7.4.3: Suppression and Alteration of Microbiota by Antimicrobials - Biology

7.4.3: Suppression and Alteration of Microbiota by Antimicrobials - Biology

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Our bodies depend upon, and host, a vast number of complex microbial flora that can be affected negatively by antimicrobial treatments.

Learning Objectives

  • Describe the role and function of the microbiota

Key Points

  • The intestinal system has many different species of microbes and huge numbers of individual microbes; we rely on these microbes for proper metabolism of food.
  • The use of antimicrobial agents to slow down or kill pathogenic microbes can often kill beneficial bacteria, causing deleterious health effects.
  • Our body hosts some microbes that inhibit the growth of pathogenic microbes; using antimicrobial agents can alter the flora allowing pathogenic microbes to overgrow and cause diseases.

Key Terms

  • Candidal vulvovaginitis: Candidal vulvovaginitis or vaginal thrush, or yeast infection, is an infection of the vagina’s mucous membranes by Candida albicans.
  • microbiota: The microbial flora harbored by normal, healthy individuals.
  • pathogenic bacteria: Bacteria which infect and cause deleterious health effects.

The human body hosts thousands of different species of microbial organisms, known as the microbial flora or microbiota. Microbiota serve many functions in our body; most notable is the gut flora, crucial for the proper digestion of food, carbohydrate fermentation, and nutrient absorption. The gut flora in the human intestinal system has hundreds of species of microbes and over 100 trillion individual microbes; in comparison, the human body has around 10 trillion cells. Most of these microbes are bacterial and fungal. This is especially a problem when broad-spectrum antimicrobial agents are used, as antimicrobial treatments while helping to clear up pathogenic microbes from the body will often kill symbiotic bacteria. In addition, some microbial infections are due to translocation, the movement of advantageous bacteria to parts of the body where they might be harmful. An example is gut flora getting into the body’s blood stream. The treatment of translocated or pathogenic bacteria may necessitate the use of antibiotics that will kill symbiotic bacteria. Antimicrobial agents which can kill beneficial gut flora can reduce the numbers of individual microbes or reduce the species of beneficial bacteria. In the case of the gut flora, this may impair the ability of a patient to properly metabolize food. If advantageous bacteria do not repopulate the intestine, this can lead to serious malnutrition problems.

In addition to serving a necessary function as gut flora due in metabolism of food, some microbiota in our bodies serve the function of keeping pathogenic microbes from inhabiting or dominating other flora at locations in our body. This is exemplified by Candida albicans, a yeast which is often found on humans. C. albicans is normally harmless, but when women take some antibiotics this can kill beneficial bacteria, specifically lactobacilli, in the vulvo-vaginal area. Without lactobacilli, C. albicans growth is not suppressed and can thus overgrow. This causes candidal vulvovaginitis, or yeast infections, a potentially painful infection of the vaginal mucous membranes by overgrown C. albicans. Yeast infections can be caused by antibiotics, as well as using aggressive topical cleaning agents such as detergents which again kill off beneficial lactobacilli allowing C. albicans to overgrow.

Fortunately there are antimicrobial agents that specifically target pathogenic bacterial species, which opposed to broad-spectrum treatments can reduce harmful effects on beneficial microbes. Sometimes the use of broad-spectrum antimicrobial agents is unavoidable; in these situations, consuming foods such as yogurt which contains beneficial bacteria can replenish the body’s symbiotic microbes. In extreme cases microbes can be transplanted from a healthy individual to someone with whose symbiotic microbes have been compromised.

Albugo-imposed changes to tryptophan-derived antimicrobial metabolite biosynthesis may contribute to suppression of non-host resistance to Phytophthora infestans in Arabidopsis thaliana

Background: Plants are exposed to diverse pathogens and pests, yet most plants are resistant to most plant pathogens. Non-host resistance describes the ability of all members of a plant species to successfully prevent colonization by any given member of a pathogen species. White blister rust caused by Albugo species can overcome non-host resistance and enable secondary infection and reproduction of usually non-virulent pathogens, including the potato late blight pathogen Phytophthora infestans on Arabidopsis thaliana. However, the molecular basis of host defense suppression in this complex plant-microbe interaction is unclear. Here, we investigate specific defense mechanisms in Arabidopsis that are suppressed by Albugo infection.

Results: Gene expression profiling revealed that two species of Albugo upregulate genes associated with tryptophan-derived antimicrobial metabolites in Arabidopsis. Albugo laibachii-infected tissue has altered levels of these metabolites, with lower indol-3-yl methylglucosinolate and higher camalexin accumulation than uninfected tissue. We investigated the contribution of these Albugo-imposed phenotypes to suppression of non-host resistance to P. infestans. Absence of tryptophan-derived antimicrobial compounds enables P. infestans colonization of Arabidopsis, although to a lesser extent than Albugo-infected tissue. A. laibachii also suppresses a subset of genes regulated by salicylic acid however, salicylic acid plays only a minor role in non-host resistance to P. infestans.

Conclusions: Albugo sp. alter tryptophan-derived metabolites and suppress elements of the responses to salicylic acid in Arabidopsis. Albugo sp. imposed alterations in tryptophan-derived metabolites may play a role in Arabidopsis non-host resistance to P. infestans. Understanding the basis of non-host resistance to pathogens such as P. infestans could assist in development of strategies to elevate food security.

Keywords: Albugo Arabidopsis thaliana Camalexin Glucosinolates Non-host resistance Phytophthora infestans Salicylic acid.


