Risk factors of Shiga toxin-producing Escherichia coli in livestock raised on diversified small-scale farms in California

The increasing number of diversified small-scale farms (DSSF) that raise outdoor-based livestock in the USA reflects growing consumer demand for sustainably produced food. Diversified farms are small scale and raise a combination of multiple livestock species and numerous produce varieties. This 2015–2016 cross-sectional study aimed to describe the unique characteristics of DSSF in California, estimate the prevalence of Shiga toxin-producing Escherichia coli (STEC) in livestock and evaluate the association between risk factors and the presence of STEC in livestock, using generalised linear mixed models. STEC prevalence was 13.62% (76/558). Significant variables in the mixed-effect logistic regression model included daily maximum temperature (OR 0.95; CI95% 0.91–0.98), livestock sample source (cattle (OR 4.61; CI95% 1.64–12.96) and sheep (OR 5.29; CI95% 1.80–15.51)), multiple species sharing the same barn (OR 6.23; CI95% 1.84–21.15) and livestock having contact with wild areas (OR 3.63; CI95% 1.37–9.62). Identification of STEC serogroups of public health concern (e.g. O157:H7, O26, O103) in this study indicated the need for mitigation strategies to ensure food safety by evaluating risk factors and management practices that contribute to the spread and prevalence of foodborne pathogens in a pre-harvest environment on DSSF.

Introduction
The increasing number of diversified small-scale farms (DSSF) that raise outdoor-based (i.e. grass-fed, pasture-raised) livestock in the USA reflects growing consumer demand for sustainably produced local foods, including animal products such as meat and eggs [1][2][3][4][5]. California is the top producer in the USA of agricultural products, as well as organic food sales, which includes products from DSSF [3,6]. However, there is a lack of science-based information characterizing the risk factors associated with the prevalence of foodborne pathogens, such as Shiga toxin-producing Escherichia coli (STEC), in livestock raised on DSSF.
Diversified farms are most often small scale and can raise a combination of multiple livestock species and numerous produce varieties, with the intent of selling specialised (e.g. organically grown) animal products directly to consumers [5]. Some DSSF integrate livestock and crop production (i.e. integrated crop-livestock farms) by using their animals to graze crop residues or cover crops before planting a field with fresh produce [1,7]. Grazing improves soil fertility and provides farm owners with another source of revenue through fibre or meat products [1,8]. Additionally, many countries promote diversified farming systems as an alternative to intensive agriculture, displaying a willingness to transition to agroecology, in line with the UN Sustainable Development Goal number 12: 'Ensure sustainable consumption and production patterns' [9]. This goal may lead to an increase in the number of DSSF and products globally.
Many consumers perceive produce and meat raised on small-scale farms as more natural or safer (e.g. pesticide-free) than food grown on large-scale conventional farms or meat animals raised in confinement systems [2,10]. However, livestock are asymptomatic reservoirs for foodborne pathogens, and without adequate mitigation strategies, these pathogenic microorganisms may enter the food supply [11][12][13][14][15][16]. Livestock are intermittent shedders of enteric pathogens and shedding may increase under certain conditions, such as during specific seasons or periods of stress (e.g. transportation), or due to certain management practices (e.g. stocking rate) [7,[17][18][19]. STEC can survive in the soil for extended periods of time and spread to humans directly through contact with livestock or indirectly via contaminated food (e.g. fresh produce, meat) or water [15,17,[20][21][22]. STEC remains one of the main enteric pathogens associated with foodborne outbreaks in the USA [20,21]. The top seven STEC O-serogroups that cause the most severe illness in humans include O157:H7, O26, O103, O111, O121, O145 and O45 [23]. Beef products and vegetables are common sources of infection [20,21]. However, outbreaks linked to organic animal products have been reported in small numbers [10]. Fresh produce consumed raw are considered high-risk crops, and when contaminated by livestock or wildlife faeces containing STEC, they can become a vehicle for these pathogens to enter the food supply [21,22,24]. For instance, cattle grazing uphill from a produce field was likely the causative factor for the 2019 E. coli O157: H7 romaine lettuce outbreak [25].
The aim of this study was to (a) describe the unique characteristics of DSSF, (b) estimate the prevalence of STEC in livestock and (c) evaluate the association between risk factors and the presence of STEC in livestock raised on DSSF located in California.

Study design and farm enrolment
During 2015-2016, we conducted a repeated cross-sectional study in California to test for Campylobacter spp., Salmonella spp., non-O157 STEC and E. coli O157:H7 in faecal samples collected from livestock raised on DSSF [11]. Enrolment criteria for participating DSSF was based on the United States Department of Agriculture Economic Research Service (USDA-ERS) definition of small-scale farms [4], but adjusted to fit the unique growing conditions of California operations: (1) small-to medium-scale farm (i.e. poultry producers raising <1000 birds annually or livestock producers with an annual gross-sales <$500 000 and that raised a maximum of 500 goats/sheep and/or 100 cattle and/or 100 pigs); (2) raised a diversity of crops and/or multiple species of livestock and poultry; (3) marketed farm products directly to consumers (e.g. farmers markets, community-supported agriculture programs); and (4) willingness to participate. We enrolled farms from four regions of northern and central California that corresponded to the diversity of bioregions and that enabled an acceptable time between sample collection and laboratory processing [26]. Farm recruitment occurred through personal communications, announcements at agriculture outreach seminars and contact at farmers' markets or agricultural fairs.

Sample collection
Farms were visited twice between May 2015 and June 2016, once during each of the following periods: summer/autumn or winter/ spring, which reflect California's growing seasons and the seasonality of STEC shedding [11]. Livestock species sampled in this study included dairy and beef cattle, dairy and meat goats, pigs and sheep. Sample sizes were calculated using Epitools [27] based on the total number of animals on each farm, with an assumed STEC prevalence of 5% and 10% precision error, stratified by each livestock species. Individual fresh faecal samples were collected from the ground of barns or pastures. Samplers wore gloves and placed approximately 50-100 g of faeces into each sterile whirl-pak bag (Nasco, Modesto, CA, USA). Bags were immediately placed into plastic coolers containing ice packs, transported to the laboratory at the end of the sampling day and most samples were processed within 24 h.
Escherichia coli O157:H7 and non-O157 STEC culture and PCR STEC was isolated from faecal samples as described previously with modifications [28]. In brief, 10 g of faecal material was placed in 90 ml of Tryptic Soy Broth (TSB; Becton, Dickinson and Company, Franklin Lakes, NJ, USA). Samples were then incubated for 2 h at 25°C with 100 rpm agitation, followed by 8 h at 42°C with 100 rpm agitation and held overnight at 6°C, using a Multitron programmable shaking incubator (Eppendorf, Hauppauge, NY, USA). For detecting E. coli O157:H7, immunomagnetic separation using Dynal anti-E. coli O157 beads (Invitrogen/Dynal, Carlsbad, CV, USA) was performed on TSB enrichment broths with the automated Dynal Bead Retriever (Invitrogen) per the manufacturer's instructions. After incubation and washing, 50 μl of the resuspended beads were plated onto Rainbow agar O157 (Biolog, Hayward, CA, USA) with novobiocin (20 mg/l) and tellurite (0.8 mg/l) (MP Biomedicals, Solon, OH, USA). Fifty microlitres of the resuspended beads were also plated onto MacConkey II Agar using sorbitol supplemented with potassium tellurite (2.5 mg/l) and Cefixime (0.05 mg/l) (CT-SMAC); plates were streaked for isolation and incubated for 24 h at 37°C . Suspect E. coli O157:H7 isolates were confirmed using traditional PCR for the rfbE gene [29].
To detect non-O157 STEC, 1 ml of TSB enrichment broth was incubated in mEHEC selective media (Biocontrol, Bellevue, WA, USA) for 12 h at 42°C followed by plating and incubating on Chromagar STEC (DRG International Inc., Springfield, NJ, USA) at 37°C overnight. Up to eight presumptive STEC-positive colonies, demonstrating mauve colour and glowing under UV light, were confirmed for the presence of stx1 and/or stx2 genes by real-time PCR [28]. Confirmed STEC isolates were then characterised for virulence genes (stx1, stx2, eaeA, hlyA and ehxA) using conventional PCR [29]. After PCR testing, one isolate with a unique virulence gene profile from each positive sample was submitted to the Pennsylvania State University E. coli Reference Center to confirm O-serogroups [28,30].

Farmer questionnaire and environmental factors
A 41-question questionnaire, consisting of mainly closed-ended questions, was given to farm owners mostly in person. The questionnaire included sections regarding farm demographics, animal health and farm management practices. Variables analysed for model building included risk factors from the farmer questionnaire, sample day factors (e.g. temperature) and farm-level characteristics (e.g. whether a farm raised multiple types of livestock). Weather data from the nearest California Irrigation Management Information System weather station (http://www. cimis.water.ca.gov) within a similar microclimate provided environmental factors to be included in model building (e.g. average relative humidity, daily maximum temperature) [31]. Also, USDA plant hardiness zones, which are based on the average annual minimum winter temperature, were included as a proxy for the many microclimates in California [32]. Only three zones were necessary to categorise our participating farms: zone 7b (−15 to −12.2°C), 9a (−6.7 to −3.9°C) and 9b (−3.9 to −1.1°C).

