Altered infective competence of the human gut microbiome in COVID-19

BACKGROUND

Infections with SARS-CoV-2 have a pronounced impact on the gastrointestinal tract and its resident microbiome. Clear differences between severe cases of infection and healthy individuals have been reported, including the loss of commensal taxa. We aimed to understand if microbiome alterations including functional shifts are unique to severe cases or a common effect of COVID-19. We used high-resolution systematic multi-omic analyses to profile the gut microbiome in asymptomatic-to-moderate COVID-19 individuals compared to a control group.

RESULTS

We found a striking increase in the overall abundance and expression of both virulence factors and antimicrobial resistance genes in COVID-19. Importantly, these genes are encoded and expressed by commensal taxa from families such as Acidaminococcaceae and Erysipelatoclostridiaceae, which we found to be enriched in COVID-19-positive individuals. We also found an enrichment in the expression of a betaherpesvirus and rotavirus C genes in COVID-19-positive individuals compared to healthy controls.

CONCLUSIONS

Our analyses identified an altered and increased infective competence of the gut microbiome in COVID-19 patients. Video Abstract.

Glacier-Fed Stream Biofilms Harbor Diverse Resistomes and Biosynthetic Gene Clusters

Antimicrobial resistance (AMR) is a universal phenomenon the origins of which lay in natural ecological interactions such as competition within niches, within and between micro- to higher-order organisms. To study these phenomena, it is crucial to examine the origins of AMR in pristine environments, i.e., limited anthropogenic influences. In this context, epilithic biofilms residing in glacier-fed streams (GFSs) are an excellent model system to study diverse, intra- and inter-domain, ecological crosstalk. We assessed the resistomes of epilithic biofilms from GFSs across the Southern Alps (New Zealand) and the Caucasus (Russia) and observed that both bacteria and eukaryotes encoded twenty-nine distinct AMR categories. Of these, beta-lactam, aminoglycoside, and multidrug resistance were both abundant and taxonomically distributed in most of the bacterial and eukaryotic phyla. AMR-encoding phyla included Bacteroidota and Proteobacteria among the bacteria, alongside Ochrophyta (algae) among the eukaryotes. Additionally, biosynthetic gene clusters (BGCs) involved in the production of antibacterial compounds were identified across all phyla in the epilithic biofilms. Furthermore, we found that several bacterial genera (Flavobacterium, Polaromonas, Superphylum Patescibacteria) encode both atimicrobial resistance genes (ARGs) and BGCs within close proximity of each other, demonstrating their capacity to simultaneously influence and compete within the microbial community. Our findings help unravel how naturally occurring BGCs and AMR contribute to the epilithic biofilms mode of life in GFSs. Additionally, we report that eukaryotes may serve as AMR reservoirs owing to their potential for encoding ARGs. Importantly, these observations may be generalizable and potentially extended to other environments that may be more or less impacted by human activity. IMPORTANCE Antimicrobial resistance is an omnipresent phenomenon in the anthropogenically influenced ecosystems. However, its role in shaping microbial community dynamics in pristine environments is relatively unknown. Using metagenomics, we report the presence of antimicrobial resistance genes and their associated pathways in epilithic biofilms within glacier-fed streams. Importantly, we observe biosynthetic gene clusters associated with antimicrobial resistance in both pro- and eukaryotes in these biofilms. Understanding the role of resistance in the context of this pristine environment and complex biodiversity may shed light on previously uncharacterized mechanisms of cross-domain interactions.

Effects of Resistant Starch on Symptoms, Fecal Markers, and Gut Microbiota in Parkinson's Disease - The RESISTA-PD Trial

The composition of the gut microbiota is linked to multiple diseases, including Parkinson's disease (PD). Abundance of bacteria producing short-chain fatty acids (SCFAs) and fecal SCFA concentrations are reduced in PD. SCFAs exert various beneficial functions in humans. In the interventional, monocentric, open-label clinical trial "Effects of Resistant Starch on Bowel Habits, Short Chain Fatty Acids and Gut Microbiota in Parkinson'sDisease" (RESISTA-PD; ID: NCT02784145), we aimed at altering fecal SCFAs by an 8-week prebiotic intervention with resistant starch (RS). We enrolled 87 subjects in three study-arms: 32 PD patients received RS (PD + RS), 30 control subjects received RS, and 25 PD patients received solely dietary instructions. We performed paired-end 100 bp length metagenomic sequencing of fecal samples using the BGISEQ platform at an average of 9.9 GB. RS was well-tolerated. In the PD + RS group, fecal butyrate concentrations increased significantly, and fecal calprotectin concentrations dropped significantly after 8 weeks of RS intervention. Clinically, we observed a reduction in non-motor symptom load in the PD + RS group. The reference-based analysis of metagenomes highlighted stable alpha-diversity and beta-diversity across the three groups, including bacteria producing SCFAs. Reference-free analysis suggested punctual, yet pronounced differences in the metagenomic signature in the PD + RS group. RESISTA-PD highlights that a prebiotic treatment with RS is safe and well-tolerated in PD. The stable alpha-diversity and beta-diversity alongside altered fecal butyrate and calprotectin concentrations call for long-term studies, also investigating whether RS is able to modify the clinical course of PD.

