Evaluation of the Microba Community Profiler for Taxonomic Profiling of Metagenomic Datasets From the Human Gut Microbiome

Effects of a prebiotic formulation on the composition of the faecal microbiota of people with functional constipation

PURPOSE

Prebiotics are defined as substances which selectively promote beneficial gut microbes leading to a health benefit for the host. Limited trials have been carried out investigating their effect on the microbiota composition of individuals afflicted by functional constipation with equivocal outcomes. In a 21-day randomised, controlled clinical trial involving 61 adults with functional constipation, a prebiotic formulation with partially hydrolysed guar gum and acacia gum as its main ingredients, significantly increased complete spontaneous bowel motions in the treatment group. This follow-up exploratory analysis investigated whether the prebiotic was associated with changes to the composition, richness, and diversity of the faecal microbiota.

METHODS

Participants provided a faecal specimen at baseline and on day 21 of the intervention period. Whole genome metagenomic shotgun sequencing comprehensively assessed taxonomic and functional composition of the microbiota.

RESULTS

Linear mixed effects regression models adjusted for potential confounders showed a significant reduction in species richness of 28.15 species (95% CI - 49.86, - 6.43) and Shannon diversity of 0.29 units (95% CI - 0.56, - 0.02) over the trial period in the prebiotic group. These changes were not observed in the control group, and functional composition was unchanged in both groups.

CONCLUSION

In adults with functional constipation, the intake of a prebiotic formulation was associated with a decline of species richness and Shannon diversity. Further research regarding the associations between prebiotics and the composition and function of the gut microbiota is warranted.

Mock community taxonomic classification performance of publicly available shotgun metagenomics pipelines

Shotgun metagenomic sequencing comprehensively samples the DNA of a microbial sample. Choosing the best bioinformatics processing package can be daunting due to the wide variety of tools available. Here, we assessed publicly available shotgun metagenomics processing packages/pipelines including bioBakery, Just a Microbiology System (JAMS), Whole metaGenome Sequence Assembly V2 (WGSA2), and Woltka using 19 publicly available mock community samples and a set of five constructed pathogenic gut microbiome samples. Also included is a workflow for labelling bacterial scientific names with NCBI taxonomy identifiers for better resolution in assessing results. The Aitchison distance, a sensitivity metric, and total False Positive Relative Abundance were used for accuracy assessments for all pipelines and mock samples. Overall, bioBakery4 performed the best with most of the accuracy metrics, while JAMS and WGSA2, had the highest sensitivities. Furthermore, bioBakery is commonly used and only requires a basic knowledge of command line usage. This work provides an unbiased assessment of shotgun metagenomics packages and presents results assessing the performance of the packages using mock community sequence data.

Reintroducing a keystone bioturbator can facilitate microbial bioremediation in urban polluted sediments

Anthropogenic environmental stressors have significantly reduced biodiversity and the capacity of remnant natural habitats to deliver ecosystem functions and services in urban areas. To mitigate these impacts and recover biodiversity and function, ecological restoration strategies are needed. While habitat restoration is proliferating in rural and peri-urban areas, strategies purposely designed to succeed under the environmental, social and political pressures of urban areas are lacking. Here, we propose that ecosystem health in marine urban areas can be improved by restoring biodiversity to the most dominant habitat, unvegetated sediments. We reintroduced a native ecosystem engineer, the sediment bioturbating worm Diopatra aciculata, and assessed their effects on microbial biodiversity and function. Results showed that worms can affect the diversity of microbes, but effects varied between locations. Worms caused shifts in microbial community composition and function at all locations. Specifically, the abundance of microbes capable of chlorophyll production (i.e. benthic microalgae) increased and the abundance of microbes capable of methane production decreased. Moreover, worms increased the abundances of microbes capable of denitrification in the site with lowest sediment oxygenation. Worms also affected microbes capable of degrading the polycyclic aromatic hydrocarbon toluene, although the direction of that effect was site-specific. This study provides evidence that a simple intervention such as the reintroduction of a single species can enhance sediment functions important for the amelioration of contamination and eutrophication, although further studies are needed to understand the variation in outcomes between sites. Nevertheless, restoration strategies targeting unvegetated sediments provide an opportunity to combat anthropogenic stressors in urban ecosystems and may be used for precondition before more traditional forms of habitat restoration such as seagrass, mangrove and shellfish restoration.

