Agent-based modeling predicts RAC1 is critical for ovarian cancer metastasis.

Experimental and computational studies pinpoint rate-limiting step(s) in metastasis governed by Rac1. Using ovarian cancer cell and animal models, Rac1 expression was manipulated, and quantitative measurements of cell-cell and cell-substrate adhesion, cell invasion, mesothelial clearance, and peritoneal tumor growth discriminated the tumor behaviors most highly influenced by Rac1. The experimental data were used to parameterize an agent-based computational model simulating peritoneal niche colonization, intravasation, and hematogenous metastasis to distant organs. Increased ovarian cancer cell survival afforded by the more rapid adhesion and intravasation upon Rac1 overexpression is predicted to increase the numbers of and the rates at which tumor cells are disseminated to distant sites. Surprisingly, crowding of cancer cells along the blood vessel was found to decrease the numbers of cells reaching a distant niche irrespective of Rac1 overexpression or knockdown, suggesting that sites for tumor cell intravasation are rate limiting and become accessible if cells intravasate rapidly or are displaced due to diminished viability. Modeling predictions were confirmed through animal studies of Rac1-dependent metastasis to the lung. Collectively, the experimental and modeling approaches identify cell adhesion, rapid intravasation, and survival in the blood as parameters in the ovarian metastatic cascade that are most critically dependent on Rac1.

Machine learning prediction and tau-based screening identifies potential Alzheimer's disease genes relevant to immunity.

With increased research funding for Alzheimer's disease (AD) and related disorders across the globe, large amounts of data are being generated. Several studies employed machine learning methods to understand the ever-growing omics data to enhance early diagnosis, map complex disease networks, or uncover potential drug targets. We describe results based on a Target Central Resource Database protein knowledge graph and evidence paths transformed into vectors by metapath matching. We extracted features between specific genes and diseases, then trained and optimized our model using XGBoost, termed MPxgb(AD). To determine our MPxgb(AD) prediction performance, we examined the top twenty predicted genes through an experimental screening pipeline. Our analysis identified potential AD risk genes: FRRS1, CTRAM, SCGB3A1, FAM92B/CIBAR2, and TMEFF2. FRRS1 and FAM92B are considered dark genes, while CTRAM, SCGB3A1, and TMEFF2 are connected to TREM2-TYROBP, IL-1ß-TNFα, and MTOR-APP AD-risk nodes, suggesting relevance to the pathogenesis of AD.

GLUT3 inhibitor discovery through in silico ligand screening and in vivo validation in eukaryotic expression systems.

The passive transport of glucose and related hexoses in human cells is facilitated by members of the glucose transporter family (GLUT, SLC2 gene family). GLUT3 is a high-affinity glucose transporter primarily responsible for glucose entry in neurons. Changes in its expression have been implicated in neurodegenerative diseases and cancer. GLUT3 inhibitors can provide new ways to probe the pathophysiological role of GLUT3 and tackle GLUT3-dependent cancers. Through in silico screening of an ~ 8 million compounds library against the inward- and outward-facing models of GLUT3, we selected ~ 200 ligand candidates. These were tested for in vivo inhibition of GLUT3 expressed in hexose transporter-deficient yeast cells, resulting in six new GLUT3 inhibitors. Examining their specificity for GLUT1-5 revealed that the most potent GLUT3 inhibitor (G3iA, IC50 ~ 7 µM) was most selective for GLUT3, inhibiting less strongly only GLUT2 (IC50 ~ 29 µM). None of the GLUT3 inhibitors affected GLUT5, three inhibited GLUT1 with equal or twofold lower potency, and four showed comparable or two- to fivefold better inhibition of GLUT4. G3iD was a pan-Class 1 GLUT inhibitor with the highest preference for GLUT4 (IC50 ~ 3.9 µM). Given the prevalence of GLUT1 and GLUT3 overexpression in many cancers and multiple myeloma's reliance on GLUT4, these GLUT3 inhibitors may discriminately hinder glucose entry into various cancer cells, promising novel therapeutic avenues in oncology.

