, 2006). Pseudomonas fluorescens 2P24 is an effective biocontrol agent of plant disease caused by soilborne pathogens (Wei et al., 2004b; Yan et al., 2004). The antibiotic 2,4-DAPG is a major biocontrol determinant in strain 2P24 (Wei et al., 2004a). The luxI and luxR homologues pcoI and pcoR have been shown to be involved in biofilm formation, colonization signaling pathway of wheat

rhizosphere and in suppressing wheat take-all (Wei & Zhang, 2006). In the present study, we describe the identification and characterization of the hfq gene, a global regulator that influences the production of 2,4-DAPG and the expression of the PcoI–PcoR QS system in P. fluorescens 2P24. The bacterial strains and plasmids used in this study are described in Table 1. Pseudomonas fluorescens strains were cultivated

in Luria–Bertani (LB; Sambrook et al., 1989), King’s B (KB; King et al., 1954) or AB minimal medium (ABM; Chilton et al., 1974) at 30 °C. Escherichia coli strains were grown in LB at 37 °C. Agrobacterium tumefaciens NTL4 (pZLR4) indicator strain (Cha et al., 1998) was grown in ABM at 30 °C. When required, the growth media were supplemented with ampicillin (50 μg mL−1), kanamycin (50 μg mL−1), LGK-974 supplier tetracycline (20 μg mL−1), gentamycin (30 μg mL−1), streptomycin sulfate (200 μg mL−1) or 5-bromo-4-chloro-3-indolyl-β-d-galacto-pyranoside (X-Gal) (40 μg mL−1). Plasmid DNA extractions and other molecular assays were performed according to standard procedures (Sambrook et al., 1989). Electroporation of bacterial cells with plasmid DNA was performed as described previously (Wei & Zhang, 2006). Sirolimus clinical trial Nucleotide sequencing was performed by Sunbiotechnology Co. Ltd (Beijing, China). Nucleotide and deduced amino acid sequences were analyzed using programs of the National Center for Biotechnology Information

blast server (Altschul et al., 1997) (http://www.ncbi.nlm.nih.gov/BLAST). The promoter region of phlA was amplified by PCR using primers phl2267 and phl3010 (Supporting Information, Table S1) and was cloned ahead of a promoterless lacZ gene in pRG970Gm (Table 1) derived from pRG970b (Van den Eede et al., 1992). The resulting plasmid p970Gm-phlA was used for phlA promoter analysis. To screen for novel regulators of antibiotic production, strain 2P24 carrying a phlA-lacZ transcriptional fusion in the pGm970-phlA vector was subjected to random mini-Tn5 insertion mutagenesis using the mini-Tn5 suicide plasmid pUT-Km, following a method described by Herrero et al. (1990). Approximately 10 000 gentamycin- and kanamycin-resistant P. fluorescens colonies carrying Tn5 were incubated on ABM plates containing X-Gal. Colony color and intensity were visually assessed after 18–36 h of growth at 30 °C. Colonies with decreased β-galactosidase activity (indicated by a more intense white color) were selected and purified.

For generating HopF1 expression vector, HopF1 encoding sequence was amplified from genomic DNA of Psp 1449B race 7 with sequence-specific primers, and then inserted into pGG7R2-V. Primers and corresponding enzyme restriction sites used are listed in Table S2. In vitro transcription and rub-inoculation of bean leaves was carried out according to Kachroo et al. (2008). Following inoculation, plants were maintained in the growth chamber

at 25 °C with a photoperiod of 16 h. Total RNA was extracted from bean leaves click here with TRIzol reagent (Invitrogen). Transcript levels of RIN4 and HopF1 were determined by reverse transcriptase (RT)-PCR or Northern blotting. For RT-PCR, cDNA was synthesized from total RNA through a Thermoscript RT-PCR system (Invitrogen), with oligo(dT)18 primers, following the manufacturer’s instructions. RT-PCR was performed using Taq DNA polymerase (TaKaRa) with the

