Time Series Experiments Research Articles

Pore formation during dehydration of a polycrystalline gypsum sample observed and quantified in a time-series synchrotron X-ray micro-tomography experiment

Abstract. We conducted an in-situ X-ray micro-computed tomography heating experiment at the Advanced Photon Source (USA) to dehydrate an unconfined 2.3 mm diameter cylinder of Volterra Gypsum. We used a purpose-built X-ray transparent furnace to heat the sample to 388 K for a total of 310 min to acquire a three-dimensional time-series tomography dataset comprising nine time steps. The voxel size of 2.2 μm3 proved sufficient to pinpoint reaction initiation and the organization of drainage architecture in space and time. We observed that dehydration commences across a narrow front, which propagates from the margins to the centre of the sample in more than four hours. The advance of this front can be fitted with a square-root function, implying that the initiation of the reaction in the sample can be described as a diffusion process. Novel parallelized computer codes allow quantifying the geometry of the porosity and the drainage architecture from the very large tomographic datasets (20483 voxels) in unprecedented detail. We determined position, volume, shape and orientation of each resolvable pore and tracked these properties over the duration of the experiment. We found that the pore-size distribution follows a power law. Pores tend to be anisotropic but rarely crack-shaped and have a preferred orientation, likely controlled by a pre-existing fabric in the sample. With on-going dehydration, pores coalesce into a single interconnected pore cluster that is connected to the surface of the sample cylinder and provides an effective drainage pathway. Our observations can be summarized in a model in which gypsum is stabilized by thermal expansion stresses and locally increased pore fluid pressures until the dehydration front approaches to within about 100 μm. Then, the internal stresses are released and dehydration happens efficiently, resulting in new pore space. Pressure release, the production of pores and the advance of the front are coupled in a feedback loop.

Additional role of O‐acetylserine as a sulfur status‐independent regulator during plant growth

O-acetylserine (OAS) is one of the most prominent metabolites whose levels are altered upon sulfur starvation. However, its putative role as a signaling molecule in higher plants is controversial. This paper provides further evidence that OAS is a signaling molecule, based on computational analysis of time-series experiments and on studies of transgenic plants conditionally displaying increased OAS levels. Transcripts whose levels correlated with the transient and specific increase in OAS levels observed in leaves of Arabidopsis thaliana plants 5-10 min after transfer to darkness and with diurnal oscillation of the OAS content, showing a characteristic peak during the night, were identified. Induction of a serine-O-acetyltransferase gene (SERAT) in transgenic A.thaliana plants expressing the genes under the control of an inducible promoter resulted in a specific time-dependent increase in OAS levels. Monitoring the transcriptome response at time points at which no changes in sulfur-related metabolites except OAS were observed and correlating this with the light/dark transition and diurnal experiments resulted in identification of six genes whose expression was highly correlated with that of OAS (adenosine-5'-phosphosulfate reductase 3, sulfur-deficiency-induced 1, sulfur-deficiency-induced 2, low-sulfur-induced 1, serine hydroxymethyltransferase 7 and ChaC-like protein). These data suggest that OAS displays a signalling function leading to changes in transcript levels of a specific gene set irrespective of the sulfur status of the plant. Additionally, a role for OAS in a specific part of the sulfate response can be deduced.

Intercultural Relationship, Prejudice and Ethnocentrism in a Computer-Mediated Communication (CMC): A Time-Series Experiment

This paper investigated the effects of relationship types (intracultural versus intercultural conditions) on relational development in computer-mediated communications (CMC) and face-to-face settings by controlling for the effect of prejudice and ethnocentrism. A total of 140 participants were involved in the experiment, and they were randomly assigned to the face-to-face, synchronous and asynchronous computer-mediated channels. Within each group, they were assigned to intracultural versus intercultural conditions and were required to interact in pairs with their zero-history partners. All participants underwent four experimental sessions.. The results exhibited a significant main effect of relationship types on relational progression in face-to-face and asynchronous CMC groups. No significant main effect of relationship types was observed in the synchronous CMC group. The results showed no significant effects of prejudice and ethnocentrism on relational development in all groups. The findings of the study provide partial support for the “equalization” view of CMC.

