Explorative characterization and taxonomy-aligned comparison of alterations in lipids and other biomolecules in Antarctic bacteria grown at different temperatures.

Temperature significantly impacts bacterial physiology, metabolism and cell chemistry. In this study, we analysed lipids and the total cellular biochemical profile of 74 fast-growing Antarctic bacteria grown at different temperatures. Fatty acid diversity and temperature-induced alterations aligned with bacterial classification-Gram-groups, phylum, genus and species. Total lipid content, varied from 4% to 19% of cell dry weight, was genus- and species-specific. Most bacteria increased lipid content at lower temperatures. The effect of temperature on the profile was complex and more species-specific, while some common for all bacteria responses were recorded. Gram-negative bacteria adjusted unsaturation and acyl chain length. Gram-positive bacteria adjusted methyl branching (anteiso-/iso-), chain length and unsaturation. Fourier transform infrared spectroscopy analysis revealed Gram-, genus- and species-specific changes in the total cellular biochemical profile triggered by temperature fluctuations. The most significant temperature-related alterations detected on all taxonomy levels were recorded for mixed region 1500-900 cm-1 , specifically the band at 1083 cm-1 related to phosphodiester groups mainly from phospholipids (for Gram-negative bacteria) and teichoic/lipoteichoic acids (for Gram-positive bacteria). Some changes in protein region were detected for a few genera, while the lipid region remained relatively stable despite the temperature fluctuations.

Raman spectroscopy online monitoring of biomass production, intracellular metabolites and carbon substrates during submerged fermentation of oleaginous and carotenogenic microorganisms.

BACKGROUND: Monitoring and control of both growth media and microbial biomass is extremely important for the development of economical bioprocesses. Unfortunately, process monitoring is still dependent on a limited number of standard parameters (pH, temperature, gasses etc.), while the critical process parameters, such as biomass, product and substrate concentrations, are rarely assessable in-line. Bioprocess optimization and monitoring will greatly benefit from advanced spectroscopy-based sensors that enable real-time monitoring and control. Here, Fourier transform (FT) Raman spectroscopy measurement via flow cell in a recirculatory loop, in combination with predictive data modeling, was assessed as a fast, low-cost, and highly sensitive process analytical technology (PAT) system for online monitoring of critical process parameters. To show the general applicability of the method, submerged fermentation was monitored using two different oleaginous and carotenogenic microorganisms grown on two different carbon substrates: glucose fermentation by yeast Rhodotorula toruloides and glycerol fermentation by marine thraustochytrid Schizochytrium sp. Additionally, the online FT-Raman spectroscopy approach was compared with two at-line spectroscopic methods, namely FT-Raman and FT-infrared spectroscopies in high throughput screening (HTS) setups. RESULTS: The system can provide real-time concentration data on carbon substrate (glucose and glycerol) utilization, and production of biomass, carotenoid pigments, and lipids (triglycerides and free fatty acids). Robust multivariate regression models were developed and showed high level of correlation between the online FT-Raman spectral data and reference measurements, with coefficients of determination (R2) in the 0.94-0.99 and 0.89-0.99 range for all concentration parameters of Rhodotorula and Schizochytrium fermentation, respectively. The online FT-Raman spectroscopy approach was superior to the at-line methods since the obtained information was more comprehensive, timely and provided more precise concentration profiles. CONCLUSIONS: The FT-Raman spectroscopy system with a flow measurement cell in a recirculatory loop, in combination with prediction models, can simultaneously provide real-time concentration data on carbon substrate utilization, and production of biomass, carotenoid pigments, and lipids. This data enables monitoring of dynamic behaviour of oleaginous and carotenogenic microorganisms, and thus can provide critical process parameters for process optimization and control. Overall, this study demonstrated the feasibility of using FT-Raman spectroscopy for online monitoring of fermentation processes.

Comment on "Dying in the Sun: Direct evidence for elevated UV-B radiation at the end-Permian mass extinction".

