Background correction using dinucleotide affinities improves the performance of GCRMA

BACKGROUND: High-density short oligonucleotide microarrays are a primary research tool for assessing global gene expression. Background noise on microarrays comprises a significant portion of the measured raw data, which can have serious implications for the interpretation of the generated data if not estimated correctly. RESULTS: We introduce an approach to calculate probe affinity based on sequence composition, incorporating nearest-neighbor (NN) information. Our model uses position-specific dinucleotide information, instead of the original single nucleotide approach, and adds up to 10% to the total variance explained (R(2)) when compared to the previously published model. We demonstrate that correcting for background noise using this approach enhances the performance of the GCRMA preprocessing algorithm when applied to control datasets, especially for detecting low intensity targets. CONCLUSION: Modifying the previously published position-dependent affinity model to incorporate dinucleotide information significantly improves the performance of the model. The dinucleotide affinity model enhances the detection of differentially expressed genes when implemented as a background correction procedure in GeneChip preprocessing algorithms. This is conceptually consistent with physical models of binding affinity, which depend on the nearest-neighbor stacking interactions in addition to base-pairing

MedZIM: Mediation analysis for Zero-Inflated Mediators with applications to microbiome data

The human microbiome can contribute to the pathogenesis of many complex diseases such as cancer and Alzheimer's disease by mediating disease-leading causal pathways. However, standard mediation analysis is not adequate in the context of microbiome data due to the excessive number of zero values in the data. Zero-valued sequencing reads, commonly observed in microbiome studies, arise for technical and/or biological reasons. Mediation analysis approaches for analyzing zero-inflated mediators are still lacking largely because of challenges raised by the zero-inflated data structure: (a) disentangling the mediation effect induced by the point mass at zero; and (b) identifying the observed zero-valued data points that are actually not zero (i.e., false zeros). We develop a novel mediation analysis method under the potential-outcomes framework to fill this gap. We show that the mediation effect of the microbiome can be decomposed into two components that are inherent to the two-part nature of zero-inflated distributions. The first component corresponds to the mediation effect attributable to a unit-change over the positive relative abundance and the second component corresponds to the mediation effect attributable to discrete binary change of the mediator from zero to a non-zero state. With probabilistic models to account for observing zeros, we also address the challenge with false zeros. A comprehensive simulation study and the applications in two real microbiome studies demonstrate that our approach outperforms existing mediation analysis approaches.Comment: Corresponding: Zhigang L

Stochastic changes over time and not founder effects drive cage effects in microbial community assembly in a mouse model

Maternal transmission and cage effects are powerful confounding factors in microbiome studies. To assess the consequences of cage microenvironment on the mouse gut microbiome, two groups of germ-free (GF) wild-type (WT) mice, one gavaged with a microbiota harvested from adult WT mice and another allowed to acquire the microbiome from the cage microenvironment, were monitored using Illumina 16S rRNA sequencing over a period of 8 weeks. Our results revealed that cage effects in WT mice moved from GF to specific pathogen free (SPF) conditions take several weeks to develop and are not eliminated by the initial gavage treatment. Initial gavage influenced, but did not eliminate a successional pattern in which Proteobacteria became less abundant over time. An analysis in which 16S rRNA sequences are mapped to the closest sequenced whole genome suggests that the functional potential of microbial genomes changes significantly over time shifting from an emphasis on pathogenesis and motility early in community assembly to metabolic processes at later time points. Functionally, mice allowed to naturally acquire a microbial community from their cage, but not mice gavaged with a common biome, exhibit a cage effect in Dextran Sulfate Sodium-induced inflammation. Our results argue that while there are long-term effects of the founding community, these effects are mitigated by cage microenvironment and successional community assembly over time, which must both be explicitly considered in the interpretation of microbiome mouse experiments

Microbial genomic analysis reveals the essential role of inflammation in bacteria-induced colorectal cancer

Enterobacteria, especially Escherichia coli, are abundant in patients with inflammatory bowel disease or colorectal cancer (CRC). However, it is unclear whether cancer is promoted by inflammation-induced expansion of E. coli and/or changes in expression of specific microbial genes. Here we use longitudinal (2, 12 and 20 weeks) 16S rRNA sequencing of luminal microbiota from ex-germ free mice to show that inflamed Il10−/− mice maintain a higher abundance of Enterobacteriaceae than healthy wild-type mice. Experiments with mono-colonized Il10−/− mice reveal that host inflammation is necessary for E. coli cancer-promoting activity. RNA-sequence analysis indicates significant changes in E. coli gene catalogue in Il10−/− mice, with changes mostly driven by adaptation to the intestinal environment. Expression of specific genes present in the tumor-promoting E. coli pks island are modulated by inflammation/CRC development. Thus, progression of inflammation in Il10−/− mice supports Enterobacteriaceae and alters a small subset of microbial genes important for tumor development

VSL#3 probiotic modifies mucosal microbial composition but does not reduce colitis-associated colorectal cancer

