s2fieldsofstudy: Biology Chemistry

text:
Sequential Astrocytic 5-HT2B Receptor Stimulation, [Ca2+]i Regulation, Glycogenolysis, Glutamate Synthesis, and K+ Homeostasis are Similar but Not Identical in Learning and Mood Regulation

Citation: Chen Y, Du T, Peng L, Gibbs ME and Hertz L (2016) Sequential Astrocytic 5-HT2B Receptor Stimulation, [Ca ]i Regulation, Glycogenolysis, Glutamate Synthesis, and K Homeostasis are Similar but Not Identical in Learning and Mood Regulation. Front. Integr. Neurosci. 9:67. doi: 10.3389/fnint.2015.00067 Sequential Astrocytic 5-HT2B Receptor Stimulation, [Ca]i Regulation, Glycogenolysis, Glutamate Synthesis, and K Homeostasis are Similar but Not Identical in Learning and Mood Regulation

INTERRELATION BETWEEN ANXIETY AND MEMORY
A close interrelation between anxiety and memory was first suggested by Kalueff and Nutt (1996), reviewing effects of γ-aminobutyrate (GABA) on both conditions. Wall and Messier (2000) showed subsequently that pretreatment with an opioid kappa receptor antagonist was anxiogenic and disrupted working memory. Additional papers supporting interactions between anxiety and memory were cited by Kalueff (2007). They included demonstration of memory improvement by serotonin, whereas a decreased ability to increase serotonin is a model of anxiety.

SEROTONIN AND THE 5-HT 2B RECEPTOR
Serotonin acts on many different receptors. The present paper specifically deals with the 5-HT 2B receptor, which is expressed in human brain (Schmuck et al., 1994;Bonhaus et al., 1995). Its mRNA expression is two times higher in freshly isolated (Lovatt et al., 2007) mouse astrocytes than in neurons . It is necessary for consolidation of one-trial aversive learning in day-old chickens (Gibbs and Hertz, 2014) at an early stage of memory consolidation. It is also required for the therapeutic effect of serotonin-specific reuptake inhibitors (SSRIs) in major depression (Diaz et al., 2012;Li et al., 2012;Hertz et al., 2015b), a disease often accompanied by anxiety. The 5-HT 2B receptor in cultured astrocytes is stimulated by fluoxetine (Li et al., 2008: Qiao et al., 2015 and all other SSRIs (Zhang et al., 2010). Chronic treatment of mice with fluoxetine for 14 days upregulates the astrocytic, but not the neuronal 5-HT 2B receptor, although this receptor is expressed in both cell types Hertz et al., 2015b). Decrease of its astrocytic gene expression parallels development of a depressive phenotype in a mouse model of Parkinson's disease , and Pitychoutis et al. (2015) reported schizophrenia-like symptoms in mice lacking the 5-HT 2B receptor gene or treated with a receptor inhibitor. Schizophrenia is often associated with depressed mood (Fortunati et al., 2015). 5-HT 2B RECEPTOR, [Ca 2+ ] i , GLYCOGENOLYSIS, GLUTAMATE, K + , AND LEARNING Inhibition of learning by a 5-HT 2B/C receptor antagonist (SB221284) and equipotent rescue of impaired learning by the 5-HT 2B receptor agonists fluoxetine and paroxetine (Gibbs and Hertz, 2014) injected intracerebrally at specific times shows the importance of this receptor for establishment of memory soon after training. The similar potency of the two drugs is important, because they have widely different affinities for SERT (Wong and Bymaster, 1995) whereas all SSRIs have similar affinity for the 5-HT 2B receptor (Zhang et al., 2010). Another SSRI, citalopram, counteracts spatial memory deficits . Mice lacking the 5-HT 2B receptor gene show learning disabilities (Pitychoutis et al., 2015).
Glycogenolysis is also required for formation of glutamate, and its metabolite GABA ( Figure 1B) in the brain in vivo (Gibbs et al., 2007(Gibbs et al., , 2008 at a time when glutamate production must be evoked by 5-HT 2B stimulation (Gibbs and Hertz, 2014). It also increases uptake of glutamate into cultured astrocytes and neurons as well as release of lactate from astrocytes (Sickmann et al., 2009). Glutamate is synthesized intracerebrally from receptor-mediated effects on [Ca 2+ ] i , glycogenolysis, and glutamate content in astrocytes (blue) and effects of glutamate transfer to neurons (green arrow) and of glycogenolysis-evoked release of lactate on neurons (red). These effects are acutely important for learning, and drug-induced chronic effects of 5-HT 2B receptor stimulation have therapeutic effect, also on impaired memory, in major depression (fluoxetine) and in schizophrenia (clozapine). However, as discussed in "Concluding remarks" in these situations it appears that it is a decreased effect on the receptor or on [Ca 2+ ] i that is therapeutically effective.
glucose. This can only occur in astrocytes, because neurons lack an enzyme, pyruvate carboxylase, needed for its synthesis (reviewed by Gibbs et al., 2008;Hertz, 2013). Glutamate is subsequently converted to glutamine and carried to neurons (green arrow in Figure 1B) in an extremely active glutamineglutamate/GABA cycle, which also returns released transmitter glutamate to neurons after its initial astrocytic accumulation (reviewed by Hertz, 2013;Hertz and Rothman, in press). The importance of glutamate receptor activity for memory is beyond doubt (Riedel et al., 2003), and interruption of the glutamineglutamate/GABA cycle by inhibition of either glutamine synthetase (Kant et al., 2014) or astrocytic glutamate uptake (Gibbs et al., 2004) abolishes learning. GABA is also important for learning (Kalueff and Nutt, 1996;Gibbs and Bowser, 2009), and besides its neuronal effects stimulates glycogenolysis in cultured astrocytes and brain slices (Xu et al., 2014a).
A major role of glutamate ( Figure 1B) is stimulation of postsynaptic glutamate receptors, leading to increases in brain metabolism (Howarth et al., 2012) and in extracellular K + concentration (Hertz et al., 2015c and references therein). Cellular re-accumulation of K + includes an initial uptake mediated by the astrocytic Na + ,K + -ATPase (MacAulay and Zeuthen, 2012; Hertz et al., 2015c), release of astrocytically accumulated K + by Kir4.1 channels (Bay and Butt, 2012) and neuronal reuptake. The astrocytic Na + ,K + -ATPase is important for learning (Moseley et al., 2007;Schaefer et al., 2011;Hertz et al., 2013;Tadi et al., 2015). Extracellular K + concentrations high enough to stimulate the Na + , K + , 2 Cl − co-transporter NKCC1 (>10-12 mM) also causes release of gliotransmitters (Song et al., 2014;Xu et al., 2014b;Liu et al., 2015). Both astrocytic release of glutamate (Lee et al., 2014) and ATP (Gibbs et al., 2011;Stehberg et al., 2012) are crucial for learning. Facilitation of learning by K + -mediated depolarization of oligodendrocytes and increased myelination at high extracellular K + concentration (Roitbak, 1984) and attributed to increased ability of the myelinated axon to carry out rapid impulse conduction has recently been confirmed and characterized by Yamazaki et al. (2014).