From defaults to databases: parameter and database choice dramatically impact the performance of metagenomic taxonomic classification tools

In metagenomic analyses of microbiomes, one of the first steps is usually the taxonomic classification of reads by comparison to a database of previously taxonomically classified genomes. While different studies comparing metagenomic taxonomic classification methods have determined that different tools are 'best', there are two tools that have been used the most to-date: Kraken (k-mer-based classification against a user-constructed database) and MetaPhlAn (classification by alignment to clade-specific marker genes), the latest versions of which are Kraken2 and MetaPhlAn 3, respectively. We found large discrepancies in both the proportion of reads that were classified as well as the number of species that were identified when we used both Kraken2 and MetaPhlAn 3 to classify reads within metagenomes from human-associated or environmental datasets. We then investigated which of these tools would give classifications closest to the real composition of metagenomic samples using a range of simulated and mock samples and examined the combined impact of tool-parameter-database choice on the taxonomic classifications given. This revealed that there may not be a one-size-fits-all 'best' choice. While Kraken2 can achieve better overall performance, with higher precision, recall and F1 scores, as well as alpha- and beta-diversity measures closer to the known composition than MetaPhlAn 3, the computational resources required for this may be prohibitive for many researchers, and the default database and parameters should not be used. We therefore conclude that the best tool-parameter-database choice for a particular application depends on the scientific question of interest, which performance metric is most important for this question and the limit of available computational resources.

Bioinformatic Tools for NGS-Based Metagenomics to Improve the Clinical Diagnosis of Emerging, Re-Emerging and New Viruses

Epidemics and pandemics have occurred since the beginning of time, resulting in millions of deaths. Many such disease outbreaks are caused by viruses. Some viruses, particularly RNA viruses, are characterized by their high genetic variability, and this can affect certain phenotypic features: tropism, antigenicity, and susceptibility to antiviral drugs, vaccines, and the host immune response. The best strategy to face the emergence of new infectious genomes is prompt identification. However, currently available diagnostic tests are often limited for detecting new agents. High-throughput next-generation sequencing technologies based on metagenomics may be the solution to detect new infectious genomes and properly diagnose certain diseases. Metagenomic techniques enable the identification and characterization of disease-causing agents, but they require a large amount of genetic material and involve complex bioinformatic analyses. A wide variety of analytical tools can be used in the quality control and pre-processing of metagenomic data, filtering of untargeted sequences, assembly and quality control of reads, and taxonomic profiling of sequences to identify new viruses and ones that have been sequenced and uploaded to dedicated databases. Although there have been huge advances in the field of metagenomics, there is still a lack of consensus about which of the various approaches should be used for specific data analysis tasks. In this review, we provide some background on the study of viral infections, describe the contribution of metagenomics to this field, and place special emphasis on the bioinformatic tools (with their capabilities and limitations) available for use in metagenomic analyses of viral pathogens.

Evaluation of taxonomic classification and profiling methods for long-read shotgun metagenomic sequencing datasets

BACKGROUND

Long-read shotgun metagenomic sequencing is gaining in popularity and offers many advantages over short-read sequencing. The higher information content in long reads is useful for a variety of metagenomics analyses, including taxonomic classification and profiling. The development of long-read specific tools for taxonomic classification is accelerating, yet there is a lack of information regarding their relative performance. Here, we perform a critical benchmarking study using 11 methods, including five methods designed specifically for long reads. We applied these tools to several mock community datasets generated using Pacific Biosciences (PacBio) HiFi or Oxford Nanopore Technology sequencing, and evaluated their performance based on read utilization, detection metrics, and relative abundance estimates.

RESULTS

Our results show that long-read classifiers generally performed best. Several short-read classification and profiling methods produced many false positives (particularly at lower abundances), required heavy filtering to achieve acceptable precision (at the cost of reduced recall), and produced inaccurate abundance estimates. By contrast, two long-read methods (BugSeq, MEGAN-LR & DIAMOND) and one generalized method (sourmash) displayed high precision and recall without any filtering required. Furthermore, in the PacBio HiFi datasets these methods detected all species down to the 0.1% abundance level with high precision. Some long-read methods, such as MetaMaps and MMseqs2, required moderate filtering to reduce false positives to resemble the precision and recall of the top-performing methods. We found read quality affected performance for methods relying on protein prediction or exact k-mer matching, and these methods performed better with PacBio HiFi datasets. We also found that long-read datasets with a large proportion of shorter reads (< 2 kb length) resulted in lower precision and worse abundance estimates, relative to length-filtered datasets. Finally, for classification methods, we found that the long-read datasets produced significantly better results than short-read datasets, demonstrating clear advantages for long-read metagenomic sequencing.

