Individual Final Project

SECTION 1: ABSTRACT

Provide a concise, self-contained summary of your project (minimum 150 words). The abstract should allow a reader to understand the purpose, approach, and expected outcomes of the work without referring to other sections.

1. Your abstract should briefly address the following elements:

a. Significance: What problem or question does the project address, and why is it important?

b. Broad Objective: What is the overall goal of the project?

c. Hypothesis: What prediction or principle is the project testing or demonstrating?

d. Specific Aims: What key steps or milestones will be completed to achieve the objective?

e. Methods: What experimental or technical approaches will be used?

SECTION 2: PROJECT AIMS

Aim 1: Experimental Aim - In Silico Design & Laboratory Validation

The first aim is to design a synthetic rhizosphere consortium in silico and establish the experimental foundation for its laboratory validation. Using Benchling as the primary platform, we will construct modular, salt-responsive genetic circuits for three complementary organisms: Pseudomonas fluorescens (osmoprotection), Azospirillum brasilense (nitrogen fixation), and Bacillus subtilis (biofilm formation). A conditional kill switch will be incorporated into each construct to ensure biosafety and environmental containment.

Phase 1A - In Silico Genetic Circuit Design

• Design salt-responsive genetic constructs for each organism in Benchling:
  • P. fluorescens: P_algU → betA → betB (glycine betaine production)
  • A. brasilense: P_nifH → nifHDK (nitrogen fixation under salinity stress)
  • B. subtilis: P_epsA → epsA → tapA (biofilm matrix formation)
• Incorporate auxotrophic kill switches in all three constructs for biosafety.
• Perform codon optimization of heterologous genes (betA, betB) for P. fluorescens using the IDT Codon Optimization Tool.
• Validate metabolic feasibility of glycine betaine production in P. fluorescens via Flux Balance Analysis (COBRApy, model iJN1411).
• Simulate consortium interactions between P. fluorescens and B. subtilis using BacArena to assess metabolic compatibility and predict inter-species metabolite exchange.

Phase 1B - Laboratory Validation (Future Experimental Work)

• Synthesize DNA constructs via Twist Bioscience and obtain expression plasmids from Addgene.
• Transform each bacterial strain:
  • P. fluorescens: electroporation
  • A. brasilense: electroporation
  • B. subtilis: competent cell transformation
• Confirm successful transformation via colony PCR.
• Validate individual strain performance under saline conditions:
  • P. fluorescens: glycine betaine quantification via HPLC
  • A. brasilense: nitrate production via colorimetric assay
  • B. subtilis: exopolysaccharide production via crystal violet staining
• Validate kill switch functionality: CFU assay with and without DAP (P. fluorescens) and tryptophan (B. subtilis).
• Validate consortium performance: culture all three strains together under saline stress and measure combined outputs.

Aim 2: Development Aim - Plant Validation Under Salinity Stress

The second aim extends the engineered consortium into a plant-based validation system, evaluating its capacity to improve growth and stress tolerance under saline conditions. Arabidopsis thaliana due to its well-annotated genome, short life cycle and availability of salt-responsive mutant lines (Chu et al., 2019). Results obtained with A. thaliana will inform future translation to native Bolivian Altiplano crops.

Measurable outcomes:

• Number of seeds germinated.
• Number of flowers and siliques produced.
• Rosette diameter and leaf area.
• Photosynthetic capacity (chlorophyll fluorescence).
• Fresh and dry biomass of roots and shoots.
• Comparison: Consortium-inoculated plants vs non-inoculated controls under saline vs normal conditions.
• Microbial community monitoring: 16S rRNA sequencing to confirm consortium stability in the rhizosphere.
• Optimize microbial ratios between the three strains for maximum plant consortium.

Delivery: Two delivery strategies will be evaluated in parallel:

• Seed coating: lyophilized consortium applied directly to seeds prior to germination.
• Soil inoculant: liquid consortium applied to soil at planting.

Lyophilization is the preferred long-term delivery format as it eliminates cold chain requirements and extends shelf life — critical for deployment in remote regions such as the Bolivian Altiplano.

Aim 3: Visionary Aim - Directed Evolution for the Bolivian Altiplano

The Core Problem: Ecological Fragility of Synthetic Consortia

In natural soil ecosystems, plants actively recruit microorganisms through root exudates, generating selective rhizosphere microbiomes adapted to local environmental pressures. In the Bolivian Altiplano, native crops such as quinua (Chenopodium quinoa) coexist with microbial communities shaped by chronic salinity, drought, nutrient limitation and high UV exposure. Consequently, a purely synthetic consortium composed of engineered bacterial strains may exhibit limited ecological persistence once introduced into real agricultural soils.

Synthetic microbial communities are particularly vulnerable to competitive exclusion by native halotolerant microorganisms, instability across successive plant generations and incompatibility with host-specific rhizosphere signaling (Chang et al., 2021). Additionally, microbial communities are inherently dynamic systems whose composition changes continuously through ecological succession, potentially causing loss of engineered functionality over time (Sánchez et al., 2021).

Previous studies on artificial microbiome selection demonstrated that engineered communities frequently experience functional drift, reduced heritability, and declining performance after repeated propagation cycles (Blouin et al., 2015; Chang et al., 2021). These limitations are especially relevant in saline soils, where environmental fluctuations strongly influence microbial community composition and plant–microbe interactions.

Proposed Solution: Rational Directed Iterative Evolution

To address these ecological limitations, we propose a rational directed iterative evolution strategy inspired by top-down microbiome engineering frameworks (Chang et al., 2021; Sánchez et al., 2021). Rather than relying exclusively on bottom-up rational assembly, this approach integrates ecological selection, enrichment, perturbation, and adaptive stabilization to evolve a field-compatible microbial consortium.

Directed evolution of microbial communities can be conceptualized as a guided exploration of an “ecological structure–function landscape,” in which microbial composition and collective functions co-evolve under selective environmental pressures (Sánchez et al., 2021).

This iterative framework mimics natural ecological adaptation while preserving the desired engineered traits. Environmental perturbations such as increasing salinity, nutrient limitation, and osmotic stress can shift community composition toward more resilient stable states (Sánchez et al., 2021). Furthermore, migration from native microbial pools may replenish functional diversity and prevent evolutionary stagnation during selection cycles (Chang et al., 2021).

Ecological and Evolutionary Rationale:

Traditional bottom-up synthetic biology approaches assume that microbial interactions can be fully predicted from individual strain behavior. However, microbial communities frequently exhibit higher-order interactions, emergent properties, and multistable ecological states that cannot be anticipated from pairwise interactions alone (Guo & Boedicker, 2016; Sánchez-Gorostiaga et al., 2019).

Directed evolution bypasses this limitation by allowing beneficial ecological interactions to emerge naturally through selection. According to Sánchez et al. (2021), effective community-level evolution requires:

• Phenotypic variation between microbial communities
• Heritable transmission of community-level functions
• Stabilization of ecological succession dynamics
• Recurrent perturbation and selection cycles

Importantly, generational stability is critical because microbial communities continuously change through ecological succession. Communities must therefore reach stable equilibrium states before their functions can be reliably inherited across generations. Several ecological engineering strategies described by Chang et al. (2021) and Sánchez et al. (2021) directly support our proposed workflow, including:

• Migration from native microbial pools
• Dilution-to-extinction bottlenecking
• Community coalescence
• Environmental pulse perturbations
• Selective enrichment under stress conditions

Functional Targets for Directed Evolution:

Salt Stress Adaptation

Salinity imposes osmotic imbalance, ion toxicity, oxidative stress, and water limitation on both plants and microorganisms. Therefore, evolved consortia will be selected for enhanced halotolerance through:

• Increased biosynthesis of osmoprotectants:

-trehalose -glycine betaine -ectoine

• Enhanced Na⁺ extrusion and ion homeostasis systems
• Exopolysaccharide (EPS) production for soil water retention
• Reduction of reactive oxygen species (ROS) accumulation
• Improved membrane stability under osmotic stress

Nitrogen Fixation Under Salinity

Nitrogen fixation efficiency is highly sensitive to oxidative and osmotic stress because nitrogenase enzymes are easily inhibited under saline conditions. Consequently, selection will favor microbial communities capable of maintaining diazotrophic activity despite environmental stress.

Selection targets include:

• Nitrogenase activity under high salinity
• Stable conversion of atmospheric N₂ into ammonium
• Improved nitrogen assimilation by quinua plants
• Maintenance of metabolic cooperation between consortium members
• Increased nutrient availability in nutrient-poor Altiplano soils

Artificial microbiome selection has previously been used to enhance plant-associated functions under abiotic stress, including drought and salinity tolerance (Mueller et al., 2016; Jochum et al., 2019).

Biofilm Stability and Rhizosphere Persistence

Long-term rhizosphere persistence represents one of the principal challenges for engineered microbiomes. Biofilms provide structural stability, facilitate nutrient exchange, and protect microbial communities against environmental fluctuations.

The consortium will therefore be evolved for:

• Increased extracellular matrix production
• Enhanced root adhesion and colonization
• Cooperative metabolic interactions
• Improved protection against desiccation and salinity
• Resistance to invasion by competing microorganisms
• Stable persistence across successive plant generations

Chang et al. (2021) demonstrated that communities evolved through iterative perturbation and selection exhibit greater resistance to ecological invasion compared to rationally assembled synthetic consortia. This property is essential for field deployment in highly competitive native soils.

Expected Impact

This Aim seeks to integrate ecological adaptation with synthetic biology to create a next-generation agricultural microbiome optimized for saline soils of the Bolivian Altiplano. Instead of producing a static engineered consortium, this strategy aims to generate an ecologically stabilized and evolutionarily conditioned rhizosphere microbiome capable of long-term persistence under extreme environmental stress.

By combining rational engineering with directed community evolution, the final consortium is expected to provide:

• Sustained nitrogen fixation under salinity
• Enhanced plant osmotic stress tolerance
• Greater rhizosphere colonization efficiency
• Long-term ecological resilience
• Improved quinua productivity in saline soils

The ultimate vision of this project is a modular, open-source microbial platform for climate-resilient agriculture that can be:

• Adapted to different saline environments beyond the Bolivian Altiplano.
• Scaled through low-cost fermentation and lyophilization infrastructure.
• Deployed directly by farming communities without dependence on cold chain logistics or chemical fertilizers.
• Iteratively improved through continued directed evolution as environmental conditions change with climate.

In the long term, this approach represents a replicable model for engineering resilient agricultural microbiomes in response to the growing global challenge of soil salinization driven by climate change and unsustainable irrigation practices.

SECTION 3: BACKGROUND

Background and Literature Context

Provide background research that explains the current state of knowledge and identifies the gap in knowledge or capability that your project addresses.

1. Briefly summarize two peer-reviewed research citations relevant to your research (minimum four sentences).

Citation 1

Hiernaux, P., et al. (2024). Complementarity of Sentinel-1 and Sentinel-2 data for soil salinity monitoring to support sustainable agriculture practices in the Central Bolivian Altiplano. Sustainability, 16(14), 6200. https://www.mdpi.com/2071-1050/16/14/6200

The Bolivian Altiplano is a remote endorheic region that suffers from the major problem of soil salinization, threatening the sustainability of agriculture activity. Located at an average elevation of 3,700 meters above sea level, the region around Lake Poopó in the central Altiplano faces severe and progressive salt accumulation driven by high evaporation rates and the endorheic nature of the basin. This study monitored soil salinity across a five-year period using satellite imagery and machine learning, revealing the extent and seasonal dynamics of salinization in the region. The findings highlight an urgent need for mitigation strategies that are accessible to remote farming communities, precisely the gap this project addresses through the development of a deployable microbial inoculant. Globally, soil salinization poses a critical threat to agricultural productivity, ecosystem resilience, and regional resource sustainability, with primary and secondary salinization processes intensifying under climate change and unsustainable land-use practices. Together, these findings establish the Bolivian Altiplano as one of the most vulnerable agricultural regions in South America and justify the urgent development of low-cost, biologically based salinity mitigation strategies.

