Species Identity, Life History, and Geographic Distance Influence Gut Bacterial Communities in Lab-Reared and European Field-Collected Culicoides Biting midges.

Bacteria are part of the insect gut system and influence many physiological traits of their host. Gut bacteria may even reduce or block the transmission of arboviruses in several species of arthropod vectors. Culicoides biting midges are important arboviral vectors of several livestock and wildlife diseases, yet limited information is available on their gut bacterial communities. Addressing this gap will help inform how these communities can be manipulated and ultimately used as novel tools to control pathogens. To assess how bacterial communities change during the life stages of lab-reared C. nubeculosus and C. sonorensis, endosymbiotic bacteria were identified using Illumina sequencing of 16S rRNA and taxonomically characterised. Analyses were conducted to determine how gut bacterial communities in adults are influenced by species identity and geographic distance among biting midge populations. Communities of the two lab-reared Culicoides species significantly changed after pupation and with maturation into 6-day-old adults. Pseudomonas, Burkholderiaceae and Leucobacter bacteria were part of a core community that was trans-stadially transmitted and found throughout their life cycle. Among field-collected biting midges, the bacterial communities were unique for almost each species. Cardinium, Rickettsia and Wolbachia were some of the most abundant bacteria in midges collected from wetlands. Only Pseudomonas was present in high relative abundance in all field-collected species. In this study, species identity, as well as geographic distance, influenced the gut bacterial communities and may partly explain known inter- and intra-species variability in vector competence. Additionally, stably associated bacterial species could be candidates for paratransgenic strategies to control vector-borne pathogens.

Chitin- and Keratin-Rich Soil Amendments Suppress Rhizoctonia solani Disease via Changes to the Soil Microbial Community.

Enhancing soil suppressiveness against plant pathogens or pests is a promising alternative strategy to chemical pesticides. Organic amendments have been shown to reduce crop diseases and pests, with chitin products the most efficient against fungal pathogens. To study which characteristics of organic products are correlated with disease suppression, an experiment was designed in which 10 types of organic amendments with different physicochemical properties were tested against the soilborne pathogen Rhizoctonia solani in sugar beet seedlings. Organic amendments rich in keratin or chitin reduced Rhizoctonia solani disease symptoms in sugar beet plants. The bacterial and fungal microbial communities in amended soils were distinct from the microbial communities in nonamended soil, as well as those in soils that received other nonsuppressive treatments. The Rhizoctonia-suppressive amended soils were rich in saprophytic bacteria and fungi that are known for their keratinolytic and chitinolytic properties (i.e., Oxalobacteraceae and Mortierellaceae). The microbial community in keratin- and chitin-amended soils was associated with higher zinc, copper, and selenium, respectively.IMPORTANCE Our results highlight the importance of soil microorganisms in plant disease suppression and the possibility to steer soil microbial community composition by applying organic amendments to the soil.

Outcome prediction of head and neck squamous cell carcinoma by MRI radiomic signatures.

OBJECTIVES: Head and neck squamous cell carcinoma (HNSCC) shows a remarkable heterogeneity between tumors, which may be captured by a variety of quantitative features extracted from diagnostic images, termed radiomics. The aim of this study was to develop and validate MRI-based radiomic prognostic models in oral and oropharyngeal cancer. MATERIALS AND METHODS: Native T1-weighted images of four independent, retrospective (2005-2013), patient cohorts (n = 102, n = 76, n = 89, and n = 56) were used to delineate primary tumors, and to extract 545 quantitative features from. Subsequently, redundancy filtering and factor analysis were performed to handle collinearity in the data. Next, radiomic prognostic models were trained and validated to predict overall survival (OS) and relapse-free survival (RFS). Radiomic features were compared to and combined with prognostic models based on standard clinical parameters. Performance was assessed by integrated area under the curve (iAUC). RESULTS: In oral cancer, the radiomic model showed an iAUC of 0.69 (OS) and 0.70 (RFS) in the validation cohort, whereas the iAUC in the oropharyngeal cancer validation cohort was 0.71 (OS) and 0.74 (RFS). By integration of radiomic and clinical variables, the most accurate models were defined (iAUC oral cavity, 0.72 (OS) and 0.74 (RFS); iAUC oropharynx, 0.81 (OS) and 0.78 (RFS)), and these combined models outperformed prognostic models based on standard clinical variables only (p < 0.001). CONCLUSIONS: MRI radiomics is feasible in HNSCC despite the known variability in MRI vendors and acquisition protocols, and radiomic features added information to prognostic models based on clinical parameters. KEY POINTS: • MRI radiomics can predict overall survival and relapse-free survival in oral and HPV-negative oropharyngeal cancer. • MRI radiomics provides additional prognostic information to known clinical variables, with the best performance of the combined models. • Variation in MRI vendors and acquisition protocols did not influence performance of radiomic prognostic models.

