Revealing Unknown Benefits of Existing Medications to Aid the Discovery of New Treatments for Post-Traumatic Stress Disorder.

Objective: To systematically identify novel pharmacological strategies for preventing or treating post-traumatic stress disorder (PTSD) by leveraging large-scale analysis of real-world observational data. Methods: Using a self-controlled study design, the association between 1399 medications and the incidence of PTSD across four US insurance claims databases covering commercially insured, Medicare eligible, and Medicaid patients was examined. A validated algorithm for identifying PTSD in claims data was used, and medications were identified by their RxNorm ingredient. Medications used to treat PTSD or its symptoms (e.g., antidepressants, antipsychotics) were excluded. Medications associated with ≥30% reduction in risk of PTSD in ≥2 databases were identified. Results: A total of 137,182,179 individuals were included in the analysis. Fifteen medications met the threshold criteria for a potential protective effect on PTSD; six were categorized as "primary signals" while the remaining nine were considered "potential signals". The primary signals include a beta blocker that has been previously studied for PTSD, and five medications used to treat attention-deficit/hyperactivity disorder. The potential signals include four medications used to treat substance use disorders and five medications used to treat sleep disorders. Discussion: The medications identified in this analysis provide targets for further research in studies that are designed to examine specific hypotheses regarding these medications and the incidence of PTSD. This work may aid in discovering novel therapeutic approaches to treat PTSD, wherein new and effective treatments are badly needed.

Immune targets for therapeutic development in depression: towards precision medicine.

Over the past two decades, compelling evidence has emerged indicating that immune mechanisms can contribute to the pathogenesis of major depressive disorder (MDD) and that drugs with primary immune targets can improve depressive symptoms. Patients with MDD are heterogeneous with respect to symptoms, treatment responses and biological correlates. Defining a narrower patient group based on biology could increase the treatment response rates in certain subgroups: a major advance in clinical psychiatry. For example, patients with MDD and elevated pro-inflammatory biomarkers are less likely to respond to conventional antidepressant drugs, but novel immune-based therapeutics could potentially address their unmet clinical needs. This article outlines a framework for developing drugs targeting a novel patient subtype within MDD and reviews the current state of neuroimmune drug development for mood disorders. We discuss evidence for a causal role of immune mechanisms in the pathogenesis of depression, together with targets under investigation in randomized controlled trials, biomarker evidence elucidating the link to neural mechanisms, biological and phenotypic patient selection strategies, and the unmet clinical need among patients with MDD.

Application of Real-World Data and the REWARD Framework to Detect Unknown Benefits of Memantine and Identify Potential Disease Targets for New NMDA Receptor Antagonists.

BACKGROUND: Observational data may inform novel drug development programs by identifying previously unappreciated, clinical benefits of existing drugs. Several preclinical and clinical studies have suggested emergent therapeutic utility of drugs acting on the N-methyl-D-aspartate (NMDA) receptor, a subtype of glutamate receptors, including the antidementia drug memantine. METHODS: Using a self-controlled cohort study design, the association of exposure to the NMDA receptor antagonist memantine with the incidence of all observed disease outcomes in four US administrative claims databases, spanning from January 2000 through January 2019, was assessed. The databases used in this study were the IBM MarketScan® Commercial Database (CCAE), the IBM MarketScan® Multi-State Medicaid Database (MDCD), the IBM MarketScan® Medicare Supplemental Database (MDCR), and the Optum©â€¯De-Identified Clinformatics® Data Mart Database. Outcomes were defined according to the unique Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) classification system codes and required a diagnosis on two or more distinct dates. Of 20,953 outcomes assessed, only those for which memantine was associated with a ≥ 50% reduction in risk in two or more databases were included. A meta-analysis with random effects was used to pool data across the databases. RESULTS: Overall, 312,336 patients were exposed to memantine during the study. After removing conditions related to dementia and memory loss, 60 outcomes met the threshold criteria. Results fell into five disease categories: mental disorders, substance use disorders, pain, gastrointestinal and colon disorders, and demyelinating disease. The bulk of findings fell into the first two groups, with 28 outcomes related to mental disorders and 24 related to substance use disorders. CONCLUSION: The present results confirm that NMDA receptor antagonism may have broader therapeutic utility than previously recognized. Further observational and clinical research may be warranted to explore the therapeutic benefit of NMDA antagonists for the outcomes found in this study.