Citation 2

Bukhat, S., et al. (2021). Potential of plant growth promoting bacterial consortium for improving the growth and yield of wheat under saline conditions. Frontiers in Plant Science. PMC9557047. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9557047/

Research on multi-strain bacterial consortia demonstrated that co-inoculation of PGPR strains, including Pseudomonas fluorescens, caused significant positive impacts on shoot length, root length, fresh weight, and biochemical traits of wheat at the highest salinity levels tested, outperforming any single-strain inoculation. The multi-strain consortium significantly improved chlorophyll content, relative water content and K/Na ratio, which are key indicators of salinity tolerance in plants. These results provide direct experimental evidence that combining PGPR strains with complementary growth-promoting traits produces synergistic effects that cannot be achieved by a single organism alone. This finding is foundational to the design rationale of this project, which combines three functionally distinct organisms: Pseudomonas fluorescens for osmoprotection, Azospirillum brasilense for nitrogen fixation and Bacillus subtilis for biofilm formation, each addressing a different dimension of salinity stress simultaneously. The study also tested bacterial compatibility prior to consortium development, a methodological consideration directly reflected in the BacArena simulation component of this project.

Citation 3

Gerbore, J., et al. (2024). A “love match” score to compare root exudate attraction and feeding of the plant growth-promoting rhizobacteria Bacillus subtilis, Pseudomonas fluorescens, and Azospirillum brasilense. Frontiers in Microbiology. PMC11456545. https://pmc.ncbi.nlm.nih.gov/articles/PMC11456545/

A study evaluating the exact three organisms used in this project, Bacillus subtilis, Pseudomonas fluorescens and Azospirillum brasilense, found that P. fluorescens and A. brasilense responded more efficiently to plant root exudates than B. subtilis and proposed a quantitative “love match” score to evaluate plant-PGPR pair compatibility based on chemotaxis and bacterial growth responses. This research is particularly significant for this project because it directly evaluates the three chosen organisms together, confirming that they are well-established biofertilizers with documented responses to plant signals. The study found that all three organisms responded positively to root exudates from multiple plant species, suggesting that the consortium could be compatible with a range of host plants, including Arabidopsis thaliana. Furthermore, the quantitative framework proposed by this study could be directly applied in future experimental work to predict and optimize the rhizosphere performance of this consortium.

Citation 4

Ilyas, N., et al. (2024). Plant growth-promoting bacteria (PGPB)-induced plant adaptations to stresses: an updated review. PeerJ. https://peerj.com/articles/17882/

A comprehensive review documented that microbial consortia of Bacillus subtilis and Azospirillum brasilense upregulate root and shoot development, carbon dioxide uptake, transpiration, and leaf chlorophyll index under stress conditions, with inoculation improving grain yield and nitrogen accumulation in wheat. Additionally, Bacillus subtilis, Azospirillum brasilense and Pseudomonas fluorescens have been shown to increase the concentration of stress-protective compounds in plants under abiotic stress. This review consolidates a growing body of literature confirming that the three organisms selected for this project have individually and collectively demonstrated plant growth-promoting properties under stress conditions. The review also highlights that PGPR function through multiple complementary mechanisms, including osmoprotectant production, nitrogen fixation, exopolysaccharide synthesis and phytohormone modulation, underscoring why a multi-organism consortium targeting each of these mechanisms simultaneously represents a more robust strategy than single-strain inoculants. This mechanistic diversity is the biological foundation of the consortium design presented in this project.

2. Explain how your project is novel or innovative. (Minimum 3 sentences.)

This project represents a significant departure from conventional approaches to agricultural salinity management, which have historically relied on either chemical soil amendments, such as gypsum application and leaching or the development of genetically modified salt-tolerant crop varieties. Both approaches face critical limitations: chemical amendments are costly, require specialized equipment and are inaccessible to smallholder farmers in remote regions, while transgenic crop development involves lengthy regulatory processes and raises socioeconomic concerns about seed sovereignty.

This project proposes a different paradigm because instead of modifying the plant or the soil chemistry, it engineers the invisible microbial community at the root-soil interface, the rhizosphere, to actively buffer the plant against salt stress from the outside in.

The novelty of this project lies first in its systems-level design philosophy. Rather than introducing a single organism with a single function, this project engineers three phylogenetically distinct bacteria, a Pseudomonad, a Spirillum and a Firmicute, each assigned a specific, non-redundant functional role within the consortium: osmoprotection (P. fluorescens), nitrogen fixation (A. brasilense), and soil stabilization through biofilm (B. subtilis). This division of labor mirrors the functional redundancy and specialization observed in natural soil ecosystems but imposes synthetic genetic control over when and how each function is expressed. The use of salt-responsive promoters, P_algU in P. fluorescens and P_epsA in B. subtilis, ensures that the consortium activates its protective functions precisely when the plant needs them most.

Second, the integration of synthetic biology tools, including Gibson Assembly, codon optimization and auxotrophic kill switches into a multi-organism agricultural consortium represents an application of tools primarily developed for single-organism chassis systems. While Gibson Assembly, auxotrophic containment and salt-responsive promoters have each been used individually in synthetic biology contexts, their combined application across three different host organisms in a coordinated consortium designed for open-field agriculture is unprecedented. The auxotrophic kill switch design in particular addresses one of the most persistent barriers to the field deployment of engineered microorganisms which are biosafety and ecological containment. By requiring an external metabolite supplement (DAP, tryptophan or aromatic amino acids) for survival, each organism is intrinsically confined to controlled environments with a robust containment strategy that does not depend on active gene expression or environmental triggers.

Third, its long-term vision includes the use of controlled adaptive evolution under contained or semi-contained conditions to improve the environmental robustness of the synthetic consortium while maintaining biosafety safeguards. By integrating engineered functions with insights from native Altiplano microbiota, the project aims to develop biologically resilient systems that are both effective and ecologically responsible. In doing so, it expands synthetic biology beyond static engineered organisms toward more adaptive and environmentally integrated biotechnological platforms.

3. Explain why your project matters and what impact it could have. (Minimum 5 sentences.)

Soil salinization is one of the most pressing and underaddressed threats to global food security. Secondary salinization now threatens 20% of irrigated lands worldwide, with projections suggesting 50% of croplands may be affected by 2050 — driven by climate change, decreased precipitation, and poor irrigation practices (Butcher et al., 2016). The FAO’s first major global assessment of salt-affected soils in 50 years estimates that over 1,381 million hectares, 10.7% of total global land area, are currently affected with models indicating that increasing aridity could expand this to between 24 and 32% of total land surface, with the vast majority of aridification expected to occur in developing countries (FAO, 2024). In the Bolivian Altiplano specifically, this problem is not a future projection but a present reality. This remote endorheic region that suffers from the major problem of soil salinization, threatening the sustainability of agriculture activity (Hiernaux et al., 2024). The farming communities of the Altiplano depend on crops like quinua, fava bean, barley, cañahua, etc; as their primary food and income sources. This project directly targets this gap, not with a solution designed for industrial agriculture but with one designed for resource-limited, remote and extreme environments.

The critical barrier this project addresses is salinity itself and the absence of accessible, biological and deployable solutions for smallholder farmers in saline environments. Soil salinity stress is considered highly detrimental for agriculture because of its devastating effects on productivity and food security, in addition to having important ecological and socioeconomic repercussions, with salinity negatively affecting microbial diversity in the plant rhizosphere and limiting water conductance, soil porosity, and aeration (Ilyas et al., 2021).

Existing biofertilizer products on the market typically contain a single PGPR strain and are designed for temperate, well-irrigated agricultural systems and not for the extreme conditions of high-altitude endorheic basins like the Altiplano. A lyophilized, multi-functional microbial consortium that can be applied as a seed coating, without cold chain, without specialized equipment, represents a solution designed from the ground up for accessibility, scalability and ecological compatibility.

Beyond its agricultural application, this project has the potential to contribute to broader societal goals in several dimensions. Nitrogen fertilizer production and use account for approximately 5% of global greenhouse gas emissions and food security’s reliance on synthetic nitrogen fertilizers represents one of the most significant environmental costs of modern agriculture (Cassman & Dobermann, 2022). By providing biological nitrogen fixation through A. brasilense, this consortium could meaningfully reduce dependence on synthetic nitrogen fertilizers in Altiplano farming systems. A biological alternative that delivers fixed nitrogen directly to the rhizosphere could dramatically improve this efficiency. Furthermore, the lyophilized delivery format envisions a product that farming communities could store and apply themselves, potentially creating local biotechnology capacity in regions historically excluded from the benefits of the bioeconomy. PubMed CentralPubMed

In soil microbiology, it provides a framework for integrating genome-scale metabolic modeling (COBRApy, model iJN1411) and agent-based consortium simulation (BacArena) to predict the behavior of engineered microbial communities before committing to costly wet lab experiments. The use of multi-strain co-inoculation strategies has been shown to cause significant positive impacts on shoot length, root length, fresh weight and biochemical traits. Moreover, the compatibility of strains must be assessed prior to consortium development, a methodological consideration directly reflected in the BacArena simulation component of this project (Bukhat et al., 2021). The directed evolution component of the long-term vision further pushes the boundary of what is currently possible in agricultural biotechnology, proposing a rational, iterative approach to adapting synthetic consortia to specific ecological contexts.

Finally, research evaluating the compatibility of B. subtilis, P. fluorescens, and A. brasilense together has already proposed quantitative frameworks for predicting plant-PGPR pair performance, establishing a scientific foundation for the rational design of multi-organism consortia (Gerbore et al., 2024), and this project builds on that foundation by adding synthetic genetic control, environmental responsiveness and biosafety containment, moving PGPR science from empirical strain selection toward rational consortium engineering. Moreover, if a lyophilized synthetic consortium can be shown to improve crop resilience in one of the world’s most extreme agricultural environments, it opens the door to a new generation of biological products designed specifically for the frontlines of climate change, arid regions, saline soils and degraded lands, where the need is greatest and current solutions are most inadequate.

4. Describe the ethical implications associated with your project and identify relevant ethical principles (e.g., non-maleficence, beneficence, justice, or responsibility). (Minimum 2 paragraphs.)

This project sits at the intersection of synthetic biology, food security and environmental justice, each of these dimensions carries significant ethical weight. The deliberate engineering of microorganisms for open-environment deployment raises immediate concerns related to the principle of non-maleficence: the obligation to avoid causing harm.

Introducing genetically engineered bacteria into the rhizosphere of the Bolivian Altiplano, a complex, poorly characterized and ecologically fragile ecosystem carries the risk of unintended consequences, including competitive displacement of native soil microbiota, horizontal gene transfer of engineered constructs to non-target organisms and disruption of existing nitrogen cycling and soil microbiome dynamics that local crops have co-evolved with over centuries. The principle of justice is equally central to this project: the Bolivian Altiplano is home to Indigenous Quechua and Aymara farming communities whose agricultural systems, cultural identities and food sovereignty are deeply intertwined. Any biotechnology deployed in this context must be developed with, not for, these communities, ensuring that the benefits of the technology are equitably distributed and that local knowledge and consent are respected throughout the research process. Finally, the principle of responsibility demands transparency about what this technology can and cannot do: this project is an in silico design and its real-world efficacy remains unvalidated. Overstating its potential could mislead policymakers, funders or communities into premature adoption of an unproven system.

