Proximity-Dependent Biotinylation to Elucidate the Interactome of TNK2 Nonreceptor Tyrosine Kinase.

Nonreceptor tyrosine kinases (NRTKs) represent an important class of signaling molecules driving diverse cellular pathways. Aberrant expression and hyperphosphorylation of TNK2, an NRTK, have been implicated in multiple cancers. However, the exact proteins and cellular events that mediate phenotypic changes downstream of TNK2 are unclear. Biological systems that employ proximity-dependent biotinylation methods, such as BioID, are being increasingly used to map protein-protein interactions, as they provide increased sensitivity in discovering interaction partners. In this study, we employed stable isotope labeling with amino acids in cell culture and BioID coupled to the biotinylation site identification technology (BioSITe) method that we recently developed to quantitatively explore the interactome of TNK2. By performing a controlled comparative analysis between full-length TNK2 and its truncated counterpart, we were able to not only identify site-level biotinylation of previously well-established TNK2 binders and substrates including NCK1, NCK2, CTTN, and STAT3, but also discover several novel TNK2 interacting partners. We also performed co-immunoprecipitation and immunofluorescence analysis to validate the interaction between TNK2 and CLINT1, a novel TNK2 interacting protein. Overall, this work reveals the power of the BioSITe method coupled to BioID and highlights several molecules that warrant further exploration to assess their functional significance in TNK2-mediated signaling.

Toward the human cellular microRNAome.

MicroRNAs are short RNAs that serve as regulators of gene expression and are essential components of normal development as well as modulators of disease. MicroRNAs generally act cell-autonomously, and thus their localization to specific cell types is needed to guide our understanding of microRNA activity. Current tissue-level data have caused considerable confusion, and comprehensive cell-level data do not yet exist. Here, we establish the landscape of human cell-specific microRNA expression. This project evaluated 8 billion small RNA-seq reads from 46 primary cell types, 42 cancer or immortalized cell lines, and 26 tissues. It identified both specific and ubiquitous patterns of expression that strongly correlate with adjacent superenhancer activity. Analysis of unaligned RNA reads uncovered 207 unknown minor strand (passenger) microRNAs of known microRNA loci and 495 novel putative microRNA loci. Although cancer cell lines generally recapitulated the expression patterns of matched primary cells, their isomiR sequence families exhibited increased disorder, suggesting DROSHA- and DICER1-dependent microRNA processing variability. Cell-specific patterns of microRNA expression were used to de-convolute variable cellular composition of colon and adipose tissue samples, highlighting one use of these cell-specific microRNA expression data. Characterization of cellular microRNA expression across a wide variety of cell types provides a new understanding of this critical regulatory RNA species.

Integrating transcriptomic and proteomic data for accurate assembly and annotation of genomes.

Complementing genome sequence with deep transcriptome and proteome data could enable more accurate assembly and annotation of newly sequenced genomes. Here, we provide a proof-of-concept of an integrated approach for analysis of the genome and proteome of Anopheles stephensi, which is one of the most important vectors of the malaria parasite. To achieve broad coverage of genes, we carried out transcriptome sequencing and deep proteome profiling of multiple anatomically distinct sites. Based on transcriptomic data alone, we identified and corrected 535 events of incomplete genome assembly involving 1196 scaffolds and 868 protein-coding gene models. This proteogenomic approach enabled us to add 365 genes that were missed during genome annotation and identify 917 gene correction events through discovery of 151 novel exons, 297 protein extensions, 231 exon extensions, 192 novel protein start sites, 19 novel translational frames, 28 events of joining of exons, and 76 events of joining of adjacent genes as a single gene. Incorporation of proteomic evidence allowed us to change the designation of more than 87 predicted "noncoding RNAs" to conventional mRNAs coded by protein-coding genes. Importantly, extension of the newly corrected genome assemblies and gene models to 15 other newly assembled Anopheline genomes led to the discovery of a large number of apparent discrepancies in assembly and annotation of these genomes. Our data provide a framework for how future genome sequencing efforts should incorporate transcriptomic and proteomic analysis in combination with simultaneous manual curation to achieve near complete assembly and accurate annotation of genomes.

