Assessing the short-term impact of a high-fat, high-salt diet on the gut bacteria and related pathophysiology in mice

doi:10.21203/rs.3.rs-3341945/v1

Download PDF

Short Report

Assessing the short-term impact of a high-fat, high-salt diet on the gut bacteria and related pathophysiology in mice

https://doi.org/10.21203/rs.3.rs-3341945/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Current research shows that consuming high-fat and salt that are now the main ingredients of modern diets over a period of time can disrupt the gut ecosystem, leading to metabolic imbalances and metabolic diseases. However, which component of modern diets, such as high-fat and high-salt consumption for a short duration, is more harmful to health based on its impact on gut bacteria and associated health outcomes is still poorly explored. This study aimed to determine which of high fat or high salt is more detrimental to health by feeding mice HFD and HSD diets for a short period of 3 weeks. To address these wide knowledge gaps, we conducted a high-throughput sequencing study to see how gut microbiota profile changes in HFD or HSD-fed mice. Further, we also investigated whether high fat or high salt is more detrimental to health. In this study, the mice were fed a standard chow diet (CD), HFD and HSD for 3 weeks. Animals were euthanized and examined of haemato-biochemical and histopathological attributes. We also used 16S rRNA sequencing followed by bioinformatics analysis to evaluate the changes in gut microbiota ecology. Interestingly, this study found that HFD or HSD feeding for a short duration induces the pathophysiological attributes of a typical metabolic syndrome as indicated by serum biochemistry and significantly modifies gut microbiota in mice. We concluded that HSD causes significantly more changes in gut bacteria than HFD due to a diminution of beneficial gut bacteria and an enrichment of harmful gut bacteria. We found that HFD led to a more significant increase in plasma total cholesterol (TC), a known risk factor for heart disease, stroke, and atherosclerosis. While HSD is more detrimental to the kidneys, since an increase in creatinine levels indicates kidney disease. Furthermore, mice fed HFD or HSD for a short duration showed minimal and insignificant pathological changes in their hearts, livers, and kidneys.

High-Salt Diet

High fat diet

Gut microbiota and Metabolic syndrome

A global problem of high fat and high salt intake as key components of the modern diet has seriously impacted human health by developing or advancing diseases such as metabolic disorders, immune-related disorders, and dysfunction of neurobehavioral traits [1, 2]. A healthy diet should not exceed 30% of total energy from total fat and saturated fat and trans-fat should not exceed 10% and 1% of food energy, respectively [3–8]. Further, daily salt consumption should not exceed 5 g (equivalent to less than 2 g of sodium daily) [9]. As a result, HFD and HSD consumption over a period leads to chronic low-grade systemic inflammation, which disrupts the gut bacteria landscape by producing circulatory metabolites, such as inflammatory mediators, circulating free fatty acids, and endotoxins, which ultimately damage the target organs [10–13]. People around the world consume food based on taste characteristics like fat, salty, sweet, sour, bitter, and umami [14–24]. High fat and salt are universal flavour enhancers. There is broad agreement that high fat and high salt consumption over a long period induce metabolic syndrome pathology, including abnormal cholesterol levels, elevated fasting blood sugar, and increased triglyceride levels, as well as chronic inflammation linked to alterations in gut ecology [25–28]. As a result, this knowledge is crucial because the modern diet is generally associated with higher fat and salt levels than what is recommended and widely consumed. In spite of this, it is not well understood which component of modern diets, such as high fat and salt consumption for a short duration, is more harmful to health on the basis of the impact on gut bacteria and the associated health. This study aimed to determine which of high fat or high salt is more detrimental to health by feeding mice HFD and HSD diets for a short period of 3 weeks. We first showed that HFD and HSD intake for only 3 weeks disrupted the gut ecological system and led to the development of partial pathophysiology of metabolic syndrome related inflammation. After that, we also critically evaluated the haemato-biochemical profile and pathological changes in vital organs, namely the heart, liver, and kidney, in the mouse model.

Animal model

All experimental procedures defined were approved and performed according to relevant guidelines outlined by the Institutional Animal Ethics Committee (IAEC) of All India Institute of Medical Sciences (AIIMS), New Delhi, India. In this study, 18 male C57BL/6J mice aged 6–8 weeks were fed ad-libitum and maintained under standard environmental conditions (22±3°C, 12 h light/dark cycles) with free access to water at AIIMS, New Delhi.

Study design

Male mice from the same cohort were randomly assigned into three groups (N = 6 for each group) and fed experimental diets ad-libitum for 3 weeks. The experimental groups were fed a control chow (CD) diet (10% energy from fat and 0.4% NaCl in chow); high fat (HFD) diet (73% including lard, cholesterol, and veg oil) and high salt (HSD) diet (4% NaCl in chow). The mice were euthanized by CO2 inhalation after a 12-hours overnight fast at the end of the study period.

Blood, tissue, and caecal sample collection

Terminal bleeding was performed by cardiocentesis using a 1-mL tuberculin syringe. Afterward the heart, liver, and kidney were collected for histopathological analysis. Caecal content samples were collected and stored at -80 °C for future analysis.

Serum biochemistry and hematology

According to the manufacturer's protocol, blood biochemistry was carried out on the serum auto-analyzer Screen Master 3000, Tulip, Alto Santa Cruz, India, using the Coral GPO-PAP kit (CORAL Clinical Systems, Goa, India). An automated vet hematology counter (Melet Schloesing Laboratories, Guwahati, India) was used to analyse blood samples in accordance with the manufacturer's instructions.

Histopathology

For histological analysis, sections of vital organs namely heart, liver and kidney of mice from CD, HFD and HSD were fixed in 10 % neutral buffered formalin for 24 h and embedded in paraffin. Tissue sections were deparaffinized and stained with haematoxylin-eosin (H&E). The digital images were taken using light microscopy (Olympus CX-29: Olympus Optical Co. Ltd, Tokyo, Japan) and a camera (Magnus DC 10).

High throughput 16S rRNA gene amplicon sequencing

According to the manufacturer's recommendation, total microbial genomic DNA from caecal content samples was extracted using Qiagen DNA Stool Mini Kit. The extracted DNA was forward for high throughput 16S rRNA gene amplicon sequencing and genus analysis (DNA Xperts Private Limited, India). By using the universal 16s PCR primer specific for V3–V4 region included: 341F 5′- CCTAYGGGRBGCASCAG-3′ and 806R 5′-GGACTACNNGGGTATCTAAT-3′, the samples were expanded following the Illumina Miseq high-throughput sequencer usage guide [29].

Microbial Bioinformatics Analysis

The 16 S rRNA raw data of all samples were processed and analysed using the QIIME pipeline (v1.9.1). Trimmomatic and Fast QC were used to trim and align the paired-end reads with tags with an average read length of 252 bp [30]. The sequences were assigned to operational taxonomic units (OTU) with a 97% similarity threshold [31]. Rarefaction curves assessed the sufficient sequence depth of all samples. Alpha-diversity was calculated using Chao1, observed species, Shannon and Simpson indices [32].

Statistical Analysis

Data are expressed as the means ± Standard Deviation (SD). The differences in quantitative data of groups were statistically analyzed by one-way analysis of variance (ANOVA). After confirmation of significant differences among the groups, post-hoc comparisons were made by the Bonferroni test. GraphPad Prism version 9.2.0 (3.2.0) for Windows, Graph Pad Software, San Diego, California USA, www.graphpad.com" was used for the statistical analysis of experimental data. The results were considered significant at p < 0.05.

Short period HSD consumption significantly more decrease of white blood cells (WBC) compared to in HFD fed mice

This study first critically evaluated the comparative impact of HSD and HFD on hematological parameters in mice (Figure 1 and Table 1). Compared to HFD fed mice, we observed a more significant decrease in WBC and thrombocytes in HSD fed mice (p=0.0001).