Statistical data analysis and model building
Descriptive statistics (e.g. mean, range) were calculated for all data. STEC prevalence was estimated for the overall study and per livestock species. Generalised linear mixed models were used to assess the association between STEC presence in faecal samples and risk factors. The binary outcome of interest was whether each faecal sample was STEC-positive or negative. Univariate analysis assessed the distribution of variables. During bivariate analysis, variables with low variability, small cell sizes 2 Laura Patterson et al.
(<5) or large standard errors were either modified, collapsed if appropriate or discarded from model building. Correlation of numeric variables was measured with Spearman's rank correlation coefficient; those variables that were correlated 0.80 or more were highlighted during the model-building phase to evaluate for multicollinearity. To identify possible confounders, each variable was evaluated using a directed acyclic graph. The glmer function was used from the lme4 package in R to build models, with farm added as a random effect to account for farm-level clustering effects [33]. Manual two-way stepwise variable selection was employed for model building. Variance inflation factors (VIF) identified possible multicollinearity and variables with a VIF over five were assessed for removal. Top models were compared and a final model was chosen based on the lowest Akaike Information Criterion (AIC) and smallest deviance, relative to the other models. Intraclass correlation (ICC) was calculated, which accounts for the proportion of the model variance explained by clustering and indicated whether a random effect was necessary. Diagnostic plots from the DHARMa package in R were used to assess the final model and included fitted vs. binned residuals, a Q-Q plot and the Kolmogorov-Smirnov test statistic [34]. Odds ratios and 95% confidence intervals (CI 95% ) were calculated for variables in the final model. All data analyses were performed using R Statistical Software version 1.4.1036 © [35].

Study participants and farm characteristics
Sixteen farms participated in our study and were located in four regions of California: Central Valley (n = 10), Central Coast (n = 3), Shasta Cascade (n = 2) and the North Coast (n = 1). Four of the 16 farms (25.00%) raised livestock only, nine farms (56.25%) raised a combination of produce and livestock and used the animals to graze crop fields and three produce farms (18.75%) rented sheep seasonally to graze cover crops and did not raise any other animals. The rented sheep were present during the sampling day. Four farms were not organic (25.00%), 56.25% (9/16) were certified organic and 18.75% (3/16) used organic practices but were not certified by a third party.

STEC prevalence
A total of 558 faecal samples were collected from 16  Escherichia coli O157:H7 was found only in cattle, with a prevalence of 5.31% (6/113). Of the 76 positive STEC faecal samples, 72 were non-O157 STEC, six were E. coli O157:H7 and one was untypeable. Three samples from beef cattle were positive for both E. coli O157:H7 and a non-O157 STEC. All six O157:H7 positive isolates were from one farm and contained virulence factors stx2, ehxA, hlyA and eaeA, but not stx1. O26 was the most prevalent O-serogroup, accounting for 27.85% (22/79) of positive isolates. Stx2 was identified in 16.46% (13/79) of isolates, stx1 was identified in 69.62% (55/79) and 13.92% (11/79) contained both stx1 and stx2. In Table 1, isolates that shared the same virulence genes were from the same farm except the following, which originated from two farms each: O22 in dairy cattle, O103 in swine and O26 in swine. Additionally, O26 in goats was collected from three farms.

Risk factor analysis
Of the 16 participating farms, two were excluded from model building, due to incomplete questionnaires, leaving a total of 502 samples for model building. The mean, median and range of selected continuous variables assessed for inclusion during model building are shown in Table 2. Farms in this study ranged from two to 500 acres and had been farming 2-30 years. Stocking density rate was calculated by dividing the total number of livestock species, excluding poultry, by the total number of farm acres. Selected categorical variables, stratified by whether they were STEC-positive or negative, are presented in Table 3, and P-values were reported for χ 2 test or Fisher's exact test if cell sizes were less than five. For instance, 28.99% (20/69) of positive samples came from farms that mixed livestock species within a barn, whilst only 15.70% (68/433) of negative samples came from farms with shared barns (P-value 0.012). Moreover, 72.46% (50/69) of positive samples were from farms that allowed livestock to have contact with bordering wild areas (e.g. streams, forest) (P-value 0.026).

Final multivariable model results
The final mixed-effect multivariable logistic regression model is shown in Table 4 and had an AIC of 369.5, and a deviance of 351.5. The highest VIF for any variable in the model was only 2.02, which was below our threshold of five.
Significant risk factors associated with the presence of STEC in the final model included: for every increase in the daily maximum temperature°C, the odds of a STEC-positive sample decreased (OR 0.95; CI 95% 0.91-0.98). The odds of a positive STEC sample were 4.61 times higher for cattle (OR 4.61; CI 95% 1.64-12.96) compared to swine and more than five times greater in sheep (OR 5.29; CI 95% 1.80-15.51). The odds of STEC increased by 6.23 times for those farms that housed multiple livestock species within the same barn vs. those farms that kept livestock separately (OR 6.23; CI 95% 1.84-21.15). The odds of a positive STEC sample were more than three times greater (OR 3.63; CI 95% 1.37-9.62) for a farm that allowed its livestock contact with wild areas. The effect of the number of years a farm had been in operation was not significant (P-value 0.076; CI 95% 0.13-1.11), but was included in the model as a possible confounder.
Diagnostically, the simulated residuals vs. predicted values did not show any significant problems: the Q-Q plot from DHARMa simulated scaled residuals was linear with no major deviations, and the Kolmogorov-Smirnov test indicated no deviation from uniform distribution of the scaled residuals [34]. The adjusted ICC was 0.08 for the final model, which signified the proportion of the variance that was explained by farm clustering and indicated the need for a farm random effect. The isSingular test function in the lme4 package was false, which indicated no singularities existed in the final model.

Discussion
This is one of the first studies to describe the unique characteristics of DSSF in California while ascertaining significant associations between risk factors and the prevalence of STEC. This study detected STEC on more than half of the enrolled farms and typed O-serogroups including ones that cause serve illness in humans, including O157:H7, O26, O103 and O111 [23]. Significant risk factors for the presence of STEC included a negative association with the daily maximum temperature, whether multiple livestock species shared a barn, the host species of the collected faecal sample, and whether livestock had contact with wild areas.
Overall STEC prevalence measured for the 16 farms in this study was 13.62%. Six of the 16 farms had 0% STEC prevalence; however, due to the intermittent shedding of STEC which is affected by many factors, this result does not necessarily indicate that their livestock were not STEC reservoirs. Although STEC prevalence in livestock raised on large farms has been measured frequently in past studies, evaluation of STEC prevalence and associated risk factors estimated on DSSF is less common [28,[36][37][38]. However, a study conducted by USDA-APHIS collected faecal samples from dairy cows in 21 states and stratified E. coli O157:H7 prevalence between large dairies (i.e. 500 or more cows) and small dairies (i.e. 100 cows or less) and reported that small ranches had 29.4% E. coli O157:H7 and large dairies had 53.9% prevalence [39]. Although this report indicated that small farms have less E. coli O157:H7 than large farms, the 29.4% prevalence they detected on small dairies is still larger than the 18.18% we identified in dairy cattle. Risk factors for the transmission of foodborne pathogens on large farms may be different, especially if they only raise one crop or livestock type, instead of a diversity of species as seen on DSSF.
One of our previous studies measured a 4.17% STEC prevalence in sheep raised on a mixed crop-livestock organic farm in California, which was lower than the 13.4% prevalence in sheep identified in this study [40]. A 2005 California study that screened livestock at the California state fair, which usually hosts livestock raised on small-scale operations, observed a 3% prevalence of E. coli O157:H7 in pigs, but did not find O157:H7 in any other livestock samples including dairy cows, whereas our study identified O157:H7 in cattle but not pigs [41]. A 2002 study that also collected faecal samples at fairs in three states, identified an E. coli O157:H7 prevalence of 11.4% in cattle, 1.2% in swine and 3.6% in sheep and goats, whereas we measured a 5.31% (6/113) E. coli O157:H7 in all cattle (i.e. combined dairy and beef cattle samples) [42]. Animals shown at fairs are subjected to stress and comingling and may experience increased STEC shedding. Differing STEC prevalence in these aforementioned studies may reflect different management practices or other climate or animallevel factors, as reflected in our results. Additionally, since ruminants are the main reservoirs for STEC, our results indicating that STEC prevalence in swine is lower comparatively than the sampled ruminants are in agreement with previous research. However, pigs are still a livestock species of public health concern, as they harbour E. coli O157:H7 as indicated by many studies [36,42,43]. Our model results also showed that cattle (OR 4.61) and sheep (OR 5.29) are significant factors in STEC presence. However, differences in location, laboratory methods and sampling methods make comparison between studies challenging.
More than half of the typed isolates in this study belonged to an O-group that is listed on the CDC list of the top 7 STEC of concern for public health, including six O157:H7, 22 O26, nine O103 and one O111 [23]. Stx2 was identified in 16.46% (13/79) of isolates, and 13.92% (11/79) contained both stx1 and stx2. Subtyping of stx1 and stx2 was not conducted in this study, therefore direct implications to public health due to virulence subtypes cannot be inferred. The eaeA gene, which allows STEC bacteria to attach to human host cells, was detected in 55.69% (44/79) of positive STEC samples, contrary to a study conducted by Dewsbury et al. (2015), which rarely reported eaeA in their non-O157 isolates from cattle faecal samples [44][45][46][47]. The ehxA gene, which is reported in severe human cases of STEC infection, was detected in 88.61% of the positive isolates (70/79) [46]. Compared to a study conducted by Djordjevic et al. (2004) in adult sheep and lambs, they detected stx1, stx2 and ehxA in 78.2% of their positive isolates, vs. our study which only identified those three genes in 1.27% (1/79) of positive isolates. However, they reported 0.8% of their isolates had just stx2 and ehxA genes, whereas, in this current study, 11.39% (9/79) of the positive isolates contained these two virulence genes [46]. The pathogenic STEC O-serogroups, genes and virulence factors identified in this study highlight the need for continued studies on DSSF, as well as the implementation of outreach efforts to stakeholders regarding pre-harvest food safety risks and the development of on-farm mitigation strategies.
Significant risk factors identified by the final mixed-effect model included daily maximum temperature°C, which ranged from 11.7 to 39.80°C during the study period. An experiment that measured the decline of E. coli O157:H7 in inoculated manure at four temperatures, 7, 16, 23 and 33°C, reported that E. coli O157:H7 declined significantly faster in manure at 23 and 33°C than at 7 and 16°C, for both oscillating and constant temperatures [48]. These study results confirm our model findings, which suggested that as the daily maximum temperature increased within our temperature range, the odds of finding  a USDA zone information can be found online: https://planthardiness.ars.usda.gov. Three USDA zones were used to categorise participating farms: zone 7b (−15 to −12.2°C), 9a (−6.7 to −3.9°C) and 9b (−3.9 to −1.1°C). b Organic: some farms use organic practices, but were 'not certified' organic by a third party. c Two of the 16 farm surveys were not completed. *Indicates statistical significance with a P-value < 0.05. STEC in a faecal sample were less likely. A study by Franklin et al. (2013) also identified daily maximum temperature as a significant risk factor, when conducting a study of STEC in wild ungulates in Colorado [49]. They detected a positive association between temperature and STEC presence in faecal samples, whereas our study identified a negative association with the daily maximum temperature [49]. However, the range of daily maximum temperatures in their analysis was narrower than our recorded temperatures, which may account for this difference. Although many studies indicated that higher STEC shedding occurs during summer months, California microclimates differ from each other and from the majority of seasons in other states [32]. California valleys and foothills experience low humidity and temperatures above 37.78°C in the summer and autumn, which may affect STEC shedding [39]. For example, to compensate for the numerous microclimates in California in our study on Campylobacter spp., which included the same farms from this current study, we divided the California summer season into Coastal and Inland and the season was a significant risk factor in that final multilevel logistic regression model [11]. Differences in weather (i.e. humidity, temperature range) between states in the USA reveal a need to report the full range of measurements collected for studies estimating the effect of seasons on STEC shedding in livestock. For instance, a study that collected samples from conventional dairy and beef cattle in Michigan revealed that high average temperatures (more than 28.9°C) measured 1-5 days before sampling had a 2.5 times greater odds of STEC than lower temperatures, which differs from our study results that suggested that STEC survival is less likely at higher maximum temperatures [50]. These Michigan results contradicted ours; however, the highest maximum daily temperature measured in our study (i.e. 39.8°C) is not a temperature normally observed in many areas of the USA. The range of daily maximum temperatures for the Michigan study was 22.78-32.2°C, with one 36.11°C outlier. Additionally, our study included winter temperatures, while their study was only conducted in summer, and summers in Michigan include precipitation events, while little rainfall is observed during California summers. High temperatures or other weather discrepancies observed in different parts of the world may affect conclusions and interpretations of results. Stanford et al. (2017) reported the effects of severe weather events on STEC shedding in Canadian cattle [51]. Although they also observed that STEC prevalence increased when ambient temperatures were higher than 28.9°C, a separate finding indicated that the O-serogroup O26 had a significant reduction in prevalence during extreme heat in July and August [51]. Almost 28% of the O-serogroups in our study were O26, and the final model results may have been influenced by this strain. The survival of non-O157 STEC strains under different environmental conditions may account for variations in results between studies [51]. Moreover, physiological and behavioural changes in the host species during various temperature fluctuations or extreme weather events should also be considered [52]. For instance, Dawson et al. (2018) measured behavioural changes in cattle during increased temperatures, such as increased water consumption or change in grazing habits, as a possible driver of varying STEC prevalence [52]. Their simulation results indicated that higher summer temperatures may encourage more resting by cattle in crowded areas, such as under shade trees, which can lead to direct transmission of STEC. Since the aforementioned studies differ in conclusions regarding the direction of temperature on STEC shedding in livestock, this risk factor needs further investigation as perhaps there are underlying mechanisms accounting for the difference between results, including microclimates or animal-level factors [19,51].

