GIMAP6 regulates autophagy, immune competence, and inflammation in mice and humans

The goat pan-genome reveals patterns of gene loss during domestication

BACKGROUND

Unveiling genetic diversity features and understanding the genetic mechanisms of diverse goat phenotypes are pivotal in facilitating the preservation and utilization of these genetic resources. However, the total genetic diversity within a species can't be captured by the reference genome of a single individual. The pan-genome is a collection of all the DNA sequences that occur in a species, and it is expected to capture the total genomic diversity of the specific species.

RESULTS

We constructed a goat pan-genome using map-to-pan assemble based on 813 individuals, including 723 domestic goats and 90 samples from their wild relatives, which presented a broad regional and global representation. In total, 146 Mb sequences and 974 genes were identified as absent from the reference genome (ARS1.2; GCF_001704415.2). We identified 3,190 novel single nucleotide polymorphisms (SNPs) using the pan-genome analysis. These novel SNPs could properly reveal the population structure of domestic goats and their wild relatives. Presence/absence variation (PAV) analysis revealed gene loss and intense negative selection during domestication and improvement.

CONCLUSIONS

Our research highlights the importance of the goat pan-genome in capturing the missing genetic variations. It reveals the changes in genomic architecture during goat domestication and improvement, such as gene loss. This improves our understanding of the evolutionary and breeding history of goats.

Spatial multiomics of arterial regions from cardiac allograft vasculopathy rejected grafts reveal novel insights into the pathogenesis of chronic antibody-mediated rejection

Cardiac allograft vasculopathy (CAV) causes late graft failure and mortality after heart transplantation. Donor-specific antibodies (DSAs) lead to chronic endothelial cell injury, inflammation, and arterial intimal thickening. In this study, GeoMx digital spatial profiling was used to analyze arterial areas of interest (AOIs) from CAV+DSA+ rejected cardiac allografts (N = 3; 22 AOIs total). AOIs were categorized based on CAV neointimal thickening and underwent whole transcriptome and protein profiling. By comparing our transcriptomic data with that of healthy control vessels of rapid autopsy myocardial tissue, we pinpointed specific pathways and transcripts indicative of heightened inflammatory profiles in CAV lesions. Moreover, we identified protein and transcriptomic signatures distinguishing CAV lesions exhibiting low and high neointimal lesions. AOIs with low neointima showed increased markers for activated inflammatory infiltrates, endothelial cell activation transcripts, and gene modules involved in metalloproteinase activation and TP53 regulation of caspases. Inflammatory and apoptotic proteins correlated with inflammatory modules in low neointima AOIs. High neointima AOIs exhibited elevated TGFβ-regulated transcripts and modules enriched for platelet activation/aggregation. Proteins associated with growth factors/survival correlated with modules enriched for proliferation/repair in high neointima AOIs. Our findings reveal novel insight into immunological mechanisms mediating CAV pathogenesis.

Identification and validation of GIMAP family genes as immune-related prognostic biomarkers in lung adenocarcinoma

Background

The GIMAP family genes play a key role in immune function. Increasing evidence suggests that GIMAP genes were implicated in the tumorigenesis of lung adenocarcinoma (LUAD). This study aimed to investigate the clinical significance of GIMAP family genes in LUAD.

Methods

In this study, we explored the expression, mutation, prognostic value of GIMAP family genes and the correlation with immune microenvironment in LUAD. We further investigated the relationship between GIMAP family genes expression and immunotherapy response in GEO LUAD and melanoma cohorts.

Results

Among the GIMAP family genes, the expression levels of GIMAP1, GIMAP2, GIMAP4, GIMAP5, GIMAP6, GIMAP7, and GIMAP8 were significantly lower in LUAD tumor tissues than normal tissues. Most GIMAP genes were closely related to age, tumor grade and T stage, but not significantly related to sex, N stage and M stage. In the overall population, patients with high expression of GIMAP family genes had a significant longer overall survival (OS). GO and KEGG enrichment analysis showed that GIMAP family genes were highly enriched in immune-related biological process. The expression of GIMAP family genes was positively correlated with immune cell infiltration and immune checkpoint molecules. Furthermore, high expression of GIMAP family genes were correlated with therapeutic response to immunotherapy in LUAD and melanoma patients.

