A novel exploration technique using the microbial fingerprint of shallow sediment to detect hydrocarbon microseepage and predict hydrocarbon charge — An Argentinian case study

The fundamental assumption of many successful geochemical and geomicrobial technologies developed in the last 80 years is that hydrocarbons leak from subsurface accumulations vertically to the surface. Driven by buoyancy, the process involves sufficiently large volumes directly measurable or indirectly inferable from their surface expressions. Even when the additional hydrocarbons are not measurable, their presence slightly changes the environment, where complex microbial communities live, and acts as an evolutionary constraint on their development. Since the ecology of this ecosystem is very complicated, we propose to use the full-microbiome analysis of the shallow sediments samples instead of targeting a selected number of known species, and the use of machine learning for uncovering the meaningful correlations in these data. We achieve this by sequencing the microbial biomass and generating its “DNA fingerprint”, and by analyzing the abundance and distribution of the microbes over the dataset. The proposed technology uses machine learning as an accurate tool for determining the detailed interactions among the various microorganisms and their environment in the presence or absence of hydrocarbons, thus overcoming data complexity. In a proof-of-technology study, we have taken more than 1000 samples in the Neuqu謠Basin in Argentina over three distinct areas, namely, an oil field, a gas field, and a dry location outside the basin, and created several successful predictive models. A subset of randomly selected samples was kept outside of the training set and blinded by the client operator, providing the means for objectively validating the prediction performance of this methodology. Uncovering the blinded dataset after estimating the prospectivity revealed that most of these samples were correctly predicted. This very encouraging result shows that analyzing the microbial ecosystem in the shallow sediment can be an additional de-risking method for assessing hydrocarbon prospects and improving the Probability Of Success(POS) of a drilling campaign.

Prevalence and Genetic Relationship of Predominant Escherichia coli Serotypes Isolated from Poultry, Wild Animals, and Environment in the Mekong Delta, Vietnam

Avian pathogenic Escherichia coli (APEC) is the main causative agent of avian colibacillosis, which is an important systemic disease of profound economic and clinical consequences for the poultry industry worldwide. In this study, 975 E. coli strains were isolated from 2,169 samples collected from cloacal swabs of chickens, in-farm wild animals (ants, geckos, flies, and rats), and environment. The highest proportion of E. coli isolation was obtained from chicken cloacal swabs with 71.05% (95% confidence interval (CI) 66.69–75.05%) followed by the proportions of 38.15% (95% CI 35.41–40.97%) and 38.11% (95% CI 34.15–42.24%) from wild animals or environment, respectively. Distribution of O-antigen serotypes of the E. coli isolates, including O1, O2, O18, and O78, was determined by PCR. The most predominant serotype was O18 (10.56%) followed by O2 (9.44%), O1 (7.79%), and O78 (6.56%). Of note, serotype O18 was more likely distributed in the examined wild animals, especially in geckos. Polymorphic DNA fingerprints, generated by ERIC-PCR, of representative E. coli strains of each serotype revealed genetic heterogeneity of the examined E. coli, and O18 was more divergent with 63 clusters formed from 66 isolates. Furthermore, several E. coli strains from different sample sources shared high DNA fingerprint relatedness, suggesting that there exists complex transmission of E. coli from chickens to wild animals and environment and vice versa in poultry husbandry settings. Although pathotypes of the examined E. coli were not determined in this study, our results provided important findings of epidemiological and genetic characteristics of E. coli in the Mekong Delta and highlighted the prerequisite of stricter biocontainment to reduce the prevalence and consequences of APEC in poultry production.

Evaluation and Characterization of High Yielding Cassava Mosaic Resistant Variety YTP2

The cassava variety YTP2 (Me 681) has been developed through selection from Thondamuthur type at Tapioca and Castor Research Station, TNAU, Yethapur. The performance of YTP2 in the Adaptive Research Trial (ART) and On Farm Trial (OFT) in the farmer’s field inferred that this new variety is well adapted to cassava growing districts of Tamil Nadu. In addition to the above, YTP2 was found to be resistant to cassava mosaic disease incidence (CMD). Plants are erect, medium growing and non-branching type and suitable for growing under irrigated and rainfed conditions. The internodal length is shorter and the leaf size is medium with sufficient canopy. The leaves of the plants droop down to reduce the transpiration loss which is more advantageous to overcome or escape from drought and heat stress during summer season. It is a dual purpose variety wherein the tubers contain high starch content which is much favourable for the manufacture of starch, sago and also suited for table purpose. The overall performance of this variety showed higher tuber yield (42.20 t ha-1) and starch content (28.40%) which is 15.94% and 18.20% increase over the check varieties YTP1 and H226 respectively. The results of DNA fingerprint data involving SSR markers (SSRY235, NS169 and NS928) showed that it is genetically distinct from the existing commercial varieties viz., YTP1, H226 and Sree Athulya.

