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.

Improper data practices erode the quality of global ecological databases and impede the progress of ecological research

The scientific community has entered an era of big data. However, with big data comes big responsibilities, and best practices for how data are contributed to databases have not kept pace with the collection, aggregation, and analysis of big data. Here, we rigorously assess the quantity of data for specific leaf area (SLA) available within the largest and most frequently used global plant trait database, the TRY Plant Trait Database, exploring how much of the data were applicable (i.e., original, representative, logical, and comparable) and traceable (i.e., published, cited, and consistent). Over three-quarters of the SLA data in TRY either lacked applicability or traceability, leaving only 22.9% of the original data usable compared with the 64.9% typically deemed usable by standard data cleaning protocols. The remaining usable data differed markedly from the original for many species, which led to altered interpretation of ecological analyses. Though the data we consider here make up only 4.5% of SLA data within TRY, similar issues of applicability and traceability likely apply to SLA data for other species as well as other commonly measured, uploaded, and downloaded plant traits. We end with suggested steps forward for global ecological databases, including suggestions for both uploaders to and curators of databases with the hope that, through addressing the issues raised here, we can increase data quality and integrity within the ecological community.

Safety Assessment of the CP4 EPSPS and NPTII Proteins in Eucalyptus

Glyphosate herbicide treatment is essential to sustainable Eucalyptus plantation management in Brazil. Eucalyptus is highly sensitive to glyphosate, and Suzano/FuturaGene has genetically modified eucalyptus to tolerate glyphosate, with the aim of both protecting eucalyptus trees from glyphosate application damage and improving weed management. This study presents the biosafety results of the glyphosate-tolerant eucalyptus event 751K032, which expresses the selection marker neomycin phosphotransferase II (NPTII) enzyme and CP4-EPSPS, a glyphosate-tolerant variant of plant 5-enolpyruvyl-shikimate-3-phosphate synthase enzyme. The transgenic genetically modified (GM) event 751K032 behaved in the plantations like conventional non-transgenic eucalyptus clone, FGN-K, and had no effects on arthropods and soil microorganisms. The engineered NPTII and CP4 EPSPS proteins were heat-labile, readily digestible, and according to the bioinformatics analyses, unlikely to cause an allergenic or toxic reaction in humans or animals. This assessment of the biosafety of the glyphosate-tolerant eucalyptus event 751K032 concludes that it is safe to be used for wood production.

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.

False discovery rates of qpAdm-based screens for genetic admixture

Although a broad range of methods exists for reconstructing population history from genome-wide single nucleotide polymorphism data, just a few methods gained popularity in archaeogenetics: principal component analysis (PCA); ADMIXTURE, an algorithm that models individuals as mixtures of multiple ancestral sources represented by actual or inferred populations; formal tests for admixture such as f3-statistics and D/f4-statistics; and qpAdm, a tool for fitting two-component and more complex admixture models to groups or individuals. Despite their popularity in archaeogenetics, which is explained by modest computational requirements and ability to analyze data of various types and qualities, protocols relying on qpAdm that screen numerous alternative models of varying complexity and find "fitting" models (often considering both estimated admixture proportions and p-values as a composite criterion of model fit) remain untested on complex simulated population histories in the form of admixture graphs of random topology. We analyzed genotype data extracted from such simulations and tested various types of high-throughput qpAdm protocols ("rotating" and "non-rotating", with or without temporal stratification of target groups and proxy ancestry sources, and with or without a "model competition" step). We caution that high-throughput qpAdm protocols may be inappropriate for exploratory analyses in poorly studied regions/periods since their false discovery rates varied between 12% and 68% depending on the details of the protocol and on the amount and quality of simulated data (i.e., >12% of fitting two-way admixture models imply gene flows that were not simulated). We demonstrate that for reducing false discovery rates of qpAdm protocols to nearly 0% it is advisable to use large SNP sets with low missing data rates, the rotating qpAdm protocol with a strictly enforced rule that target groups do not pre-date their proxy sources, and an unsupervised ADMIXTURE analysis as a way to verify feasible qpAdm models. Our study has a number of limitations: for instance, these recommendations depend on the assumption that the underlying genetic history is a complex admixture graph and not a stepping-stone model.

