Estimation of a Common Effect Parameter from Follow-Up Data When There Is No Mechanistic Interaction

In a stratified analysis, the results from different strata if homogeneity assumption is met are pooled together to obtain a single summary estimate for the common effect parameter. However, the effect can appear homogeneous across strata using one measure but heterogeneous using another. Consequently, two researchers analyzing the same data can arrive at conflicting conclusions if they use different effect measures. In this paper, the author draws on the sufficient component cause model to develop a stratified-analysis method regarding a particular effect measure, the ‘peril ratio’. When there is no mechanistic interaction between the exposure under study and the stratifying variable (i.e., when they do not work together to complete any sufficient cause), the peril ratio is constant across strata. The author presents formulas for the estimation of such a common peril ratio. Three real data are re-analyzed for illustration. When the data is consistent with peril-ratio homogeneity in a stratified analysis, researchers can use the formulas in this paper to pool the strata.     

Risk-Based Learning Games Improve Long-Term Retention of Information among School Pupils

Risk heightens motivation and, if used appropriately, may have the potential to improve engagement in the classroom. We have developed a risk-based learning game for school pupils in order to test whether such learning games can improve later recall of information. The study was performed during a series of public engagement workshops delivered by undergraduate students. Undergraduate neuroscience students delivered 90-minute science workshops to 9–10 year old school pupils (n = 448) that were divided into ‘Risk’, ‘No risk’ and ‘Control’ classes. ‘Risk’ classes received periodic multiple-choice questions (MCQs) during the workshops which required small teams of pupils to assign tokens to the answer(s) they believed to be correct. Tokens assigned to the correct answer were returned to the group and an equal number given back as a prize; tokens assigned to incorrect answers were lost. Participation was incentivised by the promise of a brain-related prize to the team with the most tokens at the end of the workshop. ‘No risk’ classes received MCQs without the risk component whilst the ‘Control’ classes received no MCQs. When presented with a neuroscience quiz based on workshop content at the end of the workshop, pupils in the ‘Risk’ classes exhibited significantly greater recall of information one week later. Quiz scores were higher than scores from the day of the workshop which suggested pupils may have discussed the workshop content outside of the classroom, thereby increasing knowledge over and above what was learned during the workshop. This is supported by feedback from pupils in ‘Risk’ classes which indicated that ‘Risk’ workshops were more interesting than ‘No risk’ and ‘Control’ workshops. These data suggest that there is a role for risk in the classroom but further investigations are required to elucidate the causal mechanisms of improved retention of information.     

Biotic Interactions in the Face of Climate Change: A Comparison of Three Modelling Approaches

Climate change is expected to alter biotic interactions, and may lead to temporal and spatial mismatches of interacting species. Although the importance of interactions for climate change risk assessments is increasingly acknowledged in observational and experimental studies, biotic interactions are still rarely incorporated in species distribution models. We assessed the potential impacts of climate change on the obligate interaction between Aeshna viridis and its egg-laying plant Stratiotes aloides in Europe, based on an ensemble modelling technique. We compared three different approaches for incorporating biotic interactions in distribution models: (1) We separately modelled each species based on climatic information, and intersected the future range overlap (‘overlap approach’). (2) We modelled the potential future distribution of A. viridis with the projected occurrence probability of S. aloides as further predictor in addition to climate (‘explanatory variable approach’). (3) We calibrated the model of A. viridis in the current range of S. aloides and multiplied the future occurrence probabilities of both species (‘reference area approach’). Subsequently, all approaches were compared to a single species model of A. viridis without interactions. All approaches projected a range expansion for A. viridis. Model performance on test data and amount of range gain differed depending on the biotic interaction approach. All interaction approaches yielded lower range gains (up to 667% lower) than the model without interaction. Regarding the contribution of algorithm and approach to the overall uncertainty, the main part of explained variation stems from the modelling algorithm, and only a small part is attributed to the modelling approach. The comparison of the no-interaction model with the three interaction approaches emphasizes the importance of including obligate biotic interactions in projective species distribution modelling. We recommend the use of the ‘reference area approach’ as this method allows a separation of the effect of climate and occurrence of host plant.     

