When agrochemical and pharmaceutical companies develop new products, they must test extensively for potential toxicity before obtaining regulatory approval. This testing usually involves lengthy and expensive animal studies.
A research team at University of Illinois has developed a gene biomarker identification technique that cuts the testing process down to a few days while maintaining a high level of accuracy.
“The aim of this research was to identify the smallest set of indicators from the liver to predict toxicity and potential liver cancer,” says Zeynep Madak-Erdogan, associate professor in the Department of Food Science and Human Nutrition at U of I and a lead author on the study.
“The agrochemical industry has a pipeline where they test new compounds in terms of toxicity-related endpoints. Liver toxicity is one of the most important endpoints, because the liver is the organ that receives the blood supply and cleans it, making it one of the biggest targets in terms of environmental toxic action,” Madak-Erdogan explains.
Normally, companies do this through long-term animal experiments, she adds. They track animals for up to a year to see if they develop liver cancer after exposure to these compounds. The studies require thousands of mice or rats, and a lot of human time taking care of the animals, collecting samples, and analyzing the data.
The study, published in Scientific Reports, identifies a biomarker gene signature that indicates potential liver toxicity just 24 hours after exposure.
Madak-Erdogan and her colleagues analyzed information from a large database maintained by the National Institute of Environmental Health Sciences.
In collaboration with scientists at the U of I National Center for Supercomputing Applications (NCSA), they used machine learning approaches to identify gene biomarkers in messenger RNA to predict future toxicity.
“From designing new molecules to identifying novel biological targets, machine learning approaches are playing a key role in accelerating drug target identification and validation,” explains Colleen Bushell, director of NCSA’s Healthcare Innovation Program Office and co-author on the study.
While this study is not the first to employ such techniques, it is the most comprehensive, Madak-Erdogan says. The researchers used a large amount of data and multiple machine learning techniques in order to identify the methods that provide the fastest and most accurate results.
“We are assessing the best prediction techniques and finding the best indicators for liver toxicity. Instead of going for months or years, now we can just treat a few mice for 24 hours, collect livers, look at the biomarkers we identified, and predict whether the animal will potentially develop liver cancer or not,” she explains.
The study’s results can be used broadly by toxicologists and other scientists, and can help the agrochemical and pharmaceutical industry improve their testing capabilities.
“Our findings show machine learning approaches are definitely very valuable in analyzing the vast amount of biological data that we create in our research activities. Collaboration between life sciences and computer sciences is very important for this work,” Madak-Erdogan concludes.
microRNAs (miRNAs) represent a class of small, non-coding RNAs comprising of 17–25 nucleotides [1], whose main role is to regulate mRNA by leading to its degradation and also to adjust the protein levels [1–4].
Their discovery was first published in 1993 and they were described as “mediators of temporal pattern formation” in Caenorhabditis elegans [5–9]. Previous studies have shown that miRNA encoding sequences form up to 1% of the human genome [10].
Biogenesis of miRNA begins in the nucleus, where the transcription of its precursor, primary miRNA or pri-miRNA takes place under the influence of RNA polymerases II and III [11,12]. The resulting molecule is a hairpin-like structure, which contains a loop at one end [11]. This primordial mi-RNA precursor that is usually made up of hundreds of nucleotides is then processed consecutively by two RNase III enzymes [13–15].
The first enzyme to act upon the pri-miRNA, which still resides in the nucleus, is called Drosha or DCGR8, and turns it into a new hairpin-like structure of approximately 70 nucleotides, the Precursor-miRNA or pre-miRNA. The latter is then transported to the cytoplasm, with the help of Exportin-5, where it is cleaved again by the Ago2/Dicer complex leading to the short, mature miRNA double strands [16].
Further on, one of the strands, usually known as the guide strand, will be integrated into the RNA-induced silencing complex (RISC), while the other one, known as the passenger strand, is going to be degraded, even though in some occasions it has been found to be also functional [17].
In most cases, the strand that contains the less stable 5, end or a uracil at the beginning is more likely to be selected as the guide strand [18–20]. In those situations, where the passenger strand is not degraded and both get incorporated into the miRISC complex, the mature miRNA in the guide strand will be the dominant one [21,22].
The main role of miRNA in the human body is gene regulation [23] by mediating the degradation of mRNA and also by regulating transcription and translation through canonical and non-canonical mechanisms [4]. The canonical mechanism means that the miRISC complex containing the miRNA guide strand is exerting its action by binding to the target mRNA through its 3,-untranslated region (3,-UTR) [3].