On the other hand, the measures proposed to ensure this project is conducted ethically operate at multiple levels. At the biosafety level, all three engineered organisms incorporate auxotrophic kill switches, requiring external metabolite supplementation for survival, providing a passive, robust containment mechanism that does not depend on active monitoring or enforcement. This directly addresses the risk of uncontrolled environmental persistence. At the research conduct level, any future experimental validation should be preceded by contained greenhouse studies, followed by small-scale field trials with continuous monitoring of soil microbial community composition using 16S rRNA sequencing, before any open-field deployment is considered. At the community engagement level, the project envisions the development of the lyophilized inoculant as an open-source, non-proprietary product, explicitly designed to prevent corporate capture of a technology developed for marginalized communities.

Potential unintended consequences that must be acknowledged include: the possibility that the synthetic consortium outcompetes native PGPR strains that local crops depend on; that the kill switch fails under unexpected environmental conditions; or that the directed evolution component introduces unforeseen traits into the hybrid consortium. Critically, this project assumes that salinity is the primary limiting factor for Altiplano agriculture, an assumption that may be incorrect, as water availability, frost, UV radiation and socioeconomic factors also constrain productivity. Alternative approaches that avoid genetic engineering entirely, such as metagenomics-guided enrichment of native salt-tolerant consortia or the development of biochar-based soil amendments should be evaluated in parallel as safer, more ecologically conservative options. Ultimately, the ethical imperative of this project is not just to do no harm but to actively advance the well-being of communities on the front lines of climate change, making beneficence not merely a principle but a design requirement.

SECTION 4: EXPERIMENTAL DESIGN, TECHNIQUES, TOOLS, AND TECHNOLOGY

1. Create a detailed experimental plan for your final project. Include a timeline for each part of your experimental plan (i.e., how long you would expect each step in your final project to take). (min. 15 lines/sentences—a numbered list is acceptable)

1. In silico genetic circuit design - P. fluorescens (Days 1-2, ~5 hours)

Design of the osmoprotection circuit in Benchling using a synthetic AlgU-responsive promoter (P_algU, based on Firoved & Deretic 2003 consensus: -35 box GAACTT, 16 nt spacer, -10 box TCTGA, 31 bp total), betA (choline dehydrogenase, 1,671 bp) and betB (betaine aldehyde dehydrogenase, 1,425 bp) coding sequences from E. coli K-12 (NCBI accession X52905), RBS BBa_B0034 (12 bp, iGEM Parts Registry), and terminator BBa_B0015 (129 bp, iGEM Parts Registry), assembled into the broad host-range backbone pBBR1MCS2 (Addgene #26702, KanR, 5,148 bp) via Gibson Assembly with 40 bp overlaps.

Expected result: complete annotated plasmid map in Benchling with status “Ready to assemble.”

2. Codon optimization of betA and betB (Day 2)

Codon optimization of both heterologous genes for P. fluorescens using the IDT Codon Optimization Tool, with P. aeruginosa as reference organism due to shared GC content (~60-63%) and codon usage preferences. Both genes were re-uploaded to Benchling replacing the original E. coli sequences.

Expected result: optimized sequences replacing rare codons with Pseudomonas-preferred synonymous alternatives, improving translation efficiency without altering the amino acid sequence or enzymatic function of BetA and BetB.

3. Auxotrophic kill switch - P. fluorescens (Day 2)

Design of biosafety containment for P. fluorescens through deletion of dapA (4-hydroxy-tetrahydrodipicolinate synthase, 465 bp, locus tag CPH89_RS26560), an essential gene for diaminopimelate (DAP) biosynthesis and cell wall formation. Without DAP, P. fluorescens cannot synthesize peptidoglycan and undergoes cell lysis. Documented in Benchling with dual annotations: CDS (wildtype) and Misc Feature (DELETION TARGET). In wet lab, deletion would be performed using lambda Red recombination or CRISPR/Cas9.

Expected result: passive containment because bacteria cannot survive in natural Altiplano soil where free DAP is unavailable.

4. In silico genetic circuit design - B. subtilis (Days 2-3, ~5 hours)

Design of the biofilm circuit in Benchling using the native P_epsA promoter (NCBI NC_000964, complement region 3529856-3530056, 201 bp), which responds to salt stress through the Spo0A/SinR regulatory cascade, epsA (BSU_34370, 705 bp, modulator of EpsB kinase) and tapA (BSU_34400, 762 bp, TasA anchoring protein essential for biofilm matrix assembly) from B. subtilis 168, assembled into pBP_Pveg backbone (Addgene #112776, CmR, 2,204 bp) via Gibson Assembly with 40 bp overlaps. Both genes were located on the complementary strand of NC_000964 and reverse complemented using a custom Python script to obtain correct 5’ to 3’ coding sequences.

Expected result: complete B. subtilis plasmid map with status “Assembled.”

5. Auxotrophic kill switch - B. subtilis (Day 3)

Design of biosafety containment for B. subtilis through deletion of trpC (indole-3-glycerol phosphate synthase, 750 bp, locus tag BSU_24310) — an essential gene for tryptophan biosynthesis. Without tryptophan, B. subtilis cannot synthesize essential proteins and dies. Documented in Benchling with dual annotations: CDS (wildtype) and Misc Feature (DELETION TARGET).

Expected result: passive containment because bacteria cannot survive in natural Altiplano soil where free tryptophan is unavailable in sufficient concentrations.

6. In silico genetic circuit design - A. brasilense (Day 4, ~5 hours)

Design of the nitrogen fixation circuit using the native P_nifH promoter (~200 bp upstream of nifH, sigma-54 and NifA-dependent) and nifHDK operon from A. brasilense Sp7 (NCBI GCF_008274965.1): nifH (nitrogenase reductase, 882 bp), nifD (nitrogenase alpha subunit, 1,440 bp), and nifK (nitrogenase beta subunit). All three genes form a polycistronic operon controlled by a single P_nifH promoter. Since A. brasilense is a native diazotroph, FeMo cofactor biosynthesis genes (nifB, nifE, nifN) are already present in the chromosome and there is no need to include them in the plasmid. Assembled into pBBR1MCS2 via Gibson Assembly.

Expected result: complete A. brasilense plasmid map with status “Assembled.”

7. Auxotrophic kill switch - A. brasilense (Day 4, integrated)

Design of biosafety containment for A. brasilense through deletion of aroA (3-phosphoshikimate 1-carboxyvinyltransferase), an essential gene for biosynthesis of all three aromatic amino acids: phenylalanine, tyrosine, and tryptophan. Without aromatic amino acids, A. brasilense cannot synthesize essential proteins and structural components. Documented in Benchling with dual annotations: CDS (wildtype) and Misc Feature (DELETION TARGET).

Expected result: passive containment because bacteria cannot survive in natural Altiplano soil where free aromatic amino acids are unavailable.

8. Metabolic feasibility analysis - COBRApy (Day 5, ~5 hours)

Flux Balance Analysis (FBA) performed using COBRApy (Ebrahim et al., 2013) in Google Colab, with the P. putida KT2440 model iJN746 (Nogales et al., 2008) as a metabolic proxy for P. fluorescens, justified by shared Pseudomonas genus and conserved central metabolic pathways. Choline uptake (EX_chol_e) was set to -10 mmol/gDW/h as carbon substrate. CHOLD (betA) and BETALDHx (betB) were assigned minimum flux constraints of 1.0 mmol/gDW/h to simulate constitutive P_algU-driven expression under salt stress. Salinity stress was modeled by restricting the biomass reaction (BiomassKT_TEMP) to 100%, 70%, and 40% of maximum growth rate, corresponding to 0, 75, and 150 mM NaCl respectively. Glycine betaine export (EX_glyb_e) and transport reactions (GLYBtex, GLYBabcpp) were opened to allow secretion.

Expected result: glycine betaine production sustained at 10 mmol/gDW/h across all salinity conditions, with growth rate decreasing from 2.64 h⁻¹ (normal) to 1.47 h⁻¹ (severe stress), confirming that the betA/betB pathway is metabolically feasible in Pseudomonas without causing metabolic collapse.

9. Future dry lab - Consortium interaction simulation - BacArena (Day 6, ~5 hours)

Agent-based metabolic simulation of P. fluorescens and B. subtilis co-culture using BacArena in R, with genome-scale metabolic models iJN746 (P. putida proxy) and iBB1018 (B. subtilis 168) respectively. A. brasilense was excluded due to the absence of a validated genome-scale metabolic model in any public database (BiGG, BioModels). Simulation will assess inter-species metabolite exchange, competitive exclusion risk and consortium stability under saline conditions.

Expected result: confirmation of metabolic compatibility between P. fluorescens and B. subtilis with no predicted competitive inhibition, supporting the feasibility of the three-organism consortium.

10. Future wet lab - DNA synthesis and transformation (Weeks 1-2)

Order codon-optimized constructs from Twist Bioscience. Transform P. fluorescens and A. brasilense by electroporation (2.5 kV, 25 µF, 200 Ω) and B. subtilis by competent cell transformation using the standard two-step starvation protocol. Select transformants on LB agar with appropriate antibiotics: kanamycin 50 µg/mL (P. fluorescens and A. brasilense) and chloramphenicol 5 µg/mL (B. subtilis). Confirm transformation by colony PCR using construct-specific primers, visualized by 1% agarose gel electrophoresis.

Expected result: antibiotic-resistant colonies confirmed by band of expected size.

11. Future wet lab - Individual strain validation under saline stress (Weeks 3-4)

Grow each transformed strain in liquid media supplemented with 0, 75, and 150 mM NaCl. Measure functional outputs: glycine betaine production by HPLC or colorimetric assay (P. fluorescens), nitrogenase activity by Acetylene Reduction Assay (ARA) — measuring ethylene production as a proxy for N₂ fixation (A. brasilense), and biofilm formation by crystal violet staining quantified at OD₅₉₀ (B. subtilis). Each condition performed in biological triplicates.

Expected result: statistically significant increase (p < 0.05, Student’s t-test) in each functional output under salt stress compared to non-transformed wild-type controls.

12. Future wet lab - Kill switch validation (Week 4)

CFU assays for each strain grown in media with and without supplementation of DAP (0.3 mM, P. fluorescens), tryptophan (0.5 mM, B. subtilis), and aromatic amino acids (0.5 mM each, A. brasilense). Plate on LB agar after 24h growth and count colonies. Perform live/dead staining (SYTO 9 / propidium iodide) as secondary validation.

Expected result: complete growth inhibition (0 CFU/mL) without metabolite supplementation in all three strains, confirming kill switch functionality and biosafety containment.

13. Future wet lab - Promoter validation using GFP reporter (Week 5)

Insert GFP downstream of P_algU (P. fluorescens) and P_epsA (B. subtilis) as transcriptional reporters. Grow under NaCl gradient (0, 50, 100, 150, 200 mM). Measure fluorescence intensity (excitation 488 nm, emission 507 nm) using plate reader and confirm by fluorescence microscopy.

Expected result: dose-dependent increase in GFP fluorescence with increasing NaCl concentration — directly confirming salt-responsive promoter activation in vivo.

