Amit Kaushal

A Transcriptome Database for Astrocytes, Neurons, and Oligodendrocytes: A New Resource for Understanding Brain Development and Function

Understanding the cell–cell interactions that control CNS development and function has long been limited by the lack of methods to cleanly separate neural cell types. Here we describe methods for the prospective isolation and purification of astrocytes, neurons, and oligodendrocytes from developing and mature mouse forebrain. We used FACS (fluorescent-activated cell sorting) to isolate astrocytes from transgenic mice that express enhanced green fluorescent protein (EGFP) under the control of an S100β promoter. Using Affymetrix GeneChip Arrays, we then created a transcriptome database of the expression levels of >20,000 genes by gene profiling these three main CNS neural cell types at various postnatal ages between postnatal day 1 (P1) and P30. This database provides a detailed global characterization and comparison of the genes expressed by acutely isolated astrocytes, neurons, and oligodendrocytes. We found that Aldh1L1 is a highly specific antigenic marker for astrocytes with a substantially broader pattern of astrocyte expression than the traditional astrocyte marker GFAP. Astrocytes were enriched in specific metabolic and lipid synthetic pathways, as well as the draper/Megf10 and Mertk/integrin αvβ5 phagocytic pathways suggesting that astrocytes are professional phagocytes. Our findings call into question the concept of a “glial” cell class as the gene profiles of astrocytes and oligodendrocytes are as dissimilar to each other as they are to neurons. This transcriptome database of acutely isolated purified astrocytes, neurons, and oligodendrocytes provides a resource to the neuroscience community by providing improved cell-type-specific markers and for better understanding of neural development, function, and disease.

The Mouse Blood-Brain Barrier Transcriptome: A New Resource for Understanding the Development and Function of Brain Endothelial Cells

The blood-brain barrier (BBB) maintains brain homeostasis and limits the entry of toxins and pathogens into the brain. Despite its importance, little is known about the molecular mechanisms regulating the development and function of this crucial barrier. In this study we have developed methods to highly purify and gene profile endothelial cells from different tissues, and by comparing the transcriptional profile of brain endothelial cells with those purified from the liver and lung, we have generated a comprehensive resource of transcripts that are enriched in the BBB forming endothelial cells of the brain. Through this comparison we have identified novel tight junction proteins, transporters, metabolic enzymes, signaling components, and unknown transcripts whose expression is enriched in central nervous system (CNS) endothelial cells. This analysis has identified that RXRalpha signaling cascade is specifically enriched at the BBB, implicating this pathway in regulating this vital barrier. This dataset provides a resource for understanding CNS endothelial cells and their interaction with neural and hematogenous cells.

High Dynamic Range Characterization of the Trauma Patient Plasma Proteome

Although human plasma represents an attractive sample for disease biomarker discovery, the extreme complexity and large dynamic range in protein concentrations present significant challenges for characterization, candidate biomarker discovery, and validation. Herein we describe a strategy that combines immunoaffinity subtraction and subsequent chemical fractionation based on cysteinyl peptide and N-glycopeptide captures with two-dimensional LC-MS/MS to increase the dynamic range of analysis for plasma. Application of this “divide-and-conquer” strategy to trauma patient plasma significantly improved the overall dynamic range of detection and resulted in confident identification of 22,267 unique peptides from four different peptide populations (cysteinyl peptides, non-cysteinyl peptides, N-glycopeptides, and non-glycopeptides) that covered 3654 different proteins with 1494 proteins identified by multiple peptides. Numerous low abundance proteins were identified, exemplified by 78 “classic” cytokines and cytokine receptors and by 136 human cell differentiation molecules. Additionally a total of 2910 different N-glycopeptides that correspond to 662 N-glycoproteins and 1553 N-glycosylation sites were identified. A panel of the proteins identified in this study is known to be involved in inflammation and immune responses. This study established an extensive reference protein database for trauma patients that provides a foundation for future high throughput quantitative plasma proteomic studies designed to elucidate the mechanisms that underlie systemic inflammatory responses.

Shotgun Proteomics Identifies Proteins Specific for Acute Renal Transplant Rejection

Purpose: Acute rejection (AR) remains the primary risk factor for renal transplant outcome; development of non‐invasive diagnostic biomarkers for AR is an unmet need.

Determination of burn patient outcome by large-scale quantitative discovery proteomics.

Objectives:Emerging proteomics techniques can be used to establish proteomic outcome signatures and to identify candidate biomarkers for survival following traumatic injury. We applied high-resolution liquid chromatography-mass spectrometry and multiplex cytokine analysis to profile the plasma proteome of survivors and nonsurvivors of massive burn injury to determine the proteomic survival signature following a major burn injury. Design:Proteomic discovery study. Setting:Five burn hospitals across the United States. Patients:Thirty-two burn patients (16 nonsurvivors and 16 survivors), 19–89 years old, were admitted within 96 hours of injury to the participating hospitals with burns covering more than 20% of the total body surface area and required at least one surgical intervention. Interventions:None. Measurements and Main Results:We found differences in circulating levels of 43 proteins involved in the acute-phase response, hepatic signaling, the complement cascade, inflammation, and insulin resistance. Thirty-two of the proteins identified were not previously known to play a role in the response to burn. Interleukin-4, interleukin-8, granulocyte macrophage colony-stimulating factor, monocyte chemotactic protein-1, and &bgr;2-microglobulin correlated well with survival and may serve as clinical biomarkers. Conclusions:These results demonstrate the utility of these techniques for establishing proteomic survival signatures and for use as a discovery tool to identify candidate biomarkers for survival. This is the first clinical application of a high-throughput, large-scale liquid chromatography-mass spectrometry-based quantitative plasma proteomic approach for biomarker discovery for the prediction of patient outcome following burn, trauma, or critical illness.

