Total RNA analysis of the active microbiome on moving bed biofilm reactor carriers under incrementally increasing micropollutant concentrations.

Micropollutants are increasingly prevalent in the aquatic environment. A major part of these originates from wastewater treatment plants since traditional treatment technologies do not remove micropollutants sufficiently. Moving bed biofilm reactors (MBBRs), however, have been shown to aid in micropollutant removal when applied to conventional wastewater treatment as a polishing step. Here, we used Total RNA sequencing to investigate both the active microbial community and functional dynamics of MBBR biofilms when these were exposed to increasing micropollutant concentrations over time. Concurrently, we conducted batch culture experiments using biofilm carriers from the MBBRs to assess micropollutant degradation potential. Our study showed that biofilm eukaryotes, in particular protozoa, were negatively influenced by micropollutant exposure, in contrast to prokaryotes that increased in relative abundance. Further, we found several functional genes that were differentially expressed between the MBBR with added micropollutants and the control. These include genes involved in aromatic and xenobiotic compound degradation. Moreover, the biofilm carrier batch experiment showed vastly different alterations in benzotriazole and diclofenac degradation following the increased micropollutant concentrations in the MBBR. Ultimately, this study provides essential insights into the microbial community and functional dynamics of MBBRs and how an increased load of micropollutants influences these dynamics.

A novel genus of Pectobacterium bacteriophages display broad host range by targeting several species of Danish soft rot isolates.

The bacterial diseases black leg and soft rot in potatoes cause heavy losses of potatoes worldwide. Bacteria within the genus Pectobacteriaceae are the causative agents of black leg and soft rot. The use of antibiotics in agriculture is heavily regulated and no other effective treatment currently exists, but bacteriophages (phages) have shown promise as potential biocontrol agents. In this study we isolated soft rot bacteria from potato tubers and plant tissue displaying soft rot or black leg symptoms collected in Danish fields. We then used the isolated bacterial strains as hosts for phage isolation. Using organic waste, we isolated phages targeting different species within Pectobacterium. Here we focus on seven of these phages representing a new genus primarily targeting P. brasiliense; phage Ymer, Amona, Sabo, Abuela, Koroua, Taid and Pappous. TEM image of phage Ymer showed siphovirus morphotype, and the proposed Ymer genus belongs to the class Caudoviricetes, with double-stranded DNA genomes varying from 39kb to 43kb. In silico host range prediction using a CRISPR-Cas spacer database suggested both P. brasiliense, P. polaris and P. versatile as natural hosts for phages within the proposed Ymer genus. A following host range experiment, using 47 bacterial isolates from Danish tubers and plants symptomatic with soft rot or black leg disease verified the in silico host range prediction, as the genus as a group were able to infect all three Pectobacterium species. Phages did, however, primarily target P. brasiliense isolates and displayed differences in host range even within the species level. Two of the phages were able to infect two or more Pectobacterium species. Despite no nucleotide similarity with any phages in the NCBI database, the proposed Ymer genus did share some similarity at the protein level, as well as gene synteny, with currently known phages. None of the phages encoded integrases or other genes typically associated with lysogeny. Similarly, no virulence factors nor antimicrobial resistance genes were found, and combined with their ability to infect several soft rot-causing Pectobacterium species from Danish fields, demonstrates their potential as biocontrol agents against soft rot and black leg diseases in potatoes.

Airborne DNA reveals predictable spatial and seasonal dynamics of fungi.

Fungi are among the most diverse and ecologically important kingdoms in life. However, the distributional ranges of fungi remain largely unknown as do the ecological mechanisms that shape their distributions1,2. To provide an integrated view of the spatial and seasonal dynamics of fungi, we implemented a globally distributed standardized aerial sampling of fungal spores3. The vast majority of operational taxonomic units were detected within only one climatic zone, and the spatiotemporal patterns of species richness and community composition were mostly explained by annual mean air temperature. Tropical regions hosted the highest fungal diversity except for lichenized, ericoid mycorrhizal and ectomycorrhizal fungi, which reached their peak diversity in temperate regions. The sensitivity in climatic responses was associated with phylogenetic relatedness, suggesting that large-scale distributions of some fungal groups are partially constrained by their ancestral niche. There was a strong phylogenetic signal in seasonal sensitivity, suggesting that some groups of fungi have retained their ancestral trait of sporulating for only a short period. Overall, our results show that the hyperdiverse kingdom of fungi follows globally highly predictable spatial and temporal dynamics, with seasonality in both species richness and community composition increasing with latitude. Our study reports patterns resembling those described for other major groups of organisms, thus making a major contribution to the long-standing debate on whether organisms with a microbial lifestyle follow the global biodiversity paradigms known for macroorganisms4,5.

