Microbiome amplicon sequencing data are distorted by multiple protocol-dependent biases from bacterial DNA extraction, contamination, sequence errors, and chimeras, hindering clinical microbiome applications. In particular, extraction bias is a major confounder in sequencing-based microbiome analyses, with no correction method available to date. Here, we suggest using mock community controls to computationally correct extraction bias based on bacterial morphological properties. We compared dilution series of 3 cell mock communities with an even or staggered composition. DNA of these mock, and additional skin microbiome samples, was extracted with 8 different extraction protocols (2 buffers, 2 extraction kits, 2 lysis conditions). Extracted DNA was sequenced (V1–V3 16S rRNA gene) together with corresponding DNA mocks. Microbiome composition was significantly different between extraction kits and lysis conditions, but not between buffers. Independent of the extraction protocol, chimera formation increased with higher input cell numbers. Contaminants originated mostly from buffers, and considerable cross-contamination was observed in low-input samples. Comparing the microbiome composition of the cell mocks to corresponding DNA mocks revealed taxon-specific protocol-dependent extraction bias. Strikingly, this extraction bias per species was predictable by bacterial cell morphology. Morphology-based computational correction of extraction bias significantly improved resulting microbial compositions when applied to different mock samples, even with different taxa. Equivalent correction of the skin samples showed a substantial impact on microbiome compositions. Our results indicate that higher DNA density increases chimera formation during PCR amplification. Furthermore, we show that computational correction of extraction bias based on bacterial cell morphology would be feasible using appropriate positive controls, thus constituting an important step toward overcoming protocol biases in microbiome analysis.

Radiodermatits is commonly observed during radiotherapy in post-surgery breast cancer patients, reducing their quality of life. To date, neither the inter-indivdual differences nor the pathomechanism are sufficiently understood. To investigate the role of the skin microbiome in the development of severe radiodermatitis, we conducted a longitudinal pilot study with 20 female breast cancer patients undergoing radiotherapy. At 9 visits, the skin physiology was assessed and skin swabs for next-generation sequencing of the V1-V3 region of the 16S rRNA and quantitative PCR on both the affected and non-affected bodysides were taken on a weekly basis before, during and after radiotherapy (360 samples). All patients developed mild (n=7), moderate (n=9) or severe (n=4) radiodermatitis. Strikingly, low (<5%) relative abundance of skin commensals (Staphylococcus epidermidis, Staphylococcus hominis, Cutibacterium acnes) before radiotherapy was significantly predictive for the development of severe radiodermatitis with an accuracy of 100%. Instead, severe patients were characterized by higher Corynebacteriaceae relative abundance which was correlated with increased skin pH. Interestingly, severe patients only showed an increase in total bacterial load before the onset of severe radiodermatitis symptoms estimated by qPCR of the 16S rRNA copies in contrast to stable bacterial load in mild and moderate radiodermatitis patients. Summarizing, we observed a link between low commensals relative abundance before radiotherapy with an increase in total bacterial load in the early phase of radiation leading to severe radiodermatitis symptoms in the late phase of radiation. Our results hint towards a direct influence of microbes in the pathogenesis of severe radiodermatitis.

Importance The interindividual differences in severity of acute radiation dermatitis are not well understood. To date, the pathomechanism and interplay of microbiome and radiodermatitis before and during treatment remain largely unknown. Objective To assess the association of skin microbiome baseline composition and dynamics with severity of radiodermatitis in patients undergoing adjuvant radiotherapy for breast cancer. Design, Setting, and Participants A longitudinal prospective pilot observational study was conducted between January 2017 and January 2019. Sequencing results were received in March 2021, and the data were analyzed from August 2021 to March 2023. This study was performed at an urban academic university cancer center. A total of 21 female patients with breast cancer after surgery were consecutively approached, of which 1 patient withdrew consent before the study started. Exposure Adjuvant radiotherapy for breast cancer for 7 weeks. Main Outcomes and Measures The main outcome was the association of baseline skin microbiome composition and its dynamics with the severity of radiodermatitis. A total of 360 skin microbiome samples from patients were analyzed, taken before, during, and after radiotherapy, from both the treated and contralateral healthy sides. The skin microbiome samples were analyzed using 16S (V1-V3) amplicon sequencing and quantitative polymerase chain reaction bacterial enumeration. Results Twenty female patients with breast cancer after surgery who underwent radiotherapy enrolled in the study had a median (range) age of 61 (37-81) years. The median (range) body mass index of the patients was 24.2 (17.6-38.4). The 16S sequencing revealed that low (<5%) relative abundance of commensal skin bacteria (Staphylococcus epidermidis, Staphylococcus hominis, Cutibacterium acnes) at baseline composition was associated with the development of severe radiodermatitis with an accuracy of 100% (sensitivity and specificity of 100%, P < .001). Furthermore, in patients with severe radiodermatitis, quantitative polymerase chain reaction bacterial enumeration revealed a general non-species-specific overgrowth of skin bacterial load before the onset of severe symptoms. Subsequently, the abundance of commensal bacteria increased in severe radiodermatitis, coinciding with a decline in total bacterial load. Conclusions and Relevance The findings of this observational study indicated a potential mechanism associated with the skin microbiome for the pathogenesis of severe radiodermatitis, which may be a useful biomarker for personalized prevention of radiodermatitis in patients undergoing adjuvant radiotherapy for breast cancer.

