Results Integration of multiplatform data revealed somatic SDHC methylation and loss of the 1q23.3 region containing the SDHC gene. This correlated with decreased SDHC messenger RNA (mRNA) and protein levels. Furthermore, another genetic event found affected the VHL gene, which showed a decreased DNA copy number, associated with low VHL mRNA levels, and an absence of VHL protein detected by immunohistochemistry. In addition, the tumor displayed a pseudohypoxic phenotype consisting in overexpression of the hypoxia-inducible factor (HIF)-1 α and miR-210, as well as downregulation of the iron-sulfur cluster assembly enzyme (ISCU) involved in SDHB maturation. This profile resembles that of SDHx- or VHL-mutated PGLs but not of PGLs with decreased VHL copy number, pointing to SDHC rather than VHL as the pathogenic driver. Paragangliomas (PGLs) and pheochromocytomas (PCCs) are rare neuroendocrine tumors of the parasympathetic and sympathetic nervous system (). Parasympathetic PGLs typically develop in the head and neck region, whereas sympathetic PGLs are frequently located at the abdomen or thorax.

PCCs are a special type of PGLs derived from the chromaffin cells at the adrenal medulla. Parasympathetic PGLs are typically treated surgically, the first-line therapeutic choice, or by radiotherapy. Unfortunately, the complexity of the anatomy of the skull base and the proximity of tumors to main arteries and nerves lead, inevitably, to a high rate of severe iatrogenic morbidity even through the use of safe embolization protocols and sophisticated surgical approaches. The limited knowledge of the molecular basis of these tumors has precluded the development of effective drug-based therapies. Although the molecular mechanisms involved in tumor development are not completely understood, the fact that ∼40% of tumors are hereditary has shed some light on the pathogenesis of this disease.

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Mutations in SDHB, SDHC, SDHA, and SDHD genes increase the risk of developing hereditary PCC/PGLs (). These genes (collectively SDHx genes) encode the four core subunits of the mitochondrial succinate dehydrogenase (SDH) complex, which links the Krebs cycle and the oxidative phosphorylation pathway. In addition, mutations in the SDHAF2 gene, encoding an auxiliary subunit required for SDH function, have also been associated with hereditary PGLs/PCCs ().

Other PGL/PCC susceptibility genes include RET, VHL, NF1, TMEM127, MAX, KIF1B, and EGLN1 (, ). Mutations in any of these genes could lead to the development of sympathetic PGLs/PCCs, whereas parasympathetic PGLs are more specifically linked to mutations in the SDHx genes (). Aside from the hereditary component, less is known about the genetic drivers of the “sporadic” PGLs/PCCs developing in the absence of known hereditary pathogenic mutations. Nevertheless, multiplatform molecular characterization studies have revealed that sporadic PCCs/PGLs may also be driven by somatic alterations affecting the same PGL/PCC-susceptibility genes (, ). Mutations in the SDHx genes abolish SDH’s ability to convert succinate to fumarate, leading to succinate accumulation in the cell.

It has been hypothesized that succinate accumulation leads to activation of the hypoxia-inducible factor (HIF) (), known to be involved in abnormal cell growth and tumor formation (). Regarding activation of HIF, recent reports showed either moderated or strong activation of HIF-related pathways in PGLs/PCCs associated with SDHx or VHL mutations, respectively (). Among the different targets of the HIF transcription factor, the microRNA, miR-210, is thought to play a major role in PGLs/PCCs because of its induced downregulation of the iron-sulfur (Fe-S) cluster assembly enzyme (ISCU), which is required for maturation of mitochondrial proteins containing Fe-S clusters such as SDHB (). Recent reports have shown that the SDHx-mutated PGLs/PCCs can be easily identified in surgical tumor specimens by immunohistochemistry.

Specifically, SDHx-mutated tumors show absence of SDHB protein detected by immunohistochemistry (), and SDHA-mutated tumors also have loss of SDHA immunostaining (). Although the mechanism involved in loss of SDHB is not fully understood, it is plausible that SDHx mutations lead to decreased preassembly of the SDHC-SDHD or SDHA-SDHB complexes at the inner mitochondrial membrane and that this interferes with maturation of the heterotetrameric protein complex, leading to destabilization of the SDHB protein. On the other hand, upon mitochondrial import, SDHA and SDHB proteins mature by flavination of SDHA by SDHAF2 and the insertion of three Fe-S clusters generated by ISCU. Thus, modification of the protein levels or molecular structure of SDHAF2 or ISCU may also alter SDH complex stability (). In this same line, some SDHB mutations cause impaired Fe-S cluster incorporation into SDHB, thus rendering the protein unstable ().

