Andrew D. Fernandes

Microbiome Profiling by Illumina Sequencing of Combinatorial Sequence-Tagged PCR Products

We developed a low-cost, high-throughput microbiome profiling method that uses combinatorial sequence tags attached to PCR primers that amplify the rRNA V6 region. Amplified PCR products are sequenced using an Illumina paired-end protocol to generate millions of overlapping reads. Combinatorial sequence tagging can be used to examine hundreds of samples with far fewer primers than is required when sequence tags are incorporated at only a single end. The number of reads generated permitted saturating or near-saturating analysis of samples of the vaginal microbiome. The large number of reads allowed an in-depth analysis of errors, and we found that PCR-induced errors composed the vast majority of non-organism derived species variants, an observation that has significant implications for sequence clustering of similar high-throughput data. We show that the short reads are sufficient to assign organisms to the genus or species level in most cases. We suggest that this method will be useful for the deep sequencing of any short nucleotide region that is taxonomically informative; these include the V3, V5 regions of the bacterial 16S rRNA genes and the eukaryotic V9 region that is gaining popularity for sampling protist diversity.

Deep sequencing of the vaginal microbiota of women with HIV

Background Women living with HIV and co-infected with bacterial vaginosis (BV) are at higher risk for transmitting HIV to a partner or newborn. It is poorly understood which bacterial communities constitute BV or the normal vaginal microbiota among this population and how the microbiota associated with BV responds to antibiotic treatment. Methods and Findings The vaginal microbiota of 132 HIV positive Tanzanian women, including 39 who received metronidazole treatment for BV, were profiled using Illumina to sequence the V6 region of the 16S rRNA gene. Of note, Gardnerella vaginalis and Lactobacillus iners were detected in each sample constituting core members of the vaginal microbiota. Eight major clusters were detected with relatively uniform microbiota compositions. Two clusters dominated by L. iners or L. crispatus were strongly associated with a normal microbiota. The L. crispatus dominated microbiota were associated with low pH, but when L. crispatus was not present, a large fraction of L. iners was required to predict a low pH. Four clusters were strongly associated with BV, and were dominated by Prevotella bivia, Lachnospiraceae, or a mixture of different species. Metronidazole treatment reduced the microbial diversity and perturbed the BV-associated microbiota, but rarely resulted in the establishment of a lactobacilli-dominated microbiota. Conclusions Illumina based microbial profiling enabled high though-put analyses of microbial samples at a high phylogenetic resolution. The vaginal microbiota among women living with HIV in Sub-Saharan Africa constitutes several profiles associated with a normal microbiota or BV. Recurrence of BV frequently constitutes a different BV-associated profile than before antibiotic treatment.

Side-by-Side Comparison of Five Commercial Media Systems in a Mouse Model: Suboptimal In Vitro Culture Interferes with Imprint Maintenance

Assisted reproductive technologies (ARTs) are becoming increasingly prevalent and are generally considered to be safe medical procedures. However, evidence indicates that embryo culture may adversely affect the developmental potential and overall health of the embryo. One of the least studied but most important areas in this regard is the effects of embryo culture on epigenetic phenomena, and on genomic imprinting in particular, because assisted reproduction has been linked to development of the human imprinting disorders Angelman and Beckwith-Wiedemann syndromes. In this study, we performed side-by-side comparisons of five commercial embryo culture systems (KSOMaa, Global, Human Tubal Fluid, Preimplantation 1/Multiblast, and G1v5PLUS/G2v5PLUS) in relation to a best-case (in vivo-derived embryos) and a worst-case (Whitten culture) scenario. Imprinted DNA methylation and expression were examined at three well-studied loci, H19, Peg3, and Snrpn, in mouse embryos cultured from the 2-cell to the blastocyst stage. We show that embryo culture in all commercial media systems resulted in imprinted methylation loss compared to in vivo-derived embryos, although some media systems were able to maintain imprinted methylation levels more similar to those of in vivo-derived embryos in comparison to embryos cultured in Whitten medium. However, all media systems exhibited loss of imprinted H19 expression comparable to that using Whitten medium. Combined treatment of superovulation and embryo culture resulted in increased perturbation of genomic imprinting, above that from culture alone, indicating that multiple ART procedures further disrupt genomic imprinting. These results suggest that time in culture and number of ART procedures should be minimized to ensure fidelity of genomic imprinting during preimplantation development.