Table 1 Comparative short-term high fat (73% energy from fat) and high salt (4%) diet effect on haematological parameters in mice

Parameters	Groups			Significance (p value)
Parameters	CD	HFD	HSD	CD vs HFD	CD vs HSD	HFD vs HSD
Hb (g/dl)	15.46±1.1	14.26±0.6	15.33±0.5	NS	NS	NS
RBC (10⁶ cells/ml)	9.40±0.3	8.77±0.24	9.26±0.4	NS	NS	NS
HCT (%)	58.23±3.8	52.56±2.1	55.43±2.4	0.0226	NS	NS
MCV (g)	61.9±1.8	60.0±1.04	60.5±0.1	0.0218	NS	NS
MCH (pg)	16.36±0.5	16.2±0.3	16.5±0.2	NS	NS	NS
MCHC(g/dl)	26.5±0.2	27.1±0.6	27.3±0.2	NS	NS	NS
RDW (%)	10.2.0.34	10.3±0.57	10.2±0.52	NS	NS	NS
WBC (10⁶ cells/ml)	9.41±1.1	6.95±1.3	6.45±0.8	0.0128	0.0016	0.0001
Lymphocytes (10⁶ cells/ml)	96.3±0.7	95.0±2.7	95.96±0.5	NS	NS	NS
Monocytes (%)	1.06±0.1	1.33±0.7	2.3±1.9	NS	NS	NS
Granulocytes (%)	0.26±0.08	0.27±0.1	0.18±0.03	NS	NS	NS
Thrombocytes 10³/microL)	407.66±76.2	630.66±55.08	664±25.2	<0.0001	<0.0001	NS

Values are expressed as the means ± SD. Data were analysed by one-way ANOVA, followed by Bonferroni test, n = 6, p≤ 0.05, NS= Not significant, CD: Standard- chow diet for 3 weeks; HFD: High-fat diet for 3 weeks HSD: High-salt diet for 3 weeks

Short term HSD consumption significantly higher increases in serum creatinine levels whereas HFD diet higher increases cholesterol levels

After hematological examination, we assessed the impact of HSD and HFD on the biochemical parameters in the mice (Figure 2 andTable 2 and ). Compared to HSD-fed mice, our study found a more significant increase in cholesterol (p <0.0001) in HFD-fed mice. While we observed significantly higher levels of creatinine in HSD-fed mice compared to HFD-fed mice (p <0.0001). Both HFD and HSD fed mice showed a significant increase in glucose levels compared to CD fed mice. However, we found no significant difference between HFD-fed mice and HSD-fed mice. Moreover, HFD-fed mice and HSD-fed mice showed a significant decrease in urea level compared to CD-fed mice, but HSD-fed mice showed a more significant decrease than HFD-fed mice. A significant difference was not observed for ALT, AST in HFD-fed mice or HSD-fed mice compared to CD-fed mice.

Table 2 Comparative short-term high-fat (73% energy from fat) and high-salt (4%) diets effect on biochemical parameters in mice

Parameters	Groups			Significance (p-value)
Parameters	CD	HFD	HSD	CD vs HFD	CD vs HSD	HFD vs HSD
Cholesterol (mg/dl)	71.49±3.6	127.39±5.5	108.91±9.8	<0.0001	<0.0001	<0.0001
Triglycerides (mg/dl)	122.99±7.5	92.79±7.1	127.21±9.4	<0.0001	NS	<0.0001
ALT (IU)	56.80±4.5	53.12±2.1	47.86±6.5	NS	NS	NS
AST (IU)	74.47±4.8	83.08±2.49	75.19±7.08	NS	NS	NS
Creatinine (mg/dl)	0.36±0.06	0.33±0.05	0.75±0.06	NS	<0.0001	<0.0001
Urea (mg/dl)	72.21±4.5	59.57±1.9	54.99+0.8	<0.0001	<0.0001	0.0469
Glucose mg/dl)	219.80±13.08	340.05±14.7	329.20±23.6	<0.0001	<0.0001	NS

Values are expressed as the means ± SD. Data were analysed by one-way ANOVA, followed by Bonferroni test, n = 6, p≤ 0.05, NS= Not significant, CD: Standard- chow diet for 3weeks; HFD: High-fat diet for 3 weeks HSD: High-salt diet for 3 weeks

Short period HSD consumption increases more richness of gut bacteria with more depletion of different gut bacteria compared to HFD intake

Considering the significant impact of HFD and HSD on the key metabolic parameters of the pathophysiology of metabolic syndrome, we further investigated that these metabolic changes may be a sign of the changes in gut bacteria associated with the pathophysiology of metabolic syndrome. Therefore, we next examined how HFD and HSD can impact gut microbiota diversity. Next, we examined the comparative impact on gut bacteria diversity by using the Alpha diversity indices. We obtained an average of 280656 reads per sample after filtering, 561312 high-quality sequences. At 97% similarity, we found 14388 OTUs as shown in Table 3. The Shannon-Wiener curve in Figure 3 reached asymptotes that reflect the adequate sequencing depth of these samples. Tables 3 and Figure 3 summarize estimates of alpha diversity. We found a more significant increase in Chao1 (p=0.0205) in HSD fed mice compared to HFD fed mice which indicates a higher level of richness in the HSD fed mice group. On the other hand, the Shannon index was significantly lower in the HSD group (p<0.0001) than the HFD, indicating HSD decreased the diversity of specific gut bacteria compared to HSD-fed mice.

Table 3 Comparative effect of high-fat (73% energy from fat) and high-salt (4%) diet effect on alpha diversity

Alpha diversity indices	Groups			Significance (p value)
Alpha diversity indices	CD	HFD	HSD	CD vs HFD	CD vs HSD	HFD vs HSD
OTUs	6468.76±568.64	6543.45±634.15	7844.45±1004.47	NS	0.0331	0.0205
Chaos	8682.73±42.695	9354.48±651.47	10901.16±150.73	0.0229	<0.0001	<0.0001
Shannon	6.99±0.0132	8.19±0.055	7.86±0.029	<0.0001	<0.0001	<0.0001
Simpson	0.933±1.110	0.979±0.00	0.968±0.00	NS	NS	NS

Values are expressed as the means ± SD. One-way ANOVA, followed by Bonferroni test, n = 6, CD vs. HFD; CD vs HFD; HFD vs HSD, P ≤ 0.05, NS= Not significant, CD = Standard chow diet, HFD= High-fat diet, HSD=High- salt diet

Short term HFD consumption induce pathophysiology of metabolic disorder related inflammation more while HSD cause gut inflammation more by changing of gut ecology

After assessing the richness and diversity of gut bacteria involved in HFD and HSD treatment groups, we next investigated the relative abundance of the microbiota at the phylum level (Figure 4 and Table 4). The F/B ratio in HFD-fed mice was significantly higher by accounting for an increase in the abundance of Firmicutes and a depletion of Bacteroidetes compared with that of HSD-fed mice. The relative abundance of Proteobacteria was increased more in mice who received HSD than mice given HFD. The abundance of TM7 and Tenericutes was significantly higher in HSD-fed mice than in HFD-fed mice. Verrucomicrobia was significantly higher in HSD-fed mice than HFD-fed mice.

Table 4 Comparative short-term high fat (73% energy from fat) and high-salt (4%) diets effect on the relative abundance of major phyla in mice

Phylum	Relative abundance (%)			*Significance (P-value)*
Phylum	CD	HFD	HSD	CDvsHFD	CDvsHSD	HFDvsHSD
Firmicutes	80.4±0.396	67.70± 0.467	47.40±0.499	<0.0001	<0.0001	<0.0001
Proteobacteria	7.50±0.263	13.80±.0.344	16.20±0.368	<0.0001	<0.0001	<0.0001
Bacteroidetes	5.20±0.233	1.40±0.117	2.30±0.149	<0.0001	<0.0001	<0.0001
Actinobacteria	3.80±0.191	9.60 ±0.294	9.30±0.290	<0.0001	<0.0001	NS
Verrucomicrobia	0.20±0.044	0.30±0.054	6.40±0.244	NS	<0.0001	<0.0001
Acidobacteria	0.40±0.063	0.60 ±0.077	1.00±0.099	0.0020	<0.0001	<0.0001
TM7	0.10±0.031	3.30±0.178	11.60±0.320	<0.0001	<0.0001	<0.0001
Tenericutes	0.00±0.000	0.00±0.000	0.50±0.070	NS	<0.0001	<0.0001
F/B ratio	15.74±0.363	48.35±0.449	20.6±0.404	<0.0001	<0.0001	<0.0001

Values are expressed as the means ± SD. Data were analysed one-way ANOVA, followed by Bonferroni test, n = 6, p ≤ 0.05, NS= Not significant, CD: Standard- chow diet for 3 weeks: High-fat diet for 3 weeks; HSD+ A.: High-salt diet for 3 weeks.

We identified 45, 63, and 71 families in each of the CD, HFD, and HSD groups, at the family level (Figure 5 and Table 5). HFD-fed mice showed a significantly higher increase in the abundance of Lactobacillaceae, Desulfovibrionaceae and Coriobacteriaceae in comparison to HSD-fed mice. While HSD-fed mice showed a significantly higher increase in the abundance of Erysipelotrichiciae, F 16 and Verrucomicrobiaceae. Further, Lachnospiraceae and S24-7 were depleted more in HFD-fed mice compared to HSD-fed mice. While HSD-fed mice more observed a significant decrease in the abundance of Clostridiaceae, and Ruminococcaceae as compared to HFD-fed mice. There was no significant difference in the relative abundance of Enterobacteriaceae in HFD-fed mice and HSD-fed mice. Interestingly, the relative abundance of Bacteroidaceae did not differ between HFD-fed mice and HSD-fed mice.

