A Network-Based Approach to Prioritize Results from Genome-Wide Association Studies

Genome-wide association studies (GWAS) are a valuable approach to understanding the genetic basis of complex traits. One of the challenges of GWAS is the translation of genetic association results into biological hypotheses suitable for further investigation in the laboratory. To address this challenge, we introduce Network Interface Miner for Multigenic Interactions (NIMMI), a network-based method that combines GWAS data with human protein-protein interaction data (PPI). NIMMI builds biological networks weighted by connectivity, which is estimated by use of a modification of the Google PageRank algorithm. These weights are then combined with genetic association p-values derived from GWAS, producing what we call ‘trait prioritized sub-networks.’ As a proof of principle, NIMMI was tested on three GWAS datasets previously analyzed for height, a classical polygenic trait. Despite differences in sample size and ancestry, NIMMI captured 95% of the known height associated genes within the top 20% of ranked sub-networks, far better than what could be achieved by a single-locus approach. The top 2% of NIMMI height-prioritized sub-networks were significantly enriched for genes involved in transcription, signal transduction, transport, and gene expression, as well as nucleic acid, phosphate, protein, and zinc metabolism. All of these sub-networks were ranked near the top across all three height GWAS datasets we tested. We also tested NIMMI on a categorical phenotype, Crohn’s disease. NIMMI prioritized sub-networks involved in B- and T-cell receptor, chemokine, interleukin, and other pathways consistent with the known autoimmune nature of Crohn’s disease. NIMMI is a simple, user-friendly, open-source software tool that efficiently combines genetic association data with biological networks, translating GWAS findings into biological hypotheses

Microbial applications for sustainable space exploration beyond low Earth orbit

Abstract With the construction of the International Space Station, humans have been continuously living and working in space for 22 years. Microbial studies in space and other extreme environments on Earth have shown the ability for bacteria and fungi to adapt and change compared to “normal” conditions. Some of these changes, like biofilm formation, can impact astronaut health and spacecraft integrity in a negative way, while others, such as a propensity for plastic degradation, can promote self-sufficiency and sustainability in space. With the next era of space exploration upon us, which will see crewed missions to the Moon and Mars in the next 10 years, incorporating microbiology research into planning, decision-making, and mission design will be paramount to ensuring success of these long-duration missions. These can include astronaut microbiome studies to protect against infections, immune system dysfunction and bone deterioration, or biological in situ resource utilization (bISRU) studies that incorporate microbes to act as radiation shields, create electricity and establish robust plant habitats for fresh food and recycling of waste. In this review, information will be presented on the beneficial use of microbes in bioregenerative life support systems, their applicability to bISRU, and their capability to be genetically engineered for biotechnological space applications. In addition, we discuss the negative effect microbes and microbial communities may have on long-duration space travel and provide mitigation strategies to reduce their impact. Utilizing the benefits of microbes, while understanding their limitations, will help us explore deeper into space and develop sustainable human habitats on the Moon, Mars and beyond

Digital Health Interventions for Depression and Anxiety Among People With Chronic Conditions: Scoping Review

BackgroundChronic conditions are characterized by their long duration (≥1 year), need for ongoing medical attention, and limitations in activities of daily living. These can often co-occur with depression and anxiety as common and detrimental comorbidities among the growing population living with chronic conditions. Digital health interventions (DHIs) hold promise in overcoming barriers to accessing mental health support for these individuals; however, the design and implementation of DHIs for depression and anxiety in people with chronic conditions are yet to be explored. ObjectiveThis study aimed to explore what is known in the literature regarding DHIs for the prevention, detection, or treatment of depression and anxiety among people with chronic conditions. MethodsA scoping review of the literature was conducted using the Arksey and O’Malley framework. Searches of the literature published in 5 databases between 1990 and 2019 were conducted in April 2019 and updated in March 2021. To be included, studies must have described a DHI tested with, or designed for, the prevention, detection, or treatment of depression or anxiety in people with common chronic conditions (arthritis, asthma, diabetes mellitus, heart disease, chronic obstructive pulmonary disease, cancer, stroke, and Alzheimer disease or dementia). Studies were independently screened by 2 reviewers against the inclusion and exclusion criteria. Both quantitative and qualitative data were extracted, charted, and synthesized to provide a descriptive summary of the trends and considerations for future research. ResultsDatabase searches yielded 11,422 articles across the initial and updated searches, 53 (0.46%) of which were included in this review. DHIs predominantly sought to provide treatment (44/53, 83%), followed by detection (5/53, 9%) and prevention (4/53, 8%). Most DHIs were focused on depression (36/53, 68%), guided (32/53, 60%), tailored to chronic physical conditions (19/53, 36%), and delivered through web-based platforms (20/53, 38%). Only 2 studies described the implementation of a DHI. ConclusionsAs a growing research area, DHIs offer the potential to address the gap in care for depression and anxiety among people with chronic conditions; however, their implementation in standard care is scarce. Although stepped care has been identified as a promising model to implement efficacious DHIs, few studies have investigated the use of DHIs for depression and anxiety among chronic conditions using such models. In developing stepped care, we outlined DHI tailoring, guidance, and intensity as key considerations that require further research

A library of analytic indicators to evaluate effective engagement with consumer mHealth apps for chronic conditions: Scoping review