PyQuant: A Versatile Framework for Analysis of Quantitative Mass Spectrometry Data.

Quantitative mass spectrometry data necessitates an analytical pipeline that captures the accuracy and comprehensiveness of the experiments. Currently, data analysis is often coupled to specific software packages, which restricts the analysis to a given workflow and precludes a more thorough characterization of the data by other complementary tools. To address this, we have developed PyQuant, a cross-platform mass spectrometry data quantification application that is compatible with existing frameworks and can be used as a stand-alone quantification tool. PyQuant supports most types of quantitative mass spectrometry data including SILAC, NeuCode, (15)N, (13)C, or (18)O and chemical methods such as iTRAQ or TMT and provides the option of adding custom labeling strategies. In addition, PyQuant can perform specialized analyses such as quantifying isotopically labeled samples where the label has been metabolized into other amino acids and targeted quantification of selected ions independent of spectral assignment. PyQuant is capable of quantifying search results from popular proteomic frameworks such as MaxQuant, Proteome Discoverer, and the Trans-Proteomic Pipeline in addition to several standalone search engines. We have found that PyQuant routinely quantifies a greater proportion of spectral assignments, with increases ranging from 25-45% in this study. Finally, PyQuant is capable of complementing spectral assignments between replicates to quantify ions missed because of lack of MS/MS fragmentation or that were omitted because of issues such as spectra quality or false discovery rates. This results in an increase of biologically useful data available for interpretation. In summary, PyQuant is a flexible mass spectrometry data quantification platform that is capable of interfacing with a variety of existing formats and is highly customizable, which permits easy configuration for custom analysis.

Long non-coding RNA expression in primary human monocytes.

Long non-coding RNAs (lncRNAs) have been shown to possess a wide range of functions in both cellular and developmental processes including cancers. Although some of the lncRNAs have been implicated in the regulation of the immune response, the exact function of the large majority of lncRNAs still remains unknown. In this study, we characterized the lncRNAs in human primary monocytes, an essential component of the innate immune system. We performed RNA sequencing of monocytes from four individuals and combined our data with eleven other publicly available datasets. Our analysis led to identification of ~8000 lncRNAs of which >1000 have not been previously reported in monocytes. PCR-based validation of a subset of the identified novel long intergenic noncoding RNAs (lincRNAs) revealed distinct expression patterns. Our study provides a landscape of lncRNAs in monocytes, which could facilitate future experimental studies to characterize the functions of these molecules in the innate immune system.

Annotation of the zebrafish genome through an integrated transcriptomic and proteomic analysis.

Accurate annotation of protein-coding genes is one of the primary tasks upon the completion of whole genome sequencing of any organism. In this study, we used an integrated transcriptomic and proteomic strategy to validate and improve the existing zebrafish genome annotation. We undertook high-resolution mass-spectrometry-based proteomic profiling of 10 adult organs, whole adult fish body, and two developmental stages of zebrafish (SAT line), in addition to transcriptomic profiling of six organs. More than 7,000 proteins were identified from proteomic analyses, and ∼ 69,000 high-confidence transcripts were assembled from the RNA sequencing data. Approximately 15% of the transcripts mapped to intergenic regions, the majority of which are likely long non-coding RNAs. These high-quality transcriptomic and proteomic data were used to manually reannotate the zebrafish genome. We report the identification of 157 novel protein-coding genes. In addition, our data led to modification of existing gene structures including novel exons, changes in exon coordinates, changes in frame of translation, translation in annotated UTRs, and joining of genes. Finally, we discovered four instances of genome assembly errors that were supported by both proteomic and transcriptomic data. Our study shows how an integrative analysis of the transcriptome and the proteome can extend our understanding of even well-annotated genomes.

