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.

Aberrant fecal flora observed in guinea pigs with pressure overload is mitigated in animals receiving vagus nerve stimulation therapy.

Altered gut microbial diversity has been associated with several chronic disease states, including heart failure. Stimulation of the vagus nerve, which innervates the heart and abdominal organs, is proving to be an effective therapeutic in heart failure. We hypothesized that cervical vagus nerve stimulation (VNS) could alter fecal flora and prevent aberrations observed in fecal samples from heart failure animals. To determine whether microbial abundances were altered by pressure overload (PO), leading to heart failure and VNS therapy, a VNS pulse generator was implanted with a stimulus lead on either the left or right vagus nerve before creation of PO by aortic constriction. Animals received intermittent, open-loop stimulation or sham treatment, and their heart function was monitored by echocardiography. Left ventricular end-systolic and diastolic volumes, as well as cardiac output, were impaired in PO animals compared with baseline. VNS mitigated these effects. Metagenetic analysis was then performed using 16S rRNA sequencing to identify bacterial genera present in fecal samples. The abundance of 10 genera was significantly altered by PO, 8 of which were mitigated in animals receiving either left- or right-sided VNS. Metatranscriptomics analyses indicate that the abundance of genera that express genes associated with ATP-binding cassette transport and amino sugar/nitrogen metabolism was significantly changed following PO. These gut flora changes were not observed in PO animals subjected to VNS. These data suggest that VNS prevents aberrant gut flora following PO, which could contribute to its beneficial effects in heart failure patients.

Structural insights into the function of a unique tandem GTPase EngA in bacterial ribosome assembly.

Many ribosome-interacting GTPases, with proposed functions in ribosome biogenesis, are also implicated in the cellular regulatory coupling between ribosome assembly process and various growth control pathways. EngA is an essential GTPase in bacteria, and intriguingly, it contains two consecutive GTPase domains (GD), being one-of-a-kind among all known GTPases. EngA is required for the 50S subunit maturation. However, its molecular role remains elusive. Here, we present the structure of EngA bound to the 50S subunit. Our data show that EngA binds to the peptidyl transferase center (PTC) and induces dramatic conformational changes on the 50S subunit, which virtually returns the 50S subunit to a state similar to that of the late-stage 50S assembly intermediates. Very interestingly, our data show that the two GDs exhibit a pseudo-two-fold symmetry in the 50S-bound conformation. Our results indicate that EngA recognizes certain forms of the 50S assembly intermediates, and likely facilitates the conformational maturation of the PTC of the 23S rRNA in a direct manner. Furthermore, in a broad context, our data also suggest that EngA might be a sensor of the cellular GTP/GDP ratio, endowed with multiple conformational states, in response to fluctuations in cellular nucleotide pool, to facilitate and regulate ribosome assembly.

Dissecting the in vivo assembly of the 30S ribosomal subunit reveals the role of RimM and general features of the assembly process.

Ribosome biogenesis is a tightly regulated, multi-stepped process. The assembly of ribosomal subunits is a central step of the complex biogenesis process, involving nearly 30 protein factors in vivo in bacteria. Although the assembly process has been extensively studied in vitro for over 40 years, very limited information is known for the in vivo process and specific roles of assembly factors. Such an example is ribosome maturation factor M (RimM), a factor involved in the late-stage assembly of the 30S subunit. Here, we combined quantitative mass spectrometry and cryo-electron microscopy to characterize the in vivo 30S assembly intermediates isolated from mutant Escherichia coli strains with genes for assembly factors deleted. Our compositional and structural data show that the assembly of the 3'-domain of the 30S subunit is severely delayed in these intermediates, featured with highly underrepresented 3'-domain proteins and large conformational difference compared with the mature 30S subunit. Further analysis indicates that RimM functions not only to promote the assembly of a few 3'-domain proteins but also to stabilize the rRNA tertiary structure. More importantly, this study reveals intriguing similarities and dissimilarities between the in vitro and the in vivo assembly pathways, suggesting that they are in general similar but with subtle differences.

Structural dynamics of the MecA-ClpC complex: a type II AAA+ protein unfolding machine.

The MecA-ClpC complex is a bacterial type II AAA(+) molecular machine responsible for regulated unfolding of substrates, such as transcription factors ComK and ComS, and targeting them to ClpP for degradation. The six subunits of the MecA-ClpC complex form a closed barrel-like structure, featured with three stacked rings and a hollow passage, where substrates are threaded and translocated through successive pores. Although the general concepts of how polypeptides are unfolded and translocated by internal pore loops of AAA(+) proteins have long been conceived, the detailed mechanistic model remains elusive. With cryoelectron microscopy, we captured four different structures of the MecA-ClpC complexes. These complexes differ in the nucleotide binding states of the two AAA(+) rings and therefore might presumably reflect distinctive, representative snapshots from a dynamic unfolding cycle of this hexameric complex. Structural analysis reveals that nucleotide binding and hydrolysis modulate the hexameric complex in a number of ways, including the opening of the N-terminal ring, the axial and radial positions of pore loops, the compactness of the C-terminal ring, as well as the relative rotation between the two nucleotide-binding domain rings. More importantly, our structural and biochemical data indicate there is an active allosteric communication between the two AAA(+) rings and suggest that concerted actions of the two AAA(+) rings are required for the efficiency of the substrate unfolding and translocation. These findings provide important mechanistic insights into the dynamic cycle of the MecA-ClpC unfoldase and especially lay a foundation toward the complete understanding of the structural dynamics of the general type II AAA(+) hexamers.

