The gut microbiome has important effects on human health, yet its importance in human ageing remains unclear. In the present study, we demonstrate that, starting in mid-to-late adulthood, gut microbiomes become increasingly unique to individuals with age. We leverage three independent cohorts comprising over 9,000 individuals and find that compositional uniqueness is strongly associated with microbially produced amino acid derivatives circulating in the bloodstream. In older age (over ~80 years), healthy individuals show continued microbial drift towards a unique compositional state, whereas this drift is absent in less healthy individuals. The identified microbiome pattern of healthy ageing is characterized by a depletion of core genera found across most humans, primarily Bacteroides. Retaining a high Bacteroides dominance into older age, or having a low gut microbiome uniqueness measure, predicts decreased survival in a 4-year follow-up. Our analysis identifies increasing compositional uniqueness of the gut microbiome as a component of healthy ageing, which is characterized by distinct microbial metabolic outputs in the blood.

We have developed an improved method of straightening DNA molecules for use in optical restriction mapping. The DNA was straightened on 3-aminopropyltriethoxysilane-coated glass slides using surface tension generated by a moving meniscus. In our method the meniscus motion was controlled mechanically, which provides advantages of speed and uniformity of the straightened molecules. Variation in the affinity of the silanized surfaces for DNA was compensated by precoating the slide with single-stranded non-target blocking DNA. A small amount of MgCl2 added to the DNA suspension increased the DNA-surface affinity and was necessary for efficient restriction enzyme digestion of the straightened surface-bound DNA. By adjusting the amounts of blocking DNA and MgCl2, we prepared slides that contained many straight parallel DNA molecules. Straightened lambda phage DNA (48 kb) bound to a slide surface was digested by EcoRI restriction endonuclease, and the resulting restriction fragments were imaged by fluorescence microscopy using a CCD camera. The observed fragment lengths showed excellent agreement with their predicted lengths.

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Deep sequencing of transcriptomes has become an indispensable tool for biology, enabling expression levels for thousands of genes to be compared across multiple samples. Since transcript counts scale with sequencing depth, counts from different samples must be normalized to a common scale prior to comparison. We analyzed fifteen existing and novel algorithms for normalizing transcript counts, and evaluated the effectiveness of the resulting normalizations. For this purpose we defined two novel and mutually independent metrics: (1) the number of “uniform” genes (genes whose normalized expression levels have a sufficiently low coefficient of variation), and (2) low Spearman correlation between normalized expression profiles of gene pairs. We also define four novel algorithms, one of which explicitly maximizes the number of uniform genes, and compared the performance of all fifteen algorithms. The two most commonly used methods (scaling to a fixed total value, or equalizing the expression of certain ‘housekeeping’ genes) yielded particularly poor results, surpassed even by normalization based on randomly selected gene sets. Conversely, seven of the algorithms approached what appears to be optimal normalization. Three of these algorithms rely on the identification of “ubiquitous” genes: genes expressed in all the samples studied, but never at very high or very low levels. We demonstrate that these include a “core” of genes expressed in many tissues in a mutually consistent pattern, which is suitable for use as an internal normalization guide. The new methods yield robustly normalized expression values, which is a prerequisite for the identification of differentially expressed and tissue-specific genes as potential biomarkers.

We present an ultrafast method for comparing personal genomes. We transform the standard genome representation (lists of variants relative to a reference) into “genome fingerprints” via locality sensitive hashing. The resulting genome fingerprints can be meaningfully compared even when the input data were obtained using different sequencing technologies, processed using different pipelines, represented in different data formats and relative to different reference versions. Furthermore, genome fingerprints are robust to up to 30% missing data. Because of their reduced size, computation on the genome fingerprints is fast and requires little memory. For example, we could compute all-against-all pairwise comparisons among the 2504 genomes in the 1000 Genomes data set in 67 s at high quality (21 μs per comparison, on a single processor), and achieved a lower quality approximation in just 11 s. Efficient computation enables scaling up a variety of important genome analyses, including quantifying relatedness, recognizing duplicative sequenced genomes in a set, population reconstruction, and many others. The original genome representation cannot be reconstructed from its fingerprint, effectively decoupling genome comparison from genome interpretation; the method thus has significant implications for privacy-preserving genome analytics.

