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Journal of Life Sciences
Volume 5, Number 8, August 2011 (Serial Number 40)
Contents
Research Papers
581 Quantifying Neuromuscular Electrical Stimulation Dosage after Knee Arthroplasty
Adam R. Marmon and Lynn Snyder-Mackler
584 The Role of Mouse Wnt9a in MA891 Breast Cancer Cell Proliferation
Xueqin Zheng, Xiaonian Zhong, Wenjing Meng, Chengneng Mi, Shuangmei Liu, Yuehui Li, Shen Yu, Jie Zhao, Lin Zhang, Dongxiang Li, Dongsong Nie and Yang Xiang
590 A Family of Origin Scale in Mothers of Children with Autistic Spectrum Disorder-Preliminary Report
Piotr W. Gorczyca, Agnieszka Kapinos-Gorczyca, Maciej Kapinos, Aleksandra Leksowska, Katarzyna Ziora, Joanna Oświęcimska and Jarosław Sobiś
593 Modified Multiple Scale/Segment Entropy (MMPE) Analysis of Heart Rate Variability of NHH, CHF & AF Subjects
Chodavarapu Renu Madhavi and Alevoor Gopal Krishnachar Ananth
598 Proteome Profiles of Longissimus and Biceps Femoris Porcine Muscles Related to Exercise and Resting
Marinus F.W. Te Pas, Els Keuning, Dick J.M. Van De Wiel, Jette F. Young, Niels Oksbjerg and Leo Kruijt
609 Effects of Sublethal Doses of Chlorfluazuron on Ovarioles in the Common Cutworm, Spodoptera
Litura (F.) (Lepidoptera: Noctuidae)
Farzana Perveen
614 Potassium Concentration by Natural 40K-40Ar γ Rays Detection in Four Basic Diet Products (Milk, Eggs, Wheat and Corn)
Juan Manuel Navarrete, Trinidad Martínez, Luis Cabrera, Pilar Lizárraga, Miguel Angel Zúñiga and Michelle Camacho
618 Influence of Saline Stress on Ionic Balance of Wheat (Triticum Aestivum) and Its Wild Congeners
Nina Terletskaya, Batyrbek Sarsenbayev and Yerlan Kirshibayev
625 The Research on the Distribution, Abundance and Some Ecological Characteristics of Neuroterus
Species (Hymenoptera: Cynipidae) in Oak Forest of West Azerbaijan (Iran)
634 Effect of Natural Infection with Onion Yellow Dwarf Virus (OYDV) on Yield of Onion and Garlic Crops in Egypt
Salah Elnagar, Mohamed Abdel-Kader El-Sheikh and Abeer Salah El-Deen Abd El-Wahab
639 In Vitro and In Silico Studies of Lunacridine from Lunasia Amara Blanco as Anticancer
Zubair M. Sulaiman, Subehan Lallo and Natsir Djide
646 The Influence of the “Terroir” Concerning the Quantity and Quality of Grapes Yield at White Grapevine Varieties Growing in the Iasi Vineyard
Liliana Rotaru, Vasile Stoleru, Feodor Filipov, Mihai Mustea and Gabriela Petrea
654 Food Color Memory and Names–A Linguistic Vantage
Jodi Louise Sandford
661 Analytical Study of Ground Painting Layers and Conservation Processes of an Egyptian Painted Coffin
Hala Afifi Mahmood and Mostafa Ahmed AbdEl Fatah
670 An Investigation of Environmental Education Knowledge for Sustainable Development in High School Sectors in UK
Quantifying Neuromuscular Electrical Stimulation
Dosage after Knee Arthroplasty
Adam R. Marmon and Lynn Snyder-Mackler
Department of Physical Therapy, University of Delaware, Newark, DE 19716, USA
Received: February 25, 2011 / Accepted: May 13, 2011 / Published: August 30, 2011.
Abstract: Recovering functional ability after total knee arthroplasty (TKA) requires recovery of strength and voluntary activation. Short-term recovery of strength and activation are enhanced following a protocol combining strength training with neuromuscular electrical stimulation (NMES). The purpose of the study was to determine if a dose response curve could be constructed for patients who received NMES as part of their treatment after TKA. NMES dosage was quantified as the electrically evoked knee extensor torque, expressed as a percentage of the subject’s maximal voluntary contraction. Dose-response curves were generated, with the associations between NMES training intensity and quadriceps strength, voluntary activation, and lean muscle cross-sectional area examined using Pearson Product-Moment Correlation Coefficients. Significantly, linear correlations were observed between NMES training intensity and both quadriceps strength and voluntary activation, but not lean muscle cross-sectional area. These results suggest that maximizing the elicited training force during rehabilitation will enhance short-term recovery following TKA.
Key words: Quadriceps strength, voluntary activation, total knee arthroplasty, neuromuscular electrical stimulation, rehabilitation.
1. Introduction
Total knee arthroplasty (TKA) procedures are expected to increase substantially over the next 20 years [1]; however, consensus on treatment regimens following TKA remains equivocal. Quadriceps strength is a primary determinant of lower extremity functional ability [2]; with a larger portion of the quadriceps weakness observed in patients following TKA attributable to reductions voluntary activation than to muscle atrophy [3]. Improvements in both quadriceps strength and voluntary activation have been demonstrated after surgery with the use of progressive quadriceps strength training combined with neuromuscular electrical stimulation (NMES) [4]. Therefore, it is important to understand how the training dosage of NMES used for patients who have undergone TKA influences quadriceps strength, size
Corresponding author: Adam R. Marmon, Ph.D., postdoctoral researcher, research fields: applied and basic concepts in neuromuscular physiology. E-mail: [email protected].
and voluntary activation. The purpose of this study was to establish a dose-response curve and determine how the intensity of NMES treatment after TKA influences the improvement in quadriceps strength, atrophy, and activation. We hypothesized a positive dose-response relationship for all.
2. Materials and Methods
2.1 Subjects
Seventy individuals (29 women; 51-82 yrs) scheduled for unilateral TKA who were included in the NMES arm of the trial were the subjects for this study
[4]. Exclusion criteria included severe obesity (BMI ≥
surgery), midway through the intervention (Mid), and after completing the intervention (End). Atrophy was assessed before and after the intervention with magnetic resonance imaging (MRI). The study was approved by the University of Delaware’s Human Subject Review Board and subjects provided informed consent.
2.2 Neuromuscular Electrical Stimulation Training
Subjects received 6-weeks of progressive strength training [6] with neuromuscular electrical stimulation (2-3 times/week) beginning 3-4 weeks after surgery [4]. The NMES was performed on an electromechanical dynamometer (KinCom; Chattanooga Corporation, Chattanooga, TN), with the knee flexed to 60° and the hip flexed to ~90°. The protocol comprised 10 electrically evoked contractions of the quadriceps delivered through two self-adhesive electrodes (7.62 × 12.70 cm; ConMed; Utica, NY) for 10 s, with a 2.5 kHz sinusoidal wave at 50 Hz (Electro Med Health Industries; Miami, FL). Stimulus intensity was determined by each subject’s maximum tolerance, with target dosage of at least 30% of the subject’s maximal voluntary contraction (MVC). The dose of each contraction was recorded as a percentage of the involved knee MVC. Dose was defined as the average recorded dose over the 18 sessions.
2.3 Quadriceps Strength, Voluntary Activation, and Size
Quadriceps strength and voluntary activation were
also recorded on a KinCom, with hip flexed ~90° and
knee flexed ~75°. The axis of rotation was aligned with
the lateral femoral condyle and the distal end of the lever arm secured to the lower leg ~2 cm above the lateral malleolus. Subjects completed submaximal and maximal knee extension contractions for familiarization and warm-up, with strength recorded as the peak force during a MVC; subjects received visual feedback and strong verbal encouragement. Analysis of changes in quadriceps strength was evaluated using the quadriceps index (QI = involved MVC/uninvolved MVC).
