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Surface-enhanced Raman scattering (SERS) is a powerful and sensitive technique for the detection of fingerprint sig-nals of molecules and for the investigation of a series of surface chemical reactions. Many studies introduced quantita-tive applications of SERS in various fields and several SERS methods have been implemented for each specific applica- tion, ranging in performance characteristics, analytes used, instruments, and analytical matrices. In general, very few methods have been validated according to international guidelines. As a consequence, the application of SERS in high-ly- regulated environments is still considered risky and the perception of a poorly reproducible and insufficiently robust analytical technique has persistently retarded its routine implementation. Collaborative trials are a type of interlabora-tory study (ILS) frequently performed to ascertain the quality of a single analytical method. The idea of an ILS of quan- tification with SERS arose within the framework of Working Group 1 (WG1) of the COST Action BM1401 Ra-man4Clinics32 in an effort to overcome the problematic perception of quantitative SERS methods. Here we report the first interlaboratory SERS study ever conducted, involving 15 laboratories and 41 researchers. In this study we tried to define a methodology to assess the reproducibility and trueness of a quantitative SERS method, and to compare differ- ent methods. In our opinion, this is a first important step toward a “standardization” process of SERS protocols, not proposed by a single laboratory but by a larger community.

Liver plays an important role in drug metabolism, so studying the grade of maturation of this organ would help to develop more appropriate dosing regimens for paediatric populations. Nevertheless, considering the invasive nature of liver analyses there are obvious ethical boundaries, particularly in babies and children. In this work, we investigated the suitability of blood plasma as an alternative matrix to evaluate the biological age of liver. With this aim, we studied the correlation of plasma and liver metabolomic profiles obtained by HPLC-TOF-MS for piglets of different ages (newborns, neonates and infants). By means of Pearson correlation analysis we observed that 360 and 1784 pairs of metabolite features were significantly correlated in positive and negative ionization mode, respectively. Procrustes analysis was applied in order to assess the similarity of the clustering resulting from the data obtained from the two matrices and the two ionisation modes. The Procrustes distances were low for both ESI+ (0.3753) and ESI- (0.3673) and, hence, liver and plasma are expected to provide similar discriminatory information. Furthermore, we found that Multiblock Principal Component Analysis (MB-PCA) readily allowed us to combine the data obtained from both matrices and to better understand the clustering according to the three study groups. Considering all these results, we suggest that plasma can provide valuable insight into the maturation grade of liver in order to provide accurate dosing in paediatric population.

Detecting food adulteration has always been an important task for food safety, especially when grapefruit is the adulterant as components in the juice have undesired interactions with many medicines. In this study we employed a handheld Raman device to detect adulteration of orange juices with grapefruit juices. Fresh fruits of orange and grapefruit were purchased from five different sources and fruit juices were made using a handheld juicer. The extracted juices were then mixed in a way that concentrations of grapefruit juices varied from 0% to 100% in 5% increments. In order to study the impact of the different sources of the fruits, three different sets of mixtures were prepared based on their spectral similarity and dissimilarity. Raman spectra were collected using a handheld instrument with an excitation laser at 785 nm and data analysed using principal component analysis (PCA), principal component-discriminant function analysis (PC-DFA) and partial least squares regression (PLS-R). PLS-R models were trained and validated on: (i) the full data set from the three different mixture sets, and (ii) each set of the three mixtures separately. The results showed that a good calibration model was obtained using full data which had a coefficient of determination (

Patients suspected of ventilator-associated lower respiratory tract infections (VA-LRTIs) commonly receive broad-spectrum antimicrobial therapy unnecessarily. We tested whether exhaled breath analysis can discriminate between patients suspected of VA-LRTI with confirmed infection, from patients with negative cultures. Breath from 108 patients suspected of VA-LRTI was analysed by gas chromatography-mass spectrometry. The breath test had a sensitivity of 98% at a specificity of 49%, confirmed with a second analytical method. The breath test had a negative predictive value of 96% and excluded pneumonia in half of the patients with negative cultures. Trial registration number: UKCRN ID number 19086, registered May 2015.

Introduction The diagnosis of COVID-19 is normally based on the qualitative detection of viral nucleic acid sequences. Properties of the host response are not measured but are key in determining outcome. Although metabolic profiles are well suited to capture host state, most metabolomics studies are either underpowered, measure only a restricted subset of metabolites, compare infected individuals against uninfected control cohorts that are not suitably matched, or do not provide a compact predictive model. Objectives Here we provide a well-powered, untargeted metabolomics assessment of 120 COVID-19 patient samples acquired at hospital admission. The study aims to predict the patient’s infection severity (i.e., mild or severe) and potential outcome (i.e., discharged or deceased). Methods High resolution untargeted UHPLC-MS/MS analysis was performed on patient serum using both positive and negative ionization modes. A subset of 20 intermediary metabolites predictive of severity or outcome were selected based on univariate statistical significance and a multiple predictor Bayesian logistic regression model was created. Results The predictors were selected for their relevant biological function and include deoxycytidine and ureidopropionate (indirectly reflecting viral load), kynurenine (reflecting host inflammatory response), and multiple short chain acylcarnitines (energy metabolism) among others. Currently, this approach predicts outcome and severity with a Monte Carlo cross validated area under the ROC curve of 0.792 (SD 0.09) and 0.793 (SD 0.08), respectively. A blind validation study on an additional 90 patients predicted outcome and severity at ROC AUC of 0.83 (CI 0.74–0.91) and 0.76 (CI 0.67–0.86). Conclusion Prognostic tests based on the markers discussed in this paper could allow improvement in the planning of COVID-19 patient treatment.

In this study, we used stimulated Raman spectroscopy (SRS) microscopy to collect Raman signatures from live Saccharomyces cerevisiae cells in the spectral range 2804−3060 cm−1 and 830−2000 cm−1 with a spectral resolution of 8 cm−1. To effect this, we tuned the pump beam to several distinct wavelengths and thus acquired a series of chemical maps in order to reconstruct SRS spectra based on the intensity of the pixels, an approach also referred as hyperspectral SRS (hsSRS). One of the advantages of hsSRS over spontaneous Raman is that it is not overtly plagued by fluorescence and so fluorescent samples like yeast can be analysed. We show however that Raman signatures acquired by this approach may be subject to spectral artefacts that manifest as drops in intensity of Raman signal due to the movement of lipid droplets (LDs) within the yeast cells. To overcome this issue, yeast cells were chemically fixed with 4% formaldehyde and no artefacts were observed in the Raman signatures acquired from ‘stationary’ samples. Our findings indicate that caution must be applied when analysing SRS signatures obtained through hsSRS from mobile LDs and/or any other moving target within a system, whether biological or not.

