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Abstract Background Hereditary genetic mutations causing predisposition to colorectal cancer are accountable for approximately 30% of all colorectal cancer cases. However, only a small fraction of these are high penetrant mutations occurring in DNA mismatch repair genes, causing one of several types of familial colorectal cancer (CRC) syndromes. Most of the mutations are low-penetrant variants, contributing to an increased risk of familial colorectal cancer, and they are often found in additional genes and pathways not previously associated with CRC. The aim of this study was to identify such variants, both high-penetrant and low-penetrant ones. Methods We performed whole exome sequencing on constitutional DNA extracted from blood of 48 patients suspected of familial colorectal cancer and used multiple in silico prediction tools and available literature-based evidence to detect and investigate genetic variants. Results We identified several causative and some potentially causative germline variants in genes known for their association with colorectal cancer. In addition, we identified several variants in genes not typically included in relevant gene panels for colorectal cancer, including CFTR, PABPC1 and TYRO3, which may be associated with an increased risk for cancer. Conclusions Identification of variants in additional genes that potentially can be associated with familial colorectal cancer indicates a larger genetic spectrum of this disease, not limited only to mismatch repair genes. Usage of multiple in silico tools based on different methods and combined through a consensus approach increases the sensitivity of predictions and narrows down a large list of variants to the ones that are most likely to be significant.

Abstract Background Detection of copy number variation (CNV) in genes associated with disease is important in genetic diagnostics, and next generation sequencing (NGS) technology provides data that can be used for CNV detection. However, CNV detection based on NGS data is in general not often used in diagnostic labs as the data analysis is challenging, especially with data from targeted gene panels. Wet lab methods like MLPA (MRC Holland) are widely used, but are expensive, time consuming and have gene-specific limitations. Our aim has been to develop a bioinformatic tool for CNV detection from NGS data in medical genetic diagnostic samples. Results Our computational pipeline for detection of CNVs in NGS data from targeted gene panels utilizes coverage depth of the captured regions and calculates a copy number ratio score for each region. This is computed by comparing the mean coverage of the sample with the mean coverage of the same region in other samples, defined as a pool. The pipeline selects pools for comparison dynamically from previously sequenced samples, using the pool with an average coverage depth that is nearest to the one of the samples. A sliding window-based approach is used to analyze each region, where length of sliding window and sliding distance can be chosen dynamically to increase or decrease the resolution. This helps in detecting CNVs in small or partial exons. With this pipeline we have correctly identified the CNVs in 36 positive control samples, with sensitivity of 100% and specificity of 91%. We have detected whole gene level deletion/duplication, single/multi exonic level deletion/duplication, partial exonic deletion and mosaic deletion. Since its implementation in mid-2018 it has proven its diagnostic value with more than 45 CNV findings in routine tests. Conclusions With this pipeline as part of our diagnostic practices it is now possible to detect partial, single or multi-exonic, and intragenic CNVs in all genes in our target panel. This has helped our diagnostic lab to expand the portfolio of genes where we offer CNV detection, which previously was limited by the availability of MLPA kits.

Summary: High secretion of the metabolites citrate and spermine is a unique hallmark for normal prostate epithelial cells, and is reduced in aggressive prostate cancer. However, the identity of the genes controlling this biological process is mostly unknown. In this study, we have created a gene signature of 150 genes connected to citrate and spermine secretion in the prostate. We have computationally integrated metabolic measurements with multiple transcriptomics datasets from the public domain, including 3826 tissue samples from prostate and prostate cancer. The accuracy of the signature is validated by its unique enrichment in prostate samples and prostate epithelial tissue compartments. The signature highlights genes AZGP1, ANPEP and metallothioneins with zinc-binding properties not previously studied in the prostate, and the expression of these genes are reduced in more aggressive cancer lesions. However, the absence of signature enrichment in common prostate model systems can make it challenging to study these genes mechanistically.

Abstract Objective Properties of gene products can be described or annotated with Gene Ontology (GO) terms. But for many genes we have limited information about their products, for example with respect to function. This is particularly true for long non-coding RNAs (lncRNAs), where the function in most cases is unknown. However, it has been shown that annotation as described by GO terms to some extent can be predicted by enrichment analysis on properties of co-expressed genes. Results GAPGOM integrates two relevant algorithms, lncRNA2GOA and TopoICSim, into a user-friendly R package. Here lncRNA2GOA does annotation prediction by co-expression, whereas TopoICSim estimates similarity between GO graphs, which can be used for benchmarking of prediction performance, but also for comparison of GO graphs in general. The package provides an improved implementation of the original tools, with substantial improvements in performance and documentation, unified interfaces, and additional features.