CONCLUSIONS

Our critical assessment of available methods provides best-practice recommendations for current research using long reads and establishes a baseline for future benchmarking studies.

Cultivation-independent genomes greatly expand taxonomic-profiling capabilities of mOTUs across various environments

BACKGROUND

Taxonomic profiling is a fundamental task in microbiome research that aims to detect and quantify the relative abundance of microorganisms in biological samples. Available methods using shotgun metagenomic data generally depend on the deposition of sequenced and taxonomically annotated genomes, usually from cultures of isolated strains, in reference databases (reference genomes). However, the majority of microorganisms have not been cultured yet. Thus, a substantial fraction of microbial community members remains unaccounted for during taxonomic profiling, particularly in samples from underexplored environments. To address this issue, we developed the mOTU profiler, a tool that enables reference genome-independent species-level profiling of metagenomes. As such, it supports the identification and quantification of both "known" and "unknown" species based on a set of select marker genes.

RESULTS

We present mOTUs3, a command line tool that enables the profiling of metagenomes for >33,000 species-level operational taxonomic units. To achieve this, we leveraged the reconstruction of >600,000 draft genomes, most of which are metagenome-assembled genomes (MAGs), from diverse microbiomes, including soil, freshwater systems, and the gastrointestinal tract of ruminants and other animals, which we found to be underrepresented by reference genomes. Overall, two thirds of all species-level taxa lacked a reference genome. The cumulative relative abundance of these newly included taxa was low in well-studied microbiomes, such as the human body sites (6-11%). By contrast, they accounted for substantial proportions (ocean, freshwater, soil: 43-63%) or even the majority (pig, fish, cattle: 60-80%) of the relative abundance across diverse non-human-associated microbiomes. Using community-developed benchmarks and datasets, we found mOTUs3 to be more accurate than other methods and to be more congruent with 16S rRNA gene-based methods for taxonomic profiling. Furthermore, we demonstrate that mOTUs3 increases the resolution of well-known microbial groups into species-level taxa and helps identify new differentially abundant taxa in comparative metagenomic studies.

CONCLUSIONS

We developed mOTUs3 to enable accurate species-level profiling of metagenomes. Compared to other methods, it provides a more comprehensive view of prokaryotic community diversity, in particular for currently underexplored microbiomes. To facilitate comparative analyses by the research community, it is released with >11,000 precomputed profiles for publicly available metagenomes and is freely available at: https://github.com/motu-tool/mOTUs . Video Abstract.

expam-high-resolution analysis of metagenomes using distance trees

Summary

Shotgun metagenomic sequencing provides the capacity to understand microbial community structure and function at unprecedented resolution; however, the current analytical methods are constrained by a focus on taxonomic classifications that may obfuscate functional relationships. Here, we present expam, a tree-based, taxonomy agnostic tool for the identification of biologically relevant clades from shotgun metagenomic sequencing.

Availability and implementation

expam is an open-source Python application released under the GNU General Public Licence v3.0. expam installation instructions, source code and tutorials can be found at https://github.com/seansolari/expam.

Supplementary information

Supplementary data are available at Bioinformatics online.

Effect of a reduced fat and sugar maternal dietary intervention during lactation on the infant gut microbiome

Objective

A growing body of literature has shown that maternal diet during pregnancy is associated with infant gut bacterial composition. However, whether maternal diet during lactation affects the exclusively breastfed infant gut microbiome remains understudied. This study sets out to determine whether a two-week of a reduced fat and sugar maternal dietary intervention during lactation is associated with changes in the infant gut microbiome composition and function.

Design

Stool samples were collected from four female and six male (n = 10) infants immediately before and after the intervention. Maternal baseline diet from healthy mothers aged 22–37 was assessed using 24-h dietary recall. During the 2-week dietary intervention, mothers were provided with meals and their dietary intake was calculated using FoodWorks 10 Software. Shotgun metagenomic sequencing was used to characterize the infant gut microbiome composition and function.

