Search PubMed⌕ Search

Biomedical subjects

Nathan Salomonis

Publications and source records attributed to Nathan Salomonis.

7 recordsLinked to original sources
PubMed2013

The identification of novel potential injury mechanisms and candidate biomarkers in renal allograft rejection by quantitative proteomics.

Early transplant dysfunction and failure because of immunological and nonimmunological factors still presents a significant clinical problem for transplant recipients. A critical unmet need is the noninvasive detection and prediction of immune injury such that acute injury can be reversed by proactive immunosuppression titration. In this study, we used iTRAQ -based proteomic discovery and targeted ELISA validation to discover and validate candidate urine protein biomarkers from 262 renal allograft recipients with biopsy-confirmed allograft injury. Urine samples were randomly split into a training set of 108 patients and an independent validation set of 154 patients, which comprised the clinical biopsy-confirmed phenotypes of acute rejection (AR) (n = 74), stable graft (STA) (n = 74), chronic allograft injury (CAI) (n = 58), BK virus nephritis (BKVN) (n = 38), nephrotic syndrome (NS) (n = 8), and healthy, normal control (HC) (n = 10). A total of 389 proteins were measured that displayed differential abundances across urine specimens of the injury types (p < 0.05) with a significant finding that SUMO2 (small ubiquitin-related modifier 2) was identified as a "hub" protein for graft injury irrespective of causation. Sixty-nine urine proteins had differences in abundance (p < 0.01) in AR compared with stable graft, of which 12 proteins were up-regulated in AR with a mean fold increase of 2.8. Nine urine proteins were highly specific for AR because of their significant differences (p < 0.01; fold increase >1.5) from all other transplant categories (HLA class II protein HLA-DRB1, KRT14, HIST1H4B, FGG, ACTB, FGB, FGA, KRT7, DPP4). Increased levels of three of these proteins, fibrinogen beta (FGB; p = 0.04), fibrinogen gamma (FGG; p = 0.03), and HLA DRB1 (p = 0.003) were validated by ELISA in AR using an independent sample set. The fibrinogen proteins further segregated AR from BK virus nephritis (FGB p = 0.03, FGG p = 0.02), a finding that supports the utility of monitoring these urinary proteins for the specific and sensitive noninvasive diagnosis of acute renal allograft rejection.

Acute Kidney Injury↗
PubMed2005

Gene expression patterns define key transcriptional events in cell-cycle regulation by cAMP and protein kinase A.

Although a substantial number of hormones and drugs increase cellular cAMP levels, the global impact of cAMP and its major effector mechanism, protein kinase A (PKA), on gene expression is not known. Here we show that treatment of murine wild-type S49 lymphoma cells for 24 h with 8-(4-chlorophenylthio)-cAMP (8-CPT-cAMP), a PKA-selective cAMP analog, alters the expression of approximately 4,500 of approximately 13,600 unique genes. By contrast, gene expression was unaltered in Kin- S49 cells (that lack PKA) incubated with 8-CPT-cAMP. Changes in mRNA and protein expression of several cell-cycle regulators accompanied cAMP-induced G1-phase cell-cycle arrest of wild-type S49 cells. Within 2 h, 8-CPT-cAMP altered expression of 152 genes that contain evolutionarily conserved cAMP-response elements within 5 kb of transcriptional start sites, including the circadian clock gene Per1. Thus, cAMP through its activation of PKA produces extensive transcriptional regulation in eukaryotic cells. These transcriptional networks include a primary group of cAMP-response element-containing genes and secondary networks that include the circadian clock.

Animals↗
PubMed2005

Identifying genetic networks underlying myometrial transition to labor.

BACKGROUND: Early transition to labor remains a major cause of infant mortality, yet the causes are largely unknown. Although several marker genes have been identified, little is known about the underlying global gene expression patterns and pathways that orchestrate these striking changes. RESULTS: We performed a detailed time-course study of over 9,000 genes in mouse myometrium at defined physiological states: non-pregnant, mid-gestation, late gestation, and postpartum. This dataset allowed us to identify distinct patterns of gene expression that correspond to phases of myometrial 'quiescence', 'term activation', and 'postpartum involution'. Using recently developed functional mapping tools (HOPACH (hierarchical ordered partitioning and collapsing hybrid) and GenMAPP 2.0), we have identified new potential transcriptional regulatory gene networks mediating the transition from quiescence to term activation. CONCLUSIONS: These results implicate the myometrium as an essential regulator of endocrine hormone (cortisol and progesterone synthesis) and signaling pathways (cyclic AMP and cyclic GMP stimulation) that direct quiescence via the transcriptional upregulation of both novel and previously associated regulators. With term activation, we observe the upregulation of cytoskeletal remodeling mediators (intermediate filaments), cell junctions, transcriptional regulators, and the coordinate downregulation of negative control checkpoints of smooth muscle contractile signaling. This analysis provides new evidence of multiple parallel mechanisms of uterine contractile regulation and presents new putative targets for regulating myometrial transformation and contraction.

Animals↗
PubMed2005

ANOSVA: a statistical method for detecting splice variation from expression data.

MOTIVATION: Many or most mammalian genes undergo alternative splicing, generating a variety of transcripts from a single gene. New information on splice variation is becoming available through technology for measuring expression levels of several exons or splice junctions per gene. We have developed a statistical method, ANalysis Of Splice VAriation (ANOSVA) to detect alternative splicing from expression data. Since ANOSVA requires no transcript information, it can be applied when the level of annotation is poor. When validated against spiked clone data, it generated no false positives and few false negatives. We demonstrated ANOSVA with data from a prototype mouse alternative splicing array, run against normal adult tissues, yielding a set of genes with evidence of tissue-specific splice variation. AVAILABILITY: The results are available at the supplementary information site. SUPPLEMENTARY INFORMATION: The results are available at the supplementary information site https://bioinfo.affymetrix.com/Papers/ANOSVA/

Alternative Splicing↗
PubMed2003

Time- and exercise-dependent gene regulation in human skeletal muscle.

BACKGROUND: Skeletal muscle remodeling is a critical component of an organism's response to environmental changes. Exercise causes structural changes in muscle and can induce phase shifts in circadian rhythms, fluctuations in physiology and behavior with a period of around 24 hours that are maintained by a core clock mechanism. Both exercise-induced remodeling and circadian rhythms rely on the transcriptional regulation of key genes. RESULTS: We used DNA microarrays to determine the effects of resistance exercise (RE) on gene regulation in biopsy samples of human quadriceps muscle obtained 6 and 18 hours after an acute bout of isotonic exercise with one leg. We also profiled diurnal gene regulation at the same time points (2000 and 0800 hours) in the non-exercised leg. Comparison of our results with published circadian gene profiles in mice identified 44 putative genes that were regulated in a circadian fashion. We then used quantitative PCR to validate the circadian expression of selected gene orthologs in mouse skeletal muscle. CONCLUSIONS: The coordinated regulation of the circadian clock genes Cry1, Per2, and Bmal1 6 hours after RE and diurnal genes 18 hours after RE in the exercised leg suggest that RE may directly modulate circadian rhythms in human skeletal muscle.

Animals↗
PubMed2003

MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data.

MAPPFinder is a tool that creates a global gene-expression profile across all areas of biology by integrating the annotations of the Gene Ontology (GO) Project with the free software package GenMAPP http://www.GenMAPP.org. The results are displayed in a searchable browser, allowing the user to rapidly identify GO terms with over-represented numbers of gene-expression changes. Clicking on GO terms generates GenMAPP graphical files where gene relationships can be explored, annotated, and files can be freely exchanged.

Animals↗