Genome-wide inhibition of pro-atherogenic gene expression by multi-STAT targeting compounds as a novel treatment strategy of CVDs

Cardiovascular diseases (CVDs), including atherosclerosis, are globally the leading cause of death. Key factors contributing to onset and progression of atherosclerosis include the pro-inflammatory cytokines Interferon (IFN)a and IFN? and the Pattern Recognition Receptor (PRR) Toll-like receptor 4 (TLR4). Together, they trigger activation of Signal Transducer and Activator of Transcription (STAT)s. Searches for compounds targeting the pTyr-SH2 interaction area of STAT3, yielded many small molecules, including STATTIC and STX-0119. However, many of these inhibitors do not seem STAT3-specific. We hypothesized that multi-STAT-inhibitors that simultaneously block STAT1, STAT2, and STAT3 activity and pro-inflammatory target gene expression may be a promising strategy to treat CVDs. Using comparative in silico docking of multiple STAT-SH2 models on multi-million compound libraries, we identified the novel multi-STAT inhibitor, C01L-F03. This compound targets the SH2 domain of STAT1, STAT2, and STAT3 with the same affinity and simultaneously blocks their activity and expression of multiple STAT-target genes in HMECs in response to IFNa. The same in silico and in vitro multi-STAT inhibiting capacity was shown for STATTIC and STX-0119. Moreover, C01L-F03, STATTIC and STX-0119 were also able to affect genome-wide interactions between IFN? and TLR4 by commonly inhibiting pro-inflammatory and pro-atherogenic gene expression directed by cooperative involvement of STATs with IRFs and/or NF-κB. Moreover, we observed that multi-STAT inhibitors could be used to inhibit IFN?+LPS-induced HMECs migration, leukocyte adhesion to ECs as well as impairment of mesenteric artery contractility. Together, this implicates that application of a multi-STAT inhibitory strategy could provide great promise for the treatment of CVDsThis publication was supported by grants UMO-2015/17/B/NZ2/00967 (HB) and UMO-2015/16/T/NZ2/00055 (MS) from National Science Centre Poland. This work was supported by the KNOW RNA Research Centre in Poznan (No. 01/KNOW2/2014) and in part by PL-Grid Infrastructure (MS

Genome-Wide Inhibition of Pro-atherogenic Gene Expression by Multi-STAT Targeting Compounds as a Novel Treatment Strategy of CVDs

Cardiovascular diseases (CVDs), including atherosclerosis, are globally the leading cause of death. Key factors contributing to onset and progression of atherosclerosis include the pro-inflammatory cytokines Interferon (IFN)α and IFNγ and the Pattern Recognition Receptor (PRR) Toll-like receptor 4 (TLR4). Together, they trigger activation of Signal Transducer and Activator of Transcription (STAT)s. Searches for compounds targeting the pTyr-SH2 interaction area of STAT3, yielded many small molecules, including STATTIC and STX-0119. However, many of these inhibitors do not seem STAT3-specific. We hypothesized that multi-STAT-inhibitors that simultaneously block STAT1, STAT2, and STAT3 activity and pro-inflammatory target gene expression may be a promising strategy to treat CVDs. Using comparative in silico docking of multiple STAT-SH2 models on multi-million compound libraries, we identified the novel multi-STAT inhibitor, C01L_F03. This compound targets the SH2 domain of STAT1, STAT2, and STAT3 with the same affinity and simultaneously blocks their activity and expression of multiple STAT-target genes in HMECs in response to IFNα. The same in silico and in vitro multi-STAT inhibiting capacity was shown for STATTIC and STX-0119. Moreover, C01L_F03, STATTIC and STX-0119 were also able to affect genome-wide interactions between IFNγ and TLR4 by commonly inhibiting pro-inflammatory and pro-atherogenic gene expression directed by cooperative involvement of STATs with IRFs and/or NF-κB. Moreover, we observed that multi-STAT inhibitors could be used to inhibit IFNγ+LPS-induced HMECs migration, leukocyte adhesion to ECs as well as impairment of mesenteric artery contractility. Together, this implicates that application of a multi-STAT inhibitory strategy could provide great promise for the treatment of CVDs

Temporal information processing and its relation to executive functions in elderly individuals

Normal aging triggers deterioration in cognitive functions. Evidence has shown that these age-related changes concern also executive functions (EF) as well as temporal information processing (TIP) in a millisecond range. A considerable amount of literature data has indicated that each of these two functions sets a frame for our mental activity and may be considered in terms of embodied cognition due to advanced age. The present study addresses the question whether in elderly subjects the efficiency of TIP is related to individual differences in EF. The study involved 53 normal healthy participants aged from 65 to 78. In these subjects TIP was assessed by sequencing abilities measured with temporal-order threshold (TOT). It is defined as the minimum time gap separating two auditory stimuli presented in rapid succession which is necessary for a subject to report correctly their temporal order, thus the relation ‘before-after’. The EF were assessed with regard to the efficiency of the executive planning measured with the Tower of London-Drexel University (TOLDX) which has become a well-known EF task. Using Spearman’s rank correlations we observed two main results. Firstly, the indices of the TOLDX indicated a coherent construct reflecting the effectiveness of executive planning in the elderly. Initiation time seemed dissociated from these coherent indices, which suggested a specific strategy of mental planning in the elderly based on on-line planning rather than on preplanning. Secondly, TOT was significantly correlated with the indices of TOLDX. Although some of these correlations were modified by subject’s age, the correlation between TOT and the main index of TOLDX (‘Total Move Score’) was rather age resistant. These results suggest that normal aging may be characterized by an overlapping of deteriorated TIP and deteriorated EF

Identification of STAT1 and STAT3 specific inhibitors using comparative virtual screening and docking validation.

