Unfolding and de-confounding: biologically meaningful causal inference from longitudinal multi-omic networks using METALICA.

A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state of the art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing tools. The results lend support to predictions about biological models and processes by which microbial taxa interact with each other in a microbiome. The unrolling process helps identify putative intermediaries (genes and/or metabolites) to explain the interactions between microbes; the de-confounding process identifies putative common causes that may lead to spurious relationships to be inferred. METALICA was applied to the networks inferred by existing causal discovery, and network inference algorithms were applied to a multi-omics data set resulting from a longitudinal study of IBD microbiomes. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases. IMPORTANCE: We have developed a suite of tools and techniques capable of inferring interactions between microbiome entities. METALICA introduces novel techniques called unrolling and de-confounding that are employed to uncover multi-omic entities considered to be confounders for some of the relationships that may be inferred using standard causal inferencing tools. To evaluate our method, we conducted tests on the inflammatory bowel disease (IBD) dataset from the iHMP longitudinal study, which we pre-processed in accordance with our previous work. From this dataset, we generated various subsets, encompassing different combinations of metagenomics, metabolomics, and metatranscriptomics datasets. Using these multi-omics datasets, we demonstrate how the unrolling process aids in the identification of putative intermediaries (genes and/or metabolites) to explain the interactions between microbes. Additionally, the de-confounding process identifies potential common causes that may give rise to spurious relationships to be inferred. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.

Differences in gut microbiota between Dutch and South-Asian Surinamese: potential implications for type 2 diabetes mellitus.

Gut microbiota, or the collection of diverse microorganisms in a specific ecological niche, are known to significantly impact human health. Decreased gut microbiota production of short-chain fatty acids (SCFAs) has been implicated in type 2 diabetes mellitus (T2DM) disease progression. Most microbiome studies focus on ethnic majorities. This study aims to understand how the microbiome differs between an ethnic majority (the Dutch) and minority (the South-Asian Surinamese (SAS)) group with a lower and higher prevalence of T2DM, respectively. Microbiome data from the Healthy Life in an Urban Setting (HELIUS) cohort were used. Two age- and gender-matched groups were compared: the Dutch (n = 41) and SAS (n = 43). Microbial community compositions were generated via DADA2. Metrics of microbial diversity and similarity between groups were computed. Biomarker analyses were performed to determine discriminating taxa. Bacterial co-occurrence networks were constructed to examine ecological patterns. A tight microbiota cluster was observed in the Dutch women, which overlapped with some of the SAS microbiota. The Dutch gut contained a more interconnected microbial ecology, whereas the SAS network was dispersed, i.e., contained fewer inter-taxonomic correlational relationships. Bacteroides caccae, Butyricicoccus, Alistipes putredinis, Coprococcus comes, Odoribacter splanchnicus, and Lachnospira were enriched in the Dutch gut. Haemophilus, Bifidobacterium, and Anaerostipes hadrus discriminated the SAS gut. All but Lachnospira and certain strains of Haemophilus are known to produce SCFAs. The Dutch gut microbiome was distinguished from the SAS by diverse, differentially abundant SCFA-producing taxa with significant cooperation. The dynamic ecology observed in the Dutch was not detected in the SAS. Among several potential gut microbial biomarkers, Haemophilus parainfluenzae likely best characterizes the ethnic minority group, which is more predisposed to T2DM. The higher prevalence of T2DM in the SAS may be associated with the gut dysbiosis observed.

Genomic and metabolic versatility of Pseudomonas aeruginosa contributes to its inter-kingdom transmission and survival.