Background and Purpose: Intracranial dural arteriovenous fistulas (DAVFs) are abnormal shunts between dural arteries and dural venous sinus or cortical veins. We report our experience with endovascular therapy of primary complex DAVFs using modern embolic agents. Methods: This is a retrospective analysis of patients with DAVFs treated between 2015 and 2019. Patient demographics and technical aspects including the use of embolic agent, access to the fistula, number of treatments, occlusion rates, and complications were addressed. Angiographic treatment success was defined as complete occlusion (CO) of the DAVF. Results: Fifty patients were treated endovascularly. Median age was 61 years and 66% were men. The most common symptom was pulsatile tinnitus in 17 patients (34%). The most frequent location of the DAVF was the transverse-sigmoid sinus (40%). Thirty-six fistulas (72%) had cortical venous reflux. Nonadhesive and adhesive liquid agents were used in 92% as a single material or in combination. CO was achieved in 48 patients (96%). In 28 individuals (56%), only 1 procedure was necessary. Nonadhesive liquid agents were exclusively used in 14 patients (28%) with CO attained in every case. For CO of tentorial DAVFs, multiple sessions were more often required than at the other locations (55 vs. 14%, p = 0.0051). Among 93 procedures, the overall complication rate was 3%. The procedure-related mortality rate was 0%. Conclusion: Endovascular treatment of intracranial DAVFs is feasible, safe, and effective with high rates of CO. In more than half of the patients, the DAVF was completely occluded after a single procedure. However, in tentorial DAVFs, multiple sessions were more often required.

Abstract Background Diseases like cancer will lead to changes in gene expression, and it is relevant to identify key regulatory genes that can be linked directly to these changes. This can be done by computing a Regulatory Impact Factor (RIF) score for relevant regulators. However, this computation is based on estimating correlated patterns of gene expression, often Pearson correlation, and an assumption about a set of specific regulators, normally transcription factors. This study explores alternative measures of correlation, using the Fisher and Sobolev metrics, and an extended set of regulators, including epigenetic regulators and long non-coding RNAs (lncRNAs). Data on prostate cancer have been used to explore the effect of these modifications. Results A tool for computation of RIF scores with alternative correlation measures and extended sets of regulators was developed and tested on gene expression data for prostate cancer. The study showed that the Fisher and Sobolev metrics lead to improved identification of well-documented regulators of gene expression in prostate cancer, and the sets of identified key regulators showed improved overlap with previously defined gene sets of relevance to cancer. The extended set of regulators lead to identification of several interesting candidates for further studies, including lncRNAs. Several key processes were identified as important, including spindle assembly and the epithelial-mesenchymal transition (EMT). Conclusions The study has shown that using alternative metrics of correlation can improve the performance of tools based on correlation of gene expression in genomic data. The Fisher and Sobolev metrics should be considered also in other correlation-based applications.

Abstract Background Prostate cancer (PCa) has the highest incidence rates of cancers in men in western countries. Unlike several other types of cancer, PCa has few genetic drivers, which has led researchers to look for additional epigenetic and transcriptomic contributors to PCa development and progression. Especially datasets on DNA methylation, the most commonly studied epigenetic marker, have recently been measured and analysed in several PCa patient cohorts. DNA methylation is most commonly associated with downregulation of gene expression. However, positive associations of DNA methylation to gene expression have also been reported, suggesting a more diverse mechanism of epigenetic regulation. Such additional complexity could have important implications for understanding prostate cancer development but has not been studied at a genome-wide scale. Results In this study, we have compared three sets of genome-wide single-site DNA methylation data from 870 PCa and normal tissue samples with multi-cohort gene expression data from 1117 samples, including 532 samples where DNA methylation and gene expression have been measured on the exact same samples. Genes were classified according to their corresponding methylation and expression profiles. A large group of hypermethylated genes was robustly associated with increased gene expression (UPUP group) in all three methylation datasets. These genes demonstrated distinct patterns of correlation between DNA methylation and gene expression compared to the genes showing the canonical negative association between methylation and expression (UPDOWN group). This indicates a more diversified role of DNA methylation in regulating gene expression than previously appreciated. Moreover, UPUP and UPDOWN genes were associated with different compartments — UPUP genes were related to the structures in nucleus, while UPDOWN genes were linked to extracellular features. Conclusion We identified a robust association between hypermethylation and upregulation of gene expression when comparing samples from prostate cancer and normal tissue. These results challenge the classical view where DNA methylation is always associated with suppression of gene expression, which underlines the importance of considering corresponding expression data when assessing the downstream regulatory effect of DNA methylation.

Mitochondrial activity in cancer cells has been central to cancer research since Otto Warburg first published his thesis on the topic in 1956. Although Warburg proposed that oxidative phosphorylation in the tricarboxylic acid (TCA) cycle was perturbed in cancer, later research has shown that oxidative phosphorylation is activated in most cancers, including prostate cancer (PCa). However, more detailed knowledge on mitochondrial metabolism and metabolic pathways in cancers is still lacking. In this study we expand our previously developed method for analyzing functional homologous proteins (FunHoP), which can provide a more detailed view of metabolic pathways. FunHoP uses results from differential expression analysis of RNA-Seq data to improve pathway analysis. By adding information on subcellular localization based on experimental data and computational predictions we can use FunHoP to differentiate between mitochondrial and non-mitochondrial processes in cancerous and normal prostate cell lines. Our results show that mitochondrial pathways are upregulated in PCa and that splitting metabolic pathways into mitochondrial and non-mitochondrial counterparts using FunHoP adds to the interpretation of the metabolic properties of PCa cells.