Epidemiology and Infection
Our study also indicated that livestock sharing a barn with other animals resulted in 3.5 greater odds of a positive STEC sample. Multiple livestock housed in a barn could share pathogens by cross-contamination of food or water troughs or persistence of STEC in a barn environment that may not be subjected to regular cleaning [17,53,54]. Other studies have indicated that STEC persists for long periods of time in barns or on surfaces within a farm environment. For instance, one study swabbed multiple barn surfaces at a dairy ranch and measured 14.9-19.1% STEC in samples from feeders and 11.3-18.0% on other surfaces [55]. Another study implicated water troughs as harbouring E. coli O157:H7 and inferred that shared water troughs play a role in the persistence and maintenance of continued E. coli O157:H7 infections in cattle [56]. A British study reported that housed beef cattle shed more STEC than unsheltered and suggested that this may be due to shared water sources or feeding bins or an accumulation of pathogens in a shared environment [53].
Finally, the final multivariable model included livestock in contact with wild areas, such as forests or wetlands, as a risk factor for the presence of STEC in faecal samples. Wildlife including feral pigs, deer and birds are known reservoirs of STEC [57][58][59][60][61]. A study conducted in California identified a low prevalence of E. coli O157:H7 in rodents (0.2%); however, they did not test for non-O157 STEC in samples, which may have a higher prevalence in rodents [62]. A 2016 published study discovered the stx2 gene in over 20% of Canada geese faecal samples and 7% of nearby water samples from Lake Eric bordering Ohio, USA [58]. Another study of wild birds sampled on a California produce farm detected a low prevalence of 0.34% for O157:H7 and 0.5% for non-O157 STEC, which included O-serogroups O103 and O26 [61]. A case-control study conducted after 15 human cases of E. coli O157:H7 identified the source of STEC as those who ate fresh strawberries contaminated by deer faeces [60]. Livestock that graze in wild areas may be exposed to indirect sources of STEC, for instance through environmental contamination of soil or water, or because wildlife that live in these bordering wild areas enter agricultural areas and contaminate the pastures grazed by animals [24,63].
Limitations of this study included a small sample size of farms and an inability to randomise sampled farms, so the model results are not generalisable to other regions and farms. Variables that should be included in future studies to increase our knowledge regarding carriage of foodborne pathogens on DSSF include the age of the animal and whether livestock have direct or indirect contact with neighbouring livestock.

Conclusion
Many consumers perceive DSSF and outdoor-raised livestock as safer than food grown on large-scale conventional farms or meat animals raised in confinement systems. However, identification of STEC O-serogroups that are of public health concern indicates the need for mitigation strategies on DSSF, such as housing livestock species separately and restricting access to wild areas, in order to prevent the transmission of foodborne pathogens in a pre-harvest environment. for statistical advice, and the staff at Western Center for Food Safety and Pires lab for fieldwork and technical support.
Financial support. This project was supported by start-up funds provided by the School of Veterinary Medicine, the Division of Agriculture Natural Resources (UC ANR), University of California, and by the National Institute of Food and Agriculture, US Department of Agriculture, under Award No. 2011-51181-30767. Any opinions, findings, conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the US Department of Agriculture.


s2fieldsofstudy: Biology Medicine

text:
Refining a Protocol for Faecal Microbiota Engraftment in Animal Models After Successful Antibiotic-Induced Gut Decontamination

Background There is mounting evidence for the therapeutic use of faecal microbiota transplant (FMT) in numerous chronic inflammatory diseases. Germ free mice are not always accessible for FMT research and hence alternative approaches using antibiotic depletion prior to FMT in animal studies are often used. Hence, there is a need for standardising gut microbiota depletion and FMT methodologies in animal studies. The aim of this study was to refine gut decontamination protocols prior to FMT engraftment and determine efficiency and stability of FMT engraftment over time. Methods Male C57BL/6J mice received an antibiotic cocktail consisting of ampicillin, vancomycin, neomycin, and metronidazole in drinking water for 21 days ad libitum. After antibiotic treatment, animals received either FMT or saline by weekly oral gavage for 3 weeks (FMT group or Sham group, respectively), and followed up for a further 5 weeks. At multiple timepoints throughout the model, stool samples were collected and subjected to bacterial culture, qPCR of bacterial DNA, and fluorescent in-situ hybridisation (FISH) to determine bacterial presence and load. Additionally, 16S rRNA sequencing of stool was used to confirm gut decontamination and subsequent FMT engraftment. Results Antibiotic treatment for 7 days was most effective in gut decontamination, as evidenced by absence of bacteria observed in culture, and reduced bacterial concentration, as determined by FISH as well as qPCR. Continued antibiotic administration had no further efficacy on gut decontamination from days 7 to 21. Following gut decontamination, 3 weekly doses of FMT was sufficient for the successful engraftment of donor microbiota in animals. The recolonised animal gut microbiota was similar in composition to the donor sample, and significantly different from the Sham controls as assessed by 16S rRNA sequencing. Importantly, this similarity in composition to the donor sample persisted for 5 weeks following the final FMT dose. Conclusions Our results showed that 7 days of broad-spectrum antibiotics in drinking water followed by 3 weekly doses of FMT provides a simple, reliable, and cost-effective methodology for FMT in animal research.