Major Depressive Disorder Is Associated With Differential Expression of Innate Immune and Neutrophil-Related Gene Networks in Peripheral Blood: A Quantitative Review of Whole-Genome Transcriptional Data From Case-Control Studies.

BACKGROUND: Whole-genome transcription has been measured in peripheral blood samples as a candidate biomarker of inflammation associated with major depressive disorder. METHODS: We searched for all case-control studies on major depressive disorder that reported microarray or RNA sequencing measurements on whole blood or peripheral blood mononuclear cells. Primary datasets were reanalyzed, when openly accessible, to estimate case-control differences and to evaluate the functional roles of differentially expressed gene lists by technically harmonized methods. RESULTS: We found 10 eligible studies (N = 1754 depressed cases and N = 1145 healthy controls). Fifty-two genes were called significant by 2 of the primary studies (published overlap list). After harmonization of analysis across 8 accessible datasets (n = 1706 cases, n = 1098 controls), 272 genes were coincidentally listed in the top 3% most differentially expressed genes in 2 or more studies of whole blood or peripheral blood mononuclear cells with concordant direction of effect (harmonized overlap list). By meta-analysis of standardized mean difference across 4 studies of whole-blood samples (n = 1567 cases, n = 954 controls), 343 genes were found with false discovery rate <5% (standardized mean difference meta-analysis list). These 3 lists intersected significantly. Genes abnormally expressed in major depressive disorder were enriched for innate immune-related functions, coded for nonrandom protein-protein interaction networks, and coexpressed in the normative transcriptome module specialized for innate immune and neutrophil functions. CONCLUSIONS: Quantitative review of existing case-control data provided robust evidence for abnormal expression of gene networks important for the regulation and implementation of innate immune response. Further development of white blood cell transcriptional biomarkers for inflamed depression seems warranted.

Effects of immunomodulatory drugs on depressive symptoms: A mega-analysis of randomized, placebo-controlled clinical trials in inflammatory disorders.

Activation of the innate immune system is commonly associated with depression. Immunomodulatory drugs may have efficacy for depressive symptoms that are co-morbidly associated with inflammatory disorders. We report a large-scale re-analysis by standardized procedures (mega-analysis) of patient-level data combined from 18 randomized clinical trials conducted by Janssen or GlaxoSmithKline for one of nine disorders (N = 10,743 participants). Core depressive symptoms (low mood, anhedonia) were measured by the Short Form Survey (SF-36) or the Hospital Anxiety and Depression Scale (HADS), and participants were stratified into high (N = 1921) versus low-depressive strata based on baseline ratings. Placebo-controlled change from baseline after 4-16 weeks of treatment was estimated by the standardized mean difference (SMD) over all trials and for each subgroup of trials targeting one of 7 mechanisms (IL-6, TNF-α, IL-12/23, CD20, COX2, BLγS, p38/MAPK14). Patients in the high depressive stratum showed modest but significant effects on core depressive symptoms (SMD = 0.29, 95% CI [0.12-0.45]) and related SF-36 measures of mental health and vitality. Anti-IL-6 antibodies (SMD = 0.8, 95% CI [0.20-1.41]) and an anti-IL-12/23 antibody (SMD = 0.48, 95% CI [0.26-0.70]) had larger effects on depressive symptoms than other drug classes. Adjustments for physical health outcome marginally attenuated the average treatment effect on depressive symptoms (SMD = 0.20, 95% CI: 0.06-0.35), but more strongly attenuated effects on mental health and vitality. Effects of anti-IL-12/23 remained significant and anti-IL-6 antibodies became a trend after controlling for physical response to treatment. Novel immune-therapeutics can produce antidepressant effects in depressed patients with primary inflammatory disorders that are not entirely explained by treatment-related changes in physical health.