Multi-Omics Integration Reveals the Crucial Role of Fusobacterium in the Inflammatory Immune Microenvironment in Head and Neck Squamous Cell Carcinoma

The tumor microbiome is believed to have a profound impact on tumor progression owing to its local colonization in the tumor microenvironment (TME). Using the Cancer Microbiome Atlas (TCMA), a database of curated, decontaminated microbial profiles for 3,689 oropharyngeal, esophageal, gastrointestinal, and colorectal tissue samples from 1,772 patients, we conducted a comprehensive multi-omics analysis to reveal microbial signatures among various cancers and the potential mechanisms involved in tumor progression of head and neck squamous cell carcinoma (HNSC). We found that compared with other cancer types, the tumor-resident microbiome of HNSC accounted for the highest bacterial abundance and strongest association with host TME signatures. Fusobacterium was found to be enriched in HNSC tissues, which was associated with an increased inflammatory effect and inferior prognosis. Moreover, we revealed that the microbiota-associated inflammatory TME was attributed to the competing endogenouse RNA (ceRNA) network and chromatin accessibility. IMPORTANCE Studies on revealing the composition and potential mechanisms of the tumor microbiome are still at an initial stage. We uncovered the potential contribution of the tumor-resident microbiota on the immunosuppressive microenvironment in HNSC, which will provide a new perspective for tumor microbiome research and yield valuable insights into the clinical management of HNSC.

Exploring methods to summarize gut microbiota composition for microbiability estimation and phenotypic prediction in swine

The microbial composition resemblance among individuals in a group can be summarized in a square covariance matrix and fitted in linear models. We investigated eight approaches to create the matrix that quantified the resemblance between animals based on the gut microbiota composition. We aimed to compare the performance of different methods in estimating trait microbiability and predicting growth and body composition traits in three pig breeds. This study included 651 purebred boars from either breed: Duroc (n = 205), Landrace (n = 226), and Large White (n = 220). Growth and body composition traits, including body weight (BW), ultrasound backfat thickness (BF), ultrasound loin depth (LD), and ultrasound intramuscular fat (IMF) content, were measured on live animals at the market weight (156 ± 2.5 days of age). Rectal swabs were taken from each animal at 158 ± 4 days of age and subjected to 16S rRNA gene sequencing. Eight methods were used to create the microbial similarity matrices, including four kernel functions (Linear Kernel, LK; Polynomial Kernel, PK; Gaussian Kernel, GK; Arc-cosine Kernel with one hidden layer, AK1), two dissimilarity methods (Bray-Curtis, BC; Jaccard, JA), and two ordination methods (Metric Multidimensional Scaling, MDS; Detrended Correspondence analysis, DCA). Based on the matrix used, microbiability estimates ranged from 0.07 to 0.21 and 0.12 to 0.53 for Duroc, 0.03 to 0.21 and 0.05 to 0.44 for Landrace, and 0.02 to 0.24 and 0.05 to 0.52 for Large White pigs averaged over traits in the model with sire, pen, and microbiome, and model with the only microbiome, respectively. The GK, JA, BC, and AK1 obtained greater microbiability estimates than the remaining methods across traits and breeds. Predictions were made within each breed group using four-fold cross-validation based on the relatedness of sires in each breed group. The prediction accuracy ranged from 0.03 to 0.18 for BW, 0.08 to 0.31 for BF, 0.21 to 0.48 for LD, and 0.04 to 0.16 for IMF when averaged across breeds. The BC, MDS, LK, and JA achieved better accuracy than other methods in most predictions. Overall, the PK and DCA exhibited the worst performance compared to other microbiability estimation and prediction methods. The current study shows how alternative approaches summarized the resemblance of gut microbiota composition among animals and contributed this information to variance component estimation and phenotypic prediction in swine.

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.

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.

Multi-dimensional leaf phenotypes reflect root system genotype in grafted grapevine over the growing season

BACKGROUND

Modern biological approaches generate volumes of multi-dimensional data, offering unprecedented opportunities to address biological questions previously beyond reach owing to small or subtle effects. A fundamental question in plant biology is the extent to which below-ground activity in the root system influences above-ground phenotypes expressed in the shoot system. Grafting, an ancient horticultural practice that fuses the root system of one individual (the rootstock) with the shoot system of a second, genetically distinct individual (the scion), is a powerful experimental system to understand below-ground effects on above-ground phenotypes. Previous studies on grafted grapevines have detected rootstock influence on scion phenotypes including physiology and berry chemistry. However, the extent of the rootstock's influence on leaves, the photosynthetic engines of the vine, and how those effects change over the course of a growing season, are still largely unknown.

