CRITICAL STEPS FOR HUMAN GUT EXFOLIOME RNA PROFILING ANALYSIS USING NON-INVASIVE STOOL SAMPLES

Introduction: Dietary exposure and drug treatments influence gut cellular pathways and hence growth and potentially even the gut-brain-microbiome axis. Since eukaryotic mRNA presents poly A sequence that distinguishes them from the prokaryotes mRNA, we could analyze the gene expression of human gut cells using exfoliated gut cells available in stool samples. However, the impact of the critical steps of these non-invasive methods must be analyzed. Methods: We tested prokaryote contamination in all the steps of different procedures to analyze human exfoliome by microarrays and the influence of the fecal sampling collection process. Results & Conclusion: The least bacterial contamination was found using RNA amplified with oligo dT from GeneChip 3´ IVT Pico Reagent kit or using RNA purified by both Oligotex® + oligodT. RNA later® collection of feces affects the microarray results compared to directly frozen fecal samples, although both methods produce similar cDNA quality. This technique is a potential non-invasive diagnostic tool that can be applied to larger studies to quantify intestinal gene expression in humans with non-invasive samples, but samples should always be collected and analyzed under the same procedure.

G2S: A New Deep Learning Tool for Predicting Stool Microbiome Structure From Oral Microbiome Data

Deep learning methodologies have revolutionized prediction in many fields and show the potential to do the same in microbial metagenomics. However, deep learning is still unexplored in the field of microbiology, with only a few software designed to work with microbiome data. Within the meta-community theory, we foresee new perspectives for the development and application of deep learning algorithms in the field of the human microbiome. In this context, we developed G2S, a bioinformatic tool for taxonomic prediction of the human fecal microbiome directly from the oral microbiome data of the same individual. The tool uses a deep convolutional neural network trained on paired oral and fecal samples from populations across the globe, which allows inferring the stool microbiome at the family level more accurately than other available approaches. The tool can be used in retrospective studies, where fecal sampling was not performed, and especially in the field of paleomicrobiology, as a unique opportunity to recover data related to ancient gut microbiome configurations. G2S was validated on already characterized oral and fecal sample pairs, and then applied to ancient microbiome data from dental calculi, to derive putative intestinal components in medieval subjects.

Herd-Level and Individual Differences in Fecal Lactobacilli Dynamics of Growing Pigs

We studied the fecal lactobacilli count and species diversity of growing pigs along with immune parameters associated with intestinal lactobacilli. Thirty pigs categorized as small (S, n = 12) or large (L, n = 18) at birth were followed from birth to slaughter in two commercial herds, H1 and H2. Herds differed in terms of their general management. We determined sow colostrum quality, colostrum intake, piglet serum immunoglobulins, and pig growth. We took individual fecal samples from pigs in the weaning and finishing units. We studied lactobacilli count and identified their diversity with 16S PCR. Total lactobacilli count increased in H1 and decreased in H2 between samplings. Lactobacilli species diversity was higher in H1 in both fecal sampling points, whereas diversity decreased over time in both herds. We identified altogether seven lactobacilli species with a maximum of five (one to five) species in one herd. However, a relatively large proportion of lactobacilli remained unidentified with the used sequencing technique. Small pigs had higher lactobacilli counts in both herds but the difference was significant only in H2 (p = 0.01). Colostrum quality was numerically better in H1 than in H2, where colostrum intake tended to be associated with total lactobacilli count (p = 0.05).

ACUTE FLACCID PARALYSIS REGISTERED FOR THE PERIOD 2012-2020, IN STARA ZAGORA REGION

Acute Flaccid Paralysis (AFP) is a clinical syndrome. There are many infectious and non-infectious causes of AFP. Poliomyelitis caused by the wild polio virus (the natural circulating strain of polio) is one of the causes of AFP. As a part of the worldwide campaign to eradicate polio, all countries do surveillance for polio by looking for clinical cases of AFP. The purpose of the study is to describe the cases of Acute Flaccid Paralysis, without Paresis Nervi facialis, in the Stara Zagora region for the period 2012-2020. Methods: A retrospective, descriptive analysis was performed on the parameters: diagnosis, seasonality, sex, residence, age, clinical data, and comorbidities, results of follow-up examinations, polio vaccine administration, and timeliness of studies. Results: There were 9 cases of AFP, without Paresis nervi facialis, registered and reported for the Stara Zagora region, for the period 2012 – 2020. The children in 3 years old were more affected- 4 patients. About the coverage with polio vaccine: 7 of the patients had 4 doses, 1- had 6 doses, 1 child had 5 doses, and + 1 zero dose. 100% of children are covered, according to their age, with IPV. Follow-up in 6 of the patients is without residual paresis and complications. Residual paresis was found in 1 of the cases. In 2 patients no control examination was performed due to migration. Conclusion: The correct epidemiological diagnosis is the way to timely and corrects clinical diagnosis. AFP Surveillance is of particular importance as well as fecal sampling up to 48 hours, from the onset of paralysis, and follow-up after the 60th day in children <15 years.