Functional meta-omics provide critical insights into long- and short-read assemblies

Real-world evaluations of metagenomic reconstructions are challenged by distinguishing reconstruction artifacts from genes and proteins present in situ. Here, we evaluate short-read-only, long-read-only and hybrid assembly approaches on four different metagenomic samples of varying complexity. We demonstrate how different assembly approaches affect gene and protein inference, which is particularly relevant for downstream functional analyses. For a human gut microbiome sample, we use complementary metatranscriptomic and metaproteomic data to assess the metagenomic data-based protein predictions. Our findings pave the way for critical assessments of metagenomic reconstructions. We propose a reference-independent solution, which exploits the synergistic effects of multi-omic data integration for the in situ study of microbiomes using long-read sequencing data.

PathoFact: a pipeline for the prediction of virulence factors and antimicrobial resistance genes in metagenomic data

BACKGROUND

Pathogenic microorganisms cause disease by invading, colonizing, and damaging their host. Virulence factors including bacterial toxins contribute to pathogenicity. Additionally, antimicrobial resistance genes allow pathogens to evade otherwise curative treatments. To understand causal relationships between microbiome compositions, functioning, and disease, it is essential to identify virulence factors and antimicrobial resistance genes in situ. At present, there is a clear lack of computational approaches to simultaneously identify these factors in metagenomic datasets.

RESULTS

Here, we present PathoFact, a tool for the contextualized prediction of virulence factors, bacterial toxins, and antimicrobial resistance genes with high accuracy (0.921, 0.832 and 0.979, respectively) and specificity (0.957, 0.989 and 0.994). We evaluate the performance of PathoFact on simulated metagenomic datasets and perform a comparison to two other general workflows for the analysis of metagenomic data. PathoFact outperforms all existing workflows in predicting virulence factors and toxin genes. It performs comparably to one pipeline regarding the prediction of antimicrobial resistance while outperforming the others. We further demonstrate the performance of PathoFact on three publicly available case-control metagenomic datasets representing an actual infection as well as chronic diseases in which either pathogenic potential or bacterial toxins are hypothesized to play a role. In each case, we identify virulence factors and AMR genes which differentiated between the case and control groups, thereby revealing novel gene associations with the studied diseases.

CONCLUSION

PathoFact is an easy-to-use, modular, and reproducible pipeline for the identification of virulence factors, bacterial toxins, and antimicrobial resistance genes in metagenomic data. Additionally, our tool combines the prediction of these pathogenicity factors with the identification of mobile genetic elements. This provides further depth to the analysis by considering the genomic context of the pertinent genes. Furthermore, PathoFact's modules for virulence factors, toxins, and antimicrobial resistance genes can be applied independently, thereby making it a flexible and versatile tool. PathoFact, its models, and databases are freely available at https://pathofact.lcsb.uni.lu . Video abstract.

Evaluating the Use of Circulating MicroRNA Profiles for Lung Cancer Detection in Symptomatic Patients

Importance

The overall low survival rate of patients with lung cancer calls for improved detection tools to enable better treatment options and improved patient outcomes. Multivariable molecular signatures, such as blood-borne microRNA (miRNA) signatures, may have high rates of sensitivity and specificity but require additional studies with large cohorts and standardized measurements to confirm the generalizability of miRNA signatures.

Objective

To investigate the use of blood-borne miRNAs as potential circulating markers for detecting lung cancer in an extended cohort of symptomatic patients and control participants.

Design, Setting, and Participants

This multicenter, cohort study included patients from case-control and cohort studies (TREND and COSYCONET) with 3102 patients being enrolled by convenience sampling between March 3, 2009, and March 19, 2018. For the cohort study TREND, population sampling was performed. Clinical diagnoses were obtained for 3046 patients (606 patients with non-small cell and small cell lung cancer, 593 patients with nontumor lung diseases, 883 patients with diseases not affecting the lung, and 964 unaffected control participants). No samples were removed because of experimental issues. The collected data were analyzed between April 2018 and November 2019.

Main Outcomes and Measures

Sensitivity and specificity of liquid biopsy using miRNA signatures for detection of lung cancer.

Results

A total of 3102 patients with a mean (SD) age of 61.1 (16.2) years were enrolled. Data on the sex of the participants were available for 2856 participants; 1727 (60.5%) were men. Genome-wide miRNA profiles of blood samples from 3046 individuals were evaluated by machine-learning methods. Three classification scenarios were investigated by splitting the samples equally into training and validation sets. First, a 15-miRNA signature from the training set was used to distinguish patients diagnosed with lung cancer from all other individuals in the validation set with an accuracy of 91.4% (95% CI, 91.0%-91.9%), a sensitivity of 82.8% (95% CI, 81.5%-84.1%), and a specificity of 93.5% (95% CI, 93.2%-93.8%). Second, a 14-miRNA signature from the training set was used to distinguish patients with lung cancer from patients with nontumor lung diseases in the validation set with an accuracy of 92.5% (95% CI, 92.1%-92.9%), sensitivity of 96.4% (95% CI, 95.9%-96.9%), and specificity of 88.6% (95% CI, 88.1%-89.2%). Third, a 14-miRNA signature from the training set was used to distinguish patients with early-stage lung cancer from all individuals without lung cancer in the validation set with an accuracy of 95.9% (95% CI, 95.7%-96.2%), sensitivity of 76.3% (95% CI, 74.5%-78.0%), and specificity of 97.5% (95% CI, 97.2%-97.7%).

