Data-driven patient stratification of UK Biobank cohort suggests five endotypes of multimorbidity.

Multimorbidity generally refers to concurrent occurrence of multiple chronic conditions. These patients are inherently at high risk and often lead a poor quality of life due to delayed treatments. With the emergence of personalized medicine and stratified healthcare, there is a need to stratify patients right at the primary care setting. Here we developed multimorbidity analysis pipeline (MulMorPip), which can stratify patients into multimorbid subgroups or endotypes based on their lifetime disease diagnosis and characterize them based on demographic features and underlying disease-disease interaction networks. By implementing MulMorPip on UK Biobank cohort, we report five distinct molecular subclasses or endotypes of multimorbidity. For each patient, we calculated the existence of broad disease classes defined by Charlson's comorbidity classification using the International Classification of Diseases-10 encoding. We then applied multiple correspondence analysis in 77 524 patients from UK Biobank, who had multimorbidity of more than one disease, which resulted in five multimorbid clusters. We further validated these clusters using machine learning and were able to classify 20% model-blind test set patients with an accuracy of 97% and an average Jaccard similarity of 84%. This was followed by demographic characterization and development of interlinking disease network for each cluster to understand disease-disease interactions. Our identified five endotypes of multimorbidity draw attention to dementia, stroke and paralysis as important drivers of multimorbidity stratification. Inclusion of such patient stratification at the primary care setting can help general practitioners to better observe patients' multiple chronic conditions, their risk stratification and personalization of treatment strategies.

Immuno-informatics analysis predicts B and T cell consensus epitopes for designing peptide vaccine against SARS-CoV-2 with 99.82% global population coverage.

The current global pandemic due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has taken a substantial number of lives across the world. Although few vaccines have been rolled-out, a number of vaccine candidates are still under clinical trials at various pharmaceutical companies and laboratories around the world. Considering the intrinsic nature of viruses in mutating and evolving over time, persistent efforts are needed to develop better vaccine candidates. In this study, various immuno-informatics tools and bioinformatics databases were deployed to derive consensus B-cell and T-cell epitope sequences of SARS-CoV-2 spike glycoprotein. This approach has identified four potential epitopes which have the capability to initiate both antibody and cell-mediated immune responses, are non-allergenic and do not trigger autoimmunity. These peptide sequences were also evaluated to show 99.82% of global population coverage based on the genotypic frequencies of HLA binding alleles for both MHC class-I and class-II and are unique for SARS-CoV-2 isolated from human as a host species. Epitope number 2 alone had a global population coverage of 98.2%. Therefore, we further validated binding and interaction of its constituent T-cell epitopes with their corresponding HLA proteins using molecular docking and molecular dynamics simulation experiments, followed by binding free energy calculations with molecular mechanics Poisson-Boltzmann surface area, essential dynamics analysis and free energy landscape analysis. The immuno-informatics pipeline described and the candidate epitopes discovered herein could have significant impact upon efforts to develop globally effective SARS-CoV-2 vaccines.

muSignAl: An algorithm to search for multiple omic signatures with similar predictive performance.

Multidimensional omic datasets often have correlated features leading to the possibility of discovering multiple biological signatures with similar predictive performance for a phenotype. However, their exploration is limited by low sample size and the exponential nature of the combinatorial search leading to high computational cost. To address these issues, we have developed an algorithm muSignAl (multiple signature algorithm) which selects multiple signatures with similar predictive performance while systematically bypassing the requirement of exploring all the combinations of features. We demonstrated the workflow of this algorithm with an example of proteomics dataset. muSignAl is applicable in various bioinformatics-driven explorations, such as understanding the relationship between multiple biological feature sets and phenotypes, and discovery and development of biomarker panels while providing the opportunity of optimising their development cost with the help of equally good multiple signatures. Source code of muSignAl is freely available at https://github.com/ShuklaLab/muSignAl.

ATRPred: A machine learning based tool for clinical decision making of anti-TNF treatment in rheumatoid arthritis patients.

