Update on perineuronal net staining with Wisteria floribunda agglutinin (WFA)

As chemically specialized forms of the extracellular matrix in the central nervous system, polyanionic perineuronal nets (PNs) contain diverse constituents, including chondroitin sulfate proteoglycans (CSPGs), hyaluronic acid, and tenascins. They are detectable by various histological approaches such as colloidal iron binding and immunohistochemical staining to reveal, for instance, the CSPGs aggrecan, neurocan, phosphacan, and versican. Moreover, biotin, peroxidase, or fluorescein conjugates of the lectins Vicia villosa agglutinin and soybean agglutinin enable the visualization of PNs. At present, the N-acetylgalactosamine-binding Wisteria floribunda agglutinin (WFA) is the most widely applied marker for PNs. Therefore, this article is largely focused on methodological aspects of WFA staining. Notably, fluorescent WFA labeling allows, after its conversion into electron-dense adducts, electron microscopic analyses. Furthermore, the usefulness of WFA conjugates for the oftentimes neglected in vivo and in vitro labeling of PNs is emphasized. Subsequently, we discuss impaired WFA-staining sites after long-lasting experiments in vitro, especially in autoptic brain samples with long postmortem delay and partial enzymatic degradation, while immunolabeling of aggrecan and CSPG link proteins under such conditions has proven more robust. In some hippocampal regions from perfusion-fixed mice, more PNs are aggrecan immunoreactive than WFA positive, whereas the retrosplenial cortex displays many WFA-binding PNs devoid of visible aggrecan immunoreactivity. Additional multiple fluorescence labeling exemplarily revealed in ischemic tissue diminished staining of WFA-binding sites and aquaporin 4 and concomitantly upregulated immunolabeling of neurofilament, light chains, and collagen IV. Finally, we briefly discuss possible future staining approaches based on nanobodies to facilitate novel technologies revealing details of net morphology

Joint Analysis of Multiple Metagenomic Samples

The availability of metagenomic sequencing data, generated by sequencing DNA pooled from multiple microbes living jointly, has increased sharply in the last few years with developments in sequencing technology. Characterizing the contents of metagenomic samples is a challenging task, which has been extensively attempted by both supervised and unsupervised techniques, each with its own limitations. Common to practically all the methods is the processing of single samples only; when multiple samples are sequenced, each is analyzed separately and the results are combined. In this paper we propose to perform a combined analysis of a set of samples in order to obtain a better characterization of each of the samples, and provide two applications of this principle. First, we use an unsupervised probabilistic mixture model to infer hidden components shared across metagenomic samples. We incorporate the model in a novel framework for studying association of microbial sequence elements with phenotypes, analogous to the genome-wide association studies performed on human genomes: We demonstrate that stratification may result in false discoveries of such associations, and that the components inferred by the model can be used to correct for this stratification. Second, we propose a novel read clustering (also termed “binning”) algorithm which operates on multiple samples simultaneously, leveraging on the assumption that the different samples contain the same microbial species, possibly in different proportions. We show that integrating information across multiple samples yields more precise binning on each of the samples. Moreover, for both applications we demonstrate that given a fixed depth of coverage, the average per-sample performance generally increases with the number of sequenced samples as long as the per-sample coverage is high enough

Gene prediction in metagenomic fragments: A large scale machine learning approach

<p>Abstract</p> <p>Background</p> <p>Metagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positive predictions.</p> <p>Results</p> <p>We introduce a novel gene prediction algorithm for metagenomic fragments based on a two-stage machine learning approach. In the first stage, we use linear discriminants for monocodon usage, dicodon usage and translation initiation sites to extract features from DNA sequences. In the second stage, an artificial neural network combines these features with open reading frame length and fragment GC-content to compute the probability that this open reading frame encodes a protein. This probability is used for the classification and scoring of gene candidates. With large scale training, our method provides fast single fragment predictions with good sensitivity and specificity on artificially fragmented genomic DNA. Additionally, this method is able to predict translation initiation sites accurately and distinguishes complete from incomplete genes with high reliability.</p> <p>Conclusion</p> <p>Large scale machine learning methods are well-suited for gene prediction in metagenomic DNA fragments. In particular, the combination of linear discriminants and neural networks is promising and should be considered for integration into metagenomic analysis pipelines. The data sets can be downloaded from the URL provided (see Availability and requirements section).</p

