Secretory MPP3 reinforce myeloid differentiation trajectory and amplify myeloid cell production.

Hematopoietic stem cells (HSC) and downstream lineage-biased multipotent progenitors (MPP) tailor blood production and control myelopoiesis on demand. Recent lineage tracing analyses revealed MPPs to be major functional contributors to steady-state hematopoiesis. However, we still lack a precise resolution of myeloid differentiation trajectories and cellular heterogeneity in the MPP compartment. Here, we found that myeloid-biased MPP3 are functionally and molecularly heterogeneous, with a distinct subset of myeloid-primed secretory cells with high endoplasmic reticulum (ER) volume and FcγR expression. We show that FcγR+/ERhigh MPP3 are a transitional population serving as a reservoir for rapid production of granulocyte/macrophage progenitors (GMP), which directly amplify myelopoiesis through inflammation-triggered secretion of cytokines in the local bone marrow (BM) microenvironment. Our results identify a novel regulatory function for a secretory MPP3 subset that controls myeloid differentiation through lineage-priming and cytokine production and acts as a self-reinforcing amplification compartment in inflammatory stress and disease conditions.

Identification of a pleiotropic effect of ADIPOQ on cardiac dysfunction and Alzheimer's disease based on genetic evidence and health care records.

Observations of comorbidity in heart diseases, including cardiac dysfunction (CD) are increasing, including and cognitive impairment, such as Alzheimer's disease and dementia (AD/D). This comorbidity might be due to a pleiotropic effect of genetic variants shared between CD and AD/D. Here, we validated comorbidity of CD and AD/D based on diagnostic records from millions of patients in Korea and the University of California, San Francisco Medical Center (odds ratio 11.5 [8.5-15.5, 95% Confidence Interval (CI)]). By integrating a comprehensive human disease-SNP association database (VARIMED, VARiants Informing MEDicine) and whole-exome sequencing of 50 brains from individuals with and without Alzheimer's disease (AD), we identified missense variants in coding regions including APOB, a known risk factor for CD and AD/D, which potentially have a pleiotropic role in both diseases. Of the identified variants, site-directed mutation of ADIPOQ (268 G > A; Gly90Ser) in neurons produced abnormal aggregation of tau proteins (p = 0.02), suggesting a functional impact for AD/D. The association of CD and ADIPOQ variants was confirmed based on domain deletion in cardiac cells. Using the UK Biobank including data from over 500000 individuals, we examined a pleiotropic effect of the ADIPOQ variant by comparing CD- and AD/D-associated phenotypic evidence, including cardiac hypertrophy and cognitive degeneration. These results indicate that convergence of health care records and genetic evidences may help to dissect the molecular underpinnings of heart disease and associated cognitive impairment, and could potentially serve a prognostic function. Validation of disease-disease associations through health care records and genomic evidence can determine whether health conditions share risk factors based on pleiotropy.

Analysis of the docking property of host variants of hACE2 for SARS-CoV-2 in a large cohort.

The recent novel coronavirus disease (COVID-19) outbreak, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is threatening global health. However, an understanding of the interaction of SARS-CoV-2 with human cells, including the physical docking property influenced by the host's genetic diversity, is still lacking. Here, based on germline variants in the UK Biobank covering 502,543 individuals, we revealed the molecular interactions between human angiotensin-converting enzyme 2 (hACE2), which is the representative receptor for SARS-CoV-2 entry, and COVID-19 infection. We identified six nonsense and missense variants of hACE2 from 2585 subjects in the UK Biobank covering 500000 individuals. Using our molecular dynamics simulations, three hACE2 variants from 2585 individuals we selected showed higher levels of binding free energy for docking in the range of 1.44-3.69 kcal/mol. Although there are diverse contributors to SARS-CoV-2 infections, including the mobility of individuals, we analyzed the diagnosis records of individuals with these three variants of hACE2. Our molecular dynamics simulations combined with population-based genomic data provided an atomistic understanding of the interaction between hACE2 and the spike protein of SARS-CoV-2.

Identifying the Risk of Sepsis in Patients With Cancer Using Digital Health Care Records: Machine Learning-Based Approach.