14. Future wet lab - Consortium assembly and plant inoculation (Weeks 5-8)

Co-culture the three engineered strains at optimized ratios (1:1:1 initial, then optimized based on individual strain data). Lyophilize consortium and apply as seed coating to Arabidopsis thaliana Col-0 seeds. Grow on 0.5x MS agar plates with 0 and 150 mM NaCl for 14 days, then transfer to soil for 21 days. Measure germination rate, rosette diameter, number of leaves, fresh and dry biomass, and chlorophyll fluorescence (Fv/Fm ratio by pulse amplitude modulation fluorometry). Compare consortium-inoculated plants vs non-inoculated controls.

Expected result: statistically significant improvement (p < 0.05) in all plant performance metrics under saline conditions in inoculated plants.

15. Future wet lab - Microbial community monitoring (Weeks 6-8)

Extract DNA from rhizosphere soil of inoculated and non-inoculated A. thaliana plants. Perform 16S rRNA amplicon sequencing (V3-V4 region) using Illumina MiSeq platform. Analyze community composition using QIIME2 pipeline.

Expected result: all three engineered strains detectable and stable in rhizosphere of inoculated plants throughout the experiment, with no significant displacement of native microbiota composition.

16. Future wet lab - Opentrons automation (Year 1)

Translate optimal microbial ratios and NaCl screening conditions identified in plant inoculation experiments into Opentrons OT-2 Python liquid handling protocols for high-throughput combinatorial screening of consortium performance in 96-well plates. Measure OD₆₀₀ and functional outputs automatically.

Expected result: automated, reproducible preparation of consortium inoculants at multiple microbial ratios, eliminating pipetting error and enabling rapid optimization of consortium composition.

from opentrons import protocol_api

metadata = {
    'protocolName': 'HTGAA2026 - Rhizosphere Consortium Screening',
    'author': 'Ian Sebastian Teran Garcia',
    'description': '''Combinatorial screening of synthetic rhizosphere consortium
                      (P. fluorescens + A. brasilense + B. subtilis) at multiple
                      microbial ratios under NaCl salinity stress conditions.
                      Bolivian Altiplano Project - HTGAA 2026''',
    'apiLevel': '2.13'
}

def run(protocol: protocol_api.ProtocolContext):

    # ── LABWARE ──────────────────────────────────────────────────
    tiprack_1 = protocol.load_labware('opentrons_96_tiprack_300ul', 1)
    tiprack_2 = protocol.load_labware('opentrons_96_tiprack_300ul', 2)
    
    # Bacterial stocks — each organism in separate tube
    tube_rack = protocol.load_labware('opentrons_24_tuberack_eppendorf_2ml_safelock_snapcap', 3)
    
    # NaCl solutions — different concentrations
    reservoir = protocol.load_labware('nest_12_reservoir_15ml', 4)
    
    # 96-well plate for screening
    plate = protocol.load_labware('nest_96_wellplate_200ul_flat', 5)
    
    # Pipettes
    p300_multi = protocol.load_instrument('p300_multi_gen2', 'left', tip_racks=[tiprack_1])
    p300_single = protocol.load_instrument('p300_single_gen2', 'right', tip_racks=[tiprack_2])

    # ── REAGENTS ─────────────────────────────────────────────────
    # Tube rack positions
    Pf_stock  = tube_rack['A1']  # P. fluorescens — OD600 = 1.0
    Ab_stock  = tube_rack['A2']  # A. brasilense  — OD600 = 1.0
    Bs_stock  = tube_rack['A3']  # B. subtilis    — OD600 = 1.0
    media     = tube_rack['A4']  # LB media base

    # Reservoir — NaCl solutions
    NaCl_0mM   = reservoir['A1']   # 0 mM NaCl   — normal conditions
    NaCl_75mM  = reservoir['A2']   # 75 mM NaCl  — moderate stress
    NaCl_150mM = reservoir['A3']   # 150 mM NaCl — severe stress

    # ── PLATE LAYOUT ─────────────────────────────────────────────
    # Columns 1-4:   0 mM NaCl
    # Columns 5-8:   75 mM NaCl
    # Columns 9-12:  150 mM NaCl
    # 
    # Row A: Pf only (100% Pf, 0% Ab, 0% Bs)
    # Row B: Ab only (0% Pf, 100% Ab, 0% Bs)
    # Row C: Bs only (0% Pf, 0% Ab, 100% Bs)
    # Row D: Pf + Ab (50:50)
    # Row E: Pf + Bs (50:50)
    # Row F: Ab + Bs (50:50)
    # Row G: Full consortium 1:1:1
    # Row H: Full consortium 2:1:1 (Pf dominant)

    # ── BACTERIAL RATIOS (µL per well, total volume = 200 µL) ────
    ratios = {
        'A': {'Pf': 100, 'Ab': 0,   'Bs': 0},    # Pf only
        'B': {'Pf': 0,   'Ab': 100, 'Bs': 0},    # Ab only
        'C': {'Pf': 0,   'Ab': 0,   'Bs': 100},  # Bs only
        'D': {'Pf': 50,  'Ab': 50,  'Bs': 0},    # Pf + Ab
        'E': {'Pf': 50,  'Ab': 0,   'Bs': 50},   # Pf + Bs
        'F': {'Pf': 0,   'Ab': 50,  'Bs': 50},   # Ab + Bs
        'G': {'Pf': 33,  'Ab': 33,  'Bs': 34},   # Full 1:1:1
        'H': {'Pf': 67,  'Ab': 17,  'Bs': 16},   # Full 2:1:1
    }

    nacl_cols = {
        '0mM':   [1, 2, 3, 4],
        '75mM':  [5, 6, 7, 8],
        '150mM': [9, 10, 11, 12]
    }

    nacl_sources = {
        '0mM':   NaCl_0mM,
        '75mM':  NaCl_75mM,
        '150mM': NaCl_150mM
    }

    # ── STEP 1: ADD NaCl MEDIA TO ALL WELLS ──────────────────────
    protocol.comment("Step 1: Dispensing NaCl media to all wells")

    for condition, cols in nacl_cols.items():
        source = nacl_sources[condition]
        for col in cols:
            p300_multi.pick_up_tip()
            p300_multi.aspirate(100, source)
            p300_multi.dispense(100, plate.columns()[col - 1][0])
            p300_multi.drop_tip()

    # ── STEP 2: ADD BACTERIA AT SPECIFIED RATIOS ─────────────────
    protocol.comment("Step 2: Adding bacterial strains at specified ratios")

    rows = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']
    bacteria_sources = {
        'Pf': Pf_stock,
        'Ab': Ab_stock,
        'Bs': Bs_stock
    }

    for row in rows:
        ratio = ratios[row]
        for bacteria, volume in ratio.items():
            if volume > 0:
                source = bacteria_sources[bacteria]
                for col in range(1, 13):
                    well = plate.wells_by_name()[f'{row}{col}']
                    p300_single.pick_up_tip()
                    p300_single.aspirate(volume, source)
                    p300_single.dispense(volume, well)
                    p300_single.mix(3, 50, well)  # mix 3x after dispensing
                    p300_single.drop_tip()

    # ── STEP 3: SEAL AND INCUBATE ─────────────────────────────────
    protocol.comment("""
    Step 3: Protocol complete.
    Seal plate with breathable membrane.
    Incubate at 28°C for 48 hours with shaking at 200 rpm.
    Measure OD600 at 0h, 24h, and 48h.
    After incubation, perform:
    - Crystal violet staining for biofilm (B. subtilis)
    - Colorimetric assay for glycine betaine (P. fluorescens)
    - Acetylene Reduction Assay for N2 fixation (A. brasilense)
    """)

Opentrons OT-2 - Plate Design

The 96-well plate is organized along two axes, rows and columns, creating a fully factorial experimental design that simultaneously tests all microbial combinations across all salinity conditions in a single automated run.

Columns - Salinity Conditions (the environmental variable)

The 12 columns are divided into three groups of four replicates each, representing the three salinity conditions used throughout this project:

• Columns 1-4: 0 mM NaCl - normal growth conditions, used as baseline control
• Columns 5-8: 75 mM NaCl - moderate salinity stress, equivalent to early-stage soil salinization
• Columns 9-12: 150 mM NaCl - severe salinity stress, representative of Bolivian Altiplano conditions

Having four replicates per condition ensures statistical robustness and allows detection of well-to-well variability.

Rows - Microbial Combinations (the biological variable)

The 8 rows test every possible combination of the three organisms, from individual strains to the full consortium, allowing direct comparison of single-strain vs multi-strain performance.

• Row A: P. fluorescens only (100%) - isolates osmoprotection contribution
• Row B: A. brasilense only (100%) - isolates nitrogen fixation contribution
• Row C: B. subtilis only (100%) - isolates biofilm formation contribution
• Row D: P. fluorescens + A. brasilense (50:50) - tests osmoprotection + N₂ fixation synergy
• Row E: P. fluorescens + B. subtilis (50:50) - tests osmoprotection + biofilm synergy
• Row F: A. brasilense + B. subtilis (50:50) - tests N₂ fixation + biofilm synergy
• Row G: Full consortium 1:1:1 - equal ratio of all three organisms
• Row H: Full consortium 2:1:1 (P. fluorescens dominant) - tests whether increasing osmoprotection capacity improves overall performance

This factorial layout answers several key scientific questions simultaneously:

1. Which organism contributes most to salinity tolerance?

Comparing rows A, B, C across columns.

2. Do pairs of organisms perform better than individuals?

Comparing rows D, E, F vs A, B, C.

3. Does the full consortium outperform any pair?

Comparing rows G, H vs D, E, F.

4. What is the optimal microbial ratio?

Comparing rows G vs H.

5. How does each combination respond to increasing salinity?

Reading across columns 1-4, 5-8, 9-12.

Abbreviations

• Pf = Pseudomonas fluorescens
• Ab = Azospirillum brasilense
• Bs = Bacillus subtilis

Salt stress conditions

• 0 mM NaCl → Control
• 75 mM NaCl → Moderate salt stress
• 150 mM NaCl → High salt stress

Readouts measured after 48h incubation at 28°C:

• OD₆₀₀ at 0h, 24h, and 48h → growth dynamics
• Crystal violet staining → biofilm quantification (B. subtilis)
• Colorimetric assay → glycine betaine production (P. fluorescens)
• Acetylene Reduction Assay (ARA) → nitrogenase activity (A. brasilense)

17. Future long-term - Directed evolution with native Altiplano microbiota (Year 2)

Collect rhizosphere soil samples from native quinua (Chenopodium quinoa) fields across multiple sites in the Bolivian Altiplano, specifically targeting soils with documented high salinity (>150 mM NaCl equivalent). Create a native microbiome library by culturing samples under progressive saline stress conditions (75, 150, 300 mM NaCl) to enrich for naturally salt-tolerant PGPR. Co-culture this native microbiome library with the synthetic consortium in iterative selection cycles of 4-6 weeks each — selecting combinations that maximize quinua germination rate, root biomass, and nitrogen content under saline stress. After each iteration, characterize the winning microbial combinations by 16S rRNA amplicon sequencing and whole genome sequencing to identify which native strains integrated successfully with the engineered consortium. Repeat for a minimum of 4 iterations until consortium performance stabilizes.

Expected result: a hybrid consortium combining the precision of the engineered strains with the ecological robustness of naturally Altiplano-adapted microorganisms, outperforming the synthetic-only consortium in quinua growth metrics.

18. Future long-term - Field translation and lyophilized product development (Year 3)

Test the optimized hybrid consortium on quinua under greenhouse conditions mimicking the Bolivian Altiplano environment: altitude equivalent pressure, high UV radiation, temperature fluctuations between -5°C and 20°C, and saline soil substrate collected directly from the Altiplano. Optimize lyophilization protocol, including cryoprotectant selection (trehalose, skim milk) and storage conditions to maximize consortium viability after freeze-drying. Formulate final product as a lyophilized seed coating applicable by hand without specialized equipment. Conduct small-scale open field trials in collaboration with local Altiplano farming communities, with continuous monitoring of soil microbial composition (16S rRNA), plant performance, and consortium persistence over one full growing season.

