Gut microbiome of multiple sclerosis patients and paired household healthy controls reveal associations with disease risk and course.

Changes in gut microbiota have been associated with several diseases. Here, the International Multiple Sclerosis Microbiome Study (iMSMS) studied the gut microbiome of 576 MS patients (36% untreated) and genetically unrelated household healthy controls (1,152 total subjects). We observed a significantly increased proportion of Akkermansia muciniphila, Ruthenibacterium lactatiformans, Hungatella hathewayi, and Eisenbergiella tayi and decreased Faecalibacterium prausnitzii and Blautia species. The phytate degradation pathway was over-represented in untreated MS, while pyruvate-producing carbohydrate metabolism pathways were significantly reduced. Microbiome composition, function, and derived metabolites also differed in response to disease-modifying treatments. The therapeutic activity of interferon-ß may in part be associated with upregulation of short-chain fatty acid transporters. Distinct microbial networks were observed in untreated MS and healthy controls. These results strongly support specific gut microbiome associations with MS risk, course and progression, and functional changes in response to treatment.

NanoDeep: a deep learning framework for nanopore adaptive sampling on microbial sequencing.

Nanopore sequencers can enrich or deplete the targeted DNA molecules in a library by reversing the voltage across individual nanopores. However, it requires substantial computational resources to achieve rapid operations in parallel at read-time sequencing. We present a deep learning framework, NanoDeep, to overcome these limitations by incorporating convolutional neural network and squeeze and excitation. We first showed that the raw squiggle derived from native DNA sequences determines the origin of microbial and human genomes. Then, we demonstrated that NanoDeep successfully classified bacterial reads from the pooled library with human sequence and showed enrichment for bacterial sequence compared with routine nanopore sequencing setting. Further, we showed that NanoDeep improves the sequencing efficiency and preserves the fidelity of bacterial genomes in the mock sample. In addition, NanoDeep performs well in the enrichment of metagenome sequences of gut samples, showing its potential applications in the enrichment of unknown microbiota. Our toolkit is available at https://github.com/lysovosyl/NanoDeep.

Benchmarking genome assembly methods on metagenomic sequencing data.

Metagenome assembly is an efficient approach to reconstruct microbial genomes from metagenomic sequencing data. Although short-read sequencing has been widely used for metagenome assembly, linked- and long-read sequencing have shown their advancements in assembly by providing long-range DNA connectedness. Many metagenome assembly tools were developed to simplify the assembly graphs and resolve the repeats in microbial genomes. However, there remains no comprehensive evaluation of metagenomic sequencing technologies, and there is a lack of practical guidance on selecting the appropriate metagenome assembly tools. This paper presents a comprehensive benchmark of 19 commonly used assembly tools applied to metagenomic sequencing datasets obtained from simulation, mock communities or human gut microbiomes. These datasets were generated using mainstream sequencing platforms, such as Illumina and BGISEQ short-read sequencing, 10x Genomics linked-read sequencing, and PacBio and Oxford Nanopore long-read sequencing. The assembly tools were extensively evaluated against many criteria, which revealed that long-read assemblers generated high contig contiguity but failed to reveal some medium- and high-quality metagenome-assembled genomes (MAGs). Linked-read assemblers obtained the highest number of overall near-complete MAGs from the human gut microbiomes. Hybrid assemblers using both short- and long-read sequencing were promising methods to improve both total assembly length and the number of near-complete MAGs. This paper also discussed the running time and peak memory consumption of these assembly tools and provided practical guidance on selecting them.

Detection of Mpox Virus Using Microbial Cell-Free DNA: The Potential of Pathogen-Agnostic Sequencing for Rapid Identification of Emerging Pathogens.