5-HT 2B RECEPTOR, [Ca 2+ ] i , GLYCOGENOLYSIS, GLUTAMATE, K + , AND MOOD DISORDERS
Fluoxetine is better known for its antidepressant effect, which in contrast to the acute stimulation of the 5-HT 2B receptor during learning takes several weeks to materialize. During this time many changes occur in gene expression and editing, as shown in mice chronically treated with fluoxetine. Studies in neuronal and astrocytic cell fractions freshly obtained from these mice (Lovatt et al., 2007) showed that most of these alterations occurred in astrocytes, although some neuronal changes also took place Peng et al., 2014;Hertz et al., 2015b). This finding suggests that astrocytes play a major role in the antidepressant effects of SSRIs Hertz et al., 2015b), a conclusion in agreement with results by many other authors (e.g., Ongür et al., 1998;Kugaya and Sanacora, 2005;Ongür et al., 2007;Valentine and Sanacora, 2009;Bernstein et al., 2015;Hertz et al., 2015b and references therein). It is especially interesting that Bechtholt-Gompf et al. (2010) found that blockade of astrocytic glutamate uptake in rats induces signs of anhedonia (a component of depression that is easily measurable in animals) and impaired spatial memory.
Some of the editing changes reduced normally occurring effects of transmitters. Li et al. (2011) showed that in astrocyte cultures treated for sufficient length of time with fluoxetine, the effects on [Ca 2+ ] i by acute administration of several transmitters or ryanodine receptor agonists are reduced or abolished. On the other hand, the effect of an increased extracellular concentration of K + was increased. Thus, chronic treatment with an SSRI diminishes or alters some of the normal responses of the 5-HT 2B receptor to stimulation. This might partly be explained by inhibition of capacitative Ca 2+ entry, mediated by glycogenolysis-dependent (Müller et al., 2014) TRPC1 channels, which causes depletion of Ca 2+ stores. Due to this inhibition refilling of depleted Ca 2+ stores by addition of 2 mM CaCl 2 to the medium was greatly reduced (Li et al., 2011). All effects of chronic fluoxetine administration could be replicated by TRPC1 channel antibody. However, the expression of Ca v 1.2, a gene of an L-channel for Ca 2+ which is stimulated by elevations in extracellular K + concentrations of at least 10 mM is increased (Du et al., 2014), probably explaining the enhanced K + effect on [Ca 2+ ] i described above. The 5-HT 2B receptor itself is also edited by chronic fluoxetine treatment, rapidly reducing the effects of its stimulation of the IP 3 receptor . Since chronic SSRI treatment improves memory in depressed patients (Table  in Krysta et al., 2015), inhibition of glutamate-induced increase in astrocytic [Ca 2+ ] i and thus in release of gliotransmitter glutamate  has no deleterious effect on learning, at least not when combined with [Ca 2+ ] i increase by elevation of the extracellular K + concentration. In this connection it seems of considerable interest that Medina et al. (2015) described downregulation of mRNA expression of glutamate transporters, K + channels and gap junction proteins in hippocampus of patients having suffered from major depression. Most of these genes are selectively expressed in astrocytes. Abnormalities of Na + ,K + -ATPase function in depressed patients have been described by De Lores Arnaiz and Ordieres (2014)

RELATED ASTROCYTIC MECHANISMS IN SCHIZOPHRENIA
Schizophrenia is treatable both by the dopamine antagonist haloperidol and atypical antipsychotics like clozapine, which is an antagonist at the 5-HT 2B receptor in the fundus of the stomach (Villazón et al., 2003). Again, acute stimulation of the 5-HT 2B receptor is likely to increase [Ca 2+ ] i , glycogenolysis and glutamate formation ( Figure 1B). An increase in [Ca 2+ ] i by stimulation of astrocytic dopamine receptors is reduced by exposure to clozapine (Reuss and Unsicker, 2001), and this seems also to be the case after clozapine activation of 5-HT 2B receptors. A resulting reduced production of glutamate ( Figure 1B) in mice lacking 5-HT 2B receptors may explain a decreased content of glutamate in some brain areas (Pitychoutis et al., 2015), which may contribute to the impairment of learning.

CONCLUDING REMARKS
Activation of the astrocytic 5-HT 2B receptor stimulates an increase in [Ca 2+ ] i , glycogenolysis, glutamate formation, and the effect of glutamate on extracellular K + , all of which are involved in learning ( Figure 1B). However, Sibille et al. (2015) found that acute inhibition of Ca 2+ signaling in astrocytes by [Ca 2+ ] i chelation potentiates excitatory synaptic transmission. This apparent contradiction may be explained by the complexity of astrocytic [Ca 2+ ] i regulation (Volterra et al., 2014). An important difference between Gibbs and Hertz (2014) and Sibille et al. (2015) is that the latter authors elicited astrocytic increase in [Ca 2+ ] i in response to adjacent neuronal activity during GABA A receptor inhibition, whereas the former described transmitter-induced, glycogenolytic (and thus Ca 2+dependent) effects on learning without GABA A receptor inhibition.
Drugs used for treatment of symptoms of major depression (fluoxetine) and of schizophrenia (clozapine), which included memory impairment, interfered with 5-HT 2B receptoractivated functions, but in different manners: the SSRI fluoxetine edited and thereby reduced some normal effects of this receptor, whereas clozapine caused a decrease in [Ca 2+ ] i . This effect is consistent with the enhancement of excitatory synaptic transmission described by Sibille et al. (2015). Disposition to both major depression and schizophrenia is probably inborn, and perhaps these patients display quantitative and/or qualitative abnormalities in 5-HT 2B -mediated signaling, which might also affect learning processes. In agreement with this notion 5-HT 2B receptors play a major role during brain development (Lauder et al., 2000).

