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  • Research article
  • Open Access

Mapping gastrointestinal gene expression patterns in wild primates and humans via fecal RNA-seq

  • 1,
  • 2, 3,
  • 2, 3,
  • 2, 3,
  • 2, 4,
  • 1,
  • 5,
  • 6,
  • 7,
  • 8,
  • 9, 10,
  • 2, 11, 12Email author and
  • 1Email author
BMC Genomics201920:493

https://doi.org/10.1186/s12864-019-5813-z

  • Received: 3 December 2018
  • Accepted: 20 May 2019
  • Published:

Abstract

Background

Limited accessibility to intestinal epithelial tissue in wild animals and humans makes it challenging to study patterns of intestinal gene regulation, and hence to monitor physiological status and health in field conditions. To explore solutions to this limitation, we have used a noninvasive approach via fecal RNA-seq, for the quantification of gene expression markers in gastrointestinal cells of free-range primates and a forager human population. Thus, a combination of poly(A) mRNA enrichment and rRNA depletion methods was used in tandem with RNA-seq to quantify and compare gastrointestinal gene expression patterns in fecal samples of wild Gorilla gorilla gorilla (n = 9) and BaAka hunter-gatherers (n = 10) from The Dzanga Sangha Protected Areas, Central African Republic.

Results

Although only a small fraction (< 4.9%) of intestinal mRNA signals was recovered, the data was sufficient to detect significant functional differences between gorillas and humans, at the gene and pathway levels. These intestinal gene expression differences were specifically associated with metabolic and immune functions. Additionally, non-host RNA-seq reads were used to gain preliminary insights on the subjects’ dietary habits, intestinal microbiomes, and infection prevalence, via identification of fungi, nematode, arthropod and plant RNA.

Conclusions

Overall, the results suggest that fecal RNA-seq, targeting gastrointestinal epithelial cells can be used to evaluate primate intestinal physiology and gut gene regulation, in samples obtained in challenging conditions in situ. The approach used herein may be useful to obtain information on primate intestinal health, while revealing preliminary insights into foraging ecology, microbiome, and diet.

Keywords

  • Gene expression
  • Nonhuman primate
  • RNA-seq
  • Noninvasive method

Background

Difficulties in obtaining nucleic acids from representative samples make it necessary to implement non-invasive approaches to explore genetic traits in wild animals and humans [1, 2]. In wild apes, non-invasive genotyping methods from feces have been invaluable in determining long term variation in social group dynamics, estimating population abundance, and performing individual tracking [37]. Other applications of feces-based, noninvasive genetic methods in wild non-human primates include inferences on evolutionary history [8], seed dispersal patterns [9], and assessment of diet [1012]; all facilitated by advances in high throughput genomic methods [13].

The application of other noninvasive genetic methods from fecal samples, such as transcriptome analyses, more directly reflective of intestinal immune or metabolic phenotype at the moment of sample collection, are more challenging [14]. Apart from difficulties in obtaining representative tissue samples, significant challenges associated with these analyses include preservation and integrity of samples and nucleic acids in field conditions, contamination with other biological materials in feces (e.g. microbes, dietary materials) and hence, inability to detect nucleic acid signals reflecting animal physiological status. Nonetheless, transcriptomic (mRNA) analyses from fecal samples in humans and animals, targeting intestinal epithelial cells (IECs) via RT-qPCR or RNA-seq, have been previously used to monitor inflammatory biomarkers during diarrhea [15], to profile intestinal metabolism and immunity after birth [16, 17], to assess colon cancer risk [18], and to monitor mucosal transcriptomics in response to non-steroidal anti-inflammatory drug enteropathy [19]. In addition, other techniques, such as droplet digital PCR, have been useful in detecting transcriptional markers of inflammation from human fecal samples [20].

Thus, transcriptomic analyses from fecal samples can be a promising, non-invasive approach to assess intestinal function, when obtaining tissue samples is a challenging task. However, this approach is much less common in wild or captive animals, or human populations living in challenging field conditions, where this noninvasive analysis tool could be invaluable in monitoring for physiological and health status in the context of host health and conservation. Therefore, this study focuses on a preliminary assessment of this noninvasive approach to profile intestinal function in wild primates and human populations in situ. To that end, we validate RNA-seq analyses from fecal samples, with the goal of quantifying differential intestinal gene expression patterns between wild western lowland gorillas (Gorilla gorilla gorilla) and a human forager population; the BaAka hunter-gatherers, from the Dzanga Sangha Protected Areas, Central African Republic. We chose these two closely related, but different primate species, expecting to detect wide distinctions in intestinal gene expression, while validating the potential usefulness of the technique for monitoring health and physiological status in habituated or captive wild primates, and isolated human populations when repeated sampling is possible.

The RNA-seq pipeline followed herein sought to preserve the integrity of intestinal epithelial cells in field conditions, while maximizing the amount of transcripts (mRNA reads) obtained by comparing different eukaryotic mRNA enrichment methods (poly(A) mRNA enrichment and/or rRNA depletion). Our data showed that although the proportion of gorilla and human mRNA signals recovered from fecal samples via RNA-seq is significantly small, differential immune and metabolic gene expression patterns denoting intestinal functional distinctions between the two closely related primate species can still be detected.

