Systematic Identification of Cyclic-di-GMP Binding Proteins in Vibrio cholerae Reveals a Novel Class of Cyclic-di-GMP-Binding ATPases Associated with Type II Secretion Systems

Funding for Open Access provided by the UMD Libraries Open Access Publishing Fund.Cyclic-di-GMP (c-di-GMP) is a ubiquitous bacterial signaling molecule that regulates a variety of complex processes through a diverse set of c-di-GMP receptor proteins. We have utilized a systematic approach to identify c-di-GMP receptors from the pathogen Vibrio cholerae using the Differential Radial Capillary Action of Ligand Assay (DRaCALA). The DRaCALA screen identified a majority of known c-di-GMP binding proteins in V. cholera and revealed a novel c-di-GMP binding protein, MshE (VC0405), an ATPase associated with the mannose sensitive hemagglutinin (MSHA) type IV pilus. The known c-di-GMP binding proteins identified by DRaCALA include diguanylate cyclases, phosphodiesterases, PilZ domain proteins and transcription factors VpsT and VpsR, indicating that the DRa- CALA-based screen of open reading frame libraries is a feasible approach to uncover novel receptors of small molecule ligands. Since MshE lacks the canonical c-di-GMP-binding motifs, a truncation analysis was utilized to locate the c-di-GMP binding activity to the N-terminal T2SSE_N domain. Alignment of MshE homologs revealed candidate conserved residues responsible for c-di-GMP binding. Site-directed mutagenesis of these candidate residues revealed that the Arg9 residue is required for c-di-GMP binding. The ability of c-di-GMP binding to MshE to regulate MSHA dependent processes was evaluated. The R9A allele, in contrast to the wild type MshE, was unable to complement the ΔmshE mutant for the production of extracellular MshA to the cell surface, reduction in flagella swimming motility, attachment to surfaces and formation of biofilms. Testing homologs of MshE for binding to c-di-GMP identified the type II secretion ATPase of Pseudomonas aeruginosa (PA14_29490) as a c-di-GMP receptor, indicating that type II secretion and type IV pili are both regulated by c-di-GMP

Identifying beliefs underlying pre-drivers’ intentions to take risks: an application of the theory of planned behaviour

Novice motorists are at high crash risk during the first few months of driving. Risky behaviours such as speeding and driving while distracted are well-documented contributors to crash risk during this period. To reduce this public health burden, effective road safety interventions need to target the pre-driving period. We use the Theory of Planned Behaviour (TPB) to identify the pre-driver beliefs underlying intentions to drive over the speed limit (N = 77), and while over the legal alcohol limit (N = 72), talking on a hand-held mobile phone (N = 77) and feeling very tired (N = 68). The TPB explained between 41% and 69% of the variance in intentions to perform these behaviours. Attitudes were strong predictors of intentions for all behaviours. Subjective norms and perceived behavioural control were significant, though weaker, independent predictors of speeding and mobile phone use. Behavioural beliefs underlying these attitudes could be separated into those reflecting perceived disadvantages (e.g., speeding increases my risk of crash) and advantages (e.g., speeding gives me a thrill). Interventions that can make these beliefs safer in pre-drivers may reduce crash risk once independent driving has begun

TEFM variants impair mitochondrial transcription causing childhood-onset neurological disease

Mutations in the mitochondrial or nuclear genomes are associated with a diverse group of human disorders characterized by impaired mitochondrial respiration. Within this group, an increasing number of mutations have been identified in nuclear genes involved in mitochondrial RNA biology. The TEFM gene encodes the mitochondrial transcription elongation factor responsible for enhancing the processivity of mitochondrial RNA polymerase, POLRMT. We report for the first time that TEFM variants are associated with mitochondrial respiratory chain deficiency and a wide range of clinical presentations including mitochondrial myopathy with a treatable neuromuscular transmission defect. Mechanistically, we show muscle and primary fibroblasts from the affected individuals have reduced levels of promoter distal mitochondrial RNA transcripts. Finally, tefm knockdown in zebrafish embryos resulted in neuromuscular junction abnormalities and abnormal mitochondrial function, strengthening the genotype-phenotype correlation. Our study highlights that TEFM regulates mitochondrial transcription elongation and its defect results in variable, tissue-specific neurological and neuromuscular symptoms

MicroRNA-Integrated and Network-Embedded Gene Selection with Diffusion Distance

Gene network information has been used to improve gene selection in microarray-based studies by selecting marker genes based both on their expression and the coordinate expression of genes within their gene network under a given condition. Here we propose a new network-embedded gene selection model. In this model, we first address the limitations of microarray data. Microarray data, although widely used for gene selection, measures only mRNA abundance, which does not always reflect the ultimate gene phenotype, since it does not account for post-transcriptional effects. To overcome this important (critical in certain cases) but ignored-in-almost-all-existing-studies limitation, we design a new strategy to integrate together microarray data with the information of microRNA, the major post-transcriptional regulatory factor. We also handle the challenges led by gene collaboration mechanism. To incorporate the biological facts that genes without direct interactions may work closely due to signal transduction and that two genes may be functionally connected through multi paths, we adopt the concept of diffusion distance. This concept permits us to simulate biological signal propagation and therefore to estimate the collaboration probability for all gene pairs, directly or indirectly-connected, according to multi paths connecting them. We demonstrate, using type 2 diabetes (DM2) as an example, that the proposed strategies can enhance the identification of functional gene partners, which is the key issue in a network-embedded gene selection model. More importantly, we show that our gene selection model outperforms related ones. Genes selected by our model 1) have improved classification capability; 2) agree with biological evidence of DM2-association; and 3) are involved in many well-known DM2-associated pathways

Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin

Recent genomic analyses of pathologically-defined tumor types identify “within-a-tissue” disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head & neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multi-platform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All datasets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies

The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review

Machine learning uses historical data to make predictions about new data. It has been frequently applied in healthcare to optimise diagnostic classification through discovery of hidden patterns in data that may not be obvious to clinicians. Congenital Heart Defect (CHD) machine learning research entails one of the most promising clinical applications, in which timely and accurate diagnosis is essential. The objective of this scoping review is to summarise the application and clinical utility of machine learning techniques used in paediatric cardiology research, specifically focusing on approaches aiming to optimise diagnosis and assessment of underlying CHD. Out of 50 full-text articles identified between 2015 and 2021, 40% focused on optimising the diagnosis and assessment of CHD. Deep learning and support vector machine were the most commonly used algorithms, accounting for an overall diagnostic accuracy > 0.80. Clinical applications primarily focused on the classification of auscultatory heart sounds, transthoracic echocardiograms, and cardiac MRIs. The range of these applications and directions of future research are discussed in this scoping review

Use of Potentially Harmful Medications and Health-Related Quality of Life among People with Dementia Living in Residential Aged Care Facilities

Background: Use of potentially harmful medications (PHMs) is common in people with dementia living in Residential Aged Care Facilities (RACFs) and increases the risk of adverse health outcomes. Debate persists as to how PHM use and its association with quality of life should be measured. We designed this study to determine the association of exposure to PHM, operationalized by three different measures, with self-reported Health-Related Quality of Life among people with dementia residing in RACFs. Methods: Cross-sectional study of 351 people aged >65 years diagnosed with dementia residing in RACFs and with MMSE ≤24. The primary outcome measure was the self-rated Quality of Life – Alzheimer’s disease questionnaire (QoL-AD). We collected data on patients’ medications, age, gender, MMSE total score, Neuropsychiatric Inventory total score, and comorbidities. Using regression analyses, we calculated crude and adjusted mean differences between groups exposed and not exposed to PHM according to potentially inappropriate medications (PIMs; identified by Modified Beers criteria), Drug Burden Index (DBI) >0 and polypharmacy (i.e. ≥5 medications). Results: Of 226 participants able to rate their QoL-AD, 56.41% were exposed to at least one PIM, 82.05% to medication contributing to DBI >0, and 91.74% to polypharmacy. Exposure to PIMs was not associated with self-reported QoL-AD ratings, while exposure to DBI >0 and polypharmacy were (also after adjustment); exposure to DBI >0 tripled the odds of lower QoL-AD ratings. Conclusion: Exposure to PHM, as identified by DBI >0 and by polypharmacy (i.e. ≥5 medications), but not by PIMs (Modified Beers criteria), is inversely associated with self-reported health-related quality of life for people with dementia living in RACFs

Two-year course of cognitive function and mood in adults with congestive heart failure and coronary artery disease: The Heart-Mind Study

Background: Congestive heart failure (CHF) has been associated with impaired cognitive function, but it is unclear if these changes are specific to CHF and if they get worse with time. We designed this study to determine if adults with CHF show evidence of cognitive decline compared with adults with and without coronary artery disease (CAD). Methods: A longitudinal study was carried out of 77 adults with CHF (ejection fraction, EF < 0.4), 73 adults with a clinical history of CAD and EF > 0.6, and 81 controls with no history of CAD. The Cambridge Cognitive Examination of the Elderly (CAMCOG) was the primary outcome measure. Secondary measures included the California Verbal Learning Test (CVLT), digit coding and copying, Hospital Anxiety and Depression Scale (HADS), and the short form health survey (SF36). Endpoints were collected at baseline and after 12 and 24 months. Results: The adjusted CAMCOG scores of CHF participants declined 0.9 points over two years (p = 0.022) compared with controls without CAD. There were no differences between the groups on other cognitive measures. Participants with CHF and with CAD experienced similar changes in cognitive function over two years. Left ventricular EF and six-minute walk test results could not explain the observed associations. Conclusions: The changes in cognitive function and mood associated with CHF over two years are subtle and not specific to CHF. © International Psychogeriatric Association 2011

Dietary patterns are associated with cognition among older people with mild cognitive impairment

There has been increasing interest in the influence of diet on cognition in the elderly. This study examined the cross-sectional association between dietary patterns and cognition in a sample of 249 people aged 65-90 years with mild cognitive impairment (MCI). Two dietary patterns; whole and processed food; were identified using factor analysis from a 107-item; self-completed Food Frequency Questionnaire. Logistic regression analyses showed that participants in the highest tertile of the processed food pattern score were more likely to have poorer cognitive functioning; in the lowest tertile of executive function (OR 2.55; 95% CI: 1.08-6.03); as assessed by the Cambridge Cognitive Examination. In a group of older people with MCI; a diet high in processed foods was associated with some level of cognitive impairment. © 2012 by the authors; licensee MDPI, Basel, Switzerland