Selection of Anabaena sp. PCC 7938 as a Cyanobacterium Model for Biological ISRU on Mars.

Crewed missions to Mars are expected to take place in the coming decades. After short-term stays, a permanent presence will be desirable to enable a wealth of scientific discoveries. This will require providing crews with life-support consumables in amounts that are too large to be imported from Earth. Part of these consumables could be produced on site with bioprocesses, but the feedstock should not have to be imported. A solution under consideration lies in using diazotrophic, rock-weathering cyanobacteria as primary producers: fed with materials naturally available on site, they would provide the nutrients required by other organisms. This concept has recently gained momentum but progress is slowed by a lack of consistency across contributing teams, and notably of a shared model organism. With the hope to address this issue, we present the work performed to select our current model. We started with preselected strains from the Nostocaceae family. After sequencing the genome of Anabaena sp. PCC 7938-the only one not yet available-we compared the strains' genomic data to determine their relatedness and provide insights into their physiology. We then assessed and compared relevant features: chiefly, their abilities to utilize nutrients from Martian regolith, their resistance to perchlorates (toxic compounds present in the regolith), and their suitability as feedstock for secondary producers (here a heterotrophic bacterium and a higher plant). This led to the selection of Anabaena sp. PCC 7938, which we propose as a model cyanobacterium for the development of bioprocesses based on Mars's natural resources. IMPORTANCE The sustainability of crewed missions to Mars could be increased by biotechnologies which are connected to resources available on site via primary producers: diazotrophic, rock-leaching cyanobacteria. Indeed, this could greatly reduce the mass of payloads to be imported from Earth. The concept is gaining momentum but progress is hindered by a lack of consistency across research teams. We consequently describe the selection process that led to the choice of our model strain, demonstrate its relevance to the field, and propose it as a shared model organism. We expect this contribution to support the development of cyanobacterium-based biotechnologies on Mars.

Hybrid computing using a neural network with dynamic external memory.

Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning, but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer. Like a conventional computer, it can use its memory to represent and manipulate complex data structures, but, like a neural network, it can learn to do so from data. When trained with supervised learning, we demonstrate that a DNC can successfully answer synthetic questions designed to emulate reasoning and inference problems in natural language. We show that it can learn tasks such as finding the shortest path between specified points and inferring the missing links in randomly generated graphs, and then generalize these tasks to specific graphs such as transport networks and family trees. When trained with reinforcement learning, a DNC can complete a moving blocks puzzle in which changing goals are specified by sequences of symbols. Taken together, our results demonstrate that DNCs have the capacity to solve complex, structured tasks that are inaccessible to neural networks without external read-write memory.

Overcoming catastrophic forgetting in neural networks.

The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand-written digit dataset and by learning several Atari 2600 games sequentially.

The phosphorylation flow of the Vibrio harveyi quorum-sensing cascade determines levels of phenotypic heterogeneity in the population.

UNLABELLED: Quorum sensing (QS) is a communication process that enables a bacterial population to coordinate and synchronize specific behaviors. The bioluminescent marine bacterium Vibrio harveyi integrates three autoinducer (AI) signals into one quorum-sensing cascade comprising a phosphorelay involving three hybrid sensor kinases: LuxU; LuxO, an Hfq/small RNA (sRNA) switch; and the transcriptional regulator LuxR. Using a new set of V. harveyi mutants lacking genes for the AI synthases and/or sensors, we assayed the activity of the quorum-sensing cascade at the population and single-cell levels, with a specific focus on signal integration and noise levels. We found that the ratios of kinase activities to phosphatase activities of the three sensors and, hence, the extent of phosphorylation of LuxU/LuxO are important not only for the signaling output but also for the degree of noise in the system. The pools of phosphorylated LuxU/LuxO per cell directly determine the amounts of sRNAs produced and, consequently, the copy number of LuxR, generating heterogeneous quorum-sensing activation at the single-cell level. We conclude that the ability to drive the heterogeneous expression of QS-regulated genes in V. harveyi is an inherent feature of the architecture of the QS cascade. IMPORTANCE: V. harveyi possesses one of the most complex quorum-sensing (QS) cascades known, using three different autoinducers (AIs) to control the induction of, e.g., bioluminescence, virulence factors, and biofilm and exoprotease production. We constructed various V. harveyi mutants to study the impact of each component and subsystem of the QS signaling cascade on QS activation at the population and single-cell levels. We found that the output was homogeneous only in the presence of all AIs. In the absence of any one AI, QS activation varied from cell to cell, resulting in phenotypic heterogeneity. This study elucidates a molecular design principle which enables a tightly integrated signaling cascade to control the expression of diverse phenotypes within a genetically homogeneous population.

