Uncovering the Horseshoe Effect in Microbial Analyses

Systematic analysis on the horse-shoe-like effect in PCA plots of scRNA-seq data

Motivation

In single-cell studies, principal component analysis (PCA) is widely used to reduce the dimensionality of dataset and visualize in 2D or 3D PC plots. Scientists often focus on different clusters within PC plot, overlooking the specific phenomenon, such as horse-shoe-like effect, that may reveal hidden knowledge about underlying biological dataset. This phenomenon remains largely unexplored in single-cell studies.

Results

In this study, we investigated into the horse-shoe-like effect in PC plots using simulated and real scRNA-seq datasets. We systematically explain horse-shoe-like phenomenon from various inter-related perspectives. Initially, we establish an intuitive understanding with the help of simulated datasets. Then, we generalized the acquired knowledge on real biological scRNA-seq data. Experimental results provide logical explanations and understanding for the appearance of horse-shoe-like effect in PC plots. Furthermore, we identify a potential problem with a well-known theory of 'distance saturation property' attributed to induce horse-shoe phenomenon. Finally, we analyse a mathematical model for horse-shoe effect that suggests trigonometric solutions to estimated eigenvectors. We observe significant resemblance after comparing the results of mathematical model with simulated and real scRNA-seq datasets.

Availability and implementation

The code for reproducing the results of this study is available at: https://github.com/najeebullahshah/PCA-Horse-Shoe.

Functional data geometric morphometrics with machine learning for craniodental shape classification in shrews

This work proposes a functional data analysis approach for morphometrics in classifying three shrew species (S. murinus, C. monticola, and C. malayana) from Peninsular Malaysia. Functional data geometric morphometrics (FDGM) for 2D landmark data is introduced and its performance is compared with classical geometric morphometrics (GM). The FDGM approach converts 2D landmark data into continuous curves, which are then represented as linear combinations of basis functions. The landmark data was obtained from 89 crania of shrew specimens based on three craniodental views (dorsal, jaw, and lateral). Principal component analysis and linear discriminant analysis were applied to both GM and FDGM methods to classify the three shrew species. This study also compared four machine learning approaches (naïve Bayes, support vector machine, random forest, and generalised linear model) using predicted PC scores obtained from both methods (a combination of all three craniodental views and individual views). The analyses favoured FDGM and the dorsal view was the best view for distinguishing the three species.

Regional Differences in Microbial Infiltration of Brain Tissue from Alzheimer's Disease Patients and Control Individuals

Alzheimer's disease (AD) is characterized by cognitive decline and neuropathology including amyloid beta (Aβ) plaques and neurofibrillary tangles (tau). Factors initiating or driving these pathologies remain unclear, though microbes have been increasingly implicated. Our data and others' findings indicate that microbes may be common constituents of the brain. It is notable that Aβ and tau have antimicrobial properties, suggesting a response to microbes in the brain. We used 16S rRNA sequencing to compare major bacterial phyla in post-mortem tissues from individuals exhibiting a range of neuropathology and cognitive status in two brain regions variably affected in AD. Our data indicate that strong regional differences exist, driven in part by the varied presence of Proteobacteria and Firmicutes. We confirmed our data using ELISA of bacterial lipopolysaccharide (LPS) and lipoteichoic acid in the same brain tissue. We identified a potential association between the composition of phyla and the presence of neuropathology but not cognitive status. Declining cognition and increasing pathology correlated closely with serum LPS, but not brain levels of LPS, although brain LPS showed a strong negative correlation with cerebral amyloid angiopathy. Collectively, our data suggest a region-specific heterogeneity of microbial populations in brain tissue potentially associated with neurodegenerative pathology.

Considerations for the inclusion of metabarcoding data in the plant protection product risk assessment of the soil microbiome

There is increasing interest in further developing the plant protection product (PPP) environmental risk assessment, particularly within the European Union, to include the assessment of soil microbial community composition, as measured by metabarcoding approaches. However, to date, there has been little discussion as to how this could be implemented in a standardized, reliable, and robust manner suitable for regulatory decision-making. Introduction of metabarcoding-based assessments of the soil microbiome into the PPP risk assessment would represent a significant increase in the degree of complexity of the data that needs to be processed and analyzed in comparison to the existing risk assessment on in-soil organisms. The bioinformatics procedures to process DNA sequences into community compositional data sets currently lack standardization, while little information exists on how these data should be used to generate regulatory endpoints and the ways in which these endpoints should be interpreted. Through a thorough and critical review, we explore these challenges. We conclude that currently, we do not have a sufficient degree of standardization or understanding of the required bioinformatics and data analysis procedures to consider their use in an environmental risk assessment context. However, we highlight critical knowledge gaps and the further research required to understand whether metabarcoding-based assessments of the soil microbiome can be utilized in a statistically and ecologically relevant manner within a PPP risk assessment. Only once these challenges are addressed can we consider if and how we should use metabarcoding as a tool for regulatory decision-making to assess and monitor ecotoxicological effects on soil microorganisms within an environmental risk assessment of PPPs. Integr Environ Assess Manag 2024;20:337-358. © 2023 SETAC.

Multi-omic approaches for host-microbiome data integration

The gut microbiome interacts with the host through complex networks that affect physiology and health outcomes. It is becoming clear that these interactions can be measured across many different omics layers, including the genome, transcriptome, epigenome, metabolome, and proteome, among others. Multi-omic studies of the microbiome can provide insight into the mechanisms underlying host-microbe interactions. As more omics layers are considered, increasingly sophisticated statistical methods are required to integrate them. In this review, we provide an overview of approaches currently used to characterize multi-omic interactions between host and microbiome data. While a large number of studies have generated a deeper understanding of host-microbiome interactions, there is still a need for standardization across approaches. Furthermore, microbiome studies would also benefit from the collection and curation of large, publicly available multi-omics datasets.

Ancient dental calculus reveals oral microbiome shifts associated with lifestyle and disease in Great Britain

The prevalence of chronic, non-communicable diseases has risen sharply in recent decades, especially in industrialized countries. While several studies implicate the microbiome in this trend, few have examined the evolutionary history of industrialized microbiomes. Here we sampled 235 ancient dental calculus samples from individuals living in Great Britain (∼2200 BCE to 1853 CE), including 127 well-contextualized London adults. We reconstructed their microbial history spanning the transition to industrialization. After controlling for oral geography and technical biases, we identified multiple oral microbial communities that coexisted in Britain for millennia, including a community associated with Methanobrevibacter, an anaerobic Archaea not commonly prevalent in the oral microbiome of modern industrialized societies. Calculus analysis suggests that oral hygiene contributed to oral microbiome composition, while microbial functions reflected past differences in diet, specifically in dairy and carbohydrate consumption. In London samples, Methanobrevibacter-associated microbial communities are linked with skeletal markers of systemic diseases (for example, periostitis and joint pathologies), and their disappearance is consistent with temporal shifts, including the arrival of the Second Plague Pandemic. This suggests pre-industrialized microbiomes were more diverse than previously recognized, enhancing our understanding of chronic, non-communicable disease origins in industrialized populations.

LMdist: Local Manifold distance accurately measures beta diversity in ecological gradients

MOTIVATION

Differentiating ecosystems poses a complex, high-dimensional problem constrained by capturing relevant variation across species profiles. Researchers use pairwise distances and subsequent dimensionality reduction to highlight variation in a few dimensions. Despite popularity in analysis of ecological data, these low-dimensional visualizations can contain geometric abnormalities such as "arch" and "horseshoe" effects, potentially obscuring the impact of environmental gradients. These abnormalities appear in ordination but are in fact a product of oversaturated large pairwise distances.

RESULTS

We present Local Manifold distance (LMdist), an unsupervised algorithm which adjusts pairwise beta diversity measures to better represent true ecological distances between samples. Beta diversity measures can have a bounded dynamic range in depicting long environmental gradients with high species turnover. Using a graph structure, LMdist projects pairwise distances onto a manifold and traverses the manifold surface to adjust pairwise distances at the upper end of the beta diversity measure's dynamic range. This allows for values beyond the range of the original measure. Not all datasets will have oversaturated pairwise distances, nor will capture variation that resembles a manifold, so LMdist adjusts only those pairwise values which may be undervalued in the presence of a sampled gradient. The adjusted distances serve as input for ordination and statistical testing. We demonstrate on real and simulated data that LMdist effectively recovers distances along known gradients and along complex manifolds such as the Swiss roll dataset. LMdist enables more powerful statistical tests for gradient effects and reveals variation orthogonal to the gradient.

