News Article | March 4, 2016
Mice were housed in the Unit for Laboratory Animal Medicine at the Whitehead Institute for Biomedical Research and Koch Institute for Integrative Cancer Research. The following strains were obtained from the Jackson Laboratory: Lgr5-EGFP-IRES-CreERT2 (strain name: B6.129P2-Lgr5tm1(cre/ERT2)Cle/J, stock number 008875), Rosa26-lacZ (strain name: B6.129S4-Gt(ROSA)26Sortm1Sor/J, stock number 003474), db/db (strain name: B6.BKS(D)-Leprjb/J, stock number 000697), PpardL/L (strain name: B6.129S4-Ppardtm1Rev/J, stock number 005897). Apcloxp exon 14 (ApcL/L) has been previously described41. Villin-CreERT2 was a gift from S. Robine. Long-term HFD was achieved by feeding male and female mice a dietary chow consisting of 60% kcal fat (Research Diets D12492) beginning at the age of 8–12 weeks and extending for a period of 9–14 months. Control mice were sex- and age-matched and fed standard chow ad libitum. GW501516 (Enzo) was reconstituted in DMSO at 4.5 mg ml−1 and diluted 1:10 in a solution of 5% PEG400 (Hampton Research), 5% Tween80 (Sigma), 90% H O for a daily intraperitoneal injection of 4 mg kg−1. Apc exon 14 was excised by tamoxifen suspended in sunflower seed oil (Spectrum S1929) at a concentration of 10 mg ml−1 and 250 μl per 25 g of body weight, and administered by intraperitoneal injection twice over 4 days before collecting tissue. PpardL/L mice were administered 4–5 intraperitoneal injections of tamoxifen on alternate days. Mice were analysed within 2 weeks of the last tamoxifen injection. BrdU was prepared at 10 mg ml−1 in PBS, passed through a 0.22-μm filter and injected at 100 mg kg−1. As previously described1, tissues were fixed in 10% formalin, paraffin embedded and sectioned. Antigen retrieval was performed with Borg Decloaker RTU solution (Biocare Medical) in a pressurized Decloaking Chamber (Biocare Medical) for 3 min. Antibodies used: rat anti-BrdU (1:2,000 (immunohistochemistry (IHC)), 1:1,000 (immunofluorescence (IF)) Abcam 6326), rabbit chromogranin A (1:4,000 (IHC), 1:250 (IF), Abcam 15160), rabbit monoclonal non-phospho β-catenin (1:800 (IHC), 1:400 (IF), CST 8814S), mouse monoclonal β-catenin (1:200, BD Biosciences 610154), rabbit polyclonal lysozyme (1:250, Thermo RB-372-A1), rabbit polyclonal MUC2 (1:100, Santa Cruz Biotechnology 15334), rabbit monoclonal OLFM4 (1:10,000, gift from CST, clone PP7), Biotin-conjugated secondary donkey anti-rabbit or anti-rat antibodies were used from Jackson ImmunoResearch. The Vectastain Elite ABC immunoperoxidase detection kit (Vector Labs PK-6101) followed by Dako Liquid DAB+ Substrate (Dako) was used for visualization. For immunofluorescence, Alexa Fluor 568 secondary antibody (Invitrogen) was used with Prolong Gold (Life Technologies) mounting media. All antibody incubations involving tissue or sorted cells were performed with Common Antibody Diluent (Biogenex). Organoids were fixed with 4% paraformaldehyde, permabilized with 0.5% Triton X-100 in PBS, rinsed with 100 mM glycine in PBS, blocked with 10% donkey serum in PBS, incubated overnight with primary antibody at 4 °C, rinsed and incubated with Alexa Fluor 568 secondary antibody (Invitrogen), and mounted with Prolong Gold (Life Technologies) mounting media. The in situ hybridization probes used in this study correspond to expressed sequence tags or fully sequenced cDNAs obtained from Open Biosystems. The accession numbers (IMAGE mouse cDNA clone in parenthesis) for these probes are as follows: mouse Olfm4 BC141127 (9055739), mouse Crp4 BC134360 (40134597). Both sense and antisense probes were generated to ensure specificity by in vitro transcription using DIG RNA labelling mix (Roche) according to the manufacturer’s instructions and to previously published detailed methods23, 42. Single-molecule in situ hybridization was performed using Advanced Cell Diagnostics RNAscope 2.0 HD Detection Kit. Adult mice were exposed to 15 Gy of ionizing irradiation from a 137-caesium source (GammaCell) and euthanized after 72 h. The number of surviving crypts per length of the intestine was enumerated from haematoxylin-and-eosin-stained sections15. Antibodies: rabbit polyclonal anti-PPAR-δ (1:100, Thermo PA1-823A), rabbit polyclonal anti-CPT1a (1:250, ProteinTech 15184-1-AP), rabbit polyclonal anti-HMGCS2 (1:500, Sigma AV41562), rabbit monoclonal anti-FABP1 (1:1,000, Abcam ab129203), NF-κB Sampler Pathway Kit (CST, 9936S), mouse monoclonal anti-STAT-3 (CST, 9139P), rabbit monoclonal anti-P-STAT3 (Y705) XP (CST, 9145P), mouse monoclonal anti-CREB (CST, 86B10), mouse monoclonal anti-β-catenin (1:200, BD Biosciences 610154), rabbit polyclonal anti-γ-tubulin (1:1,000, Sigma T5192). For immunoprecipitation assays, crypts were collected and nuclear extraction was carried out using Abcam nuclear extraction kit (ab113474) following manufacturer’s instructions. Nuclear extracts were incubated with 5 μg anti-PPAR-δ antibody (Thermo), or anti-rabbit IgG control antibody (Santa Cruz) overnight at 4 °C followed by 2 h of incubation with Dynabeads Protein G for immunoprecipitation. Protein complexes bound to antibody and beads were washed five times and eluted with Laemmli sample buffer. Samples were resolved by SDS–PAGE. Protein interaction was analysed by immunoblotting. Lgr5-GFPhi ISCs or Lgr5-GFPlow progenitors were sorted directly into Laemmli sample buffer and boiled for 5 min. Samples were resolved by SDS–PAGE and analysed by immunoblotting with horseradish peroxidase (HRP)-conjugated IgG secondary antibodies (1:10,000, Santa Cruz Biotechnology sc-2054) and Western Lightning Plus-ECL detection kit (Perkin Elmer NEL104001EA) As previously reported and briefly summarized here, small intestines and colons were removed, washed with cold PBS without magnesium chloride and calcium (PBS−/−) opened longitudinally, and then cut into 3–5-mm fragments. Pieces were washed several times with cold PBS−/− until clean, washed 2–3 with PBS−/− EDTA (10 mM), incubated on ice for 90–120 min, and gently shook at 30-min intervals. Crypts were then mechanically separated from the connective tissue by more rigorous shaking, and then filtered through a 70-μm mesh into a 50-ml conical tube to remove villus material (for small intestine) and tissue fragments. Crypts were removed from this step for crypt culture experiments and embedded in Matrigel with crypt culture media. For ISC isolation, the crypt suspensions were dissociated to individual cells with TrypLE Express (Invitrogen). Cell labelling consisted of an antibody cocktail comprising CD45-PE (eBioscience, 30-F11), CD31-PE (Biolegend, Mec13.3), Ter119-PE (Biolegend, Ter119), CD24-Pacific Blue (Biolegend, M1/69), CD117-APC/Cy7 (Biolegend, 2BS), and EPCAM-APC (eBioscience, G8.8). ISCs were isolated as Lgr5-EGFPhiEpcam+CD24low/−CD31−Ter119−CD45−7-AAD−. EGFPlow progenitors were isolated as EGFPlowEpcam+CD24low/−CD31−Ter119−CD45−7-AAD−, and Paneth cells from small intestine were isolated as CD24hiSidescatterhiLgr5-EGFP−Epcam+CD31−Ter119−CD45−7-AAD− with a BD FACS Aria II SORP cell sorter into supplemented crypt culture medium for culture. Dead cells were excluded from the analysis with the viability dye 7-AAD (Life Technologies). When indicated, populations were cytospun (Thermo Cytospin 4) at 800 r.p.m. for 2 min, or allowed to settle at 37 °C in fully humidified chambers containing 5% CO onto poly-l-lysine-coated slides (Polysciences). The cells were subsequently fixed in 4% paraformaldehyde (pH 7.4, Electron Microscopy Sciences) before staining. Isolated crypts were counted and embedded in Matrigel (Corning 356231 growth factor reduced) at 5–10 crypts per μl and cultured in a modified form of medium as described previously13. Unless otherwise noted, Advanced DMEM (Gibco) was supplemented by EGF 40 ng ml−1 (R&D), Noggin 200 ng ml−1 (Peprotech), R-spondin 500 ng ml−1 (R&D or Sino Biological), N-acetyl-l-cysteine 1 μM (Sigma-Aldrich), N2 1X (Life Technologies), B27 1X (Life Technologies), Chiron 10 μM (Stemgent), Y-27632 dihydrochloride monohydrate 20 ng ml−1 (Sigma-Aldrich). Colonic crypts were cultured in 50% conditioned medium derived from L-WRN cells supplemented with Y-27632 dihydrochloride monohydrate 20 ng ml−1, as described43. Approximately 25–30 μl droplets of Matrigel with crypts were plated onto a flat bottom 48-well plate (Corning 3548) and allowed to solidify for 20–30 min in a 37 °C incubator. Three hundred microlitres of crypt culture medium was then overlaid onto the Matrigel, changed every 3 days, and maintained at 37 °C in fully humidified chambers containing 5% CO . Clonogenicity (colony-forming efficiency) was calculated by plating 50–300 crypts and assessing organoid formation 3–7 days or as specified after initiation of cultures. Palmitic acid (Cayman Chemical Company 10006627 conjugated to BSA), oleic acid (Sigma O1008), lipid mixture (Sigma L0288), or GW501516 (Enzo) were added immediately to cultures at 30 μM (palmitic acid, oleic acid), 2% (lipid mixture), and 1 μM (GW501516). 