Rheumatoid arthritis (RA) is a chronic autoimmune condition, characterised by joint pain, damage and disability, which can be addressed in a high proportion of patients by timely use of targeted biologic treatments. However, the patients, non-responsive to the treatments often suffer from refractoriness of the disease, leading to poor quality of life. Additionally, the biologic treatments are expensive. We obtained plasma samples from N = 144 participants with RA, who were about to commence anti-tumour necrosis factor (anti-TNF) therapy. These samples were sent to Olink Proteomics, Uppsala, Sweden, where proximity extension assays of 4 panels, containing 92 proteins each, were performed. A total of n = 89 samples of patients passed the quality control of anti-TNF treatment response data. The preliminary analysis of plasma protein expression values suggested that the RA population could be divided into two distinct molecular sub-groups (endotypes). However, these broad groups did not predict response to anti-TNF treatment, but were significantly different in terms of gender and their disease activity. We then labelled these patients as responders (n = 60) and non-responders (n = 29) based on the change in disease activity score (DAS) after 6 months of anti-TNF treatment and applied machine learning (ML) with a rigorous 5-fold nested cross-validation scheme to filter 17 proteins that were significantly associated with the treatment response. We have developed a ML based classifier ATRPred (anti-TNF treatment response predictor), which can predict anti-TNF treatment response in RA patients with 81% accuracy, 75% sensitivity and 86% specificity. ATRPred may aid clinicians to direct anti-TNF therapy to patients most likely to receive benefit, thus save cost as well as prevent non-responsive patients from refractory consequences. ATRPred is implemented in R.

Hardware acceleration of genomics data analysis: challenges and opportunities.

The significant decline in the cost of genome sequencing has dramatically changed the typical bioinformatics pipeline for analysing sequencing data. Where traditionally, the computational challenge of sequencing is now secondary to genomic data analysis. Short read alignment (SRA) is a ubiquitous process within every modern bioinformatics pipeline in the field of genomics and is often regarded as the principal computational bottleneck. Many hardware and software approaches have been provided to solve the challenge of acceleration. However, previous attempts to increase throughput using many-core processing strategies have enjoyed limited success, mainly due to a dependence on global memory for each computational block. The limited scalability and high energy costs of many-core SRA implementations pose a significant constraint in maintaining acceleration. The Networks-On-Chip (NoC) hardware interconnect mechanism has advanced the scalability of many-core computing systems and, more recently, has demonstrated potential in SRA implementations by integrating multiple computational blocks such as pre-alignment filtering and sequence alignment efficiently, while minimizing memory latency and global memory access. This article provides a state of the art review on current hardware acceleration strategies for genomic data analysis, and it establishes the challenges and opportunities of utilizing NoCs as a critical building block in next-generation sequencing (NGS) technologies for advancing the speed of analysis.

Response to interferons and antibacterial innate immunity in the absence of tyrosine-phosphorylated STAT1.

Signal transducer and activator of transcription 1 (STAT1) plays a pivotal role in the innate immune system by directing the transcriptional response to interferons (IFNs). STAT1 is activated by Janus kinase (JAK)-mediated phosphorylation of Y701. To determine whether STAT1 contributes to cellular responses without this phosphorylation event, we generated mice with Y701 mutated to a phenylalanine (Stat1(Y701F)). We show that heterozygous mice do not exhibit a dominant-negative phenotype. Homozygous Stat1(Y701F) mice show a profound reduction in Stat1 expression, highlighting an important role for basal IFN-dependent signaling. The rapid transcriptional response to type I IFN (IFN-I) and type II IFN (IFNγ) was absent in Stat1(Y701F) cells. Intriguingly, STAT1Y701F suppresses the delayed expression of IFN-I-stimulated genes (ISG) observed in Stat1(-/-) cells, mediated by the STAT2/IRF9 complex. Thus, Stat1(Y701F) macrophages are more susceptible to Legionella pneumophila infection than Stat1(-/-) macrophages. Listeria monocytogenes grew less robustly in Stat1(Y701F) macrophages and mice compared to Stat1(-/-) counterparts, but STAT1Y701F is not sufficient to rescue the animals. Our studies are consistent with a potential contribution of Y701-unphosphorylated STAT1 to innate antibacterial immunity.

