Placental determinants of fetal growth: identification of key factors in the insulin-like growth factor and cytokine systems using artificial neural networks.
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-alpha 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.


CONCLUSIONS
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.
Department of Pediatrics, University of Parma, 43100 Parma, Italy. mariaelisabeth.street@unipr.it
Maria E
ME
Street
Enzo
E
Grossi
Cecilia
C
Volta
Elena
E
Faleschini
Sergio
S
Bernasconi
D003198:Computer Simulation; D016207:Cytokines / Q000378:metabolism*; D005260:Female; D047109:Fetal Development / Q000502:physiology*; D006801:Humans; D007231:Infant, Newborn; D008423:Maternal Age; D008954:Models, Biological; D016571:Neural Networks, Computer; D010920:Placenta / Q000378:metabolism*; D011247:Pregnancy; D025941:Protein Interaction Mapping; D012016:Reference Values; D013002:Somatomedins / Q000378:metabolism*
D016207:Cytokines; D013002:Somatomedins