Comparative evaluation of cDNA library construction approaches for RNA-Seq analysis from low RNA-content human specimens

Comparative transcriptomic analysis of Staphylococcus epidermidis associated with periprosthetic joint infection under in vivo and in vitro conditions

Staphylococcus epidermidis is part of the commensal microbiota of the skin and mucous membranes, though it can also act as a pathogen in certain scenarios, causing a range of infections, including periprosthetic joint infection (PJI). Transcriptomic profiling may provide insights into mechanisms by which S. epidermidis adapts while in a pathogenic compared to a commensal state. Here, a total RNA-sequencing approach was used to profile and compare the transcriptomes of 19 paired PJI-associated S. epidermidis samples from an in vivo clinical source and grown in in vitro laboratory culture. Genomic comparison of PJI-associated and publicly available commensal-state isolates were also compared. Of the 1919 total transcripts found, 145 were from differentially expressed genes (DEGs) when comparing in vivo or in vitro samples. Forty-two transcripts were upregulated and 103 downregulated in in vivo samples. Of note, metal sequestration-associated genes, specifically those related to staphylopine activity (cntA, cntK, cntL, and cntM), were upregulated in a subset of clinical in vivo compared to laboratory grown in vitro samples. About 70% of the total transcripts and almost 50% of the DEGs identified have not yet been annotated. There were no significant genomic differences between known commensal and PJI-associated S. epidermidis isolates, suggesting that differential genomics may not play a role in S. epidermidis pathogenicity. In conclusion, this study provides insights into phenotypic alterations employed by S epidermidis to adapt to infective and non-infected microenvironments, potentially informing future therapeutic targets for related infections.

Techniques, procedures, and applications in microbiome analysis

Microbiota is a complex community of microorganisms living in a defined environment. Until the 20th century, knowledge of microbiota was partial, as the techniques available for their characterization were primarily based on bacteriological culture. In the last twenty years, the development of DNA sequencing technologies, multi-omics, and bioinformatics has expanded our understanding of microorganisms. We have moved from mainly considering them isolated disease-causing agents to recognizing the microbiota as an essential component of host biology. These techniques have shown that the microbiome plays essential roles in various host phenotypes, influencing development, physiology, reproduction, and evolution. This chapter provides researchers with a summary of the primary concepts, sample collection, experimental techniques, and bioinformatics analysis commonly used in microbiome research. The main features, applications in microbiome studies, and their advantages and limitations are included in each section.

Cost and time-efficient construction of a 3'-end mRNA library from unpurified bulk RNA in a single tube

The major drawbacks of RNA sequencing (RNA-seq), a remarkably accurate transcriptome profiling method, is its high cost and poor scalability. Here, we report a highly scalable and cost-effective method for transcriptomics profiling called Bulk transcriptOme profiling of cell Lysate in a single poT (BOLT-seq), which is performed using unpurified bulk 3'-end mRNA in crude cell lysates. During BOLT-seq, RNA/DNA hybrids are directly subjected to tagmentation, and second-strand cDNA synthesis and RNA purification are omitted, allowing libraries to be constructed in 2 h of hands-on time. BOLT-seq was successfully used to cluster small molecule drugs based on their mechanisms of action and intended targets. BOLT-seq competes effectively with alternative library construction and transcriptome profiling methods.

Understanding human health through metatranscriptomics

Metatranscriptomics has revolutionized our ability to explore and understand transcriptional programs in microbial communities. Moreover, it has enabled us to gain deeper and more specific insight into the microbial activities in human gut, respiratory, oral, and vaginal communities. Perhaps the most important contribution of metatranscriptomics arises, however, from the analyses of disease-associated communities. We review the advantages and disadvantages of metatranscriptomics analyses in understanding human health and disease. We focus on human tissues low in microbial biomass and conditions associated with dysbiotic microbiota. We conclude that a more widespread use of metatranscriptomics and increased knowledge on microbe activities will uncover critical interactions between microbes and host in human health and provide diagnostic basis for culturing-independent, direct functional pathogen identification.