Polyethylenimine-Based Amphiphilic Core–Shell Nanoparticles: Study of Gene Delivery and Intracellular Trafficking

Amphiphilic core-shell nanoparticle, which is composed of a hydrophobic core and a branched polyethylenimine (PEI) shell, has been designed and synthesized as a novel gene delivery nanocarrier. In our previous study, we demonstrated that the core-shell nanoparticle was not only able to efficiently complex with plasmid DNA (pDNA) and protect it against enzymatic degradation, but also three times less cytotoxic, and threefold more efficient in gene transfection than branched 25 kDa PEI. This paper reports our further studies in the following three aspects: (1) the ability of the PEI-based nanoparticles to deliver gene in various mammalian cell lines; (2) intracellular distributions of the nanoparticles and their pDNA complexes in HeLa cells; and (3) incorporation of nuclear targeting agent into the nanoparticle/pDNA complexes to enhance the nuclear targeting ability. The PEI-based nanoparticles were able to transfect both human and non-human cell lines and their transfection efficiencies were cell-dependent. Within our four tested cell lines (MCF-7, BEL 7404, C6 and CHO-K1), gene transfer using PEI-based core-shell nanoparticles displayed gene expression levels comparable to, or even better than, the commercial Lipofectamine™ 2000. Confocal laser scanning microscopy showed that the nanoparticles and their pDNA complexes were effectively internalized into the HeLa cells. The in vitro time series experiments illustrated that both the nanoparticle/pDNA complexes and PEI-based nanoparticles were distributed in the cytoplasmic region after transfection for 10 and 60 min, respectively. Nuclear localization was also observed in both samples after transfection for 20 and 60 min, respectively. Incorporation of the high mobility group box 1 (HMGB1) protein for nuclear targeting has also been demonstrated with a simple approach: electrostatic complexation between the PEI-based nanoparticles and HMGB1. In the in vitro transfection study in MCF-7 cells, the expression level of the firefly luciferase gene encoded by the pDNA increased remarkably by up to eightfold when the HMGB1 protein was incorporated into the nanoparticle/pDNA complexes. Our results demonstrate that the PEI-based core-shell nanoparticles are promising nanocarriers for gene delivery.

On seasonal signals in geodetic time series

We explore implications for modeling and noise analysis of stochastic seasonal processes of climatic origin in geodetic time series. Seasonal signals are generally modeled as sinusoids with annual periods (and harmonics thereof), each with constant amplitude and phase. However, environmental noise that underlies the seasonal signal in geodetic time series has a reddened power spectral density (PSD). We investigate the form of the PSD of a time series having a stochastic seasonal component and find that for frequencies greater than the nominal seasonal frequency, the PSD of the time series reflects the PSD of the seasonal amplitudes. For example, if the PSD of the seasonal amplitudes can be expressed as an inverse power law, then the PSD of the time series will behave as an inverse power law for high frequencies. Stochastic seasonal variability will also induce a peak near the nominal seasonal frequency in addition to that of the mean seasonal signal and will be relatively flat below this frequency. It is therefore possible that some of the noise in Global Navigation Satellite Systems (GNSS) time series reported by others may be associated with neglecting the stochastic component of the seasonal signal. We use a GNSS time series from site ZIMM as an example to demonstrate the existence of a variable seasonal signal (without attributing its cause), and we use an example Gravity Recovery and Climate Experiment (GRACE) time series from Alaska to demonstrate that use of a nonstochastic seasonal model can have a significant impact on the value and uncertainty of time‐variable rates estimated from the time series.

