Association between γ-Glutamyl Transpeptidase and SARS-CoV-2 Spike Antibody Titers among BNT162b2 Vaccine Recipients.

BACKGROUND: Increased γ-glutamyl transpeptidase (GGT) levels can deplete plasma glutathione, which in turn impairs immune regulation; however, evidence on GGT levels and post-vaccine immunogenicity is lacking. OBJECTIVE: To examine the association between GGT and SARS-CoV-2 spike IgG antibodies. METHODS: Participants were 1479 medical staff (aged 21 to 75 years) who received a SARS-CoV-2 antibody test after their second vaccine and whose GGT levels were measured before the vaccine rollout. Elevated and highly elevated GGT levels were defined as 51-80 and ≥81 U/L, respectively. Multivariable linear regression was used to calculate the means of SARS-CoV-2 spike IgG. RESULTS: In a basic model, both elevated and highly elevated GGT levels were associated with significantly lower antibody titers. The ratio of mean (95% CI) was 0.83 (0.72-0.97) and 0.69 (0.57-0.84) for elevated and highly elevated GGT levels, respectively. However, these associations were largely attenuated after additional adjustment for potential confounders. An inverse association between GGT levels and antibody titers was found in women [0.70 (0.51-0.97)], normal-weight adults [0.71 (0.51-0.98)], and non-drinkers [0.73 (0.46-1.14)] but not in men, overweight adults, and alcohol drinkers. CONCLUSIONS: Circulating GGT concentrations were associated with the humoral immune response after COVID-19 vaccination, but this relationship could be ascribed to confounders such as sex, BMI, and alcohol drinking rather than GGT per se.

Macrophage migration inhibitory factor and stearoyl-CoA desaturase 1: potential prognostic markers for soft tissue sarcomas based on bioinformatics analyses.

The diagnosis and treatment of soft tissue sarcomas (STSs) has been particularly difficult, because STSs are a group of highly heterogeneous tumors in terms of histopathology, histological grade, and primary site. Recent advances in genome technologies have provided an excellent opportunity to determine the complete biological characteristics of neoplastic tissues, resulting in improved diagnosis, treatment selection, and investigation of therapeutic targets. We had previously developed a novel bioinformatics method for marker gene selection and applied this method to gene expression data from STS patients. This previous analysis revealed that the extracted gene combination of macrophage migration inhibitory factor (MIF) and stearoyl-CoA desaturase 1 (SCD1) is an effective diagnostic marker to discriminate between subtypes of STSs with highly different outcomes. In the present study, we hypothesize that the combination of MIF and SCD1 is also a prognostic marker for the overall outcome of STSs. To prove this hypothesis, we first analyzed microarray data from 88 STS patients and their outcomes. Our results show that the survival rates for MIF- and SCD1-positive groups were lower than those for negative groups, and the p values of the log-rank test are 0.0146 and 0.00606, respectively. In addition, survival rates are more significantly different (p = 0.000116) between groups that are double-positive and double-negative for MIF and SCD1. Furthermore, in vitro cell growth inhibition experiments by MIF and SCD1 inhibitors support the hypothesis. These results suggest that the gene set is useful as a prognostic marker associated with tumor progression.

Improved heterologous protein production by a tripeptidyl peptidase gene (AosedD) disruptant of the filamentous fungus Aspergillus oryzae.

Proteolytic degradation is one of the serious bottlenecks limiting the yields of heterologous protein production by Aspergillus oryzae. In this study, we selected a tripeptidyl peptidase gene AosedD (AO090166000084) as a candidate potentially degrading the heterologous protein, and performed localization analysis of the fusion protein AoSedD-EGFP in A. oryzae. As a result, the AoSedD-EGFP was observed in the septa and cell walls as well as in the culture medium, suggesting that AoSedD is a secretory enzyme. An AosedD disruptant was constructed to investigate an effect of AoSedD on the production level of heterologous proteins and protease activity. Both of the total protease and tripeptidyl peptidase activities in the culture medium of the AosedD disruptant were decreased as compared to those of the control strain. The maximum yields of recombinant bovine chymosin (CHY) and human lysozyme (HLY) produced by the AosedD disruptants showed approximately 2.9- and 1.7-fold increases, respectively, as compared to their control strains. These results suggest that AoSedD is one of the major proteases involved in the proteolytic degradation of recombinant proteins in A. oryzae.

Gene expression analysis of soft tissue sarcomas: characterization and reclassification of malignant fibrous histiocytoma.