AUTHOR CONTRIBUTIONS
All authors planned or carried out reviewed experiments. YC and LH wrote the paper and MEG edited it.


extfieldsofstudy: Biology Medicine

text:
Nested PCR Biases in Interpreting Microbial Community Structure in 16S rRNA Gene Sequence Datasets

Background Sequencing of the PCR-amplified 16S rRNA gene has become a common approach to microbial community investigations in the fields of human health and environmental sciences. This approach, however, is difficult when the amount of DNA is too low to be amplified by standard PCR. Nested PCR can be employed as it can amplify samples with DNA concentration several-fold lower than standard PCR. However, potential biases with nested PCRs that could affect measurement of community structure have received little attention. Results In this study, we used 17 DNAs extracted from vaginal swabs and 12 DNAs extracted from stool samples to study the influence of nested PCR amplification of the 16S rRNA gene on the estimation of microbial community structure using Illumina MiSeq sequencing. Nested and standard PCR methods were compared on alpha- and beta-diversity metrics and relative abundances of bacterial genera. The effects of number of cycles in the first round of PCR (10 vs. 20) and microbial diversity (relatively low in vagina vs. high in stool) were also investigated. Vaginal swab samples showed no significant difference in alpha diversity or community structure between nested PCR and standard PCR (one round of 40 cycles). Stool samples showed significant differences in alpha diversity (except Shannon’s index) and relative abundance of 13 genera between nested PCR with 20 cycles in the first round and standard PCR (P<0.01), but not between nested PCR with 10 cycles in the first round and standard PCR. Operational taxonomic units (OTUs) that had low relative abundance (sum of relative abundance <0.167) accounted for most of the distortion (>27% of total OTUs in stool). Conclusions Nested PCR introduced bias in estimated diversity and community structure. The bias was more significant for communities with relatively higher diversity and when more cycles were applied in the first round of PCR. We conclude that nested PCR could be used when standard PCR does not work. However, rare taxa detected by nested PCR should be validated by other technologies.

applied in the first round of PCR. We conclude that nested PCR could be used when standard PCR does not work. However, rare taxa detected by nested PCR should be validated by other technologies.

Background
Polymerase chain reaction (PCR) amplification of 16S rRNA genes followed by next generation sequencing to characterize microbial communities is now the norm in the fields of human health and environmental sciences. The research on human microbial communities has focused on material such as feces, oral and vaginal swabs that have relatively large bacterial populations and few human cells [1,2]. Bacterial communities from body sites such as lung, esophagus and stomach are more difficult to assess by standard PCR due to sparser bacterial populations and relatively high abundance of human DNA (e. g. from human tissue biopsies). Nested PCR is thus necessary for studies on certain human tissue microbiota since it can amplify the target DNA with concentrations several-fold lower than standard PCR [3][4][5][6][7]. Nested PCR involves two rounds of PCR reactions with the first round targeting a wide DNA region and the second round targeting a narrower sub-region of the products of the first round which is used as a template. Barcoded primers have a low efficacy for binding and amplification when targeted sequences are sparse. In nested PCRs, barcoded primers are only used in the second round of PCR when the template is a 100% match and in higher abundance, and amplify the target with greater efficacy. Therefore, nested PCR works for the samples with sparse targets while standard PCR does not. The drawback to the nested PCR is that the bias due to preferential amplification may be greater when two successive PCR reactions are applied [8]. To date, the potential bias of nested PCR combined with next generation sequencing technologies on the interpretation of microbial diversity and structure has not been rigorously examined.
In this study, we evaluated the bias of nested PCR in estimating microbial diversity and community structure by comparing it with standard PCR. We investigated the influence of microbial diversity (relatively low vs. high) and the number of cycles in the first and second round of nested PCR.

Sample collection
Vaginal swabs (17 samples) and fecal samples (12 samples) were collected from adults as previously described [9,10]. The study of vaginal swab samples was approved by the Institutional Review Board at the University of Maryland Baltimore. The study of fecal samples was approved by the National Cancer Institute Special Studies Institutional Review Board. All participants provided written informed consent.

DNA extraction, PCR amplification and sequencing of 16S rRNA
The DNA from previously published studies was used [9,11]. The designs for nested and standard PCRs are shown in Table 1 [12]. All PCRs had 40 cycles in total. The nested PCRs had either 10 or 20 cycles for the first round of amplifications, with 30 or 20 cycles, respectively, for the second round. These are referred to as Nested_10-30 and Nested_20-20, respectively. For comparison, each nested PCR had a standard PCR with either 10-30 or 20-20 cycles, thereby mimicking the nested PCR cycles but using only primer pair 2. These are referred to as Standard_10-30 and Standard_20-20, respectively. An additional Standard_40 PCR used 40 cycles with primer pair 2.
For the nested PCRs, primer pair 1 for the first round of amplifications was 515F: GTGCCAGCMGCCGCGGTAA, 1492R: TACCTTGTTACGACTT, and primer pair 2 for the second round was tagged 515F (AATGATACGGCGACCACCGAGATCTACAC TATGG TAATT GT GTGCCAGCMGCCGCGGTAA, tagged 806R CAAGCAGAAGACGGCATACG AGAT XXXXXXXXXXXX AGTCAGTCAG CC GGACTACHVGGGTWTCTAAT). For standard PCR, only primer pair 2 was used. The italic parts of primer pair 2 were the Illumina adapter, primer pad and linker. All PCRs were performed in a reaction mixture (25 μL) containing 1 μL of dissolved DNA (10 ng), 0.5 μl of each primer (10 μM concentration), 12.5 μl 2x Phusion Mastermix with HF Buffer, 0.75 μl DMSO and water. The amplification program used was as follows: 98°C for 1 min; number of cycles of 98°C for 15 s, 55°C for 15 s, and 72°C for 30 s; and 72°C for 1 min. For nested PCR and the standard PCRs that mimic nested PCRs, the first round of PCR products were cleaned up using ExoSAP-IT according to the manufacturer's recommendations. The PCR products for sequencing were cleaned up and normalized using the SequalPrep DNA Normalization Kit (Invitrogen) according to the manufacturer's recommendations and eluted in 20 μl elution buffer.
Sequencing was performed on an Illumina MiSeq platform using the 250 bp paired end protocol. The details were described previously [12]. The raw sequence data were deposit in Short Sequence Archive database at National Center for Biotechnology Information (Accession, SRP058353).