4-OH tamoxifen (Calbiochem, 579002, 10 nM) was added to organoid cultures derived from PpardL/L; Villin-CreERT2 (Ppard IKO) crypts to ensure Ppard excision in the ex vivo fatty acid or GW501516 experiments. Isolated ISCs or progenitor cells were centrifuged for 5 min at 250g, re-suspended in the appropriate volume of crypt culture medium (500–1,000 cells μl−1), then seeded onto 25–30 μl Matrigel (Corning 356231 growth factor reduced) containing 1 μM Jagged (Ana-Spec) in a flat bottom 48-well plate (Corning 3548). Alternatively, ISCs and Paneth cells were mixed after sorting in a 1:1 ratio, centrifuged, and then seeded onto Matrigel. The Matrigel and cells were allowed to solidify before adding 300 μl of crypt culture medium. The crypt media was changed every second or third day. Organoids were quantified on days 3, 7 and 10 of culture, unless otherwise specified. For secondary organoid assays, either individual primary organoids or many primary organoids were mechanically dissociated and then replated, or organoids were dissociated for 10 min in TrypLE Express at 32 °C, resuspended with SMEM (Life Technologies), centrifuged (5 min at 250g) and then resuspended in cold SMEM with the viability dye 7-AAD. Live cells were sorted and seeded onto Matrigel as previously described in standard crypt media (not supplemented with lipids or GW501516). Secondary organoids were enumerated on day 4, unless otherwise specified. Human biopsies were obtained from patients with informed consent undergoing intestinal resection at the Massachusetts General Hospital (MGH). The MGH Institutional Review Board committee and Massachusetts Institute of Technology Committee on the Use of Humans as Experimental Subjects approved the study protocols. Crypts were isolated43, embedded in Matrigel and subsequently exposed to lipid mixture, palmitic acid or GW501516 (as described in earlier). Cultures were passaged weekly and maintained for 3–4 weeks. To passage, equal numbers of organoids from each condition were disrupted with trypsin/EDTA. Numbers of organoids were counted 4–7 days after passaging into control media. Counts were normalized to numbers of organoids present in control wells and plotted. Statistical significance was calculated by performing analysis of variance (ANOVA) multiple comparisons of the means for each group. For quantitative RNA expression analysis, organoids were dissociated, cells were selected as a live population by flow cytometry (7-AAD, Life Technologies), and sorted into Tri Reagent (Life Technologies) for RNA isolation. After 5 days of culturing, intestinal organoids were placed into Karnovsky’s KII solution (2.5% glutaraldehyde, 2.0% paraformaldehyde, 0.025% calcium chloride, in a 0.1 M sodium cacodylate buffer, pH 7.4) and fixed overnight. Subsequently, they were post-fixed in 2.0% osmium tetroxide, stained en bloc with uranyl acetate, dehydrated in graded ethanol solutions, infiltrated with propylene oxide/Epon mixtures, flat embedded in pure Epon, and polymerized overnight at 60 °C. Then 1-μm sections were cut, stained with toluidine blue, and examined by light microscopy. Representative areas were chosen for electron microscopic study and the Epon blocks were trimmed accordingly. Thin sections were cut with an LKB 8801 ultramicrotome and diamond knife, stained with Sato’s lead, and examined in a FEI Morgagni transmission electron microscope. Images were captured with an AMT (Advanced Microscopy Techiques) 2K digital CCD camera. For RNA sequencing (RNA-seq), total RNA was extracted from 200,000 sorted Lgr5-GFPhi ISCs and Lgr5-GFPlow progenitors by pooling 2–5 71-week-old HFD male or control mice using Tri Reagent (Life Technologies) according to the manufacturer’s instructions, except for an overnight isopropanol precipitation at −20 °C. From the total RNA, poly(A)+ RNA was selected using Oligo(dT) -Dynabeads (Life technologies) according to the manufacturer’s protocol. Strand-specific RNA-seq libraries were prepared using the dUTP-based, Illumina-compatible NEXTflex Directional RNA-Seq Kit (Bioo Scientific) according to the manufacturer’s directions. All libraries were sequenced with an Illumina HiSeq 2000 sequencing machine. For RNA-seq data analysis, raw stranded reads (40 nucleotides) were trimmed to remove adaptor and bases with quality scores below 20, and reads shorter than 35 nucleotides were excluded. High-quality reads were mapped to the mouse genome (mm10) with TopHat version 1.4.1 (ref. 44), using known splice junctions from Ensembl Release 70 and allowing at most two mismatches. Genes were quantified with htseq-count (with the ‘intersect strict’ mode) using Ensembl Release 70 gene models. Gene counts were normalized across all samples using estimateSizeFactors from the DESeq R/Bioconductor package45. Differential expression analysis was also performed between two samples of interest with DESeq. GSEA (http://software.broadinstitute.org/gsea/index.jsp) was performed by using the pre-ranked (according to their ratios) 8,240 differentially expressed genes as the expression data set. Motif Analysis was performed using Haystack motif enrichment tool: http://github.com/lucapinello/Haystack46. In total, 24 single Lgr5-GFPhi ISCs and 72 single Lgr5-GFPlow progenitor cells were sorted from control or HFD-fed mice (n = 2 mice per group) for single-cell gene expression analysis. For one-tube single-cell sequence-specific preamplification, individual primer sets of β-catenin target genes (total of 96, Supplementary Table 2) were pooled to a final concentration of 0.1 mM for each primer. Single cells were directly sorted into 96-well plates containing 5 μl RT–PCR master mix (2.5 μl CellsDirect reaction mix, Invitrogen; 0.5 μl primer pool; 0.1 μl reverse transcriptase/Taq enzyme, Invitrogen; 1.9 μl nuclease-free water) in each well. Immediately after, plates were placed on PCR machine for preamplification. Sequence-specific preamplification PCR protocol was as following: 60 min at 50 °C for cell lysis and sequence-specific reverse transcription; then 3 min at 95 °C for reverse transcriptase inactivation and Taq polymerase activation. cDNA was then amplified by 20 cycles of 15 s at 95 °C for initial denaturation, 15 min at 60 °C for annealing and elongation. After preamplificiation, samples were diluted 1:5 before high-throughput microfluidic real-time PCR analysis using Fluidigm platform. Amplified single-cell cDNA samples were assayed for gene expression using individual qRT–PCR primers and 96.96 dynamic arrays on a BioMark System by following manufacturers protocol (Fluidigm). To confirm PPAR-δ-mediated induction of the most upregulated genes (n = 3 mice, 24 ISCs and 72 progenitors per group), or for single-cell analysis of organoid composition (n = 3 mice, 48 cells per group) and db/db mice (n = 3, 48 cells per group) standard single-cell qRT–PCR was performed using preamplified cDNA with corresponding primers. For Fluidigm analysis, threshold cycle (C ) values were calculated using the BioMark Real-Time PCR Analysis software (Fluidigm). See Supplementary Information for raw gene expression data. Gene expression levels were estimated by subtracting the C values from the background level of 35, which approximately represent the log gene expression levels. The t-Distributed stochastic neighbour embedding (t-SNE) analysis47 was performed using the MATLAB toolbox for dimensionality reduction. Differential expression analysis was conducted using the two-sided Wilcoxon–Mann–Whitney rank sum test implemented in the R coin package (https://www.r-project.org). P values were adjusted for multiple testing48 using the p.adjust function in R with method = ‘fdr’ option. Fold changes were calculated as the difference of median of log expression levels for the two cell populations. Split violin plots were generated using the vioplot package and the vioplot2 function in R (https://gist.github.com/mbjoseph/5852613). The heatmap for β-catenin target genes was generated with the MultiExperiment Viewer (MeV) program (http://www.tm4.org/mev.html) using the correlation-based distance and average linkage method as parameters of the unsupervised hierarchical clustering of genes. The heatmap for organoid composition was generated using MATLAB. The percentages of Jag1/Jag2-upregulated cells were calculated based on the number of single cells whose log expression was above 15. Approximately 25,000 cells were sorted into Tri Reagent (Life Technologies) and total RNA was isolated according to the manufacturer’s instructions with following modification: the aqueous phase containing total RNA was purified using the RNeasy plus kit (Qiagen). RNA was converted to cDNA with the cDNA synthesis kit (Bio-Rad). qRT–PCR was performed with diluted cDNA (1:5) in three wells for each primer and SYBR green master mix (Bio-Rad) on Bio-Rad iCycler RT–PCR detection system. For organoid experiments, 1,000 live cells were sorted and qRT–PCR optimized for low cell numbers (<1,000) was performed after sequence specific pre-amplification (cDNA diluted 1:200 in three wells for each primer) as described in single-cell gene expression analysis. All qRT–PCR experiments were repeated at least three independent times. Primers used are listed on Supplementary Table 1. ApcL/L; Lgr5-EGFP-IRES-CreERT2 mice were treated with vehicle or GW501516 for 1 month, and then injected with two intraperitoneal doses of tamoxifen. Four days later, Apc-null Lgr5-GFPhi ISCs and Lgr5-GFPlow progenitors were sorted by flow cytometry, as described earlier. For primary cell transplantations, 10,000 Apc-null Lgr5-GFPhi ISCs and Lgr5-GFPlow progenitors were resuspended into 90% crypt culture media (as described) and 10% Matrigel, then transplanted into the colonic lamina propria of C57BL/6 recipient mice by optical colonoscopy using a custom injection needle (Hamilton Inc., 33-gauge, small Hub RN NDL, 16 inches long, point 4, 45 degree bevel, like part number 7803-05), syringe (Hamilton Inc. part number 7656-01), and transfer needle (Hamilton Inc. part number 7770-02). Optical colonoscopy was performed using a Karl Storz Image 1 HD Camera System, Image 1 HUB CCU, 175 Watt Xenon Light Source, and Richard Wolf 1.9mm/9.5 Fr Integrated Telescope (part number 8626.431). Four injections were performed per mouse. Mice then underwent colonoscopy 8 weeks later to assess tumour formation. Colonoscopy videos and images were saved for offline analysis. Following sacrifice, the distal colons were excised and fixed in 10% formalin, then examined by haematoxylin and eosin section to identify adenomas. Histology images were reviewed by gastrointestinal pathologists who were blinded to the treatment groups (S.S., V.D. and Ö.H.Y.). All experiments reported in Figs 1, 2, 3, 4, 5 were repeated at least three independent times, except for Figs 3a, 4c, d, which were repeated twice. All samples represent biological replicates. For mouse organoid assays, 2–4 wells per group with at least 3 different mice were analysed. For human organoid assays, 4 wells per group with 4 different patient samples were analysed and experiments were repeated 4 times. All centre values shown in graphs refer to the mean. For statistical significance of the differences between the means of two groups, we used two-tailed Student’s t-tests. Statistical significance in Fig. 3k was calculated by performing ANOVA multiple comparisons of the means for each group. No samples or animals were excluded from analysis, and sample size estimates were not used. Animals were randomly assigned to groups. Studies were not conducted blinded, with the exception of all histological analyses and Fig. 5c, h. All experiments involving mice were carried out with approval from the Committee for Animal Care at MIT and under supervision of the Department of Comparative Medicine at MIT.
