more RD-related publications and more other types of resources as a next step.

Artificial Intelligence in Medical Imaging and its Impact on the Rare Disease Community: Threats, Challenges and Opportunities.

Almost 1 in 10 individuals can suffer from one of many rare diseases (RDs). The average time to diagnosis for an RD patient is as high as 7 years. Artificial intelligence (AI)-based positron emission tomography (PET), if implemented appropriately, has tremendous potential to advance the diagnosis of RDs. Patient advocacy groups must be active stakeholders in the AI ecosystem if we are to avoid potential issues related to the implementation of AI into health care. AI medical devices must not only be RD-aware at each stage of their conceptualization and life cycle but also should be trained on diverse and augmented datasets representative of the end-user population including RDs. Inability to do so leads to potential harm and unsustainable deployment of AI-based medical devices (AIMDs) into clinical practice.

Scientific evidence based rare disease research discovery with research funding data in knowledge graph.

BACKGROUND: Limited knowledge and unclear underlying biology of many rare diseases pose significant challenges to patients, clinicians, and scientists. To address these challenges, there is an urgent need to inspire and encourage scientists to propose and pursue innovative research studies that aim to uncover the genetic and molecular causes of more rare diseases and ultimately to identify effective therapeutic solutions. A clear understanding of current research efforts, knowledge/research gaps, and funding patterns as scientific evidence is crucial to systematically accelerate the pace of research discovery in rare diseases, which is an overarching goal of this study. METHODS: To semantically represent NIH funding data for rare diseases and advance its use of effectively promoting rare disease research, we identified NIH funded projects for rare diseases by mapping GARD diseases to the project based on project titles; subsequently we presented and managed those identified projects in a knowledge graph using Neo4j software, hosted at NCATS, based on a pre-defined data model that captures semantics among the data. With this developed knowledge graph, we were able to perform several case studies to demonstrate scientific evidence generation for supporting rare disease research discovery. RESULTS: Of 5001 rare diseases belonging to 32 distinct disease categories, we identified 1294 diseases that are mapped to 45,647 distinct, NIH-funded projects obtained from the NIH ExPORTER by implementing semantic annotation of project titles. To capture semantic relationships presenting amongst mapped research funding data, we defined a data model comprised of seven primary classes and corresponding object and data properties. A Neo4j knowledge graph based on this predefined data model has been developed, and we performed multiple case studies over this knowledge graph to demonstrate its use in directing and promoting rare disease research. CONCLUSION: We developed an integrative knowledge graph with rare disease funding data and demonstrated its use as a source from where we can effectively identify and generate scientific evidence to support rare disease research. With the success of this preliminary study, we plan to implement advanced computational approaches for analyzing more funding related data, e.g., project abstracts and PubMed article abstracts, and linking to other types of biomedical data to perform more sophisticated research gap analysis and identify opportunities for future research in rare diseases.

Expanding of ST11 Carbapenemase-Producing Klebsiella pneumoniae Subclones in a Chinese Hospital, Shenzhen, China.

BACKGROUND: ST11 is the most prevalent sequence type of clinical Klebsiella pneumoniae in China. METHODS: We investigated the characteristics of the ST11 subclones using core genome multi-locus sequence typing (cgMLST). Ninety-three carbapenemase-producing K. pneumoniae isolates were collected at Shenzhen People's Hospital. Then, whole-genome sequencing and cgMLST were used to discriminate apparent subclones within the ST11 group. RESULTS: We analyzed the prevalence and genetic relationships of these subclones. ST11 and K. pneumoniae carbapenemase (KPC-2) were the predominant genotype and carbapenemase, respectively, in the clinical carbapenemase-producing K. pneumoniae strains. cgMLST scheme genotyping divided the ST11 group into two clades across seven complex types (CTs). CT1313 was the most prevalent subclone. The deletion of galF and a high frequency of SNPs in genes associated with the stress- and SOS-responses were found in CT1291 and CT2405 over time, respectively. CONCLUSION: Our results indicated that the subclones of the ST11 group had different patterns of prevalence. Highly discriminatory genotyping techniques, such as cgMLST scheme, should be used in further molecular epidemiology investigations.

Genomic surveillance of Nevada patients revealed prevalence of unique SARS-CoV-2 variants bearing mutations in the RdRp gene.

