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Posts Tagged ‘GWAS’


Renal (Kidney) Cancer: Connections in Metabolism at Krebs cycle  and Histone Modulation

Curator: Demet Sag, PhD, CRA, GCP

Through Histone Modulation

Renal cell carcinoma accounts for only 3% of total human malignancies but it is still the most common type of urological cancer with a high prevalence in elderly men (>60 years of age).

ICD10 C64
ICD9-CM 189.0
ICD-O M8312/3
OMIM 144700 605074
DiseasesDB 11245
MedlinePlus 000516
eMedicine med/2002

Most kidney cancers are renal cell carcinomas (RCC). RCC lacks early warning signs and 70 % of patients with RCC develop metastases. Among them, 50 % of patients having skeletal metastases developed a dismal survival of less than 10 % at 5 years.

There are three main histopathological entities:

  1. Clear cell RCC (ccRCC), dominant in histology (65%)
  2. Papillary (15-20%) and
  3. Chromophobe RCC (5%).

There are very rare forms of RCC shown in collecting duct, mucinous tubular, spindle cell, renal medullary, and MiTF-TFE translocation carcinomas.

Subtypes of clear cell and papillary RCC, and a new subtype, clear cell papillary http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399969/bin/nihms380694f6.jpg

Different subtypes of clear cell RCC can be defined by HIF patterns as well as by transcriptomic expression as defined by ccA and ccB subtypes. Papillary RCC also demonstrates distinct histological subtypes. A recently described variant denoted as clear cell papillary RCC is VHL wildtype (VHL WT), while other clear cell tumors are characterized by VHL mutation, loss, or inactivation (VHL MT).

KEY POINTS

  • Renal cell cancer is a disease in which malignant (cancer) cells form in tubules of the kidney.
  • Smoking and misuse of certain pain medicines can affect the risk of renal cell cancer.
  • Signs of renal cell cancer include
  • Blood in your urine, which may appear pink, red or cola colored
  • A lump in the abdomen.
  • Back pain just below the ribs that doesn’t go away
  • Weight loss
  • Fatigue
  • Intermittent fever

 

Factors that can increase the risk of kidney cancer include:

  • Older age.
  • High blood pressure (hypertension).
  • Treatment for kidney failure.(long-term dialysis to treat chronic kidney failure)
  • Certain inherited syndromes.
  • von Hippel-Lindau disease

Tests that examine the abdomen and kidneys are used to detect (find) and diagnose renal cell cancer.

The following tests and procedures may be used:

There are 3 treatment approaches for Renal Cancer:

Stages of Renal Cancer:

Stage I Tumour of a diameter of 7 cm (approx. 23⁄4 inches) or smaller, and limited to the kidney. No lymph node involvement or metastases to distant organs.
Stage II Tumour larger than 7.0 cm but still limited to the kidney. No lymph node involvement or metastases to distant organs.
Stage III
any of the following
Tumor of any size with involvement of a nearby lymph node but no metastases to distant organs. Tumour of this stage may be with or without spread to fatty tissue around the kidney, with or without spread into the large veins leading from the kidney to the heart.
Tumour with spread to fatty tissue around the kidney and/or spread into the large veins leading from the kidney to the heart, but without spread to any lymph nodes or other organs.
Stage IV
any of the following
Tumour that has spread directly through the fatty tissue and the fascia ligament-like tissue that surrounds the kidney.
Involvement of more than one lymph node near the kidney
Involvement of any lymph node not near the kidney
Distant metastases, such as in the lungs, bone, or brain.
Grade Level Nuclear Characteristics
Grade I Nuclei appear round and uniform, 10 μm; nucleoli are inconspicuous or absent.
Grade II Nuclei have an irregular appearance with signs of lobe formation, 15 μm; nucleoli are evident.
Grade III Nuclei appear very irregular, 20 μm; nucleoli are large and prominent.
Grade IV Nuclei appear bizarre and multilobated, 20 μm or more; nucleoli are prominent

 

GENETICS:

90% or more of kidney cancers are believed to be of epithelial cell origin, and are referred to as renal cell carcinoma (RCC), which are further subdivided based on histology into clear-cell RCC (75%), papillary RCC (15%),

chromophobe tumor (5%), and oncocytoma (5%).

Nephrectomy continues to be the cornerstone of treatment for localized renal cell carcinoma (RCC). Research is still underway to developed targeted agents against the vascular endothelial growth factor (VEGF) molecule and related pathways as well as inhibitors of the mammalian target of rapamycin (mTOR),

clear cell RCC (ccRCC) doesn’t respond well to radiation chemotherapy due to high radiation resistancy.  The hallmark genetic features of solid tumors such as KRAS or TP53 mutations are also absent. However, there is a well-designed association presented between ccRCC and mutations in the VHL gene

Hereditary RCC, accounts for around 4% of cases, has been a relatively dominant area of RCC genetics.

Causative genes have been identified in several familial cancer syndromes that predispose to RCC including

  • VHLmutations in von Hippel-Lindau disease that predispose to ccRCC and VHL is somatically mutated in up to 80% of ccRCC
  • METmutations in familial papillary renal cancer,
  • dominantly activating kinase domainMET mutation reported in 4–10% of sporadic papillary RCC[2].
  • FH (fumarate hydratase) mutations in hereditary leiomyomatosis and renal cell cancer that predispose to papillary RCC
  • FLCN(folliculin) mutations in Birt-Hogg-Dubé syndrome that predispose to primarily chromophobe RCC.

In addition, there are germline mutations:

  • in theTSC1/2 genes predispose to tuberous sclerosis complex where approximately 3% of cases develop ccRCC,
  • in the SDHB(succinate dehydrogenase type B) in patients with paraganglioma syndrome shows elevated risk to develop multiple types of RCC.

GWAS in almost 6000 RCC cases demonstrated that loci on 2p21 and 11q13.3 play a role in RCC. Although EPAS1 gene encoding a transcription factor operative in hypoxia-regulated responses in  2p21 , 11q13.3 has no known coding genes.

There has been, however, comparatively less progress in the elaboration of the somatic genetics of sporadic RCC.

Absent mutations in sporadic RCC:

  • somaticFH mutations
  • somatic mutations ofTSC12 and SDHB

Present mutations in sporadic ccRCC (chromophobe RCC) are

  • TSC1mutations occur in 5% of ccRCCs and
  • somatic mutations inFLCN  rare
  • may predict for extraordinary sensitivity to mTORC1 inhibitors clinically.

The COSMIC database reports somatic point mutations in TP53 in 10% of cases, KRAS/HRAS/NRAS combined ≤1%, CDKN2A 10%, PTEN 3%, RB1 3%, STK11/LKB1 ≤1%, PIK3Ca ≤1%, EGFR1% and BRAF ≤1% in all histological samples. Further information can be found at (http://www.sanger.ac.uk/ genetics/CGP/cosmic/) for the  RCC somatic genetics.

HIF- and hypoxia-mediated epigenetic regulation work together due to histone modification because HIF activate several chromatin demethylases, including JMJD1A (KDM3A), JMJD2B (KDM4B), JMJD2C (KDM4C) and JARID1B (KDM5B), all of which are directly targeted by HIF.

Overview of Histone 3 modifications implicated in RCC genetics http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399969/bin/nihms380694f1.jpg

A number of histone modifying genes are mutated in renal cell carcinoma. These include the H3K36 trimethylase SETD2, the H3K27 demethylase UTX/KDM6A, the H3K4 demethylase JARID1C/KDM5C and the SWI/SNF complex compenent PBRM1, shown in this cartoon to represent their relative activities on Histone H3.

Hyper-methylation is observed on RASSF1 highly (50% f RCC) yet less on VHL and CDKN2A, yet there is a methylation and silencing observed on TIMP3 and secreted frizzled-related protein 2.

RCC is ONE OF THE “CILIOPATHIES” among Polycystic Kidney Disease (PKD), Tuberous Sclerosis Complex (TSC) and VHL Syndrome. The main display of cysts is dysfunctional primary cilia.

Mol Cancer Res. Author manuscript; available in PMC 2013 Jan 1.

Mol Cancer Res. 2012 Jul; 10(7): 859–880. Published online 2012 May 25. doi:  10.1158/1541-7786.MCR-12-0117

pVHL mutants are categorized as Class A, B and C depending on the affected step in pVHL protein quality control http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399969/bin/nihms380694f2.jpg

VHL proteostasis involves the chaperone mediated translocation of nascent VHL peptide from the ribosome to the TRiC/CCT chaperonin, where folding occurs in an ATP dependent process. The VBC complex is formed while VHL is bound to TRiC, and the mature complex is then released. Three different classes of mutation exist: Class A mutations prevent binding of VHL to TRiC, and abrogate folding into a mature complex. Class B mutations prevent association of Elongins C and B to VHL. Class C mutations inhibit interaction between VHL and HIF1 a.

# 193300. VON HIPPEL-LINDAU SYNDROME; VHL ICD+, Links
VON HIPPEL-LINDAU SYNDROME, MODIFIERS OF, INCLUDED
Cytogenetic locations: 3p25.3 , 11q13.3
Matching terms: lindau, disease, von, hippellindau, hippel
  • Birt-Hogg-Dube syndrome,
# 135150. BIRT-HOGG-DUBE SYNDROME; BHD ICD+, Links
Cytogenetic location: 17p11.2 
Matching terms: birthoggdube, syndrome, birt, hogg, dube
  • tuberous sclerosis
# 191100. TUBEROUS SCLEROSIS 1; TSC1 ICD+, Links
Cytogenetic location: 9q34.13 
Matching terms: tuber, sclerosi, tuberous
  • familial papillary renal cell carcinoma.
# 144700. RENAL CELL CARCINOMA, NONPAPILLARY; RCC ICD+, Links
NONPAPILLARY RENAL CARCINOMA 1 LOCUS, INCLUDED
Cytogenetic locations: 3p25.3 3p25.3 3q21.1 8q24.13 12q24.31 17p11.2 17q12 
Matching terms: renal, familial, papillary, carcinoma, cell

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4358399/bin/467fig3.jpg

Model for the control of the fate of nephron progenitor cells. Eya1 lies genetically upstream of Six2. Six2 labels the nephron progenitor cells, which can either maintain a progenitor state and self-renew or differentiate via the Wnt4-mediated MET. Wnt4 expression is under the direct control of Wt1. β-Catenin is involved in both progenitor cell fates through activation of different transcriptional programs. Active nuclear phosphorylated Yap/Taz shifts the progenitor balance toward the self-renewal fate. Eya1 and Six2 interact directly with Mycn, leading to dephosphorylation of Mycn pT58, stabilization of the protein, increased proliferation, and potentially a shift of the nephron progenitor toward self-renewal. Genes activated in Wilms’ tumors are depicted in green, and inactivated genes are in blue. Deregulation of Yap/Taz in Wilms’ tumors results in phosphorylated Yap not being retained in the cytoplasm as it should, but it translocates to the nucleus and thus shifts the progenitor cell balance toward self-renewal. This model is likely a simplification, as it presumes that all Wilms’ tumors, regardless of causative mutation, are caused by the same mechanism.

Epigenetic aberrations associated with Wilms’ tumor

Chinese Case Study: PMCID: PMC4471788

They u8ndertook this study based on association of low circulating adiponectin concentrations with a higher risk of several cancers, including renal cell carcinoma. Thus they demonstrated that by case–control study that ADIPOQ rs182052 is significantly associated with ccRCC risk.

They investigated the frequency of three single nucleotide polymorphisms (SNPs), rs182052G>A, rs266729C>G, rs3774262G>A, in the adiponectin gene (ADIPOQ).  1004 registered patients with clear cell renal cell carcinoma (ccRCC) compared with 1108 healthy subjects (= 1108).

The first table presents the characteristics of 1004 patients with clear cell renal cell carcinoma and 1108 cancer-free controls from a Chinese Han population. The Second and third table shows the SNP results.

Table 1: The characteristics of the examined population.

Variable Cases, n (%) Controls, n (%) P-value
1004 (100) 1108 (100)
Age, years
 ≤44 195 (19.4) 230 (20.8) 0.559
 45–64 580 (57.8) 644 (58.1)
 ≥65 229 (22.8) 234 (21.1)
Sex
 Male 711 (70.8) 815 (73.6) 0.160
 Female 293 (29.2) 293 (26.4)
BMI, kg/m2
 <25 480 (47.8) 589 (53.2) 0.014
 ≥25 524 (52.2) 519 (46.8)
Smoking status
 Never 455 (45.3) 529 (47.7) 0.265
 Ever/current 549 (54.7) 579 (52.3)
Hypertension
 No 639 (63.6) 780 (70.4) 0.001
 Yes 365 (36.4) 328 (29.6)
Fuhrman grade
 I 40 (4.0)
 II 380 (37.8)
 III 347 (34.6)
 IV 175 (17.4)
 Missing 62 (6.2)
Stage at diagnosis
 I 738 (73.5)
 II 71 (7.1)
 III 19 (1.9)
 IV 176 (17.5)

Pearson’s χ2-test.

Table 2:

Association between ADIPOQ single nucleotide polymorphisms (SNP) and clear cell renal cell carcinoma risk

SNP HWE Cases, n(%) Controls, n(%) Crude OR (95% CI) P-value Adjusted OR (95% CI) P-value
rs182052
 GG 0.636 249 (24.8) 315 (28.4) 1.00 1.00
 AG 485 (48.3) 544 (49.1) 1.13 (0.92–1.39) 0.253 1.11 (0.90–1.37) 0.331
 AA 270 (26.9) 249 (22.5) 1.37 (1.08–1.75) 0.010 1.36 (1.07–1.74) 0.013
 AG/AA versusGG 1.20 (0.99–1.46) 0.060 1.19 (0.98–1.45) 0.086
 AA versusGG/AG 1.28 (1.04–1.57) 0.019 1.27 (1.04–1.56) 0.019
rs266729
 CC 0.143 502 (50.0) 572 (51.6) 1.00 1.00
 CG 398 (39.6) 434 (39.2) 1.05 (0.88–1.25) 0.635 1.05 (0.87–1.26) 0.633
 GG 104 (10.4) 102 (9.2) 1.16 (0.86–1.57) 0.324 1.17 (0.86–1.58) 0.307
 CG/GG versusCC 1.07 (0.91–1.29) 0.456 1.07 (0.90–1.27) 0.445
 GG versus CC/CG 1.19 (0.83–1.59) 0.377 1.15 (0.86–1.54) 0.353
rs3774262
 GG 0.106 482 (48.0) 523 (47.2) 1.00 1.00
 AG 420 (41.8) 459 (41.4) 0.99 (0.83–1.20) 0.938 0.99 (0.82–1.19) 0.905
 AA 102 (10.2) 126 (11.4) 0.88 (0.66–1.17) 0.381 0.90 (0.67–1.20) 0.463
 AG/AA versusGG 0.98 (0.80–1.16) 0.711 0.97 (0.82–1.15) 0.722
 AA versusGG/AG 0.88 (0.67–1.18) 0.372 0.90 (0.68–1.19) 0.465

Bold values indicate significance.

Adjusted for age, sex, BMI, smoking status, and hypertension. CI, confidence interval; OR, odds ratio; HWE, Hardy–Weinberg equilibrium.

Table 3:

Association between ADIPOQ single nucleotide polymorphisms (SNP) and clear cell renal cell carcinoma risk

SNP HWE Cases, n(%) Controls, n(%) Crude OR (95% CI) P-value Adjusted OR (95% CI) P-value
rs182052
 GG 0.636 249 (24.8) 315 (28.4) 1.00 1.00
 AG 485 (48.3) 544 (49.1) 1.13 (0.92–1.39) 0.253 1.11 (0.90–1.37) 0.331
 AA 270 (26.9) 249 (22.5) 1.37 (1.08–1.75) 0.010 1.36 (1.07–1.74) 0.013
 AG/AA versusGG 1.20 (0.99–1.46) 0.060 1.19 (0.98–1.45) 0.086
 AA versusGG/AG 1.28 (1.04–1.57) 0.019 1.27 (1.04–1.56) 0.019
rs266729
 CC 0.143 502 (50.0) 572 (51.6) 1.00 1.00
 CG 398 (39.6) 434 (39.2) 1.05 (0.88–1.25) 0.635 1.05 (0.87–1.26) 0.633
 GG 104 (10.4) 102 (9.2) 1.16 (0.86–1.57) 0.324 1.17 (0.86–1.58) 0.307
 CG/GG versusCC 1.07 (0.91–1.29) 0.456 1.07 (0.90–1.27) 0.445
 GG versus CC/CG 1.19 (0.83–1.59) 0.377 1.15 (0.86–1.54) 0.353
rs3774262
 GG 0.106 482 (48.0) 523 (47.2) 1.00 1.00
 AG 420 (41.8) 459 (41.4) 0.99 (0.83–1.20) 0.938 0.99 (0.82–1.19) 0.905
 AA 102 (10.2) 126 (11.4) 0.88 (0.66–1.17) 0.381 0.90 (0.67–1.20) 0.463
 AG/AA versusGG 0.98 (0.80–1.16) 0.711 0.97 (0.82–1.15) 0.722
 AA versusGG/AG 0.88 (0.67–1.18) 0.372 0.90 (0.68–1.19) 0.465

Bold values indicate significance.

Adjusted for age, sex, BMI, smoking status, and hypertension. CI, confidence interval; OR, odds ratio; HWE, Hardy–Weinberg equilibrium.

Molecular Genetics Level for Physiology (Function):

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4503866/bin/10585_2015_9731_Fig6_HTML.jpg

a The protein–protein interaction for the identified 8 proteins in STRING (10 necessary proteins/genes were added into the network so as to find the potential strong connection among them. The red dotted lines circled three main pathways. b The ingenuity pathway analysis (IPA) for all these 18 genes showing that oxidative phosphorylation, mitochondria dysfunction and granzyme A are the significantly activated pathways (fold change over 1.5, P < 0.05). c The possible mechanism related mitochondria functions: unspecific condition like inflammation, carcinogens, radiation (ionizing or ultraviolet), intermittent hypoxia, viral infections which is carcinogenesis in our study that damages a cell’s oxidative phosphorylation. Any of these conditions can damage the structure and function of mitochondria thus activating a respiratory chain changes (Complex I, II, III, IV) and also cytochrome c release. When the mitochondrial dysfunction persists, it produces genome instability (mtDNA mutation), and further lead to malignant transformation (metastasis) via increased ROS and apoptotic resistance. (Color figure online)

RENAL CELL CARCINOMA AND METABOLISM goes hand to hand in genes encoding enzymes of the Krebs cycle suppress tumor formation in kidney cells. This includes Succinate dehydrogenase (SDH), Fumarate hydratase (FH).  As a result of accumulation of succinate or fumarate causes the inhibition of a family of 2-oxoglutarate-dependent dioxygeneases.

The FH and SDH genes function as two-hit tumor suppressor genes.

SDH has a complex of 4 different polypeptides (SDHA-D) function in electron transfer, catalyzes the conversion of succinate to fumarate. Furthermore, heterozygous germline mutations in SDHsubunits predispose to pheochromocytoma/paraganglioma. FH function to convert fumarate to malate.  When its mutations presented as heterozygous germline, it predisposes hereditary leiomyomatosis and renal cell cancer (HLRCC). Among them about 20–50% of HLRCC families are typically papillary-type 2 (pRCC-2) and overwhelmingly aggressive.RCC is increasingly being recognized as a metabolic disease, and key lesions in nutrient sensing and processing have been detected.

Regulation of Prolyl Hydroxylases and Keap1 by Krebs cycle http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399969/bin/nihms380694f4.jpg

Regulation of Prolyl Hydroxylases by Tricarboxylic Acid (TCA) Cycle Intermediates. Prolyl hydroxylases use TCA cycle intermediates to help catalyze the oxygen, iron and ascorbate dependent- addition of a hydroxyl side chain to a Pro402 and Pro564 of HIF alpha subunits, leading to VHL binding and degradation. Defects in either fumarate hydratase or succinate dehydrogenase will drive up levels of fumarate and succinate, which competitively bind prolyl hydroxylases, and prevent HIF prolyl hydroxylation. This results in higher intracellular HIF levels.

Regulation of mTORC1 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399969/bin/nihms380694f5.jpg

HIF regulation and mTOR pathway connections. Hypoxia blocks HIF expression in a TSC1/2 and REDD dependent pathway [155]. HIF1α appears to be both TORC1 and TORC2 dependent, whereas HIF2α is only TORC2 dependent [275]. Signaling via TORC2 appears to upregulate HIF2α in an AKT dependent manner [69].

TREATMENT:

Based on the types of renal cancers the treatment method may vary but the general scheme is:

 

Drugs Approved for Kidney (Renal Cell) Cancer

Food and Drug Administration (FDA) approved drugs for kidney (renal cell) cancer. Some of the drug names link to NCI’s Cancer Drug Information summaries.

T cell regulation in RCC http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399969/bin/nihms380694f7.jpg

Immune regulation of renal tumor cells. A: When an antigen presenting cell (APC) engages a T-cell via a cognate T-cell receptor (TCR) and CD28, T-cell cell activation occurs. B: Early and late T-cell inhibitory signals are mediated via CTLA-4 and PD-1 receptors, and this occurs via engagement of the APC via B7 and PD-L1, respectively. C: Inhibitory antibodies against CTLA-4 and PD-1 can overcome T-cell downregulation and once again allow cytokine production.

Phase III Trials of Targeted Therapy in Metastatic Renal Cell Carcinoma

Trial Number
of
patients
Clinical setting RR (%) PFS (months) OS (months)
VEGF-Targeted Therapy
*AVOREN

Bevacizumab +
IFNa
vs.IFNa[270]

649 First-line 31 vs. 12 10.2 vs. 5.5
(p<0.001)
23.3 vs. 21.3
(p=0.129)
*CALBG 90206

Bevacizumab +
IFNa
vs.IFNa[271]

732 First-line 25.5 vs. 13 8.4 vs. 4.9
(p<0.001)
18.3 vs. 17.4
(p=0.069)
Sunitinib vs.
IFNa[248]
750 First-line 47 vs. 12 11 vs. 5
(p=0.0001)
26.4 vs. 21.8
(p=0.051)
*TARGET

Sorafenib vs.
Placebo[272]

903 Second-line

(post-cytokine)

10 vs. 2 5.5 vs. 2.8
(p<0.01)
17.8vs.15.2
(p=0.88)
Pazopanib vs.
placebo[273]
435 First line/second line

(post-cytokine)

30 vs. 3 9.2 vs. 4.2
(p<0.0001)
22.9 vs. 20.5
(p=0.224)
*AXIS

Axitinib vs.
sorafenib [269]

723 Second line

(post-sunitinib, cytokine,
bevacizumab or
temsirolimus)

19 vs. 9
(p=0.0001)
6.7 vs. 4.7
(p<0.0001)
Not reported
mTOR-Targeted Therapy
*ARCC
Temsirolimus
vs. Tem + IFNa
vs. IFNa[249]
624 First line, ≥ 3 poor risk
featuresa
9 vs. 5 3.8 vs. 1.9 for
IFNa
monotherapy
(p=0.0001)
10.9 vs. 7.3 for
IFNa(p=0.008)
*RECORD-1
Everolimus vs.
placebo [274]
410 Second line
(post sunitinib and/or
sorafenib)
2 vs. 0 4.9 vs. 1.9

(p<0.0001)

14.8 vs. 14.5

RCC renal cell carcinoma, RR response rate, OS overall survival, PFS progression free survival, VEGFvascular endothelial growth factor, IFNa interferon alphamTOR mammalian target of rapamycin. AVORENAVastin fOr RENal cell cancer, CALBG Cancer and Leukemia Group B. TARGET Treatment Approaches in Renal Cancer Global Evaluation Trial. AXIS Axitinib in Second Line. ARCC Advanced Renal-Cell Carcinoma. RECORD-1 REnal Cell cancer treatment withOral RAD001 given Daily.

aIncluding serum lactate dehydrogenase level of more than 1.5 times the upper limit of the normal range, a hemoglobin level below the lower limit of the normal range; a corrected serum calcium level of more than 10 mg per deciliter (2.5 mmol per liter), a time from initial diagnosis of renal-cell carcinoma to randomization of less than 1 year, a Karnofsky performance score of 60 or 70, or metastases in multiple organs.

PMC full text: Open Access J Urol. Author manuscript; available in PMC 2013 Jul 8.

Open Access J Urol. 2010 Aug; 2010(2): 125–141. doi:  10.2147/RRU.S7242

Table: RCC-Associated Antigens (RCCAA) Recognized by T Cells.

