Modeling Data Journeys to Inform the Digital Transformation of Kidney Transplant Services: Observational Study.

BACKGROUND: Data journey modeling is a methodology used to establish a high-level overview of information technology (IT) infrastructure in health care systems. It allows a better understanding of sociotechnical barriers and thus informs meaningful digital transformation. Kidney transplantation is a complex clinical service involving multiple specialists and providers. The referral pathway for a transplant requires the centralization of patient data across multiple IT solutions and health care organizations. At present, there is a poor understanding of the role of IT in this process, specifically regarding the management of patient data, clinical communication, and workflow support. OBJECTIVE: To apply data journey modeling to better understand interoperability, data access, and workflow requirements of a regional multicenter kidney transplant service. METHODS: An incremental methodology was used to develop the data journey model. This included review of service documents, domain expert interviews, and iterative modeling sessions. Results were analyzed based on the LOAD (landscape, organizations, actors, and data) framework to provide a meaningful assessment of current data management challenges and inform ways for IT to overcome these challenges. RESULTS: Results were presented as a diagram of the organizations (n=4), IT systems (n>9), actors (n>4), and data journeys (n=0) involved in the transplant referral pathway. The diagram revealed that all movement of data was dependent on actor interaction with IT systems and manual transcription of data into Microsoft Word (Microsoft, Inc) documents. Each actor had between 2 and 5 interactions with IT systems to capture all relevant data, a process that was reported to be time consuming and error prone. There was no interoperability within or across organizations, which led to delays as clinical teams manually transferred data, such as medical history and test results, via post or email. CONCLUSIONS: Overall, data journey modeling demonstrated that human actors, rather than IT systems, formed the central focus of data movement. The IT landscape did not complement this workflow and exerted a significant administrative burden on clinical teams. Based on this study, future solutions must consider regional interoperability and specialty-specific views of data to support multi-organizational clinical services such as transplantation.

A Blockchain-Based Dynamic Consent Architecture to Support Clinical Genomic Data Sharing (ConsentChain): Proof-of-Concept Study.

BACKGROUND: In clinical genomics, sharing of rare genetic disease information between genetic databases and laboratories is essential to determine the pathogenic significance of variants to enable the diagnosis of rare genetic diseases. Significant concerns regarding data governance and security have reduced this sharing in practice. Blockchain could provide a secure method for sharing genomic data between involved parties and thus help overcome some of these issues. OBJECTIVE: This study aims to contribute to the growing knowledge of the potential role of blockchain technology in supporting the sharing of clinical genomic data by describing blockchain-based dynamic consent architecture to support clinical genomic data sharing and provide a proof-of-concept implementation, called ConsentChain, for the architecture to explore its performance. METHODS: The ConsentChain requirements were captured from a patient forum to identify security and consent concerns. The ConsentChain was developed on the Ethereum platform, in which smart contracts were used to model the actions of patients, who may provide or withdraw consent to share their data; the data creator, who collects and stores patient data; and the data requester, who needs to query and access the patient data. A detailed analysis was undertaken of the ConsentChain performance as a function of the number of transactions processed by the system. RESULTS: We describe ConsentChain, a blockchain-based system that provides a web portal interface to support clinical genomic sharing. ConsentChain allows patients to grant or withdraw data requester access and allows data requesters to query and submit access to data stored in a secure off-chain database. We also developed an ontology model to represent patient consent elements into machine-readable codes to automate the consent and data access processes. CONCLUSIONS: Blockchains and smart contracts can provide an efficient and scalable mechanism to support dynamic consent functionality and address some of the barriers that inhibit genomic data sharing. However, they are not a complete answer, and a number of issues still need to be addressed before such systems can be deployed in practice, particularly in relation to verifying user credentials.

Cage and maternal effects on the bacterial communities of the murine gut.