Recent reports highlighted the existence of a small subset of PGLs/PCCs that lack SDHB protein but do not harbor SDHx mutations, suggesting that mechanisms independent of SDHx mutations may be involved in SDHB protein silencing (,, ). In this report, we have explored the molecular mechanism involved in SDHB silencing of a parasympathetic PGL that lacked mutations in SDHx genes. In addition, we have compared the data in that tumor to those of healthy paraganglia and other PGLs with or without SDHx mutations. The putative involvement of the HIF-1 α/miR-210/ISCU pathway was also analyzed in the tumor under analysis. We describe the first published case of a parasympathetic PGL harboring SDHC methylation likely involved in tumorigenesis. Materials and Methods Experimental design Our previous work showed an absence of SDHB immunostaining in some PGLs lacking SDHx and VHL mutations (). This was striking and suggestive of pathogenic mechanisms alternative to SDHx mutations impinging on SDH activity.

This study was aimed at deciphering molecular mechanisms involved in silencing of SDHB protein in one of these tumors, hereafter designated as PGLmx. Exome sequencing, array–comparative genomic hybridization (array-CGH) targeting tumor suppressor genes, genome-wide methylation analysis, quantitative reverse transcription polymerase chain reaction (PCR), and immunohistochemistry studies were performed in PGLmx. Data were compared with those of healthy paraganglia (carotid body) and other PGLs lacking or not the SDHx mutation (clinical and genetic features are described in Supplemental Table S2). The selected case was a jugular PGL developed in a man at age 29 years. This patient lacked a family history of PCCs/PGLs. The tumor had been surgically treated and incompletely removed. Five years later, magnetic resonance imaging showed a rounded lump that included the mastoids and partially covered the internal carotid artery compatible with tumor recurrence.

At the retroperitoneal space, two lesions with diameters ∼10 and 11 mm each were identified with moderated contrast uptake (). These lesions were compatible with PGLs. Scintigraphy and Tc99m-Tektrotyd photon emission computed tomography studies revealed pathological uptake in the mastoid region but not in the retroperitoneal space.

Diagnosis was recurrence of jugular PGL and two putative nonfunctional retroperitoneal PGLs. The patient was asymptomatic and the therapeutic strategy was wait-and-see. Two years later, in October 2017, he remained asymptomatic.

Magnetic resonance imaging studies in PGLmx. Vamsam Serial Roja Facebook. Magnetic resonance images of the (A) coronal and (B) axial planes of the abdomen and skull base, respectively, of PGLmx showing one lesion in the mastoid region and two in the retroperitoneal space. Diameters of those lesions are shown in centimeters. Tumor specimens Tumor and blood samples were obtained from patients with PGLs or PCCs, diagnosed and treated between 2005 and 2016 in the Hospital Universitario Central de Asturias.

For DNA-based studies, fragments were obtained from the core of the tumor and contained >60% tumor cells. Tumor specimens were snap-frozen at the time of surgical resection and stored at −80°C in RNAlater (Ambion, Thermo Fisher Scientific, Waltham, MA) until processed. Informed consent was obtained from the patients, and the study was approved by the ethical committee of our institution. Mutation analysis Genomic DNA was isolated using the QIAmp DNA Mini kit (Qiagen, Inc., Chatsworth, CA) and subsequently treated with RNase A (1 U/mL) at 37°C for 5 minutes. Mutation analysis of SDHA, SDHB, SDHC, SDHD, and VHL genes was performed by direct sequencing as previously described. SDHx and VHL genes were analyzed for the presence of large deletions in tumor DNA using the multiplex ligation-dependent probe amplification (MLPA) method as recommended by the manufacturer (MRC Holland, Amsterdam, The Netherlands). Array-CGH Screening of genome-wide copy number variants (CNVs) was carried out by array-CGH using the OncoNIM Familial Cancer platform, a 60K Agilent-based custom array-CGH (Nimgenetics, Madrid, Spain).

This custom array covers the whole genome with a median spatial resolution of 1 probe per 150 kb, with high-density coverage in 20 genes related to familial cancer (100-bp median spatial resolutions for these genes). Hybridizations were performed according to the manufacturer’s protocols. A commercially available male DNA sample (Promega, Madison, WI) was used as reference DNA. Microarray data were extracted and visualized using the Feature Extraction Software v10.7 and Agilent Genomic Workbench v.5.0 (Agilent Technologies, Santa Clara, CA) using ADAM-2 (windown 0.5 Mb, A = 10) as an aberration detection statistic. Genomic build NCBI37 (Hg19) was used for delineating the genomic coordinates of the detected CNVs.