INTRODUCTION
The human gut harbours trillions of microbes which include bacteria, archaea, viruses, and eukaryotes. Bacteria and archaea make up most of the gut microbiome, and studies have demonstrated a role for the gut microbiota in influencing disease processes in many organs (1,2). Unsurprisingly, alterations in the richness, diversity, composition, and function of the gut microbiota have been associated with risks of a broad range of diseases, ranging from gastrointestinal inflammatory and metabolic conditions to neurological, cardiovascular, and respiratory illnesses (2)(3)(4)(5)(6). Hence, targeted manipulation of the gut microbiota for personalised nutrition and precision medicine has emerged as an attractive and potential therapeutic strategy (6)(7)(8)(9). Indeed, increasing evidence suggests gut manipulation through faecal microbiota transplantation (FMT) has the potential to be beneficial in many diseases (10)(11)(12).
Animal models are increasingly used to study the effect of gut microbiome manipulation by FMT on disease pathogenesis, and gut based therapeutic interventions. Although microbial similarity between mice and humans at the phylum level is remarkable, at the genus level 85% of murine sequences represent species that have not been detected in humans (13). "Humanization" of the mouse microbiota is frequently used to overcome this limitation, and it has been shown that mice humanised with different human donors generate a similar microbiome composition and metabolomic profile to that of the donor, with preservation of individual-specific features (14). Thus, for effective translation of therapeutics targeting the gut microbiota, and to explore the mechanisms underlying hostmicrobe interactions in animals, reconstitution of animals with human donor samples through FMT is key to translational research in this area.
For animal-based FMT studies, recipient animals are required to be either germ free (GF) or depleted of gut bacteria using antibiotics. Germ free mice are considered the gold standard as they are completely devoid of microbes and are therefore perfect candidates for receiving FMT. However, GF animals display an immature and underdeveloped lymphoid system compared with animals living in a conventional microbiological environment (15). In addition, maintaining GF animals requires specialised facilities and may not be feasible in many institutions. Furthermore, GF mice administered FMT also harbour chronic alterations that result in reduced intestinal absorptive function (16). As such, GF animals may not be suitable for all studies, especially when assessing the impact of FMT on host physiology and immunology.
The antibiotic-based approach is usually preferred, as it avoids several issues seen in GF models, allowing mice to undergo normal colonisation and full postnatal development of their immune system (17,18). However, a consistent protocol is needed for the composition, dose, frequency, length, and mode of antibiotic administration, as highlighted by Kennedy,King (17). Most studies based on antibiotic-induced microbiota depletion of gut use a cocktail comprising of vancomycin, neomycin, ampicillin, and metronidazole. Nonetheless, the duration of antibiotic administration in drinking water varies substantially from 3 days up to 10 weeks (8,17,(19)(20)(21). Prolonged antibiotic use can have an adverse impact on animal health and potentially alter host physiology (19). Hence, it is important to determine the minimum duration of antibiotic treatment required to achieve gut clearance with minimal side-effects.
Similarly, in animal studies, the frequency and duration of FMT administration have varied from a single administration (22), to once daily for 3 days (16), to three doses a week for 3 weeks (23), or once or twice weekly for 4 weeks (22) and up to twice daily for 12 weeks (20). Furthermore, not all studies have analysed microbial composition after completion of FMT administrations to ensure that engraftment of the human microbiota was stable. Consequently, a simple, quick, costeffective, reproducible, and translational model for reconstituting the animal gut with human gut microbiota is required.
In this study we aimed to address these gaps in the literature, by developing a gut decontamination protocol and determining the minimum duration of antibiotic treatment required to efficiently deplete mice of their commensal gut microbiota. This then enables us to determine the effectiveness of FMT and engraftment stability of human donor microbiota until the end of our follow up period.

Animals
Four-week-old male C57BL/6J mice were obtained from Australian Biological Resources (NSW, Australia) and allowed to acclimatise for 7 days. Mice were co-housed in a temperaturecontrolled facility (22-24 • C), 12:12 hrs light/dark cycle, with standard irradiated chow (Mouse Maintenance Diet; Speciality Feeds, WA, Australia) and water available ad libitum. All mice (n = 20) received antibiotic treatment for 21 days. Day 0 corresponded to pre-antibiotics phase. After antibiotic treatment mice were randomised to faecal microbiota transplant (FMT) or Sham groups (n = 10 mice per group) ( Figure 1A). Animals received either FMT or saline by weekly oral gavage for 3 weeks (FMT group or Sham group, respectively). Mice were monitored and samples collected for a further 5 weeks after oral gavage.
Animal experiments were approved by the Animal Care and Ethics Committee (UNSW Sydney, Ethics ID: 18/79B) and conducted in compliance with the NSW Animal Research Act 1985 and the National Health and Medical Research Council (24).

Antibiotic Treatment Protocol
Mice received an antibiotic cocktail consisting of Ampicillin 1 g/L, Vancomycin 0.5 g/L, Neomycin 1 g/L, and Metronidazole 1 g/L, in drinking water supplemented with 10% sucrose for 21 days ad libitum. This combination of antibiotics is the most commonly used cocktail in literature (17) (reviewed by) and is effective in depleting the microbiome with minimal effects on the morbidity and mortality of the animal (21,25). The antibiotic solution was changed three times a week. Water intake was monitored throughout the 21 days of antibiotic administration. Following completion of the antibiotic protocol,

Faecal Microbiota Transplant (FMT) Protocol
Faecal microbiota solution was prepared from fresh human stool sample. Stool was collected and homogenised in phosphate buffered saline (PBS) (0.1 g/mL) within an hour of collection. The mixture was passed through a 100 µm cell strainer, followed by a 40 µm cell strainer, and was stored in 15% glycerol/PBS at −80 o C. After 21 days of antibiotics, ∼0.2 mL (a maximum volume of 10 mL/Kg) of faecal solution (FMT group) or an equivalent volume of Saline (Sham group) was administered to mice via oral gavage once a week for 3 weeks.

Murine Stool Collection
Stool samples from individual mice (at least three pellets per mouse) were collected aseptically for bacterial culture, fluorescence in-situ hybridisation (FISH), qPCR and 16S rRNA sequencing. Stool samples were collected before starting antibiotics on day 0 and every 3-4 days during antibiotic treatment (days 3, 7, 10, 14, 17 and 21). Stool samples were also collected before inoculation of faecal microbiota (F0) and after each of the three doses of FMT which were administrated once a week for 3 weeks (F1, F2, F3, respectively). Post FMT completion, to determine efficiency and stability of gut engraftment over time, stool samples continued to be analysed weekly, for a further 5 weeks, corresponding to the engraftment time points (E)1-E5 ( Figure 1A). Stool pellets were weighed individually, and frozen at −80 • C until used.

Stool Culture
To assess bacterial levels in stool pellets, freshly collected pellets were homogenised with 20x volume of PBS, using Qiagen Tissuelyzer II (30 Hz for 1 min). The homogenate was then centrifuged at 300 x g for 2 min and the supernatant resuspended in 15% glycerol. A 1:1000 dilution of the stool homogenate was prepared and streaked onto blood agar plates [Columbia Agar Base (Sigma Aldrich Australia) + horse blood (10% vol/vol)] and incubated at 37 • C under aerobic, anaerobic and microaerophilic conditions for 48 h before being observed for growth.

Fluorescent in-situ Hybridisation-Fish
A universal 16S rRNA probe for bacteria was used for hybridisation (EUB338). Stool pellets were initially homogenised with 20x volume of PBS. FISH was performed as described previously (26,27). In brief, samples were fixed in 4% PFA (final dilution of 1:100). For slide preparation, 10 µL of sample was added to a gelatine coated slide and left to air dry at room temperature. Slides were then fixed in 95% ethanol for 10 min. Another 10 µL of fixed sample was added to the slide and dried in a hybridisation oven at 50 • C. Bacteria was fixed by soaking the slide for 3 min each in 60, 80 and 95% ethanol. Finally, hybridisation master mix was prepared with 1 µL of probe and 9 µL of hybridisation buffer (0.9 M NaCl; 20 mM Tris pH 8.0; 0.1% SDS). Ten microliters of probe/hybridisation mix were added on top of the cells and hybridised for 90 min at 50 • C. Slides were washed in dH 2 O, air dried, and mounted with soft mounting media.

DNA Extraction
DNA from stool samples was extracted using the PSP Spin Stool DNA plus kit (Stratec Molecular, Berlin, Germany) according to the manufacturer's instructions. Total genomic DNA concentration was determined fluorometrically using Qubit dsDNA broad range assay kit (Qubit 3.0 Fluorometer; Thermo Fisher Scientific, United States).

Quantitative PCR
Assessment of bacterial load remaining following antibiotic treatment was performed by qPCR. This was performed using PowerUp SYBR Green Master Mix (Life Technologies, TX, USA) with universal 16S primers 926F and 1062R (5'-AAACTCAAAKGAATTGACGG-3' and 5'-CTCACRRCACGAGCTGAC-3' , respectively) on the QuantStudio 7 Flex (Life Technologies, CA, USA) with the following cycling conditions: initial denaturation at 95 • C for 10 min followed by 40 cycles of 95 • C for 15 s and 60 • C for 1 min, with a ramp rate of 1 • C/s. This was followed by a melt curve analysis from 65 to 95 • C at a rate of 0.5 • C/s. Each sample was analysed in duplicate. A positive and negative control were run in each plate. Based on the amplification curve, Ct values for each sample were calculated by the instrument when Ct values were below 40.

Microbiome Analysis
16S rRNA gene forward and reverse reads were imported into Qiime2 (28). The DADA2 pipeline (29) was used for detecting and correcting Illumina amplicon sequences, removal of primers and chimeric reads, and assembly into sequence variants (SV)/operational taxonomic units (OTUs) (30). Taxonomy was assigned using a naïve Bayes classifier trained on the RefSeq database (31). Alpha diversity metrics investigated included Faith's Phylogenetic Diversity (PD), Evenness, Operational Taxonomy Units (OTUs) and Shannon's diversity index which were calculated using qiime2-q2-diversity. Beta-diversity metrics were calculated using Qiime2. Distance metrics calculated included weighted and unweighted UniFrac50 and Bray-Curtis dissimilarity index. Data was visualised using principal coordinate analysis (PCoA) plots and alpha diversity plots generated within R version 4.0.2 using ggplot2 (32) and phyloseq (33). Other packages used included dplyr (34) and qiime2R (35).