Pseudomonas aeruginosa is one of the most versatile bacteria with renowned pathogenicity and extensive drug resistance. The diverse habitats of this bacterium include fresh, saline and drainage waters, soil, moist surfaces, taps, showerheads, pipelines, medical implants, nematodes, insects, plants, animals, birds and humans. The arsenal of virulence factors produced by P. aeruginosa includes pyocyanin, rhamnolipids, siderophores, lytic enzymes, toxins and polysaccharides. All these virulent elements coupled with intrinsic, adaptive and acquired antibiotic resistance facilitate persistent colonization and lethal infections in different hosts. To date, treating pulmonary diseases remains complicated due to the chronic secondary infections triggered by hospital-acquired P. aeruginosa. On the contrary, this bacterium can improve plant growth by suppressing phytopathogens and insects. Notably, P. aeruginosa is one of the very few bacteria capable of trans-kingdom transmission and infection. Transfer of P. aeruginosa strains from plant materials to hospital wards, animals to humans, and humans to their pets occurs relatively often. Recently, we have identified that plant-associated P. aeruginosa strains could be pathologically similar to clinical isolates. In this review, we have highlighted the genomic and metabolic factors that facilitate the dominance of P. aeruginosa across different biological kingdoms and the varying roles of this bacterium in plant and human health.

Detection of SARS-CoV-2 in high-efficiency particulate air (HEPA) filters of low-cost air purifiers in community-based organizations.

This study aims to investigate the presence of SARS-CoV-2 in public spaces and assess the utility of inexpensive air purifiers equipped with high-efficiency particulate air (HEPA) filters for viral detection. Samples were collected from six community-based organizations in underserved minority neighborhoods in Northwest Miami, Florida, from February to May 2022. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to detect SARS-CoV-2 in air purifier filters and surface swabs. Among 32 filters tested, three yielded positive results, while no positive surface swabs were found. Notably, positive samples were obtained exclusively from child daycare centers. These findings highlight the potential for airborne transmission of SARS-CoV-2 in indoor air, particularly in child daycare centers. Moreover, the study demonstrates the effectiveness of readily available HEPA filters in detecting the virus. Improving indoor ventilation and implementing air filtration systems are crucial in reducing COVID-19 transmission where people gather. Air filtration systems incorporating HEPA filters offer a valuable approach to virus detection and reducing transmission risks. Future research should explore the applicability of this technology for early identification and mitigation of viral outbreaks.

Microbiome depiction through user-adapted bioinformatic pipelines and parameters.

Introduction. The role of the microbiome in health and disease continues to be increasingly recognized. However, there is significant variability in the bioinformatic protocols for analysing genomic data. This, in part, has impeded the potential incorporation of microbiomics into the clinical setting and has challenged interstudy reproducibility. In microbial compositional analysis, there is a growing recognition for the need to move away from a one-size-fits-all approach to data processing.Gap Statement. Few evidence-based recommendations exist for setting parameters of programs that infer microbiota community profiles despite these parameters significantly impacting the accuracy of taxonomic inference.Aim. To compare three commonly used programs (DADA2, QIIME2, and mothur) and optimize them into four user-adapted pipelines for processing paired-end amplicon reads. We aim to increase the accuracy of compositional inference and help standardize microbiomic protocol.Methods. Two key parameters were isolated across four pipelines: filtering sequence reads based on a whole-number error threshold (maxEE) and truncating read ends based on a quality score threshold (QTrim). Closeness of sample inference was then evaluated using a mock community of known composition.Results. We observed that raw genomic data lost were proportionate to how stringently parameters were set. Exactly how much data were lost varied by pipeline. Accuracy of sample inference correlated with increased sequence read retention. Falsely detected taxa and unaccounted for microbial constituents were unique to pipeline and parameter. Implementation of optimized parameter values led to better approximation of the known mock community.Conclusions. Microbial compositions generated based on the 16S rRNA marker gene should be interpreted with caution. To improve microbial community profiling, bioinformatic protocols must be user-adapted. Analysis should be performed with consideration for the select target amplicon, pipelines and parameters used, and taxa of interest.

Attention Deficit Hyperactivity Disorder (ADHD) and the gut microbiome: An ecological perspective.