Exome sequencing Genomic DNA (3 μg) was sheared and used for the construction of a paired-end sequencing library as described in the protocol provided by Illumina (San Diego, CA). Enrichment of exonic sequences was then performed for each library using the SureSelect Human All Exon 50-Mb kit (Agilent, Santa Clara, CA) following the manufacturer’s instructions. Exon-enriched DNA was precipitated with magnetic beads coated with streptavidin (Invitrogen, Carlsbad, CA), washed, and eluted. An additional 18 cycles of amplification were then performed on the captured library. Exon enrichment was validated by real-time PCR in a 7300 Real-Time PCR System (Applied Biosystems, Foster City, CA) using a set of two pairs of primers to amplify exons and one pair to amplify an intron. Enriched libraries were sequenced in one lane of the Illumina Gene Analyzer II× sequencer, using the standard protocol. Read mapping and data processing were done by DreamGenics S.L.

Mean and median coverage were >40 for all SDHx genes and SDHAF2 and VHL. Genome-wide methylation analysis DNA was extracted with phenol/chloroform/isoamyl alcohol and bisulfite converted before genome-wide analysis of methylation with the Infinium Human Methylation450 BeadChip array (Illumina, San Diego, CA) in the Spanish “Centro Nacional de Genotipado” CEGEN-ISCIII National Genotyping Center, National Health Institute Carlos. Raw data files were imported and preprocessed using R/Bioconductor package minfi (version 1.14.0) (). Methylation raw signals were normalized using the SWAN (subset-quantile within array normalization) method ().

A methylation measure was defined to be defective if it had a detection P value >0.01. Probes with >2 defective measures and samples with >5000 defective measures were removed from the data set.

Methylation was described as a β value, which ranges between 0 (no methylation) and 1 (full methylation). The β values of probes located 2000 bp upstream and 200 bp downstream the transcription start site (TSS; defined as promoter regions) of the parasympathetic PGL-susceptibility genes ( SDHA, SDHB, SDHC, SDHD, SDHAF2, and VHL) were extracted and analyzed in comparison with healthy carotid bodies.

The methylation level of selected CpGs was validated by pyrosequencing (PyromarkQ24 Advanced System). Primers used for PCR amplification and sequencing were designed with the PyroMark assay designer (Supplemental Table S3). The genomic region included in the analysis has been previously reported (, ).

The statistical significance of the differences between groups of tumors was assessed using a t test with a significance threshold of 0.05. Immunohistochemistry SDHB, SDHC, SDHA, VHL, and HIF-1 α expression was evaluated by immunohistochemistry in tissue sections from the surgically resected PGLs as previously described (). Following published recommendations (), both the percentage of immunostained cells and the intensity of staining were used to quantify HIF-1 α protein expression. SDHC antibody (Abcam, Cambridge, United Kingdom) showed granular cytoplasmic staining similar to that obtained with SDHA or SDHB antibodies, as previously reported (). Data were reviewed independently by three investigators. MicroRNA/messenger RNA quantification Total RNA was isolated with the mirVana miRNA Isolation Kit (Ambion, Thermo Fisher Scientific, Waltham, MA) according to the manufacturer’s instructions.

TaqMan assay (Applied Biosystems, Foster City, CA) was used to analyze the expression of the mature human miR-210. For microRNA quantification, 10 ng total RNA was used in the reverse transcriptase reaction, and the transcribed complementary DNA was then used for subsequent PCR amplification using the TaqMan 2X Universal PCR Master Mix, No AmpErase UNG (Applied Biosystems, Foster City, CA) as described by the manufacturer. RNU44 expression was assayed for normalization.

For SDHx and VHL messenger RNA (mRNA) quantification, PCR reactions were performed by using the SYBR Green PCR Master Mix (Applied Biosystems, Foster City, CA) and the thermocycler conditions recommended by the manufacturer. Each sample was analyzed for cyclophilin A mRNA to normalize for RNA input amounts and to perform relative quantification.

All reactions were performed in triplicate, and relative microRNA/mRNA expression was normalized against endogenous controls using the comparative ΔΔ CT method. Statistical analysis All statistical analyses were performed using SPSS statistical software version 19 (SPSS, Inc., Chicago, IL) as previously described. Heterozygous loss of SDHC and VHL genes in PGLmx tumor. (A, B) Array-CGH screening of genomic DNA in PGLmx.

Profiles are displayed as normalized log2 signal intensity ratios of each spot on the array to the genomic position at (A) chromosome 1 and (B) chromosome 3. Genes with high density of probes in the array are indicated. VHL points to the chr3:1013904 (Human GRCh37/hg19) genomic region.