Two Albugo species compromise plant…

Two Albugo species compromise plant immunity and enable sporulation of Phytophthora infestans. a–f…

Genes differentially expressed in expression…

Genes differentially expressed in expression profiling experiment. The number of differentially expressed genes…

The tryptophan-derived metabolite pathway. Simplified…

The tryptophan-derived metabolite pathway. Simplified schematic of the tryptophan-derived metabolite pathway, adapted from…

A strategy to decrease vectorial competence of dengue mosquito Aedes aegypti by alteration of its gut microbiota using Indian traditional medicinal plants

In the present study, the sensitivity of the gut microbes of dengue fever mosquito Aedes aegypti to Indian traditional medicinal plants was evaluated. The microbes were isolated from the midgut of laboratory-reared fourth instar larvae of Ae. aegypti and grown on Luria-Bertini agar plates at an optimum temperature of 27ºC. The microbial colonies were differentiated based on their characteristics such as size, shape, opacity, elevation, consistency, and rate of growth. The axenic culture of different strains was obtained by streaking method. Ten different types of microbial clones were identified up to species level using Biolog’s advanced phenotypic technology. Five Indian traditional plants: Ocimum sanctum, Azadirachta indica, Catharanthus roseus, Curcuma longa, and Syzygium aromaticum and an invasive obnoxious weed Lantana camara were used in the present study. The extracts of the specific parts of individual plants were prepared in ethanol and hexane using ‘Soxhlet apparatus’. The extracts were screened for their antimicrobial activities by disc diffusion assay. The results indicate that the plants used in the present study possessed antimicrobial activities against gut microbes of Ae. aegypti. However, the sensitivity of different microbes to the extracts varied. The zone of inhibition observed after 24 h of incubation revealed that ethanol extract of A. indica has the most potent antibacterial activity followed by that of S. aromaticum, C. longa and O. sanctum. L. camara and C. roseus were least effective against gut microbes of Ae. aegypti.

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Modification of the intestinal microbiota by the application of probiotics


According to the Food and Agricultural Organization of the United Nations and the World Health Organization, probiotics are defined as ‘living microorganisms, which when administered in adequate amounts confer health benefits on the host’ [Food and Agriculture Organization of the United Nations et al. 2006]. Nobel laureate Elie Metchnikoff introduced the concept of probiotics to the scientific community. He published a seminal report linking the longevity of Bulgarians with consumption of fermented milk products containing viable Lactobacilli [Metchnikoff and Mitchell, 1907]. This observation suggested that certain microbes, when ingested, could be beneficial for human health. Since then, probiotics had been widely marketed and consumed, mostly as dietary supplements or functional foods. Mechanisms of probiosis include manipulation of intestinal microbial communities, suppression of pathogens, immunomodulation, stimulation of epithelial cell proliferation and differentiation and fortification of the intestinal barrier ( Figure 3 ) [Thomas and Versalovic, 2010].

Probiotic mechanisms in the human gastrointestinal tract. Probiotics may manipulate intestinal microbial communities and suppress growth of pathogens by inducing the host’s production of β-defensin and IgA. Probiotics may be able to fortify the intestinal barrier by maintaining tight junctions and inducing mucin production. Probiotic-mediated immunomodulation may occur through mediation of cytokine secretion through signaling pathways such as NF㮫 and MAPKs, which can also affect proliferation and differentiation of immune cells (such as T cells) or epithelial cells. Gut motility and nociception may be modulated through regulation of pain receptor expression and secretion of neurotransmitters. APRIL, a proliferation-inducing ligand hsp, heat shock protein IEC, intestinal epithelial cell Ig, immunoglobulin MAPK, mitogen-activated protein kinase NF㮫, nuclear factor-kappaB pIgR, polymeric immunoglobulin receptor STAT, signal transducer and activator of transcription Treg, T regulatory cell. (Reproduced with permission from Thomas and Versalovic [2010].)

Dysbiosis and human diseases

The intestinal microbiome plays an important role in the function and integrity of the gastrointestinal tract, maintenance of immune homeostasis and host energy metabolism [Pflughoeft and Versalovic, 2012]. Perturbations in the composition of microbial communities, also known as dysbiosis, may result in disrupted interactions between microbes and its host. These changes in microbiome composition and function may contribute to disease susceptibility [Frank et al. 2011]. Several studies have demonstrated associations between intestinal dysbiosis and chronic low-grade inflammation [Cani and Delzenne, 2009] and metabolic disorders [Jumpertz et al. 2011], ultimately resulting in metabolic syndrome, obesity and diabetes [Claus et al. 2008 Larsen et al. 2010 Pflughoeft and Versalovic, 2012]. Alterations in the composition of the intestinal microbiome have been associated with infections in the gastrointestinal tract, inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS) [Pflughoeft and Versalovic, 2012 Saulnier et al. 2011]. Treatment modalities to manipulate and restore the balance in the richness and diversity of intestinal microbiome are being explored [Sonnenburg and Fischbach, 2012]. Probiotics may introduce beneficial functions into the gastrointestinal tract or enhance the functionality of existing microbial communities. Probiotics may also affect the composition and function of microbial communities by competition for nutrients, production of growth substrates or inhibitors and modulation of intestinal immunity [O’Toole and Cooney, 2008]. This concept is supported by results from randomized controlled clinical trials showing the benefits of probiotics during the treatment of gastrointestinal diseases (extensively reviewed by Preidis, Thomas and Versalovic [Preidis and Versalovic, 2009 Thomas and Versalovic, 2010]).

How probiotics alter the intestinal microbiota?

