extfieldsofstudy: Biology Medicine

text:
Mechanisms of Foot-and-Mouth Disease Virus Tropism Inferred from Differential Tissue Gene Expression

Foot-and-mouth disease virus (FMDV) targets specific tissues for primary infection, secondary high-titer replication (e.g. foot and mouth where it causes typical vesicular lesions) and long-term persistence at some primary replication sites. Although integrin αVβ6 receptor has been identified as primary FMDV receptors in animals, their tissue distribution alone fails to explain these highly selective tropism-driven events. Thus, other molecular mechanisms must play roles in determining this tissue specificity. We hypothesized that differences in certain biological activities due to differential gene expression determine FMDV tropism and applied whole genome gene expression profiling to identify genes differentially expressed between FMDV-targeted and non-targeted tissues in terms of supporting primary infection, secondary replication including vesicular lesions, and persistence. Using statistical and bioinformatic tools to analyze the differential gene expression, we identified mechanisms that could explain FMDV tissue tropism based on its association with differential expression of integrin αVβ6 heterodimeric receptor (FMDV receptor), fibronectin (ligand of the receptor), IL-1 cytokines, death receptors and the ligands, and multiple genes in the biological pathways involved in extracellular matrix turnover and interferon signaling found in this study. Our results together with reported findings indicate that differences in (1) FMDV receptor availability and accessibility, (2) type I interferon-inducible immune response, and (3) ability to clear virus infected cells via death receptor signaling play roles in determining FMDV tissue tropism and the additional increase of high extracellular matrix turnover induced by FMDV infection, likely via triggering the signaling of highly expressed IL-1 cytokines, play a key role in the pathogenesis of vesicular lesions.

Introduction
Foot and mouth disease (FMD) is one of the most contagious and economically devastating viral animal diseases. It is caused by FMD virus (FMDV), a positive-sense, single stranded RNA virus member of the family Picornaviridae (genus Aphtovirus). Susceptible hosts include several domesticated (e.g. cattle, Asian buffalo, sheep, goats, and swine) and wild (e.g. African buffalo and wild boar) cloven-hoofed animals. The morbidity is very high but the mortality is generally low in adult hosts. The infection in cattle commonly occurs via the respiratory route by aerosolized virus [1]. After infection, the virus replicates locally in primary replication sites such as the nasopharynx and lung during the pre-viremic phase [2], [3], [4], [5]. The infection then spreads via the bloodstream (viremic phase) to secondary replication sites where the virus grows to high titers and causes typical blisters, erosions, and ulcers at specific regions of the oral cavity, feet, and occasionally other sites [1]. Persistent infection can occur for long periods (30 days -5 years) with virus persisting at certain primary infection sites (e.g. nasopharynx) in a subset of infected animals [6], [7]. The mechanisms that determine FMDV tissue tropism including target sites for primary, secondary, and persistent infection remain undetermined despite extensive research on this virus discovered over 100 years ago.
Several host integrins including avb1, avb3, avb5, avb6 and avb8 have been shown to be receptors for FMDV in cultured cells and the mechanism of binding has been shown to involve an Arg-Gly-Asp (RGD) amino acid sequence motif present on the FMDV VP1 [8], [9], [10], [11], [12], [13], [14]. However, several lines of evidence suggest that avb6 is the primary FMDV receptor in animals and this receptor expression is associated with FMDV tropism [15], [16], [17], [18]. Despite this evidence, the tissue expression patterns of the integrins do not fully explain the tropism and pathogenesis as many non-susceptible tissues also express the avb6 integrin [18]. Results from several studies using other piconaviruses also suggest that viral receptors are not the sole determinants of virus tropism [19]. Interestingly, the site-specific location of vesicular lesions is not unique to FMDV. Vesicular stomatitis virus, swine vesicular disease virus, and vesicular exanthema of swine virus have nearly identical lesion sites and clinical signs as FMD in cattle and swine, suggesting that these targeted tissues possess certain biological characteristics rendering them susceptible to a common pathway associated with these viral vesicular diseases.
Like other pathogenic viruses, the FMDV genome encodes several proteins that facilitate evasion of the innate immune response of infected cells. For example, the leader protease (Lpro) can inhibit host cap-dependent translation by cleaving translation initiator factor eIF4G but has no effects on viral cap-independent translation [20]. Lpro is also found to enter the cell nucleus and inhibit the induction of IFNb and some interferon stimulated genes [21], [22]. Additionally, there are several reports showing that altering some host cellular factors such as Rab5 and RNA helicase A can inhibit FMDV replication [23], [24]. These findings indicate that host factors other than the virus receptors may be contributing to FMDV tissue tropism.
Because cells differentiate by alternating their gene expression, differential tissue gene expression may provide valuable insights into the molecular mechanisms involved in determining FMDV tissue tropism. We designed a custom bovine whole genome expression microarray and used microarray analysis to identify genes differentially transcribed among tissues targeted and not targeted by FMDV for primary, secondary, and persistent infection in infected and non-infected cattle. The bioinformatic analyses of the differentially expressed genes lead to the identification of several biological processes/pathways which activity could be affected by the differential expression. Based on the expression of the genes involved in these biological processes and on relevant findings from the literature, we propose molecular bases that help explain the mechanisms of FMDV tissue tropism.

Animal Inoculations, Tissue Collection, and RNA Preparation
All animal studies were conducted under an approved Institutional Animal Care and Use Committee protocol. Five Holstein steers weighing 225 to 300 kg were obtained from an accredited experimental-livestock provider (Thomas-Morris Inc, Reisterstown, MD). Two of the steers were aerosol-inoculated as previously described [5] with FMDV-A24-Cruzeiro and three were mock inoculated with sterile cell culture media. All steers were euthanized at 72 hours post infection which was coincident with the first observation of generalized FMD (vesicles) in the two infected animals. Necropsies and tissue collections were performed immediately after euthanasia as previously described [3]. For the sites with observed vesicular lesion, tissues with ,0.75 centimeter away from the vesicular lesion were sampled to avoid the impact of tissue necrosis on RNA quality. These tissues were immediately frozen in liquid nitrogen and stored in 270uC. Total tissue RNA was isolated from the frozen tissues using a Qiagen RNA isolation kit (Qiagen, Germantown, MD). The quantity and integrity of RNA samples were examined with a NanoDrop 1000 (Thermo Fisher Scientific, Waltham, MA) and a Bioanalyzer 2000 (Agilent Technologies, Santa Clara CA), respectively. Viral RNA in sera and nasal secretions was quantitated using real-time RT-PCR as previously described [3].

Description and Classification of Analyzed Tissues
Selected tissues were collected based on pathogenesis studies as previously described [3]. The dorsal soft palate (DSP), dorsal nasal pharynx (DNP), and mid anterior lung (LNG) have been shown to be the primary replication sites (PRS) based upon consistent localization of FMDV to these tissues when collected from previremic steers infected with FMDV [3]. The nasal turbinate epithelium (NTE), previously shown not to be infected during the early phase of FMD pathogenesis, was used individually as a negative control for PRS. The interdigital cleft skin (IDC), coronary band (CB), and tongue epithelium (TE) are secondary replication sites (SRS) where the virus can replicate to high titers and cause vesicular lesions. Despite close proximity to the feet, the metacarpal skin (MCS) has never been described as a FMD lesion site in cattle and was therefore chosen as a negative control for SRS. For data analyses, MCS and NTE were also grouped as not targeted sites (NTS) based on low or no viral replication compared to their corresponding targeted tissues. DSP and DNP were also grouped as persistent infection sites (PIS) to contrast all other tissues where FMDV persistence does not occur.

DNA Microarray Design
A bovine whole genome expression microarray was designed based on all bovine expressed sequence tags and RNA sequences from the NCBI database and assembled into unique sequences with the CAP3 program [25]. The assembled sequences were aligned to the bovine genome sequences and displayed with the UCSC genome browser [26]. Non-redundant bovine expressed sequences and sequences with homology to genes in other species but no bovine EST or RNA were selected for probe design. The custom bovine whole genome expression microarray comprised approximately 42,000 60-oligonucleaotide sense probes and was designed with ArrayDesigner 4.0 (PREMIER Biosoft International, Palo Alto CA) from the selected bovine sequences. The probes were designed with locations biased to the 39-end of RNA sequences in order to produce high signal intensity for fluorescent labeling chemistry using poly-T priming. The annotation of the bovine microarray was based on the results of BLAST against human sequences, manual annotation based on the probe sequence alignments in the bovine genome sequence (Baylor 4.0/bosTau4 October 2007 release), and other genetic information displayed on the UCSC genome browser (http://genome. ucsc.edu/cgi-bin/hgGateway). The DNA probes for all available FMDV genome sequences were also designed and included in the array to detect FMDV RNA levels.

Microarray Analysis
The custom bovine microarrays used in this study were manufactured by Agilent technologies. Agilent Quick Amp RNA labeling kit (Agilent Technologies, Santa Clara CA) was used for the sample labeling. Total RNA prepared from a metacarpal skin sample was labeled with Cy5 and used as a universal control for cross-array data normalization, whereas all samples including 8 tissues from each animal (40 samples) were labeled individually with Cy3. Each Cy3 labeled sample together with the control was hybrided to a microarray. The entire procedure from RNA quality check to microarray hybridization and washing was conducted using reagents and protocols provided by Agilent Technologies. The microarrays were scanned at 5 mm resolution with Cy3:Cy5 signal ratio of between 0.9 to 1 using a GenePix 4000B scanner and GenePix Pro 6.0 program (Molecular Devices, Sunnyvale, CA).