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer.

Inhibiting protein kinases (PKs) that cause cancers has been an important topic in cancer therapy for years. So far, almost 8% of >530 PKs have been targeted by FDA-approved medications, and around 150 protein kinase inhibitors (PKIs) have been tested in clinical trials. We present an approach based on natural language processing and machine learning to investigate the relations between PKs and cancers, predicting PKs whose inhibition would be efficacious to treat a certain cancer. Our approach represents PKs and cancers as semantically meaningful 100-dimensional vectors based on word and concept neighborhoods in PubMed abstracts. We use information about phase I-IV trials in ClinicalTrials.gov to construct a training set for random forest classification. Our results with historical data show that associations between PKs and specific cancers can be predicted years in advance with good accuracy. Our tool can be used to predict the relevance of inhibiting PKs for specific cancers and to support the design of well-focused clinical trials to discover novel PKIs for cancer therapy.

Identification of new GLUT2-selective inhibitors through in silico ligand screening and validation in eukaryotic expression systems.

Glucose is an essential energy source for cells. In humans, its passive diffusion through the cell membrane is facilitated by members of the glucose transporter family (GLUT, SLC2 gene family). GLUT2 transports both glucose and fructose with low affinity and plays a critical role in glucose sensing mechanisms. Alterations in the function or expression of GLUT2 are involved in the Fanconi-Bickel syndrome, diabetes, and cancer. Distinguishing GLUT2 transport in tissues where other GLUTs coexist is challenging due to the low affinity of GLUT2 for glucose and fructose and the scarcity of GLUT-specific modulators. By combining in silico ligand screening of an inward-facing conformation model of GLUT2 and glucose uptake assays in a hexose transporter-deficient yeast strain, in which the GLUT1-5 can be expressed individually, we identified eleven new GLUT2 inhibitors (IC50 ranging from 0.61 to 19.3 µM). Among them, nine were GLUT2-selective, one inhibited GLUT1-4 (pan-Class I GLUT inhibitor), and another inhibited GLUT5 only. All these inhibitors dock to the substrate cavity periphery, close to the large cytosolic loop connecting the two transporter halves, outside the substrate-binding site. The GLUT2 inhibitors described here have various applications; GLUT2-specific inhibitors can serve as tools to examine the pathophysiological role of GLUT2 relative to other GLUTs, the pan-Class I GLUT inhibitor can block glucose entry in cancer cells, and the GLUT2/GLUT5 inhibitor can reduce the intestinal absorption of fructose to combat the harmful effects of a high-fructose diet.

InContext: curation of medical context for drug indications.

Accurate and precise information about the therapeutic uses (indications) of a drug is essential for applications in drug repurposing and precision medicine. Leading online drug resources such as DrugCentral and DrugBank provide rich information about various properties of drugs, including their indications. However, because indications in such databases are often partly automatically mined, some may prove to be inaccurate or imprecise. Particularly challenging for text mining methods is the task of distinguishing between general disease mentions in drug product labels and actual indications for the drug. For this, the qualifying medical context of the disease mentions in the text should be studied. Some examples include contraindications, co-prescribed drugs and target patient qualifications. No existing indication curation efforts attempt to capture such information in a precise way. Here we fill this gap by presenting a novel curation protocol for extracting indications and machine processable annotations of contextual information about the therapeutic use of a drug. We implemented the protocol on a reference set of FDA-approved drug product labels on the DailyMed website to curate indications for 150 anti-cancer and cardiovascular drugs. The resulting corpus - InContext - focuses on anti-cancer and cardiovascular drugs because of the heightened societal interest in cancer and heart disease. In order to understand how InContext relates with existing reputable drug indication databases, we analysed it's overlap with a state-of-the-art indications database - LabeledIn - as well as a reputable online drug compendium - DrugCentral. We found that 40% of indications sampled from DrugCentral (and 23% from LabeledIn) respectively, could not be accounted for in InContext. This raises questions about the veracity of indications not appearing in InContext. The additional contextual information curated by InContext about disease mentions in drug SPLs provides a foundation for more precise, structured and formal representations of knowledge related to drug therapeutic use, in order to increase accuracy and agreement of drug indication extraction methods for in silico drug repurposing.

