Biology subjects

Hildebrand, F.

Publications and source records attributed to Hildebrand, F..

15 recordsLinked to original sources

bioRxiv2026

Protal: Ultra-fast metagenomic profiling and strain-resolved analysis

Large-scale metagenomic studies increasingly require taxonomic profiles that are sensitive, precise, strain-resolved and computationally tractable. Existing profilers typically trade taxonomic breadth, sensitivity, precision and speed against one another, limiting their utility for high-resolution microbiome analyses. Here we present protal -- profiling through alignment -- an ultra-fast alignment-based method for species-and strain-resolved profiling of metagenomes. Protal combines a newly developed alignment algorithm, machine-learning-based classification and conserved bacterial marker genes to profile species represented in the standardized and regularly updated GTDB taxonomy. Protal reliably profiles all 143,614 bacterial and archaeal species in GTDB r226 and achieved higher precision on CAMI2 benchmarks than all tested contemporary profilers, including MetaPhlAn 4, mOTUs4, sylph and Kraken2+Bracken (mean species-level precision 98.3% versus 97.5% for the next-best profiler, sylph). In custom benchmarks, protal showed particularly strong gains for rare species represented by a single reference genome and for highly complex communities containing 10,000 species (F1-score 9% and 14% higher than the second best profiler, respectively). At the strain level, protal reconstructs intraspecific phylogenetic relationships among detected bacteria with similar accuracy as StrainPhlAn 4; because protal produces precise alignments, the phylogenies can be de novo produced without reliance on reference strain collections. Unlike dedicated strain-profiling workflows, however, protal performs strain analysis concurrently with species-level profiling, making it up to 40-fold faster without requiring additional steps. Together, these features make strain-resolved profiling of thousands of metagenomes feasible on commodity hardware. The software, databases and tutorials are available at https://github.com/4less/protal and http://protal.earlham.ac.uk.

bioinformatics↗

bioRxiv2026

Enriching the Human Stool Microeukaryotes for Shotgun Sequencing

The human gut microbiome harbours a diverse community of microeukaryotes, predominantly fungi, which may potentially play important roles in gut ecology and homeostasis. Despite their potential, the study of gut microeukaryotes has been hampered by the limited sensitivity of standard sequencing approaches, which struggle to capture DNA from low-abundance microorganisms against the overwhelming background of bacterial biomass. To address this, we developed a method to selectively enrich for microeukaryotic cells in human faecal samples by depleting bacterial cells prior to metagenomic sequencing. Through systematic comparison and optimisation at each processing step, we established a robust standard operating procedure (SOP) for microeukaryotic cell enrichment. By benchmarking this SOP across eight human faecal samples with three technical replicates each, we showed that it consistently increased microeukaryote representation in metagenomic libraries, greater microeukaryotic taxonomic diversity, and a reduced proportion of unclassified taxa. Together, these improvements enabled substantially deeper characterisation of the microeukaryotic fraction of the human gut microbiome.

microbiology↗

bioRxiv2026

REMAG: recovery of eukaryotic genomes from metagenomic data using contrastive learning

Metagenome-assembled genomes (MAGs) are central to exploring microbial communities. Yet, despite the relevance of protists and fungi to diverse ecosystems, eukaryotic MAG recovery lags behind that of prokaryotes. A major bottleneck is that most state-of-the-art binning pipelines exclusively rely on prokaryotic single-copy core gene reference databases and are optimized for smaller genomes. To address this gap, we present REMAG (Recovery of Eukaryotic MAGs), a tool designed to recover high-quality eukaryotic genomes suited for longread metagenomic data. REMAG leverages finetuned HyenaDNA genomic foundation models to efficiently filter eukaryotic contigs. It then employs a dual-encoder Siamese network trained with Barlow Twins contrastive loss to learn a shared embedding space by integrating contig composition and differential coverage. Finally, high-quality bins are extracted using greedy iterative Leiden clustering optimized with eukaryotic single-copy core gene constraints. In benchmarks based on simulated mixed prokaryotic/eukaryotic communities and real datasets of varying sizes and origin, we demonstrate REMAGs ability to recover more near-complete eukaryotic genomes than existing state-of-the-art tools, which often produce highly fragmented eukaryotic bins. REMAG provides an automated eukaryotic binning method that scales effectively with the increasing size and sequencing depth of metagenomic datasets.

bioinformatics↗

bioRxiv2025

Optimizing storage, high-molecular weight DNA extraction and genome reconstructions from human faecal samples.

