A Bayesian approach to Mendelian randomization using summary statistics in the univariable and multivariable settings with correlated pleiotropy.

Mendelian randomization uses genetic variants as instrumental variables to make causal inferences on the effect of an exposure on an outcome. Due to the recent abundance of high-powered genome-wide association studies, many putative causal exposures of interest have large numbers of independent genetic variants with which they associate, each representing a potential instrument for use in a Mendelian randomization analysis. Such polygenic analyses increase the power of the study design to detect causal effects; however, they also increase the potential for bias due to instrument invalidity. Recent attention has been given to dealing with bias caused by correlated pleiotropy, which results from violation of the "instrument strength independent of direct effect" assumption. Although methods have been proposed that can account for this bias, a number of restrictive conditions remain in many commonly used techniques. In this paper, we propose a Bayesian framework for Mendelian randomization that provides valid causal inference under very general settings. We propose the methods MR-Horse and MVMR-Horse, which can be performed without access to individual-level data, using only summary statistics of the type commonly published by genome-wide association studies, and can account for both correlated and uncorrelated pleiotropy. In simulation studies, we show that the approach retains type I error rates below nominal levels even in high-pleiotropy scenarios. We demonstrate the proposed approaches in applied examples in both univariable and multivariable settings, some with very weak instruments.

Using genetic association data to guide drug discovery and development: Review of methods and applications.

Evidence on the validity of drug targets from randomized trials is reliable but typically expensive and slow to obtain. In contrast, evidence from conventional observational epidemiological studies is less reliable because of the potential for bias from confounding and reverse causation. Mendelian randomization is a quasi-experimental approach analogous to a randomized trial that exploits naturally occurring randomization in the transmission of genetic variants. In Mendelian randomization, genetic variants that can be regarded as proxies for an intervention on the proposed drug target are leveraged as instrumental variables to investigate potential effects on biomarkers and disease outcomes in large-scale observational datasets. This approach can be implemented rapidly for a range of drug targets to provide evidence on their effects and thus inform on their priority for further investigation. In this review, we present statistical methods and their applications to showcase the diverse opportunities for applying Mendelian randomization in guiding clinical development efforts, thus enabling interventions to target the right mechanism in the right population group at the right time. These methods can inform investigators on the mechanisms underlying drug effects, their related biomarkers, implications for the timing of interventions, and the population subgroups that stand to gain the most benefit. Most methods can be implemented with publicly available data on summarized genetic associations with traits and diseases, meaning that the only major limitations to their usage are the availability of appropriately powered studies for the exposure and outcome and the existence of a suitable genetic proxy for the proposed intervention.

Noise-augmented directional clustering of genetic association data identifies distinct mechanisms underlying obesity.

Clustering genetic variants based on their associations with different traits can provide insight into their underlying biological mechanisms. Existing clustering approaches typically group variants based on the similarity of their association estimates for various traits. We present a new procedure for clustering variants based on their proportional associations with different traits, which is more reflective of the underlying mechanisms to which they relate. The method is based on a mixture model approach for directional clustering and includes a noise cluster that provides robustness to outliers. The procedure performs well across a range of simulation scenarios. In an applied setting, clustering genetic variants associated with body mass index generates groups reflective of distinct biological pathways. Mendelian randomization analyses support that the clusters vary in their effect on coronary heart disease, including one cluster that represents elevated body mass index with a favourable metabolic profile and reduced coronary heart disease risk. Analysis of the biological pathways underlying this cluster identifies inflammation as potentially explaining differences in the effects of increased body mass index on coronary heart disease.

HAM-ART: An optimised culture-free Hi-C metagenomics pipeline for tracking antimicrobial resistance genes in complex microbial communities.