BACKGROUND: The 2022 global outbreak of Monkeypox virus (MPXV) highlighted challenges with polymerase chain reaction detection as divergent strains emerged and atypical presentations limited the applicability of swab sampling. Recommended testing in the United States requires a swab of lesions, which arise late in infection and may be unrecognized. We present MPXV detections using plasma microbial cell-free DNA (mcfDNA) sequencing. METHODS: Fifteen plasma samples from 12 case-patients were characterized through mcfDNA sequencing. Assay performance was confirmed through in silico inclusivity and exclusivity assessments. MPXV isolates were genotyped using mcfDNA, and phylodynamic information was imputed using publicly available sequences. RESULTS: MPXV mcfDNA was detected in 12 case-patients. Mpox was not suspected in 5, with 1 having documented resolution of mpox >6 months previously. Six had moderate to severe mpox, supported by high MPXV mcfDNA concentrations; 4 died. In 7 case-patients, mcfDNA sequencing detected coinfections. Genotyping by mcfDNA sequencing identified 22 MPXV mutations at 10 genomic loci in 9 case-patients. Consistent with variation observed in the 2022 outbreak, 21 of 22 variants were G > A/C > T. Phylogenetic analyses imputed isolates to sublineages arising at different time points and from different geographic locations. CONCLUSIONS: We demonstrate the potential of plasma mcfDNA sequencing to detect, quantify, and, for acute infections with high sequencing coverage, subtype MPXV using a single noninvasive test. Sequencing plasma mcfDNA may augment existing mpox testing in vulnerable patient populations or in patients with atypical symptoms or unrecognized mpox. Strain type information may supplement disease surveillance and facilitate tracking emerging pathogens.

Rapid turnover and short-term blooms of Escherichia coli in the human gut.

Escherichia coli (E. coli) is a common microorganism that is widely present in the environment and closely related to human health. The extent of E. coli presence in the human gut has been a subject of ongoing debate. Through whole-genome shotgun metagenomic sequencing, our study revealed that E. coli exists in the human body at a low abundance (average abundance 1.21%), with occasional short-term bursts leading to temporary increases in abundance, with the highest recorded at 50.91%. Further investigations into the factors contributing to these short-term blooms of E. coli showed significant variations in strain types and genomes within fecal samples collected from the same individuals at different time points. Evolutionary tree analysis indicated that samples from different individuals crossed, suggesting a change in the dominant E. coli strains within the human gut. Therefore, it can be inferred that E. coli in the human body are more likely to be transient bacteria rather than permanent residents in the gut. The rapid rate of turnover among months (87.5% within a month) and short-term blooms of E. coli in the human body can establish "latent infections" of nonpathogenic strains in healthy individuals while also posing a potential risk of introducing pathogenic strains, thereby impacting human health. In summary, our study revealed the variation in E. coli abundance and strains within the human gut, influenced by geographic area and temporal factors. These findings contribute to a better understanding of the relationship between E. coli, the gut microbiota, and human health. IMPORTANCE Escherichia coli (E. coli) is a microorganism closely linked to human health, and its presence in the human gut has been a topic of debate. Our study, using whole-genome shotgun metagenomic sequencing, revealed that E. coli exists at a low abundance in the human body, with occasional short-term bursts leading to temporary increases. Strain and genome variations were observed within fecal samples from the same individuals at different time points, suggesting transient rather than permanent residence of E. coli in the gut. The rapid turnover rate and short-term blooms of E. coli can establish latent infections while also posing a risk of introducing pathogenic strains. These findings enhance our understanding of the relationship between E. coli, the gut microbiota, and human health.

Chronic and Neurotropic: A Paradigm-Challenging Case of Dengue Virus Encephalitis in a Patient With Advanced HIV Infection.

A 32-year-old female with advanced human immunodeficiency virus infection presented to an Australian hospital with subacute, worsening symptoms of encephalitis. Metagenomic sequencing and Dengue NS3 antigen staining of brain tissue confirmed active dengue virus (DENV) encephalitis. The most recent possible DENV exposure was months prior in West Africa, indicating chronicity.

Gut microbiome in association with chemotherapy-induced toxicities among patients with breast cancer.