BACKGROUND: Sepsis is diagnosed in millions of people every year, resulting in a high mortality rate. Although patients with sepsis present multimorbid conditions, including cancer, sepsis predictions have mainly focused on patients with severe injuries. OBJECTIVE: In this paper, we present a machine learning-based approach to identify the risk of sepsis in patients with cancer using electronic health records (EHRs). METHODS: We utilized deidentified anonymized EHRs of 8580 patients with cancer from the Samsung Medical Center in Korea in a longitudinal manner between 2014 and 2019. To build a prediction model based on physical status that would differ between sepsis and nonsepsis patients, we analyzed 2462 laboratory test results and 2266 medication prescriptions using graph network and statistical analyses. The medication relationships and lab test results from each analysis were used as additional learning features to train our predictive model. RESULTS: Patients with sepsis showed differential medication trajectories and physical status. For example, in the network-based analysis, narcotic analgesics were prescribed more often in the sepsis group, along with other drugs. Likewise, 35 types of lab tests, including albumin, globulin, and prothrombin time, showed significantly different distributions between sepsis and nonsepsis patients (P<.001). Our model outperformed the model trained using only common EHRs, showing an improved accuracy, area under the receiver operating characteristic (AUROC), and F1 score by 11.9%, 11.3%, and 13.6%, respectively. For the random forest-based model, the accuracy, AUROC, and F1 score were 0.692, 0.753, and 0.602, respectively. CONCLUSIONS: We showed that lab tests and medication relationships can be used as efficient features for predicting sepsis in patients with cancer. Consequently, identifying the risk of sepsis in patients with cancer using EHRs and machine learning is feasible.

MPI-GWAS: a supercomputing-aided permutation approach for genomewide association studies.

Permutation testing is a robust and popular approach for significance testing in genomic research that has the advantage of reducing inflated type 1 error rates; however, its computational cost is notorious in genome-wide association studies (GWAS). Here, we developed a supercomputing-aided approach to accelerate the permutation testing for GWAS, based on the message-passing interface (MPI) on parallel computing architecture. Our application, called MPI-GWAS, conducts MPI-based permutation testing using a parallel computing approach with our supercomputing system, Nurion (8,305 compute nodes, and 563,740 central processing units [CPUs]). For 107 permutations of one locus in MPI-GWAS, it was calculated in 600 s using 2,720 CPU cores. For 107 permutations of ~30,000-50,000 loci in over 7,000 subjects, the total elapsed time was ~4 days in the Nurion supercomputer. Thus, MPI-GWAS enables us to feasibly compute the permutation-based GWAS within a reason-able time by harnessing the power of parallel computing resources.

Condensed trajectory of the temporal correlation of diseases and mortality extracted from over 300,000 patients in hospitals.

Understanding mortality, derived from debilitations consisting of multiple diseases, is crucial for patient stratification. Here, in systematic fashion, we report comprehensive mortality data that map the temporal correlation of diseases that tend toward deaths in hospitals. We used a mortality trajectory model that represents the temporal ordering of disease appearance, with strong correlations, that terminated in fatal outcomes from one initial diagnosis in a set of patients throughout multiple admissions. Based on longitudinal healthcare records of 10.4 million patients from over 350 hospitals, we profiled 300 mortality trajectories, starting from 118 diseases, in 311,309 patients. Three-quarters (75%) of 59,794 end-stage patients and their deaths accrued throughout 160,360 multiple disease appearances in a short-term period (<4 years, 3.5 diseases per patient). This overlooked and substantial heterogeneity of disease patients and outcomes in the real world is unraveled in our trajectory map at the disease-wide level. For example, the converged dead-end in our trajectory map presents an extreme diversity of sepsis patients based on 43 prior diseases, including lymphoma and cardiac diseases. The trajectories involving the largest number of deaths for each age group highlight the essential predisposing diseases, such as acute myocardial infarction and liver cirrhosis, which lead to over 14,000 deaths. In conclusion, the deciphering of the debilitation processes of patients, consisting of the temporal correlations of diseases that tend towards hospital death at a population-wide level is feasible.