Replicable and Coupled Changes in Innate and Adaptive Immune Gene Expression in Two Case-Control Studies of Blood Microarrays in Major Depressive Disorder.

BACKGROUND: Peripheral inflammation is often associated with major depressive disorder (MDD), and immunological biomarkers of depression remain a focus of investigation. METHODS: We used microarray data on whole blood from two independent case-control studies of MDD: the GlaxoSmithKline-High-Throughput Disease-specific target Identification Program [GSK-HiTDiP] study (113 patients and 57 healthy control subjects) and the Janssen-Brain Resource Company study (94 patients and 100 control subjects). Genome-wide differential gene expression analysis (18,863 probes) resulted in a p value for each gene in each study. A Bayesian method identified the largest p-value threshold (q = .025) associated with twice the number of genes differentially expressed in both studies compared with the number of coincidental case-control differences expected by chance. RESULTS: A total of 165 genes were differentially expressed in both studies with concordant direction of fold change. The 90 genes overexpressed (or UP genes) in MDD were significantly enriched for immune response to infection, were concentrated in a module of the gene coexpression network associated with innate immunity, and included clusters of genes with correlated expression in monocytes, monocyte-derived dendritic cells, and neutrophils. In contrast, the 75 genes underexpressed (or DOWN genes) in MDD were associated with the adaptive immune response and included clusters of genes with correlated expression in T cells, natural killer cells, and erythroblasts. Consistently, the MDD patients with overexpression of UP genes also had underexpression of DOWN genes (correlation > .70 in both studies). CONCLUSIONS: MDD was replicably associated with proinflammatory activation of the peripheral innate immune system, coupled with relative inactivation of the adaptive immune system, indicating the potential of transcriptional biomarkers for immunological stratification of patients with depression.

The effects of interleukin-6 neutralizing antibodies on symptoms of depressed mood and anhedonia in patients with rheumatoid arthritis and multicentric Castleman's disease.

Cytokines, including interleukin-6 (IL-6), modulate neuronal plasticity and stress coping. Depressive symptoms and major depressive disorder (MDD) have been associated with changes in cytokines and their signaling. The current study examined the effect of IL-6 monoclonal antibody administration on depressive symptoms in patients with rheumatoid arthritis (RA) or multicentric Castleman's disease (MCD). The data were obtained from two phase 2, double-blind, placebo-controlled trials designed to test the efficacy of sirukumab in RA (N=176) or of siltuximab in MCD (N=65), and were analyzed post hoc to investigate the effects of these IL-6 antibodies on depressive symptoms. The SF-36 questionnaire items on depressed-mood and anhedonia were combined as the measure for depressive symptoms. The study participants were grouped by the presence/absence of prevalent depressed mood and anhedonia (PDMA, meaning either depressed mood or anhedonia was present at least 'most of the time' and the other at least 'some of the time' for four weeks) at baseline; 26.1% of the RA sample and 15.4% of the MCD sample met criteria for PDMA at baseline. Compared with placebo, sirukumab and siltuximab produced significantly greater improvements on depressive symptoms. To account for an effect on mood due to changes in RA or MCD, the analysis was (1) adjusted for symptom severities using DAS28-CRP for RA and MCDOS for MCD alone or together with bodily pain and physical functioning, and (2) performed within RA and MCD non-responders. Improvement in depressive symptoms remained significant in the treated group for both drugs. The significance over placebo was also observed in the siltuximab study. The improvement in depressive symptoms by sirukumab correlated positively with the baseline soluble IL-6 receptor levels. The data together suggest that the IL-6 antibodies improve depressive symptoms in patients with RA and MCD. Further studies are needed to elucidate to what extents the IL-6 antibodies improve depressive symptoms through improving primary disease dependent and independent mechanisms, especially in RA patients, and the brain mechanisms underlying depressive symptom improvements.