‘RNA walk’ a novel approach to study RNA–RNA interactions between a small RNA and its target

In this study we describe a novel method to investigate the RNA–RNA interactions between a small RNA and its target that we termed ‘RNA walk’. The method is based on UV-induced AMT cross-linking in vivo followed by affinity selection of the hybrid molecules and mapping the intermolecular adducts by RT–PCR or real-time PCR. Domains carrying the cross-linked adducts fail to efficiently amplify by PCR compared with non-cross-linked domains. This method was calibrated and used to study the interaction between a special tRNA-like molecule (sRNA-85) that is part of the trypanosome signal recognition particle (SRP) complex and the ribosome. Four contact sites between sRNA-85 and rRNA were identified by ‘RNA walk’ and were further fine-mapped by primer extension. Two of the contact sites are expected; one contact site mimics the interaction of the mammalian Alu domain of SRP with the ribosome and the other contact sites include a canonical tRNA interaction. The two other cross-linked sites could not be predicted. We propose that ‘RNA walk, is a generic method to map target RNA small RNAs interactions in vivo.     

Filamentous Biopolymers on Surfaces: Atomic Force Microscopy Images Compared with Brownian Dynamics Simulation of Filament Deposition

Nanomechanical properties of filamentous biopolymers, such as the persistence length, may be determined from two-dimensional images of molecules immobilized on surfaces. For a single filament in solution, two principal adsorption scenarios are possible. Both scenarios depend primarly on the interaction strength between the filament and the support: i) For interactions in the range of the thermal energy, the filament can freely equilibrate on the surface during adsorption; ii) For interactions much stronger than the thermal energy, the filament will be captured by the surface without having equilibrated. Such a ‘trapping’ mechanism leads to more condensed filament images and hence to a smaller value for the apparent persistence length. To understand the capture mechanism in more detail we have performed Brownian dynamics simulations of relatively short filaments by taking the two extreme scenarios into account. We then compared these ‘ideal’ adsorption scenarios with observed images of immobilized vimentin intermediate filaments on different surfaces. We found a good agreement between the contours of the deposited vimentin filaments on mica (‘ideal’ trapping) and on glass (‘ideal’ equilibrated) with our simulations. Based on these data, we have developed a strategy to reliably extract the persistence length of short worm-like chain fragments or network forming filaments with unknown polymer-surface interactions.     

Joint Effect of Education and Main Lifetime Occupation on Late Life Health: A Cross-Sectional Study of Older Adults in Xiamen,China

Background

The effects of education and occupation on health have been well documented individually, but little is known about their joint effect, especially their cumulative joint effect on late life health.

Methods

We enrolled 14,292 participants aged 60+ years by multistage sampling across 173 communities in Xiamen, China, in 2013. Heath status was assessed by the ability to perform six basic activities of daily life. Education was classified in four categories: ‘Illiterate’, ‘Primary’, ‘Junior high school’ and ‘Senior high school and beyond’. Main lifetime occupation was also four categorized: ‘Employed’, ‘Farmer’, ‘Jobless’ and ‘Others’. Odds ratios (ORs) were estimated by random-intercept multilevel models regressing health status on education and main lifetime occupation with or without their interactions, adjusting by some covariates.

Results

Totally, 13,880 participants had complete data, of whom 12.5% suffered from disability, and ‘Illiterate’ and ‘Farmer’ took up the greatest proportion (33.01% and 42.72%, respectively). Participants who were higher educated had better health status (ORs = 0.62, 0.46, and 0.44 for the ‘Primary’, ‘Junior high school’, and ‘Senior high school and beyond’, respectively, in comparison with ‘Illiterate’). Those who were long term jobless in early life had poorest heath (ORs = 1.88, 95% CI 1.47 to 2.40). Unexpectedly, for the farmers, the risk of poor health gradually increased in relation to higher education level (ORs = 1.26, 1.28, 1.40 and 2.24, respectively). For the ‘Employed’, similar ORs were obtained for the ‘Junior high school’ and ‘Senior high school and beyond’ educated (both ORs = 1.01). For the ‘Farmer’ and ‘Jobless’, participants who were ‘Illiterate’ and ‘Primary’ educated also showed similar ORs.

Conclusions

Both education and main lifetime occupation were associated with late life health. Higher education was observed to be associated with better health, but such educational advantage was mediated by main lifetime occupation.     

