Analysis of variation of amplitudes in cell cycle gene expression

BACKGROUND: Variation in gene expression among cells in a population is often considered as noise produced from gene transcription and post-transcription processes and experimental artifacts. Most studies on noise in gene expression have emphasized a few well-characterized genes and proteins. We investigated whether different cell-arresting methods have impacts on the maximum expression levels (amplitudes) of a cell cycle related gene. RESULTS: By introducing random noise, modeled by a von Mises distribution, to the phase angle in a sinusoidal model in a cell population, we derived a relationship between amplitude and the distribution of noise in maximum transcription time (phase). We applied our analysis to Whitfield's HeLa cell cycle data. Our analysis suggests that among 47 cell cycle related genes common to the 2(nd )experiment (thymidine-thymidine method) and the 4(th )experiment (thymidine-nocodazole method): (i) the amplitudes of CDC6 and PCNA, which are expressed during G1/S phase, are smaller in the 2(nd )experiment than in the 4(th), while the amplitude of CDC20, which is expressed during G2/M phase, is smaller in the 4(th )experiment; and (ii) the two cell-arresting methods had little impact on the amplitudes of the other 43 genes in the 2(nd )and 4(th )experiments. CONCLUSION: Our analysis suggests that procedures that arrest cells in different stages of the cell cycle differentially affect expression of some cell cycle related genes once the cells are released from arrest. The impact of the cell-arresting method on expression of a cell cycle related gene can be quantitatively estimated from the ratio of two estimated amplitudes in two experiments. The ratio can be used to gauge the variation in the phase/peak expression time distribution involved in stochastic transcription and post-transcriptional processes for the gene. Further investigations are needed using normal, unperturbed and synchronized HeLa cells as a reference to compare how many cell cycle related genes are directly and indirectly affected by various cell-arresting methods

Combined pigment and metatranscriptomic analysis reveals highly synchronized diel patterns of phenotypic light response across domains in the open oligotrophic ocean

Sunlight is the most important environmental control on diel fluctuations in phytoplankton activity, and understanding diel microbial processes is essential to the study of oceanic biogeochemical cycles. Yet, little is known about the in situ temporal dynamics of phytoplankton metabolic activities and their coordination across different populations. We investigated diel orchestration of phytoplankton activity in photosynthesis, photoacclimation, and photoprotection by analyzing pigment and quinone distributions in combination with metatranscriptomes in surface waters of the North Pacific Subtropical Gyre (NPSG). We found diel cycles in pigment abundances resulting from the balance of their synthesis and consumption. These dynamics suggest that night represents a metabolic recovery phase, refilling cellular pigment stores, while photosystems are remodeled towards photoprotection during daytime. Transcript levels of genes involved in photosynthesis and pigment metabolism had synchronized diel expression patterns among all taxa, reflecting the driving force light imparts upon photosynthetic organisms in the ocean, while other environmental factors drive niche differentiation. For instance, observed decoupling of diel oscillations in transcripts and related pigments indicates that pigment abundances are modulated by environmental factors extending beyond gene expression/regulation reinforcing the need to combine metatranscriptomics with proteomics and metabolomics to fully understand the timing of these critical processes in situ

Troponin-only Manchester Acute Coronary Syndromes (T-MACS) decision aid: single biomarker re-derivation and external validation in three cohorts.

BACKGROUND: The original Manchester Acute Coronary Syndromes model (MACS) 'rules in' and 'rules out' acute coronary syndromes (ACS) using high sensitivity cardiac troponin T (hs-cTnT) and heart-type fatty acid binding protein (H-FABP) measured at admission. The latter is not always available. We aimed to refine and validate MACS as Troponin-only Manchester Acute Coronary Syndromes (T-MACS), cutting down the biomarkers to just hs-cTnT. METHODS: We present secondary analyses from four prospective diagnostic cohort studies including patients presenting to the ED with suspected ACS. Data were collected and hs-cTnT measured on arrival. The primary outcome was ACS, defined as prevalent acute myocardial infarction (AMI) or incident death, AMI or coronary revascularisation within 30 days. T-MACS was built in one cohort (derivation set) and validated in three external cohorts (validation set). RESULTS: At the 'rule out' threshold, in the derivation set (n=703), T-MACS had 99.3% (95% CI 97.3% to 99.9%) negative predictive value (NPV) and 98.7% (95.3%-99.8%) sensitivity for ACS, 'ruling out' 37.7% patients (specificity 47.6%, positive predictive value (PPV) 34.0%). In the validation set (n=1459), T-MACS had 99.3% (98.3%-99.8%) NPV and 98.1% (95.2%-99.5%) sensitivity, 'ruling out' 40.4% (n=590) patients (specificity 47.0%, PPV 23.9%). T-MACS would 'rule in' 10.1% and 4.7% patients in the respective sets, of which 100.0% and 91.3% had ACS. C-statistics for the original and refined rules were similar (T-MACS 0.91 vs MACS 0.90 on validation). CONCLUSIONS: T-MACS could 'rule out' ACS in 40% of patients, while 'ruling in' 5% at highest risk using a single hs-cTnT measurement on arrival. As a clinical decision aid, T-MACS could therefore help to conserve healthcare resources