Forebrain knock-out of torsinA reduces striatal free-water and impairs whole-brain functional connectivity in a symptomatic mouse model of DYT1 dystonia.

Multiple lines of evidence implicate striatal dysfunction in the pathogenesis of dystonia, including in DYT1, a common inherited form of the disease. The impact of striatal dysfunction on connected motor circuits and their interaction with other brain regions is poorly understood. Conditional knock-out (cKO) of the DYT1 protein torsinA from forebrain cholinergic and GABAergic neurons creates a symptomatic model that recapitulates many characteristics of DYT1 dystonia, including the developmental onset of overt twisting movements that are responsive to antimuscarinic drugs. We performed diffusion MRI and resting-state functional MRI on cKO mice of either sex to define abnormalities of diffusivity and functional connectivity in cortical, subcortical, and cerebellar networks. The striatum was the only region to exhibit an abnormality of diffusivity, indicating a selective microstructural deficit in cKO mice. The striatum of cKO mice exhibited widespread increases in functional connectivity with somatosensory cortex, thalamus, vermis, cerebellar cortex and nuclei, and brainstem. The current study provides the first in vivo support that direct pathological insult to forebrain torsinA in a symptomatic mouse model of DYT1 dystonia can engage genetically normal hindbrain regions into an aberrant connectivity network. These findings have important implications for the assignment of a causative region in CNS disease.

Functional activity of the sensorimotor cortex and cerebellum relates to cervical dystonia symptoms.

Cervical dystonia (CD) is the most common type of focal dystonia, causing abnormal movements of the neck and head. In this study, we used noninvasive imaging to investigate the motor system of patients with CD and uncover the neural correlates of dystonic symptoms. Furthermore, we examined whether a commonly prescribed anticholinergic medication in CD has an effect on the dystonia-related brain abnormalities. Participants included 16 patients with CD and 16 healthy age-matched controls. We collected functional MRI scans during a force task previously shown to extensively engage the motor system, and diffusion and T1-weighted MRI scans from which we calculated free-water and brain tissue densities. The dystonia group was also scanned ca. 2 h after a 2-mg dose of trihexyphenidyl. Severity of dystonia was assessed pre- and post-drug using the Burke-Fahn-Marsden Dystonia Rating Scale. Motor-related activity in CD was altered relative to controls in the primary somatosensory cortex, cerebellum, dorsal premotor and posterior parietal cortices, and occipital cortex. Most importantly, a regression model showed that increased severity of symptoms was associated with decreased functional activity of the somatosensory cortex and increased activity of the cerebellum. Structural imaging measures did not differ between CD and controls. The single dose of trihexyphenidyl altered the fMRI signal in the somatosensory cortex but not in the cerebellum. Symptom severity was not significantly reduced post-treatment. Findings show widespread changes in functional brain activity in CD and most importantly that dystonic symptoms relate to disrupted activity in the somatosensory cortex and cerebellum. Hum Brain Mapp 38:4563-4573, 2017. © 2017 Wiley Periodicals, Inc.

Free-water imaging in Parkinson's disease and atypical parkinsonism.

