Merging FT-IR and NGS for simultaneous phenotypic and genotypic identification of pathogenic Candida species

Global biochemical profiling of fast-growing Antarctic bacteria isolated from meltwater ponds by high-throughput FTIR spectroscopy

Fourier transform infrared (FTIR) spectroscopy is a biophysical technique used for non-destructive biochemical profiling of biological samples. It can provide comprehensive information about the total cellular biochemical profile of microbial cells. In this study, FTIR spectroscopy was used to perform biochemical characterization of twenty-nine bacterial strains isolated from the Antarctic meltwater ponds. The bacteria were grown on two forms of brain heart infusion (BHI) medium: agar at six different temperatures (4, 10, 18, 25, 30, and 37°C) and on broth at 18°C. Multivariate data analysis approaches such as principal component analysis (PCA) and correlation analysis were used to study the difference in biochemical profiles induced by the cultivation conditions. The observed results indicated a strong correlation between FTIR spectra and the phylogenetic relationships among the studied bacteria. The most accurate taxonomy-aligned clustering was achieved with bacteria cultivated on agar. Cultivation on two forms of BHI medium provided biochemically different bacterial biomass. The impact of temperature on the total cellular biochemical profile of the studied bacteria was species-specific, however, similarly for all bacteria, lipid spectral region was the least affected while polysaccharide region was the most affected by different temperatures. The biggest temperature-triggered changes of the cell chemistry were detected for bacteria with a wide temperature tolerance such Pseudomonas lundensis strains and Acinetobacter lwoffii BIM B-1558.

Analysis and comparison of machine learning methods for species identification utilizing ATR-FTIR spectroscopy

Accurate identification of insect species holds paramount significance in diverse fields as it facilitates a comprehensive understanding of their ecological habits, distribution range, and impact on both the environment and humans. While morphological characteristics have traditionally been employed for species identification, the utilization of empty pupariums for this purpose remains relatively limited. In this study, ATR-FTIR was employed to acquire spectral information from empty pupariums of five fly species, subjecting the data to spectral pre-processing to obtain average spectra for preliminary analysis. Subsequently, PCA and OPLS-DA were utilized for clustering and classification. Notably, two wavebands (3000-2800 cm-1 and 1800-1300 cm-1) were found to be significant in distinguishing A. grahami. Further, we established three machine learning models, including SVM, KNN, and RF, to analyze spectra from different waveband groups. The biological fingerprint region (1800-1300 cm-1) demonstrated a substantial advantage in identifying empty puparium species. Remarkably, the SVM model exhibited an impressive accuracy of 100 % in identifying all five fly species. This study represents the first instance of employing infrared spectroscopy and machine learning methods for identifying insect species using empty pupariums, providing a robust research foundation for future investigations in this area.

Multi-resistant diarrheagenic Escherichia coli identified by FTIR and machine learning: a feasible strategy to improve the group classification

The identification of multidrug-resistant strains from E. coli species responsible for diarrhea in calves still faces many laboratory limitations and is necessary for adequately monitoring the microorganism spread and control. Then, there is a need to develop a screening tool for bacterial strain identification in microbiology laboratories, which must show easy implementation, fast response, and accurate results. The use of FTIR spectroscopy to identify microorganisms has been successfully demonstrated in the literature, including many bacterial strains; here, we explored the FTIR potential for multi-resistant E. coli identification. First, we applied principal component analysis to observe the group formation tendency; the first results showed no clustering tendency with a messy sample score distribution; then, we improved these results by adequately selecting the main principal components which most contribute to group separation. Finally, using machine learning algorithms, a predicting model showed 75% overall accuracy, demonstrating the method's viability as a screaming test for microorganism identification.