Antigen Antigen
Category
Frequency of
Expression
Among RCC
Tumors (%)
CD8+ T cell
recognition:
Patients with
HLA Class I
Allele(s)
CD4+ T cell
recognition:
Patients with
HLA Class II
Allele(s)
References found in Open Access J Urol. Author manuscript; available in PMC 2013 Jul 8.
Survivina ML 100 Multiple Multiple 114
OFA-iLR OF 100 A2 NR 115116
IGFBP3ab ML 97 NR Multiple 117118
EphA2a ML > 90 A2 DR4 1744119
RU2AS Antisense
transcript
> 90 B7 NR 120
G250
(CA-IX) ab
RCC 90 A2, A24 Multiple 4751
EGFRab ML 85 A2 NR 121122
HIFPH3a ML 85 A24 NR 123
c-Meta ML > 80 A2 NR 124
WT-1a ML 80 A2, A24 NR 125128
MUC1ab ML 76 A2 DR3 46129130
5T4 ML 75 A2, Cw7 DR4 54131133
iCE aORF 75 B7 NR 134
MMP7a ML 75 A3 Multiple 117135136
Cyclin D1a ML 75 A2 Multiple 117137138
HAGE b CT 75 A2 DR4 139
hTERT ab ML > 70 Mutliple Multiple 140142
FGF-5 Protein splice variant > 60 A3 NR 143
mutVHLab ML > 60 NR NR 144
MAGE-A3 b CT 60 Multiple Multiple 145
SART-3 ML 57 Mulitple NR 146149
SART-2 ML 56 A24 NR 150
PRAME b CT 40 Multiple NR 151154
p53ab Mutant/WT
ML
32 Mutliple Multiple 155156
MAGE-A9b CT >30 A2 NR 157
MAGE-A6b CT 30 Mutliple DR4 18158
MAGE-D4b CT 30 A25 NR 159
Her2/neua ML 1030 Multiple Multiple 45160164
SART-1a ML 25 Multiple NR 165167
RAGE-1 CT (ORF2/5) 21 Mutliple Multiple 151157168169
TRP-1/ gp75 ML 11 A31 DR4 151170172

A summary is provided for RCCAA that have been defined at the molecular level. RCCAA are characterized with regard to their antigen category, their prevalence of (over)expression among total RCC specimens evaluated, whether RCCAA expression is modulated by hypoxia or tumor DNA methylation status, and which HLA class I and class II alleles have been reported to serve as presenting molecules for T cell recognition of peptides derived from a given RCCAA.

Abbreviations: CT = Cancer-Testis Antigens; ML = Multi-lineage Antigens; NR = Not Reported; OF = Oncofetal Antigen; aORF = altered open reading frame; ORF = open reading frame; RCC = Renal cell carcinoma; WT = Wild-Type;

aHypoxia-Induced;

bHypomethylation-Induced.

PMC full text: Open Access J Urol. Author manuscript; available in PMC 2013 Jul 8.

Open Access J Urol. 2010 Aug; 2010(2): 125–141. doi:  10.2147/RRU.S7242

Expected Impact on Teff versus Suppressor Cells
Co-Therapeutic Agent Teff
priming
Teff
function
Teff
survival
Teff
(TME)
Treg/
MDSC
References found in Open Access J Urol. Author manuscript; available in PMC 2013 Jul 8.
Cytokines
IL-2 +/− ↑ (Treg) 173175
IL-7 ↑ (Treg) 176178
IL-12 – (Treg), ↓ (MDSC) 179181
IL-15 ↑ (Treg)* 182183
IL-18 ↓ (Treg) 184186
IL-21 ? +/− (Treg) 187190
IFN-α +/− (Treg) 175191194
IFN-γ -? ? ↑ ↑ (Treg); ↑ ?(MDSC) 195197
GM-CSF ? ↑ (Treg); ↑(MDSC) 198202
Coinhibitory Antagonist
CTLA-4 ? ↓ (Treg) 203204
PD1/PD1L ↓ (Treg) 205207
Costimulatory Agonist
CD40/CD40L ↑ (Treg); ↑(MDSC) 208211
GITR/GITRL ↓ (Treg); ↓ (MDSC) 212213
OX40/OX86 ↑↓ (Treg); ↓ (MDSC) 214219
4-1BB/4-1BBL ↑ (Treg) 220224
TLR Agonists
Imiquimod (TLR7) ? 225227
Resiquimod (TLR8) ? ? 228229
CpG (TLR9) ↓ (Treg) 230232
Anti-Angiogenic
VEGF-Trap ? ? 233
Sunitinib ? ↓ (Treg/MDSC) 98100234
Sorafenib ? ↓ (MDSC) 235
Bevacizumab ? ? ↓ (MDSC) 236237
Gefitinib (IRESSA) ? ? ? ? ? 238239
Cetuximab ? ? ? ? 240
mTOR Inhibitors
Temsirolimus/Everolimus ? ↓ (Treg) 241
Treg/MDSC Inhibitors
Iplimumab (CTLA-4) ? ↓ (Treg) 242243
ONTAK (CD25) +/− +/− ? ? ↓ (Treg) 244
Anti-TGFβ/TGFβR ↓ (Treg) 245247
Anti-IL10/IL10R +/− ↓ (Treg) 248249
Anti-IL35/IL35R ↑? ↑? ↑? ↑? ↓ (Treg) 250
1-methyl trytophan ? ? ↓ (MDSC) 251
ATRA ? ? ↑ (Treg), ↓ (MDSC) 9093

Agents that are currently or soon-to-be in clinical trials are summarized with regard to their anticipated impact(s) on Type-1 anti-tumor T cell (Te) activation, function, survival and recruitment into the TME. Additional anticipated effects of drugs on suppressor cells (Treg and MDSC) are also summarized. Key: ↑, agent is expected to increase parameter; ↓, agent is expected to inhibit parameter; +/−, minimal increase or decrease is expected in parameter as a consequence of treatment with agent; ?, unknown effect of agent on parameter.

Abbreviations: ATRA, all-trans retinoic acid; CTLA-4, cytotoxic T Lymphocyte antigen 4; GITR(L), glucocorticoid-induced TNF receptor (ligand); GM-CSF, granulocyte-macrophage colony stimulating factor; IFN, interferon; IL, interleukin; MDSC, myeloid-derived suppressor cell; PD1/PD1L, programmed cell death 1 (ligand); TGF-β(R), tumor necrosis factor-β(receptor); TLR, Toll-like receptor; TME, tumor microenvironment; Treg, regulatory T cell; VEGF, vascular endothelial growth factor.

Alternative and Complementary Therapies for Cancer:

  • Art therapy
  • Dance or movement therapy
  • Exercise
  • Meditation
  • Music therapy
  • Relaxation exercises

Mol Cancer Res. 2012 Jul; 10(7): 859–880. Published online 2012 May 25. doi:  10.1158/1541-7786.MCR-12-0117 PMCID: PMC3399969 NIHMSID: NIHMS380694

State-of-the-science: An update on renal cell carcinoma

Eric Jonasch,1 Andrew Futreal,1 Ian Davis,2 Sean Bailey,2 William Y. Kim,2 James Brugarolas,3 Amato Giaccia,4 Ghada Kurban,5 Armin Pause,6 Judith Frydman,4 Amado Zurita,1 Brian I. Rini,7 Pam Sharma,8Michael Atkins,9 Cheryl Walker,8,* and W. Kimryn Rathmell2,*

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[Discovery Medicine; ISSN: 1539-6509; Discov Med 18(101):341-350, December 2014.Copyright © Discovery Medicine. All rights reserved.]

 

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Precision Medicine for Future of Genomics Medicine is The New Era

Demet Sag, PhD, CRA, GCP

 

Are we there yet?  Life is a journey so the science.

Governor Brown announced Precision Medicine initiative for California on April 14, 2015.  UC San Francisco is hosting the two-year initiative, through UC Health, which includes UC’s five medical centers, with $3 million in startup funds from the state. The public-private initiative aims to leverage these funds with contributions from other academic and industry partners.

With so many campuses spread throughout the state and so much scientific, clinical and computational expertise, the UC system has the potential to bring it all together, said Atul Butte, MD, PhD, who is leading the initiative.

At the beginning of 2015 President Obama signed this initiative and assigned people to work on this project.

Previously NCI Director Harold Varmus, MD said that “Precision medicine is really about re-engineering the diagnostic categories for cancer to be consistent with its genomic underpinnings, so we can make better choices about therapy,” and “In that sense, many of the things we’re proposing to do are already under way.”

The proposed initiative has two main components:

  • a near-term focus on cancers and
  • a longer-term aim to generate knowledge applicable to the whole range of health and disease.

Both components are now within our reach because of advances in basic research, including molecular biology, genomics, and bioinformatics. Furthermore, the initiative taps into converging trends of increased connectivity, through social media and mobile devices, and Americans’ growing desire to be active partners in medical research.

Since the human genome is sequenced it became clear that actually there are few genes than expected and shared among organisms to accomplish same or similar core biological functions.  As a result, knowledge of the biological role of such shared proteins in one organism can be transferred to another organism.

I remember when I was screening the X-chromosome by using deletion/duplication mapping and using P elements and bar balancers as a tool to keep the genome stable to identify transregulating elements of ovo gene, female germline specific Drosophila melanogaster germline sex determination gene. At the time for my dissertation, I screened X-chromosome using 45 deficiency strains, I found that these trans-regulating regions were grouped into 12 loci based on overlapping cytology. Five regions were trans-regulating activators, and seven were trans-regulating repressors; extrapolating to the entire genome, this result predicted nearly 85 loci. This one gene may expressed three proteins at different time of development and activate/downregulate various regions to accommadate proper system development in addition to auto-regulate and gene dose responses. Drosophila has only four chromosomes but the cellular interactions and signaling mechanisms are still complicated yet as not complicated as human. I do appreciate the new applications and upcoming changes.

Now, the technology is much better and precision is the key to establish to use in clinics.  However, we have new issues to overcome like computing such a big data, align properly, analyze effectively, compare and contrast the outcomes to identify the variations that may function in on  population, or two etc. At the end of the day collaboration, standardization, and data sharing are few of the key factors.

It is necessary to generate a dynamic yet controlled standardized collection of information with ever changing and accumulating data so  Gene Ontology Consortium is created. Three independent ontologies can be reached at  (http://www.geneontology.org) developed based on :

  1. biological process,
  2. molecular function and
  3. cellular component.

Precision-Medicine-Timeline2

We need a common language for annotation for a functional conservation. Genesis of the grand biological unification made it possible to complete the genomic sequences of not only human but also the main model organisms and more. some examples include:

  • the budding yeast, Saccharomyces cerevisiae,
  • the nematode worm Caenorhabditis elegans
  • the fruitfly Drosophila melanogaster,
  • the flowering plant Arabidopsis thaliana
  • fission yeast Schizosaccharomyces pombe
  • the  mouse , Mus musculus

On the other hand, as we know there are allelic variations that underlie common diseases and complete genome sequencing for many individuals with and without disease is required.  However, there are advantages and disadvantages as we can carry out partial surveys of the genome by genotyping large numbers of common SNPs in genome-wide association studies but there are problems such as computing the data efficiently and sharing the information without tempering privacy. Therefore we should be mindful about few main conditions including:

  1. models of the allelic architecture of common diseases,
  2. sample size,
  3. map density and
  4. sample-collection biases.

This will lead into the cost control and efficiency while identifying genuine disease-susceptibility loci. The genome-wide association studies (GWAS) have progressed from assaying fewer than 100,000 SNPs to more than one million, and sample sizes have increased dramatically as the search for variants that explain more of the disease/trait heritability has intensified.

In addition, we must translate this sequence information from genomics locus of the genes to function with related polymorphism of these genes so that possible patterns of the gene expression and disease traits can be matched. Then, we may develop precision technologies for:

  1. Diagnostics
  2. Targeted Drugs and Treatments
  3. Biomarkers to modulate cells for correct functions

With the knowledge of:

  1. gene expression variations
  2. insight in the genetic contribution to clinical endpoints ofcomplex disease and
  3. their biological risk factors,
  4. share etiologic pathways

therefore, requires an understanding of both:

  • the structure and
  • the biology of the genome.

These studies demonstrated hundreds of associations of common genetic variants with over 80 diseases and traits collected under a controlled online resource.  However, identifying published GWAS can be challenging as a simple PubMed search using the words “genome wide association studies”  may be easily populated with unrelevant  GWAS.

National Human Genome Research Institute (NHGRI) Catalog of Published Genome-Wide Association Studies (http://www.genome.gov/gwastudies), an online, regularly updated database of SNP-trait associations extracted from published GWAS was developed.

Therefore, sequencing of a human genome is a quite undertake and requires tools to make it possible:

  • to explore the genetic component in complex diseases and
  • to fully understand the genetic pathways contributing to complex disease

Examples of Gene Ontology

The rapid increase in the number of GWAS provides an unprecedented opportunity to examine the potential impact of common genetic variants on complex diseases by systematically cataloging and summarizing key characteristics of the observed associations and the trait/disease associated SNPs (TASs) underlying them.

 

With this in mind, many forms can be established:

  1. to describe the features of this resource and the methods we have used to produce it,
  2. to provide and examine key descriptive characteristics of reported TASs such as estimated risk allele frequencies and odds ratios,
  3. to examine the underlying functionality of reported risk loci by mapping them to genomic annotation sets and assessing overrepresentation via Monte Carlo simulations and
  4. to investigate the relationship between recent human evolution and human disease phenotypes.

 

This procedure has no clear path so there are several obstacles in the actual functional variant that is often unknown. This may be due to:

  1. trait/disease associated SNPs (TASs),
  2. a well known SNP+ strong linkage disequilibrium (LD) with the TAS,
  3. an unknown common SNP tagged by a haplotype
  4. rare single nucleotide variant tagged by a haplotype on which the TAS occurs, or
  5. Copy Number variation (CNV), a linked copy number variant.

 

There can be other factors such as

  • Evolution,
  • Natural Selection
  • Environment
  • Pedigree
  • Epigenetics

 

Even though heritage is another big factor, the concept of heritability and its definition as an estimable, dimensionless population parameter as introduced by Sewall Wright and Ronald Fisher almost a century ago.

 

As a result, heritability gain interest since it allows us to compare of the relative importance of genes and environment to the variation of traits within and across populations. The heritability is an ongoing mechanism and  remains as a main factor:

 

  • to selection in evolutionary biology and agriculture, and
  • to the prediction of disease risk in medicine.

Reported TASs associated with two or more distinct traits

Chromosomal region Rs number(s) Attributed genes Associated traits reported in catalog
1p13.2 rs2476601, rs6679677 PTPN22 Crohn’s disease, type 1 diabetes, rheumatoid arthritis
1q23.2 rs2251746, rs2494250 FCER1A Serum IgE levels, select biomarker traits (MCP1)
2p15 rs1186868, rs1427407 BCL11A Fetal hemoglobin, F-cell distribution
2p23.3 rs780094 GCKR CRP, lipids, waist circumference
6p21.33 rs3131379, rs3117582 HLA / MHC region Systemic lupus erythematosus, lung cancer, psoriasis, inflammatory bowel disease, ulcerative colitis, celiac disease, rheumatoid arthritis, juvenile idiopathic arthritis, multiple sclerosis, type 1 diabetes
6p22.3 rs6908425, rs7756992, rs7754840, rs10946398, rs6931514 CDKAL1 Crohn’s disease, type 2 diabetes
6p25.3 rs1540771, rs12203592, rs872071 IRF4 Freckles, hair color, chronic lymphocytic leukemia
6q23.3 rs5029939, rs10499194 TNFAIP3 Systemic lupus erythematosus, rheumatoid arthritis
7p15.1 rs1635852, rs864745 JAZF1 Height, type 2 diabetes*
8q24.21 rs6983267 Intergenic Prostate or colorectal cancer, breast cancer
9p21.3 rs10811661, rs1333040, rs10811661, rs10757278, rs1333049 CDKN2A, CDKN2B Type 2 diabetes, intracranial aneurysm, myocardial infarction
9q34.2 rs505922, rs507666, rs657152 ABO Protein quantitative trait loci (TNF-α), soluble ICAM-1, plasma levels of liver enzymes (alkaline phosphatase)
12q24 rs1169313, rs7310409, rs1169310, rs2650000 HNF1A Plasma levels of liver enzyme (GGT), C-reactive protein, LDL cholesterol
16q12.2 rs8050136, rs9930506, rs6499640, rs9939609, rs1121980 FTO Type 2 diabetes, body mass index or weight
17q12 rs7216389, rs2872507 ORMDL3 Asthma, Crohn’s disease
17q12 rs4430796 TCF2 Prostate cancer, type 2 diabetes
18p11.21 rs2542151 PTPN2 Type 1 diabetes, Crohn’s disease
19q13.32 rs4420638 APOE, APOC1, APOC4 Alzheimer’s disease, lipids

* The well known association of JAZF1 with prostate cancer was reported with a p value of 2 × 10−6, which did not meet the threshold of 5 × 10−8 for this analysis.

PMC full text: Proc Natl Acad Sci U S A. 2009 Jun 9; 106(23): 9362–9367.Published online 2009 May 27. doi:  10.1073/pnas.0903103106

.

Allele-Frequency Data for Nine Reproducible Associations

frequency
gene diseasea SNP associated alleleb Europeand Africane δf FST reference(s)c
CTLA4 T1DM Thr17Ala Ala .38 (1,670) .209 (402) .171 .06 Osei-Hyiaman et al. 2001; Lohmueller et al. 2003
DRD3 Schizophrenia Ser9Gly Ser/Ser .67 (202) .116 (112) .554 .458 Crocq et al. 1996; Lohmueller et al.2003
AGT Hypertension Thr235Met Thr .42 (3,034) .91 (658) .49 .358 Rotimi et al. 1996; Nakajima et al.2002
PRNP CJD Met129Val Met .72 (138) .556 (72) .164 .049 Hirschhorn et al. 2002; Soldevila et al. 2003
F5 DVT Arg506Gln Gln .044 (1,236) .00 (251) .044 .03 Rees et al. 1995; Hirschhorn et al.2002
HFE HFE Cys382Tyr Tyr .038 (2,900) .00 (806) .038 .024 Feder et al. 1996; Merryweather-Clarke et al. 1997
MTHFR DVT C677T T .3 (188) .066 (468) .234 .205 Schneider et al. 1998; Ray et al.2002
PPARG T2DM Pro12Ala Pro .925 (120) 1.0 (120) .075 .067 Altshuler et al. 2000HapMap Project
KCNJ11 T2DM Asp23Lys Lys .36 (96) .09 (98) .27 .182 Florez et al. 2004

aCJD = Creutzfeldt-Jacob disease; DVT = deep venous thrombosis; HFE = hemochromatosis; T1DM = type I diabetes; T2DM = type II diabetes.

bThe associated allele is the SNP associated with disease, regardless of whether it is the derived or the ancestral allele. The frequencies for this allele are given.

cThe reference that claims this to be a reproducible association, as well as the reference from which the allele frequencies were taken. For allele frequencies obtained from a meta-analysis, only the reference claiming reproducible association is given.

dAllele frequency obtained from the literature involving a European population. Either the general population frequency or the frequency in control groups in an association study was used. To reduce bias, when a control frequency was used for Europeans, a control frequency was also used for Africans. The total number of chromosomes surveyed is given in parentheses after each frequency.

eAllele frequency obtained from the literature involving a West African population. The total number of chromosomes surveyed is given in parentheses after each frequency.

fδ = The difference in the allele frequency between Europeans and Africans.

 

PMC full text:

Am J Hum Genet. 2006 Jan; 78(1): 130–136.Published online 2005 Nov 16. doi:  10.1086/499287Copyright/License ►Request permission to reuse

Allele-Frequency Data for 39 Reported Associations

frequency
gene disease/phenotypea SNP associated alleleb Europeand Africane δf FST referencec
ADRB1 MI Arg389Gly Arg .717 (46) .467 (30) .251 .1 Iwai et al. 2003
ALOX5AP MI, stroke rs10507391 T .682 (44) .159 (44) .523 .425 Helgadottir et al. 2004
CAT Hypertension −844 (C/T) Tg .714 (42) .659 (44) .055 0 Jiang et al. 2001
CCR2 AIDS susceptibility Ile64Val Val .87 (46) .813 (48) .057 0 Smith et al. 1997
CD36 Malaria Y to stop Stop 0 (46) .083 (48) .083 .062 Aitman et al. 2000
F13 MI Val34Leu Val .762 (42) .795 (44) .033 0 Kohler et al. 1999
FGA Pulmonary embolism Thr312Ala Ala .2 (40) .5 (42) .3 .159 Carter et al. 2000
GP1BA CAD Thr145Met Met .022 (46) .167 (48) .145 .095 Gonzalez-Conejero et al.1998
ICAM1 MS Lys469Glu Lys .643 (42) .875 (48) .232 .12 Nejentsev et al. 2003
ICAM1 Malaria Lys29Met Met 0 (46) .354 (48) .354 .335 Fernandez-Reyes et al.1997
IFNGR1 Hp infection −56 (C/T) T .455 (44) .604 (48) .15 .023 Thye et al. 2003
IL13 Asthma −1055 (C/T) T .196 (46) .25 (44) .054 0 van der Pouw Kraan et al. 1999
IL13 Bronchial asthma Arg110Gln Gln .273 (44) .119 (42) .154 .05 Heinzmann et al. 2003
IL1A AD −889 (C/T) T .295 (44) .391 (46) .096 0 Nicoll et al. 2000
IL1B Gastric cancer −31 (C/T) T .826 (46) .375 (48) .451 .335 El-Omar et al. 2000
IL3 RA −16 (C/T) C .739 (46) .875 (48) .136 .037 Yamada et al. 2001
IL4 Asthma −590 (T/C) T .174 (46) .708 (48) .534 .436 Noguchi et al. 1998
IL4R Asthma Gln576Arg Arg .295 (44) .565 (46) .27 .118 Hershey et al. 1997
IL6 Juvenile arthritis −174 (C/G) G .5 (44) 1 (46) .5 .494 Fishman et al. 1998
IL8 RSV bronchiolitis −251 (T/A) Th .659 (44) .229 (48) .43 .301 Hull et al. 2000
ITGA2 MI 807 (C/T) T .316 (38) .25 (48) .066 0 Moshfegh et al. 1999
LTA MI Thr26Asn Asn .357 (42) .5 (44) .143 .018 Ozaki et al. 2002
MC1R Fair skin Val92Met Met .068 (44) 0 (44) .068 .047 Valverde et al. 1995
NOS3 MI Glu298Asp Asp .5 (44) .136 (44) .364 .247 Shimasaki et al. 1998
PLAU AD Pro141Leu Pro .659 (44) .979 (48) .32 .287 Finckh et al. 2003
PON1 CAD Arg192Gln Arg .174 (46) .727 (44) .553 .461 Serrato and Marian 1995
PON2 CAD Cys311Ser Ser .826 (46) .762 (42) .064 0 Sanghera et al. 1998
PTGS2 Colon cancer −765 (G/C) C .238 (42) .292 (48) .054 0 Koh et al. 2004
PTPN22i RA Arg620Trp Trp .084 (1,120) .024 (818) .059 .03 Begovich et al. 2004
SELE CAD Ser128Arg Arg .091 (44) .021 (48) .07 .025 Wenzel et al. 1994
SELL IgA nephropathy Pro238Ser Ser .065 (46) .333 (48) .268 .183 Takei et al. 2002
SELP MI Thr715Pro Thr .864 (44) .977 (44) .114 .063 Herrmann et al. 1998
SFTPB ARDS Ile131Thr Thr .5 (44) .348 (46) .152 .025 Lin et al. 2000
SPD RSV infection Met11Thr Met .568 (44) .478 (46) .09 0 Lahti et al. 2002
TF AD Pro570Ser Pro .957 (46) .935 (46) .022 0 Zhang et al. 2003
THBD MI Ala455Val Ala .87 (46) .848 (46) .022 0 Norlund et al. 1997
THBS4 MI Ala387Pro Pro .341 (44) .083 (48) .258 .166 Topol et al. 2001
TNFA Infectious disease −308 (A/G) A .182 (44) .205 (44) .023 0 Bayley et al. 2004
VCAM1 Stroke in SCD Gly413Ala Gly 1 (46) .938 (48) .063 .041 Taylor et al. 2002

aAD = Alzheimer disease; AIDS = acquired immunodeficiency syndrome; ARDS = acute respiratory distress syndrome; CAD = coronary artery disease; Hp = Helicobacter pylori; MI = myocardial infarction; MS = multiple sclerosis; RA = rheumatoid arthritis; RSV = respiratory syncytial virus; SCD = sickle cell disease.

bThe associated allele is the SNP associated with disease, regardless of whether it is the derived or the ancestral allele. The frequencies for this allele are given.

cThe reference that reported association with the listed disease/phenotype.

dFrequency obtained from the Seattle SNPs database for the European sample. The total number of chromosomes surveyed is given in parentheses after each frequency.

eFrequency obtained from the Seattle SNPs database for the African American sample. The total number of chromosomes surveyed is given in parentheses after each frequency.

fδ = The difference in the allele frequency between African Americans and Europeans.

gAssociated allele in database is A.

hAssociated allele in reference is A.

iThis SNP was not from the Seattle SNPs database; instead, allele frequencies from Begovich et al. (2004) were used.

 

They reported that “The SNPs associated with common disease that we investigated do not show much higher levels of differentiation than those of random SNPs. Thus, in these cases, ethnicity is a poor predictor of an individual’s genotype, which is also the pattern for random variants in the genome. This lends support to the hypothesis that many population differences in disease risk are environmental, rather than genetic, in origin. However, some exceptional SNPs associated with common disease are highly differentiated in frequency across populations, because of either a history of random drift or natural selection. The exceptional SNPs given  are located in AGT, DRD3, ALOX5AP, ICAM1, IL1B, IL4, IL6, IL8, and PON1.

Of note, evidence of selection has been observed for AGT (Nakajima et al. 2004), IL4(Rockman et al. 2003), IL8 (Hull et al. 2001), and PON1 (Allebrandt et al. 2002). Yet, for the vast majority of the common-disease–associated polymorphisms we examined, ethnicity is likely to be a poor predictor of an individual’s genotype.”

