Rigorous Donor Selection for Fecal Microbiota Transplantation in Active Ulcerative Colitis: Key Lessons From a Randomized Controlled Trial Halted for Futility.

BACKGROUND & AIMS: Rigorous donor preselection on microbiota level, strict anaerobic processing, and repeated fecal microbiota transplantation (FMT) administration were hypothesized to improve FMT induction of remission in ulcerative colitis (UC). METHODS: The RESTORE-UC trial was a multi-centric, double-blind, sham-controlled, randomized trial. Patients with moderate to severe UC (defined by total Mayo 4-10) were randomly allocated to receive 4 anaerobic-prepared allogenic or autologous donor FMTs. Allogenic donor material was selected after a rigorous screening based on microbial cell count, enterotype, and the abundance of specific genera. The primary endpoint was steroid-free clinical remission (total Mayo ≤2, no sub-score >1) at week 8. A pre-planned futility analysis was performed after 66% (n = 72) of intended inclusions (n = 108). Quantitative microbiome profiling (n = 44) was performed at weeks 0 and 8. RESULTS: In total, 72 patients were included, of which 66 received at least 1 FMT (allogenic FMT, n = 30 and autologous FMT, n = 36). At week 8, respectively, 3 and 5 patients reached the primary endpoint of steroid-free clinical remission (P = .72), indicating no treatment difference of at least 5% in favor of allogenic FMT. Hence, the study was stopped due to futility. Microbiome analysis showed numerically more enterotype transitions upon allogenic FMT compared with autologous FMT, and more transitions were observed when patients were treated with a different enterotype than their own at baseline (P = .01). Primary response was associated with lower total Mayo scores, lower bacterial cell counts, and higher Bacteroides 2 prevalence at baseline. CONCLUSION: The RESTORE-UC trial did not meet its primary endpoint of increased steroid-free clinical remission at week 8. Further research should additionally consider patient selection, sterilized sham-control, increased frequency, density, and viability of FMT prior to administration. CLINICALTRIALS: gov, Number: NCT03110289.

Effects of fecal microbiota transplantation for recurrent Clostridium difficile infection in children on kidney replacement therapy: a pilot study.

BACKGROUND: Recurrent Clostridium difficile infection (rCDI) is a rising problem in children with chronic diseases. Fecal microbiota transplantation (FMT) is a recent alternative for rCDI patients who do not respond to conventional treatment. FMT could have an additional positive effect on the intestinal dysbiosis and accumulation of uremic retention molecules (URM) associated with chronic kidney disease (CKD). Our aim was to investigate the clinical efficacy of FMT for rCDI in children with CKD together with the effect on dysbiosis and URM levels. METHODS: We analyzed stool and blood samples before and until 3 months after FMT in 3 children between 4 and 8 years old with CKD and rCDI. The microbiome was analyzed by 16 s rRNA sequencing. URM were analyzed with ultra-performance liquid chromatography-tandem mass spectrometry. CRP and fecal calprotectin were analyzed as parameters for systemic and gut inflammation, respectively. RESULTS: CDI resolved after FMT in all three without adverse events; one patient needed a second FMT. No significant effect on CRP and calprotectin was observed. Stool samples demonstrated a reduced richness and bacterial diversity which did not improve after FMT. We did observe a trend in the decrease of specific URM up to 3 months after FMT. CONCLUSION: FMT is an effective treatment for rCDI in patients with CKD. Analysis of the microbiome showed an important intestinal dysbiosis that, besides a significant reduction in Clostridium difficile, did not significantly change after FMT. A trend for reduction was seen in some of the measured URM after FMT.

Diversity Analysis in Viral Metagenomes.

Viruses are the most abundant and diverse biological entity in the earth. Nowadays, there are several viral metagenomes from different ecological niches which have been used to characterize new viral particles and to determine their diversity. However, viral metagenomic data have the disadvantage to be high-dimensional compositional and sparse. This type of data renders many of the conventional multivariate statistical analyses inoperative. Fortunately, different libraries and statistical packages have been developed to deal with this problem and perform the different ecological and statistical analyses. In the present chapter, it is analyzed simulated viral metagenomes, based on real human gut-associated viral metagenomes, using different R and python packages. The example presented here includes the estimation and comparison of different indexes of diversity, evenness, and richness; perform different ordination and statistical analysis using different dissimilarity metrics; determine the optimal cluster configuration and perform biomarker discovery. The scripts and the simulated datasets are in https://github.com/jorgevazcast/Viromic-diversity.