Gut microbiota-mediated polyphenol metabolism is restrained by parasitic whipworm infection and associated with altered immune function in mice.

Polyphenols are phytochemicals commonly found in plant-based diets which have demonstrated immunomodulatory and anti-inflammatory properties. However, the interplay between polyphenols and pathogens at mucosal barrier surfaces has not yet been elucidated in detail. Here, we show that proanthocyanidin (PAC) polyphenols interact with gut parasites to influence immune function and gut microbial-derived metabolites in mice. PAC intake inhibited mastocytosis during infection with the small intestinal roundworm Heligmosomoides polygyrus, and altered the host tissue transcriptome at the site of infection with the large intestinal whipworm Trichuris muris, with a notable enhancement of type-1 inflammatory and interferon-driven gene pathways. In the absence of infection, PAC intake promoted the expansion of Turicibacter within the gut microbiota, increased fecal short chain fatty acids, and enriched phenolic metabolites such as phenyl-γ-valerolactones in the cecum. However, these putatively beneficial effects were reduced in PAC-fed mice infected with T. muris, suggesting concomitant parasite infection can attenuate gut microbial-mediated PAC catabolism. Collectively, our results suggest an inter-relationship between a phytonutrient and infection, whereby PAC may augment parasite-induced inflammation (most prominently with the cecum dwelling T. muris), and infection may abrogate the beneficial effects of health-promoting phytochemicals.

21st century accelerating neurological deaths in UK and major Western countries: - Demographic and/or multiple-interactive-environmental causes?

OBJECTIVE: We examine whether the rise in neurological death rates over the 21st century are solely explained by the Gompertzian hypothesis. STUDY DESIGN: We examine two data-sets. First, Office of National Statistics (ONS, 2022) for nineteen mortality categories in England/Wales, including Alzheimer's, Dementias and Parkinson's Disease. Secondly, WHO (2020) Combined Neurological Mortality (CNM), from WHO Global mortality categories, Nervous Disease Deaths, and Alzheimer's & Other Dementias. METHODS: Based on ONS data we investigate trends in Age-Standardised Mortality Rates (ASMR) of CNM 2000-2022. Based on WHO data we examine rates of Early Deaths (55-74) and ASMR, for CNM between 2000 and 2015 in the ten Major 'Western' economies: Australia, Canada, France, Germany, Italy, Japan, Netherlands, Spain, UK, and the USA. RESULTS: In England & Wales death rates have increased 348% for Alzheimer's, 235% for Dementias, and 105% for Parkinson's Disease in contrast with falls in most other cause mortality. Early Adults Deaths CNM rates increased in eight countries, an average of 19%. Neurological ASMR rose in every country, averaging 43%, the highest was the UK 95%. CONCLUSION: We reject the Gompertzian hypothesis as an all-encompassing explanation for these marked increases in ASMR. Increases in early adult neurological deaths suggests this cannot be solely explained by an aging population. Furthermore, increases in mortality could be related to an increased prevalence of neurological conditions in this age group. Action is urgently needed to investigate factors - whether environmental, lifestyle or health systems - that could explain these findings.

Non-target analysis of Danish wastewater treatment plant effluent: Statistical analysis of chemical fingerprinting as a step toward a future monitoring tool.

In an attempt to discover and characterize the plethora of xenobiotic substances, this study investigates chemical compounds released into the environment with wastewater effluents. A novel non-targeted screening methodology based on ultra-high resolution Orbitrap mass spectrometry and nanoflow ultra-high performance liquid chromatography together with a newly optimized data-processing pipeline were applied to effluent samples from two state-of-the-art and one small wastewater treatment facility. In total, 785 molecular structures were obtained, of which 38 were identified as single compounds, while 480 structures were identified at a putative level. Most of these substances were therapeutics and drugs, present as parent compounds and metabolites. Using R packages Phyloseq and MetacodeR, originally developed for bioinformatics, significant differences in xenobiotic presence in the wastewater effluents between the three sites were demonstrated.