Expected result: field-ready lyophilized microbial inoculant with demonstrated efficacy under real Altiplano conditions, shelf-stable for at least 12 months without refrigeration and deployable directly by local farmers as a seed coating.

2. We discussed and practiced various techniques related to synthetic biology throughout the semester. Place a check next to the techniques relevant to your project.

• Pipetting (future - bacterial transformation, colony PCR, plant inoculation assays)
• Lab Safety (biosafety level 1 organisms, GMO containment protocols, kill switch design)
• Bioethical Considerations
• DNA Construct Design
• DNA Sequencing (future validation — Sanger sequencing of constructs)
• DNA Editing (kill switch deletions via CRISPR or recombination)
• Databases (NCBI, GenBank, iGEM Parts Registry, BiGG, BioModels)
• Lab Automation (Opentrons OT-2 protocol design)
• Creating Code for Laboratory Automation (Opentrons Python protocol)
• Using Liquid Handling Robots (Opentrons OT-2 - 96-well consortium screening)
• Designing a Twist Order (codon-optimized constructs for future synthesis)
• Use of Benchling (primary platform for all circuit design)
• Models and Notebooks (COBRApy FBA + BacArena consortium simulation)
• Chassis Selection (P. fluorescens, A. brasilense, B. subtilis)
• Registry of Standard Biological Parts (BBa_B0034, BBa_B0015)
• Plasmid Preparation (future - pBBR1MCS2 and pBP_Pveg)
• Bacterial Culturing (future - transformation and validation)
• Gibson Assembly (all three constructs assembled via Gibson)
• CRISPR/Cas9 (future - kill switch gene deletions)
• Gel Electrophoresis (future - colony PCR verification)
• Primer Design or Selection (future - construct verification primers)
• PCR Reactions (future - colony PCR confirmation)

Techniques - Expanded Description

1. Use of Benchling

Benchling served as the primary platform for all genetic circuit design in this project. For each of the three organisms, Pseudomonas fluorescens, Azospirillum brasilense and Bacillus subtilis, individual genetic parts were imported from NCBI and the iGEM Parts Registry, annotated with their functional roles (promoter, RBS, CDS, terminator) and assembled into complete circular plasmid constructs using the Gibson Assembly workflow. The plasmid map visualization tool was used to verify part order, topology, overlap regions, and annotation colors, creating a complete and reproducible digital record of the consortium’s genetic architecture.

Additionally, Benchling Notebook was used to document all design decisions, part sources, circuit logic, kill switch rationale, and measurable outcomes for each organism, providing a structured experimental record that mirrors the documentation standards of professional biotechnology research :))

2. Gibson Assembly

Gibson Assembly was selected as the assembly method for all three constructs due to its flexibility with multi-fragment assemblies, compatibility with any DNA sequence regardless of restriction sites and high efficiency for constructs in the 5-12 kb range. For each construct, 40 bp overlaps were designed between adjacent fragments, P_algU promoter, RBS, coding sequences, terminator and backbone. They were verified using the Benchling Assembly Wizard, which automatically calculated overlap regions and confirmed construct integrity with “Ready to assemble” or “Assembled” status. In future wet lab validation, Gibson Assembly reactions will be performed using the NEB HiFi Assembly Master Mix at 50°C for 60 minutes, followed by transformation into competent cells and antibiotic selection. Constructs will be confirmed by Sanger sequencing prior to transformation into the final host organisms, P. fluorescens, A. brasilense and B. subtilis, ensuring sequence accuracy before any functional validation experiments.

Industry Council Connections

• Addgene: Source of expression backbones used in all three constructs: pBBR1MCS2 (#26702) for P. fluorescens and A. brasilense, and pBP_Pveg (#112776) for B. subtilis
• Twist Biosciences: Planned DNA synthesis partner for codon-optimized betA and betB constructs for future wet lab validation
• Opentrons: Opentrons OT-2 liquid handling robot protocols were designed for high-throughput combinatorial screening of consortium performance under multiple NaCl concentrations in 96-well plates.
• Ginkgo Bioworks: Potential partner for scale-up fermentation, consortium production, and autonomous lab validation of engineered strains
• Biome Consortia: Direct alignment with the project’s core concept of engineering microbial consortia for agricultural and environmental applications
• New England Biolabs: Planned supplier of NEB HiFi Assembly Master Mix for future Gibson Assembly reactions and Q5 polymerase for colony PCR verification
• Thermo Fisher Scientific: Planned supplier of reagents for HPLC glycine betaine quantification, crystal violet biofilm staining, and live/dead staining for kill switch validation
• SecureDNA: Relevant to the biosafety and environmental containment framework of this project, particularly the kill switch design and GMO release protocols
• Waters Corporation: HPLC instrumentation for future quantification of glycine betaine production by engineered P. fluorescens.

SECTION 5: Results & Quantitative Expectations

1. You are required to validate at least one aspect of your final project aims. This is to ensure that you are able to successfully apply a relevant synthetic biology technique to your project.

Include figures if you have them—accuracy is critical in figures, tables, and graphs

Here is a non-exhaustive list of acceptable validations:

1. What aspect of your final project did you choose to validate? (min. 2 sentences)

To anyone, feel free to check the genetics constructs on Benchling :D:

https://benchling.com/editor

This project validated two complementary aspects of the synthetic rhizosphere consortium design. First, the complete in silico genetic circuit design for all three organisms, Pseudomonas fluorescens, Azospirillum brasilense and Bacillus subtilis, was validated through Gibson Assembly simulation in Benchling, confirming that all genetic parts are correctly ordered, properly annotated, and have sufficient 40 bp overlap regions for successful assembly.

Second, the metabolic feasibility of glycine betaine biosynthesis in Pseudomonas was computationally validated using Flux Balance Analysis (FBA) in COBRApy, demonstrating that the engineered betA/betB pathway can sustain production of 10 mmol/gDW/h of glycine betaine across all salinity conditions tested (0, 75, and 150 mM NaCl) without causing metabolic collapse, even as growth rate decreased by 44% under severe saline stress. Together,

These two validations confirm both the structural integrity of the genetic constructs and the metabolic viability of the osmoprotection strategy, providing a strong computational foundation for future experimental validation.

Genetic Constructs

Genetic Parts Summary

Pseudomonas fluorescens:

Bacillus subtilis:

Azospirillum brasilense:

2. Write down a detailed protocol of how you validated this aspect of your final project. (Numbered list or paragraph is fine)

Validation Protocol

Part 1 - Genetic Circuit Design and Gibson Assembly Validation in Benchling

1. Created a new project in Benchling named HTGAA2026_RhizosphereConsortium with eight folders: 01_Pf_Osmoprotectant, 02_Pf_KillSwitch, 03_Registry_Parts, 04_Notebook, 05_Bs_Biofilm, 06_Bs_KillSwitch, 07_Ab_NitrogenFixation and 08_Ab_KillSwitch.

2. Imported all genetic parts into the Registry folder as individual DNA sequences:

   • BBa_B0034 (RBS, 12 bp) and BBa_B0015 (terminator, 129 bp) from the iGEM Parts Registry
   • betA (1,671 bp) and betB (1,425 bp) from NCBI accession X52905 (E. coli K-12)
   • epsA (705 bp) and tapA (762 bp) from NCBI NC_000964 (B. subtilis 168)
   • nifH (882 bp), nifD (1,440 bp), and nifK from NCBI GCF_008274965.1 (A. brasilense Sp7)
   • Backbone pBBR1MCS2 (5,148 bp) from Addgene #26702
   • Backbone pBP_Pveg (2,204 bp) from Addgene #112776

3. For all genes located on the complementary strand of their respective genomes (epsA, tapA, nifH, nifD, nifK), performed reverse complementation using a custom Python script to obtain the correct 5’ to 3’ coding sequences before importing into Benchling.

4. Designed a synthetic AlgU-responsive promoter for P. fluorescens (31 bp) based on the published consensus sequence from Firoved & Deretic (2003): -35 box GAACTT, 16 nt spacer, -10 box TCTGA. Extracted the native P_epsA promoter (201 bp) from the region complement(3529856..3530056) of NC_000964, and the native P_nifH promoter (~200 bp upstream of nifH) from GCF_008274965.1.

5. Performed codon optimization of betA and betB for Pseudomonas using the IDT Codon Optimization Tool with P. aeruginosa as reference organism. Updated sequences in Benchling replacing original E. coli sequences.

6. Assembled each construct as a new DNA sequence in its respective folder, concatenating parts in the correct order:

   • P. fluorescens: P_algU → BBa_B0034 → betA → BBa_B0034 → betB → BBa_B0015
   • B. subtilis: P_epsA → BBa_B0034 → epsA → BBa_B0034 → tapA → BBa_B0015
   • A. brasilense: P_nifH → BBa_B0034 → nifH → BBa_B0034 → nifD → BBa_B0034 → nifK → BBa_B0015

7. Annotated each part within the construct with its functional type (Promoter, RBS, CDS, Terminator) and color-coded for visualization.

8. Designed kill switch modules for each organism:

   • P. fluorescens: ΔdapA (465 bp, CPH89_RS26560) - DAP auxotrophy
   • B. subtilis: ΔtrpC (750 bp, BSU_24310) - tryptophan auxotrophy
   • A. brasilense: ΔaroA - aromatic amino acid auxotrophy
   • Each documented with dual annotations: CDS (wildtype) and Misc Feature (DELETION TARGET)

9. Configured Gibson Assembly for each construct in Benchling using the Assembly Wizard:

   • Added backbone and insert fragments
   • Set overlap length to 40 bp
   • Clicked Autopopulate to calculate overlaps automatically
   • Verified “Ready to assemble” or “Assembled” status for all three constructs

10. Verified final plasmid maps in circular view, confirming correct part order, topology, and annotation for all three constructs.

Part 2 - Metabolic Feasibility Validation using COBRApy

1. Opened Google Colab and created a new notebook named HTGAA2026_COBRApy_Pf_GlycineBetaine.

2. Installed COBRApy using !pip install cobra -q and imported required libraries: cobra, requests, pandas, and matplotlib.

3. Downloaded the P. putida KT2440 genome-scale metabolic model iJN746 from BioModels (MODEL1507180068) and loaded it using cobra.io.read_sbml_model(). Used iJN746 as a metabolic proxy for P. fluorescens due to the absence of a validated P. fluorescens model in public databases, justified by shared Pseudomonas genus and conserved central metabolic pathways.

4. Explored the model to identify glycine betaine-related reactions, confirming the presence of:

   • CHOLD - choline dehydrogenase (equivalent to betA)
   • BETALDHx - betaine aldehyde dehydrogenase (equivalent to betB)
   • EX_glyb_e - glycine betaine export reaction
   • GLYBtex and GLYBabcpp - glycine betaine transport reactions

5. Configured the model for FBA simulation:

   • Set choline uptake EX_chol_e lower bound to -10 mmol/gDW/h
   • Set EX_chol_e upper bound to 0 (uptake only)
   • Opened glycine betaine export and transport reactions
   • Set minimum flux constraints of 1.0 mmol/gDW/h on CHOLD and BETALDHx to simulate P_algU-driven expression
   • Set objective function to maximize EX_glyb_e (glycine betaine export)

6. Ran FBA under three salinity conditions by restricting the biomass reaction (BiomassKT_TEMP) to simulate growth inhibition:

   • 0 mM NaCl: 100% maximum growth rate (3.6794 h⁻¹)
   • 75 mM NaCl: 70% maximum growth rate
   • 150 mM NaCl: 40% maximum growth rate

7. Recorded glycine betaine production, growth rate, and solver status for each condition.

8. Generated a two-panel bar chart using matplotlib showing glycine betaine production and growth rate across all three salinity conditions, saved as FBA_glycine_betaine_salinity.png.

