Batch effects account for the main findings of an in utero human intestinal bacterial colonization study

AbstractA recent study by Rackaityte et al. reported evidence for a low level of bacterial colonization, specifically of Micrococcus luteus, in the intestine of second trimester human fetuses. We have re-analyzed their sequence data and identified a batch effect which violates the underlying assumptions of the bioinformatic method used for contamination removal. This batch effect resulted in Micrococcus not being identified as a contaminant in the original work and being falsely assigned to the fetal samples. We further provide evidence that the micrographs presented by Rackaityte et al. are unlikely to show Micrococci or other bacteria as the size of the particles shown exceeds that of related bacterial cells. Finally, phylogenetic analysis showed that the microbes cultured from the fetal samples differed significantly from those detected by sequencing. Overall, our findings show that the presence of Micrococcus in the fetal gut is not supported by the primary sequence data. Our findings underline important aspects of the nature of contamination for both sequencing and culture approaches in microbiome studies and the appropriate use of automated contamination identification tools.

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Batch Effect Removal via Batch-Free Encoding

10.1101/380816 ◽

2018 ◽

Cited By ~ 1

Author(s):

Uri Shaham

Keyword(s):

Deep Learning ◽

Biological Properties ◽

Batch Effect ◽

Training Data ◽

Rna Seq ◽

Batch Effects ◽

Training Time ◽

Learning Techniques ◽

Downstream Analysis ◽

Biological Patterns

AbstractBiological measurements often contain systematic errors, also known as “batch effects”, which may invalidate downstream analysis when not handled correctly. The problem of removing batch effects is of major importance in the biological community. Despite recent advances in this direction via deep learning techniques, most current methods may not fully preserve the true biological patterns the data contains. In this work we propose a deep learning approach for batch effect removal. The crux of our approach is learning a batch-free encoding of the data, representing its intrinsic biological properties, but not batch effects. In addition, we also encode the systematic factors through a decoding mechanism and require accurate reconstruction of the data. Altogether, this allows us to fully preserve the true biological patterns represented in the data. Experimental results are reported on data obtained from two high throughput technologies, mass cytometry and single-cell RNA-seq. Beyond good performance on training data, we also observe that our system performs well on test data obtained from new patients, which was not available at training time. Our method is easy to handle, a publicly available code can be found at https://github.com/ushaham/BatchEffectRemoval2018.

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Multilayer Polyelectrolyte Films Functionalized by Insertion of Defensin: a New Approach to Protection of Implants from Bacterial Colonization

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.48.10.3662-3669.2004 ◽

2004 ◽

Vol 48 (10) ◽

pp. 3662-3669 ◽

Cited By ~ 131

Author(s):

O. Etienne ◽

C. Picart ◽

C. Taddei ◽

Y. Haikel ◽

J. L. Dimarcq ◽

...

Keyword(s):

Antimicrobial Peptide ◽

Micrococcus Luteus ◽

Bacterial Colonization ◽

Streaming Potential ◽

Multilayer Films ◽

Microbial Infection ◽

Host Protection ◽

New Strategy ◽

Local Host ◽

Positively Charged

ABSTRACT Infection of implanted materials by bacteria constitutes one of the most serious complications following prosthetic surgery. In the present study, we developed a new strategy based on the insertion of an antimicrobial peptide (defensin from Anopheles gambiae mosquitoes) into polyelectrolyte multilayer films built by the alternate deposition of polyanions and polycations. Quartz crystal microbalance and streaming potential measurements were used to follow step by step the construction of the multilayer films and embedding of the defensin within the films. Antimicrobial assays were performed with two strains: Micrococcus luteus (a gram-positive bacterium) and Escherichia coli D22 (a gram-negative bacterium). The inhibition of E. coli D22 growth at the surface of defensin-functionalized films was found to be 98% when 10 antimicrobial peptide layers were inserted in the film architecture. Noticeably, the biofunctionalization could be achieved only when positively charged poly(l-lysine) was the outermost layer of the film. On the basis of the results of bacterial adhesion experiments observed by confocal or electron microscopy, these observations could result from the close interaction of the bacteria with the positively charged ends of the films, which allows defensin to interact with the bacterial membrane structure. These results open new possibilities for the use of such easily built and functionalized architectures onto any type of implantable biomaterial. The modified surfaces are active against microbial infection and represent a novel means of local host protection.

