Bioinformatics 2008, 24:i7–13.PubMedCrossRef 33. Meyer F, Paarmann D, D’Souza M, Olson R, Glass EM, Kubal M, Paczian

T, Rodriguez A, Stevens R, Wilke A, Wilkening J, Edwards RA: The Metagenomics RAST server – A public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics 2008, 9:386.PubMedCrossRef 34. Cole JR, Chai B, Farris RJ, Wang Q, Kulam-Syed-Mohidee AS, McGarrell DM, Bandela AM, Cardenas E, Garrity GM, Tiedje JM: The ribosomal database project (RDP-II): introducing myRDP space and quality controlled public data. Nucleic Acids Res 2007, 35:169–172.CrossRef 35. Pruess E, LY2606368 Quast C, Knittel K, Fuchs B, Ludwig W, Peplies J, Glöckner FO: SILVA: a comprehensive see more online resource for quality checked and aligned ribosomal

RNA sequence data compatible with ARB. Nuc Acids Res 2007, 35:7188–7196.CrossRef 36. DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL: Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 2006, 72:5069–5072.PubMedCrossRef 37. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. J Mol Biol 1990, 215:403–410.PubMed 38. Kristiansson E, Hugenholtz P, Dalevi D: ShotgunFunctionalizeR: An R-package for functional analysis of metagenomic data. Bioinformatics 2009, 25:2737–2738.PubMedCrossRef 39. Parks DH, Beiko RG: Identifying biologically relevant differences between metagenomic C59 communities. Bioinformatics 2010, 26:715–721.PubMedCrossRef 40. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, Stres B, Thallinger

GG, Van Horn DJ, Weber CF: Introducing mothur: open source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 2009, 75:7537–41.PubMedCrossRef 41. Overbeek R, Begley T, Butler RM, Choudhuri JV, Chuang HY, Cohoon M, de Crécy-Lagard V, Diaz N, Disz T, Edward R, Fonstein M, Frank ED, Gerdes S, Glass EM, Goesmann A, Hanson A, Iwata-Reuyl D, Jensen R, Jamshidi N, Krause L, Kubal M, Larsen N, Linke B, McHardy AC, Meyer F, Neuweger H, Olsen G, Olson R, Osterman A, Portnoy V, Pusch GD, Rodionov DA, Rückert C, Steiner J, Stevens R, Thiele I, Vassieva O, Ye Y, Zagnitko O, Vonstein V: The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res 2005, 33:5691–702.PubMedCrossRef 42. Clarke KR, Gorley RN: PRIMER-E. PRIMER-E Ltd, Plymout, UK; 2001. Authors’ contributions RL carried out sample collection, sample processing, bioinformatic analyses, and manuscript preparation. JSD conceived of the study, and participated in its design and coordination and helped to draft the manuscript. SG participated in bioinformatic and statistical analyses.

010X + 1.318 0.89 ROS-neutralised Y= − 0.012X + 1.380 0.89 Effect of humic acid Figure 6 shows the log inactivation of A. hydrophila for water samples with or without humic acid at 10 mg L-1 through the TFFBR system. Water samples with humic acid showed almost 0. 4 log inactivation in both aerobic and ROS-neutralised condition. On the other hand water samples without humic acid showed almost 1.3 log inactivation in both conditions. This is close to a ten fold difference in the actual level of inactivation between these samples. Both water samples had initial counts of 1.4 × 105 CFU mL-1 whereas without humic acids this BMN 673 molecular weight dropped to 1.0 × 104 CFU mL-1 after

TFFBR while with humic acids this stayed high at 5.0 × 104 CFU mL-1 after TFFBR. Under full sunlight condition in the TFFBR, there was negligible cell injury observed, since similar counts were obtained under aerobic and ROS-neutralised conditions.

