Impact of Amoxicillin-Clavulanate followed by Autologous Fecal Microbiota Transplantation on Fecal Microbiome Structure and Metabolic Potential

Overview.This was a prospective, randomized controlled pilot study (NCT 02046525) to determine the effect of autoFMT on the intestinal microbiome versus placebo (saline enema) after 5 days of Amox-Clav, administered at 875 mg twice per day. The study participants and investigators were blinded to the treatment assignment until after all analyses were completed. This study was approved by the Washington University School of Medicine Human Research Protection Office. Written informed consent was obtained from all study participants.

Study participants.Ten healthy volunteers were recruited to participate. Written informed consent was obtained from all study participants. Inclusion criteria included being generally healthy and between 21 and 70 years of age. Exclusion criteria included a history of allergic reaction to beta-lactam antimicrobials or contraindications to amoxicillin-clavulanic acid; any nontopical antimicrobial exposure or tube feeds as a primary source of nutrition in the six months prior to enrollment; pregnancy or risk of becoming pregnant during the study period; gastroenteritis in the last three months; any nonelective hospitalization in the previous 12 months; incontinency of feces; prior resection or alteration of the stomach, small bowel, or colon; unwillingness to receive an enema/FMT; known colonization with an MDRO; anticipated change in diet, medications, or elective surgery during the study period; or a history of an intestinal disorder.

Study procedures.Participants submitted stool samples to study investigators at enrollment, immediately post-Amox-Clav, and at days 1, 7, 30, and 90 postenema. Once the preantimicrobial specimen was obtained, the participant was instructed to take 5 days of Amox-Clav at 875 mg twice daily (BID). Participants were requested to return the bottles of Amox-Clav to confirm all doses had been taken. The participant was then randomized in a 1:1 fashion to 100 ml of placebo (nonbacteriostatic saline) or autoFMT product. The autoFMT product was thawed overnight at room temperature. The study enema was administered 24 to 48 h after the last dose of Amox-Clav. The participants were instructed to retain the material for as long as possible. All participants retained >90% of the material. Study participants were monitored for 30 min after the enema. Previous studies in FMT for treatment of C. difficile infection have demonstrated that FMT effectiveness is not dependent on FMT volume (40). In order to maintain blinding of the study participant, the study enema was delivered to the study clinic in an opaque bag, and the participant was instructed to not turn around when the enema was being prepared and administered. A vial of the participant’s autoFMT product was opened in the room, regardless if randomized to placebo or autoFMT, prior to preparing the enema for administration in order to prevent the participant from attempting to guess which study group she or he had been assigned to based on the odor during the procedure. The investigators doing the sequencing work remained blinded to the study group until after the sequencing was completed.

Fecal processing.Participants were provided with sealable feces collection devices. After the participant collected a bowel movement, samples were delivered within 2 h of collection. Upon receipt, the feces was immediately processed with 1.5 g reserved for feces culture, 1 g for Clostridium difficile culture, and approximately 23 g for feces pulverization and sequence-based analyses, and 50 g of the subject’s first sample was used to prepare the FMT product.

FMT product preparation.To prepare the FMT product, approximately 50 g of feces was weighed and transferred to a sterile container. Nonbacteriostatic saline was added to the feces in a volume of twice the weight of the feces. A sterile spatula was used to emulsify the mixture for 3 to 5 min. Then the mixture was allowed to rest for 5 min. The feces-saline mixture was then poured through a stainless steel strainer to remove large particulate matter. Four 2-ml aliquots were frozen for genomic analysis of the stored FMT product. The remaining filtrate was drawn into 60-ml syringes (50 ml filtrate each). If the patient was randomized to saline, two 60 -ml syringes were filled with nonbacteriostatic saline. The filled syringes and aliquots were stored at −80°C.

Quantitative culturomics.Fresh feces (1.5 g) was added to an equal amount of 1× PBS and mixed thoroughly. Immediately, six 10-fold serial dilutions were made from the homogenized specimen. Ten microliters and 100 μl of the 1/10 and 1/100 dilutions were plated to two each of the following media: TSA II with 5% sheep blood (BAP; BBL BD, Franklin Lakes, NJ), Columbia colistin nalidix agar with 5% sheep blood (CNA; BBL BD), MacConkey (MAC; BBL BD), and chocolate agar (CHOC; BBL BD). The BAP and CHOC were incubated at 35°C in CO₂. The CNA and MAC were incubated at 35°C in air. The plates were read at 24 h and 72 h. Ten microliters and 100 μl of the 1/10³ and 1/10⁶ dilutions were plated to two each of the following media: brucella blood agar (BBA; Anaerobe Systems, Morgan Hill, CA), Bacteroides bile esculin agar (BBE; Anaerobe Systems), laked blood with kanamycin and vancomycin (LKV; Anaerobe Systems), cycloserine-cefoxitin fructose agar with horse blood and taurocholate (CCFA_HT; Anaerobe Systems), and phenylethyl alcohol blood agar (PEA; Anaerobe Systems). These plates were incubated at 35°C anaerobically for 7 days. The plates were read at 48 h, 4 to 5 days, and 7 days. One gram of fresh feces was processed for culture of C. difficile, as previously described (41).