Inflammation is strongly implicated in both injury and repair processes occurring after stroke. In this exploratory study we assessed the feasibility of repeated sampling of exhaled volatile organic compounds and performed an untargeted metabolomic analysis of plasma collected at multiple time periods after stroke. Metabolic profiles were compared with the time course of the inflammatory markers C-reactive protein (CRP) and interleukin-6 (IL-6). Serial breath sampling was well-tolerated by all patients and the measurement appears feasible in this group. We found that exhaled decanal tracks CRP and IL-6 levels post-stroke and correlates with several metabolic pathways associated with a post-stroke inflammatory response. This suggests that measurement of breath and blood metabolites could facilitate development of novel therapeutic and diagnostic strategies. Results are discussed in relation to the utility of breath analysis in stroke care, such as in monitoring recovery and complications including stroke associated infection.

1. Plant-microbe interactions are critical for ecosystem functioning and drive rhizosphere processes. Root exudates are an important soil carbon (C) input, as well as a mechanism for communication between plants and rhizosphere microbes, but are notoriously difficult to extract and characterise. Common methods produce either substantial noise from the soil or do not mimic natural systems. Optimising methods for root exudate collection in soil is crucial for advancing our understanding of root-microbe interactions under changing environmental conditions. 2. Hybrid root exudate collection methods, where plants are grown in soil and transferred to hydroponics for exudate collection after root washing, might offer an ecologically relevant alternative to existing approaches. However, this method causes potential root damage as well as osmosis and subsequent leaking of cell contents. Here, we assessed different ‘root recovery’ periods after root washing and before hybrid root exudate collection, by comparing root exudate quantity and quality with both damaged root extracts and with leachates collected from the intact root-soil system. This was done across three common grassland species representing three functional groups. 3. We found that root exudate profiles of the shortest recovery period (0 days) were similar to damaged root extracts and were very high in C. With an increasing period of root recovery, profiles were more similar to leachates collected from the intact root-soil system, and C concentrations decreased. While both hybrid and leachate collection methods separated species by their root exudate profiles, the hybrid method was less variable in terms of the amount of C measured and provided a more diverse and abundant metabolome with better identification of metabolites. 4. Our results show that a recovery period after root washing of at least 3 days is critical to prevent root damage bias in hybrid collection methods, and that our hybrid method yields exudates that discriminate between species. Our data also suggest that exudates collected with this hybrid method are ecologically valid, which is vital for gaining a mechanistic understanding of their role in ecosystem functioning., Highlights • Root-washing method is suitable for ecologically sound root exudate collection. • It allows accurate measures of exudate quantity and composition. • Damage affects must be accounted for and use of a recovery protocol is recommended. • The method permits identification of metabolite specificity between species. • Leachate complements, provides rhizosphere chemical info and species specificity., Tweetable abstract: We introduce a root-washing method for ecologically sound root exudate collection to minimise root damage and distinguish species.

The authenticity of olive oil has been a significant long-term challenge. Extra virgin olive oil (EVOO) is the most desirable of these products and commands a high price, thus unscrupulous individuals often alter its quality by adulteration with a lower grade oil. Most analytical methods employed for the detection of food adulteration require sample collection and transportation to a central laboratory for analysis. We explore the use of portable conventional Raman and spatially-offset Raman spectroscopy (SORS) technologies as non-destructive approaches to assess the adulteration status of EVOO quantitatively and for SORS directly through the original container, which means that after analysis the bottle is intact and the oil would still be fit for use. Three sample sets were generated, each with a different adulterant and varying levels of chemical similarity to EVOO. These included EVOO mixed with sunflower oil, pomace olive oil, or refined olive oil. Authentic EVOO samples were stretched/diluted from 0% to 100% with these adulterants and measured using two handheld Raman spectrometers (excitation at 785 or 1064 nm) and handheld SORS (830 nm). The PCA scores plots displayed clear trends which could be related to the level of adulteration for all three mixtures. Conventional Raman (at 785 or 1064 nm) and SORS (at 830 nm with a single spatial offset) conducted in sample vial mode resulted in prediction errors for the test set data ranging from 1.9–4.2% for sunflower oil, 6.5–10.7% for pomace olive oil and 8.0–12.8% for refined olive oil, with the limit of detection (LOD) typically being 3–12% of the adulterant. Container analysis using SORS produced very similar results: 1.4% for sunflower, 4.9% for pomace, and 10.1% for refined olive oil, with similar LODs ranging from 2–14%. It can be concluded that Raman spectroscopy, including through-container analysis using SORS, has significant potential as a rapid and accurate analytical method for the non-destructive detection of adulteration of extra virgin olive oil.

Simple Summary Due to the non-specific symptoms of brain cancer (e.g., headaches or memory changes), gliomas will often remain undetected until they are larger or at a higher grade, reducing the patient’s likelihood of a good clinical outcome. Earlier detection and diagnosis of brain tumours is vital to improve patient outcomes, leading to safer surgeries and earlier treatments. A liquid biopsy for brain tumour would prove revolutionary however in order to detect disease earlier the liquid biopsy needs to be able to detect smaller tumours; and current liquid biopsies perform worse when detecting smaller or earlier stage tumours. Here, for the first time, we confirm the applicability of a validated spectroscopic liquid biopsy approach to detect both small and low-grade gliomas proving that the spectroscopic liquid biopsy approach is insensitive to tumour volume unlike other liquid biopsies. Abstract Background: To support the early detection and diagnosis of brain tumours we have developed a rapid, cost-effective and easy to use spectroscopic liquid biopsy based on the absorbance of infrared radiation. We have previously reported highly sensitive results of our approach which can discriminate patients with a recent brain tumour diagnosis and asymptomatic controls. Other liquid biopsy approaches (e.g., based on tumour genetic material) report a lower classification accuracy for early-stage tumours. In this manuscript we present an investigation into the link between brain tumour volume and liquid biopsy test performance. Methods: In a cohort of 177 patients (90 patients with high-grade glioma (glioblastoma (GBM) or anaplastic astrocytoma), or low-grade glioma (astrocytoma, oligoastrocytoma and oligodendroglioma)) tumour volumes were calculated from magnetic resonance imaging (MRI) investigations and patients were split into two groups depending on MRI parameters (T1 with contrast enhancement or T2/FLAIR (fluid-attenuated inversion recovery)). Using attenuated total reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy coupled with supervised learning methods and machine learning algorithms, 90 tumour patients were stratified against 87 control patients who displayed no symptomatic indications of cancer, and were classified as either glioma or non-glioma. Results: Sensitivities, specificities and balanced accuracies were all greater than 88%, the area under the curve (AUC) was 0.98, and cancer patients with tumour volumes as small as 0.2 cm3 were correctly identified. Conclusions: Our spectroscopic liquid biopsy approach can identify gliomas that are both small and low-grade showing great promise for deployment of this technique for early detection and diagnosis.