A High-Performance Database Management System for Managing and Analyzing Large-Scale SNP Data in Plant Genotyping and Breeding Applications

A DNA fingerprint database is an efficient, stable, and automated tool for plant molecular research that can provide comprehensive technical support for multiple fields of study, such as pan-genome analysis and crop breeding. However, constructing a DNA fingerprint database for plants requires significant resources for data output, storage, analysis, and quality control. Large amounts of heterogeneous data must be processed efficiently and accurately. Thus, we developed plant SNP database management system (PSNPdms) using an open-source web server and free software that is compatible with single nucleotide polymorphism (SNP), insertion–deletion (InDel) markers, Kompetitive Allele Specific PCR (KASP), SNP array platforms, and 23 species. It fully integrates with the KASP platform and allows for graphical presentation and modification of KASP data. The system has a simple, efficient, and versatile laboratory personnel management structure that adapts to complex and changing experimental needs with a simple workflow process. PSNPdms internally provides effective support for data quality control through multiple dimensions, such as the standardized experimental design, standard reference samples, fingerprint statistical selection algorithm, and raw data correlation queries. In addition, we developed a fingerprint-merging algorithm to solve the problem of merging fingerprints of mixed samples and single samples in plant detection, providing unique standard fingerprints of each plant species for construction of a standard DNA fingerprint database. Different laboratories can use the system to generate fingerprint packages for data interaction and sharing. In addition, we integrated genetic analysis into the system to enable drawing and downloading of dendrograms. PSNPdms has been widely used by 23 institutions and has proven to be a stable and effective system for sharing data and performing genetic analysis. Interested researchers are required to adapt and further develop the system.

Screening of 200 Core SNPs and the Construction of a Systematic SNP-DNA Standard Fingerprint Database with More Than 20,000 Maize Varieties

To strengthen the management of maize varieties and the protection of intellectual property rights to new varieties, we constructed a comprehensive single nucleotide polymorphism (SNP)-DNA standard fingerprint database of 20,075 materials covering nationally and provincially approved maize hybrid lines, hybridized combinations, and inbred lines. The database was based on 200 core SNPs selected from 60 K SNPs distributed in intragenic regions, including 106 (53.0%) located in exons. Average minor allele frequencies (MAF) of the 200 SNPs in 6755 maize hybrids, 7837 hybridized combinations, and 3478 inbred lines were 0.385, 0.350, and 0.378, respectively, with corresponding average polymorphism information content (PIC) values of 0.354, 0.335, and 0.351. Heterozygous genotype frequencies of maize hybrids, hybridized combinations, and inbred lines averaged 0.48, 0.47, and 0.012, respectively. The number of different loci in the three different maize groups ranged from one up to 164, 160, and 140, respectively. The percentage of different SNPs within 5% (the number of difference SNPs is less than 10) accounted for 0.013%, 0.011%, and 0.030% among pairwise comparisons of samples within hybrid lines, hybridized combinations and inbred lines, respectively. Genetic distances between varieties based on the 200 core SNPs were highly correlated with those obtained using 60 K SNPs, with a correlation coefficient of 0.82 and 0.87 in in inbred and hybrid lines, respectively. The maize SNP-DNA fingerprint database established in this study can play an important role in variety authentication, purity determination and the protection of variety rights, thereby providing reliable, comprehensive data support for use in the seed industry.

