Summary: Biosurfactants are produced by hydrocarbon‐degrading marine bacteria in response to the presence of water‐insoluble hydrocarbons. This is believed to facilitate the uptake of hydrocarbons by bacteria. However, these diffusible amphiphilic surface‐active molecules are involved in several other biological functions such as microbial competition and intra‐ or inter‐species communication. We report the isolation and characterization of a marine bacterial strain identified as C obetia sp. MM1IDA2H ‐1, which can grow using the sulfur‐containing heterocyclic aromatic hydrocarbon dibenzothiophene ( DBT ). As with DBT , when the isolated strain is grown in the presence of a microbial competitor, it produces a biosurfactant. Because the obtained biosurfactant was formed by hydroxy fatty acids and extracellular lipidic structures were observed during bacterial growth, we investigated whether the biosurfactant at its critical micelle concentration can interfere with bacterial communication systems such as quorum sensing. We focused on A eromonas salmonicida subsp. salmonicida , a fish pathogen whose virulence relies on quorum sensing signals. Using biosensors for quorum sensing based on C hromobacterium violaceum and V ibrio anguillarum , we showed that when the purified biosurfactant was mixed with N ‐acyl homoserine lactones produced by A . salmonicida , quorum sensing was inhibited, although bacterial growth was not affected. In addition, the transcriptional activities of A . salmonicida virulence genes that are controlled by quorum sensing were repressed by both the purified biosurfactant and the growth in the presence of C obetia sp. MM1IDA2H ‐1. We propose that the biosurfactant, or the lipid structures interact with the N ‐acyl homoserine lactones, inhibiting their function. This could be used as a strategy to interfere with the quorum sensing systems of bacterial fish pathogens, which represents an attractive alternative to classical antimicrobial therapies in fish aquaculture.

Kefir beverage is a probiotic food associated with health benefits, containing probiotic microorganisms and biomolecules produced during fermentation. The microbial composition of these beverages varies among countries, geographical regions, and the substrates, therefore, the characterization of kefir beverages is of great relevance in understanding their potential health-promoting and biotechnological applications. Therefore, this study presents the metagenomic and functional characterization of two Chilean kefir beverages, K02 and K03, through shotgun and amplicon-based metagenomic, microbiological, chemical, and biochemical studies. Results show that both beverages’ microbiota were mainly formed by Bacteria (>98%), while Eukarya represented less than 2%. Regarding Bacteria, the most abundant genera were Acetobacter (93.43% in K02 and 80.99% in K03) and Lactobacillus (5.72% in K02 and 16.75% in K03), while Kazachstania was the most abundant genus from Eukarya (42.55% and 36.08% in K02 and K03). Metagenomic analyses revealed metabolic pathways for lactose and casein assimilation, biosynthesis of health-promoting biomolecules, and clusters for antibiotic resistance, quorum sensing communication, and biofilm formation. Enzymatic activities, microbial β-amyloids, and short-chain fatty acids (acetic acid and propionic acid) were also detected in these beverages. Likewise, both kefir beverages inhibited biofilm formation of the opportunistic pathogen Pseudomonas aeruginosa.

Cobetia sp. strain MM1IDA2H-1 is a marine bacterium isolated from seawater samples that uses the heterocyclic aromatic hydrocarbon dibenzothiophene as the sole carbon source and produces a biosurfactant that inhibits bacterial quorum sensing. The Cobetia sp. MM1IDA2H-1 genome was sequenced, processed, assembled, and annotated for basic and applied studies.

This study proposes an extension of Seasonal Autoregressive Integrated Moving Average models with exogenous regressors (SARIMAX) by incorporating skew-normal and zero-inflated skew-normal error structures to better accommodate asymmetry and excess zeros in time series data. The proposed framework demonstrates improved flexibility and robustness compared to traditional Gaussian-based models. Simulation experiments reveal that the skewness parameter significantly affect forecasting accuracy, with reductions in mean absolute error (MAE) and root mean square error (RMSE) observed across both positively and negatively skewed scenarios. Notably, in negative-skew contexts, the model achieved an MAE of 0.40 and RMSE of 0.49, outperforming its symmetric-error counterparts. The inclusion of zero-inflation probabilities further enhances model performance in sparse datasets, yielding superior values in goodness-of-fit criteria such as the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). To illustrate the practical value of the methodology, a real-world case study is presented involving the modeling of optical density (OD₆₀₀) data from Escherichia coli during stationary-phase growth. A SARIMAX(1,1,1) model with skew-normal errors was fitted to 200 time-stamped absorbance measurements, revealing significant positive skewness in the residuals. Bootstrap-derived confidence intervals confirmed the significance of the estimated skewness parameter ($\alpha =14.033$ with 95% CI [12.07, 15.99]). The model outperformed the classical ARIMA benchmark in capturing the asymmetry of the stochastic structure, underscoring its relevance for biological, environmental, and industrial applications in which non-Gaussian features are prevalent.

The Quintero Bay, located along the central coast of Chile, has suffered different oil spills during the past 5 years, impacting marine ecosystems. This report describes the microbial community structure of seawater samples obtained from the Quintero Bay through 16S rRNA amplicon sequencing.