Table 5 Comparative short-term high fat (73% energy from fat) and high salt (4%) diets effect on the relative abundance microbiota at family level in mice

Family	Relative abundance (%)			*Significance (p-value)*
Family	CD	HFD	HSD	CDvsHFD	CDvsHSD	HFDvsHSD
Lactobacillaceae	4.50±0.207	15.20±0.359	8.50±0.278	<0.0001	<0.0001	<0.0001
Unc Clostridiales	39.0±0.487	32.60±0.468	15.5±0.361	<0.0001	<0.0001	<0.0001
Clostridiaceae	5.90±0.235	4.90±0.215	2.10±0.143	<0.0001	<0.0001	<0.0001
Lachnospiraceae	12.40±0.329	5.70±0.231	11.80±0.322	<0.0001	0.0098	<0.0001
Ruminococcaceae	17.40±0.379	6.30±0.242	2.70±0.162	<0.0001	<0.0001	<0.0001
Erysipelotrichaceae	0.30±0.054	0.80±0.089	5.20±0.222	<0.0001	<0.0001	<0.0001
Desulfovibrionaceae	3.00±0.170	7.50±0.263	6.80±0.251	<0.0001	<0.0001	0.0003
Enterobacteriaceae	0.70±0.083	0.90±0.094	1.00±0.099	0.0057	0.0001	NS
Bacteroidaceae	0.20±0.044	0.20±0.044	0.20±0.044	NS	NS	NS
S24-7	4.10±0.198	0.30±0.054	1.00±0.099	<0.0001	<0.0001	<0.0001
Coriobacteriaceae	1.40±0.117	6.70±0.250	5.10±0.219	<0.0001	<0.0001	<0.0001
Verrucomicrobiaceae	0.10±0.031	0.10±0.031	6.40±0.244	NS	<0.0001	<0.0001
F16	0.10±0.031	3.30±0.178	11.60±0.320	<0.0001	<0.0001	<0.0001

Values are expressed as the means ± SD. Data were analysed by one-way ANOVA, followed by Bonferroni test, n = 6, p ≤ 0.05, NS= Not significant, CD: Standard- chow diet for 3 weeks: High-fat diet for 3 weeks; HSD+ A.: High-salt diet for 3 weeks.

A total of 21, 38, and 44 genera were found in CD, HFD, and HSD group samples, respectively (Figure 6 and Table 6). The species detected in these groups are presented in Table 7. HFD-fed mice showed a significantly higher increase in the relative abundance of Granulicatella, Lactobacillus, Streptococcus, Turicibacte, Dorea, and Desulfovibrio, as compared to HSD-fed mice. Further, HFD-fed mice showed a significantly higher abundance of Rothia mucilaginosa compared to HFD-fed mice. HSD-fed mice showed a significantly higher increase in the relative abundance of Clostridium, [Ruminococcus], Allobaculum, Klebsiella, Haemophilus, Neisseria, Prevotell, and Akkermansia in comparison to HFD-fed mice. Moreover, the relative abundance of [Ruminococcus] gnavus, Akkermansia muciniphila, Prevotella melaninogenica and Neisseria subflava was significantly higher in HSD-fed mice compared to HFD-fed mice. HSD-fed mice showed a significant higher decrease in the relative abundance of Oscillospira and Ruminococcus than HFD-fed mice. Interestingly the relative abundance of Bacteroides remained the same in all groups.

Table 6 Comparative short-term effect of high-fat (73% energy from fat) and high-salt (4%) diets effect on the relative abundance microbiota at genus level in mice

Genus	Relative abundance at the genus level (%)			*Significance (p-value)*
Genus	CD	HFD	*HSD*	CDvsHFD	CDvsHSD	*CDvsHSD*
Unc Gemellaceae	0.00±0.000	0.20±0.044	0.10±0.031	<0.0001	0.0002	0.0002
Granulicatella	0.00±0.000	0.20±0.044	0.10±0.031	<0.0001	0.0002	0.0002
Enterococcus	0.00±0.000	0.10±0.031	0.00±0.000	<0.0001	NS	<0.0001
Lactobacillus	4.50±0.207	15.2±0.359	8.50±0.278	<0.0001	<0.0001	<0.0001
Streptococcus	0.00±0.000	0.60±0.077	0.50±0.070	<0.0001	<0.0001	0.0341
Turicibacter	0.00±0.000	0.50±0.070	0.10±0.031	<0.0001	0.0041	<0.0001
Unc Clostridiales	39.10±0.489	32.70±0.469	15.50±0.361	<0.0001	<0.0001	<0.0001
Unc Clostridiaceae	0.00±0.000	0.00±0.000	0.50±0.070	NS	<0.0001	<0.0001
Candidatus Arthromitus	5.70±0.232	4.80±0.213	0.10±0.031	<0.0001	<0.0001	<0.0001
Clostridium	0.00±0.000	0.10±0.031	1.40±0.117	NS	<0.0001	<0.0001
Dehalobacterium	0.60±0.077	0.10±0.031	0.10±0.031	<0.0001	<0.0001	NS
Unc Lachnospiraceae	9.50±0.293	2.50±0.156	1.00±0.099	<0.0001	<0.0001	<0.0001
Blautia	0.10±0.031	0.10±0.031	0.00±0.000	NS	<0.0001	<0.0001
Coprococcus	0.80±0.089	0.80±0.089	1.00±0.099	NS	0.0058	0.0058
[Ruminococcus]	1.10±0.104	1.90±0.136	9.60±0.294	<0.0001	<0.0001	<0.0001
Unc Peptostreptococcaceae	0.00±0.000	0.10±0.031	0.10±0.031	0.0002	0.0002	NS
Unc Ruminococcaceae	12.10±0.326	2.60±0.159	2.10±0.143	<0.0001	<0.0001	0.0042
Dorea	0.00±0.000	0.20±0.044	0.00±0.000	<0.0001	NS	<0.0001
Oscillospira	1.80±0.132	3.10±0.173	0.30±0.054	<0.0001	<0.0001	<0.0001
Ruminococcus	1.90±0.136	0.60±0.077	0.30±0.054	<0.0001	<0.0001	0.0465
Veillonella	0.00±0.000	0.10±0.031	0.10±0.031	<0.0001	<0.0001	NS
Unc Erysipelotrichaceae	0.20±0.044	0.30±0.054	1.90±0.136	NS	<0.0001	<0.0001
Allobaculum	0.00±0.000	0.40±0.063	3.10±0.173	<0.0001	<0.0001	<0.0001
Neisseria	0.00±0.000	0.00±0.000	0.40±0.063	NS	<0.0001	<0.0001
Eubacterium	0.10±0.031	0.20±0.044	0.20±0.044	0.0018	0.0018	NS
Rhodobacter	0.00±0.000	0.30±0.054	0.10±0.031	<0.0001	0.0013	<0.0001
Bilophila	0.00+0.000	0.10+0.031	0.00+0.000	<0.0001	NS	<0.0001
Desulfovibrio	3.00±0.170	7.40±0261	6.80±0.251	<0.0001	<0.0001	0.0013
Unc Enterobacteriaceae	0.20±0.044	0.20±0.044	0.30±0.054	NS	0.0072	0.0072
Klebsiella	0.50±0.070	0.60±0.077	0.80±0.089	NS	<0.0001	0.0016
Haemophilus	0.00±0.000	0.30±0.054	0.40±0.063	<0.0001	<0.0001	0.0082
Bacteroides	0.20±0.044	0.20±0.044	0.20±0.044	NS	NS	NS
Prevotella	0.00±0.000	0.00±0.000	0.10±0.031	NS	<0.0001	<0.0001
Unc S24-7	4.10±0.115	0.30±0.054	1.00±0.999	<0.0001	<0.0001	<0.0001
Unc Acidimicrobiales	0.10±0.031	0.10±0.031	0.60±0.077	NS	<0.0001	<0.0001
Unc C111	0.70±0.083	0.40±0.063	0.70±0.083	<0.0001	NS	<0.0001
Unc ACK-M1	0.80±0.089	0.50±0.070	0.60±0.077	<0.0001	0.0016	NS
Rothia	0.00+0.000	0.70±0.083	0.40±0063	<0.0001	<0.0001	<0.0001
Bifidobacterium	0.00+0.000	0.00+0.000	0.20±0.044	NS	<0.0001	<0.0001
Unc Coriobacteriaceae	0.10±0.031	2.70±0.162	1.60±0.125	<0.0001	<0.0001	<0.0001
Adlercreutzia	1.30±0.113	3.90±0.193	3.40±0.181	<0.0001	<0.0001	0.0003
Akkermansia	0.00+0.000	0.00±0.000	6.40±0.244	NS	<0.0001	<0.0001
Unc F16	0.10±0.031	3.30±0.178	11.60±0.320	<0.0001	<0.0001	<0.0001
Unc RF 39	0.00+0.000	0.00+0.000	0.50±0.070	NS	<0.0001	<0.0001

Table 7 Comparative short-term effect of high-fat (73% energy from fat) and high-salt (4%) diet on the relative abundance microbiota at the species level in mice

Species	Relative abundance (%)			*Significance (p-value)*
Species	CD	HFD	HSD	CD vs HFD	CD vs HSD	HFD vs HSD
Blautia producta	0.10±0.039	0.10±0.039	0.00±0.000	NS	0.0002	0.0002
[Ruminococcus] gnavus	1.10±0.104	1.90±0.136	9.60±0.294	<0.0001	<0.0001	<0.0001
Brevundimonas diminuta	0.00±0.000	0.10±0.039	0.10±0.039	NS	<0.0001	NS
Neisseria subflava	0.00±0.000	0.00±0.000	0.30±0.054	NS	<0.0001	<0.0001
Prevotella melaninogenica	0.00±0.000	0.00±0.000	0.40±0.063	NS	<0.0001	<0.0001
Rothia mucilaginosa	0.00±0.000	0.70±0.083	0.40±0.063	<0.0001	<0.0001	<0.0001
Akkermansia muciniphila	0.00±0.000	0.00±0.000	6.40±0.244	NS	<0.0001	<0.0001

Short term HFD and HSD consumption cause non-significant histopathological changes in vital organs

In order to determine whether HFD or HSD diets have a greater negative impact on essential organs, we performed histopathological studies on the heart, liver, and kidney (Figure 7). Heart photomicrographs of HFD-fed mice showed mild congestion in blood vessels while a slight cardiomyocyte hypertrophy and degenerative tissue changes were noticed in HSD-treated mice. The liver histology of mice fed HFD revealed mild congestion of the sinusoidal, central, and portal veins. There was a decrease in cell proliferation as well as oddly shaped cells in the liver of mice observations, however, were insufficient to conclude that HFD or HSD were more determinantal in the context of histopathological changes in vital organs.