Attention Deficit Hyperactivity Disorder (ADHD) is an increasingly prevalent neuropsychiatric disorder characterized by hyperactivity, inattention, and impulsivity. Symptoms emerge from underlying deficiencies in neurocircuitry, and recent research has suggested a role played by the gut microbiome. The gut microbiome is an ecosystem of interdependent taxa involved in an exponentially complex web of interactions, plus host gene and reaction pathways, some of which involve neurotransmitters with roles in ADHD neurocircuitry. Studies have analyzed the ADHD gut microbiome using macroscale metrics such as diversity and differential abundance, and have proposed several taxa as elevated or reduced in ADHD compared to Control. Few studies have delved into the complex underlying dynamics ultimately responsible for the emergence of such metrics, leaving a largely incomplete, sometimes contradictory, and ultimately inconclusive picture. We aim to help complete this picture by venturing beyond taxa abundances and into taxa relationships (i.e. cooperation and competition), using a publicly available gut microbiome dataset (targeted 16S, v3-4 region, qPCR) from an observational, case-control study of 30 Control (15 female, 15 male) and 28 ADHD (15 female, 13 male) undergraduate students. We first perform the same macroscale analyses prevalent in ADHD gut microbiome literature (diversity, differential abundance, and composition) to observe the degree of correspondence, or any new trends. We then estimate two-way ecological relationships by producing Control and ADHD Microbial Co-occurrence Networks (MCNs), using SparCC correlations (p ≤ 0.01). We perform community detection to find clusters of taxa estimated to mutually cooperate along with their centroids, and centrality calculations to estimate taxa most vital to overall gut ecology. We finally summarize our results, providing conjectures on how they can guide future experiments, some methods for improving our experiments, and general implications for the field.

Raising awareness of antimicrobial resistance: development of an 'antibiotic footprint calculator'.

Non-academic partners can be vital in successful public engagement activities on antimicrobial resistance. With collaboration between academic and non-academic partners, we developed and launched an open-access web-based application, the 'antibiotic footprint calculator', in both Thai and English. The application focused on a good user experience, addressing antibiotic overuse and its impact, and encouraging immediate action. The application was unveiled in joint public engagement activities. From 1 Nov 2021 to 31 July 2022 (9 month period), 2554 players estimated their personal antibiotic footprint by using the application.

Lower respiratory tract microbiome composition and community interactions in smokers.

The lung microbiome impacts on lung function, making any smoking-induced changes in the lung microbiome potentially significant. The complex co-occurrence and co-avoidance patterns between the bacterial taxa in the lower respiratory tract (LRT) microbiome were explored for a cohort of active (AS), former (FS) and never (NS) smokers. Bronchoalveolar lavages (BALs) were collected from 55 volunteer subjects (9 NS, 24 FS and 22 AS). The LRT microbiome composition was assessed using 16S rRNA amplicon sequencing. Identification of differentially abundant taxa and co-occurrence patterns, discriminant analysis and biomarker inferences were performed. The data show that smoking results in a loss in the diversity of the LRT microbiome, change in the co-occurrence patterns and a weakening of the tight community structure present in healthy microbiomes. The increased abundance of the genus Ralstonia in the lung microbiomes of both former and active smokers is significant. Partial least square discriminant and DESeq2 analyses suggested a compositional difference between the cohorts in the LRT microbiome. The groups were sufficiently distinct from each other to suggest that cessation of smoking may not be sufficient for the lung microbiota to return to a similar composition to that of NS. The linear discriminant analysis effect size (LEfSe) analyses identified several bacterial taxa as potential biomarkers of smoking status. Network-based clustering analysis highlighted different co-occurring and co-avoiding microbial taxa in the three groups. The analysis found a cluster of bacterial taxa that co-occur in smokers and non-smokers alike. The clusters exhibited tighter and more significant associations in NS compared to FS and AS. Higher degree of rivalry between clusters was observed in the AS. The groups were sufficiently distinct from each other to suggest that cessation of smoking may not be sufficient for the lung microbiota to return to a similar composition to that of NS.

