FR3078166A1 - METHOD OF DETERMINING A PATHOLOGY OR SPECIES BY ANALYSIS OF FECAL MATERIAL BY MALDI-TOF TYPE MASS SPECTROMETRY - Google Patents

Abstract

The present invention relates to a method for determining a pathology of a human individual or a species of an animal by analysis of a faecal sample of said human or animal individual by MALDI-type mass spectrometry. TOF, wherein: a) a combination of a plurality of discriminant peaks is determined for which intensity value thresholds have been determined which make it possible to distinguish the spectra of faecal samples from individuals having said pathology or respective animals; said animal species with respect to the spectra of faecal samples of individuals not having said pathology or of animals not belonging to said animal species, respectively, as a function of the intensity values of said discriminating peaks; b) the spectrum of a sample of feces to be analyzed is analyzed, and c) it is checked whether the spectrum of said fecal sample to be analyzed comprises said discriminating peaks and if the values of the intensities of the discriminating peaks correspond to a combination of values of the intensities corresponding to the spectra of individuals presenting the said pathology or of individuals not having the said pathology or respectively to the spectra of animals belonging to the said animal species or not belonging to the said animal species, as determined in step a).

Description

Method for determining a pathology or a species by analysis of faecal material by MALDI-TOF type mass spectrometry.

The present invention relates to a method for determining a pathology of a human individual or of a species of animal by analysis of a sample of faeces of said human or animal individual respectively by mass spectrometry of MALDI type TOF.

MALDI-TOF type mass spectrometry is a technique which allows the desorption of proteins from a biological product, subjecting them to an electric field and measuring the time of flight of peptides ("AM of flight") which is determined both by their weight and by their electrical charge. This technique has many uses and has been widely used for bacterial identification [1] but also for the identification of animals including arthropods and mammals [2-5].

More specifically, mass spectrometry is a physical analysis technique used to detect and identify molecules of interest by measuring their mass. Its principle lies in the separation in the gas phase of charged molecules (ions) according to their mass / charge ratio (m / z). It also makes it possible to characterize the chemical structure of molecules by fragmenting them. Its use, easy, fast and inexpensive, has spread widely in many scientific fields, including in biology. This is how, combined with other approaches, it allows the study of the proteome. Recently extended to medicine, it makes it possible to study the patient's serum, a complex mixture of proteins, which can be useful in the diagnosis of various diseases. Mass spectrometry under its MALDI-TOF variant (matrix-assisted laser desorption ionization-time-of-flight) has recently entered microbiology laboratories. It allows the detection of bacterial toxins. It also makes it possible to identify and classify different bacterial types using intact bacteria. Indeed, among the various components identified in a MALDI-TOF mass spectrometry spectrum, some seem specific to a specific bacterial species. Thus, it was possible to identify a large number of bacterial species in clinical samples by using databases made from each of these species.

Mass spectrometry of the MALDI-TOF type has imposed itself in recent years in clinical microbiology laboratories for the identification of colonies of bacteria or yeasts isolated on solid culture medium from human samples. Indeed, its advantages are numerous: (1) speed of identification (in a few minutes); (2) precision of identification (without prior need to know the type of microorganism sought); and (3) low cost per sample (less than 50 euro cents per sample).

Among the mass spectrometer manufacturers, two complete MALDI-TOF systems are available for sale, marketed by the companies Bruker Daltonics (Germany) and Biomérieux (France). Each instrument is accompanied by control software, a database obtained from colonies isolated in solid medium and an expert system allowing the identification of the microorganism based on the analysis of the spectrum generated by the spectrometer of mass. An application of this technology to the identification of bacteria currently involves the isolation of colonies (obtained after 24 - 48 h of culture) and the analysis of whole bacteria or protein extracts of isolated bacteria, said bacteria being isolated directly to from patient samples.

In WO 2016066930, a method of identifying microorganisms has been described directly from samples of urine from patients, without a culture step, in order to be able to obtain, very quickly, the identification of pathogenic microorganisms in samples from patients .

Finally, MALDI-TOF mass spectrometry has made it possible to characterize different spores of fungi, yeasts and pollens, in particular in US 2003/027231.

In WO 2011154650, it has been described that it was possible to identify, that is to say to characterize in a specific manner, from whole cells (that is to say cells not previously lysed before analysis). types of mammalian eukaryotic cells by MALDI-TOF type mass spectrometry. This was made possible by taking into account all the peaks of the spectra, that is to say by a combinatorial analysis of the spectra involving the comparison spectrum to spectrum and not the search for specific peaks in themselves taken separately. This specific characterization results from the fact that, during a mass spectrometry of the MALDI-TOF type, said intracellular proteins are in sufficient number and their nature and their relative proportion are sufficiently distinct and variable and therefore specific between the different eukaryotic cell types for allow a specific characterization of the cell type by an analysis of the mass spectrum of this combination of proteins, provided that no attempt is made to identify said cells from specific peaks, corresponding to specific proteins, but by analysis combinatorics of peaks, namely by comparison of complete spectra, in particular of at least 70 peaks.

The study of the digestive microbiota carried out by metagenomic molecular biology techniques but also by culture techniques called culturomics [6, 7] reveals a diversity of microbes obtained in a certain number of pathological situations and / or according to animal species.

According to the present invention, it has been discovered that the direct use of faeces, which mainly consist of microbes, can give specific signatures of an animal species and of specific pathologies in humans even in the absence of identification of a specific pathogen concerned by said pathology.

To do this, the present invention consists of the in vitro analysis by the MALDI-TOF mass spectrometry technique of faeces without prior treatment followed by the analysis of the spectra obtained by normalization treatment, smoothing and calibration of the spectra after definition. quality criteria, allowing groupings corresponding perfectly to the identity of animal species and pathologies of the subjects whose stool samples were tested. Thus, it was discovered that it was possible, on the one hand, to identify an animal species from the direct analysis of samples of excrement from it, especially for different species of monkeys around the world. Thus, for example baboons from Saudi Arabia and Senegal, chimpanzees from Senegal, bonobos from the Democratic Republic of Congo, gorillas from Congo Brazzaville, green monkeys and red monkeys from Senegal could be tested thanks to this technical. At the same time, we tested as a control group, on the one hand humans and on the other hand dogs.

Interestingly, we used a molecular tool to recognize individuals and this showed that at this level of distinction, it was however impossible to recognize the individual in the context of this analysis of fecal matter.

It was discovered that it was possible to identify a said animal species or a pathology due to the modifications of the microbiota involved in this disease, even in the absence of detection of a protein extract of one or more specific proteins. of said species or said pathology.

More specifically, the present invention provides a method for determining a pathology of a human individual or of a species of animal by analysis of a sample of faeces of said human or animal individual respectively by mass spectrometry of MALDI-TOF type, in which:

a) a combination of a plurality of discriminating peaks is determined for which intensity value thresholds are determined making it possible to distinguish the spectra of fecal samples from individuals with said pathology or respectively animals belonging to said animal species by relation to the spectra of samples of faeces of individuals not presenting said pathology or respectively of animals not belonging to said animal species, as a function of the values of the intensities of said discriminating peaks;

b) the spectrum of a sample of faeces to be analyzed is produced, and

c) it is checked whether the spectrum of said sample of faeces to be analyzed comprises the said discriminating peaks and whether the values of the intensities of the discriminating peaks correspond to a combination of values of the intensities corresponding to the spectra of individuals presenting said pathology or of individuals not presenting said pathology or respectively to the spectra of animals belonging to said animal species or not belonging to said animal species as determined in step a).

The inventors consider it probable without being bound by this theory that this discovery of a specificity of discrimination allowing the determination of a pathology of a human individual or of an animal species by analysis of a sample of faeces by spectrometry. MALDI-TOF type mass could result from the fact that the protein composition of faeces is conditioned by the composition of the microbial ecosystem of the digestive tract and eating habits which in practice would therefore be correlated with the pathology of the individual and his species.

More particularly, the spectrum of a said sample of crude feces dissolved in a matrix solution preferably of saturated solution of alpha-cyano-4-hydroxycinnamic acid in 50% acetonitrile and 2.5% tri-fluoracetic acid.

More particularly still, in step a) a combination of a plurality of discriminating peaks is determined, characterized in that ranges of intensity values are determined making it possible to distinguish the spectra of samples from individuals with said pathology or respectively belonging to the said animal species and the spectra of samples of individuals not presenting the said pathology or respectively not belonging to the said animal species, according to the different values of the intensities of the discriminating peaks concerned in the spectrum of a sample to be analyzed thus making it possible to determine the presence or not of said pathology or respectively belonging or not to said species depending on whether for said discriminating peaks the intensity of each discriminating peak is specifically inside or outside of at least one range of intensity values, said ranges being able to be identical or of pre different reference for the different discriminating peaks.

In one embodiment, a said range consists of the values above or below a threshold of intensity values.

More particularly, according to a first embodiment illustrated in Examples 1 to 3, in step a), the presence or absence of said pathology is determined or, respectively, whether or not it belongs to said species, depending on whether for said discriminating peaks, the intensity values are included in a first range of intensity values to determine the presence of said pathology or respectively belonging to said species or within a second range of values of intensities to determine the absence of said pathology or respectively the non-belonging to said species, said first range and second range not overlapping, and this for each said discriminating peak.

More particularly still, in this first embodiment, in step a), the probability of the presence of said pathology or respectively of belonging to said species is all the greater as the number nor of said discriminating peaks for which the relative intensity values are inside said first ranges, is greater and greater than the number n2 of said discriminating peaks for which the relative intensity values are inside said second ranges.

More particularly still, in this first embodiment, an identification score S is determined from “-1” to “+1”, “1” corresponding to 100% certainty of the presence of said pathology or belonging to said species and “-1” corresponding to 100% certainty of the absence of said pathology or not belonging to said species, S being calculated by the sum of the scores “if” with i = l to p, for the peaks sum which is divided by the number p of peaks, such that for each of the discriminating peaks, the score si is of values "1" if the intensity of the peak is in the said first range, "-1" if l the intensity of the peak is in said second range and “0” if the intensity of the peak is not included in said first and second ranges.

More particularly still in this first embodiment, in step a), the probability of the presence of said pathology or belonging to said species is 100% if for all said discriminating specific peaks, the values of relative intensities are at inside said first ranges, and the probability of the absence of said pathology or not belonging to said species is 100% if for all said discriminating peaks the values of relative intensities are inside said second ranges.

In practice, we manage to discriminate a group of subjects as presenting a pathology or belonging to an animal species with a number of 1 to 15 discriminating peaks, preferably 3 to 10 discriminating peaks.

More particularly still according to this first embodiment, the presence or absence of the disease consisting of a colitis resulting from an infection with Clostridium difficile is determined for which, in step a), 8 so-called first and second ones were identified. ranges of intensity values of 8 discriminating peaks.