(C) Upper panel shows a schematic representation of the structure of the VHL gene; arrows indicate location of the probes included in the MLPA. Capillary gel electrophoresis images depicting the MLPA assay for VHL in tumor DNA. (D, E) Normalized exome coverage data are determined for each individual capturing exon in the PGLmx tumor sample and blood sample from the same patient. The tumor/normal ratio is calculated for each probe. Copy number alterations in PGLmx are indicated by dark gray lines.

DNA copies below or above two are indicated by red or blue dots, respectively. Heterozygous loss of SDHC and VHL genes in PGLmx tumor. (A, B) Array-CGH screening of genomic DNA in PGLmx. Profiles are displayed as normalized log2 signal intensity ratios of each spot on the array to the genomic position at (A) chromosome 1 and (B) chromosome 3. Genes with high density of probes in the array are indicated. VHL points to the chr3:1013904 (Human GRCh37/hg19) genomic region. (C) Upper panel shows a schematic representation of the structure of the VHL gene; arrows indicate location of the probes included in the MLPA.

Capillary gel electrophoresis images depicting the MLPA assay for VHL in tumor DNA. (D, E) Normalized exome coverage data are determined for each individual capturing exon in the PGLmx tumor sample and blood sample from the same patient. The tumor/normal ratio is calculated for each probe.

Copy number alterations in PGLmx are indicated by dark gray lines. DNA copies below or above two are indicated by red or blue dots, respectively. Epigenetic alterations DNA methylation arrays of the tumor genomic DNA were performed and compared with that of three samples of human healthy carotid bodies and 17 PCCs/PGLs. Promoter regions of SDHB, SDHD, SDHA, SDHAF2, or VHL were not found differentially methylated either in PGLmx or in the other PGLs included in the array. In contrast, the data showed high methylation percentages in the promoter of SDHC in PGLmx ().

This epigenetic alteration was not identified in two tumors lacking SDHx mutations and carrying somatic deletion of VHL exon 1 (). Methylation status of SDHC gene around the TSS in PGLmx was verified by pyrosequencing (). Pyrosequencing of VHL CpG sites also confirmed the absence of differential methylation in PGLmx compared with healthy carotid body or other PGLs lacking SDHx alterations (n = 6 tumors) ().

SDHC TSS methylation in PGLmx. (A) Unsupervised hierarchical cluster analysis of normalized methylation β values of healthy carotid bodies (N) and parasympathetic PGLs carrying SDHB (B), SDHD (D), or no SDHx mutations (0). PGLmx sample is indicated by red 0*. Quantification (mean ± standard deviation value) of the β values corresponding to the cg12036621 ( SDHC TSS) and cg13672843 ( VHL promoter) probes in the indicated group of tumors is shown below the SDHC and VHL data heatmap, respectively.

(B, C) Schematic representations of the (B) SDHC and (C) VHL genes indicating the location of the CpG sites identified by the indicated probes and selected for validation. SDHC TSS methylation percentages for the indicated genomic sites were determined by directed pyrosequencing in (B, left panel) tumor and blood samples from PGLmx and (B, right panel) three other parasympathetic PGLs (PGLs 1 to 3) harboring SDHB-positive immunostaining in the absence of SDHx mutations. VHL promoter methylation percentages (mean ± standard deviation value) for the indicated genomic regions determined by pyrosequencing in tumor samples from one healthy carotid body (CB); five, four, and two PGLs without SDHx mutations [wild-type (wt) SDHx]; or with SDHD or SDHB mutations, respectively. SDHC TSS methylation in PGLmx. (A) Unsupervised hierarchical cluster analysis of normalized methylation β values of healthy carotid bodies (N) and parasympathetic PGLs carrying SDHB (B), SDHD (D), or no SDHx mutations (0). PGLmx sample is indicated by red 0*. Quantification (mean ± standard deviation value) of the β values corresponding to the cg12036621 ( SDHC TSS) and cg13672843 ( VHL promoter) probes in the indicated group of tumors is shown below the SDHC and VHL data heatmap, respectively.

(B, C) Schematic representations of the (B) SDHC and (C) VHL genes indicating the location of the CpG sites identified by the indicated probes and selected for validation. SDHC TSS methylation percentages for the indicated genomic sites were determined by directed pyrosequencing in (B, left panel) tumor and blood samples from PGLmx and (B, right panel) three other parasympathetic PGLs (PGLs 1 to 3) harboring SDHB-positive immunostaining in the absence of SDHx mutations.

