Editorial: Microbiome in an immunocompromised host- a jungle of challenges or a glacier of hidden opportunities?
Bibliographic record
Abstract
It has been over 20 years since Pirofski and Casadevall distinguished the terms “infection,” “colonization,” and “commensalism” by establishing the “damage response framework” where microbial pathogenesis is the outcome of an interaction between host and microorganism. Accordingly, the resulting amount of damage to the host, as a function of time, can be used to define and characterize the outcome of infection as the respective states of commensalism, colonization, latency or disease(1). This concept appears especially relevant in the era of multi-omic analyses and the increasing number of patients diagnosed with an inborn error of immunity (IEI) or affected by secondary, including iatrogenic, immunodeficiency. It is widely accepted that the microbiome plays a critical role in the maintenance of immune homeostasis, but it can also be an important driver of immune dysregulation, predisposing individuals to infections and metabolic disorders. Although relatively unexplored, the role of the microbiome in the context of impaired immunity has been garnering significant interest. Our objective is to highlight the potential implications of host-microbiome interaction profiles in the context of immune deficiency and multi-omic technology. We take this a step further by describing the practical diagnostic and therapeutic potential provided by using a metagenomic next-generation sequencing (mNGS) approach in the immunocompromised host. We therefore present a diverse and synergistic collection of studies on the composition and function of the microbiome from different sites in immunocompromised hosts comprising three original papers, two reviews and one case study. To compare the impact of acquired versus inherited immunodeficiency on the composition and function of the microbiome, we included metagenomic studies in patients with IEIs and secondary immunodeficiencies. Gastrointestinal inflammatory disorders are frequent in patients with IEIs, and this is especially true in common variable immunodeficiency (CVID), one of the most prevalent IEIs. Previous studies have highlighted the important role of the gut microbiome in CVID. This is likely impacted by the intestinal microbiota, which, in turn, is influenced by mucosal alterations induced by the underlying immune defect itself. Although fecal microbiota transplantation (FMT) has been considered as a therapeutic option in CVID, there have been concerns over increased intestinal barrier permeability and microbial translocation in these patients. (2,3) So far, microbiome-modifying interventions have been reported only in animal models (4). Among the studies included in our collection, Napiórkowska-Baran et al. present, to our knowledge, the first reported case of successful FMT in a patient with CVID and secondary immunodeficiency post-splenectomy. As suggested by the authors, intestinal dysbiosis underlies gastrointestinal disorders associated with CVID that are likely resistant to immunoglobulin supplementation and can be successfully treated with FMT. (Napiórkowska-Baran et al. 2024)We next explore the use of mNGS in clinical diagnostics and highlight the possible advantages of promoting a hypothesis-free microbiome-focused approach in the clinical setting. This is especially true when considering bacterial pathogenicity as a continuum depending on host immunity, as outlined in the “damage response framework”. In our collection, Ye et al. present the outcomes of a metagenomic diagnostic investigation in kidney allograft recipients. The authors indicate the simultaneous presence of identical microorganisms in different anatomical sites while recognizing complex microbial profiles in specific locations. They also highlight molds such as Mucor and Aspergillus spp. as clinically relevant infectious agents albeit less prevalent in the context of kidney transplantation. (Ye at al. 2024) Correspondingly, other contributors review the potential application of metagenomics in patients with hematological diseases at high risk of febrile neutropenia (Wang et al.2024) and in patients with hematological conditions both with and without neutropenia (Chen et al.2024). As suggested by Wang et al., identifying the causal pathogen during febrile neutropenia remains challenging when using traditional microbiological diagnostic methods, while mNGS enables the proper identification of the spectrum of microorganisms coexisting in one patient as it related to their clinical characteristics and outcomes. Similar to Ye et al., the authors drew attention to lesser-considered but clinically significant fungi and viruses. Similarly, Chen et al. present the outcomes of a metagenomic diagnostic investigation in the setting of hematological disorders and their complications. In line with the other two studies, the authors indicate the usefulness of mNGS in this specific clinical context and emphasize its capacity to identify various microorganisms within one anatomical site. Previous studies have largely investigated the use of metagenomics as a diagnostic tool in particularly challenging clinical conditions such as central nervous system diseases where it can be used to identify a spectrum of viruses and bacteria in cerebrospinal fluid (5,6). The diagnostic application of metagenomics in immunodeficiency has also been investigated prospectively, which demonstrated the potential for a hypothesis-free approach in identifying microbial communities in primary immune deficiencies, solid organ transplantation and hematological disorders (7). In particular, its capacity to identify the causal pathogen has been found to be more significant in both primary and secondary immune deficiencies compared to in immunocompetent hosts(7). Nevertheless, our intention is not to assess mNGS as a diagnostic method itself as it definitely needs further standardization, appropriate controls for environmental contaminants, and improved scalability. Rather, we seek to point out the unique opportunity to identify the broader spectrum of microorganisms in previously considered culture-negative conditions while providing additional insight into their metabolic function and pathogenicity. This places mNGS at the cutting edge between the scientific and applied understanding of the role of microbiome. As highlighted in the aforementioned contributed studies, this approach allowed for tailored treatment interventions.Given that both primary and secondary immune deficiencies can impact the microbiota at different body sites, which, in the context of solid organ transplantation, can further impact end-organ function, we included a review from Elsayed et al. focusing on the urine microbiome (urobiome) in the context of impaired immune responses. (Elsayed et al. 2024) The authors present findings linking the urobiome and different immunologic conditions affecting the transplanted kidney, such as allograft fibrosis. Of particular interest are their discussions on the urine virome in kidney transplantation. The authors also discuss the implications of the urine metabolome in immune dysregulation and autoimmunity. Indeed, the metabolites produced by the constituents of the microbiota are key mediators of local and systemic immune responses in immune deficiency. However, research targeting the complexity of the microbiome-metabolome axis has been relatively limited in immunocompromised hosts, particularly in the context of inborn errors of immunity. Nevertheless, a specific group of short-chain fatty acids (SCFAs) has been of particular interest due to its potential immunoregulatory effects. The review from Jardou et al., which focused on solid organ transplantation, provides insight into the connection between SCFAs, post-transplant dysbiosis, and immunosuppression. The authors indicate the impact of decreased SCFAs production under immunosuppressive therapy on low-grade inflammation and, therefore, address the issue of non-specific allograft injury in relation to microbial shifts. (Jardou et al. 2024) In summary, at the beginning of this journey, we posed an open question about the opportunities and challenges hidden within the microbiome of immunocompromised hosts. We have only scratched the tip of the iceberg while the boundary between traditionally perceived “pathogenicity” and “commensalism” remains blurred. This is even more evident in the context of an impaired immune system, where the damage response framework remains highly relevant. (8) However, the ever evolving and increasingly available multi-omics tools are uncovering opportunities to elucidate the impact of immune deficiency on microbial composition and function identifying biomarkers with diagnostic and prognostic value, as well as novel therapeutic targets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.016 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".