Regulation of gene expression involves the orchestrated interaction of a large number of proteins with transcriptional regulatory elements in the context of chromatin. Our understanding of gene regulation is limited by the lack of a protein measurement technology that can systematically detect and quantify the ensemble of proteins associated with the transcriptional regulatory elements of specific genes. Here, we introduce a set of selected reaction monitoring (SRM) assays for the systematic measurement of 464 proteins with known or suspected roles in transcriptional regulation at RNA polymerase II transcribed promoters in Saccharomyces cerevisiae. Measurement of these proteins in nuclear extracts by SRM permitted the reproducible quantification of 42% of the proteins over a wide range of abundances. By deploying the assay to systematically identify DNA binding transcriptional regulators that interact with the environmentally regulated FLO11 promoter in cell extracts, we identified 15 regulators that bound specifically to distinct regions along ∼600 bp of the regulatory sequence. Importantly, the dataset includes a number of regulators that have been shown to either control FLO11 expression or localize to these regulatory regions in vivo. We further validated the utility of the approach by demonstrating that two of the SRM-identified factors, Mot3 and Azf1, are required for proper FLO11 expression. These results demonstrate the utility of SRM-based targeted proteomics to guide the identification of gene-specific transcriptional regulators. C ritical to understanding gene regulation is the ability to determine the composition of the regulatory complexes that assemble at specific genes and to determine how the composition of these complexes change in response to cellular, genetic, and environmental signals. Despite considerable efforts to address these key questions, the lack of methods for routine analysis of the ensemble of transcription factors (TFs) associated with specific transcriptional regulatory elements (TREs) remains a significant limitation.Current approaches for studying TF-TRE interactions include the EMSA (1, 2), protein binding microarrays (PBMs) (3), the yeast one-hybrid method (4), and chromatin immunoprecipitation (ChIP)-based methods (5, 6). Although each of these methods can provide information about TF-DNA interactions, their utility for routine analysis of TF-DNA interactions and complexes assembled at TREs is limited by the need for genetic engineering, protein detection reagents, and/or purified proteins. Notably, ChIP-chip has been used to systematically study the localization of most transcriptional regulators (TRs) across the yeast genome (7), but this tour de force required the creation and independent assay of 203 TR-specific, epitope-tagged strains.Another approach for studying TF-TRE interactions that does not require genetic engineering or protein detection reagents, and can readily provide information about the ensemble of TFs associated with a TRE, is DNA affinity purification fo...

Biomedical research requires protein detection technology that is not only sensitive and quantitative, but that can reproducibly measure any set of proteins in a biological system in a high throughput manner. Here we report the development and application of a targeted proteomics platform termed index-ion triggered MS2 ion quantification (iMSTIQ) that allows reproducible and accurate peptide quantification in complex mixtures. The key feature of iMSTIQ is an approach called index-ion triggered analysis (ITA) that permits the reproducible acquisition of full MS2 spectra of targeted peptides independent of their ion intensities. Accurate quantification is achieved by comparing the relative intensities of multiple pairs of fragment ions derived from isobaric targeted peptides during MS2 analysis. Importantly, the method takes advantage of the favorable performance characteristics of the LTQ-Orbitrap, which include high mass accuracy, resolution, and throughput. As such it provides an attractive targeted proteomics tool to meet the demands of systems biology research and biomarker studies.

The identification of DNA copy numbers from short-read sequencing data remains a challenge for both technical and algorithmic reasons. The raw data for these analyses are measured in tens to hundreds of gigabytes per genome; transmitting, storing, and analyzing such large files is cumbersome, particularly for methods that analyze several samples simultaneously. We developed a very efficient representation of depth of coverage (150–1000× compression) that enables such analyses. Current methods for analyzing variants in whole-genome sequencing (WGS) data frequently miss copy number variants (CNVs), particularly hemizygous deletions in the 1–100 kb range. To fill this gap, we developed a method to identify CNVs in individual genomes, based on comparison to joint profiles pre-computed from a large set of genomes. We analyzed depth of coverage in over 6000 high quality (>40×) genomes. The depth of coverage has strong sequence-specific fluctuations only partially explained by global parameters like %GC. To account for these fluctuations, we constructed multi-genome profiles representing the observed or inferred diploid depth of coverage at each position along the genome. These Reference Coverage Profiles (RCPs) take into account the diverse technologies and pipeline versions used. Normalization of the scaled coverage to the RCP followed by hidden Markov model (HMM) segmentation enables efficient detection of CNVs and large deletions in individual genomes. Use of pre-computed multi-genome coverage profiles improves our ability to analyze each individual genome. We make available RCPs and tools for performing these analyses on personal genomes. We expect the increased sensitivity and specificity for individual genome analysis to be critical for achieving clinical-grade genome interpretation.