 

In 2002 the International HapMap Project was launched:

  • to provide a public resource
  • to accelerate medical genetic research.

Two Hapmap projects were completed.

In phase I the objective was to genotype at least one common SNP every 5 kilobases (kb) across the euchromatic portion of the genome in 270 individuals from four geographically diverse population.

In Phase II of the HapMap Project, a further 2.1 million SNPs were successfully genotyped on the same individuals.

The re-mapping of SNPs from Phase I of the project identified 21,177 SNPs that had an ambiguous position or some other feature indicative of low reliability. These are not included in the filtered Phase II data release. All genotype data are available from the HapMap Data Coordination Center located at (http://www.hapmap.org) and dbSNP (http://www.ncbi.nlm.nih.gov/SNP).

In the Phase II HapMap we identified 32,996 recombination hotspots (an increase of over 50% from Phase I) of which 68% localized to a region of≤5 kb. The median map distance induced by a hotspot is 0.043 cM (or one crossover per 2,300 meioses) and the hottest identified, on chromosome 20, is 1.2 cM (one crossover per 80 meioses). Hotspots account for approximately 60% of recombination in the human genome and about 6% of sequence.

In addition to many previously identified regions in HapMap Phase I including LARGESYT1 andSULT1C2 (previously called SULT1C1), about  200 regions identified from the Phase II HapMap that include many established cases of selection, such as the genes HBB andLCT, the HLA region, and an inversion on chromosome 17. Finally, in the future, whole-genome sequencing will provide a natural convergence of technologies to type both SNP and structural variation. Nevertheless, until that point, and even after, the HapMap Project data will provide an invaluable resource for understanding the structure of human genetic variation and its link to phenotype.

HMM libraries, such as PANTHER, Pfam, and SMART, are used primarily

  • to recognize and
  • to annotate conserved motifs in protein sequences.

In the genomic era, one of the fundamental goals is to characterize the function of proteins on a large scale.

PANTHER, for relating protein sequence relationships to function relationships in a robust and accurate way under two main parts:

  • the PANTHER library (PANTHER/LIB)- collection of “books,” each representing a protein family as a multiple sequence alignment, a Hidden Markov Model (HMM), and a family tree.
  • the PANTHER index (PANTHER/X)- ontology for summarizing and navigating molecular functions and biological processes associated with the families and subfamilies.

 

PANTHER can be applied on three areas of active research:

  • to report the size and sequence diversity of the families and subfamilies, characterizing the relationship between sequence divergence and functional divergence across a wide range of protein families.
  • use the PANTHER/X ontology to give a high-level representation of gene function across the human and mouse genomes.
  • to rank missense single nucleotide polymorphisms (SNPs), on a database-wide scale, according to their likelihood of affecting protein function.

PRINTS is ” a compendium of protein motif ‘fingerprints’. A fingerprint is defined as a group of motifs excised from conserved regions of a sequence alignment, whose diagnostic power or potency is refined by iterative databasescanning (in this case the OWL composite sequence database)”.

The information contained within PRINTS is distinct from, but complementary to the consensus expressions stored in the widely-used PROSITE dictionary of patterns.

However, the position-specific amino acid probabilities in an HMM can also be used to annotate individual positions in a protein as being conserved (or conserving a property such as hydrophobicity) and therefore likely to be required for molecular function. For example, a mutation (or variant) at a conserved position is more likely to impact the function of that protein.

In addition, HMMs from different subfamilies of the same family can be compared with each other, to provide hypotheses about which residues may mediate the differences in function or specificity between the subfamilies.

Several computational algorithms and databases for comparing protein sequences developed and matured profile methods (Gribskov et al. 1987;Henikoff and Henikoff 1991Attwood et al. 1994):

  1. particularly Hidden Markov Models (HMM;Krogh et al. 1994Eddy 1996) and
  2. PSI-BLAST (Altschul et al. 1997),

The profile has a different amino acid substitution vector at each position in the profile, based on the pattern of amino acids observed in a multiple alignment of related sequences.

Profile methods combine algorithms with databases:

A group of related sequences is used to build a statistical representation of corresponding positions in the related proteins. The power of these methods therefore increases as new sequences are added to the database of known proteins.

Multiple sequence alignments (Dayhoff et al. 1974) and profiles have allowed a systematic study of related sequences. One of the key observations is that some positions are “conserved,” that is, the amino acid is invariant or restricted to a particular property (such as hydrophobicity), across an entire group of related sequences.

The dependence of profile and pattern-matching approaches (Jongeneel et al. 1989) on sequence databases led to the development of databases of profiles

  1. BLOCKS,Henikoff and Henikoff 1991;
  2. PRINTS,Attwood et al. 1994) and
  3. patterns (Prosite,Bairoch 1991) that could be searched in much the same way as sequence databases.

 

Among the most widely used protein family databases are

  1. Pfam (Sonnhammer et al. 1997;Bateman et al. 2002) and
  2. SMART (Schultz et al. 1998;Letunic et al. 2002), which combine expert analysis with the well-developed HMM formalism for statistical modeling of protein families (mostly families of related protein domains).

Either knowing its family membership to predict its function, or subfamily within that family is enough (Hannenhalli and Russell 2000).

  • Phylogenetic trees (representing the evolutionary relationships between sequences) and
  • dendrograms (tree structures representing the similarity between sequences) (e.g.,Chiu et al. 1985Rollins et al. 1991).

 

The PANTHER/LIB HMMs can be viewed as a statistical method for scoring the “functional likelihood” of different amino acid substitutions on a wide variety of proteins. Because it uses evolutionarily related sequences to estimate the probability of a given amino acid at a particular position in a protein, the method can be referred to as generating position-specific evolutionary conservation” (PSEC) scores.

 

The process for building PANTHER families include:

  1. Family clustering.
  2. Multiple sequence alignment (MSA), family HMM, and family tree building.
  3. Family/subfamily definition and naming.
  4. Subfamily HMM building.
  5. Molecular function and biological process association.

Of these, steps 1, 2, and 4 are computational, and steps 3 and 5 are human-curated (with the extensive aid of software tools).

 

Conclusion:

Precision medicine effort is the beginning of a new journey to provide better health solutions.

 

Further Reading and References:

Human Phenome Project:

Freimer N., Sabatti C. The human phenome project. Nat. Genet. 2003;34:15–21.

Jones R., Pembrey M., Golding J., Herrick D. The search for genenotype/phenotype associations and the phenome scan. Paediatr. Perinat. Epidemiol. 2005;19:264–275.

Stearns F.W. One hundred years of pleiotropy: A retrospective. Genetics.2010;186:767–773.

Welch J.J., Waxman D. Modularity and the cost of complexity. Evolution.2003;57:1723–1734.

Albert A.Y., Sawaya S., Vines T.H., Knecht A.K., Miller C.T., Summers B.R., Balabhadra S., Kingsley D.M., Schluter D. The genetics of adaptive shape shift in stickleback: Pleiotropy and effect size. Evolution. 2008;62:76–85.

Brem R.B., Yvert G., Clinton R., Kruglyak L. Genetic dissection of transcriptional regulation in budding yeast. Science. 2002;296:752–755.

Morley M., Molony C.M., Weber T.M., Devlin J.L., Ewens K.G., Spielman R.S., Cheung V.G. Genetic analysis of genome-wide variation in human gene expression. Nature. 2004;430:743–747. [PMC free article] [PubMed]

Wagner G.P., Zhang J. The pleiotropic structure of the genotype-phenotype map: The evolvability of complex organisms. Nat. Rev. Genet. 2011;12:204–213.

Cooper Z.N., Nelson R.M., Ross L.F. Informed consent for genetic research involving pleiotropic genes: An empirical study of ApoE research. IRB. 2006;28:1–11.

Model Organisms:

Worm Sequencing Consortium. The C. elegans Sequencing Consortium Genome sequence of the nematode C. elegans: a platform for investigating biology. Science.1998;282:2012–2018.

Adams MD, et al. The genome sequence of Drosophila melanogasterScience.2000;287:2185–2195.

Meinke DW, et al. Arabidopsis thaliana: a model plant for genome analysis. Science. 1998;282:662–682. [PubMed]

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Genomics & Genetics of Cardiovascular Disease Diagnoses: A Literature Survey of AHA’s Circulation Cardiovascular Genetics, 3/2010 – 3/2013

 

Curators: Aviva Lev-Ari, PhD, RN and Larry H. Bernstein, MD, FCAP

 

348 articles that appeared in AHA’s Circulation Cardiovascular Genetics, 3/2010 – 3/2013 were classified by the curators of this article into the following TEN categories. The first 9, represent DIAGNOSES of cardiovascular diseases, the last, deals with Pharmacogenomics.

The Cardiovascular Diagnoses that were covered in the period of 3/2010 – 3/2013, include the following:

  • Preventative Cardiology
  • MicroRNA in Serum as Bimarker for Cardiovascular Pathologies: acute myocardial infarction, viral myocarditis, diastolic dysfunction, and acute heart failure
  • Genetic Determinants of Potassium Sensitivity and Hypertension
  • Heart and Aging Research in Genomic Epidemiology: 1700 MIs and 2300 coronary heart disease events among about 29 000 eligible patients
  • Genetics of CVD and Hyperlipidemia, Hyper Cholesterolemia, Metabolic Syndrome
  • Genomics and Valvular Disease
  • Pharmacogenomics

Introductions

Larry H. Bernstein, MD, FCAP

 

The curation of this large amount of material in 10 categories begins with a first chapter on preventative cardiology, which has had much public attention for the last decade.  Much of the concern with preventive cardiology has emphasized diet and exercise.  There is much to be said about this in articles not yet written.  However, there are several decades of research on the amino acid composition of foods, and the essential fatty acids, that indicates an essential balance between proinflammatory and antiinflammatory fatty acids in polyunsaturated fatty acids, and of the harmful effects of saturated fats.  There is also much to be said of essential amino acids, and in particular, those essential for methylation processes, and sulfur metabolism.

The next eight chapters are all concerned with genomics in cardiovascular disease.  This is in no small part a follow up on the completion of the genetic code in 2003, a seminal event.  Let us look at these in clusters.

[1]   microRNA in serum is now considered for a biomarker for cardiovascular disease.  It can be measured at very low levels, but we don’t yet know where it fits.   It might be more revealing once we understand the adaptive mechanism in development of congestive heart failure, renal hypertension, and post-genomic events.

[2]  It appears to me that potassium sensitivity and hypertension approached from the genomic side is more complicate.  Why is that?   The kidney excretes a sodium load and in metabolic acidosis, the serum potassium rises with a metabolic acidemia that can’t be compensated by the respiratory loss of CO2 through the carbonic anhydrase mechanism.

[3]  Heart and aging research is a rich area for work on the long term post-genomic changes, and it involves a large population base.

[4][5]  The genomics of cardiac dysrrhytmias and cardiomyopathies will open new doors into our understanding of the mechanisms of these diseases, and perhaps find therapeutic targets.  There has been a large volume of work on lipid synthesis, the role of the liver in generating apolipoproteins, and this has new answers on the way.  The most important feature, not readily accepted is the measurement of particles, which has now been done by a monoclonal antibody.  Metabolic syndrome brings together adipose tissue metabolism, endocrine and changes in CRP and IL-1.

[6]   Vascular pathologies and coagulation, hyperviscosity has had an enormous increase in intensity of research.  The concept of plaque rupture to account for all AMIs is being modified, and the high sensitivity cardio-specific troponins have become the most widely use test.

[7]  The genomics of valvular disease fits with the increased surgical procedures for valvular disease related to atheroschlerosis and advent of minimally invasive surgical procedures for the reapir and replacement of valves, procedure called TAVR vs. Openhealrt surgery for valve replacement.

[8]  Inherited cardiovascular disease is an older family of disorders, going back to Victor McKusik, and also the “Blue Baby” operation, both at Johns Hopkins.

[9] Pharmacogenomics is a vary active field of investigation and has uncovered inter-individual differences in handling Warfarin as a starter.

 

 

Preventative Cardiology

 

Methods in Genetics and Clinical Interpretation Randomized Trial of Personal Genomics for Preventive Cardiology Design and Challenges

Joshua W. Knowles, MD, PhD, Themistocles L. Assimes, MD, PhD, Michaela Kiernan, PhD, Aleksandra Pavlovic, BS, Benjamin A. Goldstein, PhD, Veronica Yank, MD, Michael V. McConnell, MD, Devin Absher, PhD, Carlos Bustamante, PhD, Euan A. Ashley, MD, DPhil and John P.A. Ioannidis, MD, DSc

Author Affiliations

From the Division of Cardiovascular Medicine (J.W.K., T.L.A., A.P., M.V.M., E.A.A.), Stanford Prevention Research Center (M.K., V.Y., J.P.A.I.), Division of General Medical Disciplines (V.Y.), Department of Genetics (C.B.), Department of Health Research and Policy (J.P.A.I.), Stanford University School of Medicine, Stanford, CA; Quantitative Sciences Unit, Stanford University School of Medicine, Palo Alto, CA (B.A.G.); HudsonAlpha Institute for Biotechnology, Huntsville, AL (D.A.); Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, CA (J.P.A.I.).

Correspondence to Joshua W. Knowles, MD, PhD, Stanford University School of Medicine, Division of Cardiovascular Medicine, Falk CVRC, 300 Pasteur Dr, Stanford, CA 94305. E-mail knowlej@stanford.edu

Background

Genome-wide association studies (GWAS) have identified more than 1500 disease-associated single nucleotide polymorphisms (SNPs), including many related to atherosclerotic cardiovascular disease (CVD). Associations have been found for most traditional risk factors (TRFs), including lipids,1,2 blood pressure/hypertension,3,4 weight/body mass index,5,6 smoking behavior,7 and diabetes.8–13 GWAS have also identified susceptibility variants for coronary heart disease (CHD). The first and, so far, strongest of these signals was found in the 9p21.3 locus, where common variants in this region increase the relative risk of CVD by 15% to 30% per risk allele in most race/ethnic groups.13–20 Subsequent large-scale GWAS meta-analyses and replication studies in largely white/European populations have led to the reliable identification of an additional 26 loci conferring susceptibility to CHD,2,20–23 all with substantially lower effects sizes compared with the 9p21 locus. Many of these CVD susceptibility loci appear to be conferring risk independent of TRFs and thus cannot currently be assessed by surrogate clinical measures (Table 1). Among the 27 independent loci identified in the most recent large meta-analyses of CVD, 21 were reported not to be associated with any of the TRFs.20,21

 SOURCE

Circulation: Cardiovascular Genetics 2012; 5: 368-376

doi: 10.1161/ CIRCGENETICS.112.962746

 

 

MicroRNA in Serum as Bimarker for Cardiovascular Pathologies: acute myocardial infarction, viral myocarditis,  diastolic dysfunction, and acute heart failure

Increased MicroRNA-1 and MicroRNA-133a Levels in Serum of Patients With Cardiovascular Disease Indicate Myocardial Damage

 

Yasuhide Kuwabara, MD, Koh Ono, MD, PhD, Takahiro Horie, MD, PhD, Hitoo Nishi, MD, PhD, Kazuya Nagao, MD, PhD, Minako Kinoshita, MD, PhD, Shin Watanabe, MD, PhD, Osamu Baba, MD, Yoji Kojima, MD, PhD, Satoshi Shizuta, MD, Masao Imai, MD, Toshihiro Tamura, MD, Toru Kita, MD, PhD and Takeshi Kimura, MD, PhD

Author Affiliations

From the Department of Cardiovascular Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan (Y. Kuwabara, K.O., T.H., H.N., K.N., M.K., S.W., O.B., Y. Kojima, S.S., M.I., T.T., T. Kimura); and Kobe City Medical Center General Hospital, Kobe, Japan (T. Kita).

Correspondence to Koh Ono, MD, PhD, Department of Cardiovascular Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin-kawahara-cho, Sakyo-ku, Kyoto, Japan 606-8507. E-mail kohono@kuhp.kyoto-u.ac.jp

 

Abstract

Background—Recently, elevation of circulating muscle-specific microRNA (miRNA) levels has been reported in patients with acute myocardial infarction. However, it is still unclear from which part of the myocardium or under what conditions miRNAs are released into circulating blood. The purpose of this study was to identify the source of elevated levels of circulating miRNAs and their function in cardiovascular diseases.

Conclusions—These results suggest that elevated levels of circulating miR-133a in patients with cardiovascular diseases originate mainly from the injured myocardium. Circulating miR-133a can be used as a marker for cardiomyocyte death, and it may have functions in cardiovascular diseases.

SOURCE:

Circulation: Cardiovascular Genetics. 2011; 4: 446-454

Published online before print June 2, 2011,

doi: 10.1161/ CIRCGENETICS.110.958975

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Circulating MicroRNA-208b and MicroRNA-499 Reflect Myocardial Damage in Cardiovascular Disease

Maarten F. Corsten, MD, Robert Dennert, MD, Sylvia Jochems, BSc, Tatiana Kuznetsova, MD, PhD, Yvan Devaux, PhD, Leon Hofstra, MD, PhD, Daniel R. Wagner, MD, PhD, Jan A. Staessen, MD, PhD, Stephane Heymans, MD, PhD and Blanche Schroen, PhD

Author Affiliations

From the Center for Heart Failure Research (M.F.C., R.D., S.J., S.H., B.S.), Cardiovascular Research Institute, Maastricht, The Netherlands; the Division of Hypertension and Cardiovascular Rehabilitation (T.K., J.A.S.), Department of Cardiovascular Diseases, University of Leuven, Leuven, Belgium and Department of Epidemiology, Maastricht University Medical Center, Maastricht, The Netherlands; Centre de Recherche Public–Santé, Luxembourg (Y.D., D.R.W.), Luxembourg; Maastricht University Medical Center (L.H.), Maastricht, The Netherlands; and Centre Hospitalier Luxembourg (D.R.W.), Luxembourg.

Correspondence to Blanche Schroen, PhD, Center for Heart Failure Research, Cardiovascular Research Institute Maastricht, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands. E-mail b.schroen@cardio.unimaas.nl

Drs Heymans and Schroen contributed equally to this work.

Abstract

Background— Small RNA molecules, called microRNAs, freely circulate in human plasma and correlate with varying pathologies. In this study, we explored their diagnostic potential in a selection of prevalent cardiovascular disorders.

Methods and Results— MicroRNAs were isolated from plasmas from well-characterized patients with varying degrees of cardiac damage:

(1) acute myocardial infarction,

(2) viral myocarditis,

(3) diastolic dysfunction, and

(4) acute heart failure.

Plasma levels of selected microRNAs, including heart-associated (miR-1, -133a, -208b, and -499), fibrosis-associated (miR-21 and miR-29b), and leukocyte-associated (miR-146, -155, and -223) candidates, were subsequently assessed using real-time polymerase chain reaction. Strikingly, in plasma from acute myocardial infarction patients, cardiac myocyte–associated miR-208b and -499 were highly elevated, 1600-fold (P<0.005) and 100-fold (P<0.0005), respectively, as compared with control subjects. Receiver operating characteristic curve analysis revealed an area under the curve of 0.94 (P<1010) for miR-208b and 0.92 (P<109) for miR-499. Both microRNAs correlated with plasma troponin T, indicating release of microRNAs from injured cardiomyocytes. In viral myocarditis, we observed a milder but significant elevation of these microRNAs, 30-fold and 6-fold, respectively. Plasma levels of leukocyte-expressed microRNAs were not significantly increased in acute myocardial infarction or viral myocarditis patients, despite elevated white blood cell counts. In patients with acute heart failure, only miR-499 was significantly elevated (2-fold), whereas no significant changes in microRNAs studied could be observed in diastolic dysfunction. Remarkably, plasma microRNA levels were not affected by a wide range of clinical confounders, including age, sex, body mass index, kidney function, systolic blood pressure, and white blood cell count.

Conclusions— Cardiac damage initiates the detectable release of cardiomyocyte-specific microRNAs-208b and -499 into the circulation.

SOURCE:

Circulation: Cardiovascular Genetics. 2010; 3: 499-506

Published online before print October 4, 2010,

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Genetic Determinants of Potassium Sensitivity and Hypertension

 

Integrated Computational and Experimental Analysis of the Neuroendocrine Transcriptome in Genetic Hypertension Identifies Novel Control Points for the Cardiometabolic Syndrome

Ryan S. Friese, PhD, Chun Ye, PhD, Caroline M. Nievergelt, PhD, Andrew J. Schork, BS, Nitish R. Mahapatra, PhD, Fangwen Rao, MD, Philip S. Napolitan, BS, Jill Waalen, MD, MPH, Georg B. Ehret, MD, Patricia B. Munroe, PhD, Geert W. Schmid-Schönbein, PhD, Eleazar Eskin, PhD and Daniel T. O’Connor, MD

Author Affiliations

From the Departments of Bioengineering (R.S.F., G.W.S.-S.), Medicine (R.S.F., A.J.S., F.R., P.S.N., D.T.O.), Pharmacology (D.T.O.), and Psychiatry (C.M.N.), the Bioinformatics Program (C.Y.), and the Institute for Genomic Medicine (D.T.O.), University of California at San Diego; the VA San Diego Healthcare System, San Diego, CA (D.T.O.); the Departments of Computer Science & Human Genetics, University of California at Los Angeles (E.E.); the Department of Biotechnology, Indian Institute of Technology Madras, Chennai, India (N.R.M.); Clinical Pharmacology and The Genome Centre, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom (P.B.M.); Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (G.B.E.); and Scripps Research Institute, La Jolla, CA (J.W.).

Correspondence to Daniel T. O’Connor, MD, Department of Medicine, University of California at San Diego School of Medicine, VASDHS (0838), Skaggs (SSPPS) Room 4256, 9500 Gilman Drive, La Jolla, CA 92093-0838. E-mail doconnor@ucsd.edu

Abstract

Background—Essential hypertension, a common complex disease, displays substantial genetic influence. Contemporary methods to dissect the genetic basis of complex diseases such as the genomewide association study are powerful, yet a large gap exists betweens the fraction of population trait variance explained by such associations and total disease heritability.

Methods and Results—We developed a novel, integrative method (combining animal models, transcriptomics, bioinformatics, molecular biology, and trait-extreme phenotypes) to identify candidate genes for essential hypertension and the metabolic syndrome. We first undertook transcriptome profiling on adrenal glands from blood pressure extreme mouse strains: the hypertensive BPH (blood pressure high) and hypotensive BPL (blood pressure low). Microarray data clustering revealed a striking pattern of global underexpression of intermediary metabolism transcripts in BPH. The MITRA algorithm identified a conserved motif in the transcriptional regulatory regions of the underexpressed metabolic genes, and we then hypothesized that regulation through this motif contributed to the global underexpression. Luciferase reporter assays demonstrated transcriptional activity of the motif through transcription factors HOXA3, SRY, and YY1. We finally hypothesized that genetic variation at HOXA3, SRY, and YY1 might predict blood pressure and other metabolic syndrome traits in humans. Tagging variants for each locus were associated with blood pressure in a human population blood pressure extreme sample with the most extensive associations for YY1 tagging single nucleotide polymorphism rs11625658 on systolic blood pressure, diastolic blood pressure, body mass index, and fasting glucose. Meta-analysis extended the YY1 results into 2 additional large population samples with significant effects preserved on diastolic blood pressure, body mass index, and fasting glucose.

Conclusions—The results outline an innovative, systematic approach to the genetic pathogenesis of complex cardiovascular disease traits and point to transcription factor YY1 as a potential candidate gene involved in essential hypertension and the cardiometabolic syndrome.

 SOURCE:

Circulation: Cardiovascular Genetics.2012; 5: 430-440

Published online before print June 5, 2012,

doi: 10.1161/ CIRCGENETICS.111.962415

Genome-Wide Linkage and Positional Candidate Gene Study of Blood Pressure Response to Dietary Potassium Intervention

The Genetic Epidemiology Network of Salt Sensitivity Study

Tanika N. Kelly, PhD, James E. Hixson, PhD, Dabeeru C. Rao, PhD, Hao Mei, MD, PhD, Treva K. Rice, PhD, Cashell E. Jaquish, PhD, Lawrence C. Shimmin, PhD, Karen Schwander, MS, Chung-Shuian Chen, MS, Depei Liu, PhD, Jichun Chen, MD, Concetta Bormans, PhD, Pramila Shukla, MS, Naveed Farhana, MS, Colin Stuart, BS, Paul K. Whelton, MD, MSc, Jiang He, MD, PhD and Dongfeng Gu, MD, PhD

Author Affiliations

From the Department of Epidemiology (T.N.K., H.M., C.-S.C., J.H.), Tulane University School of Public Health and Tropical Medicine, and Department of Medicine (J.H.), Tulane University School of Medicine, New Orleans, La; Department of Epidemiology (J.E.H., L.C.S., C.B., P.S., N.F., C.S.), University of Texas School of Public Health, Houston, Tex; Division of Biostatistics (D.C.R., T.K.R., K.S.), Washington University School of Medicine, St Louis, Mo; Division of Prevention and Population Sciences (C.E.J.), National Heart, Lung, Blood Institute, Bethesda, Md; National Laboratory of Medical Molecular Biology (D.L.), Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Cardiovascular Institute and Fuwai Hospital (J.C., D.G.), Chinese Academy of Medical Sciences and Peking Union Medical College and Chinese National Center for Cardiovascular Disease Control and Research, Beijing, China; and Office of the President (P.K.W.), Loyola University Health System and Medical Center, Maywood, Ill.