Human norovirus (NoV) remains the most common cause of viral gastroenteritis and the leading cause of viral foodborne outbreaks globally. NoV is highly pathogenic with an estimated median viral infective dose (ID50) ranging from 18 to 1015 genome copies. For NoV detection, the only reliable and sensitive method available for detection and quantification is reverse transcription quantitative polymerase chain reaction (RTqPCR). NoV detection in food is particularly challenging, requiring matrix specific concentration of the virus and removal of inhibitory compounds to detection assays. Hence, the RTqPCR method poses some challenges for rapid in-field or point-of-care diagnostic applications. We propose a new colorimetric NanoZyme aptasensor strategy for rapid (10 min) and ultrasensitive (calculated Limit of Detection (LoD) of 3 viruses per assay equivalent to 30 viruses/mL of sample and experimentally demonstrated LoD of 20 viruses per assay equivalent to 200 viruses/mL) detection of the infective murine norovirus (MNV), a readily cultivable surrogate for NoV. Our approach combines the enzyme-mimic catalytic activity of gold nanoparticles with high target specificity of an MNV aptamer to create sensor probes that produce a blue color in the presence of this norovirus, such that the color intensity provides the virus concentrations. Overall, our strategy offers the most sensitive detection of norovirus or a norovirus surrogate achieved to date using a biosensor approach, enabling for the first time, the detection of MNV virion corresponding to the lower end of the ID50 for NoV. We further demonstrate the robustness of the norovirus NanoZyme aptasensor by testing its performance in the presence of other nontarget microorganisms, human serum and shellfish homogenate, supporting the potential of detecting norovirus in complex matrices. This new assay format can, therefore, be of significant importance as it allows ultrasensitive norovirus detection rapidly within minutes, while also offering the simplicity of use and need for nonspecialized laboratory infrastructure.

Here, we applied NMR spectroscopy in combination with chemometrics to quantify the adulteration of fresh coconut water, stretched with water-sugar mixtures. Coconut water was extracted from young Costa Rican coconuts and adulterated with concentrations of various sugar solutions. A total of 45 samples were analysed by 1D proton NMR spectroscopy and chemometrics. Results showed highly sensitive quantification, with a limit of detection of adulteration with sugars of 1.3% and a root-mean-squared error of prediction of 0.58%. Interestingly, we identified a regular drift in the chemical shift and a change in the lineshape of malic acid signals concomitant with increasing levels of adulteration. On further investigation, this was found to originate from changes in the concentration of divalent cations, such as magnesium, within the samples. It can be concluded that 1 H NMR spectroscopy enables accurate quantification for the degree of adulteration in this product, with the added discovery finding that the shift and lineshape of the malic acid signal can be utilised as a potential diagnostic marker for partial substitution of fresh coconut water with extrinsic components such as sugar mixtures.

Microbial communities play essential functions which drive various ecosystems supporting animal and aquatic life. However, linking bacteria with specific metabolic functions is difficult, since microbial communities consist of numerous and phylogenetically diverse microbes. Stable isotope probing (SIP) combined with single-cell tools has emerged as a novel culture-independent strategy for unravelling microbial metabolic roles and intertwined interactions in complex communities. In this study, we applied Raman and Fourier-transform infrared (FT-IR) spectroscopies, secondary ion mass spectrometry (SIMS) with SIP to probe the rate of 13C incorporation in Escherichia coli at 37 and 25 °C. Our results indicate quantitative enrichment and flow of 13C into E. coli at various time points. Multivariate and univariate analyses of Raman and FT-IR data demonstrated distinctive 13C concentration-dependent trends that were due to vibrational bands shifting to lower frequencies and these shifts were a result of incubation time and metabolic rate. SIMS results were in complete agreement with the spectroscopy findings, and confirmed the detected levels of 13C incorporation into microbial biomass at the investigated conditions. Having established that FT-IR and Raman spectroscopy with SIP can measure metabolism kinetics in this simple system, we have applied the kinetics concept to study the metabolism of phenol by Pseudomonas putida and metabolic interactions within a two-species consortia with E. coli that could not degrade phenol. Raman spectroscopy combined with SIP identified quantitative shifts in P. putida due to temporal assimilation of phenol. Although E. coli was unable to grow on phenol, in co-culture with P. putida, general metabolic probing using deuterated water for SIP revealed that E. coli displayed increasing metabolic activity, presumably due to cross feeding from metabolites generated by P. putida. This study clearly demonstrates that Raman and FT-IR combined with SIP provide rapid and sensitive detection of carbon incorporation rates and microbial interactions. These novel findings may guide the identification of primary substrate consumers in complex microbial communities in situ, which is a key step towards the characterisation of novel genes, enzymes and metabolic flux analysis in microbial consortia.