Sonicate Fluid Cellularity Predicted by Transcriptomic Deconvolution Differentiates Infectious from Non-Infectious Arthroplasty Failure

BACKGROUND

Although cellularity is traditionally assessed morphologically, deep sequencing approaches being used for microorganism detection may be able to provide information about cellularity. We hypothesized that cellularity predicted using CIBERSORTx (Stanford University), a transcriptomic-based cellular deconvolution tool, would differentiate between infectious and non-infectious arthroplasty failure.

METHODS

CIBERSORTx-derived cellularity profiles of 93 sonicate fluid samples, including 53 from subjects who underwent failed arthroplasties due to periprosthetic joint infection (PJI) (abbreviated for the purpose of this study as PJIF) and 40 from subjects who had undergone non-infectious arthroplasty failure (abbreviated NIAF) that had been subjected to bulk RNA sequencing were evaluated.

RESULTS

Samples from PJIF and NIAF subjects were differentially clustered by principal component analysis based on the cellularity profile. Twelve of the 22 individual predicted cellular fractions were differentially expressed in the PJIF cases compared with the NIAF cases, including increased predicted neutrophils (mean and standard error, 9.73% ± 1.06% and 0.81% ± 0.60%), activated mast cells (17.12% ± 1.51% and 4.11% ± 0.44%), and eosinophils (1.96% ± 0.37% and 0.42% ± 0.21%), and decreased predicted M0 macrophages (21.33% ± 1.51% and 39.75% ± 2.45%), M2 macrophages (3.56% ± 0.52% and 8.70% ± 1.08%), and regulatory T cells (1.57% ± 0.23% and 3.20% ± 0.34%). The predicted total granulocyte fraction was elevated in the PJIF cases (32.97% ± 2.13% and 11.76% ± 1.61%), and the samples from the NIAF cases had elevated predicted total macrophage and monocyte (34.71% ± 1.71% and 55.34% ± 2.37%) and total B cell fractions (5.89% ± 0.30% and 8.62% ± 0.86%). Receiver operating characteristic curve analysis identified predicted total granulocytes, neutrophils, and activated mast cells as highly able to differentiate between the PJIF cases and the NIAF cases. Within the PJIF cases, the total granulocyte, total macrophage and monocyte, M0 macrophage, and M2 macrophage fractions were differentially expressed in Staphylococcus aureus compared with Staphylococcus epidermidis -associated samples. Within the NIAF cases, the predicted total B cell, naïve B cell, plasma cell, and M2 macrophage fractions were differentially expressed among different causes of failure.

CONCLUSIONS

CIBERSORTx can predict the cellularity of sonicate fluid using transcriptomic data, allowing for the evaluation of the underlying immune response during the PJIF and NIAF cases, without a need to phenotypically assess cell composition.

Human transcriptomic response to periprosthetic joint infection

Periprosthetic joint infection (PJI), a devastating complication of total joint replacement, is of incompletely understood pathogenesis and may sometimes be challenging to clinically distinguish from other causes of arthroplasty failure. We characterized human gene expression in 93 specimens derived from surfaces of resected arthroplasties, comparing transcriptomes of subjects with infection- versus non-infection-associated arthroplasty failure. Differential gene expression analysis confirmed 28 previously reported potential biomarkers of PJI, including bactericidal/permeability increasing protein (BPI), cathelicidin antimicrobial peptide (CAMP), C-C-motif chemokine ligand 3 (CCL3), 4(CCL4) and C-X-C-motif chemokine ligand 2 (CXCL2), colony stimulating factor 2 receptor beta (CSF2RB), colony stimulating factor 3 (CSF3), alpha-defensin (DEFA4), Fc fragment of IgG receptor 1B (CD64B), intercellular adhesion molecule 1 (ICAM1), interferon gamma (IFNG), interleukin 13 receptor subunit alpha 2 (IL13RA2), interleukin 17D (IL17D), interleukin 1 (IL1A, IL1B, IL1RN), interleukin 2 receptors (IL2RA, IL2RG), interleukin 5 receptor (IL5RA), interleukin 6 (IL6), interleukin 8 (IL8), lipopolysaccharide binding protein (LBP), lipocalin (LCN2), lactate dehydrogenase C (LDHC), lactotransferrin (LTF), matrix metallopeptidase 3 (MMP3), peptidase inhibitor 3 (PI3), and vascular endothelial growth factor A (VEGFA), and identified three novel molecules of potential diagnostic use for detection of PJI, namely C-C-motif chemokine ligand CCL20, coagulation factor VII (F7), and B cell receptor FCRL4. Comparative analysis of infections caused by staphylococci versus bacteria other than staphylococci and Staphylococcus aureus versus Staphylococcus epidermidis showed elevated expression of interleukin 13 (IL13), IL17D, and MMP3 in staphylococcal infections, and of IL1B, IL8, and platelet factor PF4V1 in S. aureus compared to S. epidermidis infections. Pathway analysis of over-represented genes suggested activation of host immune response and cellular maintenance and repair functions in response to invasion of infectious agents. The data presented provides new potential targets for diagnosis of PJI and for differentiation of PJI caused by different infectious agents.