Abstract Background Almost 16,000 human long non-coding RNA (lncRNA) genes have been identified in the GENCODE project. However, the function of most of them remains to be discovered. The function of lncRNAs and other novel genes can be predicted by identifying significantly enriched annotation terms in already annotated genes that are co-expressed with the lncRNAs. However, such approaches are sensitive to the methods that are used to estimate the level of co-expression. Results We have tested and compared two well-known statistical metrics (Pearson and Spearman) and two geometrical metrics (Sobolev and Fisher) for identification of the co-expressed genes, using experimental expression data across 19 normal human tissues. We have also used a benchmarking approach based on semantic similarity to evaluate how well these methods are able to predict annotation terms, using a well-annotated set of protein-coding genes. Conclusion This work shows that geometrical metrics, in particular in combination with the statistical metrics, will predict annotation terms more efficiently than traditional approaches. Tests on selected lncRNAs confirm that it is possible to predict the function of these genes given a reliable set of expression data. The software used for this investigation is freely available.

Background: Many types of data from genomic analyses can be represented as genomic tracks, i.e. features linked to the genomic coordinates of a reference genome. Examples of such data are epigenetic DNA methylation data, ChIP-seq peaks, germline or somatic DNA variants, as well as RNA-seq expression levels. Researchers often face difficulties in locating, accessing and combining relevant tracks from external sources, as well as locating the raw data, reducing the value of the generated information. Description of work: We propose to advance the application of FAIR data principles (Findable, Accessible, Interoperable, and Reusable) to produce searchable metadata for genomic tracks. Findability and Accessibility of metadata can then be ensured by a track search service that integrates globally identifiable metadata from various track hubs in the Track Hub Registry and other relevant repositories. Interoperability and Reusability need to be ensured by the specification and implementation of a basic set of recommendations for metadata. We have tested this concept by developing such a specification in a JSON Schema, called FAIRtracks, and have integrated it into a novel track search service, called TrackFind. We demonstrate practical usage by importing datasets through TrackFind into existing examples of relevant analytical tools for genomic tracks: EPICO and the GSuite HyperBrowser. Conclusion: We here provide a first iteration of a draft standard for genomic track metadata, as well as the accompanying software ecosystem. It can easily be adapted or extended to future needs of the research community regarding data, methods and tools, balancing the requirements of both data submitters and analytical end-users.

The k -Nearest Neighbor ( k NN) classifier represents a simple and very general approach to classification. Still, the performance of k NN classifiers can often compete with more complex machine-learning algorithms. The core of k NN depends on a “guilt by association” principle where classification is performed by measuring the similarity between a query and a set of training patterns, often computed as distances. The relative performance of k NN classifiers is closely linked to the choice of distance or similarity measure, and it is therefore relevant to investigate the effect of using different distance measures when comparing biomedical data. In this study on classification of cancer data sets, we have used both common and novel distance measures, including the novel distance measures Sobolev and Fisher, and we have evaluated the performance of k NN with these distances on 4 cancer data sets of different type. We find that the performance when using the novel distance measures is comparable to the performance with more well-established measures, in particular for the Sobolev distance. We define a robust ranking of all the distance measures according to overall performance. Several distance measures show robust performance in k NN over several data sets, in particular the Hassanat, Sobolev, and Manhattan measures. Some of the other measures show good performance on selected data sets but seem to be more sensitive to the nature of the classification data. It is therefore important to benchmark distance measures on similar data prior to classification to identify the most suitable measure in each case.

Germline variants inactivating the mismatch repair (MMR) genes MLH1, MSH2, MSH6 and PMS2 cause Lynch syndrome that implies an increased cancer risk, where colon and endometrial cancer are the most frequent. Identification of these pathogenic variants is important to identify endometrial cancer patients with inherited increased risk of new cancers, in order to offer them lifesaving surveillance. However, several other genes are also part of the MMR pathway. It is therefore relevant to search for variants in additional genes that may be associated with cancer risk by including all known genes involved in the MMR pathway. Next-generation sequencing was used to screen 22 genes involved in the MMR pathway in constitutional DNA extracted from full blood from 199 unselected endometrial cancer patients. Bioinformatic pipelines were developed for identification and functional annotation of variants, using several different software tools and custom programs. This facilitated identification of 22 exonic, 4 UTR and 9 intronic variants that could be classified according to pathogenicity. This study has identified several germline variants in genes of the MMR pathway that potentially may be associated with an increased risk for cancer, in particular endometrial cancer, and therefore are relevant for further investigation. We have also developed bioinformatics strategies to analyse targeted sequencing data, including low quality data and genomic regions outside of the protein coding exons of the relevant genes.

Abstract Background The relationship between cholesterol and prostate cancer has been extensively studied for decades, where high levels of cellular cholesterol are generally associated with cancer progression and less favorable outcomes. However, the role of in vivo cellular cholesterol synthesis in this process is unclear, and data on the transcriptional activity of cholesterol synthesis pathway genes in tissue from prostate cancer patients are inconsistent. Methods A common problem with cancer tissue data from patient cohorts is the presence of heterogeneous tissue which confounds molecular analysis of the samples. In this study we present a general method to minimize systematic confounding from stroma tissue in any prostate cancer cohort comparing prostate cancer and normal samples. In particular we use samples assessed by histopathology to identify genes enriched and depleted in prostate stroma. These genes are then used to assess stroma content in tissue samples from other prostate cancer cohorts where no histopathology is available. Differential expression analysis is performed by comparing cancer and normal samples where the average stroma content has been balanced between the sample groups. In total we analyzed seven patient cohorts with prostate cancer consisting of 1713 prostate cancer and 230 normal tissue samples. Results When stroma confounding was minimized, differential gene expression analysis over all cohorts showed robust and consistent downregulation of nearly all genes in the cholesterol synthesis pathway. Additional Gene Ontology analysis also identified cholesterol synthesis as the most significantly altered metabolic pathway in prostate cancer at the transcriptional level. Conclusion The surprising observation that cholesterol synthesis genes are downregulated in prostate cancer is important for our understanding of how prostate cancer cells regulate cholesterol levels in vivo. Moreover, we show that tissue heterogeneity explains the lack of consistency in previous expression analysis of cholesterol synthesis genes in prostate cancer.

