The era of genomic epidemiology

Whole genome enrichment approach for genomic surveillance of Toxoplasma gondii

Pathogenic bacteria, viruses, fungi, and protozoa can cause food and waterborne diseases. Surveillance methods must therefore screen for these pathogens at various stages of water distribution and of food from production to consumption. Detection using nucleic acid amplification methods offer rapid identification, but such methods have limited utility for characterizing populations, variant types or virulence traits of pathogens. Whole genome sequencing (WGS) can be used to determine this information. However, pathogens must be isolated and cultured to yield sufficient DNA for WGS, which is laborious or not feasible for certain stages of parasites like oocysts of Toxoplasma gondii. We previously developed the Circular Nucleic acid Enrichment Reagent (CNER) method to make whole genome enrichment (WGE) baits for difficult-to-grow bacterial pathogens. WGE using CNERs facilitates direct sequencing of pathogens from samples without the need to isolate and grow them. Here, we made WGE-CNERs for T. gondii to demonstrate the use of the CNER method to make baits to enrich the large genomes of water and foodborne protozoan pathogens. By sequencing, we detected as few as 50 parasites spiked in an oyster hemolymph matrix. We discuss the use of WGE-CNERs for genomic surveillance of food and waterborne pathogens.

Epi-Gene: An R-Package for Easy Pan-Genome Analysis

The main aim of this study was to develop a set of functions that can analyze the genomic data with less time consumption and memory. Epi-gene is presented as a solution to large sequence file handling and computational time problems. It uses less time and less programming skills in order to work with a large number of genomes. In the current study, some features of the Epi-gene R-package were described and illustrated by using a dataset of the 14 Aeromonas hydrophila genomes. The joining, relabeling, and conversion functions were also included in this package to handle the FASTA formatted sequences. To calculate the subsets of core genes, accessory genes, and unique genes, various Epi-gene functions have been used. Heat maps and phylogenetic genome trees were also constructed. This whole procedure was completed in less than 30 minutes. This package can only work on Windows operating systems. Different functions from other packages such as dplyr and ggtree were also used that were available in R computing environment.

The Experience of Greece as a Model to Contain COVID-19 Infection Spread

The severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) emerged in late 2019 and has caused a pandemic known as corona virus disease 2019 (COVID-19), responsible for the death of more than 2 million people worldwide. The outbreak of COVID-19 has posed an unprecedented threat on human lives and public safety. The aim of this review is to describe key aspects of the bio-pathology of the novel disease, and discuss aspects of its spread, as well as targeted protective strategies that can help shape the outcome of the present and future health crises. Greece is used as a model to inhibit SARS-COV-2 spread, since it is one of the countries with the lowest fatality rates among nations of the European Union (E.U.), following two consecutive waves of COVID-19 pandemic. Furthermore, niche research technological approaches and scientific recommendations that emerged during the COVID-19 era are discussed.

Genomic Epidemiology and Recent Update on Nucleic Acid-Based Diagnostics for COVID-19

Purpose of the Review

The SARS-CoV-2 genome has been sequenced and the data is made available in the public domain. Molecular epidemiological investigators have utilized this information to elucidate the origin, mode of transmission, and contact tracing of SARS-CoV-2. The present review aims to highlight the recent advancements in the molecular epidemiological studies along with updating recent advancements in the molecular (nucleic acid based) diagnostics for COVID-19, the disease caused by SARS-CoV-2.

Recent Findings

Epidemiological studies with the integration of molecular genetics principles and tools are now mainly focused on the elucidation of molecular pathology of COVID-19. Molecular epidemiological studies have discovered the mutability of SARS-CoV-2 which is of utmost importance for the development of therapeutics and vaccines for COVID-19. The whole world is now participating in the race for development of better and rapid diagnostics and therapeutics for COVID-19. Several molecular diagnostic techniques have been developed for accurate and precise diagnosis of COVID-19.

Summary

Novel genomic techniques have helped in the understanding of the disease pathology, origin, and spread of COVID-19. The whole genome sequence established in the initial days of the outbreak has enabled to identify the virus taxonomy. Several rapid, accurate, and sensitive diagnostic methods have been developed; those are based on the principle of detecting SARS-CoV-2 nucleic acids in clinical samples. Most of these molecular diagnostics are based on RT-PCR principle.

