Sc exhibits the sulfoxidation of residue M213 as a covalent signature. Subsequent computational analysis predicted that the presence of sulfoxide groups at both Met residues 206 and 213 destabilize the α-fold, suggesting oxidation may facilitate the conversion of PrPC into PrPSc. To further study the effect of oxidation on prion formation, we generated pAbs to linear PrP peptides encompassing the Helix-3 region, as opposed to the non-linear complexed epitope of IPC2. We now show that pAbs, whose epitopes comprise Met residues, readily detected PrPC, but could not recognize most PrPSc bands unless they were vigorously reduced. Next, we showed that the α-Met pAbs did not recognize newly formed PrPSc, as is the case for the PK resistant PrP present in lines of prion infected cells. In addition, these reagents did not detect intermediate forms such as PK sensitive and partially aggregated PrPs present in infected brains. Finally, we show that PrP molecules harboring the pathogenic mutation E200K, which is linked to the most common form of familial CJD, may be spontaneously oxidized. We conclude that the oxidation of methionine residues in Helix-3 represents an early and important event in the conversion of PrPC to PrPSc. We believe that further investigation into the mechanism and role of PrP oxidation will be central in finally elucidating the mechanism by which a normal cell protein converts into a pathogenic entity that causes fatal brain degeneration.While elucidating the peculiar epitope of the α-PrP mAb IPC2, we found that PrP C into an aggregated and protease resistant β-sheet rich PrPSc form. Until recently, no covalent modification was known to be associated with such a conversion, making it difficult to follow the individual fate of each PrP form or to associate cellular events as stress-response or inflammation with the formation of prions. We now show that before PrPC initiates its conversion from proteinase K sensitive to resistant and from soluble to aggregated in the pathway to becoming PrPSc, it first undergoes oxidation of the most hidden Met residues located in a protein region exhibiting sequence identity for all species. While the cellular events promoting such oxidation in this transmissible disease remain unclear, we present evidence that PrP molecules carrying a mutation ascribed to the most common familial prion disease spontaneously oxidizes at these same Met residues. Our data provide new insights into the mechanism underlying familial Creutzfeld Jacob disease (CJD) and contribute to our general understanding of the fundamental processes related to prion pathogenesis.The protein only theory, a widely accepted model describing the prion agent, assumes that the mechanism underlying prion disease pathogenesis includes a conformational change of the α-helix rich, soluble and protease sensitive PrP Sc, a misfolded form of the GPI-anchored glycoprotein termed PrPCC has not been fully elucidated, it has been suggested that this protein plays a role in the protection of cells from copper-induced oxidative stress C, and largely a β-sheet assembly for PrPScSc, M213, is differentially oxidized Sc was also confirmed by chemical reduction experiments, state of the art mass spectrometry and detection by an antibody generated against a MetO rich maize protein Sc was first reported in the seminal work of Stahl et al. following sequencing of the PrP27-30 endoLysC peptides Sc is particularly intriguing since they are the most buried residues among methionines in the 3D PrP α-fold and thus are less accessible to reactive oxygen species (ROS) Prions are infectious agents that cause neurodegenerative diseases, such as scrapie, bovine spongiform encephalopathy (BSE) and CJD. They are believed to be composed mainly of PrPSc is formed and accumulated in brain cells, then Met oxidation, while being an interesting covalent marker of PrPSc, may not participate in the sequence of events leading to prion formation and disease manifestation. Conversely, if Met oxidation occurs on the PrPC form and mediates the subsequent conformational change, then methionine oxidation may constitute an early and important step in prion formation. Along these lines, theoretical investigations have predicted that the polarity increase of Met 206 and 213 residues upon sulfoxidation may induce destabilization of the PrP helical conformation . To further establish the role sulfoxidation in PrPSc formation, we aimed to generate pAb antibodies against linear PrP human/mouse sequences, which include reduced and oxidized Helix-3 Met residues. As opposed to the complex IPC2 epitope, which precludes simple recognition of most PrP forms upon disulfide bond reduction .The time course of Helix-3 Met oxidation as related to PrP conformational conversion is of great mechanistic importance. If this specific oxidation takes place after PrPSc and most mouse PrPSc chains were not detected by antibodies generated against reduced PrP Helix-3 Met residues unless these brain proteins were previously reduced by strong chemical reagents. In addition, our antibodies did not detect PrPSc expressed in prion-infected cells or partially aggregated PrPs present in gradient fractions of prion-infected brains, indicating that both newly formed PrPSc as well as intermediate PrP forms could be oxidized. Intriguingly, this was also the case for a PK-sensitive mutant PrP form linked to the most prevalent familial prion disease rHuPrP(23–231) E200KC into proteinase K (PK) resistant PrPSc.Consistent with this prediction, we now show that all human PrP2O2 after synthesis and coupling. Following several rounds of immunization . Properties of the other serum samples are described in sequence .2O2-oxidized form. In contrast, the activity of RTC was inhibited by the TM peptide (201–205), suggesting that this pAb recognizes the TVDK or TVDKM sequence (present in Hu and Mo PrP), N-terminally to the relevant Met residues. This finding implies that both oxidized PrP peptides failed to generate an immune response to the oxidized Met rich region, consistent with investigations in other fields indicating that charged and oxidized epitopes are mostly unrecognized by T-cell receptors As opposed to the similarity in species reactivity of the pAbs RTC and RVC, the RTC and RVC epitopes on the PrP sequence were found to be different, as determined by the inhibition of the PrP immunoblotting signal with an array of peptides. While the activity of the pAb RVC was inhibited by the KM peptide, which includes the three Met residues, this same peptide did not affect recognition of PrP by the pAb RTC in either the reduced or HSc forms. To this effect, we immunoblotted brain samples from normal and prion infected brains (digested in the presence or absence of proteinase K at 37°C) with a panel of antibodies generated against PrP epitopes upstream and downstream of the helix 3 Met area to ensure that PrPSc was present in its full length under these conditions. Sc and PrPC) the pAb RVC could not detect all of HuPrPSc and detected only low levels of MoPrPSc after PK digestion.Next, we examined the capacity of the RTC and RVC pAbs to recognize PrPSc forms indeed resulted from Met oxidation, we subjected PK digested extracts from prion-infected human and mouse brains to N-methylmercaptoacetamide (MMA), a specific MetO reducing agent Sc at detection levels similar to those of the α-PrP mAb 6H4 both before and after MMA treatment. Similar but less striking results were obtained for detection of the reduced samples by IPC2 because, as described in our previous publication To confirm that the recognition pattern of the pAb RVC vis a vis proteinase K resistant PrPC to PrPSc, we asked whether Met oxidation occurs first on PrPC or whether oxidation is a delayed effect related to the long-term accumulation and reduced clearance of proteinase K resistant and misfolded prion protein in the brains of the affected subjects. To separate these mechanistic possibilities, we studied by pAb RCV the oxidation status of newly formed PK resistant PrPSc generated in cells permanently infected with prions, such as ScN2a Sc produced by them can be considered relatively new, as opposed to the PrPSc molecules that accumulate in infected brains.To establish whether oxidation of Met residues is essential for the conversion of PrPSc before proteinase K digestion. These results indicate that newly made PrPSc may be quantitatively oxidized, as was shown here for PrP from two different cell lines and for two prion stains . Similar results were obtained for an RML infected GT1 line expressing chimeric Mo-Ha PrP Sc in the cells, and as depicted also in Sc in infected mouse brains as well as some prion-related bands in the undigested parallel samples. Therefore, we conclude that in infected murine brains, as opposed to infected human brains or infected mouse cells, low levels of proteinase K resistant PrP are present in a fully or partially reduced form. Whether such PrPSc molecules are formed independently or join a seed of oxidized PrPSc molecules after formation is currently unknown.For this experiment, extracts from ScN2a cells (infected with the RML mouse prion strain) and from the ScGT1 cell line (infected with both the RML and the 22L prion strains) Sc, detection of cell PrPSc by the pAb RVC could be restored in both cell lines when samples were reduced by MMA before being subjected to immunoblotting  or PrP* Sc properties. Intermediate PK sensitive PrP forms may be present as aggregates and require extensive or partial denaturation to be recognized by anti-PrP antibodies, as is the case for PrPScC in the lighter fractions (1–3) and PrPSc in the heavier fractions. After proteinase K digestion, only the heaviest gradient fractions (mostly fractions 9–10) presented any form of PrP signal, indicating that while proteinase K resistant PrPSc is the most aggregated, partially aggregated PrP-sen forms (fractions 4–7) may also present the PrPSc banding pattern Sc in the heaviest fraction. Following proteinase K digestion, the PrP signal mostly disappeared form all infected fractions, except traces in the last fraction of the mouse gradient, consistent with the experiments described in Sc-sen forms are as oxidized as the PrPSc-res forms, we conclude that oxidation of PrP accompanies