Building the taxonomic profile of the Riniaie Marwah hot spring of Kishtwar in Jammu and Kashmir: the first high-throughput sequencing-based metagenome study

Background and Objectives

Rinaie Marwah hot spring Kishtwar (RMHSK) is one of the geothermal springs located at 33°51'51″N 75°32'07″E with an elevation of 2134 meters above sea level in Jammu and Kashmir, India. We aimed to study the microbial diversity of this geothermal spring using metagenomics.

Materials and Methods

In the present study, physiochemical parameters including temperature (65-75°C), pH (6. 9-8. 8), hardness (250 ppm), and mineral content was measured along with the microbial diversity using Illumina MiSeq metagenome-based 16s amplicon sequencing (V3-V4). The sequence reads were classified taxonomically into 31 phyla, 71 classes, 152 orders, 256 families, 410 genus, and 665 species. QIIME 2 (Quantitative Insights into Microbial Ecology), an extensible, powerful, and decentralized analytical tool, was used for taxonomic analysis.

Results

Bacteroidota (32. 57%) was the dominant phylum, Bacteroidia (32. 51%) the dominant class, Bacteroidales (16. 6%) the dominant order, and Lentimicrobiaceae (14. 23%) was the dominant family per the abundance analysis. Shannon (2. 28) and Chao 1 (87. 0) diversity indices support the existence of higher microbial diversity in RMHSK (50717 OTUs).

Conclusion

The microbial diversity of RMHSK is reported for the first time through a metagenomic study. Identification of microorganisms with characteristics that are relevant to industries.

Pharmacovariome scanning using whole pharmacogene resequencing coupled with deep computational analysis and machine learning for clinical pharmacogenomics

BACKGROUND

This pilot study aims to identify and functionally assess pharmacovariants in whole exome sequencing data. While detection of known variants has benefited from pharmacogenomic-dedicated bioinformatics tools before, in this paper we have tested novel deep computational analysis in addition to artificial intelligence as possible approaches for functional analysis of unknown markers within less studied drug-related genes.

METHODS

Pharmacovariants from 1800 drug-related genes from 100 WES data files underwent (a) deep computational analysis by eight bioinformatic algorithms (overall containing 23 tools) and (b) random forest (RF) classifier as the machine learning (ML) approach separately. ML model efficiency was calculated by internal and external cross-validation during recursive feature elimination. Protein modelling was also performed for predicted highly damaging variants with lower frequencies. Genotype-phenotype correlations were implemented for top selected variants in terms of highest possibility of being damaging.

RESULTS

Five deleterious pharmacovariants in the RYR1, POLG, ANXA11, CCNH, and CDH23 genes identified in step (a) and subsequent analysis displayed high impact on drug-related phenotypes. Also, the utilization of recursive feature elimination achieved a subset of 175 malfunction pharmacovariants in 135 drug-related genes that were used by the RF model with fivefold internal cross-validation, resulting in an area under the curve of 0.9736842 with an average accuracy of 0.9818 (95% CI: 0.89, 0.99) on predicting whether a carrying individuals will develop adverse drug reactions or not. However, the external cross-validation of the same model indicated a possible false positive result when dealing with a low number of observations, as only 60 important variants in 49 genes were displayed, giving an AUC of 0.5384848 with an average accuracy of 0.9512 (95% CI: 0.83, 0.99).

CONCLUSION

While there are some technologies for functionally assess not-interpreted pharmacovariants, there is still an essential need for the development of tools, methods, and algorithms which are able to provide a functional prediction for every single pharmacovariant in both large-scale datasets and small cohorts. Our approaches may bring new insights for choosing the right computational assessment algorithms out of high throughput DNA sequencing data from small cohorts to be used for personalized drug therapy implementation.