The Norwegian e-Infrastructure for Life Sciences (NeLS) has been developed by ELIXIR Norway to provide its users with a system enabling data storage, sharing, and analysis in a project-oriented fashion. The system is available through easy-to-use web interfaces, including the Galaxy workbench for data analysis and workflow execution. Users confident with a command-line interface and programming may also access it through Secure Shell (SSH) and application programming interfaces (APIs). NeLS has been in production since 2015, with training and support provided by the help desk of ELIXIR Norway. Through collaboration with NorSeq, the national consortium for high-throughput sequencing, an integrated service is offered so that sequencing data generated in a research project is provided to the involved researchers through NeLS. Sensitive data, such as individual genomic sequencing data, are handled using the TSD (Services for Sensitive Data) platform provided by Sigma2 and the University of Oslo. NeLS integrates national e-infrastructure storage and computing resources, and is also integrated with the SEEK platform in order to store large data files produced by experiments described in SEEK. In this article, we outline the architecture of NeLS and discuss possible directions for further development.

Shiga toxin-producing Escherichia coli (STEC) cause both sporadic infections and outbreaks of enteric disease in humans, with symptoms ranging from asymptomatic carriage to severe disease like haemolytic uremic syndrome (HUS). Bacterial virulence factors like subtypes of the Shiga toxin (Stx) and the locus of enterocyte effacement (LEE) pathogenicity island, as well as host factors like young age, are strongly associated with development of HUS. However, these factors alone do not accurately differentiate between strains that cause HUS and those that do not cause severe disease, which is important in the context of diagnosis, treatment, as well as infection control. We have used RNA sequencing to compare transcriptomes of 30 stx2a and eae positive STEC strains of non-O157 serogroups isolated from children

Psoriasis is a chronic cutaneous inflammatory disease. The immunopathogenesis is a complex interplay between T cells, dendritic cells and the epidermis in which T cells and dendritic cells maintain skin inflammation. Anti-tumour necrosis factor (anti-TNF)-α agents have been approved for therapeutic use across a range of inflammatory disorders including psoriasis, but the anti-inflammatory mechanisms of anti-TNF-α in lesional psoriatic skin are not fully understood. We investigated early events in skin from psoriasis patients after treatment with anti-TNF-α antibodies by use of bioinformatics tools. We used the Human Gene 1.0 ST Array to analyse gene expression in punch biopsies taken from psoriatic patients before and also 4 and 14 days after initiation of treatment with the anti-TNF-α agent adalimumab. The gene expression was analysed by gene set enrichment analysis using the Functional Annotation Tool from DAVID Bioinformatics Resources. The most enriched pathway was visualised by the Pathview Package on Kyoto Encyclopedia of Genes and Genomes (KEGG) graphs. The analysis revealed new very early events in psoriasis after adalimumab treatment. Some of these events have been described after longer periods of anti-TNF-α treatment when clinical and histological changes appear, suggesting that effects of anti-TNF-α treatment on gene expression appear very early before clinical and histological changes. Combining microarray data on biopsies from psoriasis patients with pathway analysis allowed us to integrate in vitro findings into the identification of mechanisms that may be important in vivo. Furthermore, these results may reflect primary effect of anti-TNF-α treatment in contrast to studies of gene expression changes following clinical and histological changes, which may reflect secondary changes correlated to the healing of the skin.

Molecular analysis of patient tissue samples is essential to characterize the in vivo variability in human cancers which are not accessible in cell-lines or animal models. This applies particularly to studies of tumor metabolism. The challenge is, however, the complex mixture of various tissue types within each sample, such as benign epithelium, stroma and cancer tissue, which can introduce systematic biases when cancers are compared to normal samples. In this study we apply a simple strategy to remove such biases using sample selections where the average content of stroma tissue is balanced between the sample groups. The strategy is applied to a prostate cancer patient cohort where data from MR spectroscopy and gene expression have been collected from and integrated on the exact same tissue samples. We reveal in vivo changes in cancer-relevant metabolic pathways which are otherwise hidden in the data due to tissue confounding. In particular, lowered levels of putrescine are connected to increased expression of SRM, reduced levels of citrate are attributed to upregulation of genes promoting fatty acid synthesis, and increased succinate levels coincide with reduced expression of SUCLA2 and SDHD. In addition, the strategy also highlights important metabolic differences between the stroma, epithelium and prostate cancer. These results show that important in vivo metabolic features of cancer can be revealed from patient data only if the heterogeneous tissue composition is properly accounted for in the analysis.