Results

In all but one participant, maternal fat and sugar intake during the intervention were significantly lower than at baseline. The functional capacity of the infant gut microbiome was significantly altered by the intervention, with increased levels of genes associated with 28 bacterial metabolic pathways involved in biosynthesis of vitamins (p = 0.003), amino acids (p = 0.005), carbohydrates (p = 0.01), and fatty acids and lipids (p = 0.01). Although the dietary intervention did not affect the bacterial composition of the infant gut microbiome, relative difference in maternal fiber intake was positively associated with increased abundance of genes involved in biosynthesis of storage compounds (p = 0.016), such as cyanophycin. Relative difference in maternal protein intake was negatively associated with Veillonella parvula (p = 0.006), while positively associated with Klebsiella michiganensis (p = 0.047). Relative difference in maternal sugar intake was positively associated with Lactobacillus paracasei (p = 0.022). Relative difference in maternal fat intake was positively associated with genes involved in the biosynthesis of storage compounds (p = 0.015), fatty acid and lipid (p = 0.039), and metabolic regulator (p = 0.038) metabolic pathways.

Conclusion

This pilot study demonstrates that a short-term maternal dietary intervention during lactation can significantly alter the functional potential, but not bacterial taxonomy, of the breastfed infant gut microbiome. While the overall diet itself was not able to change the composition of the infant gut microbiome, changes in intakes of maternal protein and sugar during lactation were correlated with changes in the relative abundances of certain bacterial species.

Clinical trial registration: Australian New Zealand Clinical Trials Registry (ACTRN12619000606189).

RiboTaxa: combined approaches for rRNA genes taxonomic resolution down to the species level from metagenomics data revealing novelties

Metagenomic classifiers are widely used for the taxonomic profiling of metagenomics data and estimation of taxa relative abundance. Small subunit rRNA genes are a gold standard for phylogenetic resolution of microbiota, although the power of this marker comes down to its use as full-length. We aimed at identifying the tools that can efficiently lead to taxonomic resolution down to the species level. To reach this goal, we benchmarked the performance and accuracy of rRNA-specialized versus general-purpose read mappers, reference-targeted assemblers and taxonomic classifiers. We then compiled the best tools (BBTools, FastQC, SortMeRNA, MetaRib, EMIRGE, VSEARCH, BBMap and QIIME 2’s Sklearn classifier) to build a pipeline called RiboTaxa. Using metagenomics datasets, RiboTaxa gave the best results compared to other tools (i.e. Kraken2, Centrifuge, METAXA2, phyloFlash, SPINGO, BLCA, MEGAN) with precise taxonomic identification and relative abundance description without false positive detection (F-measure of 100% and 83.7% at genus level and species level, respectively). Using real datasets from various environments (i.e. ocean, soil, human gut) and from different approaches (e.g. metagenomics and gene capture by hybridization), RiboTaxa revealed microbial novelties not discerned by current bioinformatics analysis opening new biological perspectives in human and environmental health.

Characterization and Demonstration of Mock Communities as Control Reagents for Accurate Human Microbiome Community Measurements

Standardization and quality assurance of microbiome community analysis by high-throughput DNA sequencing require widely accessible and well-characterized reference materials. Here, we report on newly developed DNA and whole-cell mock communities to serve as control reagents for human gut microbiota measurements by shotgun metagenomics and 16S rRNA gene amplicon sequencing. The mock communities were formulated as near-even blends of up to 20 bacterial species prevalent in the human gut, span a wide range of genomic guanine-cytosine (GC) contents, and include multiple strains with Gram-positive type cell walls. Through a collaborative study, we carefully characterized the mock communities by shotgun metagenomics, using previously developed standardized protocols for DNA extraction and sequencing library construction. Further, we validated fitness of the mock communities for revealing technically meaningful differences among protocols for DNA extraction and metagenome/16S rRNA gene amplicon library construction. Finally, we used the mock communities to reveal varying performance of metagenome-based taxonomic profilers and the impact of trimming and filtering of sequencing reads on observed species profiles. The latter showed that aggressive preprocessing of reads may result in substantial GC-dependent bias and should thus be carefully evaluated to minimize unintended effects on species abundances. Taken together, the mock communities are expected to support a myriad of applications that rely on well-characterized control reagents, ranging from evaluation and optimization of methods to assessment of reproducibility in interlaboratory studies and routine quality control.

