Investigating the thermal state of permafrost with Bayesian inverse modeling of heat transfer

Long-term measurements of permafrost temperatures do not provide a complete picture of the Arctic subsurface thermal regime. Regions with warmer permafrost often show little to no long-term change in ground temperature due to the uptake and release of latent heat during freezing and thawing. Thus, regions where the least warming is observed may also be the most vulnerable to permafrost degradation. Since direct measurements of ice and liquid water contents in the permafrost layer are not widely available, thermal modeling of the subsurface plays a crucial role in understanding how permafrost responds to changes in the local energy balance. In this work, we first analyze trends in observed air and permafrost temperatures at four sites within the continuous permafrost zone, where we find substantial variation in the apparent relationship between long-term changes in permafrost temperatures (0.02–0.16 K yr−1) and air temperature (0.09–0.11 K yr−1). We then apply recently developed Bayesian inversion methods to link observed changes in borehole temperatures to unobserved changes in latent heat and active layer thickness using a transient model of heat conduction with phase change. Our results suggest that the degree to which recent warming trends correlate with permafrost thaw depends strongly on both soil freezing characteristics and historical climatology. At the warmest site, a 9 m borehole near Ny-Ålesund, Svalbard, modeled active layer thickness increases by an average of 13 ± 1 cm K−1 rise in mean annual ground temperature. In stark contrast, modeled rates of thaw at one of the colder sites, a borehole on Samoylov Island in the Lena River delta, appear far less sensitive to temperature change, with a negligible effect of 1 ± 1 cm K−1. Although our study is limited to just four sites, the results urge caution in the interpretation and comparison of warming trends in Arctic boreholes, indicating significant uncertainty in their implications for the current and future thermal state of permafrost.</p

Repeat length of C9orf72-associated glycine–alanine polypeptides affects their toxicity

G4C2 hexanucleotide repeat expansions in a non-coding region of the C9orf72 gene are the most common cause of familial amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). G4C2 insertion length is variable, and patients can carry up to several thousand repeats. Dipeptide repeat proteins (DPRs) translated from G4C2 transcripts are thought to be a main driver of toxicity. Experiments in model organisms with relatively short DPRs have shown that arginine-rich DPRs are most toxic, while polyGlycine–Alanine (GA) DPRs cause only mild toxicity. However, GA is the most abundant DPR in patient brains, and experimental work in animals has generally relied on the use of low numbers of repeats, with DPRs often tagged for in vivo tracking. Whether repeat length or tagging affect the toxicity of GA has not been systematically assessed. Therefore, we generated Drosophila fly lines expressing GA100, GA200 or GA400 specifically in adult neurons. Consistent with previous studies, expression of GA100 and GA200 caused only mild toxicity. In contrast, neuronal expression of GA400 drastically reduced climbing ability and survival of flies, indicating that long GA DPRs can be highly toxic in vivo. This toxicity could be abolished by tagging GA400. Proteomics analysis of fly brains showed a repeat-length-dependent modulation of the brain proteome, with GA400 causing earlier and stronger changes than shorter GA proteins. PolyGA expression up-regulated proteins involved in ER to Golgi trafficking, and down-regulated proteins involved in insulin signalling. Experimental down-regulation of Tango1, a highly conserved regulator of ER-to Golgi transport, partially rescued GA400 toxicity, suggesting that misregulation of this process contributes to polyGA toxicity. Experimentally increasing insulin signaling also rescued GA toxicity. In summary, our data show that long polyGA proteins can be highly toxic in vivo, and that they may therefore contribute to ALS/FTD pathogenesis in patients

Rapid climate change drives soil temperature warming and permafrost thaw on Svalbard