Proposed mechanisms of probiosis include effects on composition and function of the intestinal microbiome. Probiotics produce antimicrobial agents or metabolic compounds that suppress the growth of other microorganisms [Spinler et al. 2008 O’Shea et al. 2011], or compete for receptors and binding sites with other intestinal microbes on the intestinal mucosa [Collado et al. 2007]. Probiotic Lactobacillus strains enhance the integrity of the intestinal barrier, which may result in maintenance of immune tolerance, decreased translocation of bacteria across the intestinal mucosa, and disease phenotypes such as gastrointestinal infections, IBS and IBD [Lee and Bak, 2011]. Moreover, probiotics can modulate the intestinal immunity and alter the responsiveness of the intestinal epithelia and immune cells to microbes in the intestinal lumen [Thomas and Versalovic, 2010 Bron et al. 2011]. The effects of probiotics on the composition, diversity and function of the gut microbiota have been studied using different tools and techniques ranging from targeted, culture-dependent methods to metagenomic sequencing. However, not many studies have demonstrated associations of altered microbiota following treatment with probiotics. A clinical study demonstrated decreased pain and flatulence in patients with IBS that received a 4-week treatment with a rose-hip drink containing 5 × 10 7 colony-forming units (CFU)/ml of L. plantarum DSM 9843 per day [Nobaek et al. 2000]. This improvement in clinical symptoms was associated with the presence of L. plantarum in rectal biopsies of patients, along with reduced amounts of enterococci in fecal specimens. A more recent study focusing on patients with diarrhea-dominant IBS (IBS-D) yielded symptomatic relief in patients treated with a probiotic mixture of L. acidophilus, L. plantarum, L. rhamnosus, Bifidobacterium breve, B. lactis, B. longum and Streptococcus thermophilus. Interestingly, analyses of the fecal microbiota of these patients using denaturing gradient gel electrophoresis (DGGE) revealed that the similarity of the microbial composition was more similar in probiotics-treated patients than that of the placebo group. This observation suggested that microbial community composition was more stable during the period of probiotics treatment [Ki Cha et al. 2011].

Recent technological innovations in DNA sequencing and advancements in bioinformatics have provided scientists with tools to explore research questions related to the human microbiome and how treatment modalities affect changes in global composition and function of the microbial communities. A recent study [Cox et al. 2010] using a high-throughput, culture-independent method analyzed the fecal microbiota of 6-month-old infants treated with daily supplements of L. rhamnosus (LGG). Results showed an abundance of LGG and an increased index of evenness in the fecal microbiota of these infants, suggesting ecological stability. The ability of probiotics to induce changes in intestinal microbial communities was demonstrated by a recent study, which explored the effects of L. reuteri on microbial community composition in a neonatal mouse model using 16S rRNA metagenomic sequencing. Results from this study demonstrated a transient increase in community evenness and diversity of the distal intestinal microbiome in animals treated with L. reuteri compared with that of vehicle-treated animals [Preidis et al. 2012]. The diversity in microbial communities was shown to be associated with increased ecological stability [Eisenhauer et al. 2012]. The loss of species in a community, although not immediately visible, can result in diminished ecological resilience after a stress-related perturbation [Peterson et al. 1998]. Interestingly, reduced microbial diversity was associated with diseases such as Crohn’s disease [Manichanh et al. 2006] and eczema in early life [Forno et al. 2008]. Probiotics may induce changes in the intestinal microbiota and stabilize microbial communities. However, further studies in humans are needed to assess whether probiotics can make the same impact on the human intestinal microbiome and whether the changes are associated with clinical benefits in the host.

In addition to directly affecting the composition of the intestinal microbiota, probiotics may also modulate the global metabolic function of intestinal microbiomes. Fermented milk products containing several probiotics did not alter the composition of intestinal bacterial communities in gnotobiotic mice and monozygotic twins [McNulty et al. 2011]. However, fecal metatranscriptomic analysis of probiotics-treated animals demonstrated significant changes in expression of microbial enzymes, especially enzymes involved in carbohydrate metabolism. Moreover, mass spectrometric analysis of urinary metabolites revealed altered abundance of several carbohydrate metabolites. These observations suggested that probiotics may affect the global metabolic function of the intestinal microbiome.

Alterations of the Gut Microbiome by Antibiotics

Antibiotics, as lifesaving medicines for over a century, have been in the front line for combating infections, preventing various medical conditions, and promoting animal growth [54••]. However, they present certain disadvantages including antimicrobial resistance (AR) and adverse drug events (ADEs) [55]. As gut microbiota is characterized by multiple shifts from the endometrial period till the end of life, antibiotics are suggested to represent one of the most pivotal factors for these alterations stimulating or promoting various diseases.

Since 1940, it has been known that antimicrobials may affect the intestinal microbiota. In 1950, terramycin proved to alter the gut microbiota in patients submitted to bowel surgery [56, 57]. Dysbiosis is closely related to the use of medication, being characterized by (i) the flourishing of the pathobionts, i.e., resident microbes with pathogenic potential (ii) the loss of α-diversity, i.e., the mean species diversity in the intestinal tract (iii) the recruitment of inflammatory cells (iv) the ‘leaky gut’ syndrome and (v) the impaired protection against pathogens [54••]. Figure 2 depicts the main mechanisms interconnecting gut dysbiosis triggered by environmental exposures such as diet and antibiotics and obesity.

Gut dysbiosis triggered by environmental exposures such as diet and antibiotics plays an important role in disrupting molecular metabolism and impacting on obesity outcomes. In obesity, the adipose tissue is infiltrated with inflammatory immune cells that produce high amounts of proinflammatory cytokines and chemokines. The gut barrier is disrupted causing gut antigens and PAMPs such as LPS to enter the tissue and stimulate inflammation. DC: dendritic cells, GABA: gamma aminobutyric acid, Mono: monocytes, PYY: peptide YY, PMNs: polymorphonuclear neutrophils, Th: T helper cells 5HT: 5-hydroxytryptamine

Several studies have demonstrated that the use of antibiotics during pregnancy, infancy, and childhood is strongly correlated to short-term consequences including antibiotic-related diarrhea, infection from Clostridium difficile, and AR emergence, while the long-term effects may comprise allergic, autoimmune, and metabolic disorders [54••, 58,59,60,61,62,63,64]. The human intestinal microbiome may also harbor antimicrobial resistance genes (ARGs) as a reservoir undergoing changes in its resistome, i.e., the ARGs from both pathogenic and non-pathogenic bacteria after antibiotic treatment [64, 65]. Moreover, a handful of studies have proposed a transitory dysbiosis, whereas other studies have shown that antibiotics may cause permanent disturbances of the intestinal microbial communities [54••, 65]. Actually, antibiotics lessen the microbial diversity in short-term usage, while they present a variable behavior concerning their long-term effects [66••].

