Innate lymphoid cells are derived from a common Id2-dependent precursor and appear to influence immunity, inflammation and tissue repair and homeostasis in health and disease. Ongoing studies are examining the influence of host- and environmental-derived signals on the development and functions of innate lymphoid cells. The findings of these studies offer the potential to identify new therapeutic targets to limit infection, chronic inflammation and autoimmune diseases.
Metagenomic sequencing approaches in patient populations have revealed dysbiosis alterations of commensal bacterial communities in patients suffering from multiple inflammatory diseases including arthritis, multiple sclerosis, psoriasis or inflammatory bowel disease. Studies in murine model systems support a causal relationship between alterations in commensal bacteria and chronic inflammation.
Studies in the Artis lab are employing germ-free mice and selective antibiotic treatment, coupled with pyrosequencing of bacterial communities, to interrogate the mechanisms through which signals derived from commensal bacteria can influence expression of proinflammatory cytokines and pathogenesis of chronic inflammatory diseases at multiple barrier surfaces.
The MinION system has been used for sequencing the genomes of infectious agents, including the analysis of bacterial antibiotic resistance islands , the influenza virus and genome surveillance of the Ebola virus The advancements in high-throughput sequencing technologies now provide the opportunity to choose different sequencing platforms to conduct microbiome research. Human gut microbiome signatures exhibit individual specificity.
There is a high degree of inter individual variation that is based on both host genetics and environmental factors , The high degree of individual specificity, however, has hampered our understanding of function of the gut microbiome and its importance in health and disease. The human gut microbiome exhibits a high degree of plasticity, mainly in response to dietary changes that support a healthy gut ecosystem and minimize disease risk The onset of new methodologies, including NGS and bioinformatic pipelines, have resulted in a paradigm shift in the fields of clinical microbiology and infectious diseases due to the realization of the complex interactions that occur within the microbiome.
The relationship between human pathogens, infectious diseases, and the gut microbiome are slowly being revealed. Several studies have examined the correlation between the human gut microbiome and health status , Reports have indicated that while the gut microbiome appears to be relatively stable under healthy conditions, any qualitative or quantitative changes in the gut microbiome can result in functional modifications and disease as reported , — A rich level of bacterial diversity is considered to be an indicator of a healthy status, while a low level of bacterial diversity is correlated with inflammatory, immune, and obesity-related diseases 58 , , — Several studies have indicated that the human microbiota plays a crucial role in human health and disease 68 , — Studies have also revealed that microbial symbiosis plays a central role in the development of a number of diseases, including liver diseases , metabolic disorders , gastrointestinal GI malignancy , respiratory diseases , autoimmune diseases , and mental or psychological diseases Johnson et al.
Yiu et al. In discussing chronic IBD, Frick and Wehkamp outlined some of the available therapeutic interventions that can be used to alter mucosal immunity and the composition of the microbiome. While studying the molecular aspects of human gut-brain interactions, Lee et al. The advances in NGS have resulted in the production of massive datasets that are increasingly difficult to analyse As larger datasets are generated, more sophisticated computational resources and bioinformatic tools are required. The interpretation and understanding of metagenomic studies depend on the computational tools that can be used to analyse enormous data sets and mine valuable, useful, and valid information regarding the microbial communities being studied.
Bioinformatic tools used for metagenomic analysis, especially for translating raw sequences into meaningful data, are continually developing with the aim of providing the ability to examine both the taxonomic and the functional composition of diverse metagenomes , A number of the specialized software programs available for analysing the metagenomic data are listed Table 2. Based on the list provided, an example of a comparative analysis pipeline is presented in the present review that takes into consideration user friendliness, ease of access, open source availability, ability to analyse metagenomic datasets, and ability to provide graphical representations of the analyzed data Figure 5.
The four pipelines share several steps during the analysis such as quality control, clustering, and annotation Figure 5. Figure 5. Table 3. Comparative workflow of the four most commonly used bioinformatics pipeline for analyzing metagenomic datasets. As comprehensive metagenomic studies are becoming more common, they are yielding novel and important insights into the microbial communities in diverse environments; from terrestrial to aquatic ecosystems and from human skin to the human gastrointestinal tract.
Advances in NGS have made it more possible than ever for researchers to conduct whole genome sequencing. The analysis of the datasets obtained from NGS is complex and require an intelligent and systematic approach to process the data efficiently. The results obtained from any metagenomic study relies on in silico computational tools that can analyse large data sets and can mine and highlight various aspects about the community being examined.
Although the tools and databases developed to investigate the taxonomic composition of a microbial community and provide information on the functional aspects of the community are becoming more elaborate and complex, though CLC microbial genomic package offered by Qiagen are good for these analysis. Nanopore sequencing technology has presented an option for an analysis pipeline, with novel options for assembly and annotation.