Statistical and Bioinformatic Analyses
Microarray data were extracted from the signal intensity of Cy3 andCy5 usingGenPix Pro 6.0program anddata imported intoa SQL database created with Microsoft SQL Server 2000 and AcuityH 4.0 Enterprise Microarray Informatics software (Molecular Devices, Sunnyvale, CA). The data of extracted gene expression signals were background-corrected with the average signals of nearby negative control features. All negative intensity readings were adjusted as 0. The expression data were normalized with the signal of Cy5-labeled universal control to have equal averaged signal intensity. After normalization, all expression data were added with an arbitrary small value of 1 as suggested in the EDGE program user manual (http:// www.genomine.org/edge/edgehelp.pdf) to avoid statistical analysis involved with division by zero. The normalized signal data were square-root-transformed and analyzed using t-test. Differentially expressed genes (DEG) were defined as having a difference of $75% or 1.75 fold with a P value of 0.05 or smaller in all pair-wise two tissue comparisons between tissue groups. The significant differences obtained from bovine sequences without known human homologous genes were not included in the gene sets and were excluded from further analysis. Eight gene sets including up-and down-regulated in non-targeted sites (NTS) vs targeted sites (SRS+PRS), up-and downregulated in secondary replication sites (SRS) vs other tissues (NTS+PRS), up-and down-regulated in persistent infection sites (PIS) vs others (NTS+SRS+LNG), and up-and down-regulated in primary replication sites (PRS) vs nasal turbinate epithelium comparisons were created from data obtained from three not infected animals and identified as NTS-up, NTS-down, SRS-up, SRS-down, PIS-up, PIS-down, PRS-up, and PRS-down, respectively ( Table 1).
The gene sets were submitted to a BIOBASE web site (http:// explain30.biobase-international.com/cgi-bin/biobase/ExPlain_3. 0/bin/start.cgi) using a subscribed account to identify the pathways/networks that can regulate the differential expression by analyzing over-represented transcription factor binding sites in the promoter sequences of the identified DEG and can be significantly affected by the differential gene expression using ExPlain TM program (BIOBASE, Wolfenbüttel, Germany). To identify over-represented transcription factor binding sites, all bovine gene sequences used to design the microarray probes were mapped to human genes based on the result of BLAST or annotation from the bovine genome sequence displayed in the UCSC genome browser. The promoter sequences in 2500 and +100 bases of transcription starting sites of human genes homologous to the identified bovine DEG were compared to a human house-keeping gene set provided by BIOBASE as a control. The over-represented transcription factor binding sites not shared in compared gene sets were considered as the sites of interests, and signal transduction pathways that can activate transcription factors to bind to these sites are considered to regulate the differential expression. To detect biological networks that may be significantly affected by the differential gene expressions, the gene sets were analyzed to identify those with significant more DEG than that may occur randomly (false discover rate #0.05) using statistical tests included in the ExPlain TM program. Both up-and down-stream networks of key nodes were analysis to identify DEG regulating and regulated by the key nodes, respectively.

Biological Inferences
Once candidate pathways/networks were detected, all genes known to be involved in these biological processes were identified and their expression levels were examined in terms of up-or down-regulation and the magnitudes of the differences between tissue groups. Significant differences (P = or ,0.05) were analyzed with t-test. Biological impact of differential gene expression was evaluated based on known biological functions of the genes with an assumption that higher transcription levels are associated with higher biological activity of the genes. The tissue activity of indentified pathways/networks was determined based on the expression levels and functions of the genes in the pathways. Biological inferences were made according to the impact of predicted differences in the activity of the pathways on FMDV infection. The data from three non-infected and two infected animals were used to make the inferences.

Estimate of Integrin aVb6 Receptor Level
RGD-binding integrins were selected based on Hynes [27], which include 1 a subunit (ITGAV) and 5 b subunits (ITGB1, ITGB3, ITGB5, ITGB6, and ITGB8). According to Hynes [27], ITGB1 also forms heterodimeric receptors with ITGA1, ITGA2, ITGA3, ITGA4, ITGA6, ITGA7, ITGA9, ITGA10, and ITGA11, and ITGB3 also binds to ITGA2B. The portion of ITGB1 that may binds to ITGAV (ITGB1_v) was estimated by multiplying the signal intensity of ITGB1 with the percentage of ITGAV intensity in the total intensity of all ITGB1-binding a subunits. Similarly, the portion of ITGB3 binding to ITGAV (ITGB3_v) was calculated by multiplying the level of ITGB3 with the percentage of ITGAV level in the sum of ITGAV and ITGA2B. Finally, the expression level of integrins aVb6 was estimated based on the product of ITGAV and the percentage of ITGB6 in the sum of ITGB1_v, ITGB3_v, ITGB5, ITGB6, and ITGB8.

Clinical Observations
Steers inoculated with sterile media did not develop fever, vesicular lesions, or any other signs of disease or complications from the aerosolization procedure. The two steers inoculated with FMDV-A24 had very similar clinical syndromes including fever

Signal Intensity of FMDV Target Sequences
Varying signal intensities of FMDV microarray features were observed in the infected tissues (Table 1), whereas the tissues of the non-infected animals displayed signal intensities nearly identical to the negative control. As expected, the signal intensities of FMDV RNA from the infected secondary replication sites (sampled away from vesicular lesions) were the highest among the tissues tested. There were also differences among the SRS tissues with significantly lower signals in the coronary band than other secondary replication tissues. Not targeted tissues such as the metacarpal skin and nasal turbinate epithelium displayed 3-4 fold lower FMDV RNA signal than their corresponding targeted tissues. The signal intensity of the metacarpal skin was more than 5 times higher than that of nasal turbinate epithelium. The interdigital cleft skin and lung had the highest signal intensity of the SRS and PRS, respectively; however, the viral RNA signal intensity in the interdigital cleft skin was over 33fold higher than that in the lung. These results are consistent with viral titers and tissue distribution observed in infected animals in previous studies [2].

Differentially Expressed Genes
Whole genome gene expression profiles of the tissues from the three naïve animals were compared across four tissue categories selected according to their classification as not targeted sites, primary infection, secondary replication, or persistence sites. Tissue categories were based upon previously published studies describing tissue-specific FMDV loads as stated earlier. Eight gene sets were created from the tissue comparisons based on the statistical analysis described earlier (Table 2). In the tissue comparison of not targeted sites versus others, most of the DEG were up-regulated (n = 36) and only two genes were downregulated (Table 2). Similarly, when comparing the persistent infection site versus other tissues, there were 73 up-regulated and only 4 down-regulated genes. When the SRS or PRS were compared to the rest of the tissues, there were 412 and 220 upregulated and 103 and 360 down-regulated genes in the secondary and primary replication sites, respectively. These gene sets were statistically analyzed using the ExPlain TM program to infer the biological pathways that could be potentially involved in determining FMDV tissue tropism.

Expression of Integrins aVb6 and Fibronectin
Integrin b subunits may compete with each other for ITGAV if the expression of ITGAV is a limiting factor. Our results show that the signal intensity of ITGAV was significantly lower than the total of all RGD-binding integrins b (Table 3). Therefore, differential expression of other integrins b besides ITGAV and ITGB6 may also affect the amount of heterodimeric integrins aVb6 (FMDV receptor). Additionally, the binding between integrins aVb6 and the ligand, fibronectin (FN1), may hinder the binding of FMDV to the receptors. Therefore, the expressions of integrin aVb6 heterodimeric receptor and FN1 were examined to determine if the expression of the receptor could explain the mechanisms of FMDV tropism.
Although the expression of ITGAV was not significantly different among tissues, the SRS expressed higher ITGB6 and lower FN1 than other tissues in infected and non-infected animals ( Table 3). The expression of ITGB6 in the SRS increased after FMDV infection, while the expression of FN1 decreased when the infected and non-infected tissues were compared ( Table 3). The total expression of RGD-binding integrin b subunits (ITGBs) was the lowest in the SRS of infected and not infected animals (Table 3). Among them, ITGB3 and ITGB8 were DEG in the SRS-down gene set. The estimated level of integrin aVb6 dimeric receptor was significantly higher in the infected and non-infected SRS than that in other tissues (Table 3). However, the difference was not observed between primary replication sites and the control tissue. Thus, higher number of integrin aVb6 heterodimeric receptor and lower expression of fibronectin in the SRS especially in the infected animals indicate that the expression levels of FMDV receptor and the ligand are determining factors of FMDV tropism in the secondary infection.

Bioinformatics of Differential Expression
Because there were less than 5 genes in the NTS-down and PISdown gene sets, only other six gene sets were used in the bioinformatic analysis to infer biological mechanisms of FMDV tissue tropism. Analysis of the promoter sequences of the DEG identified 33 over-represented transcription factor binding sites ( Table 4). Six of these binding sites (V$AP1_Q2_01, V$IRF_Q6, V$IRF2_01, V$ISRE_01, V$STAT_Q6, and V$STAT1_01) are known to play important roles in inducing antiviral innate immune response. V$ISRE_01 (a binding site for transcription factor complex activated by type 1 interferon signaling pathway) was found to be over-represented in the NTS-up, SRS-down, and PISup gene sets. V$IRF_Q6 and V$IRF2_01 (binding sites for interferon response factors) were more frequent in the SRS-down gene set, while AP1_Q2_01 (a binding site for transcription factors activated by IL-1cytokine family) appeared more in the SRS-up gene set. V$STAT1_01 (a binding site for STAT1 activated by IFNc signaling) and V$STAT_Q6 (a site for STATs stimulated by the cytokine receptor-kinase complex) were over-represented in the PRS-up gene set. These results indicate that transcription factors binding to these binding sites may play roles in differential gene expression; therefore, pathways such as interferons and IL-1 cytokines that activate these transcription factors may be involved in determining FMDV tropism. Using the ExPlain TM program to identify networks that may be significantly affected by the differential expression, we detected several networks relevant to those identified using the promoter sequences of the DEG and to FMDV infection. Several network key nodes associated with type I interferon signaling were detected in the analysis using the SRS-down (PKR, IFNa, indoleacetaldehyde, Jak1), PRS-down (IFNa), and PRS-up (IFNa, Jak1) gene sets, whereas some involved in IL-1 signaling were found using the SRS-down (p38a, p38b2), PRS-down (p38a), and PRS-up (FOS, IkB-a, IKK-b:IkB-a, IL18, IL1B) gene sets (Table 5). In the SRS-up and SRS-down sets, significant numbers of DEG are in a network regulating uPAR-integrin pathways and a network associated with integrins, which may be relevant to FMDV receptors (Table 5). In the PIS-up gene set, 10 DEG are in a network up-stream of survivin (BRIC5, an inhibitor of apoptosis) and two DEG in the PIS-down gene set were in the down-stream of TNF-alpha regulated network, suggesting the involvement of clearing virus infected cells via inducing cell death. Based on the results of these bioinformatic analyses and their relevance to FMDV infection, we decided to further examine the expression of genes involved in extracellular matrix turnover and IL-1, interferon, and death receptor signaling to determine if the expression could explain the mechanisms of FMDV tropism (biological inferences).