the conformational change required for PrP aggregation and precedes the acquisition of proteinase K resistance during PrPSc formation. The fact that the low levels of reduced mouse PrPSc were detected by PVC only in the most aggregated fraction, both before and after PK digestion, further suggests that non-oxidized mouse PrP may join the prion seed following its formation from oxidized PrP molecules.Previous studies on prion-infected cells demonstrated that the formation of PrPctions 1– and PrPSC in normal human brains and undigested PrP in the brains of sporadic CJD patients, PrP was poorly detected in brains of heterozygous E200K PrP fCJD patients C from PrPSc and that the only difference between the mutant and wt PrP proteins could be the mutation itself, it was concluded that in heterozygous E200K patients only the mutant protein (K at codon 200) acquires the proteinase K resistance property during disease Sc and since the peptide used for generation of the RGM antibody comprised both the 200 residue and the Helix-3 methionines, we now investigated whether this reagent does not recognize E200K PrPSc specifically or otherwise cannot detect all forms of human PrPSc, as described above for pAb RVC. To this effect, we immunoblotted brain homogenates from RML infected-mice as well as from sporadic or familial E200K CJD human cases and analyzed them in parallel with both anti-PrP mAb 6H4 and pAb RGM. As depicted in Sc from infected brains, but did not detect PrPSc from infected cells lines, as depicted here for GT1 cells expressing chimeric Mo-Ha PrP. Similar results were obtained for PrPSc from ScN2a cells (not shown).Based on the results described above for the pAb RVC, demonstrating that antibodies directed against Helix-3 methionines may not recognize PrPSc, as predicted by the theoretical studies.To assess whether pAb RGM has separate recognition sites for E at position 200 and for the Helix 3 Met residues, which may explain why this antibody did not detect 200K PrP in fibroblasts from E200K homozygous subjects Sc as well as other oxidized proteins To investigate why pAb RGM was unable to recognize the mutated PrP even though its epitope does not include the 200 residue, we examined the recognition of wt and rHuPrP E200K by RGM as well as by a panel of antibodies designed to detect oxidized and non-oxidized PrP forms. As shown in Sc forms. This finding applied to most PrPSc accumulated in scrapie-infected mouse brains and for all PrPSc accumulated in human CJD brains, as well as for all newly formed PrPSc in several prion-infected cell lines. Since reduction by MMA restored the recognition of brain and cell's PrPSc by these antibodies, we conclude that most Helix 3 Met residues in PrPSc, both as newly made in cells, or as long term accumulated in infected brains are oxidized. Our results also indicate that Met oxidation is also present in intermediate PrP forms, such as proteinase K sensitive and partially aggregated PrPs found in human and mouse infected brains, indicating that oxidation accompanies aggregation and precedes acquisition of proteinase K resistance by the nascent PrPSc molecules. In addition, we show here that pathogenic mutant PrP forms, as is the case for E200K PrP C into proteinase K resistant PrPSc and thus in prion formation and subsequent disease pathogenesis.We have shown here that antibodies generated against reduced Helix-3 PrP Met residues could not recognize the majority of PrPFrom a structural point of view, Met oxidation involves the transformation from a moderated hydrophobic to a hydrophilic side chain. While in protein exposed residues this chemical change may not have major structural effects, sulfoxidation of buried Met may impact the stabilization interactions maintaining the proteins 3D fold. Indeed, this intuitive prediction is in agreement with our theoretical studies, which showed that changing the sulfur atom of Met206 and M213, both single or in combination, by a sulfoxide destabilizes the native α-folded C is not a difficult task. The Met rich area in Helix-3 appears to be quite immunogenic, as deduced by the fact that even immunization of rabbits with the relatively large peptide spanning amino acids 195–213 yielded antibodies against the Met rich area , they can transmit prion infectivity to wt animals While our results suggest that the oxidation of PrP forms may play a role in the formation of PrPC is not associated with prion disease pathogenesis. However, we show here that oxidation of Met residues on PrPC, which may relate to its proposed antioxidative function Sc conformation. In fact, the association between oxidative stress and PrP conversion may link the activity of the prion proteins with other neurodegenerative conditions affected by stress and oxidation, such as ALS, AD and Parkinson's diseases Based mainly on the fact that PrP ablated mice did not suffer from severe neurological damage Animal experiments were conducted under the guidelines and supervision of the Hebrew University Ethical Committee, which approved the methods employed in this project. Brain human samples were received following postmortem examinations from the Pathology Department of the Hadassah University Hospital. Immunobloting analysis, as that described in this manuscript (in search of PrPSc), is part of the routine pathological protocol applied on brains from suspected CJD cases. Our laboratory in the Hadassah Department of Neurology is the national referral center for CJD diagnosis . The testing of these samples was approved by both the safety and ethical authorities of the Hadassah University Hospital. Since all cases of CJD and alike negative controls are unable to sign for such tests long before their death due to their medical condition, the relatives of these patients provided informed written consent for PM studies. Enabling close relatives to provide such consent is the standard policy of the Israeli Ministry of Health.PrP peptides were synthesized on a Liberty peptide synthesizer with a Discover single mode microwave module, using standard Fmoc chemistry. Amino acids were purchased from Luxembourg Bio Technologies, except for Fmoc-Met(O)-OH, which was purchased from Novabiochem. Peptides were cleaved from the resin by treatment with a mixture 95% trifluoroacetic acid, 2.5% water, 2.5% triisopropylsilane, and precipitation with cold diethylether. The peptides were purified on a Vydac C8 semipreparative column using gradients of 5% to 60% acetonitrile in water, with 0.1% trifluoroacetic acid (TFA) in both solvents. The mass of the peptides was measured using an Applied Biosystems Voyager-DE Pro MALDI TOF mass spectrometer and verified to be within ±1 Da of the theoretical mass. The purified peptides were lyophilized with 30% acetic acid to remove residual TFA.5′-GAAGTTCACCAAGACCGACGTTAAG-3′ (forward) and 5′–CTTAACGT CGGTCTTGGTGAACTTC-3′ (reverse). Before their use, proteins were equilibrated by dialysis in 10 mM NaAc pH 4.5 containing 50 mM NaCl and 0.5 mM citrate and characterized both as monomers by dynamic light scattering using DynaPro Titan spectroscatter (Wyatt Technology). CD spectra were recorded using a Jasco-810 spectropolarimeter operating at 25°C, and using 0.1 cm pathlength cuvettes and about 13 µM protein concentration solutions. Thermal denaturation experiments were performed by following the changes in the ellipticity at 220 nm as the samples were heated from 15°C to 85°C at the rate of 1 degree/min.Recombinant HuPrP(23–230) wt (with M129) and E200K chains were produced, purified and refolded into the α-form from their pET11a constructs using oxidized glutathione for disulfide bond formation and including 2 mM Met in refolding buffers 2O2. After 15 min incubation at 37°C, the reaction was quenched by addition of 20 mM of free methionine before addition to the adjuvant. Following 3 lines of immunization, serum samples from all immunized rabbits were tested for their anti-PrP activity. Rabbits with positive sera were immunized once again before final collection of blood. Antisera were purified by affinity chromatography, using for retention either peptides (RVC antibody) or Protein A (RTC and RGM antibodies).Designated PrP peptides were coupled to activated KLH (Sigma) and inoculated into rabbits while emulsified into Complete Freund's Adjuvant for the first immunization and Incomplete Freund's Adjuvant for subsequent injections. Most peptide immuniziations were performed at the animal facility of the Hebrew University-Medical School, except the ones for the RVC antibody which was produced by GenScript Inc . KLH coupled with the Cys-KM peptide was first oxidized with 20 mM HBrain samples of normal humans and patients with confirmed sporadic and genetic E200K CJD were obtained from Hadassah University Hospital Pathology department. Brains from mice infected with the RML scrapie prions and from golden hamsters inoculated with Sc237 prions were provided by the Animal Facility of the Hebrew University-Medical school. Brain homogenates (10% w/v) were prepared by repeated extrusion through an 18-gauge followed by a 21-gauge needle in phosphate-buffered saline (PBS), aliquoted and maintained at −70°C until use.Naïve and scrapie infected ScN2aNormal and prion-containing brain samples were homogenated at 10% (W/V) in 10 mM Tris, pH 7.4 and 0.3 M sucrose. Proteinase K digestions were performed by incubating 30 µl of 10% prion-infected brain homogenates with 2% sarkosyl for 30 min at 37°C with 40 µg/ml protease. Control samples were incubated at 37°C in the absence of proteinase K). After boiling in sample buffer, samples were subjected to SDS PAGE and immunoblotting with the diverse anti-PrP antibodies. For the inhibition experiments, nitrocellulose sheets comprising the transferred proteins were subjected either to a 1∶2000 dilution of the designated antibody alone or preincubated for at least 2 hours with the appropriate synthetic peptide (2 µg/ml). Immunoblots were developed with α mouse or α rabbit antibodies AP or HRP-conjugated secondary antibodies .Proteinase K digested prion-infected cells or brain homogenates were treated with 6 M N-methylmercaptoacetamide (MMA) Sarkosyl extracted brain extracts from human and mouse , Mouse Prion Protein: P04925 (PRIO_MOUSE), Hamster Prion Protein: P04273 (PRIO_MESAU).