It is clear that a humic acid content of 10 mg L-1 has a major negative effect on solar photocatalysis at high sunlight and low flow rate conditions. Figure 6 Effect of humic acid (HA) on the inactivation of Aeromonas hydrophila ATCC 35654. Experiments were carried out using the TFFBR under an average value of global irradiance of 1037 W m-2at low flow rate 4.8 L h-1. Enumeration was performed under aerobic (unshaded bars) and ROS neutralised (shaded bars) conditions Comparison of selleck screening library inactivation of A. hydrophila in pond water and spring water Figure 7 shows the differences in the inactivation levels of A. hydrophila inoculated into aquaculture Rutecarpine pond waters (filtered and unfiltered) and spring water and then run across the TFFBR plate under high solar irradiance conditions. Filtered pond water and spring water showed a similar level of A. hydrophila inactivation within a range of 1.22 – 1.32 log inactivation under both aerobic and ROS-neutralised

conditions, where the initial count was 5.1 log CFU mL-1. On the other hand, with the same experimental conditions, unfiltered pond water showed a log inactivation of 0.2 under aerobic condition and 0.15 log CFU mL-1 under ROS-neutralised condition. During the experiments, several water quality variables (pH, salinity conductivity and turbidity levels) were measured before and after treating the water samples through the TFFBR (Table 2). Figure 7 Comparison of log inactivation of A. hydrophila ATCC 35654 inoculated in pond water (filtered, un-filtered) and spring water. Experiments were carried out using the TFFBR under an average value of global irradiance of 1021 W m-2 at low flow rate 4.8 L h-1. Enumeration was carried out at under aerobic (unshaded bars) and ROS neutralised (shaded bars) conditions Table 2 Experimental conditions of different variables while conducting the inactivation of A .hydrophila through TFFBR Experiment No.

Sp17 was found in 66% of endometrial cancers (11), and 61% see more of cervical cancers [14] in our previous work. As the expression of Sp17 in normal tissue is limited and its function is obscure, it is reasonable to predict that aberrant expression of Sp17 in malignant tumors could be a molecular marker for tumor imaging diagnosis and targeting therapy of the diseases. Molecular imaging methods permit noninvasive detection of cellular and molecular events by using highly specific probes and gene reporters in living animals, some of which can be directly translated to patient studies. A novel optical imaging technique in cancer is the use of near-infrared (NIR) light (700 to 900 nm) to monitor

the site and size of the cancers [15]. The fundamental advantage of imaging in the NIR range is that photon penetration into living tissue is higher because of lower photon absorption and scatter [16]. An additional advantage is that tissue emits limited intrinsic fluorescence (i.e., autofluorescence) in the 700 nm to 900 nm range. Therefore, fluorescence contrast

agents that emit in the NIR range demonstrate a favorable signal-to-background ratio(SBR) when Tariquidar research buy used in animal models or for patient care, especially for endoscopy. Optical imaging is a very versatile, sensitive, and powerful tool for molecular imaging in small animals. The near infrared fluorescence dye ICG-Der-02 (indocyanine Green derivative 02) is a derivative of indocyanine green (ICG), which was approved by the FDA (Food and Drug Administration) to be used in human subjects. Compared to ICG, the self-synthesized ICG-Der-02 organic dye holds favorable hydrophilicity Arachidonate 15-lipoxygenase and higher fluorescence quantum yield with excitation and emission peaks at 780 nm and 810 nm,

respectively. ICG-Der-02 offers one carboxyl functional group on the side chain which enables the dye to be covalently conjugated to the biomarker for in vivo optical imaging [17]. In this study, we first demonstrated the overexpression of Sp17 in the hepatocellular carcinoma cell line SMMC-7721 and in xenografts in mice. After synthesis of anti-Sp17-ICG-Der-02, we evaluated the targeting effect of anti-Sp17-ICG-Der-02 on tumors in vivo with a whole-body optical imaging system in animal models. Materials and methods Cell line and monoclonal antibody The human hepatocellular carcinoma cell line SMMC-7721 expresses high levels of Sp17 and was used for in vitro and in vivo experiments, Sp17- HO8910 ovarian cancer cell line used as negative control. The cells were cultured in RPMI 1640 medium (Invitrogen) supplemented with 10% fetal bovine serum (Hyclone) in a humidified incubator maintained at 37°C with 5% CO2 atmosphere and medium was replaced every 3 days. The anti-human Sp17 monoclonal antibody clone 3C12 was produced in our laboratory as previously described [14].

