Placental determinants of fetal growth

Identification of key factors in the insulin-like growth factor and cytokine systems using artificial neural networks

Maria E. Street, Enzo Grossi, Cecilia Volta, Elena Faleschini, Sergio Bernasconi

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

Background: Changes and relationships of components of the cytokine and IGF systems have been shown in placenta and cord serum of fetal growth restricted (FGR) compared with normal newborns (AGA). This study aimed to analyse a data set of clinical and biochemical data in FGR and AGA newborns to assess if a mathematical model existed and was capable of identifying these two different conditions in order to identify the variables which had a mathematically consistent biological relevance to fetal growth. Methods: Whole villous tissue was collected at birth from FGR (N = 20) and AGA neonates (N = 28). Total RNA was extracted, reverse transcribed and then real-time quantitative (TaqMan) RT-PCR was performed to quantify cDNA for IGF-I, IGF-II, IGFBP-1, IGFBP-2 and IL-6. The corresponding proteins with TNF-α in addition were assayed in placental lysates using specific kits. The data were analysed using Artificial Neural Networks (supervised networks), and principal component analysis and connectivity map. Results: The IGF system and IL-6 allowed to predict FGR in approximately 92% of the cases and AGA in 85% of the cases with a low number of errors. IGF-II, IGFBP-2, and IL-6 content in the placental lysates were the most important factors connected with FGR. The condition of being FGR was connected mainly with the IGF-II placental content, and the latter with IL-6 and IGFBP-2 concentrations in placental lysates. Conclusion: These results suggest that further research in humans should focus on these biochemical data. Furthermore, this study offered a critical revision of previous studies. The understanding of this system biology is relevant to the development of future therapeutical interventions possibly aiming at reducing IL-6 and IGFBP-2 concentrations preserving IGF bioactivity in both placenta and fetus.

Original languageEnglish
Article number24
JournalBMC Pediatrics
Volume8
DOIs
Publication statusPublished - Jun 17 2008

Fingerprint

Somatomedins
Fetal Development
Insulin-Like Growth Factor Binding Protein 2
Cytokines
Interleukin-6
Insulin-Like Growth Factor II
Placenta
Insulin-Like Growth Factor Binding Protein 1
Systems Biology
Principal Component Analysis
Insulin-Like Growth Factor I
Fetus
Theoretical Models
Complementary DNA
Parturition
RNA
Polymerase Chain Reaction
Serum
Research
Proteins

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Placental determinants of fetal growth : Identification of key factors in the insulin-like growth factor and cytokine systems using artificial neural networks. / Street, Maria E.; Grossi, Enzo; Volta, Cecilia; Faleschini, Elena; Bernasconi, Sergio.

In: BMC Pediatrics, Vol. 8, 24, 17.06.2008.

Research output: Contribution to journalArticle

@article{4e57ecf200a841febad3a724e491bfb6,
title = "Placental determinants of fetal growth: Identification of key factors in the insulin-like growth factor and cytokine systems using artificial neural networks",
abstract = "Background: Changes and relationships of components of the cytokine and IGF systems have been shown in placenta and cord serum of fetal growth restricted (FGR) compared with normal newborns (AGA). This study aimed to analyse a data set of clinical and biochemical data in FGR and AGA newborns to assess if a mathematical model existed and was capable of identifying these two different conditions in order to identify the variables which had a mathematically consistent biological relevance to fetal growth. Methods: Whole villous tissue was collected at birth from FGR (N = 20) and AGA neonates (N = 28). Total RNA was extracted, reverse transcribed and then real-time quantitative (TaqMan) RT-PCR was performed to quantify cDNA for IGF-I, IGF-II, IGFBP-1, IGFBP-2 and IL-6. The corresponding proteins with TNF-α in addition were assayed in placental lysates using specific kits. The data were analysed using Artificial Neural Networks (supervised networks), and principal component analysis and connectivity map. Results: The IGF system and IL-6 allowed to predict FGR in approximately 92{\%} of the cases and AGA in 85{\%} of the cases with a low number of errors. IGF-II, IGFBP-2, and IL-6 content in the placental lysates were the most important factors connected with FGR. The condition of being FGR was connected mainly with the IGF-II placental content, and the latter with IL-6 and IGFBP-2 concentrations in placental lysates. Conclusion: These results suggest that further research in humans should focus on these biochemical data. Furthermore, this study offered a critical revision of previous studies. The understanding of this system biology is relevant to the development of future therapeutical interventions possibly aiming at reducing IL-6 and IGFBP-2 concentrations preserving IGF bioactivity in both placenta and fetus.",
author = "Street, {Maria E.} and Enzo Grossi and Cecilia Volta and Elena Faleschini and Sergio Bernasconi",
year = "2008",
month = "6",
day = "17",
doi = "10.1186/1471-2431-8-24",
language = "English",
volume = "8",
journal = "BMC Pediatrics",
issn = "1471-2431",
publisher = "BMC",

