Introduction

Genomic and structure

Epidemiology

Clinical characteristics

Treatments for 2019-nCoV

Discussion

Acknowledgement

References

Abstract

A novel coronavirus designated as 2019-nCoV hit the central Chinese city of Wuhan in late December 2019, and subsequently spread rapidly to all provinces of China and multiple countries. Up to 0:00 am February 9, 2020, a total of 37,287 cases have been confirmed infection of 2019-nCoV in China mainland, and 302 cases have also been cumulatively reported from 24 countries. According to the latest data, a total of 813 deaths occurred in China mainland, with the mortality reaching approximately 2.2%. At present, there is no vaccine or specific drugs for human coronavirus, so that it’s critical to understand the nature of the virus and its clinical characteristics to response to the 2019-nCoV outbreak. Thus, we summarize the not much but timely reports on the 2019-nCoV in the present study, briefly but comprehensively.

Introduction

Coronaviruses (CoVs) are enveloped, single positive stranded RNA virus, which belong to the subfamily Coronavirinae. The CoVs genome, ranging from 26 to 32 kilobases in length, is probably the largest viral RNA known (1,2). Previously, there are six CoVs known to cause human diseases and can be divided into low pathogenic and highly pathogenic CoVs (2,3). The low pathogenic CoVs, including 229E, HKU1, OC43 and NL63, account for 10% to 30% of upper respiratory tract infections and typically cause mild respiratory diseases (3,4). In contrast, the highly pathogenic CoVs, including Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) CoV, predominantly infect lower airways and cause fatal pneumonia (2,5).

SARS-CoV emerged as a new human infection in South China in November 2002 and ended in July 2003. It infected 8,096 people and caused 774 deaths with an overall mortality rate of about 9.6% (6,7). MERS-CoV, another highly pathogenic CoV, first emerged in Saudi Arabiawas, has caused a total of 2494 laboratory-confirmed cases and 858 deaths from 27 countries (mortality rate, 34.4%) since September 2012. (http://www.who.int/emergencies/mers-cov/en/) (8). These two highly pathogenic β-CoVs have posed a substantial threat to public health.

Background: Virtually all eukaryotic cells contain spatially distinct genomes, a single nuclear genome that harbours the vast majority of genes and much smaller genomes found in mitochondria present at thousands of copies per cell. To generate a coordinated gene response to various environmental cues, the genomes must communicate with each another. Much of this bi-directional crosstalk relies on epigenetic processes, including DNA, RNA, and histone modification pathways. Crucially, these pathways, in turn depend on many metabolites generated in specific pools throughout the cell, including the mitochondria. They also involve the transport of metabolites as well as the enzymes that catalyse these modifications between nuclear and mitochondrial genomes. Scope of review: This study examines some of the molecular mechanisms by which metabolites influence the activity of epigenetic enzymes, ultimately affecting gene regulation in response to metabolic cues. We particularly focus on the subcellular localisation of metabolite pools and the crosstalk between mitochondrial and nuclear proteins and RNAs. We consider aspects of mitochondrial-nuclear communication involving histone proteins, and potentially their epigenetic marks, and discuss how nuclear-encoded enzymes regulate mitochondrial function through epitranscriptomic pathways involving various classes of RNA molecules within mitochondria. Major conclusions: Epigenetic communication between nuclear and mitochondrial genomes occurs at multiple levels, ultimately ensuring a coordinated gene expression response between different genetic environments. Metabolic changes stimulated, for example, by environmental factors, such as diet or physical activity, alter the relative abundances of various metabolites, thereby directly affecting the epigenetic machinery. These pathways, coupled to regulated protein and RNA transport mechanisms, underpin the coordinated gene expression response. Their overall importance to the fitness of a cell is highlighted by the identification of many mutations in the pathways we discuss that have been linked to human disease including cancer.