Bioinformatics for Whole-Genome Shotgun Sequencing of Microbial Communities

The application of whole-genome shotgun sequencing to microbial communities represents a major development in metagenomics, the study of uncultured microbes via the tools of modern genomic analysis. In the past year, whole-genome shotgun sequencing projects of prokaryotic communities from an acid mine biofilm, the Sargasso Sea, Minnesota farm soil, three deep-sea whale falls, and deep-sea sediments have been reported, adding to previously published work on viral communities from marine and fecal samples. The interpretation of this new kind of data poses a wide variety of exciting and difficult bioinformatics problems. The aim of this review is to introduce the bioinformatics community to this emerging field by surveying existing techniques and promising new approaches for several of the most interesting of these computational problems

Joint Inference in Weakly-Annotated Image Datasets via Dense Correspondence

We present a principled framework for inferring pixel labels in weakly-annotated image datasets. Most previous, example-based approaches to computer vision rely on a large corpus of densely labeled images. However, for large, modern image datasets, such labels are expensive to obtain and are often unavailable. We establish a large-scale graphical model spanning all labeled and unlabeled images, then solve it to infer pixel labels jointly for all images in the dataset while enforcing consistent annotations over similar visual patterns. This model requires significantly less labeled data and assists in resolving ambiguities by propagating inferred annotations from images with stronger local visual evidences to images with weaker local evidences. We apply our proposed framework to two computer vision problems, namely image annotation with semantic segmentation, and object discovery and co-segmentation (segmenting multiple images containing a common object). Extensive numerical evaluations and comparisons show that our method consistently outperforms the state-of-the-art in automatic annotation and semantic labeling, while requiring significantly less labeled data. In contrast to previous co-segmentation techniques, our method manages to discover and segment objects well even in the presence of substantial amounts of noise images (images not containing the common object), as typical for datasets collected from Internet search

Cervicovaginal mucus barrier properties during pregnancy are impacted by the vaginal microbiome

Introduction Mucus in the female reproductive tract acts as a barrier that traps and eliminates pathogens and foreign particles via steric and adhesive interactions. During pregnancy, mucus protects the uterine environment from ascension of pathogens and bacteria from the vagina into the uterus, a potential contributor to intrauterine inflammation and preterm birth. As recent work has demonstrated the benefit of vaginal drug delivery in treating women’s health indications, we sought to define the barrier properties of human cervicovaginal mucus (CVM) during pregnancy to inform the design of vaginally delivered therapeutics during pregnancy. Methods CVM samples were self-collected by pregnant participants over the course of pregnancy, and barrier properties were quantified using multiple particle tracking. 16S rRNA gene sequencing was performed to analyze the composition of the vaginal microbiome. Results Participant demographics differed between term delivery and preterm delivery cohorts, with Black or African American participants being significantly more likely to delivery prematurely. We observed that vaginal microbiota is most predictive of CVM barrier properties and of timing of parturition. Lactobacillus crispatus dominated CVM samples showed increased barrier properties compared to polymicrobial CVM samples. Discussion This work informs our understanding of how infections occur during pregnancy, and directs the engineering of targeted drug treatments for indications during pregnancy

Exploring evolution of maximum growth rates in plankton

Evolution has direct and indirect consequences on species–species interactions and the environment. However, Earth systems models describing planktonic activity invariably fail to explicitly consider organism evolution. Here we simulate the evolution of the single most important physiological characteristic of any organism as described in models—its maximum growth rate (μm). Using a low-computational-cost approach, we incorporate the evolution of μm for each of the plankton components in a simple Nutrient-Phytoplankton-Zooplankton -style model such that the fitness advantages and disadvantages in possessing a high μm evolve to become balanced. The model allows an exploration of parameter ranges leading to stresses, which drive the evolution of μm. In applications of the method we show that simulations of climate change give very different projections when the evolution of μm is considered. Thus, production may decline as evolution reshapes growth and trophic dynamics. Additionally, predictions of extinction of species may be overstated in simulations lacking evolution as the ability to evolve under changing environmental conditions supports evolutionary rescue. The model explains why organisms evolved for mature ecosystems (e.g. temperate summer, reliant on local nutrient recycling or mixotrophy), express lower maximum growth rates than do organisms evolved for immature ecosystems (e.g. temperate spring, high resource availability)

Cervicovaginal mucus barrier properties during pregnancy are impacted by the vaginal microbiome

IntroductionMucus in the female reproductive tract acts as a barrier that traps and eliminates pathogens and foreign particles via steric and adhesive interactions. During pregnancy, mucus protects the uterine environment from ascension of pathogens and bacteria from the vagina into the uterus, a potential contributor to intrauterine inflammation and preterm birth. As recent work has demonstrated the benefit of vaginal drug delivery in treating women’s health indications, we sought to define the barrier properties of human cervicovaginal mucus (CVM) during pregnancy to inform the design of vaginally delivered therapeutics during pregnancy.MethodsCVM samples were self-collected by pregnant participants over the course of pregnancy, and barrier properties were quantified using multiple particle tracking. 16S rRNA gene sequencing was performed to analyze the composition of the vaginal microbiome.ResultsParticipant demographics differed between term delivery and preterm delivery cohorts, with Black or African American participants being significantly more likely to delivery prematurely. We observed that vaginal microbiota is most predictive of CVM barrier properties and of timing of parturition. Lactobacillus crispatus dominated CVM samples showed increased barrier properties compared to polymicrobial CVM samples.DiscussionThis work informs our understanding of how infections occur during pregnancy, and directs the engineering of targeted drug treatments for indications during pregnancy