Search results for: Megasphaera elsdenii

Authors: M. S. Alatas, H. D. Umucalilar

Abstract:

SARA is a common and serious metabolic disorder in early lactation in dairy cattle and in finishing beef cattle, caused by diets with high inclusion of cereal grain. This experiment was performed to determine the efficacy of Megasphaera elsdenii, a major lactate-utilizing bacterium in prevention/treatment of SARA in vivo. In vivo experimentation, it was used eight ruminally cannulated rams and it was applied the rapid adaptation with the mixture of grain based on wheat (%80 wheat, %20 barley) and barley (%80 barley, %20 wheat). During the systematic adaptation, it was followed the probability of SARA formation by being measured the rumen pH with two hours intervals after and before feeding. After being evaluated the data, it was determined the ruminal pH ranged from 5,2-5,6 on the condition of feeding with 60 percentage of grain mixture based on barley and wheat, that assured the definite form of subacute acidosis. In four days SARA period, M. elsdenii (1010 cfu ml-1) was inoculated during the first two days. During the SARA period, it was observed the decrease of feed intake with M. elsdenii inoculation. Inoculation of M. elsdenii was caused to differentiation of rumen pH (P < 0,0001), while it was found the pH level approximately 5,55 in animals applied the inoculation, it was 5,63 pH in other animals. It was observed that total VFA with the bacterium inoculation tended to change in terms of grain feed (P < 0,07). It increased with the effect of total VFA inoculation in barley based diet, but it was more stabilized in wheat based diet. Bacterium inoculation increased the ratio of propionic acid (18,33%-21,38%) but it caused to decrease the butyric acid, and acetic/propionic acid. During the rapid adaptation, the concentration of lactic acid in the rumen liquid increased depending upon grain level (P<0,0001). On the other hand bacterium inoculation did not have an effect on concentration of lactic acid. M. elsdenii inoculation did not affect ruminal ammonia concentration. In the group that did not apply inoculation, the level of ruminal ammonia concentration was higher than the others applied inoculation. M. elsdenii inoculation did not changed protozoa count in barley-based diet whereas it decreased in wheat-based diet. In the period of SARA, it was observed that the level of blood glucose, lactate and hematocrit increased greatly after inoculation (P < 0,0001). When it is generally evaluated, it is seen that M. elsdenii inoculation has not a positive impact on rumen parameters. Therefore, to reveal the full impact of the inoculation with different strains, feedstuffs and animal groups, further research is required.

Keywords: In vivo, Subactute ruminal acidosis, Megasphaera elsdenii, Rumen fermentation

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Authors: M. C. Muya, L. J. Erasmus

Abstract:

Twenty four new born male Holstein calves were divided into two treatments groups and used to evaluate the effects of M. elsdenii NCIMB 41125. The first groups were dosed with 50 ml containing 108 CFU/mL of M. elsdenii NCIMB 41125 (Me) and the control calves were not dosed. Within each of the two treatments groups, calves were divided into three treatment groups (Not dosed: 7 d, 14 d and 21 d vs dosed Me 7 d, Me14 and Me21 d (treatments), each groups contained 4 calves within which two calves were euthanized at 24 h and two calves at 72 h. Calves entered the trial until euthanize at whether 24 or 72 H after dosing time. After receiving colostrum for 3 consecutive days after birth, calves were fed whole milk and had free access to a commercial calf starter pellet and fresh water. Fecal grab samples were taken from each calf in duplicate +24 h or +72 h relative to dosing. Immediately after euthanizing, the digestive tract was harvested, and duplicate rumen and colon digesta samples collected for VFA’s determination and DNA extraction for bacteria count using 16s RNA PCR probe technique. Independent two t-test was performed to compare mean volatile fatty acids. Mixed-effects linear regressions were performed to establish relationships between: 1) M. elsdenii and Me, and between VFA’s and Me using SAS (2009). M. elsdenii NCIMB 41125 was detected in the faeces, colon and rumen of dosed calves at both +24H and +72H and ranged from 1.6 x 106 to 4.9 x 109 cfu/ml, indicating its potential to colonize in the digestive tract of calves. There was a strong positive relationship (R²=0.96; P < 0.0001) between M. elsdenii NCIMB 41125 and M. elsdenii population (cfu/ml) in the rumen, suggesting that the increase in M. elsdenii was due to increased M. elsdenii NCIMB 41125. An increase in butyrate was observed from +24 h to +72 h when calves were dosed on both d 7 and 14. Results showed that Me presented a positive relationship with butyrate (P < 0.001, R² = 0.43) and a concomitant negative relationship with acetate (P = 0.017, R² = -0.33). These results suggest that dosing pre-weaned dairy calves with M. elsdenii NCIMB 41125 has the potential to alter ruminal VFA production through increasing proportions of butyrate at the expense of propionate.

Keywords: calves, megasphaera elsdenii, rumen fermentation, bacteria

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Authors: S. Alqarni, J. C. McCann, A. Palladino, J. J. Loor

Abstract:

Seven fistulated Holstein cows were used from 3 weeks prepartum to 4 weeks postpartum to determine the relative abundance of 7 different species of ruminal microorganisms. The prepartum diet was based on corn silage. In the postpartum, diet included ground corn, grain by-products, and alfalfa haylage. Ruminal digesta were collected at five times: -14, -7, 10, 20, and 28 days around parturition. Total DNA from ruminal digesta was isolated and real-time quantitative PCR was used to determine the relative abundance of bacterial species. Eubacterium ruminantium and Selenomonas ruminantium were not affected by time (P>0.05). Megasphaera elsdenii and Prevotella bryantii increased significantly postpartum (P<0.001). Conversely, Butyrivibrio proteoclasticus decreased gradually from -14 through 28 days (P<0.001). Fibrobacter succinogenes was affected by time being lowest at day 10 (P=0.02) while Anaerovibrio lipolytica recorded the lowest abundance at -7 d followed by an increase by 20 days postpartum (P<0.001). Overall, these results indicate that changes in diet after parturition affect the abundance of ruminal bacteria, particularly M. elsdenii (a lactate-utilizing bacteria) and P. bryantii (a starch-degrading bacteria) which increased markedly after parturition likely as a consequence of a higher concentrate intake.

