Recent Secular Trends of Body Mass Index in Individuals With Bipolar Disorders and in the General Population.

OBJECTIVE: The aims of this study were to investigate secular trends and distribution of body mass index (BMI) among individuals with bipolar disorders and the general population between 2008 and 2019. METHODS: Data were from the Swedish National Quality Register for Bipolar Disorder, where 24,423 adults with bipolar disorders were identified, and from the national Swedish Living Conditions Surveys, where 77,485 adults from the general population were identified. Quantile regression was used to compare the 15th, 50th, and 85th percentiles of BMI across age and study years. RESULTS: The study sample included 22,127 individuals with bipolar disorders (mean age, 48 years; 63% women) and 71,894 individuals from the general population (mean age, 52 years; 51% women). BMI percentiles were higher among individuals with bipolar disorders. At the 50th percentile, the BMI group differences were 1.1 (95% CI=0.8-1.14) for men and 1.8 (95% CI=1.5-2.1) for women. The gap was widest at the 85th BMI percentile: men, 2.3 (95% CI=1.8-2.8); women, 4.1 (95% CI=3.7-4.6). BMI increased over time in both study groups, but more in the group with bipolar disorders. The changes per decade in mean BMI were 0.4 (95% CI=0.3-0.5) among men in the general population, 1.1 (95% CI=0.7-1.4) among men with bipolar disorders, 0.6 (95% CI=0.5-0.7) among women in the general population, and 1.4 (95% CI=1.1-1.7) among women with bipolar disorders. Women with bipolar disorders had the highest prevalence and the greatest rate of increase of obesity. In 2019, the obesity prevalence was 33% among women and 29% among men with bipolar disorders, compared with 13% and 15%, respectively, among women and men in the general population. CONCLUSIONS: Adults with bipolar disorders had a higher BMI and a higher prevalence of obesity than the general population, indicating a higher cardiometabolic risk. Annually, BMI increased more in the group with bipolar disorders than in the general population, particularly among women and among those with high BMI.

Diagnostic yield for video capsule endoscopy in gastrointestinal graft- versus -host disease: a systematic review and metaanalysis.

BACKGROUND: The gastrointestinal tract is the second most involved organ for graft-versus-host disease where involvement of the small intestine is present in 50% of the cases. Therefore, the use of a non-invasive investigation i.e., video capsule endoscopy (VCE) seems ideal in the diagnostic work-up, but this has never been systematically evaluated before. OBJECTIVE: The aim of this systematic review was to determine the efficacy and safety of VCE, in comparison with conventional endoscopy in patients who received hematopoietic stem cell transplantation. METHOD: Databases searched were PubMed, Scopus, EMBASE, and Cochrane CENTRAL. All databases were searched from their inception date until June 17, 2022. The search identified 792 publications, of which 8 studies were included in our analysis comprising of 232 unique patients. Efficacy was calculated in comparison with the golden standard i.e., histology. Risk of bias assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. RESULTS: The pooled sensitivity was higher for VCE at 0.77 (95% CI: 0.60-0.89) compared to conventional endoscopy 0.62 (95% CI: 0.47-0.75) but the difference was not statistically significant (p = 0.155, Q = 2.02). Similarly, the pooled specificity was higher for VCE at 0.68 (95% CI: 0.46-0.84) than for conventional endoscopy at 0.58 (95% CI: 0.40-0.74) but not statistically significant (p = 0.457, Q = 0.55). Moreover, concern for adverse events such as intestinal obstruction or perforation was not justified since none of the capsules were retained in the small bowel and no perforations occurred in relation to VCE. A limitation to the study is the retrospective approach seen in 50% of the studies. CONCLUSION: The role of video capsule endoscopy in diagnosing or dismissing graft-versus-host disease is not yet established and requires further studies. However, the modality appears safe in this cohort.

What Is the Role of the Environment in the Emergence of Novel Antibiotic Resistance Genes? A Modeling Approach.

It is generally accepted that intervention strategies to curb antibiotic resistance cannot solely focus on human and veterinary medicine but must also consider environmental settings. While the environment clearly has a role in transmission of resistant bacteria, its role in the emergence of novel antibiotic resistance genes (ARGs) is less clear. It has been suggested that the environment constitutes an enormous recruitment ground for ARGs to pathogens, but its extent is practically unknown. We have constructed a model framework for resistance emergence and used available quantitative data on relevant processes to identify limiting steps in the appearance of ARGs in human pathogens. We found that in a majority of possible scenarios, the environment would only play a minor role in the emergence of novel ARGs. However, the uncertainty is enormous, highlighting an urgent need for more quantitative data. Specifically, more data is most needed on the fitness costs of ARG carriage, the degree of dispersal of resistant bacteria from the environment to humans, and the rates of mobilization and horizontal transfer of ARGs. This type of data is instrumental to determine which processes should be targeted for interventions to curb development and transmission of ARGs in the environment.

