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Posts Tagged ‘STAT3’


Leptin signaling in mediating the cardiac hypertrophy associated with obesity

Larry H Bernstein, MD, FCAP, Reviewer, and Aviva Lev-Ari, PhD, RN

 

There has been a lot of interest in leptins and both insulin resistance and obesity for the last decade.  The association between obesity and cardiac hypertrophy is also known, but what drives this association.  We have covered heart disease from many aspects in a long series of articles.  The next is a pleasure to take in.

Importance of leptin signaling and signal transducer and activator of transcription-3 activation in mediating the cardiac hypertrophy associated with obesity

Maren Leifheit-Nestler12, Nana-Maria Wagner13, Rajinikanth Gogiraju1,Michael Didié14, Stavros Konstantinides15, Gerd Hasenfuss1 and Katrin Schäfer1*

1Department of Cardiology and Pulmonary Medicine, Heart Research Center, Georg August University Medicine Goettingen, Robert Koch Strasse 40, D-37075, Göttingen, Germany

2Current address: Department of Pediatric Kidney, Liver and Metabolic Diseases, Hannover Medical School, Hannover, Germany

3Current address: Clinic for Anesthesiology and Intensive Care Medicine, University Medicine Rostock, Rostock, Germany

4Department of Pharmacology, Georg August University Medicine Goettingen, Goettingen, Germany

5Current address: Center for Thrombosis and Hemostasis, University Medicine Mainz, Mainz, Germany

J Translational Medicine: Cardiovascular, Metabolic and Lipoprotein Translation. 2013; 11:170.  http://www.translational-medicine.com/content/11/1/170

http://dx.doi.org/10.1186/1479-5876-11-170

This is an Open Access article distributed under the terms of the Creative Commons Attribution License 
http://creativecommons.org/licenses/by/2.0

Abstract

Background

The adipokine leptin and its receptor are expressed in the heart, and

  • leptin has been shown to promote cardiomyocyte hypertrophy in vitro.

Obesity is associated with

  • hyperleptinemia 
  • hypothalamic leptin resistance and
  • an increased risk to develop cardiac hypertrophy and heart failure.

However, the role of cardiac leptin signaling in mediating the cardiomyopathy associated with increased body weight is unclear, in particular, whether it develops subsequently to cardiac leptin resistance or overactivation of hypertrophic signaling pathways via elevated leptin levels.

Methods

The cardiac phenotype of high-fat diet (HFD)-induced obese wildtype (WT) mice was examined and compared to age-matched genetically obese leptin receptor (LepR)-deficient (LepRdb/db) or lean WT mice. To study the role of leptin-mediated STAT3 activation during obesity-induced cardiac remodeling,

  • mice in which tyrosine residue 1138 within LepR had been replaced with a serine (LepRS1138) were also analyzed.

Results

Obesity was associated with hyperleptinemia and elevated cardiac leptin expression in both diet-induced and genetically obese mice.

  • Enhanced LepR and STAT3 phosphorylation levels were detected in hearts of obese WT mice, but not in those with LepR mutations, and
  • exogenous leptin continued to induce cardiac STAT3 activation in diet-induced obese mice.

Although echocardiography revealed signs of cardiac hypertrophy in all obese mice,

  • the increase in left ventricular (LV) mass and diameter was significantly more pronounced in LepRS1138 animals.

LepRS1138 mice also exhibited an increased activation of signaling proteins downstream of LepR, including Jak2 (1.8-fold), Src kinase (1.7-fold), protein kinase B (1.3-fold) or C (1.6-fold). Histological analysis of hearts revealed that the inability of leptin to activate STAT3 in LepRdb/db and LepRS1138 mice

  • was associated with reduced cardiac angiogenesis as well as increased apoptosis and fibrosis.

Conclusions

Our findings suggest that hearts from obese mice continue to respond to elevated circulating or cardiac leptin, which

  • may mediate cardioprotection via LepR-induced STAT3 activation, whereas
  • signals distinct from LepR-Tyr1138 promote cardiac hypertrophy.

On the other hand, the presence of cardiac hypertrophy in obese mice with complete LepR signal disruption indicates that additional pathways also play a role.

Keywords:

Heart; Hypertrophy; Leptin; Obesity; Signal transduction; STAT3

Background

Obesity is frequently associated with elevated circulating leptin levels [1] and an increased risk to develop cardiac hypertrophy [2,3] or heart failure [4]. Clinical studies demonstrated a positive correlation between serum leptin levels and left ventricular (LV) mass or wall thickness [5,6], independent of blood pressure levels, suggesting a direct role for leptin in the pathogenesis of obesity-associated cardiomyopathy. Furthermore, leptin was shown to promote hypertrophy of isolated rat or human ventricular cardiomyocytes [7,8], and

  • this effect could be prevented using neutralizing antibodies [9].

Cardiac hypertrophy also develops in obese rodents fed high-fat diet (HFD)[10,11], and

  • studies in mice with (functional) leptin deficiency suggested that the cardiac hypertrophy developing in states of chronic hyperleptinemia
  • may result from the inability to transduce anti-hypertrophic and/or cardioprotective effects of the adipokine [12,13].

The effects of leptin on cell shortening and intracellular Ca2+ transients were abrogated in cardiomyocytes isolated from HFD-fed obese rats [14], but then others found

  • a preserved signal transduction in response to leptin in hyperleptinemic obese mice [15,16] or rats[17].

The leptin receptor (LepR) belongs to the family of cytokine type I receptors that signal via activation of

  • Janus kinase (Jak)-2 and
  • signal transducer and
  • activator of transcription (STAT)-3 [18].

Analysis of cardiomyocytes ex vivo revealed leptin promotes hypertrophy via activation of p38 and p42/44 MAP kinases as well as protein kinase B (Akt) [19,20]. But it is unknown whether STAT-3 activation downstream of LepR is required to transmit the cardiac effects of leptin and whether it may be involved in mediating protective (i.e. anti-apoptotic, anti-fibrotic or pro-angiogenic) signals, as previously reported in mice with cardiomyocyte-specific STAT-3 deletion [21,22].

In this study, we examined the cardiac phenotype of diet-induced (i.e. with hypothalamic leptin resistance) and genetically obese (i.e. with systemic leptin receptor deficiency) hyperleptinemic mice, developing with age or after continuous β-adrenergic stimulation. Moreover, we determined

  • the importance of leptin-mediated STAT-3 activation
  • for the development of cardiac hypertrophy in obesity
  • by analyzing mice with targeted mutation of the STAT3 binding site within LepR.

Methods

Animals

C57Bl6/J leptin receptor-deficient db/db (LepRdb/db; BKS.Cg Leprdb/Leprdb) mice and C57Bl6/J wildtype (WT) controls were obtained from Harlan Winkelmann, Germany. Mice heterozygous mutant for the LepRS1138 allele (on the congenic B6.129/J background; 98- > 99% homozygous for C57Bl/6; [23]) were obtained from Professor Martin Myers (University of Michigan Medical School, Ann Arbor, USA) and bred at the animal facility of the University of Goettingen, Germany, to generate homozygous mutant obese LepRS1138 mice. Age- and gender-matched WT (LepR+/+) and heterozygous (LepRS/+) littermates were used as controls. To induce obesity, 3 months-old mice were switched to high-fat diet (HFD; D12451) for 4 months, while controls were maintained on normal rodent chow (D12450B; both Research Diets Inc.). The composition of both diets is shown in Additional file 1: Table S1. To examine the cardiac response to hypertrophic stimuli other than leptin, osmotic minipumps (Alzet®; model 2002; Charles River Laboratories) were filled with isoprenaline hydrochloride (Sigma; 20 mg/kg body weight [BW] per day) and implanted for 14 days under the dorsal skinfold of 2 months-old, 2% isoflurane anesthetized mice. At the time of tissue harvest, mice were weighed followed by intraperitoneal anesthesia with a mixture of 2% xylazine (6 mg/kg BW) and 10% ketamine hydrochloride (100 mg/kg BW), and blood was drawn by cardiac puncture. Hearts were rapidly excised, the atria removed and ventricles immediately processed for protein isolation or cryoembedding, respectively. All animal care and experimental procedures had been approved by the institutional Animal Research Committee and complied with national guidelines for the care and use of laboratory animals.

