Chlorophyll fluorescence as a valuable multitool for microalgal biotechnology

Light footprint of microalgae cultivation system addressed by modified Cornet model

The cultivation of microalgae is significantly influenced by light intensity and utilization efficiency. This study developed a modified Cornet (M-Cornet) model to assess the distribution of light intensity and flux in microalgae cultivation systems. Algal biofilm cultivation represents a more concentrated approach of algal suspension cultivation. Both follow the M-Cornet model and exhibit the same growth rates when cultured under identical conditions. Algal pigments and morphologies greatly impact the light absorption and scattering, resulting in light attenuation in intensity, penetration distance and light flux distribution. Algae varieties exhibit diverse light flux characteristics. 37% - 90% of the incident light is absorbed, of which, 80% to 90% is dissipated as heat. 10% to 63% of the incident light is scattered off the photobioreactor. The overall light utilization efficiency ranges 6% to 13%. The light footprint using the M-Cornet model offers valuable insights for photobioreactors designing and cultivation operating.

Tailoring molecularly imprinted polymer on titanium-multiwalled carbon nanotube functionalized gold electrode for enhanced chlorophyll determination in microalgae health assessment

A unique method for determining chlorophyll content in microalgae is devised employing a gold interdigitated electrode (G-IDE) with a 10-µm gap, augmented by a nano-molecularly imprinted polymer (nano-MIP) and a titanium dioxide/multiwalled carbon nanotube (TiO2/MWCNT) nanocomposite. The nano-MIP, produced using chlorophyll template voids, successfully trapped chlorophyll, while the TiO2/MWCNT nanocomposite, synthesized by the sol-gel technique, exhibited a consistent distribution and anatase crystalline structure. The rebinding of procured chlorophyll powder, which was used as a template for nano-MIP synthesis, was identified with a high determination coefficient (R2 = 0.9857). By combining the TiO2/MWCNT nanocomposite with nano-MIP, the G-IDE sensing method achieved a slightly better R2 value of 0.9892 for detecting chlorophyll in microalgae. The presented G-IDE sensor showed a significant threefold enhancement in chlorophyll detection compared with commercially available chlorophyll powder. It had a detection limit of 0.917 mL (v/v) and a linear range that spanned from 10-6 to 1 mL. The effectiveness of the sensor in detecting chlorophyll in microalgae was confirmed through validation of its repeatability and reusability.

Sudden sulfamethoxazole shock leads to nitrite accumulation in microalgae-nitrifying bacteria consortia: Physiological responses and light regulating strategy

Antibiotic shock may potentially impact the performance of promising microalgae-nitrifying bacteria consortia (MNBC) processes. This study investigated physiological behaviors of MNBC under sulfamethoxazole (SMX) shock (mg/L level) and verified a light regulating strategy for improving process performance. Results showed that SMX shock did not affect ammonium removal but caused nitrite accumulation, resulting from combined effects of excessive reactive oxidative species (ROS) production, inhibited microalgal photosynthetic activity, upregulated expressions of amoA and hao, and downregulated expression of nxrA. Moreover, high ammonium concentration aggravated nitrite accumulation and reduced ammonium removal owing to significantly reduced dissolved oxygen (DO). Increasing light intensity enhanced microalgal photo-oxygenation and promoted expressions of all nitrification-related genes, thus improving ammonium removal and alleviating nitrite accumulation. A central composite design coupled with response surface methodology (CCD-RSM) further demonstrated the negative impacts of SMX shock and high ammonium on MNBC and the effectiveness of the light regulation in maintaining stable process performance. This study provides theoretical basis for physiological responses and regulatory strategy of the MNBC process facing short-term antibiotic shock.

Competence of algal consortia under municipal wastewater: remediation efficiency, photosynthetic performance, antioxidant defense mechanisms and biofuel production