As can be seen, CH4 was the main product, whereas H2, CO, and CH3OH (vapors) were also obtained during the reaction when using either Ti-KIT-6 (dried, Si/Ti = 200) or Ti-KIT-6(dried, Si/Ti = 100) materials. However, H2 increased and CH4 decreased when Ti-KIT-6 (dried,

Si/Ti = 50) was used. As already mentioned in the characterization part pertaining to the UV-vis, TEM, and XPS analyses, this phenomenon might be due to the TiO2 cluster formation caused by the increased Ti content in the Si/Ti ratio of 50, which favors a greater H2 formation [15]. Figure 6 Comparison of fuel formation after a 3-h photocatalytic reduction of CO 2 and H 2 O vapors. (a- c) Ti-KIT-6, dried, Si/Ti = 200, 100, and 50 ratios and (d- f) Ti-KIT-6, calcined, Si/Ti = 200, 100, and 50 ratios. A similar trend

of activity was also observed when Ti-KIT-6 (calcined, Si/Ti = 200, NU7026 molecular weight 100, and 50 ratios) was used. However, overall, the Ti-KIT-6 (calcined, Si/Ti = 200, 100, and 50 ratios) materials show higher activity than the Ti-KIT-6 (dried, Si/Ti = 200, 100, and 50 ratios) materials. This might be due to the fact that some of the Ti species in Ti-KIT-6 (dried, Si/Ti = 200, 100, and 50 ratios) materials which were not accessible on the surface for the reaction might have been trapped in the bulk dried KIT-6 powder during the synthesis. However, PF-4708671 purchase this might not be the problem in the case of Ti-KIT-6 (calcined, Si/Ti = 200, 100, and 50 ratios), where the 3-D pore structure was fully developed in the calcined KIT-6. Therefore, the greater number of accessible active sites in Ti-KIT-6 (calcined, Si/Ti = 200, 100, and 50 ratios) than that in Ti-KIT-6 (dried, Si/Ti = 200, 100, and 50 ratios) may have caused higher activity. Moreover, it is clear that Ti-KIT-6 (calcined or dried, Si/Ti = 100) shows a higher activity than the Si/Ti ratios of 200 and 50, because of the combined contribution of the high dispersion

state of the Ti oxide species, which is due to the large pore size with a 3-D channel structure, and the lower formation of Ti-O-Ti or TiO2 agglomerates, Obeticholic Acid clinical trial as confirmed by UV-vis, TEM, and XPS analyses. Moreover, the high production of CH4 for Ti-KIT-6 (Si/Ti = 100) with greater concentrations of the OH groups (2 nm−1) than the other ratios (Si/Ti = 200 and 50 = 1.5 and 1.2, respectively) obtained from the FT-IR of the materials actually affects the adsorption properties of the water on the catalyst surface [16]. Competitive adsorption between the H2O vapors and CO2 is another parameter that can determine the selectivity of CH4 or CH3OH. CH4 formation selectivity becomes higher as H2O vapor adsorption increases due to the greater concentration of OH groups or hydrophilicity of the material [4].

03 99 cd38 7 811821-34 2 2 0.03 100 a Accesstion number in Genbank is AM180355. b Identified previously by Marsh et al. [13] and van den Berg et al. [14]. c This locus contains incomplete repeat and is denoted by the size of array. Capillary gel electrophoresis-based PCR ribotyping Of the 142 isolates, capillary gel electrophoresis-based PCR-ribotyping identified 57 independent types, including 32 singletons. The most common types were R45, R4, R10, R14, and R17 (UK017), containing 7, 17, 11, 11 and 9 isolates, respectively (Figure 1). The R27 (UK 027) virulent type was not found among the local strains. AZD8186 Figure 1 Comparison of PCR riboytpe and MLVA groups for 142 C. difficile isolates. Dendrogram

is based on UPGMA analysis of capillary electrophoresis-based PCR ribotyping, and the vertical line is the cutoff point for identifying PCR-ribotype groups. Corresponding PCR-ribotype GANT61 mouse groups, MLVA34 groups, MLVA10 groups, and number of isolates are shown. MLVA groups are identified by minimum-spanning tree: one group is defined by MLVA type with less than two loci difference.

