life
Article
Age-Dependent Changes in Protist and Fungal Microbiota in a
Peruvian Cattle Genetic Nucleus
Richard Estrada 1 , Yolanda Romero 1 , Carlos Quilcate 1, Deisy Dipaz 1, Carol S. Alejos-Asencio 1, Silvia Leon 1,
Wuesley Yusmein Alvarez-García 1 , Diorman Rojas 1, Wigoberto Alvarado 2, Jorge L. Maicelo 2
and Carlos I. Arbizu 3,*
1 Dirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Lima 15024,
Peru; richard.estrada.bioinfo@gmail.com (R.E.); yolanda.bioinfo@gmail.com (Y.R.);
ceqp2374@yahoo.com (C.Q.); dipazdeysi8@gmail.com (D.D.); alejoscarito1@gmail.com (C.S.A.-A.);
sleont@gmail.com (S.L.); walvarez@inia.gob.pe (W.Y.A.-G.); diormanr@gmail.com (D.R.)
2 Facultad de Ingeniería Zootecnista, Agronegocios y Biotecnología, Universidad Nacional Toribio Rodríguez
de Mendoza de Amazonas (UNTRM), Cl. Higos Urco 342, Chachapoyas 01001, Peru;
wigoberto.alvarado@untrm.edu.pe (W.A.)
3 Facultad de Ingeniería y Ciencias Agrarias, Universidad Nacional Toribio Rodríguez de Mendoza de
Amazonas (UNTRM), Cl. Higos Urco 342, Chachapoyas 01001, Peru
* Correspondence: carlos.arbizu@untrm.edu.pe
Abstract: In this research, the connection between age and microbial diversity in cattle was explored,
revealing significant changes in both protist diversity and fungal microbiota composition with age.
Using fecal samples from 21 Simmental cattle, microbial communities were analyzed through 18S
rRNA gene sequencing. Results indicated significant differences in alpha protist diversity among the
three age groups, while fungal composition varied notably with age and was linked to hematological
parameters. Despite the stability of fungal alpha diversity, compositional changes suggest the gut
as a stable niche for microbial colonization influenced by diet, clinical parameters, and microbial
Citation: Estrada, R.; Romero, Y.; interactions. All cattle were maintained on a consistent diet, tailored to meet the specific nutritional
Quilcate, C.; Dipaz, D.; needs of each age group. These findings emphasize the importance of understanding age-related
Alejos-Asencio, C.S.; Leon, S.; microbial dynamics to enhance livestock management and animal health, contributing to broader
Alvarez-García, W.Y.; Rojas, D.; ecological and biomedical research. This study was limited by the lack of comprehensive metabolic
Alvarado, W.; Maicelo, J.L.; et al. analyses correlating microbiota changes with specific age-related variations, indicating a need for
Age-Dependent Changes in Protist further research in this area.
and Fungal Microbiota in a Peruvian
Cattle Genetic Nucleus. Life 2024, 14, Keywords: microbial diversity; cattle; hematological parameters; gut microbiota
1010. https://doi.org/10.3390/
life14081010
Academic Editors: Pabulo
H. Rampelotto and Einar Ringø 1. Introduction
Received: 27 June 2024 The gut microbiota constitutes a complex and diverse ecosystem, composed of various
Revised: 21 July 2024 microorganisms such as bacteria, methanogenic archaea, ciliates, anaerobic fungi, viruses,
Accepted: 13 August 2024 and bacteriophages [1]. These symbiotic communities degrade dietary components like
Published: 14 August 2024 plant fibers, carbohydrates, proteins, and lipids, generating volatile fatty acids (VFAs) that
are crucial for the animal, meeting up to 70–80% of its energy requirements [2]. The gut
microbiome, closely related to various physiological functions of the host, is fundamental
to the health and performance of livestock [3,4]. This complex system, influenced by
Copyright: © 2024 by the authors. factors such as diet, genetics, and the age of the host and its environment, has significant
Licensee MDPI, Basel, Switzerland. implications for the animal’s health and development [5,6].
This article is an open access article The composition of the microbiota in ruminants varies significantly with age, influenc-
distributed under the terms and ing their health and metabolic processes [7]. In young cattle, the microbiota is diverse and
conditions of the Creative Commons becomes more complex as the animals mature, which is crucial for efficient digestion and
Attribution (CC BY) license (https:// overall health [8]. Specifically, in cattle, the ruminal microbiota changes with age, affecting
creativecommons.org/licenses/by/ the diversity and stability of intestinal microbes, impacting digestion and general health [9].
4.0/).
Life 2024, 14, 1010. https://doi.org/10.3390/life14081010 https://www.mdpi.com/journal/life
Life 2024, 14, 1010 2 of 17
Diet continues to be the primary factor determining the structure and composition of the
ruminal microbiota [10]. Clinical parameters such as glucose and cholesterol are linked to
age-induced variations in the microbiota, reflecting metabolic adjustments as the animal
matures [11]. Additionally, the diet of animals is influenced by geographical location and
the type of production system. Scientific findings demonstrate that lipid supplements can
reduce enteric methane production, providing viable options to mitigate the environmental
impact of animal production [12].
The intestinal microbiota of cattle includes diverse fungal and protist communi-
ties [13,14]. Dominant fungal phyla in cattle gut microbiota are Ascomycota, Basidiomycota,
and Neocallimastigomycota [15]. These fungal phyla play crucial roles in various gut func-
tions [13]. Additionally, prominent protist phyla include Ciliophora and Apicomplexa,
which are integral to nutrient metabolism and overall gut health [14,16].