Adaption of microbial communities to the hostile environment in the Doce River after the collapse of two iron ore tailing dams

In November 2015, two iron ore tailing dams collapsed in the city of Mariana, Brazil. The dams' collapse generated a wave of approximately 50 million m³ of a mixture of mining waste and water. It was a major environmental tragedy in Brazilian history, which damaged rivers, and cities 660 km away in the Doce River basin until it reached the ocean coast. Shortly after the incident, several reports informed that the concentration of metals in the water was above acceptable legal limits under Brazilian laws. Here the microbial communities in samples of water, mud, foam, and rhizosphere of Eichhornia from Doce River were analyzed for 16S and 18S rRNA-based amplicon sequencing, along with microbial isolation, chemical and mineralogical analyses. Samples were collected one month and thirteen months after the collapse. Prokaryotic communities from mud shifted drastically over time (33% Bray-Curtis similarity), while water samples were more similar (63% Bray-Curtis similarity) in the same period. After 12 months, mud samples remained with high levels of heavy metals and a reduction in the diversity of microeukaryotes was detected. Amoebozoans increased in mud samples, reaching 49% of microeukaryote abundance, with Discosea and Lobosa groups being the most abundant. The microbial communities’ structure in mud samples changed adapting to the new environment condition. The characterization of microbial communities and metal-tolerant organisms from such impacted environments is essential for understanding the ecological consequences of massive anthropogenic impacts and strategies for the restoration of contaminated sites such as the Doce River.

Impact of nutrient addition on diversity and fate of fecal bacteria

Understanding the variations in the microorganisms associated with human fecal pollution in different types of water is necessary to manage water quality and predict human health risks. Using an Illumina sequencing method, we investigated variations in the fecal bacteria originating from fresh human feces and their decay trends in nutrient-supplemented water and natural river water. Nutrient addition contributed to the growth of heterotrophic bacteria like Comamonadaceae, Cytophagaceae, and Sphingobacteriaceae, but led to lower concentrations for Bacteroidaceae, Lachnospiraceae, and Ruminococcaceae. This result suggests that the utilization of nutrients by high-activity bacteria may suppress other bacteria via depletion of the available nutrient resources. As we did not observe proliferation of Bacteroidales, Lactobacillales, Clostridiales, or Ruminococcaceae in either supplemented or river water, we consider these groups suitable for use as indicators to determine the level of fecal pollution. Moreover, we tested the persistence of Bacteroidales markers, including general-Bacteroidales marker GenBac and human-specific Bacteroidales marker qHS601, by quantitative PCR. We observed similar trends in the decay of the Bacteroidales markers GenBac and qHS601 in the nutrient-supplemented water and natural river water, and the high R² values of the GenBac (R²_{nutrient-supplemented} = 0.93, R²_{natural river} = 0.81) and qHS601 (R²_{nutrient-supplemented} = 0.93, R²_{natural river} = 0.91) suggests they are a good fit for the first-order decay model. We also found stronger correlations between the markers and potential pathogenic anaerobes in the different types of water, demonstrating the validity of the use of GenBac and qHS601 from Bacteroidales for the identification of human-associated pollution sources.

Two listeria outbreaks caused by smoked fish consumption-using whole-genome sequencing for outbreak investigations

Listeria monocytogenes may contaminate and persist in food production facilities and cause repeated, seemingly sporadic, illnesses over extended periods of time. We report on the investigation of two such concurrent outbreaks. We compared patient isolates and available isolates from foods and food production facilities by use of whole-genome sequencing and subsequent multilocus sequence type and single nucleotide polymorphism analysis. Outbreak cases shared outbreak strains, defined as Listeria monocytogenes isolates belonging to the same sequence type with fewer than five single nucleotide polymorphism differences. We performed routine food consumption interviews of L. monocytogenes patients and compared outbreak cases with sporadic cases. Two outbreaks were defined, each consisting of ten outbreak cases in the period 2013-15. Seven outbreak cases and a fetus in gestational week 38 died. Listeria monocytogenes isolates from cold smoked or gravad fish products or their two respective production environments were repeatedly found to belong to the outbreak strains. Outbreak cases more often than sporadic cases stated that they consumed the relevant fish products, odds ratio 10.7. Routine collection and typing of food isolates was key to solving the outbreaks. Furthermore, these outbreaks illustrate the value of whole-genome sequencing for outbreak definition and investigation. Whole-genome sequencing combined with epidemiological investigations provided the discriminatory power to recognize low-intensity, extended time-period outbreaks and link them to food products from two different contaminated production facilities with sufficient strength for food authorities to intervene on. Cold smoked and gravad fish constitute risk products and may be responsible for more listeriosis cases than previously recognized.