Microbiome Dysbiosis Shows Strong Association of Gut-Derived Altered Metabolomic Profile in Gulf War Chronic Multisymptom Illness Symptom Persistence Following Western Diet Feeding and Development of Obesity.

The pathophysiology of Gulf War Illness (GWI) remains elusive even after three decades. The persistence of multiple complex symptoms along with metabolic disorders such as obesity worsens the health of present Gulf War (GW) Veterans often by the interactions of the host gut microbiome and inflammatory mediators. In this study, we hypothesized that the administration of a Western diet might alter the host metabolomic profile, which is likely associated with the altered bacterial species. Using a five-month symptom persistence GWI model in mice and whole-genome sequencing, we characterized the species-level dysbiosis and global metabolomics, along with heterogenous co-occurrence network analysis, to study the bacteriome-metabolomic association. Microbial analysis at the species level showed a significant alteration of beneficial bacterial species. The beta diversity of the global metabolomic profile showed distinct clustering due to the Western diet, along with the alteration of metabolites associated with lipid, amino acid, nucleotide, vitamin, and xenobiotic metabolism pathways. Network analysis showed novel associations of gut bacterial species with metabolites and biochemical pathways that could be used as biomarkers or therapeutic targets to ameliorate symptom persistence in GW Veterans.

Unfolding and De-confounding: Biologically meaningful causal inference from longitudinal multi-omic networks using METALICA.

A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state-of-the-art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing tools. The results lend support to predictions about biological models and processes by which microbial taxa interact with each other in a microbiome. The unrolling process helps to identify putative intermediaries (genes and/or metabolites) to explain the interactions between microbes; the de-confounding process identifies putative common causes that may lead to spurious relationships to be inferred. METALICA was applied to the networks inferred by existing causal discovery and network inference algorithms applied to a multi-omics data set resulting from a longitudinal study of IBD microbiomes. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.

Plant-associated Pseudomonas aeruginosa strains harbour multiple virulence traits critical for human infection.

Introduction. Pseudomonas aeruginosa causes fatal infections in immunocompromised individuals and patients with pulmonary disorders.Gap Statement. Agricultural ecosystems are the vast reservoirs of this dreaded pathogen. However, there are limited attempts to analyse the pathogenicity of P. aeruginosa strains associated with edible plants.Aim. This study aims to (i) elucidate the virulence attributes of P. aeruginosa strains isolated from the rhizosphere and endophytic niches of cucumber, tomato, eggplant and chili;and (ii) compare these phenotypes with that of previously characterized clinical isolates.Methodology. Crystal-violet microtitre assay, swarm plate experiment, gravimetric quantification and sheep blood lysis were performed to estimate the biofilm formation, swarming motility, rhamnolipid production and haemolytic activity, respectively, of P. aeruginosa strains. In addition, their pathogenicity was also assessed based on their ability to antagonize plant pathogens (Xanthomonas oryzae, Pythium aphanidermatum, Rhizoctonia solani and Fusarium oxysporum) and kill a select nematode (Caenorhabditis elegans).Results. Nearly 80 % of the plant-associated strains produced rhamnolipid and exhibited at least one type of lytic activity (haemolysis, proteolysis and lipolysis). Almost 50 % of these strains formed significant levels of biofilm and exhibited swarming motility. The agricultural strains showed significantly higher and lower virulence against the bacterial and fungal pathogens, respectively, compared to the clinical strains. In C. elegans, a maximum of 40 and 100% mortality were induced by the agricultural and clinical strains, respectively.Conclusion. This investigation shows that P. aeruginosa in edible plants isolated directly from the farm express virulence and pathogenicity. Furthermore, clinical and agricultural P. aeruginosa strains antagonized the tested fungal phytopathogens, Pythium aphanidermatum, Rhizoctonia solani and Fusarium oxysporum. Thus, we recommend using these fungi as simple eukaryotic model systems to test P. aeruginosa pathogenicity.

