The Microbiome: A Reservoir to Discover New Antimicrobials Agents

2020 ◽  
Vol 20 (14) ◽  
pp. 1291-1299
Author(s):  
Sébastien Boutin ◽  
Alexander H. Dalpke
Keyword(s):  
Data Mining ◽  
Next Generation Sequencing ◽  
Antimicrobial Compounds ◽  
Microbial World ◽  
Functional Roles ◽  
The Past ◽  
Culture Independent ◽  
New Antibiotics ◽  
Micro Organisms ◽  
Generation Sequencing

Nature offered mankind the first golden era of discovery of novel antimicrobials based on the ability of eukaryotes or micro-organisms to produce such compounds. The microbial world proved to be a huge reservoir of such antimicrobial compounds which play important functional roles in every environment. However, most of those organisms are still uncultivable in a classical way, and therefore, the use of extended culture or DNA based methods (metagenomics) to discover novel compounds promises usefulness. In the past decades, the advances in next-generation sequencing and bioinformatics revealed the enormous diversity of the microbial worlds and the functional repertoire available for studies. Thus, data-mining becomes of particular interest in the context of the increased need for new antibiotics due to antimicrobial resistance and the rush in antimicrobial discovery. In this review, an overview of principles will be presented to discover new natural compounds from the microbiome. We describe culture-based and culture-independent (metagenomic) approaches that have been developed to identify new antimicrobials and the input of those methods in the field as well as their limitations.

scholarly journals Next-generation sequencing to confirm clinical familial hypercholesterolemia

2020 ◽  
pp. 204748732094299
Author(s):  
Laurens F Reeskamp ◽  
Tycho R Tromp ◽  
Joep C Defesche ◽  
Aldo Grefhorst ◽  
Erik SG Stroes ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Familial Hypercholesterolemia ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Pathogenic Variant ◽  
Low Density ◽  
Low Density Lipoprotein Cholesterol ◽  
The Past ◽  
Cholesterol Levels ◽  
Generation Sequencing

Background Familial hypercholesterolemia is characterised by high low-density lipoprotein-cholesterol levels and is caused by a pathogenic variant in LDLR, APOB or PCSK9. We investigated which proportion of suspected familial hypercholesterolemia patients was genetically confirmed, and whether this has changed over the past 20 years in The Netherlands. Methods Targeted next-generation sequencing of 27 genes involved in lipid metabolism was performed in patients with low-density lipoprotein-cholesterol levels greater than 5 mmol/L who were referred to our centre between May 2016 and July 2018. The proportion of patients carrying likely pathogenic or pathogenic variants in LDLR, APOB or PCSK9, or the minor familial hypercholesterolemia genes LDLRAP1, ABCG5, ABCG8, LIPA and APOE were investigated. This was compared with the yield of Sanger sequencing between 1999 and 2016. Results A total of 227 out of the 1528 referred patients (14.9%) were heterozygous carriers of a pathogenic variant in LDLR (80.2%), APOB (14.5%) or PCSK9 (5.3%). More than 50% of patients with a Dutch Lipid Clinic Network score of ‘probable’ or ‘definite’ familial hypercholesterolemia were familial hypercholesterolemia mutation-positive; 4.8% of the familial hypercholesterolemia mutation-negative patients carried a variant in one of the minor familial hypercholesterolemia genes. The mutation detection rate has decreased over the past two decades, especially in younger patients in which it dropped from 45% in 1999 to 30% in 2018. Conclusions A rare pathogenic variant in LDLR, APOB or PCSK9 was identified in 14.9% of suspected familial hypercholesterolemia patients and this rate has decreased in the past two decades. Stringent use of clinical criteria algorithms is warranted to increase this yield. Variants in the minor familial hypercholesterolemia genes provide a possible explanation for the familial hypercholesterolemia phenotype in a minority of patients.

scholarly journals Recombinant GII.P16/GII.4 Sydney 2012 Was the Dominant Norovirus Identified in Australia and New Zealand in 2017

Viruses ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 548 ◽  
Author(s):  
Jennifer Lun ◽  
Joanne Hewitt ◽  
Grace Yan ◽  
Daniel Enosi Tuipulotu ◽  
William Rawlinson ◽  
...  
Keyword(s):  
New Zealand ◽  
Next Generation Sequencing ◽  
Virus Infection ◽  
Phylogenetic Analyses ◽  
Rt Pcr ◽  
Molecular Surveillance ◽  
Recombinant Viruses ◽  
The Past ◽  
Wastewater Samples ◽  
Generation Sequencing