Shotgun metagenomics is a powerful tool to identify antimicrobial resistance (AMR) genes in microbiomes but has the limitation that extrachromosomal DNA, such as plasmids, cannot be linked with the host bacterial chromosome. Here we present a comprehensive laboratory and bioinformatics pipeline HAM-ART (Hi-C Assisted Metagenomics for Antimicrobial Resistance Tracking) optimised for the generation of metagenome-assembled genomes including both chromosomal and extrachromosomal AMR genes. We demonstrate the performance of the pipeline in a study comparing 100 pig faecal microbiomes from low- and high-antimicrobial use pig farms (organic and conventional farms). We found significant differences in the distribution of AMR genes between low- and high-antimicrobial use farms including a plasmid-borne lincosamide resistance gene exclusive to high-antimicrobial use farms in three species of Lactobacilli. The bioinformatics pipeline code is available at https://github.com/lkalmar/HAM-ART.

Identification of Campylobacter jejuni and Campylobacter coli genes contributing to oxidative stress response using TraDIS analysis.

BACKGROUND: Campylobacter jejuni and Campylobacter coli are the major causative agents of bacterial gastroenteritis worldwide and are known obligate microaerophiles. Despite being sensitive to oxygen and its reduction products, both species are readily isolated from animal food products kept under atmospheric conditions where they face high oxygen tension levels. RESULTS: In this study, Transposon Directed Insertion-site Sequencing (TraDIS) was used to investigate the ability of one C. jejuni strain and two C. coli strains to overcome oxidative stress, using H2O2 to mimic oxidative stress. Genes were identified that were required for oxidative stress resistance for each individual strain but also allowed a comparison across the three strains. Mutations in the perR and ahpC genes were found to increase Campylobacter tolerance to H2O2. The roles of these proteins in oxidative stress were previously known in C. jejuni, but this data indicates that they most likely play a similar role in C. coli. Mutation of czcD decreased Campylobacter tolerance to H2O2. The role of CzcD, which functions as a zinc exporter, has not previously been linked to oxidative stress. The TraDIS data was confirmed using defined deletions of perR and czcD in C. coli 15-537360. CONCLUSIONS: This is the first study to investigate gene fitness in both C. jejuni and C. coli under oxidative stress conditions and highlights both similar roles for certain genes for both species and highlights other genes that have a role under oxidative stress.

A novel family of defensin-like peptides from Hermetia illucens with antibacterial properties.

BACKGROUND: The world faces a major infectious disease challenge. Interest in the discovery, design, or development of antimicrobial peptides (AMPs) as an alternative approach for the treatment of bacterial infections has increased. Insects are a good source of AMPs which are the main effector molecules of their innate immune system. Black Soldier Fly Larvae (BSFL) are being developed for large-scale rearing for food sustainability, waste reduction and as sustainable animal and fish feed. Bioinformatic studies have suggested that BSFL have the largest number of AMPs identified in insects. However, most AMPs identified in BSF have not yet undergone antimicrobial evaluation but are promising leads to treat critical infections. RESULTS: Jg7197.t1, Jg7902.t1 and Jg7904.t1 were expressed into the haemolymph of larvae following infection with Salmonella enterica serovar Typhimurium and were predicted to be AMPs using the computational tool ampir. The genes encoding these proteins were within 2 distinct clusters in chromosome 1 of the BSF genome. Following removal of signal peptides, predicted structures of the mature proteins were superimposed, highlighting a high degree of structural conservation. The 3 AMPs share primary sequences with proteins that contain a Kunitz-binding domain; characterised for inhibitory action against proteases, and antimicrobial activities. An in vitro antimicrobial screen indicated that heterologously expressed SUMO-Jg7197.t1 and SUMO-Jg7902.t1 did not show activity against 12 bacterial strains. While recombinant SUMO-Jg7904.t1 had antimicrobial activity against a range of Gram-negative and Gram-positive bacteria, including the serious pathogen Pseudomonas aeruginosa. CONCLUSIONS: We have cloned and purified putative AMPs from BSFL and performed initial in vitro experiments to evaluate their antimicrobial activity. In doing so, we have identified a putative novel defensin-like AMP, Jg7904.t1, encoded in a paralogous gene cluster, with antimicrobial activity against P. aeruginosa.

Causal Relationship Between Kidney Function and Cancer Risk: A Mendelian Randomization Study.