Fragmentation and Coverage Variation in Viral Metagenome Assemblies, and Their Effect in Diversity Calculations.

Metagenomic libraries consist of DNA fragments from diverse species, with varying genome size and abundance. High-throughput sequencing platforms produce large volumes of reads from these libraries, which may be assembled into contigs, ideally resembling the original larger genomic sequences. The uneven species distribution, along with the stochasticity in sample processing and sequencing bias, impacts the success of accurate sequence assembly. Several assemblers enable the processing of viral metagenomic data de novo, generally using overlap layout consensus or de Bruijn graph approaches for contig assembly. The success of viral genomic reconstruction in these datasets is limited by the degree of fragmentation of each genome in the sample, which is dependent on the sequencing effort and the genome length. Depending on ecological, biological, or procedural biases, some fragments have a higher prevalence, or coverage, in the assembly. However, assemblers must face challenges, such as the formation of chimerical structures and intra-species variability. Diversity calculation relies on the classification of the sequences that comprise a metagenomic dataset. Whenever the corresponding genomic and taxonomic information is available, contigs matching the same species can be classified accordingly and the coverage of its genome can be calculated for that species. This may be used to compare populations by estimating abundance and assessing species distribution from this data. Nevertheless, the coverage does not take into account the degree of fragmentation, or else genome completeness, and is not necessarily representative of actual species distribution in the samples. Furthermore, undetermined sequences are abundant in viral metagenomic datasets, resulting in several independent contigs that cannot be assigned by homology or genomic information. These may only be classified as different operational taxonomic units (OTUs), sometimes remaining inadvisably unrelated. Thus, calculations using contigs as different OTUs ultimately overestimate diversity when compared to diversity calculated from species coverage. In order to compare the effect of coverage and fragmentation, we generated three sets of simulated Illumina paired-end reads with different sequencing depths. We compared different assemblies performed with RayMeta, CLC Assembly Cell, MEGAHIT, SPAdes, Meta-IDBA, SOAPdenovo, Velvet, Metavelvet, and MIRA with the best attainable assemblies for each dataset (formed by arranging data using known genome coordinates) by calculating different assembly statistics. A new fragmentation score was included to estimate the degree of genome fragmentation of each taxon and adjust the coverage accordingly. The abundance in the metagenome was compared by bootstrapping the assembly data and hierarchically clustering them with the best possible assembly. Additionally, richness and diversity indexes were calculated for all the resulting assemblies and were assessed under two distributions: contigs as independent OTUs and sequences classified by species. Finally, we search for the strongest correlations between the diversity indexes and the different assembly statistics. Although fragmentation was dependent of genome coverage, it was not as heavily influenced by the assembler. The sequencing depth was the predominant attractor that influenced the success of the assemblies. The coverage increased notoriously in larger datasets, whereas fragmentation values remained lower and unsaturated. While still far from obtaining the ideal assemblies, the RayMeta, SPAdes, and the CLC assemblers managed to build the most accurate contigs with larger datasets while Meta-IDBA showed a good performance with the medium-sized dataset, even after the adjusted coverage was calculated. Their resulting assemblies showed the highest coverage scores and the lowest fragmentation values. Alpha diversity calculated from contigs as OTUs resulted in significantly higher values for all assemblies when compared with actual species distribution, showing an overestimation due to the increased predicted abundance. Conversely, using PHACCS resulted in lower values for all assemblers. Different association methods (random-forest, generalized linear models, and the Spearman correlation index) support the number of contigs, the coverage, and fragmentation as the assembly parameters that most affect the estimation of the alpha diversity. Coverage calculations may provide an insight into relative completeness of a genome but they overlook missing fragments or overly separated sequences in a genome. The assembly of a highly fragmented genomes with high coverage may still lead to the clustering of different OTUs that are actually different fragments of a genome. Thus, it proves useful to penalize coverage with a fragmentation score. Using contigs for calculating alpha diversity result in overestimation but it is usually the only approach available. Still, it is enough for sample comparison. The best approach may be determined by choosing the assembler that better fits the sequencing depth and adjusting the parameters for longer accurate contigs whenever possible whereas diversity may be calculated considering taxonomical and genomic information if available.