Formation and function of phosphatidylserine and phosphatidylethanolamine in mammalian cells

Phosphatidylserine (PS) and phosphatidylethanolamine (PE) are metabolically related membrane aminophospholipids. In mammalian cells, PS is required for targeting and function of several intracellular signaling proteins. Moreover, PS is asymmetrically distributed in the plasma membrane. Although PS is highly enriched in the cytoplasmic leaflet of plasma membranes, PS exposure on the cell surface initiates blood clotting and removal of apoptotic cells. PS is synthesized in mammalian cells by two distinct PS synthases that exchange serine for choline or ethanolamine in phosphatidylcholine (PC) or PE, respectively. Targeted disruption of each PS synthase individually in mice demonstrated that neither enzyme is required for viability whereas elimination of both synthases was embryonic lethal. Thus, mammalian cells require a threshold amount of PS. PE is synthesized in mammalian cells by four different pathways, the quantitatively most important of which are the CDP-ethanolamine pathway that produces PE in the ER, and PS decarboxylation that occurs in mitochondria. PS is made in ER membranes and is imported into mitochondria for decarboxylation to PE via a domain of the ER [mitochondria-associated membranes (MAM)] that transiently associates with mitochondria. Elimination of PS decarboxylase in mice caused mitochondrial defects and embryonic lethality. Global elimination of the CDP-ethanolamine pathway was also incompatible with mouse survival. Thus, PE made by each of these pathways has independent and necessary functions. In mammals PE is a substrate for methylation to PC in the liver, a substrate for anandamide synthesis, and supplies ethanolamine for glycosylphosphatidylinositol anchors of cell-surface signaling proteins. Thus, PS and PE participate in many previously unanticipated facets of mammalian cell biology. This article is part of a Special Issue entitled Phospholipids and Phospholipid Metabolism.     

Cyclin D1 regulates p27 stability in B cells

p27^Kip1 is a cyclin-dependent kinase inhibitor that plays a critical role in regulating G₁/S transition, and whose activity is, in part, regulated through interactions with D-type cyclins. We have generated the BD1-9 cell line, a BaF3 pro-B cells derivative in which cyclin D1 can be induced rapidly and reversibly by ponasterone A. The induction of cyclin D1 expression leads to a targeted p27^Kip1 accumulation in both cytoplasmic and nuclear compartments. But, only the p27^Kip1 form phosphorylated on serine 10 (pSer10-p27^Kip1) accumulates in BD1-9 cells. We found that the binding of cyclin D1 and pSer10-p27^Kip1 prevents p27^Kip1 degradation by the cytoplasmic Kip1 ubiquitylation-promoting complex (KPC) proteosomic pathway. Importantly, the nuclear CDK2 activity which is crucial for G₁/S transition is not altered by p27^Kip1 increase. Using siRNA techniques, we revealed that p27^Kip1 inhibition does not affect the distribution of BD1-9 cells in the different phases of the cell cycle. Our study demonstrates that aberrant cyclin D1 expression acts as a p27^Kip1 trap in B lymphocytes but does not induce p27^Kip1 relocation from the nucleus to the cytoplasm and does not modulate the G₁/S transition. Since our cellular model mimics what observed in aggressive lymphomas, our data bring new insights into the understanding of their physiopathology.     

Changes in electron transport pathways in endoplasmic reticulum of rapeseed in response to cold