Additional file 1: Table S1. Diet composition.
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Serum analysis

Freshly drawn blood was allowed to clot at room temperature (RT) for 30 min, followed by centrifugation for 10 min at 3,000 rpm. The supernatant was stored at -80°C pending analysis of serum leptin levels using specific enzyme-linked immunoassays (ELISA; R&D Systems).

Echocardiography

Echocardiography was performed by a blinded examiner at the day before tissue harvest in mice under 1.5% isoflurane anesthesia using the VisualSonics Vevo 2100 system (Visualsonics) equipped with a 30 MHz center frequency ultrasound transducer, as previously described [24]. M-mode echocardiographical recordings were used to determine the end-diastolic and end-systolic LV diameter (EDD and ESD, respectively) and the ventricular wall thickness (WTh), corresponding to the mean of the anterior and posterior WTh. LV mass was calculated using the formula: 1.055 × ([AWTh + EDD + PWTh]3 – EDD3). Fractional shortening (FS) was calculated as (EDD – ESD)/EDD × 100. B-mode echocardiography images were used to calculate the heart weight, using the equation: 1.05 × (5/6) × ((Episyst × (Lsyst + ((AWThsyst + PWThsyst)/2))) – (Areasyst × Lsyst)).

Histology and immunohistochemistry

Histochemical analyses were performed on 5 μm-thick frozen cross sections through the LV. For each mouse, 4 sections (approx. 500 μm apart) and 4 randomly selected viewing fields (at 200-fold magnification) per section were analyzed and findings averaged. Cardiac fibrosis was determined after overnight incubation in Bouin’s fixative followed by Masson’s trichrome (MTC) stain. Monoclonal rat antibodies against mouse CD31 (Santa Cruz Biotechnology) were used to detect endothelial cells [24,25]. Their number was manually counted by a person blinded to the mouse genotype and expressed per mm2 or cardiomyocyte, respectively.

Single cardiomyocytes were visualized by incubation with fluorescein-labeled wheat germ agglutinin (WGA; Molecular Probes), followed by determination of the cardiomyocyte cross-sectional area (CSA) using image analysis software (Image ProPlus). Per cross section, at least 10 randomly selected cardiomyocytes were evaluated and results averaged. Apoptosis was analyzed using the ‘In Situ Cell Death Detection kit’ (Roche). Cell nuclei were visualized using 4′,6-diamidino-2-phenylindole (DAPI; Sigma).

Immunoprecipitation and western blot analysis

Membranes were blocked in 1% bovine serum albumine (in TBS, containing 0.1% Tween-20) prior to incubation with antibodies against phosphorylated (p)-Akt (S473) and total Akt, p-Jak2 (Y1007/1008) and total Jak2, p-p38 (T180/Y182) and total p38, p-p42/44 (T202/Y204) and total p42/44, p-Src (Y416) and total Src, p-STAT3 (Y705) and total STAT3, or p-PKC (pan), respectively (all Cell Signaling Technologies), or against leptin (R&D Systems) and GAPDH (Biotrend), respectively. Protein bands were visualized using HRP-conjugated secondary antibodies (Amersham Biosciences), followed by detection with SuperSignal® West Pico Substrate (Pierce). For the analysis of LepR phosphorylation, 100 μg total heart tissue lysates were immunoprecipitated under rotation at 4°C with 2 μg anti-LepR antibody (against an internal domain present in the short and long isoforms of murine LepR; Santa Cruz Biotechnology) plus 50 μL nProtein A Sepharose™ 4 Fast Flow beads (GE Healthcare) followed by detection of phosphorylated tyrosines (p-Tyr [PY20]; Santa Cruz Biotechnology) or LepR. For the analysis of STAT3 phosphorylation in response to acute elevations of circulating leptin, mice were fasted overnight, injected with recombinant murine leptin (1 mg/kg BW i.p.) and hearts harvested 30 min later.

Statistical analysis

Quantitative data are presented as mean ± standard error of the mean (SEM). Normal data distribution was tested using the D’Agostino & Pearson omnibus normality test. When three or more groups were compared, ANOVA was employed, if samples were normally distributed, or Kruskal-Wallis test, if not. For post-hoc comparisons, ANOVA was followed by Bonferroni’s and Kruskal-Wallis by Dunn’s multiple comparison test. Differences before and after isoprenaline infusion were tested using Student’s t-test for paired means. Statistical significance was assumed when P reached a value less than 0.05. All statistical analyses were performed using GraphPad PRISM software, version 4.01 (GraphPad Software Inc).

Results

Clinical and experimental studies revealed that obesity is associated with LV hypertrophy [10,11], an important risk factor for the development of heart failure. As shown in Tables 1 and 2, WT mice fed HFD for 4 months (WT + HFD; mean body weight [BW], 44±1.9 g) to induce obesity exhibited a non-significant trend towards an increased mean heart weight, LV mass and WTh compared to age-matched lean controls fed normal chow (BW, 29±1.0 g). Marked LV hypertrophy was observed in 7 months-old obese LepRdb/db mice (Table 1 and 2), consistent with a previous report [12]. Longitudinal sections through hearts of WT, WT + HFD and LepRdb/db mice are shown in Figure 1A, representative M-mode echocardiography recordings in Figure 1B and cardiac cross-sections after WGA staining to delineate cardiomyocyte borders in Figure 1C. Of note, adiposity in mice with LepR deficiency was more pronounced compared to age-matched WT + HFD mice (Table 1; P < 0.001), in which obesity develops as result of hypothalamic resistance to chronically elevated leptin levels[26].

1479-5876-11-170-1  F1 Cardiac phenotype of lean and obese WT

Figure 1.Cardiac phenotype of lean and obese WT, WT + HFD, LepRS1138 and LepRdb/db mice.
(A)
Representative H&E-stained longitudinal sections through hearts of 7 months-old mice are shown. Magnification, ×10.(B) Representative M-mode echocardiographic recordings.(C) Representative images of wheat germ agglutinin (WGA)-stained myocardial cross sections. The mean cardiomyocyte cross-sectional areas are given in Table 1.

Table 1.Body, visceral fat and heart weights in 7 months-old mice

Table 2.Echocardiographic parameter in 7 months-old mice

The presence of cardiac hypertrophy in LepR-deficient and, to a lesser extent also in diet-induced obese mice, suggests that it develops as a result of the heart’s inability to respond to elevated systemic (Table 1) and/or cardiac (Figure 2A) leptin levels. In this regard, Western blot analysis revealed increased levels of phosphorylated (p-) LepR (Figure 2B) and STAT-3 (Figure 2C) protein in hearts of HFD-induced obese mice (P < 0.05 vs. WT for both), whereas findings in LepRdb/db mice did not differ from those in lean controls or were reduced compared to those in WT + HFD mice (P < 0.05 for differences in LepR phosphorylation). Moreover, both lean and HFD-induced obese WT mice responded to a single i.p. injection of recombinant murine leptin with a significant increase in the cardiac STAT-3 phosphorylation (Figure 2D), suggesting a preserved cardiac leptin signal transduction in hyperleptinemic, diet-induced obese mice.

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Figure 2.Cardiac leptin expression and signal transduction in lean and obese mice.
Protein was extracted from hearts of 7 months-old mice (n = 8 per group) and analyzed for the expression of
(A) leptin, (B)phosphorylated LepR (using immunoprecipitation of LepR, followed by the detection of total phosphotyrosines and LepR) and (C) phosphorylated STAT3. (D) Cardiac STAT3 phosphorylation in response (30 min later) to a single injection of recombinant murine leptin (1 mg/kg BW i.p.) was examined in WT (n = 4) and WT + HFD (n = 6) mice. Results are expressed as -fold increase of controls (black bars) after normalization for total protein and GAPDH expression. The mean ± SEM as well as representative Western blot results are shown. *P < 0.05 and **P < 0.01 vs. WT mice; #P < 0.05 vs. WT + HFD mice.

