Differences In Disease States Research Articles

Unified Mouse and Human Kidney Single-Cell Expression Atlas Reveal Commonalities and Differences in Disease States.

Mouse models have been widely used to understand kidney disease pathomechanisms and play an important role in drug discovery. However, these models have not been systematically analyzed and compared. The authors characterized 18 different mouse kidney disease models at both bulk and single-cell gene expression levels and compared single-cell gene expression data from diabetic kidney disease (DKD) mice and from patients with DKD. Although single cell-level gene expression changes were mostly model-specific, different disease models showed similar changes when compared at a pathway level. The authors also found that changes in fractions of cell types are major drivers of bulk gene expression differences. Although the authors found only a small overlap of single cell-level gene expression changes between the mouse DKD model and patients, they observed consistent pathway-level changes. Mouse models have been widely used to understand kidney disease pathomechanisms and play an important role in drug discovery. However, these models have not been systematically analyzed and compared. We analyzed single-cell RNA sequencing data (36 samples) and bulk gene expression data (42 samples) from 18 commonly used mouse kidney disease models. We compared single-nucleus RNA sequencing data from a mouse diabetic kidney disease model with data from patients with diabetic kidney disease and healthy controls. We generated a uniformly processed mouse single-cell atlas containing information for nearly 300,000 cells, identifying all major kidney cell types and states. Our analysis revealed that changes in fractions of cell types are major drivers of differences in bulk gene expression. Although gene expression changes at the single-cell level were mostly model-specific, different disease models showed similar changes when compared at a pathway level. Tensor decomposition analysis highlighted the important changes in proximal tubule cells in disease states. Specifically, we identified important alterations in expression of metabolic and inflammation-associated pathways. The mouse diabetic kidney disease model and patients with diabetic kidney disease shared only a small number of conserved cell type-specific differentially expressed genes, but we observed pathway-level activation patterns conserved between mouse and human diabetic kidney disease samples. This study provides a comprehensive mouse kidney single-cell atlas and defines gene expression commonalities and differences in disease states in mice. The results highlight the key role of cell heterogeneity in driving changes in bulk gene expression and the limited overlap of single-cell gene expression changes between animal models and patients, but they also reveal consistent pathway-level changes.

Non-Pharmacological Therapeutic Options for the Treatment of Alzheimer's Disease.

Alzheimer’s disease is a growing global crisis in need of urgent diagnostic and therapeutic strategies. The current treatment strategy mostly involves immunotherapeutic medications that have had little success in halting disease progress. Hypotheses for pathogenesis and development of AD have been expanded to implicate both organ systems as well as cellular reactions. Non-pharmacologic interventions ranging from minimally to deeply invasive have attempted to address these diverse contributors to AD. In this review, we aim to delineate mechanisms underlying such interventions while attempting to provide explanatory links between the observed differences in disease states and postulated metabolic or structural mechanisms of change. The techniques discussed are not an exhaustive list of non-pharmacological interventions against AD but provide a foundation to facilitate a deeper understanding of the area of study.

Length of PM2.5 exposure and alterations in the serum metabolome among women undergoing infertility treatment.

Background:Both acute and chronic exposure to fine particulate matter (PM2.5) have been linked to negative health outcomes. Studies have used metabolomics to describe the biological pathways linking PM2.5 with disease but have focused on a single exposure window. We compared alterations in the serum metabolome following various short- and long-term PM2.5 exposures.Methods:Participants were women undergoing in vitro fertilization at a New England fertility clinic (n = 200). Women provided their residential address and provided a blood sample during controlled ovarian stimulation. PM2.5 exposure was estimated in the 1, 2, and 3 days, 2 weeks, and 3 months prior to blood collection using a validated spatiotemporal model. We utilized liquid chromatography with high-resolution mass spectrometry. We used generalized linear models to test for associations between metabolomic features and PM2.5 exposures after adjusting for potential confounders. Significant features (P < 0.005) were used for pathway analysis and metabolite identification.Results:We identified 17 pathways related to amino acid, lipid, energy, and nutrient metabolism that were solely associated with acute PM2.5 exposure. Fifteen pathways, mostly, pro-inflammatory, anti-inflammatory, amino acid, and energy metabolism, were solely associated with long-term PM2.5 exposure. Seven pathways were associated with the majority of exposure windows and were mostly related to anti-inflammatory and lipid metabolism. Among the significant features, we confirmed seven metabolites with level-1 evidence.Conclusions:We identified serum metabolites and metabolic pathways uniquely associated with acute versus chronic PM2.5 exposure. These different biologic pathways may help explain differences in disease states when investigating different lengths of PM2.5 exposure.

