Plasma proteomic associations with genetics and health in the UK Biobank.

The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public-private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics1.

Six reference-quality genomes reveal evolution of bat adaptations.

Bats possess extraordinary adaptations, including flight, echolocation, extreme longevity and unique immunity. High-quality genomes are crucial for understanding the molecular basis and evolution of these traits. Here we incorporated long-read sequencing and state-of-the-art scaffolding protocols1 to generate, to our knowledge, the first reference-quality genomes of six bat species (Rhinolophus ferrumequinum, Rousettus aegyptiacus, Phyllostomus discolor, Myotis myotis, Pipistrellus kuhlii and Molossus molossus). We integrated gene projections from our 'Tool to infer Orthologs from Genome Alignments' (TOGA) software with de novo and homology gene predictions as well as short- and long-read transcriptomics to generate highly complete gene annotations. To resolve the phylogenetic position of bats within Laurasiatheria, we applied several phylogenetic methods to comprehensive sets of orthologous protein-coding and noncoding regions of the genome, and identified a basal origin for bats within Scrotifera. Our genome-wide screens revealed positive selection on hearing-related genes in the ancestral branch of bats, which is indicative of laryngeal echolocation being an ancestral trait in this clade. We found selection and loss of immunity-related genes (including pro-inflammatory NF-κB regulators) and expansions of anti-viral APOBEC3 genes, which highlights molecular mechanisms that may contribute to the exceptional immunity of bats. Genomic integrations of diverse viruses provide a genomic record of historical tolerance to viral infection in bats. Finally, we found and experimentally validated bat-specific variation in microRNAs, which may regulate bat-specific gene-expression programs. Our reference-quality bat genomes provide the resources required to uncover and validate the genomic basis of adaptations of bats, and stimulate new avenues of research that are directly relevant to human health and disease1.

The use of stool specimens reveals Helicobacter pylori strain diversity in a cohort of adolescents and their family members in a developed country.

Helicobacter pylori infection occurs within families but the transmission route is unknown. The use of stool specimens to genotype strains facilitates inclusion of complete families in transmission studies. Therefore, we aimed to use DNA from stools to analyze strain diversity in H. pylori infected families. We genotyped H. pylori strains using specific biprobe qPCR analysis of glmM, recA and hspA. Concentration of H. pylori organisms before DNA isolation enhanced subsequent DNA amplification. We isolated H. pylori DNA from 50 individuals in 13 families. Tm data for at least 2 of the 3 genes and sequencing of the glmM amplicon were analyzed. Similar strains were commonly found in both mothers and children and in siblings. However, 20/50 (40%) individuals had multiple strains and several individuals harbored strains not found in other family members, suggesting that even in developed countries sources of infection outside of the immediate family may exist. Whether infection occurs multiple times or one transmission event with several strains occurs is not known but future studies should aim to analyze strains from children much closer to infection onset. The presence of multiple stains in infected persons has implications for antibiotic sensitivity testing and treatment strategies.

Effectiveness of interventions to directly support food and drink intake in people with dementia: systematic review and meta-analysis.