The response of the gut microbiome to antibiotic treatment is a multifactorial process and depends upon the type and spectrum of activity, the route of administration, the duration, the number of doses, the age of subject, the genetic susceptibility and lifestyle, the pharmacological action, and the target bacteria [54••]. Because of the abovementioned factors, there are numerous existing patterns of microbiome shifts due to the use of antibiotics in humans and mouse models as depicted in Table 1 [81, 82••]. In a recent systematic review, it has been shown that changes in the gut microbiome from metronidazole and clarithromycin lasted the longest (4 years), followed by clindamycin (2 years), and ciprofloxacin (1 year). Additionally, antibiotics, particularly macrolides, amoxicillin, amoxicillin/clavulanate, quinolones, clindamycin, lipopolyglycopeptides, ketolides, tigecycline, fosfomycin, and cephalosporins, were associated with elevated numbers of Enterobacteriaceae other than E. coli (mainly Citrobacter spp., Enterobacter spp., and Klebsiella spp.) [82••]. Noteworthy, different classes of antibiotics have variable effects on gut microbiota for example, β-lactams decrease the abundance of Actinobacteria and Firmicutes and increase Proteobacteria and Bacteroidetes [68, 83]. Elevated abundance of Enterococcus spp. is promoted by amoxicillin, piperacillin and ticarcillin, carbapenems, lipoglycopeptides, and cephalosporins (except fifth generation cephalosporins), while decreased abundance is stimulated by macrolides and doxycycline. Piperacillin and ticarcillin, carbapenems, clindamycin, macrolides, and quinolones reduce significantly the abundance of anaerobic bacteria [65]. There are also studies demonstrating that the intestinal microbiome is resilient after short-term exposure to broad-spectrum antibiotics, but the changes differ among individuals while the restoration of the diversity and composition varies as well [54••, 65, 79, 84].

Overall, antibiotics lead to microbiome perturbations and create gut dysbiosis mainly by the increase of the abundance of Proteobacteria (considered as pathobionts) and the decrease of Actinobacteria and Bacteroidetes (considered as synbionts), with great varieties regarding severity and resilience. To overcome the collateral damage of the antibiotic usage, such as dysbiosis and AR, efforts have focused on personalized strategies, including understanding of microbiota-host interactions, rational use of antibiotics, vaccines and non-conventional antimicrobial agents, specifically bacteriophages, antimicrobial peptides, nucleoside-based antibiotics, and monoclonal antibodies [54••].


NAFLD is now the most common cause of liver disease in Western countries. This Review explores the links between NAFLD, the metabolic syndrome, dysbiosis, poor diet and gut health. Animal studies in which the gut microbiota are manipulated, and observational studies in patients with NAFLD, have provided considerable evidence that dysbiosis contributes to the pathogenesis of NAFLD. Dysbiosis increases gut permeability to bacterial products and increases hepatic exposure to injurious substances that increase hepatic inflammation and fibrosis. Dysbiosis, combined with poor diet, also changes luminal metabolism of food substrates, such as increased production of certain short-chain fatty acids and alcohol, and depletion of choline. Changes to the microbiome can also cause dysmotility, gut inflammation and other immunological changes in the gut that might contribute to liver injury. Evidence also suggests that certain food components and lifestyle factors, which are known to influence the severity of NAFLD, do so at least in part by changing the gut microbiota. Improved methods of analysis of the gut microbiome, and greater understanding of interactions between dysbiosis, diet, environmental factors and their effects on the gut–liver axis should improve the treatment of this common liver disease and its associated disorders.

Author information


Institut de Cancérologie Gustave Roussy Cancer Campus (GRCC), 114 rue Edouard Vaillant, 94805, Villejuif, France

Laurence Zitvogel, Romain Daillère, María Paula Roberti & Bertrand Routy

Institut National de la Santé Et de la Recherche Medicale (INSERM), U1015, GRCC, 94805, Villejuif, France

Laurence Zitvogel, Romain Daillère, María Paula Roberti & Bertrand Routy

University of Paris-Saclay, 94270, Kremlin Bicêtre, France

Laurence Zitvogel, Romain Daillère, María Paula Roberti & Bertrand Routy

Center of Clinical Investigations CIC1428, GRCC, 94805, Villejuif, France

Laurence Zitvogel & Guido Kroemer

Equipe 11 labelisée par la Ligue Nationale contre le Cancer, INSERM U1138, Centre de Recherche des Cordeliers, Paris, 75006, France

University of Paris Descartes, Sorbonne Paris Cité, Paris, 75006, France

University of Pierre et Marie Curie, Paris, 75006, France

Pôle de Biologie, Hôpital Européen Georges Pompidou, Paris, 75015, France

Metabolomics and Cell Biology Platforms, GRCC, 94805, Villejuif, France

Department of Women's and Children's Health, Karolinska Institute, Karolinska University Hospital, Stockholm, 17176, Sweden


