Hepatitis C virus in peripheral blood mononuclear cells of individuals with isolated anti‐hepatitis C virus antibody reactivity†
Bibliographic record
Abstract
We were intrigued by the recent work by Bernardin and colleagues,1 who concluded that peripheral blood mononuclear cells (PBMCs) are unlikely to be a long-lived reservoir of hepatitis C virus (HCV) in individuals reactive for anti-HCV antibodies and negative for HCV RNA by transcription-mediated amplification assay (TMA) (sensitivity of 30 HCV RNA copies/mL; GenProbe Inc., San Diego, CA). Because this finding is in contrast with those previously reported by our and other groups,2-6 we want to briefly outline the most important methodological differences which might have led to this opposing conclusion. In the study by Bernardin et al.,1 HCV RNA was evaluated in cryopreserved, unstimulated PBMCs by so-called cell-associated TMA and reverse transcription–nested polymerase chain reaction (RT-nPCR) with sensitivities arguably between 2-50 and 15-150 virus copies/5 × 106 PBMCs, respectively. In our investigations, RT-nPCR followed by nucleic acid hybridization (NAH) analysis of amplicons (that is, Southern blotting), in order to validate specificity and enhance sensitivity of the signal detection by ≈10-fold over RT-nPCR, was applied.2, 7 This RT-nPCR/NAH method consistently detects <10 HCV RNA copies/mL or <5 copies/106 PBMCs, knowing that 1 × 106 PMBCs gives on average 1 μg RNA. Nonetheless, a more fundamental discrepancy concerns the amount of RNA used for analysis. In the work cited, RNA extracted with the RNeasy minikit (Qiagen, Valencia, CA) from an equivalent of, at most, 1.6 × 105 PBMCs (assuming, albeit unlikely, complete cell recovery after thawing and washing) was used for RT-nPCR. In contrast, we analyzed RNA from 1 × 106 to 2 × 106 of PBMCs that were unmanipulated after cryopreservation and extracted with Trizol (Invitrogen Life Technologies, Burlington, Canada), paying meticulous attention to the maximum recovery of high-quality RNA.2, 7 Furthermore, our experience indicates that RNA recovery after extraction with Trizol surpasses that with the RNeasy minikit by two-fold to three-fold. Therefore, the amount of template used for RT-nPCR by Bernardin and colleagues1 was 10-20 times lower than that in our studies, which also applied an overall more sensitive assay. Our investigations of several cohorts of individuals who were reactive to anti-HCV antibodies long after resolution of hepatitis C revealed HCV RNA positive strands in unstimulated PBMCs in ≈30% of cases at levels between 10 and 104 copies/107 cells.7 However, a brief culture of PBMCs with mitogens and cytokines that activated immune cells facilitated HCV RNA detection in as much as 75% of initially HCV-negative cases.7 Further, in up to 20% of cases, the HCV genome can be found in stimulated PBMCs but not in parallel serum. The simultaneous detection of an HCV RNA replicative strand, HCV protein, and unique HCV variants served to authenticate active virus replication in PBMCs, irrespective of serum HCV RNA status, as our recent study reassured.6 The above approach to HCV RNA identification was not employed by Bernardin and colleagues.1 Thus, as they alluded, it remains a distinct possibility that ex vivo stimulation of PBMCs might have augmented HCV detection in their study. Nevertheless, we recently demonstrated that replication of HCV can be confined to particular immune cell subsets, while total PBMCs appear nonreactive,6 suggesting that PBMC negativity does not necessarily exclude low-level HCV infection in this compartment. In summary, substantial differences in the methodology by which PBMCs and RNA were prepared, in the amount of template analyzed, and in the overall assay sensitivity are the most probable reasons behind the contradictory findings described by Bernardin et al.1 and others.2-6 As such, standardization of methods for HCV RNA detection in circulating immune cells based on the most sensitive approaches uncovered should be considered in future studies on this subject. Tram N.Q. Pham Ph.D.*, Tomasz I. Michalak M.D., Ph.D.*, * Molecular Virology and Hepatology Research Group, Faculty of Medicine, Health Science Center, Memorial University, St. John's, Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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; both teacher heads agree on what is shown here.
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".