Extracellular Matrix (ECM) Turnover
Cell attachment involves two types of interactions; cell-ECM and cell-cell adhesion. Cell-ECM interaction is mediated by RGDbinding integrins and regulated by urokinase plasminogen activator receptor (PLAUR or uPAR)-integrin pathways [28]. PLAUR and plasminogen (PLG), the activators of ECM degradation, were expressed significantly higher in the high-titer secondary replication sites, SRS, than those in the negative control tissue ( Figure 1A and 1B). On the other hand, SERPINE and SERPINF, the inhibitors of urokinase plasminogen activator (PLAU) and PLG, respectively, were expressed at significantly lower levels in the SRS than those in the control. These differential expressions between the SRS and the control were observed in both infected and non-infected animals. Plasmin can also activate metalloproteases (MMPs) to degrade ECM. Although the average expression of MMPs in the SRS was 1.4 and 2 fold lower than that in the control, the expression of the inhibitors of MMPs (TIMPs) in the control was 2.6 and 5.3 fold higher than that in the SRS in not infected and infected animals, respectively ( Figure 1A and 1B). Interestingly, SERPINE and SERPINF were also expressed significantly lower in the non-infected primary replication site, PRS, (3 fold in PIS and 4.6 fold in the lung) than those in the negative control, nasal turbinate epithelium, whereas PLAU, another activator of ECM degradation, was expressed at significantly higher level in both infected and not infected PRS (at least 2.6 fold in PIS and 10.6 fold in the lung) than that in the control ( Figure 1A and 1B).
When the expression of these individual genes was normalized to be equally weighted, the SRS expressed significantly higher activators and lower inhibitors of ECM degradation than the negative control tissue, whereas the PRS had significantly lower expression of the inhibitors than their negative control tissue in the not infected animals and the activators were expressed significantly higher in the lung than the control in the infected and non-infected animals. These results indicate that tissues susceptible to primary and secondary infection have gene expression profiles promoting higher ECM turnover.  Table 4. Over-represented transcription factor binding sites in the promoters of genes differentially expressed between tissue groups of three non-infected animals.

IL-1 Gene Family
In order to evaluate the impact of differential IL-1 cytokine expression, we examined the expression ratios of agonist(s) vs their antagonist(s) instead of the individual genes according to Johnston et al. [29] because there are agonists and antagonist for several receptors and cytokines of the IL-1 gene family [30], [31]. The SRS had significantly higher ratios of IL1R1 vs IL1RII, IL18 vs IL18BP+IL18RN, and IL36 vs IL36RNs but expressed significantly less IL18R1 and IL33 than the negative control, MCS, in the infected and non-infected animals (Figure 2A and 2B). Similarly, primary replication sites had higher IL1 vs IL1RN ratio than their negative control tissue in the infected and noninfected animals (Figure 2A and 2B) and also expressed higher other agonists or have higher agonist vs antagonist ratios than the control in the non-infected animals (Figure 2A). In the infected animals, the differences between the lung and the control, NTE, were smaller and not significant ( Figure 2B). When the gene expression was evaluated based on the average Log 2 ratio of targeted vs not targeted tissues, both SRS/MCS and PRS/NTE ratios were at least 1.4 (equivalent to ,2.6 fold difference between the SRS and MCS) and 0.7 (,1.6 fold) for IL-1 cytokines in the infected and not infected animals, respectively, and the differences for the receptors were much smaller than those for the cytokines, indicating that susceptibility to FMDV infection in the primary and secondary replication sites was associated with relatively higher local expression of IL-1 agonist cytokines.

Interferon Signaling
We examined the expression of genes in the interferon JAK-STAT and PI3K-AKT pathways (regulating transcription and translation, respectively) according to Kroczynska et al. [32] and found that four of the seven genes in the interferon JAK-STAT signaling pathway were expressed lower in the non-infected SRS than those in the control tissue (three were statistically different) ( Figure 3A). Three of four genes known to enhance interferon signing [33] were also expressed significantly lower in the SRS than in the control tissue ( Figure 3A). The differential expression was also observed in the infected animals ( Figure 3B). The average signal intensity of these genes appeared to be lower in the SRS than the control in the not infected animal but was significantly smaller in the infected tissues. Unlike the SRS, the PRS especially the lung appeared to have higher expression in the genes involved in interferon signaling than their control tissue, NTE (Figure 3). Similarly, differential expression for the genes in the interferon PI3K-AKT signaling pathway was also obtained as those in the JAK-STAT pathway. Five kinases (PIK3CA, PIK3CG, PIK3R1, AKT3, and RPS6KB1) in the pathway were expressed lower in the SRS than those in the control tissue in both infected and noninfected animals. In contrast to the SRS, the expression in the PRS appeared to higher than that in their control tissue ( Figure 4A and 4B).
To compare the status of interferon signaling pathways among tissues, 48 interferon-inducible antiviral effector genes were identified from the Gene Database (http://www.ncbi.nlm.nih. gov/sites/entrez?db = gene) based on the functions discussed by Saddler and Williams [34]. The average expression of these genes was significantly lower in the SRS than that in the control tissue in both infected and not infected animals ( Figure 5A). There were no significant differences in overall expression of type I interferon between the SRS and metacarpal skin, though the SRS appeared to have higher type I interferons in the infected animals ( Figure 5B), indicating that the differential expression of antiviral effectors between the SRS and the control tissue was mainly due to differences in genes involved in interferon signal transduction.   Unlike the SRS, there were no significant differences in the expression of interferon-inducible antiviral effectors between the PRS and the negative control in the non-infected animals ( Figure 5A). However, the lung had higher type I interferon expression than the PIS and negative control ( Figure 5B) in the not infected animals and expressed significantly higher antiviral effectors than the PIS and the control in the infected cattle ( Figure 5A). As in the non-infected animals, the lung of the infected animals appeared to have higher type I interferon expression than the PIS and the control ( Figure 5B), but the differences was not statistically significant. Interferons a and v in non-infected animals and interferons b in the infected animals accounted for most of the observed differences (data not shown). Taken together with the results of genes involved in the interferon signaling, our results strongly suggest that high-titer FMDV replication and susceptibility to FMDV persistence in primary replication sites are associated with relatively weaker type I interferon signaling when targeted and not targeted tissues were compared.

Death Receptors and the Ligands
It is well known that several members of the TNF super family including LTA, TNF, FASLG, TNFSF10, TNFSF12, and TNFSF15 play important roles in clearing virus by inducing apoptosis and/or necrosis of infected cells via binding to death receptors such as TNFRSF1, FAS, TNFRSF10, TNFRSF21, and TNFRSF25 [35], [36], [37], [38]. There were significant differences in the expression of the death receptors and the ligands between the SRS and the negative control and between the PRS and the control (data not provided). Interestingly, TNFSF10 was the only ligand which expression was significantly induced by FMDV infection in all tissues tested and was significantly higher (5.6 fold in not infected and 7.7 in the infected animals) in the lung than that in those susceptible to persistent infection. When the expression of the receptors and the ligands was added together, the SRS had lower overall expression of death receptor ligands in both infected and not infected animals ( Figure 6A) and lower death receptor expression (excluding TNFRSF10 and TNFRSF25, which probes were not in our microarray) in the infected animals ( Figure 6B) than the negative control. The overall expression of the ligands in the lung was at least 5 fold higher than that in the PIS and nasal turbinate epithelium of both infected and not infected animals ( Figure 6A). There was no difference in the ligand expression between the PIS and nasal turbinate epithelium; however, the PIS had significantly lower overall expression of death receptors tested than the lung and nasal turbinate epithelium in the non-infected cattle and remained lower in the infected animals ( Figure 6B). These results suggest that FMDV tropism particularly at persistence sites is associated with relatively lower expression of death receptors and the ligands.
In summary, we found that a large number of genes were differentially expressed between tissues with different FMDV tropism. Analysis of the differentially expressed genes leads to identification of several biological processes that may be affected by the differential expression. Further examination focused on those relevant to FMDV infection according to reported findings shows that FMDV tissue tropism was associated with significantly differential expression in integrin aVb6 heterodimeric receptor, fibronectin, IL-1 cytokines, death receptors and the ligands, and multiple genes involved in ECM turnover and type I interferon signaling.