Liu et al. present evidence of increased abundance of UV-B-absorbing compounds in fossilized sporomorphs at the end-Permian mass extinction based on Fourier transform infrared (FTIR) spectroscopy. Their approach assumes that UV-B-absorbing compounds are present in the fossilized sporomorphs spanning the extinction interval and that they can be quantified using FTIR. However, further analysis reveals that the signal that they aim to focus on is weak and poorly resolved against nonrandom background interference most likely associated with water vapor. We also show that the peak detection methods that they use are inappropriate for use on these fossil sporomorphs because their methods select only 3.9% of the spectra at the target waveband of interest. The reconstruction that they present is based on baseline variations in the spectra and cannot be confidently attributed to variations in UV-B-absorbing compounds. "Direct" evidence for UV-B radiation at the end-Permian mass extinction cannot be claimed to have been observed in this record.

Characterisation of Cartilage Damage via Fusing Mid-Infrared, Near-Infrared, and Raman Spectroscopic Data.

Mid-infrared spectroscopy (MIR), near-infrared spectroscopy (NIR), and Raman spectroscopy are all well-established analytical techniques in biomedical applications. Since they provide complementary chemical information, we aimed to determine whether combining them amplifies their strengths and mitigates their weaknesses. This study investigates the feasibility of the fusion of MIR, NIR, and Raman spectroscopic data for characterising articular cartilage integrity. Osteochondral specimens from bovine patellae were subjected to mechanical and enzymatic damage, and then MIR, NIR, and Raman data were acquired from the damaged and control specimens. We assessed the capacity of individual spectroscopic methods to classify the samples into damage or control groups using Partial Least Squares Discriminant Analysis (PLS-DA). Multi-block PLS-DA was carried out to assess the potential of data fusion by combining the dataset by applying two-block (MIR and NIR, MIR and Raman, NIR and Raman) and three-block approaches (MIR, NIR, and Raman). The results of the one-block models show a higher classification accuracy for NIR (93%) and MIR (92%) than for Raman (76%) spectroscopy. In contrast, we observed the highest classification efficiency of 94% and 93% for the two-block (MIR and NIR) and three-block models, respectively. The detailed correlative analysis of the spectral features contributing to the discrimination in the three-block models adds considerably more insight into the molecular origin of cartilage damage.

Sparse wavelengths data in mid-infrared spectroscopy: Modelling approaches and channel sampling.

Infrared instruments with smaller and cost-effective components such as bandpass filters, single channel detectors, and laser-based light sources are being developed to provide cheaper and faster analysis of biological samples. Such instruments often provide measurements in form of sparse data, which include a collection of single-frequency channels or a collection of channels covering very narrow spectral ranges, called here multi-frequency channels. To keep costs low, the number of channels needs to be kept at a minimum. However, modelling and preprocessing of sparse data needs enough channels to perform the task. The aim of this study therefore was to understand the effect of channels sampling on data modelling results and find optimal modelling algorithm for different type of sparse data. The sparse data was simulated using Fourier Transform Infrared spectra of milk and fungi. Regression models were established to predict fatty acid composition by partial least squares regression (PLSR), multiple linear regression (MLR) and random forest (RF) methods. We observe that PLSR algorithm is very well suited for sparse data such as multi-frequency channels: excellent calibration models were obtained with only three channels comprising three wavenumbers each. The results were comparable to results obtained with full spectra. MLR and RF in turn provided similarly good results using data with single-frequency channels requiring nine channels in total.

High-throughput vibrational spectroscopy methods for determination of degree of acetylation for chitin and chitosan.

The rising demand for chitin and chitosan in chemical, agro-food, and healthcare industries is creating a need for rapid and high-throughput analysis. The physicochemical properties of these biopolymers are greatly dependent on the degree of acetylation (DA). Conventional methods for DA determination, such as LC-MS and 1H NMR, are time-consuming when performed on many samples, and therefore efficient methods are needed. Here, high-throughput microplate-based FTIR and FT-Raman methods were compared with their manual counterparts. Partial least squares regression models were based on 30 samples of chitin and chitosan with reference DA values obtained by LC-MS and 1H NMR, and the models were validated on an independent test set of 16 samples. The overall predictive accuracy of the high-throughput methods was at the same level as the manual methods and the well-established LC-MS and 1H NMR methods. Therefore, high-throughput FTIR and FT-Raman DA determination methods have great potential to serve as fast and economical substitutes for traditional methods.

Optimizing extraction solvents for deoxynivalenol analysis in maize via infrared attenuated total reflection spectroscopy and chemometric methods.