Microbiome next-generation sequencing data are distorted by multiple laboratory and bioinformatic biases. Extraction bias, sequence errors and contamination are major factors blurring true biological signals, and could potentially be corrected by jointly optimizing experimental and computational workflows. We compared dilution series (10⁸-10⁴ bacteria) of 3 mock communities with an even or staggered composition. DNA was extracted with 8 different extraction protocols (2 buffers, 2 extraction kits, 2 lysis conditions). Extracted DNA was sequenced (V1-V3 16S) together with corresponding DNA mocks. Sequences were denoised using DADA2, and annotated by exact matching against reference genomes. Independent of the extraction protocol, contamination increased with less input cells, but interestingly, chimera formation increased with higher input cells. Microbiome composition was significantly different between extraction kits and lysis conditions, but not between buffers. Differences in the skin microbiome between two participants were more pronounced than any difference between extraction protocols. Bias in microbiome composition compared to corresponding DNA mocks revealed that extraction protocols favored specific groups of bacteria. Strikingly, this extraction bias per groups of species was predictable by bacterial cell morphology. We provide novel explanations that higher DNA density increases chimera formation during PCR amplification, and present a robust link between cell morphology and extraction bias. These findings pave the road for bioinformatic correction of biases in microbiome data.

Severe Atopic dermatitis (AD) is known to be associated with a Staphylococcus aureus driven microbiome dysbiosis. However, it is not yet clear how this dysbiosis is related to the actual bacterial load, in particular to the absolute Staphylococcus aureus load, on the skin. Here, we combined the relative abundance results obtained by next-generation sequencing (NGS, 16S V1-V3) with bacterial quantification by targeted qPCR (total bacterial load = 16S, S. aureus = nuc gene) to study the composition of absolute bacterial load in AD patients. Skin (lesional and non-lesional) swabs were sampled cross-sectionally in AD patients (n=135) and in healthy controls (n=20). AD patients, both in lesional and non-lesional skin, exhibited significantly higher total bacterial load, in addition to higher S. aureus relative abundance and S. aureus cell numbers, as compared to healthy controls. Moreover, significantly more severe AD patients had high total bacterial load in lesional skin as compared to moderate and mild AD patients. Furthermore, among AD patients with high total bacterial load, significantly more severe AD patients presented with higher S. aureus cell numbers and S. aureus relative abundance and total bacterial load, in correlation with the total bacterial load, as compared to patients with mild or moderate AD. Thus, indicating that severe AD patients exhibit an S. aureus driven increased bacterial skin colonization giving rise to higher total skin bacterial load. Our results warrant that, in addition to immune modulation treatment, severe AD patient could benefit also from therapy directed at suppressing skin bacterial overgrowth.

Atopic dermatitis (AD) is an inflammatory skin disease with a microbiome dysbiosis towards a high relative abundance of Staphylococcus aureus. However, information is missing on the actual bacterial load on AD skin, which may affect the cell number driven release of pathogenic factors. Here, we combined the relative abundance results obtained by next-generation sequencing (NGS, 16S V1-V3) with bacterial quantification by targeted qPCR (total bacterial load = 16S, S. aureus = nuc gene). Skin swabs were sampled cross-sectionally (n = 135 AD patients; n = 20 healthy) and longitudinally (n = 6 AD patients; n = 6 healthy). NGS and qPCR yielded highly inter-correlated S. aureus relative abundances and S. aureus cell numbers. Additionally, intra-individual differences between body sides, skin status, and consecutive timepoints were also observed. Interestingly, a significantly higher total bacterial load, in addition to higher S. aureus relative abundance and cell numbers, was observed in AD patients in both lesional and non-lesional skin, as compared to healthy controls. Moreover, in the lesional skin of AD patients, higher S. aureus cell numbers significantly correlated with the higher total bacterial load. Furthermore, significantly more severe AD patients presented with higher S. aureus cell number and total bacterial load compared to patients with mild or moderate AD. Our results indicate that severe AD patients exhibit S. aureus driven increased bacterial skin colonization. Overall, bacterial quantification gives important insights in addition to microbiome composition by sequencing.