Prior exposure to microcystin alters host gut resistome and is associated with dysregulated immune homeostasis in translatable mouse models.

A strong association between exposure to the common harmful algal bloom toxin microcystin and the altered host gut microbiome has been shown. We tested the hypothesis that prior exposure to the cyanotoxin microcystin-LR may alter the host resistome. We show that the mice exposed to microcystin-LR had an altered microbiome signature that harbored antibiotic resistance genes. Host resistome genotypes such as mefA, msrD, mel, ant6, and tet40 increased in diversity and relative abundance following microcystin-LR exposure. Interestingly, the increased abundance of these genes was traced to resistance to common antibiotics such as tetracycline, macrolides, glycopeptide, and aminoglycosides, crucial for modern-day treatment of several diseases. Increased abundance of these genes was positively associated with increased expression of PD1, a T-cell homeostasis marker, and pleiotropic inflammatory cytokine IL-6 with a concomitant negative association with immunosurveillance markers IL-7 and TLR2. Microcystin-LR exposure also caused decreased TLR2, TLR4, and REG3G expressions, increased immunosenescence, and higher systemic levels of IL-6 in both wild-type and humanized mice. In conclusion, the results show a first-ever characterization of the host resistome following microcystin-LR exposure and its connection to host immune status and antimicrobial resistance that can be crucial to understand treatment options with antibiotics in microcystin-exposed subjects in clinical settings.

Potential Autoimmunity Resulting from Molecular Mimicry between SARS-CoV-2 Spike and Human Proteins.

Molecular mimicry between viral antigens and host proteins can produce cross-reacting antibodies leading to autoimmunity. The coronavirus SARS-CoV-2 causes COVID-19, a disease curiously resulting in varied symptoms and outcomes, ranging from asymptomatic to fatal. Autoimmunity due to cross-reacting antibodies resulting from molecular mimicry between viral antigens and host proteins may provide an explanation. Thus, we computationally investigated molecular mimicry between SARS-CoV-2 Spike and known epitopes. We discovered molecular mimicry hotspots in Spike and highlight two examples with tentative high autoimmune potential and implications for understanding COVID-19 complications. We show that a TQLPP motif in Spike and thrombopoietin shares similar antibody binding properties. Antibodies cross-reacting with thrombopoietin may induce thrombocytopenia, a condition observed in COVID-19 patients. Another motif, ELDKY, is shared in multiple human proteins, such as PRKG1 involved in platelet activation and calcium regulation, and tropomyosin, which is linked to cardiac disease. Antibodies cross-reacting with PRKG1 and tropomyosin may cause known COVID-19 complications such as blood-clotting disorders and cardiac disease, respectively. Our findings illuminate COVID-19 pathogenesis and highlight the importance of considering autoimmune potential when developing therapeutic interventions to reduce adverse reactions.

New dynamic microreactor system to mimic biofilm formation and test anti-biofilm activity of nanoparticles.

Microbial biofilms are composed of surface-adhered microorganisms enclosed in extracellular polymeric substances. The biofilm lifestyle is the intrinsic drug resistance imparted to bacterial cells protected by the matrix. So far, conventional drug susceptibility tests for biofilm are reagent and time-consuming, and most of them are in static conditions. Rapid and easy-to-use methods for biofilm formation and antibiotic activity testing need to be developed to accelerate the discovery of new antibiofilm strategies. Herein, a Lab-On-Chip (LOC) device is presented that provides optimal microenvironmental conditions closely mimicking real-life clinical biofilm status. This new device allows homogeneous attachment and immobilization of Pseudomonas aeruginosa PA01-EGFP cells, and the biofilms grown can be monitored by fluorescence microscopy. P. aeruginosa is an opportunistic pathogen known as a model for drug screening biofilm studies. The influence of flow rates on biofilms growth was analyzed by flow simulations using COMSOL® 5.2. Significant cell adhesion to the substrate and biofilm formation inside the microchannels were observed at higher flow rates > 100 µL/h. After biofilm formation, the effectiveness of silver nanoparticles (SNP), chitosan nanoparticles (CNP), and a complex of chitosan-coated silver nanoparticles (CSNP) to eradicate the biofilm under a continuous flow was explored. The most significant loss of biofilm was seen with CSNP with a 65.5% decrease in average live/dead cell signal in biofilm compared to the negative controls. Our results demonstrate that this system is a user-friendly tool for antibiofilm drug screening that could be simply applied in clinical laboratories.Key Points• A continuous-flow microreactor that mimics real-life clinical biofilm infections was developed.• The antibiofilm activity of three nano drugs was evaluated in dynamic conditions.• The highest biofilm reduction was observed with chitosan-silver nanoparticles.