Conventional single tensor diffusion analysis models have provided mixed findings in the substantia nigra of Parkinson's disease, but recent work using a bi-tensor analysis model has shown more promising results. Using a bi-tensor model, free-water values were found to be increased in the posterior substantia nigra of Parkinson's disease compared with controls at a single site and in a multi-site cohort. Further, free-water increased longitudinally over 1 year in the posterior substantia nigra of Parkinson's disease. Here, we test the hypothesis that other parkinsonian disorders such as multiple system atrophy and progressive supranuclear palsy have elevated free-water in the substantia nigra. Equally important, however, is whether the bi-tensor diffusion model is able to detect alterations in other brain regions beyond the substantia nigra in Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy and to accurately distinguish between these diseases. Free-water and free-water-corrected fractional anisotropy maps were compared across 72 individuals in the basal ganglia, midbrain, thalamus, dentate nucleus, cerebellar peduncles, cerebellar vermis and lobules V and VI, and corpus callosum. Compared with controls, free-water was increased in the anterior and posterior substantia nigra of Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy. Despite no other changes in Parkinson's disease, we observed elevated free-water in all regions except the dentate nucleus, subthalamic nucleus, and corpus callosum of multiple system atrophy, and in all regions examined for progressive supranuclear palsy. Compared with controls, free-water-corrected fractional anisotropy values were increased for multiple system atrophy in the putamen and caudate, and increased for progressive supranuclear palsy in the putamen, caudate, thalamus, and vermis, and decreased in the superior cerebellar peduncle and corpus callosum. For all disease group comparisons, the support vector machine 10-fold cross-validation area under the curve was between 0.93-1.00 and there was high sensitivity and specificity. The regions and diffusion measures selected by the model varied across comparisons and are consistent with pathological studies. In conclusion, the current study used a novel bi-tensor diffusion analysis model to indicate that all forms of parkinsonism had elevated free-water in the substantia nigra. Beyond the substantia nigra, both multiple system atrophy and progressive supranuclear palsy, but not Parkinson's disease, showed a broad network of elevated free-water and altered free-water corrected fractional anisotropy that included the basal ganglia, thalamus, and cerebellum. These findings may be helpful in the differential diagnosis of parkinsonian disorders, and thereby facilitate the development and assessment of targeted therapies.

In vivo imaging reveals impaired connectivity across cortical and subcortical networks in a mouse model of DYT1 dystonia.

Developing in vivo functional and structural neuroimaging assays in Dyt1 ΔGAG heterozygous knock-in (Dyt1 KI) mice provide insight into the pathophysiology underlying DYT1 dystonia. In the current study, we examined in vivo functional connectivity of large-scale cortical and subcortical networks in Dyt1 KI mice and wild-type (WT) controls using resting-state functional magnetic resonance imaging (MRI) and an independent component analysis. In addition, using diffusion MRI we examined how structural integrity across the basal ganglia and cerebellum directly relates to impairments in functional connectivity. Compared to WT mice, Dyt1 KI mice revealed increased functional connectivity across the striatum, thalamus, and somatosensory cortex; and reduced functional connectivity in the motor and cerebellar cortices. Further, Dyt1 KI mice demonstrated elevated free-water (FW) in the striatum and cerebellum compared to WT mice, and increased FW was correlated with impairments in functional connectivity across basal ganglia, cerebellum, and sensorimotor cortex. The current study provides the first in vivo MRI-based evidence in support of the hypothesis that the deletion of a 3-base pair (ΔGAG) sequence in the Dyt1 gene encoding torsinA has network level effects on in vivo functional connectivity and microstructural integrity across the sensorimotor cortex, basal ganglia, and cerebellum.

Free-water and BOLD imaging changes in Parkinson's disease patients chronically treated with a MAO-B inhibitor.

Rasagiline is a monoamine oxidase type B inhibitor that possesses no amphetamine-like properties, and provides symptomatic relief in early and late stages of Parkinson's disease (PD). Data in animal models of PD suggest that chronic administration of rasagiline is associated with structural changes in the substantia nigra, and raise the question whether the structure and function of the basal ganglia could be different in PD patients treated chronically with rasagiline as compared with PD patients not treated with rasagiline. Here, we performed a retrospective cross-sectional magnetic resonance imaging (MRI) study at 3 T that investigated nigrostriatal function and structure in PD patients who had taken rasagiline before testing (∼8 months), PD who had not taken rasagiline before testing, and age-matched controls. The two PD groups were selected a priori to not differ significantly in age, sex, disease duration, severity of symptoms, cognitive status, and total levodopa equivalent daily dose of medication. We evaluated percent signal change in the posterior putamen during force production using functional MRI, free-water in the posterior substantia nigra using diffusion MRI, and performance on a bimanual coordination task using a pegboard test. All patients were tested after overnight withdrawal from antiparkinsonian medication. The rasagiline group had greater percent signal change in the posterior putamen, less free-water in the posterior substantia nigra, and better performance on the coordination task than the group not taking rasagiline. These findings point to a possible chronic effect of rasagiline on the structure and function of the basal ganglia in PD. Hum Brain Mapp 37:2894-2903, 2016. © 2016 Wiley Periodicals, Inc.