Introduction

To fit within the confined space of a eukaryotic nucleus, a cell’s genome must be efficiently compacted, but in a highly ordered manner that maintains accessibility to the genetic information. This is achieved by DNA complexing with histone proteins to form nucleosome structures, which in turn further compact to form chromatin that ultimately packages entire chromosomes. Importantly, chromatin is not an inert packaging structure but rather an instructive scaffold capable of responding to various cues to regulate access of the DNA to different cellular machineries. This accessibility is fundamentally regulated by ATP-dependent chromatin remodelling activities and by covalent modification of both DNA and histone proteins. These modifications are commonly referred to as “epigenetic” modifications. Furthermore, in addition to epigenetics, the emerging field of epitranscriptomics is now adding numerous modifications of RNA molecules to the “modification repertoire,” thereby expanding our current view of epigenetics and how it regulates all DNA processes, in particular gene expression [1]. DNA and histone modifications regulate gene expression by modulating chromatin accessibility and/or providing binding sites for regulatory proteins. To date, more than 100 post-translational modifications (PTMs) have been detected on histones, including acetylation, methylation, ubiquitylation, phosphorylation, citrullination, and O-GlcNAcylation. Of these, acetylation and methylation are by far the best-studied and most characterised histone modifications (for review, see [2,3]).

18-1- Introduction to personalized nutrition: “One size does not fit all”

18-2- Genes, nutrients, and nutraceuticals in athletic performance

18-3- Phytonutritional epigenomics: Role of phytonutrients in athletic performance

18-4- Conclusion

References

Abstract

In sports nutrition, the use of genetic information to personalize a nutritional regime seems to be useful to maximize athletic performances. The genetic variability between individuals can affect physical properties (muscle strength, skeletal structure, heart and lung size, tendon elasticity) but also nutrients absorption, distribution, metabolism, and excretion. Despite genetic testing for predicting sports performance and talent identification being continuously on the rise in the market, nutrigenetic/nutrigenomic aspects are less known and applied. This is due to the complexity in the identification of associations of different polymorphisms in nutrition, especially because every polymorphism can affect directly or indirectly different other genes, proteins, or metabolic pathways. In this chapter, we analyze the latest advances on the application of nutrition and genetics in sport, from scientific evidence on the role of macro- and micronutrients in sports performance, to the application of phytonutritional epigenomics in the field and future perspectives.

Introduction to personalized nutrition: “One size does not fit all”

Since ancient times, nutrition has always been considered an essential condition for maintaining good health. Hippocrates of Kos, the father of medicine, said in 460 BC: “Let food be thy medicine and medicine be thy food.” His observations led to associative evidence between diet and health by highlighting how food is able to interfere with our body’s physiology by not only acting as an energy provider, but as a modulator of the health/disease balance in a different way for each individual (Tsiompanou and Marketos, 2013) depending on the personal characteristics. Somehow it can be considered a precursor of modern nutritional genomics. It is fundamental to consider that although nutrients act by modulating some physiologic functions in a dose-dependent manner, each individual responds differently depending on its genotypic and phenotypic characteristics (Ferguson et al., 2016). Therefore, the recommended daily doses suggested by the international nutrition guidelines, based on studies on large populations rather than specific genotypes or phenotypes, should be used with enough flexibility to account for the plethora of genetic, epigenetic, and environmental factors contributing to health and disease in each individual.

Metagenomic studies are becoming increasingly widespread, yielding important insights into microbial communities covering diverse environments from terrestrial to aquatic ecosystems. This also because genome sequencing is likely to become a routinely and ubiquitous analysis in a near future thanks to a new generation of portable devices, such as the Oxford Nanopore MinION. The main issue is however represented by the huge amount of data produced by these devices, whose management is actually challenging considering the resources required for an efficient data transfer and processing. In this paper we discuss these aspects, and in particular how it is possible to couple Edge and Cloud computing in order to manage the full analysis pipeline. In general, a proper scheduling of the computational services between the data center and smart devices equipped with low-power processors represents an effective solution.