To further study the role of leptin signaling in the development of cardiac hypertrophy and also to determine, whether the inability of leptin to activate STAT3 contributes to the cardiac maladaptation in obesity, we examined mice in which tyrosine (Tyr)1138 within LepR had been replaced by a serine (LepRS1138). In these mice, leptin cannot signal via STAT3, but continues to be able to activate Jak2 and SH2 domain-containing adapter proteins. Western blot analysis revealed that p-LepR (Figure 2B) and p-STAT3 (Figure 2C) levels in hearts of LepRS1138 mice did not significantly differ from those in WT and LepRdb/db mice. Similar to mice with complete LepR deficiency, lack of LepR-mediated STAT3 activation resulted in severe adiposity, although serum leptin levels were lower than those in LepRdb/db mice (P < 0.001; Table 1). Interestingly, obese LepRS1138 exhibited a more pronounced increase in mean heart weights not only compared to lean or diet-induced obese WT mice, but also compared to LepRdb/db mice (P < 0.001 for all comparisons; Table 1), and differences persisted after normalization for body weight (P < 0.001) or tibia length (P < 0.001). Echocardiography confirmed increased LV mass (P < 0.01) or heart weights (P < 0.001) in LepRS1138 mice compared to their LepRdb/dbcounterparts (Table 2; please also see Figure 1A-C). Moreover, hearts of LepRS1138 mice exhibited elevated levels of phosphorylated Jak2 (P < 0.001 vs. WT; Figure 3A), Src kinase (P < 0.05 vs. WT, WT + HFD and LepRdb/db; Figure 3B), Akt (P < 0.001 vs. LepRdb/db; Figure 3C), PKC (P < 0.05 vs. WT and LepRdb/db, P < 0.01 vs. WT + HFD; Figure 3D) and p38 MAPK (P < 0.01 vs. LepRdb/db; Figure 3E), suggesting that an intact, Tyr1138-independent LepR activation in the presence of elevated leptin levels may have contributed to the pronounced cardiac hypertrophy present in these mice. On the other hand, cardiac levels of p-p42/44 MAPK did not significantly differ between the mouse groups (Figure 3F).

Figure 3. Hypertrophic signal transduction

Figure 3.Hypertrophic signal transduction in hearts of lean and obese mice. Protein was isolated from hearts of 7 months-old WT (n = 15), WT + HFD (n = 12), LepRS1138(n = 15) and LepRdb/db (n = 15) mice and analyzed for the expression of phosphorylated Jak2 (A), Src kinase (B), Akt (C), PKC (D), p38 (E) and p42/44 MAPK (F). Results are expressed as -fold increase of lean control mice (after normalization for total protein [with the exception of PKC] and GAPDH expression). The mean ± SEM as well as representative findings are shown. *P < 0.05, **P < 0.01 and ***P < 0.001 vs. WT mice; #P < 0.05 and ##P < 0.01 vs. WT + HFD mice; §P < 0.05, §§P < 0.01 and §§§P < 0.001 for the difference between LepRdb/db and LepRS1138 mice.

M-mode echocardiography also revealed significantly increased enddiastolic LV diameters in LepRS1138 mice (P < 0.01 vs. WT and LepRdb/db mice; Table 2; representative findings are shown in Figure 1B), suggesting that the observed (over-)activation of LepR signaling together with the inability to induce STAT3 may result in augmented hypertrophy and maladaptive cardiac remodeling. Of note, fractional shortening (FS) was not significantly altered in HFD-induced obese WT mice (P = n.s. vs. WT mice), but found to be increased in both LepRdb/db (P < 0.01 vs. WT and P < 0.001 vs. WT + HFD mice) and LepRS1138mice (P < 0.05 vs. WT and P < 0.001 vs. WT + HFD mice). Histological analyses revealed significantly reduced numbers of CD31-positive capillary endothelial cells in LepRdb/db, and to a lesser extent also in LepRS1138 mice (Figure 4A), whereas the number of TUNEL-positive apoptotic cells (Figure 4B) and the fibrotic tissue area (Figure 4C) were found to be significantly increased in hearts of both LepRS1138 and LepRdb/db mice compared to lean and diet-induced obese WT mice.

Figure 4.Histological analysis of angiogenesis, apoptosis and fibrosis in hearts of lean and obese mice.
Serial cross sections through the LV of WT, WT + HFD, LepR
S1138 and LepRdb/db mice (n = 10 per group) were immunostained and the number of (A) CD31-positive endothelial cells and (B) TUNEL-positive apoptotic cell nuclei determined. Results are expressed per cardiomyocyte and/or mm2.(C) The degree of cardiac fibrosis was quantified after Masson’s trichrome (MTC) staining. Results are expressed as % of total tissue area (at 200-fold magnification). The mean ± SEM as well as representative findings are shown. **P < 0.01 and ***P < 0.001 vs. WT; #P < 0.05, ##P < 0.01 and ###P < 0.001 vs. WT + HFD mice.

Figure 4.Histological analysis  (unable to post)

To examine the specificity of leptin’s hypertrophic action in obesity, the cardiac response of young, i.e. 2 months-old WT (n = 12; body weight, 22 ± 0.9 g), LepRS1138 (n = 9; 34 ± 1.1 g, P < 0.001 vs. WT) and LepRdb/db mice (n = 7; 40 ± 1.3 g; P < 0.001 vs. WT and P < 0.01 vs. LepRS1138) to chronic isoprenaline infusion (20 mg/kg BW per day) was examined. Under basal conditions, similar findings as those in 7 months-old mice were observed, i.e. LepRS1138 mice exhibited an increased heart weight (P < 0.05 vs. LepRdb/db; Figure 5A), LV mass (P < 0.01 vs. WT; Figure 5B) and mean WTh (P < 0.05 vs. WT; Figure 5C), whereas other changes, such as differences in fractional shortening (Figure 5D), ESD (Figure 5E) and EDD (Figure 5F) were not (yet) detected. On the other hand, all mouse groups responded to chronic β-adrenergic stimulation with significant cardiac hypertrophy, and no differences (with the exception of heart weight; Figure 5A) were observed between LepRS1138 and LepRdb/db mice. Representative M-mode echocardiography tracings are shown in Figure 6 and summarized in Additional file 2: Table S2.

1479-5876-11-170-5  F5 Echocardiography findings

Figure 5.Echocardiography findings in young lean and obese mice before and after chronic β-adrenergic stimulation.
Isoprenaline-filled osmotic minipumps were subcutaneously implanted into 2 months-old WT (n = 12), LepR
S1138 (n = 9) and LepRdb/db (n = 7) mice to examine the cardiac response to a hypertrophic stimulus other than leptin. Echocardiography (A-F) was performed immediately before (open bars) as well as at the time of tissue harvest 14 days later (dotted bars). *P < 0.05, *P < 0.01 and ***P < 0.001 for differences vs. WT mice; §P < 0.05 for differences between LepRdb/db and LepRS1138 mice. Significance levels for differences before and after isoprenaline stimulation (as determined using Student’s t test for paired means) are indicated within the graph.

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Figure 6.Representative M-mode echocardiography recordings.

Additional file 2: Table S2. Echocardiographic parameter in 2 months-old mice before and 14 days after isoprenaline infusion.

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Discussion

The adipocytokine leptin may link obesity with cardiac hypertrophy, an important risk factor for the development of heart failure. Studies in humans [2,3] and rodents [10,11] have shown that obesity is associated with LV hypertrophy, and body mass index was identified as a strong and independent predictor of LV mass [2,3]. Importantly, cardiac hypertrophy is also observed in normotensive obese subjects [6], and plasma leptin levels are associated with increased myocardial wall thickness independent of BW or blood pressure elevations [5], suggesting a causal role for leptin in the pathogenesis of cardiac hypertrophy.

Although the major source of leptin is adipose tissue, cardiomyocytes are also capable of synthesizing leptin [27], and increased cardiac leptin levels have been reported in mice or rats following coronary ligation [13,18] or in patients with heart failure [28]. In this study, elevated circulating as well as cardiac leptin levels were detected in both diet-induced and genetically obese mice, which may have acted on cardiomyocytes as well as other, non-cardiomyocyte cells expressing leptin receptors [29]. Although leptin serum levels were higher than in previous publications [30], we explain this findings with the higher age of the mice, a factor previously found to be associated with increased circulating leptin levels [31]. Leptin has been shown to stimulate the hypertrophy of cardiomyocytes isolated from rats [7,20] or humans [8,19]. Moreover, chronic leptin infusion increased cardiac ANP expression after myocardial infarction (MI) in mice [32], whereas neutralizing LepR antibodies abrogated the hypertrophy of the surviving myocardium after coronary artery ligation in rats [33]. On the other hand and as confirmed in our analysis, cardiac hypertrophy also develops in leptin- and LepR-deficient mice and may be reversed by leptin substitution[12]. Caloric restriction experiments suggested that the anti-hypertrophic effects of leptin had occurred in addition to weight loss [12], which itself may preserve heart function and attenuate LV remodeling [34]. Thus, it is unclear whether the cardiac hypertrophy in obesity is the consequence of pro-hypertrophic effects of the adipokine or rather the result of a resistance towards leptin’s preventive effects on hypertrophic cardiac remodeling. Of note, since body weight is markedly elevated in the diet-induced and particularly, the genetically obese mice, the heart-to-body weight ratio decreases, even though the absolute heart weight is increased (but to a relatively lesser extent).