Animal model and experimental timeline

The animal protocol for the study was approved by Institutional Animal Care and the Committee on the Ethics of Animal Experiments of South China Agricultural University (Permit Number 2017-B017). Pathogen-free adult male Sprague–Dawley rats (180–220 g) were obtained from Guangdong Medical Laboratory Animal Center (Guangzhou, China). Overall, the animal study lasted for 15 weeks. All rats were acclimatised for 1 week to the laboratory environment (12 h light/dark cycle, 25 ± 1 °C, and 55–65% relative humidity), with ad libitum access to food and water each rat was housed per cage. The experimental timeline is presented in Supplementary Fig. 1. Following acclimatisation, 48 rats were randomly divided into two groups: healthy control (HC n = 12) and CUMS (n = 36), with all rats being housed independently. With the exception of HC rats, all were subjected to eight different chronic unpredictable mild stimuli for 14 weeks, including 8 weeks of depression model development and a 6-week treatment period. The CUMS-resistant rats were screened using behaviour tests (sucrose preference test (SPT), open field test (OFT) and light/dark test (LDT)) after 8 weeks of exposure to CUMS. To ensure adequate statistical power, the remaining rats were randomly divided into three groups according to body weight as follows: model (CUMS, n = 6), Ami treatment (Ami, n = 6), and Flu treatment (Flu, n = 7) groups. The Flu used in this study was purchased from LILLY (Eli Lilly and Company, IN, USA). Amitriptyline was purchased from Dongting (Hunan Dongting Pharmaceutical, Hunan, China). The antidepressant groups were administered with Ami (25 mg/kg/d) or Flu (12 mg/kg/d) for 6 weeks by oral gavage, whereas the HC and CUMS rats were administered with equal volumes of sterile water. SPT was performed during weeks 0, 9, and 15. Behaviour tests (OFT, LDT) were performed pre- and post-Ami and Flu treatment. Faecal samples were collected at weeks 9 and 15. At week 9, using 16 S rRNA gene sequencing, faecal samples from randomly chosen CUMS-induced (n = 12) and HC (n = 12) rats were collected and subjected to 16S rRNA gene sequencing analysis. In addition, faecal samples of 3 individual rats randomly chosen from each group (HC: n = 3 CUMS: n = 3 Ami: n = 3 Flu: n = 3) were subjected to metagenome analysis at week 15. At the end of the experiment, the rats were fasted for 12 h, anaesthetised using 6% (v/v) chloral hydrate, and euthanised. In these studies, no blinding was done.

Chronic unpredictable mild stress

The CUMS procedures were performed as previously described 39,40 . The protocol comprised eight stressors: food deprivation for 24 h, water deprivation for 24 h, flash stimulation (150 flashes/min for 5 min), cage tilting at 45° for 24 h, overnight illumination for 8 h, wet cage environment (200 mL water added to sawdust bedding) for 24 h, tail suspension for 5 min, 60 °C heat stimulation for 6 min, and nipped tail for 5 min. Stressors were applied at least 13 times and without repetitive stressors in two consecutive days. To prevent being influenced by the CUMS rats, the HC rats were housed in an adjacent room and had no contact with the model animals. The model rats were authenticated by behaviour tests upon completion of the model development period.

Behavioural testing

The body weights of all rats were recorded each week. Anxiety-like and depression-like behaviours were examined using SPT, OFT, and LDT. In SPT, rats were trained to adapt to a sucrose solution (1%, w/v): two bottles of sucrose solution were placed in each cage for 24 h, subsequently, each bottle of sucrose solution was replaced with water for 24 h. After adaptation, the mice were deprived of water and food for 24 h. The rats were then given ad libitum access to water and sucrose solution for 4 h, after which the remaining volume of water and sucrose solution were measured. Sucrose preference was calculated using the formula as follows: sucrose preference = sucrose consumption/ (water and sucrose consumption) × 100% 41 . In OFT, an open field consisting of a black square (80 × 80 × 60 cm) was divided into 16 equal squares. Each rat was placed in the centre of an open arena and rat behaviour was recorded for 5 min. Prior to the start of the test, 30 s was set for adaptation. The total number of crossings and rearings was recorded 42 . In LDT, each rat was placed in the centre of an apparatus (40 × 30 × 35 cm) containing two chambers of equal size, one bright and the other dark. Total time spent in the dark zone was recorded for 5 min 43 .

Gut microbiota profiling by 16S rRNA gene sequencing

To profile the microbial composition, faecal samples were subjected to total genome DNA extraction using the QIAamp DNA Stool Mini Kit following the manufacturer’s instruction (Qiagen, Venlo, The Netherlands). The V4–V5 region of 16S rRNA genes of the samples was amplified by polymerase chain reaction (PCR) (98 °C for 60 s, followed by 30 cycles at 98 °C for 10 s, 50 °C for 30 s, 72 °C for 60 s, and 72 °C for 5 min), using primers 515 F 5′-GTGCCAGCMGCCGCGGTAA-3′ and 907 R 5′-CCGTCAATTCCTTTGAGTTT-3′. The sequencing libraries of the V4–V5 region of the 16 S rRNA genes were generated using the TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, San Diego, CA, USA) following the manufacturer’s instruction, and index codes were added. The libraries were sequenced using an Illumina HiSeq 2500 platform. QIIME software (Version 1.7.0) 44 was used to analyse alpha- (within samples) and beta- (among samples) diversity. Reads were first filtered by QIIME quality filters and chimera sequences were removed using the UCHIME algorithm. The filtered sequences were then clustered into OTUs according to representative sequence using UPARSE software 45 (Version 7.0.1001) and classified against the Greengenes database 46 with a threshold of 97% sequence similarity. Alpha-diversity was applied toward analysing the complexity of species diversity for a sample through four indices including observed-species, Chao 1, Simpson, and Shannon. Principal-coordinate analysis (PCoA) based on weighted and unweighted UniFrac metrics was used to assess the variation of bacterial composition among different groups and phases. These analyses were performed using the free online Majorbio I-Sanger Cloud Platform ( The 16S rRNA sequence data for representative samples were deposited in the Sequence Read Archive (SRA) database ( 47 .

Shotgun metagenomics analysis of faecal samples

To explore the microbial metabolic function and ARGs of the rat faecal microbiota, DNA extracts of the representative samples were further subjected to shotgun metagenomics sequencing analysis on an Illumina HiSeq 4000 platform using the HiSeq 4000 PE Cluster Kit and HiSeq 4000 SBS Kits. Open reading frames (ORFs) predicted from all samples were merged and aligned to each other. Gene pairs with >95% identity (no gap allowed) and aligned reads covering over 90% of the shorter reads were grouped together. The longest ORF in each group was used to represent the group while, the other ORFs of the group were regarded as redundant sequences. ORFs with a length less than 100 bp were subsequently filtered out. Based on this reference gene set, taxonomic assignment and functional annotation were further conducted using the latest version (Version 2.2.28+) of the KEGG database 48 and Comprehensive Antibiotic Resistance Database 49 . The metagenomics datasets were deposited into the SRA database.