Introduction Microbiome amplicon sequencing data are distorted by multiple protocol-dependent biases, originating from bacterial DNA extraction, contamination, sequence errors, and chimeras. In particular, extraction bias is a major confounder in sequencing-based microbiome analyses, with no correction method available to date. Here, we suggest using mock community controls to bioinformatically correct extraction bias based on morphological properties. Methods We compared dilution series of 3 mock communities with an even or staggered composition. DNA was extracted with 8 different extraction protocols (2 buffers, 2 extraction kits, 2 lysis conditions). Extracted DNA was sequenced (V1-V3 16S rRNA gene) together with corresponding DNA mocks. Sequences were denoised using DADA2, and annotated by matching against mock reference genomes. Results Microbiome composition was significantly different between extraction kits and lysis conditions, but not between buffers. Independent of the extraction protocol, chimera formation increased with high input cell number. Contaminants originated mostly from buffers, and considerable cross-contamination was observed in low-input samples. Comparison of microbiome composition of the cell mocks to corresponding DNA mocks revealed taxon-specific protocol-dependent extraction bias. Strikingly, this extraction bias per species was predictable by bacterial cell morphology. Morphology-based bioinformatic correction of extraction bias significantly improved sample compositions when applied to different samples, even with different taxa. Conclusions Our results indicate that higher DNA density increases chimera formation during PCR amplification. Furthermore, we show that bioinformatic correction of extraction bias is feasible based on bacterial cell morphology.

BACKGROUND:Atopic eczema (atopic dermatitis, AD) is characterized by disrupted skin barrier associated with elevated skin pH and skin microbiome dysbiosis, due to high Staphylococcus aureus loads, especially during flares. Since S aureus shows optimal growth at neutral pH, we investigated the longitudinal interplay between these factors and AD severity in a pilot study.METHOD:Emollient (with either basic pH 8.5 or pH 5.5) was applied double-blinded twice daily to 6 AD patients and 6 healthy (HE) controls for 8 weeks. Weekly, skin swabs for microbiome analysis (deep sequencing) were taken, AD severity was assessed, and skin physiology (pH, hydration, transepidermal water loss) was measured.RESULTS:Physiological, microbiome, and clinical results were not robustly related to the pH of applied emollient. In contrast to longitudinally stable microbiome in HE, S aureus frequency significantly increased in AD over 8 weeks. High S aureus abundance was associated with skin pH 5.7-6.2. High baseline S aureus frequency predicted both increase in S aureus and in AD severity (EASI and local SCORAD) after 8 weeks.CONCLUSION:Skin pH is tightly regulated by intrinsic factors and limits the abundance of S aureus. High baseline S aureus abundance in turn predicts an increase in AD severity over the study period. This underlines the importance and potential of sustained intervention regarding the skin pH and urges for larger studies linking skin pH and skin S aureus abundance to understand driving factors of disease progression.

ABSTRACT Gene therapy approaches based on liver-restricted and regulated alpha interferon (IFN-α) expression, recently shown to be effective in different murine hepatitis models, appear promising alternatives to inhibit hepatitis C virus (HCV) replication in patients and minimize side effects. Tamarins ( Saguinus species) infected by GB virus B (GBV-B) are considered a valid surrogate model for hepatitis C to study the biology of HCV infection and the development of new antiviral drugs. To test the efficacy of local delivery and expression of IFN-α in this model, we have developed HD-TET-tIFN, a helper-dependent adenovirus vector expressing tamarin IFN-α (tIFN) under the control of the tetracycline-inducible transactivator rtTA2 s -S2. Expression of tIFN was successfully induced both in vitro and in vivo in rodents by doxycycline administration with consequent activation of IFN-responsive genes. More importantly, tIFN efficiently inhibited GBV-B replicon in a Huh-7 hepatoma cell line at low HD-TET-tIFN doses. A certain degree of transcriptional control of tIFN was achieved in tamarins injected with HD-TET-tIFN, but under the conditions used in this study, infection and replication of GBV-B were only delayed and not totally abrogated upon virus challenge. Hepatic delivery and regulated expression of IFN-α appear to be a possible approach for the cure of hepatitis, but this approach requires more studies to increase its efficacy. To our knowledge, this is the first report showing a regulated gene expression in a nonhuman primate hepatitis model.

Screening cDNA libraries from solid human tumors with sera of autologous patients (SEREX) has proven to be a powerful approach to identifying tumor antigens recognized by the humoral arm of the immune system. In many cases, application of this methodology has led to the discovery of novel tumor antigens as unknown gene products. We tried to improve the potency of the SEREX approach by combining it with phage-display technology. We designed a new lambda vector to express protein fragments as N-terminal fusions to the D capsid protein and generated high-complexity cDNA libraries from human breast carcinoma cell lines and solid tumors. Screening these phage-displayed libraries required limited amounts of sera from patients and efficiently identified several tumor antigens specifically reacting with sera from breast cancer patients.

Hepatitis C virus (HCV) and GB virus B (GBV-B) replicons have been reported to replicate only in Huh7 cells. Here we demonstrate that subpopulations of another human hepatoma cell line, Hep3B, are permissive for the GBV-B replicon, showing different levels of enhancement of replication from those of the unselected parental cell population. Adaptive mutations are not required for replication of the GBV-B replicon in these cells, as already demonstrated for Huh7 cells. Nonetheless, we identified a mutant replicon in one of the selected cell lines, which, although lacking the 5' end proximal stem-loop, is able to replicate in Hep3B cells as well as in Huh7 cells. This mutant indeed shows a higher replication efficiency than does wild-type replicon, especially in the Hep3B cell clone from which it was originally recovered. This indicates that the stem-loop Ia is not necessary for replication of the GBV-B replicon in human cells, unlike what occurs with HCV, and that its absence can even provide a selective advantage.