The human TERT (hTERT) gene encodes the telomerase catalytic subunit which plays a role in telomerase regulation. Telomerase is activated in more than 90% of all human malignancies and understanding how telomerase is regulated is necessary for implementation of successful anti-cancer therapies. microRNAs (miRNAs) are important regulators of gene expression in eukaryotic cells but evidence of their role in telomerase regulation has not been documented. To determine whether hTERT activity is regulated by multiple miRNAs, eight miRNAs which have putative binding sites in the hTERT 3′UTR together with miR-138-5p were evaluated in luciferase assays with a reporter containing the hTERT 3′UTR. Six miRNAs  specifically inhibited the expression of the reporter luciferase-driven constructs and let-7g*, miR-133a, miR-138-5p, and miR-491-5p also downregulated endogenous telomerase activity in cells. Moreover, all six miRNAs significantly inhibited cell proliferation. miRNAs  also have predicted binding sites within the 3′UTR of three genes involved in Wnt signaling . These miRNAs inhibited the expression of the luciferase reporter constructs containing 3′UTRs of these genes and downregulated protein expression of the TCF7 transcription factor, which mediates the canonical Wnt pathway. Together, these results suggest the existence of a miRNA regulatory network involving the hTERT and Wnt pathway. MicroRNAs (miRNAs) are a class of short non-protein-coding RNA molecules that bind with Argonaute proteins to form RNA-induced silencing complexes (RISCs) miRNAs are key regulators of embryogenesis and establishment of tissue identity in vertebrates Replicative immortality, one of the hallmarks of cancer progression, is dependent on the activation of telomerase in most human tumors Telomerase expression and activity in vertebrates is tightly controlled. Telomerase is active in embryonic tissues but downregulated in most adult somatic cells In this report we demonstrate that hTERT is regulated by multiple miRNAs and that this regulatory network is interconnected with other pathways that are also involved in oncogenesis.The miR-138-5p was previously shown to bind a site in the hTERT 3′UTR The TCF7 3′UTR has similar numbers of predicted binding sites for seven different miRNAs found in the hTERT 3′UTR, lacking only binding sites for let-7g* . The othTo test if any of the selected miRNAs could regulate hTERT gene expression through its predicted binding sites in the hTERT 3′UTR, we used a luciferase reporter assay . HeLa ceThe level of hTERT mRNA is critical for the regulation of telomerase activity in human cells. Therefore, we evaluated whether these miRNAs had the ability to inhibit telomerase activity in the HeLa adenocarcinoma cell line. miRNA precursors  were transfected into cells and telomerase activity was determined 4 hours after transfection using the telomeric repeat amplification protocol . We selemiRNAs regulating telomerase activity are expressed together in many normal tissues and their levels are significantly reduced in ovary and liver tumors suggesting that they may play role in the activation telomerase in these tissues To address the significance of regulation of telomerase activity by endogenous miRNAs we electroporated inhibitors of miR-138, miR-342, miR-491, and miR-541 into Jurkat cells in which these miRNAs are expressed Six of the miRNAs which specifically inhibited the TERT 3′UTR-driven reporter activity also had predicted binding sites in the 3′UTRs of two or all three of the Wnt pathway regulating genes  analyzed . To deteThe experiments above demonstrated that the transfection of the miRNAs  inhibit TCF7 and MSI1 3′UTR reporters, therefore, we analyzed their ability to alter the endogenous protein levels of these genes in DLD-1 cells . We usedA significant inhibition of HeLa cell proliferation following transfection of the hTERT regulating miRNAs was not observed (data not shown). HeLa cells have very low Wnt activity We observed that hTERT specific miRNAs cooperate in their ability to reduce telomerase activity. In order to determine whether miRNAs may also cooperate to inhibit cell proliferation, DLD-1 and MCF-7 cell lines were treated with three combinations of miRNAs as described in The number of genes known to be regulated by miRNAs is exponentially increasing, however, only limited information is available about the direct regulation of hTERT by miRNAs Our results indicate that the same set of miRNAs that interact with the hTERT 3′UTR also participate in the regulation of TCF7, MSI1, and PAX5 genes. TCF7 is one of the four essential transcription factors involved in the regulation of canonical Wnt signaling The inhibition of the luciferase reporter and inhibition of telomerase activity four hours after miRNA treatment suggest that interactions of miRNAs with the hTERT 3′UTR directly mediate these effects through the regulation of hTERT. Some of these miRNAs cooperate, even at low concentration, in the inhibition of telomerase activity as described previously for inhibition of other genes L) and inhibits cellular invasion of glioma cells While miRNAs can function both as oncogenes and anti-oncogenes, miRNAs that target the hTERT 3′UTR inhibit tumorigenesis and consequently are commonly downregulated in many types of cancer. miR-138 was found to be downregulated in anaplastic T cell (ATC) leukemia and in aggressive variants of papillary thyroid carcinomas (PTC), in oral and head and neck squamous cell carcinomas (OSCC and HNSCC) as well as in lung, hepatic and gastric tumors Homo sapiens, which is very similar to the number of sequence-specific transcription factors Our results suggest that more miRNAs are likely involved in the hTERT regulatory network. It is likely that there are additional miRNAs that co-regulate hTERT with other genes through binding to their 3′UTR because we did not analyze the less conserved miRNAs and TargetScan5.2 did not consider all human miRNAs known at that time. Additionally, it is likely that the rules for predicting the sequence of the binding sites are too strict and a shorter precise match in the seed sequence than currently considered may be sufficient for miRNA binding In conclusion, multiple miRNAs regulate telomerase activity, proliferation, TCF7, MSI1 and PAX5 genes. Thus, small non-coding miRNAs may be involved in a coordinated regulatory network involving the transcriptional regulation of telomerase and several genes of the Wnt pathway.Renilla luciferase reporter vector The entire 3′UTR sequence of the hTERT gene  was PCR amplified from human cDNA and cloned into the XbaI-XhoI sites of the pcDNA3.1dsRluc 4 cells per well) or 12-well plates (4×104 cells per well) and transfected after 24 hours using siPORT amine reagent  or Metafectene SI  with miRNA mimics (Ambion) at a final concentration of 60 nM . DLD-1 and Caco-2 colon carcinoma cell lines were described previously of 60 nM . All celRenilla luciferase-based hTERT 3′UTR reporter constructs using FuGENE HD transfection reagent . Transfection efficiency was controlled by co-transfection with a firefly luciferase-based control reporter pCI-firefly Renilla luciferase-based UTR reporter constructs, pCI-firefly, and pEGFP-N1 reporters, and 60 nM miRNA mimics using Metafectene Easy transfection reagent (Biontex-USA). Cells were harvested 48 hours after transfection and analyzed using a Dual-Luciferase Reporter Assay System (Promega). The Renilla luciferase signal of each sample was normalized for differences in transfection efficiency using the activity of co-transfected pCI-firefly reporter as control.The HeLa cells were transfected in 12-well plates with the TCF7 was detected with the mixture of antibodies 1D2 and 2E9 . The MSI1 protein was detected by monoclonal antibody (EP1302) . Human actin was stained by monoclonal antibody AC-15 (ab6276) (Abcam). The cells were lysed in buffers supplemented with a set of inhibitors which included: 2× concentrated complete protease inhibitor cocktail , 2 mM phenylmethylsulfonyl fluoride, and aprotinin . Prestained molecular weight markers EZ-RUN from Thermo Fisher Scientific  were employed to determine molecular weights. Proteins were separated on NuPAGE 4–12% Bis-Tris gels, electroblotted to Invitrolon PVDF membrane  and visualized with SuperSignal West Dura Chemiluminescent Substrate with (MSI1) or without (TCF7) SuperSignal Western blot enhancer (Thermo Fisher Scientific).The level of telomerase activity was evaluated using the TRAP assay Figure S1hTERT 3′UTR reporter with miRNA binding sites and mutagenized nucleotides.(PDF)Click here for additional data file.Methods S1Prediction of genes that share 3′UTR miRNA target sites with hTERT 3′UTR.(PDF)Click here for additional data file.Table S1miRNAs used in the study.(PDF)Click here for additional data file.


Early bystander cardiopulmonary resuscitation (CPR) is one of the most effective interventions in improving outcome from sudden out-of-hospital cardiac arrest. However, despite large-scale community training programs, citizen-CPR rates have been persistently low. Therefore, a recent report of the 2010 European Resuscitation Council guidelines has re-emphasized the need for dispatchers to be specifically trained in starting telephone CPR protocol for suspected cardiac arrest. In accordance, 112 Belgian dispatchers have been trained for resuscitation assistance by telephone, using a specific protocol named ALERT (Algorithme Liègeois d'Encadrement à la Réanimation Téléphonique). The present work evaluates the educational aspects of this recent implementation.n = 140). The aim was to assess the added value of the training, based on the model of Donald Kirkpatrick that allowed gathering information about perceptions of dispatchers, their satisfaction with the training and their actual ability to apply the protocol.This was a prospective multicentric study including all French-speaking dispatchers in Belgium , which was nevertheless significantly increased by the training (97%). There was a significant improvement in perceptions of dispatchers regarding their assistance skills (+44%). The training provided a significant improvement in staff perceptions on applicability of the approach on the field, and impacts for the victims. Participants (96%) were generally satisfied with the training. Finally, participants' knowledge on public health issues (33%), basic life support (+17%) and dispatching protocol (+19%) was significantly improved.French-language federal training in the 100/112 dispatching centers significantly improves dispatchers' perceptions and knowledge of assistance to resuscitation by the ALERT protocol. Such results reinforce the pivotal role of standardized protocols and training in art and science medical dispatching.