TSLP signaling network revealed by SILAC-based phosphoproteomics.

Thymic stromal lymphopoietin (TSLP) is a cytokine that plays diverse roles in the regulation of immune responses. TSLP requires a heterodimeric receptor complex consisting of IL-7 receptor α subunit and its unique TSLP receptor (gene symbol CRLF2) to transmit signals in cells. Abnormal TSLP signaling (e.g. overexpression of TSLP or its unique receptor TSLPR) contributes to the development of a number of diseases including asthma and leukemia. However, a detailed understanding of the signaling pathways activated by TSLP remains elusive. In this study, we performed a global quantitative phosphoproteomic analysis of the TSLP signaling network using stable isotope labeling by amino acids in cell culture. By employing titanium dioxide in addition to antiphosphotyrosine antibodies as enrichment methods, we identified 4164 phosphopeptides on 1670 phosphoproteins. Using stable isotope labeling by amino acids in cell culture-based quantitation, we determined that the phosphorylation status of 226 proteins was modulated by TSLP stimulation. Our analysis identified activation of several members of the Src and Tec families of kinases including Btk, Lyn, and Tec by TSLP for the first time. In addition, we report TSLP-induced phosphorylation of protein phosphatases such as Ptpn6 (SHP-1) and Ptpn11 (Shp2), which has also not been reported previously. Co-immunoprecipitation assays showed that Shp2 binds to the adapter protein Gab2 in a TSLP-dependent manner. This is the first demonstration of an inducible protein complex in TSLP signaling. A kinase inhibitor screen revealed that pharmacological inhibition of PI-3 kinase, Jak family kinases, Src family kinases or Btk suppressed TSLP-dependent cellular proliferation making them candidate therapeutic targets in diseases resulting from aberrant TSLP signaling. Our study is the first phosphoproteomic analysis of the TSLP signaling pathway that greatly expands our understanding of TSLP signaling and provides novel therapeutic targets for TSLP/TSLPR-associated diseases in humans.

Inhibition of PKCepsilon improves glucose-stimulated insulin secretion and reduces insulin clearance.

In type 2 diabetes, pancreatic beta cells fail to secrete sufficient insulin to overcome peripheral insulin resistance. Intracellular lipid accumulation contributes to beta cell failure through poorly defined mechanisms. Here we report a role for the lipid-regulated protein kinase C isoform PKCepsilon in beta cell dysfunction. Deletion of PKCepsilon augmented insulin secretion and prevented glucose intolerance in fat-fed mice. Importantly, a PKCepsilon-inhibitory peptide improved insulin availability and glucose tolerance in db/db mice with preexisting diabetes. Functional ablation of PKCepsilon selectively enhanced insulin release ex vivo from diabetic or lipid-pretreated islets and optimized the glucose-regulated lipid partitioning that amplifies the secretory response. Independently, PKCepsilon deletion also augmented insulin availability by reducing both whole-body insulin clearance and insulin uptake by hepatocytes. Our findings implicate PKCepsilon in the etiology of beta cell dysfunction and highlight that enhancement of insulin availability, through separate effects on liver and beta cells, provides a rationale for inhibiting PKCepsilon to treat type 2 diabetes.

Is Perruchet's dissociation between eyeblink conditioned responding and outcome expectancy evidence for two learning systems?

P. Perruchet (1985b) showed a double dissociation of conditioned responses (CRs) and expectancy for an airpuff unconditioned stimulus (US) in a 50% partial reinforcement schedule in human eyeblink conditioning. In the Perruchet effect, participants show an increase in CRs and a concurrent decrease in expectancy for the airpuff across runs of reinforced trials; conversely, participants show a decrease in CRs and a concurrent increase in expectancy for the airpuff across runs of nonreinforced trials. Three eyeblink conditioning experiments investigated whether the linear trend in eyeblink CRs in the Perruchet effect is a result of changes in associative strength of the conditioned stimulus (CS), US sensitization, or learning the precise timing of the US. Experiments 1 and 2 demonstrated that the linear trend in eyeblink CRs is not the result of US sensitization. Experiment 3 showed that the linear trend in eyeblink CRs is present with both a fixed and a variable CS-US interval and so is not the result of learning the precise timing of the US. The results are difficult to reconcile with a single learning process model of associative learning in which expectancy mediates CRs.