MicroRNAs 146a/b-5 and 425-3p and 24-3p are markers of antidepressant response and regulate MAPK/Wnt-system genes.

Antidepressants (ADs) are the most common treatment for major depressive disorder (MDD). However, only ∼30% of patients experience adequate response after a single AD trial, and this variability remains poorly understood. Here, we investigated microRNAs (miRNAs) as biomarkers of AD response using small RNA-sequencing in paired samples from MDD patients enrolled in a large, randomized placebo-controlled trial of duloxetine collected before and 8 weeks after treatment. Our results revealed differential expression of miR-146a-5p, miR-146b-5p, miR-425-3p and miR-24-3p according to treatment response. These results were replicated in two independent clinical trials of MDD, a well-characterized animal model of depression, and post-mortem human brains. Furthermore, using a combination of bioinformatics, mRNA studies and functional in vitro experiments, we showed significant dysregulation of genes involved in MAPK/Wnt signalling pathways. Together, our results indicate that these miRNAs are consistent markers of treatment response and regulators of the MAPK/Wnt systems.

Ketamine and Imipramine Reverse Transcriptional Signatures of Susceptibility and Induce Resilience-Specific Gene Expression Profiles.

BACKGROUND: Examining transcriptional regulation by antidepressants in key neural circuits implicated in depression and understanding the relation to transcriptional mechanisms of susceptibility and natural resilience may help in the search for new therapeutic agents. Given the heterogeneity of treatment response in human populations, examining both treatment response and nonresponse is critical. METHODS: We compared the effects of a conventional monoamine-based tricyclic antidepressant, imipramine, and a rapidly acting, non-monoamine-based antidepressant, ketamine, in mice subjected to chronic social defeat stress, a validated depression model, and used RNA sequencing to analyze transcriptional profiles associated with susceptibility, resilience, and antidepressant response and nonresponse in the prefrontal cortex (PFC), nucleus accumbens, hippocampus, and amygdala. RESULTS: We identified similar numbers of responders and nonresponders after ketamine or imipramine treatment. Ketamine induced more expression changes in the hippocampus; imipramine induced more expression changes in the nucleus accumbens and amygdala. Transcriptional profiles in treatment responders were most similar in the PFC. Nonresponse reflected both the lack of response-associated gene expression changes and unique gene regulation. In responders, both drugs reversed susceptibility-associated transcriptional changes and induced resilience-associated transcription in the PFC. CONCLUSIONS: We generated a uniquely large resource of gene expression data in four interconnected limbic brain regions implicated in depression and its treatment with imipramine or ketamine. Our analyses highlight the PFC as a key site of common transcriptional regulation by antidepressant drugs and in both reversing susceptibility- and inducing resilience-associated molecular adaptations. In addition, we found region-specific effects of each drug, suggesting both common and unique effects of imipramine versus ketamine.

Side effect profile similarities shared between antidepressants and immune-modulators reveal potential novel targets for treating major depressive disorders.