VHL promoter methylation percentages (mean ± standard deviation value) for the indicated genomic regions determined by pyrosequencing in tumor samples from one healthy carotid body (CB); five, four, and two PGLs without SDHx mutations [wild-type (wt) SDHx]; or with SDHD or SDHB mutations, respectively. We tested blood from a patient with PGLmx for SDHC TSS methylation and found no SDHC methylation (), consistent with postzygotic onset of SDHC methylation rather than germline inheritance. This suggests a process of SDHC reprogramming during tumor development. Absence of SDHC methylation in other SDHB-negative/SDHx wild-type PGLs Our previous study had identified three other parasympathetic PGLs with loss of SDHB protein in the absence of SDHx mutations (). Therefore, we analyzed whether those tumors also harbored somatic SDHC TSS methylation.

This analysis showed low methylation levels in the three tumors that were similar to those of normal carotid bodies (). Thus, SDHC methylation does not seem to be a frequent event in SDHB-negative/ SDHx wild-type parasympathetic PGLs. SDHx and VHL gene expression To determine whether gene alterations of SDHC, SDHB, and/or VHL result in deregulations at the mRNA level, quantitative reverse transcription PCR analysis of SDHC, SDHB, and VHL genes was performed in tumor complementary DNA, in a healthy carotid body, and in eight parasympathetic PGL samples [five wt SDHx (tumors lacking SDHx mutations) and three mut SDHx (tumors with SDHB or SDHD mutations)]. As shown in, decreased SDHC mRNA levels were found in the PGLmx tumor compared with wt SDHx tumors or the normal carotid body, although levels were similar to that of mut SDHB or mut SDHD tumors. In contrast, SDHB, SDHA, SDHD, and SDHAF1 mRNA levels were statistically significantly higher (threefold) in the PGLmx tumor than in the other PGLs (). Decreased VHL and SDHC mRNA levels in PGLmx.

(A, B) mRNA relative levels (mean ± standard deviation value) of (A) SDHC and (B) SDHA, SDHB, SDHD, SDHAF1, and SDHAF2 in one healthy carotid body (CB), PGLs lacking SDHx mutations [wild-type (wt) SDHx; n = 5], PGLs harboring SDHB (n = 2) or SDHD (n = 1) mutations, and PGLmx. (C) mRNA relative levels of VHL in PGLs lacking SDHx mutations and harboring (del VHL) or not (wt VHL) exon 1 deletion of VHL.

Blue arrow and green dot denotes VHL mRNA levels in PGLmx. NS, not significant; ** P. Decreased VHL and SDHC mRNA levels in PGLmx.

(A, B) mRNA relative levels (mean ± standard deviation value) of (A) SDHC and (B) SDHA, SDHB, SDHD, SDHAF1, and SDHAF2 in one healthy carotid body (CB), PGLs lacking SDHx mutations [wild-type (wt) SDHx; n = 5], PGLs harboring SDHB (n = 2) or SDHD (n = 1) mutations, and PGLmx. (C) mRNA relative levels of VHL in PGLs lacking SDHx mutations and harboring (del VHL) or not (wt VHL) exon 1 deletion of VHL. Blue arrow and green dot denotes VHL mRNA levels in PGLmx. NS, not significant; ** P.

SDHB, SDHA, SDHC, HIF-1 α, and VHL immunostainings in PGLmx. Representative images of (A) SDHB, (B) SDHA, (C, D) SDHC, (E) HIF-1 α, and (F) VHL immunostainings in (A–C, E–F) PGLmx and (D) a PGL with SDHB mutation. Inset in panel F shows a representative image of VHL immunostaining of a SDHx wild-type PGL. Scale bars = 100 µm. HIF-1 α/miR-210/ISCU signaling pathway The analysis of the HIF-1 α/miR-210/ISCU axis in the PGLmx tumor revealed high expression levels of HIF-1 α (immunoscore: 75, ), which was above the median value detected in parasympathetic PGLs (median value: 33) (). This study also showed increased levels of miR-210 and lower levels of ISCU mRNA compared with normal paraganglia ().

ISCU and miR-210 RNA levels in PGLmx. MiR-210 and ISCU mRNA levels were analyzed in PGLmx and compared with those of a human normal carotid body (CB).

