The Banff 97 working classification refines earlier schemas and represents input from two classifications most widely used in clinical rejection trials and in clinical practice worldwide. Major changes include the following: rejection with vasculitis is separated from tubulointerstitial rejection; severe rejection requires transmural changes in arteries; "borderline" rejection can only be interpreted in a clinical context; antibody-mediated rejection is further defined, and lesion scoring focuses on most severely involved structures. Criteria for specimen adequacy have also been modified. Banff 97 represents a significant refinement of allograft assessment, developed via international consensus discussions.

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A group of renal pathologists, nephrologists, and transplant surgeons met in Banff, Canada on August 2-4, 1991 to develop a schema for international standardization of nomenclature and criteria for the histologic diagnosis of renal allograft rejection. Development continued after the meeting and the schema was validated by the circulation of sets of slides for scoring by participant pathologists. In this schema intimal arteritis and tubulitis are the principal lesions indicative of acute rejection. Glomerular, interstitial, tubular, and vascular lesions of acute rejection and "chronic rejection" are defined and scored 0 to 3+, to produce an acute and/or chronic numerical coding for each biopsy. Arteriolar hyalinosis (an indication of cyclosporine toxicity) is also scored. Principal diagnostic categories, which can be used with or without the quantitative coding, are: (1) normal, (2) hyperacute rejection, (3) borderline changes, (4) acute rejection (grade I to III), (5) chronic allograft nephropathy ("chronic rejection") (grade I to III), and (6) other. The goal is to devise a schema in which a given biopsy grading would imply a prognosis for a therapeutic response or long-term function. While the clinical implications must be proven through further studies, the development of a standardized schema is a critical first step. This standardized classification should promote international uniformity in reporting of renal allograft pathology, facilitate the performance of multicenter trials of new therapies in renal transplantation, and ultimately lead to improvement in the management and care of renal transplant recipients.

High-throughput sequencing, also known as next-generation sequencing (NGS), has revolutionized genomic research. In recent years, NGS technology has steadily improved, with costs dropping and the number and range of sequencing applications increasing exponentially. Here, we examine the critical role of sequencing library quality and consider important challenges when preparing NGS libraries from DNA and RNA sources. Factors such as the quantity and physical characteristics of the RNA or DNA source material as well as the desired application (i.e., genome sequencing, targeted sequencing, RNA-seq, ChIP-seq, RIP-seq, and methylation) are addressed in the context of preparing high quality sequencing libraries. In addition, the current methods for preparing NGS libraries from single cells are also discussed.

Galectin-3 is a member of a growing family of beta-galactoside-binding animal lectins. Previous studies have demonstrated a variety of biological activities for this protein in vitro, including activation of cells, modulation of cell adhesion, induction of pre-mRNA splicing, and regulation of apoptosis. To assist in fully elucidating the physiological and pathological functions of this protein, we have generated galectin-3-deficient (gal3(-/-)) mice by targeted interruption of the galectin-3 gene. Gal3(-/-) mice consistently developed fewer inflammatory cell infiltrations in the peritoneal cavities than the wild-type (gal3(+/+)) mice in response to thioglycollate broth treatment, mainly due to lower numbers of macrophages. Also, when compared to cells from gal3(+/+) mice, thioglycollate-elicited inflammatory cells from gal3(-/-) mice exhibited significantly lower levels of NF-kappaB response. In addition, dramatically different cell-spreading phenotypes were observed in cultured macrophages from the two genotypes. Whereas macrophages from gal3(+/+) mice exhibited well spread out morphology, those from gal3(-/-) mice were often spindle-shaped. Finally, we found that peritoneal macrophages from gal3(-/-) mice were more prone to undergo apoptosis than those from gal3(+/+) mice when treated with apoptotic stimuli, suggesting that expression of galectin-3 in inflammatory cells may lead to longer cell survival, thus prolonging inflammation. These results strongly support galectin-3 as a positive regulator of inflammatory responses in the peritoneal cavity.