BACKGROUND: Little research has focused on the relationship between gut microbiome and chemotherapy-induced toxicity. METHODS: This prospective study involves 301 patients with breast cancer who had prechemotherapy stool samples collected. Gut microbiome was sequenced by shotgun metagenomics; associations with chemotherapy-induced toxicities during first-line treatment by gut microbial diversity, composition, and metabolic pathways with severe (i.e., grade ≥3) hematological and gastrointestinal toxicities were evaluated via multivariable logistic regression. RESULTS: High prechemotherapy α-diversity was associated with a significantly reduced risk of both severe hematological toxicity (odds ratio [OR] = 0.94; 95% CI, 0.89-0.99; p = .048) and neutropenia (OR = 0.94; 95% CI, 0.89-0.99; p = .016). A high abundance of phylum Synergistota, class Synergistia, and order Synergistales were significantly associated with a reduced risk of severe neutropenia; conversely, enrichment of phylum Firmicutes C, class Negativicutes, phylum Firmicutes I, and class Bacilli A, order Paenibacillales were significantly associated with an increased risk of severe neutropenia (p range: 0.012-2.32 × 10-3; false discovery rate <0.1). Significant positive associations were also observed between severe nausea/vomiting and high Chao1 indexes, ß-diversity (p < .05), 20 species belonging to the family Lachnospiraceae, Oscillospiraceae, and Ruminococcaceae (p value range: 6.14 × 10-3 to 1.33 × 10-5; false discovery rate <0.1), and three metabolic pathways involved in reductive tricarboxylic acid cycle I and cycle II, and an incomplete reductive tricarboxylic acid cycle (p < .01). Conversely, a high abundance of species Odoribacter laneus and the pathway related to the L-proline biosynthesis II were inversely associated with severe nausea/vomiting. CONCLUSIONS: Our study suggests that gut microbiota may be a potential preventive target to reduce chemotherapy-induced toxicity.

Landscapes of gut bacterial and fecal metabolic signatures and their relationship in severe preeclampsia.

BACKGROUND: Preeclampsia is a pregnancy-specific disease leading to maternal and perinatal morbidity. Hypertension and inflammation are the main characteristics of preeclampsia. Many factors can lead to hypertension and inflammation, including gut microbiota which plays an important role in hypertension and inflammation in humans. However, alterations to the gut microbiome and fecal metabolome, and their relationships in severe preeclampsia are not well known. This study aims to identify biomarkers significantly associated with severe preeclampsia and provide a knowledge base for treatments regulating the gut microbiome. METHODS: In this study, fecal samples were collected from individuals with severe preeclampsia and healthy controls for shotgun metagenomic sequencing to evaluate changes in gut microbiota composition. Quantitative polymerase chain reaction analysis was used to validate the reliability of our shotgun metagenomic sequencing results. Additionally, untargeted metabolomics analysis was performed to measure fecal metabolome concentrations. RESULTS: We identified several Lactobacillaceae that were significantly enriched in the gut of healthy controls, including Limosilactobacillus fermentum, the key biomarker distinguishing severe preeclampsia from healthy controls. Limosilactobacillus fermentum was significantly associated with shifts in KEGG Orthology (KO) genes and KEGG pathways of the gut microbiome in severe preeclampsia, such as flagellar assembly. Untargeted fecal metabolome analysis found that severe preeclampsia had higher concentrations of Phenylpropanoate and Agmatine. Increased concentrations of Phenylpropanoate and Agmatine were associated with the abundance of Limosilactobacillus fermentum. Furthermore, all metabolites with higher abundances in healthy controls were enriched in the arginine and proline metabolism pathway. CONCLUSION: Our research indicates that changes in metabolites, possibly due to the gut microbe Limosilactobacillus fermentum, can contribute to the development of severe preeclampsia. This study provides insights into the interaction between gut microbiome and fecal metabolites and offers a basis for improving severe preeclampsia by modulating the gut microbiome.