Deciphering the Plasma Proteome of Type 2 Diabetes.

With an estimated prevalence of 463 million affected, type 2 diabetes represents a major challenge to health care systems worldwide. Analyzing the plasma proteomes of individuals with type 2 diabetes may illuminate hitherto unknown functional mechanisms underlying disease pathology. We assessed the associations between type 2 diabetes and >1,000 plasma proteins in the Cooperative Health Research in the Region of Augsburg (KORA) F4 cohort (n = 993, 110 cases), with subsequent replication in the third wave of the Nord-Trøndelag Health Study (HUNT3) cohort (n = 940, 149 cases). We computed logistic regression models adjusted for age, sex, BMI, smoking status, and hypertension. Additionally, we investigated associations with incident type 2 diabetes and performed two-sample bidirectional Mendelian randomization (MR) analysis to prioritize our results. Association analysis of prevalent type 2 diabetes revealed 24 replicated proteins, of which 8 are novel. Proteins showing association with incident type 2 diabetes were aminoacylase-1, growth hormone receptor, and insulin-like growth factor-binding protein 2. Aminoacylase-1 was associated with both prevalent and incident type 2 diabetes. MR analysis yielded nominally significant causal effects of type 2 diabetes on cathepsin Z and rennin, both known to have roles in the pathophysiological pathways of cardiovascular disease, and of sex hormone-binding globulin on type 2 diabetes. In conclusion, our high-throughput proteomics study replicated previously reported type 2 diabetes-protein associations and identified new candidate proteins possibly involved in the pathogenesis of type 2 diabetes.

How to Illuminate the Druggable Genome Using Pharos.

Pharos is an integrated web-based informatics platform for the analysis of data aggregated by the Illuminating the Druggable Genome (IDG) Knowledge Management Center, an NIH Common Fund initiative. The current version of Pharos (as of October 2019) spans 20,244 proteins in the human proteome, 19,880 disease and phenotype associations, and 226,829 ChEMBL compounds. This resource not only collates and analyzes data from over 60 high-quality resources to generate these types, but also uses text indexing to find less apparent connections between targets, and has recently begun to collaborate with institutions that generate data and resources. Proteins are ranked according to a knowledge-based classification system, which can help researchers to identify less studied "dark" targets that could be potentially further illuminated. This is an important process for both drug discovery and target validation, as more knowledge can accelerate target identification, and previously understudied proteins can serve as novel targets in drug discovery. Two basic protocols illustrate the levels of detail available for targets and several methods of finding targets of interest. An Alternate Protocol illustrates the difference in available knowledge between less and more studied targets. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Search for a target and view details Alternate Protocol: Search for dark target and view details Basic Protocol 2: Filter a target list to get refined results.

Diabetes mellitus risk for 102 drugs and drug combinations used in patients with bipolar disorder.