Functional Brain Activity Relates to 0-3 and 3-8 Hz Force Oscillations in Essential Tremor.

It is well-established that during goal-directed motor tasks, patients with essential tremor have increased oscillations in the 0-3 and 3-8 Hz bands. It remains unclear if these increased oscillations relate to activity in specific brain regions. This study used task-based functional magnetic resonance imaging to compare the brain activity associated with oscillations in grip force output between patients with essential tremor, patients with Parkinson's disease who had clinically evident tremor, and healthy controls. The findings demonstrate that patients with essential tremor have increased brain activity in the motor cortex and supplementary motor area compared with controls, and this activity correlated positively with 3-8 Hz force oscillations. Brain activity in cerebellar lobules I-V was reduced in essential tremor compared with controls and correlated negatively with 0-3 Hz force oscillations. Widespread differences in brain activity were observed between essential tremor and Parkinson's disease. Using functional connectivity analyses during the task evidenced reduced cerebellar-cortical functional connectivity in patients with essential tremor compared with controls and Parkinson's disease. This study provides new evidence that in essential tremor 3-8 Hz force oscillations relate to hyperactivity in motor cortex, 0-3 Hz force oscillations relate to the hypoactivity in the cerebellum, and cerebellar-cortical functional connectivity is impaired.

High-throughput mRNA and miRNA profiling of epithelial-mesenchymal transition in MDCK cells.

BACKGROUND: Epithelial-mesenchymal transition (EMT) is an important process in embryonic development, especially during gastrulation and organ formation. Furthermore EMT is widely observed in pathological conditions, e.g., fibrosis, tumor progression and metastasis. Madin-Darby Canine Kidney (MDCK) cells are widely used for studies of EMT and epithelial plasticity. MDCK cells show an epithelial phenotype, while oncogenic Ras-transformed MDCK (MDCK-Ras) cells undergo EMT and show a mesenchymal phenotype. METHODS: RNA-Seq and miRNA-Seq analyses were performed on MDCK and MDCK-Ras cells. Data were validated by qRT-PCR. Gene signature analyses were carried out to identify pathways and gene ontology terms. For selected miRNAs target prediction was performed. RESULTS: With RNA-Seq, mRNAs of approximately half of the genes known for dog were detected. These were screened for differential regulation during Ras-induced EMT. We went further and performed gene signature analyses and found Gene Ontology (GO) terms and pathways important for epithelial polarity and implicated in EMT. Among the identified pathways, TGFß1 emerged as a central signaling factor in many EMT related pathways and biological processes. With miRNA-Seq, approximately half of the known canine miRNAs were found expressed in MDCK and MDCK-Ras cells. Furthermore, among differentially expressed miRNAs, miRNAs that are known to be important regulators of EMT were detected and new candidates were predicted. New dog miRNAs were discovered after aligning our reads to that of other species in miRBase. Importantly, we could identify 25 completely novel miRNAs with a stable hairpin structure. Two of these novel miRNAs were differentially expressed. We validated the two novel miRNAs with the highest read counts by RT-qPCR. Target prediction of a particular novel miRNA highly expressed in mesenchymal MDCK-Ras cells revealed that it targets components of epithelial cell junctional complexes. Combining target prediction for the most upregulated miRNAs and validation of the targets in MDCK-Ras cells with pathway analysis allowed us to identify two novel pathways, e.g., JAK/STAT signaling and pancreatic cancer pathways. These pathways could not be detected solely by gene set enrichment analyses of RNA-Seq data. CONCLUSION: With deep sequencing data of mRNAs and miRNAs of MDCK cells and of Ras-induced EMT in MDCK cells, differentially regulated mRNAs and miRNAs are identified. Many of the identified genes are within pathways known to be involved in EMT. Novel differentially upregulated genes in MDCK cells are interferon stimulated genes and genes involved in Slit and Netrin signaling. New pathways not yet linked to these processes were identified. A central pathway in Ras induced EMT is TGFß signaling, which leads to differential regulation of many target genes, including miRNAs. With miRNA-Seq we identified miRNAs involved in either epithelial cell biology or EMT. Finally, we describe completely novel miRNAs and their target genes.