RATIONALE & OBJECTIVE: Patients treated with kidney replacement therapy experience a 1.5 to 2-fold increased risk of cancer and cancer mortality compared to the general population. Whether this excess risk extends to people with earlier stages of chronic kidney disease is unclear. This study explored the potentially causal relationship between reduced kidney function and cancer. STUDY DESIGN: Two-sample Mendelian randomization (MR). SETTING & PARTICIPANTS: Genome Wide Association Study (GWAS) summary statistics for estimated glomerular filtration rate (eGFR) (n=567,460) and urinary albuminuria (UACR) (n=127,865) from the CKDGen consortium and cancer outcomes from the UK Biobank (n=407,329). EXPOSURES: eGFR and UACR. OUTCOMES: Overall cancer incidence, cancer-related mortality, and site-specific colorectal, lung, and urinary tract cancer incidence. ANALYTICAL APPROACH: Univariable and multivariable MR were conducted for all outcomes using inverse-variance weighted methods. RESULTS: The mean eGFR and median UACR were 91.4 mL/min/1.73m2 and 9.32 mg/g in the CKDGen and 90.4 mL/min/1.73m2 and 9.29 mg/g in the UK Biobank. There were 98,093 cases of cancer, 6,664 colorectal, 3584 lung, and 3,271 urinary tract. There were 15,850 cases of cancer-related death, The genetic instruments for eGFR and UACR comprised 34 and 38 variants, respectively. Genetically predicted kidney function (eGFR and UACR) was not associated with overall cancer risk or cancer death. No associations of genetically predicted eGFR and UACR with overall cancer incidence were observed; odds ratio (95%CI; p-value) of 0.88 (0.40-1.97; p=0.76) and 0.90 (0.78-1.04; p=0.16), respectively. An adjusted generalized additive model for eGFR and cancer demonstrated evidence of non-linearity. There was no evidence of a causal association between eGFR and cancer in a stratified MR. LIMITATIONS: To avoid overlapping samples a smaller GWAS for UACR was used, reducing the strength of the instrument and potentially introducing population stratification. CONCLUSIONS: These findings did not demonstrate a causal association of kidney function with overall cancer incidence or cancer-related death.

Plasma protein signatures of adult asthma.

BACKGROUND: Adult asthma is complex and incompletely understood. Plasma proteomics is an evolving technique that can both generate biomarkers and provide insights into disease mechanisms. We aimed to identify plasma proteomic signatures of adult asthma. METHODS: Protein abundance in plasma was measured in individuals from the Agricultural Lung Health Study (ALHS) (761 asthma, 1095 non-case) and the Atherosclerosis Risk in Communities study (470 asthma, 10,669 non-case) using the SOMAScan 5K array. Associations with asthma were estimated using covariate adjusted logistic regression and meta-analyzed using inverse-variance weighting. Additionally, in ALHS, we examined phenotypes based on both asthma and seroatopy (asthma with atopy (n = 207), asthma without atopy (n = 554), atopy without asthma (n = 147), compared to neither (n = 948)). RESULTS: Meta-analysis of 4860 proteins identified 115 significantly (FDR<0.05) associated with asthma. Multiple signaling pathways related to airway inflammation and pulmonary injury were enriched (FDR<0.05) among these proteins. A proteomic score generated using machine learning provided predictive value for asthma (AUC = 0.77, 95% CI = 0.75-0.79 in training set; AUC = 0.72, 95% CI = 0.69-0.75 in validation set). Twenty proteins are targeted by approved or investigational drugs for asthma or other conditions, suggesting potential drug repurposing. The combined asthma-atopy phenotype showed significant associations with 20 proteins, including five not identified in the overall asthma analysis. CONCLUSION: This first large-scale proteomics study identified over 100 plasma proteins associated with current asthma in adults. In addition to validating previous associations, we identified many novel proteins that could inform development of diagnostic biomarkers and therapeutic targets in asthma management.

Mendelian randomization for nephrologists.

