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The study investigated the methane production efficiency in a semi-continuous laboratory experiment with periodic feeding of wastewater sludge (WWS) as primary substrate and addition of whey (CW) and cow manure (CM). The short-term behavior of a real-scale anaerobic digester with WWS and the methane production improvements with different feeding mixtures of WWS, CW and CM were addressed. Gradual addition of CW to WWS (WWS:CW:CM = 70:20:0 to 70:55:0) increased the average daily methane production to 48.6 mL CH 4 /g COD/day and prevented reactor failure, but high VOA/TIC values showed that the reactors were conditionally stable evolution at an OLR of 8 g COD/L/day. Reactors that were additionally supplemented with CM (WWS:CW:CM = 70:55:10) achieved at least 12.3 % more methane than the reactors supplemented with WWS and CW alone. The highest methane production and process evolution in the reactors were achieved at OLRs between 7.5 and 8.7 g COD/L per day. After day 50, the addition of double the amount of CW further increased the methane production and VOA/TIC ratios. In this case, the OLR increased from 6.3 to 9.3 g COD/L/day. The concentration of propionic and acetic acid in all reactors increased above the recommended values and caused inhibition and instability. A strong positive Pearson correlation was found between the trace elements (Fe, Cu, Zn, Mn) detected by XRF. TE contributed to methane production, but to a lesser extent than TIC and NH4+-N. The simplified model successfully predicted methane production under a periodic feeding regime., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Objective: Cerebral palsy (CP) is a group of neurological disorders with profound implications for children's development. The identification of perinatal risk factors for CP may lead to improved preventive and therapeutic strategies. This study aimed to identify the early predictors of CP using machine learning (ML)., Design: This is a retrospective case-control study, using data from the two population-based databases, the Slovenian National Perinatal Information System and the Slovenian Registry of Cerebral Palsy. Multiple ML algorithms were evaluated to identify the best model for predicting CP., Setting: This is a population-based study of CP and control subjects born into one of Slovenia's 14 maternity wards., Participants: A total of 382 CP cases, born between 2002 and 2017, were identified. Controls were selected at a control-to-case ratio of 3:1, with matched gestational age and birth multiplicity. CP cases with congenital anomalies ( n =44) were excluded from the analysis. A total of 338 CP cases and 1014 controls were included in the study., Exposure: 135 variables relating to perinatal and maternal factors., Main Outcome Measures: Receiver operating characteristic (ROC), sensitivity and specificity., Results: The stochastic gradient boosting ML model (271 cases and 812 controls) demonstrated the highest mean ROC value of 0.81 (mean sensitivity=0.46 and mean specificity=0.95). Using this model with the validation dataset (67 cases and 202 controls) resulted in an area under the ROC curve of 0.77 (mean sensitivity=0.27 and mean specificity=0.94)., Conclusions: Our final ML model using early perinatal factors could not reliably predict CP in our cohort. Future studies should evaluate models with additional factors, such as genetic and neuroimaging data., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

This study aimed to compare the microbiome profiles of patients with colorectal cancer (CRC, n  = 380) and colorectal adenomas (CRA, n  = 110) against generally healthy participants ( n  = 2,461) from various studies. The overarching objective was to conduct a real-life experiment and develop a robust machine learning model applicable to the general population. A total of 2,951 stool samples underwent a comprehensive analysis using the in-house MetaBakery pipeline. This included various data matrices such as microbial taxonomy, functional genes, enzymatic reactions, metabolic pathways, and predicted metabolites. The study found no statistically significant difference in microbial diversity among individuals. However, distinct clusters were identified for healthy, CRC, and CRA groups through linear discriminant analysis (LDA). Machine learning analysis demonstrated consistent model performance, indicating the potential of microbiome layers (microbial taxa, functional genes, enzymatic reactions, and metabolic pathways) as prediagnostic indicators for CRC and CRA. Notable biomarkers on the taxonomy level and microbial functionality (gene families, enzymatic reactions, and metabolic pathways) associated with CRC were identified. The research presents promising avenues for practical clinical applications, with potential validation on external clinical datasets in future studies., Competing Interests: LD was employed by the NU B.V. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Murovec, Deutsch and Stres.)

In this study, we present MetaBakery (http://metabakery.fe.uni-lj.si), an integrated application designed as a framework for synergistically executing the bioBakery workflow and associated utilities. MetaBakery streamlines the processing of any number of paired or unpaired fastq files, or a mixture of both, with optional compression (gzip, zip, bzip2, xz, or mixed) within a single run. MetaBakery uses programs such as KneadData (https://github.com/bioBakery/kneaddata), MetaPhlAn, HUMAnN and StrainPhlAn as well as integrated utilities and extends the original functionality of bioBakery. In particular, it includes MelonnPan for the prediction of metabolites and Mothur for calculation of microbial alpha diversity. Written in Python 3 and C++ the whole pipeline was encapsulated as Singularity container for efficient execution on various computing infrastructures, including large High-Performance Computing clusters. MetaBakery facilitates crash recovery, efficient re-execution upon parameter changes, and processing of large data sets through subset handling and is offered in three editions with bioBakery ingredients versions 4, 3 and 2 as versatile, transparent and well documented within the MetaBakery Users' Manual (http://metabakery.fe.uni-lj.si/metabakery_manual.pdf). It provides automatic handling of command line parameters, file formats and comprehensive hierarchical storage of output to simplify navigation and debugging. MetaBakery filters out potential human contamination and excludes samples with low read counts. It calculates estimates of alpha diversity and represents a comprehensive and augmented re-implementation of the bioBakery workflow. The robustness and flexibility of the system enables efficient exploration of changing parameters and input datasets, increasing its utility for microbiome analysis. Furthermore, we have shown that the MetaBakery tool can be used in modern biostatistical and machine learning approaches including large-scale microbiome studies., Competing Interests: LD was employed by The Nu B.V. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Murovec, Deutsch, Osredkar and Stres.)

