Long-adapter single-strand oligonucleotide probes for the massively multiplexed cloning of kilobase genome regions.

As the catalogue of sequenced genomes and metagenomes continues to grow, massively parallel approaches for the comprehensive and functional analysis of gene products and regulatory elements are becoming increasingly valuable. Current strategies to synthesize or clone complex libraries of DNA sequences are limited by the length of the DNA targets, throughput and cost. Here, we show that long-adapter single-strand oligonucleotide (LASSO) probes can capture and clone thousands of kilobase DNA fragments in a single reaction. As a proof-of-principle, we simultaneously cloned >3,000 bacterial open reading frames (ORFs) from E. coli genomic DNA (spanning 400-5,000 bp targets). Targets were enriched up to a median of ~60-fold compared to non-targeted genomic regions. At a cutoff of 3 times the median non-target reads per kilobase of genetic element per million reads, ~75% of the targeted ORFs were successfully captured. We also show that LASSO probes can clone human ORFs from complementary DNA, and an ORF library from a human-microbiome sample. LASSO probes could be used for the preparation of long-read sequencing libraries and for massively multiplexed cloning.

Large-scale analysis of post-translational modifications in E. coli under glucose-limiting conditions.

BACKGROUND: Post-translational modification (PTM) of proteins is central to many cellular processes across all domains of life, but despite decades of study and a wealth of genomic and proteomic data the biological function of many PTMs remains unknown. This is especially true for prokaryotic PTM systems, many of which have only recently been recognized and studied in depth. It is increasingly apparent that a deep sampling of abundance across a wide range of environmental stresses, growth conditions, and PTM types, rather than simply cataloging targets for a handful of modifications, is critical to understanding the complex pathways that govern PTM deposition and downstream effects. RESULTS: We utilized a deeply-sampled dataset of MS/MS proteomic analysis covering 9 timepoints spanning the Escherichia coli growth cycle and an unbiased PTM search strategy to construct a temporal map of abundance for all PTMs within a 400 Da window of mass shifts. Using this map, we are able to identify novel targets and temporal patterns for N-terminal N α acetylation, C-terminal glutamylation, and asparagine deamidation. Furthermore, we identify a possible relationship between N-terminal N α acetylation and regulation of protein degradation in stationary phase, pointing to a previously unrecognized biological function for this poorly-understood PTM. CONCLUSIONS: Unbiased detection of PTM in MS/MS proteomics data facilitates the discovery of novel modification types and previously unobserved dynamic changes in modification across growth timepoints.

The E. coli molecular phenotype under different growth conditions.

Modern systems biology requires extensive, carefully curated measurements of cellular components in response to different environmental conditions. While high-throughput methods have made transcriptomics and proteomics datasets widely accessible and relatively economical to generate, systematic measurements of both mRNA and protein abundances under a wide range of different conditions are still relatively rare. Here we present a detailed, genome-wide transcriptomics and proteomics dataset of E. coli grown under 34 different conditions. Additionally, we provide measurements of doubling times and in-vivo metabolic fluxes through the central carbon metabolism. We manipulate concentrations of sodium and magnesium in the growth media, and we consider four different carbon sources glucose, gluconate, lactate, and glycerol. Moreover, samples are taken both in exponential and stationary phase, and we include two extensive time-courses, with multiple samples taken between 3 hours and 2 weeks. We find that exponential-phase samples systematically differ from stationary-phase samples, in particular at the level of mRNA. Regulatory responses to different carbon sources or salt stresses are more moderate, but we find numerous differentially expressed genes for growth on gluconate and under salt and magnesium stress. Our data set provides a rich resource for future computational modeling of E. coli gene regulation, transcription, and translation.

Stable association of RNAi machinery is conserved between the cytoplasm and nucleus of human cells.