Farmers, cereal suppliers and processors demand rapid techniques for the assessment of mould-associated contamination. Deoxynivalenol (DON) is among the most important Fusarium toxins and related to human and animal diseases besides causing significant economic losses. Routine analytical techniques for the analysis of DON are either based on chromatographic or immunoanalytical techniques, which are time-consuming and frequently rely on hazardous consumables. The present study evaluates the feasibility of infrared attenuated total reflection spectroscopy (IR-ATR) for the analysis of maize extracts via different solvents optimized for the determination of DON contamination along the regulatory requirements by the European Union (EU) for unprocessed maize (1750 µg kg-1). Reference analysis was done by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). The studied maize samples were either naturally infected or had been artificially inoculated in the field with Fusarium graminearum, Fusarium culmorum or Fusarium verticillioides. Principal component analysis demonstrated that water and methanol-water (70 : 30% v) were optimum solvents for differentiating DON contamination levels. Supervised partial least squares discriminant analysis resulted in excellent classification accuracies of 86.7% and 90.8% for water and methanol-water extracts, respectively. The IR spectra of samples with fungal infection and high DON contamination had distinct spectral features, which could be related to carbohydrates, proteins and lipid content within the investigated extracts.

Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach.

Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error (RMSE) was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied.

The Use of Constituent Spectra and Weighting in Extended Multiplicative Signal Correction in Infrared Spectroscopy.

Extended multiplicative signal correction (EMSC) is a widely used preprocessing technique in infrared spectroscopy. EMSC is a model-based method favored for its flexibility and versatility. The model can be extended by adding constituent spectra to explicitly model-known analytes or interferents. This paper addresses the use of constituent spectra and demonstrates common pitfalls. It clarifies the difference between analyte and interferent spectra, and the importance of orthogonality between model spectra. Different normalization approaches are discussed, and the importance of weighting in the EMSC is demonstrated. The paper illustrates how constituent analyte spectra can be estimated, and how they can be used to extract additional information from spectral features. It is shown that the EMSC parameters can be used in both regression tasks and segmentation tasks.

Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics.

The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several thousand spectral variables into datasets comprising only seven spectral variables. Different preprocessing approaches were compared, including simple baseline correction and normalization procedures, and model-based preprocessing, such as multiplicative signal correction (MSC). The optimal preprocessing was selected based on the quality of classification models established by partial least squares discriminant analysis for discriminating healthy and damaged cartilage samples. The best results for the sparse data were obtained by preprocessing using a baseline offset correction at 1800 cm-1, followed by peak normalization at 850 cm-1 and preprocessing by MSC.

Deep learning-enabled Inference of 3D molecular absorption distribution of biological cells from IR spectra.

Infrared spectroscopy delivers abundant information about the chemical composition, as well as the structural and optical properties of intact samples in a non-destructive manner. We present a deep convolutional neural network which exploits all of this information and solves full-wave inverse scattering problems and thereby obtains the 3D optical, structural and chemical properties from infrared spectroscopic measurements of intact micro-samples. The proposed model encodes scatter-distorted infrared spectra and infers the distribution of the complex refractive index function of concentrically spherical samples, such as many biological cells. The approach delivers simultaneously the molecular absorption, sample morphology and effective refractive index in both the cell wall and interior from a single measured spectrum. The model is trained on simulated scatter-distorted spectra, where absorption in the distinct layers is simulated and the scatter-distorted spectra are estimated by analytic solutions of Maxwell's equations for samples of different sizes. This allows for essentially real-time deep learning-enabled infrared diffraction micro-tomography, for a large subset of biological cells.

An automated approach for fringe frequency estimation and removal in infrared spectroscopy and hyperspectral imaging of biological samples.

In infrared spectroscopy of thin film samples, interference introduces distortions in spectra, commonly referred to as fringes. Fringes may alter absorbance peak ratios, which hampers the spectral analysis. We have previously introduced extended multiplicative signal correction (EMSC) for fringes correction. In the current article, we provide a robust open-source algorithm for fringe correction in infrared spectroscopy and propose several improvements to the Fringe EMSC model. The suggested algorithm achieves a more precise fringe frequency estimation by mean centering of the measured spectrum and applying a window function prior to the Fourier transform. It selects two frequencies from a user defined number of maxima in the Fourier domain. The improved Fringe EMSC algorithm is validated on two experimental datasets, one of them being a hyperspectral image. Techniques for separating sample spectra from background spectra in hyperspectral images, and techniques to identify spectra affected by fringes are also provided.

