Paternal sepsis induces alterations of the sperm methylome and dampens offspring immune responses—an animal study

Background: Sepsis represents the utmost severe consequence of infection, involving a dysregulated and self-damaging immune response of the host. While different environmental exposures like chronic stress or malnutrition have been well described to reprogram the germline and subsequently offspring attributes, the intergenerational impact of sepsis as a tremendous immunological stressor has not been examined yet. Methods: Polymicrobial sepsis in 12-week-old male C57BL/6 mice was induced by cecal ligation and puncture (CLP), followed by a mating of the male survivors (or appropriate sham control animals) 6 weeks later with healthy females. Alveolar macrophages of offspring animals were isolated and stimulated with either LPS or Zymosan, and supernatant levels of TNF-α were quantified by ELISA. Furthermore, systemic cytokine response to intraperitoneally injected LPS was assessed after 24 h. Also, morphology, motility, and global DNA methylation of the sepsis survivors’ sperm was examined. Results: Comparative reduced reduction bisulfite sequencing (RRBS) of sperm revealed changes of DNA methylation (n = 381), most pronounced in the intergenic genome as well as within introns of developmentally relevant genes. Offspring of sepsis fathers exhibited a slight decrease in body weight, with a more pronounced weight difference in male animals (CLP vs. sham). Male descendants of sepsis fathers, but not female descendants, exhibited lower plasma concentrations of IL-6, TNF-alpha, and IL-10 24 h after injection of LPS. In line, only alveolar macrophages of male descendants of sepsis fathers produced less TNF-alpha upon Zymosan stimulation compared to sham descendants, while LPS responses kept unchanged. Conclusion: We can prove that male—but surprisingly not female—descendants of post-sepsis fathers show a dampened systemic as well as pulmonary immune response. Based on this observation of an immune hypo-responsivity, we propose that male descendants of sepsis fathers are at risk to develop fungal and bacterial infections and might benefit from therapeutic immune modulation

The immunosuppressive face of sepsis early on intensive care unit-A large-scale microarray meta-analysis.

BACKGROUND:Sepsis is defined as a life-threatening condition, resulting from a dysregulated and harmful response of the hosts' immune system to infection. Apart from this, the (over-)compensating mechanisms counterbalancing the inflammatory response have been proven to render the host susceptible to further infections and increase delayed mortality. Our study aimed to unravel the heterogeneity of immune response in early sepsis and to explain the biology behind it. METHODS:A systematic search of public repositories yielded 949 microarray samples from patients with sepsis of different infectious origin and early after clinical manifestation. These were merged into a meta-expression set, and after applying sequential conservative bioinformatics filtering, an in-deep analysis of transcriptional heterogeneity, as well as a comparison to samples of healthy controls was performed. RESULTS:We can identify two distinct clusters of patients (cluster 1: 655 subjects, cluster 2: 294 subjects) according to their global blood transcriptome. While both clusters exhibit only moderate differences in direct comparison, a comparison of both clusters individually to healthy controls yielded strong expression changes of genes involved in immune responses. Both comparisons found similar regulated genes, with a stronger dysregulation occurring in the larger patient cluster and implicating a loss of monocyte and T cell function, co-occurring with an activation of neutrophil granulocytes. CONCLUSION:We propose a consistent-but in its extent varying-presence of immunosuppression, occurring as early in sepsis as its clinical manifestation and irrespective of the infectious origin. While certain cell types possess contradictory activation states, our finding underlines the urgent need for an early host-directed therapy of sepsis side-by-side with antibiotics

Sevoflurane depletes macrophages from the melanoma microenvironment.

BACKGROUND:With more than 18 million annual new cases, cancer belongs to the major challenges of modern healthcare. Surgical resection of solid tumours under general anaesthesia is the prime therapy. Different aspects of anaesthesia are under discussion to independently influence the long-term outcome of cancer patients. Most recently, the commonly used volatile anaesthetics like sevoflurane have entered the spotlight, as retrospective studies suggest a detrimental outcome in certain cancer aetiologies with sparse mechanistic understanding. Our objective was to investigate this concept in a murine melanoma model, herein comparing the consequence of inhalative and injection anesthesia on tumour composition and growth. METHODS:We used a murine model of malignant melanoma in male, adult C57BL/6 mice (n = 92), induced by the subcutaneous injection of B16-F10 cells. We either exposed the melanoma cells to sevoflurane before implantation or subjected the animals to single or double anaesthesia with either volatile or injection drugs. After a maximum follow-up of 4 weeks, leucocytes within the tumour microenvironment (TME) were comprehensively analysed by flow cytometry with focus on tumor-associated macrophages (TAM). RESULTS:We found that exposure of melanoma cells to sevoflurane before implantation induced long-lasting transcriptome changes and aggravated tumour growth, without extensive changes of the TME. Contrastingly, both a single and double anaesthesia with sevoflurane led to a significant reduction of TAMs (injection vs. sevoflurane: 2,0 vs. 0.3% and 1.2 vs. 0.6%, respectively), whilst increasing PD-L1 expression on the remaining cells (mean fluorescent intensity injection vs. sevoflurane: 3,804 vs. 7,143 and 9,090 vs. 32,228, respectively). No changes in tumour growth were observed in these groups. CONCLUSION:In sharp contrast to the detrimental impact of sevoflurane on patients' outcome reported in retrospective clinical studies, we propose here that sevoflurane might actually exert a beneficial effect by decreasing TAMs within the TME, rendering the tumour again susceptible for cytotoxic T cells and immunotherapies. Further research is warranted to delineate, how these results translate into the clinic