Factors That Affect Large Subunit Ribosomal DNA Amplicon Sequencing Studies of Fungal Communities: Classification Method, Primer Choice, and Error

Nuclear large subunit ribosomal DNA is widely used in fungal phylogenetics and to an increasing extent also amplicon-based environmental sequencing. The relatively short reads produced by next-generation sequencing, however, makes primer choice and sequence error important variables for obtaining accurate taxonomic classifications. In this simulation study we tested the performance of three classification methods: 1) a similarity-based method (BLAST + Metagenomic Analyzer, MEGAN); 2) a composition-based method (Ribosomal Database Project naïve Bayesian classifier, NBC); and, 3) a phylogeny-based method (Statistical Assignment Package, SAP). We also tested the effects of sequence length, primer choice, and sequence error on classification accuracy and perceived community composition. Using a leave-one-out cross validation approach, results for classifications to the genus rank were as follows: BLAST + MEGAN had the lowest error rate and was particularly robust to sequence error; SAP accuracy was highest when long LSU query sequences were classified; and, NBC runs significantly faster than the other tested methods. All methods performed poorly with the shortest 50–100 bp sequences. Increasing simulated sequence error reduced classification accuracy. Community shifts were detected due to sequence error and primer selection even though there was no change in the underlying community composition. Short read datasets from individual primers, as well as pooled datasets, appear to only approximate the true community composition. We hope this work informs investigators of some of the factors that affect the quality and interpretation of their environmental gene surveys

Critical Assessment of Metagenome Interpretation:A benchmark of metagenomics software

International audienceIn metagenome analysis, computational methods for assembly, taxonomic profilingand binning are key components facilitating downstream biological datainterpretation. However, a lack of consensus about benchmarking datasets andevaluation metrics complicates proper performance assessment. The CriticalAssessment of Metagenome Interpretation (CAMI) challenge has engaged the globaldeveloper community to benchmark their programs on datasets of unprecedentedcomplexity and realism. Benchmark metagenomes were generated from newlysequenced ~700 microorganisms and ~600 novel viruses and plasmids, includinggenomes with varying degrees of relatedness to each other and to publicly availableones and representing common experimental setups. Across all datasets, assemblyand genome binning programs performed well for species represented by individualgenomes, while performance was substantially affected by the presence of relatedstrains. Taxonomic profiling and binning programs were proficient at high taxonomicranks, with a notable performance decrease below the family level. Parametersettings substantially impacted performances, underscoring the importance ofprogram reproducibility. While highlighting current challenges in computationalmetagenomics, the CAMI results provide a roadmap for software selection to answerspecific research questions

InterCarb: a community effort to improve interlaboratory standardization of the carbonate clumped isotope thermometer using carbonate standards

Increased use and improved methodology of carbonate clumped isotope thermometry has greatly enhanced our ability to interrogate a suite of Earth-system processes. However, interlaboratory discrepancies in quantifying carbonate clumped isotope (Δ47) measurements persist, and their specific sources remain unclear. To address interlaboratory differences, we first provide consensus values from the clumped isotope community for four carbonate standards relative to heated and equilibrated gases with 1,819 individual analyses from 10 laboratories. Then we analyzed the four carbonate standards along with three additional standards, spanning a broad range of δ47 and Δ47 values, for a total of 5,329 analyses on 25 individual mass spectrometers from 22 different laboratories. Treating three of the materials as known standards and the other four as unknowns, we find that the use of carbonate reference materials is a robust method for standardization that yields interlaboratory discrepancies entirely consistent with intralaboratory analytical uncertainties. Carbonate reference materials, along with measurement and data processing practices described herein, provide the carbonate clumped isotope community with a robust approach to achieve interlaboratory agreement as we continue to use and improve this powerful geochemical tool. We propose that carbonate clumped isotope data normalized to the carbonate reference materials described in this publication should be reported as Δ47 (I-CDES) values for Intercarb-Carbon Dioxide Equilibrium Scale