Results

Finding recovery of human and gorilla mRNA reads from fecal samples using poly(a) mRNA enrichment plus rRNA depletion vs rRNA depletion alone

To evaluate two methods for eukaryotic mRNA enrichment from fecal samples, we conducted RNA-seq in 4 sample sets (1 from gorilla and 3 from humans) using poly(A) mRNA enrichment plus rRNA depletion and rRNA depletion alone, generating between 4.5–16.7 M paired end reads (Table 1). After quality control and alignment of pair-end reads to the latest genome drafts for G. g. gorilla (gorGor4.1/gorGor4) and H. sapiens (GRCh38/hg38), the percentage of reads aligning to the respective host genomes ranged from 0. 44 to 1.41% for samples undergoing poly(A) mRNA enrichment plus rRNA depletion. In contrast, samples in which only rRNA depletion alone was used showed from 0.04 to 0.27% alignment rate to the target genomes. Thus, the combined method showed from 11 to 15 times more mappability to the host genomes (paired t-test, P = 0.02), while effectively removing rRNA (0.0 to 4.4%) (Additional file 1: Table S1).
Table 1

Evaluation of mRNA enrichment methods using RNA-seq in four sample set. Read statistics along with the % of alignment to G. g. gorilla and H. sapiens genomes

Samples

Groups

Number of paired-end reads

Remaining reads after quality filtering

Singletons

Low quality reads

Low quality (%)

Total reads aligned using Kallisto

Ratio OLIGO:RIBO

Alignment (%)

KOTO-OLIGO

Haman

13,796,106

13,340,097

0

456,009

3.31

59,616

9.13

0.45

KOTO-RIBO

Human

14,066,624

13,695,055

0

371,569

2.64

6532

 

0.05

MBUSA-OLIGO

Human

16,559,894

14,492,534

1,269,002

798,358

4.82

103,239

11.85

0.71

MBUSA-RIBO

Human

16,775,019

15,644,483

845,112

285,424

1.70

8714

 

0.06

IYIKI-OLIGO

Human

4,992,286

4,390,932

437,374

163,980

3.28

57,579

7.41

1.31

IYIKI-RIBO

Human

5,677,816

5,159,806

388,422

129,588

2.28

7772

 

0.15

MALUI-OLIGO

Gorilla

4,541,457

4,086,161

334,319

120,977

2.66

57,810

5.02

1.41

MALUI-RIBO

Gorilla

4,749,986

4,193,129

418,094

138,763

2.92

11,506

 

0.27

Signletons: Number of high quailty reads for which the correspoding pairs were discarded during quality filtering

Ratio OLIGO:RIBO: Ratio of aligned reads using poly(A) mRNA enrichment plus rRNA depletion vs rRNA depletion alone

Alignment rate: Number of Kallisto aligned reads/number of high quality reads

Non-host RNA-seq reads in the fecal samples primarily map to the microeukaryotes

As poly(A) enrichment plus rRNA depletion yielded the most mappability to human and gorilla genomes, we used this approach for RNA-seq analyses on n = 10 fecal samples of BaAka foragers and n = 9 samples of wild gorillas, with sequencing depth varying from 2.2 million to 21. 52 million in all samples (Additional file 1: Table S1). However, before proceeding with differential gene expression analyses, we first determined the identity of reads not mapping to the target genomes. To that end, 10,000 randomly selected unmapped reads from each sample were aligned against the NCBI NR database. A strict identity cut-off was used to achieve maximum specificity and hence a large proportion of non-host reads remained unclassified (78.08–98%) (Additional file 2: Table S2). Out of all classified reads, most were of eukaryotic origin (~ 74.77 ± 26.16%) followed by reads mapping to bacteria (~ 15.13 ± 15.06%) (Fig. 1a, Additional file 1: Table S1). Upon closer inspection to the eukaryotic fraction, the majority of reads mapped to microeukaryotes (~ 53.4 ± 23.9% of all eukaryotic reads, excluding gorillas and humans), followed by reads mapping to metazoans (~ 11.6 ± 9.33%), plants (~ 6.4 ± 6.5%) and fungi (~ 3.2 ± 3.5%) (Fig. 1b, Additional file 1: Table S1). Of note, gorillas harbored significantly higher number of reads mapping to metazoans (q = 2.17e-05, Wilcoxon signed-rank test) and plants (q = 0.001, Wilcoxon signed-rank test), and tended to have higher percentage of reads mapping to fungi (q = 0.09, Wilcoxon test). Humans tended to show higher percentage of reads mapping to bacteria compared to gorilla (q = 0.07, Wilcoxon signed-rank test) (Fig. 1c). Further inspection of the metazoan-derived reads indicated similar proportion of reads mapping to arthropods (q = 0.68, Wilcoxon signed-rank test) and nematodes (q = 0. 51, Wilcoxon signed-rank test) in both gorillas and humans (Fig. 1d).
Fig. 1
Fig. 1

Taxonomic profiling of non-host mRNA reads, using BLAST against the NR database. BLAST on 10,000 randomly selected unmapped reads from each sample: a) Percentage relative proportions of major taxonomic groups, b) Percentage relative proportions of other eukaryotic hits (excluding gorillas and humans), c) Percentage relative proportions of all taxonomic groups identified from gorilla and human samples, d) Percentage relative proportion of Arthropods and Nematodes

Differential expression profiles in IECs of gorillas and humans

The percentage of reads mapping to human genomes in BaAka fecal samples ranged from 0.40 to 4.99% (1.37 ± 1.36%); while in gorilla fecal samples, the mapping rate to gorilla genomes ranged from 0.26 to 3.1% (1.37 ± 1.00%). Thus, no differences in fecal mappability between gorillas and human were detected (t-test, p = 0.99). From the alignments, transcripts per million (TPM) values for 45,717 gorilla transcripts and 179,974 human transcripts were obtained and then a non-redundant (NR) set of 15,602 common genes, based on gene symbol, and known function in both species, was identified. This NR gene set was used for all downstream gene expression comparisons between humans and gorillas using the DESeq2 R package [21, 22]. A principal coordinate analysis based on Bray-Curtis distances showed significant differential gene expression patterns in the gastrointestinal tract between humans and gorillas (PERMANOVA, R2 = 0.33, p < 0.001) (Fig. 2a), which was further supported by a hierarchical clustering analysis (Fig. 2b). Furthermore, to validate these differential gene expression patterns, we downloaded RNA-seq data targeting IECs shed in fecal samples of human infants. This analysis showed that intestinal gene expression profiles in fecal samples of human infants are closer to those seen in the BaAka hunter-gatherers, based on Bray-Cutis distances (Principal coordinate analysis, PERMANOVA, R2 = 0.43, p < 0.001, Wilcoxon rank sum test, p < 0.001, Fig. 2c-d).
Fig. 2
Fig. 2