Assessment of Biotechnologically Important Filamentous Fungal Biomass by Fourier Transform Raman Spectroscopy.

Oleaginous filamentous fungi can accumulate large amount of cellular lipids and biopolymers and pigments and potentially serve as a major source of biochemicals for food, feed, chemical, pharmaceutical, and transport industries. We assessed suitability of Fourier transform (FT) Raman spectroscopy for screening and process monitoring of filamentous fungi in biotechnology. Six Mucoromycota strains were cultivated in microbioreactors under six growth conditions (three phosphate concentrations in the presence and absence of calcium). FT-Raman and FT-infrared (FTIR) spectroscopic data was assessed in respect to reference analyses of lipids, phosphorus, and carotenoids by using principal component analysis (PCA), multiblock or consensus PCA, partial least square regression (PLSR), and analysis of spectral variation due to different design factors by an ANOVA model. All main chemical biomass constituents were detected by FT-Raman spectroscopy, including lipids, proteins, cell wall carbohydrates, and polyphosphates, and carotenoids. FT-Raman spectra clearly show the effect of growth conditions on fungal biomass. PLSR models with high coefficients of determination (0.83-0.94) and low error (approximately 8%) for quantitative determination of total lipids, phosphates, and carotenoids were established. FT-Raman spectroscopy showed great potential for chemical analysis of biomass of oleaginous filamentous fungi. The study demonstrates that FT-Raman and FTIR spectroscopies provide complementary information on main fungal biomass constituents.

Calcium Affects Polyphosphate and Lipid Accumulation in Mucoromycota Fungi.

Calcium controls important processes in fungal metabolism, such as hyphae growth, cell wall synthesis, and stress tolerance. Recently, it was reported that calcium affects polyphosphate and lipid accumulation in fungi. The purpose of this study was to assess the effect of calcium on the accumulation of lipids and polyphosphate for six oleaginous Mucoromycota fungi grown under different phosphorus/pH conditions. A Duetz microtiter plate system (Duetz MTPS) was used for the cultivation. The compositional profile of the microbial biomass was recorded using Fourier-transform infrared spectroscopy, the high throughput screening extension (FTIR-HTS). Lipid content and fatty acid profiles were determined using gas chromatography (GC). Cellular phosphorus was determined using assay-based UV-Vis spectroscopy, and accumulated phosphates were characterized using solid-state 31P nuclear magnetic resonance spectroscopy. Glucose consumption was estimated by FTIR-attenuated total reflection (FTIR-ATR). Overall, the data indicated that calcium availability enhances polyphosphate accumulation in Mucoromycota fungi, while calcium deficiency increases lipid production, especially under acidic conditions (pH 2-3) caused by the phosphorus limitation. In addition, it was observed that under acidic conditions, calcium deficiency leads to increase in carotenoid production. It can be concluded that calcium availability can be used as an optimization parameter in fungal fermentation processes to enhance the production of lipids or polyphosphates.

Evaluation and optimisation of direct transesterification methods for the assessment of lipid accumulation in oleaginous filamentous fungi.

BACKGROUND: Oleaginous filamentous fungi can accumulate large amount of cellular lipids and potentially serve as a major source of oleochemicals for food, feed, chemical, pharmaceutical, and transport industries. Transesterification of microbial oils is an essential step in microbial lipid production at both laboratory and industrial scale. Direct transesterification can considerably reduce costs, increase sample throughput and improve lipid yields (in particular fatty acid methyl esters, FAMEs). There is a need for the assessment of the direct transesterification methods on a biomass of filamentous fungi due to their unique properties, specifically resilient cell wall and wide range of lipid content and composition. In this study we have evaluated and optimised three common direct transesterification methods and assessed their suitability for processing of fungal biomass. RESULTS: The methods, based on hydrochloric acid (Lewis method), sulphuric acid (Wahlen method), and acetyl chloride (Lepage method), were evaluated on six different strains of Mucoromycota fungi by using different internal standards for gas chromatography measurements. Moreover, Fourier transform infrared (FTIR) spectroscopy was used for the detection of residual lipids in the biomass after the transesterification reaction/extraction, while transesterification efficiency was evaluated by nuclear magnetic resonance spectroscopy. The results show that the majority of lipids, in particular triglycerides, were extracted for all methods, though several methods had substandard transesterification yields. Lewis method, optimised with respect to solvent to co-solvent ratio and reaction time, as well as Lepage method, offer precise estimate of FAME-based lipids in fungal biomass. CONCLUSIONS: The results show that Lepage and Lewis methods are suitable for lipid analysis of oleaginous filamentous fungi. The significant difference in lipid yields results, obtained by optimised and standard Lewis methods, indicates that some of the previously reported lipid yields for oleaginous filamentous fungi must be corrected upwards. The study demonstrates value of biomass monitoring by FTIR, importance of optimal solvent to co-solvent ratio, as well as careful selection and implementation of internal standards for gas chromatography.