BACKGROUND: Side effects, or the adverse effects of drugs, contain important clinical phenotypic information that may be useful in predicting novel or unknown targets of a drug. It has been suggested that drugs with similar side-effect profiles may share common targets. The diagnostic class, Major Depressive Disorder, is increasingly viewed as being comprised of multiple depression subtypes with different biological root causes. One 'type' of depression generating substantial interest today focuses on patients with high levels of inflammatory burden, indicated by elevated levels of C-reactive proteins (CRP) and pro-inflammatory cytokines such as interleukin 6 (IL-6). It has been suggested that drugs targeting the immune system may have beneficial effect on this subtype of depressed patients, and several studies are underway to test this hypothesis directly. However, patients have been treated with both anti-inflammatory and antidepressant compounds for decades. It may be possible to exploit similarities in clinical readouts to better understand the antidepressant effects of immune-related drugs. METHODS: Here we explore the space of approved drugs by comparing the drug side effect profiles of known antidepressants and drugs targeting the immune system, and further examine the findings by comparing the human cell line expression profiles induced by them with those induced by antidepressants. RESULTS: We found 7 immune-modulators and 14 anti-inflammatory drugs sharing significant side effect profile similarities with antidepressants. Five of the 7 immune modulators share most similar side effect profiles with antidepressants that modulate dopamine release and/or uptake. In addition, the immunosuppressant rapamycin and the glucocorticoid alclometasone induces transcriptional changes similar to multiple antidepressants. CONCLUSIONS: These findings suggest that some antidepressants and some immune-related drugs may affect common molecular pathways. Our findings support the idea that certain medications aimed at the immune system may be helpful in relieving depressive symptoms, and suggest that it may be of value to test immune-modulators for antidepressant-like activity in future proof-of-concept studies.

Clinical Relevance of Sleep Duration: Results from a Cross-Sectional Analysis Using NHANES.

STUDY OBJECTIVES: To assess the clinical relevance of sleep duration, hours slept were compared by health status, presence of insomnia, and presence of depression, and the association of sleep duration with BMI and cardiovascular risk was quantified. METHODS: Cross-sectional analysis of subjects in the US National Health and Nutrition Examination Surveys using adjusted linear and logistic regressions. RESULTS: A total of 22,281 adults were included, 37% slept ≤ 6 hours, 36% were obese, and 45% reported cardiovascular conditions. Mean sleep duration was 6.87 hours. Better health was associated with more hours of sleep. Subjects with poor health reported sleeping 46 min, (95% CI -56.85 to -35.67) less than subjects with excellent health. Individuals with depression (vs. not depressed) reported 40 min less sleep, (95% CI -47.14 to -32.85). Individuals with insomnia (vs. without insomnia) reported 39 min less sleep, (95% CI -56.24 to -22.45). Duration of sleep was inversely related to BMI; for every additional hour of sleep, there was a decrease of 0.18 kg/m(2) in BMI, (95% CI -0.30 to -0.06). The odds of reporting cardiovascular problems were 6.0% lower for every hour of sleep (odds ratio = 0.94, 95% CI [0.91 to 0.97]). Compared with subjects who slept ≤ 6 h, subjects who slept more had lower odds of reporting cardiovascular problems, with the exception of subjects ≥ 55 years old who slept ≥ 9 hours. CONCLUSIONS: Long sleep duration is associated with better health. The fewer the hours of sleep, the greater the BMI and reported cardiovascular disease. A difference of 30 minutes of sleep is associated with substantive impact on clinical well-being.

Identifying amyloid pathology-related cerebrospinal fluid biomarkers for Alzheimer's disease in a multicohort study.

INTRODUCTION: The dynamic range of cerebrospinal fluid (CSF) amyloid ß (Aß1-42) measurement does not parallel to cognitive changes in Alzheimer's disease (AD) and cognitively normal (CN) subjects across different studies. Therefore, identifying novel proteins to characterize symptomatic AD samples is important. METHODS: Proteins were profiled using a multianalyte platform by Rules Based Medicine (MAP-RBM). Due to underlying heterogeneity and unbalanced sample size, we combined subjects (344 AD and 325 CN) from three cohorts: Alzheimer's Disease Neuroimaging Initiative, Penn Center for Neurodegenerative Disease Research of the University of Pennsylvania, and Knight Alzheimer's Disease Research Center at Washington University in St. Louis. We focused on samples whose cognitive and amyloid status was consistent. We performed linear regression (accounted for age, gender, number of APOE e4 alleles, and cohort variable) to identify amyloid-related proteins for symptomatic AD subjects in this largest ever CSF-based MAP-RBM study. ANOVA and Tukey's test were used to evaluate if these proteins were related to cognitive impairment changes as measured by mini-mental state examination (MMSE). RESULTS: Seven proteins were significantly associated with Aß1-42 levels in the combined cohort (false discovery rate adjusted P < .05), of which lipoprotein a (Lp(a)), prolactin (PRL), resistin, and vascular endothelial growth factor (VEGF) have consistent direction of associations across every individual cohort. VEGF was strongly associated with MMSE scores, followed by pancreatic polypeptide and immunoglobulin A (IgA), suggesting they may be related to staging of AD. DISCUSSION: Lp(a), PRL, IgA, and tissue factor/thromboplastin have never been reported for AD diagnosis in previous individual CSF-based MAP-RBM studies. Although some of our reported analytes are related to AD pathophysiology, others' roles in symptomatic AD samples worth further explorations.