Analysis of 16S rRNA gene sequence data
We removed low quality reads (reads with average quality less than 20 over 30bp window based on Phred algorithm; paired reads which have at least one with length less than 75% of its original length) and chimera reads (by UCHIME), and clustered the remaining reads into species-level Operational Taxonomy Units (OTUs) at 97% identity by Usearch method in software package Quantitative Insights into Microbial Ecology (QIIME 1.8.0) [13]. Reads without 97% identical reference in Greengenes database (release gg_13_8, http://qiime.org/home_static/ dataFiles.html) were excluded. OTUs with only one read or in only one sample were excluded. Of the reads that passed QIIME's quality filters, 3,054,521 reads from vagina samples (97% of the filtered reads) and 1,363,608 reads from stool samples (91% of the filtered reads) hit a reference sequence with at least 97% identity. In order to exclude the effect of extremely rare OTUs, we also used more strict filtering criteria. The OTUs with less than 20 reads or in less than 10% of samples were excluded. Of the reads that passed the strict criteria, 3,046,632 reads from vaginal samples and 1,356,860 reads from stool samples hit a reference sequence with at least 97% identity.
In order to minimize the difference in microbial diversity estimation due to sequencing effort, a random sample of the same sequence depth was drawn 20 times without replacement from each sample. Each participant's alpha diversity measures were based on the mean values from 20 such random samplings. The minimum sequence depth used for vagina and stool samples were 16,279 and 12,746 reads per sample respectively, and 16,214 and 12,377 per sample respectively based on the strict OTU filtering criteria.
Four measures of alpha diversity including number of OTUs, Chao1, Shannon's Index, and Phylogenetic distance (PD) whole tree were calculated for both less strict and strict OTU filtering criteria. The total number of unique OTUs does not take relative abundance of OTUs into account. Chao1 is bias-corrected for singleton OTUs [14]. Shannon's index is defined as (negative) the sum over OTUs of the product of the relative abundance of the OTU times the natural logarithm of the relative abundance [15]. PD_whole tree reflects number and relative abundance of OTUs, and phylogenetic divergence among OTUs within an individual [16]. Wilcoxon signed-rank test for paired samples was applied for the difference of alpha diversity between nested PCRs and their controls [17]. Significance level of 0.05 was applied.
A beta-diversity measure, Weighted UniFrac distance matrix, which measures the pairwise difference in microbial diversity among samples, was calculated in QIIME [18]. To provide visualization of the sample distribution patterns, a principal component analysis (PCA) was applied to transform the UniFrac distance matrices into principal coordinates.
Taxonomy was assigned using RDP classifier 2.2 [19]. The relative abundance of bacterial genera was calculated in QIIME [13]. Bray-Curtis dissimilarity matrix (a measure of dissimilarity among community states) and hierarchical clustering with average linkage hierarchical clustering were used to determine community similarity. Linear discriminant analysis (LDA) effect size (LEfSe) method was used to identify genus-level taxa with statistically significant different relative abundance between paired samples amplified by the two different PCR methods [20].  Table 1.

Difference in alpha diversity between nested and standard PCR controls
Compared with their matching standard PCRs, both nested PCRs of vagina samples had on average 3-7% fewer OTUs, 2% lower in Shannon's index and 2-8% lower in Chao1 and PD_whole tree. The differences by PCR methods were not significant except between Nested_10-30 and Standard_10-30 in number of OTUs (P = 0.02, Fig 1), and Nested_20_20 and Standard_20_20 in PD_whole tree (p = 0.04). Based on our strict OTU filtering criteria, the differences by PCR methods were only statistically significant between Nested_10-30 and Standard_10-30. These two methods differ in Number of OTUs, Chao1 and PD_whole tree (p<0.003), but not in Shannon's index (S1 Table).
Likewise, both nested PCRs of stool samples had on average 17 to 24% fewer OTUs, 2-8% lower in Shannon's index and 12-21% lower in Chao1 and PD_whole tree compared with their matching standard PCRs. All differences between Nested_20-20 and standard PCRs were statistically significant (P0.01) except for Shannon's index between Nested_20-20 and Standard_40 (shown in Fig 1 for Number of OTUs and Shannon's index). We found no statistically significant difference in alpha diversity between Nested_10-30 and the standard PCRs. The results based on strict OTU criteria are the same as the ones based on less strict OTU criteria (not shown).
Unique sets of OTUs were detected only in the nested PCRs or only in the standard PCRs. The discordant OTUs between Nested_10-30 and Standard_40 OTUs are listed in S2 Table. The average counts and sums of relative abundance of these discordant OTUs are shown in Table 2. On average, 34-51 OTUs were only detected in nested PCRs and 43-58 OTUs were only detected in the standard PCRs for vagina samples; 141-187 OTUs were only detected by nested PCR and 223-265 OTUs were only detected by standard PCRs for stool samples. The majority of the discordant OTUs had low or very low abundance (S2 Table). They comprised 30%-47% of the total number of OTUs in vagina and 27-44% in stool samples, but they only contributed 0.2-0.3% of total OTU abundance in the vagina microbiota, and slightly more (0.9-16.7%) in the stool microbiota.

Comparison of beta diversity between nested and standard PCR
Weighted UniFrac-based PCoA plots (Fig 2) revealed a strong pattern of primary clustering of community structure by subject for the vaginal swabs, but not for the stool samples. Samples from the same subject clustered together, and within-subject UniFrac distances were generally smaller than between-subject distances, suggesting the community composition of samples from the same subject were more similar to each other and consisted of bacterial lineages sharing a common evolutionary history. For the stool samples, however, several nested PCRs were outliers, far distant from their standard PCR controls (Fig 2 and Fig 3).

Relative abundance of taxa at genus level between nested and standard PCRs
The heatmap of microbial genus relative abundance (Figs 3 and 4) shows that vaginal samples were clustered by subjects while stool samples were not. Further analysis in LEfSe indicated that no taxon at the genus level was significantly different in relative abundance between nested and standard PCRs for vaginal samples (data not shown). However, compared to Standard_40 in the stool samples, 7 taxa at genus level had higher relative abundance with Nested_10-30 (comprising 1.79% relative abundance). Furthermore, 11 taxa (comprising 7.80% relative abundance) had higher relative abundance and 2 (comprising 1.73% relative abundance) had lower relative abundance with Nested_20-20 than with Standard_40 ( Fig 5). Similar distortions (not shown) were also observed between Nested_10-30 and Nested_20-20 compared to their matching standard PCRs.