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Microbial Dextran-Hydrolyzing Enzymes: Fundamentals and Applications

Microbiology and Molecular Biology Reviews ◽

10.1128/mmbr.69.2.306-325.2005 ◽

2005 ◽

Vol 69 (2) ◽

pp. 306-325 ◽

Cited By ~ 128

Author(s):

Elvira Khalikova ◽

Petri Susi ◽

Timo Korpela

Keyword(s):

Sequence Data ◽

Three Dimensional ◽

Side Reaction ◽

Sequence Information ◽

New Classification ◽

Wide Range ◽

Hydrolyzing Enzymes ◽

Complex Polymer ◽

Sequence Similarities ◽

Primary Sequence Data

SUMMARY Dextran is a chemically and physically complex polymer, breakdown of which is carried out by a variety of endo- and exodextranases. Enzymes in many groups can be classified as dextranases according to function: such enzymes include dextranhydrolases, glucodextranases, exoisomaltohydrolases, exoisomaltotriohydrases, and branched-dextran exo-1,2-α-glucosidases. Cycloisomalto-oligosaccharide glucanotransferase does not formally belong to the dextranases even though its side reaction produces hydrolyzed dextrans. A new classification system for glycosylhydrolases and glycosyltransferases, which is based on amino acid sequence similarities, divides the dextranases into five families. However, this classification is still incomplete since sequence information is missing for many of the enzymes that have been biochemically characterized as dextranases. Dextran-degrading enzymes have been isolated from a wide range of microorganisms. The major characteristics of these enzymes, the methods for analyzing their activities and biological roles, analysis of primary sequence data, and three-dimensional structures of dextranases have been dealt with in this review. Dextranases are promising for future use in various scientific and biotechnological applications.

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Stages of Infection during the Tripartite Interaction between Xenorhabdus nematophila, Its Nematode Vector, and Insect Hosts

Applied and Environmental Microbiology ◽

10.1128/aem.70.11.6473-6480.2004 ◽

2004 ◽

Vol 70 (11) ◽

pp. 6473-6480 ◽

Cited By ~ 88

Author(s):

Mathieu Sicard ◽

Karine Brugirard-Ricaud ◽

Sylvie PagÃ¯Â¿Â½s ◽

Anne Lanois ◽

Noel E. Boemare ◽

...

Keyword(s):

Extracellular Matrix ◽

Spodoptera Littoralis ◽

Fluorescent Protein ◽

Bacterial Colonization ◽

Infective Juvenile ◽

Bacterial Cells ◽

Xenorhabdus Nematophila ◽

Insect Larvae ◽

Connective Tissues ◽

Anterior Midgut

ABSTRACT Bacteria of the genus Xenorhabdus are mutually associated with entomopathogenic nematodes of the genus Steinernema and are pathogenic to a broad spectrum of insects. The nematodes act as vectors, transmitting the bacteria to insect larvae, which die within a few days of infection. We characterized the early stages of bacterial infection in the insects by constructing a constitutive green fluorescent protein (GFP)-labeled Xenorhabdus nematophila strain. We injected the GFP-labeled bacteria into insects and monitored infection. We found that the bacteria had an extracellular life cycle in the hemolymph and rapidly colonized the anterior midgut region in Spodoptera littoralis larvae. Electron microscopy showed that the bacteria occupied the extracellular matrix of connective tissues within the muscle layers of the Spodoptera midgut. We confirmed the existence of such a specific infection site in the natural route of infection by infesting Spodoptera littoralis larvae with nematodes harboring GFP-labeled Xenorhabdus. When the infective juvenile (IJ) nematodes reached the insect gut, the bacterial cells were rapidly released from the intestinal vesicle into the nematode intestine. Xenorhabdus began to escape from the anus of the nematodes when IJs were wedged in the insect intestinal wall toward the insect hemolymph. Following their release into the insect hemocoel, GFP-labeled bacteria were found only in the anterior midgut region and hemolymph of Spodoptera larvae. Comparative infection assays conducted with another insect, Locusta migratoria, also showed early bacterial colonization of connective tissues. This work shows that the extracellular matrix acts as a particular colonization site for X. nematophila within insects.

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Microbial development in distillers wet grains produced during fuel ethanol production from corn (Zea mays)

Canadian Journal of Microbiology ◽

10.1139/w07-073 ◽

2007 ◽

Vol 53 (9) ◽

pp. 1046-1052 ◽

Cited By ~ 6

Author(s):

R. Michael Lehman ◽

Kurt A. Rosentrater

Keyword(s):

