Structural biology of STAT3 and its implications for anticancer therapies development

Transcription factors are proteins able to bind DNA and induce the transcription of specific genes. Consequently, they play a pivotal role in multiple cellular pathways and are frequently over-expressed or dysregulated in cancer. Here, we will focus on a specific “signal transducer and activator of transcription” (STAT3) factor that is involved in several pathologies, including cancer. For long time, the mechanism by which STAT3 exerts its cellular functions has been summarized by a three steps process: (1) Protein phosphorylation by specific kinases, (2) dimerization promoted by phosphorylation, (3) activation of gene expression by the phosphorylated dimer. Consequently, most of the inhibitors reported in literature aimed at blocking phosphorylation and dimerization. However, recent observations reopened the debate and the entire functional mechanism has been revisited stimulating the scientific community to pursue new inhibition strategies. In particular, the dimerization of the unphosphorylated species has been experimentally demonstrated and specific roles proposed also for these dimers. Despite difficulties in the expression and purification of the full length STAT3, structural biology investigations allowed the determination of atomistic structures of STAT3 dimers and several protein domains. Starting from this information, computational methods have been used both to improve the understanding of the STAT3 functional mechanism and to design new inhibitors to be used as anticancer drugs. In this review, we will focus on the contribution of structural biology to understand the roles of STAT3, to design new inhibitors and to suggest new strategies of pharmacological intervention

Oxidation state dependent conformational changes of HMGB1 regulate the formation of the CXCL12/HMGB1 heterocomplex

High-mobility Group Box 1 (HMGB1) is an abundant protein present in all mammalian cells and involved in several processes. During inflammation or tissue damage, HMGB1 is released in the extracellular space and, depending on its redox state, can form a heterocomplex with CXCL12. The heterocomplex acts exclusively via the chemokine receptor CXCR4 enhancing leukocyte recruitment. Here, we used multi-microsecond molecular dynamics (MD) simulations to elucidate the effect of the disulfide bond on the structure and dynamics of HMGB1. The results of the MD simulations show that the presence or lack of the disulfide bond between Cys23 and Cys45 modulates the conformational space explored by HMGB1, making the reduced protein more suitable to form a complex with CXCL12

Identification of structural determinants of Iight chain amyloidosis

ln systemic immunoglobulin light chain amyloidosis (AL) disease, pathogenic immunoglobulin light chains (LCs) form toxic species and amyloid fibrils in target tissues, leading to organ failure and death. Prompt diagnosis is crucial, to avoid permanent organ damage, however, delays are common with consequent high mortality rates, as symptoms usually appear only after strong organ involvement. Predicting the onset of AL is highly challenging as each patient carries a different pathogenic LC, which is generated by genetic rearrangement and by a unique set of somatic mutations acquired during B cell affinity maturation. Due to such disease complexity, the molecular mechanism of AL amyloidosis and the determinants of LCs proteotoxicity, still need to be uncovered, further complicating the disease diagnosis. Consequently, the development of specific prediction tools would be a crucial step to anticipate AL diagnosis and improve patients' prognosis. In this thesis, we aimed at identifying determinants of AL amyloidosis analyzing LC protein sequences, starting from the hypothesis that somatic mutations may be an important driving force in the development of the disease and may be exploited as discriminative factors to classify LCs according to their clinical phenotype . To this aim, we developed LICTOR (Light Chain TOxicity predictoR), a machine leaming approach predicting LC toxicity in AL, starting from LC protein sequences. LICTOR uses somatic mutations, exploited in sequence and structural features, to automatically classify previously unseen LC sequences as either taxie or non-taxie in AL. LICTOR achieves a specificity and a sensitivity of 0.82 and 0.76, respectively, with an AUC of0.87, making it a valuable tool for early AL diagnosis. Taking advantage of LICTOR, we in-silico reverted the toxic phenotype of a LC performing only two mutations. These data were validated in a well-established Caenorhabditis elegans in-vivo model used to evaluate LC toxicity. In conclusion, LICTOR represents an unprecedented method able to accurately predict LC toxicity in AL. Hence, LICTOR may allow a timely identification of high-risk patients, paving the way for early treatment and higher survival rates

Dipping Status, Ambulatory Blood Pressure Control, Cardiovascular Disease, and Kidney Disease Progression: A Multicenter Cohort Study of CKD

Ambulatory blood pressure (ABP) monitoring allows concurrent evaluation of BP control and nocturnal BP dipping status, both related to adverse outcomes. However, very few studies have assessed the prognostic role of combining information on dipping status and achieved ABP in patients with chronic kidney disease (CKD)

Machine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity

Systemic light chain amyloidosis (AL) is caused by the production of toxic light chains and can be fatal, yet effective treatments are often not possible due to delayed diagnosis. Here the authors show that a machine learning platform analyzing light chain somatic mutations allows the prediction of light chain toxicity to serve as a possible tool for early diagnosis of AL

Expert recommendation from the Swiss Amyloidosis Network (SAN) for systemic AL-amyloidosis

Systemic amyloidosis is a heterogeneous group of diseases associated with protein misfolding into insoluble beta-sheet rich structures that deposit extracellularly in different organs, eventually compromising their function. There are more than 30 different proteins, known to be amyloidogenic with “light chain” (AL)-amyloidosis being the most common type, followed by transthyretin (ATTR)-, and amyloid protein A (AA)-amyloidosis. Systemic amyloidosis is a rare disease with an incidence of around 10 patients in 1 million inhabitants. Recently several new therapeutic options have been developed for subgroups of amyloidosis patients, and the introduction of novel therapies for plasma cell myeloma has led to an increase in the therapeutic armamentarium for plasma cell disorders, including AL amyloidosis. Among them, proteasome inhibitors, immunomodulatory agents (-imids), and monoclonal antibodies have been successfully introduced into clinical practice. Still, high-quality data from randomised controlled trials regarding the benefit of these cost-intensive drugs in AL amyloidosis are widely lacking, and due to the rarity of the disease many physicians will not gain routine experience in the management of these frail patients. The diagnosis of AL amyloidosis relies on a close collaboration between clinicians, pathologists, imaging experts, and sometimes geneticists. Diagnosis and treatment options in this complex disorder should be discussed in dedicated multidisciplinary boards. In January 2020, the first meeting of the Swiss Amyloidosis Network took place in Zurich, Switzerland. One aim of this meeting was to establish a consensus guideline regarding the diagnostic work-up and the treatment recommendations for systemic amyloidosis tailored to the Swiss health care system. Forty-five participants from different fields in medicine discussed many aspects of amyloidosis. These are the Swiss Amyloidosis Network recommendations which focus on diagnostic work-up and treatment of AL-amyloidosis