BACKGROUND: Eating and drinking difficulties are recognised sources of ill health in people with dementia. In the EDWINA (Eating and Drinking Well IN dementiA) systematic review we aimed to assess effectiveness of interventions to directly improve, maintain or facilitate oral food and drink intake, nutrition and hydration status, in people with cognitive impairment or dementia (across all settings, levels of care and support, types and degrees of dementia). Interventions included oral nutrition supplementation, food modification, dysphagia management, eating assistance and supporting the social element of eating and drinking. METHODS: We comprehensively searched 13 databases for relevant intervention studies. The review was conducted with service user input in accordance with Cochrane Collaboration's guidelines. We duplicated assessment of inclusion, data extraction, and validity assessment, tabulating data, carrying out random effects meta-analysis and narrative synthesis. RESULTS: Forty-three controlled interventions were included, disappointingly none were judged at low risk of bias. Oral nutritional supplementation studies suggested small positive short term but unclear long term effects on nutritional status. Food modification or dysphagia management studies were smaller and of low quality, providing little evidence of an improved nutritional status. Eating assistance studies provided inconsistent evidence, but studies with a strong social element around eating/drinking, although small and of low quality provided consistent suggestion of improvements in aspects of quality of life. There were few data to address stakeholders' questions. CONCLUSIONS: We found no definitive evidence on effectiveness, or lack of effectiveness, of specific interventions but studies were small and short term. People with cognitive impairment and their carers have to tackle eating problems despite this lack of evidence, so promising interventions are listed. The need remains for high quality trials tailored for people with cognitive impairment assessing robust outcomes. SYSTEMATIC REVIEW REGISTRATION: The systematic review protocol was registered (CRD42014007611) and is published, with the full MEDLINE search strategy, on Prospero.

The Potential for Digital Phenotyping in Understanding Mindfulness App Engagement Patterns: A Pilot Study.

Background: Low app engagement is a central barrier to digital mental health efficacy. With mindfulness-based mental health apps growing in popularity, there is a need for new understanding of factors influencing engagement. This study utilized digital phenotyping to understand real-time patterns of engagement around app-based mindfulness. Different engagement metrics are presented that measure both the total number of app-based activities participants completed each week, as well as the proportion of days that participants engaged with the app each week. Method: Data were derived from two iterations of a four-week study exploring app engagement in college students (n = 169). This secondary analysis investigated the relationships between general and mindfulness-based app engagement with passive data metrics (sleep duration, home time, and screen duration) at a weekly level, as well as the relationship between demographics and engagement. Additional clinically focused analysis was performed on three case studies of participants with high mindfulness activity completion. Results: Demographic variables such as gender, race/ethnicity, and age lacked a significant association with mindfulness app-based engagement. Passive data variables such as sleep and screen duration were significant predictors for different metrics of general and mindfulness-based app engagement at a weekly level. There was a significant interaction effect for screen duration between the number of mindfulness activities completed and whether or not the participant received a mindfulness notification. K-means clusters analyses using passive data features to predict mindfulness activity completion had low performance. Conclusions: While there are no simple solutions to predicting engagement with mindfulness apps, utilizing digital phenotyping approaches at a population and personal level offers new potential. The signal from digital phenotyping warrants more investigation; even small increases in engagement with mindfulness apps may have a tremendous impact given their already high prevalence of engagement, availability, and potential to engage patients across demographics.

Smartphone app engagement and clinical outcomes in a hybrid clinic.

Despite the growing prevalence of mental health-related smartphone apps, low real-world engagement has prevented these apps from transforming the mental health landscape. Integrating mental health apps into more traditional therapeutic models appears to support better clinical outcomes, but also raises questions about the relationship between app engagement, the app itself, and the coach or clinician. This study explores patient app engagement patterns and the associated clinical outcomes gathered from piloting a digital clinic. Patients with anxiety or depression completed eight clinical visits and coach visits over a median of 83 days with a standard deviation of 17.25 days. Between clinical visits, patients completed therapeutic activities on the mindLAMP app. Mean PHQ-9 and GAD-7 scores decreased from the intake visit to both visit 4 and visit 8. Patients had high app engagement, but engagement did not correlate with outcomes. From intake visit to visit 4, the interaction effects indicate significant differences in the change of both PHQ-9 and GAD-7 depending on participants' average app satisfaction and clinician/coach satisfaction (as measured by WAI-SR) with engagement. Overall, results support the feasibility of incorporating an app into a hybrid clinic.

Potential Role of Smartphone Technology in Advancing Work on Neurological Soft Signs with a Focus on Schizophrenia.