Network analysis

To investigate co-occurrence patterns of microbial community and ARGs, correlation matrices were constructed by calculating each pairwise Spearman’s rank correlation. A correlation between any two items was considered statistically robust if the Spearman’s correlation coefficient (ρ) was >0.8 and the P-value was <0.01 50 . The resulting correlation matrices were translated into an association network using Cytoscape v3. 7. 1 51 .

Statistical analysis

In this study, 2,127,824 high quality-filtered and chimera-checked sequences were generated, with an average length of 372.38 bp across all samples. The mean number of reads per sample was 68,639, ranging from 58,524 to 74,062 reads. A total of 702 OTUs (97% sequence similarity) were detected among all samples. Based on relative abundance, the taxonomic analysis revealed 16 bacteria phyla, 30 classes, 49 orders, 88 families, and 194 genera across all samples. OTUs that reached 97% similarity were used for alpha-diversity estimations, which included observed OTUs (Sobs), diversity (Shannon and Simpson indices), richness (Chao I), coverage (Good’s coverage), and rarefaction curve analysis using Mothur 52 (Version 1.30.23). Results on behaviour assessments (body weight, sucrose preference ratio, numbers of crossings and rearings, time spent in the dark zone) were compared among groups using one-way analysis of variance (ANOVA) followed by the least significant difference test using Statistical Package for Social Science programme (SPSS 22.0, Armonk, NY, USA). PCoA based on weighted and unweighted UniFrac metrics was used to assess the variation of bacterial composition among different groups and different phases. The relative abundance of faecal microbiota, ARGs, and KOs in the four groups was compared using the Kruskal–Wallis H test with Tukey’s posthoc tests was used in the case of pairwise comparison. Moreover, LEfSe analysis combining the Kruskal–Wallis test with linear discriminant analysis was used to identify the differential KEGG pathway representation between faecal microbiomes of the two groups. A threshold value >2 was used as the cut off value for statistical significance based on a P-value of 0.05.

Patterns and Processes in Parasite Co-Infection

Mark E. Viney , Andrea L. Graham , in Advances in Parasitology , 2013

4.2.2 The Microbiota

The microbiota, the bacterial composition in animal guts, has recently been recognised to have very significant effects on many aspects of an animal's biology. These effects seem to be so wide-spread that it seems inconceivable that a host's other infections are not affected by the microbiota. There are at least three means by which this could happen. Firstly, the microbiota has metabolic effects on food that a host has ingested, which therefore affects a host's actual nutrition. Thus, the microbiota can be part of the effect of host nutrition on infection. Secondly, the microbiota will affect the local physiology of the host gut, which will therefore affect the niche that intestinal parasites use. For example, commensal bacteria species can affect the establishment of pathogenic bacteria. Thirdly, the microbiota has immunological effects both local to the gut and more systemically, and these immunological effects will affect parasites and other infections. With our perspective of infracommunities, the microbiota is therefore itself an intrinsic part of that infracommunity (indeed it may have the greatest component biomass of any individual's infracommunity). This therefore clearly makes the point that an infracommunity is both the result of the processes of exposure and susceptibility, but that the infracommunity is then also an actor in within-host processes affecting susceptibility. The microbiota is therefore an example of how to view and conceptualise host infracommunities.

The microbiota has become available for analysis because of DNA sequencing approaches prior to this, much of the microbiota, which includes anaerobic species, was difficult, if not impossible, to work with using classical microbiological approaches (e.g. in vitro culture). To introduce the microbiota: in humans the microbiota consist of more than a 1000 species, present in large quantities (about a kilogram of cells 9 of every 10 cells in the human body are microbial), containing ≥200 times as many genes (i.e. the microbiome) as our own genome, with metabolic and other effects ( Candela et al., 2010 Ding et al., 2010 Eckburg et al., 2005 Heijtz et al., 2011 Lee and Mazmanian, 2010 Ley et al., 2006 Ley, 2010 Lozupone et al., 2012 Pennisi, 2010 Rajilić-Stojanović et al., 2007 Turnbaugh et al., 2006 , 2008 , 2009 ). Most gut bacteria (the microbiota) are normal commensal colonists of the gut some are always pathogenic (obligate pathogens), some are sometimes pathogens (opportunistic pathogens). Thus, the gut microbiota is a mixture of taxa of different effects and relationships to the host. The microbiota shapes the development of the ‘normal’ human gut and is important in immunological tolerance and ‘normal’ immunological function ( Brugman and Nieuwenhuis, 2010 Candela et al., 2010 Cerf-Benussan and Gaboriau-Routhiau, 2010 Chung and Kasper 2010 Hand and Belkaid, 2010 Lee and Mazmanian, 2010 ). There is growing evidence that the microbiota has a role in immune dysregulation, for example in inflammatory bowel disease ( Round and Mazmanian, 2009 ). There has been investigation of what shapes the composition of an individual's microbiota. Individuals begin to be colonised with microbes from birth ( Manco et al., 2010 ), so maternal and childhood events can have life-long effects ( Collado et al., 2010 Gareau et al., 2010 Spor et al., 2011 ). During the first year of life individual microbiotas vary, but this converges to a mix between major microbial taxonomic groups (Firmicutes, Bacteriodetes, etc.) from about 1 year of age which persists through life ( Booijink et al., 2010 Palmer et al., 2007 Turnbaugh et al., 2009 ). Notwithstanding this, there is a large degree of inter-individual variation in microbiotas, such that individuals have their own ‘fingerprint’ of microbial species ( Kuczynski et al., 2010 ). There is both an environmental (e.g. microbial exposure, diet, infection effects) and a genetic component controlling the microbiota ( Benson et al., 2010 Booijink et al., 2010 Brugman and Nieuwenhuis, 2010 De Filippo et al., 2010 Hilberbrandt et al., 2009 Kuczynski et al., 2010 Ley et al., 2005 McKnite et al., 2012 Palmer et al., 2007 Spor et al., 2011 Stecher et al., 2010 Turnbaugh et al., 2009 Walk et al., 2010 Yatsunenko et al., 2012 ).