gene-specific primers as shown in Table S2. β-Tubulin was used as standards for mRNA expression. For Northern blot analysis, 10 μg of total RNA was loaded in each lane. The RNA gel blot was hybridized with the Dig-labeled random-primed probes (Roche). A yeast two-hybrid assay was performed with the MATCHMAKER Two-Hybrid System 3 from Clontech according to the manufacturer’s handbook. HopF1 was amplified from genomic DNA of Psp race 7 by using specific primers and inserted into bait (pGADT7) Alectinib manufacturer plasmid after the same restriction. PvRIN4 proteins were amplified from common bean cDNA by using specific primers and inserted into the prey (pGBKT7) plasmid. Gene-specific primers used above are listed in Table S2. Growth of yeast strain AH109 cotransfected with constructed pGADT7 and pGBKT7 was on SD/His-Trp-Leu plates. HopF1 was amplified from genomic DNA of Psp 1449B race 7 and inserted into the pUC19-35S-FLAG-RBS (Li et al., 2005) plasmid to give the HopF1-FLAG construct. PvRIN4a and PvRIN4b were PCR amplified for from bean cDNA and inserted into

the pUC19-35S-HA-RBS (Wang et al., 2010) plasmid to generate the PvRIN4-HA construct. Gene-specific primers are listed in Table S2. Arabidopsis protoplasts were prepared and transfected with PvRIN4a/b-HA alone or in combination with HopF1-FLAG as described previously (Asai et al., 2002). Following transient expression overnight, Arabidopsis protoplasts were harvested for protein extraction with IP buffer (Wang et al., 2010). Total protein was immunoprecipitated with anti-FLAG antibody. The presence of HopF1-FLAG and PvRIN4-HA in the immunocomplex was detected by immunoblot with a monoclonal anti-FLAG antibody and anti-HA antibody (Perfect Biotechnology) respectively. Previous studies showed that HopF2 can inhibit Arabidopsis PTI responses, including ROS production, callose deposition and MAPK activation (Wang et al., 2010). HopF1 is a homolog of HopF2 in Psp.

GHSR KO mice, however, did not show these alterations despite having normal glucocorticoid responses to stress. In parallel with these changes, chronic unpredictable Cyclopamine stress caused changes in norepinephrine, dopamine and serotonin in a number of brain regions. Of these, norepinephrine neurotransmission in the arcuate nucleus and prefrontal cortex was differentially altered

in GHSR KO mice. Within the nucleus acumbens, dopamine utilization was increased in WT mice but not in GHSR KO mice. Finally, there were strain differences in serotonin neurotransmission that may explain interstrain body weight and adiposity differences. These results suggest that the metabolic changes necessary to deal with the energetic challenge presented by repeated exposure to stressors do not occur in GHSR KO mice, and they are discussed within the context of the potential vulnerability to

stress-induced pathology. “
“Department of Neurology & Neurotherapeutics, UT Southwestern Medical Center, Dallas, TX, USA Brain-derived neurotrophic factor (BDNF) plays a critical role in plasticity at glutamate synapses and in the effects of repeated cocaine exposure. We recently showed that intracranial injection of BDNF into the rat nucleus accumbens (NAc), a key region for cocaine addiction, rapidly increases α-amino-3-hyroxy-5-methyl-4-isoxazole-propionic acid receptor (AMPAR) surface expression. To further characterize Selleck Sirolimus BDNF’s role in both rapid AMPAR trafficking and slower, homeostatic changes in AMPAR surface expression, we investigated the effects of acute (30 min) and long-term (24 h) treatment with BDNF on AMPAR distribution in NAc medium spiny neurons from postnatal rats co-cultured with mouse prefrontal cortex neurons to restore excitatory inputs. Immunocytochemical

studies showed that acute BDNF treatment increased cell surface GluA1 and GluA2 levels, as well as their co-localization, on NAc neurons. This effect of BDNF, confirmed Ergoloid using a protein crosslinking assay, was dependent on ERK but not AKT signaling. In contrast, long-term BDNF treatment decreased AMPAR surface expression on NAc neurons. Based on this latter result, we tested the hypothesis that BDNF plays a role in AMPAR ‘scaling down’ in response to a prolonged increase in neuronal activity produced by bicuculline (24 h). Supporting this hypothesis, decreasing BDNF signaling with the extracellular BDNF scavenger TrkB-Fc prevented the scaling down of GluA1 and GluA2 surface levels in NAc neurons normally produced by bicuculline. In conclusion, BDNF exerts bidirectional effects on NAc AMPAR surface expression, depending on duration of exposure. Furthermore, BDNF’s involvement in synaptic scaling in the NAc differs from its previously described role in the visual cortex.