Strict control of tissue-specific gene expression plays a pivotal role during lineage commitment. The transcription factor c-Myb has an essential role in adult haematopoiesis and functions as an oncogene when rearranged in human cancers. Here we have exploited digital genomic footprinting analysis to obtain a global picture of c-Myb occupancy in the genome of six different haematopoietic cell-types. We have biologically validated several c-Myb footprints using c-Myb knockdown data, reporter assays and DamID analysis. We show that our predicted conserved c-Myb footprints are highly dependent on the haematopoietic cell type, but that there is a group of gene targets common to all cell-types analysed. Furthermore, we find that c-Myb footprints co-localise with active histone mark H3K4me3 and are significantly enriched at exons. We analysed co-localisation of c-Myb footprints with 104 chromatin regulatory factors in K562 cells, and identified nine proteins that are enriched together with c-Myb footprints on genes positively regulated by c-Myb and one protein enriched on negatively regulated genes. Our data suggest that c-Myb is a transcription factor with multifaceted target regulation depending on cell type.

Shiga toxin-producing Escherichia coli (STEC) cause infections in humans ranging from asymptomatic carriage to bloody diarrhoea and haemolytic uremic syndrome (HUS). Here we present whole genome comparison of Norwegian non-O157 STEC strains with the aim to distinguish between strains with the potential to cause HUS and less virulent strains. Whole genome sequencing and comparisons were performed across 95 non-O157 STEC strains. Twenty-three of these were classified as HUS-associated, including strains from patients with HUS (n = 19) and persons with an epidemiological link to a HUS-case (n = 4). Genomic comparison revealed considerable heterogeneity in gene content across the 95 STEC strains. A clear difference in gene profile was observed between strains with and without the Locus of Enterocyte Effacement (LEE) pathogenicity island. Phylogenetic analysis of the core genome showed high degree of diversity among the STEC strains, but all HUS-associated STEC strains were distributed in two distinct clusters within phylogroup B1. However, non-HUS strains were also found in these clusters. A number of accessory genes were found to be significantly overrepresented among HUS-associated STEC, but none of them were unique to this group of strains, suggesting that different sets of genes may contribute to the pathogenic potential in different phylogenetic STEC lineages. In this study we were not able to clearly distinguish between HUS-associated and non-HUS non-O157 STEC by extensive genome comparisons. Our results indicate that STECs from different phylogenetic backgrounds have independently acquired virulence genes that determine pathogenic potential, and that the content of such genes is overlapping between HUS-associated and non-HUS strains.

BACKGROUND: Transcription factors are important controllers of gene expression and mapping transcription factor binding sites (TFBS) is key to inferring transcription factor regulatory networks. Several methods for predicting TFBS exist, but there are no standard genome-wide datasets on which to assess the performance of these prediction methods. Also, it is believed that information about sequence conservation across different genomes can generally improve accuracy of motif-based predictors, but it is not clear under what circumstances use of conservation is most beneficial. RESULTS: Here we use published ChIP-seq data and an improved peak detection method to create comprehensive benchmark datasets for prediction methods which use known descriptors or binding motifs to detect TFBS in genomic sequences. We use this benchmark to assess the performance of five different prediction methods and find that the methods that use information about sequence conservation generally perform better than simpler motif-scanning methods. The difference is greater on high-affinity peaks and when using short and information-poor motifs. However, if the motifs are specific and information-rich, we find that simple motif-scanning methods can perform better than conservation-based methods. CONCLUSIONS: Our benchmark provides a comprehensive test that can be used to rank the relative performance of transcription factor binding site prediction methods. Moreover, our results show that, contrary to previous reports, sequence conservation is better suited for predicting strong than weak transcription factor binding sites.

Alpha-diversity indices are an essential tool for describing and comparing biodiversity. Microbial ecologists apply indices originally intended for, or adopted by, macroecology to address questions relating to taxonomy (conserved marker) and function (metagenome-based data). In this Perspective piece, I begin by discussing the nature and mathematical quirks important for interpreting routinely employed alpha-diversity indices. Secondly, I propose a metagenomic alpha-diversity index (MD) that measures the (dis)similarity of protein-encoding genes within a community. MD has defined limits, whereby a community comprised mostly of similar, poorly diverse protein-encoding genes pulls the index to the lower limit, while a community rich in divergent homologs and unique genes drives it toward the upper limit. With data acquired from an in silico and three in situ metagenome studies, I derive MD and typical alpha-diversity indices applied to taxonomic (ribosomal rRNA) and functional (all protein-encoding) genes, and discuss their relationships with each other. Not all alpha-diversity indices detect biological trends, and taxonomic does not necessarily follow functional biodiversity. Throughout, I explain that protein Richness and MD provide complementary and easily interpreted information, while probability-based indices do not. Finally, considerations regarding the unique nature of microbial metagenomic data and its relevance for describing functional biodiversity are discussed., (© The Author(s) 2024. Published by Oxford University Press on behalf of FEMS.)