OBJECTIVE: To compare the largest set of bipolar disorder pharmacotherapies to date (102 drugs and drug combinations) for risk of diabetes mellitus (DM). METHODS: The IBM MarketScan® database was used to retrospectively analyze data on 565,253 adults with bipolar disorder without prior glucose metabolism-related diagnoses. The pharmacotherapies compared were lithium, mood-stabilizing anticonvulsants, antipsychotics, and antidepressants (monotherapy and multi-class polypharmacy). Cox regression modeling included fixed pre-treatment covariates and time-varying drug exposure covariates to estimate the hazard ratio (HR) of each treatment versus "No drug". RESULTS: The annual incidence of new-onset diabetes during the exposure period was 3.09 % (22,951 patients). The HR of drug-dependent DM ranged from 0.79 to 2.37. One-third of the studied pharmacotherapies, including most of the antipsychotic-containing regimens, had a significantly higher risk of DM compared to "No drug". A significantly lower DM risk was associated with lithium, lamotrigine, oxcarbazepine and bupropion monotherapies, selective serotonin reuptake inhibitors (SSRI) mono-class therapy and several drug combinations containing bupropion and an SSRI. As additional drugs were combined in more complex polypharmacy, higher HRs were consistently observed. CONCLUSIONS: There is an increased risk of diabetes mellitus associated with antipsychotic and psychotropic polypharmacy use in bipolar disorder. The evidence of a lower-than-baseline risk of DM with lamotrigine, oxcarbazepine, lithium, and bupropion monotherapy should be further investigated.

A Selective Ligand for Estrogen Receptor Proteins Discriminates Rapid and Genomic Signaling.

Estrogen exerts extensive and diverse effects throughout the body of women. In addition to the classical nuclear estrogen receptors (ERα and ERß), the G protein-coupled estrogen receptor GPER is an important mediator of estrogen action. Existing ER-targeted therapeutic agents act as GPER agonists. Here, we report the identification of a small molecule, named AB-1, with the previously unidentified activity of high selectivity for binding classical ERs over GPER. AB-1 also possesses a unique functional activity profile as an agonist of transcriptional activity but an antagonist of rapid signaling through ERα. Our results define a class of small molecules that discriminate between the classical ERs and GPER, as well as between modes of signaling within the classical ERs. Such an activity profile, if developed into an ER antagonist, could represent an opportunity for the development of first-in-class nuclear hormone receptor-targeted therapeutics for breast cancer exhibiting reduced acquired and de novo resistance.

Comparison of 71 bipolar disorder pharmacotherapies for kidney disorder risk: The potential hazards of polypharmacy.

BACKGROUND: This study compared the largest set of bipolar disorder pharmacotherapies to date (71 drugs and drug combinations) for risk of kidney disorders (KDs). METHODS: This retrospective observational study used the IBM MarketScan® database to analyze data on 591,052 adults with bipolar disorder without prior nephropathy, for onset of KDs (of "moderate" or "high" severity) following psychopharmacotherapy (lithium, mood stabilizing anticonvulsants [MSAs], antipsychotics, antidepressants), or "No drug". Cox regression models included fixed pre-treatment covariates and time-varying drug exposure covariates to estimate the hazard ratio (HR) of each treatment versus "No drug". RESULTS: Newly observed KD occurred in 14,713 patients. No regimen had significantly lower risk of KDs than "No drug". The HR estimates ranged 0.86-2.66 for "all" KDs and 0.87-5.30 for "severe" KDs. As additional drugs were combined to compare more complex polypharmacies, higher HRs were consistently observed. Most regimens containing lithium, MSAs, or antipsychotics had a higher risk than "No drug" (p < 0.05). The risk for "all" and "severe" KDs was highest respectively on monoamine oxidase inhibitors (MAOIs) (HR = 2.66, p = 5.73 × 10-5), and a lithium-containing four-class combination (HR = 5.30, p = 2.46 × 10-9). The HR for lithium monotherapy was 1.82 (p = 4.73 × 10-17) for "severe" KDs. LIMITATIONS: The limitations inherent for an observational study were non-randomized assignment of patients to treatment groups, non-standardization of diagnostic decisions, and non-uniform quality of data collection. No correction was made for medication dosage. CONCLUSIONS: The findings support literature concerns about lithium nephrotoxicity and highlight the potential risks of MAOIs, MSAs, antipsychotics and psychotropic polypharmacy.

Proteomic analysis defines kinase taxonomies specific for subtypes of breast cancer.

