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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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The evolution of class-dependent reproductive effort in humans and other animals

Reproductive effort is a major life history trait that largely determines an organisms reproductive and survival schedule, and therefore it has a significant impact on lifetime fitness. A wealth of theoretical models have identified a wide range of factors that provide adaptive explanations for reproductive effort, including senescence, differential adult and offspring survival, and inter-generational competition. This work, however, is inadequate for explaining the levels of variation in reproductive effort found in stratified societies characterised by complex social dynamics. Rank and class-based societies are widespread in the natural world and common in social species, from insects and birds to humans and other mammals. In this article, I investigate how class and intra-generational social mobility influence the allocation of resources between fecundity and somatic tissue. I find that social mobility causes lower-class mothers to preferentially invest in survival, but only if class is associated with additional reproductive resources. If, by contrast, class is associated with extra survival resources, then upper-class mothers are always favoured to invest more in somatic maintenance, whilst lower-class mothers are always favoured to invest less in somatic maintenance, irrespective of social mobility. Moreover, I find that class-dependent reproductive effort leads to the emergence of distinct class-specific life-history syndromes, with each syndrome being associated with a suite of contrasting life-history traits. Finally, I find that these life-history syndromes are in close agreement with those observed in a human contemporary population. These findings lend support to the idea that evolutionary models can bridge the gap between the animal-human divide, and therefore be a valuable tool for public health decision-making and other human affairs.

evolutionary biology

Spatial and Temporal Analysis of the Stomach and Small Intestinal Microbiota in Fasted Healthy Humans

Although the microbiota in the proximal gastrointestinal (GI) tract has been implicated in health and disease, much of these microbes remains understudied compared to the distal GI tract. This study characterized the microbiota across multiple proximal GI sites over time in healthy individuals.\n\nAs part of a study of the pharmacokinetics of oral mesalamine administration, healthy, fasted volunteers (N=8; 10 observation periods total) were orally intubated with a four-lumen catheter with multiple aspiration ports. Samples were taken from stomach, duodenal, and multiple jejunal sites, sampling hourly ([≤]7 hours) to measure mesalamine (administered at t=0), pH, and 16S rRNA gene-based composition.\n\nWe observed a predominance of Firmicutes across proximal GI sites, with significant variation compared to stool. The microbiota was more similar within individuals over time than between subjects, with the fecal microbiota being unique from that of the small intestine. The stomach and duodenal microbiota displayed highest intra-individual variability compared to jejunal sites, which were more stable across time. We observed significant correlations in the duodenal microbial composition with changes in pH; linear mixed models identified positive correlations with multiple Streptococcus operational taxonomic units (OTU) and negative correlations with multiple Prevotella and Pasteurellaceae OTUs. Few OTUs correlated with mesalamine concentration.\n\nThe stomach and duodenal microbiota exhibited greater compositional dynamics compared to the jejunum. Short-term fluctuations in the duodenal microbiota was correlated with pH. Given the unique characteristics and dynamics of the proximal GI tract microbiota, it is important to consider these local environments in health and disease states.

microbiology

Satb1 integrates DNA sequence, shape, motif density and torsional stress to differentially bind targets in nucleosome-dense regions.

Satb1 is a genome organizer that regulates multiple cellular and developmental processes. It is not yet clear how Satb1 selects different sets of targets throughout the genome. We used live-cell single molecule imaging and deep sequencing to assess determinants of Satb1 binding-site selectivity. We found that Satb1 preferentially targets nucleosome-dense regions and can directly bind consensus motifs within nucleosomes. Some genomic regions harbor multiple regularly spaced Satb1 binding motifs (typical separation [~]1 turn of the DNA helix), characterized by highly cooperative binding. The Satb1 homeodomain is dispensable for high-affinity binding but is essential for specificity. Finally, Satb1{Leftrightarrow}DNA interactions are mechanosensitive: increasing negative torsional stress in DNA enhances Satb1 binding and Satb1 stabilizes base unpairing regions (BURs) against melting by molecular machines. The ability of Satb1 to control diverse biological programs may reflect its ability to combinatorially use multiple site selection criteria.

molecular biology

The Swiss Primary Ciliary Dyskinesia registry: objectives, methods and first results

