Vivek Kumar Gaur

Plants are associated with diverse microbial communities that play a crucial role in plant growth promotion and improving stability under stress conditions. Microbiome engineering involves the targeted manipulation, recruitment, and optimization of plant-associated microbial communities to enhance plant performance. However, the effectiveness of microbiome engineering can vary with plant genotype, environmental conditions, microbial composition, and the stability of introduced or selected communities. This review paper summarizes emerging strategies for plant microbiome engineering, including synthetic microbial consortia (SynComs) and host-mediated microbiome selection. SynCom approaches involve the rational assembly of selected microbial strains at the community scale, whereas host-mediated engineering uses plant–microbiome interactions and host genetic variation to enrich beneficial microbial communities. Evidence suggests that combining complementary microbial functions with host-mediated selection may improve microbiome establishment and functional consistency; however, translating these benefits into reliable field performance remains challenging because of context-dependent microbial interactions, competition with resident communities, and limited long-term persistence. Furthermore, this review also discusses omics technologies, including metagenomics, metabolomics, and transcriptomics, which facilitate the identification of functional microbial traits and plant–microbe interactions. Artificial intelligence and machine learning provide complementary tools for microbiome prediction and targeted intervention design. However, their predictive reliability depends on representative datasets, robust validation, and integration of environmental and biological variables. The review highlights that successful microbiome engineering requires a shift from microbial community assembly alone toward functionally validated, ecologically stable, and field-tested strategies. Integrating microbiome engineering with multi-omics, AI-driven prediction, and precision agriculture could support more predictable, resilient, and sustainable crop production.

Introduction: Stability, yield, and interactions between variables in the process, combined with the risk of contamination, pose a challenge for optimization of microbial fermentation processes. In this work, FERM-AI, a novel risk-aware machine learning surrogate framework for benchmark evaluation of cellulase fermentation, is presented, comprising of yield prediction, contamination risk assessment and an interactive decision-support interface. Methods: The framework was tested with a benchmark set of 1,000 runs of fermentation, consisting of 20 fermentation runs from experiments and 980 synthetic fermentation runs to develop the methods. Biologically relevant process optimality, stress responses and interaction effects were captured using domain-informed feature engineering, and features were then selected and expanded using polynomial feature expansion. Cellulase yield prediction was analysed using multiple regression models such as Ridge Regression, Bayesian Ridge Regression, Huber Regression, Partial Least Squares (PLS) Regression, Gradient Boosting, Extra Trees, XGBoost and ensemble models; and contamination risk was modelled using imbalance-aware classification with SMOTE and ensemble learning. Results: The Huber Regressor outperformed the other algorithms with an R ² of 0.9998, while PLS Regression performed well with an R ² of 0.9937, further validating the strength of the proposed feature representation. The soft voting ensemble classifier was 83.5% accurate and had an AUC-ROC of 0.9045 for the contamination prediction. Discussion: The integrated framework is a proof-of-concept showing the potential of simultaneously modelling productivity and contamination risks, which can aid in the evaluation of fermentation operating conditions in a risk-aware fashion. The data used in the benchmark is synthetic, so the predictive results are meant to show the validation of the proposed surrogate modelling framework, and not that the predictive ability of the framework is of industrial scale. The framework will be validated in the future with bigger fermentation datasets obtained from experiments and expanded to the real-world fermentation process optimization.

The transition toward circular and low-carbon manufacturing requires strategies that move beyond the passive management of carbon emissions and enable their productive utilization. With this notion, carbon dioxide (CO ₂ ) is increasingly being recognized as an abundant C ₁ feedstock for the sustainable synthesis of chemicals, fuels, polymers, and functional materials. Although conventional approaches such as carbon capture and storage (CCS), afforestation, and geological sequestration mitigate CO ₂ emissions, they often generate limited economic value and do not exploit CO ₂ as a renewable carbon resource. In contrast, carbon capture and utilization (CCU) has emerged as a sustainable alternative by transforming captured CO ₂ into value-added chemicals, fuels and specialty products such as methane, methanol, polymers and other materials thereby promoting carbon circularity while reducing dependence on fossil-derived resources. While CCU has traditionally focused on fuels and bulk chemicals, emerging advances increasingly position CO ₂ as a versatile carbon synthon for next-generation manufacturing, extending beyond conventional valorization routes. This review provides a comprehensive overview of recent advances in CO ₂ conversion technologies with particular emphasis on expanding the accessible chemical space from CO ₂ toward specialty chemicals, non-natural products, advanced materials, and biomedical applications. Recent state-of-the-art developments in catalytic, electrochemical, photochemical and biological transformation strategies are examined to illustrate how improved carbon selectivity, enhanced product complexity, and programmable carbon fixation are reshaping sustainable carbon utilization. Particular emphasis is placed on emerging biological interventions that expand carbon utilization beyond native metabolic constraints and enable access to structurally diverse and functionally tailored products. Beyond advances in conversion technologies, this review evaluates the factors governing industrial translation, including process integration, scalability, commercialization challenges, and techno-economic feasibility. Emerging opportunities involving functional materials, specialty products, and biomedical applications are further discussed to demonstrate the evolution of CO ₂ from an environmental burden toward a renewable feedstock for high-value manufacturing. Finally, future perspectives are presented to outline the technological and translational developments required for establishing scalable and economically viable and net zero carbon technologies.