Unbiased identification of substrates of protein tyrosine phosphatase ptp-3 in C. elegans.

The leukocyte antigen related (LAR) family of receptor-like protein tyrosine phosphatases has three members in humans - PTPRF, PTPRD and PTPRS - that have been implicated in diverse processes including embryonic development, inhibition of cell growth and axonal guidance. Mutations in the LAR family are associated with developmental defects such as cleft palate as well as various cancers including breast, neck, lung, colon and brain. Although this family of tyrosine phosphatases is important for many developmental processes, little is known of their substrates. This is partially due to functional redundancy within the LAR family, as deletion of a single gene in the LAR family does not have an appreciable phenotype, but a dual knockout is embryonically lethal in mouse models. To circumvent the inability to knockout multiple members of the LAR family in mouse models, we used a knockout of ptp-3, which is the only known ortholog of the LAR family in Caenorhabditis elegans and allows for the study of the LAR family at the organismal level. Using SILAC-based quantitative phosphoproteomics, we identified 255 putative substrates of ptp-3, which included four of the nine known annotated substrates of the LAR family. A motif analysis of the identified phosphopeptides allowed for the determination of sequences that appear to be preferentially dephosphorylated. Finally, we discovered that kinases were overrepresented in the list of identified putative substrates and tyrosine residues whose phosphorylation is known to increase kinase activity were dephosphorylated by ptp-3. These data are suggestive of ptp-3 as a potential negative regulator of several kinase families, such as the mitogen activated kinases (MAPKs), and multiple tyrosine kinases including FER, MET, and NTRK2.

PKC alpha is activated but not required during glucose-induced insulin secretion from rat pancreatic islets.

The role of protein kinase C (PKC) in glucose-stimulated insulin secretion (GSIS) is controversial. Using recombinant adenoviruses for overexpression of PKC alpha and PKC delta, in both wild-type (WT) and kinase-dead (KD) forms, we here demonstrate that activation of these two PKCs is neither necessary nor sufficient for GSIS from batch-incubated, rat pancreatic islets. In contrast, responses to the pharmacologic activator 12-O-tetradecanoylphorbol-13-acetate (TPA) were reciprocally modulated by overexpression of the PKC alpha WT or PKC alpha KD but not the corresponding PKC delta adenoviruses. The kinetics of the secretory response to glucose (monitored by perifusion) were not altered in either cultured islets overexpressing PKC alpha KD or freshly isolated islets stimulated in the presence of the conventional PKC (cPKC) inhibitor Go6976. However, the latter did inhibit the secretory response to TPA. Using phosphorylation state-specific antisera for consensus PKC phosphorylation sites, we also showed that (compared with TPA) glucose causes only a modest and transient functional activation of PKC (maximal at 2-5 min). However, glucose did promote a prolonged (15 min) phosphorylation of PKC substrates in the presence of the phosphatase inhibitor okadaic acid. Overall, the results demonstrate that glucose does stimulate PKC alpha in pancreatic islets but that this makes little overall contribution to GSIS.

Estrogen and insulin/IGF-1 cooperatively stimulate cell cycle progression in MCF-7 breast cancer cells through differential regulation of c-Myc and cyclin D1.