Primary Ciliary Dyskinesia (PCD) is a rare hereditary, multi-organ disease caused by defects in ciliary structure and function. It results in a wide range of clinical manifestations, most commonly in the upper and lower airways. Central data collection in national and international registries is essential to studying the epidemiology of rare diseases and filling in gaps in knowledge of diseases such as PCD. For this reason, the Swiss Primary Ciliary Dyskinesia Registry (CH-PCD) was founded in 2013 as a collaborative project between epidemiologists and adult and paediatric pulmonologists.\n\nThe registry records patients of any age, suffering from PCD, who are treated and resident in Switzerland. It collects information from patients identified through physicians, diagnostic facilities, and patient organisations. The registry dataset contains data on diagnostic evaluations, lung function, microbiology and imaging, symptoms, treatments, and hospitalizations.\n\nBy May 2018, CH-PCD has contacted 566 physicians of different specialties and identified 134 patients with PCD. At present this number represents an overall 1 in 63,000 prevalence of people diagnosed with PCD in Switzerland. Prevalence differs by age and region; it is highest in children and adults younger than 30 years, and in Espace Mittelland. The median age of patients in the registry is 25 years (range 5-73), and 49 patients have a definite PCD diagnosis based on recent international guidelines. Data from CH-PCD are contributed to international collaborative studies and the registry facilitates patient identification for nested studies.\n\nCH-PCD has proven to be a valuable research tool that already has highlighted weaknesses in PCD clinical practice in Switzerland. Development of centralised diagnostic and management centres and adherence to international guidelines are needed to improve diagnosis and management--particularly for adult PCD patients.

epidemiology

CropMonitor: a scalable open-source experiment management system for distributed plant phenotyping and IoT-based crop management

BackgroundHigh-quality plant phenotyping and climate data lay the foundation of phenotypic analysis as well as genotype-by-environment interactions, which is important biological evidence not only to understand the dynamics between crop performance, genotypes, and environmental factors, but also for agronomists and farmers to monitor crops in fluctuating agricultural conditions. With the rise of Internet of Things technologies in recent years, many IoT-based remote sensing devices have been applied to phenotyping and crop monitoring that generate big plant-environment datasets every day; however, it is still technically challenging to calibrate, annotate, and aggregate big data effectively, especially when they were generated in multiple locations, and often at different scales.\n\nFindingsCropSurveyor is a PHP and SQL based server platform, which provides automated data collation, storage, device and experiment management through IoT-based sensors and distributed plant phenotyping workstations. It provides a two-component solution for monitoring biological experiments and networked devices, with interfaces specifically designed for distributed IoT devices and centralised data servers. Data transfer is performed automatically though an HTTP accessible RESTful API installed on both device-side and server-side of the CropSurveyor system, which synchronise daily representative crop growth images for quick and visual-based crop assessment, as well as detailed microclimate readings for GxE studies. CropSurveyor also supports the comparison of historical and ongoing crop performance whilst different experiments are being conducted.\n\nConclusionsAs an open-source experiment and data management system, CropSurveyor can be used to maintain and collate important crop performance and microclimate datasets captured by IoT sensors and distributed phenotyping installations. It provides near real-time environmental and crop growth monitoring in addition to historical and current data comparison through a single cloud-ready server system. Accessible both locally in the field through smart devices and remotely in an office using a PC, CropSurveyor has been used in wheat field experiments for prebreeding since 2016 and has the potential to enable scalable crop management and IoT-style agricultural practices in the near future.

plant biology

Perfect Counterfactuals for Epidemic Simulations

Simulation studies are often used to predict the expected impact of control measures in infectious disease outbreaks. Typically, two independent sets of simulations are conducted, one with the intervetnion, and one without, and epidemic sizes (or some related metric) are compared to estimate the effect of the intervention. Since it is possible that controlled epidemics are larger than uncontrolled ones if there is substantial stochastic variation between epidemics, uncertainty intervals from this approach can include a negative effect even for an effective intervention. To more precisely estimate the number of cases an intervention will prevent within a single epidemic, here we develop a single world approach to matching simulations of controlled epidemics to their exact uncontrolled counterfac-tual. Our method borrows concepts from percolation approaches prune out possible epidemic histories and create potential epidemic graph that can be realized to create perfectly matched controlled and uncontrolled epidemics. We present an implementation of this method for a common class of compartmental models, and its application in a simple SIR model. Results illustrate how, at the cost of some computation time, this method substantially narrows confidence intervals and avoids non-sensical inferences.