Confounding is a major limitation of observational studies. Mendelian randomization (MR) is a powerful study design that uses genetic variants as instrumental variables to enable examination of the causal effect of an exposure on an outcome in observational data. With the emergence of large-scale genome-wide association studies in nephrology over the past decade, MR has become a popular method to establish causal inferences. However, MR is a complex and challenging methodology that requires careful consideration to ensure robust results. This review article aims to summarize the basic concepts of MR, its application and relevance in nephrology, and the methodological challenges and limitations as well as discuss the current guidelines for design and reporting. With reference to a clinically relevant example of examining the causal relationship between the estimated glomerular filtration rate and cancer, this review outlines the key steps to conducting an MR study, including the key considerations and potential pitfalls at each step. These include defining the clinical question, selecting the data sources, identifying and refining appropriate genetic variants by considering linkage disequilibrium and associations with potential confounders, harmonization of variants across data sets, validation of the genetic instrument by assessing its strength, estimation of the causal effects, confirming the validity of the findings, and interpreting and reporting results.

An efficient and robust approach to Mendelian randomization with measured pleiotropic effects in a high-dimensional setting.

Valid estimation of a causal effect using instrumental variables requires that all of the instruments are independent of the outcome conditional on the risk factor of interest and any confounders. In Mendelian randomization studies with large numbers of genetic variants used as instruments, it is unlikely that this condition will be met. Any given genetic variant could be associated with a large number of traits, all of which represent potential pathways to the outcome which bypass the risk factor of interest. Such pleiotropy can be accounted for using standard multivariable Mendelian randomization with all possible pleiotropic traits included as covariates. However, the estimator obtained in this way will be inefficient if some of the covariates do not truly sit on pleiotropic pathways to the outcome. We present a method that uses regularization to identify which out of a set of potential covariates need to be accounted for in a Mendelian randomization analysis in order to produce an efficient and robust estimator of a causal effect. The method can be used in the case where individual-level data are not available and the analysis must rely on summary-level data only. It can be used where there are any number of potential pleiotropic covariates up to the number of genetic variants less one. We show the results of simulation studies that demonstrate the performance of the proposed regularization method in realistic settings. We also illustrate the method in an applied example which looks at the causal effect of urate plasma concentration on coronary heart disease.

Application of TraDIS to define the core essential genome of Campylobacter jejuni and Campylobacter coli.

Campylobacter species are the major cause of bacterial gastroenteritis. As there is no effective vaccine, combined with the rapid increase in antimicrobial resistant strains, there is a need to identify new targets for intervention. Essential genes are those that are necessary for growth and/or survival, making these attractive targets. In this study, comprehensive transposon mutant libraries were created in six C. jejuni strains, four C. coli strains and one C. lari and C. hyointestinalis strain, allowing for those genes that cannot tolerate a transposon insertion being called as essential. Comparison of essential gene lists using core genome analysis can highlight those genes which are common across multiple strains and/or species. Comparison of C. jejuni and C. coli, the two species that cause the most disease, identified 316 essential genes. Genes of interest highlighted members of the purine pathway being essential for C. jejuni whilst also finding that a functional potassium uptake system is essential. Protein-protein interaction networks using these essential gene lists also highlighted proteins in the purine pathway being major 'hub' proteins which have a large number of interactors across the network. When adding in two more species (C. lari and C. hyointestinalis) the essential gene list reduces to 261. Within these 261 essential genes, there are many genes that have been found to be essential in other bacteria. These include htrB and PEB4, which have previously been found as core virulence genes across Campylobacter species in other studies. There were 21 genes which have no known function with eight of these being associated with the membrane. These surface-associated essential genes may provide attractive targets. The essential gene lists presented will help to prioritise targets for the development of novel therapeutic and preventative interventions.

An attacin antimicrobial peptide, Hill_BB_C10074, from Hermetia illucens with anti-Pseudomonas aeruginosa activity.

