Computational estimation of sediment symbiotic bacterial structures of seagrasses overgrowing downstream of onshore aquaculture

Validation of causal inference data using DirectLiNGAM in an environmental small-scale model and calculation settings

The development of data science has been needed in environmental fields such as marine, weather, and soil data. In general, the datasets are large in some cases, but they are often small because they contain observation data that the analyses themselves are limited. In such a case, the data are statistically evaluated by increasing or decreasing the levels of factors using differential analysis, resulting in the essential factors are estimated. However, there is no consistent approach to the means of assessing strong associations as a group between factors. Causal inference method has the possibility to output effective results for small data, and the results are expected to provide important information for understanding the potential highly association between factors, not necessarily the inference with big data. Here, we describe essential checkpoints and settings for the calculation by a direct method for learning a linear non-Gaussian structural equation model (DirectLiNGAM) and validation methods for the calculation results by using DirectLiNGAM with small-scale model data as an additional discussion of DirectLiNGAM portion of the related research article. Thus, this study provides the statistical validation methods for the association networks, treatments, and interventions for structural inference as a group of essential factors.•Causal inference with DirectLiNGAM•Validation of correlation coefficient and feature importance•Validation using causal effect object and propensity scores.

Microbial Detoxification of Sediments Underpins Persistence of Zostera marina Meadows

Eelgrass meadows have attracted much attention not only for their ability to maintain marine ecosystems as feeding grounds for marine organisms but also for their potential to store atmospheric and dissolved CO2 as blue carbon. This study comprehensively evaluated the bacterial and chemical data obtained from eelgrass sediments of different scales along the Japanese coast to investigate the effect on the acclimatization of eelgrass. Regardless of the eelgrass habitat, approximately 1% Anaerolineales, Babeliales, Cytophagales, and Phycisphaerales was present in the bottom sediment. Sulfate-reducing bacteria (SRB) were present at 3.69% in eelgrass sediment compared to 1.70% in bare sediment. Sulfur-oxidizing bacteria (SOB) were present at 2.81% and 1.10% in the eelgrass and bare sediment, respectively. Bacterial composition analysis and linear discriminant analysis revealed that SOB detoxified H2S in the eelgrass meadows and that the larger-scale eelgrass meadows had a higher diversity of SOB. Our result indicated that there were regional differences in the system that detoxifies H2S in eelgrass meadows, either microbial oxidation mediated by SOB or O2 permeation via the physical diffusion of benthos. However, since bacterial flora and phylogenetic analyses cannot show bias and/or causality due to PCR, future kinetic studies on microbial metabolism are expected.

Alteration of shoaling behavior and dysbiosis in the gut of medaka (Oryzias latipes) exposed to 2-μm polystyrene microplastics

There is global concern that microplastics may harm aquatic life. Here, we examined the effects of fine polystyrene microplastics (PS-MPs, 2-μm diameter, 0.1 mg/L, 2.5 × 107 particles/L) on the behavior and the microbiome (linked to brain-gut interaction) of a fish model using medaka, Oryzias latipes. We found that shoaling behavior was reduced in PS-MP-exposed medaka compared with control fish during the exposure period, but it recovered during a depuration period. There was no difference in swimming speed between the PS-MP-exposed and control groups during the exposure period. Analysis of the dominant bacterial population (those comprising ≥1% of the total bacterial population) in the gut of fish showed that exposure to PS-MPs tended to increase the relative abundance of the phylum Fusobacteria and the genus Vibrio. Furthermore, structural-equation modeling of gut bacteria on the basis of machine-learning data estimated strong relationship involved in the reduction of the functional bacterial species of minority (<1% of the total bacterial population) such as the genera Muribaculum (an undefined role), Aquaspirillum (a candidate for nitrate metabolism and magnetotactics), and Clostridium and Phascolarctobacterium (potential producers of short-chain fatty acids, influencing behavior by affecting levels of neurotransmitters) as a group of gut bacteria in association with PS-MP exposure. Our results suggest that fish exposure to fine microplastics may cause dysbiosis and ultimately cause social behavior disorders linked to brain-gut interactions. This effect could be connected to reduction of fish fitness in the ecosystem and reduced fish survival.

Estimation of silent phenotypes of calf antibiotic dysbiosis

Reducing antibiotic usage among livestock animals to prevent antimicrobial resistance has become an urgent issue worldwide. This study evaluated the effects of administering chlortetracycline (CTC), a versatile antibacterial agent, on the performance, blood components, fecal microbiota, and organic acid concentrations of calves. Japanese Black calves were fed with milk replacers containing CTC at 10 g/kg (CON group) or 0 g/kg (EXP group). Growth performance was not affected by CTC administration. However, CTC administration altered the correlation between fecal organic acids and bacterial genera. Machine learning (ML) methods such as association analysis, linear discriminant analysis, and energy landscape analysis revealed that CTC administration affected populations of various types of fecal bacteria. Interestingly, the abundance of several methane-producing bacteria at 60 days of age was high in the CON group, and the abundance of Lachnospiraceae, a butyrate-producing bacterium, was high in the EXP group. Furthermore, statistical causal inference based on ML data estimated that CTC treatment affected the entire intestinal environment, potentially suppressing butyrate production, which may be attributed to methanogens in feces. Thus, these observations highlight the multiple harmful impacts of antibiotics on the intestinal health of calves and the potential production of greenhouse gases by calves.