RESULTS

Here, we investigate associations between rootstock genotype and shoot system phenotypes using 5 multi-dimensional leaf phenotyping modalities measured in a common grafted scion: ionomics, metabolomics, transcriptomics, morphometrics, and physiology. Rootstock influence is ubiquitous but subtle across modalities, with the strongest signature of rootstock observed in the leaf ionome. Moreover, we find that the extent of rootstock influence on scion phenotypes and patterns of phenomic covariation are highly dynamic across the season.

CONCLUSIONS

These findings substantially expand previously identified patterns to demonstrate that rootstock influence on scion phenotypes is complex and dynamic and underscore that broad understanding necessitates volumes of multi-dimensional data previously unmet.

Common Patterns of Skull Bone Fusion and Their Potential to Discriminate Different Ontogenetic Stages in Extant Birds

The degree of sutural closure between bones generally allows for the classification of skeleton maturity in tetrapods. In mammals, the sutural closure of skull bones was previously used as proxy to evaluate the ontogenetic stage of single individuals. However, due to temporal variation, this process can be only applied among mammalian subclades, but not for all mammals in general. In contrast, the process of sutural closures in bird skulls could be a more reliable ontogenetic proxy for this clade as adult birds commonly show a generally high degree of bone fusion. To test this, we studied the process of sutural closure in ontogenetic series of 18 extant bird species regarding the presence of an ontogenetic signal and compared the results with changes in skull size and proportions. Univariate analyses indicate that bone fusion happens faster in altricial than in precocial birds. However, the use of PCoA and multivariate regressions reveal that the skull bone fusion follows a common pattern among birds and thus can be used as proxy to identify different ontogenetic stages. In general, the process of sutural closure spreads from posterior to anterior and from ventral to dorsal. In contrast, skull measurements reflect rather interspecific allometry than ontogeny. The used of bone fusion as proxy will help to better identify and compare different stages of maturation in birds, including historical material from osteological collections.

Dense time-course gene expression profiling of the Drosophila melanogaster innate immune response

BACKGROUND

Immune responses need to be initiated rapidly, and maintained as needed, to prevent establishment and growth of infections. At the same time, resources need to be balanced with other physiological processes. On the level of transcription, studies have shown that this balancing act is reflected in tight control of the initiation kinetics and shutdown dynamics of specific immune genes.

RESULTS

To investigate genome-wide expression dynamics and trade-offs after infection at a high temporal resolution, we performed an RNA-seq time course on D. melanogaster with 20 time points post Imd stimulation. A combination of methods, including spline fitting, cluster analysis, and Granger causality inference, allowed detailed dissection of expression profiles, lead-lag interactions, and functional annotation of genes through guilt-by-association. We identified Imd-responsive genes and co-expressed, less well characterized genes, with an immediate-early response and sustained up-regulation up to 5 days after stimulation. In contrast, stress response and Toll-responsive genes, among which were Bomanins, demonstrated early and transient responses. We further observed a strong trade-off with metabolic genes, which strikingly recovered to pre-infection levels before the immune response was fully resolved.

CONCLUSIONS

This high-dimensional dataset enabled the comprehensive study of immune response dynamics through the parallel application of multiple temporal data analysis methods. The well annotated data set should also serve as a useful resource for further investigation of the D. melanogaster innate immune response, and for the development of methods for analysis of a post-stress transcriptional response time-series at whole-genome scale.

Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome

The stomach is inhabited by diverse microbial communities, co-existing in a dynamic balance. Long-term use of drugs such as proton pump inhibitors (PPIs), or bacterial infection such as Helicobacter pylori, cause significant microbial alterations. Yet, studies revealing how the commensal bacteria re-organize, due to these perturbations of the gastric environment, are in early phase and rely principally on linear techniques for multivariate analysis. Here we disclose the importance of complementing linear dimensionality reduction techniques with nonlinear ones to unveil hidden patterns that remain unseen by linear embedding. Then, we prove the advantages to complete multivariate pattern analysis with differential network analysis, to reveal mechanisms of bacterial network re-organizations which emerge from perturbations induced by a medical treatment (PPIs) or an infectious state (H. pylori). Finally, we show how to build bacteria-metabolite multilayer networks that can deepen our understanding of the metabolite pathways significantly associated to the perturbed microbial communities.