AVAILABILITY AND IMPLEMENTATION

Available on GitHub at https://github.com/knights-lab/LMdist.

Soil and bark biodiversity forms discrete islands between vineyards that are not affected by distance or management regime

Within geographic regions, the existing data suggest that physical habitat (bark, soil, etc.) is the strongest factor determining agroecosystem microbial community assemblage, followed by geographic location (site), and then management regime (organic, conventional, etc.). The data also suggest community similarities decay with increasing geographic distance. However, integrated hypotheses for these observations have not been developed. We formalized and tested such hypotheses by sequencing 3.8 million bacterial 16S, fungal ITS2 and non-fungal eukaryotic COI barcodes deriving from 108 samples across two habitats (soil and bark) from six vineyards sites under conventional or conservation management. We found both habitat and site significantly affected community assemblage, with habitat the stronger for bacteria only, but there was no effect of management. There was no evidence for community similarity distance-decay within sites within each habitat. While communities significantly differed between vineyard sites, there was no evidence for between site community similarity distance-decay apart from bark bacterial communities, and no correlations with soil and bark pH apart from soil bacterial communities. Thus, within habitats, vineyard sites represent discrete biodiversity islands, and while bacterial, fungal and non-fungal eukaryotic biodiversity mostly differs between sites, the distance by which they are separated does not define how different they are.

A new method for examining the co-occurrence network of fossil assemblages

Currently, studies of ancient faunal community networks have been based mostly on uniformitarian and functional morphological evidence. As an important source of data, taphonomic evidence offers the opportunity to provide a broader scope for understanding palaeoecology. However, palaeoecological research methods based on taphonomic evidence are relatively rare, especially for body fossils in lacustrine sediments. Such fossil communities are not only affected by complex transportation and selective destruction in the sedimentation process, they also are strongly affected by time averaging. Historically, it has been believed that it is difficult to study lacustrine entombed fauna by a small-scale quadrat survey. Herein, we developed a software, the TaphonomeAnalyst, to study the associational network of lacustrine entombed fauna, or taphocoenosis. TaphonomeAnalyst allows researchers to easily perform exploratory analyses on common abundance profiles from taphocoenosis data. The dataset for these investigations resulted from fieldwork of the latest Middle Jurassic Jiulongshan Formation near Daohugou Village, in Ningcheng County of Inner Mongolia, China, spotlighting the core assemblage of the Yanliao Fauna. Our data included 27,000 fossil specimens of animals from this deposit, the Yanliao Fauna, whose analyses reveal sedimentary environments, taphonomic conditions, and co-occurrence networks of this highly studied assemblage, providing empirically robust and statistically significant evidence for multiple Yanliao habitats.

PandaGUT provides new insights into bacterial diversity, function, and resistome landscapes with implications for conservation

BACKGROUND

The gut microbiota play important roles in host adaptation and evolution, but are understudied in natural population of wild mammals. To address host adaptive evolution and improve conservation efforts of threatened mammals from a metagenomic perspective, we established a high-quality gut microbiome catalog of the giant panda (pandaGUT) to resolve the microbiome diversity, functional, and resistome landscapes using approximately 7 Tbp of long- and short-read sequencing data from 439 stool samples.

RESULTS

The pandaGUT catalog comprises 820 metagenome-assembled genomes, including 40 complete closed genomes, and 64.5% of which belong to species that have not been previously reported, greatly expanding the coverage of most prokaryotic lineages. The catalog contains 2.37 million unique genes, with 74.8% possessing complete open read frames, facilitating future mining of microbial functional potential. We identified three microbial enterotypes across wild and captive panda populations characterized by Clostridium, Pseudomonas, and Escherichia, respectively. We found that wild pandas exhibited host genetic-specific microbial structures and functions, suggesting host-gut microbiota phylosymbiosis, while the captive cohorts encoded more multi-drug resistance genes.

CONCLUSIONS

Our study provides largely untapped resources for biochemical and biotechnological applications as well as potential intervention avenues via the rational manipulation of microbial diversity and reducing antibiotic usage for future conservation management of wildlife. Video Abstract.

Variation in Temperature Dependences across Europe Reveals the Climate Sensitivity of Soil Microbial Decomposers

Temperature is a major determinant of biological process rates, and microorganisms are key regulators of ecosystem carbon (C) dynamics. Temperature controls microbial rates of decomposition, and thus warming can stimulate C loss, creating positive feedback to climate change. If trait distributions that define temperature relationships of microbial communities can adapt to altered temperatures, they could modulate the strength of this feedback, but if this occurs remains unclear. In this study, we sampled soils from a latitudinal climate gradient across Europe. We established the temperature relationships of microbial growth and respiration rates and used these to investigate if and with what strength the community trait distributions for temperature were adapted to their local environment. Additionally, we sequenced bacterial and fungal amplicons to link the variance in community composition to changes in temperature traits. We found that microbial temperature trait distributions varied systematically with climate, suggesting that an increase in mean annual temperature (MAT) of 1°C will result in warm-shifted microbial temperature trait distributions equivalent to an increase in temperature minimum (T_min) of 0.20°C for bacterial growth, 0.07°C for fungal growth, and 0.10°C for respiration. The temperature traits for bacterial growth were thus more responsive to warming than those for respiration and fungal growth. The microbial community composition also varied with temperature, enabling the interlinkage of taxonomic information with microbial temperature traits. Our work shows that the adaptation of microbial temperature trait distributions to a warming climate will affect the C-climate feedback, emphasizing the need to represent this to capture the microbial feedback to climate change. IMPORTANCE One of the largest uncertainties of global warming is if the microbial decomposer feedback will strengthen or weaken soil C-climate feedback. Despite decades of research effort, the strength of this feedback to warming remains unknown. We here present evidence that microbial temperature relationships vary systematically with environmental temperatures along a climate gradient and use this information to forecast how microbial temperature traits will create feedback between the soil C cycle and climate warming. We show that the current use of a universal temperature sensitivity is insufficient to represent the microbial feedback to climate change and provide new estimates to replace this flawed assumption in Earth system models. We also demonstrate that temperature relationships for rates of microbial growth and respiration are differentially affected by warming, with stronger responses to warming for microbial growth (soil C formation) than for respiration (C loss from soil to atmosphere), which will affect the atmosphere-land C balance.

Surface Bacterioplankton Community Structure Crossing the Antarctic Circumpolar Current Fronts

The Antarctic Circumpolar Current (ACC) is the major current in the Southern Ocean, isolating the warm stratified subtropical waters from the more homogeneous cold polar waters. The ACC flows from west to east around Antarctica and generates an overturning circulation by fostering deep-cold water upwelling and the formation of new water masses, thus affecting the Earth's heat balance and the global distribution of carbon. The ACC is characterized by several water mass boundaries or fronts, known as the Subtropical Front (STF), Subantarctic Front (SAF), Polar Front (PF), and South Antarctic Circumpolar Current Front (SACCF), identified by typical physical and chemical properties. While the physical characteristics of these fronts have been characterized, there is still poor information regarding the microbial diversity of this area. Here we present the surface water bacterioplankton community structure based on 16S rRNA sequencing from 13 stations sampled in 2017 between New Zealand to the Ross Sea crossing the ACC Fronts. Our results show a distinct succession in the dominant bacterial phylotypes present in the different water masses and suggest a strong role of sea surface temperatures and the availability of Carbon and Nitrogen in controlling community composition. This work represents an important baseline for future studies on the response of Southern Ocean epipelagic microbial communities to climate change.

Simulation Tests of Methods in Evolution, Ecology, and Systematics: Pitfalls, Progress, and Principles

Complex statistical methods are continuously developed across the fields of ecology, evolution, and systematics (EES). These fields, however, lack standardized principles for evaluating methods, which has led to high variability in the rigor with which methods are tested, a lack of clarity regarding their limitations, and the potential for misapplication. In this review, we illustrate the common pitfalls of method evaluations in EES, the advantages of testing methods with simulated data, and best practices for method evaluations. We highlight the difference between method evaluation and validation and review how simulations, when appropriately designed, can refine the domain in which a method can be reliably applied. We also discuss the strengths and limitations of different evaluation metrics. The potential for misapplication of methods would be greatly reduced if funding agencies, reviewers, and journals required principled method evaluation.

