ILP-2 modeling and virtual screening of an FDA-approved library:a possible anticancer therapy

Response Surface Methodology to Optimize the Expression Efficiency of Recombinant Reteplase

Background

Over expression of Reteplase enzyme has already been studies in the periplasmic space of Escherichia coli (E. coli). However, the role different factors in its expresssin rate remained to be elucidated.

Objectives

Optical cell density (OD), IPTG concentration, and expression time are highly effective in the protein expression rates. Therefore, we aimed to determine the optimum levels of these factors for reteplase expression using response surface methodology (RSM).

Materials and Methods

The pET21b plasmid was used to sub-clone the designed reteplase gene. Then, the gene was transformed into E. coli BL21 strain. Induction of expression was done by IPTG and analyzed by the SDS page. experiments were designed using the RMS, while the effects of different conditions were evaluated using the Real time-PCR.

Results

Sequence optimization removed all undesirable sequences of the designed gene. Transformation into E. coli BL21 was confirmed with an 1152 bp band on the agarose gel. A 39 kDa expression band on the SDS gel confirmed the gene expression. Performing 20 RSM-designed experiments, the optimum levels for IPTG concentration and OD were determined as 0.34mM and 5.6, respectively. Moreover, the optimum level of expression time was demonstrated to be 11.91 hours. The accuracy of the regression model for reteplase overexpression was confirmed by an F-value equal to 25.31 and a meager probability value [(Prob > F) < 0.0001]. The real-time-PCR results indicated that the performed calculations were highly accurate.

Conclusion

The obtained results indicate that IPTG concentration, OD, and expression time are significantly involved in the augmentation of recombinant reteplase expression. To the best of our knowledge, this is the first study to assess the combined effect of these factors on reteplase expression. Further RSM-based experiments would bring about new insights regarding the best conditions for reteplase expression.

Impact of SNPs, off-targets, and passive permeability on efficacy of BCL6 degrading drugs assigned by virtual screening and 3D-QSAR approach

B-cell lymphoma 6 (BCL6) regulates various genes and is reported to be overexpressed in lymphomas and other malignancies. Thus, BCL6 inhibition or its tagging for degradation would be an amenable therapeutic approach. A library of 2500 approved drugs was employed to find BCL6 inhibitory molecules via virtual screening. Moreover, the 3D core structure of 170 BCL6 inhibitors was used to build a 3D QSAR model and predict the biological activity. The SNP database was analyzed to study the impact on the destabilization of BCL6/drug interactions. Structural similarity search and molecular docking analyses were used to assess the interaction between possible off-targets and BCL6 inhibitors. The tendency of drugs for passive membrane permeability was also analyzed. Lifitegrast (DB11611) had favorable binding properties and biological activity compared to the BI-3802. Missense SNPs were located at the essential interaction sites of the BCL6. Structural similarity search resulted in five BTB-domain containing off-target proteins. BI-3802 and Lifitegrast had similar chemical behavior and binding properties against off-target candidates. More interestingly, the binding affinity of BI-3802 (against off-targets) was higher than Lifitegrast. Energetically, Lifitegrast was less favorable for passive membrane permeability. The interaction between BCL6 and BI-3802 is more prone to SNP-derived variations. On the other hand, higher nonspecific binding of BI-3802 to off-target proteins could bring about higher undesirable properties. It should also be noted that energetically less desirable passive membrane translocation of Lifitegrast would demand drug delivery vehicles. However, further empirical evaluation of Lifitegrast would unveil its true potential.

ILP-2: A New Bane and Therapeutic Target for Human Cancers

Inhibitor of apoptosis protein-related-like protein-2 (ILP-2), also known as BIRC-8, is a member of the inhibitor of apoptosis protein (IAPs) family, which mainly encodes the negative regulator of apoptosis. It is selectively overexpressed in a variety of human tumors and can help tumor cells evade apoptosis, promote tumor cell growth, increase tumor cell aggressiveness, and appears to be involved in tumor cell resistance to chemotherapeutic drugs. Several studies have shown that downregulation of ILP-2 expression increases apoptosis, inhibits metastasis, reduces cell growth potential, and sensitizes tumor cells to chemotherapeutic drugs. In addition, ILP-2 inhibits apoptosis in a unique manner; it does not directly inhibit the activity of caspases but induces apoptosis by cooperating with other apoptosis-related proteins. Here, we review the current understanding of the various roles of ILP-2 in the apoptotic cascade and explore the use of interfering ILP-2, and the combination of related anti-tumor agents, as a novel strategy for cancer therapy.