The gut microbiota in cattle undergoes age-related changes, influencing various aspects
of health, including metabolic, immune, and cognitive functions [17]. Probiotics play a
significant role in modulating this microbiota, aiding in healthy aging by enhancing the
intestinal barrier and improving nutrient absorption, particularly in older cattle [17]. Early
probiotic supplementation aids juvenile animals, including newborn calves, by fostering
development, improving gastrointestinal health, and boosting immunity to diseases [18].
Analyzing the microbiota from a genetic core perspective offers valuable insights into its
inherited effects on well-being. This approach enables the creation of customized probiotic
treatments tailored to various animal populations, enhancing their overall health and
productivity [19]. The diet of cattle is also shaped by geographical location and production
systems, which in turn affects the microbiota’s composition and function. Simmental cattle
are especially recognized for their dual-purpose use in meat and dairy production, due
to their fast growth and effective feed conversion. Contemporary genomic research has
discovered genetic variants linked to meat quality, concentrating on genes associated with
muscle development and growth rate [20,21]. Furthermore, Simmental cows are known
for their strong milk production capacity, making them highly suitable for dual-purpose
farming operations [22,23].
The 18S rRNA molecular marker is crucial for deciphering the intestinal microbiota of
fungi and protists due to its conserved nature among eukaryotes, allowing precise iden-
tification and classification [24]. This marker, coupled with high-throughput sequencing
technologies like Illumina, enables detailed profiling of microbial communities, including
low-abundance species [25]. This comprehensive approach is essential for linking microbial
diversity with host health, facilitating targeted probiotic therapies to enhance animal health
and productivity [26].
This study investigates how age affects the intestinal microbiota of cattle, focusing
on fungi and protists, and examines correlations with blood parameters across three age
groups within a genetic core of healthy bovines. The findings reveal that age significantly
affects the composition and diversity of the microbiota and its relationship with various
clinical parameters. These results underscore the importance of comprehending the age-
related dynamics of the intestinal microbiota and its impact on the metabolic health of cattle.
Additionally, this study underscores the significance of identifying age-specific biomarkers
and exploring personalized probiotic therapies to enhance cattle health and performance
across various stages of life.
2. Materials and Methods
2.1. Animal and Sample Collection
In Huaral, Lima, at an altitude of 128 m above sea level (11◦31′18′′ S and 77◦14′06′′ W),
21 fecal samples were gathered from Simmental cattle at the Donoso Agricultural Research
Station (EEA Donoso for its acronym in Spanish). This station hosts a government-owned
herd that serves as a cattle genetic nucleus. The conditions included natural light exposure,
a relative humidity of 88.5%, and an average temperature of 25.5 ◦C [27]. An initial pilot
study was performed to ascertain the ideal number of replications needed for this research.
Life 2024, 14, 1010 3 of 17
The designated age groups were defined as 58 to 63 months for 5Y, 18 to 21 months for
1Y8M, and 5 months for 5M. The sex ratio across all age groups was 4 females to 3 males.
The diet regimen at the Donoso Experimental Station of INIA (Peru) relies on fresh forage,
with particular supplements tailored to the age group. Based on the dietary details provided
in a previous study [28], the diet and composition details are provided in Supplementary
Table S1. Stool samples were collected directly from the rectum of each animal using
disposable gloves, transported to the laboratory in liquid nitrogen, and kept at −80 ◦C until
DNA extraction. Additionally, blood samples were drawn from each animal’s jugular vein.
Twenty-one cattle from EEA Donoso were included in this study. These animals, regularly
monitored by the veterinary unit, underwent parasitological examinations revealing no
cysts, oocysts, or larvae and were certified as healthy. Regular veterinary checks, such as
physical examinations, clinical history assessments, and laboratory analyses, are carried
out to maintain the stringent health standards necessary for semen and ovum donors.
Consequently, no unhealthy animals exist in the genetic nucleus. This study adhered to
Peruvian National Law No. 30407: “Animal Protection and Welfare”.
2.2. Clinical Parameters
According to the methodology implemented in a prior investigation [28], the clinical
parameter abbreviations are detailed in Supplementary Table S2.
2.3. DNA Extraction and 18S rRNA Gene Sequencing
Total genomic DNA was extracted from the 21 fecal samples using a commercial
kit (Norgen, Biotek Corporation, Sacramento, CA, USA) following the manufacturer’s
recommended protocol. To evaluate the quality of the extracted DNA, its concentration
was measured with the NanoDrop ND-1000 spectrophotometer (Nanodrop Technologies,
Wilmington, DE, USA), while the 260/280 absorbance ratio was also determined. Fur-
thermore, DNA integrity was verified through 1% agarose gel electrophoresis, ensuring
the reliability of the extracted samples. To construct the Illumina amplicon sequencing
library, around 10 ng of DNA from each sample was used for PCR amplification with the
528F/706R primer pair targeting the 18S rRNA gene. The PCR procedure began with an
initial denaturation at 94 ◦C for 2 min. This was followed by 5 cycles of denaturation at
94 ◦C for 45 s, annealing at 52/54 ◦C for 45 s, and extension at 72 ◦C for 1 min. An addi-
tional 35 cycles were then carried out, with a reduced annealing temperature of 50/52 ◦C.
The final step was an elongation at 72 ◦C for 10 min. The library was prepared using the
Illumina TruSeq DNA PCR-Free Library Preparation Kit (Illumina, USA), in accordance
with the manufacturer’s guidelines, incorporating index sequences. Library quality was
subsequently assessed using a Qubit 2.0 Fluorometer (Thermo Scientific, Waltham, MA,
USA). The approved libraries were eventually subjected to sequencing on the 250-bp paired-
end Illumina NovaSeq 6000 platform (Illumina Inc., San Diego, CA, USA) following the
manufacturer’s guidelines.