Signal transducers and activators of transcription (STATs) facilitate action of cytokines, growth factors and pathogens. STAT activation is mediated by a highly conserved SH2 domain, which interacts with phosphotyrosine motifs for specific STAT-receptor contacts and STAT dimerization. The active dimers induce gene transcription in the nucleus by binding to a specific DNA-response element in the promoter of target genes. Abnormal activation of STAT signaling pathways is implicated in many human diseases, like cancer, inflammation and auto-immunity. Searches for STAT-targeting compounds, exploring the phosphotyrosine (pTyr)-SH2 interaction site, yielded many small molecules for STAT3 but sparsely for other STATs. However, many of these inhibitors seem not STAT3-specific, thereby questioning the present modeling and selection strategies of SH2 domain-based STAT inhibitors. We generated new 3D structure models for all human (h)STATs and developed a comparative in silico docking strategy to obtain further insight into STAT-SH2 cross-binding specificity of a selection of previously identified STAT3 inhibitors. Indeed, by primarily targeting the highly conserved pTyr-SH2 binding pocket the majority of these compounds exhibited similar binding affinity and tendency scores for all STATs. By comparative screening of a natural product library we provided initial proof for the possibility to identify STAT1 as well as STAT3-specific inhibitors, introducing the 'STAT-comparative binding affinity value' and 'ligand binding pose variation' as selection criteria. In silico screening of a multi-million clean leads (CL) compound library for binding of all STATs, likewise identified potential specific inhibitors for STAT1 and STAT3 after docking validation. Based on comparative virtual screening and docking validation, we developed a novel STAT inhibitor screening tool that allows identification of specific STAT1 and STAT3 inhibitory compounds. This could increase our understanding of the functional role of these STATs in different diseases and benefit the clinical need for more drugable STAT inhibitors with high specificity, potency and excellent bioavailability

Binding conformations of top-scored compounds from clean leads library in the SH2 domain of hSTAT1 and hSTAT3.

<p>(A) Binding pose variation of the top-scored hSTAT1-specific inhibitor in SH2 domain of hSTAT1 and hSTAT3. (B) Binding pose variation of the top-scored hSTAT3-specific inhibitor in SH2 domain of hSTAT1 and hSTAT3. The binding pose variations are shown in line representation, colored in yellow and green. Results were obtained using Surflex-Dock 2.6 program.</p

STAT3-CBAVs of STAT3-specific inhibitors.

<p>Graph presents comparative binding affinity values of a selection of STAT3-specific inhibitors docked to models of all hSTAT monomers.</p

Structural models and phylogenetic comparison of hSTAT monomers (1, 2, 3, 4, 5A, 5B and 6) with their specific pTyr-linkers.

<p><b>(A)</b> Phylogenetic distribution of hSTATs in form of a simplified phylogenetic tree. <b>(B)</b> Models of the monomers are shown in the cartoon representation with pTyr-peptides in the stick representation. Specific domains are positioned as follows: N-domain on the top-left, coiled-coiled domain on the bottom-center, C-domain on the top-right and SH2 domain on the top-center, to facilitate visual analysis of phosphotyrosine (pTyr)-linker and the SH2 interactions. Monomers are colored according to the predicted local deviation from the real structure (the predicted error of the model), as calculated by MetaMQAP. Blue indicates low predicted deviation of Cα atoms down to 0Å, red indicates unreliable regions with deviation > 5Å, green to orange indicate intermediate values. pTyr-peptides are colored in violet, while pTyr residue is colored in pink. <b>(C)</b> Models of hSTAT dimers with the linker of monomer I in the SH2 domain of monomer II. pTyr-peptides are presented in stick representation, pY+0—pTyr binding pocket, pY-X—hydrophobic side-pocket. SH2 domains are in the surface representation, colored according to the distribution of the electrostatic surface potential, calculated with APBS. Blue indicates positively charged regions, red indicates negatively charged regions.</p

Binding conformations of top-scored compounds from natural products library in the SH2 domain of hSTAT1 and hSTAT3.

<p><b>(A)</b> Binding pose variation of the top-scored hSTAT1-specific inhibitor in SH2 domain of hSTAT1 and hSTAT3. <b>(B)</b> Binding pose variation of the top-scored hSTAT3-specific inhibitor in SH2 domain of hSTAT1 and hSTAT3. The binding pose variations are shown in line representation, colored in blue and violet. Results were obtained using Surflex-Dock 2.6 program.</p