Obesity is associated with elevated circulating leptin levels and hypothalamic resistance to the weight-reducing effects of the adipokine, whereas the existence of a peripheral (e.g. cardiac) leptin resistance is controversial. For example, reduced cardiac LepR expression has been reported in HFD-fed rats[14], whereas others demonstrated unaltered cardiac STAT3 phosphorylation in diet-induced obese rodents following acute leptin administration [1517]. Our findings also suggest that hearts from diet-induced obese mice continue to respond to leptin in the presence of chronically elevated leptin levels and that the observed elevation of serum and cardiac leptin may thus contribute to the development of cardiac hypertrophy in obesity. For example, hearts of hyperleptinemic obese WT mice (i.e. those with intact leptin receptors) exhibited signs of activated leptin signaling, including elevated levels of phosphorylated LepR and STAT3, while they were unchanged or reduced in mice with mutated or truncated forms of LepR (i.e. LepRS1138 or LepRdb/db mice). Moreover, both lean and obese WT mice responded to a single leptin injection with increased cardiac STAT3 phosphorylation. Of note, we could not spatially dissect the cardiac responsiveness to leptin, since whole heart homogenates were examined. Possible explanations underlying the discrepancy between the present and some previous studies include the animal species, as the absence of a response to leptin in obesity has been so far primarily observed in rats[14]. In addition, age, sex and feeding status of the animals or the time of recombinant leptin administration may have influenced the results. Of note, previous studies in humans have reported the existence of individuals (up to 40%) exhibiting a blunted response to leptin [35], although it is unknown, whether such phenomenon also occurs in rodents.

Interestingly, hearts from LepRS1138 mice exhibited a marked overactivation of STAT3-independent leptin signaling pathways, including Jak2, Src kinase, Akt or p38 MAPK, i.e. factors previously shown to mediate the pro-hypertrophic effects of the adipokine in cardiomyocytes [19,20]. Importantly, overactivation of leptin signaling in hearts of LepRS1138 mice was accompanied by a pronounced cardiac hypertrophy, both at the organ and the single cardiomyocyte level, despite similar adiposity. Although leptin levels were found to be lower in LepRS1138compared to LepRdb/db mice, as previously reported [23], leptin continues to be able to activate LepR signal transduction in these mice, for example via LepR-Tyr985. Similar echocardiographical findings were obtained in young (i.e. 2 months-old) and older (i.e. 7 months-old) mice, arguing against the development of cardiac hypertrophy secondary to hemodynamic or other metabolic changes associated with obesity, although we cannot exclude the possible contribution of a more pronounced hyperinsulinemia [23] to the development of cardiac hypertrophy in LepRS1138 mice. On the other hand, hypertension had not been observed in ob/ob mice [12], and heart weight increase and concentric LV hypertrophy in obese mice and humans also occurs without systolic and diastolic blood pressure elevations [5,6,36].

Although a predominant cardiac expression of the short (i.e. without STAT3 binding site) over the long LepR isoform has been reported [7,29], previous studies have shown that stimulation of neonatal rat cardiomyocytes with leptin increased STAT3 phosphorylation, nuclear translocation and DNA binding activity [32]. Also, cardiac STAT3 activation after MI was blunted in leptin-deficient mice [13]. The observation that increased cardiac STAT3 phosphorylation in hyperleptinemic, diet-induced obese mice was reduced or almost completely abolished in LepRS1138 or LepRdb/db mice suggests that cardiac STAT3 activation in obesity largely occurs downstream of elevated leptin levels and that other cytokines, also elevated in obesity and known to signal via Jak2-STAT3, may be of minor importance. On the other hand, the importance of leptin-mediated STAT3 activation in the heart and its contribution to cardioprotective signaling pathways in vivo have not been directly examined so far.

STAT3 has been implicated in cardioprotection after various injuries. For example, cardiomyocyte-specific STAT3 deletion results in dilatative cardiomyopathy, characterized by increased apoptosis and interstitial fibrosis as well as reduced myocardial capillary density [21,22]. Previous studies suggested that leptin may exert beneficial effects on the heart. For example, administration of leptin was associated with smaller infarct size after ischemia/reperfusion injury [37], whereas ischemic postconditioning failed to induce cardioprotection in mice lacking leptin or its receptor [38]. Also, leptin deficiency was associated with a worsened cardiac function and survival after coronary artery ligation, which could be improved by leptin repletion [13]. Regarding possible mechanisms, increased cardiac myocyte apoptosis was observed in hearts from leptin (receptor)-deficient mice [39,40]. Similar findings were obtained in vitro, showing that leptin protects cardiomyocytes against apoptotic cell death induced by serum starvation [41]. Our analyses also revealed significantly elevated numbers of apoptotic cells in hearts of obese LepRS1138 and LepRdb/dbmice, consistent with a reduced activation of STAT3-responsive anti-apoptotic genes [40]. Although findings in mice with systemic defects in leptin signal transduction may have been confounded by the concomitant presence of obesity and associated metabolic and inflammatory alterations, adverse cardiac remodeling after MI [42] or lethal heart failure [43] were recently reported in mice with cardiomyocyte-specific LepR deletion. On the other hand, the beneficial effects of leptin-mediated STAT3 activation may not be restricted to cardiomyocytes. For example, we and others have shown that leptin promotes the angiogenic properties of endothelial (progenitor) cells [25,44], and cardiac angiogenesis was reduced in LepRS1138 and LepRdb/db mice. In addition, hearts of obese LepRS1138 and LepRdb/db mice exhibited increased interstitial fibrosis, which may have occurred secondary to increased cardiomyocyte loss, although previous studies have shown that leptin may also directly influence myocardial matrix metabolism [45]. On the functional level, the enhanced activation of pro-hypertrophic signaling pathways in the absence of STAT3-mediated cardioprotection may have contributed to the echocardiographic finding of LV cavity dilation in LepRS1138 compared to LepRdb/db mice.

Conclusions

Taken together, our findings suggest that hearts from diet-induced obese mice continue to respond to chronically elevated leptin levels and that increased systemic and/or local leptin and enhanced cardiac LepR activation contribute the development of cardiac hypertrophy. On the other hand, chronic overactivation of hypertrophic signaling mediators together with an inabilitity to activate STAT3-dependent cardioprotective pathways may promote maladaptive cardiac remodeling. Of note, our findings also indicate that leptin signaling is not a prerequisite to develop cardiac hypertrophy in obesity and that additional pathways also contribute to the increase in LV mass associated with higher body weight.

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    J Am Coll Cardiol 1994, 24:1492-1498. PubMed Abstract |Publisher Full Text OpenURL

  3. Lauer MS, Anderson KM, Kannel WB, Levy D: The impact of obesity on left ventricular mass and geometry. The framingham heart study.

    JAMA 1991, 266:231-236. PubMed Abstract | Publisher Full Text OpenURL

  4. Kenchaiah S, Evans JC, Levy D, Wilson PW, Benjamin EJ, Larson MG, Kannel WB, Vasan RS: Obesity and the risk of heart failure.

    N Engl J Med 2002, 347:305-313. PubMed Abstract | Publisher Full Text OpenURL

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    Hypertension 1999, 34:1047-1052. PubMed Abstract |Publisher Full Text OpenURL

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CRACKING THE CODE OF HUMAN LIFE: Milestones along the Way – Part IIA

Curator: Larry H Bernstein, MD, FCAP

Introduction and purpose

This material goes beyond the Initiation Phase of Molecular Biology, Part I.

https://pharmaceuticalintelligence.com/2013/02/08/the-initiation-and-growth-of-molecular-biology-and-genomics/
Part II reviews the Human Genome Project and the decade beyond.

In a three part series:
Part IIA.  CRACKING THE CODE OF HUMAN LIFE: Milestones along the Way
Part IIB.  CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics & Computational Genomics
Part IIC.  CRACKING THE CODE OF HUMAN LIFE: Recent Advances in Genomic Analysis and Disease

Part III will conclude with Ubiquitin, it’s Role in Signaling and Regulatory Control.
Part I reviewed the huge expansion of the biological research enterprise after the Second World War. It concentrated on the

  • discovery of cellular structures,
  • metabolic function, and
  • creation of a new science of Molecular Biology.

