Early and selective localization of tau filaments to glutamatergic subcellular domains within the human anterodorsal thalamus

Widespread cortical accumulation of misfolded pathological tau proteins (ptau) in the form of paired helical filaments is a major hallmark of Alzheimer’s disease. Subcellular localization of ptau at various stages of disease progression is likely to be informative of the cellular mechanisms involving its spread. Here, we found that the density of ptau within several distinct rostral thalamic nuclei in post-mortem human tissue (n = 25 cases) increased with the disease stage, with the anterodorsal nucleus (ADn) consistently being the most affected. In the ADn, ptau-positive elements were present already in the pre-cortical (Braak 0) stage. Tau pathology preferentially affected the calretinin-expressing subpopulation of glutamatergic neurons in the ADn. At the subcellular level, we detected ptau immunoreactivity in ADn cell bodies, dendrites, and in a specialized type of presynaptic terminal that expresses vesicular glutamate transporter 2 (vGLUT2) and likely originates from the mammillary body. The ptau-containing terminals displayed signs of degeneration, including endosomal/lysosomal organelles. In contrast, corticothalamic axon terminals lacked ptau. The data demonstrate the involvement of a specific cell population in ADn at the onset of the disease. The presence of ptau in subcortical glutamatergic presynaptic terminals supports hypotheses about the transsynaptic spread of tau selectively affecting specialized axonal pathways

Development of an In Vivo RNAi Protocol to Investigate Gene Function in the Filarial Nematode, Brugia malayi

Our ability to control diseases caused by parasitic nematodes is constrained by a limited portfolio of effective drugs and a paucity of robust tools to investigate parasitic nematode biology. RNA interference (RNAi) is a reverse-genetics tool with great potential to identify novel drug targets and interrogate parasite gene function, but present RNAi protocols for parasitic nematodes, which remove the parasite from the host and execute RNAi in vitro, are unreliable and inconsistent. We have established an alternative in vivo RNAi protocol targeting the filarial nematode Brugia malayi as it develops in an intermediate host, the mosquito Aedes aegypti. Injection of worm-derived short interfering RNA (siRNA) and double stranded RNA (dsRNA) into parasitized mosquitoes elicits suppression of B. malayi target gene transcript abundance in a concentration-dependent fashion. The suppression of this gene, a cathepsin L-like cysteine protease (Bm-cpl-1) is specific and profound, both injection of siRNA and dsRNA reduce transcript abundance by 83%. In vivo Bm-cpl-1 suppression results in multiple aberrant phenotypes; worm motility is inhibited by up to 69% and parasites exhibit slow-moving, kinked and partial-paralysis postures. Bm-cpl-1 suppression also retards worm growth by 48%. Bm-cpl-1 suppression ultimately prevents parasite development within the mosquito and effectively abolishes transmission potential because parasites do not migrate to the head and proboscis. Finally, Bm-cpl-1 suppression decreases parasite burden and increases mosquito survival. This is the first demonstration of in vivo RNAi in animal parasitic nematodes and results indicate this protocol is more effective than existing in vitro RNAi methods. The potential of this new protocol to investigate parasitic nematode biology and to identify and validate novel anthelmintic drug targets is discussed

RNAi Effector Diversity in Nematodes

While RNA interference (RNAi) has been deployed to facilitate gene function studies in diverse helminths, parasitic nematodes appear variably susceptible. To test if this is due to inter-species differences in RNAi effector complements, we performed a primary sequence similarity survey for orthologs of 77 Caenorhabditis elegans RNAi pathway proteins in 13 nematode species for which genomic or transcriptomic datasets were available, with all outputs subjected to domain-structure verification. Our dataset spanned transcriptomes of Ancylostoma caninum and Oesophagostomum dentatum, and genomes of Trichinella spiralis, Ascaris suum, Brugia malayi, Haemonchus contortus, Meloidogyne hapla, Meloidogyne incognita and Pristionchus pacificus, as well as the Caenorhabditis species C. brenneri, C. briggsae, C. japonica and C. remanei, and revealed that: (i) Most of the C. elegans proteins responsible for uptake and spread of exogenously applied double stranded (ds)RNA are absent from parasitic species, including RNAi-competent plant-nematodes; (ii) The Argonautes (AGOs) responsible for gene expression regulation in C. elegans are broadly conserved, unlike those recruited during the induction of RNAi by exogenous dsRNA; (iii) Secondary Argonautes (SAGOs) are poorly conserved, and the nuclear AGO NRDE-3 was not identified in any parasite; (iv) All five Caenorhabditis spp. possess an expanded RNAi effector repertoire relative to the parasitic nematodes, consistent with the propensity for gene loss in nematode parasites; (v) In spite of the quantitative differences in RNAi effector complements across nematode species, all displayed qualitatively similar coverage of functional protein groups. In summary, we could not identify RNAi effector deficiencies that associate with reduced susceptibility in parasitic nematodes. Indeed, similarities in the RNAi effector complements of RNAi refractory and competent nematode parasites support the broad applicability of this research genetic tool in nematodes

QMI compared with STA technique.