The pathophysiology of metabolic syndrome induced by the consumption of a diet rich in fat and salt has become a major health concern due to the increasing risk of many chronic diseases such as diabetes and cardiovascular diseases [33, 34]. In the long run, a diet rich in fat and salt increases cholesterol levels, blood sugar levels, triglyceride levels, chronic inflammation, and modulation of gut ecology [35–37]. The majority of people today consume modern food consciously or unconsciously, particularly processed foods. However, the short-term damaging effect of high-fat and high-salt diets on gut bacteria is still poorly explored. This understanding is very critical today as modern food contains more fat and salt than recommended by the WHO. Additionally, little is known about the comparative damaging effects of high-fat and high-salt diets on gut bacteria and associated health. Our objective was to investigate whether a high fat or high salt diet intake for a short time period of three weeks is more harmful in terms of health in a mice model.

Our research indicates that high taxonomic richness in HSD-fed mice contributed to the exclusive appearance of different specific gut bacteria such as Unc Clostridiaceae, Neisseria, Prevotella, Bifidobacterium, Akkermansia and Unc RF 39 in comparison to HFD-fed mice and CD-fed mice. Nevertheless, HSD-fed mice showed a reduction in diversity, which contributes to inflammatory gut diseases in mice [38]. Furthermore, many research studies have found that patients with gut inflammation have an overall loss of biodiversity due to the reduction of Firmicutes and the increase of Proteobacteria [39–56]. However, diversity was higher in HFD-fed mice due to specific gut bacteria namely Enterococcus, Dorea and Bilophila compared to HSD-fed mice and CD-fed mice. Additionally, the present study showed differences in gut microbiota composition between the three study groups.

Our research indicates high taxonomic richness in HSD-fed mice contributed to the exclusive appearance of different specific gut bacteria such as Unc Clostridiaceae, Neisseria, Prevotella, Bifidobacterium, Akkermansia, Unc RF 39 in comparison to HFD-fed mice and CD-fed mice. In our study, we found that the F/B ratio was significantly higher with higher levels of Firmicutes and a decline in Bacteroidetes that could contribute to obesity-related metabolic inflammation [57, 58]. However, HSD-fed mice exhibited more increase in the relative abundance of Proteobacteria and TM7 reported as indicators of intestinal dysbiosis and causing active inflammatory bowel disease [59, 60]. Our study indicate that HFD inclined more towards in the inducing the pathophysiology of obesity while HSD caused the pathophysiology of inflammatory gut diseases. Additionally, HSD-treated mice showed an increase in Verrucomicrobia, which is crucial for maintaining host immunity [61].

In this study, HFD-fed mice showed a significantly higher increase in the relative abundance of Desulfovibrionaceae known to associate with obesity and Coriobacteriaceae considered commensal communities but some members associate with bile acid metabolism which leads to metabolic dysfunctions [33]. Further a higher reduction of Lachnospiraceae and S24-7 linked to disruption of gut epithelial barrier integrity and metabolic endotoxemia was observed in HFD-fed mice [33]. In this study HSD fed mice showed a higher abundance of Erysipelotrichaceae that coincided with inflammation-related gastrointestinal disorders [62]. Moreover, we also found in HSD-fed mice a lower abundance of Clostridiaceae, and Ruminococcaceae, which are positively associated with maintaining gut health. The significant decline of Clostridiaceae is associated with T2D while Ruminococcaceae prevail prominently in fibrolytic communities and maintain gut health [63, 64]. HSD-fed mice also showed a higher relative abundance of Verrucomicrobiaceae, which have been linked to improved immunoinflammatory functions [65].

At the genus level, we have consistently observed a high increase in the abundance of Granulicatella, Lactobacillus, Streptococcus, Turicibacte, Dorea, Desulfovibrio and Oscillospira in HFD-fed mice and at the species level [66–69]. There are also several reports suggesting that Lactobacillus abundance plays an active role in obesity and chronic inflammation associated with diabetes [33]. In addition, the majority of the above-mentioned gut microbes play a significant role in metabolic syndrome pathogenesis. Lactobacillus and Turicibacter abundance positively correlate with blood glucose levels [70]. Similarly, the increase in Desulfovibrio is associated with insulin resistance [71–73]. Likewise, the enrichment of Streptococcus, Granulicatella, and Allobaculm is linked to metabolic diseases [33]. Likewise, Desulfovibrio increase is associated with insulin resistance [33]. A similar association has been found that enrichment of Streptococcus and Granulicatella, and metabolic diseases [74–76]. A positive correlation exists between Oscillospira and fasting serum insulin levels and a reduction in the levels of mRNA expression of Zonula occludens-1 (ZO-1) that prevents solute leakage from the gastrointestinal tract [33]. A striking observation was the reduction of Lactobacillus reuteri in HFD-fed mice, which prevents pathogen growth in the intestine[70]. Further, HFD-fed mice showed a significant higher abundance of Rothia mucilaginosa, considered an emerging opportunistic pathogen [79]. Our data suggest that HFD contributes to metabolic syndrome pathophysiology of obesity-related metabolic disorders by decreasing insulin signalling and intestinal barrier integrity.

Our genus-level results confirmed that HSD causes more abundance of gut microbiota, including Clostridium, [Ruminococcus], Allobaculum, Klebsiella, Haemophilus, Neisseria, Prevotella, and Akkermansia. HSD-fed mice showed a decrease in abundance of Ruminococcus gnavus, which is known to produce a polysaccharide that stimulates inflammatory cytokines and induces inflammatory bowel disease. [34]. A well-known correlation exists between Allobaculum and ANGPTL4 expression that contributes to obesity-related metabolic disorders [76]. There are many Clostridium and Haemophilus strains that cause disease in humans [44]. There is evidence that Klibsiella can cause gut inflammation [80]. Furthermore, this study found that Neisseria subflava and Prevotella melanogenic were significantly abundant in mice fed HSD. There is growing recognition that Neisseria subflava reflects a shift in the microbial community towards acid-secreting bacteria [43]. A study of humanized mice indicated that Prevotella melanogenic causes gut inflammation [82]. Therefore, our data suggest that HSD increases the majority of bacteria known to play an active role in gut inflammatory diseases. Consistently, our study also found a significant finding that the composition of Bacteroides gut bacteria did not affect HSD diet as reported by previous studies that had a salt-tolerance gene [83]. Nevertheless, it is significant to note that even HFD feeding for 3 consecutive weeks did not increase Bacteroides counts in this study.

The gut microbiota regulates energy homeostasis and their composition and diversity are correlated with insulin sensitivity and host glycemic control [34]. There have been several studies that have found that long-term consumption of HFD or HSD has adverse effects on biochemical health [83–86]. Both HFD-fed mice and HSD-fed mice showed an increase in blood glucose levels. Importantly, we found that. HFD intake even for short periods elevated blood glucose levels contrary to previous studies that required long-term consumption of 30–90 days and 18 months [87–88]. We believe that the increase in the abundance of Granulicatella, Lactobacillus, Streptococcus, Turicibacter, Desulfovibrio and depletion of Lachnospiraceae and S24-7 in HFD-fed mice is the reason for the increase in serum glucose level because these gut bacteria have a role in the development of insulin resistance. Similarly, HSD increases the abundance of specific gut microbiota such as Lactobacillus, Streptococcus, Ruminococcus gnavus, Allobaculum, and Desulfovibrio known to decrease insulin sensitivity caused an increase in blood glucose level. HSD also increases the abundance of specific gut bacteria, such as Lactobacillus, Streptococcus, Ruminococcus gnavus, Allobaculum, and Desulfovibrio, known to decrease insulin sensitivity and raise blood glucose levels.