News Article | January 26, 2016
While this event was first predicted almost twenty years ago, evidence for it has proved elusive. Now, researchers from the University of Glasgow have demonstrated the Meselson effect for the first time in any organism at a genome-wide level, studying a parasite called Trypanosoma brucei gambiense (T.b. gambiense). Their findings are to be published in the journal eLife. The research was conducted at the Wellcome Trust Centre for Molecular Parasitology in the University's Institute of Biodiversity Animal Health and Comparative Medicine. T.b. gambiense is responsible for causing African sleeping sickness in humans, leading to severe symptoms including fever, headaches, extreme fatigue, and aching muscles and joints, which do not occur until weeks or sometimes even months after infection. These symptoms extend to neurologic problems, such as progressive confusion and personality changes, when the infection invades the central nervous system. In order to demonstrate the Meselson effect in T.b. gambiense, the research team, led by Dr. Annette Macleod, sequenced the genomes of 85 isolates of the parasite, including multiple samples from disease focus points within Guinea, Cote d'Ivoire and Cameroon, collected over fifty years from 1952 to 2004. The similarity of the genomes studied from these different locations, together with a lack of recombination in the evolution of the parasite, suggests that this sub-species emerged from a single individual within the last 10,000 years. "It was around this time that livestock farming was developing in West Africa, allowing the parasite, which was originally an animal organism, to 'jump' from one species to the other via the Tsetse fly," says lead author Dr. Willie Weir. "Since then, mutations have built up and the lack of sexual recombination in T.b. gambiense means that the two chromosomes in each pair have evolved independently of each other, demonstrating the Meselson effect." Dr. Weir adds that the parasites' inability to recombine with each other prevents genes from being exchanged between strains. This could subsequently hamper the ability of the organism to develop resistance to multiple drugs. The team also uncovered evidence that the parasite uses gene conversion to compensate for its lack of sex. This mechanism essentially repairs the inferior, or mutated, copy of a gene on a chromosome by 'copying and pasting' the superior copy from the chromosome's partner. The future challenge will be to investigate the effectiveness of this mechanism in the long term, as evolutionary theory suggests that asexual organisms should eventually face extinction. If T.b. gambiense shares this fate, the major cause of African sleeping sickness will be eliminated - although it is impossible to predict when this might happen. Explore further: Sequence is scaffold to study sleeping sickness More information: William Weir et al. Population genomics reveals the origin and asexual evolution of human infective trypanosomes, eLife (2016). DOI: 10.7554/eLife.11473
News Article | December 7, 2016
All animal work was approved by the Committee for Animal Care of the Division of Comparative Medicine at the Massachusetts Institute of Technology. Adult (3-month-old) male double transgenic 5XFAD Cre mice were produced by crossing 5XFAD transgenic mice with the transgenic PV or CW2 promoter driven Cre line. Adult (5-month-old) male and female APP/PS1 mice were gifted from the Tonegawa laboratory. Adult (4-month-old) male TauP301S mice were obtained from the Jackson Laboratory. Nine-month-old WT mice (C57Bl/6) were obtained from the Jackson Laboratory. Mice were housed in groups of three to five on a standard 12 h light/12 h dark cycle, and all experiments were performed during the light cycle. Food and water were provided ad libitum unless otherwise noted. Littermates were randomly assigned to each condition by the experimenter. The experimenter was blind to animal genotypes during tissue processing and electrophysiological recording and analysis. No animals were excluded from analysis. Adeno-associated viral (AAV) particles of serotype 5 were obtained from the Vector Core Facility at The University of North Carolina at Chapel Hill. The AAV5 virus contained a channelrhodopsin-2 (ChR2) fused to eYFP in a double-floxed, inverted, open-reading-frame (DIO) driven by the EF1α promoter (Extended Data Fig. 2a). An AAV-DIO–eYFP construct was used as a control. Three-month-old 5XFAD/PV-Cre or CW2 mice were anaesthetized with an intraperitoneal (i.p.) injection of a mixture of ketamine (1.1 mg/kg) and xylazine (0.16 mg/kg). A small craniotomy was made 2.0 mm posterior to bregma and 1.8 mm lateral to the midline on the left side. Virus was delivered through a small durotomy by a glass micropipette attached to a Quintessential Stereotaxic Injector (Stoelting). The glass micropipette was lowered to 1.2 mm below the brain surface. A bolus of 1 μL of virus (AAV-DIO-ChR2–eYFP or AAV-DIO–eYFP; 2 × 1012 viral molecules per millilitre) was injected into the CA1 region of the hippocampus at 0.075 μL min−1. The pipette remained in place for 5 min following the injection before being retracted from the brain. A unilateral optical fibre implant (300 μm core diameter; Thor Labs) was lowered to 0.9 mm below the brain surface about the injection site. Two small screws anchored at the anterior and posterior edges of the surgical site were bound with dental glue to secure the implant in place. For electrophysiological recordings, adult (3-month-old) male 5XFAD/PV-Cre and 5XFAD negative littermates (for CA1 recordings), or 5XFAD and their WT littermates (for VC recordings) mice were anaesthetized using isoflurane and placed in a stereotactic frame. The scalp was shaved, ophthalmic ointment (Puralube Vet Ointment, Dechra) was applied to the eyes, and Betadine and 70% ethanol were used to sterilize the surgical area. For CA1 recordings, a craniotomy (in millimetres, from bregma: −2 anterior/posterior, 1.8 medial/lateral) was opened to deliver 1 μL of virus to CA1 (as described above). The target craniotomy site for LFP recordings was marked on the skull (in mm, from bregma: −3.23 anterior/posterior, 0.98 medial/lateral for CA1 and 2.8 anterior/posterior, 2.5 medial/lateral for VC), three self-tapping screws (F000CE094, Morris Precision Screws and Parts) were attached to the skull, and a custom stainless steel headplate was affixed using dental cement (C&B Metabond, Parkell). On the day of the first recording session, a dental drill was used to open the LFP craniotomies (300–400 μm diameter) by first thinning the skull until ~100 μm thick, and then using a 30-gauge needle to make a small aperture. The craniotomy was then sealed with a sterile silicone elastomer (Kwik-Sil WPI) until recording that day and in between recording sessions. Two to four weeks after virus injection and implant placement (which provided time for the mice to recover and undergo behaviour training for animals used for electrophysiology, and the virus to express in the neurons), CA1 neurons were optogenetically manipulated. A 200 mW, 4,793 nm DPSS laser was connected to a patch cord with a fibre channel/physical contact connector at each end. During the experiment, 1 mW (measured from the end of the fibre) of optical stimulation was delivered for 1 h. For molecular and biochemical analyses, each animal received one of three stimulation protocols: 8 Hz, 40 Hz, or random stimulation (light pulses were delivered with a random interval determined by a Poisson process with an average frequency of 40 Hz). eYFP control animals received 40 Hz stimulation. For electrophysiological recordings, each animal received all stimulation conditions interleaved during recordings. Fifteen minutes before the experiment, 5XFAD mice were treated with saline (control) or picrotoxin (0.18 mg/kg)25. For molecular and biochemical analyses, mice were then placed in a dark chamber illuminated by a light-emitting diode (LED) bulb and exposed to one of five stimulation conditions: dark, light, 20 Hz, 40 Hz (12.5 ms light on, 12.5 ms light off, 60 W), 80 Hz flicker for 1h. For electrophysiological recordings, each animal received dark, light, 40 Hz flicker, or random (light pulses were delivered with a random interval determined by a Poisson process with an average interval of 40 Hz) stimulation conditions interleaved in 10 s blocks during recordings. For CA1 recordings, head-fixed animals ran on an 8-inch spherical treadmill supported by an air cushion through a virtual reality environment, as described in ref. 30. The motion of the spherical treadmill was measured by an optical mouse and fed into virtual reality software31, running in MATLAB (version 2013b, Mathworks). The virtual environment consisted of a linear track with two small enclosures at the ends where the animal could turn (Extended Data Fig. 1a). Animals were rewarded with sweetened condensed milk (diluted 1:2 in water) at each end of the track for alternating visits to each end of the track. Animals learned to run on the virtual linear track over approximately 1 week. The animals were left to recover from the surgery for 1 week, and habituated to handling for 1–2 days before behavioural training began. To learn to manoeuvre on the treadmill and get comfortable in the testing environment, on the first 2 days of training the animals were placed on the spherical treadmill with the virtual reality system off and were rewarded with undiluted sweetened condensed milk. On the second day of training on the spherical treadmill, the animals’ food was restricted to motivate them to run. Animals were restricted to no more than 85% of their baseline weight and typically weighed over 88% of their baseline weight. From the third day until the end of training (typically 5–7 days) the animals were placed on the treadmill for increasing amounts of time (30 min to 2 h) running in the VR linear track. Animals were rewarded with diluted (1:2) sweetened condensed milk at the end of the linear track after traversing the length of the track. Between recording sessions, animals were given refresher training sessions to maintain behavioural performance. For VC recordings, animals ran on the spherical treadmill while exposed to dark, light, or light-flickering conditions (described below in data acquisition). Before recordings, animals learned to manoeuvre on the treadmill and get comfortable in the testing environment by being placed on the spherical treadmill (with the virtual reality system off) and receiving a reward of undiluted sweetened condensed milk. For optogenetic stimulation of CA1 during recording, a 300 μm core optical fibre was advanced through the craniotomy used to deliver virus to CA1 to a depth of 900 μm into the brain. Light pulses that were 1 ms and 1 mW (measured from the end of the fibre) were delivered via a 473 nm DPSS (diode pumped solid state) laser (as described above). To avoid photoelectric artefacts, neural activity was recorded with glass electrodes. LFP electrodes were pulled from borosilicate glass pipettes (Warner) on a filament-based micropipette puller (Flaming-Brown P97, Sutter Instruments), to a fine tip, which was then manually broken back to a diameter of ~10–20 μm and filled with sterile saline. For CA1 recordings the LFP electrode was advanced through the LFP recording craniotomy at an angle 60 degrees posterior to the coronal plane and 45° inferior to the horizontal plane until clear electrophysiological signatures of the hippocampal stratum pyramidale layer were observed (~600–1000 μV theta waves while the animal was running, clearly distinguishable SWRs during immobility, and multiple spikes greater than 150 μV; Extended Data Fig. 1b). For VC recordings, the LFP electrode was advanced vertically