Image classification with symbolic hints using limited resources.

Typical machine learning classification benchmark problems often ignore the full input data structures present in real-world classification problems. Here we aim to represent additional information as "hints" for classification. We show that under a specific realistic conditional independence assumption, the hint information can be included by late fusion. In two experiments involving image classification with hints taking the form of text metadata, we demonstrate the feasibility and performance of the fusion scheme. We fuse the output of pre-trained image classifiers with the output of pre-trained text models. We show that calibration of the pre-trained models is crucial for the performance of the fused model. We compare the performance of the fusion scheme with a mid-level fusion scheme based on support vector machines and find that these two methods tend to perform quite similarly, albeit the late fusion scheme has only negligible computational costs.

Global Spore Sampling Project: A global, standardized dataset of airborne fungal DNA.

Novel methods for sampling and characterizing biodiversity hold great promise for re-evaluating patterns of life across the planet. The sampling of airborne spores with a cyclone sampler, and the sequencing of their DNA, have been suggested as an efficient and well-calibrated tool for surveying fungal diversity across various environments. Here we present data originating from the Global Spore Sampling Project, comprising 2,768 samples collected during two years at 47 outdoor locations across the world. Each sample represents fungal DNA extracted from 24 m3 of air. We applied a conservative bioinformatics pipeline that filtered out sequences that did not show strong evidence of representing a fungal species. The pipeline yielded 27,954 species-level operational taxonomic units (OTUs). Each OTU is accompanied by a probabilistic taxonomic classification, validated through comparison with expert evaluations. To examine the potential of the data for ecological analyses, we partitioned the variation in species distributions into spatial and seasonal components, showing a strong effect of the annual mean temperature on community composition.

Dual glucagon-like peptide-1 and glucagon receptor agonism reduces energy intake in type 2 diabetes with obesity.

AIMS: To establish which components of energy balance mediate the clinically significant weight loss demonstrated with use of cotadutide, a glucagon-like peptide-1 (GLP-1)/glucagon receptor dual agonist, in early-phase studies. MATERIALS AND METHODS: We conducted a phase 2a, single-centre, randomized, placebo-controlled trial in overweight and obese adults with type 2 diabetes. Following a 16-day single-blind placebo run-in, participants were randomized 2:1 to double-blind 42-day subcutaneous treatment with cotadutide (100-300 µg daily) or placebo. The primary outcome was percentage weight change. Secondary outcomes included change in energy intake (EI) and energy expenditure (EE). RESULTS: A total of 12 participants (63%) in the cotadutide group and seven (78%) in the placebo group completed the study. The mean (90% confidence interval [CI]) weight change was -4.0% (-4.9%, -3.1%) and -1.4% (-2.7%, -0.1%) for the cotadutide and placebo groups, respectively (p = 0.011). EI was lower with cotadutide versus placebo (-41.3% [-66.7, -15.9]; p = 0.011). Difference in EE (per kJ/kg lean body mass) for cotadutide versus placebo was 1.0% (90% CI -8.4, 10.4; p = 0.784), assessed by doubly labelled water, and -6.5% (90% CI -9.3, -3.7; p < 0.001), assessed by indirect calorimetry. CONCLUSION: Weight loss with cotadutide is primarily driven by reduced EI, with relatively small compensatory changes in EE.

Environmental drivers of Arctic communities based on metabarcoding of marine sediment eDNA.

Our ability to assess biodiversity at relevant spatial and temporal scales for informing management is of increasing importance given this is foundational to identify and mitigate the impacts of global change. Collecting baseline information and tracking ecological changes are particularly important for areas experiencing rapid changes and representing data gaps such as Arctic marine ecosystems. Environmental DNA has the potential to provide such data. We extracted environmental DNA from 90 surface sediment samples to assess eukaryote diversity around Greenland and Svalbard using two separate primer pairs amplifying different sections of the 18S rRNA gene. We detected 27 different phyla and 99 different orders and found that temperature and the change in temperature explained the most variation in the community in a single linear model, while latitude, sea ice cover and change in temperature explained the most variation in the community when assessed by individual non-linear models. We identified potential indicator taxa for Arctic climate change, including a terebellid annelid worm. In conclusion, our study demonstrates that environmental DNA offers a feasible method to assess biodiversity and identifies warming as a key driver of differences in biodiversity across these remote ecosystems.