Comparison of human and gorilla transcriptomic data from fecal samples using expression profile of total 15,602 common or orthologous genes: a) Principal coordinate analysis based on Bray-Curtis distances, b) Hierarchical cluster analysis using hclust function based on the distances calculated using binary distance measure, c) Comparison of human-BaAka, gorilla and human-infants (downloaded) transcriptomic data using principal coordinate analysis based on Bray-Curtis distances, d) Bray-Curtis distances between these groups

Fold change analyses on the total NR gene set supported by false discovery rate-adjusted q-values (Benjamini & Hochberg correction) identified 212 genes showing significant differential expression between gorillas and the BaAka foragers. One hundred eighty four genes were up-regulated in gorillas while only 28 genes were up-regulated in the BaAka foragers (> 5 fold, q < 0.05, Fig. 3a). The top 50 differentially expressed genes were used to construct a heatmap showing a pool of markers involved in diverse regulatory functions (Fig. 3b and Table 2). Additionally, 26 gene markers specific to gastrointestinal tract metabolism and immune responses were identified (Additional file 3: Table S3). The biological relevance of the differentially expressed genes detected in gorillas and humans was determined using a functional gene set enrichment analysis, as implemented in Ingenuity Pathway Analysis [23]. For this analysis, differentially expressed genes with an FDR q-value cut-off < 0.05 between the two groups were considered. This procedure identified 23 pathways (based on the z-scores) significantly regulated between gorillas and humans (Top 5 upregulated and downregulated pathways in each species can be seen in Fig. 4, all pathways in Table 3). The most upregulated pathway in gorillas was oxidative phosphorylation (ATP5F1E, ATP5MC3, ATP5MF, ATP5PD, COX5B, COX6B1, SDHB, UQCRB and UQCRQ), followed by phagocytosis in macrophages and monocytes (e.g., ACTA1, ACTB, ACTC1, ARPC3 and FCGR2A), dendritic cell maturation (e.g., FCGR2A, IL32, LEP, NFKBIA and PLCD3), interferon signaling (e.g., IFI6, IFITM1, IFITM2 and IFNGR2) and PI3K signaling in B lymphocytes (e.g., ATF3, CHP1, NFKBIA and PCLD3) (Fig. 4 and Table 3). In contrast, humans exhibited highly up-regulated expression of genes associated with IL-8 signaling (e.g., GNB1, LASP1 and RHOB), G-protein (GNB1 and RHOB), RhoGDI (e.g., ARHGEF12, GNB1 and RHOB) and phospholipase C (e.g., ARHGEF12, GNB1 and RHOB) signaling and production of nitric oxide and reactive oxygen species in macrophages (e.g., RHOB) (Fig. 4 and Table 3).
Fig. 3
Fig. 3

Identification of differentially expressed genes from IECs of gorillas and humans in fecal samples: a) Volcano plot of differentially expressed genes. A total 212 genes showed significant differential expression: 184 up-regulated, and 28 down-regulated, b) Heatmap representation of top 50 differentially expressed genes based on q-value <= 3.09E-12 and log2 fold change> = 13, between human and gorilla samples. Each row in the heatmap represents gene symbols, whereas each column in the heatmap represents sample names. Color gradient scaling represents the normalized z-scores

Table 2

List of selected top 50 significantly differentially expressed genes from gorilla and human fecal RNA-seq