through the LFP recording craniotomy to a depth of 600–900 μm and multiple spikes greater than 150 μV were observed. Data were acquired with a sampling rate of 20 kHz and bandpass filtered 1 Hz to 1 kHz. Animals ran on the spherical treadmill or rested for prolonged periods. For optogenetic simulation sessions, data were recorded for 30 min before any stimulation began. Then stimulation was delivered at gamma (40 Hz), random (as described under Optogenetic stimulation protocol), or theta (8 Hz) frequency for 10 s periods interleaved with 10 s baseline periods (no stimulation). In two animals, stimulation of each type or baseline was delivered for 5 min periods instead of 10 s periods. Each 30 min of stimulation recordings were followed by 5–30 min of recording with no stimulation. For visual light flicker simulation sessions, LED striplights surrounding the animal lights were flickered at gamma (40 Hz), random (described above in Visual stimulation protocol), theta (8 Hz), or 20 Hz frequency for 10 s periods, or were on continuously for 10 s periods, interleaved with 10 s periods with lights off. A few recordings were made above the brain surface during light flicker to ensure that the lights did not create electrical or photoelectric noise during recording. Recording sessions were terminated after approximately 3–5 h. Animals were 3–4 months old at the time of recording. Spikes were detected by thresholding the 300–6,000 Hz bandpassed signal. Threshold was the median of the filtered signal plus five times a robust estimator of the standard deviation of the filtered signal (median/0.675) to avoid contamination of the standard deviation measure by spikes32. Recorded traces were downsampled to 2 kHz and then bandpass filtered between 1 and 300 Hz. Activity across the hippocampal network changes markedly when animals run or sit quietly, and these changes are often referred to as different network states. These network states are clearly distinguishable by the presence or absence of LFP oscillations in different frequency bands12, 13. When animals ran, we observed large theta (4–12 Hz) oscillations in CA1 as others have shown (Extended Data Fig. 1b, left)13, 30, 33, 34. When animals sat quietly, theta oscillations were no longer visible and we recorded SWRs, high-frequency oscillations of 150–250 Hz that last around 50–100 ms and are associated with bursts of population activity, as others have observed (Extended Data Fig. 1b, right)15, 16. SWRs were detected (Fig. 1a–d and Extended Data Fig. 1d–i) when the envelope amplitude of the filtered trace was greater than four standard deviations above the mean for at least 15 ms. The envelope amplitude was calculated by taking the absolute value of the Hilbert transform of the filtered LFP. We also confirmed our results held when using a higher threshold for SWR detection, six standard deviations above the mean, which detects larger SWRs (Extended Data Fig. 1j, k). To detect theta (Extended Data Fig. 1c, d), the LFP was bandpass filtered for theta (4–12 Hz), delta (1–4 Hz), and beta (12–30 Hz) using an FIR equiripple filter. The ratio of theta to delta and beta (‘theta ratio’) was computed as the theta envelope amplitude divided by the sum of the delta and beta envelope amplitudes. Theta periods were classified as such when the theta ratio was greater than one standard deviation above mean for at least 2 s and the ratio reached a peak of at least two standard deviations above mean. Non-theta periods were classified as such when the theta ratio was less than one for at least 2 s. SWRs, theta periods, and non-theta periods were visually inspected to ensure that these criteria accurately detected SWRs, theta periods, and non-theta periods, respectively. Spectral analysis was performing using multitaper methods (Chronux toolbox, time-bandwidth product = 3, number of tapers = 5). For examining power spectra without stimulation (Extended Data Fig. 1c, d), only theta periods were included: theta periods greater than 5 s long were divided into 5 s trials and the average power spectral density was computed for each animal over these trials. For examining power spectra during optogenetic (Fig. 1e and Extended Data Fig. 1l) and visual stimulation (Fig. 4a and Extended Data Fig. 4a), data were divided into 10 s trials of each stimulation condition or baseline periods, and the average power spectral density was computed for each animal over these trials. Spectrograms were computed using multitaper methods (Chronux toolbox). The spectrogram was computed for each SWR including a window of 400 ms before and after the peak of the SWR. Then a z-scored spectrogram was computed in each frequency band using the mean and standard deviation of the spectrogram computed across the entire recording session to create a normalized measure of power in units of standard deviation (Fig. 1a and Extended Data Fig. 1e). Instantaneous frequency of gamma during SWRs was computed by bandpass filtering the LFP for 10–50 Hz, taking the Hilbert transform, then taking the reciprocal of the difference in peaks of the transformed signal (Fig. 1b and Extended Data Fig. 1f). Gamma power before, during, and after SWRs was computed by filtering the LFP for low gamma (20–50 Hz) and taking the amplitude of the envelope of the Hilbert transform to get the mean gamma power in 100 ms bins centred on the SWR peak. This was normalized by the mean and standard deviation of the amplitude of the envelope for the entire recording session to get z-scored gamma power for each bin around each SWRs (Fig. 1c and Extended Data Fig. 1g, j). Phase modulation by gamma during SWRs was computed by bandpass filtering the LFP for gamma (20–50 Hz), taking the Hilbert transform, and determining the phase of the resulting signal for each spike that occurred during SWRs (Extended Data Fig. 1h). To measure differences in phase modulation between 5XFAD and WT animals, we used resampling with replacement: a subset of 100 spikes from each recording was randomly selected to create a phase modulation distribution and this was repeated 500 times for each recording (Fig. 1d and Extended Data Fig. 1k). We then measured the depth of modulation for the spike-gamma phase distribution by computing the difference between the peak and trough divided by the sum of the peak and trough for each distribution (Fig. 1d and Extended Data Fig. 1k). To plot stimulus-evoked multiunit firing histograms, spikes were binned in 2.5 ms bins for 100 ms after the start of each light-on pulse and the fraction of spikes in each bin was computed. Mean and standard error were then computed across all light-on periods. To compute differences in multi-unit firing rate between conditions, firing rates were computed for each 10 s period of stimulation or baseline (total number of spikes divided by duration of period). Differences in firing rate were taken between nearby periods of the relevant type of stimulation (firing rate in gamma stimulation period minus baseline or random periods for optogenetic stimulation, firing rate in gamma stimulation period minus baseline, continuous on, or random periods for light flicker stimulation). Differences from all animals were plotted in histograms (Extended Data Figs 1m and 4c) and the median and quartiles of the multiunit firing rates per 40 Hz stimulation, random stimulation, and no stimulation period for each animal were plotted in box plots (Extended Data Figs 1o and 4d). Mice were perfused with 4% paraformaldehyde under deep anaesthesia, and the brains were post-fixed overnight in 4% paraformaldehyde. Brains were sectioned at 40 μm using a vibratome (Leica). Sections were permeabilized and blocked in PBS containing 0.2% Triton X-100 and 10% normal donkey serum at room temperature for 1 h. Sections were incubated overnight at 4 °C in primary antibody in PBS with 0.2% Triton X-100 and 10% normal donkey serum. Primary antibodies were anti-EEA1 (BD Transduction Laboratories; 641057), anti-β-amyloid (Cell Signaling Technology; D54D2), anti-Iba1 (Wako Chemicals; 019-19741), anti-parvalbumin (Abcam; ab32895), and anti-Rab5 (Enzo Life Sciences; ADI-KAP-GP006-E). To confirm ELISA experiments, the anti-Aβ antibody D54D2 was used because it allowed for co-labelling with EEA1 and the anti-Aβ antibody 12F4 was used because it does not react with APP, allowing us to determine whether our labelling was specific to Aβ. For co-labelling experiments, the anti-Aβ antibody 12F4 (Biolegend; 805501) was used. Primary antibodies were visualized with Alexa-Fluor 488 and Alex-Fluor 647 secondary antibodies (Molecular Probes), and cell nuclei visualized with Hoechst 33342 (Sigma-Aldrich; 94403). Images were acquired using a confocal microscope (LSM 710; Zeiss) with a 40× objective at identical settings for all conditions. Images were quantified using ImageJ 1.42q by an experimenter blind to treatment groups. For each experimental condition, two coronal sections from at least three animals were used for quantification. Scale bars are 50 μm. For CA1 imaging, the analysis was restricted to the pyramidal cell layer, except in the case of Iba1+ cells analysis, where the whole field of view was required to image an adequate number of cells. ImageJ was used to measure the diameter of Iba1+ cell bodies and to trace the processes for length measurement. In addition, the Coloc2 plugin was used to measure co-localization of Iba1 and Aβ. Imarisx64 8.1.2 (Bitplane, Zurich, Switzerland) was used for three-dimensional rendering. For counting the ‘plaque number’, deposits of at least 10 μm were included. Fixed brains were sliced into 100 μm coronal sections on a vibratome (Leica VT100S) in 1× PBS. Sections containing VC were selected, with reference to the Allen Mouse Brain Atlas, and incubated in clearing buffer (pH 8.5–9.0, 200 mM sodium dodecylsulfate, 20 mM lithium hydroxide monohydrate, 4 mM boric acid in double-distilled H O) for 2 h, shaking at 55 °C. Cleared sections were washed 3 × 10mins in 1× PBST (0.1% Triton-X100/1XPBS) and put into blocking solution (2% bovine serum albumin/1× PBST) overnight, shaking at room temperature27. Subsequently, three 1 h washes in 1× PBST were performed, shaking at room temperature. Sections were then incubated at 4 °C for 2 days, shaking, with anti-β-amyloid (Biolegend; 805501) and anti-Iba1 (Wako Chemicals; 019-19741) primary antibodies, diluted to 1:100 in 1× PBST. Another set of 3 × 1 h washes in 1× PBST was conducted before sections were incubated for 9 h, shaking at room temperature, in 1:100 1× PBS-diluted secondary antibody mixture. Fragmented Donkey Anti-Rabbit Alexa Fluor 488 (Abcam; ab175694) and Anti-Mouse 568 (Abcam; ab150101) secondary antibodies were used to visualize the primary antibody labelling. Halfway through this incubation period, Hoechst 33258 (Sigma-Aldrich; 94403) was spiked into each sample at a 1:250 final dilution. Sections were then washed overnight in 1× PBS, shaking at room temperature. Before mounting for imaging, slices were incubated in refractive index matching solution (75 g Histodenz, 20 mL 0.1 M phosphate buffer, 60 mL double-distilled H O) for 1 h, shaking at room temperature. Tissue sections were mounted onto microscopy slides with coverslips (VWR VistaVision, VWR International, LLC, Radnor, Pennsylvania, USA) using Fluromount G Mounting Medium (Electron Microscopy Sciences, Hatfield, Pennsylvania, USA). Images were acquired on a Zeiss LSM 880 microscope with the accompanying Zen Black 2.1 software (Carl Zeiss Microscopy, Jena, Germany). Section overview and cellular-level images used for three-dimensional reconstruction were taken using a Plan-Apochromat 63×/1.4 oil differential interference contrast objective. Imarisx64 8.1.2 (Bitplane, Zurich, Switzerland) was used for three-dimensional rendering and analysis. CA1 