Protocol: high throughput silica-based purification of RNA from Arabidopsis seedlings in a 96-well format

The increasing popularity of systems-based approaches to plant research has resulted in a demand for high throughput (HTP) methods to be developed. RNA extraction from multiple samples in an experiment is a significant bottleneck in performing systems-level genomic studies. Therefore we have established a high throughput method of RNA extraction from Arabidopsis thaliana to facilitate gene expression studies in this widely used plant model. We present optimised manual and automated protocols for the extraction of total RNA from 9-day-old Arabidopsis seedlings in a 96 well plate format using silica membrane-based methodology. Consistent and reproducible yields of high quality RNA are isolated averaging 8.9 μg total RNA per sample (~20 mg plant tissue). The purified RNA is suitable for subsequent qPCR analysis of the expression of over 500 genes in triplicate from each sample. Using the automated procedure, 192 samples (2 × 96 well plates) can easily be fully processed (samples homogenised, RNA purified and quantified) in less than half a day. Additionally we demonstrate that plant samples can be stored in RNAlater at -20°C (but not 4°C) for 10 months prior to extraction with no significant effect on RNA yield or quality. Additionally, disrupted samples can be stored in the lysis buffer at -20°C for at least 6 months prior to completion of the extraction procedure providing a flexible sampling and storage scheme to facilitate complex time series experiments.

Systematic identification of long noncoding RNAs expressed during zebrafish embryogenesis

Long noncoding RNAs (lncRNAs) comprise a diverse class of transcripts that structurally resemble mRNAs but do not encode proteins. Recent genome-wide studies in humans and the mouse have annotated lncRNAs expressed in cell lines and adult tissues, but a systematic analysis of lncRNAs expressed during vertebrate embryogenesis has been elusive. To identify lncRNAs with potential functions in vertebrate embryogenesis, we performed a time-series of RNA-seq experiments at eight stages during early zebrafish development. We reconstructed 56,535 high-confidence transcripts in 28,912 loci, recovering the vast majority of expressed RefSeq transcripts while identifying thousands of novel isoforms and expressed loci. We defined a stringent set of 1133 noncoding multi-exonic transcripts expressed during embryogenesis. These include long intergenic ncRNAs (lincRNAs), intronic overlapping lncRNAs, exonic antisense overlapping lncRNAs, and precursors for small RNAs (sRNAs). Zebrafish lncRNAs share many of the characteristics of their mammalian counterparts: relatively short length, low exon number, low expression, and conservation levels comparable to that of introns. Subsets of lncRNAs carry chromatin signatures characteristic of genes with developmental functions. The temporal expression profile of lncRNAs revealed two novel properties: lncRNAs are expressed in narrower time windows than are protein-coding genes and are specifically enriched in early-stage embryos. In addition, several lncRNAs show tissue-specific expression and distinct subcellular localization patterns. Integrative computational analyses associated individual lncRNAs with specific pathways and functions, ranging from cell cycle regulation to morphogenesis. Our study provides the first systematic identification of lncRNAs in a vertebrate embryo and forms the foundation for future genetic, genomic, and evolutionary studies.

Contribution of satellite laser ranging to combined gravity field models

In the framework of satellite-only gravity field modeling, satellite laser ranging (SLR) data is typically exploited to recover long-wavelength features. This contribution provides a detailed discussion of the SLR component of GOCO02S, the latest release of combined models within the GOCO series. Over a period of five years (January 2006 to December 2010), observations to LAGEOS-1, LAGEOS-2, Ajisai, Stella, and Starlette were analyzed. We conducted a series of closed-loop simulations and found that estimating monthly sets of spherical harmonic coefficients beyond degree five leads to exceedingly ill-posed normal equation systems. Therefore, we adopted degree five as the spectral resolution for real data analysis. We compared our monthly coefficient estimates of degree two with SLR and Gravity Recovery and Climate Experiment (GRACE) time series provided by the Center for Space Research (CSR) at Austin, Texas. Significant deviations in C 20 were noted between SLR and GRACE; the agreement is better for the non-zonal coefficients. Fitting sinusoids together with a linear trend to our C 20 time series yielded a rate of (−1.75 ± 0.6) × 10 −11/yr; this drift is equivalent to a geoid change from pole to equator of 0.35 ± 0.12 mm/yr or an apparent Greenland mass loss of 178.5 ± 61.2 km 3/yr. The mean of all monthly solutions, averaged over the five-year period, served as input for the satellite-only model GOCO02S. The contribution of SLR to the combined gravity field model is highest for C 20, and hence is essential for the determination of the Earth’s oblateness.