<p>The original and recovered filters using the QMI, STA and wSTA techniques. Reconstruction in the case of: natural stimulus (101x101 pixel frames) (a) filter from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0147738#pone.0147738.g001" target="_blank">Fig 1(d)</a>, (b) filter from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0147738#pone.0147738.g001" target="_blank">Fig 1(e)</a>, and white noise with (c) uniform and (d) Gaussian distributions, 16x16 pixel frame resolution input. (e) Direct comparison in accuracy between QMI and STA using the RFV projection measure (<i>Q</i>) vs. number of frames (and spikes) in the stimuli. Insets show the recovered filters for a selection of points. It is known that if the input signals are not white then the STA is a broadened version of the original filter; STA panels in (a) and (b). The attempt to remove correlations by multiplying the STA recovered filter by the inverse of the <i>a priory</i> covariance matrix (wSTA) doesn’t help much because the natural scene input is non-Gaussian, as discussed in detail in [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0147738#pone.0147738.ref025" target="_blank">25</a>].</p

Response of the eight PV-retina cell types (PV0-PV7) to three natural scene movies (labelled catMov1, catMov2 and catMov3).

<p>For each cell type a different colour is used and three individual recordings are shown in a line. Each point represents a spike and red vertical lines represent the start (at 3 s) and the end of the movies. Before and after the stimuli the retinas were exposed to uniform gray illumination. The stimulus movies were centred around each individual cell being recorded from, i.e. every single recorded cell had approximately identical input. Additional raster plots are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0147738#pone.0147738.s004" target="_blank">S4</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0147738#pone.0147738.s005" target="_blank">S5</a> Figs.</p

RFVs for: (A) PV5 and (B) PV6 cell classes.

<p>a) 2D and b) 3D RFVs with error bars and average number of spikes vs. projections of the input vectors, c) receptive field radius and d) cell’s memory estimates for cell types PV4 and PV5. Azimuth = 45°, elevation = –10° (Frame 0, -1, -2), and 10° for the rest of the frames.</p

RFV, receptive field radius and cell’s memory estimates for cell type PV7.

<p>Outer circle diameter is 250μm, the inner circle diameter 75μm. Panel a), bottom shows interpretation of the biological meaning of the discerned RFV. 3D RFV: azimuth = 37°, elevation = 20°, error: elevation: 80°. Average standard error: 0.18.</p

Receptive Field Vectors and estimates of the RFV radius and cell’s memory for: (A) PV0 and (B) PV1 cell types (results for representative cells shown).

<p>a) Single RFV that maximally separate spiking from non-spiking inputs. Brighter responses represent higher (illumination) values; color bar shown below (colourmap is Matlab parula, range is [–1,1]. Two circles (diameters 350μm and 100μm), assist in the estimation of the size of the structures. Right, average number of spikes generated vs. projections of the input vectors onto the RFV. b) 3D plots of the RFVs and standard error, estimated as described in Methods. PV0: azimuth = 10°, elevation = 80°, PV1: azimuth = -45°, elevation = 10° (frames: 0, -1, -2) and elevation = -10° (frames: -3 and -4). Standard error plots: same azimuth, elevation = 90°, note different colourmap (Matlab jet, range is [0,1]). c) MI contained within an increasing radius across the entire RFV. The decrease in the MI indicates overfitting (see text for explanation). The vertical red line represents the identified radius before the onset of the overfitting artifacts (<i>R</i>_MI). d) MI vs. number of frames that the RFV contains. The relevant receptive field history (or the Cell Memory) was estimated as in c) and marked with a red arrow.</p

Validation of using MI to estimate the Receptive Field Radius and the Cell Memory.

<p>Left column, MI contained within an increasing radius. The decrease in MI indicates overfitting (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0147738#sec002" target="_blank">Results</a>). The vertical red arrow indicates the identified radius before the onset of the overfitting artifacts (<i>R</i>_MI). Middle column, the original filter with a circle of the radius <i>R</i>_MI. Right, MI vs. number of frames that the RFV contains. The relevant receptive field history (or the Cell Memory) was estimated as for the radius and marked with a red arrow. (a) Gabor filter, same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0147738#pone.0147738.g001" target="_blank">Fig 1(a)</a>, average number of spikes 650. (b) Gabor filter: σ_<i>x</i> = 38 μm, γ = 1.3, λ = 94 μm, θ = −π/7, 〈<i>spikes</i>〉 = 642. (c) Same filter as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0147738#pone.0147738.g001" target="_blank">Fig 1(f)</a>.</p

RFVs for: (A) PV2 and (B) PV4 cell classes.

<p>a) 2D RFVs and average number of spikes vs. projections of the input vectors, b) 3D RFVs with 2D error plots c) receptive field radius and d) cell’s memory estimates. Azimuth = 45°, elevation = 10° (Frame 0, -1, -2 for PV2, and -3, -4, -5 for PV4), and -10° for the rest of the frames.</p