whole-cell lysates were prepared using tissue from 3-month-old male 5XFAD/PV-Cre mice. Tissue was homogenized in 1 ml RIPA (50 mM Tris HCl pH 8.0, 150 mM NaCl, 1% NP-40, 0.5% sodium deoxycholate, 0.1% SDS) buffer with a hand homogenizer (Sigma), incubated on ice for 15 min, and rotated at 4 °C for 30 min. Cell debris was isolated and discarded by centrifugation at 14,000 r.p.m. for 10 min. Lysates were quantitated using a nanodrop, and 25 μg protein was loaded on a 10% acrylamide gels. Protein was transferred from acrylamide gels to PVDF membranes (Invitrogen) at 100 V for 120 min. Membranes were blocked using bovine serum albumin (5% w/v) diluted in TBS:Tween. Membranes were incubated in primary antibodies overnight at 4 °C and secondary antibodies at room temperature for 90 min. Primary antibodies were anti-APP (Invitrogen; PAD CT695), anti-APP (Sigma; A8967), and anti-β-actin (Abcam; ab9485). Secondary antibodies were horseradish peroxidase-linked (GE Healthcare). Signal intensities were quantified using ImageJ 1.46a and normalized to values of β-actin. We examined tau protein solubility using sequential protein extraction as described in ref. 28. We then probed the detergent insoluble tau fraction using an antibody against Tau5 (Thermo Fisher Scientific; AHB0042). CA1 or VC was isolated from male mice, lysed with PBS or 5M Guanidine HCl, and subjected to Aβ measurement with the use of mouse (for WT experiments) or human (for all other experiments) Aβ or Aβ ELISA kit (Invitrogen) according to the manufacturer’s instructions. We lysed the tissue in phosphate-buffered saline (PBS) to extract the PBS soluble Aβ fraction. The soluble Aβ fraction probably contained monomeric and oligomeric Aβ. Tissue was further treated with guanidine HCl to extract the insoluble Aβ fraction. Aβ was below detectable levels for both flicker and control groups in WT VC and microglia-specific samples. Total RNA was extracted from CA1 isolates using the RNeasy kit (Qiagen). Purified mRNA was used for RNA-seq library preparation using the BIOO NEXTflex kit (BIOO 5138-08) according to the manufacturer’s instructions. Total mRNA (1 μg) was subject to a sequential workflow of poly-A purification, fragmentation, first strand and second strand synthesis, DNA end-adenylation, and adaptor ligation. The libraries were enriched by 15 cycles of PCR reactions and cleaned with Agencourt AMPure XP magnetic beads (Beckman Coulter). The quality of the libraries was assessed using an Advanced Analytical-fragment Analyzer. The bar-coded libraries were equally mixed for sequencing in a single lane on the Illumina HiSeq 2000 platform at the MIT BioMicro Center. The raw fastq data of 50-bp single-end sequencing reads were aligned to the mouse mm9 reference genome using TopHat2.0. The mapped reads were processed by Cufflinks 2.2 with UCSC mm9 reference gene annotation to estimate transcript abundances, and test for differential expression. An average of 26,518,345 sequencing reads was obtained from three stimulated and three non-stimulated mice. Relative abundance of transcript was measured by fragments per kilobase of exon per million fragments mapped (FPKM). Gene differential expression test between treated and untreated groups was performed using Cuffdiff module with an adjusted P value <0.05 for statistical significance (GEO accession number GSE77471). To understand the cellular and molecular mechanisms from our RNA-seq data, 14 of publicly available RNA-seq datasets35 were processed for cell-type-specific analysis. Additionally, 60 publicly available neuron-, microglia-, and macrophage-specific RNA-seq datasets under different chemical and genetic perturbations36, 37, 38, 39, 40, 41 were downloaded and processed using TopHat/Cufflinks pipeline for gene set enrichment (GSEA) statistical analysis. GSEA was used to determine whether a defined gene set from our RNA-seq data are significantly enriched at either direction of a ranked gene list from a particular perturbation study. Genes detected in the public RNA-seq datasets were ranked by log values of fold change (case versus control), from positive to negative values. A defined gene set (in our case, up- or downregulated genes upon gamma treatment) was considered significantly correlated with a perturbation-induced transcriptomic change (either up- or downregulation) when both nominal P value and false discovery rate q value were less than 0.05. The sign of the calculated normalized enrichment score (NES) indicates whether the gene set is enriched at the top or the bottom of the ranked list. The heatmap for differentially expressed genes was generated using a custom R script, and z-score values across all libraries for each gene were calculated on the basis of the gene FPKM values. The box plots for cell-type specificity analysis were also generated by the R program, on the basis of gene FPKM values. The CA1 subregion was isolated from hippocampus of 3-month-old male 5XFAD/PV-Cre mice. Tissue was rapidly frozen using liquid nitrogen and stored at −80 °C, and RNA extracted using the RNeasy kit according to the manufacturer’s protocol (Qiagen). RNA (3 μg) was treated with DNase I (4 U, Worthington Biochemical Corporation), purified using RNA Clean and Concentrator-5 Kit (Zymo Research) according to the manufacturers’ instructions, and eluted with 14 μL DEPC-treated water. For each sample, 1 μg RNA was reverse transcribed in a 20 μL reaction volume containing random hexamer mix and Superscript III reverse transcriptase (50 U, Invitrogen) at 50 °C for 1 h. First strand cDNAs were diluted 1:10 and 1 μL were used for RT-qPCR amplification in a 20 μL reaction (SsoFast EvaGreen Supermix, Bio-Rad) containing primers (0.2 μM). Relative changes in gene expression were assessed using the 2−ΔΔCt method. The primary VC (V1 region) was rapidly dissected and placed in ice-cold Hanks’ balanced salt solution (HBSS) (Gibco by Life Technologies, catalogue number 14175-095). The tissue was then enzymatically digested using the Neural Tissue Dissociation Kit (P) (Miltenyi Biotec, catalogue number 130-092-628) according to the manufacturer’s protocol, with minor modifications. Specifically, the tissue was enzymatically digested at 37 °C for 15 min instead of 35 min and the resulting cell suspension was passed through a 40 μm cell strainer (Falcon Cell Strainers, Sterile, Corning, product 352340) instead of a MACS SmartStrainer, 70 μm. The resulting cell suspension was then stained using allophycocyanin (APC)-conjugated CD11b mouse clone M1/126.96.36.199 (Miltenyi Biotec, 130-098-088) and phycoerythrin (PE)-conjugated CD45 antibody (BD Pharmingen, 553081) according to the manufacturer’s (Miltenyi Biotec) recommendations. FACS was then used to purify CD11b and CD45 positive microglial cells. The cells were sorted directly into 1× PBS (Extended Data Fig. 6a). Code is publicly available upon request from the corresponding author. For electrophysiological data that were not normally distributed, results are presented as medians and quartiles unless otherwise noted. Two-sided Wilcoxon rank sum tests for equal medians were performed to determine whether distributions were significantly different, and Wilcoxon signed rank tests were performed to determine whether distributions were significantly different from zero as these do not assume data are normally distributed. Variability was similar between the groups that were statistically compared. The Bonferroni method was used to correct for multiple comparisons. No statistical method was used to estimate sample size, but it was consistent with previous publications. Molecular and biochemical results are presented as mean + s.e.m. Percentages stated are group means. All statistical analysis used Prism GraphPad software. Normality was determined using the D’Agostino and Pearson omnibus normality test. Variability was similar between the groups that were statistically compared. Comparison data for normally distributed data consisting of two groups were analysed by two-tailed unpaired t-tests. Comparison of normally distributed data consisting of three or more groups was by one-way ANOVA followed by Tukey’s multiple comparisons test. Comparisons for non-normally distributed data were performed using Mann–Whitney tests. The statistical test, exact P values, and sample size (n) for each experiment are specified in the figure legend. For optogenetic ELISA data, two-sided unpaired Student’s t-tests were performed to compare mice from the same litter that received different conditions. No statistical method was used to estimate sample size, but it is consistent with previous publications. Molecular and biochemical analysis used a minimum of three biological replicates per condition. Data are publicly available upon request from the corresponding author.
News Article | February 15, 2017
Keeping blood sugar levels within a safe range is key to managing both type 1 and type 2 diabetes. In a new finding that could lead to fewer complications for diabetes patients, Yale School of Medicine researchers have found that changes in the size of mitochondria in a small subset of brain cells play a crucial role in safely maintaining blood sugar levels. The study is published in the Feb. 9 issue of the journal Cell Metabolism. "Low blood sugar can be as dangerous as high blood sugar," said senior author Sabrina Diano, professor in the Departments of Obstetrics, Gynecology & Reproductive Sciences, Neuroscience, and Comparative Medicine. "We've found that changes in the size of mitochondria -- small intracellular organelles responsible for energy production -- in certain cells in the brain, could be key to maintaining the blood sugar within a safe range." "This new finding adds to our understanding of how the body keeps blood sugar levels within a safe range when sugar levels drop, like during fasting, or when they spike after a meal," Diano added. Diano and her research team designed the study to help understand how neurons in the brain that regulate appetite affect systemic glucose levels. The team used mouse models in which a specific mitochondrial protein, dynamin-related protein 1 (DRP1), was either missing or present in varying amounts in the subset of brain cells that sense circulating sugar levels. The researchers found that depending on whether the mouse was hungry or not, mitochondria displayed dynamic changes in size and shape, driven by the DRP1 protein. "We found that when DRP1 activity in the neurons was missing, these neurons were more sensitive to changes in glucose levels," said Diano, who is also a member of the Program in Integrative Cell Signaling and Neurobiology of Metabolism and the director of the Reproductive Neuroscience Group at Yale University School of Medicine. "What surprised our research team was that these intracellular changes in this small subset of neurons were specifically important to increase blood sugar levels during a fasting period by activating the so-called counter-regulatory responses to hypoglycemia, in which the brain senses lower glucose levels and sends signals to peripheral organs such as the liver to increase glucose production." Diano said the findings suggest that alterations in this mechanism may be critical for the development of hypoglycemia-associated autonomic failure (HAAF), a complication of several diabetes treatments occurring most often in people with type 1 diabetes who must take insulin for survival. Diano's research team will now focus on assessing how mitochondrial morphological changes relate to mitochondrial function in this subset of neurons in the development of HAAF. Other authors on the study include Anna Santoro, Michela Campolo, Chen Liu, Hiromi Sesaki, Rosaria Meli, Zhong-Wu Liu, and Jung Dae Kim. The study was funded by the National Institutes of Health.