For the past two decades, norovirus pandemic variants have emerged every 3–5 years, and dominate until they are replaced by alternate strains. However, this scenario changed in 2016 with the co-circulation of six prevalent viruses, three of which possessed the pandemic GII.4 Sydney 2012 capsid. An increased number of institutional gastroenteritis outbreaks were reported within the Oceania region in mid-2017. This study identified emerging noroviruses circulating in Australia and New Zealand in 2017 to assess the changing dynamics of the virus infection. RT-PCR-based methods, next generation sequencing, and phylogenetic analyses were used to genotype noroviruses from both clinical and wastewater samples. Antigenic changes were observed between the capsid of pandemic Sydney 2012 variant and the two new Sydney recombinant viruses. The combination of these antigenic changes and the acquisition of a new ORF1 through recombination could both facilitate their ongoing persistence in the population. Overall, an increased prevalence of GII.P16/GII.4 Sydney 2012 viruses was observed in 2017, replacing the GII.P16/GII.2 recombinant that dominated in the region at the end of 2016. This shift in strain dominance was also observed in wastewater samples, demonstrating the reliability of wastewater as a molecular surveillance tool.

scholarly journals AmalgamScope: Merging Annotations Data across the Human Genome

2014 ◽  
Vol 2014 ◽  
pp. 1-5
Author(s):  
Georgia Tsiliki ◽  
Konstantinos Tsaramirsis ◽  
Sophia Kossida
Keyword(s):  
Next Generation Sequencing ◽  
Human Genome ◽  
Software Tool ◽  
Whole Genome ◽  
Interactive Software ◽  
Data Types ◽  
The Past ◽  
Multiple Data ◽  
Diverse Data ◽  
Generation Sequencing

The past years have shown an enormous advancement in sequencing and array-based technologies, producing supplementary or alternative views of the genome stored in various formats and databases. Their sheer volume and different data scope pose a challenge to jointly visualize and integrate diverse data types. We present AmalgamScope a new interactive software tool focusing on assisting scientists with the annotation of the human genome and particularly the integration of the annotation files from multiple data types, using gene identifiers and genomic coordinates. Supported platforms include next-generation sequencing and microarray technologies. The available features of AmalgamScope range from the annotation of diverse data types across the human genome to integration of the data based on the annotational information and visualization of the merged files within chromosomal regions or the whole genome. Additionally, users can define custom transcriptome library files for any species and use the file exchanging distant server options of the tool.

scholarly journals Precision Medicine and Precision Public Health in the Era of Pathogen Next-Generation Sequencing

2019 ◽  
Vol 221 (Supplement_3) ◽  
pp. S289-S291 ◽  
Author(s):  
Mariana Leguia ◽  
Anton Vila-Sanjurjo ◽  
Patrick S G Chain ◽  
Irina Maljkovic Berry ◽  
Richard G Jarman ◽  
...  
Keyword(s):  
Public Health ◽  
Next Generation Sequencing ◽  
Infectious Diseases ◽  
Next Generation ◽  
The Past ◽  
Infectious Disease Research ◽  
History Of ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing ◽  
Health Applications

Abstract This brief report serves as an introduction to a supplement of the Journal of Infectious Diseases entitled “Next-Generation Sequencing (NGS) Technologies to Advance Global Infectious Disease Research.” We briefly discuss the history of NGS technologies and describe how the techniques developed during the past 40 years have impacted our understanding of infectious diseases. Our focus is on the application of NGS in the context of pathogen genomics. Beyond obvious clinical and public health applications, we also discuss the challenges that still remain within this rapidly evolving field.

scholarly journals Sequencing your genome: your future is here, but are you sure you want to know it?

2014 ◽  
Vol 96 ◽  
Author(s):  
NIR PILLAR ◽  
OFER ISAKOV ◽  
NOAM SHOMRON
Keyword(s):  
Next Generation Sequencing ◽  
Research Laboratory ◽  
High Throughput Sequencing ◽  
Genomic Research ◽  
Sequencing Technology ◽  
Clinical Diagnostic Laboratory ◽  
Diagnostic Laboratory ◽  
The Past ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

Next-generation sequencing (NGS; also known as deep sequencing or ultra-high throughput sequencing) has probably been the most important tool for genomic research over the past few years. NGS has led to numerous discoveries and scientific breakthroughs in the genetic field. The sequencing technology that has entered the research laboratory in the past decade is now being introduced into the clinical diagnostic laboratory. Consequently, NGS results are becoming available in the medical arena as abundance of clinically relevant variants, conferring predisposition to disease, are being discovered at a growing rate (Stanley, 2014).

scholarly journals Observed Antibody Space: a resource for data mining next generation sequencing of antibody repertoires