Ammonia (NH 3 ) inhibition represents a major limitation to methane production during anaerobic digestion of organic material in biogas reactors. This process relies on co-operative metabolic interactions between diverse taxa at the community-scale. Despite this, most investigations have focused singularly on how methanogenic Archaea respond to NH 3 stress. With a high-NH 3 pre-adapted and un-adapted community, this study investigated responses to NH 3 inhibition both at the community-scale and down to individual taxa. The pre-adapted community performed methanogenesis under inhibitory NH 3 concentrations better than the un-adapted. While many functionally important phyla were shared between the two communities, only taxa from the pre-adapted community were robust to NH 3 . Functionally important phyla were mostly comprised of sensitive taxa (≥ 50%), yet all groups, including methanogens, also possessed tolerant individuals (10-50%) suggesting that potential mechanisms for tolerance are non-specific and widespread. Hidden Markov Model-based phylogenetic analysis of methanogens confirmed that NH 3 tolerance was not restricted to specific taxonomic groups, even at the genus level. By reconstructing covarying growth patterns via network analyses, methanogenesis by the pre-adapted community was best explained by continued metabolic interactions (edges) between tolerant methanogens and other tolerant taxa (nodes). However, under non-inhibitory conditions, sensitive taxa re-emerged to dominate the pre-adapted community, suggesting that mechanisms of NH 3 tolerance can be disadvantageous to fitness without selection pressure. This study demonstrates that methanogenesis under NH 3 inhibition depends on broad-scale tolerance throughout the prokaryotic community. Mechanisms for tolerance seem widespread and non-specific, which has practical significance for the development of robust methanogenic biogas communities. KEY POINTS: • Ammonia pre-adaptation allows for better methanogenesis under inhibitory conditions. • All functionally important prokaryote phyla have some ammonia tolerant individuals. • Methanogenesis was likely dependent on interactions between tolerant individuals., (© 2023. The Author(s).)

Water is necessary for all life on Earth. Water is so critical that organisms have developed strategies to survive in hyperarid environments. These regions with extremely low water availability are also unique analogs in which to study the physico-chemical conditions of extraterrestrial environments such as Mars. We have identified a daily, sustainable cycle of water vapor adsorption (WVA) and desorption that measurably affects soil water content (SWC) in the hyperarid region of the Atacama Desert in southern Perú. We pair field-based soil temperature and relative humidity soil profiles with laboratory simulations to provide evidence for a daily WVA cycle. Using our WVA model, we estimate that one adsorptive period-one night-increases SWC by 0.2-0.3 mg/g of soil (∼30 μm rainfall). We can plausibly rule out other water inputs during our field campaign that could account for this water input, and we provide evidence that this WVA cycle is driven by solar heating and maintained by atmospheric water vapor. The WVA may also serve to retain water from infrequent rain events in these soils. If the water provided by WVA in these soils is bioavailable, it could have significant implications for the microorganisms that are endemic to hyperarid environments.

The Black Queen hypothesis describes the evolutionary strategy to lose costly functions in favour of improving growth efficiency. This results in mutants (cheaters) becoming obligately dependent upon a provider (black queen) to produce a necessary resource. Previous analyses demonstrate black queens and cheaters reach a state of equilibrium in pair-wise systems. However, in complex communities, accumulation of cheaters likely poses a serious burden on shared resources. This should result in a Tragedy of the Commons (ToC), whereby over-utilisation of public resources risks making them growth-limiting. With a collection of differential equations, microbial communities composed of twenty prokaryote 'species' either from rhizosphere, characterised by abundant carbon and energy sources, or bulk soil, with limited carbon and energy supply, were simulated. Functional trait groups differed based on combinations of cellulase and amino acid production, growth and resource uptake. Randomly generated communities were thus composed of species that acted as cellulolytic prototrophic black queens, groups that were either cellulolytic or prototrophic, or non-cellulolytic auxotrophic cheaters. Groups could evolve to lose functions over time. Biomass production and biodiversity were tracked in 8,000 Monte Carlo simulations over 500 generations. Bulk soil favoured oligotrophic co-operative communities where biodiversity was positively associated with growth. Rhizosphere favoured copiotrophic cheaters. The most successful functional group across both environments was neither black queens nor cheaters, but those that balanced providing an essential growth-limiting function at a relatively low maintenance cost. Accumulation of loss of function mutants in bulk soil risked resulting in loss of cumulative growth by ToC, while cumulative growth increased in the rhizosphere. In the bulk soil, oligotrophic adaptations assisted species in avoiding extinction. This demonstrated that loss of function by mutation is a successful evolutionary strategy in host-associated and/or resource-rich environments, but poses a risk to communities that must co-operate with each other for mutual co-existence. It was concluded that microbial communities must follow different evolutionary and community assembly strategies in bulk soil versus rhizosphere, with bulk soil communities more dependent on traits that promote co-operative interactions between microbial species., 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 Finn, App, Hertzog and Tebbe.)

Despite adoption of high-throughput sequencing of PCR-amplified microbial taxonomic markers for ecological analyses, distinct approaches for preparing amplicon libraries exist. One approach utilises long fusion primers and a single PCR (one-step) while another utilises shorter primers in a first reaction, before transferring diluted amplicons to a second reaction for barcode index incorporation (two-step). We investigated whether transferring diluted amplicons risked creating artificially simplified, poorly diverse communities. In soils from three sites with paired cropland and forest, one-step yielded higher alpha-diversity indices, including detection of two-four times more unique taxa. Modelling expected taxa per sequence observation predicted that one-step reaches full coverage by 104 sequences per sample while two-step needs 105-109. Comparisons of rank abundance demonstrated that two-step covered only 38%-69% of distributions. Beta-diversity showed better separation of communities in response to land use change under one-step, although both approaches showed a significant effect. Driving differences was underestimation of relatively minor taxa with the two-step procedure. These taxa were low in abundance, yet play important roles in carbon cycling, secondary metabolite production, anaerobic metabolism, and bacterial predation. We conclude that one-step amplicon libraries are advisable for studies focussed on diversity or relatively minor yet functionally important taxa., (© The Author(s) 2022. Published by Oxford University Press on behalf of FEMS.)

