CFSA: Comparative flux sampling analysis as a guide for strain design.

Genome-scale metabolic models of microbial metabolism have extensively been used to guide the design of microbial cell factories, still, many of the available strain design algorithms often fail to produce a reduced list of targets for improved performance that can be implemented and validated in a step-wise manner. We present Comparative Flux Sampling Analysis (CFSA), a strain design method based on the extensive comparison of complete metabolic spaces corresponding to maximal or near-maximal growth and production phenotypes. The comparison is complemented by statistical analysis to identify reactions with altered flux that are suggested as targets for genetic interventions including up-regulations, down-regulations and gene deletions. We applied CFSA to the production of lipids by Cutaneotrichosporon oleaginosus and naringenin by Saccharomyces cerevisiae identifying engineering targets in agreement with previous studies as well as new interventions. CFSA is an easy-to-use, robust method that suggests potential metabolic engineering targets for growth-uncoupled production that can be applied to the design of microbial cell factories.

In silico analysis of design of experiment methods for metabolic pathway optimization.

Microbial cell factories allow the production of chemicals presenting an alternative to traditional fossil fuel-dependent production. However, finding the optimal expression of production pathway genes is crucial for the development of efficient production strains. Unlike sequential experimentation, combinatorial optimization captures the relationships between pathway genes and production, albeit at the cost of conducting multiple experiments. Fractional factorial designs followed by linear modeling and statistical analysis reduce the experimental workload while maximizing the information gained during experimentation. Although tools to perform and analyze these designs are available, guidelines for selecting appropriate factorial designs for pathway optimization are missing. In this study, we leverage a kinetic model of a seven-genes pathway to simulate the performance of a full factorial strain library. We compare this approach to resolution V, IV, III, and Plackett Burman (PB) designs. Additionally, we evaluate the performance of these designs as training sets for a random forest algorithm aimed at identifying best-producing strains. Evaluating the robustness of these designs to noise and missing data, traits inherent to biological datasets, we find that while resolution V designs capture most information present in full factorial data, they necessitate the construction of a large number of strains. On the other hand, resolution III and PB designs fall short in identifying optimal strains and miss relevant information. Besides, given the small number of experiments required for the optimization of a pathway with seven genes, linear models outperform random forest. Consequently, we propose the use of resolution IV designs followed by linear modeling in Design-Build-Test-Learn (DBTL) cycles targeting the screening of multiple factors. These designs enable the identification of optimal strains and provide valuable guidance for subsequent optimization cycles.

Combinatorial optimization of pathway, process and media for the production of p-coumaric acid by Saccharomyces cerevisiae.

Microbial cell factories are instrumental in transitioning towards a sustainable bio-based economy, offering alternatives to conventional chemical processes. However, fulfilling their potential requires simultaneous screening for optimal media composition, process and genetic factors, acknowledging the complex interplay between the organism's genotype and its environment. This study employs statistical design of experiments to systematically explore these relationships and optimize the production of p-coumaric acid (pCA) in Saccharomyces cerevisiae. Two rounds of fractional factorial designs were used to identify factors with a significant effect on pCA production, which resulted in a 168-fold variation in pCA titre. Moreover, a significant interaction between the culture temperature and expression of ARO4 highlighted the importance of simultaneous process and strain optimization. The presented approach leverages the strengths of experimental design and statistical analysis and could be systematically applied during strain and bioprocess design efforts to unlock the full potential of microbial cell factories.

Machine Learning-Guided Optimization of p-Coumaric Acid Production in Yeast.

Industrial biotechnology uses Design-Build-Test-Learn (DBTL) cycles to accelerate the development of microbial cell factories, required for the transition to a biobased economy. To use them effectively, appropriate connections between the phases of the cycle are crucial. Using p-coumaric acid (pCA) production in Saccharomyces cerevisiae as a case study, we propose the use of one-pot library generation, random screening, targeted sequencing, and machine learning (ML) as links during DBTL cycles. We showed that the robustness and flexibility of the ML models strongly enable pathway optimization and propose feature importance and Shapley additive explanation values as a guide to expand the design space of original libraries. This approach allowed a 68% increased production of pCA within two DBTL cycles, leading to a 0.52 g/L titer and a 0.03 g/g yield on glucose.

Biosignature Detection and MinION Sequencing of Antarctic Cryptoendoliths After Exposure to Mars Simulation Conditions.