Archaea Microbiome Dysregulated Genes and Pathways as Molecular Targets for Lung Adenocarcinoma and Squamous Cell Carcinoma

The human microbiome is a vast collection of microbial species that exist throughout the human body and regulate various bodily functions and phenomena. Of the microbial species that exist in the human microbiome, those within the archaea domain have not been characterized to the extent of those in more common domains, despite their potential for unique metabolic interaction with host cells. Research has correlated tumoral presence of bacterial microbial species to the development and progression of lung cancer; however, the impacts and influences of archaea in the microbiome remain heavily unexplored. Within the United States lung cancer remains highly fatal, responsible for over 100,000 deaths every year with a 5-year survival rate of roughly 22.9%. This project attempts to investigate specific archaeal species' correlation to lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) incidence, patient staging, death rates across individuals of varying ages, races, genders, and smoking-statuses, and potential molecular targets associated with archaea microbiome. Archaeal species abundance was assessed across lung tissue samples of 527 LUAD patients, 479 LUSC patients, and 99 healthy individuals. Nine archaeal species were found to be of significantly altered abundance in cancerous samples as compared to normal counterparts, 6 of which are common to both LUAD and LUSC subgroups. Several of these species are of the taxonomic class Thermoprotei or the phylum Euryarchaeota, both known to contain metabolic processes distinct from most bacterial species. Host-microbe metabolic interactions may be responsible for the observed correlation of these species' abundance with cancer incidence. Significant microbes were correlated to patient gene expression to reveal genes of altered abundance with respect to high and low archaeal presence. With these genes, cellular oncogenic signaling pathways were analyzed for enrichment across cancer and normal samples. In comparing gene expression between LUAD and adjacent normal samples, 2 gene sets were found to be significantly enriched in cancers. In LUSC comparison, 6 sets were significantly enriched in cancer, and 34 were enriched in normals. Microbial counts across healthy and cancerous patients were then used to develop a machine-learning based predictive algorithm, capable of distinguishing lung cancer patients from healthy normal with 99% accuracy.

Ancient dental calculus preserves signatures of biofilm succession and interindividual variation independent of dental pathology

Dental calculus preserves oral microbes, enabling comparative studies of the oral microbiome and health through time. However, small sample sizes and limited dental health metadata have hindered health-focused investigations to date. Here, we investigate the relationship between tobacco pipe smoking and dental calculus microbiomes. Dental calculus from 75 individuals from the 19th century Middenbeemster skeletal collection (Netherlands) were analyzed by metagenomics. Demographic and dental health parameters were systematically recorded, including the presence/number of pipe notches. Comparative data sets from European populations before and after the introduction of tobacco were also analyzed. Calculus species profiles were compared with oral pathology to examine associations between microbiome community, smoking behavior, and oral health status. The Middenbeemster individuals exhibited relatively poor oral health, with a high prevalence of periodontal disease, caries, heavy calculus deposits, and antemortem tooth loss. No associations between pipe notches and dental pathologies, or microbial species composition, were found. Calculus samples before and after the introduction of tobacco showed highly similar species profiles. Observed interindividual microbiome differences were consistent with previously described variation in human populations from the Upper Paleolithic to the present. Dental calculus may not preserve microbial indicators of health and disease status as distinctly as dental plaque.

Bacterioplankton communities reveal horizontal and vertical influence of an Island Mass Effect

Coral reefs are highly productive ecosystems with distinct biogeochemistry and biology nestled within unproductive oligotrophic gyres. Coral reef islands have often been associated with a nearshore enhancement in phytoplankton, a phenomenon known as the Island Mass Effect (IME). Despite being documented more than 60 years ago, much remains unknown about the extent and drivers of IMEs. Here we utilized 16S rRNA gene metabarcoding as a biological tracer to elucidate horizontal and vertical influence of an IME around the islands of Mo'orea and Tahiti, French Polynesia. We show that those nearshore oceanic stations with elevated chlorophyll a included bacterioplankton found in high abundance in the reef environment, suggesting advection of reef water is the source of altered nearshore biogeochemistry. We also observed communities in the nearshore deep chlorophyll maximum (DCM) with enhanced abundances of upper euphotic bacterioplankton that correlated with intrusions of low-density, O₂ rich water, suggesting island influence extends into the DCM.

Compositionally Aware Phylogenetic Beta-Diversity Measures Better Resolve Microbiomes Associated with Phenotype

Microbiome data have several specific characteristics (sparsity and compositionality) that introduce challenges in data analysis. The integration of prior information regarding the data structure, such as phylogenetic structure and repeated-measure study designs, into analysis, is an effective approach for revealing robust patterns in microbiome data. Past methods have addressed some but not all of these challenges and features: for example, robust principal-component analysis (RPCA) addresses sparsity and compositionality; compositional tensor factorization (CTF) addresses sparsity, compositionality, and repeated measure study designs; and UniFrac incorporates phylogenetic information. Here we introduce a strategy of incorporating phylogenetic information into RPCA and CTF. The resulting methods, phylo-RPCA, and phylo-CTF, provide substantial improvements over state-of-the-art methods in terms of discriminatory power of underlying clustering ranging from the mode of delivery to adult human lifestyle. We demonstrate quantitatively that the addition of phylogenetic information improves effect size and classification accuracy in both data-driven simulated data and real microbiome data. IMPORTANCE Microbiome data analysis can be difficult because of particular data features, some unavoidable and some due to technical limitations of DNA sequencing instruments. The first step in many analyses that ultimately reveals patterns of similarities and differences among sets of samples (e.g., separating samples from sick and healthy people or samples from seawater versus soil) is calculating the difference between each pair of samples. We introduce two new methods to calculate these differences that combine features of past methods, specifically being able to take into account the principles that most types of microbes are not in most samples (sparsity), that abundances are relative rather than absolute (compositionality), and that all microbes have a shared evolutionary history (phylogeny). We show using simulated and real data that our new methods provide improved classification accuracy of ordinal sample clusters and increased effect size between sample groups on beta-diversity distances.

Optimizing UniFrac with OpenACC Yields Greater Than One Thousand Times Speed Increase

UniFrac is an important tool in microbiome research that is used for phylogenetically comparing microbiome profiles to one another (beta diversity). Striped UniFrac recently added the ability to split the problem into many independent subproblems, exhibiting nearly linear scaling but suffering from memory contention. Here, we adapt UniFrac to graphics processing units using OpenACC, enabling greater than 1,000× computational improvement, and apply it to 307,237 samples, the largest 16S rRNA V4 uniformly preprocessed microbiome data set analyzed to date. IMPORTANCE UniFrac is an important tool in microbiome research that is used for phylogenetically comparing microbiome profiles to one another. Here, we adapt UniFrac to operate on graphics processing units, enabling a 1,000× computational improvement. To highlight this advance, we perform what may be the largest microbiome analysis to date, applying UniFrac to 307,237 16S rRNA V4 microbiome samples preprocessed with Deblur. These scaling improvements turn UniFrac into a real-time tool for common data sets and unlock new research questions as more microbiome data are collected.

Identification and classification of cis-regulatory elements in the amphipod crustacean Parhyale hawaiensis

Emerging research organisms enable the study of biology that cannot be addressed using classical 'model' organisms. New data resources can accelerate research in such animals. Here, we present new functional genomic resources for the amphipod crustacean Parhyale hawaiensis, facilitating the exploration of gene regulatory evolution using this emerging research organism. We use Omni-ATAC-seq to identify accessible chromatin genome-wide across a broad time course of Parhyale embryonic development. This time course encompasses many major morphological events, including segmentation, body regionalization, gut morphogenesis and limb development. In addition, we use short- and long-read RNA-seq to generate an improved Parhyale genome annotation, enabling deeper classification of identified regulatory elements. We discover differential accessibility, predict nucleosome positioning, infer transcription factor binding, cluster peaks based on accessibility dynamics, classify biological functions and correlate gene expression with accessibility. Using a Minos transposase reporter system, we demonstrate the potential to identify novel regulatory elements using this approach. This work provides a platform for the identification of novel developmental regulatory elements in Parhyale, and offers a framework for performing such experiments in other emerging research organisms.