Utilizing monoalgal species for wastewater treatment is facing tremendous challenges owing to changing wastewater complexity in terms of physico-chemical characteristic, nutrient and metal concentration. The environmental conditions are also fluctuating therefore, the formation of robust system is of utmost importance for concomitant sustainable wastewater treatment and bioenergy production. In the present study, the tolerance and adaptability potential of algal consortia-1 (Chlorococcum humicola and Tetradesmus sp.) and consortia-2 (Chlorococcum humicola, Scenedesmus vacuolatus and Tetradesmus sp.) treated with municipal wastewater were examined under natural environmental conditions. The results exhibited that consortia-2 was more competent in recovering nitrate-nitrogen (82.92%), phosphorus (70.47%), and heavy metals (31-73.70%) from municipal wastewater (100%) than consortia-1. The results further depicted that total chlorophyll, carbohydrate, and protein content decreased significantly in wastewater-treated consortia-1 as compared to consortia-2. However, lipid content was increased by 4.01 and 1.17 folds in algal consortia-1 and consortia-2 compared to their respective controls. Moreover, absorption peak at 1740.6 cm-1 reflected higher biofuel-producing potential of consortia-1 as compared to consortia-2 as confirmed through FTIR spectroscopy. The results also revealed that consortia-2 showed the highest photosynthetic performance which was evident from the increment in the active photosystem-II reaction center (1.724 ± 0.068), quantum efficiency (0.633 ± 0.038), and performance index (3.752 ± 0.356). Further, a significant increase in photosynthetic parameters was observed in selected consortia at lag phase, while a noteworthy decline was observed at exponential and stationary phases in consortia-1 than consortia-2. The results also showed the maximum enhancement in ascorbic acid (2.43 folds), proline (3.34 folds), and cysteine (1.29 folds) in consortia-2, while SOD (1.75 folds), catalase (2.64 folds), and GR (1.19 folds) activity in consortia-1. Therefore, it can be concluded that due to remarkable flexibility and photosynthetic performance, consortia-2 could serve as a potential candidate for sustainable nutrient resource recovery and wastewater treatment, while consortia-1 for bio-fuel production in a natural environment. Thus, formation of algal consortia as the robust biosystem tolerates diverse environmental fluctuations together with wastewater complexity and ultimately can serve appropriate approach for environmental-friendly wastewater treatment and bioenergy production.

Enhancement of carbon nanotubes on microalgal-fungal consortium formation and wastewater treatment

As a promising material with an efficient light capture capability, a low amount of carbon nanotubes can affect growth and photosynthesis by regulating microalgal cells, thereby enhancing the pollutant removal efficiency in wastewater. In this study, microalgal-fungal consortia of Chlorella vulgaris and Ganoderma lucidum were developed with different types and concentrations of carbon nanotubes. The treatment effect of microalgal-fungal consortia on simulated digestate was also studied. The results demonstrate that 1.5 mg/L of carboxylated multi-walled carbon nanotubes remarkably promoted the formation, growth and photosynthesis of consortium. The dry weight and chlorophyll a content reached 19.3 ± 0.53 mg/symbiont and 27.3 ± 0.52 μg/L, respectively. Moreover, the removal efficiency of chemical oxygen demand, total nitrogen, total phosphorus and tetracycline hydrochloride were 94.1%, 65.5%, 61.9% and 96.2%, respectively. Overall, these findings suggest a promising future for the use of carbon nanotubes in wastewater treatment by regulating microalgal-fungal consortia.

Novel Strigolactone Mimics That Modulate Photosynthesis and Biomass Accumulation in Chlorella sorokiniana

In terrestrial plants, strigolactones act as multifunctional endo- and exo-signals. On microalgae, the strigolactones determine akin effects: induce symbiosis formation with fungi and bacteria and enhance photosynthesis efficiency and accumulation of biomass. This work aims to synthesize and identify strigolactone mimics that promote photosynthesis and biomass accumulation in microalgae with biotechnological potential. Novel strigolactone mimics easily accessible in significant amounts were prepared and fully characterized. The first two novel compounds contain 3,5-disubstituted aryloxy moieties connected to the bioactive furan-2-one ring. In the second group of compounds, a benzothiazole ring is connected directly through the cyclic nitrogen atom to the bioactive furan-2-one ring. The novel strigolactone mimics were tested on Chlorella sorokiniana NIVA-CHL 176. All tested strigolactones increased the accumulation of chlorophyll b in microalgae biomass. The SL-F3 mimic, 3-(4-methyl-5-oxo-2,5-dihydrofuran-2-yl)-3H-benzothiazol-2-one (7), proved the most efficient. This compound, applied at a concentration of 10-7 M, determined a significant biomass accumulation, higher by more than 15% compared to untreated control, and improved the quantum yield efficiency of photosystem II. SL-F2 mimic, 5-(3,5-dibromophenoxy)-3-methyl-5H-furan-2-one (4), applied at a concentration of 10-9 M, improved protein production and slightly stimulated biomass accumulation. Potential utilization of the new strigolactone mimics as microalgae biostimulants is discussed.