Exhaled breath analysis is a promising new diagnostic tool, but currently no standardised method for sampling is available in mechanically ventilated patients. We compared two breath sampling methods, first using an artificial ventilator circuit, then in "real life"in mechanically ventilated patients on the intensive care unit. In the laboratory circuit, a 24-component synthetic-breath volatile organic compound (VOC) mixture was injected into the system as air was sampled: (A) through a port on the exhalation limb of the circuit and (B) through a closed endo-bronchial suction catheter. Sorbent tubes were used to collect samples for analysis by thermal desorption-gas chromatography-mass spectrometry. Realistic mechanical ventilation rates and breath pressure-volume loops were established and method detection limits (MDLs) were calculated for all VOCs. Higher yields of VOCs were retrieved using the closed suction catheter; however, for several VOCs MDLs were compromised due to the background signal associated with plastic and rubber components in the catheters. Different brands of suction catheter were compared. Exhaled VOC data from 40 patient samples collected at two sites were then used to calculate the proportion of data analysed above the MDL. The relative performance of the two methods differed depending on the VOC under study and both methods showed sensitivity towards different exhaled VOCs. Furthermore, method performance differed depending on recruitment site, as the centres were equipped with different brands of respiratory equipment, an important consideration for the design of multicentre studies investigating exhaled VOCs in mechanically ventilated patients.

Highlights • The response to nitrogen of 4 winter oat varieties in three field trials was analysed. • A novel high-resolution method was developed to profile metabolite changes. • Conditions that enhance yield do not necessarily result in higher nutritional value. • Choice of variety is of equally high importance as the nitrogen levels applied., Oats (Avena sativa L.) are a healthy food, being high in dietary fibre (e.g. β-glucans), antioxidants, minerals, and vitamins. Understanding the effect of variety and crop management on nutritional quality is important. The response of four oat varieties to increased nitrogen levels was investigated across multiple locations and years with respect to yield, grain quality and metabolites (assessed via GC- and LC- MS). A novel high-resolution UHPLC-PDA-MS/MS method was developed, providing improved metabolite enrichment, resolution, and identification. The combined phenotyping approach revealed that, amino acid levels were increased by nitrogen supplementation, as were total protein and nitrogen containing lipid levels, whereas health-beneficial avenanthramides were decreased. Although nitrogen addition significantly increased grain yield and β-glucan content, supporting increasing the total nitrogen levels recommended within agricultural guidelines, oat varietal choice as well as negative impacts upon health beneficial secondary metabolites and the environmental burdens associated with nitrogen fertilisation, require further consideration.

To support the early detection and diagnosis of brain tumors we have developed a rapid, cost-effective and easy to use spectroscopic liquid biopsy based on the absorbance of infrared radiation. We have previously reported highly sensitive results of our approach which can discriminate patients with a recent brain tumor diagnosis and asymptomatic controls. Other liquid biopsy approaches (e.g., based on tumor genetic material) report a lower classification accuracy for early-stage tumors. In this manuscript we present an investigation into the link between brain tumor volume and liquid biopsy test performance. In a cohort of 177 patients (90 patients with high-grade glioma (glioblastoma (GBM) or anaplastic astrocytoma), or low-grade glioma (astrocytoma, oligo-astrocytoma and oligodendroglioma)) tumor volumes were calculated from magnetic resonance imaging (MRI) investigations and patients were split into two groups depending on MRI parameters (T1 with contrast enhancement or T2/FLAIR (fluid-attenuated inversion recovery)). Using attenuated total reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy coupled with supervised learning methods and machine learning algorithms, 90 tumor patients were stratified against 87 control patients who displayed no symptomatic indications of cancer, and were classified as either glioma or non-glioma. Sensitivities, specificities and balanced accuracies were all greater than 88%, the area under the curve (AUC) was 0.98, and cancer patients with tumor volumes as small as 0.2 cm3 were correctly identified. Our spectroscopic liquid biopsy approach can identify gliomas that are both small and low-grade showing great promise for deployment of this technique for early detection and diagnosis. Citation Format: Alexandra Sala, Ashton G. Theakstone, Paul M. Brennan, Michael D. Jenkinson, Samantha J. Mills, Khaja Syed, Christopher Rinaldi, Yun Xu, Royston Goodacre, Holly J. Butler, David S. Palmer, Benjamin R. Smith, Matthew J. Baker. Rapid spectroscopic liquid biopsy for the early detection of brain tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5923.

Rapid and accurate classification and discrimination of bacteria is an important task and has been highlighted recently for rapid diagnostics using real-time results. Coupled with a recent report by Jim O'Neill [] that if left unaddressed antimicrobial resistance (AMR) in bacteria could kill 10 million people per year by 2050, which would surpass current cancer mortality, this further highlights the need for unequivocal identification of microorganisms. Whilst traditional microbiological testing has offered insights into the characterisation and identification of a wide range of bacteria, these approaches have proven to be laborious and time-consuming and are not really fit for purpose, considering the modern day speed and volume of international travel and the opportunities it creates for the spread of pathogens globally. To overcome these disadvantages, modern analytical methods, such as mass spectrometry (MS) and vibrational spectroscopy, that analyse the whole organism, have emerged as essential alternative approaches. Currently within clinical microbiology laboratories, matrix assisted laser desorption ionisation (MALDI)-MS is the method of choice for bacterial identification. This is largely down to its robust analysis as it largely measures the ribosomes which are always present irrespective of how the bacteria are cultured. However, MALDI-MS requires large amounts of biomass and infrared spectroscopy and Raman spectroscopy are attractive alternatives as these physicochemical bioanalytical techniques have the advantages of being rapid, reliable and cost-effective for analysing various types of bacterial samples, even at the single cell level. In this review, we discuss the fundamental applications, advantages and disadvantages of modern analytical techniques used for bacterial characterisation, classification and identification.