Figure 6 , presents the workflow involved in metagenomic analysis, and indicates all the steps and tools used for analyzing the data generated from metagenomic sequencing. The metagenomic pipeline can utilize any of the presented approaches, based on type of sequencing data targeted metagenomics or shotgun metagenomics. The flowchart summarizes the basic steps that are followed in the analysis pipeline starting from preprocessing of the sequencing data to the final extraction, storage, and presentation of the data. The most popular tools, along with the databases and algorithms employed for the analysis, are indicated.
Over the past decade, advancements in NGS have led to a significant reduction in the cost of genome sequencing. The cost estimates presented in Figure 7 represent A cost in U. S, dollars per Mb of sequence data from to , B cost in U. S, dollars per Mb from to , C cost in U. S, dollars per Genome from to , and D cost in U. S, dollars per Genome from to Although sequencing is now relatively easy and straight forward, NGS technology is not perfect and errors in the data do occur.
Moreover, some regions of the DNA have not been successfully sequenced. The underlying costs associated with different approaches to sequencing genomes are of great importance because they impact the scope and scale of genomic projects. Decreases in sequencing costs have led to the establishment of large collaborative projects with broad goals and individual laboratories targeting more specific questions.
Figure 7. Timeline showing the sequencing cost A per Mb until year , B per Mb between year and , C per genome until year , D per genome between year and The decreasing cost structure of DNA sequencing has had and will continue to have an impact on genomics and bio-computing. With the size of databases expanding continuously, the translation of data into biological insight is becoming more and more important.
As a result, data analysis a more prominent aspect in obtaining information and value from the data Significant analytical efforts are needed to gain useful insights from the generated data. The fields of microbiology, biotechnology, and medicine are already benefiting from genome sequencing efforts, and as costs continue to decrease, the practice of genome sequencing is expected to become almost routine.
For example, the Sanger Institute is sequencing the genomes of patients suffering from cancer and rare diseases as part of the , Genomes Project organized by Genomics England. Some patients have already benefitted from metagenomic-based diagnoses and treatments, and researchers are continuing to gain more knowledge about the genetic variations that cause a variety of diseases. Sequencing, however, is not the only option for genetic analysis. An important part of the Precision Medicine Initiative, organized by the US National Institute of Health, is to develop a more predictable and possibly less technically complex method of genetic analysis.
Sequencing, however, appears to be the only way to comprehensively explore the complex features of DNA that guide the initiation and progression of a number of diseases. Additionally, comprehensive sequencing also helps determine how our DNA keeps us healthy Though the field of metagenomics pre-dates NGS, modern high-throughput sequencing technologies have greatly transformed this promising field by enabling a comprehensive characterization of all microorganisms present in a sample. As metagenomic approaches become more developed and clinically corroborated, it is expected that metagenomics will be at the forefront as a method for diagnosing infectious diseases.
When a complex or unknown infectious disease is encountered, the use of multiple conventional diagnostic tests can potentially lead to unnecessary expenses; more importantly, this can also result in the delay of a diagnosis. Metagenomics can be used to identify potential pathogens, both known and novel, and can also be used to assess the state of an individual's microbiome.
As sequencing become easier, faster, and more cost-effective, it will be possible to serially characterize the human microbiota to explore changes that occur in the human microbiome over time. This knowledge could lead to the development of novel medicines and approaches for treating infectious diseases.
Indeed, metagenomic studies may become so standard that DNA sequencers could be used in homes to monitor changes in the stool microbiome of an individual to guide the maintenance of health. All forms of life on this planet are dependent on microbes. They define an environment and are in turn defined by it. Our understanding of host-pathogen systems, however, is only in its infancy. Over the past two decades, sequencing technology, along with bioinformatic tools, have improved significantly; making it feasible to explore microbial communities residing within diverse hosts.
There is a strong recognition that the microbial diversity existing in extreme habitats has largely been unexplored. NGS technologies have provided a rapid, cost-efficient means of generating sequencing data and provided sequencing platforms that can be used in large genome-sequencing centers, as well as individual laboratories. These upgrades will increase high-throughput ability and read length, while at the same time significantly reduce the cost of sequencing per base. These developments will significantly contribute to and provide exciting new opportunities to microbiologists.
The integration of several approaches to biological studies will be necessary to answer questions about the diversity and ecology of microbial flora. It is the opinion of the authors of the present review that the development of better bioinformatic tools for analysing metagenomic data is urgently needed. The vast amounts of metagenomic data that will be forthcoming will bring new challenges for analysing, storing, and transferring data.
Genome-sequencing centers and laboratories are going to become more dependent on information technology and bioinformatics. Bioinformatic expertise will increasingly be necessary to analyse large amounts of data and to mine the data for useful information about microbial diversity. Metagenomics will play an increasing role in the fields of medicine, biotechnology, and environmental science.
The authors hope that this review provides a clear overview of the sequencing platforms and bioinformatic analysis of software that are available, including their high value and limitations. AD prepared the illustrations. EA and AH edited the manuscript. MM Y was supported by the University Ph.
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