Multi-omic profiling reveals associations between the gut microbiome, host genome and transcriptome in patients with colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is the leading cancer worldwide. Microbial agents have been considered to contribute to the pathogenesis of different disease. But the underlying relevance between CRC and microbiota remain unclear. METHODS: We dissected the fecal microbiome structure and genomic and transcriptomic profiles of matched tumor and normal mucosa tissues from 41 CRC patients. Of which, the relationship between CRC-associated bacterial taxa and their significantly correlated somatic mutated gene was investigated by exome sequencing technology. Differentially expressed functional genes in CRC were clustered according to their correlation with differentially abundant species, following by annotation with DAVID. The composition of immune and stromal cell types was identified by XCELL. RESULTS: We identified a set of 22 microbial gut species associated with CRC and estimate the relative abundance of KEGG ontology categories. Next, the interactions between CRC-related gut microbes and clinical phenotypes were evaluated. 4 significantly mutated gene: TP53, APC, KRAS, SMAD4 were pointed out and the associations with cancer related microbes were identified. Among them, Fusobacterium nucleatum positively corelated with different host metabolic pathways. Finally, we revealed that Fusobacterium nucleatum modified the tumor immune environment by TNFSF9 gene expression. CONCLUSION: Collectively, our multi-omics data could help identify novel biomarkers to inform clinical decision-making in the detection and diagnosis of CRC.

Real-time application of ITS and D1-D3 nanopore amplicon metagenomic sequencing in fungal infections: Enhancing fungal infection diagnostics.

While fungal infections cause considerable morbidity and mortality, the performance of the current diagnostic tests for fungal infection is low. Even though fungal metagenomics or targeted next-generation sequencing have been investigated for various clinical samples, the real-time clinical utility of these methods still needs to be elucidated. In this study, we used internal transcribed spacer (ITS) and D1-D3 ribosomal DNA nanopore amplicon metagenomic sequencing to assess its utility in patients with fungal infections. Eighty-four samples from seventy-three patients were included and categorized into 'Fungal infection,' 'Fungal colonization,' and 'Fungal contamination' groups based on the judgement of infectious disease specialists. In the 'Fungal infection' group, forty-seven initial samples were obtained from forty-seven patients. Three fungal cases detected not by the sequencing but by conventional fungal assays were excluded from the analysis. In the remaining cases, the conventional fungal assay-negative/sequencing-positive group (n=11) and conventional fungal assay-positive/sequencing-positive group (n=33) were compared. Non-Candida and non-Aspergillus fungi infections were more frequent in the conventional-negative/sequencing-positive group (p-value = 0.031). We demonstrated the presence of rare human pathogens, such as Trichosporon asahii and Phycomyces blakesleeanus. In the 'Fungal infection' group and 'Fungal colonization' group, sequencing was faster than culturing (mean difference = 4.92 days, p-value < 0.001/ mean difference = 4.67, p-value <0.001). Compared to the conventional diagnostic methods including culture, nanopore amplicon sequencing showed a shorter turnaround time and a higher detection rate for uncommon fungal pathogens.

Gut microbes on the risk of advanced adenomas.