Ensembl Id

baseMean

log2FC

lfcSE

pvalue

padj

Gene symbol

entrez ID

Gene name

ENSG00000167996

11,610.8

−16.95

1.69

1.30E-23

3.81E-20

FTH1

2495

ferritin heavy chain 1

ENSG00000159251

10,736.4

16.84

1.76

1.20E-21

1.75E-18

ACTC1

70

actin, alpha, cardiac muscle 1

ENSG00000183304

61.38

25.02

2.68

9.69E-21

9.46E-18

FAM9A

171,482

family with sequence similarity 9 member A

ENSG00000185201

183.02

26.54

2.96

3.09E-19

2.26E-16

IFITM2

10,581

interferon induced transmembrane protein 2

ENSG00000158470

18,496.9

15.59

1.77

1.18E-18

5.77E-16

B4GALT5

9334

beta-1,4-galactosyltransferase 5

ENSG00000152977

704.66

−26.19

2.97

1.10E-18

5.77E-16

ZIC1

7545

Zic family member 1

ENSG00000235750

3610.92

15.27

1.74

1.76E-18

7.37E-16

KIAA0040

9674

KIAA0040

ENSG00000227877

91.61

25.53

2.96

6.52E-18

2.32E-15

MRLN

100,507,027

myoregulin

ENSG00000089356

85.74

25.50

2.96

7.12E-18

2.32E-15

FXYD3

5349

FXYD domain containing ion transport regulator 3

ENSG00000280831

72.47

25.28

2.96

1.38E-17

4.04E-15

RPS25

6230

ribosomal protein S25

ENSG00000169605

64.76

24.87

2.96

4.44E-17

1.18E-14

GKN1

56,287

gastrokine 1

ENSG00000164405

52.34

24.83

2.96

5.02E-17

1.23E-14

UQCRQ

27,089

ubiquinol-cytochrome c reductase complex III subunit VII

ENSG00000126709

45.85

24.65

2.96

8.44E-17

1.72E-14

IFI6

2537

interferon alpha inducible protein 6

ENSG00000233927

45.61

24.64

2.96

8.67E-17

1.72E-14

RPS28

6234

ribosomal protein S28

ENSG00000107223

53.30

24.63

2.96

8.81E-17

1.72E-14

EDF1

8721

endothelial differentiation related factor 1

ENSG00000144233

39.76

24.44

2.96

1.53E-16

2.68E-14

AMMECR1L

83,607

AMMECR1 like

ENSG00000173467

39.14

24.43

2.96

1.56E-16

2.68E-14

AGR3

155,465

anterior gradient 3, protein disulphide isomerase family member

ENSG00000129559

38.07

24.39

2.96

1.77E-16

2.77E-14

NEDD8

4738

neural precursor cell expressed, developmentally down-regulated 8

ENSG00000106153

40.39

24.38

2.96

1.80E-16

2.77E-14

CHCHD2

51,142

coiled-coil-helix-coiled-coil-helix domain containing 2

ENSG00000169189

36.70

24.35

2.96

2.00E-16

2.93E-14

NSMCE1

197,370

NSE1 homolog, SMC5-SMC6 complex component

ENSG00000163541

32.88

24.20

2.96

3.04E-16

4.24E-14

SUCLG1

8802

succinate-CoA ligase alpha subunit

ENSG00000198643

32.08

24.14

2.96

3.59E-16

4.78E-14

FAM3D

131,177

family with sequence similarity 3 member D

ENSG00000275903

29.48

24.03

2.96

4.88E-16

6.22E-14

PSMB3

5691

proteasome subunit beta 3

ENSG00000177697

36.53

23.99

2.96

5.34E-16

6.26E-14

CD151

977

CD151 molecule (Raph blood group)

ENSG00000163959

26.61

23.91

2.96

6.79E-16

7.65E-14

SLC51A

200,931

solute carrier family 51 alpha subunit

ENSG00000196914

49.35

−23.95

2.97

7.09E-16

7.69E-14

ARHGEF12

23,365

Rho guanine nucleotide exchange factor 12

ENSG00000136167

26.11

23.87

2.96

7.58E-16

7.86E-14

LCP1

3936

lymphocyte cytosolic protein 1

ENSG00000241468

25.70

23.86

2.96

7.78E-16

7.86E-14

ATP5MF

9551

ATP synthase membrane subunit f

ENSG00000182718

24.11

23.77

2.96

1.00E-15

9.77E-14

ANXA2

302

annexin A2

ENSG00000164266

38.51

23.73

2.96

1.12E-15

1.06E-13

SPINK1

6690

serine peptidase inhibitor, Kazal type 1

ENSG00000156467

22.80

23.69

2.96

1.24E-15

1.14E-13

UQCRB

7381

ubiquinol-cytochrome c reductase binding protein

ENSG00000140319

21.51

23.60

2.96

1.58E-15

1.40E-13

SRP14

6727

signal recognition particle 14

ENSG00000118972

1630.18

−14.12

1.77

1.62E-15

1.40E-13

FGF23

8074

fibroblast growth factor 23

ENSG00000110700

20.79

23.57

2.96

1.72E-15

1.44E-13

RPS13

6207

ribosomal protein S13

ENSG00000124172

18.93

23.45

2.96

2.43E-15

1.97E-13

ATP5F1E

514

ATP synthase F1 subunit epsilon

ENSG00000105404

18.29

23.39

2.96

2.81E-15

2.22E-13

RABAC1

10,567

Rab acceptor 1

ENSG00000143226

17.72

23.36

2.96

3.10E-15

2.33E-13

FCGR2A

2212

Fc fragment of IgG receptor IIa

ENSG00000197956

886.17

13.24

1.68

3.08E-15

2.33E-13

S100A6

6277

S100 calcium binding protein A6

ENSG00000116663

16.94

23.29

2.96

3.68E-15

2.70E-13

FBXO6

26,270

F-box protein 6

ENSG00000105379

16.11

23.22

2.96

4.51E-15

3.22E-13

ETFB

2109

electron transfer flavoprotein beta subunit

ENSG00000246705

15.06

23.13

2.96

5.68E-15

3.96E-13

H2AFJ

55,766

H2A histone family member J

ENSG00000206291

15.77

22.97

2.96

8.90E-15

5.79E-13

HLA-DPA1

3113

major histocompatibility complex, class II, DP alpha 1

ENSG00000103187

12.75

22.91

2.96

1.04E-14

6.51E-13

COTL1

23,406

coactosin like F-actin binding protein 1

ENSG00000117616

13.04

22.91

2.96

1.04E-14

6.51E-13

RSRP1

57,035

arginine and serine rich protein 1

ENSG00000162909

12.50

22.79

2.96

1.41E-14

8.59E-13

CAPN2

824

calpain 2

ENSG00000148180

19.65

−22.67

2.97

2.24E-14

1.34E-12

GSN

2934

gelsolin

ENSG00000165030

17.54

−22.51

2.97

3.41E-14

1.96E-12

NFIL3

4783

nuclear factor, interleukin 3 regulated

ENSG00000165704

16.69

−22.44

2.97

4.10E-14

2.31E-12

HPRT1

3251

hypoxanthine phosphoribosyltransferase 1

ENSG00000119431

15.72

−22.36

2.97

5.06E-14

2.80E-12

HDHD3

81,932

haloacid dehalogenase like hydrolase domain containing 3

ENSG00000116176

13.05

22.26

2.96

5.70E-14

3.09E-12

TPSG1

25,823

tryptase gamma 1

log2FC: log2FoldChange (Gorilla/human)

lfcSE: logfoldchangeStandard Error

padj: Adjusted p value

Fig. 4
Fig. 4

Pathway analysis (Ingenuity IPA software) was performed using differentially expressed genes. Top 5 up-regulated and top-5 down regulated pathways selected based on the IPA provided z-scores