Shape-based feature matching improves protein identification via LC-MS and tandem MS.

The characterization of proteins via liquid chromatography-mass spectrometry (LC-MS) and tandem MS is a challenge due to the large dynamic range and the high complexity of the molecules of interest. In LC-MS experiments, the inconsistent variation in the travel time of analytes in the LC column results in nonlinear shifts in the LC retention time (RT). This variability must be corrected to accurately match corresponding peptide features across samples in LC-MS experiments. Standard methods for RT alignment applied to the raw data are computationally expensive, making it impractical to process a large number of samples. More successful algorithms perform the alignment on features that matched across experiments based on pre-specified mass and RT windows. Features that match across multiple experiments are more likely to be true positives and, therefore, will be more suitable to drive the alignment correction. However, depending on the feature matching algorithm, ambiguities can arise when more than one candidate feature match falls within the specified windows which might affect the alignment performance. In addition, some of the feature-based alignment algorithms do not correct for nonlinear RT shifts. We propose a novel feature matching algorithm that incorporates wavelet-based shape information about the features. We tested our algorithm on two different applications of MS. First, we combined the feature matching algorithm with a robust nonparametric kernel-type regression to form a nonlinear feature-based alignment framework for LC-MS experiments. We validated our alignment framework on LC-MS data from complex samples with known spiked-in proteins, demonstrating our ability to correctly identify each of them with higher reproducibility and probability score when comparing with the SuperHirn software. In addition, by using our feature-based alignment framework, we were able to increase the number of matched features and improve the correlation between replicates. Second, we tested our feature matching algorithm on MALDI MS with MS/MS acquisitions. We found that using only features that matched across replicates of tandem mass spectra we could improve the identification of peptides compared with the current state-of-the-art software. Supplementary Material is available online at www.libertonline.com/cmb .

EDAR: an efficient error detection and removal algorithm for next generation sequencing data.

Genomic sequencing techniques introduce experimental errors into reads which can mislead sequence assembly efforts and complicate the diagnostic process. Here we present a method for detecting and removing sequencing errors from reads generated in genomic shotgun sequencing projects prior to sequence assembly. For each input read, the set of all length k substrings (k-mers) it contains are calculated. The read is evaluated based on the frequency with which each k-mer occurs in the complete data set (k-count). For each read, k-mers are clustered using the variable-bandwidth mean-shift algorithm. Based on the k-count of the cluster center, clusters are classified as error regions or non-error regions. For the 23 real and simulated data sets tested (454 and Solexa), our algorithm detected error regions that cover 99% of all errors. A heuristic algorithm is then applied to detect the location of errors in each putative error region. A read is corrected by removing the errors, thereby creating two or more smaller, error-free read fragments. After performing error removal, the error-rate for all data sets tested decreased (∼35-fold reduction, on average). EDAR has comparable accuracy to methods that correct rather than remove errors and when the error rate is greater than 3% for simulated data sets, it performs better. The performance of the Velvet assembler is generally better with error-removed data. However, for short reads, splitting at the location of errors can be problematic. Following error detection with error correction, rather than removal, may improve the assembly results.

Spike timing dependent plasticity: a consequence of more fundamental learning rules.

