In-silico insights on the prognostic potential of immune cell infiltration patterns in the breast lobular epithelium.

Scattered inflammatory cells are commonly observed in mammary gland tissue, most likely in response to normal cell turnover by proliferation and apoptosis, or as part of immunosurveillance. In contrast, lymphocytic lobulitis (LLO) is a recurrent inflammation pattern, characterized by lymphoid cells infiltrating lobular structures, that has been associated with increased familial breast cancer risk and immune responses to clinically manifest cancer. The mechanisms and pathogenic implications related to the inflammatory microenvironment in breast tissue are still poorly understood. Currently, the definition of inflammation is mainly descriptive, not allowing a clear distinction of LLO from physiological immunological responses and its role in oncogenesis remains unclear. To gain insights into the prognostic potential of inflammation, we developed an agent-based model of immune and epithelial cell interactions in breast lobular epithelium. Physiological parameters were calibrated from breast tissue samples of women who underwent reduction mammoplasty due to orthopedic or cosmetic reasons. The model allowed to investigate the impact of menstrual cycle length and hormone status on inflammatory responses to cell turnover in the breast tissue. Our findings suggested that the immunological context, defined by the immune cell density, functional orientation and spatial distribution, contains prognostic information previously not captured by conventional diagnostic approaches.

Functional classification of membrane transporters and channels based on filtered TM/non-TM amino acid composition.

Membrane transporters catalyze the transport of small solute molecules across biological barriers such as lipid bilayer membranes. As the experimental annotation of which proteins transport which substrates is incomplete it is highly desirable to develop computational methods that can assist in the classification and substrate annotation of putative membrane transport proteins. Here, we determined the similarity of membrane transporter sequences annotated in the Transport Classification Database (Saier et al., Nucleic Acids Res 2006, 34, D181-D186) and Arabidopsis thaliana membrane transporters annotated in the database Aramemnon (Schwacke et al., Plant Physiol 2003, 131, 16-26). The similarity measure was based on the amino acid composition either considering the full sequences or separately in the transmembrane (TM) and external parts of the sequences. We considered four different substrate sets and three different subfamilies and tried to classify the given proteins into these classes. Family or substrate prediction based on the simple amino acid frequency had an average accuracy of 76%. The differentiation between TM and non-TM regions led to an improved accuracy of 80% on average.