Distinct functional and macrostructural brain changes in Parkinson's disease and multiple system atrophy.

Parkinson's disease (PD) and the parkinsonian variant of multiple system atrophy (MSAp) are neurodegenerative disorders that can be difficult to differentiate clinically. This study provides the first characterization of the patterns of task-related functional magnetic resonance imaging (fMRI) changes across the whole brain in MSAp. We used fMRI during a precision grip force task and also performed voxel-based morphometry (VBM) on T1 -weighted images in MSAp patients, PD patients, and healthy controls. All groups were matched on age, and the patient groups had comparable motor symptom durations and severities. There were three main findings. First, MSAp and PD had reduced fMRI activation in motor control areas, including the basal ganglia, thalamus, insula, primary sensorimotor and prefrontal cortices, and cerebellum compared with controls. Second, there were no activation differences among the disease groups in the basal ganglia, thalamus, insula, or primary sensorimotor cortices, but PD had more extensive activation deficits throughout the cerebrum compared with MSAp and controls. Third, VBM revealed reduced volume in the basal ganglia, middle and inferior cerebellar peduncles, pons, and throughout the cerebrum in MSAp compared with controls and PD, and additionally throughout the cerebellar cortex and vermis in MSAp compared with controls. Collectively, these results provide the first evidence that fMRI activation is abnormal in the basal ganglia, cerebellum, and cerebrum in MSAp, and that a key distinguishing feature between MSAp and PD is the extensive and widespread volume loss throughout the brain in MSAp.

Distinct patterns of brain activity in progressive supranuclear palsy and Parkinson's disease.

The basal ganglia-thalamo-cortical and cerebello-thalamo-cortical circuits are important for motor control. Whether their functioning is affected in a similar or different way by progressive supranuclear palsy (PSP) and Parkinson's disease (PD) is not clear. A functional magnetic resonance imaging (fMRI) force production paradigm and voxel-based morphometry were used to assess differences in brain activity and macrostructural volumes between PSP, PD, and healthy age-matched controls. We found that PSP and PD share reduced functional activity of the basal ganglia and cortical motor areas, but this is more pronounced in PSP than in PD. In PSP the frontal regions are underactive, whereas the posterior parietal and occipital regions are overactive as compared with controls and PD. Furthermore, lobules I through IV, V, and VI of the cerebellum are hypoactive in PSP and PD, whereas Crus I and lobule IX are hyperactive in PSP only. Reductions in gray and white matter volume are specific to PSP. Finally, the functional status of the caudate as well as the volume of the superior frontal gyrus predict clinical gait and posture measures in PSP. PSP and PD share hypoactivity of the basal ganglia, motor cortex, and anterior cerebellum. These patients also display a unique pattern, such that anterior regions of the cortex are hypoactive and posterior regions of the cortex and cerebellum are hyperactive. Together, these findings suggest that specific structures within the basal ganglia, cortex, and cerebellum are affected differently in PSP relative to PD.

Progress in Wound-Healing Products Based on Natural Compounds, Stem Cells, and MicroRNA-Based Biopolymers in the European, USA, and Asian Markets: Opportunities, Barriers, and Regulatory Issues.

Wounds are breaks in the continuity of the skin and underlying tissues, resulting from external causes such as cuts, blows, impacts, or surgical interventions. Countless individuals suffer minor to severe injuries, with unfortunate cases even leading to death. In today's scenario, several commercial products are available to facilitate the healing process of wounds, although chronic wounds still present more challenges than acute wounds. Nevertheless, the huge demand for wound-care products within the healthcare sector has given rise to a rapidly growing market, fostering continuous research and development endeavors for innovative wound-healing solutions. Today, there are many commercially available products including those based on natural biopolymers, stem cells, and microRNAs that promote healing from wounds. This article explores the recent breakthroughs in wound-healing products that harness the potential of natural biopolymers, stem cells, and microRNAs. A comprehensive exploration is undertaken, covering not only commercially available products but also those still in the research phase. Additionally, we provide a thorough examination of the opportunities, obstacles, and regulatory considerations influencing the potential commercialization of wound-healing products across the diverse markets of Europe, America, and Asia.