Bacterial Colonisation: From Airborne Dispersal to Integration Within the Soil Community

The deposition of airborne microorganisms into new ecosystems is the first stage of colonisation. However, how and under what circumstances deposited microorganisms might successfully colonise a new environment is still unclear. Using the Arctic snowpack as a model system, we investigated the colonisation potential of snow-derived bacteria deposited onto Arctic soils during and after snowmelt using laboratory-based microcosm experiments to mimic realistic environmental conditions. We tested different melting rate scenarios to evaluate the influence of increased precipitation as well as the influence of soil pH on the composition of bacterial communities and on the colonisation potential. We observed several candidate colonisations in all experiments; with a higher number of potentially successful colonisations in acidoneutral soils, at the average snowmelt rate measured in the Arctic. While the higher melt rate increased the total number of potentially invading bacteria, it did not promote colonisation (snow ASVs identified in the soil across multiple sampling days and still present on the last day). Instead, most potential colonists were not identified by the end of the experiments. On the other hand, soil pH appeared as a determinant factor impacting invasion and subsequent colonisation. In acidic and alkaline soils, bacterial persistence with time was lower than in acidoneutral soils, as was the number of potentially successful colonisations. This study demonstrated the occurrence of potentially successful colonisations of soil by invading bacteria. It suggests that local soil properties might have a greater influence on the colonisation outcome than increased precipitation or ecosystem disturbance.

Phylogeny-Aware Analysis of Metagenome Community Ecology Based on Matched Reference Genomes while Bypassing Taxonomy

We introduce the operational genomic unit (OGU) method, a metagenome analysis strategy that directly exploits sequence alignment hits to individual reference genomes as the minimum unit for assessing the diversity of microbial communities and their relevance to environmental factors. This approach is independent of taxonomic classification, granting the possibility of maximal resolution of community composition, and organizes features into an accurate hierarchy using a phylogenomic tree. The outputs are suitable for contemporary analytical protocols for community ecology, differential abundance, and supervised learning while supporting phylogenetic methods, such as UniFrac and phylofactorization, that are seldom applied to shotgun metagenomics despite being prevalent in 16S rRNA gene amplicon studies. As demonstrated in two real-world case studies, the OGU method produces biologically meaningful patterns from microbiome data sets. Such patterns further remain detectable at very low metagenomic sequencing depths. Compared with taxonomic unit-based analyses implemented in currently adopted metagenomics tools, and the analysis of 16S rRNA gene amplicon sequence variants, this method shows superiority in informing biologically relevant insights, including stronger correlation with body environment and host sex on the Human Microbiome Project data set and more accurate prediction of human age by the gut microbiomes of Finnish individuals included in the FINRISK 2002 cohort. We provide Woltka, a bioinformatics tool to implement this method, with full integration with the QIIME 2 package and the Qiita web platform, to facilitate adoption of the OGU method in future metagenomics studies. IMPORTANCE Shotgun metagenomics is a powerful, yet computationally challenging, technique compared to 16S rRNA gene amplicon sequencing for decoding the composition and structure of microbial communities. Current analyses of metagenomic data are primarily based on taxonomic classification, which is limited in feature resolution. To solve these challenges, we introduce operational genomic units (OGUs), which are the individual reference genomes derived from sequence alignment results, without further assigning them taxonomy. The OGU method advances current read-based metagenomics in two dimensions: (i) providing maximal resolution of community composition and (ii) permitting use of phylogeny-aware tools. Our analysis of real-world data sets shows that it is advantageous over currently adopted metagenomic analysis methods and the finest-grained 16S rRNA analysis methods in predicting biological traits. We thus propose the adoption of OGUs as an effective practice in metagenomic studies.

Influence of Pre-treatment Saliva Microbial Diversity and Composition on Nasopharyngeal Carcinoma Prognosis

Background

The human microbiome has been reported to mediate the response to anticancer therapies. However, research about the influence of the oral microbiome on nasopharyngeal carcinoma (NPC) survival is lacking. We aimed to explore the effect of oral microbiota on NPC prognosis.

Methods

Four hundred eighty-two population-based NPC cases in southern China between 2010 and 2013 were followed for survival, and their saliva samples were profiled using 16s rRNA sequencing. We analyzed associations of the oral microbiome diversity with mortality from all causes and NPC.

Results

Within- and between-community diversities of saliva were associated with mortality with an average of 5.29 years follow-up. Lower Faith’s phylogenetic diversity was related to higher all-cause mortality [adjusted hazard ratio (aHR), 1.52 (95% confidence interval (CI), 1.06–2.17)] and NPC-specific mortality [aHR, 1.57 (95% CI, 1.07–2.29)], compared with medium diversity, but higher phylogenetic diversity was not protective. The third principal coordinate (PC3) identified from principal coordinates analysis (PCoA) on Bray–Curtis distance was marginally associated with reduced all-cause mortality [aHR, 0.85 (95% CI, 0.73–1.00)], as was the first principal coordinate (PC1) from PCoA on weighted UniFrac [aHR, 0.86 (95% CI, 0.74–1.00)], but neither was associated with NPC-specific mortality. PC3 from robust principal components analysis was associated with lower all-cause and NPC-specific mortalities, with HRs of 0.72 (95% CI, 0.61–0.85) and 0.71 (95% CI, 0.60–0.85), respectively.

Conclusions

Oral microbiome may be an explanatory factor for NPC prognosis. Lower within-community diversity was associated with higher mortality, and certain measures of between-community diversity were related to mortality. Specifically, candidate bacteria were not related to mortality, suggesting that observed associations may be due to global patterns rather than particular pathogens.

Assessment of human microbiota stability across longitudinal samples using iteratively growing-partitioned clustering

Microbiome research is advancing rapidly, and every new study should definitively be based on updated methods, trends and milestones in this field to avoid the wrong interpretation of results. Most human microbiota surveys rely on data captured from snapshots-single data points from subjects-and have permitted uncovering the recognized interindividual variability and major covariates of such microbial communities. Currently, changes in individualized microbiota profiles are under the spotlight to serve as robust predictors of clinical outcomes (e.g. weight loss via dietary interventions) and disease anticipation. Therefore, novel methods are needed to provide robust evaluation of longitudinal series of microbiota data with the aim of assessing intrapersonally short-term to long-term microbiota changes likely linked to health and disease states. Consequently, we developed microbiota STability ASsessment via Iterative cluStering (μSTASIS)-a multifunction R package to evaluate individual-centered microbiota stability. μSTASIS targets the recognized interindividual variability inherent to microbiota data to stress the tight relationships observed among and characteristic of longitudinal samples derived from a single individual via iteratively growing-partitioned clustering. The algorithms and functions implemented in this framework deal properly with the sparse and compositional nature of microbiota data. Moreover, the resulting metric is intuitive and independent of beta diversity distance methods and correlation coefficients, thus estimating stability for each microbiota sample rather than giving nonconsensus magnitudes that are difficult to interpret within and between datasets. Our method is freely available under GPL-3 licensing. We demonstrate its utility by assessing gut microbiota stability from three independent studies published previously with multiple longitudinal series of multivariate data and respective metadata.

Applications and Comparison of Dimensionality Reduction Methods for Microbiome Data

Dimensionality reduction techniques are a key component of most microbiome studies, providing both the ability to tractably visualize complex microbiome datasets and the starting point for additional, more formal, statistical analyses. In this review, we discuss the motivation for applying dimensionality reduction techniques, the special characteristics of microbiome data such as sparsity and compositionality that make this difficult, the different categories of strategies that are available for dimensionality reduction, and examples from the literature of how they have been successfully applied (together with pitfalls to avoid). We conclude by describing the need for further development in the field, in particular combining the power of phylogenetic analysis with the ability to handle sparsity, compositionality, and non-normality, as well as discussing current techniques that should be applied more widely in future analyses.

Effect of zero-valent iron and granular activated carbon on nutrient removal and community assembly of photogranules treating low-strength wastewater

Traditional wastewater treatment processes with high energy consumption and greenhouse gas emissions are not suitable for rural areas with low sewage strength and wide distribution. In this study, a microalgae-bacteria synergistic photogranules system was developed under the impetus of green chemical additives to address these challenges. The results showed that zero-valent iron (ZVI) or granular activated carbon (GAC) addition made successful photogranulation treating low-strength wastewater with excellent settleability and stability performance (settling velocity: 14-22 m h^-1; integrity coefficient: 0.81-6.62%), while systems without light or additives failed due to the bio-granules disintegration caused by the overgrowth of predators or phototrophic species. A better nutrient removal performance (TN < 15 mg L^-1, TP < 0.4 mg L^-1) was observed in photogranules systems, and stoichiometric and biological analysis found that the divisions of nitrogen removal by microalgae and bacteria were different for photogranules between GAC and ZVI additions. As a physical enhancer, GAC can be used as the nucleus of photogranules regenerating after granules disintegration rather than affecting the community succession process. However, ZVI addition strengthened the sedimentation ability and stability of photogranules through chemical and biological effects, focusing on enhancing bacterial community diversity, enriching biofilm formation bacteria and inhibiting the overgrowth of filamentous cyanobacteria. Notably, the photogranules process with ZVI addition could be operated under non-aeration conditions without compromising removal efficiency. There existed an ideal distribution of microalgae and bacterial functional species in the photogranules, which seemed to be essential for its self-sustained synergistic symbiosis and stability. Consequently, this work might provide engineering alternatives for realizing carbon neutrality and environmental sustainability of the decentralized wastewater treatment process for low-strength wastewater in rural areas.