Conclusions and Relevance

The findings of the study suggest that the identified patterns of miRNAs may be used as a component of a minimally invasive lung cancer test, complementing imaging, sputum cytology, and biopsy tests.

PLSDB: a resource of complete bacterial plasmids

The study of bacterial isolates or communities requires the analysis of the therein included plasmids in order to provide an extensive characterization of the organisms. Plasmids harboring resistance and virulence factors are of especial interest as they contribute to the dissemination of antibiotic resistance. As the number of newly sequenced bacterial genomes is growing a comprehensive resource is required which will allow to browse and filter the available plasmids, and to perform sequence analyses. Here, we present PLSDB, a resource containing 13 789 plasmid records collected from the NCBI nucleotide database. The web server provides an interactive view of all obtained plasmids with additional meta information such as sequence characteristics, sample-related information and taxonomy. Moreover, nucleotide sequence data can be uploaded to search for short nucleotide sequences (e.g. specific genes) in the plasmids, to compare a given plasmid to the records in the collection or to determine whether a sample contains one or multiple of the known plasmids (containment analysis). The resource is freely accessible under https://ccb-microbe.cs.uni-saarland.de/plsdb/.

Assessing the heterogeneity of in silico plasmid predictions based on whole-genome-sequenced clinical isolates

High-throughput next-generation shotgun sequencing of pathogenic bacteria is growing in clinical relevance, especially for chromosomal DNA-based taxonomic identification and for antibiotic resistance prediction. Genetic exchange is facilitated for extrachromosomal DNA, e.g. plasmid-borne antibiotic resistance genes. Consequently, accurate identification of plasmids from whole-genome sequencing (WGS) data remains one of the major challenges for sequencing-based precision medicine in infectious diseases. Here, we assess the heterogeneity of four state-of-the-art tools (cBar, PlasmidFinder, plasmidSPAdes and Recycler) for the in silico prediction of plasmid-derived sequences from WGS data. Heterogeneity, sensitivity and precision were evaluated by reference-independent and reference-dependent benchmarking using 846 Gram-negative clinical isolates. Interestingly, the majority of predicted sequences were tool-specific, resulting in a pronounced heterogeneity across tools for the reference-independent assessment. In the reference-dependent assessment, sensitivity and precision values were found to substantially vary between tools and across taxa, with cBar exhibiting the highest median sensitivity (87.45%) but a low median precision (27.05%). Furthermore, integrating the individual tools into an ensemble approach showed increased sensitivity (95.55%) while reducing the precision (25.62%). CBar and plasmidSPAdes exhibited the strongest concordance with respect to identified antibiotic resistance factors. Moreover, false-positive plasmid predictions typically contained only few antibiotic resistance factors. In conclusion, while high degrees of heterogeneity and variation in sensitivity and precision were observed across the different tools and taxa, existing tools are valuable for investigating the plasmid-borne resistome. Nevertheless, additional studies on representative clinical data sets will be necessary to translate in silico plasmid prediction approaches from research to clinical application.

The sncRNA Zoo: a repository for circulating small noncoding RNAs in animals

The repertoire of small noncoding RNAs (sncRNAs), particularly miRNAs, in animals is considered to be evolutionarily conserved. Studies on sncRNAs are often largely based on homology-based information, relying on genomic sequence similarity and excluding actual expression data. To obtain information on sncRNA expression (including miRNAs, snoRNAs, YRNAs and tRNAs), we performed low-input-volume next-generation sequencing of 500 pg of RNA from 21 animals at two German zoological gardens. Notably, none of the species under investigation were previously annotated in any miRNA reference database. Sequencing was performed on blood cells as they are amongst the most accessible, stable and abundant sources of the different sncRNA classes. We evaluated and compared the composition and nature of sncRNAs across the different species by computational approaches. While the distribution of sncRNAs in the different RNA classes varied significantly, general evolutionary patterns were maintained. In particular, miRNA sequences and expression were found to be even more conserved than previously assumed. To make the results available for other researchers, all data, including expression profiles at the species and family levels, and different tools for viewing, filtering and searching the data are freely available in the online resource ASRA (Animal sncRNA Atlas) at https://www.ccb.uni-saarland.de/asra/.

An estimate of the total number of true human miRNAs

While the number of human miRNA candidates continuously increases, only a few of them are completely characterized and experimentally validated. Toward determining the total number of true miRNAs, we employed a combined in silico high- and experimental low-throughput validation strategy. We collected 28 866 human small RNA sequencing data sets containing 363.7 billion sequencing reads and excluded falsely annotated and low quality data. Our high-throughput analysis identified 65% of 24 127 mature miRNA candidates as likely false-positives. Using northern blotting, we experimentally validated miRBase entries and novel miRNA candidates. By exogenous overexpression of 108 precursors that encode 205 mature miRNAs, we confirmed 68.5% of the miRBase entries with the confirmation rate going up to 94.4% for the high-confidence entries and 18.3% of the novel miRNA candidates. Analyzing endogenous miRNAs, we verified the expression of 8 miRNAs in 12 different human cell lines. In total, we extrapolated 2300 true human mature miRNAs, 1115 of which are currently annotated in miRBase V22. The experimentally validated miRNAs will contribute to revising targetomes hypothesized by utilizing falsely annotated miRNAs.