Table 3

Differentially regulated pathways between gorilla and humans identified using Ingenuity Pathway Analysis

Ingenuity Canonical Pathways

-log(p-value)

Ratio

z-score

Molecules

Oxidative Phosphorylation

6.02E+ 00

8.26E-02

3

COX6B1,SDHB,ATP5PD,COX5B,ATP5MF,ATP5MC3,UQCRQ,ATP5F1E,UQCRB

Fcγ Receptor-mediated Phagocytosis in Macrophages and Monocytes

2.72E+ 00

5.38E-02

2.236

FCGR2A,ACTB,ARPC3,ACTC1,ACTA1

Dendritic Cell Maturation

1.42E+ 00

2.58E-02

2.236

PLCD3,NFKBIA,LEP,FCGR2A,IL32

Interferon Signaling

3.44E+ 00

1.11E-01

2

IFNGR2,IFI6,IFITM2,IFITM1

PI3K Signaling in B Lymphocytes

1.39E+ 00

2.94E-02

2

PLCD3,ATF3,NFKBIA,CHP1

EIF2 Signaling

9.25E+ 00

7.05E-02

1.89

ATF3,ACTB,RPS23,RPLP2,RPL23,RPL10A,RPS28,EIF1,RPS13,RPS25,RPS15A,RPL36,RPL31,ACTC1,ACTA1,RPLP0

RhoA Signaling

3.74E+ 00

5.65E-02

1.633

ARHGEF12,NRP2,CFL1,ACTB,ARPC3,ACTC1,ACTA1

Regulation of Actin-based Motility by Rho

4.63E+ 00

7.78E-02

1.342

RHOB,CFL1,ACTB,ARPC3,GSN,ACTC1,ACTA1

HMGB1 Signaling

1.98E+ 00

3.60E-02

1.342

HMGB1,CXCL8,KAT6B,RHOB,IFNGR2

Integrin Signaling

3.60E+ 00

4.11E-02

1.134

ARF5,RHOB,ACTB,ARPC3,ZYX,CAPN2,GSN,ACTC1,ACTA1

Signaling by Rho Family GTPases

3.16E+ 00

3.57E-02

1.134

GNB1,CDH1,ARHGEF12,RHOB,CFL1,ACTB,ARPC3,ACTC1,ACTA1

ILK Signaling

2.56E+ 00

3.55E-02

1.134

PPP2CB,CDH1,RHOB,CFL1,ACTB,ACTC1,ACTA1

Actin Cytoskeleton Signaling

4.10E+ 00

4.29E-02

1

ARHGEF12,WASF2,CFL1,ACTB,FGF23,ARPC3,GSN,ACTC1,ACTA1,IQGAP3

Dopamine-DARPP32 Feedback in cAMP Signaling

3.01E+ 00

4.27E-02

1

PLCD3,PPP2CB,KCNJ2,KCNJ14,CHP1,PPP1R11,KCNJ3

Death Receptor Signaling

1.93E+ 00

4.30E-02

1

NFKBIA,ACTB,ACTC1,ACTA1

Tec Kinase Signaling

1.64E+ 00

2.94E-02

1

GNB1,RHOB,ACTB,ACTC1,ACTA1

Sirtuin Signaling Pathway

2.16E+ 00

2.74E-02

0.816

SCNN1A,CXCL8,CDH1,SDHB,SLC2A1,TUBA4B,HMGCS2,ATP5F1E

Neuroinflammation Signaling Pathway

1.12E+ 00

1.93E-02

0.447

HMGB1,CXCL8,CHP1,IFNGR2,GLUL,KCNJ3

Production of Nitric Oxide and Reactive Oxygen Species in Macrophages

1.42E+ 00

2.58E-02

−0.447

PPP2CB,NFKBIA,RHOB,IFNGR2,PPP1R11

Phospholipase C Signaling

1.54E+ 00

2.46E-02

−0.816

GNB1,PLCD3,ARHGEF12,RHOB,FCGR2A,CHP1

RhoGDI Signaling

5.12E+ 00

5.65E-02

−1

GNB1,CDH1,ARHGEF12,WASF2,RHOB,CFL1,ACTB,ARPC3,ACTC1,ACTA1

Gαq Signaling

1.73E+ 00

3.11E-02

−1

GNB1,RGS2,NFKBIA,RHOB,CHP1

IL-8 Signaling

1.35E+ 00

2.46E-02

−1

GNB1,CXCL8,CDH1,RHOB,LASP1

z-score(+): upregulation in gorilla, whereas z-score(−): upregulation in humans

Discussion

Here, we attempted to maximize functional genomic information from the intestinal tract of wild lowland gorillas and the BaAka foragers from the Dzanga Sangha Protected Areas in the Central African Republic. Our main goal was to determine if the non-invasive RNA-seq approach would detect regulatory signals under selection in the gastrointestinal tract of two closely related primate species, potentially allowing us to answer critical questions on gut physiology of free-ranging gorillas and syntopically living humans. The main obstacle faced by this method was the recovery of sufficient mammalian transcripts from human and gorilla fecal samples. However, as healthy humans can shed approximately 10 billion intestinal cells per day in feces, it has been suggested that detecting mRNA signals from intestinal cells in feces is possible [24, 25].