Furthermore, hyperinsulinaemia

(a characteristic of insulin resistance) is a major risk factor for coronary artery disease in non-HIV-infected individuals [34,35]. There are no generally accepted criteria for diagnosing insulin resistance in routine clinical practice. The so-called “gold-standard” of euglycaemic clamping is useful for intensive physiological studies of small numbers of subjects, but a simpler method such as use of the HOMA-IR index has proved to be robust and is more appropriate for epidemiological studies [36]. We found that HOMA-IR is a strong independent predictor of IGT or DM, which suggests that it may usefully indicate patients who should undergo an OGTT in order to investigate their glucose metabolism further. The association between low CD4 cell counts and IGT or DM is less clear and more difficult to explain. One hypothesis

is that patients with see more low CD4 cell counts have higher concentrations of pro-inflammatory cytokines, which stimulate lipolysis and inhibit adipose tissue lipogenesis, thus exacerbating Caspase inhibitor increases in fatty acid concentrations (reviewed in Florescu and Kotler [37]). We were unable to verify this hypothesis because we did not assay cytokine levels in parallel with glycaemia but, as the independent association between CD4 cell counts and IGT or DM was not confirmed by our second multivariable model, further studies are necessary to determine whether this association is strong and consistent. Unlike others [6–8,21,38], we did not find that the classic risk factors for DM, including gender, age, lipid profile DNA ligase and family history, were associated with abnormal glucose tolerance. This may be explained by the fact that most of our patients had a normal BMI and waist circumference, a negative family history of DM, and a normal lipid profile, and were <50 years old, and/or by the fact that we used the combination of IGT and DM as the dependent variable rather than full-blown

DM alone. Patients coinfected with HCV are at higher risk of developing abnormal glucose metabolism, including DM [10,39–41], and Huang et al. found a higher prevalence of OGTT-detected pre-diabetes or DM in patients with chronic HCV infection and normal FPG levels than in uninfected controls [42]. We may therefore have overestimated the risk of IGT in our HIV-infected patients because of the high prevalence of HCV coinfection; we did not find a clear association between HBV coinfection and OGTT-detected IGT or DM, but this was probably because of the small number of patients with HBV infection, the small number of patients with IGT or DM, or both. Our study has one further limitation: as there is no accepted threshold for defining abnormal HOMA-IR values, we used the median value in our population and this may not apply to different settings; further studies are necessary to identify a shared and validated threshold of this insulin-resistance index.

Biofilm formation by Bradyrhizobium was first described by Sereviratne and Jayasingherachchi (2003). Since then, both bacterial and plant surface molecules have been shown to be involved in the establishment of microbial communities on legume roots. In the symbiosis between Bradyrhizobium PR-171 purchase sp. and peanut plant, the attachment level varies

depending on the metabolic state of the rhizobia. Optimal attachment was observed when cells were harvested at the late log or the early stationary phase of growth (Dardanelli et al., 2003). A 14-kDa calcium-binding protein is important for bacterial attachment to the plant root, because root incubation with this adhesin before the attachment assay resulted in a significant, dose-dependent decrease of attachment. EDTA treatment of the cells caused the release of the rhicadhesin-like protein from the bacterial surface into the culture medium, and bacterial attachment was restored (Dardanelli et al., 2003). Plant lectins are proteins that reversibly and nonenzymatically bind specific carbohydrates (De Hoff GDC-0449 et al., 2009). They play important roles during the early stages of interaction

between the host plant and the symbiotic bacteria, particularly in the initial attachment of rhizobia to root epidermal cells. Soybean lectin causes a dose-dependent increase of attachment and biofilm formation on polystyrene surface by Bradyrhizobium japonicum wild-type USDA 110 cultures (Pérez-Giménez et al., 2009). Preincubation of rhizobia with soybean lectin increases bradyrhizobial adhesion to soybean roots (Lodeiro et al., 2000). Exopolysaccharides seem to be involved in B. japonicum biofilm formation on both inert and biotic surfaces (Pérez-Giménez et al., 2009). A mutant, which lacks UDP-Glc-4′ epimerase activity and produces second low levels of a shorter exopolysaccharide lacking galactose, showed biofilm biomass less than that of the wild-type strain. The defective phenotype was not

restored by soybean lectin addition to the mutant culture. Adhesion of mutant cells to soybean roots was significantly lower than that of the wild-type strain, indicating that complete exopolysaccharide is required for efficient colonization of B. japonicum on soybean (Pérez-Giménez et al., 2009). Attachment of R. leguminosarum to plant root hairs has two steps: primary attachment mediated by either bacterial adhesins (Smit et al., 1992) or plant lectins (Dazzo et al., 1984) and then secondary attachment via cellulose fibrils on the bacterial surface (Dazzo et al., 1984). Rhizobium leguminosarum, like many other bacteria, forms biofilms on sterile inert surfaces (Fujishige et al., 2005, 2006). The biofilm formation ability, assessed by a microtiter plate assay, was much lower in a pSym-deficient mutant than in R. leguminosarum bv.