Part II follows the race to delineation of the Human Genome, discovery methods and fundamental genomic patterns that are ancient in both animal and plant speciation. But it explores both the complexity and the systems view of the architecture that underlies and understanding of the genome.

These articles review a web-like connectivity between inter-connected scientific discoveries, as significant findings have led to novel hypotheses and many expectations over the last 75 years. This largely post WWII revolution has driven our understanding of biological and medical processes at an exponential pace owing to successive discoveries of

  • chemical structure,
  • the basic building blocks of DNA  and proteins,
  • nucleotide and protein-protein interactions,
  • protein folding, allostericity,
  • genomic structure,
  • DNA replication,
  • nuclear polyribosome interaction, and
  • metabolic control.

In addition, the emergence of methods for

  • copying,
  • removal,
  • insertion,
  • improvements in structural analysis
  • developments in applied mathematics that have transformed the research framework.

Part IIA:

CRACKING THE CODE OF HUMAN LIFE:

Milestones along the Way

A NOVA interview with Francis Collins (NHGRI) (FC), J. Craig Venter (CELERA)(JCV), and Eric Lander (EL).
RK: For the past ten years, scientists all over the world have been painstakingly trying to read the tiny instructions buried inside our DNA. And now, finally, the “Human Genome” has been decoded.
EL: The genome is a storybook that’s been edited for a couple billion years.
The following will address the odd similarity of genes between man and yeast

EL: In the nucleus of your cell the DNA molecule resides that is about 10 angstroms wide curled up, but the amount of curling is limited by the negative charges that repel one another, but there are folds upon folds. If the DNA is stretched the length of the DNA would be thousands of feet.
EL: We have known for 2000 years that your kids look a lot like you. Well it’s because you must pass them instructions that give them the eyes, the hair color, and the nose shape they have. RK: Cracking the code of those minuscule differences in DNA that influence health and illness is what the Human Genome Project is all about. Since 1990, scientists all over the world have been involved in the effort to read all three billion As, Ts, Gs, and Cs of human DNA.  It took 10 years to find the one genetic mistake that causes cystic fibrosis. Another 10 years to find the gene for Huntington’s disease. Fifteen years to find one of the genes that increase the risk for breast cancer. One letter at a time, painfully slowly…     And then came the revolution. In the last ten years the entire process has been computerized. The computations can do a thousand every second and that has made all the difference. EL: This is basically a parts list with a lot of parts. If you take an airplane, a Boeing 777, I think it has like 100,000 parts. If I gave you a parts list for the Boeing 777 in one sense you’d know 100,000 components, screws and wires and rudders and things like that.  But you wouldn’t know how to put it together, or why it flies. We now have a parts list, and that’s not enough to understand why it flies.

The Human Genome

The Human Genome (Photo credit: dullhunk)

A Quest For Clarity

Tracy Vence is a senior editor of Genome Technology
Tracy Vence @GenomeTechMag
Projects supported by the US National Institutes of Health will have produced 68,000 total human genomes — around 18,000 of those whole human genomes — through the end of this year, National Human Genome Research Institute estimates indicate. And in his book, The Creative Destruction of Medicine, the Scripps Research Institute’s Eric Topol projects that 1 million human genomes will have been sequenced by 2013 and 5 million by 2014.
Daniel MacArthur, a group leader in Massachusetts General Hospital’s Analytic and Translational Genetics Unit estimates that “From a capacity perspective … millions of genomes are not that far off. If you look at the rate that we’re scaling, we can certainly achieve that.”    The prospect of so many genomes has brought clinical interpretation into focus. But there is an important distinction to be made between the interpretation of an apparently healthy person’s genome and that of an individual who is already affected by a disease.
In an April Science Translational Medicine paper, Johns Hopkins University School of Medicine‘s Nicholas Roberts and his colleagues reported that personal genome sequences for healthy monozygotic twin pairs are not predictive of significant risk for 24 different diseases in those individuals. The researchers concluded that whole-genome sequencing was not likely to be clinically useful. Ambiguities have clouded even the most targeted interpretation efforts.

  • Technological challenges,
  • meager sample sizes,
  • a need for increased,
  • fail-safe automation and most important
  • a lack of community-wide standards for the task.

have hampered researchers’ attempts to reliably interpret the clinical significance of genomic variation.

How signals from the cell surface affect transcription of genes in the nucleus.

James Darnell, Jr., MD, Astor Professor, Rockefeller
After graduation from Washington University School of Medicine he worked with Francois Jacob at the Pasteur Institute in Paris and served as Vice President for Academic Affairs at Rockefeller in 1990-91. He is the coauthor with S.E. Luria of General Virology and the founding author with Harvey Lodish and David Baltimore of Molecular Cell Biology, now in its sixth edition. His book RNA, Life’s Indispensable Molecule was published in July 2011 by Cold Spring Harbor Laboratory Press. A member of the National Academy of Sciences since 1973, recipient of  numerous awards, including the 2003 National Medal of Science, the 2002 Albert Lasker Award.
Using interferon as a model cytokine, the Darnell group discovered that cell transcription was quickly changed by binding of cytokines to the cell surface. The bound interferon led to the tyrosine phosphorylation of latent cytoplasmic proteins now called STATs (signal transducers and activators of transcription) that dimerize by

  • reciprocal phosphotyrosine-SH2 interchange.
  • accumulate in the nucleus,
  • bind DNA and drive transcription.

This pathway has proved to be of wide importance with seven STATs now known in mammals that take part in a wide variety of developmental and homeostatic events in all multicellular animals. Crystallographic analysis defined functional domains in the STATs, and current attention is focused on two areas:

  • how the STATs complete their cycle of  activation and inactivation, which requires regulated tyrosine dephosphorylation; and how
  • persistent activation of STAT3 that occurs in a high proportion of many human cancers contributes to blocking apoptosis in cancer cells.

Current efforts are devoted to inhibiting STAT3 with modified peptides that can enter cells.

Cell cycle regulation and the cellular response to genotoxic stress

Stephen J Elledge, PhD, Gregor Mendel Professor of Genetics and Medicine, Investigator, Howard Hughes Medical Institute, Harvard Medical School
As a postdoctoral fellow at Stanford working on eukaryotic homologous recombination, he serendipitously found a family of genes known as ribonucleotide reductases. He subsequently showed that

  • these genes are activated by DNA damage and
  • could serve as tools to help scientists dissect the signaling pathways
  • through which cells sense and respond to DNA damage and replication stress.

At Baylor College of Medicine he made a second major breakthrough with the discovery of the cyclin-dependent kinase 2 gene (Cdk2), which

  • controls the G1-to-S cell cycle transition,
  • an entry checkpoint for the cell proliferation cycle and
  • a critical regulatory step in tumorigenesis.

From there, using a novel “two-hybrid” cloning method he developed, Elledge and Wade Harper, PhD, proceeded to

  • isolate several members of the Cdk2-inhibitory family.

Their discoveries included the p21 and p57 genes, mutations in the latter (responsible for Beckwith-Wiedemann syndrome), characterized by somatic overgrowth and increased cancer risk. Elledge is also recognized for his work in understanding

  • proteome remodeling through ubiquitin-mediated proteolysis.
  • they identified F-box proteins that regulate protein degradation in the cell by
  1. binding to specific target protein sequences and then
  2. marking them with ubiquitin for destruction by the cell’s proteasome machinery.

This breakthrough resulted in

  • the elucidation of the cullin ubiquitin ligase family,
  • which controls regulated protein stability in eukaryotes.

nature10774-f5.2  nature10774-f3.2   ubiquitin structures  Rn1  Rn2

Elledge’s recent research has focused on the cellular mechanisms underlying DNA damage detection and cancer using genetic technologies. In collaboration with Cold Spring Harbor Laboratory researcher Gregory Hannon, PhD, Elledge has generated complete human and mouse short hairpin RNA (shRNA) libraries for genome-wide loss-of-function studies. Their efforts have led to

  • the identification of a number of tumor suppressor proteins
  • genes upon which cancer cells uniquely depend for survival.