More particularly still, to determine the presence or absence of the disease consisting of a colitis resulting from an infection by Clostridium difficile, the so-called 8 discriminating peaks are peaks at 2710, 3372, 3399, 3408, 3445, 4374, 5658 , 11047 Da at +/- 0.5%.

More particularly still, to determine the presence or absence of the disease consisting of a colitis resulting from an infection by Clostridium difficile, the said first and second ranges of intensity values for said 8 following discriminating peaks, expressed in d values relative intensities compared to the sum of the intensity values of the spectrum normalized to the value 1, are:

-For the peak at 2710 Da,

a so-called first range of relative intensity values corresponds to the relative intensity values less than 4.56326002121813 x 10 ' ⁵ , and

a so-called second range of relative intensity values corresponds to the relative intensity values greater than 4.86226943144253x 10 '⁵; and

-For the peak at 3372 Da,

a so-called first range of relative intensity values corresponds to the relative intensity values greater than 0.000434223789748754, and

-a second range of relative intensity values corresponds to the relative intensity values less than

0.000339153329205546; and

-For the peak at 3399 Da,

a so-called first range of relative intensity values corresponds to the relative intensity values greater than 9.98606712559408 x 10 ' ⁵ , and

a so-called second range of relative intensity values corresponds to the relative intensity values less than 9.7561289741134 x 10 '⁵; and

-For the peak at 3408 Da,

a so-called first range of relative intensity values corresponds to the relative intensity values greater than 0.000118664760690361, and

-a second range of relative intensity values corresponds to the relative intensity values less than 9.78528309013007 x 10 '⁵; and

-For the peak at 3445 Da,

a so-called first range of relative intensity values corresponds to the relative intensity values greater than 0.000837188281840417, and

a second range of relative intensity values corresponds to the relative intensity values less than 0.000634234550815951; and

-For the peak at 4374 Da,

a so-called first range of relative intensity values corresponds to the relative intensity values less than 9.55157313225957 x 10 ' ⁵ , and

a so-called second range of relative intensity values corresponds to the relative intensity values greater than

0.0001002315521507 x 10 '⁵; and

-For the peak at 5658 Da

a so-called first range of relative intensity values corresponds to the relative intensity values greater than 4.3325324409441 x 10 ' ⁵ , and

a so-called second range of relative intensity values corresponds to the relative intensity values less than 5.23774442820013 x 10 ^-5 ; and

-For the peak at 11047 Da,

a so-called first range of relative intensity values corresponds to the relative intensity values less than 4.98150891075045 x 10 ' ⁵ , and

-a second range of relative intensity values corresponds to the relative intensity values greater than 5.199534671731 10 ' ⁵ .

More particularly, in a second example of pathology, Crohn's disease is determined for which in step a), 3 so-called first and second ranges of intensity values of 3 discriminating peaks have been identified.

More particularly still, in this second pathology, the so-called 3 discriminating peaks are peaks at 3343, 4134 and 4170 Da at +/- 0.5%.

More particularly, in this second example of pathology; said first and second ranges of intensity values for said following 3 discriminating peaks, expressed in relative intensity values with respect to the sum of the intensity values of the spectrum normalized to the value 1, are:

-For the peak at 3343 Da,

a so-called first range of relative intensity values corresponds to the relative intensity values greater than 0.000153184507861765, and

a so-called second range of relative intensity values corresponds to the relative intensity values less than 0.000121099556323652; and

-For the peak at 4134 Da,

a so-called first range of relative intensity values corresponds to the relative intensity values greater than 0.000425424602595293 and

a so-called second range of relative intensity values corresponds to the relative intensity values less than 5.33325181290711 x 10 '⁵; and

-For the peak at 4170 Da,

a so-called first range of relative intensity values corresponds to the relative intensity values greater than 0.000148115657553745, and

a so-called second range of relative intensity values corresponds to the relative intensity values less than 0.000115898008983965.

More particularly, in a third example of pathology, the presence or absence of the disease consisting of ulcerative hemorrhagic ulcerative colitis is determined for which, in step a), 11 so-called first and second ranges of values are identified. intensities of 11 discriminating peaks.

More particularly, in this third example of pathology, the so-called 3 specific discriminating peaks are peaks at 3324, 3345, 3444, 3482, 3493, 6451, 7268, 9599, 9620, 9636, 9707 Da at +/- 0.5%.

More particularly, in this third example of pathology, said first and second ranges of relative intensity values for said following discriminating peaks, expressed in relative intensity values with respect to the sum of the intensity values of the spectrum normalized to the value 1, are:

-For the peak at 3324 Da,

a so-called first range of relative intensity values corresponds to the relative intensity values greater than

0.000202083994783644, and

a second range of relative intensity values corresponds to the relative intensity values less than 0.000120628491201788; and

-For the peak at 3345 Da,

a so-called first range of relative intensity values corresponds to the relative intensity values greater than 9.9939643301577 x 10 ' ⁵ , and

a so-called second range of relative intensity values corresponds to the relative intensity values less than 9.29121405175384 x 10 '⁵;

-For the peak at 3444 Da,

a so-called first range of relative intensity values corresponds to the relative intensity values greater than 0.000201322490887782; and

a so-called second range of relative intensity values corresponds to the relative intensity values less than 0.00012259036150653; and

-For the peak at 3482 Da,

a so-called first range of relative intensity values corresponds to the relative intensity values greater than 0.000136036653540984, and

a second range of relative intensity values corresponds to the relative intensity values less than 0.00011930447354638; and

-For the peak at 3493 Da,

a so-called first range of relative intensity values corresponds to the relative intensity values greater than

0.000289961990980622, and

a second range of relative intensity values corresponds to the relative intensity values less than 0.000183699312236316; and

-For the peak at 6451 Da,

-a so-called first range of relative intensity values corresponds to relative intensity values less than 9.67374238926758 x 10 ' ⁵ , and

a so-called second range of relative intensity values corresponds to the relative intensity values greater than 0.000107099318660843 x 10 '⁵;

-For the peak at 7268 Da,

a so-called first range of relative intensity values corresponds to the relative intensity values less than 6.0909717149715 x 10 ' ⁵ , and

a so-called second range of relative intensity values corresponds to the relative intensity values greater than 9.51109288448326 x 10 '⁵; and

-For the peak at 9599 Da,

a so-called first range of relative intensity values corresponds to the relative intensity values less than 5.45853583475995 x 10 ' ⁵ , and

a so-called second range of relative intensity values corresponds to the relative intensity values greater than 8.01651427124604 x 10 ^-5 ; and

-For the peak at 9620 Da

a so-called first range of relative intensity values corresponds to the relative intensity values less than 5.45853583475995 x 10 ' ⁵ , and

a so-called second range of relative intensity values corresponds to the relative intensity values greater than 8.01651427124604 x 10 ^_5 ; and

-For the peak at 9636 Da,

a so-called first range of relative intensity values corresponds to the relative intensity values less than 5.48801247377731 x 10 ' ⁵ , and

-a second range of relative intensity values corresponds to relative intensity values greater than 7.5819888660436 x 10 '⁵;

-For the peak at 9707 Da,

a so-called first range of relative intensity values corresponds to the relative intensity values less than 5.75858354561268 x 10 '⁵; and

-a second range of relative intensity values corresponds to the relative intensity values greater than 7.47308753223 x 10 ' ⁵ .

In a second embodiment, in step a), one or more combinations of said discriminating peaks are determined with:

a) first ranges of intensity values of said discriminating peaks of said combination to determine the presence of said pathology or respectively belonging to said species and

b) second ranges of intensity values of said discriminating peaks of said combination to determine the absence of said pathology or respectively the non-belonging to said species,

c) at least some of the said first and second ranges overlapping for some of the said discriminating peaks, but nevertheless making it possible, by the plurality of the discriminating peaks, to determine the presence of said pathology or respectively belonging to said species by series of intensity values for said discriminating peaks of the combination depending on whether they are inside or outside of said first ranges or second ranges.

In practice, this embodiment consists in constituting a decision tree for determining the presence of said pathology or respectively belonging to said species implying the comparison of the peaks and intensity values of a spectrum to be analyzed with respect to the plurality of couples (picfou rchette (s) of intensities) of said combination.

More particularly, in this second embodiment, said first ranges and second ranges formed by values above and respectively below thresholds of intensity values are determined.

More particularly still, in this second embodiment, said thresholds of intensity values are determined by approximation to median values of intensity values of the intervals between said first and second ranges.

More particularly still, in this second embodiment, the presence or absence of the AIDS disease resulting from an HIV infection is determined for which in step a), 4 so-called first forks and second forks are determined. intensity values of 4 discriminating peaks.

More particularly still, in this second embodiment, the presence or absence of the AIDS disease resulting from an HIV infection is determined for which in step a), 4 thresholds of intensity values are determined. 4 discriminating peaks such as:

1) the disease is present if the intensity values of the 4 peaks are such that:

1.1) -the intensity of peak n ° l is greater than the intensity threshold of peak n ° l, the intensity of peak n ° 2 is lower than the intensity threshold of peak n ° 2 and the intensity of peak n ° 3 is higher than the intensity threshold of peak n ° 3, or

1.2) -the intensity of peak n ° l is greater than the intensity threshold of peak n ° l, the intensity of peak n ° 2 is lower than the intensity threshold of peak n ° 2, the intensity of peak n ° 3 is lower than the intensity threshold of picn ° 3 and the intensity of peak n ° 4 is lower than the intensity threshold of peak n ° 4; and

2) the disease is not present if the intensity values of the 4 peaks are such that:

2.1) the intensity of peak n ° l is lower than the intensity threshold of peak n ° l,

2.2) the intensity of peak n ° l is greater than the intensity threshold of peak n ° l and the intensity of peak n ° 2 is greater than the intensity threshold of peak n ° 2, and

2.3) the intensity of peak n ° l is greater than the intensity threshold of peak n ° l, the intensity of peak n ° 2 is lower than the intensity threshold of peak n ° 2, the intensity of peak n ° 3 is below the intensity threshold of peak n ° 3 and the intensity of peak n ° 4 is greater than the intensity threshold of peak n ° 4.

More particularly still, to determine the presence or absence of AIDS disease, the peaks n ° 1 to 4 are, at +/- 0.5%:

- 4160 Da for peak n ° l,

- 3319 Da for peak n ° 2,

- 3607 Da for peak n ° 3, and

- 5143 Da for peak n ° 4.