Relative and Quantitative Rhizosphere Microbiome Profiling Results in Distinct Abundance Patterns

Next-generation sequencing is one of the most popular and cost-effective ways of characterizing microbiome in multiple samples. However, most of the currently available amplicon sequencing approaches are limited, as they result in relative abundance profiles of microbial taxa, which does not represent actual abundance in the environment. Here, we combined amplicon sequencing (16S rRNA gene for bacteria and ITS region for fungi) with real-time quantitative PCR (qPCR) to characterize the rhizosphere microbiome of wheat. We show that changes in the relative abundance of major microbial phyla do not necessarily follow the same pattern as the estimated quantitative abundance. Most of the bacterial phyla linked with the rhizosphere of plants grown in soil with no history of water stress showed enrichment patterns in their estimated absolute abundance, which was in contradiction with the trends observed in the relative abundance data. However, in the case of the fungal groups (except for Basidiomycota), such an enrichment pattern was not observed and the abundance of fungi remained relatively unchanged under different soil water stress history when estimated absolute abundance was considered. Comparing relative and estimated absolute abundances of dominant bacterial and fungal phyla, as well as their correlation with the functional processes in the rhizosphere, our results suggest that the estimated absolute abundance approach gives a different and more realistic perspective than the relative abundance approach. Such a quantification approach provides complementary information that helps to better understand the rhizosphere microbiomes and their associated ecological functional processes.

Potential role for microbial ureolysis in the rapid formation of carbonate tufa mounds

Modern carbonate tufa towers in the alkaline (~pH 9.5) Big Soda Lake (BSL), Nevada, exhibit rapid precipitation rates (exceeding 3 cm/year) and host diverse microbial communities. Geochemical indicators reveal that carbonate precipitation is, in part, promoted by the mixing of calcium-rich groundwater and carbonate-rich lake water, such that a microbial role for carbonate precipitation is unknown. Here, we characterize the BSL microbial communities and evaluate their potential effects on carbonate precipitation that may influence fast carbonate precipitation rates of the active tufa mounds of BSL. Small subunit rRNA gene surveys indicate a diverse microbial community living endolithically, in interior voids, and on tufa surfaces. Metagenomic DNA sequencing shows that genes associated with metabolisms that are capable of increasing carbonate saturation (e.g., photosynthesis, ureolysis, and bicarbonate transport) are abundant. Enzyme activity assays revealed that urease and carbonic anhydrase, two microbial enzymes that promote carbonate precipitation, are active in situ in BSL tufa biofilms, and urease also increased calcium carbonate precipitation rates in laboratory incubation analyses. We propose that, although BSL tufas form partially as a result of water mixing, tufa-inhabiting microbiota promote rapid carbonate authigenesis via ureolysis, and potentially via bicarbonate dehydration and CO₂ outgassing by carbonic anhydrase. Microbially induced calcium carbonate precipitation in BSL tufas may generate signatures preserved in the carbonate microfabric, such as stromatolitic layers, which could serve as models for developing potential biosignatures on Earth and elsewhere.

Efficient computation of Faith's phylogenetic diversity with applications in characterizing microbiomes

The number of publicly available microbiome samples is continually growing. As data set size increases, bottlenecks arise in standard analytical pipelines. Faith's phylogenetic diversity (Faith's PD) is a highly utilized phylogenetic alpha diversity metric that has thus far failed to effectively scale to trees with millions of vertices. Stacked Faith's phylogenetic diversity (SFPhD) enables calculation of this widely adopted diversity metric at a much larger scale by implementing a computationally efficient algorithm. The algorithm reduces the amount of computational resources required, resulting in more accessible software with a reduced carbon footprint, as compared to previous approaches. The new algorithm produces identical results to the previous method. We further demonstrate that the phylogenetic aspect of Faith's PD provides increased power in detecting diversity differences between younger and older populations in the FINRISK study's metagenomic data.

Uniform Manifold Approximation and Projection (UMAP) Reveals Composite Patterns and Resolves Visualization Artifacts in Microbiome Data

Microbiome data are sparse and high dimensional, so effective visualization of these data requires dimensionality reduction. To date, the most commonly used method for dimensionality reduction in the microbiome is calculation of between-sample microbial differences (beta diversity), followed by principal-coordinate analysis (PCoA). Uniform Manifold Approximation and Projection (UMAP) is an alternative method that can reduce the dimensionality of beta diversity distance matrices. Here, we demonstrate the benefits and limitations of using UMAP for dimensionality reduction on microbiome data. Using real data, we demonstrate that UMAP can improve the representation of clusters, especially when the clusters are composed of multiple subgroups. Additionally, we show that UMAP provides improved correlation of biological variation along a gradient with a reduced number of coordinates of the resulting embedding. Finally, we provide parameter recommendations that emphasize the preservation of global geometry. We therefore conclude that UMAP should be routinely used as a complementary visualization method for microbiome beta diversity studies. IMPORTANCE UMAP provides an additional method to visualize microbiome data. The method is extensible to any beta diversity metric used with PCoA, and our results demonstrate that UMAP can indeed improve visualization quality and correspondence with biological and technical variables of interest. The software to perform this analysis is available under an open-source license and can be obtained at https://github.com/knightlab-analyses/umap-microbiome-benchmarking; additionally, we have provided a QIIME 2 plugin for UMAP at https://github.com/biocore/q2-umap.

Ecological Adaptation and Succession of Human Fecal Microbial Communities in an Automated In Vitro Fermentation System

Longitudinal studies of gut microbiota following specific interventions are vital for understanding how they influence host health. However, robust longitudinal sampling of gut microbiota is a major challenge, which can be addressed using in vitro fermentors hosting complex microbial communities. Here, by employing 16S rRNA gene amplicon sequencing, we investigated the adaptation and succession of human fecal microbial communities in an automated multistage fermentor. We performed two independent experiments using different human donor fecal samples, one configured with two units of three colon compartments each studied for 22 days and another with one unit of two colon compartments studied for 31 days. The fermentor maintained a trend of increasing microbial alpha diversity along colon compartments. Within each experiment, microbial compositions followed compartment-specific trajectories and reached independent stable configurations. While compositions were highly similar between replicate units, they were clearly separated between different experiments, showing that they maintained the individuality of fecal inoculum rather than converging on a fermentor-specific composition. While some fecal amplicon sequence variants (ASVs) were undetected in the fermentor, many ASVs undetected in the fecal samples flourished in vitro. These bloomer ASVs accounted for significant proportions of the population and included prominent health-associated microbes such as Bacteroides fragilis and Akkermansia muciniphila. Turnover in community compositions is likely explained by feed composition and pH, suggesting that these communities can be easily modulated. Our results suggest that in vitro fermentors are promising tools to study complex microbial communities harboring important members of human gut microbiota. IMPORTANCE In vitro fermentors that can host complex gut microbial communities are promising tools to investigate the dynamics of human gut microbiota. In this work, using an automated in vitro gut fermentor consisting of different colon compartments, we investigated the adaptation dynamics of two different human fecal microbial communities over 22 and 31 days. By observing the temporal trends of different community members, we found that many dominant members of the fecal microbiota failed to maintain their dominance in vitro, and some of the low-abundance microbes undetected in the fecal microbiota successfully grew in the in vitro communities. Microbiome compositional changes and blooming could largely be explained by feed composition and pH, suggesting that these communities can be modulated to desired compositions via optimizing culture conditions. Thus, our results open up the possibility of modulating in vitro microbial communities to predefined compositions by optimizing feed composition and culture conditions.