A receptor subunits. Subunits α4 and δ were downregulated in maternal LS, likely reflecting a reduction in the extrasynaptic, neurosteroid-sensitive α4/δ containing receptor subtype. Conversely, subunits ε and θ were increased in maternal LS. Fifteen K+ channel related genes showed altered expression, as did dopamine receptors Drd1a and Drd2 (both downregulated), hypocretin receptor 1 (Hcrtr1), kappa opioid receptor 1 (Oprk1), and transient receptor potential channel 4 (Trpc4). Expression of a large number of genes linked to developmental processes or cell differentiation were also altered in postpartum LS, including chemokine (C-X-C) motif ligand 12 (Cxcl12), fatty acid binding protein 7 (Fabp7), plasma membrane proteolipid (Pllp), and suppressor of cytokine signaling 2 (Socs2). Additional genes that are linked to anxiety, such as glutathione reductase (Gsr), exhibited altered expression. Pathway analysis also identified changes in genes related to cyclic nucleotide metabolism, chromatin structure, and the Ras gene family. The sensory presence of pups was found to contribute to the altered expression of a subset of genes across all categories. This study suggests that both large changes in neuronal signaling and the possible terminal differentiation of neuronal and/or glial cells play important roles in producing the maternal state.Coordinated gene expression changes across the CNS are required to produce the mammalian maternal phenotype. Lateral septum (LS) is a brain region critically involved with aspects of maternal care, and we recently examined gene expression of whole septum  in selectively bred maternal mice. Here, we expand on the prior study by 1) conducting microarray analysis solely on LS in virgin and postpartum mice, 2) using outbred mice, and 3) evaluating the role of sensory input on gene expression changes. Large scale changes in genes related to neuronal signaling were identified, including four GABA The establishment of the maternal phenotype requires a coordinated suite of changes in numerous biological pathways, from endocrine signaling and metabolic activity to nervous system properties and adaptive behaviors A receptors in LS alter offspring protection A receptor is assembled from a pool of 16 known subunits, resulting in a diversity of receptor subtypes with unique properties, pharmacological profiles, and distributions throughout the brain. This diversity provides a high degree of flexibility in signal transduction and allosteric modulation A receptor subunits in LS of maternal mice has yet to be studied. This study therefore has a particular focus on investigating expression changes in GABAA receptors themselves as a possible mechanism of modulating GABA signaling in the maternal LS.Lateral septum (LS) is a brain region that is centrally featured in a network of structures known to influence social and parental behavior and emotional states We recently performed a gene expression study in the whole septum of maternal mice selectively bred for high offspring protection The transition from a virgin to lactating state involves many genes across numerous biological pathways. To address this, we utilized system level approaches to analyze microarray data and detect enrichment in sets of genes with related functions. Using these methods, we were able to identify functional themes in the expression profile of the maternal LS.http://www.ncbi.nlm.nih.gov/pubmed/) and GeneCards compendium (http://www.genecards.org). This table serves as a representative sample to highlight the microarray results, but many genes of biological interest and statistical significance cannot be included in such a short list. The full list of all 35,557 targets, their relative expression, and significance is presented in Using Probe Logarithmic Intensity Error (PLIER) analysis, we identified 1,001 targets with significant expression differences (FDR-adjusted p<0.25) between the LS  of lactaA receptor subunits were confirmed by quantitative real-time PCR analysis . In the 809 significant expression changes in maternal LS relative to virgin, NIH DAVID’s functional annotation clustering tool detected enrichment in numerous, small gene sets that each included genes linked to developmental processes and neuron/glial differentiation. We consolidated these developmentally related genes into a single, larger cluster that is visualized in To assess which gene changes may be most strongly associated with continued sensory input from pups, we identified genes that exhibited significance in lactating maternal LS when compared to both virgin and pups removed groups. A receptor subtypes.This study used Affymetrix microarray and quantitative real-time PCR to identify gene expression changes occurring naturally in LS of mice in association with the transition from a virgin to postpartum state at multiple levels of analysis, from single genes to enriched networks and biological pathways. Additionally, we evaluated the degree to which expression changes are dependent on the continued sensory input from pups. The results reveal numerous changes in genes of interest that may be of particular importance as markers of the maternal phenotype, including differential regulation of GABAA receptor was selected as a protein of interest based on previous studies suggesting the importance of GABA signaling in the LS for maternal behavior. Site-directed application of GABAA receptor antagonist was shown to inhibit offspring protection in lactating maternal mice A receptor expression might be a mechanism of regulating GABA signaling in LS.The GABAA receptor subunits showed significant expression differences in lactating maternal LS relative to virgin, including δ, ε, and α4 (FDR-adjusted p<0.25). Several additional subunits, such as α1, ρ2, γ1, α5, θ, and β2, exhibited possible significance (PLIER p<0.05). The most ubiquitous and abundant GABAA receptor subtype in the brain is composed of two α1, two β2, and one γ2 subunit In the microarray analysis, three GABAA receptors than their wild type counterparts exhibited greater tonic currents and smaller GABA-mediated mIPSCs A receptors could have significant effects on the nature of net neuronal activity in the maternal LS.δ assembles with α4 endogenously to form a benzodiazepine insensitive receptor that mediates a tonic, rather than phasic, form of inhibitory current. α4/δ containing receptors have a high affinity for GABA, resist desensitization, and exhibit some degree of agonist-independent activity. They are located extrasynaptically and are positively modulated by the binding of neurosteroids A large and diverse group of genes related to neuronal signaling was enriched in the maternal LS relative to virgin, including a large number of potassium channel related genes . These c.The modulatory neurotransmitter dopamine contributes to learning motivation and reward associated behaviors The kappa opioid receptor (Oprk1) displayed a 17% decrease in maternal LS relative to virgin. Some studies report that site specific manipulations of Oprk1 can mediate anxiety-like behavior, the effects of social defeat stress, and the extent to which social play is rewarding A number of studies have indicated plasticity of the maternal brain, and it is possible that the maternal state represents an endpoint in neuronal or glial differentiation. It could be that some cells do not fully differentiate until the postpartum state, and this contributes to the emergence of the maternal phenotype. We identified changes in a number of genes with strong ties to developmental processes and neuronal/glial differentiation in the maternal LS . A subseA highly significant 32% decrease in fatty acid binding protein 7 (Fabp7) was observed by qPCR in maternal LS relative to virgin . Fabp7 iCytokine signaling in the CNS influences how stem cells respond to hormones and plays an important role in the differentiation of neural progenitor cells into either glia or neurons Functional annotation clustering revealed a small cluster of genes influencing the synthesis of cyclic nucleotides , with mo−/− knockout mice exhibit a lethal phenotype characterized by aberrant neocortical development with reduced proliferation of neuronal precursors, demonstrating developmental relevance There was a large degree of enrichment in a nucleosomal gene cluster primarily composed of histone genes. These genes were almost exclusively downregulated, but it is not clear what implications this has on the function of the maternal LS. It is possible that a downregulation of histone mRNA may reflect changes in post-transcriptional processes that influence the stability of histone mRNA transcripts The Ras related gene cluster includes members of the Ras family, which is involved with many different cellular processes. It has been widely linked to tumor formation and has been shown to contribute in conjunction with thymoma viral proto-oncogene (Akt) signaling to glioblastoma formation in the brain A receptors and other neuronal signaling genes have been linked to anxiety. Glutathione reductase (Gsr) is also linked to anxiety, and qPCR confirmed a 12% decrease in Gsr in the maternal LS compared to virgin   were used for all experiments. Nulliparous animals were split into three age-matched groups (∼70 days of age at time of dissection), designated as lactating maternal, pups removed, and virgin. For mating, females in the lactating maternal and pups removed groups were housed in polypropylene cages with a breeder male for 2 weeks. Virgin females were concurrently co-housed with one another to provide similar levels of exposure to social stimuli. After the separation of breeder males, all females (pregnant and virgin) were housed individually and provided precut nesting material until dissections. Under this schedule, all females experienced similar levels of co-housing and single housing to minimize potential effects of isolation-induced stress. Cages were changed once per week until pups were born (postpartum Day 0), after which cages were not changed again for all animals until dissection. On Day 0, pups were culled, if necessary, to standardize litter size to eleven. For females in the pups removed group, pups were removed from the cage on postpartum Day 2. The pups removed group was included in the experimental design to provide insight as to whether or not continued sensory input from pups is required, in addition to parturition, to generate expression changes characteristic of the maternal phenotype. All animals were housed in the same room with cages of each experimental group positioned in an alternating fashion on the same shelves. A 12∶12 light/dark cycle with lights on at 06∶00 h CST was used. Female mice were provided with ad lib access to breeder chow (Harlan) and tap water. Procedures were performed in strict accordance with the guidelines of the National Institutes of Health Guide for the Care and Use of Laboratory Animals, and all studies were approved by the University of Wisconsin Animal Care and Use Committee.Outbred hsd:ICR female mice  were randomly selected for use in the microarray experiment. Microarray analysis was performed with the GeneChip Mouse Gene 1.0 ST array  using targets derived from total RNA isolated from LS as described above. cDNA for array hybridization was reverse transcribed from 200 ng of total RNA using an Ambion GeneChip WT Expression Kit  in accordance with the manufacturer’s specifications. In short, total RNA was used to synthesize double-stranded cDNA, which was then used as a template to synthesize single-stranded cRNA. This cRNA was subsequently used as a template for one round of single-stranded cDNA synthesis, and the resulting DNA-RNA hybrids were then degraded with RNase H. Amplified single-stranded cDNA was fragmented and biotinylated with an Affymetrix WT Terminal Labeling Kit  according to the manufacturer’s instructions. Fragmented, labeled cDNA samples were hybridized with the arrays for 16 hours at 45°C. Hybridized arrays were washed, stained, and subsequently scanned at 570 nm on an Affymetrix GC3000 G7 Scanner. The Affymetrix Command Console v. 3.1.1.1229 was used to extract and process data from scans. cDNA synthesis, fragmentation, labeling, array hybridization, and scanning were performed by the Gene Expression Center at the University of Wisconsin-Madison.Probeset level summarization and data normalization were performed with the PLIER algorithm and GC bin background correction using Affymetrix Power Tools v. 1.12.0. The raw and summarized microarray data presented in this publication have been deposited in NCBI’s Gene Expression Omnibus A receptor subunits; α1 (Gabra1), α4 (Gabra4), δ (Gabrd), ε (Gabre), and θ (Gabrq), as well as five genes currently being evaluated as potential markers of the maternal phenotype; chemokine (C-X-C) motif ligand 12 (Cxcl12), fatty acid binding protein 7 (Fabp7), glutathione reductase (Gsr), plasma membrane proteolipid (Pllp), and suppressor of cytokine signaling 2 (Socs2). Two stable reference genes were used to normalize relative expression results in genes of interest; Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide (Ywhaz), and peptidylprolyl isomerase A (Ppia). Primer information can be viewed in To confirm expression changes detected by microarray analysis, qPCR was performed on genes of interest (n = 8 per group). Target genes included five GABADAVID’s functional annotation clustering is a tool which analyzes enrichment in a variety of precompiled gene sets assembled by shared ontology, protein domains, and biological pathways within a given list of input genes. DAVID then groups related gene sets into clusters. Enrichment scores are generated by detecting overrepresentation of a cluster within the input list compared to a reference background Table S1Full list of all microarray targets, their relative expression, and FDR-adjusted p-values.(XLSX)Click here for additional data file.Table S2Expression data summary for enriched gene clusters found in NIH DAVID functional annotation clustering.(XLSX)Click here for additional data file.Table S3Primers for genes of interest and reference genes used in real-time quantitative PCR experiments.(DOCX)Click here for additional data file.