2.4. Bioinformatics Analysis
In the QIIME2 [29] analysis, trimming and quality filtering were performed. Following
this, the paired-end reads, demultiplexed by Illumina, were analyzed with the Qiime2-
DADA2 [30] software v2023.9, leading to the creation of an Amplicon Sequence Variant
(ASV) table. To minimize the occurrence of erroneous ASVs, any sequence exhibiting fewer
than 10 reads across all samples was discarded. Plant sequences were specifically removed.
Taxonomic classification of ASVs was performed using the SILVA v138.1 database for
identifying fungi and protists through 18S sequence analysis. The high-quality sequences,
after filtering, were then aligned using the integrated MAFFT [31] aligner. Using the QIIME2
phylogenetic module, rooted and unrooted phylogenetic trees for fungi and protists were
constructed with the FastTree algorithm.
Life 2024, 14, 1010 4 of 17
2.5. Statistical Analysis
The data underwent statistical evaluation using the packages Phyloseq (v1.223) [32],
MicrobiotaProcess, and Microeco [33] in R software v4.1.1 [34]. Individual samples under-
went analysis to generate rarefaction curves, assessing the sequencing depth. Subsequently,
various measures of intestinal bacterial alpha diversity were evaluated (Pielou, Simpson,
ACE, Observed, Chao1, and Shannon indices). Beta diversity was assessed using the
Jaccard and Unweighted UniFrac methods, with the results visualized through Principal
Coordinate Analysis (PCoA). To evaluate differences in microorganism communities among
groups, a two-way PERMANOVA [35] test was conducted, utilizing 9999 permutations.
Distinctive features in gut microbiota profiles were detected using the LEfSe method, which
applies linear discriminant analysis (LDA) effect size. This method highlighted biomarkers
with the highest statistical and biological significance. Spearman rank correlation tests were
conducted on pairs of variables, including clinical parameters and fungal alpha diversity
indices. Additionally, the relationship between clinical variables and fungal community
composition was examined using Mantel tests with 999 permutations.
3. Results
For fungi, a total of 1,808,136 high-quality reads were generated, with an average of
86,101 high-quality reads, a maximum of 171,671 high-quality reads, and a minimum of
20,340 high-quality reads. Similarly, for protists, a total of 1,189,559 high-quality reads were
obtained, with an average of 56,645 high-quality reads, a maximum of 144,851 high-quality
reads, and a minimum of 4288 high-quality reads.
3.1. Impact of Age on the Diversity and Composition of Fungal and Protist Communities in the
Bovine Microbiome
The rarefaction curve demonstrated good representativity of species diversity in the
analyzed samples, indicating that the sampling was adequate for the analysis of both fungi
(Figure S1) and protists (Figure S2). The alpha diversity of fungal and protist communities
in cattle exhibited distinct patterns (Figure 1). For fungal communities, measured through
the same metrics, no significant differences were observed (Figure 1A). In contrast, the al-
pha diversity of protist communities, measured through the metrics ACE, Chao1, Observed,
Pielou, Shannon, and Simpson, exhibited significant differences between age groups. In par-
ticular, Pielou (p = 0.026) and Simpson (p = 0.026) metrics presented significant differences
between the age groups 1Y8M and 5M (Figure 1B).
In the analysis of the beta diversity of the fungal communities in cattle, significant
patterns influenced by age and the interaction of age and sex were observed. The PCoA
plots for both metrics indicated distinct clustering of fungal communities across differ-
ent age groups (Figure 2A,B). The PERMANOVA analysis for Jaccard distances (Table 1)
indicated that age (p = 0.015) and the interaction of age and sex (p = 0.007) significantly in-
fluenced the beta diversity. Similarly, the PERMANOVA for Unweighted UniFrac distances
(Table 1) demonstrated significant effects of age (p = 0.002) and the interaction of age and
sex (p = 0.002). For the protist community, using both the Jaccard index and Unweighted
UniFrac, PCoAs were generated to display the similar distribution of the samples across
different age groups (Figure 2C,D). The permutational multivariate analysis of variance
(PERMANOVA) revealed no significant disparities between the evaluated groups (Table 1).
Venn diagrams illustrated the distinct and overlapping fungal and protist ASVs among
cattle of different age ranges (Figure 3). For fungal ASVs (Figure 3A), 67 are common across
all age groups, with 7 unique to the 1Y8M group, 5 to the 5Y group, and 2 to the 5M group.
Shared ASVs between two age groups include 14 between 1Y8M and 5Y, 18 between 1Y8M
and 5M, and 15 between 5Y and 5M. For protist ASVs (Figure 3B), 54 are common across all
age groups, with 8 unique to the 1Y8M group, 12 to the 5Y group, and 17 to the 5M group.
Shared ASVs include 8 between 1Y8M and 5Y, 22 between 1Y8M and 5M, and 13 between
5Y and 5M.
LLiiffee 22002244,, 1144,, 1x0 F1O0 R PEER REVIEW 5 of 18  5 of 17
 
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Life 2024, 14, x FOR PEER REVIEW 6 of 18 
 
Life 2024, 14, 1010 6 of 17
 
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ofopf rportoistitsst.s. 