Voluntary muscle activation was assessed using the
burst super-imposition technique [7]. A Grass S8800 stimulator delivered a 12-pulse, 100-Hz train of stimuli at 135 Volts (Grass Instruments; West Warwick, RI) through two electrodes (7.2 × 12.7 cm; CONMED Corp, Utica, NY) applied proximally over the rectus femoris and distally over the vastus medialis. A program controlled the timing of stimuli to be delivered during the plateau in force of an MVC (LabView 4.01, National Instrument; Austin, TX). Voluntary activation was quantified with the central activation ratio (CAR) [4] as the percentage increase in force after stimulation above the MVC force.
Lean Muscle Cross Sectional Area (LMCSA) of the quadriceps was quantified in 29 of 70 subjects using MRI before (IE) and after (End) the intervention. Subjects were imaged axially, from greater trochanter to tibial plateau (7 mm interslice intervals) while laying supine, knees extended, in a 1.5 Tesla body coil (Signa, General Electric, Waukesha, WI) using standard sequence imaging (GE SPGR: 2-D spoiled gradient-echo, 500-ms pulse TR, 8-ms TE with a 256 X 256 encoding matrix and 480X480-mm field of view). Images were processed in IMOD software (The Boulder Laboratory for 3D Electron Microscopy of Cells; Boulder, Colorado) digitally outlining the quadriceps (Intuos 2; Wacom Corporation; Vancouver, Washington). Customized software scaled the outlines and removed intramuscular fat and connective tissue to calculate the enclosed LMCSA. The MRI image with the largest LMCSA was used for data analyses.
2.4 Data Analysis
(change in QI, CAR and LMCSA) were evaluated using Pearson Product-Moment Correlation Coefficients.
Significance for statistical analyses was P≤ 0.05.
3. Results and Discussion
The treatments produced main effects for strength
and activation (P < 0.001). Strength improved at each
time point of the intervention, evident by significant changes in the QI (IE = 49.9 ± 22.9%; Mid = 66.2 ± 24.3%; End = 81.3 ± 26.7%). Similarly, activation levels increased at each time point (IE = 78.3 ± 15.4%; Mid = 87.7 ± 12.6%; End = 89.5 ± 10.1%). The
LMCSA significantly improved from IE (30.8 ± 12.7 cm2)
to the End (36.4 ± 14.6 cm2) of treatment (P < 0.001).
The average dose exceeded the minimal target of 30% MVC for all but eight subjects; one subject’s average dose was 10% MVC, seven subjects had average doses 23-29% MVC, with the group average of 55.6 ± 28.9% MVC (range 10%-203%).
The stimulation training dosage was significantly and positively correlated with changes in QI (r = 0.480;
P < 0.001; Fig. 1A) and CAR (r = 0.574, P < 0.001; Fig.
1B), but not LMCSA (r = -0.325; P = 0.086).
Quadriceps strength, voluntary activation, and LMCA had statistically and clinically important improvements over the intervention period. NMES dose was significantly and positively correlated with strength and activation improvements, but not with increases in LMCSA. NMES overcomes activation deficits during application. Perhaps the main mechanism by which NMES contributes to muscle strength gain is via its effects on voluntary muscle activation.
Acknowledgments
The authors gratefully acknowledge the funding source for this study, The National Institutes of Health NIH R01-HD041055.
References
[1] S. Kurtz, F. Mowat, K. Ong, N. Chan, E. Lau, M. Halpern, Prevalence of primary and revision total hip and knee arthroplasty in the United States from 1990 through 2002, The Journal of Bone & Joint Surgery 87 (2005) 1487-1497.
Fig. 1 The changes in quadriceps index (QI; A) and central activation ratio (CAR; B) as a function of the average training dosage were significant, P < 0.001.
[2] R.L. Mizner, S.C. Petterson, J.E. Stevens, K. Vandenvorne, L. Snyder-Mackler, Early quadriceps strength loss after total knee arthroplasty, the contributions of muscle atrophy and failure of voluntary muscle activation, The Journal of Bone and Joint Surgery 87 (2005) 1047-1053. [3] R.L. Mizner, J.E. Stevens, L. Snyder-Mackler, Voluntary
activation and decreased force production of the quadriceps femoris muscle after total knee arthroplasty, Physical Therapy 83 (2003) 359-365.
[4] S.C. Petterson, R.L Mizner, J.E. Stevens, L. Raisis, A. Bodenstab, W. Newcomb, et al., Improved function from progressive strengthening interventions after total knee arthroplasty: A randomized clinical trial with an imbedded prospective cohort, Arthritis & Rheumatism 61 (2009) 174-183. [5] World Health Organization, Obesity: Preventing and
managing the global epidemic, Report of a WHO Consultation, WHO Technical Report Series 894, Geneva, 2000, p. 9. [6] J.E. Stevens, R.L Mizner, L. Snyder-Mackler,
Neuromuscular electrical stimulation for quadriceps muscle strengthening after bilateral total knee arthroplasty: A case series, The Journal of Orthopaedic and Sports, Physical Therapy 34 (2004) 21-29.
The Role of Mouse Wnt9a in MA891 Breast Cancer Cell
Proliferation
Xueqin Zheng, Xiaonian Zhong, Wenjing Meng, Chengneng Mi, Shuangmei Liu, Yuehui Li, Shen Yu, Jie Zhao, Lin Zhang, Dongxiang Li, Dongsong Nie and Yang Xiang
Department of Chemistry and Chemical Engineering, Hunan Institute of Science and Technology, Yueyang 414000, China
Received: March 06, 2011 / Accepted: May 04, 2011 / Published: August 30, 2011.
Abstract: Wnts are powerful regulators of cell proliferation and differentiation. Activation of Wnt signalling in many tissues has also been associated with cancer. RNAi is a process of posttranscriptional gene silencing, by which dsRNA induces sequence-specific degradation of homologous gene transcripts. In many eukaryotes, expression of nuclear-encoded mRNA can be strongly inhibited by the presence of a small double-stranded RNA corresponding to exon sequences in the mRNA. In this study the pAVU6+27 vector, which has SalI and XbaI clone sites, was used to construct the siRNA expression vectors for mouse wnt9a. Four kinds of small interfering RNA inserts were designed, synthesized and visually tested for efficacy by in situ hybridization, the results demonstrated that dramatically reduced wnt9a signals were observed in the cells transfected with U6+27 cassettes with anti-wnt9a hairpin siRNA inserts compared with the untransfected. The results of flow cytometry analysis showed that the cell proliferation was promoted after lowering expression of the mouse wnt9a in MA891 cells by RNAi. All that suggest the expression level of mouse wnt9a in breast cancer MA891 cells may play a role in adjusting the rate of proliferation.
Key words: Mouse wnt9a, RNAi, MA891, proliferation.
1. Introduction
Wnts are secreted lipid-modified signaling proteins and powerful regulators of cell proliferation and differentiation, and their signaling pathway involves proteins that directly participate in both gene transcription and cell adhesion [1]. The central player
is β-catenin, which is a transcription cofactor with T
cell factor/lymphoid enhancer factor TCF/LEF in the Wnt pathway [2] and a structural adaptor protein linking cadherins to the actin cytoskeleton in cell-cell adhesion. Wnts influence multiple processes in animal development [1]. Nineteen Wnt genes exist in mammalian genomes [2]. In many tissues, activation of Wnt signalling has also been associated with cancer [3]. Unchecked Wnt signaling and/or the loss of cell-cell
Corresponding author: Yang Xiang, Ph.D., professor, research field: molecular and cell biology. E-mail: [email protected].
