Background Viral-encoded auxiliary metabolic genes (AMGs) are important toolkits for modulating their hosts’ metabolisms and the microbial-driven biogeochemical cycles. Although the functions of AMGs have been extensively reported in numerous environments, we still know little about the drivers that shape the viral community-wide AMG compositions in natural ecosystems. Exploring the drivers of viral community-wide AMG compositions is critical for a deeper understanding of the complex interplays among viruses, hosts, and the environments. Results Here, we investigated the impact of viral lifestyles (i.e., lytic and lysogenic), habitats (i.e., water, particle, and sediment), and prokaryotic hosts on viral AMG profiles by utilizing metagenomic and metatranscriptomic techniques. We found that viral lifestyles were the most important drivers, followed by habitats and host identities. Specifically, irrespective of what habitats viruses came from, lytic viruses exhibited greater AMG diversity and tended to encode AMGs for chaperone biosynthesis, signaling proteins, and lipid metabolism, which could boost progeny reproduction, whereas temperate viruses were apt to encode AMGs for host survivability. Moreover, the lytic and temperate viral communities tended to mediate the microbial-driven biogeochemical cycles, especially nitrogen metabolism, in different manners via AMGs. When focusing on each lifestyle, we further found clear dissimilarity in AMG compositions between water and sediment, as well the divergent AMGs encoded by viruses infecting different host orders. Conclusions Overall, our study provides a first systematic characterization of the drivers of viral community-wide AMG compositions and further expands our knowledge of the distinct interactions of lytic and temperate viruses with their prokaryotic hosts from an AMG perspective, which is critical for understanding virus-host-environment interactions in natural conditions.

No abstract

<abstract> <p>Transcription involves gene activation, nuclear RNA export (NRE) and RNA nuclear retention (RNR). All these processes are multistep and biochemical. A multistep reaction process can create memories between reaction events, leading to non-Markovian kinetics. This raises an unsolved issue: how does molecular memory affect stochastic transcription in the case that NRE and RNR are simultaneously considered? To address this issue, we analyze a non-Markov model, which considers multistep activation, multistep NRE and multistep RNR can interpret many experimental phenomena. In order to solve this model, we introduce an effective transition rate for each reaction. These effective transition rates, which explicitly decode the effect of molecular memory, can transform the original non-Markov issue into an equivalent Markov one. Based on this technique, we derive analytical results, showing that molecular memory can significantly affect the nuclear and cytoplasmic mRNA mean and noise. In addition to the results providing insights into the role of molecular memory in gene expression, our modeling and analysis provide a paradigm for studying more complex stochastic transcription processes.</p> </abstract>

Previous studies assumed that the reaction processes in the chemical Brusselator model are memoryless or Markovian. However, as long as a reactant interacts with its environment, the reaction kinetics cannot be described as a memoryless process. This raises a question: how do we predict the behavior of the chemical Brusselator system with molecular memory characterized by nonexponential waiting-time distributions? Here, a novel technique is developed to address this question. This technique converts a non-Markovian question to a Markovian one by introducing effective transition rates that explicitly decode the memory effect. Based on this conversion, it is analytically shown that molecular memory can induce bifurcations and oscillations. Moreover, a set of sufficient conditions are derived, which can guarantee that the system of the rate equations for the Markovian reaction system generates oscillations via memory index-induced bifurcation. In turn, these conditions can guarantee that the original non-Markovian reaction system generates stochastic oscillations. Numerical simulation verifies the theoretical prediction. The overall analysis indicates that molecular memory is not a negligible factor affecting a chemical system’s behavior.

No abstract