Several studies have shown that gut microbiota dysbiosis directly affects cholesterol metabolism [89, 90]. Our study demonstrated that HFD modulates gut bacteria leading to high circulating serum total cholesterol levels. We found a decrease in the abundance of Lactobacillus reuteri implicated for the cholesterol-lowering effect by conversion of uncoupled bile acids into secondary bile acids to control serum cholesterol levels [91, 92]. HFD-fed mice also displayed a depletion of Lachnospiraceae families known to have a positive correlation with total and low-density lipoprotein (LDL) cholesterol [93, 94]. Further HFD-fed mice also showed an increase in the abundance of Coriobacteriaceae and Erysipelotrichaceae, known to have positive correlations with host cholesterol metabolites [95, 96]. We found that HSD-fed mice showed increased levels of serum triglycerides and glucose metabolites associated with endothelial dysfunction, which contributed to the short-term increase in total cholesterol. In addition, several studies suggest the gut microbiota can alter blood lipid composition, particularly cholesterol, via gut bacteria-related metabolites [97–99]. Also, we observed higher abundances of Lactobacillus, Enterobacteriaceae, and Streptococcus in the gut microbiota reported in cardiovascular diseases (CVD) [100]. Additionally, we found that higher abundances of Erysipelotrichaceae and Coriobacteriaceae correlated with higher plasma cholesterol levels in both human and animal models [96, 101–104, 117]. As a result of our study HFD caused a relatively higher significant increase in blood cholesterol when compared with HSD-fed mice, in which we observed increased abundances of Bifidobacterium and Lachnospiraceae implicated with reductions in total cholesterol levels [95,96.105].

There has been substantial evidence that high salt intake leads to progressive impairment of renal function either through BP-dependent or independent mechanisms [106–108]. Our study consistently demonstrated an increase in creatinine levels in mice receiving HSD [109]. In our study HSD increased metabolic factors, such as glucose and triglycerides, which cause endothelial dysfunction by increasing oxidative stress and decreasing nitric oxide bioavailability [109]. Thus, endothelial remodeling can adversely affect renal hemodynamics by reducing glomerular filtration efficiency, which leads to higher blood creatinine levels. Conclusively on the biochemical parameters, we found that HFD was associated with a greater increase in plasma total cholesterol (TC), a known risk factor for coronary heart disease, atherosclerosis, and stroke as compared to HSD. While HSD is more harmful to the kidneys because an increase in creatinine level indicates kidney failure.

Previous studies have shown a negative impact of long-term consumption of HFD and HSD on hematological parameters [83, 113–115]. This study investigated whether short-term intake of HFD or HSD has a more detrimental effect on hematological parameters. Our results indicated that both HSD and HFD adversely affect hematological profiles. However, mice fed HFD showed a slight more decline in WBCs compared to mice fed HSD. Lower WBC counts in HFD-fed mice indicate that HFD may suppress the immune system more than HSD. A photomicrograph of the heart section from mice fed either HFD or HSD did not show any significant histopathological changes. Similarly, liver and kidney of HFD and HSD-fed mice showed no significant histopathology difference. Our study indicates that short-term increases in HSD or HFD diet could have transient effects on the biochemical profile. Nonetheless, long-term consumption of HFD or HSD may cause permanent damage to vital organs.

In summary, the present study showed that even short perturbation of HFD and HSD intensively disturb the gut bacteria ecology. Further, our results indicate that HSD causes a more substantial alteration of gut bacteria than HFD. HFD change the composition and diversity of gut bacteria and, which are known to induce the pathophysiology of metabolic syndrome related inflammation. Further, HFD was linked to a greater increase in plasma total cholesterol (TC), an established risk factor for coronary heart disease, atherosclerosis, and stroke. HSD increased more the relative abundance of specific gut microbes that are known to contribute to metabolic disease-related inflammation while HSD caused an increase in the majority of those bacteria known to play an active role in gut inflammation. Moreover, we observed some beneficial bacteria being increased in the HSD diet. While, HSD is more detrimental to the kidneys, since an increase in creatinine levels indicates kidney disease. Furthermore, mice fed either HFD or HSD showed minimal and insignificant pathological changes in their hearts, livers, and kidneys. These preliminary studies, however, had several limitations. Firstly, this study was conducted on mice and cannot be applied to humans. Second, we also reduced the sample size of caecal contents per group, and tested their relative proportions in the total population of bacteria for cost-effectiveness.

CD: Standard chow diet

HSD: High-salt diet

HFD: High-fat diet

Hb: Haemoglobin

RBC: Red blood cells

MCH: Mean corpuscular haemoglobin

HCT: Haematocrit

MCV: Mean corpuscular volume

WBC: White blood cells

ALT: Alanine aminotransferase

AST: Aspartate aminotransferase

TG: Triglycerides

TC: Total cholesterol

CVD: cardiovascular diseases

LDL: Low-density lipoprotein

F/B: Firmicutes/Bacteroidetes

T2D: Type 2 diabetes

ZO-1: Zonula occludens-1

ANGPTL4: Angiopoietin-related protein

NO: Nitric oxide

OTUs: Operating taxonomic units

IAEC: Institutional Animal Ethics Committee

WHO: World Health Organization

AIIMS: All India Institute of Medical Science

SD: Standard deviation

ANOVA: Analysis of variance

Acknowledgements:

We greatly appreciate Dr. Vikram Saini (All India Institute of Medical Science, New Delhi, India) for guiding the experiment.

Data availability:

All data generated or analysed during this study are included in this manuscript. Metadata is available at: https://dataview.ncbi.nlm.nih.gov/object/PRJNA821450?reviewer= spn10eelulrond 90jtemfqqpkg in read-only format.

Author contributions

S.K. conducted the research and analyzed the data. R.P.P., C.M.C. and V.S. contributed to the conceptualization and design of the research and critically revising the manuscript. All authors read and approved the fnal manuscript.

Funding: This research was funded by VtR Inc-CGU (SCRPD1L0221), DOXABIO-CGU (SCRPD1K0131), and a CGU grant (UZRPD1L0011, UZRPD1M0081).

