Interferon regulatory factors (IRFs) are a family of homologous proteins that regulate the transcription of interferons (IFNs) and IFN-induced gene expression. As such they are important modulating proteins in the Toll-like receptor (TLR) and IFN signaling pathways, which are vital elements of the innate immune system. IRFs have a multi-domain structure, with the N-terminal part acting as a DNA binding domain (DBD) that recognizes a DNA-binding motif similar to the IFN-stimulated response element (ISRE). The C-terminal part contains the IRF-association domain (IAD), with which they can self-associate, bind to IRF family members or interact with other transcription factors. This complex formation is crucial for DNA binding and the commencing of target-gene expression. IRFs bind DNA and exert their activating potential as homo or heterodimers with other IRFs. Moreover, they can form complexes (e.g., with Signal transducers and activators of transcription, STATs) and collaborate with other co-acting transcription factors such as Nuclear factor-κB (NF-κB) and PU.1. In time, more of these IRF co-activating mechanisms have been discovered, which may play a key role in the pathogenesis of many diseases, such as acute and chronic inflammation, autoimmune diseases, and cancer. Detailed knowledge of IRFs structure and activating mechanisms predisposes IRFs as potential targets for inhibition in therapeutic strategies connected to numerous immune system-originated diseases. Until now only indirect IRF modulation has been studied in terms of antiviral response regulation and cancer treatment, using mainly antisense oligonucleotides and siRNA knockdown strategies. However, none of these approaches so far entered clinical trials. Moreover, no direct IRF-inhibitory strategies have been reported. In this review, we summarize current knowledge of the different IRF-mediated transcriptional regulatory mechanisms and how they reflect the diverse functions of IRFs in homeostasis and in TLR and IFN signaling. Moreover, we present IRFs as promising inhibitory targets and propose a novel direct IRF-modulating strategy employing a pipeline approach that combines comparative in silico docking to the IRF-DBD with in vitro validation of IRF inhibition. We hypothesize that our methodology will enable the efficient identification of IRF-specific and pan-IRF inhibitors that can be used for the treatment of IRF-dependent disorders and malignancies.

Androgen insensitivity syndrome (AIS), manifesting incomplete virilization in 46,XY individuals, is caused mostly by androgen receptor (AR) gene mutations. Therefore, a search for AR mutations is a routine approach in AIS diagnosis. However, some AIS patients lack AR mutations, which complicates the diagnosis. Here, we describe a patient suffering from partial androgen insensitivity syndrome (PAIS) and lacking AR mutations. The whole exome sequencing of the patient and his family members identified a heterozygous FKBP4 gene mutation, c.956T>C (p.Leu319Pro), inherited from the mother. The gene encodes FKBP prolyl isomerase 4, a positive regulator of the AR signaling pathway. This is the first report describing a FKBP4 gene mutation in association with a human disorder of sexual development (DSD). Importantly, the dysfunction of a homologous gene was previously reported in mice, resulting in a phenotype corresponding to PAIS. Moreover, the Leu319Pro amino acid substitution occurred in a highly conserved position of the FKBP4 region, responsible for interaction with other proteins that are crucial for the AR functional heterocomplex formation and therefore the substitution is predicted to cause the disease. We proposed the FKBP4 gene as a candidate AIS gene and suggest screening that gene for the molecular diagnosis of AIS patients lacking AR gene mutations.

Background The functions of RNA molecules are mainly determined by their secondary structures. These functions can also be predicted using bioinformatic tools that enable the alignment of multiple RNAs to determine functional domains and/or classify RNA molecules into RNA families. However, the existing multiple RNA alignment tools, which use structural information, are slow in aligning long molecules and/or a large number of molecules. Therefore, a more rapid tool for multiple RNA alignment may improve the classification of known RNAs and help to reveal the functions of newly discovered RNAs. Results Here, we introduce an extremely fast Python-based tool called RNAlign2D. It converts RNA sequences to pseudo-amino acid sequences, which incorporate structural information, and uses a customizable scoring matrix to align these RNA molecules via the multiple protein sequence alignment tool MUSCLE. Conclusions RNAlign2D produces accurate RNA alignments in a very short time. The pseudo-amino acid substitution matrix approach utilized in RNAlign2D is applicable for virtually all protein aligners.

Motivation The function of RNA molecules is mainly determined by their secondary structure. Addressing that issue requires creation of appropriate bioinformatic tools that enable alignment of multiple RNA molecules to determine functional domains and/or classify RNA families. The existing tools for RNA multiple alignment that use structural information are relatively slow. Therefore, providing a rapid tool for multiple structural alignment may improve classification of the known RNAs and reveal the function of the newly discovered ones. Results Here, we developed an extremely fast Python based RNAlign2D tool. It converts RNA sequence and structure to pseudo-amino acid sequence and uses customizable pseudo-amino acid substitution matrix to align RNA secondary structures and sequences using MUSCLE. It is suitable for RNAs containing modified nucleosides and/or pseudoknots. Our approach is compatible with virtually all protein aligners. Availability and implementation RNAlign2D is available from https://github.com/tomaszwozniakihg/rnalign2d. It has been tested on Linux and MacOSX. Supplementary information Supplementary data are available at Bioinformatics online.