IMPORTANCE Application of high-throughput DNA sequencing has greatly accelerated human microbiome research and its translation into new therapeutic and diagnostic capabilities. Microbiome community analyses results can, however, vary considerably across studies or laboratories, and establishment of measurement standards to improve accuracy and reproducibility has become a priority. The here-developed mock communities, which are available from the NITE Biological Resource Center (NBRC) at the National Institute of Technology and Evaluation (NITE, Japan), provide well-characterized control reagents that allow users to judge the accuracy of their measurement results. Widespread and consistent adoption of the mock communities will improve reproducibility and comparability of microbiome community analyses, thereby supporting and accelerating human microbiome research and development.

Synbiotics Easing Renal Failure by Improving Gut Microbiology II (SYNERGY II): A Feasibility Randomized Controlled Trial

Synbiotics have emerged as a therapeutic strategy for modulating the gut microbiome and targeting novel cardiovascular risk factors, including uremic toxins indoxyl sulfate (IS) and p-cresyl sulfate (PCS). This study aims to evaluate the feasibility of a trial of long-term synbiotic supplementation in adults with stage 3-4 chronic kidney disease (CKD). Adult participants with CKD and estimated glomerular filtration rate (eGFR) of 15-60 mL/min/1.73 m²) were recruited between April 2017 and August 2018 to a feasibility, double-blind, placebo-controlled, randomized trial of synbiotic therapy or matched identical placebo for 12 months. The primary outcomes were recruitment and retention rates as well as acceptability of the intervention. Secondary outcomes were treatment adherence and dietary intake. Exploratory outcomes were evaluation of the cardiovascular structure and function, serum IS and PCS, stool microbiota profile, kidney function, blood pressure, and lipid profile. Of 166 potentially eligible patients, 68 (41%) were recruited into the trial (synbiotic n = 35, placebo n = 33). Synbiotic and placebo groups had acceptable and comparable 12-month retention rates (80% versus 85%, respectively, p = 0.60). Synbiotic supplementation altered the stool microbiome with an enrichment of Bifidobacterium and Blautia spp., resulting in a 3.14 mL/min/1.73 m² (95% confidence interval (CI), -6.23 to -0.06 mL/min/1.73 m², p < 0.01) reduction in eGFR and a 20.8 µmol/L (95% CI, 2.97 to 38.5 µmol/L, p < 0.01) increase in serum creatinine concentration. No between-group differences were observed in any of the other secondary or exploratory outcomes. Long-term synbiotic supplementation was feasible and acceptable to patients with CKD, and it modified the gastrointestinal microbiome. However, the reduction in kidney function with synbiotics warrants further investigation.

Autism-related dietary preferences mediate autism-gut microbiome associations

There is increasing interest in the potential contribution of the gut microbiome to autism spectrum disorder (ASD). However, previous studies have been underpowered and have not been designed to address potential confounding factors in a comprehensive way. We performed a large autism stool metagenomics study (n = 247) based on participants from the Australian Autism Biobank and the Queensland Twin Adolescent Brain project. We found negligible direct associations between ASD diagnosis and the gut microbiome. Instead, our data support a model whereby ASD-related restricted interests are associated with less-diverse diet, and in turn reduced microbial taxonomic diversity and looser stool consistency. In contrast to ASD diagnosis, our dataset was well powered to detect microbiome associations with traits such as age, dietary intake, and stool consistency. Overall, microbiome differences in ASD may reflect dietary preferences that relate to diagnostic features, and we caution against claims that the microbiome has a driving role in ASD.

Diet Quality and Protein-Bound Uraemic Toxins: Investigation of Novel Risk Factors and the Role of Microbiome in Chronic Kidney Disease

OBJECTIVE

This study aims to explore the associations between diet quality, uraemic toxins, and gastrointestinal microbiota in the chronic kidney disease (CKD) population.

METHODS

This is a baseline cross-sectional study of adults with CKD participating in a randomized controlled trial of prebiotic and probiotic supplementation. Dietary intake was measured using a seven-day diet history method, administered by a specialist dietitian. Diet quality was assessed using plant-based diet index (PDI) (overall PDI, healthy PDI, and unhealthy PDI), food group analysis, protein intake, fiber intake, and dietary protein-to-fiber ratio. Serum uraemic toxins (free and total; indoxyl sulfate and p-cresyl sulfate) were determined by ultraperformance liquid chromatography. Gastrointestinal microbiota richness, diversity, composition, and functional capacity were analyzed via metagenomic sequencing.