epidemiology

Feeding the disparities: the geography and trends of breastfeeding in the United States

There is scientific consensus on the importance of breastfeeding for the present and future health of newborns, in high- and low-income settings alike. In the United States, improving breast milk access is a public health priority but analysis of secular trends are largely lacking. Here, we used data from the National Immunization Survey of the CDC, collected between 2003 and 2016, to illustrate the temporal trends and the spatial heterogeneity in breastfeeding. We also considered the effect sizes of two key determinants of breastfeeding rates. We show that, while access to breast milk both at birth and at 6 months old has steadily increased over the past decade, large spatial disparities still remain at the state level. We also find that, since 2009, the proportion of households below the poverty level has become the strongest predictor of breastfeeding rates. We argue that, because variations in breastfeeding rates are associated with socio-economic factors, public health policies advocating for breastfeeding are still needed in particular in underserved communities. This is key to reducing longer term health disparities in the U.S., and more generally in high-income countries.

epidemiology

Prediction of environmental response in field-grown rice using expression-dynamics-QTL

How genetic variations affect gene expression dynamics of field-grown plants remains unclear. Using statistical analysis of large-scale time-series RNA-sequencing of field-grown rice from chromosome segment substitution lines (CSSLs), we identified 1675 expression dynamics quantitative trait loci (edQTLs) leading to polymorphisms in expression dynamics under field conditions. Based on the edQTL and environmental information, we successfully predicted gene expression under environments different from training environments, and in rice cultivars with more complex genotypes than the CSSLs. Overall, edQTL identification helped understanding the genetic architecture of expression dynamics under field conditions, which is difficult to assess with laboratory experiments1.The prediction of expression based on edQTL and environmental information will contribute to crop breeding by increasing the accuracy of trait prediction under diverse conditions.

bioinformatics

Natural depletion of H1 in sex cells causes DNA demethylation, heterochromatin decondensation and transposon activation

Transposable elements (TEs), the movement of which can damage the genome, are epigenetically silenced in eukaryotes. Intriguingly, TEs are activated in the sperm companion cell - vegetative cell (VC) - of the flowering plant Arabidopsis thaliana. However, the extent and mechanism of this activation are unknown. Here we show that about 100 heterochromatic TEs are activated in VCs, mostly by DEMETER-catalyzed DNA demethylation. We further demonstrate that DEMETER access to some of these TEs is permitted by the natural depletion of linker histone H1 in VCs. Ectopically expressed H1 suppresses TEs in VCs by reducing DNA demethylation and via a methylation-independent mechanism. We demonstrate that H1 is required for heterochromatin condensation in plant cells and show that H1 overexpression creates heterochromatic foci in the VC progenitor cell. Taken together, our results demonstrate that the natural depletion of H1 during male gametogenesis facilitates DEMETER-directed DNA demethylation, heterochromatin relaxation, and TE activation.

