Garofalo, M. | Search bioRxiv

bioRxiv2019

Systematic development of peptide inhibitors targeting the CXCL12/HMGB1 interaction

During inflammatory reactions, the production and release of chemotactic factors guide the recruitment of selective leukocyte subpopulations. HMGB1 and the chemokine CXCL12, both released in the microenvironment, form a heterocomplex, which exclusively acts on the chemokine receptor CXCR4, enhancing cell migration and, in some pathological conditions such as Rheumatoid Arthritis, exacerbating the immune response. An excessive cell influx at the inflammatory site can be diminished by disrupting the heterocomplex. Here, we report the computationally driven identification of a novel peptide (HBP08), which binds HMGB1 with the highest affinity reported so far (Kd of 0.8 {+/-} 0.1 M), able to selectively inhibit the activity of the CXCL12/HMGB1 heterocomplex. The identification of this peptide represents an important step towards the development of innovative pharmacological tools for the treatment of severe chronic inflammatory conditions characterized by an uncontrolled immune response.

biochemistry↗

bioRxiv2019

Machine learning predicts immunoglobulin light chain toxicity through somatic mutations

In systemic light chain amyloidosis (AL), pathogenic monoclonal immunoglobulin light chains (LCs) form toxic aggregates and amyloid fibrils in target organs. Prompt diagnosis is crucial to avoid permanent organ damage. However, delays in diagnosis are common, with a consequent poor patients prognosis, as symptoms usually appear only after strong organ involvement. Here, we present LICTOR, a machine learning approach predicting LC toxicity in AL, based on the distribution of somatic mutations acquired during clonal selection. LICTOR achieved a specificity and a sensitivity of 0.82 and 0.76, respectively, with an area under the receiver operating characteristic curve (AUC) of 0.87. Tested on an independent set of 12 LCs sequences with known clinical phenotypes, LICTOR achieved a prediction accuracy of 83%. Furthermore, we were able to abolish the toxic phenotype of an LC by in silico reverting two germline-specific somatic mutations identified by LICTOR and by experimentally assessing the loss of in vivo toxicity in a Caenorhabditis elegans model. Therefore, LICTOR represents a promising strategy for AL diagnosis and reducing high mortality rates in AL.

bioinformatics↗