Haptoglobin enhances cardiac transplant rejection.

RATIONALE: Early graft inflammation enhances both acute and chronic rejection of heart transplants, but it is unclear how this inflammation is initiated. OBJECTIVE: To identify specific inflammatory modulators and determine their underlying molecular mechanisms after cardiac transplantation. METHODS AND RESULTS: We used a murine heterotopic cardiac transplant model to identify inflammatory modulators of early graft inflammation. Unbiased mass spectrometric analysis of cardiac tissue before and ≤72 hours after transplantation revealed that 22 proteins including haptoglobin, a known antioxidant, are significantly upregulated in our grafts. Through the use of haptoglobin-deficient mice, we show that 80% of haptoglobin-deficient recipients treated with perioperative administration of the costimulatory blocking agent CTLA4 immunoglobulin exhibited >100-day survival of full major histocompatibility complex mismatched allografts, whereas all similarly treated wild-type recipients rejected their transplants by 21 days after transplantation. We found that haptoglobin modifies the intra-allograft inflammatory milieu by enhancing levels of the inflammatory cytokine interleukin-6 and the chemokine MIP-2 (macrophage inflammatory protein 2) but impair levels of the immunosuppressive cytokine interleukin-10. Haptoglobin also enhances dendritic cell graft recruitment and augments antidonor T-cell responses. Moreover, we confirmed that the protein is present in human cardiac allograft specimens undergoing acute graft rejection. CONCLUSIONS: Our findings provide new insights into the mechanisms of inflammation after cardiac transplantation and suggest that, in contrast to its prior reported antioxidant function in vascular inflammation, haptoglobin is an enhancer of inflammation after cardiac transplantation. Haptoglobin may also be a key component in other sterile inflammatory conditions.

Differential expression analysis for paired RNA-Seq data.

BACKGROUND: RNA-Seq technology measures the transcript abundance by generating sequence reads and counting their frequencies across different biological conditions. To identify differentially expressed genes between two conditions, it is important to consider the experimental design as well as the distributional property of the data. In many RNA-Seq studies, the expression data are obtained as multiple pairs, e.g., pre- vs. post-treatment samples from the same individual. We seek to incorporate paired structure into analysis. RESULTS: We present a Bayesian hierarchical mixture model for RNA-Seq data to separately account for the variability within and between individuals from a paired data structure. The method assumes a Poisson distribution for the data mixed with a gamma distribution to account variability between pairs. The effect of differential expression is modeled by two-component mixture model. The performance of this approach is examined by simulated and real data. CONCLUSIONS: In this setting, our proposed model provides higher sensitivity than existing methods to detect differential expression. Application to real RNA-Seq data demonstrates the usefulness of this method for detecting expression alteration for genes with low average expression levels or shorter transcript length.

Bias detection and correction in RNA-Sequencing data.

BACKGROUND: High throughput sequencing technology provides us unprecedented opportunities to study transcriptome dynamics. Compared to microarray-based gene expression profiling, RNA-Seq has many advantages, such as high resolution, low background, and ability to identify novel transcripts. Moreover, for genes with multiple isoforms, expression of each isoform may be estimated from RNA-Seq data. Despite these advantages, recent work revealed that base level read counts from RNA-Seq data may not be randomly distributed and can be affected by local nucleotide composition. It was not clear though how the base level read count bias may affect gene level expression estimates. RESULTS: In this paper, by using five published RNA-Seq data sets from different biological sources and with different data preprocessing schemes, we showed that commonly used estimates of gene expression levels from RNA-Seq data, such as reads per kilobase of gene length per million reads (RPKM), are biased in terms of gene length, GC content and dinucleotide frequencies. We directly examined the biases at the gene-level, and proposed a simple generalized-additive-model based approach to correct different sources of biases simultaneously. Compared to previously proposed base level correction methods, our method reduces bias in gene-level expression estimates more effectively. CONCLUSIONS: Our method identifies and corrects different sources of biases in gene-level expression measures from RNA-Seq data, and provides more accurate estimates of gene expression levels from RNA-Seq. This method should prove useful in meta-analysis of gene expression levels using different platforms or experimental protocols.

GAMMA-BASED CLUSTERING VIA ORDERED MEANS WITH APPLICATION TO GENE-EXPRESSION ANALYSIS.