Multiplexed small molecule inhibitors covalently bound to Sepharose beads (MIBs) were used to capture functional kinases in luminal, HER2-enriched and triple negative (basal-like and claudin-low) breast cancer cell lines and tumors. Kinase MIB-binding profiles at baseline without perturbation proteomically distinguished the four breast cancer subtypes. Understudied kinases, whose disease associations and pharmacology are generally unexplored, were highly represented in MIB-binding taxonomies and are integrated into signaling subnetworks with kinases that have been previously well characterized in breast cancer. Computationally it was possible to define subtypes using profiles of less than 50 of the more than 300 kinases bound to MIBs that included understudied as well as metabolic and lipid kinases. Furthermore, analysis of MIB-binding profiles established potential functional annotations for these understudied kinases. Thus, comprehensive MIBs-based capture of kinases provides a unique proteomics-based method for integration of poorly characterized kinases of the understudied kinome into functional subnetworks in breast cancer cells and tumors that is not possible using genomic strategies. The MIB-binding profiles readily defined subtype-selective differential adaptive kinome reprogramming in response to targeted kinase inhibition, demonstrating how MIB profiles can be used in determining dynamic kinome changes that result in subtype selective phenotypic state changes.

A comprehensive map of molecular drug targets.

The success of mechanism-based drug discovery depends on the definition of the drug target. This definition becomes even more important as we try to link drug response to genetic variation, understand stratified clinical efficacy and safety, rationalize the differences between drugs in the same therapeutic class and predict drug utility in patient subgroups. However, drug targets are often poorly defined in the literature, both for launched drugs and for potential therapeutic agents in discovery and development. Here, we present an updated comprehensive map of molecular targets of approved drugs. We curate a total of 893 human and pathogen-derived biomolecules through which 1,578 US FDA-approved drugs act. These biomolecules include 667 human-genome-derived proteins targeted by drugs for human disease. Analysis of these drug targets indicates the continued dominance of privileged target families across disease areas, but also the growth of novel first-in-class mechanisms, particularly in oncology. We explore the relationships between bioactivity class and clinical success, as well as the presence of orthologues between human and animal models and between pathogen and human genomes. Through the collaboration of three independent teams, we highlight some of the ongoing challenges in accurately defining the targets of molecular therapeutics and present conventions for deconvoluting the complexities of molecular pharmacology and drug efficacy.

Discovery of a specific inhibitor of human GLUT5 by virtual screening and in vitro transport evaluation.

GLUT5, a fructose-transporting member of the facilitative glucose transporter (GLUT, SLC2) family, is a therapeutic target for diabetes and cancer but has no potent inhibitors. We virtually screened a library of 6 million chemicals onto a GLUT5 model and identified N-[4-(methylsulfonyl)-2-nitrophenyl]-1,3-benzodioxol-5-amine (MSNBA) as an inhibitor of GLUT5 fructose transport in proteoliposomes. MSNBA inhibition was specific to GLUT5; this inhibitor did not affect the fructose transport of human GLUT2 or the glucose transport of human GLUT1-4 or bacterial GlcPSe. In MCF7 cells, a human breast cancer cell line, MSNBA competitively inhibited GLUT5 fructose uptake with a KI of 3.2 ± 0.4 µM. Ligand docking, mutagenesis and functional studies indicate that MSNBA binds near the active site and inhibitor discrimination involves H387 of GLUT5. Thus, MSNBA is a selective and potent inhibitor of fructose transport via GLUT5, and the first chemical probe for this transporter. Our data indicate that active site differences in GLUT members could be exploited to further enhance ligand specificity.

Novel Activities of Select NSAID R-Enantiomers against Rac1 and Cdc42 GTPases.