Fungal pathogens are responsible for millions of life-threatening infections on an annual basis worldwide. The current repertoire of antifungal drugs is very limited and, worryingly, resistance has emerged and already become a serious threat to our capacity to treat fungal diseases. The first step to develop new drugs is often to identify molecular targets in the pathogen whose inhibition during infection can prevent its growth. However, the current models are not suitable to validate targets in established infections. Here, we have characterized the promising antifungal target methionine synthase in great detail, using the prominent fungal pathogen Aspergillus fumigatus as a model. We have uncovered the underlying reason for its essentiality and confirmed its druggability. Furthermore, we have optimized the use of a genetic system to show a beneficial effect of targeting methionine synthase in established infections. Therefore, we believe that antifungal drugs to target methionine synthase should be pursued and additionally, we provide a model that permits gaining information about the validity of antifungal targets in established infections., There is an urgent need to develop novel antifungals to tackle the threat fungal pathogens pose to human health. Here, we have performed a comprehensive characterization and validation of the promising target methionine synthase (MetH). We show that in Aspergillus fumigatus the absence of this enzymatic activity triggers a metabolic imbalance that causes a reduction in intracellular ATP, which prevents fungal growth even in the presence of methionine. Interestingly, growth can be recovered in the presence of certain metabolites, which shows that metH is a conditionally essential gene and consequently should be targeted in established infections for a more comprehensive validation. Accordingly, we have validated the use of the tetOFF genetic model in fungal research and improved its performance in vivo to achieve initial validation of targets in models of established infection. We show that repression of metH in growing hyphae halts growth in vitro, which translates into a beneficial effect when targeting established infections using this model in vivo. Finally, a structure-based virtual screening of methionine synthases reveals key differences between the human and fungal structures and unravels features in the fungal enzyme that can guide the design of novel specific inhibitors. Therefore, methionine synthase is a valuable target for the development of new antifungals.

Abstract A colourimetric assay for the detection of DNA fragments associated with the oil palm pathogen Ganoderma boninense and other fungi DNA is reported. The assay is based on the aggregation of DNA-nanoparticle conjugates in the presence of complementary DNA from the target organism. Here, various designs of DNA-nanoparticle conjugates were evaluated, and it was found that the best design gave a visually observable colour change with as little as 2 pmol of double-stranded DNA analyte even in the presence of a large excess of a mixture of non-complementary DNA. Overall, this label-free system is rapid, sensitive, selective, simple in design, and easy to carry out. It does not require specialist equipment or specialist training for the interpretation of the results, and therefore has the potential to be deployed for agricultural diagnostics in the field. Graphic abstract Development of a colourimetric assay based on DNA-nanoparticle conjugates for the oil palm pathogen Ganoderma boninense.

In the past two decades, metabolomics has proved to be a valuable tool with many potential applications in different areas of science. However, there are still some challenges that need to be addressed, particularly for multicenter studies. These challenges are mainly attributed to various sources of fluctuation and unwanted variations that can be introduced at pre-analytical, analytical, and/or post-analytical steps of any metabolomics experiment. Thus, this study aimed at using Streptomyces lividans TK24 as the model organism in a cross-laboratory experiment in Manchester and Leuven to evaluate the reproducibility of a standard sample preparation method, and determine the optimal sample format (cell extract or quenched biomass) required to preserve the metabolic profile of the cells during cross-lab sample transportation and storage. Principal component analysis (PCA) scores plot of the gas chromatography-mass spectrometry (GC-MS) data from both laboratories displayed clear growth-dependent clustering patterns which was in agreement with the Procrustes analysis findings. In addition, the data generated in Manchester displayed tight clustering of cell pellets (quenched biomass) and metabolite extracts, confirming the stability of both sample formats during the transportation and storage period. ispartof: METABOLITES vol:10 issue:9 ispartof: location:Switzerland status: published

Monoclonal antibodies (mAbs) represent a rapidly expanding market for biotherapeutics. Structural changes in the mAb can lead to unwanted immunogenicity, reduced efficacy, and loss of material during production. The pharmaceutical sector requires new protein characterization tools that are fast, applicable in situ and to the manufacturing process. Raman has been highlighted as a technique to suit this application as it is information-rich, minimally invasive, insensitive to water background and requires little to no sample preparation. This study investigates the applicability of Raman to detect Post-Translational Modifications (PTMs) and degradation seen in mAbs. IgG4 molecules have been incubated under a range of conditions known to result in degradation of the therapeutic including varied pH, temperature, agitation, photo, and chemical stresses. Aggregation was measured using size-exclusion chromatography, and PTM levels were calculated using peptide mapping. By combining principal component analysis (PCA) with Raman spectroscopy and circular dichroism (CD) spectroscopy structural analysis we were able to separate proteins based on PTMs and degradation. Furthermore, by identifying key bands that lead to the PCA separation we could correlate spectral peaks to specific PTMs. In particular, we have identified a peak which exhibits a shift in samples with higher levels of Trp oxidation. Through separation of IgG4 aggregates, by size, we have shown a linear correlation between peak wavenumbers of specific functional groups and the amount of aggregate present. We therefore demonstrate the capability for Raman spectroscopy to be used as an analytical tool to measure degradation and PTMs in-line with therapeutic production.

A metabolomics profiling approach was conducted to identify diagnostic biomarkers of PD from sebum, a non-invasively available biofluid. In this study, we used liquid chromatography-mass spectrometry (LC-MS) to analyse 274 samples from participants (80 drug naïve PD, 138 medicated PD and 56 well matched control subjects) and detected metabolites that could predict PD phenotype. Partial least squares-discriminant analysis (PLS-DA) models based on this sebum metabolome had correct classification rates of 70.4% and 69.7% to distinguish between drug naïve PD and medicated PD from control, respectively. Variable importance in projection (VIP) scores indicate compounds with significance belonged to sphingolipid, triacylglycerol and fatty acid/ester lipid classes. Pathway enrichment analysis showed alterations in lipid metabolism and mitochondrial dysfunction viz. the carnitine shuttle, sphingolipid metabolism and arachidonic acid metabolism. This study unveiled novel diagnostic sebum-based biomarkers for PD, and provides insight towards our current understanding of the pathogenesis of PD.

