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Record W2085260657 · doi:10.1093/infdis/jiu210

Torque Teno Virus Load as a Biomarker of Immunosuppression? New Hopes and Insights

2014· letter· en· W2085260657 on OpenAlexaff
Kathie Béland, Michael Dore-Nguyen, Marie-Josée Gagné, Nathalie Patey, Julie Brassard, Fernando Álvarez, Ugur Halac

Bibliographic record

VenueThe Journal of Infectious Diseases · 2014
Typeletter
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Virus Infections Studies
Canadian institutionsAgriculture and Agri-Food CanadaUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsTorque teno virusImmunosuppressionVirologyBiomarkerBiologyVirusViral loadMedicineImmunologyComputational biologyPolymerase chain reactionGenetics

Abstract

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To the Editor—We thank Focosi and colleagues for their comments [1] concerning our article [2]. The results presented in their letter are of major interest and the conclusions are attractive. We want to emphasize that, similar to studies in adults, our results confirmed that a clear correlation between torque teno virus (TTV) load and the intensity of immunosuppression was shown in pediatric liver transplant recipients. This was related to the number of immunosuppressive drugs. As it has been clearly established that TTV is ubiquitous but has a variable prevalence [3], we also believe that TTV infection should be evaluated mainly on the basis of the viral load, and not on the prevalence, in healthy individuals as well as in patients with chronic conditions. Focosi et al [1] suggested that a continuous relationship can be established between intensity of iatrogenic maintenance immunosuppression and increase in TTV viremia in hepatitis B virus (HBV)/hepatitis C virus (HCV)–related patients with cirrhosis undergoing orthotopic liver transplantation (OLT). Similar data were reported in patients with onco-hematological conditions [4], in HCV-infected patients [5], and after OLT [6]. However, other factors must be taken into consideration by which viral load can decrease in transplant patients, even when administered immunosuppression is high. As physicians involved in the follow-up of immunosuppressed patients (solid organ or tissue transplant, cancer, inflammatory bowel diseases, etc), we need to develop reliable and inexpensive markers of immune system function. The results of Focosi et al [1] contribute to this. However, we need to further elucidate some remaining questions: What is the influence of hepatotropic viral agents such as HBV, HCV, or hepatitis E virus (HEV) on TTV load? We found that TTV load decreases when a hepatotropic virus, such as HEV, infects immunosuppressed patients [2]. Previously, we also reported a high prevalence of chronic HEV infection in such patients [7]. Correlation between recent HEV infection and lower TTV load could result from 2 phenomena: (1) competitive use of the replication machinery, as TTV and HEV can both replicate in hepatocytes [8]; or (2) HEV-triggered liver inflammation that nonspecifically impairs TTV through hepatocyte death and increased interferon secretion. Concordant with this hypothesis, TTV is susceptible to the effects of interferon [9]. Altogether, these results suggest that, in contrast to the cooperation between TTV and some viral agents such as Epstein-Barr virus [10], TTV and HEV do not act as cofactors, and that liver inflammatory activity could impair TTV replication in the liver. The relationship with HBV, HCV, and other hepatotropic viruses needs to be further analyzed in larger studies. Viral load can decrease in patients with rejection or chronic hepatitis, as the liver is one of the sites of TTV replication. We found a lower viral load in patients with chronic hepatitis after OLT than in those with a normal histology [2]. This finding in pediatric liver transplant recipients needs further evaluation in patients receiving other organs. If we are to use TTV viremia as “biomarker,” what “range” levels should be considered normal? The difficulties of standardization of such a tool may also be highly dependent on the specific condition of the patient. An interesting population is the bone marrow recipient, since these patients are transitorily immunosuppressed following chemotherapy or in the event of treatment of graft-vs-host disease. We should also take into consideration that patients after kidney transplant have significantly different immunosuppressive regimens compared with immunocompromised patients with Crohn's disease treated with biologics or immunomodulators. Therefore, individualized management of this “biomarker,” based on each specific patient, seems necessary, making standardization difficult. It has been reported that the prevalence of TTV is geographically variable. Even if we suppose that prevalence is not an indicator of pathogenicity, could such a large variability in prevalence influence the behavior of the infection at an individual level? When TTV prevalence is almost 100% of the general population in a particular area, could the chronic exposure to this virus be a source of repetitive pathogenicity for immunosuppressed patients? Although this is purely speculative, it should be studied with large comparative and prospective studies. Altogether, we are encouraged to see that major observations are being published concerning the role of TTV viremia in patients with chronic immunosuppressive conditions. The letter by Focosi et al [1] is clinically relevant and represents an important contribution to the long-term management of immunocompromised patients. Potential conflicts of interest. All authors: No reported conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0040.005
Open science0.0020.001
Research integrity0.0160.017
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.016
GPT teacher head0.234
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

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Citations22
Published2014
Admission routes1
Has abstractno

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