To determine if these isolates showed the she PAI associated with the set1 gene, the presence of other genes contained in this PAI, the pic, sigA and sap genes, was studied. Only two isolates carried the three genes indicating the presence of the whole island, 22 showed the pic and sap genes and eight only the pic gene. This indicates the high variability Tacrolimus datasheet in the structure of this PAI. In contrast to the ShET-1 toxin, the ShET-2 toxin encoded by the sen gene was more frequent among isolates collected from patients who had taken quinolones before isolation of the bacteria. This toxin was significantly more frequent among nalidixic

acid-resistant isolates (15% vs. 6%, P=0.046), and 35% of ShET2-positive Cobimetinib molecular weight isolates belonged to phylogenetic group B1 (P=0.0001). The EAST-1 toxin was more frequently found in the E. coli isolates collected from patients with septic shock (19% vs.

8%, P=0.07). No B2 isolates had this toxin; it was more frequently found among isolates belonging to the A, B1 and D phylogenetic groups (P=0.02). Finally, the AggR transcriptional factor encoded by the aggR gene was more frequently found among isolates collected from patients with chronic renal insufficiency (37.8% vs. 12%, P=0.03) and from patients with pneumonia (33% vs. 12%, P=0.09). The presence of this transcriptional factor was not associated with any phylogenetic group, and it was more frequently found among isolates forming biofilm (18% vs. 9%, P=0.08) (Table 1). The presence of genes encoding enterotoxins and a transcriptional factor involved in virulence were analysed in E. coli isolates collected from patients with bacteraemia. The ShET-1 toxin has been described in S. flexneri 2a and has also been detected in other bacterial taxa such as Y. enterocolitica, S. typhimurium and E. coli (Al-Hasani et al., 2001). This toxin has been found in EAEC causing diarrhoea (Mohamed et al., 2007; Mendez-Arancibia et al.,

2008). In both of these studies, an association was observed between the presence of the set1 gene and biofilm production. Thus, 43% of biofilm producers presented this gene in contrast to 6% of nonbiofilm producers (P=0.0004). These results are in agreement with those obtained in the present study. This ability to form biofilm is a trait that is closely associated with bacterial persistence and virulence, and many persistent these and chronic bacterial infections are now believed to be linked to the formation of biofilm (Mohamed et al., 2007). There seems to be a relationship between the presence of the set1 gene and nalidixic acid susceptibility. In fact, set1 was more frequent among nalidixic acid-susceptible isolates. A possible explanation for this phenomenon may be that this gene is contained in the she PAI. This PAI is a chromosomal, laterally acquired, integrative element of S. flexnerii that carries genes with established or putative roles in virulence (Mohamed et al., 2007).

63, P > 0.5). We relied on neurons that had spatial selectivity for the location of the stimuli, whose discharge rate was therefore informative

about the location of the salient stimuli, and with at least three error trials learn more in the level 3 difficulty condition (Fig. 1D). A total of 63 neurons from dlPFC and 62 neurons from LIP satisfied these criteria and were used in this analysis. The time of target discrimination was computed for each area by comparing the responses to the salient stimulus in receptive field with distractors in receptive fields, using correct trials from stimulus presentations of difficulty level 1 (Fig. 2A and C) and level 3 (Fig. 2B and D). Consistent with a previous study from our laboratory that reported an early involvement of the dlPFC in bottom-up attention (Katsuki & Constantinidis, 2012a), the times of target discrimination were similar in this sample of neurons too, and in fact slightly earlier in dlPFC than LIP, for both level 1 stimulus (126 ms after stimulus onset in dlPFC, 133 ms in LIP) and level 3 stimulus

