The researchers found that the bacterial replication rates were indeed higher in the weight loss group than in the no-weight-loss group. Furthermore, bacteria belonging to the genus Prevotella were responsible to a large extent for the increased replication rate in the weight loss group. Notably, previous research has shown that individuals with higher levels of Prevotella in the gut are more likely to lose weight on a high fiber diet. Higher Prevotella levels in the gut are associated with increased levels of degradation of complex carbohydrates by fermentation, resulting in the production of short-chain fatty acids.
These short-chain fatty acids are less energy-dense than the consumed carbohydrates and can reduce inflammation. This is especially noteworthy since experts believe that obesity is likely associated with chronic low grade inflammation. In contrast, microbiome genes associated with the breakdown of complex carbohydrates and proteins and those involved in the stress response and cellular respiration were enriched in individuals resistant to weight loss.
To be precise, genes enriched in the no-weight-loss group included those coding for enzymes that degrade complex carbohydrates into simple sugars. At the same time, the lower levels of bacteria with the ability to transform these simple sugars into fermentation products in individuals resistant to weight loss may result in greater absorption of simple sugars by the host, i.
Thus, the authors hypothesize that the lower replication rates of bacteria involved in fermentation and high levels of carbohydrate-degrading enzymes may be responsible for the lack of response to weight loss interventions. He continued:. Thus, the microbiome appears to modulate the efficiency by which the host extracts calories from the diet.
Describing the strengths of the study, Dr. These factors are highly correlated because people with higher BMIs tend to lose more weight in response to an intervention. Therefore, we corrected for baseline BMI when looking for associations with weight loss. The results reported here are features associated with weight loss that are fully independent of baseline BMI.
The authors acknowledged that the study had certain limitations. To address the small sample size in the present study, the authors intend to replicate the research with larger groups of participants.
Discussing future research directions, Dr. How much influence do your gut bacteria have on your state of mind? Perhaps more than you think, according to research into the microbiome-gut-brain…. In this edition of Medical Myths, we investigate 11 misconceptions about weight loss. We cover sugar and sweeteners, skipping meals, snacking, and…. In this edition of Medical Myths, we address five persistent myths about obesity.
This article covers genetics, weight loss, diabetes, and more. Losing weight effectively and keeping it down involves a number of factors. Find out more about how to lose weight. Being a healthy weight offers many health benefits, as well as a feeling of wellbeing. In addition, a trend to higher overall species richness was observed in postoperative samples Fig.
The taxon-dependent analysis was conducted to describe the composition of the fecal microbiota via the Ribosomal Database Project RDP classifier in different groups. The alterations of the structure and relative abundance of the gut microbiota were shown at the genus level Fig. The PD surgery induced a decreased abundance in Escherichia-Shigella and Acinetobacter as well as an increase of Enterococcus.
To explore the changes of gut microbiota by somatostatin therapy, a comparative analysis was conducted before and after somatostatin therapy. Remarkably, a trend to lower overall species diversity and richness in postoperative samples was observed Fig.
However, a simply discriminated tendency was found and it could not be transformed into significant results. The abundance changes in gut microbiota were further analyzed.
At genus level, somatostatin led to a decrease in Bifidobacterium, Subdoligranulum and Dubosiella as well as an increase in Akkermansia, Enterococcus and Enterobacter Fig.
To investigate the specific alterations of microbiota in samples from POPF, the 7 patients with somatostatin therapy were assessed. This phenomenon was also found in the overall species richness Fig.
As shown in Fig. Besides, the weighted NMDS showed slight differences in bacterial composition. Notably, the proportions of Ruminococcaceae and Bifidobacteriaceae were significantly decreased, while the proportions of Enterobacteriaceae and Enterococcaceae were increased at family level Fig.
Meanwhile, the data of another 10 patients without POPF were further analyzed. The trend of diversity and richness alterations in these 10 patients were similar to those patients with POPF Fig. At the family, the proportions of Desulfovibrionaceae and Bacteroidaceae were significantly increased, while the proportion of Muribaculaceae were decreased in patients without POPF Fig. The Shannon index of the two groups was similar preoperatively. Above all, these data indicates microbial changes of certain bacteria associated with POPF.
To identify the specific bacterial taxa associated with different intervention, the compositions of the fecal microbiota were compared by the linear discriminant analysis effect size LEfSe method.