Towards an integrated food safety surveillance system: a simulation study to explore the potential of combining genomic and epidemiological metadata

Foodborne infection is a result of exposure to complex, dynamic food systems. The efficiency of foodborne infection is driven by ongoing shifts in genetic machinery. Next-generation sequencing technologies can provide high-fidelity data about the genetics of a pathogen. However, food safety surveillance systems do not currently provide similar high-fidelity epidemiological metadata to associate with genetic data. As a consequence, it is rarely possible to transform genetic data into actionable knowledge that can be used to genuinely inform risk assessment or prevent outbreaks. Big data approaches are touted as a revolution in decision support, and pose a potentially attractive method for closing the gap between the fidelity of genetic and epidemiological metadata for food safety surveillance. We therefore developed a simple food chain model to investigate the potential benefits of combining 'big' data sources, including both genetic and high-fidelity epidemiological metadata. Our results suggest that, as for any surveillance system, the collected data must be relevant and characterize the important dynamics of a system if we are to properly understand risk: this suggests the need to carefully consider data curation, rather than the more ambitious claims of big data proponents that unstructured and unrelated data sources can be combined to generate consistent insight. Of interest is that the biggest influencers of foodborne infection risk were contamination load and processing temperature, not genotype. This suggests that understanding food chain dynamics would probably more effectively generate insight into foodborne risk than prescribing the hazard in ever more detail in terms of genotype.

Global epidemiology of amyotrophic lateral sclerosis: a systematic review of the published literature

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is relatively rare, yet the economic and social burden is substantial. Having accurate incidence and prevalence estimates would facilitate efficient allocation of healthcare resources.

OBJECTIVE

To provide a comprehensive and critical review of the epidemiological literature on ALS.

METHODS

MEDLINE and EMBASE (1995-2011) databases of population-based studies on ALS incidence and prevalence reporting quantitative data were analyzed. Data extracted included study location and time, design and data sources, case ascertainment methods and incidence and/or prevalence rates. Medians and interquartile ranges (IQRs) were calculated, and ALS case estimates were derived using 2010 population estimates.

RESULTS

In all, 37 articles met the inclusion criteria. In Europe, the median incidence rate (/100,000 population) was 2.08 (IQR 1.47-2.43), corresponding to an estimated 15,355 (10,852-17,938) cases. Median prevalence (/100,000 population) was 5.40 (IQR 4.06-7.89), or 39,863 (29,971-58,244) prevalent cases.

CONCLUSIONS

Disparity in rates among ALS incidence and prevalence studies may be due to differences in study design or true variations in population demographics such as age and geography, including environmental factors and genetic predisposition. Additional large-scale studies that use standardized case ascertainment methods are needed to more accurately assess the true global burden of ALS.

Nature versus nurture: death of a dogma, and the road ahead

Interaction between the genome and the environment has been widely discussed in the literature, but has the importance ascribed to understanding these interactions been overstated? In this opinion piece, we critically discuss gene-environment interactions and attempt to answer three key questions. First, is it likely that gene-environment interactions actually exist? Second, what is the realistic value of trying to unravel these interactions, both in terms of understanding disease pathogenesis and as a means of ameliorating disease? Finally, and most importantly, do the technologies and methodologies exist to facilitate an unbiased search for gene-environment interactions? Addressing these questions highlights key areas of feasibility that must be considered in this area of research.

Towards a complete resolution of the genetic architecture of disease

After years of linear gains in the genetic dissection of human disease we are now in a period of exponential discovery. This is particularly apparent for complex disease. Genome-wide association studies (GWAS) have provided myriad associations between common variability and disease, and have shown that common genetic variability is unlikely to explain the entire genetic predisposition to disease. Here we detail how one can expand on this success and systematically identify genetic risks that lead or predispose to disease using next-generation sequencing. Geneticists have had for many years a protocol to identify Mendelian disease. A similar set of tools is now available for the identification of rare moderate-risk loci and common low-risk variants. Whereas major challenges undoubtedly remain, particularly regarding data handling and the functional classification of variants, we suggest that these will be largely practical and not conceptual.

Gene-Environment Interaction: A Genetic-Epidemiological Approach

Classical epidemiology addresses the distribution and determinants of diseases in populations, and the factors associated with disease causation, with the aim of preventing disease. Both genetic and environmental factors may contribute to susceptibility, and it is still unclear how these factors interact in their influence on risk. Genetic epidemiology is the field which incorporates concepts and methods from different disciplines including epidemiology, genetics, biostatistics, clinical and molecular medicine, and their interaction is crucial to understanding the role of genetic and environmental factors in disease processes. The study of gene-environment interaction is central in the field of genetic epidemiology. Gene-environment interaction is defined as »a different effect of an environmental exposure on disease risk in persons with different genotypes,« or, alternatively, »a different effect of a genotype on disease risk in persons with different environmental exposures.« Five biologically plausible models are described for the relations between genotypes and environmental exposures, in terms of their effects on disease risk. Therefore, the study of gene-environment interaction is important for improving accuracy and precision in the assessment of both genetic and environmental factors, especially in disorders of less defined etiology. Genetic epidemiology is also applied at the various levels of disease prevention.