MinION Sequencing of Yeast Mock Communities To Assess the Effect of Databases and ITS-LSU Markers on the Reliability of Metabarcoding Analysis

Microbial communities play key roles both for humans and the environment. They are involved in ecosystem functions, maintaining their stability, and provide important services, such as carbon cycle and nitrogen cycle. Acting both as symbionts and as pathogens, description of the structure and composition of these communities is important. Metabarcoding uses ribosomal DNA (rDNA) (eukaryotic) or rRNA gene (prokaryotic) sequences for identification of species present in a site and measuring their abundance. This procedure requires several technical steps that could be source of bias producing a distorted view of the real community composition. In this work, we took advantage of an innovative "long-read" next-generation sequencing (NGS) technology (MinION) amplifying the DNA spanning from the internal transcribed spacer (ITS) to large subunit (LSU) that can be read simultaneously in this platform, providing more information than "short-read" systems. The experimental system consisted of six fungal mock communities composed of species present at various relative amounts to mimic natural situations characterized by predominant and low-frequency species. The influence of the sequencing platform (MinION and Illumina MiSeq) and the effect of different reference databases and marker sequences on metagenomic identification of species were evaluated. The results showed that the ITS-based database provided more accurate species identification than LSU. Furthermore, a procedure based on a preliminary identification with standard reference databases followed by the production of custom databases, including only the best outputs of the first step, is proposed. This additional step improved the estimate of species proportion of the mock communities and reduced the number of ghost species not really present in the simulated communities. IMPORTANCE Metagenomic analyses are fundamental in many research areas; therefore, improvement of methods and protocols for the description of microbial communities becomes more and more necessary. Long-read sequencing could be used for reducing biases due to the multicopy nature of rDNA sequences and short-read limitations. However, these novel technologies need to be assessed and standardized with controlled experiments, such as mock communities. The interest behind this work was to evaluate how long reads performed identification and quantification of species mixed in precise proportions and how the choice of database affects such analyses. Development of a pipeline that mitigates the effect of the barcoding sequences and the impact of the reference database on metagenomic analyses can help microbiome studies go one step further.

Multiscale spectroscopic analysis of lipids in dimorphic and oleaginous Mucor circinelloides accommodate sustainable targeted lipid production

BACKGROUND

Oleaginous fungi have versatile metabolism and able to transform a wide range of substrates into lipids, accounting up to 20-70% of their total cell mass. Therefore, oleaginous fungi are considered as an alternative source of lipids. Oleaginous fungi can accumulate mainly acyl glycerides and free fatty acids which are localized in lipid droplets. Some of the oleaginous fungi possessing promising lipid productivity are dimorphic and can exhibit three cell forms, flat hyphae, swollen hyphae and yeast-like cells. To develop sustainable targeted fungal lipid production, deep understanding of lipogenesis and lipid droplet chemistry in these cell forms is needed at multiscale level. In this study, we explored the potential of infrared spectroscopy techniques for examining lipid droplet formation and accumulation in different cell forms of the dimorphic and oleaginous fungus Mucor circinelloides.

RESULTS

Both transmission- and reflectance-based spectroscopy techniques are shown to be well suited for studying bulk fungal biomass. Exploring single cells with infrared microspectroscopy reveals differences in chemical profiles and, consequently, lipogenesis process, for different cell forms. Yeast-like cells of M. circinelloides exhibited the highest absorbance intensities for lipid-associated peaks in comparison to hyphae-like cell forms. Lipid-to-protein ratio, which is commonly used in IR spectroscopy to estimate lipid yield was the lowest in flat hyphae. Swollen hyphae are mainly composed of lipids and characterized by more uniform distribution of lipid-to-protein concentration. Yeast-like cells seem to be comprised mostly of lipids having the largest lipid-to-protein ratio among all studied cell forms. With infrared nanospectroscopy, variations in the ratios between lipid fractions triglycerides and free fatty acids and clear evidence of heterogeneity within and between lipid droplets are illustrated for the first time.

CONCLUSIONS

Vibrational spectroscopy techniques can provide comprehensive information on lipogenesis in dimorphic and oleaginous fungi at the levels of the bulk of cells, single cells and single lipid droplets. Unicellular spectra showed that various cell forms of M. circinelloides differs in the total lipid content and profile of the accumulated lipids, where yeast-like cells are the fatty ones and, therefore, could be considered as preferable cell form for producing lipid-rich biomass. Spectra of single lipid droplets showed an indication of possible droplet-to-droplet and within-droplet heterogeneity.

Next-Generation Sequencing Applications for the Study of Fungal Pathogens

Next-generation sequencing (NGS) has become a widely used technology in biological research. NGS applications for clinical pathogen detection have become vital technologies. It is increasingly common to perform fast, accurate, and specific detection of clinical specimens using NGS. Pathogenic fungi with high virulence and drug resistance cause life-threatening clinical infections. NGS has had a significant biotechnological impact on detecting bacteria and viruses but is not equally applicable to fungi. There is a particularly urgent clinical need to use NGS to help identify fungi causing infections and prevent negative impacts. This review summarizes current research on NGS applications for fungi and offers a visual method of fungal detection. With the development of NGS and solutions for overcoming sequencing limitations, we suggest clinicians test specimens as soon as possible when encountering infections of unknown cause, suspected infections in vital organs, or rapidly progressive disease.