9. Interpreted results and documented findings in Google Colab notebook, linking metabolic feasibility to the Benchling circuit design.

3. What synthetic biology techniques did you utilize in validating this aspect of your final project? You can refer to the list of techniques in question 8. (min. 4 sentences)

The primary technique utilized in this project was DNA construct design in Benchling, the industry-standard platform for genetic circuit design used by leading synthetic biology companies including Ginkgo Bioworks and Twist Bioscience. All three genetic circuits were designed from scratch using sequences retrieved from databases, specifically NCBI GenBank for coding sequences (betA, betB, epsA, tapA, nifH, nifD, nifK) and the Registry of Standard Biological Parts (iGEM) for standardized RBS BBa_B0034 and terminator BBa_B0015, demonstrating the ability to navigate and extract relevant biological information from multiple public repositories simultaneously.

Gibson Assembly was used as the assembly strategy for all three constructs, with 40 bp overlaps calculated and verified through the Benchling Assembly Wizard, a technique that requires understanding of DNA homology, exonuclease activity and fragment design principles that were directly applied in the design of each construct. Models and notebooks were central to the computational validation component: COBRApy was used to perform Flux Balance Analysis (FBA) on a genome-scale metabolic model, requiring knowledge of constraint-based metabolic modeling, objective function definition and the interpretation of flux distributions, skills that bridge synthetic biology with systems biology and metabolic engineering.

Additionally, bioethical considerations were integrated throughout the entire validation process, from the design of auxotrophic kill switches as biosafety containment mechanisms, to the explicit acknowledgment of the limitations of in silico validation and the need for staged experimental testing before any field deployment of the engineered consortium.

4. You must present data as part of your final project and include some analysis of that data. The data may be collected experimentally in the lab or generated as simulated data (e.g., using the Asimov Kernel or another simulation method). (min.2 sentences)

The primary data presented in this project was generated computationally using Flux Balance Analysis (FBA) in COBRApy, a well-established simulation method in systems biology and metabolic engineering. The analysis produced quantitative flux data for glycine betaine production and growth rate across three salinity conditions (0, 75, and 150 mM NaCl), revealing that the engineered betA/betB pathway sustained maximum glycine betaine production of 10 mmol/gDW/h under all conditions tested, while growth rate decreased progressively from 2.64 h⁻¹ under normal conditions to 1.47 h⁻¹ under severe saline stress, a 44% reduction confirming the metabolic cost of salinity stress on Pseudomonas central metabolism.

These results were visualized as a two-panel bar chart comparing glycine betaine production and growth rate across all three conditions, providing clear graphical evidence that the betA/betB pathway is metabolically feasible without causing cellular collapse, directly validating the core design rationale of the P. fluorescens osmoprotection circuit designed in Benchling. The solver status remained “optimal” across all three conditions, which in FBA terms means the model successfully found a feasible solution satisfying all metabolic constraints simultaneously, confirming that glycine betaine biosynthesis can be sustained alongside core cellular functions including growth, energy generation and biosynthesis of essential metabolites.

Computational Validation: Can Pseudomonas Produce Glycine Betaine Under Salt Stress?

Objective:

The goal of this analysis was to determine whether Pseudomonas fluorescens has sufficient metabolic capacity to produce glycine betaine through the engineered betA/betB pathway under saline stress conditions, without compromising its own growth and survival.

Methods:

Flux Balance Analysis (FBA) was performed using COBRApy (Ebrahim et al., 2013) in Google Colab. Due to the absence of a validated genome-scale metabolic model for P. fluorescens in public databases, the closely related P. putida KT2440 model iJN746 was used as a metabolic proxy. Both organisms belong to the Pseudomonas genus and share highly conserved central metabolic pathways, making this a valid approximation for feasibility analysis.

Key modeling decisions:

• Choline uptake was set to -10 mmol/gDW/h as the carbon substrate for glycine betaine biosynthesis.
• CHOLD (betA) and BETALDHx (betB) were assigned minimum flux constraints to simulate constitutive expression driven by the synthetic P_algU promoter.
• BHMT, the reaction that internally consumes glycine betaine was constrained to simulate osmoprotectant accumulation under stress.
• Salinity stress was modeled by restricting the biomass reaction to 100%, 70%, and 40% of maximum growth rate, corresponding to 0, 75, and 150 mM NaCl respectively.

Results

Limitations

• FBA is a static optimization model and does not simulate gene regulation or dynamic stress responses. It cannot predict whether P_algU will activate under salt stress, that requires experimental validation.
• iJN746 is a P. putida model, not P. fluorescens. While the central metabolic pathways are conserved, strain-specific differences may exist.
• The minimum flux constraints on CHOLD and BETALDHx simulate promoter activation. This is a modeling assumption, not a direct measurement of gene expression.

Interpretation

1. The betA/betB pathway is metabolically feasible in Pseudomonas. The model remained optimal under all salinity conditions tested, meaning the bacterium can simultaneously grow and produce glycine betaine without metabolic collapse. This confirms that the heterologous expression of betA and betB from E. coli K-12 is compatible with Pseudomonas central metabolism.

2. Even under 150 mM NaCl, where growth rate dropped by 44% (from 2.64 to 1.47 h⁻¹), glycine betaine production remained at maximum capacity (10 mmol/gDW/h). This is consistent with the biological rationale of the circuit: P_algU activates betA/betB precisely when salt stress is highest.

3. The iJN746 model contains native CHOLD and BETALDHx reactions, confirming that Pseudomonas has the required cofactors, substrates, and thermodynamic conditions to support the betA/betB pathway, validating the circuit design in Benchling.

Code: Full analysis available in Google Colab.

https://colab.research.google.com/drive/10zRvkxbc8lPv3u9Fvgu1oBn0WxqwVY4g?usp=sharing

Step 1: Install COBRApy and import libraries

This step installs COBRApy and imports the required Python libraries for Flux Balance Analysis (FBA).

# Install COBRApy
!pip install cobra -q

# Import libraries
import cobra
import requests
import pandas as pd
import matplotlib.pyplot as plt

print("COBRApy installed correctly")

Step 2: Download and load the iJN746 metabolic model

This step downloads the iJN746 genome-scale metabolic model of Pseudomonas putida from BioModels and loads it into COBRApy using SBML format.

from google.colab import files

# Upload the SBML file downloaded manually from BioModels
uploaded = files.upload()

# Replace with the exact uploaded filename if different
model_file = "MODEL1507180068_urn.xml"

# Load the SBML model into COBRApy
model = cobra.io.read_sbml_model(model_file)

print("Model loaded successfully")
print(f"Reactions: {len(model.reactions)}")
print(f"Metabolites: {len(model.metabolites)}")
print(f"Genes: {len(model.genes)}")

Step 3: Explore the baseline metabolic model

This step evaluates the basal growth rate of the model and identifies reactions associated with glycine betaine biosynthesis and osmoprotection pathways.

# Basal growth rate
solution = model.optimize()

print(f"Basal growth rate: {solution.objective_value:.4f} h⁻¹")

# Search glycine betaine-related reactions
print("\n Glycine betaine-related reactions:\n")

keywords = ["betaine", "choline", "osmo", "glycine betaine", "bet"]

for rxn in model.reactions:
    for kw in keywords:
        if kw.lower() in rxn.name.lower() or kw.lower() in rxn.id.lower():

            print(f"ID: {rxn.id}")
            print(f"Name: {rxn.name}")
            print(f"Equation: {rxn.reaction}")
            print(f"Bounds: {rxn.bounds}")
            print("---")

            break

Step 4: Identify the biomass reaction

This step searches for the biomass reaction used by the model as the growth objective during Flux Balance Analysis.

# Search biomass reaction
print("Searching for biomass reaction:\n")

for rxn in model.reactions:

    if "biomass" in rxn.id.lower() or "biomass" in rxn.name.lower():

        print(f"ID: {rxn.id}")
        print(f"Name: {rxn.name}")
        print("---")

Step 5: Verify choline uptake and betA pathway activity

This step verifies whether choline can enter the metabolic network and evaluates the activity of the betA-associated reaction involved in glycine betaine biosynthesis.

with model:

    # Enable choline uptake
    model.reactions.get_by_id("EX_chol_e").lower_bound = -10
    model.reactions.get_by_id("EX_chol_e").upper_bound = 0

    # Set biomass as objective function
    model.objective = "BiomassKT_TEMP"

    # Run optimization
    solution = model.optimize()

    print(f"Status: {solution.status}")
    print(f"Growth rate: {solution.fluxes['BiomassKT_TEMP']:.4f}")

    print(f"Flux EX_chol_e: {solution.fluxes['EX_chol_e']:.4f}")
    print(f"Flux CHOLD (betA): {solution.fluxes['CHOLD']:.4f}")

Step 6: Perform Flux Balance Analysis under salinity stress

This step simulates glycine betaine production under increasing salinity stress conditions by constraining biomass formation and activating the betA/betB pathway.

results = []

with model:

    # Base configuration
    model.reactions.get_by_id("EX_chol_e").lower_bound = -10
    model.reactions.get_by_id("EX_chol_e").upper_bound = 0

    model.reactions.get_by_id("EX_glyb_e").lower_bound = 0
    model.reactions.get_by_id("EX_glyb_e").upper_bound = 999999

    model.reactions.get_by_id("GLYBtex").lower_bound = -999999
    model.reactions.get_by_id("GLYBabcpp").lower_bound = -999999

    model.reactions.get_by_id("BHMT").upper_bound = 0

    # Simulate activation of betA and betB under P_algU
    model.reactions.get_by_id("CHOLD").lower_bound = 1.0
    model.reactions.get_by_id("BETALDHx").lower_bound = 1.0

    # Set glycine betaine export as objective
    model.objective = "EX_glyb_e"

    # Salinity conditions
    salt_conditions = [
        ("0 mM NaCl (normal)", 1.0),
        ("75 mM NaCl (moderate)", 0.7),
        ("150 mM NaCl (severe)", 0.4),
    ]

    base_growth = 3.6794

    for condition, fraction in salt_conditions:

        # Constrain growth according to stress intensity
        model.reactions.get_by_id(
            "BiomassKT_TEMP"
        ).upper_bound = base_growth * fraction

        # Optimize model
        solution = model.optimize()

        # Store results
        results.append({
            "Condition": condition,
            "Glycine betaine (mmol/gDW/h)": round(solution.objective_value, 4),
            "Growth rate (h⁻¹)": round(solution.fluxes.get("BiomassKT_TEMP", 0), 4),
            "betA flux": round(solution.fluxes.get("CHOLD", 0), 4),
            "betB flux": round(solution.fluxes.get("BETALDHx", 0), 4),
            "Status": solution.status
        })

# Convert results into DataFrame
df = pd.DataFrame(results)

# Display results
print(df.to_string(index=False))

Step 7: Visualize FBA results under salinity stress

This step generates comparative plots showing glycine betaine production and growth rate under different NaCl stress conditions.