Infrared Fiber-Optic Spectroscopy Detects Bovine Articular Cartilage Degeneration.

OBJECTIVE: Joint injuries may lead to degeneration of cartilage tissue and initiate development of posttraumatic osteoarthritis. Arthroscopic surgeries can be used to treat joint injuries, but arthroscopic evaluation of articular cartilage quality is subjective. Fourier transform infrared spectroscopy combined with fiber optics and attenuated total reflectance crystal could be used for the assessment of tissue quality during arthroscopy. We hypothesize that fiber-optic mid-infrared spectroscopy can detect enzymatically and mechanically induced damage similar to changes occurring during progression of osteoarthritis. DESIGN: Bovine patellar cartilage plugs were extracted and degraded enzymatically and mechanically. Adjacent untreated samples were utilized as controls. Enzymatic degradation was done using collagenase and trypsin enzymes. Mechanical damage was induced by (1) dropping a weight impactor on the cartilage plugs and (2) abrading the cartilage surface with a rotating sandpaper. Fiber-optic mid-infrared spectroscopic measurements were conducted before and after treatments, and spectral changes were assessed with random forest, partial least squares discriminant analysis, and support vector machine classifiers. RESULTS: All models had excellent classification performance for detecting the different enzymatic and mechanical damage on cartilage matrix. Random forest models achieved accuracies between 90.3% and 77.8%, while partial least squares model accuracies ranged from 95.8% to 84.7%, and support vector machine accuracies from 91.7% to 80.6%. CONCLUSIONS: The results suggest that fiber-optic Fourier transform infrared spectroscopy attenuated total reflectance spectroscopy is a viable way to detect minor and major degeneration of articular cartilage. Objective measures provided by fiber-optic spectroscopic methods could improve arthroscopic evaluation of cartilage damage.

Animal Fat as a Substrate for Production of n-6 Fatty Acids by Fungal Solid-State Fermentation.

The method of solid-state fermentation (SSF) represents a powerful technology for the fortification of animal-based by-products. Oleaginous Zygomycetes fungi are efficient microbial cell factories used in SSF to valorize a wide range of waste and rest cereal materials. The application of this fermentation technique for utilization and biotransformation of animal-based materials represents a distinguished step in their treatment. In this study, for the first time, the strain Umbelopsis isabellina CCF2412 was used for the bioconversion of animal fat by-products to the fermented bioproducts enriched with n-6 polyunsaturated fatty acids, mainly γ-linolenic acid (GLA). Bioconversion of both cereals and the animal fat by-product resulted in the production of fermented bioproducts enriched with not just GLA (maximal yield was 6.4 mg GLA/g of fermented bioproduct), but also with high yields of glucosamine. Moreover, the fermentation on the cornmeal matrix led to obtaining bioproduct enriched with ß-carotene. An increased amount of ß-carotene content improved the antioxidant stability of obtained fermented bioproducts. Furthermore, the application of Fourier-transform infrared spectroscopy for rapid analysis and characterization of the biochemical profile of obtained SSF bioproducts was also studied.

Metal and Phosphate Ions Show Remarkable Influence on the Biomass Production and Lipid Accumulation in Oleaginous Mucor circinelloides.