Slightly acidic (pH 5.1) waste sludge with 4.7 % Total Solids (TS) was treated on a laboratory scale pined disc rotary generator of hydrodynamic cavitation (PD RGHC). Influence of four rotor discs with different number of cavitation generation units (CGUs) was investigated: 8-pins, 12-pins, 16-pins and 8-prism elements. The effect of hydrodynamic cavitation (HC) was investigated by analyzing rheological properties, surface tension, dewaterability, and particle size distribution. After subjecting the sludge to 30 cavitation passes, the dewatering ability of the sludge significantly decreased, resulting in a more than two-fold increase in Capillary Suction Time (CST). All regimes were successful in disintegrating particles to smaller sizes. A slight increase of sludge surface tension was measured post cavitation. Cavitated samples displayed a zero-shear viscosity, in contrast to the untreated sample, where viscosity noticeably increased as shear stress decreased. HC did not improve methane yield. Statistically significant correlations between physio-chemical properties and apparent viscosity at low shear stress were identified. Although there were no discernible statistical differences in sludge characteristics, some trends are visible among investigated CGU designs and warrant further research., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Climate change modifies environmental conditions, resulting in altered precipitation patterns, moisture availability and nutrient distribution for microbial communities. Changes in water availability are projected to affect a range of ecological processes, including the decomposition of plant litter and carbon cycling. However, a detailed understanding of microbial stress response to drought/flooding is missing. In this study, an intermittent lake is taken up as a model for changes in water availability and how they affect the functional pathways in microbial communities of the decomposing Phragmites australis litter. The results show that most enriched functions in both habitats belonged to the classes of Carbohydrates and Clustering-based subsystems (terms with unknown function) from SEED subsystems classification. We confirmed that changes in water availability resulted in altered functional makeup of microbial communities. Our results indicate that microbial communities under more frequent water stress (due to fluctuating conditions) could sustain an additional metabolic cost due to the production or uptake of compatible solutes to maintain cellular osmotic balance. Nevertheless, although prolonged submergence seemed to have a negative impact on several functional traits in the fungal community, the decomposition rate was not affected., (© 2023. The Author(s).)

Anaerobic digestion in wastewater treatment plants converts its unwanted end product - waste activated sludge into biogas. Even if the process is well established, pre-treatment of the sludge can further improve its efficiency. In this study, four treatment regimes for increasing methane production through prior sludge disintegration were investigated using lab-scale cavitation generator and real sludge samples. Three different cavitating (attached cavitation regime, developed cloud shedding cavitation regime and cavitation in a wake regime) and one non-cavitating regime at elevated static pressure were studied in detail for their effectiveness on physical and chemical properties of sludge samples. Volume-weighted mean diameter D[4,3] of sludge's particles decreased by up to 92%, specific surface area increased by up to 611%, while viscosity (at a shear rate of 3.0 s -1 ) increased by up to 39% in the non-cavitating and decreased by up to 24% in all three cavitating regimes. Chemical changes were more pronounced in cavitating regimes, where released soluble chemical oxygen demand (sCOD) and increase of dissolved organic matter (DOM) compounds by up to 175% and 122% were achieved, respectively. Methane production increased in all four cases, with the highest increase of 70% corresponding to 312 mL CH 4 g -1 COD. However, this treatment was not particularly efficient in terms of energy consumption. The best energy balance was found for the regime with a biochemical methane potencial increase of 43%., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Although metagenomic sequencing is now the preferred technique to study microbiome-host interactions, analyzing and interpreting microbiome sequencing data presents challenges primarily attributed to the statistical specificities of the data (e.g., sparse, over-dispersed, compositional, inter-variable dependency). This mini review explores preprocessing and transformation methods applied in recent human microbiome studies to address microbiome data analysis challenges. Our results indicate a limited adoption of transformation methods targeting the statistical characteristics of microbiome sequencing data. Instead, there is a prevalent usage of relative and normalization-based transformations that do not specifically account for the specific attributes of microbiome data. The information on preprocessing and transformations applied to the data before analysis was incomplete or missing in many publications, leading to reproducibility concerns, comparability issues, and questionable results. We hope this mini review will provide researchers and newcomers to the field of human microbiome research with an up-to-date point of reference for various data transformation tools and assist them in choosing the most suitable transformation method based on their research questions, objectives, and data characteristics., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Ibrahimi, Lopes, Dhamo, Simeon, Shigdel, Hron, Stres, D’Elia, Berland and Marcos-Zambrano.)

In this perspective analysis, we strive to answer the following question: how can we advance integrative biology research in the 21st century with lessons from animal science? At the University of Ljubljana, Biotechnical Faculty, Department of Animal Science, we share here our three lessons learned in the two decades from 2002 to 2022 that we believe could inform integrative biology, systems science, and animal science scholarship in other countries and geographies. Cultivating multiomics knowledge through a conceptual lens of integrative biology is crucial for life sciences research that can stand the test of diverse biological, clinical, and ecological contexts. Moreover, in an era of the current COVID-19 pandemic, animal nutrition and animal science, and the study of their interactions with human health (and vice versa) through integrative biology approaches hold enormous prospects and significance for systems medicine and ecosystem health.

Objectives: The aim of this study was to analyse the characteristics of typical absence seizures (AS), myoclonic AS and AS with eyelid myoclonia in children and to find associations between these characteristics and difficult to treat absence seizures (DTAS)., Methods: This was a single-center retrospective study. Electronic health records of pediatric patients with a clinical diagnosis of AS treated at a single tertiary epilepsy center between January 2013 and June 2020 were reviewed. Clinical characteristics, seizure information, ASM, and therapeutic response of patients were recorded. All patients were followed up for at least 1 year. DTAS were defined as failure to achieve remission after treatment with at least 2 anti-seizure medications (ASM), regardless of whether remission was achieved eventually in the study period., Results: Data from 131 patients were available for analysis. Remission was achieved after the first ASM treatment in 81 (61.8%) patients, and eventually in 120 (91.6%) during the study period. Epilepsy was classified as DTAS in 18 (13.7%) patients. AS were more often difficult to treat in patients with myoclonic AS and AS with eyelid myoclonia (40.0%), compared with patients with typical AS (11.4%; p = 0.012, 95% CI 1.480-25.732). A positive family history of epilepsy ( p = 0.046; 95% CI 1.021-8.572), a higher seizure frequency ( p = 0.023, 95% CI 1.009-1.126) prior to ASM treatment, and longer time between seizure onset and treatment onset ( p = 0.026; 95% CI 1.006-1.099) were also associated with DTAS., Significance: Our study suggests that several clinical characteristics of AS are associated with DTAS. One of these was the time between onset of AS and initiation of ASM treatment, which can be shortened with better care, suggesting that early diagnosis and treatment may improve prognosis in pediatric patients with AS. These findings remain to be confirmed in larger prospective studies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Gregorčič, Hrovat, Bizjak, Rener Primec, Hostnik, Stres, Perković Benedik and Osredkar.)