Clinical Resistome Screening of 1,110 Escherichia coli Isolates Efficiently Recovers Diagnostically Relevant Antibiotic Resistance Biomarkers and Potential Novel Resistance Mechanisms

Multidrug-resistant pathogens represent one of the biggest global healthcare challenges. Molecular diagnostics can guide effective antibiotics therapy but relies on validated, predictive biomarkers. Here we present a novel, universally applicable workflow for rapid identification of antimicrobial resistance (AMR) biomarkers from clinical Escherichia coli isolates and quantitatively evaluate the potential to recover causal biomarkers for observed resistance phenotypes. For this, a metagenomic plasmid library from 1,110 clinical E. coli isolates was created and used for high-throughput screening to identify biomarker candidates against Tobramycin (TOB), Ciprofloxacin (CIP), and Trimethoprim-Sulfamethoxazole (TMP-SMX). Identified candidates were further validated in vitro and also evaluated in silico for their diagnostic performance based on matched genotype-phenotype data. AMR biomarkers recovered by the metagenomics screening approach mechanistically explained 77% of observed resistance phenotypes for Tobramycin, 76% for Trimethoprim-Sulfamethoxazole, and 20% Ciprofloxacin. Sensitivity for Ciprofloxacin resistance detection could be improved to 97% by complementing results with AMR biomarkers that are undiscoverable due to intrinsic limitations of the workflow. Additionally, when combined in a multiplex diagnostic in silico panel, the identified AMR biomarkers reached promising positive and negative predictive values of up to 97 and 99%, respectively. Finally, we demonstrate that the developed workflow can be used to identify potential novel resistance mechanisms.

BusyBee Web: metagenomic data analysis by bootstrapped supervised binning and annotation

Metagenomics-based studies of mixed microbial communities are impacting biotechnology, life sciences and medicine. Computational binning of metagenomic data is a powerful approach for the culture-independent recovery of population-resolved genomic sequences, i.e. from individual or closely related, constituent microorganisms. Existing binning solutions often require a priori characterized reference genomes and/or dedicated compute resources. Extending currently available reference-independent binning tools, we developed the BusyBee Web server for the automated deconvolution of metagenomic data into population-level genomic bins using assembled contigs (Illumina) or long reads (Pacific Biosciences, Oxford Nanopore Technologies). A reversible compression step as well as bootstrapped supervised binning enable quick turnaround times. The binning results are represented in interactive 2D scatterplots. Moreover, bin quality estimates, taxonomic annotations and annotations of antibiotic resistance genes are computed and visualized. Ground truth-based benchmarks of BusyBee Web demonstrate comparably high performance to state-of-the-art binning solutions for assembled contigs and markedly improved performance for long reads (median F1 scores: 70.02–95.21%). Furthermore, the applicability to real-world metagenomic datasets is shown. In conclusion, our reference-independent approach automatically bins assembled contigs or long reads, exhibits high sensitivity and precision, enables intuitive inspection of the results, and only requires FASTA-formatted input. The web-based application is freely accessible at: https://ccb-microbe.cs.uni-saarland.de/busybee.

Prospect and challenge of detecting dynamic gene copy number increases in stem cells by whole genome sequencing

Abstract

Gene amplification is an evolutionarily well-conserved and highly efficient mechanism to increase the amount of specific proteins. In humans, gene amplification is a hallmark of cancer and has recently been found during stem cell differentiation. Amplifications in stem cells are restricted to specific tissue areas and time windows, rendering their detection difficult. Here, we report on the performance of deep WGS sequencing (average 82-fold depth of coverage) on the BGISEQ with nanoball technology to detect amplifications in human mesenchymal and neural stem cells. As reference technology, we applied array-based comparative genomic hybridization (aCGH), fluorescence in situ hybridization (FISH), and qPCR. Using different in silico strategies for amplification detection, we analyzed the potential of WGS for amplification detection. Our results provide evidence that WGS accurately identifies changes of the copy number profiles in human stem cell differentiation. However, the identified changes are not in all cases consistent between WGS and aCGH. The results between WGS and the validation by qPCR were concordant in 83.3% of all tested 36 cases. In sum, both genome-wide techniques, aCGH and WGS, have unique advantages and specific challenges, calling for locus-specific confirmation by the low-throughput approaches qPCR or FISH.

Key messages

WGS allows for the identification of dynamic copy number changes in human stem cells.

Less stringent threshold setting is crucial for detection of copy number increase.

Broad knowledge of dynamic copy number is pivotal to estimate stem cell capabilities.

Electronic supplementary material

The online version of this article (10.1007/s00109-019-01792-y) contains supplementary material, which is available to authorized users.