Biodiversity of an Antarctic rocky subtidal community and its relationship with glacier meltdown processes

Glacier meltdown is a major environmental response to climate change in the West Antarctic Peninsula. Yet, the consequences of this process for local biodiversity are still not well understood. Here, we analyse the diversity and structure of a species-rich marine subtidal macrobenthic community (consumers and primary producers) across two abiotic environmental gradients defined by the distance from a glacier (several km) and depth (between 5 and 20 m depth) in Fildes Bay, King George Island. The analysis of spatially extensive records of seawater turbidity, high-frequency temperature and salinity data, and suction dredge samples of macrobenthic organisms revealed non-linear and functional group-dependent associations between biodiversity, glacier influence, and depth. Turbidity peaked in shallow waters and in the nearby of the glacier. Temperature and salinity, on the other hand, slightly decreased in the proximity of the glacier relative to reference sites. According to the spatial pattern in turbidity, species richness of consumers was lowest in shallow waters and near to the glacier. Also, Shannon's diversity of consumers significantly decreased in the nearby of glacier across depths. Moreover, the spatial variation in community structure of consumers and primary producers depended on both glacier distance and depth. These results suggest that glacier melting can have significant effects on diversity and community structure. Therefore, the accelerated glacier meltdown may have major consequences for the biodiversity in this ecosystem.

Application of Machine Learning in Microbiology

Microorganisms are ubiquitous and closely related to people's daily lives. Since they were first discovered in the 19th century, researchers have shown great interest in microorganisms. People studied microorganisms through cultivation, but this method is expensive and time consuming. However, the cultivation method cannot keep a pace with the development of high-throughput sequencing technology. To deal with this problem, machine learning (ML) methods have been widely applied to the field of microbiology. Literature reviews have shown that ML can be used in many aspects of microbiology research, especially classification problems, and for exploring the interaction between microorganisms and the surrounding environment. In this study, we summarize the application of ML in microbiology.

Supramolecular cages as differential sensors for dicarboxylate anions: guest length sensing using principal component analysis of ESI-MS and 1H-NMR raw data

Dynamic covalent libraries (DCLs) have been widely used in the development of differential sensors. Inspired by recent studies which use supramolecular recognition systems for sensing, we report the use of a tris(-pyridylmethyl)amine (TPMA)-based supramolecular cage as a differential sensor for dicarboxylate anions. In particular, a library of molecular cages constituted by linkers differing in size and flexibility was allowed to equilibrate toward a series of guests differing in molecular size. The differential system was able to discriminate a series of dicarboxylates depending on their chain length. This differentiation was evaluated through the application of the Principal Component Analysis (PCA) method using interpolated and raw data obtained from ESI-MS. Interestingly, while the 1H NMR spectra of the differential system did not allow for the discrimination of the analytes by the naked eye, PCA of the raw data from the spectra revealed information on the chain length of the guest and also on the odd-even character of the carbon chain.

Explicit Characterization of Spatial Heterogeneity Based on Water Quality, Sediment Contamination, and Ichthyofauna in a Riverine-to-Coastal Zone

Our study aims to identify the spatial characteristics of water quality and sediment conditions in relation to fisheries resources, since the productivity of fisheries resources is closely related to the ambient conditions of the resource areas. We collected water quality samples and sediment contaminants from twenty-one sites at Gwangyang Bay, Korea, in the summer of 2018. Our study sites covered the area from the Seomjin River estuary to the inner and outer bays. To spatially characterize physicochemical features of Gwangyang Bay, we used Self-Organizing Map (SOM), which is known as a robust and powerful tool of unsupervised neural networks for pattern recognition. The present environmental conditions of Gwangyang Bay were spatially characterized according to four different attributes of water quality and sediment contamination. From the results, we put emphasis on several interesting points: (i) the SOM manifests the dominant physicochemical attributes of each geographical zone associated with the patterns of water quality and sediment contamination; (ii) fish populations appear to be closely associated with their food sources (e.g., shrimps and crabs) as well as the ambient physicochemical conditions; and (iii) in the context of public health and ecosystem services, the SOM result can potentially offer guidance for fish consumption associated with sediment heavy metal contamination. The present study may have limitations in representing general features of Gwangyang Bay, given the inability of snapshot data to characterize a complex ecosystem. In this regard, consistent sampling and investigation are needed to capture spatial variation and to delineate the temporal dynamics of water quality, sediment contamination, and fish populations. However, the SOM application is helpful and useful as a first approximation of an environmental assessment for the effective management of fisheries resources.