Correspondence to Dongfeng Gu, MD, PhD, Division of Population Genetics and Prevention, Cardiovascular Institute and Fuwai Hospital, 167 Beilishi Rd, Beijing 100037, China. E-mail gudongfeng@vip.sina.com

Abstract

Background— Genetic determinants of blood pressure (BP) response to potassium, or potassium sensitivity, are largely unknown. We conducted a genome-wide linkage scan and positional candidate gene analysis to identify genetic determinants of potassium sensitivity.

Conclusions— Genetic regions on chromosomes 3 and 11 may harbor important susceptibility loci for potassium sensitivity. Furthermore, the AGTR1 gene was a significant predictor of BP responses to potassium intake.

SOURCE:

Circulation: Cardiovascular Genetics. 2010; 3: 539-547

Published online before print September 22, 2010,

doi: 10.1161/ CIRCGENETICS.110.940635

 

Genome-Wide Association Study of Cardiac Structure and Systolic Function in African Americans

The Candidate Gene Association Resource (CARe) Study

Ervin R. Fox, MD*, Solomon K. Musani, PhD*, Maja Barbalic, PhD*, Honghuang Lin, PhD, Bing Yu, MS, Kofo O. Ogunyankin, MD, Nicholas L. Smith, PhD, Abdullah Kutlar, MD, Nicole L. Glazer, MD, Wendy S. Post, MD, MS, Dina N. Paltoo, PhD, MPH, Daniel L. Dries, MD, MPH, Deborah N. Farlow, PhD, Christine W. Duarte, PhD, Sharon L. Kardia, PhD, Kristin J. Meyers, PhD, Yan V. Sun, PhD, Donna K. Arnett, PhD, Amit A. Patki, MS, Jin Sha, MS, Xiangqui Cui, PhD, Tandaw E. Samdarshi, MD, MPH, Alan D. Penman, PhD, Kirsten Bibbins-Domingo, MD, PhD, Petra Bůžková, PhD, Emelia J. Benjamin, MD, David A. Bluemke, MD, PhD, Alanna C. Morrison, PhD, Gerardo Heiss, MD, J. Jeffrey Carr, MD, MSc, Russell P. Tracy, PhD, Thomas H. Mosley, PhD, Herman A. Taylor, MD, Bruce M. Psaty, MD, PhD, Susan R. Heckbert, MD, PhD, Thomas P. Cappola, MD, ScM and Ramachandran S. Vasan, MD

Author Affiliations

Guest Editor for this article was Barry London, MD, PhD.

Correspondence to Ervin Fox, MD MPH, FAHA, FACC, Professor of Medicine, Department of Medicine, University of Mississippi Medical Center, 2500 North State St, Jackson, MS 39216. E-mail efox@medicine.umsmed.edu

* These authors contributed equally as joint first authors.

Abstract

Background—Using data from 4 community-based cohorts of African Americans, we tested the association between genome-wide markers (single-nucleotide polymorphisms) and cardiac phenotypes in the Candidate-gene Association Resource study.

Methods and Results—Among 6765 African Americans, we related age, sex, height, and weight-adjusted residuals for 9 cardiac phenotypes (assessed by echocardiogram or magnetic resonance imaging) to 2.5 million single-nucleotide polymorphisms genotyped using Genome-wide Affymetrix Human SNP Array 6.0 (Affy6.0) and the remainder imputed. Within the cohort, genome-wide association analysis was conducted, followed by meta-analysis across cohorts using inverse variance weights (genome-wide significance threshold=4.0 ×107). Supplementary pathway analysis was performed. We attempted replication in 3 smaller cohorts of African ancestry and tested lookups in 1 consortium of European ancestry (EchoGEN). Across the 9 phenotypes, variants in 4 genetic loci reached genome-wide significance: rs4552931 in UBE2V2 (P=1.43×107) for left ventricular mass, rs7213314 in WIPI1 (P=1.68×107) for left ventricular internal diastolic diameter, rs1571099 in PPAPDC1A (P=2.57×108) for interventricular septal wall thickness, and rs9530176 in KLF5 (P=4.02×107) for ejection fraction. Associated variants were enriched in 3 signaling pathways involved in cardiac remodeling. None of the 4 loci replicated in cohorts of African ancestry was confirmed in lookups in EchoGEN.

Conclusions—In the largest genome-wide association study of cardiac structure and function to date in African Americans, we identified 4 genetic loci related to left ventricular mass, interventricular septal wall thickness, left ventricular internal diastolic diameter, and ejection fraction, which reached genome-wide significance. Replication results suggest that these loci may be unique to individuals of African ancestry. Additional large-scale studies are warranted for these complex phenotypes.

SOURCE:

Circulation: Cardiovascular Genetics. 2013; 6: 37-46

Published online before print December 28, 2012,

doi: 10.1161/ CIRCGENETICS.111.962365

 

Heart and Aging Research in Genomic Epidemiology: 1700 MIs and 2300 coronary heart disease events among about 29 000 eligible patients

 

Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium

Design of Prospective Meta-Analyses of Genome-Wide Association Studies From 5 Cohorts

Bruce M. Psaty, MD, PhD, Christopher J. O’Donnell, MD, MPH, Vilmundur Gudnason, MD, PhD, Kathryn L. Lunetta, PhD, Aaron R. Folsom, MD, Jerome I. Rotter, MD, André G. Uitterlinden, PhD, Tamara B. Harris, MD, Jacqueline C.M. Witteman, PhD, Eric Boerwinkle, PhD and on Behalf of the CHARGE Consortium

Author Affiliations

From the Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services (B.M.P.), University of Wash; Center for Health Studies, Group Health (B.M.P.), Seattle, Wash; the National Heart, Lung and Blood Institute and the Framingham Heart Study (C.J.O.D.), Framingham, Mass; Icelandic Heart Association and the Department of Cardiovascular Genetics (Y.G.), University of Iceland, Reykjavik, Iceland; Department of Biostatistics (K.L.), Boston University School of Public Health, Mass; Division of Epidemiology and Community Health (A.R.F.), University of Minnesota, Minneapolis; Medical Genetics Institute (J.I.R.), Cedars-Sinai Medical Center, Los Angeles, Calif; Departments of Internal Medicine (A.G.U.) and Epidemiology (A.G.U., J.C.M.W.), Erasmus Medical Center, Rotterdam, The Netherlands; Laboratory of Epidemiology, Demography, and Biometry (T.B.H.), Intramural Research Program, National Institute on Aging, Bethesda, Md; and Human Genetics Center and Division of Epidemiology (E.B.), University of Texas, Houston.

Guest editor for this article was Elizabeth R. Hauser, PhD.

Abstract

Background— The primary aim of genome-wide association studies is to identify novel genetic loci associated with interindividual variation in the levels of risk factors, the degree of subclinical disease, or the risk of clinical disease. The requirement for large sample sizes and the importance of replication have served as powerful incentives for scientific collaboration.

Methods— The Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium was formed to facilitate genome-wide association studies meta-analyses and replication opportunities among multiple large population-based cohort studies, which collect data in a standardized fashion and represent the preferred method for estimating disease incidence. The design of the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium includes 5 prospective cohort studies from the United States and Europe: the Age, Gene/Environment Susceptibility—Reykjavik Study, the Atherosclerosis Risk in Communities Study, the Cardiovascular Health Study, the Framingham Heart Study, and the Rotterdam Study. With genome-wide data on a total of about 38 000 individuals, these cohort studies have a large number of health-related phenotypes measured in similar ways. For each harmonized trait, within-cohort genome-wide association study analyses are combined by meta-analysis. A prospective meta-analysis of data from all 5 cohorts, with a properly selected level of genome-wide statistical significance, is a powerful approach to finding genuine phenotypic associations with novel genetic loci.

Conclusions— The Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium and collaborating non-member studies or consortia provide an excellent framework for the identification of the genetic determinants of risk factors, subclinical-disease measures, and clinical events.

Example of Coronary Heart Disease

The cohort-study methods papers provide detail about many of the phenotypes listed in Table 2. For coronary heart disease, investigators knowledgeable about the phenotype in each study decided to focus on fatal and nonfatal myocardial infarction (MI) as the primary outcome because the MI criteria differed in only trivial ways among the studies. There were some minor differences in the definition of the composite outcome of MI, fatal coronary heart disease, and sudden death, which became the secondary outcome. Only subjects at risk for an incident event were included in the analysis. MI survivors whose DNA was drawn after the event were not eligible. The primary analysis was restricted to Europeans or European Americans. Patients entered the analysis at the time of the DNA blood draw, and were followed until an event, death, loss to follow up, or the last visit. The main recommendations of the Analysis Committee were adopted, and a threshold of 5×108 was selected for genome-wide statistical significance. Analyses in progress include about 1700 MIs and 2300 coronary heart disease events among about 29 000 eligible patients. Each cohort conducted its own analysis, and results were uploaded to a secure share site for the fixed-effects meta-analysis. Even with this number of events (Supplemental Figure 2), power is good for only for relatively high minor allele frequencies (>0.25) and large relative risks (>1.3).

The authors had full access to and take full responsibility for the integrity of the data. All authors have read and agree to the manuscript as written.

Discussion

In thousands of published papers, the 5 CHARGE cohort studies and many of the collaborating studies have already characterized the risk factors for and the incidence and prognosis of a variety of aging-related and cardiovascular conditions. The analysis of the incident MI, for instance, is free from the survival bias typically associated with cross-sectional or case-control studies. The methodologic advantages of the prospective population-based cohort design, the similarity of phenotypes across 5 studies, the availability of genome-wide genotyping data in each cohort, and the need for large sample sizes to provide reliable estimates of genotype-phenotype associations have served as the primary incentives for the formation of the CHARGE consortium, which includes GWAS data on about 38 000 individuals. The consortium effort relies on collaborative methods that are similar to those used by the individual contributing cohorts.

Phenotype experts who know the studies and the data well are responsible for phenotype-standardization across cohorts. The coordinated prospectively planned meta-analyses of CHARGE provide results that are virtually identical to a cohort-adjusted pooled analysis of individual level data. This approach–the within-study analysis followed by a between-study meta-analysis–avoids the human subjects issues associated with individual-level data sharing.

Editors, reviewers, and readers expect replication as the standard in science.6 The finding of a genetic association in one population with evidence for replication in multiple independent populations provides moderate assurance against false-positive reports and helps to establish the validity of the original finding. In a single experiment, the discovery-replication structure is traditionally embodied in a 2-stage design. The CHARGE consortium includes up to 5 independent replicate samples as well as additional collaborating studies for some phenotype working groups, so that it would have been possible to set up analysis plans within CHARGE to mimic the traditional 2-stage design for replication. For instance, the 2 largest cohorts could have served as the discovery set and the others as the replication set. However, attaining the extremely small probability values expected in GWAS requires large sample sizes. For any phenotype, a prospective meta-analysis of all participating cohorts, with a properly selected level of genome-wide statistical significance to minimize the chance of false-positives, is the most powerful approach to finding new genuine associations for genetic loci.25 When findings narrowly miss the prespecified significance threshold, genotyping individuals in other independent populations provides additional evidence about the association. For findings that substantially exceed pre-established significance thresholds, the results of a CHARGE meta-analysis effectively provide evidence of a multistudy replication.

The effort to assemble and manage the CHARGE consortium has provided some interesting and unanticipated challenges. Participating cohorts often had relationships with outside study groups that predated the formation of CHARGE. Timelines for genotyping and imputation have shifted. Purchases of new computer systems for the volume of work were sometimes necessary. Each cohort came to the consortium with their own traditions for methods of analysis, organization, and authorship policies that, while appropriate for their own work, were not always optimal for collaboration with multiple external groups. Within each cohort, the investigators had often formed working groups that divided up the large number of available phenotypes in ways that made sense locally but did not necessarily match the configuration that had been adopted by other cohorts. The Research Steering Committee has attempted to create a set of CHARGE working groups that accommodate the needs and the conventions of the various cohorts. Transparency, disclosure, and professional collaborative behavior by all participating investigators have been essential to the process.

Resource limitations are another challenge. Grant applications that funded the original single-study genome-wide genotyping effort typically imagined a much simpler design. The CHS whole-genome study had as its primary aim, for instance, the analysis of data on 3 endpoints, coronary disease, stroke and heart failure. With a score of active phenotype working groups, the CHARGE collaboration broadened the scope of the short-term work well beyond initial expectations for all the participating cohorts.

One of the premier challenges has been communications among scores of investigators at a dozen sites. CHS and ARIC are themselves multi-site studies. To be successful, the CHARGE collaboration has required effective communications: (1) within each cohort; (2) between cohorts; (3) within the CHARGE working groups; and (4) among the major CHARGE committees. In addition to the traditional methods of conference calls and email, the CHARGE “wiki,” set up by Dr J. Bis (Seattle, Wash), has provided a crucial and highly functional user-driven website for calendars, minutes, guidelines, working group analysis plans, manuscript proposals, and other documents. In the end, there is no substitute for face-to-face meetings, especially at the beginning of the collaboration, and this complex meta-organization has benefited from several CHARGE-wide meetings.

The major emerging opportunity is the collaboration with other studies and consortia. Many working groups have already incorporated nonmember studies into their efforts. Several working groups have coordinated submissions of initial manuscripts with the parallel submission of manuscripts from other studies or consortia. Several working groups have embarked on plans for joint meta-analyses between CHARGE and other consortia. CHARGE has tried to acknowledge and reward the efforts of champions, who assume leadership responsibility for moving these large complex projects forward and who are often hard-working young investigators, the key to the future success of population science.

The CHARGE Consortium represents an innovative model of collaborative research conducted by research teams that know well the strengths, the limitations, and the data from 5 prospective population-based cohort studies. By leveraging the dense genotyping, deep phenotyping and the diverse expertise, prospective meta-analyses are underway to identify and replicate the major common genetic determinants of risk factors, measures of subclinical disease, and clinical events for cardiovascular disease and aging.

SOURCE:

Circulation: Cardiovascular Genetics.2009; 2: 73-80

doi: 10.1161/ CIRCGENETICS.108.829747

 

 

Genomics of Ventricular arrhythmias, A-Fib, Right Ventricular Dysplasia, Cardiomyopathy

 

Comprehensive Desmosome Mutation Analysis in North Americans With Arrhythmogenic Right Ventricular Dysplasia/Cardiomyopathy

A. Dénise den Haan, MD, Boon Yew Tan, MBChB, Michelle N. Zikusoka, MD, Laura Ibañez Lladó, MS, Rahul Jain, MD, Amy Daly, MS, Crystal Tichnell, MGC, Cynthia James, PhD, Nuria Amat-Alarcon, MS, Theodore Abraham, MD, Stuart D. Russell, MD, David A. Bluemke, MD, PhD, Hugh Calkins, MD, Darshan Dalal, MD, PhD and Daniel P. Judge, MD

Author Affiliations

From the Department of Medicine/Cardiology (A.D.d.H., B.Y.T., M.N.Z., L.I.L., R.J., A.D., C.T., C.J., N.A.-A., T.A., S.D.R., H.C., D.D., D.P.J.), Johns Hopkins University School of Medicine, Baltimore, Md; Department of Cardiology, Division of Heart and Lungs (A.D.d.H.), University Medical Center Utrecht, Utrecht, The Netherlands; and National Institutes of Health, Radiology and Imaging Sciences (D.A.B.), Bethesda, Md.

Correspondence to Daniel P. Judge, MD, Johns Hopkins University, Division of Cardiology, Ross 1049; 720 Rutland Avenue, Baltimore, MD 21205. E-mail djudge@jhmi.edu

Abstract

Background— Arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVD/C) is an inherited disorder typically caused by mutations in components of the cardiac desmosome. The prevalence and significance of desmosome mutations among patients with ARVD/C in North America have not been described previously. We report comprehensive desmosome genetic analysis for 100 North Americans with clinically confirmed or suspected ARVD/C.

Methods and Results— In 82 individuals with ARVD/C and 18 people with suspected ARVD/C, DNA sequence analysis was performed on PKP2, DSG2, DSP, DSC2, and JUP. In those with ARVD/C, 52% harbored a desmosome mutation. A majority of these mutations occurred in PKP2. Notably, 3 of the individuals studied have a mutation in more than 1 gene. Patients with a desmosome mutation were more likely to have experienced ventricular tachycardia (73% versus 44%), and they presented at a younger age (33 versus 41 years) compared with those without a desmosome mutation. Men with ARVD/C were more likely than women to carry a desmosome mutation (63% versus 38%). A mutation was identified in 5 of 18 patients (28%) with suspected ARVD. In this smaller subgroup, there were no significant phenotypic differences identified between individuals with a desmosome mutation compared with those without a mutation.

Conclusions— Our study shows that in 52% of North Americans with ARVD/C a mutation in one of the cardiac desmosome genes can be identified. Compared with those without a desmosome gene mutation, individuals with a desmosome gene mutation had earlier-onset ARVD/C and were more likely to have ventricular tachycardia.

SOURCE:

Circulation: Cardiovascular Genetics.2009; 2: 428-435

Published online before print June 3, 2009,

doi: 10.1161/ CIRCGENETICS.109.858217

 

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Large-Scale Candidate Gene Analysis in Whites and African Americans Identifies IL6R Polymorphism in Relation to Atrial Fibrillation

The National Heart, Lung, and Blood Institute’s Candidate Gene Association Resource (CARe) Project

Renate B. Schnabel, MD, MSc*, Kathleen F. Kerr, PhD*, Steven A. Lubitz, MD*, Ermeg L. Alkylbekova, MD*, Gregory M. Marcus, MD, MAS, Moritz F. Sinner, MD, Jared W. Magnani, MD, Philip A. Wolf, MD, Rajat Deo, MD, Donald M. Lloyd-Jones, MD, ScM, Kathryn L. Lunetta, PhD, Reena Mehra, MD, MS, Daniel Levy, MD, Ervin R. Fox, MD, MPH, Dan E. Arking, PhD, Thomas H. Mosley, PhD, Martina Müller-Nurasyid, MSc, PhD, Taylor R. Young, MA, H.-Erich Wichmann, MD, PhD, Sudha Seshadri, MD, Deborah N. Farlow, PhD, Jerome I. Rotter, MD, Elsayed Z. Soliman, MD, MSc, MS, Nicole L. Glazer, PhD, James G. Wilson, MD, Monique M.B. Breteler, MD, Nona Sotoodehnia, MD, MPH, Christopher Newton-Cheh, MD, MPH, Stefan Kääb, MD, PhD, Patrick T. Ellinor, MD, PhD*, Alvaro Alonso, MD*, Emelia J. Benjamin, MD, ScM*, Susan R. Heckbert, MD, PhD* and for the Candidate Gene Association Resource (CARe) Atrial Fibrillation/Electrocardiography Working Group

Correspondence to Susan R. Heckbert, MD, PhD, Cardiovascular Health Research Unit, University of Washington, 1730 Minor Ave, Suite 1360, Seattle, WA 98101. E-mail heckbert@u.washington.edu; Emelia J. Benjamin, MD, ScM, Medicine and Epidemiology, Boston University Schools of Medicine and Public Health, The Framingham Heart Study, 73 Mount Wayte Ave, Framingham, MA 01702–5827. E-mail emelia@bu.edu; Renate B. Schnabel, MD, MSc, Department of Medicine 2, Cardiology, Johannes Gutenberg University, Langenbeckstr 1, 55131 Mainz, Germany. E-mail schnabelr@gmx.de

* These authors contributed equally to the manuscript.

Abstract

Background—The genetic background of atrial fibrillation (AF) in whites and African Americans is largely unknown. Genes in cardiovascular pathways have not been systematically investigated.

Methods and Results—We examined a panel of approximately 50 000 common single-nucleotide polymorphisms (SNPs) in 2095 cardiovascular candidate genes and AF in 3 cohorts with participants of European (n=18 524; 2260 cases) or African American descent (n=3662; 263 cases) in the National Heart, Lung, and Blood Institute’s Candidate Gene Association Resource. Results in whites were followed up in the German Competence Network for AF (n=906, 468 cases). The top result was assessed in relation to incident ischemic stroke in the Cohorts for Heart and Aging Research in Genomic Epidemiology Stroke Consortium (n=19 602 whites, 1544 incident strokes). SNP rs4845625 in the IL6R gene was associated with AF (relative risk [RR] C allele, 0.90; 95% confidence interval [CI], 0.85–0.95; P=0.0005) in whites but did not reach statistical significance in African Americans (RR, 0.86; 95% CI, 0.72–1.03; P=0.09). The results were comparable in the German AF Network replication, (RR, 0.71; 95% CI, 0.57–0.89; P=0.003). No association between rs4845625 and stroke was observed in whites. The known chromosome 4 locus near PITX2 in whites also was associated with AF in African Americans (rs4611994; hazard ratio, 1.40; 95% CI, 1.16–1.69; P=0.0005).

Conclusions—In a community-based cohort meta-analysis, we identified genetic association in IL6R with AF in whites. Additionally, we demonstrated that the chromosome 4 locus known from recent genome-wide association studies in whites is associated with AF in African Americans.

 SOURCE:

Circulation: Cardiovascular Genetics.2011; 4: 557-564

Published online before print August 16, 2011,

doi: 10.1161/ CIRCGENETICS.110.959197

PITX2c Is Expressed in the Adult Left Atrium, and Reducing Pitx2c Expression Promotes Atrial Fibrillation Inducibility and Complex Changes in Gene Expression

Paulus Kirchhof, MD*, Peter C. Kahr*, Sven Kaese, Ilaria Piccini, PhD, Ismail Vokshi, BSc, Hans-Heinrich Scheld, MD, Heinrich Rotering, MD, Lisa Fortmueller, MD (vet), Sandra Laakmann, MD (vet), Sander Verheule, PhD, Ulrich Schotten, MD, PhD, Larissa Fabritz, MD and Nigel A. Brown, PhD

Author Affiliations

From the Department of Cardiology and Angiology (P.K., P.C.K., S.K., I.P., L.F., S.L., L.F.) and the Department of Thoracic and Cardiovascular Surgery (H.-H.S., H.R.), University Hospital Muenster, Germany; Division of Biomedical Sciences (P.C.K., I.V., N.A.B.), St. George’s, University of London, United Kingdom; and the Department of Physiology (S.V., U.S.), Maastricht University, The Netherlands.

Correspondence to Nigel A. Brown, PhD, Division of Biomedical Sciences, St George’s, University of London, Cranmer Terrace, London, SW17 0RE, UK. E-mail nbrown@sgul.ac.uk

* Drs Kirchhof and Kahr contributed equally to this work.

Abstract

Background— Intergenic variations on chromosome 4q25, close to the PITX2 transcription factor gene, are associated with atrial fibrillation (AF). We therefore tested whether adult hearts express PITX2 and whether variation in expression affects cardiac function.

Methods and Results— mRNA for PITX2 isoform c was expressed in left atria of human and mouse, with levels in right atrium and left and right ventricles being 100-fold lower. In mice heterozygous for Pitx2c (Pitx2c+/), left atrial Pitx2c expression was 60% of wild-type and cardiac morphology and function were not altered, except for slightly elevated pulmonary flow velocity. Isolated Pitx2c+/ hearts were susceptible to AF during programmed stimulation. At short paced cycle lengths, atrial action potential durations were shorter in Pitx2c+/ than in wild-type. Perfusion with the β-receptor agonist orciprenaline abolished inducibility of AF and reduced the effect on action potential duration. Spontaneous heart rates, atrial conduction velocities, and activation patterns were not affected in Pitx2c+/ hearts, suggesting that action potential duration shortening caused wave length reduction and inducibility of AF. Expression array analyses comparing Pitx2c+/ with wild-type, for left atrial and right atrial tissue separately, identified genes related to calcium ion binding, gap and tight junctions, ion channels, and melanogenesis as being affected by the reduced expression of Pitx2c.

Conclusions— These findings demonstrate a physiological role for PITX2 in the adult heart and support the hypothesis that dysregulation of PITX2 expression can be responsible for susceptibility to AF.

 SOURCE:

Circulation: Cardiovascular Genetics.2011; 4: 123-133

Published online before print January 31, 2011,

doi: 10.1161/ CIRCGENETICS.110.958058

 

Genetics of CVD and Hyperlipidemia, Hyper Cholesterolemia, Metabolic Syndrome

 

Genetic Loci Associated With Plasma Concentration of Low-Density Lipoprotein Cholesterol, High-Density Lipoprotein Cholesterol, Triglycerides, Apolipoprotein A1, and Apolipoprotein B Among 6382 White Women in Genome-Wide Analysis With Replication

Daniel I. Chasman, PhD*, Guillaume Paré, MD, MS*, Robert Y.L. Zee, PhD, MPH, Alex N. Parker, PhD, Nancy R. Cook, ScD, Julie E. Buring, ScD, David J. Kwiatkowski, MD, PhD, Lynda M. Rose, MS, Joshua D. Smith, BS, Paul T. Williams, PhD, Mark J. Rieder, PhD, Jerome I. Rotter, MD, Deborah A. Nickerson, PhD, Ronald M. Krauss, MD, Joseph P. Miletich, MD and Paul M Ridker, MD, MPH

Author Affiliations

From the Center for Cardiovascular Disease Prevention (D.I.C., G.P., R.Y.L.Z., N.R.C., J.E.B., L.M.R., P.M.R.) and Donald W. Reynolds Center for Cardiovascular Research (D.I.C., G.P., R.Y.L.Z., N.R.C., D.J.K., P.M.R.), Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass; Amgen, Inc, Cambridge, Mass (A.N.P., J.M.P.); Department of Genome Sciences, University of Washington, Seattle, Wash (J.D.S., M.J.R., D.A.N.); Life Science Division, Lawrence Berkeley National Laboratory, Berkeley, Calif (P.T.W., R.M.K.); Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, Calif (J.I.R.); and Children’s Hospital Oakland Research Institute, Oakland, Calif (R.M.K.).

