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The degree of cyclization, or ring index (RI), in archaeal glycerol dibiphytanyl glycerol tetraether (GDGT) lipids was long thought to reflect homeoviscous adaptation to temperature. However, more recent experiments show that other factors (e.g., pH, growth phase, and energy flux) can also affect membrane composition. The main objective of this study was to investigate the effect of carbon and energy metabolism on membrane cyclization. To do so, we cultivatedAcidianussp. DS80, a metabolically flexible and thermoacidophilic archaeon, on different electron donor, acceptor, and carbon source combinations (S⁰/Fe³⁺/CO₂, H₂/Fe³⁺/CO₂, H₂/S⁰/CO₂, or H₂/S⁰/glucose). We show that differences in energy and carbon metabolism can result in over a full unit of change in RI in the thermoacidophileAcidianussp. DS80. The patterns in RI correlated with the normalized electron transfer rate between the electron donor and acceptor and did not always align with thermodynamic predictions of energy yield. In light of this, we discuss other factors that may affect the kinetics of cellular energy metabolism: electron transfer chain (ETC) efficiency, location of ETC reaction components (cytoplasmicvs.extracellular), and the physical state of electron donors and acceptors (gasvs.solid). Furthermore, the assimilation of a more reduced form of carbon during heterotrophy appears to decrease the demand for reducing equivalents during lipid biosynthesis, resulting in lower RI. Together, these results point to the fundamental role of the cellular energy state in dictating GDGT cyclization, with those cells experiencing greater energy limitation synthesizing more cyclized GDGTs.

Some archaea make unique membrane-spanning lipids with different numbers of five- or six-membered rings in the core structure, which modulate membrane fluidity and permeability. Changes in membrane core lipid composition reflect the fundamental adaptation strategies of archaea in response to stress, but multiple environmental and physiological factors may affect the needs for membrane fluidity and permeability. In this study, we tested howAcidianussp. DS80 changed its core lipid composition when grown with different electron donor/acceptor pairs. We show that changes in energy and carbon metabolisms significantly affected the relative abundance of rings in the core lipids of DS80. These observations highlight the need to better constrain metabolic parameters, in addition to environmental factors, which may influence changes in membrane physiology in Archaea. Such consideration would be particularly important for studying archaeal lipids from habitats that experience frequent environmental fluctuations and/or where metabolically diverse archaea thrive.

 

Solving multiphysics-based inverse problems for geological carbon storage monitoring can be challenging when multimodal time-lapse data are expensive to collect and costly to simulate numerically. We overcome these challenges by combining computationally cheap learned surrogates with learned constraints. Not only does this combination lead to vastly improved inversions for the important fluid-flow property, permeability, it also provides a natural platform for inverting multimodal data including well measurements and active-source time-lapse seismic data. By adding a learned constraint, we arrive at a computationally feasible inversion approach that remains accurate. This is accomplished by including a trained deep neural network, known as a normalizing flow, which forces the model iterates to remain in-distribution, thereby safeguarding the accuracy of trained Fourier neural operators that act as surrogates for the computationally expensive multiphase flow simulations involving partial differential equation solves. By means of carefully selected experiments, centered around the problem of geological carbon storage, we demonstrate the efficacy of the proposed constrained optimization method on two different data modalities, namely time-lapse well and time-lapse seismic data. While permeability inversions from both these two modalities have their pluses and minuses, their joint inversion benefits from either, yielding valuable superior permeability inversions and CO₂plume predictions near, and far away, from the monitoring wells.

 

Archaea adjust the number of cyclopentane rings in their glycerol dibiphytanyl glycerol tetraether (GDGT) membrane lipids as a homeostatic response to environmental stressors such as temperature, pH, and energy availability shifts. However, archaeal expression patterns that correspond with changes in GDGT composition are less understood. Here we characterize the acid and cold stress responses of the thermoacidophilic crenarchaeonSaccharolobus islandicusREY15A using growth rates, core GDGT lipid profiles, transcriptomics and proteomics. We show that both stressors result in impaired growth, lower average GDGT cyclization, and differences in gene and protein expression. Transcription data revealed differential expression of the GDGT ring synthasegrsBin response to both acid stress and cold stress. Although the GDGT ring synthase encoded bygrsBforms highly cyclized GDGTs with ≥5 ring moieties,S. islandicus grsBupregulation under acidic pH conditions did not correspond with increased abundances of highly cyclized GDGTs. Our observations highlight the inability to predict GDGT changes from transcription data alone. Broader analysis of transcriptomic data revealed thatS. islandicusdifferentially expresses many of the same transcripts in response to both acid and cold stress. These included upregulation of several biosynthetic pathways and downregulation of oxidative phosphorylation and motility. Transcript responses specific to either of the two stressors tested here included upregulation of genes related to proton pumping and molecular turnover in acid stress conditions and upregulation of transposases in cold stress conditions. Overall, our study provides a comprehensive understanding of the GDGT modifications and differential expression characteristic of the acid stress and cold stress responses inS. islandicus.

 

Modern-day reservoir management and monitoring of geologic carbon storage increasingly call for costly time-lapse seismic data collection. We demonstrate how techniques from graph theory can be used to optimize acquisition geometries for low-cost sparse 4D seismic data. Based on midpoint-offset-domain connectivity arguments, our algorithm automatically produces sparse nonreplicated time-lapse acquisition geometries that favor wavefield recovery. 

We present the Seismic Laboratory for Imaging and Modeling/Monitoring open-source software framework for computational geophysics and, more generally, inverse problems involving the wave equation (e.g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations. By integrating multiple layers of abstraction, the software is designed to be both readable and scalable, allowing researchers to easily formulate problems in an abstract fashion while exploiting the latest developments in high-performance computing. The design principles and their benefits are illustrated and demonstrated by means of building a scalable prototype for permeability inversion from time-lapse crosswell seismic data, which, aside from coupling of wave physics and multiphase flow, involves machine learning. 

Geologic carbon storage represents one of the few truly scalable technologies capable of reducing the CO 2 concentration in the atmosphere. While this technology has the potential to scale, its success hinges on our ability to mitigate its risks. An important aspect of risk mitigation concerns assurances that the injected CO 2 remains within the storage complex. Among the different monitoring modalities, seismic imaging stands out due to its ability to attain high-resolution and high-fidelity images. However, these superior features come at prohibitive costs and time-intensive efforts that potentially render extensive seismic monitoring undesirable. To overcome this shortcoming, we present a methodology in which time-lapse images are created by inverting nonreplicated time-lapse monitoring data jointly. By no longer insisting on replication of the surveys to obtain high-fidelity time-lapse images and differences, extreme costs and time-consuming labor are averted. To demonstrate our approach, hundreds of realistic synthetic noisy time-lapse seismic data sets are simulated that contain imprints of regular CO 2 plumes and irregular plumes that leak. These time-lapse data sets are subsequently inverted to produce time-lapse difference images that are used to train a deep neural classifier. The testing results show that the classifier is capable of detecting CO 2 leakage automatically on unseen data with reasonable accuracy. We consider the use of this classifier as a first step in the development of an automatic workflow designed to handle the large number of continuously monitored CO 2 injection sites needed to help combat climate change.