Introduction

Genome sequencing is one of the most effective analysis technique to monitor both the human body, in physiological settings and pathological conditions, as well as the bacterial communities of different environments. Developed in the 1970s with a cost of hundred million dollars, its impressive progress [1] reduced the cost down to about $1000 dollars, and the perspective is a further reduction to about $100 for genome1 . In particular, the MinION by Oxford Nanopore [2], a miniaturized sequencing instrument device with a weight under 100g powered by its USB port, represents one of the most promising tool belonging to the thirdgeneration DNA sequencing technology [3]. Coupled with a laptop, MinION can be used on the field [4] to obtain genomic sequences, thus providing essential information for tracing back the organisms present in the environment [5]. These devices have been widely used for microbiology studies 2 , for water monitoring3 and in agriculture 4 . Portable sequencer can also be used to monitor bacteria in air-filters of hospitals, food industries, and pharmaceutical companies in order to give alarms in case pathogens are identified [6, 7]. More extreme usages of Oxford Nanopore devices have also been experimented [8, 9, 10]. This new trend is sometimes referred as Internet of Living Things (IoLT) [11]. The combination of the MinION sequencers with remote platform for data integration is still in its infancy, although some attempts have been reported in conference talks and blogs [12, 13]. Moreover, some prototypes of IoT platforms for monitoring clean water [14, 15], precision farming [16, 17, 18], livestock [19, 20] and more generally to improve agricultural productivity [21, 22, 23] have been presented. The two major obstacles affecting the utility of this kind of devices are the access to suitable computational capabilities and bandwidth, since in principle it is possible to stream a couple of gigabytes of raw data per day. Even if the raw data analysis software works reliably on most of the current laptops, it is a very resource-intensive task. The adoption of a Cloud-based approach can mitigate such issue, though it comes at the expense of broadband connections.

Strategies for biotechnology must take account of opportunities for research, innovation and business growth. At a regional level, public-private collaborations provide potential for such growth and the creation of centres of excellence. By considering recent progress in areas such as genomics, healthcare diagnostics, synthetic biology, gene editing and bio-digital technologies, opportunities for smart, strategic and specialised investment are discussed. These opportunities often involve convergent or disruptive technologies, combining for example elements of pharma-science, molecular biology, bioinformatics and novel device development to enhance biotechnology and the life sciences. Analytical applications use novel devices in mobile health, predictive diagnostics and stratified medicine. Synthetic biology provides opportunities for new product development and increased efficiency for existing processes. Successful centres of excellence should promote publicprivate business partnerships, clustering and global collaborations based on excellence, smart strategies and innovation if they are to remain sustainable in the longer term.

Introduction

The biotechnology sector operates on an increasingly global basis, with the 25 biggest drug and biotech companies coming from eight different countries (Morrison & Lhäteenmäki 2017; NASDAQ Biotech Index, 2018). The sector has however, undergone considerable consolidation, including the $130 Bn merger of Dow Chemical with Du Pont and Chem China’s bid of $43 Bn for Syngenta, leading to a smaller number of truly global players. Major revenue earners include Abvie’s Humira monoclonal antibody for arthritis, psoriasis, and Crohn’s disease therapy ($15.9 Bn), Gilead’s Harvoni small molecule for hepatitis C antiviral therapy ($13.8 Bn) and Celgene’s Revlimid for multiple myeloma and mantle cell lymphoma ($6.9 Bn; 1, illustrated in Fig. 1). Approximately 26% of revenue sales are currently spent on research and development, in what is increasingly an expensive escalating biotechnological arms race, with upwards of 40% of venture capital centred on just two areas: Boston Bay and San Francisco (Morrison & Lhäteenmäki, 2017). Amongst European companies, one of the biggest recent changes has seen Bayer bid $66 Bn for Monsanto, to create a global entity dedicated to innovation in healthcare, including pharmaceuticals, consumer & animal health and agriculture. Digital farming combining big data with the internet of things, is one example of how Bayer will meet the challenges posed by world population increasing to 10 Bn by 2050. Farmland per capita will decline by 17% and climate change is forecast to reduce yields by a further 17%. This means an effective 60% productivity increase will be necessary to meet the needs of 10 Bn global citizens (Gartland & Gartland 2016; Food & Agriculture Organisation, 2016). Sustainably producing more food with fewer inputs will require precision data on needs and opportunities for growers. Bayer hope to provide an improved quality of life by combining aspects of their current crop protection portfolio with seed, trait and climate change platforms from Monsanto (Bayer, 2017). At a national level, EU28’s largest biotechnology products pipeline comes from the UK, where €34.66 Bn was generated from pharma, medtech and biotechnology products in 2015 (PWC, 2017). This activity provides 482,000 UK jobs, €9.81 Bn tax take and equates to a Gross Value Added of €118,500 per employee, being the highest in Europe. What then are the areas of biotechnology where opportunities exist for smart, specialized strategies to emerge and be successful in research and innovation, including economic and future employment prospects?