Integrating Culture-based Antibiotic Resistance Profiles with Whole-genome Sequencing Data for 11,087 Clinical Isolates

Emerging antibiotic resistance is a major global health threat. The analysis of nucleic acid sequences linked to susceptibility phenotypes facilitates the study of genetic antibiotic resistance determinants to inform molecular diagnostics and drug development. We collected genetic data (11,087 newly-sequenced whole genomes) and culture-based resistance profiles (10,991 out of the 11,087 isolates comprehensively tested against 22 antibiotics in total) of clinical isolates including 18 main species spanning a time period of 30 years. Species and drug specific resistance patterns were observed including increased resistance rates for Acinetobacter baumannii to carbapenems and for Escherichia coli to fluoroquinolones. Species-level pan-genomes were constructed to reflect the genetic repertoire of the respective species, including conserved essential genes and known resistance factors. Integrating phenotypes and genotypes through species-level pan-genomes allowed to infer gene–drug resistance associations using statistical testing. The isolate collection and the analysis results have been integrated into GEAR-base, a resource available for academic research use free of charge at https://gear-base.com.

Comparing genome versus proteome-based identification of clinical bacterial isolates

Whole-genome sequencing (WGS) is gaining importance in the analysis of bacterial cultures derived from patients with infectious diseases. Existing computational tools for WGS-based identification have, however, been evaluated on previously defined data relying thereby unwarily on the available taxonomic information.Here, we newly sequenced 846 clinical gram-negative bacterial isolates representing multiple distinct genera and compared the performance of five tools (CLARK, Kaiju, Kraken, DIAMOND/MEGAN and TUIT). To establish a faithful 'gold standard', the expert-driven taxonomy was compared with identifications based on matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) analysis. Additionally, the tools were also evaluated using a data set of 200 Staphylococcus aureus isolates.CLARK and Kraken (with k =31) performed best with 626 (100%) and 193 (99.5%) correct species classifications for the gram-negative and S. aureus isolates, respectively. Moreover, CLARK and Kraken demonstrated highest mean F-measure values (85.5/87.9% and 94.4/94.7% for the two data sets, respectively) in comparison with DIAMOND/MEGAN (71 and 85.3%), Kaiju (41.8 and 18.9%) and TUIT (34.5 and 86.5%). Finally, CLARK, Kaiju and Kraken outperformed the other tools by a factor of 30 to 170 fold in terms of runtime.We conclude that the application of nucleotide-based tools using k-mers-e.g. CLARK or Kraken-allows for accurate and fast taxonomic characterization of bacterial isolates from WGS data. Hence, our results suggest WGS-based genotyping to be a promising alternative to the MS-based biotyping in clinical settings. Moreover, we suggest that complementary information should be used for the evaluation of taxonomic classification tools, as public databases may suffer from suboptimal annotations.

Large-scale validation of miRNAs by disease association, evolutionary conservation and pathway activity

The validation of microRNAs (miRNAs) identified by next generation sequencing involves amplification-free and hybridization-based detection of transcripts as criteria for confirming valid miRNAs. Since respective validation is frequently not performed, miRNA repositories likely still contain a substantial fraction of false positive candidates while true miRNAs are not stored in the repositories yet. Especially if downstream analyses are performed with these candidates (e.g. target or pathway prediction), the results may be misleading. In the present study, we evaluated 558 mature miRNAs from miRBase and 1,709 miRNA candidates from next generation sequencing experiments by amplification-free hybridization and investigated their distributions in patients with various disease conditions. Notably, the most significant miRNAs in diseases are often not contained in the miRBase. However, these candidates are evolutionary highly conserved. From the expression patterns, target gene and pathway analyses and evolutionary conservation analyses, we were able to shed light on the complexity of miRNAs in humans. Our data also highlight that a more thorough validation of miRNAs identified by next generation sequencing is required. The results are available in miRCarta ( https://mircarta.cs.uni-saarland.de ).

Comparison of initial oral microbiomes of young adults with and without cavitated dentin caries lesions using an in situ biofilm model

Dental caries is caused by acids released from bacterial biofilms. However, the in vivo formation of initial biofilms in relation to caries remains largely unexplored. The aim of this study was to compare the oral microbiome during the initial phase of bacterial colonization for individuals with (CC) and without (NC) cavitated dentin caries lesions. Bovine enamel slabs on acrylic splints were worn by the volunteers (CC: 14, NC: 13) for in situ biofilm formation (2 h, 4 h, 8 h, 1 ml saliva as reference). Sequencing of the V1/V2 regions of the 16S rRNA gene was performed (MiSeq). The relative abundances of individual operational taxonomic units (OTUs) were compared between samples from the CC group and the NC group. Random forests models were furthermore trained to separate the groups. While the overall heterogeneity did not differ substantially between CC and NC individuals, several individual OTUs were found to have significantly different relative abundances. For the 8 h samples, most of the significant OTUs showed higher relative abundances in the CC group, while the majority of significant OTUs in the saliva samples were more abundant in the NC group. Furthermore, using OTU signatures enabled a separation between both groups, with area-under-the-curve (AUC) values of ~0.8. In summary, the results suggest that initial oral biofilms provide the potential to differentiate between CC and NC individuals.