Potential Plasma Proteins (LGALS9, LAMP3, PRSS8 and AGRN) as Predictors of Hospitalisation Risk in COVID-19 Patients.

Background: The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has posed unprecedented challenges to healthcare systems worldwide. Here, we have identified proteomic and genetic signatures for improved prognosis which is vital for COVID-19 research. Methods: We investigated the proteomic and genomic profile of COVID-19-positive patients (n = 400 for proteomics, n = 483 for genomics), focusing on differential regulation between hospitalised and non-hospitalised COVID-19 patients. Signatures had their predictive capabilities tested using independent machine learning models such as Support Vector Machine (SVM), Random Forest (RF) and Logistic Regression (LR). Results: This study has identified 224 differentially expressed proteins involved in various inflammatory and immunological pathways in hospitalised COVID-19 patients compared to non-hospitalised COVID-19 patients. LGALS9 (p-value < 0.001), LAMP3 (p-value < 0.001), PRSS8 (p-value < 0.001) and AGRN (p-value < 0.001) were identified as the most statistically significant proteins. Several hundred rsIDs were queried across the top 10 significant signatures, identifying three significant SNPs on the FSTL3 gene showing a correlation with hospitalisation status. Conclusions: Our study has not only identified key signatures of COVID-19 patients with worsened health but has also demonstrated their predictive capabilities as potential biomarkers, which suggests a staple role in the worsened health effects caused by COVID-19.

Genomic, Proteomic, and Phenotypic Biomarkers of COVID-19 Severity: Protocol for a Retrospective Observational Study.

BACKGROUND: Health organizations and countries around the world have found it difficult to control the spread of COVID-19. To minimize the future impact on the UK National Health Service and improve patient care, there is a pressing need to identify individuals who are at a higher risk of being hospitalized because of severe COVID-19. Early targeted work was successful in identifying angiotensin-converting enzyme-2 receptors and type II transmembrane serine protease dependency as drivers of severe infection. Although a targeted approach highlights key pathways, a multiomics approach will provide a clearer and more comprehensive picture of severe COVID-19 etiology and progression. OBJECTIVE: The COVID-19 Response Study aims to carry out an integrated multiomics analysis to identify biomarkers in blood and saliva that could contribute to host susceptibility to SARS-CoV-2 and the development of severe COVID-19. METHODS: The COVID-19 Response Study aims to recruit 1000 people who recovered from SARS-CoV-2 infection in both community and hospital settings on the island of Ireland. This protocol describes the retrospective observational study component carried out in Northern Ireland (NI; Cohort A); the Republic of Ireland cohort will be described separately. For all NI participants (n=519), SARS-CoV-2 infection has been confirmed by reverse transcription-quantitative polymerase chain reaction. A prospective Cohort B of 40 patients is also being followed up at 1, 3, 6, and 12 months postinfection to assess longitudinal symptom frequency and immune response. Data will be sourced from whole blood, saliva samples, and clinical data from the electronic care records, the general health questionnaire, and a 12-item general health questionnaire mental health survey. Saliva and blood samples were processed to extract DNA and RNA before whole-genome sequencing, RNA sequencing, DNA methylation analysis, microbiome analysis, 16S ribosomal RNA gene sequencing, and proteomic analysis were performed on the plasma. Multiomics data will be combined with clinical data to produce sensitive and specific prognostic models for severity risk. RESULTS: An initial demographic and clinical profile of the NI Cohort A has been completed. A total of 249 hospitalized patients and 270 nonhospitalized patients were recruited, of whom 184 (64.3%) were female, and the mean age was 45.4 (SD 13) years. High levels of comorbidity were evident in the hospitalized cohort, with cardiovascular disease and metabolic and respiratory disorders being the most significant (P<.001), grouped according to the International Classification of Diseases 10 codes. CONCLUSIONS: This study will provide a comprehensive opportunity to study the mechanisms of COVID-19 severity in recontactable participants. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50733.