Discussion
FMDV tropism has been known for nearly half a century [39], [40]. However, the molecular basis for FMDV tissue tropism remains mostly unknown. The functional genomics approach used here allows for a systematic investigation of mechanisms involved. Our previous study demonstrated that data obtained with our custom microarray agreed with those measured using real-time PCR [41]. In this study, we used pair-wise two-tissue comparisons instead of tissue group comparisons in order to detect genes with consistent effects across the tissue tested. Various cut-offs ranging from 2 to 1.5 fold difference with P, or = 0.05 were tested. We found that the threshold of significantly differential expression at the 1.75 fold was a better cut-off in terms of balancing between false positives and false negatives and between detection power and biological impact of differential expression in order to create gene sets optimal for the pathway/network analysis. Threshold correction for multiple t-tests was not applied because it is too stringent to create the gene sets with adequate gene number for bioinformatic analysis due to diverse tissues within the tissue groups. For example, when we used threshold correction for multiple t-tests according to Leek et al. [42], less than 10 differentially expressed genes were identified (Data not shown).
We used different bioinformatic tools to analyze the gene sets to identify candidate pathways or networks. The results of different bioinformatic analyses support each other, and the results from non-infected animals mostly agree with those from infected animals. Because of substantial intrinsic differences among tissues, we do not expect that all DEG identified play roles in determining FMDV tropism. On the other hand, not all genes involved can be detected with the pair-wise tissue comparisons because differential expression of different genes can have same effect on a biological process as we found in this study. Some of detected biological processes may play roles in determining FMDV tropism but were not recognized in this study due to lack of knowledge. We used an approach based on published biological findings to interpret the gene expression results and focused on those relevant to virus/ FMDV infection to make the biological inferences. The examination of gene expression in the detected biological processes revealed that the differential expression found could explain FMDV tropism under the widely accepted assumption of the correlation between gene expression level and the functional activity.
Differential expression in integrins aVb6, fibronectin, and genes regulating ECM turnover appears to be the major factors determining FMDV tropism based on the magnitude of the differences and the biological process involved. Our results of the estimated expression of integrins aVb6 alone can only explain in part why SRS is more susceptible to FMDV infection than other tissues, but it cannot elucidate the difference between PRS and the negative control tissue. However, the results of combining the scale of the differential expression and the differentially expressed genes involved in regulating cell-ECM attachment could explain the susceptibility to both primary and secondary infections and the differences between primary and secondary replication sites in terms of virus replication titers and pathogenesis. It has been reported that ECM acts as a barrier against virus spread [43], [44], [45], [46], [47]. Because fibronectin is an ECM protein that binds to integrin aVb6 receptor with the same RGD sequence motif as FMDV, it is reasonable to hypothesize that higher ECM turnover and the degradation and decreased expression of fibronectin make the receptors more accessible for FMDV to infect cells. Based on the known mechanisms involved in cell-ECM attachment, our results show that all FMDV-susceptible tissues examined here regarding both primary and secondary infection possess similar biological characteristics (high ECM turnover) by differentially expressing the same and/or different genes. Our results could also explain why cells isolated from non-targeted tissues become susceptible to FMDV infection in cell culture and why SRS tissues are susceptible to vesicular lesions caused by several viruses.
Our results show tissue susceptibility to primary and secondary FMDV infection was also associated with relatively higher local expression of IL-1 family agonist genes. IL-1 has been reported to be an important mediator of pemphigus vulgaris [48], which can induce an acantholysis process that resembles vesicular lesions seen in FMD, and the pathogenesis of acantholysis can be mediated via uPA system [49]. More interestingly, it has been reported that high IL-1 level in nasal lavage fluids before virus challenge was associated with the onset of rhinovirus infection in humans [50], indicating high IL-1 level is a susceptible factor of viral respiratory infections. The mechanisms of IL-1 signaling involved in susceptibility to respiratory diseases are unknown. However, IL-1 signaling can induce the expression of the activators of ECM degradation [51], [52], [53]. Therefore, we hypothesize that high expression of IL-1 agonists and high ECM turnover render tissues susceptible to viral infections and vesicular lesions. Our results show that significant differences in the estimated level of integrin aVb6 heterodimeric receptor and fibronectin expression were observed only in the infected animals, indicating that IL-1 signaling triggered by FMDV infection may also play a key role in this differential expression.
Interferon pretreatment has been known to protect against FMDV infection by inducing expression of interferon-inducible antiviral effectors [54]. The data reported here show that the overall expression of interferon-inducible antiviral effector genes and several genes involved in interferon signaling were significantly lower in the SRS than that in other tissues tested. These results suggest that cells in the SRS tissues are more permissive for viral replication because of their decreased expression of antiviral effectors, which probably due to weaker response to interferon stimulation based on the lower expression of genes involved in the signaling found in this study. This agrees with a previous report showing that type 1 interferon response and the expression level of interferon-stimulated genes were the determining factors of tissue susceptibility to poliovirus infection [55]. The lungs (a primary replication site resistant to FMDV persistent infection) of the infected cattle had higher expression of interferon-inducible antiviral effectors, which was associated with higher expression of type I interferons and genes involved in interferon signaling, than the tissues of primary replication site susceptible to the persistent infection (i.e. pharynx), indicating that interferon signaling may also play roles in determining FMDV persistent infection.
Our bioinformatic analyses also led to the identification of a candidate mechanism that could explain, at least in part, why FMDV targets certain PRS for persistent infection. We have identified all bovine death receptors except TNFRSF10 and have found death domains in these receptors. TNFSF10, also named as tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), has been reported to play critical roles in the clearance of viruses by specifically targeting infected cells to induce apoptosis and/or necrosis and by facilitating cell-mediated cytotoxic immune response [38], [56], [57], [58]. We found that the expression of this cytokine was significantly induced by FMDV infection and was several fold higher in the primary replication site (the lung) resistant to FMDV persistence than that in those susceptible. In addition, there were 10 genes in the survivin network significantly up-regulated in PIS compared to other tissues. Because survivin is an inhibitor of apoptosis [59], up-regulated expressions of genes in this network may render the cells more resistant to apoptosis, which may in turn facilitate persistent infection. Studies conducted by Zhang and Alexandersen [60] and Zhang et al. [61] showed that declining rate of FMDV levels during early infection rather than the virus levels determined FMDV persistent infection. These authors proposed that either variations on the host's abilities to clear virus or differences in the support of virus replication may determine the establishment of FMDV persistent infection. Our results analyzing death receptor and interferon signaling genes provide molecular evidence supporting both hypotheses.
In conclusion, we have identified differential gene expression at a genomic scale between tissues with different FMDV tropism. FMDV tropism could not be explained by the differential expression of a single gene alone. The bioinformatic analysis of the genes differentially expressed led to identify a few biological processes or pathways. Further examination of genes in those relevant to FMDV infection according to reported findings provide the molecular basis that could explain, at least in part, the mechanisms of why FMDV targets specific tissues for primary, high-titer secondary and persistent infection and causes vesicular lesions. Based on these results, we hypothesize that multiple contributory factors including increased FMDV receptor availability and accessibility resulted from high ECM turnover and differential FMDV receptor and the ligand expression, reduced antiviral innate immunity due to low type I interferon response and/or type I interferon production, and decreased ability to clear infected cells via death receptor signaling play roles in determining FMDV tropism. We also hypothesize that additional increase in already high tissue ECM turnover induced by FMDV infection, likely via triggering the signaling of highly expressed IL-1 cytokines, plays a key role in the pathogenesis of vesicular lesions. This work provides guidance for future research to rationally test these factors and further elucidate the molecular mechanisms of tissue tropism for FMDV. The approaches used in this study may also be applied to understanding tropism of other viral agents.


s2fieldsofstudy: Biology Medicine

text:
Comparative Genomic Analysis of Mycobacterium tuberculosis Drug Resistant Strains from Russia

Tuberculosis caused by multidrug-resistant (MDR) and extensively drug-resistant (XDR) Mycobacterium tuberculosis (MTB) strains is a growing problem in many countries. The availability of the complete nucleotide sequences of several MTB genomes allows to use the comparative genomics as a tool to study the relationships of strains and differences in their evolutionary history including acquisition of drug-resistance. In our work, we sequenced three genomes of Russian MTB strains of different phenotypes – drug susceptible, MDR and XDR. Of them, MDR and XDR strains were collected in Tomsk (Siberia, Russia) during the local TB outbreak in 1998–1999 and belonged to rare KQ and KY families in accordance with IS6110 typing, which are considered endemic for Russia. Based on phylogenetic analysis, our isolates belonged to different genetic families, Beijing, Ural and LAM, which made the direct comparison of their genomes impossible. For this reason we performed their comparison in the broader context of all M. tuberculosis genomes available in GenBank. The list of unique individual non-synonymous SNPs for each sequenced isolate was formed by comparison with all SNPs detected within the same phylogenetic group. For further functional analysis, all proteins with unique SNPs were ascribed to 20 different functional classes based on Clusters of Orthologous Groups (COG). We have confirmed drug resistant status of our isolates that harbored almost all known drug-resistance associated mutations. Unique SNPs of an XDR isolate CTRI-4XDR, belonging to a Beijing family were compared in more detail with SNPs of additional 14 Russian XDR strains of the same family. Only type specific mutations in genes of repair, replication and recombination system (COG category L) were found common within this group. Probably the other unique SNPs discovered in CTRI-4XDR may have an important role in adaptation of this microorganism to its surrounding and in escape from antituberculosis drugs treatment.