A novel approach for combining the metagenome, metaresistome, metareplicome and causal inference to determine the microbes and their antibiotic resistance gene repertoire that contribute to dysbiosis.

The use of whole metagenomic data to infer the relative abundance of all its microbes is well established. The same data can be used to determine the replication rate of all eubacterial taxa with circular chromosomes. Despite their availability, the replication rate profiles (metareplicome) have not been fully exploited in microbiome analyses. Another relatively new approach is the application of causal inferencing to analyse microbiome data that goes beyond correlational studies. A novel scalable pipeline called MeRRCI (Metagenome, metaResistome, and metaReplicome for Causal Inferencing) was developed. MeRRCI combines efficient computation of the metagenome (bacterial relative abundance), metaresistome (antimicrobial gene abundance) and metareplicome (replication rates), and integrates environmental variables (metadata) for causality analysis using Bayesian networks. MeRRCI was applied to an infant gut microbiome data set to investigate the microbial community's response to antibiotics. Our analysis suggests that the current treatment stratagem contributes to preterm infant gut dysbiosis, allowing a proliferation of pathobionts. The study highlights the specific antibacterial resistance genes that may contribute to exponential cell division in the presence of antibiotics for various pathogens, namely Klebsiella pneumoniae, Citrobacter freundii, Staphylococcus epidermidis, Veilonella parvula and Clostridium perfringens. These organisms often contribute to the harmful long-term sequelae seen in these young infants.

Rhizospheric and endophytic Pseudomonas aeruginosa in edible vegetable plants share molecular and metabolic traits with clinical isolates.

AIM: Pseudomonas aeruginosa, a leading opportunistic pathogen causing hospital-acquired infections, is also commonly found in agricultural settings. However, there are minimal attempts to examine the molecular and functional attributes shared by agricultural and clinical strains of P. aeruginosa. This study investigates the presence of P. aeruginosa in edible vegetable plants (including salad vegetables) and analyses the evolutionary and metabolic relatedness of the agricultural and clinical strains. METHODS AND RESULTS: Eighteen rhizospheric and endophytic P. aeruginosa strains were isolated from cucumber, tomato, eggplant, and chili directly from the farms. The identity of these strains was confirmed using biochemical and molecular assays. The genetic and metabolic traits of these plant-associated P. aeruginosa isolates were compared with clinical strains. DNA fingerprinting and 16S rDNA-based phylogenetic analyses revealed that the plant- and human-associated strains are evolutionarily related. Both agricultural and clinical isolates possessed plant-beneficial properties, including mineral solubilization to release essential nutrients (phosphorous, potassium, and zinc), ammonification, and the ability to release extracellular pyocyanin, siderophore, and indole-3 acetic acid. CONCLUSION: These findings suggest that rhizospheric and endophytic P. aeruginosa strains are genetically and functionally analogous to the clinical isolates. In addition, the genotypic and phenotypic traits do not correlate with plant sources or ecosystems. SIGNIFICANCE AND IMPACT OF THE STUDY: This study reconfirms that edible plants are the potential source for human and animal transmission of P. aeruginosa.