Lactic Acid ◽

Lactic Acid Bacteria ◽

Heterotrophic Bacteria ◽

Micrococcus Luteus ◽

Phospholipid Fatty Acid ◽

Dry Mass ◽

Fuel Ethanol ◽

Fatty Acid Analysis ◽

Yeast Cells ◽

Bacterial Cells

Distillers grains are coproduced with ethanol and carbon dioxide during the production of fuel ethanol from the dry milling and fermentation of corn grain, yet there is little basic microbiological information on these materials. We undertook a replicated field study of the microbiology of distillers wet grains (DWG) over a 9 day period following their production at an industrial fuel ethanol plant. Freshly produced DWG had a pH of about 4.4, a moisture content of about 53.5% (wet mass basis), and 4 × 105 total yeast cells/g dry mass, of which about 0.1% were viable. Total bacterial cells were initially below detection limits (ca. 106 cells/g dry mass) and then were estimated to be ∼5 × 107 cells/g dry mass during the first 4 days following production. Culturable aerobic heterotrophic organisms (fungi plus bacteria) ranged between 104 and 105 CFU/g dry mass during the initial 4 day period, and lactic acid bacteria increased from 36 to 103 CFU/g dry mass over this same period. At 9 days, total viable bacteria and yeasts and (or) molds topped 108 CFU/g dry mass and lactic acid bacteria approached 106 CFU/g dry mass. Community phospholipid fatty acid analysis indicated a stable microbial community over the first 4 days of storage. Thirteen morphologically distinct isolates were recovered, of which 10 were yeasts and molds from 6 different genera, 2 were strains of the lactic-acid-producing Pediococcus pentosaceus and only one was an aerobic heterotrophic bacteria, Micrococcus luteus . The microbiology of DWG is fundamental to the assessment of spoilage, deleterious effects (e.g., toxins), or beneficial effects (e.g., probiotics) in its use as feed or in alternative applications.

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Concentrated bovine colostral whey proteins from Streptococcus mutans/Strep. sobrinus immunized cows inhibit the adherence of Strep. mutans and promote the aggregation of mutans streptococci

Journal of Dairy Research ◽

10.1017/s0022029998003069 ◽

1998 ◽

Vol 65 (4) ◽

pp. 599-607 ◽

Cited By ~ 22

Author(s):

VUOKKO LOIMARANTA ◽

ANETTE CARLÉN ◽

JAN OLSSON ◽

JORMA TENOVUO ◽

EEVA-LIISA SYVÄOJA ◽

...

Keyword(s):

Oral Cavity ◽

Streptococcus Mutans ◽

Bacterial Colonization ◽

Whey Proteins ◽

Mutans Streptococci ◽

Bacterial Cells ◽

Parotid Saliva ◽

Control Product ◽

Significant Difference ◽

BatchServer: a web server for batch effect evaluation, visualization and correction

10.1101/2020.03.23.996264 ◽

2020 ◽

Author(s):

Tiansheng Zhu ◽

Guo-Bo Chen ◽

Chunhui Yuan ◽

Rui Sun ◽

Fangfei Zhang ◽

...

Keyword(s):

Web Server ◽

Batch Effect ◽

Variance Component Analysis ◽

Data Sets ◽

Batch Effects ◽

Source Codes ◽

Link Type ◽

R Shiny ◽

User Friendly ◽

Web Platform

AbstractBatch effects are unwanted data variations that may obscure biological signals, leading to bias or errors in subsequent data analyses. Effective evaluation and elimination of batch effects are necessary for omics data analysis. In order to facilitate the evaluation and correction of batch effects, here we present BatchSever, an open-source R/Shiny based user-friendly interactive graphical web platform for batch effects analysis. In BatchServer we introduced autoComBat, a modified version of ComBat, which is the most widely adopted tool for batch effect correction. BatchServer uses PVCA (Principal Variance Component Analysis) and UMAP (Manifold Approximation and Projection) for evaluation and visualizion of batch effects. We demonstate its application in multiple proteomics and transcriptomic data sets. BatchServer is provided at https://lifeinfo.shinyapps.io/batchserver/ as a web server. The source codes are freely available at https://github.com/guomics-lab/batch_server.

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SMNN: Batch Effect Correction for Single-cell RNA-seq data via Supervised Mutual Nearest Neighbor Detection

10.1101/672261 ◽

2019 ◽

Cited By ~ 1

Author(s):

Yuchen Yang ◽

Gang Li ◽

Huijun Qian ◽

Kirk C. Wilhelmsen ◽

Yin Shen ◽

...

Keyword(s):