Effective processing of such signals requires developing machine learning methods capable of learning the various blockade modes for classification and knowledge discovery purposes. Here we propose a method aimed to improve our stochastic analysis capabilities to better understand the discriminatory capabilities of the observed the nanopore channel interactions with analyte.Ionic current blockade signal processing, for use in nanopore detection, offers a promising new way to analyze single molecule properties with potential implications for DNA sequencing. The We tailored our memory-sparse distributed implementation of a Mixture of Hidden Markov Models (MHMMs) to the problem of channel current blockade clustering and associated analyte classification. By using probabilistic fully connected HMM profiles as mixture components we were able to cluster the various 9 base-pair hairpin channel blockades. We obtained very high Maximum a Posteriori (MAP) classification with a mixture of 12 different channel blockade profiles, each with 4 levels, a configuration that can be computed with sufficient speed for real-time experimental feedback. MAP classification performance depends on several factors such as the number of mixture components, the number of levels in each profile, and the duration of a channel blockade event. We distribute Baum-Welch Expectation Maximization (EM) algorithms running on our model in two ways. A distributed implementation of the MHMM data processing accelerates data clustering efforts. The second, simultanteous, strategy uses an EM checkpointing algorithm to lower the memory use and efficiently distribute the bulk of EM processing in processing large data sequences (such as for the progressive sums used in the HMM parameter estimates)..The proposed distributed MHMM method has many appealing properties, such as precise classification of analyte in real-time scenarios, and the ability to incorporate new domain knowledge into a flexible, easily distributable, architecture. The distributed HMM provides a feature extraction that is equivalent to that of the sequential HMM with a speedup factor approximately equal to the number of independent CPUs operating on the data. The MHMM topology learns clusters existing within data samples via distributed HMM EM learning. A Java implementation of the MHMM algorithm is available at  Staphylococcus aureus secretes α-hemolysin monomers that bind to the outer membrane of susceptible cells. Seven monomers can oligomerize to form a very stable water-filled transmembrane channel  (the 10-fold resampling is needed to perform majority-vote classification stabilization). The resampling offers a similar stabilization on classifications, and at similar computational expense, to what is done via data-rejection in [We have learned blockade signal clusters for five different types of molecules: two such profile mixtures, learned in 50 iterations, are shown in Figure ction in . Accuracwhere True Positives (TP), True Negatives (TN), False Positives (FP) and False Negatives (FN) are among the classified data samples. We have systematically investigated how the model complexity affects accuracy as shown in Figure The accuracy of consecutive same-analyte toggle samples classification is shown in Figure The accuracy improvement is consistent with the accuracy of the previously reported classification process  as shownsingle HMM .The better resolution between 9GC and 9AT channel blockades obtained with the 50-state (used in ) may simThe MHMM analysis framework first has been implemented in a concurrent fashion on a quad-core Sun Ultra 40 M2 machine with speedup factor 3.66 as compared to a conventional implementation, and then distributed to the five machines of the same type with Java RMI with additional speedup of 4.02, which translates to the total speedup of 3.66 × 4.02 = 14.71.In our approach we used unsupervised distributed learning of nanopore ionic flow blockade toggles with an MHMM. MHMMs have a long record of successful implementations that started in speech recognition  and lateThe ionic flow blockade records were obtained from the previous studies . Two axoAnother test set was constructed from the above data files to measure accuracy of consecutive same-analyte toggle sample classification. In this instance we take all the available blockade signal coming from the test files of a certain molecule (not just the first 100 ms) and use multiple sample draws from the same signal blockade . With the 100 ms signal samples drawn from the same blockade event, we perform MAP scoring followed by majority vote (with random resolution of ties). , connected through gigabit Ethernet switch.The following parameters describe the conventional HMM implementation according to :S = {S1,..., SN} with qt being the state visited at time t,• A set of states B = {b1(o),..., bN(o)}, describing the emission probabilities bj(ot) = p(ot|qt = Sj) for 1 ≤ j ≤ N, where ot is the observation at time-point t from the sequence of observations O = {o1,..., oT},• A set of PDFs ai,j} for 1 ≤ i, j ≤ N, where ai, j = p(qt+1 = Sj|qt = Si),• The state-transition probability matrix A = {π1,..., πN}.• The initial state distribution vector ∏ = {λ =  completely specifies an HMM. Here we describe the HMM parameter update rules for the EM learning algorithm rigorously derived in [A set of parameters rived in . When trξt as the probability of being in state i at time t, and state j at time t + 1, given the model and the observation sequenceLet us define γt(i) as the probability of being in state i at time t, given the observation sequence and the modeland The HMM maximization step using these probabilities is shown in Table The objective of mixture learning is to maximize the likelihood function The Expectation step in the mixture fitting algorithm is done by computing the responsibility matrix of the components given the data points:λm and emission sequence Ok:We use Bayes' rule to find the posterior probability (responsibility) of a mixture component with parameters The Expectation step is followed by the maximization step where we re-estimate parameters.• Mixture proportions• Initial probabilitiesm given sequence Ok,where • Transitionsm given sequence Ok,where • Emissionsm given sequence Ok.where parallel implementation can greatly depend on model topology. In the speech recognition community researchers are able to use a highly parallel HMM architectures for phoneme and dictionary word recognition. Typically, when a large number of Processing Elements (PEs) is used, the utilization of each element drops due to communication overheads. Therefore, the communication overhead in any parallel architecture must be strictly managed, ideally reduced to a constellation of PEs with shared memory [As discussed in , the comd memory . In reced memory  we descrO(T N) memory and O(2T N Qmax + T (Q + E)) time, where T is the length of the observed sequence, N is the number of HMM states, Qmax is the maximum HMM node out-degree, E is the number of free emission parameters, Q is the number of free transition parameters.• Conventional EM due to Leonard E. Baum and Lloyd R.  takes O(O(N) memory and O(3T N Qmax + T (Q + E)) time,• Checkpointing EM -20 takesO(N(Q + E D)) memory and O(T NQmax(Q + E D)) time.• Linear memory EM ,21 takesSimilar improvements are also described for the HMM Viterbi implementation in linear memory . In actuThe distributed checkpointing EM algorithm is shown in Figure O into subsequences O1,..., Ot,..., λ,1. Client machine splits data sequence Ot were calculated and stored,2. Find Forward and Backward N3. Reconstruct dynamic programming tables of size 4. After calculating local parameter estimate, communicate 5. Redistribute newly found An MHMM can easily split the responsibilities calculation between several cluster nodes with minimum communication overhead in the following way:λ1,..., λm,..., λM and sequence O1,..., Ok,..., OK calculate likelihood p(Ok| λm) on the server nodes and communicate them back to the client,1. For each parameter 2. Client finds responsibilities for each mixture component and a sequence according to formula (4),3. Estimated mixture proportions λm and sequence Ok and send them back to the client,4. The server nodes find 5. On the client node these newly computed parameters are weighted according to responsibilities (6), (7), (8),6. Newly found HMM parameters There are several advantages in our approach:• Classification is highly accurate with no data dropped from consideration,• Model parameters may have intuitive physical interpretation (but not in this study),• The MHMM implementation is distributed, such that:- Learning can take a larger number of samples (for improved accuracy),- Enables real-time analyte classification, currently takes only 0.411 sec to classify 100 ms sample,- Checkpointing algorithm keeps the memory profile low both on server and client sides without compromising the running time .The need for using a mixture model beyond a simple HMM comes from the observation that generally no more than half of hairpin blockades come from the same mode of hairpin molecule interacting with nanopore . Other mode contributions require different probabilistic profiles for classification which naturally leads to a mixture analysis problem. The method shown in Figure Increasing EM-learning model complexity beyond 4 levels and 12 mixture components increases the log-likelihood of fully trained model, but does not lead to better prediction accuracy as shown in Figure Since we did not computationally exhaust all the possible parameter settings , we provide a rationale for the parameter choice we believe is optimal. With preliminary experiments learning on 9CG toggle samples with MHMM of 15 toggle clusters we have consistently exhausted the number of components, many of them converging to the same simple blockade as shown in figure IL ⇔ UL ⇔ LL ⇔ S to happen at any time during the analysis procedure. The spikes model, as described in [The number of four blockade levels corresponds to the physical model of DNA hairpin interacting with nanopore . From thribed in , could p.A demo program implementing distributed MHMM analysis framework is available free of charge on our web site The authors declare that they have no competing interests.AC conceptualized the project, implemented and tested the MHMM EM algorithm for nanopore ionic flow analysis. SWH helped with writing the manuscript and provided many valuable suggestions directing the study. All authors read and approved the final manuscript.


In addition, plant diversity has been shown to have strong effects on microbial communities and belowground processes and could potentially alter the functional capacity of denitrifiers. Here, we examined three questions: (1) Does DEA vary through the growing season? (2) If so, can we predict DEA variability with environmental variables? (3) Does plant functional diversity affect DEA variability?Denitrification is an important ecosystem service that removes nitrogen (N) from N-polluted watersheds, buffering soil, stream, and river water quality from excess N by returning N to the atmosphere before it reaches lakes or oceans and leads to eutrophication. The denitrification enzyme activity (DEA) assay is widely used for measuring denitrification potential. Because DEA is a function of enzyme levels in soils, most ecologists studying denitrification have assumed that DEA is less sensitive to ambient levels of nitrate (NOp<0.0001). Similarly, microbial biomass standardized DEA rates were significantly greater in July than May and August (p<0.0001). Of the soil variables measured—soil moisture, organic matter, total inorganic nitrogen, and microbial biomass—none consistently explained the pattern observed in DEA through time. There was no significant relationship between DEA and plant species richness or functional diversity. However, the seasonal variance in microbial biomass standardized DEA rates was significantly inversely related to plant species functional diversity (p<0.01).The study site is a restored wetland in North Carolina, US with native wetland herbs planted in monocultures or mixes of four or eight species. We found that denitrification potentials for soils collected in July 2006 were significantly greater than for soils collected in May and late August 2006 (These findings suggest that higher plant functional diversity may support a more constant level of DEA through time, buffering the ecosystem from changes in season and soil conditions. Denitrification in wetland soils is an important