Streptococcus pneumoniae. Ontological pathway analysis of LRRK2 across metabolic and kidney processes and pathological categories suggested that the thioredoxin network may play a role in perturbing these organ systems. The phenotype of the LRRK2 null rat is suggestive of a complex biology influencing metabolism, immune function and kidney homeostasis. These data need to be extended to better understand the role of the kinase domain or other biological functions of the gene to better inform the development of pharmacological inhibitors.Genetic evidence links mutations in the LRRK2 gene with an increased risk of Parkinson’s disease, for which no neuroprotective or neurorestorative therapies currently exist. While the role of LRRK2 in normal cellular function has yet to be fully described, evidence suggests involvement with immune and kidney functions. A comparative study of LRRK2-deficient and wild type rats investigated the influence that this gene has on the phenotype of these rats. Significant weight gain in the LRRK2 null rats was observed and was accompanied by significant increases in insulin and insulin-like growth factors. Additionally, LRRK2-deficient rats displayed kidney morphological and histopathological alterations in the renal tubule epithelial cells of all animals assessed. These perturbations in renal morphology were accompanied by significant decreases of lipocalin-2, in both the urine and plasma of knockout animals. Significant alterations in the cellular composition of the spleen between LRRK2 knockout and wild type animals were identified by immunophenotyping and were associated with subtle differences in response to dual infection with rat-adapted influenza virus (RAIV) and  Parkinson’s disease (PD) is the second most common neurodegenerative disease, affecting 1–2% of the population over the age of 60 LRRK2, through its kinase and/or scaffolding function, appears to regulate a variety of cellular processes critical for homeostasis and survival; it has been shown to affect protein synthesis through its interaction with the microRNA processing molecule Argonaute Predicting safety liabilities of novel therapeutic targets is challenging prior to the availability of good tool compounds. One approach is to study genetically modified animal models. Specifically, a phenotypic analysis of knockout animals may provide insight into potential effects of pharmacological inhibition. Though there are a variety of confounding factors , there is still the opportunity to gain important insights in the targeted biology. In the case of LRRK2, data generated in several LRRK2 deficient mouse strains has indicated that kidney homeostasis is perturbed in vivo following challenge with an infectious agent. Similar to previous reports in mice, LRRK2 deficient rats exhibited renal morphological and histopathological changes, with the novel finding that the renal biomarker lipocalin-2 (NGAL) was significantly reduced in both the urine and serum of knockout animals. Significant changes in the cellular composition of splenocytes were identified between genotypes, but these changes only translated to subtle differences in their response to a dual-infection insult in a host resistance study, where knockout and wild type animals were sequentially infected with rat adapted influenza virus (RAIV) and Streptococcus pneumoniae. Unique to this study was the observation that LRRK2 deficient rats experienced significant weight gain, which was accompanied by a number of metabolic and clinical pathological perturbations. Molecular pathway analysis suggested that an interaction between LRRK2 and thioredoxin-related pathways may be relevant for the kidney pathology and metabolic perturbations observed in these knockout animals.The purpose of the series of experiments presented herein was to perform a comparative analysis of LRRK2 wild type and deficient rats, characterize kidney morphology, histopathology and homeostasis as well as determine if the putative immunological changes have measurable effects 2 chamber and blood was collected via cardiac puncture from each animal for clinical pathological and plasma biomarker analyses. Gross necropsy was conducted on each animal following terminal sacrifice, including an examination of the external features of the carcass, external body orifices, the abdominal, thoracic, organs and tissues (gross pathology). Tissues were weighed and subsequently fixed in 10% neutral buffered formalin. Kidneys, spleens, bone marrow and lungs were prepared for hematoxylin and eosin (H&E) staining and histopathological assessment by a board-certified (ACVP) veterinary pathologist . Three separate cohorts of animals were received from SAGE. Animals from cohort 1 were utilized for comparative immunophenotyping in the absence of viral or bacterial challenges. Animals from cohort 2 were utilized for water and food consumption, metabolic, clinical chemical, complete blood count, and urinary and serum biomarker and histopathological analyses. Animals from cohort 3 were used in the host-resistance study. All animal work completed at Elan complied with Elan IACUC protocols and was approved by the Institutional and Animal Care and Use Committee.Three separate cohorts of LRRK2 Knockout (KO) and wildtype (WT) rats were received from Sigma Advance Genetic Engineering (SAGE) laboratories and were maintained until 12 to 16 weeks of age. Rats were group-housed in microisolator cages and provided with certified rodent diet (Harlan Test Lab #2018) and water ad libitum. Environmental controls for the animal room were set to maintain 18 to 29°C, a relative humidity of 30 to 70%, a minimum of 10 room air changes/hour, and a 12-hour light/12-hour dark cycle. Rats were acclimated for at least three days before the start of any study. Group food and water consumption were recorded once at the end of study whereas individual body weights were measured once per week. Animals were euthanized in a CORat blood was collected from 5 age-matched LRRK2 KO and WT male rats approximately 13 weeks of age. Samples were assessed for peripheral complete blood counts and plasma clinical chemical parameters within 24 hours of withdrawal . Plasma was isolated and analyzed in the Rat MetabolicMAP® and Rat KidneyMAP® panels . Urine was collected on wet ice using metabolic cages for 5 age-matched LRRK2 KO and WT male rats approximately 13 weeks of age. Urine was filtered using a 0.2 micron sterile filter and analyzed for the presence of lipocalin 2 (NGAL), osteopontin, albumin and KIM-1 using the MesoScale Discoveries rat kidney injury panel . Urine kidney biomarker data were normalized using creatinine levels.Rat spleens from WT and LRRK2 KO rats were removed during necropsy. Splenocytes were individually isolated from half of the spleen of each animal. To enrich for WBC’s, RBC were lysed using RBC lysing buffer  at RT for 15 min. WBC’s were washed with cold PBS twice, then blocked with PBS/50% rat serum at RT for 20 min. WBC’s were counted using the Vi cel , and plated at approximately 40 k/well of in a 96-well V-bottom plate. Surface antibodies  were prepared at 2 µg/ml in PBS/10% rat serum . Splenocyte WBC’s were spun down in the V-bottom plate, buffer discarded and cells resuspended in the respective cell marker and the isotype control solutions at 100 µl/well. After 30 min on ice, cells were spun down and washed with cold PBS twice. Cells were resuspended in 200 ul cold PBS and assessed on the LSRII flow cytometer  and the relative percentage each cell sub-population comprised within the spleen was calculated.S. pneumoniae infection controls, were assessed for their immunological response in a dual infection host-resistance study . Rats were acclimated for one week prior to the beginning