Correspondence to Daniel I. Chasman, Center for Cardiovascular Disease Prevention, Brigham and Women’s Hospital, 900 Commonwealth Ave E, Boston, MA 02215. E-mail dchasman@rics.bwh.harvard.edu

Abstract

Background— Genome-wide genetic association analysis represents an opportunity for a comprehensive survey of the genes governing lipid metabolism, potentially revealing new insights or even therapeutic strategies for cardiovascular disease and related metabolic disorders.

Methods and Results— We have performed large-scale, genome-wide genetic analysis among 6382 white women with replication in 2 cohorts of 970 additional white men and women for associations between common single-nucleotide polymorphisms and low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, apolipoprotein (Apo) A1, and ApoB. Genome-wide associations (P<5×108) were found at the PCSK9 gene, the APOB gene, the LPL gene, the APOA1-APOA5 locus, the LIPC gene, the CETP gene, the LDLR gene, and the APOE locus. In addition, genome-wide associations with triglycerides at the GCKR gene confirm and extend emerging links between glucose and lipid metabolism. Still other genome-wide associations at the 1p13.3 locus are consistent with emerging biological properties for a region of the genome, possibly related to the SORT1 gene. Below genome-wide significance, our study provides confirmatory evidence for associations at 5 novel loci with low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, or triglycerides reported recently in separate genome-wide association studies. The total proportion of variance explained by common variation at the genome-wide candidate loci ranges from 4.3% for triglycerides to 12.6% for ApoB.

Conclusion— Genome-wide associations at the GCKR gene and near the SORT1 gene, as well as confirmatory associations at 5 additional novel loci, suggest emerging biological pathways for lipid metabolism among white women.

 SOURCE:

Circulation: Cardiovascular Genetics.2008; 1: 21-30

doi: 10.1161/ CIRCGENETICS.108.773168

 

 

Integrated Computational and Experimental Analysis of the Neuroendocrine Transcriptome in Genetic Hypertension Identifies Novel Control Points for the Cardiometabolic Syndrome

Ryan S. Friese, PhD, Chun Ye, PhD, Caroline M. Nievergelt, PhD, Andrew J. Schork, BS, Nitish R. Mahapatra, PhD, Fangwen Rao, MD, Philip S. Napolitan, BS, Jill Waalen, MD, MPH, Georg B. Ehret, MD, Patricia B. Munroe, PhD, Geert W. Schmid-Schönbein, PhD, Eleazar Eskin, PhD and Daniel T. O’Connor, MD

Author Affiliations

From the Departments of Bioengineering (R.S.F., G.W.S.-S.), Medicine (R.S.F., A.J.S., F.R., P.S.N., D.T.O.), Pharmacology (D.T.O.), and Psychiatry (C.M.N.), the Bioinformatics Program (C.Y.), and the Institute for Genomic Medicine (D.T.O.), University of California at San Diego; the VA San Diego Healthcare System, San Diego, CA (D.T.O.); the Departments of Computer Science & Human Genetics, University of California at Los Angeles (E.E.); the Department of Biotechnology, Indian Institute of Technology Madras, Chennai, India (N.R.M.); Clinical Pharmacology and The Genome Centre, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom (P.B.M.); Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (G.B.E.); and Scripps Research Institute, La Jolla, CA (J.W.).

Correspondence to Daniel T. O’Connor, MD, Department of Medicine, University of California at San Diego School of Medicine, VASDHS (0838), Skaggs (SSPPS) Room 4256, 9500 Gilman Drive, La Jolla, CA 92093-0838. E-mail doconnor@ucsd.edu

Abstract

Background—Essential hypertension, a common complex disease, displays substantial genetic influence. Contemporary methods to dissect the genetic basis of complex diseases such as the genomewide association study are powerful, yet a large gap exists betweens the fraction of population trait variance explained by such associations and total disease heritability.

Methods and Results—We developed a novel, integrative method (combining animal models, transcriptomics, bioinformatics, molecular biology, and trait-extreme phenotypes) to identify candidate genes for essential hypertension and the metabolic syndrome. We first undertook transcriptome profiling on adrenal glands from blood pressure extreme mouse strains: the hypertensive BPH (blood pressure high) and hypotensive BPL (blood pressure low). Microarray data clustering revealed a striking pattern of global underexpression of intermediary metabolism transcripts in BPH. The MITRA algorithm identified a conserved motif in the transcriptional regulatory regions of the underexpressed metabolic genes, and we then hypothesized that regulation through this motif contributed to the global underexpression. Luciferase reporter assays demonstrated transcriptional activity of the motif through transcription factors HOXA3, SRY, and YY1. We finally hypothesized that genetic variation at HOXA3, SRY, and YY1 might predict blood pressure and other metabolic syndrome traits in humans. Tagging variants for each locus were associated with blood pressure in a human population blood pressure extreme sample with the most extensive associations for YY1 tagging single nucleotide polymorphism rs11625658 on systolic blood pressure, diastolic blood pressure, body mass index, and fasting glucose. Meta-analysis extended the YY1 results into 2 additional large population samples with significant effects preserved on diastolic blood pressure, body mass index, and fasting glucose.

Conclusions—The results outline an innovative, systematic approach to the genetic pathogenesis of complex cardiovascular disease traits and point to transcription factor YY1 as a potential candidate gene involved in essential hypertension and the cardiometabolic syndrome.

 SOURCE:

Circulation: Cardiovascular Genetics.2012; 5: 430-440

Published online before print June 5, 2012,

doi: 10.1161/ CIRCGENETICS.111.962415

 

Associations Between Incident Ischemic Stroke Events and Stroke and Cardiovascular Disease-Related Genome-Wide Association Studies Single Nucleotide Polymorphisms in the Population Architecture Using Genomics and Epidemiology Study

Cara L. Carty, PhD, Petra Bůžková, PhD, Myriam Fornage, PhD, Nora Franceschini, MD, Shelley Cole, PhD, Gerardo Heiss, MD, PhD, Lucia A. Hindorff, PhD, MPH, Barbara V. Howard, PhD, Sue Mann, MPH, Lisa W. Martin, MD, Ying Zhang, PhD, Tara C. Matise, PhD, Ross Prentice, PhD, Alexander P. Reiner, MD, MS and Charles Kooperberg, PhD

Author Affiliations

From the Public Health Sciences, Fred Hutchinson Cancer Research Center (C.L.C., S.M., R.P., C.K.); Department of Biostatistics, University of Washington, Seattle, WA (P.B.); Institute of Molecular Medicine, University of Texas Health Sciences Center at Houston, Houston, TX (M.F.); Division of Epidemiology, School of Public Health, University of Texas Health Sciences Center, Houston, TX (M.F.); Department of Epidemiology, University of North Carolina, Chapel Hill, NC (N.F., G.H.); Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX (S.C.); Office of Population Genomics, National Human Genome Research Institute, Bethesda, MD (L.A.H.); Medstar Health Research Institute, Washington, DC (B.V.H.); George Washington University School of Medicine, Washington, DC (B.V.H., L.W.M.); University of Oklahoma Health Sciences Center, Oklahoma City, OK (Y.Z.); Department of Genetics, Rutgers University, Piscataway, NJ (T.C.M.); Department of Epidemiology, University of Washington, Seattle, WA (A.P.R.).

Correspondence to Dr Cara L. Carty, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N./M3-A410, Seattle, WA 98109. E-mail ccarty@fhcrc.org

Abstract

Background—Genome-wide association studies (GWAS) have identified loci associated with ischemic stroke (IS) and cardiovascular disease (CVD) in European-descent individuals, but their replication in different populations has been largely unexplored.

Methods and Results—Nine single nucleotide polymorphisms (SNPs) selected from GWAS and meta-analyses of stroke, and 86 SNPs previously associated with myocardial infarction and CVD risk factors, including blood lipids (high density lipoprotein [HDL], low density lipoprotein [LDL], and triglycerides), type 2 diabetes, and body mass index (BMI), were investigated for associations with incident IS in European Americans (EA) N=26 276, African-Americans (AA) N=8970, and American Indians (AI) N=3570 from the Population Architecture using Genomics and Epidemiology Study. Ancestry-specific fixed effects meta-analysis with inverse variance weighting was used to combine study-specific log hazard ratios from Cox proportional hazards models. Two of 9 stroke SNPs (rs783396 and rs1804689) were associated with increased IS hazard in AA; none were significant in this large EA cohort. Of 73 CVD risk factor SNPs tested in EA, 2 (HDL and triglycerides SNPs) were associated with IS. In AA, SNPs associated with LDL, HDL, and BMI were significantly associated with IS (3 of 86 SNPs tested). Out of 58 SNPs tested in AI, 1 LDL SNP was significantly associated with IS.

Conclusions—Our analyses showing lack of replication in spite of reasonable power for many stroke SNPs and differing results by ancestry highlight the need to follow up on GWAS findings and conduct genetic association studies in diverse populations. We found modest IS associations with BMI and lipids SNPs, though these findings require confirmation.

SOURCE:

Circulation: Cardiovascular Genetics.2012; 5: 210-216

Published online before print March 8, 2012,

doi: 10.1161/ CIRCGENETICS.111.962191

 

Common Variation in Fatty Acid Genes and Resuscitation From Sudden Cardiac Arrest

Catherine O. Johnson, PhD, MPH, Rozenn N. Lemaitre, PhD, MPH, Carol E. Fahrenbruch, MSPH, Stephanie Hesselson, PhD, Nona Sotoodehnia, MD, MPH, Barbara McKnight, PhD, Kenneth M. Rice, PhD, Pui-Yan Kwok, MD, PhD, David S. Siscovick, MD, MPH and Thomas D. Rea, MD, MPH

Author Affiliations

From the Departments of Medicine (C.O.J., R.N.L., N.S., D.S.S., T.D.R.), Biostatistics (B.M., K.M.R.), and Epidemiology (D.S.S), University of Washington, Seattle; King County Emergency Medical Services, Seattle, WA (C.E.F.); and Institute of Human Genetics, University of California San Francisco (S.H., P.-Y.K.).

Correspondence to Catherine O. Johnson, PhD, MPH, Department of Medicine, University of Washington, CHRU 1730 Minor Ave, Suite 1360, Seattle, WA 98101. E-mail johnsoco@uw.edu

Abstract

Background—Fatty acids provide energy and structural substrates for the heart and brain and may influence resuscitation from sudden cardiac arrest (SCA). We investigated whether genetic variation in fatty acid metabolism pathways was associated with SCA survival.

Methods and Results—Subjects (mean age, 67 years; 80% male, white) were out-of-hospital SCA patients found in ventricular fibrillation in King County, WA. We compared subjects who survived to hospital admission (n=664) with those who did not (n=689), and subjects who survived to hospital discharge (n=334) with those who did not (n=1019). Associations between survival and genetic variants were assessed using logistic regression adjusting for age, sex, location, time to arrival of paramedics, whether the event was witnessed, and receipt of bystander cardiopulmonary resuscitation. Within-gene permutation tests were used to correct for multiple comparisons. Variants in 5 genes were significantly associated with SCA survival. After correction for multiple comparisons, single-nucleotide polymorphisms in ACSL1 and ACSL3 were significantly associated with survival to hospital admission. Single-nucleotide polymorphisms in ACSL3, AGPAT3, MLYCD, and SLC27A6 were significantly associated with survival to hospital discharge.

Conclusions—Our findings indicate that variants in genes important in fatty acid metabolism are associated with SCA survival in this population.

SOURCE:

Circulation: Cardiovascular Genetics.2012; 5: 422-429

Published online before print June 1, 2012,

doi: 10.1161/ CIRCGENETICS.111.961912

 

Genome-Wide Association Study Pinpoints a New Functional Apolipoprotein B Variant Influencing Oxidized Low-Density Lipoprotein Levels But Not Cardiovascular Events

AtheroRemo Consortium

Kari-Matti Mäkelä, BM, BSc, Ilkka Seppälä, MSc, Jussi A. Hernesniemi, MD, PhD, Leo-Pekka Lyytikäinen, MD, Niku Oksala, MD, PhD, DSc, Marcus E. Kleber, PhD, Hubert Scharnagl, PhD, Tanja B. Grammer, MD, Jens Baumert, PhD, Barbara Thorand, PhD, Antti Jula, MD, PhD, Nina Hutri-Kähönen, MD, PhD, Markus Juonala, MD, PhD, Tomi Laitinen, MD, PhD, Reijo Laaksonen, MD, PhD, Pekka J. Karhunen, MD, PhD, Kjell C. Nikus, MD, PhD, Tuomo Nieminen, MD, PhD, MSc, Jari Laurikka, MD, PhD, Pekka Kuukasjärvi, MD, PhD, Matti Tarkka, MD, PhD, Jari Viik, PhD, Norman Klopp, PhD, Thomas Illig, PhD, Johannes Kettunen, PhD, Markku Ahotupa, PhD, Jorma S.A. Viikari, MD, PhD, Mika Kähönen, MD, PhD, Olli T. Raitakari, MD, PhD, Mahir Karakas, MD, Wolfgang Koenig, MD, PhD, Bernhard O. Boehm, MD, Bernhard R. Winkelmann, MD, Winfried März, MD and Terho Lehtimäki, MD, PhD

Correspondence to Kari-Matti Mäkelä, Department of Clinical Chemistry, Finn-Medi 2, PO Box 2000, FI-33521 Tampere, Finland. E-mail kari-matti.makela@uta.fi

Abstract

Background—Oxidized low-density lipoprotein may be a key factor in the development of atherosclerosis. We performed a genome-wide association study on oxidized low-density lipoprotein and tested the impact of associated single-nucleotide polymorphisms (SNPs) on the risk factors of atherosclerosis and cardiovascular events.

Methods and Results—A discovery genome-wide association study was performed on a population of young healthy white individuals (N=2080), and the SNPs associated with a P<5×10–8 were replicated in 2 independent samples (A: N=2912; B: N=1326). Associations with cardiovascular endpoints were also assessed with 2 additional clinical cohorts (C: N=1118; and D: N=808). We found 328 SNPs associated with oxidized low-density lipoprotein. The genetic variant rs676210 (Pro2739Leu) in apolipoprotein B was the proxy SNP behind all associations (P=4.3×10–136, effect size=13.2 U/L per allele). This association was replicated in the 2 independent samples (A and B, P=2.5×10–47 and 1.1×10–11, effect sizes=10.3 U/L and 7.8 U/L, respectively). In the meta-analyses of cohorts A, C, and D (excluding cohort B without angiographic data), the top SNP did not associate significantly with the age of onset of angiographically verified coronary artery disease (hazard ratio=1.00 [0.94–1.06] per allele), 3-vessel coronary artery disease (hazard ratio=1.03 [0.94–1.13]), or myocardial infarction (hazard ratio=1.04 [0.96–1.12]).

Conclusions—This novel genetic marker is an important factor regulating oxidized low-density lipoprotein levels but not a major genetic factor for the studied cardiovascular endpoints.

 SOURCE:

Circulation: Cardiovascular Genetics.2013; 6: 73-81

Published online before print December 17, 2012,

doi: 10.1161/ CIRCGENETICS.112.964965

Genome-Wide Screen for Metabolic Syndrome Susceptibility Loci Reveals Strong Lipid Gene Contribution But No Evidence for Common Genetic Basis for Clustering of Metabolic Syndrome Traits

Kati Kristiansson, PhD, Markus Perola, MD, PhD, Emmi Tikkanen, MSc, Johannes Kettunen, PhD, Ida Surakka, MSc, Aki S. Havulinna, DSc (Tech.), Alena Stančáková, MD, PhD, Chris Barnes, PhD, Elisabeth Widen, MD, PhD, Eero Kajantie, MD, PhD, Johan G. Eriksson, MD, DMSc, Jorma Viikari, MD, PhD, Mika Kähönen, MD, PhD, Terho Lehtimäki, MD, PhD, Olli T. Raitakari, MD, PhD, Anna-Liisa Hartikainen, MD, PhD, Aimo Ruokonen, MD, PhD, Anneli Pouta, MD, PhD, Antti Jula, MD, PhD, Antti J. Kangas, MSc, Pasi Soininen, PhD, Mika Ala-Korpela, PhD, Satu Männistö, PhD, Pekka Jousilahti, MD, PhD, Lori L. Bonnycastle, PhD, Marjo-Riitta Järvelin, MD, PhD, Johanna Kuusisto, MD, PhD, Francis S. Collins, MD, PhD, Markku Laakso, MD, PhD, Matthew E. Hurles, PhD, Aarno Palotie, MD, PhD, Leena Peltonen, MD, PhD*, Samuli Ripatti, PhD and Veikko Salomaa, MD, PhD

Correspondence to Dr Kati Kristiansson, National Institute for Health and Welfare, University of Helsinki, Biomedicum, PL 104, FI-00251 Helsinki, Finland. E-mail kati.kristiansson@thl.fi

Abstract

Background—Genome-wide association (GWA) studies have identified several susceptibility loci for metabolic syndrome (MetS) component traits, but have had variable success in identifying susceptibility loci to the syndrome as an entity. We conducted a GWA study on MetS and its component traits in 4 Finnish cohorts consisting of 2637 MetS cases and 7927 controls, both free of diabetes, and followed the top loci in an independent sample with transcriptome and nuclear magnetic resonance-based metabonomics data. Furthermore, we tested for loci associated with multiple MetS component traits using factor analysis, and built a genetic risk score for MetS.

Methods and Results—A previously known lipid locus, APOA1/C3/A4/A5 gene cluster region (SNP rs964184), was associated with MetS in all 4 study samples (P=7.23×109 in meta-analysis). The association was further supported by serum metabolite analysis, where rs964184 was associated with various very low density lipoprotein, triglyceride, and high-density lipoprotein metabolites (P=0.024–1.88×105). Twenty-two previously identified susceptibility loci for individual MetS component traits were replicated in our GWA and factor analysis. Most of these were associated with lipid phenotypes, and none with 2 or more uncorrelated MetS components. A genetic risk score, calculated as the number of risk alleles in loci associated with individual MetS traits, was strongly associated with MetS status.

Conclusions—Our findings suggest that genes from lipid metabolism pathways have the key role in the genetic background of MetS. We found little evidence for pleiotropy linking dyslipidemia and obesity to the other MetS component traits, such as hypertension and glucose intolerance.

 SOURCE:

Circulation: Cardiovascular Genetics.2012; 5: 242-249

Published online before print March 7, 2012,

doi: 10.1161/ CIRCGENETICS.111.961482

 

Genetics and Vascular Pathologies and Platelet Aggregation, Cardiac Troponin T in Serum

 

 

TGFβRIIb Mutations Trigger Aortic Aneurysm Pathogenesis by Altering Transforming Growth Factor β2 Signal Transduction

Katharine J. Bee, PhD, David C. Wilkes, PhD, Richard B. Devereux, MD, Craig T. Basson, MD, PhD and Cathy J. Hatcher, PhD

Author Affiliations

From the Center for Molecular Cardiology, Greenberg Division of Cardiology, Weill Cornell Medical College, New York, NY.

Correspondence to Cathy J. Hatcher, PhD, Greenberg Division of Cardiology, Weill Cornell Medical College, 525 E. 68th St, New York, NY 10065. E-mail cjhatche@med.cornell.edu

Abstract

Background—Thoracic aortic aneurysm (TAA) is a common progressive disorder involving gradual dilation of the ascending and/or descending thoracic aorta that eventually leads to dissection or rupture. Nonsydromic TAA can occur as a genetically triggered, familial disorder that is usually transmitted in a monogenic autosomal dominant fashion and is known as familial TAA. Genetic analyses of families affected with TAA have identified several chromosomal loci, and further mapping of familial TAA genes has highlighted disease-causing mutations in at least 4 genes: myosin heavy chain 11 (MYH11), α-smooth muscle actin (ACTA2), and transforming growth factor β receptors I and II (TGFβRI and TGFβRII).

Methods and Results—We evaluated 100 probands to determine the mutation frequency in MYH11, ACTA2, TGFβRI, and TGFβRII in an unbiased population of individuals with genetically mediated TAA. In this study, 9% of patients had a mutation in one of the genes analyzed, 3% of patients had mutations in ACTA2, 3% in MYH11, 1% in TGFβRII, and no mutations were found in TGFβRI. Additionally, we identified mutations in a 75 base pair alternatively spliced TGFβRII exon, exon 1a that produces the TGFβRIIb isoform and accounted for 2% of patients with mutations. Our in vitro analyses indicate that the TGFβRIIb activating mutations alter receptor function on TGFβ2 signaling.

Conclusions—We propose that TGFβRIIb expression is a regulatory mechanism for TGFβ2 signal transduction. Dysregulation of the TGFβ2 signaling pathway, as a consequence of TGFβRIIb mutations, results in aortic aneurysm pathogenesis.

SOURCE: 

Circulation: Cardiovascular Genetics.2012; 5: 621-629

Published online before print October 24, 2012,doi: 10.1161/​CIRCGENETICS.112.964064

Matrix Metalloproteinase-9 Genotype as a Potential Genetic Marker for Abdominal Aortic Aneurysm

Tyler Duellman, BS, Christopher L. Warren, PhD, Peggy Peissig, PhD, Martha Wynn, MD and Jay Yang, MD, PhD

Author Affiliations

From the Molecular and Cellular Pharmacology Graduate Program (T.D., J.Y.) and Department of Anesthesiology (M.W., J.Y.), University of Wisconsin School of Medicine and Public Health, Madison; Illumavista Biosciences LLC, Madison, WI (C.L.W.); and Biomedical Informatics Research Center, Marshfield Clinics Research Foundation, Marshfield, WI (P.P.).

Correspondence to Jay Yang, MD, PhD, Department of Anesthesiology, University of Wisconsin SMPH, SMI 301, 1300 University Ave, Madison, WI 53706. E-mail Jyang75@wisc.edu

Abstract

Background—Degradation of extracellular matrix support in the large abdominal arteries contribute to abnormal dilation of aorta, leading to abdominal aortic aneurysms, and matrix metalloproteinase-9 (MMP-9) is the predominant enzyme targeting elastin and collagen present in the walls of the abdominal aorta. Previous studies have suggested a potential association between MMP-9 genotype and abdominal aortic aneurysm, but these studies have been limited only to the p-1562 and (CA) dinucleotide repeat microsatellite polymorphisms in the promoter region of the MMP-9 gene. We determined the functional alterations caused by 15 MMP-9 single-nucleotide polymorphisms (SNPs) reported to be relatively abundant in the human genome through Western blots, gelatinase, and promoter–reporter assays and incorporated this information to perform a logistic-regression analysis of MMP-9 SNPs in 336 human abdominal aortic aneurysm cases and controls.

Methods and Results—Significant functional alterations were observed for 6 exon SNPs and 4 promoter SNPs. Genotype analysis of frequency-matched (age, sex, history of hypertension, hypercholesterolemia, and smoking) cases and controls revealed significant genetic heterogeneity exceeding 20% observed for 6 SNPs in our population of mostly white subjects from Northern Wisconsin. A step-wise logistic-regression analysis with 6 functional SNPs, where weakly contributing confounds were eliminated using Akaike information criteria, gave a final 2 SNP (D165N and p-2502) model with an overall odds ratio of 2.45 (95% confidence interval, 1.06–5.70).

Conclusions—The combined approach of direct experimental confirmation of the functional alterations of MMP-9 SNPs and logistic-regression analysis revealed significant association between MMP-9 genotype and abdominal aortic aneurysm.