Single Cell ◽

Nearest Neighbor ◽

State Of The Art ◽

Nearest Neighbors ◽

Cell Types ◽

Batch Effect ◽

Batch Effects ◽

Cell Type ◽

Label Information ◽

Cell Type Specific

AbstractBatch effect correction has been recognized to be indispensable when integrating single-cell RNA sequencing (scRNA-seq) data from multiple batches. State-of-the-art methods ignore single-cell cluster label information, but such information can improve effectiveness of batch effect correction, particularly under realistic scenarios where biological differences are not orthogonal to batch effects. To address this issue, we propose SMNN for batch effect correction of scRNA-seq data via supervised mutual nearest neighbor detection. Our extensive evaluations in simulated and real datasets show that SMNN provides improved merging within the corresponding cell types across batches, leading to reduced differentiation across batches over MNN, Seurat v3, and LIGER. Furthermore, SMNN retains more cell type-specific features, partially manifested by differentially expressed genes identified between cell types after SMNN correction being biologically more relevant, with precision improving by up to 841%.Key PointsBatch effect correction has been recognized to be critical when integrating scRNA-seq data from multiple batches due to systematic differences in time points, generating laboratory and/or handling technician(s), experimental protocol, and/or sequencing platform.Existing batch effect correction methods that leverages information from mutual nearest neighbors across batches (for example, implemented in SC3 or Seurat) ignore cell type information and suffer from potentially mismatching single cells from different cell types across batches, which would lead to undesired correction results, especially under the scenario where variation from batch effects is non-negligible compared with biological effects.To address this critical issue, here we present SMNN, a supervised machine learning method that first takes cluster/cell-type label information from users or inferred from scRNA-seq clustering, and then searches mutual nearest neighbors within each cell type instead of global searching.Our SMNN method shows clear advantages over three state-of-the-art batch effect correction methods and can better mix cells of the same cell type across batches and more effectively recover cell-type specific features, in both simulations and real datasets.

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deepMNN: Deep Learning-Based Single-Cell RNA Sequencing Data Batch Correction Using Mutual Nearest Neighbors

Frontiers in Genetics ◽

10.3389/fgene.2021.708981 ◽

2021 ◽

Vol 12 ◽

Author(s):

Bin Zou ◽

Tongda Zhang ◽

Ruilong Zhou ◽

Xiaosen Jiang ◽

Huanming Yang ◽

...

Keyword(s):

Deep Learning ◽

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Cell Types ◽

Batch Effect ◽

Batch Effects ◽

Batch Correction ◽

Single Cell Rna Sequencing ◽

Identical Cell

It is well recognized that batch effect in single-cell RNA sequencing (scRNA-seq) data remains a big challenge when integrating different datasets. Here, we proposed deepMNN, a novel deep learning-based method to correct batch effect in scRNA-seq data. We first searched mutual nearest neighbor (MNN) pairs across different batches in a principal component analysis (PCA) subspace. Subsequently, a batch correction network was constructed by stacking two residual blocks and further applied for the removal of batch effects. The loss function of deepMNN was defined as the sum of a batch loss and a weighted regularization loss. The batch loss was used to compute the distance between cells in MNN pairs in the PCA subspace, while the regularization loss was to make the output of the network similar to the input. The experiment results showed that deepMNN can successfully remove batch effects across datasets with identical cell types, datasets with non-identical cell types, datasets with multiple batches, and large-scale datasets as well. We compared the performance of deepMNN with state-of-the-art batch correction methods, including the widely used methods of Harmony, Scanorama, and Seurat V4 as well as the recently developed deep learning-based methods of MMD-ResNet and scGen. The results demonstrated that deepMNN achieved a better or comparable performance in terms of both qualitative analysis using uniform manifold approximation and projection (UMAP) plots and quantitative metrics such as batch and cell entropies, ARI F1 score, and ASW F1 score under various scenarios. Additionally, deepMNN allowed for integrating scRNA-seq datasets with multiple batches in one step. Furthermore, deepMNN ran much faster than the other methods for large-scale datasets. These characteristics of deepMNN made it have the potential to be a new choice for large-scale single-cell gene expression data analysis.

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Characterization ofRhodopseudomonas palustrispopulation dynamics on tobacco phyllosphere and induction of plant resistance toTobacco mosaic virusinfection

10.1101/616110 ◽

2019 ◽

Author(s):

Pin Su ◽

Deyong Zhang ◽

Zhuo Zhang ◽

Ang Chen ◽

Muhammad Rizwan Hamid ◽

...

Keyword(s):

Biocontrol Agent ◽

Bacterial Colonization ◽

Rhodopseudomonas Palustris ◽

Phase Iii ◽

Bacterial Cells ◽

Plant Tolerance ◽

Beneficial Effects ◽

Biocontrol Bacteria ◽

Bacterial Aggregates ◽

Type 3

AbstractAlthough many biocontrol bacteria can be used to improve plant tolerance to stresses and to promote plant growth, the hostile environmental conditions on plant phyllosphere and the limited knowledge on bacterial colonization on plant phyllosphere minimized the beneficial effects produced by the biocontrol bacteria.Rhodopseudomonas palustrisstrain GJ-22 is known as a phyllosphere biocontrol agent. In this paper we described detailed processes of strain GJ-22 colony establishment at various colonization stages. We have shown that the preferable location sites of bacterial aggregates on leaf phyllosphere are grooves between plant epidermal cells. In this study, we categorized bacterial colonies into four phases. Analyses of expressions of plant defense-related genes showed that, starting from Phase III, bacterial cells in the Type 3 and Type 4 colonies started produce unidentified signals to induce host defense againTobacco mosaic virusinfection. To our knowledge, this is the first report focused on the colonization process of a phyllosphere biocontrol agent.

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