of the experiment. All animal work completed at Burleson Research Technologies (BRT) complied with BRT IACUC protocols and was approved by their Institutional and Animal Care and Use Committee.LRRK2 KO male rats and corresponding age-matched wild type (WT) Long Evans male controls, along with Long Evans male rats used as −2 dilution of the stock virus (approximately 2×105 plaque forming units) in a volume of 200 µl on day 0. Streptococcus pneumoniae Type 14 was inoculated into brain heart infusion (BHI) broth (day prior to infection) and incubated overnight at 37°C/5% CO2. On the day of infection, optical density (575 nm) was determined to confirm growth. Bacteria were subcultured, centrifuged and resuspended in Dulbecco’s phosphate buffered saline (D-PBS). All animals were anesthetized with isoflurane and infected intranasally with S. pneumoniae Type 14 (approximately 1×107 colony forming units [CFU] per rat) on experimental Day 28.Animals were anesthetized with isoflurane and infected intranasally with rat-adapted influenza virus (RAIV) as a 10Blood was collected to measure influenza-specific immunoglobulins IgM and IgG prior to infection with RAIV (Day -8) and post-infection to RAIV (Day 8 for IgM and Day 21 for IgG). Influenza-specific pulmonary IgM and IgG concentrations were measured by enzyme-linked immunosorbent assay (ELISA). Plates were coated with influenza A/Port Chalmers/1/73 (H3N2) virus. Standards, controls, and samples from test animals were added to the pre-coated plates. After washing to remove unbound immunoglobulin, goat anti-rat IgM and rabbit anti-rat IgG HRP conjugated  detection antibodies were added. Unbound conjugated antibodies were removed by washing and the amount of conjugate remaining in the well was measured following incubation with a TMB chromogenic substrate . The resulting absorbance was obtained using a Spectramax 340 microplate reader (Molecular Devices). All samples were run in duplicate and data analysis performed using Softmax Pro v2.2.1 software (Molecular Devices). Relative titers were interpolated from a total rat IgM and IgG standard curve and reported as the mean of duplicate samples. The baseline level of IgM antibody observed in the serum at Day -8 represents the assay background and not influenza-specific antibody.51Cr). Effector cells were obtained from whole blood using Ficoll-Paque centrifugation and adjusted to achieve the desired effector-to-target ratios of 25∶1. Effector and target cells were added in triplicate to wells of round-bottom microtiter plates. Spontaneous-release (S) and total 51Cr release (T) controls were prepared separately by adding target cells in prepared media  to the control wells. The plates were centrifuged to initiate cell contact and subsequently incubated at 37°C/5%CO2 for 4 hours. Plates were centrifuged and supernatants harvested and counted with a Cobra II Auto-Gamma counter (Packard Instruments). Percent specific 51Cr release  was calculated using the formula [(E−S)/(T−S)]×100; where E is the 51Cr release from target cells in the presence of effector cells, S is the spontaneous release of 51Cr from target cells alone, and T is the maximum release of 51Cr from target cells in the presence of Triton X-100.Blood was collected on experimental Day 2 following RAIV infection to assess natural killer cell activity. Target YAC-1 cells were labeled with Chromium-51 (6/mL. PBMCs (2×105/well) were added to triplicate wells of an ELISpot plate that was pre-coated with anti-rat IFNγ antibodies by the manufacturer. Cells were incubated in the presence or absence of Concanavalin A  for 20 hours. The ELISPOT assay was then performed according to the manufacturer’s instructions and IFNγ-positive spots were counted using an Immunospot® analyzer .Blood was collected on experimental Day 8 following RAIV infection to measure IFNγ production as a function of peripheral blood mononuclear cells (PBMCs). PBMC production of IFNγ was determined using a commercial ELISPOT kit . Blood was collected into K2 EDTA blood collection tubes followed by PBMC isolation using Ficol-Paque centrifugation. Cells were washed and resuspended in complete RPMI 1640 at 1×102. Long-Evan control animals were euthanized 1 hour post-infection on Day 28 to confirm infection with S. pneumonia. LRRK2 KO and age-matched controls were euthanized on Day 29 (24 hours post-infection with S. pneumoniae) to evaluate bacterial clearance. Lung homogenates were prepared for determination of S. pneumoniae titers. Whole lung homogenates were diluted in PBS and the dilutions plated, each in duplicate, on blood agar plates. Plates were incubated at 37°C/5% CO2 for 16–48 hours. Bacterial colonies were counted to determine the number of colony forming units (CFU). The spleen from each animal was dissected in half and preserved for flow cytometric immunophenotyping, as described above. An aliquot of the lung homogenate was cleared by filtration using 0.2 mm syringe filters to remove the bacteria and used to determine influenza virus titers using Madin Darby Canine Kidney (MDCK) cells. Dilutions of filtered lung homogenate were added to monolayers of MDCK cells and covered with an agarose overlay. Following incubation for 36–48 hours to allow plaque development, the cell monolayers were fixed with buffered formalin and stained with crystal violet. Viral plaques were counted visually to determine infectious virus titer.Animals were euthanized with COMetabolic and renal data, generated above, was used to assess potential LRRK2 networks and pathways that may help identify potential hypotheses for the observations. Network reconstruction and ontological analysis were performed in MetaCore™  with the Systems Toxicology Module. MetaCore™ comprises databases of chemicals, genes, proteins, and their molecular interactions  and biological, toxicological and disease associations for genes, proteins and compounds in the global interactome network. Molecular interaction data was derived from full-text literature curation of experimental data that permits interactome reconstruction based on high confidence interactions using directionality (a→b), mechanism  and the nature of the interaction (positive effect or negative effect) Two-tailed Student T-tests were used to assess significance between knockout and wildtype control genotypes. Microsoft Excel (2007) was used to perform these analyses.Weight measurements from several cohorts of rats demonstrated that male LRRK2 KO rats are significantly heavier than their age-matched wild type counterparts . SignifiA panel of 21 plasma-based metabolic parameters was assessed in 5 male rats of each genotype . A numbeKidneys of male LRRK2 KO rats exhibited gross morphological changes, being larger and darker in color than those from wild type animals. Histopathological analysis revealed the presence of eosinophilic hyaline droplets in the renal cortical epithelium and proximal convoluted tubules of all KO rats, but none of the wild type rats. When viewed with fluorescence microscopy, these droplets were autofluorescent . Follow-S. pneumoniae, demonstrated that significant differences in the cellular composition exists between the two genotypes  and enotypes . Under nenotypes , CD4  and Streptococcus pneumoniae were administered intranasally on Days 0 and 28, respectively. Flow cytometric immunophenotyping identified several changes that were in contrast to the non-infected cellular changes mentioned above, including a decrease in the percent of CD11b+ cells in the LRRK2 KO animals, whereas CD4+, CD8+ and CD3+ cells increased following infection .(TIF)Click here for additional data file.Table S1Relative food and water consumption for LRRK2 wildtype and knockout rats.(DOCX)Click here for additional data file.