Dongsong Nie, Ph.D., research field: molecular and cell biology. E-mail: [email protected].
adhesion are involved in cancer induction and progression. Loss of cadherin expression can also promote tumorigenesis.
being subsequently processed into siRNA by Dicer endonuclease [7]. However, the long dsRNAs provoke a strong cytotoxic response through the activation of RNA-Dependent Protein Kinase PKR in mammalian cells [8]. This non-specific cytotoxic effect can be overcome by directly applying synthetic siRNAs, pools of siRNAs, or DNA vector-expressed small RNA transcripts, including shRNAs and siRNAs. Considering the delivery of shRNA (short-hairpin RNA) into mammalian cells, the best-characterized transient expression methods are the systems based on the pAVU6+27 integration and pAVU6+27, which contains human U6 promoter, the first 27-bp of U6 RNA
coding sequence and SalI / XbaI clone sites, have been
described by Paul et al. [9]. pAVU6+27 vectors were used researing RNAi for human and mouse genes [10]. However, the emergence of gene ablation technologies based upon the RNA interference phenomenon has opened up new experimental opportunities. In particular, the generation of vectors directing the synthesis of short hairpin RNAs that are processed to small siRNAs enables suppression of endogenous gene expression [11]. Synthetic siRNA duplexes and vector-derived siRNAs have been shown to inhibit the
expression of mouse Wnt16 that targeted-Wnt16b
inhibition leads to apoptotic cell death of
lymphoblastoid leukemia cells [12]. Wnt 9a
predominantly expressed in the mural trophoblast and inner cell mass cells surrounding [13]. Wnt9a has been implicated as being a player in joint induction, based on gain-of function experiments in chicken and mouse. Wnt9a is a temporal and spatial regulator of Indian hedgehog (Ihh), a central player of skeletogenesis. Wnt9a signaling is required for joint integrity, regulation of Ihh during chondrogenesis [14], and required for joint integrity and regulation of Ihh during chondrogenesis. Wnt9a secreted from the walls of hepatic sinusoids is essential for morphogenesis, proliferation, and glycogen accumulation of chick hepatic epithelium [15]. In this study, we show that the
mouse wnt9a mRNA were targeted by transiently
expressed shRNA vector pAVU6+27/m9aRNAis,
leading to dramatic reduction of wnt9a signals
observed by in situ hybridization test as compared to
the untransfected cells. Cell cycle analysis by flow
cytometry suggested that cell proliferation was
promoted after RNAi for mouse wnt9a in MA891 cells,
and was reduced after-over expression of mouse wnt9a.
2. Materials and Methods
2.1 Construction of siRNA Expression Vectors
pAVU6+27, which contains human U6 promoter and the first 27-bp of U6 RNA coding sequence, has been described by Paul et al. [9]. A series of shRNA expression vectors were generated by inserting annealed oligos sequence into pAVU6+27 vector
between SalI and XbaI sites respectively. The
pAVU6+27 vector was digested with SalI and XbaI to
generate compatible ends for cloning. The sequences of
four siRNAs for mouse wnt9a were designed by using
the Ambion web-based criteria. Their primer pairs for RT-PCR were synthesized by Invitrogen Co. as follows:
M9A RNAi-1 F 5-TCGA aagtacagcagcaagtttgtc
CTTG gacaaacttgctgctgtactt TTTT-3,
M9A RNAi-1 R 5-CTAG AAAA
aagtacagcagcaagtttgtcCAAGgacaaacttgctgctgtactt-3;
M9A RNAi-2 F 5-TCGAaa caacctcgtgataaaggct
CTTG agcctttatcacgaggttgtt TTTT-3,
M9A RNAi-2 R 5-CTAG AAAA
aacaacctcgtgataaaggctCAAGagcctttatcacgaggttg-3;
M9A RNAi-3 F 5-TCGA aaccacttgcaaatgccatgg
CTTG ccatggcatttgcaagtggtt TTTT-3,
M9A RNAi-3 R 5-CTAG AAAA
aaccacttgcaaatgccatggCAAGccatggcatttgcaagtggtt-3;
M9A RNAi-4 F 5-TCGA aacacaaatatgagacctcgc
CTTG gcgaggtctcatatttgtgtt TTTT-3,
M9A RNAi-4 R 5-CTAG AAAA
aacacaaatatgagacctcgcCAAGgcgaggtctcatatttgtgtt-3.
inserted into the digested pAVU6+27 vectors with SalI
and XbaI clone sites. The shRNA expression vectors,
pAVU6+27/m9aRNAi-1, pAVU6+27/m9aRNAi-2, pAVU6+27/m9aRNAi-3 and pAVU6+27/m9aRNAi-4,
were transformed into E.coli DH5a and identified by
RT-PCR using sense primer,
5′-CTAACTGACACACATTCCAC-3′ and antisense
primer, 5′-GCAATAAACAAGTTACTAGTCC-3′.
The resulting products were analyzed by electrophoresis on a 2.0% agarose gel and sequenced.
2.2 Cell Culture and Transfection
A mouse MA891 breast cancer cells were maintained in RPMI-1640 medium supplemented with heat-inactivated 10% fetal calf serum (FCS) and 1% antibiotic/antimycotic solution at 37 °C in a humidified
incubator with 5% CO2. The cell line was routinely
split three to four times a week after trypsinization. Twenty-four hours before transfection, MA891 cells were splitted and seeded in 6-well culture plates at
1×105 cells per well. The cells were transiently
transfected with 2.0 μg of the empty vectors
pAVU6+27 and 2.0 μg of the RNA expression vectors
pAVU6+27/m9aRNAis using Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. The cells were splitted after one day transiently transfected and then the cells were collected at three days post-tansfection for flow
cytometry analysis, or were reserveted for in situ
hybridization.
2.3 In Situ Hybridization Analyses
Mouse wnt9ain situ hybridization kit was purchased
from Boster Bio-engineering Co.. Being longer probes were difficult to penetrate the cell, short oligonucleotide probes were used in this study. To strengthen the signals, three probes were used. The sequences of oligonucleotide probes were as follows: (1) 5’-GCACGGTGTTTCGTCCTTTCCACAAGAT ATATAAA-3’; (2) 5’-ATAGGCCCTCTTCCTGCCC GACCTTGGATCCGGCT-3’; (3) 5’-AACTCCGCCC
AGTTCCGCCCATTCTCCGCCCCATG-3’. The probes were labeled with digoxigenin. To get specific signals,
in situ hybridization was done as follows: cells were
washed in 0.1 M PBS (pH 7.2-7.6) twice, each for 5
min, treated with H2O2 mix (H2O2:methanol = 1:50) for
30 min and washed in distilled water for three times, each for 5 min. Then the cells were digested by 3% pepsin (diluted by citric acid) for 30 s, washed in 0.5 M PBS (pH 7.2-7.6) three times, each for 5 min. Washed cells in distilled water for 5 min and then post-fixed in buffered 1% paraformaldehyde in 0.1 M PBS (pH 7.2-7.6) containing 0.1% DEPC and washed cells in distilled water three times, each for 5 min. Pre-hybridization was performed for 3 h. Subsequently, hybridization was performed in a solution containing
digoxigenin-labeled mouse wnt9a probes. The control
cells were treated with the solution without wnt9a
probes. Slides were incubated at 38 °C for 16 h in a humidified chamber. Post-hybridization washes were performed at 37 °C by incubation in 2×SSC twice, each for 5min; in 0.5×SSC for 15 min and in 0.2×SSC twice, each for 15 min. The cells were incubated in blocking buffer for 30min at 37 °C and then incubated with biotined antidigoxigenin antibody at 37 °C for 2 h. The slides were washed in 0.5 M PBS (pH 7.2-7.6) four times, each for 5 min. Cells were incubated in SABC at 37 °C for 20 min. Slides were washed in 0.5 M PBS (pH 7.2-7.6) three times, each for 5 min. After that, cells were incubated in biotined peroxidase at 37 °C for 20 min. Slides were washed in 0.5 M PBS (pH 7.2-7.6) four times, each for 5min. The specific signals were visualized by incubation in DAB buffer. Water was used to end the reactions. Cells were re-dyed with haematoxylon and photographs were taken.