Statistical Analysis
Results are presented as mean ± SD. Statistical analyses for DNA quantification and qPCR were assessed by one-way ANOVA and post-hoc Tukey's multiple comparisons test. P values of <0.05 were considered statistically significant. Statistical analyses were performed with Prism v8.4 (GraphPad Software, Inc.).
A Kruskal-Wallis test with Dunn's post-hoc test was used to identity individual variations in alpha diversity between treatment groups. Distance based permutation multivariate analysis of variance (PERMANOVA) (36) was performed to test the null hypothesis that there were no differences in the microbial community structure across treatment at a significance level of P = 0.05 based on 999 permutations. Resulting P values were false discovery rate (FDR) corrected with 0.05 as the cut off. Corrected P values are presented as q-values.

Broad-Spectrum Antibiotic Regimen Did Not Impact General Health of Animals
Throughout the 3 weeks of antibiotic treatment, there were no adverse effects nor animal deaths. Animal well-being was also confirmed by measuring body weight and water intake. All mice gained weight ( Figure 1B) and had a stable water intake (Figure S1A), indicating that general health of animals was maintained. Stool weight was unchanged for the first 7 days of antibiotic treatment, with subsequent increase in stool weight observed from day 7 to day 17 compared to day 0 (baseline) (p < 0.001) (Figure S1B). At day 21, stool weight had returned to baseline (Figure S1A). No observable change in stool consistency was recorded throughout this period. Importantly, there were no differences in body weight (Figure 1B), water intake ( Figure S1A) or stool weight ( Figure S1B) when comparing animals that would subsequently receive FMT or Sham, thus providing a stable baseline for subsequent treatment comparisons.

Broad-Spectrum Antibiotic Regimen Efficiently Decontaminated the Gut After Seven Days
Next, we determined the optimal antibiotic treatment period for gut decontamination. To evaluate the efficacy of broad-spectrum antibiotic on bacterial depletion, bacterial culture, qPCR and FISH of stool aliquots were undertaken to measure bacterial load. Prior to antibiotic treatment, at day 0 (baseline), measures of high bacterial presence were observed across all assays as expected. Numerous colony forming units (CFUs) were observed on blood agar plates (Figure 2A), DNA concentration was 2.6 ± 0.8 ng/mg stool (Figure 2B), an average qPCR threshold cycle (Ct) of 16.8 was obtained (Figure 2C), and bacteria of various morphologies were confirmed by FISH (Figure 2D).
At day 3 of antibiotic treatment, no CFUs were visible on aerobic and microaerophilic plates, while a dramatic reduction in CFUs was observed on anaerobic plates (Figure 2A). Total stool DNA decreased significantly (96% reduction) when compared to baseline (P < 0.00001; Figure 2B), and average Ct values of 34.2 indicates a 100,000-fold reduction in bacterial DNA (p < 0.0001; Figure 2C). In addition, limited numbers of fluorescent bacterial cells were observed using FISH assay (Figure 2D), indicating a significant reduction in bacterial presence at day 3 of antibiotic treatment. From day 7 of antibiotic treatment, there were further reductions in bacterial presence, which remained constant until day 21. At all 5 timepoints between day 7 and day 21 (inclusive), no bacterial growth was observed (Figure 2A), total DNA concentration was reduced by 98% compared to baseline (Figure 2B), average Ct values of 39.3 were equivalent to negative controls (Figure 2C), and no bacterial cells were visible in FISH assay ( Figure 2D). Our results showed that broad-spectrum antibiotic cocktail was effective at gut decontamination following 7 days of antibiotic treatment and was maintained thereafter.

Recolonisation of the Gut After Three Doses of Human Donor FMT Remained Stable for Five Weeks
Following antibiotic gut decontamination, we next investigated the degree of recolonisation by oral administration of either donor faeces (FMT) or saline (Sham) for 3 weeks. There was no impact of FMT or Sham on body weight (Figure 1B).
In animals that received FMT, the microbial composition shifted away from the initial community structure that was present prior to oral gavage (F1-3 vs F0; q < 0.05; Figure 3A) and most resembled the donor stool (Bray Curtis dissimilarity index) after the first FMT dose, at F1 (Figures 3A,B). After subsequent FMT doses (F2 and F3), the gut microbiota composition became more dissimilar to the donor stool sample, with subsequent stabilisation during the engraftment period (E1-5) (Figure 3B). Following FMT administration weekly for 3 weeks, the community diversity was increased compared to F0 (q < 0.05) and was sustained for a further 5 weeks to the end of the study (Figures 3C,D). Taxonomic profiling of the FMT group at 5 weeks engraftment (E5) confirmed the presence of dominant taxa originating from the donor, including Firmicutes, Bacteroides, Verrucomicrobia and Proteobacteria (Figure 3E), indicating successful FMT engraftment that was stable for 5 weeks.
The microbial community of the Sham group also differed from the initial community structure that was present prior to oral gavage (q < 0.05) (Figure 3A), indicating recolonization of the gut irrespective of FMT treatment. Since all animals received antibiotic gut decontamination, this is likely due recovery of endogenous gut microbiota following antibiotic decontamination. The microbial community composition of the Sham group differed from that of the FMT group at all comparative timepoints (q < 0.05), indicating no crosscontamination of the control mice from environmental input ( Figure 3B). Taxonomic composition of the Sham group showed near total domination by Firmicutes in the following five weeks post antibiotics treatment (Figure 3E), again suggesting recovery of the gut microbiota following antibiotics.

DISCUSSION
GF mice are ideal models for gut microbiota studies. However, GF models harbour immunological defects, and the feasibility of additional housing requirements render it challenging for many studies. Thus, broad-spectrum antibiotic depletion of the gut microbiota in animals is used as an alternative. However, there are inconsistencies in the method, dose, and duration of treatment regimes. In this study, we determined the optimal duration required for effective gut decontamination as well as frequency of FMT required to achieve effective gut engraftment by donor FMT. We tested for the presence of faecal bacteria at multiple timepoints by multiple modalities using bacterial culture, qPCR, and FISH analysis. Our results showed that remnants of bacteria were present after three days, and that the optimal duration of antibiotic treatment for gut decontamination was seven days. We also showed that three weekly doses of FMT were sufficient for successful engraftment following antibiotic gut decontamination, and that following FMT, bacteria from the donor persisted in animals for the entire five weeks of follow up.
Indeed, antibiotic depletion of microbiota is necessary for the successful engraftment of FMT (37) as microbiota depleted mice have been shown to exhibit equivalent or better engraftment than GF mice (16,37). However, long periods of antibiotics may lead to unwanted side effects. For instance, 12 days of antibiotic treatment can lead to a significant overgrowth of fungi in mouse faeces (8). Hence, a shorter course of antibiotics is potentially a better approach. Our results supported the previous report indicating that a 7-day course of ampicillin, vancomycin, neomycin, and metronidazole was a viable method to deplete the gut of its native microbiota prior to FMT (8). This was achieved without any observed side-effects, such as dehydration, diarrhoea, or loss of body weight, that may be associated with extended antibiotic treatment.
We observed a reduction in bacteria (both bacterial growth and reduction in bacterial DNA load) within three days with qPCR assessment confirming optimum bacterial DNA depletion was achieved following seven days of antibiotic therapy. This was in concordance with other studies that administered antibiotics via oral gavage or drinking water and observed depletion of the gut microbiota within four days. In this setting, extending the antibiotic regimen beyond seven days did not improve the efficiency of bacteria depletion (8). Notably, the mode of delivery was also important, with similar efficacy when antibiotics were administered via drinking water or twice daily doses via oral gavage. However, a single daily dose via oral gavage did not sufficiently deplete the gut microbiota (8). We opted to deliver the antibiotic cocktail via drinking water, which minimised discomfort and stress to the animals while also being simple and consistent to perform. Taken together, our antibiotic treatment regime combined frequently used antibiotics with a simple delivery method that, when administered for seven days, was highly effective at depleting the gut of bacteria.
We noted that bacteria that remained following antibiotic treatment were not those that were initially abundant in untreated mice, but rather represented residual taxa that were resistant to antibiotic treatment. This supported previous observations that antibiotic treatment exerted a selective pressure on bacterial communities (38), leading to enrichment of communities that were initially scarce. These residual indigenous species had been shown to be able to outcompete a considerable proportion of the human donor microbiota within the first week following FMT (37).
Addressing the question of the frequency and intervals of FMT to achieve successful engraftment is also important. A singular FMT inoculation was shown to elicit no clear improvements in microbial diversity when compared to saline alone (20,39). Therefore, multiple FMT doses were necessary to maintain a stable engrafted microbiome. Previous concerns were raised that FMT dosing at short intervals, such as twice a week for several weeks, might disturb the newly established ecosystem and impair its equilibrium (22). Our results indicated that three weekly doses of FMT was able to effectively engraft the decontaminated gut with abundances of Firmicutes, Bacteroides, Verrucomicrobia and Proteobacteria similar to that of the donor. Importantly, this similarity in microbiome composition to the human donor sample remained for the entire five weeks of follow up, suggesting that the frequency and interval tested, were successful in achieving stable engraftment.
Despite our results showing good engraftment of donor microbiota, the study does have limitations. A longer follow up period could have determined the length of persistence of successful engraftment in mice. Although there is compelling evidence for the use of antibiotics in successful FMT engraftment, we did not compare our results to mice without antibiotic decontamination, nor to germ free mice due to feasibility.
Although our gut depletion protocol is likely applicable across different groups of mice, other factors need to be taken into consideration when interpreting results utilising gut depletion and subsequent FMT. We have only used young, male C57Bl/6J mice, which are the most widely used inbred, general-purpose strain of mouse as they are a stable strain and easy to breed (40,41). Nevertheless, the gut microbiota is likely to be different in different strains and in female mice, which may affect the development of disease (42). A number of studies have also demonstrated that the gut microbial structure and diversity changes with age (43)(44)(45). However, few studies utilise other mouse strains in gut depletion studies and even fewer have validated the efficacy of gut depletion following antibiotic treatment. Effectiveness of the protocol can easily be confirmed in different strains, ages, and sexes using the same methods in this study. Assessment of the colony-forming units (CFUs) from faecal samples plated in aerobic and/or anaerobic conditions on non-selective media, and qPCR of the 16S rRNA gene allows for culture-independent assessment of gastrointestinal bacterial load. These techniques are easily accessible and reproducible to quickly assess the efficacy of gut microbiota depletion.
The aim of this protocol was to deplete gut bacteria, which is arguably independent of the mouse strain, age and sex. One caveat would be the residual gut microbiota post-antibiotics may be different between mice of different strains, ages and sexes which is an inherent limitation of antibiotic-mediated gut depletion protocols. This would arise from differences in the initial colonisation of the gut microbiota prior to antibiotics treatment. These differences may result in different immune and inflammatory responses (46) and lingering effects of initial hostmicrobiota interactions cannot be discounted. Depletion of the gut microbiota with broad spectrum antibiotics also changes host immunology and has been associated with altered immune cell populations. For example, secretory IgA and various immune cell types were depleted in the intestine, while in the spleen, dendritic cells and neutrophils were depleted, but basophils were enriched (17) (reviewed by). Thus, care should be taken when planning studies to ensure the gut microbiota is profiled at different timepoints to ensure these factors are taken into consideration. Nevertheless, this protocol is likely applicable to multiple strains of mice of differing ages and sexes, but factors inherent to antibiotic-mediated gut depletion need to be considered.