SOURCE:

Circulation: Cardiovascular Genetics.2012; 5: 529-537

Published online before print August 31, 2012,

doi: 10.1161/ CIRCGENETICS.112.963082

Common Genetic Variation in the 3BCL11B Gene Desert Is Associated With Carotid-Femoral Pulse Wave Velocity and Excess Cardiovascular Disease Risk

The AortaGen Consortium

Gary F. Mitchell, MD*, Germaine C. Verwoert, MSc*, Kirill V. Tarasov, MD, PhD*, Aaron Isaacs, PhD, Albert V. Smith, PhD, Yasmin, BSc, MA, PhD, Ernst R. Rietzschel, MD, PhD, Toshiko Tanaka, PhD, Yongmei Liu, MD, PhD, Afshin Parsa, MD, MPH, Samer S. Najjar, MD, Kevin M. O’Shaughnessy, MA, BM, DPhil, FRCP, Sigurdur Sigurdsson, MSc, Marc L. De Buyzere, MSc, Martin G. Larson, ScD, Mark P.S. Sie, MD, PhD, Jeanette S. Andrews, MS, Wendy S. Post, MD, MS, Francesco U.S. Mattace-Raso, MD, PhD, Carmel M. McEniery, BSc, PhD, Gudny Eiriksdottir, MSc, Patrick Segers, PhD, Ramachandran S. Vasan, MD, Marie Josee E. van Rijn, MD, PhD, Timothy D. Howard, PhD, Patrick F. McArdle, PhD, Abbas Dehghan, MD, PhD, Elizabeth S. Jewell, MS, Stephen J. Newhouse, MSc, PhD, Sofie Bekaert, PhD, Naomi M. Hamburg, MD, Anne B. Newman, MD, MPH, Albert Hofman, MD, PhD, Angelo Scuteri, MD, PhD, Dirk De Bacquer, PhD, Mohammad Arfan Ikram, MD, PhD†, Bruce M. Psaty, MD, PhD†, Christian Fuchsberger, PhD‡, Matthias Olden, PhD‡, Louise V. Wain, PhD§, Paul Elliott, MB, PhD§, Nicholas L. Smith, PhD‖, Janine F. Felix, MD, PhD‖, Jeanette Erdmann, PhD¶, Joseph A. Vita, MD, Kim Sutton-Tyrrell, PhD, Eric J.G. Sijbrands, MD, PhD, Serena Sanna, PhD, Lenore J. Launer, MS, PhD, Tim De Meyer, PhD, Andrew D. Johnson, MD, Anna F.C. Schut, MD, PhD, David M. Herrington, MD, MHS, Fernando Rivadeneira, MD, PhD, Manuela Uda, PhD, Ian B. Wilkinson, MA, BM, FRCP, Thor Aspelund, PhD, Thierry C. Gillebert, MD, PhD, Luc Van Bortel, MD, PhD, Emelia J. Benjamin, MD, MSc, Ben A. Oostra, PhD, Jingzhong Ding, MD, PhD, Quince Gibson, MBA, André G. Uitterlinden, PhD, Gonçalo R. Abecasis, PhD, John R. Cockcroft, BSc, MB, ChB, FRCP, Vilmundur Gudnason, MD, PhD, Guy G. De Backer, MD, PhD, Luigi Ferrucci, MD, Tamara B. Harris, MD, MS, Alan R. Shuldiner, MD, Cornelia M. van Duijn, PhD, Daniel Levy, MD*, Edward G. Lakatta, MD* and Jacqueline C.M. Witteman, PhD*

Correspondence to Gary F. Mitchell, MD, Cardiovascular Engineering, Inc, 1 Edgewater Dr, Suite 201A, Norwood, MA 02062. E-mail GaryFMitchell@mindspring.com

* These authors contributed equally.

Abstract

Background—Carotid-femoral pulse wave velocity (CFPWV) is a heritable measure of aortic stiffness that is strongly associated with increased risk for major cardiovascular disease events.

Methods and Results—We conducted a meta-analysis of genome-wide association data in 9 community-based European ancestry cohorts consisting of 20 634 participants. Results were replicated in 2 additional European ancestry cohorts involving 5306 participants. Based on a preliminary analysis of 6 cohorts, we identified a locus on chromosome 14 in the 3′-BCL11B gene desert that is associated with CFPWV (rs7152623, minor allele frequency=0.42, β=−0.075±0.012 SD/allele, P=2.8×1010; replication β=−0.086±0.020 SD/allele, P=1.4×106). Combined results for rs7152623 from 11 cohorts gave β=−0.076±0.010 SD/allele, P=3.1×1015. The association persisted when adjusted for mean arterial pressure (β=−0.060±0.009 SD/allele, P=1.0×1011). Results were consistent in younger (<55 years, 6 cohorts, n=13 914, β=−0.081±0.014 SD/allele, P=2.3×109) and older (9 cohorts, n=12 026, β=−0.061±0.014 SD/allele, P=9.4×106) participants. In separate meta-analyses, the locus was associated with increased risk for coronary artery disease (hazard ratio=1.05; confidence interval=1.02–1.08; P=0.0013) and heart failure (hazard ratio=1.10, CI=1.03–1.16, P=0.004).

Conclusions—Common genetic variation in a locus in the BCL11B gene desert that is thought to harbor 1 or more gene enhancers is associated with higher CFPWV and increased risk for cardiovascular disease. Elucidation of the role this novel locus plays in aortic stiffness may facilitate development of therapeutic interventions that limit aortic stiffening and related cardiovascular disease events.

SOURCE:

Circulation: Cardiovascular Genetics.2012; 5: 81-90

Published online before print November 8, 2011,

doi: 10.1161/ CIRCGENETICS.111.959817

Genetic Variation in PEAR1 is Associated with Platelet Aggregation and Cardiovascular Outcomes

Joshua P. Lewis1, Kathleen Ryan1, Jeffrey R. O’Connell1, Richard B. Horenstein1, Coleen M. Damcott1, Quince Gibson1, Toni I. Pollin1, Braxton D. Mitchell1, Amber L. Beitelshees1, Ruth Pakzy1, Keith Tanner1, Afshin Parsa1, Udaya S. Tantry2, Kevin P. Bliden2, Wendy S. Post3, Nauder Faraday3, William Herzog4, Yan Gong5, Carl J. Pepine6, Julie A. Johnson5, Paul A. Gurbel2 and Alan R. Shuldiner7*

Author Affiliations

1University of Maryland School of Medicine, Baltimore, MD

2Sinai Hospital of Baltimore, Baltimore, MD

3Johns Hopkins University School of Medicine, Baltimore, MD

4Sinai Hospital of Baltimore & Johns Hopkins University School of Medicine, Baltimore, MD

5University of Florida College of Pharmacy, Gainesville, FL

6University of Florida College of Medicine, Gainesville, FL

7University of Maryland School of Medicine & Veterans Administration Medical Center, Baltimore, MD

* University of Maryland School of Medicine & Veterans Administration Medical Center, Baltimore, MD ashuldin@medicine.umaryland.edu

Abstract

Background-Aspirin or dual antiplatelet therapy (DAPT) with aspirin and clopidogrel is standard therapy for patients at increased risk for cardiovascular events. However, the genetic determinants of variable response to aspirin (alone and in combination with clopidogrel) are not known.

Methods and Results-We measured ex-vivo platelet aggregation before and after DAPT in individuals (n=565) from the Pharmacogenomics of Antiplatelet Intervention (PAPI) Study and conducted a genome-wide association study (GWAS) of drug response. Significant findings were extended by examining genotype and cardiovascular outcomes in two independent aspirin-treated cohorts: 227 percutaneous coronary intervention (PCI) patients, and 1,000 patients of the International VErapamil SR/trandolapril Study (INVEST) GENEtic Substudy (INVEST-GENES). GWAS revealed a strong association between single nucleotide polymorphisms on chromosome 1q23 and post-DAPT platelet aggregation. Further genotyping revealed rs12041331 in the platelet endothelial aggregation receptor-1 (PEAR1) gene to be most strongly associated with DAPT response (P=7.66×10-9). In Caucasian and African American patients undergoing PCI, A-allele carriers of rs12041331 were more likely to experience a cardiovascular event or death compared to GG homozygotes (hazard ratio = 2.62, 95%CI 0.96-7.10, P=0.059 and hazard ratio = 3.97, 95%CI 1.10-14.31, P=0.035 respectively). In aspirin-treated INVEST-GENES patients, rs12041331 A-allele carriers had significantly increased risk of myocardial infarction compared to GG homozygotes (OR=2.03, 95%CI 1.01-4.09, P=0.048).

Conclusions-Common genetic variation in PEAR1 may be a determinant of platelet response and cardiovascular events in patients on aspirin, alone and in combination with clopidogrel.

Clinical Trial Registration Information-clinicaltrials.gov; Identifiers: NCT00799396 and NCT00370045

SOURCE:

CIRCGENETICS.112.964627

Published online before print February 7, 2013,

doi: 10.1161/ CIRCGENETICS.111.964627

Association of Genome-Wide Variation With Highly Sensitive Cardiac Troponin-T Levels in European Americans and Blacks

A Meta-Analysis From Atherosclerosis Risk in Communities and Cardiovascular Health Studies

Bing Yu, MD, MSc, Maja Barbalic, PhD, Ariel Brautbar, MD, Vijay Nambi, MD, Ron C. Hoogeveen, PhD, Weihong Tang, PhD, Thomas H. Mosley, PhD, Jerome I. Rotter, MD, Christopher R. deFilippi, MD, Christopher J. O’Donnell, MD, Sekar Kathiresan, MD, Ken Rice, PhD, Susan R. Heckbert, MD, PhD, Christie M. Ballantyne, MD, Bruce M. Psaty, MD, PhD and Eric Boerwinkle, PhD on behalf of the CARDIoGRAM Consortium

Author Affiliations

From the Human Genetic Center, University of Texas Health Science Center at Houston, Houston, TX (B.Y., M.B., E.B.); Deptartment of Medicine (A.B., V.N., R.C.H., C.M.B.), and Human Genome Sequencing Center (E.B.), Baylor College of Medicine, Houston, TX; Department of Epidemiology, University of Minnesota, Minneapolis, MN (W.T.); Division of Geriatrics, University of Mississippi Medical Center, Jackson, MS (T.H.M.); Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, CA (J.I.R.); School of Medicine, University of Maryland, Baltimore, MD (C.R.D.); National Heart, Lung, and Blood Institute and Framingham Heart Study, National Institutes of Health, Bethesda, MD (C.J.O.D.); Center for Human Genetic Research & Cardiovascular Research Center, Massachusetts General Hospital and Department of Medicine, Harvard Medical School, Boston, MA (S.K.); Department of Biostatistics (K.R.), and Cardiovascular Health Research Unit & Department of Epidemiology (S.R.H.), University of Washington, Seattle, WA; and Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington & Group Health Research Institute, Group Health Cooperative, Seattle, WA (B.M.P.).

Correspondence to Eric Boerwinkle, PhD, Human Genetic Center, University of Texas School of Public Health, 1200 Herman Pressler E-447, Houston, TX 77030. E-mail Eric.Boerwinkle@uth.tmc.edu

Abstract

Background—High levels of cardiac troponin T, measured by a highly sensitive assay (hs-cTnT), are strongly associated with incident coronary heart disease and heart failure. To date, no large-scale genome-wide association study of hs-cTnT has been reported. We sought to identify novel genetic variants that are associated with hs-cTnT levels.

Methods and Results—We performed a genome-wide association in 9491 European Americans and 2053 blacks free of coronary heart disease and heart failure from 2 prospective cohorts: the Atherosclerosis Risk in Communities Study and the Cardiovascular Health Study. Genome-wide association studies were conducted in each study and race stratum. Fixed-effect meta-analyses combined the results of linear regression from 2 cohorts within each race stratum and then across race strata to produce overall estimates and probability values. The meta-analysis identified a significant association at chromosome 8q13 (rs10091374; P=9.06×109) near the nuclear receptor coactivator 2 (NCOA2) gene. Overexpression of NCOA2 can be detected in myoblasts. An additional analysis using logistic regression and the clinically motivated 99th percentile cut point detected a significant association at 1q32 (rs12564445; P=4.73×108) in the gene TNNT2, which encodes the cardiac troponin T protein itself. The hs-cTnT-associated single-nucleotide polymorphisms were not associated with coronary heart disease in a large case-control study, but rs12564445 was significantly associated with incident heart failure in Atherosclerosis Risk in Communities Study European Americans (hazard ratio=1.16; P=0.004).

Conclusions—We identified 2 loci, near NCOA2 and in the TNNT2 gene, at which variation was significantly associated with hs-cTnT levels. Further use of the new assay should enable replication of these results.

 SOURCE:

Circulation: Cardiovascular Genetics.2013; 6: 82-88

Published online before print December 16, 2012,

doi: 10.1161/ CIRCGENETICS.112.963058

 

Genomics and Valvular Disease

 

Supravalvular Aortic Stenosis Elastin Arteriopathy

 

Giuseppe Merla, PhD, Nicola Brunetti-Pierri, MD, Pasquale Piccolo, PhD, Lucia Micale, PhD and Maria Nicla Loviglio, PhD, MSc

Author Affiliations

From the Medical Genetics Unit, IRCCS Casa Sollievo Della Sofferenza Hospital, San Giovanni Rotondo, Italy (G.M., L.M., M.N.L.); Telethon Institute of Genetics and Medicine, Napoli, Italy (N.B-P., P.P.); Department of Pediatrics, Federico II University of Naples, Naples, Italy (N.B-P.); and CIG Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland (M.N.L.).

Correspondence to Giuseppe Merla, PhD, Medical Genetics Unit, IRCCS Casa Sollievo della Sofferenza, viale Cappuccini, 71013 San Giovanni Rotondo, Italy. E-mail g.merla@operapadrepio.it

Abstract

Supravalvular aortic stenosis is a systemic elastin (ELN) arteriopathy that disproportionately affects the supravalvular aorta. ELN arteriopathy may be present in a nonsyndromic condition or in syndromic conditions such as Williams–Beuren syndrome. The anatomic findings include congenital narrowing of the lumen of the aorta and other arteries, such as branches of pulmonary or coronary arteries. Given the systemic nature of the disease, accurate evaluation is recommended to establish the degree and extent of vascular involvement and to plan appropriate interventions, which are indicated whenever hemodynamically significant stenoses occur. ELN arteriopathy is genetically heterogeneous and occurs as a consequence of haploinsufficiency of the ELN gene on chromosome 7q11.23, owing to either microdeletion of the entire chromosomal region or ELN point mutations. Interestingly, there is a prevalence of premature termination mutations resulting in null alleles among ELN point mutations. The identification of the genetic defect in patients with supravalvular aortic stenosis is essential for a definitive diagnosis, prognosis, and genetic counseling.

SOURCE:

Circulation: Cardiovascular Genetics.2012; 5: 692-696

doi: 10.1161/ CIRCGENETICS.112.962860

Genetic Loci for Coronary Calcification and Serum Lipids Relate to Aortic and Carotid Calcification

Daniel Bos, MD, M. Arfan Ikram, MD, PhD, Aaron Isaacs, PhD, Benjamin F.J. Verhaaren, MD, Albert Hofman, MD, PhD, Cornelia M. van Duijn, PhD, Jacqueline C.M. Witteman, PhD, Aad van der Lugt, MD, PhD and Meike W. Vernooij, MD, PhD

Author Affiliations

From the Departments of Radiology (D.B., M.A.I., B.F.J.V., A.v.d.L., M.W.V), Epidemiology (D.B., M.A.I., A.I., B.F.J.V., A.H., C.M.v.D., J.C.M.W., M.W.V.), and Genetic Epidemiology Unit (A.I., C.M.v.D.), Erasmus MC, Rotterdam, the Netherlands.

Correspondence to Meike W. Vernooij, MD, PhD, Department of Radiology, Erasmus MC, Gravendijkwal 230, PO Box 2040, 3000CA Rotterdam, the Netherlands. E-mail m.vernooij@erasmusmc.nl

Abstract

Background—Atherosclerosis in different vessel beds shares lifestyle and environmental risk factors. It is unclear whether this holds for genetic risk factors. Hence, for the current study genetic loci for coronary artery calcification and serum lipid levels, one of the strongest risk factors for atherosclerosis, were used to assess their relation with atherosclerosis in different vessel beds.

Methods and Results—From 1987 persons of the population-based Rotterdam Study, 3 single-nucleotide polymorphisms (SNPs) for coronary artery calcification and 132 SNPs for total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides were used. To quantify atherosclerotic calcification as a marker of atherosclerosis, all participants underwent nonenhanced computed tomography of the aortic arch and carotid arteries. Associations between genetic risk scores of the joint effect of the SNPs and of all calcification were investigated. The joint effect of coronary artery calcification–SNPs was associated with larger calcification volumes in all vessel beds (difference in calcification volume per SD increase in genetic risk score: 0.15 [95% confidence interval, 0.11–0.20] in aorta, 0.14 [95% confidence interval, 0.10–0.18] in extracranial carotids, and 0.11 [95% confidence interval, 0.07–0.16] in intracranial carotids). The joint effect of total cholesterol SNPs, low-density lipoprotein SNPs, and of all lipid SNPs together was associated with larger calcification volumes in both the aortic arch and the carotid arteries but attenuated after adjusting for the lipid fraction and lipid-lowering medication.

Conclusions—The genetic basis for aortic arch and carotid artery calcification overlaps with the most important loci of coronary artery calcification. Furthermore, serum lipids share a genetic predisposition with both calcification in the aortic arch and the carotid arteries, providing novel insights into the cause of atherosclerosis.

 SOURCE:

Circulation: Cardiovascular Genetics.2013; 6: 47-53

Published online before print December 16, 2012,

doi: 10.1161/ CIRCGENETICS.112.963934

 

Joint Associations of 61 Genetic Variants in the Nicotinic Acetylcholine Receptor Genes with Subclinical Atherosclerosis in American Indians

A Gene-Family Analysis

Jingyun Yang, PhD*, Yun Zhu, MS*, Elisa T. Lee, PhD, Ying Zhang, PhD, Shelley A. Cole, PhD, Karin Haack, PhD, Lyle G. Best, BS MD, Richard B. Devereux, MD, Mary J. Roman, MD, Barbara V. Howard, PhD and Jinying Zhao, MD, PhD

Author Affiliations

From the Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (J.Y., Y. Zhu, J.Z.); Center for American Indian Health Research, University of Oklahoma Health Sciences Center, Oklahoma City, OK (E.T.L., Y. Zhang); Texas Biomedical Research Institute, San Antonio, TX (S.A.C., K.H.); Missouri Breaks Industries Research Inc, Timber Lake, SD (L.G.B.); The New York Hospital-Cornell Medical Center, New York, NY (R.B.D., M.J.R.); MedStar Health Research Institute, Hyattsville, MD (B.V.H.); and Georgetown and Howard Universities Centers for Translational Sciences, Washington, DC (B.V.H.).

Correspondence to Jinying Zhao, MD, PhD, Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal St, SL18, New Orleans, LA 70112. E-mail jzhao5@tulane.edu

* These authors contributed equally to this work.

Abstract

Background—Atherosclerosis is the underlying cause of cardiovascular disease, the leading cause of morbidity and mortality in all American populations, including American Indians. Genetic factors play an important role in the pathogenesis of atherosclerosis. Although a single-nucleotide polymorphism (SNP) may explain only a small portion of variability in disease, the joint effect of multiple variants in a pathway on disease susceptibility could be large.

Methods and Results—Using a gene-family analysis, we investigated the joint associations of 61 tag SNPs in 7 nicotinic acetylcholine receptor genes with subclinical atherosclerosis, as measured by carotid intima-media thickness and plaque score, in 3665 American Indians from 94 families recruited by the Strong Heart Family Study (SHFS). Although multiple SNPs showed marginal association with intima-media thickness and plaque score individually, only a few survived adjustments for multiple testing. However, simultaneously modeling of the joint effect of all 61 SNPs in 7 nicotinic acetylcholine receptor genes revealed significant association of the nicotinic acetylcholine receptor gene family with both intima-media thickness and plaque score independent of known coronary risk factors.

Conclusions—Genetic variants in the nicotinic acetylcholine receptor gene family jointly contribute to subclinical atherosclerosis in American Indians who participated in the SHFS. These variants may influence the susceptibility of atherosclerosis through pathways other than cigarette smoking per se.

SOURCE:

Circulation: Cardiovascular Genetics.2013; 6: 89-96

Published online before print December 22, 2012,

doi: 10.1161/ CIRCGENETICS.112.963967

 

 

Heredity of Cardiovascular Disorders Inheritance

 

A Clinical Approach to Common Cardiovascular Disorders When There Is a Family History

The Implications of Inheritance for Clinical Management

Srijita Sen-Chowdhry, MBBS, MD, FESC, Daniel Jacoby, MD and William J. McKenna, MD, DSc, FESC

Author Affiliations

From the Institute of Cardiovascular Science, University College London, London, United Kingdom (S.S-C., W.J.M.); Department of Epidemiology, Imperial College, London, London, United Kingdom (S.S-C.); Division of Cardiology, Yale School of Medicine, New Haven, CT (D.J., W.J.M.).

Correspondence to Professor William J. McKenna, MD, DSc, FESC, Institute of Cardiovascular Science, University College London, The Heart Hospital, 16-18 Westmoreland Street, London, E-mail william.mckenna@uclh.nhs.uk

Introduction

Since the advent of genotyping, recognition of heritable disease has been perceived as an opportunity for genetic diagnosis or new gene identification studies to advance understanding of pathogenesis. Until recently, however, clinical application of DNA-based testing was confined largely to Mendelian disorders. Even within this remit, predictive testing of relatives is cost-effective only in diseases in which the majority of families harbor mutations in known causal genes, such as adult polycystic kidney disease and hypertrophic cardiomyopathy, but not dilated cardiomyopathy. Confirmatory genetic testing of index cases with borderline clinical features may be economic in the still smaller subset of diseases with limited locus heterogeneity, such as Marfan syndrome. Furthermore, Mendelian diseases account for ≈5% of total disease burden.1 Genome-wide association studies have made headway in elucidating the genetic contribution to the more common, complex diseases, and high throughput techniques promise to facilitate integration of genetic analysis into clinical practice. Nevertheless, many genes remain to be identified and implementation of genomic profiling as a population screening tool would not be cost-effective at present. The implications of heredity, however, extend beyond serving as a platform for genetic analysis, influencing diagnosis, prognostication, and treatment of both index cases and relatives, and enabling rational targeting of genotyping resources. This review covers acquisition of a family history, evaluation of heritability and inheritance patterns, and the impact of inheritance on subsequent components of the clinical pathway.

SOURCE:

Circulation: Cardiovascular Genetics.2012; 5: 467-476

doi: 10.1161/ CIRCGENETICS.110.959361

Clinical Considerations of Heritable Factors in Common Heart Failure

Thomas P. Cappola, MD, ScM and Gerald W. Dorn II, MD

Author Affiliations

From the Department of Medicine, University of Pennsylvania, Philadelphia, PA (T.P.C.), and Center for Pharmacogenomics, Washington University School of Medicine, St Louis, MO (G.W.D.II.).

Correspondence to Gerald W. Dorn II, MD, Center for Pharmacogenomics, Washington University, 660 S Euclid Ave, Campus Box 8220, St Louis, MO 63110. E-mail gdorn@dom.wustl.edu

 

Introduction

Heart failure is a common condition responsible for at least 290 000 deaths each year in the United States alone.1 A small minority of heart failure cases are attributed to Mendelian or familial cardiomyopathies. The majority of systolic heart failure cases are not familial but represent the end result of 1 or many conditions that primarily injure the myocardium sufficiently to diminish cardiac output in the absence of compensatory mechanisms. Paradoxically, because they also injure the myocardium, it is the chronic actions of the compensatory mechanisms that in many instances contribute to the progression from simple cardiac injury to dilated cardiomyopathy and overt heart failure. Thus, the epidemiology of common heart failure appears to be just as sporadic as its major antecedent conditions (atherosclerosis, diabetes, hypertension, and viral myocarditis).

Familial trends in preclinical cardiac remodeling2 and risk of developing heart failure3 reveal an important role for genetic modifiers in addition to clinical and environmental factors. Candidate gene studies performed over the past 10 years have identified a few polymorphic gene variants that modify risk or progression of common heart failure.4 Whole-genome sequencing will lead to the discovery of other genetic modifiers that were not candidates.5 The imminent availability of individual whole-genome sequences at a cost competitive with available genetic tests for familial cardiomyopathy will no doubt further expand the list of putative genetic heart failure modifiers. Heart failure risk alleles along with traditional clinical factors will need to be considered by clinical cardiologists in their design of optimal disease surveillance and prevention programs and in individually tailoring heart failure management.

The use of individual genetic make-up is likely to have the earliest and greatest impact on managing patients with heart failure by tailoring available pharmacotherapeutics to optimize patient response and minimize adverse effects (ie, the area of pharmacogenetics). Modern heart failure management has been derived and directed by the results of large, randomized, multicenter clinical trials. When standard therapies are applied according to the selection criteria used in these trials, they prolong average survival across affected populations or decrease the incidence of heart failure in populations at risk.6 For this reason, standardized treatment guidelines prescribe heart failure therapies according to trial designs, aiming for the same target doses and general treatment approaches,7 and largely ignore individual characteristics. In this article, we review established and emerging knowledge of genetic influence on common heart failure and try to anticipate how these genetic factors may be best used to eschew the cookie-cutter approach to heart failure management and move toward implementing a personalized medicine approach for the treatment and prevention of this important and prevalent disease.

The Concept of Genotype-Directed Personal Medical Management in Heart Failure

Variation in clinical heart failure progression and therapeutic response (either benefits or side effects) supports the need for a more individualized approach to disease management. On the basis of clinical stratification (eg, by etiology of heart failure as ischemic versus nonischemic, functional status, comorbid disease), physicians try to match each patient’s specific heart failure syndrome with a therapeutic regime devised to provide the most benefit. Standard heart failure pharmacotherapy currently comprises a minimum of 3 medications (angiotensin-converting enzyme [ACE] inhibitors, β-blockers, and aldosterone antagonists), with consideration of additional medications (hydralazine/isosorbide, angiotensin receptor blockers) and diuretics. The recommended target dosages for these agents, derived from their respective clinical trials, is rarely achieved,8 partly because of untoward clinical side effects such as low blood pressure or renal dysfunction. Accordingly, the published guidelines most often are applied in each individual patient using ad hoc approaches derived from personal experience and the “art of medicine.”