Argonaute 2 (AGO2), the catalytic engine of RNAi, is typically associated with inhibition of translation in the cytoplasm. AGO2 has also been implicated in nuclear processes including transcription and splicing. There has been little insight into AGO2's nuclear interactions or how they might differ relative to cytoplasm. Here we investigate the interactions of cytoplasmic and nuclear AGO2 using semi-quantitative mass spectrometry. Mass spectrometry often reveals long lists of candidate proteins, complicating efforts to rigorously discriminate true interacting partners from artifacts. We prioritized candidates using orthogonal analytical strategies that compare replicate mass spectra of proteins associated with Flag-tagged and endogenous AGO2. Interactions with TRNC6A, TRNC6B, TNRC6C, and AGO3 are conserved between nuclei and cytoplasm. TAR binding protein interacted stably with cytoplasmic AGO2 but not nuclear AGO2, consistent with strand loading in the cytoplasm. Our data suggest that interactions between functionally important components of RNAi machinery are conserved between the nucleus and cytoplasm but that accessory proteins differ. Orthogonal analysis of mass spectra is a powerful approach to streamlining identification of protein partners.

Controlled Measurement and Comparative Analysis of Cellular Components in E. coli Reveals Broad Regulatory Changes in Response to Glucose Starvation.

How do bacteria regulate their cellular physiology in response to starvation? Here, we present a detailed characterization of Escherichia coli growth and starvation over a time-course lasting two weeks. We have measured multiple cellular components, including RNA and proteins at deep genomic coverage, as well as lipid modifications and flux through central metabolism. Our study focuses on the physiological response of E. coli in stationary phase as a result of being starved for glucose, not on the genetic adaptation of E. coli to utilize alternative nutrients. In our analysis, we have taken advantage of the temporal correlations within and among RNA and protein abundances to identify systematic trends in gene regulation. Specifically, we have developed a general computational strategy for classifying expression-profile time courses into distinct categories in an unbiased manner. We have also developed, from dynamic models of gene expression, a framework to characterize protein degradation patterns based on the observed temporal relationships between mRNA and protein abundances. By comparing and contrasting our transcriptomic and proteomic data, we have identified several broad physiological trends in the E. coli starvation response. Strikingly, mRNAs are widely down-regulated in response to glucose starvation, presumably as a strategy for reducing new protein synthesis. By contrast, protein abundances display more varied responses. The abundances of many proteins involved in energy-intensive processes mirror the corresponding mRNA profiles while proteins involved in nutrient metabolism remain abundant even though their corresponding mRNAs are down-regulated.

Predicting growth conditions from internal metabolic fluxes in an in-silico model of E. coli.

A widely studied problem in systems biology is to predict bacterial phenotype from growth conditions, using mechanistic models such as flux balance analysis (FBA). However, the inverse prediction of growth conditions from phenotype is rarely considered. Here we develop a computational framework to carry out this inverse prediction on a computational model of bacterial metabolism. We use FBA to calculate bacterial phenotypes from growth conditions in E. coli, and then we assess how accurately we can predict the original growth conditions from the phenotypes. Prediction is carried out via regularized multinomial regression. Our analysis provides several important physiological and statistical insights. First, we show that by analyzing metabolic end products we can consistently predict growth conditions. Second, prediction is reliable even in the presence of small amounts of impurities. Third, flux through a relatively small number of reactions per growth source (∼10) is sufficient for accurate prediction. Fourth, combining the predictions from two separate models, one trained only on carbon sources and one only on nitrogen sources, performs better than models trained to perform joint prediction. Finally, that separate predictions perform better than a more sophisticated joint prediction scheme suggests that carbon and nitrogen utilization pathways, despite jointly affecting cellular growth, may be fairly decoupled in terms of their dependence on specific assortments of molecular precursors.

Numerical study of lipid translocation driven by nanoporation due to multiple high-intensity, ultrashort electrical pulses.

The dynamical translocation of lipids from one leaflet to another due to membrane permeabilization driven by nanosecond, high-intensity (>100kV/cm) electrical pulses has been probed. Our simulations show that lipid molecules can translocate by diffusion through water-filled nanopores which form following high voltage application. Our focus is on multiple pulsing, and such simulations are relevant to gauge the time duration over which nanopores might remain open, and facilitate continued lipid translocations and membrane transport. Our results are indicative of a N(½) scaling with pulse number for the pore radius. These results bode well for the use of pulse trains in biomedical applications, not only due to cumulative behaviors and in reducing electric intensities and pulsing hardware, but also due to the possibility of long-lived thermo-electric physics near the membrane, and the possibility for pore coalescence.