Bayesian hierarchical clustering for microarray time series data with replicates and outlier measurements

BackgroundPost-genomic molecular biology has resulted in an explosion of data, providing measurements for large numbers of genes, proteins and metabolites. Time series experiments have become increasingly common, necessitating the development of novel analysis tools that capture the resulting data structure. Outlier measurements at one or more time points present a significant challenge, while potentially valuable replicate information is often ignored by existing techniques.ResultsWe present a generative model-based Bayesian hierarchical clustering algorithm for microarray time series that employs Gaussian process regression to capture the structure of the data. By using a mixture model likelihood, our method permits a small proportion of the data to be modelled as outlier measurements, and adopts an empirical Bayes approach which uses replicate observations to inform a prior distribution of the noise variance. The method automatically learns the optimum number of clusters and can incorporate non-uniformly sampled time points. Using a wide variety of experimental data sets, we show that our algorithm consistently yields higher quality and more biologically meaningful clusters than current state-of-the-art methodologies. We highlight the importance of modelling outlier values by demonstrating that noisy genes can be grouped with other genes of similar biological function. We demonstrate the importance of including replicate information, which we find enables the discrimination of additional distinct expression profiles.ConclusionsBy incorporating outlier measurements and replicate values, this clustering algorithm for time series microarray data provides a step towards a better treatment of the noise inherent in measurements from high-throughput genomic technologies. Timeseries BHC is available as part of the R package 'BHC' (version 1.5), which is available for download from Bioconductor (version 2.9 and above) via http://www.bioconductor.org/packages/release/bioc/html/BHC.html?pagewanted=all.

Integrating heterogeneous gene expression data for gene regulatory network modelling

Gene regulatory networks (GRNs) are complex biological systems that have a large impact on protein levels, so that discovering network interactions is a major objective of systems biology. Quantitative GRN models have been inferred, to date, from time series measurements of gene expression, but at small scale, and with limited application to real data. Time series experiments are typically short (number of time points of the order of ten), whereas regulatory networks can be very large (containing hundreds of genes). This creates an under-determination problem, which negatively influences the results of any inferential algorithm. Presented here is an integrative approach to model inference, which has not been previously discussed to the authors' knowledge. Multiple heterogeneous expression time series are used to infer the same model, and results are shown to be more robust to noise and parameter perturbation. Additionally, a wavelet analysis shows that these models display limited noise over-fitting within the individual datasets.

Laboratory and field evaluation of diffusive gradient in thin films (DGT) for monitoring levels of dissolved mercury in natural river water

The diffusive gradient in the thin films (DGT) technique was tested to measure dissolved mercury (Hg) both in laboratory aqueous solutions and in situ in river water. For this purpose, a commercial ready-to-use and specific-for-Hg DGT device was used. Each sampler consisted of a filter membrane-agarose gel as the diffusive layer and a Spheron-Thiol resin in polyacrylamide gel as the binding agent. Basic performance assays at the laboratory with this type of DGT unit confirmed the applicability of Fick's first law for DGT measurements. The diffusion coefficient of MeHg in the agarose diffusive gel was 8.50 × 10−6 cm2 s−1 at 25°C. Several field studies were also carried out in two different rivers of the Ebro River basin (NE Spain) affected by Hg wastes released by the chlor-alkali industry. Hg concentrations determined by DGT were generally much lower than the results obtained through direct measurements of the river water. In addition, the results of a time series experiment also performed in the field show that the amount of Hg accumulated in the resin does not increase at all with the exposure time. This may be explained by the underestimation of the truly dissolved Hg fraction due to the formation of a biofilm layer on the surface of the samplers, thus clogging the filter and preventing Hg species from diffusing through it. Consequently, it was demonstrated that the DGT technique presents important limitations for measuring Hg in polluted rivers characterised by a high biomass load (eutrophic), whereas its performance was demonstrated to be correct in oligotrophic waters.