Data Availability Statement: Not applicable

Conflicts of Interest: The authors declare no conflict of interest

M. Fitzpatrick, “Junk food,” Lancet (London, England), vol. 363, no. 9413, p. 1000, 2004, doi: 10.1016/S0140-6736(04)15815-7.
R. Ahirwar and P. R. Mondal, “Prevalence of obesity in India: A systematic review,” Diabetes Metab. Syndr. Clin. Res. Rev., vol. 13, no. 1, pp. 318–321, Jan. 2019, doi: 10.1016/J.DSX.2018.08.032.
Hooper L, Abdelhamid A, Bunn D, Brown T, Summerbell CD, Skeaff CM. Effects of total fat intake on body weight. Cochrane Database Syst Rev. 2015; (8):CD011834.
Diet, nutrition and the prevention of chronic diseases: report of a Joint WHO/FAO Expert Consultation. WHO Technical Report Series, No. 916. Geneva: World Health Organization; 2003.
Fats and fatty acids in human nutrition: report of an expert consultation. FAO Food and Nutrition Paper 91. Rome: Food and Agriculture Organization of the United Nations; 2010.
Nishida C, Uauy R. WHO scientific update on health consequences of trans fatty acids: introduction. Eur J Clin Nutr. 2009; 63 Suppl 2:S1–4.
Guidelines: Saturated fatty acid and trans-fatty acid intake for adults and children. Geneva: World Health Organization; 2018 (Draft issued for public consultation in May 2018).
REPLACE: An action package to eliminate industrially-produced trans-fatty acids. WHO/NMH/NHD/18.4. Geneva: World Health Organization; 2018
C. C. Evans et al., “Exercise prevents weight gain and alters the gut microbiota in a mouse model of high fat diet-induced obesity,” PLoS One, vol. 9, no. 3, Mar. 2014, doi: 10.1371/JOURNAL.PONE.0092193.
H. Daniel et al., “High-fat diet alters gut microbiota physiology in mice,” ISME J., vol. 8, no. 2, pp. 295–308, Feb. 2014, doi: 10.1038/ISMEJ.2013.155.
P. J. Turnbaugh, F. Bäckhed, L. Fulton, and J. I. Gordon, “Diet-induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome,” Cell Host Microbe, vol. 3, no. 4, pp. 213–223, Apr. 2008, doi: 10.1016/J.CHOM.2008.02.015.
O. Guillemot-Legris, J. Masquelier, A. Everard, P. D. Cani, M. Alhouayek, and G. G. Muccioli, “High-fat diet feeding differentially affects the development of inflammation in the central nervous system,” J. Neuroinflammation, vol. 13, no. 1, Aug. 2016, doi: 10.1186/S12974-016-0666-8.
B. Yi et al., “Effects of dietary salt levels on monocytic cells and immune responses in healthy human subjects: a longitudinal study,” Transl. Res., vol. 166, no. 1, pp. 103–110, Jul. 2015, doi: 10.1016/J.TRSL.2014.11.007.
Boesveldt S, Bobowksi N, McCrickerd K, Maitre I, Sulmont- Rosse C, Forde CG. The changing role of the senses in food choice and food intake across the lifespan. Food Qual Pref. (2018) 68:80–9. doi: 10.1016/j.foodqual.2018.02.004
Boesveldt S, de Graaf K. The differential role of smell and taste for eating behavior. Perception. (2017) 46:307–19. doi: 10.1177/0301006616685576
Clark JE. Taste and flavour: their importance in food choice and acceptance. Proc Nutr Soc. (1998) 57:639–43. doi: 10.1079/PNS19980093
Cox DN, Melo L, Zabaras D, Delahunty CM. Acceptance of health-promoting Brassica vegetables: the influence of taste perception, information and attitudes. Public Health Nutr. (2012) 15:1474–82. doi: 10.1017/S1368980011003442
Januszewska R, Pieniak Z, Verbeke W. Food choice questionnaire revisited in four countries. Does it still measure the same? Appetite. (2011) 57:94–8. doi: 10.1016/j.appet.2011.03.014
Kourouniotis S, Keast RSJ, Cicerale S. The importance of taste on dietary choice, behaviour and intake in a group of university students. J Nutr Intermed Metabo. (2014) 1:37. doi: 10.1016/j.jnim.2014.10.132
Renner B, Sproesser G, Strohbach S, Schupp HT. Why we eat what we eat. The Eating Motivation Survey (TEMS). Appetite. (2012) 59:117–28. doi: 10.1016/j.appet.2012.04.00
Steptoe A, Pollard TM, Wardle J. Development of a measure of the motives underlying the selection of food: the food choice questionnaire. Appetite. (1995) 25:267–84. doi: 10.1006/appe.1995.0061
Tuorila H, Huotilainen A, Lahteenmaki L, Ollila S, Tuomi-Nurmi S, Urala N. Comparison of affective rating scales and their relationship to variables reflecting food consumption. Food Qual Pref. (2008) 19:51–61. doi: 10.1016/j.foodqual.2007.06.007
keast RSJ, Costanzo A. Is fat the sixth taste primary? Evidence and implications. Flavour. (2015) 4:1–7. doi: 10.1186/2044-7248-4-5
Running CA, Craig BA, Mattes RD. Oleogustus: the unique taste of fat. Chem Senses. (2015) 40:507–16. doi: 10.1093/chemse/bjv036
Monteiro R, Azevedo I. (2010). Chronic inflammation in obesity and the metabolic syndrome Mediators Inflamm. 2010;2010:289645.
Festi D, Schiumerini R, Eusebi LH, Marasco G, Taddia M, Colecchia A. (2014). Gut microbiota and metabolic syndrome. World J Gastroenterol.
Lasker, S., Rahman, M.M., Parvez, F. et al. High-fat diet-induced metabolic syndrome and oxidative stress in obese rats are ameliorated by yogurt supplementation. Sci Rep 9, 20026 (2019). https://doi.org/10.1038/s41598-019-56538-0
Wang J, Cheng R, Luo Y, et al. Wei Sheng Yan Jiu. 2021;50(1):93-99. doi:10.19813/j.cnki.weishengyanjiu.2021.01.016
Caporaso J, Lauber C, Walters W, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012 doi: 10.1038/ismej.2012.8. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
Magoc T, Salzberg SL. FLASH fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 2011 doi: 10.1093/bioinformatics/btr507. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013 doi: 10.1038/nmeth.2604. [PubMed] [CrossRef] [Google Scholar]
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costellon EK, Fierer N, Peña AG, Goodrich JK, Gordon JI, Huttley GA, Kelley STN, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Knight R. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010 doi: 10.1038/nmeth.f.303. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
Kumar, S., Raj, V. S., Ahmad, A., & Saini, V. (2022). Amoxicillin modulates gut microbiota to improve short-term high-fat diet induced pathophysiology in mice. Gut pathogens, 14(1), 40. https://doi.org/10.1186/s13099-022-00513-0
Kumar, S., Perumal, N., Yadav, P.K. et al. Amoxicillin impact on pathophysiology induced by short term high salt diet in mice. Sci Rep 12, 19351 (2022). https://doi.org/10.1038/s41598-022-21270-9
R. Monteiro and I. Azevedo, “Chronic inflammation in obesity and the metabolic syndrome,” Mediators Inflamm., vol. 2010, 2010, doi: 10.1155/2010/289645.
D. Festi, R. Schiumerini, L. H. Eusebi, G. Marasco, M. Taddia, and A. Colecchia, “Gut microbiota and metabolic syndrome,” World J. Gastroenterol., vol. 20, no. 43, pp. 16079–16094, Nov. 2014, doi: 10.3748/WJG.V20.I43.16079.
S. Lasker et al., “High-fat diet-induced metabolic syndrome and oxidative stress in obese rats are ameliorated by yogurt supplementation,” Sci. Reports 2019 91, vol. 9, no. 1, pp. 1–15, Dec. 2019, doi: 10.1038/s41598-019-56538-0.
S. C. Larsen, L. Ängquist, T. I. A. Sørensen, and B. L. Heitmann, “24h Urinary Sodium Excretion and Subsequent Change in Weight, Waist Circumference and Body Composition,” PLoS One, vol. 8, no. 7, p. e69689, Jul. 2013, doi: 10.1371/JOURNAL.PONE.0069689.
C. Manichanh et al., “Reduced diversity of faecal microbiota in Crohn’s disease revealed by a metagenomic approach,” Gut, vol. 55, no. 2, p. 205, Feb. 2006, doi: 10.1136/GUT.2005.073817.
T. Clavel et al., “Intestinal microbiota in metabolic diseases: from bacterial community structure and functions to species of pathophysiological relevance,” Gut Microbes, vol. 5, no. 4, pp. 544–551, 2014, doi: 10.4161/GMIC.29331.
L. Yang, O. Bajinka, P. O. Jarju, Y. Tan, A. M. Taal, and G. Ozdemir, “The varying effects of antibiotics on gut microbiota,” AMB Express, vol. 11, no. 1, pp. 1–13, Dec. 2021, doi: 10.1186/S13568-021-01274-W/TABLES/2.
M. Gurung et al., “Role of gut microbiota in type 2 diabetes pathophysiology,” EBioMedicine, vol. 51, Jan. 2020, doi: 10.1016/J.EBIOM.2019.11.051.
S. N. Peterson et al., “The dental plaque microbiome in health and disease,” PLoS One, vol. 8, no. 3, Mar. 2013, doi: 10.1371/JOURNAL.PONE.0058487.
T. Das et al., “Alterations in the gut bacterial microbiome in people with type 2 diabetes mellitus and diabetic retinopathy,” Sci. Rep., vol. 11, no. 1, p. 2738, Dec. 2021, doi: 10.1038/S41598-021-82538-0.