Svalbard is a hotspot of climate change in the rapidly warming Arctic. The strong air temperature warming coincides with a multitude of changes in other climate variables such as liquid precipitation, snow cover, and the surface energy budget components. These changes have highly complex effects on the soil temperature and freezing conditions. We investigate seasonal patterns of change in climate and soil conditions at the Bayelva study site close to Ny-Ålesund, Svalbard for the period 1998-2020. We use Bayesian inference to estimate trends in monthly mean values of air and soil temperature, radiation fluxes, sensible and latent heat flux, liquid precipitation, snow depth, and soil moisture. We then apply PCMCI+, a recently developed causal inference framework, in order to quantify the contributions of all meteorological variables to soil warming. Air temperature at the Bayelva site rose in all months of the year in the last 23 years (1998-2020). This trend has been particularly strong in April (1.3°C/10years), September (1.5°C/10years) and October (1.9°C/10years). The strong changes in spring and autumn led to earlier snowmelt (-14 days/10 years, 2007-2020) and more snow free days (+26 days/10years, 2007-2020). We observe later soil freezing in October and lower snow depth. Furthermore, strong rain events have become more frequent in winter, which contributed to soil warming. As a result of changes in air temperature, water fluxes, and the energy budget, top soil temperature increased in particular during spring (May/June 1.4°C/10years, 1998-2020). Our results illustrate how rapid climate change drives soil warming and permafrost thaw. They can help to validate results from climate and land surface models as well as aid in future predictions of landscape changes in Svalbard

Insulin and TOR signal in parallel through FOXO and S6K to promote epithelial wound healing

The TOR and Insulin/IGF signalling (IIS) network controls growth, metabolism and ageing. Although reducing TOR or insulin signalling can be beneficial for ageing, it can be detrimental for wound healing, but the reasons for this difference are unknown. Here we show that IIS is activated in the cells surrounding an epidermal wound in Drosophila melanogaster larvae, resulting in PI3K activation and redistribution of the transcription factor FOXO. Insulin and TOR signalling are independently necessary for normal wound healing, with FOXO and S6K as their respective effectors. IIS is specifically required in cells surrounding the wound, and the effect is independent of glycogen metabolism. Insulin signalling is needed for the efficient assembly of an actomyosin cable around the wound, and constitutively active myosin II regulatory light chain suppresses the effects of reduced IIS. These findings may have implications for the role of insulin signalling and FOXO activation in diabetic wound healing

DEEPGEN TM-A novel variant calling assay for low frequency variants

Detection of genetic variants in clinically relevant genomic hot-spot regions has become a promising application of next-generation sequencing technology in precision oncology. Effective personalized diagnostics requires the detection of variants with often very low frequencies. This can be achieved by targeted, short-read sequencing that provides high sequencing depths. However, rare genetic variants can contain crucial information for early cancer detection and subsequent treatment success, an inevitable level of background noise usually limits the accuracy of low frequency variant calling assays. To address this challenge, we developed DEEPGENTM, a variant calling assay intended for the detection of low frequency variants within liquid biopsy samples. We processed reference samples with validated mutations of known frequencies (0%-0.5%) to determine DEEPGENTM's performance and minimal input requirements. Our findings confirm DEEPGENTM's effectiveness in discriminating between signal and noise down to 0.09% variant allele frequency and an LOD(90) at 0.18%. A superior sensitivity was also confirmed by orthogonal comparison to a commercially available liquid biopsy-based assay for cancer detection

The evolution of Arctic permafrost over the last 3 centuries from ensemble simulations with the CryoGridLite permafrost model

Abstract. Understanding the future evolution of permafrost requires a better understanding of its climatological past. This requires permafrost models to efficiently simulate the thermal dynamics of permafrost over the past centuries to millennia, taking into account highly uncertain soil and snow properties. In this study, we present a computationally efficient numerical permafrost model which satisfactorily reproduces the current ground temperatures and active layer thicknesses of permafrost in the Arctic and their trends over recent centuries. The performed simulations provide insights into the evolution of permafrost since the 18th century and show that permafrost on the North American continent is subject to early degradation, while permafrost on the Eurasian continent is relatively stable over the investigated 300-year period. Permafrost warming since industrialization has occurred primarily in three “hotspot” regions in northeastern Canada, northern Alaska, and, to a lesser extent, western Siberia. We find that the extent of areas with a high probability (p3 m&gt;0.9) of near-surface permafrost (i.e., 3 m of permafrost within the upper 10 m of the subsurface) has declined substantially since the early 19th century, with loss accelerating during the last 50 years. Our simulations further indicate that short-term climate cooling due to large volcanic eruptions in the Northern Hemisphere in some cases favors permafrost aggradation within the uppermost 10 m of the ground, but the effect only lasts for a relatively short period of a few decades. Despite some limitations, e.g., with respect to the representation of vegetation, the presented model shows great potential for further investigation of the climatological past of permafrost, especially in conjunction with paleoclimate modeling. </jats:p