Using sequences of life-events to predict human lives.

Here we represent human lives in a way that shares structural similarity to language, and we exploit this similarity to adapt natural language processing techniques to examine the evolution and predictability of human lives based on detailed event sequences. We do this by drawing on a comprehensive registry dataset, which is available for Denmark across several years, and that includes information about life-events related to health, education, occupation, income, address and working hours, recorded with day-to-day resolution. We create embeddings of life-events in a single vector space, showing that this embedding space is robust and highly structured. Our models allow us to predict diverse outcomes ranging from early mortality to personality nuances, outperforming state-of-the-art models by a wide margin. Using methods for interpreting deep learning models, we probe the algorithm to understand the factors that enable our predictions. Our framework allows researchers to discover potential mechanisms that impact life outcomes as well as the associated possibilities for personalized interventions.

Weighted Burgers Vector analysis of orientation fields from high-angular resolution electron backscatter diffraction.

The Weighted Burgers Vector (WBV) method can extract information about dislocation types and densities present in distorted crystalline materials from electron backscatter diffraction (EBSD) maps, using no assumptions about which slip systems might be present. Furthermore, high-angular resolution EBSD (HR-EBSD) uses a cross-correlation procedure to increase the angular precision of EBSD measurements by an order of magnitude compared to conventional EBSD. However, the WBV technique has not previously been applied to HR-EBSD data and therefore it remains unclear as to which low-angle substructures can be reliably characterised by WBV analysis of conventional EBSD data and which require additional HR-EBSD processing. To establish some practical examples that can be used to guide future data-acquisition strategies, we compare the output of the WBV method when applied to conventional EBSD data and HR-EBSD data collected from the most common minerals in Earth's lower crust (plagioclase feldspar) and upper mantle (olivine). The results demonstrate that HR-EBSD and WBV processing are complementary techniques. The increase in angular precision achieved with HR-EBSD processing allows low-angle (on the order of 0.1°) structures, which are obscured by noise in conventional EBSD data, to be analyzed quantitatively using the WBV method. Combining the WBV and HR-EBSD methods increases the precision of calculated WBV directions, which is essential when using information about active slip systems to infer likely deformation mechanisms from naturally deformed microstructures. This increase in precision is particularly important for low-symmetry crystals, such as plagioclase, that have a wide range of available slip systems that vary in relative activity with changing pressure, temperature and differential stress. Because WBV directions are calculated using no assumptions about which slip systems may be present, combining this technique with HR-EBSD to refine the precision of lattice orientation gradients is ideal for investigating complex natural materials with unknown deformation histories.

Insights into the circular: The cryptic plasmidome and its derived antibiotic resistome in the urban water systems.

Plasmids have been a concern in the dissemination and evolution of antibiotic resistance in the environment. In this study, we investigated the total pool of plasmids (plasmidome) and its derived antibiotic resistance genes (ARGs) in different compartments of urban water systems (UWSs) in three European countries representing different antibiotic usage regimes. We applied a direct plasmidome approach using wet-lab methods to enrich circular DNA in the samples, followed by shotgun sequencing and in silico contig circularisation. We identified 9538 novel sequences in a total of 10,942 recovered circular plasmids. Of these, 66 were identified as conjugative, 1896 mobilisable and 8970 non-mobilisable plasmids. The UWSs' plasmidome was dominated by small plasmids (≤10 Kbp) representing a broad diversity of mobility (MOB) types and incompatibility (Inc) groups. A shared collection of plasmids from different countries was detected in all treatment compartments, and plasmids could be source-tracked in the UWSs. More than half of the ARGs-encoding plasmids carried mobility genes for mobilisation/conjugation. The richness and abundance of ARGs-encoding plasmids generally decreased with the flow, while we observed that non-mobilisable ARGs-harbouring plasmids maintained their abundance in the Spanish wastewater treatment plant. Overall, our work unravels that the UWS plasmidome is dominated by cryptic (i.e., non-mobilisable, non-typeable and previously unknown) plasmids. Considering that some of these plasmids carried ARGs, were prevalent across three countries and could persist throughout the UWSs compartments, these results should alarm and call for attention.