Discussion
We evaluated the effect of nested PCR and other factors including cycle number in the first round of nested PCR and community diversity level on measures of microbial community structure. We observed biases by nested PCR in the estimation of microbial diversity and structure both in vaginal and stool samples and the differences were mainly observed between Nested_20-20 and standard PCRs in stool samples. Our results indicated that nested PCR introduced bias in measurements of community structure, particularly for stool samples. This might be due to the discordant OTUs that were detected only in nested PCR or only in its matching standard PCR. Such discordant OTUs were rare. They contributed 30-47% to the total number of unique OTUs, but were so rare that they contributed only a small proportion (0.002-0.003) of the total abundance of the vaginal microbiota. In the stool samples, which had an average of 631 unique OTUs, discordant OTUs  represented 27-44% of the total number of unique OTUs, and they contributed 0.01-0.17 to the total abundance in stool. Alternatively, such unique OTUs might be the true members of the community that could not be consistently amplified due to their rarity or low specificity with the primers used for PCR reactions. Other sensitive and accurate techniques will be required to characterize such rare taxa.
Our results showed significant bias of alpha diversity estimation from standard PCRs by Nested_20-20 but not by Nested _10-30 in stool samples, suggesting that the lower number of cycles in the first round of nested PCR results in less bias. However, since only 6 stool samples for Nested_10-30 were amplified, the low bias in Nested_10-30 might merely reflect limited statistical power. In addition, the total number of cycles might have an effect on the estimation of microbial diversity and structure, which is not estimated in the study.
In conclusion, our results showed that nested PCR will introduce biases in interpreting microbial community diversity and structure with Illumina MiSeq-based 16S rRNA gene profiles. The bias was more significant for high diverse communities or a high number of cycles in the first round of PCR. We suggest that nested PCR should only be applied when standard PCR is unsuccessful, and the number of cycles in the first round of PCR should be as low as possible. Rare taxa detected by nested PCR should be confirmed by other technologies.
period is the PCR method used.). Columns represent the predominant bacterial genera with mean relative abundance of 0.01 or greater. The colors in the heatmaps represent the relative abundance of each genus, as indicated in the upper left corner of each panel. Supporting Information S1  Table. List of discordant OTUs between nested_10-30 and standard PCRs; 2a), List of discordant OTUs between nested_10-30 and standard_40 for each vagina samples; 2b), List of discordant OTUs between nested_20-20 and standard_40 for each vagina samples; 2c), List of discordant OTUs between nested_10-30 and standard_40 for each stool samples; 2d), List of discordant OTUs between nested_20-20 and standard_40 for each stool samples (XLS)


extfieldsofstudy: Medicine Biology

text:
SpeedSeq: Ultra-fast personal genome analysis and interpretation

SpeedSeq is an open-source genome analysis platform that accomplishes alignment, variant detection and functional annotation of a 50× human genome in 13 hours on a low-cost server, alleviating a bioinformatics bottleneck that typically demands weeks of computation with extensive hands-on expert involvement. SpeedSeq offers competitive or superior performance to current methods for detecting germline and somatic single nucleotide variants, indels, and structural variants, and includes novel functionality for streamlined interpretation.

Our implementations of FreeBayes and CNVnator are also faster than their original counterparts. SpeedSeq's parallelized FreeBayes produced SNV and indel calls in 1.4 hours, compared to 19.9 hours for the single-threaded version and 2.2 hours with naïve parallelization by chromosome. CNVnator took only 0.5 hours, compared with 13.5 hours using the original (version 0.3), a 27-fold speed improvement.
Our parallelized software implementations demonstrate little or no difference from the original versions. SAMBLASTER produces nearly identical output to Picard Tools duplicate marking 4 . Our parallelized FreeBayes output differs from the single-threaded standard version at 1,310/4,333,589 sites (0.03%), with only 43 of these variants validating against the GIAB truth set. In our implementation of CNVnator, there were minor differences in output due to the use of a lookup table and a slight loss of floating-point precision. Copy number variable regions differed by ~2,300 bp over the entire genome and often corresponded to ambiguous CNV calls with low-amplitude copy number deviations and low confidence scores.

Supplementary Note 2: Characteristics of miscalled variants
Non-unique 100-mers harbored higher percentages of false positive and false negative SNVs, and both SNVs and indels showed increased false positive rates in satellite repeats (Supplementary Table 1). We were not able to evaluate the role of segmental duplications and simple repeats in variant calling accuracy because the Genome in a Bottle truth set excluded these features, however, we expect performance to be similarly diminished in these regions.
Clusters of nearby variants can complicate variant detection due to misalignment, and indeed false positives were enriched in their proximity to indels, with 16.9% of false positive SNVs located within 10 bp of an indel compared to 0.06% of true positives. Upon manual inspection, many of these resulted from complex variants where flanking indels caused SpeedSeq to call phantom SNVs. A smaller portion of miscalls was due to different representations of the same variant, despite our adherence to the variant normalization protocols used by the Genome in a Bottle Consortium 5 . This reflects a persistent deficiency in tools to compare VCF files, and suggests that our metrics likely underestimate SpeedSeq's true performance.
To characterize the nature of miscalled SVs, we used their composite long-read/1KGP validation status to assess whether they were true (TP) or false positives (FP), and counted the number of SVs in which either of the breakends overlapped with annotated genomic features. We expressed this count as a percentage of the number of true positives (N=3,388) or false positives (N=3,088) (Supplementary Table 1). False positives overlapped more frequently with simple repeats (TP: 10.6%, FP: 29.4%) and segmental duplications (TP: 4.5%, FP: 23.7%). However, we note the important caveat that SVs in repetitive regions of the genome are also less likely to validate by PacBio or Illumina Moleculo long-reads, and may have been under-ascertained by 1KGP SV mapping efforts, and therefore some of this enrichment may be due to technical deficiencies in validation (Supplementary Table 1).

Supplementary Note 3: De novo variant detection
Identification of de novo variants from a family trio is a common WGS application and a natural extension of SpeedSeq's multi-sample variant detection functionality. To evaluate the performance of our tools, we defined a "trio" from the CEPH 1463 pedigree in which NA12878 represented the child, NA12878's biological mother (NA12892) represented one parent, and NA12877 (an unrelated individual) represented her father (Supplementary Fig. 4). Thus, the truth set of 288,409 de novo SNVs were defined by those variants that were present in the NA12878 GIAB 2.17 variant truth set but absent from NA12877 and NA12892, as determined by an independent variant set from Real Time Genomics 6 . Using the default minimum variant quality score of 1 and no additional filtering, SpeedSeq detected 98.3% of the "de novo mutations" with a false discovery rate of 5.9%. These putative mutation calls can be further refined within GEMINI (or using ad hoc scripts) by annotating known polymorphisms (e.g., dbSNP, 1KGP, NHLBI-ESP, ExAC), and by filtering for known sources of false positives. For example, simply requiring all three samples to have a minimum read-depth of 30 at putative mutation sites -a built-in filtering parameter in GEMINI -reduced FDR to 3.2%, with a minor effect on sensitivity (97.1%). Functionally relevant de novo mutations can be prioritized by filtering on diverse annotations and variant impact scores. Of course, reducing false positives to very low levelsas required for mutation rate studies -is a more difficult problem that requires extensive probabilistic filtering 7,8 .