Driving factors influencing the rhizobacteriome community structure of plants adapted to multiple climatic stressors in edaphic savannas

The natural variation of multiple abiotic stresses in hyper-seasonal edaphic savanna provides a unique opportunity to study the rhizobacteriome community structure of plants adapted to climate change-like conditions in the humid tropics. In this study, we evaluated changes in soil, plant and rhizobacteriome community structure parameters across seasons (wet and dry) in two edaphic savannas (SV-1 and SV-5) using four dominant plant species. We then examined relationships between rhizobacteriome community structure and soil properties, plant biomass, and conventional and novel root traits. We further hypothesized that plants adapted to the Aripo Savanna had a core rhizobacteriome, which was specific to plant species and related to root foraging traits. Our results showed that cation exchange capacity (CEC) and the concentration of micronutrients (Fe, Cu and B) were the only soil factors that differed across savanna and season, respectively. Plant biomass traits were generally higher in the dry season, with a higher allocation to root growth in SV-5. Root traits were more plastic in SV-5, and network length-distribution was the only root trait which showed a consistent pattern of lower values in the dry season for three of the dominant plant species. Rhizobacterial community compositions were dominated by Proteobacteria and Acidobacteria, as well as WPS-2, which is dominant in extreme environments. We identified a shared core rhizobacteriome across plant species and savannas. Cation exchange capacity was a major driver of rhizobacterial community assemblies across savannas. Savanna-specific drivers of rhizobacterial community assemblies included CEC and Fe for SV-1, and CEC, TDS, NH₄⁺, NO₃^-, Mn, K, and network length-distribution for SV-5. Plant factors on the microbiome were minimal, and host selectivity was mediated by the seasonal changes. We conclude that edaphoclimatic factors (soil and season) are the key determinants influencing rhizobacteriome community structure in multiple stressed-environments, which are ecologically similar to the Aripo Savanna.

Potential and expression of carbohydrate utilization by marine fungi in the global ocean

BACKGROUND

Most of the research on the cycling of carbon in the open-ocean has focused on heterotrophic prokaryotes and eukaryotic phytoplankton, but the role of pelagic fungi remains largely enigmatic.

METHODS

Here, we performed a global-ocean multi-omics analysis of all pelagic fungal carbohydrate-active enzymes (CAZymes), key enzymes in the carbon cycling. We studied the occurrence, expression, diversity, functional classification, and taxonomic affiliation of the genes encoding all pelagic fungal CAZymes from the epi- and mesopelagic realm.

RESULTS

Pelagic fungi are active in carbohydrate degradation as indicated by a high ratio of CAZymes transcripts per gene. Dothideomycetes in epipelagic and the Leotiomycetes in mesopelagic waters (both from the phylum Ascomycota) are the main pelagic fungi responsible for carbohydrate degradation in the ocean. The abundance, expression, and diversity of fungal CAZymes were higher in the mesopelagic than in the epipelagic waters, in contrast to the distribution pattern of prokaryotic CAZymes.

CONCLUSIONS

Our results reveal a widespread utilization of different types of CAZymes by pelagic fungi, uncovering an active and hitherto largely unexplored participation of fungi in the pelagic C cycling, where pelagic prokaryotes and fungi occupy different ecological niches, and fungi becoming relatively more important with depth. Video abstract.

Investigation into the presence and transfer of microbiomes within a forensic laboratory setting

Microbial profiling within forensic science is an emerging field that may have applications in the identification of individuals using microbial signatures. It is important to determine if microbial transfer may occur within a forensic laboratory setting using current standard operating procedures (SOPs) for nuclear DNA recovery, to assess the suitability of such procedures for microbial profiling and establish the potential limitations of microbial profiling for forensic purposes. This preliminary study investigated the presence and potential transfer of human-associated microbiomes within a forensic laboratory. Swabs of laboratory surfaces, external surfaces of personal protective equipment (PPE) and equipment were taken before and after mock examinations of cotton swatches, which harboured microbiota transferred from direct hand-contact. Microbial profiles obtained from these samples were compared to reference profiles obtained from the participants, cotton swatches and the researcher to detect microbial transfer from the individuals and determine potential source contributions. The results revealed an apparent transfer of microbiota to the examined swatches, laboratory equipment and surfaces from the participants and/or researcher following the mock examinations, highlighting potential contamination issues regarding microbial profiling when using current laboratory SOPs for nuclear DNA recovery, and cleaning.

kernInt: A Kernel Framework for Integrating Supervised and Unsupervised Analyses in Spatio-Temporal Metagenomic Datasets

The advent of next-generation sequencing technologies allowed relative quantification of microbiome communities and their spatial and temporal variation. In recent years, supervised learning (i.e., prediction of a phenotype of interest) from taxonomic abundances has become increasingly common in the microbiome field. However, a gap exists between supervised and classical unsupervised analyses, based on computing ecological dissimilarities for visualization or clustering. Despite this, both approaches face common challenges, like the compositional nature of next-generation sequencing data or the integration of the spatial and temporal dimensions. Here we propose a kernel framework to place on a common ground the unsupervised and supervised microbiome analyses, including the retrieval of microbial signatures (taxa importances). We define two compositional kernels (Aitchison-RBF and compositional linear) and discuss how to transform non-compositional beta-dissimilarity measures into kernels. Spatial data is integrated with multiple kernel learning, while longitudinal data is evaluated by specific kernels. We illustrate our framework through a single point soil dataset, a human dataset with a spatial component, and a previously unpublished longitudinal dataset concerning pig production. The proposed framework and the case studies are freely available in the kernInt package at https://github.com/elies-ramon/kernInt.

Local community assembly mechanisms shape soil bacterial β diversity patterns along a latitudinal gradient

Biodiversity patterns across geographical gradients could result from regional species pool and local community assembly mechanisms. However, little has been done to separate the effects of local ecological mechanisms from variation in the regional species pools on bacterial diversity patterns. In this study, we compare assembly mechanisms of soil bacterial communities in 660 plots from 11 regions along a latitudinal gradient in eastern China with highly divergent species pools. Our results show that β diversity does not co-vary with γ diversity, and local community assembly mechanisms appear to explain variation in β diversity patterns after correcting for variation in regional species pools. The variation in environmental conditions along the latitudinal gradient accounts for the variation in β diversity through mediating the strength of heterogeneous selection. In conclusion, our study clearly illustrates the importance of local community assembly processes in shaping geographical patterns of soil bacterial β diversity.

The relative importance of regional species pool and local assembly processes as drivers of beta diversity is unclear. Here the authors investigate soil bacterial diversity patterns along a 3700-km latitudinal gradient in Chinese forests, finding that community assembly processes differ based on environmental heterogeneity.

Developments in Molecular Level Characterization of Naphthenic Acid Fraction Compounds Degradation in a Constructed Wetland Treatment System

The reclamation of oil sands process-affected water (OSPW) is a matter of environmental importance because of the aquatic toxicity to biota. This study describes refinements in advanced analytical methods to assess the performance of biological treatment systems for OSPW, such as constructed wetland treatment systems (CWTSs). Assessment of treatment efficiency by measurement of the degradation of naphthenic acid fraction compounds (NAFCs) in OSPW is challenging in CWTS due to potentially interfering constituents such as humic acids, organic acids, salts, and hydrocarbons. Here we have applied a previous weak anion exchange (WAX) solid-phase extraction (SPE) method and high-resolution Orbitrap-mass spectrometry (MS) to remove major interferences from the NAFC analysis. The refinements in data processing employing principal component analysis (PCA) indicates that the relative abundance of NAFCs decreased with time in the treated OSPW relative to the untreated OSPW. The most saturated NAFCs with higher carbon numbers were relatively more degraded as compared to unsaturated NAFCs. The use of Kendrick plots and van Krevelen plots for assessment of the performance of the CWTS is shown to be well-suited to detailed monitoring of the complex composition of NAFCs as a function of degradation. The developments and application of analytical methods such as the WAX SPE method and high-resolution Orbitrap-MS are demonstrated as tools enabling the advancement of CWTS design and optimization, enabling passive or semi-passive water treatment systems to be a viable opportunity for OSPW treatment.