Long-term memories are thought to depend upon the coordinated activation of a broad network of cortical and subcortical brain regions. However, the distributed nature of this representation has made it challenging to define the neural elements of the memory trace, and lesion and electrophysiological approaches provide only a narrow window into what is appreciated a much more global network. Here we used a global mapping approach to identify networks of brain regions activated following recall of long-term fear memories in mice. Analysis of Fos expression across 84 brain regions allowed us to identify regions that were co-active following memory recall. These analyses revealed that the functional organization of long-term fear memories depends on memory age and is altered in mutant mice that exhibit premature forgetting. Most importantly, these analyses indicate that long-term memory recall engages a network that has a distinct thalamic-hippocampal-cortical signature. This network is concurrently integrated and segregated and therefore has small-world properties, and contains hub-like regions in the prefrontal cortex and thalamus that may play privileged roles in memory expression. Memory retrieval is thought to involve the coordinated activation of multiple regions of the brain, rather than localized activity in a specific region. In order to visualize networks of brain regions activated by recall of a fear memory in mice, we quantified expression of an activity-regulated gene (c-fos) that is induced by neural activity. This allowed us to identify collections of brain regions where Fos expression co-varies across mice, and presumably form components of a network that are co-active during recall of long-term fear memory. This analysis suggested that expression of a long-term fear memory is an emergent property of large scale neural network interactions. This network has a distinct thalamic-hippocampal-cortical signature and, like many real-world networks as well as other anatomical and functional brain networks, has small-world architecture with a subset of highly-connected hub nodes that may play more central roles in memory expression. Long-term memories are thought to be represented by changes in the strength of connections among neurons in the brain Supplementary Note in Text S1). Here we have developed a brain-wide imaging approach to study the network organization of long-term contextual fear memories in mice  = 1.31, P = 0.21), indicating that no forgetting occurred across this time period. Control groups, that underwent the same procedure but did not receive footshocks during training, showed little freezing during testing  = 63.08, P<0.0001).In order to characterize regional activation following long-term memory recall, we used a contextual fear conditioning task in which mice learn an association between a context and an aversive event . When returned to the same context, contextual fear memory is inferred from an increase in freezing behavior c-fos, measuring Fos protein levels allows for the detection of recently activated neurons Supplementary Note in Text S1). Therefore, in order to characterize regional activation following contextual fear testing, Fos was quantified in 84 brain regions . These included cortical, thalamic, hippocampal, cerebral and midbrain nuclei and were defined according to a standard mouse brain atlas Table S1). In order to isolate changes in gene expression associated with contextual fear memory recall and control for non-specific aspects of the testing procedure, the density of Fos positive cells in each brain region in trained mice was compared to control animals that did not receive footshocks during conditioning. Importantly, multivariate task partial least squares (PLS) analyses revealed that patterns of Fos expression depended both on the training condition as well as the retention delay (significant condition × delay interaction (P<0.05). To better we understand the nature of the interaction we conducted separate ‘post-hoc’ analyses. These revealed distinct patterns of Fos expression in the trained vs. control (no shock) mice following testing at both the 1 day  or indirectly by measuring a metabolic signal such as blood flow (fMRI). Functional connectivity is then typically evaluated by assessing covariance of these signals across different brain regions during performance of a given task. In contrast, in experimental animal studies, neuronal activity is typically assessed post-mortem by evaluating, for example, changes in expression of the activity-regulated genes such as P<0.005) (P≤0.0025) or less  conservative thresholds  or alternate correlation coefficients  in order to evaluate whether network properties were stable across a range of conditions. In all resulting undirected graphs, nodes (brain regions) are connected by edges representing super-threshold inter-regional correlations.Accordingly, we next computed a complete set of inter-regional correlations in the groups of mice tested at each retention delay . Moreover, a number of other immediate early genes are regulated by neural activity, including egr-1Figure S5), consistent with previous studies showing that immediate early genes are typically expressed in the same or largely overlapping neuronal populations Figure S6 and Table S2). While direct anatomical connections are not necessary for two regions to be functionally connected , nonetheless this finding is consistent with previous analyses indicating that there is typically good correspondence between functional and underlying structural connections in brain networks The structure of these network graphs varied across conditions, but, importantly, these differences were not simply an artifact of group differences in signal strength and/or variance  may offer greater sensitivity at detecting negative correlations .Table S1) and asked whether connection strength between these major subdivisions differed at the 1 day and 36 day time-points (see Table S3). We focused in particular, on three a priori predictions of systems consolidation models Regional inactivation or lesions may disrupt expression of a previously-acquired memory. As these retrograde effects are often temporally-graded  Table S3), whereas coupling of activity between hippocampal and neocortical regions increased over time  among cortical regions was more strongly correlated at the 36-day, compared to 1-day, retention delay  play an P≤0.0025) or less  conservative thresholds were used to generate the networks .Our data suggest that recall of long-term fear memories involves coordinated activation of a broad network of brain regions. Graphical representation of these networks suggest that they are complex in nature, with the majority of brain regions (or nodes) having relatively few connections, but a minority of nodes being highly connected (Supplementary Note in Text S1). To assess segregation we initially computed the clustering coefficient for each network. The clustering coefficient measures the tendency for a node's nearest neighbors to be also connected to each other . Moreover, a similar pattern of results was found using alternate segregation measures .Many complex networks, including both anatomical and functional brain networks, have small-world organization Figure S10). Moreover, we found a similar pattern of results using path length as a measure of integration  and micro/meso-scale  brain networks Integration may be evaluated by quantifying either path length  or global efficiency . Using global efficiency as a measure of integration, we found that fear memory networks exhibited equivalent integration compared to random networks (Table S4) that appear to cluster based on their common interaction with the Cg-a. Interestingly, many of the regions within this grouping, including the Cg-a, have previously been implicated in long-term memory expression, especially at remote time-points following training (Table S5). Within the blue cluster, thalamic regions were over-represented (Table S4), and, similar to the green cluster, this grouping contained several regions that play important roles in remote memory expression, including PrL, Ca1-m, C, LD and MD (Table S5).Networks engaged by recall of a long-term fear memory were highly clustered. To examine this structure in more detail we next focused on the network engaged by memory recall at the long retention delay, as this network was densely-connected and fear memory was robustly expressed. We applied the Markov clustering algorithm to systematically organize nodes into discrete modules based on their common inter-connections. This analysis identified eight distinct clusters in the fear memory network, and, in particular, a large densely-connected central component containing two groupings  to be directly connected with one another th percentile rank in our primary network (P≤0.0025) or less  conservative thresholds . However, how any given node interacts with the rest of the network depends not only on the number of connections but also the nature of these connections. We therefore additionally ranked nodes using another measure of centrality, betweenness . Betweenness centrality computes the number of shortest paths between node pairs that pass through a given node, and nodes that have a large number of intermodular connections tend to have high betweenness centrality Figure S13). Of the highly-connected nodes, three regions—Cg-a, PrL, and Re—were also ranked above the 80th percentile in terms of betweenness in each of the low, moderate and high confidence networks  may disproportionately influence network function. In order to identify specific hub regions within our fear memory network, we therefore next ranked all nodes by degree. As expected, nodes within the large, central component were highly ranked. They accounted for the vast majority of regions >80P<0.001), suggesting that regions that show reliable connectivity with the seed regions form a functional network that is engaged during contextual fear memory expression specifically, and not engaged by other non-specific factors of the testing procedure.We next used seed PLS analysis to examine patterns of interactions of the three putative hubs   and tested their memory 1 or 36 days later. As expected, the mutants exhibited accelerated forgetting, with freezing levels greatly reduced at the longer (12.4±5.2%) compared to shorter (49.5±10.5%) retention delay. Importantly, freezing levels at the shorter retention delay were similar to WT mice  = 1.25, P = 0.23). An ANOVA confirmed the differential impact of this mutation on memory at the longer retention delay  = 10.62, P<0.001). Following testing, Fos expression was subsequently quantified in 84 brain regions, and networks generated as previously described . Moreover, compared to random control networks, mutant networks were more segregated, but equivalently integrated, at both the short and long retention delays . Finally, network density declined (rather than increased) as a function of time most likely reflecting no (or very weak) memory expression in the mutants at the remote retention delay , within the hippocampus and between the hippocampus and neocortex between groups . Therefore, time-dependent changes in the nature of functional connections might reflect the transformation of the contextual fear memory from a precise, detailed form into a less precise, generalized form What might these changes in network organization reflect? Levels of freezing were similar at the short and long delays, and therefore distinct networks are unlikely to be directly related to different levels of fear. However, while behavioral output may be similar at these two time points, it is possible that underlying context representations differ. Such representational changes are suggested by time-dependent increases in context generalization following fear conditioning Fear memory networks had a distinct thalamic-hippocampal-cortical signature, and this was most pronounced at the longer retention delay. Interestingly, electrophysiological recording studies in awake, behaving animals suggest similar networks are engaged during encoding and subsequently re-engaged during post-encoding periods such as sleep or even retrieval Table S4), consistent with the idea that hubs play more integral roles in network function. For example, loss of function manipulations  targeting the anterior cingulate cortex In our networks, functional connections reflect statistical, rather