Structural basis for the function of a small GTPase RsgA on the 30S ribosomal subunit maturation revealed by cryoelectron microscopy.

The bacterial RsgA, a circularly permutated GTPase, whose GTPase activity is dependent on the 30S ribosomal subunit, is a late-stage ribosome biogenesis factor involved in the 30S subunit maturation. The role of RsgA is to release another 30S biogenesis factor, RbfA, from the mature 30S subunit in a GTP-dependent manner. Using cryoelectron microscopy, we have determined the structure of the 30S subunit bound with RsgA in the presence of GMPPNP at subnanometer resolution. In the structure, RsgA binds to the central part of the 30S subunit, close to the decoding center, in a position that is incompatible with multiple biogenesis factors, all three translation initiation factors, as well as A-, P-site tRNAs and the 50S subunit. Further structural analysis not only provides a structural model for the RsgA-dependent release of RbfA from the nascent 30S subunit, but also indicates RsgA's role in the ribosomal protein assembly, to promote some tertiary binding protein incorporation. Moreover, together with available biochemical and genetic data, our results suggest that RsgA might be a general checkpoint protein in the late stage of the 30S subunit biogenesis, whose function is not only to release biogenesis factors (e.g., RbfA) from the nascent 30S subunit, but also to block the association of initiation factors to the premature 30S subunit.

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.

Synaptic protein ubiquitination in rat brain revealed by antibody-based ubiquitome analysis.

Protein ubiquitination is an essential post-translational modification regulating neurodevelopment, synaptic plasticity, learning, and memory, and its dysregulation contributes to the pathogenesis of neurological diseases. Here we report a systematic analysis of ubiquitinated proteome (ubiquitome) in rat brain using a newly developed monoclonal antibody that recognizes the diglycine tag on lysine residues in trypsinized peptides (K-GG peptides). Initial antibody specificity analysis showed that the antibody can distinguish K-GG peptides from linear GG peptides or pseudo K-GG peptides derived from iodoacetamide. To evaluate the false discovery rate of K-GG peptide matches during database search, we introduced a null experiment using bacterial lysate that contains no such peptides. The brain ubiquitome was then analyzed by this antibody enrichment with or without strong cation exchange (SCX) prefractionation. During SCX chromatography, although the vast majority of K-GG peptides were detected in the fractions containing at least three positive charged peptides, specific K-GG peptides with two positive charges (e.g., protein N-terminal acetylated and C-terminal non-K/R peptides) were also identified in early fractions. The reliability of C-terminal K-GG peptides was also extensively investigated. Finally, we collected a data set of 1786 K-GG sites on 2064 peptides in 921 proteins and estimated their abundance by spectral counting. The study reveals a wide range of ubiquitination events on key components in presynaptic region (e.g., Bassoon, NSF, SNAP25, synapsin, synaptotagmin, and syntaxin) and postsynaptic density (e.g., PSD-95, GKAP, CaMKII, as well as receptors for NMDA, AMPA, GABA, serotonin, and acetylcholine). We also determined ubiquitination sites on amyloid precursor protein and alpha synuclein that are thought to be causative agents in Alzhermer's and Parkinson's disorders, respectively. As K-GG peptides can also be produced from Nedd8 or ISG15 modified proteins, we quantified these proteins in the brain and found that their levels are less than 2% of ubiquitin. Together, this study demonstrates that a large number of neuronal proteins are modified by ubiquitination and provides a feasible method for profiling the ubiquitome in the brain.

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.

Mouse prostate proteomes are differentially altered by supranutritional intake of four selenium compounds.

We have shown that, in contrast to selenomethionine (SeMet) or selenite, methylseleninic acid (MSeA) and Se-methylselenocysteine (MSeC) exert prostate cancer (PCa) inhibitory effect in preclinical models. Here we investigated the prostate proteome signatures of mice treated with each selenium (Se) form for hypothesis generation concerning their potential in vivo molecular targets and cancer risk modification. Nude mice bearing subcutaneous PC-3 xenografts were treated daily with each Se form (3 mg Se/kg) orally for 45 days. Five prostates were pooled from each group. Their proteomes were profiled by LC-MS/MS with iTRAQ labeling. Of the 1,088 proteins identified, 72 were significantly modulated by one or more Se forms. MSeA and MSeC each induced separate sets of tumor suppressor proteins and suppressed different onco-proteins. Proteins induced by selenite and shared with MSeC were related to energy metabolism (e.g., fatty-acid synthase), and those induced by SeMet included vimentin and heat-shock protein-70, favoring cancer growth. While proteome changes induced by MSeA were associated with PCa risk reduction, desirable risk-reducing signatures induced by MSeC were counterbalanced by risk-promoting patterns shared with selenite and SeMet. We propose that the balance of oncogenic vs. suppressor protein patterns in the prostate may impact the direction of PCa risk modification by a given selenium.

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.

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.

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.

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.