This work led to the development of the “non-oncogene addiction” concept. This is noted as follows:

  • proteome remodeling through ubiquitin-mediated proteolysis
  • F-box proteins regulate protein degradation in the cell by binding to specific target protein sequences
  • and then marking them with ubiquitin for destruction by the cell’s proteasome machinery
  • elucidation of the cullin ubiquitin ligase family, which controls regulated protein stability in eukaryotes

Playing the dual roles of inventor and investigator, Elledge developed original techniques to define

  • what drives the cell cycle and
  • how cells respond to DNA damage.

By using these tools, he and his colleagues have identified multiple genes involved in cell-cycle regulation.

Elledge’s work has earned him many awards, including a 2001 Paul Marks Prize for Cancer Research and a 2003 election to the National Academy of Sciences. In his Inaugural Article (1), published in this issue of PNAS, Elledge and his colleagues describe the function of Fbw7, a protein involved in controlling cell proliferation (see below). Elledge studied the error-prone DNA repair mechanism in E-Coli (Escherichia coli) called SOS mutagenesis for his PhD thesis at MIT. His work identified  and described

  • the regulation of a group of enzymes now known as error-prone polymerases,
  • the first members of which were the umuCD genes in E. coli.

It was then that he developed a new cloning tool. Elledge invented a technique that allowed him to approach future cloning problems of this type with great rapidity. With the new technique, “you could make large libraries in lambda that behave like plasmids. We called them `phasmid’ vectors, like plasmid and phage together”. The phasmid cloning method was an early cornerstone for molecular biology research.

Elledge began working on homologous recombination in postdoctoral fellowship at Stanford University, an important niche in the field of eukaryotic genetics. Working with the yeast genome, Elledge searched for rec A, a gene that allows DNA to recombine homologously. Although he never located rec A, he discovered a family of genes known as ribonucleotide reductases (RNRs), which are involved in DNA production. Rec A and RNRs share the same last 4 amino acids, which caused an antibody crossreaction in one of Elledge’s experiments. Initially disappointed with the false positives in his hunt for rec A, Elledge was later delighted with his luck. He found that

  • RNRs are turned  on by DNA damage, and
  • these genes are regulated by the cell cycle.

Prior to leaving Stanford, Elledge attended a talk at the University of California, San Francisco, by Paul Nurse, a leader in cell-cycle research who would later win the 2001 Nobel Prize in medicine. Nurse described his success in isolating the homolog of a key human cell-cycle kinase gene, Cdc2, by using a mutant strain of yeast (8). Although Nurse’s methods were primitive, Elledge was struck by the message he carried: that

  • cell-cycle regulation was functionally conserved, and
  • many human genes could be isolated by looking for complimentary genes in yeast.

Elledge then took advantage of his past successes in building phasmid vectors to build a versatile human cDNA library that could be expressed in yeast. After setting up a laboratory at Baylor, he introduced this library into yeast, screening for complimentary cell-cycle genes.  He quickly identified the same Cdc2 gene isolated by Nurse. However, Elledge also discovered a related gene known as Cdk2. Elledge subsequently found that

  • Cdk2 controlled the G1 to S cell-cycle transition, a step that often goes awry in cancer. These results were published in the EMBO Journal in 1991.

He then continued to use

  • RNRs to perform genetic screens to
  • identify genes involved in sensing and responding to DNA damage.

He subsequently worked out the

  • signal transduction pathways in both yeast and humans that recognize damaged DNA and replication problems.

These “checkpoint” pathways are central to the

  • prevention of genomic instability and a key to understanding tumorigenesis.

This contribution is part of the special series of Inaugural Articles by members of the National Academy of Sciences elected on April 29, 2003.

Defective cardiovascular development and elevated cyclin E and Notch proteins in mice lacking the Fbw7 F-box protein.

Tetzlaff MT, Yu W, Li M, Zhang P, Finegold M, Mahon K , Harper JW, Schwartz RJ, and SJ Elledge. PNAS 2004; 101(10): 3338-3345. cgi doi 10.1073.  pnas.0307875101

The mammalian F-box protein Fbw7 and its Caenorhabditis elegans counterpart Sel-10 have been implicated in

  • the ubiquitin-mediated turnover of cyclin E
  • as well as the Notch Lin-12 family of transcriptional activators. Both unregulated
  1. Notch and cyclin E
  2. promote tumorigenesis, and
  3. inactivate mutations in human

Fbw7 studies suggest that it may be a tumor suppressor. To generate an in vivo system to assess the consequences of such unregulated signaling, we generated mice deficient for Fbw7.  Fbw7-null mice die around 10.5 days post coitus because of a combination of deficiencies in hematopoietic and vascular development and heart chamber mutations. The absence of Fbw7 results in elevated levels of cyclin E, concurrent with inappropriate DNA replication in placental giant trophoblast cells. Moreover, the levels of both Notch 1 and Notch 4 intracellular domains were elevated, leading to stimulation of downstream transcriptional pathways involving Hes1, Herp1, and Herp2. These data suggest essential functions for Fbw7 in controlling cyclin E and Notch signaling pathways in the mouse.

Science as an Adventure

Ubiquitins

Prof. Avram Hershko – Science as an Adventure
Prof. Avram Hershko shared the 2004 Nobel Prize in Chemistry with Aaron Ciechanover and Irwin Rose for “for the discovery of ubiquitin-mediated protein degradation.”

http://www.youtube.com/watch?v=lGJvsmG3mhw&feature=player_detailpage&list=EC8814C902ACB98559

Gene Switches

Nipam Patel is a professor in the Departments of Molecular and Cell Biology and Integrative Biology at UC Berkeley and runs a research laboratory that studies the role, during embryonic development, of homeotic genes (the genetic switches described in this feature). “Ghost in Your Genes” focuses on epigenetic “switches” that turn genes “on” or “off.” But not all switches are epigenetic; some are genetic. That is, other genes within the chromosome turn genes on or off. In an animal’s embryonic stage, these gene switches play a predominant role in laying out the animal’s basic body plan and perform other early functions;

  • the epigenome begins to take over during the later stages of embryogenesis.

Beginning as a fertilized single egg that egg becomes many different kinds of cells.  Altogether, multicellular organisms like humans have thousands of differentiated cells. Each is optimized for use in the brain, the liver, the skin, and so on. Remarkably, the DNA inside all these cells is exactly the same. What makes the cells differ from one another is that different genes in that DNA are either turned on or off in each type of cell.

Take a typical cell, such as a red blood cell. Each gene within that cell has a coding region that encodes the information used to make a particular protein. (Hemoglobin shuttles oxygen to the tissues and carbon dioxide back out to the lungs—or gills, if you’re a fish.) But another region of the gene, called “regulatory DNA,” determines whether and when the gene will be expressed, or turned on, in a particular kind of cell. This precise transcribing of genes is handled by proteins known as transcription factors, which bind to the regulatory DNA, thereby generating instructions for the coding region.

One important class of transcription factors is encoded by the so called homeotic, or Hox, genes. Found in all animals, Hox genes act to “regionalize” the body along the embryo’s anterior-to-posterior (head-to-tail) axis. In a fruit fly, for example, Hox genes lay out the various main body segments—the head, thorax, and abdomen. Amazingly, all animals, from fruit flies to mice to people, rely on the same basic Hox-gene complex. Using different-colored antibody stains, we can see exactly where and to what degree Hox genes are expressed. Each Hox gene is expressed in a specific region along the anterior-to-posterior axis of the embryo.

A fly’s body has three main divisions: head, thorax, and abdomen. We’ll focus on the thorax, which itself has three main segments. In a normal adult fly, the second thoracic segment features a pair of wings, while the third thoracic segment has a pair of small, balloon-shaped structures called halteres. A modified second wing, the haltere serves as a flight stabilizer. In order for the pair of wings and the pair of halteres (as well as all other parts of the fly) to develop properly, the fly’s suite of

  • Hox genes must be expressed in a precise way and at precise times.

During development, the fly’s two wings grow from a structure in the larva known as the wing imaginal disk. (An imago is an insect in its final, adult state.) The haltere grows from the larval haltere imaginal disk. Remember the Ubx Hox gene? Using staining again, we can detect the gene product of Ubx. This reveals that

  • the Ubx gene is naturally “off” in the wing disk—
  • and is “on” in the haltere disk.
  • Now you’ll see what happens when the Ubx gene—just one of a large number of Hox genes—is turned off in the haltere disk. What if a genetic mutation caused the Ubx gene to be turned off, during the larval stage, in the third thoracic segment, the segment that normally produces the haltere? Instead of a pair of halteres, the fly has a second set of wings. With the switch of that single Hox gene, Ubx, from on to off, the third thoracic segment becomes an additional second thoracic segment and the pair of halteres became a second pair of wings. This illustrates the remarkable ability of transcription factors like Ubx to control patterning as well as cell type during development.