Preterm birth (before 37 weeks gestation) accounts for ~10% of births worldwide and remains one of the leading causes of death in children under 5 years of age. Preterm born adults have been consistently shown to be at an increased risk for chronic disorders including cardiovascular, endocrine/metabolic, respiratory, renal, neurologic, and psychiatric disorders that result in increased death risk. Oxidative stress was shown to be an important risk factor for hypertension, metabolic syndrome and lung disease (reduced pulmonary function, long-term obstructive pulmonary disease, respiratory infections, and sleep disturbances). The aim of this study was to explore the differences between preterm and full-term male participants' levels of urine and fecal proton nuclear magnetic resonance ( 1 H-NMR) metabolomes, during rest and exercise in normoxia and hypoxia and to assess general differences in human gut-microbiomes through metagenomics at the level of taxonomy, diversity, functional genes, enzymatic reactions, metabolic pathways and predicted gut metabolites. Significant differences existed between the two groups based on the analysis of 1 H-NMR urine and fecal metabolomes and their respective metabolic pathways, enabling the elucidation of a complex set of microbiome related metabolic biomarkers, supporting the idea of distinct host-microbiome interactions between the two groups and enabling the efficient classification of samples; however, this could not be directed to specific taxonomic characteristics.

Physical inactivity is a worldwide health problem, an important risk for global mortality and is associated with chronic noncommunicable diseases. The aim of this study was to explore the differences in systemic urine 1 H-NMR metabolomes between physically active and inactive healthy young males enrolled in the X-Adapt project in response to controlled exercise (before and after the 3-day exercise testing and 10-day training protocol) in normoxic (21% O 2 ), normobaric (~1000 hPa) and normal-temperature (23 °C) conditions at 1 h of 50% maximal pedaling power output (W peak ) per day. Interrogation of the exercise database established from past X-Adapt results showed that significant multivariate differences existed in physiological traits between trained and untrained groups before and after training sessions and were mirrored in significant differences in urine pH, salinity, total dissolved solids and conductivity. Cholate, tartrate, cadaverine, lysine and N6-acetyllisine were the most important metabolites distinguishing trained and untrained groups. The relatively little effort of 1 h 50% W peak per day invested by the untrained effectively modified their resting urine metabolome into one indistinguishable from the trained group, which hence provides a good basis for the planning of future recommendations for health maintenance in adults, irrespective of the starting fitness value. Finally, the 3-day sessions of morning urine samples represent a good candidate biological matrix for future delineations of active and inactive lifestyles detecting differences unobservable by single-day sampling due to day-to-day variability.

Common reed ( Phragmites australis ) has high biomass production and is primarily subjected to decomposition processes affected by multiple factors. To predict litter decomposition dynamics in intermittent lakes, it is critical to understand how communities of fungi, as the primary decomposers, form under different habitat conditions. This study reports the shotgun metagenomic sequencing of the initial fungal communities on common reed leaves decomposing under different environmental conditions. We demonstrate that a complex network of fungi forms already on the plant persists into the decomposition phase. Phragmites australis leaves contained at least five fungal phyla, with abundant Ascomycota (95.7%) and Basidiomycota (4.1%), identified as saprotrophs (48.6%), pathotrophs (22.5%), and symbiotrophs (12.6%). Most of the correlations between fungi in fresh and decomposing leaves were identified as co-occurrences (positive correlations). The geographic source of litter and leaf age did not affect the structure and diversity of fungal communities. Keystone taxa were mostly moisture-sensitive. Our results suggest that habitat has a strong effect on the formation of the fungal communities through keystone taxa. Nevertheless, it can also alter the proportions of individual fungal groups in the community through indirect effects on competition between the fungal taxa and their exploitation of favourable conditions.

General Unified Microbiome Profiling Pipeline (GUMPP) was developed for large scale, streamlined and reproducible analysis of bacterial 16S rRNA data and prediction of microbial metagenomes, enzymatic reactions and metabolic pathways from amplicon data. GUMPP workflow introduces reproducible data analyses at each of the three levels of resolution (genus; operational taxonomic units (OTUs); amplicon sequence variants (ASVs)). The ability to support reproducible analyses enables production of datasets that ultimately identify the biochemical pathways characteristic of disease pathology. These datasets coupled to biostatistics and mathematical approaches of machine learning can play a significant role in extraction of truly significant and meaningful information from a wide set of 16S rRNA datasets. The adoption of GUMPP in the gut-microbiota related research enables focusing on the generation of novel biomarkers that can lead to the development of mechanistic hypotheses applicable to the development of novel therapies in personalized medicine.

Spinal muscular atrophy (SMA) is a genetically heterogeneous group of rare neuromuscular diseases and was until recently the most common genetic cause of death in children. The effects of 2-month nusinersen therapy on urine, serum, and liquor 1H-NMR metabolomes in SMA males and females were not explored yet, especially not in comparison to the urine 1H-NMR metabolomes of matching male and female cohorts. In this prospective, single-centered study, urine, serum, and liquor samples were collected from 25 male and female pediatric patients with SMA before and after 2 months of nusinersen therapy and urine samples from a matching healthy cohort ( n = 125). Nusinersen intrathecal application was the first therapy for the treatment of SMA by the Food and Drug Administration (FDA) and the European Medicines Agency (EMA). Metabolomes were analyzed using targeted metabolomics utilizing 600 MHz 1H-NMR, parametric and nonparametric multivariate statistical analyses, machine learning, and modeling. Medical assessment before and after nusinersen therapy showed significant improvements of movement, posture, and strength according to various medical tests. No significant differences were found in metabolomes before and after nusinersen therapy in urine, serum, and liquor samples using an ensemble of statistical and machine learning approaches. In comparison to a healthy cohort, 1H-NMR metabolomes of SMA patients contained a reduced number and concentration of urine metabolites and differed significantly between males and females as well. Significantly larger data scatter was observed for SMA patients in comparison to matched healthy controls. Machine learning confirmed urinary creatinine as the most significant, distinguishing SMA patients from the healthy cohort. The positive effects of nusinersen therapy clearly preceded or took place devoid of significant rearrangements in the 1H-NMR metabolomic makeup of serum, urine, and liquor. Urine creatinine was successful at distinguishing SMA patients from the matched healthy cohort, which is a simple systemic novelty linking creatinine and SMA to the physiology of inactivity and diabetes, and it facilitates the monitoring of SMA disease in pediatric patients through non-invasive urine collection.

