Autores
Ossa Jaramillo, C.A. (UNIVERSIDAD DE CALDAS) ; Rosero Moreano, M. (UNIVERSIDAD DE CALDAS) ; Satoshi, O. (JIRCAS) ; Jaimes Mogollon, L. (UNIVERSIDAD DE PAMPLONA) ; Ionescu, R. (ESTONIAN LIFE SCIENCES UNIVERSITY) ; Zanella, R. (UNIVERSIDADE FEDERAL DO SANTA MARIA) ; Haick, H. (TECHNION)
Resumo
The rice crop is affected by multiple diseases, whether caused by bacteria or
fungi, therefore a solution is required to control this type of affectation.
Oryza sativa or rice as it is commonly known, which is a cereal of great
importance in many culinary cultures, as well as in Latin America. The pathogenic
fungus Gaeumannomyces graminis, affects this crop. It is intended to identify
biomarkers or volatile organic compounds, which are produced when a microorganism
or plant is subjected to stress conditions. By means of HS-SPME-GC-MS, sampling
was carried out, using a red commercial fiber and Lab Made, for the in-vitro
detection of VOCs. Several secondary metabolites were identified, including
heptadecane.
Palavras chaves
drone-SPME; biomarkers; rice crops
Introdução
The rice crop is affected by multiple diseases, whether caused by bacteria or
fungi, therefore a solution is required to control this type of affectation.
This project seeks to increase the productivity and efficiency of the crops, by
making decisions in real time and based on the health of the crops, reducing
with the CitOMICs platform, the time to take decisions and the resources to
improve the health of the crops, e.g. water, pesticides, fertilizer, adjuvants.
CitOMICs is projected in the future as a platform based on Artificial
Intelligence, being a support system for agricultural crops in real time,
increasing the productivity and efficiency of agriculture, reducing losses and
generating savings in time and resources.
Oryza sativa or rice as it is commonly known, which is a cereal of great
importance in many culinary cultures, as well as in Latin America (Rodríguez,
2015). Rice grain is considered the second most produced worldwide (Rodríguez,
2015).
Rice yield is subject to the following factors: the number of panicles per unit
area, the number of spikelets or grains per panicle, and the percentage of full
grains (Rodríguez, 2015). In this order of ideas, it is also necessary to add to
these yield factors, the affectation of crops by phytopathogens (Rodríguez,
2015). Which significantly damage crops and even destroy the harvest (Rodríguez,
2015). A phytopathogen of interest in this case is the "sick foot", "orange
stain" or the pathogenic fungus Gaeumannomyces graminis. The pathology of this
fungus consists of the affectation of the root zone of the plant, this
phytopathogen is found in the soil (Tapia, 2013).
This project seeks to contribute in the future, to control pests in the
agricultural sector of the country, in mitigating the loss of crops and saving
expenses. Based on an early, rapid, non-invasive and low-cost warning. According
to the Food and Agriculture Organization of the United Nations (FAO), in their
most recent report they highlight that the goal to end hunger and malnutrition
has deviated in recent years, taking into account Covid-19 as one of the main
reasons (FAO, 2021). Factors that have influenced the negative figures to combat
famine in the world have been conflicts, extreme climate variability and
conditions, (FAO, 2021) .
A crop can be monitored by means of precision agriculture (PA), which consists
of the use of information technologies to adapt the management of soils and
crops, to the variability present within a lot (García and Flego, 2008). With
the PA, costs in personnel and supplies are minimized to a great extent, in
addition to protecting crops by making decisions in less time (García and Flego,
2008).
Living organisms release a series of substances into the environment, called
volatile organic compounds (VOCs) (Cantúa Ayala et al., 2019; Dudareva et al.,
2006). In this sense, plants, humans, animals and microorganisms, emanate this
type of substances to the environment in a predominant way, in the face of some
biotic or abiotic stress situation, understood as a response or alert mechanism
(Cantúa Ayala et al., 2019; Dudareva et al., 2006). Plants synthesize different
VOCs fulfilling an ecological role (Cantúa Ayala et al., 2019; Dudareva et al.,
2006).
Based on the above, when a crop is attacked by a pathogen, it will express
certain VOCs, in response to a stress condition (Cantúa Ayala et al., 2019;
Dudareva et al., 2006). In this order of ideas, the aim is to identify secondary
metabolites that are expressed in the rice crop attacked by the Gaeumannomyces
graminis fungus. There are four varieties of this fungus, the one that attacks
rice is called Gaeumannomyces graminis var. graminis or also known as orange
spot (Valencia, 2019). This project aims to achieve the following objectives:
GENERAL GOAL
To identify secondary metabolites (biomarkers) generated in the infection of
rice plantations affected by the Gaeumannomyces graminis fungus, through the
CitOMICs drone-SPME platform, as a strategy for early, rapid and non-invasive
detection of the disease, and that allows to build a future support system for
decision-making in crop health.
SPECIFIC GOALS
• To identify in vitro secondary metabolites (biomarkers) of the infection of
the fungus Gaeumannomyces graminis in rice crops using HS-SPME-GC-MS.
