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

Unlabelled: 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.

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.

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

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 includes pyocyanin, rhamnolipids, siderophores, lytic enzymes, toxins and polysaccharides.

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.

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.

Microbiome depiction through user-adapted bioinformatic pipelines and parameters.

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.

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.

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).

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.

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.

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.

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

causes fatal infections in immunocompromised individuals and patients with pulmonary disorders. Agricultural ecosystems are the vast reservoirs of this dreaded pathogen. However, there are limited attempts to analyse the pathogenicity of strains associated with edible plants.

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.

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.

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.

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.

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.

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.

Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data.

A key challenge in the analysis of longitudinal microbiome data is the inference of temporal interactions between microbial taxa, their genes, the metabolites that they consume and produce, and host genes. To address these challenges, we developed a computational pipeline, a pipeline for the analysis of longitudinal multi-omics data (PALM), that first aligns multi-omics data and then uses dynamic Bayesian networks (DBNs) to reconstruct a unified model. Our approach overcomes differences in sampling and progression rates, utilizes a biologically inspired multi-omic framework, reduces the large number of entities and parameters in the DBNs, and validates the learned network.

JMM Profile: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is the cause of an infection known as coronavirus infectious disease 2019 (COVID-19). COVID-19 has become a global source of morbidity, mortality and social disruption since its emergence in East Asia in late 2019 and subsequent pandemic spread. Typical symptoms include cough, sore throat, fever, and sudden loss of taste and smell.

Causal effects in microbiomes using interventional calculus.

Causal inference in biomedical research allows us to shift the paradigm from investigating associational relationships to causal ones. Inferring causal relationships can help in understanding the inner workings of biological processes. Association patterns can be coincidental and may lead to wrong conclusions about causality in complex systems.

Inferring directional relationships in microbial communities using signed Bayesian networks.

Background: Microbe-microbe and host-microbe interactions in a microbiome play a vital role in both health and disease. However, the structure of the microbial community and the colonization patterns are highly complex to infer even under controlled wet laboratory conditions. In this study, we investigate what information, if any, can be provided by a Bayesian Network (BN) about a microbial community.

So you think you can PLS-DA?

Background: Partial Least-Squares Discriminant Analysis (PLS-DA) is a popular machine learning tool that is gaining increasing attention as a useful feature selector and classifier. In an effort to understand its strengths and weaknesses, we performed a series of experiments with synthetic data and compared its performance to its close relative from which it was initially invented, namely Principal Component Analysis (PCA).

Results: We demonstrate that even though PCA ignores the information regarding the class labels of the samples, this unsupervised tool can be remarkably effective as a feature selector.

Plasmin Cascade Mediates Thrombotic Events in SARS-CoV-2 Infection via Complement and Platelet-Activating Systems.

Recently emerged beta-coronavirus SARS-CoV-2, has resulted in the current pandemic designated COVID-19. COVID-19 manifests as severe illness exhibiting systemic inflammatory response syndrome, acute respiratory distress syndrome (ARDS), thrombotic events, and shock, exacerbated further by co-morbidities and age. Recent clinical evidence suggests that the development of ARDS and subsequent pulmonary failure result from a complex interplay between cell types (endothelial, epithelial and immune) within the lung promoting inflammatory infiltration and a pro-coagulative state.