 
Life 2024, 14, x FOR PEER REVIEW 7 of 18 
 
Table 1. PERMANOVA of Jaccard and Unweighted UniFrac. * p < 0.05, ** p < 0.01 
Items Df SumOfSqs R2 F Pr (>F) 
Jaccard         
Year 2 0.397308 0.130516 1.472821 0.015 * 
Sex 1 0.197202 0.064781 1.462057 0.055 
Year/Sex 2 0.426412 0.140076 1.580706 0.007 ** 
Life 2024, 14, 1010 Residual 15 2.023202 0.664625   7 of 17
Total 20 3.044126 1   
Unweighted UniFrac         
TableY1e.aPrE RMANOVA2o f Jaccard an0d.1U6n6w84e ighted U0n.i2F0r8a5c.2* p < 0.05,2*.7* 9p7<6 0.01 0.002 ** 
Sex 1 0.04198 0.05247 1.4079 0.168 
Items Df SumOfSqs R2 F Pr (>F)
Year/Sex 2 0.14402 0.18 2.4149 0.002 ** 
Residual Jaccard 15 0.44728 0.55901   
TYoetarl 220 0..83097031028 0.1310 516 1.47 2 821 0.0 15 *
Sex 1 0.197202 0.064781 1.462057 0.055
YVeaern/nS exdiagrams il2lustrated th0.e4 2d64is1t2inct and0 .1o4v0e0r7l6apping 1fu.5n80g7a0l6 and pro0t.0is0t7 A**SVs 
Residual 15 2.023202 0.664625
amonTgo ctaalttle of differe2n0 t age rang3e.s0 4(4F1i2g6ure 3). For f1ungal ASVs (Figure 3A), 67 are com-
mon aUcrnowsesi gahllt eadgeU gnirForuapc s, with 7 unique to the 1Y8M group, 5 to the 5Y group, and 2 to 
the 5MYe garroup. Shared 2ASVs betwe0e.1n6 6tw84o age gro0u.2p08s5 i2nclude 142 .b7e9t7w6 een 1Y8M0.0 0a2nd** 5Y, 
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Ascomycota had a relative abundance of 75%, 80%, and 80% in cattle aged 1Y8M, 5Y, and
Ascomycota had a relative abundance of 75%, 80%, and 80% in cattle aged 1Y8M, 5Y, and 
5M, respectively. Mucoromycota exhibited an abundance of 20%, 15%, and 25% at the
5M, respectively. Mucoromycota exhibited an abundance of 20%, 15%, and 25% at the 
same ages. Basidiomycota had lower representation with values of 3%, 3%, and 2%, while
same ages. Basidiomycota had lower representation with values of 3%, 3%, and 2%, while 
Neocallimastigomycota presented low percentages of 2%, 2%, and 3%.
Neocallimastigomycota presented low percentages of 2%, 2%, and 3%. 
Similarly, the intestinal microbiological structure of protists exhibited significant vari-
Similarly, the intestinal microbiological structure of protists exhibited significant var-
ations across different ages (Figure 4C). Incertae_Sedis was the predominant phylum at
iations across different ages (Figure 4C). Incertae_Sedis was the predominant phylum at 
all ages, with relative abundances of 75%, 80%, and 75% in cattle aged 1Y8M, 5Y, and 5M,
arlels apgeecst,i vweilyth.  Areplaictoivmep albeuxanddaenmcoens sotfr a7t5e%d,r 8e0la%ti,v aenadb u75n%da innc ceasttolfe2 a0g%e,d1 15Y%8,Man, d5Y2,5 a%n,dw 5hMile, 
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pdliefxfear eanntda gCeisl.iophora, with other phyla exhibiting relatively low but consistent abun-
danceTsh aecrhoesast mdiaffperdeinstp alagyess.t he proportional representation of diverse protist and fungal
genera across various samples, highlighting both the most and least abundant genera
across different age groups (Figure 4B,D). In the fungi (Figure 4B), Mucor, Kluyveromyces,
 Kurtzmanella-Candida clade, Clavispora-Candida clade, Pichia, and Candida-Lodderomyces
clade were the most abundant genera in the intestinal microbiota of cattle at different
ages, exhibiting high relative abundances. In contrast, genera such as Phymatotrichopsis,
Sarocladium, Malassezia, Hanseniaspora, Kodamaea, Saturnispora, Cylamyces, Nakaseomyces-
Candida clade, Starmera-Candida clade, Cladosporium, Candida, Cyniclomyces, and Pecoramyces
were the least abundant, with low relative abundances in all age groups. With respect
to protists (Figure 4D), Blastocystis, Buxtonella, Colpodella, and Gregarina were the most
abundant genera, displaying high relative abundances across all age groups. In contrast,
Life 2024, 14, 1010 8 of 17
genera such as LKM51, Entamoeba, Platyophrya, Vahlkampfia, Syndiniales Group I, Thalassiosira,
Life 2024, 14, x FOR PEER REVIEW 8 of 18 
 Teleaulax, Colpoda, Mastigina, Eugregarinorida, Syndiniales Group II, Flamella, and Novel Clade
2 were the least abundant, with low relative abundances at all ages.
 
Figure 4. Compaarraattiivvee aannaallyyssiiss ooff ffunggaall and prrottiisstt communiittiieess accrrossss diiffffeerreentt age groups of 
ccaattttllee.. ((A)) BBaarr pplloottss rreepprreesseennttiinngg tthhee rreellaattiivvee aabbuunnddaannccee ooff ffuunnggaall ttaaxxaa.. ((B)) Heeaattmaappss pprreesseennttiinngg tthhee 
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rreellaattiivvee aabbuunnddaannccee ooff pprroottiissttt taaxxaa. .( D(D))H Heeaatmtmaappssp prerseesnetnintigngth tehed idstirsitbriubtuiotinoann adnadb aubnudnadnacencoef othf eth2e0 
20 most abundant protist genera. 
most abundant protist genera.