Dendrogram based on PCR ribotyping A phylogenetic dendrogram based on the PCR-ribotypes was constructed using the 142 C. difficile isolates (Figure 1). Of the 142 isolates, PCR-ribotype, MLVA34, and MLVA10, identified 57 types, 47 groups, and 45 groups, respectively. The PCR-ribotype was more discriminatory than the two MLVA groups (Figure 1). Using a MycoClean Mycoplasma Removal Kit threshold of >83% similarity for defining PCR-ribotype groups, all isolates were able to be divided into 47 PCR-ribotype groups, including 22 singletons. Over 87% (41/47) of the PCR-ribotype groups

were specifically recognized in the MLVA34 and MLVA10 groups. However, PCR-ribotype groups 39 and 25 were recognized together as one by both MLVA groups, with the fingerprints for these isolates sharing a 70% similarity (a four-band difference). In addition, PCR ribotype groups 26 and 49 were also identified as one by the two MLVA groups, with the fingerprints of these two isolates sharing a 78% similarity. Furthermore, PCR ribotype groups 8 and 23 were also seen as one by the two MLVA groups, with the fingerprint of these isolates sharing an 82% similarity. Taken together, these results shows that this discordance, the lack of one to one identification between PCR ribotypes and MLVA groups, mainly occurred when PCR-ribotypes shared >83% similarity. Congruence between groups of the PCR ribotype and MLVA MLVA panels with slightly limit allelic diversity generated groups highly congruent with PCR ribotyping (Table 2). To determine the most congruent groupings between MLVA panels and PCR-ribotype groups, groupings of MLVA panels consisting of VNTR loci with high to low allelic diversity were compared with the PCR-ribotype groups. MLVA34, MLVA12, and MLVA10 generated partitions (47, 45, and 45, respectively) and allelic diversity (0.959, 0.957, and 0.957, respectively) similar to those identified by PCR ribotyping (Table 2).

13.11.3) (alpha and beta), gentisate 1,2-dioxygenase (EC 1.13.11.4), homogentisate 1,2-dioxygenase (EC 1.13.11.5), protocatechuate 4,5-dioxygenase (EC 1.13.11.8) (alpha and beta), methyl-coenzyme

M reductase (EC 2.8.4.1) (alpha), methane monooxygenase (EC 1.14.13.25) (particulate: pMMO and soluble: sMMO). The metagenome reads were further compared to a protein sequence library for alkane monooxygenase (alkB) on the freely available Bioportal computer service [66]. The reference library for alkB was downloaded from Fungene (Functional gene pipeline & repository) version v6.1 [74], including only sequences with a score (bits saved) of 100 or more from the HMMER Hidden Markov Model search against NCBIs non-redundant protein database. We used blastX against the protein sequences of the enzyme library with a maximum expectation value of Selleck Inhibitor Library 10-20[67]. Maximum one alignment was reported. PCA analysis The PCA-plots were created using the vegan library in R [75–77]. The ordination was based on reads assigned at the phylum level in find more MEGAN version 4 (“Not assigned” and “No hits” were excluded)

and to level I SEED subsystems extracted from MG-RAST (“No hits” was excluded) [68, 69]. All metagenome data were given as percent of total reads. Symmetric scaling, for both parameters and sites, was used in the plot. The geochemical parameters [25] were fitted onto the ordination using the envfit function. The lengths of arrows for the fitted parameters were automatically adjusted to the physical size of the plot, and can therefore not be compared across plots. To account for the different measuring units, all geochemical parameters were normalized by dividing with the standard deviation and subtracting the smallest number from all numbers in each row. Rarefaction analysis Rarefaction analysis was performed in MEGAN version Temsirolimus 4 [68, 69]. The MEGAN program uses an LCA-algorithm

to bin reads to taxa based on their blast-hits. This results in a rooted tree. The leaves in this tree are then used as OTUs in the rarefaction analysis. The program randomly chooses 10%, 20% … 100% of the total number of reads as subsets. For each of these random subsets the number of leaves (hit with at least 5 reads (min-support)) was determined. This sub sampling is repeated 20 times for each percentage and then the average value is used for each percentage. The analysis was done for all taxa (including Bacteria, Archaea, Eukaryota, viruses and unclassified sequences) at the genus level, and at the most detailed level (typically species or strain) of the NCBI taxonomy in MEGAN. Comparison of the metagenomes Comparison tables of absolute numbers for different bacterial and archaeal taxonomic (NCBI) levels for the seven metagenomes were extracted from MEGAN [68, 69]. Likewise, comparison tables of absolute numbers of reads assigned to SEED subsystems in the seven metagenomes were extracted from MG-RAST [72, 73].