Introduction
Mycobacterium tuberculosis (MTB) is one of the most harmful worldwide human pathogens responsible for nearly 2 million deaths and 8-10 million new cases per annum [1]. In Russia, the epidemiological situation with tuberculosis (TB) is very alarming. In accordance with official statistics in 2009, the TB incidence rate was found to be 82.3 per 100000 population, and mortality rate was 2.8 per 100000 [2]. Almost 16% of newly diagnosed TB cases belonged to multidrug-resistant (MDR) TB, i.e. resistant at least to rifampicin and isoniazid (and 42.4% MDR among previously treated TB cases) [1]. The most complicated challenge is that the recently described extensively drug-resistant (XDR) TB was also reported in Russia [3][4][5].
Sequencing of the whole genomic DNA of MTB strain H37Rv in 1998 [6] provided a breakthrough in tuberculosis research, opening the way to understanding the biology, metabolism and evolution of this pathogen. Now, the development of new generation sequencing technologies reduces the cost and time required for genome sequencing, leading to the increasing availability of the whole genome sequences of microbial pathogens [7]. In recent years an increasing number of works brings us closer to the explanation of XDR TB formation. In contrast to MDR TB, molecular basis of which is well described [8,9], the origin of XDR phenotype is unclear. There were various attempts devoted to discovery and detailed analysis of canonical and novel molecular mechanisms responsible for drug resistance [8,[10][11][12][13][14][15], to identification of essential genes for MTB growth and survival [16], to clarification of morphological and physiological differences between drug susceptible, MDR and XDR M. tuberculosis strains [17,18]. A comprehensive analysis of the whole genome sequences (WGS) of a number of MTB isolates seems to be very useful for such investigations.
The first sequenced XDR strains were obtained from the KwaZulu-Natal (KZN) region of South Africa, isolated from a most notable outbreak of XDR-TB in 2005 [19][20][21]. Of these, the KZN-605 genome sequence is complete and annotated (according to the information from www.broadinstitute.org), while KZN-R506 strain has been sequenced up to 99% completion allowing an accurate detection of features (first at all, polymorphisms) relevant to the drug resistance [20,22]. To date, a huge WGS data on MTB has been uploaded into the NCBI database, including the genomic information of XDR MTB strains isolated in Samara region, Russia [23].
In this study, we performed a genome sequencing of two drugresistant clinical MTB isolates, which were isolated during a large tuberculosis outbreak in 1998-1999 in Tomsk (Siberia, Russian Federation), a region where Institutions of a Russian penitentiary system are located. In accordance with retrospective studies it was the first place in Russia where the XDR TB cases were described [3,24]. Both selected strains showed the resistant phenotypes (CTRI-3 MDR and CTRI-4 XDR , respectively). Basing on IS6110 RFLP DNA fingerprinting, they belonged to the KQ and KY families of MTB, respectively (according to the database of the Public Health Research Instutute, NJ, USA). These families are also known as Ural and Beijing respectively, and appear to be endemic to Russia [3,25,26].
Since no drug susceptible isolates of the above outbreak in Tomsk were available the drug susceptible isolate M. tuberculosis CTRI-2 SENS from Central region of the European Russia was included as an outgroup. This strain belonged to the AI (or LAM) family, which is also prevalent in Russia [27].
We hope that WGS of these M. tuberculosis strains could be one more step in filling the gap in the knowledge on a history and evolution of this pathogen, and finally will assist in understanding of the origin and possible ways of development of drug resistant MTB.

Strains, DNA Preparation and Drug Susceptibility Testing
Basic characteristics of the MTB isolates selected for current genome re-sequencing projects are presented in Table 1. The isolates under investigation were re-cultured from the laboratory bank in Dubos Broth supplemented with 5% BSA. Genomic DNA of MTB was extracted by the ''Proba-NK'' kit (''DNA-technology'' Ltd. TY 9398-01646482062-2008, Russia) in accordance with the manufacturer's instruction. The susceptibility testing of isolates was done using a BACTEC TM MGIT TM 960 Culture system (Becton Dickinson, USA) by standard protocol. All standard TB genotyping methods, including IS6110 RFLP, spoligotyping, and 24 loci MIRU-VNTR, were performed as previously described [28][29][30].

Genome Sequencing and Assembly
Genomes were sequenced on a Roche Genome Sequencer GS FLX using a standard protocol for a shotgun genome library. Assembly of raw sequencing reads with an average length of 215 bases was performed by the GS de novo assembly software version 1.1.03.24 (Roche 454 Life Science). In particular, the order of contigs was predicted by comparison with full-length genomes: H37Rv, H37Ra, F11, CDC1551. Mauve 2.3.1 was used for visualization of the data [31].
To determine the full-length genomic sequence of CTRI-2 SENS , the regions between contigs corresponding to repetitive regions or regions with no coverage were additionally sequenced on ABI PrismH 3730 Genetic Analyzer («Applied Biosystems», USA; «Hitachi», Japan).
To search for genomic rearrangements in strain CTRI-2 SENS relative to H37Rv, MUMmer 3.20 with its run-mummer3 algorithm was used for full alignment of sequences.
Annotation of coding sequences for isolate CTRI-2 SENS was completed by comparison to RefSeq annotation of M. tuberculosis strain H37Rv. Insertion sequences that were not present in the genome of H37Rv were annotated by comparison with other mycobacteria.

Multilocus Sequence/SNPs Analysis for Phylogeny
25 partially-or whole-genome sequenced MTB strains, which included both three strains studied and 22 genomes uploaded into the NCBI database (Table 2) were scanned for on the nucleotide polymorphisms in different loci of genome.
At first, all SNPs (refer to H37Rv) were extracted from genomes and used for the phylogenetic tree building. Next, we have analyzed 42 core genes on the nucleotide diversity index. Of 42, eight genes were taken away as non-variable, six were excluded because of poor quality of sequences, and in two cases the dN/dS ratio exceeded 1.0. Twenty six loci were used for the analysis of phylogenetic relationships. Furthermore, a set of DNA repair, replication and recombination (3R) genes was delineated in accordance with Dos Vultos et al. [33] and utilized for the designation of phylogenetic relationships.
BioEdit v.7.0.9.0, and MEGA v.4.0 were used for examination of sequences and phylogenetic evolutionary analysis. DNAsp v.5.10 was used for the estimation of d N /d S ratios and the nucleotide diversity of genes.

Strains
Clinical isolates of MTB obtained from patients with newly diagnosed pulmonary tuberculosis have been collected in Central Tuberculosis Research Institute (Moscow, Russia) in 1999. All isolates were tested for the susceptibility to first-line antituberculosis drugs and typed for IS6110 RFLP, and then stored in the laboratory bank.
In 2007, these isolates were subcultured and re-tested on the susceptibility to the first-line and second-line drugs including fluoroquinolone, prior to genome sequencing. Isolate CTRI-2 SENS was confirmed as drug-susceptible, while two isolates, CTRI-3 MDR and CTRI-4 XDR , were characterized as MDR and XDR, respectively. Later, the IS6110 RFLP, spoligotyping, and VNTR analysis were additionally performed (Table 1).

Genome Sequencing Data
Genomes of the three isolates were sequenced to 98% completion using 454 Whole Genome Shotgun methodology with greater than 10-fold of coverage. The GS De Novo Assembler software was used to assemble the reads, obtaining at a first step 517, 573 and 739 contigs with an N50 length of 18425, 13668 and 11896 bp for CTRI-2 SENS , CTRI-3 MDR and CTRI-4 XDR , respectively. However, mycobacterial genomes contain a lot of repetitive elements, as well as poorly readable regions of high G+C content, so in some cases we additionally used a Sanger sequencing instrumentation to get poor covered fragments.
The resulting (circular) genome of CTRI-2 SENS was obtained (GenBank accession number is CP002992). It has been composed by 4398525 nucleotides and characterized with a G+C content of 65.6%. At least, 955 isolated SNPs referring to the H37Rv genome were revealed: 862 of 955 SNPs occurred in coding regions; 324 and 538 of them were identified as synonymous (sSNPs) and nonsynonymous (nsSNPs) SNPs, respectively. Seven of these nsSNPs were nonsense mutations. 247 of 862 SNPs were common for all genomes included in our research, so we may postulate these polymorphisms as specific for H37Rv.
One set of rRNAs and 45 tRNA genes were identified in the CTRI-2 SENS genome, as well as 3946 protein-coding genes with the average gene length of 1004 bp. In total, protein-coding genes represent 90.3% of whole genome. More than 97% of coding sequences mapped to H37Rv genes.
Also, 204 indels were found in CTRI-2 SENS with respect to H37Rv genome. More details are available in Supplement file (Text S1).
The sequences of CTRI-3 MDR and CTRI-4 XDR isolates were obtained in a similar way. 814 SNPs in CTRI-3 MDR and 1188 SNPs in CTRI-4 XDR belonged to coding regions. Venn diagram showing the SNPs distribution in three genomes under study compared with the H37Rv demonstrates that all our strains appeared to be quite different ( Figure 1): only 395 polymorphisms are common, while each isolate possessed a considerable number of individual SNPs.

Genetic Markers of Drug Resistance
For studied MDR and XDR MTB isolates the pattern of mutations conferring the drug resistance was determined. According to our analysis, we found almost all genetic markers reflecting the certain susceptibility phenotype (Table 3). We also looked for compensatory mutations in rpoA and rpoC genes described recently [34], and did not find any of them in studied strains.
Unfortunately, mechanism of FQ resistance in CTRI-4 XDR isolate remains unclear but probably efflux pumps might be involved. We have checked out mutations in known genes coding the probable membrane transporters and efflux pumps [35] and found that CTRI-4 XDR (in contrast to CTRI-3 MDR ) carried some dissimilar amino acid substitutions in drugs-transport transmembrane ATP-binding protein ABC transporter Rv0194 (Pro1098-Leu), drug efflux membrane protein Rv1634 (Gly198Arg), antibiotic ABC transporter ATP-binding protein Rv2688c (Cy-s213Arg).
In former studies, the Rv2686c-Rv2687c-Rv2688c proteins were found to be responsible for active efflux of FQs outside the bacterial cell [36], and the expression of Rv1634 was found to confer a low level of FQs resistance [37]. Although there is no data how mutations in these particular proteins can effect on drug resistance, we suspect that they could have a cumulative effect on formation of FQ resistance in case of CTRI-4 XDR .

Phylogenomic Analysis
In accordance with IS6110 RFLP typing, CTRI-2 SENS M. tuberculosis isolate contained 13 copies of IS6110 and was referred to AI114 family. The drug-resistant isolates, CTRI-3 MDR and CTRI-4 XDR , contained 12 and 10 copies of IS6110, respectively, and were attributed to the KQ and KY families of MTB, which were found in the last years of XX century and are considered to be endemic for Russia [3,25,26].
The obtained WGS were used to determine the SNP Cluster Groups (SCGs) [41] for analyzed isolates, which were found SCG5, SCG3a and SCG2 for CTRI-2 SENS , CTRI-3 MDR and CTRI-4 XDR , respectively. Also, we defined the principal genetic groups (PGG) based on the determination of nucleotide polymorphisms in two codons of katG (katG463) and gyrA (gyrA95) genes [42], which were found PGG2 for both CTRI-2 SENS and CTRI-3 MDR , in contrast to PGG1 for CTRI-4 XDR . The 25 of MTB whole-or partially-genome sequences were drawn for the comparative phylogenetic analysis. In addition to three isolates under study (CTRI-2 SENS , CTRI-3 MDR and CTRI-4 XDR ), this group included twenty two genomes presented in the NCBI database (Table 2).