Metagenomic sequencing has become an essential tool for assessing the composition and functional potential of microbial communities. However, established metagenomic DNA extraction protocols are limited by trade-offs between universal taxonomic representation, DNA quality, quantity and molecule length, making them mostly unsuitable for 3rd generation sequencing. MethodsWe assessed the effectiveness of high molecular weight (HMW) DNA workflows from faecal samples, using different combinations of collection, storage and extraction kits. Faecal samples were either immediately processed, frozen at -80{degrees}C or stored in DNA/RNA Shield, GutAlive or OMNIgeneGUT and then subjected to three different DNA isolation kits, each modified for mechanical, chemical and enzymatic cell lysis steps, totalling 32 evaluated combinations. Isolated DNA was assessed for its quality and quantity, while taxonomic consistency was evaluated using metataxonomics and validated with metagenomic sequencing. ResultsThe yield of HMW DNA differed substantially between storage and DNA extraction protocols. Specifically, storage protocol had the highest impact on HMW DNA recovery, with DNA/RNA Shield storage yielding on average 51% more HMW DNA >30kb. Similarly, the sample storage had independently the effect size of R2 = 26.9% on the microbiome composition, and since the DNA extraction protocol in our study inherently combines both DNA extraction kit and lysis type, the latter had the largest effect size (R2 = 12.8%). While all kits had specific dis/advantages and biases, DNA/RNA Shield and Maxwell RSC PureFood GMO and Authentication kit performed best overall for long-read metagenomics. The protocol was validated on a) isolate genomes and b) 4 faecal samples, demonstrating both increased DNA yield at >30kb molecule length. Using the MG-TK pipeline these metagenomes yielded genomes on average, per sample, 48 Pacific Biosciences (PacBio) and 55 Oxford Nanopore Technologies (ONT) circular bacterial metagenomic assembled genomes. ConclusionThe current gold-standard of freezing faecal samples was surprisingly inconsistent and inefficient for long-read metagenomics, necessitating rethinking gut microbial study designs. We provide a simple and cheap protocol that preserves both taxonomic composition and HMW DNA quality, while our bioinformatics workflow allows for complete bacterial genome reconstruction.

molecular biology↗

bioRxiv2025

Supplementation with effective microorganisms in earthen ponds affects common carp growth but not overall microbial communities

Probiotics are increasingly explored in aquaculture to enhance fish health and growth without leaving harmful residues. However, their efficacy in real-world pond environments remains poorly understood. Here, we conducted a 103-day field experiment to assess the effects of two effective microorganisms commercial products supplementations on microbial communities and growth performance of common carp (Cyprinus carpio). Effective microorganisms were added both to feed and directly to pond water. Microbial diversity was analysed using 16S rRNA and whole-genome shotgun sequencing across three environments - water (three time points), sediment (two time points) and fish intestine (one time point) - from 25 experimental ponds. Bioinformatics processing involved QIIME2 and MG-TK pipeline with taxonomic classification based on the SILVA database. The results showed that although supplemented bacterial families did not establish significantly in pond environments, fish exposed to specific effective microorganisms treatments exhibited improved growth metrics. These findings suggest that effective microorganisms can enhance carp growth in aquaculture without significantly altering resident microbial communities, offering a promising residue-free alternative to traditional additives.

microbiology↗

bioRxiv2025

Unearthing the rhizosphere microbiome recruited by ancestral bread wheat landraces

Crop root traits that modulate the soil microbiome can turn the tide of reduced fertility in intensively farmed land by optimising nutrient availability and resilience to environmental stresses. Advantageous genetic diversity that allows adaptation to nutrient availability is present in historic crop genotypes. The Watkins collection of bread wheat landraces is an unexploited resource, carrying untapped phenotypic traits. Here, we show that the rhizosphere microbiome assembly of these landraces is distinct compared to elite varieties, specifically those that come from ancestral groups (AGs) not used in modern breeding. We used 16S rRNA sequencing to identify changes in microbial communities of rhizosphere soil collected from 81 landraces and two elite varieties. We found high similarity in microbiome recruitment between the elite cultivars and the two AGs genetically closest to the elite. The rhizosphere microbiome of five AGs genetically distant from the elite cultivars showed significant differences in the abundance of taxa involved in nitrogen and carbon turnover, keystone taxa and associations within the microbial network. Our findings suggest that genes to recruit or suppress microbial taxa in the rhizosphere are shared by landraces from these AGs. Selective breeding for traits to control microbial functions can enhance soil productivity and crop performance.

ecology↗

bioRxiv2025

Functional diversity of soil microbial communities increases with ecosystem development