Impact of Gut Bacteria on the Infection and Transmission of Pathogenic Arboviruses by Biting Midges and Mosquitoes.

Tripartite interactions among insect vectors, midgut bacteria, and viruses may determine the ability of insects to transmit pathogenic arboviruses. Here, we investigated the impact of gut bacteria on the susceptibility of Culicoides nubeculosus and Culicoides sonorensis biting midges for Schmallenberg virus, and of Aedes aegypti mosquitoes for Zika and chikungunya viruses. Gut bacteria were manipulated by treating the adult insects with antibiotics. The gut bacterial communities were investigated using Illumina MiSeq sequencing of 16S rRNA, and susceptibility to arbovirus infection was tested by feeding insects with an infectious blood meal. Antibiotic treatment led to changes in gut bacteria for all insects. Interestingly, the gut bacterial composition of untreated Ae. aegypti and C. nubeculosus showed Asaia as the dominant genus, which was drastically reduced after antibiotic treatment. Furthermore, antibiotic treatment resulted in relatively more Delftia bacteria in both biting midge species, but not in mosquitoes. Antibiotic treatment and subsequent changes in gut bacterial communities were associated with a significant, 1.8-fold increased infection rate of C. nubeculosus with Schmallenberg virus, but not for C. sonorensis. We did not find any changes in infection rates for Ae. aegypti mosquitoes with Zika or chikungunya virus. We conclude that resident gut bacteria may dampen arbovirus transmission in biting midges, but not so in mosquitoes. Use of antimicrobial compounds at livestock farms might therefore have an unexpected contradictory effect on the health of animals, by increasing the transmission of viral pathogens by biting midges.

Improved high-dimensional prediction with Random Forests by the use of co-data.

BACKGROUND: Prediction in high dimensional settings is difficult due to the large number of variables relative to the sample size. We demonstrate how auxiliary 'co-data' can be used to improve the performance of a Random Forest in such a setting. RESULTS: Co-data are incorporated in the Random Forest by replacing the uniform sampling probabilities that are used to draw candidate variables by co-data moderated sampling probabilities. Co-data here are defined as any type information that is available on the variables of the primary data, but does not use its response labels. These moderated sampling probabilities are, inspired by empirical Bayes, learned from the data at hand. We demonstrate the co-data moderated Random Forest (CoRF) with two examples. In the first example we aim to predict the presence of a lymph node metastasis with gene expression data. We demonstrate how a set of external p-values, a gene signature, and the correlation between gene expression and DNA copy number can improve the predictive performance. In the second example we demonstrate how the prediction of cervical (pre-)cancer with methylation data can be improved by including the location of the probe relative to the known CpG islands, the number of CpG sites targeted by a probe, and a set of p-values from a related study. CONCLUSION: The proposed method is able to utilize auxiliary co-data to improve the performance of a Random Forest.

Corrigendum: Animal-based factors prior to infection predict histological disease outcome in porcine reproductive and respiratory syndrome virus- and Actinobacillus pleuropneumoniae-infected pigs.

[This corrects the article DOI: 10.3389/fvets.2021.742877.].

Driving factors of influenza transmission in the Netherlands.