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships

BackgroundThe large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions.ResultsIn this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification.ConclusionHigh quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data.

Trauma‐associated human neutrophil alterations revealed by comparative proteomics profiling

Polymorphonuclear neutrophils (PMNs) play an important role in mediating the innate immune response after severe traumatic injury; however, the cellular proteome response to traumatic condition is still largely unknown.

Use of the Kidney Failure Risk Equation to Reduce Uncertainty in Predicting Time to ESRD

Today there are approximately 23.5 million individuals in the United States with chronic kidney disease (CKD), as defined by estimated glomerular filtration rate (eGFR) , 60 mL/min/1.73 m or persistent albumin-creatinine ratio . 30 mg/g. It has been demonstrated that CKD is associated with significant sequelae including progression to end-stage renal disease (ESRD), as well as increased cardiovascular morbidity and all-cause mortality. For patients progressing to ESRD, adequate preparation time is required to provide education, consider renal replacement modality options, and plan for the initiation of therapy. The optimal time to begin preparation is uncertain for a number of reasons: estimates of the rate of kidney disease progression based on eGFR slope may be imprecise for individual patients; eGFR decline may be nonlinear and episodes of acute kidney injury or cardiovascular events may unpredictably shorten the time to ESRD. Unforeseen delays also may result from the patient’s initial reluctance to begin renal replacement planning, despite professional advice. Furthermore, when such planning has begun, the time required for ESRD preparation cannot be known a priori: there may be a variable time required to establish a functional arteriovenous access or complete medical preparations in cases in which preemptive transplantation is being considered. Because of these uncertainties, patients with CKD may progress to ESRD without appropriate preparation. Unanticipated progression to ESRD results in missed opportunities for medical optimization or preemptive transplantation and can lead to initiation of dialysis therapy in emergent circumstances. An accurate and precise tool that determines which patients with CKD are likely to progress to ESRD potentially could allow patient-specific discussions regarding prognosis and a more personalized approach to planning renal replacement strategies. System-level benefits also may result from better ESRD risk prediction. Planning for dialysis and transplantation uses a significant amount of resources; unfortunately, preparing for ESRD may be futile for some patients if they die of a competing event prior to reaching ESRD. Thus, appropriately directing resources is important; it can result in timely care plans for many patients, avoid possibly futile or unnecessary interventions in others,

Inference for longitudinal data with nonignorable nonmonotone missing responses

For the analysis of longitudinal data with nonignorable and nonmonotone missing responses, a full likelihood method often requires intensive computation, especially when there are many follow-up times. The authors propose and explore a Monte Carlo method, based on importance sampling, for approximating the maximum likelihood estimators. The finite-sample properties of the proposed estimators are studied using simulations. An application of the proposed method is also provided using longitudinal data on peptide intensities obtained from a proteomics experiment of trauma patients.

Vancomycin-Associated Acute Kidney Injury with a Steep Rise in Serum Creatinine

Background: Vancomycin-associated (VA) acute kidney injury (AKI) is being increasingly recognized. A distinct pattern of rapid rise in serum creatinine (sCr) during VA-AKI has occasionally been observed. However, such scenarios remain underreported. Methods: We conducted an online survey at the American Society of Nephrology Communities forum and reviewed publications of VA-AKI via PubMed or Google searching for cases of precipitous AKI (those with rise in sCr ≥1.5 mg/dL/day) attributable to vancomycin. Results: We identified 12 original cases compiled from 6 different hospitals and 4 published cases (n = 16; 38% women, age 43.5 ± 16 years, weight 108 ± 23 kg, body mass index 35 ± 7 kg/m2) of precipitous AKI observed shortly after large cumulative doses of VA (8.8 ± 5 g). The median steepest 24-h rise in sCr was 2.6 mg/dL (range 1.5–3.5 mg/dL) and the slope of the initial 48-h sCr rise was greater than that of a control AKI (non-VA, n = 48) group (2.03 ± 0.1 vs. 0.62 ± 0.0 mg/dL/day; p < 0.0001). The steep rise in sCr in the VA-AKI was not accompanied by anuria. Overt rhabdomyolysis was absent in all cases. Further, in 3 precipitous VA-AKI cases, simultaneous serum cystatin C values did not rise precipitously, suggesting that the reductions in glomerular filtration rate were overestimated by the sCr increase. Conclusions: VA-AKI can manifest with a precipitous rise in sCr shortly after a high cumulative dose of vancomycin. True toxic tubular injury overrepresented by the sCr rise is postulated.