We have used a translational convergent functional genomics (CFG) approach to identify and prioritize genes involved in schizophrenia, by gene-level integration of genome-wide association study data with other genetic and gene expression studies in humans and animal models. Using this polyevidence scoring and pathway analyses, we identify top genes (DISC1, TCF4, MBP, MOBP, NCAM1, NRCAM, NDUFV2, RAB18, as well as ADCYAP1, BDNF, CNR1, COMT, DRD2, DTNBP1, GAD1, GRIA1, GRIN2B, HTR2A, NRG1, RELN, SNAP-25, TNIK), brain development, myelination, cell adhesion, glutamate receptor signaling, G-protein–coupled receptor signaling and cAMP-mediated signaling as key to pathophysiology and as targets for therapeutic intervention. Overall, the data are consistent with a model of disrupted connectivity in schizophrenia, resulting from the effects of neurodevelopmental environmental stress on a background of genetic vulnerability. In addition, we show how the top candidate genes identified by CFG can be used to generate a genetic risk prediction score (GRPS) to aid schizophrenia diagnostics, with predictive ability in independent cohorts. The GRPS also differentiates classic age of onset schizophrenia from early onset and late-onset disease. We also show, in three independent cohorts, two European American and one African American, increasing overlap, reproducibility and consistency of findings from single-nucleotide polymorphisms to genes, then genes prioritized by CFG, and ultimately at the level of biological pathways and mechanisms. Finally, we compared our top candidate genes for schizophrenia from this analysis with top candidate genes for bipolar disorder and anxiety disorders from previous CFG analyses conducted by us, as well as findings from the fields of autism and Alzheimer. Overall, our work maps the genomic and biological landscape for schizophrenia, providing leads towards a better understanding of illness, diagnostics and therapeutics. It also reveals the significant genetic overlap with other major psychiatric disorder domains, suggesting the need for improved nosology.

BackgroundGenomic and other high dimensional analyses often require one to summarize multiple related variables by a single representative. This task is also variously referred to as collapsing, combining, reducing, or aggregating variables. Examples include summarizing several probe measurements corresponding to a single gene, representing the expression profiles of a co-expression module by a single expression profile, and aggregating cell-type marker information to de-convolute expression data. Several standard statistical summary techniques can be used, but network methods also provide useful alternative methods to find representatives. Currently few collapsing functions are developed and widely applied.ResultsWe introduce the R function collapseRows that implements several collapsing methods and evaluate its performance in three applications. First, we study a crucial step of the meta-analysis of microarray data: the merging of independent gene expression data sets, which may have been measured on different platforms. Toward this end, we collapse multiple microarray probes for a single gene and then merge the data by gene identifier. We find that choosing the probe with the highest average expression leads to best between-study consistency. Second, we study methods for summarizing the gene expression profiles of a co-expression module. Several gene co-expression network analysis applications show that the optimal collapsing strategy depends on the analysis goal. Third, we study aggregating the information of cell type marker genes when the aim is to predict the abundance of cell types in a tissue sample based on gene expression data ("expression deconvolution"). We apply different collapsing methods to predict cell type abundances in peripheral human blood and in mixtures of blood cell lines. Interestingly, the most accurate prediction method involves choosing the most highly connected "hub" marker gene. Finally, to facilitate biological interpretation of collapsed gene lists, we introduce the function userListEnrichment, which assesses the enrichment of gene lists for known brain and blood cell type markers, and for other published biological pathways.ConclusionsThe R function collapseRows implements several standard and network-based collapsing methods. In various genomic applications we provide evidence that both types of methods are robust and biologically relevant tools.

A major challenge for kidney transplantation is balancing the need for immunosuppression to prevent rejection, while minimizing drug-induced toxicities.We used DNA microarrays (HG-U95Av2 GeneChips, Affymetrix) to determine gene expression profiles for kidney biopsies and peripheral blood lymphocytes (PBLs) in transplant patients including normal donor kidneys, well-functioning transplants without rejection, kidneys undergoing acute rejection, and transplants with renal dysfunction without rejection. We developed a data analysis schema based on expression signal determination, class comparison and prediction, hierarchical clustering, statistical power analysis and real-time quantitative PCR validation. We identified distinct gene expression signatures for both biopsies and PBLs that correlated significantly with each of the different classes of transplant patients. This is the most complete report to date using commercial arrays to identify unique expression signatures in transplant biopsies distinguishing acute rejection, acute dysfunction without rejection and well-functioning transplants with no rejection history. We demonstrate for the first time the successful application of high density DNA chip analysis of PBL as a diagnostic tool for transplantation. The significance of these results, if validated in a multicenter prospective trial, would be the establishment of a metric based on gene expression signatures for monitoring the immune status and immunosuppression of transplanted patients.