Technological advances in human genomics promise a different approach and are bringing cardiology into an era of clinically applied pharmacogenetics9 (whether we want to or not). As sequencing costs decline, it is not hard to envision that patients will present having had their entire genome already sequenced. The imperative to apply genome information in clinical settings will increase, as demonstrated by recent proof-of-concept studies.10 Our field seems poorly prepared for this type of evolution in care; Roden et al9 identified 3 major barriers: First is the absence of rapidly available genotype information in the clinical workflow. This barrier is being overcome with whole-genome sequencing, which (with proper analysis) promises a permanent and largely immutable genetic roadmap for individual disease risk and drug response at a cost comparable to many other clinical tests.11 Second, we must have the knowledge to properly apply information on genetic variants for the diseases we are managing and the drugs we are using. As we describe, this knowledge is accumulating for heart failure and for other cardiac conditions, and the rate at which we are gaining additional information and developing further expertise appears to be accelerating.

The third and perhaps most formidable barrier is the lack of clinical evidence showing how real-time application of genetic information can best benefit patients. As has been broadly communicated to the medical community and lay public, common functional gene variants in CYP2C19 can impair the transformation of clopidogrel into its active metabolite, leading to increased risk of stent thrombosis after percutaneous coronary intervention.12 The relevant question thus becomes the following: If physicians have this information at the time of clinical care and reacted by adjusting clopidogrel dose or substituting prasugrel, which is unaffected by CYP2C19 genotype,13 would there be any improvement in clinical outcome? It is also important to consider whether any observed benefits justify the additional costs of genetic testing and for the alternate drug. Studies are currently examining these questions, and similar clinical trials will prospectively examine whether a genotype-guided strategy of warfarin dosing will be superior to the standard genotype-blinded approach in reaching target anticoagulation goals. At this time, there are no similar prospective, randomized, blinded trials of genotype-guided care for common heart failure.

Emerging Variants

The variants described here are established, but new ones are emerging. Although findings in heart failure genome-wide association studies have been limited, we can expect additional common heart failure variants to emerge as sample sizes increase.65 The CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium published a genome-wide association study of incident heart failure that tested for associations between >2.4 million HapMap-imputed polymorphisms in >20 000 subjects.7 They identified 2 loci associated with heart failure, rs10519210 (15q22, containing USP3 encoding a ubiquitin-specific protease) in subjects of European ancestry and rs11172782 (12q14, containing LRIG3 encoding a leucine-rich, immunoglobulin-like domain-containing protein of uncertain function) in subjects of African ancestry.66 In a companion study using the same population and genotyping results, mortality analysis of the subgroup of individuals who developed heart failure implicated an intronic SNP in CMTM7 (CKLF-like MARVEL transmembrane domain-containing 7).67 These genetic associations require independent replication and further study to identify the underlying biological mechanisms.

A recently published genome-wide association study by a European consortium on dilated cardiomyopathy identified common variants in BAG3 (BCL2-associated athanogene 3) associated with heart failure57 and identified rare BAG3 missense and truncation mutations that segregate with familial cardiomyopathy. These findings were consistent with an earlier exome-sequencing study that identified BAG3 as a familial dilated cardiomyopathy gene and showed recapitulation of cardiomyopathy with BAG3 morpholino knockdown in zebra fish.68 Together, these studies convincingly support variation in BAG3 as a genetic risk factor of cardiomyopathy and heart failure. It is noteworthy that both common and rare functional variations were identified at this locus. A unifying hypothesis for these findings, which needs to be formally tested, is that common variants in BAG3 serve as proxies for rare functional BAG3 mutations with large effects. In this situation, the underlying genetic lesion is a rare variant with a large functional effect. This has recently been described for common variants in MYH6 that correlated with rare functional MYH6 variants to cause sick sinus syndrome.69 It is premature to speculate on the clinical applications of these newer findings.

Moving Knowledge to Practice

A small number of genomic variants have been identified that modify heart failure by affecting well-understood physiological systems. The principal barrier preventing their adoption in practice may be lack of evidence showing how application of this information can best be used for clinical benefit. Trials testing genotype targeting of antiplatelet therapy and anticoagulation will be completed in the coming years. The findings from these studies will likely determine the level of enthusiasm for conducting genotype-guided trials of β-blockers and RAAS antagonists in heart failure. Given that the lifetime risk of heart failure in the United States is estimated at 1 in 5, even a small favorable effect on heart failure prevention or outcome through use of genome-guided therapy has the potential for a large public health impact. We therefore believe that a near-term goal should be to conduct pharmacogenomic trials in heart failure based on our current understanding of heart failure variants.

Looking ahead, unbiased approaches will continue to reveal a large number heart failure-modifying variants (both common and rare). Based on experience in other complex phenotypes, such has height70 and plasma lipid levels,71 the underlying genetic mechanisms for many new heart failure variants will be completely unknown, and their sheer number will preclude detailed experimentation using murine models to figure them out. Leveraging these variants for clinical application is a challenge that we will be forced to confront.

As our ability to identify rare, disease-causing variants improves through personal genome sequencing, we will be faced with the additional problem of how best to estimate the disease risk conferred by a sequence variant for which there has been no biological validation. In probabilistic terms, because there are 3 billion nucleotides in the human genome and over twice that many humans on the planet, it is likely that a nucleotide substitution for every position is represented in someone. Obviously, it will be impossible to recombinantly express and functionally characterize every DNA variant that is going to be implicated in heart failure. Bioinformatics filters have been used to try and separate functionally significant from insignificant variants based on the likelihood of changing transcript expression or protein function. These tools are limited but will improve if we tailor their results to the known characteristics of each gene product. For example, current approaches to categorize amino acid substitutions as conservative or nonconservative based only on charge or side chains can be improved by molecular modeling that incorporates protein-specific structure-function information. This approach has been used to estimate the pathogenicity of myosin heavy chain (MHC) mutations in an effort to determine which mutations are likely to cause familial cardiomyopathy when linkage analysis is not feasible.72 In concept, this approach can be applied to any protein for which structure-function activities have been finely mapped to distinct domains.

A promising extension of this approach may be to use evolutionary genetics to infer disease causality. Again, using the MHC genes as examples, human genome data show a greater prevalence of nonsynonymous gene variants in MYH6, which encodes the minor cardiac α-MHC isoform, compared with the adjacent MYH7, which encodes the major β-MHC isoform. This disparity suggests a greater tolerance for protein changes in the α-MHC isoform and negative selection against these in β-MHC. We can infer, therefore, that amino acid changes are more likely to have adverse impacts in MYH7-encoded β-MHC. If this paradigm survives prospective testing, then the forthcoming explosion of individual genetic data not only will present a massive problem in interpretation, but also will provide the genetic information by which analyses of rare sequence variants across large unaffected populations can help to differentiate the tolerable variants from those that are more likely to alter disease risk.

Each Reference above is found in:

http://circgenetics.ahajournals.org/content/4/6/701.full

SOURCE: 

Circulation: Cardiovascular Genetics.2011; 4: 701-709

doi: 10.1161/ CIRCGENETICS.110.959379

 

Pharmacogenomics

 

Hypertension Susceptibility Loci and Blood Pressure Response to Antihypertensives

Results From the Pharmacogenomic Evaluation of Antihypertensive Responses Study

Yan Gong, PhD, Caitrin W. McDonough, PhD, Zhiying Wang, MS, Wei Hou, PhD, Rhonda M. Cooper-DeHoff, PharmD, MS, Taimour Y. Langaee, PhD, Amber L. Beitelshees, PharmD, MPH, Arlene B. Chapman, MD, John G. Gums, PharmD, Kent R. Bailey, PhD, Eric Boerwinkle, PhD, Stephen T. Turner, MD and Julie A. Johnson, PharmD

Author Affiliations

From the Department of Pharmacotherapy and Translational Research (Y.G., C.W.M., R.M.C.-D., T.Y.L., J.G.G., J.A.J.), Department of Biostatistics, College of Medicine (W.H.), Division of Cardiovascular Medicine, College of Medicine (R.M.C.-D., J.A.J.), and Department of Community Health and Family Medicine (J.G.G.), University of Florida, Gainesville, FL; Division of Epidemiology, University of Texas at Houston, Houston, TX (Z.W., E.B.); Division of Endocrinology, Diabetes and Nutrition, University of Maryland, Baltimore, MD (A.L.B.); Renal Division, Emory University, Atlanta, GA (A.B.C.); and Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN (S.T.T.).

Correspondence to Yan Gong, PhD, Department of Pharmacotherapy and Translational Research, University of Florida, PO Box 100486, 1600 SW Archer Rd, Gainesville, FL 32610. E-mail gong@cop.ufl.edu.

Abstract

Background—To date, 39 single nucleotide polymorphisms (SNPs) have been associated with blood pressure (BP) or hypertension in genome-wide association studies in whites. Our hypothesis is that the loci/SNPs associated with BP/hypertension are also associated with BP response to antihypertensive drugs.

Methods and Results—We assessed the association of these loci with BP response to atenolol or hydrochlorothiazide monotherapy in 768 hypertensive participants in the Pharmacogenomics Responses of Antihypertensive Responses study. Linear regression analysis was performed on whites for each SNP in an additive model adjusting for baseline BP, age, sex, and principal components for ancestry. Genetic scores were constructed to include SNPs with nominal associations, and empirical P values were determined by permutation test. Genotypes of 37 loci were obtained from Illumina 50K cardiovascular or Omni1M genome-wide association study chips. In whites, no SNPs reached Bonferroni-corrected α of 0.0014, 6 reached nominal significance (P<0.05), and 3 were associated with atenolol BP response at P<0.01. The genetic score of the atenolol BP-lowering alleles was associated with response to atenolol (P=3.3×10–6 for systolic BP; P=1.6×10–6 for diastolic BP). The genetic score of the hydrochlorothiazide BP-lowering alleles was associated with response to hydrochlorothiazide (P=0.0006 for systolic BP; P=0.0003 for diastolic BP). Both risk score P values were <0.01 based on the empirical distribution from the permutation test.

Conclusions—These findings suggest that selected signals from hypertension genome-wide association studies may predict BP response to atenolol and hydrochlorothiazide when assessed through risk scoring.

SOURCE:

Circulation: Cardiovascular Genetics.2012; 5: 686-691

Published online before print October 19, 2012,

doi: 10.1161/ CIRCGENETICS.112.964080

 

Genetic Determinants of Statin-Induced Low-Density Lipoprotein Cholesterol Reduction

The Justification for the Use of Statins in Prevention: An Intervention Trial Evaluating Rosuvastatin (JUPITER) Trial

Daniel I. Chasman, PhD, Franco Giulianini, PhD, Jean MacFadyen, BA, Bryan J. Barratt, PhD, Fredrik Nyberg, MD, PhD, MPH and Paul M Ridker, MD, MPH

Author Affiliations

From the Center for Cardiovascular Disease Prevention (D.I.C., F.G., J.M., P.M.R.), JUPITER Trial Coordinating Center (D.I.C., F.G., J.M., P.M.R.), Brigham and Women’s Hospital and Harvard Medical School (D.I.C., P.M.R.), Boston, MA; Personalised Healthcare and Biomarkers, AstraZeneca Research and Development, Alderley Park, United Kingdom (B.J.B.); AstraZeneca Research and Development, Mölndal, Sweden (F.N.); and Unit of Occupational and Environmental Medicine, Department of Public Health and Community Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden (F.N.).

Correspondence to Daniel I. Chasman, PhD, Center for Cardiovascular Disease Prevention, Brigham and Women’s Hospital, 900 Commonwealth Ave E, Boston, MA 02215. E-mail dchasman@rics.bwh.harvard.edu

Abstract

Background—In statin trials, each 20 mg/dL reduction in cholesterol results in a 10–15% reduction of annual incidence rates for vascular events. However, interindividual variation in low-density lipoprotein cholesterol (LDL-C) response to statins is wide and may partially be determined on a genetic basis.

Methods and Results—A genome-wide association study of LDL-C response was performed among a total of 6989 men and women of European ancestry who were randomly allocated to either rosuvastatin 20 mg daily or placebo. Single nucleotide polymorphisms (SNPs) for genome-wide association (P<5×108) with LDL-C reduction on rosuvastatin were identified at ABCG2, LPA, and APOE, and a further association at PCSK9 was genome-wide significant for baseline LDL-C and locus-wide significant for LDL-C reduction. Median LDL-C reductions on rosuvastatin were 40, 48, 51, 55, 60, and 64 mg/dL, respectively, among those inheriting increasing numbers of LDL-lowering alleles for SNPs at these 4 loci (P trend=6.2×1020), such that each allele approximately doubled the odds of percent LDL-C reduction greater than the trial median (odds ratio, 1.9; 95% confidence interval, 1.8–2.1; P=5.0×1041). An intriguing additional association with sub–genome-wide significance (P<1×10-6) was identified for statin related LDL-C reduction at IDOL, which mediates posttranscriptional regulation of the LDL receptor in response to intracellular cholesterol levels. In candidate analysis, SNPs in SLCO1B1 and LDLR were confirmed as associated with LDL-C lowering, and a significant interaction was observed between SNPs in PCSK9 and LDLR.

Conclusions—Inherited polymorphisms that predominantly relate to statin pharmacokinetics and endocytosis of LDL particles by the LDL receptor are common in the general population and influence individual patient response to statin therapy.

SOURCE:

Circulation: Cardiovascular Genetics.2012; 5: 257-264

Published online before print February 13, 2012,

doi: 10.1161/ CIRCGENETICS.111.961144

Genetic Variation in the β2 Subunit of the Voltage-Gated Calcium Channel and Pharmacogenetic Association With Adverse Cardiovascular Outcomes in the INternational VErapamil SR-Trandolapril STudy GENEtic Substudy (INVEST-GENES)

Yuxin Niu, PhD*, Yan Gong, PhD*, Taimour Y. Langaee, PhD, Heather M. Davis, PharmD, Hazem Elewa, PhD, Amber L. Beitelshees, PharmD, MPH, James I. Moss, PhD, Rhonda M. Cooper-DeHoff, PharmD, Carl J. Pepine, MD and Julie A. Johnson, PharmD

Author Affiliations

From the Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics (Y.N., Y.G., T.Y.L., H.M.D., H.E., J.I.M., R.M.C.-D., J.A.J.), College of Pharmacy, University of Florida, Gainesville, Fla; Division of Endocrinology, Diabetes and Nutrition (A.L.B.), University of Maryland School of Medicine, Baltimore, Md; and Division of Cardiovascular Medicine (R.M.C.-D., C.J.P., J.A.J.), University of Florida College of Medicine, Gainesville, Fla.

Correspondence to Julie A. Johnson, PharmD, Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, PO Box 100486, Gainesville, FL 32610. E-mail Johnson@cop.ufl.edu

* Drs Niu and Gong contributed equally to this work.

Abstract

Background— Single-nucleotide polymorphisms (SNPs) within the regulatory β2 subunit of the voltage-gated calcium channel (CACNB2) may contribute to variable treatment response to antihypertensive drugs and adverse cardiovascular outcomes.

Methods and Results— SNPs in CACNB2 from 60 ethnically diverse individuals were identified and characterized. Three common SNPs (rs2357928, rs7069292, and rs61839258) and a genome-wide association study-identified intronic SNP (rs11014166) were genotyped for a clinical association study in 5598 hypertensive patients with coronary artery disease randomized to a β-blocker (BB) or a calcium channel blocker (CCB) treatment strategy in the INternational VErapamil SR-Trandolapril STudy GENEtic Substudy (INVEST-GENES). Reporter gene assays were conducted on the promoter SNP, showing association with clinical outcomes. Twenty-one novel SNPs were identified. A promoter A>G SNP (rs2357928) was found to have significant interaction with treatment strategy for adverse cardiovascular outcomes (P for interaction, 0.002). In whites, rs2357928 GG patients randomized to CCB were more likely to experience an adverse outcome than those randomized to BB treatment strategy, with adjusted hazard ratio (HR) (CCB versus BB) of 2.35 (95% CI, 1.19 to 4.66; P=0.014). There was no evidence for such treatment difference in AG (HR, 1.16; 95% CI, 0.75 to 1.79; P=0.69) and AA (HR, 0.63; 95% CI, 0.36 to 1.11; P=0.11) patients. This finding was consistent in Hispanics and blacks. CACNB2 rs11014166 showed similar pharmacogenetic effect in Hispanics, but not in whites or blacks. Reporter assay analysis of rs2357928 showed a significant increase in promoter activity for the G allele compared to the A allele.

Conclusions— These data suggest that genetic variation within CACNB2 may influence treatment-related outcomes in high-risk patients with hypertension.

Clinical Trial Registration— URL: http://www.clinicaltrials.gov. Unique identifier: NCT00133692.

SOURCE:

Circulation: Cardiovascular Genetics.2010; 3: 548-555

doi: 10.1161/ CIRCGENETICS.110.957654

 

Hepatic Metabolism and Transporter Gene Variants Enhance Response to Rosuvastatin in Patients With Acute Myocardial Infarction

The GEOSTAT-1 Study

Kristian M. Bailey, MBChB, Simon P.R. Romaine, BSc, Beryl M. Jackson, RGN, Amanda J. Farrin, MSc, Maria Efthymiou, MSc, Julian H. Barth, MD, Joanne Copeland, BSc, Terry McCormack, MBBS, Andrew Whitehead, MSc, Marcus D. Flather, MBBS, Nilesh J. Samani, MD, FMedSci, Jane Nixon, PhD, Alistair S. Hall, MD, PhD, Anthony J. Balmforth, PhD and on behalf of the SPACE ROCKET Trial Group

Author Affiliations

From the Division of Cardiovascular and Diabetes Research (K.M.B., S.P.R.R., B.M.J., A.J.B.), and Division of Cardiovascular and Neuronal Remodelling (A.S.H.), Multidisciplinary Cardiovascular Research Centre, Leeds Institute of Genetics, Health and Therapeutics, University of Leeds, Leeds, United Kingdom; Clinical Trials Research Unit (A.J.F., M.E., J.C., J.N.), University of Leeds, Leeds, United Kingdom; Clinical Biochemistry (J.H.B.), Leeds General Infirmary, Leeds, United Kingdom; Whitby Group Practice (T.M.), Spring Vale Medical Centre, Whitby, North Yorkshire, United Kingdom; Pharmacy Department (A.W.), Leeds General Infirmary, Leeds, United Kingdom; Clinical Trials and Evaluation Unit (M.D.F.), Royal Brompton and Harefield NHS Trust and Imperial College, London, United Kingdom; and Department of Cardiovascular Sciences (N.J.S.), University of Leicester, Leicester, United Kingdom.

Correspondence to Alistair S. Hall, Clinical Cardiology, Multidisciplinary Cardiovascular Research Centre (MCRC), G Floor, Jubilee Building, Leeds General Infirmary, Leeds, LS1 3EX, United Kingdom. E-mail A.S.Hall@leeds.ac.uk

* Dr Bailey, Mr Romaine, Dr Hall, and Dr Balmforth contributed equally to this study.

Abstract

Background— Pharmacogenetics aims to maximize benefits and minimize risks of drug treatment. Our objectives were to examine the influence of common variants of hepatic metabolism and transporter genes on the lipid-lowering response to statin therapy.

Methods and Results— The Genetic Effects On STATins (GEOSTAT-1) Study was a genetic substudy of Secondary Prevention of Acute Coronary Events—Reduction of Cholesterol to Key European Targets (SPACE ROCKET) (a randomized, controlled trial comparing 40 mg of simvastatin and 10 mg of rosuvastatin) that recruited 601 patients after myocardial infarction. We genotyped the following functional single nucleotide polymorphisms in the genes coding for the cytochrome P450 (CYP) metabolic enzymes, CYP2C9*2 (430C>T), CYP2C9*3 (1075A>C), CYP2C19*2 (681G>A), CYP3A5*1 (6986A>G), and hepatic influx and efflux transporters SLCO1B1 (521T>C) and breast cancer resistance protein (BCRP; 421C>A). We assessed 3-month LDL cholesterol levels and the proportion of patients reaching the current LDL cholesterol target of <70 mg/dL (<1.81 mmol/L). An enhanced response to rosuvastatin was seen for patients with variant genotypes of either CYP3A5 (P=0.006) or BCRP (P=0.010). Furthermore, multivariate logistic-regression analysis revealed that patients with at least 1 variant CYP3A5 and/or BCRP allele (n=186) were more likely to achieve the LDL cholesterol target (odds ratio: 2.289; 95% CI: 1.157, 4.527; P=0.017; rosuvastatin 54.0% to target vs simvastatin 33.7%). There were no differences for patients with variants of CYP2C9, CYP2C19, or SLCO1B1 in comparison with their respective wild types, nor were differential effects on statin response seen for patients with the most common genotypes for CYP3A5 and BCRP (n=415; odds ratio: 1.207; 95% CI: 0.768, 1.899; P=0.415).

Conclusion— The LDL cholesterol target was achieved more frequently for the 1 in 3 patients with CYP3A5 and/or BCRP variant genotypes when prescribed rosuvastatin 10 mg, compared with simvastatin 40 mg.

Clinical Trial Registration— URL: http://isrctn.org. Unique identifier: ISRCTN 89508434.

SOURCE:

Circulation: Cardiovascular Genetics.2010; 3: 276-285

Published online before print March 5, 2010,

doi: 10.1161/ CIRCGENETICS.109.898502

 

Comprehensive Whole-Genome and Candidate Gene Analysis for Response to Statin Therapy in the Treating to New Targets (TNT) Cohort

John F. Thompson, PhD, Craig L. Hyde, PhD, Linda S. Wood, MS, Sara A. Paciga, MA, David A. Hinds, PhD, David R. Cox, MD, PhD, G. Kees Hovingh, MD, PhD and John J.P. Kastelein, MD, PhD

Author Affiliations

From the Helicos BioSciences (J.F.T.), Cambridge, Mass; Molecular Medicine (J.F.T., L.S.W., S.A.P.) and Statistical Applications (C.L.H.), Pfizer Global Research and Development, Groton, Conn; Perlegen Sciences (D.A.H., D.R.C.), Mountain View, Calif; and Department of Vascular Medicine (G.K.H., J.J.P.K.), Academic Medical Center, Amsterdam, The Netherlands.

Correspondence to John J.P. Kastelein, MD, PhD, Department of Vascular Medicine, Academic Medical Center, Meibergdreef 9, Room F4-159.2, 1105 AZ Amsterdam, The Netherlands. E-mail j.j.kastelein@amc.uva.nl or j.s.jansen@amc.uva.nl

Abstract

Background— Statins are effective at lowering low-density lipoprotein cholesterol and reducing risk of cardiovascular disease, but variability in response is not well understood. To address this, 5745 individuals from the Treating to New Targets (TNT) trial were genotyped in a combination of a whole-genome and candidate gene approach to identify associations with response to atorvastatin treatment.

Methods and Results— A total of 291 988 single-nucleotide polymorphisms (SNPs) from 1984 individuals were analyzed for association with statin response, followed by genotyping top hits in 3761 additional individuals. None was significant at the whole-genome level in either the initial or follow-up test sets for association with low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, or triglyceride response. In addition to the whole-genome platform, 23 candidate genes previously associated with statin response were analyzed in these 5745 individuals. Three SNPs in apoE were most highly associated with low-density lipoprotein cholesterol response, followed by 1 in PCSK9 with a similar effect size. At the candidate gene level, SNPs in HMGCR were also significant though the effect was less than with those in apoE and PCSK9. rs7412/apoE had the most significant association (P=6×1030), and its high significance in the whole-genome study (P=4×109) confirmed the suitability of this population for detecting effects. Age and gender were found to influence low-density lipoprotein cholesterol response to a similar extent as the most pronounced genetic effects.

Conclusions— Among SNPs tested with an allele frequency of at least 5%, only SNPs in apoE are found to influence statin response significantly. Less frequent variants in PCSK9 and smaller effect sizes in SNPs in HMGCR were also revealed.

SOURCE:

Circulation: Cardiovascular Genetics.2009; 2: 173-181

Published online before print February 12, 2009,

doi: 10.1161/ CIRCGENETICS.108.818062

Summary

Larry H. Bernstein, MD, FCAP

This review has examined a compendium of well regarded documents drawn from 248 articles in Circulation Cardiovascular Genetics from March 2010 to March 2013. The large amount of evidence obtained from large population studies identifying Genome Wide Analysis Studies (GWAS) examines a host of cardiac and vascular diseases in which there is association between specific single nucleotide peptides (SNPs), and gene loci, that may play or have no significant role in developing heart disease. It certainly is evidence of the role that the American Heart Association has is in supporting the leading research today for tomorrow’s patients.   It is too early to sort them out, but it speaks to a large volume of discovery in this area.

It raises another issue that we have been confronted with mostly since the second half of the 20th century.  What is that issue?  The issue, it appears to me, is the vast improvements in analytical technology so that “imprecision” is far less likely to be a confounder in biological measurements and this lends access to far better accuracy?  But from that question arises another! Accuracy only refers to what is measured, but does it give us better ability to explain a complex and dynamic process?  In other words, what is what we are looking at representative of in manageable events?   I think that this is the most important idea that should come out of the recent criticism of the trajectory that molecular genetics been on in the last 5 years.