Background: There is mixed evidence to support current ambitions for mobile health (mHealth) apps to improve chronic health and well-being. One proposed explanation for this variable effect is that users do not engage with apps as intended. The application of analytics, defined as the use of data to generate new insights, is an emerging approach to study and interpret engagement with mHealth interventions. Objective: This study aimed to consolidate how analytic indicators of engagement have previously been applied across clinical and technological contexts, to inform how they might be optimally applied in future evaluations. Methods: We conducted a scoping review to catalog the range of analytic indicators being used in evaluations of consumer mHealth apps for chronic conditions. We categorized studies according to app structure and application of engagement data and calculated descriptive data for each category. Chi-square and Fisher exact tests of independence were applied to calculate differences between coded variables. Results: A total of 41 studies met our inclusion criteria. The average mHealth evaluation included for review was a two-group pretest-posttest randomized controlled trial of a hybrid-structured app for mental health self-management, had 103 participants, lasted 5 months, did not provide access to health care provider services, measured 3 analytic indicators of engagement, segmented users based on engagement data, applied engagement data for descriptive analyses, and did not report on attrition. Across the reviewed studies, engagement was measured using the following 7 analytic indicators: the number of measures recorded (76%, 31/41), the frequency of interactions logged (73%, 30/41), the number of features accessed (49%, 20/41), the number of log-ins or sessions logged (46%, 19/41), the number of modules or lessons started or completed (29%, 12/41), time spent engaging with the app (27%, 11/41), and the number or content of pages accessed (17%, 7/41). Engagement with unstructured apps was mostly measured by the number of features accessed (8/10, P=.04), and engagement with hybrid apps was mostly measured by the number of measures recorded (21/24, P=.03). A total of 24 studies presented, described, or summarized the data generated from applying analytic indicators to measure engagement. The remaining 17 studies used or planned to use these data to infer a relationship between engagement patterns and intended outcomes. Conclusions: Although researchers measured on average 3 indicators in a single study, the majority reported findings descriptively and did not further investigate how engagement with an app contributed to its impact on health and well-being. Researchers are gaining nuanced insights into engagement but are not yet characterizing effective engagement for improved outcomes. Raising the standard of mHealth app efficacy through measuring analytic indicators of engagement may enable greater confidence in the causal impact of apps on improved chronic health and well-being

“I Have Eight Different Files at Eight Different Places”: Perspectives of Youths and Their Family Caregivers on Transitioning from Pediatric to Adult Rehabilitation and Community Services

Introduction: The number of young adults (youth) living with childhood-onset disabilities, and requiring transitional support to adult community and rehabilitation services, is increasing. We explored facilitators and barriers to accessing and sustaining community and rehabilitation services during the transition from pediatric to adult care. Methods: A qualitative descriptive study was conducted in Ontario, Canada. Data were collected through interviews with youth (n = 11) and family caregivers (n = 7). The data were coded and analyzed using thematic analysis. Results: Youth and caregivers face many types of transitions from pediatric to adult community and rehabilitation services, e.g., those related to education, living arrangements, and employment. This transition is marked by feelings of isolation. Supportive social networks, continuity of care (i.e., same care providers), and advocacy all contribute to positive experiences. Lack of knowledge about resources, changing parental involvement without preparation, and a lack of system responses to evolving needs were barriers to positive transitions. Financial circumstances were described as either a barrier or facilitator to service access. Conclusions: This study demonstrated that continuity of care, support from providers, and social networks all contribute markedly to the positive experience of transitioning from pediatric to adult services for individuals with childhood-onset disabilities and family caregivers. Future transitional interventions should incorporate these considerations

HNRNPC haploinsufficiency affects alternative splicing of intellectual disability-associated genes and causes a neurodevelopmental disorder

Heterogeneous nuclear ribonucleoprotein C (HNRNPC) is an essential, ubiquitously abundant protein involved in mRNA processing. Genetic variants in other members of the HNRNP family have been associated with neurodevelopmental disorders. Here, we describe 13 individuals with global developmental delay, intellectual disability, behavioral abnormalities, and subtle facial dysmorphology with heterozygous HNRNPC germline variants. Five of them bear an identical in-frame deletion of nine amino acids in the extreme C terminus. To study the effect of this recurrent variant as well as HNRNPC haploinsufficiency, we used induced pluripotent stem cells (iPSCs) and fibroblasts obtained from affected individuals. While protein localization and oligomerization were unaffected by the recurrent C-terminal deletion variant, total HNRNPC levels were decreased. Previously, reduced HNRNPC levels have been associated with changes in alternative splicing. Therefore, we performed a meta-analysis on published RNA-seq datasets of three different cell lines to identify a ubiquitous HNRNPC-dependent signature of alternative spliced exons. The identified signature was not only confirmed in fibroblasts obtained from an affected individual but also showed a significant enrichment for genes associated with intellectual disability. Hence, we assessed the effect of decreased and increased levels of HNRNPC on neuronal arborization and neuronal migration and found that either condition affects neuronal function. Taken together, our data indicate that HNRNPC haploinsufficiency affects alternative splicing of multiple intellectual disability-associated genes and that the developing brain is sensitive to aberrant levels of HNRNPC. Hence, our data strongly support the inclusion of HNRNPC to the family of HNRNP-related neurodevelopmental disorders. [Display omitted] We identified genetic variants of HNRNPC in 13 individuals with intellectual disability and global developmental delay. Through a meta-analysis of multiple cell types, we found that loss of HNRNPC affects alternative splicing, in particular of intellectual disability-associated genes. In vivo assays confirmed that neurodevelopment was affected by aberrant HNRNPC levels