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 Generalized Framework for Analyzing Taxonomic, Phylogenetic, and Functional Community Structure Based on Presence–Absence Data

Community structure as summarized by presence–absence data is often evaluated via diversity measures by incorporating taxonomic, phylogenetic and functional information on the constituting species. Most commonly, various dissimilarity coefficients are used to express these aspects simultaneously such that the results are not comparable due to the lack of common conceptual basis behind index definitions. A new framework is needed which allows such comparisons, thus facilitating evaluation of the importance of the three sources of extra information in relation to conventional species-based representations. We define taxonomic, phylogenetic and functional beta diversity of species assemblages based on the generalized Jaccard dissimilarity index. This coefficient does not give equal weight to species, because traditional site dissimilarities are lowered by taking into account the taxonomic, phylogenetic or functional similarity of differential species in one site to the species in the other. These, together with the traditional, taxon- (species-) based beta diversity are decomposed into two additive fractions, one due to taxonomic, phylogenetic or functional excess and the other to replacement. In addition to numerical results, taxonomic, phylogenetic and functional community structure is visualized by 2D simplex or ternary plots. Redundancy with respect to taxon-based structure is expressed in terms of centroid distances between point clouds in these diagrams. The approach is illustrated by examples coming from vegetation surveys representing different ecological conditions. We found that beta diversity decreases in the following order: taxon-based, taxonomic (Linnaean), phylogenetic and functional. Therefore, we put forward the beta-redundancy hypothesis suggesting that this ordering may be most often the case in ecological communities, and discuss potential reasons and possible exceptions to this supposed rule. Whereas the pattern of change in diversity may be indicative of fundamental features of the particular community being studied, the effect of the choice of functional traits—a more or less subjective element of the framework—remains to be investigated.

Table for five, please: Dietary partitioning in boreal bats

Differences in diet can explain resource partitioning in apparently similar, sympatric species. Here, we analyzed 1,252 fecal droppings from five species (Eptesicus nilssonii, Myotis brandtii, M. daubentonii, M. mystacinus, and Plecotus auritus) to reveal their dietary niches using fecal DNA metabarcoding. We identified nearly 550 prey species in 13 arthropod orders. Two main orders (Diptera and Lepidoptera) formed the majority of the diet for all species, constituting roughly 80%-90% of the diet. All five species had different dietary assemblages. We also found significant differences in the size of prey species between the bat species. Our results on diet composition remain mostly unchanged when using either read counts as a proxy for quantitative diet or presence-absence data, indicating a strong biological pattern. We conclude that although bats share major components in their ecology (nocturnal life style, insectivory, and echolocation), species differ in feeding behavior, suggesting bats may have distinctive evolutionary strategies. Diet analysis helps illuminate life history traits of various species, adding to sparse ecological knowledge, which can be utilized in conservation planning.

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.

Establishment and convergence of photosynthetic microbial biomats in shallow unit process open-water wetlands

The widespread adoption of engineered wetlands designed for water treatment is hindered by uncertainties in system reliability, resilience and management associated with coupled biological and physical processes. To better understand how shallow unit process open-water wetlands self-colonize and evolve, we analyzed the composition of the microbial community in benthic biomats from system establishment through approximately 3 years of operation. Our analysis was conducted across three parallel demonstration-scale (7500 m²) cells located within the Prado Constructed Wetlands in Southern California. They received water from the Santa Ana River (5.9 ± 0.2 mg/L NO₃-N), a water body where the flow is dominated by municipal wastewater effluent from May to November. Phylogenetic inquiry and microscopy confirmed that diatoms and an associated aerobic bacterial community facilitated early colonization. After approximately nine months of operation, coinciding with late summer, an anaerobic community emerged with the capability for nitrate attenuation. Varying the hydraulic residence time (HRT) from 1 to 4 days the subsequent year resulted in modest ecological changes across the three parallel cells that were most evident in the outlet regions of the cells. The community that established at this time was comparatively stable for the remaining years of operation and converged with one that had previously formed approximately 550 km (350 miles) away in a pilot-scale (400 m²) wetland in Northern California. That system received denitrified (20.7 ± 0.7 mg/L NO₃-N), secondary treated municipal wastewater for 5 years of operation. Establishment of a core microbiome between the two systems revealed a strong overlap of both aerobic and anaerobic taxa with approximately 50% of the analyzed bacterial sequences shared between the two sites. Additionally the same species of diatom, Stauirsa construens var. venter, was prolific in both systems as the putative dominant primary producer. Our results indicate that despite differences in scale, geographic location and source waters, the shallow open-water wetland design can select for a rapid convergence of microbial structure and functionality associated with the self-colonizing benthic biomat. This resulting biomat matures over the first growing season with operational parameters such as HRT further exerting a modest selective bias on community succession.