Soil Bacterial Communities Exhibit Strong Biogeographic Patterns at Fine Taxonomic Resolution

Bacteria have been inferred to exhibit relatively weak biogeographic patterns. To what extent such findings reflect true biological phenomena or methodological artifacts remains unclear. Here, we addressed this question by analyzing the turnover of soil bacterial communities from three data sets. We applied three methodological innovations: (i) design of a hierarchical sampling scheme to disentangle environmental from spatial factors driving turnover; (ii) resolution of 16S rRNA gene amplicon sequence variants to enable higher-resolution community profiling; and (iii) application of the new metric zeta diversity to analyze multisite turnover and drivers. At fine taxonomic resolution, rapid compositional turnover was observed across multiple spatial scales. Turnover was overwhelmingly driven by deterministic processes and influenced by the rare biosphere. The communities also exhibited strong distance decay patterns and taxon-area relationships, with z values within the interquartile range reported for macroorganisms. These biogeographical patterns were weakened upon applying two standard approaches to process community sequencing data: clustering sequences at 97% identity threshold and/or filtering the rare biosphere (sequences lower than 0.05% relative abundance). Comparable findings were made across local, regional, and global data sets and when using shotgun metagenomic markers. Altogether, these findings suggest that bacteria exhibit strong biogeographic patterns, but these signals can be obscured by methodological limitations. We advocate various innovations, including using zeta diversity, to advance the study of microbial biogeography.IMPORTANCE It is commonly thought that bacterial distributions show lower spatial variation than for multicellular organisms. In this article, we present evidence that these inferences are artifacts caused by methodological limitations. Through leveraging innovations in sampling design, sequence processing, and diversity analysis, we provide multifaceted evidence that bacterial communities in fact exhibit strong distribution patterns. This is driven by selection due to factors such as local soil characteristics. Altogether, these findings suggest that the processes underpinning diversity patterns are more unified across all domains of life than previously thought, which has broad implications for the understanding and management of soil biodiversity.

Applications of weighted association networks applied to compositional data in biology

Next-generation sequencing technologies have generated, and continue to produce, an increasingly large corpus of biological data. The data generated are inherently compositional as they convey only relative information dependent upon the capacity of the instrument, experimental design and technical bias. There is considerable information to be gained through network analysis by studying the interactions between components within a system. Network theory methods using compositional data are powerful approaches for quantifying relationships between biological components and their relevance to phenotype, environmental conditions or other external variables. However, many of the statistical assumptions used for network analysis are not designed for compositional data and can bias downstream results. In this mini-review, we illustrate the utility of network theory in biological systems and investigate modern techniques while introducing researchers to frameworks for implementation. We overview (1) compositional data analysis, (2) data transformations and (3) network theory along with insight on a battery of network types including static-, temporal-, sample-specific- and differential-networks. The intention of this mini-review is not to provide a comprehensive overview of network methods, rather to introduce microbiology researchers to (semi)-unsupervised data-driven approaches for inferring latent structures that may give insight into biological phenomena or abstract mechanics of complex systems.

The fecal microbiota of wild and captive raptors

BACKGROUND

The microorganisms populating the gastro-intestinal tract of vertebrates, collectively known as "microbiota", play an essential role in digestion and are important in regulating the immune response. Whereas the intestinal microbiota in humans and model organisms has been studied for many years, much less is known about the microbiota populating the intestinal tract of wild animals.

RESULTS

The relatively large number of raptors admitted to the Tufts Wildlife Clinic on the Cummings School of Veterinary Medicine at Tufts University campus provided a unique opportunity to investigate the bacterial microbiota in these birds. Opportunistic collection of fecal samples from raptors of 7 different species in the orders Strigiformes, Accipitriformes, and Falconiformes with different medical histories generated a collection of 46 microbiota samples. Based on 16S amplicon sequencing of fecal DNA, large β-diversity values were observed. Many comparisons exceeded weighted UniFrac distances of 0.9. Microbiota diversity did not segregate with the taxonomy of the host; no significant difference between microbiota from Strigiformes and from Accipitriformes/Falconiformes were observed. In contrast, in a sample of 22 birds admitted for rehabilitation, a significant effect of captivity was found. The change in microbiota profile was driven by an expansion of the proportion of Actinobacteria. Based on a small number of raptors treated with anti-microbials, no significant effect of these treatments on microbiota α-diversity was observed.

CONCLUSIONS

The concept of "meta-organism conservation", i.e., conservation efforts focused on the host and its intestinal microbiome has recently been proposed. The observed effect of captivity on the fecal microbiota is relevant to understanding the response of wildlife to captivity and optimizing wildlife rehabilitation and conservation efforts.

Impact of hydraulic retention time on community assembly and function of photogranules for wastewater treatment

Photogranules are dense, spherical agglomerates of cyanobacteria, microalgae and non-phototrophic microorganisms that have considerable advantages in terms of harvesting and nutrient removal rates for light driven wastewater treatment processes. This ecosystem is poorly understood in terms of the microbial community structure and the response of the community to changing abiotic conditions. To get a better understanding, we investigated the effect of hydraulic retention time (HRT) on photogranule formation and community assembly over a period of 148 days. Three laboratory bioreactors were inoculated with field samples from various locations in the Netherlands and operated in sequencing batch mode. The bioreactors were operated at four different HRTs (2.00, 1.00, 0.67, 0.33 days), while retaining the same solid retention time of 7 days. A microbial community with excellent settling characteristics (95-99% separation efficiency) was established within 2-5 weeks. The observed nutrient uptake rates ranged from 24 to 90 mg_N L^-1 day^-1 and from 3.1 to 5.4 mg_P L^-1 day^-1 depending on the applied HRT. The transition from single-cell suspension culture to floccular agglomeration to granular sludge was monitored by microscopy and 16S/18S sequencing. In particular, two important variables for driving aggregation and granulation, and for the structural integrity of photogranules were identified: 1. Extracellular polymeric substances (EPS) with high protein to polysaccharide ratio and 2. specific microorganisms. The key players were found to be the cyanobacteria Limnothrix and Cephalothrix, the colony forming photosynthetic eukaryotes within Chlamydomonadaceae, and the biofilm producing bacteria Zoogloea and Thauera. Knowing the makeup of the microbial community and the operational conditions influencing granulation and bioreactor function is crucial for successful operation of photogranular systems.

Analysis of Identification Method for Bacterial Species and Antibiotic Resistance Genes Using Optical Data From DNA Oligomers

Bacterial antibiotic resistance is becoming a significant health threat, and rapid identification of antibiotic-resistant bacteria is essential to save lives and reduce the spread of antibiotic resistance. This paper analyzes the ability of machine learning algorithms (MLAs) to process data from a novel spectroscopic diagnostic device to identify antibiotic-resistant genes and bacterial species by comparison to available bacterial DNA sequences. Simulation results show that the algorithms attain from 92% accuracy (for genes) up to 99% accuracy (for species). This novel approach identifies genes and species by optically reading the percentage of A, C, G, T bases in 1000s of short 10-base DNA oligomers instead of relying on conventional DNA sequencing in which the sequence of bases in long oligomers provides genetic information. The identification algorithms are robust in the presence of simulated random genetic mutations and simulated random experimental errors. Thus, these algorithms can be used to identify bacterial species, to reveal antibiotic resistance genes, and to perform other genomic analyses. Some MLAs evaluated here are shown to be better than others at accurate gene identification and avoidance of false negative identification of antibiotic resistance.

Towards centralized biogas plants: Co-digestion of sewage sludge and pig manure maintains process performance and active microbiome diversity

The aim of this study is to assess the performance of anaerobic digestion against co-digestion systems during the start-up stages based on key process parameters and biological indicators. Two parallel experiments treating sewage sludge alone or co-digested with low concentration of pig manure (8% vol., 2-3% in COD basis) were carried out in two lab-scale CSTR at mesophilic conditions. Same inoculant and organic loading rate sequences were applied for two consecutive runs of 79 and 90 days. According to the removal efficiencies achieved, no significant differences were encountered amongst mono-digestion and co-digestion. This observation was reinforced with the analysis of the total/active microbiome, sequencing 16S rRNA genes and transcripts. The addition of a co-substrate at low concentration had a negligible effect on the total/active microbial communities; they evolved following the same pattern. This might be an advantage in order to upgrade existing wastewater treatment plants to become centralized biogas facilities.

Molecular level study of hot water extracted green tea buried in soils - a proxy for labile soil organic matter

Understanding the composition of soil organic matter (SOM) is vital to our understanding of how soils form, evolve and respond to external stimuli. The shear complexity of SOM, an inseparable mixture of thousands of compounds hinders the determination of structure-function relationships required to explore these processes on a molecular level. Litter bags and soil hot water extracts (HWE) have frequently been used to study the transformation of labile SOM, however these are still too complex to examine beyond compound classes. In this work, a much simpler mixture, HWE buried green tea, was investigated by Nuclear Magnetic Resonance (NMR) spectroscopy and Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR-MS), as a proxy for labile SOM. Changes induced by the burial over 90 days in a grassland, woodland and two peatland sites, one damaged by drainage and one undergoing restoration by drain-blocking, were analysed. Major differences between the extracts were observed on the level of compound classes, molecular formulae and specific molecules. The causes of these differences are discussed with reference to abiotic and biotic processes. Despite the vastly different detection limits of NMR and MS, chemometric analysis of the data yielded identical separation of the samples. These findings provide a basis for the molecular level interrogation of labile SOM and C-cycling processes in soils.