Spike timing dependent plasticity (STDP) is a phenomenon in which the precise timing of spikes affects the sign and magnitude of changes in synaptic strength. STDP is often interpreted as the comprehensive learning rule for a synapse - the "first law" of synaptic plasticity. This interpretation is made explicit in theoretical models in which the total plasticity produced by complex spike patterns results from a superposition of the effects of all spike pairs. Although such models are appealing for their simplicity, they can fail dramatically. For example, the measured single-spike learning rule between hippocampal CA3 and CA1 pyramidal neurons does not predict the existence of long-term potentiation one of the best-known forms of synaptic plasticity. Layers of complexity have been added to the basic STDP model to repair predictive failures, but they have been outstripped by experimental data. We propose an alternate first law: neural activity triggers changes in key biochemical intermediates, which act as a more direct trigger of plasticity mechanisms. One particularly successful model uses intracellular calcium as the intermediate and can account for many observed properties of bidirectional plasticity. In this formulation, STDP is not itself the basis for explaining other forms of plasticity, but is instead a consequence of changes in the biochemical intermediate, calcium. Eventually a mechanism-based framework for learning rules should include other messengers, discrete change at individual synapses, spread of plasticity among neighboring synapses, and priming of hidden processes that change a synapse's susceptibility to future change. Mechanism-based models provide a rich framework for the computational representation of synaptic plasticity.

The mechanisms of differential sensitivity to an insulin-like growth factor-1 receptor inhibitor (BMS-536924) and rationale for combining with EGFR/HER2 inhibitors.

Overexpression and enhanced activity of insulin-like growth factor-I receptor (IGF-IR) in diverse tumor types make it an attractive target for cancer therapy. BMS-536924 is a potent small molecule inhibitor of IGF-IR, which shows antitumor activity in multiple tumor models, including sarcoma. To facilitate the development of IGF-IR inhibitors as cancer therapy, identification of biomarkers for selecting patients most likely to derive clinical benefit is needed. To do so, 28 sarcoma and neuroblastoma cell lines were screened for in vitro response to BMS-536924 to identify sensitive and resistant cell lines. Notably, Ewing's sarcoma, rhabdomyosarcoma, and neuroblastoma are more responsive to BMS-536924, suggesting these specific subtypes may represent potential targeted patient subpopulations for the IGF-IR inhibitor. Gene expression and protein profiling were performed on these cell lines, and candidate biomarkers correlating with intrinsic and/or acquired resistance to BMS-536924 were identified. IGF-I, IGF-II, and IGF-IR were highly expressed in sensitive cell lines, whereas IGFBP-3 and IGFBP-6 were highly expressed in resistant lines. Overexpression of epidermal growth factor receptor (EGFR) and its ligands in resistant cell lines may represent one possible resistance mechanism by the adaptation of IGF-IR-independent growth using alternative signaling pathways. Based on cross-talk between IGF-IR and EGFR pathways, combination studies to target both pathways were performed, and enhanced inhibitory activities were observed. These results provide a strategy for testing combinations of IGF-IR inhibitors with other targeted therapies in clinical studies to achieve improved patient outcomes. Further exploration of mechanisms for intrinsic and acquired drug resistance by these preclinical studies may lead to more rationally designed drugs that target multiple pathways for enhanced antitumor efficacy.

Timing and contributions of pre-synaptic and post-synaptic parameter changes during unitary plasticity events at CA3-CA1 synapses.