E. Vamanu and S. N. Rai, “The Link between Obesity, Microbiota Dysbiosis, and Neurodegenerative Pathogenesis,” Diseases, vol. 9, no. 3, p. 45, Jun. 2021, doi: 10.3390/DISEASES9030045.
J. Ouyang et al., “The Bacterium Akkermansia muciniphila: A Sentinel for Gut Permeability and Its Relevance to HIV-Related Inflammation,” Front. Immunol., vol. 11, p. 645, Apr. 2020, doi: 10.3389/FIMMU.2020.00645/BIBTEX.
M. Fatahi-Bafghi, “Characterization of the Rothia spp. and their role in human clinical infections,” Infect. Genet. Evol., vol. 93, Sep. 2021, doi: 10.1016/J.MEEGID.2021.104877.
C. Kasai et al., “Comparison of human gut microbiota in control subjects and patients with colorectal carcinoma in adenoma: Terminal restriction fragment length polymorphism and next-generation sequencing analyses,” Oncol. Rep., vol. 35, no. 1, pp. 325–333, Jan. 2016, doi: 10.3892/OR.2015.4398.
S. Lasker et al., “High-fat diet-induced metabolic syndrome and oxidative stress in obese rats are ameliorated by yogurt supplementation,” Sci. Reports 2019 91, vol. 9, no. 1, pp. 1–15, Dec. 2019, doi: 10.1038/s41598-019-56538-0.
C. Manichanh et al., “Reshaping the gut microbiome with bacterial transplantation and antibiotic intake,” Genome Res., vol. 20, no. 10, pp. 1411–1419, Oct. 2010, doi: 10.1101/GR.107987.110.
D. N. Frank, A. L. St. Amand, R. A. Feldman, E. C. Boedeker, N. Harpaz, and N. R. Pace, “Molecular-phylogenetic characterization of microbial community imbalances in human inflammatory bowel diseases,” Proc. Natl. Acad. Sci. U. S. A., vol. 104, no. 34, p. 13780, Aug. 2007, doi: 10.1073/PNAS.0706625104.
H. Sokol et al., “Specificities of the Fecal Microbiota in Inflammatory Bowel Disease,” Inflamm. Bowel Dis., vol. 12, no. 2, pp. 106–111, Feb. 2006, doi: 10.1097/01.MIB.0000200323.38139.C6.
A. M. Peterson, D. P. Nau, J. A. Cramer, J. Benner, F. Gwadry-Sridhar, and M. Nichol, “A checklist for medication compliance and persistence studies using retrospective databases,” Value Health, vol. 10, no. 1, pp. 3–12, 2007, doi: 10.1111/J.1524-4733.2006.00139. X.
S. J. Ott et al., “Reduction in diversity of the colonic mucosa associated bacterial microflora in patients with active inflammatory bowel disease,” Gut, vol. 53, no. 5, pp. 685–693, May 2004, doi: 10.1136/GUT.2003.025403.
M. Löwenberg, M. Peppelenbosch, and D. Hommes, “Biological therapy in the management of recent-onset Crohn’s disease: why, when and how?” Drugs, vol. 66, no. 11, pp. 1431–1439, 2006, doi: 10.2165/00003495-200666110-00002.
C. Lupp et al., “Host-Mediated Inflammation Disrupts the Intestinal Microbiota and Promotes the Overgrowth of Enterobacteriaceae,” Cell Host Microbe, vol. 2, no. 2, pp. 119–129, Aug. 2007, doi: 10.1016/J.CHOM.2007.06.010.
S. Stojanov, A. Berlec, and B. Štrukelj, “The Influence of Probiotics on the Firmicutes/Bacteroidetes Ratio in the Treatment of Obesity and Inflammatory Bowel disease,” Microorganisms, vol. 8, no. 11, pp. 1–16, Nov. 2020, doi: 10.3390/MICROORGANISMS8111715.
F. Magne et al., “The Firmicutes/Bacteroidetes Ratio: A Relevant Marker of Gut Dysbiosis in Obese Patients?,” Nutrients, vol. 12, no. 5, May 2020, doi: 10.3390/NU12051474.
S. F. Clarke et al., “The gut microbiota and its relationship to diet and obesity: new insights,” Gut Microbes, vol. 3, no. 3, pp. 186–202, 2012, doi: 10.4161/GMIC.20168.
T. Kuehbacher et al., “Intestinal TM7 bacterial phylogenies in active inflammatory bowel disease,” J. Med. Microbiol., vol. 57, no. Pt 12, pp. 1569–1576, Dec. 2008, doi: 10.1099/JMM.0.47719-0.
F. Lindenberg et al., “Expression of immune regulatory genes correlate with the abundance of specific Clostridiales and Verrucomicrobia species in the equine ileum and cecum,” Sci. Rep., vol. 9, no. 1, Dec. 2019, doi: 10.1038/S41598-019-49081-5.
N. O. Kaakoush, “Insights into the role of Erysipelotrichaceae in the human host,” Front. Cell. Infect. Microbiol., vol. 5, no. NOV, p. 84, 2015, doi: 10.3389/FCIMB.2015.00084/BIBTEX.
N. Larsen et al., “Gut Microbiota in Human Adults with Type 2 Diabetes Differs from Non-Diabetic Adults,” PLoS One, vol. 5, no. 2, p. e9085, Feb. 2010, doi: 10.1371/JOURNAL.PONE.0009085.
A. Biddle, L. Stewart, J. Blanchard, and S. Leschine, “Untangling the Genetic Basis of Fibrolytic Specialization by Lachnospiraceae and Ruminococcaceae in Diverse Gut Communities,” Divers. 2013, Vol. 5, Pages 627-640, vol. 5, no. 3, pp. 627–640, Aug. 2013, doi: 10.3390/D5030627
Ley, R. E. et al. Obesity alters gut microbial ecology. Proc. Natl. Acad. Sci. U. S. A. 102, 11070–11075 (2005).
Heisel T, Montassier E, Johnson A, Al-Ghalith G, Lin YW, Wei LN, Knights D, Gale CA. High-fat diet changes fungal microbiomes and interkingdom relationships in the murine gut. mSphere. 2017 doi: 10.1128/mSphere.00351-17. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
Lin H, An Y, Hao F, et al. correlations of fecal metabonomic and microbiomic changes induced by high-fat diet in the pre-obesity state. Sci Rep. 2016 doi: 10.1038/srep21618. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
Liu S, Qin P, Wang J. High-fat diet alters the intestinal microbiota in streptozotocin-induced type 2 diabetic mice. Microorganisms. 2019 doi: 10.3390/microorganisms7060176. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
Deshpande NG, Saxena J, Pesaresi TG, Carrell CD, Ashby GB, Liao MK, Freeman LR. High fat diet alters gut microbiota but not spatial working memory in early middle-aged Sprague Dawley rats. PLoS ONE. 2019 doi: 10.1371/journal.pone.0217553. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
Sun J, Qiao Y, Qi C, Jiang W, Xiao H, Shi Y, Le GW. High-fat-diet-induced obesity is associated with decreased antiinflammatory Lactobacillus reuteri sensitive to oxidative stress in mouse Peyer's patches. Nutrition. 2016 doi: 10.1016/j.nut.2015.08.020. [PubMed] [CrossRef] [Google Scholar].
Nagao-Kitamoto H, Kamada N. Host-microbial cross-talk in inflammatory bowel disease. Immune Netw. 2017 doi: 10.4110/in.2017.17.1.1. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
Lam YY, Ha CW, Campbell CR, Mitchell AJ, Dinudom A, Oscarsson J, Cook DI, Hunt NH, Caterson ID, Holmes AJ, Storlien LH. Increased gut permeability and microbiota change associate with mesenteric fat inflammation and metabolic dysfunction in diet-induced obese mice. PLoS ONE. 2012 doi: 10.1371/journal.pone.0034233. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
Zhang Q, Xiao X, Li M, Yu M, Ping F, Zheng J, Wang T, Wang X. Vildagliptin increases butyrate-producing bacteria in the gut of diabetic rats. PLoS. 2017 doi: 10.1371/journal.pone.0184735/. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
He C, Cheng D, Peng C, Li Y, Zhu Y, Lu N. High-fat diet induces dysbiosis of gastric microbiota prior to gut microbiota in association with metabolic disorders in mice. Front Microbiol. 2018 doi: 10.3389/fmicb.2018.00639. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
Yang Y, Cai Q, Zheng W, Steinwandel M, Blot WJ, Shu XO, Long J. 2019 Oral microbiome and obesity in a large study of low-income and African-American populations. J Oral Microbiol. 2019;10(1080/20002297):1650597. doi: 10.1080/20002297.2019.1650597. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
Fernández-Hernando C, Suárez Y. ANGPTL4: a multifunctional protein involved in metabolism and vascular homeostasis. Curr Opin Hematol. 2022 doi: 10.1097/MOH.0000000000000580. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
Yi H, Wang L, Xiong Y, Wen X, Wang Z, Yang X, Gao K, Jiang Z. Effects of barrier function in weaned pigs. J Anim Sci. 2018 doi: 10.1093/jas/sky129. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
Sun J, Qiao Y, Qi C, Jiang W, Xiao H, Shi Y, Le GW. High-fat-diet-induced obesity is associated with decreased antiinflammatory Lactobacillus reuteri sensitive to oxidative stress in mouse Peyer's patches. Nutrition. 2016 doi: 10.1016/j.nut.2015.08.020. [PubMed] [CrossRef] [Google Scholar]
J. Bruminhent, M. J. Tokarczyk, D. Jungkind, and J. A. DeSimone, “Rothia mucilaginosa prosthetic device infections: a case of prosthetic valve endocarditis,” J. Clin. Microbiol., vol. 51, no. 5, pp. 1629–1632, 2013, doi: 10.1128/JCM.03173-12
P. JL et al., “Microbial Colonization Coordinates the Pathogenesis of a Klebsiella pneumoniae Infant Isolate,” Sci. Rep., vol. 9, no. 1, 2019, doi: 10.1038/S41598-019-39887-8.
Peterson, S. N. et al. The dental plaque microbiome in health and disease. PLoS ONE 8, e58487 (2013).