Estrogen and insulin/insulin-like growth factor-I (IGF-I) are major mitogens for breast epithelial cells and when co-administered, synergistically induce G(1)-S phase cell cycle progression. We investigated this cooperativity by evaluating if the key cell cycle regulators, c-Myc and cyclin D1, represent points of convergence in the action of these mitogens in MCF-7 breast cancer cells. These studies demonstrated that estrogen significantly increased both c-Myc and cyclin D1 protein, while insulin predominantly increased cyclin D1 levels. This cumulative increase in c-Myc and cyclin D1 contributes to the cooperativity of these mitogens, since ectopic expression of c-Myc or cyclin D1 cooperates with either the estrogen or insulin signaling pathways to increase cell cycle progression. Inhibition of the MAPK or PI3-kinase pathways significantly reduced c-Myc and cyclin D1 protein levels and cell cycle progression. Ectopic expression of cyclin D1 partially overcame this inhibition, while ectopic expression of c-Myc partially overcame MAPK but not PI3-kinase inhibition. Therefore, estrogen and insulin/IGF-1 differentially regulate c-Myc and cyclin D1 to cooperatively stimulate breast cancer cell proliferation.

miRge - A Multiplexed Method of Processing Small RNA-Seq Data to Determine MicroRNA Entropy.

Small RNA RNA-seq for microRNAs (miRNAs) is a rapidly developing field where opportunities still exist to create better bioinformatics tools to process these large datasets and generate new, useful analyses. We built miRge to be a fast, smart small RNA-seq solution to process samples in a highly multiplexed fashion. miRge employs a Bayesian alignment approach, whereby reads are sequentially aligned against customized mature miRNA, hairpin miRNA, noncoding RNA and mRNA sequence libraries. miRNAs are summarized at the level of raw reads in addition to reads per million (RPM). Reads for all other RNA species (tRNA, rRNA, snoRNA, mRNA) are provided, which is useful for identifying potential contaminants and optimizing small RNA purification strategies. miRge was designed to optimally identify miRNA isomiRs and employs an entropy based statistical measurement to identify differential production of isomiRs. This allowed us to identify decreasing entropy in isomiRs as stem cells mature into retinal pigment epithelial cells. Conversely, we show that pancreatic tumor miRNAs have similar entropy to matched normal pancreatic tissues. In a head-to-head comparison with other miRNA analysis tools (miRExpress 2.0, sRNAbench, omiRAs, miRDeep2, Chimira, UEA small RNA Workbench), miRge was faster (4 to 32-fold) and was among the top-two methods in maximally aligning miRNAs reads per sample. Moreover, miRge has no inherent limits to its multiplexing. miRge was capable of simultaneously analyzing 100 small RNA-Seq samples in 52 minutes, providing an integrated analysis of miRNA expression across all samples. As miRge was designed for analysis of single as well as multiple samples, miRge is an ideal tool for high and low-throughput users. miRge is freely available at http://atlas.pathology.jhu.edu/baras/miRge.html.

A multi-omic analysis of human naïve CD4+ T cells.

BACKGROUND: Cellular function and diversity are orchestrated by complex interactions of fundamental biomolecules including DNA, RNA and proteins. Technological advances in genomics, epigenomics, transcriptomics and proteomics have enabled massively parallel and unbiased measurements. Such high-throughput technologies have been extensively used to carry out broad, unbiased studies, particularly in the context of human diseases. Nevertheless, a unified analysis of the genome, epigenome, transcriptome and proteome of a single human cell type to obtain a coherent view of the complex interplay between various biomolecules has not yet been undertaken. Here, we report the first multi-omic analysis of human primary naïve CD4+ T cells isolated from a single individual. RESULTS: Integrating multi-omics datasets allowed us to investigate genome-wide methylation and its effect on mRNA/protein expression patterns, extent of RNA editing under normal physiological conditions and allele specific expression in naïve CD4+ T cells. In addition, we carried out a multi-omic comparative analysis of naïve with primary resting memory CD4+ T cells to identify molecular changes underlying T cell differentiation. This analysis provided mechanistic insights into how several molecules involved in T cell receptor signaling are regulated at the DNA, RNA and protein levels. Phosphoproteomics revealed downstream signaling events that regulate these two cellular states. Availability of multi-omics data from an identical genetic background also allowed us to employ novel proteogenomics approaches to identify individual-specific variants and putative novel protein coding regions in the human genome. CONCLUSIONS: We utilized multiple high-throughput technologies to derive a comprehensive profile of two primary human cell types, naïve CD4+ T cells and memory CD4+ T cells, from a single donor. Through vertical as well as horizontal integration of whole genome sequencing, methylation arrays, RNA-Seq, miRNA-Seq, proteomics, and phosphoproteomics, we derived an integrated and comparative map of these two closely related immune cells and identified potential molecular effectors of immune cell differentiation following antigen encounter.