}

TY - JOUR

T1 - Placental determinants of fetal growth

T2 - Identification of key factors in the insulin-like growth factor and cytokine systems using artificial neural networks

AU - Street, Maria E.

AU - Grossi, Enzo

AU - Volta, Cecilia

AU - Faleschini, Elena

AU - Bernasconi, Sergio

PY - 2008/6/17

Y1 - 2008/6/17

N2 - Background: Changes and relationships of components of the cytokine and IGF systems have been shown in placenta and cord serum of fetal growth restricted (FGR) compared with normal newborns (AGA). This study aimed to analyse a data set of clinical and biochemical data in FGR and AGA newborns to assess if a mathematical model existed and was capable of identifying these two different conditions in order to identify the variables which had a mathematically consistent biological relevance to fetal growth. Methods: Whole villous tissue was collected at birth from FGR (N = 20) and AGA neonates (N = 28). Total RNA was extracted, reverse transcribed and then real-time quantitative (TaqMan) RT-PCR was performed to quantify cDNA for IGF-I, IGF-II, IGFBP-1, IGFBP-2 and IL-6. The corresponding proteins with TNF-α in addition were assayed in placental lysates using specific kits. The data were analysed using Artificial Neural Networks (supervised networks), and principal component analysis and connectivity map. Results: The IGF system and IL-6 allowed to predict FGR in approximately 92% of the cases and AGA in 85% of the cases with a low number of errors. IGF-II, IGFBP-2, and IL-6 content in the placental lysates were the most important factors connected with FGR. The condition of being FGR was connected mainly with the IGF-II placental content, and the latter with IL-6 and IGFBP-2 concentrations in placental lysates. Conclusion: These results suggest that further research in humans should focus on these biochemical data. Furthermore, this study offered a critical revision of previous studies. The understanding of this system biology is relevant to the development of future therapeutical interventions possibly aiming at reducing IL-6 and IGFBP-2 concentrations preserving IGF bioactivity in both placenta and fetus.

AB - Background: Changes and relationships of components of the cytokine and IGF systems have been shown in placenta and cord serum of fetal growth restricted (FGR) compared with normal newborns (AGA). This study aimed to analyse a data set of clinical and biochemical data in FGR and AGA newborns to assess if a mathematical model existed and was capable of identifying these two different conditions in order to identify the variables which had a mathematically consistent biological relevance to fetal growth. Methods: Whole villous tissue was collected at birth from FGR (N = 20) and AGA neonates (N = 28). Total RNA was extracted, reverse transcribed and then real-time quantitative (TaqMan) RT-PCR was performed to quantify cDNA for IGF-I, IGF-II, IGFBP-1, IGFBP-2 and IL-6. The corresponding proteins with TNF-α in addition were assayed in placental lysates using specific kits. The data were analysed using Artificial Neural Networks (supervised networks), and principal component analysis and connectivity map. Results: The IGF system and IL-6 allowed to predict FGR in approximately 92% of the cases and AGA in 85% of the cases with a low number of errors. IGF-II, IGFBP-2, and IL-6 content in the placental lysates were the most important factors connected with FGR. The condition of being FGR was connected mainly with the IGF-II placental content, and the latter with IL-6 and IGFBP-2 concentrations in placental lysates. Conclusion: These results suggest that further research in humans should focus on these biochemical data. Furthermore, this study offered a critical revision of previous studies. The understanding of this system biology is relevant to the development of future therapeutical interventions possibly aiming at reducing IL-6 and IGFBP-2 concentrations preserving IGF bioactivity in both placenta and fetus.

UR - http://www.scopus.com/inward/record.url?scp=46349084176&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=46349084176&partnerID=8YFLogxK

U2 - 10.1186/1471-2431-8-24

DO - 10.1186/1471-2431-8-24

M3 - Article

VL - 8

JO - BMC Pediatrics

JF - BMC Pediatrics

SN - 1471-2431

M1 - 24

ER -