Objective: The goal of any topical formulation is efficient transdermal delivery of its active components. However, delivery of compounds can be problematic with penetration through tough layers of fibrotic dermal scar tissue. Approach: We propose a new approach combining high-performance liquid chromatography (HPLC) and Raman spectroscopy (RS) using a topical of unknown composition against a well-known antiscar topical (as control). Results: Positive detection of compounds within the treatment topical using both techniques was validated with mass spectrometry. RS detected conformational structural changes; the 1,655/1,446 cm(−1) ratio estimating collagen content significantly decreased (p

Glycation is a protein modification prevalent in the progression of diseases such as Diabetes and Alzheimer’s, as well as a byproduct of therapeutic protein expression, notably for monoclonal antibodies (mAbs). Quantification of glycated protein is thus advantageous in both assessing the advancement of disease diagnosis and for quality control of protein therapeutics. Vibrational spectroscopy has been highlighted as a technique that can easily be modified for rapid analysis of the glycation state of proteins, and requires minimal sample preparation. Glycated samples of lysozyme and albumin were synthesised by incubation with 0.5 M glucose for 30 days. Here we show that both FTIR-ATR and Raman spectroscopy are able to distinguish between glycated and non-glycated proteins. Principal component analysis (PCA) was used to show separation between control and glycated samples. Loadings plots found specific peaks that accounted for the variation - notably a peak at 1027 cm-1 for FTIR-ATR. In Raman spectroscopy, PCA emphasised peaks at 1040 cm-1 and 1121 cm-1. Therefore, both FTIR-ATR and Raman spectroscopy found changes in peak intensities and wavenumbers within the sugar C-O/C-C/C-N region (1200-800 cm-1). For quantification of the level of glycation of lysozyme, partial least squares regression (PLSR), with statistical validation, was employed to analyse Raman spectra from solution samples containing 0 – 100% glycated lysozyme, generating a robust model with R2 of 0.99. We therefore show the scope and potential of Raman spectroscopy as a high throughput quantification method for glycated proteins in solution that could be applied in disease diagnostics, as well as therapeutic protein quality control.

Recently a species of Pseudanabaena was identified as the dominant photosynthetic organism during a bloom event in a high pH (pH ∼11.4), radioactive spent nuclear fuel pond (SNFP) at the Sellafield Ltd., United Kingdom facility. The metabolic response of a laboratory culture containing the cyanobacterium Pseudanabaena catenata, a relative of the major photosynthetic microorganism found in the SNFP, to X-ray irradiation was studied to identify potential survival strategies used to support colonization of radioactive environments. Growth was monitored and the metabolic fingerprints of the cultures, during irradiation and throughout the post-irradiation recovery period, were determined using Fourier transform infrared (FT-IR) spectroscopy. A dose of 95 Gy delivered over 5 days did not significantly affect growth of P. catenata, as determined by turbidity measurements and cell counts. Multivariate statistical analysis of the FT-IR spectral data revealed metabolic variation during the post-irradiation recovery period, with increased polysaccharide and decreased amide spectral intensities. Increases in polysaccharides were confirmed by complementary analytical methods including total carbohydrate assays and calcofluor white staining. This observed increased production of polysaccharides is of significance, since this could have an impact on the fate of the radionuclide inventory in the pond via biosorption of cationic radionuclides, and may also impact on downstream processes through biofilm formation and biofouling.

Autism spectrum disorder (ASD) is a broad and heterogeneous group of neurological developmental disorders characterized by impaired social interaction and communication, restricted and repetitive behavioural patterns, and altered sensory processing. Currently, no reliable ASD molecular biomarkers are available. Since immune dysregulation has been supposed to be related with ASD onset and dyslipidaemia has been recognized as an early symptom of biological perturbation, lipid extracts from peripheral blood mononuclear cells (PBMCs), consisting primarily of lymphocytes (T cells, B cells, and NK cells) and monocytes, of 38 children with ASD and their non-autistic siblings were investigated by hydrophilic interaction liquid chromatography (HILIC) coupled with electrospray ionization and Fourier-transform mass spectrometry (ESI-FTMS). Performances of two freeware software for data extraction and processing were compared with acquired reliable data regardless of the used informatics. A reduction of variables from 1460 by the untargeted XCMS to 324 by the semi-untargeted Alex123 software was attained. All-ion fragmentation (AIF) MS scans along with Alex123 software were successfully applied to obtain information related to fatty acyl chain composition of six glycerophospholipid classes occurring in PBMC. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were explored to verify the occurrence of significant differences in the lipid pool composition of ASD children compared with 36 healthy siblings. After rigorous statistical validation, we conclude that phospholipids extracted from PBMC of children affected by ASD do not exhibit diagnostic biomarkers. Yet interindividual variability comes forth from this study as the dominant effect in keeping with the existing phenotypic and etiological heterogeneity among ASD individuals. Graphical abstract.

The broad variability of Cucumis melo (melon, Cucurbitaceae) presents a challenge to conventional classification and organization within the species. To shed further light on the infraspecific relationships within C. melo, we compared genotypic and metabolomic similarities among 44 accessions representative of most of the cultivar-groups. Genotyping-by-sequencing (GBS) provided over 20,000 single-nucleotide polymorphisms (SNPs). Metabolomics data of the mature fruit flesh and rind provided over 80,000 metabolomic and elemental features via an orchestra of six complementary metabolomic platforms. These technologies probed polar, semi-polar, and non-polar metabolite fractions as well as a set of mineral elements and included both flavor-and taste-relevant volatile and non-volatile metabolites. Together these results enabled an estimate of “metabolomic/elemental distance” and its correlation with the genetic GBS distance of melon accessions. This study indicates that extensive and non-targeted metabolomics/elemental characterization produced classifications that strongly, but not completely, reflect the current and extensive genetic classification. Certain melon Groups, such as Inodorous, clustered in parallel with the genetic classifications while other genome to metabolome/element associations proved less clear. We suggest that the combined genomic, metabolic, and element data reflect the extensive sexual compatibility among melon accessions and the breeding history that has, for example, targeted metabolic quality traits, such as taste and flavor. Fil: Moing, Annick. Universite de Bordeaux; Francia Fil: William Allwood, J.. The James Hutton Institute; Reino Unido Fil: Aharoni, Asaph. Weizmann Institute Of Science Israel; Israel Fil: Baker, John. Rothamsted Research; Reino Unido Fil: Beale, Michael H.. Rothamsted Research; Reino Unido Fil: Ben-Dor, Shifra. Weizmann Institute Of Science Israel; Israel Fil: Biais, Benoît. Universite de Bordeaux; Francia Fil: Brigante, Federico Iván. Max Planck Institute Of Molecular Plant Physiology; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Ciencia y Tecnología de Alimentos Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Ciencia y Tecnología de Alimentos Córdoba; Argentina Fil: Burger, Yosef. Agricultural Research Organization Of Israel; Israel Fil: Deborde, Catherine. Universite de Bordeaux; Francia Fil: Erban, Alexander. Max Planck Institute Of Molecular Plant Physiology; Alemania Fil: Faigenboim, Adi. Agricultural Research Organization Of Israel; Israel Fil: Gur, Amit. Agricultural Research Organization Of Israel; Israel Fil: Goodacre, Royston. University of Liverpool; Reino Unido Fil: Hansen, Thomas H.. Universidad de Copenhagen; Dinamarca Fil: Jacob, Daniel. Universite de Bordeaux; Francia Fil: Katzir, Nurit. Agricultural Research Organization Of Israel; Israel Fil: Kopka, Joachim. Max Planck Institute Of Molecular Plant Physiology; Alemania Fil: Lewinsohn, Efraim. Agricultural Research Organization Of Israel; Israel Fil: Maucourt, Mickael. Universite de Bordeaux; Francia Fil: Meir, Sagit. Weizmann Institute Of Science Israel; Israel Fil: Miller, Sonia. Rothamsted Research; Reino Unido Fil: Mumm, Roland. Wageningen University & Países Bajos Fil: Oren, Elad. Agricultural Research Organization Of Israel; Israel Fil: Paris, Harry S.. Agricultural Research Organization Of Israel; Israel Fil: Rogachev, Ilana. Weizmann Institute Of Science Israel; Israel Fil: Rolin, Dominique. Universite de Bordeaux; Francia Fil: Saar, Uzi. Agricultural Research Organization Of Israel; Israel Fil: Schjoerring, Jan K.. Universidad de Copenhagen; Dinamarca Fil: Tadmor, Yaakov. Agricultural Research Organization Of Israel; Israel Fil: Tzuri, Galil. Agricultural Research Organization Of Israel; Israel Fil: de Vos, Ric C. H.. Wageningen University & Países Bajos Fil: Ward, Jane L.. Rothamsted Research; Reino Unido Fil: Yeselson, Elena. Agricultural Research Organization Of Israel; Israel Fil: Hall, Robert D.. Wageningen University & Países Bajos Fil: Schaffer, Arthur A.. Agricultural Research Organization Of Israel; Israel