Genetic composition of social groups influences male aggressive behaviour and fitness in natural genotypes of Drosophila melanogaster

Indirect genetic effects (IGEs) describe how an individual''s behaviour—which is influenced by his or her genotype—can affect the behaviours of interacting individuals. IGE research has focused on dyads. However, insights from social networks research, and other studies of group behaviour, suggest that dyadic interactions are affected by the behaviour of other individuals in the group. To extend IGE inferences to groups of three or more, IGEs must be considered from a group perspective. Here, I introduce the ‘focal interaction’ approach to study IGEs in groups. I illustrate the utility of this approach by studying aggression among natural genotypes of Drosophila melanogaster. I chose two natural genotypes as ‘focal interactants’: the behavioural interaction between them was the ‘focal interaction’. One male from each focal interactant genotype was present in every group, and I varied the genotype of the third male—the ‘treatment male’. Genetic variation in the treatment male''s aggressive behaviour influenced the focal interaction, demonstrating that IGEs in groups are not a straightforward extension of IGEs measured in dyads. Further, the focal interaction influenced male mating success, illustrating the role of IGEs in behavioural evolution. These results represent the first manipulative evidence for IGEs at the group level.     

Tool for Rapid & Easy Identification of High Risk Diabetic Foot: Validation & Clinical Pilot of the Simplified 60 Second Diabetic Foot Screening Tool

Background

Most diabetic foot amputations are caused by ulcers on the skin of the foot i.e. diabetic foot ulcers. Early identification of patients at high risk for diabetic foot ulcers is crucial. The ‘Simplified 60-Second Diabetic Foot Screening Tool’ has been designed to rapidly detect high risk diabetic feet, allowing for timely identification and referral of patients needing treatment. This study aimed to determine the clinical performance and inter-rater reliability of ‘Simplified 60 Second Diabetic Foot Screening Tool’ in order to evaluate its applicability for routine screening.

Methods and Findings

The tool was independently tested by n=12 assessors with n=18 Guyanese patients with diabetes. Inter-rater reliability was assessed by calculating Cronbach’s alpha for each of the assessment items. A minimum value of 0.60 was considered acceptable. Reliability scores of the screening tool assessment items were: ‘monofilament test’ 0.98; ‘active ulcer’ 0.97; ‘previous amputation’ 0.97; ‘previous ulcer’ 0.97; ‘fixed ankle’ 0.91; ‘deformity’ 0.87; ‘callus’ 0.87; ‘absent pulses’ 0.87; ‘fixed toe’ 0.80; ‘blisters’ 0.77; ‘ingrown nail’ 0.72; and ‘fissures’ 0.55. The item ‘stiffness in the toe or ankle’ was removed as it was observed in only 1.3% of patients. The item ‘fissures’ was also removed due to low inter-rater reliability. Clinical performance was assessed via a pilot study utilizing the screening tool on n=1,266 patients in an acute care setting in Georgetown, Guyana. In total, 48% of patients either had existing diabetic foot ulcers or were found to be at high risk for developing ulcers.

Conclusions

Clinicians in low and middle income countries such as Guyana can use the Simplified 60-Second Diabetic Screening Tool to facilitate early detection and appropriate treatment of diabetic foot ulcers. Implementation of this screening tool has the potential to decrease diabetes related disability and mortality.     

MAS C-Terminal Tail Interacting Proteins Identified by Mass Spectrometry- Based Proteomic Approach

Propagation of signals from G protein-coupled receptors (GPCRs) in cells is primarily mediated by protein-protein interactions. MAS is a GPCR that was initially discovered as an oncogene and is now known to play an important role in cardiovascular physiology. Current literature suggests that MAS interacts with common heterotrimeric G-proteins, but MAS interaction with proteins which might mediate G protein-independent or atypical signaling is unknown. In this study we hypothesized that MAS C-terminal tail (Ct) is a major determinant of receptor-scaffold protein interactions mediating MAS signaling. Mass-spectrometry based proteomic analysis was used to comprehensively identify the proteins that interact with MAS Ct comprising the PDZ-binding motif (PDZ-BM). We identified both PDZ and non-PDZ proteins from human embryonic kidney cell line, mouse atrial cardiomyocyte cell line and human heart tissue to interact specifically with MAS Ct. For the first time our study provides a panel of PDZ and other proteins that potentially interact with MAS with high significance. A ‘cardiac-specific finger print’ of MAS interacting PDZ proteins was identified which includes DLG1, MAGI1 and SNTA. Cell based experiments with wild-type and mutant MAS lacking the PDZ-BM validated MAS interaction with PDZ proteins DLG1 and TJP2. Bioinformatics analysis suggested well-known multi-protein scaffold complexes involved in nitric oxide signaling (NOS), cell-cell signaling of neuromuscular junctions, synapses and epithelial cells. Majority of these protein hits were predicted to be part of disease categories comprising cancers and malignant tumors. We propose a ‘MAS-signalosome’ model to stimulate further research in understanding the molecular mechanism of MAS function. Identifying hierarchy of interactions of ‘signalosome’ components with MAS will be a necessary step in future to fully understand the physiological and pathological functions of this enigmatic receptor.     