Air carries a diverse load of particulate microscopic biological matter in suspension, either aerosolized or aggregated with dust particles, the aerobiome, which is dispersed by winds from sources to sinks. The aerobiome is known to contain microbes, including pathogens, as well as debris or small-sized propagules from plants and animals, but its variability and composition has not been studied comprehensibly. To gain a dynamic insight into the aerobiome existing over a mixed-use dryland setting, we conducted a biologically comprehensive, year-long survey of its composition and dynamics for particles less than 10 μm in diameter based on quantitative analyses of DNA content coupled to genomic sequencing. Airborne biological loads were more dependent on seasonal events than on meteorological conditions and only weakly correlated with dust loads. Core aerobiome species could be understood as a mixture of high elevation (e.g. Microbacteriaceae, Micrococcaceae, Deinococci), and local plant and soil sources (e.g. Sphingomonas, Streptomyces, Acinetobacter). Despite the mixed used of the land surrounding the sampling site, taxa that contributed to high load events were largely traceable to proximal agricultural practices like cotton and livestock farming. This included not only the predominance of specific crop plant signals over those of native vegetation, but also that of their pathogens (bacterial, viral and eukaryotic). Faecal bacterial loads were also seasonally important, possibly sourced in intensive animal husbandry or manure fertilization activity, and this microbial load was enriched in tetracycline resistance genes. The presence of the native opportunistic pathogen, Coccidioides spp., by contrast, was detected only with highly sensitive techniques, and only rarely. We conclude that agricultural activity exerts a much stronger influence that the native vegetation as a mass loss factor to the land system and as an input to dryland aerobiomes, including in the dispersal of plant, animal and human pathogens and their genetic resistance characteristics., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Crop harvest followed by a fallow period can act as a disturbance on soil microbial communities. Cropping systems intended to improve alpha-diversity of communities may also confer increased compositional stability during succeeding growing seasons. Over a single growing season in a long-term (18 year) agricultural field experiment incorporating conventional (CON), conservation (CA), organic (ORG) and integrated (INT) cropping systems, temporal changes in prokaryote, fungal and arbuscular mycorrhizal fungi (AMF) communities were investigated overwinter, during crop growth and at harvest. While certain prokaryote phyla were influenced by cropping system (e.g. Acidobacteria), the community as a whole was primarily driven by temporal changes over the growing season as distinct overwinter and crop-associated communities, with the same trend observed regardless of cropping system. Species-rich prokaryote communities were most stable over the growing season. Cropping system exerted a greater effect on fungal communities, with alpha-diversity highest and temporal changes most stable under CA. CON was particularly detrimental for alpha-diversity in AMF communities, with AMF alpha-diversity and stability improved under all other cropping systems. Practices that promoted alpha-diversity tended to also increase the similarity and temporal stability of soil fungal (and AMF) communities during a growing season, while prokaryote communities were largely insensitive to management., (© The Author(s) 2021. Published by Oxford University Press on behalf of FEMS. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Tropical peatlands are hotspots of methane (CH 4 ) production but present high variation and emission uncertainties in the Amazon region. This is because the controlling factors of methane production in tropical peats are not yet well documented. Although inhibitory effects of nitrogen oxides (NO x ) on methanogenic activity are known from pure culture studies, the role of NO x in the methane cycling of peatlands remains unexplored. Here, we investigated the CH 4 content, soil geochemistry and microbial communities along 1-m-soil profiles and assessed the effects of soil NO x and nitrous oxide (N 2 O) on methanogenic abundance and activity in three peatlands of the Pastaza-Marañón foreland basin. The peatlands were distinct in pH, DOC, nitrate pore water concentrations, C/N ratios of shallow soils, redox potential, and 13 C enrichment in dissolved inorganic carbon and CH 4 pools, which are primarily contingent on H 2 -dependent methanogenesis. Molecular 16S rRNA and mcrA gene data revealed diverse and novel methanogens varying across sites. Importantly, we also observed a strong stratification in relative abundances of microbial groups involved in NO x cycling, along with a concordant stratification of methanogens. The higher relative abundance of ammonia-oxidizing archaea (Thaumarchaeota) in acidic oligotrophic peat than ammonia-oxidizing bacteria ( Nitrospira ) is noteworthy as putative sources of NO x . Experiments testing the interaction of NO x species and methanogenesis found that the latter showed differential sensitivity to nitrite (up to 85% reduction) and N 2 O (complete inhibition), which would act as an unaccounted CH 4 control in these ecosystems. Overall, we present evidence of diverse peatlands likely differently affected by inhibitory effects of nitrogen species on methanogens as another contributor to variable CH 4 fluxes., 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 Buessecker, Zamora, Sarno, Finn, Hoyt, van Haren, Urquiza Muñoz and Cadillo-Quiroz.)