Supplementary Note 4: Excluded regions
Despite the high quality of the human reference genome, artifacts remain in low-complexity regions and unannotated paralogous sequences that delay processing time and confound variant interpretation. Thus, we have excluded a static set of high-depth regions in the human genome from SNV, indel, and SV breakpoint calling modules of SpeedSeq. These regions exhibit an aberrant increase in sequencing coverage depth where reads from disparate parts of the genome accumulate, violating the diploid assumption of downstream variant calling algorithms. Most high-depth regions are caused by misassembled regions of the reference genome, where moderate or high copy repeats have been collapsed into a single (or few) copies, causing read pile-ups.
To identify these high-depth regions, we aligned reads from the CEPH 1463 pedigree to GRCh37 with BWA-MEM and measured aggregate coverage depth from all 17 family members plus one replicate. We then excluded the 15.6 Mb (0.6% of the genome) where the depth was greater than 2 × mode coverage + 3 standard deviations (Supplementary Fig. 4). While this static set of excluded regions was based on a single large family, the depth cutoff accommodates a 2-fold increase in copy number relative to the reference genome -corresponding to a homozygous duplication (4 copies) in a genomic region that is single copy in the reference -minimizing the bias toward fixed polymorphism in that family. In the future, we envision that excluded genomic regions will be defined by a much larger set of high-quality human genomes from diverse populations. For structural variation detection with LUMPY, we also exclude the mitochondrial genome, which is prone to false positive calls due to extremely high depth. These regions are not excluded from read-depth analysis using CNVnator, so it is possible (albeit technically complicated) to detect CNVs in these regions using SpeedSeq.
We tested for feature enrichment in repetitive elements from RepeatMasker and mappability from the CRG Alignability track 9,10 . As expected, the excluded regions are highly enriched in repetitive regions of the genome that are known to be poorly assembled in the reference genome including satellite repeats (54-fold enrichment), segmental duplications (9-fold), and sequences near centromeres (28-fold) and other assembly gaps (25-fold) (Supplementary Table 5


s2fieldsofstudy: Biology Medicine

text:
HPV-16 impairs the subcellular distribution and levels of expression of protein phosphatase 1γ in cervical malignancy

Background The high risk Human Papillomavirus (HPV) E6 oncoproteins play an essential role in the development of cervical malignancy. Important cellular targets of E6 include p53 and the PDZ domain containing substrates such as hScrib and Dlg. We recently showed that hScrib activity was mediated in part through recruitment of protein phosphatase 1γ (PP1γ). Methods Expression patterns of hScrib and PP1γ were assessed by immunohistochemistry of HPV-16 positive cervical intraepithelial neoplasm (CIN), classified as CIN1 (n = 4), CIN2 (n = 8), CIN3 (n = 8), cervical carcinoma tissues (n = 11), and HPV-negative cervical tissues (n = 8), as well as by subfractionation assay of the HPV-16 positive cervical cancer cell lines, CaSki and SiHa. To explore the effects of the HPV-16 oncoproteins, we have performed siRNA knockdown of E6/E7 expression, and monitored the effects on the expression patterns of hScrib and PP1γ. Results We show that PP1γ levels in HPV-16 positive tumour cells are reduced in an E6/E7 dependent manner. Residual PP1γ in these cells is found mostly in the cytoplasm as opposed to the nucleus where it is predominantly found in normal cells. We have found a striking concordance with redistribution in the pattern of expression (9/11; 81.8%) and loss of PP1γ expression in HPV-16 positive cervical tumours (2/11; 18.2%). Furthermore, this loss of PP1γ expression and redistribution in the pattern of expression occurs progressively as the lesions develop (8/8; 100%). Conclusion Together, these results suggest that PP1γ may be a novel target of the HPV-16 oncoproteins and indicate that it might be a potential novel biomarker for HPV-16 induced malignancy.

Background
Human Papillomaviruses (HPVs) are the aetiological agents of cervical cancer [1]. This is caused by infection with the high risk subset of HPV types, of which HPV-16 is the most important, being responsible for over 60% of global cervical cancer cases [2]. Cancer-causing HPVs encode two oncoproteins, E6 and E7, whose continued expression and activity is essential for maintaining the malignant phenotype, many years after the initial immortalising events [3,4]. Both viral oncoproteins function by perturbing the normal activity of a variety of different cellular control mechanisms. HPV E7 promotes cell cycle progression, in part through its association with members of the pocket protein family of tumour suppressors [5], whilst HPV E6 counteracts the pro-apoptotic effects of E7 through targeting the p53 tumour suppressor [6]. In both cases, the viral oncoproteins make efficient use of the cellular ubiquitin-proteasome machinery, with E7 targeting pRb through the cullin 2 ubiquitin ligase complex [7], whilst E6 uses the E6AP ubiquitin ligase to target p53 [8]. The effects of E6 and E7 are therefore cooperative, and this is reflected both in tissue culture systems, where they cooperate in the immortalisation of primary keratinocytes [9][10][11], and in animal models of tumourigenesis, where they cooperate in the induction of tumours in the skin and cervix [12,13].
Whilst targeting the pRb and p53 pathways is obviously very important for cervical tumourigenesis, it is also clear that E6 and E7 have a large number of other activities, many of which are also important for tumour development. In the case of high risk HPV E6 oncoproteins, an intriguing class of targets that appear to be important for HPV E6 induced malignancy are the PDZ (PSD/Dlg/ZO) domain containing substrates [14,15]. These are bound by E6 via a short stretch of amino acids within the extreme carboxy terminal region of the E6 oncoprotein. Most importantly, this PDZ binding motif (PBM) is only found in the high risk HPV E6 oncoproteins and is absent from the benign HPV E6 proteins [16,17]. Through this PBM, E6 can interact with a large number of cellular PDZ domain containing proteins, many of which are subject to E6-induced proteasomal degradation and E6-induced redistribution [16,[18][19][20][21]. One of the most important of these targets is the cellular tumour suppressor hScrib. In Drosophila Scrib was originally identified as a potential tumour suppressor [22], and more recent studies in mammalian tissues also indicate tumour suppressive potential for hScrib. Loss of Scrib cooperates with c-Myc in the development of mammary carcinogenesis and Scrib also downregulates ERK signaling, with hScrib deregulation correlating with poor cancer prognosis [23][24][25][26][27]. In cervical tumourigenesis, hScrib patterns of expression are also perturbed as lesions develop, with hScrib being completely absent in many late stage tumours [28]. We recently found that hScrib could interact with PP1γ [29] a protein phosphatase that plays a critical role in controlling chromatin organization and also has an important role in the DNA damage response pathway [30,31] This suggested that PP1γ expression patterns in cervical tumourigenesis might likewise be perturbed. Therefore we initiated a series of studies to investigate the pattern of PP1γ expression in HPV16 positive cervical tumours and derived cell lines. We show that PP1γ is indeed subject to a striking alteration in both its levels of expression and localisation, both as lesions develop, and in the tumour derived cell lines. However this altered pattern of expression is independent of hScrib, is due directly to E6/ E7 expression, and highlights PP1γ as potential novel biomarker of HPV induced neoplasia.