Phospholipase Cdelta(1) does not mediate Ca(2+) responses in neonatal rat cardiomyocytes.

Phospholipase C (PLC) activation in neonatal rat ventricular cardiomyocytes (NRVM) generates inositol(1,4,5)trisphosphate (Ins(1,4,5)P(3)) in response to elevations in Ca(2+) or inositol(1,4)bisphosphate in response to G protein stimulation. Overexpression of PLCdelta(1) increased total [(3)H]inositol phosphate (InsP) content and elevated [(3)H]Ins(1,4,5)P(3), but failed to increase [(3)H]InsP responses to the Ca(2+) ionophore A23187. Antisense PLCdelta(1) expression reduced endogenous PLCdelta(1) content but did not decrease the A23187 response. In permeabilized NRVM, [(3)H]InsP responses to elevated Ca(2+) were not inhibited by Ins(1,4,5)P(3), even at concentrations 1000-fold greater than required for selective inhibition of PLCdelta(1). Taken together these data provide evidence that PLCdelta(1) does not mediate the InsP response to elevated Ca(2+) in NRVM.

Prediction of Gene Activity in Early B Cell Development Based on an Integrative Multi-Omics Analysis.

An increasingly common method for predicting gene activity is genome-wide chromatin immuno-precipitation of 'active' chromatin modifications followed by massively parallel sequencing (ChIP-seq). In order to understand better the relationship between developmentally regulated chromatin landscapes and regulation of early B cell development, we determined how differentially active promoter regions were able to predict relative RNA and protein levels at the pre-pro-B and pro-B stages. Herein, we describe a novel ChIP-seq quantification method (cRPKM) to identify active promoters and a multi-omics approach that compares promoter chromatin status with ongoing active transcription (GRO-seq), steady state mRNA (RNA-seq), inferred mRNA stability, and relative proteome abundance measurements (iTRAQ). We demonstrate that active chromatin modifications at promoters are good indicators of transcription and steady state mRNA levels. Moreover, we found that promoters with active chromatin modifications exclusively in one of these cell states frequently predicted the differential abundance of proteins. However, we found that many genes whose promoters have non-differential but active chromatin modifications also displayed changes in abundance of their cognate proteins. As expected, this large class of developmentally and differentially regulated proteins that was uncoupled from chromatin status used mostly post-transcriptional mechanisms. Strikingly, the most differentially abundant protein in our B-cell development system, 2410004B18Rik, was regulated by a post-transcriptional mechanism, which further analyses indicated was mediated by a micro-RNA. These data highlight how this integrated multi-omics data set can be a useful resource in uncovering regulatory mechanisms. This data can be accessed at: https://usegalaxy.org/u/thereddylab/p/prediction-of-gene-activity-based-on-an-integrative-multi-omics-analysis.

Competition between an avoidance response and a safety signal: evidence for a single learning system.

Two experiments examined competition between an instrumental avoidance response and a Pavlovian safety signal for association with omission of electric shock in a human fear conditioning paradigm. Self-reported shock expectancies and skin conductance responses were consistent with blocking of learning of the instrumental contingency by prior training of the Pavlovian contingency, and vice versa. The results support the idea that a common learning mechanism underlies both Pavlovian and instrumental conditioning. The expectancy data suggest that this learning mechanism is cognitive in nature, and that Pavlovian and instrumental learning involve external and internal attributions, respectively. The procedure may thus serve as a laboratory model for attributional processes involved in the acquisition of threat expectancies in anxiety and anxiety disorders.