Identifying the Medical Lethality of Suicide Attempts Using Network Analysis and Deep Learning: Nationwide Study.

BACKGROUND: Suicide is one of the leading causes of death among young and middle-aged people. However, little is understood about the behaviors leading up to actual suicide attempts and whether these behaviors are specific to the nature of suicide attempts. OBJECTIVE: The goal of this study was to examine the clusters of behaviors antecedent to suicide attempts to determine if they could be used to assess the potential lethality of the attempt. To accomplish this goal, we developed a deep learning model using the relationships among behaviors antecedent to suicide attempts and the attempts themselves. METHODS: This study used data from the Korea National Suicide Survey. We identified 1112 individuals who attempted suicide and completed a psychiatric evaluation in the emergency room. The 15-item Beck Suicide Intent Scale (SIS) was used for assessing antecedent behaviors, and the medical outcomes of the suicide attempts were measured by assessing lethality with the Columbia Suicide Severity Rating Scale (C-SSRS; lethal suicide attempt >3 and nonlethal attempt ≤3). RESULTS: Using scores from the SIS, individuals who had lethal and nonlethal attempts comprised two different network nodes with the edges representing the relationships among nodes. Among the antecedent behaviors, the conception of a method's lethality predicted suicidal behaviors with severe medical outcomes. The vectorized relationship values among the elements of antecedent behaviors in our deep learning model (E-GONet) increased performances, such as F1 and area under the precision-recall gain curve (AUPRG), for identifying lethal attempts (up to 3% for F1 and 32% for AUPRG), as compared with other models (mean F1: 0.81 for E-GONet, 0.78 for linear regression, and 0.80 for random forest; mean AUPRG: 0.73 for E-GONet, 0.41 for linear regression, and 0.69 for random forest). CONCLUSIONS: The relationships among behaviors antecedent to suicide attempts can be used to understand the suicidal intent of individuals and help identify the lethality of potential suicide attempts. Such a model may be useful in prioritizing cases for preventive intervention.

Tracing diagnosis trajectories over millions of patients reveal an unexpected risk in schizophrenia.

The identification of novel disease associations using big-data for patient care has had limited success. In this study, we created a longitudinal disease network of traced readmissions (disease trajectories), merging data from over 10.4 million inpatients through the Healthcare Cost and Utilization Project, which allowed the representation of disease progression mapping over 300 diseases. From these disease trajectories, we discovered an interesting association between schizophrenia and rhabdomyolysis, a rare muscle disease (incidence < 1E-04) (relative risk, 2.21 [1.80-2.71, confidence interval = 0.95], P-value 9.54E-15). We validated this association by using independent electronic medical records from over 830,000 patients at the University of California, San Francisco (UCSF) medical center. A case review of 29 rhabdomyolysis incidents in schizophrenia patients at UCSF demonstrated that 62% are idiopathic, without the use of any drug known to lead to this adverse event, suggesting a warning to physicians to watch for this unexpected risk of schizophrenia. Large-scale analysis of disease trajectories can help physicians understand potential sequential events in their patients.

Deconvoluting essential gene signatures for cancer growth from genomic expression in compound-treated cells.

MOTIVATION: Essential gene signatures for cancer growth have been typically identified via RNAi or CRISPR-Cas9. Here, we propose an alternative method that reveals the essential gene signatures by analysing genomic expression profiles in compound-treated cells. With a large amount of the existing compound-induced data, essential gene signatures at genomic scale are efficiently characterized without technical challenges in the previous techniques. RESULTS: An essential gene is characterized as a gene presenting positive correlation between its down-regulation and cell growth inhibition induced by diverse compounds, which were collected from LINCS and CGP. Among 12 741 genes, 1092, 1 228 827 962, 1 664 580 and 829 essential genes are characterized for each of A375, A549, BT20, LNCAP, MCF7, MDAMB231 and PC3 cell lines (P-value ≤ 1.0E-05). Comparisons to the previously identified essential genes yield significant overlaps in A375 and A549 (P-value ≤ 5.0E-05) and the 103 common essential genes are enriched in crucial processes for cancer growth. In most comparisons in A375, MCF7, BT20 and A549, the characterized essential genes yield more essential characteristics than those of the previous techniques, i.e. high gene expression, high degrees of protein-protein interactions, many homologs and few paralogs. Remarkably, the essential genes commonly characterized by both the previous and proposed techniques show more significant essential characteristics than those solely relied on the previous techniques. We expect that this work provides new aspects in essential gene signatures. AVAILABILITY AND IMPLEMENTATION: The Python implementations are available at https://github.com/jmjung83/deconvolution_of_essential_gene_signitures. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Comparing Ethnicity-Specific Reference Intervals for Clinical Laboratory Tests from EHR Data.