It was still in an era that “BIG’ science was not the normal.  One could spend an enormous effort at stepwise purification of a protein or enzyme, or other biomolecule starting with a slurry made from 100 lbs of “chicken heart”, for example.  These separations were based on negative charges on the molecules and positive charges on the column, and the molecules of no interest were eluted by gradient elution.  Much was learned about large scale preparation from small scale trials.  But this work was not undertaken without the intent to carry out a number of investigations to understand the “functionality” of a link in a metabolic pathway.  The studies that followed the purification required kinetic investigation with a coenzyme, or with a synthetically modified coenzyme, amino acid sequencing, NMR studies, etc.  You could not put together a “mechanism” without having the minimum amount of necessary information for a reliable account.  It is probably this requirement that led to today’s “BIG” science, that is founded upon multiple methods, now large data bases, and teams of investigators across institutions and continents.  The acquisition of knowledge has been astounding, but the integration of knowledge has not caught up.

However, let’s see if we can sort out the most meaningful signals from what I too am beginning to call the “noisy channel”.  As often happens, important areas of research are opened up that are followed by significant discovery and, in the long run, many other dead end publications that have no lasting significance.  In order to do justice to the work, I’ll pick through documents I find interesting, keeping in mind there is a hidden layer of complexity of which only sufficient information leads to a better understanding.  As much literature calls attention to, much of what ails us has nothing to do with classical Mendelian genetics, and has a postgenomic component.

The most fascinating aspect of this is the withering “dark matter” of the genome. While that component may be silent or expressed, the understanding comes at a higher observed order.  The dark became light! The expression became subtle, like weak bond interactions. The underlying organization is a component of the adaptive ability of an organism or individual in an environment with plants and animals in a changing climate, at particular altitudes, with given water supplies, with disease vectors, and with endogenous sources of essential nutrients.  This brings into focus the regulatory role of the genome as just as important a factor as transmission of the genetic code, especially in somatic cell populations.

The remainder of this discussion deals specifically with my observations on cardiovascular genomics. The following conclusion is appropriate, if incomplete, at this time on circulating miRNAs, particularly miR-133a:

  • elevated levels of circulating miR-133a in patients with cardiovascular diseases originate mainly from the injured myocardium.
  • Circulating miR-133a can be used as a marker for cardiomyocyte death, and

A number of articles that cite this article suggest that it may be useful for following disease progression:
Plasma microRNAs serve as biomarkers of therapeutic efficacy and disease progression in hypertension-induced heart failure  Eur J Heart Fail  2013

MicroRNAs Within the Continuum of Postgenomics Biomarker Discovery Arterio. Thromb. Vasc. Bio. 2013;33:206-214

“Need for Rigor in Design, Reporting, and Interpretation of Transcriptomic Biomarker Studies”  J. Clin. Microbiol.. 2012;50:4192-4193

Circulating microRNAs as diagnostic biomarkers for cardiovascular diseases. Am. J. Physiol. Heart Circ. Physiol.. 2012;303:H1085-H1095,

Circulating MicroRNAs: Novel Biomarkers and Extracellular Communicators in Cardiovascular Disease? Circ. Res.. 2012;110:483-495

Circulating MicroRNAs: Biomarkers or Mediators of Cardiovascular Diseases?  Arterioscler. Thromb. Vasc. Bio. 2011;31:2383-2390,

Circulating MicroRNA-208b and MicroRNA-499 Reflect Myocardial Damage in Cardiovascular Disease MF Corsten, R Dennert, S Jochem, T Kuznetsova,  et al.

The finding refers to an association that is related to the appearance of a miRNA in the circulation of patients with acute cardiac ischemia, and particular released into the circulation of patients from injured myocardium.  This finding has to be distinguished from a finding of another miRNA released with acute injury.  In the case of miR499 (and miR208b), there is a comparison with plasma cTnT, and an ROC curve is produced.

The List of this follows:

Circulation: Cardiovascular Genetics 2010; 3: 499-506

Strikingly, in plasma from

  • acute myocardial infarction patients, cardiac myocyte–associated miR-208b and -499 were highly elevated, 1600-fold (P<0.005) and 100-fold (P<0.0005), respectively, as compared with control subjects. Receiver operating characteristic curve analysis revealed an area under the curve of 0.94 (P<10−10) for miR-208b and 0.92 (P<10−9) for miR-499. Both microRNAs correlated with plasma troponin T, indicating release of microRNAs from injured cardiomyocytes.
  • In patients with acute heart failure, only miR-499 was significantly elevated (2-fold), whereas
  • no significant changes in microRNAs studied could be observed in diastolic dysfunction.

Remarkably, plasma microRNA levels were not affected by a wide range of clinical confounders, including

  • age,
  • sex,
  • body mass index,
  • kidney function,
  • systolic blood pressure, and
  • white blood cell count.

This is miRNA with a different twist.  It appears that there are 3 types found in AMI (133a, 208b, 409).  But type 499 alone is increased with acute heart failure (no mention of chronic cardiomyopathy and no effect of estimated GFR, or of age).

If the problem was just of AMI, then we have to know what this brings to the table.  As it is the hs-troponins have yet to be shown to effectively not only increase the high sensitivity of the tests, but to decrease the confusion generated by the elevation.  The enormous improvement of a test that may be superior to the hs-ctn’s is for the patient with very indeterminiate shortness of breath, a nondefinitive ECG, and in a prodromal phase of AMI.  This happened in the past, and it may happen now, and it may account for many cases of silent MI that were found at autopsy.

Cited by
Plasma microRNAs serve as biomarkers of therapeutic efficacy and disease progression in hypertension-induced heart failure Eur J Heart Fail. 2013;0:hft018v1-hft018,
Circulating microRNAs as diagnostic biomarkers for cardiovascular diseases Am. J. Physiol. Heart Circ. Physiol.. 2012;303:H1085-H1095,

Circulation Editors’ Picks: Most Read Articles in Cardiovascular Genetics Circulation. 2012;126:e163-e169,
MicroRNAs in Patients on Chronic Hemodialysis (MINOS Study) CJASN. 2012;7:619-623,

Novel techniques and targets in cardiovascular microRNA research Cardiovasc Res. 2012;93:545-554,

Microparticles: major transport vehicles for distinct microRNAs in circulation Cardiovasc Res. 2012;93:633-644,

Profiling of circulating microRNAs: from single biomarkers to re-wired networks Cardiovasc Res. 2012;93:555-562,

Small but smart–microRNAs in the centre of inflammatory processes during cardiovascular diseases, the metabolic syndrome, and ageing   Cardiovasc Res. 2012;93:605-613,

Circulation: Heart Failure Editors’ Picks: Most Important Papers in Pathophysiology and Genetics Circ Heart Fail. 2012;5:e32-e49

Use of Circulating MicroRNAs to Diagnose Acute Myocardial Infarction   Clin. Chem. 2012;58:559-567,

Circulating microRNAs to identify human heart failure   Eur J Heart Fail. 2012;14:118-119,

Next Steps in Cardiovascular Disease Genomic Research–Sequencing, Epigenetics, and Transcriptomics  Clin. Chem. 2012;58:113-126,

Most Read in Cardiovascular Genetics on Biomarkers, Inherited Cardiomyopathies and Arrhythmias, Metabolomics, and Genomics Circ Cardiovasc Genet. 2011;4:e24-e30,

MicroRNA-126 modulates endothelial SDF-1 expression and mobilization of Sca-1+/Lin- progenitor cells in ischaemia  Cardiovasc Res. 2011;92:449-455,

The use of genomics for treatment is another matter, and has several factors, e.g., age, residual function after AMI, comorbidities

This is a lot of interesting work that opens as many questions as it answers. The observations are real, and they lead to questions relating to the heart and the circulation.  Maybe it will generate answers to very tough issues concerning hypertension, renal disease and the heart.  It is far too early to tell.  It appears that we are about to hear a cacophony of miR’s in a symphony on cardiac and circulatory diseases not be be pieced together soon. But we have many more tools at our disposal than we did when Karmen discovered and made a distinction between

  • Aspartate and Alanine aminotransferases in the late 1950s, followed in the 1960s by
  • Creatine phosphokinase, the
  • MB-isoenzyme of CK by Sobel, Shell and Kjeckshus,
  • isoenzyme-1 of lactate dehydrogenase, and later the
  • Troponins,

leading to the programs to “reduce the extent of infarct damage”.  Then came the

  • a- and b-type natriuretic peptides,

which are still not fully understood in their role in congestive heart failure and in renal disease.

One item strikes the imagination as a fruitful area of further study.   Genetic Determinants of Potassium Sensitivity and Hypertension.    Integrated Computational and Experimental Analysis of the Neuroendocrine Transcriptome in Genetic Hypertension Identifies Novel Control Points for the Cardiometabolic Syndrome

Essential hypertension, a common complex disease, displays substantial genetic influence. Contemporary methods to dissect the genetic basis of complex diseases such as the genomewide association study are powerful, yet a large gap exists betweens the fraction of population trait variance explained by such associations and total disease heritability.

The researchers

  • developed a novel, integrative method (combining animal models, transcriptomics, bioinformatics, molecular biology, and trait-extreme phenotypes)
  • to identify candidate genes for essential hypertension and the metabolic syndrome.

Method  …  transcriptome profiling on adrenal glands from blood pressure extreme mouse strains:

  1. the hypertensive BPH (blood pressure high) and
  2. hypotensive BPL (blood pressure low).

Results….   Microarray data clustering revealed

  • underexpression of intermediary metabolism transcripts in HIGH BLOOD PRESSURE.
  • The MITRA algorithm identified a conserved motif in the transcriptional regulatory regions of the underexpressed metabolic genes,
  • They decide that regulation through this motif contributed to the global underexpression.
  • Luciferase reporter assays demonstrated transcriptional activity of the motif through transcription factors
    • HOXA3,
    • SRY, and
    •  YY1.

They finally hypothesized that genetic variation at HOXA3, SRY, and YY1 might predict blood pressure and other metabolic syndrome traits in humans. Tagging variants for each locus were associated with

  • blood pressure in a human population blood pressure extreme sample with
  • the most extensive associations for YY1 tagging single nucleotide polymorphism rs11625658 on
  1. systolic blood pressure,
  2. diastolic blood pressure,
  3. body mass index, and
  4. fasting glucose.

Meta-analysis extended the YY1 results into 2 additional large population samples with significant effects preserved on diastolic blood pressure, body mass index, and fasting glucose.

It will take much more of this beautiful integrative work to open up our imagination as to what physiological processes are occurring.

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ENCODE data reveals important information from Genome Wide Association Studies relevant to understanding complex genetic diseases

Author: Ritu Saxena, Ph.D.

Introduction

“The depth, quality, and diversity of the ENCODE data are unprecedented” is what was stated by John Stamatoyannopoulos, professor of genomic sciences at the University of Washington and one of the many principle investigators of ENCODE project. ENCODE (Encyclopedia of DNA elements), indeed, was an ambitious project launched as a pilot in 2003 and then expanded in 2007 for the whole genome analysis and identification of all the functional elements of the human genome. The findings were striking as they challenged the definition of “gene” and ‘the central dogma of genetics (Gene-mRNA-protein). Infact, the non-coding part that constitutes about 80% of the genome or the so-called “junk DNA” was found to contain elements crucial for gene regulation. The elements, in large part, include RNA transcripts that are not transcribed into proteins but might have a regulatory role. For detailed reading, refer to the findings published in the issue of Nature, The ENCODE Project Consortium Nature 489, 57–74 (2012) An integrated encyclopedia of DNA elements in the human genome

Key features of the data, as explained in the National Human Genome Research Institute website (National Human Genome Research Institute News feature), include comprehensive mapping of:

  • Protein-coding genes — Proteins are molecules made of amino acids linked together in a specific sequence; the amino acid sequence is encoded by the sequence of DNA subunits called nucleotides that make up genes.
  • Non-coding genes — Stretches of DNA that are read by the cell as if they were genes but do not encode proteins. These appear to help regulate the activity of the genome.
  • Chromatin structure features — Complex physical structures made from a combination of DNA and binding proteins that make up the contents of the nucleus and affects genome function.
  • Histone modifications — Histones are the proteins that make up the chromatin structures that help shape and control the genome. In addition, histone proteins can be physically modified by adding chemical groups, such as a methyl molecule, that further regulates genomic activity.
  • DNA methylation — Just like histones, methyl groups can be added to DNA itself in a process called DNA methylation. Chemically attaching methyl groups to DNA physically changes the ability of enzymes to reach the DNA and thus alters the gene expression pattern in cells. Methylation helps cells “remember what they are doing” or alter levels of gene expression, and it is a crucial part of normal development and cellular differentiation in higher organisms.
  • Transcription factor binding sites — Transcription factors are proteins that bind to specific DNA sequences, controlling the flow (or transcription) of genetic information from DNA to mRNA. Mapping the binding sites can help researchers understand how genomic activity is controlled.

How could ENCODE be helpful in the study of complex human diseases?

Complex diseases and Genome wide association studies (GWAS)

Coronary artery disease, type 2 diabetes and many forms of cancer are complex human diseases that have a significant genetic component. Unlike mendelian disorders that have defined loci, the genetic component of complex disorders lies in the form of genetic variations in the genome making an individual susceptible to these complex diseases.

Researchers have performed Genome-wide association studies (GWAS) of the human genome, leading to the identification of thousands of DNA variants that could be linked with complex traits and diseases. However, identifying the variants, referred to as SNPs (Single Nucleotide Polymorphisms), that actually contribute to the disease, and understanding how they exert influence on a disease has been more of a mystery.

How would ENCODE solve the puzzle?

The puzzle lies in interpreting how the SNPs found in the genome affect a person’s susceptibility to a particular trait or disease and what is the mechanism behind it. As identified in the GWAS, most variants that are associated with the phenotype of the trait or disease lie in the non-coding region of the genome. Infact, in more than 400 studies compiled in the GWAS catalog only a small minority of the trait/disease-associated SNPs occur in protein-coding regions; the large majority (89%) are in noncoding regions. These variants fall in the gene deserts that lie far from protein-coding region, similar to those where cis-regulatory modules (CRMs) are found. CRMs such as promoters and enhancers are a group of binding sites for transcription factors, and the presence of transcription factors bound to these sites is a good indicator of the potential regulatory regions.

The integrative analysis of ENCODE data has give important insights to the results of GWAS studies. Investigators have employed ENCODE data as an initial guide to discover regulatory regions in which genetic variation is affecting a complex trait. Additionally, ENCODE study when examined the SNPs from GWAS that were associated with the phenotype of the trait, found that these regions are enriched in DNase-sensitive regions i.e, lie in the function-associated DNA region of the genome as it could be bound by transcription factors affecting the regulation of gene expression. Thus, the project demonstrates that non-coding regions must be considered when interpreting GWAS results, and it provides a strong motivation for reinterpreting previous GWAS findings.

Using ENCODE Data to Interpret GWAS Results

ENCODE and predisposition to CANCER:

C-Myc, a proto-oncogene, codes for a transcripton factor, when expressed constitutively leads to uninhibited cell proliferation resulting in cancer. It has been observed that common variants within a ~1 Mb region upstream of c-Myc gene have been associated with cancers of the colon, prostate, and breast. Several SNPs have been reported in this region, that although affect the phenotype, lie in the distal cis-region of the MYC gene. Alignment of the ENCODE data in this region with the significant variants from the GWAS also reveals that key variants are found in the transcription factor occupied DNA segments mapped by this consortium. One variant rs698327, lies within a DNase hypersensitive site that is bound by several transcription factors, enhancer-associated protein p300, and contains histone modifications relative to enhancers (high H3K4me1, low H3K4me3). ENCODE data indicates that non-coding regions in the human chromosome 8q24 loci are associated with cancer and as observed in the case of c-myc gene, similar studies on cancer-related genes could help explain predisposition to cancer.

ENCODE and fetal hemoglobin expression:

Another example of the use of ENCODE data is that of gene regulation of fetal hemoglobin. Several regions were predicted via ENCODE that were involved in the regulation of fetal hemoglobin. It was found that these predicted regions are close to the SNPs in the BLC11A gene that is associated with persistent expression of fetal hemoglobin.

Future perspective

As evident from the above examples, the ENCODE data shows that genetic variants do affect regulated expression of a target gene. Recently, several research groups in the UK performed a large-scale GWAS study to determine the genetic predisposition to fracture risk. The collaborative effort, published in a recent issue of the PLoS journal, was made to identify genetic variants associated with cortical bone thickness (CBT) and bone mineral density (BMD) with data from more than 10,000 subjects. http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002745 The study generated a wealth of data including the result – identification of SNPs in the WNT16 and its adjacent gene, FAM3C were found to be relevant to CBT and BMD. ENCODE data, in this case, could be helpful in interpreting more detailed information including determining additional SNPs, the regulatory information of the genes involved and much more. Thus, it could be concluded that ENCODE data could be immensely useful in interpreting associations between disease and DNA sequences that can vary from person to person.

Sources:

Research articles

An integrated encyclopedia of DNA elements in the human genome

A User’s Guide to the Encyclopedia of DNA Elements (ENCODE)

What does our genome encode?

Genome-wide Epigenetic Data Facilitate Understanding of Disease Susceptibility Association Studies

Genomics: ENCODE explained

ENCODE Project Writes Eulogy For Junk DNA

WNT16 Influences Bone Mineral Density, Cortical Bone Thickness, Bone Strength, and Osteoporotic Fracture Risk

 News articles

ENCODE project: In massive genome analysis new data suggests ‘gene’ redefinition

National Human Genome Research Institute News feature

Related posts

Expanding the Genetic Alphabet and linking the genome to the metabolome

Junk DNA codes for valuable miRNAs: non-coding DNA controls Diabetes

ENCODE Findings as Consortium

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Author: Margaret Baker, PhD, Registered Patent Agent

The Encyclopedia of DNA Elements (ENCODE) Project was launched in September of 2003. In 2007 the ENCODE project was expanded to study the entire human genome, Genome-wide association studies or GWAS, and published a Nature paper entitled “An integrated encyclopedia of DNA elements in the human genome,” this month also all data are available at http://genome.ucsc.edu/ENCODE/.  Novel functional roles have been discovered for both transcribed and non-transcribed portions of DNA.  See several articles and commentary in Science 7 September 2012: Vol. 337 no. 6099 including Maurano et al. pp. 1190-1195  DOI: 10.1126/science.1222794b

For the first time, the 3-dimensional connections that cross the genome have been mapped as long-range looping interactions between functional elements and the genes controlled. These regions of the genome, formerly referred to as “junk DNA”, have the potential to be involved in disease initiation, pathophysiology, and complications. Further, epigenetic factors may be seen to play a more direct role in the expression or silencing of protein coding genes as DNase I hot spots, nucleosomal anchor points, and DNA methylation sites are added to the map.

Non-coding transcribed DNA includes a large percentage of sequences coding for RNA. In fact, RNA encoding genes number nearly equal to the protein encoding genes- 18,400 v 20,687 – and previously unknown non-coding RNA (ncRNA) have also been characterized.

Some of the known elements that were cataloged include:

  • cis elements – promoters, transcription factor binding sites;
  • gene contiguous non-coding stretches such as introns, polyA, and UTR, splice variants;
  • pseudogenes (11,224);
  • long range gene associated elements – enhancers, insulators, suppressors, and predicted promoter flanking regions;
  • ribosomal RNA genes; and
  • sequences for 7,052 small RNAs of which 85% are small nuclear(sn)RNA, small nucleolar(sno)RNA), transfer(t)RNA, and micro(mi)RNA.

What has been found is that distinct non-coding regions, including ncRNA, can be associated with distinct disease traits. miRNA are among the non-gene encoding sequences in the genome which have already been shown to play a major post-transcriptional role in expression of multiple genes..

Most miRNA genes are intergenic or oriented antisense to neighboring genes and therefore assumed to be controlled by independent promoter units. However, in some cases a microRNA gene is transcribed together with its target gene implying coupled regulation of miRNA and protein-coding gene. About one third of miRNA genes reside in polycistronic clusters. miRNA genes can occupy the introns of protein, non-protein coding genes, or nonprotein-coding transcripts. The promoters have been shown to have some similarities in their motifs to promoters of other genes transcribed by RNA polymerase II such as protein coding genes. The ENCODE project also noted that miRNA promoters were in chromatin regions of high promiscuity. There may be up to 1000 miRNA genes in the human genome. In addition, human miRNAs show RNA editing of sequences to yield products different from those encoded by their DNA.  miRNA are implicated in cellular roles as diverse as developmental timing in worms, cell death and fat metabolism in flies, haematopoiesis in mammals, and leaf development and floral patterning in plants

The final miRNA gene product is a ∼22 nt functional RNA molecule. The mature miRNA (designated miR-#) is processed from a characteristic stem–loop sequence (called a pre-mir), which in turn may be excised from a longer primary transcript (or pri-mir). It is processed by the same enzyme (DICER) that processes short hairpin RNA, forming interfering RNA, which provides and additional level of control.

MiRNA controls gene expression by binding to complementary regions of messenger transcripts in the 3’ untranslated region to repress their translation or regulate degradation. What makes the mechanism more powerful (or complicated) is the imperfect but specific binding motif associates with a large number of mRNAs in the 3’ untranslated region having the complimentary motif.  Conversely then, each mRNA can potentially associate with a number miRNA. Mature processed cytosolic miRNA can act in a manner akin to small interfering(si)RNA, and form the RNA-induced silencing complex (RISC) to block translation. Computational methods have been used to identify potential gene targets based on complimentarity between the miRNA and mRNA sequences.

Gerstein et al. explored the “Architecture of the human regulatory network derived from ENCODE data” Nature 489:91-100 (06 Sep 2012) focusing on the regulation of transcription factors (TF) and association between TF and miRNAs, miRNA and miRNA, protein-protein interactions, and protein phosphorylation. Not surprisingly, not all TF are the upstream factor in each network.

These new and remarkably detailed examinations of the different elements within and transcribed from the human genome perhaps do more to aid our knowledge of why we have stumbled in attempts to eradicate diseases, initially by focusing on a single gene or constellation of coding regions. The miRNA wikipedia is also being re-written on a daily basis and new disease associations made*.  As an example of a pathological state that may be linked to miRNA controlled elements, in vitro as well as in small population studies have examined miRNA species in diabetogenic conditions and patients with diabetes (Type I and Type II).

Diabetes and miRNA

In adult β-cell islets, miR-375 is low when glucose is freely available and low miR-375 induces insulin secretion. Interestingly, miR-375 is found only in brain and β-cells which share a secretion pathway.

Diabetic Complications

Organ specific miRNA have been identified in liver, skeletal muscle, kidney, vascular, and adipose tissue which are responsive to transient or sustained hyperglycemia.

miR-17-5p and miR-132 were reported to show significant differences between obese and non obese omental fat and were also abnormal in the blood of obese subjects.  Altered expression of miR-17-5p and miR-132 were found to correlate significantly with BMI, fasting blood glucose and glycosylated hemoglobin. (Kloting et al. PLoS ONE 4(3), e4699 (2009).

Clinical practice related to miRNA in diabetes may be possible as one group has identified eight miRNAs (miR-144, miR-146a, miR-150, miR-182, miR-192, miR-29a, miR-30d and miR-320) as potential ‘signature miRNAs’ that could distinguish prediabetic patients from those with overt T2D (Karolina DS, Armugam A, Tavintharan S et al. MicroRNA 144 impairs insulin signaling by inhibiting the expression of insulin receptor substrate 1 in Type 2 diabetes mellitus. PLoS ONE 6(8), e22839 (2011).

Due to the autoimmune component of T1D, the constellation of miRNA would be expected to be different: upregulation of miR-510 and underexpression of miR-191 and miR-342 were observed in the Tregs (regulatory T-cells) of T1D patients (Hezova R, Slaby O, Faltejskova P et al. microRNA-342, microRNA-191 and microRNA-510 are differentially expressed in T regulatory cells of Type 1 diabetic patients. Cell. Immunol. 260(2),70–74 (2010).

Taken together with the “physical” mapping of miRNA genes in the context of the 3-dimensional genome provided by the ENCODE studies and new understanding of potential concerted regulatory mechanisms, the miRNA data for tissues and specific cell types involved in disease pathology form a new approach to either detecting or possibly correcting gene (coding or non-coding) dysregulation.  miRNA mimics and anti-miRNA agents are being developed as new therapeutic modalities.

References

Bartel, DP et al. MicroRNAs: Genomics, Biogenesis, Mechanism, and Function” Cell 2004, 116:281-297.

Fernandez-Valverde, SL et al. MicroRNAs in beta-cell Biology, insulin resistance, diabetes and its complications. Diabetes July 2011 60 (7):1825-31.

Kantharidis, et al.  Diabetes Complications: The MicroRNA Perspective http://diabetes.diabetesjournals.org/content/60/7/1832.short

MEDSCAPE Review article: “miRNAs and Diabetes Mellitus: miRNAs in Diabetic Complicatons”  http://www.medscape.org/viewarticle/763729_6

*Based on initial studies in the worm C. elegans showing the temporal appearance of 21- and 22-nt RNAs during development, a family of highly conserved micro RNA sequences (miRNA) existing in invertebrates and vertebrates, were cataloged by Tuschl et al. at the Max-Planck-Institute and others (see Eddy, SR  Non-coding RNA genes and the modern RNA world Nature Reviews Genetics, 2:920-929, 2001). The sequence-specific post-transcriptional regulatory mechanisms mediated by these miRNAs have been associated with certain disease states such as cancer miR-21) and more specifically, lung cancer (miR-124) or breast cancer (miR-7, miR-21) and new species and function continue to be found (see http://www.mirbase.org/ ).

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