Despite success in recovering information from IECs and immune cells, percent mappability obtained in this dataset is still lower in comparison to the mappability obtained in human studies (range 0.7–37.9%, mean = 9.21 ± 13.6%) [17]. These differences in mappability may be due to distinctions in sample processing and storage; for instance, we did not perform any bowel fluid preparation to filter out fecal debris nor use a direct poly(A) RNA enrichment extraction kit as described previously [26]. Also, we could not collect fecal samples immediately after voiding, which promotes cellular death. In addition, although we only selected RNA samples of high quality (2 to 5μg of RNA, with a RIN from 7 to 8), RNA purity may have also affected the number of host-derived transcripts obtained. To address these issues, we attempted to test the combined effect of rRNA depletion plus poly(A) enrichment, which improved mammalian genome mappability. In this regard, it has been shown that poly(A) enrichment and rRNA depletion provide similar rRNA removal efficiency, coverage, mappability and transcript quantification, besides providing a less biased coverage of 3′ ends in genes [27]; however, increasing sequencing effort (~ 4 times sequencing depth) may be necessary when using rRNA depletion alone at the same exonic coverage [28]. This is consistent with the increased mappability reached herein, using the combined rRNA depletion and poly(A) enrichment method, as opposed to rRNA depletion alone, at the same sequencing depth. Nonetheless, this combined approach can be considerably more costly compared to using RNA depletion alone. As such, approaches that are gene-centric and that rely on either micro-arrays or RT-qPCR to target gene markers of gastrointestinal disease [29], nutrition [30], and immune and metabolic health [31] may be more cost effective. However, targeted approaches are unable to detect additional information on extrinsic host factors (non-host derived) such as diet and microbiome..

For instance, our results showed that from all the mRNA reads unmapped to gorilla and human genomes, those of plants, metazoa and fungi were more prevalent in gorillas than in the BaAka fecal samples. Thus, non-host RNA-seq reads, as detected here, could potentially offer preliminary insights on dietary behaviors and microbiome. For example, it is a fact that, compared with humans, wild gorillas rely significantly more on plant-based diets [32]. Also, gorillas have been reported to have specialized gut fungal populations to process and ferment highly fibrous and complex foods, an adaptation believed to be of less importance for humans [32]. The observation that both gorillas and the BaAka humans showed similar proportions of arthropod-derived reads is more intriguing; but it may be connected with similar patterns of consumption of insects and insect-derived products by both gorillas and human foragers [3336]. Likewise, similar prevalence of gastrointestinal nematode infections have been reported by our group in these same gorilla groups and human populations previously [37]. Nonetheless, we were unable to determine why the proportion of all nematode reads was higher in gorillas. This issue is likely a result of the limited taxonomic information that can be recovered from such a small transcript fragment, influence of food processing by the host, and issues related to the technical challenges of this endeavour. Also, due to the liable nature of RNA, DNA-based metabarcoding may be more effective to infer dietary behaviors from feces surveys, in the context of plant and insect consumption [10]. As such, all results related with non-host factors (diet and microbiomes) presented herein warrant further investigation, and comparison with targeted approaches.

Another important consideration of the proposed noninvasive approach is the kind of functional information that can be obtained from exfoliated intestinal epithelial and immune cells in fecal samples. Exfoliated cells in fecal samples may originate from any site along the gastrointestinal tract, including stomach, villus tips in the small intestine and from crypt surfaces at colon level. This information can potentially offer valuable insights on gastrointestinal homeostasis in the context of nutrition and health [25, 38]. However the exact origin of these cells is unknown, and once detached from the extracellular matrix, cells enter a stage of programmed apoptosis and anoikis. Additionally, an important concern is the degradation of RNA coming from small intestinal cells in the lower GI tract and in fecal samples; these limitations may have caused us to miss important expression patterns.

Regardless, the results presented here, along with validation of our data by contrasting fecal mRNA reads of with a previously published human dataset, demonstrate the biological value of fecal RNA-seq to mine for differential gene expression patterns in gut tissues [17, 19]. For instance, we report a higher number of upregulated pathways in gorillas (18 pathways) than in humans (5 pathways), with oxidative phosphorylation in gorillas being the most upregulated pathway in the dataset. Colonic expression of genes involved in energy metabolism, such as oxidative phosphorylation, may be enhanced via the colonic microbiome and its metabolites, specifically butyrate [39]. Thus, it is expected that diets that promote increased fermentation selectively favor this regulatory trait [40]. This observation explains the dramatic downregulation of oxidative phosphorylation in the BaAka humans, who incorporate significantly less plant material in their diets. Other gorilla-specific pathways mainly denote maintenance of intestinal barrier integrity, cell architecture and immune functions (Table 3). [A.G2] For example, the upregulation of adaptive immune pathways in gorillas such as phagocytosis in macrophages, dendritic cell maturation and interferon signaling may reflect capacity to recognize and eliminate potential external insults and maintain a balance between tolerance and inflammation in the intestinal environment. Regulatory adaptations to counteract potential pathobionts in the gorilla gut, while tolerating commensals, may be reinforced by reliance on diets that could also inhibit pathogens and stimulate beneficial microbes, such as those rich in phenolics and fermentable dietary substrates (fiber) [41, 42]. These observations may explain why gorillas, compared with the BaAka humans, exhibit down regulation of pathways involved in proinflammation.

For instance, in the BaAka, most upregulated pathways (4 out of the 5 detected) denote increased inflammatory responses. The more upregulated pathway in the BaAka was IL-8 signaling, which mediates recruitment of pro-inflammatory cytokines (CXCL8) to sites of infection [43, 44]. In this regard, it has been reported that African foragers exhibit evolutionary adaptations to counteract increased susceptibility to intestinal infection, by enhancing immune responses to pathogens [37, 4547]. Other upregulated pathways seen in the BaAka that may support increased responses to pathogens and inflammation are Gɑq signaling and production of nitric oxide (NO) and reactive oxygen (RO) by macrophages. Gɑq signaling plays a role in Paneth cell maturation, intestinal barrier integrity and protection against luminal pathogens by secreting alpha-defensins [48], and NO and RO production in macrophages is correlated with levels of pathogenic infections in monocytes and inflammation [49, 50]. Moreover, phospholipase C signaling is also involved in protection from a hostile luminal environment and tissue restitution after intestinal epithelial cell injury, in conjunction with Gɑq [51, 52]. Based on these results, and the differential expression of pro-inflammatory pathways detected, it is tempting to speculate that humans, in comparison with nonhuman primates are evolutionarily primed for inflammatory responses in response to specific diets, environments or the microbiome; nonetheless, these hypotheses warrants further investigation.