Loss of miR-210 leads to progressive retinal degeneration in Drosophila melanogaster

miRNAs are small, non-coding RNAs that regulate gene expression post-transcriptionally. We used small RNA sequencing to identify tissue-specific miRNAs in the adult brain, thorax, gut, and fat body of Drosophila melanogaster. One of the most brain-specific miRNAs that we identified was miR-210, an evolutionarily highly conserved miRNA implicated in the regulation of hypoxia in mammals. In Drosophila, we show that miR-210 is specifically expressed in sensory organs, including photoreceptors. miR-210 knockout mutants are not sensitive toward hypoxia but show progressive degradation of photoreceptor cells, accompanied by decreased photoreceptor potential, demonstrating an important function of miR-210 in photoreceptor maintenance and survival

Tissue-specific modulation of gene expression in response to lowered insulin signalling in Drosophila

Reduced activity of the insulin/IGF signalling network increases health during ageing in multiple species. Diverse and tissue-specific mechanisms drive the health improvement. Here, we performed tissue-specific transcriptional and proteomic profiling of long-lived Drosophila dilp2-3,5 mutants, and identified tissue-specific regulation of >3600 transcripts and >3700 proteins. Most expression changes were regulated post-transcriptionally in the fat body, and only in mutants infected with the endosymbiotic bacteria, Wolbachia pipientis, which increases their lifespan. Bioinformatic analysis identified reduced co-translational ER targeting of secreted and membrane-associated proteins and increased DNA damage/repair response proteins. Accordingly, age-related DNA damage and genome instability were lower in fat body of the mutant, and overexpression of a minichromosome maintenance protein subunit extended lifespan. Proteins involved in carbohydrate metabolism showed altered expression in the mutant intestine, and gut-specific overexpression of a lysosomal mannosidase increased autophagy, gut homeostasis, and lifespan. These processes are candidates for combatting ageing-related decline in other organisms

The CryoGrid community model (version 1.0) - a multi-physics toolbox for climate-driven simulations in the terrestrial cryosphere

The CryoGrid community model is a flexible toolbox for simulating the ground thermal regime and the ice-water balance for permafrost and glaciers, extending a well-established suite of permafrost models (CryoGrid 1, 2, and 3). The CryoGrid community model can accommodate a wide variety of application scenarios, which is achieved by fully modular structures through object-oriented programming. Different model components, characterized by their process representations and parameterizations, are realized as classes (i.e., objects) in CryoGrid. Standardized communication protocols between these classes ensure that they can be stacked vertically. For example, the CryoGrid community model features several classes with different complexity for the seasonal snow cover, which can be flexibly combined with a range of classes representing subsurface materials, each with their own set of process representations (e.g., soil with and without water balance, glacier ice). We present the CryoGrid architecture as well as the model physics and defining equations for the different model classes, focusing on one-dimensional model configurations which can also interact with external heat and water reservoirs. We illustrate the wide variety of simulation capabilities for a site on Svalbard, with point-scale permafrost simulations using, e.g., different soil freezing characteristics, drainage regimes, and snow representations, as well as simulations for glacier mass balance and a shallow water body. The CryoGrid community model is not intended as a static model framework but aims to provide developers with a flexible platform for efficient model development. In this study, we document both basic and advanced model functionalities to provide a baseline for the future development of novel cryosphere models