BACKGROUND: The results of clinical laboratory tests are an essential component of medical decision-making. To guide interpretation, test results are returned with reference intervals defined by the range in which the central 95% of values occur in healthy individuals. Clinical laboratories often set their own reference intervals to accommodate variation in local population and instrumentation. For some tests, reference intervals change as a function of sex, age, and self-identified race and ethnicity. METHODS: In this work, we develop a novel approach, which leverages electronic health record data, to identify healthy individuals and tests for differences in laboratory test values between populations. RESULTS: We found that the distributions of >50% of laboratory tests with currently fixed reference intervals differ among self-identified racial and ethnic groups (SIREs) in healthy individuals. CONCLUSIONS: Our results confirm the known SIRE-specific differences in creatinine and suggest that more research needs to be done to determine the clinical implications of using one-size-fits-all reference intervals for other tests with SIRE-specific distributions.

Comprehensive analysis of normal adjacent to tumor transcriptomes.

Histologically normal tissue adjacent to the tumor (NAT) is commonly used as a control in cancer studies. However, little is known about the transcriptomic profile of NAT, how it is influenced by the tumor, and how the profile compares with non-tumor-bearing tissues. Here, we integrate data from the Genotype-Tissue Expression project and The Cancer Genome Atlas to comprehensively analyze the transcriptomes of healthy, NAT, and tumor tissues in 6506 samples across eight tissues and corresponding tumor types. Our analysis shows that NAT presents a unique intermediate state between healthy and tumor. Differential gene expression and protein-protein interaction analyses reveal altered pathways shared among NATs across tissue types. We characterize a set of 18 genes that are specifically activated in NATs. By applying pathway and tissue composition analyses, we suggest a pan-cancer mechanism of pro-inflammatory signals from the tumor stimulates an inflammatory response in the adjacent endothelium.

Precision annotation of digital samples in NCBI's gene expression omnibus.

The Gene Expression Omnibus (GEO) contains more than two million digital samples from functional genomics experiments amassed over almost two decades. However, individual sample meta-data remains poorly described by unstructured free text attributes preventing its largescale reanalysis. We introduce the Search Tag Analyze Resource for GEO as a web application (http://STARGEO.org) to curate better annotations of sample phenotypes uniformly across different studies, and to use these sample annotations to define robust genomic signatures of disease pathology by meta-analysis. In this paper, we target a small group of biomedical graduate students to show rapid crowd-curation of precise sample annotations across all phenotypes, and we demonstrate the biological validity of these crowd-curated annotations for breast cancer. STARGEO.org makes GEO data findable, accessible, interoperable and reusable (i.e., FAIR) to ultimately facilitate knowledge discovery. Our work demonstrates the utility of crowd-curation and interpretation of open 'big data' under FAIR principles as a first step towards realizing an ideal paradigm of precision medicine.

Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets.

The decreasing cost of genomic technologies has enabled the molecular characterization of large-scale clinical disease samples and of molecular changes upon drug treatment in various disease models. Exploring methods to relate diseases to potentially efficacious drugs through various molecular features is critically important in the discovery of new therapeutics. Here we show that the potency of a drug to reverse cancer-associated gene expression changes positively correlates with that drug's efficacy in preclinical models of breast, liver and colon cancers. Using a systems-based approach, we predict four compounds showing high potency to reverse gene expression in liver cancer and validate that all four compounds are effective in five liver cancer cell lines. The in vivo efficacy of pyrvinium pamoate is further confirmed in a subcutaneous xenograft model. In conclusion, this systems-based approach may be complementary to the traditional target-based approach in connecting diseases to potentially efficacious drugs.