Multi-Omics Pipeline and Omics-Integration Approach to Decipher Plant's Abiotic Stress Tolerance Responses

The present day's ongoing global warming and climate change adversely affect plants through imposing environmental (abiotic) stresses and disease pressure. The major abiotic factors such as drought, heat, cold, salinity, etc., hamper a plant's innate growth and development, resulting in reduced yield and quality, with the possibility of undesired traits. In the 21st century, the advent of high-throughput sequencing tools, state-of-the-art biotechnological techniques and bioinformatic analyzing pipelines led to the easy characterization of plant traits for abiotic stress response and tolerance mechanisms by applying the 'omics' toolbox. Panomics pipeline including genomics, transcriptomics, proteomics, metabolomics, epigenomics, proteogenomics, interactomics, ionomics, phenomics, etc., have become very handy nowadays. This is important to produce climate-smart future crops with a proper understanding of the molecular mechanisms of abiotic stress responses by the plant's genes, transcripts, proteins, epigenome, cellular metabolic circuits and resultant phenotype. Instead of mono-omics, two or more (hence 'multi-omics') integrated-omics approaches can decipher the plant's abiotic stress tolerance response very well. Multi-omics-characterized plants can be used as potent genetic resources to incorporate into the future breeding program. For the practical utility of crop improvement, multi-omics approaches for particular abiotic stress tolerance can be combined with genome-assisted breeding (GAB) by being pyramided with improved crop yield, food quality and associated agronomic traits and can open a new era of omics-assisted breeding. Thus, multi-omics pipelines together are able to decipher molecular processes, biomarkers, targets for genetic engineering, regulatory networks and precision agriculture solutions for a crop's variable abiotic stress tolerance to ensure food security under changing environmental circumstances.

Failure of Micractinium simplicissimum Phosphate Resilience upon Abrupt Re-Feeding of Its Phosphorus-Starved Cultures

Microalgae are naturally adapted to the fluctuating availability of phosphorus (P) to opportunistically uptake large amounts of inorganic phosphate (P_i) and safely store it in the cell as polyphosphate. Hence, many microalgal species are remarkably resilient to high concentrations of external P_i. Here, we report on an exception from this pattern comprised by a failure of the high P_i-resilience in strain Micractinium simplicissimum IPPAS C-2056 normally coping with very high P_i concentrations. This phenomenon occurred after the abrupt re-supplementation of P_i to the M. simplicissimum culture pre-starved of P. This was the case even if P_i was re-supplemented in a concentration far below the level toxic to the P-sufficient culture. We hypothesize that this effect can be mediated by a rapid formation of the potentially toxic short-chain polyphosphate following the mass influx of P_i into the P-starved cell. A possible reason for this is that the preceding P starvation impairs the capacity of the cell to convert the newly absorbed P_i into a "safe" storage form of long-chain polyphosphate. We believe that the findings of this study can help to avoid sudden culture crashes, and they are also of potential significance for the development of algae-based technologies for the efficient bioremoval of P from P-rich waste streams.

Machine learning methods for assessing photosynthetic activity: environmental monitoring applications

Monitoring of the photosynthetic activity of natural and artificial biocenoses is of crucial importance. Photosynthesis is the basis for the existence of life on Earth, and a decrease in primary photosynthetic production due to anthropogenic influences can have catastrophic consequences. Currently, great efforts are being made to create technologies that allow continuous monitoring of the state of the photosynthetic apparatus of terrestrial plants and microalgae. There are several sources of information suitable for assessing photosynthetic activity, including gas exchange and optical (reflectance and fluorescence) measurements. The advent of inexpensive optical sensors makes it possible to collect data locally (manually or using autonomous sea and land stations) and globally (using aircraft and satellite imaging). In this review, we consider machine learning methods proposed for determining the functional parameters of photosynthesis based on local and remote optical measurements (hyperspectral imaging, solar-induced chlorophyll fluorescence, local chlorophyll fluorescence imaging, and various techniques of fast and delayed chlorophyll fluorescence induction). These include classical and novel (such as Partial Least Squares) regression methods, unsupervised cluster analysis techniques, various classification methods (support vector machine, random forest, etc.) and artificial neural networks (multilayer perceptron, long short-term memory, etc.). Special aspects of time-series analysis are considered. Applicability of particular information sources and mathematical methods for assessment of water quality and prediction of algal blooms, for estimation of primary productivity of biocenoses, stress tolerance of agricultural plants, etc. is discussed.