In soft tissue sarcomas, the diagnosis of malignant fibrous histiocytoma (MFH) has been a very controversial issue, and MFH is now considered to be reclassified into pleomorphic subtypes of other sarcomas. To characterize MFH genetically, we used an oligonucleotide microarray to analyze gene expression in 105 samples from 10 types of soft tissue tumors. Spindle cell and pleomorphic sarcomas, such as dedifferentiated liposarcoma, myxofibrosarcoma, leiomyosarcoma, malignant peripheral nerve sheath tumor (MPNST), fibrosarcoma and MFH, showed similar gene expression patterns compared to other tumors. Samples from those five sarcoma types could be classified into respective clusters based on gene expression by excluding MFH samples. We calculated distances between MFH samples and other five sarcoma types (dedifferentiated liposarcoma, myxofibrosarcoma, leiomyosarcoma, MPNST and fibrosarcoma) based on differentially expressed genes and evaluated similarities. Three of the 21 MFH samples showed marked similarities to one of the five sarcoma types, which were supported by histological findings. Although most of the remaining 18 MFH samples showed little or no histological resemblance to one of the five sarcoma types, 12 of them showed moderate similarities in terms of gene expression. These results explain the heterogeneity of MFH and show that the majority of MFHs could be reclassified into pleomorphic subtypes of other sarcomas. Taken together, gene expression profiling could be a useful tool to unveil the difference in the underlying molecular backgrounds, which leads to a rational taxonomy and diagnosis of a diverse group of soft tissue sarcomas.

The SYT-SSX fusion protein down-regulates the cell proliferation regulator COM1 in t(x;18) synovial sarcoma.

Chromosomal translocations are frequently associated with soft-tissue sarcomas. Fusion proteins generated by such translocations often play critical roles in tumorigenesis. Therefore, it is important to understand the function of the fusion protein to develop therapeutic interventions. The t(X;18)(p11.2;q11.2) translocation found in synovial sarcomas results in a fusion between the SYT gene on chromosome 18 and an SSX gene on the X chromosome. Although SYT-SSX fusion proteins appear to trigger synovial sarcoma development, little is known about the downstream targets of SYT-SSX. We found that the SYT-SSX fusion protein produces a dominant-negative function for SYT, which is a transcriptional coactivator. We then analyzed the gene expression profiles of SYT-SSX1-expressing HeLa cells using oligonucleotide microarrays and found that the SYT-SSX1 fusion protein directly down-regulated the expression of COM1, a regulator of cell proliferation. COM1 was found to be expressed at relatively low levels in synovial sarcoma tissues and cell lines. We then investigated the impact of conditional COM1 expression in the synovial sarcoma cell line. Increased COM1 expression resulted in induced apoptosis and in reduced cell growth and colony formation activity. Our results suggested that restoration of COM1 expression may be of therapeutic benefit in synovial sarcoma.

Cancer diagnosis marker extraction for soft tissue sarcomas based on gene expression profiling data by using projective adaptive resonance theory (PART) filtering method.

BACKGROUND: Recent advances in genome technologies have provided an excellent opportunity to determine the complete biological characteristics of neoplastic tissues, resulting in improved diagnosis and selection of treatment. To accomplish this objective, it is important to establish a sophisticated algorithm that can deal with large quantities of data such as gene expression profiles obtained by DNA microarray analysis. RESULTS: Previously, we developed the projective adaptive resonance theory (PART) filtering method as a gene filtering method. This is one of the clustering methods that can select specific genes for each subtype. In this study, we applied the PART filtering method to analyze microarray data that were obtained from soft tissue sarcoma (STS) patients for the extraction of subtype-specific genes. The performance of the filtering method was evaluated by comparison with other widely used methods, such as signal-to-noise, significance analysis of microarrays, and nearest shrunken centroids. In addition, various combinations of filtering and modeling methods were used to extract essential subtype-specific genes. The combination of the PART filtering method and boosting--the PART-BFCS method--showed the highest accuracy. Seven genes among the 15 genes that are frequently selected by this method--MIF, CYFIP2, HSPCB, TIMP3, LDHA, ABR, and RGS3--are known prognostic marker genes for other tumors. These genes are candidate marker genes for the diagnosis of STS. Correlation analysis was performed to extract marker genes that were not selected by PART-BFCS. Sixteen genes among those extracted are also known prognostic marker genes for other tumors, and they could be candidate marker genes for the diagnosis of STS. CONCLUSION: The procedure that consisted of two steps, such as the PART-BFCS and the correlation analysis, was proposed. The results suggest that novel diagnostic and therapeutic targets for STS can be extracted by a procedure that includes the PART filtering method.