Repurpose terbutaline sulfate for amyotrophic lateral sclerosis using electronic medical records.

Prediction of new disease indications for approved drugs by computational methods has been based largely on the genomics signatures of drugs and diseases. We propose a method for drug repositioning that uses the clinical signatures extracted from over 13 years of electronic medical records from a tertiary hospital, including >9.4 M laboratory tests from >530,000 patients, in addition to diverse genomics signatures. Cross-validation using over 17,000 known drug-disease associations shows this approach outperforms various predictive models based on genomics signatures and a well-known "guilt-by-association" method. Interestingly, the prediction suggests that terbutaline sulfate, which is widely used for asthma, is a promising candidate for amyotrophic lateral sclerosis for which there are few therapeutic options. In vivo tests using zebrafish models found that terbutaline sulfate prevents defects in axons and neuromuscular junction degeneration in a dose-dependent manner. A therapeutic potential of terbutaline sulfate was also observed when axonal and neuromuscular junction degeneration have already occurred in zebrafish model. Cotreatment with a ß2-adrenergic receptor antagonist, butoxamine, suggests that the effect of terbutaline is mediated by activation of ß2-adrenergic receptors.

Proteome-wide remodeling of protein location and function by stress.

Protein location and function can change dynamically depending on many factors, including environmental stress, disease state, age, developmental stage, and cell type. Here, we describe an integrative computational framework, called the conditional function predictor (CoFP; http://nbm.ajou.ac.kr/cofp/), for predicting changes in subcellular location and function on a proteome-wide scale. The essence of the CoFP approach is to cross-reference general knowledge about a protein and its known network of physical interactions, which typically pool measurements from diverse environments, against gene expression profiles that have been measured under specific conditions of interest. Using CoFP, we predict condition-specific subcellular locations, biological processes, and molecular functions of the yeast proteome under 18 specified conditions. In addition to highly accurate retrieval of previously known gold standard protein locations and functions, CoFP predicts previously unidentified condition-dependent locations and functions for nearly all yeast proteins. Many of these predictions can be confirmed using high-resolution cellular imaging. We show that, under DNA-damaging conditions, Tsr1, Caf120, Dip5, Skg6, Lte1, and Nnf2 change subcellular location and RNA polymerase I subunit A43, Ino2, and Ids2 show changes in DNA binding. Beyond specific predictions, this work reveals a global landscape of changing protein location and function, highlighting a surprising number of proteins that translocate from the mitochondria to the nucleus or from endoplasmic reticulum to Golgi apparatus under stress.

Unraveling human protein interaction networks underlying co-occurrences of diseases and pathological conditions.

BACKGROUND: Human diseases frequently cause complications such as obesity-induced diabetes and share numbers of pathological conditions, such as inflammation, by dysfunctions of common functional modules, such as protein-protein interactions (PPIs). METHODS: Our developed pipeline, ICod (Interaction analysis for disease Comorbidity), grades similarities between pairs of disease-related PPIs including comorbid diseases and pathological conditions. ICod displayed a disease similarity network consisting of nodes of disease PPIs and edges of similarity value. As a proof of concept, eight complex diseases and pathological conditions, such as type 2 diabetes, obesity, inflammation, and cancers, were examined to discover whether PPIs shared between diseases were associated with comorbidities. RESULTS: By comparing Medicare reports of disease co-occurrences from 31 million patients, the disease similarity network shows that PPIs of pathological conditions, including insulin resistance, and inflammation, overlap significantly with PPIs of various comorbid diseases, including diabetes, obesity, and cancers (p < 0.05). Interestingly, maintaining connectivity between essential genes was more drastically perturbed by removing a node of a disease-related gene rather than a pathological condition-related gene, such as one related to inflammations. CONCLUSION: Thus, PPIs of pathological symptoms are underlying functional modules across diseases accompanying comorbidity phenomena, whereas they contribute only marginally to maintaining interactions between essential genes.

A nutrigenomic framework to identify time-resolving responses of hepatic genes in diet-induced obese mice.