BACKGROUND: There is a global need to develop new therapies to treat infectious diseases and tackle the rise in antimicrobial resistance. To date, the larvae of the Black Solider Fly, Hermetia illucens, have the largest repertoire of antimicrobial peptides derived from insects. Antimicrobial peptides are of particular interest in the exploration of alternative antimicrobials due to their potent action and reduced propensity to induce resistance compared with more traditional antibiotics. RESULTS: The predicted attacin from H. illucens, Hill_BB_C10074, was first identified in the transcriptome of H. illucens populations that had been fed a plant-oil based diet. In this study, recombinant Hill_BB_C10074 (500 µg/mL), was found to possess potent antimicrobial activity against the serious Gram-negative pathogen, Pseudomonas aeruginosa. Sequence and structural homology modelling predicted that Hill_BB_C10074 formed a homotrimeric complex that may form pores in the Gram-negative bacterial outer membrane. In vitro experiments defined the antimicrobial action of Hill_BB_C10074 against P. aeruginosa and transmission electron microscopy and electrochemical impedance spectroscopy confirmed the outer membrane disruptive power of Hill_BB_C10074 which was greater than the clinically relevant antibiotic, polymyxin B. CONCLUSIONS: Combining predictive tools with in vitro approaches, we have characterised Hill_BB_C10074 as an important insect antimicrobial peptide and promising candidate for the future development of clinical antimicrobials.

Development and validation of a CRISPR interference system for gene regulation in Campylobacter jejuni.

BACKGROUND: Campylobacter spp. are the leading cause of bacterial food-borne illness in humans worldwide, with Campylobacter jejuni responsible for 80% of these infections. There is an urgent need to understand fundamental C. jejuni biology for the development of new strategies to prevent and treat infections. The range of molecular tools available to regulate gene expression in C. jejuni is limited, which in turn constrains our ability to interrogate the function of essential and conditionally essential genes. We have addressed this by developing and utilising a CRISPR-based interference system known as CRISPRi in C. jejuni to control gene expression. To achieve this, a catalytically inactive ("dead") cas9 and sgRNA backbone from the Streptococcus pyogenes CRISPRi system was combined with C. jejuni-derived promoters of predetermined expression activities to develop a CRISPRi-based repression tool in C. jejuni strains M1Cam and 81-176. RESULTS: The CRISPRi tool was validated through successful repression of the arylsulphatase-encoding gene astA using a range of sgRNA target sequences spanning the astA gene. The tool was also applied to target astA in an M1Cam CRISPR-Cas9 deletion strain, which showed that the presence of an endogenous CRISPR-Cas9 system did not affect the activity of the CRISPRi-based repression tool. The tool was further validated against the hippicurase-encoding gene hipO. Following this, the flagella genes flgR, flaA, flaB and both flaA and flaB were targeted for CRISPRi-based repression, which resulted in varying levels of motility reduction and flagella phenotypes as determined by phenotypical assays and transmission electron microscopy (TEM). CONCLUSIONS: This is the first report of a CRISPRi-based tool in C. jejuni, which will provide a valuable resource to the Campylobacter community.

Modification of avian pathogenic Escherichia coli χ7122 lipopolysaccharide increases accessibility to glycoconjugate antigens.

BACKGROUND: Worldwide, an estimated 70.7 billion broilers were produced in 2020. With the reduction in use of prophylactic antibiotics as a result of consumer pressure and regulatory oversight alternative approaches, such as vaccination, are required to control bacterial infections. A potential way to produce a multivalent vaccine is via the generation of a glycoconjugate vaccine which consists of an antigenic protein covalently linked to an immunogenic carbohydrate. Protein-glycan coupling technology (PGCT) is an approach to generate glycoconjugates using enzymes that can couple proteins and glycan when produced in bacterial cells. Previous studies have used PGCT to generate a live-attenuated avian pathogenic Escherichia coli (APEC) strain capable of N-glycosylation of target proteins using a chromosomally integrated Campylobacter jejuni pgl locus. However, this proved ineffective against C. jejuni challenge. RESULTS: In this study we demonstrate the lack of surface exposure of glycosylated protein in APEC strain χ7122 carrying the pgl locus. Furthermore, we hypothesise that this may be due to the complex cell-surface architecture of E. coli. To this end, we removed the lipopolysaccharide O-antigen of APEC χ7122 pgl+ via deletion of the wecA gene and demonstrate increased surface exposure of glycosylated antigens (NetB and FlpA) in this strain. We hypothesise that increasing the surface expression of the glycosylated protein would increase the chance of host immune cells being exposed to the glycoconjugate, and therefore the generation of an efficacious immune response would be more likely. CONCLUSIONS: Our results demonstrate an increase in cell surface exposure and therefore accessibility of glycosylated antigens upon removal of lipopolysaccharide antigen from the APEC cell surface.