At individual synapses, post-synaptic responses include a mixture of "successes" and "failures" in which transmitter is released or not released, respectively. Previously we measured synaptic strength at CA3-CA1 synapses averaged over all trials, including both successes and failures, using an induction protocol that allowed us to observe potentiation and depression events as step-like changes. Here we report quantal properties of 15 of the earlier experiments, including 14 potentiation events and eight depression events. In five experiments both potentiation events and depression events were evoked at the same synapse. During potentiation, success rate increased from 0.56 +/- 0.14 (mean +/- SD) to 0.69 +/- 0.12, and during depression, success rate decreased from 0.70 +/- 0.09 to 0.51 +/- 0.10. During potentiation potency increased from 10 +/- 5 to 19 +/- 9 pA, and during depression, potency decreased from 18 +/- 12 to 12 +/- 7 pA. On average, changes in potency accounted for 76% of the change in response size in potentiation events and 60% of the change in depression events. A reduced-assumption spectral analysis method showed evidence for multiple quantal peaks in distributions of post-synaptic current amplitudes. Consistent with the observed changes in potency, estimated quantal size (Q) increased with potentiation and decreased with depression. A change in potency, which is thought to reflect post-synaptic expression mechanisms, was followed within seconds to minutes by a change in success rate, which is thought to reflect pre-synaptic expression mechanisms. Synaptic plasticity events may therefore consist of changes that occur on both sides of a synapse in a temporally coordinated fashion.

Malleability of spike-timing-dependent plasticity at the CA3-CA1 synapse.

The magnitude and direction of synaptic plasticity can be determined by the precise timing of presynaptic and postsynaptic action potentials on a millisecond timescale. In vivo, however, neural activity has structure on longer timescales. Here we show that plasticity at the CA3-CA1 synapse depends strongly on parameters other than millisecond spike timing. As a result, the notion that a single spike-timing-dependent plasticity (STDP) rule alone can fully describe the mapping between neural activity and synapse strength is invalid. We have begun to explore the influence of additional behaviorally relevant activity parameters on STDP and found conditions under which underlying spike-timing-dependent rules for potentiation and depression can be separated from one another. Potentiation requires postsynaptic burst firing at 5 Hz or higher, a firing pattern that occurs during the theta rhythm. Potentiation is measurable after only tens of presynaptic-before-postsynaptic pairings. Depression requires hundreds of pairings but has less stringent long timescale requirements and broad timing dependence. By varying these parameters, we obtain STDP curves that are long-term potentiation only, bidirectional, or long-term depression only. This expanded description of the CA3-CA1 learning rule reconciles apparent contradictions between spike-timing-dependent plasticity and previous work at CA3-CA1 synapses.

Graded bidirectional synaptic plasticity is composed of switch-like unitary events.

Biological information storage events are often rapid transitions between discrete states. In neural systems, the initiation of bidirectional plasticity by all-or-none events may help confer robustness on memory storage. Here, we report that at CA3-CA1 hippocampal synapses, individual potentiation and depression plasticity events are discrete and heterogeneous in nature. Individual synapses began from extreme high and low strength states. Unitary plasticity events were all-or-none and drove synaptic strength between extremes in <1 min. Under naïve conditions, approximately three-fourths of synapses began in a low-strength state. The timing of these unitary events can account for the time course of macroscopic synaptic plasticity.

Dissection of bidirectional synaptic plasticity into saturable unidirectional processes.

In populations of synapses, overall synaptic strength can undergo either a net strengthening (long-term potentiation) or weakening (long-term depression). These phenomena have distinct induction pathways, but the functional outcome is usually measured as a single lumped quantity. In hippocampal CA3-CA1 synapses, we took two approaches to study the activity dependence of each phenomenon in isolation. First, we selectively blocked one process by applying kinase or phosphatase inhibitors known, respectively, to block potentiation or depression. Second, we saturated depression or potentiation and examined the activity dependence of the converse process. The resulting unidirectional learning rules could be recombined to give a well-known bidirectional frequency-dependent learning rule under the assumption that when both pathways are activated kinases dominate, resulting in potentiation. Saturation experiments revealed an additional process in which potentiated synapses can be locked at high strength. Saturability of the components of plasticity implies that the amount of plasticity contributed by each pathway depends on the initial level of strength of the synapses. Variation in the distribution of initial synaptic strengths predicts a form of metaplasticity and can account for differences in learning rules observed under several physiological and genetic manipulations.