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Università della Svizzera italiana

Label-free biosensor detection of endocrine disrupting compounds using engineered estrogen receptors

La Spina, Rita ; Ferrero, Valentina E. V. ; Aiello, Venera ; Pedotti, Mattia ; Varani, Luca ; Lettieri, Teresa ; Calzolai, Luigi ; Haasnoot, Willem ; Colpo, Pascal

In: Biosensors, 2018, vol. 8, no. 1, p. 1-15

Endocrine Disrupting Compounds (EDCs) are chemical substances shown to interfere with endogenous hormones affecting the endocrine, immune and nervous systems of mammals. EDCs are the causative agents of diseases including reproductive disorders and cancers. This highlights the urgency to develop fast and sensitive methods to detect EDCs, which are detrimental even at very low concentrations....

Università della Svizzera italiana

Rationally modified estrogen receptor protein as a bio-recognition element for the detection of EDC pollutants : strategies and opportunities

Pedotti, Mattia ; Ferrero, Valentina Elisabetta Viviana ; Lettieri, Teresa ; Colpo, Pascal ; Follonier, Stephane ; Calzolai, Luigi ; Varani, Luca

In: International journal of environmental research and public health, 2015, vol. 12, no. 3, p. 2612-2621

The estrogen receptor protein (ER) can bind a vast number of organic pollutants widely spread in the environment and collectively known as Endocrine Disrupting Chemicals, EDCs. Its broad selectivity makes it an ideal bio-recognition element for the detection of EDCs. Here we describe the strategy and rationale for the design of ER based biosensors and assays that generate a signal in the...

Università della Svizzera italiana

Rational modification of estrogen receptor by combination of computational and experimental analysis

Ferrero, Valentina Elisabetta Viviana ; Pedotti, Mattia ; Chiadò, Alessandro ; Simonelli, Luca ; Calzolai, Luigi ; Varani, Luca ; Lettieri, Teresa

In: Plos one, 2014, vol. 9, no. 7, p. e102658

In this manuscript, we modulate the binding properties of estrogen receptor protein by rationally modifying the amino acid composition of its ligand binding domain. By combining sequence alignment and structural analysis of known estrogen receptor- ligand complexes with computational analysis, we were able to predict estrogen receptor mutants with altered binding properties. These predictions...