Overexpression of HOXD8 inhibits the proliferation, migration and invasion of breast cancer cells by downregulating ILP2 expression

Breast cancer is one of the most common malignant tumors in women. Although a number of homeobox (HOX) genes are known to serve an important role in breast cancer, the role of HOXD8 in breast cancer remains unclear. The aim of the present study was to investigate the role of HOXD8 in the physiological behaviors of breast cancer cells. The Gene Expression Profiling Interactive Analysis database was used to analyze the expression of HOXD8 in patients with breast cancer and in healthy subjects. Western blotting was performed to determine the expression levels of HOXD8 in several breast cancer cell lines; subsequently, HOXD8 expression was knocked down and overexpressed in MCF-7 cells. Cell Counting Kit-8, colony formation, wound healing and Transwell assays were used to evaluate the effects of HOXD8 on breast cancer cell viability, proliferation, migration and invasion, respectively. Chromatin immunoprecipitation and dual-luciferase reporter assays were conducted to identify the binding sites between HOXD8 and inhibitor of apoptosis-like protein-2 (ILP2). In addition, ILP2 expression levels were knocked down in MCF-7 cells. The results demonstrated that the expression levels of HOXD8 were significantly downregulated in breast cancer tissues and cell lines, and that the overexpression of HOXD8 inhibited the proliferation, invasion and migration of cancer cells. HOXD8 was shown to bind to the ILP2 promoter to regulate the expression of ILP2. Furthermore, ILP2 knockdown reversed the effects of HOXD8 knockdown on breast cancer cell proliferation, invasion and migration. In conclusion, the findings of the present study suggested that HOXD8 may inhibit the proliferation, migration and invasion of breast cancer cells by downregulating ILP2 expression.

Liothyronine could block the programmed death-ligand 1 (PDL1) activity: an e-Pharmacophore modeling and virtual screening study

PURPOSE

The interaction between PD-L1 on tumor cells and the programmed death 1 (PD1) on immune cells helps them to escape the immune system elimination. Therefore, developing therapeutic agents to block this interaction has garnered a lot of attention as a therapeutic approach. In the present study, we have tried to screen for an inhibitory compound to inhibit the interaction between the PD1/PD-L1 molecules.

METHODS

In this regard, the structure of PD-L1 and its inhibitor were prepared and employed to generate an e-Pharmacophore model. A library of approved compounds was prepared and toxicity analysis using Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) predictor was performed. The built e-Pharmacophore model was validated and used to screen the prepared compound library. Ligand docking and binding energy calculation were performed on the screened ligands.

RESULTS

A seven-feature e-Pharmacophore model was generated using the PD-L1 complex. All of the compounds within the library passed the ADMET criteria. Performing the virtual screening, only 79 compounds have survived the criteria to fit four pharmacophoric features. The compound with the highest binding energy was the liothyronine (T3).

CONCLUSION

The ability of T3 in PD1/PD-L1 checkpoint blockade along with its potential in T4 reduction could be a desirable combination in cancer treatment. These abilities of T3 could be used to restore the ability of the immune system to eliminate tumor cells.

In silico design and in vitro development of a highly accurate test to detect Brucella species

Background/aim

Conventional methods of detecting Brucella spp. suffer from technical and biological complications. Besides, newly characterized species of the genus Brucella could be neglected by previously designed polymerase chain reaction (PCR) tests. Therefore, a more accurate PCR-based test seems to be imminently needed.

Materials and methods

Blood samples were collected from 39 patients diagnosed with brucellosis and 25 healthy controls. Multiple sequence alignments (MSA) were performed on 500 Omp2-related protein and gene sequences. Thereafter, specific primers were designed and synthesized for the regions with highest conservancy. The collected samples were assessed by PCR test. To overcome the cross-reactivity issue, PCR thermal program was optimized regarding annealing time and temperature.