Land abandonment is the single largest process of land-use change in the Global North driving succession and afforestation at continental scales, but assessing its impacts on soil microbial communities remains a challenge. Here, we established a nationwide successional gradient of paired grassland and forest sites to track developments in microbial structure and functioning following land abandonment and gradually changing plant communities. We show that microbes generally respond through threshold dynamics, leading to increasing functional but decreasing taxonomic diversity. Succession also increased the specialization of microbial nutrient (C-N-P) cycling genes while decreasing genetic redundancy, highlighting a putative trade-off between two desirable ecosystem properties: functional diversity and functional redundancy. Increasing fungal functional diversity underpinned higher microbial C-cycling capacity, underscoring the causal link between functional traits and ecosystem processes. Changing litter quality similarly provided a mechanistic link between plant and microbial communities despite otherwise largely decoupled successional developments. Land abandonment is frequently touted as an opportunity to increase biodiversity and carbon storage. Our results show that deeper knowledge about the multifaceted development of soil microbial communities and its links to plant communities during succession may be needed to fully grasp the impacts of global land abandonment processes.

ecology↗

bioRxiv2024

One year of gluten free diet impacts gut function and microbiome in celiac disease

BACKGROUND & AIMSCurrently, the main treatment option for coeliac disease (CD) is a gluten free diet (GFD). This observational cohort study investigated the impact of CD and 1 year of GFD on gut function and microbiome. METHODS36 newly diagnosed patients and 36 healthy volunteers (HV) were studied at baseline and at 12 months follow up. Small bowel water content (SBWC), whole gut transit time (WGTT) and colon volumes were measured by MRI. Stool samples DNA was subjected to shotgun metagenomic sequencing. Species level abundances and gene functions, including carbohydrate active enzymes (CAZymes) were determined. RESULTSSBWC was significantly higher in people with CD 157{+/-}15 mL versus HVs 100{+/-}12 mL (p=0.003). WGTT was delayed in people with CD 68{+/-}8 hours versus HVs 41{+/-}5 hours (p=0.002). The differences reduced after 12 months of GFD but not significantly. Wellbeing in the CD group significantly improved after GFD but did not recover to control values. CD faecal microbiota showed high abundance of proteolytic gene functions, associated with Escherichia coli, Enterobacter and Peptostreptococcus. GFD significantly reduced Bifidobacteria and increased Blautia wexerelae. Microbiome composition correlated positively with WGTT, colonic volume and Akkermansia municphilia but negatively with B.wexerelae. Following GFD the reduction in WGTT and colonic volume significantly associated with increased abundance of B.wexerelae. There were also significant alterations in CAZyme profiles, specifically starch and arabinoxylan degrading families. CONCLUSIONSCD impacted gut function and microbiota. GFD ameliorated but did not reverse these effects, significantly reducing Bifidobacteria associated with reduced intake of resistant starch and arabinoxylan from wheat.

physiology↗

bioRxiv2023

Improved taxonomic annotation of Archaea communities using LotuS2, the Genome Taxonomy Database and RNAseq data

Metabarcoding is increasingly used to uncover diversity and characterise communities of Archaea In various habitats, but taxonomic annotation of their sequences remains more challenging than for bacteria. Fewer reference sequences are available; widely used databases do not reflect recent revisions of higher level archaeal taxonomy and a substantial fraction of their phylogenetic diversity remains to be fully characterised. We address these gaps with a systematic and tractable approach based around the Genome Taxonomy Database (GTDB). GTDB provides a standardized taxonomy with normalized ranks based on protein coding genes, allowing us to identify and remove incongruent SSU sequences. We then use this in combination with the eukaryote PR2 database to annotate a collection of near full length rRNA sequences and the Archaea SSU sequences in SILVA, creating a new reference database, KSGP (Karst, Silva, GTDB and PR2). GTDB SSUs alone provides a small improvement in annotation of an example marine Archaea OTU data set over standardized SSU databases such as SILVA and Greengenes2, while KSGP increases Class and Order assignments by 145% and 280% respectively and is likely to provide some improvement in annotation of bacterial sequences too. We make the KSGP database and a cleaned and deduplicated subset of GTDB SSU sequences available at ksgp.earlham.ac.uk; integrate them into a metabarcoding pipeline, LotuS2 and outline rapid and robust strategies to generate a set of annotated Archaea OTUs and to determine the proportion of Archaea sequences in metatranscriptomic data. We also demonstrate simple tools to visualise the completeness of database coverage and outline strategies to further understand poorly characterised components of the archaeal community which will be equally applicable to bacteria.