The heatmap displays the proportional representation of diverse protist and fungal 
genera across various samples, highlighting both the most and least abundant genera 
across different age groups (Figure 4B,D). In the fungi (Figure 4B), Mucor, Kluyveromyces, 
Kurtzmanella-Candida clade, Clavispora-Candida clade, Pichia, and Candida-Lodderomyces 
 
Life 2024, 14, 1010 9 of 17
3.3. Biomarkers Identified Based on Age in Fungi and Protists
To identify specific fungal and protist taxa associated with different age groups, a
comparative analysis of fecal microbiota compositions was performed utilizing the LEfSe
method approach (Figure 5). The taxa exhibiting the most significant differences, from
phylum to genus level, were identified based on their LDA scores. For fungi, the 1Y8M
group demonstrated enrichment in two phyla (Incertae Sedis and Mucoromycota), one
order (Mucorales), two families (Mucoraceae and Rhynchogastremataceae), two genera
(Mucor and Papiliotrema), and four species (Mucor sp., Papiliotrema sp., Candida etchellsii,
and Starmerella Candida clade). In the 5Y group, the analysis indicated enrichment in one
order (Pyxidiophorales), one class (Laboulbeniomycetes), one family (Pyxidiophoraceae),
one genus (Pyxidiophora), and one species (Pyxidiophora arvernensis) (Figure 5A). For the
5M group, the analysis demonstrated enrichment in one order (Hypocreales). In contrast,
Life 2024, 14, x FOR PEER REVIEW the 1Y8M group exhibited enrichment in one family (Saccharomycetac10e aoef )1,8 one genus
 
(Aspergillus), and one species (Aspergillus sp.) (Figure 5B).
 
FFiigguurree 55. .DDiffifefreernecnecse isni nthteh ientienstteinsatiln faulnfguanl g(Aal,B(A) a,Bnd)  apnrdotipstr o(Ctis,Dt )( Cm,Dicr)ombiioctrao abmiootnaga mdiffoenrgendti fferent ages
aingecsa itntl cea.ttBlea.r Bcahr acrhtairltl uilslutrsatrtaintinggt htheed diifffferentiiaalllyy aabbuundnadnatn ttaxta xidaeindteifinetdifi tehdrotuhgrho ulingeharli dniesacri-discriminant
maninaalynst iasn(aLlyDsAis )(.L(DAA))D. (iAff)e Dreiffnetiraelnlytiaallbyu anbduanndtanfut nfugnaglatla txaaxai nint thhee 11YY88MM aanndd 55MM grgoruopu.p (B. )( BD)ifD- ifferentially
ferentially abundant fungal taxa in the 5Y and 5M group. (C) Differentially abundant protist taxa in 
abundant fungal taxa in the 5Y and 5M group. (C) Differentially abundant protist taxa in the 5M and
the 5M and 5Y group. (D) Differentially abundant protist taxa in the 5M and 1Y8M group. 
5Y group. (D) Differentially abundant protist taxa in the 5M and 1Y8M group.
3.4. Correlation of Alpha and Beta Diversity of Fungi with Clinical Parameters 
Analyses of clinical parameters (Table S3) were performed using the Kruskal–Wallis 
test, considering age as the variable of interest. The parameters that were significant for 
age were RBC, MCV, MCHC, MCH, LYM%, NEU, SEG, LYM, EOS, and TP (Table S4). 
Subsequently, Bonferroni post hoc analysis was conducted. In the 5Y-5M comparison, sig-
nificant differences were observed in RBC, MCV, MCHC, MCH, NEU, SEG, LYM, EOS, 
LYM%, and TP. Similarly, in the 1Y8M-5M comparison, significant differences were de-
tected in RBC and TP. Finally, in the 1Y8M-5Y comparison, significant differences were 
identified in MCV and MCH (Table S5). A Spearman correlation analysis was conducted 
to examine the relationship between clinical parameters and fungal alpha diversity indices 
 
Life 2024, 14, 1010 10 of 17
For protists, the 5Y group exhibited enrichment in two phyla (Ciliophora and In-
tramacronucleata). In contrast, the 5M group demonstrated enrichment in one family
(Pseudoperkinsea), one order (Ichthyophonida), one class (Holozoa), and three genera
(Eimeria cylindrica, unclassified Ichthyosporea, and LKM51) (Figure 5C). Additionally, for the
5M group, there was further enrichment in two classes (Holozoa and Ichthyosporea), one
family (Pseudoperkinsea), and two genera (unclassified Ichthyophorea and LKM51). The
1Y8M group exhibited enrichment in one genus (Cryptosporidium) (Figure 5D).
3.4. Correlation of Alpha and Beta Diversity of Fungi with Clinical Parameters
Analyses of clinical parameters (Table S3) were performed using the Kruskal–Wallis
test, considering age as the variable of interest. The parameters that were significant for
age were RBC, MCV, MCHC, MCH, LYM%, NEU, SEG, LYM, EOS, and TP (Table S4).
Subsequently, Bonferroni post hoc analysis was conducted. In the 5Y-5M comparison,
significant differences were observed in RBC, MCV, MCHC, MCH, NEU, SEG, LYM, EOS,
Life 2024, 14, x FOR PEER REVIEW LYM%, and TP. Similarly, in the 1Y8M-5M comparison, significant differen11c eosf w1e8r e detected
 in RBC and TP. Finally, in the 1Y8M-5Y comparison, significant differences were identified
in MCV and MCH (Table S5). A Spearman correlation analysis was conducted to examine
the relationship between clinical parameters and fungal alpha diversity indices (Figure 6).