Conclusions

In summary, we advance a noninvasive approach that evaluates primate intestinal physiology, and that can be used in situ, by measuring gene expression patterns in fecal samples. Although the proportions of IEC mRNA patterns obtained in these conditions are not fully representative, compared to standard RNA-seq approaches, we show that this method has the potential to reveal critical gene expression information when comparing primates exposed to different physiological and environmental conditions. In addition to providing insights into differences among two primate species in intestinal cell function, this approach could reveal fine-grained distinctions among conspecific populations, including differences by age or sex. Broadly, these results have important implications for understanding the immune and metabolic gut environment of humans and animals in filed conditions, when invasive samples are unattainable. In the future, applying this procedure in tandem with methods that provide a more reliable and targeted picture of diet and microbiome (e.g. DNA barcoding, metabolomics and metagenomics) may offer a powerful tool to characterize diet-host-microbe interactions in the context of primate nutrition, health and conservation.

Methods

Study site and sampled objects

The study was conducted in Dzanga-Sangha Protected Areas (DSPA) in the Central African Republic. Activities in DSPA are directed by the DSPA administration under the collaborative management of the CAR Government and World Wildlife Fund. The climate is characterized by marked seasonal variation [53], with dry (November–March) and wet (April–October) months [54]. Human population density is low, estimated at one person per square kilometer, with the greatest concentration (60% of people) located in the village of Bayanga [55, 56]. In May 2016, we sampled three habituated gorilla groups (Makumba, Mayeli, Mata) around two permanent Primate Habituation Program (PHP) research camps: Bai Hokou (2° 50 N, 16° 28 E) and Mongambe (2° 55 N, 16° 23 E). The human subjects in the study included personnel working as gorilla trackers (BaAka) hired by the PHP and their female partners. The BaAka males alternated periods of time between the research camps, tracking the gorillas in the Park and their villages. Both trackers and their partners frequently did either day hunts from the villages or longer hunting trips within the DSPA.

Sample collection and preparation

Fecal samples (1 g) of lowland gorillas and humans were collected within 5 min after defecation and divided in three subsamples (to prevent repeated thawing-freezing if samples need to be analyzed more times) which were immediately in the field preserved in RNAlater (Qiagen, Hilden, Germany) (to prevent repeated thawing-freezing if samples need to be analyzed more times) and left overnight at room temperature. Next day the samples were stored in the mobile freezer powered by solar battery. The necessity to collect the sample immediately after defecation restricts this method only to habituated apes as samples from the unhabituated ones are usually several hours old. In the field, the samples were stored in a mobile freezer powered by solar battery. During the transport to the laboratory in the Czech Republic samples were kept at − 20 °C in a mobile freezer powered by a car battery, while during the international air transport, samples were transported in dry ice. After the arrival to the laboratory in the Czech Republic, samples were stored at − 80 °C and RNA isolated within a month.

RNA isolation

RNAlater-preserved and frozen feces were thawed on ice to allow collection of approximately 250 mg of feces. The RNA was isolated using the RNeasy Midi Kit (Qiagen, Hilden, Germany) following manufacturer’s instructions. For the cell lysis, β-mercaptoethanol was added to the RLT buffer and RNA was eluted in two steps to two separate tubes using 150 μL of RNAse-free water each time and the isolated RNA was immediately stored at − 80 °C.

Next-generation sequencing

Total RNA was treated with DNAse and followed by RiboZero rRNA removal kit (bacteria, MRZB12424 Illumina). Samples that yielded from 2 to 5μg of RNA, with a RIN from 7 to 8 were used for downstream applications. RNA was QC’d on the Bioanalyzer using Agilent RNA 6000 Nano Kit (5067–1511), with accepted values of remaining rRNA at 20%. The RNA samples were subjected to mRNA isolation in an effort to capture the host mRNA. The entire sample was used for mRNA isolation using NEBNext poly(A) mRNA Magnetic Isolation Module (NEB E7490L) on the Apollo 324 system. The resulting mRNA was used for generating Illumina libraries using the PrepX RNA-seq for Illumina Library Preparation Kit, 8 sample (Wafergen cat#400039) on the Apollo 324 system with the scriptSeq Index PCR primers (epicenter SSIP1234). Final libraries were side selected at 0.9X. The libraries were QC’s for proper sizing and the absence of adapter dimers on the Bioanalyzer using the Agilent DNA high sensitivity kit (5067–4626) followed by qPCR using the library quantification kit (Kapa Biosystems KK4835) on a 7900HT Fast real-time PCR system (Thermo-Fisher). The libraries were sequenced on the NextSEq 500 at 2*75 base pair read length.

Gene expression quantification

Quality reports for raw reads were generated using FastQC toolkit. Trimmomatic was used for the trimming of raw reads using “ILLUMINACLIP:$TRIMMOMATIC/adapters/TruSeq2-PE.fa:2:30:10 SLIDINGWINDOW:4:20 MINLEN:36”. Trimmed reads obtained from the gorilla samples were mapped against the gorilla genome and from the human samples were mapped against the Human genome using Kallisto [57]. Aligned counts (TPM values) for each gene from each sample were used for the subsequent analyses. Variance for each was calculated using DESeq2 package after performing the variance stabilizing transformation. Principal coordinate and hierarchical clustering analysis were performed in R. Fold changes and adjusted p-values (q-value) for Gorilla vs Human comparison were calculated using DESeq2 at alpha = 0.05 and pAdjustMethod = “BH”. Top 50 differentially expressed genes (q-value < 0.05 & log2 fold change > 10) and having maximum variance were selected for the association analysis using cluster heatmap (distfun = “euclidean”, hclustfun = “complete”).