With this perspective, it is perhaps somewhat surprising that there are rather few studies to date that have investigated how parasitic infections are affected by the host microbiota. (There are though many current studies of microbial pathogens, particularly seeking to use approaches that manipulate the microbiota to provide anti-pathogen therapy, because commensals can affect the establishment etc. of pathogenic bacteria.) Recently, though, direct effects of the host microbiota on nematode infections have been found. For example, successful establishment of T. muris in the host intestine and the viability of the nematode's eggs in-part depends on an interaction with gut bacteria ( Hayes et al., 2010 ). Furthermore, clinical syndromes such as Trichuris suis-induced diarrhoea in pigs depends on co-infection with spirochaetes ( Rutter and Beer, 1975 ). The effects are also of two-way: T. suis infection in pigs alters the pig microbiota, as does H. polygyrus infection in the microbiota of mice ( Walk et al., 2010 Wu et al., 2012 ). A relationship between a bacterial infection (Bordetella bronchiseptica in the respiratory tract) and gastrointestinal nematode infection (Graphidium strigosum) in rabbits has been found, such that the helminth infection was more abundant in co-infected animals, compared with helminth-only infected animals ( Murphy et al., 2011 Pathak et al., 2012 ). More such work is required, particularly to tease apart directionality of effects, which might require time-series experimental analyses for example (Section 5 ).

In summary, recent extensive evidence has shown how the host microbiota has pervasive effects on host biology. When parasitologists consider a host's infracommunity, it is now clear that the microbiota also needs to be considered too. It is inconceivable that the microbiota is not relevant to shaping helminth and protozoan infracommunites, and early evidence is now showing this. This is easy to envisage for intestinal parasites, but the microbiota's body-wide effects on nutrition and immunity make this relevant to parasites inhabiting all tissues.

Gut Microbiome in the Elderly Covidants: Does It Explain High Mortality Rates?

The increased rates of mortality among the elderly in COVID-19 seem to stem from alterations in gut microbiota. The potential rationale underlying increased rates of mortality among the elderly in COVID-19 due to likely higher abundance of inflammatory bacteria ( Table 1 ). The abundance of beneficial bifidobacterial may be depleted in the elderly peoples (Nagpal etਊl., 2018). Elderly people may be more susceptible to SARS-CoV-2 infection due to less diverse beneficial microorganisms.

Table 1

List of elevated bacteria in COVID-19 patients associated with inflammation and immunity.

Bacterial Genus/Species PhylumMode of action associated with inflammation
StreptococcusFirmicutesInduces secretion of pro-inflammatory cytokines such as IL-1β, IL-6, IL-8, and TNF-α from epithelial cells.
Actinomyces viscosusActinobacteriaInduces inflammatory lesions in tissues having PMNs, macrophages, and plasma cells.
BurkholderiaProteobacteriaType VI effector, TecA of B. cenocepacia induces the activation of pyrin inflammasome through the deamidation of Rho GTPases that drive inflammation
Klebsiella K. pneumoniae enhances inflammatory response in human airway epithelial cells through activation of TLR4 and TLR2 and preventing the action of host proteins such as CYLD and MKP-1 which are involved in immune homeostasis post inflammation event
Escherichia coliEnterobactin of E. coli prevents action of bacteriocidal enzyme myeloperoxidase which is secreted from the neutrophil in the inflamed gut
Acinetobacter baumanniiHisF gene of this bacteria is responsible for the reduction of innate immune response.
Acinetobacter nosocomialisOuter membrane vesicles (OMVs) induce inflammatory responses in epithelial cells

It has also been hypothesized that drugs used to treat diabetes mellitus and hypertension might upregulate the expression of ACE2 facilitating SARS-CoV-2 infection (Fang etਊl., 2020). Taking these factors into consideration, it can be easily speculated that SARS-CoV-2 infection might contribute to gut dysbiosis resulting in generalized inflammation contributing to MODS and other serious clinical worsening, especially in the elderly and patients with underlying clinical conditions.

Previous studies showed that dietary supplementation of probiotic formula with Bifidobacterium lactis in aged individuals enhanced the tumoricidal functions of natural killer (NK) cells. Probiotics such as L. johnsonii, L. fermentum, L. reuteri, L. paracasei, L. rhamnosus, L. acidophilus, L. plantarum, belonged to genera Lactobacillus and B. longum, B. breve, B. bifidum, and B. animalis subsp. lactis were involved in alleviating inflammatory manifestations via regulation of innate immune responses (Dhar and Mohanty, 2020). Probiotic bacteria like L. rhamnosus, B. lactis, and B.breve are involved in the down-regulation of inflammation through elevation of Treg cells (Feleszko etਊl., 2007).

Prebiotics such as inulin, fructo-oligosachharides (Fos), galactosachharides (Gos), and polydextrose are involved in the development of host immunity through alterations of gut microbiome. Prebiotics reportedly reduce the levels of the proinflammatory IL-6 that tends to be the prime culprit behind the hitherto described grave prognosis in COVID-19 and enhance the levels of anti-inflammatory IL-10 (West etਊl., 2017). Protein enriched diet enhances the abundance of gut commensals such as bifidobacteria and lactobacilli simultaneously reduces the pathogenic gut microbiota (Świątecka etਊl., 2011). Probiotic strains such as bifidobacteria or lactobacilli are not only involved in the clearance of virus from the respiratory tract but also augments the activity of antigen presenting cells, NK cells, T cells to drive the enhanced release of mucosal antibodies in lung fluids (Zelaya etਊl., 2016). Lactobacillus casei induces the phagocytic activity of alveolar macrophages and over expression of IgA, IFN-γ, and TNF-α in the host to protect against flu virus infections. Bifidobacterium, Lactobacillus paracasei, and Lactobacillus rhamnosus enhanced the efficacy of vaccine response against respiratory infections such as H1N1, H5N1, and H3N2 (He L. H. etਊl., 2020). Probiotic strains are involved in the regulation of proinflammatory and anti-inflammatory cytokines that likely could ameliorate ARDS complications in COVID-19.