CONCLUSION
We describe a viable and efficient protocol for gut decontamination and microbiota manipulation studies, consisting of seven days of antibiotic administration in drinking water, followed by three weekly doses of faecal transplant into recipient mice. This approach to decontaminate the gut prior to FMT was cost-effective and less invasive, providing an alternative to using GF mice. Standardising methodologies for FMT in animal research are required for the effective translation of pre-clinical findings.

DATA AVAILABILITY STATEMENT
The original contributions presented in the study are publicly available. This data can be found here: https://www.ncbi.nlm.nih. gov/bioproject/PRJNA765053.

ETHICS STATEMENT
The animal study was reviewed and approved by Animal Care and Ethics Committee, UNSW Sydney.

AUTHOR CONTRIBUTIONS
This study was performed in collaboration among all authors. NA designed the study, drafted the manuscript, and performed the statistical analyses. NA and AR collected the animal data. NA, AR, and SKh performed the experimental work. NA and EM performed the microbiome analyses. NA, GH, and AZ analysed the results and supervised the study. SS and SKo revised the manuscript and made significant changes during the review process. All authors contributed to revision of the final manuscript.

FUNDING
This study was supported by Sir Owen Glenn grant, RG182137, UNSW. The grant body had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.


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Hu Antigen R (HuR) Protein Structure, Function and Regulation in Hepatobiliary Tumors

Simple Summary Hepatobiliary tumors are a group of primary malignancies encompassing the liver, the intra- and extra-hepatic biliary tracts, and the gall bladder. Within the liver, hepatocellular carcinoma (HCC) is the most common type of primary cancer, which is, also, representing the third-most recurrent cause of cancer-associated death and the sixth-most prevalent type of tumor worldwide, nowadays. Although less frequent, cholangiocarcinoma (CCA) is, currently, a fatal cancer with limited therapeutic options. Here, we review the regulatory role of Hu antigen R (HuR), a ubiquitous member of the ELAV/Hu family of RNA-binding proteins (RBPs), in the pathogenesis, progression, and treatment of HCC and CCA. Overall, HuR is proposed as a valuable diagnostic and prognostic marker, as well as a therapeutic target in hepatobiliary cancers. Therefore, novel therapeutic approaches that can selectively modulate HuR function appear to be highly attractive for the clinical management of these types of tumors. Abstract Hu antigen R (HuR) is a 36-kDa ubiquitous member of the ELAV/Hu family of RNA-binding proteins (RBPs), which plays an important role as a post-transcriptional regulator of specific RNAs under physiological and pathological conditions, including cancer. Herein, we review HuR protein structure, function, and its regulation, as well as its implications in the pathogenesis, progression, and treatment of hepatobiliary cancers. In particular, we focus on hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), tumors where the increased cytoplasmic localization of HuR and activity are proposed, as valuable diagnostic and prognostic markers. An overview of the main regulatory axes involving HuR, which are associated with cell proliferation, invasion, metastasis, apoptosis, and autophagy in HCC, is provided. These include the transcriptional, post-transcriptional, and post-translational modulators of HuR function, in addition to HuR target transcripts. Finally, whereas studies addressing the relevance of targeting HuR in CCA are limited, in the past few years, HuR has emerged as a potential therapeutic target in HCC. In fact, the therapeutic efficacy of some pharmacological inhibitors of HuR has been evaluated, in early experimental models of HCC. We, further, discuss the major findings and future perspectives of therapeutic approaches that specifically block HuR interactions, either with post-translational modifiers or cognate transcripts in hepatobiliary cancers.

Introduction
Hu antigen R (HuR), also known as HuA or embryonic lethal abnormal vision protein 1 (ELAVL1), is a 36-kDa ubiquitous member of the ELAV/Hu family of R binding proteins (RBPs), which, also, includes HuB, HuC and HuD [1,2]. In humans protein is encoded by the ELAVL1 gene. In healthy individuals, HuR protein is, m expressed in human endocrine tissues, the respiratory system, and the gastrointe tract [3].

Regulation of HuR Protein Function
The function of HuR is regulated through transcriptional, post-transcriptional, and post-translational mechanisms, as discussed below.

Transcriptional Control of HuR
The ELAVL1 gene displays alternate transcriptional start sites, leading to at least two distinct transcripts with distinct 5 -UTRs [99,118], the shorter of them being expressed in stress conditions under the influence of bone morphogenetic protein-7 (BMP-7) and the Smad 1/5/8 route [118]. Expression of the long HuR isoform relies on nuclear factor k of activated B-cells (NF-kB). The HuR promoter region contains a target of NF-kB, to which one of its members-p65/RelA-binds upon the induction of the phosphatidylinositol 3-kinase (PI3K)/AKT signaling route, resulting in the abnormal upregulation of HuR gene expression in gastric tumors [119].

Post-Transcriptional Control of HuR
The stability of HuR mRNA is another key factor controlling its protein levels. HuR can both up-and down-regulate its expression, by interacting with its own transcript [25,94,95,120]. In addition, the existence of both positive and negative feedback loops between HuR and the protein product of its target mRNAs have been reported [121][122][123][124].
More recently, it was described that circular RNAs (circRNAs) may comprise various binding motifs for one or several RBPs, including HuR, acting as sponges, where the RBP is prevented from binding to other targets while tethered to the circRNA [167]. For instance, it was shown that circPABPN1 competes with PABP1 mRNA for binding to HuR, affecting the expression of p53, c-myc, and bcl-2, besides PABP1 [168].

HuR Protein Oligomerization
HuR and functionally related proteins, such as AUF1, form dimers and multimers both free and bound to target sequences [12,15,153,[169][170][171]. HuR dimers have been detected by fluorescence resonance energy transfer (FRET), both in the cell nucleus and cytoplasm. Importantly, the HuR oligomerization state strongly affects the stability of its target mRNAs. Fluorescence anisotropy analyses suggested that HuR RRM1-2 is able to form dimers with high affinity (K D ca. 0.1 nM). These data indicate that RRM1-2 dimerization could be an important feature of the interaction mechanism between HuR and its target RNAs [172]. Furthermore, a structural analysis revealed that RRM1 alone forms homodimers as well, since Cys13 is exposed to and capable of establishing disulfide bonds, thus enabling HuR to respond to oxidative stress [7]. Beyond dimerization, cytoplasmic multimerization of HuR has been observed in glioma cells. A thorough analysis using distinct HuR constructs bound to fluorescent and bioluminescent probes enabled a model to be constructed, in which Cys13 is responsible for covalent dimerization, although the hinge region and RRM3 are still necessary for further oligomerization [173]. Indeed, the C-terminal domain of HuR is, also, prominently involved in its dimerization [12,13,15,170], as well as in the cooperative formation of HuR oligomers on ARE motifs [12,153,170]. According to smallangle X-ray scattering (SAXS) and NMR data, a single site-directed mutation (W261E) in RRM3 was enough to hinder its dimerization, under reducing conditions [15]. Moreover, deletion of RRM3 critically impaired the ability of the RBP to stabilize reporter β-globin mRNA constructs containing different AREs [9]. Altogether, experimental evidence points to HuR dimerizing through RRM1 and RRM3 in an independent way, while requiring both domains for oligomerization.

HuR and Hepatobiliary Cancers
HuR owns the post-transcriptional control of a large number of RNAs, enabling the protein to play pivotal roles that are dictated by the molecular functions of the transcripts. HuR targets, chiefly, include many mRNAs encoding proteins involved in cell proliferation, senescence, apoptosis, differentiation, stress, and immune responses. In turn, HuR was found to be involved not only in physiological processes (e.g., adipogenesis and muscle differentiation) [69,[174][175][176] but also in disease (primarily, cancer and inflammation) [28,29,177]. Indeed, the relevance of HuR in some types of hepatobiliary tumors has been constantly described during the last decade. The major findings to date and future perspectives on the applications of HuR as a protein marker for the diagnosis, prognosis, and therapy of hepatobiliary cancers are the main topic of this review.