plant biology

Seasonal patterns of dengue fever in rural Ecuador: 2009-2016

Season is a major determinant of infectious disease rates, including arboviruses spread by mosquitoes, such as dengue, chikungunya, and Zika. Seasonal patterns of disease are driven by a combination of climatic or environmental factors, such as temperature or rainfall, and human behavioral time trends, such as school year schedules, holidays, and weekday-weekend patterns. These factors affect both disease rates and healthcare-seeking behavior. Seasonality of dengue fever has been studied in the context of climatic factors, but short-and long-term time trends are less well-understood. With 2009-- 2016 medical record data from patients diagnosed with dengue fever at two hospitals in rural Ecuador, we used Poisson generalized linear modeling to determine short-and long-term seasonal patterns of dengue fever, as well as the effect of day of the week and public holidays. In a subset analysis, we determined the impact of school schedules on school-aged children. With a separate model, we examined the effect of climate on diagnosis patterns. In the first model, the most important predictors of dengue fever were annual sinusoidal fluctuations in disease, long-term trends, day of the week, and hospital. Seasonal trends showed single peaks in case diagnoses, during April. Compared to an average day, cases were more likely to be diagnosed on Tuesdays (risk ratio (RR): 1.26, 95% confidence interval (CI) 1.05--1.51) and Thursdays (RR: 1.25, 95% CI 1.02--1.53), and less likely to be diagnosed on Saturdays (RR: 0.81, 95% CI 0.65--1.01) and Sundays (RR: 0.74, 95% CI 0.58--0.95). Public holidays were not significant predictors of dengue fever diagnoses, except for an increase in diagnoses on the day after Christmas (RR: 2.77, 95% CI 1.46--5.24). School schedules did not impact dengue diagnoses in school-aged children. In the climate model, important climate variables included the monthly total precipitation (RR: 2.14, 95% CI 1.26--3.64), an interaction between total precipitation and monthly absolute minimum temperature (RR: 0.93, 95% CI 0.88--0.98), an interaction between total precipitation and monthly precipitation days (RR: 0.90, 95% CI 0.82--0.99), and a three-way interaction between minimum temperature, total precipitation, and precipitation days (RR: 1.01, 95% CI 1.00--1.02). This is the first report of long-term dengue fever seasonality in Ecuador, one of few reports from rural patients, and one of very few studies utilizing daily disease reports. These results can inform local disease prevention efforts, public health planning, as well as global and regional models of dengue fever trends.\n\nAuthor summaryDengue fever exhibits a seasonal pattern in many parts of the world, much of which has been attributed to climate and weather. However, additional factors may contribute to dengue seasonality. With 2009-- 2016 medical record data from rural Ecuador, we studied the short-and long-term seasonal patterns of dengue fever, as well as the effect of school schedules and public holidays. We also examined the effect of climate on dengue. We found that dengue diagnoses peak once per year during April, but that diagnoses are also affected by day of the week. Dengue was also impacted by regional climate and complex interactions between local weather variables. This is the first report of long-term dengue fever seasonality in Ecuador, one of few reports from rural patients, and one of very few studies utilizing daily disease reports. This is the first report on the impacts of school schedules, holidays, and weekday-weekend patterns on dengue diagnoses. These results suggest a potential impact of human behaviors on dengue exposure risk. More broadly, these results can inform local disease prevention efforts and public health planning, as well as global and regional models of dengue fever trends.

epidemiology

Resistance diagnostics as a public health tool to combat antibiotic resistance: A model-based evaluation

Rapid point-of-care resistance diagnostics (POC-RD) are thought to be a key tool in the fight against antibiotic resistance. By tailoring drug choice to infection genotype, doctors can improve treatment efficacy while limiting costs of inappropriate antibiotic prescription. Here we combine epidemiological theory and data to assess the potential of POC-RD innovations in a public health context, as a means to limit or even reverse selection for antibiotic resistance. POC-RD can be used to impose a non-biological fitness cost on resistant strains, by triggering targeted interventions that reduce their opportunities for transmission. We assess this diagnostic-imposed fitness cost in the context of a spectrum of bacterial population biologies and POC-RD conditional strategies, and find that the expected impact varies from selection against resistance for obligate pathogens to marginal public health improvements for opportunistic pathogens with high bystander antibiotic exposure during asymptomatic carriage (e.g. the pneumococcus). We close by generalizing the notion of RD-informed strategies to incorporate both POC and carriage surveillance information, and illustrate that coupling transmission control interventions to the discovery of resistant strains in carriage can potentially select against resistance in a broad range of opportunistic pathogens.

epidemiology

Alcohol consumption is associated with widespread changes in blood DNA methylation: analysis of cross-sectional and longitudinal data