Past studies strongly connected stool consistency-as measured by Bristol Stool Scale (BSS)-with microbial gene richness and intestinal inflammation, colonic transit time and metabolome characteristics that are of clinical relevance in numerous gastro intestinal conditions. While retention time, defecation rate, BSS but not water activity have been shown to account for BSS-associated inflammatory effects, the potential correlation with the strength of a gel in the context of intestinal forces, abrasion, mucus imprinting, fecal pore clogging remains unexplored as a shaping factor for intestinal inflammation and has yet to be determined. Our study introduced a minimal pressure approach (MP) by probe indentation as measure of stool material crosslinking in fecal samples. Results reported here were obtained from 170 samples collected in two independent projects, including males and females, covering a wide span of moisture contents and BSS. MP values increased exponentially with increasing consistency (i.e., lower BSS) and enabled stratification of samples exhibiting mixed BSS classes. A trade-off between lowest MP and highest dry matter content delineated the span of intermediate healthy density of gel crosslinks. The crossectional transects identified fecal surface layers with exceptionally high MP and of <5 mm thickness followed by internal structures with an order of magnitude lower MP, characteristic of healthy stool consistency. The MP and BSS values reported in this study were coupled to reanalysis of the PlanHab data and fecal 1H-NMR metabolomes reported before. The exponential association between stool consistency and MP determined in this study was mirrored in the elevated intestinal and also systemic inflammation and other detrimental physiological deconditioning effects observed in the PlanHab participants reported before. The MP approach described in this study can be used to better understand fecal hardness and its relationships to human health as it provides a simple, fine scale and objective stool classification approach for the characterization of the exact sampling locations in future microbiome and metabolome studies.

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Moreno-Indias, Lahti, Nedyalkova, Elbere, Roshchupkin, Adilovic, Aydemir, Bakir-Gungor, Santa Pau, D’Elia, Desai, Falquet, Gundogdu, Hron, Klammsteiner, Lopes, Marcos-Zambrano, Marques, Mason, May, Pašić, Pio, Pongor, Promponas, Przymus, Saez-Rodriguez, Sampri, Shigdel, Stres, Suharoschi, Truu, Truică, Vilne, Vlachakis, Yilmaz, Zeller, Zomer, Gómez-Cabrero and Claesson.)

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Marcos-Zambrano, Karaduzovic-Hadziabdic, Loncar Turukalo, Przymus, Trajkovik, Aasmets, Berland, Gruca, Hasic, Hron, Klammsteiner, Kolev, Lahti, Lopes, Moreno, Naskinova, Org, Paciência, Papoutsoglou, Shigdel, Stres, Vilne, Yousef, Zdravevski, Tsamardinos, Carrillo de Santa Pau, Claesson, Moreno-Indias and Truu.)

Canteens represent an essential food supply hub for educational institutions, companies, and business parks. Many people in these locations rely on a guaranteed service with consistent quality. It is an ongoing challenge to satisfy the demand for sufficient serving numbers, portion sizes, and menu variations to cover food intolerances and different palates of customers. However, overestimating this demand or fluctuating quality of dishes leads to an inevitable loss of unconsumed food due to leftovers. In this study, the food waste fraction of canteen leftovers was identified as an optimal diet for black soldier fly ( Hermetia illucens ) larvae based on 50% higher consumption and 15% higher waste reduction indices compared with control chicken feed diet. Although the digestibility of food waste was nearly twice as high, the conversion efficiency of ingested and digested chicken feed remains unparalleled (17.9 ± 0.6 and 37.5 ± 0.9 in CFD and 7.9 ± 0.9 and 9.6 ± 1.0 in FWD, respectively). The oil separator waste fraction, however, inhibited biomass gain by at least 85% and ultimately led to a larval mortality of up to 96%. In addition to monitoring larval development, we characterized physicochemical properties of pre- and post-process food waste substrates. High-throughput amplicon sequencing identified Firmicutes, Proteobacteria, and Bacteroidota as the most abundant phyla, and Morganella , Acinetobacter , and certain Lactobacillales species were identified as indicator species. By using metagenome imputation, we additionally gained insights into the functional spectrum of gut microbial communities. We anticipate that the results will contribute to the development of decentralized waste-management sites that make use of larvae to process food waste as it has become common practice for biogas plants., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Klammsteiner, Walter, Bogataj, Heussler, Stres, Steiner, Schlick-Steiner and Insam.)

Background: Lignin intermediates resulting from lignocellulose degradation have been suspected to hinder anaerobic mineralisation of organic materials to biogas. Phenyl acids like phenylacetate (PAA) are early detectable intermediates during anaerobic digestion (AD) of aromatic compounds. Studying the phenyl acid formation dynamics and concomitant microbial community shifts can help to understand the microbial interdependencies during AD of aromatic compounds and may be beneficial to counteract disturbances., Results: The length of the aliphatic side chain and chemical structure of the benzene side group(s) had an influence on the methanogenic system. PAA, phenylpropionate (PPA), and phenylbutyrate (PBA) accumulations showed that the respective lignin intermediate was degraded but that there were metabolic restrictions as the phenyl acids were not effectively processed. Metagenomic analyses confirmed that mesophilic genera like Fastidiosipila or Syntrophomonas and thermophilic genera like Lactobacillus, Bacillus, Geobacillus, and Tissierella are associated with phenyl acid formation. Acetoclastic methanogenesis was prevalent in mesophilic samples at low and medium overload conditions, whereas Methanoculleus spp. dominated at high overload conditions when methane production was restricted. In medium carbon load reactors under thermophilic conditions, syntrophic acetate oxidation (SAO)-induced hydrogenotrophic methanogenesis was the most important process despite the fact that acetoclastic methanogenesis would thermodynamically be more favourable. As acetoclastic methanogens were restricted at medium and high overload conditions, syntrophic acetate oxidising bacteria and their hydrogenotrophic partners could step in for acetate consumption., Conclusions: PAA, PPA, and PBA were early indicators for upcoming process failures. Acetoclastic methanogens were one of the first microorganisms to be impaired by aromatic compounds, and shifts to syntrophic acetate oxidation coupled to hydrogenotrophic methanogenesis occurred in thermophilic reactors. Previously assumed associations of specific meso- and thermophilic genera with anaerobic phenyl acid formation could be confirmed.