More particularly still, in this second embodiment, to determine the presence or absence of the AIDS disease resulting from an HIV infection, the relative intensity thresholds of said discriminating peaks are threshold values of relative intensities expressed in relative intensity values with respect to the sum of the intensity values of the spectrum normalized to the value 1, are:

- for 0.000116691, peak # 1 at 4160 Da, a value of intensity threshold on of - for pic n ° 2 at 3319 Da, an intensity threshold value of 0.0006103814 f - for peak # 3 at 3607 Da, a value of intensity threshold on of 0.000110201, and - for peak # 4 at 5143 Da, a value of intensity threshold on of 0.000144286. More specifically again in all the shifts lisation, we determine the presence or absence of pathology e of an individual human through

analysis of a sample of faeces by mass spectrometry of the MALDI-TOF type in which the said discriminating peaks are not present in the spectra of a sample of pathogen responsible for said isolated pathology, but reflect a modification of the composition of feces associated with said pathology.

More particularly still, in the second embodiment, in step a), a combination of a plurality of discriminating peaks is determined for which first ranges and second ranges of discriminating peak intensity values have been determined making it possible to distinguish the spectra of samples of individuals belonging to said animal species and the spectra of samples of individuals not belonging to said animal species, according to the different values of the intensities of the discriminating peaks in the spectrum of a sample to be analyzed thus making it possible to constitute a binary decision tree of belonging or not to said species depending on whether for said discriminating peaks taken successively the intensity of each peak is specifically inside or outside of said first ranges or second ranges .

More particularly still, in the second embodiment, one determines whether or not one of the following 9 species of monkeys belongs: Baboons Papio hamadryas from Saudi Arabia, Baboons Papio papio from Senegal, Chimpanzees Pan troglodytes verus du Senegal, green monkeys Ch / orocebus sabaeus from Senegal, red monkeys Erythrocebus patas from Senegal, gorillas Gorilla gorilla gorilla from Republic of Congo, howler monkeys

A / ouatta senicuius from French Guiana, the Bonobos Pan panicus Loia and the Bonobos Pan panicus Eko / o from the Democratic Republic of the Congo, for which the first 9 ranges and 9 second ranges of intensity values of 9 discriminating peaks are determined.

More particularly still, one determines the membership or not to one of the 9 said species of following monkeys: The baboons Papio hamadryas of Saudi Arabia, the baboons Papio papio of Senegal, the Chimpanzees Pan troglodytes verus of Senegal, the green monkeys Chiorocebus sabaeus from Senegal, red monkeys Erythrocebus patas from Senegal, gorillas Gorilla gorilla gorilla from Republic of Congo, howler monkeys Aiouatta senicuius from Guyana, bonobos Pan panicus Loia and bonobos Pan panicus Eko / o from Democratic Republic of Congo, for which are determined 10 thresholds of intensity values of 9 discriminating peaks, such as:

1) the said species is the chimpanzee Pan troglodytes verus if the intensity of peak n ° l is greater than the intensity threshold of peak n ° l,

2) the said species is the gorilla Gorilla gorilla gorilla if the intensity of peak n ° l is less than or equal to the intensity threshold of peak n ° l, the intensity of peak n ° 2 is greater than the intensity threshold of peak 2 and the intensity of peak 3 is less than or equal to a first intensity threshold of peak 3;

3) the said species is the Papio papio baboon if the intensity of peak n ° l is less than or equal to the intensity threshold of peak n ° l, the intensity of peak n ° 2 is less than or equal to the threshold of intensity of peak 2, intensity of peak 4 is greater than the intensity threshold of peak 4 and intensity of peak 5 is less than or equal to the intensity threshold of peak 5 , or

4) the said species is the bonobo Pan panicus eko / o if the intensity of peak n ° l is less than or equal to the intensity threshold of peak n ° l, the intensity of peak n ° 2 is less than or equal to intensity threshold of peak 2 and the intensity of peak 3 is less than or equal to a second intensity threshold of peak 3; the intensity of peak 4 is less than or equal to the intensity threshold of peak 4 and the intensity of peak 6 is greater than the intensity threshold of peak 6;

5) the said species is the Pan panicus Lo / a bonobo if:

- the intensity of peak n ° l is less than or equal to the intensity threshold of peak n ° l, the intensity of peak n ° 2 is greater than the intensity threshold of peak n ° 2 and the intensity of peak n ° 3 is higher than the second intensity threshold of peak n ° 3; or

- the intensity of peak n ° l is less than or equal to the intensity threshold of peak n ° l, the intensity of peak n ° 2 is less than or equal to the intensity threshold of peak n ° 2, the intensity of peak # 3 is greater than the second intensity threshold of peak # 3; the intensity of peak 4 is less than or equal to the intensity threshold of peak 4 and the intensity of peak 6 is greater than the intensity threshold of peak 6; and

6) the said species is the red monkey Erythrocebus patas if the intensity of peak n ° l is less than or equal to the intensity threshold of peak n ° l, the intensity of peak n ° 2 is less than or equal to the threshold d intensity of peak n ° 2, intensity of peak n ° 4 is less than or equal to the intensity threshold of peak n ° 4, intensity of peak n ° 6 is less than or equal to the intensity threshold of peak n ° 6 and the intensity of peak n ° 7 is higher than the intensity threshold of peak n ° 7;

7) the said species is the Papio hamadryas baboon if:

- the intensity of peak n ° l is less than or equal to the intensity threshold of peak n ° l, the intensity of peak n ° 2 is less than or equal to the intensity threshold of peak n ° 2, the intensity of peak 4 is greater than the intensity threshold of peak 4 and the intensity of peak 5 is greater than the intensity threshold of peak 5, or

- the intensity of peak n ° l is less than or equal to the intensity threshold of picn ° l, the intensity of peak n ° 2 is less than or equal to the intensity threshold of picn ° 2, the intensity of peak n ° 4 is less than or equal to the intensity threshold of picn ° 4, the intensity of peak n ° 6 is less than or equal to the intensity threshold of picn ° 6, the intensity of peak n ° 7 is lower or equal to the intensity threshold of peak 7 and the intensity of peak 8 is greater than the intensity threshold of peak 8;

8) the said species is the green monkey Ch / orocebus sabaeus if the intensity of peak n ° l is less than or equal to the intensity threshold of peak n ° l, the intensity of peak n ° 2 is less than or equal to intensity threshold of peak n ° 2, intensity of peak n ° 4 is less than or equal to the intensity threshold of peak n ° 4, intensity of peak n ° 6 is less than or equal to the intensity threshold of peak n ° 6, the intensity of peak n ° 7 is less than or equal to the intensity threshold of peak n ° 7, the intensity of peak n ° 8 is less than or equal to the intensity threshold of peak n ° 8 and the intensity of peak 9 is greater than the intensity threshold of peak 9; and

9) the said species is the howler monkey A / ouatta senicu / us if the intensity of peak n ° l is less than or equal to the intensity threshold of peak n ° l, the intensity of peak n ° 2 is lower or equal to the intensity threshold of peak n ° 2, the intensity of peak n ° 4 is

lower or equal at threshold intensity of picn ° 4, the intensity of peak # 6 East lower or equal at threshold intensity of picn ° 6, the intensity of peak # 7 East lower or equal at threshold intensity of pic n ° 7, the intensity of peak # 8 East lower or equal at threshold intensity of peak 8 and the intensity of peak # 9 East

less than or equal to the intensity threshold of peak n ° 9.

More particularly still, the peaks n ° 1 to 9 are, at +/- 0.5%:

- 2771 Da for peak n ° l,

- 3442 Da for peak n ° 2,

- 4668 Da for peak n ° 3,

- 3830 Da for peak n ° 4,

- 8731 Da for peak n ° 5,

- 14074 Da for peak n ° 6,

- 2186 Da for peak n ° 7,

- 4540 Da for peak n ° 8, and

- 2982 Da for peak n ° 9.

More particularly still, the intensity thresholds of said discriminating peaks expressed in relative intensity values with respect to the sum of the intensity values of the spectrum normalized to the value 1, are:

- for peak No. 1 at 2771 Da, a relative intensity threshold value of 0.000974671,

- for peak n ° 2 at 3442 Da, a relative intensity threshold value of 0.0001552983,

- for peak n ° 3 at 4668 Da, a value of first relative intensity threshold of 0.0001570043 and a value of second intensity threshold of 0.000222270079,

- for the 0.0005264181, peak # 4 at 3830 da, a value of threshold intensity on of - for 1.791533 x 10 ' ⁵ , peak # 5 at 8731 da, a value of threshold intensity on of - for the 0.0001110175, peak # 6 at 14074 da, a value of threshold intensity on of - for the 0.0001926287, peak # 7 at 2186 da, a value of threshold intensity on of - for the peak 0.0001414035, and # 8 at 4540 da, a value of threshold intensity on of - for 8.152213 x 10 ^-5 . peak # 9 at 2982 da, a value of threshold intensity on of

More particularly still, to determine whether or not it belongs to a said species, said discriminating peaks are not present in the spectra of a sample of protein specific for said species or for a pathology in said species.

More particularly still, the peaks of said spectra are defined by a pair of coordinates composed of an abscissa made up of the mass / charge ratio (m / z) of 2,000 to 20,000 Da, more particularly of 3,000 to 15,000 Da, the coordinate on the ordinate being a relative intensity or in an arbitrary unit, and the 40 peaks of greatest intensities are preferably retained in the spectrum.

In the tables below:

- the values mentioned are average values of relative intensity and molecular mass,

- the values of the molecular weights m / z can vary up to ± 0.5% of the average value mentioned in the tables, and

- the relative intensity values can vary up to ± 0.5%, from the average relative intensity value mentioned in the tables, and

- In the tables and in the figures, the value m / z is reported, z representing the electric charge of the ionized species. But, in practice, m / z can be assimilated to the molecular mass m, with z = l, because the quantity of proteins which ionize 2 times (z = +2), even 3 times (z = + 3), is negligible, given the limited sensitivity of measurement techniques.

The values reported in the tables were obtained by analysis in a matrix solution of α-cyano-4-hydroxycinnamic acid. More specifically, the raw stool was mixed with a matrix solution of α-cyano-4-hydroxycinnamic acid, during said analysis by mass spectrometry and not before.

Other characteristics and advantages of the present invention will appear in the light of the examples which will follow, made with reference to FIGS. A to E and FIGS. 1 to 7 below.

Figures A to E illustrate the general method of processing spectra to identify discriminating peaks described below.

Figures IA, 2A, 3A, 4A and 6 represent the results of binary discriminant analyzes with the discriminative powers of the 40 peaks (ordinate: m / z (Da)) of best discriminant scores for the group of patients infected with clostridium difficile ( CDI) of example 1 (figure IA), the group of patients with ulcerative colitis (UC) of example 2 (figure 2A), the group of patients with Crohn's disease of example 3 (Figure 3A), the group of AIDS patients of Example 4 (Figure 4A).