Temperature- and Nutrients-Induced Phenotypic Changes of Antarctic Green Snow Bacteria Probed by High-Throughput FTIR Spectroscopy

Simple Summary

Green snow microorganisms play an important role in biogeochemical cycle and carbon sink processes and they can be a source of biotechnologically interesting cell factories. A wide temperature tolerance is a unique property of bacteria isolated from cold environments, which has received great attention in the last years. The present paper examines the growth and chemical profile flexibility for green snow bacteria exposed to different temperature and nutrient fluctuations. By applying high-throughput chemical phenotyping with FTIR spectroscopy we discovered chemical changes possessed by green snow bacteria when grown at high/low temperature and rich/minimal media.

Abstract

Temperature fluctuations and nutrient composition are the main parameters influencing green snow microbiome. In this study we investigated the influence of temperature and nutrient conditions on the growth and cellular chemical profile of bacteria isolated from green snow. Chemical profiling of the green snow bacteria was done by high-throughput FTIR spectroscopy combined with multivariate data analysis. We showed that temperature and nutrients fluctuations strongly affect growth ability and chemical profile of the green snow bacteria. The size of colonies for green snow bacteria grown at higher (25 °C) and lower (4 °C and 10 °C) than optimal temperature (18 °C) was smaller. All isolates grew on rich medium, and only 19 isolates were able to grow on synthetic minimal media. Lipid and mixed spectral regions showed to be phylogeny related. FTIR fingerprinting indicates that lipids are often affected by the temperature fluctuations. Growth on different media resulted in the change of the whole chemical profile, where lipids showed to be more affected than proteins and polysaccharides. Correlation analysis showed that nutrient composition is clearly strongly influencing chemical changes in the cells, followed by temperature.

Starch Rich Chlorella vulgaris: High-Throughput Screening and Up-Scale for Tailored Biomass Production

The use of microalgal starch has been studied in biorefinery frameworks to produce bioethanol or bioplastics, however, these products are currently not economically viable. Using starch-rich biomass as an ingredient in food applications is a novel way to create more value while expanding the product portfolio of the microalgal industry. Optimization of starch production in the food-approved species Chlorella vulgaris was the main objective of this study. High-throughput screening of biomass composition in response to multiple stressors was performed with FTIR spectroscopy. Nitrogen starvation was identified as an important factor for starch accumulation. Moreover, further studies were performed to assess the role of light distribution, investigating the role of photon supply rates in flat panel photobioreactors. Starch-rich biomass with up to 30% starch was achieved in cultures with low inoculation density (0.1 g L−1) and high irradiation (1800 µmol m−2 s−1). A final large-scale experiment was performed in 25 L tubular reactors, achieving a maximum of 44% starch in the biomass after 12 h in nitrogen starved conditions.

DNA barcoding of phytopathogens for disease diagnostics and bio-surveillance

DNA barcoding has proven to be a versatile tool for plant disease diagnostics in the genomics era. As the mass parallel and next generation sequencing techniques gained importance, the role of specific barcodes came under immense scrutiny. Identification and accurate classification of phytopathogens need a universal approach which has been the main application area of the concept of barcode. The present review entails a detailed description of the present status of barcode application in plant disease diagnostics. A case study on the application of Internal Transcribed Spacer (ITS) as barcode for Aspergillus and Fusarium spp. sheds light on the requirement of other potential candidates as barcodes for accurate identification. The challenges faced while barcoding novel pathogens have also been discussed with a comprehensive outline of integrating more recent technologies like meta-barcoding and genome skimming for detecting plant pathogens.