Crossing bacterial boundaries: The carbon catabolite repression system Crc-Hfq of Pseudomonas putida KT2440 as a tool to control translation in E. coli.

As a global regulatory mechanism, carbon catabolite repression allows bacteria and eukaryal microbes to preferentially utilize certain substrates from a mixture of carbon sources. The mechanism varies among different species. In Pseudomonas spp., it is mainly mediated by the Crc-Hfq complex which binds to the 5' region of the target mRNAs, thereby inhibiting their translation. This molecular mechanism enables P. putida to rapidly adjust and fine-tune gene expression in changing environments. Hfq is an RNA-binding protein that is ubiquitous and highly conserved in bacterial species. Considering the characteristics of Hfq, and the widespread use and rapid response of Crc-Hfq in P. putida, this complex has the potential to become a general toolbox for post-transcriptional multiplex regulation. In this study, we demonstrate for the first time that transplanting the pseudomonal catabolite repression protein, Crc, into E. coli causes multiplex gene repression. Under the control of Crc, the production of a diester and its precursors was significantly reduced. The effects of Crc introduction on cell growth in both minimal and rich media were evaluated. Two potential factors - off-target effects and Hfq-sequestration - could explain negative effects on cell growth. Simultaneous reduction of off-targeting and increased sequestration of Hfq by the introduction of the small RNA CrcZ, indicated that Hfq sequestration plays a more prominent role in the negative side-effects. This suggests that the negative growth effect can be mitigated by well-controlled expression of Hfq. This study reveals the feasibility of controlling gene expression using heterologous regulation systems.

On the growth dynamics of the cyanobacterium Anabaena sp. PCC 7938 in Martian regolith.

The sustainability of crewed infrastructures on Mars will depend on their abilities to produce consumables on site. These abilities may be supported by diazotrophic, rock-leaching cyanobacteria: from resources naturally available on Mars, they could feed downstream biological processes and lead to the production of oxygen, food, fuels, structural materials, pharmaceuticals and more. The relevance of such a system will be dictated largely by the efficiency of regolith utilization by cyanobacteria. We therefore describe the growth dynamics of Anabaena sp. PCC 7938 as a function of MGS-1 concentration (a simulant of a widespread type of Martian regolith), of perchlorate concentration, and of their combination. To help devise improvement strategies and predict dynamics in regolith of differing composition, we identify the limiting element in MGS-1 - phosphorus - and its concentration-dependent effect on growth. Finally, we show that, while maintaining cyanobacteria and regolith in a single compartment can make the design of cultivation processes challenging, preventing direct physical contact between cells and grains may reduce growth. Overall, we hope for the knowledge gained here to support both the design of cultivation hardware and the modeling of cyanobacterium growth within.

Structural shifts in sea ice prokaryotic communities across a salinity gradient in the subarctic.

Current knowledge of the processes that shape prokaryotic community assembly in sea ice across polar ecosystems is scarce. Here, we coupled culture-dependent (bacterial isolation on R2A medium) and culture-independent (high-throughput 16S rRNA gene sequencing) approaches to provide the first comprehensive assessment of prokaryotic communities in the late winter ice and its underlying water along a natural salinity gradient in coastal Hudson Bay, an iconic cryo-environment that marks the ecological transition between Canadian Subarctic and Arctic biomes. We found that prokaryotic community assembly processes in the ice were less selective at low salinity since typical freshwater taxa such as Frankiales, Burkholderiales, and Chitinophagales dominated both the ice and its underlying water. In contrast, there were sharp shifts in community structure between the ice and underlying water samples at sites with higher salinity, with the orders Alteromonadales and Flavobacteriales dominating the ice, while the abovementioned freshwater taxa dominated the underlying water communities. Moreover, primary producers including Cyanobium (Cyanobacteria, Synechococcales) may play a role in shaping the ice communities and were accompanied by known Planctomycetes and Verrucomicrobiae taxa. Culture-dependent analyses showed that the ice contained pigment-producing psychrotolerant or psychrophilic bacteria from the phyla Proteobacteria, Actinobacteriota, and Bacteroidota, likely favored by the combination of low temperatures and the seasonal increase in sunlight. Our findings suggest that salinity, photosynthesis and dissolved organic matter are the main drivers of prokaryotic community structure in the late winter ice of coastal Hudson Bay, the ecosystem with the fastest sea ice loss rate in the Canadian North.