Morphogenesis of an extended phenotype: four-dimensional ant nest architecture

Animals produce a variety of structures to modify their environments adaptively. Such structures represent extended phenotypes whose development is rarely studied. To begin to rectify this, we used micro-computed tomography (CT) scanning and time-series experiments to obtain the first high-resolution dataset on the four-dimensional growth of ant nests. We show that extrinsic features within the environment, such as the presence of planes between layers of sediment, influence the architecture of Lasius flavus nests, with ants excavating horizontal tunnels along such planes. Intrinsically, the dimensions of the tunnels are associated with individual colonies, the dynamics of excavation can be explained by negative feedback and the angular distribution of tunnels is probably a result of local competition among tunnels for miners. The architecture and dynamics of ant nest excavation therefore result from local interactions of ants with one another and templates inherent in the environment. The influence of the environment on the form of structures has been documented across both biotic and abiotic domains. Our study opens up the utility of CT scanning as a technique for observing the morphogenesis of such structures.

High temporal resolution observations of spring fast ice melt and seawater iron enrichment in East Antarctica

A time series experiment was conducted in late austral spring (November–December 2009) in coastal fast ice, East Antarctica (66°13!07S, 110°39!02E). Iron (Fe) measurements were made in sea ice, snow, brines, and underlying seawater, together with meteorological, physical, and biogeochemical measurements to investigate the processes controlling the release of Fe into the underlying water column. Warming air temperatures were clearly associated with decreasing brine volume fractions. Macronutrient profiles revealed very low (<1 mM) nitrate + nitrite concentrations in the interior of the sea ice, and the brines suggested nitrate + nitrite drawdown exceeded Redfield ratios in comparison to phosphate and silicate. In the basal ice, nitrate + nitrite and silicate were drawn down through time but did not lead to a limiting condition. We found that dissolved Fe tracked the brine volume fraction and was readily transferred from the surface/interior to the underlying water column over time. In contrast, particulate Fe did not show this clear decreasing trend and correlated with particulate organic carbon and chlorophyll a distributions. Over the 28 d of sampling, two distinct mean air temperature warming events were observed (−12.1 to −1.3°C and −6.4 to 0.8°C). This resulted in the release of 419 mmol of TDFe per m2 of sea ice from our coastal fast ice station into the underlying water column during the study period. Assuming an increase of 1 nM Fe is sufficient for Antarctic diatoms to bloom, our study site presented a fertilization potential for 419 m3 of Fe limited surface Southern Ocean seawater with TDFe and 29 m3 with dFe, per m2 of fast ice.

Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic Bayesian network analysis

Reverse engineering gene regulatory networks, especially large size networks from time series gene expression data, remain a challenge to the systems biology community. In this article, a new hybrid algorithm integrating ordinary differential equation models with dynamic Bayesian network analysis, called Differential Equation-based Local Dynamic Bayesian Network (DELDBN), was proposed and implemented for gene regulatory network inference. The performance of DELDBN was benchmarked with an in vivo dataset from yeast. DELDBN significantly improved the accuracy and sensitivity of network inference compared with other approaches. The local causal discovery algorithm implemented in DELDBN also reduced the complexity of the network inference algorithm and improved its scalability to infer larger networks. We have demonstrated the applicability of the approach to a network containing thousands of genes with a dataset from human HeLa cell time series experiments. The local network around BRCA1 was particularly investigated and validated with independent published studies. BRAC1 network was significantly enriched with the known BRCA1-relevant interactions, indicating that DELDBN can effectively infer large size gene regulatory network from time series data. The R scripts are provided in File 3 in Supplementary Material. zheng.li@monsanto.com; jingdong.liu@monsanto.com Supplementary data are available at Bioinformatics online.