ecosystem service that removes nitrogen (N) from N-polluted watersheds, buffering soil, stream and river water quality from excess N by returning N to the atmosphere before it reaches lakes or oceans 3−), producing mainly the gases dinitrogen (N2) and nitrous oxide (N2O)—a greenhouse gas 3− denitrifying bacteria (denitrifiers) also require organic carbon (OC) as an energy source and are typically most active near the “hot-spot” interface with the oxic zone where nitrification occurs potential ability to denitrify under optimal conditions yields more information about denitrifier functioning than in situ denitrification measurements—where the denitrification rate is a product of the availability of denitrification substrates in the soil environment and not a measure of microbial community's functional potential. The Denitrification Enzyme Activity (DEA) assay is a widely used method for measuring denitrification potential 3− under anoxic conditions and measures N2O production over a short incubation period in situ denitrification where the denitrification rate may be limited by substrate availability, redox status, and/or temperature.Denitrification is an anaerobic process that reduces nitrate  Does DEA vary through the growing season? (2) If so, can we predict DEA variability with environmental variables? (3) Does plant functional diversity affect DEA variability?Carex crinita, Carex lurida, Scirpus cyperinus, Juncus effusus, Panicum virgatum, Chasmanthium latifolium, Eupatorium fistulosum, Vernonia noveboracensis, Asclepias incarnata, and Lobelia cardinalis. In the plots with four species (15 plots), 25 individuals of each randomly selected species were planted. In the plots with eight species (16 plots), either 12 or 13 individuals of each randomly selected species were planted. The species in the study were selected from a list of recommended species for North Carolina stream restoration The study site is located in a restored riparian wetland in the Duke University Stream and Wetland Assessment Management Park (SWAMP), located along Sandy Creek in the Duke Forest in Durham, NC . Soils in this area are primarily Cartecay silt loams and Mayodan sandy loams 3− + NO2−) and ammonium (NH4+) 3, NO2−, and NH4+as TIN because during most of the growing season these wetland soils remained relatively oxic, which allows soil N to readily cycle between NO3− and NH4+. Therefore, TIN serves as a more integrated measure of soil N available to the microbial community than NO3− alone. We determined microbial biomass C (MBC) using a modified version of the Voroney and Winter 2SO4). Non-fumigated samples were extracted immediately. We analyzed unfumigated and fumigated samples for Total Organic Carbon (TOC) on a Shimadzu 5000 TOC analyzer  and calculated microbial biomass as the difference between the fumigated and unfumigated values of TOC Soil samples were collected in the second growing season of the experiment, 2006, in early May, mid-July, and late August. Two soil samples (2.5 cm diameter) from each plot were collected in plastic sleeves from the upper 15 cm of each plot using a piston corer. These samples were stored on ice until arrival at the lab then stored at 4°C until they could be analyzed, typically within a week of collection. Upon arrival at the laboratory, the two cores from each plot were bulked together and sieved through a 4.75 mm sieve prior to analysis. To determine soil redox status, we measured percent soil moisture by oven-drying a sub-sample of each soil at 105°C for 24 hours. A sub-sample of this dried soil was then used to determine percent soil organic matter (OM) by loss on ignition at 450°C 2H2) block technique to inhibit formation of the end product N2, such that N2O gas produced is a function of the level of enzyme in the sample 3) to ensure non-limiting substrate conditions, and 0.125 g/L chloramphenicol to inhibit protein synthesis. The slurries were made anaerobic by repeated flushing with N2 gas. Flasks were vented with a needle followed by an injection of 10 mL of acetylene into each flask 63Ni electron capture detector gas chromatograph . N2O dissolved in the slurry was corrected with the Bunsen equation The DEA assay mass-standardized DEA, which is the DEA rate per unit MBC. Groffman and Tiedje In an effort to standardize our DEA rates, we calculated We used a multivariate, trait-based metric of plant functional diversity (Petchy's FD) that includes traits that are assumed to be functionally important, meaning these traits inform about the differences between species that affect the target ecosystem function 3− and indirectly by priming the soil for decomposition of organic material, increasing N mineralization and available NO3−Both the quality and quantity of soil C have been shown to limit denitrification A third pathway by which plants may affect denitrification is through modification of the redox conditions in the soil via delivery of oxygen through radial oxygen loss. In an anaerobic wetland environment, root porosity facilitates nitrification by allowing oxygen to be released from the roots These five traits—C:N ratios of roots and aboveground biomass, root and aboveground biomass, and root porosity—were used to calculate individual measures of plant trait diversity, FD, for each plot. These traits were transformed into standard deviation units, z-scores, so that all traits would be equally weighted no matter their original units. These z-scores were used to calculate dendrograms of the ten planted species, with a calculated branch length for each species based on the five traits indicating how different each species was from the others. A longer branch length means a species is more different from the rest of the species. To calculate the final FD for each plot, the branch lengths for each species present in a plot were summed. FD scores ranged from 3.72 to 14.08. See Petchey and Gaston To determine whether DEA, mass-standardized DEA, and soil variables exhibited significant variation across sampling dates, we used ANOVAs and Tukey's HSD post-hoc test. We tested the relationship of both DEA and mass-standardized DEA with soil variables and FD using a General Linear Model (GLM) with normal error distribution for each sample date. We calculated the coefficient of variation (CV) of the three sample dates' values for DEA, mass-standardized DEA, and the soil variables to quantify variability through time. Another GLM was used to compare the CV of DEA with the CV of the soil variables and FD. This GLM was repeated replacing DEA with mass-standardized DEA. MBC was removed from the latter models since it is part of the calculation of mass-standardized DEA. TIN data were log transformed. Plot level FD values do not have a CV since it was not measured at each sample date. Statistical analyses were performed using JMP 7.0 .F = 14.15; p<0.0001; F = 10.11; p<0.0001; F = 5.88; p = 0.0036; F = 107.52; p<0.0001; Both DEA  DEA varied through time; (2) the environmental factors related to DEA shifted through time; and (3) plant FD had a significant effect on mass-standardized denitrification variability through time. These results suggest that the functional capacity of the denitrifier community is shifting over time. Environmental factors alone did not predict denitrification potential consistently through time, while higher levels of plant trait diversity were associated with reduced variability through time.3− and OC, and by making the environment anoxic. A change over time in DEA rates can be an indicator of a change in microbial community function in response to changes in the soil environment and/or plant trait diversity.Typically the microbial process of denitrification is viewed as simply a function of environmental factors  in the biogeochemical model of denitrification in situ denitrification in situ denitrification and DEA and observed strikingly different seasonal patterns between the two metrics. Parsons et al. Both DEA and mass-standardized DEA rates were significantly higher in mid-July than in early May or late August . Severalin situ denitrification varies over time due to changes in environmental factors including soil redox status and N and C availability, it has been assumed that DEA variation can be explained by these same factors over time was significant or consistent. These results suggest that changes in DEA over time cannot be explained by any one factor; rather the environmental controls on the denitrifier community are complex and can shift within a single growing season.Although it is understood that Plant FD was a significant predictor of mass-standardized DEA variability where plots with higher FD tended to have lower mass-standardized DEA variability . This fiGiven the observed FD effect on denitrification potential, we expected to see differences in DEA rates among the monocultures; but surprisingly peak DEA rates were similar for all species in monocultures and the highest overall rates of DEA were found in July in most of the monocultures . This inWhile this is one of the first studies with experimental evidence linking plant functional diversity and soil denitrification, other studies have described the relationship between plant FD, root processes, soil properties, and soil microbes. Zak et al. Given the importance of plant traits and communities on microbial processes, one promising avenue for future research should address how plant trait phenotypic plasticity impacts ecosystem function. Studies have shown that both abiotic and biotic factors can influence plant phenotypic plasticity, but the consequences of phenotypic plasticity on plant community dynamics are not well understood The seasonal DEA variation documented here indicates a change in the denitrifier community's ability to respond to resource pulses—meaning the ability of the microbial community to denitrify—is shifting over time. Our results suggest that rather than using DEA to get a single snapshot of soil microbial community functioning, we can use it as a tool to track changes in microbial community functioning in response to changes in environmental conditions through time. Researchers should consider these implications for planning and interpreting DEA assays. Although we found soil variables were related to DEA rates at single sampling times, none of them consistently predicted DEA through time despite being known as important factors in the denitrification process. The fact that we found that variation over time in DEA rates was not explained by changes in soil variables bolsters the need for microbial community dynamics to be included in biogeochemical models Even though the monoculture data did not indicate that individual species stimulated DEA at different times during the season, plant FD was a strong predictor of mass-standardized DEA variability . These rFigure S1Scatter plot of explanatory variables and DEA rates for each sample date. For every plot early May values are circles, mid-July values are triangles, and late August values are squares. A significant relationship is indicated by a * to the right of the regression line. Panels: A) Functional diversity, B) Total Inorganic N, C) MBC, D) Organic matter, and E) Soil moisture.(0.17 MB TIF)Click here for additional data file.Figure S2Scatter plot of explanatory variables and mass-standardized DEA rates for each sample date. For every plot early May values are circles, mid-July values are triangles, and late August values are squares. A significant relationship is indicated by a * to the right of the regression line. Panels: A) Functional diversity, B) Total Inorganic N, C) Organic matter, and D) Soil moisture.(0.17 MB TIF)Click here for additional data file.Figure S3The relationship between the CV of DEA rates and plant functional diversity. Scatter plot of the CV of DEA rates in early May, mid-July, and late August 2006 from each plot versus the FD value for that plot . Regression analyzed using a general linear model. Each point represents one plot.(0.03 MB TIF)Click here for additional data file.Figure S4Carex crinita, 2; Carex lurida, 1; Scirpus cyperinus, 2; Juncus effusus, 3; Panicum virgatum, 2; Chasmanthium latifolium, 1; Eupatorium fistulosum, 1; Vernonia noveboracencis, 3. Error bars indicate standard error.DEA rates in early May, mid-July, and late August for monoculture plots. There was no significant species or date by species interaction among the monoculture plots, i.e. different species did not appear to promote different patterns in DEA over time. Monoculture plots' n for: (0.07 MB TIF)Click here for additional data file.