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

fig.suptitle(
    "FBA Analysis: Glycine Betaine Production in Pseudomonas\nunder Salinity Stress",
    fontsize=13,
    fontweight="bold"
)

# Extract values
conditions = [r["Condition"] for r in results]

glyb_vals = [
    r["Glycine betaine (mmol/gDW/h)"]
    for r in results
]

growth_vals = [
    r["Growth rate (h⁻¹)"]
    for r in results
]

colors = ["#2ecc71", "#f39c12", "#e74c3c"]

# Plot 1 — Glycine betaine production
bars1 = ax1.bar(
    conditions,
    glyb_vals,
    color=colors,
    edgecolor="white",
    linewidth=1.5
)

ax1.set_title(
    "Glycine Betaine Production\n(betA/betB pathway)",
    fontweight="bold"
)

ax1.set_ylabel("mmol/gDW/h")

ax1.set_ylim(0, max(glyb_vals) * 1.3)

for bar, val in zip(bars1, glyb_vals):

    ax1.text(
        bar.get_x() + bar.get_width()/2,
        bar.get_height() + 0.1,
        f"{val:.2f}",
        ha="center",
        fontweight="bold"
    )

ax1.tick_params(axis="x", labelsize=8)

# Plot 2 — Growth rate
bars2 = ax2.bar(
    conditions,
    growth_vals,
    color=colors,
    edgecolor="white",
    linewidth=1.5
)

ax2.set_title(
    "Growth Rate under Salinity Stress",
    fontweight="bold"
)

ax2.set_ylabel("h⁻¹")

ax2.set_ylim(0, max(growth_vals) * 1.3)

for bar, val in zip(bars2, growth_vals):

    ax2.text(
        bar.get_x() + bar.get_width()/2,
        bar.get_height() + 0.02,
        f"{val:.2f}",
        ha="center",
        fontweight="bold"
    )

ax2.tick_params(axis="x", labelsize=8)

# Save figure
plt.tight_layout()

plt.savefig(
    "FBA_glycine_betaine_salinity.png",
    dpi=150,
    bbox_inches="tight"
)

plt.show()

print("Figure saved")

2. Did you encounter any unexpected challenge(s) when performing your validation? If so, describe the challenge(s) and strategies to overcome it. If not, discuss potential problems, difficulties, limitations, and/or alternative strategies to overcome challenges in your final project. (min. 4 sentences).

1. Incorrect promoter selection for P. fluorescens - resolved

The initially proposed salt-responsive promoter for P. fluorescens was the E. coli osmY promoter (P_osmY), which was later identified as sigma-S dependent, a sigma factor not conserved in Pseudomonas. This was resolved by designing a synthetic AlgU-responsive promoter based on the published consensus sequence from Firoved & Deretic (2003), which uses the native Pseudomonas salt-stress sigma factor AlgU. This challenge highlighted the importance of verifying promoter-sigma factor compatibility across species before committing to a design.

2. Taxonomic reclassification of A. brasilense Sp245 - resolved

During genome retrieval, the initially targeted strain A. brasilense Sp245 was found to have been reclassified as Azospirillum baldaniorum sp, causing confusion in NCBI database searches. This was resolved by switching to A. brasilense Sp7 (NCBI GCF_008274965.1), which retains the original A. brasilense designation and has a well-characterized nif gene cluster. This is documented in the notebook as a design note to maintain transparency.

3. Complementary strand sequences - resolved

Multiple target genes, including epsA, tapA, nifH, nifD and nifK, were located on the complementary strand of their respective genomes, meaning the sequences retrieved from NCBI were in the reverse orientation. This was resolved by writing a custom Python script to perform reverse complementation of each sequence before importing into Benchling, ensuring all coding sequences were in the correct 5’ to 3’ orientation for expression.

4. Absence of P. fluorescens genome-scale metabolic model - partially resolved

No validated genome-scale metabolic model exists for P. fluorescens in any public database (BiGG, BioModels), which prevented direct FBA analysis of the target organism. This was partially resolved by using the P. putida KT2440 model iJN746 as a metabolic proxy, justified by shared Pseudomonas genus and conserved central metabolic pathways. However, this remains a limitation of the analysis, as strain-specific metabolic differences between P. putida and P. fluorescens cannot be fully excluded.

5. BiGG database server unavailability - resolved

During the COBRApy analysis, the BiGG database server (bigg.ucsd.edu) was unavailable and refused all connections, preventing direct model download. This was resolved by retrieving the iJN746 model from BioModels (MODEL1507180068) as an alternative source, which provided the same model in SBML format compatible with COBRApy.

6. Glycine betaine production initially returning zero flux - resolved

Initial FBA runs returned zero glycine betaine production despite choline being available as a substrate. Investigation revealed two issues: first, the choline exchange reaction (EX_chol_e) had incorrect bounds that only allowed export, not uptake; and second, the internal BHMT reaction was consuming all produced glycine betaine before it could be exported. These were resolved by correcting the EX_chol_e bounds to allow uptake, opening glycine betaine transport reactions, and setting minimum flux constraints on CHOLD and BETALDHx to simulate P_algU-driven expression — ultimately achieving the expected production of 10 mmol/gDW/h.

7. Backbone availability on Addgene - resolved

The initially planned backbone for B. subtilis (pHT01, Addgene #26861) was not available on Addgene at the time of design. This was resolved by identifying an alternative backbone from the BacilloFlex toolkit, pBP_Pveg (Addgene #112776), which is specifically designed for B. subtilis expression and is part of a well-validated modular assembly system.

SECTION 6: ADDITIONAL INFORMATION

12. List all references cited in this assignment (bullet-point list)

Aim 3

• Chu, T. N., Tran, B. T. H., Van Bui, L., & Hoang, M. T. T. (2019). Plant growth-promoting rhizobacterium Pseudomonas PS01 induces salt tolerance in Arabidopsis thaliana. BMC Research Notes, 12, 1. https://doi.org/10.1186/s13104-019-4046-1
• Chang, C.-Y., Osborne, M. L., Bajic, D., & Sanchez, A. (2020). Artificially selecting microbial communities using propagule strategies. Evolution, 74(11), 2392–2403. https://doi.org/10.1111/evo.14094
• Chang, C.-Y., Vila, J. C. C., Bender, M., Li, R., Mankowski, M. C., Bassette, M., Borden, J., Golfier, S., Sanchez, P. G. L., Waymack, R., Zhu, X., Diaz-Colunga, J., Estrela, S., Rebolleda-Gomez, M., & Sanchez, A. (2021). Engineering complex communities by directed evolution. Nature Ecology & Evolution, 5(7), 1011–1023. https://doi.org/10.1038/s41559-021-01457-5 (NCBI) (PubMed)
• Blouin, M., Karimi, B., Mathieu, J., & Lerch, T. Z. (2015). Levels and limits in artificial selection of communities. Ecology Letters, 18(10), 1040–1048. https://doi.org/10.1111/ele.12482
• Guo, X., & Boedicker, J. Q. (2016). The contribution of high-order metabolic interactions to the global activity of a four-species microbial community. PLoS Computational Biology, 12(9), e1005079. https://doi.org/10.1371/journal.pcbi.1005079
• Jochum, M. D., McWilliams, K. L., Pierson, E. A., & Jo, Y.-K. (2019). Host-mediated microbiome engineering of drought tolerance in the wheat rhizosphere. PLoS ONE, 14(12), e0225933. https://doi.org/10.1371/journal.pone.0225933
• Mueller, U. G., Juenger, T. E., Kardish, M. R., Carlson, A. L., Burns, K. M., Edwards, J. A., Smith, C. C., Fang, C.-C., & Des Marais, D. L. (2021). Artificial selection on microbiomes to breed microbiomes that confer salt tolerance to plants. mSystems, 6(6), e01125-21. https://doi.org/10.1128/mSystems.01125-21 (nih) (PubMed)
• Sánchez, A., Chang, C.-Y., Díaz-Colunga, J., Estrela, S., Rebolleda-Gómez, M., & Vila, J. C. C. (2021). Directed evolution of microbial communities. Annual Review of Biophysics, 50, 323–341. https://doi.org/10.1146/annurev-biophys-101220-072829
• Sánchez-Gorostiaga, A., Bajic, D., Osborne, M. L., Poyatos, J. F., & Sanchez, A. (2019). High-order interactions distort the functional landscape of microbial consortia. PLoS Biology, 17(12), e3000550. https://doi.org/10.1371/journal.pbio.3000550

Salinity & Bolivian Altiplano

• Hiernaux, P., et al. (2024). Complementarity of Sentinel-1 and Sentinel-2 data for soil salinity monitoring to support sustainable agriculture practices in the Central Bolivian Altiplano. Sustainability, 16(14), 6200. https://www.mdpi.com/2071-1050/16/14/6200
• FAO. (2024). Global status of salt-affected soils. FAO Knowledge Repository. https://openknowledge.fao.org/handle/20.500.14283/cd3044en
• Butcher, K., et al. (2016). Soil salinity: A threat to global food security. Agronomy Journal. https://www.researchgate.net/publication/310621326

PGPR Consortium & Compatibility

• Bukhat, S., et al. (2021). Potential of plant growth promoting bacterial consortium for improving the growth and yield of wheat under saline conditions. Frontiers in Plant Science. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9557047/
• Gerbore, J., et al. (2024). A “love match” score to compare root exudate attraction and feeding of Bacillus subtilis, Pseudomonas fluorescens, and - Azospirillum brasilense. Frontiers in Microbiology. https://pmc.ncbi.nlm.nih.gov/articles/PMC11456545/
• Ilyas, N., et al. (2024). Plant growth-promoting bacteria (PGPB)-induced plant adaptations to stresses: an updated review. PeerJ. https://peerj.com/articles/17882/
• Chu, T. N., Tran, B. T. H., Van Bui, L., & Hoang, M. T. T. (2019). Plant growth-promoting rhizobacterium Pseudomonas PS01 induces salt tolerance in Arabidopsis thaliana. BMC Research Notes, 12, 1. https://doi.org/10.1186/s13104-019-4046-1

Azospirillum brasilense

• Tropaldi, L., et al. (2025). Azospirillum brasilense as a bioinoculant to alleviate the effects of salinity on quinoa seed germination. Plants, 14(24), 3829. https://www.mdpi.com/2223-7747/14/24/3829
• Wisniewski-Dye, F., et al. (2011). Azospirillum genomes reveal transition of bacteria from aquatic to terrestrial environments. PLoS Genetics. https://doi.org/10.1371/journal.pgen.1002430
• Prikryl, Z., et al. (2002). pBBR1-based vectors for monitoring Azospirillum-wheat interactions. PubMed 12084480.