Integrative visualization of temporally varying medical image patterns.

We have developed a tool for the visualization of temporal changes of disease patterns, using stacks of medical images collected in time-series experiments. With this tool, users can generate 3D surface models representing disease patterns and observe changes over time in size, shape, and location of clinically significant image patterns. Statistical measurements of the volume of the observed disease patterns can be performed simultaneously. Spatial data integration occurs through the combination of 2D slices of an image stack into a 3D surface model. Temporal integration occurs through the sequential visualization of the 3D models from different time points. Visual integration enables the tool to show 2D images, 3D models and statistical data simultaneously. As an example, the tool has been used to visualize brain MRI scans of several multiple sclerosis patients. It has been developed in Java&trade;, to ensure portability and platform independence, with a user-friendly interface and can be downloaded free of charge for academic users.

Decrease in the Autotrophic-to-Heterotrophic Biomass Ratio of Picoplankton in Oligotrophic Marine Waters Due to Bottle Enclosure

We investigated the effects of bottle enclosure on autotrophic and heterotrophic picoplankton in North and South subtropical Atlantic oligotrophic waters, where the biomass and metabolism of the microbial community are dominated by the picoplankton size class. We measured changes in both autotrophic (Prochlorococcus, Synechococcus, and picoeukaryotes) and heterotrophic picoplankton biomass during three time series experiments and in 16 endpoint experiments over 24 h in light and dark treatments. Our results showed a divergent effect of bottle incubation on the autotrophic and heterotrophic components of the picoplankton community. The biomass of picophytoplankton showed, on average, a >50% decrease, mostly affecting the picoeukaryotes and, to a lesser extent, Prochlorococcus. In contrast, the biomass of heterotrophic bacteria remained constant or increased during the incubations. We also sampled 10 stations during a Lagrangian study in the North Atlantic subtropical gyre, which enabled us to compare the observed changes in the auto- to heterotrophic picoplankton biomass ratio (AB:HB ratio) inside the incubation bottles with those taking place in situ. While the AB:HB ratio in situ remained fairly constant during the Lagrangian study, it decreased significantly during the 24 h of incubation experiments. Thus, the rapid biomass changes observed in the incubations are artifacts resulting from bottle confinement and do not take place in natural conditions. Our results suggest that short (<1 day) bottle incubations in oligotrophic waters may lead to biased estimates of the microbial metabolic balance by underestimating primary production and/or overestimating bacterial respiration.

Detection and interpretation of metabolite–transcript coresponses using combined profiling data

Motivation: Studying the interplay between gene expression and metabolite levels can yield important information on the physiology of stress responses and adaptation strategies. Performing transcriptomics and metabolomics in parallel during time-series experiments represents a systematic way to gain such information. Several combined profiling datasets have been added to the public domain and they form a valuable resource for hypothesis generating studies. Unfortunately, detecting coresponses between transcript levels and metabolite abundances is non-trivial: they cannot be assumed to overlap directly with underlying biochemical pathways and they may be subject to time delays and obscured by considerable noise.Results: Our aim was to predict pathway comemberships between metabolites and genes based on their coresponses to applied stress. We found that in the presence of strong noise and time-shifted responses, a hidden Markov model-based similarity outperforms the simpler Pearson correlation but performs comparably or worse in their absence. Therefore, we propose a supervised method that applies pathway information to summarize similarity statistics to a consensus statistic that is more informative than any of the single measures. Using four combined profiling datasets, we show that comembership between metabolites and genes can be predicted for numerous KEGG pathways; this opens opportunities for the detection of transcriptionally regulated pathways and novel metabolically related genes.Availability: A command-line software tool is available at http://www.cin.ufpe.br/~igcf/Metabolites.Contact: henning@psc.riken.jp; igcf@cin.ufpe.brSupplementary information: Supplementary data are available at Bioinformatics online.