Hepatobiliary Cancers
Hepatobiliary cancers are a group of primary malignancies encompassing the liver, the intra-and extra-hepatic biliary tracts, and the gall bladder. Within the liver, the most common primary cancer is hepatocellular carcinoma (HCC). HCC, usually, arises in patients with an underlying chronic liver disease, such as chronic infection with the hepatitis B (HBV) or C virus (HBC), cirrhosis, excessive alcohol consumption, metabolic syndrome, diabetes, and non-alcoholic fatty liver disease (NAFLD) [178]. Even though the clinical management of HCC has improved in the past ten years, particularly for patients at advanced stages, HCC is still the third-most common cause of cancer-associated death and the sixth-most prevalent type of cancer worldwide [179]. Of relevance, in the past few years, important studies on immunotherapy in HCC have been carried out, resulting in novel guidelines for its treatment [180][181][182].
On the other hand, cholangiocarcinoma (CCA) encompasses a group of malignancies, arising at any point in the hepatic biliary tree. Biliary tract cancers, including intrahepatic (ICC), perihilar (PCC), and distal cholangiocarcinoma (DCC), as well as gallbladder cancer, are low-incidence malignancies in most high-income countries, but represent a major health problem in endemic areas. Indeed, ICC is the second-leading cause of primary liver cancer [183]. Nowadays, CCA is a fatal cancer, with a survival rate beyond a year of diagnosis inferior to 5% [184]. Therapeutic options are limited and surgery is the cornerstone of cure for CCA, even though most patients present with locally advanced or metastatic disease [185]. In the past few years, druggable alterations such as fibroblast growth factor receptor 2 (FGFR2) gene fusions and rearrangements, or isocitrate dehydrogenase-1 (IDH-1) and BRAF mutations, have been widely described in CCA patients, further indicating the important differences between iCCA and PCC/DCC, as thoroughly reviewed in [186].

HuR as a Prognostic and Diagnostic Biomarker in Hepatobiliary Cancers
HuR protein is, either, overexpressed in most human cancers or overactivated, as denoted by its increased cytoplasmic localization and the translation of various mRNAs involved in carcinogenesis. Regarding liver cancer, Embade et al. [187], reported significantly higher HuR protein levels in the mouse liver progenitor 29 (MLP29) cell line and in the S-adenosylmethionine-deficient (SAMe-D) cell line, isolated from the methionine adenosyltransferase (MAT)1A knockout (MAT1A-KO) mouse model of HCC, compared with primary mouse hepatocytes. Accordingly, higher HuR expression levels were found in human hepatoma cells (e.g., HepG2, Hep3B, SNU398, SNU449, SNU182, and SNU475), than in normal CRL4020 cells [188]. Moreover, in a human liver cancer tissue microarray (TMA) of 59 liver tissue cores from 44 patients, also by Zhu et al. [188], HCC tumor tissues showed significantly higher overall and cytoplasmic HuR staining, compared to normal liver tissues, and this high HuR staining score correlated with worse survival of patients with early-stage HCC. Furthermore, immunofluorescence analyses of normal versus malignant liver tissue revealed that HuR protein is down-regulated in normal human liver samples and up-regulated in HCC samples of different aetiologies (cirrhotic patients with HCV, alcoholic steatohepatitis, and non-alcoholic steatohepatitis (NASH)), where HuR concentration increased, proportionately, to their transformation status [189]. Finally, the expression of ELAVL1 gene is highly induced in the tumor tissue of a cohort of CCA patients, according to The Cancer Genome Atlas (TCGA) mRNA expression repository [190]. Likewise, high cytoplasmic HuR levels are associated with poor survival in patients with surgically resected CCA, treated with adjuvant gemcitabine-based chemotherapy [191].
In sum, HuR overexpression, along with its cytoplasmic localization, are hallmarks of both HCC and CCA, correlating with disease progression and overall survival. Even though the role of HuR in HCC has been widely investigated, with some studies revealing HuR targets and regulators under these conditions, the function of HuR in CCA remains rather unexplored to date.

Signaling Pathways Implicated in HCC Involving HuR
The oncogenic-gene-expression programs that allow cancer cells to develop, survive, proliferate, and colonize other tissues are strongly dependent on post-transcriptional mechanisms [28]. As has been previously described, HuR is highly involved in many types of cancer, including HCC and CCA, and so are the numerous HuR-regulated RNAs, which are known to contribute to the main cancer hallmark functions (i.e., enhanced cell proliferation and survival, elevated local angiogenesis, evasion of immune recognition, facilitated cancer cell invasion, and metastasis). Furthermore, a series of transcriptional, post-transcriptional, and post-translational regulators of HuR function have been found to be altered during HCC. Herein, we aimed to analyze the main regulatory axes involving HuR that are associated with hepatobiliary tumors, which can be classified into those related with cell proliferation, invasion, and metastasis; apoptosis; and autophagy ( Figure 2). In the process, some of the previously reported HuR RNA targets and regulators of HuR function have been confirmed in the specific context of HCC, while others have been newly identified in this type of tumor. post-transcriptional, and post-translational regulators of HuR function have been found to be altered during HCC. Herein, we aimed to analyze the main regulatory axes involving HuR that are associated with hepatobiliary tumors, which can be classified into those related with cell proliferation, invasion, and metastasis; apoptosis; and autophagy ( Figure 2). In the process, some of the previously reported HuR RNA targets and regulators of HuR function have been confirmed in the specific context of HCC, while others have been newly identified in this type of tumor. The PI3K/AKT signaling route is known to be upregulated in HCC [192], and, therefore, it could be responsible for the increased expression of the long HuR mRNA isoform, through NF-kB, as it occurs in gastric tumors [119]. However, it is likely that expression of the short HuR transcript is not observed during HCC, as reduced levels of  The PI3K/AKT signaling route is known to be upregulated in HCC [192], and, therefore, it could be responsible for the increased expression of the long HuR mRNA isoform, through NF-kB, as it occurs in gastric tumors [119]. However, it is likely that expression of the short HuR transcript is not observed during HCC, as reduced levels of BMP-7 and p-Smad1/5/8 were detected in patient samples [193].
During HBV-associated HCC, the HBV-encoded X (HBx) protein upregulates HuR expression, which enhances HER2 mRNA stabilization and translation, thus contributing to the migration of HCC cells [194]. In the MAT1A knockout (MAT1A-KO) mouse model, which shows a chronic deficiency in SAMe levels and, spontaneously, develops NASH and HCC, hepatic levels of LKB1 and AMPK are activated, incrementing the cytoplasmic localization of HuR, which leads to the stabilization and expression of cyclin A2 and D1 mRNAs, and subsequent cell cycle progression [195]. Moreover, an abnormally low ratio between methylated and unmethylated HuR was revealed in HCC samples. The two HuR isoforms can associate with the 3 -UTR of MAT2A mRNA, whose activity is linked to liver cell proliferation. However, while unmethylated HuR was shown to stabilize the MAT2A transcript, an increase in its methylation status correlated with lower MAT2A mRNA levels. Hence, the loss of HuR methylation may explain the increased MAT2A mRNA and protein expression, and the subsequent loss of SAMe homeostasis that occurs during hepatocyte dedifferentiation, proliferation, and carcinogenesis [189]. Moreover, related with PTMs, NEDDylation of HuR was firstly reported in the context of liver cancer. Specifically, the E3 ligase Mdm2 catalyzes the conjugation of NEDD8 to HuR at Lys283, Lys313, and Lys326, a process that has been linked to the nuclear localization and reduced proteasomal degradation of the RBP [187]. Interestingly, the Mdm2 transcript being a described target of HuR [43,44], and considering the significantly positive correlation between Mdm2 and HuR expression in clinical HCC and human hepatoma cell lines [187], it would be highly expected that the mRNA levels of this E3 ligase were stabilized by HuR during HCC too, despite not having been verified to date.
The Wnt/β-catenin pathway has been shown to induce stearoyl-CoA desaturase (SCD) expression in liver-tumor-initiating and HCC cells, further increasing the synthesis of mono unsaturated fatty acids (MUFAs). MUFAs can block the nuclear import of HuR, thereby increasing its protein levels in the cytoplasm, where it binds to the 3 UTR of Lrp5 and Lrp6 mRNAs as well as stabilizes and stimulates their translation, further providing a positive feedback loop, by amplifying Wnt/β-catenin signaling and contributing to liver carcinogenesis [196]. Moreover, Wilms tumor 1-associated protein (WTAP) drives N6methyladenosine (m6A) RNA methylation and epigenetic silencing of ETS1, by interfering with HuR-mediated stabilization of ETS1 mRNA, further alleviating the expression of p21 and p27 G2/M checkpoint proteins, which are known downstream effectors of ETS1, and facilitating HCC progression [197]. Moreover, it has been reported that ionizing radiation activates the DNA damage response (DDR) via ATM/p38, which causes HuR shuttling to the cytoplasm in order to stabilize mitochondrial transcription factor A (TFAM) mRNA and induce its expression in HepG2 hepatoma cells. These results suggest a new pathway, which could be targeted to increase the sensitivity of liver cancer cells to radiotherapy [198].