2.4 Cell Cycle Analysis by Flow Cytometry
Cell-cycle phase distribution was analyzed by flow cytometry using PI staining. Briefly, control for treating MA891 cells were harvested after trypsinization and washing with 1×PBS, then fixed in
samples not exceeding 3×106 cells, and then stained
with 50 μg/mL propidium iodide (containing 10 mg/ml
RNase A) for 30 min at 4 ℃. Resulting DNA
distributions were analyzed on a FACSort (Becton Dickinson , San Jose , CA) with Cell Quest software (version 313) for the proportions of cells in G1, S and G2 phases of the cell cycle.
3. Results and Discussion
3.1 Construction of Expression Vector for Silence Mouse Wnt9
In this study, the authors used pAVU6+27 vector,
which contains the SalI / XbaI cloning sites, to
construct the RNAi expression vector for the mouse
wnt9a. siRNA expressed by using RNA polymerase III
from the U6+27 cassettes was expressed primarily as full-length transcripts and was located in nucleus [16]. U6+27 transcripts contain the first 27 nucleotides of mouse U6 RNA. Cassettes are designed so that the short RNA coding sequences are inserted between
unique SalI and XbaI sites. After the XbaI site, the
cassette encodes a strong stem to protect the transcripts against 3’-5’ exonuclease attack, then a poly (U) transcription termination sequence. However, the insertion sequences discussed later also contain their own UUUU terminator at the 3' end of the inserted sequences, terminating most transcription before the cassette-encoded stem/terminator region [17].
According to SalⅠ/XbaⅠ clone sites on pAVU6+27,
the primers were designed as 21 bp-target sequence. To
study the function of mouse wnt9a, we targeted four
sites in mouse wnt9a mRNA. The inserted sequences
encoded four siRNA duplexes shown in Fig. 1. To make the siRNA duplex as one short transcript, an RNA insert was used that contains the 21-nucleotide sense strand of the target, followed by a CUUG tetraloop sequence, the antisense strand, and a UUUU transcription terminator, in that order. This terminates a high percentage of the transcripts exactly at the end of the siRNA stem. The 3’-UUUU overhang after the siRNA is attacked by 3’ exonucleases, leaving from 1
Fig. 1 Four kinds of tested inserts of anti-mouse wnt9a RNA. Each would begin immediately after the SalI sequence, and most terminations occur after the UUUU at 3’ terminus of the insert.
to 4 U 3’-end overhangs [17]. Results from the experiment with synthetic siRNA [18] suggest that such 3’ overhangs can increase efficacy.
3.2 The Reduction of Mouse Wnt9a RNA Level after RNAi Examined by in situ Hybridization
To examine whether expressed shRNA duplexes
specifically target to mouse wnt9a and reduced the
expression levels, the authors generated four kinds of shRNA expression vectors. The synthetic DNA oligos encoding shRNAs were annealed and cloned downstream of U6 promoter for the expression of double-stranded shRNA. Sequence TTTTT was introduced at the 3’-end of these oligos, which served as a transcription terminator for RNA polymerase III. The empty vector or its shRNA expression vectors were transiently transfected into the mouse MA891 cells. The total RNA was isolated 48 h post-transfection and reverse-transcription PCR experiments were performed. The results demonstrated
that the expressed shRNAs reduced the mouse wnt9a
RNA level, whereas the empty vector had no effect on
the reduction of mouse wnt9a RNA level (data not
shown). Then, we used in situ hybridization to test the
effect of shRNA on mouse wnt9a. The transiently
6-well plates at 1×103 cells per well. Then, the cells
were fixed and examined by in situ hybridization for
mouse wnt9a after three days post-transfection. The
results demonstrated that the cells showed, when
U6+27 cassettes were used with anti-wnt9a hairpin
siRNA inserts, dramatic reduction of wnt9a signals
whereas as compared to the untransfected cells in the same fields under microscope (Figs. 2C-2F), whereas
no elimination of wnt9a mRNA was observed MA891
cells transiently transfected with pAVU6+27 vector (Fig. 2A). Negative control has no hybridization signal (Fig. 2B) shown the hybridization probes having
specific for mouse wnt9a.
3.3 The Influence Effect of Mouse Wnt9a on MA891
Cell Cycles
The flow cytometry analyses were applied and demonstrated the promoting proliferation of MA891
Fig. 2 The effect of U6+27-siRNAs transcripts. Mouse MA891 cells were transfected with pAVU6+27. With the U6+27-anti- mouse wnt9a cassette vector, and fixed with and probed with digoxigenin three days after transfection. The specific signals (deep brown in color) were visualized by incubation in DAB buffer. Cells were re-dyed with haematoxylon and photographs were taken. (A) transfections of pAVU6+27 without the anti- mouse wnt9a siRNA, as a control, showed that mouse wnt9a was stained into deep brown in all cells. (B) transfections of with pAVU6+27 the anti- mouse wnt9a siRNA, as a negative control, showed that the mouse wnt9a was stained lightly in all cells. (C)-(F) Transfection with U6+27 promoter cassettes containing the anti- mouse wnt9a siRNA1, siRNA2, siRNA3 and siRNA4, respectively, showed in Fig. 1. Transfected cell cytoplasms are in light color (negative stain), whereas cytoplasms are deep brown (positive stain) in untransfected cells.
cells by RNAi mouse wnt9a. Flow cytometric analysis
of the cells transfected with expression vectors pAVU6+27/m9aRNAis (Fig. 3B-3E), for DNA content showed that the number of cells in G1 phase decreased and the number of cells in S, G2/M phases increased as compared with the cells transfected with pAVU6+27 (Fig. 3A), suggesting that lower expression of mouse wnt9a in MA891 cells promotes cell proliferation (Fig. 3).
4. Conclusions
In summary, our results clearly demonstrated that the pAVU6+27/m9aRNA expression vectors were effective in inhibiting transcription of the homologous
mouse wnt9a RNA in cultured mammalian cells. Since
RNAi technique has evolved as a powerful strategy for reverse functional genomics in various organisms and with tremendous therapeutic potentials in mammals, pAVU6+27 vector should provide a simple and rapid cloning tool for expression of the small interfering RNA transcripts, which can effectively induce RNAi-mediated gene silencing in mammalian cells. It is difficult to examine the efficiency of the transient transfection by RT-PCR, Western blot and RealTime PCR, because of the low transfection efficiency (about
30%). In this study, we used in situ hybridization to
observe visually the effect of siRNA for mouse wnt9a.
The cells proliferation was promoted after lowering
expression of mouse wnt9a in MA891 cells by RNAi.
All suggest that the expression level of mouse wnt9a in
breast cancer MA891 cells may play a role in adjusting the rate of proliferation.
Acknowledgment
This work was supported by grants from the National Nature Science Foundation of China (No.31071091; No.30971570; No.31171196) and Department of Education Key Project of Hunan Province, China (No.09A035; and No.06C370).