Small ncRNA-Seq Results of Human Tissues: Variations Depending on Sample Integrity

BACKGROUND

Although mature miRNAs are relatively stable in vivo, RNA degradation can have a substantial influence on small noncoding RNA (sncRNA) profiles.

METHODS

Using different tissue storage conditions and RNA isolation procedures, we analyzed the integrity and quality of RNA isolates from human lung and heart tissues. We sequenced a total of 64 RNA samples and quantified the effect of RNA degradation, DNA contamination, and other confounding factors on the sncRNA-seq data. Besides microRNAs, other noncoding RNA species (piRNAs, tRNAs, snoRNAs, rRNAs) were investigated.

RESULTS

Consistent with previous results, we found that the tissue specificity of microRNAs is generally well preserved. The distribution of microRNA isoforms was similar to the distribution of canonical forms. New miRNAs were more frequently discovered in degraded samples. sncRNA Reads generated from degraded samples mapped frequently to piRNAs, tRNAs, snoRNAs, or scaRNAs. Sequencing reads that were depleted of sncRNAs showed an increased mapping frequency to bacterial species.

CONCLUSIONS

Our data emphasize the importance of sample integrity, especially for next-generation sequencing (NGS)-based high-throughput sncRNA profiles. For the prediction of novel miRNAs in particular, only samples with the highest RNA integrity should be used in order to avoid identification of false "miRNAs."

Gene amplification in mesenchymal stem cells and during differentiation towards adipocytes or osteoblasts

Gene amplifications are an attribute of tumor cells and have for long time been overlooked in normal cells. A growing number of investigations describe gene amplifications in normal mammalian cells during development and differentiation. Possibly, tumor cells have rescued the gene amplification mechanism as a physiological attribute of stem cells. Here, we investigated human mesenchymal stem cells (hMSCs) for gene amplification using array-CGH, single cell fluorescence in situ hybridization and qPCR. Gene amplifications were detected in mesenchymal stem cells and in mesenchymal stem cells during differentiation towards adipocytes and osteoblasts. Undifferentiated hMSCs harbor 12 amplified chromosomal regions, hMSCs that differentiated towards adipocytes 18 amplified chromosome regions, and hMSCs that differentiate towards osteoblasts 19 amplified regions. Specifically, hMSCs that differentiated towards adipocytes or osteoblasts harbor CDK4 and MDM2 amplifications both of which frequently occur in osteosarcoma and liposarcoma that are both of same cell origin. Beside the amplifications, we identified 36 under-replicated regions in undifferentiated and in differentiating hMSC cells.

miRNAs in Ancient Tissue Specimens of the Tyrolean Iceman

The analysis of nucleic acids in ancient samples is largely limited to DNA. Small noncoding RNAs (microRNAs) are known to be evolutionary conserved and stable. To gain knowledge on miRNAs measured from ancient samples, we profiled microRNAs in cryoconserved mummies. First, we established the approach on a World War One warrior, the "Kaiserjäger", which has been preserved for almost one century. Then, we profiled seven ancient tissue specimens including skeletal muscle, stomach mucosa, stomach content and two corpus organ tissues of the 5,300-year-old copper age mummy Iceman and compared these profiles to the presence of organ-specific miRNAs in modern tissues. Our analyses suggest the presence of specific miRNAs in the different Iceman's tissues. Of 1,066 analyzed human miRNAs, 31 were discovered across all biopsies and 87 miRNAs were detected only in a single sample. To check for potential microbiological contaminations, all miRNAs detected in Iceman samples and not present in ancient samples were mapped to 14,582 bacterial and viral genomes. We detected few hits (3.9% of miRNAs compared with 3.6% of miRNAs). Interestingly, the miRNAs with higher abundance across all ancient tissues were significantly enriched for Guanine (P value of 10-13) and Cytosine (P value of 10-7). The same pattern was observed for modern tissues. Comparing miRNAs measured from ancient organs to modern tissue patterns highlighted significant similarities, e.g., for miRNAs present in the muscle. Our first comprehensive analysis of microRNAs in ancient human tissues indicates that these stable molecules can be detected in tissue specimens after 5,300 years.

High-throughput qRT-PCR validation of blood microRNAs in non-small cell lung cancer

Validation of biomarkers is essential to advance the translational process to clinical application. Although there exists an increasing number of reports on small non-coding RNAs (microRNAs) as minimally-invasive markers from blood, serum or plasma, just a limited number is verified in follow-up studies. We used qRT-PCR to evaluate a known miRNA signature measured from blood that allowed for separation between patients with non-small cell lung cancer (NSCLC), COPD and healthy controls.