LEARNING OBJECTIVE AFTER PARTICIPATING IN THIS CME ACTIVITY, THE PSYCHIATRIST SHOULD BE BETTER ABLE TO: • Outline and Identify potential benefits of using neurological soft signs (NSS) as biomarkers of schizophrenia. ABSTRACT: Since the late 1960s, NSS have been a focus of study across psychiatric illnesses, including depression, bipolar disorder, and schizophrenia in particular. Utilizing these subtle neurological impairments as biomarkers of illness has numerous benefits; NSS offer a direct connection between clinical presentation and neurological functioning, and assessments are cost-effective. However, incongruent measurement scales, confounding variables, and rating system subjectivity have hindered the advancement and scalability of NSS research and clinical implementation. This article provides a brief overview of the literature on NSS as related to schizophrenia, and proposes utilizing smartphone sensing technology to create standardized NSS assessments with objective scoring. Incorporating digital phenotyping into NSS assessment offers the potential to make measurement more scalable, accessible, and directly comparable across locations, cultures, and demographics. We conducted a narrative search in PubMed and APA PsycInfo using the following keywords: neurological soft signs, schizophrenia spectrum disorders, and psychotic illnesses. No date limitations were used. There is no other direct work on NSS and new smartphone methods like digital phenotyping; though, there is related work in neurology. Harnessing advances in smartphone technology could provide greater insight into and further our understanding of specific aspects of the NSS field. For instance, it could help us distinguish trait vs. state markers and better understand how distinct groups of signs may reflect different aspects of psychiatric illness and neurological impairment. In addition, such technology can help advance research on the capabilities of NSS as an effective diagnostic tool.

Classifying and clustering mood disorder patients using smartphone data from a feasibility study.

Differentiating between bipolar disorder and major depressive disorder can be challenging for clinicians. The diagnostic process might benefit from new ways of monitoring the phenotypes of these disorders. Smartphone data might offer insight in this regard. Today, smartphones collect dense, multimodal data from which behavioral metrics can be derived. Distinct patterns in these metrics have the potential to differentiate the two conditions. To examine the feasibility of smartphone-based phenotyping, two study sites (Mayo Clinic, Johns Hopkins University) recruited patients with bipolar I disorder (BPI), bipolar II disorder (BPII), major depressive disorder (MDD), and undiagnosed controls for a 12-week observational study. On their smartphones, study participants used a digital phenotyping app (mindLAMP) for data collection. While in use, mindLAMP gathered real-time geolocation, accelerometer, and screen-state (on/off) data. mindLAMP was also used for EMA delivery. MindLAMP data was then used as input variables in binary classification, three-group k-nearest neighbors (KNN) classification, and k-means clustering. The best-performing binary classification model was able to classify patients as control or non-control with an AUC of 0.91 (random forest). The model that performed best at classifying patients as having MDD or bipolar I/II had an AUC of 0.62 (logistic regression). The k-means clustering model had a silhouette score of 0.46 and an ARI of 0.27. Results support the potential for digital phenotyping methods to cluster depression, bipolar disorder, and healthy controls. However, due to inconsistencies in accuracy, more data streams are required before these methods can be applied to clinical practice.

An optimistic protein assembly from sequence reads salvaged an uncharacterized segment of mouse picobirnavirus.

Advances in Next Generation Sequencing technologies have enabled the generation of millions of sequences from microorganisms. However, distinguishing the sequence of a novel species from sequencing errors remains a technical challenge when the novel species is highly divergent from the closest known species. To solve such a problem, we developed a new method called Optimistic Protein Assembly from Reads (OPAR). This method is based on the assumption that protein sequences could be more conserved than the nucleotide sequences encoding them. By taking advantage of metagenomics, bioinformatics and conventional Sanger sequencing, our method successfully identified all coding regions of the mouse picobirnavirus for the first time. The salvaged sequences indicated that segment 1 of this virus was more divergent from its homologues in other Picobirnaviridae species than segment 2. For this reason, only segment 2 of mouse picobirnavirus has been detected in previous studies. OPAR web tool is available at http://bioinformatics.czc.hokudai.ac.jp/opar/.