A metabolomics profiling approach was conducted to identify diagnostic biomarkers of PD from sebum, a non-invasively available biofluid. In this study, we used liquid chromatography-mass spectrometry (LC-MS) to analyse 274 samples from participants (80 drug naïve PD, 138 medicated PD and 56 well matched control subjects) and detected metabolites that could predict PD phenotype. Partial least squares-discriminant analysis (PLS-DA) models based on this sebum metabolome had correct classification rates of 70.4% and 69.7% to distinguish between drug naïve PD and medicated PD from control, respectively. Variable importance in projection (VIP) scores indicate compounds with significance belonged to sphingolipid, triacylglycerol and fatty acid/ester lipid classes. Pathway enrichment analysis showed alterations in lipid metabolism and mitochondrial dysfunction viz. the carnitine shuttle, sphingolipid metabolism and arachidonic acid metabolism. This study unveiled novel diagnostic sebum-based biomarkers for PD, and provides insight towards our current understanding of the pathogenesis of PD.

Offline breath analysis by thermal desorption gas chromatography mass spectrometry (TD-GC-MS) requires the use of sorbent traps to concentrate and store volatile compounds. The selection of which sorbent to use and best practices for managing high relative humidity are important considerations to allow for reproducible, untargeted, biomarker discovery in water saturated breath samples. This work aims to assess three commonly used sorbent materials for their use in breath volatile sampling and determine how the high relative humidity inherent in such samples effects the capture of volatile compounds of interest. TenaxGR, TenaxTA/Carbograph1TD and TenaxTA/Carbograph5TD tubes were selected as they are the most commonly used sorbents in the breath sampling literature. The recovery of 29 compounds in a standard mix loaded using high humidity gas was tested for each sorbent and compared to loading in dry gas. Water retention and dry purge rates were determined for each sorbent for 500 ml and 1000 ml breath collections. Finally, breath samples were collected simultaneously on to each sorbent type using the ReCIVA and analysed by TD-GC-MS. All three sorbents exhibited acceptable reproducibility when loaded with the standard mix in dry gas (RSD < 10%). Loading the standard mix in humid gas led to reduced recovery of compounds based on their chemical properties. Dry purging performance for each sorbent material was assessed and was shown to be 1.14, 1.13 and 0.89 mg H2O min−1 for TenaxGR, TenaxTA/Carbograph1TD and TenaxTA/Carbograph5TD respectively when flushed with 50 ml min−1 of N2. A comparison of breath profiles on different sorbents showed differences in background artefacts (sulfur dioxide, cyclopenten-1-one and 3-nonene) and endogenous breath compounds (2-methyl-furan and furfural). This work demonstrates that high relative humidity during sampling reduces the ability of sorbent tubes to capture volatile compounds and could impact method detection limits during breath sampling. Sufficient water to impair accurate analysis was retained on all tubes. Minimal differences were observed between sorbent materials when used to sample breath, however, suggestions are provided for sorbent selection for future studies.

Personalized medicine is a rapidly emerging approach to medicine guided by scientific insights into genetic make-up and molecular dynamics of health and disease. This approach relies on “-omics” technologies that aim to define molecular mechanisms of a human body in health and disease. Personalized medicine is a generic entity that has been practiced for decades within evidence-based framework. This approach is slow and potentially dangerous as it relies on the very first choice of treatment to be the most effective. In the scenario, when the first treatment is not as effective, an assessment is made following which, an alternate medicine is prescribed, which may not be the best solution always, and so the cycle goes. Metabolomics, an approach that can quantify small molecules in a given biofluid measures snapshot of health or disease. Small molecules reflecting larger changes in proteins or lipids are personalized signature's of an individual's health.