Discrete mixture models provide a well-known basis for effective clustering algorithms, although technical challenges have limited their scope. In the context of gene-expression data analysis, a model is presented that mixes over a finite catalog of structures, each one representing equality and inequality constraints among latent expected values. Computations depend on the probability that independent gamma-distributed variables attain each of their possible orderings. Each ordering event is equivalent to an event in independent negative-binomial random variables, and this finding guides a dynamic-programming calculation. The structuring of mixture-model components according to constraints among latent means leads to strict concavity of the mixture log likelihood. In addition to its beneficial numerical properties, the clustering method shows promising results in an empirical study.

Differential control of Zap1-regulated genes in response to zinc deficiency in Saccharomyces cerevisiae.

BACKGROUND: The Zap1 transcription factor is a central player in the response of yeast to changes in zinc status. We previously used transcriptome profiling with DNA microarrays to identify 46 potential Zap1 target genes in the yeast genome. In this new study, we used complementary methods to identify additional Zap1 target genes. RESULTS: With alternative growth conditions for the microarray experiments and a more sensitive motif identification algorithm, we identified 31 new potential targets of Zap1 activation. Moreover, an analysis of the response of Zap1 target genes to a range of zinc concentrations and to zinc withdrawal over time demonstrated that these genes respond differently to zinc deficiency. Some genes are induced under mild zinc deficiency and act as a first line of defense against this stress. First-line defense genes serve to maintain zinc homeostasis by increasing zinc uptake, and by mobilizing and conserving intracellular zinc pools. Other genes respond only to severe zinc limitation and act as a second line of defense. These second-line defense genes allow cells to adapt to conditions of zinc deficiency and include genes involved in maintaining secretory pathway and cell wall function, and stress responses. CONCLUSION: We have identified several new targets of Zap1-mediated regulation. Furthermore, our results indicate that through the differential regulation of its target genes, Zap1 prioritizes mechanisms of zinc homeostasis and adaptive responses to zinc deficiency.

Rgcs1, a dominant QTL that affects retinal ganglion cell death after optic nerve crush in mice.

BACKGROUND: Intrinsic apoptosis of neuronal somas is one aspect of neurodegenerative diseases that can be influenced by genetic background. Genes that affect this process may act as susceptibility alleles that contribute to the complex genetic nature of these diseases. Retinal ganglion cell death is a defining feature of the chronic and genetically complex neurodegenerative disease glaucoma. Previous studies using an optic nerve crush procedure in inbred mice, showed that ganglion cell resistance to crush was affected by the Mendelian-dominant inheritance of 1-2 predicted loci. To assess this further, we bred and phenotyped a large population of F2 mice derived from a resistant inbred strain (DBA/2J) and a susceptible strain (BALB/cByJ). RESULTS: Genome wide mapping of the F2 mice using microsatellite markers, detected a single highly significant quantitative trait locus in a 25 cM (58 Mb) interval on chromosome 5 (Chr5.loc34-59 cM). No interacting loci were detected at the resolution of this screen. We have designated this locus as Retinal ganglion cell susceptible 1, Rgcs1. In silico analysis of this region revealed the presence of 578 genes or expressed sequence tags, 4 of which are highly expressed in the ganglion cell layer of the mammalian retina, and 2 of which are suspected susceptibility alleles in chronic neurodegenerative diseases. In addition, 25 genes contain 36 known single nucleotide polymorphisms that create nonsynonymous amino acid changes between the two parental strains. Collectively, this analysis has identified 7 potential candidate genes that may affect ganglion cell death. CONCLUSION: The process of ganglion cell death is likely one of the many facets of glaucoma susceptibility. A novel dominant locus has been identified that affects sensitivity of ganglion cells to optic nerve crush. The allele responsible for this sensitivity may also be a susceptibility allele for glaucoma.

Data Pre-Processing for Label-Free Multiple Reaction Monitoring (MRM) Experiments.

Multiple Reaction Monitoring (MRM) conducted on a triple quadrupole mass spectrometer allows researchers to quantify the expression levels of a set of target proteins. Each protein is often characterized by several unique peptides that can be detected by monitoring predetermined fragment ions, called transitions, for each peptide. Concatenating large numbers of MRM transitions into a single assay enables simultaneous quantification of hundreds of peptides and proteins. In recognition of the important role that MRM can play in hypothesis-driven research and its increasing impact on clinical proteomics, targeted proteomics such as MRM was recently selected as the Nature Method of the Year. However, there are many challenges in MRM applications, especially data pre­processing where many steps still rely on manual inspection of each observation in practice. In this paper, we discuss an analysis pipeline to automate MRM data pre­processing. This pipeline includes data quality assessment across replicated samples, outlier detection, identification of inaccurate transitions, and data normalization. We demonstrate the utility of our pipeline through its applications to several real MRM data sets.