Mitochondrial dysfunction in adult midbrain dopamine neurons triggers an early immune response.

Dopamine (DA) neurons of the midbrain are at risk to become affected by mitochondrial damage over time and mitochondrial defects have been frequently reported in Parkinson's disease (PD) patients. However, the causal contribution of adult-onset mitochondrial dysfunction to PD remains uncertain. Here, we developed a mouse model lacking Mitofusin 2 (MFN2), a key regulator of mitochondrial network homeostasis, in adult midbrain DA neurons. The knockout mice develop severe and progressive DA neuron-specific mitochondrial dysfunction resulting in neurodegeneration and parkinsonism. To gain further insights into pathophysiological events, we performed transcriptomic analyses of isolated DA neurons and found that mitochondrial dysfunction triggers an early onset immune response, which precedes mitochondrial swelling, mtDNA depletion, respiratory chain deficiency and cell death. Our experiments show that the immune response is an early pathological event when mitochondrial dysfunction is induced in adult midbrain DA neurons and that neuronal death may be promoted non-cell autonomously by the cross-talk and activation of surrounding glial cells.

Tissue-specific transcriptional imprinting and heterogeneity in human innate lymphoid cells revealed by full-length single-cell RNA-sequencing.

The impact of the microenvironment on innate lymphoid cell (ILC)-mediated immunity in humans remains largely unknown. Here we used full-length Smart-seq2 single-cell RNA-sequencing to unravel tissue-specific transcriptional profiles and heterogeneity of CD127+ ILCs across four human tissues. Correlation analysis identified gene modules characterizing the migratory properties of tonsil and blood ILCs, and signatures of tissue-residency, activation and modified metabolism in colon and lung ILCs. Trajectory analysis revealed potential differentiation pathways from circulating and tissue-resident naïve ILCs to a spectrum of mature ILC subsets. In the lung we identified both CRTH2+ and CRTH2- ILC2 with lung-specific signatures, which could be recapitulated by alarmin-exposure of circulating ILC2. Finally, we describe unique TCR-V(D)J-rearrangement patterns of blood ILC1-like cells, revealing a subset of potentially immature ILCs with TCR-δ rearrangement. Our study provides a useful resource for in-depth understanding of ILC-mediated immunity in humans, with implications for disease.

Industrial wastewater treatment plant enriches antibiotic resistance genes and alters the structure of microbial communities.

Antibiotic resistance is an emerging global health crisis, driven largely by overuse and misuse of antibiotics. However, there are examples in which the production of these antimicrobial agents has polluted the environment with active antibiotic residues, selecting for antibiotic resistant bacteria and the genes they carry. In this work, we have used shotgun metagenomics to investigate the taxonomic structure and resistance gene composition of sludge communities in a treatment plant in Croatia receiving wastewater from production of the macrolide antibiotic azithromycin. We found that the total abundance of antibiotic resistance genes was three times higher in sludge from the treatment plant receiving wastewater from pharmaceutical production than in municipal sludge from a sewage treatment plant in Zagreb. Surprisingly, macrolide resistance genes did not have higher abundances in the industrial sludge, but genes associated with mobile genetic elements such as integrons had. We conclude that at high concentrations of antibiotics, selection may favor taxonomic shifts towards intrinsically resistant species or strains harboring chromosomal resistance mutations rather than acquisition of mobile resistance determinants. Our results underscore the need for regulatory action also within Europe to avoid release of antibiotics into the environment.

Magnetic phase diagram of K2Cr8O16 clarified by high-pressure muon spin spectroscopy.

The K2Cr8O16 compound belongs to a series of quasi-1D compounds with intriguing magnetic properties that are stabilized through a high-pressure synthesis technique. In this study, a muon spin rotation, relaxation and resonance (µ+SR) technique is used to investigate the pressure dependent magnetic properties up to 25 kbar. µ+SR allows for measurements in true zero applied field and hereby access the true intrinsic material properties. As a result, a refined temperature/pressure phase diagram is presented revealing a novel low temperature/high pressure (pC1 = 21 kbar) transition from a ferromagnetic insulating to a high-pressure antiferromagnetic insulator. Finally, the current study also indicates the possible presence of a quantum critical point at pC2 ~ 33 kbar where the magnetic order in K2Cr8O16 is expected to be fully suppressed even at T = 0 K.

Modelling of zero-inflation improves inference of metagenomic gene count data.