From the data of our previous microarray studies we selected a panel of 235 miRNAs related to lung cancer and COPD. We observed a high concordance between the AUC values of our initial microarray screening and the qRT-PCR data (correlation of 0.704, p < 10⁻¹⁶). Overall, 90.3% of markers were successfully validated. Among the top markers that were concordant between both studies we found hsa-miR-20b-5p, hsa-miR-20a-5p, hsa-miR-17-5p, and hsa-miR-106a-5p. The qRT-PCR analysis also confirmed that non-small cell lung cancer patients could be accurately differentiated from unaffected controls: a subset of five markers was sufficient to separate NSCLC patients from unaffected controls with accuracy of 94.5% (specificity and sensitivity of 98% and 91%). Beyond differentiation from controls, we also succeeded in separating NSCLC patients from patients with COPD. MiRNAs that were identified as relevant for the separation between lung cancer and COPD by both qRT-PCR and the array-based studies included hsa-miR-26a-5p, hsa-miR-328-3p and hsa-miR-1224-3p. Although for differentiation between NSCLC patients from COPD patients more markers were required, still high accuracy rates were obtained (5 markers: 78.8%; 10 markers: 83.9%; 50 markers: 87.6%).

Deep characterization of blood cell miRNomes by NGS

A systematic understanding of different factors influencing cell type specific microRNA profiles is essential for state-of-the art biomarker research. We carried out a comprehensive analysis of the biological variability and changes in cell type pattern over time for different cell types and different isolation approaches in technical replicates. All combinations of the parameters mentioned above have been measured, resulting in 108 miRNA profiles that were evaluated by next-generation-sequencing. The largest miRNA variability was due to inter-individual differences (34 %), followed by the cell types (23.4 %) and the isolation technique (17.2 %). The change over time in cell miRNA composition was moderate (<3 %) being close to the technical variations (<1 %). Largest variability (including technical and biological variance) was observed for CD8 cells while CD3 and CD4 cells showed significantly lower variations. ANOVA highlighted that 51.5 % of all miRNAs were significantly influenced by the purification technique. While CD4 cells were least affected, especially miRNA profiles of CD8 cells were fluctuating depending on the cell purification approach. To provide researchers access to the profiles and to allow further analyses of the tested conditions we implemented a dynamic web resource.

Longitudinal study on circulating miRNAs in patients after lung cancer resection

There is an urgent need of comprehensive longitudinal analyses of circulating miRNA patterns to identify dynamic changes of miRNAs in cancer patients after surgery. Here we provide longitudinal analysis of 1,205 miRNAs in plasma samples of 26 patients after lung cancer resection at 8 time points over a period of 18 months and compare them to 12 control patients. First, we report longitudinal changes with respect to the number of detected miRNAs over time and identified a significantly increased number of miRNAs in patients developing metastases (p = 0.0096). A quantitative analysis with respect to the expression level of the detected miRNAs revealed more significant changes in the miRNA levels in samples from patients without metastases compared to the non-cancer control patients. This analysis provided further evidence of miRNA plasma levels that are changing over time after tumor resection and correlate to patient outcome. Especially hsa-miR-197 could be validated by qRT-PCR as prognostic marker. Also for this miRNA, patients developing metastases had levels close to that of controls while patients that did not develop metastases showed a significant up-regulation.

In conclusion, our data indicate that the overall miRNome of a patient that later develops metastases is less affected by surgery than the miRNome of a patient who does not show metastases. The relationship between altered plasma levels of specific miRNAs with the development of metastases would partially have gone undetected by an analysis at a single time point only.

Prioritizing and selecting likely novel miRNAs from NGS data

Small non-coding RNAs play a key role in many physiological and pathological processes. Since 2004, miRNA sequences have been catalogued in miRBase, which is currently in its 21st version. We investigated sequence and structural features of miRNAs annotated in the miRBase and compared them between different versions of this reference database. We have identified that the two most recent releases (v20 and v21) are influenced by next-generation sequencing based miRNA predictions and show significant deviation from miRNAs discovered prior to the high-throughput profiling period. From the analysis of miRBase, we derived a set of key characteristics to predict new miRNAs and applied the implemented algorithm to evaluate novel blood-borne miRNA candidates. We carried out 705 individual whole miRNA sequencings of blood cells and collected a total of 9.7 billion reads. Using miRDeep2 we initially predicted 1452 potentially novel miRNAs. After excluding false positives, 518 candidates remained. These novel candidates were ranked according to their distance to the features in the early miRBase versions allowing for an easier selection of a subset of putative miRNAs for validation. Selected candidates were successfully validated by qRT-PCR and northern blotting. In addition, we implemented a web-server for ranking potential miRNA candidates, which is available at: www.ccb.uni-saarland.de/novomirank.

Influence of next-generation sequencing and storage conditions on miRNA patterns generated from PAXgene blood

Whole blood derived miRNA signatures determined by Next-Generation Sequencing (NGS) offer themselves as future minimally invasive biomarkers for various human diseases. The PAXgene system is a commonly used blood storage system for miRNA analysis. Central to all miRNA analyses that aim to identify disease specific miRNA signatures, is the question of stability and variability of the miRNA profiles that are generated by NGS. We characterized the influence of five different conditions on the genome wide miRNA expression pattern of human blood isolated in PAXgene RNA tubes. In detail, we analyzed 15 miRNomes from three individuals. The blood was subjected to different numbers of freeze/thaw cycles and analyzed for the influence of storage at -80 or 8 °C. We also determined the influence of blood collection and NGS preparations on the miRNA pattern isolated from a single individual, which has been sequenced 10 times. Here, five PAXGene tubes were consecutively collected that have been split in two replicates, representing two experimental batches. All samples were analyzed by Illumina NGS. For each sample, approximately 20 million NGS reads have been generated. Hierarchical clustering and Principal Component Analysis (PCA) showed an influence of the different conditions on the miRNA patterns. The effects of the different conditions on miRNA abundance are, however, smaller than the differences that are due to interindividual variability. We also found evidence for an influence of the NGS measurement on the miRNA pattern. Specifically, hsa-miR-1271-5p and hsa-miR-182-5p showed coefficients of variation above 100% indicating a strong influence of the NGS protocol on the abundance of these miRNAs.