Rho family GTPases (including Rac, Rho and Cdc42) collectively control cell proliferation, adhesion and migration and are of interest as functional therapeutic targets in numerous epithelial cancers. Based on high throughput screening of the Prestwick Chemical Library® and cheminformatics we identified the R-enantiomers of two approved drugs (naproxen and ketorolac) as inhibitors of Rac1 and Cdc42. The corresponding S-enantiomers are considered the active component in racemic drug formulations, acting as non-steroidal anti-inflammatory drugs (NSAIDs) with selective activity against cyclooxygenases. Here, we show that the S-enantiomers of naproxen and ketorolac are inactive against the GTPases. Additionally, more than twenty other NSAIDs lacked inhibitory action against the GTPases, establishing the selectivity of the two identified NSAIDs. R-naproxen was first identified as a lead compound and tested in parallel with its S-enantiomer and the non-chiral 6-methoxy-naphthalene acetic acid (active metabolite of nabumetone, another NSAID) as a structural series. Cheminformatics-based substructure analyses-using the rotationally constrained carboxylate in R-naproxen-led to identification of racemic [R/S] ketorolac as a suitable FDA-approved candidate. Cell based measurement of GTPase activity (in animal and human cell lines) demonstrated that the R-enantiomers specifically inhibit epidermal growth factor stimulated Rac1 and Cdc42 activation. The GTPase inhibitory effects of the R-enantiomers in cells largely mimic those of established Rac1 (NSC23766) and Cdc42 (CID2950007/ML141) specific inhibitors. Docking predicts that rotational constraints position the carboxylate moieties of the R-enantiomers to preferentially coordinate the magnesium ion, thereby destabilizing nucleotide binding to Rac1 and Cdc42. The S-enantiomers can be docked but are less favorably positioned in proximity to the magnesium. R-naproxen and R-ketorolac have potential for rapid translation and efficacy in the treatment of several epithelial cancer types on account of established human toxicity profiles and novel activities against Rho-family GTPases.

A Novel Pharmacologic Activity of Ketorolac for Therapeutic Benefit in Ovarian Cancer Patients.

PURPOSE: We previously identified the R-enantiomer of ketorolac as an inhibitor of the Rho-family GTPases Rac1 and Cdc42. Rac1 and Cdc42 regulate cancer-relevant functions, including cytoskeleton remodeling necessary for tumor cell adhesion and migration. This study investigated whether administration of racemic (R,S) ketorolac after ovarian cancer surgery leads to peritoneal distribution of R-ketorolac, target GTPase inhibition in cells retrieved from the peritoneal cavity, and measureable impact on patient outcomes. EXPERIMENTAL DESIGN: Eligible patients had suspected advanced-stage ovarian, fallopian tube or primary peritoneal cancer. Secondary eligibility was met when ovarian cancer was confirmed and optimally debulked, an intraperitoneal port was placed, and there were no contraindications for ketorolac administration. R- and S-ketorolac were measured in serum and peritoneal fluid, and GTPase activity was measured in peritoneal cells. A retrospective study correlated perioperative ketorolac and ovarian cancer-specific survival in ovarian cancer cases. RESULTS: Elevated expression and activity of Rac1 and Cdc42 was detected in ovarian cancer patient tissues, confirming target relevance. Ketorolac in peritoneal fluids was enriched in the R-enantiomer and peritoneal cell GTPase activity was inhibited after ketorolac administration when R-ketorolac was at peak levels. After adjusting for age, AJCC stage, completion of chemotherapy, and neoadjuvant therapy, women given perioperative ketorolac had a lower hazard of death (HR, 0.30; 95% confidence interval, 0.11-0.88). CONCLUSIONS: Ketorolac has a novel pharmacologic activity conferred by the R-enantiomer and R-ketorolac achieves sufficient levels in the peritoneal cavity to inhibit Rac1 and Cdc42, potentially contributing to the observed survival benefit in women who received ketorolac.

Ligand-directed targeting of lymphatic vessels uncovers mechanistic insights in melanoma metastasis.