microbiology↗

bioRxiv2023

A single dietary factor, daily consumption of a fermented beverage, can modulate the gut microbiome within the same ethnic community

In this study, the impact of traditional rice-based fermented alcoholic beverages (Apong) on the gut microbiome and health of the Mishing community in India was examined. Two groups that consumed one of these beverages were compared to a control group that did not consume either beverage. Gut microbial composition was analyzed by sequencing 16S rRNA of fecal metagenomes and analyzing untargeted fecal metabolites, and short-chain fatty acids (SCFAs). We also collected data on anthropometric measures and serum biochemical markers. Our results showed that Apong drinkers had higher blood pressure, but lower blood glucose and total protein levels than other non-drinkers. Also, gut microbiome composition was found to be affected by the choice of Apong, with Apong drinkers having a more diverse and distinct microbiome compared to non-drinkers. Apong drink type or being a non-drinker explained even a higher variation of fecal metabolome composition than microbiome composition and Apong drinkers had lower levels of the SCFA isovaleric acid than non-drinkers. Overall, this study shows that a single dietary factor can significantly impact the gut microbiome of a community and highlights the potential role of traditional fermented beverages in maintaining gut health.

microbiology↗

bioRxiv2021

LotuS2: An ultrafast and highly accurate tool for amplicon sequencing analysis

BackgroundAmplicon sequencing is an established and cost-efficient method for profiling microbiomes. However, many available tools to process this data require both bioinformatics skills and high computational power to process big datasets. Furthermore, there are only few tools that allow for long read amplicon data analysis. To bridge this gap, we developed the LotuS2 (Less OTU Scripts 2) pipeline, enabling user-friendly, resource friendly, and versatile analysis of raw amplicon sequences. ResultsIn LotuS2, six different sequence clustering algorithms as well as extensive pre- and post-processing options allow for flexible data analysis by both experts, where parameters can be fully adjusted, and novices, where defaults are provided for different scenarios. We benchmarked three independent gut and soil datasets, where LotuS2 was on average 29 times faster compared to other pipelines - yet could better reproduce the alpha- and beta-diversity of technical replicate samples. Further benchmarking a mock community with known taxa composition showed that, compared to the other pipelines, LotuS2 recovered a higher fraction of correctly identified genera and species (98% and 57%, respectively). At ASV/OTU level, precision and F-score were highest for LotuS2, as was the fraction of correctly reconstructed 16S sequences. ConclusionLotuS2 is a lightweight and user-friendly pipeline that is fast, precise and streamlined. High data usage rates and reliability enable high-throughput microbiome analysis in minutes. AvailabilityLotuS2 is available from GitHub, conda or via a Galaxy web interface, documented at http://lotus2.earlham.ac.uk/.

bioinformatics↗

bioRxiv2021

On the reproductive mechanisms of Gram-positive protocells

Prokaryotes are hypothesized to have evolved from more primitive protocells. Unlike present-day cells, protocells are thought to have been devoid of complex molecular biological processes. They are believed to have mediated reproduction entirely by biophysical forces under favorable environmental conditions. Despite this proposition, little is known about the actual mechanism of their reproduction. To understand the reproduction process of protocells in their native habitat, here we used a top-down approach to transform bacterial cells into a primitive lipid vesicle-like state. Given that environmental conditions are thought to have played an essential role in mediating protocell reproduction, we then studied these cells under the presumed environmental conditions of Archaean Eon Earth. Even in the absence of functioning biological processes, cells in our study reproduced in a defined sequence of steps, always leading to the formation of viable daughter cells. Their reproduction mechanism can be explained by the interaction between intracellular metabolism, physicochemical properties of cell constituents, and, most importantly, environmental conditions. Given the simplicity of this reproduction mechanism and its suitability to environmental conditions of early Earth, we propose that protocells reproduced by this process. Moreover, this method of reproduction is also in tune with the earlier theoretical propositions on protocells, the results of the top- down approach of building a minimal cell, and the paleontological record of the Achaean Eon. Our study is the first to bridge the gap between non-living systems like lipid vesicles, living cells, and the paleontology of the Archaean Eon.

biophysics↗

bioRxiv2021

Benchmark of data processing methods and machine learning models for gut microbiome-based diagnosis of inflammatory bowel disease