Influenza epidemics in temperate regions show a characteristic seasonal pattern with peak incidence occurring in winter. Previous research has shown that low absolute humidity and school holidays can both affect influenza transmission. During an epidemic, transmission is strongly influenced by the depletion of susceptibles (i.e., increase in the number of those immune). To assess how much variability in influenza transmission intensity is due to each of these driving factors, we used a long time series of the number of weekly visits to general practitioners for influenzalike illness in the Netherlands from 1970-2011 and transformed this into a time series of weekly influenza reproduction numbers, which are a measure of transmission intensity. We used statistical regression techniques to quantify how the reproduction numbers were affected by each driving factor. We found a clear ranking of importance of driving factors in explaining the variation in transmission intensity. Most of the variation (30%) was explained by the depletion of susceptibles during the season, 27% was explained by between-season effects, and 3% was explained by absolute humidity. School holidays at the Christmas period did not have a statistically significant effect on influenza transmission. Although the influence of absolute humidity was small, its seasonal fluctuations may determine when sustained influenza transmission is possible and may thus drive influenza seasonality.

Estimating the generation interval of influenza A (H1N1) in a range of social settings.

A proper understanding of the infection dynamics of influenza A viruses hinges on the availability of reliable estimates of key epidemiologic parameters such as the reproduction number, intrinsic growth rate, and generation interval. Often the generation interval is assumed to be similar in different settings although there is little evidence justifying this. Here we estimate the generation interval for stratifications based on age, cluster size, and social setting (camp, school, workplace, household) using data from 16 clusters of Novel Influenza A (H1N1) in the Netherlands. Our analyses are based on a Bayesian inferential framework, enabling flexible handling of both missing infection links and missing times of symptoms onset. The analysis indicates that a stratification that allows the generation interval to differ by social setting fits the data best. Specifically, the estimated generation interval was shorter in households (2.1 days [95% credible interval = 1.6-2.9]) and camps (2.3 days [1.4-3.4]) than in workplaces (2.7 days [1.9-3.7]) and schools (3.4 days [2.5-4.5]). Our findings could be the result of differences in the number of contacts between settings, differences in prophylactic use of antivirals between settings, and differences in underreporting.

Pitfalls in the statistical analysis of microbiome amplicon sequencing data.

Microbiome data are characterized by several aspects that make them challenging to analyse statistically: they are compositional, high dimensional and rich in zeros. A large array of statistical methods exist to analyse these data. Some are borrowed from other fields, such as ecology or RNA-sequencing, while others are custom-made for microbiome data. The large range of available methods, and which is continuously expanding, means that researchers have to invest considerable effort in choosing what method(s) to apply. In this paper we list 14 statistical methods or approaches that we think should be generally avoided. In several cases this is because we believe the assumptions behind the method are unlikely to be met for microbiome data. In other cases we see methods that are used in ways they are not intended to be used. We believe researchers would be helped by more critical evaluations of existing methods, as not all methods in use are suitable or have been sufficiently reviewed. We hope this paper contributes to a critical discussion on what methods are appropriate to use in the analysis of microbiome data.

Pairwise ratio-based differential abundance analysis of infant microbiome 16S sequencing data.

Differential abundance analysis of infant 16S microbial sequencing data is complicated by challenging data properties, including high sparsity, extreme dispersion and the relative nature of the information contained within the data. In this study, we propose a pairwise ratio analysis that uses the compositional data analysis principle of subcompositional coherence and merges it with a beta-binomial regression model. The resulting method provides a flexible and easily interpretable approach to infant 16S sequencing data differential abundance analysis that does not require zero imputation. We evaluate the proposed method using infant 16S data from clinical trials and demonstrate that the proposed method has the power to detect differences, and demonstrate how its results can be used to gain insights. We further evaluate the method using data-inspired simulations and compare its power against related methods. Our results indicate that power is high for pairwise differential abundance analysis of taxon pairs that have a large abundance. In contrast, results for sparse taxon pairs show a decrease in power and substantial variability in method performance. While our method shows promising performance on well-measured subcompositions, we advise strong filtering steps in order to avoid excessive numbers of underpowered comparisons in practical applications.

Circadian rhythms and diurnal patterns in the feed intake behaviour of growing-finishing pigs.