Glucose homeostasis in mice is transglutaminase 2 independent.

Transglutaminase type 2 (TG2) has been reported to be a candidate gene for maturity onset diabetes of the young (MODY) because three different mutations that impair TG2 transamidase activity have been found in 3 families with MODY. TG2 null (TG2(-/-)) mice have been reported to be glucose intolerant and have impaired glucose-stimulated insulin secretion (GSIS). Here we rigorously evaluated the role of TG2 in glucose metabolism using independently generated murine models of genetic TG2 disruption, which show no compensatory enhanced expression of other TGs in pancreatic islets or other tissues. First, we subjected chow- or fat-fed congenic SV129 or C57BL/6 wild type (WT) and TG2(-/-) littermates, to oral glucose gavage. Blood glucose and serum insulin levels were similar for both genotypes. Pancreatic islets isolated from these animals and analysed in vitro for GSIS and cholinergic potentiation of GSIS, showed no significant difference between genotypes. Results from intraperitoneal glucose tolerance tests (GTTs) and insulin tolerance tests (ITTs) were similar for both genotypes. Second, we directly investigated the role of TG2 transamidase activity in insulin secretion using a coisogenic model that expresses a mutant form of TG2 (TG2(R579A)), which is constitutively active for transamidase activity. Intraperitoneal GTTs and ITTs revealed no significant differences between WT and TG2(R579A/R579A) mice. Given that neither deletion nor constitutive activation of TG2 transamidase activity altered basal responses, or responses to a glucose or insulin challenge, our data indicate that glucose homeostasis in mice is TG2 independent, and question a link between TG2 and diabetes.

Both trace and delay conditioned eyeblink responding can be dissociated from outcome expectancy.

Squire and colleagues have proposed that trace and delay eyeblink conditioning are fundamentally different kinds of learning: trace conditioning requires acquisition of a conscious declarative memory for the stimulus contingencies whereas delay conditioning does not. Declarative memory in trace conditioning is thought to generate conditioned responding through the activation of a conscious expectancy for when the unconditioned stimulus (US) is going to occur. Perruchet (1985) has previously shown that in a 50% partial reinforcement design it is possible to dissociate single cue delay eyeblink conditioning from conscious expectancy for the US by examining performance over runs of reinforced and nonreinforced trials. Clark, Manns, and Squire (2001) claim that this dissociation does not occur in trace eyeblink conditioning. In the present experiment we examined the Perruchet effect for short, moderate, and long trace intervals (600, 1000, and 1400 ms) and for the equivalent interstimulus intervals (ISIs) in a delay conditioning procedure. We found evidence for a dissociation of eyeblink CRs and US expectancy over runs regardless of whether there was a delay or a trace arrangement of cues. The reasons for the Perruchet effect are still unclear, but the present data suggest that it does not depend on a separate nondeclarative system of the type proposed by Squire and colleagues.

Awareness is necessary for differential trace and delay eyeblink conditioning in humans.

Squire et al. have proposed that trace and delay eyeblink conditioning procedures engage separate learning systems: a declarative hippocampal/cortical system associated with conscious contingency awareness, and a reflexive sub-cortical system independent of awareness, respectively (Clark and Squire, 1998; Smith et al., 2005). The only difference between these two procedures is that the conditioned stimulus (CS) and the unconditioned stimulus (US) overlap in delay conditioning, whereas there is a brief interval (e.g., 1s) between them in trace conditioning. In two experiments using the same procedure as Clark and Squire's group, we observed differential conditioning only in participants who showed contingency awareness in a post-experimental questionnaire, with both trace and delay procedures. We interpret these results to suggest that, although there may be multiple brain regions involved in learning, these regions are organized as a coordinated system rather than as separate, independent systems.