Pathways Involving Non-Coding Transcripts
In addition to mRNAs, a few examples, whereby HuR interacts with non-coding transcripts during hepatobiliary tumors, have been reported. In human HCC cells subjected to hypoxic stress, HuR binds to the primary transcript of miR-199a (pri-miR-199a) blocking its processing into mature miR-199a. Interestingly, miR-199a is a negative regulator of Hk2 and Pkm2 mRNA expression. Therefore, HuR-mediated miR-199a maturation inhibition, during hypoxia, enables the metabolic reprogramming of HCC cells towards the Warburg effect, which confers favorable conditions for tumor growth, invasion, and metastasis [199]. The long intergenic noncoding RNA (lincRNA)-UFC1 plays an oncogenic role in liver cancer, by interacting with HuR, which stabilizes and induces the expression of the CTNNB1 mRNA, leading to increased cell-cycle progression as well as proliferation and reduced apoptosis in HCC cells [200]. The oncofetal lncRNA Ptn-dt appeared to be highly expressed in HCC tissue and was found to interact with HuR protein, further compromising the stabilization and expression of miR-96. Therefore, the reduced function of miR-96 on the post-transcriptional inhibition of anaplastic lymphoma kinase (Alk) protein contributed to HCC cell proliferation [201]. Another study, describing the tumor suppressor role of lncRNA-AK058003 in HCC, revealed SNCG mRNA as a potential target of HuR. It was postulated that lncRNA-AK058003 is downregulated during HCC but, if overexpressed, it can interact with HuR to suppresses its expression, further affecting SNCG translation and stability, thus inhibiting γ-synuclein-mediated HCC cell proliferation and metastasis, both in vitro and in vivo [202].
In an attempt to elucidate the function of circRNAs, a few circRNA-RBP-mRNA axes involving HuR were revealed in HCC. For example, circBACH1 acts as an oncogene during hepatic tumorigenesis, upon association with HuR, to facilitate its translocation to the cytoplasm, where the RBP inhibits p27 protein expression and allows cell cycle progression, eventually favoring HCC cell proliferation [203]. KIAA1429, a key component of the m6A methyltransferase complex, negatively regulates circRNA-DLC1 in HCC tissues. A mechanistic study revealed that circDLC1 competitively binds with HuR, thereby impairing HuR-mediated MMP1 mRNA stabilization and expression, ultimately resulting in decreased hepatoma cell proliferation and metastasis [204]. Moreover, hsa_circ_0074854 physically interacts and stabilizes HuR protein in the cytoplasm, which induces ZEB1 protein expression, thereby promoting the migration, invasion, and epithelial-mesenchymal transition (EMT) of HepG2 hepatoma cells [205].

The Role of HuR in Cell Death during HCC
In SAMe-D cells derived from MAT1A KO mice, sustained LKB1 phosphorylation contributes to increased cytoplasmic HuR localization, where it, specifically, binds to the

Therapeutic Approaches to Inhibit HuR in Hepatobiliary Tumors
Given its essential role as a regulation hub of cell-fate decisions, suppression of HuR activity has become a key objective to control tumor progression and therapy resistance. Even though RBPs were, initially, considered unsuitable for drug screening, developments during the last decade have enabled the generation of distinct approaches to target RBPs, including HuR, in cancer and other human pathologies [210].
The downregulation of HuR function may be achieved through different strategies, with small molecules being the most popular class of inhibitors for this RBP. Small molecules, directly, inhibit HuR interaction with target RNAs, oligomerization, nucleocytoplasmic shuttling, or post-translational modification, further compromising its function. Dehydromutactin, MS-444, and okicenone are three low-molecular-weight molecules, found to inhibit both HuR dimerization and RNA binding, during a screening of compounds derived from Actinomyces cultures [172]. Among them, MS-444 showed the highest affinity and has proven to affect the viability of different cell lines [173,[211][212][213]. Dihydrotanshinone-I (DHTS) [214], b40, and quercetin [215], as well as the coumarin derivative CMLD-2 [216][217][218], were, also, identified as chemical disruptors of the interaction between HuR and its target mRNAs. Other small-molecule inhibitors preventing HuR interaction with its target mRNAs include KH-3, which has been found to be promising for the treatment of breast cancer [219]. A new inhibitor is the muscone derivative ZM-32, a synthetic compound that shows antiangiogenic effects in breast cancer tumor cells, by blocking HuR association with VEGF and MMP9 mRNAs [220].
Except for ZM-32, which was tested in the human hepatoma HepG2 cell line without obtaining very successful outcomes [220], surprisingly, none of the abovementioned molecules have been tested in hepatobiliary tumors. Currently, studies reporting the use of small-molecule HuR inhibitors to tackle HCC are limited (Table 1). For instance, resveratrol (RSV) increased HuR mRNA and protein-expression levels in human liver cancer cell lines, which helped to raise MAT2B and SIRT1 expression, by stabilizing their mRNAs at the 3 -UTR. Importantly, the induced HuR, SIRT1, and MATβ proteins interact and stabilize each other, while compromising the binding of MATβ to MATα2. Eventually, this leads to an increase in SAMe levels, which might favor cell growth suppression and apoptosis during liver cancer, upon treatment with RSV [221]. In another study, N-Benzylcantharidinamide impaired HuR translocation to the cytosol and decreased the stability of MMP-9 mRNA and its expression, further inhibiting the invasive potential in metastatic Hep3B cells [222]. Additionally, since the nucleocytoplasmic transport of HuR depends on the interaction of this RBP with cytoskeletal proteins, the naturally occurring cytoskeletal inhibitors latrunculin A and blebbistatin have shown to exert antitumorigenic properties in human hepatoma cells, by interfering with the intracellular trafficking of HuR and its mRNA cargo [223]. Finally, the NEDDylation inhibitor Pevonedistat was shown to exert antitumoral effects in vitro and in vivo in liver cancer, partially through HuR destabilization. Importantly, overexpression of HuR in hepatoma cells offered resistance to pharmacological NEDDylation inhibition, while low levels of HuR sensitized cells to the treatment, suggesting that HuR levels determine the druggability of the NEDDylation pathway in HCC [224].
Notably, antisense oligonucleotides (ASOs) [225] and small interfering RNAs (siRNAs) exist for HuR. The latter have been widely used in the numerous studies mentioned throughout this review, in connection with HCC [187][188][189]194,197,199,204,206,208]. Related with siRNA technology as well, targeted delivery is, often, highly desirable and can be achieved thanks to the use of nanotechnology, for example, by means of the folate-receptortargeted nanoparticle delivery of HuR siRNA, to reach lung cancer cells [226].

Future Perspectives
Herein, we have reviewed the current knowledge on the role of HuR in hepatobiliary tumors, including HCC and CCA. Considering increased HuR protein expression, cytoplasmic localization, and its relationship with patient outcome, HuR is regarded as a valuable diagnostic as well as a prognostic marker in HCC. Even though we recognize the growing attention of liquid biopsy in HCC, only invasive approaches have been carried out, to address the diagnosis and prognosis of HuR in these types of tumors, to date. With regard to CCA, high cytoplasmic expression and elevated tumor-gene levels of HuR have been described. However, special care should be taken when using cytological and pathological approaches, due to the highly desmoplastic nature of these types of tumors. In fact, in biliary tract carcinomas, biopsy samples are often inadequate for molecular profiling. Additionally, tissue sampling has reported high specificity but low sensitivity in diagnosis. Thereby, to further validate the relevance of HuR as a diagnostic and prognostic marker in CCA, further in vitro studies and in vivo pre-clinical mouse models are required.
Even though the role of HuR in CCA remains, largely, unexplored, novel mechanisms underlying HuR regulation have been addressed in HCC, with special focus on the importance of the PTM NEDDylation in the stabilization of HuR, preventing it from proteasome-mediated degradation [187]. Consequently, the relevance of HuR in pathological conditions, including cancer, has gained interest, with the advent of many naturally occurring and synthetic inhibitors of HuR. Nevertheless, given the lack of studies, using the currently available HuR inhibitors for the treatment of hepatobiliary tumors, and considering the heterogenous nature of HuR as a target affecting multiple pathways in many types of human cancers, it is, somehow, expected that the inhibition of HuR may be associated with undesired side-effects. Indeed, it has been described that HuR whole-body knock-out leads to embryonic lethality in mice, suggesting that HuR is, most probably, involved in regulating the fate of mRNAs encoding proteins implicated in key processes, such as organ development and tissue homeostasis [227].
Therefore, to take advantage of HuR as a therapeutic target, we propose that it would be critical to, specifically, inhibit the diverse interactions of HuR with its transcripts, which are uniquely relevant in the context of hepatobiliary cancers. For this purpose, the first step would be to interrogate the HuR RNA-binding signature in hepatobiliary tumors, which could be achieved, for example, by using RNA sequencing (RNA-Seq) approaches after the immunoprecipitation (IP) of the RNAs interacting with HuR, in the tumor and adjacent tissues, of large cohorts of HCC and CCA patients. Additionally, considering the relevance of HuR PTMs in liver disease, the screening of HuR PTM signature, including the residues susceptible of modification in liver cancer can unravel new unexpected and druggable regulatory mechanisms of HuR function. The screening of novel HuR RNA targets and PTMs in liver cancer as well as their clustering, according to the degree of liver disease, may, eventually, reveal subtypes of patients where certain HuR-related therapeutic approaches may be more suitable, paving the way for a more-personalized medicine. In other words, from this population-based study, we could, potentially, treat liver cancer by inhibiting the interactions of HuR either with (1) specific target RNAs or (2) peptides responsible for its PTM, which are ascribed to liver cancer. Indeed, short RNAs that compete with HuR for the binding to its target mRNAs, without affecting other HuR-mRNA binding interactions, had been previously suggested as a novel strategy for the management of HCC [228]. In this context, aptamers have, recently, emerged as small single-stranded DNA or RNA molecules that fold in a specific way, which is optimal to interact with a given target [229]. Thus, after elucidating HuR RNA targets and its PTMs in the context of liver cancer, we believe it would be possible to obtain aptamers blocking HuR interaction with both RNA and the peptides associated with PTMs, in a highly specific manner, minimizing potential adverse effects. This might be achieved through the screening of aptamer libraries, using the Systematic Evolution of Ligands by Exponential Enrichment (SELEX) selection method. For this purpose, the understanding of HuR protein structure is of great benefit to predict whether the conformation of the aptamer would be suitable to inhibit the binding of HuR to its target.

Concluding Remarks
In conclusion, even though, in the past few years, great efforts have been carried out, in order to better understand the function of HuR and its regulation in hepatobiliary tumors, further studies are necessary to address the therapeutic potential of HuR inhibitors or, alternatively, a more-personalized treatment based on the selected inhibition of HuR and its RNA or the protein targets in HCC and CCA.

Conflicts of Interest:
The authors declare no conflict of interest.