Comparative meta-RNA-seq of the vaginal microbiota and differential expression by Lactobacillus iners in health and dysbiosis

BackgroundBacterial vaginosis (BV), the most common vaginal condition of reproductive-aged women, is associated with a highly diverse and heterogeneous microbiota. Here we present a proof-of-principle analysis to uncover the function of the microbiota using meta-RNA-seq to uncover genes and pathways that potentially differentiate healthy vaginal microbial communities from those in the dysbiotic state of bacterial vaginosis (BV).ResultsThe predominant organism, Lactobacillus iners, was present in both conditions and showed a differing expression profile in BV compared to healthy. Despite its minimal genome, L. iners differentially expressed over 10% of its gene complement. Notably, in a BV environment L. iners increased expression of a cholesterol-dependent cytolysin, and of mucin and glycerol transport and related metabolic enzymes. Genes belonging to a CRISPR system were greatly upregulated suggesting that bacteriophage influence the community. Reflective of L. iners, the bacterial community as a whole demonstrated a preference for glycogen and glycerol as carbon sources under BV conditions. The predicted end-products of metabolism under BV conditions include an abundance of succinate and other short-chain fatty-acids, while healthy conditions are predicted to largely contain lactic acid.ConclusionsOur study underscores the importance of understanding the functional activity of the bacterial community in addition to characterizing the population structure when investigating the human microbiome.

Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis.

BackgroundExperimental designs that take advantage of high-throughput sequencing to generate datasets include RNA sequencing (RNA-seq), chromatin immunoprecipitation sequencing (ChIP-seq), sequencing of 16S rRNA gene fragments, metagenomic analysis and selective growth experiments. In each case the underlying data are similar and are composed of counts of sequencing reads mapped to a large number of features in each sample. Despite this underlying similarity, the data analysis methods used for these experimental designs are all different, and do not translate across experiments. Alternative methods have been developed in the physical and geological sciences that treat similar data as compositions. Compositional data analysis methods transform the data to relative abundances with the result that the analyses are more robust and reproducible.ResultsData from an in vitro selective growth experiment, an RNA-seq experiment and the Human Microbiome Project 16S rRNA gene abundance dataset were examined by ALDEx2, a compositional data analysis tool that uses Bayesian methods to infer technical and statistical error. The ALDEx2 approach is shown to be suitable for all three types of data: it correctly identifies both the direction and differential abundance of features in the differential growth experiment, it identifies a substantially similar set of differentially expressed genes in the RNA-seq dataset as the leading tools and it identifies as differential the taxa that distinguish the tongue dorsum and buccal mucosa in the Human Microbiome Project dataset. The design of ALDEx2 reduces the number of false positive identifications that result from datasets composed of many features in few samples.ConclusionStatistical analysis of high-throughput sequencing datasets composed of per feature counts showed that the ALDEx2 R package is a simple and robust tool, which can be applied to RNA-seq, 16S rRNA gene sequencing and differential growth datasets, and by extension to other techniques that use a similar approach.

ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-Seq.

Experimental variance is a major challenge when dealing with high-throughput sequencing data. This variance has several sources: sampling replication, technical replication, variability within biological conditions, and variability between biological conditions. The high per-sample cost of RNA-Seq often precludes the large number of experiments needed to partition observed variance into these categories as per standard ANOVA models. We show that the partitioning of within-condition to between-condition variation cannot reasonably be ignored, whether in single-organism RNA-Seq or in Meta-RNA-Seq experiments, and further find that commonly-used RNA-Seq analysis tools, as described in the literature, do not enforce the constraint that the sum of relative expression levels must be one, and thus report expression levels that are systematically distorted. These two factors lead to misleading inferences if not properly accommodated. As it is usually only the biological between-condition and within-condition differences that are of interest, we developed ALDEx, an ANOVA-like differential expression procedure, to identify genes with greater between- to within-condition differences. We show that the presence of differential expression and the magnitude of these comparative differences can be reasonably estimated with even very small sample sizes.