Discussion Complete loss of SDHB protein expression, as determined by immunohistochemistry, is a common feature of SDHx-mutated tumors. However, recent reports have alerted about the existence of SDHB loss in the absence of SDHx mutations (). The molecular scrutiny of one such tumor, described here, has revealed the presence of two major genetic alterations consisting of (1) the somatic epigenetic silencing and loss of heterozygosis of the SDHC gene, accompanied by low SDHC expression at the mRNA and protein levels, and (2), decreased copy number of the VHL gene, low VHL mRNA levels, and absence of VHL protein in the tumor. Recent reports described for the first time that SDHC methylation could be involved in tumorigenesis (,,, ). In line with the case described here, all those tumors shared a similar phenotype: deficiency of SDH activity in the absence of SDHx mutations. The first works reported that gastrointestinal stromal tumors (GISTs) that developed in patients with (9 patients) or without (14 patients) the Carney triad are caused by hypermethylation of the SDHC promoter (,, ). Two of the patients with the Carney triad also developed sympathetic PGLs, which carried SDHC hypermethylation ().

Compound heterozygosity (monoallelic methylation and mutation of SDHC) or homozygous SDHC methylation was found in 4 and 12 GISTs, respectively (). SDHC loss of heterozygosis has not been reported for the remaining cases (,, ). SDHC epigenetic alteration was more recently found in two sympathetic PGLs that developed in one patient, apparently out of the context of the Carney triad (). Similarly to some GISTs, loss of heterozygosis was not identified in those two PGLs.

However, the fact that epigenetic modification of the SDHC gene was the only event identified in those patients suggested that this genetic alteration was a plausible mechanism of functional impairment of the SDH complex and tumorigenesis. The case reported here has similarities but also some differences with those GIST and PGL cases previously reported. The tumor displays loss of SDHB protein, absence of SDHx mutations, and SDHC TSS methylation, which was consistent with the low levels of SDHC mRNA and of the SDHC protein detected by immunostaining. We also found that the epigenetic modification of SDHC was accompanied by the heterozygous loss of the chromosomal region containing the SDHC gene, consistent with the Knudson two-hit model for tumorigenesis.

Recent clinical review (7 years after diagnosis of the jugular paraganglioma) revealed the absence of other tumors related to the Carney triad or Carney-Stratakis syndrome such as GISTs or pulmonary chondroma. Thus, this case adds to the previously reported PGL case not associated with Carney triad and represents the first case involving SDHC TSS methylation and loss of heterozygosis in a parasympathetic PGL.

However, even though the SDHC genetic events fulfill the two-hit model to attribute it a pathogenic role, the unambiguous claiming of its involvement in PGL tumorigenesis is not a straightforward conclusion. This is because, in contrast to the other reported cases, we found that the PGLmx tumor harbored an additional genetic defect concerning another PGL-related gene. This consisted of decreased VHL gene dosage and mRNA levels, as well as absence of VHL protein detected by immunohistochemistry. These types of VHL deregulations have been previously reported in a subtype of parasympathetic PGLs, the so-called del- VHL-PGLs ().

A previous report also described the coexistence of alterations in two susceptibility genes (somatic deletion of the VHL gene and somatic SDHC mutation) in sporadic PCCs (). The functional significance of partial loss of VHL is, thus far, unknown.

Indeed, del- VHL-PGLs do not display the distinctive pseudohypoxic phenotype of VHL- or SDHx-related tumors as, for example, the strong or moderated activation of the HIF-1 α/miR-210/ISCU pathway found in VHL-mutated or SDHx-mutated PGLs, respectively (). Furthermore, del- VHL-PGLs do not harbor the low levels of SDHB protein typically found in VHL-mutated PGLs ().

Importantly, in contrast to del- VHL-PGLs, the case described here harbors activation of the HIF-1 α/miR-210/ISCU pathway and lacks SDHB protein, thus resembling SDHx-mutated rather than del- VHL-PGLs. These features tip the balance toward a pathogenic role of SDHC rather than VHL, although they do not allow definitely ruling out a not yet identified role of VHL protein.

In any case, irrespective of whether it is VHL, SDHC, or both, the drivers of the activation of the HIF pathway, the fact is that the PGLmx tumor displays reduced levels of ISCU protein, which is known to participate in the maturation of SDHB by the insertion of three Fe-S clusters (). Thus, it is tempting to speculate that loss of SDHB protein was due to an additive effect of loss of both SDHC and ISCU. Although the mechanism involved in aberrant SDHC hypermethylation is unknown, a causative role of VHL deletion does not seem likely given that our study included two “sporadic” parasympathetic PGLs that carry a decreased VHL DNA copy number and decreased VHL mRNA levels (as PGLmx) but lack SDHC hypermethylation. Somatic mosaicism has been proposed as a mechanism for SDHC hypermethylation in patients with GIST given that low-levels SDHC hypermethylation had been found in blood and saliva of patients (). In contrast to those studies, SDHC hypermethylation was not identified in the blood DNA of the patient analyzed in the present work. Nevertheless, a role of tissue-specific mosaicism epigenetically affecting the SHC gene cannot be ruled out.