Findings from gut microbiome studies are strongly influenced by both experimental and analytical factors that can unintentionally bias their interpretation. Environment is also critical. Both co-housing and maternal effects are expected to affect microbiomes and have the potential to confound other manipulated factors, such as genetics. We therefore analysed microbiome data from a mouse experiment using littermate controls and tested differences among genotypes (wildtype versus colitis prone-mdr1a-/-), gut niches (stool versus mucus), host ages (6 versus 18 weeks), social groups (co-housed siblings of different genotypes) and maternal influence. We constructed a 16S phylogenetic tree from bacterial communities, fitting random forest models using all 428,234 clades identified. Models discriminated all criteria except host genotype, where no community differences were found. Host social groups differed in abundant, low-level, taxa whereas intermediate phylogenetic and abundance scales distinguished ages and niches. Thus, a carefully controlled experiment treating evolutionary clades of microbes equivalently without reference to taxonomy, clearly identifies whether and how gut microbial communities are distinct across ecologically important factors (niche and host age) and other experimental factors, notably cage effects and maternal influence. These findings highlight the importance of considering such environmental factors in future microbiome studies.

The role of ß2 integrin in dendritic cell migration during infection.

BACKGROUND: Dendritic cells (DCs) play a key role in shaping T cell responses. To do this, DCs must be able to migrate to the site of the infection and the lymph nodes to prime T cells and initiate the appropriate immune response. Integrins such as ß2 integrin play a key role in leukocyte adhesion, migration, and cell activation. However, the role of ß2 integrin in DC migration and function in the context of infection-induced inflammation in the gut is not well understood. This study looked at the role of ß2 integrin in DC migration and function during infection with the nematode worm Trichuris muris. Itgb2tm1Bay mice lacking functional ß2 integrin and WT littermate controls were infected with T. muris and the response to infection and kinetics of the DC response was assessed. RESULTS: In infection, the lack of functional ß2 integrin significantly reduced DC migration to the site of infection but not the lymph nodes. The lack of functional ß2 integrin did not negatively impact T cell activation in response to T. muris infection. CONCLUSIONS: This data suggests that ß2 integrins are important in DC recruitment to the infection site potentially impacting the initiation of innate immunity but is dispensible for DC migration to lymph nodes and T cell priming in the context of T. muris infection.

Glucocorticoids rapidly inhibit cell migration through a novel, non-transcriptional HDAC6 pathway.

Glucocorticoids (GCs) act through the glucocorticoid receptor (GR, also known as NR3C1) to regulate immunity, energy metabolism and tissue repair. Upon ligand binding, activated GR mediates cellular effects by regulating gene expression, but some GR effects can occur rapidly without new transcription. Here, we show that GCs rapidly inhibit cell migration, in response to both GR agonist and antagonist ligand binding. The inhibitory effect on migration is prevented by GR knockdown with siRNA, confirming GR specificity, but not by actinomycin D treatment, suggesting a non-transcriptional mechanism. We identified a rapid onset increase in microtubule polymerisation following GC treatment, identifying cytoskeletal stabilisation as the likely mechanism of action. HDAC6 overexpression, but not knockdown of αTAT1, rescued the GC effect, implicating HDAC6 as the GR effector. Consistent with this hypothesis, ligand-dependent cytoplasmic interaction between GR and HDAC6 was demonstrated by quantitative imaging. Taken together, we propose that activated GR inhibits HDAC6 function, and thereby increases the stability of the microtubule network to reduce cell motility. We therefore report a novel, non-transcriptional mechanism whereby GCs impair cell motility through inhibition of HDAC6 and rapid reorganization of the cell architecture.This article has an associated First Person interview with the first author of the paper.

Hardy-Weinberg Equilibrium in the Large Scale Genomic Sequencing Era.

Hardy-Weinberg Equilibrium (HWE) is used to estimate the number of homozygous and heterozygous variant carriers based on its allele frequency in populations that are not evolving. Deviations from HWE in large population databases have been used to detect genotyping errors, which can result in extreme heterozygote excess (HetExc). However, HetExc might also be a sign of natural selection since recessive disease causing variants should occur less frequently in a homozygous state in the population, but may reach high allele frequency in a heterozygous state, especially if they are advantageous. We developed a filtering strategy to detect these variants and applied it on genome data from 137,842 individuals. The main limitations of this approach were quality of genotype calls and insufficient population sizes, whereas population structure and inbreeding can reduce sensitivity, but not precision, in certain populations. Nevertheless, we identified 161 HetExc variants in 149 genes, most of which were specific to African/African American populations (∼79.5%). Although the majority of them were not associated with known diseases, or were classified as clinically "benign," they were enriched in genes associated with autosomal recessive diseases. The resulting dataset also contained two known recessive disease causing variants with evidence of heterozygote advantage in the sickle-cell anemia (HBB) and cystic fibrosis (CFTR). Finally, we provide supporting in silico evidence of a novel heterozygote advantageous variant in the chromodomain helicase DNA binding protein 6 gene (CHD6; involved in influenza virus replication). We anticipate that our approach will aid the detection of rare recessive disease causing variants in the future.