Infectious salmon anemia virus (ISAV) causes a multisystemic disease responsible for severe losses in salmon aquaculture. Better understanding of factors that explain variations in resistance between individuals and families is essential for development of strategies for disease control. To approach this, we compared global gene expression using microarrays in fish dying early and late in the time course following infection from a highly pathogenic ISAV.Tissues  from infected Atlantic salmon  were collected from three stages over the time course of the experiment; early , 21–25 days post-infection (DPI)), intermediate  and late  mortality. Viral loads were equal in EM and IM but dropped markedly in LM fish. Gene expression analyses using a 1.8 K salmonid fish cDNA microarray (SFA2.0) and real-time qPCR revealed a large group of genes highly up-regulated across tissues in EM, which were mainly implicated in innate antiviral responses and cellular stress. Despite equal levels of MHC class I in EM and LM, increase of splenic and cardiac expression of immunoglobulin-like genes was found only in LM while a suite of adaptive immunity markers were activated already in IM. The hepatic responses to ISAV were characterized by difference between EM and LM in expression of chaperones and genes involved in eicosanoid metabolism. To develop classification of high and low resistance phenotypes based on a small number of genes, we processed results from qPCR analyses of liver using a linear discriminant analysis. Four genes  were sufficient for correct assignment of individuals to EM, LM and uninfected groups, while IM was inseparable from EM. Three of four prognostic markers are involved in metabolism of inflammatory regulators.This study adds to the understanding of molecular determinants for resistance to acute ISAV infection. The most susceptible individuals were characterized by high viral replication and dramatic activation of innate immune responses, which did not provide protection. The ability to endure high levels of infection for sustained periods could be associated with lower inflammatory responses while subsequent protection and viral clearance was most likely conferred by activation of adaptive immunity. Isavirus within the family Orthomyxoviridae. It mainly infects endothelial cells and leukocytes and causes a multisystemic disease characterized by high mortality with exophthalmia, pale gills, ascites, hemorrhagic liver necrosis, renal interstitial hemorrhage and tubular nephrosis  is a negative single-stranded RNA virus assigned to the genus iewed in ). Severaiewed in ,4. The fiewed in ,6. Nonetiewed in , which iiewed in ,9. Thereiewed in . CollectIn addition to the scientific interest, comparative studies of salmon with high and low resistance are of great importance for aquaculture. At present selective breeding and vaccination are key approaches to disease control, which employ respectively the innate and acquired protective mechanisms. Innate defence involves generalized antiviral responses, such as IFN and IFN-independent proteins and pathogen-specific antiviral factors referred to as restriction factors ,12. AdapTo obtain a more comprehensive understanding and elucidation of important molecular determinants for survival towards infectious salmon anemia disease we have compared differences in global gene expression in resistant and susceptible fish using microarrray technology. Survival time is commonly used as an estimate of susceptibility and resistance in pathogen challenge tests. Atlantic salmon were infected with a common acute ISAV isolate by cohabitance, which mimics the natural mode of virus transfer. Tissues from fish within early (EM), intermediate (IM) and late mortality (LM) groups were sampled and viral loads were determined by real-time qPCR. We used a high-density salmonid fish cDNA microarray (SFA2.0 immunochip) designed for studies of responses to pathogens and stressors (GEO GPL1212) ,14. In cThe cohabitant transmission of virus from fish injected with a previously tested viral load enabled the investigation of the immune response in fish after a natural route of infection. The chosen ISAV strain (Glesvaer 2/90) is well characterized and is classified among isolates inducing acute disease progression . A cumul50 [T (cycle threshold) values within each stage indicated that viral RNA levels were similar between individuals and justified the comparison of gene expression between the three groups of mortalities. In general, there was a high viral replication in EM and IM stages with no significant difference between these groups, while a marked reduction was observed in LM (except for liver). Levels in survivor fish  were similar to LM (data not shown). When transforming CT values into viral particles based on a standard curve of CT from in vitro infected cells this corresponded to a 1000-fold reduction of viral particles in spleens only within 6 days (from 31–37 DPI), given that cohabitants were infected simultaneously. No significant difference was observed between levels of two ISAV segments (HA and NS1) (data not shown).The ISAV pathology in sampled fish was confirmed by clinical examination. Levels of infection in four random fish from the EM, IM and LM stages were determined by real-time qPCR analysis of viral RNA in all tissues Fig. . This is50 . Low staMicroarray analyses were conducted on gill, heart, liver and spleen samples pooled from six individuals for each tissue, and on individual samples of liver (n = 6). The former indicated tissue-specificity of responses while the latter allowed for statistical assessment of differences between the early and late mortalities (EM and LM). Primary data was submitted to GEO and the processed results are available in Additional file Many genes were regulated across all tissues and a large fraction of these showed markedly higher expression levels in EM compared to LM Fig. . Among tA group of genes induced at the LM versus EM stage was of major interest since they could point to responses important for resistance or late survival. Microarray analyses of heart, spleen and gills revealed a coordinated induction of Ig-related genes Fig. . A gene + CTL response; CD8α, CD4+ TH1 response; IFN-γ and TH2 response; TGF-β) immunity. For this analysis we also included fish in the IM stage and increased the number of individuals (6 per stage) to see if the viral clearance observed in LM was explained by a general activation of adaptive immunity in IM. The results showed that IgM and IgZ, but not IgD (data not shown), were induced at all stages versus control but in opposite manners  was generally down-regulated in late survivors except for up-regulation in heart LM, the tissue with highest viral RNA levels.To further detail the features of the adaptive immune responses observed in LM we designed qPCR primers for well-known genes of humoral  and T-cell mediated  showed consistently lower expression levels in liver from LM compared to EM Table . ChaperoIn liver ISAV also affected genes involved in steroid and lipid metabolism Table . FunctioMicroarray screening with subsequent qPCR analyses of candidate genes is a widely used approach for developing prognostic markers. We wished to try a hypothesis-free strategy meaning that gene expression profiles were used with no surmise on gene functions. Therefore the candidate genes Table  were choWe searched for the host factors that may account for different susceptibility and resistance to ISAV in Atlantic salmon. This question is of great importance for the development of protective strategies in aquaculture. With the exception of one study that reported possible association of specific MHC class I alleles with survival  there isApart from active protection, survival time may depend on the tolerance or ability to endure high viral loads, and comparison of the hepatic responses tended to support this possibility. Regulation of heat shock proteins plus other chaperones regarded as evidence for cellular stress, is a common feature of various viral diseases . These cH2 marker), in IM and LM fish which correlated with viral clearance in LM fish. An optimal TH1 response, consisting of virus-specific (IFN-γ)-secreting CD4+ T cells and cytotoxic CD8+ T cells that lyse virus-infected cells [+ T cells, was consistently induced in all tissues and stages. These results also support recent observations that ISAV survival correlated more strongly to the level of cell-mediated responses than did humoral responses [Microarray analyses revealed only one group of genes (immunoglobulin-related) that were higher induced in LM compared to EM. Changes in levels of these transcripts could be due to maturation and/or migration of immune cells to the heart and spleen, the two most heavily infected organs, and suggested an activation of adaptive immune responses in late survivors. The microarray includes eleven Ig clones corresponding to seven Unigene clusters. The exact roles of these salmon genes are not known, however their putative mammalian homologs are involved in early B-cell development. Apart from mediating antibody responses, mature B cells are crucial for activation of effector helper T cells . Equallyed cells , is cruced cells . FurtherCollectively, these findings demonstrate the power of functional genomics in untangling the complexities of virus-host interactions and viral pathogenesis. From analysis of gene expression profiles we suggest that common features of highly pathogenic ISAV include high viral replication, dramatic induction of innate immunity without protection and subsequent activation of cell-mediated immunity and reduced inflammation associated with viral clearance. Similarities to influenza pathogenesis are striking; fatalities of the 1918 influenza virus is caused by rapid replication kinetics resulting in an excessively vigorous innate immune and inflammatory response that contributes to severe tissue damage, disease and death ,52.The need for prognostic and diagnostic tools for control of aquaculture diseases is urgent. We compared consequences of the acute and protracted forms of ISAV infection, but to search for prognostic markers our experimental design was not appropriate as analyses should have been conducted before challenge. Nonetheless, we regard this study as a step towards this task, which can help to clarify a number of important questions. An ideal strategy for prediction of resistance would be an in vitro test, such as stimulation of primary cultures  with ISAV and subsequent gene expression analysis. However because we did not find any broad-tissue responses associated with improved resistance the feasibility of such approach is ambiguous. The LM group was characterized by activation of adaptive immune responses which are systemic by nature and require complex interactions of different cellular elements and humoral factors. Given the pivotal role of acquired responses, the development of animal models will be essential for studies on fish resistance to viral pathogens. We used materials from this experiment as a pilot study for the development of predictors based on microarray and qPCR analyses. Four genes were sufficient for accurate class assignment and importantly, this was achieved using linear discriminate analyses, a gold standard approach to tasks of this kind.In conclusion, three groups with different survival times were characterized by distinct phenotypes suggesting that resistance at early and late stages of ISAV could be explained by different molecular host determinants. Early mortality associated with highly pathogenic form of ISAV was characterized by high viral replication, dramatic up-regulation of innate