No abstract

For the assessment of cardiac function, heartbeat represents one key parameter. Current methods of heartbeat measurements in the zebrafish larvae usually require larval immobilization, fluorescent transgenic strains and a confocal microscope, costly commercial software for analysis, or strong programming skills if the software is open-source. Here, we present a simple yet powerful method of heartbeat analysis using untethered, unlabeled zebrafish larva using ImageJ (open-source software), which does not require programming skills. We named it as ZebraPace for Zebrafish Precise Algorithm for Cardiac-rhythm Estimation. ZebraPace works directly with AVI videos and requires no image processing steps. ZebraPace uses pixel intensity change in a grayscale video to count the number of beats. We have validated the ZebraPace method by pharmacological alterations of the heartbeat in zebrafish larvae of 48 and 72 hpf stages. We have also determined beat-to-beat interval, which relates to rhythmicity of heartbeat. The results obtained by using ZebraPace corroborates well with the heartbeat values previously reported for similarly aged larvae as determined by using specialized software. We believe that the ZebraPace method is simple, cost-effective, and easy to grasp as it involves fewer steps. It not only reduces the manual workload but also eliminates sample preparation time and researcher subjectivity.

Whole genome sequencing (WGS) provides the highest resolution for genome-based species identification and can provide insight into the antimicrobial resistance and virulence potential of a single microbiological isolate during the diagnostic process. In contrast, metagenomic sequencing allows the analysis of DNA segments from multiple microorganisms within a community, either using an amplicon- or shotgun-based approach. However, WGS and shotgun metagenomic data are rarely combined, although such an approach may generate additive or synergistic information, critical for, e.g., patient management, infection control, and pathogen surveillance. To produce a combined workflow with actionable outputs, we need to understand the pre-to-post analytical process of both technologies. This will require specific databases storing interlinked sequencing and metadata, and also involves customized bioinformatic analytical pipelines. This review article will provide an overview of the critical steps and potential clinical application of combining WGS and metagenomics together for microbiological diagnosis.

Mounting evidence points towards a pivotal role of gut microbiota in multiple sclerosis (MS) pathophysiology. Yet, whether disease-modifying treatments alter microbiota composition and whether microbiota shape treatment response and side-effects remain unclear. In this prospective observational pilot study, we assessed the effect of dimethyl fumarate (DMF) on gut microbiota and on host/microbial metabolomics in a cohort of 20 MS patients. Combining state-of-the-art microbial sequencing, metabolome mass spectrometry, and computational analysis, we identified longitudinal changes in gut microbiota composition under DMF-treatment and an increase in citric acid cycle metabolites. Notably, DMF-induced lymphopenia, a clinically relevant safety concern, was correlated with distinct baseline microbiome signatures in MS patients. We identified gastrointestinal microbiota as a key therapeutic target for metabolic properties of DMF. By characterizing gut microbial composition as a candidate risk factor for DMF-induced lymphopenia, we provide novel insights into the role of microbiota in mediating clinical side-effects.