Further work on other tissues could shed some light into this relevant issue. Taken together, the identification of the SDHC TSS methylation and heterozygous loss of SDHC gene fulfills the two-hit Knudson model for cancer initiation, providing an explanation for the tumorigenesis of sporadic parasympathetic PGLs lacking SDHB protein expression. Furthermore, we also provide data suggesting the activation of the HIF-1 α/miR-210/ISCU axis, which could be also involved in tumorigenesis of the SDHC-epimutated PGLs. Importantly, this type of tumors could be identified in surgical specimens by SDHB and SDHC immunohistochemistry, which should be negative or weak positive for both.

This is of clinical relevance given that a histopathological analysis could allow the identification of patients carrying SDHC methylation. Finally, our data raise the possibility that patients with SDHC-epimutated PGLs could benefit from hypomethylating drugs and/or agents targeting HIF.

Further research on a wide series of PGLs with negative SDHB immunostaining and absence of SDHx mutations is indicated to get to know the real extent of this type of genetic alterations in sporadic tumors. Abbreviations: •.

Climate change, habitat loss, and harvesting are potential drivers of species extinction. These factors are unlikely to act on isolation, but their combined effects are poorly understood. We explored these effects in Catopsis compacta, an epiphytic bromeliad commercially harvested in Oaxaca, Mexico.

We analyzed local climate change projections, the dynamics of the vegetation patches, the distribution of Catopsis in the patches, together with population genetics and demographic information. A drying and warming climate trend projected by most climate change models may contribute to explain the poor forest regeneration.

Catopsis shows a positive mean stochastic population growth. A PVA reveals that quasi-extinction probabilities are not significantly affected by the current levels of harvesting or by a high drop in the frequency of wet years (2%) but increase sharply when harvesting intensity duplicates. Genetic analyses show a high population genetic diversity, and no evidences of population subdivision or a past bottleneck. Colonization mostly takes place on hosts at the edges of the fragments.

Over the last 27 years, the vegetation cover has being lost at a 0.028 years −1 rate, but fragment perimeter has increased 0.076 years −1. The increases in fragment perimeter and vegetation openness, likely caused by climate change and logging, appear to increase the habitat of Catopsis, enhance gene flow, and maintain a growing and highly genetically diverse population, in spite of harvesting. Our study evidences conflicting requirements between the epiphytes and their hosts and antagonistic effects of climate change and fragmentation with harvesting on a species that can exploit open spaces in the forest. A full understanding of the consequences of potential threatening factors on species persistence or extinction requires the inspection of the interactions of these factors among each other and their effects on both the focus species and the species on which this species depends. Population genetic analyses Our samples for population genetic analyses were collected in the Santa Catarina Ixtepeji forests, as in the demographic analysis.

We differentiate the area in the two vegetation types described above, the oak forest and the oak-chaparral. Fresh leaf samples were collected from 71 and 63 randomly selected individuals of the oak forest and the chaparral, respectively. The leaves were extracted within 24 h after collecting and ground with liquid N 2. The proteins were extracted with the study described by Soltis et al.

() extraction buffer. Horizontal starch gel electrophoresis was performed on 17 isozymes of which 11 could be scored and interpreted (). We used the pH 5.7 histidine/citrate buffer of Cheliak and Pitel (), and the pH 8.3 Tris-citrate/lithium borate buffer of Conkle et al. () in 12.5% and 12.8% starch gels, respectively.

Gels run between 3–5 h at 50 mA. We analyzed the percentage of polymorphic loci, the mean number of alleles per loci, allelic richness, and observed and expected heterozygosity under Hardy–Weinberg assumptions using the FSTAT program (Goudet ). Theory predicts that a bottleneck in populations under mutation-drift equilibrium causes both heterozygosity and allelic diversity to decline compared with the original population before the bottleneck. However, in recent bottlenecks of sufficient magnitude, allelic richness declines faster than heterozygosity. We looked for such transient heterozygosity excess relative to the current levels of allelic diversity with the Cornuet and Luikart () bottleneck software (ver. We run 1000 bootstrap permutations at 95% significance, under the infinite allele mutation model, which best fits to isozymes.

Spatial distribution of the individuals and deforestation patterns With the aid of GPS's, a team of 4–5 people recorded the position of the host individuals of C. Compacta in the forests and shrublands of Santa Catarina through parallel walks starting at the edge of the vegetation. The trend in deforestation was analyzed through the classification of 1979 Landsat MSS and 2006 Landsat ETM+ images, spanning 27 years. The 2006 Landsat images were geometrically, radiometrically, and topographically corrected. The 1979 image was classified using the same control sites from the 2006 image and were geometrically corrected with the aid of aerial orthophotographs from 1970 of the study region.