BackgroundInflammatory bowel disease (IBD) patients wait months and undergo numerous invasive procedures between the initial appearance of symptoms and receiving a diagnosis. In order to reduce time until diagnosis and improve patient wellbeing, machine learning algorithms capable of diagnosing IBD from the gut microbiomes composition are currently being explored. To date, these models have had limited clinical application due to decreased performance when applied to a new cohort of patient samples. Various methods have been developed to analyze microbiome data which may improve the generalizability of machine learning IBD diagnostic tests. With an abundance of methods, there is a need to benchmark the performance and generalizability of various machine learning pipelines (from data processing to training a machine learning model) for microbiome-based IBD diagnostic tools. ResultsWe collected fifteen 16S rRNA microbiome datasets (7707 samples) from North America to benchmark combinations of gut microbiome features, data normalization methods, batch effect reduction methods, and machine learning models. Pipeline generalizability to new cohorts of patients was evaluated with four binary classification metrics following leave-one dataset-out cross validation, where all samples from one study were left out of the training set and tested upon. We demonstrate that taxonomic features obtained from QIIME2 lead to better classification of samples from IBD patients than inferred functional features obtained from PICRUSt2. In addition, machine learning models that identify non-linear decision boundaries between labels are more generalizable than those that are linearly constrained. Prior to training a non-linear machine learning model on taxonomic features, it is important to apply a compositional normalization method and remove batch effects with the naive zero-centering method. Lastly, we illustrate the importance of generating a curated training dataset to ensure similar performance across patient demographics. ConclusionsThese findings will help improve the generalizability of machine learning models as we move towards non-invasive diagnostic and disease management tools for patients with IBD.

microbiology↗

bioRxiv2020

A combined flow injection/reversed phase chromatography - high resolution mass spectrometry workflow for accurate absolute lipid quantification with 13C- internal standards

We propose a fully automated novel workflow for lipidomics based on flow injection-followed by liquid chromatography high resolution mass spectrometry (FI/LC-HRMS). The workflow combined in-depth characterization of the lipidome achieved via reversed phase LC-HRMS with absolute quantification as obtained by a high number of lipid species-specific- and/or retention time (RT) matched/class-specific calibrants. The lipidome of 13C labelled yeast (LILY) provided a cost efficient, large panel of internal standards covering triacylglycerols (TG), steryl esters (SE), free fatty acids (FA), diacylglycerols (DG), sterols (ST), ceramides (Cer), hexosyl ceramides (HexCer), phosphatidylglycerols (PG), phosphatidylethanolamines (PE), phosphatidic acids (PA), cardiolipins (CL), phosphatidylinositols (PI), phosphatidylserines (PS), phosphatidylcholines (PC), lysophosphatidylcholines (LPC) and lysophosphatidylethanolamines (LPE). In order to exploit the full potential of isotopically enriched biomass, LILY was absolutely quantified on demand via reversed isotope dilution analysis using FI-HRMS. Subsequent LC-HRMS analysis integrated different calibration strategies including lipid species-specific standards for >90 lipids. Extensive measures on quality control allowed to rank the calibration strategies and to automatically selected the calibration strategy of highest metrological order for the respective lipid species. Overall, the workflow enabled a streamlined analysis pipeline (identification and quantification in separate analytical runs) and provided validation tools together with absolute concentration values for >350 lipids in human plasma on a species level with an analytical run-time of less than 25 min per sample. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/367987v1_ufig1.gif" ALT="Figure 1"> View larger version (58K): org.highwire.dtl.DTLVardef@127472borg.highwire.dtl.DTLVardef@1b17e98org.highwire.dtl.DTLVardef@1ee6ea6org.highwire.dtl.DTLVardef@a28477_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2019

Disentangling the impact of environmental and phylogenetic constraints on prokaryotic strain diversity

Microbial organisms inhabit virtually all environments and encompass a vast biological diversity. The pan-genome concept aims to facilitate an understanding of diversity within defined phylogenetic groups. Hence, pan-genomes are increasingly used to characterize the strain diversity of prokaryotic species. To understand the interdependency of pan-genome features (such as numbers of core and accessory genes) and to study the impact of environmental and phylogenetic constraints on the evolution of conspecific strains, we computed pan-genomes for 155 phylogenetically diverse species using 7000 high-quality genomes. We show that many pan-genome features such as functional diversity and core genome nucleotide diversity are correlated to each other. Further, habitat flexibility as approximated by species ubiquity is associated with several pan-genome features, particularly core genome size. In general, environment had a stronger impact on pan-genome features than phylogenetic signal. Similar environmental preferences led to convergent evolution of pan-genomic features in distant phylogenetic clades. For example, the soil environment promotes expansion of pan-genome size, while host-associated habitats lead to its reduction.

genomics↗