Patients with signs of COVID-19 were tested through diagnostic RT-PCR for SARS-CoV-2 using RNA extracted from the nasopharyngeal/nasal swabs. To determine the variants of SARS-CoV-2 circulating in the state of Nevada, specimens from 200 COVID-19 patients were sequenced through our robust sequencing platform, which enabled sequencing of SARS-CoV-2 from specimens with even very low viral loads, without the need of culture-based amplification. High genome coverage allowed the identification of single and multi-nucleotide variants in SARS-CoV-2 in the community and their phylogenetic relationships with other variants present during the same period of the outbreak. We report the occurrence of a novel mutation at 323aa (314aa of orf1b) of nsp12 (RNA-dependent RNA polymerase) changed to phenylalanine (F) from proline (P), in the first reported isolate of SARS-CoV-2, Wuhan-Hu-1. This 323F variant was present at a very high frequency in Northern Nevada. Structural modeling determined this mutation in the interface domain, which is important for the association of accessory proteins required for the polymerase. In conclusion, we report the introduction of specific SARS-CoV-2 variants at very high frequency in distinct geographic locations, which is important for understanding the evolution and circulation of SARS-CoV-2 variants of public health importance, while it circulates in humans.

Effects of Fish Oil Combined with Selenium and Zinc on Learning and Memory Impairment in Aging Mice and Amyloid Precursor Protein Processing.

Alzheimer's disease is characterized by the aggregation of amyloid-beta (Aß) peptide into plaques and neurofibrillary tangles. Aß peptide is generated by the cleavage of the ß-amyloid precursor protein (APP) by ß- and γ-secretase. The present study was conducted to investigate the effects of fish oil (or eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA)), selenium, and zinc on learning and memory impairment in an aging mouse model and on APP. We performed the Morris water maze and platform recorder tests on male Kunming mice (10/group) grouped as control and D-galactose-induced aging model mice treated with vehicle, fish oil, fish oil + selenium, fish oil + selenium + zinc, and positive control (red ginseng extract). Fish oil + zinc + selenium for 7 weeks significantly improved learning and memory impairments in aging model animals in the Morris water maze and platform recorder tests, as evidenced by shortened incubation periods and number of errors. In vitro analysis of Aß1-40 content in APP695-transfected CHO cells revealed a decrease after treatment with EPA, DHA, and their combinations with selenium or selenium and zinc. Assaying ß- and γ-secretase activities revealed a decrease in PC12 cells and mouse serum as well as a decrease in ß-site APP-cleaving enzyme 1 and presenilin 1 protein levels in the PC12 cells and mouse serum. Taken together, our results show that fish oil combined with selenium and zinc inhibited APP processing and alleviated learning and memory impairment in a mouse model of aging.

Genomic surveillance of Nevada patients revealed prevalence of unique SARS-CoV-2 variants bearing mutations in the RdRp gene.

Patients with signs of COVID-19 were tested with CDC approved diagnostic RT-PCR for SARS-CoV-2 using RNA extracted from nasopharyngeal/nasal swabs. In order to determine the variants of SARS-CoV-2 circulating in the state of Nevada, 200 patient specimens from COVID-19 patients were sequenced through our robust protocol for sequencing SARS-CoV-2 genomes. Our protocol enabled sequencing of SARS-CoV-2 genome directly from the specimens, with even very low viral loads, without the need of culture-based amplification. This allowed the identification of specific nucleotide variants including those coding for D614G and clades defining mutations. These sequences were further analyzed for determining SARS-CoV-2 variants circulating in the state of Nevada and their phylogenetic relationships with other variants present in the united states and the world during the same period of the outbreak. Our study reports the occurrence of a novel variant in the nsp12 (RNA dependent RNA Polymerase) protein at residue 323 (314aa of orf1b) to Phenylalanine (F) from Proline (P), present in the original isolate of SARS-CoV-2 (Wuhan-Hu-1). This 323F variant is found at a very high frequency (46% of the tested specimen) in Northern Nevada. Functional significance of this unique and highly prevalent variant of SARS-CoV-2 with RdRp mutation is currently under investigation but structural modeling showed this 323aa residue in the interface domain of RdRp, which is required for association with accessory proteins. In conclusion, we report the introduction of specific SARS-CoV-2 variants at a very high frequency within a distinct geographic location, which is important for clinical and public health perspectives in understanding the evolution of SARS-CoV-2 while in circulation.