All growth was observed and recorded semiquantitatively. All distinct colonies were identified using Vitek MS IVD v2.0 MALDI-TOF MS (bioMérieux). For any isolate that was not identified, a Gram stain was performed. All isolates were stored in TSB with glycerol at −80°C.

Metagenomic DNA extraction and sequencing.Metagenomic DNA was extracted from 0.5 g of feces via phenol-chloroform for each sample as previously described (18). DNA from each sample was sheared to 500 to 600 bp using the Covaris E220 sonicator (intensity, 4; duty cycle, 10%; cycles per burst, 200; treatment time, 75 s; temperature, 4°C; sample volume, 130 µl). The sonication product was purified with the Qiagen PCR purification kit and eluted in 63 μl nuclease-free water (prewarmed at 50°C).

End repair and barcode ligation reactions were performed in triplicate for each sample. End repair was performed in a Bio-Rad thermocycler using the following reagents: 2.5 μl T4 DNA ligase buffer with 10 mM ATP (10×) (NEB; B0202S), 1 μl dNTP (1 mM), 0.5 μl T4 polymerase (NEB; M0203S), 0.5 μl T4 PNK (NEB; M0201S), and 0.5 μl Taq polymerase (NEB; M0267S).

The barcode ligation reaction was performed by adding 2.5 μl of unique sequencing barcode at 1 μM to 500 ng of end-repaired DNA. An 0.8-μl amount of T4 DNA ligase (NEB; M0202M) was incubated with the barcode and sample DNA mixture in a Bio-Rad thermocycler. Following barcode ligation, samples were pooled into groups containing 6 barcodes. Pools were purified using the Qiagen PCR purification kit and MinElute columns and eluted in 15 μl of EB.

Fragment size selection was performed using a Use 0.5 TBE 1.5% agarose gel and visualized with SYBR Safe DNA stain. Barcoded DNA fragments sized 400 to 900 bp were cut from the gel, purified with the Qiagen Gel Extraction kit and MinElute columns, and eluted in 12 μl buffer EB. Two microliters of the eluted mixture was used for PCR enrichment of size-selected products. The enrichment reaction was prepared as follows: 12.5 μl 2× Phusion HF Master Mix, 9.5 μl nuclease-free water, 1 μl Illumina PCR primer mix (F+R) (10 μM), and 2 μl gel-purified DNA. The reaction was performed in a Bio-Rad thermocycler using the following program: 17 cycles of 98°C for 30 s (0:30), 65°C for 30 s (0:30), and 72°C for 30 s (0:30); 72°C for 5 min; and 4°C forever.

Size selection of the enriched products was performed using a Use 0.5 TBE 1.5% agarose gel, and products were visualized with SYBR Safe DNA stain. Fragments sized 400 to 900 bp were cut from the gel, purified with the Qiagen Gel Extraction kit and MinElute columns, and eluted in 15 μl of EB. DNA concentration was quantified using a Qbit fluorometer, and all sample pools were combined at equal concentrations for sequencing. Prior to sequencing, pooled fragment size was assessed with a Bioanalyzer trace and barcode read distribution was assessed using a spike-in run on the Illumina sequencing platform.

To generate metagenomic sequencing reads, the Illumina NextSeq platform was used with the high output kit and settings to generate a minimum of 400 million paired-end reads per run. In total, 70 samples were sequenced at least 1 million reads per sample to allow microbiome and resistome analyses. The depth of 1 million reads was established using previous studies of the gut microbiome (42–44).

Multiplexed Illumina paired-end shotgun metagenomic sequence reads were demultiplexed by barcode. Reads without exact match to barcode were discarded. The remaining reads were quality filtered using Trimmomatic v0.35 and parameters optimized by the Dantas lab (seed mismatches, 2; palindrome clip threshold, 30; simple clip threshold, 10; minimum adapter length, 1; keep both reads, TRUE; window size, 4; required quality, 20; leading, 10; trailing, 10; minimum length, 60).

Microbiome taxonomic composition prediction.Taxonomic composition was determined by comparing unique indicator sequences from sequencing reads to clade-specific marker genes from approximately 17,000 reference genomes using MetaPhlAn 2.6.0 (18). Analysis was performed using metaphlan2/2.6.0 with the following parameters: –blastdb./metaphlan2/blastdb–input_type multifasta. The difference in species diversity was calculated using the Shannon index function from the R vegan package. The difference in species composition was calculated using Bray-Curtis distance between samples, and significance was tested with type II Adonis.