immune mechanisms and cellular stress. Intermediate mortalities shared common features with EM  in contrast to the late group of survivors where a significant viral clearance coincided with activation of adaptive cellular immunity and reduced inflammation. Overall, development of adaptive immune responses in salmonid fish requires several weeks after pathogen challenge, which is consistent with our results. Ability to survive to this point is likely to have crucial importance for the outcome of disease.3 TCID50 of a third passage of ISAV strain Glesvaer 2/90 . A separate tank contained 40 unvaccinated control fish of same size and origin which were kept under identical conditions  as challenged fish. Standardized tissue sampling  was performed from 12, 24 and 12 moribund fish from respectively EM (0–10% CM), IM (35–55% CM) and LM (75–84% CM) stages (cohabitants) and similarly from half the number of controls at the same time-points as challenged fish. Samples were immediately stored in >10× excess volume of RNAlater  at 4°C overnight following -20°C until RNA extraction. All fish were negatively tested for immune status by ELISA testing for Vibrio salmonicidae, Vibrio anguillarum O1 and O2, Moritella viscosa and IPNV before challenge. Moribund fish were confirmed clinically positive for ISAV. No mortality was observed among control fish.Tissue samples from control and ISAV infected fish originated from a reference challenge trial performed at VESO Vikan . The trial was approved by The National Animal Research Authority (see Availability and requirements section for URL) according to the 'European Convention for the Protection of Vertebrate Animals used for Experimental and other Scientific Purposes' (EST 123). Unvaccinated juvenile Atlantic salmon (average size 22.8 gram at start) were kept in separate tanks at 12°C under controlled conditions  and acclimatised for one week before challenged. In one tank, 300 fish (hereafter referred to as cohabitants) were cohabitated with 60 marked fish each i.p.-injected with a virus dose of 7.2 × 10free™, Ambion, Austin, TX, USA) total RNA using TaqMan® Reverse Transcription reagents  and random hexamer primers, according to manufacturer's protocol.Total RNA from tissue was isolated using PureLink Micro-Midi kit (Invitrogen) using manufacturer's protocols and guidelines. RNA quantity was measured by Nanodrop  and integrity confirmed by gel electrophoresis. Samples were precipitated and stored under ethanol at -70°C. cDNA synthesis was performed on 2 μg DNAse-treated  and images were processed with GenePix Pro 6.0 . The spots were filtered by criterion (I-B)/(SI+SB) ≥ 0.6, where I and B are the mean signal and background intensities and SI, SB are the standard deviations. The low quality spots were excluded from analysis and genes presented with less than three high quality spots on a slide were discarded. After subtraction of median background from median signal intensities, the expression ratios (ER) were calculated. Lowess normalization was performed first for the whole slide and next for twelve rows and four columns per slide. The differential expression was assessed by difference of the mean log-ER from zero . The log-ER values in individual liver samples were compared with t-test (p < 0.05). Data were submitted to GEO (GPL6154).The salmonid fish cDNA microarray SFA2.0 immunochip contains 1800 unique clones printed each in six spot replicates. The genes were selected by their functional roles and the platform is enriched in a number of functional classes such as immune response (236 genes), cell communication (291 genes), signal transduction (245 genes) and receptor activity (126 genes), apoptosis (120 genes), cell cycle (76 genes), protein catabolism (90 genes) and folding (70 genes) and response to oxidative stress (39 genes). The gene composition and sequences are provided in GEO (GPL6154). Microarray analyses were conducted on fish from EM and LM stages. A common reference design of hybridization was applied and RNA pooled from 6 uninfected fish per tissue and per stage was used as control. Pooled samples of gill, liver, heart and spleen  were analyzed in a dye-swap design; two slides were used for each sample with reverse assignment of dyes. Individual comparisons (6 fish from EM and 6 fish from LM) were done on liver samples in a single-slide format and one slide per each sample was used. The samples (20 μg RNA in each) were labeled with Cy3-dUTP and Cy5-dUTP  using the SuperScript™ Direct cDNA Labeling System (Invitrogen). The cDNA synthesis was performed at 43°C for 3 hours in a 20 μl reaction volume, followed with RNA degradation with 0.2 M NaOH at 37°C for 15 min and alkaline neutralization with 0.6 M Hepes. Labeled cDNA was purified with Microcon YM30 . The slides were pretreated with 1% BSA fraction V, 5 × SSC, 0.1% SDS for 30 min at 50°C and washed with 2 × SSC for 3 min and 0.2 × SSC for 3 min at room temperature and hybridized overnight at 60°C in a cocktail containing 1.3 × Denhardt's, 3 × SSC, 0.3% SDS, 0.67 μg μl® Green Master Mix (Roche Diagnostics) and varying amounts of cDNA and primer concentrations. Optimal PCR conditions in a 12 μl reaction volume were 3 μl 1:10 diluted cDNA for all assays (1:2000 for 18S rRNA). Primer concentrations were 0.4–0.6 μM. PCR was performed in duplicates (triplicates for E-curves) in 96-well optical plates on LightCycler 480 (Roche Diagnostics). Running conditions were 5–10 min pre-incubation following 40 cycles of 95°C for 5 sec, 60°C for 15 sec, 72°C for 15 sec. Cycle threshold (CT) values were calculated using both the fit points and second derivative methods (Roche Diagnostics), with respective rejection of CT values above 37 and 35. Relative expression of mRNA was calculated using the ΔΔCT method adjusted for E. Statistics was calculated using Unistat version 5.5 . Difference between groups was analyzed with ANOVA with subsequent Newman-Keuls test (p < 0.05). The design of analyses and numbers of samples are indicated in Table qPCR primers Table  were desT values as determined by qPCR were processed with linear discriminant analyses using Statistica 6.0. The prediction function was verified with independent samples from all study groups .Eight genes were chosen for the development of predictors based on results from microarray analyses of the liver Table . A trainThe National Animal Research Authority: The author(s) declare that they have no competing interests.SMJ and AK designed the study, conducted gene expression analyses and drafted the manuscript. SA developed software for processing of microarray data and performed the statistical analyses. All authors read and approved the final manuscript.complete results of microarray analyses. log-expression ratios (ER) and respective p-valuesClick here for file


Drosophila melanogaster is well established as a model system in the study of human neurodegenerative diseases. Utilizing RNAi, we have carried out a high-throughput screen for modifiers of aggregate formation in Drosophila larval CNS-derived cells expressing mutant human Huntingtin exon 1 fused to EGFP with an expanded polyglutamine repeat (62Q). 7200 genes, encompassing around 50% of the Drosophila genome, were screened, resulting in the identification of 404 candidates that either suppress or enhance aggregation. These candidates were subjected to secondary screening in normal length (18Q)-expressing cells and pruned to remove dsRNAs with greater than 10 off-target effects (OTEs). De novo RNAi probes were designed and synthesized for the remaining 68 candidates. Following a tertiary round of screening, 21 high confidence candidates were analyzed in vivo for their ability to modify mutant Huntingtin-induced eye degeneration and brain aggregation. We have established useful models for the study of human HD using the fly, and through our RNAi screen, we have identified new modifiers of mutant human Huntingtin aggregation and aggregate formation in the brain. Newly identified modifiers including genes related to nuclear transport, nucleotide processes, and signaling, may be involved in polyglutamine aggregate formation and Huntington disease cascades.The fruitfly  Huntington Disease (HD) is a late-onset, autosomal dominant neurodegenerative disorder characterized at the genetic level by expansion of a CAG repeat in the huntingtin (htt) gene. HD, together with 8 other diseases including the Spinocerebellar Ataxias (SCAs), DRPLA and SBMA, is classified as a CAG repeat disease. Expansion of the CAG repeat in exon 1 of the htt gene to greater than 35 repeats results in a disease-causing expanded polyglutamine tract in the Htt protein. Mutant Htt is aggregation-prone, and acquires a toxic gain-of-function (GOF) by interfering with, or disrupting, normal cellular pathways such as the ubiquitin-proteasome system (UPS), transcriptional regulation, and signaling pathways Drosophila melanogaster to study human disease, particular diseases of the nervous system. The fly is particularly amenable to sophisticated genetic approaches and the ability to temporally and spatially control the expression of transgenes is a powerful advantage of this system. CAG repeat diseases, caused by a toxic GOF, can be modeled in the fly by expression of the mutant protein. Drosophila models for SCA 1, SCA 3, MJD, SMBA and DRPLA have been successfully established and mimic various aspects of this group of diseases such as the presence of mutant protein aggregates, progressive neurodegeneration and behavioural abnormalities Researchers have long been aware of the benefits of using In recent years, RNAi has become a powerful approach to study gene loss-of-function (LOF). The relative ease with which genes can be ablated in insect cells has lead researchers to carry out large-scale and even genome-wide analyses to study the effects of gene LOF on various cellular pathways in vivo candidate validation to identify potential new regulators of mutant human Htt aggregation. Although there are some controversies about polyglutamine aggregate and cellular toxicity, aggregate formation is a main pathological feature and detecting modulators of aggregation may help our understanding of the pathological process of Huntington disease. By establishing an in vitro model of HD in Drosophila cells stably expressing htt exon 1 fused to EGFP with an expanded polyQ tract (62Q), we screened 7200 dsRNA molecules for their effects on mutant htt aggregation. We identified 404 candidate dsRNAs that could either suppress or enhance aggregation. We carried out secondary and tertiary screening in our cell culture model to select the strongest candidates and investigated these candidates further using in vivo models of HD established for this purpose. Transgenic flies were established that express htt exon1 fused to EGFP with either normal length (18Q) or expanded (62Q or 152Q) and either with or without an NLS for nuclear targeting.We have combined a high throughput RNAi screen with Drosophila RNAi screen for modifiers of a mutant human aggregating protein. We have identified and thoroughly validated 21 candidate genes in our study using in vivo fly models of HD. These candidate genes may shed light on the underlying pathways important in the development and pathogenesis of human HD.To our knowledge, this is the first Drosophila cells to identify genes that modify aggregation of polyglutamine-expanded human huntingtin exon 1, we established an in vitro cell culture model of human HD as described in To utilise high throughput RNAi screening in Drosophila cell culture model, we chose to screen the Open Biosystems RNAi library because it is enriched for Drosophila orthologues of mammalian proteins. We