ENCODE

A. Data Suggests “Gene” Redefinition

As part of a huge collaborative effort called ENCODE (Encyclopedia of DNA Elements), a research team led by Cold Spring Harbor Laboratory (CSHL) Professor Thomas Gingeras, PhD, publishes a genome-wide analysis of RNA messages, called transcripts, produced within human cells.
Their analysis—one component of a massive release of research results by ENCODE teams from 32 institutes in 5 countries, with 30 papers appearing in 3 different high-level scientific journals—shows that three-quarters of the genome is capable of being transcribed.  This indicates that nearly all of our genome is dynamic and active.  It stands in marked contrast to consensus views prior to ENCODE’s comprehensive research efforts, which suggested that

  • only the small protein-encoding fraction of the genome was transcribed.

The vast amount of data generated with advanced technologies by Gingeras’ group and others in the ENCODE project changes the prevailing understanding of what defines a gene. The current outstanding question concerns

  • the nature and range of those functions.  It is thought that these
  • “non-coding” RNA transcripts act something like components of a giant, complex switchboard, controlling a network of  many events in the cell by
  1. regulating the processes of
  2. replication,
  3. transcription
  4. and translation

– that is, the copying of DNA and the making of proteins is based on information carried by messenger RNAs.  With the understanding that so much of our DNA can be transcribed into RNA comes the realization that there is much less space between what we previously thought of as genes, Gingeras points out.

The full ENCODE Consortium data sets can be freely accessed through

  • the ENCODE project portal as well as at the University of California at Santa Cruz genome browser,
  • the National Center for Biotechnology Information, and
  • the European Bioinformatics Institute.

Topic threads that run through several different papers can be explored via the ENCODE microsite page at http://Nature.com/encode.    Date: September 5, 2012   Source: Cold Spring Harbor Laboratory

1000 Genomes Project Team Reports on Variation Patterns

(from Phase I Data) October 31, 2012 GenomeWeb

In a study appearing online today in Nature, members of the 1000 Genomes Project Consortium presented an integrated haplotype map representing the genomic variation present in more than 1,000 individuals from 14 human populations.  Using data on 1,092 individuals tested by

  • low-coverage whole-genome sequencing,
  • deep exome sequencing, and/or
  • dense genotyping,

the team looked at the nature and extent of the rare and common variation present in the genomes of individuals within these populations. In addition to population-specific differences in common variant profiles, for example, the researchers found distinct rare variant patterns within populations from different parts of the world — information that is expected to be important in interpreting future disease studies. They also encountered a surprising number of the variants that are expected to impact gene function, such as

  • non-synonymous changes,
  • loss-of-function variants, and, in some cases,
  • potentially damaging mutations.

ENCODE was designed to pick up where the Human Genome Project left off.
Although that massive effort revealed the blue­print of human biology, it quickly became clear that the instruction manual for reading the blueprint was sketchy at best. Researchers could identify in its 3 billion letters many of the regions that code for proteins, but they make up little more than 1% of the genome, contained in around 20,000 genes. ENCODE, which started in 2003, is a massive data-collection effort designed to catalogue the

  • ‘functional’ DNA sequences,
  • learn when and in which cells they are active and
  • trace their effects on how the genome is
  1. packaged,
  2. regulated and
  3. read.

After an initial pilot phase, ENCODE scientists started applying their methods to the entire genome in 2007. That phase came to a close with the publication of 30 papers, in Nature, Genome Research and Genome Biology. The consortium has assigned some sort of function to roughly 80% of the genome, including

  • more than 70,000 ‘promoter’ regions — the sites, just upstream of genes, where proteins bind to control gene expression —
  • and nearly 400,000 ‘enhancer’ regions that regulate expression of  distant genes (see page 57)1. But the job is far from done.

Junk DNA? What Junk DNA?

New data reveals that at least 80% of the human genome encodes elements that have some sort of biological function. [© Gernot Krautberger – Fotolia.com] Far from containing vast amounts of junk DNA between its protein-coding genes, at least 80% of the human genome encodes elements that have some sort of biological function, according to newly released data from the Encyclopedia of DNA Elements (Encode) project, a five-year initiative that aims to delineate all functional elements within human DNA. The massive international project, data from which are published in 30 different papers in Nature, Genome Research, Genome Biology, the Journal of Biological Chemistry, Science, and Cell, has identified four million gene switches, effectively

  • regulatory regions in the genome where
  • proteins interact with the DNA to control gene expression.

Overall, the Encode data define regulatory switches that are scattered all over the three billion nucleotides of the genome. In fact, the data suggests,

  • the regions that lie between gene-coding sequences contain a wealth of previously unrecognized functional elements,Including
  • nonprotein-coding RNA transcribed sequences,
  • transcription factor binding sites,
  • chromatin structural elements, and
  • DNA methylation sites.

The combined results suggest that 95% of the genome lies within 8 kb of a DNA-protein interaction, and 99% lies within 1.7 kb of at least one of the biochemical events, the researchers say. Importantly, given the complex three-dimensional nature of DNA, it’s also apparent that

  • a regulatory element for one gene may be located quite some ‘linear’ distance from the gene itself.

“The information processing and the intelligence of the genome reside in the regulatory elements,” explains Jim Kent, director of the University of California, Santa Cruz Genome Browser project and head of the Encode Data Coordination Center. “With this project, we probably went from understanding less than 5% to now around 75% of them.”
The ENCODE results also identified SNPs within regulatory regions that are associated with a range of diseases, providing new insights into the roles that

  • noncoding DNA plays in disease development.

“As much as nine out of 10 times, disease-linked genetic variants are not in protein-coding regions,” comments Mike Pazin, Encode program director at the National Human Genome Research Institute.  “Far from being junk DNA, this regulatory DNA clearly makes important contributions to human disease.”

Other Related Articles on this Open Access Online Scientific Journal, include the following: 

Big Data in Genomic Medicine LHB

https://pharmaceuticalintelligence.com/2012/12/17/big-data-in-genomic-medicine/

BRCA1 a tumour suppressor in breast and ovarian cancer – functions in transcription, ubiquitination and DNA repair S Saha
http://pharmaceuticalintelligence.com/2012/12/04/brca1-a-tumour-suppressor-in-breast-and-ovarian-cancer-functions-in-transcription-ubiquitination-and-dna-repair/

Computational Genomics Center: New Unification of Computational Technologies at Stanford A Lev-Ari
http://pharmaceuticalintelligence.com/2012/12/03/computational-genomics-center-new-unification-of-computational-technologies-at-stanford/

Personalized medicine gearing up to tackle cancer ritu saxena
http://pharmaceuticalintelligence.com/2013/01/07/personalized-medicine-gearing-up-to-tackle-cancer/

Differentiation Therapy – Epigenetics Tackles Solid Tumors sj Williams
http://pharmaceuticalintelligence.com/2013/01/03/differentiation-therapy-epigenetics-tackles-solid-tumors/

Mechanism involved in Breast Cancer Cell Growth: Function in Early Detection & Treatment A Lev-Ari
http://pharmaceuticalintelligence.com/2013/01/17/mechanism-involved-in-breast-cancer-cell-growth-function-in-early-detection-treatment/

The Molecular pathology of Breast Cancer Progression tilde barliya`
http://pharmaceuticalintelligence.com/2013/01/10/the-molecular-pathology-of-breast-cancer-progression/

Paradigm Shift in Human Genomics – Predictive Biomarkers and Personalized Medicine – Part 1 (pharmaceuticalintelligence.com) A Lev-Ari

http://pharmaceuticalintelligence.com/2013/01/13/paradigm-shift-in-human-genomics-predictive-biomarkers-and-personalized-medicine-part-1/

LEADERS in Genome Sequencing of Genetic Mutations for Therapeutic Drug Selection in Cancer Personalized Treatment: Part 2 A Lev-Ari
http://pharmaceuticalintelligence.com/2013/01/13/leaders-in-genome-sequencing-of-genetic-mutations-for-therapeutic-drug-selection-in-cancer-personalized-treatment-part-2/