Human bony labyrinth is an indicator of population history and dispersal from Africa

The cavity system of the inner ear—the so-called bony labyrinth—houses the senses of balance and hearing. This structure is embedded in dense petrous bone, fully formed by birth and generally well preserved in human skeletal remains, thus providing a rich source of morphological information about past populations. Here we show that labyrinthine morphology tracks genetic distances and geography in an isolation-by-distance model with dispersal from Africa. Because petrous bones have become prime targets of ancient DNA recovery, we propose that all destructive studies first acquire high-resolution 3D computed-tomography data prior to any invasive sampling. Such data will constitute an important archive of morphological variation in past and present populations, and will permit individual-based genotype–phenotype comparisons.

The dispersal of modern humans from Africa is now well documented with genetic data that track population history, as well as gene flow between populations. Phenetic skeletal data, such as cranial and pelvic morphologies, also exhibit a dispersal-from-Africa signal, which, however, tends to be blurred by the effects of local adaptation and in vivo phenotypic plasticity, and that is often deteriorated by postmortem damage to skeletal remains. These complexities raise the question of which skeletal structures most effectively track neutral population history. The cavity system of the inner ear (the so-called bony labyrinth) is a good candidate structure for such analyses. It is already fully formed by birth, which minimizes postnatal phenotypic plasticity, and it is generally well preserved in archaeological samples. Here we use morphometric data of the bony labyrinth to show that it is a surprisingly good marker of the global dispersal of modern humans from Africa. Labyrinthine morphology tracks genetic distances and geography in accordance with an isolation-by-distance model with dispersal from Africa. Our data further indicate that the neutral-like pattern of variation is compatible with stabilizing selection on labyrinth morphology. Given the increasingly important role of the petrous bone for ancient DNA recovery from archaeological specimens, we encourage researchers to acquire 3D morphological data of the inner ear structures before any invasive sampling. Such data will constitute an important archive of phenotypic variation in present and past populations, and will permit individual-based genotype–phenotype comparisons.

Fungal Endophyte Communities in Begonia Species from the Brazilian Atlantic Rainforest

Tropical plants represent hotspots of endophytic fungal species diversity. Based on culture-dependent methods, we evaluated the endophytic fungal communities in leaves of three plant species found in the Brazilian Atlantic Rainforest: Begonia fischeri, Begonia olsoniae, and Begonia venosa. These species are found in two distant sites: a continental region and an insular area. A total of 426 fungal endophytes in 19 genera were isolated in pure culture including Colletotrichum (51.6% of isolates) and Diaporthe (22.5%) as the most abundant, followed by Phyllosticta (3.5%), Neopestalotiopsis (1.8%), Stagonospora (1.8%), and Nigrospora (1.6%) among the genera found in minor abundance. The diversity and composition of fungal taxa differed across plant hosts. Richness and diversity of fungi were higher in B. fischeri in comparison to B. olsoniae and B. venosa. Discriminatory analysis revealed that fungal communities are structured according to hosts, which means that each plant species had its distinct endophytic communities, but dominated by common fungal taxa. This is the first study to report fungal endophytes in begonia leaves and characterize their communities.

Analysis of community-level mesocosm data based on ecologically meaningful dissimilarity measures and data transformation

The principal response curve (PRC) method is a constrained ordination method developed specifically for the analysis of community data collected in mesocosm experiments, which provides easily understood summaries and graphical representations of community response to stress. It is a redundancy analysis method and is usually performed on log-transformed abundance data. The choice of a measure of dissimilarity between samples and the choice of the data transformation significantly affect the results of multivariate analysis. Dissimilarity measures that are more ecologically meaningful than the Euclidean distance can be incorporated into the PRC using distance-based redundancy analysis. The present study investigates the ordinations produced by a small selection of dissimilarity measures: the Euclidean distance using log-transformed and Hellinger-transformed data and the Bray-Curtis dissimilarity using raw and log-transformed data. It compares 2 data sets from experiments on the effect of the anti-inflammatory drug diclofenac and the insecticide chlorpyrifos on macroinvertebrate communities. The choice of dissimilarity measure can determine the outcome of a risk assessment. For the diclofenac data set, the PRCs were different depending on the dissimilarity measure: the community no-effect concentration was lowest for the Bray-Curtis on log-transformed data and Hellinger dissimilarity measures. For chlorpyrifos, however, the PRCs were similar for all dissimilarity measures. Environ Toxicol Chem 2017;36:1667-1679. © 2016 SETAC.

Uncovering the Horseshoe Effect in Microbial Analyses

The horseshoe effect is often considered an artifact of dimensionality reduction. We show that this is not true in the case for microbiome data and that, in fact, horseshoes can help analysts discover microbial niches across environments.