COGs Analysis
To study new molecular mechanisms probably associated with virulence and drug resistance formation of TB the list of unique individual SNPs was formed for each sequenced isolate. Whereas the studied isolates -drug-susceptible, MDR and XDR, appeared to be rather genetically heterogenic, we have considered only SNPs present in certain isolate and absent in other genetically related MTB strains. Along this line only full sequenced and well characterized strains were considered (Table 2). Thus for CTRI-2 SENS such genetically related strains were assigned as F11, KZN 1435, and KZN 605. For CTRI-3 MDR , this group consisted of CDC1551 and Haarlem, and during the analysis of SNPs present in CTRI-4 XDR the ones from CCDC5079 and CCDC5180 were excluded.
The distribution of individual SAPs in certain COGs was analyzed comparatively for three isolates. The number of SAPs was normalized with respect to the number of genes of a certain class present in MTB and then to the total number of individual SAPs identified in certain isolates ( Figure 3). The calculated average number of SAPs was found 5.00, and a standard deviation was 3.81.
All isolates possessed very few SAPs mapped to O (posttranslational modification, protein turnover, chaperones) COG, suggesting that the genes in this category may be under evolutionary pressure. Additionally, the CTRI-4 XDR isolate had significantly fewer SAPs mapped to F (nucleotide transport and metabolism) COG than the other ones, and revealed no SAPs in proteins belonging to M (Cell wall/membrane/envelope biogenesis) group.
In all cases, the significant numbers of SAPs were found in proteins from N (cell motility) COG. It is not surprising because they belong to the PE/PPE protein family which reflects the natural variability of surface structures. One also can see the expose of the relative density of mutations in proteins of U  (Intracellular trafficking, secretion, and vesicular transport) COG for CTRI-2 SENS and CTRI-4 XDR , and of V (defense mechanisms) COG for CTRI-4 XDR only. The last observation may be directly related to the formation of the XDR phenotype. In case of XDR tuberculosis, it was rather interesting to analyze other mutations, different from related to drug resistance ones, because they can play an important role in survival, adaptation and spreading of such strains in microbial population. Thus, the unique SAPs in CTRI-4 XDR were analyzed more precisely. Along this line, we reduced the full list of mutations by excluding PE/ PPE protein family, hypothetical proteins, and also proteins with homogenous amino acid substitutions (Table S4). All SNPs presented in the table S4 were verified by Sanger sequencing.
As it might have been expected the known mutations in rpoB, katG, pnzA, embB and rpsL genes, conferring resistance to RIF, INH, PZA, EMB, and STR respectively, fell into this list. We also found a mutation in thyA gene, which leads to Asp117Gly amino acid substitution. Recently, the mutations in thymidylate synthase A, encoded by the thyA gene and required for a thymine biosynthesis in the folate pathway, were shown to be associated with para-aminosalicylic acid (PAS) resistance in MTB [46]. However, this particular Asp117Gly alteration was not described yet neither in clinical M. tuberculosis strains resistant to PAS nor in PAS-resistant spontaneous mutants [47]. It should be mentioned that susceptibility testing of the CTRI-4 XDR strain to this drug was not done either.
In contrast to CTRI-2 SENS and CTRI-3 MDR , it is noteworthy that CTRI-4 XDR isolate exposed much more mutations in proteins belonging to the categories H (coenzyme transport and metabolism), I (lipid transport and metabolism), J (translation, ribosomal structure and biogenesis), and V (defense mechanisms) (Figure 1).
It is rather difficult to interpret mutations in J COG, mostly happened in ribosomal proteins, but the observed aberrations in proteins belonged to I and V classes along with mutant enzymes from C (energy production and conversion) COG may play an important role in adaptation of the microorganism to its surrounding. This way, we can speculate that some of the listed mutations might be related to survival of mycobacteria under stress, including the drug treatment. So we observed a lot of mutations in proteins belonged to oxidation-reduction enzymes -NADH dehydrogenase, enoyl-CoA hydratase, acyl-CoA dehydrogenases, electron transfer protein FdxB. Earlier, the disorder in functioning of a redox system was shown for some human cell lines revealed the resistance to anticancer drugs [48]. Aware of the illegitimacy of the parallels between pro-and eukaryotes we can suggest that some features of the internal MTB metabolism promote a better adaptation of these cells and create more comfortable conditions for the formation of resistance to antituberculosis agents. Perhaps the further contribution to this process makes changes in DNA polymerase III, one of the SOS response enzymes. As far as mutations in ribosomal proteins are concerned, we can suspect their involvement in translation fidelity. Besides of substitutions in rpsL and rpsA genes associated with drug resistance that has been already noted above, we indicated Lys69Arg replacement in 30S ribosomal protein S17 encoding by rpsQ gene. It's one of the primary rRNA binding proteins; it binds specifically to the 59-end of 16S ribosomal RNA and also plays a role in translation fidelity. The altered form of this protein may affect the accuracy of translation, and thereby leads to errors in the process of protein synthesis.
Other features of CTRI-4 XDR genomic DNA sequences were mutations in ParA and RodA proteins, controlling certain stages of cell division. Since the expression of parA gene is essential for the growth of mycobacteria [49], discovery of a mutant protein in CTRI-4 XDR isolate allows us to explain the slightly slower accumulation of cell biomass in the liquid medium culture observed earlier. Perhaps recorded mutations lead to uncompleted cell division, previously described as a feature of XDR TB strains [17].
When we used the same algorithm to identify the unique mutations characteristic for XDR TB from KwaZulu-Natal region of South Africa (see Table S5), it became obvious that they have a little in common with CTRI-4 XDR with the exception of mutations in pks genes cording the polyketide synthases (we did not consider mutations in rpo gene). Although the different genes were found to be altered, pks1 and pks7 for CTRI-4 XDR and pks12 for KZNs, their products both belonged to similar multifunctional enzymes each of which contains a b-ketoacyl synthase, and an acyltransferase activities (www.kegg.org). Generally, they catalyze polymerization of simple fatty acids into branched-chain ones. In spite of the MTB genome has revealed a remarkable array of polyketide synthases, no polyketide product has been isolated thus far. Most of the polyketide synthases genes have been implicated in the biosynthesis of complex lipids, and also in a lipopolysaccharide biosynthesis. In light of this one can assume that the wrong activity of these enzymes leads to disorders in cell wall structure and in its permeability for anti-tuberculosis agents.

Occurrence of Selected for CTRI-4 XDR Isolate Mutations among the other Beijing MTB Strains
To solve the question if there are other MTB strains carried any mutations from the list formed for CTRI-4 XDR (Table S4) we marked out a group of eight Beijing TB strains (Table 2) which draft genomic sequences were publicly available from NCBI web site. Additionally, we tried to find these SNPs in WGS data for 26 MDR and 13 XDR M. tuberculosis strains from the work of Casali N. et al [23].
Throughout analysis revealed that the mutations in Rv0068 and Rv1266c genes should be assigned as polymorphisms because of their presence in the most of strains.
As well, we identified a large group of mutations (24 from 76; 31.5%) shared by CTRI-4 XDR , 02_1987, and R1207 TB strains. All belonged to the Beijing sublineage [39,40]. Taking into account that we were failed to find any information on 02_1987 phenotype, presumably it is a drug susceptible strain, while the R1207 is known to be MDR, our attention was mostly caught by five mutations in Rv0667 (rpoB ), Rv1589 (bioB), Rv1908c (katG), Rv1934c (fadE17) and Rv3158 (nuoN) genes shared by CTRI-4 XDR and R1207 only. Among them, the canonical mutations in rpoB and katG genes conferring the RIF and INH resistance are not so interesting, while the other ones in fadE17, and nuoN genes can support the drug resistance formation via the changes in functioning of a redox system. Besides CTRI-4 XDR as well as R1207 carried a mutant variant of an enoyl-CoA hydratase (echA10), another enzyme belonged to a redox system. However in this case, the amino acid substitutions were different.
To support the idea that some of these thought-out mutations of CTRI-4 XDR actually involved in formation of XDR phenotype; we have sequenced the additional XDR M. tuberculosis isolates (n = 14) collected in the different regions of Russian Federation in the period 2006-2010 years. Thirteen of them belonged to the modern Beijing group in accordance with spoligotyping and mutT2 gene analysis [50].
All strains were sequenced for mutations in the following genes of interest: nuoN, icd2, dnaE, rodA, parA, enhA1, enhA10, bioB, fadB3, fadE17, fdxB, rpsL and gyrA ( Table 4). The latter two were included to check of the potential XDR genotype. Nearly all strains possessed any mutations in Ala90 or/and Asp94 of GyrA, and Lys43Arg substitution in ribosomal protein S12 encoding by rpsL gene, which is different from CTRI-4 XDR strain. The other mutations we were looking for were absent in all additional strains, suggesting that CTRI-4 XDR evolved by its own individual way.