BACKGROUND: More than 90% of colorectal cancer (CRC) arises from advanced adenomas (AA) and gut microbes are closely associated with the initiation and progression of both AA and CRC. OBJECTIVE: To analyze the characteristic microbes in AA. METHODS: Fecal samples were collected from 92 AA and 184 negative control (NC). Illumina HiSeq X sequencing platform was used for high-throughput sequencing of microbial populations. The sequencing results were annotated and compared with NCBI RefSeq database to find the microbial characteristics of AA. R-vegan package was used to analyze α diversity and ß diversity. α diversity included box diagram, and ß diversity included Principal Component Analysis (PCA), principal co-ordinates analysis (PCoA), and non-metric multidimensional scaling (NMDS). The AA risk prediction models were constructed based on six kinds of machine learning algorithms. In addition, unsupervised clustering methods were used to classify bacteria and viruses. Finally, the characteristics of bacteria and viruses in different subtypes were analyzed. RESULTS: The abundance of Prevotella sp900557255, Alistipes putredinis, and Megamonas funiformis were higher in AA, while the abundance of Lilyvirus, Felixounavirus, and Drulisvirus were also higher in AA. The Catboost based model for predicting the risk of AA has the highest accuracy (bacteria test set: 87.27%; virus test set: 83.33%). In addition, 4 subtypes (B1V1, B1V2, B2V1, and B2V2) were distinguished based on the abundance of gut bacteria and enteroviruses (EVs). Escherichia coli D, Prevotella sp900557255, CAG-180 sp000432435, Phocaeicola plebeiuA, Teseptimavirus, Svunavirus, Felixounavirus, and Jiaodavirus are the characteristic bacteria and viruses of 4 subtypes. The results of Catboost model indicated that the accuracy of prediction improved after incorporating subtypes. The accuracy of discovery sets was 100%, 96.34%, 100%, and 98.46% in 4 subtypes, respectively. CONCLUSION: Prevotella sp900557255 and Felixounavirus have high value in early warning of AA. As promising non-invasive biomarkers, gut microbes can become potential diagnostic targets for AA, and the accuracy of predicting AA can be improved by typing.

Microbial carbohydrate active enzyme (CAZyme) genes and diversity from Menagesha Suba natural forest soils of Ethiopia as revealed by shotgun metagenomic sequencing.

BACKGROUND: The global over-reliance on non-renewable fossil fuels has led to the emission of greenhouse gases, creating a critical global environmental challenge. There is an urgent need for alternative solutions like biofuels. Advanced biofuel is a renewable sustainable energy generated from lignocellulosic plant materials, which can significantly contribute to mitigating CO2 emissions. Microbial Carbohydrate Active Enzymes (CAZymes) are the most crucial enzymes for the generation of sustainable biofuel energy. The present study designed shotgun metagenomics approaches to assemble, predict, and annotate, aiming to gain an insight into the taxonomic diversity, annotate CAZymes, and identify carbohydrate hydrolyzing CAZymes from microbiomes in Menagesha suba forest soil for the first time. RESULTS: The microbial diversity based on small subunit (SSU) rRNA analysis revealed the dominance of the bacterial domain representing 81.82% and 92.31% in the studied samples. Furthermore, the phylum composition result indicated the dominance of the phyla Proteobacteria (23.08%, 27.27%), Actinobacteria (11.36%, 20.51%), and Acidobacteria (10.26%, 15.91%). The study also identified unassigned bacteria which might have a unique potential for biopolymer hydrolysis. The metagenomic study revealed that 100,244 and 65,356 genes were predicted from the two distinct samples. A total number of 1806 CAZyme genes were identified, among annotated CAZymes, 758 had a known enzyme assigned to CAZymes. Glycoside hydrolases (GHs) CAZyme family contained most of the CAZyme genes with known enzymes such as ß-glucosidase, endo-ß-1,4-mannanase, exo-ß-1,4-glucanase, α-L-arabinofuranosidase and oligoxyloglucan reducing end-specific cellobiohydrolase. On the other hand, 1048 of the identified CAZyme genes were putative CAZyme genes with unknown enzymatical activity and the majority of which belong to the GHs family. CONCLUSIONS: In general, the identified putative CAZymes genes open up an opportunity for the discovery of new enzymes responsible for hydrolyzing biopolymers utilized for biofuel energy generation. This finding is used as a first-hand piece of evidence to serve as a benchmark for further and comprehensive studies to unveil novel classes of bio-economically valuable genes and their encoded products.

Metagenomic profiling of gut microbiota in Fall Armyworm (Spodoptera frugiperda) larvae fed on different host plants.