The horseshoe effect is a phenomenon that has long intrigued ecologists. The effect was commonly thought to be an artifact of dimensionality reduction, and multiple techniques were developed to unravel this phenomenon and simplify interpretation. Here, we provide evidence that horseshoes arise as a consequence of distance metrics that saturate—a familiar concept in other fields but new to microbial ecology. This saturation property loses information about community dissimilarity, simply because it cannot discriminate between samples that do not share any common features. The phenomenon illuminates niche differentiation in microbial communities and indicates species turnover along environmental gradients. Here we propose a rationale for the observed horseshoe effect from multiple dimensionality reduction techniques applied to simulations, soil samples, and samples from postmortem mice. An in-depth understanding of this phenomenon allows targeting of niche differentiation patterns from high-level ordination plots, which can guide conventional statistical tools to pinpoint microbial niches along environmental gradients.

IMPORTANCE The horseshoe effect is often considered an artifact of dimensionality reduction. We show that this is not true in the case for microbiome data and that, in fact, horseshoes can help analysts discover microbial niches across environments.

Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning

Recent models of movement generation in motor cortex have sought to explain neural activity not as a function of movement parameters, known as representational models, but as a dynamical system acting at the level of the population. Despite evidence supporting this framework, the evaluation of representational models and their integration with dynamical systems is incomplete in the literature. Using a representational velocity-tuning based simulation of center-out reaching, we show that incorporating variable latency offsets between neural activity and kinematics is sufficient to generate rotational dynamics at the level of neural populations, a phenomenon observed in motor cortex. However, we developed a covariance-matched permutation test (CMPT) that reassigns neural data between task conditions independently for each neuron while maintaining overall neuron-to-neuron relationships, revealing that rotations based on the representational model did not uniquely depend on the underlying condition structure. In contrast, rotations based on either a dynamical model or motor cortex data depend on this relationship, providing evidence that the dynamical model more readily explains motor cortex activity. Importantly, implementing a recurrent neural network we demonstrate that both representational tuning properties and rotational dynamics emerge, providing evidence that a dynamical system can reproduce previous findings of representational tuning. Finally, using motor cortex data in combination with the CMPT, we show that results based on small numbers of neurons or conditions should be interpreted cautiously, potentially informing future experimental design. Together, our findings reinforce the view that representational models lack the explanatory power to describe complex aspects of single neuron and population level activity.

The question of how the brain generates movement has been extensively studied, yet multiple competing models exist. Representational approaches relate the activity of single neurons to movement parameters such as velocity and position, approaches useful for the decoding of movement intentions, while the dynamical systems approach predicts that neural activity should evolve in a predictable way based on population activity. Existing representational models cannot reproduce the recent finding in monkeys that predictable rotational patterns underlie motor cortex activity during reach initiation, a finding predicted by a dynamical model in which muscle activity is a direct combination of neural population rotations. However, previous simulations did not consider an essential aspect of representational models: variable time offsets between neurons and kinematics. Whereas these offsets reveal rotational patterns in the model, these rotations are statistically different from those observed in the brain and predicted by a dynamical model. Importantly, a simple recurrent neural network model also showed rotational patterns statistically similar to those observed in the brain, supporting the idea that dynamical systems-based approaches may provide a powerful explanation of motor cortex function.

Why is there a lack of consensus on molecular subgroups of glioblastoma? Understanding the nature of biological and statistical variability in glioblastoma expression data

INTRODUCTION

Gene expression patterns characterizing clinically-relevant molecular subgroups of glioblastoma are difficult to reproduce. We suspect a combination of biological and analytic factors confounds interpretation of glioblastoma expression data. We seek to clarify the nature and relative contributions of these factors, to focus additional investigations, and to improve the accuracy and consistency of translational glioblastoma analyses.

METHODS

We analyzed gene expression and clinical data for 340 glioblastomas in The Cancer Genome Atlas (TCGA). We developed a logic model to analyze potential sources of biological, technical, and analytic variability and used standard linear classifiers and linear dimensional reduction algorithms to investigate the nature and relative contributions of each factor.

RESULTS

Commonly-described sources of classification error, including individual sample characteristics, batch effects, and analytic and technical noise make measurable but proportionally minor contributions to inconsistent molecular classification. Our analysis suggests that three, previously underappreciated factors may account for a larger fraction of classification errors: inherent non-linear/non-orthogonal relationships among the genes used in conjunction with classification algorithms that assume linearity; skewed data distributions assumed to be Gaussian; and biologic variability (noise) among tumors, of which we propose three types.