A comparison of performance of plant miRNA target prediction tools and the characterization of features for genome-wide target prediction

Background

Deep-sequencing has enabled the identification of large numbers of miRNAs and siRNAs, making the high-throughput target identification a main limiting factor in defining their function. In plants, several tools have been developed to predict targets, majority of them being trained on Arabidopsis datasets. An extensive and systematic evaluation has not been made for their suitability for predicting targets in species other than Arabidopsis. Nor, these have not been evaluated for their suitability for high-throughput target prediction at genome level.

Results

We evaluated the performance of 11 computational tools in identifying genome-wide targets in Arabidopsis and other plants with procedures that optimized score-cutoffs for estimating targets. Targetfinder was most efficient [89% ‘precision’ (accuracy of prediction), 97% ‘recall’ (sensitivity)] in predicting ‘true-positive’ targets in Arabidopsis miRNA-mRNA interactions. In contrast, only 46% of true positive interactions from non-Arabidopsis species were detected, indicating low ‘recall’ values. Score optimizations increased the ‘recall’ to only 70% (corresponding ‘precision’: 65%) for datasets of true miRNA-mRNA interactions in species other than Arabidopsis. Combining the results of Targetfinder and psRNATarget delivers high true positive coverage, whereas the intersection of psRNATarget and Tapirhybrid outputs deliver highly ‘precise’ predictions. The large number of ‘false negative’ predictions delivered from non-Arabidopsis datasets by all the available tools indicate the diversity in miRNAs-mRNA interaction features between Arabidopsis and other species. A subset of miRNA-mRNA interactions differed significantly for features in seed regions as well as the total number of matches/mismatches.

Conclusion

Although, many plant miRNA target prediction tools may be optimized to predict targets with high specificity in Arabidopsis, such optimized thresholds may not be suitable for many targets in non-Arabidopsis species. More importantly, non-conventional features of miRNA-mRNA interaction may exist in plants indicating alternate mode of miRNA target recognition. Incorporation of these divergent features would enable next-generation of algorithms to better identify target interactions.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-348) contains supplementary material, which is available to authorized users.     

Red:far-red light conditions affect the emission of volatile organic compounds from barley (Hordeum vulgare), leading to altered biomass allocation in neighbouring plants

Background and Aims Volatile organic compounds (VOCs) play various roles in plant–plant interactions, and constitutively produced VOCs might act as a cue to sense neighbouring plants. Previous studies have shown that VOCs emitted from the barley (Hordeum vulgare) cultivar ‘Alva’ cause changes in biomass allocation in plants of the cultivar ‘Kara’. Other studies have shown that shading and the low red:far-red (R:FR) conditions that prevail at high plant densities can reduce the quantity and alter the composition of the VOCs emitted by Arabidopsis thaliana, but whether this affects plant–plant signalling remains unknown. This study therefore examines the effects of far-red light enrichment on VOC emissions and plant–plant signalling between ‘Alva’ and ‘Kara’.Methods The proximity of neighbouring plants was mimicked by supplemental far-red light treatment of VOC emitter plants of barley grown in growth chambers. Volatiles emitted by ‘Alva’ under control and far-red light-enriched conditions were analysed using gas chromatography–mass spectrometry (GC-MS). ‘Kara’ plants were exposed to the VOC blend emitted by the ‘Alva’ plants that were subjected to either of the light treatments. Dry matter partitioning, leaf area, stem and total root length were determined for ‘Kara’ plants exposed to ‘Alva’ VOCs, and also for ‘Alva’ plants exposed to either control or far-red-enriched light treatments.Key Results Total VOC emissions by ‘Alva’ were reduced under low R:FR conditions compared with control light conditions, although individual volatile compounds were found to be either suppressed, induced or not affected by R:FR. The altered composition of the VOC blend emitted by ‘Alva’ plants exposed to low R:FR was found to affect carbon allocation in receiver plants of ‘Kara’.Conclusions The results indicate that changes in R:FR light conditions influence the emissions of VOCs in barley, and that these altered emissions affect VOC-mediated plant–plant interactions.     