Multi-Run Concrete Autoencoder to Identify Prognostic lncRNAs for 12 Cancers.

BACKGROUND: Long non-coding RNA plays a vital role in changing the expression profiles of various target genes that lead to cancer development. Thus, identifying prognostic lncRNAs related to different cancers might help in developing cancer therapy. METHOD: To discover the critical lncRNAs that can identify the origin of different cancers, we propose the use of the state-of-the-art deep learning algorithm concrete autoencoder (CAE) in an unsupervised setting, which efficiently identifies a subset of the most informative features. However, CAE does not identify reproducible features in different runs due to its stochastic nature. We thus propose a multi-run CAE (mrCAE) to identify a stable set of features to address this issue. The assumption is that a feature appearing in multiple runs carries more meaningful information about the data under consideration. The genome-wide lncRNA expression profiles of 12 different types of cancers, with a total of 4768 samples available in The Cancer Genome Atlas (TCGA), were analyzed to discover the key lncRNAs. The lncRNAs identified by multiple runs of CAE were added to a final list of key lncRNAs that are capable of identifying 12 different cancers. RESULTS: Our results showed that mrCAE performs better in feature selection than single-run CAE, standard autoencoder (AE), and other state-of-the-art feature selection techniques. This study revealed a set of top-ranking 128 lncRNAs that could identify the origin of 12 different cancers with an accuracy of 95%. Survival analysis showed that 76 of 128 lncRNAs have the prognostic capability to differentiate high- and low-risk groups of patients with different cancers. CONCLUSION: The proposed mrCAE, which selects actual features, outperformed the AE even though it selects the latent or pseudo-features. By selecting actual features instead of pseudo-features, mrCAE can be valuable for precision medicine. The identified prognostic lncRNAs can be further studied to develop therapies for different cancers.

Effect of metronidazole on vaginal microbiota associated with asymptomatic bacterial vaginosis.

Vaginal dysbiosis-induced by an overgrowth of anaerobic bacteria is referred to as bacterial vaginosis (BV). The dysbiosis is associated with an increased risk for acquisition of sexually transmitted infections. Women with symptomatic BV are treated with oral metronidazole (MET), but its effectiveness remains to be elucidated. This study used whole-genome sequencing (WGS) to determine the changes in the microbiota among women treated with MET. WGS was conducted on DNA obtained from 20 vaginal swabs collected at four time points over 12 months from five randomly selected African American (AA) women. The baseline visit included all women who were diagnosed with asymptomatic BV and were untreated. All subjects were tested subsequently once every 2 months and received a course of MET for each BV episode during the 12 months. The BV status was classified according to Nugent scores (NSs) of vaginal smears. The microbial and resistome profiles were analysed along with the sociodemographic metadata. Despite treatment, none of the five participants reverted to normal vaginal flora - two were consistently positive for BV, and the rest experienced episodic cases of BV. WGS analyses showed Gardnerella spp. as the most abundant organism. After treatment with MET, there was an observed decline of Lactobacillus and Prevotella species. One participant had a healthy vaginal microbiota based on NS at one follow-up time point. Resistance genes including tetM and lscA were detected. Though limited in subjects, this study shows specific microbiota changes with treatment, presence of many resistant genes in their microbiota, and recurrence and persistence of BV despite MET treatment. Thus, MET may not be an effective treatment option for asymptomatic BV, and whole metagenome sequence would better inform the choice of antibiotics.

Journal of Medical Microbiology: global footprint and appointment of a regional Editorial Board.

The Journal of Medical Microbiology has a global presence with an international Editorial Board. Asian countries such as PR China, India and Iran are prolific in the submission of manuscripts. Overall, the acceptance rate has been highest for European countries, the USA, Canada and Australia, and lowest for African, Asian and Latin American (LATAM) countries. The creation of regional Editors to assist the authors from these countries would serve the scientific community.