We studied changes induced by cold on electron transfer pathways (linked to NADH or NADPH oxidation) in endoplasmic reticulum of rapeseed hypocotyls (Brassica napus L.) from a freezing-sensitive variety (ISL) and freezing-tolerant variety (Tradition). Plantlets were grown at 22 degrees C then submitted to a cold shock of 13 or 35 days at 4 degrees C. We measured the content in NADH, NADPH, NAD and NADP of the hypocotyls and the redox power was estimated by the reduced versus oxidized nucleotide ratio. The contents in cytochromes b (5) and P-450, electron acceptors of NADH and NADPH respectively, were determined by differential spectrophotometry. Finally, activity of both NADH-cytochrome b (5) reductase (E.C.1.6.2.2) and NADPH cytochrome P-450 reductase (E.C.1.6.2.4) was determined by reduction of exogenous cytochrome c. Results show that during cold shock, along with an increase of linolenic acid content, there was a general activation of the NADPH pathway which was observed more quickly in Tradition plantlets than in ISL ones. Due to transfer of electrons that can occur between NADPH reductase and cytochrome b (5), this could favor fatty acid desaturation in Tradition, explaining why linolenic acid accumulation was more pronounced in this variety. Besides, more cytochrome P-450 accumulated in ISL that could compete for electrons needed by the FAD3 desaturase, resulting in a relative slower enrichment in 18:3 fatty acid in these plantlets.     

Association of Eosinophilic Inflammation with FKBP51 Expression in Sputum Cells in Asthma

Background

Airway eosinophilia is a predictor of steroid responsiveness in steroid-naïve asthma. However, the relationship between airway eosinophilia and the expression of FK506-binding protein 51 (FKBP51), a glucocorticoid receptor co-chaperone that plays a role in steroid insensitivity in asthma, remains unknown.

Objective

To evaluate the relationship between eosinophilic inflammation and FKBP51 expression in sputum cells in asthma.

Methods

The FKBP51 mRNA levels in sputum cells from steroid-naïve patients with asthma (n = 31) and stable asthmatic patients on inhaled corticosteroid (ICS) (n = 28) were cross-sectionally examined using real-time PCR. Associations between FKBP51 levels and clinical indices were analyzed.

Results

In steroid-naïve patients, the FKBP51 levels were negatively correlated with eosinophil proportions in blood (r = −0.52) and sputum (r = −0.57), and exhaled nitric oxide levels (r = −0.42) (all p<0.05). No such associations were observed in patients on ICS. In steroid-naïve patients, improvement in forced expiratory volume in one second after ICS initiation was correlated with baseline eosinophil proportions in blood (r = 0.74) and sputum (r = 0.76) and negatively correlated with FKBP51 levels (r = −0.73) (all p<0.0001) (n = 20). Lastly, the FKBP51 levels were the lowest in steroid-naïve asthmatic patients, followed by mild to moderate persistent asthmatic patients on ICS, and the highest in severe persistent asthmatic patients on ICS (p<0.0001).

Conclusions

Lower FKBP51 expression in sputum cells may reflect eosinophilic inflammation and glucocorticoid responsiveness in steroid-naïve asthmatic patients.     

Methods to Develop an Electronic Medical Record Phenotype Algorithm to Compare the Risk of Coronary Artery Disease across 3 Chronic Disease Cohorts

Background

Typically, algorithms to classify phenotypes using electronic medical record (EMR) data were developed to perform well in a specific patient population. There is increasing interest in analyses which can allow study of a specific outcome across different diseases. Such a study in the EMR would require an algorithm that can be applied across different patient populations. Our objectives were: (1) to develop an algorithm that would enable the study of coronary artery disease (CAD) across diverse patient populations; (2) to study the impact of adding narrative data extracted using natural language processing (NLP) in the algorithm. Additionally, we demonstrate how to implement CAD algorithm to compare risk across 3 chronic diseases in a preliminary study.

Methods and Results

We studied 3 established EMR based patient cohorts: diabetes mellitus (DM, n = 65,099), inflammatory bowel disease (IBD, n = 10,974), and rheumatoid arthritis (RA, n = 4,453) from two large academic centers. We developed a CAD algorithm using NLP in addition to structured data (e.g. ICD9 codes) in the RA cohort and validated it in the DM and IBD cohorts. The CAD algorithm using NLP in addition to structured data achieved specificity >95% with a positive predictive value (PPV) 90% in the training (RA) and validation sets (IBD and DM). The addition of NLP data improved the sensitivity for all cohorts, classifying an additional 17% of CAD subjects in IBD and 10% in DM while maintaining PPV of 90%. The algorithm classified 16,488 DM (26.1%), 457 IBD (4.2%), and 245 RA (5.0%) with CAD. In a cross-sectional analysis, CAD risk was 63% lower in RA and 68% lower in IBD compared to DM (p<0.0001) after adjusting for traditional cardiovascular risk factors.