(Figure 6). The ObTsheervOebds,e ArvCedE,, AanCdE ,CahnadoC1 hinaod1iciensd wicesrew seirgensiifigcnaifinctlayn ntlyegnaetgivateilvye lcyorcroerlraetlaetde d with TG.
with TG. The PielTohue iPnideleoxu winadse xsiwgnaisfisciganitfilyca pnotlsyitpiovseiltyiv ceolyrrceolrarteeladt ewdiwthit LhYLMYM%%. T. Thhee CChhaao1 index was
index was significsaignntilfiyc panotsliytipvoesliyt icvoelryrecloartreedla wtedithw MithCMHCCH. C.
 
Figure 6. Clinical parameters and fungal alpha diversity were analyzed using Spearman correlations.
Figure 6. Clinical pPaorsaitmiveetceorsrr ealnatdio fnusnagreali nadlpichatae ddiinvebrrsoiwtyn ,wwehriel eanneaglaytziveedc uorsrienlgat iSopnseaarrme raenp rceosernretelda-in dark blue.
tions. Positive correTlhaetimonastr iaxrien cinluddiecsatoendly inst abtrisotwicanl,l ywshiginleifi ncaengtactiovrere claotriorenlsa(tpio-vnasl uaerse< re0.p0r5e).sented in 
dark blue. The matrix includes only statistically significant correlations (p-values < 0.05). 
Several significant results were identified in the correlation analysis of clinical variables
Several signiwfiictahnbte rteasduilvtse rwsietyref oidr efunntigfiie(dTa ibnl eth2e). cTohrereMlaatniotenl atensatluysins gotfh celiJnaicccaalr dvairnidae-x revealed
bles with beta disviegrnsiifitcya nfot rc ofrurnelgait io(Tnasbfoler M2)C. VTh(pe =M0a.0n3t)ealn tdesMt CuHsin(pg =th0e.0 2J)a.cTchaerdP airntdiaelxM raen-tel test also
vealed significantd ceomrornelsatrtaiotends sfiogrn iMficCanVt c(opr =re 0la.t0i3o)n sanfodr MMCCVH( p(p= =0 .001.042) )a.n TdhMe CPHar(tpia=l 0M.0a1n).tAeld ditionally,
test also demonsttrhaeteMda snitgenl itfiecstanwti tchortrheelaUtinownse igfohrt eMd CUVni F(pra =c 0in.0d1e4x) iannddic aMteCdHsi g(pn ifi= c0a.n0t1)c.o rrelations
for MCH (p = 0.045) and LYM% (p = 0.048). The Partial Mantel test did not reveal any
Additionally, thes iMgnainfitcealn ttecsotr rweliatthio tnhse.  Unweighted UniFrac index indicated significant 
correlations for MCH (p = 0.045) and LYM% (p = 0.048). The Partial Mantel test did not 
reveal any significant correlations. 
Table 2. Correlation of clinical parameters with beta diversity (Jaccard and Unweighted UniFrac) 
using Mantel and Partial Mantel Tests. Only significant variables are presented. * p < 0.05. 
 Jaccard Unweighted UniFrac 
  Mantel Test Partial Mantel Test Mantel Test Partial Mantel Test 
Variables r p r p r p r p 
MCV 0.196815555 0.03 * 0.209922522 0.014 * 0.137675537 0.07 0.104071883 0.124 
MCH 0.177721795 0.022 * 0.192394859 0.01 * 0.149206478 0.045 * 0.114890751 0.082 
LYM% 0.192477483 0.064 0.194205056 0.068 0.195319744 0.048 * 0.178444189 0.094 
4. Discussion 
In this study, the relationship between age and microbial diversity in cattle was in-
vestigated, revealing that protist diversity and the composition of the fungal microbiota 
change significantly with age. Variations were observed in the fungal composition and its 
relationship with hematological parameters. These findings are crucial as they highlight 
the dynamic nature of the gut microbiota and its interaction with host physiology over 
time. 
Age did not exhibit a significant correlation with fungal alpha diversity, which is 
consistent with previous findings in tigers [36] and goats [37]. Conversely, age was 
 
Life 2024, 14, 1010 11 of 17
Table 2. Correlation of clinical parameters with beta diversity (Jaccard and Unweighted UniFrac)
using Mantel and Partial Mantel Tests. Only significant variables are presented. * p < 0.05.
Jaccard Unweighted UniFrac
Mantel Test Partial Mantel Test Mantel Test Partial Mantel Test
Variables r p r p r p r p
MCV 0.196815555 0.03 * 0.209922522 0.014 * 0.137675537 0.07 0.104071883 0.124
MCH 0.177721795 0.022 * 0.192394859 0.01 * 0.149206478 0.045 * 0.114890751 0.082
LYM% 0.192477483 0.064 0.194205056 0.068 0.195319744 0.048 * 0.178444189 0.094
4. Discussion
In this study, the relationship between age and microbial diversity in cattle was
investigated, revealing that protist diversity and the composition of the fungal microbiota
change significantly with age. Variations were observed in the fungal composition and its
relationship with hematological parameters. These findings are crucial as they highlight the
dynamic nature of the gut microbiota and its interaction with host physiology over time.
Age did not exhibit a significant correlation with fungal alpha diversity, which is
consistent with previous findings in tigers [36] and goats [37]. Conversely, age was signifi-
cantly correlated with fungal composition, a phenomenon also reported in other mammals,
such as humans [38,39] and monkeys [40]. This could be due to the intestine providing a
relatively stable niche for fungal colonization [41], where predominant species establish
early and maintain their presence throughout the host’s life [42]. On the other hand, fungal
composition may be more influenced by changes in the host’s internal environment as it
ages, such as dietary alterations, immune changes, and interactions with other microbio-
tas [43]. These factors can affect which fungal species are more abundant or dominant at
different life stages, without necessarily changing the total number of species present [44].