Early detection of drug-resistant Streptococcus pneumoniae and Haemophilus influenzae by quantitative flow cytometry

Early detection of drug resistance contributes to combating drug-resistant bacteria and improving patient outcomes. Microbial testing in the laboratory is essential for treating infectious diseases because it can provide critical information related to identifying pathogenic bacteria and their resistance profiles. Despite these clinical requirements, conventional phenotypic testing is time-consuming. Additionally, recent rapid drug resistance tests are not compatible with fastidious bacteria such as Streptococcus and Haemophilus species. In this study, we validated the feasibility of direct bacteria counting using highly sensitive quantitative flow cytometry. Furthermore, by combining flow cytometry and a nucleic acid intercalator, we constructed a highly sensitive method for counting viable fastidious bacteria. These are inherently difficult to measure due to interfering substances from nutrients contained in the medium. Based on the conventional broth microdilution method, our method acquired a few microliter samples in a time series from the same microplate well to exclude the growth curve inconsistency between the samples. Fluorescent staining and flow cytometry measurements were completed within 10 min. Therefore, this approach enabled us to determine antimicrobial resistance for these bacteria within a few hours. Highly sensitive quantitative flow cytometry presents a novel avenue for conducting rapid antimicrobial susceptibility tests.

Clinical characteristics and germline mutation spectrum of RB1 in Chinese patients with retinoblastoma: A dual-center study of 145 patients

Retinoblastoma (Rb) is the most common primary intraocular childhood malignancy and one of the main causes of blindness in children. In China, most tumors are diagnosed at an advanced stage and have relatively poor outcomes compared to developed countries. Here, we aimed to update the clinical manifestations and RB transcriptional corepressor 1 (RB1) mutation spectrum in Chinese Rb patients. Medical charts of 184 eyes in 145 Chinese Rb patients belonging to unrelated families were reviewed. Genomic DNA was isolated from peripheral blood of the patients and their parents. Mutation analysis of whole coding regions, promoter regions and flanking splice sites in the RB1 gene was performed. In addition, multiplex ligation-dependent probe amplification (MLPA) was done to detect gross aberrations. Germline RB1 mutations were observed in 37.2% (54/145) of Rb patients. RB1-mutated patients presented with earlier age of diagnosis (p = 0.019), with a significantly larger proportion of bilateral cases (p = <0.001) and secondary malignancies (p = 0.027) relative to those without RB1 mutations. For ocular clinical presentations, RB1-mutated retinoblastomas presented with a larger proportion of ectropion uveae (p = 0.017) and iris neovascularization (p = 0.001). These RB1 mutations comprised of 13 (24.1%) nonsense mutation, 13 (24.1%) splicing mutations, 11 (20.4%) frameshift deletions, 11 (20.4%) gross mutations, 3 (5.6%) missense mutations, 2 (3.7%) promoter mutations and 1 (1.9%) non-frameshift deletion. In addition, 8 novel RB1 mutations were identified. These germline RB1 mutations were not related to age at diagnosis or laterality. Here, we provide a comprehensive spectrum of RB1 germline mutations in Chinese Rb patients and describe correlations between RB1 mutations and clinical presentations. Our study also provides new evidence that will inform management and genetic counselling of Rb patients and families.

Isolation and characterization of fast-growing green snow bacteria from coastal East Antarctica

Snow microorganisms play a significant role in climate change and affecting the snow melting rate in the Arctic and Antarctic regions. While research on algae inhabiting green and red snow has been performed extensively, bacteria dwelling in this biotope have been studied to a much lesser extent. In this study, we performed 16S rRNA gene amplicon sequencing of two green snow samples collected from the coastal area of the eastern part of Antarctica and conducted genotypic and phenotypic profiling of 45 fast-growing bacteria isolated from these samples. 16S rRNA gene amplicon sequencing of two green snow samples showed that bacteria inhabiting these samples are mostly represented by families Burkholderiaceae (46.31%), Flavobacteriaceae (22.98%), and Pseudomonadaceae (17.66%). Identification of 45 fast-growing bacteria isolated from green snow was performed using 16S rRNA gene sequencing. We demonstrated that they belong to the phyla Actinobacteria and Proteobacteria, and are represented by the genera Arthrobacter, Cryobacterium, Leifsonia, Salinibacterium, Paeniglutamicibacter, Rhodococcus, Polaromonas, Pseudomonas, and Psychrobacter. Nearly all bacterial isolates exhibited various growth temperatures from 4°C to 25°C, and some isolates were characterized by a high level of enzymatic activity. Phenotyping using Fourier transform infrared (FTIR) spectroscopy revealed a possible accumulation of intracellular polymer polyhydroxyalkanoates (PHA) or lipids in some isolates. The bacteria showed different lipids/PHA and protein profiles. It was shown that lipid/PHA and protein spectral regions are the most discriminative for differentiating the isolates.