Reference
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in animal development, Genes Dev. 11 (1997) 3286-3305. [3] T. Reya1, H. Clevers, Wnt signalling in stem cells and
cancer, Nature 434 (2005) 843-850.
[4] A. Fire, S. Xu, M.K. Montgomery, Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans, Nature 391 (1998) 806-811. [5] P.D. Zamore, T. Tuschl, P.A. Sharp, RNAi:
Double-stranded RNA directs the ATP-dependent cleavage of mRNA at 21 to 23 nucleotide intervals, Cell 101 (2000) 5-33.
[6] N. Tavernarakis, S.L. Wang, M. Dorovkov, Heritable and inducible genetic interference by double-stranded RNA encoded by transgenes, Nat. Genet. 24 (2000) 180-183. [7] E. Bernstein, A.A. Caudy, S.M. Hammond, Role for a
bidentate ribonuclease in the initiation step of RNA interference, Nature 409 (2001) 363-366.
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vector-based RNAi systems in mammalian cells, Biochemical and Biophysical Research Communications 330 (2005) 53-59.
[9] C.P. Paul, P.D. Good, I. Winer, Active expression of small interfering RNA in mouse cells, Nat. Biotechnol. 20 (2002) 505-508.
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A Family of Origin Scale in Mothers of Children with
Autistic Spectrum Disorder-Preliminary Report
Piotr W. Gorczyca1, Agnieszka Kapinos-Gorczyca2, Maciej Kapinos3, Aleksandra Leksowska1, Katarzyna Ziora4,
Joanna Oświęcimska4 and Jarosław Sobiś1
1. Department of Psychiatry, Medical University of Silesia in Katowice, Tarnowskie Góry 42-612, Poland 2. Daily Psychiatric Ward for Children and Adolescents, NZOZ FENIKS, Gliwice 44-100, Poland 3. Psychiatric Hospital, Rybnik 44-200, Poland
4. Department of Pediatrics, Medical University of Silesia in Katowice, Zabrze 41-800, Poland
Received: April 11, 2011 / Accepted: April 26, 2011 / Published: August 30, 2011.
Abstract: The influence of the family of origin is often described in the aetiology of different psychiatric disorders. The majority of papers concerning the families of autistic children concentrate on the quality of their lives. The aim of our study was to compare the experiences from the family of origin of mothers of children with Autistic Spectrum Disorders (ASD) and from mothers of healthy children. In our study a Family of Origin Scale (FOS) was used. This scale consists of 10 constructs: clarity of expression, responsibility, respect for others, openness to others, acceptance of separation/loss, range of feelings, mood and tone, conflict resolution, empathy and trust. It was a pilot study. The examined group consisted of 9 mothers of children with ASD, the control group-7 mothers of healthy children. We found that both groups differed in a statistically significant way as for the construct called responsibility. Our research was a pilot study and it required further investigations.
Key words: Autism Spectrum Disorders, mothers characteristic, Family of Origin Scale.
1. Introduction
The influence of The Family of Origin Scale (FOS) had been described in the some psychiatric problems [1, 2]. The majority of the papers concerning the families of autistic children concentrated on the quality of their life [3-5].There was a large number of evidences from family and twins studies suggesting the etiology of autism was mainly genetic [6]. Also it was accepted that there was a need to assess the individual relationships in families over time. The dimension that had been the most influential was that of ‘control’ or ‘overprotection’ [7]. Some studies had demonstrated an association among low care and high overprotection and a range of adult psychiatric disorders [8]. There were some scales that measure the problems in families.
Corresponding author: Piotr Gorczyca, Ph.D., psychiatrist, research field: psychology. E-mail: [email protected].
One of them was a family of Origin Scale (FOS), which was what we used in this study. This scale consists of 40 items: ten subscale self-report instruments designed to assess perceptions of family health and based on the dimensions of Autonomy and Intimacy [9]. FOS was used among the adolescents psychiatric inpatients. However, there were not any studies/reports using FOS in Medline database. The aim of this study was to compare the experiences from The Family of Origin Scale of mothers of children with Autistic Spectrum Disorders (ASD) and from mothers of healthy children as the control group.
2. Materials and Methods
separation/loss, range of feelings, mood and tone, conflict resolution, empathy and trust. It was a pilot study. The examined group consisted of 9 mothers of children with ASD, the control group-7 mothers of healthy children.
To diagnose ASD we used DSM IV TR.
Statistical analysis was conducted by the use of
Wald-Wolfowitz test, Kołmogorov-Smirnov test and U
Mann Whitney test. A level of P < 0.05 was accepted as
statistically significant. Calculations were conducted by using “Statistica 5.0 Pl” software (StatSoft INC., USA).
3. Results
Both groups differed in a statistically significant way
as for the construct called responsibility (P = 0.04).
According to Tables 1 and 2 there was a significant difference between two examined groups as for the concept of responsibility.
4. Discussion
The significant differences in the item responsibility could be in connection with overprotection. And there were no statistically significant differences between the two examined groups as for the other items.
In the past, the theories that connected the cause of autism with mother-child relation were popular
although controversial. The “refrigerator mother”
hypothesis of autism was firstly used [11], and then there was a theory that focused on abnormal mother’s response to child signals [12]. The item of overprotection could refer somehow to the Bettelheim’s theory. The scale, therefore, does not attempt to distinguish between objective-factual or interpretive-subjective views of the family of origin [10]. On the other hand, mothers of autistic children could develop the feature of overprotection in the relation with the child illness. Other question is discussing an argument in case of statistically significant differences were observed for the other items. For example, the so-called double-hit hypothesis which is characteristic for the families with schizophrenic member may concern to significant
differences in item clarity of expression. In so-called
psycho-somatic families with anorexia nervosa
occurrence the differences in items respect of others
and openness to others may appear. If there was in fact such a result of these groups investigation then we could identify the important risk factor for the certain diseases development. That would also mean the
Table 1 Results in Family of Origin Scale in mothers of autistic children (examined group).
Scale/subscales Mothers of children with ASD, n = 9
1 2 3 4 5 6 7 8 9
Autonomy concept
1.Clarity of expression 12 10 4 14 18 15 10 5 19
2. Responsibility 10 10 5 12 19 16 14 8 8
3. Respect for others 10 8 5 14 16 13 10 4 4
4. Openness to others 13 13 10 11 19 14 12 6 13
5. Acceptance of separation/loss 10 12 4 14 17 13 14 8 16
Total of subscale 55 53 28 65 89 71 60 31 59
Intimacy concept
6. Range of feelings 11 13 6 12 15 15 12 8 16
7. Mood&tone 10 11 6 13 18 15 13 4 20
8. Conflict resolution 9 9 4 12 15 13 11 4 16
9. Empathy 9 12 5 10 18 14 13 4 8
10. Trust 9 12 9 13 18 14 11 12 8
Total of subscale 48 55 30 60 84 71 60 32 68
Table 2 Results in Family of Origin Scale in mothers of healthy children (control group).
Scale/subscales control group, n = 7 p (between examined and
control group)
forgotten theories restoration to a high degree. Conclusively, if the mentioned results refered to the large groups of families with a certain illness, then we could investigate for the defined connection in genetic research.
5. Conclusions
The construct called responsibility could have
certain influence on the development of autistic disorder. Our research was a pilot study and it required further investigations. Conducting wider research in the families of patients with other illnesses could bring important data on FOS possible application.
Referencess
[1] P. Yelsma, A.J. Hovestadt, W.T. Anderson, J.E. Nilsson, Family of origin expressiveness: Measurement, meaning, and relationship to alexithymia, Journal of Marital and Family Therapy 26 (3) (2000) 353-363.