The feeding behaviour of growing-finishing pigs is an important indicator of performance, health and welfare, but this use is limited by its large, poorly-understood variation. We explored the variation in basal feed intake of individual pigs by detecting circadian rhythms, extracting features of diurnal patterns and assessing consistency over time, from day-to-day and across age. Hourly feed intake data of individual pigs (n = 110) was obtained during one growing-finishing phase, using electronic feeding stations. We applied wavelet analysis to assess rhythms and a hurdle generalised additive model to extract features of diurnal patterns. We found that circadian rhythms could be detected during 58 ± 3% (mean ± standard error) of days in the growing-finishing phase (range 0-100%), predominantly at older ages. Although the group diurnal intake pattern was alternans (small morning peak, larger afternoon peak), individual pigs showed a range of diurnal patterns that changed with age, differing mostly in the extent of night fasting and day-to-day consistency. Our results suggest that the type, day-to-day consistency and age development of diurnal patterns in feed intake show general group patterns but also differ between pigs. Using this knowledge, promising features may be selected to compare against production, health and welfare parameters.

Genetic Mapping of the Root Mycobiota in Rice and its Role in Drought Tolerance.

BACKGROUND: Rice is the second most produced crop worldwide, but is highly susceptible to drought. Micro-organisms can potentially alleviate the effects of drought. The aim of the present study was to unravel the genetic factors involved in the rice-microbe interaction, and whether genetics play a role in rice drought tolerance. For this purpose, the composition of the root mycobiota was characterized in 296 rice accessions (Oryza sativa L. subsp. indica) under control and drought conditions. Genome wide association mapping (GWAS) resulted in the identification of ten significant (LOD > 4) single nucleotide polymorphisms (SNPs) associated with six root-associated fungi: Ceratosphaeria spp., Cladosporium spp., Boudiera spp., Chaetomium spp., and with a few fungi from the Rhizophydiales order. Four SNPs associated with fungi-mediated drought tolerance were also found. Genes located around those SNPs, such as a DEFENSIN-LIKE (DEFL) protein, EXOCYST TETHERING COMPLEX (EXO70), RAPID ALKALINIZATION FACTOR-LIKE (RALFL) protein, peroxidase and xylosyltransferase, have been shown to be involved in pathogen defense, abiotic stress responses and cell wall remodeling processes. Our study shows that rice genetics affects the recruitment of fungi, and that some fungi affect yield under drought. We identified candidate target genes for breeding to improve rice-fungal interactions and hence drought tolerance.

Effect of Type of Cow-Calf Contact on Health, Blood Parameters, and Performance of Dairy Cows and Calves.

Prolonged cow-calf contact (CCC) could potentially improve dairy calf welfare. However, it is currently unknown how different types of CCC affect animals' biological functions. We evaluated health and performance parameters of dairy calves and their dams, where calves: (i) had no contact with their dam (NC), in which the calf was removed from the dam directly after birth (n = 10); (ii) were allowed to have partial contact (PC) with their dam, in which the calf was housed in a calf pen adjacent to the cow area allowing physical contact on the initiative of the dam but no suckling (n = 18); (iii) were allowed to have full contact (FC) with their dam, including suckling, in which calves were housed together with their dams in a free-stall barn (n = 20). Throughout the first 7 weeks postpartum, data were collected on the health status, fecal microbiota, hematological profile, immune and hormonal parameters, and growth rates of calves, and on the health status, metabolic responses, and performance of dams. Overall, FC calves had more health issues (P = 0.02) and a tendency for higher antibiotic usage (P = 0.07) than NC calves. Additionally, FC calves showed elevated levels of erythrocytes, hematocrit, hemoglobin, and leukocytes on day 49 compared to NC calves (P < 0.001). Calf fecal microbiota changed over time, and we found preliminary evidence that fecal microbiota is affected by the type of CCC, as reflected by differences in relative abundances of taxa including Lactobacillus in FC calves compared to NC and PC calves except on days 7 and 66. The FC calves had a greater average daily gain in body weight than NC and PC calves (P = 0.002). Cow health was not affected by the type of CCC, although in the first 7 weeks of lactation FC cows had a lower machine-gained milk yield accompanied by a lower fat percentage than NC and PC cows (P < 0.001). These results indicate that full contact posed a challenge for calf health, presumably because the housing conditions of FC calves in this experimental context were suboptimal. Secondly, ad libitum suckling leads to higher weight gains and negatively affected milk fat content besides machine-gained yields. More research into strategies to improve cow-calf housing and management in CCC systems is warranted.