What makes a blood cell based miRNA expression pattern disease specific?--a miRNome analysis of blood cell subsets in lung cancer patients and healthy controls

There is evidence of blood-borne miRNA signatures for various human diseases. To dissect the origin of disease-specific miRNA expression in human blood, we separately analyzed the miRNome of different immune cell subtypes, each in lung cancer patients and healthy individuals. Each immune cell type revealed a specific miRNA expression pattern also dependinging on the cell origin, line of defense, and function. The overall expression pattern of each leukocyte subtype showed great similarities between patients and controls. However, for each cell subtype we identified miRNAs that were deregulated in lung cancer patients including hsa-miR-21, a well-known oncomiR associated with poor lung cancer prognosis that was up-regulated in all leukocyte subtype comparisons of cancer versus controls. While the miRNome of cells of the adaptive immune system allowed only a weak separation between patients and controls, cells of the innate immune system allowed perfect or nearly perfect classification. Leukocytes of lung cancer patients show a cancer-specific miRNA expression profile. Our data also show that cancer specific miRNA expression pattern of whole blood samples are not determined by a single cell type. The data indicate that additional blood components, like erythrocytes, platelets, or exosomes might contribute to the disease specificity of a miRNA signature.

Posttranscriptional deregulation of signaling pathways in meningioma subtypes by differential expression of miRNAs

BACKGROUND

Micro (mi)RNAs are key regulators of gene expression and offer themselves as biomarkers for cancer development and progression. Meningioma is one of the most frequent primary intracranial tumors. As of yet, there are limited data on the role of miRNAs in meningioma of different histological subtypes and the affected signaling pathways.

METHODS

In this study, we compared expression of 1205 miRNAs in different meningioma grades and histological subtypes using microarrays and independently validated deregulation of selected miRNAs with quantitative real-time PCR. Clinical utility of a subset of miRNAs as biomarkers for World Health Organization (WHO) grade II meningioma based on quantitative real-time data was tested. Potential targets of deregulated miRNAs were discovered with an in silico analysis.

RESULTS

We identified 13 miRNAs deregulated between different subtypes of benign meningiomas, and 52 miRNAs deregulated in anaplastic meningioma compared with benign meningiomas. Known and putative target genes of deregulated miRNAs include genes involved in epithelial-to-mesenchymal transition for benign meningiomas, and Wnt, transforming growth factor-β, and vascular endothelial growth factor signaling for higher-grade meningiomas. Furthermore, a 4-miRNA signature (miR-222, -34a*, -136, and -497) shows promise as a biomarker differentiating WHO grade II from grade I meningiomas with an area under the curve of 0.75.

CONCLUSIONS

Our data provide novel insights into the contribution of miRNAs to the phenotypic spectrum in benign meningiomas. By deregulating translation of genes belonging to signaling pathways known to be important for meningioma genesis and progression, miRNAs provide a second in line amplification of growth promoting cellular signals. MiRNAs as biomarkers for diagnosis of aggressive meningiomas might prove useful and should be explored further in a prospective manner.

miRNAs can be generally associated with human pathologies as exemplified for miR-144

BACKGROUND

miRNA profiles are promising biomarker candidates for a manifold of human pathologies, opening new avenues for diagnosis and prognosis. Beyond studies that describe miRNAs frequently as markers for specific traits, we asked whether a general pattern for miRNAs across many diseases exists.

METHODS

We evaluated genome-wide circulating profiles of 1,049 patients suffering from 19 different cancer and non-cancer diseases as well as unaffected controls. The results were validated on 319 individuals using qRT-PCR.

RESULTS

We discovered 34 miRNAs with strong disease association. Among those, we found substantially decreased levels of hsa-miR-144* and hsa-miR-20b with AUC of 0.751 (95% CI: 0.703-0.799), respectively. We also discovered a set of miRNAs, including hsa-miR-155*, as rather stable markers, offering reasonable control miRNAs for future studies. The strong downregulation of hsa-miR-144* and the less variable pattern of hsa-miR-155* has been validated in a cohort of 319 samples in three different centers. Here, breast cancer as an additional disease phenotype not included in the screening phase has been included as the 20th trait.

CONCLUSIONS

Our study on 1,368 patients including 1,049 genome-wide miRNA profiles and 319 qRT-PCR validations further underscores the high potential of specific blood-borne miRNA patterns as molecular biomarkers. Importantly, we highlight 34 miRNAs that are generally dysregulated in human pathologies. Although these markers are not specific to certain diseases they may add to the diagnosis in combination with other markers, building a specific signature. Besides these dysregulated miRNAs, we propose a set of constant miRNAs that may be used as control markers.