In silico design of a T-cell epitope vaccine candidate for parasitic helminth infection.

Trichuris trichiura is a parasite that infects 500 million people worldwide, leading to colitis, growth retardation and Trichuris dysentery syndrome. There are no licensed vaccines available to prevent Trichuris infection and current treatments are of limited efficacy. Trichuris infections are linked to poverty, reducing children's educational performance and the economic productivity of adults. We employed a systematic, multi-stage process to identify a candidate vaccine against trichuriasis based on the incorporation of selected T-cell epitopes into virus-like particles. We conducted a systematic review to identify the most appropriate in silico prediction tools to predict histocompatibility complex class II (MHC-II) molecule T-cell epitopes. These tools were used to identify candidate MHC-II epitopes from predicted ORFs in the Trichuris genome, selected using inclusion and exclusion criteria. Selected epitopes were incorporated into Hepatitis B core antigen virus-like particles (VLPs). Bone marrow-derived dendritic cells and bone marrow-derived macrophages responded in vitro to VLPs irrespective of whether the VLP also included T-cell epitopes. The VLPs were internalized and co-localized in the antigen presenting cell lysosomes. Upon challenge infection, mice vaccinated with the VLPs+T-cell epitopes showed a significantly reduced worm burden, and mounted Trichuris-specific IgM and IgG2c antibody responses. The protection of mice by VLPs+T-cell epitopes was characterised by the production of mesenteric lymph node (MLN)-derived Th2 cytokines and goblet cell hyperplasia. Collectively our data establishes that a combination of in silico genome-based CD4+ T-cell epitope prediction, combined with VLP delivery, offers a promising pipeline for the development of an effective, safe and affordable helminth vaccine.

GeVIR is a continuous gene-level metric that uses variant distribution patterns to prioritize disease candidate genes.

With large-scale population sequencing projects gathering pace, there is a need for strategies that advance disease gene prioritization1,2. Metrics that provide information about a gene and its ability to tolerate protein-altering variation can aid in clinical interpretation of human genomes and can advance disease gene discovery1-4. Previous reported methods analyzed the total variant load in a gene1-4, but did not analyze the distribution pattern of variants within a gene. Using data from 138,632 exome and genome sequences2, we developed gene variation intolerance rank (GeVIR), a continuous gene-level metric for 19,361 genes that is able to prioritize both dominant and recessive Mendelian disease genes5, that outperforms missense constraint metrics3 and that is comparable-but complementary-to loss-of-function (LOF) constraint metrics2. GeVIR is also able to prioritize short genes, for which LOF constraint cannot be estimated with confidence2. The majority of the most intolerant genes identified here have no defined phenotype and are candidates for severe dominant disorders.

Gut eosinophils and their impact on the mucus-resident microbiota.

The gut has the largest commensal bacterial population in the body and its composition can be impacted by host factors such as production of immunoglobulin A (IgA). Eosinophils in the gut have been implicated in the production of antibacterial factors and maintenance of IgA-secreting plasma cells. We used an eosinophil-deficient mouse (∆dblGATA-1-/- ) and littermate controls to investigate the role of eosinophils in the regulation of the microbiota, with particular emphasis on mucus-resident species in the small and large intestine. We found no differences in IgA production or IgA-expressing plasma cells between naive littermates in the small or large intestine. However, denaturing gel gradient electrophoresis revealed differences in the bacterial communities of the mucus and stools between wild-type mice and ∆dblGATA-1-/- mice, with the greatest separation between the mucus microbial communities. Mucus-resident bacteria in ∆dblGATA-1-/- mice had reduced diversity in the mucus compared with the stools. A quantitative PCR panel of selected bacteria showed that the most significant differences in the microbiota were between mucus-resident bacteria and those in stool, such as the abundance of Clostridiales and Bacteroides. Our data implicate eosinophils in the regulation of the microbiota, especially the bacteria most hyperlocal to the gut barrier. Although we see differences between host genotypes in the overall microbial communities, further work is required to establish specifically which bacteria are different between these groups. Most importantly, the data revealed that the mucus and stool microbiota are discrete communities. Stool analysis alone may be insufficient to comprehensively explore and define the role of the gut microbiota in health and disease.