Resistance gene prediction.The metagenomic DNA sequence was analyzed for both known and sequence-novel antimicrobial resistance genes using functional metagenomic selections and curated resistance gene databases for resistance gene identification and ShortBRED for resistance gene abundance estimation (45). To supplement known resistance gene markers available via the well-curated CARD database, markers from novel, cryptic resistance gene unique to the studied samples were obtained by performing functional metagenomic selections on bacterial metagenomic DNA pooled by participant (46). Functional metagenomic identification of genes that conferred resistance to Amox-Clav was performed as previously described (18, 47, 48) by randomly shearing metagenomic DNA from each pool of feces into fragment libraries. These libraries were cloned into the natively pan-susceptible host Escherichia coli DH10B using vector PZE21. For each sample, the host cells containing library fragments were selected against amoxicillin and ampicillin at concentrations lethal to the untransformed host. Surviving colonies were pooled, and the inserted fragments were sequenced via the Illumina MiSeq platform (2 × 150 bp). Sequencing reads were assembled into contigs with the PARFUMS pipeline (49). The contigs were searched for open reading frames with MetaGeneMark (50) and annotated by hmmscan function of HMMER3 (51) against the Resfams core database (52), Pfams (46), and TIGRFAMS (46). Resulting annotations were then hand curated (46) in the following manner. Selections were excluded if >100 contigs were assembled, because this suggests a failure in the assembly or selection since <100 unique resistance contigs are expected per selection. Within each contig, annotations were ranked by specificity to the selective agents and lowest E value with preference given to Resfams annotations over Pfams or TIGRFAMS. In the absence of a clear specific causative gene annotation (i.e., a beta-lactamase), the two best annotations with <90% overlap were accepted. The sequences corresponding to accepted annotations were then pooled with known antimicrobial resistance gene sequences from CARD 2017 (46). The genes were then quantified in unassembled metagenomic sequence using the ShortBRED pipeline with a clustering identity of 1.

Metabolic pathway prediction.The metabolic potential of the fecal microbial communities was inferred through functional potential profiling. The presence and abundance of metabolic pathways in the microbial communities were assessed using HUMAnN2, which maps unassembled shotgun sequencing reads to functionally annotated species pangenomes in order to predict function (53). Default parameters were used, and details can be found at http://huttenhower.sph.harvard.edu/humann2.

In order to assess metabolic perturbations, we implemented an index of metabolic capacity (IMC) from the metagenomic sequencing data. To form this index, we grouped genes with metabolic functions into 37 metabolic pathway categories using Gene Ontology (GO) terms. GO term grouping was performed using ASaiM (54) and custom scripts. The ability of the predicted metabolic capacity to discriminate between sample groups was visualized using principal component analysis (PCA), and significance was tested using type II Adonis of Bray-Curtis distance between samples. This test of significance was conducted with and without participants 5 and 8 to determine whether any difference observed was driven by the large taxonomic perturbations seen in those participants. Each metabolic category comprising the IMC was tested for contribution to the difference between enrollment and post-Amox-Clav IMC using a Random Forests model. This was implemented using the Boruta package in R (55). Factors were ranked according to their contribution to this difference.

Statistical analysis.Significance of differences between taxonomic composition at baseline and post-Amox-Clav was calculated by determining Bray-Curtis distance between the communities and using a type II Adonis with a significance level of P < 0.05 with n = 10. Significance of differences between IMC at enrollment and post-Amox-Clav was calculated similarly. Bray-Curtis distance between IMCs was calculated, and a type II Adonis with a significance level of P < 0.05 with n = 10 was used. Because these are permutation tests, iteration cutoffs were set to detect significance up to P < 0.01. Saline and autoFMT treatment group taxonomy and IMC were compared to enrollment for return to baseline comparison. Enrichment of beta-lactamase genes in postantimicrobial samples relative to enrollment was calculated using a two-tailed t test and a significance level of P < 0.05. This test was also applied to the comparison between saline and autoFMT to enrollment baseline.

Index of metabolic capacity validation.To determine the likelihood of obtaining a significant difference in IMC between groups of microbiome samples by chance, we performed bootstrap analysis using the Human Microbiome Project 2 (HMP2) Inflammatory Bowel Disease (IBD) cohort. This publicly available data set contains 364 samples from 27 healthy participants and 375 samples from 38 ulcerative colitis patients (UC) (56). We randomly selected 2 groups of 10 healthy samples (without replacement) and saw a difference in IMC (P < 0.05) in 39 of 1,000 such iterations. This is consistent with the expectation that we are not likely to see a significant difference in IMC by chance alone. Note that we selected 10 samples for each group to mirror the autoFMT study design, where a significant difference in IMC (P < 0.01) was noted between the 10 enrollment and 10 post-Amox-Clav samples.

Accession number(s).All nucleotide sequences generated during this study have been uploaded to NCBI under BioProject accession no. PRJNA446061.