Researchers present a Bayesian inference method for heterogeneous systems that integrates prior information with noisy experimental data.

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.

Infection with severe acute respiratory syndrome coronavirus 2 associates with diverse symptoms, which can persist for months. While antiviral antibodies are protective, those targeting interferons and other immune factors are associated with adverse coronavirus disease 2019 (COVID-19) outcomes. Here we discovered that antibodies against specific chemokines were omnipresent post-COVID-19, were associated with favorable disease outcome and negatively correlated with the development of long COVID at 1 yr post-infection. Chemokine antibodies were also present in HIV-1 infection and autoimmune disorders, but they targeted different chemokines compared with COVID-19. Monoclonal antibodies derived from COVID-19 convalescents that bound to the chemokine N-loop impaired cell migration. Given the role of chemokines in orchestrating immune cell trafficking, naturally arising chemokine antibodies may modulate the inflammatory response and thus bear therapeutic potential.

Emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants diminishes the efficacy of vaccines and antiviral monoclonal antibodies. Continued development of immunotherapies and vaccine immunogens resilient to viral evolution is therefore necessary. Using coldspot-guided antibody discovery, a screening approach that focuses on portions of the virus spike glycoprotein that are both functionally relevant and averse to change, we identified human neutralizing antibodies to highly conserved viral epitopes. Antibody fp.006 binds the fusion peptide and cross-reacts against coronaviruses of the four genera, including the nine human coronaviruses, through recognition of a conserved motif that includes the S2′ site of proteolytic cleavage. Antibody hr2.016 targets the stem helix and neutralizes SARS-CoV-2 variants. Antibody sd1.040 binds to subdomain 1, synergizes with antibody rbd.042 for neutralization, and, similar to fp.006 and hr2.016, protects mice expressing human angiotensin-converting enzyme 2 against infection when present as a bispecific antibody. Thus, coldspot-guided antibody discovery reveals donor-derived neutralizing antibodies that are cross-reactive with Orthocoronavirinae, including SARS-CoV-2 variants.

Impaired cellular cholesterol efflux is a key factor in the progression of renal, cardiovascular, and autoimmune diseases. Here we describe a class of 5-arylnicotinamide compounds, identified through phenotypic drug discovery, that upregulate ABCA1-dependent cholesterol efflux by targeting Oxysterol Binding Protein Like 7 (OSBPL7). OSBPL7 was identified as the molecular target of these compounds through a chemical biology approach, employing a photoactivatable 5-arylnicotinamide derivative in a cellular cross-linking/immunoprecipitation assay. Further evaluation of two compounds (Cpd A and Cpd G) showed that they induced ABCA1 and cholesterol efflux from podocytes in vitro and normalized proteinuria and prevented renal function decline in mouse models of proteinuric kidney disease: Adriamycin-induced nephropathy and Alport Syndrome. In conclusion, we show that small molecule drugs targeting OSBPL7 reveal an alternative mechanism to upregulate ABCA1, and may represent a promising new therapeutic strategy for the treatment of renal diseases and other disorders of cellular cholesterol homeostasis.

In systemic light chain amyloidosis (AL), pathogenic monoclonal immunoglobulin light chains (LC) form toxic aggregates and amyloid fibrils in target organs. Prompt diagnosis is crucial to avoid permanent organ damage, but delayed diagnosis is common because 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 achieves 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 achieves a prediction accuracy of 83%. Furthermore, we are 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.