We explored the metabolic makeup of urine in prescreened healthy male participants within the PlanHab experiment. The run-in (5 day) and the following three 21-day interventions [normoxic bedrest (NBR), hypoxic bedrest (HBR), and hypoxic ambulation (HAmb)] were executed in a crossover manner within a controlled laboratory setup (medical oversight, fluid and dietary intakes, microbial bioburden, circadian rhythm, and oxygen level). The inspired O 2 (F i O 2 ) fraction next to inspired O 2 (P i O 2 ) partial pressure were 0.209 and 133.1 ± 0.3 mmHg for the NBR variant in contrast to 0.141 ± 0.004 and 90.0 ± 0.4 mmHg (approx. 4,000 m of simulated altitude) for HBR and HAmb interventions, respectively. 1 H-NMR metabolomes were processed using standard quantitative approaches. A consensus of ensemble of multivariate analyses showed that the metabolic makeup at the start of the experiment and at HAmb endpoint differed significantly from the NBR and HBR endpoints. Inactivity alone or combined with hypoxia resulted in a significant reduction of metabolic diversity and increasing number of affected metabolic pathways. Sliding window analysis (3 + 1) unraveled that metabolic changes in the NBR lagged behind those observed in the HBR. These results show that the negative effects of cessation of activity on systemic metabolism are further aggravated by additional hypoxia. The PlanHab HAmb variant that enabled ambulation, maintained vertical posture, and controlled but limited activity levels apparently prevented the development of negative physiological symptoms such as insulin resistance, low-level systemic inflammation, constipation, and depression. This indicates that exercise apparently prevented the negative spiral between the host's metabolism, intestinal environment, microbiome physiology, and proinflammatory immune activities in the host., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Šket, Deutsch, Prevoršek, Mekjavić, Plavec, Rittweger, Debevec, Eiken and Stres.)

An organism's gut microbiome handles most of the metabolic processes associated with food intake and digestion but can also strongly affect health and behavior. A stable microbial core community in the gut provides general metabolic competences for substrate degradation and is robust against extrinsic disturbances like changing diets or pathogens. Black Soldier Fly larvae (BSFL; Hermetia illucens ) are well known for their ability to efficiently degrade a wide spectrum of organic materials. The ingested substrates build up the high fat and protein content in their bodies that make the larvae interesting for the animal feedstuff industry. In this study, we subjected BSFL to three distinct types of diets carrying a low bioburden and assessed the diets' impact on larval development and on the composition of the bacterial and archaeal gut community. No significant impact on the gut microbiome across treatments pointed us to the presence of a predominant core community backed by a diverse spectrum of low-abundance taxa. Actinomyces spp., Dysgonomonas spp., and Enterococcus spp. as main members of this community provide various functional and metabolic skills that could be crucial for the thriving of BSFL in various environments. This indicates that the type of diet could play a lesser role in guts of BSFL than previously assumed and that instead a stable autochthonous collection of bacteria provides the tools for degrading of a broad range of substrates. Characterizing the interplay between the core gut microbiome and BSFL helps to understand the involved degradation processes and could contribute to further improving large-scale BSFL rearing., (Copyright © 2020 Klammsteiner, Walter, Bogataj, Heussler, Stres, Steiner, Schlick-Steiner, Arthofer and Insam.)

Background: Proteinaceous wastes exhibit high theoretical methane yields and their residues are considered valuable fertilisers. The routine anaerobic degradation of proteins often raises problems like high aromatic compound concentrations caused by the entry of aromatic amino acids into the system. A profound investigation of the consequences of aromatic compound exposure on various microorganisms, which cascade-like and interdependently degrade complex molecules to biogas, is still pending., Results: In mesophilic samples, methane was predominantly produced via acetoclastic methanogenesis. The highest positive correlation was observed between phenylacetate (PAA) and Psychrobacter spp. and between phenylpropionate (PPA) and Haloimpatiens spp. Moreover, Syntrophus spp. negatively correlated with PAA (Spearman's rank correlations coefficient (rs) = - 0.46, p  < 0.05) and PPA concentrations (rs = - 0.44, p  < 0.05) and was also associated with anaerobic benzene ring cleavage. In thermophilic samples, acetate was predominantly oxidised by Tepidanaerobacter spp. or Syntrophaceticus spp. in syntrophic association with a hydrogenotrophic methanogen. The genera Sedimentibacter and Syntrophaceticus correlated positively with both PAA and PPA concentrations. Moreover, Sedimentibacter spp., Tepidanaerobacter spp., Acetomicrobium spp., and Sporanaerobacter spp. were significant LEfSe (linear discriminant analysis effect size) biomarkers for high meso- as well as thermophilic phenyl acid concentrations. Direct negative effects of phenyl acids on methanogenic properties could not be proven., Conclusions: Anaerobic phenyl acid formation is not restricted to specific microbial taxa, but rather done by various meso- and thermophilic bacteria. The cleavage of the highly inert benzene ring is possible in methanogenic batch reactors-at least in mesophilic fermentation processes. The results indicated that phenyl acids rather affect microorganisms engaged in preceding degradation steps than the ones involved in methanogenesis., Competing Interests: Competing interestsThe authors declare that they have no competing interests., (© The Author(s) 2020.)