Cell lines and culture
HPV positive cervical cancer cell lines, CaSki, SiHa and HeLa plus HPV negative C33A (cervical cancer derived) and HaCaT (human keratinocytes) cells were cultured in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum at 37°C in a humidified incubator with 5% CO 2 [32]. The effect of proteasome inhibitor was determined 24 hours posttransfection after 3 hours of treatment with 10 μM MG132 (Calbiochem).
For plasmid transfection, 293 cells were transfected using TransIT-293 transfection reagent (Mirus Bio) and HaCaT cells were transfected using Lipofectamine 2000 (Invitrogen), according to the manufacturer's instructions, with pcDNA-HPV-16 E6. A plasmid expressing βgalactosidase was included in each transfection and pcDNA was used to equalize the input DNA.

siRNA transfection
The HPV-positive cervical cancer cells were seeded on 6 cm dishes and transfected using Lipofectamine 2000 (Invitrogen) with control siRNA against Luciferase (siLuc), or siRNA against HPV-16 and 18 E6 sequences (Dharmacon) described previously by Kranjec C et al., 2011. 72 hours post-transfection cells were harvested and total cell extracts or cell fractionated extracts were then analysed by western blotting. Alexa 568 labeled negative control siRNA (Qiagen) was used to measure transfection efficiency. The transfection efficiency was determined to be over 70% for each cell line.

Subcellular fractionation assays
Differential extraction of the cells to obtain cytoplasmic, membrane, cytoskeleton, and nuclear fractions was performed using the Calbiochem Proteo Extract Fractionation Kit according to the manufacturer's instructions. To inhibit phosphatase activity during the preparation of cell lysates, phosphatase inhibitors (1 mM Na 3 VO 4 , 1 mM β-Glycerophosphate, 2.5 mM Sodium Pyrophosphate, 1 mM Sodium Fluoride) were also included.

Immunohistochemistry
All tissue samples were fixed in formalin and embedded in paraffin (obtained from patients under Institutional Review Board approval through the University of Tokyo Hospital). For all antibodies, immunostaining was performed according to standard techniques using an autostainer (BenchMark XT; Ventana Medical Systems, Inc., Tucson, AZ, USA). Immunoreactivity was interpreted based on the negative control, which was incubated without the primary antibody. Detection of hScrib expression was evaluated based on the existence of basolateral membrane staining as described previously [28]. For PP1γ, the expression was evaluated by nuclear staining. The immunostaining patterns of each sample were evaluated independently and blindly by pathologists specializing in gynaecological pathology, and cytology.

PCR-based HPV DNA testing
DNA was extracted from cervical smear samples by using the QIAGEN® DNeasy® Blood & Tissue Kits. PCR-based HPV DNA testing was performed using the PGMY-CHUV assay. Briefly, standard PCR was conducted using the PGMY09/11 L1 consensus primer sets and HLA-dQ primer sets. Reverse blotting hybridization was subsequently performed as described previously [33].

Results
Distribution patterns of hScrib and PP1γ in HPV-16 positive cervical intraepithelial neoplasm (CIN) and cervical carcinoma tissues Previous studies had highlighted hScrib as a potential biomarker for HPV-16 induced malignancy [19,28,34]. We reasoned that if PP1γ was also regulated directly by hScrib, this should be similarly affected in HPV-16 induced malignancy. In order to investigate this we performed IHC analysis of hScrib and PP1γ expression in HPV-16 positive cervical tumours and control cervix.
The results obtained are shown in Figure 1 and Table 1.
In normal tissue hScrib is found primarily at cell-cell junctions, with high levels of expression as the cells begin to differentiate. However, hScrib distribution is altered significantly in all the HPV-16 positive tumours, with significant redistribution in the pattern of expression in 5/11 tumours and a complete loss of expression in 6/11 tumours. These results are largely in agreement with previous studies [28,35]. In the case of PP1γ, this displays a largely nuclear pattern of expression and this is present throughout the differentiating epithelium in the normal cervical tissue. In contrast, in the cervical tumours there is a complete loss of expression of PP1γ in 2/11 cases, with a striking redistribution in the pattern of expression in the remaining 9 samples, where there was a shift from a nuclear localisation to a cytoplasmic pattern of expression.
We were then interested in investigating whether perturbation in the pattern of PP1γ expression was an early or late event during HPV-induced neoplastic progression. To do this we repeated the PP1γ IHC analysis on lesions exhibiting different grades of CIN. The lesions were classified as CIN1 (n = 4), CIN2 (n = 8), CIN3 (n = 8). As shown in Figure 2, there is a marked loss in nuclear PP1γ expression, which is already apparent in CIN2, and this is more evident in the CIN3 lesion, where there are also much lower levels of PP1γ expression. Interestingly, PP1γ positive cells were distributed only in the lower third of the epithelial layer in CIN1 cases (4/4) and 8/8 of patients with CIN3 had PP1γ positive cells distributed in the lower, middle, and upper third of the epithelium ( Figure 2B). In the case of hScrib, there is a similar perturbation in the pattern of expression as the lesions develop, but similar to what has been reported previously, there is a tendency in some lower grade lesions to find highly overexpressed hScrib in regions of the epithelium.
These results indicate that hScrib and PP1γ, whilst both being perturbed during the progression to malignancy, are altered in a manner that is not interdependent, suggesting that PP1γ might be an independent marker for cervical tumour development. Indeed, the pattern and expression levels of PP1γ declined with an almost linear relationship from normal tissue, through increasing grades of CIN lesion, to invasive cancer.