Contamination Issue in Viral Metagenomics: Problems, Solutions, and Clinical Perspectives

We describe the most common internal and external sources and types of contamination encountered in viral metagenomic studies and discuss their negative impact on sequencing results, particularly for low-biomass samples and clinical applications. We also propose some basic recommendations for reducing the background noise in viral shotgun metagenomic (SM) studies, which would limit the bias introduced by various classes of contaminants. Regardless of the specific viral SM protocol, contamination cannot be totally avoided; in particular, the issue of reagent contamination should always be addressed with high priority. There is an urgent need for the development and validation of standards for viral metagenomic studies especially if viral SM protocols will be more widely applied in diagnostics.

Transcriptomic analysis of Streptococcus agalactiae periprosthetic joint infection

Although Streptococcus agalactiae periprosthetic joint infection (PJI) is not as prevalent as staphylococcal PJI, invasive S. agalactiae infection is not uncommon. Here, RNA-seq was used to perform transcriptomic analysis of S. agalactiae PJI using fluid derived from sonication of explanted arthroplasties of subjects with S. agalactiae PJI, with results compared to those of S. agalactiae strain NEM316 grown in vitro. A total of 227 genes with outlier expression were found (164 upregulated and 63 downregulated) between PJI sonicate fluid and in vitro conditions. Functional enrichment analysis showed genes involved in mobilome and inorganic ion transport and metabolism to be most enriched. Genes involved in nickel, copper, and zinc transport, were upregulated. Among known virulence factors, cyl operon genes, encoding β-hemolysin/cytolysin, were consistently highly expressed in PJI versus in vitro. The data presented provide insight into S. agalactiae PJI pathogenesis and may be a resource for identification of novel PJI therapeutics or vaccines against invasive S. agalactiae infections.

Comparative Transcriptomic Analysis of Staphylococcus aureus Associated with Periprosthetic Joint Infection under in Vivo and in Vitro Conditions

Transcriptomic analysis can provide insight as to how Staphylococcus aureus adapts to the environmental niche of periprosthetic joint infection (PJI), a challenging clinical infection. Here, in vivo RNA expression of eight S. aureus PJIs was compared with expression of the corresponding isolates in planktonic culture using a total RNA-sequencing approach. Expression varied among isolates, with a common trend showing increased expression of several ica-independent biofilm formation genes, including sdr, fnb, ebpS, and aaa; genes encoding enzymes and toxins, including coa, nuc, hlb, and hlgA/B/C; and genes facilitating acquisition of iron via the iron-binding molecule siderophore B (snb) and heme consumption protein (isd) pathways in PJI. Several antimicrobial resistance determinants were detected; although their presence correlated with phenotypic susceptibility of the associated isolates, no difference in expression between in vivo and in vitro conditions was identified.

Protocol Improvement for RNA Extraction From Compromised Frozen Specimens Generated in Austere Conditions: A Path Forward to Transcriptomics-Pathology Systems Integration