BackgroundDNA methylation may be one of the mechanisms by which alcohol consumption is associated with the risk of disease. We conducted a large-scale, cross-sectional, genome-wide DNA methylation association study of alcohol consumption and a longitudinal analysis of repeated measurements taken several years apart.\n\nMethodsUsing the Illumina Infinium HumanMethylation450 BeadChip, DNA methylation measures were determined using baseline peripheral blood samples from 5,606 adult Melbourne Collaborative Cohort Study (MCCS) participants. For a subset of 1,088 of them, these measures were repeated using blood samples collected at follow-up, a median of 11 years later. Associations between alcohol intake and blood DNA methylation were assessed using linear mixed-effects regression models adjusted for batch effects and potential confounders. Independent data from the LOLIPOP (N=4,042) and KORA (N=1,662) cohorts were used to replicate associations discovered in the MCCS.\n\nResultsCross-sectional analyses identified 1,414 CpGs associated with alcohol intake at P<10-7, 1,243 of which had not been reported previously. Of these 1,243 novel associations, 1,078 were replicated (P<0.05) using LOLIPOP and KORA data. Using the MCCS data, we also replicated (P<0.05) 403 of 518 associations that had been reported previously. Interaction analyses suggested that associations were stronger for women, non-smokers, and participants genetically predisposed to consume less alcohol. Of the 1,414 CpGs, 530 were differentially methylated (P<0.05) in former compared with current drinkers. Longitudinal associations between the change in alcohol intake and the change in methylation were observed for 513 of the 1,414 cross-sectional associations.\n\nConclusionOur study indicates that, for middle-aged and older adults, alcohol intake is associated with widespread changes in DNA methylation across the genome. Longitudinal analyses showed that the methylation status of alcohol-associated CpGs may change with changes in alcohol consumption.

epidemiology

Nutritional status and morbidity profile of children with contact to leprosy in the rural community.

IntroductionChildhood leprosy reflects upon the disease transmission in the community. So, this study aimed to find out the children with contact to leprosy in their surroundings, prevalence of leprosy or subclinical infections in them and to assess their nutritional status. The study was conducted for 2 months and analysed 70 children in the rural community who were living with a household contact of leprosy.\n\nMethods70 children in the rural areas surrounding Chengalpattu living with leprosy contacts were selected for carrying out the study. Information regarding their demographic characteristics, socioeconomic factors, environmental conditions, feeding practices, food habits and any present health problems or in the recent past were collected. The children were then subjected to anthropometric measurements. The children were clinically evaluated by a dermatologist qualified in paediatric leprosy and children who were diagnosed as cases of leprosy were classified according to Ridley-Jopling classification. Slit skin smears for acid fast bacilli was done in all children with suspicious skin lesions.\n\nResultsOut of the 70 children taken into the study, 41 were boys and 29 were girls. 7-22% of boys and 3-6% of girls and overall,4-15% children are severely malnourished. 19 out of the 70 children had clinical pallor. Among the 70 leprosy contact children, 3 children were diagnosed to have leprosy (4.28%).Of the 3, 2 children had multibacillary leprosy while 1 had paucibacillary leprosy, according to the WHO classification and all 3 were classified as cases of Borederline Tuberculoid Leprosy according to Ridley-Jopling classification. All these 3 children had contact to leprosy for 10 or more years living with them.\n\nConclusionIt can be concluded that malnutrition, the closeness and duration of contact to leprosy are significant risk factors for leprosy. Regular contact screening and early case detection are essential strategies to prevent further transmission in the endemic areas. Diagnostic methods for detection of subclinical infection in contacts needs further research.

epidemiology

Bayesian learning ecosystem dynamics with delayed dependencies from incomplete multiple source data : an application to plant epidemiology

Ecosystem dynamics forecasting is central to major problems in ecology, society, and economy. The existing models serve as decision tools but their parameters valitity are usually not confronted to real data in a formalized approach. Dynamics bayesian network inference is promissing but limited when dealing with incomplete multiple source time series with delayed time dependencies. We propose here a temporal bayesian network with time delay and aproximate inference algorithm, to learn altogether cryptic ecosystem variables, missing data, and model parameters. The novelty in the approach is that it combines simulation-based and likelihood-based aproximate bayesian inference. The advantage of simulation based is that it allows to sample hidden processes. The advantage of likelihood based is that it provides a summary statistics that is really representing the model we are interested in. The ecosystem variables and the missing data are simulated from indicator variables using the probabilistic indicator-ecosystem model. The likelihood is estimated by averaging the probability of observed-simulated data over simulations, the parameter space is sampled with Metropolis Hasting algorithm. Another innovative proposition is to parametrize the network structure in order to learn model structure within a space provided by prior distribution. We apply to plant epidemiology.