RESULTS

Sixty-eight adults [median age: 70 (interquartile range: 58-75) years, 66% male] with an estimated glomerular filtration rate of 34 ± 11 mL/min/1.73 m² were included, with 40 participants completing the optional fecal substudy. Dietary fiber intake was associated with lower levels of total indoxyl sulfate, whereas the healthy plant-based diet index was associated with lower levels of free p-cresyl sulfate. A higher protein-to-fiber ratio was associated with an increased relative abundance of unclassified members of order Oscillospirales. Intake of vegetables and whole grains was correlated with Subdoligranulum formicile, whereas an unclassified Prevotella species was correlated with potatoes and food items considered discretionary, including sweet drinks, sweet desserts, and animal fats.

CONCLUSIONS

Diet quality may influence uraemic toxin generation and gut microbiota diversity, composition, and function in adults with CKD. Well-designed dietary intervention studies targeting the production of uraemic toxins and exploring the impact on gut microbiome are warranted in the CKD population.

Critical evaluation of faecal microbiome preservation using metagenomic analysis

The ability to preserve microbial communities in faecal samples is essential as increasing numbers of studies seek to use the gut microbiome to identify biomarkers of disease. Here we use shotgun metagenomics to rigorously evaluate the technical and compositional reproducibility of five room temperature (RT) microbial stabilisation methods compared to the best practice of flash-freezing. These methods included RNALater, OMNIGene-GUT, a dry BBL swab, LifeGuard, and a novel method for preserving faecal samples, a Copan FLOQSwab in an active drying tube (FLOQSwab-ADT). Each method was assessed using six replicate faecal samples from five participants, totalling 180 samples. The FLOQSwab-ADT performed best for both technical and compositional reproducibility, followed by RNAlater and OMNIgene-GUT. LifeGuard and the BBL swab had unpredictable outgrowth of Escherichia species in at least one replicate from each participant. We further evaluated the FLOQSwab-ADT in an additional 239 samples across 10 individuals after storage at -20 °C, RT, and 50 °C for four weeks compared to fresh controls. The FLOQSwab-ADT maintained its performance across all temperatures, indicating this method is an excellent alternative to existing RT stabilisation methods.

Characteristics of the gastrointestinal microbiota in paired live kidney donors and recipients

BACKGROUND

There are few studies that have examined whether dysbiosis occurs in kidney donors and transplant recipients following kidney transplant surgery.

AIM

To ascertain whether changes occur in the gastrointestinal microbiota of the kidney donor and recipient following kidney transplantation.

METHODS

Kidney transplant recipients and their donors were prospectively enrolled in a pilot study to collect one faecal sample prior to, and another faecal sample between four to eight weeks following surgery. Gastrointestinal microbiota richness, Shannon diversity measures and functional assessments of kidney donors and recipients were analysed via metagenomic sequencing.

RESULTS

The study included 12 donors (median age 56 years, 6 females) and 12 recipients (median age 51 years, 3 females). Donor microbiota showed no significant changes in gastrointestinal microbiota richness, Shannon diversity, or functional assessments before and after nephrectomy. Recipient microbiota was altered post-transplant, reflected in reductions of the mean (±SD) richness values (156 ± 46.5 to 116 ± 38.6, p = 0.002), and Shannon diversity (3.57 ± 0.49 to 3.14 ± 0.52, p = 0.007), and a dramatic increase in Roseburia spp. abundance post-transplant (26-fold increase from 0.16 ± 0.0091 to 4.6 ± 0.3; p = 0.006; FDR = 0.12). Functionally, the post-transplant microbial community shifted towards those taxa using the glycolysis pathway (1.2-fold increase; p = 0.02; FDR = 0.26) for energy metabolism, while those functions involved with reactive oxygen species degradation decreased (2.6-fold; p = 0.006; FDR = 0.14).

CONCLUSION

Live donor kidney transplantation and standard care post-transplant result in significant alterations in gut microbiota richness, diversity, composition and functional parameters in kidney transplant recipients but not in their kidney donors.