FIGS. 1B, 2B, 3B and 4B represent the distributions of the values of the intensities of the peaks having a discriminating power of 100% for the group of patients infected with clostridium difficile (CDI) of Example 1 (FIG. 1B). patients with ulcerative colitis (UC) of Example 2 (Figure 2B), the group of patients with Crohn's disease of Example 3 (Figure 3B), the group of AIDS patients Example 4 (Figure 4B).

FIG. 5 represents the binary classification tree from 4 specific peaks discriminating for HIV disease as a function of the intensities with respect to threshold values, of Example 4.

FIG. 6 represents the results of the binary discriminating analysis with the discriminating power of the 40 peaks of best score for the group of patients reached for the 9 groups of primates tested in example 5.

FIG. 7 represents the binary decision tree for 9 primate species from the 9 discriminating peaks as a function of the intensities with respect to threshold values, of Example 5.

In Examples 1 to 5 below, the following materials and methods have been used.

The method according to the present invention called “MERDI-TOF method” (MERDI from the Latin merda which means feces and TOF, from the English time of flight), by reference to the MALDI-TOF mass spectrometry technique consists in the analysis by this MALDI-TOF mass spectrometry technique of faeces without prior treatment followed by analysis of the spectra.

A) Preparation of stool samples.

The stool samples are placed directly on a MALDI-TOF spectrometer plate (Bruker Daltonics, Bremen, Germany). For each sample, four deposits are made on the plate and then covered with 2 μl of a matrix solution (saturated solution of alpha-cyano-4hydroxycinnamic acid in 50% acetonitrile and 2.5% tri-fluoracetic acid) .

The plate is dried for five minutes and then analyzed by Microflex type mass spectrometry (Bruker Daltonics, Bremen, Germany).

The MERDI-TOF method was evaluated on two types of stool: a "normal" stool, and a diarrheal stool. For each stool, a 50th dilution range was made on 10 tubes with PBS (Phosphate Buffered Saline). Each dilution as well as the undiluted saddle was directly deposited on a MALDI-TOF plate and then identified as described above. PBS was used as a negative control. For each saddle, the spectra obtained from the first to the 10th dilution were identical to the spectrum of the saddle without dilution which differed from the spectrum of the negative control. Thus we validated the method and its application to stools of any consistency.

B) The spectra acquisition method includes the following stages and characteristics:

1- introduce the plate (target Microflex reference model 224989 called MSP 96) containing the deposits in the Microflex LT mass spectrometer,

2- start the computer associated with the spectrometer to launch the analyzes, and

3- launch the acquisition of MALDI-TOF mass spectra.

4- the parameters applied to the mass spectrometer are as follows:

5- positive linear mode, IS1 electrode: 20.00 kV, IS2 electrode: 18.05 kV, Focusing electrode: 6 kV, Laser frequency: 60 Hz, Detector gain: 8.8 X, Post-ion-extraction (PIE): 120 ns, Mass range: from 700 to 20,000 Da.

6- the spectra are obtained via an automatic method controlled by the FLEXCONTROL® software from the manufacturer BRUKER DALTONICS® the parameters are as follows for each spot:

7- series numbers of 40 shots: 6 on different positions of the spot (6x40 = 240 spectra),

8- so that the shots have swept the entire sample homogeneously the acquisition is made in 6 different positions according to a hexagonal geometry,

9- pre-shots: 10 shots at 40% of the maximum laser power are used to desalt the sample (contamination by mineral salts),

10- laser power: 30% to 45% regulated via the FLEXCONTROL® software,

11- spectrum selection: over a mass range from 2000 to 20,000 Da.

C) Processing and analysis of spectra

The purpose of this processing and analysis is to identify the combination of peaks making it possible to discriminate the groups of samples and to allow the subsequent identification of a sample.

This analysis is carried out with a MALDI-TOF spectrum analysis program written in the free programming language R [R Development Core Team (2008). A: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org], and requiring the additional “MALDIquant” statistical libraries [S. Gibb and K. Strimmer (2012)

MALDIquant: a versatile R package for the analysis of mass spectrometry data. R package version 1.17], “binda” [Sébastian Gibb and Korbinian Strimmer (2015) binda: Multi-Class Discriminant Analysis using Binary Predictors. R package version 1.0.3] and “C50” [Max Kuhn, Weston Steve, Culp Mark, Coulter Nathan, Ross Quinlan (2017). C50: C5.0 Decision Trees and Rule-Based Models. R package version 0.1.1].

Spectrum files are copied from the disks of MALDI-TOF mass spectrometry machines. For each spectrum, two files are necessary: the file called "fid" by the automaton and containing the spectrum data and the file called "acquired" by the automaton and containing the parameters of spectrum acquisition made by the FLEXCONTROL software.

The processing of the raw spectra (FIG. A) from MALDITOF Brucker Microflex spectrometers comprises the following usual steps:

1) Normalization of the spectra:

1.1) Selection of the protein band from 2 to 20 kDa

1.2) Smoothing the spectrum to eliminate the random peaks (figure B) by the sliding average technique with a half-window of 12 Daltons.

1.3) Estimation of the baseline shift of the raw spectrum linked to the experimental conditions by the “Statistics-sensitive Non-linear Iterative Peak-clipping” algorithm [Morhac M, Matousek V. Peak clipping algorithms for background estimation in spectroscopic data. Spectrosc app. 2008 Jan; 62 (l): 91-106. doi: 10.1366 / 000370208783412762] set to 100 iterations, then correction of the baseline of the spectrum which is aligned (figure C).

1.4) We perform a calibration (scale correction) of the intensity of the spectra to make the spectra comparable: The total intensity of each spectrum is normalized so that the sum of the area of the signal constituting the spectrum is equal to 1 (figure D).

1.5) The different spectra are then aligned using reference peaks of remarkable intensities to correct the shifts in peaks caused by fluctuations in experimental conditions from one acquisition to another.

1.6) Detection of peaks relative to background noise, the latter being estimated by calculating the median of the absolute deviations from the median (MAD) with a half window of 8 Da and a signal-to-noise ratio of 4.

1.7) Fusion of the 4 spectra of the 4 samples from the same sample are merged by constructing the average of the spectra.

2) Construction of the intensity matrix (figure E).

2.1) Detection of peaks relative to background noise on the basis of a signal-to-noise ratio of 4 with a fluctuation tolerance of 8 Da.

2.2) Filtering of the spectra by retaining only the detected peaks (S / N greater than or equal to 4) and the peaks present in at least 30% of the spectra of the batch of spectra studied in each of examples 1 to 5, namely spectra d 'samples of subjects belonging to the species or presenting the pathology concerned and controls.

2.3) construction of the matrix in which each line is a spectrum with the relative intensities of the peaks, each column corresponding to a peak in the list of masses m / z of the protein peaks of the first line, the relative intensity of the peaks corresponding to the area of said peak with respect to the sum of the area of the peaks of said spectrum. This intensity matrix is the experimental material which will be the subject of the following spectral analyzes.

3) Search for discriminating peaks

The lines of the intensity matrix are first of all associated with the groups of samples which one wishes to discriminate (group of subjects presenting the pathology concerned vs group controls of healthy subjects or group of subjects belonging to said species vs subject belonging to another species).

The first step aims to identify the peaks with the greatest discriminating power between the groups. It is based on the iterative application of a binary discriminant analysis on each peak, that is to say the search for each peak of the relative intensity threshold value which makes it possible to maximize the separation of the groups and of the discrimination score (measurement of overlapping or separation of intensities) of groups. This score makes it possible to construct the list of peaks according to their decreasing discriminating power.

At the end of this first step, we look to see if there is a set of peaks with 100% contrast (that is to say without overlapping), that is to say characteristics of each group. If this is the case, this set of peaks is retained, with the threshold values identified by the binary discriminant analysis. The spectra are identified here by a score resulting from a linear combination of presence of peaks (linear score of resemblance with respect to a standard) as illustrated in examples 1 to 3.

If this is not the case it is then necessary to proceed to a second step in order to find the combinations of cascading peaks which make it possible to differentiate the groups (examples 4 and 5). This combination is organized in the form of a binary decision tree, each leaf of which is the determination of a group to be discriminated and each node is a decision rule of the form: "if the value of the peak is greater than or equal to the threshold x then choice n ° l otherwise choice n ° 2 ”. Each choice can lead either to another decision rule or lead to another sheet (determination of another group to discriminate). The decision rules can thus be linked in cascade until the identification of a group. These binary decision trees are identified by applying the Quinlan C5.0 symbolic learning and data mining algorithm mentioned above. To control the application of this algorithm by reducing its search space and force it to use the peaks of optimal discriminating power, we will restrict the intensity matrix which will be treated to only the peaks with the highest scores of the binary discriminant analysis previous.

Such a use of binary trees in this context of identifying MALDI-TOF spectra as illustrated in Examples 4 and 5 has never been proposed.

Example 1: Identification of Clostridium difficile ulcerative colitis.

Clostridium difficile ulcerative colitis is a very serious disease, probably killing more than 100,000 people a year worldwide, 30,000 in Europe, 30,000 in the United States. This disease is due to the association of a disturbance of the faecal flora with the presence of a Clostridium difficile microbe secreting toxins. The gut microbiota was studied in apparently healthy controls and in patients with Clostridium difficile ulcerative colitis detected by molecular techniques to suggest that Clostridium difficile and its toxin were present in patients with signs typical clinics of this disease.

Nine stools were used from a patient with C. difficile colitis (CDI) defined by the presence of diarrhea concomitantly with the detection of C. difficile toxin in the stool. The presence of C. difficile toxin was sought by real-time PCR (RT-PCR) with the GeneXpert® IV kit (Cepheid, Sunnyvale, United States) detecting the tcdB gene for toxin B, the cdt gene for binary toxin and deletion 117 of the regulatory locus tcdC making it possible to detect strains of genotype 027.

Eight control samples from healthy subjects were used as a control group. The test for C. difficile toxin by RT-PCR was negative in these stools.

All samples used in this analysis were stored at -80 ° C before analysis.

The binary discriminant analysis of these 17 samples made it possible to prioritize the 984 protein peaks retained in a matrix of relative intensities obtained by the pretreatment described above. The result of the binary discriminant analysis showing the 40 peaks of the highest discriminant scores is presented in Figure IA in which the discriminant score is quantified from -4 to +4 for the infected group (CDI) and the control group (control) . A positive score corresponds to the presence of the peak for the group considered and vice versa.

The results of discriminant analysis of FIG. 1A result from the distributions of values of the relative intensities including the distributions of values for the most discriminating peaks shown in FIG. 1B.

Among the 50 peaks with the highest discriminating score, we looked for those with 100% contrast, that is to say with a sensitivity and specificity of 1. A total of 8 peaks could be identified, and are described in Table 1.

Table 1: Values of the discriminating thresholds for peaks with 100% contrast. These thresholds are expressed in relative intensity values with respect to the surface normalized to the value 1 of the total surface of the spectrum.