This finding

suggests that the full virulence of E. coli RS218 requires both chromosomal and plasmid-located genes. Further studies including in depth analysis of RS218 chromosome will advance our understanding of NMEC pathogenesis. Conclusions Incomplete understanding of NMEC pathogenesis is a major hindrance that has been identified and pointed find more out by many scientists particularly in relation to formulation of novel therapeutic and prevention strategies for neonatal meningitis. The plasmid pRS218 in NMEC RS218 strain belongs to IncFIB/IIA subset of virulence plasmids in pathogenic E. coli. These plasmids harbor many virulence traits that are required for bacterial survival inside the host. The nucleotide sequence of pRS218 showed buy BMS202 a greater similarity to the plasmids of E. coli associated with acute cystitis than the plasmids from NMEC. However, the prevalence of pRS218 virulence-related

genes was significantly higher in NMEC strains tested than fecal commensal E. coli. We have also demonstrated that the pRS218 is involved in NMEC pathogenesis using both in vivo and in vitro experiments. Future studies on pRS218 transcriptome analysis, identification of plasmid-located genes responsible for current observations and in-depth analysis of E. coli RS218 whole genome will likely broaden our knowledge of NMEC pathogenesis. Methods Bacterial strains and media The prototype NMEC strain E. coli RS218 (O18: H7: K1) and NMEC strain EC10 (O7: K1) were kindly provided Resminostat by Dr. James Johnson (Department of Medicine, University of Minnesota, Minneapolis, MN). Both E. coli RS218 and EC10 strains have been isolated from cerebrospinal fluid of neonates diagnosed with bacterial meningitis (15). A total of 51 NMEC strains which were isolated from neonatal meningitis cases were also obtained from Dr. K.S. Kim

(School of Medicine, John Hopkins University, Baltimore, MD) and 49 fecal E. coli strains isolated from feces of healthy individuals were obtained from the E. coli Reference Center (Pennsylvania State University, University Park, PA). All E. coli were stored in Luria Bertani broth (LB) at -80°C until further use. Bacteria were grown in MacConkey agar or LB broth. All bacteriologic media were purchased from Becton, Dickinson and Company (BD), Sparks, MD. Plasmid isolation, sequencing, assembly and annotation Sequencing of pRS218 was performed as a part of a project that sequenced the whole genome of E. coli RS218. The genomic DNA including the plasmid DNA was extracted using phenol-chloroform method as described previously [33]. The DNA preparation was further cleaned using Genomic Tips (Qiagen, Valencia, CA) [33]. Whole genome sequencing was performed using Ion Torrent PGM Technology (Life Technologies, Carlsbad, CA) at the Genomics Core Facility (Pennsylvania State University, University Park, PA).

Pathways in cancer and Wnt signalling pathways were ranked first in the KEGG and Panther pathway lists, respectively, highlighting the essential roles of miRNAs in cancer development. Third, there should be adequate information about the pattern of expression of the miRNAs in different types of specimens. It has been indicated that circulating miRNAs

in plasma could be more tissue-specific than tumour-specific [41, 42]. In the context of the vast inconsistency between tissue-based and plasma-based results [23], we focused on KPT 330 studies that analysed miRNA expression between PDAC tissues and noncancerous pancreatic tissues in humans. Last but not least, rigorous validation and demonstration of reproducibility in an independent cohort of patients are necessary to confirm the diagnostic value of miRNAs. With this in mind, we experimentally validated 10 candidate miRNAs in PDAC samples and confirmed that these 10 miRNAs were differentially expressed between PDAC tissues and noncancerous pancreatic tissues. Considering that miRNA expression is able to successfully discriminate normal from cancerous