Analysis of PP1γ expression in HPV-16-positive cells
In order to determine whether perturbation of PP1γ expression was a direct result of HPV-16 oncoprotein function, we proceeded to examine the pattern of PP1γ expression in cell lines derived from HPV-16 positive cervical tumours. To do this we analysed the pattern of PP1γ expression in HPV-16 positive CaSki and SiHa

cells, and compared this with HPV negative HaCaT cells.
To determine whether any alterations might be HPVspecific, we also transfected the cells with siRNA E6/E7 and siLuc as a control. After 72 hours the cells were harvested and cells fractionated into cytosolic, membrane, nuclear and cytoskeletal pools, such that the pattern of PP1γ subcellular distribution could be monitored. The pattern of PP1γ expression was then ascertained by western blotting and the results obtained are shown in Figure 3. PP1γ is found predominantly within the nucleus in HaCaT cells ( Figure 3A), whilst in the HPV-16 positive cells it is found weakly re-localised both in nuclear and cytoplasmic locations. However when E6/E7 expression is ablated there is a dramatic redistribution in the pattern of PP1γ expression, with much higher levels being found within the nuclear fraction of the cells ( Figure 3B). In contrast, we found no difference in PP1γ transcript levels after siRNA E6/E7 treatment in HPV-16 positive cells (data not shown). These results suggest that loss of nuclear PP1γ expression in HPV positive tumour cells is a direct result of the expression of the HPV E6/E7 oncoproteins.

PP1γ is subject to degradation in HPV-16 positive cells
Interestingly, the fractionation studies indicate that whilst there is a significant increase in nuclear PP1γ in the absence of E6/E7, there is not a significant loss of cytoplasmic PP1γ, suggesting that some of the loss of nuclear expression may be due to proteasome mediated degradation. Therefore we were first interested in determining whether E6/E7 expression could affect the total levels of PP1γ expression. To do this we analysed the levels of PP1γ expression in total cell extracts from CaSki and SiHa cells previously transfected with siRNA E6/E7 or siLuc as a control. After 72 hours the cells were extracted and the levels of PP1γ expression monitored by western blotting. The results in Figure 4A show that loss of E6/E7 expression induces a marked increase in the total levels of PP1γ expression in HPV-16 positive cells, in a manner similar to that seen for restoration of p53 levels, which served as a positive control for efficient ablation of E6/E7 expression. We also monitored the efficiency of E6/E7 knockdown by RT-PCR and found that  , and cytoskeleton (F4) pools and hScrib and PP1γ were detected by western blotting. α-tubulin was a loading control for the cytoplasmic fraction, E-Cadherin was a loading control for the membrane fraction, p84 was a loading control for the nuclear fraction, and Vimentin was a loading control for the cytoskeleton fraction.
E6/E7 transcripts were reduced by around 60% following siRNA transfection (data not shown). In contrast to the change in PP1γ protein levels, we found no difference in PP1γ transcript levels after siRNA E6/E7 treatment in HPV-16 positive cells. Furthermore, to determine whether the cell type or E6 expression contributed to the alterations in PP1γ expression levels, we compared the ability of E6 to direct the degradation of PP1γ in 293 and HaCaT cells. First 293 cells were transfected with increasing amounts of HPV-16 E6, as indicated in Figure 4B. Then, we performed the same analysis using HaCaT cells ( Figure 4C) cells were harvested and the levels of PP1γ expression ascertained by western blotting. As can be seen from Figure 4D, there are minimal changes in the levels of PP1γ expression following proteasome inhibition, regardless of the presence or absence of HPV DNA sequences, whilst there is efficient rescue of p53 following proteasome inhibition in HPV positive cells. These results indicate that the effects of E6 upon PP1γ patterns of expression are most likely proteasome independent.

Discussion
PP1 is a major serine/threonine protein phosphatase, normally regulating the phosphorylation status of a large number of important cellular regulatory proteins [36][37][38][39]. Important activities include the regulation of chromosome structure during mitosis and also following DNA damage, through de-phosphorylation of histones [30,40], and the control of centrosome disjunction through antagonism of Nek2A kinase activity [41].
In this study we have identified PP1γ as a potential new biomarker of HPV-16 induced malignancy. Using HPV-16 positive cervical tumour derived cell lines, IHC analysis of HPV-16 positive cervical tumours and CIN lesions, we present compelling evidence that PP1γ expression patterns are perturbed as a result of infection with HPV-16.
We originally considered that the PP1γ/hScrib complex might be a general target for HPV-16 E6, based on our previous studies showing complex formation between hScrib and PP1γ. However, analysis of the expression patterns of PP1γ and hScrib in cervical tissues indicate that this is not the case. Most importantly however, this highlights PP1γ as an independent target of the HPV-16 oncoproteins. In the normal cervix, PP1γ is expressed throughout the differentiating cervical epithelium, with a predominantly nuclear pattern of expression, which is consistent with previous studies [42]. To our surprise, we found that in all the HPV-16 positive cervical tumours analysed, this nuclear localisation of PP1γ was undetectable. Low levels of PP1γ can still be found within the cytoplasm of many cells within the majority of the cervical tumours that we analysed, although in 2/11 cases all PP1γ expression appeared to be lost. Similarly, perturbation in the pattern of PP1γ expression is apparent in CIN2 lesions, and this becomes more marked as the lesions progress to CIN3, suggesting that perturbation in the pattern of PP1γ expression is an early event in the development of cervical malignancy.
In order to understand whether these effects on PP1γ expression patterns were a direct consequence of E6/E7 activity, we then focused our attention on cells derived from HPV-16 positive cervical tumours. Again we found striking parallels with the IHC data, with very little PP1γ expression in the nucleus of HPV-16 positive CaSki or SiHa cells. In contrast, readily detectable nuclear PP1γ was observed in HaCaT cells. Most strikingly, siRNA ablation of E6/E7 expression resulted in a dramatic rescue of PP1γ expression within the nucleus of the HPV-16 positive cells, which appeared very similar to the effects seen upon the pattern of p53 expression. In contrast to p53 however, the alteration in the levels and pattern of PP1γ expression by E6 does not appear to involve the proteasome in cells derived from cervical tumours. Obviously further studies will be required to elucidate the precise mechanisms by which HPV-16 targets PP1γ.

Conclusions
Currently we have no information as to whether the HPV-16 E6/E7 oncoproteins can modulate any of these phosphorylation events in a PP1γ dependent manner, it is nonetheless intriguing that all of these pathways are perturbed to some extent in cells containing the HPV-16 oncoproteins. Future studies will investigate these aspects further, but it is tempting to speculate that targeting of the nuclear forms of PP1γ might contribute directly towards the generation of genome instability, chromatin remodeling and tumour progression. The cellular redistribution of PP1γ seems to have an important role in the development of centrosome abnormalities and chromosomal instability at early stages of cervical carcinogenesis. Taken together this study highlights the potential value of PP1γ as a novel biomarker for HPVinduced cervical neoplasia.