than necessarily physical, relationships between brain regions. Accordingly, our network analyses allowed us to generate hypotheses about which brain regions may play privileged roles in memory expression, with highly-connected hub-like regions likelier to influence overall network function. In particular, our analysis revealed that the network engaged by recall of a fear memory more than one month after training contains a densely-interconnected central core of highly-connected hub-like regions. Importantly, this central core included many brain regions that have previously been implicated in the expression of contextual fear memories at such remote time-points +/− mice exhibited equivalent levels of conditioned freezing compared to WT littermates at the short delay, yet underlying functional networks differed considerably. Together, this suggests that a population of network solutions may support contextual fear memory, and raise two related issues. First, because of this degeneracy, pre-training manipulations of key brain regions may not necessarily prevent the formation of a contextual fear memory. For example, pre-training lesions of the hippocampus surprisingly do not always impair acquisition of a contextual fear memory +/− mice were able to form a contextual fear memory, but this memory faded rapidly over time. This highlights that not all degenerate solutions necessarily have the same robustness, although it remains to be determined which specific alterations in network organization in α-CaMKII+/− mice might be causally related to premature forgetting.One interesting observation was that the same (or very similar) behavioral output was associated with distinct functional networks, suggesting that there is a degree of degeneracy at the network level . Following the last footshock mice remained in the context for an additional minute, and then were returned to their home cage. Separate groups of mice were tested either 1 day  or 36 days  later. During testing, mice were placed back in the conditioning chamber for two minutes, and freezing was assessed. Control mice underwent the same procedure but did not receive the footshocks during the conditioning session .Context fear conditioning experiments were conducted in a windowless room containing four conditioning chambers. The chambers were stainless steel . The shock-grid floor consisted of stainless steel shock-grid bars (diameter 3.2 mm) that were spaced 7.9 mm apart. The front, top, and back of the chamber were made of clear acrylic and the two sides made of modular aluminum. Mouse freezing behavior was monitored via four overhead cameras. Freezing was assessed using an automated scoring system , which digitized the video signal at 4 Hz and compared frame by frame movement to determine the amount of time spent freezing. During training, WT and α-CaMKIINinety minutes following the completion of testing, mice were deeply anesthetized then perfused transcardially with phosphate buffered saline (PBS) followed by 4% paraformaldehyde (PFA). The brains were removed, fixed overnight in PFA, then transferred to 30% sucrose solution and stored at 4°C. Fifty µm coronal cryostat sections were cut from the anterior to the posterior of the brain. Every fourth section contributed to a set, creating four sets with sections spaced 200 µm apart. One set was used for Fos and another for Egr-1 immunohistochemical staining. Brains were prepared for immunocytochemistry using an anti Fos (1∶20000) or an anti Egr-1 (1∶7500) primary rabbit polyclonal antibody. A biotinylated goat anti-rabbit antibody (1∶2000) was used as a secondary antibody. Staining was revealed using the avidin-biotin peroxidase method (ABC kit) coupled to diaminobenzidine as a chromogen, as previously described Table S1) and included cortical, thalamic, hippocampal, cerebral and midbrain nuclei. The borders of regions were defined manually according to the Franklin and Paxinos mouse brain atlas Figure S1).Fos expression was analyzed in 84 brain regions . For the majority of brain regions three sections were quantified bilaterally and then a mean count was computed for each animal. These sections were evenly spaced, and typically did not include the most anterior and posterior portions of a region in order to avoid sampling outside the region of interest. In a small subset of regions, counts were based on <3 sections, usually because the anterior-posterior extent of the region ≤600 µm .Quantitative analysis of Fos-positive and Egr-1-positive nuclei was performed on 8-bit grey scale images using Image J software . The threshold for detection of positive nuclei was set at a consistent level for each brain region, and only nuclei 25–125 µmPLS is a multivariate statistical technique that is used to identify optimal patterns of functional activity or connectivity that differentiate conditions Statistical assessment of PLS was performed by using permutation testing for LVs and bootstrap estimation of standard error for the brain region saliences. For the LV, significance was assessed by permutation testing; resampling without replacement by shuffling which condition (trained vs. control) each mouse was assigned to. Following each resampling, the PLS was recalculated. This was done 500 times in order to determine whether the effects represented in a given LV were significantly different to random noise. For brain region salience, reliability was assessed using bootstrap estimation of standard error. Bootstrap tests were performed by resampling 500 times with replacement, while keeping the subjects assigned to their conditions. This reflects the reliability of the contribution of that brain region to the LV. Brain regions with a bootstrap ratio greater than 3  were considered as reliably contributing to the pattern. An advantage to using this approach over univariate methods is that no corrections for multiple comparisons are necessary because the brain region saliences are calculated on all of the brain regions in a single mathematical step +/−/1 day, α-CaMKII+/−/36 days), all possible pairwise correlations between the Fos signal in the 84 regions were determined by computing Pearson correlation coefficients . Each complete set of correlations was computed from a vector of size 8, and were displayed as color-coded correlation matrices using Matlab software . A correlation matrix was also constructed for analysis of Egr-1 expression in 8 regions in the WT/36 day group.Within each of the four experimental groups of animals . We then contrasted mean correlation strength between different major subdivisions at the short vs. long retention delays. These contrasts were selected P<0.005, uncorrected for multiple comparisons. Higher and lower confidence networks were constructed as well to insure that network properties were not dependent on threshold level selection. A threshold of r≥0.8697  was used to generate the high confidence networks and a threshold of r ≥0.7887  was used to generate the low confidence networks. In addition, networks were constructed using Spearman's rank correlation coefficient, with a threshold of r ≥0.8343, which corresponds to a one tailed significance level of P<0.005. The nodes in the networks represent brain regions and the correlations that survived thresholding were considered connections. NetDraw  was used to visualize networks, with node size set proportional to degree (number of connections) and connection line weights reflecting the strength of the correlation. While potentially interesting, we did not consider negative correlations in the current network analyses.Networks were constructed by thresholding inter-regional correlations in each group of animals. The primary networks were constructed by considering correlations with Pearson's r ≥0.8343, which corresponds to a one tailed significance level of https://www.brain-connectivity-toolbox.net/). Definitions and formulae for these graph theory measures have been described in elsewhere Graph theoretical measures were used to characterize properties of the long-term memory networks. For each primary, low and high confidence fear memory network, 1000 random, null hypothesis networks were generated with the same number of active nodes, connections and same degree distribution. Then long-term memory network properties were contrasted with averaged values from these corresponding random, control networks. Network measures and random null hypothesis networks were generated using functions from Olaf Sporns' brain connectivity toolbox . Additionally, we validated our results using a number of alternate clustering algorithms, including community clustering, clusterONE, affinity propagation, connected components and k means clustering. These yielded mostly overlapping results. However, we chose to present results based on Markov clustering since previous studies indicated that this procedure is significantly more tolerant to noise and behaves more robustly than other algorithms http://www.cytoscape.org/). The assortativity coefficient is a correlation coefficient between the degrees of all directly-connected node pairs. A positive assortativity coefficient indicates that nodes tend to link to other nodes with the same or similar degree Cluster analysis was conducted with the Markov Cluster Algorithm (inflation parameter set at 2.6), a scalable, unsupervised cluster algorithm for networks based on simulation of stochastic flow in graphs  Seed PLS is used in the analysis of functional connectivity to explore if seed regions exhibit task-related changes in activity in relation to the rest of the brain. Like task PLS described above, seed PLS identifies LVs through singular value decomposition of correlation maps +/− 1 day retention delay groups, all individual correlations were compared with no a priori predictions (3486 comparisons). Significance differences in individual correlations were determined with permutation testing. Permutation testing involved shuffling subject labels to produce several permuted combinations of the original two groups of data. Following each permutation the test statistic (correlation) was recalculated. The number of events that exceeded the observed correlation statistic was determined and a probability of the number of observed events being greater than expected was assigned. The false discovery rate was controlled at the 5% level because of the number of multiple comparisons.In order to compare correlation matrices for the WT and α-CaMKIIFigure S1Fos quantification. Sample images of Fos immunohistochemical staining from a representative brain section. Images were captured with the 2× objective (red border) and 10× objective (blue border). Higher magnification images are shown for specific hippocampal (CA3), cortical (S1Tr), cerebral nuclei (Ce), hypothalamic (DM) and thalamic (VPM) regions. Dotted lines indicate region borders.(TIF)Click here for additional data file.Figure S2High and low confidence networks in WT mice. Networks formed from thresholding inter-regional Fos correlations in mice tested at the 1 (upper) and 36 day (lower) retention delay using either a high confidence threshold of r >0.87 (left) or a low confidence threshold of r >0.79 (right). Brain regions are grouped by major brain subdivision and node size is proportional to the number of connections (degree) while the weight of the connection is proportional to correlation strength.(TIF)Click here for additional data file.Figure S3Spearman rank correlation networks in WT mice.A. Matrices showing inter-regional Spearman rank correlations for Fos expression at the 1 (upper) and 36 day (lower) retention delays. Axes are numbered, and correspond to brain regions listed in Table S1. Colors reflect correlation strength . B. Network graphs were generated by considering only the strongest correlations (Spearmans's rs≥0.83). In these graphs, regions are grouped by major brain subdivision and node size is proportional to the number of connections (degree) while the weight of the connection is proportional to correlation strength.