Risk based culling for highly infectious diseases of livestock.

The control of highly infectious diseases of livestock such as classical swine fever, foot-and-mouth disease, and avian influenza is fraught with ethical, economic, and public health dilemmas. Attempts to control outbreaks of these pathogens rely on massive culling of infected farms, and farms deemed to be at risk of infection. Conventional approaches usually involve the preventive culling of all farms within a certain radius of an infected farm. Here we propose a novel culling strategy that is based on the idea that farms that have the highest expected number of secondary infections should be culled first. We show that, in comparison with conventional approaches (ring culling), our new method of risk based culling can reduce the total number of farms that need to be culled, the number of culled infected farms (and thus the expected number of human infections in case of a zoonosis), and the duration of the epidemic. Our novel risk based culling strategy requires three pieces of information, viz. the location of all farms in the area at risk, the moments when infected farms are detected, and an estimate of the distance-dependent probability of transmission.

High probability of avian influenza virus (H7N7) transmission from poultry to humans active in disease control on infected farms.

An epizootic of avian influenza (H7N7) caused a large number of human infections in The Netherlands in 2003. We used data from this epizootic to estimate infection probabilities for persons involved in disease control on infected farms. Analyses were based on databases containing information on the infected farms, person-visits to these farms, and exposure variables (number of birds present, housing type, poultry type, depopulation method, period during epizootic). Case definition was based on self-reported conjunctivitis and positive response to hemagglutination inhibition assay. A high infection probability was associated with clinical inspection of poultry in the area surrounding infected flocks (7.6%; 95% confidence interval [CI], 1.4%-18.9%) and active culling during depopulation (6.2%; 95% CI, 3.7%-9.6%). Low probabilities were estimated for management of biosecurity (0.0%; 95% CI, 0.0%-1.0%) and cleaning assistance during depopulation (0.0%; 95% CI, 0.0%-9.2%). No significant association was observed between the probability of infection and the exposure variables.

Log-ratio analysis of microbiome data with many zeroes is library size dependent.

Microbiome composition data collected through amplicon sequencing are count data on taxa in which the total count per sample (the library size) is an artefact of the sequencing platform, and as a result, such data are compositional. To avoid library size dependency, one common way of analysing multivariate compositional data is to perform a principal component analysis (PCA) on data transformed with the centred log-ratio, hereafter called a log-ratio PCA. Two aspects typical of amplicon sequencing data are the large differences in library size and the large number of zeroes. In this study, we show on real data and by simulation that, applied to data that combine these two aspects, log-ratio PCA is nevertheless heavily dependent on the library size. This leads to a reduction in power when testing against any explanatory variable in log-ratio redundancy analysis. If there is additionally a correlation between the library size and the explanatory variable, then the type 1 error becomes inflated. We explore putative solutions to this problem.

Animal-Based Factors Prior to Infection Predict Histological Disease Outcome in Porcine Reproductive and Respiratory Syndrome Virus- and Actinobacillus pleuropneumoniae-Infected Pigs.

A large variety of clinical manifestation in individual pigs occurs after infection with pathogens involved in porcine respiratory disease complex (PRDC). Some pigs are less prone to develop respiratory disease symptoms. The variation in clinical impact after infection and the recovery capacity of an individual animal are measures of its resilience. In this paper, we examined which ones of a range of animal-based factors (rectal temperature, body weight, skin lesion scores, behavior, natural antibody serum levels, serum levels of white blood cells, and type of T and granulocyte subsets) when measured prior to infection are related to disease severity. These animal-based factors and the interaction with housing regimen of the piglets (conventional or enriched) were modeled using linear regression to predict disease severity using a dataset acquired from a previous study using a well-established experimental coinfection model of porcine reproductive and respiratory syndrome virus (PRRSV) and Actinobacillus pleuropneumoniae. Both PRRSV and A. pleuropneumoniae are often involved in PRDC. Histological lung lesion score of each animal was used as a measure for PRDC severity after infection. Prior to infection, higher serum levels of lymphocytes (CD3+), naïve T helper (CD3+CD4+CD8-), CD8+ (as well as higher relative levels of CD8+), and memory T helper (CD3+CD4+CD8+) cells and higher relative levels of granulocytes (CD172a) were related to reduced disease severity in both housing systems. Raised serum concentrations of natural IgM antibodies binding to keyhole limpet hemocyanin (KLH) were also related to reduced disease severity after infection. Increased levels of skin lesions at the central body part (after weaning and before infection) were related to increased disease severity in conventional housing systems only. High resisters showed a lower histological lung lesion score, which appeared unrelated to sex. Body temperature, behavior, and growth prior to infections were influenced by housing regimen but could not explain the variation in lung lesion scores after infection. Raised basal lymphocyte counts and lower skin lesion scores are related to reduced disease severity independent of or dependent on housing system, respectively. In conclusion, our study identifies intrinsic animal-based measures using linear regression analysis that predicts resilience to infections in pigs.