Peak (m / z) Maximum relative intensity value for absence of peak Minimum relative intensity value for presence of the peak 2710 4.56326002121813 e-05 4.86226943144253 e-05 3372 0.000339153329205546 0.000434223789748754 3399 9.7561289741134 e-05 9.98606712559408 e-05 3408 9.78528309013007 e-05 0.000118664760690361 3445 0.000634234550815951 0.000837188281840417 4374 9.55157313225957 e-05 0.0001002315521507 5658 4.3325324409441 e-05 5.23774442820013 e-05 11047 4.98150891075045 e-05 5.199534671731 e-05

The method of identifying a spectrum from these discriminating peaks is as follows.

After normalization, only the values of the 8 discriminating peaks are retained, with a tolerance of the value m / z of the peaks which can be fixed by default at 5% o.

Calculation of the identification score:

For each of the 8 peaks:

- “1” if the intensity of the peak is in the case value zone;

- “-1” if the intensity of the peak is in the value range of the controls;

- "0" if the intensity of the peak is between the 2 value zones.

We do the sum (score), which is in the interval [-8, 8]. The normalized discriminating score is the score divided by the number of discriminating peaks (1: certainty of resemblance to the cases, -1: certainty of resemblance to witnesses).

None of the specific discriminating peaks correspond to a Clostridium difficile protein currently identified in international reference databases such as SWISSPROT.

Example 2: Identification of stools of subjects suffering from ulcerative colitis (UC).

Four samples of patients with stable ulcerative colitis (UC) and 15 control samples were analyzed.

The binary discriminant analysis of these 19 samples made it possible to prioritize the 157 protein peaks in the intensity matrix obtained by the pretreatment.

The result of the binary discriminant analysis showing the 40 peaks of the highest discriminant scores is presented in Figure 2A with a score of -4 to +4 for the patient group (RCH) and the control group (control). A positive score corresponds to the presence of the peak for the group considered.

Among the 50 peaks with the highest discriminating score, we looked for those with 100% contrast, that is to say with a sensitivity and specificity of 1. A total of 11 peaks could be identified, and are described in Table 2.

Their statistical distribution of the peaks with the highest discriminating powers is presented in Figure 2B.

Table 2: Values of the discriminating intensity thresholds for peaks with

100% contrast. These thresholds are expressed in relative intensity values with respect to the surface normalized to the value 1 of the total surface of the spectrum.

Peak (m / z) Maximum value of relative intensities for absence of peak Minimum value of relative intensities for presence of the peak 3324 0.000120628491201788 0.000202083994783644 3345 9.29121405175384 e-05 9.9939643301577 e-05 3444 0.00012259036150653 0.000201322490887782 3482 0.00011930447354638 0.000136036653540984 3493 0.000183699312236316 0.000289961990980622 6451 9.67374238926758 e-05 0.000107099318660843 7268 6.0909717149715 e-05 9.51109288448326 e-05 9599 5.45853583475995 e-05 8.01651427124604 e-05 9620 5.48801247377731 e-05 7.5819888660436 e-05 9636 5.75858354561268 e-05 7.47308753223 e-05 9707 4.958713534478e-05 7.32265878998493 e-05

The discriminant analysis results of FIG. 2A result from the distributions of values of the relative intensities including the distributions of values for the most discriminating peaks shown in FIG. 2B.

The method of identifying a spectrum from these discriminating peaks is as follows.

After normalization, only the values of the 8 discriminating peaks are retained, with a tolerance of the value m / z of the peaks which can be fixed by default at 5% o.

An identification score is calculated as follows: Each peak 3324, 3345, 3444, 3482, 3493 higher than its threshold from the previous table and each peak 6451, 7268, 9599, 9620, 9636, 9707 lower than its threshold from the previous table is worth 1 point, 0 if the intensity of the peak is between the 2 value zones, and -1 if the intensity is in the opposite value zone.

The sum is made (score). The normalized score is the score divided by the number of discriminating peaks (1: certainty of resemblance to the cases, -1: certainty of resemblance to the controls).

Example 3: Identification of stools of subjects suffering from Crohn's disease.

We analyzed 13 samples of patients with Crohn's disease (including 7 treated and stable, 5 in therapeutic failure, and 1 untreated but stable) and 29 control samples.

The binary discriminant analysis of these 19 samples made it possible to prioritize the 116 protein peaks retained by the pretreatment. The result of the binary discriminant analysis showing the 40 peaks of the highest discriminant scores is presented in Figure 3A.

Among the 50 peaks with the highest discriminating score, we looked for those with 100% contrast, that is to say with a sensitivity and specificity of 1. A total of 3 peaks could be identified, and are described in Table 3.

The statistical distribution of the 8 best peaks, presented in Figure 3B, shows that peak 4134 has the highest discriminating power.

In FIG. 3A, the discriminating scores are quantified from -4 to +4 for the patient group (RCH) and the control group (control). A positive score corresponds to the presence of the peak for the group considered.

Table 3: Values of the discriminating thresholds for peaks with 100% contrast. These thresholds are expressed in relative intensity values with respect to the surface normalized to the value 1 of the total surface of the spectrum.

Peak (m / z) Maximum value of relative intensities for absence of peak Minimum value of relative intensities for presence of the peak 3443 0.000121099556323652 0.000153184507861765 4134 5.33325181290711 e-05 0.000425424602595293 4170 0.000115898008983965 0.000148115657553745

The so-called discriminating peaks are not present in the spectra of a sample of pathogens currently identified in international reference databases such as SWISSPROT.

The method of identifying a spectrum from these discriminating peaks is as follows.

After normalization, only the values of the 3 discriminating peaks are retained, with a tolerance of the value m / z of the peaks which can be fixed by default at 5% o.

We calculate an identification score: Each peak 3443.4134.4170 above its threshold in the previous table is worth 1 point, 0 if the intensity of the peak is between the 2 value zones, and -1 if the intensity is within the zone of opposite values.

We do the sum (score). The normalized score is the score divided by the number of discriminating peaks (1: certainty of resemblance to the cases, -1: certainty of resemblance to the controls)

EXAMPLE 4 Identification of Stools of Subjects with HIV

The samples were taken from 54 HIV positive patients, one HIVFree patient, and 37 controls. The 396 raw spectra were subjected to the treatments described above. The peaks making it possible to discriminate the HIV positive patients (cases) from the controls were sought by the second treatment described. The result of the binary discriminant analysis with the 40 most discriminating peaks is reported in Figure 4A.

FIG. 4B shows the distribution of the relative intensity values of the 10 peaks of the highest discriminating score obtained by the analysis of FIG. 6. No peak makes it possible to discriminate a group with 100% of contrast. In this case we sought and found a combination of couples (peak intensity threshold) to determine membership in an HIV group or control group constituting a decision tree shown in Figure 8 by applying the method described above based on Quinlan's C5.0 algorithm.

Thus, this binary classification tree search made it possible to identify 4 peaks with their threshold values allowing a classification of the two groups with an accuracy of 96.8% (3.2% incorrect classification) presented in Figure 6.

Table 4: Values of the discriminating intensity thresholds for the discriminating peaks. These thresholds are expressed in relative intensity values with respect to the surface normalized to the value 1 of the total surface of the spectrum.

Peak (m / z) Minimum value for presence of peak = maximum value for absence of peak Pl = 4160 Da 0.000116891 P2 = 3319 Da 0.0006103814 P3 = 3607 Da 0.000110201 P4 = 5143 Da 0.0001444286

Said discriminating peaks are not present in the spectra of a sample of the HIV virus responsible for said isolated pathology currently identified in international reference databases such as SWISSPROT.

Figure 4A shows the results of the binary discriminant analysis on samples from HIV patients (Control = control patients, boxes = HIV patients). In FIG. 4B, the discriminating scores are quantified from -5 to +5 for the group of patients (HIV) and the control group (control). A positive score corresponds to the presence of the peak for the group considered.

Figure 6 shows the binary classification tree obtained by applying the C5.0 algorithm (Control = control patients, boxes = HIV patients).

The presence or absence of the AIDS disease resulting from an HIV infection is determined for which in step a), 4 thresholds of 4 specific peaks have been identified such as:

1) the disease is present if the relative intensity values of the 4 peaks are such that:

1.1) -the relative intensity of the peak PI is greater than the intensity threshold of 0.000116691, the relative intensity of the peak P2 is less than the intensity threshold of 0.0006103814 and the relative intensity of the peak P3 is greater than the threshold of intensity of 0.000110201, or

1.2) -the relative intensity of the peak PI is greater than the intensity threshold of 0.000116691, the relative intensity of the peak P2 is less than the intensity threshold of 0.0006103814, the relative intensity of the peak P3 is less than the threshold of intensity of 0.000110201 and the relative intensity of peak P4 is below the intensity threshold of 0.0001444286; and

2) the disease is not peaks are such that: present if the intensity values relative of the 4 2.1) relative intensity of the peak PI East less than threshold intensity of 0.000116691, 2.2) relative intensity of the peak PI East greater than threshold intensity of 0.000116691 and the relative intensity of the peak P2 < is greater than the threshold it intensity of 0.0006103814, 2.3) relative intensity of the peak PI East greater than threshold intensity of

0.000116691, the relative intensity of peak P2 is less than the intensity threshold of 0.0006103814, the relative intensity of peak P3 is less than the intensity threshold of peak P3 of 0.000110201 and the relative intensity of peak P4 is greater than threshold P4 peak intensity of 0.0001444286.

Example 5: Monkeys

Stools have been collected from seven groups of primates (baboons, bonobos, chimpanzees, gorillas, green monkeys, red monkeys and howler monkeys) in West Africa (Senegal), Central Africa (Republic of Congo or Congo Brazzaville and Democratic Republic Congo or DRC), Saudi Arabia and French Guiana. In Senegal, stool samples were collected from 12 Guinea baboons (Papio papio), 23 green monkeys (Chiorocebus sabaeus), 23 red monkeys (Erythrocebus patas) and 26 chimpanzees (Pan troglodytes verus). In Congo-Brazzaville, 17 stools of western lowland gorillas (Gorilla gorilla) were sampled. In the DRC, 10 samples of bonobos (Pan panicus) were collected. In Saudi Arabia, 14 stool samples of hamadrya baboons (Papio hamadryas) were collected. In French Guiana, 9 samples of saddles of howler monkeys (Aiouatta senicuius) were also collected. 17 stool samples of companion dogs of different breeds living in France and 24 samples of human stool from Senegal, Congo-Brazzaville, DRC, Saudi Arabia and France were used as controls. The information relating to the authorizations and places of collection of the different samples is detailed in the appendices.

171 primate stool samples and 41 control stools were analyzed, including 24 human and 17 canine stools. A total of 684 spectra were obtained, including 520 (76%) from primates, 96 (14%) from humans and 68 (10%) from dogs.