In silico prediction of HIV-1-host molecular interactions and their directionality

Human immunodeficiency virus type 1 (HIV-1) continues to be a major cause of disease and premature death. As with all viruses, HIV-1 exploits a host cell to replicate. Improving our understanding of the molecular interactions between virus and human host proteins is crucial for a mechanistic understanding of virus biology, infection and host antiviral activities. This knowledge will potentially permit the identification of host molecules for targeting by drugs with antiviral properties. Here, we propose a data-driven approach for the analysis and prediction of the HIV-1 interacting proteins (VIPs) with a focus on the directionality of the interaction: host-dependency versus antiviral factors. Using support vector machine learning models and features encompassing genetic, proteomic and network properties, our results reveal some significant differences between the VIPs and non-HIV-1 interacting human proteins (non-VIPs). As assessed by comparison with the HIV-1 infection pathway data in the Reactome database (sensitivity > 90%, threshold = 0.5), we demonstrate these models have good generalization properties. We find that the ‘direction’ of the HIV-1-host molecular interactions is also predictable due to different characteristics of ‘forward’/pro-viral versus ‘backward’/pro-host proteins. Additionally, we infer the previously unknown direction of the interactions between HIV-1 and 1351 human host proteins. A web server for performing predictions is available at http://hivpre.cvr.gla.ac.uk/.     

Resilience to Leaking — Dynamic Systems Modeling of Information Security

Leaking of confidential material is a major threat to information security within organizations and to society as a whole. This insight has gained traction in the political realm since the activities of Wikileaks, which hopes to attack ‘unjust’ systems or ‘conspiracies’. Eventually, such threats to information security rely on a biologistic argument on the benefits and drawbacks that uncontrolled leaking might pose for ‘just’ and ‘unjust’ entities. Such biological metaphors are almost exclusively based on the economic advantage of participants. Here, I introduce a mathematical model of the complex dynamics implied by leaking. The complex interactions of adversaries are modeled by coupled logistic equations including network effects of econo-communication networks. The modeling shows, that there might arise situations where the leaking envisioned and encouraged by Wikileaks and the like can strengthen the defending entity (the ‘conspiracy’). In particular, the only severe impact leaking can have on an organization seems to originate in the exploitation of leaks by another entity the organization competes with. Therefore, the model suggests that leaks can be used as a `tactical mean’ in direct adversary relations, but do not necessarily increase public benefit and societal immunization to ‘conspiracies’. Furthermore, within the model the exploitation of the (open) competition between entities seems to be a more promising approach to control malicious organizations : divide-et-impera policies triumph here.     

Detection of Subclinical Atherosclerosis in Asymptomatic Subjects Using Ultrasound Radiofrequency-Tracking Technology

Objective

Atherosclerosis is a chronic and systemic disease and its developmental process involves the synergism of multiple risk factors such as hypertension, dyslipidemia, diabetes, obesity and smoking. The diagnosis of subclinical atherosclerosis is essential for strategic guidance towards suitable treatments and efficient prevention against acute cardiovascular events. This study employed ultrasound radiofrequency (RF) tracking technology to characterize human carotid arteries in vivo in terms of intima-media thickness (IMT) and artery stiffness, and evaluated the statistical correlation between carotid IMT and stiffness, and the number of risk factors for atherosclerosis.

Methods

A total of 160 asymptomatic subjects were enrolled. Ultrasound RF-tracking technology was employed to acquire carotid IMT and stiffness parameters: maximum IMT (^MAXIMT), RF Quality IMT (^RFQIMT), distensibility coefficient (), compliance coefficient (), index, index and local pulse wave velocity (). The subjects were categorized in four groups in terms of the number of risk factors: ‘zero’, ‘single’, ‘double’, and ‘multiple’, and statistical analyses of carotid IMT and stiffness parameters were performed between these different groups.

