Regularized S-Map Reveals Varying Bacterial Interactions

Abstract

There is a growing awareness that the bacterial interactions should follow a highly nonlinear pattern in reality. However, it is challenging to tract the varying bacterial interactions using the pair-wise correlation analysis, which fails to explore their potential effects on the behavior of microbes. Here, we utilize the regularized S-map to capture the varying interspecific interactions from the time-series data of bacterial community under the exposure to nitrite. Our results show that the bacterial interactions are highly variable and asymmetric interactions dominate the interaction pattern in community. Furthermore, we propose a Jacobian coefficient-based statistical method to predict the harmony level of a bacterial community at each successive ecosystem state. The result shows that bacterial community exhibits higher harmony level in nitrite-treated samples than the control group. We show that the community harmony level is positively associated with the specific endogenous respiration rates and biofilm formation of the culture. In addition, the community tends to process lower diversity and structural stability under zero and high nitrite stresses. We demonstrate that harmony level, other than structural stability, is a useful index to unveil the underlying mechanism of bacterial performance. Overall, the regularized S-map can help us to understand the bacterial interactions in eco-systems more accurately than previous approaches. Importance It has long been acknowledged that bacterial interactions play important roles in the community structure and function. Revealing the interaction variability can allow an understanding of how bacteria response to perturbation and why bacterial community performance changes. Such information should improve our skills to engineer the bacterial community (e.g., wastewater treatment plant) and achieve better removal performance and lower energy consumption.

Causal networks of phytoplankton diversity and biomass are modulated by environmental context

“…Furthermore, our analyses based on CCM cannot access the sign of feedbacks (i.e., positive or negative), although it is known that the sign is important in determining the response of feedbacks to external perturbations (e.g., amplified or dampened). Although methods to estimate the sign of interactions were proposed (e.g., S-map 22 , 57 ), the robustness of these methods has not been thoroughly examined 58 . Lastly, due to limitations of data availability, our analysis only quantified causal strength across systems at a consensus monthly scale, acknowledging that state-space reconstruction methods (e.g., CCM) are scale-dependent 59 , e.g., one causal driver dominated monthly might not necessarily dominate at other time scales.…”

Convergency and Stability Responses of Bacterial Communities to Salinization in Arid and Semiarid Areas: Implications for Global Climate Change in Lake Ecosystems

“…This is in agreement with the results of our study, in which positive interactions accounted for approximately 87.36% of the interactions within the four networks. Crucially, however, the balance of mutualistic and antagonistic interactions in bacterial communities is likely environment-dependent ( Yu et al, 2020 ). The previous stress gradient hypothesis (SGH) predicts that a harsh environment intensifies antagonistic interactions, while a benign environment favors mutualistic interactions ( Bertness and Callaway, 1994 ).…”

“…A possible reason is that mutualism leads to unbounded positive feedbacks, which create instability ( Coyte et al, 2015 ). To dampen these unbounded positive feedbacks, antagonistic interactions limit the benefits that a focal species receives from its mutualist partners ( Qian and Akcay, 2020 ) and thus increase community stability ( Yu et al, 2020 ). Induced by salinization, decreasing biodiversity and increasing mutualistic interactions collectively destabilize bacterial communities.…”

Time‐lagged interspecies interactions prevail during biofilm development in moving bed biofilm reactor

“…PCR amplification of the bacterial 16S rRNA gene V3‐V4 region was performed using primers 338F (5‐ACTCCTACGGGAGGCAGCAG‐3) and 806R (5‐GGACTACHVGGGTWTCTAAT‐3). The PCR components and reactions were performed in accordance with our previous study (Yu et al, 2020). The PCR products were purified using Agencourt AMPure Beads (Beckman Coulter) and quantified using the PicoGreen dsDNA Assay Kit (Invitrogen).…”