(TIF)Click here for additional data file.Figure S4Relationship between magnitude and variance of regional Fos signal and correlation strength.A. Overall Fos signal levels  in WT and α-CaMKII+/− mice tested at the 1 and 36 day delay. Fos levels were elevated in the WT mice that were tested at the long retention delay. B. Scatterplot of mean correlation strength (r2) vs. magnitude of Fos signal for each brain region. Data points are taken from all 4 groups tested. There was no relationship between correlation strength and the magnitude of the Fos signal, indicating that group differences in number of functional connections (or network density) cannot be attributed to overall differences in Fos expression. C. Overall coefficient of variation (standard deviation/mean Fos) in WT and α-CaMKII+/− mice tested at the 1 and 36 day delay. Variability did not differ across groups. D. Scatterplot of mean correlation strength (r2) vs. coefficient of variation for each brain region. Data points are taken from all 4 groups tested. While correlation strength typically increased as a function of variance (or coefficient of variation), variance was equivalent across groups and therefore cannot account for increased network connectivity in WT mice at the long retention delay.(TIF)Click here for additional data file.Figure S5Patterns of inter-regional correlations derived from Fos and Egr-1 expression are similar. Matrices showing inter-regional correlations for (A) Fos and (B) Egr-1 expression in a subset of brain regions in WT mice tested at the 36 day retention delay. Colors reflect correlation strength . Overall correlation strength did not differ in the Fos vs. Egr-1 matrices , nor were any individual inter-regional correlations different .(TIF)Click here for additional data file.Figure S6Correspondence between structural and functional connectivity for the reuniens thalamic nucleus (Re). Of the 84 regions investigated, 47 demonstrated direct structural (afferent or efferent) connectivity with the Re in published tract tracing studies in rodents . Regions that demonstrate functional connectivity with the Re in the high (left), primary (center) and low (right) confidence long term memory networks are represented by red circles. For the high and primary networks, all functional connections had corresponding structural connections. For the low confidence network, the majority (22/24) of functional connections had corresponding structural connections.(TIF)Click here for additional data file.Figure S7Degree distribution in high and low confidence networks. Histogram showing degree distribution for fear memory networks in WT mice tested at the 1 day (upper) and 36 day (lower) retention delay. These networks were constructed by imposing either (A) more  or (B) less  stringent thresholds on correlation matrices.(TIF)Click here for additional data file.Figure S8Segregation in high and low confidence networks. Mean clustering coefficient constructed by imposing (A) more  or (B) less  stringent thresholds on correlation matrices for WT mice tested at the 1 day (upper) and 36 day (lower) delay. The bootstrapped mean clustering coefficient for the fear memory networks are compared to the average of 1000 random networks matched for node, degree and degree distribution. Error bars represent 95% confidence intervals.(TIF)Click here for additional data file.Figure S9Alternate measures of segregation for WT mice tested at the 1 and 36 day delay. (A) Transitivity and (B) mean local efficiency measured in fear memory networks for WT mice tested at the 1 day (upper) and 36 day (lower) retention delay. The mean of bootstrapped measures in the fear memory networks are compared to the average of 1000 random networks matched for node, degree and degree distribution. Error bars represent 95% confidence intervals. These alternate measures indicate that fear memory networks are more segregated than would be expected by chance.(TIF)Click here for additional data file.Figure S10Integration measures in high and low confidence networks. Mean global efficiency in graphs constructed by imposing (A) more  or (B) less  stringent thresholds on correlation matrices for WT mice tested at the 1 day (upper) and 36 day (lower) delay. The bootstrapped mean global efficiency for the fear memory networks are compared to the average of 1000 random networks matched for node, degree and degree distribution. Error bars represent 95% confidence intervals.(TIF)Click here for additional data file.Figure S11Alternate measure of integration for WT mice tested at the 1 and 36 day delay. Characteristic path length for fear memory networks for WT mice tested at the 1 day (upper) and 36 day (lower) retention delay. The bootstrapped characteristic path length in the fear memory networks is compared to the average of 1000 random networks matched for node, degree and degree distribution. Error bars represent 95% confidence intervals. Fear memory and random networks had similar characteristic path length, suggesting they have equivalent levels of integration.(TIF)Click here for additional data file.Figure S12Ranked centrality measures in high and low confidence networks. Brain regions are ranked in descending order for degree and betweenness in the (A) high and (B) low confidence long term memory networks. Regions to the left of the hatched line are ranked above the 80th percentile and dark colors indicate regions that are additionally ranked above 80th percentile for both degree and betweenness. These regions were used for hub identification, shown in (TIF)Click here for additional data file.Figure S13Nodes ranked high in betweenness differ from those in random networks. Mean betweenness values for nodes in 100 random networks matched for node, degree and degree distribution is superimposed on the ranked betweenness values in the primary (A), high (B) and low (C) confidence networks. High betweenness nodes differ from those derived from the random networks.(TIF)Click here for additional data file.Figure S14+/− mice.Networks have small-world properties in α-CaMKIIA. Histogram showing degree distribution for fear memory networks corresponding to 1 day (upper) and 36 day (lower) retention delay. B. Mean clustering coefficient for fear memory vs. random network . At both short (upper) and long (lower) retention delays the fear memory network was more clustered. C. Mean global efficiency for fear memory vs. random network. At both short (upper) and long (lower) retention delays global efficiency (or integration) was equivalent in the fear memory vs. random networks. Error bars represent 95% confidence intervals.(TIF)Click here for additional data file.Figure S15Context generalization increases as a function of retention delay.A. Experimental design. Mice were trained in an identical way as in the main experiment (5 footshocks in context A). Freezing was then assessed either 1 day (n = 12) or 36 days (n = 14) later in the training context (context A) and an alternate context (context B). B. Percent time freezing in contexts A and B at the short (1 day) vs. long (36 day) retention delay. C. Context discrimination ([freezingA−freezingB]/[freezingA+freezingB]) declined as a function of retention delay.(TIF)Click here for additional data file.Table S1List of brain regions in which Fos was quantified with corresponding abbreviations (according to supplementary reference  in (PDF)Click here for additional data file.Table S2Direct afferent and efferent connections with the reuniens thalamic nucleus (Re). Connections were identified from published tract tracing studies in rodents (see supplementary references Text S1). Brain structure from which each connection originates from and terminates are listed, together with tracer method and strength of projection (if reported).(PDF)Click here for additional data file.Table S3Mean correlation coefficients within or between major brain subdivisions for WT mice tested 1 vs. 36 days after training, with corresponding 95% confidence interval of the difference. Time-dependent changes in correlation strength were only considered significant if the 95% confidence interval of the difference was greater than zero (in red). mPFC = medial prefrontal cortex .(PDF)Click here for additional data file.Table S4Over-representation of cortical, hippocampal and thalamic regions in the densely-interconnected central component. Using a clustering algorithm, the fear memory network (for the WT/36 day group) was organized into eight distinct clusters that included, in particular, a large densely-connected central component containing two groupings (green and blue nodes). This table reports the proportion of brain regions in major brain subdivisions with respect to the total number of regions analyzed, as well as the proportion of brain regions in major brain subdivisions with respect to the total number of regions found in the blue and green clusters. Cortical and hippocampal regions were over-represented in the green cluster, and thalamic regions were over-represented in the blue cluster (highlighted in red).(PDF)Click here for additional data file.Table S5Brain regions implicated in long-term memory expression at remote time-points following training. A pubmed-based search was conducted to identify brain regions where targeted, loss-of-function manipulations affect expression of remote memory expression (see supplementary references Text S1). Behavioral paradigms were considered if acquisition and/or initial memory expression  were dependent upon the hippocampus. Loss-of-function manipulations included anatomical lesions, pharmacological inactivation, DNA demethylation, and testing occurred at least 25 days after the completion of training. DNMT = DNA methyltransferases. adorsal hippocampus, bventral hippocampus, cdorsal+ventral hippocampus.(PDF)Click here for additional data file.Text S1Supplementary methods, results, notes and references.(PDF)Click here for additional data file.


Nematostella vectensis (starlet sea anemone) provides a molecular genetic view into the first nervous systems, which appeared in a late common ancestor of cnidarians and bilaterians. Nematostella has a surprisingly large and diverse set of neuronal signaling genes including paralogs of most neuronal signaling molecules found in higher metazoans. Several ion channel gene families are highly expanded in the sea anemone, including three subfamilies of the Shaker K+ channel gene family: Shaker (Kv1), Shaw (Kv3) and Shal (Kv4). In order to better understand the physiological significance of these voltage-gated K+ channel expansions, we analyzed the function of 18 members of the 20 gene Shaker subfamily in Nematostella. Six of the Nematostella Shaker genes express functional homotetrameric K+ channels in vitro. These include functional orthologs of bilaterian Shakers and channels with an unusually high threshold for voltage activation. We identified 11 Nematostella Shaker genes with a distinct “silent” or “regulatory” phenotype; these encode subunits that function only in heteromeric channels and serve to further diversify Nematostella Shaker channel gating properties. Subunits with the regulatory phenotype have not previously been found in the Shaker subfamily, but have evolved independently in the Shab (Kv2) family in vertebrates and the Shal family in a cnidarian. Phylogenetic analysis indicates that regulatory subunits were present in ancestral cnidarians, but have continued to diversity at a high rate after the split between anthozoans and hydrozoans. Comparison of Shaker family gene complements from diverse metazoan species reveals frequent, large scale duplication has produced highly unique sets of Shaker channels in the major metazoan lineages.The genome of the cnidarian  NematostellaDrosophila and Caenorhabditis elegans, voltage-gated channel families are often represented by a single or only a few extant genes + channel families The classical view of cnidarian nervous system as a simple, diffuse nerve net has been eroded by recent anatomical and genetic findings. Sequencing of the Nematostella vectensis (anthozoan) and Hydra magnipapillata (hydrozoan) genomes revealed that the molecular building blocks of the nervous system  are conserved between cnidarians and bilaterians Part of the answer to this question is undoubtedly that cnidarian nervous systems are more complex than originally thought. For instance, neural development in Nematostella appears to be driven by a complex cascade of highly conserved neuronal transcription factors Nematostella Shaker, or Kv1, potassium channel subfamily. The Drosophila Shaker gene was the first K+ channel cloned + channels which includes four closely related gene subfamilies (Shaker (Kv1), Shab (Kv2), Shaw (Kv3) and Shal (Kv4)) +-selective pore; Tetrameric assembly in Shaker family channels is promoted by a unique N-terminal cytoplasmic domain, T1 + channels in vitro. Bilaterian Shaker subfamily channels