News Article | February 28, 2017
New Haven, Conn.-- More than 8 million individuals in the United States have gout, a disease that can cause intense recurrent episodes of debilitating pain, inflammation, and fever. The cause of gout is the accumulation of urate crystals in joints, which continuously reactivate the immune system, leading to activation of the most common type of immune cell in the blood, neutrophils. These periods of immune reactivation are known as flares, and are driven by a protein complex called the NLRP3 inflammasome. Recent work from the laboratory of Vishwa Deep Dixit, Professor of Comparative Medicine and Immunobiology, has shown that the ketone body β-hydroxybutyrate can specifically inhibit the NLRP3 inflammasome. Ketones are byproducts of fat break down in the liver that can serve as alternative metabolic fuels for the brain and heart during periods of low carbohydrate intake, such as fasting, or ketogenic diet. To test if elevating ketones protected against inflammation during gout, a Postdoctoral Fellow in Dixit's lab, Emily Goldberg, and Associate Research Scientist and Clinical Veterinarian in Comparative Medicine, Jennifer Asher, and their colleagues collaborated to develop a novel model of gout flares in rats. They found that feeding rats a high-fat, low-carbohydrate ketogenic diet increased β-hydroxybutyrate levels and protected rats from joint swelling, tissue damage, and systemic inflammation normally seen during gout. "In isolated neutrophils, β-hydroxybutyrate completely blocked NLRP3 inflammasome activation, even when provided at low concentrations that are physiologically achievable through dietary modification," said Goldberg. She speculated that specifically targeting the NLRP3 inflammasome to reduce inflammation during a flare could improve gout patients' outcomes, but more studies need to be performed to test this possibility.
News Article | March 23, 2016
The APP/PS1 (ref. 10) double-transgenic AD mice, originally described as Line 85, were obtained from Jackson Laboratory (stock number 004462). Under the control of mouse prion promoter elements, these mice express a chimaeric mouse/human APP transgene containing Swedish mutations (K595N/M596L) as well as a mutant human PS1 transgene (delta exon 9 variant). To label memory engram cells in APP/PS1 mice, we generated a triple-transgenic mouse line by mating c-Fos-tTA11, 28 transgenic mice with APP/PS1 double-transgenic mice. The PS1/APP/tau18 triple-transgenic AD mice were obtained from Jackson Laboratory (stock number 004807). These 3×Tg-AD mice express a mutant human PS1 transgene (M146V), a human APP transgene containing Swedish mutations (KM670/671NL) and a human MAPT transgene harbouring the P301L mutation. All mouse lines were maintained as hemizygotes. Mice had access to food and water ad libitum and were socially housed in numbers of two to five littermates until surgery. After surgery, mice were singly housed. For behavioural experiments, all mice were male and 7–9 months old. For optogenetic experiments, mice had been raised on food containing 40 mg kg−1 DOX for at least 1 week before surgery, and remained on DOX for the remainder of the experiments except for the target engram labelling days. For in vitro electrophysiology experiments, mice were 24–28 days old at the time of surgery. All experiments were conducted in accordance with US National Institutes of Health (NIH) guidelines and the Massachusetts Institute of Technology Department of Comparative Medicine and Committee of Animal Care. No statistical methods were used to predetermine sample size. Our previously established method11 for labelling memory engram cells combined c-Fos-tTA transgenic mice with a DOX-sensitive adeno-associated virus (AAV). However, in this study, we modified the method using a double-virus system to label memory engram cells in the early AD mice, which already carry two transgenes. The pAAV-c-Fos-tTA plasmid was constructed by cloning a 1 kb fragment from the c-Fos gene (550 bp upstream of c-Fos exon I to 35 bp into exon II) into an AAV backbone using the KpnI restriction site at the 5′ terminus and the SpeI restriction site at the 3′ terminus. The AAV backbone contained the tTA-Advanced29 sequence at the SpeI restriction site. The pAAV-TRE-ChR2-eYFP and pAAV-TRE-eYFP constructs were previously described11, 12. The pAAV-TRE-oChIEF-tdTomato20 plasmid was constructed by replacing the ChR2-eYFP fragment from the pAAV-TRE-ChR2-eYFP plasmid using NheI and MfeI restriction sites. The pAAV-CaMKII-oChIEF-tdTomato plasmid was constructed by replacing the TRE fragment from the pAAV-TRE-oChIEF-tdTomato plasmid using BamHI and EcoRI restriction sites. The pAAV-TRE-DTR-eYFP25 plasmid was constructed by replacing the ChR2 fragment from the pAAV-TRE-ChR2-eYFP plasmid using EcoRI and AgeI restriction sites. AAV vectors were serotyped with AAV coat proteins and packaged at the University of Massachusetts Medical School Gene Therapy Center and Vector Core. Viral titres were 1.5 × 1013 genome copy (GC) ml−1 for AAV -c-Fos-tTA, AAV -TRE-ChR2-eYFP and AAV -TRE-eYFP, 1 × 1013 GC ml−1 for AAV -TRE-oChIEF-tdTomato, 4 × 1013 GC ml−1 for AAV -CaMKII-oChIEF-tdTomato and 2 × 1013 GC ml−1 for AAV -TRE-DTR-eYFP. Mice were anaesthetized with isoflurane or 500 mg kg−1 avertin for stereotaxic injections14. Injections were targeted bilaterally to the DG (−2.0 mm anteroposterior (AP), ±1.3 mm mediolateral (ML), −1.9 mm dorsoventral (DV)), MEC (−4.7 mm AP, ±3.35 mm ML, −3.3 mm DV) and LEC (−3.4 mm AP, ±4.3 mm ML, −4.0 mm DV). Injection volumes were 300 nl for DG and 400 nl for MEC and LEC. Viruses were injected at 70 nl min−1 using a glass micropipette attached to a 10 ml Hamilton microsyringe. The needle was lowered to the target site and remained for 5 min before beginning the injection. After the injection, the needle stayed for 10 min before it was withdrawn. A custom DG implant containing two optic fibres (200 mm core diameter; Doric Lenses) was lowered above the injection site (−2.0 mm AP, ±1.3 mm ML, −1.7 mm DV). The implant was secured to the skull with two jewellery screws, adhesive cement (C&B Metabond) and dental cement. An opaque cap derived from the top part of an Eppendorf tube protected the implant. Mice were given 1.5 mg kg−1 metacam as analgesic and allowed to recover for 2 weeks before behavioural experiments. All injection sites were verified histologically. As criteria, we only included mice with virus expression limited to the targeted regions. For seizure experiments11, mice were taken off DOX for 1 day and injected intraperitoneally with 15 mg kg−1 kainic acid (KA). Mice were returned to DOX food 6 h after KA treatment and perfused the next day for immunohistochemistry procedures. Mice were dispatched using 750–1,000 mg kg−1 avertin and perfused transcardially with PBS, followed by 4% paraformaldehyde (PFA). Brains were extracted and incubated in 4% PFA at room temperature overnight. Brains were transferred to PBS and 50-μm coronal slices were prepared using a vibratome. For immunostaining14, each slice was placed in PBS + 0.2% Triton X-100 (PBS-T), with 5% normal goat serum for 1 h and then incubated with primary antibody at 4 °C for 24 h. Slices then underwent three wash steps for 10 min each in PBS-T, followed by 1 h incubation with secondary antibody. After three more wash steps of 10 min each in PBS-T, slices were mounted on microscope slides. All analyses were performed blind to the experimental conditions. Antibodies used for staining were as follows: to stain for ChR2–eYFP, DTR–eYFP or eYFP alone, slices were incubated with primary chicken anti-GFP (1:1,000, Life Technologies) and visualized using anti-chicken Alexa-488 (1:200). For plaques, slices were stained using primary mouse anti-β-amyloid (1:1,000; Sigma-Aldrich) and secondary anti-mouse Alexa-488 (1:500). c-Fos was stained with rabbit anti-c-Fos (1:500, Calbiochem) and anti-rabbit Alexa-568 (1:300). Adult newborn neurons were stained with guinea pig anti-DCX (1:1,000; Millipore) and anti-guinea-pig Alexa-555 (1:500). Neuronal nuclei were stained with mouse anti-NeuN (1:200; Millipore) and Alexa-488 (1:200). DG mossy cell axons were stained with mouse anti-CR (1:1,000; Swant) and Alexa-555 (1:300). To characterize the expression pattern of ChR2–eYFP, DTR–eYFP, eYFP alone and oChIEF-tdTomato in control and AD mice, the number of eYFP+/tdTomato+ neurons were counted from 4–5 coronal slices per mouse (n = 3–5 mice per group). Coronal slices centred on coordinates covered by optic fibre implants were taken for DG quantification and sagittal slices centred on injection coordinates were taken for MEC and LEC. Fluorescence images were acquired using a Zeiss AxioImager.Z1/ApoTome microscope (×20). Automated cell counting analysis was performed using ImageJ software. The cell body layers of DG granule cells (upper blade), MEC or LEC cells were outlined as a region of interest (ROI) according to the DAPI signal in each slice. The number of eYFP+/tdTomato+ cells per section was calculated by applying a threshold above background fluorescence. Data were analysed using Microsoft Excel with the Statplus plug-in. A similar approach was applied for quantifying amyloid-β plaques, c-Fos+ neurons and adult newborn (DCX+) neurons. Total engram cell reactivation was calculated as ((c-Fos+ eYFP+)/(total DAPI+)) × 100. Chance overlap was calculated as ((c-Fos+/total DAPI+) × (eYFP+/total DAPI+)) × 100. Percentage of adult newborn neurons expressing neuronal markers was calculated as ((NeuN+ DCX+)/(total DCX+) × 100. DAPI+ counts were approximated from five coronal/sagittal slices using ImageJ. All counting experiments were conducted blind to experimental group. Researcher 1 trained the animals, prepared slices and randomized images, while researcher 2 performed semi-automated cell counting. Statistical comparisons were performed using unpaired t-tests: *P < 0.05, **P < 0.01, ***P < 0.001. Engram cells were labelled using c-Fos-tTA-driven synthesis of ChR2–eYFP or eYFP alone. The eYFP signal was amplified using immunohistochemistry procedures, after which fluorescence z-stacks were taken by confocal microscopy (Zeiss LSM700) using a ×40 objective. Maximum intensity projections were generated using ZEN Black software (Zeiss). Four mice per experimental group were analysed for dendritic spines. For each mouse, 30–40 dendritic fragments of 10-μm length were quantified (n = 120–160 fragments per group). To measure spine density of DG engram cells with a focus on entorhinal cortical inputs, distal dendritic fragments in the middle-to-outer molecular layer (ML) were selected. For CA3 and CA1 engram cells, apical and basal dendritic fragments were selected. To compute spine density, the number of spines counted on each fragment was normalized by the cylindrical approximation of the surface of the specific fragment. Experiments were conducted blind to experimental group. Researcher 1 imaged dendritic fragments and randomized images, while