Occurrence of Yeasts in White-Brined Cheeses: Methodologies for Identification, Spoilage Potential and Good Manufacturing Practices

Yeasts are generally recognized as contaminants in the production of white-brined cheeses, such as Feta and Feta-type cheeses. The most predominant yeasts species are Debaryomyces hansenii, Geotrichum candidum, Kluyveromyces marxianus, Kluyveromyces lactis, Rhodotorula mucilaginosa, and Trichosporon spp. Although their spoilage potential varies at both species and strain levels, yeasts will, in case of excessive growth, present a microbiological hazard, effecting cheese quality. To evaluate the hazard and trace routes of contamination, the exact taxonomic classification of yeasts is required. Today, identification of dairy yeasts is mainly based on DNA sequencing, various genotyping techniques, and, to some extent, advanced phenotypic identification technologies. Even though these technologies are state of the art at the scientific level, they are only hardly implemented at the industrial level. Quality defects, caused by yeasts in white-brined cheese, are mainly linked to enzymatic activities and metabolism of fermentable carbohydrates, leading to production of metabolites (CO2, fatty acids, volatile compounds, amino acids, sulfur compounds, etc.) and resulting in off-flavors, texture softening, discoloration, and swelling of cheese packages. The proliferation of spoilage yeast depends on maturation and storage conditions at each specific dairy, product characteristics, nutrients availability, and interactions with the co-existing microorganisms. To prevent and control yeast contamination, different strategies based on the principles of HACCP and Good Manufacturing Practice (GMP) have been introduced in white-brined cheese production. These strategies include milk pasteurization, refrigeration, hygienic sanitation, air filtration, as well as aseptic and modified atmosphere packaging. Though a lot of research has been dedicated to yeasts in dairy products, the role of yeast contaminants, specifically in white-brined cheeses, is still insufficiently understood. This review aims to summarize the current knowledge on the identification of contaminant yeasts in white-brined cheeses, their occurrence and spoilage potential related to different varieties of white-brined cheeses, their interactions with other microorganisms, as well as guidelines used by dairies to prevent cheese contamination.

Do Metabolomics and Taxonomic Barcode Markers Tell the Same Story about the Evolution of Saccharomyces sensu stricto Complex in Fermentative Environments?

Yeast taxonomy was introduced based on the idea that physiological properties would help discriminate species, thus assuming a strong link between physiology and taxonomy. However, the instability of physiological characteristics within species configured them as not ideal markers for species delimitation, shading the importance of physiology and paving the way to the DNA-based taxonomy. The hypothesis of reconnecting taxonomy with specific traits from phylogenies has been successfully explored for Bacteria and Archaea, suggesting that a similar route can be traveled for yeasts. In this framework, thirteen single copy loci were used to investigate the predictability of complex Fourier Transform InfaRed spectroscopy (FTIR) and High-performance Liquid Chromatography–Mass Spectrometry (LC-MS) profiles of the four historical species of the Saccharomyces sensu stricto group, both on resting cells and under short-term ethanol stress. Our data show a significant connection between the taxonomy and physiology of these strains. Eight markers out of the thirteen tested displayed high correlation values with LC-MS profiles of cells in resting condition, confirming the low efficacy of FTIR in the identification of strains of closely related species. Conversely, most genetic markers displayed increasing trends of correlation with FTIR profiles as the ethanol concentration increased, according to their role in the cellular response to different type of stress.

Microcultivation and FTIR spectroscopy-based screening revealed a nutrient-induced co-production of high-value metabolites in oleaginous Mucoromycota fungi