In the present study, notable variations in the Simpson and Pielou indices of alpha
diversity of protists were observed across the 1Y8M cohort and the 5M cohort, but not
when comparing the 5Y group and the 5M group. This observation suggests the importance
of considering age as a complex and non-linear factor in microbiota diversity. The first two
years of life represent a critical developmental period during which the gut microbiota
undergoes significant changes, which could explain the observed difference between 1Y8M
and 5M [45]. After this period, alpha diversity tends to decrease, where in the early
stages of life there is a rapid growth in alpha diversity, but the growth rate gradually
decreases as age increases, as demonstrated in studies of microorganisms in cattle [46] and
other mammals such as humans [47,48]. Furthermore, factors such as environment and
individual physiology can influence these results, suggesting that the relationship between
age and alpha diversity is more complex [49]. Conversely, no significant differences were
observed in protist beta diversity. Although specific studies on the relationship between age
and alpha protist diversity are scarce, some research has reported significant differences in
protist microbiota diversity in age-related contexts. For instance, lower eukaryotic diversity
has been documented in patients with Parkinson’s disease [50], and it is suggested that
eukaryotic biomass and diversity may be influenced by lifestyle and diet in populations
of different ages [51]. Furthermore, a study in alpacas identified significant differences in
the diversity of alpha protists between different age groups and health states [52]. These
findings underscore the need for studies specifically focused on the influence of age on
eukaryotic diversity to better understand how this factor can affect the microbiota in
different species and contexts.
The fungal phyla prevalent across the three age categories are comparable, encom-
passing Ascomycota, Mucoromycota, Basidiomycota, and Neocallimastigomycota. These
results align with previous research on cattle and other ruminant species, including
bovines [16,53], goats [54], and alpacas [55]. Similarly, the analysis of protists revealed that
the dominant phyla were Incertae Sedis, Apicomplexa, and Ciliophora, which have also
been reported in humans [51,55] and primates [56]. In this study, Incertae Sedis includes
Life 2024, 14, 1010 12 of 17
Blastocystis sp., which is predominant among the identified genera. This finding has also
been reported in cattle [57], goats [58], camels [59], and humans [60].
The most abundant genus identified and recognized as a biomarker in the 1Y8M cattle
group was Mucor. This aligns with studies indicating that this genus is more prevalent in
non-obese individuals and its abundance increases with weight loss, suggesting a favorable
metabolic health state [61]. This genus was also common in the human gastrointestinal tract,
associated with intestinal health [62], and has been reported in a higher proportion in adults
than in young people [63]. Furthermore, in yaks with diarrhea, Mucor was not detected,
indicating that its growth is restricted in the presence of diarrhea [64]. The genus Blastocystis
was the most prevalent genus in this study and is known to confer beneficial effects on the
host immune system, such as stimulating mucus production through the cytokine IL-22,
which improves intestinal health and alleviates colitis symptoms [60]. Additionally, recent
studies have associated Blastocystis colonization with greater bacterial diversity in the gut
microbiota, indicative of a healthy microbiota [60,65]. The presence of Blastocystis correlates
negatively with Bacteroides levels and positively with increased bacterial diversity, which
is often linked to better health and reduced risk of inflammatory conditions [66]. These
findings suggest that the high prevalence of Mucor and Blastocystis in the three age groups
of healthy cattle could be related to a balanced and beneficial gut microbiome.
In the 5Y age group, Aspergillus was identified as a biomarker. This genus is common
in the gastrointestinal tract of several animals and has been associated with beneficial
roles such as fiber digestion. It also has potential pathogenic implications under certain
conditions [67]. Metabolites produced by Aspergillus may play a role in maintaining a
beneficial microbial balance in the absence of disease-triggering factors [68]. The presence
of Aspergillus in healthy cattle may be related to its ability to interact beneficially with other
microbial species, promoting a balanced and healthy intestinal microbiome [68].
In the 1Y8M group, Eimeria cylindrica was identified as a biomarker. Despite its
presence, the animals did not exhibit clinical symptoms, suggesting that under specific
conditions, this pathogen can be present without causing disease [69]. This observation
aligns with other studies that have detected Eimeria spp. in cattle without clinical symptoms,
indicating that factors such as host immunity and environmental conditions can influence
the pathogenicity of these parasites [70,71]. In the 5M age group, Cryptosporidium was
identified as a distinctive biomarker. This genus Cryptosporidium is generally associated
with severe diarrhea in young ruminants [72]. Its presence in asymptomatic animals has
been reported in lambs with Cryptosporidium xiaoi and Cryptosporidium ubiquitum [73]. This
highlights the variability in the pathogenicity of Cryptosporidium depending on the species
and the host’s immune status [74,75]. The detection of Cryptosporidium in asymptomatic
individuals underscores the need to interpret its presence in diagnostic and epidemiological
studies with caution, as it does not always indicate active disease [76,77].
A significant negative correlation was identified between alpha diversity indices
Observe, Chao1, and Ace and triglycerides, suggesting that lower fungal diversity is associ-
ated with higher triglyceride levels. Certain fungi can help maintain healthy triglyceride
levels [61]. The abundance of Mucor racemosus has also been observed to significantly influ-
ence fasting triglyceride levels, suggesting its potential as a biomarker for cardiovascular
risk [78]. Furthermore, hypertriglyceridemia in older individuals has been associated with
a reduction in gut mycobiome diversity [79]. A significant positive correlation between
fungal alpha diversity and lymphocytes suggests that higher gut fungal diversity is associ-
ated with increased lymphocyte counts, highlighting the importance of gut mycobiome
homeostasis for host immune functions [80]. For instance, colonization by fungi such as
Candida albicans can stimulate the proliferation of Th17 cells and IL-17 feedback, aiding
in the fight against infections [81]. Additionally, a reduction in intestinal fungi has been
correlated with decreases in immune factors in the blood, such as lymphocytes, under-
scoring the protective role of symbiotic fungi in calibrating the immune response [82].