Obesity and its related complications have emerged as global health problems; however, the pathophysiological mechanism of obesity is still not fully understood. In this study, C57BL/6J mice were fed a normal (ND) or high-fat diet (HFD) for 0, 2, 4, 6, 8, 12, 20, and 24 weeks and the time course was systemically analyzed specifically for the hepatic transcriptome profile. Genes that were differentially expressed in the HFD-fed mice were clustered into 49 clusters and further classified into 8 different expression patterns: long-term up-regulated (pattern 1), long-term downregulated (pattern 2), early up-regulated (pattern 3), early down-regulated (pattern 4), late up-regulated (pattern 5), late down-regulated (pattern 6), early up-regulated and late down-regulated (pattern 7), and early down-regulated and late up-regulated (pattern 8) HFD-responsive genes. Within each pattern, genes related with inflammation, insulin resistance, and lipid metabolism were extracted, and then, a protein-protein interaction network was generated. The pattern specific sub-network was as follows: pattern 1, cellular assembly and organization, and immunological disease, pattern 2, lipid metabolism, pattern 3, gene expression and inflammatory response, pattern 4, cell signaling, pattern 5, lipid metabolism, molecular transport, and small molecule biochemistry, pattern 6, protein synthesis and cell-to cell signaling and interaction and pattern 7, cell-to cell signaling, cellular growth and proliferation, and cell death. For pattern 8, no significant sub-networks were identified. Taken together, this suggests that genes involved in regulating gene expression and inflammatory response are up-regulated whereas genes involved in lipid metabolism and protein synthesis are down-regulated during diet-induced obesity development.

Prioritization of SNPs for genome-wide association studies using an interaction model of genetic variation, gene expression, and trait variation.

The identification of true causal loci to unravel the statistical evidence of genotype-phenotype correlations and the biological relevance of selected single-nucleotide polymorphisms (SNPs) is a challenging issue in genome-wide association studies (GWAS). Here, we introduced a novel method for the prioritization of SNPs based on p-values from GWAS. The method uses functional evidence from populations, including phenotype-associated gene expressions. Based on the concept of genetic interactions, such as perturbation of gene expression by genetic variation, phenotype and gene expression related SNPs were prioritized by adjusting the p-values of SNPs. We applied our method to GWAS data related to drug-induced cytotoxicity. Then, we prioritized loci that potentially play a role in druginduced cytotoxicity. By generating an interaction model, our approach allowed us not only to identify causal loci, but also to find intermediate nodes that regulate the flow of information among causal loci, perturbed gene expression, and resulting phenotypic variation.

Predicting tissue-specific expressions based on sequence characteristics.

In multicellular organisms, including humans, understanding expression specificity at the tissue level is essential for interpreting protein function, such as tissue differentiation. We developed a prediction approach via generated sequence features from overrepresented patterns in housekeeping (HK) and tissue-specific (TS) genes to classify TS expression in humans. Using TS domains and transcriptional factor binding sites (TFBSs), sequence characteristics were used as indices of expressed tissues in a Random Forest algorithm by scoring exclusive patterns considering the biological intuition; TFBSs regulate gene expression, and the domains reflect the functional specificity of a TS gene. Our proposed approach displayed better performance than previous attempts and was validated using computational and experimental methods.

Prediction of cancer prognosis with the genetic basis of transcriptional variations.

Phenotypes of diseases, including prognosis, are likely to have complex etiologies and be derived from interactive mechanisms, including genetic and protein interactions. Many computational methods have been used to predict survival outcomes without explicitly identifying interactive effects, such as the genetic basis for transcriptional variations. We have therefore proposed a classification method based on the interaction between genotype and transcriptional expression features (CORE-F). This method considers the overall "genetic architecture," referring to genetically based transcriptional alterations that influence prognosis. In comparing the performance of CORE-F with the ensemble tree, the best-performing method predicting patient survival, we found that CORE-F outperformed the ensemble tree (mean AUC, 0.85 vs. 0.72). Moreover, the trained associations in the CORE-F successfully identified the genetic mechanisms underlying survival outcomes at the interaction-network level.