Microbial species play important roles in different environments and the production of high-quality genomes from metagenome data sets represents a major obstacle to understanding their ecological and evolutionary dynamics. Metagenome-Assembled Genomes Orchestra (MAGO) is a computational framework that integrates and simplifies metagenome assembly, binning, bin improvement, bin quality (completeness and contamination), bin annotation, and evolutionary placement of bins via detailed maximum-likelihood phylogeny based on multiple marker genes using different amino acid substitution models, next to average nucleotide identity analysis of genomes for delineation of species boundaries and operational taxonomic units. MAGO offers streamlined execution of the entire metagenomics pipeline, error checking, computational resource distribution and compatibility of data formats, governed by user-tailored pipeline processing. MAGO is an open-source-software package released in three different ways, as a singularity image and a Docker container for HPC purposes as well as for running MAGO on a commodity hardware, and a virtual machine for gaining a full access to MAGO underlying structure and source code. MAGO is open to suggestions for extensions and is amenable for use in both research and teaching of genomics and molecular evolution of genomes assembled from small single-cell projects or large-scale and complex environmental metagenomes., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

The development of the gut microbiome is influenced by several factors. It is acquired during and after birth and involves both maternal and environmental factors as well as the genetic disposition of the offspring. However, it is unclear if the microbiome development is directly triggered by the mode of delivery and very early contact with the mother or mostly at later stages of initial development mainly by breast milk provided by the mother. To investigate to what extent the gut microbiome composition of the offspring is determined by the nursing mother, providing breast milk, compared to the birth mother during early development, a cross-fostering experiment involving two genetically different mouse lines was developed, being prone to be obese or lean, respectively. The microbiome of the colon was analyzed by high-throughput 16S rRNA gene sequencing, when the mice were 3 weeks old. The nursing mother affected both α- and β-diversity of the offspring's gut microbiome and shaped its composition. Especially bacterial families directly transferred by breast milk, like Streptococcaceae, or families which are strongly influenced by the quality of the breast milk like Rikenellaceae, showed a strong response. The core microbiome transferred from the obese nursing mother showed a higher robustness in comparison to the microbiome transferred from the lean nursing mother. Overall, the nursing mother impacts the gut microbial composition of the offspring during early development and might play an important role for health and disease of the animals at later stages of life.

The European cave salamander Proteus anguinus is a charismatic amphibian endemic to the concealed and inaccessible subterranean waters of the Dinaric Karst. Despite its exceptional conservation importance not much is known about its ecology and interactions with the groundwater microbiome. The cutaneous microbiota of amphibians is an important driver of metabolic capabilities and immunity, and thus a key factor in their wellbeing and survival. We used high-throughput 16S rRNA gene sequencing based on seven variable regions to examine the bacteriome of the skin of five distinct evolutionary lineages of P. anguinus and in their groundwater environment. The skin bacteriomes turned out to be strongly filtered subsamples of the environmental microbial community. The resident microbiota of the analyzed individuals was dominated by five bacterial taxa. Despite an indicated functional redundancy, the cutaneous bacteriome of P. anguinus presumably provides protection against invading microbes by occupying the niche, and thus could serve as an indicator of health status. Besides conservation implications for P. anguinus, our results provide a baseline for future studies on other endangered neotenic salamanders.

In this study, nuclear magnetic resonance ( 1 H NMR) spectroscopic profiling was used to provide a more comprehensive view of microbial metabolites associated with poor reactor performance in a full-scale 4 MW mesophilic agricultural biogas plant under fully operational and also under inhibited conditions. Multivariate analyses were used to assess the significance of differences between reactors whereas artificial neural networks (ANN) were used to identify the key metabolites responsible for inhibition and their network of interaction. Based on the results of nm-MDS ordination the subsamples of each reactor were similar, but not identical, despite homogenization of the full-scale reactors before sampling. Hence, a certain extent of variability due to the size of the system under analysis was transferred into metabolome analysis. Multivariate analysis showed that fully active reactors were clustered separately from those containing inhibited reactor metabolites and were significantly different. Furthermore, the three distinct inhibited states were significantly different from each other. The inhibited metabolomes were enriched in acetate, caprylate, trimethylamine, thymine, pyruvate, alanine, xanthine and succinate. The differences in the metabolic fingerprint between inactive and fully active reactors observed in this study resembled closely the metabolites differentiating the (sub) acute rumen acidosis inflicted and healthy rumen metabolomes, creating thus favorable conditions for the growth and activity of pathogenic bacteria. The consistency of our data with those reported before for rumen ecosystems shows that 1 H NMR based metabolomics is a reliable approach for the evaluation of metabolic events at full-scale biogas reactors., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

A start-up of 4 MW agricultural biogas plant in Vučja vas, Slovenia, was monitored from 2011 to 2014. The start-up was carried out in 3 weeks with the intake of biomass from three operating full-scale 1-2 MW donor agricultural biogas plants. The samples were taken from donor digesters and from two serial digesters during the start-up over the course of 2.5 years. Bacterial and Archaeal microbial communities progressively diverged from the composition of donor digesters during the start-up phase. The rate of change of Bacterial community decreased exponentially over the first 2.5 years as dynamics within the first 70 days was comparable to that of the next 1.5 years, whereas approximately constant rate was observed for Archaea. Despite rearrangements, the microbial communities remained functionally stable and produced biogas throughout the whole 2.5 years of observation. All systems parameters measured were ordered according to their Kernel density (Gaussian function) ranging from the most dispersed (substrate categories used as cosubstrates, quantities of each cosubstrate, substate dry and volatile matter, process parameters) towards progressively least dispersed (trace metal and ion profiles, aromatic-polyphenolic compounds, biogas plant functional output (energy)). No deficiency was detected in trace metal content as the distribution of metals and elements fluctuated within the suggested limits for biogas over 2.5 year observation. In contrast to the recorded process variables, Bacterial and Archaeal microbial communities exhibited directed changes oriented in time. Variation partitioning showed that a large fraction of variability in the Bacterial and Archaeal microbial communities (55% and 61%, respectively) remained unexplained despite numerous measured variables (n = 44) and stable biogas production. Our results show that the observed reorganization of microbial communities was not directly associated with impact on the full-scale biogas reactor performance. Novel parameters need to be determined to elucidate the variables directly associated with the reorganization of microbial communities and those relevant for sustained function such as the more in-depth interaction between TSOC, trace metal profiles, aromatic-polyphenolic compounds and ionic strength (e.g. electrical conductivity)., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