This process happens in accordance with the seed sequence of the miRNA, the first 2-7 nucleotides from the 5, end, and it is followed by mRNA deadenylation, translation suppression and finally, degradation [24–26]. However, in human cells, about 60% of the interactions between the miRISC complex and mRNA are non-canonical [27], which means that their chains are not always entirely complementary [28].
This leads to the idea that a single miRNA could potentially target numerous mRNAs, while at the same time, one mRNA could contain multiple binding sites for miRNAs, turning this into a possibility that vast number of biological processes could be regulated by this interaction [3].
Another important role played by miRNA is intercellular signaling. Even though most of the miRNAs are found inside the cell, there is a big proportion that migrates outside it and can be found in bodily fluids [29–33]. These are the so-called circulating miRNAs and they are discharged in blood, urine, saliva, seminal fluid, breast milk [30,34] and other fluids through tissue damage, apoptosis, and necrosis [4], or through active passage, in microvesicles, exosomes, or through bonding to a protein [35,36].
The question has also been raised regarding the existence of exogenous miRNA in the blood of healthy subjects [37,38], its origin being assigned to bacteria, food and fungi primarily from the gut [3]. The possible pathological effects of these exogenous miRNA are also taken into consideration. Previous studies have shown that about 10% of the circulating miRNAs are secreted in exosomes, while the other 90% form complexes with proteins like argonaute 2 (Ago2), nucleophosmin 1 (NPM 1) and high density lipoprotein (HDL) [36,39,40].
This kind of packaging is essential in order to prevent the digestion of miRNA, by the RNases known to be found in the bodily fluids [41].
Having a diameter of 50–100 nm, exosomes are a form endosome-derived microvesicles fusing with the membrane, which contains, amongst others, miRNA. They are secreted by numerous cells, both in vitro and in vivo and can be encountered in most of the fluids in the body [30,34].
Several previous studies have demonstrated in vitro the assimilation of exosomes by neighboring cells, thus leading to the idea of a “message in a bottle” type intercellular communication [42–44]. Furthermore, another hypothesis supported by recent findings states that miRNAs could be selectively designated to certain exosomes because of the fact that the miRNA variety found in these vesicles tends to be different from that of their cell of origin [42,45–47].
As mentioned earlier, the transport with the help of a protein of circulating miRNAs is estimated at around 90% of the total. Some papers suggest that this type of transport could be ATP-dependent [36], miRNAs have first been established as biomarkers for cancer in 2008, when Lawrie et al. utilized them for the examination of diffuse large B-cell lymphoma in the serum of patients [84,85], and ever since, their potential use as biomarkers has been mentioned in literature for numerous diseases.
This novel class of molecules possesses an array of advantages that could turn them into ideal candidates for biomarkers in a variety of afflictions. As mentioned before, the ideal biomarker needs to be easily accessible, a condition that applies to miRNAs that can easily be extracted through liquid biopsies from blood, urine and other bodily fluids.
It also has a high specificity for the tissue or cell type of provenance and it is sensitive in the way that it varies according to the disease progression, being used in several studies for the differentiation of the cancer stages [86] and even for the measurement of the therapy responsiveness [87].
Moreover, the technologies for the detection of nucleic acids already exist and the development of new assays requires less time and lower costs in comparison to producing new antibodies for protein biomarkers.
Another advantage of miRNAs lies in their potential for being used as multimarker models for accurate diagnosis, guided treatment and evaluating responsiveness to treatment.
While running many protein markers may be both expensive and time-consuming, using multimarker panels composed of numerous miRNAs may provide a non-invasive method for diagnosis and prediction of disease progression. For instance, identifying the urinary miRNA signature of lupus nephritis has promoted the early detection of renal fibrosis [88]. This is especially important in cancer, a thoroughly heterogenous disease, where a multimarker approach would be preferable. To this extent, a nine-miRNA multimarker panel for breast carcinoma has already been shown to significantly improve the reliability of breast cancer diagnosis [89].
However, the research of miRNAs as biomarkers is still in its early stages, therefore at the moment, the findings generally lack reproducibility. There are several discordances reported between different teams that have analyzed the same tumors [90]. In order to resolve this issue, standardized protocols must be developed both for the initial stages of the process, like sample collection, transport, and storage, as well as data analyzing for the diversity of technological methods used.
reference link : doi:10.3390/cells9020276
More information: Brandi Patrice Smith et al, Identification of early liver toxicity gene biomarkers using comparative supervised machine learning, Scientific Reports (2020). DOI: 10.1038/s41598-020-76129-8