Linking Microbial Community Structure to Trait Distributions and Functions Using Salinity as an Environmental Filter

The structure and function of microbial communities vary along environmental gradients; however, interlinking the two has been challenging. In this study, salinity was used as an environmental filter to study how it could shape trait distributions, community structures, and the resulting functions of soil microbes. The environmental filter was applied by salinizing nonsaline soil (0 to 22 mg NaCl g^-1). Our targeted community trait distribution (salt tolerance) was determined with dose-response relationships between bacterial growth and salinity. The bacterial community structure responses were resolved with Illumina 16S rRNA gene amplicon sequencing, and the microbial functions determined were respiration and bacterial and fungal growth. Salt exposure quickly resulted in filtered trait distributions, and stronger filters resulted in larger shifts. The filtered trait distributions correlated well with community composition differences, suggesting that trait distribution shifts were driven at least partly by species turnover. While salt exposure decreased respiration, microbial growth responses appeared to be characterized by competitive interactions. Fungal growth was highest when bacterial growth was inhibited by the highest salinity, and it was lowest when the bacterial growth rate peaked at intermediate salt levels. These findings corroborated a higher potential for fungal salt tolerance than bacterial salt tolerance for communities derived from a nonsaline soil. In conclusion, by using salt as an environmental filter, we could interlink the targeted trait distribution with both the community structure and resulting functions of soil microbes.IMPORTANCE Understanding the role of ecological communities in maintaining multiple ecosystem processes is a central challenge in ecology. Soil microbial communities perform vital ecosystem functions, such as the decomposition of organic matter to provide plant nutrition. However, despite the functional importance of soil microorganisms, attribution of ecosystem function to particular constituents of the microbial community has been impeded by a lack of information linking microbial processes to community composition and structure. Here, we apply a conceptual framework to determine how microbial communities influence ecosystem processes, by applying a "top-down" trait-based approach. By determining the dependence of microbial processes on environmental factors (e.g., the tolerance to salinity), we can define the aggregate response trait distribution of the community, which then can be linked to the community structure and the resulting function performed by the microbial community.

Robust and automatic definition of microbiome states

Analysis of microbiome dynamics would allow elucidation of patterns within microbial community evolution under a variety of biologically or economically important circumstances; however, this is currently hampered in part by the lack of rigorous, formal, yet generally-applicable approaches to discerning distinct configurations of complex microbial populations. Clustering approaches to define microbiome "community state-types" at a population-scale are widely used, though not yet standardized. Similarly, distinct variations within a state-type are well documented, but there is no rigorous approach to discriminating these more subtle variations in community structure. Finally, intra-individual variations with even fewer differences will likely be found in, for example, longitudinal data, and will correlate with important features such as sickness versus health. We propose an automated, generic, objective, domain-independent, and internally-validating procedure to define statistically distinct microbiome states within datasets containing any degree of phylotypic diversity. Robustness of state identification is objectively established by a combination of diverse techniques for stable cluster verification. To demonstrate the efficacy of our approach in detecting discreet states even in datasets containing highly similar bacterial communities, and to demonstrate the broad applicability of our method, we reuse eight distinct longitudinal microbiome datasets from a variety of ecological niches and species. We also demonstrate our algorithm's flexibility by providing it distinct taxa subsets as clustering input, demonstrating that it operates on filtered or unfiltered data, and at a range of different taxonomic levels. The final output is a set of robustly defined states which can then be used as general biomarkers for a wide variety of downstream purposes such as association with disease, monitoring response to intervention, or identifying optimally performant populations.

Calour: an Interactive, Microbe-Centric Analysis Tool

Calour allows us to identify interesting microbial patterns and generate novel biological hypotheses by interactively inspecting microbiome studies and incorporating annotation databases and convenient statistical tools. Calour can be used as a first-step tool for microbiome data exploration.

Microbiome analyses can be challenging because microbial strains are numerous, and often, confounding factors in the data set are also numerous. Many tools reduce, summarize, and visualize these high-dimensional data to provide insight at the community level. However, they lose the detailed information about each taxon and can be misleading (for example, the well-known horseshoe effect in ordination plots). Thus, multiple methods at different levels of resolution are required to capture the full range of microbial patterns. Here we present Calour, a user-friendly data exploration tool for microbiome analyses. Calour provides a study-centric data model to store and manipulate sample-by-feature tables (with features typically being operational taxonomic units) and their associated metadata. It generates an interactive heatmap, allowing visualization of microbial patterns and exploration using microbial knowledge databases. We demonstrate the use of Calour by exploring publicly available data sets, including the gut and skin microbiota of habitat-switched fire salamander larvae, gut microbiota of Trichuris muris-infected mice, skin microbiota of different human body sites, gut microbiota of various ant species, and a metabolome study of mice exposed to intermittent hypoxia and hypercapnia. In these cases, Calour reveals novel patterns and potential contaminants of subgroups of microbes that are otherwise hard to find. Calour is open source under the Berkeley Software Distribution (BSD) license and available from https://github.com/biocore/calour.

IMPORTANCE Calour allows us to identify interesting microbial patterns and generate novel biological hypotheses by interactively inspecting microbiome studies and incorporating annotation databases and convenient statistical tools. Calour can be used as a first-step tool for microbiome data exploration.

A Novel Sparse Compositional Technique Reveals Microbial Perturbations

The central aims of many host or environmental microbiome studies are to elucidate factors associated with microbial community compositions and to relate microbial features to outcomes. However, these aims are often complicated by difficulties stemming from high-dimensionality, non-normality, sparsity, and the compositional nature of microbiome data sets. A key tool in microbiome analysis is beta diversity, defined by the distances between microbial samples. Many different distance metrics have been proposed, all with varying discriminatory power on data with differing characteristics. Here, we propose a compositional beta diversity metric rooted in a centered log-ratio transformation and matrix completion called robust Aitchison PCA. We demonstrate the benefits of compositional transformations upstream of beta diversity calculations through simulations. Additionally, we demonstrate improved effect size, classification accuracy, and robustness to sequencing depth over the current methods on several decreased sample subsets of real microbiome data sets. Finally, we highlight the ability of this new beta diversity metric to retain the feature loadings linked to sample ordinations revealing salient intercommunity niche feature importance. IMPORTANCE By accounting for the sparse compositional nature of microbiome data sets, robust Aitchison PCA can yield high discriminatory power and salient feature ranking between microbial niches. The software to perform this analysis is available under an open-source license and can be obtained at https://github.com/biocore/DEICODE; additionally, a QIIME 2 plugin is provided to perform this analysis at https://library.qiime2.org/plugins/deicode/.

Daylight exposure modulates bacterial communities associated with household dust

BACKGROUND

Microbial communities associated with indoor dust abound in the built environment. The transmission of sunlight through windows is a key building design consideration, but the effects of light exposure on dust communities remain unclear. We report results of an experiment and computational models designed to assess the effects of light exposure and wavelengths on the structure of the dust microbiome. Specifically, we placed household dust in replicate model "rooms" with windows that transmitted visible, ultraviolet, or no light and measured taxonomic compositions, absolute abundances, and viabilities of the resulting bacterial communities.

RESULTS

Light exposure per se led to lower abundances of viable bacteria and communities that were compositionally distinct from dark rooms, suggesting preferential inactivation of some microbes over others under daylighting conditions. Differences between communities experiencing visible and ultraviolet light wavelengths were relatively minor, manifesting primarily in abundances of dead human-derived taxa. Daylighting was associated with the loss of a few numerically dominant groups of related microorganisms and apparent increases in the abundances of some rare groups, suggesting that a small number of microorganisms may have exhibited modest population growth under lighting conditions. Although biological processes like population growth on dust could have generated these patterns, we also present an alternate statistical explanation using sampling models from ecology; simulations indicate that artefactual, apparent increases in the abundances of very rare taxa may be a null expectation following the selective inactivation of dominant microorganisms in a community.

CONCLUSIONS

Our experimental and simulation-based results indicate that dust contains living bacterial taxa that can be inactivated following changes in local abiotic conditions and suggest that the bactericidal potential of ordinary window-filtered sunlight may be similar to ultraviolet wavelengths across dosages that are relevant to real buildings.