PSI-13 Evaluation of acid insoluble ash as a digestibility marker in feedlot diets containing corn-milling byproducts

We evaluated the accuracy of acid insoluble ash (AIA) as a digestibility marker in feedlot cattle diets containing corn-milling byproducts and examined the effect of fecal sampling frequency on digestibility estimates. Steers (n = 6) were used in a crossover split-plot design where 3 steers per period were assigned to 1 of 2 diets [receiving (REC) containing 19% roughage and 38% Sweet Bran™ or finishing (FIN) containing 8% roughage and 20% Sweet Bran] typical of those used in the beef feedlot industry. Steers were limit fed at 2.0% of initial body weight. After a 21-d adaptation period, steers were housed in metabolism crates for 7 d of total collection (TC) of feed and feces and simultaneous collection of fresh manure grab samples twice daily to calculate nutrient digestibility from AIA. Grab samples were then averaged to represent 7 (7dAIA), 5 (5dAIA), 3 (3dAIA) and 1 (1dAIA) days of the collection period. No interactions (P ≥ 0.13) were observed for DM, OM or NDF digestibility between diet and method of estimating digestibility. Digestibility of DM and OM were greater (P &lt; 0.01) for FIN than for REC, and NDF digestibility was less (P &lt; 0.01) for FIN than REC. Both DM and OM digestibility were greater (P &lt; 0.01) for AIA estimates than for TC; however, estimates with 7dAIA were less than 3dAIA and 1dAIA, but not different from 5dAIA. Similarly, NDF digestibility was greater (P &lt; 0.01) for all AIA estimates than for TC, but 7dAIA and 5dAIA were less than 1dAIA and not different from 3dAIA. A treatment × method interaction (P = 0.02) occurred for ADF digestibility. These data suggest that AIA over-estimated digestibility of beef feedlot diets containing corn-milling byproducts, but accuracy improved with greater grab sampling frequency.

203 Efficiency of SID lysine utilization and maximum SID lysine retention for gilts in early, mid and late gestation

Efficiency of amino acid (AA) use is presumed constant across gestation but may not reflect changes in metabolic demand during gestation nor consider changes in efficiency depending on level of AA intake. Two experiments were conducted to determine efficiency of SID Lys utilization in gilts during early (d 48-52), mid (d 75-79) and late gestation (d 103-107). Each experiment provided 4 isocaloric (3,335 kcal ME/kg) and isoproteic (11.75 % CP) diets containing 4 SID Lys levels (Table 1). Diets were randomly assigned to 45 gilts (PIC 1050, 158.0 ± 8.0 kg at d 39.4 ± 1 of gestation) in Exp. 1 and 27 gilts (PIC 1050, 169.0 ± 7.5 kg at d 41 ± 1 of gestation) in Exp. 2. Dietary indispensable AA were set to meet or exceed 100% of AA:Lys ratios in both experiments. The SID Lys retention was estimated from whole body nitrogen (N) retention balance studies in each period (7 d diet adaptation, 5 d total urine collection and grab fecal sampling) according to the NRC (2012) equations. The relationship between SID Lys intake and SID Lys retention was determined by nonlinear regression models using the CurveExpert Professional software. According to the Hoerl regression model: E(y)=exp(β 0+β 1X)[Xβ2] best-fitting line, maximum efficiency of SID Lys utilization (i.e. g SID Lys retention/g SID Lys intake) was 65%, 57%, and 53% in early, mid and late gestation and occurred at 6.6, 8 and 12 g of SID Lys intake/d, respectively. Maximum SID Lys retention occurred at 8.1 and 9.8 g of SID Lys intake/d for early and mid-gestation. The SID Lys retention did not reach a maximum value in late gestation. These results suggest that efficiency of SID Lys utilization is not constant across gestation and that maximal efficiency occurs at intake below current recommendations.

Bacterial “Virulence” Traits and Host Demographics Predict Escherichia coli Colonization Behaviors Within Households

Background Although intestinal colonization precedes most extraintestinal Escherichia coli infections, colonization-promoting factors are incompletely understood. We compared within-household E. coli colonization patterns with host and bacterial traits. Methods Twenty-two veterans with a clinical E. coli isolate and their 46 human and animal household members underwent longitudinal fecal sampling. Distinct E. coli strains were characterized for phylogenetic background, virulence genes, antibiotic resistance, and colonization behaviors. Host and bacterial traits were assessed statistically as predictors of colonization behaviors. Results Among the 139 unique-by-household fecal E. coli strains, univariable predictors of colonization behavior included (i) host demographics, (ii) matching the index clinical isolate, and (iii) bacterial characteristics (2 phylogroups, 5 clonal lineages, 18 virulence genes, and molecular extraintestinal pathogenic E. coli status). Multivariable predictors of colonization behavior included veteran host, spouse host, matching the index clinical isolate, phylogroup F, ST73, hlyD (alpha hemolysin), hlyF (variant hemolysin), H7 fliC (flagellar variant), vat (vacuolating toxin), and iha (adhesin-siderophore). Conclusions Host demographics, multiple bacterial “virulence” traits, and matching the index clinical isolate predicted E. coli fecal colonization behaviors. Thus, certain bacterial characteristics may promote both colonization and pathogenicity. Future interventions directed toward such traits might prevent E. coli infections both directly and by disrupting antecedent colonization.