researcher 2 performed manual spine counting. After isoflurane anaesthesia, brains were quickly removed and used to prepare sagittal slices (300 μm) in an oxygenated cutting solution at 4 °C with a vibratome14. Slices were incubated at room temperature in oxygenated artificial cerebrospinal fluid (ACSF) until the recordings. The cutting solution contained (in mM): 3 KCl, 0.5 CaCl , 10 MgCl , 25 NaHCO , 1.2 NaH PO , 10 d-glucose, 230 sucrose, saturated with 95% O –5% CO (pH 7.3, osmolarity of 340 mOsm). The ACSF contained (in mM): 124 NaCl, 3 KCl, 2 CaCl , 1.3 MgSO , 25 NaHCO , 1.2 NaH PO , 10 d-glucose, saturated with 95% O –5% CO (pH 7.3, 300 mOsm). Individual slices were transferred to a submerged experimental chamber and perfused with oxygenated ACSF warmed at 35 °C (±0.5 °C) at a rate of 3 ml min−1 during recordings. Current or voltage clamp recordings were performed under an IR-DIC microscope (Olympus) with a ×40 water immersion objective (0.8 NA), equipped with four automatic manipulators (Luigs & Neumann) and a CCD camera (Hamamatsu). Borosilicate glass pipettes (Sutter Instruments) were fabricated with resistances of 8–10 MΩ. The intracellular solution (in mM) for current clamp recordings was: 110 K-gluconate, 10 KCl, 10 HEPES, 4 ATP, 0.3 GTP, 10 phosphocreatine, 0.5% biocytin (pH 7.25, 290 mOsm). Recordings used two dual channel amplifiers (Molecular Devices), a 2 kHz filter, 20 kHz digitization and an ADC/DAC data acquisition unit (Instrutech) running on custom software in Igor Pro (Wavemetrics). Data acquisition was suspended whenever the resting membrane potential was depolarized above −50 mV or the access resistance (RA) exceeded 20 MΩ. Optogenetic stimulation was achieved using a 460 nm LED light source (Lumen Dynamics) driven by TTL input with a delay onset of 25 μs (subtracted offline for latency estimation). Light power on the sample was 33 mW mm−2. To test oChIEF expression, EC cells were stimulated with a single light pulse of 1 s, repeated 10 times every 5 s. DG granule cells were held at −70 mV. Optical LTP protocol: 5 min baseline (10 blue light pulses of 2 ms each, repeated every 30 s) was acquired before the onset of the LTP protocol (100 blue light pulses of 2 ms each at a frequency of 100 Hz, repeated 5 times every 3 min) and the effect on synaptic amplitude was recorded for 30 min (1 pulse of 2 ms every 30 s). Using the 5 min baseline recording data, EPSPs were normalized (Fig. 3j). Potentiation was observed in 6 out of 30 cells and results were statistically confirmed using a two-tailed paired t-test. Experiments were performed in the presence of 10 μM gabazine (Tocris) and 2 μM CGP55845 (Tocris). Recorded cells were recovered for morphological identification using streptavidin CF633 (Biotium). Multi-unit responses to optical stimulation were recorded in the DG of mice injected with a cocktail of AAV -c-Fos-tTA and AAV -TRE-oChIEF-tdTomato viruses into MEC/LEC. Mice were anaesthetized (10 ml kg−1) using a mixture of ketamine (100 mg ml−1)/xylazine (20 mg ml−1) and placed in the stereotactic system. Anaesthesia was maintained by booster doses of ketamine (100 mg kg−1). An optrode consisting of a tungsten electrode (0.5 MΩ) attached to an optic fibre (200-μm core diameter), with the tip of the electrode extending beyond the tip of the fibre by 300 μm, was used for simultaneous optical stimulation and extracellular recording. The power intensity of light emitted from the optrode was calibrated to about 10 mW, consistent with the power used in behavioural assays. oChIEF+ cells were identified by delivering 20-ms light pulses (1 Hz) to the recording site every 50–100 μm. After light-responsive cells were detected, multi-unit activity in response to trains of light pulses (200 ms) at 100 Hz was recorded. Data acquisition used an Axon CNS Digidata 1440A system. MATLAB analysis was performed, as previously described12. Experiments were conducted during the light cycle (7 a.m. to 7 p.m.). Mice were randomly assigned to experimental groups for specific behavioural assays immediately after surgery. Mice were habituated to investigator handling for 1–2 min on three consecutive days. Handling took place in the holding room where the mice were housed. Before each handling session, mice were transported by wheeled cart to and from the vicinity of the behaviour rooms to habituate them to the journey. For natural memory recall sessions, data were quantified using FreezeFrame software. Optogenetic stimulation interfered with the motion detection, and therefore all light-induced freezing behaviour was manually quantified. All behaviour experiments were analysed blind to experimental group. Unpaired Student’s t-tests were used for independent group comparisons, with Welch’s correction when group variances were significantly different. Given behavioural variability, initial assays were performed using a minimum of 10 mice per group to ensure adequate power for any observed differences. Experiments that resulted in significant behavioural effects were replicated three times in the laboratory. Following behavioural protocols, brain sections were prepared to confirm efficient viral labelling in target areas. Animals lacking adequate labelling were excluded before behaviour quantification. Two distinct contexts were employed14. Context A was 29 × 25 × 22 cm chambers with grid floors, opaque triangular ceilings, red lighting, and scented with 1% acetic acid. Four mice were run simultaneously in four identical context A chambers. Context B consisted of four 30 × 25 × 33 cm chambers with perspex floors, transparent square ceilings, bright white lighting, and scented with 0.25% benzaldehyde. All mice were conditioned in context A (two 0.60 mA shocks of 2 s duration in 5 min), and tested (3 min) in contexts A and B 1 day later. Experiments showed no generalization in the neutral context B. All experimental groups were counter-balanced for chamber within contexts. Floors of chambers were cleaned with quatricide before and between runs. Mice were transported to and from the experimental room in their home cages using a wheeled cart. The cart and cages remained in an anteroom to the experimental rooms during all behavioural experiments. For engram labelling, mice were kept on regular food without DOX for 24 h before training. When training was complete, mice were switched back to food containing 40 mg kg−1 DOX. Spontaneous motor activity was measured in an open field arena (52 × 26 cm) for 10 min. All mice were transferred to the testing room and acclimated for 30 min before the test session. During the testing period, lighting in the room was turned off. The apparatus was cleaned with quatricide before and between runs. Total movements (distance travelled and velocity) in the arena were quantified using an automated infrared (IR) detection system (EthoVision XT, Noldus). The tracking software plotted heat maps for each mouse, which was averaged to create representative heat maps for each genotype. Raw data were extracted and analysed using Microsoft Excel. For light-induced freezing behaviour, a context distinct from the CFC training chamber (context A) was used. These were 30 × 25 × 33 cm chambers with perspex floors, square ceilings, white lighting, and scented with 0.25% benzaldehyde. Chamber ceilings were customized to hold a rotary joint (Doric Lenses) connected to two 0.32-m patch cords. All mice had patch cords fitted to the optic fibre implant before testing. Two mice were run simultaneously in two identical chambers. ChR2 was stimulated at 20 Hz (15 ms pulse width) using a 473 nm laser (10–15 mW), for the designated epochs. Testing sessions were 12 min in duration, consisting of four 3 min epochs, with the first and third as light-off epochs, and the second and fourth as light-on epochs. At the end of 12 min, the mouse was detached and returned to its home cage. Floors of chambers were cleaned with quatricide before and between runs. One day after CFC training and engram labelling (DG plus PP terminals) in control and early AD groups, mice were placed in an open field arena (52 × 26 cm) after patch cords were fitted to the fibre implants. After a 15 min acclimatization period, mice with oChIEF+ PP engram terminals in the DG received the optical LTP23 protocol (100 blue light pulses of 2 ms each at a frequency of 100 Hz, repeated 5 times every 3 min). This in vivo protocol was repeated 10 times over a 3 h duration. After induction, mice remained in the arena for an additional 15 min before returning to their home cage. To apply optical LTP to a large portion of excitatory MEC neurons, an AAV virus expressing oChIEF-tdTomato under the CaMKII promoter, rather than a c-Fos-tTA/TRE virus (that is, engram labelling), was used. For protein synthesis inhibition experiments, immediately after the in vivo LTP induction protocol mice received 75 mg kg−1 anisomycin (Aniso) or an equivalent volume of saline intraperitoneally. Mice were then returned to their home cages. An hour later, a second injection of Aniso or saline was delivered. A 30 × 28 × 34 cm unscented chamber with transparent square ceilings and intermediate lighting was used. The chamber consisted of two sections, one with grid flooring and the other with a white light platform. During the conditioning session (1 min), mice were placed on the light platform, which is the less preferred section of the chamber (relative to the grid section). Once mice entered the grid section of the chamber (all four feet), 0.80 mA shocks of 2 s duration were delivered. On average, each mouse received 2–3 shocks per training session. After 1 min, mice were returned to their home cage. The next day, latency to enter the grid section of the chamber as well as total time on the light platform was measured (3 min test). Spatial memory was measured in a white plastic chamber (28 × 28 cm) that had patterns (series of parallel lines or circles) on opposite walls. The apparatus was unscented and intermediate lighting was used. All mice were transferred to the behavioural room and acclimated for 30 min before the training session. On day 1, mice were allowed to explore the chamber with patterns for 15 min. On days 2 and 3, mice were introduced into the chamber that had an object (7-cm-tall glass flask filled with metal beads) placed adjacent to either patterned wall. The position of the object was counter-balanced within each genotype. On day 4, mice were placed into the chamber with the object either in the same position as the previous exposure (familiar) or at a novel location based on wall patterning. Frequency of visits to the familiar and novel object locations was quantified using an automated detection system (EthoVision XT, Noldus). Total time exploring the object was also measured (nose within 1.5 cm of object). The tracking software plotted heat maps based on exploration time, which was averaged to create representative heat maps for each genotype. Raw data were extracted and analysed using Microsoft Excel.
