Archivos de la categoría Bioinformatics

Virtual Cell testing integrates bioinformatic alternatives to replace animal trials

Virtual Cell testing integrates bioinformatic alternatives to replace animal trials

 

Step into a world where scientific discovery doesn’t require animal testing opens up endless possibilities for ethical and innovative research. It may sounds like sci-fi, but today it is a feasible reality, where virtual cells can actually replace live animal testing.

Virtual cell testing is based on the data obtained from in vitro trials using cell lines, which enables the development of in silico prediction models. By leveraging in vitro experiments, one can gain a deeper understanding of the interactions between time, external stimuli, and compound concentration-dependent mechanisms, enabling the prediction of outcomes through computer models.

But how does this data is transformed into virtual predictions? How can we develop accurate prediction methods and learning algorithms?

Firstly, a large amount of in vitro experimental data must be incorporated into cutting-edge computer simulations to generate accurate virtual organisms. This integration enables the anticipation of the effects of various compounds and/or environmental conditions on cells, leading to significant advancements in preventing animal testing.

In vitro testing

Featured image: DNY59, iStock license.

Secondly, the ever-evolving advancements in biotechnology, including organ-on-a-chip technology, high-throughput screening experiments, multi-omics, and mathematical biology, facilitate the implementation of automatic learning algorithms. The incorporation these innovations not only provide a holistic view of an organism’s potential response to treatments. They can also be used to futher refine the prediction of outcomes.

Virtual Cell testing holds the potential to transform animal health and nutrition trials by accelerating research advancements.

The virtual cell is a promising approach to effectively reduce animal trial costs, enable comparisons of a greater number of treatments and conditions, and speed up the results. However, like any new technology, virtual cell testing also has drawbacks and limitations. These include the dependency on animal cell culture protocols and limited data availability across different species and organs.

To overcome these limitations, a vast amount of data derived from in vitro approaches needs to be leveraged in order to develop more robust in silico prediction models.

For instance, the availability of culture cell lines that retain several properties observed in live rainbow trout (Oncorhynchus mykiss), has facilitated deeper insights into the basic functions of the digestive tract and the effects of functional feed ingredients, host intestinal immune response, barrier function, digestion and complexity of intestinal microenvironment in this species.

Recent exciting news from the Swiss Aquatic Research Institute Eawag and the University of Utrecht has introduced the idea of a «virtual animal». This concept entails building a virtual test using in vitro data from fish gill cells by recollecting data from the major organs’ cells into a single computer model. So, by observing how toxic certain chemicals are to fish cells, researchers were able to build a computer model that enables the in silico prediction on how the chemical could affect a living fish.

Whereas more research is in progress to add to the test more important organs, such intestine and nervous system, the test based on rainbow trout gill cells has been already released by the OECD as the last guideline in the field of environmental toxicology.

Moreover, multi-omics techniques such as transcriptomics, proteomics and metabolomics, can be further explored to improve learning algorithms and prediction models. With extensive molecular databases and cell lines at our disposal, we are able to advance toward more humane and precise research practices, ultimately reducing our dependence on animal testing.

In conclusion, the virtual cell is an exciting journey into a new era of scientific innovation toward the application of the 3R principles (Replacement, Reduction, and Refinement) in animal experimentation.

 

3R principles in animal experimentation

Do you work in animal nutrition, health or reproduction? Are you thinking of developing in silico prediction model to study new feed effects on gut health and reproduction traits? Are you wondering how omics technologies can be further explored in vitro and in silico studies?

 

 

Reach out to us, and let’s bring your vision to reality!

The Potential of Hi-c Sequencing for Unlocking Plasmid-Bacteria Host Interactions in Animal Health Research

The Game-Changing Potential of Hi-c Sequencing for Unlocking Plasmid-Bacteria Host Interactions in Animal Health Research

 

At DataOmics, we are always eager to learn about new technologies and techniques in bioinformatics with application to animal science. That’s why we are thrilled to explore the potential of Hi-c sequencing applications in microbiome and animal health research.

So, if you are interested in applying the latest and most creative solutions to develop more effective strategies for promoting animal health, we invite you to join us and learn more about Hi-C sequencing and its benefits.

Hi-C sequencing combines Chromosome Conformation Capture (3C) technology with next-generation sequencing (NGS) to study the three-dimensional structure of chromosomes. This method gives a detailed and extensive view at how genes interact throughout the whole genome, allowing researchers to understand the importance of genome organization on various levels for different organisms and cell types.

One of the most promising applications of Hi-C sequencing is elucidating plasmid-bacteria host interactions. This method explores the cross-links between plasmid genes (and other elements)  and their host bacteria and how these interactions affect genetic processes such as gene regulation and expression.

Plasmids are mobile genetic elements that can carry not only genes related to beneficial traits but also antibiotic resistance genes among others, which are particularly relevant to animal health.  Therefore, as a practical example, the Hi-c proximity-ligation method can helps us recognize the bacterial hosts more likely to carry antibiotic resistance genes and track how these genes spread among bacterial populations.

Moreover, the Hi-c chromatin-level contact probability maps can also be used to reconstruct the individual genomes of microbial species obtained from metagenomic shotgun sequencing. Hi-C data provides intracellular contiguity information and contains both intrachromosomal and interchromosomal data, making it a powerful tool for species-level deconvolution of microbiota that inhabits an animal gut.

Featured figure adapted from Maximiliam et al. 2017: copyright available under a CC-BY-NC-ND 4.0 International license. Closer look at the chromatin-level DNA cross-links, e.g. plasmid genes and bacteria chromosome (red highlighted), in a microbiota metagenome and sequencing linkages between DNA contigs or scaffolds are used to deconvolute DNAs derived from the same organisms.

Featured figure adapted from Maximiliam et al. 2017: copyright available under a CC-BY-NC-ND 4.0 International license. Closer look at the chromatin-level DNA cross-links, e.g. plasmid genes and bacteria chromosome (red highlighted), in a microbiota metagenome and sequencing linkages between DNA contigs or scaffolds are used to deconvolute DNAs derived from the same organisms.

But not only that; Hi-C sequencing combined with long-read sequencing holds the potential to improve De novo genome assembly for species without a high-quality reference genome. For instance, it can be particularly useful in aquaculture R&D, as demonstrated by recent studies. The potential of incorporating Hi-c data to perform De novo assemblies was successfully exemplified by the high-quality genome assemblies of Trachidermus fasciatue and Pelteobagrus vachelli genomes.

 

Additionally, ultra-long-range Hi-c chromatin interaction data used by a phasing tool enables the generation of haplotype-resolved genome assemblies. It works as an alternative to other complex and unfeasible protocols, such as cultured cells that contain a single-haplotype (haploid) genome, single cells where haplotypes are separated, or co-sequencing of parental genomes in a trio-based approaches.

There is no doubt that Hi-c sequencing is a game-changing technology with a broad range of applications. It is specially true in the genomic research of non-model species and exploring the effect of feeds and additives on animal gut microbiota and health.

Although Hi-c data can be highly complex and require careful experimental design, appropriate data processing, and bioinformatic analysis, DataOmics team has experience working with Hi-c sequencing data and can provide the necessary computational resources and bioinformatics services to handle the demanding nature of this technology.

 

If you’re interested in Hi-C technology, DataOmics experts can help your team overcome these challenges and make the most of this groundbreaking technology for your research.

 

Contact us!