Current Viewpoint on Female Urogenital Microbiome-The Cause or the Consequence?

An increasing amount of evidence implies that native microbiota is a constituent part of a healthy urinary tract (UT), making it an ecosystem on its own. What is still not clear is whether the origin of the urinary microbial community is the indirect consequence of the more abundant gut microbiota or a more distinct separation exists between these two systems. Another area of uncertainty is the existence of a link between the shifts in UT microbial composition and both the onset and persistence of cystitis symptoms. Cystitis is one of the most common reasons for antimicrobial drugs prescriptions in primary and secondary care and an important contributor to the problem of antimicrobial resistance. Despite this fact, we still have trouble distinguishing whether the primary cause of the majority of cystitis cases is a single pathogen overgrowth or a systemic disorder affecting the entire urinary microbiota. There is an increasing trend in studies monitoring changes and dynamics of UT microbiota, but this field of research is still in its infancy. Using NGS and bioinformatics, it is possible to obtain microbiota taxonomic profiles directly from urine samples, which can provide a window into microbial diversity (or the lack of) underlying each patient's cystitis symptoms. However, while microbiota refers to the living collection of microorganisms, an interchangeably used term microbiome referring to the genetic material of the microbiota is more often used in conjunction with sequencing data. It is this vast amount of sequences, which are truly "Big Data", that allow us to create models that describe interactions between different species contributing to an UT ecosystem, when coupled with machine-learning techniques. Although in a simplified predator-prey form these multi-species interaction models have the potential to further validate or disprove current beliefs; whether it is the presence or the absence of particular key players in a UT microbial ecosystem, the exact cause or consequence of the otherwise unknown etiology in the majority of cystitis cases. These insights might prove to be vital in our ongoing struggle against pathogen resistance and offer us new and promising clinical markers.

Application of Causality Modelling for Prediction of Molecular Properties for Textile Dyes Degradation by LPMO.

The textile industry is one of the largest water-polluting industries in the world. Due to an increased application of chromophores and a more frequent presence in wastewaters, the need for an ecologically favorable dye degradation process emerged. To predict the decolorization rate of textile dyes with Lytic polysaccharide monooxygenase (LPMO), we developed, validated, and utilized the molecular descriptor structural causality model (SCM) based on the decision tree algorithm (DTM). Combining mathematical models and theories with decolorization experiments, we have elucidated the most important molecular properties of the dyes and confirm the accuracy of SCM model results. Besides the potential utilization of the developed model in the treatment of textile dye-containing wastewater, the model is a good base for the prediction of the molecular properties of the molecule. This is important for selecting chromophores as the reagents in determining LPMO activities. Dyes with azo- or triarylmethane groups are good candidates for colorimetric LPMO assays and the determination of LPMO activity. An adequate methodology for the LPMO activity determination is an important step in the characterization of LPMO properties. Therefore, the SCM/DTM model validated with the 59 dyes molecules is a powerful tool in the selection of adequate chromophores as reagents in the LPMO activity determination and it could reduce experimentation in the screening experiments.

Modeling of Urinary Microbiota Associated With Cystitis.

A decade ago, when the Human Microbiome Project was starting, urinary tract (UT) was not included because the bladder and urine were considered to be sterile. Today, we are presented with evidence that healthy UT possesses native microbiota and any major event disrupting its "equilibrium" can impact the host also. This dysbiosis often leads to cystitis symptoms, which is the most frequent lower UT complaint, especially among women. Cystitis is one of the most common causes of antimicrobial drugs prescriptions in primary and secondary care and an important contributor to the problem of antimicrobial resistance. Despite this fact, we still have trouble distinguishing whether the primary cause of majority of cystitis cases is a single pathogen overgrowth, or a systemic disorder affecting entire UT microbiota. There are relatively few studies monitoring changes and dynamics of UT microbiota in cystitis patients, making this field of research still an unknown. In this study variations to the UT microbiota of cystitis patients were identified and microbial dynamics has been modeled. The microbial genetic profile of urine samples from 28 patients was analyzed by 16S rDNA Illumina sequencing and bioinformatics analysis. One patient with bacterial cystitis symptoms was prescribed therapy based on national guideline recommendations on antibacterial treatment of urinary tract infections (UTI) and UT microbiota change was monitored by 16S rDNA sequencing on 24 h basis during the entire therapy duration. The results of sequencing implied that a particular class of bacteria is associated with majority of cystitis cases in this study. The contributing role of this class of bacteria - Gammaproteobacteria, was further predicted by generalized Lotka-Volterra modeling (gLVM). Longitudinal microbiota insight obtained from a single patient under prescribed antimicrobial therapy revealed rapid and extensive changes in microbial composition and emphasized the need for current guidelines revision in regards to therapy duration. Models based on gLVM indicated protective role of two taxonomic classes of bacteria, Actinobacteria and Bacteroidia class, which appear to actively suppress pathogen overgrowth.