characteristically activate near action potential threshold with rapid kinetics and are thus well suited to regulate action potential threshold and repolarization + channel subunits function only in heteromeric channels and have previously been identified in the mammalian Shab and Polyorchis Shal subfamilies In order to better understand the physiological role of voltage-gated ion channel diversity in cnidarians, we functionally characterized the highly expanded Nematostella vectensis husbandry was carried out according to best practices developed in the Nematostella community to optimize animal health.Drosophila melanogaster and Caenorhabditis elegans have been well established and were downloaded from RefSeq Nematostella vectensis  Hydra magnipapillata  Trichoplax adhaerens (Placozoa) Caenorhabditis briggsae (Nematoda) Helobdella robusta (Annelida) Capitella teleta (Annelida) Lottia gigantea (Mollusca) Daphnia pulex (Arthropoda) Anopheles gambiae (Arthropoda) Strongylocentrotus purpuratus (Echinodermata) Branchiostoma floridiae  Polyorchis penicillatus (Hydrozoa) Shaker family sequences were also included in phylogenetic analysis  using standard techniques. Multiple independent clones were fully sequenced for each gene to verify the coding sequence. Predicted amino acid sequences from verified sequences are included in Clones of full coding sequences were shuttled to the pOX expression vector - solution , pH 7.2). Bath clamp circuitry was isolated using a 1 M NaCl-agarose bridge. Electrodes were filled with 3 M KCl and had resistances of 0.4–1 MΩ.Whole cell two-electrode voltage clamp (TEV) currents were recorded using a Dagan CA–1B amplifier  and the pClamp acquisition suite . Currents were leak subtracted using P/N protocol to remove linear leak (less than 50 nA) and capacitive transients. Recordings were carried out under constant perfusion of a low Cl+, NaMES was replaced with KMES. Grounds were bridged as described for TEV. Currents were recorded using a Multiclamp 700B amplifier and the pClamp acquisition suite . Pipette capacitance and series resistance were compensated, and bath offsets were cancelled prior to seal formation. Patch pipettes were coated with Sticky Wax  to reduce capacitance and fire polished to a resistance of 0.6–1.5 MΩ.For excised inside out patch recordings, oocytes were first shrunk in a hypertonic stripping solution 1-A2)/(1+ e((V-V50)/s)) + A2, where g(V) is the conductance at voltage V, 50V is the half-maximal conductance value, s is the slope factor and 1A and 2A are the minimum and maximum, respectively. Normalized data are displayed as mean ± S.E.M. with a simulated Boltzmann fit (G/Gmax = 1/(1+ e-((V-V50)/s)) in which V50 and s are fixed to the mean values obtained from fits of data from individual cells. Single exponential fits of the late phase of activation (defined as the last 50% of current rise) and tail current decay used the equation I(t) = Ii+Ae-t/Τ, where I(t) is the current at time t, Ii is the initial current, A is the amplitude of the fit and Τ is the time constant. Sigmoidal delay of activation was quantified as the time between the start of the voltage pulse and the time at which an exponential fit of the late phase of activation intercepted the zero current line. Exponential fitting was carried out in Clampfit (Molecular Devices) and Boltzmann fitting was carried out in Origin .Boltzmann fits of voltage activation and steady state inactivation data from individual cells used the equation g(V) = . Sequences used and source citations (where applicable) are given in We identified 20 Nematostella Shaker subfamily genes using mouse Shaker amino acid sequences to BLAST query Nematostella Shaker ORFs revealed 14 intronless gene loci  + sensitive. This result suggests that NvShak4 may inactivate by both N-type and C-type mechanisms, similar to the Drosophila Shaker A splice variant + (data not shown).Intrinsic inactivation in Shaker channels depends on an N-terminal ball and chain mechanism (N-type) or a K+ to maximize inward tail currents and to reduce C-type inactivation in NvShak5. We observed two distinct activation phenotypes in the Nematostella channels. One group of channels, consisting of NvShak1 and NvShak4-6 had low activation thresholds and V50 values ranging from −20 to −30 mV; this range of voltage activation is similar to (but slightly more depolarized than) that of typical bilaterian Shaker channels such as mouse Kv1.2  family contains an expansion of 10 genes that do not functionally express as homomultimers We reasoned that the 12 Nematostella Shaker genes that did not express functional homomultimeric channels Five Nematostella Shakers  did indeed introduce an inactivating component when expressed with NvShak3 . Since wSeven of Nematostella Shaker regulatory subunits measurably altered activation and/or deactivation of NvShak3, including NvShakR8. Examples of whole cell currents recorded in response to depolarizing voltage steps  are shown for each heteromeric combination compared to homomeric NvShak3 in Only one of the putative regulatory subunits, NvShakR10, failed to produce a measurable alteration of NvShak3. Our experiments do not rule out a regulatory functional role for NvShakR10; it may co-assemble with other NvShak subunits or regulate functional properties that we could not easily measure in this biophysical assay. For instance, we would not have detected a shift of activation to more depolarized potentials because of contamination with homomeric NvShak3 currents. NvShakR subunits could alternatively also affect modulation, trafficking or subcellular localization or of Shaker channels. For instance, inclusion of the mammalian Shaker channel subunit Kv1.5 in heteromeric channels confers regulation by Src-family tyrosine kinases in vivo, as these will depend on which subunits are co-expressed and whether NvShakR subunits are able to functionally co-assemble with multiple NvShak1-6 partners. We cannot rule out that co-expression of multiple NvShakR channels together would result in functional channels, but mammalian Shab-related regulatory subunits do not co-assemble into functional channels (data not shown). Nevertheless, combinatorial expression of NvShak1-6 with regulatory subunits could clearly provide a huge diversity of Shaker channel phenotypes in Nematostella.These experiments do not address what heteromeric channel combinations may exist HTTV), NvShak5 (-QTSV) NvShak6 (-DGFV) and NvShakR10 (-EFTV) differ from this consensus by a single conservative substitution (underlined); each substitution has been observed in a few PDZ-binding motifs Most mammalian Shaker genes (Kv1.1-1.7) contain C-terminal PDZ-binding motifs that anchor the channels to synapses and axons through interaction with MAGUK (membrane-associated guanylate kinase) family PDZ proteins such as PSD-95 and PSD-93 in situ hybridization , Shab (Kv2), Shaw (Kv3) and Shal (Kv4) gene families) using amino acid sequences from mammals, amphioxus, sea urchin, insects, lophotrochozoans, nematodes, trichoplax, and the cnidarians Nematostella, Hydra and Polyorchis. The Shaker subfamily branch of the phylogeny is shown in + channels is conserved across cnidarian orders. However, we found only a few cases of clear orthology between Nematostella and Hydra channels suggesting that much of the Shaker diversity in these species might therefore derive from separate anthozoan and hydrozoan gene expansions. Only 7 strongly supported branches in the Shaker subfamily tree contain sequences from both species  diversification occurred after the anthozoan/hydrozoan split. We assumed for this analysis that NvShakR10 has a regulatory phenotype since it does not express as a homomultimer. We also assumed that NvShakR3 and NvShakR13, two incomplete and thus untested clones, also are most likely regulatory subunits because they group closely and exclusively with proven regulatory subunits in the phylogeny. Only two clades exclusively contain Nematostella regulatory subunits and also contain sequences from Hydra. This indicates that the cnidarian-specific regulatory phenotype was present in ancestral cnidarians, but that much of the elaboration of the Nematostella regulatory subunit set occurred after the divergence from Hydrozoa. Late divergence is strongly supported by several instances of separate clustering for Nematostella and Hydra regulatory subunits. However, it is possible that the fast evolutionary divergence observed for regulatory subunits obscures some ancestral relationships between clusters. Cnidarian-specific gene expansions observed in the Shaw and Shal subfamilies also show a high degree of separate duplication in Nematostella and Hydra , S3, andth putative ancestral channel in the Shal subfamily is indicated by the close grouping of one Nematostella and one hydrozoan branch with bilaterian sequences separate from other cnidarian sequences. The Polyorchis member of this group is required for functional expression of Polyorchis Shal channels and is functionally orthologous to bilaterian Shal channels Analysis of the entire Shaker family tree suggests the presence of at least 13 Shaker family genes  in ancestral cnidarians , S2, S3.The presence of Shaker, Shab and Shaw orthologs in the placozoan Trichoplax adhaerens suggests that the Shaker family may predate the origin of true nervous systems. The placozoan genome suggests that these simple animals belong to the eumetaozan clade despite the apparent absence of a nervous system de novo emergences of subunits with a regulatory phenotype in the cnidarian Shaker and Shal subfamilies and the vertebrate Shab subfamily. Thus the major metazoan phyla have very unique sets of Shaker family genes.The results presented here show that a high functional diversity of Shaker currents has been preserved throughout eumetazoan evolution. Fast activation, diverse inactivation rates, N-type and C-type inactivation mechanisms and PDZ-dependent clustering may be universal functional properties of the Shaker subfamily within Metazoa. Vertebrates, protostomes and cnidarians all have distinct molecular strategies for producing kinetically diverse Shaker currents. Surprisingly, functional diversity of the Shaker family appears to be highest in cnidarians. Cnidarians have functional orthologs of diverse bilaterian Shaker channels and have so far cnidarian-specific high threshold channels and regulatory subunits. Amphioxus and some lophotrochozoans also have large Shaker gene expansions , but the+ channels in cnidarians may be normal rather than an exception Nematostella could enable this question to be answered.Our results from studies of the Shaker family indicate that the surprising genomic diversity of voltage-gated ion channels in cnidarians does indeed translate into extensive functional diversity of voltage-gated currents. How this functional diversity contributes to cnidarian neurophysiology is not yet known, but is a topic of considerable interest. Whole genomic analysis suggests the genetic complexity we see in voltage-gated KFigure S1Expanded view of the Shab subfamily tree from the Shaker family phylogeny. The Shab subfamily clade is shown with Nematostella highlighted in red, Hydra in green and mammals in blue. The circled number labels a single ancestral cnidarian branch and is numbered consecutively relative to the Shaker subfamily clade which contains ancestral branches 1–7 (ches 1–7 . Bars at(PDF)Click here for additional data file.Figure S2Expanded view of the Shaw subfamily tree of the Shaker phylogeny. Three ancestral cnidarian branches (9–11) are labeled with circled numbers and 6 species-restricted expansions of >3 genes  are indicated with bars. Color schemes, branch labels, gene name species codes and scale bar are identical to those used in (PDF)Click here for additional data file.Figure S3Full Shal subfamily tree of the Shaker phylogeny. Two putative ancestral Cnidarian branches  are labeled with circled numbers and 4 species-restricted expansions of >3 genes are indicated with bars. Color schemes, scales, branch labels and gene names follow the conventions of previous phylogeny figures.(PDF)Click here for additional data file.Table S1Amino acid sequences for metazoan Shaker family channels.(XLSX)Click here for additional data file.Table S2DNA sequences for the coding regions of Nematostella Shaker subfamily channels.(XLSX)Click here for additional data file.Table S3Summary of Nematostella Shaker expression patterns.(PDF)Click here for additional data file.