Data journeys: Identifying social and technical barriers to data movement in large, complex organisations.

Managers in complex organisations often have to make decisions on whether new software developments are worth undertaking or not. Such decisions are hard to make, especially at an enterprise level. Both costs and risks are regularly underestimated, despite the existence of a plethora of software and systems engineering methodologies aimed at predicting and controlling them. Our objective is to help managers and stakeholders of large, complex organisations (like the National Health Service in the UK) make better informed decisions on the costs and risks of planned new software systems that will reuse or extend their existing information infrastructure. We analysed case studies describing new software developments undertaken by providers of health care services in the UK, looking for common points of risk and high cost. The results highlighted the movement of data within and between organisations as a key factor. Data movement can be hindered by numerous technical barriers, but also by other challenges arising from social aspects of the organisation. These latter aspects are often harder to predict, and are ignored by many of the more common software engineering methodologies. In this paper, we propose data journey modelling, a new method aiming to predict places of high cost and risk when existing data needs to move to a new development. The method is lightweight and combines technical and social aspects, but relies only on information that is likely to be already known to key stakeholders, or will be cheap to acquire. To assess the effectiveness of our method, we conducted a retrospective evaluation in an NHS Foundation Trust hospital. Using the method, we were able to predict most of the points of high cost/risk that the hospital staff had identified, along with several other possible directions that the staff did not identify for themselves, but agreed could be promising.

Britain Breathing: using the experience sampling method to collect the seasonal allergy symptoms of a country.

Objective: Allergies are increasing, but the reasons for this are unclear. Although environmental factors are thought to be important, there is a lack of data on how they contribute to symptom development. To understand this relationship better, we need accurate data about both symptoms and environmental factors. Our objective here is to ascertain whether experience sampling is a reliable approach for collecting allergy symptom data in the general population, allowing us to map symptoms and understand etiology. Materials and Methods: We conducted a 32-week cross-sectional study where individuals reported their seasonal allergy symptoms and severity via a mobile application. Symptom geographical location and timestamp were also collected automatically. Results: The experience sampling method reliably infers the incidence of seasonal allergies as indicated by the strong correlation (r = 0.93, P < .003) between the reported lack of wellness and the number of antihistamines prescribed by General Practitioners. Discussion and Conclusion: The project has resulted in the first dataset to map allergy symptoms over time and place and reveals periods of peak hay fever symptoms in the UK.

Compositional Changes in the Gut Mucus Microbiota Precede the Onset of Colitis-Induced Inflammation.

BACKGROUND: Inflammatory bowel disease (IBD) is associated with an inappropriate immune response to the gut microbiota. Notably, patients with IBD reportedly have alterations in fecal microbiota. However, the colonic microbiota occupies both the gut lumen and the mucus covering the epithelium. Thus, information about mucus-resident microbiota fails to be conveyed in the routine microbiota analyses of stool samples. Further, studies analyzing microbiota in IBD have mainly focused on stool samples taken after onset of inflammation. Our objective was to investigate both temporal and spatial changes in colonic microbiota communities preceding the onset of colitis. METHODS: We studied mucus and stool microbiota using a spontaneous model of colitis, the mdr1a mouse, and their respective wild-type littermate controls in a time series mode. RESULTS: Using this approach we have shown that microbial dysbiosis was evident in the mucus but not stools, with reduced abundance of Clostridiales evident in the mucus but not stools, of colitis-prone mice mdr1a mice 12 weeks before the onset of detectable inflammation. This altered microbial composition was coupled with a significantly thinner mucus layer. On emergence of inflammation, dysbiosis was evident in the stools and at this time point, the spatial segregation between microbiota and host tissue was also disrupted, correlating with worsened inflammation. Our results reveal that microbial dysbiosis is detectable before changes in the stools. Importantly, dysbiosis in the mucus layer preceded development of colitis. CONCLUSIONS: Our data reveal the importance of mucus sampling for understanding the underlying etiology of IBD and fundamental processes underlying disease progression.

