Impact of MICA 3'UTR allelic variability on miRNA binding prediction, a bioinformatic approach.

MicroRNAs (miRNAs) are small non-coding RNAs that participate as powerful genetic regulators. MiRNAs can interfere with cellular processes by interacting with a broad spectrum of target genes under physiological and pathological states, including cancer development and progression. Major histocompatibility complex major histocompatibility complex class I-related chain A (MICA) belongs to a family of proteins that bind the natural-killer group 2, member D (NKG2D) receptor on Natural Killer cells and other cytotoxic lymphocytes. MICA plays a crucial role in the host's innate immune response to several disease settings, including cancer. MICA harbors various single nucleotide polymorphisms (SNPs) located in its 3'-untranslated region (3'UTR), a characteristic that increases the complexity of MICA regulation, favoring its post-transcriptional modulation by miRNAs under physiological and pathological conditions. Here, we conducted an in-depth analysis of MICA 3'UTR sequences according to each MICA allele described to date using NCBI database. We also systematically evaluated interactions between miRNAs and their putative targets on MICA 3'UTR containing SNPs using in silico analysis. Our in silico results showed that MICA SNPs rs9266829, rs 1880, and rs9266825, located in the target sequence of miRNAs hsa-miR-106a-5p, hsa-miR-17-5p, hsa-miR-20a-5p, hsa-miR-20b-5p, hsa-miR-93, hsa-miR-1207.5p, and hsa-miR-711 could modify the binding free energy between -8.62 and -18.14 kcal/mol, which may affect the regulation of MICA expression. We believe that our results may provide a starting point for further exploration of miRNA regulatory effects depending on MICA allelic variability; they may also be a guide to conduct miRNA in silico analysis for other highly polymorphic genes.

Major Histocompatibility Complex Class I-Related Chain A (MICA) Allelic Variants Associate With Susceptibility and Prognosis of Gastric Cancer.

Gastric cancer (GC) is the fifth most prevalent type of cancer worldwide. Gastric tumor cells express MICA protein, a ligand to NKG2D receptor that triggers natural killer (NK) cells effector functions for early tumor elimination. MICA gene is highly polymorphic, thus originating alleles that encode protein variants with a controversial role in cancer. The main goal of this work was to study MICA gene polymorphisms and their relationship with the susceptibility and prognosis of GC. Fifty patients with GC and 50 healthy volunteers were included in this study. MICA alleles were identified using Sanger sequencing methods. The analysis of MICA gene sequence revealed 13 MICA sequences and 5 MICA-short tandem repeats (STR) alleles in the studied cohorts We identified MICA*002 (*A9) as the most frequent allele in both, patients and controls, followed by MICA*008 allele (*A5.1). MICA*009/049 allele was significantly associated with increased risk of GC (OR: 5.11 [95% CI: 1.39-18.74], p = 0.014). The analysis of MICA-STR alleles revealed a higher frequency of MICA*A5 in healthy individuals than GC patients (OR = 0.34 [95% CI: 0.12-0.98], p = 0.046). Survival analysis after gastrectomy showed that patients with MICA*002/002 or MICA*002/004 alleles had significantly higher survival rates than those patients bearing MICA*002/008 (p = 0.014) or MICA*002/009 (MICA*002/049) alleles (p = 0.040). The presence of threonine in the position MICA-181 (MICA*009/049 allele) was more frequent in GC patients than controls (p = 0.023). Molecular analysis of MICA-181 showed that the presence of threonine provides greater mobility to the protein than arginine in the same position (MICA*004), which could explain, at least in part, some immune evasion mechanisms developed by the tumor. In conclusion, our findings suggest that the study of MICA alleles is crucial to search for new therapeutic approaches and may be useful for the evaluation of risk and prognosis of GC and personalized therapy.

Optimization of culture conditions for the expression of three different insoluble proteins in Escherichia coli.

Recombinant protein expression for structural and therapeutic applications requires the use of systems with high expression yields. Escherichia coli is considered the workhorse for this purpose, given its fast growth rate and feasible manipulation. However, bacterial inclusion body formation remains a challenge for further protein purification. We analyzed and optimized the expression conditions for three different proteins: an anti-MICA scFv, MICA, and p19 subunit of IL-23. We used a response surface methodology based on a three-level Box-Behnken design, which included three factors: post-induction temperature, post-induction time and IPTG concentration. Comparing this information with soluble protein data in a principal component analysis revealed that insoluble and soluble proteins have different optimal conditions for post-induction temperature, post-induction time, IPTG concentration and in amino acid sequence features. Finally, we optimized the refolding conditions of the least expressed protein, anti-MICA scFv, using a fast dilution protocol with different additives, obtaining soluble and active scFv for binding assays. These results allowed us to obtain higher yields of proteins expressed in inclusion bodies. Further studies using the system proposed in this study may lead to the identification of optimal environmental factors for a given protein sequence, favoring the acceleration of bioprocess development and structural studies.