A significant positive correlation has also been observed between fungal alpha diversity
Chao1 and MCHC, which is positively associated with DNA levels and intestinal colo-
Life 2024, 14, 1010 13 of 17
nization by Candida albicans [83]. Since MCHC reflects the concentration of hemoglobin in
red blood cells, these results suggest that greater fungal diversity could be linked to better
hematological function.
Correlation analysis demonstrated that several hematological parameters, such as
MCV and MCH, are significantly correlated with fungal beta diversity. These correlations
suggest that variations in the composition and hemoglobin content of red blood cells could
be linked to alterations in the intestinal mycobiota composition. Previous studies have
indicated that hematological health can influence the gut mycobiota, affecting both the
diversity and abundance of certain fungal species [84]. Furthermore, fungal dysbiosis
has been linked to inflammatory conditions and hematological diseases, highlighting the
interaction between hematological status and intestinal health [85]. This underscores
the importance of investigating how variations in hematological parameters influence
the diversity and functionality of the intestinal mycobiota, which could have therapeutic
implications for improving intestinal homeostasis and systemic health [38,85].
This study does not provide a comprehensive metabolic analysis linking microbiota
changes to specific age-related variations in fungi and protists. Further studies are re-
quired to explore the metabolic pathways and their interactions with microbiota com-
ponents at various life stages. This underscores crucial areas for future investigation to
deepen our understanding of the complex relationships among microbiota, aging, and
reproductive status.
5. Conclusions
This study examined how age influences microbial diversity in cattle and found
that the composition and diversity of the gut microbiota change with age. While the
alpha diversity of fungi remained constant, that of protists showed significant variations
between age groups only in the Simpson and Pielou indices, suggesting the need for further
research to clarify the impact of protists on the intestinal microbiota. Significant changes
were observed in the composition of fungal communities but not in protists, and fungi
composition was associated with some hematological parameters. Spearman correlations
indicated a negative relationship between triglycerides and certain mycobiota diversity
indices and a positive relationship between lymphocytes and fungal alpha diversity. These
results suggest a dynamic interaction between fungal microbiota and host physiology
over time. Additional studies are needed to correlate microbiota changes with age-related
variations through comprehensive metabolic analyses. These findings underscore the
importance of understanding how microbial dynamics evolve with age to enhance cattle
management and health, given the uniform diet composition with varying concentrations
across all cattle.
Supplementary Materials: The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/life14081010/s1, Figure S1: Rarefaction curves of species richness
illustrate the sequencing depth of fungi data obtained from gut samples. Figure S2: Rarefaction
curves of species richness illustrate the sequencing depth of protist data obtained from gut samples.
Table S1: Diet and composition. Table S2: List of abbreviations. Table S3: Clinical parameters of three
age groups. Table S4: Kruskal–Wallis results for clinical parameters in cattle by age. Table S5: Post
hoc Bonferroni results for clinical parameters in cattle by age.
Author Contributions: Conceptualization, R.E., Y.R., C.Q., C.S.A.-A. and C.I.A.; methodology, R.E.,
Y.R., C.Q., D.D. and D.R.; software, R.E., Y.R. and C.I.A.; validation, R.E.,Y.R., S.L., W.Y.A.-G., W.A.
and J.L.M.; formal analysis, R.E., Y.R., C.Q., S.L. and C.I.A.; investigation, R.E., Y.R., C.Q., D.R.
and C.I.A.; resources, D.D., S.L., D.R., J.L.M. and C.I.A.; data curation, R.E., Y.R., C.Q., C.S.A.-A.,
W.Y.A.-G. and W.A.; writing—original draft preparation, R.E., Y.R., C.Q., W.Y.A.-G., D.R. and C.I.A.;
writing—review and editing, R.E., Y.R., C.S.A.-A., S.L. and C.I.A.; visualization, R.E., Y.R., D.D.,
W.Y.A.-G., W.A., J.L.M. and C.I.A.; supervision, R.E., Y.R., C.Q. and W.A.; project administration, R.E.,
Y.R., C.Q., D.D., S.L. and D.R.; funding acquisition, C.Q., D.R., J.L.M. and C.I.A. All authors have
read and agreed to the published version of the manuscript.
Life 2024, 14, 1010 14 of 17
Funding: This research was funded by the following research project: “Mejoramiento de la disponi-
bilidad de material genético de ganado bovino con alto valor a nivel nacional 7 departamentos” of
the Ministry of Agrarian Development and Irrigation (MIDAGRI) of the Peruvian Government, with
grant number CUI 2432072.
Institutional Review Board Statement: The animal study protocol was approved by the Institutional
Ethics Committee of the National University Toribio Rodríguez de Mendoza de Amazonas (CIEI-
N◦0072, 20 June 2024).
Informed Consent Statement: Not applicable.
Data Availability Statement: The raw data supporting the conclusions of this article will be made
available by the authors on request.
Acknowledgments: We acknowledge the “Promeg Nacional” team for supporting the logistic
activities. C.I.A. thanks Vicerrectorado de Investigación of UNTRM.
Conflicts of Interest: The authors declare no conflicts of interest.
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