Conclusions

We developed and validated a CAD algorithm that performed well across diverse patient populations. The addition of NLP into the CAD algorithm improved the sensitivity of the algorithm, particularly in cohorts where the prevalence of CAD was low. Preliminary data suggest that CAD risk was significantly lower in RA and IBD compared to DM.     

Automatic Prediction of Rheumatoid Arthritis Disease Activity from the Electronic Medical Records

Objective

We aimed to mine the data in the Electronic Medical Record to automatically discover patients'' Rheumatoid Arthritis disease activity at discrete rheumatology clinic visits. We cast the problem as a document classification task where the feature space includes concepts from the clinical narrative and lab values as stored in the Electronic Medical Record.

Materials and Methods

The Training Set consisted of 2792 clinical notes and associated lab values. Test Set 1 included 1749 clinical notes and associated lab values. Test Set 2 included 344 clinical notes for which there were no associated lab values. The Apache clinical Text Analysis and Knowledge Extraction System was used to analyze the text and transform it into informative features to be combined with relevant lab values.

Results

Experiments over a range of machine learning algorithms and features were conducted. The best performing combination was linear kernel Support Vector Machines with Unified Medical Language System Concept Unique Identifier features with feature selection and lab values. The Area Under the Receiver Operating Characteristic Curve (AUC) is 0.831 (σ = 0.0317), statistically significant as compared to two baselines (AUC = 0.758, σ = 0.0291). Algorithms demonstrated superior performance on cases clinically defined as extreme categories of disease activity (Remission and High) compared to those defined as intermediate categories (Moderate and Low) and included laboratory data on inflammatory markers.

Conclusion

Automatic Rheumatoid Arthritis disease activity discovery from Electronic Medical Record data is a learnable task approximating human performance. As a result, this approach might have several research applications, such as the identification of patients for genome-wide pharmacogenetic studies that require large sample sizes with precise definitions of disease activity and response to therapies.     

Modeling Disease Severity in Multiple Sclerosis Using Electronic Health Records

Objective

To optimally leverage the scalability and unique features of the electronic health records (EHR) for research that would ultimately improve patient care, we need to accurately identify patients and extract clinically meaningful measures. Using multiple sclerosis (MS) as a proof of principle, we showcased how to leverage routinely collected EHR data to identify patients with a complex neurological disorder and derive an important surrogate measure of disease severity heretofore only available in research settings.

Methods

In a cross-sectional observational study, 5,495 MS patients were identified from the EHR systems of two major referral hospitals using an algorithm that includes codified and narrative information extracted using natural language processing. In the subset of patients who receive neurological care at a MS Center where disease measures have been collected, we used routinely collected EHR data to extract two aggregate indicators of MS severity of clinical relevance multiple sclerosis severity score (MSSS) and brain parenchymal fraction (BPF, a measure of whole brain volume).

Results

The EHR algorithm that identifies MS patients has an area under the curve of 0.958, 83% sensitivity, 92% positive predictive value, and 89% negative predictive value when a 95% specificity threshold is used. The correlation between EHR-derived and true MSSS has a mean R² = 0.38±0.05, and that between EHR-derived and true BPF has a mean R² = 0.22±0.08. To illustrate its clinical relevance, derived MSSS captures the expected difference in disease severity between relapsing-remitting and progressive MS patients after adjusting for sex, age of symptom onset and disease duration (p = 1.56×10⁻¹²).

Conclusion

Incorporation of sophisticated codified and narrative EHR data accurately identifies MS patients and provides estimation of a well-accepted indicator of MS severity that is widely used in research settings but not part of the routine medical records. Similar approaches could be applied to other complex neurological disorders.