We explored the metagenomic, metabolomic and trace metal makeup of intestinal microbiota and environment in healthy male participants during the run-in (5 day) and the following three 21-day interventions: normoxic bedrest (NBR), hypoxic bedrest (HBR) and hypoxic ambulation (HAmb) which were carried out within a controlled laboratory environment (circadian rhythm, fluid and dietary intakes, microbial bioburden, oxygen level, exercise). The fraction of inspired O 2 (F i O 2 ) and partial pressure of inspired O 2 (P i O 2 ) were 0.209 and 133.1 ± 0.3 mmHg for the NBR and 0.141 ± 0.004 and 90.0 ± 0.4 mmHg (~4,000 m simulated altitude) for HBR and HAmb interventions, respectively. Shotgun metagenomes were analyzed at various taxonomic and functional levels, 1 H- and 13 C -metabolomes were processed using standard quantitative and human expert approaches, whereas metals were assessed using X-ray fluorescence spectrometry. Inactivity and hypoxia resulted in a significant increase in the genus Bacteroides in HBR, in genes coding for proteins involved in iron acquisition and metabolism, cell wall, capsule, virulence, defense and mucin degradation, such as beta-galactosidase (EC3.2.1.23), α-L-fucosidase (EC3.2.1.51), Sialidase (EC3.2.1.18), and α-N-acetylglucosaminidase (EC3.2.1.50). In contrast, the microbial metabolomes, intestinal element and metal profiles, the diversity of bacterial, archaeal and fungal microbial communities were not significantly affected. The observed progressive decrease in defecation frequency and concomitant increase in the electrical conductivity (EC) preceded or took place in absence of significant changes at the taxonomic, functional gene, metabolome and intestinal metal profile levels. The fact that the genus Bacteroides and proteins involved in iron acquisition and metabolism, cell wall, capsule, virulence and mucin degradation were enriched at the end of HBR suggest that both constipation and EC decreased intestinal metal availability leading to modified expression of co-regulated genes in Bacteroides genomes. Bayesian network analysis was used to derive the first hierarchical model of initial inactivity mediated deconditioning steps over time. The PlanHab wash-out period corresponded to a profound life-style change (i.e., reintroduction of exercise) that resulted in stepwise amelioration of the negative physiological symptoms, indicating that exercise apparently prevented the crosstalk between the microbial physiology, mucin degradation and proinflammatory immune activities in the host.

We explored the assembly of intestinal microbiota in healthy male participants during the randomized crossover design of run-in (5 day) and experimental phases (21-day normoxic bed rest (NBR), hypoxic bed rest (HBR) and hypoxic ambulation (HAmb) in a strictly controlled laboratory environment, with balanced fluid and dietary intakes, controlled circadian rhythm, microbial ambiental burden and 24/7 medical surveillance. The fraction of inspired O2 (FiO2) and partial pressure of inspired O2 (PiO2) were 0.209 and 133.1 ± 0.3 mmHg for NBR and 0.141 ± 0.004 and 90.0 ± 0.4 mmHg for both hypoxic variants (HBR and HAmb; ~4000 m simulated altitude), respectively. A number of parameters linked to intestinal environment such as defecation frequency, intestinal electrical conductivity (IEC), sterol and polyphenol content and diversity, indole, aromaticity and spectral characteristics of dissolved organic matter (DOM) were measured (64 variables). The structure and diversity of bacterial microbial community was assessed using 16S rRNA amplicon sequencing. Inactivity negatively affected frequency of defecation and in combination with hypoxia increased IEC (p < 0.05). In contrast, sterol and polyphenol diversity and content, various characteristics of DOM and aromatic compounds, the structure and diversity of bacterial microbial community were not significantly affected over time. A new in-house PlanHab database was established to integrate all measured variables on host physiology, diet, experiment, immune and metabolic markers (n = 231). The observed progressive decrease in defecation frequency and concomitant increase in IEC suggested that the transition from healthy physiological state towards the developed symptoms of low magnitude obesity-related syndromes was dose dependent on the extent of time spent in inactivity and preceded or took place in absence of significant rearrangements in bacterial microbial community. Species B. thetaiotamicron, B. fragilis, B. dorei and other Bacteroides with reported relevance for dysbiotic medical conditions were significantly enriched in HBR, characterized with most severe inflammation symptoms, indicating a shift towards host mucin degradation and proinflammatory immune crosstalk.

We explored the assembly of intestinal microbiota in healthy male participants during the run-in (5 day) and experimental phases [21-day normoxic bed rest (NBR), hypoxic bedrest (HBR)], and hypoxic ambulation (HAmb) in a strictly controlled laboratory environment, balanced fluid, and dietary intakes, controlled circadian rhythm, microbial ambiental burden, and 24/7 medical surveillance. The fraction of inspired O 2 (F i O 2 ) and partial pressure of inspired O 2 (P i O 2 ) were 0.209 and 133.1 ± 0.3 mmHg for NBR and 0.141 ± 0.004 and 90.0 ± 0.4 mmHg for both hypoxic variants (HBR and HAmb; ~4,000 m simulated altitude), respectively. A number of parameters linked to intestinal transit spanning Bristol Stool Scale, defecation rates, zonulin, α 1 -antitrypsin, eosinophil derived neurotoxin, bile acids, reducing sugars, short chain fatty acids, total soluble organic carbon, water content, diet composition, and food intake were measured (167 variables). The abundance, structure, and diversity of butyrate producing microbial community were assessed using the two primary bacterial butyrate synthesis pathways, butyryl-CoA: acetate CoA-transferase ( but ) and butyrate kinase ( buk ) genes. Inactivity negatively affected fecal consistency and in combination with hypoxia aggravated the state of gut inflammation ( p < 0.05). In contrast, gut permeability, various metabolic markers, the structure, diversity, and abundance of butyrate producing microbial community were not significantly affected. Rearrangements in the butyrate producing microbial community structure were explained by experimental setup (13.4%), experimentally structured metabolites (12.8%), and gut metabolite-immunological markers (11.9%), with 61.9% remaining unexplained. Many of the measured parameters were found to be correlated and were hence omitted from further analyses. The observed progressive increase in two immunological intestinal markers suggested that the transition from healthy physiological state toward the developed symptoms of low magnitude obesity-related syndromes was primarily driven by the onset of inactivity (lack of exercise in NBR) that were exacerbated by systemic hypoxia (HBR) and significantly alleviated by exercise, despite hypoxia (HAmb). Butyrate producing community in colon exhibited apparent resilience toward short-term modifications in host exercise or hypoxia. Progressive constipation (decreased intestinal motility) and increased local inflammation marker suggest that changes in microbial colonization and metabolism were taking place at the location of small intestine.