Discussion
In spite of the hard efforts of many researchers in the world to gain insight into the origin and nature of M. tuberculosis drugresistant strains, it still remains obscure. In early XXI century, an attention of clinicians and scientists has been re-focused from the multidrug-resistant tuberculosis to the extensively drug-resistant one. Although the definition of XDR-TB was agreed by the WHO in October 2006 [51], from 1993 through 2006 forty-nine cases of XDR TB have just been reported in the United States [52]. Such strains, which show the resistance to the most of routinely used anti-tuberculosis drugs, are seriously hindering a successful TB control. Understanding the molecular basis of XDR/MDR phenotypes could help in the developing of new approaches to the diagnosis, treatment and control of this disease.
Today, against the background of the implementation of numerous genome projects, particularly in the genomics of microorganisms, the main expectations in understanding of the molecular mechanisms of virulence and resistance of pathogens, as well as in discovery new targets for chemotherapy confer on the interpretation of the WGS data.
Under this study, we hoped to come closer to understanding of the XDR phenotype formation mechanisms by means of comparative analysis of WGS data obtained for two drug-resistant isolates CTRI-3 MDR and CTRI-4 XDR , attributed to the unique KY and KQ families of MTB by IS6110 RFLP DNA fingerprinting, presumably endemic for Russia. Additionally drug-susceptible isolate CTRI-2 SENS was introduced as an outgroup. Both resistant strains were selected from the collection of M. tuberculosis isolates gathered in Tomsk, Siberia, Russia, during the well-known outbreak in 1998-2002 years [3].
Earlier, the study of similar design was done for MTB strains isolated in KwaZulu-Natal province, South Africa [20]. Although there is evidence that in South Africa XDR TB strains represented at least seven different genotype families [53], all randomly chosen XDR isolates from KwaZulu-Natal which WGS data now is publicly available belong to the same LAM family suggested that one XDR strain had spread throughout the province. The LAM family is known to be rare in European and Asian countries [54]; therefore the attention to MTB strains belonging to other genetic groups, especially drug resistant ones will dramatically extend our knowledge of the TB features.
In the frame of this work, all three MTB strains possessed drug susceptible, MDR and XDR phenotype and picked up for WGS were found to belong to three different lineage. According to the simple classification by katG463-gyrA95 SNP analysis [42], two of three strains under study (CTRI-2 SENS and CTRI-3 MDR ) fall into the principal genetic group 2 (PGG2), while CTRI-4 XDR carrying the Arg-Leu polymorphism in the katG gene -into PGG 1. SNPs typing based on the combination of 212 nucleotide markers [41] put our CTRI-2 SENS , CTRI-3 MDR , and CTRI-4 XDR isolates in the SCG-5, SCG-3a, and SCG-2 SNP cluster groups, respectively. Among these, SCG-2 is strongly associated with Beijing family and had the high prevalence of resistance to the most of the antituberculosis drugs [55]. Both of these classifications and phylogenetic analysis based on the comparison of overall SNPs dataset of genomes, 29 housekeeping genes or 75 genes of a mycobacterial 3R system, gives the robust grouping of each of our isolates under study in the particular clusters. We provisionally called this groups ''CTRI-2 SENS -'', ''CTRI-3 MDR" -'' and ''CTRI-4 XDR -'' clusters. In accordance with the excellent classification system for the humanadopted MTBC given by Gagneux with the co-workers [56][57][58][59] based on the analysis of genomic deletions, also known as large sequence polymorphisms (LSPs) [58], and, later, on the extended MLST scheme, including 89 genes of mycobacterial genome [57], all members of ''CTRI-4 XDR cluster'' -02_1987, 94_M4241A, 210, HN878, W-148, X122, T85, CCDC5079 and CCDC5180 strains -belongs to the phylogenetic lineage 2, which is well-known East Asian/Beijing lineage. Both susceptible CTRI-2 SENS and multidrug-resistant CTRI-3 MDR strains fall into the Euro-American lineage 4. These two lineages belong to the modern MTBC lineages, and they were believed to spread in the world during relatively recent human history. According to these data, the direct comparison of their sequences was hardly to yield the answer on drug resistance mechanisms.
However, we were lucky enough as the WGS data for drug susceptible and drug resistant TB strains, the members of the same genetic group as CTRI-4 XDR sequenced under current investigation, have been recently published [60]. Thus, we could analyze the pattern of mutations that distinguishes our sequenced XDR from other genetically related strains with drug susceptible and MDR phenotypes. The implementation of this approach gave us an opportunity to find unique polymorphisms that could explain the nature of XDR phenotype formation.
It should be noted that the simple analysis of genomic data obtained for CTRI-3 MDR , and CTRI-4 XDR isolates in the light of modern knowledge on the molecular mechanisms of drug resistance formation allowed to elucidate the characteristic phenotypes of these isolates at the genetic basis. Whereas we were looking for additional XDR specific mutations, for further investigation, we applied the approach, which removed from the consideration all single-nucleotide substitutions shared by closely related strains of the same genetic family. It allowed to create a list of 76 non-synonymous polymorphisms that were unique to CTRI-4 XDR . Expectedly all known mutations conferring the resistance to any first-and second-line drugs fall into this list. During the subsequent comparative analysis of the CTRI-4 XDR mutations, we focused on potential mechanisms preceding the acquisition of drug resistance in MTB strains which research is of a major interest, especially in the case of XDR.
The data obtained recently showed potential contribution of bacterial cell redox systems in antibacterial action of bactericidal antibiotics [61]. The main damaging agents in this situation are radicals of fatty acids and chemically reactive molecules containing oxygen, so-called reactive oxygen species (ROS) -superoxide (O 2 N 2 ), hydrogen peroxide (H 2 O 2 ) and hydroxyl radicals (OHN). Molecular oxygen from media diffuses into the cell and interacts with biomolecules. Particularly, ROS formation is those interactions between O 2 and proteins like respiratory flavoproteins, which have accessible catalytic redox cofactors within their active sites and participate in electron transfer reactions with O 2 . Additionally, the general mechanism of oxidative stress formation involves tricarboxylic acid (TCA) cycle metabolism and a transient depletion of NADH.
So far, in literature available there is no data about influence of amino acid substitutions in redox system enzymes on action of anti-tuberculosis drugs on MTB cells. However, in case of Pseudomonas aeruginosa, it is shown that mutations in TCA cycle metabolism and respiratory electron transport chain components decreased killing of this bacterium by aminoglycoside antibiotics [62].
It is worth to note, that genome sequence of CTRI-4 XDR isolate contains distinct nonsynonymous substitutions in genes coding enzymes of ferredoxin and flavin containing reductases as well as TCA cycle enzymes. Thus, we described mutations in flavoprotein fumarate reductase (frdA gene), isocitrate dehydrogenase (icd2 gene), NADH dehydrogenase (nuoN gene), and enzyme, responsible for reduced/oxidation state of iron (trxB2 gene).
Probably changes in activity of these enzymes influence the metabolism of mycobacteria and support the formation of resistance. Along with specific changes in genes related to SOS response [63] either seems to be characteristic for whole Beijing group (mutations in polA and recD genes) [33,64], or unique for CTRI-4 XDR (mutation in dnaE gene) it also may possibly serve as a condition for appearance of mutable phenotype, characterized by significant resistance to anti-tuberculosis drugs.
It should be noted that particular mutations in enzymes of a redox system are known to provide resistance to isoniazid and ethambutol due to substitutions in katG and embB genes, respectively, and the mechanism of their influence is well studied. Mutations found in other enzymes of a redox system, although we do not have direct evidence of alterations in their activities, prove that careful study of regularities in formation of resistance to antibacterial agents in mycobacteria is necessary and actual. Close attention should also be paid to a role of SOS response in so-called induced mutagenesis. It is quite clear, that SOS response and ROS, which induce it, influence the mutational activity of the cell. Non-synonymous replacements found in this study in genes of 3R system and a certain amount of replacements in genes of a redox system on the one-hand may determine decrease in individual sensitivity of mycobacterial cells in case of antibacterial agent's impact, and on the other hand, increased mutability in MTB and further generation of XDR phenotype.
Under this investigation, we have additionally tested two groups of MDR/XDR strains. The pattern of mutations which were analyzed touched on 13 genes of interest especially cording the enzymes of redox system, TCA cycle and SOS response. All of them were picked up in accordance with CTRI-4 XDR genome sequence. Unfortunately we failed to find any XDR-specific mutations within this group. Although the additional XDRs belong to the same Beijing family as is CTRI-4 XDR they share no other mutations than in genes of 3R system characteristic for whole family [33,64]. It seems that in case of CTRI-4 XDR , we deal with the individual features of this strain.
At this stage of scientific knowledge, we cannot draw a conclusion about the existence of some bias in the formation of XDR phenotype. While the genetic mutations associated with first-line and second-line drug resistant TB have been well described, the efforts to outline molecular character specific for MDR and XDR TB are ongoing. Thus WGS of MDR and XDR TB strains from KwaZulu-Natal revealed novel mutations not previously associated with drug resistance, but a further study determined that the majority of them came from a common ancestor, suggesting that XDR strains can evolve independently without fitness changes or XDR-specific mutations [65].
We hope that the further analysis of newly re-sequenced MTB genomes collected worldwide will give us the inestimable benefits allowing to understand an origin and evolution of the bacterial pathogens, including the evolution of drug resistance. The larger number of mutations observed in CTRI-4 XDR strain suggests that it can be a potential mechanism for the evolution of drug resistance in mycobacteria. Future work on systems-level analysis of these strains may throw a more definitive light on this important area.

Data Access
The genomic sequences of CTRI-2 SENS and CTRI-4 XDR are located at NCBI database under GenBank accession no. CP002992 and AIIE01000000, respectively. Raw sequence data for CTRI-3 MDR have been deposited in the NCBI Sequence Read Archive with Accession Number: SRA051492.     Text S1 Insertion/deletion polymorphism of CTRI-2 SENS genome. (DOC)


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Genetic Variations of GAK in Two Chinese Parkinson’s Disease Populations: A Case-Control Study

Cyclin G-associated kinase (GAK) modifies α–synuclein expression levels and affects the susceptibility of Parkinson’s disease (PD). The single-nucleotide polymorphism (SNP) rs1564282 of GAK gene has a significant association to the risk of PD among Caucasian populations. To date there is only one data with regards to ethnic Chinese from Mainland China. Here, we conducted a case-control study in two independent cohorts of Han Chinese populations from Taiwan and Singapore to validate this association. A total of 1,755 subjects (871 PD patients and 884 controls) were recruited. The results showed that neither the CT, TT genotypes nor the minor allele T of SNP rs1564282 were associated with PD among the subjects from Taiwan and Singapore as well as in the pooled analysis. Differences in our study population with regards to published literature may be due to epigenetic factors and gene-gene or gene-environmental interactions. Further studies in other Chinese populations will be of interest to validate these findings.