A cladogram represented the structures of the fecal microbiota and the predominant bacteria. The LEfSe analysis was performed to determine the microbial clade differences related to PD surgery at the taxonomical level. In order to explore the specific communities associated with somatostatin, the compositions of the gut microbiota in pre-Som and post-Som patients were compared.
These results confirm that microbiota can be applied for predicting POPF during the postoperative clinical practice. Cladogram representation of the different abundant taxa. The root of the cladogram represented the domain bacteria and the size of each node represents their relative abundance.
No different group was labeled by yellow and significant difference were showed by blue. The circle size represented the abundance and the thickness of the line showed the correlated strength. The diameter of the nodes was proportional to the relative abundance. We then analyzed the difference at genus level for better understanding the dynamics of gut microbiota following somatostatin treatment. These differential genera were used to construct an interaction network depicting the correlations between somatostatin and POPF-associated microbial markers.
The gut microbiota communicates to maintain dynamic equilibrium and the interactions among different genera help to understand the important roles of these species in POPF. Our data suggest the gut microbiota as a key mediator and potential therapeutic target for POPF.
It is therefore unsurprising that several procedures have been utilized to reduce its risk, such as the prophylactic administration of Somatostatin Analogs SAs These studies have approached inconsistent views and the value of somatostatin for POPF is unclear.
A prospective study was performed in our single center, and pancreatic texture, pancreatic duct size and somatostatin therapy were found to be related with POPF. According to the Fistula Risk Score, patient-derived risk factors of POPF included soft pancreatic texture, small pancreatic duct, high-risk pathology and excessive blood loss.
Meanwhile, our data confirmed the preventive effect of somatostatin on POPF. The gut microbiota interacts extensively with the host by the co-metabolism of substrates and metabolic exchanges to preserve a healthy status and normal functions of the body The combination of personalized medicine and understanding about the microbiota has naturally led to identify microbial factors related to clinical outcomes.
Previous studies have emphasized the significance of gut microbiota in the progression of pancreatic diseases Recent studies found that tumor resection affects the structure of the gut microbiota, which appeared to promote postoperative complications development Rogers et al.
Similar patterns were observed within pancreatic tissue, bile and jejunal samples. The notion that the microbiota contributes to POPF represents a novel way to be lost in thought of an old matter.
Our data indicated that the variations of gut microbiota in PD patients corresponds to the progression of the POPF and somatostatin therapy. The alterations of gut microbiota imply that the identified microbial signature may be a potential strategy for predicting POPF and assessing the alterations in the gut microbiota after somatostatin therapy.
Two common OUTs were observed preoperatively and none remained after the somatostatin therapy. This alteration may be associated with the treatment of somatostatin. Furthermore, there was a trend toward higher overall microbial richness and diversity Shannon index in preoperative samples of PD patients.
Conversely, a relative lack of diversity was observed in the gut microbiota of patients with POPF and treated with somatostatin. Previous studies have shown that the usage of antibiotics induces a dramatic reduction in the diversity of gut microbiota 22 , which is similar to our results that the gut microbial diversity is dramatically decreased after somatostatin therapy.
Higher microbial diversity always links to the temporal stability of gut microbiota. Decreased gut diversity is associated with early adverse outcomes, including vulnerable resistance against invading pathogens, intestinal infections, which may result in the disruption of microbiota balance 23 , In addition, the effect of somatostatin on POPF is associated with the change of microbial composition.
In previous studies, the fecal samples were allocated into three different microbial communities by the structure of the gut microbiota.
Schmitt et al. A microbial composition resembling the specific community were found to have a significantly higher risk for developing POPF. Comparing with samples from the American Gut Project, Rogers et al. Postoperative samples from patients with POPF contained increased Klebsiella and decreased commensal anaerobes, including Ruminococcus. Many studies have demonstrated the possibility of using gut microbiota as a non-invasive biomarker To identify the specific communities related with somatostatin, the LEfSe analysis showed that abundance of commensal beneficial genera, such as Bifidobacteriaceae and Bifidobacterium, were decreased after somatostatin therapy.
In recent studies 28 , the influence of Bifidobacteria on gut microbiota dysbiosis showed a potential relationship with symptoms of metabolic disorders. Zhu et al. Somatostatin therapy induces the reduction of some dominant bacterial groups, which may aggravate the instability of postoperative state in gut microbiota.