Background: The Himalaya with its altitude and geographical position forms a barrier to atmospheric transport, which produces much aqueous-particle monsoon precipitation and makes it the largest continuous ice-covered area outside polar regions. There is a paucity of data on high-altitude microbial communities, their native environments and responses to environmental-spatial variables relative to seasonal and deglaciation events., Methodology/principal Findings: Soils were sampled along altitude transects from 5000 m to 6000 m to determine environmental, spatial and seasonal factors structuring bacterial communities characterized by 16 S rRNA gene deep sequencing. Dust traps and fresh-snow samples were used to assess dust abundance and viability, community structure and abundance of dust associated microbial communities. Significantly different habitats among the altitude-transect samples corresponded to both phylogenetically distant and closely-related communities at distances as short as 50 m showing high community spatial divergence. High within-group variability that was related to an order of magnitude higher dust deposition obscured seasonal and temporal rearrangements in microbial communities. Although dust particle and associated cell deposition rates were highly correlated, seasonal dust communities of bacteria were distinct and differed significantly from recipient soil communities. Analysis of closest relatives to dust OTUs, HYSPLIT back-calculation of airmass trajectories and small dust particle size (4-12 µm) suggested that the deposited dust and microbes came from distant continental, lacustrine and marine sources, e.g. Sahara, India, Caspian Sea and Tibetan plateau. Cyanobacteria represented less than 0.5% of microbial communities suggesting that the microbial communities benefitted from (co)deposited carbon which was reflected in the psychrotolerant nature of dust-particle associated bacteria., Conclusions/significance: The spatial, environmental and temporal complexity of the high-altitude soils of the Himalaya generates ongoing disturbance and colonization events that subject heterogeneous microniches to stochastic colonization by far away dust associated microbes and result in the observed spatially divergent bacterial communities.

Degradation of brewery spent grain as a novel test substrate was explored in routine biochemical methane potential assays (BMP) using three different inocula. Significant differences in the initial biogas production rates from spent grain, methane yield coefficients and final spent grain degradation were observed between inocula. Initial and developed communities degrading novel substrate showed significant differences in archaeal community fingerprints. Differences were observed irrespective of substrate identity (no substrate, glucose, spent grain) providing evidence of a significant general influence of BMP incubation on the microbial phylotypes. A linear relationship between microbial community flexibility in BMP assay and corresponding initial biogas production rates was identified as a novel parameter to diagnose anaerobic processes, particularly under dynamic conditions like start-up.

Few studies have been conducted on adaptations of microbial communities to low and fluctuating temperatures using environmentally relevant conditions. In this study, six Himalayan and two temperate soils were selected as candidates for low-temperature/freeze-thaw (FT)-adapted and susceptible soils, respectively. Redundancy analysis with forward selection was used to create a model of environmental parameters explaining variability in the initial microbial abundance and 4 °C activities. The best predictor was soil carbon, explaining more than 74% of data variability (P=0.002), despite significant differences in the soil characteristics and environmental history. We tested the hypothesis that the reproduced Himalayan FT fluctuations select physiologically similar communities in distinct soils. Microcosms were experimentally subjected to two separate 50 and 60 FT cycle (FTC) experiments. A significant decrease in abundance, 4 °C basal respiration and drastic rearrangements in community-level physiological profiles (CLPP) were observed in microcosms with temperate soils until 40 FTC. CLPP remained distinct from those of the Himalayan soils. Minor changes were observed in the Himalayan soils, confirming that microbial populations with physiological traits consistent with the noncontinuous permafrost conditions reside in the Himalayan soils, whereas the surviving temperate soil microorganisms actively adjusted to novel environmental conditions., (© 2010 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.)

mothur aims to be a comprehensive software package that allows users to use a single piece of software to analyze community sequence data. It builds upon previous tools to provide a flexible and powerful software package for analyzing sequencing data. As a case study, we used mothur to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the alpha and beta diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments. This analysis of more than 222,000 sequences was completed in less than 2 h with a laptop computer.

Summary: BEsTRF (Best Estimated T-RF) provides a standalone environment for analyzing primers-enzymes-gene section combinations used in terminal-restriction fragment length polymorphism (T-RFLP) for its optimal resolution. User-defined sequence databases of several hundred thousand DNA sequences can be explored and the resolution of user-specified sets of primers and restriction endonucleases can be analyzed on either forward or reverse terminal fragments. Sequence quality, primer mismatches, insertions and deletions can be controlled and each primer pair-specific sequence collections can be exported for downstream analyses. The configuration for a novel T-RFLP population profiling using rpoB gene (DNA-directed RNA polymerase, beta subunit) on forward fluorescently labeled primer are presented., Availability: BEsTRF is freely available at http://lie.fe.uni-lj.si/bestrf and can be downloaded from the same site. The online protocol, numerous primer and enzyme dictionaries, sequence collections and results generated during this work for various genes are available at our website http://lie.fe.uni-lj.si/bestrf.

In this study, microcosms were used to investigate the influence of temperature (4 and 28 degrees C) and water content (45% and 90% WHC) on microbial communities and activities in carbon-rich fen soil. Bacterial, archaeal and denitrifier community composition was assessed during incubation of microcosms for 12 weeks using terminal restriction fragment length polymorphism (T-RFLP) profiling of 16S rRNA and nitrous oxide reductase (nosZ) genes. In addition, microbial and denitrifier abundance, potential denitrification activity and production of greenhouse gases were measured. No detectable changes were observed in prokaryote or denitrifier abundance. In general, cumulatively after 12 weeks more carbon was respired at the higher temperature (3.7 mg CO(2) g(-1) soil), irrespective of the water content, whereas nitrous oxide production was greater under wet conditions (98-336 microg N(2)O g(-1) soil). After an initial lag phase, methane emissions (963 microg CH(4) g(-1) soil) were observed only under warm and wet conditions. T-RFLP analyses of bacterial 16S rRNA and nosZ genes revealed small or undetectable community changes in response to temperature and water content, suggesting that bacterial and denitrifying microbial communities are stable and do not respond significantly to seasonal changes in soil conditions. In contrast, archaeal microbial community structure was more dynamic and was strongly influenced by temperature.