(171 ms in dlPFC and 183 ms in LIP). Behavioral outcomes were categorized into two groups, corresponding to correct and error trials. Only trials with lever errors following the match or non-match periods were identified as error trials for this analysis; errors Selleck Epigenetic inhibitor due to breaks in fixation at any point, or releases of the lever before the offset of the stimulus, were excluded from analysis. Average firing rates of correct trials (dlPFC, 1140 trials; LIP, 1208 trials) and error trials (dlPFC, 525 trials; LIP, 832 trials) were plotted separately for each area (Fig. 3A and B). On average, the firing rates of error trials were lower than those of the correct trials in both dlPFC and LIP. To quantify the relationship between behavioral choices and neuronal responses, we performed a ROC analysis to compute the probability of distinguishing between the distributions of error and correct trials, involving identical stimulus

conditions, a quantity also known as choice probability (Britten et al., 1996), based on signal detection theory (see ‘Materials and methods’). The area under the ROC curve using the firing rate of correct trials Methamphetamine and error trials represents the choice probability for each neuron. The choice probability was computed in a time-resolved fashion, in 250-ms windows, sliding in 50-ms intervals (Fig. 3C). The average dlPFC choice probability was significantly different from 0.5 for the cue and delay period (t-test; Cue, t62 = 5.15, P < 10−5; Delay, t62 = 4.25, P < 10−4), while significantly higher LIP choice probability than 0.5 was observed in all three task epochs (t-test; Fixation, t61 = 3.91, P < 0.001; Cue, t61 = 5.31, P < 10−5; Delay, t61 = 7.05, P < 10−8). A significant difference was present between areas in terms of choice probability.

63, P > 0.5). We relied on neurons that had spatial selectivity for the location of the stimuli, whose discharge rate was therefore informative

about the location of the salient stimuli, and with at least three error trials find more in the level 3 difficulty condition (Fig. 1D). A total of 63 neurons from dlPFC and 62 neurons from LIP satisfied these criteria and were used in this analysis. The time of target discrimination was computed for each area by comparing the responses to the salient stimulus in receptive field with distractors in receptive fields, using correct trials from stimulus presentations of difficulty level 1 (Fig. 2A and C) and level 3 (Fig. 2B and D). Consistent with a previous study from our laboratory that reported an early involvement of the dlPFC in bottom-up attention (Katsuki & Constantinidis, 2012a), the times of target discrimination were similar in this sample of neurons too, and in fact slightly earlier in dlPFC than LIP, for both level 1 stimulus (126 ms after stimulus onset in dlPFC, 133 ms in LIP) and level 3 stimulus

(171 ms in dlPFC and 183 ms in LIP). Behavioral outcomes were categorized into two groups, corresponding to correct and error trials. Only trials with lever errors following the match or non-match periods were identified as error trials for this analysis; errors Birinapant due to breaks in fixation at any point, or releases of the lever before the offset of the stimulus, were excluded from analysis. Average firing rates of correct trials (dlPFC, 1140 trials; LIP, 1208 trials) and error trials (dlPFC, 525 trials; LIP, 832 trials) were plotted separately for each area (Fig. 3A and B). On average, the firing rates of error trials were lower than those of the correct trials in both dlPFC and LIP. To quantify the relationship between behavioral choices and neuronal responses, we performed a ROC analysis to compute the probability of distinguishing between the distributions of error and correct trials, involving identical stimulus

conditions, a quantity also known as choice probability (Britten et al., 1996), based on signal detection theory (see ‘Materials and methods’). The area under the ROC curve using the firing rate of correct trials Paclitaxel and error trials represents the choice probability for each neuron. The choice probability was computed in a time-resolved fashion, in 250-ms windows, sliding in 50-ms intervals (Fig. 3C). The average dlPFC choice probability was significantly different from 0.5 for the cue and delay period (t-test; Cue, t62 = 5.15, P < 10−5; Delay, t62 = 4.25, P < 10−4), while significantly higher LIP choice probability than 0.5 was observed in all three task epochs (t-test; Fixation, t61 = 3.91, P < 0.001; Cue, t61 = 5.31, P < 10−5; Delay, t61 = 7.05, P < 10−8). A significant difference was present between areas in terms of choice probability.

Its elements are specific BMS-354825 nmr for subgroups or even single strains and are likely acquired by horizontal gene transfer (HGT). Similarities of the accessory genomic elements to DNA from other bacterial species, mainly the DNA of γ- and β-proteobacteria, indicate a role of interspecies HGT. In this study, we analysed the expression of the accessory genome in 150 clinical P. aeruginosa isolates as uncovered by transcriptome sequencing and the presence of accessory genes in eleven additional isolates.