Of the 2,802 peaks with S / N> 2, 327 peaks were retained in the intensity matrix obtained by the pretreatment. The spectra of the 130 monkey samples were assigned to each represented species: 23 Papio hamadryas baboons from Saudi Arabia, 12 Papio papio baboons from Senegal, 24 Chimpanzees Pan troglodytes verus from Senegal, 24 green monkeys Ch / orocebus sabaeus from Senegal, 23 red monkeys Erythrocebus patas from Senegal, 17 gorillas Gorilla gorilla gorilla from Republic of Congo, 9 howler monkeys A / ouatta senicu / us from Guyana, 5 bonobos Pan panicus Loia and 5 bonobos Pan panicus Ekoio from Democratic Republic of Congo.

The binary discriminant analysis of these 11 groups made it possible to order the peaks on their discriminating power and to dichotomize the peaks on the basis of the discrimination threshold. The result for the 40 peaks with the highest discriminating power is presented in Figure 6.

An examination of the intensity matrix shows that no combination of peaks allows 100% separation of the groups, which is confirmed by combinatorial calculation.

The 42 highest score peaks were used for processing by the C5.0 algorithm, which represents the first quintile of the cumulative distribution of scores. The processing of the resulting intensity matrix (130 rows and 42 columns) by the C5.0 algorithm makes it possible to identify a binary decision tree which is represented in Figure 7.

A total of 9 peaks are required to discriminate between groups. The thresholds retained for the decision nodes of the tree are described in Table 5. These thresholds are expressed in values of relative intensities with respect to the surface normalized to the value 1 of the total surface of the spectrum. The attribution to a group of a spectrum of unknown origin is done by traversing the tree with the peak values observed for this spectrum.

In FIG. 7, the discriminating scores are quantified from -10 to +10 for the group of each species of the 9 groups of primates tested. A positive score corresponds to the presence of the peak for the group considered. The positive scores correspond to the presence of the peak for the group considered.

Table 5: Values of the discriminating thresholds of the decision tree. These thresholds are expressed in relative intensity values with respect to the surface normalized to the value 1 of the total surface of the spectrum.

Tree knot Peak (m / z) Maximum value for absence of peak Minimum value for presence of the peak Median value = threshold 1 Pl = 2771 0.0005685608 0.001380781 Sl = 0.000974671 2 P2 = 3442 0.0001384863 0.0001721103 S2 = 0.0001552983 3 P4 = 3830 0.0004409823 0.000611854 S4 = 0.0005264181 4 P6 = 14074 9.077747 e-05 0.0001312575 S6 = 0.0001110175 5 P7 = 2186 0.0001884132 0.0001968443 S7 = 0.0001926287 6 P8 = 4540 0.0001110016 0.0001718054 S8 = 0.0001414035 7 P9 = 2982 7.133635 e-05 9.170791 e-05 S9 = 8.152213 e-05 12 P3 = 4668 7.373744 e-05 0.0002403486 S3 = 0.000157043 15 P5 '= 8731 1.582126 e-05 2.00094 e-05 S5 = l.791533 e-05 18 P3 = 4668 0.0001328107 0.0003126051 S3 = 0.0002227079

For the, peak P3 ′ two distinct maximum, minimum and median threshold values were determined at nodes 12 and 18.

A peak is considered absent if its value is less than column 3 and present if its value is greater than column 4. However, the median value is an estimate of a discrimination threshold on the sample validated experimentally by the large number of discriminating peaks.

Thus, as shown in FIG. 7, one determines whether or not one of the 9 said species of monkeys belongs using the 10 thresholds of median intensity values of the 9 discriminating peaks, such as:

1) the said species is the chimpanzee Pan troglodytes verus if the intensity of the peak PI 'is greater than the intensity threshold SI of 0.000974671,

2) the said species is the gorilla Gorilla gorilla gorilla if the relative intensity of the peak PI 'is less than or equal to the intensity threshold SI, the intensity of the peak P2' is greater than the intensity threshold S2 of 0.0001552983 and l 'relative intensity of peak P3' is less than or equal to a first intensity threshold S3 of 0.000157043;

3) the said species is the Papio papio baboon if the relative intensity of the peak Pl 'is less than or equal to the intensity threshold SI, the relative intensity of the peak P2' is less than or equal to the intensity threshold S2, l 'relative intensity of peak P4' is greater than intensity threshold S4 of 0.0005264181 and relative intensity of peak P5 'is less than or equal to intensity threshold S5 of 0.0001926287, or

4) the said species is the bonobo Pan panicus eko / o if the relative intensity of the peak Pl 'is less than or equal to the intensity threshold SI, the relative intensity of the peak P2' is less than or equal to the intensity threshold S2 and the relative intensity of the peak P3 'is less than or equal to a second intensity threshold S3' of 0.0002227079; the relative intensity of the peak P4 'is less than or equal to the intensity threshold S4 and the relative intensity of the peak P6' is greater than the intensity threshold S6 of 0.0001414035;

5) the said species is the Pan Panicus Loia bonobo if:

- the relative intensity of peak n ° l Pl 'is less than or equal to the intensity threshold SI, the relative intensity of peak P2' is greater than the intensity threshold S2 and the relative intensity of peak P3 'is greater at the second intensity threshold S3 'of 0.0002227079; or

the relative intensity of the peak Pl 'is less than or equal to the intensity threshold SI, the relative intensity of the peak P2' is less than or equal to the intensity threshold S2, the relative intensity of the peak P3 'is greater than the second intensity threshold S3 '; the relative intensity of the peak P4 'is less than or equal to the intensity threshold S4 and the relative intensity of the peak P6' is greater than the intensity threshold S6; and

6) the said species is the red monkey Erythrocebus patas if the relative intensity of peak n ° l Pl 'is less than or equal to the intensity threshold SI, the relative intensity of peak P2' is less than or equal to the threshold of intensity S2, the relative intensity of the peak P4 'is less than or equal to the intensity threshold S4, the relative intensity of the peak P6' is less than or equal to the intensity threshold S6 and the relative intensity of the peak P7 'is greater than the S7 intensity threshold of 0.0001926287;

7) the said species is the Papio hamadryas baboon if:

the relative intensity of the peak PI 'is less than or equal to the intensity threshold SI, the relative intensity of the peak P2' is less than or equal to the intensity threshold S2, the relative intensity of the peak P4 'is greater than the intensity threshold S4 and the relative intensity of the peak P5 'is greater than the intensity threshold S5, or

- the relative intensity of peak PI 'is i lower < OF equal at threshold intensity SI, the relative intensity of the peak P2 ' East lower or equal at threshold intensity S2, the relative intensity of the peak P4 ' East : lower or equal at threshold intensity S4, the relative intensity of the peak P6 ' East lower or equal at threshold intensity S6, the relative intensity of the peak P7 ' East lower or equal at threshold intensity S7 and relative intensity of the peak P8 ' East higher at intensity threshold S8 of 0.0001414035;

8) the said species is the green monkey Chlorocebus sabaeus if the relative intensity of the peak PI 'is less than or equal to the intensity threshold SI, the relative intensity of the peak P2' is less than or equal to the intensity threshold S2, the relative intensity of the peak P4 'is less than or equal to the intensity threshold S4, the relative intensity of the peak P6' is less than or equal to the intensity threshold S6, the relative intensity of the peak P7 'is less than or equal at the intensity threshold S7, the relative intensity of the peak P8 'is less than or equal to the intensity threshold S8 and the relative intensity of the peak P9' is greater than the intensity threshold S9 of 8.152213 x 10 '⁵; and

9) the said species is the howler monkey A / ouatta senicu / us if the relative intensity of the peak PI 'is less than or equal to the threshold of intensity SI, the relative intensity of the peak P2' is less than or equal to the threshold d 'intensity S2, relative intensity of peak P4' is less than or equal to intensity threshold S4, relative intensity of peak P6 'is less than or equal to intensity threshold S6, relative intensity of peak P7' is less than or equal to the intensity threshold S7, the relative intensity of the peak P8 'is less than or equal to the intensity threshold S8 and the relative intensity of the peak P9' is less than or equal to the intensity threshold S9.

Said discriminating peaks are not present in the spectra of a sample of protein specific for said species or of pathogenic agent responsible for pathology in said isolated species currently identified in international reference databases such as SWISSPROT.

Bibliographic references (1) Seng P, Drancourt M, Gouriet F, et al. Ongoing revolution in bacteriology: routine identification of bacteria by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Clin Infect Dis 2009 Aug 15; 49 (4): 543-51.

(2) Yssouf A, Flaudrops C, Drali R, et al. Matrix-assisted laser desorption ionization-time of flight mass spectrometry for rapid identification of tick vectors. J Clin Microbiol 2013 Feb; 51 (2): 522-8.

(3) Yssouf A, Socolovschi C, Leulmi H, et al. Identification of flea species using MALDI-TOF / MS. Comp Immunol Microbiol Infect Dis 2014 May; 37 (3): 153-7.

(4) Yssouf A, Socolovschi C, Flaudrops C, et al. Matrix-assisted laser desorption ionization - time of flight mass spectrometry: an emerging tool for the rapid identification of mosquito vectors. PLoS One 2013; 8 (8): e72380.

(5) Tran TN, Aboudharam G, Gardeisen A, et al. Classification of ancient mammal individuals using dental pulp MALDI-TOF MS peptide profiling. PLoS One 2011 Feb 25; 6 (2): el7319.

(6) Lagier JC, Armougom F, Million M, et al. Microbial culturomics: paradigm shift in the human gut microbiome study. Clin Microbiol Infect 2012 Dec; 18 (12): 1185-1193.

(7) Lagier JC, Khelaifia S, Tidjani-Alou M. Culture of previously uncultured members of the human gut microbiota by culturomics. Nat Microbiol. [in press] 2016.

(8) Hocquart M, Lagier JC, Cassir N, et al. Early Faecal Microbiota Transplantation Improves Survival in Severe Clostridium difficile Infections. Clin Infect Dis 2017 Aug 24.