BACKGROUND: The fall armyworm (FAW, Spodoptera frugiperda) is a polyphagous pest known for causing significant crop damage. The gut microbiota plays a pivotal role in influencing the biology, physiology and adaptation of the host. However, understanding of the taxonomic composition and functional characteristics of the gut microbiota in FAW larvae fed on different host plants remains limited. METHODS: This study utilized metagenomic sequencing to explore the structure, function and antibiotic resistance genes (ARGs) of the gut microbiota in FAW larvae transferred from an artificial diet to four distinct host plants: maize, sorghum, tomato and pepper. RESULTS: The results demonstrated significant variations in gut microbiota structure among FAW larvae fed on different host plants. Firmicutes emerged as the dominant phylum, with Enterococcaceae as the dominant family and Enterococcus as the prominent genus. Notably, Enterococcus casseliflavus was frequently observed in the gut microbiota of FAW larvae across host plants. Metabolism pathways, particularly those related to carbohydrate and amino acid metabolism, played a crucial role in the adaptation of the FAW gut microbiota to different host plants. KEGG orthologs associated with the regulation of the peptide/nickel transport system permease protein in sorghum-fed larvae and the 6-phospho-ß-glucosidase gene linked to glycolysis/gluconeogenesis as well as starch and sucrose metabolism in pepper-fed larvae were identified. Moreover, the study identified the top 20 ARGs in the gut microbiota of FAW larvae fed on different host plants, with the maize-fed group exhibiting the highest abundance of vanRC. CONCLUSIONS: Our metagenomic sequencing study reveals significant variations in the gut microbiota composition and function of FAW larvae across diverse host plants. These findings underscore the intricate co-evolutionary relationship between hosts and their gut microbiota, suggesting that host transfer profoundly influences the gut microbiota and, consequently, the adaptability and pest management strategies for FAW.

Metagenomic gut microbiome analysis of Japanese patients with multiple chemical sensitivity/idiopathic environmental intolerance.

BACKGROUND: Although the pathology of multiple chemical sensitivity (MCS) is unknown, the central nervous system is reportedly involved. The gut microbiota is important in modifying central nervous system diseases. However, the relationship　between the gut microbiota and MCS remains unclear. This study aimed to identify gut microbiota variations associated with MCS using shotgun metagenomic sequencing of fecal samples. METHODS: We prospectively recruited 30 consecutive Japanese female patients with MCS and analyzed their gut microbiomes using shotgun metagenomic sequencing. The data were compared with metagenomic data obtained from 24 age- and sex-matched Japanese healthy controls (HC). RESULTS: We observed no significant difference in alpha and beta diversity of the gut microbiota between the MCS patients and HC. Focusing on the important changes in the literatures, at the genus level, Streptococcus, Veillonella, and Akkermansia were significantly more abundant in MCS patients than in HC (p < 0.01, p < 0.01, p = 0.01, respectively, fold change = 4.03, 1.53, 2.86, respectively). At the species level, Akkermansia muciniphila was significantly more abundant (p = 0.02, fold change = 3.3) and Faecalibacterium prausnitzii significantly less abundant in MCS patients than in HC (p = 0.03, fold change = 0.53). Functional analysis revealed that xylene and dioxin degradation pathways were significantly enriched (p < 0.01, p = 0.01, respectively, fold change = 1.54, 1.46, respectively), whereas pathways involved in amino acid metabolism and synthesis were significantly depleted in MCS (p < 0.01, fold change = 0.96). Pathways related to antimicrobial resistance, including the two-component system and cationic antimicrobial peptide resistance, were also significantly enriched in MCS (p < 0.01, p < 0.01, respectively, fold change = 1.1, 1.2, respectively). CONCLUSIONS: The gut microbiota of patients with MCS shows dysbiosis and alterations in bacterial functions related to exogenous chemicals and amino acid metabolism and synthesis. These findings may contribute to the further development of treatment for MCS. TRIAL REGISTRATION: This study was registered with the University Hospital Medical Information Clinical Trials Registry as UMIN000031031. The date of first trial registration: 28/01/2018.

Simultaneous identification of DNA and RNA pathogens using metagenomic sequencing in cases of severe acute respiratory infection.