[2] C.L. Niedermeier, H.R. Searight, P.J. Handal, C.M. Manley, N.Y. Brown, Perceived a family functioning among adolescent psychiatric inpatients: Validity of the Family-of-Origin Scale, Child Psychiatry and Human Development 25 (4) (1995) 253-265.
[3] D. Mugno, L. Ruta, V.G. D’Arrigo, L. Mazzone, Impairment of quality of life in parents of children and adolescents with pervasive developmental disorders, Health and Quality
of Life Outcomes 27 (2007) 5: 22 (reprint).
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[6] J. Piven, The broad autism phenotype: A complementary strategy for molecular genetic studies of autism, American Journal of Medical Genetics 105 (1) (2001) 34-35. [7] J. Hill, E. Mackie, L. Banner, H. Kondryn, Relationship
with Family of Origin Scale (REFAMOS), interrater reliability and associations with childhood experiences, British Journal of Psychiatry 175 (1999) 565-570. [8] G. Parker, L. Kiloh, L. Hayward, Parental representations
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[11] B. Bettelheim, Love is not Enough: The Treatment of Emotionally Disturbed Children, Free Press, Glencoe, 1950. [12] B. Rimland, Infantile Autism: The Syndrome and Its
Modified Multiple Scale/Segment Entropy (MMPE)
Analysis of Heart Rate Variability of NHH, CHF & AF
Subjects
Chodavarapu Renu Madhavi and Alevoor Gopal Krishnachar Ananth
Rastreeya Vidyalaya. College of Engineering, Bangalore, Karnataka 560059, India
Received: August 13, 2010 / Accepted: September 03, 2010 / Published: August 30, 2011.
Abstract: Nonlinear analysis of heart rate variability (HRV) has become important as heart behaves as a complex system. In this work, the approximate entropy (ApEn) has been used as a nonlinear measure. A new concept of estimating the ApEn in different segments of long length of the recorded data called modified multiple scale (segment) entropy (MMPE) is introduced. The idea of estimating the approximate entropy in different segments is useful to detect the nonlinear dynamics of the heart present in the entire length of data. The present work has been carried out for three cases namely the normal healthy heart (NHH) data, congestive heart failure (CHF) data and Atrial fibrillation (AF) data and the data are analyzed using MMPE techniques. It is observed that the mean value of ApEn for NHH data is much higher than the mean values for CHF data and AF data. The ApEn profiles of CHF, AF and NHH data for different segments obtained using MPE profiles measures the heart dynamism for the three different cases. Also the power spectral density is obtained using fast fourier transform (FFT) analysis and the ratio of LF/HF (low frequency/high frequency) power are computed on multiple scales/segments namely MPLH (multiple scale low frequency to high frequency) for the NHH data, CHF data and AF data and analyzed using MPLH techniques. The results are presented and discussed in the paper.
Key words: Multiple scale/segment, heart rate variability, approximate entropy, congestive heart failure, atrial fibrillations.
1. Introduction
The heart rate variability (HRV) is defined as the change of heart rate about its mean value. The heart behaves as a complex system and the heart rate can be modulated over its mean value by the sympathetic and parasympathetic branches of autonomous nervous system (ANS). The HRV exhibits non-regularity and dynamic behaviour for a healthy individual, HRV is more regular for the diseased subjects [1-3]. Nonlinear measurement techniques have been applied for the analysis of heart’s complex behavior. The complexity and regularity of physiological time series data is measured using approximate entropy (ApEn), a concept introduced by Pincus [4, 5]. The ApEn
Corresponding author: Chodavarapu Renu Madhavi, associate professor, research field: biomedical engineering. E-mail: [email protected].
of NHH, CHF & AF Subjects
processing time; instead of taking huge data on each scale it is proposed to estimate the entropy on different scales handling smaller length of data by Chodavarapu et al. [7]. The entire data is uniformly divided into different scales by dividing the total length of data by N (N = 8) and entropy is estimated in each scale [11]. This method has the drawback of getting unequal lengths of scale data even if 12 hour recorded data is taken; this may be due to the different sampling frequencies used. In this paper, the scale is assigned to the data for every one hour and the entropy is estimated. The approximate entropy (ApEn) is used as a regularity measure of physiological time series data [4-6]. The ApEn values estimated over limited period may not give the appropriate representation of the dynamic nature of the heart. Hence the concept of multi-scale entropy (MSE) is introduced and ApEn are estimated over different scales [9, 10]. The multiple scale entropy (MPE) was estimated for normal healthy heart (NHH) and congestive heart failure (CHF) subjects, where the scales/segments are divided based on the data length [12]. In the present paper, the dynamism of the heart is captured by dividing the data into different scales/segments which are separated by equal periods and estimating the ApEn of the data in each segment, called modified multiple scale/segment entropy (MMPE). The segments are uniformly separated by one hour.
2. Methodology and Data Analysis
Three sets of RR interval data of CHF subjects, NHH subjects and of Atrial Fibrillations (AF) subjects of different age groups each recorded at different points of time of a day is obtained from Physionet [13, 14]. Multi-scale entropy (MSE) estimated [9, 10] requires minimum data length as 30,000 and scale-1 represented that entire data. The data is divided by the respective scale value, such that the scale 20 has 1,500 data values, and for each scale data the entropy was estimated. The values of MSE can’t indicate the behaviour at various points of time [12].
In order to overcome these limitations and to further monitor the dynamism of heart throughout the length of the recoded data, multiple scale (segment) entropy technique is applied (MPE) where in nearly 35,000 samples of RR intervals have been considered for the NHH, CHF subjects obtained from Physionet [13, 14]. The entire data is divided into 7 segments each segment having ~5,000 values of RR intervals, and each segment is denoted by a scale, and MPE was estimated using sample entropy [12, 14]. The limitation of this method is that the duration of each segment may not be the same for different data lengths as the sampling frequency of each data may be different. To overcome this, a new method called modified multiple scale/segment entropy (MMPE) is introduced where nearly 35,000 samples of RR intervals have been considered for all the three NHH, CHF and AF subjects obtained from Physionet [13, 14]. The entire data is divided into 8 segments each segment having values of RR intervals recorded for 15 minutes, and each segment is denoted by a scale. The segments are divided based on the data recorded intervals. For every one hour data recorded for 15 minutes, a segment or scale is assigned. This classification is essential for studying the nonlinear dynamic behaviour of the data over the entire period of the recorded data. The data samples taken in different scales (segments) are shown in Table 1. The first column of Table 1 indicates the data sample duration/range. Column 2 is the corresponding scales
Table 1 Classification of data into different ranges and scale index.
for each range of data. The value of ApEn estimated in each scale or segment are defined as modified multiple scale/segment entropy (MMPE). The ApEn is computed using concepts of Pincus [5-8].
Approximate entropy (ApEn) is a measure used to quantify regularity in data about which no knowledge about the system generating the data is available. ApEn is found to be useful parameter for finding the hidden changes which could not be detected by the time series analysis. It detects changes in underlying dynamic nature not seen in the signal measurements. The algorithm is implemented in C++ language and MATLAB. The algorithm is capable of handling 1024 or more data points. With m = 2 considering two elements the correlation integral Cm (i) is calculated then its logarithm value is estimated and Øm(r) is calculated as Eq. (1) given below [5-8, 11, 12]:
∑
− +The correlation integral is calculated using m = 3 and Øm+1(r) is estimated using Eq. (1) replacing m = m+1.