Remarkably, despite the large number of P. aeruginosa strains that have been sequenced to date, we found new strain-specific compositions of accessory genomic elements and a high portion (10–20%) of genes without P. aeruginosa homologues. Although some genes were detected to be expressed/present in several isolates, individual patterns regarding the genes, their functions and the possible origin of the DNA were widespread among the tested strains. Our results demonstrate the unaltered potential to discover new traits within the P. aeruginosa population and underline that the P. aeruginosa pangenome is likely to increase with increasing sequence information. “
“Depending on the genetic background

of Saccharomyces strains, a wide range of phenotypic adhesion identities can be directly attributed to the FLO11-encoded glycoprotein, which includes asexual flocculation, invasive growth and pseudohyphal formation, flor formation and adhesion to biotic and abiotic surfaces. In a previous study, we reported that AZD5363 supplier HSP30-mediated stationary-phase expression of the native chromosomal FLO11 ORF in two nonflocculent commercial Saccharomyces cerevisiae wine yeast strains, BM45 or VIN13 did not generate a flocculent phenotype second under either standard laboratory media or synthetic MS300 must fermentation conditions. In the present study, the BM45- and

VIN13-derived HSP30p-FLO11 wine yeast transformants were observed to be exclusively and strongly flocculent under authentic red wine-making conditions, thus suggesting that this specific fermentation environment specifically contributes to the development of a flocculent phenotype, which is insensitive to either glucose or mannose. Furthermore, irrespective of the strain involved this phenotype displayed both Ca2+-dependent and Ca2+-independent flocculation characteristics. A distinct advantage of this unique FLO11-based phenotype was highlighted in its ability to dramatically promote faster lees settling rates. Moreover, wines produced by BM45-F11H and VIN13-F11H transformants were significantly less turbid than those produced by their wild-type parental strains. Primarily driven by the economic importance of flocculation to downstream processing in the brewing industry, a concerted attempt was made to understand the genetics of flocculation.

In

contrast, the glial scar, evaluated by glial fibrillary acidic protein staining, showed its highest intensity 21 BTK inhibitor days post-injury in both models. The number of apoptotic oligodendrocytes, detected by CC1/caspase-3 co-labeling, was increased in both models in all evaluated regions. Finally, the numbers of OPCs, evaluated with the markers Tcf4 and Olig2, were increased from day 2 (Olig2) or day 7 (Tcf4) post-injury (P ≤ 0.05). Our results indicate that TBI induces oligodendrocyte apoptosis and widespread myelin loss, followed by a concomitant increase in the number of OPCs. Prevention of myelin loss and oligodendrocyte death may represent novel therapeutic targets for TBI. “
“Working memory (WM) performance in humans can be improved by structuring and organizing the material to be remembered. For visual and verbal information, this process of structuring has been associated with the involvement of a prefrontal–parietal network, but for non-verbal auditory material, the brain areas that facilitate WM for structured information have remained elusive. Using functional magnetic resonance imaging, this study compared neural correlates underlying encoding and rehearsal of auditory WM for structured and unstructured material.

Musicians and non-musicians performed a WM task on five-tone sequences that were either tonally structured (with all tones KU-60019 cost belonging to one tonal key) or tonally unstructured (atonal) sequences. Functional differences were observed for musicians (who are experts in the music domain), but not for non-musicians – The right

pars orbitalis was activated more strongly in musicians during the encoding of unstructured (atonal) vs. structured (tonal) sequences. In addition, data for musicians showed that a lateral (pre)frontal–parietal network (including the right premotor cortex, right inferior precentral sulcus and left intraparietal sulcus) was activated during WM rehearsal of structured, as compared with unstructured, sequences. Our findings indicate that this network plays a role in strategy-based WM for non-verbal auditory information, corroborating previous results Fenbendazole showing a similar network for strategy-based WM for visual and verbal information. “
“Parkinson’s disease is most commonly modelled via unilateral infusion of the neurotoxin 6-hydroxydopamine (6-OHDA) in the rat, but recent work has been aimed to translate the reproducibility and reliability of the model to the mouse. Here we present the effects of unilateral 6-OHDA lesions to either the medial forebrain bundle or the substantia nigra (SN) in mice, which were trained on a lateralised choice reaction time (RT) task.