News Article | December 2, 2016
The group has published their findings in a study today in the Journal of Fish Diseases, including data showing that a simple measurement procedure could be used to detect Atlantic salmon infected with salmonid alpha virus, which causes pancreas disease. Pancreas disease – which is not an issue for product consumption and is harmless to humans – can cause significant losses in farmed Atlantic salmon due to morbidity, mortality and reduced production. The researchers found that salmon with pancreas disease had a major change in the proteins present in the blood, and further to that, that these protein changes could be detected using a simple procedure. The test, called a selective precipitation reaction (SPR), has been patented by the team and could potentially be developed into a rapid analysis system allowing the disease to be diagnosed much earlier than is currently possible. This would mean that the test could be applied at a fish farm, allowing for quick diagnosis of the disease and early treatment. Current testing requires sample submissions being sent to laboratories, a process that can take several days before results are available. Professor David Eckersall, Professor of Veterinary Biochemistry and leader of the research team at the Institute of Biodiversity, Animal Health and Comparative Medicine, said: "The serendipitous discovery of the SPR has allowed a potentially powerful diagnostic test to be developed that could have significant applications in the future. "This collaborative study, funded by a BBSRC CASE PhD studentship for our colleague Mark Braceland and supported by the aquaculture industry, has made a major contribution to the health and welfare of salmon. If this SPR test can be applied to other diseases and species of fish then the benefit will be even greater. This is an excellent example of the benefit of academia-industry links supported by the BBSRC CASE studentship scheme." Pancreas disease can, according to Aunsmo et al (2012), cause a loss of up to £1.43m for a single fish farm, so early detection is a vital component of the health care of salmon in aquaculture. The SPR test may also be useful in detecting other salmon diseases, or even diseases in other fish. Dr Mark Braceland, who now is in Prince Edward Island (Canada) at the Center for Aquaculture Technologies, said: "One of the persistent challenges faced by the industry is monitoring of stocks and defining what healthy stocks are. Marine aquaculture is a very unique and relatively new form of livestock culture, and as such, diagnostic and prognostic tools available for this industry are lacking. "The SPR has some great potential in complementing pathogen screening by allowing the industry to identify clinical stages of disease process, thus giving valuable information for health practitioners. I also see it as a valuable tool for establishing the efficacy of treatment and disease prevention technologies and hope it shall be utilized in this way in the future." Dr John Tinsley of BioMar Ltd said: "The collaboration with Professor Eckersall and the University of Glasgow has been a great success and we would like it to continue. The project not only developed a highly applicable diagnostic test for the industry, but produced numerous peer reviewed articles and advanced our knowledge of fish health and welfare." Dr Dave Cockerill (MRCVS) of Marine Harvest (Scotland) Ltd said: "SPR gives us an opportunity to put in place an early warning system for detection of significant pathology in fish. In particular it appears to be a non-specific indicator of this type of disease and this sets it apart from other diagnostic tools which test for specific known disease agents. SPR could become the early indicator that further specific investigation is required." More information: 'Selective Precipitation Reaction: A Novel Diagnostic Test for Tissue Pathology in Atlantic Salmon, Salmo salar, infected with Salmonid Alpha-Virus,' Journal of Fish Diseases
News Article | December 2, 2015
The new findings, based on wild house sparrows, and published today, show how changes in DNA that are linked to ageing and lifespan take place as body size gets bigger. Although larger types of animals tend to live longer than smaller ones – elephants live longer than mice – within many species the bigger individuals have shorter life spans than their smaller counterparts – a Jack Russell has a much longer life than a St Bernard. In humans, a recent study has shown that taller people are more prone to diseases including cancer. But biologists haven't been able to fully explain why. Research into telomeres, special DNA structures that all animals have at the ends of their chromosomes, described as functioning like "the protective plastic caps at the end of shoelaces" may provide the answer. The study, conducted jointly by the University of Glasgow's Institute of Biodiversity, Animal Health & Comparative Medicine and the Centre of Biodiversity Dynamics at the Norwegian University of Science and Technology, focused on a population of wild house sparrows on the isolated island of Leka in Norway. The research, published in the Proceedings of the Royal SocietyB: Biological Sciences, found that skeletally bigger house sparrows had shorter telomeres. This relationship was maintained during a period when a selective breeding programme on the island resulted in the sparrows becoming even larger. In tandem, their telomeres became even shorter. Everyone's telomeres erode over time, and telomere shortening has been linked to ageing and disease risk including cancer. Having naturally longer telomeres appears to give individuals an advantage when it comes to health and the biological aging process. The results shed light on a paradox that has puzzled biologists for a long time. If being bigger gives you a competitive advantage, why don't animals just get bigger and bigger? Part of the answer is that growing big can mean more telomere loss and faster ageing. Professor Pat Monaghan, Regius Chair of Zoology at the University of Glasgow, who supervised the telomere analysis, said: "Growing a bigger body means that cells have to divide more. As a result, telomeres become eroded faster and cells and tissues function less well as a result. "The reason why the bigger individuals have shorter telomeres might also be related to increased DNA damage due to growing faster. Being big can have advantages, of course, but this study shows that it can also have costs." Associate professor in population ecology Thor Harald Ringsby at Norwegian University of Science and Technology who was running the fieldwork together with his colleagues in Norway said: 'The results from this study are very exciting and broad reaching. It is especially interesting that we obtained these results in a natural population. The reduction in telomere size that followed the increase in body size suggests one important mechanism that limits body size evolution in wild animal populations" The study, entitled 'On being the right size: increased body size is associated with reduced telomere length under natural conditions' is published in the Proceedings of the Royal Society B: Biological Sciences journal. The research was funded by the European Research Council and the Research Council of Norway. Explore further: Researchers show telomere lengths predict life expectancy in the wild More information: On being the right size: increased body size is associated with reduced telomere length under natural conditons, Proceedings of the Royal Society B: Biological Sciences, rspb.royalsocietypublishing.org/lookup/doi/10.1098/rspb.2015.2331
Chung S.,Sections on Lipid science |
Chung S.,University of Nebraska - Lincoln |
Chung S.,University of Florida |
Cuffe H.,Sections on Lipid science |
And 9 more authors.
Arteriosclerosis, Thrombosis, and Vascular Biology | Year: 2014
OBJECTIVE - Excessive caloric intake is associated with obesity and adipose tissue dysfunction. However, the role of dietary cholesterol in this process is unknown. The aim of this study was to determine whether increasing dietary cholesterol intake alters adipose tissue cholesterol content, adipocyte size, and endocrine function in nonhuman primates. APPROACH AND RESULTS - Age-matched, male African Green monkeys (n=5 per group) were assigned to 1 of 3 diets containing 0.002 (low [Lo]), 0.2 (medium [Med]), or 0.4 (high [Hi]) mg cholesterol/kcal. After 10 weeks of diet feeding, animals were euthanized for adipose tissue, liver, and plasma collection. With increasing dietary cholesterol, free cholesterol (FC) content and adipocyte size increased in a stepwise manner in visceral, but not in subcutaneous fat, with a significant association between visceral adipocyte size and FC content (r=0.298; n=15; P=0.035). In visceral fat, dietary cholesterol intake was associated with (1) increased proinflammatory gene expression and macrophage recruitment, (2) decreased expression of genes involved in cholesterol biosynthesis and lipoprotein uptake, and (3) increased expression of proteins involved in FC efflux. CONCLUSIONS - Increasing dietary cholesterol selectively increases visceral fat adipocyte size, FC and macrophage content, and proinflammatory gene expression in nonhuman primates. Visceral fat cells seem to compensate for increased dietary cholesterol by limiting cholesterol uptake/synthesis and increasing FC efflux pathways. © 2014 American Heart Association, Inc.
Cirone P.,Comparative Medicine |
Andresen C.J.,Comparative Medicine |
Eswaraka J.R.,Comparative Medicine |
Lappin P.B.,Drug Safety Research and Development DSRD |
Bagi C.M.,Comparative Medicine
Cancer Chemotherapy and Pharmacology | Year: 2014
Background: Metastatic bladder cancer is a serious condition with a 5-year survival rate of approximately 14 %, a rate that has remained unchanged for almost three decades. Thus, there is a profound need to identify the driving mutations for these aggressive tumors to better determine appropriate treatments. Mutational analyses of clinical samples suggest that mutations in either the phosphoinositide-3 kinase (PI3K)-AKT-mammalian target of rapamycin (mTOR) or RAS/MEK/ERK pathways drive bladder cancer progression, although it remains to be tested whether the inhibition of either (or both) of these pathways can arrest PI3K/mTOR- or Ras-driven proliferation. Methods: Herein, we used several bladder cancer cell lines to determine drug sensitivity according to genetic background and also studied mouse models of engrafted UM-UC-3 cells and patient-derived xenografts (PDXs) to test PI3K/mTOR and MEK inhibition in vivo. Results: Inhibition of these pathways utilizing PF-04691502, a PI3K and mTOR inhibitor, and PD-0325901, a MEK inhibitor, slowed the tumor growth of PDX models of bladder cancer. The growth inhibitory effect of combination therapy was similar to that of the clinical maximum dose of cisplatin; mechanistically, this appeared to predominantly occur via drug-induced cytostatic growth inhibition as well as diminished vascular endothelial growth factor secretion in the tumor models. Kinase arrays of tumors harvested after treatment demonstrated activated p53 and Axl as well as STAT1 and STAT3. Conclusion: Taken together, these results indicate that clinically relevant doses of PF-04691502 and PD-0325901 can suppress bladder tumor growth in PDX models, thus offering additional potential treatment options by a precision medicine approach. © 2014 Springer-Verlag.