Claims (7)

1. Method for determining a pathology of a human individual or of a species of animal by analysis of a sample of faeces of said human or animal individual respectively by mass spectrometry of MALDI-TOF type, in which : a) a combination of a plurality of discriminating peaks is determined for which intensity value thresholds are determined making it possible to distinguish the spectra of fecal samples from individuals with said pathology or respectively animals belonging to said animal species by relation to the spectra of samples of faeces of individuals not presenting said pathology or respectively of animals not belonging to said animal species, as a function of the values of the intensities of said discriminating peaks; b) the spectrum of a sample of faeces to be analyzed is produced, and c) it is checked whether the spectrum of said sample of faeces to be analyzed comprises the said discriminating peaks and whether the values of the intensities of the discriminating peaks correspond to a combination of values of the intensities corresponding to the spectra of individuals presenting said pathology or of individuals not presenting said pathology or respectively to the spectra of animals belonging to said animal species or not belonging to said animal species, as determined in step at). 2. Method according to claim 1, characterized in that one realizes the spectrum of a said sample of crude faeces dissolved in a matrix solution preferably of solution saturated with alpha-cyano-4hydroxycinnamic acid in 50% of acetonitrile and 2.5% trifluoroacetic acid. 3. Method according to claim 1 or 2, characterized in that in step a), a combination of a plurality of discriminating peaks is determined, characterized in that ranges of intensity values are determined making it possible to distinguish the spectra of samples from individuals with said pathology or respectively belonging to said animal species and the spectra of samples of individuals not presenting said pathology or respectively not belonging to said animal species, as a function of the different values of the intensities of the discriminating peaks concerned in the spectrum of a sample to be analyzed, thus making it possible to determine the presence or absence of said pathology or respectively belonging or not to said species depending on whether for said discriminating peaks the intensity of each discriminating peak is specifically inside or outside at least one range of values d intensities, the said ranges being able to be identical or preferably different for the different s discriminating peaks. 4. Method according to claim 3, characterized in that in step a), the presence or absence of said pathology or the belonging or not belonging to said species is respectively determined, depending on whether for said discriminating peaks, the intensity values are included in a first range of intensity values to determine the presence of said pathology or respectively belonging to said species or within a second range of intensity values to determine the absence of said pathology or respectively the non-belonging to said species, said first range and second range not overlapping, and this for each said discriminating peak. 5. Method according to claim 4, characterized in that in step a), the probability of the presence of said pathology or respectively of belonging to said species is all the greater as the number nor of said discriminating peaks for which the relative intensity values are inside said first ranges, is greater and greater than the number n2 of said discriminating peaks for which the relative intensity values are inside said second ranges. 6. Method according to claim 5, characterized in that in step a), the probability of the presence of said pathology or belonging to said species is 100% if for all said specific discriminating peaks, the intensity values relative are inside said first ranges, and the probability of absence of said pathology or not belonging to said species is 100% if for all said discriminating peaks the values of relative intensities are inside said second forks. 7. Method according to one of claims 4 to 6, characterized in that the presence or absence of the disease consisting of a colitis resulting from an infection with Clostridium difficile for which in step a) is determined, 8 so-called first and second ranges of intensity values of 8 discriminating peaks are determined. 8. Method according to claim 7, characterized in that the said 8 discriminating peaks are peaks at 2710, 3372, 3399, 3408, 3445, 4374, 5658, 11047 Da at +/- 0.5%. 9. Method according to claim 8, characterized in that said first and second ranges of intensity values for said following 8 discriminating peaks, expressed in relative intensity values with respect to the sum of the intensity values of the normalized spectrum at the value 1, are: -For the peak at 2710Da, a so-called first range of relative intensity values corresponds to the relative intensity values less than 4.56326002121813 x 10 ' ⁵ , and a so-called second range of relative intensity values corresponds to the relative intensity values greater than 4.86226943144253x 10 '⁵; and -For the peak at 3372Da, a so-called first range of relative intensity values corresponds to the relative intensity values greater than 0.000434223789748754, and a so-called second range of relative intensity values corresponds to the relative intensity values less than 0.000339153329205546; and -For the peak at 3399Da, a so-called first range of relative intensity values corresponds to the relative intensity values greater than 9.98606712559408 x 10 ' ⁵ , and a so-called second range of relative intensity values corresponds to the relative intensity values less than 9.7561289741134 x 10 '⁵; and -For the peak at 3408Da, a so-called first range of relative intensity values corresponds to the relative intensity values greater than 0.000118664760690361, and a so-called second range of relative intensity values corresponds to the relative intensity values less than 9.78528309013007 x 10 '⁵; and -For the peak at 3445Da, a so-called first range of relative intensity values corresponds to the relative intensity values greater than 0.000837188281840417, and a so-called second range of relative intensity values corresponds to the relative intensity values less than O.000634234550815951; and -For the peak at 4374Da a so-called first range of relative intensity values corresponds to the relative intensity values less than 9.55157313225957 x 10 ' ⁵ , and a so-called second range of relative intensity values corresponds to the relative intensity values greater than 0.0001002315521507 x 10 '⁵; and -For the peak at 5658 Da, a so-called first range of relative intensity values corresponds to the relative intensity values greater than 4.3325324409441 x 10 '⁵; and -a second range of relative intensity values corresponds to the relative intensity values less than 5.23774442820013 x 10 '⁵; and -For the peak at 11047 Da, a so-called first range of relative intensity values corresponds to the relative intensity values less than 4.98150891075045 x 10 ' ⁵ , and -a second range of relative intensity values corresponds to the relative intensity values greater than 5.199534671731X 10 ' ⁵ . 10. Method according to one of claims 4 to 6, characterized in that Crohn's disease is determined for which in step a), three so-called first and second ranges of intensity values of 3 discriminating peaks are determined. . 11. Method according to claim 10, characterized in that the said 3 discriminating peaks are peaks at 3343, 4134 and 4170 Da at +/- 0.5%. 12. Method according to claim 11, characterized in that said first and second ranges of intensity values for said 3 following discriminating peaks, expressed in relative intensity values with respect to the sum of the intensity values of the normalized spectrum at the value 1, are: -For the peak at 3343 Da, -a so-called first range of relative intensity values matches to values intensities on higher at 0.000153184507861765, and -a said second fork of relative intensity values matches to values intensities on lower at 0.000121099556323652; and -For the peak at 4134 Da, a so-called first range of relative intensity values corresponds to the relative intensity values greater than 0.000425424602595293, and -a second range of relative intensity values corresponds to the relative intensity values less than 5.33325181290711 x 10 '⁵; and -For the peak at 4170 Da, a so-called first range of relative intensity values corresponds to the relative intensity values greater than 0.000148115657553745, and a so-called second range of relative intensity values corresponds to the relative intensity values less than 0.000115898008983965. 13. Method according to one of claims 4 to 6, characterized in that one determines the presence or absence of the disease consisting of ulcerative hemorrhagic ulcerative colitis for which in step a), 11 is determined say first and second ranges of intensity values of 11 discriminating peaks. 14. Method according to claim 13 characterized in that the said 3 specific discriminating peaks are peaks at 3324, 3345, 3444, 3482, 3493, 6451, 7268, 9599, 9620, 9636, 9707 Da at +/- 0.5%. 15. Method according to claim 14, characterized in that said first and second ranges of relative intensity values for said following discriminating peaks, expressed in relative intensity values with respect to the sum of the intensity values of the normalized spectrum at the value 1, are: -For the peak at 3324 Da, a so-called first range of relative intensity values corresponds to the relative intensity values greater than 0.000202083994783644, and a second range of relative intensity values corresponds to the relative intensity values less than 0.000120628491201788; and -For the peak at 3345 Da, a so-called first range of relative intensity values corresponds to the relative intensity values greater than 9.9939643301577 x 10 ' ⁵ , and a so-called second range of relative intensity values corresponds to the relative intensity values less than 9.29121405175384 x 10 '⁵; -For the peak at 3444 Da, a so-called first range of relative intensity values corresponds to the relative intensity values greater than 0.000201322490887782; and a so-called second range of relative intensity values corresponds to the relative intensity values less than 0.00012259036150653; and -For the peak at 3482 Da, a so-called first range of relative intensity values corresponds to the relative intensity values greater than 0.000136036653540984, and a second range of relative intensity values corresponds to the relative intensity values less than 0.00011930447354638; and -For the peak at 3493 Da, a so-called first range of relative intensity values corresponds to the relative intensity values greater than 0.000289961990980622, and a second range of relative intensity values corresponds to the relative intensity values less than 0.000183699312236316; and -For the peak at 6451 Da, a so-called first range of relative intensity values corresponds to the relative intensity values less than 9.67374238926758 x 10 ' ⁵ , and a so-called second range of relative intensity values corresponds to the relative intensity values greater than 0.000107099318660843 x 10 '⁵; -For the peak at 7268 Da, a so-called first range of relative intensity values corresponds to the relative intensity values less than 6.0909717149715 x 10 ' ⁵ , and a so-called second range of relative intensity values corresponds to the relative intensity values greater than 9.51109288448326 x 10 '⁵; and -For the peak at 9599 Da, a so-called first range of relative intensity values corresponds to the relative intensity values less than 5.45853583475995 x 10-5, and a so-called second range of relative intensity values corresponds to the relative intensity values greater than 8.01651427124604 x 10 ^-5 ; and -For the peak at 9620 Da, a so-called first range of relative intensity values corresponds to the relative intensity values less than 5.45853583475995 x 10 ' ⁵ , and a so-called second range of relative intensity values corresponds to the relative intensity values greater than 8.01651427124604 x 10 ^_5 ; and -For the peak at 9636 Da, a so-called first range of relative intensity values corresponds to the relative intensity values less than 5.48801247377731 x 10 ' ⁵ , and -a second range of relative intensity values corresponds to relative intensity values greater than 7.5819888660436 x 10 '⁵; -For the peak at 9707 Da, a so-called first range of relative intensity values corresponds to the relative intensity values less than 5.75858354561268 x 10 '⁵; and -a second range of relative intensity values corresponds to relative intensity values greater than 7.47308753223 x 10 ' ⁵ . 