Mucoromycota fungi possess a versatile metabolism and can utilize various substrates for production of industrially important products, such as lipids, chitin/chitosan, polyphosphates, pigments, alcohols and organic acids. However, as far as commercialisation is concerned, establishing industrial biotechnological processes based on Mucoromycota fungi is still challenging due to the high production costs compared to the final product value. Therefore, the development of co-production concept is highly desired since more than one valuable product could be produced at the time and the process has a potentially higher viability. To develop such biotechnological strategy, we applied a high throughput approach consisting of micro-titre cultivation and FTIR spectroscopy. This approach allows single-step biochemical fingerprinting of either fungal biomass or growth media without tedious extraction of metabolites. The influence of two types of nitrogen sources and different levels of inorganic phosphorus on the co-production of lipids, chitin/chitosan and polyphosphates for nine different oleaginous Mucoromycota fungi was evaluated. FTIR analysis of biochemical composition of Mucoromycota fungi and biomass yield showed that variation in inorganic phosphorus had higher effect when inorganic nitrogen source-ammonium sulphate-was used. It was observed that: (1) Umbelopsis vinacea reached almost double biomass yield compared to other strains when yeast extract was used as nitrogen source while phosphorus limitation had little effect on the biomass yield; (2) Mucor circinelloides, Rhizopus stolonifer, Amylomyces rouxii, Absidia glauca and Lichtheimia corymbifera overproduced chitin/chitosan under the low pH caused by the limitation of inorganic phosphorus; (3) Mucor circinelloides, Amylomyces rouxii, Rhizopus stolonifer and Absidia glauca were able to store polyphosphates in addition to lipids when high concentration of inorganic phosphorus was used; (4) the biomass and lipid yield of high-value lipid producers Mortierella alpina and Mortierella hyalina were significantly increased when high concentrations of inorganic phosphorus were combined with ammonium sulphate, while the same amount of inorganic phosphorus combined with yeast extract showed negative impact on the growth and lipid accumulation. FTIR spectroscopy revealed the co-production potential of several oleaginous Mucoromycota fungi forming lipids, chitin/chitosan and polyphosphates in a single cultivation process.

Extended multiplicative signal correction for FTIR spectral quality test and pre-processing of infrared imaging data

Spectral quality control is an important step in the analysis of infrared spectral data, however, often neglected in scientific literature. A frequently used quality test that was originally developed for infrared spectra of bacteria is provided by OPUS software from Bruker Optik GmbH. In this study, the OPUS quality test is applied to a large number of spectra of bacteria, yeasts and moulds and hyperspectral images of microorganisms. It is shown that the use of strict thresholds for parameters of the OPUS quality test leads to discarding too many spectra. A strategy for optimizing parameters thresholds of the OPUS quality test is provided and a novel approach for spectral quality testing based on extended multiplicative signal correction (EMSC) is suggested. For all the data sets considered in our study, the EMSC quality test is shown to be the best among different alternatives of OPUS quality test provided.

Metabolomic Alterations Do Not Induce Metabolic Burden in the Industrial Yeast M2n[pBKD2-Pccbgl1]-C1 Engineered by Multiple δ-Integration of a Fungal β-Glucosidase Gene

In the lignocellulosic yeast development, metabolic burden relates to redirection of resources from regular cellular activities toward the needs created by recombinant protein production. As a result, growth parameters may be greatly affected. Noteworthy, Saccharomyces cerevisiae M2n[pBKD2-Pccbgl1]-C1, previously developed by multiple δ-integration of the β-glucosidase BGL3, did not show any detectable metabolic burden. This work aims to test the hypothesis that the metabolic burden and the metabolomic perturbation induced by the δ-integration of a yeast strain, could differ significantly. The engineered strain was evaluated in terms of metabolic performances and metabolomic alterations in different conditions typical of the bioethanol industry. Results indicate that the multiple δ-integration did not affect the ability of the engineered strain to grow on different carbon sources and to tolerate increasing concentrations of ethanol and inhibitory compounds. Conversely, metabolomic profiles were significantly altered both under growing and stressing conditions, indicating a large extent of metabolic reshuffling involved in the maintenance of the metabolic homeostasis. Considering that four copies of BGL3 gene have been integrated without affecting any parental genes or promoter sequences, deeper studies are needed to unveil the mechanisms implied in these metabolomic changes, thus supporting the optimization of protein production in engineered strains.

Clinical characteristics and mutation Spectrum of NF1 in 12 Chinese families with orbital/periorbital plexiform Neurofibromatosis type 1

Background

Orbital/periorbital plexiform neurofibroma (OPPN) can compromise physical appearance and visual function. However, the clinical characteristics and NF1 mutation landscape in patients with heritable OPPN have not been reported.

Methods

The medical charts of 26 Chinese patients with OPPN from 12 families were reviewed. Mutation analysis of the entire coding region and flanking splice sites of the NF1 gene was performed using next-generation sequencing (NGS). Novel NF1 mutations were confirmed by Sanger sequencing.