A Low-Pressure, N2/CO2 Atmosphere Is Suitable for Cyanobacterium-Based Life-Support Systems on Mars.

The leading space agencies aim for crewed missions to Mars in the coming decades. Among the associated challenges is the need to provide astronauts with life-support consumables and, for a Mars exploration program to be sustainable, most of those consumables should be generated on site. Research is being done to achieve this using cyanobacteria: fed from Mars's regolith and atmosphere, they would serve as a basis for biological life-support systems that rely on local materials. Efficiency will largely depend on cyanobacteria's behavior under artificial atmospheres: a compromise is needed between conditions that would be desirable from a purely engineering and logistical standpoint (by being close to conditions found on the Martian surface) and conditions that optimize cyanobacterial productivity. To help identify this compromise, we developed a low-pressure photobioreactor, dubbed Atmos, that can provide tightly regulated atmospheric conditions to nine cultivation chambers. We used it to study the effects of a 96% N2, 4% CO2 gas mixture at a total pressure of 100 hPa on Anabaena sp. PCC 7938. We showed that those atmospheric conditions (referred to as MDA-1) can support the vigorous autotrophic, diazotrophic growth of cyanobacteria. We found that MDA-1 did not prevent Anabaena sp. from using an analog of Martian regolith (MGS-1) as a nutrient source. Finally, we demonstrated that cyanobacterial biomass grown under MDA-1 could be used for feeding secondary consumers (here, the heterotrophic bacterium E. coli W). Taken as a whole, our results suggest that a mixture of gases extracted from the Martian atmosphere, brought to approximately one tenth of Earth's pressure at sea level, would be suitable for photobioreactor modules of cyanobacterium-based life-support systems. This finding could greatly enhance the viability of such systems on Mars.

Single Cell Analysis of a Bacterial Sender-Receiver System.

Monitoring gene expression dynamics on the single cell level provides important information on cellular heterogeneity and stochasticity, and potentially allows for more accurate quantitation of gene expression processes. We here study bacterial senders and receivers genetically engineered with components of the quorum sensing system derived from Aliivibrio fischeri on the single cell level using microfluidics-based bacterial chemostats and fluorescence video microscopy. We track large numbers of bacteria over extended periods of time, which allows us to determine bacterial lineages and filter out subpopulations within a heterogeneous population. We quantitatively determine the dynamic gene expression response of receiver bacteria to varying amounts of the quorum sensing inducer N-3-oxo-C6-homoserine lactone (AHL). From this we construct AHL response curves and characterize gene expression dynamics of whole bacterial populations by investigating the statistical distribution of gene expression activity over time. The bacteria are found to display heterogeneous induction behavior within the population. We therefore also characterize gene expression in a homogeneous bacterial subpopulation by focusing on single cell trajectories derived only from bacteria with similar induction behavior. The response at the single cell level is found to be more cooperative than that obtained for the heterogeneous total population. For the analysis of systems containing both AHL senders and receiver cells, we utilize the receiver cells as 'bacterial sensors' for AHL. Based on a simple gene expression model and the response curves obtained in receiver-only experiments, the effective AHL concentration established by the senders and their 'sending power' is determined.

Simulation of stochastic network dynamics via entropic matching.

The simulation of complex stochastic network dynamics arising, for instance, from models of coupled biomolecular processes remains computationally challenging. Often, the necessity to scan a model's dynamics over a large parameter space renders full-fledged stochastic simulations impractical, motivating approximation schemes. Here we propose an approximation scheme which improves upon the standard linear noise approximation while retaining similar computational complexity. The underlying idea is to minimize, at each time step, the Kullback-Leibler divergence between the true time evolved probability distribution and a Gaussian approximation (entropic matching). This condition leads to ordinary differential equations for the mean and the covariance matrix of the Gaussian. For cases of weak nonlinearity, the method is more accurate than the linear method when both are compared to stochastic simulations.