Effectiveness of personal protective equipment and oseltamivir prophylaxis during avian influenza A (H7N7) epidemic, the Netherlands, 2003.

We analyzed the effectiveness of personal protective equipment and oseltamivir use during the 2003 avian influenza A (H7N7) epidemic in the Netherlands by linking databases containing information about farm visits, human infections, and use of oseltamivir and personal protective equipment. Using a stringent case definition, based on self-reported conjunctivitis combined with a positive hemagglutination-inhibition assay, we found that prophylactic treatment with oseltamivir significantly reduced the risk for infection per farm visit from 0.145 (95% confidence interval [CI] 0.078-0.233) to 0.031 (95% CI 0.008-0.073). The protective effect was ≈79% (95% CI 40%-97%). These results are comparable with the reported effect of prophylactic treatment with oseltamivir on human seasonal influenza. No significant protective effect was found for use of respirators or safety glasses, possibly because of limitations of the data.

Learning from a lot: Empirical Bayes for high-dimensional model-based prediction.

Empirical Bayes is a versatile approach to "learn from a lot" in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, stored in public repositories. We review applications of a variety of empirical Bayes methods to several well-known model-based prediction methods, including penalized regression, linear discriminant analysis, and Bayesian models with sparse or dense priors. We discuss "formal" empirical Bayes methods that maximize the marginal likelihood but also more informal approaches based on other data summaries. We contrast empirical Bayes to cross-validation and full Bayes and discuss hybrid approaches. To study the relation between the quality of an empirical Bayes estimator and p, the number of variables, we consider a simple empirical Bayes estimator in a linear model setting. We argue that empirical Bayes is particularly useful when the prior contains multiple parameters, which model a priori information on variables termed "co-data". In particular, we present two novel examples that allow for co-data: first, a Bayesian spike-and-slab setting that facilitates inclusion of multiple co-data sources and types and, second, a hybrid empirical Bayes-full Bayes ridge regression approach for estimation of the posterior predictive interval.

Heterosubtypic cross-reactivity of HA1 antibodies to influenza A, with emphasis on nonhuman subtypes (H5N1, H7N7, H9N2).

Epidemics of influenza A vary greatly in size and age distribution of cases, and this variation is attributed to varying levels of pre-existing immunity. Recent studies have shown that antibody-mediated immune responses are more cross-reactive than previously believed, and shape patterns of humoral immunity to influenza A viruses over long periods. Here we quantify antibody responses to the hemagglutinin subunit 1 (HA1) across a range of subtypes using protein microarray analysis of cross-sectional serological surveys carried out in the Netherlands before and after the A/2009 (H1N1) pandemic. We find significant associations of responses, both within and between subtypes. Interestingly, substantial overall reactivity is observed to HA1 of avian H7N7 and H9N2 viruses. Seroprevalence of H7N7 correlates with antibody titers to A/1968 (H3N2), and is highest in persons born between 1954 and 1969. Seroprevalence of H9N2 is high across all ages, and correlates strongly with A/1957 (H2N2). This correlation is most pronounced in A/2009 (H1N1) infected persons born after 1968 who have never encountered A/1957 (H2N2)-like viruses. We conclude that heterosubtypic antibody cross-reactivity, both between human subtypes and between human and nonhuman subtypes, is common in the human population.