P603 Patient characteristics and adverse effects in biologic treatment of Crohn’s Disease and Ulcerative Colitis: A nationwide Danish cohort study years 2015–2018

Abstract Background Biological therapies have demonstrated effectiveness in the treatment of Crohn’s Disease (CD) and Ulcerative Colitis (UC), but comparative analyses of the various treatments remain limited. Methods Using Danish national registries, we performed a nationwide cohort study including all bio-naïve individuals diagnosed with UC or CD initiating treatment with infliximab (IFX), adalimumab (ADA), vedolizumab (VDZ), golimumab (GOL), or ustekinumab (UST) during 2015–2018. The hazard ratios (HR) of hospitalization, surgery, and start of corticosteroid therapy were explored using Cox regression adjusted for health-related and socio-economic parameters at treatment initiation. Results More than 90% of 1,836 CD and 1,886 UC patients had IFX as first-line treatment. Patients with ADA, VDZ, UST or GOL as first-line treatment were older at diagnosis and start of biologic treatment. A larger proportion were women, had higher education, and had previously had IBD surgery. We observed a higher risk of all-cause hospitalization (HR: 1.56 (95% CI: 1.16; 2.10)) among CD patients with ADA as first-line treatment compared to IFX. No risk difference was observed of IBD hospitalization, IBD related surgery, or use of corticosteroids between ADA- and IFX-treated CD patients. We observed no risk difference between ADA- and IFX-treated UC patients for any outcomes. For both CD and UC patients, analyses of VDZ were based on very low number of cases and the results were inconclusive. Analyses of GOL and UST were not possible because of low numbers. Second-line treatment with VDZ compared to ADA among CD patients previously treated with IFX showed no risk difference of all-cause hospitalization (1.32 (0.91; 1.92)), IBD hospitalization (1.27 (0.68; 2.35)), IBD related surgery (1.41 (0.69; 2.85)), or use of corticosteroids (0.70 (0.27; 1.82)). Among UC patients, second-line VDZ was associated with an increased risk of IBD related surgery (1.94 (1.01; 3.71)), but not of all-cause hospitalization (0.77 (0.48; 1.24)), IBD hospitalization (0.75 (0.38; 1.46)), or corticosteroid use (0.92 (0.37; 2.28)) compared to ADA. Conclusion We observed increased risk of hospitalization among CD patients treated with ADA compared to IFX, and increased risk of IBD-related surgery in UC patients receiving VDZ as second line treatment compared to ADA. The differences in key baseline characteristics patients initiating biologic treatment with ADA, VDZ, UST or GOL compared to IFX might reflect differences in disease state, that justify deviations from treatment guideline. It should be further investigated if the associations are an effect of differences in treatment efficacy and safety or confounding by differences in disease state at treatment start.

Machine Learning in Medical Imaging

Advances in both imaging and computers have synergistically led to a rapid rise in the potential use of artificial intelligence in various radiological imaging tasks, such as risk assessment, detection, diagnosis, prognosis, and therapy response, as well as in multi-omics disease discovery. A brief overview of the field is given here, allowing the reader to recognize the terminology, the various subfields, and components of machine learning, as well as the clinical potential. Radiomics, an expansion of computer-aided diagnosis, has been defined as the conversion of images to minable data. The ultimate benefit of quantitative radiomics is to (1) yield predictive image-based phenotypes of disease for precision medicine or (2) yield quantitative image-based phenotypes for data mining with other -omics for discovery (ie, imaging genomics). For deep learning in radiology to succeed, note that well-annotated large data sets are needed since deep networks are complex, computer software and hardware are evolving constantly, and subtle differences in disease states are more difficult to perceive than differences in everyday objects. In the future, machine learning in radiology is expected to have a substantial clinical impact with imaging examinations being routinely obtained in clinical practice, providing an opportunity to improve decision support in medical image interpretation. The term of note is decision support, indicating that computers will augment human decision making, making it more effective and efficient. The clinical impact of having computers in the routine clinical practice may allow radiologists to further integrate their knowledge with their clinical colleagues in other medical specialties and allow for precision medicine.