Finally, the ApEn value is calculated from the Eq. (2): ApEn(m,r,N) = Øm(r) – Øm+1(r) (2)
3. Results and Discussion
3.1 Modified Multiple Scale Entropy (MMPE)
The ApEn values are determined for each scale index for all the three NHH, CHF and AF subjects as shown in Fig. 1. The figure shows ApEn values for different scale index corresponding to different data ranges. The ApEn values for CHF subject’s indicates lower values of entropies compared to the NHH subjects. These results are in agreement with Refs. [9, 10]. It is clearly evident from the figure that ApEn value determined from different scale ranges shows consistently higher values for NHH data when compared to CHF data.
Further it can be seen from the Fig. 1 that the ApEn values for AF data also show higher values when compared CHF data. However, ApEn values of NHH data is significantly higher compared to both CHF and
AF data in all the intervals representing different scales. It is possible to use these results for monitoring different dynamic status of the heart. For NHH subjects ApEn values are high and the functioning of the heart is very dynamic and HRV is not very regular. For the CHF subjects the ApEn values remain low indicating that the heart is less active and HRV becomes more regular. But for the AF data the ApEn values being high and fluctuating around the same value indicates the fibrillations and erratic heart behaviour. It is reported in the Refs. [1-6] that the heart rate variations are not regular for healthy subjects indicating a highly dynamic heart and become more regular and indicate uncorrelated random behaviour for disease subjects.
In order to consolidate the significance of the results, the mean MMPE values have been calculated using the data over all the scales/segments for NHH, CHF and AF data which is shown in Fig. 2. It can be seen that for NHH subjects the mean ApEn ~1.3, for AF subjects the mean ApEn ~1.1 and for CHF subjects mean ApEn ~0.6. The MMPE value for NHH subjects is significantly high suggesting that for normal subjects
Fig. 1 ApEn values for different scale index (MMPE) plotted for NHH, CHF, and AF data.
of NHH, CHF & AF Subjects
the heart activity is more dynamic when compared to that of MMPE values for congestive heart failure subjects. The heart affected by AF shows more erratic variations than the normal subjects indicating the presence of fibrillations [2-4].
3.2 Multiple Scale Low Frequency/High Frequency (MpLH)
Since the HRV exhibits strong periodicity in time, the HRV data has been subjected to power spectrum analysis using fast fourier transform (FFT) techniques to identify the distinct periodicities present in the HRV data in the frequency domain. The components of HRV have been separated into three frequency ranges namely very low frequency VLF (0-0.04 Hz), low frequency LF (0.04-0.15 Hz) and high frequency HF (0.15-0.4 Hz) [14]. The frequency spectrum is determined for the three NHH, CHF and AF subjects. The power spectral density distribution as a function of frequency are plotted for the three cases and shown in Figs. 3-5, respectively.
It is observed form the Fig. 3 that, for NHH subjects the power spectral density distributions [14, 15] shows strong peaks corresponding to frequency F = 0.09 Hz (~ 73 beats/minute). This is in accordance with the HRV observed for NHH subjects. It can be seen that the power density distribution is maximum at LF bands and minimum in HF bands. Fig. 4 shows the frequency spectrum for the CHF subjects. The power density distribution shows strong peaks at two frequencies corresponding to frequencies F1~0.01Hz (~74 beats/minute) and F2~0.18Hz (~62 beats/minute). It is evident that the cardiac behaviour of the CHF subjects are not normal and shows periodicities in HF which needs more investigations.
It can also be seen from the Fig. 3 that the power density distribution is comparable at both lower and higher frequencies and the power is distributed equally at both LF and HF ranges. Further it can be seen from Fig. 5 that the power spectral distribution for AF subjects is highly varying and noisy throughout the frequency spectrum indicating the disturbed state with
Fig. 3 FFT of NHH data.
Fig. 4 FFT of CHF data.
Fig. 5 FFT of AF data.
a higher level of cardiac activity. From the results presented on the power spectrum distribution of HRV indicates entire different behaviour for the spectral distribution at lower and higher frequencies for the three different type subjects selected for this analysis.
Table 2 The mean LF/HF values of NHH, CHF, Af data. Subjects Mean ratio LF/HF
NHH 2 CHF 0.65 AF 8.6
from all segments/scales is ~2. For CHF subjects the LF component of power density is low and HF component is high indicating that the ratio of LF/HF < 1 having the mean determined as 0.65. Further for AF subjects the power densities observed for LF are very high compared to that of HF and the mean ratio determined is LF/HF ~8.6. The low and high frequency distribution of power densities and the corresponding LF/HF ratios exhibits very clear distinction among the cardiac disease and healthy.
4. Conclusions
The MMPE and MPLH methods can be used as a non-invasive tools for studying the dynamics of the heart for healthy and cardiac disease subjects. The results of the analysis can be used to arrive at the following conclusions:
(1) The ApEn is very high for NHH subjects compared to that of CHF and AF subjects indicating a highly dynamic activity of the heart for NHH subjects. The ApEn is high for AF subjects compared to that of CHF subjects indicating high cardiac activity for the AF subjects and low cardiac activity for the CHF subjects.
(2) The ApEn profiles seen for multiple scales indicate consistent behaviour and can be used for monitoring the cardiac activities over the recorded period. The frequency spectrum of the power density can be used as an effective tool for determining the cardiac status both in case of healthy and diseased subjects.
References
[1] R.U. Acharya, N. Kannathal, S.M. Krishnan, Comprehensive analysis of cardiac health using heart rate
signals, Physiol. Meas. J. 25 (2004) 1130-1151.
[2] R.U. Acharya, N. Kannathal, O.W. Sing, L.Y. Ping, T.L. Chua, Heart rate analysis in normal subjects of various age groups, Biomedical Engineering Online 3 (2004) 24. [3] R.U. Acharya, P.S. Bhat, N. Kannathal, L.C. Min, S.
Laxminarayan, Cardiac health diagnosis using wavelet transformation and phase space plots, in: Proceedings of the IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, 2005, pp. 3868-3871. [4] S.M. Pincus, Approximate entropy as a measure of system complexity, Proc. Natl. Acad. Sci. USA 88 (1991) 2297-2301.
[5] S.M. Pincus, I.M. Gladstone, R.A. Ehrenkranz, A regularity statistic for medical data analysis, J. Clin. Monitor 7 (1991) 335-345.
[6] S.M. Pincus, Greater signal regularity may indicate increased system isolation, Math. Biosci 22 (1992) 161-181.
[7] C.R. Madhavi, A.G.K. Ananth, Estimation of approximate entropy and investigating the effect of meditation on it, in: Proceedings of ICSAP 2010, Bangalore, India, 2010, pp. 304-306.
[8] J.S. Richman, J.R. Moorman, Physiological time-series analysis using approximate entropy and sample entropy, Am. J. Physiol. Heart Circ. Physiol. 278 (2000) 2039-2049.
[9] M. Costa, A.I. Goldberger, C.K. Peng, Multiscale entropy analysis of complex physiologic time series, Physical Review Letters 89 (2002) 1-4.
[10] M. Costa, A.I. Goldberger, C.K. Peng, Multiscale entropy analysis of biological signals, Phys. Rev. E 71 (2005) 1-18.
[11] C.R. Madhavi, A.G.K. Ananth, Multiplescaleentropy (MPE) estimation and analysis of normal and congestive heart failure subjects, Journal of Materials Science and Engineering 4 (2010) 66-71.
[12] C.R. Madhavi, A.G.K. Ananth, Estimation of multiple scale entropy (MPE) of heart rate variability of normal and congestive heart failure data by choosing suitable ‘m’ and ‘r’ value, in: Proceedings of ICNB, Vishakhapatnam, India, 2010, p. 151.
[13] Available online at: www.physionet.com\physiobank\ Physiobank Archieves \AFdata.