Pseudomonas fluorescens - Promoter & Osmoprotection

• Firoved, A. M., & Deretic, V. (2003). Genomic analysis of AlgU regulon in Pseudomonas aeruginosa. Journal of Bacteriology. https://doi.org/10.1128/JB.185.4.1071-1081.2003
• Lamark, T., et al. (1991). DNA sequence of bet genes encoding enzymes for the osmoregulatory choline-glycine betaine pathway in Escherichia coli. Molecular Microbiology. https://doi.org/10.1111/j.1365-2958.1991.tb00748.x

Bacillus subtilis - Biofilm

• Kearns, D. B., et al. (2005). A master regulator for biofilm formation by Bacillus subtilis. Molecular Microbiology. https://doi.org/10.1111/j.1365-2958.2005.04440.x
• Wicke, N., et al. (2017). BacilloFlex: A modular DNA assembly toolkit for Bacillus subtilis. bioRxiv. https://www.addgene.org/112776/

Nitrogen Fixation

• Rubio, L. M., & Ludden, P. W. (2008). Biosynthesis of the iron-molybdenum cofactor of nitrogenase. Annual Review of Microbiology. https://doi.org/10.1146/annurev.micro.62.081307.162737

Nitrogen Fertilizers & Environmental Impact

• Menegat, S., Ledo, A., & Tirado, R. (2022). Greenhouse gas emissions from global production and use of nitrogen synthetic fertilisers in agriculture. Scientific Reports. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9411506/
• Cassman, K., & Dobermann, A. (2022). Nitrogen and the future of agriculture. Nature Food. https://www.nature.com/articles/s43016-023-00698-w
• Planet Tracker. (2023). Fixing Nitrogen: Nitrogen fertiliser production outstrips global needs. https://planet-tracker.org/nitrogen-fertiliser-production-outstrips-global-needs

Computational Tools

• Ebrahim, A., Lerman, J. A., Palsson, B. Ø., & Hyduke, D. R. (2013). COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Systems Biology, 7, 74. https://doi.org/10.1186/1752-0509-7-74
• Nogales, J., Palsson, B. Ø., & Thiele, I. (2008). A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory. BMC Systems Biology, 2, 79. https://doi.org/10.1186/1752-0509-2-79
• Freilich, S., et al. (2011). BacArena: individual-based metabolic modelling of heterogeneous microbes in complex communities. PLoS Computational Biology. https://doi.org/10.1371/journal.pcbi.1002020

Genetic Parts & Tools

• iGEM Parts Registry. BBa_B0034 — Ribosome Binding Site. https://parts.igem.org/Part:BBa_B0034
• iGEM Parts Registry. BBa_B0015 — Transcriptional Terminator. https://parts.igem.org/Part:BBa_B0015
• Addgene. pBBR1MCS-2 (#26702). https://www.addgene.org/26702/
• Addgene. pBP-Pveg (#112776). https://www.addgene.org/112776/

NCBI Sequences Used

• E. coli K-12 bet operon (betA, betB): NCBI Accession X52905
• B. subtilis 168 chromosome: NCBI Accession NC_000964
• A. brasilense Sp7 genome: NCBI Accession GCF_008274965.1
• P. fluorescens ATCC 13525 (dapA): NCBI Gene CPH89_RS26560

13. Create a supply list and budget for your project (bullet-point list)

• What supplies, equipment, and budget is needed for your project to work?

Supply List and Budget

DNA Design & Synthesis

Plasmids & Backbones

Enzymes & Assembly

Bacterial Strains

Antibiotics & Kill Switch Supplements

Culture Media & Lab Consumables

Validation Assays

Plant Experiments — A. thaliana

Sequencing & Bioinformatics

Equipment (if not available at host institution)

Budget Summary

Note: Equipment costs assume no access to shared core facility infrastructure. Most academic institutions provide access to electroporators, plate readers, centrifuges, and growth chambers, reducing the total budget to approximately $4,500-4,800 USD for reagents and consumables only.

Thank you for reading my project! :)

To close, thank you to my HTGAA 2026 node in Quito, Ecuador and to all the TAs who helped make this happen. Thank you for opening the doors of synthetic biology to Latin America. We have so much to contribute and projects like this one are just the beginning.

XXOO Ian Teran

Group Final Project

PROJECT OBJECTIVE

• Engineer the L protein of the MS2 phage to increase structural stability.
• Disrupt or reduce its interaction with the bacterial chaperone DnaJ.
• Preserve the C-terminal lysis domain to maintain lytic function.
• Avoid mutations that interfere with structurally or evolutionarily coupled residues.

Phase 1: Mapping the DnaJ Interaction Interface

Since the exact binding interface between the L protein and DnaJ is unknown, the first step is to identify it computationally rather than introducing arbitrary mutations.

• Use AlphaFold-Multimer to model the complex between L protein and DnaJ.
• Generate multiple structural predictions and select the top-ranked models.
• Identify consensus interface residues that consistently appear in the predicted binding interface.
• Perform in silico alanine scanning of the N-terminal residues in the complex to determine which residues significantly contribute to binding energy (ΔΔG).
• Analyze whether the N-terminal region resembles known DnaJ-binding motifs, typically hydrophobic residues flanked by basic amino acids.

This phase defines which residues are critical for interaction and should not be mutated randomly.

Phase 2: Targeted N-Terminal Redesign

Instead of deleting regions or performing extensive random substitutions, introduce controlled chemical modifications to disrupt interaction while preserving structural stability.

• Focus on charge inversion strategies:

  • Basic residues (K, R) → Acidic residues (E, D)
  • Acidic residues (E, D) → Basic residues (K, R)

• Disrupt hydrophobic interaction patches:

  • Hydrophobic residues (L, I, V, F) → Polar residues (S, T, N, Q)
  • Aromatic residues (F, Y, W) → Aliphatic or small residues

• Generate a graded library of variants:

  • Minor charge modifications
  • Moderate interface perturbations
  • Strong hydrophobic disruption

This creates a Pareto front of variants balancing reduced DnaJ interaction and preserved protein stability.

Phase 3: Stability and Functional Filtering

To ensure that redesigned variants remain structurally viable and functionally relevant:

• Use Rosetta or FoldX to calculate ΔΔG and verify that mutations do not destabilize the overall protein fold.

• Confirm that mutations in the N-terminal region do not propagate structural stress toward the C-terminal lysis domain.

• Perform co-evolutionary analysis (e.g., EVcouplings):

  • Identify residue pairs that co-evolved between the N-terminal and C-terminal regions.
  • Avoid mutating co-evolved residues independently to prevent functional disruption.

• Evaluate aggregation propensity using tools such as Aggrescan3D to ensure that mutations do not create exposed hydrophobic patches leading to cytoplasmic aggregation.

• Assess sequence plausibility using protein language models such as ESM to filter out unlikely or non-natural variants.

Key Limitations:

• The DnaJ binding mode may be transient or dynamic, reducing AlphaFold-Multimer accuracy.
• Protein language model scores do not guarantee in vivo functionality.
• Intrinsically disordered regions may not be accurately modeled.
• Computational predictions must ultimately be validated experimentally.

From WEEK 5 HW:

High level summary: The objective of this assignment is to improve the stability and auto-folding of the lysis protein of a MS2-phage. This mechanism is key to the understanding of how phages can potentially solve antibiotic-resistance.

Lysis Protein Sequence (UniProtKB ID:

https://www.uniprot.org/uniprotkb/P03609/entry)

METRFPQQSQQTPASTNRRRPFKHEDYPCRRQQRSSTLYVLIFLAIFLSKFTNQLLLSLLEAVIRTVTTLQQLLT

Note: Lysis protein contains a soluble N-terminal domain followed by a transmembrane protein (blue/last 35 residues). Transmembrane protein affects the lysis activity. The soluble domain (green) is the domain responsible for interaction with DnaJ.

L-Protein Engineering | Option 1: Mutagenesis

STEP 1:

A multiple sequence alignment of homologous L-protein sequences was performed using Clustal Omega to identify conserved and variable regions across related bacteriophages. The alignment revealed that the transmembrane region, located in the C-terminal portion of the protein, is highly conserved, particularly in residues forming a hydrophobic helix (LVLIFLAIFLSKFTNQLLLSLL). This high level of conservation suggests a critical functional role in membrane insertion and pore formation during bacterial lysis. In contrast, the N-terminal soluble region displayed greater sequence variability, indicating a higher tolerance to mutations. Based on these observations, conserved residues were avoided during mutational design, while more variable positions, especially in the soluble domain, were prioritized as potential targets for mutation.

STEP 2:

To evaluate the effect of mutations across the L-protein sequence, a protein language model (ESM-2) was used to compute log-likelihood ratio (LLR) scores for all possible amino acid substitutions at each position. This approach estimates how favorable a mutation is relative to the wild-type residue based on learned sequence patterns from large protein datasets. Positive LLR scores indicate mutations that are more likely to be tolerated or beneficial for protein stability, while negative scores suggest deleterious effects. The results were compiled into a ranked list of candidate mutations, allowing the identification of positions and substitutions with the highest predicted improvement. These scores were then used as a primary filter to guide mutation selection, in combination with conservation analysis from the multiple sequence alignment.

The protein language model identified several mutations with high positive LLR scores, indicating potentially favorable substitutions. The top-ranked mutations included K50L (LLR = 2.56), C29R (LLR = 2.39), Y39L (LLR = 2.24), C29S (LLR = 2.04), and S9Q (LLR = 2.01). Additional high-scoring mutations were observed at positions within both the soluble and transmembrane regions, such as T52L (LLR = 1.81), N53L (LLR = 1.86), and A45L (LLR = 1.54), particularly favoring substitutions to hydrophobic residues in the transmembrane domain. These results suggest that increasing hydrophobicity in the membrane region and selecting tolerated substitutions in variable regions may improve protein stability and folding.

STEP 3:

To assess how well the model predictions reflect real functional outcomes, the LLR scores were compared with available experimental lysis data for L-protein mutants. While some overlap between high-scoring mutations and experimentally tested variants was observed, many of the top-ranked mutations identified by the model were not present in the experimental dataset. Therefore, the experimental data was used when available, but for many candidate mutations, selection relied primarily on LLR scores in combination with conservation analysis.

STEP 4:

Based on the combined analysis of LLR scores, sequence conservation, and structural considerations, five mutations were selected as potential candidates for improving the L-protein. In the soluble region, the mutations S9Q and K23R were chosen due to their high LLR scores and location in more variable regions, suggesting a higher tolerance for substitutions that may improve folding stability. In the transmembrane region, K50L and T52L were selected, as both mutations introduce more hydrophobic residues, which is consistent with the conserved nature of this domain and may enhance membrane insertion and pore formation. Additionally, a combined mutant (S9Q + K50L) was designed to explore potential additive effects between improved folding in the soluble region and enhanced hydrophobicity in the transmembrane domain.

FASTA SEQUENCES:

>WT_L_protein METRFPQQSQQTPASTNRRRPFKHEDYPCRRQQRSSTLYVLIFLAIFLSKFTNQLLLSLLEAVIRTVTTLQQLLT

pTM = 0.44

>S9Q METRFPQQQQQTPASTNRRRPFKHEDYPCRRQQRSSTLYVLIFLAIFLSKFTNQLLLSLLEAVIRTVTTLQQLLT

pTM = 0.43

>K23R METRFPQQSQQTPASTNRRRPFRHEDYPCRRQQRSSTLYVLIFLAIFLSKFTNQLLLSLLEAVIRTVTTLQQLLT

pTM = 0.43

>K50L METRFPQQSQQTPASTNRRRPFKHEDYPCRRQQRSSTLYVLIFLAIFLSLFTNQLLLSLLEAVIRTVTTLQQLLT

pTM = 0.43

>T52L METRFPQQSQQTPASTNRRRPFKHEDYPCRRQQRSSTLYVLIFLAIFLSKFLNQLLLSLLEAVIRTVTTLQQLLT

pTM = 0.46

>S9Q_K50L METRFPQQQQQTPASTNRRRPFKHEDYPCRRQQRSSTLYVLIFLAIFLSLFTNQLLLSLLEAVIRTVTTLQQLLT

pTM = 0.43

AlphaFold predictions were used to assess the structural impact of the selected mutations. The wild-type protein showed a pTM score of 0.44, while most mutants exhibited similar values around 0.43, indicating no significant structural disruption. Notably, the T52L mutant showed a slightly higher pTM score of 0.46, suggesting a modest improvement in structural stability. This result is consistent with the introduction of a more hydrophobic residue in the transmembrane region, which may favor membrane insertion. Overall, these findings indicate that the proposed mutations are structurally tolerated and may contribute to improved protein stability.