Keywords: rumen bacteria, transition cows, rumen metabolism, peripartal period

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Authors: Sheng-Yu Lee

Abstract:

Aim: Opioid use disorder is often characterized by repetitive drug-seeking and drug-taking behaviors with severe public health consequences. Animal model showed that opioid-induced perturbations in the gut microbiota causally relate to neuroinflammation, deficits in reward responding, and opioid tolerance, possibly due to changes in gut microbiota. Therefore, we propose that the dysbiosis of gut microbiota can be associated with pathogenesis of opioid dependence. In this current study, we explored the differences in gut microbiota between patients and normal controls and in patients before and after initiation of methadone treatment program for 12 weeks. Methods: Patients with opioid use disorder between 20 and 65 years were recruited from the methadone maintenance outpatient clinic in 2 medical centers in the Southern Taiwan. Healthy controls without any family history of major psychiatric disorders (schizophrenia, bipolar disorder and major depressive disorder) were recruited from the community. After initial screening, 15 patients with opioid use disorder joined the study for initial evaluation (Week 0), 12 of them completed the 12-week follow-up while receiving methadone treatment and ceased heroin use (Week 12). Fecal samples were collected from the patients at baseline and the end of 12th week. A one-time fecal sample was collected from the healthy controls. The microbiota of fecal samples were investigated using 16S rRNA V3V4 amplicon sequencing, followed by bioinformatics and statistical analyses. Results: We found no significant differences in species diversity in opioid dependent patients between Week 0 and Week 12, nor compared between patients at both points and controls. For beta diversity, using principal component analysis, we found no significant differences between patients at Week 0 and Week 12, however, both patient groups showed significant differences compared to control (P=0.011). Furthermore, the linear discriminant analysis effect size (LEfSe) analysis was used to identify differentially enriched bacteria between opioid use patients and healthy controls. Compared to controls, the relative abundance of Lactobacillaceae Lactobacillus (L. Lactobacillus), Megasphaera Megasphaerahexanoica (M. Megasphaerahexanoica) and Caecibacter Caecibactermassiliensis (C Caecibactermassiliensis) were increased in patients at Week 0, while Coriobacteriales Atopobiaceae (C. Atopobiaceae), Acidaminococcus Acidaminococcusintestini (A. Acidaminococcusintestini) and Tractidigestivibacter Tractidigestivibacterscatoligenes (T. Tractidigestivibacterscatoligenes) were increased in patients at Week 12. Conclusion: In conclusion, we suggest that the gut microbiome community maybe linked to opioid use disorder, such differences may not be altered even after 12-week of cessation of opioid use.

Keywords: opioid use disorder, gut microbiota, methadone treatment, follow up study

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Authors: Diego Santibañez Oyarce, Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

Abstract:

The advent of high-throughput sequencing technologies has revolutionized genomics, generating vast amounts of genetic data that challenge traditional bioinformatics methods. Machine learning addresses these challenges by leveraging computational power to identify patterns and extract information from large datasets. However, biological sequence data, being symbolic and non-numeric, must be converted into numerical formats for machine learning algorithms to process effectively. So far, some encoding methods, such as one-hot encoding or k-mers, have been explored. This work proposes additional approaches for encoding DNA sequences in order to compare them with existing techniques and determine if they can provide improvements or if current methods offer superior results. Data from the 16S rRNA gene, a universal marker, was used to analyze eight bacterial groups that are significant in the pulmonary environment and have clinical implications. The bacterial genes included in this analysis are Prevotella, Abiotrophia, Acidovorax, Streptococcus, Neisseria, Veillonella, Mycobacterium, and Megasphaera. These data were downloaded from the NCBI database in Genbank file format, followed by a syntactic analysis to selectively extract relevant information from each file. For data encoding, a sequence normalization process was carried out as the first step. From approximately 22,000 initial data points, a subset was generated for testing purposes. Specifically, 55 sequences from each bacterial group met the length criteria, resulting in an initial sample of approximately 440 sequences. The sequences were encoded using different methods, including one-hot encoding, k-mers, Fourier transform, and Wavelet transform. Various machine learning algorithms, such as support vector machines, random forests, and neural networks, were trained to evaluate these encoding methods. The performance of these models was assessed using multiple metrics, including the confusion matrix, ROC curve, and F1 Score, providing a comprehensive evaluation of their classification capabilities. The results show that accuracies between encoding methods vary by up to approximately 15%, with the Fourier transform obtaining the best results for the evaluated machine learning algorithms. These findings, supported by the detailed analysis using the confusion matrix, ROC curve, and F1 Score, provide valuable insights into the effectiveness of different encoding methods and machine learning algorithms for genomic data analysis, potentially improving the accuracy and efficiency of bacterial classification and related genomic studies.

Keywords: DNA encoding, machine learning, Fourier transform, Fourier transformation

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