pancreatic tissues, it is tempting to speculate selleck that miRNAs could also predict cancer prognosis. However, our results do not exclude the possibility that other miRNAs are associated with prognosis, as we only studied a meta-signature of 10 miRNAs in a limited number of PDAC samples (n=78). The main reason for the possible association between miRNAs not within this meta-signature and prognosis may centre on the relatively small sample size in our study and others [25, 27]. It is quite unrealistic to include all the miRNAs in Kaplan-Meier survival analyses, as it would be very laborious and time-consuming. Thus, commonly, C-X-C chemokine receptor type 7 (CXCR-7) only the candidate miRNAs with the greatest fold changes are included. As mentioned above, although there were no strong disagreements between the individual miRNA profiling studies, the top lists varied considerably from study to study. To remedy this problem, it was critical to identify

the most differentially expressed miRNAs. We used a meta-review approach, which combines the results of several individual studies to increase statistical power and to subsequently resolve the inconsistency among different profiling studies. A meta-signature of seven up- and three down-regulated miRNAs was identified. Then, in independent patient samples, miR-21, miR-31 and miR-375 were found to be associated with cancer prognosis. From our point of view, great caution should be taken in future research in this field. To start, sample sizes should be increased to minimise random sampling error. Next, as it is impossible for every researcher to use the same platform, reliable microarray platforms should be employed in all experiments.

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06% of the original inoculum was obtained for non-stressed C. jejuni. Pre-exposure of bacteria to heat, starvation or osmotic stresses exacerbated the bacterial susceptibility to intracellular killing, since a significant

decline of the number of surviving bacteria was observed upon pre-exposure to these stresses 5 h post-gentamicin treatment (Figure  3B). At 24 h post gentamicin selleck inhibitor treatment, a few internalized bacteria (~1.5 × 103 CFU/ml) were observed with non-stressed inoculum. No bacteria that had been pre-exposed to heat, starvation or osmotic stress were detected. In contrast, pre-exposure to oxidative stress had no impact on internalization or intracellular survival of C. jejuni under the conditions and time frame studied. Effect of pre-exposure to stress on sub-cellular beta-catenin phosphorylation location of internalized bacteria A detailed observation of C. jejuni cells internalized within the amoebae was carried out by confocal laser scanning microscopy (CLSM). In the absence of any stress, live C. jejuni cells were detected by CellTracker Red staining inside the trophozoites immediately after gentamicin treatment (Figure  4A, B). The intracellular bacteria were distributed as clusters within acidic vacuoles as

observed by the simultaneous staining of acidic vacuoles by LysoSensor Green DND-189 (Figure  4C, D). Pre-exposure of bacteria to low-nutrient, heat, osmotic or oxidative stresses did not qualitatively alter the sub-cellular location of internalized bacteria, as all were also recovered in acidic vacuoles (Figure  4E to T). Figure 4 Confocal microscopy

analysis of stressed and non-stressed C. jejuni cells within acidic organelles of A. castellanii observed immediately after gentamicin treatment. Control Phosphoglycerate kinase C. jejuni (A-D), C. jejuni pre-exposed to osmotic stress (E-H), heat stress (I-L), hydrogen peroxide (M-P), or starvation stress (Q-T). The multiplicity of infection was 100:1 (bacteria:amoeba). (A, E, I, M, Q) differential interference contrast image; (B, F, J, N, R) C. jejuni stained with CellTracker Red; (C, G, K, O, S) acidic amoeba organelles stained with LysoSensor Green; (D, H, L, P, T) corresponding overlay. Scale bar = 5 μm. In addition to the viable count assay for the quantification of intracellular bacteria and CLSM analyses reported above, TEM was also used to more precisely assess the effect of heat stress on intracellular location of C. jejuni within A. castellanii. Heat stress was selected for TEM studies because it decreased intracellular survival of C. jejuni, but it did not affect uptake. Therefore this heat stress allowed visualization of numerous internalized bacteria at early time points. As shown in Figure  5, sections of infected A. castellanii cells obtained right after gentamicin treatment showed that C. jejuni cells were confined to tight vacuoles within the amoebae, whether they had been heat-stressed or not prior to co-culture with amoebae (Figure  5A, C).