Robert R. Kitchen

Comparative analysis of the transcriptome across distant species.

The transcriptome is the readout of the genome. Identifying common features in it across distant species can reveal fundamental principles. To this end, the ENCODE and modENCODE consortia have generated large amounts of matched RNA-sequencing data for human, worm and fly. Uniform processing and comprehensive annotation of these data allow comparison across metazoan phyla, extending beyond earlier within-phylum transcriptome comparisons and revealing ancient, conserved features. Specifically, we discover co-expression modules shared across animals, many of which are enriched in developmental genes. Moreover, we use expression patterns to align the stages in worm and fly development and find a novel pairing between worm embryo and fly pupae, in addition to the embryo-to-embryo and larvae-to-larvae pairings. Furthermore, we find that the extent of non-canonical, non-coding transcription is similar in each organism, per base pair. Finally, we find in all three organisms that the gene-expression levels, both coding and non-coding, can be quantitatively predicted from chromatin features at the promoter using a ‘universal model’ based on a single set of organism-independent parameters.

The PsychENCODE project

Recent research on disparate psychiatric disorders has implicated rare variants in genes involved in global gene regulation and chromatin modification, as well as many common variants located primarily in regulatory regions of the genome. Understanding precisely how these variants contribute to disease will require a deeper appreciation for the mechanisms of gene regulation in the developing and adult human brain. The PsychENCODE project aims to produce a public resource of multidimensional genomic data using tissue- and cell type–specific samples from approximately 1,000 phenotypically well-characterized, high-quality healthy and disease-affected human post-mortem brains, as well as functionally characterize disease-associated regulatory elements and variants in model systems. We are beginning with a focus on autism spectrum disorder, bipolar disorder and schizophrenia, and expect that this knowledge will apply to a wide variety of psychiatric disorders. This paper outlines the motivation and design of PsychENCODE.

Tracking Distinct RNA Populations Using Efficient and Reversible Covalent Chemistry.

We describe a chemical method to label and purify 4-thiouridine (s(4)U)-containing RNA. We demonstrate that methanethiosulfonate (MTS) reagents form disulfide bonds with s(4)U more efficiently than the commonly used HPDP-biotin, leading to higher yields and less biased enrichment. This increase in efficiency allowed us to use s(4)U labeling to study global microRNA (miRNA) turnover in proliferating cultured human cells without perturbing global miRNA levels or the miRNA processing machinery. This improved chemistry will enhance methods that depend on tracking different populations of RNA, such as 4-thiouridine tagging to study tissue-specific transcription and dynamic transcriptome analysis (DTA) to study RNA turnover.

Diverse human extracellular RNAs are widely detected in human plasma

There is growing appreciation for the importance of non-protein-coding genes in development and disease. Although much is known about microRNAs, limitations in bioinformatic analyses of RNA sequencing have precluded broad assessment of other forms of small-RNAs in humans. By analysing sequencing data from plasma-derived RNA from 40 individuals, here we identified over a thousand human extracellular RNAs including microRNAs, piwi-interacting RNA (piRNA), and small nucleolar RNAs. Using a targeted quantitative PCR with reverse transcription approach in an additional 2,763 individuals, we characterized almost 500 of the most abundant extracellular transcripts including microRNAs, piRNAs and small nucleolar RNAs. The presence in plasma of many non-microRNA small-RNAs was confirmed in an independent cohort. We present comprehensive data to demonstrate the broad and consistent detection of diverse classes of circulating non-cellular small-RNAs from a large population.

Quality control for quantitative PCR based on amplification compatibility test.

Quantitative qPCR is a routinely used method for the accurate quantification of nucleic acids. Yet it may generate erroneous results if the amplification process is obscured by inhibition or generation of aberrant side-products such as primer dimers. Several methods have been established to control for pre-processing performance that rely on the introduction of a co-amplified reference sequence, however there is currently no method to allow for reliable control of the amplification process without directly modifying the sample mix. Herein we present a statistical approach based on multivariate analysis of the amplification response data generated in real-time. The amplification trajectory in its most resolved and dynamic phase is fitted with a suitable model. Two parameters of this model, related to amplification efficiency, are then used for calculation of the Z-score statistics. Each studied sample is compared to a predefined reference set of reactions, typically calibration reactions. A probabilistic decision for each individual Z-score is then used to identify the majority of inhibited reactions in our experiments. We compare this approach to univariate methods using only the sample specific amplification efficiency as reporter of the compatibility. We demonstrate improved identification performance using the multivariate approach compared to the univariate approach. Finally we stress that the performance of the amplification compatibility test as a quality control procedure depends on the quality of the reference set.

Maternal separation with early weaning: A rodent model providing novel insights into neglect associated developmental deficits

Child neglect is the most prevalent form of child maltreatment in the United States, and poses a serious public health concern. Children who survive such episodes go on to experience long-lasting psychological and behavioral problems, including higher rates of post-traumatic stress disorder symptoms, depression, alcohol and drug abuse, attention-deficit/hyperactivity disorder, and cognitive deficits. To date, most research into the causes of these life-long problems has focused on well-established targets such as stress responsive systems, including the hypothalamus-pituitary-adrenal axis. Using the maternal separation and early weaning model, we have attempted to provide comprehensive molecular profiling of a model of early-life neglect in an organism amenable to genomic manipulation: the mouse. In this article, we report new findings generated with this model using chromatin immunoprecipitation sequencing, diffuse tensor magnetic resonance imaging, and behavioral analyses. We also review the validity of the maternal separation and early weaning model, which reflects behavioral deficits observed in neglected humans including hyperactivity, anxiety, and attentional deficits. Finally, we summarize the molecular characterization of these animals, including RNA profiling and label-free proteomics, which highlight protein translation and myelination as novel pathways of interest.