In the search for life in our Solar System, Mars remains a promising target based on its proximity and similarity to Earth. When Mars transitioned from a warmer, wetter climate to its current dry and freezing conditions, any putative extant life probably retreated into habitable refugia such as the subsurface or the interior of rocks. Terrestrial cryptoendolithic microorganisms (i.e., those inhabiting rock interiors) thus represent possible modern-day Mars analogs, particularly those from the hyperarid McMurdo Dry Valleys in Antarctica. As DNA is a strong definitive biosignature, given that there is no known abiotic chemistry that can polymerize nucleobases, we investigated DNA detection with MinION sequencing in Antarctic cryptoendoliths after an ∼58-sol exposure in MARTE, a Mars environmental chamber capable of simulating martian temperature, pressure, humidity, ultraviolet (UV) radiation, and atmospheric composition, in conjunction with protein and lipid detection. The MARTE conditions resulted in changes in community composition and DNA, proteins, and cell membrane-derived lipids remained detectable postexposure. Of the multitude of extreme environmental conditions on Mars, UV radiation (specifically UVC) is the most destructive to both cells and DNA. As such, we further investigated if a UVC exposure corresponding to ∼278 martian years would impede DNA detection via MinION sequencing. The MinION was able to successfully detect and sequence DNA after this UVC radiation exposure, suggesting its utility for life detection in future astrobiology missions focused on finding relatively recently exposed biomarkers inside possible martian refugia.

In Situ Real-Time Monitoring for Aseptic Drilling: Lessons Learned from the Atacama Rover Astrobiology Drilling Studies Contamination Control Strategy and Implementation and Application to the Icebreaker Mars Life Detection Mission.

In 2019, the Atacama Rover Astrobiology Drilling Studies (ARADS) project field-tested an autonomous rover-mounted robotic drill prototype for a 6-Sol life detection mission to Mars (Icebreaker). ARADS drilled Mars-like materials in the Atacama Desert (Chile), one of the most life-diminished regions on Earth, where mitigating contamination transfer into life-detection instruments becomes critical. Our Contamination Control Strategy and Implementation (CCSI) for the Sample Handling and Transfer System (SHTS) hardware (drill, scoop and funnels) included out-of-simulation protocol testing (out-of-sim) for hardware decontamination and verification during the 6-Sol simulation (in-sim). The most effective five-step decontamination combined safer-to-use sterilants (3%_hydrogen-peroxide-activated 5%_sodium-hypochlorite), and in situ real-time verification by adenosine triphosphate (ATP) and Signs of Life Detector (SOLID) Fluorescence Immunoassay for characterization hardware bioburden and airborne contaminants. The 20- to 40-min protocol enabled a 4-log bioburden reduction down to <0.1 fmoles ATP detection limit (funnels and drill) to 0.2-0.7 fmoles (scoop) of total ATP. The (post-cleaning) hardware background was 0.3 to 1-2 attomoles ATP/cm2 (cleanliness benchmark background values) equivalent to ca. 1-10 colony forming unit (CFU)/cm2. Further, 60-100% of the in-sim hardware background was ≤3-4 bacterial cells/cm2, the threshold limit for Class <7 aseptic operations. Across the six Sols, the flux of airborne contaminants to the drill sites was ∼5 and ∼22 amoles ATP/(cm2·day), accounting for an unexpectedly high Fluorescence Intensity (FI) signal (FI: ∼6000) against aquatic cyanobacteria, but negligible anthropogenic contribution. The SOLID immunoassay also detected microorganisms from multiple habitats across the Atacama Desert (anoxic, alkaline/acidic microenvironments in halite fields, playas, and alluvial fans) in both airborne and post-cleaning hardware background. Finally, the hardware ATP background was 40-250 times lower than the ATP in cores. Similarly, the FI peaks (FImax) against the microbial taxa and molecular biomarkers detected in the post-cleaned hardware (FI: ∼1500-1600) were 5-10 times lower than biomarkers in drilled sediments, excluding significant interference with putative biomarker found in cores. Similar protocols enable the acquisition of contamination-free materials for ultra-sensitive instruments analysis and the integrity of scientific results. Their application can augment our scientific knowledge of the distribution of cryptic life on Mars-like grounds and support life-detection robotic and human-operated missions to Mars.

Síndrome nefrótico en esclerodermia / Nephrotic syndrome in Scleroderma

La afección renal es infrecuente en la esclerosis sistémica, a diferencia de otras colagenosis. La aparición de síndrome nefrótico se ha relacionado con el uso de fármacos, especialmente la D-penicilamina, o raramente como manifestación de amiloidosis secundaria, bastante infrecuente en la esclerosis sistémica. Presentamos un caso de síndrome nefrótico en una paciente con esclerosis sistémica, producido por una glomerulonefritis membranosa, descrito de forma excepcional en la literatura (AU)

The renal affectation is infrequent in scleroderma, unlike other collagen diseases. The appearance of nephrotic syndrome has been related to the drug use, specially the D-penicilamine, or rarely as a manifestation of secondary amilodosis, quite infrequent in scleroderma. We report a case of nephrotic syndrome in a patient with systemic scleroderma, produced by a membranous glomerulonephritis, exceptionally described in literature (AU)