PglB function and glycosylation efficiency is temperature dependent when the pgl locus is integrated in the Escherichia coli chromosome.

BACKGROUND: Campylobacter is an animal and zoonotic pathogen of global importance, and a pressing need exists for effective vaccines, including those that make use of conserved polysaccharide antigens. To this end, we adapted Protein Glycan Coupling Technology (PGCT) to develop a versatile Escherichia coli strain capable of generating multiple glycoconjugate vaccine candidates against Campylobacter jejuni. RESULTS: We generated a glycoengineering E. coli strain containing the conserved C. jejuni heptasaccharide coding region integrated in its chromosome as a model glycan. This methodology confers three advantages: (i) reduction of plasmids and antibiotic markers used for PGCT, (ii) swift generation of many glycan-protein combinations and consequent rapid identification of the most antigenic proteins or peptides, and (iii) increased genetic stability of the polysaccharide coding-region. In this study, by using the model glycan expressing strain, we were able to test proteins from C. jejuni, Pseudomonas aeruginosa (both Gram-negative), and Clostridium perfringens (Gram-positive) as acceptors. Using this pgl integrant E. coli strain, four glycoconjugates were readily generated. Two glycoconjugates, where both protein and glycan are from C. jejuni (double-hit vaccines), and two glycoconjugates, where the glycan antigen is conjugated to a detoxified toxin from a different pathogen (single-hit vaccines). Because the downstream application of Live Attenuated Vaccine Strains (LAVS) against C. jejuni is to be used in poultry, which have a higher body temperature of 42 °C, we investigated the effect of temperature on protein expression and glycosylation in the E. coli pgl integrant strain. CONCLUSIONS: We determined that glycosylation is temperature dependent and that for the combination of heptasaccharide and carriers used in this study, the level of PglB available for glycosylation is a step limiting factor in the glycosylation reaction. We also demonstrated that temperature affects the ability of PglB to glycosylate its substrates in an in vitro glycosylation assay independent of its transcriptional level.

Pleiotropy robust methods for multivariable Mendelian randomization.

Mendelian randomization is a powerful tool for inferring the presence, or otherwise, of causal effects from observational data. However, the nature of genetic variants is such that pleiotropy remains a barrier to valid causal effect estimation. There are many options in the literature for pleiotropy robust methods when studying the effects of a single risk factor on an outcome. However, there are few pleiotropy robust methods in the multivariable setting, that is, when there are multiple risk factors of interest. In this article we introduce three methods which build on common approaches in the univariable setting: MVMR-Robust; MVMR-Median; and MVMR-Lasso. We discuss the properties of each of these methods and examine their performance in comparison to existing approaches in a simulation study. MVMR-Robust is shown to outperform existing outlier robust approaches when there are low levels of pleiotropy. MVMR-Lasso provides the best estimation in terms of mean squared error for moderate to high levels of pleiotropy, and can provide valid inference in a three sample setting. MVMR-Median performs well in terms of estimation across all scenarios considered, and provides valid inference up to a moderate level of pleiotropy. We demonstrate the methods in an applied example looking at the effects of intelligence, education and household income on the risk of Alzheimer's disease.

Multivalent poultry vaccine development using Protein Glycan Coupling Technology.