• To identify in the experimental farm in situ, volatile organic compounds
(VOCs) in rice cultivation using drone-SPME.
• To perform comparative chemometric analysis of correlation between secondary
metabolites in vitro with those in situ.
• To train an array of nanosensors with above biomarkers identified by machine
learning for pattern recognition of crop disease
• To build up platform CitOMICs by communication amongs the trained nanosensors
in target farms by seeding the analysis into the cloud computation to explore
the disease annotation for early diagnosis and control.
Material e métodos
1. VOC Acquisition
1.1 Tuning of the validated methodology of HS-SPME-GC-MS analysis with biomarker
standards described in literature (Validated Method)
Chromatographic conditions
A gas chromatograph coupled to Shimadzu Q2010plus mass spectrometry (GC-MS) was
used. The analysis is started with the oven at 40°C, and a heating time of 1
minute, then the temperature is raised to 120°C at a rate of 10°C/min and a
heating time of 1 minute at 120°C, then temperature is now brought to 180 °C
with a speed of 10 °C/min and a heating time of 5 minutes, the temperature is
now brought to 230 °C at a speed of 10 °C and a heating time of 5 minutes at 230
°C, continues then with the increase to 250 °C and then to 280 °C, in both cases
at a rate of 10 °C/min and a heating time of 5 minutes for both temperatures.
Both the ion source and interface temperatures were 280 °C and He gas was used
as carrier gas. Columna: Zebron ZB-5 30.0 m x 0.25 mm i.d; film thickness 0.25
µm. The temperature at the injection port was 250 °C.
1.2 Sampling in vitro (cases and controls) safely in Petri box and transport to
the laboratory (Sampling and Reliable and safe laboratory samples)
SPME fibers, a red commercial fiber and Lab Made Fiber were used.
5 g of rice was placed in an Erlenmeyer flask with water and left covered for
several days, for the growth of microorganisms. Samplings were carried out daily
with the different fibers. The SPME fiber was left in head space (HS) for 30
min. Then put the fiber in HS in the Erlenmeyer and was exposed in the injection
port for 15 min, for the desorption of the analytes.
2. VOC Identification
2.1 Reading of samples in situ (cases and controls) by drone-SPME-GC-MS
(Chromatographic and spectrometric analysis database consolidated)
Desing and build up the SPME fibers. Biomarkers identification
Lab Made Fiber, is coated from a thin film of montmorillonite clay, modified
with ionic liquids, and this coating is generated by means of a deposition PVD
vapor physics by R.F. magnetron sputtering. This fiber is made up of nitinol
wire (Nitinol arches ϕ 0.012 inch × 5 cm length) and offers little memory
effect. With regard to biochemical pathways, websites such as Gene Ontology
AmiGO and the NCBI have been used so far to tentatively track.
3. VOC Signal/noise substraction
From this item onwards, are the pending points to be carried out
3.1 Annotation of pesticide crops signal/noise information analysis by GC-MS and
LC-MS for substraction of biomarkers non target stressing crop (Chromatographic
and spectrometric analysis database consolidated)
Crops monitoring. Pesticide crops signal/noise biomarkers supression
4. VOC Modelling
4.1 Multivariate analysis
4.2 Metabolomic approach through the use of bioinformatic platforms
5. VOC Training
5.1 Design of array nanosensors by training with above biomarkers identified
5.2 Pilot tests of device operation + citOMICs platform with targeted training
for available biomarker standards-Machine learning AI
Resultado e discussão
VOCs compounds are produced by fungi, bacteria and plants, they are
distinguished by having low molecular weight and high vapor pressure and are
generated under certain environmental conditions or stress within the body (DO
AMARAL ET AL., 2020). In this sense, in the different samplings carried out so
far, heptadecane, n-heptadecanol and dotriacontyl isopropyl ether were
identified.
Regarding heptadecane, after several days, when performing the GC-MS analysis,
n-heptadecanol appeared, that is, an oxidation of this compound possibly
occurred. The metabolic pathway by which alkanes are processed is determined by
the regiospecificity of alkane hydroxylase, which oxidizes alkanes at the
terminal or subterminal carbon (SKINNER, 2007).
During the terminal oxidation of alkanes, the activation of the molecule occurs
through hydroxylation events that result in the production of primary alcohols
(SKINNER, 2007).
The red commercial SPME fibers and the Lab Made, helped to detect the different
VOCs. In this order of ideas, the fibers that are made in the GICTA group of the
University of Caldas, are an option compared to commercial fibers, for the
implementation of microextraction techniques.
Conclusões
Until now, some VOCs or secondary metabolites generated in the reaction medium
have been identified, being heptadecane, n-heptadecanol and dotriacontyl isopropyl
ether.
After several days, heptadecane no longer appeared, but n-heptadecanol, due to
possible metabolic processes.
It remains to carry out more tests and samplings, with certified fungal strains
and the definitive identification of them as biomarkers of interest, those
responsible for pathogenicity, in rice cultivation.
The Lab Made fibers of the GICTA Group are an alternative to commercial fibers.
Agradecimentos
FEDEARROZ
MINCIENCIAS
Referências
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