Functional Genomic and Proteomic Analysis Reveals Disruption of Myelin-Related Genes and Translation in a Mouse Model of Early Life Neglect

Early life neglect is an important public health problem which can lead to lasting psychological dysfunction. Good animal models are necessary to understand the mechanisms responsible for the behavioral and anatomical pathology that results. We recently described a novel model of early life neglect, maternal separation with early weaning (MSEW), that produces behavioral changes in the mouse that persist into adulthood. To begin to understand the mechanism by which MSEW leads to these changes we applied cDNA microarray, next-generation RNA-sequencing (RNA-seq), label-free proteomics, multiple reaction monitoring (MRM) proteomics, and methylation analysis to tissue samples obtained from medial prefrontal cortex to determine the molecular changes induced by MSEW that persist into adulthood. The results show that MSEW leads to dysregulation of markers of mature oligodendrocytes and genes involved in protein translation and other categories, an apparent downward biasing of translation, and methylation changes in the promoter regions of selected dysregulated genes. These findings are likely to prove useful in understanding the mechanism by which early life neglect affects brain structure, cognition, and behavior.

Differential global gene expression in cystic fibrosis nasal and bronchial epithelium

Respiratory epithelium is the target of therapies, such as gene therapy, for cystic fibrosis (CF) lung disease. To determine the usefulness of the nasal epithelium as a pre-screen for lung-directed therapies, we profiled gene expression in CF and non-CF nasal and bronchial epithelium samples using Illumina HumanRef-8 Expression BeadChips. 863 genes were differentially expressed between CF and non-CF bronchial epithelium but only 15 were differentially expressed between CF and non-CF nasal epithelium (≥1.5-fold, P≤0.05). The most enriched pathway in CF bronchial epithelium was inflammatory response, whereas in CF nasal epithelium it was amino acid metabolism. We also compared nasal and bronchial epithelium in each group and identified differential expression of cellular movement genes in CF patients and cellular growth genes in non-CF subjects. We conclude that CF and non-CF nasal and bronchial epithelium are transcriptionally distinct and CF nasal epithelium is not a good surrogate for the lung respiratory epithelium.

Correcting for intra-experiment variation in Illumina BeadChip data is necessary to generate robust gene-expression profiles

BackgroundMicroarray technology is a popular means of producing whole genome transcriptional profiles, however high cost and scarcity of mRNA has led many studies to be conducted based on the analysis of single samples. We exploit the design of the Illumina platform, specifically multiple arrays on each chip, to evaluate intra-experiment technical variation using repeated hybridisations of universal human reference RNA (UHRR) and duplicate hybridisations of primary breast tumour samples from a clinical study.ResultsA clear batch-specific bias was detected in the measured expressions of both the UHRR and clinical samples. This bias was found to persist following standard microarray normalisation techniques. However, when mean-centering or empirical Bayes batch-correction methods (ComBat) were applied to the data, inter-batch variation in the UHRR and clinical samples were greatly reduced. Correlation between replicate UHRR samples improved by two orders of magnitude following batch-correction using ComBat (ranging from 0.9833-0.9991 to 0.9997-0.9999) and increased the consistency of the gene-lists from the duplicate clinical samples, from 11.6% in quantile normalised data to 66.4% in batch-corrected data. The use of UHRR as an inter-batch calibrator provided a small additional benefit when used in conjunction with ComBat, further increasing the agreement between the two gene-lists, up to 74.1%.ConclusionIn the interests of practicalities and cost, these results suggest that single samples can generate reliable data, but only after careful compensation for technical bias in the experiment. We recommend that investigators appreciate the propensity for such variation in the design stages of a microarray experiment and that the use of suitable correction methods become routine during the statistical analysis of the data.

Decoding neuroproteomics: integrating the genome, translatome and functional anatomy

The immense intercellular and intracellular heterogeneity of the CNS presents major challenges for high-throughput omic analyses. Transcriptional, translational and post-translational regulatory events are localized to specific neuronal cell types or subcellular compartments, resulting in discrete patterns of protein expression and activity. A spatial and quantitative knowledge of the neuroproteome is therefore critical to understanding both normal and pathological aspects of the functional genomics and anatomy of the CNS. Improvements in mass spectrometry allow the profiling of proteins at a sufficient depth to complement results from high-throughput genomic and transcriptomic assays. However, there are challenges in integrating proteomic data with other data modalities and even greater challenges in obtaining comprehensive neuroproteomic data with cell-type specificity. Here we discuss how proteomics should be exploited to enhance high-throughput functional genomic analysis by tighter integration of data analyses. We also discuss experimental strategies to achieve finer cellular and subcellular resolution in transcriptomic and proteomic studies of neural tissues.