prepared dsRNA using dsDNA templates provided in the library, as described in Drosophila cells. Cells treated with diap1 dsRNA had significantly reduced cell viability (22% compared with plate average) demonstrating that the method of treatment and quality of dsRNAs was sufficient for screening EGFP cells. We tested the efficacy of RNAi by treating BG2- Nhtt(18Q)EGFP cells with dsRNA against EGFP. Efficient knockdown was achieved within 48 hours of treatment using the bathing method  and led creening . We therOf the 7200 dsRNAs screened, we identified 404 candidates that modified Nhtt(62Q)EGFP aggregation. Of these, 32 had positive z scores (≥2) indicating that the dsRNA could enhance aggregation, and 372 candidates had negative z scores (≤−2) resulting from suppression of aggregation by dsRNA treatment . Figure Drosophila RNAi experiments. Furthermore, dsRNAs with many predicted OTEs are overrepresented amongst our list of candidates, with 44% having more than 10 potential OTEs compared with 5.7% of the entire RNAi library  are notoriously sensitive to potential OTEs due the prevalence of trinucleotide repeats in many TF-encoding genes. Not surprisingly, therefore, we found that the category representing ‘Specific Transcription’ was the most severely reduced when candidates with more than 10 predicted OTEs were excluded . Exclusi library . It is tin vitro. We also confirmed that the potential off-target gene (pcx) had some effect on EGFP expression and aggregation (data not shown). We could not distinguish between candidate dsRNAs that reduced the Nhtt(18Q)EGFP fluorescence by enhancing proteolytic pathways or by general transcriptional reduction. Thus, some genuine candidates may have been excluded following secondary screening. Functional groups of enhancer and suppressor dsRNA following OTE pruning and secondary screening are shown in To eliminate aggregate-suppressing dsRNAs that reduced the number of aggregates simply by reducing transgene expression in our stable cell line, we treated BG2-Nhtt(18Q)EGFP with dsRNAs identified as candidates in our primary screen. Excepting the different cell line used, treatment conditions were the same as for primary screening and ArrayScan® data for the percentage of EGFP-positive cells and EGFP intensity was obtained. We decided to take a stringent approach to candidate pruning and eliminated candidates that did not reduce the percentage of EGFP-positive cells by more than 10% compared to the average for a LacZ control. Treatment with a LacZ dsRNA alone reduced the % EGFP-positivity compared to untreated cells. At the time of secondary screening, the problem of off-target effects was not fully known. Since this issue came to light, we discovered that the LacZ control dsRNA used in our secondary screen had one predicted off-target. This potentially resulted in the retention of more candidates following secondary screening because the LacZ control showed some decrease in EGFP positivity. We therefore designed a new LacZ control dsRNA (NewLacZ) for all subsequent tertiary screening and biochemical analyses De novo probes were designed using the E-RNAi software To further confirm the effect of candidate dsRNAs that passed through previous rounds of screening, we independently designed and synthesized dsRNA probes, spanning a different region of the gene from the library dsRNA. Tertiary screen candidates were also tested for their effects on the expression level of BG2-Nhtt(18Q)EGFP cells. Based on tertiary screening results, we selected 21 candidates that modified mutant Htt aggregation in the direction consistent with primary screening results and eliminated candidates that drastically altered EGFP fluorescence. These 21 candidates were placed into 3 groups, based on the percentage difference in expression level of normal length htt (18Q). Group 1 candidates differed in 18Q expression by no more than 10%, group 2 by no more than 20% and group 3 by no more than 30% . ArrayScTo further validate the effect of each candidate on mutant Htt aggregation, we treated BG2-Nhtt(62Q)EGFP cells with candidate groups 1–3 dsRNAs and prepared whole cell lysates for Western blotting. Although the immunoreactive signal of SDS-insoluble Nhtt(62Q)EGFP residing the gel top is only a qualitative way to assess the amount of mutant Htt aggregation, we consistently observed differences in the level of gel top material by treatment with our candidate dsRNAs , adding in vivo, we established transgenic flies for expression of EGFP-tagged Nhtt with either a normal (18Q) or expanded (152Q) polyglutamine tract and either lacking or containing a nuclear localization signal (NLS). We established UAS-NhttEGFP lines for driving transgene expression in various cell types. We have found that heterozygous expression of non-nuclear mutant Htt using the GMR-Gal4 driver does not produce a strong external eye phenotype, compared to a moderate loss of pigmentation phenotype caused by NLS-containing mutant Htt (data not shown). For genetic modification of Htt-induced cellular toxicity, we established a Drosophila line that expresses mutant Htt in both the cytoplasm and nucleus of all neurons and other cell types of the developing and adult compound eye using the GMR-Gal4 driver. We confirmed the presence of Nhtt(48Q)EGFPNLS inclusions in the nucleus and Nhtt(152Q)EGFP inclusions in the cytosol by confocal microscopic imaging of 3rd instar larval eye discs EGFP aggregation in cultured cells when ablated by RNAi . CG1109  fly eye . The UAS fly eye . We founse flies . Overexphenotype .We further confirmed the validity of CG5537 and CG4738 RNAi by quantification of mutant Nhtt(98Q)EGFP brain aggregates. 4 week old male brains, from Elav-Gal4, UAS- Nhtt(98Q)EGFP recombinant lines were dissected and stained with anti-elav antibody and Hoechst 33345. We quantified the number and size of brain aggregates from stacked images of 5 µm confocal sections, using ImageJ software. We observed a significant decrease in the average number of visible inclusions in each brain by expression of UAS-CG4738 RNAi and UAS-CG5537 RNAi . Althougin vivo models of HD we have validated the effect of several candidate genes identified through a large-scale RNAi screen for modifiers of mutant Htt aggregation.In summary, by using various Drosophila cells to gene knockdown by RNAi, we aimed to screen a library of dsRNA molecules covering around half of the fly genome and enriched for fly genes with mammalian homologues, for modifiers of Htt(62Q)EGFP aggregation. We carried out several rounds of screening to identify such modifier dsRNAs and carefully confirmed our screen results by addressing issues such as non-specific transcript reduction and potential gene off-targeting. Following our stringent screening method, our final 3 groups of candidates were assessed biochemically by Western Blot analysis and visually by confocal microscopy.To make use of the amenability of cultured Our candidates fell into several functional groups, the most notable being transport molecules, including those involved in nuclear transport, and nucleotide processing including RNA metabolism. Nuclear transport has been reported to be important in mutant Htt pathogenesis, with Htt itself reportedly shuttling between the nucleus and the cytoplasm Our screen was biased towards detecting suppressors due to the high aggregation propensity of our cell line. Investigation of aggregation-enhancing dsRNAs with z scores slightly below our +2SD cut-off (z scores between 1.5 and 2) revealed several UPS dsRNAs of the ubiquitin-ligase class. This is consistent with the role played by the UPS system in targeting mutant Htt for degradation. This group of enhancers can be viewed as an excel spreadsheet in in vivo models of HD to investigate whether or not these genes are likely to be involved in mutant Htt toxicity and/or modification of mutant Htt aggregation in vivo. The Drosophila compound eye has long been utilized to assess potential genetic interactions particularly in the fields of apoptosis and degeneration in part because the eye is sensitive to cell loss and produces a visible phenotype when the highly organized ommatidial structure is disrupted. Furthermore, the eye is not essential for viability, allowing genetic interactions to be investigated in the adult, even with highly toxic gene products. We were able to validate the role of several of our gene candidates using the fly eye as a model system. Expression of UAS-CG4738 RNAi and UAS-CG5537 RNAi transgenes resulted in a noticeable and consistent suppression of the loss of pigmentation phenotype caused by heterozygous expression of Nhtt(48Q)EGFPNLS and Nhtt(152Q)EGFP EGFP . We alsoin vivo validation of candidates to the eye raised several problems. Although the eye is valuable as a toxicity model, the relationship between polyglutamine aggregation and toxicity is not clear in the eye. Overexpression of the heat shock protein 40 homologue, dhdJ1, demonstrates a drastic suppression of ataxin 1-induced toxicity Limiting our We were unable to detect a significant reduction in mutant Htt inclusions in the brain by overexpression of dhdJ1. It is possible that dhdJ1 requires some cofactor for its function as a heat shock protein that may be lacking in elav-positive neurons in the fly brain. However, given that so many studies use the eye for validation of polyglutamine modifiers, it should be noted that perhaps not all such modifiers will prove valid in models for mutant Htt aggregation. Our strongest candidates, CG4738 and CG5537 were validated by two independent RNAi stocks in the eye model  supplemented with 10% heat-inactivated FBS (Sigma) and 10 µg/ml insulin (Sigma). Medium was supplemented with 0.5 mM CuSO4 for induction of NhttEGFP expression. Following initial selection, HD cell lines were not maintained in Blasticidin.We established stable, single colony-isolated BG2 cell lines using the DES® system (Invitrogen) as per the manufacturer's instructions. Briefly, larval central nervous system-derived parental cells, BG2-c2 cells In vitro transcription reactions were set up in 96 well U-bottom plates (Cellstar) using Ambion T7 megascript kits to simultaneously synthesize sense and antisense RNA strands in one reaction and purified as described in TI High Content Screening (HSC) Reader  using Target Activation Bio Application (TABa). TABa analyzes images acquired by an HSC Reader and provides measurements of the intracellular fluorescence intensity and localization on a cell-by-cell basis.Fixed, stained cells were analyzed by ArrayScan®VIn each well, several thousand cells were counted and quantified for the number and size of Nhtt(62)EGFP inclusions. Nuclei stained by Hoechst 33285 provided the autofocus target and scored the number of quantified cells. Screening consisted of two scans using Hoechst and FITC (for EGFP) fluorescence. At first, the number of aggregates was calculated. Fluorescent spots of at least 5 pixels in size (magnification 40×) with an average EGFP intensity of more than 1500 were labeled as inclusions. Secondly, nuclei were defined as the objects of interest and their number was determined. EGFP intensity in each cell was calculated in the perinuclear region within the distance of 3 pixels from the nucleus and when the average intensity exceeded 250, the cell was considered as EGFP-positive. The percentage of the cells with aggregates was calculated.ArrayScan® data was used to calculate the number of inclusions per cell (inclusion #/cell) and the inclusion load per cell (inclusion load/cell), which takes into account the inclusion size .Detailed methods of our large-scale RNAi screen including methodology can be found in 1118 embryos to establish transgenic flies. Driver lines used in our analysis were obtained from the Bloomington stock centre and were recombined with our transgene(s) for stable expression. Due to CAG repeat instability, some fly stocks were injected with constructs with repeat lengths other than 152Q. In such cases, where expressed protein sizes assessed by Western blotting were inconsistent with a 152Q repeat, the transgene was amplified by RT-PCR and the CAG repeat sequenced. UAS-dhdJ1 have been previously described and were kindly provided by Prof. Kazemi-Esfarjani NhttEGFP transgenes with either an 18Q or 152Q polyglutamine repeat and either with or without a nuclear localization signal (NLS) were subcloned into pUAST plasmid and injected by standard methods into wDrosophila Resource Screening Center (DRSC) OTE search tool to predict the number of potential Off-Target Effects with a 19 nt match.The amplicon sequence for each dsRNA in the Open Biosystems library was automatically retrieved from the FLIGHT database and entered into the de novo RNAi probes using the E-RNAi tool . Where possible, probes were selected to target all possible transcripts of a given gene and had no predicted 21 nt OTEs.The transcript sequences for the final candidates were retrieved from FlyBase and the region targeted by the Open Biosystems dsRNA was manually highlighted. Transcript sequence not targeted by the library was used to design T7-tagged oligos for amplication of in vitro transcription (IVT), total RNA was prepared from 200 liquid N2 freeze-dried whole w1118 flies using TRIzol® reagent (Invitrogen). Oligo d(T)-primed cDNA was synthesized from 2 µg total RNA, using First Strand cDNA synthesis kit according to the Manufacturer's directions (Novagen). 