Cotadutide promotes glycogenolysis in people with overweight or obesity diagnosed with type 2 diabetes.

Cotadutide is a dual glucagon-like peptide 1 and glucagon receptor agonist under development for the treatment of non-alcoholic steatohepatitis and type 2 diabetes mellitus (T2DM) and chronic kidney disease. Non-alcoholic steatohepatitis is a complex disease with no approved pharmacotherapies, arising from an underlying state of systemic metabolic dysfunction in association with T2DM and obesity. Cotadutide has been shown to improve glycaemic control, body weight, lipids, liver fat, inflammation and fibrosis. We conducted a two-part, randomized phase 2a trial in men and women with overweight or obesity diagnosed with T2DM to evaluate the efficacy and safety of cotadutide compared with placebo and liraglutide. The primary endpoints were change from baseline to day 28 of treatment in postprandial hepatic glycogen (part A) and to day 35 of treatment in fasting hepatic glycogen (part B) with cotadutide versus placebo. Secondary endpoints in part B were changes in fasting hepatic glycogen with cotadutide versus the mono glucagon-like peptide 1 receptor agonist, liraglutide, and change in hepatic fat fraction. The trial met its primary endpoint. We showed that cotadutide promotes greater reductions in liver glycogen and fat compared with placebo and liraglutide. Safety and tolerability findings with cotadutide were comparable to those of previous reports. Thus, this work provides evidence of additional benefits of cotadutide that could be attributed to glucagon receptor engagement. Our results suggest that cotadutide acts on the glucagon receptor in the human liver to promote glycogenolysis and improve the metabolic health of the liver. ClinicalTrials.gov registration: NCT03555994 .

Cessation of grazing causes biodiversity loss and homogenization of soil food webs.

There is widespread concern that cessation of grazing in historically grazed ecosystems is causing biotic homogenization and biodiversity loss. We used 12 montane grassland sites along an 800 km north-south gradient across the UK, to test whether cessation of grazing affects local α- and ß-diversity of below-ground food webs. We show cessation of grazing leads to strongly decreased α-diversity of most groups of soil microbes and fauna, particularly of relatively rare taxa. By contrast, the ß-diversity varied between groups of soil organisms. While most soil microbial communities exhibited increased homogenization after cessation of grazing, we observed decreased homogenization for soil fauna after cessation of grazing. Overall, our results indicate that exclusion of domesticated herbivores from historically grazed montane grasslands has far-ranging negative consequences for diversity of below-ground food webs. This underscores the importance of grazers for maintaining the diversity of below-ground communities, which play a central role in ecosystem functioning.

Widespread and largely unknown prophage activity, diversity, and function in two genera of wheat phyllosphere bacteria.

Environmental bacteria host an enormous number of prophages, but their diversity and natural functions remain largely elusive. Here, we investigate prophage activity and diversity in 63 Erwinia and Pseudomonas strains isolated from flag leaves of wheat grown in a single field. Introducing and validating Virion Induction Profiling Sequencing (VIP-Seq), we identify and quantify the activity of 120 spontaneously induced prophages, discovering that some phyllosphere bacteria produce more than 108 virions/mL in overnight cultures, with significant induction also observed in planta. Sequence analyses and plaque assays reveal E. aphidicola prophages contribute a majority of intraspecies genetic diversity and divide their bacterial hosts into antagonistic factions engaged in widespread microbial warfare, revealing the importance of prophage-mediated microdiversity. When comparing spontaneously active prophages with predicted prophages we also find insertion sequences are strongly correlated with non-active prophages. In conclusion, we discover widespread and largely unknown prophage diversity and function in phyllosphere bacteria.

Machine-learning vs. logistic regression for preoperative prediction of medical morbidity after fast-track hip and knee arthroplasty-a comparative study.