Results

The subjects (n = 145) with ^MAXIMT smaller than 1.0 mm matched the IMT criteria for non-atherosclerosis and were named as NA-subjects. Spearman’s rho correlation analysis of the whole group and the NA-subjects both showed that ^MAXIMT correlated positively with ^RFQIMT, , , and , and negatively with and (p<0.01). The analysis of covariance of NA-subjects showed significant differences between subjects with and without risk factors, and also showed significant differences between the ‘zero’, ‘single’, ‘double’, and ‘multiple’ groups.

Conclusions

The carotid IMT and stiffness parameters obtained by the ultrasound RF-tracking technology were demonstrated to possess significant statistical correlation with the number of risk factors from 160 subjects, and these anatomical and mechanical parameters may potentially be used together with the IMT criteria to support subclinical atherosclerosis diagnosis.     

Association between Obesity and Cardiometabolic Health Risk in Asian-Canadian Sub-Groups

Objectives

To quantify and compare the association between the World Health Organizations’ Asian-specific trigger points for public health action [‘increased risk’: body mass index (BMI) ≥23 kg/m², and; ‘high risk’: BMI ≥27.5 kg/m²] with self-reported cardiovascular-related conditions in Asian-Canadian sub-groups.

Methods

Six cycles of the Canadian Community Health Survey (2001–2009) were pooled to examine BMI and health in Asian sub-groups (South Asians, Chinese, Filipino, Southeast Asians, Arabs, West Asians, Japanese and Korean; N = 18 794 participants, ages 18–64 y). Multivariable logistic regression, adjusting for demographic, lifestyle characteristics and acculturation measures, was used to estimate the odds of cardiovascular-related health (high blood pressure, heart disease, diabetes, ‘at least one cardiometabolic condition’) outcomes across all eight Asian sub-groups.

Results

Compared to South Asians (OR = 1.00), Filipinos had higher odds of having ‘at least one cardiometabolic condition’ (OR = 1.29, 95% CI: 1.04–1.62), whereas Chinese (0.63, 0.474–0.9) and Arab-Canadians had lower odds (0.38, 0.28–0.51). In ethnic-specific analyses (with ‘acceptable’ risk weight as the referent), ‘increased’ and ‘high’ risk weight categories were the most highly associated with ‘at least one cardiometabolic condition’ in Chinese (‘increased’: 3.6, 2.34–5.63; ‘high’: 8.9, 3.6–22.01). Compared to normal weight South Asians, being in the ‘high’ risk weight category in all but the Southeast Asian, Arab, and Japanese ethnic groups was associated with approximately 3-times the likelihood of having ‘at least one cardiometabolic condition’.

Conclusion

Differences in the association between obesity and cardiometabolic health risks were seen among Asian sub-groups in Canada. The use of WHO’s lowered Asian-specific BMI cut-offs identified obesity-related risks in South Asian, Filipino and Chinese sub-groups that would have been masked by traditional BMI categories. These findings have implications for public health messaging, especially for ethnic groups at higher odds of obesity-related health risks.     

Association Signals Unveiled by a Comprehensive Gene Set Enrichment Analysis of Dental Caries Genome-Wide Association Studies

Gene set-based analysis of genome-wide association study (GWAS) data has recently emerged as a useful approach to examine the joint effects of multiple risk loci in complex human diseases or phenotypes. Dental caries is a common, chronic, and complex disease leading to a decrease in quality of life worldwide. In this study, we applied the approaches of gene set enrichment analysis to a major dental caries GWAS dataset, which consists of 537 cases and 605 controls. Using four complementary gene set analysis methods, we analyzed 1331 Gene Ontology (GO) terms collected from the Molecular Signatures Database (MSigDB). Setting false discovery rate (FDR) threshold as 0.05, we identified 13 significantly associated GO terms. Additionally, 17 terms were further included as marginally associated because they were top ranked by each method, although their FDR is higher than 0.05. In total, we identified 30 promising GO terms, including ‘Sphingoid metabolic process,’ ‘Ubiquitin protein ligase activity,’ ‘Regulation of cytokine secretion,’ and ‘Ceramide metabolic process.’ These GO terms encompass broad functions that potentially interact and contribute to the oral immune response related to caries development, which have not been reported in the standard single marker based analysis. Collectively, our gene set enrichment analysis provided complementary insights into the molecular mechanisms and polygenic interactions in dental caries, revealing promising association signals that could not be detected through single marker analysis of GWAS data.     