Glucocorticoid receptor isoforms direct distinct mitochondrial programs to regulate ATP production.

The glucocorticoid receptor (GR), a nuclear receptor and major drug target, has a highly conserved minor splice variant, GRγ, which differs by a single arginine within the DNA binding domain. GRγ, which comprises 10% of all GR transcripts, is constitutively expressed and tightly conserved through mammalian evolution, suggesting an important non-redundant role. However, to date no specific role for GRγ has been reported. We discovered significant differences in subcellular localisation, and nuclear-cytoplasmic shuttling in response to ligand. In addition the GRγ transcriptome and protein interactome was distinct, and with a gene ontology signal for mitochondrial regulation which was confirmed using Seahorse technology. We propose that evolutionary conservation of the single additional arginine in GRγ is driven by a distinct, non-redundant functional profile, including regulation of mitochondrial function.

A statistical analysis of murine incisional and excisional acute wound models.

Mice represent the most commonly used species for preclinical in vivo research. While incisional and excisional acute murine wound models are both frequently employed, there is little agreement on which model is optimum. Moreover, current lack of standardization of wounding procedure, analysis time point(s), method of assessment, and the use of individual wounds vs. individual animals as replicates makes it difficult to compare across studies. Here we have profiled secondary intention healing of incisional and excisional wounds within the same animal, assessing multiple parameters to determine the optimal methodology for future studies. We report that histology provides the least variable assessment of healing. Furthermore, histology alone (not planimetry) is able to detect accelerated healing in a castrated mouse model. Perhaps most importantly, we find virtually no correlation between wounds within the same animal, suggesting that use of wound (not animal) biological replicates is perfectly acceptable. Overall, these findings should guide and refine future studies, increasing the likelihood of detecting novel phenotypes while reducing the numbers of animals required for experimentation.

Novel SNP Discovery in African Buffalo, Syncerus caffer, using high-throughput Sequencing.

The African buffalo, Syncerus caffer, is one of the most abundant and ecologically important species of megafauna in the savannah ecosystem. It is an important prey species, as well as a host for a vast array of nematodes, pathogens and infectious diseases, such as bovine tuberculosis and corridor disease. Large-scale SNP discovery in this species would greatly facilitate further research into the area of host genetics and disease susceptibility, as well as provide a wealth of sequence information for other conservation and genomics studies. We sequenced pools of Cape buffalo DNA from a total of 9 animals, on an ABI SOLiD4 sequencer. The resulting short reads were mapped to the UMD3.1 Bos taurus genome assembly using both BWA and Bowtie software packages. A mean depth of 2.7× coverage over the mapped regions was obtained. Btau4 gene annotation was added to all SNPs identified within gene regions. Bowtie and BWA identified a maximum of 2,222,665 and 276,847 SNPs within the buffalo respectively, depending on analysis method. A panel of 173 SNPs was validated by fluorescent genotyping in 87 individuals. 27 SNPs failed to amplify, and of the remaining 146 SNPs, 43-54% of the Bowtie SNPs and 57-58% of the BWA SNPs were confirmed as polymorphic. dN/dS ratios found no evidence of positive selection, and although there were genes that appeared to be under negative selection, these were more likely to be slowly evolving house-keeping genes.

Differential gene expression in mutant mice overexpressing or deficient in the serotonin transporter: a focus on urocortin 1.

Transcriptome analyses were performed in the anterior raphe area of mutant mice deficient in the serotonin transporter (5-HTT KO) or overexpressing this protein (5-HTT TG), which exhibit opposite changes in anxiety-related behavior. Among genes with altered expression, the gene encoding the neuropeptide urocortin 1 was down-regulated in 5-HTT KO and up-regulated in 5-HTT TG mice. Expression of the gene encoding cocaine-and-amphetamine-related-peptide, which colocalizes with urocortin 1, was also increased in 5-HTT TG mutants. Real-time RT-PCR confirmed these data and immunoautoradiographic labeling showed that parallel changes in neuropeptide levels were confined to the non-preganglionic Edinger-Westphal nucleus. Thus, 5-HTT expression correlates with that of urocortin 1, suggesting that this peptide can be involved in the behavioral changes observed in 5-HTT mutant mice.