M. EV et al., “Suppression of Inflammatory Arthritis by Human Gut-Derived Prevotella histicola in Humanized Mice,” Arthritis Rheumatol. (Hoboken, N.J.), vol. 68, no. 12, 2016, doi: 10.1002/ART.39785
H. E. De Wardener and G. A. MacGregor, “Harmful effects of dietary salt in addition to hypertension,” J. Hum. Hypertens., vol. 16, no. 4, pp. 213–223, 2002, doi: 10.1038/SJ.JHH.1001374.
Culligan, E. P., Marchesi, J. R., Hill, C. & Sleator, R. D. Combined metagenomic and phenomic approaches identify a novel salt tolerance gene from the human gut microbiome. Front. Microbiol. 5, 189 (2014). S. R. Emami, M. Jafari, R. Haghshenas, and A. Ravasi, “Impact of eight weeks endurance training on biochemical parameters and obesity-induced oxidative stress in high fat diet-fed rats,” J. Exerc. Nutr. Biochem., vol. 20, no. 1, p. 29, Mar. 2016, doi: 10.20463/JENB.2016.03.20.1.5.
E. Pruszyńska-Oszmałek et al., “The long-term effects of high-fat and high-protein diets on the metabolic and endocrine activity of adipocytes in rats,” Biology (Basel)., vol. 10, no. 4, Apr. 2021, doi: 10.3390/BIOLOGY10040339/S1.
A. I. Olushola, K. O. Aderibigbe, S. O. Stephen, and O. S. Ayodeji, “Biochemical Effects of Aqueous Extract of Persea americana (Mill) on the Myocardium of Left Ventricle of High Salt-Fed Adult Wistar Rats,” J. Evid. Based. Complementary Altern. Med., vol. 22, no. 4, pp. 765–769, Oct. 2017, doi: 10.1177/2156587217733884.
X. L. Louis et al., “Treatment with low-dose resveratrol reverses cardiac impairment in obese prone but not in obese resistant rats,” J. Nutr. Biochem., vol. 23, no. 9, pp. 1163–1169, Sep. 2012, doi: 10.1016/J.JNUTBIO.2011.06.010.
C. D. Agardh and B. Ahrén, “Switching from high-fat to low-fat diet normalizes glucose metabolism and improves glucose-stimulated insulin secretion and insulin sensitivity but not body weight in C57BL/6J mice,” Pancreas, vol. 41, no. 2, pp. 253–257, Mar. 2012, doi: 10.1097/MPA.0B013E3182243107.
Araújo JR, Tomas J, Brenner C, Sansonetti PJ. Impact of high-fat diet on the intestinal microbiota and small intestinal physiology before and after the onset of obesity. Biochimie. (2017) 141:97–106. doi: 10.1016/j.biochi.2017.05.019
Ananthakrishnan AN, Luo C, Yajnik V, Khalili H, Garber JJ, Stevens BW, Cleland T, Xavier RJ. Gut Microbiome Function Predicts Response to Antiintegrin Biologic Therapy in Inflammatory Bowel Diseases. Cell Host Microbe. (2017) 21:603–610.e3. doi: 10.1016/j.chom.2017.04.010
Taranto MP, Medici M, Perdigon G, Ruiz Holgado AP, Valdez GF. Effect of Lactobacillus reuteri on the prevention of hypercholesterolemia in mice. J Dairy Sci. (2000) 83:401–3. doi: 10.3168/jds.S0022-0302(00)74895-8
Kumar M, Nagpal R, Kumar R, Hemalatha R, Verma V, Kumar A, et al. Cholesterol-lowering probiotics as potential biotherapeutics for metabolic diseases. Exp Diabetes Res. (2012) 2012:902917. doi: 10.1155/2012/902917
Koren O, Spor A, Felin J, Fåk F, Stombaugh J, Tremaroli V, et al. Human oral, gut, and plaque microbiota in patients with atherosclerosis. Proc Natl Acad Sci U S
A. 2011;108(Suppl 1):4592–4598. doi: 10.1073/pnas.1011383107. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
Le Roy T, Lécuyer E, Chassaing B, Rhimi M, Lhomme M, Boudebbouze S, Ichou F, Haro Barceló J, Huby T, Guerin M, Giral P, Maguin E, Kapel N, Gérard P, Clément K, Lesnik P. The intestinal microbiota regulates host cholesterol homeostasis. BMC Biol. 2019 Nov 27;17(1):94. doi: 10.1186/s12915-019-0715-8. PMID: 31775890; PMCID: PMC6882370
Martínez I, Perdicaro DJ, Brown AW, Hammons S, Carden TJ, Carr TP, Eskridge KM, Walter J. Diet-induced alterations of host cholesterol metabolism are likely to affect the gut microbiota composition in hamsters. Appl Environ Microbiol. 2013 Jan;79(2):516-24. doi: 10.1128/AEM.03046-12. Epub 2012 Nov 2. PMID: 23124234; PMCID: PMC3553751.
Vourakis M, Mayer G, Rousseau G. The Role of Gut Microbiota on Cholesterol Metabolism in Atherosclerosis. Int J Mol Sci. 2021;22(15):8074. Published 2021 Jul 28. doi:10.3390/ijms22158074
Yu Y, Raka F, Adeli K. The Role of the Gut Microbiota in Lipid and Lipoprotein Metabolism. J Clin Med. 2019;8(12):2227. Published 2019 Dec 17. doi:10.3390/jcm8122227
Schoeler, M., Caesar, R. Dietary lipids, gut microbiota and lipid metabolism. Rev Endocr Metab Disord 20, 461–472 (2019). https://doi.org/10.1007/s11154-019-09512-0
Yoshida, N., Yamashita, T. & Hirata, K. Gut microbiome and cardiovascular diseases. Diseases. 6, 56 (2018).
Rabot, S. et al. Germ-free C57BL/6J mice are resistant to high-fat-diet-induced insulin resistance and have altered cholesterol metabolism. FASEB J. 24, 4948–4959 (2010).
Martínez, I. et al. Diet-induced alterations of host cholesterol metabolism are likely to affect the gut microbiota composition in hamsters. Appl. Environ. Microbiol. 79, 516–524 (2013).
Claus, S. P. et al. Colonization-induced host-gut microbial metabolic interaction. MBio 2, 1–8 (2011).
Lahti, L. et al. Associations between the human intestinal microbiota, Lactobacillus rhamnosus GG and serum lipids indicated by integrated analysis of high-throughput profiling data. PeerJ 1, e32 (2013).
Singh RK, Chang HW, Yan D, Lee KM, Ucmak D, Wong K, Abrouk M, Farahnik B, Nakamura M, Zhu TH, Bhutani T, Liao W. Influence of diet on the gut microbiome and implications for human health. J Transl Med. 2017 Apr 8;15(1):73. doi: 10.1186/s12967-017-1175-y. PMID: 28388917; PMCID: PMC5385025
Sanders, P. W. Effect of salt intake on progression of chronic kidney disease. Curr. Opin. Nephrol. Hypertens. 15, 54–60 (2006).
Lin, J., Hu, F. B. & Curhan, G. C. Associations of diet with albuminuria and kidney function decline. Clin. J. Am. Soc. Nephrol. 5, 836–843 (2010).
Cianciaruso, B. et al. Salt intake and renal outcome in patients with progressive renal disease. Miner. Electrolyte Metab. 24, 296–301 (1998).
Manning, J. A. et al. Dietary sodium modulates nephropathy in Nedd4-2-deficient mice. Cell Death Differ. 27, 1832–1843 (2020).
A. C. Cepeda-Lopez, I. Aeberli, and M. B. Zimmermann, “Does obesity increase risk for iron deficiency? A review of the literature and the potential mechanisms,” Int. J. Vitam. Nutr. Res., vol. 80, no. 4–5, pp. 263–270, 2010, doi: 10.1024/0300-9831/A000033.
J. M. Rojas et al., “The Effect of Diet-induced Obesity on Toxicological Parameters in the Polygenic Sprague-Dawley Rat Model,” Toxicol. Pathol., vol. 46, no. 7, pp. 777–798, Oct. 2018, doi: 10.1177/0192623318803557
T. Nagamma, A. Konuri, C. D. Nayak, S. U. Kamath, P. E. G. Udupa, and Y. Nayak, “Dose-dependent effects of fenugreek seed extract on the biochemical and haematological parameters in high-fat diet-fed rats,” J. Taibah Univ. Med. Sci., vol. 14, no. 4, pp. 383–389, Aug. 2019, doi: 10.1016/J.JTUMED.2019.05.003
G. Wang et al., “Liver Fibrosis Can Be Induced by High Salt Intake through Excess Reactive Oxygen Species (ROS) Production,” J. Agric. Food Chem., vol. 64, no. 7, pp. 1610–1617, Feb. 2016, doi: 10.1021/ACS.JAFC.5B05897
O. E. Ofem, A. Udefa, A. Archibong, and G. O. Ujong, “Comparative effect of vitamin c and high calcium diet on some serum bio-markers of liver function in high salt loaded rats Effect of crude extract of viscum album (mistletoe)on plasma lipids:an insight into its possible antihyperglycemic and antihypertensive properties View project my project View project,” 2017, doi: 10.21276/jpb.2017.7.2.5.
E. Bragulat, E., A. de la Sierra, M. T. Antonio, & A. Coca, “Endothelial Dysfunction in Salt-Sensitive Essential Hypertension”, Hypertension, vol 37, no. 2, pp. 444–448, Feb. 2001, doi.org/10.1161/01.HYP.37.2.444.
Kumar S, Perumal N, Yadav PK, Pandey RP, Chang CM, Raj VS. Amoxicillin impact on pathophysiology induced by short term high salt diet in mice. Sci Rep. 2022 Nov 11;12(1):19351.
Pandey RP, Mukherjee R, Priyadarshini A, Gupta A, Vibhuti A, Leal E, Sengupta U, Katoch VM, Sharma P, Moore CE, Raj VS, Lyu X. Potential of nanoparticles encapsulated drugs for possible inhibition of the antimicrobial resistance development. Biomed Pharmacother. 2021 Sep;141:111943.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Assessing the short-term impact of a high-fat, high-salt diet on the gut bacteria and related pathophysiology in mice

Status:

Version 1

Abstract

Figures

Introductions

Methods

Discussion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1