Similarly, other studies demonstrated that decreased abundance levels of Ruminococcus plays a crucial role in participating in POPF. Our data also indicated that both of Verrucomicrobia and Akkermansia could be used as microbial markers for distinguishing the patients without POPF preoperatively. Akkermansia muciniphila is a gram-negative anaerobic bacterium, which is the single representative member of the Verrucomicrobia phylum in the human intestinal tract.
Recently, it has been considered as a promising probiotics. The pasteurized Akkermansia improves metabolic dysfunctions and the integrity of intestinal barrier and reduces plasma lipopolysaccharide levels in obese human volunteers 30 , Thus, the Akkermansia preparation could be supplemented properly to maintain the stable state of gut microbiota after somatostatin therapy, which may reduce the incidence of POPF.
Overall, our study reveals that the changes of microbiota could serve as potential novel biomarkers for monitoring and diagnosis of POPF and evaluate the intestinal health after somatostatin therapy.
This is the first prospective study to explore the effects of somatostatin on gut microbiota composition after PD. Although our investigations attempt to provide a comprehensive insight into potential contribution of the gut microbiota related to somatostatin, several limitations are to be addressed.
Firstly, our analysis showed that the incidence of POPF was Secondly, the pathological types of patients were different, which may affect the baseline of microbiota. Further studies should recruit more patients to validate the predictive power of hose biomarkers. Thirdly, the gut microbiota could be influenced by many factors during the perioperative period, such as timing of enteral nutrition, postoperative diet and antibiotics therapy.
Further studies are required with multiple samples, multicenter designs and the usage of advent research techniques to discover the potential mechanisms to improve outcomes of PD patients and discover diagnostic biomarker for POPF In conclusion, we elucidate that the prophylactic usage of somatostatin could reduce the incidence of POPF. The specific communities related with somatostatin and POPF-associated microbial markers are identified and gut microbiota may be considered as valuable biomarker for predicting POPF and evaluating the postoperative microenvironment of PD patients.
Accordingly, fully understanding the mechanisms of microbial changes will bring more potentially valuable for the diagnosis and treatment of POPF. This prospective study was approved by the Ethics Committee of the First Affiliated Hospital of Harbin Medical University China , and all participants signed the informed consent before intervention. All the data of patients were prospectively collected.
The baseline clinical characteristics of two groups had no significant difference Table 3. A total of 50 patients were enrolled and 5 patients were excluded in this prospective study.
Finally, our prospective study subjects comprised 50 fecal samples from 17 patients in somatostatin therapy group and 8 patients in control group.
The preoperative fecal sample was collected to determine the baseline microbiota without surgical stress, or antibiotic use, and postoperative samples were taken to evaluate the longitudinal changes in the 5—7 days after the operation. The enrolled patients were randomized to either non-somatostatin intervention group control group or 5-day treatment with somatostatin after PD somatostatin therapy group.
The clinical effects of somatostatin and occurrence of POPF will be subsequently evaluated. A total of 50 fecal samples from 25 patients were collected and each sample was delivered immediately to the laboratory in an insulated box. PCR products was mixed in equidensity ratios. Shanghai, China. The library quality was assessed on the Qubit 2. Paired-end reads were assigned to samples based on their unique barcode and were truncated by cleaving the barcode and primer sequence.
FLASH was designed to merge paired-end reads when at least some of the reads overlap the read generated from the opposite end of the same DNA fragment, and the splicing sequences were named raw tags.
Quality filtering of the raw tags was performed under specific filtering conditions to obtain high-quality clean tags according to the QIIME V1. The tags were compared with a reference database the Gold database using the UCHIME algorithm to detect chimaera sequences, and the chimaera sequences were then removed The effective tags were finally obtained. Sequence analysis was performed using Uparse software Uparse v7.
Representative sequences for each OTU were screened for further annotation. Sequencing reads were demultiplexed and filtered, and the identified taxonomy was then aligned by Greengenes database The Greengenes database was used for each representative sequence based on the RDP classifier Version 2. The development of the endoderm is a multistage process. From the initial specification of the endodermal domain in the embryo to the final regionalization of the gut, there are multiple stages that require the involvement of complex gene regulatory networks.
In one concrete case, the sea urchin embryo, some of these stages and their genetic control are relatively well understood.
Several studies have underscored the relevance of individual transcription factor activities in the process, but very few have focused the attention on gene interactions within specific gene regulatory networks GRNs.
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