Results

The MSA results indicated that the N terminus region of the Omp2 protein (DNA 5’ end) is associated with highest conservancy. Primers with highest specificity were designed and synthesized. A two-step PCR reaction was successfully designed and optimized. The desirable bands were observed in clinical samples with high accuracy.

Conclusion

It should be pointed out that using a precisely designed primer pair would bring about early infection detection, more success to detect all natural variants and higher cost-to-efficacy ratio in comparison to other detection methods.

A qualitatively validated mathematical-computational model of the immune response to the yellow fever vaccine

Background

Although a safe and effective yellow fever vaccine was developed more than 80 years ago, several issues regarding its use remain unclear. For example, what is the minimum dose that can provide immunity against the disease? A useful tool that can help researchers answer this and other related questions is a computational simulator that implements a mathematical model describing the human immune response to vaccination against yellow fever.

Methods

This work uses a system of ten ordinary differential equations to represent a few important populations in the response process generated by the body after vaccination. The main populations include viruses, APCs, CD8+ T cells, short-lived and long-lived plasma cells, B cells and antibodies.

Results

In order to qualitatively validate our model, four experiments were carried out, and their computational results were compared to experimental data obtained from the literature. The four experiments were: a) simulation of a scenario in which an individual was vaccinated against yellow fever for the first time; b) simulation of a booster dose ten years after the first dose; c) simulation of the immune response to the yellow fever vaccine in individuals with different levels of naïve CD8+ T cells; and d) simulation of the immune response to distinct doses of the yellow fever vaccine.

Conclusions

This work shows that the simulator was able to qualitatively reproduce some of the experimental results reported in the literature, such as the amount of antibodies and viremia throughout time, as well as to reproduce other behaviors of the immune response reported in the literature, such as those that occur after a booster dose of the vaccine.

A novel recombinant anti-epidermal growth factor receptor peptide vaccine capable of active immunization and reduction of tumor volume in a mouse model

Over-expression of epidermal growth factor receptor (EGFR) has been reported in a number of human malignancies. Strong expression of this receptor has been associated with poor survival in many such patients. Active immunizations that elicit antibodies of the desired type could be an appealing alternative to conventional passive immunization. In this regard, a novel recombinant peptide vaccine capable of prophylactic and therapeutic effects was constructed. A novel fusion recombinant peptide base vaccine consisting of L2 domain of murine extra-cellular domain-EGFR and EGFR mimotope (EM-L2) was constructed and its prophylactic and therapeutic effects in a Lewis lung carcinoma mouse (C57/BL6) model evaluated. Constructed recombinant peptide vaccine is capable of reacting with anti-EGFR antibodies. Immunization of mice with EM-L2 peptide resulted in antibody production against EM-L2. The constructed recombinant peptide vaccine reduced tumor growth and increased the survival rate. Designing effective peptide vaccines could be an encouraging strategy in contemporary cancer immunotherapy. Investigating the efficacy of such cancer immunotherapy approaches may open exciting possibilities concerning hyperimmunization, leading to more promising effects on tumor regression and proliferation.

Mathematical modeling based on ordinary differential equations: A promising approach to vaccinology

New contributions that aim to accelerate the development or to improve the efficacy and safety of vaccines arise from many different areas of research and technology. One of these areas is computational science, which traditionally participates in the initial steps, such as the pre-screening of active substances that have the potential to become a vaccine antigen. In this work, we present another promising way to use computational science in vaccinology: mathematical and computational models of important cell and protein dynamics of the immune system. A system of Ordinary Differential Equations represents different immune system populations, such as B cells and T cells, antigen presenting cells and antibodies. In this way, it is possible to simulate, in silico, the immune response to vaccines under development or under study. Distinct scenarios can be simulated by varying parameters of the mathematical model. As a proof of concept, we developed a model of the immune response to vaccination against the yellow fever. Our simulations have shown consistent results when compared with experimental data available in the literature. The model is generic enough to represent the action of other diseases or vaccines in the human immune system, such as dengue and Zika virus.