BACKGROUND: Poultry is the world's most popular animal-based food and global production has tripled in the past 20 years alone. Low-cost vaccines that can be combined to protect poultry against multiple infections are a current global imperative. Glycoconjugate vaccines, which consist of an immunogenic protein covalently coupled to glycan antigens of the targeted pathogen, have a proven track record in human vaccinology, but have yet to be used for livestock due to prohibitively high manufacturing costs. To overcome this, we use Protein Glycan Coupling Technology (PGCT), which enables the production of glycoconjugates in bacterial cells at considerably reduced costs, to generate a candidate glycan-based live vaccine intended to simultaneously protect against Campylobacter jejuni, avian pathogenic Escherichia coli (APEC) and Clostridium perfringens. Campylobacter is the most common cause of food poisoning, whereas colibacillosis and necrotic enteritis are widespread and devastating infectious diseases in poultry. RESULTS: We demonstrate the functional transfer of C. jejuni protein glycosylation (pgl) locus into the genome of APEC χ7122 serotype O78:H9. The integration caused mild attenuation of the χ7122 strain following oral inoculation of chickens without impairing its ability to colonise the respiratory tract. We exploit the χ7122 pgl integrant as bacterial vectors delivering a glycoprotein decorated with the C. jejuni heptasaccharide glycan antigen. To this end we engineered χ7122 pgl to express glycosylated NetB toxoid from C. perfringens and tested its ability to reduce caecal colonisation of chickens by C. jejuni and protect against intra-air sac challenge with the homologous APEC strain. CONCLUSIONS: We generated a candidate glycan-based multivalent live vaccine with the potential to induce protection against key avian and zoonotic pathogens (C. jejuni, APEC, C. perfringens). The live vaccine failed to significantly reduce Campylobacter colonisation under the conditions tested but was protective against homologous APEC challenge. Nevertheless, we present a strategy towards the production of low-cost "live-attenuated multivalent vaccine factories" with the ability to express glycoconjugates in poultry.

Size-Exclusion Chromatography Separation Reveals That Vesicular and Non-Vesicular Small RNA Profiles Differ in Cell Free Urine.

Urinary extracellular vesicles (EVs) and their RNA cargo are a novel source of biomarkers for various diseases. We aimed to identify the optimal method for isolating small (<200 nm) EVs from human urine prior to small RNA analysis. EVs from filtered healthy volunteer urine were concentrated using three methods: ultracentrifugation (UC); a precipitation-based kit (PR); and ultrafiltration (UF). EVs were further purified by size-exclusion chromatography (SEC). EV preparations were analysed with transmission electron microscopy (TEM), Western blotting, nanoparticle tracking analysis (NTA) and an Agilent Bioanalyzer Small RNA kit. UF yielded the highest number of particles both before and after SEC. Small RNA analysis from UF-concentrated urine identified two major peaks at 10-40 nucleotides (nt) and 40-80 nt. In contrast, EV preparations obtained after UC, PR or SEC combined with any concentrating method, contained predominantly 40-80 nt sized small RNA. Protein fractions from UF+SEC contained small RNA of 10-40 nt in size (consistent with miRNAs). These data indicate that most of the microRNA-sized RNAs in filtered urine are not associated with small-sized EVs, and highlights the importance of removing non-vesicular proteins and RNA from urine EV preparations prior to small RNA analysis.

Multiple change point detection and validation in autoregressive time series data.

It is quite common that the structure of a time series changes abruptly. Identifying these change points and describing the model structure in the segments between these change points is of interest. In this paper, time series data is modelled assuming each segment is an autoregressive time series with possibly different autoregressive parameters. This is achieved using two main steps. The first step is to use a likelihood ratio scan based estimation technique to identify these potential change points to segment the time series. Once these potential change points are identified, modified parametric spectral discrimination tests are used to validate the proposed segments. A numerical study is conducted to demonstrate the performance of the proposed method across various scenarios and compared against other contemporary techniques.

Wellbeing and burnout among medical students in Wales.

Medical students' wellbeing and mental health requires nurturing in order for them to build success in their careers, help people while doing so, and to be happy. To better characterise the current state of wellbeing in medical schools in Wales, medical students from Cardiff and Swansea Universities were asked to complete an online survey as part of a larger international survey regarding their mental health and wellbeing. 266 students responded out of approximately 2150, a rate of 12%. 44 students received a mental health diagnosis whilst at medical school (predominantly depression or anxiety), 65 scored threshold for concerning alcohol consumption using the CAGE questionnaire, and 224 and 230 students reached threshold for the disengagement and exhaustion components of the Oldenburg Burnout Scale, respectively.