Minimal biophysical model of combined antibiotic action

Phenomenological relations such as Ohm’s or Fourier’s law have a venerable history in physics but are still scarce in biology. This situation restrains predictive theory. Here, we build on bacterial “growth laws,” which capture physiological feedback between translation and cell growth, to construct a minimal biophysical model for the combined action of ribosome-targeting antibiotics. Our model predicts drug interactions like antagonism or synergy solely from responses to individual drugs. We provide analytical results for limiting cases, which agree well with numerical results. We systematically refine the model by including direct physical interactions of different antibiotics on the ribosome. In a limiting case, our model provides a mechanistic underpinning for recent predictions of higher-order interactions that were derived using entropy maximization. We further refine the model to include the effects of antibiotics that mimic starvation and the presence of resistance genes. We describe the impact of a starvation-mimicking antibiotic on drug interactions analytically and verify it experimentally. Our extended model suggests a change in the type of drug interaction that depends on the strength of resistance, which challenges established rescaling paradigms. We experimentally show that the presence of unregulated resistance genes can lead to altered drug interaction, which agrees with the prediction of the model. While minimal, the model is readily adaptable and opens the door to predicting interactions of second and higher-order in a broad range of biological systems.     

Molecular Mechanisms,Thermodynamics, and Dissociation Kinetics of Knob-Hole Interactions in Fibrin

Polymerization of fibrin, the primary structural protein of blood clots and thrombi, occurs through binding of knobs ‘A’ and ‘B’ in the central nodule of fibrin monomer to complementary holes ‘a’ and ‘b’ in the γ- and β-nodules, respectively, of another monomer. We characterized the A:a and B:b knob-hole interactions under varying solution conditions using molecular dynamics simulations of the structural models of fibrin(ogen) fragment D complexed with synthetic peptides GPRP (knob ‘A’ mimetic) and GHRP (knob ‘B’ mimetic). The strength of A:a and B:b knob-hole complexes was roughly equal, decreasing with pulling force; however, the dissociation kinetics were sensitive to variations in acidity (pH 5–7) and temperature (T = 25–37 °C). There were similar structural changes in holes ‘a’ and ‘b’ during forced dissociation of the knob-hole complexes: elongation of loop I, stretching of the interior region, and translocation of the moveable flap. The disruption of the knob-hole interactions was not an “all-or-none” transition as it occurred through distinct two-step or single step pathways with or without intermediate states. The knob-hole bonds were stronger, tighter, and more brittle at pH 7 than at pH 5. The B:b knob-hole bonds were weaker, looser, and more compliant than the A:a knob-hole bonds at pH 7 but stronger, tighter, and less compliant at pH 5. Surprisingly, the knob-hole bonds were stronger, not weaker, at elevated temperature (T = 37 °C) compared with T = 25 °C due to the helix-to-coil transition in loop I that helps stabilize the bonds. These results provide detailed qualitative and quantitative characteristics underlying the most significant non-covalent interactions involved in fibrin polymerization.     

A One-Degree-of-Freedom Test for Supra-Multiplicativity of SNP Effects

Deviation from multiplicativity of genetic risk factors is biologically plausible and might explain why Genome-wide association studies (GWAS) so far could unravel only a portion of disease heritability. Still, evidence for SNP-SNP epistasis has rarely been reported, suggesting that 2-SNP models are overly simplistic. In this context, it was recently proposed that the genetic architecture of complex diseases could follow limiting pathway models. These models are defined by a critical risk allele load and imply multiple high-dimensional interactions. Here, we present a computationally efficient one-degree-of-freedom “supra-multiplicativity-test” (SMT) for SNP sets of size 2 to 500 that is designed to detect risk alleles whose joint effect is fortified when they occur together in the same individual. Via a simulation study we show that the SMT is powerful in the presence of threshold models, even when only about 30–45% of the model SNPs are available. In addition, we demonstrate that the SMT outperforms standard interaction analysis under recessive models involving just a few SNPs. We apply our test to 10 consensus Alzheimer’s disease (AD) susceptibility SNPs that were previously identified by GWAS and obtain evidence for supra-multiplicativity () that is not attributable to either two-way or three-way interaction.