epidemiology

Irregular chromatin: packing density, fiber width and occurrence of heterogeneous clusters

How chromatin is folded in the lengthscale of a gene is an open question. Recent experiments have suggested that, in vivo, chromatin is folded in an irregular manner and not as an ordered fiber with a width of 30 nm expected from theories of higher order packaging. Using computational methods, we examine how the interplay between DNA-bending non histone proteins, histone tails, intra-chromatin electrostatic and other interactions decide the nature of packaging of chromatin. We show that while the DNA-bending non histone proteins make the chromatin irregular, they may not alter the packing density and size of the fiber. We find that the length of the interacting region and intra-chromatin electrostatic interactions influence the packing density, clustering of nucleosomes, and the width of the chromatin fiber. Our results suggest that the actively maintained heterogeneity in the interaction pattern will play an an important role in deciding the nature of packaging of chromatin.

biophysics

Biofilm forming capabilities and protein secretion systems distinguish ecologically diverse lineages of Xanthomonas from rice

Xanthomonas oryzae is a devastating pathogen of rice worldwide, however, X. sontii and X. maliensis are its non-pathogenic counterparts from the same host. So far, these non-pathogenic isolates were overlooked due to their less economic importance and lack of genomic information. We have carried out detailed ecological and evolutionary study focusing on diverse lifestyles of these strains. Phylogenomic analysis revealed two major lineages corresponding to X. sontii (ML-I) and X. oryzae (ML-II) species. Interestingly, one of the non-pathogenic Xanthomonas strains belonging to X. maliensis is intermediary to both the major lineages/species suggesting on-going diversification and selection. Accordingly, pangenome analysis revealed large number of lifestyle specific genes with atypical GC content indicating role of horizontal gene transfer in genome diversification. Our comprehensive comparative genomic investigation of major lineages has revealed that impact of recombination is more for X. sontii as compared to X. oryzae. Acquisition of type III secretion system and its effectome along with a type VI secretion system also seem to have played a major role in the pathogenic lineage. Other known key pathogenicity clusters or genes like biofilm forming cluster, cellobiohydrolase and non-fimbrial adhesin (yapH) are exclusive to pathogenic lineage. However, commonality of loci encoding exopolysacharide, rpf signalling molecule, iron-uptake, xanthomonadin pigment, etc. suggests their essentiality in host adaptation. Overall, this study reveals evolutionary history of pathogenic and non-pathogenic strains and will further open up a new avenue for better management of pathogenic strains for sustainable cultivation of a major staple food crop.

evolutionary biology

Electrospray sample injection for single-particle imaging with X-ray lasers

The possibility of imaging single proteins constitutes an exciting challenge for X-ray lasers. Despite encouraging results on large particles, imaging small particles has proven to be difficult for two reasons: not quite high enough pulse intensity from currently available X-ray lasers and, as we demonstrate here, contamination of the aerosolised molecules by non-volatile contaminants in the solution. The amount of contamination on the sample depends on the initial droplet-size during aerosolisation. Here we show that with our electrospray injector we can decrease the size of aerosol droplets and demonstrate virtually contaminant-free sample delivery of organelles, small virions, and proteins. The results presented here, together with the increased performance of next generation X-ray lasers, constitute an important stepping stone towards the ultimate goal of protein structure determination from imaging at room temperature and high temporal resolution.

biophysics

HLA*PRG:LA - HLA typing from linearly projected graph alignments

SummaryHLA*PRG:LA implements a new graph alignment model for HLA type inference, based on the projection of linear alignments onto a variation graph. It enables accurate HLA type inference from whole-genome (99% accuracy) and whole-exome (93% accuracy) Illumina data; from long-read Oxford Nanopore and Pacific Biosciences data (98% accuracy for whole-genome and targeted data); and from genome assemblies. Computational requirements for a typical sample vary between 0.7 and 14 CPU hours per sample.\n\nAvailability and ImplementationHLA*PRG:LA is implemented in C++ and Perl and freely available from https://github.com/DiltheyLab/HLA-PRG-LA (GPL v3).\n\nContactalexander.dilthey@med.uni-duesseldorf.de\n\nSupplementary informationSupplementary data are available online.

bioinformatics