Metastasis is the most lethal step of cancer progression in patients with invasive melanoma. In most human cancers, including melanoma, tumor dissemination through the lymphatic vasculature provides a major route for tumor metastasis. Unfortunately, molecular mechanisms that facilitate interactions between melanoma cells and lymphatic vessels are unknown. Here, we developed an unbiased approach based on molecular mimicry to identify specific receptors that mediate lymphatic endothelial-melanoma cell interactions and metastasis. By screening combinatorial peptide libraries directly on afferent lymphatic vessels resected from melanoma patients during sentinel lymphatic mapping and lymph node biopsies, we identified a significant cohort of melanoma and lymphatic surface binding peptide sequences. The screening approach was designed so that lymphatic endothelium binding peptides mimic cell surface proteins on tumor cells. Therefore, relevant metastasis and lymphatic markers were biochemically identified, and a comprehensive molecular profile of the lymphatic endothelium during melanoma metastasis was generated. Our results identified expression of the phosphatase 2 regulatory subunit A, α-isoform (PPP2R1A) on the cell surfaces of both melanoma cells and lymphatic endothelial cells. Validation experiments showed that PPP2R1A is expressed on the cell surfaces of both melanoma and lymphatic endothelial cells in vitro as well as independent melanoma patient samples. More importantly, PPP2R1A-PPP2R1A homodimers occur at the cellular level to mediate cell-cell interactions at the lymphatic-tumor interface. Our results revealed that PPP2R1A is a new biomarker for melanoma metastasis and show, for the first time to our knowledge, an active interaction between the lymphatic vasculature and melanoma cells during tumor progression.

Ligand-directed profiling of organelles with internalizing phage libraries.

Phage display is a resourceful tool to, in an unbiased manner, discover and characterize functional protein-protein interactions, create vaccines, and engineer peptides, antibodies, and other proteins as targeted diagnostic and/or therapeutic agents. Recently, our group has developed a new class of internalizing phage (iPhage) for ligand-directed targeting of organelles and to identify molecular pathways within live cells. This unique technology is suitable for applications ranging from fundamental cell biology to drug development. This unit describes the methods for generating and screening the iPhage display system, and explains how to select and validate candidate internalizing homing peptide.

Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients.

A key prerequisite for precision medicine is the estimation of disease progression from the current patient state. Disease correlations and temporal disease progression (trajectories) have mainly been analysed with focus on a small number of diseases or using large-scale approaches without time consideration, exceeding a few years. So far, no large-scale studies have focused on defining a comprehensive set of disease trajectories. Here we present a discovery-driven analysis of temporal disease progression patterns using data from an electronic health registry covering the whole population of Denmark. We use the entire spectrum of diseases and convert 14.9 years of registry data on 6.2 million patients into 1,171 significant trajectories. We group these into patterns centred on a small number of key diagnoses such as chronic obstructive pulmonary disease (COPD) and gout, which are central to disease progression and hence important to diagnose early to mitigate the risk of adverse outcomes. We suggest such trajectory analyses may be useful for predicting and preventing future diseases of individual patients.

A novel receptor cross-talk between the ATP receptor P2Y2 and formyl peptide receptors reactivates desensitized neutrophils to produce superoxide.

Neutrophils express several G-protein coupled receptors (GPCRs) and they cross regulate each other. We described a novel cross-talk mechanism in neutrophils, by which signals generated by the receptor for ATP (P2Y2) reactivate desensitized formyl peptide receptors (FPRs) so that these ligand-bound inactive FPRs resume signaling. At the signaling level, the cross-talk was unidirectional, i.e., P2Y2 ligation reactivated FPR, but not vice versa and was sensitive to the phosphatase inhibitor calyculinA. Further, we show that the cross talk between P2Y2 and FPR bypassed cytosolic Ca(2+) transients and did not rely on the actin cytoskeleton. In summary, our data demonstrate a novel cross-talk mechanism that results in reactivation of desensitized FPRs and, an amplification of the neutrophil response to ATP.