Conclusion

In this study, we identified that GIMAP family genes were significantly associated with immune cell infiltration and immune checkpoint molecules. They potentially play a critical role in anti-tumor immunity and serve as immunotherapy biomarkers.

Investigation and verification of GIMAP6 as a robust biomarker for prognosis and tumor immunity in lung adenocarcinoma

BACKGROUND AND AIM

According to previous reports, GTPase of immunity-associated protein 6 (GIMAP6) is essential for autophagy. However, it is unclear how GIMAP6 affects the development and tumor immunity of lung adenocarcinoma (LUAD).

METHODS

In the present study, the role of GIMAP6 in vivo and in vitro was examined using reverse transcription-quantitative PCR, western blotting, and Cell Counting Kit-8, colony formation and Transwell assays. Datasets from The Cancer Genome Atlas and Genotype-Tissue Expression databases were thoroughly analyzed using R software. A nomogram was created using GIMAP6 and prognostic characteristics. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes and Gene Set Enrichment Analysis were applied to explore the potential mechanism of GIMAP6 in lung cancer. The link between GIMAP6 and the immunological landscape was studied using single-cell RNA sequencing datasets from Tumor Immune Estimation Resource (TIMER) 2.0 and Tumor Immune Single-cell Hub.

RESULTS

Patients with high GIMAP6 expression had improved overall and disease-specific survival compared with those patients with low GIMAP6 expression. According to the receiver operating characteristic and calibration curve, the nomogram based on T stage, N stage and GIMAP6 had predictive value for prognosis. According to functional enrichment analysis, GIMAP6 was primarily involved in T-cell receptor signaling pathway, chemokine signaling pathway, cytokine and cytokine receptor interaction. GIMAP6 was shown to be favorably linked with the infiltration of immune cells and immune-related molecules, including cytotoxic T-lymphocyte associated protein 4, programmed death-ligand 1, and T cell immunoreceptor with Ig and ITIM domains, by single-cell sequencing and TIMER2.0 analysis. The role of GIMAP6 in lung cancer cell proliferation, invasion, migration and immunity was experimentally verified.

CONCLUSION

These findings confirmed that GIMAP6 was an effective prognostic molecule that was involved in the regulation of the immune microenvironment of LUAD, and may become a predictor for the efficacy of immunotherapy.

Screening and identifying a novel M-MDSCs-related gene signature for predicting prognostic risk and immunotherapeutic responses in patients with lung adenocarcinoma

Background: Lung adenocarcinoma (LUAD) shows intratumoral heterogeneity, a highly complex phenomenon that known to be a challenge during cancer therapy. Considering the key role of monocytic myeloid-derived suppressor cells (M-MDSCs) in the tumor microenvironment (TME), we aimed to build a prognostic risk model using M-MDSCs-related genes. Methods: M-MDSCs-related genes were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Utilized univariate survival analysis and random forest algorithm to screen candidate genes. A least absolute shrinkage and selection operator (LASSO) Cox regression analysis was selected to build the risk model. Patients were scored and classified into high- and low-risk groups based on the median risk scores. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis along with R packages "estimate" and "ssGSEA" were performed to reveal the mechanism of risk difference. Prognostic biomarkers and tumor mutation burden (TMB) were combined to predict the prognosis. Nomogram was carried out to predict the survival probability of patients in 1, 3, and 5 years. Results: 8 genes (VPREB3, TPBG, LRFN4, CD83, GIMAP6, PRMT8, WASF1, and F12) were identified as prognostic biomarkers. The GEO validation dataset demonstrated the risk model had good generalization effect. Significantly enrichment level of cell cycle-related pathway and lower content of CD8⁺ T cells infiltration in the high-risk group when compared to low-risk group. Morever, the patients were from the intersection of high-TMB and low-risk groups showed the best prognosis. The nomogram demonstrated good consistency with practical outcomes in predicting the survival rate over 1, 3, and 5 years. Conclusion: The risk model demonstrate good prognostic predictive ability. The patients from the intersection of low-risk and high-TMB groups are not only more sensitive response to but also more likely to benefit from immune-checkpoint-inhibitors (ICIs) treatment.