Central recirculation zone induced by the DBD plasma actuation.

Central recirculation zone (CRZ) is commonly formed in the near field of the injector exit by the vane swirler and used to stabilize the flame. In our experiment, a CRZ induced by the DBD plasma actuation was observed in the low swirl burner configuration for the first time, which clearly demonstrated that the mechanism of the combustion control by the plasma swirler is mainly through the aerodynamic effect. Three dielectric barrier discharge (DBD) actuators are placed in a circular array around the axis to generate ionic wind in the circumferential direction of the injector. Characteristics of the flow field have been measured using Laser Doppler Anemometry. It is found that a central recirculation zone with the shape of an ellipsoid is formed in the non-reacting flow field with the plasma actuation. The position of the upstream stagnation point was determined by the strength of the actuation. Although the CRZ disappears in the reacting flow field as the result of combustion heat release, the influence of the discharge on the flame lift-off height is noticeable. The results demonstrate that swirl enhancement by the plasma swirler is feasible, flexible and effective as a non-intrusive measure for flow control.

Preparation and Irreversible Inhibition Mechanism Insight into a Recombinant Kunitz Trypsin Inhibitor from Glycine max L. Seeds.

Soybean Kunitz trypsin inhibitor (SKTI), extracted from soybean (Glycine max L.) seeds, possesses insect resistance and anti-tumor properties. But its specific mechanisms of action are not yet known. This article reports an efficient method to produce recombinant SKTI (rSKTI) in Escherichia coli, reveals some biochemical properties of rSKTI, and discusses the inhibition mechanism of SKTI. The rSKTI was expressed as inclusion body in E. coli BL21 (DE3). After refolding, the active rSKTI was obtained and was further purified with anion-exchange chromatography (DEAE-FF) efficiently. There were similar biochemical properties between SKTI and rSKTI. The optimum pH and the optimum temperature were pH 8.0 and 35 °C, respectively, being stable during pH 7.0-11.0 and below 37 °C. The activity against trypsin was inhibited by Co2+, Mn2+, Fe3+, Al3+, and epoxy chloropropane. Inhibition kinetic assay of SKTI against trypsin as Lineweaver-Burk plots analysis both showed an unchanged Km and a decreased Vmax with N-benzoyl-L-arginine ethyl ester (BAEE) as substrate. Molecular modeling showed Arg63 of SKTI (active residue of SKTI) that interacts with four residues of trypsin, including three catalytic site (His57, Asp102, and Ser195) and one binding site (Asp189), forming five interactions. These provide reference for understanding the inhibition mechanism of such kind of Kunitz trypsin inhibitors.

Peroxynitrite nitrates adenine nucleotide translocase and voltage-dependent anion channel 1 and alters their interactions and association with hexokinase II in mitochondria.

Cardiac ischemia and reperfusion (IR) injury induces excessive emission of deleterious reactive O2 and N2 species (ROS/RNS), including the non-radical oxidant peroxynitrite (ONOO-) that can cause mitochondria dysfunction and cell death. In this study, we explored whether IR injury in isolated hearts induces tyrosine nitration of adenine nucleotide translocase (ANT) and alters its interaction with the voltage-dependent anion channel 1 (VDAC1). We found that IR injury induced tyrosine nitration of ANT and that exposure of isolated cardiac mitochondria to ONOO- induced ANT tyrosine, Y81, nitration. The exposure of isolated cardiac mitochondria to ONOO- also led ANT to form high molecular weight proteins and dissociation of ANT from VDAC1. We found that IR injury in isolated hearts, hypoxic injury in H9c2 cells, and ONOO- treatment of H9c2 cells and isolated mitochondria, each decreased mitochondrial bound-hexokinase II (HK II), which suggests that ONOO- caused HK II to dissociate from mitochondria. Moreover, we found that mitochondria exposed to ONOO- induced VDAC1 oligomerization which may decrease its binding with HK II. We have reported that ONOO- produced during cardiac IR injury induced tyrosine nitration of VDAC1, which resulted in conformational changes of the protein and increased channel conductance associated with compromised cardiac function on reperfusion. Thus, our results imply that ONOO- produced during IR injury and hypoxic stress impeded HK II association with VDAC1. ONOO- exposure nitrated mitochondrial proteins and also led to cytochrome c (cyt c) release from mitochondria. In addition, in isolated mitochondria exposed to ONOO- or obtained after IR, there was significant compromise in mitochondrial respiration and delayed repolarization of membrane potential during oxidative (ADP) phosphorylation. Taken together, ONOO- produced during cardiac IR injury can nitrate tyrosine residues of two key mitochondrial membrane proteins involved in bioenergetics and energy transfer to contribute to mitochondrial and cellular dysfunction.