Increasing peptide identifications and decreasing search times for ETD spectra by pre-processing and calculation of parent precursor charge.

BACKGROUND: Electron Transfer Dissociation [ETD] can dissociate multiply charged precursor polypeptides, providing extensive peptide backbone cleavage. ETD spectra contain charge reduced precursor peaks, usually of high intensity, and whose pattern is dependent on its parent precursor charge. These charge reduced precursor peaks and associated neutral loss peaks should be removed before these spectra are searched for peptide identifications. ETD spectra can also contain ion-types other than c and z˙. Modifying search strategies to accommodate these ion-types may aid in increased peptide identifications. Additionally, if the precursor mass is measured using a lower resolution instrument such as a linear ion trap, the charge of the precursor is often not known, reducing sensitivity and increasing search times. We implemented algorithms to remove these precursor peaks, accommodate new ion-types in noise filtering routine in OMSSA and to estimate any unknown precursor charge, using Linear Discriminant Analysis [LDA]. RESULTS: Spectral pre-processing to remove precursor peaks and their associated neutral losses prior to protein sequence library searches resulted in a 9.8% increase in peptide identifications at a 1% False Discovery Rate [FDR] compared to previous OMSSA filter. Modifications to the OMSSA noise filter to accommodate various ion-types resulted in a further 4.2% increase in peptide identifications at 1% FDR. Moreover, ETD spectra when searched with charge states obtained from the precursor charge determination algorithm is shown to be up to 3.5 times faster than the general range search method, with a minor 3.8% increase in sensitivity. CONCLUSION: Overall, there is an 18.8% increase in peptide identifications at 1% FDR by incorporating the new precursor filter, noise filter and by using the charge determination algorithm, when compared to previous versions of OMSSA.

Automatic annotation of experimentally derived, evolutionarily conserved post-translational modifications onto multiple genomes.

New generation sequencing technologies have resulted in significant increases in the number of complete genomes. Functional characterization of these genomes, such as by high-throughput proteomics, is an important but challenging task due to the difficulty of scaling up existing experimental techniques. By use of comparative genomics techniques, experimental results can be transferred from one genome to another, while at the same time minimizing errors by requiring discovery in multiple genomes. In this study, protein phosphorylation, an essential component of many cellular processes, is studied using data from large-scale proteomics analyses of the phosphoproteome. Phosphorylation sites from Homo sapiens, Mus musculus and Drosophila melanogaster phosphopeptide data sets were mapped onto conserved domains in NCBI's manually curated portion of Conserved Domain Database (CDD). In this subset, 25 phosphorylation sites are found to be evolutionarily conserved between the three species studied. Transfer of phosphorylation annotation of these conserved sites onto sequences sharing the same conserved domains yield 3253 phosphosite annotations for proteins from coelomata, the taxonomic division that spans H. sapiens, M. musculus and D. melanogaster. The method scales automatically, so as the amount of experimental phosphoproteomics data increases, more conserved phosphorylation sites may be revealed.

Ultrashort electric pulse induced changes in cellular dielectric properties.

The interaction of nanosecond duration pulsed electric fields (nsPEFs) with biological cells, and the models describing this behavior, depend critically on the electrical properties of the cells being pulsed. Here, we used time domain dielectric spectroscopy to measure the dielectric properties of Jurkat cells, a malignant human T-cell line, before and after exposure to five 10ns, 150kV/cm electrical pulses. The cytoplasm and nucleoplasm conductivities decreased dramatically following pulsing, corresponding to previously observed rises in cell suspension conductivity. This suggests that electropermeabilization occurred, resulting in ion transport from the cell's interior to the exterior. A delayed decrease in cell membrane conductivity after the nsPEFs possibly suggests long-term ion channel damage or use dependence due to repeated membrane charging and discharging. This data could be used in models describing the phenomena at work.