2018 ◽  
Author(s):  
Aleksandr Kovaltsuk ◽  
Jinwoo Leem ◽  
Sebastian Kelm ◽  
James Snowden ◽  
Charlotte M. Deane ◽  
...  
Keyword(s):  
Data Mining ◽  
Immune System ◽  
Next Generation Sequencing ◽  
Sequence Diversity ◽  
Immunoglobulin Gene ◽  
Next Generation ◽  
Natural Diversity ◽  
Antibody Repertoires ◽  
Generation Sequencing

AbstractAntibodies are immune system proteins that recognize noxious molecules for elimination. Their sequence diversity and binding versatility have made antibodies the primary class of biopharmaceuticals. Recently it has become possible to query their immense natural diversity using next-generation sequencing of immunoglobulin gene repertoires (Ig-seq). However, Ig-seq outputs are currently fragmented across repositories and tend to be presented as raw nucleotide reads, which means nontrivial effort is required to reuse the data for analysis. To address this issue, we have collected Ig-seq outputs from 53 studies, covering more than half a billion antibody sequences across diverse immune states, organisms and individuals. We have sorted, cleaned, annotated, translated and numbered these sequences and make the data available via our Observed Antibody Space (OAS) resource at antibodymap.org. The data within OAS will be regularly updated with newly released Ig-seq datasets. We believe OAS will facilitate data mining of immune repertoires for improved understanding of the immune system and development of better biotherapeutics.

scholarly journals Next-generation sequencing to confirm clinical familial hypercholesterolemia

2020 ◽  
Vol 28 (8) ◽  
pp. 875-883 ◽  
Author(s):  
Laurens F Reeskamp ◽  
Tycho R Tromp ◽  
Joep C Defesche ◽  
Aldo Grefhorst ◽  
Erik S G Stroes ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Familial Hypercholesterolemia ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Pathogenic Variant ◽  
Low Density ◽  
Low Density Lipoprotein Cholesterol ◽  
The Past ◽  
Cholesterol Levels ◽  
Generation Sequencing

Abstract Background Familial hypercholesterolemia is characterised by high low-density lipoprotein-cholesterol levels and is caused by a pathogenic variant in LDLR, APOB or PCSK9. We investigated which proportion of suspected familial hypercholesterolemia patients was genetically confirmed, and whether this has changed over the past 20 years in The Netherlands. Methods Targeted next-generation sequencing of 27 genes involved in lipid metabolism was performed in patients with low-density lipoprotein-cholesterol levels greater than 5 mmol/L who were referred to our centre between May 2016 and July 2018. The proportion of patients carrying likely pathogenic or pathogenic variants in LDLR, APOB or PCSK9, or the minor familial hypercholesterolemia genes LDLRAP1, ABCG5, ABCG8, LIPA and APOE were investigated. This was compared with the yield of Sanger sequencing between 1999 and 2016. Results A total of 227 out of the 1528 referred patients (14.9%) were heterozygous carriers of a pathogenic variant in LDLR (80.2%), APOB (14.5%) or PCSK9 (5.3%). More than 50% of patients with a Dutch Lipid Clinic Network score of ‘probable’ or ‘definite’ familial hypercholesterolemia were familial hypercholesterolemia mutation-positive; 4.8% of the familial hypercholesterolemia mutation-negative patients carried a variant in one of the minor familial hypercholesterolemia genes. The mutation detection rate has decreased over the past two decades, especially in younger patients in which it dropped from 45% in 1999 to 30% in 2018. Conclusions A rare pathogenic variant in LDLR, APOB or PCSK9 was identified in 14.9% of suspected familial hypercholesterolemia patients and this rate has decreased in the past two decades. Stringent use of clinical criteria algorithms is warranted to increase this yield. Variants in the minor familial hypercholesterolemia genes provide a possible explanation for the familial hypercholesterolemia phenotype in a minority of patients.

scholarly journals Translational Biomedical Informatics in the Cloud: Present and Future

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Jiajia Chen ◽  
Fuliang Qian ◽  
Wenying Yan ◽  
Bairong Shen
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Next Generation Sequencing ◽  
Clinical Data ◽  
High Throughput ◽  
Exponential Growth ◽  
Translational Bioinformatics ◽  
Translational Biomedical Informatics ◽  
Generation Sequencing

Next generation sequencing and other high-throughput experimental techniques of recent decades have driven the exponential growth in publicly available molecular and clinical data. This information explosion has prepared the ground for the development of translational bioinformatics. The scale and dimensionality of data, however, pose obvious challenges in data mining, storage, and integration. In this paper we demonstrated the utility and promise of cloud computing for tackling the big data problems. We also outline our vision that cloud computing could be an enabling tool to facilitate translational bioinformatics research.

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