Results

Compared to the parental generation, a significantly larger proportion of OPPN patients in the successive generation presented with earlier onset (p = 0.001), amblyopia (p = 0.034), motility disorders (p = 0.009) and bony orbital expansion (p = 0.019). Six novel NF1 mutations were identified in 11 (91.67%) families, including 6 (42.9%) single-base substitutions, 4 (28.5%) splicing mutations, 3 (21.4%) frameshift deletions, and 1 (7.14%) intron mutation.

Conclusions

The successive generation of OPPN patients presented with earlier onset and exhibited more severe ocular signs than did their parents or grandparents. Special attention should be paid to successive generations of OPPN patients. Considering that 6 mutations were novel, comprehensive NF1 mutation analysis is required or necessary or proposed for genetic counselling.

Electronic supplementary material

The online version of this article (10.1186/s12881-019-0877-9) contains supplementary material, which is available to authorized users.

Spectroscopic Characterization of Bovine, Avian and Johnin Purified Protein Derivative (PPD) with High-Throughput Fourier Transform InfraRed-Based Method

Tuberculins purified protein derivatives (PPDs) are obtained by precipitation from heat treated mycobacteria. PPDs are used in diagnosis of mycobacterial infections in humans and animals. Bovine PPD (PPDB) is obtained from Mycobacterium bovis (Mycobacterium tuberculosis complex), while Avian PPD (PPDA) and Johnin PPD (PPDJ) are extracted, respectively, from Mycobacterium avium and M. avium subsp. paratuberculosis (M. avium complex). PPDB and PPDA are used for bovine tuberculosis diagnosis, while PPDJ is experimentally used in the immunodiagnosis of paratuberculosis. Although PPDs date back to the 19th Century, limited knowledge about their composition is currently available. The goal of our study was to evaluate Fourier Transform InfraRed (FTIR) spectroscopy as a tool to differentiate PPDB, PPDA, and three PPDJs. The results highlighted that the three PPDs have specific profiles, correlated with phylogenetic characteristics of mycobacteria used for their production. This analysis is eligible as a specific tool for different PPDs batches characterization and for the assessment of their composition. The entire PPD production may be efficiently controlled, since the N content of each preparation is related to IR spectra, with a reference spectrum for each PPD and a standardized analysis protocol.

Whole-Organism Analysis by Vibrational Spectroscopy

Vibrational spectroscopy has contributed to the understanding of biological materials for many years. As the technology has advanced, the technique has been brought to bear on the analysis of whole organisms. Here, we discuss advanced and recently developed infrared and Raman spectroscopic instrumentation to whole-organism analysis. We highlight many of the recent contributions made in this relatively new area of spectroscopy, particularly addressing organisms associated with disease with emphasis on diagnosis and treatment. The application of vibrational spectroscopic techniques to entire organisms is still in its infancy, but new developments in imaging and chemometric processing will likely expand in the field in the near future.

FTIR and Raman Spectroscopy-Based Biochemical Profiling Reflects Genomic Diversity of Clinical Candida Isolates That May Be Useful for Diagnosis and Targeted Therapy of Candidiasis

Despite the fact that Candida albicans is documented to be the main cause of human candidiasis, non-C. albicans Candida (NCAC) species, such as Candida glabrata and Candida tropicalis, are also suggested to be implicated in the etiopathogenesis of opportunistic fungal infections. As biology, epidemiology, pathogenicity, and antifungal resistance of NCAC species may be affected as a result of genomic diversity and plasticity, rapid and unambiguous identification of Candida species in clinical samples is essential for proper diagnosis and therapy. In the present study, 25 clinical isolates of C. albicans, C. glabrata, and C. tropicalis species were characterized in terms of their karyotype patterns, DNA content, and biochemical features. Fourier transform infrared (FTIR) spectra- and Raman spectra-based molecular fingerprints corresponded to the diversity of chromosomal traits and DNA levels that provided correct species identification. Moreover, Raman spectroscopy was documented to be useful for the evaluation of ergosterol content that may be associated with azole resistance. Taken together, we found that vibrational spectroscopy-based biochemical profiling reflects the variability of chromosome patterns and DNA content of clinical Candida species isolates and may facilitate the diagnosis and targeted therapy of candidiasis.