Association of HLA-G 3'UTR polymorphisms and haplotypes with severe sepsis in a Brazilian population

Use of machine learning-based integration to develop a monocyte differentiation-related signature for improving prognosis in patients with sepsis

BACKGROUND

Although significant advances have been made in intensive care medicine and antibacterial treatment, sepsis is still a common disease with high mortality. The condition of sepsis patients changes rapidly, and each hour of delay in the administration of appropriate antibiotic treatment can lead to a 4-7% increase in fatality. Therefore, early diagnosis and intervention may help improve the prognosis of patients with sepsis.

METHODS

We obtained single-cell sequencing data from 12 patients. This included 14,622 cells from four patients with bacterial infectious sepsis and eight patients with sepsis admitted to the ICU for other various reasons. Monocyte differentiation trajectories were analyzed using the "monocle" software, and differentiation-related genes were identified. Based on the expression of differentiation-related genes, 99 machine-learning combinations of prognostic signatures were obtained, and risk scores were calculated for all patients. The "scissor" software was used to associate high-risk and low-risk patients with individual cells. The "cellchat" software was used to demonstrate the regulatory relationships between high-risk and low-risk cells in a cellular communication network. The diagnostic value and prognostic predictive value of Enah/Vasp-like (EVL) were determined. Clinical validation of the results was performed with 40 samples. The "CBNplot" software based on Bayesian network inference was used to construct EVL regulatory networks.

RESULTS

We systematically analyzed three cell states during monocyte differentiation. The differential analysis identified 166 monocyte differentiation-related genes. Among the 99 machine-learning combinations of prognostic signatures constructed, the Lasso + CoxBoost signature with 17 genes showed the best prognostic prediction performance. The highest percentage of high-risk cells was found in state one. Cell communication analysis demonstrated regulatory networks between high-risk and low-risk cell subpopulations and other immune cells. We then determined the diagnostic and prognostic value of EVL stabilization in multiple external datasets. Experiments with clinical samples demonstrated the accuracy of this analysis. Finally, Bayesian network inference revealed potential network mechanisms of EVL regulation.

CONCLUSIONS

Monocyte differentiation-related prognostic signatures based on the Lasso + CoxBoost combination were able to accurately predict the prognostic status of patients with sepsis. In addition, low EVL expression was associated with poor prognosis in sepsis.

HLA Class I Molecules as Immune Checkpoints for NK Cell Alloreactivity and Anti-Viral Immunity in Kidney Transplantation

Natural killer (NK) cells are innate lymphocytes that can kill diseased- or virally-infected cells, mediate antibody dependent cytotoxicity and produce type I immune-associated cytokines upon activation. NK cells also contribute to the allo-immune response upon kidney transplantation either by promoting allograft rejection through lysis of cells of the transplanted organ or by promoting alloreactive T cells. In addition, they protect against viral infections upon transplantation which may be especially relevant in patients receiving high dose immune suppression. NK cell activation is tightly regulated through the integrated balance of signaling via inhibitory- and activating receptors. HLA class I molecules are critical regulators of NK cell activation through the interaction with inhibitory- as well as activating NK cell receptors, hence, HLA molecules act as critical immune checkpoints for NK cells. In the current review, we evaluate how NK cell alloreactivity and anti-viral immunity are regulated by NK cell receptors belonging to the KIR family and interacting with classical HLA class I molecules, or by NKG2A/C and LILRB1/KIR2DL4 engaging non-classical HLA-E or -G. In addition, we provide an overview of the methods to determine genetic variation in these receptors and their HLA ligands.

HLA-G and single nucleotide polymorphism (SNP) associations with cancer in African populations: Implications in personal medicine

The immune system plays an important role in protecting the body against malignancy. During cancer immunoediting, the immune system can recognize and keep checking the tumor cells by down-expression of some self-molecules or by increasing expression of some novel molecules. However, the microenvironment created in the course of cancer development hampers the immune ability to recognize and destroy the transforming cells. Human Leukocyte Antigen G (HLA-G) is emerging as immune checkpoint molecule produced more by cancer cells to weaken the immune response against them. HLA-G is a non-classical HLA class I molecule which is normally expressed in immune privileged tissues as a soluble or membrane-bound protein. HLA-G locus is highly polymorphic in the non-coding 3′ untranslated region (UTR) and in the 5′ upstream regulatory region (5′ URR). HLA-G expression is controlled by polymorphisms located in these regions, and several association studies between these polymorphic sites and disease predisposition, response to therapy, and/or HLA-G protein expression have been reported. Various polymorphisms are demonstrated to modulate its expression and this is increasingly finding more significance in cancer biology. This review focuses on the relevance of the HLA-G gene and its polymorphisms in cancer development. We highlight population genetics of HLA-G as evidence to espouse the need and importance of exploring potential utility of HLA-G in cancer diagnosis, prognosis and immunotherapy in the currently understudied African population.

HLA-G: Function, polymorphisms and pathology

HLA-G immune modulatory genes and molecules are presently being studied by a widespread number of research groups. In the present study, we do not aim to be exhaustive since the number of manuscripts published every year is overwhelming. Instead, our aim is pointing out facts about HLA-G function, polymorphism and pathology that have been confirmed by several different researchers, together with exposing aspects that may have been overlooked or not sufficiently remarked in this productive field of study. On the other hand, we question whether performing mainly studies on HLA-G and disease associations is going to give a clear answer in the future, since 40 years of study of classical HLA molecules association with disease has still given no definite answer on this issue.