Metagenomics enables the study of gene abundances in complex mixtures of microorganisms and has become a standard methodology for the analysis of the human microbiome. However, gene abundance data is inherently noisy and contains high levels of biological and technical variability as well as an excess of zeros due to non-detected genes. This makes the statistical analysis challenging. In this study, we present a new hierarchical Bayesian model for inference of metagenomic gene abundance data. The model uses a zero-inflated overdispersed Poisson distribution which is able to simultaneously capture the high gene-specific variability as well as zero observations in the data. By analysis of three comprehensive datasets, we show that zero-inflation is common in metagenomic data from the human gut and, if not correctly modelled, it can lead to substantial reductions in statistical power. We also show, by using resampled metagenomic data, that our model has, compared to other methods, a higher and more stable performance for detecting differentially abundant genes. We conclude that proper modelling of the gene-specific variability, including the excess of zeros, is necessary to accurately describe gene abundances in metagenomic data. The proposed model will thus pave the way for new biological insights into the structure of microbial communities.

Comparison of normalization methods for the analysis of metagenomic gene abundance data.

BACKGROUND: In shotgun metagenomics, microbial communities are studied through direct sequencing of DNA without any prior cultivation. By comparing gene abundances estimated from the generated sequencing reads, functional differences between the communities can be identified. However, gene abundance data is affected by high levels of systematic variability, which can greatly reduce the statistical power and introduce false positives. Normalization, which is the process where systematic variability is identified and removed, is therefore a vital part of the data analysis. A wide range of normalization methods for high-dimensional count data has been proposed but their performance on the analysis of shotgun metagenomic data has not been evaluated. RESULTS: Here, we present a systematic evaluation of nine normalization methods for gene abundance data. The methods were evaluated through resampling of three comprehensive datasets, creating a realistic setting that preserved the unique characteristics of metagenomic data. Performance was measured in terms of the methods ability to identify differentially abundant genes (DAGs), correctly calculate unbiased p-values and control the false discovery rate (FDR). Our results showed that the choice of normalization method has a large impact on the end results. When the DAGs were asymmetrically present between the experimental conditions, many normalization methods had a reduced true positive rate (TPR) and a high false positive rate (FPR). The methods trimmed mean of M-values (TMM) and relative log expression (RLE) had the overall highest performance and are therefore recommended for the analysis of gene abundance data. For larger sample sizes, CSS also showed satisfactory performance. CONCLUSIONS: This study emphasizes the importance of selecting a suitable normalization methods in the analysis of data from shotgun metagenomics. Our results also demonstrate that improper methods may result in unacceptably high levels of false positives, which in turn may lead to incorrect or obfuscated biological interpretation.

Untreated urban waste contaminates Indian river sediments with resistance genes to last resort antibiotics.

Efficient sewage treatment is critical for limiting environmental transmission of antibiotic-resistant bacteria. In many low and middle income countries, however, large proportions of sewage are still released untreated into receiving water bodies. In-depth knowledge of how such discharges of untreated urban waste influences the environmental resistome is largely lacking. Here, we highlight the impact of uncontrolled discharge of partially treated and/or untreated wastewater on the structure of bacterial communities and resistome of sediments collected from Mutha river flowing through Pune city in India. Using shotgun metagenomics, we found a wide array (n = 175) of horizontally transferable antibiotic resistance genes (ARGs) including carbapenemases such as NDM, VIM, KPC, OXA-48 and IMP types. The relative abundance of total ARGs was 30-fold higher in river sediments within the city compared to upstream sites. Forty four ARGs, including the tet(X) gene conferring resistance to tigecycline, OXA-58 and GES type carbapenemases, were significantly more abundant in city sediments, while two ARGs were more common at upstream sites. The recently identified mobile colistin resistance gene mcr-1 was detected only in one of the upstream samples, but not in city samples. In addition to ARGs, higher abundances of various mobile genetic elements were found in city samples, including integron-associated integrases and ISCR transposases, as well as some biocide/metal resistance genes. Virulence toxin genes as well as bacterial genera comprising many pathogens were more abundant here; the genus Acinetobacter, which is often associated with multidrug resistance and nosocomial infections, comprised up to 29% of the 16S rRNA reads, which to our best knowledge is unmatched in any other deeply sequenced metagenome. There was a strong correlation between the abundance of Acinetobacter and the OXA-58 carbapenemase gene. Our study shows that uncontrolled discharge of untreated urban waste can contribute to an overall increase of the abundance and diversity of ARGs in the environment, including those conferring resistance to last-resort antibiotics.

HirBin: high-resolution identification of differentially abundant functions in metagenomes.