Introduction
Parkinson's disease (PD) is one of the most common neurodegenerative diseases, with a prevalence of ,1% over 60 years of age [1]. It is characterized by the cardinal features of bradykinesia, muscular rigidity, 4-6 Hz resting tremor, postural instability and non-motor symptoms such as cognitive impairment, depression, anosmia, dysautonomia, sleep disorders [2], all of which can significantly decrease patient's quality of life. Definite diagnosis of PD relies on pathology finding of a-synuclein immunopositive Lewy neurites and Lewy bodies [3]. However, PD is diagnosed typically based on clinical criteria, which is based on the presence of a combination of cardinal motor features, associated and exclusionary symptoms, and response to levodopa [2]. Damage to substantia nigra pars compacta (SNc), with severe obliteration of their neuromelanin-laden projection neurons, is frequently considered to be the most important hallmark of PD [3]. Moreover, mitochondrial dysfunction, oxidative stress, and protein mishandling play major roles in PD pathogenesis [1]. In search of disease etiology, gene mutations cause only a small proportion of all cases and that in most cases, non-genetic factors play a part, probably in interaction with susceptibility genes [1]. About 90% of cases are apparently sporadic, monogenetic mutations are now estimated to cause about 10% of PD cases [1]. Several pathogenic genes and susceptibility loci of Parkinson's disease (PD) have been identified in both familial and sporadic cases in the last decade [4]. Genomewide association studies (GWAS) have identified several susceptible genes contributing to PD. Thirteen loci showed genome-wide significant (p,5610 28 ) association with disease risk: BST1, CCDC62/HIP1R, DGKQ/GAK, GBA, ITGA8, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, SYT11/ RAB25, and RIT2 have been found [5][6][7].
Cyclin G-associated kinase (GAK) is a 160 kDa serine/ threonine kinase, located p16.3 on chromosome 4. It is a regulator of clathrin-mediated membrane traffic, also controls centrosome integrity and chromosome congression [8]. Its' vital role in cell proliferation and receptor trafficking was revealed in osteosarcoma [9]. It has been shown to up regulate genes in SNc of Parkinson's disease [10]. The clathrin-binding C-terminal domain of GAK binds to pre-cathepsin D (CTSD), which is implicated as the main lysosomal enzyme involved in a-synuclein degradation [11][12][13]. CTSD mutations can induce the pathological accumulation of asynuclein. Taken together, GAK modifies a-synuclein expression levels and toxicity in PD, that reduced GAK function enhances asynuclein-mediated toxicity [14]. Recently, a significant association between the single-nucleotide polymorphism (SNP) rs1564282 of GAK gene and risk of PD was described in several Caucasian studies [15][16][17][18][19], and only one in Han Chinese from Mainland China [20]. To understand the genetic effects across different populations, we aimed to investigate the genetic susceptibilities of SNP rs1564282 in GAK among Han Chinese in the Taiwan and Singapore populations.

Patient population and Ethics statement
Patients diagnosed with PD were recruited from the neurology clinics of the Chang Gung Memorial Hospital (CGMH) in Taiwan and Singapore General Hospital in Singapore. The diagnosis of PD was based on the UK PD Society Brain Bank clinical diagnostic criteria [21]. Healthy adult volunteers, as the control group, were matched for age, gender, ethnic origin, and area of residences. This study was performed according to a protocol approved by the institutional review boards of Chang Gung Memorial Hospital (ethical license No:101-2097C), and all examinations were performed after obtaining written informed consents. This study also approved by Singhealth Institution Review Board in Singapore. Written informed consent was obtained from every subject too.

Genetic analysis
In Taiwan's cohort, genotyping of the GAK SNP (rs1564282) was performed using TaqManH Assays commercially available (Applied Biosystems Inc., California, USA). TaqManH PCR was performed according to the manufacturer's standard PCR protocol. Briefly, 20 ng genomic DNA was mixed with the supplied 2X TaqMan Universal PCR Master Mix No AmpErase UNG and 40X TaqMan Assay Mix to a final volume of 5ml in a 384-well plate. Each sample underwent 50 amplification cycles on GeneAmpH PCR System 9700 (Applied Biosystems Inc.). Fluorescent signals of the two probes were analyzed end-point fluorescent data on ABI PRISMH 7900HT Sequence Detection System (Applied Biosystems Inc.). Genotype was determined automatically by Sequence Detection Software (Applied Biosystems Inc.).
In Singapore's cohort, this SNP was screened on a MALDI-TOF mass spectrometry using the Sequenom MassARRAYTM system (San Diego, CA). Briefly, multiplex genotyping assays were designed using the Sequenom DESIGNER software (San Diego, CA). Initial PCR (5 ng of genomic DNA) and primer extension reactions were carried out according to the Sequenom genotyping assay iPLEX TM protocol. After purification, 15 nl of primer extension product was analyzed with a MassARRAY Sequenom-Bruker Spectrometer (Bruker Biosciences, San Diego, CA). Sequence analysis was used to confirm the genotypes for representative samples. Dideoxy chain terminator sequencing was carried out according to the manufacturers' instructions (BigDye, Applied Biosystems, Inc.) and the products were electrophoresed on an ABI 3100 automated DNA sequencer (Applied Biosystems, Inc.). Although the genotyping methods were different between Taiwan and Singapore cohorts, the consistency of the methods was verified by sequencing.

Statistical analysis
Chi square test was used to compare the frequency of the allele and genotypes in both cases and controls. The genotypes of the cases and controls followed the Hardy Weinberg equilibrium. Odds ratios and 95% confidence intervals were estimated [22]. Power calculation was carried out using QUANTO software [23], ver. 1.2.4. In the present case-control study, at the 5% significance level, we had a power greater than 0.8 to identify an association when the per-allele genetic effect was greater than an odds ratio of 1.3 and the disease allele frequency was greater than 0.1.

Results
A total of 1,755 subjects (871 PD patients and 884 controls) from Taiwan and Singapore were included. From Taiwan, the study enrolled 483 patients (46.2% females) diagnosed with PD by two neurologists that specialized in movement disorders (YR Wu and CM Chen) in Taiwan. Only one proband with familial PD cases in the same family was recruited. The mean age at onset (AAO) of PD symptoms was 63.0611.2 years (range 19,93) and the mean age of recruitment of 495 controls (53.1% female) was 60.2613.2 years (range 17,91). The frequencies of rs1564282 genotypes and alleles were similar in both PD patients and controls (Table 1).
In the Singapore cohort, there were 388 PD and 389 controls. The mean AAO of the 388 PD patients (44% female) was 63.0610.0 years (range 21-90) and age at sampling of 389 controls (47.0% female) was 60.0610.5 years (range 20-90). Data for the GAK SNP regarding the eighty cases and fifty controls had been previously used in another study [24].
There were also no significant differences between genotypes and alleles in the Singapore's cohort, even though there was a slight increase in the odds ratio of 1.12. A pooled analysis of our two Asian cohorts also did not reveal any significant differences between PD and controls (Table 1). Stratification by age at onset (,50 and § 50 years) and sex also did not show differences in the minor T allele frequency in each case control cohort (data not shown). We had a power greater than 0.8 to identify an association at 5% significance level for our case-control study.

Discussion
GAK was first recognized as the susceptible gene associated with PD in a cohort of familial PD study [15]. Subsequent studies showed the association of GAK in PD risk appears to be present in sporadic PD as well [16][17][18][19]. However, all the studies were done utilizing primarily Caucasian populations such as Americans, the British, the French, and the Dutch [15][16][17][18][19]. Across different populations, our study showed the SNP rs1564282 was not associated with PD among Han Chinese in Taiwan and Singapore. There was no significant difference neither in CT, TT genotypes nor minor allele T. Pooled analysis of our two Asian cohorts did not reveal any significant differences between PD and controls.
In contrast, one study conducted in Mainland China showed that the rs1564282 variant of GAK increases the risk of PD in Han Chinese. The minor allele displayed an OR = 1.36 (95% CI = 1.09, 1.69, P = 0.007), and CT + TT genotypes had an OR of 1.34 (95% CI = 1.05, 1.72, P = 0.017) in their cohort [20]; our combined data showed the minor allele had an OR = 1.035 (95% CI = 0.84, 1.28, P = 0.75), and CT + TT genotypes displayed an OR of 1.005 (95% CI = 0.80, 1.27, P = 0.99). The genotyping characteristic was different in these two study populations. The minor allele has higher frequency in cases (12.9% vs 11.1%) and lower in controls (9.8% vs 10.8%) than our merged data. The subjects enrolled in this study were much younger than our study, in which the mean age of PD patients and controls were 53.60611.38 and 53.77614.37, respectively. After pooling all three sets of data, only the allele frequency showed a marginal significance, OR = 1.18 (95% CI = 1.01, 1.37, P = 0.04), and CT + TT genotypes did not reach a level of significant difference, OR = 1.15 (95% CI = 0.9-1.36, P = 0.09) ( Table 1). Further studies in other Han Chinese populations would be of interest.
The roles of epigenetic factors, gene-gene or gene-environmental interactions have not been evaluated and they could be confounding variables. One potential issue is that our power calculation was based on the assumption that the typed SNP is the causal SNP. At this moment, we do not fully understand the linkage disequilibrium structure of the unknown casual SNP and hence further replication studies may be required to determine if the actual casual SNP is identified. Moreover, only one SNP rs1564282 was analyzed which does not rule out the association of other regions within or around the gene, such as rs11248051, rs6599388, chr4:911311. In addition, such differences may be due to population-specific heterogeneity in PD [24].
In conclusion, we showed that GAK SNP rs1564282 does not contribute to the risk of PD among Han Chinese in Taiwan and Singapore. However, further studies of this and other SNPs within the GAK gene in other ethnic Chinese populations may be useful to validate our findings.