3 µl were used in a standard PCR reaction using KODPlus DNA polymerase  and using the primer pairs designed as described above and synthesized by Operon. PCR products were purified using the Vacuum Manifold® system (Millipore) and a sample was checked by agarose gel electroporesis. Purified PCR products containing T7 promoters at each end were used as templates for in vitro transcription using the Megascript T7 kit (Ambion) according to the manufacturer's directions. dsRNA is automatically made as each strand is synthesized in a single reaction. dsRNAs were purified using the Millipore Vacuum Manifold system and the concentration calculated by spectrophotometry. In cases where multiple bands were observed in the PCR product, the band of the correct size was excised and purified using the Wizard® Gel and PCR Clean-Up kit (Promega) before being used as a template for IVT.To prepare the template for PCR amplification of the target regions for et al, 2000 we brought each dsRNA to 860 nM stock and aliquoted the appropriate amounts for 43 nM and 108 nM treatments into 96 well plates. Cells were treated with each de novo dsRNA probe in duplicate on separate experimental days and in different well positions. Each plate was arrayed with controls against NewLacZ, diap1 and dhdJ1 (hsp40). Scores were normalized by dividing the inclusions/cell value by the NewLacZ control value. Averaged, normalized scores for # inclusions/cell and inclusion load/cell were calculated.Based on calculations of Clemens 4 for 16 hours. Cells were washed in PBS and then harvested in 1% SDS/PBS supplemented with Complete® Protease Inhibitor Cocktail (Roche), and divided into two for WB analysis and prepareation of total RNA. Cells for WB analysis were lysed by sonication, gently centrifuged and the protein concentration measured by BCA assay. 3 µg of whole cell lysates were boiled in LDS sample buffer/DTT and electrophoresed at 200 V through a 4–12% NuPAGE gel (Invitrogen) in MOPS buffer. Proteins were transferred wet onto PVDF (Millipore) membrane, blocked in 10% skim milk and blotted with EM48 MAB5374 Huntingtin primary antibody (Millipore) and Mouse IgG Peroxidase  secondary antibody before detection using ECL. Images were captured using LAS-1000 (Fujifilm). Blots were stripped and re-probed with E7 β-tubulin antibody (Hybridoma Bank) and Mouse IgG Peroxidase (GE). The presence of aggregates in whole cell lysates makes quantification difficult, resulting in some apparent loading differences between samples.BG2-Nhtt(62Q)EGFP cells were treated with 43 nM dsRNAs for 48 hours in 12 well plates and the culture medium was replaced with induction medium containing 0.5 mM CuSO2 and decapitated. Fly heads were aligned on a slide for imaging the left eye and viewed using an Olympus SZX16 dissecting microscope and an external light source (Kenko). Images were captured using a NIKON digital sight DS-L1 camera.Fly progeny were collected every 24 hours over a 5 day period and aged for 3 weeks (21–22 days). Flies were randomly selected, anesthetized with COFor quantification of inclusions in the adult brain, brains from male flies aged for 4 weeks (28–29 days) were dissected in PBS and immediately fixed in 4% PFA for 30–60 minutes. Brains were fixed in paraformaldehyde and stained with elav antibody . Using a 10X objective lens, 5 µm z sections were imaged using a SP2nLeica Confocal and images were converted to greyscale JPEG images using Photoshop and then opened in ImageJ Text S1Supporting Materials and Methods(0.03 MB DOC)Click here for additional data file.Figure S1RNAi screening validation and overview. The efficacy of RNAi treatment in a Drosophila cell culture model of HD was tested using dsRNA against GFP. Cells expressing BG2-Nhtt(18Q)EGFP cells treated with 37 nM GFP dsRNA for 48 hours and visualized by fluorescence microscopy show ablation of GFP (A). BG2-Nhtt(62Q)EGFP cells were treated with GFP dsRNA and analyzed by ArrayScan®. Reduction of Nhtt(62Q)EGFP by GFP dsRNA reduced the number of EGFP-positive cells (B) and the number of EGFP-positive intracellular inclusions (C) detected by ArrayScan®. Screening in 96 well plate format was validated using a screen plate arrayed with random dsRNAs including dsRNA against diap1. Loss of diap1 results in widespread apoptosis as shown by the reduced viability in cells treated with diap1 dsRNA (D). An overview of our approach to screening for modifiers of mutant Htt aggregation, including several rounds of screening in vitro, followed by validation in vivo, is shown (E).(2.33 MB TIF)Click here for additional data file.Figure S2OTE pruning following primary screening. Top pie charts show the functional categorization of all candidates following primary screening in BG2-Nhtt(62)EGFP cells. These candidates were pruned to eliminated dsRNAs with more than 10 predicted OTEs. The Specific Transcription category, including many transcription factors (TFs) was the most drastically reduced following OTE pruning, consistent with the fact that many TFs have repetitive trinucleotide repeats that are sensitive to off-targeting.(1.81 MB TIF)Click here for additional data file.Figure S3Off-target effects reduce cell viability and contribute to false positive candidates. The mean cell viability values for candidates following primary screening were plotted against the number of predicted 19 nt OTEs, demonstrating a significant negative correlation between cell viability and the number of potential OTEs (A). dsRNAs with multiple predicted OTEs are over-represented amongst our candidates following the primary screen. 54% of the dsRNA target sequences in the Open Biosystems library have no predicted 19 nt OTEs, with only 5.7% having more than 10 predicted OTEs (B). In contrast, 30% of our candidates lacked any predicted OTEs, with 44% having greater than 10 potential off-targets (C). The presence of off-target sequences causes inconsistencies in assay results. The percentage of candidates that modified Nhtt(62Q)EGFP consistently from the primary screen, using library dsRNAs, and the tertiary screen, using de novo designed dsRNAs are shown in yellow. The percentage of candidates producing an opposite effect is shown in green, while pink shows the percentage of candidates that gave inconsistent results within the tertiary screen duplicates using de novo dsRNAs with no 21 nt OTEs. The majority of candidates with no more than 1 OTE consistently modified mutant Htt aggregation in vitro. Increasing OTEs increased the likelihood of inconsistent results (D).(2.19 MB TIF)Click here for additional data file.Figure S4de novo RNAi probes. Target sequences were amplified with primers harboring the T7 promoter sequence. PCR products were purified using the Millipore Vacuum manifold system and checked for size and product specificity by agarose gel electrophoresis (top panel). In cases where more than one product was amplified, the product of the correct size was excised from the gel and purified. These products were then checked again by agarose gel electrophoresis (top right panel). To confirm the integrity of dsRNA synthesized in vitro from the PCR product templates, we ran 1 µg dsRNA on a denaturing formaldehyde gel (lower panel). Predominant bands are consistent with the predicted size for denatured RNA. We suspect the minor slower-migrating bands are non-denatured dsRNAs (A). By using RTPCR in BG2-Nhtt(62Q)EGFP cells treated with candidate groups 1–3 dsRNAs, we confirmed that in each case, the target gene was reduced upon dsRNA treatment. All results were from the same experiment except ATPsyn-b (boxed), which was from a different experiment (B).(1.59 MB TIF)Click here for additional data file.Figure S5Confocal microscopy of RNAi-treated cells. BG2-Nhtt(62Q)EGFP cells treated with candidate groups 1–3 aggregation-suppressing (A) and aggregation-enhancing (B) dsRNAs. To demonstrate the increase in diffuse-expressing cells among aggregation-suppressors, the EGFP gain was increased to 400 compared with a gain setting of 300 for imaging the aggregation-enhancing dsRNAs.(6.00 MB TIF)Click here for additional data file.Figure S6Confocal projection images of 3rd instar larval eye imaginal discs. Wandering 3rd instar larval eye discs were dissected in PBS, fixed in 4% PFA, stained with Hoechst and mounted onto a microscope slide in 80% glycerol for imaging using a Leica SP2 confocal microscope. Flies expressing Nhtt(18Q)EGFP together with Nhtt(18Q)EGFPNLS show localization of the protein in the nucleus (white arrow heads) and in the cytoplasm (white arrows) (left panels). Flies expressing mutant Nhtt(152Q)EGFP together with Nhtt(48Q)EGFPNLS show the presence of EGFP-positive nuclear (arrow heads) and cytoplasmic (white arrows) inclusions in larval eye imaginal discs (right panels). Images represent projection stacks of 5 µm z sections.(2.47 MB TIF)Click here for additional data file.Figure S7Correlation between inclusion number/load and cell viability. Based on the data shown in (2.46 MB TIF)Click here for additional data file.Figure S8Functional grouping of mammalian orthologues of the candidate genes. The mammalian orthologues of the candidate genes  identifi(3.79 MB TIF)Click here for additional data file.Data S1Excel file: Full List Functional Assign Worksheet: 2SDs full list All candidates with z scores of greater than 2 or less than −2 are shown in this worksheet, with data for Gene Ontology categorization and manual functional grouping. The effect on mutant Htt aggregation is abbreviated as either enhancing aggregation (en) or suppressing aggregation (su). The predicted number of 19 nt OTEs and the mean % viability values are shown. Given the continual updating of public databases, current information may differ from information obtained at the time this spreadsheet was made. Worksheet: en 1.5 to 2 Weaker enhancer dsRNAs with z scores between 1.5 and 2 are shown with all the data entries as above.(0.51 MB XLS)Click here for additional data file.