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BackgroundRecurrent bacterial and fungal infections, eczema, and increased serum IgE levels characterize patients with the hyper-IgE syndrome (HIES). Known genetic causes for HIES are mutations in signal transducer and activator of transcription 3 (STAT3) and dedicator of cytokinesis 8 (DOCK8), which are involved in signal transduction pathways. However, glycosylation defects have not been described in patients with HIES. One crucial enzyme in the glycosylation pathway is phosphoglucomutase 3 (PGM3), which catalyzes a key step in the synthesis of uridine diphosphate N-acetylglucosamine, which is required for the biosynthesis of N-glycans.ObjectiveWe sought to elucidate the genetic cause in patients with HIES who do not carry mutations in STAT3 or DOCK8.MethodsAfter establishing a linkage interval by means of SNPchip genotyping and homozygosity mapping in 2 families with HIES from Tunisia, mutational analysis was performed with selector-based, high-throughput sequencing. Protein expression was analyzed by means of Western blotting, and glycosylation was profiled by using mass spectrometry.ResultsMutational analysis of candidate genes in an 11.9-Mb linkage region on chromosome 6 shared by 2 multiplex families identified 2 homozygous mutations in PGM3 that segregated with disease status and followed recessive inheritance. The mutations predict amino acid changes in PGM3 (p.Glu340del and p.Leu83Ser). A third homozygous mutation (p.Asp502Tyr) and the p.Leu83Ser variant were identified in 2 other affected families, respectively. These hypomorphic mutations have an effect on the biosynthetic reactions involving uridine diphosphate N-acetylglucosamine. Glycomic analysis revealed an aberrant glycosylation pattern in leukocytes demonstrated by a reduced level of tri-antennary and tetra-antennary N-glycans. T-cell proliferation and differentiation were impaired in patients. Most patients had developmental delay, and many had psychomotor retardation.ConclusionImpairment of PGM3 function leads to a novel primary (inborn) error of development and immunity because biallelic hypomorphic mutations are associated with impaired glycosylation and a hyper-IgE-like phenotype.

Wetlands are the largest natural source of methane (CH4) emissions globally. Northern wetlands (>45° N), accounting for 42 % of global wetland area, are increasingly vulnerable to carbon loss, especially as CH4 emissions may accelerate under intensified high-latitude warming. However, the magnitude and spatial patterns of high-latitude CH4 emissions remain relatively uncertain. Here we present estimates of daily CH4 fluxes obtained using a new machine learning-based wetland CH4 upscaling framework (WetCH4) that applies the most complete database of eddy covariance (EC) observations available to date, and satellite remote sensing informed observations of environmental conditions at 10-km resolution. The most important predictor variables included near-surface soil temperatures (top 40 cm), vegetation reflectance, and soil moisture. Our results, modeled from 138 site-years across 26 sites, had relatively strong predictive skill with a mean R2 of 0.46 and 0.62 and a mean absolute error (MAE) of 23 nmol m-2 s-1 and 21 nmol m-2 s-1 for daily and monthly fluxes, respectively. Based on the model results, we estimated an annual average of 20.8 ±2.1 Tg CH4 yr-1 for the northern wetland region (2016–2022) and total budgets ranged from 13.7–44.1 Tg CH4 yr-1, depending on wetland map extents. Although 86 % of the estimated CH4 budget occurred during the May–October period, a considerable amount (1.4 ±0.2 Tg CH4) occurred during winter. Regionally, the West Siberian wetlands accounted for a majority (51 %) of the interannual variation in domain CH4 emissions. Significant issues with data coverage remain, with only 23 % of the sites observing year-round and most of the data from 11 wetland sites in Alaska and 10 bog/fen sites in Canada and Fennoscandia, and in general, Western Siberian Lowlands are underrepresented by EC CH4 sites. Our results provide high spatiotemporal information on the wetland emissions in the high-latitude carbon cycle and possible responses to climate change. Continued, all-season tower observations and improved soil moisture products are needed for future improvement of CH4 upscaling. The dataset can be found at https://doi.org/10.5281/zenodo.10802154 (Ying et al., 2024). [ABSTRACT FROM AUTHOR]