BACKGROUND: Gene-centric analysis of metagenomics data provides information about the biochemical functions present in a microbiome under a certain condition. The ability to identify significant differences in functions between metagenomes is dependent on accurate classification and quantification of the sequence reads (binning). However, biological effects acting on specific functions may be overlooked if the classes are too general. METHODS: Here we introduce High-Resolution Binning (HirBin), a new method for gene-centric analysis of metagenomes. HirBin combines supervised annotation with unsupervised clustering to bin sequence reads at a higher resolution. The supervised annotation is performed by matching sequence fragments to genes using well-established protein domains, such as TIGRFAM, PFAM or COGs, followed by unsupervised clustering where each functional domain is further divided into sub-bins based on sequence similarity. Finally, differential abundance of the sub-bins is statistically assessed. RESULTS: We show that HirBin is able to identify biological effects that are only present at more specific functional levels. Furthermore we show that changes affecting more specific functional levels are often diluted at the more general level and therefore overlooked when analyzed using standard binning approaches. CONCLUSIONS: HirBin improves the resolution of the gene-centric analysis of metagenomes and facilitates the biological interpretation of the results. HirBin is implemented as a Python package and is freely available for download at http://bioinformatics.math.chalmers.se/hirbin .

Variability in Metagenomic Count Data and Its Influence on the Identification of Differentially Abundant Genes.

Metagenomics is the study of microorganisms in environmental and clinical samples using high-throughput sequencing of random fragments of their DNA. Since metagenomics does not require any prior culturing of isolates, entire microbial communities can be studied directly in their natural state. In metagenomics, the abundance of genes is quantified by sorting and counting the DNA fragments. The resulting count data are high-dimensional and affected by high levels of technical and biological noise that make the statistical analysis challenging. In this article, we introduce an hierarchical overdispersed Poisson model to explore the variability in metagenomic data. By analyzing three comprehensive data sets, we show that the gene-specific variability varies substantially between genes and is dependent on biological function. We also assess the power of identifying differentially abundant genes and show that incorrect assumptions about the gene-specific variability can lead to unacceptable high rates of false positives. Finally, we evaluate shrinkage approaches to improve the variance estimation and show that the prior choice significantly affects the statistical power. The results presented in this study further elucidate the complex variance structure of metagenomic data and provide suggestions for accurate and reliable identification of differentially abundant genes.

Strategies to improve usability and preserve accuracy in biological sequence databases.

Biology is increasingly dependent on large-scale analysis, such as proteomics, creating a requirement for efficient bioinformatics. Bioinformatic predictions of biological functions rely upon correctly annotated database sequences, and the presence of inaccurately annotated or otherwise poorly described sequences introduces noise and bias to biological analyses. Accurate annotations are, for example, pivotal for correct identification of polypeptide fragments. However, standards for how sequence databases are organized and presented are currently insufficient. Here, we propose five strategies to address fundamental issues in the annotation of sequence databases: (i) to clearly separate experimentally verified and unverified sequence entries; (ii) to enable a system for tracing the origins of annotations; (iii) to separate entries with high-quality, informative annotation from less useful ones; (iv) to integrate automated quality-control software whenever such tools exist; and (v) to facilitate postsubmission editing of annotations and metadata associated with sequences. We believe that implementation of these strategies, for example as requirements for publication of database papers, would enable biology to better take advantage of large-scale data.

Statistical evaluation of methods for identification of differentially abundant genes in comparative metagenomics.

BACKGROUND: Metagenomics is the study of microbial communities by sequencing of genetic material directly from environmental or clinical samples. The genes present in the metagenomes are quantified by annotating and counting the generated DNA fragments. Identification of differentially abundant genes between metagenomes can provide important information about differences in community structure, diversity and biological function. Metagenomic data is however high-dimensional, contain high levels of biological and technical noise and have typically few biological replicates. The statistical analysis is therefore challenging and many approaches have been suggested to date. RESULTS: In this article we perform a comprehensive evaluation of 14 methods for identification of differentially abundant genes between metagenomes. The methods are compared based on the power to detect differentially abundant genes and their ability to correctly estimate the type I error rate and the false discovery rate. We show that sample size, effect size, and gene abundance greatly affect the performance of all methods. Several of the methods also show non-optimal model assumptions and biased false discovery rate estimates, which can result in too large numbers of false positives. We also demonstrate that the performance of several of the methods differs substantially between metagenomic data sequenced by different technologies. CONCLUSIONS: Two methods, primarily designed for the analysis of RNA sequencing data (edgeR and DESeq2) together with a generalized linear model based on an overdispersed Poisson distribution were found to have best overall performance. The results presented in this study may serve as a guide for selecting suitable statistical methods for identification of differentially abundant genes in metagenomes.