A Call for Investment in Education of US Minorities in the 21st Century

A Call for Investment in Education of US Minorities in the 21st Century

The Dose-Dependent Effect of S(+)-Ketamine on Cardiac Output in Healthy Volunteers and Complex Regional Pain Syndrome Type 1 Chronic Pain Patients

Ketamine is used as an analgesic for treatment of acute and chronic pain. While ketamine has a stimulatory effect on the cardiovascular system, little is known about the concentration-effect relationship. We examined the effect of S(+)-ketamine on cardiac output in healthy volunteers and chronic pain patients using a pharmacokinetic-pharmacodynamic modeling approach. In 10 chronic pain patients (diagnosed with complex regional pain syndrome type 1 [CRPS1] with a mean age 43.2 ± 13 years, disease duration 8.4 years, range 1.1 to 21.7 years) and 12 healthy volunteers (21.3 ± 1.6 years), 7 increasing IV doses of S(+)-ketamine were given over 5 minutes at 20-minute intervals starting with 1.5 mg with 1.5-mg increments. Cardiac output (CO) was calculated from the arterial pressure curve obtained from an arterial catheter in the radial artery. Ketamine and norketamine plasma concentrations were measured. A novel pharmacokinetic-pharmacodynamic model was constructed to quantify the direct stimulatory effect of ketamine on CO and the following adaptation/inhibition. Significant differences in pharmacokinetic estimates were observed between study groups with 15% and 40% larger S(+)-ketamine S(+)-norketamine concentrations in healthy volunteers compared to CRPS1 patients. S(+)-ketamine had a dose-dependent stimulatory effect on CO in patients and volunteers. After infusion an inhibitory effect on CO was observed. Pharmacodynamic model parameters did not differ between CRPS1 patients and healthy volunteers. The concentration of S(+)-ketamine causing a 1 L/min increase in CO was 243 ± 54 ng/mL with an onset/offset half-life of 1.3 ± 0.21 minutes. The inhibitory component was slow (time constant of 67.2 ± 17.0 minutes). S(+)-ketamine pharmacokinetics but not pharmacodynamics differed between study populations, related to differences in disease state (CRPS1 or not) or age. The dose-dependent effect of S(+)-ketamine on CO was well described by the biphasic dynamic model. The effect of S(+)-ketamine on CO was similar between study groups with respect to its stimulatory and inhibitory components, despite group differences in age and health.

Abstract LB-255: A mass spectrometry platform to quantitatively profile cancer cell metabolism from cells, tumors, and fixed tissue

Abstract The metabolic requirements of cancer and proliferating cells are different from that of normal differential tissue and may have diverse applications in the treatment of cancers. However, many of the molecular mechanisms that reorganize metabolism to support cell proliferation are unknown. To study cancer cell metabolism, we implemented a mass spectrometry based platform to quantitatively profile endogenous metabolites from proliferating cell lines, tumor tissues and formalin fixed paraffin embedded (FFPE) tissue. Cell lines are derived from several cancers including lung, multiple myeloma, prostate, and gliobastoma (GBM). In some cases, these were compared to a drosophila reference cell line. Patients were also profiled for disease classification from their cerebrospinal fluid (CSF). We also were successful in extracting polar metabolites from FFPE tissue more than five years old stored at room temperature. For FFPE tissue samples, we show that we can observe differences in disease states involving PI3K-TSC-TOR pathway and we compared different extraction methods for acquiring metabolomics data from FFPE blocks. We show that we can cluster GBM versus normal patients from analyzing their CSF. We target more than 255 unique metabolites using selected reaction monitoring (SRM) based analyses with an AB/Sciex 5500 QTRAP mass spectrometer coupled to a Shimadzu UFLC using normal phase chromatography. For a single 18 min run, our platform allows for unprecedented sensitivity, quantitation and coverage of metabolites that comprise of diverse metabolic pathways from as little as a single 6 cm tissue culture dish of cells or approximately 2–3 million cells from tissue samples. We find that amide XBridge columns (Waters) at 275 uL/min perform well in both negative and positive ion switching mode and that the sampling rate of the instrument is sufficiently fast (cycle time of 1.6 sec with 3 msec dwell times) to effectively capture up to 300 metabolite targets without scheduled SRM runs. Peak areas of metabolites are integrated using MultiQuant 1.1 software (Applied Biosystems). Peak areas from triplicate runs are hierarchically clustered and statistical analyses are applied to generate P values for metabolite changes over different biological conditions. We also probed metabolic flux in pathways by targeting a set of 13C glucose labeled metabolites. In addition, we have also analyzed a model cell line after stimulation with Insulin and EGF to examine if growth factor induced metabolic changes are evolutionarily conserved. Using metabolic inhibitors, such as Iodoacetic acid, KCN, etc., we have been able to characterize the consequences of inhibiting glycolysis and oxidative phosphorylation, respectively. Finally, we considered kinase inhibitors and measured their effects on metabolism in proliferating cancer cell lines. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr LB-255. doi:10.1158/1538-7445.AM2011-LB-255