Identification of pathogens from native urine samples by MALDI-TOF/TOF tandem mass spectrometry.

BACKGROUND: Reliable high-throughput microbial pathogen identification in human urine samples is crucial for patients with cystitis symptoms. Currently employed methods are time-consuming and could lead to unnecessary or inadequate antibiotic treatment. Purpose of this study was to assess the potential of mass spectrometry for uropathogen identification from a native urine sample. METHODS: In total, 16 urine samples having more than 105 CFU/mL were collected from clinical outpatients. These samples were analysed using standard urine culture methods, followed by 16S rRNA gene sequencing serving as control and here described culture-independent MALDI-TOF/TOF MS method being tested. RESULTS: Here we present advantages and disadvantages of bottom-up proteomics, using MALDI-TOF/TOF tandem mass spectrometry, for culture-independent identification of uropathogens (e.g. directly from urine samples). The direct approach provided reliable identification of bacteria at the genus level in monobacterial samples. Taxonomic identifications obtained by proteomics were compared both to standard urine culture test used in clinics and genomic test based on 16S rRNA sequencing. CONCLUSIONS: Our findings indicate that mass spectrometry has great potential as a reliable high-throughput tool for microbial pathogen identification in human urine samples. In this case, the MALDI-TOF/TOF, was used as an analytical tool for the determination of bacteria in urine samples, and the results obtained emphasize high importance of storage conditions and sample preparation method impacting reliability of MS2 data analysis. The proposed method is simple enough to be utilized in existing clinical settings and is highly suitable for suspected single organism infectious etiologies. Further research is required in order to identify pathogens in polymicrobial urine samples.

Microreactor Production by PolyJet Matrix 3D-Printing Technology: Hydrodynamic Characterization§.

This work investigates the methodology of producing a 3D-printed microreactor from the acrylic resin by PolyJet Matrix process. The PolyJet Matrix technology employs different materials or their combinations to generate 3D-printed structures, from small ones to complex geometries, with different material properties. Experimental and numerical methods served for the evaluation of the geometry and production of the microreactor and its hydrodynamic characterization. The operational limits of the single-phase flow in the microchannels, further improvements and possible applications of the microreactor were assessed based on the hydrodynamic characterization.

Fungal secretomes enhance sugar beet pulp hydrolysis.

The recalcitrance of lignocellulose makes enzymatic hydrolysis of plant biomass for the production of second generation biofuels a major challenge. This work investigates an efficient and economic approach for the enzymatic hydrolysis of sugar beet pulp (SBP), which is a difficult to degrade, hemicellulose-rich by-product of the table sugar industry. Three fungal strains were grown on different substrates and the production of various extracellular hydrolytic and oxidative enzymes involved in pectin, hemicellulose, and cellulose breakdown were monitored. In a second step, the ability of the culture supernatants to hydrolyze thermally pretreated SBP was tested in batch experiments. The supernatant of Sclerotium rolfsii, a soil-borne facultative plant pathogen, was found to have the highest hydrolytic activity on SBP and was selected for further hydrolyzation experiments. A low enzyme load of 0.2 mg g(-1) protein from the culture supernatant was sufficient to hydrolyze a large fraction of the pectin and hemicelluloses present in SBP. The addition of Trichoderma reesei cellulase (1-17.5 mg g(-1) SBP) resulted in almost complete hydrolyzation of cellulose. It was found that the combination of pectinolytic, hemicellulolytic, and cellulolytic activities works synergistically on the complex SBP composite, and a combination of these hydrolytic enzymes is required to achieve a high degree of enzymatic SBP hydrolysis with a low enzyme load.

Integrated hydrolyzation and fermentation of sugar beet pulp to bioethanol.

Sugar beet pulp is an abundant industrial waste material that holds a great potential for bioethanol production owing to its high content of cellulose, hemicelluloses, and pectin. Its structural and chemical robustness limits the yield of fermentable sugars obtained by hydrolyzation and represents the main bottleneck for bioethanol production. Physical (ultrasound and thermal) pretreatment methods were tested and combined with enzymatic hydrolysis by cellulase and pectinase to evaluate the most efficient strategy. The optimized hydrolysis process was combined with a fermentation step using a Saccharomyces cerevisiae strain for ethanol production in a single-tank bioreactor. Optimal sugar beet pulp conversion was achieved at a concentration of 60 g/l (39% of dry weight) and a bioreactor stirrer speed of 960 rpm. The maximum ethanol yield was 0.1 g ethanol/g of dry weight (0.25 g ethanol/g total sugar content), the efficiency of ethanol production was 49%, and the productivity of the bioprocess was 0.29 g/l·h, respectively.