AI-MARRVEL - A Knowledge-Driven AI System for Diagnosing Mendelian Disorders.

BACKGROUND: Diagnosing genetic disorders requires extensive manual curation and interpretation of candidate variants, a labor-intensive task even for trained geneticists. Although artificial intelligence (AI) shows promise in aiding these diagnoses, existing AI tools have only achieved moderate success for primary diagnosis. METHODS: AI-MARRVEL (AIM) uses a random-forest machine-learning classifier trained on over 3.5 million variants from thousands of diagnosed cases. AIM additionally incorporates expert-engineered features into training to recapitulate the intricate decision-making processes in molecular diagnosis. The online version of AIM is available at https://ai.marrvel.org. To evaluate AIM, we benchmarked it with diagnosed patients from three independent cohorts. RESULTS: AIM improved the rate of accurate genetic diagnosis, doubling the number of solved cases as compared with benchmarked methods, across three distinct real-world cohorts. To better identify diagnosable cases from the unsolved pools accumulated over time, we designed a confidence metric on which AIM achieved a precision rate of 98% and identified 57% of diagnosable cases out of a collection of 871 cases. Furthermore, AIM's performance improved after being fine-tuned for targeted settings including recessive disorders and trio analysis. Finally, AIM demonstrated potential for novel disease gene discovery by correctly predicting two newly reported disease genes from the Undiagnosed Diseases Network. CONCLUSIONS: AIM achieved superior accuracy compared with existing methods for genetic diagnosis. We anticipate that this tool may aid in primary diagnosis, reanalysis of unsolved cases, and the discovery of novel disease genes. (Funded by the NIH Common Fund and others.).

Simvastatin is associated with superior lipid and glycaemic control to atorvastatin and reduced levels of incident Type 2 diabetes, in men and women, in the UK Biobank.

INTRODUCTION: Cardiovascular disease (CVD) is the leading cause of mortality in people with Type 2 diabetes mellitus (T2DM). Statins reduce low-density lipoproteins and positively affect CVD outcomes. Statin type and dose have differential effects on glycaemia and risk of incident T2DM; however, the impact of gender, and of individual drugs within the statin class, remains unclear. AIM: To compare effects of simvastatin and atorvastatin on lipid and glycaemic control in men and women with and without T2DM, and their association with incident T2DM. METHODS: The effect of simvastatin and atorvastatin on lipid and glycaemic control was assessed in the T2DM DiaStrat cohort. Prescribed medications, gender, age, BMI, diabetes duration, blood lipid profile and HbA1c were extracted from Electronic Care Record, and compared in men and women prescribed simvastatin and atorvastatin. Analyses were replicated in the UKBiobank in those with and without T2DM. The association of simvastatin and atorvastatin with incident T2DM was also investigated in the UKBiobank. Cohorts where matched for age, BMI and diabetes duration in men and women, in the UKBioBank analysis, where possible. RESULTS: Simvastatin was associated with better LDL (1.6 ± 0.6 vs 2.1 ± 0.9 mmol/L, p < .01) and total cholesterol (3.6 ± 0.7 vs 4.2 ± 1.0 mmol/L, p < .05), and glycaemic control (62 ± 17 vs 67 ± 19 mmol/mol, p < .059) than atorvastatin specifically in women in the DiaStrat cohort. In the UKBiobank, both men and women prescribed simvastatin had better LDL (Women: 2.6 ± 0.6 vs 2.6 ± 0.7 mmol/L, p < .05; Men: 2.4 ± 0.6 vs 2.4 ± 0.6, p < .01) and glycaemic control (Women:54 ± 14 vs 56 ± 15mmol/mol, p < .05; Men, 54 ± 14 vs 55 ± 15 mmol/mol, p < .01) than those prescribed atorvastatin. Simvastatin was also associated with reduced risk of incident T2DM in both men and women (p < .0001) in the UKBiobank. CONCLUSIONS: Simvastatin is associated with superior lipid and glycaemic control to atorvastatin in those with and without T2DM, and with fewer incident T2DM cases. Given the importance of lipid and glycaemic control in preventing secondary complications of T2DM, these findings may help inform prescribing practices.