BACKGROUND: Machine-learning models may improve prediction of length of stay (LOS) and morbidity after surgery. However, few studies include fast-track programs, and most rely on administrative coding with limited follow-up and information on perioperative care. This study investigates potential benefits of a machine-learning model for prediction of postoperative morbidity in fast-track total hip (THA) and knee arthroplasty (TKA). METHODS: Cohort study in consecutive unselected primary THA/TKA between 2014-2017 from seven Danish centers with established fast-track protocols. Preoperative comorbidity and prescribed medication were recorded prospectively and information on length of stay and readmissions was obtained through the Danish National Patient Registry and medical records. We used a machine-learning model (Boosted Decision Trees) based on boosted decision trees with 33 preoperative variables for predicting "medical" morbidity leading to LOS > 4 days or 90-days readmissions and compared to a logistical regression model based on the same variables. We also evaluated two parsimonious models, using the ten most important variables in the full machine-learning and logistic regression models. Data collected between 2014-2016 (n:18,013) was used for model training and data from 2017 (n:3913) was used for testing. Model performances were analyzed using precision, area under receiver operating (AUROC) and precision recall curves (AUPRC), as well as the Mathews Correlation Coefficient. Variable importance was analyzed using Shapley Additive Explanations values. RESULTS: Using a threshold of 20% "risk-patients" (n:782), precision, AUROC and AUPRC were 13.6%, 76.3% and 15.5% vs. 12.4%, 74.7% and 15.6% for the machine-learning and logistic regression model, respectively. The parsimonious machine-learning model performed better than the full logistic regression model. Of the top ten variables, eight were shared between the machine-learning and logistic regression models, but with a considerable age-related variation in importance of specific types of medication. CONCLUSION: A machine-learning model using preoperative characteristics and prescriptions slightly improved identification of patients in high-risk of "medical" complications after fast-track THA and TKA compared to a logistic regression model. Such algorithms could help find a manageable population of patients who may benefit most from intensified perioperative care.

Field and laboratory guidelines for reliable bioinformatic and statistical analysis of bacterial shotgun metagenomic data.

Shotgun metagenomics is an increasingly cost-effective approach for profiling environmental and host-associated microbial communities. However, due to the complexity of both microbiomes and the molecular techniques required to analyze them, the reliability and representativeness of the results are contingent upon the field, laboratory, and bioinformatic procedures employed. Here, we consider 15 field and laboratory issues that critically impact downstream bioinformatic and statistical data processing, as well as result interpretation, in bacterial shotgun metagenomic studies. The issues we consider encompass intrinsic properties of samples, study design, and laboratory-processing strategies. We identify the links of field and laboratory steps with downstream analytical procedures, explain the means for detecting potential pitfalls, and propose mitigation measures to overcome or minimize their impact in metagenomic studies. We anticipate that our guidelines will assist data scientists in appropriately processing and interpreting their data, while aiding field and laboratory researchers to implement strategies for improving the quality of the generated results.

Geochemical landscapes as drivers of wildlife reproductive success: Insights from a high-Arctic ecosystem.

The bioavailability of essential and non-essential elements in vegetation is expected to influence the performance of free-ranging terrestrial herbivores. However, attempts to relate the use of geochemical landscapes by animal populations directly to reproductive output are currently lacking. Here we measured concentrations of 14 essential and non-essential elements in soil and vegetation samples collected in the Zackenberg valley, northeast Greenland, and linked these to environmental conditions to spatially predict and map geochemical landscapes. We then used long-term (1996-2021) survey data of muskoxen (Ovibos moschatus) to quantify annual variation in the relative use of essential and non-essential elements in vegetated sites and their relationship to calf recruitment the following year. Results showed that the relative use of the geochemical landscape by muskoxen varied substantially between years and differed among elements. Selection for vegetated sites with higher levels of the essential elements N, Cu, Se, and Mo was positively linked to annual calf recruitment. In contrast, selection for vegetated sites with higher concentrations of the non-essential elements As and Pb was negatively correlated to annual calf recruitment. Based on the concentrations measured in our study, we found no apparent associations between annual calf recruitment and levels of C, Mn, Co, Zn, Cd, Ba, Hg, and C:N ratio in the vegetation. We conclude that the spatial distribution and access to essential and non-essential elements are important drivers of reproductive output in muskoxen, which may also apply to other wildlife populations. The value of geochemical landscapes to assess habitat-performance relationships is likely to increase under future environmental change.