Application of Microsatellite and Inter Simple Sequence Markers for Certifying Trueness to Type of Grafted Ramets and Clustering of Persian Walnut Varieties

The utility of seventeen Microsatellite (SSR) markers and fifteen inter simple sequence repeats (ISSR) markers for the identification of twenty eight ramets of 11 varieties of walnut (Juglans regia) was explored. Thirty nine individual genomes were screened using 61 and 38 scorable fragments from SSR and ISSR markers, respectively. The least polymorphic SSR locus was WGA004 (two alleles) and the most polymorphic (5 alleles) was WGA276. Polymorphism information content values ranged from 0.08 (WGA004) to 0.43 (WGA032) in SSR markers and from 0.11 (AGA (AC)7) to 0.49 (CAC(TGT)5) in ISSR markers, with an average of 0.29 and 0.19, respectively. In most cases, grafted varieties with identical names also had the same microsatellites profile. The principal coordinate analysis and clustering (UPGMA) based on the combined marker set emphasized two failures in grafting or off-types, ramets identified as Serr 4 (S4) and Vina 1 (V1). The presence of two off-type ramets in the walnut research orchard emphasizes the importance of using molecular certification for proving true-to-type of walnut orchards. Using 13 polymorphic SSRs, we tabulated a DNA fingerprint chart of 11 walnut varieties. Except for ‘Chandler’, each cultivar could be distinguished using a combination of only two SSR loci. The 13 SSRs markers evaluated in this study could be used in future to identify clones produced from the varieties.

Police cooperation and exchange of information under the EU–UK Trade and Cooperation Agreement

Brexit has led to a realignment of police cooperation and information exchange between the EU and the UK. This has been affected by Titles II-V and IX of Part III of the Trade and Cooperation Agreement. The terms governing the exchange of DNA, fingerprint and vehicle registration data, the transfer and processing of passenger name record data, cooperation on operational information, membership of Europol and the exchange of criminal record information are henceforth governed by that instrument. This article describes the changes and comments upon how future EU-UK police cooperation may be impacted.

Variability of Chemical Profile in Almonds (Prunus dulcis) of Different Cultivars and Origins

Almonds show a great variability in their chemical composition. This variability is a result of the existence of a diverse range of almond cultivars, the self-incompatibility of most almond cultivars, and the heterogeneous harvesting conditions found around the different locations where almons are grown. In the last years, the discrimination among almond cultivars has been the focal point of some research studies to avoid fraud in protected geographical indications in almond products and also for selecting the best cultivars for a specific food application or the most interesting ones from a nutritional point of view. In this work, a revision of the recent research works related to the chemical characterization and classification of almond cultivars from different geographical origins has been carried out. The content of macronutrients, tocopherols, phytosterols, polyphenols, minerals, amino acids, and volatile compounds together with DNA fingerprint have been reported as possible cultivar and origin markers. The analysis of the results showed that no individual almond compound could be considered a universal biomarker to find differences among different almond cultivars. Hence, an adequate selection of variables or the employment of metabolomics and the application of multivariate statistical techniques is necessary when classification studies are carried out to obtain valuable results. Meanwhile, DNA fingerprinting is the perfect tool for compared cultivars based on their genetic origin.

Multiple clones of colistin-resistant Salmonella enterica carrying mcr-1 plasmids in meat products and patients in Northern Thailand

Salmonella spp. is an important foodborne pathogen associated with consumption of contaminated food, especially livestock products. Antimicrobial resistance (AMR) in Salmonella has been reported globally and increasing AMR in food production is a major public health issue worldwide. The objective of this study was to describe the genetic relatedness among Salmonella enterica isolates, which displayed identical DNA fingerprint profiles. Ten S. enterica isolates were selected from meat and human cases with an identical rep-PCR profile of serovars Rissen (n=4), Weltevreden (n=4), and Stanley (n=2). We used long-read whole genome sequencing (WGS) on the MinION sequencing platform to type isolates and investigate in silico the presence of specific AMR genes. Antimicrobial susceptibility testing was tested by disk diffusion and gradient diffusion method to corroborate the AMR phenotype. Multidrug resistance and resistance to more than one antimicrobial agent were observed in eight and nine isolates, respectively. Resistance to colistin with an accompanying mcr-1 gene was observed among the Salmonella isolates. The analysis of core genome and whole genome MLST revealed that the Salmonella from meat and human salmonellosis were closely genetic related. Hence, it could be concluded that meat is one of the important sources for Salmonella infection in human.HighlightsColistin resistance detected in 2 clones from 2 different Salmonella enterica serovars (Rissen and Weltevreden) with accompanying plasmid-borne mcr-1 gene from the food production chain and human clinical salmonellosis.High prevalence of multidrug resistant isolates and resistance to more than one antimicrobial agent.MinION has potential for mobile, rapid and accurate application in veterinary genomic epidemiology studies.