Polyadenylation (Poly(A)) plays crucial roles in gene regulation, especially in messenger RNA metabolism, protein diversification and protein localization. Accurate prediction of polyadenylation sites and identification of motifs that controlling polyadenylation are fundamental for interpreting the patterns of gene expression, improving the accuracy of genome annotation and comprehending the mechanisms that governing gene regulation. Despite considerable advances in using machine learning techniques for this problem, its efficiency is still limited by the lack of experiences and domain knowledge to carefully design and generate useful features, especially for plants. With the increasing availability of extensive genomic datasets and leading computational techniques, deep learning methods, especially convolutional neural networks, have been applied to automatically identify and understand gene regulation directly from gene sequences and predict unknown sequence profiles. Here, we present DeepPolyA, a new deep convolutional neural network-based approach, to predict polyadenylation sites from the plant Arabidopsis thaliana gene sequences. We investigate various deep neural network architectures and evaluate their performance against classical machine learning algorithms and several popular deep learning models. Experimental results demonstrate that DeepPolyA is substantially better than competing methods regarding various performance metrics. We further visualize the learned motifs of DeepPolyA to provide insights of our model and learned polyadenylation signals.

INTRODUCTION

Polyadenylation is a vital process that occurs after gene transcription and produces mature messenger RNA (mRNA) for translation by synthesizing the polyadenylation tail at the RNA’s 3’-end [1]. Recent discoveries have revealed that the 3’-end of most protein-coding and long-noncoding RNAs (lncRNAs; noncoding transcripts of 200 nucleotides or longer) is cleaved and polyadenylated [2]. In addition, alternative polyadenylation (APA) is prevalent in all eukaryotic species and plays critical roles in gene regulation, especially in the processes such as mRNA metabolism, protein diversification and protein localization [3]. Specifically, in addition to conducing to the intricacy of transcriptome by producing isoforms of distinct properties, it can regulate the translation efficiency, function, stability and localization of target RNAs [2], [4]. The polyadenylation site, also called the poly(A) site, is defined by surrounding RNA segments and conserved across metazoans with some minor variations in mammals [1]–[3]. Accurate prediction of poly(A) sites and identification of motifs that controlling them are fundamental for interpreting the patterns of gene expression, improving the accuracy of genome annotation and comprehending the mechanisms that governing gene regulation [5], [6]. However, this remains a challenging problem, especially for plants, to precisely identify the poly(A) signals and predict poly(A) sites. Unlike animals, plants possess much less conserved signal sequences in such regions [7]. For example, the upstream element signal “AAUAAA” (or “AATAAA” in DNA sequence), which has been identified as the best signal in plants, can only be found in approximately 10% of Arabidopsis genes [8], [9]. In contrast, the same signal is utilized by 50% of human genes [10]. The variable structures composed of functional motifs [11], [12] also increase the difficulty in identifying poly(A) sites. In addition, because of the epidemic presence of alternative polyadenylation in intron and coding sequence (CDS), the poly(A) sites may locate in the genomic regions other than 3’ untranslated region (3’-UTR). Thus, an ideal predictive model should be powerful and robust enough to overcome all barriers as mentioned above to achieve decent performance.