Although probiotics have shown success in preventing the development of experimental colitis-associated colorectal cancer (CRC), beneficial effects of interventional treatment are relatively unknown. Here we show that interventional treatment with VSL#3 probiotic alters the luminal and mucosally-adherent microbiota, but does not protect against inflammation or tumorigenesis in the azoxymethane (AOM)/Il10−/− mouse model of colitis-associated CRC. VSL#3 (109 CFU/animal/day) significantly enhanced tumor penetrance, multiplicity, histologic dysplasia scores, and adenocarcinoma invasion relative to VSL#3-untreated mice. Illumina 16S sequencing demonstrated that VSL#3 significantly decreased (16-fold) the abundance of a bacterial taxon assigned to genus Clostridium in the mucosally-adherent microbiota. Mediation analysis by linear models suggested that this taxon was a contributing factor to increased tumorigenesis in VSL#3-fed mice. We conclude that VSL#3 interventional therapy can alter microbial community composition and enhance tumorigenesis in the AOM/Il10−/− model

Accurate Estimates of Microarray Target Concentration from a Simple Sequence-Independent Langmuir Model

Background: Microarray technology is a commonly used tool for assessing global gene expression. Many models for estimation of target concentration based on observed microarray signal have been proposed, but, in general, these models have been complex and platform-dependent. Principal Findings: We introduce a universal Langmuir model for estimation of absolute target concentration from microarray experiments. We find that this sequence-independent model, characterized by only three free parameters, yields excellent predictions for four microarray platforms, including Affymetrix, Agilent, Illumina and a custom-printed microarray. The model also accurately predicts concentration for the MAQC data sets. This approach significantly reduces the computational complexity of quantitative target concentration estimates. Conclusions: Using a simple form of the Langmuir isotherm model, with a minimum of parameters and assumptions, and without explicit modeling of individual probe properties, we were able to recover absolute transcript concentrations with high R 2 on four different array platforms. The results obtained here suggest that with a ‘‘spiked-in’ ’ concentration serie

Application of Equilibrium Models of Solution Hybridization to Microarray Design and Analysis

Background: The probe percent bound value, calculated using multi-state equilibrium models of solution hybridization, is shown to be useful in understanding the hybridization behavior of microarray probes having 50 nucleotides, with and without mismatches. These longer oligonucleotides are in widespread use on microarrays, but there are few controlled studies of their interactions with mismatched targets compared to 25-mer based platforms. Principal Findings: 50-mer oligonucleotides with centrally placed single, double and triple mismatches were spotted on an array. Over a range of target concentrations it was possible to discriminate binding to perfect matches and mismatches, and the type of mismatch could be predicted accurately in the concentration midrange (100 pM to 200 pM) using solution hybridization modeling methods. These results have implications for microarray design, optimization and analysis methods. Conclusions: Our results highlight the importance of incorporating biophysical factors in both the design and the analysis of microarrays. Use of the probe ‘‘percent bound’ ’ value predicted by equilibrium models of hybridization is confirmed to be important for predicting and interpreting the behavior of long oligonucleotide arrays, as has been shown for shor

Background correction using dinucleotide affinities improves the performance of GCRMA

Abstract Background High-density short oligonucleotide microarrays are a primary research tool for assessing global gene expression. Background noise on microarrays comprises a significant portion of the measured raw data, which can have serious implications for the interpretation of the generated data if not estimated correctly. Results We introduce an approach to calculate probe affinity based on sequence composition, incorporating nearest-neighbor (NN) information. Our model uses position-specific dinucleotide information, instead of the original single nucleotide approach, and adds up to 10% to the total variance explained (R2) when compared to the previously published model. We demonstrate that correcting for background noise using this approach enhances the performance of the GCRMA preprocessing algorithm when applied to control datasets, especially for detecting low intensity targets. Conclusion Modifying the previously published position-dependent affinity model to incorporate dinucleotide information significantly improves the performance of the model. The dinucleotide affinity model enhances the detection of differentially expressed genes when implemented as a background correction procedure in GeneChip preprocessing algorithms. This is conceptually consistent with physical models of binding affinity, which depend on the nearest-neighbor stacking interactions in addition to base-pairing.</p

Prolonged restraint stressor exposure in outbred CD-1 mice impacts microbiota, colonic inflammation, and short chain fatty acids.

Stressor-exposure has been shown to exacerbate inflammation and change the composition of the gastrointestinal microbiota; however stressor-induced effects on microbiota-derived metabolites and their receptors are unknown. Thus, bacterial-produced short chain fatty acids (SCFAs), as well as microbial community composition, were assessed in the colons of mice exposed to stress during infection with Citrobacter rodentium. Mice were exposed to overnight restraint on 7 consecutive nights, or left undisturbed as a control. After the first exposure of restraint, mice were orally challenged with C. rodentium or with vehicle. Microbial community composition was assessed using 16S rRNA gene sequencing and SCFA levels measured using gas chromatography-mass spectrometry (GC-MS). Pathogen levels and colonic inflammation were also assessed 6 days post-infection. Results demonstrated that the microbial community structure and SCFA production were significantly affected by both stressor exposure and C. rodentium-infection. Exposure to prolonged restraint in the absence of infection significantly reduced SCFAs (acetic acid, butyric acid, and propionic acid). Multiple bacterial taxa were affected by stressor exposure, with the relative abundance of Lactobacillus being significantly reduced and directly correlated with propionic acid. Lactobacillus abundances were inversely correlated with colonic inflammation, supporting the contention that Lactobacillus helps to regulate mucosal inflammatory responses. Our data indicates that restraint stressor can have significant effects on pathogen-induced colonic inflammation and suggest that stressor-induced changes in the microbiota, microbial-produced SCFAs and their receptors may be involved