Northern peatlands provide a globally important carbon (C) store. Since the beginning of the 20th century, however, large areas of natural peatlands have been drained for biomass production across Fennoscandia. Today, drained peatland forests constitute a common feature of the managed boreal landscape, yet their ecosystem C balance and associated climate impact are not well understood, particularly within the nutrient‐poor boreal region. In this study, we estimated the net ecosystem carbon balance (NECB) from a nutrient‐poor drained peatland forest and an adjacent natural mire in northern Sweden by integrating terrestrial carbon dioxide (CO2) and methane (CH4) fluxes with aquatic losses of dissolved organic C (DOC) and inorganic C based on eddy covariance and stream discharge measurements, respectively, over two hydrological years. Since the forest included a dense spruce‐birch area and a sparse pine area, we were able to further evaluate the effect of contrasting forest structure on the NECB and component fluxes. We found that the drained peatland forest was a net C sink with a 2‐year mean NECB of −115 ± 5 g C m−2 year−1 while the adjacent mire was close to C neutral with 14.6 ± 1.7 g C m−2 year−1. The NECB of the drained peatland forest was dominated by the net CO2 exchange (net ecosystem exchange [NEE]), whereas NEE and DOC export fluxes contributed equally to the mire NECB. We further found that the C sink strength in the sparse pine forest area (−153 ± 8 g C m−2 year−1) was about 1.5 times as high as in the dense spruce‐birch forest area (−95 ± 8 g C m−2 year−1) due to enhanced C uptake by ground vegetation and lower DOC export. Our study suggests that historically drained peatland forests in nutrient‐poor boreal regions may provide a significant net ecosystem C sink and associated climate benefits. [ABSTRACT FROM AUTHOR]

Because of the continuing debates about statistics, many researchers may feel confused about how to analyze and interpret data. Current guidelines in psychology advocate the use of effect sizes and confidence intervals (CIs). However, researchers may be unsure about how to extract effect sizes from factorial designs. Contrast analysis is helpful because it can be used to test specific questions of central interest in studies with factorial designs. It weighs several means and combines them into one or two sets that can be tested with t tests. The effect size produced by a contrast analysis is simply the difference between means. The CI of the effect size informs directly about direction, hypothesis exclusion, and the relevance of the effects of interest. However, any interpretation in terms of precision or likelihood requires the use of likelihood intervals or credible intervals (Bayesian). These various intervals and even a Bayesian t test can be obtained easily with free software. This tutorial reviews these methods to guide researchers in answering the following questions: When I analyze mean differences in factorial designs, where can I find the effects of central interest, and what can I learn about their effect sizes?

To understand patterns in CO2 partial pressure (PCO2) over time in wetlands' surface water and porewater, we examined the relationship between PCO2 and land–atmosphere flux of CO2 at the ecosystem scale at 22 Northern Hemisphere wetland sites synthesized through an open call. Sites spanned 6 major wetland types (tidal, alpine, fen, bog, marsh, and prairie pothole/karst), 7 Köppen climates, and 16 different years. Ecosystem respiration (Reco) and gross primary production (GPP), components of vertical CO2 flux, were compared to PCO2, a component of lateral CO2 flux, to determine if photosynthetic rates and soil respiration consistently influence wetland surface and porewater CO2 concentrations across wetlands. Similar to drivers of primary productivity at the ecosystem scale, PCO2 was strongly positively correlated with air temperature (Tair) at most sites. Monthly average PCO2 tended to peak towards the middle of the year and was more strongly related to Reco than GPP. Our results suggest Reco may be related to biologically driven PCO2 in wetlands, but the relationship is site-specific and could be an artifact of differently timed seasonal cycles or other factors. Higher levels of discharge do not consistently alter the relationship between Reco and temperature normalized PCO2. This work synthesizes relevant data and identifies key knowledge gaps in drivers of wetland respiration. [ABSTRACT FROM AUTHOR]