Integrative Visualization of Temporally Varying Medical Image Patterns

Summary We have developed a tool for the visualization of temporal changes of disease patterns, using stacks of medical images collected in time-series experiments. With this tool, users can generate 3D surface models representing disease patterns and observe changes over time in size, shape, and location of clinically significant image patterns. Statistical measurements of the volume of the observed disease patterns can be performed simultaneously. Spatial data integration occurs through the combination of 2D slices of an image stack into a 3D surface model. Temporal integration occurs through the sequential visualization of the 3D models from different time points. Visual integration enables the tool to show 2D images, 3D models and statistical data simultaneously. As an example, the tool has been used to visualize brain MRI scans of several multiple sclerosis patients. It has been developed in Java™, to ensure portability and platform independence, with a user-friendly interface and can be downloaded free of charge for academic users.

Predictability of bacterial activity and denitrification in aquatic sediments with continuous measurements of redox potential

Redox potential has been adopted as a qualitative parameter for interpreting solubility changes of nutrients and contaminants and the biological activity within wetland systems for several decades. The majority of studies considering the redox geochemistry in sediments used measurements of bulked material and single point measurement of biogeochemical parameters for interpretation, yet it remains questionable whether this information is reliable for environments that are very dynamic, such as wetlands. In this study it is evaluated whether variations in redox potential reflect dynamics of denitrification and overall bacterial respiration using continuous measurements of redox potential in time-series experiments in laboratory microcosms, in which the biogeochemical variation was enhanced by bioturbation. The results presented here suggest that measurements of redox potential have predictive potential in approximating rates of denitrification and overall bacterial respiration in aquatic sediments. The data clearly suggest that, while sediment bulk measurements and measurements of single profiles of redox potential, denitrification and bacterial activity often fail to provide ecological relevant information in dynamic systems, measurements of spatial and temporal redox potential profiles provide a useful parameter that reflects biogeochemical processes and functioning of sediments.

Bioavailability of soil organic matter and microbial community dynamics upon permafrost thaw

Amplified Arctic warming could thaw 25% of the permafrost area by 2100, exposing vast amounts of currently fixed organic carbon to microbially mediated decomposition and release of greenhouse gasses through soil organic matter (SOM) respiration. We performed time-series incubation experiments with Holocene permafrost soils at 4°C for up to 11 days to determine changes in exoenzyme activities (EEAs) (i.e. phosphatase, β-glucosidase, aminopeptidase) as a measure for the bioavailability of SOM in response to permafrost thaw. We also profiled SSU rRNA transcripts to follow the qualitative and quantitative changes in viable prokaryotes and eukaryotes during incubation. EEA, amount of rRNA transcripts and microbial community structures differed substantially between the various soil intervals in response to thaw: after 11 days of incubation, the active layer became slightly depleted in C and P and harboured bacterial phyla indicative of more oligotrophic conditions (Acidobacteria). A fast response in phosphatase and β-glucosidase upon thaw, and a predominance of active copiotrophic Bacteroidetes, showed that the upper permafrost plate serves as storage of easily degradable carbon derived from the overlying thawed active layer during summer. EEA profiles and microbial community dynamics furthermore suggest that the deeper and older permafrost intervals mainly contain recalcitrant SOM, and that extracellular soil-bound exoenzymes play a role in the initial cleavage of biopolymers, which could kick-start microbial growth upon thaw. Basidiomycetous fungi and Candidate Subdivision OP5 bacteria were the first to respond in freshly thawed deeper permafrost intervals, and might play an important role in the decomposition of recalcitrant SOM to release more labile substrates to support the major bacterial phyla (β-Proteobacteria, Actinobacteria, Firmicutes), which predominated thereafter.