Enhanced Detection of Autoantibodies on Protein Microarrays Using a Modified Protein Digestion Technique

High-throughput studies to determine differential immune (humoral) response to diseases are becoming of increasing interest because the information they provide can help in early diagnosis as well as monitoring of therapeutics. Protein microarrays are a high-throughput and convenient technology that can be applied to the study of the humoral response. Proteins can be arrayed on slides and then probed with serum from different classes of patients to observe differences that may exist among autoantibodies that reflect differences in disease states. However, such studies may be difficult to interpret due to the weak overall signal response of such protein microarrays. We propose that this weak signal response is due to the physical positioning of the disease proteins that renders them sterically hindered from binding partners in the serum. In this study, we hypothesize that reducing the complexity and size of the disease proteins by chemical digestion using cyanogen bromide (CNBr) may enhance the overall signal from the humoral response and facilitate visualization of disease-specific responses in various classes of serum. A modified protein microarray methodology using CNBr digestion is presented here. The new workflow was applied to a set of 10 serum samples from healthy subjects, 10 from patients with chronic pancreatitis and 10 from patients diagnosed with pancreatic cancer and the results were compared to results obtained in the absence of CNBr digestion. CNBr digestion allowed the identification of 10 additional autoantibodies that responded to serum, 5 of which were unique to pancreatitis and cancer sera. This new methodology may increase the sensitivity of microarray studies measuring autoantibodies in serum.

Changes in priorities for improvement in patients with rheumatoid arthritis during 1 year of anti-tumour necrosis factor treatment

Objectives:To examine priorities for health status improvement in patients with active rheumatoid arthritis (RA) during anti-tumour necrosis factor (TNF) treatment.Methods:Data were used from 173 patients with RA starting treatment with...

Diagnostic Value of Maximal Exercise Tidal Volume

Though breathing pattern is frequently analyzed during clinical exercise testing, there is little information regarding its usefulness in the differential diagnosis of impaired exercise tolerance. This study tested the hypothesis that differences in peak tidal volume during exercise between patients with different cardiorespiratory diseases are related largely to differences in severity of respiratory mechanical impairment (vital capacity), not to differences in disease state. Patients with chronic obstructive pulmonary disease, restrictive lung disease, bronchial asthma, and heart disease (mitral valve disease or left ventricular dysfunction) were studied. Subjects selected had one and only one of the above diagnoses. All subjects performed maximal (symptom-limited) incremental exercise on a cycle ergometer. Multiple linear regression of all subjects (n = 30) in all four groups showed a significant correlation between VTmax and VC: VTmax = 0.55, VC -0.09 L (r = 0.827, p less than 0.0001). The VTmax/VC (x 100) was (mean +/- SD) 44 +/- 15, 54 +/- 11, 56 +/- 11, and 54 +/- 12 for the COPD, RLD, BA and HD patients respectively. There was no significant difference between any of the groups. We concluded that differences in VTmax between different patients are related largely to differences in VC (ie, differences in severity of respiratory mechanical impairment), not to differences in disease state. Measurement of VTmax or the VTmax/VC ratio has little value in the differential diagnosis of exertional dyspnea.