Several software packages containing diverse algorithms are available for processing Liquid Chromatography-Mass Spectrometry (LC–MS) chromatographic data and within these deconvolution packages different parameters settings can lead to different outcomes. XCMS is the most widely used peak picking and deconvolution software for metabolomics, but the parameter selection can be hard for inexpert users. To solve this issue, the automatic optimization tools such as Isotopologue Parameters Optimization (IPO) can be extremely helpful. To evaluate the suitability of IPO as a tool for XCMS parameters optimization and compare the results with those manually obtained by an exhaustive examination of the LC–MS characteristics and performance. Raw HPLC-TOF–MS data from two types of biological samples (liver and plasma) analysed in both positive and negative electrospray ionization modes from three groups of piglets were processed with XCMS using parameters optimized following two different approaches: IPO and Manual. The outcomes were compared to determine the advantages and disadvantages of using each method. IPO processing produced the higher number of repeatable (%RSD

Profiling skin microbiome and metabolome has been utilised to gain further insight into wound healing processes. The aims of this multi-part temporal study in 11 volunteers were to analytically profile the dynamic wound tissue and headspace metabolome and sequence microbial communities in acute wound healing at days 0, 7, 14, 21 and 28, and to investigate their relationship to wound healing, using non-invasive quantitative devices. Metabolites were obtained using tissue extraction, sorbent and polydimethylsiloxane patches and analysed using GCMS. PCA of wound tissue metabolome clearly separated time points with 10 metabolites of 346 being involved in separation. Analysis of variance-simultaneous component analysis identified a statistical difference between the wound headspace metabolome, sites (P = 0.0024) and time points (P

Surface-enhanced Raman scattering (SERS) generates molecularly specific fingerprints of analytes and when the experimental conditions are carefully controlled this is highly quantitative. This review critiques the development of quantitative SERS from simple univariate assessment of single vibrational modes to multivariate analysis of the whole spectrum for improved quantification. SERS has also been developed for direct multiplex detection and quantification of multiple analytes and this is also discussed, as is the need for LC-SERS for analyte separation should multivariate chemometric approaches fail to effect quantification. Finally, to effect absolute quantification with SERS, the concepts of isotopologues is introduced along with the standard addition method (SAM) and suitable examples that have been developed and exploit these techniques are presented. We believe that SERS will be routinely used for quantitative analysis and it is only a matter of time before this technique translates from the laboratory to the clinical environment.

In directed evolution (DE) the assessment of candidate enzymes and their modification is essential. In this study we have investigated genetic algorithms (GAs) in this context and conducted a systematic study of the behavior of GAs on 20 fitness landscapes (FLs) of varying complexity. This has allowed the tuning of the GAs to be explored. On the basis of this study, recommendations for the best GA settings to use for a GA-directed high-throughput experimental program (in which populations and the number of generations is necessarily low) are reported. The FLs were based upon simple linear models and were characterized by the behavior of the GA on the landscape as demonstrated by stall plots and the footprints and adhesion of candidate solutions, which highlighted local optima (LOs). In order to maximize progress of the GA and to reduce the chances of becoming stuck in a LO it was best to use: 1) a large number of generations, 2) high populations, 3) removal of duplicate sequences (clones), 4) double mutation, and 5) high selection pressure (the two best individuals go to the next generation), and 6) to consider using a designed sequence as the starting point of the GA run. We believe that these recommendations might be appropriate starting points for studies employing GAs within DE experiments.

Raman spectroscopy has attracted considerable interest during the past two decades as a vibrational technique used for the molecular characterisation of different molecules. Whilst the Raman effect is known to be generally weak, it is also known that this can be greatly improved using surface-enhanced Raman scattering (SERS). Indeed, in recent years, the power of SERS for rapid identification and quantification of target analytes in a wide range of applications has been repeatedly demonstrated in multiple studies. Moreover, the application of SERS in combination with an isotopically labelled compound (ILC), as an internal standard, has also very recently shown promising results for quantitative SERS measurements, by improving both its accuracy and precision. This is due to the 12C and 13C or 1H and 2H (D) having similar physicochemical properties. The use of these internal standards results in the reduction of any influences due to the number of nanoparticles within the analysis zone and fluctuations in laser fluence. Thus, in this study we have employed SERS for quantitative detection of tryptophan (Trp) and caffeine. These have been chosen because Trp is readily available as the deuterated form and caffeine is available in both 12C and 13C. Quantum chemical calculations based on density functional theory (DFT) have been utilized to determine the vibrational characteristics of the target analytes. For SERS analysis incorporating isotopologues of tryptophan three independent experiments were conducted with three different batches of nanoparticles over a 12 month period; our results show that the use of this internal standard improves quantification of this target molecule. In particular for the independent test sets (i.e., samples not used in quantitative partial least squares regression (PLSR) model construction) we observed improvements in the linearity for test set predictions, as well as lower errors in test set predictions, when isotope internal standards were used during SERS for both deuterated tryptophan as well as 13C caffeine. This work is an extension of and a natural progression from our earlier studies. By exploring additional analytes of interest, allowing for the assessment of the different types of stable isotopes as internal standards, and demonstrating the transfer/robustness of isotopologues for use with SERS, we believe this approach could be readily extended to other biologically-relevant compounds.

It is clear that investigating how bacterial cells work by analysing their functional roles in microbial communities is very important in environmental, clinical and industrial microbiology. The benefits of linking genes to their respective functions include the reliable identification of the causative agents of various diseases, which would permit appropriate and timely treatment in healthcare systems. In industrial and municipal wastewater treatment and management, such knowledge may allow for the manipulation of microbial communities, such as through bioaugmentation, in order to improve the efficiency and effectiveness of bioremediation processes. Stable isotope probing coupled with identification techniques has emerged to be a potentially reliable tool for the discrimination, identification and characterization of bacteria at community and single cell levels, knowledge which can be utilized to link microbially mediated bioprocesses to phylogeny. Development of the surface-enhanced Raman scattering (SERS) technique offers an exciting alternative to the Raman and Fourier-transform infrared spectroscopic techniques in understanding the metabolic processes of microorganismsin situ. SERS employing Ag and Au nanoparticles can significantly enhance the Raman signal, making it an exciting candidate for the analysis of the cellular components of microorganisms. In this study,Escherichia colicells were cultivated in minimal medium containing different ratios of12C/13C glucose and/or14N/15N ammonium chloride as the only carbon and nitrogen sources respectively, with the overall final concentrations of these substrates being constant. After growth, theE. colicells were analyzed with SERS employing anin situsynthesis of Ag nanoparticles. This novel investigation of the SERS spectral data with multivariate chemometrics demonstrated clear clusters which could be correlated to the SERS spectral shifts of biomolecules from cells grown and hence labelled with13C and15N atoms. These shifts reflect the isotopic content of the bacteria and quantification of the isotope levels could be established using chemometrics based on partial least squares regression.