[14] C.R. Madhavi, A.G.K. Ananth, Multiplescaleentropy (MPE) estimation and analysis of normal and congestive heart failure subjects, Journal of Materials Science and Engineering 4 (2010) 66-71.
Proteome Profiles of
Longissimus
and
Biceps Femoris
Porcine Muscles Related to Exercise and Resting
Marinus F.W. Te Pas1, Els Keuning1, Dick J.M. Van De Wiel1, Jette F. Young2, Niels Oksbjerg2 and Leo Kruijt1
1. Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, Lelystad 8200AB, The Netherlands 2. Aarhus University, Faculty of Agricultural Sciences, Department of Food Science, Tjele 8830, Denmark
Received: January 03, 2011 / Accepted: March 21, 2010 / Published: August 30, 2011.
Abstract: Exercise affects muscle metabolism and composition in the untrained muscles. The proteome of muscle tissue will be affected by exercise and resting. This is of economic importance for pork quality where transportation relates to exercise of untrained muscles. Rest reverses exercise effects. The objective of this research was to develop potential protein biomarkers that predict the optimal resting time after exercise related to optimal pork quality. Ten litters of four female pigs were within litter allocated to the four treatment groups: exercise by running on a treadmill for 27 minutes followed by rest for 0, 1, or 3 h; control pigs without exercise. Proteome profiles and biochemical traits measuring energy metabolism and meat quality traits expected to be related to exercise were determined in the Longissimus and the Biceps femoris of the pigs. The results indicated associations between protein abundances in muscles and exercise, resting, and biochemical traits.
Key words: Exercise, muscle tissue, proteomics profiles, Sus scrofa, biochemical energy metabolism, meat quality traits, rest.
1. Introduction
Exercise changes muscle metabolism and related muscle composition, especially in untrained muscles. Preslaughter pig transportation can be regarded as exercise of untrained muscles. The resulting biochemical changes affects proteome muscle composition and biochemical processes related to energy metabolism, which do also affect post mortem processes affecting meat quality.
Consumers’ perception of pork quality relies on the stage in the entire consumption process. When buying raw pork the appearance of the meat is important-including technological meat quality traits such as color, drip loss, inter and intra muscular fat, and texture. In the cooking process cooking loss is critical and during consumption the taste and texture perceptions of the pork are most important for the
Corresponding author: Marinus F.W. te Pas, Ph.D., senior researcher, research fields: genomics, bioinformatics and systems biology. E-mail: [email protected].
The objective of this research was to investigate the relations among the abundance of proteome profiles and biochemical meat quality traits and exercise followed by rest before slaughter, in samples removed immediately after slaughter in two different pig muscles.
2. Materials and Methods
2.1 Animal Treatment and Sampling
Animal management and experimental conditions were as previously reported [14]. Briefly, the experiment was conducted with ten litters of four female pigs (Duroc × (Landrace and Yorkshire). Within litter, pigs were assigned to one of four treatments: control pigs without experimental exercise (group 1, control group), or pre-slaughter exercise (on average 27 min running on a treadmill) followed by 0 h (group 2), or 1 h (group 3) or 3 h (group 4) of rest [14]. Muscle biopsies were taken within 1 min after bleeding. The left longissimus dorsi (LD) was biopsy sampled 4 cm cranially to the last rib and the biceps femoris (BF) 20 cm from the knee joint in the caudal direction towards the tail base. Samples were snap frozen in liquid nitrogen, transported on dry ice, and stored at -80 ℃ until use for proteome isolation.
2.2 Isolation of Proteome and Proteomics Data Generation
Isolation of the water soluble muscle proteome fraction, the sarcoplasmic proteome, was done as previously described [15, 16]. In Ref. [14] pH differences between the sample groups were described and the isolation buffer has low buffering capacity. However, the pH of the small sample size (30-50 mg) could be buffered by the large buffer volume (1.5 mL). Furthermore, no protein concentration differences were noted. Proteomics profiles were generated using the SELDI-TOF PCS4000 Enterprise (BioRAD, Veenendaal, The Netherlands) equipment and the array types CM10, IMAC30, and Q10 each with different binding characteristics (BioRad).
2.3 Statistical Analyses of Proteomics Data in Relation
with Meat Quality Data and Stress
The proteome profiles of all animals were analyzed for differences in abundance of individual peaks between the treatment groups using the SELDI-TOF (BioRAD) software. Clustering was done using the cluster algorithm that first ensures high signal to noise peaks and than generates two-dimensional arrays of peaks across multiple spectra. This cluster map was used for all downstream analyses.
Proteins were recognized by the M/Z values of their peaks in the proteome profiles generated by the SELDI-TOF equipment. Statistical difference (P < 0.05) of the abundance of a protein between groups one and two was taken as an indication of immediate response to exercise, differences between groups two and three, and between groups two and four were taken as an indication of a reaction to rest after exercise.
The proteome profiles were compared using principal component analysis (PCA) using the SELDI analysis software package. The procedure was done as indicated by the manufacturer and the four exercise-rest groups were visualized.
Association between the abundance of a protein and meat quality or exercise-related biochemical traits [14] was studied using the SELDI-TOF software. The software calculates P-values using the Kruskal-Wallis non-parametric test. Afterwards these results were used for comparison of the abundances of the proteins in the four treatment groups. The P-values were P < 0.05 for significant results and P < 0.1 for a tendency of effect. These final data indicate the relation of a protein (represented by its M/Z value) to meat quality and/or biochemical traits, as well as exercise/rest of the pigs.
3. Results
3.1 Proteomics Profiles of LD and BF Muscles
136 and the peaks vary from 1-278 kDa. The CM 10, a more general protein binding array type showed the highest number of peaks. For a given array type the samples of all animals were run on the equipment in a single run making peak height as a measure for expression level better comparable and there is no variation in number of peaks between the animals. In a good replicate this variation is usually less than 5%. Except for the Q10 array type the number of peaks within the profiles for the two muscles show good similarity. When analyzing the peak identity based on m/z values the similarity of the peaks between the two muscles is 70-86 percent.
3.2 Relation between Proteome Peak Patterns and
Stress
The effect of exercise on the abundance of muscle proteins can be evaluated with different parameters. First, we measured the number of proteins with different abundance between the groups without exercise (control, group 1) and the group exercised on a treadmill without rest before slaughtering (group 2). In the LD muscle six protein peaks showed significant differences with five proteins showing increased abundances and one protein with decreased abundance after exercise. In the BF muscle ten protein peaks were significantly different with five increased and five decreased abundances. A small protein peak of 1.2 kDa was similar for LD and BF.
Using a cluster plot analysis method we evaluated the whole pattern of changed protein abundances from control (group 1) to exercised pigs (group 2), and from exercised pigs to those rested for 1 (group 3) and 3 hours (group 4) before slaughter. It should be noted that the direct effect of exercise - i.e. comparison of group 1
Table 1 Proteome profile data of the LD and BF muscles.
Array type Peak numbers Similar peaks LD BF LD - BF
CM10 136 133 95
IMAC30 92 94 68
Q10 95 111 82
Total 323 338 245
(control) and group 2 (exercised pigs) was not always statistically significant due to delayed reaction to stress. Indeed, the abundance patterns in especially the BF muscle indicated that a delayed reaction to stress could be measured with this method in groups 3 and 4 (Figs. 1C and 1F).
In the cluster plot analysis shown for the LD muscle seven proteins showed statistically different abundance in the groups of exercise followed by rest for different time before slaughtering. Four proteins had increased abundance after exercise and three peaks showed reduced abundance after exercise. The BF muscle showed that the abundance of 17 protein peaks differed between the four groups, nine of which showed increased abundance and three of them showed decreased abundance after exercise. The remaining five
A B
C D
E F