Elderly individuals with hypertension, obesity, and diabetes are more prone to develop severe symptoms due to COVID-19 infections because dysbiosis of the gut microbiome reduces the integrity of the gut barrier, which in turn allows other pathogens to bind the enterocytes. Disruption of the integrity of tight junctions in between enterocytes of the gut called “leaky gut” in COVID-19 patients is responsible for the development of diarrhea, and inflammation due to higher levels of IL-6 in plasma and fecal calprotectin. This also allows SARS-CoV-2 to enter into the blood stream and bind with ACE2 of other body parts. F. prausnitzii belonging to class Clostridia and family Ruminococcaceae is responsible for the synthesis of a short-chain fatty acid (SCFA) such as butyric acid in the gut ( Figure 1 ). The abundance of this bacteria was reduced in COVID-19 patients. Butyric acid maintains the integrity of gut barrier and shows anti-inflammatory activity through inhibition of NF-㮫 activity, activation of G protein-coupled receptors such as GPR41 and GPR43, suppression of histone deacetylase activity, and activation of regulatory T cells (Treg) cells. Fecal microbiota transplantation (FMT), and enhancement of abundance of next-generation probiotics such as butyrate-producing gut bacteria through daily intake of dietary fiber may be used to prevent inflammation and severity in COVID-19 patients (Kim, 2021). Protein extracts of whey and pea enhanced the abundance of Bifidobacterium, and Lactobacillus whereas reduced the abundance of pathogenic bacteria Bacteroides fragilis and Clostridium perfringens (Świątecka etਊl., 2011). SCFAs mainly acetate, propionate, and butyrate which are produced by the gut microbiota through the metabolism of resistant starches and dietary fibers provide energy to gut epithelial cells, maintain the integrity of the gut barrier, and suppressed inflammation by blocking the action of LPS and prevention of proinflammatory cytokine productions (Corrສ-Oliveira etਊl., 2016). Acetate may provide protection against respiratory syncytial virus (RSV) in the lung through the activation of IFN-β via GPR43 and IFNAR (Antunes etਊl., 2019).

Azithromycin which is a commonly used antibiotic for COVID-19 treatment reduced Shannon diversity index of bacterial communities particularly the abundance of Bifidobacterium genus. Other drugs such as metformin, statins, and psychiatric drugs are also involved in the alteration of the gut microbiota as well as enhance the risk of viral infections. Combinatorial approaches of probiotics, prebiotics, and natural products are used to control the balance of gut bacteria. Probiotics suppressed diarrhea by blocking the TLR expression and controlling the humoral and cellular immune responses. Bacterial genera such as Lactobacillus and Bifidobacterium showed strong antiviral action against influenza virus type A. These probiotics suppressed the growth of candida, E. coli, pseudomonas, and staphylococci during antibiotic administration in COVID-19 patients. Prebiotics and probiotics inhibit viral replication and infection via production of interferon (IFN) by activating plasmacytoid DCs via TLR9. LPS of Gram-negative and peptidoglycans (PG) of Gram-positive bacteria interact with viral proteins (Kiousi etਊl., 2019 Donati Zeppa etਊl., 2020). Gut microbiota effects on ACE2 at the gut and lung in such a way that probiotics may control the severity of the disease. Following gut colonization, probiotics could contribute to development of immunity against viral infections. Probiotics strains such as Lactobacillus rhamnosus GG and Bifidobacterium longum are involved in compressing the infection of ICU patients. Bacteriocins which are produced by Lactobacilli and Bifidobacteria are effective against pathogenic bacteria and viruses. Probiotic Lactobacillus sp. augments gut immunity through the synthesis of antiviral agents such as mucins and mucus in the intestine. Probiotics control innate and adaptive antiviral immunity through an interaction with dendritic cells, monocytes/macrophages, and lymphocytes. Lactic acid bacteria induces the synthesis of cytokines or chemokines through binding with intestinal epithelial cells via toll-like receptors. This also drives the abundance of IgA producing cells of bronchus, mammary glands and intestine which in turn stimulates mucosal immune system. Probiotics stimulate the secretion of IgG and IL-10 from the activated T-cells. It is essential to use probiotics along with prebiotics for the treatment of COVID-19 individuals (Din etਊl., 2021). Bacteriocin compounds such as staphylococcin 188, enterocin AAR-74, erwiniocin NA4 showed antiviral activity against HIV, HSV, Coliphage, influenza virus, and H1N1 virus (Gohil etਊl., 2021).

Together, gut microbiome alterations may play a paramount role in determining the clinical outcome of clinical COVID-19 with underlying co-morbid conditions like T2D, cardiovascular disorders, obesity, etc. Research is warranted to manipulate the profile of gut microbiota in COVID-19 by employing combinatorial approaches such as use of prebiotics, probiotics and symbiotics. Prediction of gut microbiome alterations in SARS-CoV-2 infection may likely permit the development of effective therapeutic strategies. Novel and targeted interventions by manipulating gut microbiota indeed represents a promising therapeutic approach against COVID-19 immunopathogenesis and associated co-morbidities. The impact of SARS-CoV-2 on host innate immune responses associated with gut microbiome profiling is likely to contribute to development of key strategies for application and has seldom been attempted, especially in the context of symptomatic as well as asymptomatic COVID-19 disease.

Watch the video: 33. Bacteria and Antibiotic Resistance (August 2022).