Metagenomic next-generation sequencing (mNGS) is a valuable technique for identifying pathogens. However, conventional mNGS requires the separate processing of DNA and RNA genomes, which can be resource- and time-intensive. To mitigate these impediments, we propose a novel method called DNA/RNA cosequencing that aims to enhance the efficiency of pathogen detection. DNA/RNA cosequencing uses reverse transcription of total nucleic acids extracted from samples by using random primers, without removing DNA, and then employs mNGS. We applied this method to 85 cases of severe acute respiratory infections (SARI). Influenza virus was identified in 13 cases (H1N1: seven cases, H3N2: three cases, unclassified influenza type: three cases) and was not detected in the remaining 72 samples. Bacteria were present in all samples. Pseudomonas aeruginosa, Klebsiella pneumoniae, and Acinetobacter baumannii were detected in four influenza-positive samples, suggesting coinfections. The sensitivity and specificity for detecting influenza A virus were 73.33% and 95.92%, respectively. A κ value of 0.726 indicated a high level of concordance between the results of DNA/RNA cosequencing and SARI influenza virus monitoring. DNA/RNA cosequencing enhanced the efficiency of pathogen detection, providing a novel capability to strengthen surveillance and thereby prevent and control infectious disease outbreaks.

Treatment response in rheumatoid arthritis is predicted by the microbiome: a large observational study in UK DMARD-naïve patients.

OBJECTIVES: Disease-modifying antirheumatic drugs (DMARDs) are first line treatment in rheumatoid arthritis (RA). Treatment response to DMARDs is patient-specific, dose efficacy is difficult to predict and long-term results variable. The gut microbiota are known to play a pivotal role in prodromal and early-disease RA, manifested by Prevotella spp. enrichment. The clinical response to therapy may be mediated by microbiota, and large-scale studies assessing the microbiome are few. This study assessed whether microbiome signals were associated with, and predictive of, patient response to DMARD-treatment. Accurate early identification of those who will respond poorly to DMARD therapy would allow selection of alternative treatment (e.g. biologic therapy), and potentially improve patient outcome. METHODS: A multicentre, longitudinal, observational study of stool- and saliva microbiome was performed in DMARD-naïve, newly diagnosed RA patients during introduction of DMARD treatment. Clinical data and samples were collected at baseline (n = 144) in DMARD-naïve patients and at six weeks (n = 117) and 12 weeks (n = 95) into DMARD-therapy. Samples collected (n = 365 stool, n = 365 saliva) underwent shotgun sequencing. Disease activity measures were collected at each timepoint and minimal clinically important improvement determined. RESULTS: In total, 26 stool microbes were found to decrease in those manifesting a minimal clinically important improvement. Prevotella spp. and Streptococcus spp. were the predominant taxa to decline following six weeks and 12 weeks of DMARDs, respectively. Furthermore, baseline microbiota of DMARD-naïve patients were indicative of future response. CONCLUSION: DMARDs appear to restore a perturbed microbiome to a eubiotic state. Moreover, microbiome status can be used to predict likelihood of patient response to DMARD.

Metagenomic analysis revealed the potential of lactic acid bacteria in improving natural saline-alkali land.

Management and improving saline-alkali land is necessary for sustainable agricultural development. We conducted a field experiment to investigate the effects of spraying lactic acid bacteria (LAB) on the cucumber and tomato plant soils. Three treatments were designed, including spraying of water, viable or sterilized LAB preparations to the soils of cucumber and tomato plants every 20 days. Spraying sterilized or viable LAB could reduce the soil pH, with a more obvious effect by using viable LAB, particularly after multiple applications. Metagenomic sequencing revealed that the soil microbiota in LAB-treated groups had higher alpha-diversity and more nitrogen-fixing bacteria compared with the water-treated groups. Both viable and sterilized LAB, but not water application, increased the complexity of the soil microbiota interactive network. The LAB-treated subgroups were enriched in some KEGG pathways compared with water or sterilized LAB subgroups, such as environmental information processing-related pathways in cucumber plant; and metabolism-related pathways in tomato plant, respectively. Redundancy analysis revealed association between some soil physico-chemical parameters (namely soil pH and total nitrogen) and bacterial biomarkers (namely Rhodocyclaceae, Pseudomonadaceae, Gemmatimonadaceae, and Nitrosomonadales). Our study demonstrated that LAB is a suitable strategy for decreasing soil pH and improving the microbial communities in saline-alkali land.