Identifying and Seeing beyond Multiple Sequence Alignment Errors Using Intra-Molecular Protein Covariation

Background There is currently no way to verify the quality of a multiple sequence alignment that is independent of the assumptions used to build it. Sequence alignments are typically evaluated by a number of established criteria: sequence conservation, the number of aligned residues, the frequency of gaps, and the probable correct gap placement. Covariation analysis is used to find putatively important residue pairs in a sequence alignment. Different alignments of the same protein family give different results demonstrating that covariation depends on the quality of the sequence alignment. We thus hypothesized that current criteria are insufficient to build alignments for use with covariation analyses. Methodology/Principal Findings We show that current criteria are insufficient to build alignments for use with covariation analyses as systematic sequence alignment errors are present even in hand-curated structure-based alignment datasets like those from the Conserved Domain Database. We show that current non-parametric covariation statistics are sensitive to sequence misalignments and that this sensitivity can be used to identify systematic alignment errors. We demonstrate that removing alignment errors due to 1) improper structure alignment, 2) the presence of paralogous sequences, and 3) partial or otherwise erroneous sequences, improves contact prediction by covariation analysis. Finally we describe two non-parametric covariation statistics that are less sensitive to sequence alignment errors than those described previously in the literature. Conclusions/Significance Protein alignments with errors lead to false positive and false negative conclusions (incorrect assignment of covariation and conservation, respectively). Covariation analysis can provide a verification step, independent of traditional criteria, to identify systematic misalignments in protein alignments. Two non-parametric statistics are shown to be somewhat insensitive to misalignment errors, providing increased confidence in contact prediction when analyzing alignments with erroneous regions because of an emphasis on they emphasize pairwise covariation over group covariation.

Functionally Compensating Coevolving Positions Are Neither Homoplasic Nor Conserved in Clades

We demonstrated that a pair of positions in phosphoglycerate kinase that score highly by three nonparametric covariation measures are important for function even though the positions can be occupied by aliphatic, aromatic, or charged residues. Examination of these pairs suggested that the majority of the covariation scores could be explained by within-clade conservation. However, an analysis of diversity showed that the conservation within clades of covarying pairs was indistinguishable from pairs of positions that do not covary, thus ruling out both clade conservation and extensive homoplasy as means to identify covarying positions. Mutagenesis showed that the residues in the covarying pair were epistatic, with the type of epistasis being dependent on the initial pair. The results show that nonconserved covarying positions that affect protein function can be identified with high precision.

The SWI/SNF protein ATRX co-regulates pseudoautosomal genes that have translocated to autosomes in the mouse genome

BackgroundPseudoautosomal regions (PAR1 and PAR2) in eutherians retain homologous regions between the X and Y chromosomes that play a critical role in the obligatory X-Y crossover during male meiosis. Genes that reside in the PAR1 are exceptional in that they are rich in repetitive sequences and undergo a very high rate of recombination. Remarkably, murine PAR1 homologs have translocated to various autosomes, reflecting the complex recombination history during the evolution of the mammalian X chromosome.ResultsWe now report that the SNF2-type chromatin remodeling protein ATRX controls the expression of eutherian ancestral PAR1 genes that have translocated to autosomes in the mouse. In addition, we have identified two potentially novel mouse PAR1 orthologs.ConclusionWe propose that the ancestral PAR1 genes share a common epigenetic environment that allows ATRX to control their expression.

A unified genetic, computational and experimental framework identifies functionally relevant residues of the homing endonuclease I-BmoI

Insight into protein structure and function is best obtained through a synthesis of experimental, structural and bioinformatic data. Here, we outline a framework that we call MUSE (mutual information, unigenic evolution and structure-guided elucidation), which facilitated the identification of previously unknown residues that are relevant for function of the GIY-YIG homing endonuclease I-BmoI. Our approach synthesizes three types of data: mutual information analyses that identify co-evolving residues within the GIY-YIG catalytic domain; a unigenic evolution strategy that identifies hyper- and hypo-mutable residues of I-BmoI; and interpretation of the unigenic and co-evolution data using a homology model. In particular, we identify novel positions within the GIY-YIG domain as functionally important. Proof-of-principle experiments implicate the non-conserved I71 as functionally relevant, with an I71N mutant accumulating a nicked cleavage intermediate. Moreover, many additional positions within the catalytic, linker and C-terminal domains of I-BmoI were implicated as important for function. Our results represent a platform on which to pursue future studies of I-BmoI and other GIY-YIG-containing proteins, and demonstrate that MUSE can successfully identify novel functionally critical residues that would be ignored in a traditional structure-function analysis within an extensively studied small domain of ∼90 amino acids.