Scientific poster presented at the conference ASM Microbe. Metagenomic analyses are crucial to study the diversity, activity and dynamics of uncultivable microbes. An integral part in the analyses is the polymerase chain reaction (PCR), which can also present a bottleneck, such as when amplifying parts of 16S rRNA genes. Often in agarose electrophoresis gels we see smeared DNA bands of wrong size, which are considered to be non-specific PCR errors and are eliminated. However, if these amplicons are not errors, then a part of the sample’s true diversity is lost. Therefore, we explored if band smearing after PCR is in fact caused by imperfectly paired strands of the amplified DNA. Using synthetic oligonucleotides that mimic 16S rRNA in PCR, we determined that the amount of smear in agarose gels was proportional to DNA sequence heterogeneity of the 16S rRNA variable regions. Since in denaturing alkaline gels, lack of smear showed that amplified DNA had a uniform size, we suspected that two separate groups of structures had formed - correctly and imperfectly paired DNA strands. This was confirmed, by isolating and sequencing the two groups of structures using a newly developed electroelution procedure and characterizing the pairing of the sequenced DNA strands using a bioinformatics approach. When amplifying highly heterogeneous target DNA, such as 16S rRNA, imperfect pairing of the amplified DNA can lead to band smearing in agarose gels, which is not an indicator of low specificity of the PCR. Since the smear in agarose gels is only a structural part of the correctly amplified DNA, it carries important information about the richness and diversity of the analysed microbial communities. Incorrect handling of 16S rRNA samples, e.g. eliminating smearing by increasing PCR amplification stringency or excision of amplified DNA, thus leads to an underestimation of the richness and diversity of microbial species.
COBISS.SI-ID: 1539178692
Scientific poster presented at the conference ASM Microbe. Increased incidence of antimicrobial resistance (AMR) in bacteria has raised global awareness and will continue to do so, due to the decreased rate of introduction of new antibiotics. One approach to treat AMR infections is to use the most appropriate antibiotic combinations that inhibit the transfer of AMR genes, since inappropriate treatments increase AMR incidence. In human pathogens, AMR genes spread frequently by transfer of mobile conjugative plasmids from the large environmental AMR pool. Each plasmid can be hosted in a particular repertoire of appropriate bacterial hosts, characterized by the plasmid’s MOB group. Currently to determine the MOB group, a mobile plasmid must be sequenced, the sequence of its relaxase must be determined and conjugation experiments must performed. We have presently considered the following. The most important components for plasmid transfer are oriT regions and conjugative relaxases. The oriT site is a substrate for the relaxase, which nicks the DNA and initiates transfer. Since each relaxase is specific to one type of oriT, DNA in each oriT region must have specific physicochemical properties that most efficiently attract and enable activity of the particular relaxase. We thus developed a bioinformatic framework, incorporating prediction of DNA physicochemical properties of oriT regions according to current models and machine learning algorithms. Based on our computational analysis of physicochemical properties of oriT regions, we were able to successfully sort 64 mobile plasmids into their corresponding MOB groups (classification accuracy of 99%). By using this method we only needed to obtain the identity of a DNA sequence up to 240 bp long, with which the most probable hosts can be determined. In the future, this approach will help clinicians determine the right antibiotic treatment based on the present mobile elements and their host ranges, as well as develop new approaches for inhibition of horizontal AMR gene transfer within polymicrobial infections.
COBISS.SI-ID: 1539178948
Whole-cell biosensors are still the method of choice when measuring bioavailable mercury, though their implementation in environmental monitoring is limited by low sensitivity, lack of portability and use of environmentally irrelevant bacteria. To address these issues, we have engineered a new luminescence-based whole-cell mercury biosensor, as part of a standalone fully automated portable device. Our method allows the incorporation of any environmentally relevant bacterial cell, which has been modified to translate the concentration of biologically available mercury into a dose-dependent luminescent signal. The use of environmentally relevant bacteria, Pseudomonas putida for fresh waters and Allivibrio fischeri for salt waters, demonstrated that environmental samples will not exhibit toxic effects, when appropriate microorganisms are implemented. Additionally, by assuring efficient aeration of the medium and thus sufficient oxygenation of sensor cells during generation of the luminescence signal, we obtained a clear dose dependent response and observed an increased sensitivity of the method up to 100-times (the LOD was determined to be as low as ~10 ng L1). Finally, using our automated device, we demonstrated that in the environment the biologically available fraction of mercury can (i) represent an important part of the total mercury content (40-70 %) and (ii) it can correspond to the changes of total mercury content, which results in higher bioavailability of mercury closer to the source of mercury contamination.
COBISS.SI-ID: 30060071