CONCLUSIONS

Our analysis of the TCGA data demonstrates a contributory role for technical factors in molecular classification inconsistencies in glioblastoma but also suggests that biological variability, abnormal data distribution, and non-linear relationships among genes may be responsible for a proportionally larger component of classification error. These findings may have important implications for both glioblastoma research and for translational application of other large-volume biological databases.

Hegemonic and polemical beliefs: culture and consumption in the social representation of wine

Wine, in France, is a cultural product. However, the issue of wine consumption has been at the centre of a recurring social debate. We decided to focus our study on the effect of consumption practices on this social representation as well as the variations in position-taking in very different normative contexts. Results revealed two distinct social representations according to consumption practice. Moreover, Guttman effect in principal component analysis uncovered a unique phenomenon which showed that participants (consumer vs. non consumer) were inclined to act differently only in the case of polemical issues when they perceived the investigator as a consumer vs. non consumer. Indeed, in the case of hegemonic beliefs they were inclined to act in the same way and their answers were not influenced by the status of the investigator. Results are discussed around the question of the links between social representations and social identity.

THREaD Mapper Studio: a novel, visual web server for the estimation of genetic linkage maps

The estimation of genetic linkage maps is a key component in plant and animal research, providing both an indication of the genetic structure of an organism and a mechanism for identifying candidate genes associated with traits of interest. Because of this importance, several computational solutions to genetic map estimation exist, mostly implemented as stand-alone software packages. However, the estimation process is often largely hidden from the user. Consequently, problems such as a program crashing may occur that leave a user baffled. THREaD Mapper Studio (http://cbr.jic.ac.uk/threadmapper) is a new web site that implements a novel, visual and interactive method for the estimation of genetic linkage maps from DNA markers. The rationale behind the web site is to make the estimation process as transparent and robust as possible, while also allowing users to use their expert knowledge during analysis. Indeed, the 3D visual nature of the tool allows users to spot features in a data set, such as outlying markers and potential structural rearrangements that could cause problems with the estimation procedure and to account for them in their analysis. Furthermore, THREaD Mapper Studio facilitates the visual comparison of genetic map solutions from third party software, aiding users in developing robust solutions for their data sets.

Culturable bacterial populations associated with ectomycorrhizae of Norway spruce stands with different degrees of decline in the Czech Republic

The aim of this study was to determine which species of culturable bacteria are associated with ectomycorrhizae (ECM) of Norway spruce (Picea abies (L.) Karst) in the Sudety Mountains, exposed for years to atmospheric pollutants, acid rain, and climatic stress, and to identify particular species that have adapted to those conditions. Biolog identification was performed on bacterial species from ECM of adult spruce trees and seedlings of stands with low, intermediate, and high forest decline. Bacterial diversity in ECM associated with adult spruce trees, seedlings, and seedlings grown on monoliths was calculated; although the expected values appeared to vary widely, no significant differences among sites were observed. Dendrograms based on the identified bacterial species showed that stands with low forest decline clustered separately from the others. Principal component analysis of the normalized data for ECM-associated species showed a clear separation between stands with high forest decline and stands with low forest decline for seedlings and a less evident separation for adult spruce trees. In conclusion, shifts in ECM-associated culturable bacterial populations seem to be associated with forest decline in Norway spruce stands. Some bacterial species were preferentially associated with mycorrhizal roots depending on the degree of forest decline; this was more evident in seedlings where the species Burkholderia cepacia and Pseudomonas fluorescens were associated with, respectively, ECM of the most damaged stands and those with low forest decline.

Interpreting principal component analyses of spatial population genetic variation

Nearly 30 years ago, Cavalli-Sforza et al. pioneered the use of principal component analysis (PCA) in population genetics and used PCA to produce maps summarizing human genetic variation across continental regions. They interpreted gradient and wave patterns in these maps as signatures of specific migration events. These interpretations have been controversial, but influential, and the use of PCA has become widespread in analysis of population genetics data. However, the behavior of PCA for genetic data showing continuous spatial variation, such as might exist within human continental groups, has been less well characterized. Here, we find that gradients and waves observed in Cavalli-Sforza et al.'s maps resemble sinusoidal mathematical artifacts that arise generally when PCA is applied to spatial data, implying that the patterns do not necessarily reflect specific migration events. Our findings aid interpretation of PCA results and suggest how PCA can help correct for continuous population structure in association studies.