Differential gene expression in a rat model of depression based on persistent differences in exploratory activity.

Affective disorders are often accompanied by changes in motivation and anxiety. We investigated the genome-wide gene expression patterns in an animal model of depression that separates Wistar rats belonging into clusters of persistently high anxiety/low motivation to explore and low anxiety/high motivation to explore (low explorers and high explorers, LE and HE, respectively), in three brain regions previously implicated in mood disorders (raphe, hippocampus and the frontal cortex). Several serotonin-, GABA-, and glutamatergic genes were differentially expressed in LE- and HE-rats. The analysis of Gene Ontology biological process terms associated with the differentially regulated genes identified a significant overrepresentation of genes involved in the neuron development, morphogenesis, and differentiation; the most enriched pathways from the Kyoto Encyclopedia of Genes and Genomes were the Wnt signalling, MAPK signalling, long-term potentiation, and long-term depression pathways. These findings corroborate some expression data from other models of depression, and suggest additional targets.

ExprAlign--the identification of ESTs in non-model species by alignment of cDNA microarray expression profiles.

BACKGROUND: Sequence identification of ESTs from non-model species offers distinct challenges particularly when these species have duplicated genomes and when they are phylogenetically distant from sequenced model organisms. For the common carp, an environmental model of aquacultural interest, large numbers of ESTs remained unidentified using BLAST sequence alignment. We have used the expression profiles from large-scale microarray experiments to suggest gene identities. RESULTS: Expression profiles from approximation 700 cDNA microarrays describing responses of 7 major tissues to multiple environmental stressors were used to define a co-expression landscape. This was based on the Pearsons correlation coefficient relating each gene with all other genes, from which a network description provided clusters of highly correlated genes as 'mountains'. We show that these contain genes with known identities and genes with unknown identities, and that the correlation constitutes evidence of identity in the latter. This procedure has suggested identities to 522 of 2701 unknown carp ESTs sequences. We also discriminate several common carp genes and gene isoforms that were not discriminated by BLAST sequence alignment alone. Precision in identification was substantially improved by use of data from multiple tissues and treatments. CONCLUSION: The detailed analysis of co-expression landscapes is a sensitive technique for suggesting an identity for the large number of BLAST unidentified cDNAs generated in EST projects. It is capable of detecting even subtle changes in expression profiles, and thereby of distinguishing genes with a common BLAST identity into different identities. It benefits from the use of multiple treatments or contrasts, and from the large-scale microarray data.

A systematic strategy for the discovery of candidate genes responsible for phenotypic variation.

It is increasingly common to combine genome-wide expression data with quantitative trait mapping data to aid in the search for sequence polymorphisms responsible for phenotypic variation. By joining these complex but different data types at the level of the biological pathway, we can take advantage of existing biological knowledge to systematically identify possible mechanisms of genotype-phenotype interaction. With the development of web services and workflows, this process can be made rapid and systematic. Our methodology was applied to a use case of resistance to African trypanosomiasis in mice. Workflows developed in this investigation, including a guide to loading and executing them with example data, are available at http://www.myexperiment.org/users/43/workflows .

A systematic strategy for large-scale analysis of genotype phenotype correlations: identification of candidate genes involved in African trypanosomiasis.

It is increasingly common to combine Microarray and Quantitative Trait Loci data to aid the search for candidate genes responsible for phenotypic variation. Workflows provide a means of systematically processing these large datasets and also represent a framework for the re-use and the explicit declaration of experimental methods. In this article, we highlight the issues facing the manual analysis of microarray and QTL data for the discovery of candidate genes underlying complex phenotypes. We show how automated approaches provide a systematic means to investigate genotype-phenotype correlations. This methodology was applied to a use case of resistance to African trypanosomiasis in the mouse. Pathways represented in the results identified Daxx as one of the candidate genes within the Tir1 QTL region. Subsequent re-sequencing in Daxx identified a deletion of an amino acid, identified in susceptible mouse strains, in the Daxx-p53 protein-binding region. This supports recent experimental evidence that apoptosis could be playing a role in the trypanosomiasis resistance phenotype. Workflows developed in this investigation, including a guide to loading and executing them with example data, are available at http://workflows.mygrid.org.uk/repository/myGrid/PaulFisher/.