At the heart of the phenome-to-genome approach is high throughput assays, which are liable to produce false results. This risk can be mitigated by minimizing the sample bias, specifically, recycling the same tissue specimen for both phenotypic and genotypic investigations. Therefore, our aim is to suggest a methodology of obtaining robust results from frozen specimens of compromised quality, particularly if the sample is produced in conditions with limited resources. For example, generating samples at the International Space Station (ISS) is challenging because the time and laboratory footprint allotted to a project can get expensive. In an effort to be economical with available resources, snap-frozen euthanized mice are the straightforward solution; however, this method increases the risk of temperature abuse during the thawing process at the beginning of the tissue collection. We found that prolonged immersion of snap frozen mouse carcass in 10% neutral buffered formalin at 4°C yielded minimal microscopic signs of ice crystallization and delivered tissues with histomorphology that is optimal for hematoxylin and eosin (H&E) staining and fixation on glass slides. We further optimized a method to sequester the tissue specimen from the H&E slides using an incubator shaker. Using this method, we were able to recover an optimal amount of RNA that could be used for downstream transcriptomics assays. Overall, we demonstrated a protocol that enables us to maximize scientific values from tissues collected in austere condition. Furthermore, our protocol can suggest an improvement in the spatial resolution of transcriptomic assays.

3'Pool-seq: an optimized cost-efficient and scalable method of whole-transcriptome gene expression profiling

BACKGROUND

The advent of Next Generation Sequencing has allowed transcriptomes to be profiled with unprecedented accuracy, but the high costs of full-length mRNA sequencing have posed a limit on the accessibility and scalability of the technology. To address this, we developed 3'Pool-seq: a simple, cost-effective, and scalable RNA-seq method that focuses sequencing to the 3'-end of mRNA. We drew from aspects of SMART-seq, Drop-seq, and TruSeq to implement an easy workflow, and optimized parameters such as input RNA concentrations, tagmentation conditions, and read depth specifically for bulk-RNA.

RESULTS

Thorough optimization resulted in a protocol that takes less than 12 h to perform, does not require custom sequencing primers or instrumentation, and cuts over 90% of the costs associated with TruSeq, while still achieving accurate gene expression quantification (Pearson's correlation coefficient with ERCC theoretical concentration r = 0.96) and differential gene detection (ROC analysis of 3'Pool-seq compared to TruSeq AUC = 0.921). The 3'Pool-seq dual indexing scheme was further adapted for a 96-well plate format, and ERCC spike-ins were used to correct for potential row or column pooling effects. Transcriptional profiling of troglitazone and pioglitazone treatments at multiple doses and time points in HepG2 cells was then used to show how 3'Pool-seq could distinguish the two molecules based on their molecular signatures.

CONCLUSIONS

3'Pool-seq can accurately detect gene expression at a level that is on par with TruSeq, at one tenth of the total cost. Furthermore, its unprecedented TruSeq/Nextera hybrid indexing scheme and streamlined workflow can be applied in several different formats, including 96-well plates, which allows users to thoroughly evaluate biological systems under several conditions and timepoints. Care must be taken regarding experimental design and plate layout such that potential pooling effects can be accounted for and corrected. Lastly, further studies using multiple sets of ERCC spike-ins may be used to simulate differential gene expression in a system with known ground-state values.

Comprehensive analysis of dynamic gene expression and investigation of the roles of hydrogen peroxide during adventitious rooting in poplar

BACKGROUND

Adventitious roots (ARs) are roots that are generated from nonrooting tissues. ARs are usually produced both during normal development and in response to stress conditions, such as flooding, nutrient deprivation, heavy metal stress and wounding. The ability of plants to form ARs is a key trait that enables plant propagation, especially for most tree species.

RESULTS

Here, the kinetics of AR formation in a tissue culture of a hybrid variety of poplar were investigated. AR formation mainly occurred during the first 8 days and both pre- and newly- formed primordia contributed to AR formation in poplar by histological study. RNA-Seq-based transcriptome analysis was performed for stem bases collected at 0, 2, 4, 6 and 8 days after excision (DAE). Based on the data, the expression patterns of 8 phytohormone-related genes were investigated, and their influences on AR formation were considered. Subsequent gene expression cluster analysis showed a number of biological processes involved in AR formation. Among these biological pathways, genes involved in H₂O₂ homeostasis showed enrichment in one cluster that was highly upregulated from DAE0 to DAE8. Pharmacological assay confirmed that an appropriate content of H₂O₂ in stem bases could accelerate the formation of ARs in poplar.

CONCLUSIONS

Based on the results of this study, we were able to predict a regulatory network for 7 phytohormones that are involved in poplar AR formation. The influence of H₂O₂ on AR formation was also confirmed_. These results enhance our understanding of the regulation of AR formation in tree species.