In addition to field points, the information was completed with field walks and high-resolution 2008 Google Earth images, which were georeferenced with ArcGis 9.2 and adjusted to the Landsat images. The 2006 Landsat image was classified with the maximum-likelihood method.

The classification was verified with independent control points and grouped in areas with and without vegetation. The position of the host individuals was included in the 2006 Landsat image.

We generated maps of vegetation cover for 1979 and 2006, and use FRAGSTAT (version 3.2, McGarigal et al. ) to compute the area (ha) and perimeter (km) of the vegetation patch. The rates of change in these metrics, R, during 1979–2006 were calculated as follows. Demography When the temporal variability in demographic behavior was incorporated, the mean stochastic population growth rate, λ, of C. Compacta was 1.047 (95% CI 1.045, 1.048), giving an intrinsic rate of increase r = 0.046.

This λ-value estimates the expected asymptotic growth rate of the population considering the year to year oscillations detected. Thus, the population eventually will be growing at this rate if the population parameters remain constant. It is likely that the actual population growth rate of the population is close to this figure. We did not find evidence that the observed population structures in 2006, 2007, and 2008 deviate significantly from the predicted population structure by the 2005–2006, 2006–2007, and 2007–2008 matrices, respectively, either from their first iteration (χ 2 4 ≤ 0.44, P ≥ 0.98), or from the asymptotic population structure (χ 2 4 ≤ 0.46, P ≥ 0.96) (see ). The population has not colonized all the potential hosts ().

Therefore, it is unlikely that it has approached its carrying capacity. The elasticity matrices showed that the λ-value was more dependent on the destiny of the juvenile 1 and the adult stages (a), while the demographic processes that have the strongest effect on λ-value are stasis and growth (b). Harvesting appears to have a larger influence on C. Compacta demography than the increase in drought predicted by climate change.

The quasi-extinction probabilities are zero without harvesting or with a harvest intensity of 10% in all the climate scenarios analyzed. However, when harvest intensity increases to 20% the quasi-extinction probabilities increased sharply after 40 years (). Population genetic analyses We could interpret and score 11 loci and 36 alleles from five enzymatic systems in the entire population analyzed.

We did not find evidence of population subdivision between the fragments of the oak forest and the chaparral, as the Fst estimate (θ) was not significantly different from the Hardy–Weinberg expectation based on the Weir and Cockerham F-statistics analysis for multiple loci (θ = 0.023, 95% CI: -0.003, 0.048). Based on 900 randomizations and adjusted to nine multiple tests at 5% nominal level of significance, we could detect that seven of the nine polymorphic isozymes (77.8%) did not deviate significantly from the Hardy–Weinberg expectation. Only one of the nine polymorphic enzymes found, AAT-3, showed a significant deficit of heterozygosity, whereas PGM-1 showed a significant excess of heterozygosity. However, the multilocus Fit estimate revealed a significant deficit of heterozygosity (0.272, 95% CI: 0.044, 0.479), most likely attributed to a significant deficit of heterozyogosity detected at the level of individuals between subpopulations, Fis (0.254, 95% CI: 0.033. All the standard measures of genetic diversity were high compared with those reported for other species of Bromeliaceae (). The Cornuet and Luikart test shows that the population is at mutation-drift equilibrium and, therefore, do not provide evidence of a recent bottleneck.

Download Skystar 2 Driver For Windows 8. Spatial distribution and plant cover dynamics A categorical analysis of variance using proc CATMOD of SAS 9.1 revealed that: (1) trees colonized by at least one individual of C. Compacta are more common in the chaparral than in the oak forest (χ 2 1 = 7.69, P = 0.0055); and (2) in both kinds of vegetation, trees at the border of the fragments are more likely to be colonized by C. Compacta than trees at the interior of the fragments (χ 2 1 = 7.69, P = 0.0004) ().

Based on GIS analysis, the border of the fragment was defined by trees in which at least 50% of its canopy horizontal projection is separated from the canopy horizontal projection of other trees by at least 7 m, which is the average canopy diameter of the trees. Otherwise, the trees were considered as located in the interior of the fragment. The analysis of deforestation revealed that the oak forest and oak-chaparral decreased at an annual rate of 5.09 and 5.60%, respectively, while the perimeter of border of the these two vegetation types increased at an annual rate of 4.70 and 3.43%, respectively, based on the analyses of satellite imagery spanning 27 years (1979–2006).