The biomass of Mucor circinelloides, a dimorphic oleaginous filamentous fungus, has a significant nutritional value and can be used for single cell oil production. Metal ions are micronutrients supporting fungal growth and metabolic activity of cellular processes. We investigated the effect of 140 different substrates, with varying amounts of metal and phosphate ions concentration, on the growth, cell chemistry, lipid accumulation, and lipid profile of M. circinelloides. A high-throughput set-up consisting of a Duetz microcultivation system coupled to Fourier transform infrared spectroscopy was utilized. Lipids were extracted by a modified Lewis method and analyzed using gas chromatography. It was observed that Mg and Zn ions were essential for the growth and metabolic activity of M. circinelloides. An increase in Fe ion concentration inhibited fungal growth, while higher concentrations of Cu, Co, and Zn ions enhanced the growth and lipid accumulation. Lack of Ca and Cu ions, as well as higher amounts of Zn and Mn ions, enhanced lipid accumulation in M. circinelloides. Generally, the fatty acid profile of M. circinelloides lipids was quite consistent, irrespective of media composition. Increasing the amount of Ca ions enhanced polyphosphates accumulation, while lack of it showed fall in polyphosphate.

Biotransformation of Animal Fat-By Products into ARA-Enriched Fermented Bioproducts by Solid-State Fermentation of Mortierella alpina.

Solid-state fermentation (SSF) is a powerful fermentation technology for valorizing rest materials and by-products of different origin. Oleaginous Zygomycetes fungi are often used in SSF as an effective cell factory able to valorize a wide range of hydrophilic and hydrophobic substrates and produce lipid-enriched bioproducts. In this study, for the first time, the strain Mortierella alpina was used in SSF for the bioconversion of animal fat by-products into high value fermented bioproducts enriched with arachidonic acid (ARA). Two cereals-based matrixes mixed with four different concentrations of animal fat by-product were evaluated for finding optimal conditions of a fat-based SSF. All obtained fermented bioproducts were found to be enriched with ARA. The highest substrate utilization (25.8%) was reached for cornmeal and it was almost double than for the respective wheat bran samples. Similarly, total fatty acid content in a fermented bioproduct prepared on cornmeal is almost four times higher in contrast to wheat bran-based bioproduct. Although in general the addition of an animal fat by-product caused a gradual cessation of ARA yield in the obtained fermented bioproduct, the content of ARA in fungal biomass was higher. Thus, M. alpina CCF2861 effectively transformed exogenous fatty acids from animal fat substrate to ARA. Maximum yield of 32.1 mg of ARA/g of bioproduct was reached when using cornmeal mixed with 5% (w/w) of an animal fat by-product as substrate. Furthermore, implementation of attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy in characterization of obtained SSF bioproducts was successfully tested as an alternative tool for complex analysis, compared to traditional time-consuming methods.

The influence of phosphorus source and the nature of nitrogen substrate on the biomass production and lipid accumulation in oleaginous Mucoromycota fungi.

Oleaginous filamentous fungi grown under the nitrogen limitation, accumulate high amounts of lipids in the form of triacylglycerides (TAGs) with fatty acid profiles similar to plant and fish oils. In this study, we investigate the effect of six phosphorus source concentrations combined with two types of nitrogen substrate (yeast extract and ammonium sulphate), on the biomass formation, lipid production, and fatty acid profile for nine oleaginous Mucoromycota fungi. The analysis of fatty acid profiles was performed by gas chromatography with flame ionization detector (GC-FID) and the lipid yield was estimated gravimetrically. Yeast extract could be used as both nitrogen and phosphorus source, without additional inorganic phosphorus supplementation. The use of inorganic nitrogen source (ammonium sulphate) requires strain-specific optimization of phosphorus source amount to obtain optimal lipid production regarding quantity and fatty acid profiles. Lipid production was decreased in ammonium sulphate-based media when phosphorus source was limited in all strains except for Rhizopus stolonifer. High phosphorus source concentration inhibited the growth of Mortierella fungi. The biomass (22 g/L) and lipid (14 g/L) yield of Umbelopsis vinacea was the highest among all the tested strains. KEY POINTS: • The strain specific P requirements of Mucoromycota depend on the nature of N source. • Yeast extract leads to consistent biomass and lipid yield and fatty acids profiles. • Umbelopsis vinacea showed the highest biomass (22 g/L) and lipid (14 g/L) yield. • High P source amounts inhibit the growth of Mortierella fungi.