Differential expression analysis between “Cluster 1” and individuals from the healthy control group.

<p>(A) Heatmap of processed expression values for 368 dysregulated genes showing absolute logFC ≥ 1 (adj. <i>p</i>-value < 0.05). (B) Volcano plot of differentially expressed genes (solid black color indicates absolute logFC ≥ 1.0, adj. <i>p</i>-value < 0.05; numbers indicate up- (orange) or down-regulated (blue) genes). Results of GO-term analysis for enriched biological processes separately for up-regulated (top panel) and down-regulated genes (bottom panel) above defined threshold.</p

Differential expression analysis between “Cluster 2” and individuals from the healthy control group.

<p>(A) Volcano plot of differentially expressed genes (solid black color indicates absolute logFC ≥ 1.0, adj. <i>p</i>-value < 0.05; numbers indicate up- (orange) or down-regulated (blue) genes) and results of GO-term analysis for enriched biological processes separately for down-regulated (left panel) and up-regulated genes (right panel) above defined threshold (B).</p

Hierarchical cluster analysis of microarray expression data provided for the 5,000 most-variable gene symbols in the full dataset of 1084 subjects.

<p>Dendrogram and color track (origin) illustrate the sample re-arrangement of the clusters produced: New cluster 1 (blue) consists of 839 subjects, while 245 individuals are attributed to cluster 2 (green). In comparison to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198555#pone.0198555.g002" target="_blank">Fig 2A</a>, new cluster 1 identity is unchanged in most cases (99.4% retention rate), while adding new subjects from original cluster 2. The 245 individuals assigned to new cluster 2 cover both original cluster 2 samples as well as the vast majority of healthy controls (96.3% assignment rate).</p

Flowchart of microarray data selection.

<p>Data series collected from GEO and ArrayExpress were subjected to a selection process resulting in 14 data series from 12 studies. Samples of patients with sepsis and healthy controls were further assessed to meet various standards for analysis.</p

Overview of included studies with indication of available metadata and number of samples (both retrievable and published).

<p>BS: Bloodstream, CAP: community-acquired pneumonia, FP: fecal peritonitis.</p

Networks of differentially expressed genes between defined clusters and healthy controls.

<p>(A) Top network of differentially regulated genes between patients of “Cluster 1” and healthy individuals. (B) Manually selected network consisting of differentially regulated immune-related genes. Nodes showing an orange color implicate up-regulation for the conditions in contrast, while blue elements represent down-regulation. (C) and D) Overlays of the respective expression data for “Cluster 2” subjects in comparison to individuals from control group. (E) Heatmap showing the results of data deconvolution to identify cell origin of signals. Orange color represents an up-regulated “cell abundance”, representing more signals deriving from this cell type compared to healthy controls, blue color vice versa. Only informative cell types were visualized. Mega: megakaryocyte; Ery: erythroid; HSC: hematopoetic stem cell.</p

Pathophysiological model of sepsis genomic response.

<p>The early blindspot of sepsis (blue box) spans the highly individual timeframe from infection to clinical manifestation of symptoms. The quantitative and qualitative kinetic of response depends on both host and pathogen attributes. Our results originating from samples of patients early after ICU admission for sepsis prove the presence of (at least) two molecular signatures of sepsis (Cluster 1 and Cluster 2), with Cluster 1 implicating a higher degree of dysregulation towards immunosuppression than Cluster 2. Within the clusters, different cell types are likely to have contradictory or even ambivalent activation states, e.g. monocytes (Mo) with impaired cytokine production but with maintained migratory function. Neut: neutrophilic granulocytes; NK: natural killer cells; T: T cells.</p