16. Method according to claim 3, characterized in that in step a), one or more combinations of said discriminating peaks are determined with: a) first ranges of intensity values of said discriminating peaks of said combination to determine the presence of said pathology or respectively belonging to said species and b) second ranges of intensity values of said discriminating peaks of said combination to determine the absence of said pathology or respectively the non-belonging to said species, c) at least some of the said first and second ranges overlapping for some of the said discriminating peaks, but nevertheless making it possible, by the plurality of the discriminating peaks, to determine the presence of said pathology or respectively belonging to said species by series of intensity values for said discriminating peaks of the combination depending on whether they are inside or outside of said first ranges or second ranges. 17. Method according to claim 16, characterized in that said first and second ranges are determined constituted by values above and respectively below thresholds of intensity values. 18. Method according to claim 17, characterized in that said thresholds of intensity values are determined by approximation to median values of intensity values of the intervals between said first and second ranges. 19. Method according to claim 16 or 17, characterized in that one determines the presence or absence of the AIDS disease resulting from an HIV infection for which in step a), four so-called first ones are determined and second ranges of intensity values of 4 discriminating peaks. 20. Method according to claim 19, characterized in that the presence or absence of the AIDS disease resulting from an HIV infection is determined for which in step a), 4 thresholds of d values are determined '' intensities of 4 discriminating peaks such as: 1) the disease is present if the intensity values of the 4 peaks are such that: 1.1) the intensity of peak n ° l is greater than the intensity threshold of peak n ° l, the intensity of peak n ° 2 is lower than the intensity threshold of peak n ° 2 and the intensity of peak n ° 3 is higher than the intensity threshold of peak n ° 3, or 1.2) the intensity of peak n ° l is greater than the intensity threshold of peak n ° l, the intensity of peak n ° 2 is lower than the intensity threshold of peak n ° 2, the intensity of peak n ° 3 is below the intensity threshold of picn ° 3 and the intensity of peak n ° 4 is below the intensity threshold of peak n ° 4; and 2) the disease is not present if the intensity values of the 4 peaks are such that: 2.1) the intensity of peak n ° l is lower than the intensity threshold of peak n ° l, 2.2) the intensity of peak n ° l is greater than the intensity threshold of peak n ° l and the intensity of peak n ° 2 is greater than the intensity threshold of peak n ° 2, and 2.3) the intensity of peak n ° l is greater than the intensity threshold of peak n ° l, the intensity of peak n ° 2 is lower than the intensity threshold of peak n ° 2, the intensity of peak n ° 3 is below the intensity threshold of peak n ° 3 and the intensity of peak n ° 4 is greater than the intensity threshold of peak n ° 4. 21. Method according to claim 20 characterized in that the peaks n ° l to 4 are, at +/- 0.5%: - 4160 Da for peak n ° l, - 3319 Da for peak n ° 2, - 3607 Da for peak n ° 3, and - 5143 Da for peak n ° 4. 22. Method according to claims 20 and 21, characterized in that the relative intensity thresholds of said discriminating peaks are relative intensity threshold values expressed in relative intensity values relative to the sum of the intensity values of the spectrum normalized to the value 1, are: - for peak n ° 1 at 4160 Da, a relative intensity threshold value of 0.000116691, - for peak n ° 2 at 3319 Da, an intensity threshold value of 0.0006103814, - for peak n ° 3 at 3607 Da, a relative intensity threshold value of 0.000110201, and - for peak No. 4 at 5143 Da, a relative intensity threshold value of 0.000144286. 23. Method according to one of claims 1 to 22, characterized in that the presence or absence of a pathology of a human individual is determined by analysis of a sample of feces by mass spectrometry of the type MALDI-TOF in which the said discriminating peaks are not present in the spectra of a sample of pathogenic agent responsible for said isolated pathology, but reflect a modification of the composition of the faeces associated with said pathology. 24. Method according to one of claims 16 to 18, characterized in that in step a), a combination of a plurality of discriminating peaks is determined for which first ranges and second ranges of values are determined. discriminant peak intensities making it possible to distinguish the spectra of samples of individuals belonging to the said animal species and the spectra of samples of individuals not belonging to the said animal species, as a function of the different values of the intensities of the discriminant peaks in the spectrum of a sample to be analyzed, thus making it possible to constitute a binary decision tree of belonging or not to the said species depending on whether for said discriminating peaks taken successively the intensity of each peak is specifically inside or outside the say first forks or second forks. 25. Method according to claim 24 characterized in that one determines whether or not it belongs to one of the 9 said species of monkeys: Baboons Papio hamadryas from Saudi Arabia, baboons Papio papio from Senegal, Chimpanzees Pan troglodytes verus from Senegal, green monkeys Chiorocebus sabaeus from Senegal, red monkeys Erythrocebus patas from Senegal, gorillas Gorilla gorilla gorilla from Republic of Congo, howler monkeys Aiouatta senicuius from Guyana, bonobos Pan panicus Loia and bonobos Pan panicus Ekoio from la Democratic Republic of the Congo, for which the first 9 ranges and 9 second ranges of intensity values of 9 discriminating peaks are determined. 26. Method according to claims 18 and 25 characterized in that one determines whether or not it belongs to one of the 9 said species of monkeys: Baboons Papio hamadryas from Saudi Arabia, baboons Papio papio from Senegal, Chimpanzees Pan troglodytes verus from Senegal, green monkeys Chiorocebus sabaeus from Senegal, red monkeys Erythrocebus patas from Senegal, gorillas Gorilla gorilla gorilla from Republic of Congo, howler monkeys Aiouatta senicuius from Guyana, bonobos Pan panicus Lo / a and bonobos Pan Ekoio panicus from the Democratic Republic of Congo, for which 10 thresholds of intensity values of 9 discriminating peaks are determined, such as: 1) the said species is the chimpanzee Pan troglodytes verus if the intensity of peak n ° l is greater than the intensity threshold of peak n ° l, 2) the said species is the gorilla Gorilla gorilla gorilla if the intensity of peak n ° l is less than or equal to the intensity threshold of peak n ° l, the intensity of peak n ° 2 is greater than the intensity threshold of peak # 2 and the intensity of peak # 3 is less than or equal to a first intensity threshold of peak # 3; 3) the said species is the Papio papio baboon if the intensity of peak n ° l is less than or equal to the intensity threshold of peak n ° l, the intensity of peak n ° 2 is less than or equal to the threshold of intensity of peak 2, intensity of peak 4 is greater than the intensity threshold of peak 4 and intensity of peak 5 is less than or equal to the intensity threshold of peak 5 , or 4) the said species is the bonobo Pan panicus ekoio if the intensity of peak n ° l is less than or equal to the intensity threshold of peak n ° l, the intensity of peak n ° 2 is less than or equal to the threshold d the intensity of peak 2 and the intensity of peak 3 is less than or equal to a second intensity threshold of peak 3; the intensity of peak 4 is less than or equal to the intensity threshold of peak 4 and the intensity of peak 6 is greater than the intensity threshold of peak 6; 5) the said species is the Pan Panicus Loia bonobo if: - the intensity of peak n ° l is less than or equal to the intensity threshold of peak n ° l, the intensity of peak n ° 2 is greater than the intensity threshold of peak n ° 2 and the intensity of peak n ° 3 is higher than the second intensity threshold of peak n ° 3; or - the intensity of peak n ° l is less than or equal to the intensity threshold of peak n ° l, the intensity of peak n ° 2 is less than or equal to the intensity threshold of peak n ° 2, the intensity of peak # 3 is greater than the second intensity threshold of peak # 3; the intensity of peak 4 is less than or equal to the intensity threshold of peak 4 and the intensity of peak 6 is greater than the intensity threshold of peak 6; and 6) the said species is the red monkey Erythrocebus patas if the intensity of peak n ° l is less than or equal to the intensity threshold of peak n ° l, the intensity of peak n ° 2 is less than or equal to the intensity threshold of peak 2, the intensity of peak 4 is less than or equal to the intensity threshold of peak 4, the intensity of peak 6 is less than or equal to the intensity threshold of peak 6 and the intensity of peak 7 is greater than the intensity threshold of peak 7; 7) the said species is the Papio hamadryas baboon if: - the intensity of peak n ° l is less than or equal to the intensity threshold of peak n ° l, the intensity of peak n ° 2 is less than or equal to the intensity threshold of peak n ° 2, the intensity of peak 4 is greater than the intensity threshold of peak 4 and the intensity of peak 5 is greater than the intensity threshold of peak 5, or - the intensity of peak n ° l is less than or equal to the intensity threshold of peak n ° l, the intensity of peak n ° 2 is less than or equal to the intensity threshold of peak n ° 2, the intensity of peak # 4 is less than or equal to the intensity threshold of peak # 4, the intensity of peak # 6 is less than or equal to the intensity threshold of pic # 6, the intensity of peak # 7 is less than or equal to the intensity threshold of peak 7 and the intensity of peak 8 is greater than the intensity threshold of pic 8; 8) the said species East the monkey green Chlorocebus sabaeus si intensity of peak No. l East lower or equal to threshold intensity of peak n ° l, intensity of peak # 2 East lower or equal to threshold intensity of peak n ° 2, intensity of peak # 4 East lower or equal to threshold intensity of peak n ° 4, the intensity of peak # 6 East lower or equal to threshold intensity of peak n ° 6, intensity of peak # 7 East lower or equal to threshold intensity of peak n ° 7, intensity of peak # 8 East lower or equal to threshold intensity of peak n ° 8 and the intensity of pic n i ° 9 is higher than the intensity threshold of peak n ° 9; and 9) the said species is the monkey howler A / ouatta senicu / us if intensity of peak No. l East lower or equal to threshold intensity of peak n ° l, intensity of peak # 2 East less than or equal to threshold intensity of peak n ° 2, intensity of peak # 4 East lower or equal to threshold intensity of picn ° 4, intensity of peak # 6 East lower or equal to threshold intensity of picn ° 6, intensity of peak # 7 East lower or equal to threshold intensity of peak n ° 7, intensity of peak # 8 East lower or equal to threshold intensity of peak n ° 8 and the intensity of peak 9 is less than or equal to the intensity threshold of peak 9. 27. Method according to claim 25 or 26 characterized in that the peaks n ° l to 9 are, at +/- 0.5%: - 2771 Da for peak n ° l, - 3442 Da for peak n ° 2, - 4668 Da for peak n ° 3, - 3830 Da for peak n ° 4, - 8731 Da for peak n ° 5, - 14074 Da for peak n ° 6, - 2186 Da for peak n ° 7, - 4540 Da for peak n ° 8, and - 2982 Da for peak n ° 9. 28. Method according to claim 26 or 27, characterized in that the intensity thresholds of said discriminating peaks, expressed in relative intensity values with respect to the sum of the intensity values of the spectrum normalized to the value 1, are : - for peak No. 1 at 2771 Da, a relative intensity threshold value of 0.000974671, - for peak n ° 2 at 3442 Da, a relative intensity threshold value of 0.0001552983, - for peak n ° 3 at 4668 Da, a value of first relative intensity threshold of 0.0001570043 and a value of second intensity threshold of 0.000222270079, - for peak n ° 4 at 3830 Da, a relative intensity threshold value of 0.0005264181, - for peak 5 at 8731 Da, a relative intensity threshold value of 1.791533 x 10-5, - for peak n ° 6 at 14074 Da, a relative intensity threshold value of 0.0001110175, - for peak n ° 7 at 2186 Da, a relative intensity threshold value of 0.0001926287, - for peak n ° 8 at 4540 Da, a relative intensity threshold value of 0.0001414035, and - for peak n ° 9 at 2982 Da, a relative intensity threshold value of 8.152213 x 10 ^-5 . 29. Method according to one of claims 16 to 18 and 24 to 28, characterized in that said discriminating peaks are not present in the spectra of a sample of protein specific for said species or of a pathology in the said species. 5 30. Method according to one of claims 1 to 29, characterized in that one succeeds in discriminating a group of subjects presenting a said pathology or belonging to a said animal species with a number of 1 to 15 discriminating peaks, preferably from 3 to 10 discriminating peaks.