Personalized Medicine: An Institute Profile – Coriell Institute for Medical Research: Part 3 A Lev-Ari
http://pharmaceuticalintelligence.com/2013/01/13/personalized-medicine-an-institute-profile-coriell-institute-for-medical-research-part-3/

Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders @ http://pharmaceuticalintelligence.com ALA
http://pharmaceuticalintelligence.com/2013/01/13/7000/Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders/

GSK for Personalized Medicine using Cancer Drugs needs Alacris systems biology model to determine the in silico effect of the inhibitor in its “virtual clinical trial” A Lev-Ari
http://pharmaceuticalintelligence.com/2012/11/14/gsk-for-personalized-medicine-using-cancer-drugs-needs-alacris-systems-biology-model-to-determine-the-in-silico-effect-of-the-inhibitor-in-its-virtual-clinical-trial/

Recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes in serous endometrial tumors S Saha
http://pharmaceuticalintelligence.com/2012/11/19/recurrent-somatic-mutations-in-chromatin-remodeling-and-ubiquitin-ligase-complex-genes-in-serous-endometrial-tumors/

Personalized medicine-based cure for cancer might not be far away ritu saxena
http://pharmaceuticalintelligence.com/2012/11/20/personalized-medicine-based-cure-for-cancer-might-not-be-far-away/

Human Variome Project: encyclopedic catalog of sequence variants indexed to the human genome sequence A Lev-Ari
http://pharmaceuticalintelligence.com/2012/11/24/human-variome-project-encyclopedic-catalog-of-sequence-variants-indexed-to-the-human-genome-sequence/

Prostate Cancer Cells: Histone Deacetylase Inhibitors Induce Epithelial-to-Mesenchymal Transition sjwilliams
http://pharmaceuticalintelligence.com/2012/11/30/histone-deacetylase-inhibitors-induce-epithelial-to-mesenchymal-transition-in-prostate-cancer-cells/

Inspiration From Dr. Maureen Cronin’s Achievements in Applying Genomic Sequencing to Cancer Diagnostics A Lev-Ari
http://pharmaceuticalintelligence.com/2013/01/10/inspiration-from-dr-maureen-cronins-achievements-in-applying-genomic-sequencing-to-cancer-diagnostics/

The “Cancer establishments” examined by James Watson, co-discoverer of DNA w/Crick, 4/1953 A Lev-Ari
http://pharmaceuticalintelligence.com/2013/01/09/the-cancer-establishments-examined-by-james-watson-co-discover-of-dna-wcrick-41953/

Directions for genomics in personalized medicine lhb
http://pharmaceuticalintelligence.com/2013/01/27/directions-for-genomics-in-personalized-medicine/

How mobile elements in “Junk” DNA promote cancer. Part 1: Transposon-mediated tumorigenesis. SJwilliams
http://pharmaceuticalintelligence.com/2012/10/31/how-mobile-elements-in-junk-dna-prote-cancer-part1-transposon-mediated-tumorigenesis/

Mitochondria: More than just the “powerhouse of the cell” eritu saxena
http://pharmaceuticalintelligence.com/2012/07/09/mitochondria-more-than-just-the-powerhouse-of-the-cell/

Mitochondrial fission and fusion: potential therapeutic targets? Ritu saxena
http://pharmaceuticalintelligence.com/2012/10/31/mitochondrial-fission-and-fusion-potential-therapeutic-target/

Mitochondrial mutation analysis might be “1-step” away ritu saxena
http://pharmaceuticalintelligence.com/2012/08/14/mitochondrial-mutation-analysis-might-be-1-step-away/

mRNA interference with cancer expression lhb
http://pharmaceuticalintelligence.com/2012/10/26/mrna-interference-with-cancer-expression/

Expanding the Genetic Alphabet and linking the genome to the metabolome LHB
http://pharmaceuticalintelligence.com/2012/09/24/expanding-the-genetic-alphabet-and-linking-the-genome-to-the-metabolome/

Breast Cancer, drug resistance, and biopharmaceutical targets lhb
http://pharmaceuticalintelligence.com/2012/09/18/breast-cancer-drug-resistance-and-biopharmaceutical-targets/

Breast Cancer: Genomic profiling to predict Survival: Combination of Histopathology and Gene Expression Analysis A Lev-Ari
http://pharmaceuticalintelligence.com/2012/12/24/breast-cancer-genomic-profiling-to-predict-survival-combination-of-histopathology-and-gene-expression-analysis/

Gastric Cancer: Whole-genome reconstruction and mutational signatures A Lev-Ari
http://pharmaceuticalintelligence.com/2012/12/24/gastric-cancer-whole-genome-reconstruction-and-mutational-signatures-2/

Ubiquinin-Proteosome pathway, autophagy, the mitochondrion, proteolysis and cell apoptosis lhb
http://pharmaceuticalintelligence.com/2012/10/30/ubiquinin-proteosome-pathway-autophagy-the-mitochondrion-proteolysis-and-cell-apoptosis/

Genomic Analysis: FLUIDIGM Technology in the Life Science and Agricultural Biotechnology A Lev-Ari
http://pharmaceuticalintelligence.com/2012/08/22/genomic-analysis-fluidigm-technology-in-the-life-science-and-agricultural-biotechnology/

Reveals from ENCODE project will invite high synergistic collaborations to discover specific targets A. Sarkar

https://pharmaceuticalintelligence.com/2012/09/30/reveals-from-encode-project-will-lead-to-confusion-or-specific-target/

ENCODE: the key to unlocking the secrets of complex genetic diseases R. Saxena

https://pharmaceuticalintelligence.com/2012/09/26/encode-the-key-to-unlocking-the-secrets-of-complex-genetic-diseases/

Impact of evolutionary selection on functional regions: The imprint of evolutionary selection on ENCODE regulatory elements is manifested between species and within human populations s Saha

https://pharmaceuticalintelligence.com/2012/09/20/impact-of-evolutionary-selection-on-functional-regions-the-imprint-of-evolutionary-selection-on-encode-regulatory-elements-is-manifested-between-species-and-within-human-populations/

ENCODE Findings as Consortium A Lev-Ari

https://pharmaceuticalintelligence.com/2012/09/10/encode-findings-as-consortium/

Genomics Orientations for Personalized Medicine SJH, ALA, LHB

https://pharmaceuticalintelligence.com/biomed-e-books/genomics-orientations-for-personalized-medicine/

2013 Genomics: The Era Beyond the Sequencing of the Human Genome: Francis Collins, Craig Venter, Eric Lander, et al.

https://pharmaceuticalintelligence.com/2013/02/11/2013-genomics-the-era-beyond-the-sequencing-human-genome-francis-collins-craig-venter-eric-lander-et-al/

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Reporter: Prabodh Kandala, PhD

A team of University of Hawaii Cancer Center scientists led by James Turkson, Ph.D. have created a new type of anti-cancer drug named BP-1-102. The drug, which can be orally administered, targets a key protein that triggers the development of many types of cancer including lung, breast and skin cancers.

The development of BP-1-102 was guided by the research teams computer based molecular analysis of the cancer causing Stat 3 protein that causes cancer by promoting abnormal cell growth in otherwise healthy cells.

“The molecular structure of the hyperactive Stat3 protein basically resembles two cars that are joined together side-by-side,” said Professor Turkson. “We then utilized a computer program that creates molecular models of potential drugs engaging in binding to the Stat3 protein to craft the BP-1-102 drug which literally pulls apart the Stat3 protein rendering it ineffective in causing cancer.”

A unique feature of BP-1-102 is that it remains highly effective against cancer even when administered in oral form. Presently, most anti-cancer drugs require intravenous (IV) administration in a clinic or hospital setting which increases the financial, physical and emotional burdens on cancer patients. In its experimental form, BP-1-102 has shown promise in treating breast and lung cancers.

Currently, breast and lung cancers are two of the most commonly diagnosed cancers accounting for nearly half a million cases per year in the United States with over 200,000 deaths attributed to these diseases. In Hawaii, there is an average of 1500 cases diagnosed and over 600 deaths attributed to breast and lung cancers every year.

Professor Turkson is a recent and welcomed addition to the UH Cancer Center faculty. His innovative and ground breaking research focuses on developing novel anticancer drugs based on targeting signal transduction and apoptosis pathways.

Ref:

http://www.sciencedaily.com/releases/2012/05/120522115252.htm

http://www.pnas.org/content/109/2/600

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