New insights into honey bee viral and bacterial seasonal infection patterns using third-generation nanopore sequencing on honey bee haemolymph.

Honey bees are rapidly declining, which poses a significant threat to our environment and agriculture industry. These vital insects face a disease complex believed to be caused by a combination of parasites, viruses, pesticides, and nutritional deficiencies. However, the real aetiology is still enigmatic. Due to the conventional analysis methods, we still lack complete insights into the honey bee virome and the presence of pathogenic bacteria. To fill this knowledge gap, we employed third-generation nanopore metagenomic sequencing on honey bee haemolymph to monitor the presence of pathogens over almost a year. This study provides valuable insights into the changes in bacterial and viral loads within honey bee colonies. We identified different pathogens in the honey bee haemolymph, which are not included in honey bee screenings. These pathogens comprise the Apis mellifera filamentous virus, Apis rhabdoviruses, and various bacteria such as Frischella sp. and Arsenophonus sp. Furthermore, a sharp contrast was observed between young and old bees. Our research proposes that transgenerational immune priming may play a role in shaping infection patterns in honey bees. We observed a significant increase in pathogen loads in the spring, followed by a notable decrease in pathogen presence during the summer and autumn months. However, certain pathogens seem to be able to evade this priming effect, making them particularly intriguing as potential factors contributing to mortality. In the future, we aim to expand our research on honey bee transgenerational immune priming and investigate its potential in natural settings. This knowledge will ultimately enhance honey bee health and decrease colony mortality.

Gaps in diagnosing suspected infection in immunocompromised children with cancer: A systematic review.

While the survival of children with cancer has improved over time, infection remains a major morbidity and mortality risk. We conducted a systematic literature review to determine the unmet needs in diagnosing infection in immunocompromised children with cancer. The comprehensive search strategy followed the guidelines established by the Cochrane Handbook for Systematic Reviews of Interventions and the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 statement, and spanned multiple bibliographic databases and other public sources from January 1, 2012 to June 23, 2022. From 5188 records, 34 unique pediatric-focused studies met inclusion criteria. This review highlights the lack of published data on infectious disease testing in pediatric oncology patients, and the need for well-designed clinical impact and cost-effectiveness studies of both existing and novel diagnostic platforms. Such studies are necessary to optimize diagnostic and antimicrobial stewardship, leading to improvement in patient outcomes.

One Health Threat of Treated Wastewater Discharge in Urban Ohio Rivers: Implications for Surface Water and Fish Gut Microbiome and Resistome.

Wastewater treatment plants (WWTPs) are thought to be a major disseminating source of antibiotic resistance (AR) to the environment, establishing a crucial connection between human and environmental resistome. The objectives of this study were to determine how wastewater effluents impact microbiome and resistome of freshwater and fish, and identify potential AR-carrying clinically relevant pathogens in these matrices. We analyzed wastewater influent and effluent from four WWTPs in three metropolitan areas of Ohio, USA via shotgun metagenomic sequencing. We also sequenced river water and fish guts from three reaches (upstream, at the WWTP outfall, and downstream). Notably, we observed a decline in microbiome diversity and AR gene abundance from wastewater to the receiving river. We also found significant differences by reach and trophic level (diet) in beta-diversity of the fish gut microbiomes. SourceTracker revealed that 0.443 and 0.248 more of the of the fish gut microbiome was sourced from wastewater effluent in fish from the outfall and downstream locations, respectively, compared to upstream fish. Additionally, AR bacteria of public health concern were annotated in effluent and river water samples, indicating potential concern for human exposure. In summary, our findings show the continued role of wastewater as a significant AR reservoir and underscores the considerable impact of wastewater discharge on aquatic wildlife, which highlights the One Health nature of this issue.