Functional, physiological traits are the underlying drivers of niche differentiation. A common framework related to niches occupied by terrestrial prokaryotes is based on copiotrophy or oligotrophy, where resource investment is primarily in either rapid growth or stress tolerance, respectively. A quantitative trait-based approach sought relationships between taxa, traits and niche in terrestrial prokaryotes. With 175 taxa from 11 Phyla and 35 Families (n = 5 per Family), traits were considered as discrete counts of shared genome-encoded proteins. Trait composition strongly supported non-random functional distributions as preferential clustering of related taxa via unweighted pair-group method with arithmetic mean. Trait similarity between taxa increased as taxonomic rank decreased. A suite of Random Forest models identified traits significantly enriched or depleted in taxonomic groups. These traits conveyed functions related to rapid growth, nutrient acquisition and stress tolerance consistent with their presence in copiotroph-oligotroph niches. Hierarchical clustering of traits identified a clade of competitive, copiotrophic Families resilient to oxidative stress versus glycosyltransferase-enriched oligotrophic Families resistant to antimicrobials and environmental stress. However, the formation of five clades suggested a more nuanced view to describe niche differentiation in terrestrial systems is necessary. We suggest considering traits involved in both resource investment and acquisition when predicting niche., (© The Author(s) 2021. Published by Oxford University Press on behalf of FEMS.)

Niche is a fundamental concept in ecology. It integrates the sum of biotic and abiotic environmental requirements that determines a taxon's distribution. Microbiologists currently lack quantitative approaches to address niche-related hypotheses. We tested four approaches for the quantification of niche breadth and overlap of taxa in amplicon sequencing datasets, with the goal of determining generalists, specialists and environmental-dependent distributions of community members. We applied these indices to in silico training datasets first, and then to real human gut and desert biological soil crust (biocrust) case studies, assessing the agreement of the indices with previous findings. Implementation of each approach successfully identified a priori conditions within in silico training data, and we found that by including a limit of quantification based on species rank, one could identify taxa falsely classified as specialists because of their low, sparse counts. Analysis of the human gut study offered quantitative support for Bacilli, Gammaproteobacteria and Fusobacteria specialists enriched after bariatric surgery. We could quantitatively characterise differential niche distributions of cyanobacterial taxa with respect to precipitation gradients in biocrusts. We conclude that these approaches, made publicly available as an R package (MicroNiche), represent useful tools to assess microbial environment-taxon and taxon-taxon relationships in a quantitative manner., (© FEMS 2020.)

Tropical peatlands are globally important carbon reservoirs that play a crucial role in fluxes of atmospheric greenhouse gases. Amazon peatlands are expected to be large source of atmospheric methane (CH 4 ) emissions, however little is understood about the rates of CH 4 flux or the microorganisms that mediate it in these environments. Here we studied a mineral nutrient gradient across peatlands in the Pastaza-Marañón Basin, the largest tropical peatland in South America, to describe CH 4 fluxes and environmental factors that regulate species assemblages of methanogenic and methanotrophic microorganisms. Peatlands were grouped as minerotrophic, mixed and ombrotrophic categories by their general water source leading to different mineral nutrient content (rich, mixed and poor) quantified by trace elements abundance. Microbial communities clustered dependent on nutrient content (ANOSIM p < 0.001). Higher CH 4 flux was associated with minerotrophic communities compared to the other categories. The most dominant methanogens and methanotrophs were represented by Methanobacteriaceae , and Methylocystaceae , respectively. Weighted network analysis demonstrated tight clustering of most methanogen families with minerotrophic-associated microbial families. Populations of Methylocystaceae were present across all peatlands. Null model testing for species assemblage patterns and species rank distributions confirmed non-random aggregations of Methylococcacae methanotroph and methanogen families ( p < 0.05). We conclude that in studied amazon peatlands increasing mineral nutrient content provides favorable habitats for Methanobacteriaceae , while Methylocystaceae populations seem to broadly distribute independent of nutrient content., (Copyright © 2020 Finn, Ziv-El, van Haren, Park, del Aguila-Pasquel, Urquiza–Muñoz and Cadillo-Quiroz.)

A 56-year-old woman with acquired, common variable immunodeficiency was found to have persistent gastrointestinal as well as pulmonary infection with Strongyloides stercoralis. Repeated courses of treatment with thiabendazole led to marked reduction or loss of Strongyloides stercoralis larvae, but cessation of treatment always led to recurrence of Strongyloides infection. Several small bowel biopsies showed normal villous architecture and little inflammatory response to presence of larvae. Interestingly, no definite symptomatology could be attributed to the Strongyloides infection. It was postulated that the lack of signs and symptoms of strongyloidiasis, as well as poor response to treatment, was related to the immunodeficiency state. With low-dose, long-term interrupted courses of thiabendazole treatment, the Strongyloides infection finally seemed to be cured.

A review of the medical records of 123 persons with Legionnaires' disease hospitalized in the 1976 Philadelphia epidemic showed that the manifestations of infection ranged from mild grippe to a severe pneumonia that also involved other organ systems. Early in the illness, constitutional symptoms predominated. Fever, malaise, myalgia, rigors, confusion, headache, and diarrhea were usually followed by nonproductive cough and dyspnea. Physical examination showed few abnormalities other than rales. Moderate leukocytosis with left shift, elevated erythrocyte sedimentation rate, elevation of serum levels of liver enzymes, and hematuria and proteinuria were characteristic. Chest radiograph showed patchy, often nodular, areas of consolidation. Progression of pneumonia led to respiratory failure and the need for mechanical ventilatory assistance for 19 patients; renal failure, primarily after shock, occurred in 18 persons. Twenty-six patients died. Treatment with erythromycin or tetracycline resulted in the lowest case-fatality ratios, but the associations were not statistically significant.