C-terminal fragments of amyloid precursor proteins increase cofilin phosphorylation by LIM kinase in cultured rat primary neurons.

Amyloid precursor proteins (APPs) are processed by ß-, γ-, and ε-secretases and caspase-3 to generate C-terminal fragments of APP (APP-CTFs), which may contribute to the pathology of Alzheimer's disease (AD). In addition to amyloid plaques and neurofibrillary tangles, AD brains contain Hirano bodies, which are rod-like structures mostly composed of actin and the actin-binding protein, cofilin. However, the mechanisms underlying the formation of cofilin-actin rods are still unknown. In this study, we aim to elucidate the effects of APP-CTFs on the actin-depolymerizing factor [(ADF)/cofilin]. Our data indicate that transfection with APP-CT99 and APP-CT57 may increase the phosphorylation level of Ser3 of ADF/cofilin and Thr508 of LIM-kinase 1 in rat primary cortical neuronal cultures. S3 peptide, a synthetic peptide competitor of LIM-kinase 1 for ADF/cofilin phosphorylation and an inhibitor of APP-CTFs, induced ADF/cofilin phosphorylation. In comparison with the wild-type mouse, the APP-CT transgenic mouse showed increased immunoreactivity of phosphorylated cofilin (p-cofilin) in the brain. Treatment with DAPT, an inhibitor of γ-secretase, resulted in a decrease in p-cofilin protein level in the group transfected with full-length APP-695. Transfection with the mutant APP-CTF with a deleted YENPTY domain resulted in no significant increase in p-cofilin level. Thus, APP-CTFs induced cofilin phosphorylation to facilitate nuclear translocation. These results suggest a relationship between APP-CTFs and ADF/cofilin that may be suggestive of a new toxic pathway in the pathology of AD.

S-Nitrosoglutathione Reductase Dysfunction Contributes to Obesity-Associated Hepatic Insulin Resistance via Regulating Autophagy.

Obesity is associated with elevated intracellular nitric oxide (NO) production, which promotes nitrosative stress in metabolic tissues such as liver and skeletal muscle, contributing to insulin resistance. The onset of obesity-associated insulin resistance is due, in part, to the compromise of hepatic autophagy, a process that leads to lysosomal degradation of cellular components. However, it is not known how NO bioactivity might impact autophagy in obesity. Here, we establish that S-nitrosoglutathione reductase (GSNOR), a major protein denitrosylase, provides a key regulatory link between inflammation and autophagy, which is disrupted in obesity and diabetes. We demonstrate that obesity promotes S-nitrosylation of lysosomal proteins in the liver, thereby impairing lysosomal enzyme activities. Moreover, in mice and humans, obesity and diabetes are accompanied by decreases in GSNOR activity, engendering nitrosative stress. In mice with a GSNOR deletion, diet-induced obesity increases lysosomal nitrosative stress and impairs autophagy in the liver, leading to hepatic insulin resistance. Conversely, liver-specific overexpression of GSNOR in obese mice markedly enhances lysosomal function and autophagy and, remarkably, improves insulin action and glucose homeostasis. Furthermore, overexpression of S-nitrosylation-resistant variants of lysosomal enzymes enhances autophagy, and pharmacologically and genetically enhancing autophagy improves hepatic insulin sensitivity in GSNOR-deficient hepatocytes. Taken together, our data indicate that obesity-induced protein S-nitrosylation is a key mechanism compromising the hepatic autophagy, contributing to hepatic insulin resistance.