Ingenuity pathway analysis

Ingenuity Pathway Analysis (IPA) version 2.0 was used to perform the functional enrichment analysis (QIAGEN Inc., https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis/). All differentially expressed genes (q-value < 0.05) were uploaded along with their log2 fold change information. Out of total 212 genes, 184 genes significantly enriched in gorilla (log2 fold change > 5) and 28 genes significantly enriched in humans (log2 fold change > 5) were used for this analysis. The z-score was used to infer the activation state which was calculated using the log2 fold change values of each gene.

Identification of different taxonomic groups

To identify the taxonomic fractions other than the host genome, 10,000 randomly selected unmapped reads from each sample were aligned against the NCBI NR database using BLAST (default parameters)[58]. The reason for selecting this database was to predict the maximum signals from the non-host RNA reads as a preliminary assessment. Resultant blast best hits were selected using the identity threshold of > 99%. The taxonomic IDs were imported from NCBITaxa using ETE toolkit to obtain the complete taxonomic lineage of each hit [59].

Dataset downloaded for the comparison

Infants RNA-seq reads for six individual samples were downloaded from the published study [SRA:PRJNA182262] [17]. Total 300 million, 50 bp single end reads were processed using the same pipeline used in our study to generate the expression profiles.

Abbreviations

FDR: 

False discovery rate

IECs: 

Intestinal Epithelial Cells

mRNA: 

Messenger RNA

PI3K: 

Phosphoinositide 3-kinase

RNA-seq: 

RNA sequencing

rRNA: 

Ribosomal RNA

TPM: 

Transcripts per million

Declarations

Acknowledgments

We thank to the Section of Virology of the Department of Infectious Diseases and Microbiology, University of Veterinary and Pharmaceutical Sciences Brno, Czech Republic for using their facilities for RNA isolation. We thank to Mgr. Anna Bryjová and Mgr. Dagmar Čížková, Ph.D. for sharing their experience and knowledge with RNA isolation. We would like to express our gratitude to the government of the Central African Republic and the World Wildlife Fund for granting permission to conduct our research; the Ministre de l’Education Nationale, de l’Alphabetisation, de l’Enseignement Superieur, et de la Recherche for providing research permits; and the Primate Habituation Programme for providing logistical support in the field. Finally, yet importantly, we would like to thank all of the trackers and assistants in DSPA. We also want to thank Aaron Becker and the University of Minnesota Genomic Center staff for their valuable comments during the planning of this research; Mark Adams for their input on sequencing procedures and data analyses; and Joshua Baller at The Minnesota Super computer Institute for advice on bioinformatics analyses. We also thank Dr. Milena Saqui-Salces and Dr. Christopher Faulk from the University of Minnesota, Department of animal science, for their valuable comments in previous drafts and data analyses of this manuscript. AG wants to thank Ana and Martina for their continued support. This work was performed in part using computational resources at the Minnesota Supercomputing Institute.

Funding

The Leakey foundation (San Francisco, CA) and The Czech-American Scientific cooperation - by project number (LH15175) supported from the Ministry of Education, Youth and Sports of The Czech Republic provided funding for this project. Apart from providing funds, they were not involved in the study design, data analyses, interpretation, or manuscript writing.

Authors’ contributions

KJP and AG conceived, designed and the coordinated the study with help of JK. BP collected the field data under supervision of TF. KV and BC carried out molecular lab work in the Czech Republic. AKS carried out computational and statistical analyses. AKS, AG and KJP wrote the manuscript. AKS, BP, KV, BC, JK, KB, TF, SRL, MBB, RB, KP and AG revised the manuscript. All authors read the publication and gave final approval for publication.

Ethics approval and consent to participate

The research complied with the legal requirements of the Central African Republic and adhered to the research protocol of DSPA. All sample collection from humans was approved by the Czech Academy of Sciences (CAS). Only a verbal non-recorded consent was obtained from all sampled persons and the samples were anonymized. The institutional review board of CAS approved this consent procedure. The collection of fecal samples from gorillas did not cause any observable distress to the animals.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Animal Science, University of Minnesota, Twin Cities, USA
(2)
The Czech Academy of Sciences, Institute of Vertebrate Biology, Květná 8, 603 65 Brno, Czech Republic
(3)
Department of Pathology and Parasitology, Faculty of Veterinary Medicine, University of Veterinary and Pharmaceutical Sciences Brno, Palackého tř. 1946/1, 612 42 Brno, Czech Republic
(4)
Department of Zoology, Faculty of Science, Charles University, Viničná 7, 128 44 Praha, Czech Republic
(5)
Vanderbilt University medical center Technologies for Advanced Genomics, Vanderbilt University medical center, Nashville, TN, USA
(6)
WWF Central African Republic, Bangui, Central African Republic
(7)
Department of Anthropology, University of Colorado, Boulder, CO, USA
(8)
Loyola University Chicago, Quinlan Life Sciences Building, Chicago, IL, USA
(9)
Department of Genetics, Cell Biology, and Development, University of Minnesota, Twin Cities, MN, USA
(10)
Department of Ecology, Evolution and Behavior, University of Minnesota, Twin Cities, MN, USA
(11)
The Czech Academy of Sciences, Biology Centre, Institute of Parasitology, Branišovská 31, 370 05 České Budějovice, Czech Republic
(12)
Liberec Zoo, Lidové sady 425/1, 460 01 Liberec, Czech Republic

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