Pesticide contamination in urban runoff, stormwater, and peri-urban drainage networks is an increasing concern because of the persistence, mobility, and ecological toxicity of many pesticide residues and their transformation products. Nature-based solutions (NBSs), including constructed wetlands, bioretention systems, biofilters, and permeable reactive bio-barriers, provide low-energy and ecologically compatible platforms for urban water treatment; however, their performance is often constrained by limited sorption capacity, substrate saturation, variable hydraulic loading, and incomplete degradation of persistent pesticides. Biochar offers a multifunctional amendment for strengthening these systems because its tunable porosity, surface functionality, mineral composition, redox activity, and microbial habitat-forming capacity can support pesticide adsorption, catalytic transformation, and biodegradation. This review critically evaluates biochar-integrated NBSs for pesticide-contaminated urban water systems by linking biochar production and modification strategies with pesticide removal mechanisms, biochar–microbe interactions, engineered treatment configurations, and field-scale applicability. A comparative synthesis is provided across material-level mechanisms, system-level performance, machine learning-assisted prediction, techno-economic feasibility, life-cycle impacts, and environmental risk considerations. By integrating material properties, removal mechanisms, NBS configurations, predictive modeling, sustainability assessment, and risk considerations, this review provides a broader comparative basis than previous studies focused mainly on individual aspects of biochar-based pesticide remediation. Future priorities include standardized biochar production, long-term field validation, spent-biochar management, ecotoxicological assessment, and data-driven optimization of biochar-assisted NBSs.

Rhamnolipid biosurfactants have garnered significant attention as renewable, biodegradable alternatives to conventional, petroleum-derived surfactants, aligning closely with global sustainable development goals. Nevertheless, their commercial viability remains hampered by intrinsic production challenges mainly suboptimal production titers, expensive downstream recovery, and biosafety constraints of native producers. This review synthesizes current advances within an integrated framework that couples metabolic pathway optimization with strategies to enhance titer, rate, and yield (TRY), high-cell-density cultivation, and cost-effective downstream processing. Key innovations in pathway engineering such as elimination of competing routes and cofactor rebalancing are examined alongside dynamic regulatory systems that decouple growth from product formation. Process intensification techniques, including in situ foam control and membrane-based separations, are evaluated for their impact on purification efficiency and overall process economics. Critical research gaps and limitations are identified, with recommendations for future efforts to bridge metabolic landscape and scale-up implementation. Finally, potential market-ready rhamnolipid formulations are discussed, illustrating their application spectrum from agriculture and personal care to environmental remediation and healthcare. By contextualizing synthetic biology breakthroughs within rigorous techno-economic and life-cycle assessments, this review maps a clear trajectory toward the industrial deployment of truly sustainable glycolipid biosurfactants.

Lignocellulosic biomass (LCB) is an abundant, low-cost feedstock, but its conversion is limited by structural recalcitrance and cost-intensive unit operations, especially downstream processing. This review examines rhamnolipids as both products of LCB valorization and process additives that improve LCB deconstruction. It highlights the native microbial production of rhamnolipids from diverse LCB-derived streams and the associated titer, rate and yield limits. Special emphasis is placed on different metabolic engineering strategies to increase the complete utilization of lignocellulosic derived carbon and highlights its dual role in pretreatment/saccharification and fermentation, including enzyme protection from non-specific lignin binding, inhibitor mitigation, and gains in sugar and ethanol yields. Further, the techno-economic constraints related to feedstock quality and cost, fermentation productivity, and other the dominant factors that can lower the overall cost and enhance productivity. The review marks rhamnolipid amongst the top ranked biosurfactant and underscores the factors that can prove to be game changer for upcoming biorefineries.

Sophorolipid (SL) are glycolipid biosurfactants with growing industrial relevance as sustainable alternatives to petrochemical surfactants. This review highlights the advances in SL genetics and pathway architecture, transcriptional and process level regulation, and comparative performance of native producers versus recombinant platforms. It emphasizes the transition from empirical optimization to rational, systems guided strategies integrating advance metabolic engineering strategies including pathway optimization to divert carbon flux toward the SL module, balance redox/energy demands with growth, and tailor congener profiles. We further evaluated the current industrial feasibility, technology used by several companies highlighting progress increasing titers and productivities alongside persistent constraints in production, reliance on costly feedstocks, different fermentation methods, process parameters, and challenging downstream recovery. Key research gaps include incomplete understanding of regulatory control, limited systematic flux redistribution, and insufficient techno-economic integration. We outline future priorities for CRISPR enabled and omics guided rewiring, secretion and tolerance engineering, deployment of low cost/waste substrates, and standardized, scalable purification. These directions define a roadmap to robust, cost competitive SL manufacturing and clarify where recombinant hosts can complement or extend capabilities beyond Starmerella bombicola.

Microplastics transport toxins, disrupt microbial and nutrient cycles, bioaccumulate to cause oxidative stress and endocrine disruption, jeopardizing ecosystems and human health. Despite understanding microplastic origins, distribution, and microbial degradation biotechnological remediation efforts remain fragmented and largely at the proof-of-concept stage. Recent high-throughput meta-omics has uncovered diverse plastisphere associated enzymes, while metabolic engineering platforms have demonstrated programmable biofilm trap-and-release mechanism and enzymatic upcycling of PET monomers; however, the translation of these technologies to diverse polymer classes and field applications is limited. Machine learning is emerging as a powerful tool to uncover efficient microplastic degradation strategies, a domain previously underexplored. This review critically synthesizes these interdisciplinary advances spanning microbial and enzymatic remediation evolution, metabolic-engineering architectures for capture and valorization, and AI-driven monitoring to identify persistent bottlenecks and propose a unified roadmap for deploying sustainable, biotechnology-driven solutions that can be scaled to address the global microplastic crisis. By bridging these domains, we aim to inform future research priorities and accelerate the translation of laboratory findings into industrial scale mitigation strategies.