1- Introduction

2- A generic meta-process for selective re-computation

3- Related work

4- Experimental setting and blind re-computation baseline

5- Data differences

6- Differential execution

7- Partial re-execution

8- Identifying the scope of change

9- A blueprint for a generic and automated re-computation framework – challenges

10- Conclusions and future work

References

Abstract

The value of knowledge assets generated by analytics processes using Data Science techniques tends to decay over time, as a consequence of changes in the elements the process depends on: external data sources, libraries, and system dependencies. For large-scale problems, refreshing those outcomes through greedy re-computation is both expensive and inefficient, as some changes have limited impact. In this paper we address the problem of refreshing past process outcomes selectively, that is, by trying to identify the subset of outcomes that will have been affected by a change, and by only re-executing fragments of the original process. We propose a technical approach to address the selective re-computation problem by combining multiple techniques, and present an extensive experimental study in Genomics, namely variant calling and their clinical interpretation, to show its effectiveness. In this case study, we are able to decrease the number of required re-computations on a cohort of individuals from 495 (blind) down to 71, and that we can reduce runtime by at least 60% relative to the naïve blind approach, and in some cases by 90%. Starting from this experience, we then propose a blueprint for a generic re-computation meta-process that makes use of process history metadata to make informed decisions about selective re-computations in reaction to a variety of changes in the data.

Introduction

In Data Science applications, the insights generated by resourceintensive data analytics processes may become outdated as a consequence of changes in any of the elements involved in the process. Changes that cause instability include updates to reference data sources, to software libraries, and changes to system dependencies, as well as to the structure of the process itself. We address the problem of efficiently restoring the currency of analytics outcomes in the presence of instability. This involves a trade-off between the recurring cost of process update and re-execution in the presence of changes on one side, and the diminishing value of its obsolete outcomes, on the other. Addressing the problem therefore requires knowledge of the impact of a change, that is, to which extent the change invalidates the analysis, as well as of the cost involved in upgrading the process and running the analysis again. Additionally, it may be possible to optimise the re-analysis given prior outcomes and detailed knowledge of, and control over, the analysis process.

Cotton (Gossypium hirsutum) is a foundation of the global economy, prized for its important renewable fiber resource. It serves as an ideal plant for different biological studies such as genome evolution, polyploidization and single-celled biological processes (Qin and Zhu, 2011; Shan et al., 2014). Decoding cotton’s genome provides useful understanding about the agronomic and functional importance of polyploidy and genome size variations within the genus Gossypium (Chen et al., 2007). However, evolution and function of allopolyploid cotton genome is complicated by the presence of two subgenomes (AT and DT) in its nucleus. About 5-10 million years ago (MYA), the African derived ‘A’ diploid genome diverged from the eudicot progenitor simultaneously with the diploid genome ‘D’ which was native to Mexican (Wendel, 1989; Wendel and Albert, 1992). Then around 1 -2 MYA, these two species were reunited together by the transoceanic dispersal of an A -genome ancestor (Gossypium arboreum ) to the New World and hybridized with a D -genome ancestor (Gossypium raimondii) followed by chromosome doubling, which produced the allotetraploid cotton (Wendel, 1989). These well -established relationships between the allotetraploid and diploid cotton genomes help us to explore the evolution of gene expression, because most of gene functions are highly conserved between wild as well as diploid and tetraploid cotton species. Whole genome sequencing is a fundamental component for comprehensive molecular analysis and for several thousands of plant species, genome sequencing projects are now complete or underway. Compared with model plants i.e. Arabidopsis, rice and maize , the whole genome sequencing of cotton was lack behind. During last decade, sequenced genomes of tetraploid cotton (Li et al., 2015; Liu et al., 2015; Yuan et al., 2015; Zhang et al., 2015 b ) and their diploid progenitors (Li et al., 2014b; Paterson et al., 2012; Wang et al., 2012), has been released that provide critical understanding of the evolution and differentiation of genome structures. Though, knowledge of the precise sequences and position of all the genes of an organism is an initial step to explore how biological systems work together. Previously, various studies have performed to compare the structural variations in genomes which showed the differences in the expression pattern rather than in the absence and presence of genes (Gingle et al., 2006). In this respect functional genomics is the main approach which is generally referred as “development and application of global experimental approaches to evaluate gene function s by using the information and reagents obtained from structural genomics”.